esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Studies
### Validation of Prediction Model Validity and Generalization  
It is proposed to take EX2-3 and EX2-4 as examples to fully verify the effectiveness and generalization of this prediction model by comparing the prediction results with the current mainstream prediction models (BP, Ridge, XGBoost).  
The EX2-3 prediction effect is shown in Figure 12a below, the prediction residual box line diagram is shown in Figure 12b, the evaluation index effect diagram is shown in Figure 13, and the improvement degree of each model compared with BP is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elevation** & **nMAPE** & **RMSE** & \(R^{2}\) \\ \hline ConvLSTM & 19.1\% & 47.7\% & 1.1\% \\ LSTM + MHA & 35.7\% & 58.4\% & 1.6\% \\ MHA-ConvLSTM & 57.5\% & 72.5\% & 2.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The lifting degree of each model compared with plain LSTM.  
Figure 11: Comparison of evaluation indexes of each prediction model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elevation** & **nMAPE** & **RMSE** & \(R^{2}\) \\ \hline Ridge & \(-\)1.3\% & \(-\)0.7\% & \(-\)0.2\% \\ XGBoost & 62.3\% & 58.3\% & 9.4\% \\ MHA-ConvLSTM & 69.6\% & 66.4\% & 10.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lifting degree of each model compared with BP.  
The effect of EX2-4 prediction is shown in Figure 14a below, the residual box line of prediction is shown in Figure 14b, and the effect of the evaluation index is shown in Figure 15.  
Figure 12: (**a**) Forecast of different forecasting models. (**b**) Distribution of residuals for each model prediction.  
Figure 13: Comparison of evaluation indexes of each prediction model.  
The combined results demonstrate that, in terms of performance for fitting the nonlinear dam deformation data, the combined MHA-LSTM model greatly surpasses the other widely used models. Among them, BP and Ridge showed model distortion in the early stages of the prediction process for EX2-3 and EX2-4, and the predicted values significantly deviated from the measured values. However, in the middle and later stages of the prediction, the predicted values gradually fit with the measured values, and this situation was tentatively considered to be a poor acceptance of the base model for the information feedback from the attention mechanism. In contrast, the current model may exhibit great fitting effects for each peak, further demonstrating that it can successfully concentrate on the hidden state of the deformation data in the time dimension and then identify their true properties. The prediction performance is stable and excellent in EX2-3 and EX2-4, which  
Figure 14: (**a**) Forecast of different forecasting models. (**b**) Distribution of residuals for each model prediction.  
Figure 15: Comparison of evaluation indexes of each prediction model.  
verifies the effectiveness and generalization of the model. Table 4 displays the improvement degree of each model compared with BP. The nMAPE and RMSE are more than 50% better than the plain LSTM model in this set of tests, and the prediction performance is stable and excellent in EX2-3 and EX2-4.
# The Characteristics of Seismic Rotations in VTI Medium
## 5 Conclusions  
We simulated seismic waves in VTI media with different anisotropic parameters under different sources and analyzed the similarities and differences between the translational components and the rotational components, especially focusing on the effects of anisotropic parameters on the rotational motions. We drew the following conclusions from these synthetic examples:  
1. The energy of \(S\) waves is significantly stronger than that of \(P\) waves in the rotational components, and the rotational components have more high-frequency information.
2. The wave fields of rotations propagate with different phase distributions under different source mechanisms in space, and the vertical concentrated force source and shear source cause much stronger rotations than the others.
3. The amplitude and phase characteristics of the rotations are more complex in VTI media, which is helpful to distinguish VTI media from isotropic media. With the increase of \(\varepsilon\) and \(\delta\), the energy of the rotational components increases. With the increase of \(\varepsilon\), the eccentricity of the wavefronts gradually increases, while with the increase of \(\delta\) the eccentricity of the ellipse gradually decreases to near-circular.  
