esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Interference Process of Liquid Radial Reflux on the Stability of a Shaped Charge Jet
## 3 Simulation Analysis  
### Simulation Modeling  
The numerical simulation of jet penetration in the liquid-filled single-cell structure (LFSCS) was conducted by using Euler and Euler-Lagrange coupling algorithms with the AUTODYN solver to observe the shock wave propagation and liquid reflux intuitively. The simulation was divided into two stages: (1) The Euler algorithm was used to simulate the jet formation of a \(\varphi\)56 mm shaped charge (SC) [30], (2) the Euler-Lagrange coupling algorithm was adapted to calculate the jet penetration in the LFSCS. In the first stage, one Euler part was built to obtain the formed SCJ. Meanwhile, in the second stage, the formed SCJ in stage 1 was initially mapped to the Lagrange algorithm, and then one Euler part filled with water and another Lagrange part filled with Q235 were established.  
The SC finite element models in the numerical research are shown in Figure 6. The diameter and height of the SC were 56 and 73.3 mm, respectively. The thickness of the oxygen-free high-conductivity copper (OFHC) liner was 1 mm. The high explosive of the SC was JH-2, with a density of 1.717 g/cm\({}^{3}\). The initiation mode of the SC was top center initiation. The JWL equation of state (EOS) and the shock EOS were adapted for JH-2 and OFHC, respectively. The EOS of air was the ideal gas. The Euler domain was set to a gradient mesh of 0.25 mm \(\times\) 0.25 mm to 0.25 mm \(\times\) 1 mm to ensure the accuracy of jet formation.  
The shape of the SCJ at 24 \(\upmu\)s obtained by simulation is shown in Figure 7, and the jet tip velocity was 6506 m/s.
## 3 Proposed Universal Filter
### Operation in Current- and Trans-Impedance-Modes  
In CM mode, the input voltage (\(V_{\text{IN}}\)) is set to zero and input currents are applied according to Table 4 to obtain all five filter responses. Equations (7) and (8) give the transfer functions of the CM modes.  
\[I_{\text{OUT(CM)}}=\frac{(s^{2}C_{1}C_{2}+sC_{1}g_{\text{m2}}+g_{\text{m1}}g_ {\text{m3}})I_{3}-sC_{1}g_{\text{m2}}I_{2}-g_{\text{m1}}g_{\text{m2}}I_{1}}{s ^{2}C_{1}C_{2}+sC_{1}g_{\text{m2}}+g_{\text{m1}}g_{\text{m3}}}, \tag{7}\]  
\[V_{\text{OUT(TIM)}}=R_{1}\Bigg{[}\frac{(s^{2}C_{1}C_{2}+sC_{1}g_{\text{m2}}+g _{\text{m1}}g_{\text{m3}})I_{4}-sC_{1}g_{\text{m2}}I_{2}-g_{\text{m1}}g_{ \text{m2}}I_{1}}{s^{2}C_{1}C_{2}+sC_{1}g_{\text{m2}}+g_{\text{m1}}g_{\text{m3 }}}\Bigg{]}. \tag{8}\]  
In the current- and trans-impedance-modes of operation, only three input currents will be used. For CM, currents \(I_{1}\), \(I_{2}\), \(I_{3}\) and for TIM currents \(I_{1}\), \(I_{2}\), \(I_{4}\) will be applied, respectively. In addition, to realize AP response \(I_{2}\) with double magnitude is required. It can be easily generated by applying two currents of equal magnitude at the input node  
Figure 2: Proposed MISO mixed-mode universal filter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multicolumn{3}{c}{**Inputs**} \\ \cline{2-4}  & \(V_{1}\) & \(V_{2}\) & \(V_{3}\) \\ \hline LP & \(V_{\text{in}}\) & 0 & 0 \\ \hline HP & 0 & \(V_{\text{in}}\) & 0 \\ \hline BP & 0 & 0 & \(V_{\text{in}}\) \\ \hline BR & \(V_{\text{in}}\) & \(V_{\text{in}}\) & 0 \\ \hline AP & \(V_{\text{in}}\) & \(V_{\text{in}}\) & \(V_{\text{in}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input voltage excitation sequence.  
