Search Results (168)

As the number of objects in orbit increases every year, the number of objects re-entering Earth’s atmosphere grows as well. Re-entry path prediction is tricky, as atmospheric modeling lacks accuracy and requires constant monitoring of the object during re-entry. Ground-based sensors face limitations due to the field of view and weather. This paper explores the novel idea of using star trackers in very low Earth orbit to image Resident Space Objects (RSOs) that are on re-entry path and provides a comparison of different star trackers to determine the most effective parameters. A simulation with 1000 Resident Space Objects on re-entry path was performed and detectability analysis was run using AURICAM, SAGITTA, PCO, IDS, and FAI sensors placed in orbit between 200 and 600 km in altitude. The results show that all star trackers at any altitude were capable of detecting at least three RSOs on re-entry path and making at least 47 detections during the simulation period. In particular, instruments with larger aperture diameters such as SAGITTA and FAI and quantum efficiency performed better, making up to 134 detections and detecting up to 10 unique RSOs. They also detected a higher average signal-to-noise ratio. Detectability of RSOs is higher when the sensor is placed closer to the objects, with the most effective performance recorded at 300–400 km altitude. Future work should include practical testing of this technique. Full article

On-Orbit Servicing (OOS) represents a viable strategy toward a sustainable and extended exploitation of the Low-Earth-Orbit (LEO) environment. The design of OOS missions requires optimizing both the scheduling of visited objects and the transfer trajectory between each pair of orbits, resulting in the great complexity of the global mission planning problem. This research considers a servicing spacecraft equipped with a high-thrust propulsion system, required to perform multiple orbit transfers to visit several Resident Space Objects (RSOs) in a given time frame with minimum fuel consumption. The proposed method leverages a two-stage approach: (i) first, the optimal transfers are computed for all pairs of orbits and discretized dates, and the associated overall velocity changes are stored in a cost matrix; (ii) then, the problem of visiting all RSOs is cast as a search problem, and the solution space is explored through an A${}^{*}$ algorithm. The transfer strategy exploits intermediate drift orbits to increase the differential precession due to the $J_2$ harmonic of the Earth’s gravitational potential. Moreover, the A${}^{*}$ procedure leverages a heuristic function based on a modified version of the Held–Karp algorithm, which is proven to be admissible and consistent, meaning that the optimal solution is always reached. The proposed strategy is integrated within a flexible architecture, where operational constraints on phasing and servicing activities can be enforced as well. Finally, the methodology at hand is successfully applied to a case study from the literature involving three successive missions, in charge of visiting 5 RSOs each. Different discretization grids are considered, and the results are compared in terms of overall velocity change and computational time. Full article

Objective: The objectives of this study were to characterise induction-phase regional cerebral oxygen saturation (rSO₂)–mean arterial pressure (MAP) dynamics during near-infrared spectroscopy (NIRS)-guided selective carotid endarterectomy (CEA) and to examine whether the Awake→Intubated pressure–oxygenation pattern may represent an early adjunctive physiological signal of subsequent cross-clamp-related ipsilateral cerebral desaturation. Methods: In this retrospective observational cohort study, 322 consecutive elective CEAs managed with an NIRS-guided selective shunting protocol between October 2019 and February 2025 were analysed, after excluding patients considered for routine pre-emptive shunting because of contralateral internal carotid artery occlusion or ≥70% stenosis. Standardised MAP and bilateral rSO₂ values were extracted at the Awake, Intubated, and Clamp stages, defined as 3 min after carotid cross-clamping. Awake→Intubated ipsilateral ΔrSO₂/ΔMAP was evaluated as a continuous, exploratory pressure–oxygenation index, with MAP–rSO₂ directional change classified as concordant or discordant. Clamp-related desaturation was defined as a ≥20% ipsilateral rSO₂ decrease from Awake to Clamp. Discrimination and adjusted associations were evaluated using receiver operating characteristic analysis and multivariable logistic regression, respectively. Results: Clamp-related ≥20% ipsilateral rSO₂ desaturation occurred in 43 patients (13.4%). The Awake→Intubated ipsilateral ΔrSO₂/ΔMAP ratio differed significantly between patients with and without ≥20% desaturation and showed significant discrimination on receiver operating characteristic analysis, with an area under the curve (AUC) of 0.799 (95% confidence interval [CI] 0.723–0.876; p < 0.001). Concordant pressure–oxygenation change was more frequent among patients with ≥20% desaturation (31/43, 72.1%), whereas discordant change predominated among those without desaturation (228/279, 81.7%; p < 0.001). In multivariable analysis, Awake→Intubated ipsilateral ΔrSO₂/ΔMAP remained associated with clamp-related ≥20% desaturation after adjustment (adjusted odds ratio [OR] 1.63, 95% CI 1.15–2.33; p = 0.006), along with symptomatic presentation and 50–69% contralateral stenosis. Postoperative stroke occurred in 4/322 patients (1.2%), and no 30-day mortality occurred. Conclusions: During NIRS-guided selective CEA, induction-phase rSO₂–MAP dynamics were associated with subsequent cross-clamp-related ipsilateral cerebral desaturation. As the outcome was a NIRS-defined desaturation rather than an independent clinical, neurological, or imaging endpoint, these findings indicate association with a surrogate marker rather than prediction of clinically relevant cerebral ischaemia. The Awake→Intubated ΔrSO₂/ΔMAP ratio and directional pressure–oxygenation pattern may represent early adjunctive physiological signals associated with clamp-related desaturation. These findings are hypothesis-generating and require prospective validation with systematic multimodal monitoring. Full article

