esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation and Preliminaries
### RBF Neural Networks  
RBFNNs, proposed by [PERSON] and [PERSON] [28] in 1989, are motivated by the locally tuned responses in biologic neurons. In general, an RBFNN consists of three layers: the input layer, the hidden layer, and the output layer, respectively. Figure 2 shows the structure of a multiple-input multiple-output RBFNN.  
Figure 1: Pareto front (PF) of two goals.  
The output of an RBFNN is obtained by  
\[y_{j}=\sum_{i=1}^{N}w_{ji}\phi_{i}(\left\|\textbf{x}-\textbf{c}_{i}\right\|)+ \xi_{j},\,j=1,2,\ldots,m \tag{2}\]  
where \(y_{j}\) is the output of \(j\)th neuron, \(m\) is the number of output neurons; \(\textbf{x}=\left[x_{1},x_{2},\ldots,x_{n}\right]^{T}\) is an input vector, and \(\textbf{x}\in\Re^{n\times 1}\); \(\textbf{c}_{i}\in\Re^{n\times 1}\) is the center vector of \(i\)th hidden neuron, \(N\) is the number of hidden neurons; \(\left\|\cdot\right\|\) denotes the Euclidean distance between \(\textbf{x}\) and \(\textbf{c}_{i}\); \(w_{ji}\in\Re^{m\times N}\) is the connection weight between \(i\)th hidden neuron and \(j\)th output neuron; \(\xi_{j}\) is the residual sequence of \(j\)th output neuron; \(\phi_{i}(\cdot)\) is a radial basis function that maps the data from input space to the hidden space, and generally a normalized Gaussian function is chosen [29]  
\[\phi_{i}(\left\|\textbf{x}-\textbf{c}_{i}\right\|)=\exp(-\frac{\left\|\textbf {x}-\textbf{c}_{i}\right\|^{2}}{\sigma_{i}^{2}}) \tag{3}\]  
where \(\sigma_{i}\) denotes the width of \(i\)th hidden neuron and controls the response of the neurons. The training of RBFNN can be briefly described as a process to obtain an approximate functional relationship between inputs and outputs as good as possible in the high-dimensional space [30].
## 3 Classification Method
### Signal Window Detection  
The algorithm for sEMG ROI detection--depicted in the top portion of Figure 3--consisted of computing statistics on audio signal amplitudes over 75 ms moving windows. Then, the amplitude mean (\(m\)) and standard deviation (\(std\)) were used to define a threshold for voice (and hence sEMG) activity given by: \(mean\ +\ \delta\times std\), where \(\delta\) was a sensitivity adjustment for the detection among different subjects. The actual starting and ending points of the ROI were automatically shifted forward and backward, respectively, by 100 ms in order to eliminate muscle pre-activation and post-relaxation due to jaw movements.
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 4 Performance Analysis
### Suppression Capability
Using asymmetric coefficients, ECA-LF can apply higher-order filters with the same delay to achieve steeper edges, which is attractive in practical real-time processing.
## 4 Security Issues and Solutions for CAVs
### Cyber-Attacks on Intra-Vehicle Systems
#### 4.1.2 Attack Impacts on Intra-Vehicle Systems  
It is typically the first step that cyber-attacks remotely compromise a module of a vehicle. To successfully manipulate specific operation functions of the vehicle, attackers need to further determine the proprietary nature of different messages on the network, which usually involves the reverse engineering of ECU firmware. This is also why attacks on intra-vehicle systems can cause considerable damage to the vehicle. They directly tampered the control message on the system and manipulated the control modules (ECUs) of the system, which would quickly kill some functions of the vehicle. In [3; 27], the researchers provided the packet analysis results and their effects on different control modules of the vehicle, which are partially presented in Table 6.  
After attackers know the nature of different communication packets on intra-vehicle system and their effects on the corresponding control modules well, they can implement diverse types of attacks on the intra-vehicle system remotely. Specific attacks include (a) Denial of Service (DoS) [86] and fuzzing attack [87]; (b) code modification and injections [2]; (c) replay attack [6; 23]; and (d) malware injection. Even though these types of attacks may be presented in the previous section, this section focuses on the direct impact of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Message Code** & **Controlled Function** & **Impacted Control Module** \\ \hline
07 AE\_.1F 87 & Continuously activates lock replay & Body control module \\
07 AE\_CE 32 & Temporary RPM increase & Engine control module \\
07 AE\_.25 2B & Engages front-left brake & Electronic brake control \\
00 00\_00 00 & Falsify speedometer reading & Other modules \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different packets of in-vehicle networks and their effects on control modules.  
