esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Adaptive Sliding-Mode Control Approach for Precise Tracking of Robot Manipulators
## 3 Simulation
### Simulation Result
By contrast, the existing ASMC approach improves the tracking performance against unmatched and matched disturbances. Moreover, it is not sufficiently affected by the command joint angle trajectory with and without payload on the end-effector of the robot manipulator. However, the existing ASMC approach only depends on the tracking performance of the TDC approach in the transient sections because there is no interaction between the switching gain and the control input, as in Figure 5. Furthermore, in the two control approaches described above, i.e., the TDC approach and existing ASMC approach, it can be seen that some chattering is slightly caused by the unmatched disturbances. Besides, given that the existing ASMC approach employs the switching gain with the excessive adaptation rate, its control parameters are not easy to set to be adjusted appropriately and therefore may also cause some chattering. By contrast, it is remarkably observed that the proposed SS-ASMC approach works well without undesirable side effects shown in two control approaches. Especially, as the decreasing switching gains of the proposed SS-ASMC approach does not become zero instantaneously, they help to improve the nominal tracking performance in transient sections  
Figure 6: Sliding variables generated by the proposed SS-ASMC approach: (**a**) Joint 1. (**b**) Joint 2. Figure 4:0 Comparison of the tracking errors of time-delayed control (TDC) approach (dotted line), adaptive sliding-mode control (ASMC) approach (dotted dashed line), and the proposed SS-ASMC approach (solid line): (**a**) Joint 1. (**b**) Joint 2.  
where the disturbances does not occur directly. Owing to fast and stable adaptation rate, it can quickly cope with undesirable side effects caused by unmatched and matched disturbances and hence can enhance the robust tracking performance. Moreover, it can be observed that the proposed one derives the natural tracking performance irrespective of payload on the end-effector of the robot manipulator. From the above results, it can be said that the proposed SS-ASMC approach offers more precise nominal and robust tracking performance than other two control approaches. The root-mean-square (RMS) tracking errors are given in Figure 4:1
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
###### Abstract  
To better address the reconfiguration problem of distribution networks with distributed generation (DG), a dynamic reconfiguration model is developed that accounts for the time-varying characteristics of both load demand and DG output. First, an enhanced fuzzy C-means clustering method is proposed for load period partitioning, which integrates spatiotemporal load features and optimal network structure similarity to improve clustering accuracy. Second, an adaptive ordered loop-based feasibility judgment model is developed to filter infeasible and low-quality solutions based on operational constraints. Third, an improved Equilibrium Optimizer (IEO), integrating Tent chaotic initialization, elite sorting, and mutation-crossover strategies, is proposed for multi-objective optimization. The proposed framework is validated on IEEE 33- and 69-bus systems. In the IEEE 33-bus system, it achieves a 44.8% reduction in power losses and a 35.9% improvement in voltage deviation. In the IEEE 69-bus system, power loss is reduced by 40.1%, and voltage deviation by 40.5%, demonstrating the proposed method's robustness, adaptability, and effectiveness across systems of varying scales.  
distribution network reconfiguration; load period partitioning; distributed generation; adaptive feasibility model; modified equilibrium optimizer; multi-objective optimization +
Footnote †: journal: Applied sciences  
15 April 2025  
Revised: 30 May 2025  
Accepted: 5 June 2025  
Published: 7 June 2025  
**Citation:** [PERSON]; [PERSON] Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer. _Appl. Sci._**2025**, 15, 6423. [[https://doi.org/10.3390/appl5126423](https://doi.org/10.3390/appl5126423)]([https://doi.org/10.3390/appl5126423](https://doi.org/10.3390/appl5126423))  
**Copyright:** 62025 by the authors. Liconses MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# Battery Management System Hardware Concepts: An Overview
## 6 Safety and Reliability
### Insulation Measurement
#### 6.1.1 Measurement Method  
In _ECE-TRANS2010_[81], _ISO6469-1_[82] and _ECE-R100_[40], the measurement method is standardized like depicted in Figure 8b. A well-known additional resistor and a switch are used to create different states in voltage between chassis and HV system. In addition, _ISO6469-1_[82] and _ECE-TRANS2010_ claim a minimum inner resistance of \(10\,\mathrm{M}\Omega\) of the voltage measurement  
Figure 8: Insulation measurement: (**a**) insulation in IT connection; (**b**) insulation measurement schematic.  
