esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Technologies for Detecting Falls  
In the diagnosis and treatment of disorders, health monitoring is crucial. Fall detection devices, for example, can help detect anomalies and transmit real-time signals to health and social care professionals about the abnormalities [10]. Fall detection systems based on wearable devices have been increasingly popular in recent years due to various advantages, such as being lightweight, low-cost, energy-saving, and non-intrusive. In recent years, the growth of fall detection and prevention approaches has been a prevalent study area. For the development of such systems, a variety of methodologies are used [11]. Artificial intelligence (AI), Internet of Things (IoT), and cloud computing-based systems are the three primary categories in which these technologies fall.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### Above Earth's Surface Target Detection  
The capability to detect targets above the Earth's surface using GNSS-R data from TDS-1 was introduced in [138], with a proof of concept using UK TDS-1 data. Following that work, an investigation conducted by authors in [139] studied unusually bright reflected signals occurring at delays shorter than the specular reflection point over the Earth's surface. The authors examined seven possible causes of these anomalies and concluded that they were likely occurring due to signals being reflected from objects located above the Earth's surface. The positions of these objects were determined using delay and Doppler information and appropriate geometry assumptions. The satellite database from the Union of Concerned Scientists (UCS) was then used to search for satellite objects matching the delay and Doppler conditions, resulting in the discovery of three matching objects. Simulation studies have covered the required calibrations for large bandwidth GNSS signals to detect maritime targets, such as ships [140]. These capabilities have also been studied for the detection of aerial vehicles crossing the GNSS field of view. The study in [141] presents the experimental results on the use of forward-scattered GPS signals to detect air targets.
# Erlang-U: Blocking Probability of UAV-Assisted Cellular Systems
## 3 UAV Dwelling Times
In [18], the authors analyze a network that uses UAVs to offload traffic from hotspots, considering that drones are connected to a power source from a ground station, and thus prevent the activation time of the drone from being conditioned by its energy consumption. The network performance is a function of the coverage probability, which depends on the length of the tether, the density of access points and the density of accessible buildings. In [19], the authors analyze a cellular system assisted by UAVsbased on stochastic geometry to find the coverage probability in both the access link and backhaul link. In particular, the downlink is analyzed considering that the distribution of the UAVs and the macro-base stations are modeled as two independent Poisson Point Processes, with line of sight links and non-line of sight links. The results show that the average performance depends strongly on the backhaul links, which are expected to be guaranteed. In [20], the authors simulate four frequency channel allocation algorithms in a multicellular network with the use of UAVs. The results show that the Forward-Looking Game (FLG) algorithm has a better performance in terms of the average capacity of the network when there are different values of SINR and the number of users. Note that these works have mainly focused on the coverage improvement provided by the drones but they have not studied the blocking probability reduction when drones are present in the region of interest. In view of this, we develop a mathematical model that considers the average times that the UAV is assisting the cellular system, which directly depends on the energy consumption of the drones, and the times that the drone is outside this region, where the only available channels are the ones provided by the base station. Furthermore, we consider these dwelling times to be exponentially distributed. This assumption is twofold, firstly, because we do not know of any measurements regarding these times, and the exponential distribution may be used as a first approximation to this environment, and secondly, this model can be easily extended to consider different statistic characteristics of these dwelling times. Specifically, if these times are found to have a Coefficient of Variation (CoV) lower or higher than 1 (the exponential distribution has a CoV = 1), this approximation can be extended to the Erlang and Hyper-Exponential distributions, respectively. This would only require minor modifications to the exponential model presented in this work.
## 2 Experiments and Analysis
### Experimental Uncertainty Analysis  
#### 2.3.1 Possible Errors  
* Errors from experimental equipment design.  
Due to frictional drag in the pipeline, there is a slight pressure loss of the jet. When designing the test device, the length of the pipeline should be minimized, and the pressure gauge should be installed near the nozzle. In order to avoid the influence of operating water tank size on the flow field, a suitable size is adopted. According to the research of [2; 17], this size will not affect the cavitation effect of the nozzle.
* Errors from experimental materials.  