Alzheimer’s disease (AD) is a serious neurodegenerative disorder that can severely affect behavior and thinking patterns, and is accompanied by frequent memory loss. The early diagnosis of AD is essential, as this can benefit the patient, but detecting AD is a complex process due to the nature of its associated clinical data. Electroencephalography (EEG) serves as a promising and cost-effective technique for analyzing AD-related brain activity patterns. In this work, a consolidated framework for detecting AD using EEG signals and hybrid models is proposed that uses a dataset that is available online. For the feature extraction module, five efficient techniques—Principal Component Analysis (PCA), Kernel Partial Least Squares (KPLS), Kriging Model, Isomap, and K-means clustering—are used. For feature selection, with the help of biomimetics-based concepts, three efficient algorithms are used: hybrid Cuckoo Search Optimization–Rat Swarm Optimization (CSO-RSO), Zebra Optimization (ZOA), and hybrid Gravitational Search Algorithm–Particle Swarm Optimization (GSA-PSO). Four interesting hybrid classifiers are utilized here to detect AD using EEG signals—hybrid Extreme Learning Machine–Adaboost (ELM–Adaboost), hybrid Classification and Regression Trees–Adaboost (CART–Adaboost), and hybrid weighted broad learning system-based Adaboost (HWBLSA), followed by a hybrid machine learning classification model with a soft voting technique—and, finally, these are compared with other standard machine learning classifiers. The highest classification accuracy of 98.71% is found when the Kriging Model feature extraction concept is combined with the hybrid GSA-PSO feature selection method and classified with the ELM–Adaboost classifier. Full article

Lipolysis is an interfacial reaction. Lecithins are natural emulsifiers containing a mixture of phospholipids (PL). Lecithin composition can be modified via enzymatic hydrolysis of PLs to produce lysophospholipids (LPL). The quantities of PL and LPL and the PL/LPL ratio are related to the emulsifying properties and interfacial activity of digestive lipases. This study aims to: (i) produce oil-in-water nanoemulsions of rapeseed oil (RSO) with soybean lecithin (SBL) and hydrolyzed lecithin (HL) at different concentrations and homogenization pressures and measure the mean droplet diameter (MDD) and polydispersity index (PdI) by dynamic light scattering; (ii) hydrolyze the emulsions in vitro with intestinal extracts of rainbow trout and estimate the degree of hydrolysis of lipids (DH) by the pH-stat method; and (iii) model the results on MDD, PdI, and DH through the response surface methodology (RSM). When HL was used as an emulsifier, DH, MDD, and PdI were fitted to polynomial quadratic, two-factor interaction, and linear models, respectively. MDD, PdI, and DH were fitted to polynomial quadratic SBL models. The optimal conditions were emulsifier concentrations of 0.45% and 0.76% w/w and homogenization pressures of 10,790 and 10,781 psi for HL and SBL, respectively. Under these conditions, DH = 34.9% and 33.08%, MDD = 241.9 and 543.6 nm, and PdI = 0.29 and 0.52 for HL and SBL, respectively. Full article

