esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis
### Scenario 3  
As shown in our SHAP and FR analyses (Section 4.2.2), an NDVI value of 0.8 was identified as a key threshold. Based on this, in Scenario 3, non-landslide samples with NDVI values below 0.8 were first identified and removed. Then, an equal number of non-landslide samples were added in areas where the NDVI value exceeded 0.8. This threshold-based adjustment strategy helps optimize the selection of non-landslide samples, ensuring a more balanced distribution and enhancing the model's predictive accuracy and reliability. In Scenario 3 (Figure 11), the landslide susceptibility maps show similar spatial distribution patterns to those in Scenario 2. This similarity can be attributed to the strong correlation between landslide susceptibility and the NDVI in both scenarios. High-susceptibility areas are still concentrated in regions with low NDVI values, especially in the north and east mountainous areas. In contrast, low-susceptibility areas are mainly found in flatter regions with higher NDVI values. Scenario 3 adopts a more conservative prediction approach, capturing more high-susceptibility areas.  
Figure 10: Landslide susceptibility mapping for Scenario 2.  
According to the results shown in Figure 12, Scenario 2 achieves an AUC value of 0.979. Scenario 3, which applies an LCF-based correction method, reaches the highest AUC value of 0.986, higher than Scenario 2.  
Figure 11: Landslide susceptibility mapping of Scenario 3.  
Figure 12: ROC curves for Scenario 2 and Scenario 3.
# Hardware Acceleration of Tsunami Wave Propagation Modeling in the Southern Part of Japan
## 3 Numerical Results
### Parameters of the Artificial Tsunami Source  
Tsunami sources of area \(200\times 100\) km presenting sea bed displacement due to an earthquake of magnitude 8.0 were used in our computational experiments. The suggested seismic mechanism is similar to the historical tsunamigenic earthquakes at the Nankai-Tokai subduction zone. The initial water surface displacement (taking the same as in [5]) with a maximum elevation of 400 cm and lowest subsidence of \(-200\) cm caused by such an earthquake is presented in Figure 5. The model source was built according to the elastic-plastic model proposed in [17]. This model is currently used by the NOAA tsunami warning services to calculate the initial displacement in a tsunami source area on the basis of seismic monitoring data. The input data for calculating the ocean floor displacement field are the basic parameters describing the earthquake seismic mechanism. In this paper we used this displacement as the sea surface disturbance at the initial time moment.
## 2 Mathematical Formulation of the 2L-VRPSC
### Stochastic Programming Model of the 2L-VRPSC
#### 2.2.1 Customer Uncertainty Modeling Based on Monte Carlo Simulation  
Due to the fact that the complexity of simulation grows exponentially with the number of customers, we adopt the Monte Carlo method [27; 28] to generate scenarios to simulate the uncertainty of customers according to the discrete probability distribution of all customers. Specifically, for each customer, assuming that the probability that customer \(i\) requires service is \(p_{i}\), a random number \(u_{i}\in[0,1]\) is generated by using the random sampling method. If \(u_{i}>p_{i}\), customer \(i\) requires service; otherwise, the customer does not need service. For each scenario, each customer is sampled to generate the probability that requires service; namely, each scenario \(s\) corresponds to a set of random variables \(\{u_{1},u_{2},\cdots,u_{i},\cdots,u_{n-1},u_{n}\}_{i\in V_{0}}\). After a finite number of samples, a large number of scenarios are generated to simulate the uncertainty of customers.  
However, for a given solution \(\tau\) with \(n\) customers, the number of the scenario can be up to \(2^{n}\), and such large-scale scenarios will lead to a heavy computation burden. To balance the diversity and fidelity, the size of the scenarios is reduced via the scenario-reduction techniques according to the probability and the correlation among scenarios [29; 30]. The simultaneous backward reduction technique [31] is adopted to approximate the original system with high confidence and promote the diverse characteristics of the generated scenarios, which is described in Algorithm 1 below. In Algorithm 1, terms \(s\) and \(s^{{}^{\prime}}\) are used to denote different scenarios. Specifically, the distances between all the scenarios are calculated (line 1). Here the distance between scenario \(s\) and \(s^{{}^{\prime}}\) is defined as \(DT_{s,s^{{}^{\prime}}}=\sqrt{\sum_{i=1}^{n}(a_{i}^{s}-a_{i}^{s^{{}^{\prime}}}) ^{2}},i\in V_{0}\), where \(a_{i}^{s}\) is a binary variable that indicates whether customer \(i\) is served in scenario \(s\). Afterward, the probability of each pair of the nearest scenarios is calculated, and the pair with the lowest probability is selected (Algorithm 1 line 4). The scenario pair with the lowest probability will be eliminated, and the probability of all other scenarios will be updated (Algorithm 1 line 5). This process is repeated until the desired number of remaining scenarios is obtained.  
