esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 2 Materials and Methods
### Inter-Layer Knowledge Transfer  
To regularize the student network learning flow of the solution process (FSP) of the teacher network, an Inter-KT module is designed. This module calculates the pair-wisefeature relationship within the teacher network layer by layer to regulate features from the student network. A detailed description of the Inter-KT module is as follows:  
The Inter-KT module evaluates the feature interdependencies between pairs within the teacher network on a layer-by-layer basis, aiming to govern both the prolonged and immediate development of feature attributes in the student network. Below is an elaborative explanation of the Inter-KT module's functionality.  
Firstly, the FSP matrices need to be generated layer by layer. For any two general features \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\), their FSP matrix \(\mathrm{F}(\mathbf{X}_{1},\mathbf{X}_{2})\in\mathbb{R}^{m\times n}\) can be calculated through channel-wise inner product:  
\[\mathrm{F}(\mathbf{X}_{1},\mathbf{X}_{2})=\sum_{x=1}^{h}\sum_{y=1}^{w}\frac{ \mathbf{X}_{1}(x,y)\cdot\mathbf{X}_{2}(x,y)}{h\cdot w}, \tag{6}\]  
It is worth noting that the FSP matrix is computed on features with the same resolution. However, the features in \(\mathbf{T}\) have different resolutions. To address this issue, the resolution of all features in \(\mathbf{T}\) is adjusted to that of \(\mathbf{t}_{4}\) through max pooling so as to simultaneously reduce the computational cost. The adjusted features of \(\mathbf{t}_{i}\) are denoted as \(\mathrm{R}(\mathbf{t}_{i})\). Similarly, corresponding resolution adjustment is applied to features in feature set \(\mathbf{E}\), so as to align them with the resolution of \(\mathbf{t}_{i}\).  
In this study, the FSP matrices of adjacent features are calculated in order to grasp the evolution of features. In particular, an FSP matrix \(\mathrm{F}(\mathrm{R}(\mathbf{t}_{i}),\mathrm{R}(\mathbf{t}_{i+1}))\) is generated for \((\mathbf{t}_{i},\mathbf{t}_{i+1})\) in the teacher network. Similarly, the FSP matrix \(\mathrm{F}(\mathrm{R}(\mathbf{e}_{i}),\mathrm{R}(\mathbf{e}_{i+1}))\) is also calculated for \((\mathbf{e}_{i},\mathbf{e}_{i+1})\) of in the student network. Finally, the computation of the loss function for Inter-RT can be carried out using the following:  
\[\mathbf{L}_{\mathrm{inter}}=\sum_{i=1}^{7}\lVert\mathrm{F}(\mathrm{R}(\mathbf{ t}_{i}),\mathrm{R}(\mathbf{t}_{i+1}))-\mathrm{F}(\mathrm{R}(\mathbf{e}_{i}), \mathrm{R}(\mathbf{e}_{i+1}))\rVert^{2}, \tag{7}\]  
By reducing the gap among these FSP matrices, the knowledge in the teacher network SCSNet can be effectively transferred into the student network 1/n-SCSNet.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 4 Experiments  
### Experimental Settings  
The performance of the proposed DRL-DM approach is evaluated against a variety of heuristic and DRL-based baselines on both the TSP and the CVRP, using synthetic and real-world routing datasets. For the synthetic dataset, we adopt widely used protocols [22; 23] to generate instances containing \(20,50\), and \(100\) nodes, respectively, where node coordinates are uniformly sampled from the unit square \([0,1]\times[0,1]\). In the CVRP setting, customer demands are randomly selected from the integer set \(\{1,2,\cdots,9\}\), while vehicle capacities are set to \(30\), \(40\), and \(50\) for instances with \(20\), \(50\), and \(100\) nodes, respectively. For real-world benchmarks, we utilize instances from the TSPLIB and CVRPLIB repositories, which feature diverse node distributions and problem scales distinct from those of the synthetic dataset. To assess routing performance, we report the mean route length, optimality gap, and average running time. Specifically, the optimality gap is defined as the relative difference between the average route length obtained by the method and the corresponding optimal solution.  
The proposed method is implemented within an encoder-decoder architecture, employing a node embedding dimension of \(d_{g}=128\). The encoder comprises \(L=3\) stacked layers, each consisting of an MHA sublayer with \(Y=8\) attention heads, followed by an FFN sublayer with a hidden dimension of \(512\). The decoder includes an 8-head attention block followed by a single-head attention block. To enhance training stability, the hyperbolic tangent function is clipped using a constant \(\mathcal{C}=10\). The number of parallel rollout trajectories is fixed at \(\Psi=3\). For both TSP and CVRP tasks, a total of 128,000 instances are generated on the fly for training, with an additional 1000 independently generated instances used for evaluation. The model is trained for 100 epochs, with each epoch comprising 1000 iterations and a batch size of 512. Optimization is performed using the Adam optimizer, initialized with a learning rate of \(1\times 10^{-4}\) and a decay factor of 0.995 per epoch. For consistency and reproducibility, the random seed was fixed at 1234 across all learning-based models. An overview of the training procedure is depicted in Figure 3. All algorithms are implemented on a personal computer equipped with a GeForce RTX 4080-Super GPU (16 GB VRAM) and an Intel(r) Ultra U9-285K CPU.
