esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Datasets and Signals
### Signals
#### 4.2.1 Accelerometer Data  
As pointed out, one common signal for road anomaly detection and classification is obtained through an accelerometer. An accelerometer is a device that measures the acceleration in an object (e.g., a vehicle, rocket, or aircraft) relative to the g-force. The output measurements of these devices can be viewed as a time series sampled at a specific frequency. This time series varies along time due to the movements of the analyzed object, which in the context of road anomaly detection will be the vehicle in a three-dimensional space [48]. When accelerometers are used for detecting and classifying road anomalies, it is expected that vehicle acceleration in different directions varies when the vehicle passes through the anomaly. This variation is sampled by the accelerometer embedded in smartphones [21]. One key factor that needs to be considered before working with accelerometer data is the minimum sampling frequency required to obtain a reliable time-domain representation of the signal and avoid aliasing problems. In this regard, the literature concerning a specific sampling rate is not concrete. For instance, in Ref. [49] a 50 Hz sampling frequency was chosen; however, other authors have worked with 95 or 100 Hz [5; 37; 43]. The selection of an adequate sampling rate is crucial to have a correct signal representation following the Nyquist criterion [50] and to realize a correct signal transformation and analysis through the use of either the FT or wavelet transform. The above also requires real-time embedded systems to assure a deterministic sampling procedure. Despite their relatively easy use, accelerometer sensors have certain disadvantages that are essential to point out related to the noisy nature of the signals generated from these devices. This noisy nature difficulties road anomaly detection and classification since the feature extraction process could be complicated and, in some cases, even impossible [48]. To remove the low-frequency noise from the acceleration signals, what has been proposed is to use high-pass filters, such as Butterworth filters as proposed by [PERSON] et al. [42] and [PERSON] et al. [26]. The above authors, in particular, proposed to use 11 th-order Butterworth high-pass filters. Moreover, discrete wavelet transform (DWT) has been used for denoising acceleration and gyroscope signals, as proposed in the study of [PERSON] et al. [37]. [PERSON] et al. [8] proposed to use the wavelet packet denoising technique to accelerometer and gyroscope data collected from a smartphone for road condition monitoring. In addition, while working with accelerometer data, it is necessary to apply a reorientation process of the accelerometer's coordinate system into the vehicle's coordinate system [26]. The above can be achieved with the use of Euler angles [51]. [PERSON] introduced in his rotation theorem that any rotation can be described by employing only three angles. The rotations of a rigid object can be expressed in terms of rotation matrices labeled as \(D\), \(C\), and \(B\); consequently, the general rotation \(A\) can be expressed as shown in
# A Real-Time Sound Field Rendering Processor
## 3 System Architecture
### Interfaces  
The INTERFACES are provided for system scalability. When multiple processors are cascaded, control instructions and temporary data during computation need to exchange with the neighbor processors. Through the INTERFACES, multiple processors are easily connected to each other to extend the rendered sound space. As shown in Figure 4, the INTERFACE consists of receiver, decoder, and transmitter. And they are introduced more detail as follows.  
* Receiver. The receiver receives serial data from the neighbor processors, checks the data type (data or control instructions), and stores data to the first in first out (FIFO) buffer or buffer according to the data type. The receiver is composed of a data type detector (RX_FHD) and a state machine (RX_STATE) to control the receiving flow. As shown in Figure 5, the system firstly detects the 8-bit type flag, which is AB and 54 in hexadecimal for pure data and the control instructions, respectively. After the type flag is received, system then receives the data length, which is 8-bit and denotes the data length in byte. Finally, data are received and stored in the RX_FIFO for the control instructions or IN_BUF for the pure data.  
Figure 3: Computing unit.  
Figure 2: System diagram of the PE.  
* Decoder. The DECODER decodes and executes the control instructions. Eleven control instructions are defined for system configuration and data communication. Furthermore, an automatic instruction forwarding mechanism is provided to forward the control instructions to other processors when multiple processors are cascaded.