```
Input: the initial set of scenarios \(I\) Output: the preserved set of scenarios \(S\)
1 Calculate the distances between all of the scenarios. whilethe predefined size of remaining scenarios is not metdo
2 Find other nearest scenario \(s\) to each scenario \(s^{{}^{\prime}}\) (i.e., the distance \(DT_{s,s^{{}^{\prime}}}\) between scenario \(s\) and \(s^{{}^{\prime}}\) is smallest). Compute the probability for each pair of nearest scenarios and choose the one with the lowest probability.
3 Eliminate the scenarios and update the probability of the remaining scenarios.
4 end while
```  
**Algorithm 1**Scenario Reduction.
## 3 Results
### Magnetized Construction Materials and Observatory Data Quality
Note that the standard deviation for period III (after the LED set replacement by AC lighting bulbs) is more than 2 arc min smaller than the one for period II when the LED set was used. For a proper analysis, we include the geomagnetic activity information for the mentioned period. The planetary \(K\) index data [19], superimposed on each plot in Figure 7, displays the overall geomagnetic activity during the measurements. Single \(D\) values quite far from the rest correspond to increased geomagnetic activity periods when they were observed (this is a raw dataset without any adoption yet, and later the disturbed \(D\) values were excluded before the process of quasi-definitive data calculation [20]). A series of measurements during a disturbed period in November 2018, resulting in relatively large \(D\) absolute values seen on the plots, likely shows a combined effect of a natural geomagnetic disturbance and the mentioned artificial magnetic effect. However, most of the disturbances of the absolute data were related to the LED-driven magnetic field and not to the storm periods. In December 2018, it was decided to increase the number of measurements to three times a week and four measurements within a day (two measurements performed using the offset method and two performed using the null method, with consequent independent comparison of results obtained using these methods). Summarizing this result, when installing the light bulbs in the absolute pavilion, it is recommended to place them on the ceiling, not right above the theodolite, but around it, close to Eastern, Western, Northern and Southern directions from the pillar in order to provide proper lighting for the theodolite limbs at the corresponding positions of the absolute measurement process without bringing disturbances close to the pillar (an example is displayed in Figure 6b). The positions and angles of the bulbs, providing the best lighting, can be adjusted during the observation of the limbs of the theodolite. In our practice, we now use non-magnetic halogen light bulbs with ceramic bases.
## 5 Experiments
### MOS Evaluation  
We performed an evaluation to determine which are the objective measures that lead to the best speakers according to people's preferences. For each measure, we selected the 5 best ranked synthetic voices, i.e., the 15 voices shown in Table 2. Then, 10 synthetic sentences from the corpus used to compute the objective scores were selected for each voice (some samples used in the evaluation can be found at [[https://aholab.ehu.eus/users/agustin/demos/mdpi21/](https://aholab.ehu.eus/users/agustin/demos/mdpi21/)]([https://aholab.ehu.eus/users/agustin/demos/mdpi21/](https://aholab.ehu.eus/users/agustin/demos/mdpi21/)) accessed on 10 February 2022). Each participant scored the quality of 5 randomly selected sentences per donor's voice in a MOS scale (a total of 75 sentences). The participants were asked to score the sentences from 1 to 5 according to how suitable they found the voice to be used as a communication voice by people with speech impairments. A total of 25 people took part in the evaluation.  
Figure 2 shows the MOS scores obtained by each voice, together with the 95% confidence interval. The voices are ordered left-to-right from the highest to the lowest MOS. For each voice, the measure (STOI, ESTOI, SIIB or NISQA) with that voice among the best five is also shown. For example, SPK02 was among the first five positions for STOI, ESTOI, and NISQA.  
As it can be seen, the best voices according to MOS are selected by different objective measures. Table 4 shows how the scores obtained by the objective measures correlate with the evaluated MOS. The most correlated measure is ESTOI, which has its selected best voices among the top seven positions in Figure 2. It is followed by STOI with its 5 selected voices among the 13 top positions, NISQA with its 5 selected voices among the top 14, and finally SIIB among the top 15.  
