esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Water Tightness  
The watertightness test was carried out on cubes with dimensions 15 \(\times\) 15 \(\times\) 15 cm, with the use of a standard apparatus for testing the watertightness of concrete (Figure 11). The samples were dried to obtain a constant weight before the test. The experiment consisted of pressuring water with controlled pressure under 1.2 MPa on the specimen area withe 100 mm diameter. The constant temperature of water and air was established and equal 18 \(\pm\) 2 \({}^{\circ}\)C. The possible water leaks were observing during the test on the specimen surface.  
During the test, the samples were subjected to water pressure from 0.2 MPa to 1.2 MPa. The pressure was increased from 0.2 MPa by 0.2 MPa every 24 h. The final pressure value was kept during 24 h. By the end of the test, no leakages were detected on the samples. In order to assess the degree of saturation, the samples were split during the compressive strength test and the height of saturation at the centre and at the edges of the samples was measured (Figure 12). The results were summarized in Table 3.  
Figure 11: Samples during watertightness test.  
Figure 12: Measuring watertightness SC; **a**—measuring at the edge, **b**—measurement in the middle.  
According to the requirements of the standard [40], all samples shall be classified in W12 watertightness class. The value behind the letter \"W\" means the ten times water pressure value during the for the specimen without the leak. At the same time, on the basis of the conducted test, it can be stated that the highest average tightness was observed for samples made on the basis of the SC aggregate, by about 21% higher to CR and about 51% higher to BC.
# Analysis of 'Pre-Fit' Datasets of gLAB by Robust Statistical Techniques
## 5 Conclusions  
The position solution computed by means of satellite measurements strongly depends on how the data are processed. The present study is concerned with theStandard point positioning employed in the mass-market receivers, and exploits the pre-fit residuals of datasets referred to the position of permanent receivers. The normal flow of data through software packages for GNSS processing is considered by using gLAB as a reference. In the main steps of this flow of data, the treatment of pre-fit residuals comes immediately before the final FILTER module that outputs the computed coordinates. The experiments done in this specific case show positive results and pave the way for a number of further improvements and studies, as indicated in Sections 4.2 and 4.3, on the potential use of robust statistics for GNSS positioning. These improvements are in line with a present trend in the use of robust statistics for GNSS positioning, as briefly mentioned in Section 1. The element of novelty added in this work is in the use of forward search techniques and in the exploitation of pre-fit residuals. The results on positioning presented in Section 4.1 prove that the robust statistical methods used correctly identify the actual outliers present in the GNSS measurements, contributing in this way to the improvement of accuracy and the reliability of satellite-based positioning services. Among the points listed in Section 4.2 for further reflection, it was remarked once more that users shall, as in any other positioning method, observe the GDOP metric to decide whether the coordinates are being estimated with a weak geometry, especially in the event of discarding multiple satellites.  
Conceptualization, A.R.-G. and C.F.; methodology, M.T.A. and D.P.; software, D.I.S. and E.S.; All authors contributed equally to the writing, review and preparation of the manuscript, agreeing to the published version of the manuscript.  
This work was supported in part by the Spanish Ministry of Science, Innovation and Universities project RTI2018-094295-B-I00 and in part by the Horizon 2020 Marie Sklodowska-Curie Individual Global Fellowship 797461 NAVSCIN.  
Not applicable.  
Reference to IGS data is reported in Section 2.  
The authors acknowledge the use of data and products provided by the International GNSS Service.  
The authors declare no conflict of interest.
# Development of a Lightweight Prosthetic Hand for Patients with Amputated Fingers
## 2 Design of the Prosthetic Hand
### Control System  
To drive the prosthetic hand, the user wears an operating part on his or her thumb, as illustrated in Figure 6a. The control system consists of an MCU (Arduino Nano), two dual-motor drives (TB6612 FNG), two gyro sensors (MPU-6500), and a switch for controlling four actuators. The switch and one gyro sensor are attached to the operating part, and the other components are installed in the mounting part.  
Figure 4: (**a**) Natural and self-adaptive grasping motions (**b**) using two linear springs in the proximal interphalangeal (PIP) and metacarpophalangeal (MCP) joints and (**c**) using two linear springs and one torsional spring in the PIP and MCP joints.  
Figure 5: Assembly schematic diagram and all mechanical parts in the prosthetic hand.  
