esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Constellation Station Maintenance Analysis
### Contents of Station Keeping  
Ideally, for low-orbit near-circular orbit satellites, the nominal orbital semi-major axes and orbital inclinations of satellites in the same orbital plane within a constellation should be consistent. If only the perturbation of the Earth's non-spherical \(J_{2}\) term and atmospheric drag are considered, the perturbation and drift laws of the orbital elements of low-orbit constellation satellites are also consistent. However, in general practical engineering, there are initial deviations in the capture of satellite orbital elements (among them, for the orbital inclination, according to practical engineering applications, the method of pre-biasing is generally adopted, and the initial deviation amount can be ignored). Under the influence of the Earth's non-spherical \(J_{2}\) term and atmospheric drag, the decay of the satellite's semi-major axis and the phase change will be inconsistent, which in turn causes the constellation configuration to drift.  
After the constellation configuration is established, under the influence of atmospheric drag and the perturbation of the Earth's non-spherical \(J_{2}\) term, the deviation of the satellite's semi-major axis will lead to the inconsistent decay of the satellite's semi-major axis. Within an orbital period, the differences in the running speeds of satellites will cause the drift of the phase angle. When the deviation of the phase angle exceeds the allowable theoretical boundary value, the satellite's phase angle needs to be adjusted. Therefore, the content of constellation station keeping is the maintenance of the satellite's phase.
# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
## 4 Discussion
### Merging Road Scene
#### 4.2.3 Effectiveness of Driving Strategies  
Comparison of decision-making model was based on DGAIL and A3C in the same simulation scenes and parameter settings. The training results and vehicle motion parameter curves of the two different algorithms for the vehicle in the merging road scene during simulation progress are shown in Figures 13 and 14.  
As show in Figure 13, in the 300 rounds, both algorithms were in the initial learning stage, and the decision-making model had high randomness in the selection of actions. In the 300-800 rounds, the DGAIL algorithm started to make correct decisions based on expert knowledge, the success rate increased rapidly to 96%, and the reward value increased to 12.76. In contrast, the A3C algorithm was still in the exploration stage; the randomness of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Reward Value** & **Round Duration** & **Training Times** \\ \hline DQN & 9.5 & 11 & 1782 \\ GAIL & 4.8 & 11 & 2000 \\ A3C & 11.76 & 15 & 1189 \\ DGAIL & 12.9 & 15 & 855 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training time cost for merging road.  
Figure 13: Comparison of two methods’ training results for the merging road.  
the action selection strategy was larger, and the success rate and reward value were lower. In the 800-1200 rounds, the DGAIL algorithm continued to learn the empirical knowledge that was generated from the interaction with the environment, the decision-making model was approaching the expert level, and the success rate and reward value were in the stable rising stage. At this time, the A3C algorithm also began to learn the correct action decision, the randomness of the action selection strategy was reduced, and the success rate and reward value continued to rise. In the 1200-1600 rounds, both algorithms had learned the optimal strategy, the decision-making model entered a stable period, and the driving decision obtained was able to meet the needs of the straight road scene. As shown in Figure 14, compared with the A3C algorithm, the DGAIL algorithm had a slightly higher average vehicle speed, fewer lane changes, more frequent acceleration and deceleration, and higher total rewards value considering the influence of the reward guidance factor.
## 5 Comparison of Results  
Results obtained analytically (deterministic--Section 2 and stochastic--Section 3--solution) and experimentally (Section 4) were compared.  
The comparison of the analytically calculated and measured axial force is shown in Figure 17. From the graph, it is evident that in the linear elastic region of up to 1.75 turns of the Herbert screw, where the thread is not sheared, the agreement between the analytical solution and measurement is very good. For this reason, the probabilistic solution is also performed for this number of turns.  
