esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In some island regions, there may not be enough control point data between the two ICESat-2 tracks for inversion. Therefore, this study considers combining other satellite data for multi-source data fusion to improve the quality of water depth inversion results. [PERSON] et al. proposed a downscaling bathymetric mapping approach (DBMA) based on multi-temporal Landsat-8 images to obtain high-resolution water depth images in the absence of in situ water depth data [20]. Building on [PERSON]'s research, this method was further expanded into DBMA-RGB, which achieved the fusion of Landsat-8 images and RGB images exported from Google Earth Pro to produce ultra-high-resolution water depth images [10]. However, it was also noted that the significant resolution difference between Landsat-8 and RGB images could introduce errors. Therefore, this study considers combining the 2-m high spatial resolution images from GF-1 satellites with the 10-m images from Sentinel-2, which are closer in resolution, for water depth inversion, aiming to achieve more precise high-resolution water depth results. This study is aimed at the issue of high costs associated with high-resolution satellite remote sensing imagery data, which makes it difficult to obtain high-resolution bathymmetric data on a large scale. We propose a method based on scale-invariant effects to achieve high-resolution water depth inversion through multi-source data fusion. This method leverages the advantages of high-resolution images from GF-1 satellites and the abundant data from Sentinel-2 satellites, aiming to enhance the spatial resolution of bathymmetric mapping. Specifically, we first fuse the remote sensing data from ICESat-2 and Sentinel-2 to invert and obtain lower-resolution bathymmetric data. Then, based on scale-invariant effects, we apply downscaling techniques to ultimately achieve high-resolution bathymmetric data at a spatial resolution of 2 m. We apply this method to four islands in the Pacific Island region and validate its effectiveness through ICESat-2 data. This study aims to demonstrate the feasibility and effectiveness of using GF-1B/C/D series satellite data for high-resolution bathymmetric mapping through multi-source remote sensing data fusion, providing a reference for future related research. Additionally, we hope to further expand the application potential of GF-1 satellites in the field of bathymmetric mapping.
# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 1 Introduction
In a recent development, these classifiers have been extended to act as convolutional filters for the supervised generation of features maps for single object detection in remote sensing imagery [38]. Compared to standard CNNs, CSVM introduces a new convolution trick based on SVMs and does not rely on the backpropagation algorithm for training. Basically, this network is based on several alternating convolution and reduction layers followed by a classification layer. Each convolution layer uses a set of linear SVMs as filter banks, which are convolved with the feature maps produced by the precedent layer to generate a new set of feature maps. For the first convolution layer, the SVM filters are convolved with the original input images. Then the SVM weights of each convolution layer are computed in a supervised way by training on patches extracted from the previous layer. The feature maps produced by the convolution layers are then fed to a pooling layer. Finally, the high-level representations obtained by the network are fed again to a linear SVM classifier for carrying out the classification task. In this work, we extend them to the case of multi-label classification. In particular, we introduce a novel multi-branch CSVM architecture, where each branch will be used to detect one object in the image. We exploit the correlation between the objects present in the image by fusing the intermediate outputs provided by the convolution layers of each branch by means of an opportune fusion mechanism. In the experiments, we validate the method on images obtained from different indoor and outdoor spaces. The rest of this paper is organized as follows. In Section 2, we provide a description of the proposed multi-label CSVM (M-CSVM) architecture. The experimental results and discussions are presented in Sections 3 and 4, respectively. Finally, conclusions and future developments are reported in Section 5.
## 2 Transient Temperature Field Model of Wear Band on the Flank of End Mills
### Verification of Temperature Field Model  
The temperature experimental data in [PERSON] et al. [34] reflects the temperature variation law in the cutting area during the milling process, namely, it reflects the law of temperature change near the cutting edge of the end mills, and near the cutting edge of the end mills means that it includes the tool rake face that is near the cutting edge of end mills and the wear band on the flank of the end mills. Therefore, the accuracy of the established transient temperature field model of the wear band on the flank of end mills was verified by referring to the experimental data from [PERSON] et al. [34]. The processing site of the milling experiment is shown in Figure 13.  
