esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The Aksu region, situated in the arid northwest of China, experiences a warm, temperate, arid climate, which makes it particularly sensitive to climate change and vulnerable to vegetation degradation [48; 49]. Vegetation dynamics in Aksu are influenced by a complex interplay between climate variability and human activities, with anthropogenic factors playing a particularly prominent role. Previous studies have demonstrated the utility of combining NDVI and nighttime light data to assess human impacts on ecological systems [50; 51; 52]. For instance, [PERSON] et al. explored the influence of ecological restoration efforts on economic development across China using nighttime light and NDVI data [51], while [PERSON] et al. employed time-series nighttime light data and MODIS NDVI to monitor urban expansion in the Yangtze River Delta from 2000 to 2020, validating the use of nighttime light as an effective indicator of human activity changes [53]. These studies support the methodology of our research by underscoring the effectiveness of nighttime light as a proxy for urbanization and human impact, particularly in regions where direct observational data are limited. By integrating NDVI and nighttime light data over a two-decade period, this study provides a comprehensive analysis of the drivers of vegetation change in Aksu, contributing valuable insights into the region's ecological dynamics under both natural and anthropogenic influences.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Select Operation
#### 5.3.4 Different Selects with Multiple Joins and Different Functions
The query presented in Table 12 uses two left joins and multiple functions and returns each user of each company and the number of their customers ordered by the date of their creation. For each function in relational MySQL, there is a method in document-based MySQL with its name taking the fields we want to use as a direct parameter. To take several fields from the existing object, inside the _fields()_ method, we must use a comma for all the desired fields. In order to use the _groupBy()_ method, we must take the field we want to group and then use it as a parameter in this method. If grouping is attempted using any fields other than those in the _fields()_, an exception such as \"Invalid empty projection list for grouping\" will appear. Figure 11: Performance time (ms) for select operation with multiple joins and functions. The query that lasts the longest and where we can observe the optimizations conducted is the one where we obtain the number of customers for each user of each business. This query uses a group by clause to group the customers according to the user they belong to and an order by clause to obtain the users created in ascending order. This query does not use any primary key or foreign key, and only the user _id_ is used in the group. Therefore, the response times when using relational MySQL are very long and increase significantly with the number of items because the tables are scanned in their entirety, no index can be used on any primary key, and the deleted field must be checked. When using document-based MySQL, all documents that are not deleted are selected and then sorted by their creation date. The _group by_ function only takes projections as a parameter, and the field or fields used in _groupBy()_ must be selected first with the _fields()_ method; otherwise, this method will not work. Therefore, first, we selected the fields we wanted to obtain and, in addition, the field user _id_ based on which the grouping was conducted. Additionally, in the case of this database, this is the query that lasts the longest, but the differences between the databases are very large, as shown in Figure 12.  
After optimizing the database structure and adding indexes, the response times when using relational MySQL decrease, but not by very much when there are many elements; however, by adding compound indexes, the times are greatly reduced, decreasing from 10-12 to 1-3 s, which is very good. By adding the index composed in the user table consisting of deleted, _created_at_, _id_, and _business id_, the query can use this index when it takes the data and filters them according to the conditions specified.
