esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Random Analysis
### Effect of Mean Value \(\mu_{E}\)  
Figure 14 presents the variation in the mean values of normalized maximum tunnel responses with increasing \(\mu_{E}\) under different \(\theta_{E}\) (\(\theta_{E}\) = 1D, 3D, 10D). The mean values of \(w_{\rm m}\), \(M_{\rm m}\), and \(Q_{\rm m}\) all gradually increase with the increase in \(\mu_{E}\) from 6.49 MPa to 46.49 MPa. According to the two-stage analysis method, the response of the existing tunnel is controlled by two components, i.e., the greenfield soil settlement caused by the new tunnel and the soil-existing tunnel interaction, which can be understood as the ability of the existing tunnel to resist the greenfield soil movement. [PERSON] et al. introduced the concept of the \"tradeoff\" between the greenfield measurements and the interaction analysis [43]. The relative importance of the greenfield soil settlement and the soil-structure interaction may change significantly under different construction or soil conditions. The increase in \(\theta_{E}\) weakens the effect of the soil-tunnel interaction on the tunnel responses in the random field, and the existing tunnel tends to follow the greenfield settlement profile more and more under each single realization, which leads to the increases in the mean values of \(w_{\rm m}\), \(M_{\rm m}\), and \(Q_{\rm m}\).  
Figure 13: Effect of COV\({}_{E}\) on COVs of normalized maximum tunnel responses under different \(\theta_{E}\) values: (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.  
Figure 15 illustrates the variations in the COVs of the normalized maximum tunnel responses with increasing \(\theta_{E}\) under different \(\theta_{E}\) (\(\theta_{E}=1D\), \(3D\), \(10D\)). The increases in \(\mu_{E}\) result in reduction in the COVs of the normalized tunnel responses, indicating that the normalized maximum tunnel responses under different random field realizations become closer to each other. Therefore, it can be concluded that the effect of spatial variability of the soil elastic modulus becomes less obvious for the soil with higher values of \(\mu_{E}\). Moreover, the decrease in \(\theta_{E}\) slightly reduces the slope of variation in the COVs of normalized tunnel responses, which means that the decreasing \(\theta_{E}\) weakens the effect of the changes in \(\mu_{E}\) on the variability of the normalized tunnel responses.  
Figure 14: Effect of \(\mu_{E}\) on mean values of normalized maximum tunnel responses under different \(\theta_{E}\): (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.
## 2 Interference Signal Models
* Single-Tone Interference  
Single-tone interference is one of the most fundamental forms of suppression jamming. It modulates a waveform of a single frequency onto a specific carrier frequency, resulting in concentrated high energy at that frequency while leaving other frequencies unaffected. This type of interference is typically used when the communication frequency band is known in advance, serving a targeted suppression function. In the time domain, it appears as a sinusoidal waveform, while in the frequency domain, it manifests as a single spectral peak.
* Multi-Tone Interference  
Multi-tone interference consists of multiple single-tone components distributed across different frequency locations. In the frequency domain, it manifests as several pulses with high energy at specific frequencies. It is generated by the superposition of several sinusoidal signals with different frequencies and can be used to interfere with one or multiple communication channels simultaneously.
* Partial-Band Interference  
Partial-band interference concentrates signal energy within a specific bandwidth. Based on the ratio of the interference bandwidth to the total system bandwidth, it can be classified as narrowband or wideband interference. Narrowband interference is more efficient and targeted, whereas wideband interference can disrupt a broader frequency range. Typically, partial-band interference is generated by passing a noise signal through a digital bandpass filter. By adjusting parameters such as the filter's bandwidth and center frequency, interference with different bandwidth characteristics can be produced.  
Efficient Differential Amplitude Interference Detection Algorithm Based on Non-Uniform Adaptive Segmentation
## 3 Convexification and Discretization of the Re-Entry Trajectory Generation Problem
### Constraint Convexity and Relaxation  
(1) Constraints of state variables  
The model needs to establish certain trust region constraints on the latest iterative trajectory to ensure that the deviation is not too large, which can reduce the error between the convex model and the real model.  
\[\left\{\left|X-X^{(k)}\right|\right.\leq\xi_{X} \tag{29}\]  
where \(\xi_{X}\) is the trust region of state.  
