esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Example Building Structures with Semi-Active Control System  
Two structural models with semi-active control systems shown in Figure 4 were used in this study. Figure 4a shows the first model (Example 1), which was an 11-story building with STMD installed  
Figure 3: Outline of the seismic response simulation model of a seismic-excited structure with semi-active control system: (**a**) Conventional model; (**b**) RNN-based model. Figure 2: Various architecture of RNN models: (**a**) one-to-one; (**b**) one-to-many; (**c**) many-to-one; (**d**) many-to-many. on the top story; meanwhile, Figure 4b shows the second model (Example 2), which was a 26-story building with SMIS installed between the 11 th and 12 th stories. In the two example structures, the MR damper was used as a semi-active control device. The Bouc-Wen model, which is the most commonly used parametric model, was used to present the nonlinear behavior of the MR damper. Table 1 lists the structural properties of the two example buildings, which were obtained from previous studies [33,34]. The horizontal stiffness of each story shown in the table was modeled by the equivalent shear spring. Table 1 shows the mass of each story, which has one DOF without torsion. Structural damping of both models according to Rayleigh assumptions was considered to be 2%. A mass ratio of 2% for STMD was used for vibration control in Example 1. The first mode natural period of Example 1 was equal to 0.89 s. The period of STMD was tuned to that of Example 1. The structural properties of Example 2 were obtained from Shiodome Sumitomo building in Tokyo, Japan [34], which has a mid-story isolation system that is composed of multi-rubber bearing, lead damper, and steel damper. Two hysteresis-type dampers, i.e., lead and steel dampers, were replaced by MR dampers in Example 2. The Bouc-Wen model parameters for MR dampers were selected from the experiment conducted at Washington State University [35], and these parameters were scaled to have the appropriate maximum MR damper force for each model. A parameter study was performed by changing the maximum capacity of the MR damper force, and the values of 500 and 2750 kN were selected for Examples 1 and
###### Abstract
The above work demonstrates that LEO enhances the POD accuracy of GNSS satellites, and that the LEO enhancement method is feasible [25]. However, to the best of the authors' knowledge, there are relatively few studies on the enhancement of the POD accuracy of BDS-3 by large LEO satellite constellations [26; 27]. In this paper, constellations containing different numbers of LEO satellites are simulated, and ground-based monitoring station observations, LEO satellite observations, and ISL observations are generated in simulation. Firstly, the effect of ISL data on BDS-3 POD was analyzed by using the ISL and ground-station-integrated orbit determination, and secondly, the influence of different LEO satellite constellations on enhancing BDS-3 satellites POD was discussed by using LEO satellite and ground-station-integrated orbit determination. The second section details the processing method and orbit determination strategy of integrated orbit determination between the simulated LEO constellation and the ground station. Then, in the third section, the experiment of integrated orbit determination between the ISL and the ground station, and the experiment of integrated orbit between the LEO satellites and ground station, were carried out, and the POD accuracy and time synchronization accuracy of BDS-3 satellites were analyzed. The results are discussed in Section 4. Conclusions are presented in Section 5.
## High-Strain-Rate Compressive Behavior of UHMWPE Fiber Laminate
###### Abstract  
Ultra-high-molecular-weight polyethylene (UHMWPE) fiber laminate is currently widely used in ballistic protection for its exceptional physical and mechanical properties. However, the dynamic compressive mechanism of UHMWPE laminate remains poorly understood. Therefore, the stress-strain relationship, the influence of different thickness, area, and shape, and the maximum stress and fracture stress are studied in both out-of-plane and in-plane directions under quasi-static and dynamic loading using a universal test machine, Split Hopkinson pressure bar (SHPB), and high-speed camera. Furthermore, numerical models with cohesive elements are developed. The results indicate a dependency on strain rate and loading direction. Firstly, the stress-strain curve of dynamic testing can be divided into different zones according to different loading directions and strain rates. Secondly, with the increase of the strain rate in the dynamic testing, the maximum stress and fracture stress increase as well; relatively speaking, the fracture stress in the out-of-plane direction is greater than the fracture stress in the in-plane direction. Thirdly, both experiment and simulation indicate that the thickness does not influence the modulus clearly the in out-of-plane direction but influences the modulus in the in-plane direction. Fourthly, the fracture stress of dynamic testing is higher than the fracture stress of quasi-static testing in both directions. Finally, the numerical results show good agreement with the experiment in terms of the maximum stress and failure form.  
