Search Results (30)

Accurate rainfall monitoring is vital for hydrology and environmental sensing. This study presents a physics-aware deep learning framework using E-band (71–86 GHz) commercial microwave links (CMLs). Using the extensive urban CML dataset and methodology, a bi-directional Long Short-Term Memory (Bi-LSTM) model is developed to classify wet and dry periods under a temporal generalization framework across heterogeneous link configurations. The approach integrates physical signal decomposition, including baseline estimation, gaseous attenuation correction, and wet antenna attenuation (WAA) modeling, with sequence-based learning. Results demonstrate that the temporal deep learning model outperforms classical threshold-based and physical k–R approaches when evaluated over independent temporal validation blocks, effectively reducing sensitivity to path-length-related variability on heterogeneous paths. The model maintains stable performance (loss < 3%) under moderate signal-level noise. SHapley Additive exPlanations (SHAP) confirm the model relies on physical features, such as signal volatility and temporal trends, to reliably differentiate rainfall from WAA. This framework highlights the potential of E-band infrastructure as a distributed sensing network for integrated sensing and communication (ISAC) architectures. Full article

IEEE 802.11be, commercially known as Wi-Fi 7, extends wireless local area network (WLAN) capability through wider channel bandwidths, higher-order modulation, and tri-band operation. However, realised indoor performance is still strongly affected by radio propagation conditions. This study presents a controlled empirical assessment of Wi-Fi 7 behaviour in a multi-storey university building by examining throughput and received signal strength (RSS) across the 2.4 GHz, 5 GHz, and 6 GHz bands using a single-link measurement setup. Six experimental scenarios were used to examine distance variation, wall penetration, line-of-sight (LOS) obstruction, floor separation, antenna orientation, and microwave interference. The measured RSS values were compared with the free-space, two-ray ground reflection, and log-distance shadowing models using mean absolute error (MAE). Six experimental scenarios were designed to isolate dominant indoor impairments, including distance variation, wall penetration, line-of-sight obstruction, floor separation, antenna orientation, and microwave interference. Measured RSS values were evaluated against free-space, two-ray, and log-distance shadowing models using mean absolute error as the comparison metric. Results show that 2.4 GHz retains greater penetration at lesser capacity, while 6 GHz offers the maximum short-range throughput under clear line-of-sight conditionsbut rapidly deteriorates with structural attenuation. Performance in all bands is greatly diminished by multi-wall blockage and line-of-sight loss. A single propagation model cannot adequately capture the divergence introduced by increasing distance and indoor attenuation, while short-range line-of-sight conditions more closely resemble deterministic predictions in terms of measured RSS alignment. Overall, the results highlight the trade-off between Wi-Fi 7’s capacity and coverage, and provide helpful advice for choosing frequencies, positioning access points, and organizing indoor coverage. The research findings provide insights into the practical deployment of next-generation Wi-Fi in multi-story buildings and residential houses. Full article

Commercial Microwave Links (CMLs) have emerged as one of the most widely utilized opportunistic sensors for rainfall monitoring. However, rainfall retrieval using microwave links continues to face significant challenges in terms of accuracy, particularly for shorter path lengths. In recent years, a statistical approach has been demonstrated to effectively enhance retrieval accuracy. Concurrently, studies have shown that the selection of localized parameters can further optimize CML retrieval results. In this study, we evaluate and calibrate the probabilistic–statistical retrieval method proposed in a previous study for the Chinese region. Following their framework, we replace the global parameters with a Gamma rainfall distribution derived from local rain gauge observations, making the method more suitable for local climatic conditions. To validate the effectiveness of the improved method, we deployed three experimental microwave links with path lengths ranging from 420 m to 3.50 km and simultaneously recorded path attenuation along with rainfall data from surrounding rain gauges. The results show that the coefficient of determination and correlation coefficient between the proposed method and rain gauge observations reach 0.85 and 0.86, respectively, indicating a significant improvement over traditional models. The calibrated method performs particularly well during high-intensity rainfall events, demonstrating the importance of parameter localization for improving retrieval accuracy. Full article

Wireless Power Transfer (WPT) technologies are rapidly maturing, offering alternatives to traditional wired connections in applications ranging from consumer electronics to industrial automation. This review provides a technical analysis of WPT methodologies published between 2010 and 2025, explicitly distinguishing between non-radiative near-field techniques (specifically Inductive Power Transfer [IPT] and Capacitive Power Transfer [CPT]) and radiative far-field systems (Microwave Power Transfer [MPT] and Laser Power Transfer [LPT]). Unlike previous reviews that categorize primarily by coupling mechanism, this paper proposes a novel multi-parametric classification framework incorporating efficiency, alignment sensitivity, and emerging operational paradigms such as AI-optimized tuning and acoustic transfer. The analysis evaluates the engineering trade-offs between short-range, high-efficiency inductive systems and long-range, lower-efficiency radiative links. Furthermore, the paper identifies critical technical barriers to commercialization, specifically focusing on electromagnetic compatibility (EMC), biological safety (SAR) limits, and end-to-end system efficiency. Finally, the review extends beyond the physics to provide a rigorous economic analysis of the Total Cost of Ownership (TCO) for electric vehicle infrastructure and industrial IoT, highlighting the strategic viability of WPT in future smart grids. Full article