(2) Convexification of process constraint  
If the process constraint (8) of the re-entry glide vehicle is expanded on the R-V (H-V) profile  
\[\left\{\begin{array}{l}r\geq\left(2*H_{0}\ln\left(\rho_{0}(v*vc)^{3.15}C/Q_{ \max}\right)+R_{e}\right)/R_{e}=r_{\dot{Q}}\\ r\geq\left(H_{0}\ln\left(\rho_{0}(v*vc)^{2}/2q_{\max}\right)+R_{e}\right)/R_{e}=r _{q}\\ r\geq\left(H_{0}\ln\left(S\rho_{0}(v*vc)^{2}\sqrt{C_{D}^{2}+C_{L}^{2}}/(2n_{ \max}mg_{0})\right)+R_{e}\right)/R_{e}=r_{n}\end{array}\right. \tag{30}\]  
where, \(r_{\dot{Q}},r_{q},r_{n}\) represents thermal density, dynamic pressure and overload height.  
Expand Equation (30) in the last iterative trajectory  
\[\left\{\begin{array}{l}r_{\tilde{Q}}=r_{\tilde{Q}}^{(k)}+\left.\frac{\partial r _{\tilde{Q}}}{\partial v}\right|_{X^{(k)}}(v-v^{(k)})\\ r_{q}=r_{q}^{(k)}+\left.\frac{\partial r_{q}}{\partial v}\right|_{X^{(k)}}(v-v^{ (k)})\\ r_{n}=r_{n}^{(k)}+\left.\frac{\partial r_{n}}{\partial v}\right|_{X^{(k)}}(v-v^ {(k)})+\left.\frac{\partial r_{\tilde{n}}}{\partial k}\right|_{X^{(k)}}(u_{3}- u_{3}^{(k)})\end{array}\right. \tag{31}\]  
\[\left\{\begin{array}{l}\left.\frac{\partial r_{\tilde{Q}}}{\partial v}\right| _{X^{(k)}}=6.3H_{0}/\left(R_{e}v^{(k)}\right)\\ \left.\frac{\partial r_{q}}{\partial v}\right|_{X^{(k)}}=2H_{0}/\left(R_{e}v^ {(k)}\right)\\ \left[\left.\frac{\partial r_{n}}{\partial v}\right|_{X^{(k)}},\left.\frac{ \partial r_{n}}{\partial v}\right|_{X^{(k)}}\right]=\left[2H_{0}/\left(R_{e}v ^{(k)}\right),\frac{H_{0}}{R_{e}}\frac{C_{0}^{(k)}(2 cd_{3}a^{(k)}+cd_{2})+C_{ L}^{(k)}d_{2}}{C_{D}^{(k)}+c_{L}^{(k)}}\right]\end{array}\right. \tag{32}\]  
where \(H_{0}\) represents the air density calculation constant.  
(3) Convexification of the no-fly zones constraint  
The no-fly zones constraint (9) is non-convex and cannot be solved directly and effectively, so it needs to be convex.  
\[\begin{array}{l}\left(\phi^{(k)}-\phi_{bi}\right)^{2}+\left(\varphi^{(k)}- \phi_{bi}\right)^{2}+2\left(\phi^{(k)}-\phi_{bi}\right)\left(\phi-\phi^{(k)}\right) \\ +2\left(\varphi^{(k)}-\phi_{bi}\right)\left(\varphi-\varphi^{(k)}\right)-r_{ bi}^{2}\geq 0\end{array} \tag{33}\]
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 6 Open Issues and Research Challenges/ Future Challenges and Research Opportunities
### Multi-EV Charging Standards  
_What is the challenge?_  
Organizations or companies worldwide have developed several charging standards with different specifications, i.e., the number of pins, configuration, communication protocol, etc. Every manufacturer has their own interest to develop precise charging standards based on the requirement and the demand of EVs in different countries. Moreover, it is quite complicated and costly for manufacturers to develop or manage the multiple charging standards.  
_Possible solution: Inclusion of charging standards_We propose a possible idea to resolve the issue of multiple charging standards for EVs. Multiple charging standards can be incorporated to develop an inclusive charging standard that can fulfill the criteria of the design requirements of any EVs worldwide. However, the development of an inclusive charging standard can be a complex task to achieve as per the fulfillment of the specifications and features of multiple charging standards.