To minimize the effect of working water medium on the cavitation effect, after the storage water tank is filled with water, allow it to settle for two hours before starting the experiment. The same batch of refractory bricks are used as impact targets to ensure that the surface of each refractory brick has the same characteristic, and soaking in water for one hour after cleaning the surface impurities to ensure that refractory bricks contain the same water content. The operating water tank is always kept full of water to ensure that the erosion effect is not affected by the free surface during the test. The depth of the nozzle underwater is also fixed to maintain a constant pressure around the nozzle outlet.
* Errors from experimental operation.  
At the beginning of the test, pump pressure is unstable. Therefore, after the pump attains stability, the jet pressure is quickly adjusted to reduce its effect on test results during pressure changes. When the pressure reaches the setting conditions and the work is stable, test timing is started to reduce the error caused by pressure fluctuations. As the test progresses, more and more debris is washed away, which may affect the effect of cavitation. Therefore, after a large amount of debris appears, the ambient water in the working water tank is replaced to reduce the effect of turbidity on the test results. To improve the accuracy of the test results, multiple tests are performed for each working condition, and the average result is taken.
* Errors from reading data.  
Due to the limitation of the accuracy level of the pressure gauge, there is a slight precision error. After the test, the morphology of erosion damage on the refractory brick is irregular. For the maximum erosion depth, multiple measurements with a vernier caliper are required to reduce measurement errors.
## 2 Preparation and Applications of Chitosan-Based Heterogeneous Catalysts
### Photocatalysts  
In addition to its application in organic synthesis, chitosan has been studied extensively to act as a high-capacity adsorbent, for the efficient removal of various contaminants from wastewater, such as anionic and cationic dyes as well as heavy metal ions. In a comparative review study for the adsorption and removal of pollutants using different materials, chitosan was demonstrated to be a more effective adsorbent than even activated carbon [56]. Heterogeneous photocatalysts, such as titanium dioxide (TiO\({}_{2}\)) and zinc oxide (ZnO), have also been proven to effectively degrade many aromatic compounds to safer end products such as CO\({}_{2}\) and water [57; 58]. Chitosan was also used to immobilize photocatalysts of zinc tetraamino phthalocyanine and Ag nanoparticles for ease recovery after photocatalysis [27]. Considering the abovementioned advantages of chitosan and photocatalysts, [PERSON] _et al._[57] first explored the combination of using TiO\({}_{2}\) together with chitosan for the synergistic treatment of dye-containing wastewater under visible light illumination. The as-prepared catalyst was coated onto glass plates for a convenience mean of wastewater treatment. Recent studies also exhibited multifunctional performance of chitosan and TiO\({}_{2}\) or ZnO in heterogeneous photocatalysis applications, including increased adsorption capacity and enhanced photocatalytic activity [59; 60; 61].  
Right after [PERSON] _et al._ successfully implemented the use of immobilized chitosan/TiO\({}_{2}\) catalysts for dye treatment, [PERSON] _et al._[60] proposed a simple bilayer system consisting of TiO\({}_{2}\) and chitosan onto a glass plate for the removal of phenol. The authors also showed that the addition of epoxidized natural rubber (ENR\({}_{50}\)) and phenol-formaldehyde resin in the coating formula improved the deposition properties of TiO\({}_{2}\) powder. The porous structure on as-prepared TiO\({}_{2}\) layers allowed better diffusion of the target pollutants as well as increased light penetration for photocatalytic reaction. Their later study utilized a similar system for the decolorization of reactive red 4 dye (RR4) solution [62]. A faster decolorization rate of RR4 by TiO\({}_{2}\)/chitosan/glass was observed and the rate was highly dependent on the TiO\({}_{2}\) loading and the initial pH of the solution.  
A hybrid photocatalyst combining chitosan and ZnO was also proposed [63]. The catalyst was prepared by chemical precipitation method and revealed high photocatalytic activity for degradation of methylene blue dye under UV irradiation. [PERSON] _et al_. [64] managed to synthesize TiO\({}_{2}\)/ZnO/chitosan nanocomposite thin films by entrapping zinc ions and nanosized TiO\({}_{2}\) in chitosan thin films using solution casting approach. High-resolution TEM results revealed that the TiO\({}_{2}\)/ZnO were well-dispersed and uniform nanocomposite with diameter of 5-15 nm in the chitosan films. The catalyst also demonstrated high photocatalytic activity for decolorization of methyl orange.
