esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Diversity, Landslide Dynamics, and Mountainscapes  
Earlier, we posited that remotely sensed hyperspectral data may help understand relationships between functional diversity, ecosystem processes, landsl, and, more broadly,disturbance dynamics of mountainousscapes (Figure 1). The ecological and geomorphic significance of these relationships remains poorly understood, but for certain, a focus on areas disturbed by landslide activity as well as undisturbed areas [162; 163; 164; 165] is key to understanding the dynamics of mountainousscapes. First, landslides expose soil horizons exhumed all the way down to altered bedrock or even minimally weathered bedrock upon which communities and ecosystems reorganize and recover [163; 164]. Second, landsliding may contribute to the diversity [166; 167; 168; 169; 170] and functioning [171; 172; 173; 174] of montane ecosystems through the creation of unique habitats and landscape configurations that favor subsets of unique species and the alteration of abiotic conditions that directly influence ecosystem processes such as carbon and nutrient cycling and rock weathering. Third, the newly exposed substrates are heterogeneous at local (individual landslides), regional (landslide populations), and landscape (landslide assemblages) scales. Lastly, landslides leave long-lasting legacies that may influence landscape memory and therefore landsl itself [175; 176]. Studying undisturbed slopes becomes equally important because ecosystems may influence slope resistance and susceptibility to failure in complex ways given the dynamic nature of vegetation-soil-saprolite interactions during ecosystem development [163]. Advances in hyperspectral sensors and platforms (Files S1 and S2: Table S1) as well as data analytical approaches (Figure 2) may help characterize the vegetation based on functional traits and functional diversity (Figure 3), and the underlying soils and lithology based on physical and geochemical attributes. Together this has the potential to inform about the diversity and functioning of montane ecosystems at scales commensurate with landslide activity (Figure 1).  
Figure 3: Functional and spectral traits, and corresponding diversity indices, derived from PRISMA hyperspectral data. (**a**) large region of the Sierra de las Minas of Guatem (SLM), including focal areas (**b**–**d**), impacted by landslides. (**a**,**b**) are true-color RGB images TCI, (**c**) RGB composite of three  
functional traits (FT; vegetation indices) (**left**) and derived functional diversity metric (FD\({}_{\alpha}\); **right**), and (**d**) RGB composite of three spectral traits (derived from principal component analyses) (**left**) and corresponding spectral diversity metric (SD\({}_{\alpha}\); **right**). The three vegetation indices include the normalized difference water index (NDWI), Anthocyanin Content Index (ACI), and Vogelman Index 2 (VOG). The three components correspond to PC10, PC3, and PC1.
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 5 Conclusions  
The CWI affects the DVB-S2 receiver in four main stages: the signal constellation, the symbol time recovery, the frame synchronization, and the carrier frequency recovery. Each stage is crucial in the functioning of the receiver and if one fails, the whole chain is broken. First, the CWI causes the signal constellations to become circles with a radius proportional to the interference power. SNR can decrease as much as 6 dB when the JSR is \(-15\) dB. Then, interferences with high frequencies increase the timing error. The frame synchronization is also affected depending on the center frequency of the interference. A JSR of \(-7\) dB can cause as much as \(18\%\) packet loss depending on the center frequency. Finally, CWI close to Doppler frequency greatly affects the data-aided carrier frequency recovery. When the CWI center frequency is close to the Doppler frequency, the Doppler error estimation will rise exponentially, well above \(15\%\) which is enough to cause the whole CFR to fail.  
In the second part, the effect of the CWI removal with a notch filter was analyzed on a signal with and without interference as a reference. The method used here is an adaptive IIR first order notch filter preceded by a center frequency detector. Although the notch filter partially degrades the signal, it does amazing work to reduce the jamming caused by the CWI. In general, the JSR is reduced by 15 dB with only a single CWI, and, when both the AWGN and the CWI are combined, the SNR is improved by 10 dB for 0 dB JSR. An important factor that affects the improvement is the r parameter. By varying it, one can measure different improvements of the JSR and SNR.  
Without filtering, the BER is \(10^{-2}\) for an Eb/N0 ratio of 12 dB. However, after CWI removal by the notch filter, that value goes down to \(10^{-6}\), much closer to the theoretical one which is \(10^{-8}\).  
On the other hand, the notch filter has a few setbacks. Since it deletes a range of frequencies, it also deletes a part of the information. This is even more noticeable when the filter's bandwidth is larger (smaller \(r\) value). In addition, for very low JSR, the improvements are marginal and, depending on the bandwidth, the SNR could be worse.  
