esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method of Seismic Signal Detection Using Waveform Features
## 4 Implementation
### Modeling  
As shown in Table 3, we obtain the hyperparameters and the marginal likelihood value of each feature, using gradient ascent. During the training process, we can obtain appropriate hyperparameters with a strategy that assigns the initial hyperparameters more than once. Among the hyperparameters, \(l\) represents the length scale. When \(l\) is small, the swing of distribution will increase, and the performance will be reduced. Therefore, it is necessary to control the corresponding termination threshold of the training process. The magnitude of the marginal likelihood is related to the number of training sets and the size of the feature scale. Generally, the amount of training data is more substantial, while the log-marginal likelihood is higher. When the amount of data increases, the complexity of the model increments. However, the complexity of the model decreases as the length scale increases. The training results from Table 3 also illustrate the above analysis.  
From the parameters in Table 3, the mean and covariance of each feature distribution can be achieved, as shown in Table 4. Table 1 lists the statistical distributions of several features. Every row shows the statistical feature distribution of the signal training set, signal test set, and noise test set. We can see from Table 1 that it is consistent between the distribution of seismic train set and seismic test set, while there are vast differences in the distribution of signals and noise. Figures 6-9 shows two curves, one is the statistical distribution, and the other denotes Gaussian distribution. The shape of the statistical distribution depends only on the number and size of the data training set. However, the Gaussian process uses the prior information of the location and waveform features to construct the distribution. The seismic signals for testing are closer to events in the training set, and the feature distributions of test signals are consistent with the training distributions, so the probability of being judged as a real seismic signal is high.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Mean** & **Covariance** \\ \hline \(\alpha\) & 5.82 & 0.40 \\ \(\rho\) & 5.22 & 0.54 \\ \(\log(\gamma)\) & 4.24 & 1.39 \\ \(\log(\lambda)\) & 8.67 & 2.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gaussian distribution of each waveform feature.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & \(\sigma_{f}^{2}\) & \(\sigma_{n}^{2}\) & \(l\) & **L(\(\theta\))** \\ \hline \(\alpha\) & 10.95 & 1.77 & 694.49 & \(-\)221.47 \\ \(\rho\) & 10.38 & 1.57 & 568.57 & \(-\)217.74 \\ \(\log(\gamma)\) & 25.12 & 6.15 & 599.51 & \(-\)294.29 \\ \(\log(\lambda)\) & 49.91 & 6.87 & 530.10 & \(-\)307.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameters of seismic waveform characteristics.
## 2 Dual-Color Theory  
The thermal signal \(G_{realj}(T_{obj},\varepsilon_{obj})\), the infrared radiation reaching the detector of the camera for several passband filters, can be obtained by the integration of the [PERSON]'s law for a real surface in the working spectral range of the IR camera (\(\lambda^{\prime}\)\(e\)\(\lambda^{\prime}\)) and considering transmissivity functions, respectively, for the sensor (\(Rt(\lambda)\)), the optics (\(Rot(\lambda)\)), the applied filter (\(F(\lambda)\)), and for the attenuators (\(A(\lambda)\))\[G_{reali}\Big{(}T_{obj},\epsilon_{obj}\Big{)}=\int_{\lambda^{\prime}}^{\lambda^{ \prime\prime}}\epsilon_{obj}\Big{(}T_{obj},\lambda\Big{)}\cdot R_{t}(\lambda) \cdot R_{ot}(\lambda)\cdot F_{i}(\lambda)\cdot\frac{C_{1}}{\lambda^{5}\cdot \Bigg{(}e^{\frac{C_{2}}{\lambda^{5}T_{obj}}}-1\Bigg{)}}d\lambda\ \bigg{(}\frac{W}{m^{2}\cdot sr}\bigg{)} \tag{1}\]  
where \(\lambda\) is the wavelength of the radiation, \(\epsilon_{obj}\) is the surface emissivity, \(T_{obj}\) is the surface temperature, and \(C_{1}\) and \(C_{2}\) are the first and second Planck radiation constants, respectively.  
