esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
### Objective-Related Conclusions  
Based on the results presented in Section 4, the discussion reported in Section 5, and considering that the main objective of the present study was to improve the accuracy of the hydrodynamic loads' (mainly the drag force) estimation of the DT, the following objective-related conclusions can be established:  
1. The accuracy of the DT on the towline tension estimation of the ocean cleanup System 002 in wide span (\(SR^{*}\sim 0.8\)) was improved from having several GPGP data outside of the 15% increase area. Moreover, the DT for the wide span was validated against the GPGP data, with sufficient overestimation (respectively, 6% and 8% mean deviations), at the 2 nd and 3 rd validation cycles.
2. The accuracy of the DT estimation of the towline tension of the cleanup System 002 in the nominal span (\(SR^{*}\sim 0.6\)) was improved from from having most of the GPGP data outside of the 15% increase area. Moreover, the OF model (i.e., the DT) for the nominal span was validated against the GPGP data at the third validation cycle, with a mean deviation of 7%.
3. The accuracy of the DT on the towline tension estimation of the ocean cleanup System 002 in the narrow span (\(SR^{*}\sim 0.02\)) was improved. An initial deviation of 276% with respect to the GPGP data was reduced in the 2 nd cycle to \(-10\%\) and in the 3 rd cycle to 97%. In spite of the OF model for the narrow span having not been validated yet and that more GPGP data points are required for a more-robust validation, a combined deviation of 43% can be considered for engineering and design purposes, once the average of the 2 nd and 3 rd cycle curves is taken.
4. The results obtained for System 002 can be either directly applied to System 03 and future systems, if the netting and system ratio inputs are the same, or easily reevaluated, recalculated, and remodeled in case the inputs are different (cf. Figure 21). Therefore, considering the same input, System 03 DT can be also considered as validated for wide and nominal spans.
5. The previous conclusions have important implications for The Ocean Cleanup development. First, the DT of System 002 and System 03, provided that the latter has the same net and SR characteristics of the former, can be used to improve the efficiency of the operations. As an example, the towing configuration can be dynamically optimized through a trip, to sail with a large span when the plastic area density is high (plastic hot-spots) and in a short span in low-density areas. The under-designing and over-designing of the cleanup system were minimized, further reducing capital expenditure, but also operational expenditure by limiting the case of under-designed systems experiencing failures during operation. The maneuverability of the system can also be improved. As an example, the effect of optimizing the netting sizes can be accurately estimated. That will also improve efficiency since the system will reach and be swept through plastic hot-spots faster. Ultimately, all these improvements will bring the KPIs to values that are below the projected targets.
## 4 Materials and Methods
### Measurement of AsA Contents by HPLC  
The AsA concentration was measured according to a method used in previous studies [46; 47], with minor modifications. Approximately 1.0 g frozen samples were ground to a fine powder in liquid nitrogen and extracted with 5 mL 0.1% metaphosphoric acid solution. The supernatants and AsA standards (GWL8-54 KE, Beijing, China) were neutralized with 1 M NaOH, and after centrifugation, the final pH of all samples was adjusted between 5 and 6. Then, 5 mM of DL-dithothreitol (DTT) was added to the neutralized supernatants for a 30 min pre-treatment at room temperature. The supernatants were filtered using a 0.22 \(\upmu\)m filter, and 10.0 \(\upmu\)L of each filtered supernatant was injected into an Acela 1250 HPLC system (Thermo Fisher Scientific, Boston, MA, USA) equipped with a monomeric C18 column (WONDASIL C18, COLUMNS 4.6 \(\times\) 150 mm, GL Sciences Inc., Shanghai, China) with a mobile phase of 0.1% metaphosphoric acid and acetonitrile (98/2, \(v/v\)) and a flow rate of 0.5 mL/min. Finally, standard curves of AsA were generated as a reference for quantifying AsA concentrations in the measured samples.
# Photovoltaics on Landmark Buildings with Distinctive Geometries
## 4 Results and Discussions
### PV Contributing to Specific Geometry of Architectural Buildings  
In the presented samples, many of the PV systems contribute to the geometry of the buildings. Such examples are the Theme Pavilion on Shanghai Expo, Powerhouse Bratterkaia, Paris Courthouse, Umwelt Arena, Kaohsiung National Stadium and the Copenhagen International School. In the case of the Theme Pavilion, the trapezoid PV system enriched the geometry of the building roof. Visible only from the top view, it is a valuable part of the building's fifth facade. In the case of Powerhouse Bratterkaia, a sloped, south oriented surface cuts the volume of the building, acting as a roof and a facade at the same time. The Paris Courthouse is a building of relatively simple geometry, consisting of several cuboids. The PV sunshades emphasize the vertical and horizontal lines of the building facade. Opposite to the Courthouse, the Umwelt Arena and the Kaohsiung National Stadium are buildings of a complex geometry with a dominant huge PV roof. Finally, the Copenhagen International School is a building with a BIPV facade and geometry which characterizes the entire building.
