esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Propagation of Particle Rotation Waves
### Dispersion Relations  
According to the wave Equations (39)-(50), we can obtain four sets of dispersion relations for particle rotation waves, including one set of rotation longitudinal waves related to the kinematic components \(\varpi_{1}\), \(\overline{B}\), and \(B^{\text{D}}\) (Equations (39)-(41)), two sets of rotation transverse waves related to the kinematic components \(\varpi_{2}\), \(B_{(12)}\), and \(B_{[12]}\) (Equations (42)-(44)) and \(\varpi_{3}\), \(B_{(13)}\), and \(B_{[13]}\) (Equations (45)-(47)), and one set of rotation in-plane shear waves related to the kinematic components \(B_{(23)}\), \(B_{[23]}\), and \(B_{22}-B_{33}\) (Equations (48)-(50)). Their dispersion equations are as follows:  
(1) Rotation Longitudinal Waves: associated with \(\varpi_{1}\)\(\overline{B}\), and \(B^{\text{D}}\)  
\[\left|\begin{matrix}L_{11}k^{2}-\frac{\rho\beta}{l^{2}N_{p}}\omega^{2}&L_{12 }k&L_{13}k\\ L_{21}k&L_{22}k^{2}+L^{\prime}{}_{22}-\frac{3\rho\gamma}{l^{2}N_{p}}\omega^{2}& L_{23}k^{2}+L^{\prime}{}_{23}\\ L_{31}k&L_{23}k^{2}+L^{\prime}{}_{23}&L_{33}k^{2}+L^{\prime}{}_{33}-\frac{3 \rho\gamma}{2l^{2}N_{p}}\omega^{2}\end{matrix}\right|=0 \tag{51}\]  
(2) Rotation Transverse Waves: related to \(\varpi_{2}\), \(B_{(12)}\), and \(B_{[12]}\)  
\[\left|\begin{matrix}T_{11}k^{2}-\frac{\rho\beta}{l^{2}N_{p}}\omega^{2}&T_{12} k&T_{13}k\\ T_{21}k&T_{22}k^{2}+T^{\prime}{}_{22}-\frac{2\rho\gamma}{l^{2}N_{p}}\omega^{2}&T_{ 23}k^{2}\\ T_{31}k&T_{23}k^{2}&T_{33}k^{2}+T^{\prime}{}_{33}-\frac{2\rho\gamma}{l^{2}N_{p} }\omega^{2}\end{matrix}\right|=0 \tag{52}\]  
(3) Rotation Transverse Waves: related to \(\varpi_{3}\), \(B_{(13)}\), and \(B_{[13]}\)  
\[\left|\begin{matrix}T_{11}k^{2}-\frac{\rho\beta}{l^{2}N_{p}}\omega^{2}&T_{12} k&T_{13}k\\ T_{21}k&T_{22}k^{2}+T^{\prime}{}_{22}-\frac{2\rho\gamma}{l^{2}N_{p}}\omega^{2}&T_{ 23}k^{2}\\ T_{31}k&T_{23}k^{2}&T_{33}k^{2}+T^{\prime}{}_{33}-\frac{2\rho\gamma}{l^{2}N_{p} }\omega^{2}\end{matrix}\right|=0 \tag{53}\](4) Rotation in-plane Shear Waves: related to \(B_{(23)}\), \(B_{[23]}\), and \(B_{22}-B_{33}\)  
\[\omega_{(23)}=\omega_{22-33}=\sqrt{\frac{(S_{11}k^{2}+S^{\prime}_{11})l^{2}N_{p} }{\rho\gamma}},\,\omega_{[23]}=\sqrt{\frac{(S_{22}k^{2}+S^{\prime}_{22})l^{2}N_ {p}}{\rho\gamma}}. \tag{54}\]  
Since the granular materials are considered transversely isotropic, the dispersion relations for the two sets of rotation transverse waves are the same. For brevity, hence, we only analyze the characteristics of rotation transverse waves related to the kinematic components \(\omega_{2}\), \(B_{(12)}\), and \(B_{[12]}\) in the following text.
