esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proteolytic Digestion and Mass Spectrometry Analysis  
Crude enzyme was analyzed in protein bands cut from SDS denaturing gel and zymogram and in purified form using trypsin proteolysis and matrix-assisted laser desorption/ionization-Fourier transform mass spectrometry analysis. Protein bands were cut from gel, cut into small pieces, and decolorized in sonic bath at 60 \({}^{\circ}\)C several times with 0.1 M 4-ethylmorpholine acetate (pH 8.1) in 50% acetonitrile (ACN). After complete destaining, proteins were reduced by 50 mM tris-(2-Carboxymethyl)phosphine in 0.1 M 4-ethylmorpholine acetate (pH 8.1) for 5 min at 80 \({}^{\circ}\)C and alkylated using 50 mM iodoacetamide in 0.1 M 4-ethylmorpholine acetate (pH 8.1) for 30 min in the dark at room temperature. Then, the gel was washed with water, shrunk by dehydration with ACN, and reswollen in water. The rehydration and dehydration of the gel was repeated twice. Next, the gel was reswollen in 0.05 M 4-ethylmorpholine acetate (pH 8.1) in 50% ACN and then the gel was partly dried using a SpeedVac concentrator (Savant, Holbrook, NY, USA). Finally, the gel was reconstituted with cleavage buffer containing 0.01% 2-mercaptoethanolhanol, 0.05 M 4-ethylmorpholine acetate (pH 8.1), 10% ACN, and sequencing grade trypsin (Promega, 10 ng/uL). Digestion was carried out overnight at 37 \({}^{\circ}\)C, the resulting peptides were extracted with 30% ACN/0.1% trifluoroacetic acid (TFA) and subjected to mass spectrometric analysis.  
Mass spectra were acquired in the positive ion mode on a MALDI-FTMS APEX-Ultra (Bruker Daltonics, Bremen, Germany) equipped with a 9.4 T superconducting magnet and a SmartBeam laser. The acquisition mass range was 700-3500 \(m/z\) and 512k data points were collected. A 280 V potential was applied to the MALDI plate. The cell was opened for 2500 ms, 4 experiments were collected for one spectrum, where one experiment corresponds to 300 laser shots. The instrument was externally calibrated using PepMix II peptide standard (Bruker Daltonics, Bremen, Germany), resulting in a typical mass accuracy below 2 ppm. A saturated solution of \(\alpha\)-cyano-4-hydroxy-cinnamic acid in 50%ACN/0.2% TFA was used as a MALDI matrix. 1 uL of matrix solution was mixed with 1 uL of the sample on the target and the droplet was allowed to dry at ambient temperature. After analysis, the spectra were apodized using square sin apodization with one zero fill. The interpretation of mass spectra was performed using DataAnalysis version 3.4 and BioTools 3.2 software packages (Bruker Daltonics, Billerica, MA, USA). Proteins were identified by peptide mass fingerprinting (PMF) using the search algorithm MASCOT (Matrix Science, Boston, MA, USA).  
The characteristic spectrum was matched to the complete genome of _C. paraputritrium_ J4. For purified crChit62J4 the characteristic spectrum was matched reliably with predicted fragments of putative chitinase from the bacterial genome (peg1890, Supplementary Figure S2).
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 3 The Proposed Coding Optimization Algorithm and Network Architecture  
### Algorithm Overview  
We propose a coding optimization algorithm for cloud gaming videos with reference frame enhancement through a deep learning network. Figure 2 shows an overview of the proposed optimization compression pipeline. The blue boxes represent the conventional codec (i.e., H.265), which is applied to compress the cloud gaming videos. The cyan blocks indicate the proposed CMGNet, which is responsible for generating the enhanced reference frame. There are at most four reconstructed frames in the reference frame list under the LDP configuration. We use the four reference frames and the camera motions to generate an enhanced reference frame through the CMGNet and then add it to the reference frame list to participate in the inter-prediction process of the current frame. It is noted that there are less than four reference frames in the reference frame list of the first three predictive frames, so we copy the last reference frame with the smallest Picture Order Count (POC) in the list to make up the four reference frames. The camera motion information is signaled along with the conventional codec's bitstream. On the decoder side, the operation is consistent with the encoder side, and it is not necessary to transmit additional labels to mark the generated reference frames.