###### Abstract  
Millimeter-wave frequencies are crucial for meeting the high-capacity, low-latency demands of 5G communication systems, thereby driving the need for compact, high-gain antenna arrays capable of efficient beamforming. This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 \(\times\) 6 linear series-fed microstrip patch antenna array for 5G millimeter-wave communication operating at 28 GHz. The proposed antenna is fabricated on a low-loss Rogers RO3003 substrate and incorporates an integrated symmetric two-way microstrip power divider to ensure balanced feeding and phase uniformity across elements. The antenna achieves a simulated peak gain of 11.5 dBi and a broad simulated impedance bandwidth of 30.21%, with measured results confirming strong impedance matching and a return loss better than \(-\)20 dB. The far-field radiation patterns demonstrate a narrow, highly directive beam in the E-plane, and the H-plane results reveal beam tilting behavior, validating the antenna's capability for passive beam steering through feedline geometry and element spacing (\(\sim\)0.5A). Surface current distribution analysis confirms uniform excitation and efficient radiation, further validating the design's stability. The fabricated prototype shows excellent agreement with the simulation, with minor discrepancies attributed to fabrication tolerances. These results establish the proposed antenna as a promising candidate for applications requiring compact, high-gain, and beam-steerable solutions, such as 5G mm-wave wireless communication systems, point-to-point wireless backhaul, and automotive radar sensing.  
milimeter-wave; microstrip patch array; series-fed array; beamforming; passive beam steering; power divider integration; 5G communication; Ka-band (28 GHz) applications; high-gain antenna; surface current distribution; antenna array fabrication; directional radiation pattern 20252025142933
## 2 Results and Discussion
### Principal Components Analysis (PCA)  
A PCA analysis was performed to identify the relation between each fermentation process and the concentration of the seven marker volatile compounds. In Figure 5, the PCA is presented, which explained 71.2% of data in the first and second component. There was an affinity between the seven marker volatile compounds with aerobic and anaerobic fermentation treatment (Figure 5a). This is due to the fact that the microorganisms, such as yeast, _Lactobacillus_ spp., _Streptococcus_ spp., and _Enterobacteriaceae_ were anaerobic facultative, which means that they can develop with oxygen. Nevertheless, in an anaerobic environment, these microorganisms fermented the mucilage's sugars [37]. In another situation, this investigation demonstrated that fermentation in an atmosphere modified with CO2 had a negative relation with the seven marker volatile compounds (Figure 5b),due to a decrease in enzymatic activity from microorganism inactivation by the presence of CO\({}_{2}\)[25].  
2-Methylpyrazine (C2) showed a better affinity with aerobic fermentation at 24 and 48 h, because there was an inversely proportional relation between this compound and pH [48]. As for acetic acid (C1), its concentration was significantly increased in aerobic fermentation treatments by the heterogeneous aerobic microflora that produced lactic and acetic acid causing a decrease in the coffee mucilage pH, because simple sugars were consumed to produce organic acids [41].  
Volatile compounds had a tendency to decrease at 96 h of fermentation, as during the fermentation time, the volatile compounds decreased due to the reduction in precursor substrates and the increase in acid in the fermentation media [10], which explains why the 72 and 96 h aerobic fermentation process and the 96 h anaerobic fermentation process demonstrated a better affinity with acetic acid.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Time** & **pH** & **Acidity** \\ \hline Time & 1 & \(-\)0.83 *** & 0.90 *** \\ pH & \(-\)0.83 *** & 1 & \(-\)0.92 *** \\ Acidity & 0.90 *** & \(-\)0.92 *** & 1 \\ C1 & 0.41 ** & \(-\)0.62 *** & 0.59 *** \\ C2 & \(-\)0.33 * & 0.03 ns & \(-\)0.25 ns \\ C3 & \(-\)0.20 ns & \(-\)0.02 ns & \(-\)0.04 ns \\ C4 & \(-\)0.40 * & 0.16 ns & \(-\)0.34 * \\ C5 & \(-\)0.49 ** & 0.24 ns & \(-\)0.21 ns \\ C6 & \(-\)0.37 * & 0.11 ns & \(-\)0.14 ns \\ C7 & \(-\)0.13 ns & \(-\)0.05 ns & 0.19 ns \\ \hline \hline \end{tabular}  
* Not significant.
