esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stretch Forming
#### 3.1.4 One-Step Stretch Forming  
Figure 18 shows the comparison between the stretch forming of FML01-RG2.0 with and without pre-bonding in terms of the drawing force-depth and strain evolution at 235 \({}^{\circ}\)C. There were three repetitions for each condition. It was clearly seen that the difference in force and depth, as well as the strain distribution between the specimens with and without pre-bonding, was ignorable. A drawing depth of FML01-RG2.0 without pre-bonding is 30.7 \(\pm\) 1.8 mm was similar to 31.1 \(\pm\) 1.6 mm for the pre-bonded one. With this one-step thermoforming process, comparable forming limit was reached while saving the step for producing the sandwich semi-products. In this regard, the results of the investigations in this study with the pre-bonded FML could be a benchmark for the one-step thermal forming without pre-bonding of FML, which is beneficial for further investigation into the 3D shaping of FML through the one-step thermoforming process, such as the top-hat profile manufacturing described in [44]. In addition, both pre-bonded and unbonded specimens  
Figure 16: Deformation behaviour of organosheets (**a**) RGUD2.0 and (**b**) RG2.0 as core layer in FML01 after stretch forming at 235 \({}^{\circ}\)C.  
Figure 17: Crack patterns of FML01-RGUD2.0 at (**a**) RT and (**b**) 235 \({}^{\circ}\)C, of FML01-RG2.0 at (**c**) RT and (**d**) 235 \({}^{\circ}\)C.  
showed good interlaminar bonding after cooling at the end of the experiment, except for the area where the steel sheet fractured, which showed local delamination due to lack of backing support and spring-back deformation of the cover sheets.
## 4 Performance and Simulation Results
### The Modified BA Protocol, MBA
#### 4.3.3 The Impact of Total Number of Tags  
In this subsection, a simulation of n tags that existed in the entire zone of Figure 3 moving at any velocity for GBT, TBT, GQT1, and GQT2 protocols was conducted at the following conditions: velocities of 1 m/frame (blocking velocity) and 3.5 m/frame (non-blocking velocity) for the modified BA protocol MBA. Figure 8a shows that the number of success cycles (the identified tags) increases when the total number of tags rises from 100 to 500. The increasing of the total number of tags in the simulated area yields an increasing number of tags located in the reader zone. Figure 8b,c show that the collision and idle cycles increase (except the idle cycles for the uniform distributions of GQT1 and GQT2) as the total number of tags rises, because the number of tags located in the reader zone increase, but with a different ratio of increasing for each protocol. For GQT1 and GQT2 with uniform distributions, the idle cycles never increase when changing the total number of tags, since the empty leaves are nearly constant, because the success cycles never branches the identification tree for full reader prefixes. As a result, GBT, TBT, and MBA in Figure 8d have constant efficiencies when changing n from 100 to 500, which are 30% (MBA with 3.5 m/frame), 35% (GBT & TBT), and 60% (MBA with 1 m/frame). Adversely, the efficiencies for GQT1 and GQT2 are increased when n is increased, so that GQT1 and GQT2 with uniform distribution have an efficiency of 48% when n is 500 but only 41% for n equals 100. GQT1 with non-uniform distribution has also an efficiency of 28% when n is 500 but only 22% for n equals 100. Moreover, GQT2 with non-uniform distribution has an efficiency of 34% when n is 500 but only 28% for n equals 100. As a result, MBA outperforms the other protocols in consuming fewer total number of cycles with constant high efficiency when changing n from 100 to 500 with low tag mobility.  
Figure 8: _Cont._  
Figure 7: The impact of interference: (**a**) success cycles, (**b**) collision cycles, (**c**) interference cycles, (**d**) idle cycles, and (**e**) total cycles.