## 5 Conclusions  
This study proposed a deep learning framework for high-frequency signal forecasting that integrates structural modeling, cross-scale temporal encoding, and macro-level semantic modeling. Structural relationships among signals are captured via graph neural networks, while a multi-scale Transformer architecture extracts behavioral patterns at short-, mid-, and long-term horizons. In addition, macroeconomic policy texts are processed  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Feature Type** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **MSE (\(\times 10^{-4}\))** & **MAE (\(\times 10^{-2}\))** \\ \hline Low-Freq Only & 88.3 & 87.5 & 87.9 & 2.47 & 1.23 \\ High-Freq Only & 89.6 & 88.9 & 89.2 & 2.34 & 1.15 \\ Multi-Freq & 92.4 & 91.6 & 92.0 & 1.76 & 0.96 \\ \hline \end{tabular}
\end{table}
Table 5: Impact of different frequency feature inputs on model performance.  
using LLMs to generate semantic embeddings, which enhance the system's sensitivity to external shocks. These three representations are fused within a unified architecture to support end-to-end signal modeling and forecasting. The experimental results demonstrate that the proposed method outperformed seven strong baseline models--including LSTM, TCN, FinBERT, and StockGCN--in both classification and regression tasks. The framework achieved higher accuracy, better generalization, and greater robustness. Ablation studies further confirmed the independent contributions of each module, particularly the structural modeling and semantic alignment mechanisms in improving predictive stability. The scientific contributions of this work are fourfold: (1) a unified framework integrating structural, temporal, and semantic paths; (2) a multi-scale parallel Transformer design that enhances the modeling of non-stationary dynamics; (3) the first incorporation of LLM outputs for macro-level signal conditioning; and (4) the implementation of a deployable, automated forecasting system. The current model still relies on simple concatenation and attention mechanisms for interactions between modules, which may lead to limited coordination. Future research could explore reinforcement learning-based feedback or multi-task prediction structures to further improve adaptability and risk-awareness in high-frequency environments.  
Conceptualization, X.Z., L.Z., S.H., Y.H. and C.L.; Data curation, Y.H. and H.Y.; Formal analysis, T.L. and Y.J.; Funding acquisition, C.L.; Investigation, Y.J. and H.Y.; Methodology, X.Z., L.Z. and S.H.; Project administration, C.L.; Resources, Y.H. and H.Y.; Software, X.Z., L.Z., S.H. and T.L.; Supervision, C.L.; Validation, Y.J.; Visualization, T.L. and H.Y.; Writing--original draft, X.Z., L.Z., S.H., T.L., Y.H., Y.J. and C.L.; [PERSON], [PERSON], [PERSON] and T.L. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 61202479.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Experiments
### Comparative Experiments
Next, we compare and verify some common semantic segmentation models based on CNN frameworks and some dual-stem hybrid network models, and the experimental results are shown in Table 9. Among them, Conformer [47] and MobileFormer [48] are both hybrid networks of CNN and Transformer in parallel, which carry out bidirectional convergence and interaction at each stage, and MobileFormer's CNN framework adopts the MobileNet lightweight network. CMT is a hybrid network of CNN and Transformer serial [52]. The experimental data presented in Table 9 vividly illustrate that the method proposed in this study exhibits significant superiority in cloud and cloud shadow detection within Transformer-based networks. When juxtaposed with CNN based networks and CNN+Transformer dual-branch networks, it consistently achieves the most optimal segmentation outcomes. It outperforms the others by accurately identifying and segmenting clouds and cloud shadows, highlighting its robustness and effectiveness in handling such remote-sensing-related segmentation tasks. Due to space limitations, we selected one of the three methods based on Transformer, CNN, and CNN+Transformer for comparative effect. These three methods are CSWin-T, OCRNet, and Conformer, which have the best cloud segmentation effects among the same type of methods. Figure 12 shows the comparative segmentation of the model in this study against the complex remote sensing background. For targets with a discrete distribution and small area, as shown in the first row of Figure 12, the model in this study has a better segmentation effect than CSWin-T. However, when the background is similar to the target, as shown in Figure 12, the dark vegetation in the second row is easy to confuse with cloud shadows, and the CNN network, such as OCRNet, is prone to misjudgment. In the case where the road and white roof are similar to clouds in the background of the towns in Figure 12 in the third row, the other three types of models have more or less mispositives, but the models in this study balance the global and local expressions, thus reducing such misjudgments. For boundary detection, as shown in Figure 12, the junction of clouds in the fourth row and their boundaries, the models in this study are better than the other three types of models. Based on the above experimental data and comparative prediction graphs, the model designed in this study  
Figure 11: Segmentation prediction plots compared with CNN and Transformer backbone: (**a**) original image; (**b**) labels; (**c**) CSWin-T; (**d**) ResNet-50; (**e**) our model. shows an excellent segmentation effect on Cloud and Cloud Shadow datasets, which also verifies the effectiveness of the internal structure of the model.
