esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Wormhole Attack  
A wormhole attack is a type of network security threat that affects wireless networks, including flying ad hoc networks (FANETs). In particular, FANETs consist of a group of unmanned aerial vehicles (UAVs) that communicate with each other to form a self-organizing and self-healing network. Due to their dynamic topology, limited resources, and open nature, FANETs are particularly vulnerable to various security threats, including wormhole attacks. In a wormhole attack, an attacker creates a tunnel between two malicious nodes, capturing packets at one end and replaying them at the other end almost instantly [32; 33]. Wormhole attacks creates the illusion of a shorter and more efficient route between the malicious nodes. When any transmitted packet reaches one of the attacker nodes, that node forwards the packet to its distant malicious counterpart through legitimate intermediate nodes. Although these intermediate nodes are not directly involved in the communication, their resources get drained because of their unwitting participation in the wormhole attack.  
In the multicast routing process, each destination node sends a join request to the cluster head (CH) within its cluster. The source node sends a multicast route request (MRREQ) via unicast to each CH to find multiple destination nodes. Upon receiving the MRREQ, the UAV gateway node (UGW) updates its routing table and rebroadcasts the MRREQ to neighboring nodes. When a CH with a relevant multicast group ID receives an MRREQ, it updates its routing table and sends a Route Reply (RREP) packet back to the source node via unicast. Wormhole attackers also forward MRREQ and RREP packets. They exploit this routing process by sending fake RREPs with significantly higher sequence numbers than normal nodes. This makes the attackers appear to offer the freshest route to the destination, even without consulting their routing table. As a result, other nodes update their routing tables based on this misleading information, causing severe disruptions in network functionality.
# Reactive Powder Concrete: Durability and Applications
## 2 Experimental  
Mercury intrusion porosimetry (MIP) was utilized to analyze the total accessible porosity. This technique relies on employing high pressure to force mercury into capillary pore spaces to measure its porosity. Air permeability testing was performed by the method described in [33]. The experimental device used to measure air is composed of two metallic cells placed at each side of the specimen. In the first one, inlet air was held at 287,658 N \(\times\) m\({}^{-2}\) by means of a compressor and a precision pressure regulator. In the second one, the passing air was measured at atmospheric pressure in a cylinder with a piston in which the outflow was collected. Finally, the airflow rate was measured, and the air permeability coefficient was calculated according to the Hagen-Poiseuille equation, for laminar flow under steady state conditions of a compressible fluid, through a porous material composed of a network of small capillary pores.  
In addition, to assess the chloride penetration resistance, the apparent chloride diffusion coefficient was determined by a migration method consisting of applying a potential difference of 12 V between the two faces of a concrete disc of 5 mm [4]. Natural carbonation testing was performed in samples exposed to lab conditions, sheltered from rain at 20 \(\pm\) 2 \({}^{\circ}\)C and 50% RH for two years. Electrochemical impedance spectroscopy (EIS) measurements were carried out to assess the corrosion kinetic of the electrolyte (concrete), which addresses the rate of electrode reactions, by using potential control and measuring the corresponding current response. The equipment used was a Solartron type 1250 digital frequency response analyzer and Solartron type 1286 electrochemical interface with a three-electrode arrangement. A Faraday cage and a frequency analog filter KEMO type VBF 8 were utilized to keep the concrete specimens free from noise [5].  
Tests were performed on the reactive powder concrete RPC200 provided by Bouygues and defined in Table 3. For comparison purposes, a conventional concrete, C30, and a high strength concrete, C80, were tested.
# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 2 Materials and Methods
### Data Quality Assessment Method Development  
The evaluation results were quantified when the data quality was evaluated. For this purpose, the result is expressed as the ratio of error data with the data that passed or did not pass the rule, and the error rate that reflects the weight of each data rule evaluated by experts. Four indices were developed as an evaluation index: NPR, WPR, NDPR, and WDPR.  
* NPR: this is a data quality evaluation index that does not reflect the weights for data errors. The NPR is calculated by subtracting 100 from the total error rate, which is the result of the data quality evaluation for each institution, and by adding the error rates of error and warning;
* WPR: this is a level evaluation index of data in which weights are applied to errors or warnings among data errors;
* NDPR: this is a data level evaluation index that does not reflect the weight of each index for each data error after quality is evaluated by the five dimensions. The NDPR is calculated by subtracting the error rate for each total dimension error rate, which is the data quality evaluation result for each dimension, 100;
* WDPR: this is a data level evaluation index that reflects the weights for data errors in each of the five dimensions. The WDPR evaluates the importance of each dimension by experts and sets the weight after finding the average value. Thereafter, the value obtained by multiplying the total dimension error rate by each dimension of each weight was calculated as the WDPR.
