esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Secondary Landslide Risk  
The LOS average deformation rate (Figure 13) and time-series deformation (Figure 14) around South Lhonak Lake after the GLOF were computed using the Sentinel-1 SLC data from October 2023 to March 2024 from the descending track. Significant deformation was observed both in the landslide area and its eastern vicinity, with the deformation within the landslide area notably surpassing that in its eastern counterpart. Six points were selected within the zone of significant deformation, comprising P5-P7 within the landslide region and P8-P10 situated to the east of it. The average slope of the eastern region, as depicted in Figure 12, is considerably smaller than that of the landslide area. From Figure 13, it is apparent that the average deformation rate surrounding the crack at the trailing edge of the landslide is relatively small. In Figure 14, the deformation trend of P5-P7 appears relatively consistent, without discernible points of acceleration. This suggests that the larger deformation observed in the landslide area may be attributed to the movement of surface debris and floating soil, indicating that the landslide zone is relatively stable. The cumulative deformation of P8-P10 is smaller than that of P5-P7, and since the end of December 2023, the deformation rate has noticeably decreased, with the curve remaining relatively flat, indicating a current state of relative stability in this region as well. Upon observing the Landsat 8 optical images after the disaster, no prominent secondary landslides were detected in the landslide area, and there were no observable landslide occurrences in the eastern area, aligning with our initial inference.  
Figure 14: Time-series deformation of selected points obtained from descending images after the GLOF. (**a**) P5–P7; (**b**) P8–P10.  
Figure 13: The LOS average deformation rate around South Lhonak Lake after the GLOF was calculated using the Sentinel-1 SLC data from October 2023 to March 2024 from the descending track. The yellow line delineates the landslide area, and points P5–P10 were selected for further analysis of the time-series deformation within this area after the GLOF.  
However, [PERSON] et al. (2023) [51] pointed out that the cracks in the top area of landslides serve as significant indicators of the instability of the landslide headscarp and have a potential role in landslide hazard warning. Hence, a risk of secondary landslides in the area is inferred. The volume of earthwork for potential secondary landslides was measured and estimated using GF-2 images, resulting in approximately \(2.5\times 10^{6}\) m\({}^{3}\). Future monitoring of surface deformation in this area will continue.
## 4 Results and Discussion  
### Effect of Forming Angles on Geometric Accuracy  
Figure 6 demonstrates the maximum thinning rate and standard deviation of wall thickness of truncated pyramids component under different forming angles. When the forming angle is 51\({}^{\circ}\), the maximum thinning rate and standard deviation are 58.66% and 0.220 respectively. As the forming angle increases, the maximum thinning rate and standard deviation gradually decrease. This indicates that with larger forming angles, the deformation is more uniform. The contact area between the forming tool and the thin sheet decreases as the forming angle increases, which leads to a point-by-point reduction in the rolling effect of the forming tool on the thin sheet during the forming process [16; 19]. This is also the reason why larger forming angles result in more uniform deformation.
# A Polarity Capturing Sphere for Word to Vector Representation
## 5 Results and Discussion
### Comments on the Results  
We can observe that the proposed word embedding approach gives more reasonable similarity scores than some of the major approaches, such as word2 vec, GloVe, and fastText. These methods have a particular deficit dealing with antonyms, and distinguishing between antonyms and unrelated words. The failure of some of them in assigning the right similarity score to the pair \"yes\" and \"no\" is case in point. Our algorithm also fared better on the two benchmarks tested. It also did well compared to other algorithms that are specifically designed to deal with antonyms, on the GRE antonym dataset. Furthermore, our approach proved its efficiency in discriminating between antonyms and synonyms as compared to other published word vectors' models. Our algorithm can handle composite words (like \"fairy tale\"). It could potentially also handle words with multiple meanings, such as \"bat\" (the animal) and \"bat\" (a club). The way to tackle these is to consider them as different words, like \"bat-1\" and \"bat-2\". The challenge facing all word embedding methods is to distinguish words with multiple meanings from the context of the sentence.
