esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure Analysis of the Fractionator Overhead Vapor Line of a Delayed Coker Unit
## 3 Seismic and Wind Load Analysis
### Analysis of Seismic and Wind Effects  
There are three main factors that cause pipeline vibration: (1) wind, (2) earthquake, and (3) equipment (e.g., pump). Owing to the lack of actual data, the equipment factors are not considered in this study. The vibrations caused by earthquakes and winds are considered below.  
In terms of the wind effect on the pipeline vibration, the main effect is the horizontal acceleration caused by the wind pressure on the pipeline system, as shown in Table 2.  
The frontal area (in north-south direction) of the pipeline system is about 60.3 m\({}^{2}\). The mass of the pipeline system is about 32,189 kg. The horizontal acceleration caused by the wind load above magnitude 2 (inclusive) is taken from the upper limit of the wind load for each wind magnitude, as listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Beaufort Scale** & & & \\
**Number** & **Description** & **Wind Speed (m/s)** & **Wind Load (kgf/m\({}^{2}\))** \\ \hline
1 & Light air & 0.0–1.5 & \textless{}1 \\
2 & Light breeze & 1.6–3.3 & 1 \\
3 & Gentle breeze & 3.4–5.4 & 1–3 \\
4 & Moderate breeze & 5.5–7.9 & 3–7 \\
5 & Fresh breeze & 8.0–10.7 & 7–14 \\
6 & Strong breeze & 10.8–13.8 & 14–23 \\
7 & Near gale & 13.9–17.1 & 23–35 \\
8 & Gale (Mild typhoon) & 17.2–20.7 & 35–52 \\
9 & Severe gale (Mild typhoon) & 20.8–24.4 & 52–72 \\
10 & Storm (Mild typhoon) & 24.5–28.4 & 72–97 \\
11 & Violent storm (Mild typhoon) & 28.5–32.6 & 97–128 \\
12 & Hurricane (Moderate typhoon) & 32.7–36.9 & 128–164 \\
13 & Moderate typhoon & 37.0–41.4 & 164–206 \\
14 & Moderate typhoon & 41.5–46.1 & 206–256 \\
15 & Moderate typhoon & 46.2–50.9 & 256–312 \\
16 & Severe typhoon & 51.0–56.0 & 312–377 \\
17 & Severe typhoon & 56.1–61.2 & 377–499 \\ \hline \end{tabular}
\end{table}
Table 2: Beaufort wind scale [32].  
Figure 4: Fixed points for stress analysis of the pipeline system.  
In terms of the seismic impact, the seismic classification is listed in Table 4.  
In this study, the upper limit of the acceleration for each earthquake magnitude is used as the acceleration. For the magnitude 7 earthquake, 1000 gal (10 m/s\({}^{2}\)) is taken as the value of acceleration for the subsequent analysis.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 2 Hysteresis Model and Inverse Model of PAM
### The FN-QUPI Model
Furthermore, \(m\) and \(n\) indicate the number of neurons in the input and hidden layers of the FNN separately. In this paper, the activation function \(\varphi\) is chosen as an upward-opening univariate quadratic function. This is because the increase in the function value as the independent variable increases correspond roughly to the trend in the first half of the hysteresis, while the decrease in the function value as the independent variable decreases aligns with the trend in the latter half of the hysteresis. The FN-QUPI model is a composite model that combines a neural network with an improved UPI operator to address the hysteresis nonlinearity present in PAM systems. This model offers significant advantages in modeling complex hysteresis effects, enhancing control accuracy, and improving system adaptability. (1) High-precision hysteresis compensation: The hysteresis phenomenon in PAM systems often leads to reduced control accuracy. The FN-QUPI model, with its refined hysteresis modeling capabilities, can effectively compensate for this issue. (2) Strong robustness and adaptability under complex conditions: The robustness of the FN-QUPI model is reflected in its ability to adapt well to various operating conditions. Whether under sinusoidal input, complex harmonic input, or step signal input, the FN-QUPI model can maintain high prediction accuracy and stability. This robustness is especially beneficial in dynamic industrial or medical environments. When faced with sudden load changes, pressure variations, or other disturbance factors, the FN-QUPI model can quickly adapt, ensuring the system continues to operate normally. (3) Reduced system lag and improved dynamic response speed: Due to the inherent lag in PAM systems, there is often a noticeable response delay under rapid dynamic input. The FN-QUPI model, by accurately predicting the relationship between displacement and pressure, reduces the lag effect, enabling the system to quickly follow input signals.
