esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Supervised Process Monitoring Method
#### 4.1.3 Adaptive Modeling Method  
The adaptive modeling method continuously adds new process data to the modeling data matrix to make algorithms track new process modes and update the monitoring model in real-time. [PERSON] et al. [21] proposed a recursive and iterative PCA model that adapts to changes in modes by adding new sample data to the model through a recursive and iterative algorithm. Based on external analysis, the authors of [22; 23; 24] introduced adaptive monitoring, which decomposes the process variables into main and external variables, and gradually eliminates the influence of external variables using a least square regression algorithm to perform process monitoring. The authors of [25] proposed an adaptive monitoring method based on a hierarchical clustering structure, assuming that each mode has the same covariance structure. However, this assumption greatly limits its application. [PERSON] et al. [26] also proposed an adaptive monitoring method to obtain a new regression model by updating the mean and covariance structure in real-time. [PERSON] and [PERSON] et al. [27; 28] combined just-in-time learning (JITL) with PCA and least square support vector regression (LSSVR) and used JITL to model the residual sequences formed by the differences between the predicted value outputted from the process and the sample data, thus realizing real-time monitoring. [PERSON] et al. [29; 30; 31] introduced the idea of applying an adaptive dictionary learning algorithm to multi-mode process monitoring. The original data is classified and trained by dictionary learning, and the K-singular value decomposition (KSVD) algorithm is introduced to process the dictionary. After the training data is sparsely represented by the dictionary, the model is constructed for monitoring and eliminating the mode noise. The limitation of this method is that the modal data can be used by default. [PERSON] et al. [32] proposed an adaptive process monitoring method, called distributed adaptation local outlier factor (DALOF), to improve the performance of monitoring statistics. The method divides the source sample training data into the source and target domains, weights them, and verifies the monitoring accuracy by comparing the maximum mean discrepancy (MMD) between the source domain and target domain. This method is suitable for complex processes with non-linear characteristics.  
Generally, the adaptive modeling method is suitable for process monitoring in the case of slow mode changes (i.e., \"slow time-varying\"). This method involves heavy modeling data and computation demands, causing a response lag of the monitoring model to the current monitoring sample and cannot reflect the state of the current sample in time; it also depends on a mechanism model and prior knowledge of the process. Therefore, it is not frequently applied in practice.
###### Abstract  
The present research was carried out to study drought and its effects upon water resources using remote sensing data. To this end, the tropical rainfall measuring mission (TRMM) satellite precipitation, the synoptic stations, and fountain discharge data were employed. For monitoring of drought in the study area, in Kermanshah province, Iran, the monthly precipitation data of the synoptic stations along with TRMM satellite precipitation datasets were collected and processed in the geographic information system (GIS) environment. Statistical indicators were applied to evaluate the accuracy of TRMM precipitation against the meteorological stations' data. Standardized precipitation index, SPI, and normalized fountain discharge were used in the monitoring of drought conditions, and fountains discharge, respectively. The fountains were selected so that in addition to enjoying the most discharge rates, they spread along the study area. The evaluation of precipitation data showed that the TRMM precipitation data were of high accuracy. Studies in temporal scale are indicative of the strike of drought in this region to the effect that for most months of the year, frequency and duration in dry periods are much more than in wet periods. As for seasonal scales, apart from winter, the frequency and duration of drought in spring and autumn have been longer than in wet years. Moreover, the duration of these periods was different. A comparison between the results of changes in fountain discharges and drought index in the region has verified that the drought has caused a remarkable decline in the fountain discharges.  
s 2019 9 5377 [1]Applied
## 2 Related Work
We conduct one of the first systematic evaluations of DVS security within an AV context, implementing novel attack vectors like event flooding and directly comparing the sensor's resilience to that of a traditional RGB camera under attack. Although our DVS integration is for research and analysis purposes and not part of the active driving model, this investigation provides crucial, forward-looking insights into the security profile of this promising technology. In addressing the limitations inherent in simulation-based studies, we position our work as a necessary and responsible precursor to real-world implementation. The complexity, cost, and safety risks associated with testing cyberattacks on physical vehicles necessitate thorough validation in a controlled environment. By using the leading open-source CARLA simulator [36], we provide a replicable and quantifiable baseline that is essential for guiding future hardware-in-the-loop and on-road experiments, which require initial research studies based on such foundational work. Our findings, particularly on the limited effectiveness of RGB defenses (30-45% trajectory drift) and the complete mitigation of GPS spoofing, offer a clear roadmap for prioritizing the development of real-world defense mechanisms. This work, therefore, serves as a critical stepping stone, providing the initial validation and security analysis required before these advanced sensor systems can be safely deployed on public roads. In summary, this paper distinguishes itself from prior work through three main contributions. First, it provides the first systematic, comparative analysis of RGB and DVS sensor resilience in a unified cybersecurity context using the CARLA simulator. Second, it introduces and validates a tailored, high-performance anomaly detection system for these specific sensor streams, demonstrating its practical effectiveness. Finally, by quantifying the limitations of existing defenses, it provides a crucial, foundational dataset and analysis that directly informs the next stages of research and paves the way for the development of robust, real-world security frameworks.