## 6 Conclusions  
1. The analysis of morphologies revealed that under high scanning speed or low single pulse energy conditions, the surface of diamond coating experiences material melting and re-solidification progress. While with the decrease of scanning speed or rise of pulse energy (both result in a harsh condition), the ablation of diamond coating turns into a tri-stage mechanism of melting-graphitization-evaporation.
2. With respect to surface roughness and MRR evaluation, both surface roughness Ra and MRR show a strong dependence on the variation of scanning speed and pulse energy, yet they show the same tendency with scanning speeds compared to different ones when changing with single pulse energies. In addition, in the investigation of the co-effect of pulse energy and repetition rate, we found that the ablation results produced under different pulse energies or repetition rates can be figured out directly by examining the value of average laser power.
3. Concerning carbon phase transition, when withstanding harsh conditions (low scanning speed or high pulse energy), the diamond phase is weakened and disappears eventually when scanning speed decreases to 1.2 mm/s or pulse energy reaches 80 \(\upmu\)J. Another conclusion can be drawn from the shift pattern of diamond peaks that the compressive stress in the diamond coating can be released by fs laser irradiation.
4. In the investigation of the interaction mechanism, EUV exhibits acceptable reliability referring to surface roughness and carbon phase composition by embracing all three processing parameters. The significant rank in the MRR response is repetition rate \(>\) pulse energy \(>\) scanning speed, and the Ra response ratio follows repetition rate \(>\) scanning speed \(>\) pulse energy.
5. The calculated ablation threshold of CVD diamond coating at effective pulses of 90 is 0.138 J/cm\({}^{2}\), which is much smaller than that of SCD or PCD.  
Conceptualization, Y.M.; methodology, Y.M., C.W.; validation, Y.H., Y.Z. and L.Y.; formal analysis, C.W., Y.H.; investigation, C.W., Y.Z.; resources, Y.M., X.C.; data curation, L.Y.; writing--original draft preparation, C.W.; writing--review and editing, Y.M., C.W.; visualization, C.W.; supervision, Y.M., X.C.; moist administration, Y.M, X.C.; funding acquisition, Y.M., X.C.  
This research was funded by the National Natural Science Foundation of China, grant number 51375011, and Natural Science Foundation of Anhui Province, grant number 1208085 ME63, 1908085 ME129, and Natural Science Foundation of Anhui Education Department, grant number KJ2015 A050, KJ2015 A013.  
The authors declare no conflict of interest.
## 3 Results
### Distances between the COM and the MMA
The post hoc analysis revealed significant differences in the minimal mediolateral for all pairwise comparisons, with the exception of the comparison between young and older adults in the fast speed condition (\(p>0.05\)). The range of the distances along the anteroposterior and mediolateral axes for all phases and over the entire stepping are presented in Table 3.  
Along the anteroposterior axis, a significant group effect was found for the ranges of the distance COM-MMA in all phases, with the exception of P1 (\(p>0.05\)). Compared to young adults, older adults exhibited larger range of the anteroposterior distance for P2, P3, P4, P5 and throughout the entire stepping. In addition, the results highlighted a significant effect of the speed on the ranges of the distance along the anteroposterior axis. Ranges of the distances increased with speed for all the phases. Statistical analysis revealed no effect of group \(\times\) speed interaction for the ranges of the distance between COM and MMA along the anteroposterior axis (\(p>0.05\)). Along the mediolateral axis, no group effect was revealed for the range of the distance between COM and MMA. However, the ANOVA revealed a significant speed effect for the ranges of the distance along the mediolateral axis, with an increase in this distance with increasing speed for all phases. Furthermore, a significant effect of the Group \(\times\) Speed interaction was observed for the ranges of mediolateral distance during P2 and P3. For these two phases, post hoc analysis indicated significant differences in the mediolateral distance for all pairwise comparisons, except for the comparison between young and older adults at fast speed (\(p>0.05\)). Figure 1: Means of the dimensionless anteroposterior and mediolateral distances between the COM and the MMA for older and young participants in both speed conditions. The five phases (P1 to P5) are represented from left to right. Each phase is normalized from 0 to 100%. Minimal distances are observed at the end of the first swing phase (P2). Maximal distances are observed at the transition between the phases P3 and P4. The amplitudes on the ordinate axes are different in the anteroposterior [\(-0.04\); \(0.04\)] and mediolateral [\(-0.02\); \(0.01\)] directions.
