esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Discussions for Large Number of Outliers
)**} & \multicolumn{2}{c}{**AMFR1-Score**} \\ \cline{3-10}  & & **PCA** & **HQ-PCA** & \(I_{1}\)-PCA & **RPCA** & **OM-RPCA** & **AM-PCA** & **TC-PCA** \\ \hline \multirow{4}{*}{**Yale**} & \multirow{2}{*}{**Overall**} & 0.804 & 0.80 & 0.278 & 0.970 & **0.821** & 0.80 & 0.802 & 0.806 \\  & & (\(-\)) & (25) & (27.5) & (22.70) & **(28.80)** & (28.70) & (24.30) & (31.40) \\  & & [0.799] & [0.796] & [0.754] & [0.792] & **[0.821]** & [0.796] & [0.798] & [0.801] \\ \cline{2-10}  & \multirow{2}{*}{Non-standard} & 0.339 & 0.33 & 0.317 & 0.335 & 0.313 & 0.335 & 0.33 & **0.332** \\  & & [0.361] & [0.308] & [0.313] & [0.313] & [0.315] & [0.304] & **[0.325]** \\ \cline{2-10}  & \multirow{2}{*}{Standard} & 0.907 & 0.904 & 0.856 & 0.899 & **0.934** & 0.903 & 0.906 & 0.907 \\  & & **[0.998]** & [0.897] & [0.845] & [0.89] & **[0.93]** & [0.894] & [0.899] & [0.897] \\ \hline \multirow{4}{*}{AR (Normal + Lighting)} & \multirow{2}{*}{Overall} & **0.785** & 0.702 & 0.648 & 0.702 & 0.742 & 0.739 & 0.746 & 0.71 \\  & & [0.781] & [0.697] & [0.64] & [0.695] & [0.742] & [0.733] & [0.741] & [0.705] \\ \cline{2-10}  & \multirow{2}{*}{Non-standard} & **0.367** & 0.283 & 0.257 & 0.283 & 0.24 & 0.303 & 0.297 & 0.28 \\  & & **[0.313]** & [0.267] & [0.242] & [0.267] & [0.225] & [0.282] & [0.283] & [0.253] \\ \cline{2-10}  & \multirow{2}{*}{Standard} & **0.874** & 0.7392 & 0.732 & 0.791 & 0.849 & 0.832 & 0.843 & 0.802 \\  & & **[0.868]** & [0.781] & [0.719] & [0.778] & [0.846] & [0.822] & [0.833] & [0.792] \\ \hline \multirow{4}{*}{AR (Normal + Sunglasses)} & \multirow{2}{*}{Overall} & **0.793** & 0.774 & 0.643 & 0.731 & 0.726 & 0.754 & 0.712 & 0.74 \\  & & (\(-\)) & (14.90) & [0.80) & (13.30) & (14.20) & (16.20) & (14.20) & (12.90) \\ \cline{1-1}  & \multirow{2}{*}{Non-standard} & **0.791** & [0.771] & [0.622] & [0.723] & [0.723] & [0.752] & [0.708] & [0.737] \\ \cline{1-1}  & \multirow{2}{*}{Non-standard} & **0.427** & **0.427** & 0.313 & 0.38 & 0.32 & 0.413 & 0.367 & 0.353 \\ \cline{1-1}  & \multirow{2}{*}{Non-standard} & **0.409** & [0.391] & [0.203] & [0.303] & [0.29] & [0.384] & [0.324] & [0.335] \\ \cline{1-1}  & \multirow{2}{*}{Standard} & **0.871** & 0.849 & 0.703 & 0.806 & 0.813 & 0.827 & 0.786 & 0.823 \\ \cline{1-1}  & \multirow{2}{*}{Overall} & **0.865** & [0.844] & [0.687] & [0.795] & [0.804] & [0.822] & [0.779] & [0.814] \\ \hline \multirow{4}{*}{AR (Normal + Scarves)} & \multirow{2}{*}{Overall} & **0.767** & 0.645 & 0.851 & 0.582 & 0.486 & 0.676 & 0.655 & 0.669 \\ \cline{1-1}  & & (\(-\)) & (6.10) & (7.40) & (7.90) & (11.20) & (10.19) & (11.60) \\ \cline{1-1}  & \multirow{2}{*}{Non-standard} & **0.767** & [0.643] & [0.578] & [0.578] & [0.48] & [0.674] & [0.656] & [0.667] \\ \cline{1-1}  & \multirow{2}{*}{Non-standard} & **0.28** & 0.213 & 0.187 & 0.167 & 0.18 & 0.253 & 0.227 & 0.2 \\ \cline{1-1}  & \multirow{2}{*}{Non-standard} & **0.271** & [0.185] & [0.158] & [0.158] & [0.154] & [0.242] & [0.219] & [0.191] \\ \cline{1-1}  & \multirow{2}{*}{Standard} & **0.871** & 0.737 & 0.66 & 0.671 & 0.551 & 0.767 & 0.477 & **0.77** \\ \cline{1-1}  & \multirow{2}{*}{Standard} & **0.865** & [