* Slightly significant. *
* Moderately significant. **
* Highly significant. C1: Acetic acid. C2: 2-methylpyrazine. C3: 2-furancarboxaldehyde. C4: 2-furanmethanol. C5: 2,6-dimethylpyrazine. C6: 5-methylfurfural. C7: 2-methoxy-4-vinylphenol.  
\end{table}
Table 2: Correlation matrix and significance level between variables and compounds.
## 2 Evaluation Methodology
### V2X Communication  
Vehicle to vehicle (V2V) communication aims to reduce the number of collisions between close vehicles circulating in the same road network [44; 45]. The main purpose is to send warning information through the vehicles, such as information about a sudden speed change in nearby vehicles, to help decision-making during navigation. In such a way, the emergency warning propagation message delay is significantly reduced over conventional approaches [36; 46; 47; 48]. On the other hand, vehicle to infrastructure (V2I) communication is proposed to optimize fuel consumption, vehicular pollution emission, and vehicular transit speeds. The infrastructure plays the coordinator role, gathering information on traffic and road conditions to suggest optimal behaviors to a determined group of vehicles [49; 50].
## 4 Thermal Design Investigations with the Validated CFD Model
### Effects of Airflow Velocity  
Air velocity plays a crucial role in the efficiency of air-cooling systems. In the conducted simulations, air speeds from 10 to 100 m/s were tested. The conditions for the ambient temperature and turbulence intensity were standardised at 288 K (15 \({}^{\circ}\)C) and 0.5%, respectively. The temperature variation of maximum temperature increments across the range of air speeds is presented in Figure 9. Generally, the increment in hotspot temperature relative to the ambient temperature decreased as the air speed increased. However, the rate of the temperature change relative to the air speed diminished at higher air velocities.  
It is noted that in all instances the temperature at the upstream end of the winding is marginally cooler than at the downstream end, attributed to the influence of airflow. This temperature differential becomes less pronounced with higher air speeds, owing to the diminished temperature increase.
## 5 Method
### Problem Modeling
#### 5.3.1 Rescue Area Model
At this time, it is necessary to transmit the computation task to the MEC server at the task edge layer for processing via the upstream, while 5G or 4G LTE networks all adopt orthogonal frequency division multiple access technology, and the data can be transmitted in parallel and without interference between subcarriers of the upstream channel. Thus, the transmission rate of the user terminal \(i\) in sub-band \(w_{i}\) is  
\[R_{i}^{j}=w_{i}\text{log}_{2}(1+\frac{P_{i}\left|h_{i}^{j}\right|^{2}}{\sigma^ {2}}) \tag{4}\]  
In Equation (4), \(w_{i}\) is the sub-band bandwidth of the upstream transmission of the rescue device, \(P_{i}\) is the transmit power of the rescue device \(i\), \(\left|h_{i}^{j}\right|^{2}\) is the channel gain between the rescue device \(i\) and the edge node \(j\), and \(\sigma^{2}\) denotes the background noise variance, the offloading transmission delay of the device \(i\) can be expressed as  
\[T_{i,trans}^{j}=\frac{D_{i}}{R_{i}^{j}} \tag{5}\]
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 4 Datasets
### Taguchi's Character Recognition  
It is a feature selection technique in character recognition proposed by [39] in which feature extraction of a character is based on the instances of variation and abundance items. Figure 7 illustrates an example of variation and abundance instances of a character \"5\". Variation is defined as the number of switches between white-to-grey or grey-to-white as represented by the small circle; while abundance is the number of square grey boxes as the arrow passes through each row in the index (see Figure 7). These variation and abundance items act as the variables of interest in MTS for classification purposes. [PERSON]. [23] provides a detailed explanation of these concepts and examples of how they are deployed in the MTS methodology. In this paper, pattern recognition for character \"5\" is selected for analysis in the study.  
[PERSON]. [23] demonstrated the use of this method in recognizing character number \"5\" out of several \"normal\" and \"abnormal\" samples that formed several shapes similar and not similar to a numeral \"5\" respectively. The data were published in 2012 which consist of 14 variables (7 abundance instances and 7 variation instances). A number of 18 normal (resemblance of character \"5\") and 46 abnormal (no resemblance to numeral \"5\") samples were collected for the study.
