esa-sceva/satcom-chunk-collection huggingface dataset:
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 2 Related Work
### Utilization of Inter-Session Information in SBR  
Many scholars have recognized that valuable inter-session information can complement the limited intra-session information, thereby enabling more precise recommendations [15; 16; 25].  
FGNN-BCS [15] first generates a global item graph to learn extra information outside the session. It has achieved better performance than the models that only consider intra-session information at that time. Global Context Enhanced Graph Neural Networks (GCE-GNNs) [30] construct both global item graphs and intra-session graphs, with the information learned by the former being leveraged to assist the latter in making predictions. An Intra- and Intersession Interaction-aware Graph-enhanced Network (I3 GN) [19] connects items outside the current session and items inside, so it establishes an inter-session graph. Convolutions on this graph help nodes obtain inter-session information. Dual Channel Hypergraph Convolutional Networks (DHCNs) [18] design a novel contrastive learning paradigm, which implicitly incorporates inter-session information into the embedding learning process. COTREC [17] is an improved version of DHCN, and it combines contrastive learning with co-training, and the latter further facilitates the exchange of information inside and outside the session.  
While these models have shown promising results in SBR with the use of additional information, they still have failed to address the retention of useful information and the filtering of irrelevant information when utilizing inter-session data. We believe that the discreet utilization of inter-session information can solve the problem and then lead to better recommendation performance.
## 1 Introduction
### Contributions
#### 1.1.2 Methodological Innovations  
* A robust data preprocessing pipeline is implemented, addressing class imbalance through a hybrid NearMiss-SMOTE strategy and reducing feature redundancy using LASSO feature selection.
* Explainability is prioritized through the application of SHAP values (for tabular and time-series data) and Grad-CAM (for imaging data), offering model interpretability that is essential for clinical adoption.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 4 Packaging Design and Measurement of a Spaceborne Ka-Band Receiver
Figure 24: Schematic diagram and photo of the Ka band receiving SIP. (**a**) Schematic diagram of the receiving SIP; (**b**) Photo of the receiving SIP. After the new interconnection structure is applied to the metal 3D-SiP for aerospace, the improvement and optimization characteristics of the interconnection structure can only be evaluated through the performance of the entire SIP, especially the amplitude flatness and noise figure indicators. If the new transition structure is replaced by a gold-ribbon lap bonding structure, its amplitude flatness will deteriorate to 1.3 dB, and the noise coefficient will be 2.9 dB. Therefore, it can be seen that SiP based on the new transition structure has good matching characteristics. On the other hand, according to the metallographic analysis of the solder joints in the new SiP after the temperature cycling test, the contact is still good, as shown in the Figure 25.
## Materials and Methods
### Artificial Neural Network Learning Model (ANN-LM)
#### 2.5.1 Radial Basis Function Neural Network (RBFNN)  
The RBFNN network stands out for its simple construction, quick training and learning unity, and accurate approximation of nonlinear processes. It is a proactive network with three layers and one hidden layer. The \(m\) input nodes, \(P\) hidden neuron layers, and \(n\) output layers of the RBFNN neural network are shown in Figure 7.  
The following formula, \(X=[x_{1},x_{2}\dots x_{m}]^{T}\in R^{n}\), denoted the data input while deriving the following formula, \(Y=[y_{1},y_{2}\dots y_{m}]^{T}\), indicated the data output. This is apart from the radial basis utility of the layer hidden using the regular gaussian utility, the response output. Radial basis functions (RBFs), which have radial symmetry, are the activation functions utilised as basic functions in the network's hidden layer [29]. It is possible to express the widely used Gaussian function as Equation (7).  
\[R_{i}(x)=exp\left(-\frac{\|x-C_{i}\|^{2}}{2\sigma_{i}^{2}}\right),i=1,2,\dots,P \tag{7}\]  
In the above network, the activation function of the hidden layer is represented by \(R_{i}(x)\) for the input \(x_{i}\), and the centre is designated as \(c_{i}\) of the \(i\)-basis function. As stated in [30], the network's hidden and output layers have a linear relationship, whereas the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refences** & **Method** & **Computational Complexity** & **Scalability** & **Accuracy (\%)** & **Flexibility** \\ \hline
[27] & Interference Cancellation & High & Low & 82.3 & High \\
[28] & Random Forest & Moderate & Moderate & 92.7 & High \\
[28] & Support Vector Machines & High & Moderate & 89.5 & Low \\  & Proposed ANN-LMs & Low & High & 100 & Moderate \\ \hline \hline \end{tabular}
\end{table}
Table 5: The proposed ANN-LMs alongside three other methods.  
