esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Height Factor Model for GNSS Tomography
### Tropospheric Transmission of the GNSS Signal  
Troposphere-induced path delays, having an evident effect on the analysis of GNSS, InSAR, Very Long Baseline Interferometry (VLBI), and other space sensors [26; 28; 29], are generally split into two parts: a dry component and wet component. Just as its name implies, the dry delay determined from surface pressure is caused by dry gases. On the contrary, water vapor is the primary cause of the wet delay, which can be estimated from the GNSS processing, for instance, GAMIT/GLOBK or Bernese software [16].  
The wet delay along the GNSS ray path \(S\), known as SWD, is derived from the following equation [30]:  
\[SWD=\int\ N_{w}ds, \tag{1}\]  
where \(s\) represents the signal path from the satellite to the receiver, and \(N_{w}\) denotes the wet refractivity along the path. In general, \(SWD\) consists of isotropic and anisotropic parts [31]. The former, related to the ZWD of GNSS stations, is calculated with the wet mapping function \(mf_{w}(\varepsilon)\) (e.g., VFM1 employed in this paper [32]), depending on the elevation angle \(\varepsilon\). The latter, caused by the flow of atmospheric water vapor, can be estimated by the north-south wet gradient \(G_{NS}^{w}\) and the east-west wet gradient \(G_{EW}^{w}\), as well as the horizontal gradient function \(mf_{g}(\varepsilon)\)[33].  
\[SWD=\underbrace{mf_{w}(\varepsilon)\cdot ZWD}_{isotropic}+\underbrace{mf_{g}( \varepsilon)\cdot\left(G_{NS}^{w}\cdot\cos(\alpha)+G_{EW}^{w}\cdot\sin(\alpha )\right)}_{anisotropic}, \tag{2}\]  
where \(ZWD\) can be extracted from the zenith total delay (ZTD) estimated in the GNSS data processing [34]. \(\alpha\) represents the azimuth angle of the GNSS signals. \(G_{NS}^{w}\) and \(G_{EW}^{w}\), which represent north and east wet gradients, determine the variation of the delay with various azimuth angles. The SWV is practically proportional to the SWD, as follows [30]:  
\[SWV=SWD\cdot\Pi, \tag{3}\]  
where \(\Pi=\frac{10^{5}}{R_{V}\left(\frac{k_{3}}{T_{m}}+k_{2}^{\prime}\right)}\) denotes the conversion factor. \(k_{2}^{\prime}\) and \(k_{3}\) are the constant factors with values of 16.48 K/hPa and \(3.75\times 10^{5}\) K\({}^{2}\)/hPa from [35], respectively, and \(R_{V}=461.53\) J/(kg\(\cdot\)K) represents the specific gas constants for water vapor. The weighted mean temperature \(T_{m}\) is obtained using the following equation [30]:  
\[T_{m}=70.2+0.72\cdot T_{0}, \tag{4}\]  
where \(T_{0}\) is the surface temperature, which is available from the local ground meteorological station.
## 2 Materials and Methods
### New Range-Energy Relations  
As mentioned above, range-energy relations are needed to calculate, via the convolution integral of Equation (4), the absorbed fraction and subsequently the S factor by Equation (2). The calculation of the convolution integral is greatly facilitated by the availability of analytic expressions for both dE/dR and R(E). The Cole-Howell relations for dE/dR and R(E) conveniently cover the energy range up to 100 keV. However, for energies above 100 keV, [PERSON] does not provide an analytic expression for R(E), and, therefore, the residual range R(E\({}_{\rm i}\))-r has to be calculated numerically from E(R). Therefore, to facilitate calculations of the absorbed fraction and S factor by the convolution integral of Equation (4), we have fitted the experimental range-energy data deduced from Equation (8). These new (fitted) range-energy relations are analytic and valid for monoenergetic electrons over the energy range 100 keV to 20 MeV, which covers the \(\beta\)-emitters used in RPT (<4 MeV) as well as most applications of electron beams in EBRT (up to \(\sim\)20 MeV). The analytic form of this new set of range-energy relations, R(E), is presented below (E in keV and R in \(\rm\mu m\)).  