Conceptualization, M.H.S.; methodology, M.H.S.; writing\(-\)original draft preparation, M.H.S.; writing\(-\)review and editing, G.G. ([PERSON]), P.I., G.G. ([PERSON]); supervision, R.L.J. All authors have read and agreed to the published version of the manuscript.  
This research is part of the project entitled AVIO-601 in LASSENA Lab (Ecole de Technologie Supérieure) named Interference Mitigation in Satellite Communication. It is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Thales, Telesat, VIGILANT GLOBAL, CRIAQ, and Atem Canada. Special thanks to CMC for providing the required equipment to succeed with this project.  
The authors declare no conflict of interest.
## 2 Background and Related Work
### Feature Extraction  
Feature extraction is the key step for signature verification and can be generally classified into two types of methods: handcrafted feature extraction and feature learning methods [1]. In the handcrafted feature extraction methods, users design the extractor based on subjective perception. A number of survey papers [1; 4; 8; 9; 10; 11] have reviewed the handcrafted feature extraction and classification methods used in signature verification. [PERSON] et al. [12] used a wavelet-based feature extractor to calculate the curvature features of signatures. [PERSON] et al. [13] took the local binary patterns (LBP) and uniform local binary patterns (ULBP) as their texture-based feature extraction method.  
On the other hand, feature learning methods can extract features automatically without human intervention. This kind of method is commonly referred to as CNN and other deep learning approaches and has shown excellent performance compared to handcrafted features in many computer vision areas. [PERSON] et al. [14] proposed a deep CNN approach to learning features directly from signature image pixels for writer classification. [PERSON] et al. [15] proposed a CNN-based method that can further learn stable features from variable-size signatures.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 5 Discussion
### Practical Implications  
This study has several practical implications for investors, policymakers, and system developers:  
For bond traders and investors: The RF model offers a reliable tool for tactical asset allocation and risk management. Accurate short-term yield forecasts are crucial for pricing fixed-income securities and adjusting portfolio duration.  
For policymakers: Understanding the leading indicators of bond yields helps assess market expectations and the transmission of monetary and fiscal policy. For example, persistent yield curve inversion could signal recession risks and influence rate-setting decisions.  
For financial system developers: The integration of RF or MLP models into trading platforms or risk assessment systems can enhance decision support by delivering timely, data-driven forecasts [11; 13].
# Seismic Performance of Curved Bridges with Longitudinal Slope
## 4 Test Results and Discussion
### Displacement Response of Bearing  
Figure 11 shows that the displacement response of bearing increased from Site A to E. When the seismic wave was excited in the X\({}_{\text{P}}\)-direction, the main girder generated rotation, leading to the relative displacement of the bearing remarkably at pier 1#. The bearing displacement at pier 4# was greater than that at pier1# when the seismic wave was excited in the Y\({}_{\text{P}}\)-, X\({}_{\text{P}}\) + Y\({}_{\text{S}}\)-, and X\({}_{\text{S}}\) + Y\({}_{\text{P}}\)-directions. The displacement response of the bearing at the high pier was greater than that of the lower pier because the rotation effect of the main girder was relatively small and the structural response was evident at the high pier.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Site** & **Site A** & **Site B** & **Site C** & **Site D** & **Site E** & **Site A** & **Site B** & **Site C** & **Site D** & **Site E** \\ \hline Position A & & \multicolumn{4}{c}{Unidirectional input (Xp)} & \multicolumn{4}{c}{Bidirectional input (Xp + Ys)} \\ \hline
1\# & 3.62 & 4.64 & 6.41 & 8.12 & 10.72 & 5.12 & 6.11 & 7.33 & 8.91 & 16.19 \\
4\# & 3.15 & 4.14 & 5.49 & 6.94 & 8.64 & 5.95 & 6.94 & 8.10 & 10.21 & 16.59 \\ \hline Position B & & \multicolumn{4}{c}{Unidirectional input (Yp)} & \multicolumn{4}{c}{Bidirectional input (Yp + Xs)} \\ \hline
1\# & 4.06 & 4.14 & 5.47 & 6.93 & 9.08 & 4.64 & 5.94 & 7.62 & 9.71 & 17.69 \\
4\# & 4.86 & 4.97 & 6.68 & 8.94 & 10.28 & 6.75 & 5.19 & 7.41 & 10.80 & 14.61 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Displacement response of beam end (unit.mm).  
