esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification of Predication Model  
We needed to check whether the data are reliable enough. In statistics, according to the central limit theorem, the sample size has to be 30 or greater. The t-distribution and the standard normal distribution will be the same with an infinitely large sample size. Additionally, for sample sizes greater than 30, they will be similar, but the t-distribution will be somewhat more conservative. We used 120 sample data with a sample size of more than 120, which means that the sample data are reliable according to the central limit theorem. Another way to find that the data sample are reliable is by using the \(t\)-test. We used paired sample \(t\)-tests since we have two independent data sets. The paired sample  
Figure 12: Power generation prediction result per hour.  
Figure 13: Power generation prediction result per day.  
\(t\)-test is good when two numeric data are compared. The significance level for this test is set at \(\alpha=0.05\). If the \(p\) value is larger than \(\alpha\), the real power generation and the predicted power generation have little similarity. The result of the paired sample \(t\)-test is shown in Table 2.  
The test statistic is the difference between the average of real power generation and the average of predicted power generation. The degree of freedom (df) is the number of power generation data. Alternative hypothesis uses a two-sided test. The \(p\) value is \(1.621\times 10^{-14}\), that is, \(0.00000000000001621\). Therefore, \(p\) value is less than \(\alpha\) (0.05). This means that the result is statistically significant. So, we can say that the sample data are reliable through these tests. Since data samples are reliable, we can verify that the predicted model is close enough to the measured value of the current power.  
In order to verify our prediction model, we used a statistical method called the mean absolute percentage error (MAPE). The MAPE is the most common measure used to forecast error and works best when there are no extremes to the data (and no zeros) [20]. The MAPE formula is:  
\[M=\frac{100}{n}\sum_{t=1}^{n}\left|\frac{A_{t}-F_{t}}{A_{t}}\right|\]  
We used the extracted data to verify whether the model is accurate enough or not. \(A_{t}\) is the real power generation value. \(F_{t}\) is the predicted power generation value. We set 95% of reliability degree to estimate that the predict model is successfully predicted for power generation. First, we subtracted two data samples and found the absolute value. Then, all the values obtained are added through sigma and multiplied by 100 to obtain the percentage. Second, in order to find the error rate of all values, we divided by n, which is 120. The error rate obtained through this process is 3.647%. Therefore, the similarity (reliability degree) between the predicted value and the real measured value is 96.353%, that is, 100 minus the error rate (3.647%). As determined above, the similarity between the two data samples is more than 95%. As a result, we can recognize that the predicted data are successfully predicted.
## 3 Results
### High-Resolution Remote Sensing Data
#### 3.2.2 Numerical Evaluation of the Changes  
The analysis of these slopes before and after the 2015 earthquake confirmed the results derived from the processing of medium and high-resolution remote sensing imagery. The statistics of the five slope classes (i.e., 0-5, 5.01-15, 15.01-30, 30.01-45, and higher than 45.01 degrees) were calculated and the diagrams for the areas of Egremni, Okeanos, Gialos, and Komilio are presented in Figures 18-21, respectively. Generally, an increase in the  
Figure 16: On the (**left**), a Pleiades orthophoto of the Komilio coast in 2016 is displayed. On the (**right**), the same area has been presented in a 2015 Pleiades orthophoto. The backwards movement of the cliff is evident on the left image. The green dashed line indicates areas with deep erosion, while the blue line shows areas with debris accumulation.  
Figure 17: On the (**left**), the slope map of the Komilio coast in 2016 is displayed. In the (**middle**), the same area has been presented in the year 2015. Color changes indicate the changes to the cliff inclination before and after the 2015 earthquake. On the (**right**) image, the slope difference values are presented.  
inclination values ranging from 15 to 30 degrees was observed in all the study areas. At the same time, an increase at higher class slope values (above 45 degrees) was detected, which was determined to be associated with the occurrence of mass movements after the 2015 earthquake, modifying the relief in a steeper manner. Meanwhile, a decrease in the areas with slope values ranging from 30 to 45 degrees was detected in the Egremni, Gialos, and Okeanos coastal areas, resulting in the steeper transformation of the relief along the subsequent accumulation of the debris flow in areas with lower inclination values (15-30 degrees). Komilio coast is an exception to the aforementioned procedure. In particular, these specific areas were also affected by the 2003 earthquake, meaning therefore that less debris material was prone to the sliding phenomena. In fact, slope analysis confirmed this observation since minor slope changes between 2015 and 2016 were detected.  
