esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 6 Discussion
### Robustness Testing of SCS  
The single-chain strategy means that when more than one neighbor exists on one side of the satellite in constellation, one of them is selected to build the link. The above experiments all use the closest distance as the selection rule. This experiment tests the effect of the single-chain strategy on the convergence and the communication volume of the algorithm when the communication link is partially damaged, requiring the construction of the path as shown in Figure 8c. Additionally, it provides a comparison with the c-CBBA (SCS) algorithm utilizing the communication topology shown in Figure 8b, as well as the basic CBBA. The algorithm in a partially damaged communication environment is represented by SCS (k), where k represents the number of damaged communication links. Construct this environment by randomly deleting adjacent communication links. Figure 13a,b show the test results in local-1080-90 and global-1080-90 scenarios, respectively.  
As can be seen from Figure 13, the SCS policy in Figure 8c can still converge quickly and will not have a significant impact on communication volume. That is, the SCS can effectively reduce the communication volume when using different communication topology. This indicates that this strategy does not reduce system robustness due to pruning communication links, and it still has high adaptability to the partially damaged communication environment.  
Figure 12: Changes in various indicators during algorithm convergence. (a) Indicator of the number of repeated tasks; (b) indicator of the average task bundle length; (c) indicator of the total profit. The suffix local and global refers to the task distribution in the example being local or global.
## 3 Methodology
### Performance Metrics
#### 3.6.3 Average Precision (AP) and Mean Average Precision mAP  
AP (average precision) is calculated by averaging the precision for recall value over 0 to 1. In other words, AP is the area under curve for a precision-recall curve [45]. The equation for AP is given in Equation (3) [46]:  
\[AP_{i}=\int_{0}^{1}P(R)d(R) \tag{3}\]  
where \(P\) and \(R\) denote precision and recall, respectively. Finding the mean of AP for all classes gives us the mAP Equation (4) [46]:  
\[mAP=\frac{1}{cls}\sum_{i=cls}AP(i) \tag{4}\]  
where cls represents the number of classes.
## 2 Materials and Methods
### Experimental Setup  
Figure 11 shows the composition of the test bench. The electronic load of 1 kW capacity defines the current value at 0.1 Amper precision during the test study. The flowmeter calculates the total fuel consumption instantly and transmits the data via wireless connection to the main computer. Measuring tolerance of the flowmeter is \(\pm\)0.1 L/min. The air compressor or fan is controlled with the fuel cell control unit. The temperatures of the fuel cell, supercapacitor, compressor outlet, and ambient air are measured by four different thermal probes that have 0.1 \({}^{\circ}\)C precision. The test was conducted in two stages. The same current profile was applied for both the compressor and fan models. The maximum peak current value was 5 A. Subsequently, tests were conducted for the same current profile for both fixed-speed and variable-speed scenarios of the compressor. Two voltage sensors with a 0.1 V precision and a current sensor with a 0.1 A precision were used to measure the fuel cell and supercapacitor.  
Figure 11: _Cont._  
Figure 10: Flow chart of EMS and TMS.  
As seen in Figure 12, real-time test studies will be carried out with the referenced prototype hydrogen electric vehicle. The total mass of the vehicle is 180 kg with a driver. One in-wheel motor is used, including a one-stage planetary gearbox for propulsion. There is a torquemeter for measuring the speed and torque values in real time. Also, a flowmeter senses the consumed hydrogen and sends the data to the onboard computer. Thus, energy source to wheel efficiency can be calculated through real-time vehicle tests. GPS data are used for slope calculation, depicted in Figure 13.  
Figure 11: Fuel cell test bench.  
Figure 12: The prototype vehicle test.  
Figure 13: Circuit properties from GPS data.