without requiring any additional hardware. Furthermore, the gain of the TIM response can be adjusted using the resistor \(R_{1}\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analog-to-Digital Converter \\ BER & Bit Error Rate \\ FOV & Field of View \\ FSO & Free-Space Optical \\ LOS & Line of Sight \\ MIMO & Multiple Input Multiple Output \\ NPSIR & Normalized Power Signal to Interference Ratio \\ NRZ & No Zero Return \\ OCC & Optical Camera Communication \\ OOK & On-Off Keying \\ ROI & Region of interest \\ SDMA & Space Division Multiple Access \\ SIR & Signal to Interference Ratio \\ SINR & Signal to Interference and Noise Ratio \\ SNR & Signal to Noise Ratio \\ VLC & Visible Light Communication \\ WSN & Wireless Sensor Networks \\ \end{tabular}
## 2 Methodology
### Restriction Based on Structural Integrity  
The structural integrity of a machined component is calculated based on the loads to which it is subjected. It is known that stresses near the points of application of concentrated loads reach values much higher than the component's average. A typical example of this type of loading condition is geometric discontinuities, such as those obtained in the study process. Figure 6 illustrates the applied and resisted load diagram of the workpiece of interest.  
The load analysis developed on the workpiece takes into consideration the effects of the cutting forces applied to the workpiece and their effects on the stress concentrator of a machined part in a roughing operation by rolling. Other types of mechanical effects due to the application of the load are not taken into consideration, such as the unbalance effect due to geometric singularities or buckling due to load eccentricities, and the nature of the work material, or vibratory phenomena due to the geometric nature of the workpiece and the driving loads. The study analysis is only stationary due to eccentric loads defined by the diagram in Figure 6.  
From the diagram, it can be seen that all geometric factors interact, such as the major and minor diameters of the part, the lengths of the mechanical components, and the direction of load application, to formulate the resulting stresses in the concentrator, defined as the starting point of the discontinuity, defined by the minimum radius of curvature, in this case, the tool radius \(R_{c}\).  
Figure 6: Load and stress distribution diagram on the workpiece.  
It has been observed that the concentration results depend only on the ratios of the geometric parameters involved. Figure 7 illustrates the parameters of interest for constructing the concentration curve, from which the maximum stress of interest to the designer is defined.  
The stress concentrator is defined by Equation (13) [26]:  
\[K_{\sigma}=\frac{\sigma_{max}}{\sigma_{prom}} \tag{13}\]  
It is observed that the curves that define the value of the stress concentrator are entered by defining the relationship \(D_{c}/d_{m}\) and \(R_{c}/d_{m}\).
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 3 Security Threats in Modern Agriculture  
The rapid evolution and the use of smart communication technologies [60] as well as the integration of IoT in addition to the digitization and automation of business, bring new risks and dangers in terms of ICT security in the global market [61]. Potential attacks in various different smart agricultural systems can lead to serious security issues in the dynamic and distributed cyber-physical environment [60; 62]. These types of threats and attacks can result in severe disruptions of interconnected businesses. Furthermore, in the heavily mechanized landscape of agriculture, smart technologies and remote administration used in PA and smart farming are something brand new for its stakeholders, with most of the new threats in this specific domain being strongly connected with similar threats that exist in other industries [63]. These threats are mostly related to cybersecurity, data integrity and data loss [64; 65]. In addition, because of the fact that the PA sector utilizes heavy machinery connected online, there are many emerging vulnerabilities that can potentially lead to disastrous consequences [65].  
The next paragraphs of this section offer a detailed approach on the security threats met in modern agriculture. The extensive literature review performed in this section is focused on the cybersecurity  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Area** & **Studies** \\ \hline Continuous land monitoring & [23; 24; 25; 26; 27] \\ Water management & [1; 28; 29; 30] \\ Monitoring and reporting of crop growth & [25; 31; 32; 33; 34; 35] \\ Identification and management of soil characteristics & [36; 37; 38; 39; 40] \\ Detection and recognition of diseases in crops and/or plants & [41; 42; 43; 44; 45] \\ Enhanced food preservation and quality control & [46; 47; 48; 49] \\ Smart livestock & [50; 51; 52; 53] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of Internet of Things (IoT) usage and integration areas and corresponding studies.  
and IoT-related threats in agriculture, which are nonetheless interwoven strongly with PA, Agriculture 4.0 and smart farming [64].