If \(G_{bb,1}(T)\) and \(G_{bb,2}(T)\) are the infrared radiations that reach the detector (considering a black body as source) associated with filters \(F1\) and \(F2\), we have  
\[G_{bb,i}(T)=\int_{\lambda^{\prime}}^{\lambda^{\prime\prime}}R_{t}(\lambda) \cdot R_{ot}(\lambda)\cdot F_{i}(\lambda)\cdot\frac{C_{1}}{\lambda^{5}\cdot \Bigg{(}e^{\frac{C_{2}}{\lambda^{5}T_{obj}}}-1\Bigg{)}}d\lambda\ \bigg{(}\frac{W}{m^{2}\cdot sr}\bigg{)}, \tag{2}\]  
which is only a temperature function and the calibration curves can be obtained by calculating the ratio as  
\[SR(T)=G_{bb,1}(T)/G_{bb,2}(T). \tag{3}\]  
naturally, the real signal ratio between the radiation coming from a real target captured by two adjacent filters can be expressed as  
\[SR_{real}(T)=G_{real1}(T)/G_{real2}(T) \tag{4}\]  
Now if we make a grey body assumption the Equation (3) can be used also to determine the SR (signal ratio) for a real surface whether the two filters are chosen in order to remove the emissivity dependence. So, the important thing is always to evaluate the accuracy committed by the DC technique applied to the thermography for different classes of materials and non-stationary conditions by using Equations (3) and (4).
## 3 Method Description
### List of Hashtags  
The collection derived from the previous step might contain a large number of hashtags; therefore, a subset must be chosen. Multiple approaches are possible, but the one proposed here combines a certain number of \"hot\" topics with a few \"emerging\" ones, that will be used as an example for the selection (although this is also customizable by the researcher), as described in Algorithm 2.  
```
Data:\(C_{t}\) and \(C_{t-1}\) \(Stopwords\) is a list of undesired or spurious hashtags Result: Twitter Stream tracking (at most) the top \(T\) Hashtags, composed by \(H\) hot topics and \(E\) emerging trends begin /* Generate the list of Words(Hashtags) to follow */ Remove \(Stopwords\) from \(C_{t}\) and \(C_{t-1}\) Hot_hashtags = Top \(H\) Topics in \(C_{t}\) Emerging_hashtags = Top \(E\) Topics in \((C_{t}-C_{t-1})\) and not in Hot_hashtags \(Track\_hashtags\) = Hot_hashtags + Emerging_hashtags /* Note that \((H+E)\leq T\) */ /* \(T\) is the maximum number of hashtags to be tracked */ /* Generate Stream 2 */ Create Stream 2 tracking Track_hashtags end
```  
**Algorithm 2**Tweet Extractor  
The following steps are carried out in order to establish the hashtags that will be tracked in the extraction script:  
1. In each window, the top \(H\) hashtags (referred as \"hot\") in the list derived from the monitor step are selected according to their weight. The reasoning is that these are the keywords that the authorities are most strongly using on their posts;
2. Additionally, for each window, the current list (\(C_{t}\)) is compared to the previous period (\(C_{t-1}\)); from the difference between them, a list of emerging terms (labeled as \(E\)) is obtained (as a customization, this method could look at the \(n\) previous windows in case it was appropriate for the application). The emerging hashtags are a list of keywords that represent topics that could easily become \"hot\" in the following windows (as they exhibit a fast growth) but they have not reached their peak yet. This way, the method is able to implement an early detection and tracking of newer topics;
3. The combination of hot and emerging hashtags from the previous steps forms a final set of \(T\) total hashtags (of at most \(H+E\) elements). This step is graphically described in Figure 2;
4. Adding a set of stop words is another customization that might be useful for most of the applications in order to avoid capturing common daily spurious expressions used widely by Twitter users (e.g., #happysunday, #goodnight or #followfriday).
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 1 Introduction
Aiming at the above problems, this paper designs and constructs an adaptive expression recognition network based on channel weighting, as shown in Figure 1. The main contributions of this paper are as follows:  
1. Design a multi-channel feature extraction network MFE, and construct an expression recognition network in terms of scale, information integrity and fusion methods;
2. Design and use a channel-weighted module ACW, and in the auxiliary branch of MFE, assigns weights to small features from small convolutions to help improve recognition accuracy;
3. Design and modify the spatial attention module and integrate the ACW mechanism to obtain an adaptive spatial attention module ASA, which assists in optimizing the overall network structure to improve the final expression recognition accuracy.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 5 Tests and Their Interpretation  
### Evaluation Procedure  
To comprehensively assess and compare the performance of the trained agents and heuristic lift control strategies, we prepared 30 test simulations, each spanning 1000 time-steps. All heuristic algorithms, including Longest Queue First (LQF), Collective Algorithm (CA), and Last Mission Algorithm (LM) described in [PERSON] and Barto [3], as well as strategies developed by the agents, were used to control the lift in each simulation. This approach allowed us to calculate mean and standard deviations for each performance metric, providing a more detailed understanding of their effectiveness.  