Grasping is the main target motion. Four grasping patterns are preprogrammed in the controller, and the pattern mode is selected by users with a simple switch. In mode 1, only the index finger moves. In mode 2, the index and middle fingers move together. In mode 3, the ring finger moves together with the index and middle fingers. Finally, all fingers move simultaneously in mode 4. Of course, different combinations can be easily added to the controller to comply with users' needs. With these various patterns, the user can select the desired grasping motion depending on the environment. In each mode, the finger motion is initiated according to the movement of the thumb. In other words, the thumb is used to give the \"start\" command to the controller. The movement of the thumb is estimated from two gyro sensors: one is attached to the operating part, and the other is in the mounting part. By using the relative value, when the hand is moved without thumb movement, the individual finger modules are not moved, whereas the finger modules are moved when the thumb is moved and the direction of rotation of the actuators is decided depending on the signs of the measured relative values. We control each finger motor at its current mode control. The control concept and schematic diagram are shown in Figure 6b.  
Figure 6: (**a**) Electrical components in the mounting and operating parts and (**b**) control schematic diagram.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 2 Related Work
### DRL-Based Methods  
The rapid advancement of artificial intelligence and deep learning has spurred significant interest in leveraging deep reinforcement learning (DRL) to address complex routing challenges in logistics and transportation. By framing the routing problem as a sequential decision-making process, DRL learns routing strategies by identifying patterns across massive instances. Existing DRL-based approaches can be broadly categorized into construction-based and improvement-based methods, each differing in their route generation mechanisms. Construction-based methods iteratively build a solution by selecting and appending unvisited nodes to a growing partial route. A key breakthrough in this area is the attention model (AM) [22], proposed by [PERSON] et al., which pioneers the application of transformer networks to routing problems such as TSPs and CVRPs, achieving superior performance. Building on this foundation, [PERSON] et al. [23] exploit the inherent circularity of routing solutions to develop POMO, a framework that trains policies with multiple optimal candidates to enhance solution diversity and robustness. In comparison, improvement-based methods start with a complete route and refine it through iterative modifications using local search operators such as node swaps and 2-opt moves. [PERSON] et al. [24] introduce a novel encoding scheme and network structure to learn improvement policies, employing an actor-critic framework that yields strong routing performance. Similarly, [PERSON] et al. [25] develop a cross-aspect attention mechanism for node embeddings and integrate proximal policy optimization to accelerate model convergence and enhance stability. These advances highlight the growing potential of DRL in optimizing routing solutions at scale.
# Effect of Strain Hardening on Increase in Collapse Pressure during the Manufacture of ERW Pipe
## Change in the Material Stress-Strain Curve during Manufactature
### Simulation of the Manufacture Process for ERW Pipe  
As shown in Figure 2, a three-dimensional finite element model was constructed to simulate the manufacture process from breakdown to sizing. The rollers were rigid, and the dimensions, arrangement and spacing of the rollers were determined based on the information obtained from the manufacturer. The specific information of the rollers provided by the company is confidential. The steel plate was modeled using an eight-node linear brick element with reduced integration, which is referred to as C3D8R in ABAQUS [14]. When uncoiled, the steel plates are much longer than their width. However, the length of the steel plate in the simulation was limited to seven times the width. Seven layers of elements were applied through the thickness direction, and 52 and 110 equally spaced elements were applied along the width and the length directions, respectively. Convergence testing confirmed that the number of elements in each direction was sufficient to provide converged stresses.  
The ERW pipe is formed by passing a steel plate through the rollers in a fixed position. Numerical simulation, however, is conducted in the opposite way. Rigid rollers are passed over the  
Figure 1: Adopted stress–strain curve in simulation.  
Figure 2: Simulation of the manufacture process using a three-dimensional finite element method.  
steel plate to avoid any development of stresses caused by moving the steel plate. As shown in Figure 3, the front surface of the plate was constrained in the longitudinal direction, and movement in the transverse direction of the cross-section along the center line was also constrained. The roller was moved at a velocity of 30 m/min, which is within the range used in practice. Explicit dynamic analysis was performed using a time increment (\(\Delta t\)) of \(2.0\times 10^{-6}\) s, which was confirmed by convergence testing.  
During the simulation, as shown in Figure 4, two surfaces were tied together to simulate welding in the squeezing process. Neither separation nor slip occurred after the tying. Elastic friction was applied between the rollers and the steel plate. The friction coefficient was determined using various factors such as the roughness of the roller surface, the moving speed of the plate, and the type of lubricant. In this study, a friction coefficient of 0.15 was applied [16; 17].
