esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Set  
Two different data sets were used to evaluate the proposed approach. The first data set is the public eWINE data from [35], which was chosen because it was recently adopted by other researchers for ML classification of LOS/NLOS channel conditions [36; 37]. It has a rich set of samples (6000) for each of the seven scenarios included in the data set. The second data set is a radar data set created by the author and used in [38] wherein a weather radar pulse signal is submitted to different fading conditions. To be consistent with the eWINE data set, two fading conditions were used: LOS (no fading) and NLOS.  
The eWINE data set was created using the SNPN-UWB board with the DecaWave DWM1000 UWB radio module (DecaWave has now been acquired by QORVO) by transmitting UWB pulses in seven different scenarios: Office 1, Office 2, a small apartment, a small workshop, a kitchen with a living room, a bedroom, and a boiler room. In each scenario, 3000 LOS samples and 3000 NLOS samples were taken by using one DecaWave DWM1000 UWB radio module as an anchor and a second one as a tag. Only the CIR data were used in this study and no other information was used, as in [36], to make it a purely ML problem.  
The radar data set (called Radar in the rest of this paper) was created by the author using a weather radar pulse signal, which was submitted to a fading condition inspired by the 3 GPP tap-delay-line (TDL) configurations defined in the 3 GPP standard [39], but which have been customized with a Nakagami-m fading distribution (3 GPP-like fading models). As described more in detail in [38], a signal generator was used to generate a weather radar test signal defined in [40] with a sampling frequency of 40.00 MHz and a pulse width of 1 microsecond. The carrier frequency for the generation of radar pulses was set to 5650 MHz. Then, the pulse signals were transmitted through a channel emulator that emulates two different conditions: the LOS no fading condition and the NLOS TDL-C from [39] with a Nagakami-m fading model with values of m = 0.5, 1, 2, 3, 4. For each condition, 3000 samples were created (600 samples for each of the five values of m of the Nagakami-m fading model) for a total of 6000 samples. Only two conditions were selected to be consistent with the eWINE data set.  
As described in the subsequent section of this paper, the training portion of the data set (which is poisoned) was three-fourths of the entire data set while one-fourth was reserved for the classification step. This distribution was used for both data sets.
## 3 PSO Algorithm  
The PSO algorithm [38] is a biological evolutionary algorithm, which was inspired by the foraging process of birds, randomly initializing a team of particles in the solution area of the optimization problem. Each particle represents a feasible solution in the goal function, and its position is decided by way of the particle motion velocity, which is affected by using the particle history optimal solution and the group history optimal solution.  
In the \(n\)-dimensional search space, the position of the particle \(i\) (\(i=1,2,\cdots,N\)) is denoted using \(x_{i}(k)=(x_{i1}(k),x_{i2}(k),\cdots,x_{in}(k))\), and the velocity is denoted using \(v_{i}(k)=(v_{i1}(k),v_{i2}(k),\cdots,v_{in}(k))\). The parameter of the personal best position searched via the particle \(i\) is \(p_{best}=(p_{i1},p_{i2},\cdots,p_{in})\), and the parameter of the global best position of the population is \(g_{best}=(p_{g1},p_{g2},\cdots,p_{gu})\). We use the acquired global best and personal best position information to calculate the corrected position and velocity of the particle, as shown in (10) and (11):  
\[v_{i}(k+1)=\omega v_{i}(k)+c_{1}r_{1}(p_{i}(k)-x_{i}(k))+c_{2}r_{2}(p_{g}(k)-x_ {i}(k)) \tag{10}\]  
\[x_{i}(k+1)=x_{i}(k)+v_{i}(k+1) \tag{11}\]  
where \(t\) is the number of iterations, \(\omega\) is the inertia weight, \(c_{1}\) and \(c_{2}\) are learning factors, and \(c_{1}=c_{2}=1.5\), \(r_{1}\) and \(r_{2}\) are random numbers.  
