esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--SHAP Technique  
SHAP is a black-box mathematical model revealer that can uncover the hidden operations in the outcome of any ML model. The explanation of the output of the ML model is governed by the game theory concept; it involves finding each feature's contribution to the prediction. This method provides interpretability and explainability in both formats: global and local contexts. The Shapley value, which is fundamental to this approach, reflects the average expected marginal contribution of a feature across all possible feature combinations. The impact of the most significant feature and its contribution to the prediction is governed by the following equation [34]:  
\[\varphi_{j}(val)=\sum_{S\subseteq\{x_{1},\ldots,x_{p}\}\setminus\{x_{j}\}}|S|! \frac{(p-|S|-1)!}{p!}\left(val\,\left(S\cup\{x_{j}\}\right)-val(S)\right) \tag{6}\]  
The term \(\varphi_{j}\in\mathrm{R}\) represents the Shapley value for feature j. \(S\in\{0,1\}\) denotes the subset of features included in the model, \(x\) is the vector of feature values for the instance being explained, \(p\) is the total number of features, \(val(S)\) is the prediction for the feature values in set \(S\), marginalized over the features not included in \(S\). The XAI method is applied to all ML models, and based on the SHAP algorithm, the prediction of each class is computed; the influences of these individual features are plotted in ascending order in Figure 7(a). For the frequency domain features, the normalized spectral kurtosis has provided the dominant value, followed by single-frequency energy aggregation, and the least important features are identified as the ratio of the minimum peak to the maximum normalized peak and the normalized spectral bandwidth. Similarly, for the time domain attributes, entropy and the root mean square error have played a significant role in predicting the output classes. The less important features are the skewness, median, maximum-to-mean ratio, and mean absolute deviation, which contributed very little to the prediction. A feature ranking based on the relative importance is given in Table 2(a). Figure 8: _Cont._  
Figure 8: _Cont_. The SHAP value of the RF model's output explains how the time and frequency domain features impact the output of the model. In Figure 8b, the impacts of the features on the GNSS signal disruption classes are stacked to create a feature importance plot. The  
Figure 8: (**a**) Feature contributions based on frequency domain attributes: (**i**) decision tree, (**ii**) AdaBoost, (**iii**) KNN, (**iv**) random forest, and (**v**) SVM. (**b**) Plots of contributions of combinations of time domain features for different types of GNSS signal disruptions: (**i**) decision tree, (**ii**) AdaBoost, (**iii**) KNN, (**iv**) random forest, and (**v**) SVM.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 3 Research and Development of Millimeter-Grade Gangue Grouting Filling Material
### Collapse Performance of Different Gangue Grades and Slurry Concentrations  
Slump refers to the difference between the height of the test bucket and the maximum height of the filled slurry after the collapse and is the simplest method to measure the flow performance of a slurry [30; 31]. In this experiment, a standard slump cylinder with an upper diameter of 50 mm, a lower diameter of 100 mm, and a height of 150 mm was used to scale the slump of the different gangue grades and slurry concentrations. Twenty-five sets of slump data were measured, as shown in Table 3 and Figure 4.  
Since gangue slurry is a super-fluidized paste, the slump value of a high-slump gangue slurry should be greater than 220 mm without segregation [32; 33]. Therefore, considering the proportional scaling of the micro slump cylinder, the slump value of the high-slump gangue slurry in this experiment had to be higher than 110 mm.  