UHMWPE fiber laminate; direction dependency; high-strain-rate compression; size effect; cohesive element
## 2 Mission and Satellite Design
### Imaging Modes  
The imaging modes of the S-STEP SAR offer operational modes that utilize the advantages of an active phased-array antenna, and these operational modes are simple for a small satellite and demonstration mission of the S-STEP. In particular, the S-STEP SAR can be operated in multiple modes, including stripmap and ScanSAR, to provide SAR images with varying resolutions, swath widths, or sensitivities for diverse applications [29]. Subsequently, the antenna beam scheduling is adjusted to operate the stripmap and ScanSAR modes based on the requirements of the SAR image quality. The essential performance parameters of the S-STEP SAR payload for high-resolution and wide-mode SAR image quality are listed in Table 2.
# Electrospark Method in Galvanic Wastewater Treatment for Heavy Metal Removal
## 2 Materials and Methods
The experimental reactor (Figure 5a, position 4) is made in the form of a rectangular hollow parallelepiped with flat steel electrodes (position 7), a perforated bottom (position 9), and a prism-shaped cavity under it. The tank (position 1) was filled with galvanic wastewater. The shut-off valve (position 6) was set to the turned-off position. When the pump (position 2) was turned on by a valve (position 3), the required volumetric flow rate of the liquid was established. The volume of the reactor is about 20 dm\({}^{3}\), and the tested hydraulic retention times is about 300 s.  
Figure 4: The electrical circuit of the experimental low-voltage source of discharge currents. Figure 5: (**a**) The hydraulic scheme and (**b**) the appearance of the reactor. The galvanic wastewater entered the reactor passing through the holes of the perforated bottom, then through the metal loading layer (Figure 5b, position 8), in which multichannel spark discharges between metal granules formed at a given frequency. After that, through the nozzle, depending on the position of the valve (Figure 5a, position 5), the treated galvanic wastewater was poured into a container for the next analysis or returned to a repeated treatment cycle. The determination of the content of heavy metals in the start and treated water was carried out in accordance with the current regulatory documents [52; 53; 54]. The hydrogen index (pH) of the start galvanic wastewaters and the treated liquid was measured by an Ionomer I-160 M.  
The efficiency of the galvanic wastewaters treatment process was studied by varying the specific processing energy, the amount of stored energy, the pulse parameters, and the height of the metal load.
## 4 Results and Analysis
\[\text{Changing rate}=\frac{x_{10}-x_{0}}{x_{0}}\times 100\% \tag{2}\]  
where \(x_{0}\) is the value from the initial solution and \(x_{10}\) is the value from the IKQT final solution. To validate the trend of improvements in service quality during the IKQT iterations, two major influence indicators (total waiting time of all charging service and total number of unserved taxis in all charging stations) were selected for further analysis. The total number of unserved taxis in all charging stations of each iteration is illustrated in Figure 5. Figure 5 illustrates the variation in the total number of unserved taxis over different iterations. Initially, upon the first iteration, there is a notable decrease in the number of unserved taxis. As the number of iterations increases, while there are some minor fluctuations, a general downward trend is observed. Figure 5 depicts the progressive improvement in the number of taxis being served as the IKQT iterations proceed, demonstrating the effectiveness of the IKQT in reducing the number of unserved taxis over time. The total waiting time for all charging services in each iteration is demonstrated in Figure 6. Figure 6 illustrates the variation in the total waiting time (in minutes) for all charging services across different iterations of the IKQT. It is evident that the total waiting time experiences a downward trend as the number of IKQT iterations increases. Initially, the total waiting time is relatively high, suggesting potential congestion or inefficiencies in the charging service system. As the iterations progress, the total waiting time decreases, reflecting improved efficiency. It is noteworthy that despite some fluctuations, the general trend remains consistent--a decline in waiting time with each successive iteration. The last few iterations show a more stable waiting time, possibly indicating that the system has reached a state of optimal performance. Figure 5: Trends in total number of unserved taxis in all charging stations. As depicted in Figures 5 and 6, the changes between each IKQT iteration in these two pieces of data are slightly different. Importantly, these indicators are not positively correlated, meaning that a decrease in total waiting time may not necessarily correspond to a decrease in the number of unserved taxis. However, when both indicators decline simultaneously, this clearly demonstrates an improvement in charging service quality, as drivers experience shorter waiting times and a lower likelihood of not being served. The downward trends shown by both dotted lines are very similar and obvious, suggesting that the IKQT algorithm effectively optimized the deployment of charging stations, thereby enhancing the overall charging service quality.
