esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.1.1 Signal Amplification  
The amplification block is shown in Figure 2. This stage is designed using two INA128 instrumentation amplifiers. The first amplifier has a gain of 200, a slew rate of 4 V/us, and a common-mode rejection ratio (CMRR) of 111 dB; the second amplifier has a gain of 10,000, a slew rate of 4 V/us, and a CMRR of 125 dB, as the voltage amplitude of the EEG signal ranges from 5 to 300 \(\mu\)V [27; 28]. The gains of the instrumentation amplifiers are calculated by the following:  
\[G=1+\frac{R_{x}}{R_{G}}, \tag{1}\]  
where \(R_{x}\) is a constant whose value is 50 k\(\Omega\) and \(R_{G}\) represents a resistance value from 0.005 to 50 k\(\Omega\) to provide a given gain. If \(R_{G}=1.02\) k\(\Omega\), a gain value of 50 is obtained for signal amplification.  
An AD705 operational amplifier with a CMRR of 114 dB and a slew rate of 0.15 V/us is used to stabilize the offset voltage generated at the output of the first instrumentation amplifier.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 4 Performance Measure
### Bit Error Rate  
The likelihood that a bit may be wrongly received due to noise is calculated using the BER. It is calculated by deducting the quantity of unintentionally received bits from the total amount of bits supplied. This equation is used to compute the \(BER\):  
\[BER=\frac{Errors}{Total\ Number\ of\ Bits} \tag{28}\]
# An Interference Cancellation Scheme for High Reliability Based on MIMO Systems
## 2 Conventional Two-Path Relay System Model

Table 1: The configuration for transmission symbols of conventional scheme. Figure 1: The conventional two-path relay system. \[x_{\text{R}_{2}}(n)=\frac{y_{\text{D}}(n)-z_{\text{D}}(n)}{h_{\text{R}_{2}\text{D} }}. \tag{6}\]  
Substituting Equation (6) into (4), \(y_{\text{D}}(n+1)\) is as follows,  
\[y_{\text{D}}(n+1)=\beta_{1}x_{\text{s}}(n)h_{\text{SR}_{1}}h_{\text{R}_{1}}+ \frac{\beta_{1}h_{\text{R}_{1}\text{R}_{2}}h_{\text{R}_{1}\text{D}}}{h_{\text{R }_{2}\text{D}}}y_{\text{D}}(n)+\widetilde{z}(n), \tag{7}\]  
where \(\widetilde{z}(n)\) is a new noise as follows,  
\[\widetilde{z}(n)=\beta_{1}h_{\text{R}_{1}\text{D}}z_{\text{R}_{1}}(n)+z_{ \text{D}}(n+1)-\frac{\beta_{1}h_{\text{R}_{1}\text{R}_{2}}h_{\text{R}_{1} \text{D}}}{h_{\text{R}_{2}\text{D}}}z_{\text{D}}(n). \tag{8}\]  
In Equation (7), the second term of the right-hand side is IRI and it is cancelled at D by subtracting \(\frac{\beta_{1}h_{\text{R}_{1}\text{R}_{2}}h_{\text{R}_{1}\text{D}}}{h_{\text {R}_{2}\text{D}}}y_{\text{D}}(n)\)[6]. Finally, IRI free symbol \(\widetilde{y}_{\text{D}}\) is as follows,  
\[\widetilde{y}_{\text{D}}(n+1)=\beta_{1}x_{\text{s}}(n)h_{\text{SR}_{1}}h_{ \text{R}_{1}\text{D}}+\widetilde{z}(n). \tag{9}\]  
Similarly, if \(n\) is an even number, the received symbol at D including IRI is as follows,  
\[y_{\text{D}}(n+1)=\beta_{2}x_{\text{s}}(n)h_{\text{SR}_{2}}h_{\text{R}_{2} \text{D}}+\frac{\beta_{2}h_{\text{R}_{2}\text{R}_{1}}h_{\text{R}_{2}\text{D} }}{h_{\text{R}_{1}\text{D}}}y_{\text{D}}(n)+\widetilde{z}(n), \tag{10}\]  
where \(\beta_{2}\) is amplifying factor as follows,  
\[\beta_{2}=\sqrt{\frac{R_{\text{R}_{2}}}{\left|h_{\text{SR}_{2}}\right|^{2}P_{ \text{S}}+\left|h_{\text{R}_{2}\text{R}_{1}}\right|^{2}P_{\text{R}_{1}}+ \sigma^{2}}}. \tag{11}\]  
The conventional scheme removes IRI at a destination by subtracting IRI from the received symbol. However, the error performance is decreased severely when two relay nodes are close to each other because the effects of interference are increased and are propagated to a destination node. Also, the conventional two-path relay system removes interferences not at a relay but a destination. So, when the conventional two-path relay system uses the DF scheme at a relay, it has high probability that the regenerated signals at a relay have many errors and it causes severe error propagations at a destination. So, the proposed scheme uses MIMO signal detection at the relays to remove IRI at the relays (not at a destination) and the DF scheme can be used at the relays due to IRI free symbol. For removal of IRI, the proposed scheme uses low-complexity QRD-\(M\) which has optimal error performance and very lower complexity than the conventional QRD-\(M\). Finally, the source and the relays use cyclic delay diversity (CDD)[19] which is simple spatial diversity scheme to obtain spatial diversity gain and higher error performance.