## 4 Convergence Analysis of the Revised CIM-PNMCC  
The mean and convergence for the CIM-PNMCC is performed based on an approximation approach. For simple description, our proposed CIM-PNMCC is rewritten as  
\[\widehat{\mathbf{w}}\left(n+1\right)=\widehat{\mathbf{w}}\left(n\right)+\dfrac {\chi_{1}\mathbf{G}\left(n\right)f\left(e\left(n\right)\right)e\left(n\right) \mathbf{x}\left(n\right)}{\mathbf{x}^{T}\left(n\right)\mathbf{G}\left(n\right) \mathbf{x}\left(n\right)+\varepsilon_{\text{CIM}}}+\rho_{\text{CIM}}m\left(n \right), \tag{30}\]  
where \(f\left(e\left(n\right)\right)=\exp\left(-\dfrac{e^{2}\left(n\right)}{2\sigma^{ 2}}\right)\) and \(m\left(n\right)=-\frac{1}{N\sigma_{1}^{3}\sqrt{2\pi}}\widehat{\mathbf{w}}\left( n\right)\exp\left(-\frac{\left\|\widehat{\mathbf{w}}(n)\right\|^{2}}{2\sigma_{1}^{2}}\right)\). For simple convergence analysis, we give some assumptions that are used frequently in [51; 52; 53; 54].  
We have the following assumptions (A): A1: \(\mathbf{x}\left(n\right)\) with zero mean is i. i. d.  
A2: \(v\left(n\right)\) is independent with \(\mathbf{x}\left(n\right)\), and it is zero mean, and its variance is \(\sigma_{v}^{2}\).  
A3: \(f\left(e\left(n\right)\right)\) is independent with \(\mathbf{x}\left(n\right)\).  
A4: \(\widehat{\mathbf{w}}\left(n\right)\) and \(m\left(n\right)\) are independent with \(\mathbf{x}\left(n\right)\).  
A5: The expectation of a ration of two random variables is equal to the ration of the expectation of each random variable. Moreover, the expectation is existing, and \(\mathrm{E}\left[\mathbf{x}^{T}\left(n\right)\mathbf{G}\left(n\right)\mathbf{x }\left(n\right)+\varepsilon_{\text{CIM}}\right]=N\sigma_{x}^{2}+\varepsilon_{ \text{CIM}}\), and \(\mathrm{E}\left[\left(\mathbf{x}^{T}\left(n\right)\mathbf{G}\left(n\right) \mathbf{x}\left(n\right)+\varepsilon_{\text{CIM}}\right)^{2}\right]=\left(N \sigma_{x}^{2}+\varepsilon_{\text{CIM}}\right)^{2}\)[55].
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Body Risk Parameters
#### 2.1.6 Chemical Corrosion Factors  
Cable chemical corrosion is a process whereby the metal sheath or outer jacket of a cable reacts with chemicals in the surrounding environment, resulting in the gradual destruction of the cable material. In certain areas of the channel that are close to chemical plants or have peripheral leaks, the cable channel material and the cable body are susceptible to corrosion. External environmental factors such as moisture and chemical corrosives can have an impact on the cable, reducing its service life and increasing the failure rate.
## 2 Preliminaries
### Principal Components Analysis (PCA)  
PCA is an important technique that can transform multiple variables into a few main components (comprehensive variables) by means of dimensional reduction, increasing interpretability while minimizing information loss [24]. These main components are usually expressed as linear combinations of the original variables, which can represent most of the information of the whole dataset.  
For original data \(\mathbf{X}=(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{i},\ldots,\mathbf{x}_{n})\) and \(\mathbf{X}\in\mathbb{R}^{k*n}\), we can get the covariance matrix \(\mathbf{C}_{\mathrm{x}}\):  
\[\mathbf{C}_{\mathrm{x}}=E[(\mathbf{X}-E[\mathbf{X}])(\mathbf{X}-E[\mathbf{X}])^{\mathrm{T}}] \tag{1}\]  
After centralizing the data, the mean function \(E[\mathbf{X}]\) is zero and:  
\[\mathbf{C}_{\mathrm{x}}=\frac{1}{n}\mathbf{X}\mathbf{X}^{\mathrm{T}} \tag{2}\]  
Assuming that there is a matrix \(\mathbf{P}\) (\(\mathbf{P}\in\mathbb{R}^{k^{\prime}*}\)), through which we can transform the original sample data matrix \(\mathbf{X}\) into a dimensionality-reduced matrix \(\mathbf{Y}\) (\(\mathbf{Y}\in\mathbb{R}^{k^{\prime}*n}\)):  
\[\mathbf{Y}=\mathbf{P}\mathbf{X} \tag{3}\]  
Then the original data dimension is successfully reduced from \(k\) to \(k^{\prime}\), wherein the first \(k^{\prime}\) principal components can explain most of the variance.  
