esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 3 Data Analysis and Prediction Experiments
The evaluation index results are shown in Table 3. It is worth pointing out that these forecasting practices are  
Figure 4: Correlation between early warning time sequence and real earthquake time sequence. Figure 3: The gradients of geomagnetic anomalies from 10 November 2021 to 7 January 2022 (red columnar line). still too few, and the calculation of their accuracy, false alarm rate, and missed alarm rate has no statistical significance. By analyzing the above prediction results and combining them with the actual occurrence of local earthquakes, we find that because of the continuous earthquakes during this period, the interval between the two earthquakes was too close, and the geomagnetic anomaly affected each other, causing the geomagnetic anomaly to extend to about a week before the earthquake, and resulting in false and missing reports. Let us observe another group of less dense earthquake data again: the data from 1 April 2022 to 1 June 2022. According to the data released by the China Earthquake Administration Network Center (Table 4), a total of seven earthquakes occurred during this period. The results obtained using the same data processing method and threshold are shown in Figure 6. In this figure, the blue column is sequence \(A\), the column with the red circle is sequence \(B\), and the black dotted line is sequence \(C\). From Figure 6, we can see that seven earthquakes were predicted six times with six correct predictions, no false alarm, and one missed, so the accuracy rate is 85%, the false rate is 0, and the missed rate is 14%, the evaluation index result is shown in Table 5. The predicted time reaches 5 days before the earthquake. It is worth pointing out that both the recent earthquakes in Ya'an, Sichuan, China, were predicted 5 days in advance. For the Ya'an M4.8 earthquake on 20 May, the gradient value of geomagnetic anomaly exceeded the threshold twice on 15 and 17 May, reaching \(\pm\)400 respectively. For the Ms6.1 Ya'an earthquake on 1 June, the gradient value of the geomagnetic anomaly exceeded the threshold value by 250 on 27 May. Figure 5: Schematic diagram of early warning points from 10 November 2021 to 7 January 2022 (the red circle is the early warning time point). \begin{table}
\begin{tabular}{c c c} \hline
**Precision** & **Recall** & **F1 Score** \\ \hline
75\% & 63\% & 72\% \\ \hline \end{tabular}
\end{table} 
Table 3: The evaluation index results (10 November 2021–January 2022).
# A Near-Ultraviolet Photodetector Based on the TaC: Cu/4 H Silicon Carbide Heterostructure
## 2 Materials and Methods  
### Material Preparation  
This section describes the fabrication process of the TaC: Cu/4H SiC photodetector. The n-type 4H SiC substrate, with a doping level of 1 \(\times\) 10\({}^{18}\) cm\({}^{-3}\), a thickness of 350 \(\upmu\)m, and dimensions of 10 mm \(\times\) 10 mm, was procured from MSE Supplies (Tucson, AZ, USA). High-purity tantalum carbide (TaC) with a 2:1 atomic ratio and copper (Cu) targets, each with 99.9% purity and a 4-inch diameter, were acquired from royal Target Tech. Prior to deposition, the 4H SiC sample underwent rigorous cleaning to eliminate surface contamination and native oxides. This involved the immersion of the 4H SiC substrate in an ultrasonic bath with acetone and deionized water for twenty minutes, followed by treatment with buffered oxide etchant. The cleaned substrate was then dried using high-purity nitrogen gas.
## 3 Combined Numerical Forecast Model and Machine Learning
### WRF Physical Modeling  
The WRF model version 4.0 was installed and operated using UEMS ([[https://www.wrfems.info/index.php](https://www.wrfems.info/index.php)]([https://www.wrfems.info/index.php](https://www.wrfems.info/index.php)), accessed on 26 March 2024) version 19.8 to produce atmospheric forecasts. UEMS is a collection of scripts that simplifies model configuration, defines new domains, and downloads input data for the WRF preprocessor (WPS). WRF is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. Since the aim of this study was to test a postprocessing method, we used the default WRF configuration. The use of a default WRF configuration results in lower accuracy of WRF predictions, but it offers the advantage of saving optimization efforts and allows for testing the robustness of ML postprocessing. To achieve a high-resolution forecast (illustrated in Figure 3b), we ran three nested domains in increasing resolutions (9, 3, and 1 km grid spacing). We used one-way nesting, where information only flowed from the outer domain to the inner domain [69].  