## 4 A Method of Improving the Radiation Efficiency of an MAA
The feed structure of the metamaterial aperture antenna is shown in Figure 8a below. The array consists of four uniform feed structures. The feed structure contains one impedance matching capacitor and four impedance matching inductive pins, and a coaxial feed pin is at the center of the structure. The measurement was performed using near-field planar-scanning techniques and near-to-far-field transformation [20]. The measured far-field patterns across the frequency band from 33 to 37 GHz are shown in Table 1. It can be seen that the designed MAA has frequency-diverse far-field patterns. Figure 8: Geometry of the fabricated MAA: (**a**) feed structure (the black capacitor part is dielectric, and the remaining part is copper); (**b**) total view; (**c**) local view. Figure 6: The schematic of simulated metamaterial aperture antenna (MAA) with different ratios of CJC elements: (**a**) 0%; (**b**) 20%; (**c**) 50%. Figure 7: (**a**) Calculated average mutual coherence \(\mu_{g}\) and (**b**) the radiation efficiency of different MAAs with different filling ratios of CJC elements.
## 4 Discussion
### Correlation between NRT and PSA  
No significant association was found between the average of all NRT thresholds and PSA at 65 dB in our study. A systematic review of compound action potentials as predictors of cochlear implant performance by [PERSON] et al. [40] overviewed 25 articles, out of which seven made a statement about the NRT threshold. [PERSON] et al. [41] found no significant association between the NRT threshold and speech perception in adults and children, [PERSON] et al. [42] concluded the same for the adult population and four other studies [43, 44, 45, 46] also found no significant correlation between the NRT threshold and speech perception [40]. All of the above-mentioned studies support our findings. They measured the NRT threshold from different numbers of electrodes and different electrodes were picked. [PERSON] et al. [41] used electrodes E5, E10, E15, and E20; [PERSON] et al. [42] measured E2, E4, E6, E8, E10, and E12; [PERSON] et al. [43] captured E4 and E8; and [PERSON] et al. [45] used electrodes E3, E5, E10, E15, and E20.  
In a Chinese study, lower intraoperative NRT thresholds were associated with a lower NRT threshold in postoperative mapping and the latter significantly affected speech and tone perception [25]. On the other hand, [PERSON] et al. [25] also concluded that intraoperative NRT thresholds are not significantly associated with postoperative speech and tone perception, which supports our claim. In the future, it would be interesting to find measurable parameters during the implantation that could suggest the potential of cochlear injury and its effect on postoperative results.
## 4 Mechanical Test Results and Discussion
### Tensile Behavior
#### 4.2.6 Crack Patterns  
Figure 16 shows the representative cracking patterns of the UHP-SHCCs tested in this study. Table 4 summarizes the number of cracks, the number of drops in the tensile stress in the strain-hardening section, strain capacity, and energy absorption capacity. The number of cracks in each specimen was obtained by averaging the numbers of cracks counted on the top and bottom of the gauge region. Furthermore, the number of drops in the tensile stress was counted from the tensile stress-strain curve of the individual specimens for each variable and averaged.  
Figure 15: Ratios of 7 d–28 d cured for test parameters.  
During the tensile test, it was observed that the cracks formed when there were drops in the tensile stress and that the cracking behavior varied according to the type of mineral additives and curing time. Table 4 suggests that the specimens ranging from A7 to E7 using OPC, silica flour, or GGBS had cracks less than 13 ea, whereas the specimens ranging from F7 to I7 using SF or CKD had cracks ranging between 17 and 29 ea. All the 28 d cured specimens, on the other hand, had more cracks ranging between 27 and 39 ea. It was noted that specimens F, H, and I that had the highest number of cracks among the 7 d cured specimens did not have the highest number of cracks among the 28 d cured specimens. The specimens C28 and D28 using silica flour or GGBS had the most cracks, measured at 39 ea.  
