esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The article presents the process of creation of a signals database for the development of a train speed-estimation method for an axle counter system. In the article the authors present the need for information about the train speed on the railway lines and the possible applications for which the information about the train speed may be used, as well as address the concerns related with the evaluation of the information in axle counter systems. The main goal of the paper is to present the creation process of an axle counter signals database and how to emulate the real signals using the laboratory-obtained ones. A database that has been created for a specific wheel detector is available on request. It contains a variety of signals that are necessary for the development and tests of wheel detector algorithms, in particular the estimation of train speed. Signals were generated for the most commonly used rail profile in Europe and the wheels that are defined in the technical specification of interoperability. Furthermore, the signals were transformed to emulate various speeds, the possible accelerations and to include the disturbance observed in the field.  
axle counter; wheel detector signals; wheel sensor; speed measurement; wheel detection Article
# A Study on the Heat Transfer of Surrounding Rock-Supporting Structures in High-Geothermal Tunnels
## 4 Results and Discussion
### The Partitions of Thermal Response in Surrounding Rock Temperature Field  
The change law of the surrounding rock temperature field was proven to be basically the same by the calculation of heat transfer and temperature change under different initial rock temperature conditions. Moreover, all the temperature change results under different conditions were taken into consideration and summarized. Taking the initial ground temperature at 55 \({}^{\circ}\)C as an example, the variation of the surrounding rock temperature with time at different depths away from the side wall is shown in Figure 12. The decrease of rock temperature near the tunnel sidewall was faster in the first 14 days and then became slower gradually. However, the surrounding rock far away from the tunnel sidewall had no obvious change in temperature. Overall, the rock temperature around the tunnel showed three ways of change and they are corresponding to the three influence areas.  
1. [label=(0)]
2. Undulate types (strong influence area): the temperature of the surrounding rock near the tunnel sidewall decreased rapidly in 0-14 days and the cooling rate became slow after 14 days. Affected by the secondary lining at 50 days, the temperature fluctuations slightly rise, and then the temperature slowly decreases. At 50 days, affected by the secondary lining, the temperature increased slightly, and then the temperature slowly decreased.
3. Decline type (medium influence area): this part is located in the central surrounding rock. The temperature drop curve varies gradually, and the temperature change is little affected by the secondary lining.
4. Stable type (weak influence area): this part is located in the surrounding rock far away from the tunnel sidewall. The temperature decreased linearly and slowly during the whole process.  
Figure 12: Influence of excavation and cooling measurement on the surrounding rock temperature.  
The excavation and the application of the secondary lining had almost no effect on the surrounding rock temperature.
# Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters
## 3 Cluster Validity and Visualization Techniques
### Advantages of Membership Degrees  
One form of clustering is fuzzy clustering. Each data point can belong to more than one cluster. To distinguish which point belongs to which cluster, each point gets its own membership degree [17]. The advantage is that a data point close to the cluster center has a high membership degree, and another data point that is far away from the cluster center has a low membership degree. With different degrees of membership, it is possible to assign an object to multiple clusters. This enables a flexible representation of the membership relationships. In contrast to hard clustering methods, where each data point is assigned to exactly one cluster, membership degrees allow a more gradual membership. This allows a more realistic modeling of uncertainties in the data and leads to robust clustering results, especially when the boundaries between clusters are vague [18]. In addition, membership degrees allow a differentiated view of outliers that may not be clearly assigned to a specific group. Overall, the advantages of membership degrees help to strengthen the flexibility and adaptability of fuzzy cluster analysis, which is particularly useful in complex data sets and real-world applications.