When the PSO algorithm is in the stagnation stage of evolution, the particles in the particle swarm will \"aggregate\", and the particles will not be dispersed until the stagnation of evolution is broken. Equation (12) can be obtained by transforming Equations (10) and (11):  
\[x_{i}(k+1)=x_{i}(k)+\omega\times[x_{i}(k)-x_{i}(k-1)]+\tau\times[\sigma-x_{i}( k)] \tag{12}\]  
in, \(\tau=c_{1}r_{1}+c_{2}r_{2}\), \(\sigma=\frac{c_{1}r_{1}p_{1,1}+c_{2}r_{2}p_{2,1}}{c_{1}r_{1}+c_{2}r_{2}}\).  
Equation (12) further leads to (13):  
\[\mathsf{x}_{i}(k+1)=(1+\omega-\tau)\times x_{i}(k)+\omega\times x_{i}(k-1)+\tau \times\sigma \tag{13}\]  
According to (13), we obtain:  
\[\lim_{t\rightarrow+\infty}x_{i}(k)=\sigma=\frac{c_{1}r_{1}p_{best}+c_{2}r_{2} g_{best}}{c_{1}r_{1}+c_{2}r_{2}} \tag{14}\]  
Since \(r_{1}\) and \(r_{2}\) are uniformly distributed, Equation (15) can be obtained:  
\[\lim_{t\rightarrow+\infty}x_{i}(k)=\sigma=\frac{c_{1}p_{best}+c_{2}g_{best}}{ c_{1}+c_{2}} \tag{15}\]  
Since \(c_{1}=c_{2}=1.5>0\), Equation (16) can be obtained:  
\[\lim_{t\rightarrow+\infty}x_{i}(k)=\sigma=\frac{c_{1}p_{best}+c_{2}g_{best}}{ c_{1}+c_{2}}=\frac{p_{best}+g_{best}}{2} \tag{16}\]  
It can be seen from (10) to (16) that the particles will gather on the extreme values obtained by their extreme value \(p_{best}\) and the global extreme value \(g_{best}\) of the group. If the particle cannot discover a better position \(p_{best}\), the evolution will be in a stagnant state. The particles will gather at \((p_{best}+g_{best})/2\) and converge to the local optimal solution.
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 3 Results
### Scenario 3  
This scenario retains the same four TRs as in Scenario 2. Four SRs (\(SR_{i}\), where \(i=2,\ldots,5\)) covering the AoI are added, each with different detection range and different beam openings.  
Figure 8 illustrates the beams of all five SRs and the resulting beam openings due to the presence of the four TRs. The triangulated detection areas are shown in transparent red. The area of these transparent red common coverage areas is, for this specific scenario, calculated equal to 9591 km\({}^{2}\) (84.88% of the AoI).
## 5 Discussion
### Feasibility of Representing 3D Flow in 3D Space
The survey pattern causes measurements that are spatially close together to not necessarily be adjacent in the time series of the measurement. While each sample has two temporally adjacent samples, other samples in the immediate spatial vicinity are from a different line in the zig-zag pattern and, therefore, are temporally more distant. Some noise may therefore be introduced by high-frequency non-stationary properties of the flow, which may cause apparent noise, even without measurement errors.
## 2 Materials and Methods
### Measurement Tools
#### Pressure Pain Threshold (PPT)  
The PPT was measured with a pressure algometer (Somedic, Algometer, Sweden). The stimulation area was 1 cm\({}^{2}\) and the pressure increase rate was 30 kPa/s. The participants were asked to push a button as soon as the pressure sensation became painful and that was defined as the PPT. Five pressure stimuli were applied at 5 locations close to each other, and the mean of the 3 closest PPT ratings was used to define the mean PPT. The locations of the selected 3 stimuli were marked for further measurements [23; 24].  