For matrix \(\mathbf{Y}\), its covariance matrix can be expressed through original matrix \(\mathbf{X}\) as:  
\[\mathbf{C}_{y}=\frac{1}{n}\mathbf{Y}\mathbf{Y}^{T}=\frac{1}{n}(\mathbf{P}\mathbf{X})(\mathbf{P}\mathbf{X} )^{\mathrm{T}}=\frac{1}{n}\mathbf{P}\mathbf{X}\mathbf{X}^{\mathrm{T}}\mathbf{P}^{T}=\mathbf{P} \mathbf{\frac{1}{n}}\mathbf{X}\mathbf{X}^{\mathrm{T}}\mathbf{P}^{\mathrm{T}}=\mathbf{P}\mathbf{C}_{ \mathrm{x}}\mathbf{P}^{\mathrm{T}} \tag{4}\]  
It is obvious from Equation (4) that \(\mathbf{C}_{\mathrm{x}}\) is guaranteed to be a non-negative definite matrix and thus is diagonalizable by some unitary matrix. Then the objective optimization is transformed to find an orthonormal transformation matrix \(\mathbf{P}\). Normally, we can use eigenvalue decomposition or singular value decomposition to solve the matrix \(\mathbf{P}\), and the first \(k^{\prime}\)-dimensional new features corresponding to \(k^{\prime}\) eigenvalues can represent the whole data best.
## 4 Results
### Sensitivity Analysis  
As shown in Equation (6), the net benefit of participants is determined by the service price and electricity tariff. The service price is decided by the implemented VESS capacity. The results according to the VESS capacity change show the effect of the service price.  
To show the effect of the electricity tariff, the ROI is measured while varying the energy price. Using the energy price in Table 2 as the baseline, the variance of the energy price is changed, as shown in Table 3. The price factor is defined as the ratio of the standard deviation of the baseline to the changed energy price during the billing period.  
As shown in Figure 8, when the price factor increases, i.e., the variance of the energy price increases, the ROI improves. This is because the VESS operational availability is enhanced by increasing the variance of the electricity tariff. This shows that VESS is a more economically efficient method when the electricity tariff volatility is large. Moreover, in all cases of price factor, the proposed VESS operation method using **P1** outperforms the VESS operation using **P0**. When the price factor is less than 1, the ROI by applying the VESS operation using **P0** is 1. This means that the participants cannot achieve additional benefits using the VESS by **P0** in this range.  
Figure 6: Percentage of participants using the VESS service. The participant rate is measured as the number of units who participant the VESS service among all SEC units, i.e., 127 units.  
Figure 7: Pearson’s linear correlation coefficient (PLCC) between the characteristics of the participants and the allocated VESS capacity with varying implemented VESS capacity: **(a)** PLCC between the original electricity bill and the allocated VESS capacity; **(b)** PLCC between the peak demand and the allocated VESS capacity.
# Precision Analysis and Design of Rotating Coil Magnetic Measurements System
## 3 Experimental Setup
### A Magnetic Measurement Equipment Setup  
Figure 9 displays a magnetic field measuring device with two important components: (a) A rotating coil drive that consists of a cylinder rod for packing coils made from G10 material (diameter 63.5 mm, length 470 mm); (1) a Printed Circuit Board(PCB) board with a search coil inside (width 53 mm, length 430 mm); (2) multi-row angular contact ball bearings placed on a base made of aluminum as a support point for the cylinder rod; (3) a DC motor worm gear (100 W, 24 V, with a maximum current 4.6 A for driving the rotating coil); (4) flexible coupling for conveying power from the motor to the rotating coils (5). (b) Measurement and data collection, which consisted of electromagnet dipole magnets sized 0.4 Tesla (6), magnetic current sensor and Agilent 3440a for reading the current supplied to the electromagnet (7), slip ring coil voltage and NI-9238 voltage module for measuring the electromotive force obtained by measuring the magnetic field (8), incremental rotating encoder and NI-9505 Module for measuring the speed and angular position of the rotating coil (9), and a computer for recording the results of the experiment with the LabVIEW 2017 program (10).  
Figure 9: Magnetic measurement equipment set up using a rotating coil.
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 2 Methodology and Results
### Flow Simulation  
#### 2.3.1 Governing Equation and Boundary Condition  
To examine the unsteady flow around the underwater vehicle, every cell in the control volume adheres to the governing equations, which encompass the continuity equation, formulated as:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\left(\rho u_{j}\right)}{ \partial x_{j}}=0 \tag{15}\]  
and the Reynolds-averaged Navier-Stokes (RANS) momentum equation, expressed as:  
\[\frac{\partial}{\partial t}(\rho u_{j})+\frac{\partial}{\partial x_{j}}(\rho u _{i}u_{j})=-\frac{\partial P}{\partial x_{j}}+\mu\frac{\partial}{\partial x_{ i}}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}} \right)+F_{j} \tag{16}\]  
where \(u\), \(P\), and \(F\) denote the velocity, pressure, and external force, respectively. A commercial program, STAR-CCM+ ver. 13.06.012, was used for the flow analysis of the underwater vehicle in specific motions. The diffusion and convection terms of the governing equations discretize to second-order accuracy, and the velocity and pressure were analyzed using the SIMPLE (semi-implicit method for pressure-linked equations) algorithm. A turbulence model applying the k-\(\omega\) SST (shear stress transport) model and the wall functions uses the submerged body's boundary condition.
