esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Nanosecond Laser Ablation of 40 Cr13 Die Steel Based on ANOVA and BP Neural Network
## 3 Effects of Process Parameters on Ablation Results
### Effects of Process Parameters on Ablation Depth  
Figure 4 shows the effect of laser power and scan speed on D for a pulse frequency of 50 kHz. It can be observed that D decreases with an increase in scan speed at the same laser power. With an increase in scan speed, the coincidence rate of the laser pulses decreases according to Equation (2): therefore, the energy absorbed by the material per unit length decreases.  
Figure 5 shows the effect of laser power and pulse frequency on D for a scan speed of 150 mm/s. It can be observed that D increases with an increase in pulse frequency. With an increase in pulse frequency, the coincidence rate of the laser pulses increases according to Equation (2): therefore, the energy absorbed by the material per unit length increases.  
Figures 4 and 5 show that D increases significantly with laser power. Under the same repetition frequency, the single-pulse energy increases with an increase in laser power, according to Equation (1). This study conducted ANOVA to investigate the influence of laser power, scan speed, and pulse frequency on D. The results are presented in Table 5. The \(p\)-values of the three process parameters are all 0, which indicates that all three parameters affect D. Laser power has the highest degree of influence, at 52.5%, followed by the pulse frequency and scan speed, at 28.9% and 18.6%, respectively.  
Figure 4: Effect of laser power and scan speed on D.  
Figure 3: Schematic of laser pulses.
## 2 Materials and Methods
### Hardware Design of Temperature Control System  
The temperature control system comprises five primary functional modules (Figure 5). The main control module performs data fusion, processing, and real-time monitoring while outputting control strategies to regulate end-effectors. The temperature acquisition module handles analog signal inputs for environmental temperature, evaporator-side temperature, nutrient solution temperature, and root-zone temperature, converting these signals into digital format. Utilizing 4G transmission technology, the data communication module enables real-time uploading of nutrient solution temperature and related parameters to the cloud platform. The output control module transmits control signals through controller ports to relays, regulating solenoid valve operations and subsequently controlling the heat pump unit, circulation pumps, and agitators via thermal relays and AC contacts. The human-machine interface and display module facilitate target temperature setting, operational mode switching, and system data exportation while providing real-time visualization of environmental conditions, nutrient solution temperature, and equipment status. The name, specification and description of the system hardware product are shown in Table 1.  
The picture of the interior of the control cabinet is shown in Figure 6. During application, certain components may be added or modified based on the actual application environment and requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product Name** & **Specification or Model** & **Product Description** \\ \hline Single Chip Microcomputer & STM32F103 RCT6 & Based on the ARM 32-bit Cortex-M architecture. \\ Resistive Temperature Sensor & PT100 & Measurement range: \(-\)50\({}^{\circ}\)C. \\  & & Accuracy: \(\pm\) 0.1 \({}^{\circ}\)C. \\ Signal Transfer Module & MAX6675 & Convert analog signals into digital signals. \\
4G Module & Air724 UG & Provide fast and reliable wireless connectivity. \\ AC Contactor & LC1N50 & High-voltage power controller. \\ Thermal Relay & LRN357N & Overload protection. \\ Heat Pump Unit & BLD-60 AH & Input power: 49.8 kW. \\ Solution Agitator & BLD10-1.5 & Refrigerant: R410 A. \\  & & Impeller diameter: 400 mm. \\ Circulation Pump & 25 CDL2-200 & Input power: 2.2 kW. \\  & & Lift: 150 m. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Name, specification and description of system hardware product.  
Figure 5: Design of hardware module of control system.