Solar photovoltaic (PV)-powered electric vehicles (EVs) have gained greater significance in the present-day era of transportation across the globe. This proposed work presents an analysis of a five-level reduced switched-capacitor multilevel inverter (RSC-MLI)-powered permanent magnet synchronous motor (PMSM) drive for solar PV-powered battery vehicles enabled by a rat swarm optimization (RSO) maximum power point tracking (MPPT) control mechanism. The system proposed in this paper integrates solar PV arrays and battery storage systems for efficient power transfer to EVs for propulsion. In order to achieve fast, accurate tracking of the optimal maximum power point, the RSO technique is used. A five-level RSC-MLI is used in this study, which enables boosting the voltage and lowering switching losses in the system. The performance of the PMSM is further analyzed to obtain constant parameters, such as the velocity and torque of the electric vehicle. Full article

There is increasing interest in space domain awareness (SDA), motivating the use of non-traditional sensors for space surveillance. One such sensor is the Buckland Park Stratospheric–Tropospheric (BPST) very high frequency (VHF) radar, which has demonstrated an ability to detect over 2000 resident space objects (RSO) daily. A by-product of the RSO observations is the measurement of ionospheric group retardation, which can be used to estimate the total electron content (TEC) between the ground and the satellite altitude. This paper describes the use of BPST radar observations of Starlink satellites to measure vertical TEC (vTEC) from the ground to 490 km and from the ground to 560 km. The variation in BPST radar vTEC is demonstrated for both geomagnetically quiet and storm periods. The results are combined with global ionospheric TEC maps to calculate the ratio of the ionospheric to plasmaspheric (or LEO to GPS) vTEC. This allows investigation of the diurnal and annual variation in the LEO to GPS vTEC for the radar location at a temporal resolution unavailable to LEO satellite-based measurements. The results indicate that the RMS uncertainty of the BPST radar vTEC estimates is 0.41 TEC units (TECU), comparing favorably with the ≈2 TECU RMS uncertainty typically measured by GNSS receivers. The technique described in this paper may be applied to any ST or boundary layer (BL) radar without the need for hardware changes. Full article

Background: Cardiopulmonary bypass (CPB) is an essential technique for cardiac surgery but significantly increases the risk of perioperative neurological complications. Electroencephalography (EEG) enables real-time monitoring of brain function and provides sensitive biomarkers for early detection of cerebral injury. However, a systematic synthesis of how CPB-related physiological, pharmacological, and technical factors influence EEG signals, and how these insights can be integrated into clinical decision-making, is still lacking. Objective: To systematically review the effects of temperature management, mean arterial pressure (MAP), hemodilution, anesthetic agents, embolization, and systemic inflammatory response during CPB on EEG parameters (including frequency bands, Bispectral Index (BIS), quantitative EEG metrics such as burst suppression ratio (BSR), spectral edge frequency (SEF), etc.), and to evaluate the associations between EEG changes and postoperative delirium (POD) and stroke. Methods: Following the PRISMA 2020 guidelines, we searched PubMed, Web of Science, and related databases for original English-language articles published between February 1974 and September 2025. Inclusion criteria: adult patients (≥18 years) undergoing cardiac surgery with CPB and intraoperative EEG monitoring (raw or processed). Exclusion criteria: reviews, case reports, animal studies, pediatric populations, and articles with inaccessible full texts. Two reviewers independently screened the literature and extracted data; a narrative synthesis was performed. Results: Fifty-one studies were included. Main findings: (1) Hypothermia: BIS decreases linearly with temperature (≈1.12 units/°C); electrocerebral silence occurs during deep hypothermic circulatory arrest; EEG recovery dynamics during rewarming predict POD. (2) MAP and cerebral perfusion: The rate of MAP decline (≥0.66 mmHg/s) is a stronger predictor of EEG abnormalities than the absolute MAP value; under fixed pump flow, some patients exhibit coexisting cerebral overperfusion and metabolic suppression. (3) Hemodilution: Maintaining hemoglobin ≥9.4 g/dL prevents EEG slowing; a drop below 9.2 g/dL significantly increases the risk of slowing. A ≥10% decrease in regional cerebral oxygen saturation (rSO₂) is associated with a 1.5-fold increased risk of burst suppression. (4) Anesthetic agents: Propofol maintains flow-metabolism coupling, and BSR reflects deep anesthesia better than BIS; sevoflurane and isoflurane impair autoregulation and suppress EEG. (5) Embolization and inflammation: EEG epileptiform discharges increase the risk of POD five-fold; a decrease in LIR predicts stroke (AUC 0.771) and POD (AUC 0.779); persistent EEG changes increase the risk of POD 2.65-fold. Conclusions: CPB-related factors affect EEG signals through distinct mechanisms, and specific EEG patterns (slowing, burst suppression, asymmetry, epileptiform discharges) are significantly associated with postoperative neurological complications. Multimodal monitoring (EEG + cerebral oximetry + hemodynamics) with clear intervention thresholds facilitates individualized brain protection. Future interventional studies using real-time EEG feedback are needed to confirm improvements in long-term neurological outcomes. Full article

