esa-sceva/satcom-chunk-collection huggingface dataset:
# Slotting Optimization Model for a Warehouse with Divisible First-Level Accommodation Locations
## 4 Global Solution Model  
In this section, the global model of SLAP-VH is presented, which will be denominated \(P_{1}\), as can be seen in Figure 6. The problem begins by solving the first subproblem, which determines the number of locations through the demand for the SKUs, to continue with the second subproblem, and start the construction of the two different Slotting scenarios. Once the results obtained are known, we proceed to the third subproblem, defining which locations should be divided. In parallel, the fourth subproblem is solved to later introduce the results obtained in the global model of \(P_{1}\). The specifications of each subproblem are defined in the next section. It is necessary to clarify that the sequentiality in the development of the problem is determined from the key decisions considered in Section 5, since the simultaneous resolution of the analyzed subproblems does not allow obtaining feasible scenarios. Finally, the SLAP-VH solution is reflected in two slotting layout scenarios, which are evaluated to determine the optimal solution in terms of distance and costs. These scenarios show the three types of storage strategies that have been applied in the warehouse under study. Since locations without racks already have a defined strategy, the two scenarios defined in Section 7 are generated.  
Figure 6: Solution framework for the study problem.  
The mathematical model proposed for \(P_{1}\) aims to determine which is the best position for each SKU within the warehouse that minimizes the travel distance of cranes, considering the ABC classification and the stackability priority. The relevant parameters and decision variables are presented in Appendix A.  
The formulation of the model \(P_{1}\) is presented below:  
\[Min\ \sum_{I}\sum_{J}\sum_{K}\Bigl{(}Y_{ji}\cdot D_{jk}\cdot value\_sku_{i} \Bigr{)} \tag{1}\]  
Subject to  
\[\sum_{S}\sum_{I}(Y_{ji}\cdot sku\_subfamily_{si}\cdot position\_subfamily_{ js})=1,\ \forall\ j\ \in J \tag{2}\]  
\[\sum_{J}(Y_{ji}\cdot type_{jo})\geq sku\_type_{io},\ \ \forall\ i\ \in I,\ o\ \in O \tag{3}\]  
The objective of the proposed problem is to secure to each SKU at least one location, considering the minimum distance from the warehouse exit. Constraint set (2) establishes that each location/position must have a single SKU, and in set (3) it is determined that an SKU can have more than one location/position. Both restrictions consider the type of operation that position has, and the family to which it corresponds.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions  
The results across various scenarios were evaluated by considering the effects of data availability, noise type and severity, rotational speed variations, and unbalance levels. This provides a comparative assessment of both models, offering insights into their strengths and limitations under different conditions.  
The overall results are presented in Figure 6, with detailed explanations and discussions in the following subsections. The table includes classification accuracy, sensitivity, and specificity values for both the ML and TL models across all investigated scenarios. Accuracy levels are color-coded, with red indicating the lowest values and green the highest. Sensitivity and specificity are also highlighted for a more straightforward interpretation, with green denoting values above 0.95 and red ones below 0.6. Scenarios are grouped by noise type, with bold font indicating strong noise cases and regular font indicating weak noise cases. Scenario Zero, highlighted in gray, serves as the baseline (source-to-source transfer), while the remaining scenarios correspond to source-to-target transfer tasks.  
Key observations from the results indicate that the TL model consistently outperforms the ML model, achieving a higher classification accuracy across most of the investigated scenarios. The ML model performed particularly poorly in cases with CN and WGN, demonstrating its susceptibility to noise disruptions, especially in the case of strong noise severity. The TL model exhibited exceptionally high specificity, effectively identifying non-faulty cases.
## 3 ADRC Design and Parameterization  
The SMO-based motor control method, on the one hand, invokes discontinuous switching functions in the SMO bringing about high-frequency jitter. The presence of jitter means the rotor angle estimated by the observer has a large error, and this error also affects the motor speed at all times. On the other hand, traditional PI controllers are utilized in both the speed and current loops of this control method to regulate the speed. The PI controller only roughly selects the deviation between the input and output, which leads to a significant speed deviation during motor startup, and thus highly prone to overshoot.  