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 3 Module Construction
### Share Resharing
Then, using the S.Share algorithm,_ \(P_{i}\) _computes_ \(P_{i}(x_{j})=sk_{i,j}=sk_{i}+\sum_{k=1}^{t-1}c_{ijk}x_{j}^{k}\) _and sends_ \(sk_{ij}\) _to party_ \(P_{j}\) _for all_ \(j\in[N],j\
eq i\)_._
* \(RS.Rescire\left(\{sk_{j,1}\}_{j\in[N],j\
eq i}\right)\rightarrow\stackrel{{ \sim}}{{sk}}_{i}\) _Each party_ \(P_{i}\) _receives the secret key shares from the other parties_ \(P_{i}\) _and computes_ \(\stackrel{{\sim}}{{sk}}_{i}=\sum_{j=1,j\
eq i}^{N}sk_{j,i}\)_._
* \(RS.Combine\left(\stackrel{{\sim}}{{sk}}_{i},x_{1},\ldots,x_{t} \right)\to sk_{i}^{\prime}\)_: For the subset of parties_ \(P^{\prime}\subseteq P\) _with_ \(|P^{\prime}|\geq t\)_, each party_ \(P_{i}\in P^{\prime}\) _computes_ \(sk_{i}^{\prime}=\stackrel{{\sim}}{{sk}}_{i}\prod_{j=1,j\
eq i}^{t }\frac{x_{j}}{x_{j}-x_{i}}\stackrel{{\sim}}{{sk}}_{i}\lambda_{i}\)_._In Section 2.2, we thoroughly explained the definition of the Shamir secret sharing scheme and demonstrated its properties. Building on this, we prove that the additive secret key shares \(sk_{i}^{\prime}\) obtained through share resharing can meet the correctness requirements for reconstructing the ideal secret key \(sk\) during threshold decryption. For a subset of parties \(P^{\prime}\) in the threshold access structure \(\mathbb{A}_{t}\), each party \(P_{i}\in P^{\prime}\) can locally precompute its secret key share \(sk_{i}^{\prime}\) and use it to compute partial decryption results during the threshold decryption phase. By summing the partial decryption results, the secret key \(sk\) can be reconstructed. The proof of the computation process is as follows:  
\[\sum_{i=1}^{t}sk_{i}^{\prime}=\sum_{i=1}^{t}\lambda_{i}\sum_{j=1}^{N}P_{j}(x_ {i})=\sum_{j=1}^{N}\sum_{i=1}^{t}P_{j}(x_{i})\lambda_{i}=\sum_{j=1}^{N}sk_{j}=sk.\]
## 4 Experimental Evaluation
### Comparison of Actual Test Results  
Figure 5 shows a visualization of the different algorithms' detection processes using the Egohands dataset. Comparing column 1, where the backgrounds are darker and there are incomplete gestures present, all gestures were correctly identified by the comparison algorithms. However, the YOLOv7 algorithm's recognition accuracy dropped to 80% for the incomplete gesture in the bottom right corner, and the identification of the rest of the gestures was also less accurate than with other comparison methods. In column 2, where the pictures have skin-like backgrounds and incomplete gestures, the YOLOv7 algorithm missed a detection. In column 3, we can see that the left gestures are blurred by motion. Despite this, all of the comparison methods accurately recognized the blurred gesture, except for the YOLOv7 algorithm, which had a slightly lower recognition accuracy. This extreme case was not recognized by any of the other comparison methods.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Hand Classes** & **Precision/\%** & **Recall/\%** & **mAP/\%** \\ \hline fist & 97.6 & 99.3 & 66.8 \\
1 & 100 & 100 & 58.3 \\ OK & 96.5 & 95.2 & 70.9 \\ palm & 96.5 & 100 & 69.7 \\ pointer & 96.7 & 100 & 56.2 \\ thumb down & 99.1 & 100 & 69.9 \\ thumb up & 97.4 & 100 & 70.0 \\ \hline \end{tabular}
\end{table}
Table 4: Each gesture recognition result with the TinyHGR dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Precision/\%** & **Recall/\%** & **mAP/\%** \\ \hline YOLOv3-tiny-T & 94.1 & 97.6 & 65.1 \\ YOLOv5l & 97.4 & 98.3 & 65.3 \\ YOLOv7 & 96.2 & 98.6 & 64.5 \\ Proposed method & 97.7 & 99.3 & 66.8 \\ \hline \end{tabular}
\end{table}
Table 3: Experimental results of different methods with the TinyHGR dataset.
# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
## 2 Materials, Methods, and Models
### Capacity Allocation
A prevailing observation in studies on VaR is the method's failure to fulfill the sub-layering condition, leading to its classification as an inconsistent risk measure. The subdivision property, an essential component in the definition of a risk measure, is not satisfied by VaR. However, conditional value-at-risk (CVaR) is a consistent risk measure as it satisfies the subdivision property. The CVaR of a portfolio can be defined as the average amount that the portfolio would lose in the worst-case scenario, i.e., X%. On the other hand, VaR can be defined as the best of the worst cases. [PERSON] and [PERSON] defined CVaR in Equation (3) [19]. \[\left[ \begin{array}{cc}CVaR\end{array} \right]_{\alpha}\ [X]=\ [\ \min]\  
The selection of the model to be used as an artificial neural network (ANN) was determined by a series of experiments involving models with different hidden layers, neuron counts, transfer functions, and training functions. The model that yielded the highest R\({}^{2}\) value was identified as the optimal selection for our study. The results of the tested models can be found in Table 3.  
To ensure the appropriate allocation of capacity, a statistical evaluation of the results produced by the ANN and CVaR methods was conducted, with a comparison to real data. The statistical results obtained are presented in Table 4. An evaluation of the statistical results of the models revealed that the ANN model created using the NARX model yielded superior results. Consequently, the ANN model was employed in the capacity allocation segment of the study.
## 1 Introduction  
Industry 4.0 heralds the era of'smart factories', driving interconnected, flexible systems capable of autonomous decision making and real-time adaptation which foster global innovation and competitiveness [1]. Reconfigurable manufacturing systems (RMSs) exemplify this paradigm, designed to swiftly adapt to fluctuating market demands while ensuring sustained operations [2]. The responsiveness of RMSs is pivotal for managing product variety and accommodating variable demand. Firstly, RMS designs prioritize product variety by catering to product families rather than individual items, ensuring system flexibility. Secondly, an RMS operates as a modular system, enabling rapid reconfiguration to meet changing demand dynamics. At the heart of responsive manufacturing systems (RMSs) lies the reconfigurable machine tool (RMT), a cornerstone facilitating adaptability at the process design level [3]. This pivotal component epitomizes the agility to swiftly adapt and reconfigure its functions and capabilities, aligning with evolving production requirements and dynamic market demands. By leveraging reconfigurable machine tools (RMTs), RMSs can adeptly respond to evolving manufacturing needs, optimizing production processes and enhancing overall operational flexibility and efficiency [4]. Recent advancements in the literature, as discussed in [5], introduced modules enabling reconfigurable machines, termed multi-spindle RMTs, to perform operations simultaneously in parallel. Consequently, reconfigurable manufacturing systems (RMSs) can encompass a blend of multi-spindle machines (MRMTs) and single-spindle machines (SMRMTs), facilitating diverse operational configurations. The design intricacies of RMSs incorporating both single- and multi-spindle reconfigurable machines are explored in depth in [6]. The authors' research delved into optimizing the configuration of RMSs to capitalize on the capabilities of both single- and multi-spindle RMTs, thus enhancing overall manufacturing versatility and adaptability.  
This study delves into assessing the performance of reconfigurable manufacturing systems (RMSs) incorporating reconfiguration policies, with a particular focus on the influence of multi-spindle reconfiguration machines. Simulation models are crafted to evaluate the impact of introducing one or two multi-spindle machines within an RMS under various reconfiguration policies. The factors under scrutiny include part inter-arrival times, processing time variability, and the occurrence of failures. Through simulations, decision makers can glean insights to optimize the configuration of RMSs for enhanced performance.  
