esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamentals of the DC Microgrid
### Electrical Power Flow  
Figure 1 illustrates a schematic representation of a DC microgrid model operating in islanded mode and consisting of multiple DERs. In the context of an isolated direct current microgrid comprising an \(M\) number of DERs linked together in parallel via separate DC-DC converter to a common DC bus, each DER can be depicted as Thevenin's equivalents, which are linked to the DC bus through line impedances. The mathematical representation of the power flow model for the \(i\)th DER is as follows:  
\[\left\{\begin{array}{l}\begin{array}{l}L_{i}\frac{dL_{i}}{dt_{i}}=d_{i} \cdot U_{x_{i}}-U_{i},\\ C_{i}\frac{dV_{i}}{dt}=I_{i_{i}}-I_{0_{i}},\\ U_{dc}=U_{i}-R_{line}I_{0_{i}},\end{array}\right. \tag{1}\]where \(d\) denotes the converter duty cycle, \(U\) represents the output voltage of the DER, \(L\) and \(C\) stands for the inductance and capacitance of the filter, respectively, \(U_{s}\) represents the DER voltage source, \(I_{L}\) represents the current flowing through the filter inductor, \(I_{o}\) depicts the DER current output, \(U_{dc}\) represents the DC bus voltage, and \(R_{line}\) signifies the line impedance between the DC bus and the \(i\)th distributed source. It is important to highlight that the impact of the line impedance is disregarded in this scenario, as its effect is negligible due to the present LC filter. The power flow Equation in (1) can be recomputed for a DC microgrid comprising a total of \(M\) DERs as follows:  
\[\left\{\begin{array}{l}\mathbf{L}\frac{d\mathbf{I_{L}}}{dt}=\mathbf{d} \cdot\mathbf{U_{s}}-\mathbf{U},\\ \mathbf{C}\frac{d\mathbf{U}}{dt}=\mathbf{I_{L}}-\mathbf{I_{o}},\\ U_{dc}=\mathbf{U}-\mathbf{R_{line}}\mathbf{I_{o}},\end{array}\right. \tag{2}\]  
where \(\mathbf{d}=[d_{1},d_{2},\ldots,d_{M}]^{T}\), \(\mathbf{I_{L}}=[I_{L_{1}},I_{L_{2}},\ldots,I_{L_{M}}]^{T}\), \(\mathbf{I_{o}}=[I_{o_{1}},I_{o_{2}},\ldots,I_{o_{M}}]^{T}\), \(\mathbf{U}=[U_{1},U_{2},\ldots,U_{M}]^{T}\), \(\mathbf{R_{line}}=diag\{R_{line_{i}}\}\), \(\mathbf{U_{s}}=diag\{U_{s_{i}}\}\), \(\mathbf{C}=diag\{C_{i}\}\), and \(\mathbf{L}=diag\{L_{i}\}\).
## 3 Algorithm Simulation Comparison  
### Focus of Algorithm Research  
This study first analyzes the performance of various multi-taper power spectral density (PSD) estimation algorithms, including LPSD and LISA-LPSD, focusing on their limitations in low-frequency resolution in the context of gravitational wave detection. The analysis is based on simulations of the amplitude spectral density (ASD) estimation. The preliminary research aims to observe and evaluate the practical performance of these algorithms, providing foundational data and initial conclusions to support subsequent in-depth studies and optimizations.  
As shown in Figure 1, the simulation involves incident laser light undergoing sinusoidal jitter in the yaw direction at frequencies of 100 mHz, 10 mHz, and 1 mHz, each with an amplitude of 50 \(\upmu\)rad, to observe the performance of the ASD estimation algorithms. The peaks at 1 mHz, 10 mHz, and 100 mHz indicate that the improved adaptive sine multi-taper algorithm provides better resolution than the LPSD, original adaptive sine multi-taper, LISA-LPSD, and Thomson algorithms. This high-resolution advantage reduces interference caused by signal energy spreading to other frequencies, thereby minimizing the spectral leakage effect, which is crucial for accurately capturing weak signals. The improved adaptive sine multi-taper algorithm shows significantly better resolution than its original version, while the Thomson algorithm has low-frequency resolution issues. Therefore, this study focuses on systematically evaluating the performance of the LPSD, LISA-LPSD, adaptive sine multi-taper algorithm, and its original version in the field of gravitational wave detection.  