# Stationary 3D Scanning System for IoT Applications
## 9 Summary  
The research presented here describes the mechanical design and control system of a low-cost, stationary 3D scanning system along with an analysis of the accuracy of object scanning and, therefore, the possibility of its use in industrial conditions. The authors present a model of point cloud acquisition, a complex algorithm for the operation and processing of the collected input and output signals. Based on a literature review in the field of three-dimensional digitization, the authors developed and built their own 3D scanning system, the effectiveness of which, taking into account the components used and the budget, fully met expectations.  
The 3D scanning system described in the article provides a solid basis for further research to improve the measurement accuracy of the scanned surface profile along with the possibility of obtaining a significantly larger volume of the point cloud with a lower scanning time. It can be successfully applied in IoT systems.  
The infrared sensor with analog measurements introduces inherent limitations in accuracy to external interference like ambient lighting. In order to increase the efficiency of a given 3D scanning system, it would be necessary to change the technology of the main sensor itself, for example, to \"ToF\" (\"Time of Flight\"), which consists of measuring the time between transmitting and receiving an impulse reflected from a specific scanned surface [21]. A great advantage of these types of sensors is the use of a laser, which in this case would work much better, due to its resistance to the influence of external light, its ability to scan reflective surfaces, and the sheer accuracy it is able to offer, even at longer distances [22].  
In further iterations of the prototype device, materials with better mechanical properties and resistance to temperature changes compared to PLA are planned. It is also planned to increase the measurement range by increasing the size of the rotating platform.  
Conceptualization, M.K. and D.R.; methodology, M.K. and D.R.; software, M.K.; validation, M.K.; formal analysis, M.K., A.M., and D.R.; investigation, M.K.; resources, M.K.; data curation, M.K.; writing--original draft preparation, M.K. and D.R.; writing--review and editing, M.K., A.M., and D.R.; visualization, M.K.; supervision, A.M. and D.R.; project administration, M.K.,A.M., and D.R.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Polish Ministry of Science and Higher Education, under grant no.: No. 0614/SBAD/1586.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Motivation for the Research  
This paper is devoted to the practical implementation and field testing of FM-ACSK communication system, which employs continuous-time spreading for the modulation and chaotic synchronization for the despreading of the received information bits. Although the theme of the practical implementation of analog or digital chaos generators is revealed relatively well in the scientific literature, the practical implementation of communication systems is addressed scarcely. The given work is motivated by the necessity to explore the real-world performance of FM-ACSK systems, developed in previous works [26; 28; 39; 40] of our research group. Moreover, the given contribution aims to prove the effectiveness and superiority of chaotic synchronization for the demodulation of chaotic spread-spectrum signals.
## 5 Sensitivity Analysis  
The circuit analyzed in Section 4.3, Circuit de Barcelona-Catalunya, was selected for a sensitivity analysis in this section. First, an analysis was carried out on the percentage of rear braking and further on the mass of the system (motorcycle plus rider).  
The results are compared in Table 7 for several percentages of rear braking (R. Braking), meaning, for different percentages of braking, which is regenerative.  
The realistic values of 25 to 35% of regenerative braking show energy recovery values of 9.93% and 14.63%, respectively. When the rear braking is responsible for 25% of the total braking force, a distance of 39.001 km is driven by the vehicle. If, however, 35% of the total braking force is regenerative, a distance of 40.301 km can be driven, an increase of 0.666 km from the 39.635 km driven with 30% regenerative braking.  
