esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### BTDM Decoder
#### 3.2.2 Subject-Object Alignment  
This section only introduces entity pair extraction in the \(s2p\) direction. The subtask of subject-object alignment is to identify all possible objects corresponding to all subjects extracted in the previous phase. In order to do this, we iterated the subject of detected subjects set \(Y_{s}(S)\). We used the attention mechanism to mine the relation between the identified subject, object features, and relational features, translating and decoding all possible entities corresponding to each detected subject.  
Specifically, we obtain a fused representation of specific subject, relation features, and object features, then used the self-attention mechanism to obtain a selective representation. The detailed operations are as follows:  
\[\begin{split}\mathbf{Q}&=\mathbf{W}_{query}(\mathbf{v}_{ sub\_k}\circ\mathbf{ho}+\mathbf{hr})+b_{query}\\ \mathbf{K}&=\mathbf{W}_{key}(\mathbf{v}_{sub\_k}\circ\mathbf{ho}+ \mathbf{hr})+b_{key}\\ \mathbf{V}&=\mathbf{W}_{value}(\mathbf{v}_{sub\_k}\circ\mathbf{ho}+ \mathbf{hr})+b_{value}\\ \mathbf{A}&=\text{Softmax}(\frac{\mathbf{Q}\mathbf{K}^{T}}{ \sqrt{d_{k}}})\mathbf{V}\end{split} \tag{4}\]  
where \(d_{k}\) is the dimension of the attention network, and \(\circ\) denotes a hadamard product operation. Note that the subject is frequently composed of more than one token. We take the maxpool vector representation of the \(k\)-th subject as \(v_{sub\_k}\).  
Next, a binary classifier is used to identify the object, including its start and end position. The detailed operations are as follows:  
\[\begin{split} p_{i}^{s2p\_start}&=\sigma(\mathbf{W}_{ s2p\_start}\mathbf{A_{i}}+\mathbf{b}_{s2p\_start})\\ p_{i}^{s2p\_end}&=\sigma(\mathbf{W}_{s2p\_end}\mathbf{A_{i} }+\mathbf{b}_{s2p\_end}) \tag{5}\]  
where \(p_{i}^{s2p\_start}\) and \(p_{i}^{s2p\_end}\) represent the possibility that the \(i\)-th token in the sentence is the start or end position of the object corresponding to the subject, respectively.  
Finally, the final set of entity pairs \(Y_{(s,o)}(S)\) is formed by fusing the two sets of entity pairs identified in both directions.
## 3 Results and Discussion
### Electrochemical Characterization  
The galvanostatic measurements were performed on cells with structured (LS-200 \(\upmu\)m) and unstructured (reference) electrodes by applying increasing C-rates as basic information for LIBS measurements.  
Figure 2a revealed that both types of cells provide a similar specific capacity close to the theoretic capacity of graphite during the formation step (first three cycles). It is obvious that the capacity of the reference cell dropped with increased cycle numbers at 1 C. The specific discharge capacity of the cell with unstructured electrodes (reference cell) is significantly reduced during cycles. In comparison to the reference cells, the cells with line structures (LS-200 \(\upmu\)m) exhibited an excellent high-rate capability. For the thick electrodes, the diffusion pathway of lithium ions is limited. The lithium ions could be trapped within the graphite electrodes at a high C-rate and become inactive for the following cycles. It could be attributed to the capacity fading at a high C-rate. In [15; 16] it is reported that the high-rate capability depends on the lithium-ion diffusion kinetic, which is influenced by the electrode thickness and electrode design. An increased active interface between the electrode and liquid electrolyte by means of 3D electrode architectures can reduce the cell polarization and the overall impedance. In addition, cyclic voltammetry measurement was carried out with a scan rate of 10 \(\upmu\)Vs\({}^{-1}\) (Figure 2b). The result is in good agreement with [17].  
Figure 1: Scanning electron microscopy (SEM) images of laser-generated structures with a period of (**a**) 200 \(\upmu\)m and (**b**) micro pillars with a dimension of 600 \(\upmu\)m \(\times\) 600 \(\upmu\)m.  
Figure 2: (**a**) Specific discharge capacity of cells with structured (LS-200\(\upmu\)m) and unstructured (reference) electrodes as a function of cycle number; (**b**) cyclic voltammetry curve of the cell with an unstructured graphite electrode.
