esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 TCAPLL Architecture
### GPS Assisted PLL
Therefore, taking into consideration that satellite velocity errors are corrected by the ephemeris, and that the LOS vector change is negligible, \(e_{f-f}\) can be written as:  
\[e_{f-f}=3\sigma_{f_{\text{adaptive}}}=3\sqrt{\sigma_{f_{\text{ adaptive}}}^{2}+\sigma_{f_{\text{nonlinear}}}^{2}} \tag{15}\]  
where \(\sigma_{f_{\text{nonlinear}}}^{2}\) can be taken directly from the EKF covariance matrix, and \(\sigma_{f_{\text{adaptive}}}^{2}\) for the satellite \(S\) is computed as [10]:\[\sigma^{2}_{f_{input_{avg}}}=\frac{LOS_{S}P_{\
u}LOS_{S}^{T}}{\lambda_{\
u 1}^{2}} \tag{16}\]  
where \(LOS_{K}\) is the LOS vector projection between the receiver and satellite \(S\) in the ECEF frame. \(\mathbf{P_{v}}\) is the covariance matrix of the computed velocity. It is important to note that there is no analytical model for \(\mathbf{P_{v}}\) because it depends on several factors such as the dynamics of the receiver, and the GPS observables that update the covariance matrix. Therefore it will be extracted from the TCAPLL EKF covariance matrix throughout the receiver trajectory. Using Equation (13), the resulting theoretical maximum allowable error by the PLL to maintain tracking is plotted in Figure 4 for different loop bandwidths and integration times (Practically, values that are close to, but not above, these maximums can be obtained). As it will be clarified in the next section, this maximum is taken as the reference in the monitoring system to enable and disable the feed-forward component.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 4 Related Research: Modified Watermarking Approaches in the IoT Applications
### Hashing  
Regarding the data integrity, the hash function is used in the watermarking method in [101; 102; 103]. The authors in [101] propose the double-authentication strategy in the watermarking technology in order to secure the transfer of data from the source-drone along to the edge-router. The first authentication occurs between the source-drone and master-drone. The master-drone is a cluster technology that allows neighbouring drones to identify a suitable one and use it as a broker in sending and receiving data. This authentication uses hash with a key in order to compute the watermarking sequence. Following that, it inserts it with the source-data within the embedding process. However, the watermark embedding position is accounted for based on the timestamp of the work. The second authentication executes between the master-drone and the edge-route. In the master-done, data are received and aggregated where the fragile watermarking algorithm is applied for generating a random key. This random key is embedded with the data that are aggregated before they are sent through to the edge-router. When the edge-router receives packets, it starts to verify authenticity and integrity, and extracts the original data. This proposal demonstrates the resistance to tampering and data replay. In [102], the researchers propose a lightweight watermark algorithm by using a homomorphic encryption and hash function along with the WSN data in order to embed the watermarking data. In [103], the watermark embedding locations and mechanisms are determined based on different channel conditions for ensuring the watermark security and energy efficiency. One of these methods: such as DCT, DFT and DWT will be randomly selected in the embedding process. After that, a hashing algorithm is used to generate a synchronised coefficient variation factor, which defines how different the watermarked image is compared to the original image.
## 4 Conclusions  
To improve the high-temperature performance and anti-aging performance of bio-asphalt, this paper proposed a preparation method for RAMB. The DSR test, BBR test, anti-aging performance test and FTIR test were carried out. From the research, the primary conclusions are as follows:  
1. Through the method in the paper, the high-temperature performance of bio-asphalt is significantly improved. The low-temperature cracking resistance can meet the requirements of SHRP for low-temperature performance of asphalt binder.
2. The anti-aging performance of RAMB is much better than that of bio-asphalt. The distilled water treatment removes some light components in bio-oil and the addition of rock asphalt and montmorillonite increases the absorption peaks of two functional groups, C-H and Si-O-Si, respectively.
3. Based on the results of FTIR, the mixing of rock asphalt, montmorillonite and bio-asphalt is a physical blending system. The modification effect and road performance of this mixture still need further experimental research.  
M.M.: Methodology, Conceptualization, Writing--original draft preparation, Validation, Visualization, Formal analysis. C.L.: Conceptualization, Resources, Data curation, Writing--reviewing and editing, Funding acquisition, Project administration. Z.L.: Software, Validation, Investigation, Formal analysis. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52208420). Science and technology project of Guangxi Zhuang Autonomous Region (2020 AB42007). The Natural Science Foundation of Hunan province of China (2022 JJ40484). Open Fund of National Engineering Research Center of Highway Maintenance Technology (Changsha University of Science & Technology) (kfj210108). Technological innovation projects of enterprises in Wuhan (2020020602012145).  