# Image Analysis Applications for Building Inter-Story Drift Monitoring
## 2 Methods
Calculate the vector of errors \(\boldsymbol{e}\), which represents the difference between the projected coordinates and the actual coordinates of all reference points and tracking points. The vector \(\boldsymbol{e}\) is a 1-by-(\(2M+2N\)) vector where \(M\) and \(N\) represent the number of reference points and tracking points, respectively. \[\boldsymbol{e}^{t}=\left\langle\left(\boldsymbol{I}_{Ref(m)}^{t}-\boldsymbol {I}_{Ref(m)}^{t}\right)\right|\left(\boldsymbol{I}_{Trk(n)}^{t}-\boldsymbol{I }_{Trk(n)}^{t}\right)\right\rangle\] (12)
* Adjust the guessed inter-story responses and the camera movement \(\boldsymbol{d}^{t}\) according to the error vector \(\boldsymbol{e}^{t}\). In this work, the adjustment is based on the Gauss-Newton algorithm [57; 58]. We define a function \(f\) to integrate the calculations from Step 4 to 9: \[\boldsymbol{e}^{t}=\left.f\big{(}\boldsymbol{d}^{t}\right)\] (13) A Jacobian matrix \(\boldsymbol{J}\) can be calculated. The Jacobian matrix is obtained by applying a finite difference calculation, where the associated calculation procedures can be obtained in many numerical analysis textbooks (e.g., [57; 58]) and is not repeated here. The solution of \(\Delta\boldsymbol{d}^{t}\) (i.e., the story responses and camera movements) would have a unique solution if there more than three targets on both of the ceiling and the floor, and the targets are not on a straight line. If so, every story displacement and camera movement would have a unique appearance on the camera, making the Jacobian matrix nonsingular, and theoretically there would be a unique solution. \[j_{ij}=\frac{\partial\boldsymbol{e}_{i}}{\partial d_{j}},\] (14) where \(j_{ij}\) is the element of the \(i\)th row and the \(j\)th column of matrix \(\boldsymbol{J}\), \(\boldsymbol{e}_{i}\) is the \(i\)th element of vector \(\boldsymbol{e}^{t}\), and \(\boldsymbol{d}_{j}\) is the \(j\)th element of vector \(\boldsymbol{d}^{t}\). The adjustment of \(\boldsymbol{d}^{t}\) is given as \[\Delta\boldsymbol{d}^{t}=\left.\left(-\big{(}\boldsymbol{J}^{t}\boldsymbol{J} \big{)}^{-1}\boldsymbol{J}^{t}\boldsymbol{e}^{t}\right)^{\mathrm{T}}\right.\] (15) \(\boldsymbol{d}^{t}\) is then replaced with \(\boldsymbol{d}^{t}+\Delta\boldsymbol{d}^{t}\). * If the adjustment \(\Delta\boldsymbol{d}^{t}\) is sufficiently close to a zero vector, such as which norm is below a small number like \(10^{-6}\), go to the next step. Otherwise, go to Step 6 for the next iteration. * Output the data by writing to files, plotting to graphs, and displaying on the screen. The data include the inter-story responses, camera movement, and many detailed data aforementioned. The inter-story responses can be further used for structural safety early warning; however, this is not in the scope of this paper. * Go to Step 4 until we stop the monitoring.
# Micro-Satellite Systems Design, Integration, and Flight
## 5 Conclusions  
The work performed for the Alpha mission used a systems approach to improve the TRL of the ChipSat RF system and the ACS system. The progress followed the standard \"Vee\" diagram format. Requirements were defined throughout the design lifecycle, by breaking down the concept into subsystem and component solutions. Requirements were traced into experimental testing, where verification and validation confirmed prototype functionality.  
Using interface and network diagrams, the system was divided into the system and component levels. At these levels, the traceability of resources and signals was examined, and requirements between systems were established. The theory of the model was tested by verifying that each subsystem met its intended use. Finally, full system validation could be performed through mission testing.  
