esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Six-Minute Walk Test Dynamics  
The changes in the 6 MWT results before and after the interventions are summarized in Table 2 and illustrated in Figure 2. The results are presented separately for the intention-to-treat and per-protocol analyses.  
* Cadence: In the intention-to-treat analysis, significant improvements in cadence were observed in the CE group compared with the CG (_p_ = 0.009), with the HIIT group showing nonsignificant changes. The per-protocol analysis further confirmed these findings, with the CE group showing significantly greater improvements than both the HIIT and CG groups did (_p_ = 0.002).
* Distance: While the intention-to-treat analysis revealed nonsignificant differences in walking distance among the groups (_p_ = 0.133), the per-protocol analysis revealed significant improvements in the CE group compared with the other groups (_p_ = 0.034).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Overall** & **CE** & **HIIT** & **CG** \\  & **n = 76** & **n = 25** & **n = 25** & **n = 26** \\ \hline Age (years) & 69.78 (0.95) & 70.64 (1.65) & 71.08 (1.21) & 67.69 (1.93) & 0.282 \\ Sex, n (\%) & & & & 0.025 \\ Male & 37 (48.7) & 8 (32.0) & 11 (44.0) & 18 (69.23) & \\ Female & 39 (51.3) & 17 (68.0) & 14 (56.0) & 8 (30.77) & \\ Cadence (steps/min) & 114.35 (2.10) & 106.20 (4.31) & 115.23 (3.11) & 121.35 (2.83) & 0.010 \\ Distance (meters) & 425.13 (12.31) & 367.67 (23.89) & 450.77 (20.12) & 455.73 (15.44) & 0.004 \\ HRmax (beats/min) & 101.99 (2.21) & 96.08 (2.05) & 102.56 (3.30) & 107.12 (5.16) & 0.122 \\ Exercise intensity (\%) & 67.55 (1.50) & 64.08 (1.63) & 68.84 (2.68) & 69.65 (3.15) & 0.265 \\ \hline \hline Mean (SE) and n (\%). CE: combined exercise; HIIT: high-intensity interval training; CG: control group; HRmax: maximum heart rate. ANOVA was used for continuous variables, and the chi-square test was used for categorical variables. & & & \\ \end{tabular}
\end{table}
Table 1: Baseline sample characteristics.  
HRmax: Compared with the other groups, the CE group presented a significant increase in HRmax in both the intention-to-treat (\(p\) = 0.006) and per-protocol analyses (\(p\) < 0.001).
* Exercise Intensity: In the intention-to-treat analysis, exercise intensity increased significantly in the CE group compared with the CG (\(p\) = 0.004). The per-protocol analysis also revealed a significant increase in the CE group compared with both the HIIT and CG groups (\(p\) < 0.001).
# Metal Additive Manufacturing for Satellites and Rockets
## 8 Conclusions and Outlook  
As this brief overview of recent developments in metal additive manufacturing shows, many satellite and spacecraft components can already be prepared by these 3D printing techniques, while several challenges still remain. Translating the fast progress of polymer- and metal-based additive manufacturing technologies and the steady development of new techniques into the near future, it can be assumed that many of the recent problems, e.g., related to surface roughness or insufficient mechanical properties, will be solved by the expected progress of the technologies themselves and by developing strategies to make new metals printable by these techniques.  
An important point which has to be taken into account is the redesign for a specific additive manufacturing process, taking into account its limitations and making use of new freedom of design in order to optimize the mechanical, morphological, temperature-related, and other properties of the 3D printed objects.  
Additionally, future research has to concentrate on further developing the 3D printing techniques described here in order to reduce shape deviations from the design and improve the mechanical properties of additively manufactured objects toward the values reached by the wrought material. Further, optimizing the printing parameters for the materials especially well suited for space applications is necessary. In this way, it will be possible to reduce not only the mass of several components, but also development time and costs.  