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 4 Experiments and Results
### Performance Measure  
We use precision, recall, F1-score, and detection accuracy as our evaluation metrics for the comparison of different models. The _Precision_, _Recall_, and _F1-score_ are defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1\text{-}score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{10}\]  
where \(TP\) (True Positive) is the number of samples correctly classified as an anomaly class, \(FN\) (False Negative) is the number of samples incorrectly classified as not belonging to an anomaly class, and \(FP\) (False Positive) is the number of samples incorrectly classified as an anomaly class. Also, detection accuracy can be defined as the proportion of correctly classified regions by a model in total regions as follows:  
\[Detection\ accuracy=\frac{1}{|P|}\sum_{i}^{|P|}S_{i}\,,\text{where }S_{i}= \begin{cases}1&\text{if }IoU_{i}>Threshold\\ 0&\text{otherwise}\end{cases} \tag{11}\]  
where \(P\) is the set of peaks from test set and \(IoU_{i}\) is the intersection over union (\(IoU\)) for \(i\)-th peak which can be defined as follows:  
\[IoU_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{12}\]  
where \(TP_{i}\), \(FP_{i}\), and \(FN_{i}\) are \(TP\), \(FP\), and \(FN\) for the region of \(i\)-th peak. In our experiment, threshold for IOU is set to 0.55.
## 1 Introduction
Thus, this article employs sea surface wind data observed by multiple satellite microwave scatterometers to (1) extract TC center positions and (2) derive R\({}_{\rm 34 kt}\). We then build TC category-dependent statistical models of R\({}_{0}\) as functions of R\({}_{\rm 34 kt}\). Section 2 describes the data. Section 3 presents the methodology and the TC cases. Section 4 shows results, including (a) center positioning and (b) regression models between R\({}_{0}\) and scatterometer-derived R\({}_{\rm 34 kt}\) for multiple intensity categories (tropical storms, category-1, -2, and 3+). Section 5 summarizes the main findings and discusses further research.
# DDR4 BER Degradation Due to Crack in FBGA Package Solder Ball
## 6 Discussion  
The cracks in FBGA solder balls change the electrical model of the solder ball and introduce a parallel capacitance. This results in signal rise time degradation due to attenuation of higher frequencies and reflections due to discontinuity. The data eye becomes smaller and the voltage and timing margin decreases for the system. The effect of crack was measured for a running system by loading the DDR4 data line with a 0.2 pF capacitor. The data eye measurements show that there is 36 ps more data eye closure for data line with crack defect in comparison to no defect data line. This results in higher bit error rate as shown by the bathtub plots. The increase in bit error rate results in system reliability degradation and there will be more intermittent errors in the system.  
Figure 12: (**a**) Data eye for DQ3 channel with no defect (**b**) data eye for DQ2 channel with cracked solder ball.  
Figure 13: Bathtub plots for DQ2 (crack defect in solder ball) and DQ3 (no defect) data channel.  