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 4 Use-Cases
### Performance
#### 4.4.2 Performance of a Single Test
Based on these, the complete formula shows that the main factor to consider is the execution time Felix queries:  
\[\tau_{\text{test}}=\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+\mathcal{O}(1)+ \mathcal{O}(d_{\text{PHY}})+\mathcal{O}(d_{\text{PHY}}\cdot|\text{terms}|)+ \mathcal{O}(d_{\text{PHY}})=\mathcal{O}(d_{\text{PHY}}\cdot|\text{terms}|).\]  
In the case of our sample network, shown in Figure 11, where \(|\text{terms}|=668\) and \(d=d_{\text{PHY}}=6\), different runs of testing a path result in the following execution time estimations:  
\[\tau_{\text{test}}^{\text{worst case}}\leq 4\cdot 1.23+4\cdot 0.1 \cdot 668+\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+\mathcal{O}(T_{\text{write}}^{ \text{PM}})=272,12\,\text{ms}+\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+1\, \text{ms}\] \[\tau_{\text{test}}^{\text{correct path}}\leq 4\cdot 1.23+0.1 \cdot 668+\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+\mathcal{O}(T_{\text{write }}^{\text{PM}})=71,72\,\text{ms}+\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+1\, \text{ms}\] \[\tau_{\text{test}}^{\text{link}}\leq 1.23\cdot 6+\mathcal{O}(T_{\text{Im}}^{ \text{PM}})+\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+\mathcal{O}(T_{\text{write }}^{\text{PM}})=4.92\,\text{ms}+\mathcal{O}(\text{RTT}(d_{\text{PHY}}))+2\, \text{ms}.\]  
As these numbers show, in order to achieve a shorter response time when locating long paths in the network, it would be beneficial to use the Path matrix instead of Felix. In the current implementation, the Path matrix does not have the apparatus to recognize if a link is part of an operational or a backup path, which Felix can do. With modifications on the Path matrix component, this could be achieved; thus, the framework could skip recalculation of paths in the network. Such modifications would, however, have an adverse effect on every operation of the component: Initial setup, update with test results, and failure queries as well. Analysis of such options is to be covered by future work. Since performing tests requires modifications on forwarding rules and actively contributing to network traffic, these can have an adverse effect on network performance. SDN traceroute works with adding high-priority flow entries to every switch in the network. However, the number of such entries is less than the maximum degree of the network's graph which, in a usual case, adds only a small amount of new entries to flow tables. The number of test packets to send can be reduced by either applying the methods discussed in [13; 14; 15; 16] or taking into consideration the non-test traffic between hosts. The framework can instruct Felix's measurement service to detect packets generated in either way. SDN traceroute sends out only a single test packet per tested path, which again has a small contribution to network strain. Test packet generation can be made more effective by merging host-based and SDN traceroute specific test packet generation. However, this involves the modification of SDN traceroute, which is outside of the scope of the current work.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 2 Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
### Refinement and Fusion of Light Intensity Edge and Degree of Polarization Edge  
In order to avoid the phenomenon that the edge of light intensity and the edge of polarization degree do not coincide due to factors such as smoothing, the edge needs to be refined. The schematic diagram of edge refinement is shown in Figure 4.  
Assuming that the edge of the light intensity image is \(EA\), and the edge of the degree of polarization image is \(EB\), since the gradient is limited to the four directions of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\), then take the pixel point \(EA\) (\(i,j\)) on the edge of the light intensity as the center, and judge whether there is an edge of the polarization image in the 3 \(\times\) 3 neighborhood of the vertical light intensity gradient direction. If it exists, it needs to be removed; let \(EB\) (\(i,j\)) = 0, which can remove non-edge pixels, avoid the situation that the edge is too wide, and keep the thin edge.  