Figure 4: Average waiting time of all served passengers in subsequent episodes across three Experience Replay learning variants.  
Figure 3: Number of illegal moves in subsequent episodes across three Experience Replay learning variants.  
During each test simulation, the trained agents utilized the strategy \(\pi\) derived from their learned \(Q\) function values:  
\[\pi(s)=\operatorname*{arg\,max}_{a}Q(s,a). \tag{6}\]  
This strategy implies that the agent always chooses action that, based on its gathered knowledge, results in the highest expected cumulative reward, prioritizing environment exploitation over exploration.
# Investigation of Functionality of Vehicle Crumple Zones Recovered after a Traffic Accident
## 3 Methods
### Finite Element (FE) Models of crumple Zones  
In this paper, FE models were developed for the simulation of quasi-static experiments according to the geometric measurements of the specimen used in the in situ experiments with the FE software LS-Dyna V.971. Nodes located on the bottom of the specimen were fixed in all directions \(x\), \(y\), and \(z\). For nodes located at another end of the specimen, the command 'BOUNDARY_PRESCRIBED_MOTION_SET' [49] was used to set the constant motion speed (\(v=5\) m/s) in the vertical direction y, and maximum possible displacement was set for the nodes. During deformation, the command 'CONTACT_AUTOMATIC_SINGLE_SURFACE' [49] was used for simulation of the contacting surfaces of the element. Potential displacement during straightening out of the longitudinal member was introduced as the deflection directed inside. These geometric deviations were simulated in the model by sliding the respective nodes in \(x\) and/or \(z\) directions.  
For modelling the dynamic loading experiments, the nodes located on the bottom of the specimen were connected to the fixed plate referred to as S4 in the model. Moreover, plate S3 was designed to impact the investigated specimen. The load was created by attributing the required mass and initial velocity 'INITIAL_VELOCITY' to plate S3 nodes. Spot-welded joints 'CONSTRAINED_SPOTWELD_ID' were designed in the model. For each joint, parameters SN (force in axial direction) and SS (force in shear direction) were entered. In the model, one spot-welded joint had eight nodes, i.e., four pairs of two inter connected nodes. In this case, the value entered for SN and SS in the LS-Dyna subprogram was equal to \(\frac{1}{4}\) of the value of the forces obtained during the in situ experiments.  
During modelling of buckling of the thin-walled structural element, Belytschko-Tsay finite shell elements were used and calculated according to the thick-walled Mindlin-Reissner plate theory. With the deformation velocity increasing, a zero deformation energy (hourglass) effect may emerge in the model, and additional control was set for the elements to avoid this effect. To assess the effect of finite element mesh density, numerical models corresponding to the specimen simulated in Section 2 were designed for elements of different sizes. As a result of the numerical experiments, the finite element mesh density was found to not have any substantial effect on the form of specimen deformation. Figure 14 presents 30 mm, 60 mm and shapes of the specimen deformed at the maximum length. Based on the studies conducted, a 4 mm finite element was selected for development of subsequent models. The selection was based on the following criteria:  
* The results obtained with the models were very close to the results of the in situ experiments;
* Enabled determination of fabricated joints as accurately as possible;
* Acceptable duration of computer calculation.
## 3 Materials and Methods
### Design of the CNN-LSTM-Attention-Based Digital Twin Fault Prediction Model  
#### 3.5.1 CNN-LSTM-Attention Model Architecture Design  
The model framework proposed in this study is illustrated in Figure 11, with an overall structure containing three core components. First, a convolutional neural network is used to construct a feature extraction module, which primarily processes normalized time-series feature data that has been preprocessed and subjected to Pearson correlation analysis. It then extracts potential deep features from the SCADA data through convolutional operations. Subsequently, a temporal modeling module is designed based on a long short-term memory network. The features extracted by the convolutional layers are fed into the LSTM units, and an attention mechanism is incorporated to dynamically assign weights to the temporal features, thereby enhancing the model's capability for modeling time dependencies. Finally, a residual analysis module is constructed to generate the time-series prediction results through the output layer, and to conduct statistical analysis on the residual sequence of the prediction errors.
## 2 Method of Analysis
### Displacement Solutions for Surrounding Soil due to Pile Penetration  
Field measurements and theoretical research [5; 39; 40] show that the penetration of the static-pressure pile mainly causes radial displacement of the soil around the pile. Therefore, the penetration of the new pile will cause lateral compression on the old pile and will not cause pile torsion. One of the key steps to analyze the stress and deformation of the old pile is to obtain the radial displacement of the soil around the new pile.  