**Author Contributions:** Conceptualization, M.W. and S.B.; methodology, M.W. and S.B.; software, M.W. and G.B.; validation, M.W. and J.K.; formal analysis, M.W.; investigation, M.W., G.B.; resources, G.B., J.K.; data curation, M.W.; writing--original draft preparation, M.W.; writing--review and editing, M.W.; visualization, M.W.; supervision, S.B.; project administration, S.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2020R1H1A2103043).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Analysis  
### Verification of Analytical Results and Response Analysis  
The low-order natural frequency of the pipe is low when it is a unilateral constraint, which is prone to wind-vortex-induced vibration. An example of a pipe with the unilateral constraint of a jacket platform is analyzed. As shown in Figure 2, \(L\) is the length of the pipe, and \(d\) is the distance from the installation location of an NES to the pipe constraint. The construction period of the deep-water jacket is long, and the wind-vortex-induced vibration during construction may affect its overall fatigue life. The pipe and its parameters [26] are shown in Table 1.  
The vortex frequency, Strouhal number and lift and drag coefficient at different wind speeds were calculated by the CFD method [16]. When the wind velocity is 2.7 m/s, the vortex shedding frequency is 0.796 Hz, which is close to the natural frequency of pipe, and the corresponding \(C_{L}\) = 0.4535 and \(S_{t}\) = 0.225.  
Two methods were used to solve Equation (3); that is, Runge-Kutta numerical integration and the approximate analytical solution described in Section 3.1. The results were compared, as shown in Figure 3. (It is notable that the motion responses of the structure under different \(\kappa\) conditions have been calculated in this paper. In order to reflect the characteristics of the comparison of results, \(\kappa\) = 24.425, \(\kappa\) = 97.97 and \(\kappa\) = 367.38 are selected for subsequent analysis. Therefore, the question of how to determine the \(\kappa\) value will not be repeated later).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Parameters** \\ \hline Mass & 247 kg \\ Natural frequency & 0.78 Hz \\ Damping ratio & 0.0015 \\ Velocity of wind flow & 2.5–3.1 m/s \\ Diameter & 0.762 m \\ Density of air & 1.225 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the pipe.  
Figure 2: A pipe with unilateral constraint of a jacket platform.  
Figure 3 shows the time history of the system motion at a wind speed of 2.7 m/s with NES parameters of \(\zeta_{2}=0.0085\) and \(\rho_{2}=0.02\). The approximate analytical solution is extremely close to the numerical solution, which verifies the correctness of the derivation process of the analytical method described in Section 3.1.  
The motion of the pipe is quasi-periodic motion, showing a \"beat\" response. The relative motion \(Z\) between an NES and the pipe is significantly greater than that of the pipe itself, and most of the energy of the pipe is transferred to an NES.  
The Fourier transform was performed on the time histories in Figure 3. As shown in Figure 4, the results show that the pipe displacement x\({}_{1}\) and the pipe-NES relative displacement \(Z\) have multiple frequency components, which are mainly concentrated in the natural frequency of 0.78 Hz of the pipe.
# Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System
## 5 Subject Matter Validation
### Results
#### 5.2.1 Notification  
The infographic displaying the locations where events have occurred, and if multiple events have been occurring at the same location, could be misleading, since it only gives an indication of the frequency of events as opposed to the severity. This could lead to situations where one significant event (e.g., the chipping of a pantograph contact strip due to a failed dropper) could go unnoticed because it is overshadowed by many insignificant events (e.g., lateral acceleration limits of the contact wire repeatedly being exceeded during periods of high wind).  
One participant mentioned that the data in the table needed to be readable quickly, and also that using contact force as an indicator of fault severity requires understanding and experience with the system. Allowing customizability and allowing the user to specify their own limits, whilst highlighting values exceeding these, could improve rapid interpretation of the information whilst also teaching inexperienced users the range of values to expect.  
A participant also suggested that including the speed of the train would help users gauge the severity of faults, and that including the ambient air temperature would also assist users in recognising faults that are more likely to occur in certain temperatures (arcing is unavoidable and much more common in icy weather [21]).
# Malicious Powershell Detection Using Graph Convolution Network
## 3 Malicious Powershell Detection Method Using Graph Convolution Network (GCN)  
In this section, we propose a new method for detecting malicious powershells using a GCN. First, in Section 3.1, we introduce GCN. Second, in Section 3.2, we propose a method to generate an adjacency matrix using Jaccard similarity between powershell scripts and provide a method to detect malicious powershells using the adjacency matrix.
