esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determine the Optimal Features Spaces
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Species** & **Features** & **ACC** & **MCC** & **SN** & **SP** & **Size** \\ \hline _C. elegans_ & original & 0.844 & 0.689 & 0.852 & 0.837 & 2628 \\  & optimal & 0.859 & 0.718 & 0.866 & 0.853 & 214 \\ _D. melanogaster_ & original & 0.846 & 0.692 & 0.864 & 0.828 & 2628 \\  & optimal & 0.859 & 0.719 & 0.875 & 0.844 & 228 \\ _A. thaliana_ & original & 0.787 & 0.574 & 0.784 & 0.790 & 2628 \\  & optimal & 0.804 & 0.609 & 0.799 & 0.809 & 184 \\ _E. coli_ & original & 0.854 & 0.709 & 0.874 & 0.835 & 2628 \\  & optimal & 0.903 & 0.807 & 0.922 & 0.884 & 194 \\ _G. subterruneus_ & original & 0.840 & 0.681 & 0.832 & 0.847 & 2628 \\  & optimal & 0.864 & 0.729 & 0.846 & 0.882 & 241 \\ _G. pickergini_ & original & 0.867 & 0.735 & 0.858 & 0.877 & 2628 \\  & optimal & 0.902 & 0.805 & 0.888 & 0.917 & 232 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the original features and optimal features. Figure 4: Performance of the original features and optimal features on the Accuracy and Matthews correlation coefficient metrics. positive and negative samples are significantly more clustered. This result makes it easier for the predictor to place positive and negative samples in the same category in the process of drawing decision boundaries, thus improving classification accuracy. It indicates that the genomic sequence information after determining the optimized features may somehow better distinguish between non-4 mC sites and 4 mC sites. The information of the other species is shown in Supplementary Figure S2.
# A Photonically-Excited Leaky-Wave Antenna Array at E-Band for 1-D Beam Steering
## 4 Measurements and Results
### Electronic Measurements  
The set of electronic measurements was performed on a two-row array prototype with a GSG probe (Form Factor ACP 110-T), and an Agilent N5242A vector network analyzer (VNA) with a mm-wave extension head (OML V10 VNA2). The prototype included a tapered interconnection from the GSG pads to G-CPW to excite the antenna, as it can be seen in Appendix A Figure 10,e.  
For the reflection coefficient measurements, the procedure described in Appendix A was employed to de-embed the probe and interconnection. Measurement results are shown in Figure 7. The antenna was well adapted throughout the operation band and did not present an increased reflection coefficient at the frequency of broadside emission (81 GHz). The disagreement between simulation and measurement around 78 GHz could be attributed to an inexact value of the relative permittivity of the substrate used in the simulation, and to errors associated with the electrical connection between the probe and the PCB. Finally, tolerances in the fabrication of the PCB could also have impacted the performance. More precisely, VIA holes were positioned with an accuracy of \(\pm\) 25 \(\upmu\)m, which is comparable to the VIA-center to edge distance (150 \(\upmu\)m).  
For pattern measurements, a spherical 3D scanner and a receiving horn were additionally used. Figure 7(a) shows the normalized co-polar directivity of the two-row array at different frequencies. A frequency-scanning range of 22\({}^{\circ}\) was achieved. Note that the cross-polarization component, emitted largely off the scanning range of the array, vanished in the complete structure (Figure 4(b)). Additionally, the SLL of the two-row array was higher than in the complete structure (Figure 4(b)) because the patterns were optimized for the prototype excited photonically. The SLL could be improved by including more periods in the row. Figure 7(b) shows the simulated and the measured peak gain for the two-row prototype adapted for probe measurements. The largest deviation between simulated and measured data equaled 1.4 dB, which was acceptable considering the method used to excite the antenna. This value of measured gain was also consistent with the simulated radiation efficiency (77% at broadside). In the measurements, a standard gain horn was first characterized as a reference to retrieve absolute values of gain. Additionally, the incident power upon the antenna, using the  
Figure 7: Magnitude of the reflection coefficient for the two-row prototype adapted for probe measurements. The black dots represent the measurement taken directly from the VNA, whereas the blue curve represents the measurement after de-embedding the probe and the interconnection.  
de-embedded impedance values, was recalculated. Note the antenna did not present degraded gain at the frequency of broadside emission due to the good input matching observed in Figure 7. Therefore, the operation range was extended from the backward quadrant (\(\theta<0^{\circ}\)) to the broadside and the forward quadrant.