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results
### Traffic Sign Detection
The AUC values of the two PR curves were 99.33% and 97.96%, respectively, and the results implied that the detection module alone was a good detector:  
\[Precision=\frac{true\ positives\ detected}{all\ detections}\times 100\% \tag{10}\]
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 2 Preliminary
### HBase  
HBase is an open-source distributed key-value database. The technology is derived from Google's Bigtable concept. It dispersedly stores the data of a table over a cluster of nodes, in order to provide scalable storage to massive data.  
Different from traditional relational data model, data in HBase is stored in tables, and each data cell is indexed by rowkey, column family, column qualifier, and time stamp. Among them, rowkey is the unique index for directly accessing data, it is closely related to data storage and query efficiency, and plays a very important role in data processing.  
On the physical structure, HBase uses Region as the minimum unit for distributed storage. An HBase table is usually divided horizontally into multiple Regions scattered in each work node (named as RegionServer), each Region stores a certain range of data rows. A Region is decided to be split when store file size goes above threshold or according to defined region split policy, in order to make the data distribution of Regions more uniform. Split operation takes a row as the split point, and the rowkey range of the split Region changes after split [18].  
In order to leverage the parallelism of HBase clusters, a coprocessor mechanism is introduced in HBase 0.92, which is similar to a lightweight MapReduce framework, enabling the execution of custom code in RegionServers. The coprocessor can be divided into two kinds, one is Observer, and the other is Endpoint. The Observer can be thought of like database triggers, and provides event hooks of table operation to capture and process certain events, while Endpoint is similar to the storage process of traditional database. One can invoke an Endpoint at any time from the client, the Endpoint implementation will then be executed remotely in RegionServers, and results from those executions will be returned to the client [19].
# Mapping Wild Leek through the Forest Canopy Using a UAV
## 3 Results  
The dense point clouds generated with Pix4D reveal a thick forest canopy in all three areas, but the removal of non-ground points was successful in eliminating points produced by the canopy (Figure 4). Visual inspection of the point clouds shows that wild leek and other spring ephemerals grow low enough on the forest floor to be classified as ground features.  
The classification using the GCC threshold produced a map that effectively distinguishes wild leek from other vegetation and non-vegetation features (Figure 5a-c). Because this study aims at capturing the changes in distribution of the wild leek, other species were not classified. Therefore, the map consists of two classes: wild leek (WL) and not wild leek (N-WL), the latter including all other types of ground vegetation. Wild leek patches are recognizable by their shape and bright green color in the orthomosaics and this is especially noticeable when conducting a visual inspection of Area 1 at full  
Figure 4: Comparison of point clouds in (**a**) Area 1 and (**b**) Area 2 before (left) and after (right) classification and removal of non-ground points with MCC-LIDAR.  
resolution (Figure 5c). Figure 6 shows, using data for Area 2, a comparison of the 1-cm orthomosaics produced from smartphone videos and the 5-cm orthomosaics produced from the UAV, as well as the wild level classification derived from the UAV data. Wild leek is clearly recognizable in both types of orthomosaics, where it appears in bright green patches.  
The confusion matrices and F1-scores for Area 1 and Area 2 are presented in Table 1. The four corners were combined when calculating statistics for each area. The GCC threshold produced a map with the F1-score of 0.69/0.76 for wild leek in Area 1/Area 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multicolumn{6}{c}{**Reference Data**} \\ \cline{2-9}  & **Classification** & **WL** & **N-WL** & **Total** & **F1 Score** & **TPR** & **FPR** \\ \hline \multirow{3}{*}{Area 1} & WL & 79 & 25 & 104 & \multirow{3}{*}{0.69} & \multirow{3}{*}{0.64} & \multirow{3}{*}{0.09} \\ \cline{2-9}  & N-WL & 45 & 251 & 296 & & & \\ \cline{2-9}  & Total & 124 & 276 & 400 & & & \\ \hline \multirow{3}{*}{Area 2} & WL & 33 & 14 & 47 & \multirow{3}{*}{0.76} & \multirow{3}{*}{0.83} & \multirow{3}{*}{0.04} \\ \cline{2-9}  & N-WL & 7 & 346 & 353 & & & \\ \cline{2-9}  & Total & 40 & 360 & 400 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrices and F1-score for the classification of wild leek in Area 1 and Area 2, based on a GCC threshold of 0.350. WL—wild leek, N-WL—not wild leek, TPR—True Positive Rate, FPR—False Positive Rate.  
