esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Future Directions
The applications of MMSs are vast and continue to grow, spanning natural resource management, urban planning, precision agriculture, cultural heritage documentation, and autonomous vehicle navigation. The ability of MMSs to capture time-lapsed information and integrate additional data layers, such as thermal or multi-spectral imaging, further enhances their utility in addressing complex environmental and societal challenges. As MMS technology continues to evolve, it is expected to play an increasingly critical role in the development of smart cities, sustainable resource management, and autonomous systems. Integration of AI and ML methods in an MMS still requires more investment from the scientific and engineering communities. Despite considerable attention to advanced topics such as high-accuracy or real-time ML methods, there still seems to be a lag among MMS developers in terms of smart implements. Challenges such as performance, suitability of hardware and method, efficient usage of cloud computing, high-speed wireless communications, and low-cost, low-energy in-house implementations should be considered by the researchers and developers of future generations of MMSs. In conclusion, the future of MMSs is bright, with immense potential for innovation and impact. It lies in the continued integration of advanced sensors, computational methods, and AI-driven analytics. The development of standardized, modular, and scalable MMS platforms will be essential in unlocking the full potential of this technology. As we move forward, interdisciplinary collaboration and open-source initiatives will be key to driving innovation and ensuring that MMS technologies remain at the forefront of geospatial science and engineering. By addressing the challenges and opportunities outlined in this paper, the geospatial community can continue to push the boundaries of what is possible with MMSs, driving progress in science, industry, and society. Conceptualization, E.K. ; methodology, E.K. and S.N. ; investigation, E.K. and S.N. ; resources, E.K. ; writing--original draft preparation, E.K. ; writing--review and editing, E.K., S.N., P.P., Y.C., E.H. and A.H.; visualization, E.K. ; supervision E.K. ; project administration, E.K. ; funding acquisition, E.K. All authors have read and agreed to the published version of the manuscript. This research project is funded by Visionium Oy (Helsinki, Finland) through the project number 2025-1000313-1 \"promote and facilitate civil applications of Mobile Mapping Systems in Finland\". Authors [PERSON] and [PERSON] are co-founders of the company Visionium Oy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Fatigue Life Estimation
### Fatigue Life Prediction  
According to the elastic/plastic finite element analysis under the simulated load, the stress and strain response could be obtained to calculate fatigue life of welded A-type frame. The cyclic material properties were based on Table 3, and the linear cumulative damage rule was still used in this estimation. Taking the mean stress into account, the Smith-Waston-Topper (SWT) correction method was utilized in the MSC software. Fatigue was considered to have good precision in high plastic strain field. Then, the lifetime contour of welded A-type frame was acquired, as shown in Figure 23. The minimum fatigue life of this welded A-type frame was 11,203 cycles (about 24 cycles a day) and located at the bottom corner between front plates and back plates. Visual inspections of the welded A-type frame revealed the crack (seen in Figure 2) at the right bottom weld toe after the truck ran for  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **A1-1** & **A1-2** & **A2-1** & **A2-2** & **A3-1** & **A3-2** & **A4-1** & **A4-2** & **A5** \\ \hline Simulated stress & 101 & 117 & 79 & 98 & 333 & 301 & 75 & 81 & 109 \\ Tested stress & 92 & 105 & 71 & 92 & 316 & 285 & 69 & 78 & 97 \\ Error & 9.8\% & 11.4\% & 11.3\% & 6.5\% & 5.4\% & 5.6\% & 8.7\% & 5.1\% & 12.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between simulated and tested principal stress (MPa).  
Figure 21: Schematic diagram of measuring points of welded A-type frame.  
Figure 22: Layout of measuring point A1-2.  
13 months at the downhill and turning condition, and the simulated results was in good agreement with the tested ones. According to the calculated results, the fatigue life of welded A-type frame has to be increased, because its current lifetime was not in compliance with engineering requirements.
