esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper investigates dual-hop Reconfigurable Intelligent Surface (RIS) wireless communication systems with malicious jamming, where the destination node faces jamming from a malicious jammer with a RIS-Equipped Unmanned Aerial Vehicle (UAV) relay. We model the channel gains for Tx-RIS and Jammer-RIS links with a Rician distribution, while the RIS-Rx link follows a Nakagami-m distribution, and the jamming status is modeled as a Bernoulli-distributed random variable. We derived and provided closed-form expressions for the probability density functions (PDFs) of the legitimate channel and jamming channel in RIS-Equipped UAV wireless communication systems. Additionally, a new closed-form expression for the PDF of the received signal-to-jamming ratio (SJR) is derived. Using the Gauss-Laguerre Approximation method, we calculate the Average Bit Error Rate (ABER) under Binary Phase Shift Keying (BPSK) and Quadrature Amplitude Modulation (QAM) schemes. We analyze the effects of jamming probability, the location of the legitimate RIS, and different channel conditions on ABER performance through theoretical analyses and simulation results. Our theoretical analyses and simulation results indicate that an increase in the probability of malicious jamming significantly raises the ABER. For example, under favorable channel conditions, the ABER for BPSK modulation was observed to be as low as \(10^{-5}\), whereas under poor channel conditions, the ABER increased to \(10^{-2}\). Additionally, by reducing the distance between the transmitter and the RIS, the ABER can be improved. The legitimate RIS performs better when closer to the transmitter. These findings highlight the critical impact of channel conditions and the deployment of the RIS on the overall system's performance. Our results offer valuable insights into designing and evaluating the performance of RIS-Equipped UAV wireless communication systems in the presence of malicious jamming, aiding in the development of countermeasures to enhance system resilience and security. The derived expressions are validated through Monte Carlo simulations.  
reconfigurable intelligent surface (RIS); jamming; wireless communication systems; unmanned aerial vehicle (UAV)
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Routing Protocols
#### 3.2.2 Optimized Link State Routing (OLSR)  
The protocol is a proactive type depending on routing table for data access; it allows data dissemination by connecting nodes with a hello message and link-state information. The next-hop is computed based on the shortest forwarding path. Then each node rebroadcasts link-state information and also tracks information from other nodes.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 5 Results and Discussion
### Number of Route Breakages  
The source node selects a route based on the priority given by the CC. However, the priority of routes with D2D communication changes with the presence of PUs and successful data transmission to the destination node. For each data transmission cycle, a route breakage occurs when a PU reappears in a selected route, and this causes the source node to switch to another route.  
Figure 12 shows a comparison of the cumulative route breakage between DRL and TRL with a fixed \(\alpha=0.5\). Although TRL has a better performance with less route breakage at the beginning, the source node learns more about routes with higher successful transmission rates as time goes by, contributing to the improvement in DRL. When the PU OFF time increases from 50 to 250 s: (a) the route breakage of TRL reduces from 15.6 to 5.5 and (b) the route breakage of DRL reduces from 16.4 to 5.1.
## 7 GPU-Based Embedded Platforms for Real Time Object Detection
### GPU-Based CNN Object Detection  
The GPU parallel processing capability decreases the needed processing time, allowing a better system performance when compared with the obtained CPU times. The GPU allows a programmable and parallel implementation, but we need to ensure correct synchronization and memory access. To create high-performance GPU-accelerated applications with parallel programming, a variety of development platforms, such as compute unified device architecture (CUDA) [264] and open computing language (OpenCL) [265] are utilized for GPU-accelerated embedded systems.  
For a system with a single machine and multi-GPUs working on separate tasks, one can directly access any available GPU without coding in CUDA. On the other hand, for multi-GPUs working on shared tasks, such as training several models with different hyperparameters, distributed training is needed. Nvidia provides distributed training and is now supported by many popular deep learning frameworks, such as Pytorch, Caffe, TensorFlow, etc. These techniques reduce computational time linearly with the number of GPUs [266]. Nvidia Jetson is a leading low-power embedded platform that enables server-grade computing performance on edge devices.  