## 4 Permeability Coefficient Inversion Based on LSSVM
### Training Effect and Inversion Predicted Values
During the low water level operation period, the average monitored GWL (data from the year 2012 to 2018 in Figure 3) at each monitoring boreholes were as follows: JC1: 155.83 m; JC3: 165.57 m; JC8: 179.65 m. Then, this set of monitoring data were used as test values to invert the landslide permeability coefficients using three different algorithms. Table 6 shows the inverse values of the permeability coefficients. Subsequently, the inversion values obtained from the three methods were input into the finite element model to calculate the simulated GWL of the landslide, as shown in Table 7. Introducing a relative error to evaluate the inversion effectiveness, we observe that all inversion calculations of the three algorithms had relatively small relative errors, which satisfies the accuracy requirements for permeability calculations. Specifically, the PSO-LSSVM algorithm achieved the best inversion performance, followed by LSSVM, and WOA-LSSVM was slightly inferior. Thus, WOA-LSSVM is prone to premature convergence or overfitting during training, which results in relatively larger relative errors in the inversion calculations. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{k_{i}}\)} & \multicolumn{2}{c}{**LSSVM**} & \multicolumn{4}{c}{**PSO-LSSVM**} & \multicolumn{4}{c}{**WOA-LSSVM**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Inverse**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**Inverse**} & \multicolumn{1}{c}{**Values**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**Inverse**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**RMSE**} \\  & \multicolumn{1}{c}{**Values**} & & & & & & & & \\ \hline k1 & 4.732 \(\times 10^{-2}\) & 0.99909 & 7.795 \(\times 10^{-4}\) & 4.385 \(\times 10^{-2}\) & 0.9998 & 3.668 \(\times 10^{-4}\) & 5.135 \(\times 10^{-2}\) & 0.9999 & 2.556 \(\times 10^{-4}\) \\ k2 & 5.772 \(\times 10^{-3}\) & 0.99912 & 7.727 \(\times 10^{-5}\) & 5.819 \(\times 10^{-3}\) & 0.9998 & 3.645 \(\times 10^{-5}\) & 5.650 \(\times 10^{-3}\) & 0.9999 & 2.540 \(\times 10^{-5}\) \\ k3 & 6.119 \(\times 10^{-4}\) & 0.99909 & 7.784 \(\times 10^{-6}\) & 6.284 \(\times 10^{-4}\) & 0.99979 & 3.733 \(\times 10^{-6}\) & 5.816 \(\times 10^{-4}\) & 0.9999 & 2.518 \(\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Permeability coefficient inverse values, training performance RMSE and R2. Figure 5: Evolution of the optimal fitness curve for the optimization of LSSVM.
## 4 DRTO: Deep Reinforcement Learning for Task Offloading
### Update the Offloading Policy  
Due to the rapid changes of satellite-terrestrial channel states, in order to reduce the offloading cost, the offloading policy should be updated in time. Different from traditional deep learning, the training samples of DRTO are composed of the latest channel state \(\mathbf{h}\) and offloading location \(\mathbf{x}^{*}\). Since the current offloading location is generated according to the policy in the last time frame, the training samples in adjacent time frames are strongly correlated. If the latest samples are used to train the DNN immediately, the network will be updated in an inefficient way, and the offloading policy may not even converge. Thanks to the experience replay mechanism [39] proposed by Google DeepMind, the newly obtained state-location pair \((\mathbf{h},\mathbf{x}^{*})\) is added to the replay memory, and replaces the oldest one if the memory is full. Subsequently, a random batch is sampled from the memory to improve the DNN. The cross-entropy loss is reduced by utilizing the Adam optimizer [40]. Such iterations repeat, and the policy of the DNN is gradually improved.  
By utilizing the experience replay mechanism, we construct a dynamic training dataset for DNN. Thanks to the random sampling, the convergence is fastened because the correlation between training samples is reduced. Since the memory space is finite, the DNN is updated only according to the recent experience, and the offloading policy \(\pi\) is always adapted to the recent channel changes.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 5 Evaluation Results
### Effect of \(P^{\text{H}}\)  
Figure 3 shows the change of the average aggregated data rate \(\zeta_{R}\) and the energy outage probability \(\zeta_{E}\) according to the energy harvesting probability \(P^{H}\). From Figure 3, we can observe that NA-NOMA can achieve the highest average aggregated data rate \(\zeta_{R}\) while keeping the acceptable energy outage probability (i.e., 0.05). This is because IoT devices in NA-NOMA determine whether to transmit data to BS and their transmission powers by considering the energy level and other devices' actions (i.e., transmission powers). For example, a specific IoT device can transmit its data with high transmission power to increase the average aggregated data rate, when its energy level is high and other IoT devices transmit data with low transmission power (or do not transmit data). Moreover, if an IoT device has a low energy level, it does not transmit data to avoid an energy outage.  