For energies from 100 keV to 1 MeV:  
\[\mathrm{R(E)}=1.281\mathrm{E^{0.7}}-1.534\mathrm{E^{1.33}}+1.042\mathrm{E^{1.5}}- \ 0.06626\mathrm{E^{1.77}}\ -\ 0.4652, \tag{14}\]  
For energies from 1 MeV to 4 MeV:  
\[\mathrm{R(E)}=2.167\mathrm{E^{0.7}}+0.4123\mathrm{E^{1.4}}-\ 0.01055\mathrm{E^{1.77}}- 60.93, \tag{15}\]  
For energies from 4 MeV to 20 MeV:  
\[\mathrm{R(E)}=4.910\mathrm{E^{1.05}}-\ 0.1336\mathrm{E^{1.33}}-\ 368.9, \tag{16}\]  
These new set of expressions, Equations (14)-(16), may be combined with the Cole-Howell expressions, Equations (5)-(7) and (10)-(13), to calculate the absorbed fraction over a very broad energy range from 20 eV to 20 MeV.
#### 2.3.3 Survey Site Selection and Survey Preparation  
The survey sites were selected in varied geological and hydrogeological settings enabling method performance testing across a range of representative settings common in Malawi. Survey lines had to be perpendicular to lineaments and 2-D structures, such as lithological changes and hills. The sites had to be accessible by car and provide a line clear enough, sufficiently straight, long enough and that would not be crossed by traffic in order to install electrodes and cables. For a survey of \(n\) soundings, the required line length is equal to [\(50\times(n-1)+400\)] m. Such lines could only be found in roadsides; however, roads had to be located outside villages, in order to avoid high road activity, to be sufficiently wide to allow a truck passing next to the cables and avoid crossroads. The rural location meant there were no buried pipes or cables to avoid. Moreover, water boreholes cased with PVC in the study area (now common across Malawi) meant surveys could be acquired adjacent to them.  
Survey site preparation comprised a desk study and field reconnaissance characterisation, and setting up a specific acquisition plan that defined the survey layout (line location and orientation, number of soundings and sounding mid-point position). Site selection and survey preparation were interdependent and iterative processes, with site selection being guided and updated by the findings of the desk studies and field reconnaissance.  
Desk studies and site selection were based on the analysis of 30 m SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model) NASA data [65] and of true colour satellite imagery [80] under free GIS software QGIS 3.4.4 [81], as well as on geomorphological, geological and hydrogeological findings made during an initial two-week period of forensic surveying of community water point boreholes. Unfortunately, no borehole logs were available in the particular study area, as available boreholes were relatively old and drilled when record keeping was poor (recognising data availability is variable throughout Malawi but improving). Detailed national geological mapping and reporting in maps and memoirs by the Geological Survey of Malawi published in the 1960s-1970s remain a vital resource and were available for the study area and surroundings and consulted [63; 66; 67]. The only aquifer pumping test results available at a site accessible for resistivity surveying were from the Malaza well (again, availability of this data type is variable throughout Malawi). Field reconnaissance consisted of walkover surveys, geomorphological observations, quick outcrop analyses and asking village communities about the performance of their water points.  
Although the above represents less than the ideal to \"ground-truth\" the geophysical profiles obtained, the information sourced is representative of the baseline of information often found throughout Malawi.
## 2 Dynamic Power Management  
Figure 1 shows the set of dynamic power management techniques, which have become quite common in modern low power chips. These techniques are used during chip operation to continually adjust the power consumption based on the dynamically varying operating requirements.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 3 PM2.5 Time Series Prediction Model
### Phase Space Reconstruction  
Phase space reconstruction is a very important step in time series analysis and prediction. In order to extract useful information from time series, [PERSON] [27] proposed embedding theorem of phase space reconstruction. In the actual calculation, since the numerical differentiation is sensitive to the error. Therefore, phase space reconstruction of time series generally adopts coordinate delay method. The essence is to construct \(m\)-dimensional vector by different delay times of one-dimensional time series. Its calculation formula is as follows:  
\[\mathbf{U}_{i}(n)=[u_{i}(n),u_{i}(n+\tau_{i}),\cdots,u_{i}(n+(m_{i}-1)\tau_{i})] \tag{8}\]  
where \(\{u_{i}(n)\big{|}i=1,2,\cdots,K\}\) are input time series, \(m_{i}\) is the embedding dimension, and \(\tau_{i}\) is the delay time. The reconstructed phase space contains all the evolution information of original system.  