# The Sliding-Aperture Transform and Its Applicability to Deep-Level Transient Spectroscopy
## 1 Introduction  
Exponential transients are common in nature. The decay rates and initial values of these transients offer valuable insight into the underlying physics of their source. In semiconducting systems, both optical and electrical transients are used to characterize both bulk material and discrete devices. Deep-level transient spectroscopy (DLTS) is an experimental technique which analyzes exponentially decaying capacitance transients resulting from the emission of trapped charges from defects found in the depletion region of a diode [1]. Methods used to extract pertinent results from these transients are the basis of this and other works [1; 2; 3; 4; 5; 6; 7].  
Semiconductor-based components in harsh environments, such as infrared sensors in space, accumulate electrically active defects due to radiation damage [8; 9; 10; 11; 12; 13; 14]. Such defects act as recombination and charge compensation centers and thereby affect minority carrier lifetime and free carrier concentration, which degrade circuit functionality. The ability to observe and understand the formation, evolution, and mitigation of defects in semiconductors is essential to prolonging the useful life of electronics and optoelectronics in harsh environments [15; 16; 17]. Accuracy and the ability to resolve closely spaced energy levels are important for a fundamental understanding of these defects. This paper presents an improved technique for analyzing capacitance transients at constant stable temperatures. The technique allows defect-level determination with higher accuracy and resolution than current approaches.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## 4 Results and Discussion
### Experiment 2: Impact of Data Augmentation on the Performance
#### 4.2.2 Discussion  
The comparison of Tables 3 and 5 clearly indicates that all indices are improved. The number of additional tests is substantially reduced, thereby increasing the overall detection accuracy. For example, in the second set of test data in Figure 8, \"mild: 0.59\" in the model of the original data training is an additional test building. In addition, the comparison of the damaged houses detected in the Figure 9 indicates that the confidence level of the proposed detection method is evidently higher than that of the original method.  
The data of the first, third, and fourth groups in Figure 9 show the ruin missed by the detection in the complex scene. The proposed method can effectively detect the debris in the shadow and near the house and sand and stone.  
Humans may subjectively believe that the image processing method is used to rotate or mirror an image, while each generated image must be detected with the same target. However, for a machine a new feature is considered as long as the coordinates and direction of the training data are different from the original image. Data augmentation does not cause over-fitting when training the model. To increase the adaptability of the model to complex scenes, Gaussian blur and Gaussian filtering are appropriately added in the training set. We validated the above viewpoints using the dataset of Hurricane Irma, and the results are shown in Table 7. From this result, it can be seen that data augmentation is an effective way to improve the effect of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mF1** & **mAP** \\ \hline
**Original dataset** & 40.39 & 34.19 \\ \hline
**Data augmentation** & 56.99 & 51.59 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Verification result of using Hurricane Irma dataset.  
Figure 9: Object test results for both models; (**a**) is the result of the data augmentation method; (**b**) is the result of the original dataset method.
# Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks
## 2 Timing for TSCH and RPL
### Active Channel Scan Method  
The configuration of the parameters that are involved in the synchronization process is not described in the standard, and its optimization will depend on use case restrictions, such as fast deployment or energy savings. For the scanning phase, the standard proposes different variations that could speed up the process of receiving Enhanced Beacons, an active and a passive mechanism. In the passive scanning mechanism, a node scans in the different channels until it receives a beacon correctly. On the other hand, the active method allows a beacon request to be sent every time the channel is changed, so synchronized nodes can respond to the request with an Enhanced Beacon.  
In order to improve the synchronization process and reduce the connection times for TSCH networks, a brief test has been carried out to validate the viability of an active scan method in TSCH. For these tests, the TSCH implementation in the nodes was modified in order to force the activation of transmitting radio, as the normal behaviour of the Medium Access Control (MAC) layer is to discard any packet in the outbound buffer if the node is not synchronized with a TSCH network. Without bypassing this filter, the nodes were not able to send any request without previously receiving an EB, and therefore rendering the active method useless. The implemented behaviour can be seen in Figure 2.  