Conceptualization, T.B., G.E. and A.E.; investigation, T.B., G.E. and A.E.; writing--original draft preparation, A.E.; writing--review and editing, T.B. and G.E. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Silesian University of Technology Rector's Grant no. 14/030/RGJ21/00110.  
Not applicable.  
No data were created in this review.  
The authors declare no conflict 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.
## 4 Simulation Results and Analysis
### Validation of the DPLL Module  
In the DPLL module, the time-domain waveform of the communication ranging code obtained after demodulation is shown in Figure 16. In the DPLL module, due to the limitations of the designed DPLL performance, the demodulated communication ranging code will experience attenuation and will have residual noise that was not fully filtered out. Additionally, since the communication ranging code in the BPSK module is first converted into a bipolar signal before phase modulation, the demodulated communication ranging code in the DPLL module will also be a bipolar signal.  
As shown in Figure 16, although the communication ranging code obtained after DPLL demodulation experiences attenuation and residual noise, its fluctuations remain consistent with the original communication ranging code. Therefore, the phase demodulation functionality in the designed DPLL module is correct.  
Figure 14: Comparison of the time–domain signals of the low–depth BPSK modulated signal and the communication ranging code.  
Figure 15: Comparison of the frequency spectrum of the signal after low–depth BPSK modulation and the frequency spectrum of the signal after noise addition.
# Lineal Energy of Proton in Silicon by a Microdosimetry Simulation
## 3 Results and Discussions
### Effect of Various Physics Models on Secondary Yields  
[PERSON] and [PERSON] et al. showed that the displacement damage in silicon from single particle interaction is dependent on the secondary particles type and energy deposition [41; 42; 43]. In this study, we also investigate if different physics models may affect the secondary particle yield. Figure 6 shows the secondary particle yields using various intra-nuclear cascade models in Geant4, and their comparison to [PERSON]'s results [8]. The [PERSON] results come very close to the results of using the Bertini model. However, the peak in \(Z=6\) in this study was much higher than that from [PERSON]'s result. In [PERSON]'s study, all the secondary particles are from primary proton and they were calculated by the LAHET code system using Bertini cascade model. In the LET calculation part, [PERSON]'s study did not consider the decay and further nuclear interaction. In our study, the primary proton will generate the first filial and first filial can still has a nuclear interaction and decay to generate the second or further filial. That is, a heavier secondary particle (\(Z=12\), \(13\), or \(14\)) may break into two middle-sized fragments (\(Z=6\)) during its transportation as also shown by [PERSON]'s result [44].  
Compared to the differences between the binary and the Bertini cascade, it is difficult to say that they are systematically different in Figure 6 and measurements are needed. In addition, for \(10^{9}\) incident particles, the high-precision physical model in Geant4 gave a negligible difference in secondary particle yields.  
Figure 6: Secondary particle yields in 100 nm silicon irradiated by a 200 MeV proton beam using various physics models. BIC represents the Binary cascade model. BERT represents Bertini cascade model. HP represents high precision add-on.
## Hardware and Measurement System
### Single-Board Computer Platform
A shared memory circular buffer is used to transport the acquired sensor data to a non-time-critical handler process, which is used to store the data on the local file system or to stream it over the network. This approach allows stable sensor polling with a frequency of more than 1 kHz. The handler process can be configured to perform initial pre-processing, such as smoothing or downsampling of the sensor data before it is further processed. Figure 2: Additional sensor boards. (**Left**) A sensor board equipped with four ADS1115 16 bit ADCs [22], port headers to connect up to 12 channels of analog sensors, and four additional ports for the I\({}^{2}\)C-connected sensor hardware. (**Right**) A sensor board equipped with a battery-backed CMOS real time clock [23], four port headers to connect SPI-based sensors, and two additional ports for the I\({}^{2}\)C-connected sensor hardware. The hardware facing software component is designed as a plugin mechanism to facilitate the rapid development of a variety of optimized sensor drivers. A driver module can be independently deployed and loaded during run-time depending on the attached sensor hardware and the task at hand. Currently, driver modules that have been created for the sensor hardware are listed in Table 1.