## 2 Results and Discussion
### In silico Predictions of potential antioxidative peptides from linear organs hydrolysates.
According to the most recent studies, more than 50% of the identified ACE inhibitory peptides from gelatin hydrolysates had proline residues in the C-terminal position whereas more than 60% had one proline residue in one of the three C-terminal positions [29; 30]. Most of the identified peptides in the present study also had this GP sequence at their C-terminal. Almost all predicted ACE inhibitory peptides in sea cucumber protein hydrolysates were found to have the proline residue in each of their sequences. This \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Petide Sequence** & **Name of Peptide** & **Bioactive Sequence** & **Location of** \\  & & & & **Fragmentation** \\ \hline Internal organs & GPPGPQWPLDF & ACE inhibitor from Alaskan pollack skin & GP & (1–2), (4–5) \\  & & ACE inhibitor from Alaskan pollack skin & PL & (8–9) \\  & & ACE inhibitor & PG & (3–4) \\  & & ACE inhibitor & GPP & (1–3) \\  & & ACE inhibitor & PP & (2–3) \\  & & ACE inhibitor & PQ & (5–6) \\  & & ACE inhibitor & DF & (10–11) \\  & GEFPKF & ACE inhibitor & FP & (4–5) \\  & & ACE inhibitor from & GEP & (1–3) \\  & & Tricholoma giganteum & ACE inhibitor & GE & (1–2) \\  & & ACE inhibitor & KF & (6–7) \\  & & ACE inhibitor & PFP & (3–5) \\  & & ACE inhibitor & FP & (7–8) \\  & & ACE inhibitor & PR & (8–9) \\  & & AFP & (6–8) \\  & & ACE inhibitor & AF & (6–7) \\  & & ACE inhibitor & AP & (2–3) \\  & & ACE inhibitor & DM & (4–5) \\ PGGPGPGM & ACE inhibitor from Alaskan pollack skin & GP & (3–4), (5–6) \\  & & ACE inhibitor & GM & (7–8) \\  & & ACE inhibitor & GG & (2–3) \\ GPGMMGP & ACE inhibitor & PG & (1–2), (4–5), (6–7) \\  & & ACE inhibitor from Alaskan pollack skin & GP & (2–3), (7–8) \\  & & ACE inhibitor & GM & (4–5) \\  & & ACE inhibitor & MG & (6–7) \\  & & ACE inhibitor & PG & (3–4) \\  & & ACE inhibitor & MM & (5–6) \\  & & ACE inhibitor & MGP & (6–8) \\ DPIFFPS & ACE inhibitor & FP & (5–6) \\  & & ACE inhibitor & IF & (3–4) \\ \hline \hline \end{tabular}  
* G—Glycine, P—Proline, Q—Glutamine, W—Tryptophan, L—Leucine, D—Aspartic acid, F—Phenylalanine, T—Threonine, M—Methionine, A—Alanine, R—Arginine, S—Serine, I—Isoleucine, K—Lysine.  
\end{table} 
Table 5: In silico predictions of potential ACE inhibitory active peptides from internal organs hydrolysates. further confirms the potential of peptides derived from the North Atlantic Sea cucumber towards ACE inhibition. However, it has been suggested that ACE inhibitory property of protein hydrolysates could be considered as a collective effect from various peptide chains rather than from a single bioactive peptide [7; 29; 31]. In addition, it is always recommended to investigate the potential resistance to gastric enzymes of these identified bioactive peptides, once they are expected to exert a beneficial physiological effect after digestion. Thus, all predicted peptides from sea cucumber protein hydrolysates were subjected to in silico simulated gastrointestinal digestion to determine their efficacy upon digestion.
# Safety Design of Rotary Drilling Rig Mast Based on Multi-Condition Analysis
## 3 Fuzzy Comprehensive Evaluation
### Method of Weight Assignment
#### 3.3.2 CRITIC Method  
Common objective weighting methods include the entropy method [28], CRITIC method [29], and others. In this study, the CRITIC method is adopted. The CRITIC method is a weighting decision technique based on objective data and scientific analysis. This method objectively evaluates and quantifies the importance of each evaluation indicator to determine the weight of each indicator in the final evaluation or decision. The calculation process is as follows:  
1. First, construct the evaluation matrix. Supposing there are several elements and multiple evaluation indicators, an evaluation matrix can be constructed based on this information, as shown in Equation (14).  
\[B=\begin{pmatrix}b_{11}&\dots&b_{1n}\\ \vdots&\ddots&\vdots\\ b_{m1}&\dots&b_{mn}\end{pmatrix} \tag{14}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Implication** \\ \hline
1 & Equally important \\
3 & Slightly more important \\
5 & Significantly more important \\
7 & Very important \\
9 & Extremely important \\ \hline \hline \end{tabular}
\end{table}
Table 8: Judgment matrix scale.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Indicators** & **3** & **4** & **5** & **6** & **7** \\ \hline \hline \(RI\) & 0.539 & 0.916 & 1.143 & 1.292 & 1.375 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The average consistency index.  