# New Flexible Protective Coating for Printed Smart Textiles
## 2 Materials and Methods
### Choice of the Protective Coating
#### 2.2.2 Layered Silicates  
Two kinds of layered silicates have been considered to reduce the water vapor permeability of the coating: LAPONITE-SL25 (BYK-Chemie GmbH) and NTS-5 (Topy industries). These liquid dispersions were mixed with the polyurethane dispersion Takelac WPB-341 A (Mitsui Chemicals), which presents sufficient water vapor barrier properties for food packaging. The addition of LAPONITE-SL25 resulted in a significant increase in the viscosity of the mixture. For this reason, the amount of LAPONITE added remained at 14% whereas NTS was added at about 50%. A thin film of each mixture was applied on a PET sheet, and its water vapor permeability was measured.  
According to Figure 2, the addition of NTS layered silicates strongly reduced the water vapor permeability of the dried film beneath 1 g/m\({}^{2}\) d and confirmed that layered silicates can be used to improve barrier properties. On the other hand, the addition of these layered silicates caused the film to become much more brittle and to require a long drying time, which reduces its suitability with smart textiles applications or with an economically viable printing process. The addition of LAPONITE layered silicates did not have a significant impact on the water vapor permeability of the dried film, but nevertheless affected screen-printing rheological paste properties and managed to form a flexible film with a reasonable drying time. For this reason, LAPONITE-SL25 layered silicates were further used in this study.  
Figure 2: Influence of layered silicates on the water vapor permeability of protective films.
###### Abstract  
Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features by applying the Mean Decrease Accuracy method of Random Forest. Finally, we test the best indicators with a K-Nearest Neighbor classifier. For the data collection, a real bogie test bench has been used to simulate crack faults on the railway axles, and vibration signals from both the left and right sides of the axle were measured. The results not only show the performance of condition indicators in different domains, but also show that the fusion of condition indicators works well together to detect a crack fault in railway axles.  
Keywords:railway axles; crack detection; condition monitoring; time-domain features; frequency-domain features; random forest classifier; feature extraction; feature selection +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, an electromagnetic-thermal coupling numerical study of a synchronous generator with 2G HTS armatures is conducted. The main results in this paper are summarized as follows:  
First, by coupling the field part model with the circuit part model, the AC loss of HTS coils during rated operation is calculated. Second, the transient electromagnetic-thermal behavior of the HTS coil under the worst short circuit fault is studied. The results show that although the total current is the same for each turn of the HTS coil during the short circuit fault, the critical current value of each turn is different due to their diverse surrounding magnetic fields. This difference in the critical current value at the beginning leads to their subsequently different copper layer shunting and temperature rise phenomena. The turn with the lowest critical current will eventually quench completely, and the temperature rise is obvious, but it is not high enough to exceed the melting points of the materials of the HTS coil. It is concluded from this work that, if the generator short circuit protection device can remove the fault as soon as possible, the HTS coil in this high-speed HTS synchronous generator will not suffer unrecoverable quench and be damaged. Last but not least, the simulation results show that a thicker copper stabilizer improves the thermal stability of the HTS coil in the event of a short circuit fault, but for the HTS generator designer, this requires the use of a simulation model to make trade-offs between the engineering current density and the thermal stability of the HTS tapes.  
Conceptualization, X.H.; methodology, X.H.; investigation, W.L.; writing--original draft preparation, X.X.; writing--review and editing, Z.H.; supervision, Z.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Project No. 51707120), the Shanghai Rising-Star Program (20 QC1401400) and the Shanghai Marine Equipment Research Institute (Project No. 18H100000488). The authors declare no conflict of interest.
## 3 Materials and Method
### Evaluation Metrics  
To evaluate the performance of the proposed method comprehensively, multiple important metrics were employed, including accuracy, precision, recall, and Frames Per Second (FPS). These metrics assess the model's performance in different dimensions, focusing on both classification accuracy and the efficiency of model operation. Accuracy is defined as the ratio of correctly classified samples to the total number of samples:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{33}\]  
where \(TP\) represents true positive samples, \(TN\) denotes true negative samples, \(FP\) is the false positive sample count, and \(FN\) is the false negative sample count. Accuracy reflects the overall correctness of the model, but it may be limited in cases of severely imbalanced classes, requiring the use of other metrics for comprehensive evaluation. Precision measures the proportion of predicted positive samples that are actually positive. Its mathematical definition is  
\[\text{Precision}=\frac{TP}{TP+FP}. \tag{34}\]  
A high precision indicates that the model's predictions for positive classes are reliable and help reduce false positives. This is particularly important in medical imaging diagnostics, where the aim is to avoid misdiagnosing healthy samples as patients. Recall describes the proportion of actual positive samples that are correctly predicted by the model, defined as  
\[\text{Recall}=\frac{TP}{TP+FN}. \tag{35}\]  
Recall reflects the model's ability to identify positive samples. A high recall means the model can identify as many actual positive samples as possible, which is crucial for disease screening tasks. For example, in cancer detection, a high recall minimizes the possibility of missed diagnoses. FPS is an important metric for assessing the efficiency of model execution, representing the number of samples the model can process per unit of time. Given that the model processes \(n\) samples in time \(t\), FPS is defined as:  
\[\text{FPS}=\frac{n}{t}. \tag{36}\]  
A higher FPS indicates higher model efficiency, which is particularly crucial for real-time applications such as financial market predictions or dynamic medical image analysis.