## 2 Materials and Methods  
### Climate and Vegetation Index for deciduous-Broadleaved Forests and grasslands  
We obtained meteorological data of 674 stations (Figure 1) with total precipitation, \(P\) (mm), maximum (\(T_{\text{max}}\)) and minimum (\(T_{\text{min}}\)) temperatures (\({}^{\circ}\)C) in the period of 2000-2015 from the China Meteorological Administration [7]. The daily data were averaged for temperatures or summed for precipitation for each month and each wet year (from 1 October of previous year to 30 September of current year, or called water year) [33]. We then interpolated the monthly precipitation using kriging and the monthly maximum and minimum temperatures using cokriging with elevation as covariate at the spatial resolution of 1 km (ArcGIS 10, ESRI, Redlands, CA, USA).  
Large patches of deciduous-broadleaved forest and grassland without land-cover change were identified based on the annual land-cover maps of 2001-2015 for China, classified from MODIS imagery with the virtual ground-truth interpreted from high-resolution Google Earth images [34; 35; 36], with an area larger than 21 km\({}^{2}\). An inward buffer zone of 900 m was applied to minimize edge effect. In total, we identified 569 patches of deciduous-broadleaved forest (average size of 119 \(\pm\) 10 km\({}^{2}\)) and 311 grassland patches (average size of 117 \(\pm\) 14 km\({}^{2}\)). For each patch, we extracted time series of MODIS enhanced vegetation index (EVI, product MOD13Q1 V006) following the quality control  
Figure 1: Locations of temperate deciduous-broadleaved forest (DBF) and grassland (GRASS) patches selected in Northern China. Dots indicate weather stations.  
recommendation from the data provider [37] and averaged for the patch by means of Google Earth Engine [38]. This Level 3 product provides EVI and pixel quality assurance (QA) generated for every 16 days at 250 m. The vegetation indices are computed from the atmospherically corrected surface reflectance and masked for clouds and their shadows, water, and heavy aerosols, and the best available value during the 16-day period is used for low clouds and view angle but high EVI value [37].
# Monte Carlo Analysis of the Intensification Factor of Design Response Spectra for Hoisted Loads
## 6 Conclusions  
The use of idealized response spectra as a design driver for systems in seismically active regions is well established. Response spectra provide a concise yet powerful tool for assessing transient vibrations in linear systems. However, non-linear systems demand alternative, often more intricate, approaches.  
Among these, hoisted loads under cranes present a particularly relevant case, as they can experience non-linear dynamic effects when strong accelerations momentarily slacken the supporting rope. Given that cranes frequently handle delicate and mission-critical components, understanding these effects is essential.  
This hands-on study derived an intensification factor relative to the linear response spectrum, reaching a maximum of 1.65 at low damping levels. Notably, this factor remains largely independent of the specific shape of the response spectrum, provided the spectrum  
Figure 12: Intensification factors (+1\(\sigma\)) of the hoist load as a function of the damping.  
Figure 13: Non-linear peak acceleration with two different RS shapes (i.e., bandwidth of the plateau).  
retains a linear trend in the low-frequency range--an assumption that holds in typical engineering practice.  
A key finding is the critical role of internal damping in mitigating peak accelerations. High damping significantly reduces the intensification factor, reinforcing the importance of design strategies that enhance energy dissipation in suspended loads.  
In future developments of this work, we plan to perform tests with actual cranes.  
This research received no external funding.  
Not applicable for studies not involving humans or animals.  
**Data Availability Statement:** Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The author declares no conflicts of interest.