The test implemented included six nodes: five of them actively scanning and sending requests, and one central node sending EBs, but configured with an infinite period so the new nodes never  
Figure 1: Enhanced Beacon (EB) scan process in Time Slotted Channel Hopping (TSCH) [6].  
Figure 2: Active scanning method implemented for TSCH.  
receive an unrequested (passive) beacon. In this way, counting the number of transmitted and received requests allows for the probability of receiving beacon requests from non-synchronized nodes to be calculated. In view of the results that were obtained, as seen in Table 1 it can be concluded that an active scanning method is not useful in TSCH networks, since the probability that synchronized nodes receive a beacon request is too low.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 2 The Proposed Image Compensation-Based R-D Model
### Solving Polynomial Coefficients of Image Compensation Functions
\tag{5}\]  
\(X_{a}\) is the vector of the parameters of the six-parameter model for the image column coordinates, as follows:  
\[X_{a}=[a_{0},a_{1},a_{2},a_{3},a_{4},a_{5}]^{T}. \tag{6}\]  
The unknown parameters in the form of vector \(X_{a}\) are solved using the least squares method, as follows:  
\[X_{a}=\Big{(}A^{T}A\Big{)}^{-1}\Big{(}A^{T}L_{\text{C}}\Big{)}. \tag{7}\]  
Similarly, the parameters of the image compensation model for row coordinates can be solved as follows:  
\[X_{b}=[b_{0},b_{1},b_{2},b_{3},b_{4},b_{5}]^{T}=\Big{(}A^{T}A\Big{)}^{-1}\Big{(} A^{T}L_{r}\Big{)}, \tag{8}\]  
where \(L_{r}=[lr_{1},lr_{2},\ldots\ldots lr_{n}]^{T}\) is the constant term vector for the image row coordinates. Thus, the original image coordinates and the object space coordinates have the following equations:  
\[(c+\Delta c\,r+\Delta r)=RD(X,Y,Z). \tag{9}\]
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 2 Related Work  
### Multi-Branch High-Resolution Human Pose Estimation Networks  
Multi-branch high-resolution networks typically consist of multiple parallel branches, with each branch being responsible for extracting features at different levels in order to achieve more precise pose estimation results. The objective of this network architecture is to address the limitations of traditional methods when dealing with complex backgrounds, occlusion issues, and pose variations. Among the most representative works in this regard is HRNet [4], which was proposed by [PERSON] et al. in 2019. Its structure is shown in Figure 1. HRNet starts from a high-resolution subnetwork in the first stage, and a parallel low-resolution subnetwork is added in each subsequent stage. Within each stage, the information from different resolution subnetworks is repeatedly fused. This strategy for preserving high-resolution features significantly enhances the accuracy of human pose estimation.  
HigherHRNet [9] is an improved version of HRNet that introduces a hierarchical multi-resolution pyramid structure. HigherHRNet, compared to HRNet, expands the pyramid structure further by incorporating additional branches with varying resolutions. This strategy aims to enhance the model's ability to handle a larger range of scale variations and improve the model's detection capabilities for small objects and distant objects. The Cascade Prediction Fusion (CPF) [10] achieves the fusion of prediction results by organizing multiple prediction models into a cascaded structure. The cascading process is employed in CPF to progressively refine prediction results. Firstly, the base predictor generates initial prediction results. Subsequently, the cascading fusion module combines these results to produce a more accurate prediction. This output can then serve as input for a new round of cascading fusion, enabling further enhancement of prediction accuracy. Similarly, Cascaded Feature Aggregation (CFA) [11] employs iterative feature aggregation at multiple levels to capture both local and global information in images while preserving contextual details at different scales. It is based on deep convolutional neural networks and constructs a cascaded structure by stacking multiple subnetworks. This study adopts the HRNet model architecture as the foundation for a proposed novel RFSP module designed to expand the receptive field of the model and enhance its prediction capability for occluded joints. Additionally, a substantial augmentation of the network's multi-scale representation renders the model capable of extracting a greater amount of contextual information, thereby allowing it to better attend to edge details and improve the localization accuracy of keypoints.
