esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Thevenin Model for a Rectenna and Its Application to an RF Harvester with MPPT
## Appendix B Simulations of the Rectenna Efficiency with and without an L-Matching Network  
In order to highlight the benefits of using a matching network, we report simulation results of the rectenna efficiency with and without an L-matching network. For the simulations, the ADS software (Version 2017, Keysight, Santa Rosa, CA, USA) was used. The simulated circuit with the matching network was that of Figure 2 with a resistor \(R_{\mathrm{o}}\) connected at its output, in parallel with \(C_{\mathrm{o}}\). Ideal components were used for the matching network with the values reported in Section 4. The diode was modelled without parasitic elements (null series resistance and junction capacitance) but with a saturation current of 3 \(\mu\)A (that corresponding to the HSMS-2850 diode used for the implemented rectenna). For the RF source, a frequency of 808 MHz (that used for the experiments) was used with \(P_{\mathrm{av}}\) ranging from \(-10\) to 50 dBm in steps of 20 dB. For the circuit without the matching network, the right terminal of \(R_{\mathrm{a}}\) was directly connected to the diode anode (node A). For both circuits, a harmonic balance analysis was performed with \(P_{\mathrm{av}}\) and \(R_{\mathrm{o}}\) as sweeping parameters.  
Figure 11 shows the simulation results of the rectenna efficiency (\(\eta_{\mathrm{rect}}=P_{\mathrm{o}}/P_{\mathrm{av}}\)) plotted against \(R_{\mathrm{o}}\) for different values of \(P_{\mathrm{av}}\). The left graph shows the results for the circuit with the L-matching network. As can be seen, \(\eta_{\mathrm{rect}}\) steeply increases for increasing values of \(P_{\mathrm{av}}\), achieving around 99% at \(P_{\mathrm{av}}\) = 50 dBm. At \(-10\) dBm, efficiency is higher than the corresponding results of Figure 7 because ideal components were used for the simulation. On the other hand, the right graph shows the results for the circuit without the L-matching network. As can be seen, \(\eta_{\mathrm{rect}}\) steeply increases for increasing values of \(P_{\mathrm{av}}\) but now reaches a maximum value around 46%, as predicted theoretically in [35], due to the additional losses at \(R_{\mathrm{a}}\) originated by the current
## 6 Connection-Aware Scheduling Heuristics
### Reliability Score  
To ensure that the stability of the devices in the network is considered, we suggest introducing a new score that the heuristics should consider. This score, similar to the battery level, aims to influence the decision-making process used by the heuristic. We refer to this score as the \"reliability score\", denoted by \(Reliability\). It is calculated as follows:  
\[Reliability=\begin{cases}curr\_execution\_time&\text{if }noffset\_disconnection\\ avg\_connection\_time&\text{otherwise}\end{cases} \tag{1}\]  
where \(first\_disconnection\) is a variable that each device has to express if it has disconnected from the network for the first time; the variable \(curr\_execution\_time\) represents the amount of time in milliseconds executed since the start of the execution to the time the \(Reliability\) score is calculated; and finally, \(avg\_connection\_time\) corresponds to the average time that the device has been connected to the network, taking into consideration these connection events can happen multiple times over a simulation and that the metric is calculated only for the devices connected to the network.  
The following example demonstrates the calculation of the reliability score. In a hypothetical case, we have two devices. The first one has the subsequent connection and disconnection events: \(t=1\) (connect), \(t=2\) (disconnect), \(t=5\) (connect), \(t=10\) (disconnect). The second device connects at \(t=0\) and disconnects at \(t=10\). Assuming the simulation clock is at \(t=11\), the reliability score for the first device will be \(3=(1+5)/2\) and \(10\) for the second device. In this example, we can see what the reliability score aims to incorporate as new information about how stable a device is for the time it plays the role of a worker node within a cluster. The proxy calculates the reliability score when a job arrives and needs to be distributed; in this way, it can obtain the ranking for each device. Also, every time a device disconnects, its last connection duration is stored to be used for the following calculation of the reliability score.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 2 Related Works and Backgrounds
### UAV-Based Warning System  
UAVs deliver timely disaster warnings to public officers and support rescue and recovery operations. Due to their ability to fly autonomously and rapidly acquire sensing data on emergency areas that are inaccessible to humans, UAVs have become a viral candidate platform for emergency monitoring and response systems. Drones are being used by organizations such as customs control, the coast guard, environmental agencies and private businesses for a variety of purposes including safety systems in the gas and chemical industry, fire detection, tactical surveillance, search and rescue, environmental monitoring, and many more [8; 12; 27; 28; 29].  