## Appendix C Characterization by Optical Methods of BMFPO Films Deposited on LAO Substrate  
### Optical Characteristics of Surfaces  
Optical characteristics of the BPFMO films deposited on LAO substrates were deduced from standard and Differential Interference Contrast (DIC) microscopy (BX41 microscope, Olympus, Tokyo, Japan), and by spectral reflectivity on a Filmetrics instrument (F20, San Diego, CA, USA).  
The surfaces of the samples observed under bright field and dark field with high magnification (X50, X100 magnification objectives) are shown in Figure 14. These surfaces do not appear to be fully reflective, and the scattered light shows inhomogeneities whatever the magnification and whatever the Pr substitution concentration, although significantly weaker for the lowest Pr substitution concentration (5%). Such optical inhomogeneities with granular aspect, typically at micrometric scale, can be reasonably attributed to texture consisting of crystallites. Indeed, if the crystallites are not fully oriented, they exhibit a high visibility (contrast), which is even more pronounced thanks to the birefringence character of the material, which could be as large as \(n_{\mathrm{o}}=2.80\) (ordinary refractive index) and \(n_{\mathrm{e}}=2.66\) (extraordinary refractive index) for undoped BiFeO\({}_{3}\) at 830 nm [44]. It seems that, although the films deposited on LAO substrate exhibit preferential orientation (see Appendix B), the contribution from randomly oriented grains still has a substantial destructive influence on some optical measurements.  
In addition to these aspects at high magnification, the surfaces observed at low magnification (X10 objective) by DIC clearly show domain structures with specific orientations and even intricated orientations (Figure 15). As is illustrated in Figure 15 by a particular example of the film with 12.5% Pr substitution, these domains could even be perpendicularly oriented on the surface of the film. Their sizes typically range from about 20 to 200 \(\upmu\)m in width and are hundreds of \(\upmu\)m in length. As already reported [47; 48; 49], these observations could be attributed to the domain structure of LaAlO\({}_{3}\) substrate resulting from its phase transition on cooling to room temperature after crystal growth [47]. The surfaces are therefore not fully flat, and subsequently thickness variations may also occur at these scales. Such intricated structures of LAO may explain why the cleaved samples seen by SEM (see Appendix E) for high Pr substitution concentration do not show sections with sharp edges.
## 3 System Model
### An Architecture for SDN-Enabled Integrated Satellite-Terrestrial Networks  
In this subsection, an SDN-enabled ISTN model is briefly introduced. As illustrated in Figure 1, the considered ISTN consisted of two logical parts, the data plane and the control plane. In the control plane, numerous SDN controllers hosted on the physical nodes of the data plane provided logically-centralized control and management functions. On the other hand, in the data plane, the user equipment of all types connected to the terrestrial networks through the gNBs (gNodeBs) and the RNs (Relay Nodes). The terrestrial networks mainly relied on optical fiber to establish connections between the SDN nodes in the backhaul, as well as the Radio Access Network (RAN) and core network. Besides, a high throughput Geostationary orbit (GEO) satellite was set as a complementary part of the terrestrial networks in the model. It is noted that satellite communications with the ground nodes were implemented through the satellite gateways and RNs.  
Since the main interest was optimizing the joint gateway and controller placement to maximize the network reliability with network latency constraints, the analysis was simplified, and neither the core network nor RAN were considered in the proposed architecture.
## 2 Materials and Methods
### Experimental Design--Taguchi Method and Taguchi Analysis  
The orthogonal Taguchi array designed for the study (summarised in Table 1 and Table S1) was constructed and analysed using Minitab 18 Statistical Software. The array consists of five prime design parameters (control factors) for the Fenton oxidation process, which were selected to design an array. The stoichiometric equation of MPs disintegration (as given in Equation (16)) was derived to determine the H2O2 dose required to initiate the reaction. From given equations, terbutryn requires maximum 45:1 (in molar ratio, H2O2 to the compound) to disintegrate. When MPs were mixed together in a solution, the H2O2 were dosed at 10, 30, 50 and 100 times of 45:1 (in molar ratio, H2O2 to the compound), depending on the solution initial concentrations.  