Therefore, in order to reduce the speed overshoot during motor starting and the high-frequency vibration caused by SMO, we further introduced ADRC to improve the motor speed ring. The three closed-loop control links are reduced by ADRC compared to SMO, and the entire control system's immunity is improved. In order to achieve high-precision speed tracking while dealing with a sudden shift in PMSM load, the ADRC's accurate estimation and compensating capabilities are also put to use.  
ADRC consists of three parts: TD, ESO, and NLSEF. Figure 3 displays the system's general control block diagram. Figure 3 is the overall schematic diagram of the ADRC control system.  
The PMSM model of Equation (2) is first expressed as a \"series-integral type\", which is obtained by transforming the two equations in Equation (2):  
\[\frac{d^{2}\omega_{m}}{dt^{2}}=-\frac{dT_{L}}{Jdt}-\frac{Bd\omega_{m}}{Jdt}- \frac{K_{t}R_{s}}{JL_{s}}i_{q}-\frac{n_{p}K_{t}\varphi_{f}}{JL_{s}}\omega_{m}+ \frac{K_{t}}{JL_{s}}u_{q} \tag{24}\]  
Equation (4) is converted to the following form:  
\[\frac{d^{2}\omega_{m}}{dt^{2}}=bu_{q}+a(t) \tag{25}\]  
In the above equation, \(b=\frac{K_{t}}{JL_{s}}\) denotes the control gain in the motor model; \(a(t)\) denotes the total disturbance. Its specific form is shown in Equation (26):  
\[a(t)=-\frac{dT_{L}}{Jdt}-\frac{Bd\omega_{m}}{Jdt}-\frac{K_{t}R_{s}}{JL_{s}}i_{ q}-\frac{n_{p}K_{t}\varphi_{f}}{JL_{s}}\omega_{m}+f(t) \tag{26}\]  
Figure 3: PMSM speed control system based on ADRC.  
where \(f(t)\) is the other unknown perturbation, which is not taken into account in the current algorithm.  
ADRC is the standard-type structure that realizes Equation (7) by estimating the total perturbation \(a(t)\) in real time using an expansion observer and compensating it.  
\[\frac{d^{2}\omega_{m}}{dt^{2}}=bu_{q} \tag{27}\]  
Therefore, the estimation performance of the expansion observer is a key indicator of the control performance of the whole system. In the following, each of the three parts of the ADRC will be designed to analyze and determine the parameters to be adjusted and the direction of adjustment.
## 2 The Proposed Approach
### Verification of Results with Simulation  
To identify the regional modal shape causing the fracture, a modal test simulation was performed on the alternator model, and the von Mises stress values at the fracture location were obtained. The modal test simulation revealed that the fifth mode at 496.17 Hz observed in the vibration test was a local mode in the heatsink B+ zone, which matched the local mode detected at 460.21 Hz, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Frequency [Hz]** & **Amplitude [g]** & **Type** \\ \hline
1 & 202.93 & 27.311 & Structural Mode \\
2 & 321.05 & 13.539 & Local Mode \\
3 & 383.28 & 25.356 & Structural Mode \\
4 & 447.80 & 11.989 & Local Mode \\
5 & 496.17 & 9.681 & Local Mode (B+) \\ \hline \hline \end{tabular}
\end{table}
Table 1: First 5 resonance frequencies of alternator.  
Figure 4: Heatsink fracture after durability test.  
Figure 5: Alternator heatsink B+ zone local resonance modal shape.  
The simulation study (Figure 6) confirmed that the fracture observed during the test was due to the high stress at the heatsink bolt hole caused by the 10 g excitation at the local natural frequency in the \(Z\)-axis direction.
## 7 Conclusions and Future Work  
Our approximation ratio of 6 for the minimum dominating set problem in triangle-free graphs represents a significant improvement compared to previous results in the distributed setting. It is important to note that while sequential algorithms can achieve better approximation factors (as low as 5), our 6-approximation algorithm is the current best for distributed algorithms in the LOCAL model. Our analysis of the structure of triangle-free graphs allowed for a more precise estimation of the number of external vertices, which was crucial for achieving a better approximation ratio. Our approach is worth comparing with the recent work by [PERSON] et al. [27], who reached an approximation ratio of \((8+\epsilon)\) for triangle-free planar graphs.  