Next, perform standardization on the input matrix as shown in Equation (15), denoted as the standard matrix. This operation normalizes the original matrix into a standardized form.  
\[c_{ij}=\frac{b_{ij}}{\sqrt{\sum\limits_{j=1}^{n}b_{ij}^{2}}} \tag{15}\]
3. Finally, based on the standardized matrix, calculate the standard deviation \(s_{j}\) of each indicator (see Equation (16)), the correlation coefficient \(r_{ij}\) between each pair of indicators (see Equation (17)), and the potential conflict indicator \(e_{j}\) (see Equation (18)). The weight of each indicator can then be calculated as shown in Equation (19).  
\[s_{i}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(\overline{c_{i}}-c_{ij}\right)^{2 }}{m-1}} \tag{16}\]  
\[r_{ij}=\frac{\text{cov}(c_{i},c_{j})}{s_{i}s_{j}} \tag{17}\]  
\[e_{i}=\sum\limits_{i=1}^{m}\left(1-r_{ij}\right) \tag{18}\]  
\[\omega_{i}=\frac{s_{i}e_{i}}{\sum\limits_{i=1}^{m}s_{i}e_{i}} \tag{19}\]  
In the equation, \(\overline{c_{i}}\) represents the average value of the elements in the \(i\)-th row of the standardized matrix; \(\text{cov}(c_{i},c_{j})\) denotes the covariance between the \(i\)-th and \(j\)-th rows of the matrix.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 4 Experiments and Results
### Experiments of State-of-the-Art Algorithms on WAID
#### 4.2.3 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & _Precision_ & _Recall_ & _mAP_ \\ \hline YOLOv5 & CSPDarknet53 & 0.949 & 0.916 & 0.956 \\ YOLOv7 & CSPDarknet53 & 0.964 & 0.953 & 0.972 \\ YOLOv8 & CSPDarknet53 & 0.948 & 0.906 & 0.958 \\ Faster-RCNN & ResNet50 & 0.517 & 0.855 & 0.779 \\ Faster-RCNN & VGG16 & 0.279 & 0.756 & 0.610 \\ SSD & MobileNetv2 & 0.757 & 0.446 & 0.591 \\ SSD & VGG16 & 0.930 & 0.430 & 0.739 \\
**SE-YOLO** & **CSPDarknet53** & **0.978** & **0.969** & **0.983** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of object detection using different algorithms. environments with relatively complex backgrounds, the detection performance of Faster R-CNN improves noticeably. For example, with the ResNet50 backbone network, Camelus achieves 91% and Kiang achieves 82.4%. These results suggest that structure of backbone is necessary to be modified in the small target detection, as we have done with our proposed SE-YOLO method. To demonstrate the recognition performance of SE-YOLO with minimal targets with less than 50 pixel points, the case study has been conducted. As shown in Figure 9, the SE-YOLO works well and accurately detects these minimal targets of about 20 pixels. Meanwhile, the experimental results show that when SE-YOLO detects targets smaller than these, the mAP value drops rapidly by more than 5%. We consider this to be a turning point in the size of targets detected by the SE-YOLO method. Hence, 20 pixels is the lower limit of minimal targets that can be detected by the proposed SE-YOLO. Furthermore, as shown in Table 5, we also compared the recognition performance of SE-YOLO and YOLOv7 on these minimal targets. As seen from Table 5, the recognition performance of SE-YOLO for minimal targets with 20-50 pixels offer 10% more performance (mAP) than YOLOv7, and achieves 0.926, 0.894, and 0.929 in terms of mAP, recall, and precision, respectively. At the same time, as can be seen in Tables 3 and 5, the YOLOv7 performance drops rapidly by 15% (the mAP drops from 0.972 to 0.822) when encountering minimal targets, whereas the SE-YOLO drops by only 5% and still maintains an overall performance above 0.92 (mAP = 0.926). Therefore, the results suggest that the SE-YOLO works effectively on minimal targets of 20-50 pixels. Figure 8: The results and comparison of wildlife detection in aerial image using the different network models.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 3 Cluster Analysis  
Object detection provides the means to localize pedestrians from video frames. These raw data do not provide a lot of useful information on their own and require further processing. A highly efficient way of organizing and evaluating data involves the use of cluster analyses. Clustering algorithms are used to divide data points into useful groups, so-called clusters. Assigning data points into groups is based on similarity, given a certain metric, e.g., distance or density. This section categorizes different clustering algorithms based on the categories chosen by [PERSON] [27] in a contribution from 2015.