## 2 Results and Discussion
### Polyphenolic Compounds Analysis by HPLC Supported by FTIR and Confocal Microscopy
#### 2.1.2 FTIR Analysis
For each point, 3 samples were analyzed and are shown in the figure. \(y\)-axis = transmittance (%), \(x\)-axis = wavelength (cm\({}^{-1}\)). wine (1442 cm\({}^{-1}\)). The intense peak at 1447 cm\({}^{-1}\) was due to the C=C-C stretching, typical of aromatic systems and found in kiwifruit wine. The peaks at 1323, 1357, and 1339 cm\({}^{-1}\) for ferulic acid, rutin, and kiwifruit wine, and at 1230-1200, 1211, 1217, and 1223 cm\({}^{-1}\) for ferulic acid, persimmon, pomeranate, and kiwifruit wines suggested the bending vibration of the O-H group. The peak at 1324 cm\({}^{-1}\) may be attributed to the O-H bend of phenols alcohols [21] and was only found in kiwifruit wine. The peak at 1224 cm\({}^{-1}\) corresponded to the C-OH of phenols and was found in kiwifruit wine (1223 cm\({}^{-1}\)). The spectra in kiwifruit wine showed -CH bending and -CH\({}_{2}\) wagging at 1339 cm\({}^{-1}\). The peak at 1065 cm\({}^{-1}\) can be ascribed to the C-OH group of sugars in glycosylated phenols and was shown for persimmon wine at 1076 cm\({}^{-1}\). The peak at 1016 cm\({}^{-1}\) can be ascribed to the phenolic C-OH and appeared in pomergnate and kiwifruit wines at 1020 and 1014 cm\({}^{-1}\), respectively. In very recent reports [2; 3] of the same types of wines, the obtained results showed that FTIR spectra (1800-900 cm\({}^{-1}\)) of different fruit wines can be used as main indices of the year of vintage and quality of fruit wines, of wine authentication, and of its fingerprint. The findings, based on polyphenols from fruits and fruit wines, their bioactivity, and their health properties, correspond to the main shifts in the polyphenol region of the present report. FTIR analysis can be effectively supported by monitoring fluctuations in fluorescence intensity that arise from active compounds diffusing through an exceedingly small illumination volume, which is typically the focus of a confocal microscope. Analyzing these fluctuations through a correlation analysis provides insights into the diffusion coefficients, fluorescence brightness, and concentration of these compounds. These parameters are crucial for evaluating the size, aggregation behavior, and interactions of the active compounds in fruit wines, which are significant for understanding their impact on wine stability. Thus, to broaden and complete the data from the FTIR examination, a confocal microscopy examination of the samples was performed as the next step of the analysis.
## 6 Conclusions  
CMLs possess three random features: spatial-distribution, orientation, and length. All three were addressed in this study and empirical conclusions were derived. The simplest of the three is the orientation, appearing to be statistically independent of the other two features, and distributed uniformly across all angles [0\({}^{\circ}\), 180\({}^{\circ}\)]. The CMLs' spatial distribution was empirically found to be dependent on the environment type in the sense of population density. The denser the population is in the observed region, the denser the network is. This finding which relates population density to network density supports prior studies regarding backhaul BSs densities [10]. The CMLs' lengths were found to suit an exponential random variable. Moreover, CMLs' mean length, \(l\), which we claim corresponds to the distribution parameter \(\theta\) (see Equation (1)), was found to be dependent on population density as well. The denser the population, the shorter the CMLs. Thus, by association, statistical dependence is suggested between the CMLs' lengths and their spatial density. Such dependence was modeled using a non-linear model.  
Figure 7: Portraying the main attributes of the synthetic CMLs. (**a**) spatial distribution, (**b**) lengths distribution, (**c**) angles distributions [6].  
The ability to apply statistical models to CMLs allows a much needed understanding of the study of CML-based precipitation monitoring. Through these models one may design reconstruction algorithms engineered for the nature of these random projections. Moreover, as presented in Section 5, these statistical models allow for the computational simulations of CMLs.  