[PERSON], [PERSON] et al. [7] propose a monitoring system for emergency responses. Their system consists of both an aerial and a ground component. The aerial aspect includes a UAV platform, sensors and supporting modules, while the ground aspect includes vehicles and a receiving and processing system. The aerial component receives sensor data from the environment and transmits it to the ground component. Sensor data and control commands are transmitted automatically in real time.  
[PERSON], [PERSON] et al. [9] propose a dynamic routing protocol, a method for managing mobile nodes to enable reliable, real-time data transmission. In [9], to support early warning, UAVs monitor the area and collect sensor data, transferring it to the data center for analysis. [PERSON], [PERSON] et al. [10] propose a web-based platform for automatic and continuous monitoring of the Rotolon catchment in the Italian Alps for potential landslides. [PERSON], [PERSON] et al. [11] propose the use of UAVs to make their wireless sensor network more resilient to failures for river monitoring.  
Most warning systems based on UAVs focus on natural disasters, and such systems are unsuitable for application in security monitoring. In the IoT environment, each individual device and sensor must contain a security module to protect it from malicious interference. Indeed, in an emergency response system for a building, it is most important to prevent malicious attacks on the communications network. Therefore, we customize security modules from the AllJoyn Security Platform to secure every component of our system.
## 2 Materials and Methods  
### Test Area  
The test area covers an irrigation perimeter close to the town of Wako-Kungo (11\({}^{\circ}\)25\({}^{\prime}\)33\({}^{\prime\prime}\)S, 15\({}^{\circ}\)06\({}^{\prime}\)10\({}^{\prime\prime}\)E), which is located in the Cela Municipality, South-Kwanza Province, Angola (Figure 1). The test area has an area of approximately 960 km\({}^{2}\), with 40 km in the west-east direction and 24 km in the north-south direction. The Wako-Kungo region is located on a plateau zone higher than 1200 m. This region has a warm temperate climate with a marked wet season from October to April and an average annual precipitation of approximately 1250 mm. The dry season (Cacimbo) occurs from May to August. The average annual temperature is approximately 21 \({}^{\circ}\)C, ranging between 22 \({}^{\circ}\)C in September and October and 18 \({}^{\circ}\)C in June [23; 24; 25].  
The Cela area has a dense hydrographic network that is limited by the River Nhia in the north and the River Queve in the south. There is also the River Kusonhi and a large number of streams with appreciable flow during the months of the rainy season that contribute to the available water in the region. This region is dominated by ferralitic soils in association with para-ferralitic soils, which occur in hilly areas, especially in foothills. The area climate conditions combined with the soil types present in the region make the area soils fertile for agriculture. The main crops in the rainy season are maize, rice, some vegetables and pasture, with dairy farming being the main activity. The crops and pastures in this region require irrigation during the dry season [23; 24; 25].  
Figure 1: (**a**) Regional context of the test area (solid blue rectangle) with the location of the Kibala and Wako-Kungo weather stations of the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet; (**b**) test area with the locations of the ground truth areas (yellow polygons) collected in April 2015.
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 3 Materials and Methods
### Feature Graph Generation
#### 3.4.2 Mel Frequency Cepstrum Coefficient  
Mel frequency is more suitable for simulating human auditory behavior than other audio-processing methods, and it is widely used in the field of human voice (speech recognition, speaker recognition) [56]. The following steps are involved in obtaining the Mel frequency cepstrum coefficient: pre-emphasis, framing, windowing, fast Fourier transform, Mel filter banks, and discrete cosine transform [33]. The formula for converting an ordinary frequency to a Mel frequency is as follows [57]:  
\[mel(f)=2595\times\log_{10}(1+f/700) \tag{6}\]  
The discrete cosine transform is used to convert the logarithmic Mel spectrum back to the time domain, and the result is known as MFCC, as shown in Formula (7).  
\[\text{MFCC}(n_{1})=\sum_{m=1}^{M}\log[E(m,k)]\cdot\cos\bigg{(}\frac{(m-0.5)}{M }\cdot n_{1}\cdot\pi\bigg{)},\quad n_{1}=1,2,\cdots,L \tag{7}\]  
where, \(M\) is the number of filters, \(n_{1}\) is the order, and \(E(m,k)\) is the average energy of the kth frequency band.  