The continuous growth of the population of Resident Space Objects (RSOs) poses increasing challenges for Space Situational Awareness (SSA), particularly in terms of detection capability and collision risk mitigation. Ground-based radar systems represent a primary class of remote sensing instruments for RSO observation; however, their deployment is often constrained by cost and infrastructure requirements. In this context, the reuse of existing large radio astronomy facilities as radar receivers offers an innovative and potentially cost-effective alternative. This paper presents a fully model-based feasibility study of S-band bi-static radar observations of RSOs using the Sardinia Radio Telescope (SRT) as a high-sensitivity ground-based receiver. The analysis is entirely analytical and is conducted in the absence of experimental radar measurements. A bi-static radar equation framework is adopted to evaluate received signal power and the resulting signal-to-noise ratio (SNR) as functions of target size, range, and observation geometry. The model explicitly incorporates thermal noise, integration time and target dynamics, radio-frequency interference (RFI), atmospheric and environmental clutter contributions, and the realistic antenna radiation pattern of the SRT through a Gaussian beam approximation. Detection thresholds, maximum observable ranges, and performance envelopes are derived for representative RSO dimensions, and the impact of off-boresight reception on detectability is quantified. The results define the operational conditions under which RSOs may be detected in an S-band bi-static configuration and demonstrate the potential of the SRT as a non-conventional ground-based instrument for space object observation, supporting future developments in SSA and space debris monitoring strategies. Full article

The adjustable speed hydro-generating unit has a complex three-phase alternating current excitation structure. The existing rotor winding short circuit (RWSC) fault diagnosis methods are generally difficult to use to locate the fault location and identify the severity of the fault. Therefore, an offline diagnosis method for the internal RWSC of an adjustable speed hydro-generating unit is proposed in this paper. Firstly, after the unit is shut down, the low-voltage pulse signal is repeatedly injected into the rotor winding by the pulse generator. By comparing and analyzing the voltage response characteristics under different types of short circuit faults, an identification method of rotor winding short circuit fault type and fault phase based on detecting the reverse polarity sub-spike is proposed. Furthermore, the short circuit fault point can be accurately located by combining ensemble empirical mode decomposition (EEMD) with the Teager energy operator (TEO). Finally, the fault factor is constructed based on the area between the characteristic waveform and the zero line, and the quantitative evaluation of the severity of the short circuit fault is realized based on this. The effectiveness of the proposed fault diagnosis and location method is verified by the simulation results. Full article