Figure 14: Snapshots of wave fields at 0.05 s. (**a**) Radial concentrated force source. (**b**) Vertical concentrated force source. (**c**) Shear source.  
Rotations are influenced more significantly by anisotropic parameters than translations, which indicates that the response of the rotations to the anisotropy is more sensitive.  
From the simulations and comparisons introduced in this paper, we can deduce that rotation will be beneficial to distinguish VTI media from isotropic media and to predict their anisotropic parameters.  
Conceptualization, L.S. and Y.W. ([PERSON]); investigation, W.L.; writing--original draft preparation, L.S.; writing--review and editing, [PERSON] and Y.W. ([PERSON]); visualization, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U1839208 and 41425017.  
Not applicable.  
We gratefully acknowledge financial support by the National Natural Science Foundation of China (U1839208, 41425017).  
The authors declare no conflict of interest.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 3 Graphs for Signal Processing in Wireless Networks
### Types of Graphs  
Several types of standard graph models [16] relevant for the analysis in this article are described below.  
* General/Complete graph--This category includes various kinds of graphs that are defined using the physical relationships between the wireless network's nodes (such as interference, social connections between users, and others). As for the complete graph, each of its vertices is connected to all of the rest. Accordingly, all non-diagonal entries of \(A\) will be one, and all of the diagonal ones will be zero. An example is illustrated in Figure 3a. Depending on the physical relationships described via the model, some of the graphs are not necessarily complete.
* Bipartite (Kuratowski) graph--its vertices can be divided into two subsets \(\mathcal{H}\) and \(\mathcal{E}\), such that there are edges between the nodes of each subset, but not those within the subset (Figure 3b). If all vertices in \(\mathcal{H}\) are connected to those in \(\mathcal{E}\), the bipartite graph is complete. This type of graph may be extended to more than two subsets (a tripartite graph, for example, is composed of three subsets--two on each side and one between them), the vertices of each being connected to those of the adjacent subset.
* Star graph--a bipartite graph for which one subset contains only one vertex, while the other consists of the remaining vertices. In this way, the graph is formed in the shape of a star (Figure 3c) with one vertex connected to all of the others, whereas they do not have connections between themselves.
* Path graph--its first and last vertices are connected to only one other vertex (i.e., they have a degree of connectivity \(\mathcal{J}=1\)), whereas all the rest connect to two (\(\mathcal{J}=2\)). A path graph is directed if all of its edges have directions which point to the succeeding vertices (Figure 3d).
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 3 Results
### Design Specification
#### 3.2.2 Impedance Matching Network
The L-network in combination with the microstrip line network is equivalent to the T-network. Figure 6 illustrates an example of the previously mentioned design. In [34], a triple band differential rectenna was designed with a maximum efficiency of 56% at 3.5 GHz. The impedance matching network of the differential rectenna was consisted of a combination of radial stubs, open stubs, and mea  
Figure 5: Different types of impedance matching networks with lumped elements: (**a**) T-Network (**b**) L-Network (**c**) \(\pi\)-Network. Figure 6: L-network in combination with the microstrip line network is equivalent to the T- Impedance Matching Network (IMN). approach to the impedance matching network with the previous circuit is applied in the dual-band rectifier in [35]. Table 2 lists the aforementioned works of this section.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 5 Determination of Safe Vibration Thresholds and Development of Mitigation Strategies
### Development and Application of Mitigation Strategies  
Effective vibration mitigation strategies are essential for ensuring the safety of surrounding structures and construction operations in tunnel projects. This study proposes targeted mitigation measures based on numerical simulations and field data analysis.  
Adjustments to various blasting parameters revealed the following measures significantly reduce vibration impacts.  
Delayed Detonation: Using digital electronic detonators for staged delayed detonation effectively disperses vibration waves and reduces peak vibration levels.  
Hole Spacing Design: Optimizing blast hole spacing to 0.8 m and hole depth to 1.2 m, combined with controlled charge per hole, minimizes vibration amplitudes.  