## 5 Discussion  
The brake frame physically depicts plastic behavior as the displacements cannot be recovered to the initial stages when the external forces are removed after the occurrence of buckling and fracture behavior (phases 1-5, as shown in Figure 4). However, in this study, the proposed equivalent model was axial connection type based on nonlinear elasticity in which the displacements can be recovered to the initial stages after the removal of external forces. Therefore, when the net protection system is analyzed using the proposed equivalent model, the obtained results must be used at the development point of maximum tensile and reaction forces in the wire ropes without considering the results related to restoring forces.  
As compared to the cases where the brake frames were used, the tensile forces in each wire rope were observed to be at least 1.4-18 times greater in the system without using a brake frame, except in the case of wire rope 6, as shown in Figure 16. The results show that the maximum tensile forces in the wire ropes obtained in the system with brake frame were reduced by 75%, as compared to those obtained in the system without brake frame. The results also show that the sum of tensile forces in all the wire ropes obtained in the system with the brake frame were reduced by 74%, as compared to those obtained in the system without the brake frame.  
When the brake frame is used, the load distribution in the upper wire ropes was observed to increase as compared to the high load distribution in the center portion of the wire ropes when the brake frame was not used due to the reason that the deformation in the wire ropes increases when brake frames were used. In both the cases, the wire rope having maximum tensile force encompassed 28.8-30.6% of the load distribution. Furthermore, in both cases, two of the wire ropes comprising the most tensile forces acting in them were observed to be encompassing 58-61% of the load distribution. It is thus deduced from the results that the concentration of the load distribution in some of the wire ropes must be considered for the design of each individual case.  
Figure 15: Stress distribution in the net protection system after impact, with using a brake frame, (**a**) 0.12 s after impact; (**b**) 0.21 s after impact; (**c**) 0.27 s after impact; and (**d**) 0.315 s after impact.
## 2 Materials and Test Methods
### Experimental Design Plan  
Laboratory short-term aging was carried out using the rolling thin-film oven test (RTFO, 163 \({}^{\circ}\)C, 1.0 MPa, 85 min) to simulate aging during storage, mixing, transport, and placing. Thereafter, the pressurized aging vessel test (PAV, 100 \({}^{\circ}\)C, 2.1 MPa, 20 h) was performed to roughly simulate 5 years of field aging [28] in accordance with ASTM D2872 [29] and ASTM D6521 [30]. The virgin SBSMA was subjected to RTFO and PAV aging to obtain the first aged SBSMA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technical Index** & **Unit** & **Virgin** & **RTFO** & **PAV** & **Residue** & **Test Method** \\ \hline Penetration (25 \({}^{\circ}\)C, 100 g, 5 s) & 0.1 mm & 50.6 & 35.0 & 22.4 & ASTM D5 \\ Softening point, T\({}_{\text{R}\text{R}\text{R}}\) & \({}^{\circ}\)C & 78.9 & 81.0 & 83.2 & ASTM D36 \\ Ductility (5 \({}^{\circ}\)C, 5 cm/min) & cm & 25.2 & 14.7 & 2.0 & ASTM D113 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical indices of SBS modified asphalt-binder (SBSMA).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technical Index** & **Unit** & **Type I** & **Type II** & **Type III** & **Test Method** \\ \hline Viscosity @25 \({}^{\circ}\)C & SFS & 25–150 & 20–60 & 25–150 & ASTM D7496 \\ Residue & \% & \textgreater{}64 & 60–65 & \textgreater{}65 & ASTM D6934 \\ Particle charge & — & Positive & Positive & Positive & ASTM D7402 \\ Asphaltenes & \% & \textless{}11.5 & 2–4 & \textless{}11 & ASTM D2006 \\ Maltene distribution ratio & — & 0.8–1.4 & 0.5–0.9 & 0.7–1.1 & ASTM D2006 \\ (PC/S ratio & — & \textgreater{}1.2 & \textgreater{}1.2 & \textgreater{}0.5 & ASTM D2006 \\ \hline \hline \end{tabular} \({}^{\dagger}\) PC = Polar Compound; A1 = First Acidaffins; A2 = the Second Acidaffins; S = Saturated Hydrocarbons.  