Their algorithm works in three steps: first, they pick vertices that are \"hard to cover\" (meaning vertices whose neighborhood together with themselves cannot be dominated by a small number of vertices, e.g., two). Next, they build special sequences of vertices that cover many others, and, finally, they use linear programming to cover any remaining vertices. It is important to note that their method is a general algorithm that can be applied to any planar graph with an approximation ratio of \((11-\epsilon)\), not just triangle-free graphs. Our algorithm takes a different approach to selecting vertices. We use the special bunch structure of triangle-free planar graphs rather than their more general method. This approach helps us obtain a better approximation ratio (6 instead of \(8+\epsilon\)) while still running in constant time in the LOCAL model. Our algorithm is also easier to understand and implement becauseit directly selects vertices based on their degree, instead of using complex sequences and linear programming methods.  
It is worth noting that the bunch technique can also be applied to improve the approximation ratio for planar graphs in general. Using this technique, we achieved an approximation ratio of 19, which improved upon the previous result of 52. However, since this result was worse than the 11-approximation achieved in the work of [PERSON] et al. [27], it was not presented in detail in this paper. Perhaps with a more thorough analysis of the bunch structure and appropriate modifications to the algorithm, there is potential for further improvements. Further research in this direction may potentially lead to additional results.  
An interesting aspect to investigate would be the computational complexity of the presented algorithm in various distributed computing models, particularly in the CONGEST model, where only O(log n) bits can be exchanged in one round between vertices. It would be interesting to question whether using so-called short messages can achieve the identical approximation ratio.  
In summary, the presented technique constitutes a promising step toward more efficient algorithms for the minimum dominating set problem in graphs with special structure and could serve as a starting point for further research in this field.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 2 Materials and Methods
### Dynamic Recognition Model for Rock Fragment Size
#### 2.4.5 Model Training  
In this study, images were collected under different weather and lighting conditions from the actual working conditions at the Husab Uranium Mine in Namibia. Combined with mining blast pile images and an open-source rock dataset, and through data augmentation, a diversified training dataset was constructed. This dataset includes various scenes and complex backgrounds of mine rubble, ensuring that the algorithm can handle diverse rock fragmentation recognition needs in real-world environments. The algorithm used the YOLOv8x-seg.pt pre-trained model during training, which is a powerful instance segmentation model trained on a large-scale public dataset. The model was trained using rich  
Figure 12: Labeling results: (**a**) before annotation; (**b**) after annotation; (**c**) partial annotation collection.  
Figure 13: Data augmentation: (**a**) original image; (**b**) brightness adjustment; (**c**) center cropping; (**d**) vertical flip; (**e**) horizontal flip; (**f**) saturation adjustment.  
feature data, accumulating recognition capabilities for various objects. Through transfer learning, a model suitable for rock fragmentation size recognition was further trained on the basis of this pre-trained model. The parameters of the model are shown in Table 2. The model can quickly extract and recognize the boundaries and details of rubble, efficiently detecting rubble targets in images and generating independent masks for each fragment, thus enabling fine instance segmentation and providing technical support for intelligent recognition of blast fragmentation in subsequent steps.
## 3 Results
### Multi-Agent Systems and Distributed Control
#### 3.2.2 Originality  
Recent advancements in MAS have introduced innovative approaches to improve coordination among agents and optimize energy management [44,45]. One of the most notable contributions is the integration of DRL and FRL techniques to balance privacy, efficiency, and scalability in decentralized systems [38,46,47,48]. For instance, the use of federated learning within MAS allows for decentralized training without compromising data privacy. By utilizing abstract critic networks and deep deterministic policy gradient methods, FRL-based systems achieve robust coordination in MES, providing economic and environmental benefits [42]. Another innovative approach involves the application of MADRL, which incorporates advanced techniques such as prioritized experience replay and multi-actor attention critics. These enhancements enable prosumer agents to make informed decisions in dynamic and uncertain environments, addressing the limitations of model-based approaches. The method has been validated in large-scale real-world settings, demonstrating significant improvements in the trading efficiency and operational stability [5].  