Figure 11: Relative displacement of bearing: (**a**) position A: curved bridge parallel to the direction of the principal ground motion; (**b**) position B: curved bridge perpendicular to the direction of the principal ground motion.  
In summary, the main girder is rotated under X\({}_{\rm P}\)-direction seismic excitation, and the bearings at the low pier are likely to fall off. When the seismic wave is excited in the Y\({}_{\rm P}\)-, X\({}_{\rm P}\) + Y\({}_{\rm S}\)-, and X\({}_{\rm S}\) + Y\({}_{\rm P}\)-directions, the bearings at the high pier easily falls off. Therefore, the influence of the seismic excitation direction on the bearing displacement should be considered in the seismic analysis.
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Other Detection Methods  
Only a few literature reports involve other non-destructive detection methods of WRs, such as eddy current detection [73; 74] and ray detection [75; 76], and are in the laboratory stage for many reasons. The detection principles are depicted in Figure 6. Figure 6a represents the eddy current method and Figure 6b explains the ray method.  
Eddy current non-destructive testing technology takes eddy current effect produced by metal conductor in alternating magnetic field as its working principle. It has the advantages of high sensitivity, fast detection speed, non-contact, etc. [73]. Using low frequency transmission eddy current testing method, [PERSON] et al. [73] designed an adjustable annular eddy current testing device with symmetrical probe arrangement along the radial direction to perform the quantitative detection analysis of WR breakage defects. Meanwhile, [PERSON] et al. [74] developed an experimental eddy current sensor and computer measuring system to obtain characteristic data for rope samples made in laboratory. The characteristic data are identified by the RBF network, and the identification results show the proposed evaluation method is feasible.  
When non-destructive testing of WR is carried out by ray method, the damage situation inside and outside of the WR can be clearly presented on the negative film because of the different absorption ability of radiation rays between the surface/internal defect and non-defect of the WR [75; 76]. For this reason,  
Figure 6: The other detection method principles. (**a**) Eddy current method [74]; (**b**) ray method [75].  
[PERSON] et al. [75] proposed a new WR detection system based on X-ray digital imaging technology, and introduced the X-ray digital imaging technology, system structure and working principle. A detection system of bridge cables using gamma ray is proposed by [PERSON] et al. [76]. The exposure time and sensitivity of steel cables in gamma ray detection were studied. Meanwhile, the comparison between the ray detection method and the electromagnetic detection method was performed. The actual measurement demonstrated that the research results can improve the safety of WR and ensure the bridge safety assessment.
## 5 Discussion
### Adaptation Depending on the Environment
#### 5.2.1 Urban or Rural Context  
At this point, it is necessary to observe the population environment where the building is located. Depending on the location, the distance to facilities such as hospitals, fire stations, or police stations, should be studied. At the same time, the width of the communication routes with the population center must be observed, the condition and dimensions of the roads leading to the building, and the width of the existing space in the perimeter of the building that facilitates the approach and performance of foreign aid teams. If necessary, a landing point for helicopters must be located.
## 4 Experiments and Results
### Limitations and Prospects  
**Audio duration.** Our experiments used fixed 10 s segments following the DCASE protocol. In real-world applications, we recommend to perform padding to audio samples that are less than 10 s and cutting to those that are more than 10 s. While this simplifies training, it may impact performance. Future work will explore adaptive segmentation.  
**Low-SNR case and volume change.** Our MIMI-DUE dataset has considered natural factory noise (\(SNR\in[5,15]\) dB) and volume change, but extreme noise scenarios (e.g., \(SNR=-5\) dB) were not considered in this research. The system may degrade in such very low-SNR scenarios.  
**Prospects.** For multi-channel audio, extending the encoder to process spatial dimensions via cross-channel attention can be a solution. Additionally, multi-sensor fusion is feasible through early or late integration strategies. Early fusion directly merges the original data and learns cross-modal features through a unified encoder, while late fusion independently processes the data of each sensor and then fuses the decision, which is more resistant to interference but may lose fine-grained associations.  