\end{table}
Table 2: Technical indices of the rejuvenators.  
For the purpose of investigating the influence of multiple aging and rejuvenation cycles on the SBSMA in the field, 6%, 10%, and 14% by weight (wt%) of the asphalt-binder were selected as the rationale dosage of the rejuvenator for the first, second, and third rejuvenation, respectively, in accordance with previous research studies [26]. The first aged SBSMA was heated to 140\(\sim\)160 \({}^{\circ}\)C for melting and blended with three rejuvenators for 30 min to prepare the first rejuvenated SBSMA. Thereafter, the first rejuvenated SBSMA was directly placed in the PAV (100 \({}^{\circ}\)C, 2.1 MPa, 20 h) for long-term aging (rather than RTFO first) to obtain the second aged asphalt-binder on account of the in-place penetrating rejuvenation without the need for heated construction. The second rejuvenated asphalt-binder was prepared by adding 10 wt% of the rejuvenator to the second aged SBSMA. The third aged asphalt-binder and the third rejuvenated asphalt-binder were obtained by the same method. Figure 2 illustrates the experimental design plan for this research study.  
Figure 1: FTIR spectra of the three rejuvenators.  
Figure 2: Experimental flow chart.
## 6 Challenges and Future Research Scopes
### Edge Controller Placement  
One of the major issues in SDN-enabled edge cloud network architecture is controller placement. The key objective of controller placement is to minimize latency and bandwidth optimization, finding the optimum number and position of control nodes in an SDN-enabled network [59; 83; 84]. Some of the research works introduce the clustering approach and machine learning-based approaches to deal with this challenge. [PERSON] et al. proposed an optimized K-means network partitioning algorithm to address this issue. In this algorithm, they partitioned the network by distributing the vertex in one of the K clusters according to the following equation [60]:  
\[v\;\in\;cluster_{i},\quad\text{ if }d(v,\,c_{i})<d\big{(}v,\,c_{j}\big{)}, \quad\forall_{j}\in\{1,2,\ldots k\} \tag{8}\]  
where \(d(u,\,v)\) denotes the shortest path between two nodes. After clustering, the algorithm updates the centroids to minimize the sum of the shortest paths from all points in the \(cluster_{i}\). To minimize the maximum latency between the centroid and its associated switches within subnetworks, the following formula is used.  
\[\text{Min}\{max\{d(\theta_{i},\,S_{i})\}\}\quad(\forall_{i}\in k)\quad\text{ such that }\theta_{i},\,S_{i}\in\,SDN_{i} \tag{9}\]  
where \(\theta_{i}\), and \(S_{i}\) represents controller and switches in the \(i^{th}\) subnetwork.  
[PERSON] et al. [84] proposed a deep reinforcement learning (DRL)-based SDN controller placement algorithm for a MEC system to reduce the average communication delay. Finding the optimal number of SDN controllers and their placement using deep learning is another crucial research area in edge cloud networks.
## 3 DRFM System Model
### Radar Transmitter  
The radar transmitter implementation, shown in Figure 2, is capable of generating three classes of radar signals that cover most airborne radar threats [20]. The waveforms generated include unmodulated pulses, linear frequency modulation, and Phase Shift Keying (PSK) for Barker codes. Examples are shown in Figure 3.  
The waveforms are generated in MATLAB using customized functions with user adjustable parameters, such as pulse width, bandwidth, and PSK sequence (specifies the order Barker code). The resulting array is then used in the Simulink testbed LFM and PSK radar signal blocks, which are read out the digitized waveform from memory every sample period from a look-up table.  
Figure 1: The system model for the Digital Radio Frequency Memory (DRFM) jammer and its testbed.