## 4 Results and Discussion
### Evaluation Metrics
#### 4.4.5 False-Positive Rate (FPR)  
The FPR is the proportion of the number of negative objects wrongly detected of the total count of actual negative objects, and it is given by  
\[FPR=\frac{F_{p}}{F_{p}+T_{n}} \tag{47}\]  
Here, \(F_{p}\) and \(T_{n}\) denote as false-positive and true-negative, respectively.
# Impulse Controllability for Singular Hybrid Coupled Systems
## 2 Notations and Preliminaries
: _Let \(A\in\mathbf{R}^{m\times n}\), \(B\in\mathbf{R}^{m\times h}\), and \(C\in\mathbf{R}^{l\times n}\)\(K\in\mathbf{R}^{h\times l}\),_  
\[\max_{K}\bigg{(}A+BKC\bigg{)}=\min\bigg{\{}\mathrm{rank}\bigg{(}\left[ \begin{array}{cc}A&B\end{array} \right]\bigg{)},\mathrm{rank}\bigg{(}\left[ \begin{array}{c}A\\ C\end{array} \right]\bigg{)}\bigg{\}}. \tag{16}\]  
\[S_{R}=\bigg{\{}K:\mathrm{rank}(A+BKC)=\max_{K\in\mathbf{R}^{h\times l}}(A+BKC) \bigg{\}}.\]  
**Lemma 4** ([25]). : _Let \(A\in\mathbf{R}^{m\times n}\), \(P_{i}\in\mathbf{R}^{m\times h_{i}}\), and \(Q_{i}\in\mathbf{R}^{l\times n}\) be fixed real matrices, and \(K_{i}\in\mathbf{R}^{h_{i}\times l_{i}}\)\((i=1,2,...,N)\). In that way,_  
\[\max_{K_{1},...,K_{N}}(A+\sum\limits_{i=1}^{N}P_{i}K_{i}Q_{i}) \tag{17}\]  
**Lemma 5** ([26]). : _If \(M\in\mathbf{R}^{m\times n}\), \(q=\mathrm{rank}(M)\), and there exists a reversible matrix \(P\), \(Q\), then_  
\[PMQ=\left[ \begin{array}{cc}I_{q}&0\\ 0&0\end{array} \right],\]  
_and_  
\[E\{1\}=\bigg{\{}Q\bigg{[} \begin{array}{cc}I_{q}&\mathrm{X}\\ Y&Z\end{array} \bigg{]}P\bigg{\}}.\]  
**Lemma 6** ([27]). : _If \(A\in\mathbf{R}^{n\times n}\), and \(tr[H(A)]<-\sqrt{(n-1)tr[H(A)]^{2}}\), then the eigenvalues of \(A\) are all distributed in the left half of the complex plane._
# Experimental Studies of Scale Effect on the Shear Strength of Coarse-Grained Soil
## 3 Results
### Derivation of Empirical Equation of Shear Strength of Coarse-Grained Soil  
The relationship between cohesion \(c\), internal friction angle \(\varphi\), and grading area \(S\) of CGS at a given maximum particle size satisfies Equation (9). It is mentioned that \(b_{1}\) and \(b_{2}\) in Equation (9) respectively represent the cohesion \(c\) and internal friction angle \(\varphi\) of the soil sample when the maximum particle size \(d_{\max}\) is 5 mm. Therefore, the relationship between \(b_{1}\), \(b_{2}\), and \(S\) could be also expressed by Equation (10). That is, the influence of the maximum particle size \(d_{\max}\) and grading structure on the shear strength of CGS in the scale effect could be quantitatively described by Equation (11):  
\[\left\{\begin{array}{l}c=a_{1}\ln d_{\max}+(P_{1}+b_{1}S)/(c_{1}+d_{1}S)\\ \varphi=a_{2}\ln d_{\max}+b_{2}/(1+c_{2}S+d_{2}S^{2})\end{array}\right. \tag{11}\]  
where, \(a_{1}\), \(b_{1}\), \(c_{1}\), \(d_{1}\), \(a_{2}\), \(b_{2}\), \(c_{2}\) and \(d_{2}\) are taken as the equation parameters. \(a_{1}\) and \(a_{2}\), respectively, represent the change rate of the cohesion and the internal friction angle of the sample when the maximum particle size is 1 mm, and the units are kPa/ln(mm) and (\({}^{\circ}\))/ln(mm), respectively. The unit of \(P_{1}\) is kPa, where \(P_{1}\) is 1 kPa. The units of \(c_{1}\), \(d_{1}\) and \(d_{2}\) are dimensionless.  