unit. Nevertheless, _ECE-R100_ claims a minimum inner resistance of \(1\,\mathrm{M}\Omega\). This contrasts with _DIN61557-8_[83], where a minimum resistance of \(30\,\frac{\Omega}{\mathrm{V}}\) and a minimum total resistance of \(15\,\mathrm{k}\Omega\) is claimed. Moreover, _ISO6469-1_ specifies the additional resistor to at least 100 to 500 \(\frac{\Omega}{\mathrm{V}}\) with a minimum accuracy of 2%, where _ECE-R100_ specifies a minimum value of 500 \(\frac{\Omega}{\mathrm{V}}\). The measuring time to capture the insulation resistance is allowed to be at most 100 s as defined in _DIN61557-8_.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 2 Optimization of Trusses
### Simultaneous Topology and Discrete Dimension Optimization  
The first attempts to simultaneously optimize both the truss topology and the discrete dimensions of members have been presented at the beginning of this century. The problem was either simplified/linearized and was solved using the Mixed-Integer Linear Programming (MILP) and the Mixed-Logic Linear Programming (MLLP) [44] or in the non-linear form using the relative quotient algorithm [45]. In both cases [44; 45], the topology was optimized by using the previously discussed method of allowing a small lower bound on the cross-section areas. In the next attempt, the truss problem was solved by employing a mixed variable formulation (MILP), where truss member buckling constraints became linear [46].  
The simultaneous topology and discrete cross-section optimization of trusses was also solved by using the NLP with heuristics [47], the relative difference quotient algorithm [48], the genetic algorithm [49; 50], the ant colony approach [51; 52], the improved genetic algorithm with two-level approximation [53], the mixed-integer second-order cone programming approach [54], the integrated particle swarm optimizer [55], the modified meta-heuristics with random mutation [56], the advanced Jaya algorithm [57], and by the mixed integer linear optimization (MILO) [58].
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 2 Materials and Methods
### Hyperspectral Images
The loading(s) of the principal component(s) (PC) that showed to be the most sensitive to the effect of the salinity was retained such as the vector of projection (or model) that was applied to the totality of hyperspectral images: those used to compute the average spectra (\(n\) = 28 hyperspectral images) of the calibration set, and those that have not been implied at all in the definition of the model and that allow to carry out its external validation (\(n\) = 12 hyperspectral images). The hyperspectral images (SG D2 SNV preprocessed) were projected onto the chosen principal component(s) producing the corresponding artificial images of scores. As a result of these analyses and taken into account the pattern of the loadings obtained in the PCA, an index that computes an approximation to the second derivative at the red edge region was proposed. It was named Level Salinity Index (LSI). This index was also applied to all the hyperspectral images of the leaves (\(n\) = 40 hyperspectral images), thus obtaining the corresponding virtual images of the index. In order to evaluate the ability of the proposed index in comparison to other previously applied, a collection of indexes described in the literature were computed on a representative group of spectra of the calibration set. Only 7000 spectra per treatment (28,000 spectra in total) were considered in order to evaluate the ability of the indexes in a worst scenario, in systems with poorer spatial resolution and consequently lower number of pixels per image. Some of them are specifically related to salinity and others related to water, pigments and chlorophyll content under stress conditions. One-way Analysis of Variance (ANOVA) and Tukey's least significant difference procedures were carried out for each index comparing the salinity treatments. In addition, scores resulting from PCA of preprocessed spectra was included in such comparison. Similarly to the procedure followed in the calibration set, in the validation set of virtual images of scores of PCA and of the index proposed in this work, 3000 pixels per treatment were randomly selected and one-way ANOVA's and Tukey's least significant difference procedures were applied. In addition to ANOVA's, the virtual images (PCA scores and LSI) of the calibration and validation set were also used for comparison, evaluating visually the differences between salinity classes and the spatial distribution of the effects of the salinity on the leaves. In order to improve the visualization of these virtual images, the pixels of the nerves were not considered. For that a segmentation procedure was applied to the images. Firstly, artificial images were computed based on the combination of planes 730, 680 and 550 nm, where the main differences between nerve and green area were observed. Then the Otsu method was applied to these images to obtain the mask that separated the green areas and the nerves. Finally, these masks were applied onto the scores and index images.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 3 Results
### SVM-Based Classifier with Raw Signals  
The performance of the SVM-based classifier on raw Bluetooth signals was evaluated using the confusion matrix shown in Table 2. The SVM-based classifier, when applied to completely raw Bluetooth RF bursts, was able to reliably identify only a few device classes. In the Uzundurukan smartphone twins, Class 14 achieved a diagonal entry of 0.87 and Class 15 reached 0.97, demonstrating that those two units could be distinguished from all others with high confidence. A moderate result was also observed for class 18 (a smartphone of the Uzundurukan set), which scored 0.33 on the diagonal.  