## 3 Data and Empirical Results
### Empirical Results
#### 3.2.2 Evaluation of the Performance of the Prediction Models  
Since traditional VAR models can only handle linear and symmetric dependence structures, we developed novel CD-Vine COPAR models, which allowed for asymmetric modeling of serial and between-series dependencies to advocate superior predictive ability of the CD-Vine COPAR models over the classical VAR models for benchmarking. The prediction performance of the D-Vine COPAR model was compared to that of a classical VAR based model employing the out-sample (2012-2016). For evaluation of performance, the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) were measured, as shown in Table 2. The CD Vine-COPAR had better prediction accuracy with smaller RMSE and MPE.  
Furthermore, to ensure the statistical significance, we also conducted the block bootstraping procedure, which involved iteratively resampling the dataset with replacement to examine the statistical significance though the specific time periods. The number of bootstraping replications (R) was 25 and the replicated time series were simulated using the type of block resampling with fixed block lengths (l = 20) over the samples of 22 elements, obtaining the total number of 550 experiments. The bootstrap-based CDVine COPAR models were slightly better than the CD-Vine COPAR models and the classical VAR models with respect to the RMSE.
## 2 Materials and Methods
### Body Pose Reconstruction Method  
The position and orientation of each solid was reconstructed from the trajectories of the markers obtained experimentally. The orientation of the solids was defined using the recommendations of the ISB [31].  
The joint angles were defined as the relative orientation of a solid with respect to its adjacent one. To obtain them, the procedure described in the study by [PERSON] et al. [32] was used. This procedure was based on the use of rotation matrices for the calculation of the joint angles. The rotation matrices were obtained by determining the local reference systems of each solid, which are explained below. The reconstruction procedure was implemented in a custom-made calculation routine using Matlab R2024a (The MathWorks, Inc., Natick, MA, USA).  
Because no kinematic constraints were imposed, it was necessary to use at least three non-collinear markers to reconstruct the local system of each solid.  
The pelvis' Lateral-Medial (LM) axis was determined from the direction between the RPSI and LPSI markers. The Antero-Posterior (AP) axis was defined as the normal to the plane formed by the RPSI, LPSI and SACR markers. The Cranio-Caudal (CC) axis was defined perpendicular to the AP and LM axes.  
The thigh had only two physical markers; so, to obtain the local system for this solid, virtual markers were also used. In this case, the CC axis was calculated from the RHJC and RKJC markers. The AP axis was determined as the normal to the plane defined by the RGTC, RHJC and RKNE markers. The LM axis was defined perpendicular to the CC and AP axes.  
The local leg system had to be defined using virtual markers in addition to the physical markers. The CC axis was formed from the RKJC and RAJC markers. The AP axis was defined from the normal to the plane formed by the RKJC, RKJA and RANK markers. The condition of perpendicularity to the CC and AP axes was established to determine the LM axis.  
Virtual markers were also used, in this case, following the methodology established by [PERSON] et al. [24]. The AP axis for this solid was calculated from the position of the RHEE and RTOE markers. The LM axis was obtained from the normal to the plane formed by the RHEE, RTOE and RAJC markers. The CC axis was defined perpendicular to the LM and AP axes.  
The procedures above have been explained with the markers of the right leg. For the left leg, the procedure was equivalent.
