esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 4 DWT-Based Channel Estimation Algorithm
\begin{array}{cc}\text{sgn}\Big{(}D_{j,k}\Big{)} \left(\Big{|}D_{j,k}\Big{|}-T_{\text{DWT}}\right),&\Big{|}D_{j,k}\Big{|}\geq T _{\text{DWT}},\\ 0,&\Big{|}D_{j,k}\Big{|}<T_{\text{DWT}},\end{array} \right. \tag{30}\]  
where \(D^{\prime}_{j,k}\) is the detailed wavelet coefficient after threshold denoising, and \(\text{sgn}(\cdot)\) is the symbolic function. The wavelet reconstruction equation can be expressed as [16]:  
\[C^{\prime}_{j-1,k}=\sum_{m}C_{j,m}h(m-2k)+\sum_{m}D^{\prime}_{j,m}S(m-2k). \tag{31}\]  
\(\hat{H}_{\text{LS}}\) is denoised by the discrete wavelet transform to obtain \(\hat{H}_{\text{DWT}}\), which satisfies \(\hat{H}_{\text{DWT}}=C^{\prime}_{0}\). The specific steps of the DWT channel estimation algorithm are as follows:  
Step 1: Based on the received signal and the training sequence, use the LS algorithm to estimate the channel frequency domain response \(\hat{H}_{\text{LS}}\);  
Step 2: Perform a two-level wavelet decomposition on \(\hat{H}_{\text{LS}}\) and obtain \(C_{j,k}\) and \(D_{j,k}\);  
Step 3: Based on Equations (29) and (30), \(D_{j,k}\) performs the threshold decision and soft threshold processing to obtain \(D^{\prime}_{j,k}\), where the number of zero-set wavelet coefficients is \(L_{0}\);  
Step 4: Use \(C_{j,k}\) and \(D^{\prime}_{j,k}\) for wavelet reconstruction to obtain the frequency domain response of the denoised channel \(\hat{H}_{\text{DWT}}\). In the wavelet domain, the useful signal power is concentrated in some larger wavelet coefficients, and the noise power is distributed across the whole wavelet domain [15]. Moreover, the noise still behaves as additive white Gaussian noise in the wavelet domain. Like the windowed DFT algorithm, the MSE expression of the DWT-based channel estimation algorithm can also be regarded as the total noise power in the system minus the noise power on the zero-set wavelet coefficients. Therefore, the wavelet-denoising MSE of \(k\)th subcarrier can be approximately expressed as:  
\[\widetilde{\text{MSE}}_{\text{DWT}}[k]\approx\frac{L_{\text{total}}-L_{0}}{L _{\text{total}}}\frac{1}{\text{SNR}}=\frac{L_{\text{total}}-L_{0}}{L_{\text{ total}}}\sigma_{wf}^{2}. \tag{32}\]
# Characteristics of Changes in Contractile Properties of Muscle after Anaerobic Exercise
## 3 Results  
Table 2 illustrates the outcomes of the Wingate test conducted. According to the study, the test subjects achieved an average maximum pedal speed of 79.82 \(\pm\) 6.19 (km/h) while generating a peak power of 848.37 \(\pm\) 132.42 (W). The numerical characteristics of the TMG parameters obtained before and immediately after anaerobic exercise are presented together with their statistics in Table 3. Significant statistical changes were observed among individual TMG parameters in the values obtained before and immediately after anaerobic exercise in the following muscles: gastrocnemius lateralis, gastrocnemius medialis, rectus femoris, soleus, vastus lateralis, and vastus medialis. A significant decrease in Tc (\(p=0.0051\); r = 0.63) was observed in the soleus muscle, where the value of this parameter differed from the pre-exercise value by 25.93% (\(-8.88\) ms). In contrast, the values of the parameter Ts after anaerobic exercise took significantly lower \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(\tilde{x}\) & **Min** & **Max** & **Me** & **SD** & **V** \\ \hline Vmax (km/h) & 79.82 & 66.00 & 92.50 & 79.25 & 6.19 & 7.75 \\ PPO (W) & 848.37 & 520.30 & 1002.60 & 761.45 & 132.42 & 15.61 \\ tPPO (s) & 4.53 & 3.00 & 6.10 & 4.55 & 0.80 & 17.69 \\ AP (W/kg) & 11.34 & 9.20 & 14.20 & 11.70 & 1.12 & 9.84 \\ AC (W/kg) & 8.91 & 7.60 & 10.70 & 9.15 & 0.76 & 8.49 \\ FI (\%) & 14.80 & 7.00 & 22.10 & 14.55 & 3.87 & 26.14 \\ \hline \hline \end{tabular} \(\tilde{x}\)—mean; min—minimum; max—maximum; Me—median; SD—standard deviation; V—coefficient of variation; Vmax—maximal velocity; PPO—peak power output; tPPO—time to peak power; AP—anaerobic power; AC—anaerobic capacity; FI—fatigue index.  