input and hidden layers display a nonlinear relationship. Equation (8) can be used to represent the network's output.  
\[y_{i}=\sum_{i=1}^{P}\omega_{ki}R_{i}(x),k=1,2,\ldots,q \tag{8}\]  
where \(q\) is the number of output layer nodes, and \(\omega_{ki}\) is the adjustment weight across the output and hidden layers.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 2 Methodology
### Spatiotemporal Graph Attention (ST-GAT) Model
#### 2.1.2 Temporal Layer  
Wall deflection prediction, as a time-series problem, is significantly influenced by historical data. The GRU model [48], a variant of RNN, excels in handling such problems and is thus chosen as the temporal layer for the constructed ST-GAT neural network. As shown in Figure 2, this model uses update gates and reset gates to control the flow of information: the update gate \(z_{t}\) determines how much of the past information needs to be passed along to the future, while the reset gate \(r_{t}\) decides how much of the past information to forget. These operations depend on the current input \(x_{t}\), the previous hidden state \(h_{t-1}\), and the influence of the reset gate. The hidden state \(h_{t}\) is then updated by combining the new input with the past hidden state as moderated by the update and reset gates. This makes the GRU model less complex and often faster to train compared to LSTM while maintaining similar predictive accuracy. The formulas for the GRU model are shown as (6) to (9).  
\[z_{t} =\sigma(W_{z}[h_{t-1},x_{t}]+b_{z}) \tag{6}\] \[r_{t} =\sigma(W_{r}[h_{t-1},x_{t}]+b_{r})\] (7) \[\widetilde{h}_{t} =\tanh(W[r_{t}\odot h_{t-1},x_{t}]+b)\] (8) \[h_{t} =(1-z_{t})\odot\widetilde{h}_{t}+z_{t}\odot h_{t-1} \tag{9}\]  
where \(\sigma\) denotes the sigmoid function which limits outputs to a range between \(0\) and \(1\); \(\tanh\) represents the hyperbolic tangent function that scales outputs between \(-1\) and \(1\); \(x_{t}\) represents the current input; \(h_{t}\) denotes the hidden state; \(\odot\) is the Hadamard product; weight matrices \(W_{z}\), \(W_{r}\), and \(W\) correspond to the update gate, reset gate, and candidate hidden state; while \(b_{z}\), \(b_{r}\), and \(b\) denote the bias terms for these gates. The candidate hidden state vector is represented by \(\widetilde{h_{t}}\). The operation \"1-\" signifies the subtraction of the update gate's output from \(1\), which calculates how much old information is kept in the new hidden state.  
The flowchart of the ST-GAT model, as shown in Figure 3, comprises five steps: \"Data Collection\", \"Graph Construction\", \"Graph Attention Layer\", \"Temporal Layer\", and \"Output Prediction\". The predicted deflection values derived from this process are then input into the PID controller.
## 3 Experiments and Results
### Performance Analysis
#### 3.2.1 Test Scenario-1  
Figure 3 presents the computed classification (testing) accuracies of the applied algorithms for different lengths of shapelets considering \(p\) as 1, 5, 9 and 19, respectively, while keeping \(i_{max}=15\). Here, legends represent- Ensemble, binary decision tree (DT), discriminant analysis (DA), naive Bayes (NB) and \(k\)-nearest neighbors (\(k\)-NN).  