For static-pressure pile penetration, any micro-segment of the pile can be considered as a spherical cavity expansion of the same volume, which is shown in Figure 2. It can be expressed as:  
\[\frac{1}{4}\cdot\pi\cdot{d_{0}}^{2}\cdot dh=\frac{4}{3}\cdot\pi{a_{0}}^{3} \tag{3}\]  
where \(d_{0}\) is the diameter of the static-pressure pile; \(a_{0}\) is the radius of the equivalent spherical cavity, and it can be expressed as:  
\[a_{0}=\left(\frac{3}{16}\cdot{d_{0}}^{2}\cdot{d_{0}}\right)^{\frac{1}{3}} \tag{4}\]  
Figure 1: Stress boundary for semi-infinite cavity expansion correction. \(\sigma_{z}\) is the vertical stress; \(r\) and \(z\) are the horizontal and vertical positions, respectively; \(\tau_{\rm r}\) is the shear stress.  
The squeezing displacement of a pile (assuming a length of \(L\)) pressed into the soil can be calculated as the integration of \(h\) from 0 to \(L\):  
\[u_{r}=\int_{0}^{L}\frac{{d_{0}}^{2}}{16}\cdot\left(\frac{r}{R_{1}^{3}}+(3-4\
u) \frac{r}{R_{2}^{3}}-\frac{6\tau z(z+h)}{R_{2}^{5}}\right)\mathrm{d}h \tag{5a}\] \[u_{z}=\int_{0}^{L}\frac{{d_{0}}^{2}}{16}\cdot\left(\frac{z-h}{R_{1}^{3}}-(3-4 \
u)\frac{z+h}{R_{2}^{3}}+\frac{2z}{R_{2}^{3}}-\frac{6z(z+h)^{2}}{R_{2}^{5}} \right)\mathrm{d}h \tag{5b}\]  
where \(u_{r}\) and \(u_{z}\) are the lateral displacement and the vertical displacement, respectively; \(L\) is the pile length; and \(r\) and \(z\) are the coordinates of the calculated point position.
## 3 Proposed Solution to Predict the AoA
### System Overview and Assumptions
O-RAN Category B, designed for massive MIMO and intelligent beam management, aligns closely with our research goal: to realise a parallel, distributed prediction and fusion architecture leveraging DE-ACSW-R. In this system, the O-RU performs clustered jump-aware Weighted Linear Regression (WLR) for fast, local AoA prediction, while the O-DU concurrently executes curvature-adaptive SVR and applies dynamic dominance-based error fusion (dominance-enforced override). This dual-pipeline approach ensures that the most reliable predictor (LR or SVR) is adaptively privileged based on instantaneous window performance, while also supporting rapid window clustering in the presence of sudden AoA shifts. The result is robust low-latency beamforming, fully exploiting the flexibility and modularity of Split 7-2x O-RAN. While the distributed processing model of Split 7-2x enables centralised beamforming, it also introduces the following challenges:  
* The O-DU lacks an immediate physical-layer awareness of rapid changes at the O-RU. * CSI-RS-based feedback loops incur latency and cannot adapt quickly in high-mobility scenarios [30; 31]. * Codebook-based beam selection often underperforms in multipath and NLoS (non-line-of-sight) conditions. Nevertheless, to fully appreciate the motivation behind predictive AoA estimation within the O-RAN architecture, it is essential to contrast it with conventional beamforming techniques defined by 3 GPP standards [32; 33]. While Split 7-2x enables a more responsive and modular beamforming pipeline, legacy NR systems continue to rely heavily on feedback-driven mechanisms, which are susceptible to significant latency and cannot react promptly to abrupt changes in user direction or channel conditions. Our results and analysis demonstrate that, in high-mobility and densely urban scenarios, the time lag and granularity of CSI-RS-based feedback are insufficient to maintain beam alignment, resulting in increased prediction error and degraded SNR. This contrast underscores the need for real-time prediction-enhanced frameworks such as our proposed DE-ACSW-R dual-pipeline architecture, which proactively adapts to user mobility by (1) dynamically clustering prediction windows after AoA jumps, (2) enforcing dominance-based fusion according to error metrics, and (3) fusing physical-layer trends with adaptive learning models for robust, anticipatory beam management.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 3 VPNFilter Malware in the Smart Home Network
### Affected Vendors  
Based on the scale of this research, it was initially believed that only Linksys, Netgear, TP-Link, and MikroTik routers were vulnerable, but the number of routers is increasing. Figure 4 illustrates most of the devices that are already affected by this new malware attack. Table 2 summarizes the specific routers' features and their comparison.  