\end{table} 
Table 2: Characteristics of the Wingate parameters (N = 20). values in the gastrocnemius lateralis (\(p\) = 0.0012; r = 0.74) and gastrocnemius medialis (\(p\) = 0.0054; r = 0.64) muscles, where the difference between the measurement values was \(-42.26\) ms and \(-44.47\) ms, respectively. Only in the rectus femoris muscle was a significantly higher (\(p\) = 0.0276; r = 0.49) value of the Ts parameter observed, differing from the baseline value by 16.31 ms (Table 3). The parameter Tr was significantly different (\(p\) = 0.478; r = 0.44) in the rectus femoris muscle, where its value after anaerobic exercise increased by 39.23%. A statistically significant decrease in the parameter Dm was recorded in the vastus lateralis muscle (\(p\) = 0.0365; r = 0.47) in which the radial muscle belly displacement decreased by \(-0.35\) mm. The parameter Td reached significantly higher post-exercise values in almost all muscles, with the highest statistically significant difference observed in the vastus medialis muscle (\(p\) = 0.0031; r = 0.66), where the delay time increased by 1.21 ms (Table 3).
## 3 Security Attacks on AV Sensors: A Closer Examination of GPS Systems
### The Susceptibility of Inertial Measurement Units (IMUs) to Security Attacks  
The inertial measurement unit (IMU), an integral part of an autonomous vehicle's sensor ecosystem, integrates the functionalities of a gyroscope and an accelerometer. This combination produces vital information regarding the vehicle's orientation, acceleration, and velocity. The IMU also monitors changes in environmental dynamics, such as the gradient of the road, enabling the vehicle to navigate varied terrains efficiently.  
Nevertheless, the IMU is not invulnerable to potential direct security threats. For example, an attacker could manipulate the sensor data to misrepresent the road's gradient, disrupting the autonomous vehicle's ability to interpret and react to its environment correctly. Such an attack could lead to significant safety risks, as the vehicle's behavior might not align with the actual environmental conditions.  
This potential threat was exemplified by the work reported in [44], where the authors developed the CarShark tool to monitor data flow in the vehicle's controller area network (CAN) bus system. Thorough packet analysis and modification, the tool enabled the simulation of a man-in-the-middle attack on the CAN network. By altering the data packet, they could manipulate the sensor's readings, demonstrating the potential implications of such an attack on autonomous vehicles.  
A variety of countermeasures might help mitigate such a security threat. One potential strategy involves implementing encrypted communication within the vehicle's network, adding a layer of protection against unauthorized data manipulation. Another approach could be the use of additional, redundant sensors to provide backup measurements. This could involve using GPS data to verify the vehicle's inclination, providing an extra check against erroneous readings from a compromised IMU.  
In the context of the emphasis of this article on emotional intelligence, it is essential to extend this concept to how a self-driving vehicle interprets and responds to sensor data. Similar to how humans use emotional intelligence to traverse complex social situations, an autonomous vehicle could use perceptual intelligence to recognize and respond adequately to potential hazards to its sensor inputs. This additional intelligence could considerably contribute to the overall safety and dependability of autonomous vehicles while also opening up new research avenues in the field.
# Instability Characteristics of and Control Techniques for Mudstone-Clay Composite Roof Roadways
## 4 Deformation and Instability Law and Prevention Technology of Double Soft Composite Roof
### Prevention Techniques for Roadway Deformation  
#### 4.3.1 Principle of Roof Support Prevention and Control  
The relative moisture content from the above orthogonal test is the main sensitivity factor. Figure 13 displays a geological survey of the 18,511 th working face of the material roadway, which indicates that higher moisture content and thicker clay interlayers, compared to other borehole surveys, result in greater deformation of the roadway roof. Therefore, a roadway support design is developed.  
The mudstone-clay composite rock layer has multiple layers, which have a significant impact on the anchoring effect of anchor rods/cables. Using numerical analysis methods, we explore the stress distribution in the roof with multiple support configurations of anchor rod-short anchor cable-long anchor cable, as shown in Figure 14.  