## 3 Network Intrusion Detection Model Based on CNN and GRU
### Model Structure
#### 3.2.3 Model Structure  
The intrusion detection model proposed in this paper consists of three main parts: in order to comprehensively and finitely learn the features of the data, the features are firstly assigned different weights through the CBAM attention mechanism module based on residual; secondly, the spatial features are extracted through the CNN module, and the spatial information is further aggregated using fused Averagepooling and Maxpooling; then the temporal features are extracted through GRU, and finally the classification is carried out through the Softmax function, the specific structure of which is shown in Figure 7.  
1. The grayscale map obtained after pre-processing is input to the CBAM module based on residual, and the features are given different weights to obtain the output \(F\).
2. The new feature map \(F\) is input into the CNN module for feature extraction, after which the spatial information is aggregated using Maxpooling and Averagepooling to obtain the new feature map \(F_{\text{C}}\).
3. Pass \(F_{\text{C}}\) to the GRU unit to extract the dependencies between features and obtain the output \(F_{\text{G}}\).
4. Pass \(F_{\text{G}}\) to the fully connected layer, which uses Softmax as the activation function to achieve the classification of intrusion detection behavior.  
Figure 6: CNN structure.  
Figure 7: Intrusion detection model structure diagram. \(\otimes\) denotes element-wise multiplication and \(\oplus\) denotes feature concatenate.
###### Abstract  
In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption.  
Keywords:model predictive control; mixed integer programming; smart appliance scheduling; demand-side-management +
Footnote †: journal: Applied Soft
## 4 Experiment 1 Discussion of Findings  
Results from study 1 suggest adults differ in ratings for likely and unlikely test events. Participants rated the bright ball likely test event higher than the bright ball unlikely test event (large effect). Interestingly, participants rated the dark ball unlikely test event higher than the dark ball likely test event. This partly supports our hypothesis that collision event outcomes aligned with the mass cue of brightness would be rated higher (i.e., more likely) than collision event outcomes that did not. However, our hypothesis was only supported for the bright ball test events. Adults displayed higher ratings for the dark ball unlikely test event compared to dark ball likely test event, but the magnitude of effect was only moderate for dark ball events, and large for bright ball events. We are therefore inclined to suggest that participants considered other cues to mass, such as size of object A, size of object B, pre-collision velocity, post-collision velocity, coefficient of restitution, and collision sound more than the mass cue of brightness. It appears the event whereby the cube travelled a shorter distance was perceived as the most likely, for reasons we cannot conclusively determine. However, this effect was stronger for bright ball events than dark ball events, suggesting some participants considered object A brightness, at least in some part. For that reason, we conclude adults sometimes use brightness to judge mass in collision events.  
Future research could benefit from examining the mass cue of object brightness in collision events to determine under what circumstances object brightness influences decision making. In conclusion, results from this experiment indicate adults sometimes expect certain collision outcomes based on brightness of objects but consider other visual cues more. Expectation of collision event outcomes may not be limited to exclusively visual object cues, so Experiment 2 tested the effect of auditory object cues to mass on expectations of collision event outcomes.  
Figure 2: Ratings of bright and dark ball likely and unlikely test event outcomes. Data are presented as grouped dot plots and medians. AU = arbitrary units.
## 4 Results
### Numerical Simulation Results of Slope Deformation and Failure
#### 4.5.3 Shear Strain Distributions  
Many engineering cases demonstrate that the magnitude of shear strain increments can be indicative of potential slip surfaces or locations of deformation damage [42; 43]. Typically, zones with concentrated shear strain increments occur at the depth of the damage area on the slope. As illustrated in Figure 28a, the shear strain cloud map after 100 calculation steps reveals that the region with concentrated shear strain increments is situated within the middle and lower superficial layers of the pit slope, with the maximum value reaching 4.697 \(\times\) 10\({}^{-2}\). As the number of calculation steps increases, the region of concentrated shear stress shifts downward, and its impact area gradually expands. Simultaneously, the strain values consistently increase (Figure 28b-d). This progression signifies a gradual decrease in slope stability over time following excavation.  
Figure 27: Displacement cloud of profile I in the x-direction: (**a**) 100 steps; (**b**) 1000 steps; (**c**) 2000 steps; (**d**) 3000 steps.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 4 Applications and Benefits of the IoV
### Intelligent Management and Service of Vessels  
With the help of the IoV system, managers could get the accurate and complete real-time movement of the ships and predict the potential traffic problems. As a result, they can make reasonable plans and project emergencies to prevent disasters and reduce property loss. Based on the VTS, AIS and GIS, [PERSON] [65] proposed a fuzzy logic method to predict vessel collision risk to improve the safety of navigation and the efficiency of the ship management. [PERSON] [66] took advantage of artificial neural network and logistic regression to distinguish the smuggling ships. Based on the classification of different ships, by following their types, drafts and destinations, [PERSON] [67] analyzed the routes of thousands ships to realize the predicted behavior for the target ship in crowded water areas.  