Not applicable.  
Data Availability Statement: Data available on request due to restrictions e.g., privacy or ethical.  
This work was supported by the National Natural Science Foundation of China (52078063).  
The authors declare no conflict of interest.  
Figure 20: Infrared spectroscopy of rock asphalt, montmorillonite, bio-asphalt and RAMB.
## 3 Simulation Results
### As an AC-DC Converter  
Electric vehicles require an AC-DC converter to recharge their auxiliary systems. This comprises two stages: First, the AC-DC converter (diode bridge rectifier) is cascaded with the DC-DC converter. The proposed hybrid converter is employed to regulate the output voltage. To simulate the AC-DC converter, a full-bridge diode rectifier is connected to the grid to achieve a positive rectified voltage. After that, the rectified voltage is chopped by the proposed hybrid converter to achieve the desired output voltages. This application is shown in Figure 22.  
Figure 17: Inductor L\({}_{01}\) current.  
Figure 20: Inductor L\({}_{01}\) voltage.  
Figure 19: Inductor L\({}_{in}\) voltage.  
As shown in Figure 23a, the output voltage Vo1 is regulated around 100 V. The other output voltage, V\({}_{\rm o2}\), is regulated to \(-100\) V, see Figure 23b. Finally, the output voltage, V\({}_{\rm o3}\), is set to 200 V, see Figure 23c. Since the load is DC load and its value is 1.5 \(\Omega\), the load current is in phase with the load voltage. It provides an approximately 130 A load voltage, see Figure 24.  
Figure 23: The output voltages of the proposed hybrid converter. (**a**) the load 1 output voltage. (**b**) the load 2 output voltage. (**c**) the load 3 output voltage.  
Figure 22: The proposed hybrid converter in EV adapter applications.  
The rectified voltage of the grid voltage is seen in Figure 25a. This rectified voltage is regulated by the proposed hybrid converter. The grid current is affected by the switching behavior of the grid current. This could disturb the grid current, see the average grid current in Figure 25b. To estimate the power factor (PF) of the grid current, the following equation could be used:  
\[PF=Distortion\ Factor\times Displacement\ Factor \tag{14}\]  
In this case, the load is a DC load; therefore, the displacement factor is 1 (cos \(\theta\) = 1). Thus, the distortion factor is estimated in this case only by calculating:  
\[Distortion\ Factor\ (DF)=\frac{I_{in,1,rms}}{I_{in,t,rms}} \tag{15}\]  
where \(I_{in,1,rms}\) is the fundamental RMS grid current, and \(I_{in,t,rms}\) is the total RMS grid current. Finally, the total harmonic distortion (\(THD\)) is calculated by:  
\[THD=\sqrt{\frac{1}{DF^{2}}}-1 \tag{16}\]  
The \(THD\) of the grid current is illustrated in Figure 26. It can be seen that the \(THD\) of the grid current is about 21.11%. This value is within the IEEE 519 standards [38], but in future works, it could be possible to further reduce this value because the main contributor is the third harmonic. To reduce this value, a proper filter design could be the solution. However, the proposed hybrid converter has a low distortion effect on the electrical grid.  
Figure 25: Rectified grid voltage and grid current. (**a**) Rectified voltage. (**b**) Grid current.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 4 Machine-Learning-Based Models
### K-Nearest Neighbor Regression Model  
The K-nearest neighbors algorithm is a supervised machine learning technique used to solve regression and classification problems. It is simple to understand the concept of the KNN algorithm and its application. However, it has a significant drawback of becoming significantly slow if the data size in use is increased. The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other. The KNN algorithm works by calculating the distances between a query and all the points in the data, choosing a particular number of neighbors (\(K\)) closest to the query, and then voting for the most frequent label (in the case of classification) or averaging the labels (in the case of regression). Selecting the correct number of neighbors (\(K\)) can lead to the best fit in the case of regression or classification, which can be performed by applying various \(K\)'s and selecting the one that gives the best results. For this hyperparameter, the best fit obtained was \(K=2\), and the distance adopted was based on _Minkowski_'s formula. This was achieved by using the Optuna technique using different numbers and selecting the one that best fitted the measured data. Figure 3 represents how the KNN method predicts a query using two neighbors.  