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 2 Methodologies
### Numerical Simulations
#### 2.4.2 Body Force Exerted by Plasma Actuators  
The body force and the power exerted by the actuators were modeled by including the quantities \(s_{x}\), \(s_{y}\), \(s_{z}\) and \(us_{x}+vs_{y}+ws_{z}\) in the Navier-Stokes equations. For the body-force vector, the expression as follows was used:  
\[\left(\begin{array}{c}s_{x}\\ s_{y}\\ s_{z}\end{array}\right)=A\rho_{\rm c}(x,y,z)\left(\begin{array}{c}\varphi_{x} (x,y,z)\\ \varphi_{y}(x,y,z)\\ \varphi_{z}(x,y,z)\end{array}\right)\sin^{2}(2\pi f_{\rm pa}t), \tag{4}\]  
\[A=1\;(\sin(2\pi f_{\rm pa}t)\leq 0),\;-0.4(\sin(2\pi f_{\rm pa}t)>0), \tag{5}\]  
where \(\rho_{\rm c}\) is the plasma density, and \(\varphi\) is the electric field. The square of the sine function in Equation (4) means that the plasma discharge occurs twice in one period, as in the body-force model of [9].  
The distributions of plasma density and electric field were computed using the method proposed by [PERSON] [17]. In computing the plasma density, the Debye length was taken to be 0.7 mm, and the distributions of plasma density between the exposed upper electrodes were determined from the luminescence of the plasma.  
Figure 7 shows the distributions of plasma density determined from the luminescence measurements along with those predicted near the wall (\(y=0.05\) mm), where the horizontal direction is the spanwise position from the midpoint of the embedded electrode (\(z=z_{\rm pa}\)), which is non-dimensionalized using the pitch of the actuators. As shown in the figure, the predicted distributions agree well with those determined in the experiments.  
The coefficient \(A\) in Equation (4) was introduced to represent the change of the amount and direction body forces between positive and negative applied voltages. The value of \(A\) was set to be the same as that in [18], where simulations by the above-described method were employed to reproduce the induced flow in an experiment by [PERSON] [19].  
The simulated distributions of the body forces are shown in Figure 8. The most intense body force is generated around the edges of the exposed electrodes. The non-dimensional maximum body force was taken to be \(D_{\rm c}=\rho_{\rm c,max}E_{\rm pa}/(\rho_{0}{U_{0}}^{2}/L)=2.8\times 10^{-6}\) so that control effects on the mean velocity profile in the cavity were in good agreement with those measured in the experiments discussed in Section 3.1.
## 2 Description of the Multi-Sensor Monitoring System  
### Miniature Low-Power Radar  
This section describes the small, low-cost K-band radars that we use to detect and characterize targets. The RF transceivers are MACS-007802-0M1 RSV manufactured by M/A-COM (Tyco Electronics, Figure 1) [7], and are used primarily for automotive applications, e.g., front and rear-end collision detection, ground speed measurements, as well as motion detection, e.g., automatic door openers. We have also used MACS-007802-0M1R1V from the same manufacturer, which has a slightly higher output power than MACS-007802-0M1 RSV. The radar utilizes a Gunn diode oscillator and transmits a continuous wave at 24.125 GHz. It also has 0.3 GHz of bandwidth that can be controlled by applying external voltage, making it capable of estimating target range and velocity. The low-power consumption and small size make these devices excellent candidates for autonomous, compact sensors in a distributed network.  
An external voltage pulse ramp using a waveform generator is applied to the radar, which emits a continuous frequency modulated K-band signal with 300 MHz bandwidth. The received energy is internally mixed with the transmitted signal and low-pass filtered to supply in-phase and quadrature output components on separate pins. Fast Fourier Transform (FFT) is performed over samples within the chirp (fast-time) to extract range information about a target. A second FFT is performed over a series of chirps (slow-time) to estimate the velocity of the target. Table 1 shows relevant radar parameters [7].  