Heat Pain Threshold (HPT)  
The HPT was measured by the Thermal Sensory Analyzer (TSA, Medoc, Ramat-Tishay, Israel) using the 30 \(\times\) 30 mm thermode. The baseline temperature was 32 \({}^{\circ}\)C, and the temperature was increased at a rate of 1 \({}^{\circ}\)C, with a maximal temperature of 50.5 \({}^{\circ}\)C. The participants were asked to press a computer mouse button when the warm sensation became painful. Five different stimuli were applied with 15 s inter-stimulus intervals, and the mean of the 3 closest readings was defined as the mean HPT [23,24].  
Tonic Heat Pain (THP)  
THP was also measured by the TSA with the 30 \(\times\) 30 mm thermode. A constant stimulus of 46.5\({}^{\circ}\) was delivered for 2 min. Every 10 s, the participants were asked to grade the pain intensity using the 0-100 numerical pain scale (NPS) where '0' denoted \"no pain\" and '100' denoted \"maximum pain imaginable\". The mean of the THP ratings was calculated [25].  
The order of the pain threshold measures was randomly assigned for each participant and was repeated in the same order each time. The THP was always delivered immediately after the HPT.
## 2 Materials and Methods
### Growth Media and Fungal Isolation  
To isolate and identify non-_Saccharomyces_ yeasts, a combination of microbiological culture-based approaches with molecular methods facilitates the identification of species. Fungal isolation was conducted as outlined in [PERSON] et al. (2017) and [PERSON] et al. (2019) with minor modifications related to type of selective agar and sampling timepoint [14; 30]. To isolate grape-associated fungal populations, appropriate dilutions (\(10^{-1}\)\(\sim\)\(10^{-4}\) dilutions) of fermented Chambourcin must at 0 and 24 h were plated on Dichloran Rose Bengal Chloramphenicol (DRBC) agar based on manufacturer's instructions (Difco, Sparks, MD, USA). DRBC agar is recommended for the enumeration and selection of yeasts and molds in food and dietary supplements. It contains peptone as a source of carbon and nitrogen, dextrose as a sugar source, and magnesium sulfate to provide trace elements [31]. Chloramphenicol is added to inhibit bacterial growth resulting in better recovery of fungal cells, and rose bengal is added to increase the selectivity of non-_Saccharomyces_ yeast by suppression of rapidly growing molds such as _Neurospora_ and _Rhizopus_ spp. [32]. Dichloran is added to inhibit the spreading of molds by reducing colony diameters [33]. Culture plates were incubated at 25 \({}^{\circ}\)C for 5 days. Twenty colonies were selected based on unique colony morphology observed from 0 and 24 h samples across all three regional wineries. Five milliliter enrichments were prepared for individual isolates using liquid yeast peptone dextrose (YPD) medium (1% yeast extract (Difco, Sparks, MD, USA), 2% peptone (Difco, Sparks, MD, USA) and 2% dextrose (VWR International, Radnor, PA, USA)), and grown at 25 \({}^{\circ}\)C for 24 h with shaking at 200 rpm (standard laboratory conditions). Yeast Peptone Dextrose is a complete medium used for cultivation of a wide range of yeasts, including _Saccharomyces cerevisiae_[34]. Four milliliters of enrichment culture (YPD + single colony yeast isolate grown for 24 h) was centrifuged at 3000\(\times\)\(g\) for 3 min, supernatant was discarded, and cell pellets were kept frozen at \(-80\)\({}^{\circ}\)C until further use. Remaining enrichment cultures were stored at \(-80\)\({}^{\circ}\)C as a yeast cryo-stock supplemented with 30% (\(v/v\)) glycerol and deposited into our laboratory's culture collection. _Saccharomyces cerevisiae_ BY4742 [ATCC 4040004, YVC1] was used as a reference strain, grown in liquid YPD medium under standard laboratory conditions, and stored with 30% (\(v/v\)) glycerol at \(-80\)\({}^{\circ}\)C.