## 4 Electron Beam Characterisation
Numerical modelling [30] based upon particle tracing in the mapped magnetic field was implemented to describe the performance of the spectrometer to obtain the dispersion curve and the intrinsic resolution of both the imaging acquisition system and the LANEX screen. The code also accounts for errors introduced by beam pointing instability and space-charge effects along propagation. The results obtained by the magnetic spectrometer were confirmed by independent measurements carried out using an energy spectrometer consisting of sandwiched Radiochromic films [31] (RCF). A sample spectrum obtained with the magnetic spectrometer with N\({}_{2}\) gas-jet is displayed in Figure 3.  
According to this spectrum, electrons up to 10 MeV were detected, with an overall spectral distribution characterised by a broad peak with a maximum between 5 and 6 MeV. In some shots, narrower spectral components were found, though with a poor reproducibility. A summary of the results obtained in our experimental condition and for both He and N\({}_{2}\) gas-jet is reported in Table 1. Also included in the table is a list of the main parameters characterising our experimental setup, including laser and gas-jet parameters. These results confirm that, in spite of the very low laser intensity compared with most of the experiments available in literature, the electrons accelerated in our experimental conditions  
Figure 2: Schematic layout of the experimental setup: side view showing the main interaction geometry of the laser beam relative to the gas-jet, the magnetic electron spectrometer and the LANEX screen for detection of electrons. are well in the multi-MeV region, with evidence of mono-energetic components emerging clearly from the broad energy spectrum. Remarkably, a stable production of electron bunches was obtained when using the N\({}_{2}\) gas-jet, while the use of He resulted in a less reproducible operation, with narrower beam divergence.
## 3 Drone Routing Algorithms
### Security Algorithms Used in Drone Routing
#### 3.3.4 IDS and Blockchain-Based Delivery Framework for Drone-Delivered Services (DeliveryCoin)  
Recently intrusion detection techniques have been emphasized for detecting any external hacker and attackers attempt to penetrate the system [59]. To deliver privacy preserving drone-based delivery systems, the authors in [60] proposed a blockchain-enabled intrusion detection technique to enable package delivery among the drones. The framework consists of five entities, namely, package buyer, package vendor, delivery service, autonomous vehicle, and macro eNB. First, as package buyers, both customer and store center can buy certain packages from package vendor. Next, the package delivery service handles the transactions between the buyer and vendor through blockchain-enabled digital ledger, which is propagated through all the nodes. During the transactions, to detect any false transactions and attacks, machine learning techniques are utilized to ensure that the transactions remain secure inside the framework. The proposed study considers both drones and vehicles as part of the framework. Finally, a terrestrial cellular network is considered to serve the aerial as well as ground users. To achieve a consensus in the blockchain-framework, they employed a drone-based forwarding mechanism named pBFTF, which mainly updates the transactions. Next, in the intrusion detection phase, several machine learning classifiers such as Random Forest, Linear Regression, and Naive Bayes are utilized to detect network attacks and false transactions. The authors consider the combination of signatures with hash functions to achieve consensus in the delivery framework. The proposed system can detect network attacks and treat them as false transactions. Thus, the overall framework is secured. Moreover, sufficient consensus latency and accuracy are ensured.  
Figure 3: Taxonomy of algorithms for drone routing in drone-based delivery systems: A-Ptr-Net [6], JRCS [12], FSTSP-STT [41], RL-TDCD [42], CRP-TD [15], EHTDDP [16], ODCS [44], CBPTCS [11], DDC [17], CDNC [51], Covadel [56], PA-NOP [57], GaRuDa [58], DeliveryCoin [60].
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 4 Improved Dynamic Window Approach
### Key Improvements to the Dynamic Window Approach
#### 4.2.2 Adaptive Heading Angle  
The path quality of DWA is closely related to the initial heading angle. Traditional DWA usually selects a fixed value or uses the line between the start and end points as the initial heading angle. However, this approach is prone to detours, thereby adversely affecting path quality in complex environments. In this paper, we propose a novel initial heading angle by incorporating local target points. The optimized initial heading angle calculation is shown in Formula (27):  
\[\theta=\arctan\left(\frac{y_{1}-y_{0}}{x_{1}-x_{0}}\right) \tag{27}\]  
where \((x_{0},y_{o})\) is the starting point, and \((x_{1},y_{1})\) is the first local target point.  