Using combined support of anchor rods and short anchor cables increases the bearing area above the roadway. This combination generates stress superposition extending to the anchoring end of the short anchor cable, enhancing overall bearing performance. When incorporating long anchor cables with anchor rods and short anchor cables, the main bearing expands substantially. The combined action of the anchor rods and long and short anchor cables creates stress superposition, extending the bearing area towards the depth of the overlying rock and penetrating the anchoring end of the anchor cable. This enlarged bearing area forms a more effective support structure for the mudstone-clay composite roof.  
Figure 13: Column chart of roadway roof.  
The support mechanism operates as follows: (1) A non-ribbed prestressed anchor rod is installed on the roadway roof, creating compressive stress zones at both ends (Figure 15). (2) Installation of prestressed short anchor cables forms a compressive stress zone in the middle rock layer of the roof. This enhances the self-supporting ability of the middle layer, promotes the downward movement of the composite roof's \"neutral axis\", and increases the bending resistance of the roof rock layer. Truss anchor cables simultaneously apply horizontal pressure to the overlying rock, improving the integrity of the soft and fractured roof (Figure 16). (3) The combination of supporting anchor rods and short anchor cables in the lower roof's surrounding rock creates a double-layer reinforced arch, effectively enhancing the roadway roof's stability. Long anchor cables installed in the overlying rock further tighten the double-layer arch, forming a multi-layer stress superposition structure (Figure 17). In the Figures 15-17, the 1, 2 and 3 respectively represent the range of the natural balance arch, the invisible balance arch and the expanded invisible balance arch.  
Figure 14: Stress field of overlying rock with multiple support configurations.
## 2 Materials and Methods  
The ceramic layers deposited on a metal substrate by plasma spraying are based on alumina and zirconia materials, which are considered corrosion-protective materials in comparison to conventional metallic materials [20]. The structural, phase, chemical and mechanical characteristics of these ceramic covering layers were previously presented in two studies by the main author [21; 22]. An analysis of corrosion resistance was conducted on round working electrodes with a diameter of 10 mm. The samples with ceramic layers were wired and the metal surface was polished to facilitate contact with the working electrode. The tests were conducted at room temperature, specifically at 25 degrees Celsius. Three experiments were conducted for each sample, comprising the control and three samples with ceramic layers containing varying proportions of YSZ in alumina. The samples were classified as follows: Sample 1: metal substrate + Al\({}_{2}\)O\({}_{3}\)-ZrO\({}_{2}\) layer (12.5% wt), Sample 2: (metal substrate + Al\({}_{2}\)O\({}_{3}\)-ZrO\({}_{2}\) layer (25% wt), and Sample 3: metal substrate + Al\({}_{2}\)O\({}_{3}\)-ZrO\({}_{2}\) layer (37.5% wt)).  
A corrosion cell containing three electrodes (a platinum electrode, a calomel-saturated electrode and the working electrode) was employed for both potentiodynamic measurements and electrochemical impedance spectroscopy determinations, as illustrated in Figure 1. The samples were positioned within the work cell using a Teflon washer with an inner diameter of 7 mm, ensuring that the surface area of the working electrode (the portion of the sample exposed to the corrosion environment) was consistent across all samples at 0.385 cm\({}^{2}\). All potential readings were taken in relation to the reference electrode. The solution employed was naturally aerated and comprised H\({}_{2}\)SO\({}_{4}\) and HNO\({}_{3}\), with a pH level of 3, representative of an acid rain-type solution [23; 24].  
The following work sequence was employed as part of the measurement process:  
* Linear polarization for the Tafel method: potential range (\(-200\)) \(\div\) (+200) mV compared to the open circuit potential, potential scanning speed: dE/dt = 1 mV/s;
* Cyclic polarization: potential range (\(-500\)) \(\div\) (+600) mV, potential scanning speed = 10 mV/s;
* Electrochemical impedance spectroscopy measurements: working potential = open circuit potential, frequency range = 105 \(\div\) 0.1 Hz, current amplitude = 10 mV.  
X-ray diffraction was performed with X'Pert Pro MPD equipment (Almelo, The Netherlands), Cu-X-ray tube, scan range of 20\({}^{\circ}\)-90\({}^{\circ}\).  
The surface condition of the experimental samples was analysed by scanning electron microscopy (SEM VegaTescan LMH II, SE detector, 30 kV heating gun, TESCAN, Brno-Kohoutovice, Czech Republic) and X-ray energy spectroscopy (EDS) using an EDS detector from Bruker (Xflash 6/10, automatic mode detection and mapping, Bruker, Billerica, MA, USA).  