Figure 4: Infected router.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**FeaturesRouters** & **Assn RF-Ac66U** & **D-link DSR-[44,46]** & **Huawei** & **Mikorik** & **Netgar** & **Ubiqutial PRE** & **Tp-link** & **ZTE ZAGIN** \\
**[45]** & **500N(K2)** & **HC824S [47]** & **RB952 [48]** & **WNR2000 [49]** & **MS [50]** & **TL-WR/41 ND [51]** & **[44,52] H10 SN** \\ \hline \hline \begin{tabular}{c} Wifi speed \\ \end{tabular} & 130 Mbps & 300 Mbps & 54 Mbps & 10/100 Mbps & 300 Mbps & 200 Mbps & 150 Mbps & 300 Mbps \\ \hline \begin{tabular}{c} Wifi standard \\ \end{tabular} & 802.11 ac & 802.11 b/gn & 802.11 b/gn & 802.11 b/gn & 802.11 b/gn & 802.11 b/gn & 802.11 b/gn & 802.11 b/gn \\ \hline \begin{tabular}{c} Network mapping \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \begin{tabular}{c} Easy Q6x management \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \begin{tabular}{c} VPN support \\ Security vulnerabilities \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \begin{tabular}{c} VEP, WPA, \\ Security \\ \end{tabular} & \begin{tabular}{c} WPA-pk, \\ WPA-pk, \\ WPA-pk \\ \end{tabular} & \begin{tabular}{c} WEP, WPA, \\ WPA2-peripher \\ \end{tabular} & \begin{tabular}{c} WPA2-AES \\ WPA2-peripher \\ \end{tabular} & \begin{tabular}{c} WPA-WPA2 \\ WPA2-peripher \\ \end{tabular} & \begin{tabular}{c} WPA-WPA2 \\ WPA2-peripher \\ \end{tabular} & \begin{tabular}{c} WPA-WPA2 \\ WPA2-peripher \\ \end{tabular} & \
\begin{tabular}{c} WPA-WPA2 \\ WPA2-peripher \\ \end{tabular} \\ \hline
\begin{tabular}{c} LAN port \\ \end{tabular} & 4 & 4 & 4 & 5 & 4 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of router features.
## 4 Discussion
### Study Limitations  
This study has several limitations. First, its retrospective design precludes the establishment of causal relationships, and the absence of randomization introduces the possibility of confounding variables influencing the outcomes.  
Second, while the VAS was used consistently to assess sensory changes, it is inherently subjective and does not capture the more nuanced functional impairments such as two-point discrimination or tactile threshold shifts.  
Third, important psychological and socioeconomic factors that may significantly affect the treatment outcomes were not formally evaluated. The inclusion of validated psychometric instruments (e.g., PHQ-9, GAD-7) and socioeconomic profiling in future studies would enable a more holistic understanding of patient recovery trajectories [40,41].  
Fourth, the relatively small sample sizes in certain subgroups, particularly those receiving surgical or combined therapies, may have limited the statistical power to detect significant intergroup differences. Furthermore, the treatment modalities were not standardized and were influenced by both patient preference and clinician judgment, which may have introduced a selection bias.  
Lastly, as this investigation was conducted at a single academic institution, the findings may not be generalizable to the broader population or other healthcare systems. Future prospective, multicenter studies employing standardized diagnostic criteria, uniform treatment protocols, and comprehensive longitudinal follow-up--including both psychophysical and patient-reported outcomes--are warranted to refine the prognostic models and optimize the clinical management of PTTNP.
# Using Imaging Techniques to Analyze the Microstructure of
## 3 Scanning Electron Microscope (SEM)
### Discussion  
SEM has the capacity to analyze the micro-structure of AC samples and provide a better understanding of fracture and self-healing mechanisms in the samples. The high-resolution images from SEM can reveal important details about the arrangement and distribution of aggregates, asphalt binder, and air voids, as well as any defects or damage present. By examining the microstructure, SEM can help identify the initiation and propagation of micro-cracks, new air voids, and surface roughness.  
However, SEM utilizes an electron beam that cannot deeply penetrate the sample surface; therefore, it can only analyze the sample structure up to a shallower depth. SEM is a destructive imaging technique and appropriate for small-size sample screening. SEM requires contact with the sample, which makes this technique unsuitable for investigating the properties of bituminous materials at high temperatures. The application of the SEM technique for the investigation of AC microstructure is limited due to operational, sample preparation, and cost requirements. Table 2 summarizes the features of the SEM imaging technique to analyze AC microstructure.