\[\text{Diuron: C}_{9}\text{H}_{10}\text{Cl}_{2}\text{N}_{2}\text{O}+26\text{H}_{ 2}\text{O}_{2}\to 9\text{CO}_{2}+29\text{H}_{2}\text{O}+2\text{HNO}_{3}+2 \text{HCl} \tag{16}\]  
\[\text{Gabapentin: C}_{9}\text{H}_{17}\text{NO}_{2}+27\text{H}_{2}\text{O}_{2} \to 9\text{CO}_{2}+35\text{H}_{2}\text{O}+\text{HNO}_{3} \tag{17}\]  
\[\text{Sulfamethoxazole: C}_{10}\text{H}_{11}\text{N}_{3}\text{O}_{3}\text{S}+3 \text{H}_{2}\text{O}_{2}\to 10\text{CO}_{2}+36\text{H}_{2}\text{O}+3\text{HNO}_{3} +\text{H}_{2}\text{SO}_{4} \tag{18}\]  
\[\text{Terbutryn: C}_{10}\text{H}_{19}\text{N}_{5}\text{S}+45\text{H}_{2}\text{O}_{2 }\to 10\text{CO}_{2}+5\text{H}_{2}\text{O}+5\text{HNO}_{3}+\text{H}_{2} \text{SO}_{4} \tag{19}\]  
\[\text{Terbutylazine: C}_{8}\text{H}_{16}\text{Cl}_{5}+38\text{H}_{2}\text{O}_{2 }\to 9\text{CO}_{2}+43\text{H}_{2}\text{O}+5\text{HNO}_{3}+\text{HCl} \tag{20}\]  
Figure 1: Schematic layout of the methodology.  
Four factors namely, the reaction time, initial MPs concentration in mixed solution, H\({}_{2}\)O\({}_{2}\) dose and molar ratio of H\({}_{2}\)O\({}_{2}\):Fe(0)/FeSO\({}_{4}\) were assigned to four levels (written as 1, 2, 3 and 4) and solution initial pH was assigned to two levels (1: acidic, 2: neutral; shown in Table 2).  
The orthogonal array layout with 16 experimental runs is given in Supplementary Materials S1. For each treatment, FeSO\({}_{4}\) and Fe(0), experiments were made in duplicate to consider any manual errors or other environmental noise factors.  
The obtained dissolved organic carbon (DOC) reduction (mineralisation) data were used to find the SN ratios. The optimal conditions obtained after the Taguchi analysis were validated after triplicating confirmatory runs. Among the three Taguchi conditions discussed in the introduction, smaller is better was chosen to obtain the minimum DOC reduction factor (ratio of final to initial DOC concentrations in MPs mixture solution), expressed as [DOCf/i]) thus, maximising the mineralisation efficiency. The SN ratios for the smaller is the better condition is shown as:  
\[\text{SN ratios}=-10\times\text{log}_{10}(\text{sum}\,(y^{2})/n) \tag{21}\]  
where \(y\) is the response variable and \(n\) is the number of replicates.
# Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier
## 4 Experimental Setting and Results
### Performance Parameters and Evaluations  
#### 4.2.1 Experiment I: The Hand Detection Accuracies  
In this experiment, the hand detection accuracies on different hand gestures over IPN Hand dataset and Jester dataset are shown in Tables 2 and 3, respectively. Table 2 represents the results of the hand gestures of the IPN Hand dataset on both plain and complex backgrounds. We took 30 samples of each hand gesture in the plain and complex background and obtained 97.1% accuracy on plain background samples and 94.3% accuracy on complex background samples.  