The KNN calculation uses the average of the numerical target of the \(K\) nearest neighbors, applying one of the following distance functions:  
\[\text{Euclidean:}\quad D=\sqrt{\sum_{i=1}^{K}(X_{i}-Y_{i})^{2}}, \tag{19}\]  
\[\text{[PERSON]:}\quad D=\sum_{i=1}^{K}|X_{i}-Y_{i}|, \tag{20}\]  
\[\text{[PERSON]:}\quad D=\left(\sqrt{\sum_{i=1}^{K}(|X_{i}-Y_{i}|)^{q}} \right)^{1/q},\quad q\geq 1, \tag{21}\]  
where \(X\) and \(Y\) are the original path loss and the predicted path loss, respectively.
# Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
## 3 Methodology
### Advantage and Disadvantage  
From No Free Lunch theorem (NFL) it is known that no single algorithm can be best for all problems [50]. After analyzing the major attributes of QBA and QPSO algorithm, three key points for its success can be underscored in the following:  
* Better performance can be obtained from quantum-behaved algorithms compared to PSO, GA, CSA, GSA, and DE to carry out highly nonlinear, multi-modal optimization problem as quantum-behaved algorithms has the inherent capability to increase diversity in its population.
* As PSO and GA update their location depending not only on personal best information but also on explicit global best, premature convergence is common in theses algorithms and to avoid this, mean best is used in this algorithm.  
According to the No Free Lunch theorem (NFL), a single algorithm cannot be the best solution for every problem [50]. Table 1 delineates the overall advantage and disadvantages of these algorithms in brief.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Techniques** & \multicolumn{1}{c}{**Advantage**} & \multicolumn{1}{c}{**Disadvantage**} \\ \hline \multirow{4}{*}{GA} & Easy to incorporate discrete & Tuning the algorithm is hard. \\  & decision variables. & The convergence and stability linked with \\  & Initializing itself from possible solutions. & the crossover and mutation rates. \\  & Higher NPV is achieved than GA & Less efficient than PSO. \\ \hline \multirow{4}{*}{PSO} & Less parameter to tune. & \multirow{2}{*}{Trapped in local optima due to weak \\  & Simple structure and less dependent on & local search. \\ \cline{1-1}  & initial points. & \\ \cline{1-1}  & Incorporating the discrete variable & A high standard deviation and low \\ \cline{1-1}  & is easy. & efficiency are observed. \\ \hline \multirow{4}{*}{DE} & DE provides better local search & \multirow{2}{*}{Grater variance in Net present value.} \\  & Good balance between exploration & \\ \cline{1-1}  & and exploitation. & \\ \hline CSA & One parameter needs to be tuned. & Unable to avoid local optima. \\ \hline BA & Fast convergence. & Unable to avoid local optima. \\ \hline GSA & High Exploitation rate. & Unable to avoid local optima \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of discussed techniques.
# Attention-Guided Network Model for Image-Based Emotion Recognition
## 2 Materials and Methods
### Attention Modules
#### 2.2.2 Convolutional Block Attention Model  
Convolution block attention models (CBAMs) are lightweight and can also be integrated easily into pre-existing network architectures with minimal impact on the number of parameters. They have a wide application range and have shown improvements in classification tasks [6]. The CBAM attention module extends the concept of SE blocks by considering the spatial information's importance in deciding \"where\" to focus, as well as the inclusion of a global max pooling (GMP) operator to gather features to infer finer channel attention [6].  
Figure 2: Schematic of the SE attention block (blue dashed lines).  
A CBAM block is composed of two steps. The first step is where channel-wise attention is computed and processed. In the second step, the spatial attention is calculated.  
In the original CBAM architecture channel-wise attention module, the features of the convolution layer are fed into both GAP and GMP in parallel. The output features are then fed into weight-shared multi-layer perceptron (MLP) layers with one hidden layer that reduces the dimension and a second which rescales it back to C. This process produces vectors which are then summed and passed through a Sigmoid activation function to form the channel-wise attention vector. The output of this process is then multiplied with the input feature space to form the channel-wise refined features.  
Next, for spatial attention, the refined channel-wise features are fed into channel-wise global max pooling (CGMP) and channel-wise global average pooling (CGAP) in parallel. The outputs are then concatenated across the channel dimension and fed into a convolution layer with a filter size of seven before finally passing through a Sigmoid activation function to form the refined spatial features. The refined CBAM features are then the result of element-wise multiplication between the spatial attention features and the refined channel-wise features.  