Model runs were conducted in 2021, and the initialization and boundary conditions were extracted from the GFS 0.25 deg archive [70]. The WRF was run every summer day from June to August of the years 2015-2021. The model was initialized once per day at midnight and ran for 18 h. The first 10 h were used as a spin-up period, so ML modeling was conducted from 10:00 to 18:00. The ML model was trained with only the input from the 9 km resolution domain (WRP9) and the 1 km resolution domain (WRF1). The attributes of WRF1 and WRF9 resolutions are summarized in Table 1.
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
## 3 Time Distribution Network Attack Vector
### Attacks against the PTP Protocol and Packets  
Recent scientific literature report several documented examples of attacks against timing protocols, including attacks against PTP [28; 35; 36]. These attacks are usually categorized as Denial-of-Service (DoS) attacks, feasible at various network layers, and Man-In-The-Middle (MITM), including clock masquerade, replay and filtering attacks [28; 35; 36]. For instance, [PERSON] and [PERSON] [37] proposed specific attack strategies and implementations: packet content manipulation, packet removal, packet delay manipulation, time source degradation, master spoofing, slave spoofing, compromised Best Master Clock Algorithm (BMCA), packet replay, and DoS. In addition, [PERSON] et al. [28] recently proposed and experimentally demonstrated a novel class of insider threats, including two variants of DoS spamming attacks, capable to incorrectly steer or permanently skew the slave's clock, and a master clock takeover attack.  
Mitigation techniques and solutions against the previous categories of attacks have also been proposed and discussed. It is worth mentioning that IEEE-1588-2008 standard [18] defined an experimental security extension (Annex K) in order to protect a PTP network. However, a number of weaknesses and drawbacks in this approach have been identified and, today, Anne K is deprecated [35]. Further on, a new version of the standard IEEE-1588-2019 has recently been published. It contains a new security extension in the Anne P, based on a multi-pronged approach [19]. Apart this remarkable standardization effort, the scientific literature includes other recent solutions. Among the others, it is worth to point out a proposed extension for PTP of the key management mechanism used in Network Time Security (NTS) [38] and an identity-based authentication system, i.e., First Packet Authentication with Transport Access Control [28].
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 4 Solutions
### Post-Production Analysis  
Structural weak spots introduced into a product during manufacturing by means of a cyber-physical attack could easily escape discovery during the manufacturing process. If proper post-production quality testing is not performed on products leaving the production line, there is a great risk that if a product has been structurally compromised, it can enter the market without ever being suspected of any compromised structural integrity. As has been demonstrated in [13], structural weak points that are maliciously introduced into a product's design can very easily evade detection by even the trained eye. There is thus a need for a unique approach to the post-production analysis of manufactured products.  
The use of computed tomography, Raman spectroscopy and other similar material scanning techniques could easily detect any defects in manufactured products before they leave the production line [56]. The authors in [56] suggest a post-production verification technique where a series of markers that are easily detectable during material scanning are embedded into the product as an integral part of the product design. By determining the positions of these embedded markers within the manufactured product, a digital model can be generated based on the exact locations of these embedded markers against which the manufactured product can be verified. The introduction of such embedded markers also opens up the possibility for the detection of counterfeit products.
## 4 Numerical Evaluation and Engineering Application
### Engineering Application  
The optimized scheme of pre-construction techniques was timely applied in the later triple tunnel excavation. In practical use, the pipe diameter, the arrangement angle, and the pipe length of the PRPS were 108 mm, 140\({}^{\circ}\), and 39 m, respectively. The pipe amount of the left, middle, and right tunnels are 45, 43, and 45, respectively. Due to the urgency of the construction period, the design scheme of CGPR was not entirely adopted. Nevertheless, the grouting design still provided significant reference. The rock pillar and the middle tunnel were finally selected as the key grouting zones because they were the most vulnerable area according to the preliminary design.  
For the installation of PRPS, the concrete arch was cast first to provide strong support for the pipe inserting. The steel pipe was installed by the cased-drilling method [3]. The drilling jumbo was used to drill boreholes and insert steel pipe simultaneously. After the drilling process, the front casing pipe remained in place as the permanent support. The steel pipe was grouted with cement-based slurry, and the slurry diffusion radius was not less than 0.6 times the pipe spacing.  
For the execution of CGPR, the 1.0 m thick concrete wall was built first, the combination of the forward grouting and the backward grouting was adopted. Sulfoaluminate cement single slurry (water:cement = 0.8:1) was used as the main cement-based slurry, and the cement-sodium silicate binary slurry was used as the auxiliary slurry. The grouting process should be stopped when the grouting pressure achieved final grouting pressure (3-5 MPa) and the grouting speed was lower than 5 L/min.  