We coincided the light intensity edge with the polarization edge, and observed the 3*3 neighborhood of the vertical light intensity gradient direction to determine whether there is a polarization edge to refine the edge. Assume that the pixel point on the edge of the light intensity is \(EA\) (\(i,j\)). If the gradient direction of this point is \(0^{\circ}\), judge whether \(EB\) (\(i-1,j\)) and \(EB\) (\(i+1,j\)) exist in the direction of \(90^{\circ}\). If so, set their values to 0.  
Finally, we fused the refined intensity edge with the polarization edge. The fusion rules are as follows:  
\[\left\{\begin{array}{l}E(i,j)=\alpha_{1}EA(i,j)+\alpha_{2}EB(i,j)\\ \alpha_{1}=\alpha_{2}=0.5\end{array}\right., \tag{14}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the weighting factors, and \(E\) is the fused edge.  
Figure 4: Schematic diagram of edge refinement.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 3 Developed Measurement Techniques and Their Imaging Evolution
### Measurement Techniques Using the Time-Of-Flight Principle  
#### 3.3.1 Time Measurement  
In 1968, [PERSON] realized a laser-based 0d1c flow measurement system by using two parallel laser beams with known distance and evaluating the transit time of the scattering particle that passes both beams [97; 98]. This measurement technique is known as laser-2-focus anemometry (L2F). The approach was later extended for 2c and 3c measurements, but it remained a 0d measurement technique to date [34; 99].  
Instead of structuring the illumination, [PERSON] proposed in 1963 a structured receiving aperture in 1963 [100]. The approach was validated for flow experiments by [PERSON] in 1964 [101]. When the image of the scattering particle crosses the parallel-slit reticle, the temporal distance between the two detected light pulses is directly proportional to the flow velocity. The technique is named spatial filter velocimetry (SFV). Reviews of the SFV development from 1987 and 2006 are contained in [102] and [103], respectively. As an example, [PERSON] and [PERSON] described in 1996 an SFV technique that avoids the blocking of light by directly using the columns of the camera pixels as the spatial filter [104]. This approach was later extended by employing the evaluation of quadrature signals [105]. In 2004, [PERSON] and [PERSON] proposed to use the orthogonal grating structure of the pixel matrix of a CMOS camera and to evaluate subsections of the image time series that enables 2d2c flow measurements [106]. A detailed system theoretic description of this SFV measurement technique is contained in [107]. Furthermore, tomographic SFV approaches for 3d3c flow measurements with multiple cameras were theoretically presented by [PERSON] et al. in 2009 [108] and realized by [PERSON] et al. in 2013 [109].  
Similar to the Doppler techniques with frequency-based signal evaluation, several image frames are necessary to obtain the flow velocity information. In addition, the frequency of the intensity modulation increases with an increasing flow velocity. For this reason, current SFV systems are typically restricted to flow velocities well below \(1\,\mathrm{m/s}\) and flow imaging rates below the kHz range. However, the image resolution and the measurement rate are not well documented. Realizing a signal (pre-)processing directly on the camera chip as for instance was presented by [PERSON] and [PERSON] 2011 [110] should be a subject for future research. Eventually, the SFV is an advantageous technique for imaging flow velocity fields with a high seeding particle concentration.
## 2 Materials and Methods
### Method
#### 2.3.3 Analyzing the Impacts and Operational Mechanism of LPRO's Parameters on Its Macro Effects  
On the basis of the traditional ordinary least squares (OLS) regression method, this research uses geographically weighted regression (GWR) to analyze the spatial characteristics of the impact of various elements of LPRO on its macro effects and to explore the direction and explanatory power of the independent variables over the dependent variable and the spatial differences in the role of the independent variables.  
\[\mathrm{y_{i}}=\beta_{0}(\mathrm{u_{i}},\mathrm{v_{i}})+\sum_{k}\beta_{k}( \mathrm{u_{i}},\mathrm{v_{i}})x_{ik}+\varepsilon_{i} \tag{1}\]  
where (\(\mathrm{u_{i}}\), \(\mathrm{v_{i}}\)) is the geographical center of the \(i\)-th sample space unit and \(\beta_{k}\) (\(\mathrm{u_{i}}\), \(\mathrm{v_{i}}\)) represents the value of the continuous function \(\beta_{k}\)(\(\mathrm{u}\), \(\mathrm{v}\)) in sample space \(i\).  