Figure 17 demonstrates that the number of cracks was linearly proportional to the test parameters, namely, the number of drops in tensile stress, strain capacity, and energy absorption capacity. It is observed in Table 4 that the number of drops in tensile stress-strain curves was consistently higher than the number of cracks. The drops of the tensile stress have occurred because of the crack formation and the deterioration of the fiber-matrix interactions. As the tensile stress increases high enough to damage certain fiber-matrix interfaces at the most vulnerable section, the weakest interface would be broken and the fiber would abruptly be pulled out. Then, the section fails to hold the drastically increased tensile stress until the other fibers seize it and transfer the increased stress [26]. The process may produce cracks but may not all the time. This was the reason why the numbers of drops in curves were higher than the number of cracks observed on the tested specimens.  
Figure 16: Representative cracking behaviors of (**a**) 7 d and (**b**) 28 d cured specimens.  
Figure 17: Correlation of #cracks with (**a**) #drops in tensile stress–strain curve; (**b**) strain capacity; and (**c**) and energy absorption capacity.
# Laboratory Evaluation of Rejuvenating Agent on Reclaimed SBS Modified Asphalt Pavement
## 2 Materials and Methods  
### Materials  
The RSMAP material used in this paper was obtained from Beijing, and the aged modified asphalt extraction from the mixture was performed by Abson method [22]. The aged asphalt content of RSMAP is 3.6%, and the main properties of the aged modified asphalt and virgin SBS modified asphalt used in the study are shown in Table 1.  
Three rejuvenating agents with different viscosity were selected in this study in order to evaluate the recycling effect on the aged modified asphalt, and represented by H, S, G respectively. The main properties of the rejuvenating agents were tested according to ASTM D4552 and the results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Aged Modified Asphalt** & **Virgin Modified Asphalt** \\ \hline Penetration (25 \({}^{\circ}\)C)/0.1 mm & 36.3 & 48.1 \\ Softening point/\({}^{\circ}\)C & 68.8 & 69.3 \\ Ductility (5 \({}^{\circ}\)C)/cm & 0.1 & 30.7 \\ Viscosity (135 \({}^{\circ}\)C)/Pa\(\cdot\)s & 2.24 & 1.05 \\ Maximum tension/N & 599.9 & 332.7 \\ Toughness test & Toughness/N\(\cdot\)m & 17.74 & 13.52 \\  & Tenacity/N\(\cdot\)m & 6.65 & 9.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main properties of modified asphalt.  
In the process of aggregate gradation design of recycled mixtures with different RSMAP contents, the gradation should be as similar as possible. AC-13 is selected as the type of recycled mixtures. The virgin coarse aggregate is basalt, the crushing value is 14.3%, and the Los Angeles abrasion value is 13.5% [23]. The virgin fine aggregate is limestone. Table 3 shows the aggregate gradation and asphalt content of recycled mixtures with different RSMAP contents (0%, 30%, 50% and 70%). The type of rejuvenating agent for recycled mixtures used in this study is S.
## 2 Materials and Methods
### Test Overview and Recorded Signals  
Test subjects performed various physical activities during which acceleration signals were measured and recorded using the Shimmer sensor system. The sensor was located on the left wrist for feasibility of implementing the results in an activity tracker worn on the wrist. Even though using multiple sensors has been shown to increase the classification performance [25; 26], having a wearable system with only one sensor is more comfortable and convenient for the user.  
Each test subject conducted activities based on a precise schedule, where each activity was carried out for a fixed amount of time, shown in Table 1. For classification, these activities were grouped into different activity types, shown in Table 2. Indoor activities were divided into three different parts, during which each activity was performed for 3 min, with the exception of lying down, which lasted 4 min. There were short pauses between each activity, which were later discarded from the signals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indoor Test 1** & **Indoor Test 2** &
\begin{tabular}{c} **Indoor Test 3** \\ **(\% Shows Angle)** \\ \end{tabular} & **Outdoor Test** \\ \hline Walking (3) & Sitting on chair (3) & Walking (3 km/h) (3) & Cycling (14) \\ Running (3) & Lying on bed (4) & Walking (5 km/h) (3) & Cycling uphill (4) \\ Walking upstairs (3) & Typing on computer (3) & Walking (3 km/h, 10\%) (3) & Cycling downhill (1) \\ Walking downstairs (3) & Folding clothes (3) & Walking (5 km/h, 10\%) (3) & Cycling downhill (1) \\  & Cleaning surface (3) & Running (6 km/h) (3) & Running (10 km/h) (3) \\  & & Running (12 km/h) (3) & Running (12 km/h) (3) \\  & & Running (6 km/h, 10\%) (3) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conducted activities and their duration in minutes.  
In the first part, test subjects walked in a corridor, ran in the corridor, walked upstairs, and walked downstairs. Altogether, a total of 12 min of acceleration signals were used from this part.  
The second part consisted of sitting on a chair, lying on a bed, typing on a computer while sitting, standing, folding clothes while standing, and cleaning a surface while standing. A total of 19 min of signals were used from the second part.  