Figure 1: Miniature M/A-COM MACS-007802-0M1 RSV K-Band Doppler transceiver [7].  
Figure 2 demonstrates results of a simple moving dismount experiment performed with the radar. A small K-band antenna was secured to the waveguide flange of the radar. The radar was supplied with a ramp waveform (between 0.5 and 10 volts) at a frequency of 1 KHz, and a human dismount walked toward the radar at velocity of approximately 0.5 m/s. At a target range of 3 m, the complex output waveforms of the radar were captured on an oscilloscope over 10 ramp cycles (chirps). The recorded data were processed and consequently displayed in the form of the range-rate (velocity) _vs._ range intensity plot, see Figure 2(a). As seen in Figure 2, the energy is concentrated at the location representing scattering from the moving dismount, _i.e._, the max is at values of range 3 m and velocity 0.5 m/s. The range-rate (velocity) has maximum in the negative region of the graph as the target is moving toward the radar and distance was decreasing. The reverse situation is observed when the target moves away from the radar. Experiments have been completed to systematically evaluate the performance of the radar, in combination with other sensor nodes, as described in consecutive sections.
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 2 All-in-Focused Image Combination
### Consistency Verification (CV)  
In order to improve the combination effect, we employ a CV process [4] to the block map of the fused image _MAP_(_x,y_). We improve the _MAP_(_x,y_) accuracy by utilizing a majority filter in the window around _MAP_(_x,y_) as shown in Figure 5. Therefore, the CV is applied as post-processing, after the image combination process, to improve the quality of the output image and reduce the error due to unsuitable block selection. This process succeeds in both quality and complexity. Then, the DCT coefficients of the fused image _FDCT_(_x,y_) will be updated by following the improved _MAP_(_x,y_) values, as shown in Figure 6.  
Figure 4: The initial _FDCT_(_x,y_) process.  
Figure 5: Majority filter in consistency verification to improve _MAP_(_x,y_).  
Figure 3: The initial _MAP_(_x,y_) process.  
Finally, the all-in-focused image is reconstructed by applying the inverse DCT to the updated \(FDCT(x,y)\). The inverse DCT coefficients of each block are computed by  
\[I(x,y)\ =\ \sum\
olimits_{u=0}^{n-1}\sum\
olimits_{v=0}^{n-1}D(u,v)\cos\!\left( \frac{\pi u}{2n}(2x+1)\right)\cos\!\left(\frac{\pi v}{2n}(2y+1)\right). \tag{7}\]
# Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic
## 1 Introduction
The major contribution of this research is the design and implementation of an in-vehicle traffic light system based on fuzzy logic that does not rely on roadside units to resolve grid lock conflicts at unsignalized four-legged intersections involving autonomous and classical vehicles. The system uses GPS coordinates in a self-organizing vehicular network to classify vehicle positions at the intersections and vote to disperse cars in one lane. To the best of our knowledge, this is the first in-vehicle traffic light system underpinned by fuzzy logic that operates independently of any roadside infrastructure.
## 5 Analysis of the Parasitic Impedance Effect  
In this section, the effect of various parasitic impedances of the employed VDBA on the performance of the proposed mixed-mode universal filter in Figure 3 is to be analyzed. In practice, the non-ideal VDBA model with its various terminal parasitics is represented in Figure 4. It appears that the finite parasitic resistances and capacitances at the p, n, and z terminals are in the form [\(R_{p}//(1/sC_{p})\)], [\(R_{n}//(1/sC_{n})\)], and [\(R_{z}//(1/sC_{z})\)], respectively, while the low-value serial resistance (\(R_{w}\)) appears at the w terminal. Ideally, these parasitic values are assumed to be \(R_{p}=R_{n}=R_{z}=\infty\), \(R_{w}=0\), and \(C_{p}=C_{n}=C_{z}=0\). Under the effect of these parasitics, the non-ideal denominator of all transfer functions in all four working modes becomes:  