It is evident from Figure 3 that accuracy of any classifier is comparatively low when the length \(m\) of the shapelets is 30. As the length increases upto 90, accuracy tends to increase at first. But, after 130, there seems no significant increase in accuracy with the increase in the value of \(m\). Therefore, it can be implied that the searching region for evaluating subsequences can be confined within 90 to 130 of \(m\). Also, it can be noticed that ensemble classifier gives better result for most of the time.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Dynamic Adaptive Topology Management  
The MANET created by the drones has a _tree_ structure: all connections start at the base (root) and, as explained in Section 3.2, any node in the network will ultimately connect to a single root parent node (namely the one with the highest LQM). This allows for multiple children from a single parent while (a) ensuring full connectivity (consider: connections _grow_ from the root and nodes only have a LQM when connected to the root in the first place) and (b) preventing loops.  
Nodes assess, at regular intervals, their connections based on distance and the number of hops required to reach the root. To do so, nodes consider all potential parents within their reach and assess their suitability based on (1) the distance between them and (2) their height in the tree (number of hops to the root). Node \(i\) will trigger a change from its current parent \(p\) to a new parent \(j\) when the following condition is met (Equation (9)):  
\[r_{i,j}\times\sqrt{1+l_{j}}<0.75\times r_{i,p}\times\sqrt{l_{i}} \tag{9}\]  
This starts at 1.06 and rapidly increases as (a) both nodes \(i\) and \(j\) are further from the root and (b) the hop difference between the two nodes increases, cf Table 1.  
This is meant to prevent excessive rewiring for marginal benefits. As mentioned before, the equation can be tuned and tailored by changing the value 0.75.
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.2.1 Application Plane  
In SDN, there are two types of applications. First, the end application that is supported by the infrastructure which, in our proposed model, is switching the autonomous vehicle to teleoperation mode upon detection of a drowsy driver. Second, the control application that specifies the network behavior, which is in our proposed model, is giving priority to control commands traffic going back and forth between the autonomous vehicle and the remote control center (RCC) in terms of bandwidth. In this work, we deploy our previously proposed adaptive quality of service algorithm [14] that was tailored to the investigated vehicular application.
## 3 Security Evaluation Model
### Comprehensive Empoucement Based on AHP-Improved Entropy Weight Method
#### 3.1.2 Determine the Objective Weight  
The entropy weighting method is objective. First, an evaluation matrix was constructed, and the index was normalized and forwarded. Second, the entropy value was determined and normalized, and the weight value was determined.  
Normalization of entropy value:  
\[X_{j}=-\ln\frac{1}{n}\sum_{i=1}^{m}\bigl{(}p_{ij}\ln p_{ij}\bigr{)} \tag{1}\]  
Determination of weight value:  
\[{M^{\prime\prime}}_{j1}=\frac{1-X_{j}}{\sum_{j=1}^{n}\bigl{(}1-X_{j}\bigr{)}} \tag{2}\]  
Figure 3: Combination weight determination flow chart.  
When calculating the weight, there may be a situation in which the entropy value \(e_{j}\) approaches 1. However, the entropy weighting method has certain limitations. The specific details are listed in Table 2.  
From Table 2, we can see that the difference in entropy values in each group is 0.0001, 0.001, 0.01, and 0.1, respectively, but the calculation results of the final entropy weight are the same. Entropy is a measure of the amount of information required. The amount of information shown by the entropy values \"0.9997 and 0.9999\" is different from that of \"0.7 and 0.9\", and the entropy weight will also be different. Therefore, the weight distribution obtained using the traditional entropy weight method is unreasonable. The entropy weight method was modified and improved to improve the data rationality. The improved entropy weight method [29] is calculated as follows:  
\[{M^{\prime\prime}}_{j}=\left\{\begin{matrix}\left(1-\overline{X}_{j}^{n} \right){M^{\prime\prime}}_{j1}+\overline{X}_{j}^{n}{M^{\prime\prime}}_{j2},X_{ j}<1\\ 0,~{}~{}X_{j}=1\end{matrix}\right. \tag{3}\]  
In the formula, \(\overline{X}_{j}\) is the average of all non-1 entropy values and \(M_{j2}^{\prime\prime}\) is calculated as follows:  
\[{M^{\prime\prime}}_{j2}=\frac{\left(1+\overline{X}_{j}-X_{j}\right)}{\sum_{k =1}^{n}\left(1+\overline{X}_{j}-E_{k}\right)} \tag{4}\]
# The Circadian Effect Versus Mesopic Vision Effect in Road Lighting Applications
## 2 Materials and Methods  
### Materials  
To test the circadian effect and mesopic efficiency under a dim road lighting environment, we calculated the CCT, S/P ratio, EML, CL\({}_{\text{A}}\), and CS of a large set light source and analyzed their relationships. We adopted the light source data from IES TM30-15 for 318 light sources [43], as shown in Figure 1, including all kinds of light sources, such as incandescent lamps, fluorescent lamps, high-pressure intensity discharge lamps, and LEDs, to assess EML, CL\({}_{\text{A}}\), and CS in mesopic and near photopic vision. We set the photopic adaptation illuminance levels at 1, 3, 10, 30, and 100 lx. These illuminance levels are equal to 0.1, 0.3, 1, 3, and 10 cd/m\({}^{2}\) for asphalt or concrete roads with a reflectance rate equal to 0.3, covering both mesopic and near photopic vision luminance ranges. In the assessment of the melanopic effect, retinal illuminance is required. For simplicity, we assume that retinal illuminance is equal to mesopic illuminance in mesopic vision and photopic adaptation illuminance in photopic vision.  