# A Backwards-Tracking Lagrangian-Eulerian Method for Viscoelastic Two-Fluid Flows
## 2 Governing Equations  
Viscoelastic fluid flow is described by the incompressible momentum and continuity equations  
\[\rho\Big{(}\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\
abla \mathbf{u}\Big{)} =-\
abla p+\
abla\cdot(2\mu\mathbf{S}+\boldsymbol{\tau})+\mathbf{ f}, \tag{1}\]  
\[\
abla\cdot\mathbf{u}=0, \tag{2}\]where \(\rho\) is the density, \(\mathbf{u}\) velocity, \(\mu\) the solvent viscosity, \(\mathbf{S}=\frac{1}{2}(\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})\) the strain rate tensor, \(\boldsymbol{\tau}\) the viscoelastic stress tensor, and \(\mathbf{f}\) a body force. The viscoelastic stress is governed by a constitutive equation of the general form  
\[\lambda\overset{\bigtriangledown}{\boldsymbol{\tau}}+F(\boldsymbol{\tau}) \boldsymbol{\tau}=2\eta\mathbf{S}, \tag{3}\]  
where \(\lambda\) is the relaxation time, \(\eta\) polymeric viscosity, and \(F\) a scalar-valued function. The operator \(\overset{\bigtriangledown}{(\bullet)}\) denotes the upper-convected derivative of \(\boldsymbol{\tau}\), which expands to  
\[\overset{\bigtriangledown}{\boldsymbol{\tau}}=\frac{D\boldsymbol{\tau}}{ Dt}-(\
abla\mathbf{u})^{T}\cdot\boldsymbol{\tau}-\boldsymbol{\tau}\cdot\
abla \mathbf{u}, \tag{4}\]  
where  
\[\frac{D\boldsymbol{\tau}}{Dt}=\frac{\partial\boldsymbol{\tau}}{\partial t}+ \mathbf{u}\cdot\
abla\boldsymbol{\tau}, \tag{5}\]  
is the Lagrangian, or material, time derivative of \(\boldsymbol{\tau}\). The Lagrangian time derivative describes the rate of change of \(\boldsymbol{\tau}\) in an infinitesimal material element, which moves with the flow. Convected time derivatives, such as \(\overset{\bigtriangledown}{(\bullet)}\), appear in viscoelastic constitutive equations and they ensure frame-invariance for the transport of tensorial properties. It is remarked that, while the form of (3) is not applicable for all available differential constitutive equations, it covers the constitutive models discussed in this work.  
In general, the viscoelastic stress tensor may be modeled as a sum of the contribution of \(N\) modes as  
\[\boldsymbol{\tau}=\sum_{k=1}^{N}\boldsymbol{\tau}_{k}, \tag{6}\]  
where \(\boldsymbol{\tau}_{k}\) is the stress tensor that corresponds to the \(k\)th mode. Each stress tensor \(\boldsymbol{\tau}_{k}\) is then governed by a constitutive equation on the form of (3) with an individual set of model parameters.
## 3 Proposed C-HARQ Protocol Design
### The Cooperation Mechanism  
The main cooperation mechanism of C-HARQ protocol is basically similar to that of CARQ. A success in the original data transmission is acknowledged, by the destination, by sending a positive acknowledgement (_ACK_) signal to the source; whereas on reception of erroneous data, destination opts for a cooperation process instead of immediately sending a negative acknowledgement (_NACK_) signal. Meanwhile, some (or all) of the _CNodes_ becomes available for cooperation (holding either a data packet, for CARQ, or FEC packets, for C-HARQ) depending on whether they succeeded or failed in correctly overhearing the broadcasted data. When the destination calls for cooperation, these available _CNodes_ let the destination know about their availability by a proper signaling mechanism (described later in Section 3.4). The destination, then, asks the available _CNodes_, one-by-one, to transmit the packets they hold until the retransmission/recovery is successful. A successful recovery of original data from the cooperation process is conveyed to the source and the _CNodes_ by transmitting an _ACK_. However, if the cooperation process fails too, a _NACK_ is transmitted to ask the source to retransmit the data, which starts the whole process from the beginning.