Figure 5: Comparison of the original orthomosaic (left) and the classification of wild leek based on the GCC threshold (right) for (**a**) Area 1, (**b**) Area 2, and (**c**) for a portion of Area 1.
## 3 Results
### Verbal Protocol Analysis (VPA)  
VPA was devised and used by [PERSON] et al. (2018) as a method for distributing different verbalisations used in comparing musical timbres based on their semantic meaning. In the context of the present research, the subjective nature of the elicited data requires this type of analysis in order to observe different characteristics such as the sensory modality. The analysis of the verbalisations is carried out on three different levels: their logical sense, stimulus-relatedness and semantic aspects. This method of analysis was previously used by [PERSON] and [PERSON] (2018) and [PERSON] et al. (2018) for evaluating constructs resulted from RGT elicitations. VPA was also used by [PERSON] and [PERSON] (2018) in combination with a QDA elicitation methodology.  
The constructs elicited during this study were analysed based on the VPA Level 3 Features (\"Semantic aspects of verbal units\") as presented in Figure 5. As in [PERSON] et al. (2018), VPA was used for observing the ratio between descriptive and affective features with the final goal of filtering only descriptive attributes for clustering. The constructs were initially classified as either \"descriptive\" (dfe) or \"attitudinal\" (afe) features. The descriptive features were subsequently categorised as either \"unimodal\" (umd--attributes referring only to the auditory modality) or \"polymodal\" (pmd--attributes referring to multiple sensory modalities). The attitudinal features were also classified as either \"emotional-evaluative\" (emv--describing particular emotions about the stimulus) or descriptors referring to the naturalness of the sound (ntl).  
As can be observed from Table 1, 55 out of 56 constructs (98.2%) were considered descriptive features for the in-situ test and 96 out of 98 (98%) for the lab elicitation test. The participants' previous experiences with spatial acoustic evaluation and the assessed space seem to be reflected in the low number of attitudinal features elicited (good/bad spatial quality, pleasant/unpleasant listener envelopment, close/distant claustrophobia), suggesting that participants were mainly focused on describing the spatial and timbral aspects of the sound events rather than subjective and emotional aspects. Unlike [PERSON]'s study (2018), it was decided not to discard the attitudinal features from analysis in the present research to offer a better understanding of any emotional values associated with particular clusters of descriptors.  
Figure 5: Verbal protocol analysis (VPA) Level 3 Features (after [PERSON] et al. (2018)).
###### Abstract  
The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method's effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length  
trajectory determination rather than for global planning. Therefore, some methods that work effectively in local collision avoidance systems may be ineffective in applications of global path planning systems.  
This article presents a new approach focused on calculating the path using the standard A* method with map preprocessing and postprocessing. The algorithm is designed to overcome the shortcomings resulting from the large dimensions of the area in which the path is calculated. The proposed method involves reducing the map's dimensions by a known factor and then performing an image processing operation, determining the path based on the received map, and multiplying the obtained coordinates by the same factor. The map preparation and postprocessing procedure used in the proposed algorithm is straightforward to implement and significantly reduces the computational cost while maintaining satisfactory path length and safety levels from the view of mission planning for AUVs/ASVs.  
The specific research objectives of this study are as follows:  
* To develop a novel approach for global path planning by integrating the A* algorithm with image processing techniques aimed at reducing computation time;
* To validate the effectiveness of the proposed method in large-scale environments through numerical simulations;
* To compare the performance of the proposed method with the standard A* algorithm in terms of path safety, length, and computation time.  
The article is organised as follows: Section 2 presents the analysis of solutions related to improving the efficiency of the A* algorithm. Section 3 describes the standard and proposed approach to global path planning using the A* method and specifies the algorithm for creating an environment map. Section 4 discusses the research results based on a large-dimensional map of the underwater environment located in the port area. The final conclusions are discussed in Section 5.