The MFCC feature of the KS signal is extracted using 26 Mel filters, the frame length is set to 256, and the frame shift is set to 128; the signal is framed using the Hamming window function W(n) to avoid sidelobe leakage [58]. The calculated MFCC coefficient matrix (167 \(\times\) 26) is converted to the RGB format matrix and saved as an image of 224 \(\times\) 224 using the same processing method as the CWT coefficient matrix.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 3 Methods  
Our proposed method follows the workflow of the automatic segmentation of OD and OC, as shown in Figure 2. Firstly, fundus images are localized with OD via a morphological image processing method to locate the center of the OD. Secondly, image enhancement operations on the original image due to the contrast of the photographs taken on a 66 Vision-Tech fundus camera are not enough. Figure 3 shows that image enhancement is achieved using the SRGAN image generation network by alternately training the generator and discriminator to convert low-resolution images to high-resolution images to improve the quality and clarity of the images. In addition, the boundary parts of the OD are blurred, and the data enhancement can clarify the boundary parts of the OD to improve the contrast of the image. Thirdly, the original image is cropped to a region of interest (ROI). Finally, the cropped optic disk images and labels are trained on the BEAC-Net segmentation network to complete the segmentation.
## 3 Results
### Technical Validation  
The core assumptions of our method are that a probability density of pixel values represents useful information about the classes of interest, and that we can learn reliable estimates of those based on individual parcels. We first validate these visually in Figure 4 for the SAR data. The top row shows that estimates computed from all pixels of a given class are visually distinct, whereas the bottom row shows that estimates computed based on pixels of individual parcels resemble the class-level information. The figure also illustrates the difficulty of the problem; the densities are distinct but highly similar in the sense that simpler representations like mean pixel value are unlikely to be sufficient for separating the classes.  
For accuracy evaluation between the method variants, we use balanced subsets of parcel data described in Section 2.1.1 to make the results easier to interpret. We consider only balanced classification problems with equally many observations for each class so that classification accuracy can directly be interpreted as quality of the method, and we only consider the classes _ploughed_, _grass_ and _stubble_ to avoid issues with classification of the three minor classes that are difficult to separate from each other. For all of the technical experiments we use a fixed randomly chosen subset of 300 parcels per class (900 samples in total) for testing, whereas the size of the used subset of training data is a parameter for many of the experiments, seen on the horizontal axis as \"Number of training samples/class\". This is to investigate model performance with respect to data size.
## 5 Simulation Results
### Analysis of the Adjustment Effect of RMVSC-STATCOM in Capacitive Operation  
To verify performance under capacitive conditions, an inductive load of \(P_{\mathrm{L}}+jQ_{\mathrm{L}}=10\) MW + \(j10\) Mvar is set, with a simulation duration of 1 s. At 0.5 s, the RMVSC-STATCOM is connected to track the reactive power command for compensation. The voltage and current of the three phases before compensation are shown in Figure 10a. There is a phase difference between voltage and current; the current leads the voltage, and the power factor is 0.707. At 0.5 s, the RMVSC-STATCOM is engaged, and after tracking the compensation command, it eventually outputs 0.997 Mvar of reactive power. Following reactive power compensation,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline rated capacity (S\({}_{\mathrm{t}}\)) & 10 MVA \\ rated voltage (\(L_{\mathrm{b}}\)) & 10 kV \\ rated current (\(I_{\mathrm{b}}\)) & 577.35 A \\ grid frequency (\(f_{S}\)) & 50 Hz \\ main bridge switching frequency (\(f\)) & 50 Hz \\ reinjection circuit switching frequency (\(f_{r}\)) & 300 Hz \\ connecting inductor (\(L_{s}\)) & 4.775 mH \\ Submodule capacitor (\(C\)) & 5000 μF \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.  
the reactive power on the power supply side is nearly 0, and the power factor improves to nearly 1, with the voltage and current phases on the power supply side aligned. The voltage and current waveforms of the three phases after compensation are shown in Figure 10b.  
Figure 9: Simulation results of using circulating pulse modulation strategy. **(a)** Y rejection voltage. **(b)** D rejection voltage. **(c)** RMVSC output three-phase voltage. **(d)** RMVSC output three-phase current. **(e)** The spectrum of the A-phase voltage. **(f)** The spectrum of the A-phase current. **(g)** Y-bridge No. 1 IGBT voltage and its drive signal. **(h)** Y-bridge No. 1 IGBT current and its drive signal.  
Dynamic performance indicators are used to evaluate the adjustment effect of RMVSC-STATCOM under capacitive conditions, including dynamic response time \(t_{\text{r}}\), adjustment time \(t_{\text{s}}\), overshoot \(\sigma\), and steady-state error. The tracking of the compensated reactive power \(Q\) to the reactive power command value \(Q_{\text{ref}}\) is shown in Figure 10c. From 0 to 0.5 s, the RMVSC-STATCOM is not engaged, and the reactive power command value is 0, at which point the compensated reactive power Q = 0. At 0.5 s, the RMVSC-STATCOM is engaged, and after a response time of \(t_{\text{r}}\) = 0.08 s, the change in \(Q\) reaches 90% of the difference between the reactive power command value \(Q_{\text{ref}}\) and the initial value. After an adjustment time of \(t_{\text{s}}\) = 0.1 s, \(Q\) remains within the range of 95% to 105% of the reactive power command value \(Q_{\text{ref}}\), with an overshoot \(\sigma\) of 0, delivering reactive power of 10 Mvar, and the steady-state error with respect to the reactive power command value is 0. The power factor waveform is shown in Figure 10d. After the RMVSC-STATCOM is engaged, the power factor increases from 0.707 to 1.