Rainfall intensity prediction based on commercial microwave link data has received significant attention in recent years due to the higher spatial resolution and lower energy consumption. However, the predictive performance is inferior to the model based on meteorological data by reason of the high noise in commercial microwave link data, further exacerbated by the imbalance in the number of samples across different rainfall intensities. Hence, a cost-sensitive rainfall intensity prediction model (CSRFP) is proposed to achieve better predictive performance in high-noise commercial microwave link data. First, the spatiotemporal scene information is encoded, and its weights are trained to provide the model with correlations between signal data from different stations, which helps the model to better capture potential patterns between the data and thus reduce the effect of noise. Next, the rainfall cross-entropy loss based on the rainfall distribution provides the model with the probability of different rainfall intensities occurring and back-calculates the signal attenuation at a specific rainfall intensity, assigning more reasonable weights to different samples considering signal attenuation, which makes the model cost-sensitive and can address the class imbalance problem. Extensive experiments are carried out on high-noise communication data and imbalanced rainfall data in Fuzhou. Compared to typical prediction methods such as RNN applied to rainfall and communication data, CSRFP improves Recall, Precision, AUC_ROC, AUC_PR and F₁ and Accuracy by approximately 19%, 37%, 8%, 22%, 30%, and 17%, respectively. Significantly, the model’s prediction accuracy for heavy rain with the smallest number of samples improves by about 13%. Full article

As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance. Full article

Obtaining accurate rainfall measurements is highly important in urban areas, having a significant impact on different aspects in city life. Opportunistic rainfall sensing utilizing measurements collected by existing microwave and mmWave-based wireless networks has been researched in the last two decades and can be considered as an opportunistic integrated sensing and communication (ISAC) approach. In this paper, we compare two methods that utilize received signal level (RSL) measurements obtained by an existing smart-city wireless network deployed in the city of Rehovot, Israel, for rain estimation. The first method is a model-based approach using the RSL measurements from short links, in which two design parameters are calibrated empirically. This method is combined with a known wet/dry classification method, which is based on the rolling standard deviation of the RSL. The second method is a data-driven approach, based on a recurrent neural network (RNN), which is trained to estimate rainfall and classify wet/dry periods. We compare the results of rainfall classification and estimation from both methods and show that the data-driven approach slightly outperforms the empirical model and that the improvement is most significant for light rainfall events. Furthermore, we apply both methods to construct high-resolution 2D maps of accumulated rainfall in the city of Rehovot. The ground-level rainfall maps constructed over the city area are compared for the first time with weather radar rainfall maps obtained from the Israeli Meteorological Service (IMS). The rain maps generated by the smart-city network are found to be in agreement with the average rainfall depth obtained from the radar, demonstrating the potential of using existing smart-city networks as a source for constructing 2D high-resolution rainfall maps. Full article

In this work, a novel electrically reconfigurable phase shifter based on a half-mode substrate integrated waveguide (HM-SIW) is proposed. SIW is a guided transmission line topology, and by using half-mode excitation, a smaller size can be achieved. Phase shifters are electronic devices that change the phase of transmission for a wide range of applications, including inverse scattering and sensing. The tunability of PIN diodes is applied here to achieve a reconfigurable design. The proposed single-layer structure does not require extra wiring layers for the bias circuit on the suggested printed circuit board. Its principle consists in the integration, in the HM-SIW, of three parallel lines, each connecting the edge of the HM-SIW and linked to a PIN diode and a radial stub. Here we present the results of measurements for a frequency band from 4.5 to 7 GHz that demonstrate how the experiment agrees with simulations. Insertion loss was less than −10 dB, and port coupling was less than −2 dB for both simulation and measurement solutions. The proposed half-mode structure is around half the size of a typical SIW line. With the proposed design, the seven states of the PIN diodes can be validated (ON and OFF), with a wide band adaptation and a relatively constant phase difference across a broad frequency range (44%). A key benefit of the proposed design for a microwave component is the reduction of extra biasing layers for the PIN diodes. This is in addition to the reduced size of the transmission line compared to a commercial SIW. In the annexed section, simulation software is used for a more comprehensive analysis involving more phase shift values and parametric studies. Full article

Brewer’s spent yeast (BSY) mannoproteins have been reported to possess thickening and emulsifying properties. The commercial interest in yeast mannoproteins might be boosted considering the consolidation of their properties supported by structure/function relationships. This work aimed to attest the use of extracted BSY mannoproteins as a clean label and vegan source of ingredients for the replacement of food additives and protein from animal sources. To achieve this, structure/function relationships were performed by isolating polysaccharides with distinct structural features from BSY, either by using alkaline extraction (mild treatment) or subcritical water extraction (SWE) using microwave technology (hard treatment), and assessment of their emulsifying properties. Alkaline extractions solubilized mostly highly branched mannoproteins (N-linked type; 75%) and glycogen (25%), while SWE solubilized mannoproteins with short mannan chains (O-linked type; 55%) and (1→4)- and (β1→3)-linked glucans, 33 and 12%, respectively. Extracts with high protein content yielded the most stable emulsions obtained by hand shaking, while the extracts composed of short chain mannans and β-glucans yielded the best emulsions by using ultraturrax stirring. β-Glucans and O-linked mannoproteins were found to contribute to emulsion stability by preventing Ostwald ripening. When applied in mayonnaise model emulsions, BSY extracts presented higher stability and yet similar texture properties as the reference emulsifiers. When used in a mayonnaise formulation, the BSY extracts were also able to replace egg yolk and modified starch (E1422) at 1/3 of their concentration. This shows that BSY alkali soluble mannoproteins and subcritical water extracted β-glucans can be used as replacers of animal protein and additives in sauces. Full article