## 1 Introduction
In this study, overvoltages, electrostatic induction and electromagnetic induction changes, which occur due to LEMPs, indirectly affecting power plants, were investigated using [PERSON]'s equations. The surge protection systems on power transmission lines exposed to indirect effects did not function as intended. This is because surge arresters are designed to protect against the main shock pulse of an LEMP. In our study, the issue lay in the propagation of the main shock pulse to the ground. We investigated the situations arising from the induction of this propagated shock current on nearby lines. We view this situation as an interference problem from an electromagnetic compatibility perspective. The research is believed to be beneficial for future studies in the fields of power systems and electromagnetic compatibility, particularly in terms of grounding and electromagnetic shielding. In this study, we analyze the indirect effects of LEMPs occurring close to power transmission lines. The motivation for this analysis stems from the inadequacy of surge arrester systems in fully mitigating indirect effects. While surge arresters offer protection against the main shock pulse of the LEMP when it directly impacts the line, they fall short in addressing indirect effects. Our study focuses on investigating the parasitic currents, electric field and magnetic field values contributing to this issue. The [PERSON] model served as the coupling model for this study's energy transmission line. [PERSON]'s equations are commonly used to analyze the indirect effects of lightning electromagnetic pulses on power distribution and transmission lines. This is because the LEMP channel is typically assumed to be vertical in such analyses. Compared to other modeling approaches, [PERSON]'s expressions can be used in engineering studies without any disadvantages. Additionally, the analytical expression provided by [PERSON] enables easy calculation of the electric field from the return pulse of the LEMP. The Heidler Wave model was exemplified as the source of LEMPs. Substantial results were obtained by examining the relationship between the areas and the line according to the [PERSON] formulation. This study was conducted mathematically. Mathematical modeling was performed using MATLAB version R2023A. The main focus of this study was the lack of sufficient research in the field of electromagnetic compatibility for the LEMP phenomenon. This situation causes significant damage to power transmission lines, especially in countries where LEMP strikes are intensive and in high-altitude regions. Particularly in the regions we examined, the transmission lines supply power to industrial areas, and LEMP attacks in these areas often lead to prolonged power outages. This is one of the driving factors behind this research. Considering the intensity of studies in the field of power systems, we believe that protective methods in terms of electromagnetic compatibility (such as electromagnetic shielding and satellite grounding applications) could be effective at preventing these indirect effect faults on the lines.
# A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT
## 3 Background Knowledge
### Local Differential Privacy  
Traditional data privacy protection techniques, such as k-anonymity [29] and generalization [30], lack universal applicability due to their absence of a strong mathematical foundation to define data privacy and data loss. Moreover, they are ineffective against attackers armed with background knowledge. The emergence of differential privacy [31; 32; 33] has effectively addressed this issue. This model is a robust privacy protection technique based on mathematical theory. Differential privacy is unconcerned with an attacker's background knowledge, even if the attacker possesses information on all records except one, that single record's privacy remains uncompromised. Local differential privacy is a distributed variant of differential privacy that allows each user to locally perturb the raw data to protect privacy before uploading it. It is defined as follows [34]:  
**Definition 1** (\(\epsilon\)-Local Differential Privacy): _If there exists a randomized algorithm \(M\), for any two distinct tuples \(v_{i}\) and \(v^{\prime}_{i}\) in dataset \(D\) and any potential output \(y\in Y\) (\(Y\) being the output domain of \(M\)), that satisfies_  
\[\Pr[M(v_{i})Y]\leq e^{\epsilon}\times\Pr[M\big{(}v^{\prime}_{i}\big{)}Y], \tag{1}\]  
_then the randomized algorithm \(M\) is said to satisfy \(\epsilon\)-local differential privacy. Here, \(\Pr[\bullet]\) indicates the probability of the output result. \(\epsilon\) is referred to as the privacy budget, which represents the level of data privacy protection. The smaller its value, the closer the probabilities over adjacent datasets, and the higher the level of data privacy protection._  
A commonly employed technique to realize local differential privacy is the randomized response mechanism [35]. The principle behind this mechanism is that when users respond to sensitive Boolean questions, they answer truthfully with probability \(P\) and oppositely with a probability of 1-\(P\). Local differential privacy is built on a rigorous mathematical foundation that ensures data privacy protection even when the attacker has maximum background knowledge.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 11 _Sarcocystis_
### Prevalence of Sarcocystis spp. in Food-Producing Animals  
Cattle are the intermediate host of several _Sarcocystis_ species in which the infection manifests as bovine eosinophil myositis (BEM), an inflammatory myopathy most often caused by _S. cruzi_ and _S. hominis, S. bovifelis_, and _S. hirsuta_[258, 260]. It appears as diffuse grey-green patches on masticatory muscles, tongue, heart, and diaphragm, and in severe cases, all striated muscles can be involved [261], causing carcass depreciation and economic losses [258].  
In a Belgian slaughterhouse, it was recently found that female dairy cattle had the highest _Sarcocystis_ occurrence rate (91%), and this reached 100% in animals older than 7 years. Beef cattle males had the lowest _Sarcocystis_ occurrence rate (22%), partially explained by the lower age of the animals. _S. cruzi_ was the most prevalent species, followed by _S. hominis_ and _S. bovifelis_. Moreover, _S. heydorni_, _S. bovini_, and _S. hirsuta_ were found in female dairy cattle. Other species occurring in cattle are _S. rommeli_, _S. bovini_, _S. sinensis_, _S. gigantea_, _S. fusiformis_, _S. hjorti_, and _S. tenella_[261]. _S. cruzi_ is the most pathogenic species in cattle, and acute infection can lead to weakness and reduced milk yield [262]. Though theprevalence of _Sarcocystis_ in cattle is high, the prevalence of BEM is quite low, ranging from 0.002% to 5% worldwide. However, the association of _Sarcocystis_ with BEM was confirmed in a study in which _Sarcocystis_ DNA was detected in 91.7% of the lesions [260].  