The embedding theorem shows that for one-dimensional time series of infinitely long, noise-free \(m\)-dimensional chaotic attractors, an \(m\)-dimensional phase space can be found, as long as the embedding dimension satisfies \(m\geq 2d+1\), where \(d\) is dimension of the dynamic system [28]. However, the existing time series are finite-length sequences with noise. The embedding dimension and delay time cannot be arbitrarily selected, otherwise the phase space quality of the reconstruction will be seriously affected.
## 4 Development of Tree-Based Techniques
### DT Model  
To obtain the most effective DT model, several models were developed using different numbers of parameters. Table 4 reports the parameters used for modelling in this study. Upon experimenting with the values of the number of instances in leaves, minimum limit of the split subset, and maximal tree depth, the most effective DT model with the optimal value of these parameters was obtained. In addition, Figure 6 shows the tree flowchart of the proposed DT model for classifying slope stability.  
In the training phase, 75% of the dataset was used (525 slope cases), which is similar to a study conducted by [PERSON] and [PERSON] [70]. The data was selected randomly, and the input parameters were inserted into the model. In the testing phase, 25% of the dataset was used, which corresponds to 175 slope cases. Figure 7 shows the results of the DT model in the classification of the FOS for training and testing sets. According to the training set, the DT model classified 300 safe slopes and 162 unsafe slopes accurately, while classifying 12 safe slopes and 21 unsafe slopes, wrongly. In addition, in the case of the testing set, the DT model classified 109 safe slopes and 47 unsafe slopes accurately, while classifying 3 safe slopes and 16 unsafe slopes, wrongly. Later, the results of the DT from both phases were observed using the performance indicators accuracy, precision, recall, F1-score, and ROC curve.
###### Abstract  
The aim of this paper was the evaluation of hybrid porous materials, named SUNSPACE (\"SUstaiNable materials Synthesized from by-Products and Alignates for Clean air and better Environment\"), realized with raw materials such as silica fume (SUNSPACE SF) and bottom ash derived from municipal solid waste incineration (SUNSPACE BA), compared to cement and leaf for particulate matter (PM) entrapment. SUNSPACE BA was synthesized to overcome the limited applicability of the original material due to its dark grey color. The modification of raw materials used for its realization allows one to obtain a light color in comparison to the corresponding SUNSPACE SF, more suitable to be used as a coating on the buildings' facades for aesthetic reasons. Moreover, another great advantage was obtained by the synthesis of SUNSPACE BA in the frame of circular economy principles; indeed, it was obtained by using a waste material (derived from waste incineration), opening new possibilities for its reuse. Experimental tests to evaluate the particles entrapment capability of the material were realized for the first time by using a nanoparticles generator. TiO\({}_{2}\) suspension with a size of 300 nm and a concentration of 3 g/L was used to simulate a monodisperse nanoparticles flux. To compare the quantity of TiO\({}_{2}\) adsorbed by each specimen, both the exposed and the pristine samples were digested and then analyzed by total X-ray fluorescence (TXRF). The results showed a high adsorption capacity of SUNSPACE BA (3526 \(\pm\) 30 mg/kg).  
Keywords:airborne PM; nanoparticles; air quality; SUNSPACE; porous material; waste material; circular economy +
Footnote †: journal: Applied Physics Letters
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 4 Experimental Results
### Time-Frequency Analysis of EMG Signals  
EMG signals were captured from the Rectus Femoris and Vastus Lateralis. This was achieved by varying the AFE gains, highlighting a clear activation signal, particularly at gains of 8 and 12, with a high signal-to-noise ratio. The frequency spectrum analysis revealed that the signal was predominantly within 3 Hz to 250 Hz, attenuating to 2.8 kHz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & \multicolumn{2}{c}{**Baud Rate: 115,200 bps**} & \multicolumn{2}{c}{**Baud Rate: 230,400 bps**} \\  & **Byte-Level** & **Burst-Level** & **Byte-Level** & **Burst-Level** \\ \hline Max. Latency (ms) & 6.22 & 6.20 & 7.18 & 7.2 \\ Min. Latency (\(\mu\)s) & 0.4 & 17.9 & 0.3 & 14.8 \\ Mean Latency (ms) & 0.25 & 0.85 & 0.13 & 0.43 \\ Median Latency (ms) & 0.002 & 0.98 & 0.001 & 0.090 \\ Standard Deviation (ms) & 0.43 & 0.34 & 0.31 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical statistical results of latency measurements using two different baud rates: 115,200 bps and 230,400 bps.  