Conceptualization, L.G. and H.M.; Formal Analysis, L.G.; Funding Acquisition, H.M. and L.G.; Methodology, L.G.; Software, L.G.; Validation, L.G.; Investigation, H.M. and L.G.; Writing Original Draft Preparation, L.G.; Visualization, L.G.; Supervision, H.M.  
This research was funded in part by the German Research Foundation through the Integrating Microwave Link Data for Analysis of Precipitation in Complex Terrain: Theoretical Aspects and Hydrometeorological Applications project, grant number 04340365168, and in part by the Israel Water Authority on behalf of the Israeli Government, grant number 4500963260.  
We deeply thank our research team members in Tel Aviv University, and especially [PERSON] for his fruitful cooperation and discussions. We are thankful to the Israel Water Authority for supporting and funding this research on behalf of the Israeli government. This research is also related to the German Research Foundation (DFG), the Integrating CML data for the Analysis of Precipitation in complex terrains project. We thank our friends in the Israeli cellular providers: Cellcom, Pelephone, and PHI who provided data. In Pelephone, [PERSON], [PERSON] and [PERSON]. In Cellcom, [PERSON], [PERSON], [PERSON], and [PERSON]. In PHI, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
The authors declare no conflicts of interest.
# Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
## 3 Overview of Hybrid Communication Debugging System  
To repair the wireless communication lost node, we designed HCDS based on VLC. As shown in Figure 1, the system consists of a COTS smartphone and a low-cost sensor node. The uplink and downlink of HCDS employ different visible light signal receivers. In system downlink, a regular ambient light sensor equipped by sensor node is used to sense continuous light pulses which debugging codes are modulated into. In system uplink, the optical camera as a standard configuration of the smartphone is taken as the light signal receiver. The camera adopts rolling shutter technology to capture several light pulses in one picture and further realize the optical camera communication. Finally, the flashlight and LED can be taken as an available light signal transmitter.  
Debugging codes are commonly generated by the cloud, which executes cross-compilation for different types of sensor nodes. The smartphone as a VLC gateway collects the feedback debugging information from the sensor node and sends the debugging codes to it. The large memory space of the smartphone allows frequently-used debugging codes to be stored in the smartphone. Therefore, after the smartphone receives the sensor node's debugging information, it confirms whether corresponding debugging codes are available locally. If so, the smartphone sends the local debugging codes directly through VLC; otherwise, it forwards the debugging information to the cloud and requests new debugging codes by regular radio wave communication.  
Actually, HCDS is applied in an extreme situation in which the node has been malicious intruded or lost, and by normal wireless communication cannot complete a debugging procedure in safe way. At the same time, the system does not require any modification in the hardware of COTS smartphone and sensor node. Based on the above, the one-to-one synchronous communication mode and low transmission rate of HCDS can be acceptable to only transmit essential relevant-debugging data, unlike existing researches which can employ dedicated devices to achieve high transmission rate and realize complex coding scheme for broadcast or multicast [27; 28; 29; 30]. In the other hand, limited to the performance of the VLC components equipped by COTS devices, the communication range of HCDS is difficult to effectively improve by software-level optimization. In practice, maintainers can carry the portable smartphone close to the target sensor node and control the  
Figure 1: System overview.  
communication range for completing the debugging integration. With respect to sensor nodes deployed in inaccessible areas, the drone can be taken as VLC gateway to execute debugging task. We will study this debugging scenario in our future work.
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Individual Mobility
#### 3.1.3 Protocols for Specific Mobility  
The existing specific individual mobility models for WBANs are largely dependent on one application, patient monitoring, which is based on the patient mobility model [34] to generate the movement trace of patient in the hospital. In addition, the few protocols proposed to maintain network topology are as follows.  