\[D_{n}\big{(}s\big{)}=\big{(}s^{2}\ R^{\prime}_{2}R_{z1}\ C^{\prime}_{1}\ C^{ \prime}_{2}+sR_{z1}\ C^{\prime}_{1}+s\ R^{\prime}_{2}\ C^{\prime}_{2}+1\big{)} \big{(}sR_{w1}C_{p2}+1\big{)}+g_{m1}g_{m2}\ R^{\prime}_{2}R_{z1}, \tag{27}\]  
where \(R^{\prime}_{2}=R_{2}\ /\ R_{z2}\ /\ R_{nz1}\ C^{\prime}_{1}=C_{1}+C_{z1}\), and \(C^{\prime}_{2}=C_{2}+C_{z2}+C_{n1}\). Equation (27) illustrates that the order of the filter function is modified due to the parasitic pole \(\omega_{parasite}\) (i.e., \(\omega_{parasite}=1/R_{w1}C_{p2}\)). However, this effect can be diminished if the proposed circuit is designed to operate at a useful frequency much less than \(\omega_{parasite}\) or under the following condition: \(\omega<<0.1\ \omega_{parasite}\). As the term (\(sR_{w1}C_{p2}+1\)) is made close to unity, Equation (27) can be further simplified to  
\[D_{n}(s)\cong s^{2}+\bigg{(}\frac{1}{R^{\prime}_{2}\ C^{\prime}_{2}}+\frac{1 }{R_{z1}\ C^{\prime}_{1}}\bigg{)}s+\bigg{(}\frac{g_{m1}g_{m2}+\frac{1}{R^{ \prime}_{2}R_{z1}}}{C^{\prime}_{1}\ C^{\prime}_{2}}\bigg{)}\quad. \tag{28}\]  
From \(D_{n}(s)\), the expressions for \(\omega_{o}\) and \(Q\) in the presence of parasitic impedances are thus obtained as:  
\[\omega_{o}\ =\ \sqrt{\frac{g_{m1}g_{m2}+\frac{1}{R^{\prime}_{2}R_{z1}}}{C^{ \prime}_{1}\ C^{\prime}_{2}}}\quad, \tag{29}\]  
Figure 4: Non-ideal equivalent circuit of the VDBA.  
and  
\[Q\;=\;\left(\frac{R^{\prime}{}_{2}R_{z1}\;C^{\prime}{}_{1}}{R_{z1}\;C^{\prime}{}_{ 1}+\;R^{\prime}{}_{2}\;C^{\prime}{}_{2}}\right)\sqrt{\frac{\left(g_{m1}g_{m2}+ \frac{1}{R^{\prime}{}_{2}R_{z1}}\right)\;C^{\prime}{}_{2}}{C^{\prime}{}_{1}}}\; \;\;. \tag{30}\]  
Therefore, it may be concluded that the parasitic effects on the \(\omega_{o}\) and \(Q\) would be alleviated if the following designs must be satisfied:  
\[\text{maximum }(R_{1},R_{2})<<\text{ parasitic resistances }(R_{n1},R_{p2},R_{z2}), \tag{31}\]  
and  
\[\text{minimum }(C_{1},C_{2})>>\text{ parasitic capacitances }(C_{n1},C_{z1},C_{z2}). \tag{32}\]
## 2 Materials and Methods
### Determination of Volatile Flavor Substances  
Headspace solid phase microextraction was employed to extract VOCs from the fermented grain samples. The detailed method was carried out as follows: in a headspace vial with a capacity of 20 mL equipped with a magnetic stirrer, 3 g of the fermented grain sample, 2 g of sodium chloride, 20 uL of amyl acetate (0.5275 mg/mL) as an internal standard solution, and 4 mL of boiled ultra-pure water were combined. The top of the glass bottle was sealed with a silicon diaphragm to create a sealed headspace environment. The headspace glass bottle was then heated to 50 \({}^{\circ}\)C, and the tea sample was stirred at a speed of 1100 rpm using the magnetic stirrer. After a 10 min pre-balance period, a 50/30 \(\upmu\)m DVB/CAR/PDMS fiber from American Supelco was inserted into the headspace vial to extract the VOCs. The extraction process lasted for 45 min. Subsequently, the fiber was desorbed for five minutes at the injection port of the GC-MS, operating in splitless injection mode at 250 \({}^{\circ}\)C.  
Head space solid phase microextraction (HS-SPME):  
The gas chromatography conditions were as follows: column, Hp-5 ms, ultra-inert (-60\(\sim\)325 \({}^{\circ}\)C) 30 m \(\times\) 250 mm \(\times\) 0.25 \(\upmu\)m; injection temperature, 250 \({}^{\circ}\)C in splitless mode; heating procedure, a starting temperature of 50 \({}^{\circ}\)C, maintained for 2 min, increased to 230 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and maintained for 5 min; and column carrier gas, helium at a constant flow rate of 1 mL/min.  