## 3 FEM Analysis and Results
### The Model Results of Linear Location Technique for AE Source Positioning  
The average positioning error was calculated for the 1D linear location method with the varied location of the AE source. Figure 10 showed the results of the AE sources linear location coordinates obtained by FE-modeling. The histogram of the location pattern was performed by discretizing the coordinate axis with a step of 10 mm.  
It can be seen from Figure 10 that the largest scatter \(\Delta X\) was defined when the AE source was located at \(X\) = 100 and 700 mm. Therefore, when the acoustic emission source got closer to the AE sensor, the \(\Delta X\) achieved 200 mm. With the AE source moving away from the AE sensors, the difference between the calculated coordinates significantly decreased and did not exceed \(\Delta X\) = 30 mm. Thus, in the coordinates \(X\) = 400 and 500 mm, the value of the location error was less than 5%. Moreover, it even reached \(\gamma_{av}\) = 2% in the center of the studied area. The highest value of location error was observed when AE sources located at 100 mm from one of the AE sensor \(\gamma_{av}\) = 8.6%.
# A New Model of Quasigeoid for the Baltic Sea Area
###### Abstract  
The Space Research Centre in Warsaw is participating in the ESA project \"Geodetic SAR for Height System Unification and Sea Level Research\". To observe the absolute sea level and enable the unification of the height systems, the physical heights of the tide gauge stations referring to a common equipotential surface (quasigeoid/geoid) are needed. This paper describes the new quasigeoid model for the area of the Baltic sea. The quasigeoid calculation was carried out according to the Helmert method, in which the topography is condensed on a layer lying on the geoid. Airborne gravity anomalies from the Baltic area and terrestrial anomalies from Sweden, Finland, Denmark, Lithuania, Latvia, and Poland were used. The necessary terrain corrections have been computed from a digital terrain model based on the SRTM30 model. To compute the long-wavelength part of the quasigeoid, the geopotential models GOCE-DIR6, GOCO06s, and EIGEN-6C4 were used; therefore, the three solutions have been obtained. All calculations were done in a zero-tide system. The new quasigeoid model is obtained on a regular \(1.5^{\prime}\times 3.0^{\prime}\) grid in the GR80 reference system, covering the Baltic Sea and the surrounding area \(52^{\prime}<\phi<68^{\circ}\) and \(11^{\circ}<\lambda<30^{\circ}\). These gravimetric quasigeoids were compared to quasigeoid undulations derived at 29 GNSS/leveling points of the ASG-EUPOS permanent network, located in the study area. Our calculations show that the accuracy of the calculated quasigeoids is almost the same in all three cases and is about \(\pm 0.04\) meters. Finally, quasigeoid anomalies were interpolated at the Polish tide gauge stations. The new gravimetric quasigeoid solution could be very important for height system unification, for geophysical purposes as well as for engineering purposes.  
gravity; quasigeoid; Helmert method; FFT; GNSS/leveling; Baltic Sea; SAR Article  
A New Model of Quasigeoid for the Baltic Sea Area  
1  
1  
[PERSON], [PERSON], [PERSON] A New Model of Quasigeoid for the Baltic  
Polish quasigeoid for the Baltic Sea was computed in 2019 [10]. Several geoid/quasigeoid computations for the Baltic countries and Baltic Sea were performed in the last 20 years, e.g., [11; 12; 13] and others.  
These consecutive geoid models have been progressively improved over the years by implementing a better theory, conducting gravity surveys to fill large data void areas, compiling more precise Digital Terrain Models (DTM) [14], and embedding more accurate global gravity models derived from dedicated GOCE (gravity field and steady-state ocean circulation explorer) and GRACE (gravity recovery and climate experiment) satellite gravity missions [15].  
The paper aims to calculate an accurate quasigeoid for the Baltic area. The purpose of this work is to calculate the quasigeoid model in the Baltic sea area to explore the possibilities of SAR technology for Baltic height system unification and Baltic sea level research.