High CMRR of the differential amplifier rejected mains frequency interference, negating the need for additional filtering. Figure 4 shows the time (top) and frequency (bottom) representations of the EMG signals received by the AFE at gain 12. It is worth noting that captures 1 and 2 were taken at different times, but they represent the acquisition of the EMG for the Rectus Femoris, which is the muscle most activated by the movement of the protocol.  
In terms of SNR, Capture 1 achieved an SNR of 34.66 dB, while Capture 2 recorded an SNR of 26.75 dB. The SNR in decibels (dB) is calculated as shown in (3), where (\(P_{\text{signal}}\)) represents the power of the signal, and (\(P_{\text{noise}}\)) represents the power of the noise.  
\[\text{SNR (dB)}=10\cdot\log_{10}\biggl{(}\frac{P_{\text{signal}}}{P_{\text{ noise}}}\biggr{)} \tag{3}\]  
The results demonstrate that the SNR values for both captures are high, confirming that the EMG signals were recorded with minimal noise interference. This is crucial for the accurate analysis and interpretation of muscle activity, as it ensures that the data reflect true physiological signals rather than artifacts or noise.
## 3 Case Study (Levels and Options of Factors (Step 1 in Figure 3))
### Additional Factors for MASS (6-10 of Step 1 in Figure 3)  
#### 3.3.1 Level of Autonomy (LoA) and Level of Remote Control (LoRC)  
LoA and LoRC were categorised into four and three levels, respectively, based on the function of autonomy and remote-control systems (see Table 3). LoA4 was eliminated in this case study because this level does not involve any operators. The autonomy functions of LoA-2 and LoA-3 are as follows:  
* LoA-2: The system can identify non-AIS ships (e.g., small boats) and objects (e.g., driftwoods), make some warning on the existence of these objects, and advise on appropriate collision avoidance routes;
* LoA-3: The system can make autonomous collision avoidance in addition to LoA2. The system takes preventive action for collision avoidance before the target ships enter the obstacle zone by the target;
* Both systems are highly reliable, although there is a slight chance of failure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Option** & **Navigation Area** & **Expected Area** & **Frequency of Approaching Ship(s)** & **Non-AIS-Ships** & **Visibility (Weather)** & **Average Wave Height** \\ \hline NC-1 & Open Sea & Pacific Ocean & A vessel every 4 h & No & Visibility of 3 miles ahead (Rain) & 3 m \\ \hline NC-2 & & & & & Visibility of 7 miles ahead (Clear) & 1 m \\ \hline NC-3 & & Coast water & Coast of Boso Peninsula & Two vessels every one hour at the same time & Small fishing vessels Leisure crafts & Visibility of 1 mile ahead (Heavy rain) & 3 m \\ \hline NC-4 & Channel & Uraga Channel & Five vessels every one hour at the same time & Visibility of 1 mile ahead (Fog) & 1 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Navigational condition in the case study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LoA** & **Detail** & **LoRC** & **Detail** \\ \hline LoA-1 & No Autonomy & LoRC-1 & No remote control \\ \hline \multirow{4}{*}{LoA-2} & Navigation Support System (NSS) & & \\ \cline{2-3}  & The system can identify non-AIS ships (e.g., small boats) and objects (e.g., driftwood), make a warning, and advise appropriate collision avoidance routes. & LoRC-2 & Support of Navigation \\ \hline \multirow{4}{*}{LoA-3} & Autonomous Navigation System (ANS) with monitoring & & \\ \cline{2-3}  & The system can make autonomous collision avoidance in addition to LoA-2, and is monitored by responsible officers (ROs) defined in Section 3.3.2. & LoRC-3 & Navigation \\ \hline \multirow{4}{*}{LoA-4} & Autonomous Navigation System (ANS) without monitoring & & \\ \cline{2-3}  & This system is totally autonomous without any human monitoring (not applicable in the case study). & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: LoA and LoRC in the case study.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sampling Location and Duration
#### 3.1.1 Selection of Sampling Interval  
To ensure high temporal resolution and data accuracy, a 5 s sampling interval is selected for water quality monitoring. As shown in the frequency spectrum analysis (Figure 7), the 5 s interval provides higher signal amplitude in the low-frequency range while reducing noise in the high-frequency domain. In contrast, data collected at a 30 s interval show rapid attenuation in the low-frequency region, which may lead to information loss. The higher temporal resolution of the 5 s interval allows the system to capture rapid variations in water quality parameters, such as transient contamination events or sudden anomalies.  