## 2 Materials and Methods
### Rzwqm2 Model  
The RZWQM2 model can simulate daily scale crop _ET_[44]. Before _ET_ simulation with this model, it is necessary to establish meteorological, soil, and field management data files. In this study, the data from the growth periods of cabbages in 2020 and 2021 were input into the RZWQM2 model, with the parameters being calibrated using the trial and error method. The physical parameters of soil at different soil depths are shown in Table 2:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Depth** & \multicolumn{3}{c}{**Soil Texture (\%)**} & \multicolumn{3}{c}{**Bulk Density**} & **Saturated Hydraulic** & **Field Capacity** & **Available Water** \\ \cline{2-7}
**(cm)** & & **Sand** & **Silt** & **Clay** & **(g cm\({}^{-3}\))** & **Conductivity (cm h\({}^{-1}\))** & **Water Content** & **Capacity (cm\({}^{3}\))** \\ \hline
0–20 & silty loam & 30.81 & 58.67 & 10.52 & 1.65 & 0.88 & 0.30 & 0.191 \\
20–40 & silty loam & 19.43 & 68.25 & 12.32 & 1.69 & 0.64 & 0.29 & 0.185 \\
40–60 & silty loam & 13.22 & 70.58 & 16.20 & 1.29 & 1.30 & 0.29 & 0.183 \\
60–80 & silty loam & 10.38 & 74.87 & 14.75 & 1.48 & 0.75 & 0.32 & 0.209 \\
80–100 & silty loam & 11.81 & 76.53 & 11.66 & 1.45 & 0.63 & 0.32 & 0.207 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters of soil at different depths.  
The accuracy of \(ET\) simulated by the RZWQM2 model is evaluated by the determination coefficient (\(R^{2}\)) and root mean square error (\(RMSE\)) using the following formulae:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P_{i}-O_{i})^{2}} \tag{7}\]  
\[R^{2}=\big{[}\frac{\sum_{i=1}^{n}\big{(}O_{i}-O_{avg}\big{)}\big{(}P_{i}-P_{avg }\big{)}}{\sqrt{\sum_{i=1}^{n}\big{(}O_{i}-O_{avg}\big{)}^{2}}\sqrt{\sum_{i=1}^ {n}\big{(}P_{i}-P_{avg}\big{)}^{2}}}\big{]}^{2} \tag{8}\]  
where, \(P_{i}\) is the \(i\)-th simulated value, and \(O_{i}\) is the \(i\)-th observed value; \(O_{avg}\) and \(P_{avg}\) are the average of simulated and observed values; \(n\) is the number of observed or simulated values.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Buffer System Implementation  
To address potential latency issues associated with real-time random number generation, we implement a buffer system that pre-generates and stores a pool of random frequencies. The buffer operates as a circular queue with a size \(B\), determined based on the maximum expected hopping rate and the RPU's generation capabilities. The algorithm for buffer management, as described in Algorithm 1, ensures that the buffer is replenished proactively, maintaining a threshold level \(B_{\min}\) to prevent underrun. This approach decouples the random number generation process from the frequency hopping mechanism, allowing for continuous operation even if momentary delays occur in the RPU output.  
```
1:Initialization:
2:Set buffer size \(B\) and minimum threshold \(B_{\min}\).
3:Initialize buffer pointer \(p\gets 0\).
4:while True do
5:if Buffer_Size \(\leq B_{\min}\)then\(\triangleright\) Buffer Replenishment
6: Generate random number \(R_{o}\) from RPU.
7: Map \(R_{o}\) to frequency \(f_{o}\).
8: Store \(f_{o}\) in buffer at position \(p\).
9: Update pointer \(p\leftarrow(p+1)\mod B\).
10:endif
11:if Time to Hop then\(\triangleright\) Frequency Hopping
12: Retrieve next frequency \(f_{\text{hop}}\) from buffer.
13: Transmit using frequency \(f_{\text{hop}}\).
14:endif
15:endwhile
```  
**Algorithm 1** Algorithm for Buffer Management
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 3 Topological Perception Theory  
Topology, a branch of mathematics sometimes referred to as \"rubber sheet geometry\", explores the equivalence of two objects when they can be continuously deformed into each other through spatial motions like bending, twisting, stretching, and shrinking [27]. This process excludes tearing apart or gluing together parts. The primary focus of topology lies in identifying properties that persist despite these continuous deformations. Although it shares similarities with geometry, topology diverges in that geometrically equivalent objects often share numerically measured quantities, such as lengths or angles, whereas topologically equivalent objects exhibit qualitative resemblances.  