Figure 12: Interpretability analysis of the proposed model. The red rectangle highlights the anomalous region detected by our method. The blue curve represents the frequency amplitude of anomaly audio, while the orange curve denotes the frequency amplitude of normal audio.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AUC** & **pAUC** \\ \hline w/o PA & 76.26\% & 67.79\% \\ w/o \(\mathcal{D}\) & 61.27\% & 55.51\% \\ \(w_{1}=50,w_{2}=1,w_{3}=1\) & 77.38\% & 67.89\% \\ \(w_{1}=1,w_{2}=50,w_{3}=1\) & **80.67\%** & **70.33\%** \\ \(w_{1}=1,w_{2}=1,w_{3}=50\) & 71.72\% & 61.33\% \\ \(w_{1}=1,w_{2}=50,w_{3}=50\) & 75.68\% & 67.02\% \\ \(w_{1}=50,w_{2}=1,w_{3}=50\) & 73.93\% & 65.34\% \\ \(w_{1}=50,w_{2}=50,w_{3}=1\) & 71.25\% & 61.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation study experiments.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 3 Finite Element-Based Analytical Model of Phase-to-Ground Line Spacing for Ice-Covered Lines
### Calculation of Ice and Wind Loads on Conductors
#### 3.3.2 Wind Load  
Given the large stall distance and complex structure of overhead lines, load size varies due to differences in component structure and location. When the angle \(\theta\) degree between the guide line's axial direction and the horizontal direction is considered, the wind pressure acting orthogonally to the unit length of the guide line is referred to as horizontal wind load \(L_{n}\) (N/m). Due to the complexity of wind loads in real-world environments, various factors need to be considered when calculating wind loads [13; 14]. The airflow carries kinetic energy, which is converted into static pressure, or velocity pressure, forming part of the air pressure in the Bernoulli equation. This calculation yields the baseline wind pressure. Additionally, it is essential to consider the effects of span length, wind direction, and surface roughness on wind speed. Horizontal wind load per unit \(L_{n}\) (N/m) and specific load \(\gamma_{n}\) (N/m) are computed as follows:  
(1) Without icing  
\[L_{n}=W_{o}D\alpha\beta_{c}\mu_{sc}\mu_{s}\mu_{\theta}\times 10^{-3} \tag{7}\]  
\[\gamma_{n}=\frac{\text{L}_{n}}{A} \tag{8}\]  
(2) Icing  
\[L_{y}=W_{o}(D+2\text{b})\alpha\beta_{c}\mu_{sc}\mu_{s}\mu_{\theta}\times 10^{-3} \tag{9}\]  
\[\gamma_{y}=\frac{L_{y}}{A} \tag{10}\]  
Figure 5: Cross-section of the iced conductor.  
where \(W_{o}\) denotes the standard value of reference wind pressure, with \(W_{o}=\frac{V^{2}}{1.6}\), N/m\({}^{2}\); \(\alpha\) signifies the unevenness coefficient of wind pressure; \(\beta_{\rm c}\): represents 500 kV line conductor wind load adjustment factor; \(\mu_{\rm sc}\) refers to conductor-ground wire body shape factor; \(\mu_{\rm s}\) is the coefficient of variation of wind pressure height; \(\mu_{\theta}\) denotes the coefficient of variation of wind pressure with wind direction due to the angle between the wind direction and the axis of the guide line, \(\mu_{\theta}=\sin^{2}\theta\).
# Sensorless Posture Detection of Reluctance Spherical Motor Based on Mutual Inductance Voltage
## 2 Basic Principle of Detecting Rotation Angle from Mutual Inductance Voltage  
### Mutual Inductance Voltage Characteristics of Switched Reluctance Motor  
Due to the doubly salient structure of a switched reluctance motor, the mutual inductance among windings changes with the rotor position angle. When coil A of the reluctance  
Figure 1: (**a**) Reluctance spherical motor, (**b**) structure of rotor.  
motor is energized with AC, the mutual inductance voltage would be generated in an unenergized open coil B:  
\[u_{B}=e_{B}=-\frac{d\Psi_{AB}}{dt} \tag{1}\]  
\[\Psi_{AB}=L_{AB}i_{A} \tag{2}\]  
where \(\Psi_{AB}\) is the mutual inductance flux linkage between coil A and B, and \(L_{AB}\) is the mutual inductance between coil A and B. Substituting (2) into (1) obtains  
\[u_{B}=-L_{AB}\frac{di_{A}}{dt}-i_{A}\frac{dL_{AB}}{dt}=-L_{AB}\frac{di_{A}}{dt }-\omega i_{A}\frac{dL_{AB}}{d\theta} \tag{3}\]  
where \(\omega\) is the rotor angular velocity. For the spherical motor which runs in a repeated start-stop or ultra-low speed state, it can be considered that \(\omega=0\). According to the voltage equation of the switched reluctance motor [12; 13; 14; 15; 16], considering the input voltage of phase A winding, the following can be obtained  
\[u_{A}=r_{A}i_{A}-e_{A}=r_{A}i_{A}+\frac{d\Psi_{A}}{dt}=r_{A}i_{A}+L_{A}\frac{ di_{A}}{dt}+\omega i_{A}\frac{dL_{A}}{d\theta} \tag{4}\]  
Then,  
\[\frac{di_{A}}{dt}=\frac{1}{L_{A}}(u_{A}-r_{A}i_{A}) \tag{5}\]  
Substituting (5) into (3) obtains  
\[u_{B}=-\frac{L_{AB}}{L_{A}}(u_{A}-r_{A}i_{A}) \tag{6}\]  
Equation (6) indicates that, under the condition of a stable excitation phase voltage, the relative position between stator and rotor salient poles can be indirectly judged through detecting the mutual inductance voltage values of other windings. Therefore, stator coils can be used instead of position sensors to measure the spatial position of the rotor.