In addition, drawing on the idea of the threshold regression model of [PERSON] (1978) and the relevant methods of [PERSON] et al. (2016), this study designs a threshold model of the distance to the city center, land price, advanced industrial utilization index, land marketization level and LPRO's macro effects to explore the threshold and cycle characteristics of the factors influencing different links in the LPRO system.  
\[y_{it}=\gamma_{1}x_{it}I(q_{it}\leq\tau)+\gamma_{2}x_{it}I(q_{it}\geq\tau)+u_{ i}+e_{it} \tag{2}\]  
where \(x_{it}\) indicates the core explanatory variable, \(q_{it}\) indicates the threshold variable, \(\tau\) indicates the threshold value, and \(I(\cdot)\) represents the indicator function. Let the dummy variable \(I_{it}(\gamma)=\{q_{it}\leq\tau\}\); when \(q_{it}\leq\tau\), \(I=1\); otherwise, \(I=0\). \(\gamma_{1}\) and \(\gamma_{2}\) are the influencing coefficients of the explanatory variables on the explained variables when the threshold variables are \(q_{it}\leq\tau\) and \(q_{it}>\tau\), respectively.
## 3 Results  
The experimental tests and combustion process analysis performed focused on continuing previously conducted research by extending the valve phases and selecting different engine loads instead of a fixed throttle position. In the prior study, a constant throttle angle for all valve phases was used [45], while this research focused on changing the intake valve closing time at various loads.  
In the experimental tests, IVC timings were chosen from 24\({}^{\circ}\) to 59\({}^{\circ}\) aBDC in 7\({}^{\circ}\) increments. Three engine loads (BMEP = 0.31, 0.55 and 0.79 MPa) were tested using both liquid and gaseous fuels, which better represent real-world driving and allow for a more accurate assessment of engine performance at a fixed engine load than throttle position.  
The throttle position is consistently higher when operating on natural gas (NG) compared to gasoline (G) at all BMEP levels (Figure 3). Natural gas contains more hydrogen, and in order to keep a constant excess air ratio (\(\lambda\) = 1) during experimental tests, the complete combustion of NG requires more air (AFR 17.1:1) compared to gasoline (14.24:1). This difference results in a larger throttle opening to ensure sufficient VE and cylinder filling with fuel-delivered energy. It is observed that at low and medium engine loads, a slight increase in the throttle valve (up to \(\sim\)0.8%) is visible after replacing G with NG, but at maximum load (BMEP = 0.79 MPa), the throttle valve opens \(\sim\)1.8% and 2.4% more when comparing 2000 and 3000 rpm. A stoichiometric mixture of natural gas and air has a lower energy density than the gasoline-air case; therefore, to produce a comparable power output, a larger volume flow rate of NG is required. The lower density of the natural gas-air mixture also influences the larger throttle opening, which reduces the inertia of the gas flow and the VE.  
This observation is consistent with findings, where reported throttle openings in a spark-ignited (SI) engine were even up to 15% higher in NG compared to gasoline operation over a wide range of operating conditions [46]. The relationship between throttle position and BMEP shows a positive correlation for gasoline and NG, most prominently at low  
Figure 3: Influence of engine speed, load, and fuel type on throttle position.  
engine speeds. A higher BMEP means a heavier load on the engine, and therefore a higher air and fuel quantity (volume flow), meaning a larger throttle opening. However, at increased engine speeds, the relationship between throttle position and BMEP becomes less linear as a result of the combined effects of improved VE, altered gas flow dynamics, changes in combustion phasing and engine control strategies. This aligns with the results of other authors from an engine experiment, which reported a linear relationship between BMEP and throttle position at low speeds, but nonlinearities as the speed increased [16; 47].
### QoE Assessment Model Based on Machine Learning
While accounting for the fluctuation in the quality of a video caused due to bitrate switching on the QoE, other factors, such as video content characteristics, video quality and video fluency, are combined together to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to describe the relationship between the input feature vector and value of the MOS. The structure of the paper is as follows. Section 2 describes the QoE assessment model proposed in this paper. Section 3 describes the experimental results and analysis. Finally, Section 4 draws the conclusion.