The intelligent service of vessels from the IoV system allows service providers to identify the customers' (ship owners, ship crews) explicit and implicit demands, and then meet their requirements efficiently [68]. For instance, according to the information from the IoV, it can provide personalized management (Visa, cargo loading and food configuration, sailing schedule setting), set the optimal route for ships, develop monitoring and adjust the plan of energy consumption for the ships. With funding from the European Union, the European institutions are accelerating the construction of IoV systems to realize electronic freight, which is shown in Figure 12[69].  
Figure 11: The principle of the ship automatic collision avoidance.
# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ1(a-b)
#### 5.1.2 RQ1b: Which Aspects of Trustworthiness Are Explored in LLMs?  
**Objective:** Explore specific dimensions of trustworthiness that researchers focus on.  
**Results:** Figure 5 presents a bar chart illustrating the frequency and percentages of trustworthiness aspects considered in LLM research. The most frequently addressed dimension is _accuracy_, which accounts for 1,496 studies (29.40%). This is followed by _data and data governance_, representing 809 studies (15.90%), and _transparency_, with 927 studies (18.22%).  
Other notable dimensions include _human oversight_ (723 studies, 14.21%) and _robustness_ (597 studies, 11.73%). In contrast, less explored aspects are _cybersecurity_ (74 studies, 1.45%) and _record-keeping_ (60 studies, 1.18%). A broader focus on _trustworthiness_ was observed in 403 studies (7.92%).  
**Major Trend:** The predominance of _accuracy_ and _transparency_ underscores their critical role in ensuring the reliability and interpretability of LLMs. However, the relatively lower emphasis on dimensions such as _cybersecurity_ and _record-keeping_ suggests underexplored areas that warrant greater attention in future research. This trend highlights a potential gap in addressing comprehensive trustworthiness within the LLM domain, particularly in aspects that contribute to systemic security and compliance.  
Figure 4: Frequency of studies exploring trustworthiness aspects for specific LLMs.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 3 Analysis of Simulation Results
### Performance Analysis of the System without Preprocessing in Shallow Water Channels  
In the test stage, the input size of the information sequence in each time step of the neural network model is determined by the symbol length of the signal. By comparing the output of the neural network model with the source information, the BER curve is obtained and the performance of the DL-based CSK-SS UWA communication system is evaluated.  
In CSK-SS modulation, the number of bits carried by each symbol determines the symbol rate of the system. When each symbol carries 4 bit, 3 bit, and 2 bit information, the symbol rates are 275.86 bps, 206.89 bps, and 137.9 bps, respectively. The simulation results of the DL-based CSK-SS communication system in shallow water acoustic channels are shown in Figure 11. Due to the random selection of several UWA channels which are not in the range of training samples in the test stage (like the green circle in Figure 6), the BER curves of the conventional system and the DL-based system represent the mean BER of various channels under different SNR.  
Figure 11: BER curve of DL-based system and conventional system (Con-S) without preprocessing.  
Figure 10: The predicted transmission loss of transmitter at different depths. (**a**) Transmitter depth is 1 m. (**b**) Transmitter depth is 6 m. (**c**) Transmitter depth is 11 m. (**d**) Transmitter depth is 16 m. (**e**) Transmitter depth is 21 m. (**f**) Transmitter depth is 26 m.  
Without channel equalization preprocessing, the performance of DL-based systems on two different neural network models is better than that of the conventional system in the SNR range of \(-14\) dB to \(0\) dB. Meanwhile, the anti-noise ability of the LSTM neural network model is improved by \(4.5\) dB, \(8\) dB, and \(9\) dB when the symbol rate is \(275.86\) bps, \(206.89\) bps, and \(137.9\) bps, and the magnitude of the BER is \(10^{-2}\). Compared with the conventional system, the anti-noise ability of the BiLSTM neural network model is improved by \(7\) dB, \(10\) dB, and \(10\) dB. It can be seen that the BiLSTM neural network model has better performance than the LSTM neural network model.  