_S. miescheriana_ is associated with wild bours. In those hunted in northern Italy _Sarcocystis_ spp. showed a prevalence of 97% [263; 264; 265]. Species that are associated with goats and sheep, _S. capracanis_ and _S. tenella_, which do not have humans as the definitive host, can cause anorexia, weight loss, fever, anemia, hair loss, abortion, premature birth, neurologic signs, myositis, and death in sheep [266; 267; 268].  
Boiling water that could be contaminated and avoiding the consumption of raw or undercooked beef and pork are measures to prevent _Sarcocystis_ spp. infection. Sarcocystis in pork can be destroyed by heat treatment at 60 \({}^{\circ}\)C for 20 min, 70 \({}^{\circ}\)C for 15 min, 100 \({}^{\circ}\)C for 5 min, or freezing at \(-4\)\({}^{\circ}\)C for 2 days or \(-20\)\({}^{\circ}\)C for 24 h. Sporocysts die when heated to 60 \({}^{\circ}\)C for 1 min, 55 \({}^{\circ}\)C for 15 min, or 50 \({}^{\circ}\)C for one hour but can survive freezing. Commonly used disinfectants--e.g., 1% iodine, 10% formalin, 12% phenol, and 2% chlorhexidine--fail to kill sporocysts, but 5.25% sodium hydroxide is effective [8].
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 5 Simulation Results and Discussions
### Impact of Maintenance Strategy  
The influence of the maintenance strategy on the O&M of the OWT is investigated in the following sections. In the calculations, corrective maintenance, condition-based maintenance, and full inspection following the recovery of the subsystem from failure are all adopted. In order to keep the wind turbine operating at high efficiency in the long term and prevent the accelerated degradation of the wind turbine components, the \"basic service\" is assumed to be carried out every six months in the simulation. The time interval of the \"advanced service\" is assumed to be a variable, ranging from 3 months to 5 years. Herein, it is worth noting that \"basic service\" is conducted only when its time interval is shorter than the time interval of the \"advanced service\".
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 5 Malware Detection Techniques  
The purpose of incorporating malware detection methods is to secure computer systems from the potentiality of data loss and vulnerability by detecting and deterring malicious intrusions caused by malicious software. The system is further fortified by the malware detector's endeavours to pinpoint malicious activities. While signature-based, behaviour-based, model-based, and heuristic-based detection, as well as newer techniques, such as deep learning-, cloud-, mobile device-, and Internet of Things-based methods, are effective for quickly and accurately detecting previously identified viruses, signature-based detection systems are inadequate when it comes to identifying unknown malware. Due to the absence of files to evaluate and, as a result, no VirusTotal submission, it can be difficult for security software to detect fileless malware. In contrast, due to the dearth of contemporary research and methodologies, creating an effective secure system presents a daunting task [46]. The various types of malware analysis and their detection techniques have been given in Figures 5 and 6.  