## 1 Introduction and Research Aims
### Aims of the Research  
In such a scientific context, this work is part of the FarmBuiLD (Farm Building Landscape Design) research project, aiming to develop methods and tools that can lend support to the design and evaluation of rural buildings and to the building management process as well, for an optimal landscape integration of buildings, as it was broadly discussed in Reference [32].  
In particular, the authors have developed a methodological framework [32] including a set of parameters for the physiognomic characterization of both historical and contemporary rural buildings [33]. Moreover, the former paper also addressed the topical aspects related to materials, color and texture of the external surfaces of rural buildings; the latter also illustrated in detail the reasons for the selection of the physiognomic parameters. The methodology was tested on a sample of historic rural buildings appropriate for surveying the architectural features [34]. This analysis was performed also taking into account the original functions of the historic rural buildings, which is closely related to their architectonic typologies. Then, the methodology was validated through the application to a set of contemporary rural buildings [35]. In this study in particular the authors pointed out and discussed the topic of the parametric tools definition, based on the interpretation of the relationships among building dimensions (height, length, and width), full and empty volumes, and exterior surfaces. This stage was developed according to the literature on the perception of the landscape and the relationships between buildings and the surrounding environment [6].  
The general goal of the research conducted in this work is to define a meta-design framework to carry out actions suitable to improve the rural landscape quality.  
This study has the following specific aims:  
* to perform a numerical analysis of the formal characters of historic rural buildings with reference to their typological features;
* to analyze the relationships between the architectonic characteristics of historic rural buildings and contemporary ones and to quantify the level of discontinuity.  
This study was carried out on two samples of rural buildings (historical and contemporary) selected within a pilot study area of the Emilia-Romagna Region (Italy), namely the Imola District in the metropolitan city of Bologna.
## 4 Current Analytical Techniques
(2018). The PTR-MS technique detects VOCs in parts per billion (ppb). Researchers in the fields of environmental science, food technology, and medical diagnosis have reported its effectiveness. For instance, in the PTR-MS technique with O\({}_{2}\) ion in operation mode, ethylene was detected in apples, nectarines, apricots fruits, _Arundo donax_ leaves, and _Pseudomonas syringae_ pv. actinidia bacterium at concentrations as low as one ppb. It has the limitation of a lack of specificity, especially in the presence of complex matrices when other compounds could interfere with the signals for ethylene [PERSON] et al. (2018). Overall, PTR-MS is a powerful tool for evaluating the quality, stage of ripening, and differentiation among cultivars of fruits and vegetables, using their VOCs as indicators rather than for detecting microbial contamination. Before the analysis, the VOCs are extracted from the sample using two methods. The first method requires solvents, and, in some cases, the sample is heated. It is very efficient for extracting VOCs but can also provoke compound degradation and cause the volatilome profile to be wrong. The second method involves the use of solvent-free techniques. In terms of this method, the \"Headspace (HS)\" is an alternative procedure known to be fast, universal, sensitive, solvent-free, and economical. It is widely used to trap VOCs in agricultural products. The HS of a sample can be recovered with the use of a tight syringe and the solid-phase micro-extraction (SPME) procedure. Notably, SPME is recognized as a solvent-free procedure that reduces artifact formation and is suitable for recovering VOCs. It is based on the concentration of VOCs onto a fiber coated with the appropriate stationary phase, which is transported to the detector and subsequently desorbed into the hot GC injection port. Equilibrium between the amount of analyte in the sample matrix, in the headspace above the sample, and the stationary phase coated on a fused silica fiber is established in the case of headspace solid-phase microextraction (HS-SPME). The quantity of compounds absorbed depends on the compound partitioning between the HS and the agricultural product matrix, the partitioning between the fiber coating and the HS, and the coating phases in SPME. These coating phases in SPME are of vital importance; PA fiber and DVB fiber are better when polar compounds are predominant in the samples, and PDMS or CAR/PDMS fibers are more suitable for extracting non-polar compounds. Finally, CAR/PDMS or DVB/CARB/PDMS fiber coatings can extract polar and non-polar compounds [PERSON] et al. (2018). Figure 1 summarizes the release processes and analysis of VOCs in the case of horticultural products.