## 3 Experimental Setup
### TPC Operation Principle  
The operating principle of the optical readout and ARIADNE prototype TPC in GAr conditions is shown on the left of Figure 7. All G-THGEM/THGEM behaviours were characterised by monitoring the intensity of the S2 light produced in the THGEM holes. The primary scintillation/ionisation was induced by alpha particle/GAr interactions. The emitted S2 light was imaged with the EMCCD.  
An emitted alpha particle enters the TPC active region and interacts with GAr, resulting in the emission of prompt scintillation light (S1) and the creation of free electrons by ionisation. The S1 light is detected by the PMT at the bottom of the detector and is used for \(\tau_{2}\) purity monitoring, which is further discussed in Section 3.5.  
Free ionisation electrons drift upwards in response to a uniform electric field applied between the cathode and the grounded G-THGEM/THGEM bottom electrode. When electrons reach the top of the field cage, they enter the G-THGEM/THGEM holes. Each electron is then accelerated by the high electric field applied across the THGEM, between bottom and top electrodes. At sufficiently high fields, this acceleration causes the electron to further ionise the GAr, resulting in a Townsend discharge. This exponentially increases the number of electrons. In addition, this process also produces secondary scintillation light--the S2 light signal [41; 42], through the excitation of GAr atoms. Depending on the applied field, on the order of 100s of photons are produced per accelerated electron [4]. The EMCCD camera images this S2 light, which has been wavelength shifted using TPB to the high quantum efficiency range of the camera.
## 3 Experiment Results
### Aerodynamic Force  
The measurement results of aerodynamic force are shown in Figures 12-14. According to Figures 12, 13a and 14a, the lift coefficient curves of the three skin wings have high coincidence. At individual angles of attack, the lift coefficient of the photovoltaic membrane skin wing has a small loss but does not affect the overall slope of the lift coefficient curve. Therefore, it can be considered that the membrane deformation will not affect the lift performance below \(6^{\circ}\) angle of attack. As shown in Figures 12, 13b and 14b, the skin materials have obvious influence on the drag coefficient curve. Among them, the drag coefficient curves of polyimide skin and rigid skin wing intersect, which are shown as: when \(\alpha<0^{\circ}\), the drag coefficient of polyimide skin wing is smaller; when \(\alpha>0^{\circ}\), the rigid skin wing has a smaller drag coefficient; when \(\alpha=0^{\circ}\), the drag coefficients of the two airfoils are almost the same. The variation of the above drag coefficient curve is related to the change of airfoil camber and thickness. As shown in Figures 12c, 13c and 14c, skin deformation will also bring significant differences to the pitch moment coefficient curve. Comparing the \(C_{m}\)-\(\alpha\) curves of rigid skin and polyimide skin wings, it can be seen that except for individual angles of attack, the skin deformation increases the moment coefficient, and the greater the wing load, the larger the increment. Combined with the analysis of Figure 8, the greater the wing load, the more curvature increment of the section airfoil, and the moment coefficient will increase accordingly.  
In the experiment, the drag and pitch moment coefficients of photovoltaic membrane-skin wings portray great differences. Under the three wind speeds, the drag coefficient curves are all significantly higher than the other two wings. Combined with the experiment phenomenon, the reasons are explained as follows: (a) the surface of photovoltaic cells is uneven, which increases the friction resistance; (b) there are gaps in the arrangement of photovoltaic cells, resulting in periodic changes in skin modulus, bringing irregular deformation of the membrane; (c) and the photovoltaic skin has experienced the process of  
Figure 14: Aerodynamic characteristics of wings with three different skins \(v=11.0\) m/s: (**a**) \(C_{L}\)-\(\alpha\); (b) \(C_{D}\)-\(\alpha\); (c) \(C_{M}\)-\(\alpha\).  