Figure 1: Algorithm Performance Comparison Chart.
# XR Technology Deployment in Value Creation
## 4 Deployment Purposes of XR Technologies in Value Creation
### Assistance  
XR technologies can be deployed for the user in a process flow or the user's perception of a technology-enhanced experience [8]. XR technology deployment can furthermore be classified in perceptual presence, behavioral interactivity, and technology embodiment from the customer perspective to distinguish their capability to assist (i.e., support or enhance) the core customer experience [8].  
A significant field of XR technology deployment for assistance in value creation appears in the form of AR to support industrial processes. Especially in terms of assistance, the deployment of XR technologies as AR experiences for industrial applications has a focus on guiding the performance of manual activities, mostly independent from the industrial sector [56]. [PERSON] et al. reviewed the scientific literature on existing use cases in AR-based assembly systems that guide the user and highlight the relevance of context awareness and the interactivity of AR solutions for assembly guidance to add value by assisting the user [29]. [PERSON] et al. executed an analysis to provide a classification of VR and AR applications in construction safety that add value through assistance for safety inspections and building assessments by reducing users' effort to access required information through the inspection process [60]. [PERSON] et al. introduced a system framework that enables AR for adaptive support assistance, which consists of a specific AR technology setup with a haptic tracking methodology and demonstrates the viability of a maintenance system based on AR [21]. The value added is generated through the scenario by providing feedback to the user for adaptive support and assisting with the manual process tasks, improving execution speed and error avoidance. [PERSON] et al. introduced a methodology to optimize the text documentation of attachments to translate them into two-dimensional graphic symbols and map them with AR's help to support humans during maintenance work [13]. The methodology focuses on the technology aspect of content creation and enables the automated conversion of existing analog manuals into AR content. By providing the user with the manual content through AR, the user is assisted with the manual process execution. Furthermore, the framework offers a content-authoring concept to assist in the administration of the provided solution [13].  
The purpose of assistance through XR technologies in value creation can be aggregated to provide value-relevant data and information to the user. In this context, XR technologies are deployed so that the user can perform his manual process activities by providing data and performing his physical work process. Thus, the focus is on the empowerment of the execution, which distinguishes this category from visualization.
## 3 Results
### Shading Effect on the Fruit Quality  
Among the fruit quality parameters studied only the firmness, TSS and Croma of the fruits were significantly different between treatments (Table 2). Fruit weight registers show a decrease in the values when increasing shadow percentage area. However no statistically significant differences were found. The highest values of TSS were founded in 0% and 15% treatments, with values of 4.49 and 4.60 \({}^{\circ}\)Brix respectively. Among this, these treatments showed significant differences with 30% and 50% shadow treatments, where values registered were 4.43 and 4.40 \({}^{\circ}\)Brix. The pH of the fruit was very similar in the different treatments. However, the firmness of the fruit increased significantly with increasing shading treatments. Firmness of the fruit was significantly lower in the control treatment (0% shade) with a 4.3 N value, among 30% and 50% shadow treatments, which showed 4.7 and 4.8 N respectively.  
Fruit color showed significant differences between shadow treatments. The intensity of the fruit color, represented by the croma, increased when shadow percentage was reduced (Table 2). The control treatment showed the highest color intensity, with a 30.15 croma value, besides this asseveration, it could be seen with the highest values of a* and b* (Figure 15).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\% Shade** & **AFW (g)** & **Croma** & **Firmness (N)** & **TSS (*Brix)** & **pH** \\ \hline
0\% & 101.6 & 30.15\({}^{\mathrm{a}}\) & 4.3c & 4.49a & 4.40 \\
15\% & 97.0 & 27.92b & 4.5 bc & 4.60a & 4.38 \\
30\% & 95.6 & 27.43c & 4.7 ab & 4.43b & 4.40 \\
50\% & 94.9 & 27.40c & 4.8a & 4.40b & 4.45 \\ Significance & n.s. & *** & *** & *** & n.s. \\ \hline \hline \end{tabular}  
* AFW: Average Fruit Weight. TSS: Total Solids Solubles. Analysis of the variance is performed according to the Yij model = u + xi + \(\beta\)j + \(c\)ij. The nomenclature n.s., *** indicates not significant or significant for \(p\leq 0.001\). Numerical values for each column followed by different letters denote statistical significance for \(p<0.05\) according to the corrected minimum significant differences (LSD) test.  