Figure 19: Analysis of the slope classes between 2015 and 2016 for the Okeanos cliff.  
Figure 20: Analysis of the slope classes between 2015 and 2016 for the Gialos cliff.
## 3 Finite Element Analysis and Field Monitoring
### Force Characteristics of Each Rise-Span Ratios
#### 4.2.3 Analysis of the Bending Moment in the Top Arch  
The variation in the bending moment for each rise-span ratio is derived from the FEA software, as shown in Figures 15-17.  
The bending moment at the top of the arch varies as shown in Figures 15 and 16. The maximum value of the bending moment occurs at the foot of the arch, the bending moment at the top of the arch and the bending moment at the foot of the arch have opposite directions, and the bending moment at the shoulder of the arch has the smallest value. From Figure 15, it can be seen that the bending moment at the top of the arch and the rise-span ratio are negatively correlated, and as the rise-span ratio increases, the maximum axial force at the top of the arch decreases, and the drop also decreases, tending to converge. The maximum moment value of the arch structure decreases from 824.85 (kN\(\cdot\)m) at \(\mu=0.22\) to 261 (kN\(\cdot\)m) at \(\mu=0.27\), a reduction of 68% of the maximum moment value. The rise-span ratio has a relatively greater effect on the bending moment than the axial force. The overall bending moment cloud is shown in Figure 17. In terms of the overall structural analysis,  
Figure 16: (**a**) Bending moment cloud map (\(\mu=0.22\)); (**b**) Bending moment cloud map (\(\mu=0.37\)).  
Figure 15: (**a**) Cross-sectional the top arch bending moment diagram; (**b**) Maximum bending moment values for each rise–span ratios.  
the larger bending moments are found at the joints between the floor slab and the side and centre piles.
## 5 Discussion  
Early detection is a topical issue in PWD control, and many studies focus on remote sensing. Ground remote-sensing methods collect data consisting of samples from the entire tree and provide more accurate data for individual pine trees. However, PWD usually affects trees in large areas, making ground-acquired data not feasible. Moreover, satellite remote sensing could collect large-scale data, but the low spatial resolution makes the individual tree discrimination impossible. Therefore, UAV remote sensing is a more promising and practical candidate for future large-scale forest management. For UAV remote sensing, data acquisition is easily affected by shadows, overlaps and weather conditions, which may induce measurement noise. In the future, more UAV data acquisition methods such as multispectral and LiDAR equipment will be combined to generate better PWD detection results.  
The study results suggest that the accuracy of the GA and SVM classification model is higher than that of KNN, FR and SVM, which could extract spatial features from UAV HI. However, it failed to make full use of the spatial features of the HIs. On the other hand,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **OA(\%)** & **KAPPA** \\ \hline KNN & 76.19 & 0.5714 \\ RF & 76.19 & 0.6182 \\ SVM & 85.71 & 0.7649 \\ GA and SVM & 95.24 & 0.9234 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The overall accuracy (OA) and the Kappa coefficient for different classification models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Healthy Stage**} & \multicolumn{2}{c}{**Early Stage**} & \multicolumn{2}{c}{**Middle Stage**} & \multicolumn{2}{c}{**Serious Stage**} \\ \cline{2-9}  & **PA(\%)** & **UA(\%)** & **PA(\%)** & **UA(\%)** & **PA(\%)** & **UA(\%)** & **UA(\%)** \\ \hline KNN & 0 & NAN & 100 & 62.5 & 91.67 & 84.62 & 0 & NAN \\ RF & 0 & 0 & 80 & 50 & 83.33 & 100 & 100 & 100 \\ SVM & 100 & 100 & 100 & 71.43 & 83.33 & 90.91 & 50 & 100 \\ GA and SVM & 100 & 100 & 100 & 100 & 91.67 & 100 & 100 & 66.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The producer’s accuracy (PA) and user’s accuracy (UA) in different PWD infection stages using KNN, RF, SVM and GA and SVM model.  