Meanwhile, from Figure 3a, it can be observed that \(\zeta_{R}\) of NA-NOMA increases as \(P^{H}\) increases. This is explained in the following. High \(P^{H}\) indicates that IoT devices can transmit data without any concern about the energy outage. In this situation, IoT devices in NA-NOMA aggressively exploit high transmission power. Meanwhile, because the other schemes do not adjust their transmission policies by considering the energy harvesting probability, their average aggregated data rate is constant regardless of \(P^{H}\) (see Figure 3a), and their energy outage probabilities decrease as \(P^{H}\) increases (see Figure 3b).  
Meanwhile, as shown in Figure 3a, the average aggregated data rates of the comparison schemes are similar to each other. This is because all IoT devices in each comparison scheme operate with the same policy, which can degrade the expected SINR. For example, even if a specific IoT device transmits its data with the maximum transmission power, the other IoT  
Figure 2: Process that the policies of IoT devices converge to the integrated optimal policy (i.e., Nash equilibrium).  
devices probably transmit their data with the maximum transmission power, which causes low expected SINR.
## 2 Materials and Methods
### Development of the Predictive Models  
After defining the dataset for the modelling process, a predictive model is essential for analyzing the performance of the proposed approaches. Linear regression, a statistical technique applied to model relationships between variables, is used to create a basic linear model. It can handle both simple and multiple regression analyses, making it suitable for developing a predictive model for earphone design parameters and sound quality outcomes. Linear regression is widely used in data analysis and machine learning for supervised and unsupervised learning. It works by fitting a linear equation to data points, with the goal of finding the best-fitting line that minimizes the squared differences between predicted and actual values. However, it is important to check the assumptions of linearity, homoscedasticity, normality of residuals, and independence of errors for reliable and valid results. Despite its limitations, linear regression serves as a baseline method for evaluation, with applications in prediction, trend analysis, hypothesis testing, and outlier detection. It can model relationships between variables, predict future values, analyze trends over time, and identify outliers. While it is crucial to ensure that the variables' relationship can be described by a linear equation, linear regression remains a powerful tool for data analysis and machine learning. The formulation of the parameters and sound quality performance is shown in the following. The formulation revealed that the type of secondary driver has minimal impact on sound quality performance, so it was excluded in practical applications.
## 1 Introduction
It will seek to analyze various proposals of constraints in the reconfiguration process, main errors during the system adaptation process, or also some notion on how to maintain the stability and reliability of the system during the evolution of variability, ensuring the consistency of the software concerning the models and the system in operation [3]. The research will address the challenges mentioned by [5], specifically variability management through component reconfiguration. For this purpose, a systematic mapping study (SMS) for the most relevant articles of the last 11 years will be carried out. This SMS pretends to collect as much data as possible on the proposals to analyze the methodological approach used to manage runtime variability by reconfiguring the solution domain of software in an execution environment. We expect the results of this systematic mapping contribute to the research-oriented scientific community of SPLs and, eventually, DSPLs, synthesizing what has happened during the last 11 years in the evolution of runtime reconfiguration approaches for DSPLs in the context of self-adaptive systems, both at the theoretical-practical and bibliometric levels. The research questions (RQs) corresponding to this systematic mapping study are as follows:  
* What approach was used to apply constraints during software reconfigurations in DSPL? * What methodologies are currently used to manage DSPL variability during reconfigurations? * What are the current challenges in the management of DSPL? Answering RQ1 will allow us to catalog the main approaches for managing runtime variability through software reconfigurations. In this sense, we want to understand how DSPL variability is currently managed, e.g., whether constraint languages, reasoning engines, and optimization models, among others, are occupied. RQ2 will provide us with information on the execution environments of DSPLs, analyzing, for example, whether proprietary architectures, execution environments under some autonomous control loop, or third-party software are proposed. RQ3 will focus on the search and analysis of the main constraints in developing a DSPL, whose primary focus is runtime variability. The study will be conducted between 2010 and 2021 since the systematic mapping study by [PERSON] et al. examines variability management for DSPL from 2006 to 2015 [9]. The authors expose that the area of runtime variability development is not explored in-depth, concluding that most articles do not address the reconfiguration process. Furthermore, in the bibliometric analysis of the articles, an increase in the number of articles is obtained from 2010 onwards. The remainder of this paper is structured as follows. We present the background of the DSPLs and their context in Section 2. Section 3 presents some related work relative to SPLs or DSPLs. Section 4 presents the methodology for the SMS. Sections 5 and 6 present the results and discussion, respectively. Finally, Section 7 presents the conclusions and future work.