## 2 Proposed Integrated Design
### High-Band (mmWave 5G) MIMO Configuration  
The initial design of the antenna is based on Equations (1)-(3). The same equations were used initially to design the mmWave patch antenna operating at 28 GHz. The antenna was then optimized by the use of the empirical multi-parametric study. The final design was optimized for the mmWave 24.25-28 GHz band. Afterwards, the antenna was used to create a 4-port MIMO configuration. The final single element design, along with the final MIMO design, is shown in the Figure 5.  
The S-parameters for the single-element and the MIMO configuration are reported in Figure 6. It can be noted that the designed single element operates from 24.06 to 28 GHz and has an operational \(-\)10 dB bandwidth of 3.94 GHz, therefore covering the entire mmWave band-1 (24.25-27.5 GHz) of group 30 (GHz). The maximum IEEE gain of the antenna is 8.57 dBi.  
The reflection parameters for the MIMO configurations are also reported in Figure 6a. It can be noted that the operational frequency range for the configuration is from 24.15 to 28 GHz and has an operational \(-\)10 dB bandwidth of 3.85 GHz. The MIMO configuration still operates in the required band with maximum IEEE gain of 8.57 dBi and the MIMO sum gain of 13.8 dBi. The transmission parameters for the proposed design are reported in  
Figure 4: Scattering parameters for the mid-band (4G LTE and lower 5G) antenna design: (**a**) reflection; (**b**) transmission.  
Figure 3: Mid-band MIMO final antenna design progression stages: (**a**) single element, front; (**b**) single element, back; (**c**) array, front; (**d**) array, back; (**e**) MIMO antenna configuration, front; (**f**) MIMO antenna configuration, back.  
the Figure 6b. It can be noted that, even in the worst-case scenario, the minimum isolation of 30 dB was achieved, which is significantly high.  
The distance between the antennas in the MIMO configuration is provided in Figure 7. It can be seen, from Figure 7, that the minimum distance between the two antennas in the mentioned MIMO configuration is 45 mm and the maximum distance between any two antenna elements is 72.31 mm.
## 6 Security and Privacy Issues  
Security and privacy of users in VANETs are very important but, at the same time, an objective and realistic definition of security and privacy is also needed. This means that the level of security and  
Figure 3: Vehicle swarm concept.  
privacy assured in a VANET can be no more than it is in the real world [13]. In the real world, a vehicle can be identified by its registration plate and then tracked by authorities via video cameras as is the practice nowadays. Similarly, there are cases (e.g., stolen car, accident, or misbehaving vehicle) when the whereabouts of a vehicle in a VANET have to be determined by the authorities, e.g., in case of accidents, which implies traceability. The US Department of Transportation (DOT) has identified many safety applications which require each vehicle to periodically transmit its current location to its one-hop neighbours at a rate of up to 10 Hz [14]. These messages, called Cooperative Awareness Messages (CAMs), are susceptible to an eavesdropper who can then get the location of that vehicle. Therefore, it is necessary that the messages are secured from giving away the user's location. The security and privacy goals and how they are achieved in VANETs are shown in Figure 4 and are discussed below in detail.  
It is essential for VANET security that the location of a vehicle is transmitted while preserving the location privacy of the user. This location information can be used by adversaries in tracking vehicles or routes. As discussed previously, location privacy requires unlinkability, which ensures anonymity. As the information in VANETs is very critical for safety, it is imperative that it should be reliable. Reliability is achieved through authentication, which ensures the integrity of the messages, _i.e._, they have not been tampered with. Another requirement for VANETs is non-repudiation, which means that users should not be able to deny sending a message so that they can be tracked and penalized in case of a false message. This is achieved by making the messages traceable but only by the authorities so that they can be revoked. It has been proposed [15] that the tracing should be done by multiple authorities in order to provide extra security and privacy. Different security schemes have been used to protect privacy or achieve anonymity and, based on the underlying security mechanisms they use, these schemes can be classified as:  
* Pseudonyms coupled with public key infrastructure (PKI)
* Trust-based schemes
* Group signatures  
Figure 4: Security and privacy goals in VANETs and how they are achieved.  
* k-anonymity schemes
* Identity-based signature schemes  
There are various issues associated with each of these schemes which are discussed in the next section.