## 2 Methodological Background
### PID Control  
The PID controller design is used in this study to control the air temperature and flow. A PID control consists of three units: proportional (\(K_{p}\)), integral (\(K_{i}\)), and derivative (\(K_{d}\)). The so-called control tunes the gain values of these three units. \(u(t)\) is defined as the output function of the controller at time \(t\), and then a PID controller can be expressed as the following mathematical formula [2]:  
\[u(t)=K_{p}E(t)+K_{i}\int_{0}^{t}E(\tau)d\tau+K_{d}\frac{d}{dt}E(t) \tag{3}\]  
\[u(s)\ =(K_{p}+\frac{K_{i}}{s}+K_{d}s)E(s) \tag{4}\]  
where  
\(K_{p}\): coefficient of the proportional term.  
\(K_{i}\): coefficient of the integral term.  
\(K_{d}\): coefficient of the derivative term.  
\(E\): error = set value \(-\) feedback value  
\(t\): current time.  
\(\tau\): integral variable, with values from 0 to \(t\).  
Equation (4) is an expression defined in the continuous s-domain.
## 2 Device Description  
In this study, two NWT structures are used; single lateral NWTs and vertically stacked lateral NWTs. The purpose is to determine the optimal AR, and Figure 1 shows a 3D representation and material details for the initial structure of the NWTs. The Si channel is wrapped in a 0.4 nm SiO\({}_{2}\) interfacial layer and a 0.8 nm HfO\({}_{2}\) (High-k) layer is added at the gate region. The doping concentration in the channel is \(10^{14}\)/cm\({}^{3}\), in the extensions \(10^{20}\)/cm\({}^{3}\), and in the source/drain 4 \(\times\)\(10^{20}\)/cm\({}^{3}\). The charge transport's direction is organized along with \(x\)-axis, and Table 1 outlines the cross-sectional dimensions for 9 elongated elliptical nanowires (each with the same cross-sectional area amounting to approximately \(10\pi\) nm\({}^{2}\)). In addition, the diameter of the NWT along the \(y\)-axis ranges from 4.44 nm to 9.0 nm, whereas the matching diameter along the \(z\)-axis ranges from 9.0 nm to 4.44 nm. Resultantly, variance with respect to the matching aspect ratio is from 2.020 to 0.490. It should also be noted that the cross-sectional dimensions include wires with Phi and 1/Phi values. For the estimation of NWTs' performance after determining the optimal AR, state-of-the-art technology keys such as source/drain doping profile, predictive contact resistance, and gate length were utilized for both single NWT and vertically stacked lateral NWT structures.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 4 Statistical Analysis for Normally Distributed Model Parameters
### Probabilistic Cost and Input Constraint  
Problem 1 with the stochastic state Equation (10) and the joint distribution (13) results in a stochastic optimal control problem, where both the cost function (7) and the input constraint are probabilistic. Therefore, the inputs and the states are meant to be found such that they minimize the _expected cost_, namely:  
\[J(M,S,V)=\sum_{k=0}^{T-1}\lVert C\mu_{k+1}^{x}-r_{k+1}\rVert_{Q}^{ 2}+\sum_{k=0}^{T-2}\lVert\mu_{k+1}^{u}-\mu_{k}^{u}\rVert_{R}^{2} \tag{14}\] \[\quad+\sum_{k=0}^{T-1}\operatorname{Tr}\big{(}Q\,C\,\Sigma_{k+1}^ {x}\,C^{\top}\big{)}+\sum_{k=0}^{T-2}\operatorname{Tr}\big{(}R\,K(\mu_{k+1}^{x })\,\Sigma_{k+1}^{x}\,K^{\top}(\mu_{k+1}^{x})\big{)}\] \[\quad+\sum_{k=0}^{T-2}\operatorname{Tr}\big{(}R\,K(\mu_{k}^{x})\, \Sigma_{k}^{x}\,K^{\top}(\mu_{k}^{x})-R\,\operatorname{He}\Big{\{}K(\mu_{k+1}^ {x})\,\operatorname{Cov}(\hat{x}_{k+1},\hat{x}_{k})\,K^{\top}(\mu_{k}^{x}) \Big{\}}\Big{)}. \tag{15}\]  
The expanded Formula (14) of the expectation of cost (7) is derived in Appendix A.  