To practically evaluate the engineering application, DK 67 + 950 was selected as another monitoring section. The distribution of the surrounding rock pressure is shown in Figure 16. The stable crown settlements of triple tunnels range from DK67 + 940 to DK67 + 980 are shown in Figure 17. The comparisons of representative measured data of two cases are listed in Table 5.  
Figure 16: Distribution of surrounding rock pressure at section DK67 + 950 (Unit kPa).  
It can be observed that the surrounding rock pressure at the crown and shoulders of each tunnel at DK67 + 950 were mainly concentrated between 200-400 kPa. The mean surrounding rock pressure of triple tunnels at DK67 + 950 was 134.8 kPa, 33.4% lower than that (202.5 kPa) at DK68 + 220. The MCS reduced by 58.7% after pre-support and pre-reinforcement (from 44.5 to 18.4 mm). The tunnel supports were in the good working state, no more steel frame distortion or concrete cracking was observed, and the primary lining leakage was also well controlled. It implied that the combined implementation of PRPS and CGPR greatly improved the mechanical state of the rock-support system.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 3 Preliminaries
### SD Trajectory Sequence  
We define the SD trajectory sequence of an individual user \(u\)'s nth trajectory sequence as \(\Gamma_{u}^{n}=(P_{u}^{n},y_{u}^{n})\). \(P_{u}^{n}\) is the \(n^{th}\) trajectory sequence associated with user \(u\), which can be denoted as a sequence of \(p_{u,i}^{n}=\{(v_{u,i}^{n},lat_{u,i}^{n},lot_{u,i}^{n},t_{u,i}^{n}(in),t_{u,i}^ {n}(out))|1\leq i\leq 2k+1\}\). Here \(v_{u,i}^{n},(lat_{u,i}^{n},lot_{u,i}^{n})\) respectively, represents the ID and coordinates of a base station, where \(lat_{u,i}^{n}\) represents latitude, while \(lot_{u,i}^{n}\) represents longitude, and \(t_{u,i}^{n}(in),t_{u,i}^{n}(out)\) represents user \(u\)'s time of entering and leaving the detection range of base station. \(y_{u}^{n}\) indicates the precise location corresponding to \(P_{u}^{n}\), represented by the latitude and longitude \((lat_{u,i}^{on},log_{u}^{on})\) at a specific timestamp \(t_{u,i}^{on}\), and \(t_{u,k}^{n}\) refers to coarse trajectory record that match the precise location. Taking signaling data as an example, this means that the user's precise location is \((lat_{u,i}^{on},log_{u}^{on})\) at \(t_{u,i}^{on}\), while this user is also detected by the base station at \(lat_{u,i}^{n},tot_{u,i}^{n}\), and we find this record's last k records and next k records, and thus obtain a coarse trajectory sequence and map it to a precise location.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Studies for Time-Series Sequential Modeling** & **Publications** \\ \hline RNN-based methods & [29; 37; 38] \\ \hline Transformer-based methods & [30; 31; 32; 33] \\ \hline other methods & [34; 35; 36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Studies about time sequential modeling.
# Recent Advances in Positive-Instance Driven Graph Searching +
## 4 A Unifying Take on Positive-Instance Driven Graph Searching
### Simplifying the Game  
Before we define all the locations of potential gladiator fights in graph theoretic terms, let us start with some simplifications of the game. We restrict the fugitive as follows: Since she is invisible, there is no need for her to take regular actions. Instead, the only moment when she is actually active is when the searchers perform a reveal. If \(C\) is the set of contaminated vertices, consisting of the induced components \(C_{1},\ldots,C_{\ell}\), a reveal will uncover the component in which the fugitive hides and, as a result, reduce \(C\) to \(C_{i}\) for some \(1\leq i\leq\ell\). The only task of the fugitive is to answer a reveal with such a number \(i\). The complete process of the searcher performing a reveal, the fugitive answering it, and finally of reducing \(C\) to \(C_{i}\) is called a _reveal-move_.  
We also restrict the searchers by the concept of _implicit searcher removal_. Let \(S\subseteq V(G)\) be the set of vertices currently occupied by the searchers, and let \(C\subseteq V(G)\) be the set of contaminated vertices. We call a vertex \(v\in S\)_covered_ if every path between \(v\) and \(C\) contains a vertex \(w\in S\) with \(w\
eq v\).  