## 5 Theory Analysis and Discussion
### Effect of Velocity Skewness on the Travel Distance
\[t=-\frac{112D}{85}\frac{(\rho_{s}+\rho/2)}{\rho C_{D}}\frac{1}{\sqrt{u_{b0}^{2 }-\frac{64C_{D}u}{51C_{D}}}}\Bigg{(}arctan\frac{u_{b}-u_{x}}{\sqrt{u_{b0}^{2 }-\frac{64C_{D}u}{51C_{D}}}}-arctan\frac{u_{b}-u_{0}}{\sqrt{u_{b0}^{2}-\frac{64 C_{D}u}{51C_{D}}}}\Bigg{)} \tag{31}\]  
further solution:  
\[\frac{dx}{dt}=u_{b}+matn\Big{(}\frac{p}{2}mt+arctan\frac{u_{b}-u_{0}}{m}\Big{)} \tag{32}\]  
where \(p=\frac{85\rho C_{D}}{56D(\rho_{s}+\rho/2)},m=\sqrt{u_{b0}^{2}-\frac{64C_{D}u }{51C_{D}}}\). When \(t=0\), \(x=0\), Equation (32) is solved:  
\[x=u_{b}t-\frac{2}{p}ln\Bigg{|}\frac{cos\Big{(}\frac{p}{2}mt+arctan\frac{u_{b} -u_{0}}{m}\Big{)}}{cos\Big{(}arctan\frac{u_{b}-u_{0}}{m}\Big{)}}\Bigg{|}. \tag{33}\]  
The influence of the flow velocity skewness on travel distance of the particle under deceleration was also analyzed. The initial conditions that \(a=0\)-\(0.28\) m/s\({}^{2}\), \(C_{D}=0.4\), \(C_{L}=0.1\), \(\rho=1000\) kg/m\({}^{3}\)  
Figure 14: Relationship between the travel distance and velocity skewness of sediment particles. \(\rho_{s}=2650\) kg/m\({}^{3}\), \(t=0.3\) s, \(D=2R=1\)\(\sim\)2 mm \(\Delta=0.5\) mm, \(u_{b}=0.5\) m/s, and \(u_{0}=0.2\) m/s are given to deal with the travel distance Equation (33), deriving the relationship between the travel distance and the velocity skewness in the deceleration phase, as is shown in Figure 15. In this figure, theoretical results are compared with experimental data in the study, which can be found that the theoretical results are agreed with experimental data well. It was indicated that the travel distance of the rolling motion of the sediment particles increased with the increase of the flow skewness during the deceleration process, and the growth rate of the single particle travel distance rose up with the drop of reverse flow velocity. The impact of the velocity skewness on the movement distance of the sediment particles on the bed surface was thus gradually becoming notable.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 3 Results
### Validation of the Drying Model
#### 3.2.4 Application of the Model  
Figure 13a shows the simulated moisture content during the relevant months in the dry season and the rainy season for the 10-year period (2008-2017). Figure 13b summarizes the average drying time for the dry season and the rainy season, where 14% MCwb was achieved. The prediction shows that during the dry season, the target moisture content can be reached within 10 h, while during the rainy season, the drying time increases, and the drying time may be higher than 30 h.  
Figure 12: Experimental and simulated moisture content in the ISD during paddy rice drying of three experimental batches (red line indicates target MCwb = 14%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Batch** & **Date** & **Drying Period h** & **Initial MCwb. \%** & **Variables** & **R\({}^{2}\)** & **RMSE \({}^{2}\)C, \%** & **MAPE \%** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{30 October 2013} & \multirow{3}{*}{27.0} & \multirow{3}{*}{22.3} & \(T_{abs}\) & 0.85 & 2.7 & 1.5 \\  & & & & \(T_{pad}\) & 0.73 & 5.8 & 6.5 \\  & & & & \(MC_{w,b}\) & 0.82 & 1.4 & 7.4 \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{5 November 2013} & \multirow{3}{*}{29.0} & \multirow{3}{*}{22.5} & \(T_{abs}\) & 0.91 & 2.7 & 0.2 \\  & & & & \(T_{pad}\) & 0.79 & 6.6 & 6.3 \\  & & & & \(MC_{w,b}\) & 0.84 & 1.2 & 3.1 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{15 November 2013} & \multirow{3}{*}{29.5} & \multirow{3}{*}{22.5} & \(T_{abs}\) & 0.90 & 2.2 & 0.4 \\  & & & & \(T_{pad}\) & 0.85 & 5.1 & 6.9 \\  & & & & \(MC_{w,b}\) & 0.96 & 0.5 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model constants and statistic criteria (moisture content wet basis—MCwb, coefficient of determination—R\({}^{2}\), root mean square error root mean square error—RMSE and the mean absolute percentage error—MAPE).