The mass spectrometric conditions were as follows: ionization source, electron impact (EI); ion source temperature, 230 \({}^{\circ}\)C; EI energy, 70 eV; quadrupole temperature, 150 \({}^{\circ}\)C; scan mode, constant pressure full scan; and mass spectrometric scan range, 30-600 amu. We compared the mass spectrometry information of each chromatographic peak obtained from GC-MS analysis with the reference compounds in the NIST 17 library to identify unknown compounds [11]. To calculate the retention index (RI) for each peak, the retention time (RT) of \(n\)-alkanes (C7\(\sim\)C21) under the same GC-MS conditions was utilized. The identification of VOCs was accomplished by comparing the calculated RI values and the mass spectra of all detected metabolites with those present in the NIST 17 library. For the quantification of VOCs, the peak area ratio of the VOCs to the internal standard ethyl caprate was multiplied by the concentration of ethyl caprate.
## 5 Algorithm Description
### Areas  
The corresponding binary images (\(BW_{r}\) and \(BW_{s}\)) are computed from the real-world and the synthetic ones. We call \(A_{s}\) the area of \(BW_{s}\), which is the number of pixels whose value is greater than 0. A dilated version of \(BW_{s}\), called \(BW_{sd}\), is also computed using a disk with a diameter of 7 pixels as the morphological structuring element. Let us indicate with \(BW_{d}\) the difference between \(BW_{sd}\) and \(BW_{s}\):  
\[BW_{d}=BW_{sd}-BW_{s}. \tag{4}\]  
\(A_{d}\) is the area of \(BW_{d}\).  
Now, it is possible to compute the images \(M_{a}\) and \(M_{d}\), i.e., the result of the pixel-wise multiplication between \(BW_{r}\) and \(BW_{s}\), and between \(BW_{r}\) and \(BW_{d}\):  
\[M_{rs} = BW_{r}\cdot BW_{s}, \tag{5}\] \[M_{rd} = BW_{r}\cdot BW_{d}. \tag{6}\]  
Figure 6 shows the images involved in the computations.  
We can compute the score relative to the areas with the following equation:  
\[S_{A}=1.0-A_{rs}/A_{s}+A_{rd}/A_{d}, \tag{7}\]  
where \(A_{rs}\) and \(A_{rd}\) are the corresponding areas of \(M_{rs}\) and \(M_{rd}\).  
Figure 5: The images involved in the computation of the cost function term relative to edges. (**a**) \(E_{r}\), edges of the real-world image. (**b**) \(E_{s}\), edges of the synthetic image. (**c**) \(E_{sg}\), edges of the synthetic image after the Gaussian filter. (**d**) \(E_{m}\), pixel-wise multiplication between \(E_{r}\) and \(E_{sg}\).
# Absolute Distance Measurement Based on Self-Mixing Interferometry Using Compressed Sensing
## 3 Experimental Results
The maximum number of iterations \(m\) was set to 100 and the residuals' norm \(\left\|\mathbf{R}\right\|_{2}\) with and without acceleration were recorded. The change in residual is depicted in Figure 6.  
Figure 6 shows that as the iteration index increased, the residual values became smaller. Residuals with acceleration dropped faster, indicating that the improved algorithm could recover the signal more quickly. The tendency of the residual values slowed down when the iteration index was greater than 50, so this value was used as the maximum number of iterations in our experiments. Figure 4: Spectra of raw and recovered data: (**a**) all spectrum data; (**b**) detail of non-zero values; (**c**) detail of zero values. Figure 5: Differences in distance between raw data and CS recovery data.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 3 System Model  
This paper performs a simulation in the indoor environment suggested by 3 GPP. The environment suggested by 3 GPP is shown in Figure 1[14].  
As shown in Figure 1, the suggested indoor environment is a space of 120 m \(\times\) 50 m. There is a total of 12 APs for positioning in the environment.  
The indoor environment is based on Wi-Fi and uses RSSI values for positioning the user's location. The RSSI value can be obtained by the following (1):  
\[RSSI_{d}=TXpower-Pathloss \tag{1}\]  
where \(RSSI_{d}\) is the received power between the AP and the receiver for distance \(d\). Further, the pathloss value defined in 3 GPP is used as it is. The pathloss model is as follows:  
\[Pathloss=32.4+17.3\log_{10}d+20\log_{10}f \tag{2}\]  
where \(f\) represents the frequency of Wi-Fi (we use 2.4 GHz in this paper). Further, the shadow fading standard deviation is denoted by \(\sigma_{SF}\), with a value of 3 dB.