esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 3 Nonlinear Dynamic Incipient Fault Detection Method
### The Deep Decomposition Principle  
In order to even better extract the feature of the weak fault in the vibration signal, combining the deep decomposition theorem, the original space is decomposed into multiple data processing layers, and the weak fault information in the signal is deeply mined to achieve the effect of incipient fault detection.  
According to the method in Section 2.1, the dynamic kernel matrix was decomposed into the principal component space and the residual space is shown in the Formula (9):  
\[X_{K}=X_{K11}+X_{K12} \tag{9}\]where, \(X_{K11}\) and \(X_{K12}\) represent the first-order subprincipal component space and subsresidual space of the original dynamic kernel matrix \(X_{K}\), respectively. It can be expressed as Formula (10):  
\[X_{K11}=P_{11}{P_{11}}^{T}X_{K}\]  
\[X_{K12}=\Big{(}I-P_{11}{P_{11}}^{T}\Big{)}X_{K} \tag{10}\]  
where, \(P_{11}\) is the principal feature vector of \(X_{K}\). Continue to divide \(X_{K11}\) and \(X_{K12}\) to obtain the second-order subprincipal component space \(X_{K21}\), \(X_{K23}\) and subsresidual space \(X_{K22}\), \(X_{K24}\). By analogy, the original dynamic kernel matrix \(X_{K}\) can be expressed as the sum of \(2^{i}\) subspaces, where \(i\) represents the order. The subspace \(X_{Kij}\) can be expressed as Formula (11):  
\[X_{Kij}=\left\{\begin{array}{l}P_{ij}{P_{ij}}^{T}X_{K\ i-1\,\left(j+1\right)/2}\ j \text{ {is odd}}\\ \Big{(}I-P_{ij}{P_{ij}}^{T}\Big{)}X_{K\ i-1\,\ \frac{i}{2}}\ j\text{ {is even}} \end{array}\right. \tag{11}\]  
By increasing the order of the Deep ADKPCA method, extremely weak fault information can be detected, thereby achieving incipient fault detection. The structure of Deep ADKPCA is shown in Figure 2:
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 3 Materials and Methods
### Field Monitoring  
A total of 25 cisterns (6 Fesguia and 19 Majel) have been selected for erosion measurement by monitoring solid deposits after rainfall events (Figure 5). In the context of RUSLE simulation, calibration and validation were conducted during the assessment period. Erosion was estimated by measurement of deposited sediment in selected cisterns sediment settlement basins in the Koutine catchment. Cisterns are often built to catch and store rainwater. Fesguia and Majel are two common types of cisterns. Each unit consists of three main parts: the impluvium, the sediment settlement basin and the storage reservoir (Figure 6). They are distinguished according to the form of their reservoirs. Generally, Fesguia has a larger storage capacity, and therefore its construction cost is relatively higher than Majel. In our case study, the measurement concerned only deposited sediment in the settlement basin of each infrastructure (Fesguia and Majel), which depends only on the topography, soil and land cover of the impluvium. Therefore, the differences between Fesguia and Majel have no influence on the measured deposited sediment. Majel is commonly implemented in the upstream area (mountains), while Fesguia is generally constructed in the downstream area of the catchment characterized by a gentle slope (plain).  
Figure 5: Monitoring cisterns used in the study (25 measurement points: 6 Fesguia and 19 Majel).  
The measurement sites were defined by three Cartesian coordinates (x, y, z) in UTM projection Zone 32N (Figure 4). During field campaigns, we measured the thickness and the weight of the solid deposit in the sediment settlement basin. Before cleaning sediments for the next observation time, a sample is taken for laboratory analysis (soil humidity, organic matter). Sediments data for the entire network are collected and reported in the measurement sheet. These ratios account for every cistern and characterize the sediments. The eroded soils are transported towards the cisterns sediment settlement basins. According to field measurements, it is important to mention that erosion values were significant only for precipitation events that exceed 20 mm [57].  
Sediments in cisterns settlement basins represent an important record of relatives' sub-catchments erosion. In fact, they give information about the main exceptional rainfall events responsible for erosion delivery. From this point of view, the measurements were used to estimate average and maximum erosion rates for each sub-catchment. The average erosion rate was calculated at the end of the year using measurements after rainfall events of solid deposits. From these data, an annual average erosion (t/ha) was determined. These calculations were executed for all twenty-five (25) cisterns sediment settlement basins.
# Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
## 2 Characteristics of VCSEL  
### Principle of VCSEL  
VCSELs emit a laser beam perpendicularly from the top surface. As shown in Figure 1, the structure of the VCSEL can be configured with the top and bottom parts for the current injection, the p-type and n-type mirrors and the active area between them. The distributed Bragg reflectors (DBR) with thin dielectric materials and different reflectivity are stacked.  
Typically, these materials include layers of semiconductors with alloys or compositions such as AlGaAs, AlGaInAs, InP or InGaAsSP [20].  
By employing these multilayer reflectors, VCSELs can be used with thin-film lenses, taking advantage of such a thin-layer structure [28]. When the reflectivity of the two mirrors is adjusted differently, the light propagation direction can also be adjusted to diverge toward the less reflective part. The active area makes a quantum well (QW), and, as shown in the inset of Figure 1, by bonding different materials and the proportion of constituent substances, the difference in the energy level between the elements is constructed. Hence, the difference in the energy level generates photons with the corresponding wavelengths. Then, it goes through the oxide layers.  
For the infrared VCSELs, DBR pairs of InP/AlGaAs (850 to 980 nm) or AlGaAs/AlGaAs (1300 nm to 1550 nm) are used [20]. In [29], it was shown that the wave between 650 and 700 nm wavelengths can be obtained from the VCSEL with AlGaInP/GaInP MQW. Additionally, the authors in [30] showed blue VCSELs with five multiple quantum well (MQW) layers emitting the 0.7 mW maximum outputs power at room temperature with a 1.5 mA threshold current and a 3.3 V threshold voltage. Modifying the composition or utilizing different materials for the purpose of enhancing the performance of VCSELs has been actively investigated [31; 32]. Further, there have been many studies exploiting the advantages of VCSELs, such as its low cost, low threshold and the narrow beam, which are under investigation in a wide range of applications [33; 34; 35; 36].
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 5 Evaluation
### Packet Recovery Capability in Intermittent Network  
During the disaster response phase, the network is not reliable. Packet recovery capability is a key feature to avoid valuable information loss. We kept comparing our system with the NDN Publish/Subscribe system and NDN-DM to show the advantage of using KubeEdge to recover packet loss in an intermittent network. In the cross-regional topology, we disconnected several network links, performed 50 packets exchange between cloud and devices. Then, we monitored the successful status exchange ratio between them after reconnecting the network links (the amount of received status over the amount of sent status). The result is shown in Figure 18.  
The figure shows that KubeEdge can successfully recover every dropped packet caused by network disconnection. Meanwhile, for the other two systems, the successful percentage of information exchange gradually drops down when the number of disconnected links increases. This result shows the effectiveness of the cloud-edge transmission reliability design of KubeEdge which can resend packets after network links are recovered. Information exchange through the other two NDN networks suffers from packet loss because there is no recovery support.  
Figure 18: Packet recovery capability comparison between our system versus NDN Publish/Subscribe and NDN-DM.
# Hybrid Artificial Protozoa-Based JADE for Attack Detection
## 5 Case Study: Application of APO-JADE for Attack Detection
### Performance Assessment Metrics  
To discern the robustness and adaptability of our proposed design, we employed a repertoire of performance metrics. These metrics are essential in gauging the congruence between the model's predicted outcomes and the actual ground truths. Central to our evaluation methodology are the constructs of True Positives (TPs), False Positives (FPs), True Negatives (TNs), and False Negatives (FNs).  
* **True Positives (TPs)**: Quantify instances where the model accurately identifies actual intrusions.
* **False Positives (FPs)**: Correspond to instances where the model erroneously labels normal activities as intrusions.
* **True Negatives (TNs)**: Represent instances where the model correctly identifies benign behaviors.
* **False Negatives (FNs)**: Occur when the model fails to detect actual intrusive activities.  
Building upon these foundational metrics, subsequent measures such as accuracy, precision, recall, and the F1 score are derived to provide a comprehensive snapshot of the model's performance. These metrics collectively offer a holistic view of the model's effectiveness in correctly classifying and identifying various activities, illuminating its strengths and areas for improvement in the context of intrusion detection.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 8 Evaluation  
The proposed system was evaluated in a virtualized environment designed to replicate a typical enterprise network. The environment included a centralized logging system, a Security Information and Event Management (SIEM) system, and various legacy security systems, such as firewalls and Intrusion Detection Systems (IDS). These components were configured to generate and collect security logs, which served as the primary input for testing the system's ability to predict and respond to security incidents.  