A non-standard semi-artificial thermocouple consisting of titanium alloy and constantan strip was used to measure the changes in temperature during milling (Figure 14).  
This experiment used DAEWOOACE-V500 NC Machining Center for the dry milling of titanium alloy. The specific tool parameters are listed in Table 1.  
Figure 13: Experimental setup [34].  
1.1. Prediction Results for the Transient Temperature Field Model of Wear Band on the Flank of End Mills  
The temperature of the end mills varies periodically during the peripheral milling process, and the changes in milling temperature are positively correlated with the changes in undeformed chip thickness, as shown in Figure 15a,b. The temperature difference between different positions on the wear band is 23 \({}^{\circ}\)C, as presented in graphic (A) in Figure 15c. The temperature distribution along the middle position of the wear band on the flank is demonstrated in Figure 15c, when the instantaneous contact angle is 80\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parameters of End Mills**} \\ \hline
**Number of Tooth** & **Rake Angle** & **Diameter** & **Flank Angle** & **Helix Angle** \\ \hline
4 & 10\({}^{\circ}\) & 20 mm & 14\({}^{\circ}\) & 38\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tool parameters for experiment [34].  
Figure 14: A schematic of semi-artificial thermocouple temperature measurement device.  
Figure 15: Transient temperature field simulation of wear band on the flank of end mills.
# Neural-Network-Based Quark-Gluon Plasma Trigger for the CBM Experiment at FAIR
## 4 Conclusions  
The results of this study indicate that the neural network classifiers manage to identify patterns in the raw data simulated using the transport model with and without a quark-gluon plasma (QGP) formation model. Among the four architectures tested, the CNN achieves the highest accuracy of approximately 95%. The potential of using neural network classifiers to identify QGP formation in heavy-ion collisions was shown. Moving forward, the ANN4 FLES package will be integrated into the physics analysis module of the FLES package and will be used as a QGP trigger for event selection for the CBM experiment. Future work will continue to explore the performance of various neural network architectures within the ANN4 FLES package across different types of input data. Another objective will be understanding the patterns these neural network classifiers learn and whether they match with our physics models.  
Figure 9: Results for same models implemented in ANN4 FLES (red) and PyTorch (blue) for both training (dashed line) and testing (solid line) datasets. The accuracy for MLP models with 0 hidden layer ((**a**), Training and testing accuracy for MLP without hidden layers), 1 hidden layer ((**b**), Training and testing accuracy for MLP with 1 hidden layer) and 2 hidden layer ((**c**), Training and testing accuracy for MLP with 2 hidden layers) fits the training dataset well as the training accuracy reaches 100% as compared to the testing dataset where the accuracy saturates around 80%. For CNN ((**d**), Training and testing accuracy for CNN network) the generalization error is reduced compared to that of the MLP models and shows it is more capable of learning the right features for classification.  
**Author Contributions:** Conceptualization, A.B., I.K. and R.L.; Methodology, A.B. and I.K.; Software, A.B., R.L. and A.M.; Validation, A.B. and I.K.; Formal analysis, A.B. and A.M.; Investigation, A.B. and R.L.; Resources, I.K.; Data curation, I.K.; Writing--original draft, A.B., R.L. and A.M.; Writing--review & editing, A.M.; Supervision, I.K.; Project administration, I.K.; Funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by grants from Bundesministerium fur Bildung und Forschung grant number 01 IS21092 and Helmholtz Forschungszademie Hessen fur FAIR, Darmstadt, Germany (HFFF Project ID: 2.1.4.2.5).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Outcome Measurement  
EMG signals were recorded simultaneously from six shoulder muscles using a surface EMG. The surface EMG used for the recording was a portable 4-channel BTS-FREEEMG Muscle Tester (BTS Bioengineering Corp, Seoul, Republic of Korea) with wireless electrodes. Electrodes were positioned on the belly of the deltoids (anterior, middle, posterior), upper trapezius, infraspinatus, and supraspinatus of the dominant side. Arm dominance was determined by the arm used for writing. The electrodes were positioned according to previous studies [24; 25], and the detailed position of each muscle is presented in Table 1. The distance between the electrodes was a minimum of 2 cm. The same investigator was responsible for placing all electrodes in order to ensure the consistency of the measurement. Signals were cleared using a bandwidth frequency ranging from 10 to 1500 Hz. A computer equipped with BTS-FREEEMG (BTS Bioengineering Corp, Seoul, Republic of Korea) was used to calculate the root mean square, record it, and save the data automatically. Before the recording, skin preparation to reduce impedance was conducted by a licensed physiotherapist by initially cleaning the recording skin surface with 70% alcohol wipes to ensure that the skin was clean.  
