esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
developed CsiPriNet, combining a 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network, for frequency-domain channel prediction in UWA downlink OFDMA systems. Despite the advancements in DL approaches, these models often exhibit high latency and demand large storage and computational resources for training. Achieving robust channel estimation in impulsive noise environments for UAC systems remains an ongoing research endeavor. Conventional channel estimation methods lack resilience against significant outliers, and current approaches to handling impulsive noise often require prior statistical knowledge of the channel and noise, including assumptions about their distribution [32; 33]. Additionally, the high computational demands of these methods limit their applicability and effectiveness in real-time applications. To address the challenges outlined above, this work aims to develop a robust and efficient method for UWA channel estimation in impulsive noise environments. Our main contributions are as follows:  
1. We introduce a robust algorithm by reformulating the channel estimation problem as an \(\ell_{1}-\ell_{1}\) optimization problem, which offers enhanced resilience to outliers. This new optimization problem is adeptly tackled using the ADMM framework with the Accelerated Proximal Gradient (APG) method [34]. Furthermore, we incorporate a non-monotone line search strategy to increase the convergence speed as well as improve the robustness, particularly for ill-conditioned problems. The proposed method is low in complexity and has robust performance in challenging noise conditions. 2. We evaluate the performance of the proposed algorithm in various scenarios including estimating Channel Impulse Response (CIR) and Delay-Doppler (DD) spread functions for single-carrier systems through simulations as well as at-sea experimental validations for channel estimation in OFDM systems. The results demonstrate the fast convergence speed and high accuracy of the proposed method in both AWGN and impulsive noise environments, making it well-suited for robust channel estimations for diverse UWA channel models and communication schemes in practical applications. The remainder of the paper is structured as follows. Section 2 briefly discusses the channel estimation problems in UAC systems. Section 3 details the proposed method, including its implementation and complexity analysis. Numerical simulations and experimental results are in Sections 4 and 5, respectively. Finally, the conclusion is drawn in Section 6. **Notations**: Vectors and matrices are represented by boldfaced lower-case and upper-case letters, respectively. The notations \((\cdot)^{*}\)\((\cdot)^{T}\), and \((\cdot)^{H}\) represent complex conjugate, transpose, and Hermitian transpose, respectively. \(\langle\cdot,\cdot\rangle\) is an inner product operator, and \(\|\cdot\|_{p}\) represents \(\ell_{p}\)-norm.
## 5 Conclusions  
In this study, a data-balancing model based on GAN-GRU and an FDIA detection model with the Transformer architecture was introduced. Specifically, false data injection attacks were formulated with multiple attack modes contingent upon the quantum of network information at the disposal of potential attackers. To address the prevalent issue of data imbalance in practical scenarios, a data-balancing model rooted in GAN-GRU was employed. Through this approach, the generation of requisite small-scale data samples is facilitated, culminating in a balanced dataset. The outcomes of this treatment led to a pronounced improvement in the accuracy and dependability of the FDIA detection model. In the subsequent phase of experimentation, the FDIA detection model based on the Transformer was established, utilizing diverse attack vectors as inputs. A multi-label classification strategy was employed by the model to assign suitable labels to each dataset. Subsequently, the FDIA detection and classification performance of the Transformer was assessed independently using both balanced and unbalanced test datasets. The empirical  
Figure 10: Accuracy of four deep learning methods under different attack intensities.  
findings substantiated the superior performance and detection rate of the Transformer-based detection model when contrasted against traditional counterparts.  
In summary, these experimental investigations affirm the effectiveness of the data-balancing model based on GAN-GRU and the FDIA detection model based on the Transformer by providing evidence of its increased accuracy and dependability. This substantiates their potential for pertinent applications and paves the way for future research in the realm of network security. Nonetheless, it is important to note that while this study focuses on the performance of the FDIA detection model, practical applications necessitate a consideration of factors such as real-time responsiveness and scalability of the model.