To assess the effectiveness of the proposed system, attack traffic was simulated using scenarios based on the MITRE ATT&CK framework. The simulation focused on replicating common attack vectors, including credential dumping, data exfiltration, and phishing attacks. These scenarios were selected to reflect real-world adversarial behaviors and were implemented using network traffic generation tools such as Snort. The generated traffic logs were then analyzed to determine whether the system could identify patterns associated with the simulated attacks. This approach allowed for a comprehensive evaluation of the system's capacity to detect and mitigate potential threats in a realistic operational setting.  
The dataset used for evaluation consisted of a combination of real-time logs generated within the experimental environment and data collected through network asset scanning using Nmap. While publicly available datasets such as CICIDS2017 or KDD Cup 99 were not employed, the internal log data provided a sufficiently diverse and representative sample of potential attack scenarios. Future iterations of this study aim to incorporate publicly available datasets to enable broader benchmarking and validation of the system's performance.  
The evaluation demonstrated the proposed system's ability to effectively identify and analyze security incidents under simulated conditions. However, additional testing in live operational environments and with a wider range of datasets will be necessary to further validate its scalability and adaptability to evolving cyber threats.
## 4 Experiments
### Viewpoint Estimation  
For viewpoint estimation, the angle error between the predicted rotation vector and the ground truth rotation vector is measured as  
\[\Delta\Big{(}R_{pred},R_{GT}\Big{)}=\frac{\left\|\log(R_{pred}^{T}R_{GT})\right\| _{F}}{\sqrt{2}} \tag{8}\]  
\(R\) is the rotation matrix along X, Y, and Z axis. We consider Median Error and Accuracy as two evaluation criteria that are commonly applied in the literature. Median Error is the median of the rotation angle error, and Accuracy at \(\theta\) is the percentage of objects whose error is less than \(\theta\). \(\theta\) is set as \(\pi/6\) in this paper. Any image of multi-class targets can be processed by the method proposed with only one network with the deformable model matching processing. For comparison purposes, the results of each category are counted in Table 1.  
Figure 9: Fault tolerance test. We artificially added error keypoints to test the fault tolerance, as shown in the first and second column. There is no error keypoint in (**a**). (**b**) and (**c**) show cases with error keypoints added. Through the corresponding process presented in the second column, the method proposed can identify the mistaken points shown as the third column. For the method proposed, the deviation between wireframe model projection and ground truth of 2D keypoints does not change with the increase of mistaken points as displayed in the fourth column. The last two columns compare the fault tolerance of method in [11] and the method proposed on viewpoint estimation, which using evaluation criteria in Equation (8). From Table 1 it can be seen that the method proposed performs as well as mainstream approaches in viewpoint estimation. It should be noted that the first two methods are class-specific, while ours and [PERSON]'s methods are designed for multi-class objects and have better universality. From the result, the accuracy of the method proposed is similar to [PERSON], but we take a completely different solution. The 3D keypoints from the network are used to estimate viewpoint directly in [11], while 3D keypoints are only used to obtain semantic meaning and shape initial value. Viewpoint estimation is conducted in deformable model matching in our method. As a result, the method in [11] relies heavily on 3D keypoints and is vulnerable to mistaken points, while the method proposed here provides better fault tolerance ability. This conclusion is verified by the following two experiments. Firstly, the ability to identify mistaken keypoints is compared in Figure 10. It can be seen that the method proposed can distinguish mistaken keypoints by the optimization process, while it is difficult for the method in [11]. Next, we evaluate the fault tolerance ability of viewpoint estimation. As shown in the last two columns in Figure 9, viewpoint estimation of the method in [11] is sensitive to mistaken keypoints, while our method provides better fault tolerance. More quantitative results are shown in Figure 11.