## 4 Results and Discussion
### Cluster Analysis
Cluster 2 includes four monitoring stations in the middle part of the LHW and groups monitoring stations in this cluster as MS4, MS5, MS15, and MS19. Four point sources, specifically BGI, Pepsi Factory, Referral Hospital, and Industrial Park monitoring stations, were assigned to this cluster. Consequently, this cluster is characterized by comparatively heavy pollution.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 5 GUI MicroStripOptim for Analysis of Microstrip Filters  
MicroStripOptim is a developed GUI for designing, studying and optimizing microstrip low pass filters. This tool is totally compatible with both MATLAB and Octave. The code for version 1.0 of MicroStripOptim can be found in a repository at the following link [[https://github.com/[PERSON]/MicroStripOptim/blob/main/src/MicroStripOptim.m](https://github.com/[PERSON]/MicroStripOptim/blob/main/src/MicroStripOptim.m)]([https://github.com/[PERSON]/MicroStripOptim/blob/main/src/MicroStripOptim.m](https://github.com/[PERSON]/MicroStripOptim/blob/main/src/MicroStripOptim.m)) (accessed on 8 December 2024).  
The GUI consists of a window with edit-boxes to set the parameters to analyze one specific filter, as well as edit-boxes to analyze the variation in the parameters in the filter response. In Figure 6 the GUI is shown with data for one specific filter (left-upper side) and a range of values to analyze the variation in the parameters (left-lower side). In the upper left side, there are edit-boxes to configure the properties for one specific filter. These properties are as follows:  
* Dielectric constant, \(e_{r}\);
* Height of the substrate, \(h\), in mm;
* Cutoff frequency, \(f_{c}\), in GHz;
* Filter order, \(n\), from 3 to 9;
* Source impedance, \(Z_{0}\), in \(\Omega\);
* Width of high impedance line, \(W_{L}\), in mm;
* Width of low impedance line, \(W_{C}\), in mm.  
In the middle of the window there is a popup-menu to select the type of filter, with the following options:  
* Butterworth--0.5 dB;
* Butterworth--3 dB;
* Butterworth--MAXIMALLY FLAT;
* Chebyshev--\(L_{Ar}=0.01\) dB;
* Chebyshev--\(L_{Ar}=0.04321\) dB;
* Chebyshev--\(L_{Ar}=0.1\) dB.  
In the lower-left side, there are edit-boxes to set the range of values to conduct sensitivity analysis on the overall filter response. These options will be explained further.  
In the right side of the window, there are 8 buttons, labeled as: \"Scattering\", \"Print data\", \"Find limits\", \"Limits vs. \(Z_{0}\)\", \"\(TL\) vs. \(f_{c}\)\", \"Simulate\", \"\(TL\), \(L_{A}\), \(L_{A}^{\prime}\)\" and \"\(TL\) const\".  
The functions of the buttons are explained in the next section.
## 3 Modeling and Error Analysis of the Navigation Process in RINS
### Error Model of a Navigation System
Velocity error model: the position error equation can be expressed as follows:  
\[\delta\dot{p}=M_{pv}\delta v+M_{pp}\delta p \tag{17}\]where the calculation matrix can be expressed as  
\[M_{pv}= \begin{bmatrix}0&1/R_{M\hbar}&0\\ \sec L/R_{N\hbar}&0&0\\ 0&0&1\end{bmatrix}  \tag{18}\]  
\[M_{pp}= \begin{bmatrix}0&0&-v_{N}/R_{M\hbar}^{2}\\ v_{E}\sec L\tan L/R_{N\hbar}&0&-v_{E}\sec L/R_{N\hbar}^{2}\\ 0&0&0\end{bmatrix}  \tag{19}\]  
According to the error model described above, the total error model of the inertial navigation system could be expressed with Equations (15)-(17). In summary, the navigation accuracy is influenced by various parameters, and they are all coupled to each other. In order to describe the influence of an individual error source, a numerical analysis is conducted to observe the influence of an individual parameter.
