esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## Materials and Methods
### Datasets
#### 2.2.3 Topography  
The topographic information was mainly used to analyze the effect of the topography on vegetation restoration in this study. The topographic information was obtained from the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) and downloaded from the National Aeronautics and Space Administration's (NASA) Reverb Data Portal ([[http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/)]([http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/))). The estimated product accuracies for vertical and horizontal data were 20 m and 30 m, respectively, at a 95% confidence level [41]. During an aspect analysis in ArcGIS, orientations between NW (315) and NE (45) were classified as north-facing slopes and those between SE (135) and SW (225) were classified as south-facing slopes [42, 43]. We mainly discuss the vegetation restoration effects on pixels within the burn scar areas on north- and south-facing slopes; pixels that did not fall within these ranges were excluded.
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 1 Introduction
reported a CW laser diode end-pumped Nd:YAG regenerative amplifier, which was seeded by a SESAM mode-locked all-fiber seed source. The mode-locked pulse trains with a repetition rate of 38 MHz were injected into the 1 kHz regenerative amplifier, and a single pulse energy of 1.5 mJ was obtained [29]. The linear cavity SESAM passively mode-locked picosecond all-fiber oscillator can realize stable mode-locked pulses at a lower repetition rate; thus, that it has a sufficient resonant cavity length, while ensuring a simple and compact system structure. In addition, it is insensitive to external temperature and air disturbances, which further improves the stability of the system. It can be seen that the combination of an all-fiber seed source based on SESAM mode locking and a multistage Nd:YAG solid-state laser amplifier is an effective method to establish a highly stable 10 mJ-level picosecond laser source. Furthermore, the pulse width of hundreds of picoseconds can be obtained by fiber stretcher, thereby further reducing the volume of the whole system. In this paper, we first designed a compact hundred-picosecond fiber-solid hybrid laser source operating at 500 Hz repetition rate. It consists of a home-made SESAM mode-locked picosecond fiber seed source, a quasi-continuous-wave laser diode side-pumped Nd:YAG regenerative amplifier, and a double-pass amplifier. Meanwhile, we study the gain linewidth matching of Yb-doped fiber seed source and Nd:YAG laser amplifiers, and the thermal effects compensation in solid-state laser amplifier. Consequently, a laser pulse with a single pulse energy of 14.58 mJ and a pulse duration of 228 ps at central wavelength of 1064.1 nm is obtained, corresponding to a 3 dB spectrum bandwidth of 0.47 nm. The beam quality factors along the \(x\)-axis and the \(y\)-axis are 1.49 and 1.51, respectively. Thanks to high pulse energy and beam quality of this laser source, we realize the high-quality damage effect on copper material in the latter theoretical simulation. This laser source with extremely high compact and stability, which delivers laser pulse with a millijoule-level and hundred-picosecond profile, supplies a promising light source for fine processing and laser-induced damage.
## 3 Results
### Measured Volume Flows  
The mean values obtained for two indirect methods and the direct method are displayed in Table 2. They show that SM1 has the lowest mean (however, still higher than direct method on 58%) and again, SM2 shows the highest differences with the direct method (higher on 123%).  
Figure 4 shows the calculated pairwise differences of measured volume flow values with the three methods as boxplots. Comparison obtained for the pairs Direct-SM1, Direct-SM2, and SM1-SM2 is displayed. It is seen that boxplots are overlapping, That is why a Bonferroni t-test was carried out in order to identify the existence of significant differences between the mean values in the groups (Direct and SM1; Direct and SM2; SM1 and SM2). The \(p\)-values obtained using the Bonferroni t-test showed significance with the confidence interval of 95%, presented on top of the figure. Looking more specifically on each part, we can conclude that the pair \"Dir-SM1\" shows the lowest differences and again, Q values obtained via SM1 corresponds better with the direct method. The pair \"Dir-SM2\" shows higher differences and less correspondence with the direct method. The pair \"SM1-SM2\" shows that even between the indirect methods, the Q values are highly different.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stat.Type** & **Direct** & **SM1** & **SM\({}_{RD}\)1** & **SM2** & **SM\({}_{RD}\)2** \\ \hline Corr & - & 0.74 & - & 0.78 & - \\ \hline RMSE (exp) & - & 642,763 & - & 974,368 & - \\ \hline sd (exp) & 370,063 & 677,419 & 83\% & 767,976 & 107\% \\ \hline mean (exp) & 644,400 & 1,020,816 & 58\% & 1,440,346 & 123\% \\ \hline median (exp) & 587,589 & 837,680 & 42\% & 1,306,364 & 122\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical characteristics obtained for \(Q_{dir}\) and \(Q\) m\({}^{3}\) h\({}^{-1}\) from two indirect methods for the research period. \(RD\)—means relative difference in %. Parameters with the notation (exp) were transformed back to their initial units using exponential transformation.  