Altera's Cyclone V FPGA achieves a speed of 24.3 fps, for real-time lane detection using Hough transform algorithm [267]. Object detection and tracking system using the FPGA Zynq XC7Z020 board achieves a speed of 30 fps using a modified background subtraction algorithm [268]. Real-time object detection on Zynq UltraScale + MPSoC zcu102 evaluation board using a fully pipelined binarized deep convolutional neural network (BCNN) achieved a processing speed of 34.9 fps [261]. YOLOv2 object detection algorithm running on an NVIDIA Jetson TX2 achieved a processing speed of 9.57 fps [269].  
[PERSON] et al. [270] implemented real-time object detection and tracking from a UAV and tested it on a Jetson Xavier board using various deep learning techniques and has it compared with Jetson variants. [PERSON] et al. [271] implemented YOLO with TensorRT 5.0; it reduces network size which in turn increases speed compared with thw YOLO + Darknet model implementation. [PERSON] et al. [272] proposed a combination of Faster RCNN + deformable convolution + FPN + weight standardization techniques for object detection on low capacity GPU systems. [PERSON] et al. [273] implemented a Deep CNN YOLO object detector on both the PYNQ FPGA board and USB-GPU called Movidius GPU-based accelerated system.  
Table 13 shows the performance results in terms of speed; i.e., seconds per frame (FPS). A quantitative comparison between Jetson variants and GPU-based offline work station shows that the comparison changes with the dimensions of input image. One can choose the best algorithm and system for a specific application with the help of this table.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 4 Conclusions  
In this work, we present a survey of recently proposed routing protocols for underwater acoustic sensor networks. The protocols are analyzed based on an exclusive set of parameters that cover most of the fundamental aspects of a routing protocol. Moreover, the routing techniques of each of the surveyed protocols are also briefly explained, along with their merits and demerits. The surveyed schemes have been divided into two main categories, which are: (a) Localization-based routing protocols, i.e., the routing protocols that need information about the geographic coordinates of nodes to carry out their operations successfully, and (b) Localization-free routing protocol, which refer to the routing protocols that do not need information about the geographic coordinates of nodes to carry out their operations successfully. The protocols in each of these categories are further divided based on mobility.
## 5 Case Study and Discussion  
### Data Description  
The testing data were acquired from a 1490 kWh (rated capacity) PV system that starts generating (usually between 6:00 and 19:00) when the sun rises in Yeonseong-gun, Gyeonggi-do, South Korea. The historical data were downloaded from the Korea Open Data Portal and then used as the input data from January 2015 to October 2018. To reduce the average prediction error, we excluded abnormal data and filled in missing data due to PV system failure and data loss. The original data consisted of four different parameters: SPVG (kWh), solar PV irradiation (W/m\({}^{2}\)), device surface temperature (\({}^{\circ}\)C; temperature range on the surface of the solar cell), and ambient temperature (\({}^{\circ}\)C; ambient temperature). In addition, the number of data points was 1342 (32,208 h), which was divided into training and testing datasets. Figure 6 depicts the SPVG (kWh), with the range of the highest density when the solar PV irradiation (W/m\({}^{2}\)) peaks (usually between 12:00 and 14:00). Furthermore, the device surface temperature (\({}^{\circ}\)C) has similar growth patterns in relation to the atmospheric temperature (\({}^{\circ}\)C). At peak times, the device surface temperature is 10 to 20 \({}^{\circ}\)C higher than the atmospheric temperature, with a similar seasonal pattern.  
Figure 5: Process of artificial neural network (ANN) estimation, with target selection for the proposed framework.
## 3 Derivation of the CRB for Short Observation Intervals  
In this section, we derive the CRB given the output samples of the receiver's front-end, whose signal model was defined in the previous section, over a total observation time \(T_{obs}\) of up to 1 s. After introducing the CRB, we derive the general solution for \(M\) propagation paths. Then, we particularize the solution obtained for our scenario, described in Section 2, i.e., with \(M=2\), corresponding to the LOS signal plus a single coherent specular reflection.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Concentrating Systems
#### 2.1.2 Compound Parabolic Collectors (CPC)  
A variant of the PTC concentrator described in the previous section is the CPC. With respect to PTCs, they offer the advantage of concentrating on the receiver all the radiation that arrives at the collector within a determined angle of acceptance, thus exploiting also part of the diffuse radiation. The concentration factor (C\({}_{\text{CPC}}\)) of a two dimensions CPC is related to the angle of acceptance, as per Equation (1):  
\[\text{C}_{\text{CPC}}=1/\text{sin}\theta_{a}=a/2\pi r \tag{1}\]  
where \(\theta_{a}\) is the angle of acceptance, \(a\) is the mirror aperture and \(r\) is the reactor tube radius. Previous investigations point that a reactor diameter between 25 and 50 mm is optimal [19].  