## 3 Results' Comparisons  
To verify the rationality of the design, we fabricated and tested the SP-fed broadband CP circular slot array antenna loaded with an AMC. The field test environment is shown in Figure 7. The fabricated antenna sample is shown in Figure 8. As we can see, the air layer between the antenna and the AMC is supported by plastic posts; both are fixed with insulating tape and sideloaded with SMA connectors. Figure 9 presents the simulated and measured results of the design. Figure 9a depicts that the impedance bandwidth of the measurement is 77.7% (2.74-6.22 GHz), while the simulation is from 2.74 GHz to 5.91 GHz. The simulated and measured AR bandwidths (AR < 3 dB) is in Figure 9b are 2.82-5.82 GHz and 3.00-5.90 GHz, respectively. From the simulated results, the CP operating bandwidth has 94.63% coverage for the whole operating band in our design. Figure 9c displays the in-band gain, where the measured gain peak is 11.1 dBi at 4.4 GHz. From the comparison of the above measurement, there is a small difference compared with the simulated result; the first reason for the difference is the unstable positioning of the antenna air layer; the second reason is the processing error and the deviation of the FR4 substrate dielectric constant. Figure 10 displays the radiation direction of the antenna at three frequency points. The performance comparisons between the designed antenna and others are listed in Table 1. Part of the antennas proposed have simple structure and low profile, and obtain good broadband performance at the same time [9; 18; 21]. For backward radiation, most of the designs use the AMC structure or PEC as the reflecting surface [31; 33; 34; 36]. In [37; 38; 39; 40], CMA was used to design and optimize the working mode of the antenna, so that the bandwidth and directivity of the antenna were significantly improved, and the antenna sidelobe was extremely suppressed. The  
Figure 8: The fabricated antenna sample. Figure 7: Field test environment. radiation direction pattern of the designed antenna was very symmetrical. In [39; 40] both use multiple linear polarization (LP) modes to improve antenna CP bandwidth. Compared with [39], the performance of [40] is further improved. The antenna unit adopts the stack structure to obtain up to 5 LP modes. Finally, SRT is used to form the 2 \(\times\) 2 array antenna, and the circular polarization bandwidth reaches 74%. The antenna bandwidth performance designed in this paper is better than in [37; 38; 39] and slightly less than in [40]. At the same time, compared with the above literature, the antenna design for sidelobe suppression and direction pattern optimization need to be improved. CMA needs to be used to further analyze the antenna working mode to optimize the antenna structure.
## 2 Basic Method Principles
### Inverse Process of Landslide Seepage  
Using the standard LSSVM, PSO-LSSVM and WOA-LSSVM to find the optimal parameters for LSSVM learning involves minimizing the difference between predicted permeability and sample permeability as the fitness function (Equation (9)) and calculating the optimal kernel function parameters to optimize the training effect for the support vector machine model. The specific steps of the inversion process are as follows. The flowchart is shown in Figure 1.  
1. Define the permeability coefficient range for the landslide mass to be inverted and use a uniform design to establish the sample calculation scheme.
2. Establish a finite element model and calculate the transient seepage over one reservoir operation cycle. The calculated GWL data were used as input, and the corresponding permeability coefficients were used as output \(k_{i}\) to form learning samples.  
Figure 1: Flowchart of landslide seepage inverse process.  
3. Train the LSSVM model: determine the optimal parameters (\(C\), \(\sigma^{2}\)) of the standard LSSVM model using cross-validation and grid search. For LSSVM models optimized by swarm intelligence, obtain the optimal parameter values via iterative optimization using both the PSO algorithm and the WOA. Specifically, taking PSO as an example, the steps are as follows. 1. Initialize the swarm intelligence algorithm, including the swarm size, weight factors for each particle, number of iterations, randomly generated particle swarm vectors, and corresponding \(C\) and \(\sigma^{2}\) values for each particle vector. Use the learning sample set as training and validation samples. Set the individual extremes of each particle to the current position, input them into LSSVM for training, and obtain the corresponding predicted permeability coefficients. 2. Calculate the average relative error between corresponding predicted values and actual values for each particle and use it as the fitness value for each particle. Then, iterate the calculation, update the velocity and position parameters of each particle, and memorize the best fitness values that correspond to individuals and the global optimum. When the initially set maximum number of iterations is reached, terminate the calculation and remember the best (\(C\), \(\sigma^{2}\)). 4. Substitute the optimal parameters (\(C\), \(\sigma^{2}\)) obtained from step 3b into the LSSVM model to establish the nonlinear mapping relationship between the permeability of the landslide mass and the GWL. 5. Use GWL monitoring data from landslides to predict the permeability coefficients of the landslide mass. Incorporate these coefficients into the transient analysis simulation to invert the landslide seepage field.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 6 Discussion
### Challenges Related to Smart Home Technologies  
The need for energy management systems is thus motivated by (i) the possibility of efficiently managing energy flows by using intelligent commands and a supervision system that is capable of interacting with both loads and generations to balance demand and supply, and (ii) the possibility of interacting with the external network to plan the production levels that are necessary to benefit economically from exchanging energy with the grid [111].  