In contrast, Rusins portable devices (Classes 1-7 and 22) fared much worse without any preprocessing. Class 5 achieved the highest diagonal entry among them at 0.40, while Class 3 reached 0.38. Classes 1 and 2 both managed only 0.15, each with large off-diagonal confusions: Class 1 errors were divided between Classes 3 and 5, and Class 2 errors between Classes 3 and 5. Class 4 scored 0.18, Class 6 only 0.04, Class 7 reached 0.25, and Class 22 barely broke 0.12, with its predictions scattered across Classes 2, 3, and 5.  
Even within the Uzundurukan twins beyond classes 14 and 15, there was significant overlap. For example, class 10 (one iPhone 6 unit) scored a mere 0.03 on its own diagonal and was most often mistaken for classes 8 and 9 (the iPhone 5 twins), while Class 11 (the other iPhone 6) likewise hovered around 0.03 with its samples dispersed into neighboring smartphone-model classes (see Table 2).  
These widespread of diagonal assignments drag down the overall metrics: the diagonal accuracy is 30.59%, and the effective accuracy remains in the 33.33% range. In other words, raw RF signals--absent any spectral or temporal preprocessing--do not provide sufficiently unique fingerprints for reliable multi-class classification across a heterogeneous mix of Bluetooth devices.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments
### Network Comparative Experiments  
Under the condition of the same training environment and training parameters, we conducted comparative experiments on various semantic segmentation networks. Figure 12 shows the prediction results of the network. From top to bottom are the original image, U-Net, HRNet, DeepLabv3+, EMANet, PSPNet, our proposed method, and the final truth image.  
Figure 11: Network training loss curve.  
Figure 12: Experimental results and comparison with other methods.  
Overall, our network shows better segmentation results on these three types of objects, and the masks are smoother and fuller. To measure the segmentation effect of the network more accurately, we choose mean intersection over union (MIoU) as the evaluation metric and use the test set for quantitative analysis. When the value of MIoU is closer to 1, it indicates that the segmentation result is more accurate. The calculation formula of MIoU is as follows:  
\[\text{MIoU}=\frac{1}{k-1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum _{j=0}^{k}p_{ji}-p_{ii}} \tag{6}\]  
In the above formula, \(k\) represents the number of categories, including the background, \(i\) represents the true value of the pixel category, and \(j\) represents the pixel prediction result. \(p_{ii}\) represents the number of correctly classified pixels, and \(p_{jj}\) represents the total number of pixels for which \(i\) is predicted to be \(j\). \(p_{ji}\) represents the total number of pixels for which \(j\) is predicted to be \(i\). During training, we set up checkpoints to ensure that the highest MIoU weights are recorded. Meanwhile, our training MIoU can reach more than 94% after 200 epochs of training. We quantitatively evaluated the above networks on the test set, and the results are shown in Table 4 below.  