The design of a CPC-based system and an example picture are shown in Figure 2.  
Several CPC type configurations have been realized and tested by companies such as AO SOL and Energias Renovaveis Lda, and have been central to recent research efforts such as Project CADOX, Almeria, Spain [46; 47]. Finally, it is worth mentioning an extremely simplified version of the CPC, the compound triangular collector, consisting of two reflective surfaces forming a V-shape. This configuration is of very simple construction and easy maintenance, but it nonetheless recently demonstrated good performance in the treatment of bacterial contaminants in municipal wastewaters [48].
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 4 Materials and Methods
### Model and Methodology
#### 4.3.2. Vegetation Restoration Potential Methodology  
In general, areas with similar natural conditions have similar landscape and vegetation cover [4; 19]. When confronted with uniform topographic parameters such as elevation, slope, and aspect, along with climatic factors including precipitation, temperature, and humidity, it was foreseeable that the natural landscape and vegetation cover would display congruence. Any variance in vegetation cover detected within the same range of natural conditions signifies an inherent potential, enabling the computation of localized vegetation restoration potential [65]. In order to determine the optimal vegetation growth under natural conditions, the indeterminate window method was used to construct similar habitats [66].  
\[VRP_{ij}=NDVI_{kl}(V_{1},V_{2}\cdots V_{n})max \tag{4}\]  
where \(VRP_{ij}\) denotes the vegetation restoration potential in row \(i\) and column \(j\); \(NDVI_{kl}(V_{1},V_{2}\cdots V_{n})max\) represents the maximum \(NDVI\) in the window where the point was located; and \(V_{1},V_{2}\cdots V_{n}\) represent \(n\) environmental variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Degree** & **SPEI Value** & **Class** \\ \hline
1 & SPEI \(>-0.5\) & Near normal \\
2 & \(-1\) + SPEI \(\leq-0.5\) & Mildly dry \\
3 & \(-1.5\) + SPEI \(\leq-1\) & Moderately dry \\
4 & \(-2\) + SPEI \(\leq-1.5\) & Severely dry \\
5 & SPEI \(\leq-2\) & Extremely dry (drought) \\ \hline \hline \end{tabular}
\end{table}
Table 6: List of drought index division.
## 3 Results and Discussion
### Pore Evolution  
Nuclear magnetic resonance (NMR) is a widely utilized technique for the measurement of the size and distribution of pores in rocks [42; 43]. The principle underpinning this method is the interaction between hydrogen nuclei in pore fluids (such as free and bound water) and an external magnetic field. The application of radio frequency pulses induces nuclear magnetic resonance, thereby enabling the acquisition of the relaxation time distribution [44]. The relaxation time distribution of fluids in porous media can be expressed by Equation (5), which demonstrates that the pore radius is proportional to the relaxation time [45]. In accordance with the extant research methodologies and in conjunction with the properties of the data of this specimen [45; 46; 47; 48], the classification of rock pores is as follows: micropores (\(T_{2}\leq 1\) ms), small pores (\(1\) ms \(\leq T_{2}\leq 100\) ms), medium pores (\(100\) ms \(\leq T_{2}\leq 1000\) ms), and large pores (\(T_{2}\geq 1000\) ms).  
\[\frac{1}{T_{2}}\approx\frac{1}{T_{2S}}=\rho(\frac{S}{V})=\rho(\frac{F_{S}}{r}) \tag{5}\]  
Figure 14: Macroscopic failure patterns of coarse and fine granite in variable-angle shear tests.  
where \(T_{2}\) is the total relaxation time (ms), \(T_{28}\) is the surface relaxation time (ms), \(\rho\) is the surface relaxation strength, \(S\) is the surface area of the pores (\(\mu\)m\({}^{2}\)), \(F_{\rm S}\) is the shape factor of the rock, and \(r\) is the radius of the pores (\(\mu\)m).