Conventional cardiopulmonary resuscitation (CPR) is guided primarily by process metrics that do not directly quantify cerebral hemodynamics or perfusion. Near-infrared spectroscopy (NIRS) provides continuous, non-invasive monitoring of regional tissue oxygenation and has emerged as a candidate modality for physiologic feedback during low-flow states. However, CPR applications vary across devices and signal processing. This scoping review maps how NIRS has been implemented during conventional CPR in humans and porcine models, with emphasis on instrumentation characteristics, signal processing, acquisition bandwidth, artifact handling, physiologic associations, and feasibility constraints. From 1048 records, 39 studies met the inclusion criteria. Most used forehead-based cerebral rSO₂ monitoring (30/39). Rising cerebral oxygenation trajectories were consistently associated with return of spontaneous circulation (ROSC). In contrast, persistently low or non-increasing patterns were associated with non-ROSC, and absolute thresholds varied substantially across devices and studies. A minority of investigations derived compression-rate or waveform features from hemoglobin signals. Feasibility findings emphasized rapid probe placement without interrupting compressions but highlighted motion artifact, workflow constraints, and incomplete acquisition reporting. Overall, during conventional CPR, NIRS primarily serves as a dynamic monitor of oxygenation trends rather than a validated prognostic tool. Emerging waveform-based and hemodynamic analyses suggest the potential to evaluate CPR efficiency using perfusion-responsive optical features. Full article

Unmanned Aerial Vehicle (UAV) computing clusters face severe operational constraints due to limited computing capabilities and battery capacities, which complicate the simultaneous optimization of low offloading latency, long task endurance, and high cluster efficiency. To address these challenges, this paper proposes a Multi-Objective Reinforcement Learning framework based on Latency and Power Balance (MORL-LAPB). Instead of broad situational awareness descriptions, our framework directly combines a reward-shaping reinforcement learning algorithm with an evolutionary mechanism to construct a closed-loop optimization paradigm. Crucially, in this context, ’balancing’ extends beyond traditional computational workload distribution; it represents a joint optimization that balances task allocation to ensure short service delays while simultaneously equating the energy depletion rates across UAV nodes to maximize overall cluster efficiency and operational duration. By efficiently identifying Pareto optimal trade-offs, MORL-LAPB dynamically regulates UAV energy allocation and computational resource scheduling. Experimental results demonstrate that, compared to RSO, NSO, and DRLSO baselines, the proposed MORL-LAPB significantly reduces offloading latency, extends effective task execution duration, and improves cluster energy efficiency. The framework offers flexible adaptability and long-term sustainability for diverse operational scenarios under strict multi-objective constraints. Full article

The proliferation of Resident Space Objects (RSOs), including satellites, rocket bodies, and debris, poses escalating challenges for Space Situational Awareness (SSA). Optical light curves capture temporal brightness variations influenced by factors such as attitude variation, viewing geometry, and surface properties. When appropriately processed and analyzed, these data can support RSO characterization and classification. This paper presents a scoping review of machine learning (ML) and deep learning (DL) methods for RSO classification using light-curve data. From 297 peer-reviewed studies published between 2014 and 2025, a screened subset of 29 works is selected for detailed methodological comparison. We trace the methodological evolution from handcrafted feature engineering toward convolutional, recurrent, and self-supervised models that learn representations directly from photometric time series. An analysis of three publicly accessible databases, Mini Mega TORTORA, Space Debris Light-Curve Database, and Ukrainian Database, reveals pronounced class imbalance, with payloads comprising over 80% of observations. While models trained on simulated data routinely achieve 95 to 99% accuracy, performance on measured light curves degrades to 75 to 92%, exposing a persistent gap between simulation and observation. We further identify data scarcity, repeated observations of the same objects, and inconsistent evaluation protocols as key barriers to reproducible benchmarking. Future progress will require benchmark-ready, sensor-aware datasets spanning diverse orbital regimes and viewing geometries, alongside physics-informed and transfer-learning approaches that improve robustness across sensors and between synthetic and observational domains. Full article