Figure 4: Boxplots for \(Q_{dir}\) and \(Q_{ind}\) m\({}^{3}\) h\({}^{-1}\) obtained for the differences between direct and two indirect methods (SM1 and SM2) employed in this study. Red boxplot stands for the pair differences Direct–SM1; green boxplot stands for the pair differences Direct–SM2, blue boxplot stands for the pair differences SM1–SM2. Values on top of the figure present p-values obtained using the Bonferroni t-test for the paired differences of the mean values. The confidence interval is 95%.  
Looking at each method separately and their statistical values presented in Table 2, we can conclude that the direct method shows the lowest spread of the values with the lowest mean value. SM1 shows higher values than the direct method by 58% and generally lower values than SM2. SM2 has a wide range of values, with a much greater spread. In this case, SM1 shows values (See Table 2) much closer to the direct method in comparison with SM2.
# Physical-Mechanical Properties of Cupola Slag Cement Paste
## 2 Materials and Methods
### Methods
#### 3.2.4. Setting Time  
Figure 9 shows a graph comparing the initial and final setting time values for the reference cement (CEM III 32.5), for a cement consisting of 80% CEM I + 20% CSF and for another cement consisting of 50% CEM I + 50% CSF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Density [g/cm\({}^{3}\)]** & **Blaine Surface [cm\({}^{2}\)/gl]** \\ \hline \hline Cupula slag filler (CSF) & 2.810 & 5112 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physical properties of cupola slag.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Unit** & **Limit Value** & **Test Results** & **Parameter** & **Unit** & **Limit Value** & **Test Results** \\ \hline Humidity & \% & - & 0.1 & Barium & mg/kg m s. & 20 & \textless{}0.10 \\ pH & \% & - & 10.41 (21.6 \({}^{\circ}\)C) & Cadmium & mg/kg m s. & 0.04 & \textless{}0.01 \\ Conductivity & \(\mu\)S/cm & - & 72.8 (20 \({}^{\circ}\)C) & Copper & mg/kg m s. & 2 & \textless{}0.17 \\ COD & mg/kg m s. & 500 & \textless{}50 & Total chromium & mg/kg m s. & 0.5 & \textless{}0.06 \\ Chlorides & mg/kg m s. & 800 & \textless{}50 & Mercury & mg/kg m s. & 0.01 & \textless{}0.002 \\ Phenols & mg/kg m s. & 1 & \textless{}1 & Molybdenum & mg/kg m s. & 0.5 & \textless{}0.20 \\ STD & mg/kg m s. & 4 & 480 & Nickel & mg/kg m s. & 0.4 & \textless{}0.08 \\ Sulphates & mg/kg m s. & 1000 & 60.4 & Lead & mg/kg m s. & 0.5 & \textless{}0.12 \\ Antimony & mg/kg m s. & 0.06 & \textless{}0.01 & Zinc & mg/kg m s. & 4 & \textless{}0.55 \\ Arsenic & mg/kg m s. & 0.5 & \textless{}0.02 & Selenium & mg/kg m s. & 0.1 & \textless{}0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Leachate test results.  
Figure 9: Cement setting times without setting retarder.  
Based on the results shown in Figure 9, it can be concluded that increasing the percentage replacement of CEM I with CSF delays both the start and end of the setting. To ensure that the cement with 50% CEM I + 50% CSF could be used for silver application, it was decided to use the same type of cement, but with a setting activator (Na\({}_{2}\)O), see Figure 10.  
As can be seen in Figure 10 the increase in the start and end of set time caused by replacing 50% CEM I with CSF can be corrected by using a setting time activator.