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 1 Introduction
also experimentally verified the concept of the thermal superposition effect based on the relationship between the cooling efficiency of two hotspots and their relative positions [19]. Proper thermal modeling and analysis are crucial for the successful design of chiplet 2.5D integration; the current research mainly focuses on thermal interaction within a monolithic chip, and there is no relevant research on thermal interaction in chiplet 2.5D integration. The influence of thermal interaction on chiplet 2.5D integration is still unclear. It is necessary to study the mechanism and influencing parameters of thermal interaction in chiplet integration and the methods to mitigate it. In this work, a collective parameter model and a distribution parameter model are proposed to analyze the thermal interaction in chiplet 2.5D integration in Section 2. The mechanism of thermal interaction and the influence parameters are elucidated. In Section 3, through finite element simulation, the effects of different parameters on thermal interaction are analyzed and the trend predicted by the parameter model is also verified. In Section 4, the performance of a near-chip cooling scheme with two different TIMs (silicone grease vs. liquid metal) is experimentally investigated.
## 3 Results
### Optimization of Magnetic Bead + EGCG Complex Concentration: Comparative Efficiency Analysis  
Western blot analysis was performed to evaluate the effect of different concentration ratios of EGCG and T-Fe\({}_{3}\)O\({}_{4}\) magnetic beads (0.1\(\times\), 1\(\times\), and 10\(\times\)) on the efficiency of exosome isolation from plasma samples (Figure 5). The detected levels of exosomal marker proteins were analyzed in comparison with those obtained using the conventional PEG precipitation method.  
Figure 4: Analysis results of synthesized magnetic bead + ECGC complex according to mix ratio.  
The Western blot results showed that high levels of albumin (ALB, -50 kDa) were detected in the PEG method samples and the 0.1\(\times\) concentration samples, but low levels were found in the 1\(\times\) and 10\(\times\) concentration samples. This suggests that high concentrations of EGCG may reduce the isolation of non-specific proteins such as albumin. The tetraspanin family protein CD9 (\(\sim\)22 kDa) showed stronger bands using the beads at all concentrations (0.1\(\times\), 1\(\times\), and 10\(\times\)) compared to the PEG method, with the most pronounced band observed with 0.1\(\times\). CD63 (-45 kDa) showed high and similar levels across all the samples. CD81 (\(\sim\)20-25 kDa) levels were the highest in the PEG method samples and lower in the magnetic bead samples. ALLX (-75 kDa) levels were the highest using the PEG method and showed similar levels across the magnetic bead samples (0.1\(\times\), 1\(\times\), and 10\(\times\)). For TSG101 (-45 kDa), the highest level was observed using the PEG method, while among the magnetic bead samples, the 1\(\times\) and 10\(\times\) samples showed somewhat higher levels than the 0.1\(\times\) samples.  
These results demonstrate that the EGCG-modified magnetic bead method can be particularly effective for isolating specific exosomal markers such as CD9, while the detection efficiency of other marker proteins may vary depending on the bead concentration ratio.  
A Western blot analysis of the exosomal marker proteins isolated from urine samples using the conventional PEG precipitation method and EGCG-modified magnetic beads at three different concentration ratios (0.1\(\times\), 1\(\times\), and 10\(\times\)) was performed. The levels of typical exosomal markers (CD9, CD63, CD81, ALLX, and TSG101) and albumin (ALB) were compared across the different isolation methods. The molecular weights are indicated on the left in kilodaltons (kDa).
## 4 Proposed PMFAN-GDSO Approach
### Traffic Flow Prediction Based on PMFAN
#### 4.2.1 Temporal Feature Extraction with Attention Mechanism  
The service traffic bandwidth nowadays in optical networks exhibits complex nonlinear characteristics due to factors such as bursts and randomness. Traditional mathematical modeling algorithms, such as ARIMA, struggle to provide accurate predictions. Therefore, we have chosen to utilize an RNN composed of LSTM units as the foundation of our prediction model. This network can capture the long-term dependencies between the service traffic bandwidth data, capture the nonlinear features in the bandwidth sequence via its nonlinear components, and mitigate the issues of gradient vanishing and explosion during long sequence prediction. In order to further improve the accuracy of traffic prediction, considering that the prediction target often exhibits stronger correlations with specific time steps in the historical data, we introduce an attention mechanism to identify these time steps with stronger correlations and assign them higher weights, thus enhancing the accuracy of the prediction results.  