\end{table}
Table 2: Shading effects on the fruit quality.  
Figure 14: Shading effect on the number of fruits (total, commercial and non-commercial) by m\({}^{-2}\), harvested in the last harvesting period (239 d.a.t). Results comes from the analysis of the variance that is performed according to the Yij model = u + \(\alpha\)i + \(\beta\)j + \(c\)ij. The nomenclature n.s., *, ** and *** indicates not significant or significant for \(p\leq 0.05\), 0.01 and 0.001, respectively. Numerical values for each column followed by different letters denote statistical significance for \(p<0.05\) according to the corrected minimum significant differences (LSD) test.
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 2 Materials and Methods
### SSD: Single Shot Detector
#### 2.3.1 MobileNetV1  
MobileNetV1 is a lightweight model designed for use in mobile devices that typically has limited computing resources and memory. The main idea for achieving this goal is the implementation of a depthwise separable convolution. Depthwise separable convolution factorizes a conventional convolution into depthwise and pointwise convolutions (i.e., a \(1\times 1\) convolution). MobileNetV1 uses \(3\times 3\) depthwise separable convolutions, which require eight to nine times less computation than standard convolutions, with only slightly lower accuracy [15].
# End-Effectors Developed for Citrus and Other Spherical Crops
## 4 Conclusions  
In order to improve the picking performance of the picking robot, according to the characteristics of citrus fruit easy to form \"clumps\", this paper designed high-speed citrus picking three-finger grasping end-effector that can be positioned near the center of the fruit; the device produces a human-like grasp through the precise control of the joint manual picking process motor, greatly improving the quality and efficiency of the picking part. The operating parameters of the end-effector were analyzed and optimized through finite element analysis and picking performance tests, and finally, the method was shown to be robust through tests, as summarized below.  
1. The end-effector is mainly composed of a clamping mechanism, cutting mechanism, and stepper motor. The clamping mechanism adopts the principle of three-finger clamping, which can carry out stable clamping according to the different sizes of the fruits. The cutting mechanism is fixedly installed on both sides of the clamping fingers, which can cut the stem of the fruit quickly and effectively.
2. Through the simulation analysis of the picking process of the citrus picking end-effector, it is concluded that the clamping fingers can complete the stable picking of citrus fruits within the safe clamping force for citrus fruits, and the cutting blades can complete the cutting of citrus fruit stems stably, and the overall picking performance of the end-effector is stable.
3. The single-factor test of the end-effector's picking performance shows that the picking performance is best when the stepper motor speed is 250 r/min, the end-effector speed is 160 mm/min, and the picking angle is 0\({}^{\circ}\). Multi-factor or orthogonal tests showed that the factors affecting the end-effector picking performance were end-effector speed, stepper motor speed, and picking angle, in that order.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Codebook Design  
For codebook matrices, the computational cost depends on the way they are designed. Besides being easy to multiply to a given vector, a codebook matrix should be designed such that pairs of columns are not collinear. A column that is collinear to another one is almost obsolete: It hardly helps to reduce the distortion while it needs to compute additions. In an early conference version of this work [42], we proposed to find the codebook matrix by sparse quantization of the target matrix. While this results in significant savings of computational cost over the state of the art, there are even better designs for codebook matrices. Three of them are detailed in the sequel.
## 4 Numerical Results
### Non-Parametric Statistical Tests  
To further assess and quantify the performance of the algorithms, we carry out nonparametric statistical tests [92, 93]. Firstly, we apply the Friedman test to the average values of the benchmark functions (Table 2), and the average values of the array design case (Table 3). The algorithm ranking results obtained by the Friedman test are listed in Table 4. We can easily conclude that WOA achieves the highest rank among all other algorithms under test. GWO scores the second-highest rank, whereas FA has the lowest ranking.  