* Transmitter. The transmitter (TX_STATE) transmits the control instructions or pure data to the neighbor processors. Data are transmitted in the manner of data frame, which consists of type flag, data length, chip ID, and the relevant pure data or instructions. Before data transmission, the bus is checked. If it is free, transmission is started; otherwise, the system waits for some clock cycles and checks again until the bus is free. Three types of data are transmitted, which are pure data, forwarded instructions, and the acknowledgement instruction. To transmit the pure data or the forwarded instructions, the data valid instruction is firstly sent out. Then, the system waits for the acknowledgment signal from the receiver. After the signal is received, the related data frame is transmitted serially. When the system receives the data valid instruction from the neighbor processors, it responds the data communication request by sending back the acknowledgement instruction. The whole procedure is shown in Figure 6 and is controlled by a state machine.  
Figure 4: Diagram of the INTERFACE.  
Figure 5: Data receiving.
## 5 Discussion
For this reason, this research compared different numbers of particles in order to find the optimal number for wind speed estimation, as presented in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**RMSE (m/s)**} \\ \cline{2-7}
**Number of** & \multicolumn{3}{c}{**30\%**} & \multicolumn{3}{c}{**70\%**} \\ \cline{2-7}
**Neurons** & **WS \textless{} 20** & **WS \textgreater{} 20** & **Overall** & **WS \textless{} 20** & **WS \textgreater{} 20** & **Overall** \\ \hline
6 & 3.5606 & 5.9404 & 3.7926 & 3.1049 & 5.5643 & 3.3540 \\
8 & 3.5511 & 5.8782 & 3.7771 & 3.0445 & 5.2631 & 3.2650 \\
10  
The RMSE results shown in Figure 13 are the average value from the 30% and 70% training samples with a computation time of one iteration. With 50 particles, the RMSE was approximately the same as for 100 and 150 particles, but less computation time was required. The linear regression model was used to compute the \(R^{2}\) value of each lag for the delay-related and spectral-related observables. According to Figure 14, the \(R^{2}\) values of data collected from the two periods of time increased after the peak of the final power waveform. As a result, this research selected the data from lags 26 to 37 as the input information for the ANN approach. The relationship of in situ wind speed measurement and mean square slope is shown in Figure 15. The function of the sea surface roughness model for computing the up and cross wind speed in Equation (9) will depend on three conditions as shown in Equation (10). For the first condition, the result of wind speed calculated from the model is related to in situ wind speed measurement. For the second condition, the result will change with logarithm function. Finally, for the third condition, the result will change with static gain. Figure 14: \(R^{2}\) values of the delay-related and spectral-related observables. Figure 13: Comparison of the root mean square error (RMSE) (cyan) with the computation time (orange) of the particle filter.
# The Assessment of COVID-19 Vulnerability Risk for Crisis Management
## 2 Materials and Methods  
The spatiotemporal analysis approach applied in this research was based on Spatial Multicriteria Analysis (SMCA) with Analytical Hierarchy Process (AHP) for weights calculation described in. The used methodology is presented in Figure 3. The general concept of SMCA was described in [87,88]. In this article, SMCA allows for the determination of COVID-19 Vulnerability risk--defined as a situation where the risk of exposure to the hazard might be increased [89]. The presented approach allows for the estimation of the COVID-19 Vulnerability risk index (CVI) of the selected area and its characteristics over time. The test field of the solution was Germany.  
AHP methodology allows for the importance estimation by calculating the weight of selected criteria by means of pairwise comparisons of each evaluation criterion.  
Figure 3: The methodology of spatiotemporal index estimation performed in research (own study).  
The application of the AHP methodology is based on a value-function type and as such requires an estimation of the value function and criterion weights to determine the summary statistics on the selected area as below [87]:  
\[\text{CVI}=\sum_{i=1}^{n}W_{i}\times S_{n} \tag{1}\]  
where CVI is COVID-19 Vulnerability Index, \(W_{i}\) is normalized weight, and \(S\) is the Vulnerability Score of the area on the selected layer (\(n\)) as value function. Value function and weights are obligatory for estimation. The function values in the paper case study were determined by calculation based on the available dataset. The weights were estimated by pairwise comparisons of each evaluation criteria. This determines the relationship strength between the criteria, that was used to rank selected criteria based to the [90].  