Detonation Sequence Optimization: Initiating detonation from the center outward reduces wave superposition effects.  
FLAC3D software was utilized to predict vibration velocities in areas not directly measurable, such as the roof, providing a basis for optimizing blast designs.  
The model predicted a maximum vibration velocity of 1.537 cm/s at the roof, higher than that of the diaphragm wall and central pillar but still within the safety range. Numerical simulation results offered scientific guidance for refining subsequent construction plans.  
Based on the analysis of vibration impacts, the central pillar is identified as the most affected structural component. The following measures are recommended: Strengthen real-time monitoring of the central pillar during construction by installing high-precision vibration geophones to ensure it remains within safe limits. Implement additional reinforcement measures, such as temporary supports or vibration isolation devices, when necessary to further mitigate risks.  
The optimized blasting plan was implemented on-site, and results showed that all measured vibration velocities were below 2.0 cm/s, demonstrating the effectiveness of the mitigation strategies. The proposed measures are applicable to similar urban metro projects and significantly reduce vibration risks.
## 3 The Proposed Network Model
### Framework of the Whole Algorithm  
The entire framework contains several modules with dedicated functionalities, including initial pre-estimation of IR, detection of correlation area and introduction of auxiliary information. The general flow of the algorithm is as follows:  
1. Two-line input, one is low resolution IR and the other is registered high-resolution RGB image. First, we use bicubic interpolation to up sample the infrared image to the same size as RGB.
2. The most IR related channel of the RGB image is selected.
3. Next, we perform preliminary information extraction on the two images.
4. Taking the high-resolution RGB image as a guide, the main features such as edge texture are extracted by convolution.
5. The corresponding low-resolution IR is used to extract the main features such as edge texture by convolution.
6. Fuse the values of (4) and (5), we compare the fused results with the high-definition IR corresponding to the training and calculate the loss function.
7. Back propagation is used to update the network weight parameters and to constantly optimize the network model.
8. We then judge whether all images have been executed. If yes, the training is stopped. If not, loop back to step 1.
9. Finally, the network model of training optimization is obtained.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
###### Abstract  
Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be processed and calibrated manually as done in the past. Here, we describe a methodology to process the SAR and optical data in a synergistic fashion and automatically calibrate, mosaic, and integrate these data sets together into seamless, ice-sheet-wide, products. We employ this approach to produce annual mosaics of ice motion in Antarctica and Greenland with all available data acquired on a particular year. We find that the precision of a Landsat-8 pair is lower than that of its SAR counterpart, but due to the large number of Landsat-8 acquisitions, combined with the high persistency of optical surface features in the Landsat-8 data, we obtain accurate velocity products from Landsat that integrate well with the SAR-derived velocity products. The resulting pool of remote sensing products is a significant advance for observing changes in ice dynamics over the entire ice sheets and their contribution to sea level. In preparation for the next generation sensors, we discuss the implications of the results for the upcoming NASA-ISRO SAR mission (NISAR).  
Ice velocity mapping; Landsat-8; Sentinel-1; RADARSAT-2 2017
# Identification by GC-MS Analysis of Organics in Manufactured Articles through a D-Optimal Design
## 2 Materials and Methods
### Chemometric Model  
For this study, an experimental design was applied to choose the two best extraction methods. A D-optimal design [23] was used to understand how many analyses were necessary to obtain the best result, to simplify the development of the analytical method, and to reduce the number of experiments to be performed. The D-optimal design strategy was also chosen because of its particular features allowing its further tuning even during the development of the methodology; indeed, it was applied after performing some experiments, which were later included in the model, too.  
The model was built considering five independent variables (factors): type of extraction, three kinds of solvent (where every type was considered as a single factor), and time of GC-MS analysis. The extraction was performed involving Soxhelt or sonication processes; the solvent was selected from acetone, dichloromethane, and heptane, while the time of the GC-MS analysis could vary from 24 up to 30 min. Therefore, two levels were selected for every independent variable.  