some studies employed 2D-CNNs, such as Faster R-CNN and YOLOv3, to extract spatial features such as color distribution and texture [12]; however, it failed to employ the spectral information. In recent years, the 3D-CNN has been extensively applied in HI classification in the forestry field [41]. It combines 1D spectral information and 2D spatial information, which could extract spectral and spatial features simultaneously, thus exhibiting great potential in monitoring PWD infections. Therefore, a 3D-CNN model on the HI will be examined in our future study to improve the accuracy of PWD detection.
## 4 Case Study and Analysis
### Comparison of Transfer Learning Method with Other Methods  
To highlight the advantages of the transfer learning method and its effectiveness in handling the challenge of limited historical data, it is compared with other traditional prediction methods. The results are shown in Figure 7.  
From Table 3, the following can be safely concluded:  
Figure 6: Wind and solar power prediction results by TCN\(-\)ATT\(-\)LSTM transfer learning model.  
Figure 7: Comparison of transfer learning method with traditional prediction methods.  
Under conditions of limited historical data, the prediction performance of traditional deep learning models decreases significantly. Both the RNN and LSTM models show negative R\({}^{2}\) values for the wind and solar power test sets, indicating underfitting. In contrast, models using transfer learning exhibit significantly lower errors when generating wind and solar power curves, without signs of underfitting. This demonstrates the effectiveness of the transfer learning approach.
2. Compared to the pre-trained method, the proposed model reduces the MAE of wind and solar power curves by an average of 21.19% and 42.5%, respectively, while reducing RMSE by 24.11% and 37.25%. The R\({}^{2}\) values increase by 12.5% and 4.97%. This analysis shows that, while the pre-trained network effectively captures the complex spatiotemporal mapping relationships between meteorological data and renewable energy output characteristics in the source domain, it is still unable to achieve efficient prediction when directly applied to the target domain. Target domain-specific feature data adjustments are required.
3. Compared to the LSTM model after transfer learning, the proposed method reduces the MAE by an average of 32.81% for wind power and 34.75% for solar power, while RMSE decreases by 31.45% and 28.89%. The R\({}^{2}\) values increase by 16.47% and 25.82%. This analysis indicates that the LSTM method cannot fully extract and process all the features from the source domain. However, by incorporating the TCN-ATT module, the model's prediction performance is significantly enhanced.
# Comprehensive Insights into the Molecular Basis of HIV Glycoprotein
## 2 Proteins Engaged in HIV Entry  
HIV demonstrates a pronounced binding affinity between the viral glycoprotein gp120 and the initial domain of the CD4 receptor. Subsequently, CXCR4 and CCR5 coreceptors exert pivotal roles in mediating HIV entry by dictating tropism among CD4+ cells. Coreceptors interact with gp120, eliciting conformational alterations in the glycoprotein. These structural shifts lead to the dissociation of gp120 from gp41 and expose previously concealed epitopes on gp41. Subsequent exposure of these cryptic epitopes on gp41 triggers the activation of fusogenic components, initiating the process of receptor-mediated fusion known as virus-cell membrane coalescence [20; 21; 22]. Concurrently, various studies have documented the entry of HIV into receptor cells independent of CD4, CXCR4, and CCR5 receptors [9; 23; 24; 25; 26]. Therefore, HIV-1 gains entry into these cells by utilizing alternative receptors such as C-type lectin or mannose (Man) receptors in astrocytes [23; 27]. The subsequent discussion will thoroughly examine the intricate interplay between HIV and host receptors crucial for viral entry.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Space Exploration
#### 3.4.3 In-Space Assembly and Maintenance  
* Satellite Swarming [47]: Swarms of small satellites can work together to perform tasks such as Earth observation, communication, and space weather monitoring. These satellite swarms can reconfigure themselves in orbit to optimize performance. Benefits: Increases the flexibility and coverage of satellite networks, reduces the cost of individual satellites, and enhances the resilience of satellite systems.