It can be seen in Table 4 that EMANet uses ResNet-101 as the backbone and uses the expectation-maximization attention module to effectively improve the segmentation results. The combination of a deeper feature extraction network and the attention mechanism makes EMANet have better results on the test set for Spacecraft B and Spacecraft C. However, for Spacecraft A, which is noisy and has less color information, the result is not so good. Compared to U-Net and HRNet, our MIoU is higher because we have a deeper network. Compared with our baseline method, DeepLabv3+, we have greatly improved the IoU of all classes.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 4 Assessing the Level of Digital Twinning (LoDT)
### Digital Twinning Dimensions
#### 4.2.2 Connection  
In accordance with existing classifications, the data connection between the physical and digital spaces can vary in terms of automation or, in other words, of manual intervention. At the lowest level, there is no connection (e.g., in the initial phase of the asset's life cycle). Level 2 is associated with manual bidirectional connection between the physical and virtual spaces, while the maximum level, 5+, is assigned to cases where bidirectional connection occurs exclusively in an automatic way. There can be mixed cases between these levels, where one direction of connection is handled both by manual and automatic means, resulting in a semi-automatic connection. A semi-automatic data connection occurs, for example, when data is collected using automatic digital technologies (e.g., point cloud collection using drones), followed by its manual processing and transmission to the digital space.
## 5 Experimental Results and Analysis
### Experimental Setup
Through a grid search on the NSL-KDD and UNSW-NB15 datasets, we found that the best \(\sigma\) values were 250 and 50, respectively. The proposed model uses the ReLU6 [52] activation function, 1000 iterations, 0.01 learning rate and Adam learning algorithm. We experimented on both datasets with the optimal \(K\) and \(\sigma\) values to evaluate the intrusion detection performance of the proposed model. These results are shown in Tables 5 and 6. Tables 7-9 show the confusion matrix of the MDPCA-DBN on the NSL-KDD and UNSW-NB15 data sets, respectively. Based on the above research results, to demonstrate the superiority of the proposed model, six currently well-known intrusion detection models were established using the NSL-KDD and UNSW-NB15 datasets. The comparison results are shown in Tables 10-12. In addition, the performance of the proposed model was further compared with other state-of-the-art models. Table 13 depicts the comparison results based on NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21), and UNSW-NB15 datasets.
## 5 Conclusions
\begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline
**Id** & **Code** & **Description** & **Number** & **Location** & **Mass** & **Length** & **Width** & **Height** & **Diameter** & **Power** & **Reference** \\  & & & & & **(kg)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(w)** & **Reference** \\ \hline  & & & & & & & & & & & & \\
2 & BATT & Auxiliary batteries & 3 & Cph & 1 & 110 & 68 & 38 & & [90] \\  & & & & & & & & & & & \\
3 & DC-AC & Power inverter & 1 & Sph & 3.35 & 325 & 165 & 85 & & 1 & [81] \\  & & DCDC12V & DC/DC converter 24 to 12 V & 2 & Sph & 0.024 & 40 & 23 & 8 & 1 & [82] \\  & & & & & & & & & & \\
4 & DCDC3V & DC/DC converter 24 to 5 V & 2 & Sph & 0.024 & 40 & 23 & 8 & 1 & [83] \\  & & & & & & & & & & \\
5 & DCDC5V & DC/DC converter 24 to 33 V & 1 & Sph & 0.024 & 40 & 23 & 8 & 1 & [84] \\  & & STKKEX & Stack cooling heat exchanger & 1 & Hull & 5 & & & & & \\
6 & STKCOOLPMP & Stack cooling pump & 1 & Cph & 0.34 & 127 & 89 & 100 & & 48 & [85] \\  & & STKCOOLBBV & Stack cooling loop. Coolant reservoir. & 1 & Cph & 0.3052  & 80 & 80 & 80 & 80 & \\  & & MEGHVHV & MeOH tank isolation valve & 1 & Cph & 0.01 & 43 & & 10 & 2.5 & [86] \\  & & MCS & MeOH concentration sensor & 1 & Cph & 0.1 & 33 & 68 & 16 & & 0.21 & [87] \\  & & MEGHVHV & MeOH reservoir and anode separator tank & 1 & Cph & 5 & 0.375 & & & & \\