## 6 Discussion
### Limitations and Future Work  
We developed a walking environment information generation system for visually impaired individuals, focusing on four static classes. The system was designed to recognize the walking environment and generate environmental information. A pilot test was conducted with one visually impaired participant and three sighted participants. The system's contribution was assessed through comparison with previous research and by evaluating system execution time. However, this study had limitations. The experiments were conducted in a controlled laboratory environment with a small number of participants. To generalize the proposed system, it should undergo validation with a more diverse and larger group of participants, considering factors such as age, gender, and physical abilities, after obtaining IRB approval. This would allow for a more accurate assessment of the system's effectiveness and usability and help improve its performance by reflecting the actual needs of the participants.  
Additionally, for the generalization of the system, the classes should be updated to include static and dynamic objects. This means effectively processing a large and diverse amount of data and applying it to the system. Future research should focus on obtaining high-quality data to enhance the system's performance and generating various types of sentences to provide more information to visually impaired individuals.  
Conceptualization, J.L. and K.-A.C.; methodology, J.L. and K.-A.C.; software, J.L.; validation, J.L., K.-A.C. and M.L.; investigation, J.L.; data curation, J.L.; writing--original draft preparation, J.L. and K.-A.C.; writing--review and editing, J.L., K.-A.C. and M.L.; visualization, J.L.; supervision, K.-A.C.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2024S1A5A8019541).  
Not applicable.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 5 Simulation Results
Figures 9 and 10 show the average data rate and outage 5% data rate when we vary the number of UAVs. As can be seen, by introducing FFR into the UAV network, regardless of the number of UAVs, we were able to increase the data rate for users at outage while reducing the loss in the average data rate. We observe that the result in Figure 9 is a linear relationship. As each UAV has an equal capability of providing the data rate, increasing the number of UAVs (or the number of base stations) yields a linear inclination of the overall system's capacity. Subsequently, the average user rate also increases linearly. The same trend is also observed in Figure 10, with the outage rate slightly increased whenever a new UAV is added to the system. This is due to the same reason for the linear inclination of the average rate in Figure 9. There is an exception for the red curve in Figure 10, which shows the performance of frequency division per UAV scheme. The outage rate first decreases when the number of UAVs changes from three to four, then follows the aforementioned trend. The authors believe that this is because when the number of UAVs is increased from three to four, the effects of interference between neighboring UAVs overwhelm the benefits of increasing UAVs. Next, to demonstrate the benefit of the proposed mmWave-based system against the conventional microwave-band one, numerical evaluations of both systems are conducted as follows. For this purpose, the following four schemes were investigated and are shown in Figure 11.  
* Microwave band/conventional K-means-based UAV placement method;
* Microwave band/proposed UAV placement method, i.e., K-means-based smallest-circle problem;
* mmWave band/conventional K-means-based UAV placement method;
* mmWave band/proposed UAV placement method, i.e., K-means-based smallest-circle problem. These methods are numerically analyzed for 200 users distributed in a 100 m \(\times\) 100 m area, as in the previous studies, to obtain the offered data rate. This simulation was also based on 300 random generations of user distributions. The parameters used for the microwave-band simulation comparison are listed in Table 2.  
The corresponding numerical analysis results are shown in Figure 11. As can be seen, the data rates provided by the mmWave system are generally higher than those of the microwave systems. This is due to the effect of wider bandwidth when using mmWave. When comparing the two UAV placement methods, it is found that the proposed algorithm in this paper is more efficient for the mmWave system than the conventional microwave-band system. Specifically, by using the proposed K-means-based placement method with smallest-circle problem, the offered average data rate is improved by 16.3% for the mmWave band, while it is only increased by 0.4% for the microwave band. This indicates that the proposed method is particularly effective in the higher frequency band where propagation loss is remarkable.