From Figure 4, the following can be seen: (1) When the upper limit of the gangue particle size was more than 2.50 mm and the slurry concentration was below 65%, there was a risk of segregation or segregation in the gangue slurry; when the upper limit of the gangue particle size was less than 3.00 mm and the slurry concentration was above 75%, it was difficult to transport the gangue slurry. (2) As the gangue particle size upper limit increased, the gangue slurry slump showed an increasing trend. As the concentration of the gangue slurry increased, the slump of the gangue slurry showed a decreasing trend. The gangue particle size was directly proportional to the slurry transportation performance, while the slurry concentration was inversely proportional to the slurry transportation performance. (3) Within a specific range, a reasonable grading of the gangue particle size should correspond to an appropriate slurry concentration. When the upper limit of the gangue particle size was 5 mm, the appropriate slurry concentration was 70-80%; when the upper limit of the gangue particle size was 3 mm, the appropriate slurry concentration was 70-76.2%; when the upper limit of the gangue particle size was 2.5 mm, the appropriate slurry concentration was 65-75.3%; when the upper limit of the gangue particle size was 1.25 mm, the appropriate slurry concentration was 60-72%; and when the upper limit of the gangue particle size was 0.63 mm, the appropriate slurry concentration was 60-70.9%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Upper Limit of Particle Size** & **60\%** & **65\%** & **70\%** & **75\%** & **80\%** \\ \hline
5.00 mm & / & / & 141 mm & 130 mm & 120 mm \\
3.00 mm & / & / & 140 mm & 130 mm & 50 mm \\
2.50 mm & / & 145 mm & 135 mm & 115 mm & 35 mm \\
1.25 mm & 145 mm & 141 mm & 132 mm & 80 mm & 10 mm \\
0.63 mm & 142 mm & 139 mm & 125 mm & 40 mm & 10 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Slump of gangue slurry with different concentrations and particle size gradations.
## 2 Materials and Methods
### Ultrasonics: Elastic Properties  
The elastic properties of an isotropic material can be obtained using ultrasonics if its density, longitudinal speed, and shear speed are known [22; 49]. The density of a baseline sample (2:1 resin-to-hardener ratio) was obtained using a pycnometer from Micromeritics (AccuIyc II 1340). Using the ultrasonics setup previously mentioned in this section along with the cross-correlation method, a frequency sweep similar to the one performed by [PERSON] et al. [30] was used to accurately determine the longitudinal and shear sound travel times in the adhesive sample. For the longitudinal travel time, a frequency sweep from 0.9 to 2.3 MHz with a 0.1 MHz step was used, while for the shear travel time, a range from 0.5 to 1 MHz was used with the same step size. Different frequency ranges were used to obtain the longitudinal and shear travel times since a minimum amount of signal was required to perform an accurate measurement, but the magnitude of the output signal varied for each frequency and wave type. For homogenous isotropic materials, Equations (3) and (4) can be used to determine [PERSON]'s modulus and [PERSON]'s ratio, respectively [50]:  
\[E=\rho c_{s}^{2}\left(\frac{3c_{l}^{2}-4c_{s}^{2}}{c_{l}^{2}-c_{s}^{2}}\right) \tag{3}\]  
\[v=\frac{c_{l}^{2}-2c_{s}^{2}}{2c_{l}^{2}-2c_{s}^{2}} \tag{4}\]  
where \(\rho\) is the density of the material, \(c_{l}\) is the longitudinal speed in, \(c_{s}\) is the shear speed, \(E\) is the [PERSON]'s modulus, and \(v\) is the [PERSON]'s ratio.  
To compare it with destructive testing, the Young's modulus of the adhesive was also determined using tensile testing. Three tensile testing dogbone samples were manufactured and tested following ASTM D638-03 [51]. The MTS tensile tester previously mentioned was used to test the dogbone samples at a loading rate of 5 mm/min. The size of sample type I in the standard was adapted to reduce the amount of material volume used per sample. The dimensions were reduced to have a total length of 87 mm while the thickness was 4.68 mm. The width at the center of the sample and at the grips was 6.84 and 10 mm, respectively. The samples were manufactured by casting the adhesive into a PLA 3D printed mold.
## 3 Simulation Results
### Joint Design System Based on Deep Learning  
#### 3.8.1 DNN-Based Joint Design System  
To leverage DL in the joint design of communication and sensing systems, the received signal predicted through DNN application for signal detection serves a dual purpose. Firstly, it is employed to compute the BER, offering insight into the communication performance. Secondly, this predicted signal aids in assessing the radar sensing performance through the MSE evaluation within the DL framework. We assume a Rayleigh channel model with AWGN and employ a QPSK modulation scheme. The simulation parameters are kept consistent, featuring four transmit antennas (\(N_{t}=4\)) and either one or two receive antennas (\(N_{r}=1,2\)).  