Figure 5: Model of cyber-attacks on intra-vehicle systems.  
attacks on intra-vehicle systems. In addition, a type of smart attack, malware, is considered considerably threatening in the future [88] because it can modify itself to look different each time it replicates, such as polymorphic and metamorphic malware. Many existing defense approaches become fragile and unresisting to polymorphic and metamorphic malware [88; 89; 90]. To effectively defend against smart malware, some intelligent analysis and detection methods are needed [63]. Table 7 summarizes the different interfaces and types of attacks on the intra-vehicle system.
## 3 Methodology
### The Proposed Method
#### 3.2.3 LSKA Module  
In Section 3.2.2, the Backbone and Neck in YOLOv8n have been reconstructed by integrating the SAConv and SlimNeck modules. However, our experiments revealed that when confronted with massive small target instances in eggplant disease images, this structure still struggled to effectively integrate fine-grained feature information, leading to low small target detection performance. To elevate the model's efficiency in extracting important features of small disease targets, this study incorporated the LSKA modules into the SlimNeck network.  
LSKA is a large kernel separable attention module, as illustrated in Figure 9 [23]. The two depthwise convolution (DW-Conv) modules initially extract features from both the horizontal and vertical orientations of the input feature map. This step aims to generate preliminary attention maps to facilitate the model's attention to important parts of images. Subsequently, LSKA employs spatially depthwise dilated convolution (DW-D-Conv) with different dilations to further extract features, realizing long-range dependencies. These convolutional layers can cover a larger receptive field without increasing computational cost, capturing broader contextual information. After a series of convolutional operations, LSKA fuses the obtained features through the last convolutional layer to construct the ultimate attention map. Lastly, the attention map is multiplied by the input features to complete adaptive feature refinement. By this method, every element in the original feature map is assigned a weight in accordance with the values present in the attention map, which enhances the network's attention to important features.  
The output of LSKA can be calculated using Equations (2)-(5):  
\[\overline{Z}^{\text{C}}=\sum W^{\text{C}}_{(2d-1)\times(2d-1)}*F^{\text{C}} \tag{2}\]  
\[Z^{\text{C}}=\sum W^{\text{C}}_{(k/d)\times(k/d)}*\overline{Z}^{\text{C}} \tag{3}\]  
\[A^{\text{C}}=W_{1\times 1}*Z^{\text{C}} \tag{4}\]  
\[\overline{F}^{\text{C}}=A^{\text{C}}\otimes F^{\text{C}} \tag{5}\]  
where \(*\) represents convolution, \(\otimes\) represents the Hadamard operation, \(d\) is dilation rate, \(\overline{Z}^{\text{C}}\) denotes the output of a depth-wise convolution with a kernel size of \((2d-1)\times(2d-1)\), and \(Z^{\text{C}}\) signifies the output of a depth-wise dilated convolution with a kernel size of \((k/d)\times(k/d)\). Following this, a convolution operation is employed to obtain the attention map \(A^{\text{C}}\). The output of LSKA is determined by performing the Hadamard operation on the attention map \(A^{\text{C}}\) and the input feature map \(F^{\text{C}}\). When applying the LSKA module in experiments, to keep the model lightweight, this study set the kernel size parameter \(k\) to 6 and the expansion rate \(d\) to 2. Such improvements can effectively reduce the loss of fine-grained information, consequently improving the detection effect of small targets.  
Figure 9: Structure of LSKA.
## 4 Experiments and Results
### Display of Test Results  
The results shown in Figure 10 demonstrate that by cropping the original remote sensing images and inputting them into the WaveNet network for classification, the ocean internal waves in the MODIS images were successfully identified with high accuracy. This is a notable achievement as accurately identifying oceanic internal waves is a challenging task in remote sensing image analysis.  
By outlining the areas classified as \"internal waves\" in the original image, the figure provides a visual representation of the algorithm's effectiveness in detecting these features. This successful identification of oceanic internal waves has practical implications for various applications, including oceanography and environmental monitoring, where the detection and tracking of such phenomena are crucial.