Further validation is provided by power spectral density (PSD) analysis (Figure 8), which demonstrates that the PSD distribution of the 5 s interval remains relatively stable, effectively reflecting actual water quality trends. Conversely, the PSD of the 30 s interval exhibits significant fluctuations, potentially causing low-frequency information loss. The presence of large power fluctuations at certain frequency bands suggests that longer sam  
Figure 7: FFT results for different sampling intervals.  
pling intervals may introduce aliasing effects, compromising the integrity and analytical reliability of the water quality data.
## 3 Results and Discussion
### Dynamic Characteristic Analysis Results and Discussion
#### 3.2.3 Harmonic Response Analysis Results and Discussion
Under the milling working condition, the static analysis of the direct-drive turntable with two different swing frames was carried out, and the relevant settings refer to the static characteristic analysis. Their maximum deformation is shown in Figure 22:  
It can be seen from Figure 22 that the maximum deformation of the direct-drive turntable with different swing frames is similar and has a certain decrease compared with the original structure. The optimized model has better static characteristics. The mass distributions of the left and right wings of the swing frame have little effect on the deformation of the direct-drive turntable. Therefore, a structure with greater mass removal was adopted, as shown in Figure 23:  
Figure 21: Topological structure of swing frame: (**a**) opening square hole; (**b**) opening round hole. Figure 15:0 Improved swing frame. Under the milling condition, the static and dynamic characteristic data of the direct-drive turntable with an optimized swing frame were obtained after static and dynamic analysis. By comparing the two sets of data in Table 10, we can see that the mass of the optimized model is greatly reduced, reaching 13.5%. The maximum deformation and the first natural frequency also have a certain degree of optimization to achieve the purpose of optimization.
## 4 Discussion
### Limitation of the Study, Practical Applications  
It should be kept in mind that the use of other training protocols may result in different training efficiencies. In future studies, it seems necessary not only to examine the effectiveness of two separate protocols (ST vs. AIT), but also the simultaneous combination of both protocols. Our training protocol was relatively short; it would be additionally advisable to increase the duration of the training mesocycle from 6 weeks to 8 or 12 weeks, testing whether and how these training modifications would affect the variables studied. AIT was performed on cycle ergometers; other forms of activity may yield different results. Our training interventions lasted 6 weeks, during which we did not modify the loads (e.g., we did not verify 1 RM (ST) and power at VT1 and VT2 (AIT)). This could have affected the magnitude of progress in the measured performance indicators, as the loads could have become less effective over time due to training adaptation. However, re-testing would have required interrupting training for several days, which would have disrupted the entire training intervention. Manipulation of parameters in ST training, such as the number of repetitions (three to five repetitions were used in this study), to eight or twelve could also have other effects, such as in other studies [10; 11; 12; 13; 33; 36] that reported improved work economy. The study involved non-trained men, so the effectiveness of the applied training in trained individuals may be different. It also seems necessary to verify the effectiveness of these interventions in women. This study also did not investigate the physiological and biochemical mechanisms underlying the observed changes, but only evaluated the acute effects of training on aerobic and anaerobic capacity in young, untrained men. It therefore also seems necessary to assess the long-term effects. Our results suggest that, particularly in anaerobic-aerobic sports, strength training may be a training method that can simultaneously improve both anaerobic power and maximal oxygen uptake. It can also complement endurance training. Our study was not randomized. The lack of random selection of participants may have resulted in uncontrolled confounding variables also influencing the reported results.