During development, confidence tests were performed to continually verify the functionality of the solution. The RF system was already validated for Alpha by [PERSON] [42] and [PERSON] [15]. Building on their work, validation of an RTOS-based ChipSat code with a signal ready for matched filtering and FEC was achieved. RF transmission from a CC1310 microcontroller was tested during a balloon launch. Moreover, ground station connections could be established during balloon testing. Signals could be received using both a software-defined radio (RTL-SDR) and a receiver network (TinyGS). In addition, these signals could be FEC post-demodulation. For the ACS system, both validation and verification of the system were performed.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 2 Related Work
### Position-Based Routing Protocols  
GPSR is one of the most classic and famous routing protocols in location-based routing protocols [24; 25; 26]. It uses GPS positioning system of vehicle equipment for routing. Each node stores the location information of adjacent nodes, and links are created directly through the location information. It makes use of two forwarding schemes for sending packets: perimeter mode (recovery mode) and greedy mode. In greedy forwarding, the sender selects the vehicle which is nearest to the destination in neighbors as the receiver according to the location of the destination and the neighbors. Periodically, the nodes exchange this information with their one-hop neighbors through hello messages. If the greedy mode fails, the current node enters the peripheral forwarding that uses the right-handrule. GPCR [27], based on the GPSR protocol, has made relevant improvements for intersections on the internet of vehicles. GPCR divides greedy forwarding into two cases. While the next hop is in the current road segment, the node closest to the destination node is selected as the next hop. On the contrary, the node at the intersection is selected as the next-hop node. [PERSON] et al. have proposed MM-GPSR [28], based on the maximum transmission time and minimum angle. By quantifying the transmission time, the vehicle with the longest communication time is selected for the greedy forwarding, and the vehicle with the smallest angle is used to forward when the topology hole is encountered. It improves the link quality of greedy forwarding communication and reduces the redundancy of the right-hand rule in peripheral forwarding transmission. Connectivity aware routing protocol (CAR) [29] is a kind of routing scheme based on connectivity awareness. The source node finds the path information of the destination node through broadcast. In order to achieve good connectivity and link quality, the number of hops and the number of neighbor nodes will be updated to the packet in each forwarding process. After receiving, the destination node selects and responds to the source node. Although CAR solves the problem of connection, the accuracy of information along the way cannot be guaranteed, and the topology will change.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 3 Results and Discussion
### Type 3 Maize
The forecast model is constructed as a combination of models presented in Equations (15)-(18). Therefore, the dynamic structure of the type 3 maize time series is included as the effect of average temperature, precipitation, average humidity and radiation. The combination of the models was obtained through least squares and mean squares, with a resulting MSE of 0.009 and 0.011, respectively. Figure 8 presents the forecast values for a one-year time horizon given by the estimation models presented in Equations (15)-(18) and least squares and mean squares aggregation models. The associated MSEs for  
Figure 7: Representation for type 3 maize, estimation model and the residuals given by Equations (16)–(18). each estimation model presented in Equations (15)-(18) were 0.012, 0.0182, 0.022 and 0.029. The best forecast model attending to the best MSE value was obtained by least squares.
## 2 Materials and Methods
### Workflow
#### 2.1.1 Phantoms Creation  
The voxelized phantoms used in this study were constructed starting from a real patient CT (512 \(\times\) 512 matrix with a pixel size of 1.367 \(\times\) 1.367 mm\({}^{2}\) and a spacing between slices of 3.27 mm, high quality full diagnostic scan). CT contouring was performed with ITK-SNAP software [34], using a semiautomatic segmentation technique based on 3D active contours. The contoured volumes of interest (VOIs) were liver, soft tissue, lungs, adipose tissue, cortical bone, trabecular bone, air, breast prosthesis. Activity maps, with the same voxel and matrix size of the CT scan, were also constructed using ITK-SNAP, which allows the definition of the activity concentration ratio among different regions.