Furthermore, the Wilcoxon signed-rank test with a significance level of \(p=0.05\) is conducted to further evaluate the differences between WOA and the rest of the algorithms under study. Table 5 reports the obtained test results. One may notice that WOA achieves the highest rank among all the algorithms, but GWO. Although the WOA cannot be considered as significantly better than the original GWO, (yet being significantly better than the SSA, the PSO, the ABC, and the FA algorithm), it outperforms the rest of the given algorithms according to the average ranking results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **GWO** & **SSA** & **WOA** & **PSO** & **ABC** & **FA** \\ \hline Average Ranking & 2.50 & 4.42 & **1.42** & 4.50 & 3.00 & 5.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical results obtained by Friedman test (highest ranking is indicated in bold).  
Figure 6: Position-phase synthesis of a 50-element linear array with \(d_{\text{max}}=\lambda\) (**a**) obtained algorithms distribution in percentiles (boxplot), (**b**) average convergence rate of the algorithms under study, and (**c**) radiation patterns of the best arrays found for each of the given algorithms.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Reward Function  
According to the analysis in Section 3.3.2, it is clear that throughput \(o\) is a positive indicator, while the other two, \(d\) and \(l\), are negative indicators. Assuming that the reward obtained by the network state moving from \(s_{t}\) to the next state \(s_{t+1}\) is \(r_{t}\), then the reward function is defined as shown in Equation (16).  
\[r_{t}=\alpha_{o}(o/o_{\max})-\alpha_{d}\Big{(}d_{\min}/\overline{d}\Big{)}- \alpha_{l}\Big{(}\overline{l}/l_{\max}\Big{)} \tag{16}\]  
where \(o\) is the current throughput, \(o_{\max}\) is the historical maximum throughput, and \(o/o_{\max}\) represents the increased throughput after the execution of action \(a_{t}\). \(\overline{d}\) is the average delay, \(d_{\min}\) is the historical minimum delay, and \(d_{\min}/\overline{d}\) represents the reduced delay after the execution of action \(a_{t}\). \(\overline{l}\) is the average packet loss rate, \(l_{\max}\) is the historical maximum packet loss rate, and \(\overline{l}/l_{\max}\) indicates the improved packet loss rate after executing action \(a_{t}\). \(\alpha_{i}\) is the weight, reflecting the proportion of the weight of throughput, delay and packet loss to the reward, reflecting whether the optimization objective of the congestion control algorithm prefers throughput, delay or packet loss, and has as \(\sum\limits_{i=o,d,l}\alpha_{i}=1\).
## 6 Testing Stage
### Notural Testing  
In situations of low-light conditions like twilight, the system experienced a decrease in reliability. Nevertheless, this issue could be mitigated by incorporating the IR camera, coupled with a model specifically trained on images captured during nighttime as shown in Figures 22 and 23.  
Figure 21: Testing in the afternoon, at sunset.  
Figure 20: Testing at sunrise.  
Figure 22: Testing in twilight.  
During testing in low-light conditions such as night, the model exhibited an overall 100% detection accuracy for both pedestrians and traffic signs, with 90.68% sensitivity in detecting them. Additionally, we tested the system in rainy weather conditions at night, and despite the challenging circumstances, it accurately detected pedestrians and road signs in a dark, unlit street with dimmed headlights and through a rainy windscreen (as illustrated in Figure 24).
## 6 Simulation Results
### HECTOR Results
The path followed by LTP goes trough the tree root which, in this particular case, increases the path length. In Figure 13, the same simulation is run with different source and destination pairs. The tree root is also modified. We can see from this figure that the path followed by VCost falls into a dead end after  
Figure 12: Illustration of the paths followed by each algorithm with the use of \(5\) landmarks. Source is in the right side. VCost and HECTOR follow the same path while LTP and HECTOR’ passes through the tree root. Figure 13: Illustration of the paths followed by each algorithm with the use of \(5\) landmarks. VCost fails after the second hop, LTP passes through the tree root and HECTOR combines both \(T\) and \(V\) coordinates. the second hop. One may think that when VCost fails, HECTOR follows the path of LTP. We can see in Figure 13(d) that HECTOR first follows the \(V\) coordinates and then avoids the dead end encountered by VCost by using both \(T\) and \(V\) coordinates. This example shows how the combination of both \(T\) and \(V\) coordinates can guarantee delivery and optimize the path length. Once again, LTP follows the complete path in the tree and provides a very long path. It is worth noting that the landmarks and the tree root positions have a great impact on the routing process. In VCost, landmarks position may affect the success rate. In LTP, the tree root position may increase the path length, and in HECTOR, the path may be different depending on these positions.