Figure 16: Approximation curve for the 2 nd measurement for acquired \(m=1.417\) (R\({}^{2}=0.9969\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement** & \(m\) & **R\({}^{2}\)** & **F\({}_{\rm E}\)** & **n\({}_{\rm E}\)** \\ \hline
**1** & 0.8845 & 0.9817 & 268.8105 & 2.57 \\ \hline
**2** & 1.417 & 0.9969 & 232.868 & 1.98 \\ \hline
**3** & 1.631 & 0.934 & 236.9959 & 2.74 \\ \hline
**4** & 0.9146 & 0.975 & 252.8947 & 2.44 \\ \hline
**5** & 1.224 & 0.9399 & 211.88 & 2.25 \\ \hline
**6** & 0.7684 & 0.8875 & 246.3244 & 1.99 \\ \hline
**7** & 1.156 & 0.9617 & 267.5367 & 2.44 \\ \hline
**8** & 1.677 & 0.7484 & 198.9446 & 2.31 \\ \hline
**9** & 0.778 & 0.9292 & 226.2703 & 1.96 \\ \hline
**10** & 0.9052 & 0.9481 & 183.1951 & 2.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Resulting parameters of the approximation curve.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 5 Conclusions  
This work introduced an asynchronous HFL framework with bandwidth allocation and client scheduling that aims to enhance resource utilization while guaranteeing model training performance. We proposed an efficient algorithm to perform bandwidth allocation based on client mobility patterns and also load balance on edge servers. Edge servers further accelerated model uploading using the remaining bandwidth. Ultimately, compared to the existing hierarchical FL methods, our approach can reduce training time by 78.2% and achieve higher model accuracy, which represent promising potential for efficient and robust FL in dynamic environments with client mobility. Our future research endeavors will focus on safeguarding privacy in the considered asynchronous FL by exploring the integration of privacy protection techniques such as differential privacy and homomorphic encryption.  
Conceptualization, J.Y.; methodology, J.Y. and Y.Z.; validation, Y.Z.; analysis, J.Y. and Y.Z.; investigation, J.Y.; resources, J.Y. and J.Z.; writing--original draft preparation, Y.Z. and W.W.; writing--review and editing, W.W., J.Z. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by the National Natural Science Foundation of China under Grant 62201101, the Project funded by China Postdoctoral Science Foundation under Grant 2022M720020 and Grant 2022M722669, the Natural Science Foundation of Chongqing, China, under Grant cstc2021 icyj-msxmX0458, the Chongqing Technology Innovation and Application Development Special Key Project under Grant CSTB2022T1 AD-KPX0059, and the open research fund of the National Mobile Communications Research Laboratory, Southeast University, under Grant 2022D06.  
Data available within the article.  
The authors declare no conflict of interest.  
Figure 5: Previous time–current time via greedy optimization algorithm.
## 1 Introduction
The aim of this study is to evaluate and understand the potential drought drivers that have shaped the spatial-temporal variability in vegetation productivity in the region over the period of 1989-2014, and to provide valuable spatial information related to interannual variability and changes in vegetation productivity for planning and mitigation purposes, assisting managers and decision makers in maintaining biodiversity. To address these questions, we developed an assessment approach integrating seasonal climate, land cover types and topographical data with annual geospatially explicit normalized difference vegetation index (NDVI)-related productivity from 1989 to 2014 derived from 15-day composite multi-sensor NDVI time series data. All data sets used in the study were harmonized into 5.6 km spatial resolution to match the NDVI data. Figure 1: Time series of annual temperature anomalies (**top**), percent of normal annual precipitation (**middle**), and annual average Palmer drought severity index (PDSI) (**bottom**) for Arizona, Colorado, New Mexico, Utah, and the Southwest. Annual temperature anomalies and percent of normal annual precipitation are relative to 1945–2014. NOAA NCDC provided state and regional data.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 5 Application to Roller Bearings Testing
### Case 2: Analysis of NASA Outer Ring Fault Vibration Signal  
The effectiveness of the proposed method is evaluated by using bearing vibration signal data collected by NASA. The experimental platform is shown in Figure 16. Double-row Rexnord ZA-2115 roller bearings are mounted on the same spindle. PCB353B33 acceleration sensors are fitted to each bearing shaft. The rotation speed of the spindle is constant at 2000 rpm, i.e., the rotation frequency is 2000/60 Hz = 33.3 Hz. Each experiment has 20,480 data points and the sampling frequency of the acquisition system is 20 kHz. The outer ring signal in the experiment is at bearing 1 of the NO.2 dataset. In this paper, the outer ring fault vibration signal obtained at the end of the experimental failure is selected to verify the proposed method. The length of the experimental data is 2048. Among them, the diameter of the roller element \(D_{m}\) = 71.501 mm, the pitch diameter \(d_{r}\) = 8.407 mm, the number of roller elements \(n_{r}\) = 1 and the contact angle \(\theta\) = 15.17\({}^{\circ}\). BPFO = 236.4 Hz, which is computed according to Equation (18).  