In some instances, topological considerations involve ordinary objects existing in three- or lower-dimensional space. For instance, a simple loop in a plane and the boundary edge of a square in a plane are topologically equivalent. This equivalence becomes apparent when imagining the loop as a rubber band that can be stretched to snugly fit around the square. Conversely, the surface of a sphere is not topologically equivalent to a torus, the surface of a solid doughnut ring. This distinction arises because any small loop on a fixed sphere can be continuously shrunk, while staying on the sphere, to any arbitrarily small diameter. An object with this property is termed \"simply connected,\" and the quality of being simply connected remains unchanged under continuous deformation.  
Topological perception theory solves the important question of the onset of visual processes in cognitive science [28]. This theory suggests that topological perception has global geometric invariance and is prioritized over the onset of other geometric percepts [29]. Topological features such as holes and separated structures that have higher recognition rates than shape features such as circles, triangles, and squares [30]. Numerous brain fMRI experiments illustrate that topological perception is associated with the anterior temporal lobe region of the human brain, which lays the physiological foundation of topological perception [31]. In addition, studies of the apparent motion phenomenon, the illusory conjunctions effect, the structural advantage effect, and the masked priming phenomenon have all shown the importance of detecting topological properties in visual perception processes [32; 33; 34; 35]. At the same time, this idea has been supported by germline genetics and developmental psychology [36; 37; 38]. To summarize, topological perception is a fundamental geometric perception common to human beings [35].
# Silane Modification of Crumb Rubber on the Rheological Properties of Rubberized Asphalt
## 3. Experimental Result and Discussion
### Functional Group and Chemical Bond of Asphalt  
After understanding the influence of silane coupling agent on rubber powder, the mechanism of modified rubber powder on asphalt could be further analyzed. In this paper, the change in the functional group and chemical bond of asphalt caused by the silane coupling agent was revealed by the FTIR test, and the reason why the silane coupling agent enhances the rheological properties of asphalt at high and low temperatures was further explained. The FTIR test results are displayed in Table 7 and Figure 4.  
It can be seen from Figure 4 and Table 7 that the silane coupling agent had little effect on the functional groups and chemical bonds of asphalt, but had a significant effect on the absorption of individual peaks. The absorption peaks of the C=C bond, methylene C-H, and some fingerprint area wavenumbers of AR and MAR were weakened, which indicates that the relative contents of oil and resin declined, while the relative content of heavy components like asphaltene rose. The formation of C=C of MAR implies MCR strongly reacts with asphalt and produces new chemical bonds [26]. In addition, because saturated C-H is already abundant in CR, these chemical bond absorption peaks of rubberized asphalt were higher than that of AM; on the other hand, MCR had an enhanced absorption peak of the saturated C-H within MAR. As a result of the addition of the CR and MCR, absorption  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Functional Group and Chemical Bond**} & \multicolumn{3}{c}{**Absorption Peak Position (cm\({}^{-1}\))**} \\ \cline{2-4}  & **AM** & **AR** & **MAR** \\ \hline Saturated C–H & 2923.34 and 2853.12 & 2923.48 and 2853.24 & 2923.63 and 2853.33 \\ C=C & \(\backslash\) & \(\backslash\) & 2385.90 and 2316.52 \\ C=C & 1598.23 & 1599.17 & 1598.41 \\ Methylene C–H & 1459.14 and 1375.29 & 1457.48 and 1374.81 & 1457.66 and 1374.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fourier transform infrared spectroscopy (FTIR) test results.  
Figure 4: FTIR test result of AM, AR, and MAR.  
peaks of these functional groups and chemical bonds of asphalt changed, but the changes of MAR were greater, which means that the chemical reaction between MCR and asphalt was stronger.