The subsequent sections of this article are structured as follows. Section 2 provides a comprehensive overview of recent research on reconfigurable manufacturing systems, with a focus on the introduction of multi-spindle machines. Section 3 outlines the reference context and reconfiguration policy. Section 4 details the simulation models employed, while Section 5 delves into analysis of the numerical results. Finally, Section 6 offers conclusions and outlines potential avenues for future research.
# Instantaneous Networking Service Availability for Disaster Recovery
## 7 Discussion
### Limited Communication Range of LI-Cloud  
The deployable LI-Cloud, such as LACS, may adopt wireless communication based on WLANs that are widely used. The limited communication range puts a constraint to the networking service coverage. On the other hand, considering the fact that the users often gather to the emergency center in a disaster situation, the WLAN-based communication is expected to not only be easily used by users but also to promptly provide networking services with a certain coverage efficiency.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 4 Methods
### Models of Satellite Signals and Their Errors  
As raw data for GNSS/INS data joint processing we use pseudorange \(\rho^{i}\), radial velocity \(\dot{\rho}^{i}\), and single difference of carrier phase \(\Delta\phi^{i}\) received by the spaced antennas for each observed i-thsatellite. The measured pseudorange \(\rho^{iGNSS}\) and radial velocity \(\dot{\rho}^{iGNSS}\) of reference antenna for each observed \(i\)-th satellite of \(j\)-th GNSS, \(j=GPS,GLONASS\), are described by  
\[\begin{split}\rho^{iGNSS}=\rho^{i}+c\delta T^{j}+\varepsilon_{ \rho},\\ \dot{\rho}^{iGNSS}=\dot{\rho}^{i}+\frac{d\left(c\delta T^{j} \right)}{dt}+\varepsilon_{\dot{\rho}},\end{split} \tag{5}\]  
where \(\rho^{i}\) is the true distance between the satellite and the receiver, \(\dot{\rho}^{i}\) is the true radial velocity, \(c\) is the light velocity, \(\delta T^{j},j=GPS,GLONASS\) is the receiver clock error depending on the GNSS, \(\varepsilon_{\rho},\varepsilon_{\dot{\rho}}\) are the noises of GNSS receivers. Values \(\delta\rho^{j}=c\delta T^{j}\) and \(\delta v^{j}=\frac{d\left(c\delta T^{j}\right)}{dt}\) are the clock frequency shifts and velocity of their changeability with respect to j-th GNSS satellite data described by the following model:  
\[\begin{split}\dot{\delta}\rho^{j}&=\delta v^{j}+w ^{j}_{\delta\rho},\\ \dot{\delta}v^{j}&=K^{j}+w^{j}_{\delta\dot{\rho}}, \\ \dot{K}^{j}&=w^{j}_{K},\end{split} \tag{6}\]  
\(w^{j}_{\delta\rho},w^{j}_{\delta\dot{\rho}},w^{j}_{K}\) are zero-mean discrete white noises with the known variances. Factor \(K^{j}\) characterizes the clock frequency shift and is described by Wiener process.  
The measured single difference of i-th satellite carrier phase received by the spaced antennas \(L^{i}\Delta\phi^{iGNSS}\) was preliminary processed. Values--calculated as the difference between the phase difference determined at the first step for the current satellite at the current epoch and the antenna baseline with further rounding to the integer--were excluded from \(L^{i}\Delta\phi^{iGNSS}\). After that the measurements \(L^{i}\Delta\phi^{iGNSS}\) can be written as  
\[L^{i}\Delta\phi^{iGNSS}=\Delta\rho^{i}+\delta Sf^{i}+\varepsilon_{L\Delta\phi}, \tag{7}\]  
where \(\Delta\) is the operator denoting the single difference, \(L^{i}\) is the carrier wavelength, \(\delta Cf^{i}\) are the errors conditioned by the residual ambiguity of phase single differences for the satellite, \(\varepsilon_{L\Delta\phi}\) is GNSS receiver errors in the phase carrier measurement (including the multipath errors). Application of two satellite navigation receivers with a common clock excludes the receivers' clock error from the measurements [6].