The original fault vibration signal waveform of the outer ring is shown in Figure 17a. It can be seen that the NASA dataset is affected by weak noise, and the periodic pulse characteristic cannot be clearly seen in the time domain diagram. At the same time, in order to verify the effectiveness of the proposed method under the variation of noise intensity, Gaussian white noise with SNR = 10 dB is added to the original signal, and the time domain waveform of the mixed signal is shown in Figure 17b.
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 2 Theoretical Analysis  
### Mechanism of Rock Breaking  
The crushing behavior of rock via blasting is a complex energy transfer process. The crushing effect depends on the mechanical property parameters of rock and is also affected by other factors such as the surrounding environment, in situ stress, and inertia effects. The effect of rock breakage after blasting can be summarized as follows: the reflection of the shock wave causes rock tensile damage, the expansion of explosion gas leads to compressive and shear failure, and both factors influence the formation of the final crack. This view is considered comprehensive, and the obtained results align with the actual situation, which most blasting workers recognize. Therefore, it is believed that the shock wave plays a vital role in the formation of initial radial cracks in the rock, and the formation of a large number of broken rock circles is the result of the blasting gas's action [16]. The failure zone and stress state of rock blasting is shown in Figure 1, where \(\sigma\) is the radial compressive stress and \(\sigma_{\theta}\) is the tangential tensile stress.
## 2 Methodology and Data
### Model Design
\[\mathbf{ln}Y_{it}=cX_{it}+\alpha W\times X_{it}+\beta\mathbf{ln}Y_{it}+e_{1} \tag{10}\]  
\[\mathbf{ln}M_{it}=\alpha X_{it}+\eta W\times X_{it}+\delta\mathbf{ln}M_{it}+e_ {2} \tag{11}\]  
\[\mathbf{ln}Y_{it}=cX_{it}+bM_{it}+\gamma W\times X_{it}+\zeta W\times\mathbf{ ln}M_{it}+e_{3} \tag{12}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Inspection Type** & **Statistical Model** & **Statistical Value** \\ \hline \multirow{3}{*}{LR test} & Spatial lag & 8.716 \\  & Spatial error & [0] *** \\  & & 40.613 \\  & & [0] *** \\ \hline \multirow{3}{*}{Wald test} & Spatial lag & 9.409 \\  & & [0] *** \\ \cline{1-1}  & Spatial error & 87.116 \\ \hline \multirow{3}{*}{Hausman test} & 39.529 \\  & & [0] *** \\ \end{tabular} Note: Inside of the [ ] is the p-value; *, **, and *** indicate statistically significant differences at the 10%, 5%, and 1% levels, respectively; The degree of freedom of statistics is 3.  
\end{table} 
Table 4: The results of LR, Wald, and Hausman tests in the spatial panel data model. \(Y_{it}\) represents the carbon emissions; \(X_{it}\) represents the independent variable vector, that is, \(X_{it}=[\ln(P_{it}),\ln(U_{it}),\ln(G_{it}),\ln(T_{it})\ldots]\), \(e_{1}\), \(e_{2}\) and \(e_{3}\) represent the regression residual terms. \(c\) represents the total effect, \(a\), \(b\) represents the mediating effect, and \(c^{\prime}\) represents the direct effect. In this paper, the stepwise regression method is used to test the mediating effect. The test steps are as follows: (1) we determined whether the test coefficient \(c\) is significant, if it is then we proceed to the next step; (2) we check whether the coefficients \(a\), \(b\) is significant. If they are significant, we test the coefficient \(c^{\prime}\). If at least one coefficient is not significant, the _sobel_ test is performed. If it is significant, this indicates that the mediation effect is outstanding. If it is not significant, then this indicates that the mediation effect is not remarkable. (3) If the coefficient \(c^{\prime}\) is significant, it indicates that there is a portion mediating effect. If it is not significant, this indicates that there is a complete mediating effect.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 6 Conclusions  
In this paper, we developed a fully iterative analog-digital multi-user equalizer approach for wideband mmWave mMIMO SC-FDMA systems. In the proposed approach the digital and analog parts are computed to efficiently remove the residual multi-user and inter-symbol interferences. The equalizer was designed by using the sum of the MSE as the minimization metric. In the design it was assumed that the analog part is constant over the subcarriers because of hardware constraints.  