## 2 Materials and Methods
### Feature Selection  
To determine the influence of choosing characteristics at this step, samples were created with and without them. The purpose of feature selection is to identify the most significant characteristics in the SCADI dataset. Furthermore, feature selection contributes to more accurate prediction by removing or underrepresenting less significant information, saving training time, and improving learning performance [27]. There are three methods for selecting characteristics (filter, wrapper, and embedded). Filtering and wrapping approaches are employed in this experiment to identify subsets of important features.
## 4 Modified Ring Routing for the Energy Efficient Mobile Sensor Network
### Sink Mobility and the Sink Position Advertisement  
The sink mobility is based on the distance of the target from the sink node. Thus, the sink moves in a greedy fashion in the direction of the target using a controlled mobility model. The process of movement occurs in the following steps and is shown in Figure 5:  
Figure 4: Frame format of the SPI (sink position information) for the ring node.  
Figure 5: Pseudo code for the procedure of the SPI packets.  
_Step 1_: The sink broadcasts the beacon message which is updated in the ring node with a timestamp.  
_Step 2_: The Ring node then forwards the sink advertisement to the CHs and receives the acknowledgement from the CHs with the information, such as the number of nodes attached to the CH, the distance of the CH from the sink, the distance of the CH from the target and the timestamp for receiving the information.  
_Step 3_: Once the sink node receives this message from the ring node, it then computes the distance with respect to the target. \(d_{\mathit{Sink-Target}}\leq d_{\mathit{Sink-CH}}+d_{\mathit{CH-Target}}\leq 4 \mathcal{C}R_{\mathit{CH}}\), where \(\mathit{C}R_{\mathit{CH}}\) is the communication range of the CH. If the condition \(d_{\mathit{Sink-Target}}\geq d_{\mathit{Th}}\), then the sink becomes mobile with a constant velocity in the direction of the target.  
_Step 4_: Then the Sink node position is modified in the ring node using the sink node's initial position and time movement. Therefore, the message broadcasted by a network is significantly decreased while sustaining the overhead as the mobile sink merely needs to broadcast its initial position with the angular velocity to the ring node. The synchronization of the sensor clocks and the periodic calibration of the position data are needed for the mobile sink prediction shown in Figure 6.
## 4 Design of CFS Screw Connection with Gypsum Sheathing
### Load-Displacement Model of the Screw Connections  
The load-displacement curves of the screw connections at ambient and elevated temperatures are important input parameters for the elaborate simulation of the mechanical performance of CFS walls at ambient temperature or in fire conditions. A four-line degradation model was adopted by [PERSON] et al. [9] to predict the load-displacement curve of the connection at ambient temperature. However, the four-line degradation model has at least eight parameters, and it is not easy to operate. The Foschi exponential model [10] can also be used to describe the load-displacement characteristic of the connection at room temperature, but it becomes inconvenient at elevated temperatures due to its irregular change in parameters. Based on the above prediction formulas for \(F_{\text{mT}}\), \(K_{\text{eT}}\), \(\Delta_{\text{mT}}\) and \(\Delta_{\text{uT}}\), a simplified load-displacement model with the post-peak branch was developed for the screw connection at both ambient and elevated temperatures, as shown in Equation (13). The pre-peak branch of the load-displacement model was obtained by modifying the Ramberg-Osgood model [26]. The post-peak branch showed a linear decrease from \(\Delta_{\text{mT}}\) to \(\Delta_{\text{uT}}\). To simplify the operation, the exponent \(A\) in Equation (13) was given a constant value for the fastener series with the same configuration and elevated temperature, as shown in Table 5.  
\[\Delta_{T}=\left\{\begin{array}{ll}(\Delta_{mT}-\frac{F_{mT}}{k_{T}})(\frac{F _{T}}{F_{mT}})^{A}+\frac{F_{T}}{k_{T}},&F_{T}\leq F_{mT}\text{ and }\Delta_{T}\leq\Delta_{mT}\\ 5\Delta_{uT}-4\Delta_{mT}-\frac{5\Delta_{uT}-5\Delta_{mT}}{F_{mT}}F_{T},&\Delta _{mT}<\Delta_{T}\leq\Delta_{uT}\text{ and }F_{uT}\leq F_{T}<F_{mT}\end{array}\right. \tag{13}\]  
where \(\Delta_{\text{T}}\) is the connection displacement at \(T\)\({}^{\circ}\)C and \(F_{\text{T}}\) is the connection shear load at \(T\)\({}^{\circ}\)C.  