# A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions
## 2 Topology, Operation Principle and Optimization Strategy of Proposed PV/ES System
### Energy Optimization Strategy
#### 2.3.1 Energy Optimization Model  
Defining \(SOC_{j}(k)\) as the \(SOC\) of the \(j\)-th ES module at the starting time of the \(k\)-th optimization cycle [34] can be done as follows:  
\[\left\{\begin{array}{l}SOC_{j}(k+1)=SOC_{j}(k)+\frac{\left[i_{pv~{}j}(k)-i_ {\text{bus}}(k)\right]\cdot\frac{i_{\text{PV}}~{}j(k)}{i_{\text{es}}~{}j(k)} \cdot\text{T}}{360\text{-}C_{\text{es}}}\\ j=1,2,\ldots,\end{array}\right. \tag{3}\]  
where \(u_{\text{es}}\) is the voltage of the ES module, \(T\) is the time of one optimization cycle, and \(C_{\text{es}}\) is the capacity of ES module.  
To increase the charging and discharging capacity margin of the ES module at the beginning of the next optimization cycle, the objective function is designed to make the \(SOC_{s}\) of all ES modules converge to 50%. Hence, the target function is firstly designed as follows:  
\[\text{min}:F=\sum_{j=1}^{\text{J}}\left[SOC_{j}(k+1)-0.5\right]^{2} \tag{4}\]  
To prevent the ES module from deep charging and discharging, which will shorten the working life of the ES module, the upper and lower limits of \(SOC\) are set as 80% and 20%, respectively. Hence, the penalty function can be defined as follows:  
\[PEN_{j}=\left\{\begin{array}{cc}0,&0.2\leq SOC_{j}(k+1)\leq 0.8\\ SOC_{j}(k+1)-0.8,&SOC_{j}(k+1)>0.8\\ 0.2-SOC_{j}(k+1),&SOC_{j}(k+1)<0.2\end{array}\right. \tag{5}\]  
Combining the target function (4) and penalty function (5), the final objective functions of the energy optimization model can be obtained as follows:  
\[\text{min}:F=\sum_{j=1}^{\text{J}}\left[SOC_{j}(k+1)-0.5\right]^{2}+b\cdot \sum_{j=1}^{\text{J}}PEN_{j} \tag{6}\]  
where \(b\) is the penalty factor for deep charging and discharging. Assuming \(b\) is large enough, in the process of searching for the minimum fitness value of the target function (6), the optimization algorithm will automatically avoid deep charging and discharging the ES modules.  
To limit the current of the DC bus within the safety operating range, and to avoid reverse current, the constraint can be defined as follows:  
\[0<i_{\text{bus}}(k)\leq\text{I}_{\text{rated}} \tag{7}\]
## 3 Materials and Methods
### The Wireless Server Gateway  
This layer is responsible for collecting data from sensor nodes and aggregating and transmitting to the drone-LoRa layer using LoRa module, which is a long-range wide-area network technology, such as a cellular network, but designed for lower energy consumption. It enables devices to send small amounts of data over long distances, for years, running on a small battery. The range of the LoRa gateways is 15 km in rural areas and 5 km in urban areas. Table 2 shows the difference between the main low-power wide-area network (LPWAN) technologies, one of which is LoRa. Figure 6 shows communication between the HOBO U30 Weather Station Starter Kit and wireless server gateway.  
Figure 5: Sensors that attached to HOBO U30 Weather Station Starter Kit.
## 3 Instrument Characteristics and Tests
### Gain  
The radiometer gain \(G_{\rm RM}\) = \(dU_{\rm RM}\)/\(dT_{\rm RM,\;in}\) measures the response \(dU_{\rm RM}\) of the radiometer output voltage \(U_{\rm RM}\) with respect to a change \(dT_{\rm RM,\;in}\) in the input brightness temperature \(T_{\rm RM,\;in}\). If the system response is considered linear, \(G_{\rm RM}\) is:  
\[G_{\rm RM}=\frac{\left\langle U_{\rm RS}\right\rangle-\left\langle U_{\rm ACS }\right\rangle}{T_{\rm RS}-T_{\rm ACS}} \tag{10}\]  
The radiometer gains derived for the LSB and the USB channel differ by approximately 7%. This can be explained as due to small differences in the losses and gains of the components after the power splitter (Figure 2). However, the standard deviations of the \(G_{\rm RM}\) are very small for both channels. This is important to note, as it implies that \(G_{\rm RM}\) is highly stable during a measurement cycle, which lasts less than a minute, where the internal calibration and antenna are measured.