###### Abstract  
Unmanned Aerial Vehicle Remote Sensing (UAV-RS) has emerged as a transformative technology in high-resolution Earth observation, with widespread applications in precision agriculture, ecological monitoring, and disaster response. However, a systematic understanding of its scientific evolution and structural bottlenecks remains lacking. This study collected 4985 peer-reviewed articles from the Web of Science Core Collection and conducted a comprehensive scientometric analysis using CiteSpace v.6.2.R4, Origin 2022, and Excel. We examined publication trends, country/institutional collaboration networks, keyword co-occurrence clusters, and emerging research fronts. Results reveal an exponential growth in UAV-RS research since 2015, dominated by application-driven studies. Hotspots include vegetation indices, structure from motion modeling, and deep learning integration. However, foundational challenges--such as platform endurance, sensor coordination, and data standardization--remain underexplored. The global collaboration network exhibits a \"strong hubs, weak bridges\" pattern, limiting transnational knowledge integration. This review highlights the imbalance between surface-level innovation and deep technological maturity and calls for a paradigm shift from fragmented application responses to integrated systems development. Our findings provide strategic insights for researchers, policymakers, and funding agencies to guide the next stage of UAV-RS evolution.  
U
## 3 Stability Analysis for the Slipping Phase  
The first step of the control-scheduling co-design is to conduct a stability analysis for the slipping phase. In this phase, the magnitude of the clutch transmitted torque \(T_{c}\) can be actively controlled because it is generally proportional to the normal force acting on the friction interface. Further, considering that there exists \(\text{sign}(\Delta\dot{\theta}_{c})=1\) (i.e., \(\Delta\dot{\theta}_{c}>0\)) during clutch engagement, in most cases, especially when the system is stable, the piecewise nonlinearity of the clutch torque \(T_{c}(t)\), as in (7) can be linearized as \(T_{c}(t)=\overline{T}_{c}(t)\). Thereafter, the linearization allows the application of stability theory for linear systems.  
The stability analysis starts from the derivation of the transfer function of the overall system in the \(S\)-plane. For the convenience of the computation of the zero-order hold and time delay, discretization is performed by applying a \(Z\)-transform. Following that, the stability is analyzed according to the pole locations, and then the critical period can be determined. Further, the influences of the key parameters on the critical period are illustrated.
###### Abstract  
Microprocessor-controlled prosthetic knees (MPKs) improve the safety and functional capabilities of transfemoral amputees, but there is a lack of information on plantar pressure distribution and effects among individuals who have undergone transfemoral amputation of the sound limb. (1) Background: The aim of this study was to determine possible compensatory mechanisms in gait by evaluating intact extremity foot plantar pressure distribution in young MPK prosthesis users. Twenty-one patients with unilateral transfemoral amputation (TFA) and twenty-four healthy individuals were selected for the study. (2) Methods: The WalkinSense system was used to assess different foot plantar pressure distribution parameters as the participants walked at their chosen walking speed. Plantar pressure peaks and activation percentages in the eight foot regions were measured during the gait cycle. (3) Results: The pressure peaks and activation percentages in the sound limb with TFA patients and healthy subjects were measured, and statistically significant differences between the two groups were identified. The 1-, 2-, 3-, 4-, and 6-point sensor activation percentages significantly increased, whereas the 7- and 8-point sensor activation percentages decreased in the sound limb TFA participants compared with the healthy subjects. Peak plantar pressure sensor points 1, 3, 4, and 6 increased in the TFA sound limb foot in relation to healthy individuals, while they decreased in point 8. (4) Conclusion: In this study, with the use of a microprocessor knee joint TF prosthesis, in the evaluation of the underfoot pressure of intact legs, the maximum pressure point shifted to the forefoot, and it was observed that the forefoot and midfoot were more active during walking compared with the control group. This may indicate that gait compensation and plantar flexion in the sound limb are used more forcefully in the gait cycle.  