In this paper CVI calculations were extended by authors with the spatiotemporal analysis to show CVI change in time as follows:  
\[\Delta\text{CVI}=\text{CVI}_{ti}-\text{CVI}_{ti-1} \tag{2}\]  
where \(\Delta\text{CVI}\) is the change in time for summary COVID-19 Vulnerability Index for three months' interval.  
Furthermore, based on the value of CVI on the selected area the population number endangered with a certain level of vulnerability in time was estimated. This was performed with the use of GIS systems.  
The results validation consists of comparing the values of CVI with new cases over time and this is followed with the calculation of the value of the R-squared, to show the proportion of the variance for confirmed COVID-19 cases and CVI index as dependent variables.
# Withstanding Capacity of Machine Guards: Evaluation and Validation by 3D Scanners
## 2 Materials and Methods
### Deformation Tests by 3D Scanners  
Deformation measurements were performed by FreeScan UE7 by Shining 3D, a metrology grade portable 3D laser scanner (Figure 6).  
The 3D optical digitizer has the following performance parameters:  
* Resolution: 0.05-3 mm
* Single shot accuracy: 20 \(\upmu\)m  
Figure 5: Clamping frame mounting different panel sizes. (**A**) Frame for 500 \(\times\) 500 mm panels: (c) lower panel supports. (**B**) Frame for 300 \(\times\) 300 mm panels: (d) smaller panel clamping [29].  
* Volumetric accuracy: 20 \(\upmu\)m + 40 \(\upmu\)m/m  
The test guards were mounted on the clamping and support device already used during the ballistic impact tests and coated with a sublimating 3D scanning spray, to allow the optical scanning procedure on transparent surfaces. Then, high-reflective black contour targets were positioned on the frame to generate the positioning model for the scanning instrument. This allowed to have a reference frame for the measurements also when the 3D scanner was moved manually around the sample (Figure 6) to obtain a real-time 3D digital model of the panel.  
The 3D digital models of the panels were triangular meshes in STL (Standard Triangulation Language) format, and they were used to perform the deformation analysis by means of Geomagic Control X (3D Systems, Inc., Santa Clara, CA, USA), a certified 3D inspection software. Solid CAD (Computer-Aided Design) representations of all the undeformed test panels before the impact tests were generated inside the software to allow the comparison with the deformed models.  
The phases of the digital deformation analysis were:  
1. The CAD and STL files of each sample were superimposed and aligned using the undeformed edges as a reference. The undeformed areas were selected around the clamping lines. The alignment criterion was based on the best-fit mesh-to-surface.
2. 3D deformation map generation. This map highlights all the deviations between the new and the deformed panel, displaying each deformation range with a specific color.
3. Delineation of specific deformation diameters. This operation allowed to evaluate a threshold diameter corresponding to the separation between deformed and undeformed zones.
4. Characterization of deflection and thicknesses. Transversal sections of the deformed zones allowed to measure the minimum thickness of the panel after the impact and its position over the deformation zone. Furthermore, the maximum permanent deflection of the panel was measured.  
Figure 6: Sample panel during 3D scanning with FreeScan UE7.
## 1 Introduction  
Saliinity is one of the major environmental problems worldwide. The effects of salinity depend on genetic and physiological factors, such as plant species, cultivars, growth stage, salt deposition, and excess of minerals in the soil solution, which can be a substantial constraint on crop growth and yield. The devastating effects of salinity on plant growth include toxic accumulation of salts, nutrient imbalance, low osmotic potential of soil solution, and/or their combination [1, 2]. Salt concentration is adversely increasing the salinization on a global scale, with severe effects on plants that suffer significant damage when cultivated in salt-affected soils. [3]. Furthermore, the main plant-growth processes, such as photosynthesis, protein synthesis, ion balance, and lipid metabolism, are affected by salinity [4, 5, 6]. It is difficult to determine plant tolerance to salinity, because it is directly dependent on different physiological interactions within the plant [7].  