Considering that each objective measure uses different criteria, we have investigated whether a combination of the measures can produce a better estimation than the individual measures. The method and results are described in the next subsection.
# SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation
## 2 Materials and Methods
### The Proposed Sensor
Despite the simplifications, the model can give a rough but useful indication of the uncertainty affecting the measurements due to the mutual coupling. Coming back to the E-field probe proposed in this paper, it is modeled as a 3-ports (the three antennas) microwave network, where each port represents the antenna terminal. The resulting S parameter matrix is presented in (2). Figure 3: Scheme of mutual coupling in receive mode (2 antennas). \[S=\left[ \begin{array}{c}S_{11}S_{12}S_{13}\\ S_{21}S_{22}S_{23}\\ S_{31}S_{32}S_{33}\end{array} \right] \tag{2}\]  
where in \(\mathbb{S}_{\mathrm{ij}}\) is the parameter that corresponds to the wave voltage traveling from port i to port i when all the other ports are closed on perfectly matched loads. Low \(\mathbb{S}_{\mathrm{ij}}\) values are an indication of a low coupling among the antennas. Loosely speaking, it is of interest to obtain also low \(\mathbb{S}_{\mathrm{ii}}\) values, with \(\mathbb{S}_{\mathrm{ii}}\) being the i-th reflection coefficient of the i-th antenna with all the other antennas closed on a matched load. The scattering parameters can be measured using Vector Network analyzers (VNA). We used a 2-port VNA, model R&S ZNB 40, by alternatively connecting all sensor probes to the two VNA ports. During the measurements, the ports that were not connected to the VNA, were connected to a 50 \(\Omega\) impedance. The VNA was calibrated in the frequency range 0.8-6 GHz, with 100,000 measurement points being taken for each sweep. The frequency resolution in the interest bandwidth was 50 kHz. The VNA power was set to \(-\)20 dBm, which is the highest signal level usually measured around our interest RF sources by these probes. Figure 4 presents the setup used during the S parameters measurement. Finally, to evaluate the influence of the sensor on emitting equipment we have investigated the variation of the reflection S11 parameter for a custom-made patch antenna in the presence of the sensor placed at 1, 2 and 3 cm distance, respectively, in the absence of the sensor. The patch antenna was first designed using CST Studio Suite Software and then printed on a printed circuit board (PCB). Figure 4 presents the S11 measurement setup of the patch antenna using the R&S VNA.
# Observation and Numerical Simulation of a Windshear Case at an Airport in the Qinghai-Tibet Plateau
## 2 Materials and Methods
### The Wind and EDR Observations  
The DWL provides continuous measurements of the vertical wind profiles. The major wind features of interest in the present study are shown in Figure 2. The period under consideration is 30 November to 1 December 2017. Of particular importance is the occurrence of a low level easterly jet (Figure 2a), reaching 50 knots at the core of the jet. There is veering of the wind with height. In the early morning of 1 December 2017, the low level winds changed to westerly gradually, and the vertical extent of the westerly winds increased gradually with height.  
Using the DWL data, the eddy dissipation rate (EDR), a metric of turbulence for aviation application, has been calculated using the formula below ([PERSON] et al., 2004) [15]:  
\[EDR=2\pi\left(\frac{2}{3a}\right)^{3/2}\frac{\sigma_{v}^{3}}{\left(L^{2/3}-L_{ 1}^{2/3}\right)^{3/2}}\]  
where \(a\) is the universal Kolmogorov constant with a value of 1.62; \(\sigma_{v}\) is the spectrum width of Doppler velocities within the sampling time t; \(L\) represents the length of larger eddies  
Figure 2: The time–height cross-section of the wind (**(a)**, upper panel) and EDR (**(b)**, lower panel) as obtained by the DWL for this windshear case. The circle in **(a)** is the area of higher wind speed, with the arrows showing the general wind direction and the broken lines showing the locations of the rapid changes in the wind direction. To better distinguish between the easterly and the westerly winds, the former is shown with a red arrow and the latter is shown with blue arrows. Time is in Beijing Time (BJT), which is 8 h ahead of UTC. In both **(a)** and **(b)**, the height refers to the height above ground level.  
passing through the DWL beams within a time interval \(t\), and can be given as \(L\approx V_{H}\)t, \(V_{H}\) is the horizontal wind speed; and \(L_{1}\) is the horizontal length of the DWL sampling volume.  