Scalability has also been addressed through clustering strategies, as demonstrated by the multi-cluster DRL framework. This approach combines parameter sharing within clusters with diversity across clusters, allowing agents to adapt to heterogeneous prosumer characteristics while accelerating the training process. Such innovations are critical for managing the increasing complexity of DER coordination in large-scale systems [6]. The integration of V2X systems into MAS frameworks is another area of originality [49]. By developing safe operational envelopes for EV batteries and incorporating model predictive control architectures, these systems optimize the interaction between EVs and microgrids. This allows for efficient energy trading and enhances grid stability without violating operational constraints [21]. On the other hand, the introduction of human-centric trust mechanisms in P2P energy trading platforms represents a novel contribution. A fuzzy logic-based cross-layer protocol addresses the inherent vagueness of trust values while ensuring transparency and security in decentralized trading environments [50].
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Multi-Tasks and Joint Learning
#### 3.3.1 Task 1: LSTM-Based Data Prediction  
Task 1 uses an LSTM network to model the temporal dependencies of the time series data for prediction. The input is an \(M\times K\) matrix, as in Equation (9), and the output is a prediction of the next \(P\) steps, denoted \(\sigma_{\text{Task1}}\in\Re^{M}\). Task 1 is expressed as follows, where \(\theta_{1}\) are the network parameters:  
\[\sigma_{\text{Task1}}=P_{\theta 1}(X). \tag{10}\]  
The predicted data after the next \(P\) steps are \(Y_{t}\in\Re^{M}\), where  
\[Y_{t}=\begin{bmatrix}x_{t+p}^{(1)}\\ x_{t+P}^{(2)}\\ \ldots\\ x_{t+p}^{(M-1)}\\ x_{t+P}^{(M)}\end{bmatrix}\in\Re^{M}. \tag{11}\]  
The loss of the LSTMs is derived using the mean squared error (MSE) determined from the differences between the real and predicted values, i.e., \(\sigma_{\text{Task1}}\) and \(Y_{t}\), where  
\[Loss1=L(\theta_{1};X)=\sum_{i=1}^{N}{(\mathcal{O}_{\text{Task1}}-Y_{t}{}^{(i) })}^{2}. \tag{12}\]
## 3 Methodology  
In this section, we propose ST-RANet and study its mechanism. Figure 4 showcases the complete design of ST-RANet, which integrates three spatio-temporal modules and an external module. The three spatio-temporal feature modules, namely closeness, period, and trend, are showcased in the upper part of the figure. The three modules use the same network to extract spatio-temporal features, consisting of two convolutional networks before and after, two CBAM attention mechanisms, and \(k\) residual learning units. The external module, as shown in the lower part of the figure, inputs holiday information and three meteorological data (including weather types, temperature, and wind power) into two fully connected layers. Subsequently, the outputs of the first three modules are fused \(X_{Res}\) according to different weights, which are further fused with the output \(X_{Ext}\) of the external module. The resulting fusion product is transformed by the hyperbolic tangent function (tanh) to map the values onto the \([-1,1]\) scale, and ultimately obtain the model's predicted value \(Y_{t}\).  
Figure 1: Time segment sequence of closeness module.  
Figure 3: Time segment sequence of trend module.  
Figure 2: Time segment sequence of the period module.
# Neural Machine Translation of Electrical Engineering Based on Vector Fusion
## 4 Experiments and Results
### Results  
#### 4.3.1 Results of Vector Fusion  
In this paper, Transformer is used as the baseline model, and the model Res_mode of residual connection is first implemented. After that, the four fusion methods of averaging, splicing, weighting and gate mechanism were used to perform fusion experiments on the output vector of the 6-layer encoder unit of the translation model and the underlying word embedding vector, and they obtained the fusion translation models Av_fusion, Cat_fusion, Weight_fusion and Gate_fusion, respectively. The experimental results are shown in Table 1. The BLEU value of the translation model after the residual connection is 0.23 percentage points higher than that of the baseline model, and the BLEU value of the translation model after vector fusion is up to 0.8 percentage points higher than that of the baseline model.  