## 3 Methodology
### Overall Framework of BPNN Fusion Model
#### 3.1.2 Design and Establishment of DCCD-RSM  
In order to determine the appropriate range of parameters, exploratory experiments were carried out in the early stage. It is observed that the extreme conditions of the grinding parameters can lead to under-grinding or over-grinding of the workpiece surface. Underwear results in incomplete removal of surface materials, while overwear results in workpiece surface defects and damage, ultimately reducing surface roughness. In order to ensure the smoothness and integrity of surface machining, Dynamic Central Composite Design (DCCD) was used to find out the appropriate parameter range.  
Initially, a pre-experiment is carried out using the central composite design to collect response data from the region of interest and create a quadratic polynomial model of theresponse surface. By analyzing the response surface map and contour map, the region with a low response value is identified. The experimental design is modified using the data from the new experimental point, the experimental area is expanded, and the experimental points are dynamically added outside the boundary when the experimental area does not reach the optimal area. This process continues until the ideal experimental location is found.  
To model and analyze the relationship between multiple input variables and a response variable, DCCD-RSM is used, and the model equation can be expressed as:  
\[y=\beta_{0}+\sum_{i=1}^{m}\beta_{i}x_{i}+\sum_{i=1}^{m}\beta_{ii}x_{i}^{2}+ \sum_{i=1}^{m}\sum_{j=i+1}^{m}\beta_{ij}x_{i}x_{j}+\varepsilon \tag{3}\]  
where \(y\) represents the response variable, and \(x_{i}\) and \(x_{j}\) are the values of the \(i\)-th and \(j\)-th independent variables, respectively. \(\beta_{0}\) is the intercept, while \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) denote the coefficients of the linear, quadratic, and interaction terms, respectively. The error term \(\varepsilon\) accounts for any unexplained variability in the data, and \(m\) represents the number of input variables.  
To fit the model, the coefficients \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) are estimated using least squares regression by minimizing the residual sum of squares (RSS):  
\[\text{RSS}=\sum_{k=1}^{n}(y_{k}-\hat{y}_{k})^{2} \tag{4}\]  
where \(n\) is the number of data points, \(y_{k}\) is the observed value, and \(\hat{y}_{k}\) is the predicted value from the model.  
For clarity, the response surface model can also be expressed in matrix form:  
\[\hat{y}=x^{\top}Bx+b^{\top}x+\beta_{0} \tag{5}\]  
where \(x=[1,x_{1},x_{2},\ldots,x_{m}]\) is the input variable vector (with a constant term), \(B\) is a symmetric matrix containing quadratic and interaction coefficients, and \(b\) is a vector of linear coefficients.