## 6 Exploration of Findings
### Case No. 2: AHC-Based Feature Selection
#### 6.3.1 Random Forest Classifier (RFC)  
Figures 10 and 11 depict the RFC's performance with two different feature subsets, derived from AHC and RF, respectively. These figures summarize the sensitivity analysis conducted to assess the impact of each feature subset. The investigation primarily aims to determine the capability of feature selection algorithms to identify features that effectively represent the entire population. This is achieved by incrementally adding one feature at a time and evaluating the impact of each addition on the overall performance and generalizability of the model.  
Considering the trade-off between accuracy and the number of features, both figures consistently demonstrate significant improvements in estimator performance (training score, testing score, and AUC score) as more features are added. Notably, the training and testing scores displayed in the figures are very similar to each other, which indicates effective fine-tuning of RFC hyperparameters. This closeness highlights minimal overfitting and strengthens model generalizability. The highest training score for both methods is 100%, and the corresponding testing score is also 99%. The AUC score also closely follows the testing score, indicating consistent performance. However, the superiority of the developed method for feature selection based on AHC is apparent in the early appearance  
Figure 9: Comparison of accuracy and fitting time across classifiers.  
of a vertical black dashed line in Figure 10. This line denotes the point at which the model achieves its highest performance score, which occurs with fewer features compared to the RF-based method. The superiority is further supported by the relatively early stabilization of both testing and training curves in Figure 10 compared with the RF-based method shown Figure 11. On the other hand, the fitting time, as indicated by the dashed purple lines in both figures, exhibits a non-linear insignificant increase as the number of features increases, which indicates a corresponding increase in model complexity. However, the average fitting time associated with RF- and AHC-based feature selection remains relatively the same, demonstrating a comparable computational burden.
## 2 Study Area and Datasets
### Satellite Data
#### 2.3.3. Land Cover from Sentinel-1 and Sentinel-2  
A land-cover map of Western Siberia has been derived based on a combination of Sentinel-1 and 2 data [28]. Multi-spectral satellite data have been regionally employed for characterizing typical tundra landscapes reflecting moisture regimes and vegetation physiognomy previously [41]. So far, Landsat with 30 m spatial resolution has been the main data source. Further relevant information can be also  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Date** & **Day of Year** & **Day Since Thaw** & **Degree-Days of Thawing** \\ \hline  
eptember 2018 & 245 & 88 & 501.11 \\
7 October 2018 & 281 & 124 & 704.72 \\
19 October 2018 & 293 & 155 & 717.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-1 acquisition dates and corresponding air temperature derivatives (station Mare Sale).  
derived from C-band SAR data, specifically data acquired under frozen conditions [42; 43; 44]. Sentinel-1 and Sentinel-2 have been therefore combined. A two-step approach as common for large-scale land-cover mapping has been applied [41]. First an unsupervised classification was made. In situ land-cover information was then applied to assign class names and define ROIs for the final maximum likelihood classification. This is based on a dedicated vegetation survey at VD (for details see [44]), the Yamal database [24], and a survey in the Northern Ural for land-cover types in the tundra taiga transition zone as a full transect was targeted. The classification accuracy ranges between 70 and 83.3% for the VD site with largest values for the dominant land-cover type 'dry to moist prostrate to erect dwarf shrub tundra' [28]. This class corresponds to the land cover on the CALM grid and the active-layer thickness measurement sites VD-1 and VD-2. VD-3 consists of partially dry cryptogamic crust/sparse vegetation and graminoid prostrate dwarf shrub tundra (including patterned ground). Further common types are wet to waterlogged graminoid dwarf shrub tundra followed by sparse vegetation in relation to dynamical features such as seasonally inundated areas and thaw slumps. Figure 1 shows the land-cover map and the location of in situ points.