## 1 Introduction
With one-year broadcast navigation messages in 2013, [PERSON] et al. [15] evaluated the BDS-2 broadcast navigation message SIS URE and concluded that they had a 1.5 m global-average SIS URE. A comparison between BDS-2 and GPS/GLONASS/GALILEO/QZSS showed that the BDS-2 SIS URE was comparable with GLONASS and GALILEO but slightly worse than GPS and QZSS. With BDS-2 broadcast navigation messages in August 2016, 1.87 m and 2.17 m SIS UREs--considering both orbit and clock errors--were reported for the IGSO/MEO and GEO satellites, respectively [16]. [PERSON] et al. [17] analyzed the BDS-2 SIS UREs from 2013 to 2016. Their results indicated that the User Range Accuracy Index (URAI) indicator cannot properly reflect the satellite usage status and would mislead advanced receiver autonomous integrity monitoring (ARAIM) users. [PERSON] et al. [18] illustrated the BDS-2 SIS URE's influence on ARAIM false alert Probability (Pfa) with broadcast navigation messages from 2013 to 2017. Their result showed that the 2.4 m user range accuracy (URA) standard deviation can completely overbound SIS UREs for all BDS satellites, and the corresponding Pfa was \(2\times 10^{-5}\). On the one hand, a comprehensive analysis of BDS-2 satellite broadcast navigation message performance during the past six years is meaningful for regional PNT users. On the other hand, these BDS-2 satellites will start to provide global service as a critical component of the BDS-3 preliminary system from December 2018. Therefore, it is of great value and importance to assess the BDS-2 satellite broadcast navigation message performance, which is the focus of our study. This paper concerns the six-year BDS-2 satellite broadcast navigation message performance evaluation from the perspectives of broadcast message availability, anomaly detection, and SIS URE calculation. Section 2 describes the background of the current BDS-2 constellation. In Section 3, an analyzing flowchart of BDS-2 broadcast navigation messages is introduced. Six-year analyzing results are presented in Section 4. Conclusions are finally reported in Section 5.
## 3 Materials and Methods
### The Principle of LSTM-Based Risk Estimation Methodology  
#### 3.3.1 LSTM Algorithm  
The LSTM network is a special kind of Recurrent Neural Network (RNN) capable of effectively addressing the problems occurring in traditional RNNs: (1) gradient disappearance/gradient explosion; and (2) long-term dependencies. LSTM has been refined and popularized in many fields, such as time series data processing, text classification, fault prediction, etc. As mentioned in Section 3.2.1, the result of hazard identification can be described by textual descriptions, and the risk index of each hazard situation can be represented by discrete levels. Thus, the risk estimation problem can be converted into a text classification problem.  
The core idea behind LSTM network is that its cell state can run straight down the entire chain with some minor linear interactions [28,29], shown by the pink horizontal arrows in Figure 5. Thus, the unaltered transmission of information along this chain is straightforward. An LSTM cell has three gates: the forget gate (orange color in Figure 5), the input gate (blue color in Figure 5), and the output gate (green color in Figure 5); the functions of these three gates are delineated as follows.  
Figure 4: Risk estimation using risk graph tool.  
Figure 5: LSTM cell.  
The forget gate is used to determine the information to be retained in the cell state (or what information we are going to throw away from the cell state). It is controlled by a sigmoid layer called \"forget gate layer\", whose mathematical representation is as shown in Equation (1).  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
The input gate is used to determine what new information we are going to store in the cell state, consisting of two parts: a sigmoid layer called \"input layer\" is used to determine which values to update, as in Equation (2); and a \"tanh\" layer is used to create a new vector of candidate values, as in Equation (3).  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
The cell state is updated as in Equation (4): Multiply the old state \(C_{t-1}\) by \(f_{t}\), forgetting what was decided to ignore. Add \(i_{t}*\widetilde{C}_{t}\), the new candidate values, scaled by how much it was decided to update each state value.  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{4}\]  
The output gate has two steps: decide what parts of the cell state we are going to output by a sigmoid layer, put the cell state through \"tanh\", and multiply it by the output of the sigmoid gate, as in Equations (5) and (6).  