When the braking bias is 0%, all of the braking force is applied on the rear wheel. This situation, represented in Figure 19, would be ideal for regenerative braking since all braking would be regenerative. This would lead to an increase in distance from 36.097 km traveled without regeneration to 51.169 km with regeneration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **R. Braking (25\%)** & **R. Braking (30\%)** & **R. Braking (35\%)** & **R. Braking (100\%)** \\ \hline Rec. En. & 9.93\% & 12.21\% & 14.63\% & 56.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sensitivity analysis results for several rear braking percentages and a mass of 220 kg.  
Figure 17: Velocity of the motorcycle in a specified time period.  
Figure 18: Available energy over time until a 10% SOC is reached.  
A \(\pm\)5% variation of the total mass (rider + motorcycle = 220 kg) was also performed. For this, new lap times were calculated using the developed software and the recuperated energy values in Table 8 were obtained.  
If the total mass of the system were to decrease by 5% (=11 kg) to 209 kg, the percentage of recuperated energy would decrease to a value of 12.03%. However, the traveled distance would increase from 39.635 km to 40.527 km.  
If the total mass of the system increases to 231 kg, the energy recuperation results increase to a value of 12.45%, although the traveled distance decreases to 38.886 km.  
To achieve the same velocity as the motorcycle with 220 kg, more energy is required in the 231 kg scenario as kinetic energy in Equation (4) needs to increase. Hence, even though more energy is also recuperated in the 231 kg situation, the traveled distance actually decreases. The exact opposite occurs for the 209 kg scenario, where the energy required to attain the same velocity decreases and although less energy is recuperated, the traveled distance is improved.
###### Abstract  
This paper introduces a novel structural health monitoring (SHM) approach based on guided electromagnetic waves propagating in a dielectric waveguide in the frequency range from 23.5 to 26 GHz. This approach enables the detection of structural damage based on the analysis of radar signals. This paper presents the performance of the methodology through an experimental case study considering an autonomous heavy lift drone where the whole SHM system is integrated onboard. The whole data acquisition pipeline is described, and damage detection results based on a damage indicator approach are presented and discussed. Finally, this work proves the ability of guided electromagnetic wave technology to be used in flying aerial vehicles. The methodology can be applied to other aircraft structures and application cases in the future.  
damage detection; structural health monitoring; guided electromagnetic waves Accepted: 26 March 2025  
16 May 2025  
1
## 3 Datasets and Methodology
### Datasets
#### 3.1.3 Solar Flares, Geomagnetic Storms, and Coronal Mass Ejection (CME) Datasets  
For the solar flare and geomagnetic datasets, we used NOAA SWPC data (spaceweatherlive.com, accessed on 20 June 2020), which provide real-time and archived auroral and solar activity data (since June 1996), based on the space environment monitor instrument subsystem, carried on board Geostationary Operational Environmental Satellites (GOES). The dataset consists of the strongest 50 solar flares each year; for each flare, the exact level, region, and time of the occurrence are provided. We had 1250 solar flares in the dataset (regarding the years in the range of the earthquake dataset).  
For the geomagnetic activity, we used NOAA SWPC data ([[https://www.swpc.noaa.gov/products/planetary-k-index](https://www.swpc.noaa.gov/products/planetary-k-index)]([https://www.swpc.noaa.gov/products/planetary-k-index](https://www.swpc.noaa.gov/products/planetary-k-index)), accessed on 20 June 2020), which provide Kp index values, are considered excellent proxies of disturbances in the Earth's magnetic field, and are used by SWPC to determine whether geomagnetic warnings are issued. As such, we filtered out all of the relevant maps during days with high Kp index values (\(kp\geq 6\)). For the CME dataset, we used the SOHO LASCO CME catalogue ([[https://cdaw.gsfc.nasa.gov/CME_list/](https://cdaw.gsfc.nasa.gov/CME_list/)]([https://cdaw.gsfc.nasa.gov/CME_list/](https://cdaw.gsfc.nasa.gov/CME_list/)), accessed on 20 June 2020), which contains all CMEs manually identified since 1996 from the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) mission.  