Figure 18 depicts the BER for communication performance and MSE for sensing performance. This comparison occurs across the traditional and DL approaches, with fixed sensing power and varying communication power. Specifically, the scenario involves four transmit antennas (\(N_{t}=4\)) and two receive antennas (\(N_{r}=2\)).  
Figure 18: Communication and sensing performance using different approaches with 2 receive antennas.  
This figure illustrates how the communication and sensing performance vary with fixed sensing power and varying communication power for both the traditional and DL approaches. Upon examining the BER curves, it is evident that the BER versus SNR curves exhibit similar shapes, yet, consistently, the BERs obtained from the DNN method are lower than those from the traditional approach across different SNR values. This observation indicates that the DNN method consistently outperforms the traditional approach in terms of the BER under the simulation conditions across various SNR values. The lower BER values achieved by the DNN method signify its superior performance compared to the traditional approach.  
Furthermore, when scrutinizing the MSE curves, it becomes apparent that the traditional approach yields a higher MSE of the reflection coefficient compared to the DNN method. This discrepancy can be attributed to the DNN's capability to learn intricate patterns and representations from the data, thereby achieving a superior performance over the traditional approach. Additionally, upon observing the turning points in the MSE curves, it is noticeable that the DNN approach exhibits a faster and more pronounced leftward shift compared to the traditional approach. This observation underscores the positive impact of the DL approach when evaluating the radar sensing performance of the joint design system.  
Moreover, the results indicate that the BER and MSE of the reflection coefficient curves for both the traditional and DNN approaches follow the same trend in the high SNR regime, showcasing a consistent performance improvement by the DNN approach across various signal strengths. Hence, leveraging its learning capabilities, the DNN approach surpasses the traditional approach in evaluating the communication and sensing performance of the joint design system.
## 4 Proposed Approach for Forecasting the Wind Power Intervals
### Wind Power Forecasting Model Based on CNN
#### 4.1.2 The Second-Layer CNN  
Due to the randomness of the wind power data, the modes that are forecast by the first-layer CNN inevitably contain errors. Additional processing is required to minimize the errors and obtain the final point-based wind power data. The second-layer CNN is used for this purpose. The input matrix \(D\) of the second layer of the CNN consists of the mode forecasted value \(u_{\text{pred}}^{i,t}\) of all first-layer CNN sub-models and the previous \(n\) mode true values \(\left[u_{\text{meas}}^{i,t-1},u_{\text{meas}}^{i,t-2},\cdots,u_{\text{meas}}^ {i,t-n}\right]\). The details are as follows:  
\[D=\left[\begin{array}{ccc}u_{\text{meas}}^{1,t-n}&\cdots&u_{\text{meas}}^{1, t-1}&u_{\text{pred}}^{1,t}\\ u_{\text{meas}}^{2,t-n}&\cdots&u_{\text{meas}}^{2,t-1}&u_{\text{pred}}^{2,t}\\ \vdots&&\vdots&\vdots\\ u_{\text{meas}}^{p,t-n}&\cdots&u_{\text{meas}}^{p,t-1}&u_{\text{pred}}^{p,t} \end{array}\right] \tag{10}\]
## 4 Model Estimation and Results
### Measurement Models
#### 4.1.2 Divergent Validity  
The divergent validity can be tested using the average variance extracted (AVE) indicator. [PERSON] and [PERSON] [67] state that if the square root of the correlation coefficient values between the latent variables is smaller than the values of the AVE indicators related to the latent variables, then the discriminant validity is confirmed. In Table 4, these values can be observed.  