In order to further verify the effectiveness of the proposed method, this paper also sets up a comparative experiment to compare the N-1 system fusion scheme set in Reference [18] with the encoder internal vector fusion scheme proposed in this paper. With Transformer as the baseline model, the system fusion experiment and the encoder internal vector fusion experiment were carried out using four fusion methods, namely average (Av_fusion), splicing (Cat_fusion), weight (Weight_fusion) and gate mechanism (Gate_fusion). All parameter settings in the experiment were consistent, and the translation model was trained with about 190,000 parallel corpora in Chinese-English in the field of electrical engineering. The number of encoders N in the N-1 system fusion experiment was set to 2 according to Reference [18]. The results of the comparative experiment are shown in Table 2. The vector fusion experiment inside the encoder can achieve an increase of 0.8 BLEU values at most, while the N-1 system fusion scheme can only achieve an increase of 0.4 BLEU values at most.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Method** & **BLEU/\%** & \(\sigma\) \\ \hline Baseline & 36.02 & - \\ Res mode & 36.25 & 0.23 \(\uparrow\) \\ Av\_fusion & 36.17 & 0.15 \(\uparrow\) \\ Cat\_fusion & 36.43 & 0.41 \(\uparrow\) \\ Weight\_fusion & 36.82 & **0.80**\(\uparrow\) \\ Gate\_fusion & 36.41 & 0.39 \(\uparrow\) \\ \hline \hline \end{tabular} Where: \(\sigma\) = (vector fusion model BLEU value\(-\)baseline model BLEU value) \(\times\) 100.  
\end{table}
Table 1: BLEU value of vector fusion experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Fusion Method**} & \multicolumn{2}{c}{**BLEU/\%**} \\ \cline{2-3}  & **N-1 System Fusion** & **Vector Fusion** \\ \hline Baseline & - & 36.02 \\ Av\_fusion & 36.00 & 36.17 \\ Cat\_fusion & **36.42** & 36.43 \\ Weight\_fusion & 36.40 & **36.82** \\ Gate\_fusion & 36.31 & 36.41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BLEU value of comparative experiment.
## 3 Materials and Methods
### Feature Selection
#### 3.3.2 Recursive Feature Elimination  
The second feature-selection approach used was recursive feature elimination (RFE). This is a recursive process where features are ranked based on their importance [56]. RFE employs machine learning models in computing feature-relevant scores. RFE first trains the model using all features and then computes the relevance score of every feature in the dataset. All the features with lowest relevance score are ignored, and this is followed by model retraining for computation of new relevant feature scores. This process is repeated until the final desired features are obtained [57].
# A Study on the Seismic Performance of Steel H-Column and T-Beam-Bolted Joints
## 4 Parametric Analysis
### Parameter Analysis of Column Wall Thickness
The equivalent damping coefficient is \(1/2\pi\) of the ratio of the area of the hysteresis curve to the deformation value. Under the same displacement, with the increase in column wall thickness, the energy consumption of the node increases. However, when the column wall thickness reaches a certain level, the growth rate of the weekly energy consumption gradually slows down in the later stage of the node's behavior, and the increase in the equivalent viscous damping coefficient also gradually becomes smaller. Continuing to increase the column wall thickness has little impact on the improvement of energy-dissipation efficiency. Therefore, the column wall thickness should be reasonably controlled. When the column wall thickness is 12 mm (BASE), the equivalent viscous damping coefficient reaches 0.23 when the displacement is \(\pm\)104 mm. When the thickness is 18 mm (BZB-5), the value increases to 0.28 (Figure 21b). Figure 21: Energy dissipation curves of nodes with different T-plate thicknesses. (**a**) Energy-consuming capacity comparison, (**b**) equivalent viscous damping coefficient comparison. Figure 20: Node skeleton curves of different column wall thicknesses. When the column wall thickness increases from 10 mm (BZB-1) to 18 mm (BZB-5), the ductility coefficient increases significantly from 2.1 (\(\Delta_{\mathrm{u}}/\Delta_{\mathrm{y}}\) = 104/49.5) to 3.5 (\(\Delta_{\mathrm{u}}/\Delta_{\mathrm{y}}\) = 104/29.7) by 66.7% (Table 3, Figure 20). This increase indicates that as the thickness of the column wall increases, the joints shift from brittle failure (dominated by column wall buckling) to ductile failure (dominated by plastic-hinge formation at the beam end). The increase in ductility coefficient directly enhances the energy absorption capacity of the node. When the column wall thickness is 18 mm (BZB-5), the equivalent viscous damping coefficient reaches 0.28 at a displacement of \(\pm\)104 mm (Figure 21b), which is 47% higher than that of BZB-1 (0.19), indicating that the plastic hinge mechanism achieves more efficient energy dissipation through greater deformation capacity.