The results showed that the proposed multi-user equalizer is very efficient to remove the multi-user/inter-symbol interference, achieving a performance close to the digital counterpart for the both antenna array configurations, ULA and UPA, and for scenarios with and without path loss and shadowing effects. Furthermore, the proposed iterative approach outperforms the two-step one previously proposed, at the cost of some more complexity. Therefore, the proposed iterative hybrid multi-user equalizer could be an interesting approach for practical scenarios that requires high reliability links.  
**Author Contributions:** Investigation, R.M.; Supervision, D.C., A.S., and R.D.; Validation, A.G. and P.P.; Writing--original draft, R.M.; Writing--review & editing, D.C. and A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Project MASSIVE5G (PTDC/EEI-TEL/30588/2017), the project UID/EEA/50008/2019; by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020), Regional Operational Program of Lisbon, Fundacao para a ciencia e Tecnologia; PES3N: Solucoes Energeticamente Efficientes para Redes de Sensores Seguras - POCI-01-0145-FEDER-030629, and FCT grant for the first author (SFRH/BD/129395/2017).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms
#### 3.2.2 Clustering  
The use of clustering methods is reported in Figure 10. These algorithms are often used as part of more general frameworks to solve plant disease and phenology problems. For this reason, their usage is not always specifically reported.  
As an example, [PERSON] et al. in [62] use both GA and K-Means (KM) to extract vegetation indexes from a dataset of multispectral images of wheat canopy. The authors use Relief to find the initial centroid for KM to cluster the vegetation indexes: from the highest to the lowest weight, only vegetation indexes with positive precision are retained (feature selection). Instead, [PERSON] et al. in [63] combine the Otsu algorithm, a famous threshold method, used to remove the background from leaf images, with KM that performs clustering on the remaining pixels, with the goal to detect disease evidence by checking the colors of the clusters. [PERSON] et al. in [64] use KM to isolate a portion of images featuring disease symptoms from healthy ones; therefore, they improve the resulting accuracy by performing a segmentation of the area of interest and allowing subsequent algorithms to focus only on that part. KM is also used as a one-class predictor in [65] to forecast the risk of potato late blight. Other algorithms such as Spectral Clustering (SC) are successfully used to select diseased plants on hyper-spectral images: [PERSON] et al. in [66] report successful results in detecting unusual shapes, while in [67], [PERSON] et al. use Fuzzy Clustering (FC) to discriminate a target crop from others present in the area. Recently, in addition to image processing, as is shown in [68], Bayesian Clustering (BC) was used to detect genetic structures in data sampled from forest trees in the center of Europe, as part of a genetic association study to investigate the response of trees in the presence of environmental stresses.
## 3 Results and Discussion
### Texture Profile Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Textural**} & \multicolumn{6}{c}{**Ripening Time (Days)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**60**} & \multicolumn{2}{c}{**90**} & \multicolumn{2}{c}{**180**} \\ \cline{2-7}  & **TC** & **SST** & **TC** & **SST** & **TC** & **SST** & **TC** & **SST** \\ \hline Hardness & 8.28 \(\pm\)2.05\({}^{\text{a}}\) & 7.58 \(\pm\) 0.91\({}^{\text{a}}\) & 6.04 \(\pm\) 1.90\({}^{\text{a}}\) & 7.14 \(\pm\) 2.47\({}^{\text{a}}\) & 7.48 \(\pm\) 2.58\({}^{\text{a}}\) & 8.41 \(\pm\) 1.40\({}^{\text{a}}\) & 9.66 \(\pm\) 1.10\({}^{\text{a}}\) & 8.45 \(\pm\) 2.29\({}^{\text{a}}\) \\ \hline Adhesiveness & \(-31.38\pm\) & \(-23.72\pm\) & \(-22.50\pm\) & \(-18.84\pm\) & \(-24.95\pm\) & \(-28.32\pm\) & \(-33.39\pm\) & \(-40.66\pm\) \\  & 15.29\({}^{\text{a}}\) & 6.41\({}^{\text{a}}\) & 9.59\({}^{\text{a}}\) & 12.90\({}^{\text{a}}\) & 9.72\({}^{\text{a}}\) & 8.12\({}^{\text{a}}\) & 7.17\({}^{\text{a}}\) & 13.86\({}^{\text{a}}\) \\ \hline Elasticity & 1.02 \(\pm\) 0.02\({}^{\text{a}}\) & 1.22 \(\pm\) 0.36\({}^{\text{a}}\) & 1.09 \(\pm\) 0.09\({}^{\text{a}}\) & 1.21 \(\pm\) 0.20\({}^{\text{a}}\) & 1.24 \(\pm\) 0.34\({}^{\text{a}}\) & 1.18 \(\pm\) 0.30\({}^{\text{a}}\) & 