In this study's approach, some modification to the main CBAM architecture was performed. The weights of the MLP were not shared between the FC layers but rather followed the same approach of the SE block, i.e., a sequential approach. This approach was taken for the FC layers so that they could learn independently from each other for a better representation. Figure 3 represents a schematic drawing of the CBAM block used in this study.
## 2 Basic Theory
### Gaussian Filter  
The Gaussian filter is derived from the filter of the Gaussian function with the average value of \(\mu=0\). By adjusting the standard deviation and filter size, excess noise can be effectively removed. The fundamental theory of the Gaussian filter can refer to the relative investigations [51]. It is defined as:  
\[\mathrm{g}[\mathrm{n}]=\mathrm{e}^{-\mathrm{n}^{2}/2\sigma^{2}} \tag{3}\]  
\(\mathrm{n}\) is the filter index. \(\sigma\) is the standard deviation. When designing a filter, the ideal coefficient of the impulse response sum is 1, to match the input range of the input signal and the output signal, as follows:  
\[\sum_{\mathrm{n}}\mathrm{g}[\mathrm{n}]=1 \tag{4}\]
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 5 Performance Evaluation
### Clustering Results of Different Number of Sensors  
Figure 3 shows the ultimate clustering result of APSA. As clearly shown in Figure 3, the proposed algorithm divides all the sensors into five clusters. The small dots denote the sensors, and the blue lines represent the virtual link between sensors and CHs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Value** \\ \hline \(N\) & Number of nodes & 50 \\ _corbS_ & Coordinate of the base station (BS) & (40,160) \\ _PS_ & Packet Size for one communication & 2000 bits \\ \(E_{init}\) & Initial energy of each node & 2J \\ \(E_{elec}\) & Energy consumption per bit & 50 nJ/bit \\ \(\varepsilon_{\text{fs}}\) & Transmitter amplifier (Free space model) & 10 pJ/bit/m\({}^{2}\) \\ \(\varepsilon_{\text{mp}}\) & Transmitter amplifier (Multi-path model) & 0.0013 pJ/bit/m\({}^{4}\) \\ \(E_{DA}\) & Data aggregation energy & 5 nJ/bit/signal \\ \(p\) & Affinity propagation (AP) preference & \(-\)6000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Another 50 sensors are added to the network to test the presented algorithm and the simulation result is illustrated in Figure 4. APSA changes the number of CHs adaptively and it divides all sensors into six clusters.
## 2 Materials and Methods
This involves heating the surface of the printed model with a flame or heat gun, which melts the outer layer of the model and smooths out any visible imperfections. However, this technique requires care and caution as excessive heat can cause the model to warp or distort [46]. Figure 3 depicts a portable 3D printer, printing the result of the 3D scan depicted in Figure 1, scanning a glass artifact in the Laboratory of Non-Destructive Techniques, University of West Attica, Athens, Greece. SLA 3D printing is an additive manufacturing technique that uses photopolymerization to produce 3D objects layer by layer. It is one of the most precise and detailed 3D printing methods and is capable of producing extremely precise parts that are exceedingly difficult to produce using other techniques. SLA 3D printing is frequently used to create medical devices and prototype parts, in addition to jewelry, collectibles, and educational materials. SLA 3D printing begins with the transmission of a 3D model to a printer. The printer then uses a light source to solidify the poured-in liquid resin. The 3D object is constructed layer by layer as the light source hardens the resin. Following the completion of each layer, the printer advances to the next level until the entire object is printed [47]. Numerous advantages of SLA 3D printing exist, including its precision and ability to produce intricate parts that would be difficult to manufacture using other techniques. Moreover, SLA 3D printing is cost-effective and environmentally friendly, as it requires less material than conventional manufacturing processes. SLA 3D printing has revolutionized the manufacturing process and enabled the creation of intricate and complex parts with extreme precision. Its affordability and precision make it an attractive option for a variety of production and prototyping requirements. As the technology advances, we anticipate that SLA 3D printing will become more prevalent [47]. Selective Laser Sintering (SLS) 3D printing is a process that uses a laser to fuse together particles of plastic, metal, or ceramic powders into a solid object. The material is spread in thin layers on a build platform and the laser then precisely traces the cross-section of the design, fusing the particles together. The process continues, building layer upon layer, until the final object is complete. SLS 3D printing is known for its high precision and ability to produce complex geometries, making it a popular choice for prototyping and producing functional end-use parts. It is also suitable for producing parts in a variety of materials, including nylon, titanium, and aluminum. The SLS process is known for being fast and cost-effective, making it a popular choice for manufacturers looking to bring their products to market quickly and efficiently [48; 49].