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 5 PF Modelling Methodology and Numerical Implementation  
### Summary of Modelling Assumptions  
In the analytical solution and the PF model, following simplifying assumptions are made:  
1. The NaCl particle dissolves isotropically;
2. The diffusion coefficients of aqueous species in solids and in solution are constants, respectively;
3. The diffusion of all aqueous species is expressed in terms of a single ionic concentration;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Ref.** \\ \hline \(\Delta G\) & \(5.15\times 10^{5}\) & \(\
icefrac{{1}}{{m^{3}}}\) & \\ \(A\) & \(5.17\times 10^{5}\) & \(\
icefrac{{1}}{{m^{3}}}\) & \\ \(\omega\) & \(1.51\times 10^{5}\) & \(\
icefrac{{1}}{{m^{3}}}\) & \\ \(L\) & \(4.11\times 10^{-3}\) & \(\
icefrac{{m^{3}}}{{(\cdot\cdot s)}}\) & \\ \(k\) & \(1.44\times 10^{-6}\) & \(\
icefrac{{1}}{{m}}\) & \\ \(l_{0}\) & \(1.28\times 10^{-5}\) & \(m\) & \\ \(\sigma\) & \(1.10\times 10^{-1}\) & \(\
icefrac{{1}}{{m^{2}}}\) & [96] \\ \(D_{\text{L}}\) & \(1.68\times 10^{-9}\) & \(\
icefrac{{m^{2}}}{{s}}\) & [96; 97] \\ \(D_{\text{S}}\) & \(1.68\times 10^{-15}\) & \(\
icefrac{{m^{2}}}{{s}}\) & \\ \(c_{\text{solid}}\) & \(3.70\times 10^{4}\) & \(\
icefrac{{mol}}{{m^{3}}}\) & [95] \\ \(p\) & \(357\)–\(360\) & \(g/L\) & [98; 99] \\ \(\text{r}\) & \(6.41\times 10^{-4}\) & \(m\) & [95] \\ \(t\) & \(5\times 10^{2}\) & Second & [95] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters for diffusion-controlled NaCl dissolution.  
The solubility of NaCl in solution is independent of particle size.
## 4 Analysis of Response Model Results
### Model Validity Testing
In contrast, for the regression model of total fracture length, the \(F\)-value ranking is in situ stress > stress difference > explosive load, indicating that explosive fractures in the far field are mainly governed by in situ stress conditions. Furthermore, the \(p\)-values corresponding to \(F_{3}\) and \(F_{5}\) are greater than 0.05, indicating non-significant regression results. A comprehensive analysis of F-values and \(p\)-values suggests that the shape factors of the smash zone and fracture zone are primarily controlled by stress difference, which is consistent with previous studies. The borehole diameter following the L-CO\({}_{2}\) phase transition explosion is mainly influenced by explosive load, aligning with the control mechanism of the smash zone area. An error analysis was conducted for the response models constructed from the selected five response indicators, as shown in Table 10. The results exhibit high consistency with the ANOVA findings, with \(R^{2}\) values for \(F_{1}\), \(F_{2}\), and \(F_{4}\) all exceeding 0.9, indicating low discrepancies between model predictions and actual values; both \(R^{2}\)A and \(R^{2}\)p values are high and close, suggesting that the response models can adequately predict the borehole \begin{table}
\begin{tabular}{c c c} \hline
**Response** & **Regression Models** \\
**Variable** & & \\ \hline \(F_{1}\) & \(F_{1}=7.597-0.081\cdot E_{1}-0.050\cdot E_{2}+0.025\cdot E_{3}\) \\ \(F_{2}\) & \(F_{2}=1406.519-78.870\cdot E_{1}-46.932\cdot E_{2}+7.481\cdot E_{3}\) \\ \(F_{3}\) & \(F_{3}=0.521+0.042\cdot E_{1}+0.033\cdot E_{2}+0.003\cdot E_{3}\) \\ \(F_{4}\) & \(F_{4}=2238.920+143.779\cdot E_{1}-43.684\cdot E_{2}-16.977\cdot E_{3}-7.302 \cdot E_{1}E_{2}+0.006\cdot E_{1}E_{3}+0.148\cdot E_{2}E_{3}\) \\  & \(-9.615\cdot E_{1}^{2}+2.722\cdot E_{2}^{2}+0.049\cdot E_{3}^{2}\) \\ \(F_{5}\) & \(F_{5}=1.047-0.00045\cdot E_{1}+0.042\cdot E_{2}-0.00002\cdot E_{3}\) \\ \hline \end{tabular}
\end{table} 