* Spacecraft Maintenance and Repair [63]: Swarm robots can perform maintenance and repair tasks on spacecraft, space stations, and other space assets. They can inspect structures, identify issues, and perform repairs autonomously. Benefits: Reduces the need for risky extra-bricular activities (EVAs) by astronauts, ensures continuous operation of space assets, and extends the lifespan of spacecraft.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.3 Wavelet Scattering Features  
The extraction of features in the time and frequency domains might not be enough to capture the detail and variability present in the ECG signal during emotional state changes; therefore, we employed mathematical tools based on the wavelet transform to extract more complex features [37]. The wavelet scattering algorithm subdivides each signal in a determined number of scattering windows and extracts features from them using wavelet transformations. Then, each scattered window is classified independently and the classification of the original segment is performed based on a uniform weighting (voting) of the classification of each scattered window. The wavelet scattering algorithm consists of a three-stage iterative transformation of the signal: wavelet convolution, modulation, and filtering. This architecture is similar to a Convolutional Neural Network (CNN) with the difference that the convolution filters are not learned but, instead, they are predefined wavelet functions. The scattering coefficients are intended to have low variance within a class and high variance across classes. Moreover, they are insensitive to input translations on an invariance scale and have some desirable properties such as multiscale contractions, linearization of hierarchical symmetries, and produce sparse representations of data [38; 39; 40; 41]. The algorithm used in this work is the wavelet scattering function implemented in the MATLAB Signal Processing Toolbox. The function was set with an invariance scale of 20 s, with the default parameters for filters and wavelets, resulting in two filter banks with 8 and 1 wavelets per octave, respectively.
## 2 Materials and Methods
### Software Design  
When the ptp4l and phc2 sys programs in the LinuxPTP software package are running normally, the system establishes a complete clock synchronization relationship, and multi-sensor synchronous acquisition can be realized through software design. The overall design of the system software is shown in Figure 3.  
The detailed process is as follows:  
After the main controller receives the start instruction from the user, it sends the instruction to timing board 1 (i.e., the master clock) through the UDP, and timing board 1 serves as the reference source for clock synchronization.  
After receiving the start instruction, timing board 1 immediately records the timestamp RT1 of the current system clock and broadcasts the timestamp RT1 and the timed acquisition command to timing boards 2 and 3 through UDP multicast. Timing board 1 then uses the nanosleep timer to set a 1 s delay. After the delay ends, the sensors start synchronous data acquisition. After the acquisition is completed, the data is preliminarily processed, and the result is sent back to the main controller.  
When timing boards 2 and 3 receive the RT1 timestamp and the timed acquisition command, due to factors such as network delay and system interference, there may be time differences between different timing boards. To minimize this error, timing boards 2 and 3 record the timestamps RT2 and RT3 of their respective current system clocks when receiving the instructions. Then, timing boards 2 and 3 calculate and set the delay through the nanosleep timer as 1 s minus the time difference between each of them and RT1 (i.e., \(1-(\text{RTi}-\text{RT1})\) seconds, where i = 2, 3). After the delay ends, the sensors start synchronous data acquisition. After the acquisition ends, each timing board preliminarily processes the acquired data and sends the result back to the main controller.  
Figure 2: Clock-following synchronization relationship.
## 2 Measurements Campaign
### Equipment and Measurement Setup  
The measurements have been made in the time domain through a channel sounder based on a Universal Software Radio Peripheral (USRP) (Ettus Research(tm), Austin, United States). The received power level has been measured at 850 MHz and 3.5 GHz with 97,653 data points for each transmitter position (Tx). The bandwidth of the intermediate frequency filter has been 50 kHz. Omnidirectional antennas with linear (vertical) polarization have been used at the transmitter and receiver (Rx) ends. The transmitting antenna has been placed at different positions in the corridor, thus emulating the position of a moving user (U). The Rx subsystem remained in the same position, near the access to one of the buildings, with the purpose of emulating the position of an access point that offers coverage to corridor users. The transmitting and receiving antennas were located at a height of 2.05 m and 1.61 m above ground level, respectively. Figure 4 shows the top view of the corridor environment, with the location of the receiving antenna and the 11 positions of the transmitting antenna together, in a range of Tx-Rx distances from 2.00 m to 67.5 m.  