## 3 Problem Statement  
Due to distortions brought on by the intricate underwater channel and the nonlinear properties of the light source, underwater visible light communication (UVLC) devices encounter substantial difficulties. When utilizing modern modulation techniques to achieve high transmission speeds, these distortions might reduce the quality of the information received and create problems. An adaptive fuzzy logic deep-learning equalizer can be suggested as an answer to these problems in order to correct for the distortions in UVLC systems. In conclusion, the suggested adaptive fuzzy logic deep-learning equalizer offers a potentially effective way to reduce distortions in UVLC systems. The equalizer corrects for distortions, boosts performance, scales down in complexity, and adjusts to shifting undersea conditions, thereby improving UVLC communications' dependability and effectiveness. It does this by combining adaptive filtering, fuzzy logic, and deep learning approaches.
# A Hybrid Energy Equating Game for Energy Management in the Internet of Underwater Things
## 3 Preliminaries
### Energy Consumption  
According to the first order radio energy model [27], in order to attain a suitable SNR in transmitting a \(k\)-bit message over a distance \(d\), the energy spent by the radio is given by:  
\[C_{tx}(k,d)=\left\{\begin{array}{ll}E_{elc}k+kE_{fs}d^{2}&ifd<d_{0}\\ E_{elc}k+kE_{amp}d^{4}&ifd\geq d_{0}\end{array}\right. \tag{1}\]  
where, \(E_{elc}\) is the energy consumed per bit to run the transmitter or the receiver circuit, \(E_{amp}d^{4}\) and \(E_{fs}d^{2}\) represent the transmit amplifier coefficient of multi-path and free space models, and \(d\) represents the sender to the receiver distance. In case \(d\geq d_{0}\), the transmit amplify coefficient of the multi-path model \(E_{amp}d^{4}\) is used. Otherwise, the free space amplify coefficient \(E_{fs}d^{2}\) is utilized. Furthermore, we considered a symmetric channel to transmit and receive the information. When the node receives this message, it expends \(E_{Rx}(k)\) energy as:  
\[C_{rx}(k)=kE_{elc} \tag{2}\]  
In the acoustic environment, the signal attenuation does not depend on the link distance \(d\) and equally considers the frequency \(f\). Hence, an SNR for a signal which is generated under the frequency \(f\), with unit transmission power and low bandwidth is given as \(\rho(d,f)\). The acoustic signal equation is defined in Reference [27; 28; 29] for an acoustic channel with a distance \(d\) between the source and destination at a signaling frequency \(f(KHz)\) and spreading coefficient \(S\) as:  
\[A(d,f)=A_{o}[a(f)^{d}]d^{S} \tag{3}\]  
where, \(A_{o}\) denotes the unit normalization constant, \(S\) is a spreading factor the value of which can be different depending upon the propagation environment as:  
1. S = 1 for shallow water environment
2. S = 1.5 for practical environment
3. S = 3 for the deep water environment  
The absorption co-efficient \(a(f)\) is taken in \(dB/Km\) as described by using the Thorp formula [30; 31] as:  
\[10 log(a(f))=(0.11f^{2})\times\frac{1}{1+f^{2}}+(44f^{2})\times\frac{1}{4200+f} +2.75f^{2}\times 10^{-4}+0.003\quad if(f>0.4) \tag{4}\]  
\[10 log(a(f))=0.002+(0.11f)\times\frac{1}{1+f}+0.0011f\quad if(f<0.4) \tag{5}\]  
Now the energy expenditure for an FN to transmit a \(k\) bit message to another FN at a distance \(d\) with the frequency \(f\) can be computed as:  
\[C_{tx}(k,d)=\left\{\begin{array}{ll}E_{elc}k+ka(f)^{d}d^{2}&ifd<d_{0}\\ E_{elc}k+ka(f)^{d}d^{4}&ifd\geq d_{0}\end{array}\right. \tag{6}\]
## 5 Experimental Results
### Results Using the Dataset with Pathologies
#### 5.1.5. Results for Decision Tree with Evolutionary Feature Selection  
The results obtained with DT using evolutionary FS are presented below.  