Pomegranate (_Punica granatum_ L.) belongs to the _Lythracae_ family and has important economic, nutritional, and medicinal properties [8]. It is adapted to arid and semiarid regions and is widely cultivated in the Mediterranean area. Furthermore, pomegranate can be cultivated under adverse climatic and soil conditions [9], as it has been classified as medium susceptible to salinity. Pomegranate fruits are a rich source of minerals and phenolics [10] distributed in exocarp, seeds, and arils [11], and thus it is considered a'super food'. Pomegranate fruits have numerous medicinal properties due to their high phenolic content [12]. The antioxidants consist of flavonoids, anthocyanins, and tannins, which are all phenolic compounds [13]. Furthermore, pomegranate is a rich source of phytochemical compounds, such as ellagitannins, punicalagin, anthocyanins, punicic acid, and tannins [14], which have antioxidant capacity [15]. Estimations of the total phenolic and flavonoid contents and total antioxidant capacity (FRAP) under saline conditions in pomegranate cvs. are scarce. Analysis of the phenolic compounds and evaluation of the antioxidant capacity in leaves of the three pomegranate cvs. have not been conducted.  
Moreover, the amino acid proline is known to widely occur in higher plants and normally accumulates in large quantities in response to environmental stresses [16]. proline contributes to stabilizing subcellular structures (e.g., membranes and proteins), scavenging free radicals, and buffering cellular redox potential under salinity conditions [17].  
The aim of this work was (a) to investigate whether salinity stress can induce differential proline accumulation and evaluate the level of lipid peroxidation in three pomegranate cvs. that have differing responses to salinity stress; and (b) to reveal any existing tolerance mechanism, in addition to the antioxidant capacity, that is upregulated and functions to protect the salinity-tolerant cultivar.  
Hence, the present investigation contributes to the literature by looking for differences among the cvs. Wonderful, Ermioni, and Grenada, which are commercial pomegranate cvs. cultivated in Greece.
# Betatron Radiation and Bremsstrahlung in the Interaction of Intense Laser Pulse with Solid Target
## 2 Methods and Simulation Setup
### Simulation Setup  
In the simulations, a linearly polarized (in the \(y\) direction) Gaussian laser pulse of wavelength \(\lambda=800\) nm and period \(T_{L}=\lambda/c\simeq 2.67\) fs enters the simulation box from the left boundary and propagates in the \(x\) (or longitudinal) direction. The copper solid-density plasma slab is 5.0 um thick and of uniform electron density \(n_{e}=30n_{c}\), where \(n_{c}=4\pi^{2}\epsilon_{0}m_{e}c^{2}/e^{2}\lambda^{2}\simeq 1.75\times 10^{21}\) cm\({}^{-3}\) is the critical density. The electron density profile of the preplasma is \(n_{e}(x)=n_{e}(x_{0})\exp(-|x-x_{0}|/L)\), where \(n_{e}(x_{0})=30n_{c}\), \(x<x_{0}\), and \(x_{0}\) is the position of the slab's front surface. The preplasma is truncated at \(n_{e}=0.05n_{c}\).  
The rectangular simulation box is 125 and 40 \(\upmu\)m in the \(x\)- and \(y\)-directions, respectively. The spatial resolution is 0.0125 \(\upmu\)m and 0.02 \(\upmu\)m for the \(x\)- and \(y\)-directions, respectively. In each cell there are 200 macroparticles each for the electrons and copper ions. The boundary conditions are open for the fields and thermal for the particles. When a particle leaves the simulation box boundary, it is replaced by an incoming particle sampled from a Maxwellian distribution with the same temperature [45; 46]. Both synchrotron radiation and bremsstrahlung photons are recorded at their emission location, but low-energy (<10 keV) photons are ignored. Unless otherwise stated, the total simulation time is \(t_{\rm end}\sim 200T_{L}\).  