\begin{table}
\begin{tabular}{c c c} \hline
**5G Use Cases** & **Services** & **Spectrum-Coverage** \\ \hline eMMB & Areas with limited connectivity, remote offices, & Low and mid bands \\  & teleworking, commerce, indoor environments, & Medium and long range \\  & immersive technologies, and public transport. & \\ \hline mMTC & Massive IoT: smart cities, buildings, sensor & Mid band \\  & networks for agriculture, and industry. & Medium long range \\ \hline URLLC & Autonomous vehicles, Intelligent Transportation & \\  & Systems (ITS), Vehicular-to-Everything (V2X), & High bands \\  & Industries 4.0, and smart grid. & Short and medium range \\ \hline \end{tabular}
\end{table}
Table 1: A 5G use case and their corresponding spectrums and coverage range.
## 4 Discussion
### Driving Factors of NPP
The SEM indicated a promoting effect of elevated temperature and increased precipitation on NPP increase, yet the promotional effect diminished with elevation. This implies the conclusion that elevation inhibited the response of NPP to temperature and precipitation increases. On the one hand, higher temperature and increased precipitation promoted plant bioenzyme activity, lengthened plant season length, accelerated decomposition of organic matter in the soil, and facilitated plant growth [4; 34; 43]. On the other hand, elevation increase caused a rapid decrease in the environmental background values of temperature and precipitation, which counteracted the NPP increment promoted by increased temperature and precipitation. The lower elevations of the TP are mainly dominated by alpine meadows, where vegetation types are richer and more diverse, allowing the full use of different ecological niches for plant growth. When temperature and precipitation increase, the varied plants can jointly utilize these resources and optimize the use of resources through competition, symbiosis, and other ecological relationships, thus promoting NPP in the whole ecosystem [16; 55]. In contrast, the vegetation communities at higher elevations are relatively simple and are dominated by cold- and drought-tolerant alpine native steppe plants. These plants have a narrower range of adaptation to environmental changes and are unable to fully utilize the resources through multiple ecological strategies when temperature and precipitation increase, resulting in a limited promotion of NPP [19; 55]. The higher sensitivity of grassland NPP to temperature than to precipitation implied that grass growth on the TP was more susceptible to temperature regulation, which is consistent with findings that NPP on the TP is mainly temperature-limited [38; 53]. Precipitation was generally lower at higher elevations on the TP (Figure S4), but it has increased more at higher elevations over the past 22 years (Figure S6), implying a greater mitigation of precipitation-induced inhibition of vegetation growth. Temperature increased less at higher elevations, and the inhibitory effect of temperature on vegetation growth was not well alleviated, resulting in temperature being the major constraint on plant growth. We also found a stronger effect of elevation on the sensitivity of NPP response to temperature and precipitation in alpine meadows than in alpine steppes. This is related to grassland distribution. Alpine meadows are distributed at lower elevations with better hydrothermal conditions. Elevation rise can cause rapid changes in environmental conditions (Figures S1 and S4), resulting in the rapid response of NPP changes to elevation. Alpine steppes are distributed at higher elevations, and the magnitude of change in hydrothermal conditions with elevation is smaller; hence, there is a smaller impact.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs
### Scenario Description
#### 2.1.1 Basic Node Pair Representation  
\begin{tabular}{l l} \((m,\,n)\) & Originating and terminating satellite nodes in the physical topology \(G\), which also serves as the endpoints of an ISL. \\ \((i,j)\) & Originating and terminating nodes of a virtual lightpath, which traverses through several ISLs in the physical topology. Each virtual lightpath is allocated a wavelength. \\ \((s,d)\) & Source and destination nodes of an end-to-end connection request, or aggregated request, which will be routed on the virtual lightpaths. \\ \end{tabular}