The method incorporates the relationship between the robot and the local target points when calculating the initial heading angle, and by adaptively adjusting the initial heading angle, the robot can reduce unnecessary detours during the initial local path planning and bypass obstacles more safely, thereby enhancing the overall quality of the path.
## 5 Thermal Elasto-Plastic Analysis and Deformation Measurement for the Full-Frame Structure  
Thermal elasto-plastic analysis requires material properties such as the coefficient of thermal expansion, Young's modulus, and yield stress. Therefore, as shown in Figure 17, this study used material properties including nonlinear temperature history information [14]. Figure 18 shows mesh for thermal elasto-plastic analysis, and Table 10 shows the mesh properties.  
Figure 16: Temperature distribution history of the full-frame structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **Unit** & \multicolumn{4}{c}{**Range from Welding Center Line**} \\
**Distance** & **mm** & **-10** & **-5** & **-2** & **2** & **5** & **10** \\ \hline
**Temp. derived from the specimen** & \({}^{\circ}\)C & 323 & 396 & 410 & 408 & 382 & 390 \\
**Temp. to enter into the simulation** & \({}^{\circ}\)C & 315 & 390 & 410 & 410 & 390 & 315 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Temperature to enter into full structure analysis.  
Figure 15: Pre-process of heat transfer analysis: (**a**) FSW sequence; (**b**) method to enter temperature in the welding center; (**c**) moving method of heat source.  
In terms of load, the thermal analysis result data was applied to the time steps and geometry. In terms of boundary conditions, geometric constraints were applied in the X (length), Y (width), and Z (height) directions in 13 areas constrained by jigs as shown in Figure 19. Constraints were only applied in the Z direction for the bottom surface, and the X and Y directions were free of constraints.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Element Size} & \multicolumn{2}{c}{Side Frame} & 30 mm \\ \cline{2-3}  & Bottom Frame & 40 mm \\ \hline Element Shape & Triangle \\ \hline Number of Nodes & 128,986 \\ \hline Number of Elements & 67,847 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mesh properties for thermal elasto-plastic analysis.  
Figure 19: Temperature distribution history of the full-frame structure.  
Figure 17: AL6063-T5 material properties required for thermal elasto-plastic analysis: (**a**) Coefficient of thermal expansion; (**b**) Young’s modulus; (**c**) yield stress.  
Finally, fillet joint FSW was performed on the actual full-frame structure under the same conditions as the analysis to measure the deformation, as shown in Figure 20a. The measurement was performed by using a 1/100 scale dial indicator and magnetic base at a total of 20 locations for each direction at 8 points as shown in Figure 20b.
# From Discourse Relations to Network Edges: A Network Theory Approach to Discourse Analysis
## 2 Discourse Relations and Discourse Units
### Subordinating and Coordinating Relations  
Although the classification of discourse relations as subordinating or coordinating has important semantic effects in discourse interpretation, reflected in discourse anaphora and attachment availability ([39]), it is highly context-dependent, as pointed out by [38; 40], which suggests that the contexts that they appear in and the conditions that dictate their classification as either coordinating or subordinating should be further investigated. As [38] mentions, a discourse relation does not share common underlying content with a homogeneous set of other discourse relations that can be assigned to either the coordinating or the subordinating type. Essentially, the two types of discourse relations reveal an important aspect of the core of discourse structure related to the way speakers interpret the information in the discourse; this aspect is often called information packaging in the SDRT tradition.  
However, beyond the linguistic contexts that need to be studied further so that the linguistic classification tests suggested by [38] can be confirmed and/or refined, this paper aims to abstract away from the distinction between subordinating and coordinating relations and to inspect deeper properties of discourse evolvement that would probably play a role in the classification of discourse relations. We claim that the study of network motifs and other key network indices is the formal means to reveal these properties and, then, the distinction between subordinating and coordinating relations may be examined further in light of the findings in this paper. The most important feature of discourse representations that can be exploited to identify network properties is the presence of directionality in the discourse edges, as is made clear in Section 4.  