For investigating the effect of the preplasma scale length \(L\), the laser parameters are fixed at focal spot radius \(r_{0}=3.0\)\(\upmu\)m, FWHM temporal duration \(\tau_{d}=9T_{L}\), peak intensity \(I_{0}=7\times 10^{20}\) W/cm\({}^{2}\) (i.e., the laser parameter is \(a_{0}\sim 18\)), with \(L=0.15,0.5,1.5,3.0,5.0\), and 15.0 \(\upmu\)m considered. For investigating the effects of the laser parameters, we consider two control groups. First, the effect of the laser intensity is studied for focal spot radius \(r_{0}=3.0\)\(\upmu\)m. For \(L=0.5\)\(\upmu\)m, we set \(a_{0}=6.0,9.0,18.0\), and \(38.0\); and for \(L=5.0\)\(\upmu\)m we set \(a_{0}=6.0\) and \(18.0\). In addition, three 1D simulations for \(L=0.5\)\(\upmu\)m and \(a_{0}=6.0,18.0\), and 38.0 are carried out in order to see the longtime (\(t_{\rm end}\sim 5.0\) ps) behavior of bremsstrahlung. In the second control group, the effect of the laser focal spot radius \(r_{0}\) is studied for \(a_{0}=18.0\). For \(L=0.5\)\(\upmu\)m, we set \(r_{0}=0.5,1.0,1.5,2.0,2.5,3.0,4.5,6.0,7.5\), and 9.0 \(\upmu\)m; and for \(L=5.0\)\(\upmu\)m we set \(r_{0}=0.5,1.0\), and 3.0 \(\upmu\)m. The laser pulse energy is the same in all our simulations.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 3 Preliminaries on Indistinguishability Operators
### \(P\) as an Indistinguishability Operator  
Consider the family of \(t\)-norms \((T_{Dom}^{\lambda})_{\lambda\in[0,\infty]}\) due to [PERSON]. Recall, according to [15], that such a family of \(t\)-norms is given by:  
\[T_{Dom}^{\lambda}(x,y)=\left\{\begin{array}{ll}T_{D}(x,y),&\mbox{ if }\lambda=0; \\ T_{M}(x,y),&\mbox{ if }\lambda=\infty;\\ 1+\left(\left(\frac{1-x}{x}\right)^{\lambda}+\left(\frac{1-y}{y}\right)^{ \lambda}\right)^{\frac{1}{\lambda}},&\mbox{ elsewhere,}\end{array}\right.\]  
where \(T_{D}\) is the drastic \(t\)-norm (see [15]).  
According to [15], the \(t\)-norm \(T_{Dom}^{\lambda}\) is continuous and Archimedean for each \(\lambda\in]0,\infty[\). Moreover an additive generator of \(T_{Dom}^{\lambda}\) is given by  
\[f_{T_{Dom}^{\lambda}}(x)=\left(\frac{1-x}{x}\right)^{\lambda}\]for all \(x\in[0,1]\) and for each \(\lambda\in]0,\infty[\). It is not hard to verify that the pseudo-inverse of this additive generator \(f_{T_{Dom}^{\lambda}}^{(-1)}\) is given by  
\[f_{T_{Dom}^{\lambda}}^{(-1)}(y)=\frac{1}{1+y^{\frac{1}{\lambda}}}\]  
for all \(y\in[0,\infty]\) and for each \(\lambda\in]0,\infty[\).  
Taking into account the preceding facts, we are able to prove that \(P\) is in fact an indistinguishability operator constructed from a Dombi \(t\)-norm. To this end, assume that \(d\) is a distance on \(X\) and let \(T_{Dom}^{\lambda}\) be a Dombi \(t\)-norm for an arbitrary \(\lambda\in]0,\infty[\). By Theorem 3, we obtain a \(T_{Dom}^{\lambda}\)-indistinguishability operator \(E_{T_{Dom}^{\lambda}}\) that separates points by means of \(E_{T_{Dom}^{\lambda}}(x,y)=f_{T_{Dom}^{\lambda}}^{(-1)}(d(x,y))\) for all \(x,y\in X\). It follows that  
\[E_{T_{Dom}^{\lambda}}(x,y)=\frac{1}{1+d(x,y)^{\frac{1}{\lambda}}}\]  
for all \(x,y\in X\).  