## 3 Results and Discussion
### Results of SEM Analysis  
In this study, SEM analysis of metal samples was performed in the presence and absence of green corrosion inhibitors. This analysis was performed on 750 ppm extracts after 24 h and 35 \({}^{\circ}\)C in a 1 M hydrochloric acid environment. The results are shown in Figure 10. From Figure 10a (blank case), it can be seen that damage to the carbon steel sample is obvious in the absence of corrosion inhibitor. The surface of the damaged metal and the corrosion products are demonstrated in Figure 10a. As can be seen from this figure, the metal surface is damaged due to dissolution in the corrosive solution. In addition, the highly porous surface and the holes formed indicate its surface degradation in the acidic environment. Other Figure 10b-d depicts the protection of metal surfaces against corrosion using banana peel extract, rice straw extract, and the mixture of both (40:60 BPE:RSE). In the presence of the inhibitors, the rate of dissolution of the metal surface is significantly reduced, and a relatively smooth surface appears. Among these figures, the best protection and most uniform layer is observed in the presence of the mixture (Figure 10d). In this case, the resulting film is stable. Between banana peel and rice straw, the better result is shown in Figure 10c (for rice straw extract). Thus, the formation of the protective film in the presence of rice straw extract is more stable and uniform than in the presence of banana peel extract. These results are consistent with the results of the weight loss and EIS experiments.  
Figure 10: SEM analysis of steel samples in a blank case (**a**) and in the presence of BPE (**b**), RSE (**c**), and the mixture of BPE (40%) and RSE (60%) at 750 ppm (**d**).  
EDS analysis of metal samples was performed in order to determine the mass percentage of elements in the used steel after immersion in the HCl solution in the absence and presence of banana peel extract, rice straw extract, and their mixture (40:60 BPE:RSE) at 750 ppm. The analysis indicated that there was a large difference in the mass percentage of iron (the main element of steel) between the inhibited and uninhibited metal samples. The results showed the following mass percentage for the blank case, banana peel, rice straw, and their mixture: 70.12%, 88.94%, 90.07%, and 92.13%, respectively. The low iron percentage in the blank case shows the metal consumption due to corrosion. The higher iron percentage value in the steel in the presence of green inhibitors confirms their effectiveness in inhibiting corrosion. The highest iron percentage was for the case of the extract mixture, which demonstrates its higher inhibitory capacity compared to individual extracts.  
The corrosion products of steel samples in the presence and absence of green corrosion inhibitors were analyzed by Raman spectroscopy. The results showed that the main corrosion products in the uninhibited surface and in the inhibited surface at low inhibitor concentrations were in the form of oxides and chlorides due to the appearance of oxygen and chlorine peaks.
## 5 Performance Evaluation
### Packet Loss Analysis  
Figure 16 shows the change in packet loss in terms of the packet arrival rate. The packet loss rate is important in service reliability in the WoT monitoring service [12]. As Figure 16 shows, the packet loss of the proposed CoMP is less than that of MIPv6 and HMIPv6. The packet loss of both MIPv6 and HMIPv6 increases sharply as the packet arrival rate increases. In contrast, almost no packet loss occurs for CoMP because the protocol uses the hold mode of operation.  
Figure 15: Impact of wireless link delay on handover latency.  
Figure 17 shows the impact of packet loss with regard to a variety of wireless link delays. To measure the packet loss with regard to the wireless link delay, the value of \(\lambda_{\text{p}}\) was set to 10 packets/s; \(L_{WL}\) was set to 0.002 s; and \(L_{W}\) was set to vary between 10 ms and 80 ms. In MIPv6 and HMIPv6, packet loss increases as \(L_{W}\) increases. However, in CoMP, the amount of packet loss is less than that of HMIPv6 and MIPv6 under the conditions of varying wireless link delays. In CoMP, the PUT holding mechanism can be dynamically reduced. The results of both the mathematical analysis and the simulation are almost the same.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### Silex  
Emerging in 2019, Silex poses a significant threat to Linux systems and IoT devices with its multi-vector destructive capabilities. Its approach and shell commands are inspired, and some are directly borrowed from BrickerBot. After gaining root access, Silex corrupts system storage, wipes files, deletes firewall rules, and halts systems, rendering them non-functional. Silex targets both Linux systems and IoT devices, indicating an evolution in PDoS attack vectors. The malware leads to system-wide disruptions, often necessitating complete hardware replacements or reinstalls.