###### Abstract  
Land Surface Temperature (LST), as a core variable in the coupling of land-atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China's Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FT-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017-2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R\({}^{2}\)\(\geq\) 0.88, RMSE \(<\) 2.3 K). This method successfully reconstructed the FY-3C satellite's 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking.  
LST; FY-3; machine learning; fusion; downscaling; SSA +
Footnote †: journal: Applied sciences  
Article
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 3 Results and Discussion  
### S-Parameter Results  
HFSS and an Agilent N5247A vector network analyzer were used to simulate and measure the proposed UWB-MIMO antenna, and the results are provided in Figure 9. The measured S11 can cover 4.51-15.1 GHz, while S12 is below \(-\)15 dB, as shown in Figure 9. It is worthwhile to note that there are significant differences between the measured and simulated results of S12, especially in 6.5-7.5 GHz and 10-11 GHz bands, where  
Figure 8: Surface current distribution when port 1 is stimulated at (**a**) 5.4 GHz, (**b**) 6 GHz, (**c**) 8 GHz, (**d**) 11.2 GHz, (**e**) 14.6 GHz.  
Figure 7: Simulated S-parameters resulting from the tuning of L\({}_{\text{f}}\): (**a**) reflection coefficient, (**b**) transmission coefficient.  
some frequencies with a difference of more than 20 dB can be seen. Manufacturing and measurement errors might be responsible for the discrepancy between the simulated and measured S12 results, which does not affect the overall high-isolation performance of the system. From these findings, the proposed antenna offers a wide operation band with high isolation.
## 2 Channel Model
### The Channel Matrix of the RIS-Aided Parallel MIMO VLC System  
The channel gain from the \(n\)-th LED to the \(n\)-th PD is given by  
\[h_{n}=h_{n}^{\text{LoS}}+h_{n}^{\text{NLoS}}, \tag{10}\]  
We assume the LoS channel matrix is denoted by \(\mathbf{h}^{\text{LoS}}=\text{diag}(h_{1}^{\text{LoS}},h_{2}^{\text{LoS}}, \cdots,h_{N}^{\text{LoS}})\), and consequently, the channel matrix of the RIS-aided parallel MIMO VLC system is  
\[\mathbf{h}=\mathbf{h}^{\text{LoS}}+\mathbf{h}^{\text{NLoS}}=\text{diag}(h_{1},h_{2},\cdots,h_{N}), \tag{11}\]  
Meanwhile, the elements in the assignment matrix \(\mathbf{F}\) need to satisfy some constraints given by  
\[f_{n,k}\in\{0,1\}, \tag{12}\] \[\sum_{n=1}^{N}f_{n,k}\in\{0,1\},\] (13) \[\sum_{k=1}^{K}f_{n,k}=1, \tag{14}\]  
The constraint in (12) comes from the definition of \(\mathbf{F}\), and \(f_{n,k}=1\) represents that the \(k\)-th RIS element is assigned to the \(n\)-th LED and PD. The constraint in (13) is because each RIS element can be assigned to one LED at most, and the constraint in (14) guarantees that each LED will choose one RIS element to complete the NLoS channel.
###### Abstract  
In a multi-beam communication scenario where Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications coexist, the limited spectrum of resources force V2V users to reuse the orthogonal frequency bands allocated to I2V, inevitably introducing cross-layer interference between I2V and V2V. Furthermore, the adoption of a multi-beam communication architecture exacerbates beam interference, significantly degrading the overall network's communication and sensing performance. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. Specifically, IMSBA employs a joint optimization framework combining Multi-Agent Proximal Policy Optimization (MAPPO) with a Stackelberg game. Within this framework, MAPPO leverages vehicle perception data to dynamically optimize V2V beam steering and frequency selection, while the Stackelberg game reduces computational complexity through hierarchical decision-making and optimizes the joint power allocation among V2V users. Additionally, the proposed scheme incorporates a V2V cooperative sensing domain-sharing mechanism to enhance system robustness under adverse conditions. The experimental results demonstrated that, compared with existing baseline schemes, IMSBA achieved a 92.5% improvement in V2V energy efficiency while significantly enhancing both communication and sensing performance. This study provides an efficient and practical solution for spectrum-constrained scenarios in millimeter-wave Internet-of-Things (IoT), offering substantial theoretical insights and practical value for the efficient operation of intelligent transportation system (ITSs).  
a Article  
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