The shear strength index values of the 24 groups of the sand gravel test are fitted by using Equation (11), and the results are given in Table 3. The comparison between the measured values of the shear strength index of CGS in different gradation tests and the calculated values by Equation (11) are shown in Figures 12 and 13. The fitting value errors between \(c\) and \(\varphi\) are obtained by Equation (11), in which the cohesion \(c\) and internal friction angle \(\varphi\) of the corresponding test points are basically less than 7% and 3%, respectively, and the maximum errors are 10.85% and 5.81%, respectively. Notably, the determination coefficients are more than 0.865, which is in an acceptable range. Therefore, the scale-induced effect on shear strength of CGS could be quantitatively described by Equation (11).  
In summary, using Equation (11) to reflect the variation law of the shear strength of CGS with respect to the maximum particle size and gradation structure has an important application. Based on the on-site grading of CGS, a series of laboratory-scaled material shear strength tests can be conducted to determine the material parameters through Equation (11) and to deduce the shear strength of the in situ graded CGS by combining Equation (8). This contributes to the improvement of the safety and reliability for the design of earth rock dam engineering.
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 2 Review of the Algorithmic Concepts
### SVD-Based Watermarking Scheme
\begin{bmatrix}u_{1,1}&u_{1,2}&u_{1,3}&u_{1,4}\\ u_{2,1}&u_{2,2}&u_{2,3}&u_{2,4}\\ u_{3,1}&u_{3,2}&u_{3,3}&u_{3,4}\\ u_{4,1}&u_{4,2}&u_{4,3}&u_{4,4}\end{bmatrix} \times S\times \begin{bmatrix}v_{1,1 }&v_{1,2}&v_{1,3}&v_{1,4}\\ v_{2,1}&v_{2,2}&v_{2,3}&v_{2,4}\\ v_{3,1}&v_{3,2}&v_{3,3}&v_{3,4}\\ u_{4,1}&u_{4,2}&v_{4,3}&v_{4,4}\end{bmatrix} ^{T}=\sigma u_{1}v_{1}^{T} \tag{13}\]  
where \(\sigma\) is the unique singular value of diagonal matrix \(S\) of an image block of size \(n\times n\) (in this study \(n=4\)). In this case, this unique singular value is equal to the multiplication of block size '4' and matrix element '\(a\)'. With the help of Equation (13), the \(u_{1}v_{1}^{T}\) matrix can be calculated as follows:  
\[u_{1}v_{1}^{T}=\frac{A}{\sigma}=\frac{1}{4a} \begin{bmatrix}a&a&a&a\\ a&a&a&a&a\\ a&a&a&a&a\end{bmatrix} =\frac{1}{4} \begin{bmatrix}1&1&1&1\\ 1&1&1&1\\ 1&1&1&1\\ 1&1&1&1\end{bmatrix}  \tag{14}\]  
[PERSON] et al. [49] verified the correctness of the above concept via an experimental analysis of a case study and found that if difference matrix \(\Delta A\) is obtained by the above process, watermarking in the spatial domain can be achieved, with the same impact as SVD-based watermarking. Figure 2 illustrates the fundamental similarity between the SVD-based watermarking and the watermarking scheme based on the largest singular value in the spatial domain.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 3 Results  
This paper selects a water area east of the Yangtze River Estuary as the research subject. The waterway has dense traffic flow and complex vessel encounter scenarios, making the study of collision avoidance behaviors in different encounter scenarios highly significant for maritime regulatory authorities and ship operators. AIS data for vessels operating in the waterway from 1 July 2023 to 31 August 2023 were collected as experimental data to validate the effectiveness of the proposed model.  
To verify the effectiveness of the model in ship collision avoidance behavior and vessel encounter scenario identification, this study selected two sets of historical trajectories involving collision avoidance actions under different scenarios to evaluate the effectiveness of the proposed model.
## 2 Research Methodology
### Topic Coherence  
Topic modeling requires pre-specification of the number of topics. While perplexity scores have traditionally been used for this purpose, they primarily indicate model learning performance and present challenges in human interpretation [27]. Topic coherence, proposed by [28], addresses these limitations by measuring the semantic similarity of words within each topic. Higher coherence scores indicate that topics comprise more semantically related words, enhancing the interpretability of the topic modeling results. Using the Gensim library, this study calculated the CV coherence score to evaluate models with topic numbers ranging from 2 to 65. As illustrated in Figure 4, the optimal number of topics was determined to be 25, corresponding to the maximum coherence score.