Figure 2a shows the spectral sensitivity curve of the photopic vision V\({}_{\lambda}\), scotopic vision V'\({}_{\lambda}\), S-con fundamental S\({}_{\lambda}\), and melanopsin corrected with the lens, which are used in the calculations. We further  
Figure 1: Spectral power distributions of light sources.  
adopt typical road lighting sources, an HPS light source, and a low CCT phosphor-converted (PC) LED and high CCT PC LED, as shown in Figure 2b, to test their circadian and mesopic performance.
## 3 Results and Discussion
### Experimental Validation  
#### 3.2.1 Experimental Temperature Validation  
The typical simulation result is obtained under a friction force, upsetting force, friction time, upsetting time and rotating speed of 12.5 kN, 50 kN, 9 s, 5 s and 1450 rpm, respectively. The computed and measured temperatures on the AA side are plotted for comparison. As shown in Figure 9, the thermocouples were placed in the middle of the radius and the axis of the joint at distances of 20, 25 and 30 mm from the welding line (see Figure 1b). Initially, the thermocouples were positioned close to the weld interface, but they were consumed during the forging phase of the welding process. Due to the continuous 'upset' (axial shortening) of the specimens during welding, it is naturally difficult to place thermocouples that can record exact temperatures. Consequently, the first thermocouple was located about 20 mm  
Figure 8: The radial temperature distribution at different times.  
Figure 7: Thermal curves in the weld interface.  
away from the weld interface. The thermocouple wires were stuck onto the surfaces of the measuring specimen with high-temperature-resistant glue.  
Figure 9 shows the similar characteristics of the temperature course during the heating of the materials. The predicted temperature values are slightly higher than the temperature values obtained from the thermocouple readings. According to the authors [27,28], these differences could be a result of reading the delay times and the time constant of the thermocouples.  
Figure 10 shows the validation of the temperature at half of the sample radius.  
As can be seen from Figure 10, the predicted temperature is slightly higher than the temperature obtained by the experiment at distances of 20, 25 and 30 mm from bond line. The accuracy of the predicted temperature is quite good. The temperature results indicate small deviations of 3.6%, 3.5% and 3% for distances of 20, 25 and 30 mm from the interface. The maximum temperature near the welding point reached 538 \({}^{\circ}\)C in 1/2 of the sample radius for 9.5 s (Figure 10). Despite the plastic deformation of the material, this temperature did not reach the melting point of the aluminium alloy.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 6 Simulation Environment
### ACO Parameters  
In the ACO algorithm, the parameters \(\alpha\), \(\beta\) and \(\rho\) have great influence on the performance of the ACO. We make numerous attempts to find a better combination of the parameters to enhance the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Network size & 400 \(\times\) 400 m \\ Number of sensors (N) & 200 \\ Memory capacity of sensors & 500 bits \\ Communication range of sensors (R) & [0,50] m \\ Data generation ratio of sensors (I) & 1 bits/s \\ Initial energy of sensors (\(E_{0}\)) & 0.05 J \\ Mobile agent moving speed (v) & m/s \\ Mobile agent sojourn time (\(E_{0}\)) & s \\ Energy consumption on circuit (\(E_{\mathit{elice}}\)) & 50 nJ/bit \\ Free-space channel parameter (\(\epsilon_{fs}\)) & 10 pJ/bit/m\({}^{2}\) \\ Multi-path channel parameter (\(\epsilon_{\mathit{imp}}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters.  