## 2 Materials and Methods
### Sea Surface Data  
The SST and sea surface height are key parameters used for SSP inversion, with both accessible from marine remote sensing data. The data used here come from the Copernicus Program, which is a global Earth observation and environmental monitoring program led by the European Union ([[https://marine.copernicus.eu/](https://marine.copernicus.eu/)]([https://marine.copernicus.eu/](https://marine.copernicus.eu/))). The sea surface height data employed in this study come from the altimeter satellite of the Copernicus Program. The absolute sea surface height is inverted by accurately measuring the distance between the satellite and the sea surface through a radar altimeter, combined with an Earth gravity field model and the satellite's orbital parameters. The SLA data used here are integrated with the above multiple altimeter satellite data sets. After preprocessing using the optimal interpolation method, the final spatial resolution is 0.25\({}^{\circ}\). The SST data used here come from the Global High-Resolution Project, which captures short-term changes in the SST and have a spatial resolution of 0.05\({}^{\circ}\). The two sets of remote sensing data, SLA and SSTA, have daily temporal resolution. After obtaining the two types of remote sensing data, it is necessary to match the remote sensing data with the Argo float data. In the matching process, the nearest neighbor rule is adopted to match the remote sensing data with the nearest Argo float data spatially.
# Dimensional Stability of SBR-Modified Cementitious Mixtures for Use in 3D Additive Construction
## 1 Introduction  
The construction industry faces a number of problems, including declining labor efficiency, increased onsite accident rates, degraded labor quality, site management difficulties, and a reduced skilled labor force [1]. Increased automation is one solution to these problems. Automation has been rapidly advancing in the manufacturing sector, but the propagation of automation technologies in the construction industry has been slow. This slow pace is due to the fact that construction projects are generally large in scale and construction equipment is costly, among other issues [2]. One representative automated technology in the construction industry is 3D additive construction (3 DAC). This construction method builds concrete structures by stacking or layering cementitious mixtures. This 3 DAC process is operated via computer control. The specified concrete structure is built without a formwork by adding layer upon layer of a cementitious mixture [3]. Most 3 DAC technologies currently in use employ cementitious mixtures extruded through a nozzle installed on a gantry system or robotic arm.  
This construction method offers several advantages, such as reduced construction costs, accident rates, and construction time; decreased environmental contamination; and the convenient construction of complex structures [4]. Consequently, this method is considered a next-generation sustainable construction technology. This 3 DAC technology has recently been employed to construct regular buildings, pedestrian bridges, and army barracks [5; 6; 7; 8]. In such onsite applications, printing materials are considered one of the three core elements, in addition to printing machinery and 3D modeling software. Until now, ordinary Portland cement has typically been employed as the base material, along with various mineral admixtures such as silica fume and fly ash. SBR-modified cementitious mixtures exhibit excellent tensile and flexural strengths and adhesiveness, and favorable levels of resistance to water, abrasion, and chemicals (as compared to ordinary Portland cement) [9; 10; 11]. However, research on the use of SBR-modified cementitious mixtures in 3 DAC is lacking. Therefore, this study experimentally investigated the dimensional stability of SBR-modified cementitious mixtures in order to determine whether its properties are sustainable if used as a 3 DAC material.
## 5 Conclusions  
The determination of ship passage routes plays a crucial role in maritime management, contributing to improved efficiency, enhanced safety and protection of the marine environment. By properly defining a passage route, it is possible to avoid potentially dangerous areas such as shoals or reefs, significantly reducing the risk of collisions, breakdowns and other incidents that threaten both the crew and the vessel. Routing systems and algorithms should also take into account changing sea conditions, monitoring wind, currents and waves and updating the route as necessary.  
This paper presents a method for planning a ship's passage route using capability plots and an algorithm based on [PERSON]'s algorithm. By using these plots to determine transition weights between points, it was possible to find a route that minimized the impact of wind on the ship's hull. However, this does not mean that the chosen route meets other criteria such as minimizing travel time or being the shortest route. Nevertheless, for ships that are not equipped with thrusters that allow free movement along any axis, the method presented in the publication will be safer. The proposed route determination algorithm can be used to plan a route while the ship is in port. It can also monitor the ship's route during navigation, recalibrating the transition weights based on the current environmental conditions and adjusting the route accordingly.  