Figure 4: Impact of variables with SHAP, Gradient Boosting with _Stud_ selection, with pathologies.  
**GA with _Stud_ selection for Decision Tree (DT-S):** Fourteen variables were selected, obtaining an average accuracy of 0.7150 and, in the best case, up to 0.7661, as can be verified in Table 2. Among the variables used were sex, age, and seven variables related to pathologies.  
Figure 5 shows the impact of the different variables in the model Stud with Decision Tree with pathologies. Once again, age, sex and apnea are at the top of the list. In this case, diabetes appears with a similar distribution to apnea, although with less impact. The appearance of fermented beverages is also interesting.  
**GA with tournament selection for Decision Tree (DT-T):** Thanks to the feature selection, with only 16 variables, an accuracy reaching 0.7492 was achieved in the best cases and an average of 0.7103, as shown in Table 2.  
Some of the variables used for this case are metabolic syndrome, those related to cancer, fermented drinks, specifically those related to wine, and the variable heart_angina. Moreover, as in the previous cases, sex and age appear.  
Figure 6 shows a heat map representing the frequency with which the different FS models (columns) selected each of the variables (rows). Depending on the color of each cell, it is possible to get an idea of the number of times a variable was selected, with the cool colors being those cases with the lowest number of occurrences and the warm colors being those that appeared most frequently. In the case of the Decision Tree, there is greater diversity in the variables chosen by the models, since the colors are not so intense, unlike Gradient Boosting. Despite the differences between the variables chosen for the models, the extremes (top and bottom) of the heat map show a similar range of colors in most cases. The most frequent variables were sex, age, education, apnea, alcoholic beverages, both fermented and distilled, and metabolic syndrome.  
Figure 5: Impact of variables with SHAP, Decision Tree with _Stud_ selection, with pathologies.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 3 Graph Construction and Dynamic Graph Neural Network
### Classification Layer  
Finally, the pooled node representations--serving as the global representation of each graph--are mapped to the target classification space via a fully connected network, and the prediction probabilities for each class are computed using the log-softmax function:  
\[p_{i}=\log\_softmax(W_{2}\delta\big{(}W_{1}z_{g}+b_{1}\big{)}+b_{2}\big{)} \tag{19}\]  
In the formula, \(W_{1}\) represents the weight matrix of the first fully connected layer; \(b_{1}\) is the first bias vector; \(W_{2}\) is the weight matrix of the second fully connected layer; \(b_{2}\) is the second bias vector; \(p_{i}\) is the category logarithmic probability vector obtained after using log-softmax.  
Finally, a multilayer perceptron (MLP) is employed to map the GNN representation to the specific classification task, with training conducted using a loss function such as negative log-likelihood or cross-entropy. Figure 7 illustrates the detailed design of the dynamic graph neural network architecture. In this framework, a directed graph constructed via a dynamic KNN algorithm--with adaptive neighborhood relationships--is first subjected to feature projection for dimensionality reduction and subsequently used as input. An improved graph attention convolution module then efficiently aggregates local neighborhood information through a multi-head attention mechanism. Two stacked attention convolution layers, followed by ELU activation and normalization, enhance the model's capacity to represent higher-order topological structures. Finally, a fully connected layer combined with a softmax classifier accomplishes the ultimate classification task.  