First, Reliable Multi-Path Routing (RMPR) [35] belongs to reactive protocols. Its operation consists of three major phases: route discovery, route maintenance and data delivery. At the route discovery phase, a sensor node floods the Route Request (RREQ) packet to all neighbors. Intermediate node information and a new metric, String Factor (SF), are added to the packet accordingly. After receiving this RREQ message, a sink node replies with a Route Reply (RREP) packet to establish multiple paths. A source node decides the routing path with the highest SF value for the path. Once the path is established, if link quality on the routing path becomes worse, a path is replaced by one with the highest sum of SF in the route maintenance phase. In this case, multiple paths are used to transmit reply messages. Figure 5 shows the operations of RMPR. In this example, SF is set to a ratio of the received power level between two nodes where node \(a\) is source and \(g\) is sink. After RREQ and RREP packets are exchanged, three separate paths are distinguished according to SF value: (i) \(\mathrm{a-b-c-g=0.39}\); (ii) \(\mathrm{a-b-d-g=0.44}\); and (iii) \(\mathrm{a-e-f-g=0.28}\). These values are computed by multiplying SF values of all intermediate nodes along the path. Among them, path (ii) is selected because it has the highest SF value. When SF value varies due to node movement as shown in Figure 4(b). SF value on each path is updated as (i) \(\mathrm{a-b-c-g=0.5}\), (ii) \(\mathrm{a-b-d-g=0.29}\), (iii) \(\mathrm{a-e-f-g=0.28}\), respectively. According to the path selection rule, path (i) is selected for packet delivery.  
Second, Loose association Implicit reservation Protocol for Mobile WBANs (LIMB) [36] was proposed by assuming that the longer the human stays in the same position, the larger the probability of movement. To implement LIMB MAC protocol, there are two types of nodes: mobile and static node. [PERSON] et al. explained the superframe structure with three phases and association mechanism. In addition, the backbone protocol handles duplicated packets and acknowledgement processing so as not to reach maximum delay. The backbone network operates in one hop communication.
## 1 Introduction
Although the antennas presented in the study are not able to cover all GNSS bands, their designs are suitable for the thermal and vibrational constraints of space applications. One particular well-known solution that provides a full GNSS band coverage performance is the four-element ring array with different element types, such as Vivaldi, F/inverted-F, or triangle patch [15; 16]. Also, the performance of such antennas can be increased by implementing a metal cavity. However, metal cavities dramatically increase the overall weight of the antenna, which is undesirable for space applications [17]. The antenna type utilized in the design of this study is principally based on orthogonally placed symmetric crossed bowtie dipole antennas [15; 16; 17; 18; 19]. Although more complex antenna designs with asymmetric dipole configurations or with an additional metal cavity structure are presented in the literature utilizing this basic structure, the simplest form of the antenna type is preferred in this study due to the need for high simplicity and reliability in space applications [18; 19]. On the other hand, the additional requirements of a spacecraft antenna due to the application environment and operating conditions such as high robustness, radome protection, compactness, lightweight, thermal resistance, and low outgassing material usage are not considered in these studies in the literature, and the presented designs are structurally vulnerable. A more comprehensive spacecraft antenna is proposed in this study, considering the application-specific requirements that dramatically affect operational performance of the antenna design. This study presents a single-feed, stable gain, RHCP-crossed bowtie antenna covering all GNSS bands, with a compact size and lightweight structure. The proposed antenna structure is reinforced with a dielectric cavity frame (DCF) to mitigate diffracted waves while enhancing its robustness. While the preliminary prototypes previously published in [20; 21; 22] also pursued to meet the same all-band GNSS antenna goal needed in multi-band operations of space applications, the proposed final design in this study provides an improved and more stable operational performance in terms of gain, axial ratio, and cross-polarization, as well as a more compact and lightweight structural performance. Moreover, by evolving a well-known simple and delicate antenna type with space industry insight, it paves the way for its utilization in space applications and offers a strong GNSS antenna candidate for space vehicles.