Figure 2: An example of TEC maps for an earthquake day (**upper**) and for the same earthquake time in the following year (**lower**), where the red area is the the earthquake’s area, and the red point is the earthquake’s epicenter.
## 2 Methodology
### Covariance Matrix  
* Signals \(x[n]\) of 512 or 1024 samples.
* Amplitude only, no phase information.
* Several sequences \(x_{i}[n]\) of the same scene available, from 1 to 48.
* The matrix containing the input sequences \(X=[x_{1},\ldots,x_{N}]\). \[P_{MUSIC}(\tau)=\frac{1}{\sum_{i=P+1}^{N}{\left|{a^{H}(\tau){u_{i}}}\right|^{2} }}P_{MUSIC}(\tau)=\frac{1}{\sum_{i=P+1}^{N}{\left|{a^{H}(\tau){u_{i}}}\right|^{ 2}}}\] (11) \[R_{xx}=\frac{1}{N}XX^{T}=\frac{1}{N}\left[\begin{array}{cccc}x_{11}&x_{12 }&\ldots&x_{1n}\\ x_{21}&x_{22}&\ldots&x_{2n}\\ \ldots&&\\ x_{M1}&x_{M2}&\ldots&x_{Mn}\end{array}\right]\times X^{T}\] (12) \[XX^{T}=x_{1i}x_{1j}^{T}+x_{2i}x_{2j}^{T}+\ldots+x_{ip}x_{pj}^{T}=\sum_{k=1}^ {P}x_{ik}x_{kj}^{T}\] (13) \[R_{xx}=signal\times signal^{T}\] (14)  
With \([m_{R_{xx}},n_{R_{xx}}]=[ms,ms]\). The matrix \(R_{xx}\) is represented by Equation (14), it is a square matrix of dimensions and each of the two dimensions is equal to the number of samples of the signals. Since the signals are strongly correlated, the covariance matrix undergoes spatial smoothing when it is created. Spatial smoothing will allow the proper decomposition of the covariance matrix even if the signals are coherent and will improve the detection of echoes in noisy signals. The covariance matrix is previously declared and filled with 0. It is filled by adding square covariance sub-matrices, where each of the dimensions is of magnitude \(P\), the spatial smoothing parameter specified as an input parameter. Figure 3 represents the hardware architecture of covariance matrix.  
Its value is usually 16, if not somewhere between 8 and 32. Eventually, the smoothed covariance matrix is created this way:  
\[R_{xx,ls}\bigg{(}1+k\frac{P}{2}:k\frac{P}{2}+P,1+k\frac{P}{2}:k\frac{P}{2}+P \bigg{)} \tag{15}\]  
where \(k=0,1,\ldots,\big{(}2\frac{ms}{P}\big{)}-2\).  
Figure 3: Hardware architecture of covariance matrix.  
We used CORE Generator (from IPs) instead of using programs (.vhd). CORE Generator is a tool with graphical interface allowing the creation of modules of higher complexity including memories, mathematical functions, communications interfaces, etc. allowing us to optimize the modules created to exploit the unique characteristics of each Xilinx FPGA.  
We used the CORE Generator tool to generate a RAM with 256 elements with a size of 32 bits to store the S signal. Note from Table 2, the number of slices 325 is almost 2% and the No. of IOBs is 74 that is almost 18% of Virtex XCV1000e-8 bg560 FPGA.  
We also used a multiplier with the CORE Generator and you find the synthesis results in Table 3. We can also use the multiplication with the CORE Generator:  
After verification testing, we changed the design a bit using the Floating Point block and a 16 \(\times\) 32 memory block to store the data temporarily using CORE Generator.  
In this architecture, we used two blocks Floating Point and memMatrix_Rss 16 \(\times\) 32 bits.  
We used the CORE Generator tool to generate a RAM with 16 \(\times\) 32 bits to store the result of the multiplication with the Floating \(SS^{T}\) block.