\[O_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{6}\]
# Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
## 4 The Backpack Process Model (BPPM) Approach
### Data Extraction  
In the first stage of the methodology, the minimum necessary data are extracted and used for an exploratory analysis. Since the final objective is the curricular analysis, the BPPM and BPPM-S models define a table, called BPPMt, that contains a record of each course taken by each student, as well as their final grade (see Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Student ID (s)** & **Period (p)** & **Course (c)** & **Grade (g)** & **Ending Date (d)** \\ \hline
23 & 2013-1 & Algebra (A) & 2.0 & 1 July 2013 \\
23 & 2013-1 & Chemistry (Q) & 3.5 & 1 July 2013 \\
23 & 2013-1 & Calculus (C) & 4.5 & 1 July 2013 \\
23 & 2013-1 & Innovation (D) & 5.5 & 1 July 2013 \\
23 & 2013-2 & Algebra (A) & 3.4 & 1 December 2013 \\
23 & 2013-2 & Chemistry (Q) & 5.0 & 1 December 2013 \\
23 & 2013-3 & Algebra (A) & 6.5 & 1 February 2014 \\
24 & 2013-1 & Algebra (A) & 5.5 & 1 July 2013 \\
24 & 2013-1 & Chemistry (Q) & 3.5 & 1 July 2013 \\
24 & 2013-1 & Calculus (C) & 4.5 & 1 July 2013 \\
24 & 2013-1 & Innovation (D) & 6.0 & 1 July 2013 \\
24 & 2013-2 & Chemistry (Q) & 3.8 & 1 December 2013 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of table BPPMt.  
Figure 1: Example of educational trajectories, according to the BPPM and BPPM-S models. (**a**) Shows an example of BPPM. (**b**) Shows an example of BPPM-S. The darker color of the nodes represents a higher percentage of students who went through each state. The thickness of the arrows represents the percentage of students who had transitions between both states. All values are percentages in relation to the total number of students included in each model.  
Figure 2: Stages for the generation of the process model, based on an adapted version of PM\({}^{2}\) methodology [35].  
In Table 2, each row of the BPPMt table is represented by the tuple (s, p, c, g, d), where:  
* s indicates the ID of the student who took the course
* p the academic period when the course was taken
* c the identifier of the course taken
* g the final grade obtained
* d the end date of the academic period.  
For example, (23, 2013-3, Algebra, 6.5, 1 February 2014) indicates that the student with ID 23 took the algebra course in the period 2013-3, which concluded on 1 February 2014, obtaining a grade of 6.5 out of 7. The data necessary for the creation of the BPPMt table are available in the ERP systems commonly used by HEIs. It must be noted that, although the BPPMt table specifies the minimum fields as a common standardization for the next stage of the methodology, the table can be extended with additional data, such as the student's weighted GPA or current academic status.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
###### Abstract  
The Logical Range is a mission-oriented, reconfigurable environment that integrates testing, training, and simulation by virtually connecting distributed systems. In such environments, task-processing devices often experience highly dynamic workloads due to varying task demands, leading to scheduling inefficiencies and increased latency. To address this, we propose GCSG, a hybrid load forecasting model tailored for Logical Range operations. GCSG transforms time-series device load data into image representations using Gramian Angular Field (GAF) encoding, extracts spatial features via a Convolutional Neural Network (CNN) enhanced with a Squeeze-and-Excitation network (SENet), and captures temporal dependencies using a Gated Recurrent Unit (GRU). Through the integration of spatial-temporal features, GCSG enables accurate load forecasting, supporting more efficient resource scheduling. Experiments show that GCSG achieves an \(R^{2}\) of 0.86, MAE of 4.5, and MSE of 34, outperforming baseline models in terms of both accuracy and generalization.  
logical range; time-series data; load forecasting Article
# Influence of Linear Springing on the Fatigue Damage of Ultra Large Ore Carriers
## 4 Numerical Configurations and Parameters  
To systematically investigate the influence of the calculation parameters on fatigue damage, four realistic ultra large ore carriers (OC) are used, namely, 250,000 DWT OC, 300,000 DWT OC, 388,000 DWT OC, and 400,000 DWT OC, and their principle particulars are given in Table 1. The mass distributions and the structural properties are shown in Figure 3.  