Lemma 1: _A covered searcher can be removed safely._  
Proof: As we have \(N(v)\cap C=\emptyset\), the removal of \(v\) will not increase the contaminated area. Furthermore, \(v\) cannot be recontaminated at a later point, unless a neighbor of \(v\) becomes recontaminated as well (in which case the game would already be over).  
Lemma 2: _Only covered searchers can be removed safely._  
Proof: Since for any other vertex \(w\in S\) we have \(N(w)\cap C\
eq\emptyset\), the removal of \(w\) would recontaminate \(w\) and, hence, would result in a defeat of the searchers.  
Both lemmas together imply that the searchers never have to decide to remove a searcher but rather can do it _implicitly_. We restrict the possible moves of the searchers to a combined move of placing a searcher and _immediately_ removing searchers from all covered vertices. This is called a _fly-move_. Observe that the sequence of original moves mimicked by a fly-move does not contain a reveal and, thus, may be performed independently of any action of the fugitive.
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing for Controller Output under Uncertain Disturbances  
When the uncertain disturbances act on the mobile platforms, the predicted position of the controller may be different from the actual tracking position. Most of the path tracking control methods are unable to ensure that security constraints under physical limitations, especially during learning iterations. It is necessary to choose soft output constraints instead of hard constraints on the input and the rate for achieving more accurate path tracking in mobile platforms.
## 2 Membership Function Filter  
### Demands on the Filter  
Firstly, it must be emphasized that the supposed values of measured variables are predetermined (they are in known intervals) and it is necessary to appropriately eliminate extremely different values which arise from random interference during signal transmission. In the case of a long-lasting trend of measured values out of a supposed interval (for example, considerable increasing or decreasing of a measured variable) values are not eliminated. So the basic demand on the filter is that random changes would not have an effect on the filter output and, therefore, on controlled variables. Long-lasting changes out of the supposed interval must have an influence on the filter output and must be signified on the trend of measured variables.  
Further reasons for the design of the specific filter are:  
1. Considering a filter application in digital controllers with limited computing capacity, the algorithm of the proposed filter has to be simple mathematically and relatively easily programmable.
2. Using classical approximation methods by linear or non-linear trend functions is problematic, considering the huge number of random values in measured function behaviors as a result of strong interferences.  
Using typical known filters (for example the Kalman filter) is questionable, considering that mathematical description of the measured variables behavior is unknown.  
For these reasons, a filtering method using a weighted moving average was chosen with weight calculation on the basis of different membership functions.
## 2 Materials and Methods
### Statistical and Geostatistical Methods
#### 2.3.2 Spatial Autocorrelation Analysis  
The dependence of soil properties in geographic space on the observed values in adjacent regions, known as spatial autocorrelation [21], can be represented by the global Moran's \(I\) index (IN). The formula for its computation is as follows:  
\[I_{N}=\frac{N\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}W_{ij}(x_{i}-\overline{x })\big{(}x_{j}-\overline{x}\big{)}}{\left(\sum\limits_{i=1}^{N}\sum\limits_{j= 1}^{N}W_{ij}\right)\sum\limits_{i=1}^{N}\left(x_{i}-\overline{x}\right)^{2}} \tag{2}\]  
Figure 3: DEM elevation map of the study area.  
where \(N\) is the number of samples, \(x_{i}\) and \(x_{j}\) are the observed values at locations \(i\) and \(j\) in space, respectively, \(W_{ij}\) denotes the spatial weight, and \(\overline{x}\) is the sample mean.  
The range of \(I_{\text{N}}\) values lies between \(-1\) and \(1\). Values less than \(0\) indicate a negative correlation, while those greater than \(0\) suggest a positive correlation. The larger the absolute value, the more pronounced the autocorrelation. An \(I_{\text{N}}\) value of \(0\) indicates no correlation, implying a random spatial distribution. The significance level of \(I_{\text{N}}\) is tested using the following equation:  
\[Z=[I_{\text{N}}-E(I_{\text{N}})]/\sqrt{\text{var}(I_{\text{N}})} \tag{3}\]  
where \(Z\) is the statistic to test the significance of the \(I_{\text{N}}\) index, \(E(I_{\text{N}})\) represents the expected value, and \(\text{var}(I_{\text{N}})\) is the variance. Through the Z-score, its significance can be verified. At a confidence level of \(0.01\), a \(|Z|\) value of \(\geq\)1.96 indicates significant spatial autocorrelation, while a \(|Z|\) value of \(\geq\)2.58 points to highly significant spatial autocorrelation.  
Figure 4: Map of land use types in the study area.