Figure 7: Dynamic graph neural network structure.
## 3 LoRaWAN Background  
LoRaWAN defines a network architecture, shown in Figure 1, and a protocol stack, shown in Figure 2, optimized for IoT applications that provides low-cost, mobile and bi-directional communications.  
The LoRaWAN network architecture (in Figure 1) consists of a high number of LoRaWAN end-devices (EDs), a lower number of gateways and one network server, connected according to a star-of-stars topology.  
The bi-directional communication between the EDs and the network server goes through the gateways. The EDs exchange messages with the gateways through single-hop LoRa transmissions, while each gateway is connected to the network server via a standard  
Figure 1: The LoRaWAN network architecture.  
Figure 2: The LoRaWAN protocol stack (redrawn from [68]).  
IP connection. The gateways forward the messages from the EDs to the network server (uplink) and vice versa (downlink). Uplink messages are sent by an ED to the network server through one or multiple gateways. Each downlink message, instead, is sent by the network server to a single ED through one gateway.  
The protocol stack in Figure 2 shows that each LoRaWAN application runs over the LoRaWAN Link Layer, which defines three different classes of EDs (discussed in Section 3.4). The Link Layer builds upon the Regional Parameters regulations as some of the Link Layer configuration parameters depend on the geographical area in which a LoRaWAN ED works. The lowest layer, i.e., the Physical Layer, is the LoRa modulation technique (discussed in Section 3.1). A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of LoRa/LoRaWAN is presented in Section 3.5.
# Factorial Numbers and Their Practical Applications
## Application of Factorial Numbers in Permutation Problems
### Protecting Information Using Permutations  
Permutations containing \(n\) elements are capable of encrypting transmitted or stored information and thereby reliably protecting it from unauthorized access by encoding binary messages of at least integer length \(log_{2}n!\). In this case, the keys can be permutations of selected elements in permutations of all elements, carried out by factorial numbers [31]. In this case, information protection is combined with its noise-resistant coding, which makes it possible to accomplish both tasks of protection against interference and unauthorized access using permutations. Few codes have these properties today, even though they are useful for practical applications.
## 6 Performance Evaluation
### Algorithm Performance Analysis with Different \(\alpha\)  
In order to analyze the impact of \(\alpha\) on performance, we ran the simulator with different values of \(\alpha\) with a charging power of 0.3 W. Without the loss of generality, we conducted 10 simulations and selected the average of the experimental results for analysis. The corresponding results are described below.  
Figures 3 and 4 respectively show the changing trends of the average moving time and average energy consumption as \(\alpha\) increased. The average moving time is the average time that the MCGV uses to move in each round, and it was found to lengthen as \(\alpha\) increased. This is because the time for the MCGV to move was longer. The average energy consumption is the mean of the average energy consumption of all nodes in each round, and it was found to decrease as \(\alpha\) decreased. This is because the node upload distance also decreased. Based on these observations, the changing trend of lifetime could be ascertained.  
The lifetime is the round number when the first dead node appears. We found that the network lifetime increased with the increase of \(\alpha\) in the range [0, 0.4], reaching the maximum when \(\alpha=0.4\), while it decreased in the range [0.7, 1], as shown in Figure 5. This is because larger \(\alpha\) can increase the number of charging cells and, thus, reduce the average energy consumption of all nodes, which can prolong the network lifetime. However, excess charging cells increase the moving time of the MCGV and reduce the charging time in each round. The network lifetime, therefore, decreases dramatically when \(\alpha\) is high.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 4 Learning to Optimize An RIS
### Summary and Performance Comparison  
Different learning-based methods for solving problem (5) are summarized in Figure 9. For supervised learning, since the training samples are generated from conventional optimization methods, the quality of the output is determined by the properties of the solution from the employed optimization method. For the other two methods (unsupervised learning and reinforcement learning), the outputs have no such quality guarantee. To compare the performance of different learning-based methods, the first example mentioned in Section 2 is simulated, with GD optimization selected for generating training samples in supervised learning and also to serve as a performance benchmark.  