## 3 The Proposed Optimal Power Allocation Strategy
### (4) \(Sink\) Constraint  
To obtain better communication quality, SINR is an essential requirement for improving the performance of the VLC system [23]. Based on the proposed CMIMO model for the multi-color LED based VLC system, the SINR at the \(i\)th receiver is given by  
\[SIN_{i}=\frac{\mathbb{E}(I_{ii}^{2}h_{i\gamma}^{2}\gamma_{i}^{2}d_{i}^{2})}{ \sum_{j=1,j\
eq i}^{N}\mathbb{E}(I_{ii}^{2}h_{j\gamma}^{2}\gamma_{i}^{2}d_{j}^ {2})+\sigma^{2}}, \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CCT**} & \multirow{2}{*}{**Center Point**} & \multicolumn{4}{c}{**(\(\chi\), \(y\)) Tolerance Quadrangle**} \\ \cline{3-6}  & & **A** & **B** & **C** & **D** \\ \hline
3000 K & (0.4339, 0.4033) & (0.4562, 0.4260) & (0.4303, 0.4173) & (0.4150, 0.3821) & (0.4373, 0.3893) \\
3500 K & (0.4078, 0.3930) & (0.4303, 0.4173) & (0.4003, 0.4035) & (0.3895, 0.3709) & (0.4150, 0.3821) \\
4000 K & (0.3818, 0.3797) & (0.4003, 0.4035) & (0.3737,0.3880) & (0.3671, 0.3583) & (0.3895, 0.3709) \\
4500 K & (0.3613, 0.3670) & (0.3737, 0.3882) & (0.3550, 0.3754) & (0.3514, 0.3482) & (0.3672, 0.3585) \\
5000 K & (0.3446, 0.3551) & (0.3550, 0.3753) & (0.3375, 0.3619) & (0.3366, 0.3373) & (0.3514, 0.3481) \\
5700 K & (0.3287, 0.3425) & (0.3375, 0.3619) & (0.3205, 0.3476) & (0.3221, 0.3256) & (0.3366, 0.3374) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coordinate of four corners for quadrangle chromaticity constraint under different correlated color temperatures (CCTs).  
Figure 4: The chromaticity tolerance with quadrangle range for LED products in the CIE 1931 chromaticity diagram.  
where \(\mathbb{E}(d_{i}^{2})=\frac{M+1}{3(M-1)}\)[8] and we define \(\beta=\frac{M+1}{3(M-1)}\) here. To satisfy the SINR constraint, the minimum SINR requirement is set as \(\xi\). Thus, the SINR constraint can be expressed as  
\[\frac{t_{ii}^{2}t_{ii}^{2}\gamma_{i}^{2}}{\sum_{j=1,j\
eq i}^{N}t_{ij}^{2}\eta_{ j}^{2}\gamma_{j}^{2}+\frac{1}{\beta}\sigma^{2}}\geq\xi,i=1,2,\cdots,N \tag{15}\]  
For convenience, we define the following notations further:  
1. Define the matrix \(\mathbf{F}\) with entries \(f_{ij}=t_{ij}^{2}t_{ij}^{2}\).
2. Define the matrix \(\mathbf{W}\) with entries \(w_{ij}=\left\{\begin{array}{cc}0,&j=i\\ f_{ij},&j\
eq i\end{array}\right.\)
3. Define the matrix \(\mathbf{G}=diag([\frac{1}{f_{ii}},\frac{1}{f_{2i}},\cdots,\frac{1}{f_{NN}}])\).
4. Define \(e^{\mathbf{m}}=\gamma^{2}\).  
Accordingly, the SINR constraint can be equivalently rewritten as  
\[\frac{(e^{\mathbf{m}})_{i}}{(\mathbf{G}(\mathbf{W}\gamma^{2}+\frac{1}{\beta} \sigma^{2}))_{i}}\geq\xi,i=1,2,\cdots,N \tag{16}\]  
where \((\cdot)_{i}\) is the \(i\)th element in the vector.