## 3 Results and Discussion
### Analysis of Knee Flexion  
The degree of hip flexion was compared between VICON (black) and 4 DEYE (red) results using comparative analysis (Figure 5). In the hip-knee analysis graph, similar to the previous hip-flexion analysis, the degree did not increase as the frame increased, but it appeared in many subjects. In the left-side analysis, all subjects except Subject 4 showed a similar degree in the frame in the first half. For Subjects 1 and 3, the 4 DEYE sensor showed a higher degree than the VICON sensor in the second half of the frame; however, for Subject 2, the 4 DEYE sensor showed a lower degree than the VICON sensor. The VICON results for Subject 4 were comparably different from those of the 4 DEYE sensor. The hip-knee analysis on the right side showed a tendency for the degree to increase as the frame increased, that is, a tendency that is somewhat different from the previous graph trend (Subjects 1 and 4). For Subject 2, the graphs for the 4 DEYE and VICON sensors were almost identical. For Subject 3, the VICON sensor obtained a higher degree in the early frame than the 4 DEYE sensor; in the second half, the 4 DEYE sensor obtained a higher degree than the VICON sensor.  
Figure 4: Degree of hip flexion using comparative analysis between VICON (black) and 4 DEYE (red) with left and right measurements. (**a**) Subject 1, left; (**b**) Subject 1, right; (**c**) Subject 2, left; (**d**) Subject 2, right; (**e**) Subject 3, left; (**f**) Subject 3, right; (**g**) Subject 4, left; (**h**) Subject 4, right.
## 3 Materials and Methods
### Stress-ng  
To guarantee a consistent and dependable benchmark, it was essential to choose a tool that could apply a controlled and reproducible workload to the system. For this purpose, the stress-ng tool, short for Stress Next Generation, was selected. Stress-ng [18] is a powerful, Linux-based benchmarking and stress-testing tool specifically developed to evaluate the performance and stability of various hardware components under extreme conditions. It is widely used in scenarios where assessing the resilience and capability of computer systems is critical, such as in Fog (Fog Computing) and Edge Computing environments, where devices often operate under varying loads and conditions.  
Stress-ng offers an extensive range of tests that can apply loads to different parts of a system, including the CPU, memory, input/output (I/O) subsystems, and more. By simulating intense workloads, Stress-ng helps to identify potential weaknesses or bottlenecks within these subsystems. This is particularly important in Fog and Edge Computing, where hardware is often deployed in remote or resource-constrained environments, requiring systems to be both robust and reliable.  
The tool's metrics provide detailed insights into how a system performs under pressure. For instance, it can measure the CPU's ability to handle intensive computational tasks, the memory's capacity to manage large datasets, and the I/O subsystem's efficiency in processing data transfers. These metrics are crucial for understanding overall performance and identifying any areas that may require optimization. Additionally, Stress-ng can simulate hardware failures or resource exhaustion scenarios, allowing engineers to test a system's response to such conditions and ensure that it can recover gracefully or maintain functionality. This tool provides multiple options when configuring the input and output settings for the planned architecture.  
The --metrics option displays the total number of \"Bogo\" (bogus) operations performed in the stress processes, but these are not reliable indicators of performance or throughput and are not intended for benchmarking. Instead, they provide a useful way to observe system behavior under different types of load.  
Stress-ng uses an unconventional metric called \"Bogo Ops\", which measures the number of iterations the stressor completes during a run, indicating the overall amount of \"work\" done in Bogo operations (Bogo Ops). Bogo Ops provide a general idea of system performance under load conditions but are not intended to serve as precise benchmarking metrics. The other metrics used within this --metrics option are real time, usr time, sys time, Bogo Ops/s (real time) and Bogo Ops/s (usr + sys time). These metrics are explained in Table 1.  
The use of the package stress-ng is not advised for precise and conventional measurements; it is only recommended to measure tendencies. The metrics used within this tool have no transformative operation to any other real metric, such as throughput, delay or any other. We used it as a comparative tool for the different experiments planned.