Integration of the forecasting models, which are able to predict hourly power generation according to the weather forecasting inputs could make an optimal operation schedule in such a way that economically optimized power dispatch can be maintained to fulfill certain load demand. Some studies show that this would reduce daily costs by 28%. Further research could be performed to improve this method by including more relevant factors, such as the industrial and commercial profiles of a city or region [112].  
Peak demand charge is one of the major components of a customer's power bill and is the calculation of the amount of energy required during the peak demand periods. To cover the peak demand, utility companies are compelled to purchase reserves that remain untapped most of the time and are, only to be used for short periods of high demand or during failure of other reserves.  
Furthermore, smart homes are basically equipped with renewable energy generation technologies. Unfortunately, local energy generation from renewable energy sources (RES) and energy requirements are misaligned. Therefore, a smart technology might be able to modify indoor environmental conditions to time shift energy requirements from peak to off-peak hours. Moreover, if the building is equipped with an electric storage, energy from RES can be used to charge the storage when the electricity cost is low, and discharge the storage during high-cost periods. Thus, the use of storage in conjunction with RES might help optimize the cost effectiveness of a smart home. A renewable energy time shift is particularly valuable for intermittent sources [113].
## 5 Strengths and Limitations  
The present study has several strengths that contribute to the robustness of its findings. One significant strength is the utilization of both subjective and objective measurements to assess physical activity levels. By incorporating both self-report measures and objective data from accelerometers, the study provides a more comprehensive evaluation of participants' physical activity behaviors. This approach helps to mitigate potential biases or inaccuracies that may arise from relying solely on self-reported information. Moreover, the study employed validated measures of physical activity, such as the International Physical Activity Questionnaire (IPAQ) and the Actigraph accelerometer, which enhanced the validity and reliability of the obtained results.  
This study has several limitations. First, the use of a convenience sample may limit the generalizability of the findings. Second, selection bias could have occurred if those who chose to participate were more active than those who did not. Finally, the findings may not be generalizable to other healthcare professional populations or geographical locations. Future research should aim to address these limitations. Moreover, viewed from another perspective, the cross-sectional design employed restricts the ability to establish causality or determine the temporal sequence of events. Longitudinal studies would provide more insight into the dynamic nature of physical activity behaviors and their associations with various factors over time.  
Additionally, the study lacks specific information on recommended physical activities, particularly regarding strength activities and vigorous physical activities. The absence of this information limits a comprehensive understanding of the participants' adherence to different types and intensities of physical activity. Including such details would have allowed for a more nuanced interpretation of the findings and a better assessment of participants' compliance with established guidelines.  
Despite these limitations, the study's inclusion of both subjective and objective measurements, along with the use of validated assessment tools, contributes to its overall strength. However, future research incorporating longitudinal designs and more detailed information on specific types of physical activities could provide a more comprehensive understanding of the physical activity behaviors of healthcare professionals.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 3 Methodology
### MGSP for Hash Code Learning  
To enhance the cross-modal similarity consistency preserving for hash code learning, a novel technique, called Multi-Grained Similarity Preserving (MGSP) is developed, as shown in Figure 3a. It consists two key sub-modules, i.e., global similarity aggregation (GSA) and local similarity interaction (LSI), which explore multi-grained similarity information: global and local similarity structure information. Furthermore, another sub-module, named Similarity Consistency Reconstruction (SCR) is involved to narrow heterogeneity gap between original features and reconstructed features from hash codes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{\Lambda}_{ij}\) & \(\Gamma_{ij}\) & **Update** & **Rules** \\ \hline
0 & 0 & & \(S_{ij}^{r}\) \\
1 & 1 & ✓ & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Indicator matrices truth table for dual instruction fusion updating strategy.  
Figure 2: The process of dual instruction fusion updating. \(\Gamma\) is an indicator matrix that tells us where values need to be updated, when \(|S^{r}-S^{h}|>\eta_{2}\), \(\Gamma_{ij}=1\); otherwise \(\Gamma_{ij}=0\). \(\mathbf{\Lambda}_{ij}=S_{ij}^{r}\oplus S_{ij}^{h}\oplus\) is XOR operation. If \(\mathbf{I}_{ij}=1\) and \(\mathbf{\Lambda}_{ij}=0\), \(S_{ij}\) is weighted by \(S_{ij}^{r}\) and \(S_{ij}^{h}\); if \(\Gamma_{ij}=0\) and \(\mathbf{\Lambda}_{ij}=0\), then \(\mathbf{S}_{ij}=S_{ij}^{r}\); the rest of \(S\) is consistent with 0.