As we could see in the Table 4, the square roots of the correlation coefficients are lower than the AVE indicator values for all latent variables, which confirms the discriminant validity of the measurement model.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 3 The RL Environment
### Action Space  
As the goal of the environment is to train the agent to take a more advantageous position that ensures better visibility of the objects, the actions themselves are movements of the virtual camera used for rendering. Now, it is worth noting that if we intend to minimize the amount of occlusion measured in the image (using pixels or any other unit), then several trivial solutions present themselves. The agent could simply turn the camera away from the scene, thus ensuring no occlusion is seen by not seeing anything at all. Alternatively, the agent could just move the camera infinitely far away from the scene, thus reducing the visible objects to a size smaller than a single pixel, again minimizing the occlusion of the scene.  
Thankfully, both trivial solutions are easily avoided by restricting the camera position to a spherical surface with a fixed radius, while ensuring that the camera is always in a look-at orientation. For this reason, the two actions the agent can take are to move the camera along this sphere using the azimuth (\(\varphi\)) and elevation (\(\theta\)) angles. Since PyTorch3D expects the camera position to be given in the Cartesian coordinate system, the three parameters (\(\varphi,\theta\), and the radius \(\rho\)) have to be converted, and the look-at orientation has to be computed using PyTorch3D's built-in functions. The spherical-to-Cartesian conversion is calculated as follows:  
\[x=\rho sin(\varphi)cos(\theta), \tag{1}\] \[y=\rho sin(\varphi)sin(\theta),\] (2) \[z=\rho cos(\varphi). \tag{3}\]  
The initial extrinsic camera parameters in the spherical coordinate system are given by the equations below:  
\[\theta=0^{\circ}, \tag{4}\] \[\rho=4,\] (5) \[\varphi\sim U(-\pi/32;\ \pi/32). \tag{6}\]
## 3 Results and Discussion
### Restoration and Degradation Materials  
To identify materials that were due to previous restorations, FTIR spectra were made on the samples. In particular, the FTIR spectrum of the superficial part of the B1 sample (the blue sample taken from the robe of St. Andrew) is reported in Figure 12. The spectrum shows gypsum (calcium sulfate bi-hydrate) (3522, 3403, 1685, 1619, 1112, 670 cm\({}^{-1}\)) and calcite (calcium carbonate) (1418, 874 cm\({}^{-1}\)).  
We note, in addition, the features of an organic substance at 2985, 2899 cm\({}^{-1}\) (C-H stretching) and at 1732 cm\({}^{-1}\) (C=O stretching), suggesting the presence of acrylic resins, like Paraloid(tm), which could be due to previous restorations. This synthetic compound seems to have been used mostly on green and blue zones.  
In many samples, anatase, TiO\({}_{2}\), has been identified by Raman spectroscopy (Figure 6). The synthetic pigment, titanium white TiO\({}_{2}\), was introduced in recent times, around 1920, and this could indicate recent restorations. Raman spectroscopy is however very sensitive  
Figure 11: PCA score plot of wall painting samples and reference materials (painted models simulating ancient polychromies, containing G: animal glue; GE: animal glue and egg, GM: animal glue and milk, M: milk; E: egg; EM: egg and milk).  
Figure 12: FTIR spectrum of the superficial part of sample B1 (blue sample taken from the robe of St. Andrew).  
to anatase mineral, which can be present in traces in many natural compounds as impurity. Nevertheless, here its presence appeared too abundant to be considered accidental.  
The small amount of gypsum CaSO\({}_{4}\cdot 2\)H\({}_{2}\)O detected on the surface of different samples by the bands at 1132 cm\({}^{-1}\), 1007 cm\({}^{-1}\) and 414 cm\({}^{-1}\), seems to exclude its intentional use as pigment. It could be considered as a degradation product, due to the reaction of calcium carbonate with sulphuric acid, formed by the oxidation of SO\({}_{2}\) and SO\({}_{3}\), which are easy to find in urban areas.
# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 1 Introduction
As depicted in Figure 1a, evaluations of every person who has done transactions with the target can influence on target's reputation. On the other hand, trust is a subjective probability--a degree that affects an individual's decision and judgment--that is formed between a person and a target. Direct trust is derived if there had been transactions between the person and the target. Otherwise, indirect trust is derived as a second option via any other person who had relations with both parties. Figure 1b illustrates this where indirect trust from person A to the target is derived via person B. Indirect trust is not as influential as the direct trust. However, it could serve as a friends' suggestion which could help customers make a reasonable judgment about a target for the first time. In that sense, trust is often explained with a keyword friend-of-a-friend (FoaF) [17]. In this paper, we suggest a model for calculating _reputation_ and _trust_ values efficiently from a history of transactions and rating information. We maintain the history on blockchain-based payment system for its immutability, but for efficiency, our model does not parse all the previous transaction data per each transaction update that took place on the blockchain-based payment system. Instead, we utilize a small cache of key values to expedite the queries, while periodically validating and verifying those values in the background for reliability without deteriorating user  
Figure 1: Reputation and trust model. experience. We evaluate our model using both the data from our online payment system (system is real, but transaction/rating data are created for testing) as well as synthetic rating data derived from real Bitcoin transaction history (rating is synthetic, but transaction is real and massive). The purpose of using Bitcoin transaction history is to validate the notion of time in calculation with large amount of data, and also to test the query-response and processing latency for parsing real blockchain data. With rating information given by users, our model can derive reliable reputation and trust values which could be useful for the users to make decisions before buying or selling products/services on the Internet. Also, those values could help both the merchants and customers prevent abnormal transactions. The remainder of this paper is structured as follows. We first introduce related work in Section 2. Then, Section 3 discusses the design of our model and system for deriving reliable reputation and trust values from raw transactions and evaluation scores stored on blockchain-based system. In Sections 4 and 5, we show the efficiency of our model through experiments conducted using simulated dataset and actual Bitcoin transactions. Finally, Section 6 concludes the paper.
# An Initial Load-Based Green Software Defined Network +
## 2 Related Work  
At present, the energy waste problem in SDN has been considered from different aspects. In [13], [PERSON] et al. propose an energy saving scheme called ElasticTree for software defined data center networks. An optimizer is added in the controller to calculate the devices that can be put into sleep based on the traffic load. However besides the route oscillation problem, for the TCP-based traffic, when the traffic load increases, the network capacity won't be increased due to the adaptive congestion control of TCP, which will affect user experience as mentioned in [13].  
In [16], [PERSON] et al. propose an energy saving algorithm based on the switch assignment problem. In this algorithm, controllers act as switches at the same time. Links are switched off under the condition of ensuring that all switches are connected to one controller, and all the paths satisfy delay and load constraints at the same time. A severe limitation of this algorithm is that once the links that will be shut down are determined, communication demands that can't find a feasible path in the sub-topology will be rejected. This will affect the Quality of Service (QoS) of the network.  
[PERSON] et al. consider the memory size of switches, modeling the energy saving problem in SDN as an integer linear programming (ILP) problem under the constraint of limiting the number of the rules in the switches [18], and propose a greedy heuristic algorithm due to the high complexity of the problem. In this heuristic algorithm, the controller routes the traffic on the switches with fewer rules through setting the link weights according to the numbers of rules in the connected switches, and determines the links that can be set to sleep mode iteratively.  
In [15], the authors consider energy saving based on the controller placement problem, aiming at minimizing the energy consumption of the control network, but the energy consumption of the data forwarding plane was neglected.  
Two energy-saving models are proposed for software defined data center networks in [17]. One is a 0-1 integer programming model which is a superior model for small scale networks. The other one is a greedy algorithm model which is not limited by the network scale. To find the optimal paths of energy consumption for traffic demands, the two models both need to traverse all the paths from source node to destination node of all communication pairs; this causes a lot of redundant computation.  
The above works address the energy saving problem in SDN from different perspectives respectively. But as regards the energy saving problem in the data forwarding plane, most research works assume that the network is without traffic load when new traffic arrives. In our previous work [14], we proposed a real-time dynamical energy saving optimization scheme combined with routing algorithm based on the current working state of the network, and presented two routing algorithms that can be used in the scheme. In this extended version, we do a series of work to make the work more meaningful.