A manual resistance test by the same investigator was conducted to measure the baseline EMG data for the maximal voluntary isometric contraction (MVIC) for each muscle before the exercise. EMG was measured for 10 s twice with a 10 s rest between repetitions and a 2 min rest between each muscle assessment. The highest recorded muscle activity was used for normalization. The procedure of measuring the MVIC for each muscle was adapted from a previous study [26] and is described in Table 2. EMG activity was recorded at 2 weeks and 4 weeks after the exercises were performed, in the same positions for both groups, except that manual resistance was not performed. For participants in the MR group, EMG was recorded while they used the resistance of the pseudo-weight, and those in the control group used the resistance of the dumbbell.  
\begin{table}
\begin{tabular}{c c} \hline Anterior deltoid & 4 cm under the lateral clavicle, above the muscle belly a  \\ \hline Middle deltoid & At the midway point of the acromion extremity and the deltoid tubercle a  \\ \hline Posterior deltoid & 2.5 cm diagonally below the dorsal margin of the acrom a  \\ \hline Upper trapezius & At the midway point of the 7 th cervical vertebrae and the acromion process, above the muscle belly a  \\ \hline Infraspinatus & 4 cm below the scapula spine, over the intracapsular fossa, laterally but below the posterior deltoid b  \\ \hline Supraspinatus & 2.5 cm above the middle of the scapular spine and to the base of the supraspinous fossa b  \\ \end{tabular}
\end{table}
Table 1: Positions of the electromyography electrodes.  
Figure 3: Training using a HoloLens device: (**A**) shoulder scaption, (**B**) shoulder press, (**C**) shoulder external rotation.
## 2 A Survey of Bio-Inspired Algorithms for Transportation Problems
### Algorithms for Integrated Problems  
Usually, transportation in disaster relief operations involves complex interactions between different actors and tasks and, thus, can be modeled as an integrated problem that consists of a set of interdependent subproblems. This implies that we need integrated solution approaches to effectively tackle transportation problems within the response time limits.  
One of the most common models in emergency transportation is one that integrates supplies distribution and vehicle routing. [PERSON] _et al._[58] constructed such a multi-objective optimization model and designed a PSO algorithm in which every particle is encoded as a discrete-continuous vector, and the discrete variables and continuous variables are respectively changed using their own velocity/location update formulas. The fitness function for evaluating particles is defined by combining multiple objectives. However, for large-sized instances, the algorithm is sometimes easily trapped by local optima.  
[PERSON] and [PERSON] [59] considered an emergency logistics planning model, which involves dispatching commodities to distribution centers and evacuating wounded people to medical centers. They proposed an improved ACO algorithm that decomposes the problem into two phases, _i.e._, the vehicle route construction and the multi-commodity dispatch. The sub-problems are solved in an iterative manner: the first phase builds stochastic paths under the guidance of pheromone trails, while a network flow-based solver is developed in the second phase for the assignment between different types of vehicle flows and commodities. Results show that the ACO solution quality achieved within a minute of runtime is acceptable for the planner in a real emergency situation in which there is continuous uncertainty and information dynamism.  
In order to aid the reconstruction planning for the post-quake road-network, [PERSON] and [PERSON] [60] established a fuzzy multi-objective model of reconstruction scheduling. The considered problem is formulated as a bi-level network design problem: The upper level is for the work scheduling of many work troops, and the lower level is for asymmetric traffic assignment. They developed a modifiedGA for obtaining a heuristic solution that approximately optimizes the objectives on the two levels. [PERSON] and [PERSON] [61] also integrated roadway repair and relief distribution into a network flow model, which is a multi-objective, mixed-integer and multiple-commodity problem. They transformed it to a single-objective problem using the weighting method and developed a heuristic algorithm that first solves a subproblem and, then, repeatedly incorporates the remaining tasks, until a complete solution is obtained.