1 & MEGHVMP & Methanol feeding pump & 1 & Cph & 0.64 & 42 & 42 & 42 & & 9 & [88] \\  & & ANOPMP & Anode circulation pump & 1 & Cph & 0.032 & 40 & 46 & 25 & 5.8 & [99] \\  & & CO2 CSRIW & CO2 CS circulation & 1 & Cph & 0.018 & 37 & 44 & 23 & & 0.8 & [90] \\  & & CO2 CSRIX & CO2 CS heat exchanger & 1 & Hull & 10 & & & & & \\
1 & CO2 CSOPENHEX & CO2 CS open heat exchanger & 1 & Cph & 0.014 & 46 & 25 & 26 & & 5.8 & [92] \\  & & CO2 CSCORIBSY & CO2 CS cooling loop. Coolant reservoir. & 1 & Cph & 0.3652  & 80 & 80 & 80 & & & \\
1 & PRV & O2 pressure reduction valve & 1 & Cph & 0.23 & 77 & & 46 & & [93] \\  & & LFER & O2 pressure regulator & 1 & Cph & 0.454 & 49 & 32 & 111 & & 11.5 & [94] \\  & & CATBIN & Cathode circulation buffer & 1 & Cph & 0.014 & 37 & 44 & 23 & 0.8 & [90] \\  & & CATBETVNK & Cathode separation tank & 1 & Cph & 5 & 0.375 & & & 180 & & \\  & H2 OPMP & Water pump & 1 & Cph & 0.032 & 40 & 46 & 25 & 5.8 & & [99] \\  & & CATBIVLV & 3-way valves & 4 & Cph & 0.272 & 43 & 43 & 110 & 11 & [95] \\  & & PIPNIC & Nitrile piping & 1 & Cph & 0.9 & 25,000 & & & & & [96] \\ - & FLUID & Liquid fluid in pipes & 1 & Cph & 0.10 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BoP components characteristics for Model 1000. Bph, bow pressure hull. Cph, central pressure hull.
## 1 Introduction
### Contributions  
This study aims to address these identified research gaps by developing a new, simplified, and accurate dew point estimation equation based solely on ambient temperature and relative humidity. In this regard, this work provides the following contributions:  
* Development of a specific equation: This work introduces a new equation obtained through non-linear regression fitting and parametric optimization, using only ambient temperature and relative humidity. This simplifies the input requirements while maintaining high accuracy, making the model more practical and accessible. This work provides a robust tool for optimizing water harvesting and other related processes, improving overall efficiency.
* Enhanced accuracy across conditions: The equation is validated through extensive data comparison, demonstrating consistent accuracy under real environmental conditions.  
This ensures reliable performance in critical applications, especially in hot and dry areas where energy efficiency is paramount.
* An equation which is particularly useful for environmental studies: This work utilizes GIS-based tools (e.g., PVGIS), which provide localized atmospheric data with a limited number of parameters. Having a model dependent on as few parameters as possible is crucial for its applicability and real-world implementation.  
By addressing these gaps, this study offers a practical and reliable solution for DPT estimation, benefiting both scientific research and practical applications. For this purpose, this paper is organized as follows: Section 2 presents a comprehensive literature review identifying research gaps, establishing precise research objectives, and clarifying the paper's contributions. Subsequently, a description of the methodological process followed are presented in Section 3. The obtained equations are presented, analyzed, and evaluated in Section 4. Finally, Section 5 presents the conclusions of the research.
## 4 Experiments
### Ablation Study  
In order to further study the influence of each component of AEGCN on performance improvement, we designed several ablation experiments. The experimental results are shown in Table 3 (using Accuracy as the evaluation indicator).  
First, we removed the attention mechanism (w/o att) in the graph convolutional network, and the experimental results dropped slightly, indicating that using the attention mechanism to assign weights to the syntactic-related items of each node can improve the performance of the model. We removed the MHIA behind ACL and AEGCN, respectively (w/o MHIA1 and w/o MHIA2). It can be seen from the experimental results that these two components play a positive role in the process of learning semantic information. Compared with MHIA1, MHIA2 has a greater impact on the model. We believe that the reason is that the graph convolutional network will also capture noise information in the process of capturing syntactic information, which leads to a decrease in the accuracy of the model when judging sentiment polarity.