Figure 5: Malware Detection Techniques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Static Analysis** & **Dynamic Analysis** \\ \hline Reliable and quick. & Time consuming \& vulnerable. \\ Efficient in analysing the multipath malware. & The multi-stage virus is hard to analyze. \\ Cannot analyse the complicated \& polymorphic virus. & Can analyse the complicated and polymorphic virus. \\ Cannot detect new, unknown malware. & Can detect known as well as new malware. \\ High accuracy. & Low accuracy. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between static and dynamic analysis.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 3 Sources of SINR Variability
### Changes in Interfering Channels
Note that here, just as in the case of the wanted signal, we  
Figure 4: Realized interference distributions for three different \(I_{0}/I_{\text{ave}}\) dB, _vs_. realized interference powers \(1/I_{\text{ave}}\). Interference contribution from \(J=1\), \(2\), \(3\) independent sources. The distribution with \(J=1\) in red is widest and with \(J=3\) in blue most narrow for all values of \(I_{0}/I_{\text{ave}}\). Figure 3: Interference power distributions for \(J=3\) independent interferers. The average interference powers are \(I_{\text{ave},1}=I_{\text{ave},2}=I_{\text{ave},3}=I_{\text{ave}}/3\). The instantaneous interference powers at the time of measurement are \(I_{0,1}=3/2I_{0,2}=3I_{0,3}=I_{\text{ave}}/2\), so that the total measured interference power is \(I_{0}=I_{\text{ave}}\). **Left**: Theoretical Equation (10), simulation and moment matching approaches, where one moment matched non-central \(\chi^{2}\) distribution approximates the sum distribution; **Right**: Tail distribution with moment matching approaches. assume that we know both the measured power and the average power, independently for all the \(J\) interferers. The number of interferers has a great impact on backoff selection. The distribution becomes wider with a decreasing number of interferers. We proceed by considering the SINR variability caused by interference channel variability only. It is assumed that the measured signal power of the desired transmission, and the precoders of both the desired channel and interferers remain same from the time of measurement to the time \(\tau\) of transmission. Then, the outage probability with backoff \(b=\gamma_{0}/\gamma_{t}\) becomes  
\[\mathrm{P}\left(\gamma_{\tau}<\gamma_{t}\right)=1-F_{I}\left(b(I_{0}+N_{0})-N_ {0}\right) \tag{13}\]  
where \(I_{0}=\sum_{j=1}^{J}I_{0,j}\) is the total measured interference power, and the CDF of the total interference power is obtained from Equation (10) as  
\[F_{I}(i)=\sum_{n=1}^{\infty}a_{n}\left(1-Q_{J+n}\left(\sqrt{\Delta},\sqrt{ \frac{i}{\beta}}\right)\right). \tag{14}\]  
For URC, the required backoff can be evaluated at \(F_{I}(i)=0.9999\) point. The required URC backoff for three values of the total measured-to-average-interference ratio \(I_{0}/I_{\mathrm{ave}}\) is reported in Figure 5, for different values of \(I_{0}/N_{0}\). For each value of \(I_{0}/I_{\mathrm{ave}}\), there are three curves, corresponding to \(J=1,2\), \(3\). The relative power for \(J=2,3\) are shown in Figure 2:0 When there are more interferers, the backoff is systematically smaller for the same total average interference \(I_{0}\). The figure emphasizes that the backoff required due to interference variability is large when the interferers at the time of measurement are in a fade, _i.e._, when \(I_{0}/I_{\mathrm{ave}}\) is small. In these cases, it is likely that the interference will be larger at the time \(\tau\) of the realized transmission. It should be noted that here, the wanted signal does not vary at all.
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## Low-Rank Update of the Block Preconditioner
### Avoiding Complex Eigenpairs  
One possible obstacle to this approach is the fact that both \(V\) and \(\Lambda\) are likely to be complex. This is solved by constructing a real invariant subspace of the same size, as described in Algorithm 1.  
```
1:\(\Gamma=\Lambda\)
2:\(j=1\)
3:while\(j<p\)do
4:if\(|\operatorname{Im}(\lambda_{j})|>10^{-3}\)then
5:\(a=\operatorname{Re}(\lambda_{j});b=\operatorname{Im}(\lambda_{j})\)
6:\(\Gamma(j:j+1,j:j+1)=\begin{bmatrix}a&-b\\ b&a\end{bmatrix}\)
7:\(Z(:,j)=\operatorname{Re}(V(:,j))\);
8:\(Z(:,j+1)=\operatorname{Im}(V(:,j))\);
9:\(j=j+2\);
10:else
11:\(Z(:,j)=V(:,j)\);
12:\(j=j+1\);
13:endif
14:endwhile
15:if\(j==p\)then
16:\(Z(:,j)=V(:,j)\);
17:endif
```  
**Algorithm 1** Constructing a real invariant subspace for the preconditioned matrix.  
By construction matrices \(Z\) and \(\Gamma\) (which is no longer diagonal) approximately satisfy \(P^{(0)}AZ=Z\Gamma\) and the low-rank left preconditioner is defined consequently as  
\[P=P^{(0)}-Z(I_{p}-\Gamma^{-1})(Z^{T}Z)^{-1}Z^{T}P^{(0)}=P^{(0)}-ZHZ^{T}P^{(0)}, \qquad H=(I_{p}-\Gamma^{-1})(Z^{T}Z)^{-1}.\]  
Analogously the right preconditioner is modified with \(Z,\Gamma\) in place of \(V,\Lambda\).