The humidity in the atmosphere plays a crucial role in a wide range of atmospheric processes determined by the water-vapor concentration in the air. The accuracy of weather forecasts is largely dictated by the humidity field measured at low atmospheric levels. At the near-surface level, the absolute humidity variations can be large due to the variability of land cover (LC). Cities are one of the primary LCs which have a substantial impact on the humidity field. Large urban areas are expanding, causing a significant change in the near-surface humidity field. Current measurement tools, however, do not satisfactorily assess the cities’ effects on the humidity field. This paper presents an innovative method for high-resolution humidity measurements based on the cellular network. Here, the humidity field around Tel Aviv was retrieved from the cellular network during the summer of 2017. The results show a well-noticed impact of the city and other LC types on the humidity field over the Tel Aviv metropolitan area. The method presented here can offer an improved description of the humidity field at the city-canopy level and therefore provide a better assessment of the urban/LC effects on the environment, atmospheric modeling, and particularly on clouds/rain development. Full article

Since the 1990s, mobile telecommunication networks have gradually become denser around the world. Nowadays, large parts of their backhaul network consist of commercial microwave links (CMLs). Since CML signals are attenuated by rainfall, the exploitation of records of this attenuation is an innovative and an inexpensive solution for precipitation monitoring purposes. Performance data from mobile operators’ networks are crucial for the implementation of this technology. Therefore, a real-time system for collecting and storing CML power levels from the mobile phone operator “Telecel Faso” in Burkina Faso has been implemented. This new acquisition system, which uses the Simple Network Management Protocol (SNMP), can simultaneously record the transmitted and received power levels from all the CMLs to which it has access, with a time resolution of one minute. Installed at “Laboratoire des Matériaux et Environnement de l’Université Joseph KI-ZERBO (Burkina Faso)”, this acquisition system is dynamic and has gradually grown from eight, in 2019, to more than 1000 radio links of Telecel Faso’s network in 2021. The system covers the capital Ouagadougou and the main cities of Burkina Faso (Bobo Dioulasso, Ouahigouya, Koudougou, and Kaya) as well as the axes connecting Ouagadougou to these cities. Full article

We report a fiber-optic 10 GHz frequency transfer technique based on an optical–electronic joint phase compensator. A highly stable frequency signal at 10 GHz was transferred in a 50-km long fiber link by using this technique. Two key parameters of the frequency dissemination, the timing fluctuation and frequency stability were both measured. The experimental results show the root-mean-square timing fluctuation of the transferred microwave is about 103 fs within 10,000 s, and the frequency stability for the transmission link is 2.2 × 10⁻¹⁴ at 1 s and 8.5 × 10⁻¹⁷ at 2000 s. The technique proposed in this paper provides a powerful tool which can be used to transfer atomic clocks (e.g., commercial H-master and Cs clocks) in a long fiber link. Full article

The demand for accurate rainfall rate maps is growing ever more. This paper proposes a novel algorithm to estimate the rainfall rate map from the attenuation measurements coming from both broadcast satellite links (BSLs) and commercial microwave links (CMLs). The approach we pursue is based on an iterative procedure which extends the well-known GMZ algorithm to fuse the attenuation data coming from different links in a three-dimensional scenario, while also accounting for the virga phenomenon as a rain vertical attenuation model. We experimentally prove the convergence of the procedures, showing how the estimation error decreases for every iteration. The numerical results show that adding the BSL links to a pre-existent CML network boosts the accuracy performance of the estimated rainfall map, improving up to 50% the correlation metrics. Moreover, our algorithm is shown to be robust to errors concerning the virga parametrization, proving the possibility of obtaining good estimation performance without the need for precise and real-time estimation of the virga parameters. Full article

Carbotrace 480 is a commercially available fluorescent optotracer that specifically binds to cellulose’s glycosidic linkages. Herein, the use of Carbotrace 480 is reported as an analytical tool for linking cellulose content to hydrogel formation capability in defibrillated celluloses obtained from proprietary microalgae. Defibrillated celluloses obtained from acid-free hydrothermal microwave processing at low temperature (160 °C) showed poor hydrogel formation attributed to a low cellulose concentration as evidenced through the lack of Carbotrace fluorescence. High temperature (220 °C) processing afforded reasonable gels commensurate with a higher cellulose loading and stronger response to Carbotrace. Full article

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed. Full article