## 3 Communication Delay Issues with Combined TSC and TCR Controller
### Combined TSC and TCR Model  
The TSC-TCR was linked to the general grid line, as indicated in Figure 3. Six thyristors were linked to the capacitor, while the other six were connected to the inductor [37]. The thyristors were used to convert these capacitors. The reaction capacitor's capacitance, C, was 94 Mvar, while the inductor's capacitance was 109-Mvar. It only worked when the error rate between the voltage lines was more than 2%. The controller's output was alpha (\(\infty\)), which was used to modify the thyristors' pulses. The control of switching TSC and TCR for fuzzy input is shown in Figure 4. It is worth noting that this one-of-a-kind fuzzy controller could help assist other delay reduction approaches, such as PSS and PSS fuzzy.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Devices** & **Numbers** \\ \hline Substations & 11 \\ Shunt Reactor & 1 \\ Capacitor Bank & 2 \\ Buses & 69 \\ Machines & 6 \\ Loads & 20 \\ Branches & 13 \\ Two-winding transformers & 27 \\ PV System & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of components in system model.  
Figure 3: TSC-TCR 3-phase circuit diagrams with grid connection concept.
## 4 Experiments and Results
### Experimental Setup
#### 4.1.4 Evaluation Metrics  
Four evaluation standards, Accuracy (Acc), Recall (Re), Precision (Pr), and F1 measure (F1), were employed for evaluating the performance of the proposed model. These standards are widely utilized in SA and text classification tasks. These standards are computed as follows [18]:  
\[\text{Pr}=\text{TP}/\text{TP}+\text{FP}, \tag{21}\]  
\[\text{Re}=\text{TP}/\text{TP}+\text{FN}, \tag{22}\]  
\[\text{F1}=2\text{PrRe}/\text{Pr}+\text{Re}, \tag{23}\]  
\[\text{Acc}=\text{TP}+\text{TN}/\text{TP}+\text{TN}+\text{FP}+\text{FN}, \tag{24}\]  
TN, FP, TP, and FN are true negative, false positive, true positive, and false negative, respectively [18].
# Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network
## 2 System Architecture  
Figure 1 shows the proposed method for signal analysis and recognition of power quality accidents. First, the original power signal was generated by MATLAB/Simulink. Then, the original power quality signal was transformed by SDP into a polar diagram with various image features. Afterward, the power quality accident signals were identified by CNN. The proposed algorithm is described below.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 1 Introduction
In addition, our proposed optical computing network with decomposed hardware is compared with current works in this section. The main results of this work are concluded in Section 6.
## 3 Results and Discussion
### Inlet Cross-Flow Influence on Fan Blade Vibration  
#### 3.2.1 Spectral Decomposition of Surface-Averaged Vibration  
The measured response in the surface-averaged blade vibration \(v_{z}(n)\) to inlet cross-flow is shown by its spectral decomposition in Figure 6 for OP1 and in Figure 7 for OP2. The integral root mean square measures show a distinct increase in the overall blade vibration for OP1 from \(\mu=0.258\). With \(718.46\,\mathrm{mm/s}\) at OP11, \(v_{\text{rms}}\) was almost double the value of the reference configuration of OP10 (\(v_{\text{rms}}=362.52\,\mathrm{mm/s}\)). For OP2, \(\mu=0.25\) cross-flow increased \(v_{\text{rms}}\) by about \(20\%\) from \(363.40\,\mathrm{mm/s}\) (OP20) to \(436.87\,\mathrm{mm/s}\) (OP21).  
The greater overall increase in \(v_{\text{rms}}\) was also reflected by the influence of cross-flow in the spectral decomposition of \(v_{z}(n)\) in Figures 6 and 7. Amplitude gains are found for OP1 in Figure 6 for almost every integer value \(n\geq 4\), but \(n=5\) and \(n=6\) are the rotation-rate harmonics that are excited dominantly. As a result of the influence of inlet cross-flow, the blade-pass frequency was also measured to become the dominant amplitude peak \(v_{z}\) at the higher flow-rate operating point OP2, as visible in Figure 7. Amplification at \(n=6\) was also found for OP21, but to a far lesser magnitude than for OP1 (Figure 6). The previously dominant frequency \(n=3\) was not affected in amplitude by cross-flow at either operating point.  
Figure 5: Computed modal shape of second natural frequency in comparison to measured vibration at associated frequency.  
An amplification of the blade vibration around the natural frequency domains cannot be discerned from the data presented in Figures 6 and 7; the cross-flow influence appears not to have excited the fan's natural modes significantly.