In the present study, the sea states of the North Atlantic are adopted to calculate the fatigue damage to the ultra-large ore carriers. According to the results obtained for the vertical bending moment, the range of frequency 0.157 rad/s to 3.14 rad/s, which encompasses the two-node wet-mode natural frequency, is adopted for calculating the response of wave induced vibration. The wave induced vibration originates from the coupling effects of wave encounter frequency and the first order natural frequency of the ship hull. When the encounter frequency coincides with the natural frequencies of the hull, linear springing will occur. Since the encounter frequency is dependent of the ship speed, so are the numerical results. The actual ship speed is, in general, reduced in high seas. The speeds in relation with the significant wave heights explored in the present study are shown in Table 2, where \(V_{S}\) is the service speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **250,000 DWT OC** & **300,000 DWT OC** & **388,000 DWT OC** & **400,000 DWT OC** \\ \hline Length between perpendiculars \(L_{PP}\) (m) & 319.5 & 324.7 & 345.6 & 353 \\ Beam \(\mathcal{B}\) (m) & 57 & 57 & 65 & 65 \\ Depth \(\mathcal{D}\) (m) & 26.4 & 28.8 & 30.5 & 30.4 \\ Displacement in full loading condition (tonnes) & 297.917 & 340.772 & 437,054.6 & 450,851.3 \\ Displacement in ballast condition(tonnes) & 159,512 & 206,813 & 259,082.7 & 248,551.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Principal particulars of the ultra large ore carriers.  
Figure 3: Mass distribution and structural properties along the 400,000 DWT OC.  
In the calculation of the fatigue damage, inclusion of more lower-order elastic modes will improve the numerical accuracy while decrease the efficiency, the same thing goes with the mesh refinement for the hull. In order to improve the computational efficiency at the cost of negligible loss of accuracy, a convergence study has been conducted for both, and the results are presented in the Appendix A, see Figures A1-A5.
## 3 Multi-Source Fusion Structure of Wi-Fi/Acoustic/MEMS Sensors
### Multi-Source Integration Based H-IPS  
After the error evaluation of Wi-Fi and acoustic-based positioning sources using the WAI and NLOS detection algorithm, the evaluated Wi-Fi fingerprinting and acoustic ranging results are model as the observations in the final fusion phase.  
\[\delta\dot{\mathbf{X}}_{s}=\mathbf{F}_{s}\delta\mathbf{X}_{s}+\mathbf{G}_{s}\mathbf{\varepsilon}_{s} \tag{27}\]  
where \(\delta\mathbf{X}_{s}\) consist of 15 dimensions of the state vector described in Equation (5), \(\varepsilon_{s}~{}=~{}[\varepsilon_{1}\cdot\cdot\cdot\varepsilon_{15}]\) indicates the state error with the Gaussian distribution, and \(\mathbf{G}_{s}\) is the noise driven matrix.  
In the case where acoustic TOA is received, the tightly-coupled integration model is constructed according to the MEMS sensors and ADE compensated acoustic ranging results:  
\[\delta\mathbf{z}_{L}=\begin{bmatrix}\delta\mathbf{z}_{1,\text{Acoustic}}\\ \delta\mathbf{z}_{2,\text{Acoustic}}\\ \vdots\\ \delta\mathbf{z}_{n,\text{Acoustic}}\end{bmatrix}=\begin{bmatrix}L_{\text{ Acoustic}}^{1}-\zeta_{s}^{1}-d_{MEMS,1}\\ L_{\text{Acoustic}}^{2}-\zeta_{s}^{2}-d_{MEMS,2}\\ \vdots\\ L_{\text{Acoustic}}^{n}-\zeta_{s}^{n}-d_{MEMS,n}\end{bmatrix} \tag{28}\]  
where \(\delta\mathbf{z}_{n,\text{Acoustic}}\) is the compensated acoustic ranging result, and the MEMS sensor-based ranging \(d_{MEMS,n}\) is described as follows:  
\[d_{MEMS,\mu}=\sqrt{\left(E_{\text{\tiny MEMS}}^{n}-P_{n}^{E}\right)^{2}+\left( N_{\text{\tiny MEMS}}^{n}-P_{n}^{N}\right)^{2}} \tag{29}\]  
where \(\left(E_{\text{\tiny MEMS}}^{n},\text{\tiny}N^{n}_{\text{\tiny MEMS}}\right)\) represents the built-in sensors originating in the 2D location and \(\left(P_{n}^{E},P_{n}^{N}\right)\) indicates the position of the \(n^{st}\) acoustic anchor.  