The third indoor part consisted of walking on a treadmill at different speeds and angles (3 km/h, 5 km/h, 3 km/h with uphill angle 10%, 5 km/h with uphill angle 10%) and running on treadmill at different speeds and angles (6 km/h, 10 km/h, 12 km/h, 6 km/h with 10% uphill angle). A total of 24 min of signals were used from this part.  
Outdoor cycling signals were recorded separately with a different study group. These signals consist of 14 min of cycling on a plain road, 4 min of cycling uphill, and 1 min of cycling downhill.
### Reasons for Proposing Proactive Routing Protocols  
The main reason to intend proactive routing protocols is their proactive route selection strategy (pre-determined route establishment) from each node to every other node immediately after the deployment of nodes in IoT-UWSN. These routes are ready to use instantly. In addition, these propose proactive routing protocols periodically update their routing table. These mechanisms help the IoT-UWSN in E2E delay minimization. However, they dissipate high EC. In addition, bandwidth and power management ratio in proactive routing protocols is higher than reactive and hybrid routing protocols [25].  
Figure 1: Pictorial description of routing protocols in underwater wireless sensor networks.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 1 Introduction
In [28], the optimal placement and sizing of capacitor banks and inverter-based DGs was employed by biogeography-based optimization (BBO) algorithm considering the harmonic content of the DGs at different load levels. In [29], the authors presented an economic study that approved the economic benefits of using passive filters especially in the case of the nonlinear loads that exceed 1 MVA. In [30], the design of single tuned filters was carried out by using ETAP software in distribution systems with PV systems to eliminate the harmonics and their impact on the system. A comprehensive analysis of the single tuned filter parameters selection and its effect on the harmonic mitigation is presented in [16]. As illustrated in the abovementioned literature review, non-linear loads and renewable energy sources can increase the harmonic distortion in distribution systems, which leads to harmful effects on the system power quality. In this paper, a proposed method is introduced for simultaneously determine the optimal design, number, and placement of single tuned harmonic filters by using the Water Cycle Algorithm (WCA) which is implemented as a multi-objective optimization algorithm. The harmonic spectrums of the inverter-based DGs implement in the distribution system are considered beside the harmonic emissions from the nonlinear loads. This study is considered a multidimensional study as it achieves both technical and economic merits throughout the objectives that include minimizing: the total harmonic distortion, cost of power loss and that of the filter investment cost,and improvement of the voltage profile. Harmonic load flow with and without a filter is implemented for analyzing the systems' harmonics. The effect of harmonic spectrums of the DGs on the harmonic distortion in the system is studied through two cases studied. The organization of the current paper is ordered as follow: Section 2 presents the problem formulation, Section 3 presents the optimization algorithm and electric scheme representation, Section 4 presents, the proposed filters planning procedure, Section 5 presents the application of the proposed method, Section 6 presents simulation results and discussion, and Section 7 presents the conclusions.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures
### Fire Retardant
#### 5.2.2 FR Separator  
Within LIB, a separator is considered to be a critical component in securing battery safety [219; 220; 221; 222]. It acts as the separation between the cathode and the anode while providing a pathway for lithium ion migration. Once the separator fails, an internal short circuit between electrodes may occur and lead to the thermal failure of LIBs [223; 224; 225]. As shown in Figure 16, a safe separator has strict demands on the following six factors: (1) chemical/mechanical stability; (2) thickness; (3) thermal stability; (4) wettability; (5) barrier property and (6) porous property.  
As stated before, a commercialized separator typically consists of PE or PP, and has persistent drawbacks of a low melting temperature and poor wettability for liquid electrolytes which make it difficult to widely adopt in large-scale LIB. To overcome these obstacles and enhance its thermal safety, a composite separator may be a feasible solution. [PERSON] et al. [226] synthesized several FR composites (Al\({}_{2}\)O\({}_{3}\)-sputtered PE separator, Al(OH)\({}_{2}\)-composite, Mg(OH)\({}_{3}\)-composite) and verified in their experiments that these composites exhibited superior FR properties compared to traditional separator as they could release water at high temperatures. Meanwhile, bromine is another choice as a composite FR separator. [PERSON] et al. [227; 228] developed and investigated a brominated poly(2,6-dimethyl-1,4-phenylene oxide) (BPPO) coated separator to improve the FR ability and thermal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Advantages** & **Disadvantages** \\ \hline Phosphorus & Low cost; Simple synthesis process & Poor electrochemical compatibility; Low retardant efficiency \\ \hline Fluoride & Good electrochemical compatibility; Simple synthesis process & Low retardant efficiency; High cost \\ \hline Ionic liquid & Appropriate to low-temperature environment & Complicated synthesis process; Low thermal stability; Low retardant efficiency \\ \hline Composite & High retardant efficiency; Good electrochemical compatibility & Complicated synthesis process; High cost \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of several typical fire-retardant electrolyte additives.  