The resulting time-height cross section of the EDR is shown in Figure 2b. Higher turbulence is detected at two regions: first, at heights above the jet core with the veering of the wind, and second, the interface between the easterly wind and the intrusion of westerly wind.  
By taking a closer look at the easterly jet, it is found that the wind speed is not uniform with time but there are pulses of higher wind speed, as shown in Figure 3a. The time series of the wind speed at the jet core as well as above and below the jet are shown in Figure 3b. Three pulses of the wind have been identified and highlighted in Figure 3b. This pulsation is believed to give rise to the windshear that may be encountered by the aircraft at the airport.
## 4 Experimental Results and Analysis
### Encryption Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _m_th Round** & \((\mathbf{r_{0}},\mathbf{x_{0}})\) & \((\mathbf{r_{0}},\mathbf{y_{0}})\) & \(S_{A}\) & \(1 DS_{A}\) & **Size (KB)** & \(C_{R}\) \\ \hline
1 & (3.9446201, 0.1) & (3.6720488, 0.25) & 285,857,730.3 & 22,952,181.0 & 1491 & 2.696 \\
2 & (3.6978437, 0.44) & (3.9206044, 0.49) & 320,402,657.6 & 11,592,746.3 & 1592 & 2.636 \\
3 & (3.6557073, 0.03) & (3.8864578, 0.33) & 350,085,107.0 & 41,275,195.7 & 1728 & 2.606 \\
4 & (3.9037505, 0.02) & (3.7374396, 0.21) & 383,939,349.7 & 75,129,438.4 & 1826 & 2.550 \\
5 & (3.7128502, 0.56) & (3.7213353, 0.35) & 413,822,209.8 & 105,012,298.5 & 1911 & 2.492 \\
6 & (3.8007097, 0.09) & (3.9265191, 0.78) & 452,157,235.0 & 143,347,323.7 & 2031 & 2.477 \\
7 & (3.5822104, 0.46) & (3.7138054, 0.15) & 494,673,201.5 & 185,863,290.2 & 2139 & 2.453 \\
8 & (3.6359581, 0.57) & (3.9861503, 0.54) & 532,925,355.6 & 224,115,444.3 & 2220 & 2.403 \\
9 & (3.9345383, 0.58) & (3.7636595, 0.61) & 590,387,815.5 & 281,577,904.2 & 2325 & 2.382 \\
10 & (3.8741445, 0.34) & (3.900868, 0.25) & 635,882,520.3 & 327,072,609.0 & 2446 & 2.377 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The best key pairs among 10 encryption rounds for DEM B.  
Figure 11: _Cont._  
Figure 10: Ten confused DEM B. Their histograms are the same. From the two aforementioned encryption examples, we find that configurational entropy can help users choose the best-encrypted one according to specific requirements, e.g., the size of encrypted data should be as small as possible, and the encrypted image should be as complicated as possible. For instance, in consideration of transmission bandwidth, users can choose the encryption with the minimum \(S_{A}\) value. To enhance the complexity of the encrypted image, users can set larger and larger \(m\) and \(n\) values if possible. Figure 11: Three-dimensional images of encrypted DEM B. The numbering sequence is consistent with the encryption round.
## 2 Material and Methods
### Experimental Setup and PA Module
#### 2.3.2 PA Inspection Procedure  
The experimental inspection procedure consisted of an initial benchmarking of NDT characterization of low-velocity impact damages on composite coupons of two different thicknesses and laminates. The PA hardware consisted of a multi-channel pulse-receiver Sonatest (16:64) VEO+ series with simultaneous emission and reception along each channel and 16 active channels. This pulse-receiver is operated under a normal incidence pulse-echo configuration to excite the 10 MHz 64 element linear array PA transducer with a square pulse of a maximum permissible voltage of 70 V with eight active apertures. Prior to PA scanning, the hardware is pre-loaded with a specific PA configuration file, having information regarding material velocity, time-gain compensation, and scanning parameters. The receiver gain and range are set, as per the thickness of the component under inspection. These scans were performed by mounting the PA module on a simple X-Y-Z manipulator.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
###### Abstract  
Short-range noncontact sensors are capable of remotely detecting the precise movements of the subjects or wirelessly estimating the distance from the sensor to the subject. They find wide applications in our day lives such as noncontact vital sign detection of heart beat and respiration, sleep monitoring, occupancy sensing, and gesture sensing. In recent years, short-range noncontact sensors are attracting more and more efforts from both academia and industry due to their vast applications. Compared to other radar architectures such as pulse radar and frequency-modulated continuous-wave (FMCW) radar, Doppler radar is gaining more popularity in terms of system integration and low-power operation. This paper reviews the recent technical advances in Doppler radars for healthcare applications, including system hardware improvement, digital signal processing, and chip integration. This paper also discusses the hybrid FMCW-interferometry radars and the emerging applications and the future trends.  