Table 3 represents the results of the 13 hand gestures of the Jester dataset on both plain and complex backgrounds. We took 30 samples of each hand gesture in the plain and complex background and obtained 95.6% accuracy on plain background samples and 88.6% accuracy on complex background samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hand** & **Number of** & **Plain** & \multirow{2}{*}{**Accuracy (\%)**} & **Cluttered** & \multirow{2}{*}{**Accuracy (\%)**} \\
**Gestures** & **Samples** & **Background** & & & **Background** \\ \hline \hline POF & 30 & 30 & 100 & 25 & 83.3 \\ PTF & 30 & 30 & 100 & 26 & 86.6 \\ COF & 30 & 30 & 100 & 26 & 86.6 \\ CTF & 30 & 28 & 93.3 & 26 & 86.6 \\ TU & 30 & 29 & 96.6 & 29 & 96.6 \\ TD & 30 & 29 & 96.6 & 30 & 100 \\ TL & 30 & 27 & 90 & 30 & 100 \\ TR & 30 & 28 & 93.3 & 30 & 100 \\ OT & 30 & 29 & 96.6 & 30 & 100 \\ DCOF & 30 & 30 & 100 & 29 & 96.6 \\ DCTF & 30 & 30 & 100 & 29 & 96.6 \\ ZI & 30 & 30 & 100 & 28 & 93.3 \\ ZO & 30 & 29 & 96.6 & 30 & 100 \\ \hline \hline \multicolumn{5}{c}{**Mean Accuracy Rate**} & \multicolumn{2}{c}{**97.1\%**} & \multicolumn{2}{c}{**94.3\%**} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hand detection accuracies over IPN Hand dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hand** & **Number of** & **Plain** & \multirow{2}{*}{**Accuracy (\%)**} & **Cluttered** & \multirow{2}{*}{**Accuracy (\%)**} \\
**Gestures** & **Samples** & **Background** & & & **Background** \\ \hline \hline SD & 30 & 30 & 100 & 25 & 83.3 \\ SL & 30 & 29 & 96.6 & 27 & 90 \\ SR & 30 & 28 & 93.3 & 24 & 80 \\ SU & 30 & 28 & 93.3 & 26 & 86.6 \\ TD & 30 & 29 & 96.6 & 24 & 80 \\ TU & 30 & 28 & 93.3 & 30 & 100 \\ ZIF & 30 & 29 & 96.6 & 25 & 83.3 \\ ZOF & 30 & 27 & 90 & 30 & 100 \\ S & 30 & 29 & 96.6 & 25 & 83.3 \\ RF & 30 & 30 & 100 & 29 & 96.6 \\ RB & 30 & 30 & 100 & 23 & 76.6 \\ PI & 30 & 28 & 93.3 & 28 & 93.3 \\ SH & 30 & 29 & 96.6 & 30 & 100 \\ \hline \hline \multicolumn{5}{c}{**Mean Accuracy Rate**} & \multicolumn{2}{c}{**95.6\%**} & \multicolumn{2}{c}{**88.6\%**} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hand detection accuracies over Jester Hand dataset.
# A Novel Plug-in Board for Remote Insect Monitoring
## 1 Introduction
[15] published a sticky cube (or box) that consists of four polystyrene plates. Inside the box, a Raspberry Pi camera is responsible for image capture, and it can be rotated by servo motors. In this system, there are two controller devices, an Arduino and a Raspberry Pi 3 Model B+. The Arduino is used for motor movement and sensor handling, while the Raspberry Pi is responsible for image capturing and saving. In their prototype system, the circuit's components are located on a breadboard, which is placed in a plastic box. The power source is a battery, but the power consumption is not detailed. Even though we do not have information about the power consumption, presumably, this trap could operate unattended only for a limited duration. The work of [PERSON] et al. [5] presented a plug-in imaging system that is dedicated to delta traps. In this system, an Arducam development platform was used as a controller unit that is extended with a 2 MP (Megapixel) OV2640 image capture sensor. In this case, the collected images are stored on the onboard microSD card. The circuit is powered by a 350 mAh (milliampere-hour) 3.7 V lithium polymer ion battery, and its operating time is approximately 2 weeks in the case of daily image capture. The device's estimated cost is USD 33. Although the device has advantages, it is not able to process captured images, and they must be collected manually. In summary, these systems have one or more disadvantages that prevent them from being used effectively and widely, e.g., high cost, low power autonomy, low picture quality, a WIFI coverage requirement, intensive human control requirement, or poor software support. Therefore, these devices will remain in the prototype phase, and they will not become a product in their current form. The above-mentioned drawbacks of commercial and prototype traps were the main motivation for our development. Additional information about commercial and prototype traps can be found in [16]. Our proposed sensing device (controller unit plus the plug-in board) overcomes the weaknesses of earlier traps and fulfills the following requirements:  
* Programming framework available for deep-learning-based object detectors;
* Long operating time with battery power supply, even without solar charging;
* Long-range wireless communication;Cost-effectiveness. Meeting such requirements supports the wide distribution of the proposed hardware component, which is applicable not only for research purposes but also for practical goals. Since it automatizes exhausting and time-consuming manual counting, time and cost can be saved. Moreover, continuous data acquisition provides high temporal resolution, which helps to schedule spraying against insect pests more precisely. Spraying needs to be started before the insect population builds up because it is much more difficult to protect the crop against a well-established population. Moreover, the environment will be better protected due to the reduction in the frequency of spraying.