1.07 \(\pm\) 0.06\({}^{\text{a}}\) & 2.11 \(\pm\) 1.14\({}^{\text{a}}\) \\ \hline Cohesiveness & 0.37 \(\pm\) 0.03\({}^{\text{a}}\) & 0.37 \(\pm\) 0.07\({}^{\text{a}}\)The hardness of both groups of cheeses increased with time and specifically in the period of 60-180 days. This increase was enhanced by the progressive increase in the salt and ash content of the respective cheeses during the 60 th-180 th days [16], and the decrease in the fat content during the 90 th-180 th days [32]. Both cheese groups showed the lowest (6.04 \(\pm\) 1.90 N for TC and 7.14 \(\pm\) 2.47 N for SST) and the highest (9.66 \(\pm\) 1.10 N for TC and 8.45 \(\pm\) 2.29 N for SST) values on the 60 th and 180 th days, respectively. Due to the increase in hardness, the gumminess and chewiness of both cheees also increased during their ripening_preservation. The chees (TC and SST) showed the highest value in gumminess and chewiness on day 180 (3.44 \(\pm\) 0.37 N and 3.67 \(\pm\) 0.26 J for TC, and 2.99 \(\pm\) 0.86 N and 5.89 \(\pm\) 2.48 J for SST, respectively). The value of the adhesiveness (in both groups of cheeses) increased (in absolute value). Finally, the cohesiveness of the cheeses increased in both groups, showing their maximum value on the 180 th day (0.36 \(\pm\) 0.02 for TC and 0.35 \(\pm\) 0.01 for SST). As an exception, at day 90, the cohesiveness of the TC cheese decreased. Overall, the values of the textural characteristics of both TC- and SST-ripened cheeses reported here are similar to those reported by [PERSON] et al. [33], with the exception of the adhesiveness and gumminess parameters.
# Nucleofection as an Efficient Method for Alpha TC1-6 Cell Line Transfection
## 1 Introduction
Amaxa(tm) 4D-Nucleofector(tm) (Lonza, Germany) is a modular and scalable unit for electroporation-based nucleofection and is used for delivering the widest range of substrates such as DNA, RNA, proteins, and small molecules directly to nuclei of targeted cells. Amaxa(tm) 4D-Nucleofector(tm) is indicated as an easy-to-use technology and offers several benefits over traditional electroporation methods, providing high transfection efficiency for difficult-to-transfer cells, low cytotoxicity, and preservation of functionality. A specific combination of optimized pulse and solution (transfection buffers) contributes to minimal cytotoxicity of the Nucleofector(r) Technology, thus preserving the functionality of the cells. Because 4D Nucleofector(r) Technology offers virus- and reagent-free transfection, there is no reagent toxicity or immunogenic effects [15; 16]. This technology allows maintenance of phenotypic markers and differentiation potential, which is very important for research in hematopoietic and stem cells [17; 18]. The main disadvantages of nucleofection are the requirement for expensive consumables and equipment, and cell mortality under suboptimal conditions. The components of buffers are not identified, and the parameters of programs cannot be controlled by the user. Regardless of the transfection method, the crucial conditions for efficient reception of substrate are healthy and actively dividing cells [19]. Also, important parameters that could greatly influence transfection efficiency are the number of cell passages, degree of cell confluency, and contamination, as well as the quality, quantity, and size of DNA to be transfected. The efficiency of gene delivery depends on plasmid DNA size. Increasing plasmid size dramatically reduces the efficiency [20], probably due to a decreased delivery of larger DNA across the plasma/nuclear membrane [21]. The aim of this study was to determine the optimal transfection method for use in mouse pancreatic alpha TC1-6 cells. Cells were transfected with two different types of transfection methods: chemical transfection mediated with PEI, and nucleoporation with 4D-Nucleofector(tm) Technology. Cells were transfected with a plasmid-containing reporter gene for green fluorescent protein (GFP), providing information about transfection efficiency alone or in combination with more plasmid-caring information about EpiCRISPR. This study specifies that nucleoporation with 4D-Nucleofector(tm) is the preferred transfection method for in vitro transfection of mouse pancreatic alpha TC1-6 cell line. It may be useful in gene expression studies in the field of cellular reprogramming technology for pancreatic beta cell regeneration.