## 2 Security and Privacy Challenges in IoE
### Fundamental Security Risks in IoE
#### 2.1.1 Identity Management and Authorization Problems  
Authentication and access control are fundamental components of security in the IoE ecosystem [11]. However, the large-scale nature of IoE networks leads to the inadequacy of traditional identity management systems [13]. Current identity management problems include:  
* _Device spoofing and identity theft_: Since IoE devices often utilize decentralized authentication methods, malicious actors can impersonate legitimate devices [12; 36].
* _Man-in-the-Middle (MitM) attacks_: Due to the lack of robust authentication mechanisms during data transmission, attackers can intercept and manipulate communications between devices [37].
* _Challenges of centralized identity management_: Traditional authentication systems (such as OAuth, Kerberos, etc.) are not scalable for distributed environments like IoE [38; 39]. As a solution, blockchain-based identity management approaches such as DID and SSI are recommended [40; 41].
## 2 Accurate Graphic Equalizer Design and Neural Control
Figure 2: Block diagram of a second-order band filter \(H_{m}(z)\). where the input values are arbitrary user-set command gain values between \(-12\) dB and \(12\) dB and the outputs are the WLS optimized filter gains used by the EQ. For our previous work [20], we trained a fully-connected, two hidden layer feedforward neural network [29; 30] with 31 input and outputs neurons. The activation function for the hidden layers was the tanh function. One thousand five hundred input-output gain pairs were used as a training dataset, where the input gains were randomly selected, with a few manually chosen special cases, and the output gains were calculated using the ACGE3 algorithm [3; 20]. Figure 3 shows the structure of the neural network, where \(g_{c,1}\), \(g_{c,2}\),..., \(g_{c,M}\) are the input gains and \(g_{0,1}\), \(g_{0,2}\),..., \(g_{0,M}\) are the output gains, both in dB. To run the neural network in its prediction mode, the following matrix equations are used:  
\[\mathbf{g}_{c}^{\prime} =2\frac{\mathbf{g}_{c}-\mathbf{x}_{\text{min}}}{\mathbf{x}_{\text {max}}-\mathbf{x}_{\text{min}}}-1, \tag{4}\] \[\mathbf{o}^{\mathbf{1}} =\tanh(\mathbf{W}^{\mathbf{1}}\mathbf{g}_{c}^{\prime}+\mathbf{ \theta}^{\mathbf{1}}),\] (5) \[\mathbf{o}^{\mathbf{2}} =\tanh(\mathbf{W}^{\mathbf{2}}\mathbf{o}^{\mathbf{1}}+\mathbf{ \theta}^{\mathbf{2}}),\] (6) \[\mathbf{g}_{\mathbf{o}}^{\prime} =\mathbf{W}^{\mathbf{3}}\mathbf{o}^{\mathbf{2}}+\mathbf{\theta}^ {\mathbf{3}},\] (7) \[H(z)=\prod_{m=1}^{M}H_{m}(z). \tag{1}\]0  
where (4) maps the input dB gain values \(\mathbf{g}_{c}\in[-12\ 12]\) to \(\mathbf{g}_{c}^{\prime}\in[-1\ 1]\), where all \(x_{\text{min},m}=-12\) and \(x_{\text{max},m}=12\). The outputs \(\mathbf{o}^{\mathbf{1}}\) and \(\mathbf{o}^{\mathbf{2}}\) of the two hidden layers are calculated in (5) and (6) using the weighting matrices \(\mathbf{W}^{\mathbf{1}}\) and \(\mathbf{W}^{\mathbf{2}}\) and bias vectors \(\mathbf{\theta}^{\mathbf{1}}\) and \(\mathbf{\theta}^{\mathbf{2}}\). The scaled output gains \(\mathbf{g}_{\mathbf{o}}^{\prime}\in[-1\ 1]\) are given by (7), and finally, (8) converts them back to decibels using the maximum and minimum values of the training data targets \(\mathbf{t}_{\text{max}}\) and \(\mathbf{t}_{\text{min}}\), respectively. \[H(z)=\prod_{m=1}^{M}H_{m}(z). \tag{1}\]1 Neural network structure having two hidden layers. The input parameters \(g_{c,1}\), \(g_{c,2}\),..., \(g_{c,M}\) are the user-set command gains in dB; the output parameters \(g_{c,1}\), \(g_{0,2}\),..., \(g_{0,M}\) are the calculated EQ filter gains in dB; and \(M\) is the number of both the command gains and the filter gains. There are \(M=31\) input and output parameters in the third-octave and \(M=24\) parameters in the Bark band GEQ.