## 5 Measurement Verification of the Effectiveness of the Proposed Method
### The Case of a Polyharmonic Signal  
The final measurement experiment concerned the case where the input of the analyzed measurement chain was supplied with a polyharmonic input signal \(s(t)\) described by the following equation:  
\[\dot{s}(t)=D_{s,\rho}+E_{s,\rho}\frac{\mathcal{T}}{8}\sum_{i=1}^{\infty}(-1)^{i -1}(2i-1)^{-2}\sin\bigl{(}\omega_{s,\rho}t(2i-1)+\varphi_{s,\rho}\bigr{)}, \tag{52}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
11 & 213 & 210 & \(-1.26\pm 0.05\) \\
12 & 262 & 253 & \(-3.31\pm 0.34\) \\
13 & 277 & 301 & \(8.71\pm 0.04\) \\
14 & 338 & 353 & \(4.32\pm 0.04\) \\
15 & 394 & 410 & \(4.01\pm 0.08\) \\
16 & 502 & 474 & \(-5.69\pm 0.58\) \\
17 & 510 & 540 & \(5.94\pm 0.04\) \\
18 & 565 & 614 & \(8.76\pm 0.33\) \\
19 & 749 & 691 & \(-7.66\pm 0.05\) \\
20 & 670 & 776 & \(15.78\pm 0.22\) \\
21 & 777 & 868 & \(11.62\pm 0.10\) \\ \hline \end{tabular} \({}^{*}\) For 95% of all results, the values of \(\delta\) were within the range \([-7.50;17.52]\)%.  
\end{table}
Table 4: _Cont._  
Figure 8: Histograms of the realization values of the relative error in estimating the variance of the dynamic error signal (selected cases of monoharmonic signal, with expanded uncertainty at a 95% confidence level).  
where \(D_{s,o}\) is the DC component, \(E_{s,o}\) is the amplitude, \(\omega_{s,o}\) is the angular frequency, and \(\varphi_{s,o}\) is the phase shift of the fundamental harmonic of the signal. The source of the \(s(t)\) signal was the RIGOL DG1011 [49] arbitrary waveform generator, and the amplitude setpoints were verified using an additional Agilent 3458A [50] multimeter, assuming that \(E_{s,o}=\sqrt{3}E_{RMS}\), where \(E_{RMS}\) is the effective value of the \(s(t)\) signal indicated by the instrument [24].  
The experiment was performed for various frequencies of the \(s(t)\) waveform, with amplitude \(E_{s,o}=0.479\) V and constant component \(D_{s,o}=0.507\) V. The determined relative errors in estimating the variance of the dynamic error signal for this experiment are presented in Table 5, while histograms of the realization values of this quantity for selected parameters are shown in Figure 9.
## 3 Results and Discussion
### Antioxidant Capacity of Extracts
#### 3.4.1 Extracts Obtained with Shaking Solvent Extraction (SSE)  
The two-way analysis of variance showed that water and methanol extracts from apple leaves of the Ligol cultivar had the highest antioxidant capacity, amounting to 131.2 umol of Trolox/1 g of DW, and were characterised by significantly higher antioxidant activity than water extracts; however, they did not differ significantly from the other extracts. Water-methanol extracts from the leaves of the Gala and Gloster cultivars had significantly higher antioxidant capacity than their aqueous counterparts. However, all water-ethanol extracts (average 117.4 umol of Trolox/1 g of DW) differed significantly only from water extracts from the leaves of the Gala cultivar (90.2 umol of Trolox/1 g of DW). The latter were characterised by the significantly lowest antioxidant activity among extracts prepared using the SSE method; they did not differ significantly from water extracts from the leaves of the Gloster cultivar only. Also, the one-way analysis of variance confirmed that the antioxidant capacity of extracts made using the SSE extraction method is significantly influenced by both the type of solvent and the apple leaf cultivar, and in the case of apple leaf cultivars, a significant difference occurs only between the Ligol cultivar (average 120.1 umol Trolox/1 g of DW) and Gala (average 111.8 umol of Trolox/1 g of DW). In the study by [PERSON] et al. (2016), the antioxidant activity of water-alcohol extracts ranged from 120 in the Lithuanian Auksis cultivar to 142 umol of Trolox/1 g of DW in the Aldas cultivar. Therefore, very similar results were obtained.