Figure 1: Experimental setup of the work cell for corrosion analysis: electrochemical analyzer, working electrode, counter electrode, reference electrode.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.5 Data and Metadata Packets
After adding the neighbor, the received node will also do a refreshing operation where old neighbor nodes (neighbor nodes whose timestamp value is greater than a threshold value) will be removed from the neighbor list since a recent broadcast from those old nodes has not been received. The unicasting uplink metadata packet of an \(i^{th}\) node, which is sent only in the very first data-gathering cycle, consists of node ID of \(i^{th}\) node, \(\mathcal{S}_{i}\), and current timestamp value. The payload size of unicasting uplink metadata is given by \(12+4\mathcal{N}_{i}\). Thus, the unicasting uplink metadata total packet size is \(122+4\mathcal{N}_{i}\) and \(104+4\mathcal{N}_{i}\) for LTE and Ethernet communication channels, respectively. The controller will compute the \(\mathcal{B}_{i}\) and \(\mathcal{N}_{i}\) values upon receiving a unicast uplink metadata packet by a node. Broadcasting downlink metadata received by a given RSU or vehicular node consists of the full solution of the optimization, which are the values for \(z\) and \(x\) decision variables of the optimization problem provided by the optimizer at the controller. A given node should extract the \(z\) and \(x\) values corresponding to the given node from the full solution received using the node ID. Thus, the payload size of broadcasting downlink metadata is just \(2N\) bytes and the total packet size is \(110+2N\) bytes and \(94+2N\) bytes for LTE and Ethernet communication channels, respectively.
## 3 Novel Asynchronous Cooperative Diversity Scheme  
The proposed asynchronous cooperative communication system, based on RQSTBC, not only embeds OFDM but also merges the proper rotation factor into QSTBC. In the first place, the source selects half of the symbols from a signal constellation set and the other half of them from that constellation which is rotated with the optimum angle, which guarantees determinant of the coding gain distance (CGD) not being equal to zero and thus leads the diversity gain of the proposed scheme to approximate the diversity order. Second, the cycle prefix (CP) of the OFDM, performed at the source, is applied to compensate the timing delay of multi-streams (MS), which makes the signals among MS asynchronous cooperative, which is then mapped into frequency domain QSTBC at the relay nodes to ensure the full transmission rate as well as the full diversity. Finally, it implements the fast symbol-wise ML decoder for the global optimum, corresponding to the transmitted data, with the lower complexity. When it is applied to executing multi-hop communication, the working principle of it can be stated as follows.
## 4 Methodology
### Long Short-Term Memory (LSTM)  
The LSTM tends to overcome the vanishing gradient problem commonly experienced in RNNs. It operates by using three gates (input gate, forget gate, and output gate). Given a time series \(x\) as input to the LSTM block, the current state is defined as:  
\[C_{t}=\tanh W_{c}[a_{t-1},x_{t}]+b_{c} \tag{5}\]  
where \(W_{c}\) and \(b_{c}\) are the weight of the current input and block bias, respectively [27]. The input from the previous state is \(a_{t-1}\). The update gate function is obtained from the expression:  
Figure 3: Flow chart for the deep learning of river discharge process.  
\[\Gamma u=\sigma(W_{u}[a_{t-1},x_{t}]+b_{u}) \tag{6}\]  
where \(\sigma\) is the sigmoid transfer function. The forget gate \(\Gamma f\) and output gate \(\Gamma o\) determine the recurrent cell's eliminated information and output, respectively. The current state is obtained as:  
\[a_{t}=\Gamma o\times\tanh\tilde{C}_{t} \tag{7}\]  
where \(\tilde{C}_{t}\) is the self-looped cell obtained as:  
\[\tilde{C}_{t}=\Gamma u\times C_{t}+\Gamma f\times\tilde{C}_{t-1} \tag{8}\]  
In this study, 50 hidden layers were used. LSTM has been used to predict river discharge in Indonesia [19], South Africa [36], and India [30]. [PERSON] et al. [30] reported that LSTM showed better performance in river discharge over RNNs.