## 1 Introduction
An application of the above approaches to Asian regions could elucidate the changes in vegetation greening trends in Asia in a process-based manner. In this study, we used both satellite-based time-series observations and four process-based terrestrial biosphere models to identify and understand changes in terrestrial GPP in Asia (the regions are defined in Figure 1). Using the updated satellite-based vegetation index products, that is, NOAA AVHRR NDVI3g [37,38], we first quantified changes in the NDVI from 1982 to 2011 in Asia and  
Figure 1: **(a)** Land cover in the study regions. MODIS land-cover data (MCD12Q1) [35] were used. **(b)** The sub-continental division used in this study was based on the Greenhouse gases Observing Satellite (GOSAT) Level 4A product [36]. Abbreviations of the ecosystem types are evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), and mixed forest (MF). identified the regions with increasing or decreasing trends in vegetation activity. Then, we applied four different terrestrial biosphere models to simulate recent changes in terrestrial GPP; we verified these results with satellite-based NDVI variations as a proxy of terrestrial GPP at sub-continental scales. Finally, using the model simulation results, we determined the causes of the simulated trends in terrestrial GPP.
## 4 Proposed Methodology and Simulation Scenarios
### SHA-256 Algorithm  
The SHA-256 approach was developed by the National Security Agency as a successor to the SHA-1 (2001) [45] and SHA-2 [46; 47; 48; 49]. The SHA-256 algorithm is a proprietary cryptographic hash technique that produces a 256-bit output. The process of hacking is to change hashed data to make them entirely illegible. The aforementioned 256-bit hash could hardly be converted back to its original 512-bit form. The most common cause is that the sensitive material is checked twice. Hashing, for example, serves to check the completeness of safe messages and data. A hash (or cryptographic hash) is a long number that acts as a digital fingerprint of any collection of data. In IoT devices, the SHA256 hashing function is used, which generates a 64-digit hexadecimal number. For example, the cryptographic hash of the words in this paragraph is:  
C019286295F2 CDEC9958 BEE25B9603B5F94C76B2 CCC69A59 CE54872 ED26 DC479  
Data stored in the block and the hash of the previous block are put into the SHA256 function to get the value of the current block hash:  
_SHA256(Block Number, Data, Previous Block Hash) Hash_  
Now we can see how the blocks are linked. Not only does each block reference the previous block's cryptographic hash, but, in fact, that hash directly affects the value of the current block's hash.  
The timestamp is also included in the SHA256 calculation for the hash of the current block that is being mined:  
_SHA256(Block Number, Timestamp, Nonce, Data, Prev. Block Hash) -> Hash_  
In addition, since the timestamp is constantly refreshing (until the block is successfully mined), this effectively resets the Nonce range every second. SHA-256 hash function reference implementation is discussed below.  
SHA-256 hash function reference implementation:  
* (a) Generates SHA-256 hash of a string.
* (Unicode) string to be hashed.
* import Sha256 from './sha256.js';
* consthash=Sha256.hash('abc');
* ba7816 bf8f01 cfaei41410 de5 dae2223b00361a396177a9 cb410 ff61f20015 ad'
* (b) Preprocessing msg += String.fromCharCode(0x80); //add trailing '1' bit (+ 0's padding) to string [SS5.1.1] const lenHi = ((msg.length \(-\) 1)*8) / Math.pow(2, 32);  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Operating System & Contiki 2.7 \\ Simulator & COOJA \\ Computer & RAM 8 GB \\ Transmission Range & 50 m \\ Interference Range & 55 m \\ Simulation Time & 90 min \\ Routing Protocol & 6 LoWPAN \\ Number of Nodes & 06, 12, 24, 48 \\ Topology & Random \\ MAC Layer & 802.15.4 \\ Node Type & Z-mote \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
(c) Hash computation for (let i = 0; i < N; i++) {  const W = new Array(64); }  
return (x >>> n)! (x << (32 - n)); }  
(d) Logical functions  
* @private
*/  
static \(\Sigma\)0(x) { return Sha256.ROTR(2, x) ^Sha256.ROTR(13, x) ^Sha256.ROTR(22, x); }