SDRT's formalism supports directed acyclic graphs (DAGs), since, following SDRT's principled method of building graph representations of discourse, every EDU contributes to the information flow of discourse, and its advancement implies that there can be no loop constructed that could ultimately allow self-relational connections.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Detection
#### 5.1.1 Pre-Processing  
The source images are pre-processed to reduce the complexity of the image features and make the details clearer. Different pre-processing techniques are applied at this stage including (i) image resizing to maintain the same aspect ratio, (ii) converting colored images into greyscale, (iii) binarizing the resultant image by replacing its pixels with luminance values that are greater than a specified threshold by bit 1 (white) and replacing all other pixels with bit 0 (black). The threshold value is calculated as suggested in [24] using the OTSU method, (iv) dilating the binary image with a 3 \(\times\) 3 structure element to reduce the noise; this will grow the foreground regions and the noise within these regions become smaller, and (v) removing noise from the binary image with all connected pixels less than a fixed threshold. Figure 4 shows an example of applying different pre-processing techniques to a source image.  
Figure 4: Pre-processing of the source image: (**a**) RGB, (**b**) gray, (**c**) binary, (**d**) binary after dilation, and (**e**) image after noise removal.  
Figure 3: A block diagram of the proposed ALPR system with the suggested enhancements highlighted.
## 2 Mathematical Model
### Objectives  
The model involves nine objectives such that six of them are nonlinear and three are linear. These objectives are:  
\[\min \max\Bigg{\{}\sum_{i=1}^{a}M_{i},\sum_{i=1}^{a+j}z_{i,j},\sum_{i\in \mathcal{Z}}z_{i,l}\,\Big{|}\,j\in\{1,\ldots,b-1\},l\in\{b,\ldots,N\}\Bigg{\}} \tag{1}\] \[\min \max\{A_{i,j}s_{i,j}-1\,|\,i\in\mathcal{Z},j\in\mathcal{N}\}\] (2) \[\min \frac{\sum_{i\in\mathcal{Z}}\sum_{j\in\mathcal{N}}A_{i,j}x_{i,j}} {\sum_{i\in\mathcal{Z}}M_{i}}\] (3) \[\min \sum_{j\in\mathcal{N}}y_{j}\] (4) \[\min \max\{e_{OFF}^{j}\cdot j-1\,|\,j\in\mathcal{N}\}\] (5) \[\min \sum_{j\in\mathcal{N}}e_{j}\] (6) \[\min d_{CA}\sum_{j\in\mathcal{N}}y_{j}\] (7) \[\min \frac{1}{Q}d_{CA}d_{DT}\sum_{j\in\mathcal{N}}y_{j}\] (8) \[\min C_{AS}\sum_{i\in\mathcal{Z}}\sum_{j\in\mathcal{N}}A_{i,j}x_{i,j}+C _{IS}\max\{e_{OFF}^{j}\cdot j-1\,|\,j\in\mathcal{N}\}\] (9) \[+ C_{SP}\max\Bigg{\{}\sum_{i=1}^{a}M_{i},\sum_{i=1}^{a+j}z_{i,j}, \sum_{i\in\mathcal{Z}}z_{i,l}\,\Big{|}\,j\in\{1,\ldots,b-1\},l\in\{b,\ldots,N \}\Bigg{\}}\] \[+ C_{CA}\sum_{j\in\mathcal{N}}y_{j}+C_{EF}\sum_{j\in\mathcal{N}}e_ {j}+C_{DT}d_{CA}\sum_{j\in\mathcal{N}}y_{j}+C_{CT}\frac{1}{Q}d_{DT}d_{CA}\sum_ {j\in\mathcal{N}}y_{j}.\]  
Note that from nonlinear objectives, the objectives (1), (2), (5) and (9) are also nonsmooth. The objectives (1)-(3) are related to the interim storage such that (1) minimizes the maximum number of assemblies in the storage, (2) minimizes the maximum storage time, and (3) minimizes the average storage time. In the objective (1), with the first component we take into account the first \(a\) removals from the reactor where all the assemblies must be stored simultaneously. The second component handles the cases when removals are accomplished during the disposal periods. Finally, with the third component the cases when all removals are done are considered.  
The next three objectives (4)-(6) are related to the encapsulation facility. The objective (4) minimizes the total number of canisters, (5) aims to stop the disposal as early as possible, and (6) minimizes the time which the encapsulation facility is in operation.  