Next fix \(n\in\mathbb{N}\) and take \(\lambda=\frac{1}{n}\). Then, we have that \(f_{T_{Dom}^{\frac{1}{n}}\theta}\) is also an additive generator of \(T_{Dom}^{\frac{1}{n}}\), where  
\[f_{T_{Dom}^{\frac{1}{n}}\theta}(x)=\theta\cdot f_{T_{Dom}^{\frac{1}{n}}}(x)= \theta\cdot\left(\frac{1-x}{x}\right)^{\frac{1}{n}}\]  
for all \(x\in X\) and for each \(\theta\in]0,\infty[\).  
Now, we will apply Theorem 3 through \(f_{T_{Dom}^{\frac{1}{n}}\theta}\). Since the pseudo-inverse \(f_{T_{Dom}^{\frac{1}{n}}\theta}^{(-1)}\) of \(f_{T_{Dom}^{\frac{1}{n}}\theta}\) is given by  
\[f_{T_{Dom}^{\frac{1}{n}}\theta}^{(-1)}(y)=f_{T_{Dom}^{\frac{1}{n}}}^{(-1)} \left(\frac{y}{\theta}\right)=\frac{1}{1+\left(\frac{y}{\theta}\right)^{n}}= \frac{\theta^{n}}{\theta^{n}+y^{n}}.\]  
Therefore, \(E_{T_{Dom}^{\frac{1}{n}}}\) is a \(T_{Dom}^{\frac{1}{n}}\)-indistinguishability operator with  
\[E_{T_{Dom}^{\frac{1}{n}}}(x,y)=f_{T_{Dom}^{\frac{1}{n}}\theta}^{(-1)}(d(x,y) )=\frac{\theta^{n}}{\theta^{n}+d(x,y)^{n}} \tag{6}\]  
for all \(x,y\in X\). Since \(P\) given by (3) matches up with the preceding one, we conclude that the response function \(P\) is a \(T_{Dom}^{\frac{1}{n}}\)-indistinguishability operator that separates points.
# Two-View Mammogram Synthesis from Single-View Data Using Generative Adversarial Networks
## 2 Materials and Methods
### Data Sets  
We used image data from the following publicly available mammogram databases: Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) [39], INBreast database [40], and the Chinese Mammography Database (CMMD) [41]. The images in CBIS-DDSM are 16-bit digital data scanned from analog film mammograms in the Digital Imaging and Communications in Medicine (DICOM) format. The matrix sizes vary within the range from approximately 3000 \(\times\) 4500 to 4000 \(\times\) 5700. The INBreast database was acquired using a direct-conversion flat panel detector system (Siemens Mammant Novation DR). The images are 3328 \(\times\) 4084 or 2560 \(\times\) 3328 matrices, depending on the used compression plate sizes. The images were saved in DICOM format with a 14-bit contrast resolution. The CMMD database was acquired using an indirect-conversion flat panel detector system (GE Senographe DS). The images were saved in DICOM format with 8-bit contrast resolution as 1914 \(\times\) 2294 matrices. We excluded cases from the databases if they contained critical positioning errors, large artifacts (e.g., film scratches, dust particles), and foreign bodies (e.g., biopsy clip markers, pacemakers, implants). Eventually, we chose 1054 two-view mammograms (MLO- and CC-view pairs) from CBIS-DDSM, 188 pairs from INBreast, and 2542 pairs from CMMD. The right and left two-view mammograms of identical subjects were individually counted. From each database, 80% of pairs were used for training, while the remaining 20% of pairs were used for testing. We ensured that the mammograms of identical subjects were not divided into the training and test data sets, in order to avoid train-test contamination.