Taking the screw connection with the loaded edge distance of 15 mm as an example, Figure 17 compares the load-displacement curves predicted by Equation (13) to the experimental results. All of the key parameters in Equation (13), including \(F_{\text{mT}}\), \(K_{\text{eT}}\), \(\Delta_{\text{mT}}\) and \(\Delta_{\text{uT}}\), were obtained from the present prediction formulas (Equation (1) through Equations (4) and (6) through Equation (12)). The present load-displacement model can provide a reasonable estimate of the load-displacement response of the screw connection at ambient and elevated temperatures with acceptable accuracy and simple operation. In some cases, visible differences exist between the predicted curves and the experimental results, such as the load-displacement curve of the screw connection with single-layer gypsum sheathing at 500 \({}^{\circ}\)C (Figure 17a). This difference is due to the errors of the predicted \(K_{\text{eT}}\), \(\Delta_{\text{mT}}\) and \(\Delta_{\text{uT}}\), which are inevitable due to the significant scatter of these parameters in the experiments.  
Figure 17: _Cont._
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Health Applications  
In the health domain, WSNs utilize advanced medical sensors to monitor patients within a healthcare facility, as a hospital or within their home, as well as to provide real time monitoring of patient's vitals by utilizing wearable hardware. In Figure 6, the main subcategories of health applications of WSNs namely patient wearable monitoring, home assisting systems, and hospital patient monitoring are illustrated along with the types of sensors that are most commonly used in them. WSNs that have been developed for these types of health applications are examined in the following subsection.  
Figure 6: The subcategories of the health applications of WSNs and the sensors used in them.
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 5 Correctness, Security, and Performance Analysis
### Performance
The corresponding verification includes checks for linear relations, multiplication relations, and commitment consistency, and requires the same complexity of \(O\big{(}Nn^{2}\text{log}\,q\big{)}\). During the VDKeyGen process, share resharing requires each party to store an additional state \(\mathfrak{s}^{\prime}_{i}\), which is a polynomial of degree \(t-1\), incurring a space complexity of \(O(t)\). Additionally, the protocol requires each party to send and receive \(N-1\) Shamir secret shares, resulting in a communication overhead of \(O(Nt)\). The computational overhead for executing the combination algorithm when generating the public key \(pk\) and secret key shares \(sk_{i}\) is \(O(t)\). It is important to emphasize that the total overhead in the VDKeyGen protocol can be regarded as a one-time cost related to the security level. The commitments and proofs are only executed once during system initialization, and can be reused across multiple homomorphic computations without impacting subsequent rounds. Therefore, the overall impact of this verification mechanism on system performance is limited. Its communication and computation costs can be effectively amortized across many tasks, especially in scenarios involving long-term key sharing or multi-round collaborative computation. Overall, this overhead is both necessary for enhancing active security and acceptable in practice. During the encryption phase, the ciphertext size for each party is \((n+1)^{2}\lceil\log q\rceil^{2}\). The size of an individual commitment is \(n\log q\), and a proof consists of s commitments each of size \(n\log q\). In our scheme, the final ciphertext output by each party is \(ct_{i}=\big{(}ctx_{i},\pi_{ctx_{i}},com_{R_{i}},com_{n_{i}}\big{)}\). Therefore, the total size of the final output ciphertext is \((n+1)^{2}\lceil\log q\rceil^{2}+(\log q+n+9n\log q)N\log q+(s+2)n\log q\). A comparison of our scheme with several other typical fully homomorphic encryption schemes that allow multi-user joint computation includes aspects such as security, adversary model, public key size, evaluation key size, and compactness, as shown in Table 1.
## 3 Results and Discussions
### Compressive Strength
#### 3.2.2 Influence of Molarity of NaOH Solution  
The concentration of NaOH solution is considered an important parameter in the geopolymerization process of GPC, since OH ions help to dissolve the aluminosilicates in the dissolution process of raw materials. Therefore, it is important to ensure that the geopolymer matrix has a sufficient amount of OH ions for the geopolymerization process. It can also be inferred that compressive strength of geopolymer matrix is dependent on the molarity of NaOH solution [40; 41].  