The objectives (7) and (8) aim to minimize the size of the disposal facility such that (7) minimizes the total length of disposal tunnels and (8) minimizes the length of the central tunnel.  
Finally, the ninth objective (9) minimizes the total costs of the disposal process. The costs taken into account are related to the storage, cost of individual canisters, the encapsulation facility operating costs, and the building costs of the disposal and central tunnels.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 5 Challenges of SSI in Practical Application  
Research on the application of subspace methods for the damage detection of civil structures emerged in the mid-1990s. Most of the methods used in SHM presume a parametric model of a dynamic system in order to characterize structural behavior under an applied excitation load. However, civil engineering structures still have many challenges to achieve a robust SHM model. The size of civil structures does not permit a large number of sensors to be mounted on a structure. Moreover, forced-vibration is not considered to be practical due to interruption in the serviceability of structures. On the other hand, civil structures are complex in terms of geometry and their material properties involve a large range of uncertainty due to operational and environmental factors [125]. In Table 6, the problems that are faced with practical implementation of SHM systems using the SSI algorithm and the researches to resolve the associated problems are presented. Table 6 reviews the challenges of implementing the subspace algorithm in real-world applications. In practical applications of SHM, the response signal of structures is generally in the form of a non-Gaussian random signal. In such conditions, deterministic techniques result in unreliable system models. On the other hand, data loss or corruption caused by failure or loss of sensing, transmission, or storage devices during their normal use is a concern for a reliable damage detection scheme. Consequently, appropriate procedures must be considered in order to deal with the uncertainty that is caused by such instrumental failures. The accuracy of an identification algorithm is due not only to its insensitivity to environmental variation and instrumental failure, but also to the inherent performance of the estimation scheme. Studies have been conducted to improve the performance of the SSI method and enhance the modal identification process itself. The inherent performance of an estimation scheme to deal with problems, such as the short-length of a signal, non-stationarity measurement data, system non-linearity, leakage error, or different measurement setups remains a challenge for a reliable SHM. In some cases, resolving the problem and increasing the accuracy demands exhaustive expert assistance and time-consuming computation burdens. Gluing the non-simultaneously measured set-ups of sensor data is another controversial issue that needs to be considered before applying any identification platform. The aforementioned drawbacks are the topic of ongoing research in the field of SHM.
## 4 Discussion
The average mandibular rotation seen in our patients was 2.8\({}^{\circ}\), which is similar to others [19; 22; 23; 24; 25]. Regarding the dental changes, following molar intrusion we found an average increase in OB, which is similar change observed by others as well [22; 52; 53]. So far, numerous imagining techniques have been used to evaluate morphological changes of the TMJ. Panoramic radiographs have 2D limitations and low sensitivity due to structural distortion and superimposition of the zygomatic process [54]. For visualization of disc-condyle relationship Magnetic Resonance Imagining (MRI) technique has been used as the gold standard diagnostic method [55]. CT can be used to detect bony changes, however, besides its high diagnostic efficacy, CBCT has several advantages over it, such as; lower radiation dose, better accessibility, lower cost [56]. In this study we used CBCT to evaluate the pre-and posttreatment (following intrusion) condylar morphology. Among the 18 patients with orthopedic instability, flattening, surface erosion and surface irregularities were the most frequent pretreatment bony changes. This is in agreement with [PERSON] et al., who found flattening in 73.3% of TMJ related symptomatic cases [27]. When comparing pretreatment and posttreatment joints, except the sclerosis, significant improvements were seen following treatment in the three examined variables on both sides; approximately 50% of the condylar surfaces regained their convexity, cortical bone continuity and condylar position improved by about 40%. In previous study, flattening was found to show positive correlation with TMD [26]. Those studies, where no correlation has been found between TMD and morphological changes, the high prevalence of bony changes in asymptomatic patients was explained by the adaptive and compensatory potential of the TMJ [57]. Furthermore, a systematic review pointed out a potential sample selection bias among the papers comparing morphological changes between TMD and non-TMD patients; namely, in most studies patients included in those studies were referred to TMD and facial pain services, meaning that only asymptomatic and not the healthy control group could have been assured [58]. In summary, our results highlighted that occlusal splint therapy followed by orthodontic molar intrusion has a positive impact on bony changes of the TMJ.