From one perspective, the BiLSTM network increases the vertical depth of the network compared with the unidirectional LSTM network by adding a network layer that transmits information in reverse time to enhance the capability of the network. From another perspective, under the condition of a complex shallow water channel with a serious multipath effect, the BiLSTM network can use the internal relationship of ISI in sequential data to reduce the impact of ISI on performance.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 4 Communication in Distributed Systems
### Multilevel Transform Coding on Unstructured Grids for Compressed Communication  
Due to the larger gap between computing power and bandwidth, transform coding is more attractive for compressing communication in distributed systems than for in-memory compression. While methods based on Cartesian grid structures can be used for some computations, many finite element computations are performed on unstructured grids that do not exhibit the regular tensor structure exploited for designing an orthogonal transform.  
An unstructured conforming simplicial grid covers the computational domain \(\Omega\subset\mathbb{R}^{d}\) with non-overlapping simplices \(T_{i}\in\mathcal{T}\), the corners of which meet in the grid vertices \(\mathcal{N}=\{x_{i}\ |\ i=1,\ldots,m\}\); see Figure 9 for a 2D example. The simplest finite element discretization is then with piecewise linear functions, i.e., the solution is sought in the space \(V_{h}=\{u\in C^{0}(\Omega)\ |\ \forall T\in\mathcal{T}:u|_{T}\in\mathbb{P}_{1}\}\). The ubiquitous basis for \(V_{h}\) is the nodal basis \((\varphi_{i})_{i=1,\ldots,m}\) with the Lagrangian interpolation property \(\varphi_{i}(x_{j})=\delta_{ij}\), which makes all computations local and leads to sparse matrices.  
The drawback of the nodal basis is that elliptic systems then lead to ill-conditioned matrices and slow convergence of Krylov methods. Many finite element codes therefore use hierarchies of \(\ell+1\) nested grids, resulting from adaptive mesh refinement, for efficient multilevel solvers [2]. The restriction and prolongation operators implemented for those solvers realize a frequency decomposition of the solution  
\[u_{h}=\sum_{l=0}^{\ell}u_{h_{l}}, \tag{6}\]  
see Figure 4 for a 1D illustration. Using the necessary subset of the nodal basis on grid level \(l\) for representing \(u_{h_{l}}\) leads to the hierarchical basis. This hierarchical basis transform allows an efficient in-place computation of optimal complexity and with low overhead, and is readily available in many finite element codes. The transform coefficients can then be quantized uniformly according to the required accuracy and entropy coded [21], e.g., using a range coder [66]. Typically, this transform coding scheme (TCUG) takes much less than 5% of the iterative solution time, see [21; 34; 46].  
A priori error estimates for compression factors and induced distortion can be derived for functions in Lebesgue or Sobolev spaces. The analysis in [21] shows that asymptotically 2.96 bits/value (in 2D, compression factor 21.6 compared to double precision) are sufficient to achieve a reconstruction error equal to \(L^{\infty}\)-interpolation error bounds for functions with sufficient regularity, as is common in elliptic and parabolic equations. In 3D, the compression factor is slightly higher; see Figure 5.  
Figure 4: Representation of 1D linear finite element functions \(u_{h}\) in the nodal and hierarchical basis.
## 4 Simulation and Performance Evaluation
### Comparison with VGDRA
#### 4.3.2 Source Nodes Are Distributed Evenly in the Network  
In this application scenario, source nodes are evenly distributed. Each place has the same probability of source node. The parameters we set is shown as follow: (1) the number of nodes varies from 200 to 600; (2) the source nodes to all the nodes ratio is 20%; (3) _Th_ is 70%; (4) the event area changes by period, and the period is 800 s; (5) one source node produces one data packet in one second; (6) the velocity of sink is 5 m/s. Figure 21a shows the impact of nodes' number on the lifetime of network. Figure 21b shows the impact of node' number on the average residual energy. In our strategy, much energy hasn't be used when network stops to work. The residual energy is wasted. From this perspective, VGDRA has a better performance. Figure 21c shows the impact of nodes' number on the residual energy variance. We can find that our GSDCS's variance is higher than VGDRA's. When our strategy is used in this application scenario, the number of data packets sink collected from left side and right side may be almost the same. Then sink may always moves in the middle columns of network. Nodes in these middle columns may have \"hot spot\" problem. However in VGDRA, sink always moves along the boundary of network, so VGDRA can make network balanced. So VGDRA can work better in this application scenario.  
Figure 21: The comparison with VGDRA when source nodes are evenly distributed. (**a**) Lifetime of network vs. the number of nodes; (**b**) Average residual energy vs. the number of nodes; (**c**) Variance of residual energy vs. the number of nodes.  
Figure 20: The comparison with VGDRA when source nodes are distributed unevenly. (**a**) Lifetime of network vs. the number of nodes; (**b**) Average residual energy vs. the number of nodes; (**c**) Variance of residual energy vs. the number of nodes.