The influence of NaOH solution molarity on the compressive strength development of GPC mixes can be observed in Figure 4. It can be noticed that the compressive strength has been significantly increased as the molarity of NaOH solution was increased from 10 M (group A) to 12 M and 14 M (group B). The compressive strength of 12 M mixes, i.e., B1-S20 (48 MPa) and B2-S30 (62 MPa), has been increased by 20% and 17% respectively compared to their counterparts in 10 M mixes, i.e., A2-S20 (40 MPa) and A3-S30 (54 MPa). Similarly, the strength of 14 M mixes, i.e., B3-S20 (53 MPa) and B4-S30 (67 MPa), has been 33% and 28% higher respectively than the corresponding mixes of 10 M.  
The reason for increase in strength is the quick reaction of the internal Si, Al, and Ca components present in the source materials. The high alkalinity of NaOH solution due to increased molarity leads to rapid breaking of internal bonds, i.e., Si-O or Al-O, and Ca-O or Si-O, in FA and SG respectively. Furthermore, due to increased concentration of NaOH solution, more aluminosilicates can be dissolved quickly resulting in formation of stronger bonds [42].
## 2 Ground Motions Considered  
Three groups of horizontal ground motions recorded at ground surfaces with different site conditions (or soil effects) during past local and global earthquakes were adopted as horizontal acceleration inputs in this study. More precisely, the ground motion histories were mathematically substituted into \(\ddot{u}_{g}\) in Equations (1), (4) and (5). The first group (denoted as Group 1 hereafter) contained 2553 sets of local ground motion records with peak ground acceleration (PGA) values varying from 0.082 \(g\) to 1.260 \(g\) and peak ground velocity (PGV) varying from 0.006 m/s\({}^{2}\) to 2.800 m/s\({}^{2}\), which were collected by the Taiwan Strong Motion Instrument Program (TSMIP) during 29 July 1992 to 31 December 2006. Among these, 16 sets of records were classified as pulse-like near-fault ground motions [38], and the remaining were classified as far-field ground motions. The second and third groups (hereafter denoted as Group 2 and Group 3, respectively) correspondingly contained 25 sets of far-field ground motion records and 41 sets of pulse-like near-fault ground motion records [38] during past global earthquakes, which were collected by the Pacific Earthquake Engineering Research Center (PEER). The PGA and PGV values of ground motion records of Group 2 varied from 0.152 \(g\) to 0.621 \(g\) and from 0.208 m/s\({}^{2}\) to 0.704 m/s\({}^{2}\), respectively, and the PGA and PGV values of ground motion records of Group 3 varied from 0.144 \(g\) to 1.129 \(g\) and from 0.440 m/s\({}^{2}\) to 1.479 m/s\({}^{2}\), respectively. The basic information of these ground motions, including the earthquake, year of occurrence, station name (as the denotation hereafter), PGA, and PGV, is summarized in Table 1. Note that adopting Group 1 aims to more clearly identify the influences of different design parameters on wide-ranging seismic responses of SRBs, while adopting Groups 2 and 3, can further discuss and understand the individual potential merits of FPBs and SRBs subjected to far-field and pulse-like near-fault ground motions. Thus, it can be seen that a few ground motions classified into Groups 2 and 3 were duplicated in Group 1.
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 4 Application of the GSGWO in Path Planning of Obstacle-Crossing Robot
### Path Generation  
The generation and optimization steps of the path are as follows: (1) At the beginning of the generated path, a random node is first generated by a chaotic sequence in each row between the starting point and the end point of the map, and all nodes are placed in the path in order as a grey wolf individual. (2) After generating the individual, the connection between the individuals may pass through the black impassable area, so the intermediate value is inserted in the middle of each two nodes through a specific interpolation program so that the path is continuous and does not pass through the obstacle. (3) Although the interpolated path can guide the robot to move from the starting point to the end point without obstacles, the path often has redundant inflection points and too large corners, so it is necessary to perform node optimization operations. The basic logic of the node optimization operation is when the connection between two points does not pass through any impassable area, and the path cost of the connection between two points is less than the original path cost, the original path is cleared and replaced by the connection between two points, as shown in Figure 4:  
Figure 3: Environmental models and comparisons: (**a**) the map reality; (**b**) without obstacle-surmountable area; (**c**) with obstacle-surmountable area.  
The path before optimization is {1, 12, 22, 32, 42, 53, 64, 75, 86, 87, 88, 89, 100}, and the optimized path is {1, 53, 86, 100}. The detailed data are shown in Table 1. It can be seen that the optimized path length is shorter, the inflection point is less and the corner is smaller.