radar; CW; FMCW; noncontact; biomedical; gesture; microwave; Doppler; vital sign +
Footnote †: journal: Sensors
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 3 System Description
### CSI Sensing Rate and Coherence Time  
Beyond the algorithmic dependence on CSI data rates, it is important to note that Wi-Fi sensing systems require a minimum data rate to effectively understand the changing wireless channel. To better understand this, we use the concept of channel coherence time. In order to sense activity in the channel, the CSI data must be captured faster than the rate of change of the channel. The time for which the channel is considered unchanging is known as coherence time, and it can be expressed in 2.4 GHz wireless channels as follows [22]:  
\[T_{\text{coherence}}=\frac{9}{16\pi\frac{v}{\lambda}} \tag{3}\]  
where \(v\) is the speed of movement in the channel (the activity being sensed), and \(\lambda\) is the signal wavelength (0.12 m for 2.4 GHz Wi-Fi). The required CSI data rate is then the reciprocal of the coherence time, and we compute this for several common sensing applications in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Activity** & **Speed (m/s)** & \(\mathbf{T_{\text{coherence (s)}}}\) & **Req. CSI Rate (Hz)** \\ \hline Walking & 1.5 & 0.016 & 63 \\ Running & 2.7 & 0.008 & 129 \\ Typing & 4 & 0.005 & 189 \\ Occupancy & 0.7 & 0.031 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approximated data rates for various sensing applications.  
Figure 2: Wi-Fi sensing system overview.  
In summary, the accuracy of Wi-Fi sensing is influenced by the CSI data rate, which is limited by the coherence time of the activity being sensed. Additionally, the hyperparameters of the ML algorithm are governed by the data rate for training. Therefore, any fluctuations in the rate of CSI capture will negatively impact the system's performance. To prove this assertion and determine the necessary CSI data rate, we will be conducting experiments on Wi-Fi-based keystroke recognition for varying transmission rates in the following section.
## 3 Results and Discussion
### Effect of the PAL Defect Density  
The defect density in the absorption layer is one of the major factors affecting the device performance because it can act as a trap when the hole and electron carriers move to each electrode [45]. A corresponding analysis was performed in the range of \(10^{11}\)-\(10^{18}\) cm\({}^{-3}\), and the results are shown in Figures 8 and 9. Figure 8 shows each output parameter, such as the \(\rm V_{oc},J_{sc}\), FF, and PCE, according to the defect density. All output parameters showed a constant value until the defect density was \(10^{15}\)/cm\({}^{3}\) but showed a sharp decrease when it exceeded \(10^{15}\)/cm\({}^{3}\). Figure 9 presents the current density-voltage (\(J\)-\(V\)) curves for the various PAL defect densities. As the defect density increased, the \(\rm V_{oc}\), \(\rm J_{sc}\), FF, and PCE decreased significantly from 1.05 to 0.99 V, 25.6 to 11.9 mA/cm\({}^{2}\), 76.1% to 45.8%, and 20.4% to 5.44%, respectively. These results were thought to be caused by Shockley-Read-Hall (SRH) recombination [46]. According to the SRH recombination, as the defect density increases, the carrier lifetime and diffusion length decrease. This causes an increase in SRH recombination, as shown in Figure 9. Thus, the PCE of this device decreased.  
Moreover, it can be seen from Figure 10 that the recombination current increased rapidly above \(10^{16}\) cm\({}^{-3}\). Therefore, in this study, the PAL defect density was kept below \(10^{15}\) cm\({}^{-3}\).  
Figure 8: Effect of PAL defect density on (**a**) Voc, (**b**) Jsc, (**c**) FF, and (**d**) PCE.