## 3 Results
### Relationships among the PK Variables
#### 3.1.1 PCA and Correlation Analysis  
Principal component analysis was used to extract information from participants and analyze the relationships among the PK variables (Figure 2). The observations (study participants) are represented by dots in the plane produced by the two initial principal components, while the lines represent the vectors of the variables, such as AUC, Cmax, AUCinf, Tmax, etc. The loadings (I1 and I2 for the 1 st and 2 nd principal components) of all PK variables are shown next to the PCA plot.  
A correlation analysis was conducted in order to further explore the bivariate relationships between the PK variables. The Pearson's correlation coefficient (R) values were estimated (Table 1). It becomes obvious that AUC and AUCinf are fully linearly correlated, having an R very close to 1 (i.e., 0.986). In terms of the other variables, the following ranking of correlations with AUC was found: Cmax (R = 0.632), Cmax/AUC (R = \(-\)0.425), lambda (R = \(-\)0.375), Cmax/Tmax (R = 0.236), and Tmax (R = 0.114). Firstly, it should be underlined that Tmax has quite a different performance compared to the measures of extent of absorption (AUC and AUCinf). Moreover, it becomes obvious that Cmax shows a high correlation with AUC (or AUCinf) and a low correlation with the kinetic term Tmax. The Cmax/AUC ratio is related more to AUC (R = \(-\)0.425) than Tmax (R = 0.341), while Cmax/Tmax shows no association with the measures of extent of absorption but is strongly (negatively) related to Tmax (R = \(-\)0.719).
## 5 Simulation and Result of MANET-ESO in IoT Techniques
### Performance Evaluation of Packet Delivery Ratio (PDR) and Routing Overhead  
This session compares the proposed work performance to EECHS-ISSA DE and Butterfly and Ant algorithms in terms of packet delivery ratio and routing overhead. Figure 5 illustrates the evaluation of the packet delivery ratio with respect to 50, 100, and 150 nodes. Comparing this proposed method to existing ones, the packet delivery ratio is high. The clustering method and trust routing algorithm in the network reduced the packet transmission delay and transferred the packet to a short distance. As a result, all packets reach the destination node with minimum loss than other existing methods. Under 50 and 100 nodes scenarios, the packet delivery ratio of the proposed method is increased by approximately 8% compared to EECHS-ISSA DE and approximately 4% increase compared to the butterfly ant method. The PDR is increased by 8% to 9% for 50 nodes when compared to EECHS-ISSA DE and Butterfly and Ant Method. Figure 6 shows the routing overhead of the proposed work in comparison to the existing algorithms.  
Figure 3: Residual Energy vs. number of rounds.  
Figure 4: Throughput vs. number of rounds.  
Figure 5: Packet Delivery Ratio vs. Number of Nodes.  
The trusted routing method increases the route's life span and decreases routing overhead compared to other existing algorithms. In the simulation scenarios compared with 50, 100 and 150 nodes, the output result is 71% better compared to EECHS-ISSA DE and 20% better compared to the Butterfly and Ant method.  
In this Figure 7, the packet delivery ratio of the MANET-ESO in the IoT algorithm is compared with the EECHS-ISSA DE, Butterfly and Ant algorithms with respect to the percentage of malicious nodes. The number of malicious nodes in the network increased by 10 and we observed that PDR improved.  
Figure 6: Routing Overhead vs. Number of Nodes.  
Figure 7: Packet Delivery Ratio vs. Malicious Nodes (%).  
Again, the number of malicious nodes is increased to 30% and 60% respectively. Now the proposed algorithm's PDR ratio is reduced due to frequent route failures occurring from the source to the destination.  
However, compared with the existing methods, the result of PDR is increased by 24% compared to EECHS-ISSA DE, and increased by 14% compared to the Butterfly and Ant method. Figure 8 shows the performance of the proposed algorithm for routing overhead as the number of malicious nodes increase in the network. The routing overhead is increased due to frequent route failure in the network. Because of the increased malicious activity, the best route selection process requires more time to complete all packet transmission.