transemoral amputees; plantar pressure distributions; microprocessor-controlled knee joints +
Footnote †: journal: Applied Sciences
# Development of a New Sound Quality Parameter for Road Noise Perception Inside Vehicle Cabinet
## 1 Introduction
While these studies indicate that several aspects of in-cabin and emitted noise have been explored, our study focuses specifically on developing sound quality metrics for interior noise caused by rough road excitations. The sounds arriving from the internal combustion engine and the associated power units have been a way of communication of the driver with the vehicle. With the electric vehicles, this communication becomes weakened [12]. This statement leads to a necessity to design a proper interior soundscape, primarily for the road noise, as it is the most dominant component in EV's. During the target-setting stage of a new vehicle, vehicle-level targets are established with the expectation that objective measurements will align with subjective performance criteria. However, conflicts sometimes arise between subjective targets and their corresponding objective parameters. In certain cases, although the target is objectively met, customer perception remains unsatisfactory. This issue stems from the improper use or limitations of sound quality metrics. Either the objective parameters fail to adequately represent the acoustic phenomenon, or the metrics used do not correlate strongly enough with subjective evaluations of noise perception. The inconsistency between subjective evaluations and objective measurements presents a significant challenge in identifying and addressing sound quality issues throughout vehicle development. This divergence becomes particularly evident during the development of electric vehicles, where emerging noise sources like road and tire noise dominate. Current sound quality metrics often fail to capture these phenomena effectively, necessitating the development of a new metric that better reflects subjective perception. This study aims to create such a metric by combining objective measurements, subjective evaluations, and multiple linear regression analysis to develop an indicator that reliably predicts perceived road noise comfort and satisfaction.
## 5 Numerical Simulation
### Computation of the Ince-Gaussian Beams at \(p=3\)  
Figure 2 illustrates intensity distributions of the IG modes at \(p=3\) for several values of the parameter \(\varepsilon\).  
Figure 2 confirms that if the value of the parameter \(\varepsilon\) is small (\(\varepsilon\approx 0\)), the mode \(\mathbf{IG}^{\varepsilon}_{3,3}\) reduces to \(\mathbf{ReLG}_{0,3}\) with the amplitude being proportional to \(\cos 3\varphi\), i.e., the intensity contains six petals, all located on a single light ring. In addition, Figure 2 confirms that the intensity of the odd mode \(\mathbf{IG}^{\varepsilon}_{3,3}\) also contains six petals, but they are rotated by an angle of \(\pi/6\).  
As is also seen in Figure 2, the intensity pattern of the even IG beam \(\mathbf{IG}^{\varepsilon}_{3,1}\) with a small parameter \(\varepsilon\) has two petals and a ring split into two vertically elongated arcs. Correspondingly, the intensity of the odd beam \(\mathbf{IG}^{\varepsilon}_{3,1}\) with a small parameter (\(\varepsilon\approx 0\)) also has two petals that divide two light rings, but they are oriented horizontally.  
Figure 1: The curves indicating the errors between the exact and approximate functions from Equation (40): \(C_{3,0}(\varepsilon)-C_{3,0}(\varepsilon|L/(L+1)|)\) (continuous) and \(C_{1,2}(\varepsilon|L/L|)-C_{1,2}(\varepsilon)\) (dashed) for \(L=2\) and \(L=3\). We intentionally took different signs for both expressions so that the curves fall on opposite sides of the \(x\)-axis.  
At large values of the parameter \(\varepsilon\) (\(\varepsilon\gg 1\)), IG modes are defined by the HG modes and have Cartesian symmetry. It is seen that at \(\varepsilon\gg 1\), the even mode \(\mathbf{IG}_{3,1}^{\varepsilon}\) aligns in intensity with the HG mode \(\mathbf{HG}_{1,2}\) and has 6 local maxima, whereas the mode \(\mathbf{IG}_{3,3}^{\varepsilon}\) aligns with the HG mode \(\mathbf{HG}_{3,0}\) and has 4 petals along the horizontal axis. Conversely, for the odd modes at \(\varepsilon\gg 1\), the mode \(\mathbf{IG}_{3,1}^{o}\) has 4 petals on the vertical axis, while the mode \(\mathbf{IG}_{3,3}^{o}\) has 6 petals separated by two vertical and one horizontal zero-intensity lines.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 2 Elixir Programming Language
### Erlang: A Foundation for Resilience  
Erlang, introduced in 1986 by Ericsson, is a functional programming language designed for concurrent systems with the \"run forever\" philosophy [18]. This focus on robust, nonstop systems makes Erlang a natural choice for edge computing.  
Several key features contribute to Erlang's suitability:  
* Concurrent processes: Erlang runs multiple lightweight processes on the Erlang Virtual Machine (BEAM). Individual process failures are handled by automatic termination and restart, ensuring system resilience.