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Mohanrajan et al. (2022) [461] & Predicting the land use/land cover changes & That the predicted results will be more informative and trustworthy to the urban planners and forest department to take appropriate measures in the protection of the environment. & Explainability is needed to understand relationships and qualitative/quantitative impacts of input parameters in modeling mechanical properties. Explainability can also reveal new information about the effects of stress and strain on each other. \\ \hline Wang et al. (2023) [463] & ML-based decision support in medical field & Explainability is important so that medical practitioners can communicate their and ML model-made decisions to patients, therefore ensuring patient autonomy and informed consent. Explanations also help develop better ML models by revealing the model's decision-making process. Here explainability is used to evaluate the effect of feature selection on model performance. \\ \hline Pierrard et al. (2021) [464] & Medical imaging & The authors mentioned the need for transparency and human understandability in the reasoning of the model in critical scenarios where decisions based on image classification and annotation can have significant consequences. \\ \hline Praetorius et al. (2023) [465] & Detecting intramuscular fat & Explainability is needed to ensure generalizability of the ML model used in intramuscular fat detection. \\ \hline Escobar-Linero et al. (2023) [215] & Predicting withdrawal from legal process in case of violence towards woman in intimate relationship & Explainability is important so reasons behind (predicted) withdrawal from legal process can be recognized. Explainability can give new knowledge about features that affect participation and therefore help taking care of intimate relationship violence victims. The data from legal cases of intimate relationship violence is quite complex, and explainability is needed to interpret the black-box models needed to use in this prediction task. \\ \hline Pan et al. (2022) [466] & Biometric presentation attack detection & The authors mentioned that the explainability in their specific application is important to enhance the usability, security, and performance of their Facial Biometric Presentation Attack Detection system. \\ \hline \end{tabular}
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Machine Learning in WSNs
#### 1.2.1 Supervised Learning  
Supervised learning involves training models on labelled datasets to enable predictive capabilities. In WSNs, supervised learning algorithms are commonly employed for event detection (e.g., identifying fires or earthquakes), fault detection (e.g., recognising malfunction  
Figure 1: Overview of the clustering process in Wireless Sensor Networks.  
tioning nodes or communication failures), and data classification. The ability to accurately predict and classify events based on sensor data makes supervised learning an essential component in developing intelligent WSN applications [11; 12]. For example, support vector machines (SVMs) and decision trees have been used to classify sensor readings for anomaly detection or to identify faulty nodes. While supervised learning can achieve high accuracy when sufficient labelled data is available, its application to clustering in WSNs is limited. This is because clustering is an inherently unsupervised problem, and obtaining labelled data in large-scale, dynamic WSNs is often impractical. Additionally, supervised models may require significant computational resources for training and inference, which can be prohibitive for resource-constrained sensor nodes.
# Microwave Pyrolysis of Biomass: The Influence of Surface Area and Structure of a Layer
###### Abstract  
The paper presents the results of experimental research into lab-scale microwave pyrolysis of wood biomass. The influence of the surface area and the structure of the biomass layer on the characteristics of pyrolysis during microwave heating are discussed. We have established that the biomass layer structure and surface area have a significant effect on the yield of pyrolysis gas. The approach of creating artificial deformation of the biomass layer was tested. The elements of artificial porosity made it possible to increase the CO yield by 18% and 32% compared to the pyrolysis of a biomass layer with artificial channels and a uniform layer, respectively. The concentration of H\({}_{2}\) was 33% higher compared to the layer without artificial pores and 3% lower compared to artificial channels. The yield of CO\({}_{2}\) increased by 25%, and the yield of CH\({}_{4}\) doubled. The experiments showed that the distribution of biomass on a half of the bottom of the crucible and the additional porosity of the biomass layer surface effectively increase the yield of the pyrolysis gas components. Recommendations for increasing the efficiency of microwave pyrolysis of biomass were formulated.  
biomass; microwave pyrolysis; power generation; layer structure; porosity; pyrolysis gas Article