Figure 8: Communication range adjustments.  
performance of ACO. Numerous simulations are shown in Table 3, and from Table 3, we can see that the best values for \(\alpha\), \(\beta\) and \(\rho\) are 1, 2, and 0.5 respectively.
## 3 Macroscopic Transmission Control: Virtual Hub-Based Transmission
### Transmission Request and Hub Topology Construction  
When a source node \(n_{s}\) wants to transmit its data to a destination node \(n_{d}\), it broadcasts a route request (RREQ) packet to its neighborhood. Here, the RREQ packet includes following information:  
\[\text{RREQ}:=\{\text{ID},\,n_{s},\,n_{d},\,\text{DCT},\,\xi,\,\text{H-Topo}\} \tag{4}\]  
where DCT denotes the maximal end-to-end delay time and \(\xi\) denotes the probability that the delay should be guaranteed. Any hub node \(H_{i}\) that receives the RREQ packets adds its topology information to the RREQ packet and then broadcasts it:  
\[\text{RREQ}.\text{H-Topo}\leftarrow\text{RREQ}.\text{H-Topo}+\text{Topo(i)}, \tag{5}\]  
After destination node \(n_{d}\) receives some RREQ packets, it constructs the reduced hub topology graph [\(G=(N,A)\)] by merging the topology information of all the received RREQ packets. Here, \(N\) and \(A\) denote the set of hub nodes and inter-hub links  
\[[G=(N,A)]\leftarrow\bigcup_{\{\text{RREQ}.\text{ID}\}}[\text{RREQ}(\text{ RREQ}.\text{ID}).\text{H-Topo}] \tag{6}\]  
where \(\{\text{RREQ}.\text{ID}\}\) denotes the set of the ID of the received RREQs.
# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
The older people who participated in this study gave their written informed consent. There were 13 people affected by neurodegenerative diseases at the early stage and 18 healthy people, both aged in a range between 60 and 95 years. The subjects were recorded while performing the StS test in two separate acquisition sessions three months apart. Several problems emerged during the acquisition phase, as some people who participated in the first acquisition session were no longer able to perform the test independently in the second session. However, at the end of the acquisition phase, 32 videos of patients with neurodegenerative diseases and 19 videos of healthy subjects were recorded during the execution of the StS test.  
Once the video sequences of RBG images were acquired, they were appropriately processed in order to remove image distortion. Both extrinsic and intrinsic camera parameters were extracted in a calibration phase to correct the image distortion.  
Then, the videos were processed to extract the skeletons of the observed people by using the OpenPose library [39]. OpenPose is a multiperson 2D pose estimation algorithm that detects the skeletons of all people present in the scene, returning the 2D coordinates of skeleton joints. In our study, the videos had to be analyzed to extract and track only the skeleton relative to the subject performing the StS test. Figure 3 shows two sample frames of the videos acquired in the two different nursing homes. The skeletons of other persons present in the scene, such as the physiotherapist or relatives, had to discarded. For this reason, a skeleton tracking procedure was developed to maintain only the skeleton information of the subject of interest while discarding the others.  
Figure 2: System setup: the HIK Vision camera is placed in front of the subject.  
Figure 4 shows the result of the developed tracking procedure. For the sake of clarity, only the \((x,y)\) coordinates of one joint (the nose) are plotted both in the case of a sick patient (Figure 3(a)) and a healthy subject (Figure 3(b)). As can be seen, before tracking, the coordinates of the joint are misaligned among skeletons. Once the skeleton of the patient is manually indicated in the first frame, the procedure tracks the skeleton in the entire video. Thus, after tracking, the joint coordinates are correctly assigned to the skeleton of interest.