* Efficient resource allocation: Erlang's process scheduling ensures timely responsiveness for real-time tasks. Processes can migrate between execution queues, minimizing wait times and optimizing message exchange.
* Hot code loading: Updates can be applied without service interruptions via hot code loading, enabling nondisruptive operation and continuous maintenance.  
These features demonstrate Erlang's strength in building reliable and responsive systems, making it a valuable foundation for edge computing frameworks.
## 2 CDS Color System
### CDS Lookup Table  
As mentioned above, in the current version of the CDS atlas, 2740 samples are arranged on the 40 CDS hue pages, and each color sample was related, respectively, with a set of CIE chromaticity values.  
Considering that on the space of the CDS color triangle, the same lightness samples are found on a straight line perpendicular to the b-w axis, and the same chromaticess samples are found on a straight line parallel to the b-w axis, the linear interpolation method can be used to obtain the chromaticity values of the samples between two samples on the (w, b) grids. As a result, the lookup table between the notations of the CDS and the chromaticity values of the CIEXYZ and the CIELAB system can be established. The configurations of the lookup table are shown in Figure 6. In fact, there are over 400 thousand samples in the lookup table of Figure 6. Note that the illuminant for calculating the chromaticity values mentioned above is D65. Based on the CDS lookup table, the CIE chromaticity values of a color sample can be searched for using the CDS notation, and vice versa.  
Figure 5: The composition of the CDS notations.
## 3 Magnetic Characterization Instruments  
The magnetometer is a widely used device for many scopes, including biomedical purposes, geophysical studies, non-destructive testing, and catalytic materials characterization [58]. To serve the last scope, namely magnetic nanoparticle sensing, in the international literature exist several magnetometers, like the vibrating sample magnetoresistance (VSM), the optically pumped magnetometers (OPM), the magneto-optical Kerr effect magnetometers, the giant magnetoresistive sensors (GMR) [37; 59], the alternating gradient magnetometers, the Faraday rotation magnetometers, the Hall effect magnetometers, the superconducting quantum interference devices (SQUIDs), the fluxgate magnetometers, etc., each with different sensitivity and cost [60; 61]. In this paragraph, the most important types of magnetometers are presented and compared with their advantages and disadvantages.  
The main issues that determine the effectiveness of every magnetic sensor can be narrowed down to two: the inherent sensitivity of the magnetometer and its ability to be placed near the sample.  
One comparison between the different types of magnetometers derives from their field sensing range, as can be seen in Table 1[62]. Observing this table, one can categorize the magnetometers into three categories: (1) the magnetometers that detect fields lower than 1 \(\upmu\)G, (2) the magnetometers that detect fields between 1 mG to 10 G, and (3) the magnetometers that detect fields above 10 G.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Magnetic Sensor Technology** & **Detectable Field Range (Gauss)** \\ \hline Squid & \(\approx\)10\({}^{-9}\)-10\({}^{5}\) \\ Fiber-Optic & \(\approx\)10\({}^{-6}\)-10\({}^{1}\) \\ Optically Pumped & \(\approx\)10\({}^{-8}\)-10\({}^{0}\) \\ Nuclear Procession & \(\approx\)10\({}^{-7}\)-10\({}^{2}\) \\ Search-Coil & \(\approx\)10\({}^{-8}\)-10\({}^{10}\) \\ Anisotropic Magnetoresistive & \(\approx\)10\({}^{-7}\)-10\({}^{1}\) \\ Fluxgate & \(\approx\)10\({}^{-6}\)-10\({}^{2}\) \\ Magnetotransistor & \(\approx\)10\({}^{-1}\)-10\({}^{4}\) \\ Magnetodiode & \(\approx\)10\({}^{-1}\)-10\({}^{4}\) \\ Giant Magnetoresistive & \(\approx\)10\({}^{-1}\)-10\({}^{8}\) \\ Hall Effect Sensor & \(\approx\)10\({}^{1}\)-10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between different magnetometer types.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning  
6G will be able to support enhanced Mobile Broadband Communications (eMBB), Ultrareliable Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC), but with enhanced capabilities compared to 5G networks. Furthermore, will be able to support application such as Virtual Reality (VR) Augmented Reality (AR) and ultimately Extended Reality (XR). Based on the problem different ML algorithms are applied as analyzed below.