## 6 Conclusions  
In this paper, the aerodynamic shape of a arc-plate bluff body was optimized, based on the output energy of a galloping-based WEH. The Latin hypercube sampling method was employed for the design of the experiment. According to the wind tunnel test results, a Kriging surrogate model with a high accuracy was established. The variation of transverse force coefficients of the bluff body with wind attack angle was examined in detail. Experiments were employed to prove the superiority of the arc-plate-shaped bluff body for energy harvesting. The following conclusions were obtained.  
(a) Through the Latin hypercube sampling method, the arc-plate shape determined by the two variables can describe a variety of aerodynamic shapes. The Kriging surrogate model established by the wind tunnel test showed a high degree of fit with the sampling points, which laid the foundation for the subsequent shape modification.  
(b) The output power of the WEH was significantly increased by optimizing the aerodynamic shape. The WEH with the arc angle \(0.40\pi\) and tail length \(1.26D\) generated the maximum power.  
(c) The output power of the WEH was related to the transverse force coefficient. A large transverse force coefficient usually means a large output power of a galloping-based WEH.  
(d) Optimizing the aerodynamic shape of the bluff body section doubled the output power of the WEH, compared with the WEH with a prism bluff body.  
Conceptualization, G.H.; methodology, formal analysis, and investigation, T.S.; writing--original draft preparation, T.S.; writing--review and editing, L.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China, under project No. 51478369.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.  
Figure 14: The schematic of flow separation for (**a**) square cross-section and (**b**) arc-plate-shaped cross-section.
## 2 Theory Background
### Time-Frequency Analysis Based on Improved Singular Spectrum Decomposition and Hilbert Transform  
ISSD was combined with HT to perform the time-frequency on rotor vibration signal in this work. The process of this time-frequency analysis is as follows. Firstly, the original signal is decomposed into a series of SSCs by ISSD. Then, the obtained SSCs are demodulated by HT to derive their instantaneous frequency (IF) and IA.  
For a given singular spectrum component \(SSC(t)\), its HT is defined as follows [32]:  
\[H[SSC(t)]=\frac{1}{\pi}{\int_{-\infty}^{+\infty}\frac{SSC(t)}{t-\tau}d\tau}. \tag{6}\]  
We can further get the analytical signal:  
\[z(t)=SSC(t)+jH[SSC(t)]=a(t)e^{j\varphi(t)}, \tag{7}\]  
Figure 1: Schematic diagram of waveform matching.  
where, \(a(t)\) represents the IA of \(SSC(t)\) and \(\varphi(t)\) is the instantaneous phase. Their specific expressions are given in Equations (8) and (9), respectively:  
\[a(t)=\sqrt{SSC^{2}(t)+H^{2}[SSC(t)]}. \tag{8}\]  
\[\varphi(t)=\arctan\frac{H[SSC(t)]}{SSC(t)}. \tag{9}\]  
The IF of \(SSC(t)\) can be derived as:  
\[\omega(t)=\frac{d\varphi(t)}{dt}. \tag{10}\]  
The IA and IF reflects the time-frequency information of \(SSC(t)\). By integrating the time-frequency information all SSCs, the ISSD-HT TFS can be derived.