Resident Space Objects (RSOs) are a collection of both man-made and natural objects in near-Earth space. Given their large orbital velocities and rapidly increasing quantity, they pose a collision threat to space assets, necessitating better Space Situational Awareness (SSA). SSA begins with detecting these objects in the first place and can be accomplished by using space-based optical images, such as images from the Fast Auroral Imager (FAI) on the CASSIOPE satellite. However, these short-exposure images are low in resolution and contain various artifacts and noise, posing challenges to traditional source detection methods. Furthermore, the background stars and RSOs both move due to the satellite’s non-constant attitude, posing a challenge for tracking algorithms. Nevertheless, these images are a valuable source of SSA data, which can be used to develop algorithms to ultimately augment the capabilities of current SSA systems. Such augmentations include performing RSO detection as a simultaneous function on existing spacecraft or allowing dedicated SSA payloads to detect RSOs during slew maneuvers, where background stars will similarly move. This paper proposes a rules-based RSO tracking algorithm tailored for low-resolution, short-exposure, space-based imagery with non-constant spacecraft attitude, addressing the challenge of distinguishing RSOs from background stars that are also in motion. This method consists of a custom thresholding algorithm, along with the Iterative Closest Point (ICP) algorithm to correct the motion of the background stars, followed by a tracking algorithm to finally detect the RSOs within the imagery, returning their pixel positions. The algorithm was tested on an 878-image dataset, achieving 79% precision and 71% recall, while detecting 87% of all RSOs at least once. These results prove that the algorithm is a feasible method for detecting RSOs in non-constant-attitude imagery, providing a means to develop current SSA systems. Full article

Background/Objectives: Delayed cerebral ischemia (DCI) represents a major cause of morbidity and mortality after aneurysmal subarachnoid haemorrhage (aSAH). Early identification of developing cerebral ischemia is essential for timely prevention of DCI. Near-infrared spectroscopy (NIRS) provides continuous, non-invasive bedside monitoring of regional cerebral oxygen saturation (rSO₂); however, its clinical value in patients with aSAH has not yet been fully established. The primary objective of this study was to investigate whether NIRS-detected rSO₂ desaturation can serve as an early indicator of cerebral vasospasm (CV) and predict the occurrence of DCI. Secondary objectives were to examine the associations between rSO₂ changes and other cerebral deterioration events, length of intensive care unit stay, functional outcome, and in-hospital mortality. Methods: This prospective, single-centre study included 30 patients with aSAH admitted to the intensive care unit (ICU) of Riga East University Hospital between January 2019 and January 2023. Bilateral frontal near-infrared spectroscopy (NIRS) monitoring (Covidien INVOS™ 5100C-PB) was initiated within 72 h after ictus and continued for up to 7 days. Cerebral desaturation was defined as a >20% reduction from baseline (BL) or an absolute regional cerebral oxygen saturation (rSO₂) value < 50% lasting ≥30 min. CV and DCI were diagnosed according to established clinical and radiological criteria. Receiver operating characteristic (ROC) analysis was performed to evaluate the sensitivity and specificity of rSO₂ thresholds for the detection of CV, DCI, and other cerebral deterioration events. Results: CV occurred in 10 patients (33%); however, only four cases were detected during the NIRS monitoring period. NIRS demonstrated very high sensitivity (97.5%) but extremely low specificity (6%) for the early detection of CV. In contrast, diagnostic accuracy for DCI was high. An absolute rSO₂ cut-off value of 52% yielded a sensitivity of 97.5% and a specificity of 95%, whereas a decrease of ≥26% from baseline (BL) demonstrated a sensitivity of 98% and a specificity of 93%. Significant rSO₂ reductions were also observed during aneurysm re-rupture, hydrocephalus, cerebral edema, and postoperative ischemia; however, the sensitivity of NIRS for detecting these events was negligible. Patients with ≥20% desaturation tended to have longer ICU stays, and lower mean rSO₂ values as well as greater desaturation were associated with poorer functional outcomes as assessed by the modified Rankin Scale. Patients who died exhibited more pronounced rSO₂ decreases and less recovery compared with survivors. Conclusions: In this cohort, NIRS demonstrated limited specificity for the early detection of CV but showed strong associations with DCI and neurological outcome. NIRS may be useful as a non-invasive adjunct to multimodal neuromonitoring rather than as a stand-alone diagnostic tool for cerebral vasospasm. Larger, prospective studies incorporating standardized imaging protocols and optimized rSO₂ thresholds are required to more clearly define the role of NIRS in the management of aSAH. Full article