# Experimental Investigations on Laser Ablation of Aluminum in Sub-Picosecond Regimes
## 5 Conclusions  
With both crater width and depth increasing with the laser fluence, the increase in the heat affected zone was small or non-existent in the sub-picosecond regime. The aluminum sample has a high heat transfer coefficient, which could be considered in favor of the minimized heat effects. Considering future applications, for other materials, e.g., plastics, further shortening of the laser pulse might be recommended. The proposed Equation (1) for the ablation threshold can be applied in both (femto and sub-picosecond) cases, with better predictability in sub-picosecond regimes. The influence of laser wavelength is less significant in femtosecond regimes than it is in sub-picosecond regimes. This is possibly due to the involvement of non-linear absorption mechanisms (e.g., multiphoton absorption) occurring with ultrashort laser pulses. However, in our study, we focused on macroscopic results of the laser-matter interactions, without considering the specific heat transfer mechanisms. A further investigation on that matter is needed, including a broader variety of materials.  
An upper limit for the highest usable frequency is given by the interaction of the generated plasma with succeeding laser pulses, since this will distort or shield the laser beam [16]. A successful application of the pulse repetition rate in the range of 100-400 kHz in our research enables the ultrashort lasers to meet industrial demand when it comes to processing speed. Considering all of the abovementioned observations, a sub-picosecond laser would allow better parameter control in micromachining processes and more predictable results, while maintaining the advantages of the femtosecond laser micromachining.  
The obtained results allow identification of some of the ablation mechanisms in the sub-picosecond regime and show their dependence on laser pulse parameters. Therefore, they make it possible to verify the existing models and theories and increase the applicability of ultrafast lasers in commercial micro and nanomachining. A sub-picosecond laser proved to be a sufficient alternative to the micromachining of aluminum and possibly of other metals as well.  
**Author Contributions:** Conceptualization, K.G. and M.K.; methodology, K.G.; investigation, K.G.; writing--original draft preparation, K.G.; writing--review and editing, K.G. and M.K.; visualization, K.G.; supervision, M.K.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research is financed by the Institute of Fluid Flow Machinery, Polish Academy of Sciences O3/Z3/T1.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimal Agent-Based Pickup and Delivery with Time Windows and Electric Vehicles
## 3 PDPTW-EV Formulation
Equation (6) ensures that each vehicle's service start at each location is correctly determined by considering the service duration and travel time, while Equation (7) ensures that the start of service at pickup locations is not after the start of service at the corresponding delivery locations. By Equation (8), we ensure that the load on each vehicle at each location is correctly updated based on pickup and delivery actions, and by Equation (9), we ensure that the load on each vehicle at each pickup location stays within the vehicle's capacity. Next, Equation (10) ensures that the load on each vehicle at each delivery location is consistent with the load at the corresponding pickup location. The last three equations describe the electrical vehicle constraints as follows: Equation (11) ensures that the remaining battery level of each vehicle after traveling between locations is within its maximum range. Equation (12) calculates the remaining battery level of each vehicle after traveling between locations based on its initial level and travel distance. Finally, Equation (13) ensures that the flow of vehicles at charging stations is balanced, meaning that the number of vehicles entering and leaving each station is the same. Figure 1 depicts the solution example of two vehicle paths. Vehicle 1's path, colored in blue, starts from the depot (the red square) and reaches the pickup location represented by the diamond shape. Next, it reaches the delivery location and finally returns to the depot. The path of vehicle two, colored in green, depicts the path including a charging station hop (green triangle). It starts from the depot, reaches the pickup point, then reaches the delivery location. Given that the remaining battery will not guarantee it will reach the depot, it takes a stop to charge at the charging station, as represented by the green triangle. \[\sum_{k\in K}\sum_{j\in V}x^{k}_{ij}=1,\quad\forall i\in P\cup D\]3 Graphical representation of a solution for a problem instance with two pickup and two delivery points, five charging stations, and two vehicles.