Figure 16: The schematic diagram of separator and the requirement for safety.  
ability. Besides, [PERSON] et al. [229] developed a sustainable, heat-resistant and cellulose-based composite FR separator for high-performance LIB, namely FCCN, i.e., flame-retardant and thermal resistant cellulose-based composite nonwoven separator which showed superior heat tolerance and good mechanical strength.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 3 Dataset and Methods
### Improvements to SlowFast Network Structure  
In tasks related to video behavior understanding, human behavior exhibits an array of rich and diverse features, encompassing variations in the same behavior under different execution goals, and the impact of multiple factors such as shooting angle and lighting conditions. While the 3D Convolutional Neural Network (3 DCNN) proves effective in spatio-temporal modeling, it faces challenges in capturing the wealth of information embedded in videos and requires the acquisition of more fine-grained features to enhance classification accuracy. To elevate the detection accuracy of the network, this study enhances the SlowFast network in two key aspects. Firstly, for the extraction of spatial features from the video, the CoordAttention (CA) [37] attention model was incorporated into the slow branch. This enhancement strengthens the network's capacity, particularly in extracting critical information related to spatial features of behavior, thereby contributing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Videos** & **Key Frames** & **Labels** \\ \hline Farm & 215 & 1290 & 1546 \\ Manure & 192 & 1152 & 1152 \\ Seed & 245 & 1470 & 1478 \\ Sow & 202 & 1212 & 1939 \\ Spray & 185 & 1110 & 1193 \\ Water & 205 & 1230 & 1233 \\ Weed & 201 & 1206 & 1291 \\ \hline Total & 1445 & 8670 & 9832 \\ \hline farm & & & \\ seed & & & \\ manure & & & \\ soybean & & & \\ spray & & & \\ water & & & \\ weed & & & \\ \end{tabular}
\end{table}
Table 2: FWBD statistics.  
Figure 4: Dataset production process.  
Figure 5: FWBD example demonstration.  
to an overall improvement in performance. Secondly, to augment the network's proficiency in learning temporal features of behavioral motion, we introduced the action attention module in the fast branch. This module is primarily tasked with extracting such features, and its integration aims to enhance the network's capability to learn fine-grained features, thereby refining the architecture of the SlowFast network. With these two enhancements, the SlowFast network demonstrates enhanced performance in video behavior-understanding tasks. Specifically, the introduction of the CA (CoordAttention) and ACTION attention modules enables the network to more effectively capture crucial spatial and temporal motion features, thereby improving the comprehension and classification accuracy of intricate behaviors in videos. The enhanced version of SlowFast, denoted as CA-ACTION-SlowFast, is introduced herein, with its network architecture depicted in Figure 6.
# Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
## 5 Conclusions and Future Work  
In this paper, we reconsidered the UWB positioning problem by incorporating ensemble learning in the real world. According to the experimental results, our method performed better than the existing one. More specifically, \(k\)-means was introduced into the architecture for the purpose of setting the thresholds independently and automatically to remove the extreme unreasoned data, and ANN was introduced into the architecture to refine the distance information further.  
Of note, in this work, the density of obstacles was applied to assess what refinement and filtering algorithms may be better options for a specific scenario. However, the system needs to be retrained for a new scenario, and there may be more environment conditions that should be considered in practice. Hence, for future work, we intend to integrate more heterogeneous machine learning algorithms and the corresponding hyperparameters to automatically refine the UWB raw data to obtain more stable and accurate information according to various and objective environment conditions.  
Likewise, solving the positioning issues with regard to various kinds of technologies is also significant, e.g., Global Positioning System (GPS), Wi-Fi, Dedicated Short Range Communications (DSRC), cellular network, and so forth.  
Formal analysis, C.-C.C., Y.-M.O. and S.-T.T.; Methodology, C.-C.C., Y.-M.O., S.-T.T., T.-H.C. and M.-H.T.; Software, Y.-M.O. and S.-T.T.; Writing--original draft, C.-C.C.; Writing--review & editing, C.-C.C., T.-H.C. and M.-H.T. All authors have read and agreed to the published version of the manuscript.  
This research is supported by National Science and Technology Council, Taiwan, R.O.C. under grant 109-2221-E-035-067-MY3 and 109-2622-H-035-001-.  
Not applicable.  
The authors declare no conflict of interest.