## 4 Data Analysis in Maritime IoT Applications  
As the maritime industry increasingly adopts IoT solutions, the importance of data analysis in this sector is being recognized. By applying data analysis to the information collected from IoT devices, maritime stakeholders can gain meaningful insights to enhance operational efficiency, improve safety, and make strategic decisions [75].
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 2 Preliminaries
### Fully Homomorphic Encryption
A commitment scheme is defined as a tuple of two PPT algorithms \(C\) = (\(C\).Cont, \(C\).Open), as follows:  
* \(C\).\(Com\left(\textbf{x},\textbf{r}\right)\to com\): Given a message \(\textbf{x}\in\mathbb{Z}_{q}^{n}\), sample a random value \(\textbf{r}\stackrel{{ R}}{{\leftarrow}}\mathcal{D}_{\sigma}^{l_{ 1}+n+l_{2}}\). Construct the concatenated vector \(\left(0^{\mathsf{T}}\|\textbf{x}^{\mathsf{T}}\right)^{T}\in\mathbb{Z}_{q}^{l_ {1}+n}\), compute the commitment value \(com=\textbf{B}\cdot\textbf{r}+\left(0^{\mathsf{T}}\|\textbf{x}^{\mathsf{T}}\right) ^{T}\), and output com. * \(C\).\(Open\left(com,x,\textbf{r}\right)\to\left\{0,1\right\}\): Given a commitment com, the committed message \(x\), and the randomness \(r\) used in the commitment, the verifier reconstructs the vector \(\left(0^{\mathsf{T}}\|\textbf{x}^{\mathsf{T}}\right)^{T}\in\mathbb{Z}_{q}^{l_ {1}+n}\) and checks whether \(om=\textbf{B}\cdot\textbf{r}+\left(0^{\mathsf{T}}\|\textbf{x}^{\mathsf{T}}\right) ^{T}\)and whether \(r\) is bounded by a specified norm. If the check passes, the verifier outputs 1, indicating that the commitment is valid; otherwise, it outputs 0. The properties of correctness, binding, and hiding, as defined below, should be satisfied by the commitment scheme:  
Definition 12 (Correctness): A commitment scheme satisfies correctness if the \(C\).Open algorithm always accepts an honestly generated commitment for any \(x\in S_{x}\), i.e. :  
\[Pr\left[C\).\(Open(com,\textbf{x},\textbf{r})=1: \begin{array}{c}\textbf{r} \stackrel{{ R}}{{\leftarrow}}\mathcal{D}_{r}\\ com\leftarrow\mathsf{C}.Com(\textbf{x},\textbf{r})\end{array} \right]=1.\]  
Definition 13 (Binding): A commitment scheme satisfies the binding property if any PPT adversary \(\mathcal{A}\) cannot find two valid random values \(r_{0},r_{1}\) that create the same valid commitment com for two different messages \(x_{0},x_{1}\), i.e. :  
\[Pr\left[ \begin{matrix}x_{0}\
eq x_{1}\\ C.Open(com,x_{0},\textbf{r}_{0})=1:(com,\textbf{x}_{0},\textbf{r}_{0}, \textbf{x}_{1},\textbf{r}_{1})\leftarrow\mathcal{A}(params)\end{matrix}  \right]\leq negl(\lambda).\]  
Definition 14 (Hiding): A commitment scheme satisfies the hiding property if any PPT adversary \(\mathcal{A}\) given two messages \(x_{0}\) and \(x_{1}\), cannot distinguish which message the given commitment com corresponds to (the commitments are indistinguishable), i.e. :  
\[Pr\left[b=\mathcal{A}(com): \begin{array}{c}(\textbf{x}_{0},\textbf{x}_{1}) \leftarrow\mathcal{A}(params)\\ b\stackrel{{ R}}{{\leftarrow}}\{0,1\},\textbf{r}\stackrel{{ R}}{{\leftarrow}}\mathcal{D}_{r}\\ com\leftarrow C.Com(\textbf{x}_{b},\textbf{r})\end{array} \right]\leq\frac{1}{2}+ negl(\lambda).\]