## 5 Analysis
### Using Three Counterweights  
#### 5.1.1 Mechanical Characteristics  
Table 1 presents the parameters of a four-bar linkage (Figure 2). The material used for the links is steel with a density of 7800 kg/m\({}^{3}\). The counterweights are made from brass with a density (\(\rho_{c}\)) of 8500 kg/m\({}^{3}\).  
The mechanism is moved by a motor placed at point A, rotating at a constant speed of 500 rpm. Using direct kinematics, it is possible to obtain a sample of the positions (\(A_{x}\), \(A_{y}\), \(B_{x}\), \(B_{y}\), \(C_{x}\), \(C_{y}\),\(D_{x}\), \(D_{y}\) ), speeds ( \(VB_{x}\), \(VB_{y}\), \(VC_{x}\), \(VC_{y}\), \(VD_{x}\), \(VD_{y}\)) and accelerations \(AB_{x}\), \(AB_{y}\), \(AC_{x}\), \(AC_{y}\), \(AD_{x}\), \(AD_{y}\)) corresponding to each of the basic points considered in this system.  
Figure 3 shows the results of the direct kinematics using the mechanism parameters presented in Table 1. It is important to notice that Figure 3a only presents the positions of points B and D (points A and C are fixed). The same occurs with Figures 3b,c where only the velocities and the accelerations of points B and D are presented (this parameters are always zero for points A and C.)  
By replacing all known parameters in Equations (35) and (36), the balancing indexes used to define the objective function (Equation (48) can be obtained.  
According to the mechanical characteristics of the mechanism the boundaries considered for the optimization are:  
\[-0.40\ \mathrm{m}\leqslant x_{cn}\text{, }y_{cn}\leqslant 0.40\ \mathrm{m} \tag{57}\]  
\[0.005\ \mathrm{m}\leqslant t_{cn}\leqslant 0.04\ \mathrm{m} \tag{58}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Body** & **1** & **2** & **3** \\ \(n\) & **1** & **2** & **3** \\ \hline
**Mass** & & & \\ \(m_{b_{n}}\) & 2.51946901 & 4.73866901 & 3.68746901 \\
[kg] & & & \\ \hline
**Length** & & & \\ \(l_{n}\) & 0.40 & 0.78 & 0.60 \\
[m] & & & \\ \hline
**Inertia** & & & \\ \(l_{b_{n}}\) & 0.14023528 & 0.98146460 & 0.45494271 \\ \([\mathrm{kg}\ \mathrm{m}/\mathrm{s}^{2}]\) & & & \\ \hline
**Center** & & & \\ **of mass** & 0.20 & 0.39 & 0.30 \\ \(x_{b_{n}}\) & [m] & & \\ \hline
**Center** & & & \\ **of mass** & 0.00 & 0.00 & 0.00 \\ \(y_{b_{n}}\) & & & \\ \([\mathrm{m}]\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the four-bar mechanism used in the example.
# Development of a Wideband Slotted Antenna Array with Low Profile and Low Sidelobe
## 2 Design of the Antenna Array  
The configuration of the multi-layered corporate-fed 16 \(\times\) 16 slotted cavity antenna array is shown in Figure 1. It consists of four parallel copper plates, i.e., the radiation slot layer, the cavity layer, the coupling aperture layer, and the feeding layer, which are denoted by \"Layer1\", \"Layer2\", \"Layer3\", and \"Layer4\", respectively. The 256 (16 \(\times\) 16) radiation slots are regularly distributed in \"Layer1\", whose layout is particularly designed. The cavity layer \"Layer2\" consists of 8 \(\times\) 8 square air cavities, which are used to excite the \(TE_{220}\) mode [30] in the cavity. The coupling aperture layer \"Layer3\" is used to transmit the power from the feeding layer to the cavity layer. The feeding layer \"Layer4\" is designed based on a rectangular waveguide, and is composed of 6-level cascading T-junctions so that electromagnetic waves can be transmitted to the coupling layer.