The core concept of LSTM is the gate mechanism that governs the flow of information. The input, output, and forget gates collectively determine the cell state \(C_{t}\) of an LSTM memory unit. The forget gate determines what information from the previous cell state \(C_{t-1}\) will be retained. The input gate controls the current time information \(x_{t}\) and the hidden state \(h_{t-1}\). The output gate \(O_{t}\), together with the cell state \(C_{t}\), determines the output vector \(h_{t}\).  
The output vector \(h_{t}\), the input gate \(i_{t}\), the forget gate \(f_{t}\) and the output gate \(O_{t}\) can be defined as:  
\[h_{t}=O_{t}tanh(C_{t}) \tag{7}\]  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{8}\]  
\[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{9}\]  
\[O_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{10}\]  
Figure 3: The framework of proactive multi-flow assisted attention-based neural network.  
where the activation function is defined as \(\sigma\ W_{i}\ W_{f}\ W_{o}\ b_{o}\ b_{i}\ b_{f}\), which are the learnable parameters of the LSTM and will be defined when training.  
The implementation of the attention mechanism revolves around adjusting the weight coefficients assigned to the data. Each data point is associated with specific weight coefficients. By calculating and modifying these weight coefficients, the attention mechanism can represent various key information. As a result, the model's ability to capture important information is enhanced, leading to an improved prediction accuracy. The combination of the attention mechanism and LSTM is illustrated in Figure 3. The attention mechanism allows the model to dynamically focus on the most relevant time steps in the historical data, paying close attention to them for the prediction task. This enables the model to effectively capture the temporal dependencies and patterns that are crucial for accurate traffic forecasting.
## 2 Motivating Applications and Use Cases
### Big Data Analysis Use Case  
Devices used in various smart city sectors [1], such as transportation, healthcare, and agriculture, generate a huge amount of data. The appropriate understating of these data presents numerous opportunities to organizations and governments. However, the storage and real-time analysis of a vast amount of data present a huge challenge.  
Applications and use cases, such as smart homes and real-time situation analyses, require low data latencies, high data rates, vast amounts of computing and storage resources, and access to sensors and actuators. The conventional cloud computing systems are unable to fulfill the requirements of these applications [1,4,9]. The middleware platform for a heterogeneous private edge cloud system aims to fulfill the requirements of these applications.
## 3 System Model
### VLC Heterogeneous Networking Architecture
Notably, Figure 1 shows the presence of MT1 in the aforementioned region, which leads to significant degradation in its communication quality. By establishing the center points of the two VLC coverage cells as 2 m apart and considering the VLC AP coverage area as a circular region with a radius of meters, the average distance L within the overlapping region can be computed to be approximately 0.83 m.  
The VLC signal-to-noise ratio can be calculated using Equation (4) [15], where \(\Omega\) is the reflection coefficient of the photoinductive receiver, \(c\) denotes the associated VLC transmitter, \(R_{p,c}\) is the received signal power of the associated VLC, \(R_{p,i}\) is the received signal  
Figure 2: Network topology of VLC HetNet. power of the \(i\)th interfering VLC transmitter, \(W\) denotes the LED modulation bandwidth, and \(n_{0}\) denotes the noise power spectral density of the short noise. \[SINR=\frac{\left(\Omega R_{p,c}\right)^{2}}{\left(\sum_{i\
eq c}\Omega R_{p,i} \right)+n_{0}W}, \tag{4}\]  
MTs in the CCI region experience higher bit error rates (BER) due to significant drops in the signal-to-interference-plus-noise ratio (SINR). When the VLC link is blocked, SINR rapidly decreases, triggering VHO for seamless communication. For VLC networks and WLANs, we expect an average SINR of 33 dB and 25 dB, respectively. However, when the MT enters the CCI area, we anticipate an SINR of 3 dB due to the presence of co-channel interference. Furthermore, in the event of a blockage disrupting the VLC link, the SINR deteriorates to \(-20\) dB [3].