## 1 Introduction
Figure 3 shows preliminary experimental spectrum measurements and analysis of the emitted light from GDD N527 (of International Light Inc.), without MMW radiation (orange solid line) and with MMW radiation (blue solid line). Despite the many advantages mentioned above regarding the GDD and LTF combination pixel as a useful and robust MMW detector, further improvement to the detector pixel sensitivity and to the SNR are required. This paper proposes the use of an optical long-pass filter as a part of the GDD and LTF pixel component, thus enhancing the pixel detector sensitivity as well as its signal-to-noise ratio and thus improving performance of the suggested THz/MMW radiation sensor or pixel. The addition of the long-pass filter is shown here to improve the total SNR of the detected MMW signal. This improvement is due to two reasons. The first reason is the improvement in the noise level due to the filtering of the bias illumination noise of the GDD and the second reason is the increase in the signal level due to back reflection from the filter body towards the GDD cross-section. Figure 2: Typical measured spectral emission of neon lamp (**a**) and energy levels of neon. The radiated transitions are marked in red (**b**) [23; 24].
## 3 Components of VPN Architecture
### Linux Daemons  
A VPN based on IPsec on Linux systems consists of the implementation via SW of the IKE, AH, and ESP protocols, using appropriate modules made available by the kernel.  
IKE: As the name indicates, the purpose of the IKE protocol is to authenticate (using a pre-shared key, typing a public key, freeradius) the VPN peers, dynamically generate the keys, and share them with VPN peers. Keys are also used for the second phase of IPsec from IKE. Libreswan implements the IKE protocol using the project's bar program. ESP: The ESP protocol is the actual specification of the policy agreed by colleagues which is implemented in the Linux kernel's IPsec stack (NETEY/XFRM).
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 2 Adaptive Cartesian Grid Generation Method
### Grid Smoothing  
The next step involves grid quality assessment, which typically accompanies the entire process of grid refinement and coarsening throughout the computation (including the initial geometry-based and flow-based adaptive stages) and requires multiple iterative procedures aimed at eliminating any unreasonable grid configurations within the region. As illustrated in Figure 4a, \"hole elements\" [31] refer to grid cells that are flanked by neighboring cells with sub-cells on either side or top and bottom; and Figure 4b shows the scenario where the grid level difference between adjacent cells exceeds one. To address these issues, it is necessary to inspect the neighbor levels of the target grid in all directions and compare the level differences between the target grid and its neighbors. If the target grid's level is less than that of grids in multiple directions, or if the level difference with a neighbor exceeds one, then the target grid must be refined. This process involves repeated recursive checks of all grids until all irrational features are eliminated.
## 6 Space-Segment Optimization Aspects
### Orbit and Constellation Elements  
The selection of the orbital parameters for the constellation affects the overall design and mission output. In Table 5, the main parameters describing a constellation are depicted, which can be leveraged to optimize the constellation [87].  
The orbital parameters of the constellation generates its topology mainly dominated by the type and region of coverage. The coverage can be regional, zonal, or global with continuous or intermittent visibility. The coverage is typically 1-fold to 4-fold. The communication and surveillance applications usually require 1-fold coverage, and 4-fold coverage is essential for positioning and navigation applications [92].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Constellation Parameters** & & **Range** & **References** \\ \hline Altitude & \(h\) & 200–2000 km & [88; 89] \\ Number of Orbital Planes 1 & \(N_{p}\) & \(\leq\)4 & [90] \\ Number of Satellites per Plane 1 & \(N_{g}\) & \(\geq\)7 & [90] \\ Orbital Eccentricity & \(e\) & \(\approx\) 0 & [91] \\ Orbital Inclination & \(i\) & 65–85\({}^{\circ}\) & [88] \\ \hline \hline \end{tabular} \({}^{\dagger}\) These values correspond to a 1-fold coverage, namely the situation when each Earth point has at at least one satellite in view at a time.  
\end{table}
Table 5: Main Parameters Describing LEO Constellations.  
The coverage equation is modeled by  
\[\mathcal{C}=\cos(\theta+\varepsilon)=\cos\left(\frac{\varepsilon}{1+\frac{h}{R_{E} }}\right) \tag{1}\]  
where \(\mathcal{C}\) is the coverage parameter, \(\varepsilon\) is the elevation angle of the viewing cone of the satellite, \(h\) is the satellite altitude, \(R_{E}\) is the Earth radius, and \(\theta\) is the central angle of coverage [87]. An illustrative example of these parameters is provided in Figure 4.
## 4 Lunar Antenna Types
### Wire Antennas  
Wire antennas have the ability to generate a concentrated radiation pattern. Typically, these antennas are meticulously crafted to match their length with the wavelength. They find widespread use across diverse domains, including navigation, meteorology, military operations, and security applications. Examples of wire antennas encompass dipole and monopole antennas, helical antennas, and loop antennas, each with distinct strengths and weaknesses, rendering them apt for specific applications [56].