Table 8: Regression models of \(F_{1}\)–\(F_{5}\). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Type** & **Response Variable** & **Model** & \(E_{1}\) & \(E_{2}\) & \(E_{3}\) \\ \hline \multirow{6}{*}{\(F\)-value} & \(F_{1}\) & 252.40 & 50.65 & 19.62 & 686.92 \\  & \(F_{2}\) & 35.64 & 40.36 & 14.29 & 52.29 \\  & \(F_{3}\) & 2.94 & 3.99 & 6.53 & 0.06 \\  & \(F_{4}\) & 15.85 & 63.39 & 30.79 & 25.18 \\  & \(F_{5}\) & 4.49 & 0.00 & 13.41 & 0.04 \\ \hline \multirow{6}{*}{\(p\)-values} & \(F_{1}\) & \(<\)0.0001 & \(<\)0.0001 & 0.0010 & \(<\)0.0001 \\  & \(F_{2}\) & \(<\)0.0001 & \(<\)0.0001 & 0.0030 & \(<\)0.0001 \\ \cline{1-1}  & \(F_{3}\) & 0.0802 & 0.0808 & 0.0339 & 0.8172 \\ \cline{1-1}  & \(F_{4}\) & 0.0036 & 0.0005 & 0.0026 & 0.0040 \\ \cline{1-1}  & \(F_{5}\) & 0.0274 & 0.9696 & 0.0037 & 0.8408 \\ \hline \end{tabular}
\end{table} 
Table 9: Analysis of variance. diameter, smash zone area, and total fracture length. Additionally, the values for \(C_{V}\) and \(P_{A}\) also meet the required standards, collectively indicating a high level of reliability and accuracy in the experimental results.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### Resistance to Forgery Attack  
Counterfeit attack points to the attacker in the session is sent to the user, the cloud server and control server message, then the receiver will believe these messages are sent from a legal user, a cloud server, or the control server.  
In our scenario, if an attacker wants to forge a user Ui, he/she would need to forge a message M1 to pass the equation \(D3?=h(IDi\|PIDi\|ru)\). However, the attacker cannot forge \(D_{1}=A_{i}\oplus r_{u}\) because \(A_{i}=h(PIDi\|ID_{cs}\|x)\) contains the secret key \(x\) of a control server. If the attacker wants to forge a cloud server, he/she needs to fabricate two messages, \(M_{2}\) and \(M_{4}\). To pass the equation \(D_{7}?=h(SID_{j}\|PSID_{j}\|PSID_{j}\|PsID_{6})\) and \(D_{14}?=h(SK_{u}\|D_{12}\|D_{13}\|B_{i})\); however, he/she cannot forge \(D_{4}=A_{i}\oplus r_{s}\), \(D_{6}=B_{j}\oplus PsID_{j}^{\prime}\oplus h(r_{s}\|PSID_{j})\) and \(D_{14}=h(SK_{cs}\|D_{12}\|D_{13}\|h(ID_{i}\|x))\) because \(A_{j}\) and \(B_{j}\) both contain the secret key \(x\) of control server. If the attacker wants to forge the control server, he/she needs to make up a message \(M_{3}\) to pass the equation \(D_{10}?=h(SK_{s}\|D_{8}\|D_{9}\|B_{i})\). However, he/she cannot forge \(D_{8}=h(PSID_{j}^{\prime}\|ID_{cs}\|x)\oplus h(r_{s}\|PSID_{j}^{\prime})\) and \(D_{10}=h(SK_{cs}\|D_{8}\|D_{9}\|h(SID_{j}\|x))\) because those messages contain the secret key \(x\) of the control server. As a result, we provide a solution to staying away from forgery attacks.
## 5 Simulation and Experiments
The air-to-ground channel model described in [40; 41] was used from the UAV BSs to the user devices. The simulation starts with the deployment of the macrocell, UAV-BSs, and users in the scenario, then the necessary settings and models are initialized. The allocation of the users is calculated and determined by ranking based on the SINR for the macrocell and the UAV-BSs. If the user is unable to connect to the macrocell or UAV-BS that is at the top of the ranking, it moves to the next row until a stable connection is achieved. If no macrocell has a sufficient resource block to provide the required downlink throughput, the user is considered unreachable (blocked). In addition, users with an SINR value below the threshold of \(-7\) were considered unavailable. The parameters used in this work are listed in Table 2.
## 2 State-of-the-Art
### The Latency Budget of a Data Life Cycle in the Tactile Internet  
TI relies on the best response time, accessibility, dependability, and security. These objectives can be achieved by using distributed service platform architectures. Tactile applications must be implemented in locally available systems close to the clients due to the requirement for extremely low end-to-end latency [41]. Figure 9 depicts the various latency components of a typical TI system.  
Figure 8: Different network topologies and their associated latencies [38].