The use of a traditional, full-factorial design would have required, at least, the preparation of 32 experiments, without involving any replicates.  
In the present work, the use of D-optimal design allowed the preparation of 11 experiments. In actuality, 7 experiments were performed as screening; then, a further 4 experiments were performed after the D-optimal evaluation of the available factors and levels.  
The starting model was linear with interaction:  
\[\mathrm{Y}=\mathrm{b}_{0}+\mathrm{b}_{1}X_{1}+\mathrm{b}_{2}X_{2}+\mathrm{b}_ {3}X_{3}+\mathrm{b}_{12}X_{1}X_{2}+\mathrm{b}_{13}X_{1}X_{3}+\mathrm{b}_{23}X _{2}X_{3}\]  
where \(\mathrm{b}_{i}\) are the model coefficients and \(X_{i}\) are the independent variables. Subsequently, an \(X\) matrix of dimensions (\(n\times p\)) was built, where \(n\) is the number of experiments and \(p\) the number of coefficients in the model.  
With [PERSON]'s algorithm [24; 25], the best experiments were determined between the thirty-two calculated by the Chemometric Agile Tool software [26] (see Table S1). Then, the number of experiments to be prepared was determined through the logarithm of the normalized determinant \(\mathrm{M}\):  
\[\mathrm{M}=\frac{\mathrm{det}(X^{\prime}X)}{np}\]  
Since the normalized determinant weighted the information according to the experimental effort required to obtain a robust DOE model, the best results were obtained at high log(M) values.
## 3 Results
### Critical Discussion and Potential Applications  
In this section, a synthesis of potential applications for the innovative materials developed in this research is provided based on the results obtained in the experimental campaign. In Figure 13a, a possible construction detail is shown for the application of these lightweight panels with hemp addition in the construction of lightweight partitions. Additionally, in Figure 13b, a comparative visual analysis is presented between the reference material G0.65 and the various composites developed, displaying the percentage variation in each analyzed property for each dosage with respect to this traditional composite.  
In general terms, the materials developed in this research align with European objectives aimed at reducing energy consumption in buildings. Currently, the building sector accounts for approximately 40% of the total energy consumed in the European Union [65]. Therefore, the development of new lightweight partitions with a good thermal performance would contribute to mitigating this effect [66]. Lightweight partitions, as shown in Figure 13a, based on light steel framing (LSF partitions), offer significant advantages over traditional construction, including easier handling and reduced execution times, shorter transportation times and lower emissions from the logistic process, higher quality in the execution of building systems, lower volumes of waste generated during construction, increased recyclability, and the possibility of producing standardized and more reliable pieces [67; 68]. However, as demonstrated in other previous research, it is advisable to use thermal break strips to mitigate the effect of cold spots caused by the load-bearing steel structure [69].  
On the other hand, in the visual comparison shown in Figure 13b, it can be observed that the G0.65-15%F composite exhibits good behavior for its use in lightweight partitions due to its favorable combination of flexural strength, density, and thermal conductivity. In this regard, with an appropriate treatment of the hemp additions to enhance their durability, the composites developed in this study present themselves as a viable alternative to conventional lightweight plasterboards. This offers an environmental benefit by reducing gypsum consumption and the use of synthetic fibers [35; 70].  
Figure 13: (**a**) Example of application of the panels developed in the research; and (**b**) visual comparative analysis of the physical-mechanical properties.