bonding and heat treatment in the prefabrication stage, and there are inevitable wrinkles. Therefore, it can be considered that the factors affecting the aerodynamic performance of photovoltaic membrane skin wing are complex, so the influence of skin deformation on the drag coefficient and the pitch moment coefficient cannot be directly inferred from its performance in the wind tunnel. However, the results of the experiment can directly indicate the changes of aerodynamic characteristics of the wing after laying photovoltaic cells, which has reference significance for the aerodynamic and overall design of solar UAV.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 4 Taxonomy for Services Composition
### System Perspective  
In contrast to the user's perspective, the system perspective provides details about the techniques and mechanisms used to achieve service composition in terms of service publication/discovery, scheduling, and deployment. Concerning this perspective, we identified three non-exclusive approaches: the _Formal approach_, which provides the tools and formalisms that allow, for example, the formal validation or verification of a number of predefined properties; the _Structural approach_, which looks to establish data structures and access methods in formal operational protocols and languages that are often used by other approaches; the _Semantic approach_, which brings semantics to improve and optimize the composition's operations mentioned above; and, finally, the domain-specific service composition, such as a _Grid Service_ and _Geographical Information System_.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 2 Methodology
### Evaluation Indicators  
The effectiveness of the K-means-ResNet model is assessed by accuracy, precision and recall. In the multi-category problem, each category is considered as \"positive\" and the rest are considered as \"negative\".  
_Accuracy:_ The ratio of the correctly classified samples to the total samples. It is the most commonly used metric in classification.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
where \(TP\) denotes the number of the correctly classified positive samples, \(TN\) is the number of the correctly classified negative samples, \(FP\) is the number of the negative samples that are misclassified as positive and \(FN\) is the number of the positive samples that are misclassified as negative.  
_Precision:_ The ratio of the correctly classified positive samples to all positive samples.  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
_Recall:_ The ratio of the correctly classified positive samples to all positive samples.  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 4 Case Study
### Simulation on 3 \(\times\) 3 5G Macro BSs Network
#### 4.1.2 Effectiveness of the Two-Step Energy Management Model for 5G Macro BS Network
The low-priced electric energy is transferred to the high price period, thus reducing the total power electricity costs of the network. In summary, with the proposed dispatching scheme, the power consumption and electricity costs of the 5G macro BS network can be reduced by taking advantage of the spatial and temporal fluctuations of the traffic load, the thermal inertia of the cabinets, and the storage of the backup batteries.
## 3 Results and Discussion
### Impact Characteristics under Cryogenic and High Temperatures  
Based on the research detailed above, the CFRP projectile produces ballistic impacts on triaxial braided composites at cryogenic and high temperatures that most closely resemble the ballistic impacts in real engines. Table 2 presents the results of ballistic-impact tests at cryogenic and high temperatures, where \"C\" refers to cryogenic conditions, and \"H\" refers to high-temperature conditions.  
Figure 13: Impact damage to 2D triaxial braided composite targets caused by impact of CFRP projectile: (left) incident surface, (right) exit surface.  
At cryogenic temperature, the triaxial braided composite target is perforated at 130 m/s, whereas the projectile rebounds at 131.1 m/s at room temperature. At high temperature, the triaxial braided composite target is perforated at 170 m/s, whereas the projectile rebounds at 146 m/s. Based on these results, we deduce that the ballistic-limit velocity is highest at high temperature, followed by the room temperature and then low temperatures. In other words, cryogenic temperatures decrease the impact resistances of the triaxial braided composites, whereas high temperatures increase their impact resistance. Differences between the impact resistance and forms of damage under these three temperature ranges is discussed in detail below based on the test results.
# Shear Mechanical Properties of Bolt-Grout Interface under Different Bolt Surface Profiles
## 1 Introduction
Consequently, the results obtained may exhibit significant errors compared to the actual situation. To overcome these shortcomings, direct shear tests have been employed to study the mechanical characteristics of the Bolt-Grout interface. [PERSON] et al. [32] conducted shear tests containing two realistic mol profiles and found that bolt B2 with larger rib angles performed better in shear at low initial normal stresses, while bolt B1 with larger rib spacing performed better in shear at high initial normal stresses. [PERSON] et al. [1; 33] evaluated the effects of confining stress, bonding material strength, rib angle, and ribs on the interface's shear behavior. [PERSON] et al. [15] investigated the effects of bar spacing, bar height and grout proportion, and the results showed a significant correlation between the interface's peak shear strength and deformability, as well as the grout mixture and bolt profile. Previous studies have concentrated on examining the impact of individual factors on the effectiveness of bolting systems. In addition, the rib spacing and angle in the surface profile of the bolt have greater influences on the interface, but the shear mechanical proper  
Figure 1: Interface failure of Bolt-Grout in pull-out test [3]. ties and failure mechanism regarding the Bolt-Grout interface under the influence of bolt surface morphology necessitate further investigation [34]. Based on these considerations, this study has systematically conducted shear tests on Bolt-Grout specimens with varying rib spacings and angles. The above research is of great significance in enhancing the bolting effect to increase the stability and safety of the bolting project.