**Remark 3**.: _The term \(\operatorname{Cov}(\hat{x}_{k+1},\hat{x}_{k})=\operatorname{Cov}(f(\hat{x}_{k},\hat{u}_{k},v_{k},\hat{\theta}_{k}),\hat{x}_{k})\) in (14) is typically a non-quadratic function of the mean and the variance of the joint distribution (13). This term introduces a potential difficulty to the optimization, which is addressed later in Section 4.4._  
The conditions on the input can be formulated as chance constraints of the form \(\operatorname{Pr}(\hat{u}_{k}\in\mathcal{U})\geq p_{u}\), where \(p_{u}\) denotes the _probability level_ of the _confidence set_\(\mathcal{U}\). When \(\hat{u}_{k}\) comprise a single input and the input domain \(\mathcal{U}\) is an interval, the chance constraint \(\operatorname{Pr}(\hat{u}_{k}\in[\underline{u},\overline{u}])\geq p_{u}\) is equivalent to the following deterministic interval constraint [51]:  
\[\mu_{k}^{u}\in[\underline{u}+c,\overline{u}-c],\text{ with }c=\Phi^{-1}( \tfrac{p_{u}+1}{2})\sqrt{K(\mu_{k}^{x})\,\Sigma_{k}^{x}\,K^{\top}(\mu_{k}^{x})}, \tag{16}\]  
where \(\Phi:\mathbb{R}\to(0,1)\) denotes the (cumulative) distribution function of the standard normal distribution \(\mathcal{N}(0,1)\). This technique for the reformulation of a probabilistic condition is referred to as _constraint tightening_[69].
# MLMD--A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm
## 4 Evaluation
### Comparison with Existing Studies  
Table 15 shows a comparison with existing studies, mentioned earlier. It shows the accuracy and sensitivity values applicable to the best cases, using the algorithms presented. Our accuracy and sensitivity values are expressed as arithmetic mean values. They were obtained by testing during the respective iterations and during the search for the best parameters on a specially crafted dataset (25% of the original dataset).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Analysis Type** &
\begin{tabular}{c} **Dataset Size** \\ **(Malicious/Clean)** \\ \end{tabular} & **Best Algorithm** & **Accuracy (\%)** & **Sensitivity (\%)** \\ \hline [PERSON] z kol. [14] & static & 3265/1001 & RF & 97.11 & 97.43 \\ [PERSON] a kol. [15] & static & 10,521/8592 & J48 & 95.1–99.1 & 91.3–99.1 \\ [PERSON] a kol. [16] & static & 2722/2488 & RF & 98.78 & 99.0 \\ [PERSON] [17] & static & 992/771 & RF & 95.58 & 96.77 \\ [PERSON] a & static & 41,265/10,920 & ANN & 97.7 & 91 \\ kol. [18] & static & 997/490 & SVM & 95.88 & 95.9 \\ [PERSON] [20] & static & 2747/837 & XGBoost & 91.92 & _98.25_ \\ _This study_ & _dynamic_ & _2937/828_ & _XGBoost_ & _96.48_ & _98.51_ \\ [PERSON] [20] & dynamic & 997/490 & SVM & 97.16 & 97.2 \\ [PERSON] [20] & combined & 997/490 & SVM & 98.71 & 98.7 \\ [PERSON] a kol. [21] & dynamic & 220/250 & J48 & 96.8 & 95.9 \\ [PERSON] a kol. [22] & dynamic & 3130/1157 & RF & 91.4 & 91.1 \\  & & & Gaussian NB, & & \\ [PERSON] and & & & KNN, Linear SVC, & & \\ [PERSON] [23] & Ember dataset & 300 K/300 K & DT, AdaBoost, RF, & 98.5 & 0.89–0.99 \\  & & & Extra Trees, & & \\  & & & CB, XGBoost & & \\ [PERSON] and & & & NB, DT, RF, & & \\ [PERSON] [24] & Open data source & 4060/2709 & GB, XGBoost & 99.95 & - \\  & & & KNN, SVM, & & \\ [PERSON] et al. [25] & Open data source & 984/172 & [PERSON] NB, RF, & 98.2 & 92 \\  & & & DT, LR, HV & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison with existing studies.  