## 2 Problem Statement and Model Formulation
### Problem Statement  
The ISRPTWC is how to compute the optimal transmission route or set of optimal transmission routes for tasks with multiple upload windows and multiple downlink windows, given known network capacity constraints. As shown in Figure 1, first, the user defines a task at a specific moment, including the data size, the upload time window, and the downlink time window. After that, the optimal transmission routes are calculated using the known inter-satellite topology, node, and link capacity information. Finally, the tasks are downlinked to the designated locations through the constellation network. In this study, we aim to find a set of transmission routes for multiple tasks that minimizes the total transmission cost. The overall transmission cost includes the transmission route cost, the environmental loss cost, and the inherent cost of the resources.  
As shown in Figure 2, consider an inter-satellite routing network \(G=(N,E)\), in which \(N\) is a set of nodes and \(E\) is a set of connected edges. We define \(t_{ij}^{d}\), \(c_{ij}^{d}\) to denote the transmission time and transmission cost of a task \(d\) in using the edge \((i,j)\). For simplicity, we use the number of inter-satellite task forwarding hops instead of the inter-satellite task transmission time to calculate a task transmission cost [3]. Meanwhile, \(Cap_{i}\) and \(Cap_{ij}\) are used to indicate the maximum transmission capacity of nodes and links. To reduce the packet loss rate, we define the maximum congestion time \(Blo_{d}\), which is the duration of a task on a satellite. Sets, indices, and parameters of this study are summarized in Table 1. It is worth noting that all nodes in the network are divided into three categories, including the set of task upload nodes \(ST\) and the set of task downlink nodes \(AR\), the task intermediate transmission nodes \(N_{1}\cup N_{2}\), and the virtual upload node 0 and virtual downlink node \(2n+1\). The starting and ending node of all tasks are defined to the virtual upload node and virtual downlink node, respectively.  
Figure 2: Inter-satellite routing network structure. (**a**) Inter-satellite link schemes within each time slice; (**b**) Inter-satellite link schemes in space-time networks; (**c**) Routing schemes based on inter-satellite topologies.  
Before modeling, some reasonable assumptions need to be made for the ISRPTWC:  
1. The transmission process between the gateway satellite and the gateway station and between the gateway station and the ground network is not considered;
2. Access satellites are defined as satellites that receive tasks, and gateway satellites are defined as satellites that connect to ground networks;
3. A user can communicate with only two satellites at any time, including an ascending and a descending satellite;
4. Ignoring transmission failures caused by other factors, packet loss occurs only when a task transmission exceeds the maximum congestion time.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Mode Selection
#### 7.5.3 Dynamic Mode Selection  
The majority of studies consider the static network scenario. These studies primarily focus on the downlink scenarios in which the BS becomes the mediator for the D2D pairs to communicate. It is obvious that the modes that switch dynamically, either using brute force or heuristically, can bring suboptimal changes to the network performance gains. These observations show the need for a more complex mode selection scheme so that it can be dynamically used in realistic scenarios.
## 3 Results and Analysis
### Stress Wave Propagation Characteristics of Jointed Specim  
Figure 8 illustrates the stress-time curves of reflected and transmitted waves of jointed specimens with various joint inclination angles subjected to impact load at JRC of 0. The reflection and transmission stress waves exhibit a distinct stepwise variation with time. The transmitted stress waves amplify as the joint inclination angle increases from 0\({}^{\circ}\) to 90\({}^{\circ}\). The reflected stress waves display a double peak with the joint inclination angle in the range of 30\({}^{\circ}\) to 60\({}^{\circ}\). This can be attributed to the multiple reflections of stress waves within the jointed specimen caused by the presence of serrated joints during stress wave propagation.  
Figure 7: Relationship between peak stress, JRC, and joint inclination angle.  
When JRC is 5, 10, 15, or 20, the propagation characteristics of the stress wave remain consistent with those observed when JRC is 0, which is not further described.  
Figure 9 presents the stress-time curves of reflected and transmitted waves of jointed specimens with various JRCs subjected to impact load at joint inclination angle of 60\({}^{\circ}\). It is evident that the difference between the transmitted and reflected stress waves becomes more pronounced. With a constant joint inclination angle, an increase in JRC results in a weak reflected stress wave and a strong transmitted stress wave. This indicates that high JRC leads to a diminished reflection of the stress wave when the joint inclination angle is fixed.