Figure 10a shows the case of continuous phase shift. It is clear that supervised learning performs close to the GD algorithm. This is not surprising as supervised learning is mimicking the behavior of the optimization method chosen for generating the training data. However, for unsupervised learning, although it does not need training data preparation, it performs unmistakably worse than the supervised learning. Table 4 further shows the training times and inference times of GD, supervised learning, and unsupervised learning. It can be observed that the inference times of deep-learning methods are indeed short compared to the GD method, although their preparation and training times are long.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} &
\begin{tabular}{c} **Training Data** \\ **Preparation Time** \\ \end{tabular} & **Training Time** & **Inference Time** \\ \hline GD & not applicable & not applicable & 21.7 ms \\ \hline Supervised Learning & 4.8 h & 10.521 h & 87.1 μs \\ \hline Unsupervised Learning & not applicable & 11.347 h & 66.3 μs \\ \hline Reinforcement Learning & not applicable & 17.862 h & 14.3 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of learning methods for a phase shift RIS.  
Figure 9: Illustration of different learning methods. The loss functions of supervised learning and unsupervised learning are the mean squared error (MSE) between labels and predicted phases, and the expectation of the objective function of (9) over CSI, respectively.  
On the other hand, the performance of deep-learning methods under eight allowable discrete phases is shown in Figure 9(b). For the supervised learning and unsupervised learning, we simply apply quantization to the learning results. For DRL, we employ the DQN algorithm, which is trained with a DNN for 2000 epochs and 128 minibatches for each epoch. The GD method with unquantized output is also included in Figure 9(b) to show the performance limit. It can be seen from Figure 9(b) that the performance of quantization under supervised and unsupervised learning does not degrade much compared to the unquantized output in Figure 9(a). For DQN, its performance lies between supervised learning and unsupervised learning. The training and inference times of DQN are also shown in Table 4.
## 5 Evaluation
### Performance Analysis
#### 5.2.1 RINA IoT Gateway Performance  
The main goal of this performance evaluation was to measure the quantity of useful information (goodput) transmitted by the RINA IoT gateway when the number of RINA sensors connected to the RINA IoT gateway increases. Then, the RINA IoT gateway performance was measured on the shim Wi-Fi DIF and shim eth-VLAN DIF.  
The first evaluation was focused on determining how the number of flows affects the goodput of unreliable with re-transmission control flows over shim Ethernet VLAN DIF by using the rinaperf application [39]. The test consisted of allocating unreliable flows with the re-transmission control policy activated, and sending 10,000 packets of different sizes. This rinaperf configuration follows the recommendations detailed on the rlite open-source repository [39]. Figure 16 shows the goodput of unreliable flows with re-transmission in the function of the number of flows for different SDU sizes and the test for diverse SDU sizes. The curves  
Figure 14: _EdgeNode1_ log during CDAP messages interchanging.  
Figure 15: _EdgeNode1_ IRATI management console after CDAP messages.  
in the figure show the expected goodput degradation as far as the increasing number of flows that the RINA-based IoT gateway supports. Since the allocated flows are unreliable, the goodput achieved is lower than the theoretical throughput in a fast Ethernet network card and higher than the unreliable flows with transmission control. The maximum goodput achieved with 1400 bytes of SDU size is 34.5 Mbps. This is 1/3 of the theoretical throughput 100 Mbps, and this significant degradation is caused by the transmission control overhead.  
In contrast to the first test, this test allocated unreliable flows without re-transmission control, so the allocated flows are similar to a User Datagram Protocol (UDP) connection. The measurement was performed similarly to the first experiment. Figure 17 shows the goodput of unreliable flows in the function of the number of flows for different SDU sizes and the test for diverse SDU sizes. Similar to the first experiment, the curves show a degradation of goodput as far as the increasing number of flows that the RINA-based IoT gateway supports. Since the allocated flows are unreliable, the goodput achieved is lower than the theoretical throughput in a fast Ethernet network card and higher than the unreliable flows with transmission control. This variability is the expected behavior because this case does not use the transmission control mechanism that affects the goodput, as was observed in the unreliable flows with the transmission control case. The maximum goodput achieved was 72.2 Mbps with SDUs of 1400 bytes. This goodput is more than 2/3 of the theoretical throughput (100 Mbps).  
Figure 16: Goodput of unreliable flows with re-transmission control.  
Figure 17: Goodput of unreliable flows without re-transmission control.