Figure 10: The quarantine screen.  
We have achieved particularly good results in terms of the sensitivity metric, so we can classify malicious samples well, while maintaining a low false negative rate. For the dataset obtained by static sample analysis, we lag behind slightly in terms of classification accuracy--this is mainly related to the number of false positives, which we attribute to the imbalanced data. In the case of the dataset obtained by dynamic sample analysis, the results are slightly better than those obtained by static analysis, which may be due to a better selection of features to distinguish between malicious and benign samples in the case of the data obtained by dynamic analysis.
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 2 Description of Arch Bridge and Monitoring Data  
This paper takes a large-span arch bridge as the research object. The span arrangement of this large-span arch bridge is \((100+450+100)\) m, with a slenderness ratio of \(1/5\) and a total width of 45.8 m. The structural form is a center-supported steel beam composite system, with the side span arch ribs and the middle span arch ribs in the same plane, and a lateral inclination ratio of 1:5. The arch ribs are connected by transverse braces and K-braces to form a strong lateral bending rigidity to resist the wind load in the transverse direction of the bridge. The stiffening girders are supported on the arch ribs by suspender rods or pillars, and the two ends of the middle span stiffening beam are supported on the transverse girder at the intersection of the middle span arch ribs. The end supports are longitudinal sliding supports with transverse limit supports and longitudinal dampers. The side span stiffening girders are fixed to the arch ribs at the intersection of the middle span and the side span. Powerful horizontal tension members are arranged between the transverse girders at the ends of the main bridge to balance the horizontal thrust of the middle span arch ribs.  
Due to its importance as a major cross-river road, the bridge carries a huge traffic volume and suffers from serious overload. In order to continuously monitor the on-site environment and structural status of the bridge, a comprehensive SHM system has been installed; the main sensor layout is shown in Figure 1. Among the sensors, A1\(\sim\)A15 are accelerometer sensors, T1\(\sim\)T28 are temperature sensors, and D1\(\sim\)D4 are beam displacement sensors.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
###### Abstract  
Unmanned aerial vehicles (UAVs) are an emerging and promising alternative for monitoring of transmission lines in terms of flexibility, complexity, working speed, and cost. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the UAV placement with respect to the transmission grid. This work faces this challenge by developing a transmission line autonomous tracking system, which allows the placement of a commercial drone over a transmission grid using a monocular camera. This feature provides accurate positioning for the vehicle even where the Global navigation satellite system (GNSS) signal is denied, enabling to report the status of transmission lines, at any time. The system isolates transmission grid conductors in each acquired RGB-image using an image-processing algorithm based on Hough transform, morphological operations, and Gabor filters. With this information, the system computes the location of the UAV using a geometric approach that relates transmission lines building parameter and optical geometry. However, it has the problem of gradual error accumulation when the drone moves. In this regards, the estimated position of the drone is computed by the maximum likelihood estimation (MLE) by the position information estimated by visual-system, the inertial measurement unit (IMU) and GNSS. The proposed positioning system showed an efficiency of 91.44% in field experimentation in the extraction of transmission conductor, with a root mean square the error of 0.18 m in the UAV localization.  
robotics; robot vision systems; image motion analysis; transmission line inspection Article