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.3 Limited Availability of Labeled Data  
Biomedical image datasets with extensive labeled data for supervised training are often limited. DDPM's performance can be affected when trained on small datasets, potentially leading to overfitting [100].
## 3 Simulation Results
### Solution Quality and Stability of Algorithm in Terms of Convergence Time  
Convergence time is the most important metric of the problem since it represents the time to obtain the most suitable configuration in the reconfiguration problem. Therefore, it is important to examine the parameters affecting the convergence time. Table 8 gives an idea about the convergence time and stability of the algorithm between different PV array configurations. It is seen that Scenario 1 shows good performance by reaching the fastest best convergence time of 0.0002152 sec and a relatively low average convergence time of 0.858530725 sec. At the same time, the fact that its standard deviation is 1.815959142 shows that the convergence time is moderately variable. The progression from the worst convergence time to the best convergence time reveals the high performance of the algorithm in terms of convergence time. Similarly, in the PV array with four rows, high performance of the algorithm towards the best convergence value is observed in Scenario 4. However, here, the high standard deviation shows that the change in the convergence value is high. The point to be noted here is that the convergence value level of the four-row PV array is naturally higher than the three-row array due to the large solution space. This difference was previously shown in Table 1. As stated in Table 1, while the appropriate configuration space for a 3-row PV array with dual-adaptive is 249,984, this number is 4,195,023,360 for a four-row PV array. Therefore, it is natural that the convergence time in the solution of a four-row PV array configuration is longer than that of a three-row PV array. On the other  
\begin{table}
\begin{tabular}{c c c c} \hline
**PV Array Configuration** & **Number of Experiments** & **Acceptable Optimal Solutions** & **Success Rate (\%)** \\ \hline Scenario 1 & 63 & 63 & 100.0 \\ Scenario 2 & 63 & 63 & 100.0 \\ \hline Scenario 3 & 63 & 56 & 88.88 \\ Scenario 4 & 63 & 59 & 93.65 \\ \hline \end{tabular}
\end{table}
Table 6: Success rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**PV Array Configuration** & **Number of PV Array** & **Number of Experiments** & \begin{tabular}{c} **CVI (Best Fitness)** \\ \end{tabular} & \begin{tabular}{c} **Mean Fitness** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Worst Fitness** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Scenario 1 & 3 & 63 & **0.17** & 2.540 & 7.44 & 0.0 \\ Scenario 2 & 3 & 63 & 0.60 & 3.104 & 8.32 & 0.0 \\ \hline Scenario 3 & 4 & 63 & **0.35** & 4.19 & 9.69 & 0.129 \\ Scenario 4 & 4 & 63 & 0.56 & 4.28 & 10.17 & 0.101 \\ \hline \end{tabular}
\end{table}
Table 7: Best solution quality and stability of algorithm in terms of best fitness (CVI).  
hand, considering that the four-row solution space is a ten-digit number, the fact that the convergence time is less than 1 s shows the success of the proposed method algorithm.
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
###### Abstract  
Space laser communication technology has advantages such as high bandwidth and low interference, but the limitation of the load capacity of an unmanned aerial vehicle (UAV) makes it difficult for UAV clusters to have sufficient communication links to build a stable communication network. Aiming to address the problem of the low upper limit of the node degree of the self-organized network after the introduction of space laser communication technology for UAVs, a virtual node topology control (VNTC) algorithm is proposed for constructing an efficient and stable communication network structure. The algorithm is based on the unit disc graph model and achieves a lower-node-degree upper bound while maintaining connectivity through node pairing and virtual graph construction. The theoretical analysis and experimental results of the algorithm show that the algorithm can effectively deal with the topology control problem in UAV laser networks. In contrast to the XTC algorithm (exceptional topology control), the VNTC algorithm maintains the connectivity and most of the planarizability of the network, and, at the same time, it performs better in reducing the node degree and number of edges, thus providing an effective solution for low-connectivity topology control.  
U