Conceptualization, J.W. and A.L.; computations, J.W. and A.L.; methodology, J.W. and A.L.; experimental verification, J.W. and A.L.; writing text of the article, J.W. and A.L.; review and editing, J.W. and A.L.; visualization, J.W. and A.L.; supervision, J.W. and A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the research project No. WE/2024/PZ/08, Electrical Engineering Faculty, Gdynia Maritime University, Poland.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Characteristics of Magnetic Fields Induced by the Wake of an Underwater Vehicle
## 4 Results and Discussions
### Characteristics of the Wake Magnetic Field  
To facilitate the feature extraction of the magnetic field signal, we set up four wake magnetic field monitoring points before calculation, which were located 1.5 m above the submarine (b = 1.5), and the distance between the two monitoring points was 2 m (a = 2). The relationship between the monitoring point and the position of the submarine is shown in Figure 8, wherein the diagram at the top shows the relationship between the starting position of the submarine and the monitoring point, and that at the bottom shows the relationship between the end position of the submarine and the monitoring point.  
If the distance between the two monitoring points is too small, it is not conducive to analyzing the change law of the wake magnetic field with time. The distance between the two monitoring points should be greater than the distance traveled by the submarine within 0.5 s. If the distance between the two monitoring points is too large, it is not conducive to analyzing the spatial variation of the wake magnetic field. It needs to be less than half the characteristic size of the submarine. In this paper, when studying the influence of the speed on the wake magnetic field, the maximum speed was 3 m/s, and the characteristic size of the submarine was L = 4.356 m, so the distance between the two monitoring points should be 1.5 m-2.178 m. This value was selected as 2 m in this work.  
Figure 8: Setting the submarine wake magnetic field monitoring points. (**a** is the distance between the two monitoring points; **b** is the vertical distance from the monitoring point to the submarine; **A**, **B**, **C** and **D** are four wake magnetic field monitoring points).  
Figure 7: Cross sections of the wake magnetic field at different altitudes for 10 s of submarine movement.  
Theoretically, the more monitoring points, the more conducive this is to wake magnetic field feature extraction, but it will increase the computation time and data volume. For our simulation conditions and computational domain, we have found four monitoring points through several attempts that can effectively extract the wake magnetic field features.
# Parametric Investigation of Die-Sinking EDM of Ti6 Al4V Using the Hybrid Taguchi-Rams-RATMI Method
## 4 Results and Discussions
### Convergence of the Investigation  
Peak current was revealed to be the most important regulating element for achieving a satisfactory machining outcome; nevertheless, a number of other research findings were also presented to support the current findings with regard to surface morphology and machining qualities. The most important aspect for regulating the surface roughness of EDMed Ti6 Al4V with a copper electrode is currently identified by [PERSON] and [PERSON] (2014). According to [PERSON] et al. (2016), peak current utilizing a copper electrode has a beneficial impact on the MRR, TWR, and surface roughness of EDMed Ti6 Al4V. Because of the high carbide deposition at the machined surface, longer pulse-on durations show truncation of TWR and MRR readings. The surface finish is greatly degraded by longer pulse-on times. According to [PERSON] (2016), in the EDMed Ti6 Al4V utilizing a copper electrode,  
Figure 20: The SEM image revealing the existence of a different surface morphology in the R1 sample.  
peak current and pulse-on time are the most important factors; peak current contributes the most to the MRR. Increased TWR is frequently the result of increased discharge energy density produced by higher currents. [PERSON] et al. (2018) have obtained similar results. [PERSON] et al. (2018) discovered that because of the increasing heat energy in the inter-electrode gap, MRR and TWR rise with the current. However, because of carbon deposition and a long igniting delay, pulse-on time reduces MRR. Because of carbon deposition and longer heat transmission times, TWR falls with pulse-on time. Surface irregularity rises with the current and pulses punctually. According to [PERSON] et al. (2018), deep cryogenic treatment of copper tool material results in reduced crystallite size, increased micro-hardness value, refined grain, and alleviation of stress. Additionally, it decreases tool wear and enhances surface smoothness when EDMing Ti6 Al4V. White layer thickness and surface crack density were reduced by using cryogenically treated copper electrodes. According to [PERSON] et al.'s (2018) research, the input machine variable affected the density and severity of the microcracks and craters that formed. It was discovered that the primary factor influencing these attributes was pulse current.