## 3 The Existing OFDM-Based Frequency Domain Equalization Algorithm
\[X^{\prime}_{i}=Y_{i}W_{i}/B_{i} \tag{9}\]  
The subsequent simulation proves that the existing frequency domain equalization algorithm performs greatly. It guarantees communication at a low symbol error ratio provided that a channel does not contain any multipath component exceeding the length of its cyclic prefix (\(P_{2}=0\)). Nevertheless, the multipath time delay of an underwater acoustic channel often reaches dozens of milliseconds and even longer. Therefore, a longer cyclic prefix should be transmitted, severely wasting the limited resources of underwater acoustic communication including energy and bandwidth [26]. When a channel has a multipath time delay component exceeding the length of its cyclic prefix (\(P_{2}\
eq 0\)), the restored signal is still troubled by inter-subcarrier interference and inter-symbol interference as revealed by Equation (5). In this case, the performance of the existing equalization algorithm is considerably undermined [27].
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 4 Simulation Environment and Performance Metrics
### Routing Overhead  
Routing overhead (\(RO\)) represents control packets that are required to perform a specific task. Thus, it is a sum of all the control packets sent during the total simulation time:  
\[RO=\sum_{i}^{N}CP_{i} \tag{11}\]  
Here, \(CP_{i}\) represents a number of control packets sent during each iteration \(i\) and \(N\) represents the total number of iterations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of nodes & 5–60 nodes \\ Packet size & 1000 byte \\ Radio range & 150 m \\ Area & 500 m \(\times\) 500 m \\ Number of traffic sources & 4 \\ Pause time(s) at simulation & 10 s \\ Traffic type & CBR \\ Send rate of traffic & 4 packets/second \\ Routing Protocol & AODV \\ Mobility model & Random Waypoint Mobility [40] \\ Data rate & 11 Mbps \\ Simulation Time & 100 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 3 Results  
### Study Characteristics  
At the end of the search, we identify 704 studies, 509 of which were considered suitable after the title and abstract screening, and after the removal of duplicates. Then, 456 were excluded after the title and abstract screening, according to the PICO model. Thus, we  
Figure 1: Flow chart of the included studies.  
assessed 53 articles for eligibility, and we excluded 47 of them for various reasons (see Table 2 for further details).  
Therefore, we included six studies [44; 45; 46; 47; 48; 49] in this systematic review (see Figure 1 for further details). Three studies were case series [44; 45; 46], one was a prospective interventional study [47], one was a cohort study [48], and one was a case report [49]. The studies' main characteristics are described in detail in Tables 3 and 4.  
A total of 65 subjects were analyzed. Study cohorts ranged from 30 [48] to 1 [49] patients, with ages ranging from 23 years [47] to 80 years [46]. Twenty-nine subjects (45%) needed intensive care. LOS in ICU ranged from 0 days [44; 45; 46; 49] to 45 days [45], LOS in COVID-19 unit ranged from 7 days [45] to 39 days [44], and LOS in rehabilitation ranged from 21 days [49] to 90 days [45], but in two studies [46; 49], the rehabilitation program was conducted at home. During COVID-19 disease, all subjects were affected by bilateral pneumonia [44; 45; 46; 49], while pulmonary embolism was diagnosed in two (3%) subjects [44], stroke in two subjects [44; 48], peripheral neuropathy in three (5%) subjects [44], flu-like symptoms in seven (11%) subjects [45; 46], low oxygen level in six (3%) subjects [45; 46], dyspnea in seven (11%) subjects [45; 46; 47], gastrointestinal disorders in three subjects [45; 46] (5%), and cardiological and neurological symptoms in one subject (2%) [45]. Concerning the fatigue measure, one study applied the Borg Scale [46], two studies [44; 45] used the CR10, one study [46] the FSS, one study [49] the CIS fatigue scale, and one the FACIT fatigue scale [48]. Regarding intervention: one study [44] adopted increased intensity physical exercise; one study administered [45] a combination of aerobic and resistance exercise; one study [42] applied aerobic exercise, strength training of upper and lower limbs, and educational discussions program; one study used posture changes, passive mobilization of the limbs, breathing control exercises, and passive muscle stretching and pumping exercises; one study provided [47] aerobic training and breathing exercise training; and three studies [46; 49] performed an individual telerehabilitation program. No studies provided a follow-up after the end of the rehabilitative intervention.