After the Wi-Fi RSSI fingerprinting matching results acquired, the Wi-Fi and built-in sensors loosely coupled integration structure is proposed and enhanced by the WAI. In this case, the observation model is described as:  
\[\left\{\begin{array}{l}\delta\boldsymbol{z}_{r}=r_{\textit{rssi}}-\boldsymbol{ r}_{\textit{MEMS}}\\ \delta\boldsymbol{z}_{\textit{v}}=\boldsymbol{v}_{\textit{rssi}}-\boldsymbol{ v}_{\textit{MEMS}}\end{array}\right. \tag{30}\]  
where \(\boldsymbol{r}_{\textit{rssi}}\) and \(\boldsymbol{v}_{\textit{rssi}}\) indicate the matched result of Wi-Fi fingerprinting, and \(\boldsymbol{r}_{\textit{MEMS}}\) and \(\boldsymbol{v}_{\textit{MEMS}}\) represent the INS updated results.
# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 6 Conclusions  
Previous studies have found that assignment-related behavior in MOOCS can influence student achievement. However, previous studies have not fully considered the pertinent learning process. In this paper, we aim to explore the influence of the learning process on student achievement. A framework was constructed with an attention mechanism that includes data pre-processing, a DSP-based adapter, and a LSTM-based neural network to predict student achievement. We used the LSTM neural network to reflect the students' learning processes and the DSP-based adapter to enhance the significance of critical information. Some sequence patterns were mined such as the Quitter, Faineant and Cheater patterns. The experimental results also show that students who failed in the exam had common sequence patterns, while those who passed the exam did not have common sequence patterns. This means that all roads lead to Rome, but failures are always the same. Our method achieves an accuracy rate of 91% and a recall rate of 94% when predicting student achievement. Thus, our study makes an important contribution to the literature on finding the relationship between the learning process and student achievement.  
In future work, we will examine the coding content of programming assignments for higher student performance and will consider the universality of the research on MOOC courses.  
Conceptualization, S.Q. and Y.W.; Methodology, S.Q.; Software, B.W. and S.Z.; Validation, B.W.; Formal analysis, S.Q.; Investigation, S.Q.; Resources, S.Q.; Data curation, Y.W.; Writing--original draft preparation, S.Q.; Writing--review and editing, K.L.; Visualization, S.Q.; Supervision, S.Q.; Project administration, S.Q.; Funding acquisition, K.L.  
This research was funded by Key Program of the National Natural Science Foundation of China, grant number 71834001.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation Method** & **Accuracy** & **Recall** & **Cross Validation** \\ \hline MLP & 75\% & 75\% & 0.75 \\ LSTM (\(l=14\)) & 80\% & 92\% & 0.85 \\ LSTM (\(l=69\)) & 88\% & 94\% & 0.91 \\ NOSEP & 86\% & 85\% & 0.85 \\ M-F-LSTM & 91\% & 92\% & 0.92 \\ Our Method & 91\% & 94\% & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons among our method and other approaches. MLP: Multi-layer perceptron. LSTM: Long short-term memory. NOSEP: Nonoverlapping Sequence Pattern Mining. M-F-LSTM: Multi-task Fully-connected LSTM.
## 4 Numerical Simulation Analysis and Results
### Axial Stiffness and Stress Distribution Rules  
By using the above Finite Element Model for numerical simulation, the equivalent stress and the axial stress of the braided corrugated hose can be obtained, as shown in Figure 10. Figure 11 shows the equivalent stress, and the axial stress of the bellows. Figure 12 shows the axial stiffness of the braided corrugated hose.  
The following can be seen from the numerical simulation results:  
1. The equivalent stress of the braided corrugated hose is larger than the bellows under the same axial displacement. This is probably because the outer braided tube is more prone to deformation and carries more stress, as Figures 10a and 11b show. In addition, the equivalent stress changes in the range of 1-903 MPa, and the axial stress changes in the range of \(-\)561 to 517 MPa.
2. For the bellows, the stresses are more likely to be concentrated at the trough of the bellows, except for the area where the crest and the braid come into contact. The stress is unevenly distributed in the wire and is larger when the wire comes into contact with the bellows.
3. The axial stiffness is a nonlinear curve, as Figure 12 shows, when the stretching distance is within 1.5 mm. The stiffness is linear when the stretching distance exceeds 1.5 mm and increases steeply due to friction contact between the braided tube and the bellows; the inner bellows limit the radial deformation of the braided tube.  
Figure 10: Stress diagrams of the DN25 braided corrugated hose: (**a**) Equivalent stress, (**b**) Axial stress.  
Figure 9: Finite Element Model of the DN25 braided corrugated hose: (**a**) Boundary condition, (**b**) Fixed joint.