## 3 Methodology
### Data Preprocessing
#### 3.2.3 RGB Image Representation  
To encode structural semantics, we maped the three extracted files into RGB channels: DEX \(\rightarrow\) Red; Manifest \(\rightarrow\) Green; and Resources \(\rightarrow\) Blue. This enabled the model to learn inter-component relationships through a unified visual representation. During interpretability analysis, highlighted regions in each channel revealed which component contributed most to the prediction.  
To ensure the integrity of inputs for image construction, we filtered out APKs that were corrupted, malformed, or missing essential components. To maintain consistency across devices, we also excluded ZIP-level metadata--such as alignment bytes, digital signatures, and compression headers--by reading raw byte streams directly from each target file. Only valid and structurally complete samples were retained for the image generation pipeline.  
Although RGB images were stored using 8-bit pixel values in the range [0, 255], normalization was handled internally by the YOLO11 training framework. Images were globally scaled to [0, 1] during preprocessing, without per-channel or dataset-wide standardization. This approach preserves inter-channel contrast while ensuring compatibility with pretrained weights.  
The overall process is summarized in Algorithm 1.  
```
0:APK file containing classes.dex, AndroidManifest.xml, resources.arsc
0:Fixed-size RGB image of shape \(256\times 256\times 3\)
1: Extract.dex, xml,.arsc from APK
2:for each file in {DEX, Manifest, Resources} do
3: Read raw binary
4: Convert to hexadecimal string
5: Split into 2-character hex segments
6: Convert to integers in [0, 255]
7: Pad or truncate to 65,536 values
8: Reshape to \(256\times 256\) matrix
9:endfor
10: Assign matrices to R, G, B channels respectively
11: Stack to form final RGB image
12: Save to disk
13:return RGB image
```  
**Algorithm 1** Byte-to-RGB Image Conversion from APK Components
## 5 Experiments
### Ablation Experiments  
We also conducted ablation experiments, as shown in Table 5. BCELoss means the model uses the BCE loss function and keeps the other parameter unchanged. From Table 5, we can see that our proposed loss function in Formula (5) can improve the final performance. Moreover, we conducted an experiment to verify the advantage of the new loss function in terms of convergence speed. Figure 4 illustrates the results, the horizontal axis is the number of iterations, and the vertical axis represents F1 scores. From Figure 4, we can see that the new loss function could more effectively speed the convergence rate than BCE Loss. In summary, our proposed loss function not only improves the F1 score but also speeds up convergence.  
3 HS means the model uses three-times-handshaking decoding instead of twice-handshaking decoding and keeps the other parameter unchanged. Strangely, compared with 2 HS, three-times-handshaking does not bring better experimental results. Through careful analysis of test data, we found there are few sophisticated cases like the case shown in Figure 3. Although these sophisticated cases can be solved by introducing three-times-handshaking, the decoding of other simple triples becomes more stringent by shaking hands once more. From the results of the experiment, the low recall is the main factor that ultimately affects  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods** & \multicolumn{2}{c}{**COER(Exact Match)**} & \multicolumn{3}{c}{**SpanSAOKE(Partial Match)**} \\ \hline  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline Our model & 83.45 & **76.15** & **79.63** & 62.03 & **49.24** & **54.91** \\ \hline Our model & 82.91 & 75.57 & 79.07 & 60.48 & 49.29 & 53.71 \\ \hline Our model w/3 HS & **85.22** & 73.59 & 78.97 & **63.55** & 46.16 & 54.14 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of ablation study.  
the performance of F1. Although one more handshake can solve the problem in Figure 3, it improves the precision but hurts the recall and decrease F1.