esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
## 2 The Nested Structure with a Double Octagonal Inner Wall  
### The Geometric Parameter of the SCOD Tube  
The square corrugation nested tube with a double octagonal inner wall (SCOD) is shown in Figure 1. The SCOD can be divided into four sections, including the outer corrugation wall, the mid-octagon wall, the inner octagon wall, and rib plates. The four different colored lines correspond to the four thickness parameters of the sections.  
The design of the outer corrugation wall is based on a square shape with a side length of 40 mm. The corrugation on the four side lines is a cosine curve which can be represented in the Cartesian coordinate system as follows:  
\[y\,=\,A\text{cos}\bigg{(}\frac{5}{L_{0}}\pi x\bigg{)}+\frac{L_{0}}{2},x\in \bigg{[}-\frac{L_{0}}{2},\frac{L_{0}}{2}\bigg{]} \tag{1}\]  
where \(L_{0}\) denotes the length of the side; the parameter A denotes the amplitude of the cosine curve. The initial thickness \(t\) of all sections is designed to be 1 mm. To ensure adequate compression distance, the height of the nested tubes is set to 100 mm [40].  
Two octagonal sub-thin-walled structures, namely the mid-wall and inner wall, are embedded within the corrugated outer wall. The mid-wall is circumscribed from a circular shape with a diameter of 22\(\sqrt{2}\) mm; The inner wall is circumscribed from a circular shape with a diameter of 10\(\sqrt{2}\) mm. For the design of the size of octagonal sub-thin-walled structures, we refer readers to the study of Albak [35].
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 1 Introduction
### Street Ventilation  
The study of street ventilation is fundamentally linked to the investigation of the Urban Boundary Layer (UBL). The UBL is the layer of the atmosphere closest to the surface and is significantly affected by the presence of different rural and urban landscapes. This topic has been extensively studied by scientists such as [PERSON], who has conducted numerous research projects in this field [15; 16; 17], which have highlighted its dependence on the surrounding landscape [4; 14; 18]. Thanks to this research, that layer of the atmosphere is well characterised. The literature discussed this topic [19], attempting to characterise pollutant dispersion. Although this topic has been discussed in the literature, the majority of the studies usually focus on specific urban layouts under fixed external conditions, which reduces the applicability of the outcomes to very few cases. For instance, [PERSON] et al. [20] analysed the pollutant dispersion for three streets with different aspect ratios, but the results were for the pollution concentration in certain points of the street, ignoring the ventilation results related to air renovation. Nevertheless, these studies typically focus on a limited range of street morphologies, often using only one or a few aspect ratios. For example, [PERSON] et al. [21] and [PERSON] et al. [22] provide valuable information, but their findings are limited to specific cases. The effect of varying determined parameters inherent to the phenomenon is usually studied in the literature, such as the asymmetry [23], the existence of balconies [24], the Reynolds number [25], the position of the pollutant source [26], or the influence of external factors such as vegetation [27]. Since studies typically present qualitative data for variations in a single parameter, there is a lack of clear quantitative data on air flow rates, the primary mechanism of street ventilation, for a wide range of parameters [28]. It is, therefore, evident that these studies lack practical applicability, given the limited number of scenarios tested and the qualitative nature of the information obtained. Furthermore, none of the studies utilise their findings to predict or model the impacts of external factors on pollutant concentrations in different street layouts, nor do they inform decision-making in real-world scenarios.
## 3 Results
### Effect of Classifiers in the Case of Bi-Seasonal Images  
The 10-fold cross-validation accuracies obtained from the two categories of features extraction suite (very high-resolution versus ultra-resolution) by using bi-seasonal images are shown in Tables 5 and 6. Similar to the case of a single-date autumn image, the SoftVoting classifier showed slightly better performance than the other two classifiers (XGBoost and Random Forests) without substantial difference among the three classifiers in all sites.  
The variation of the classification accuracy in terms of the F1-score with the classifier using bi-seasonal images is summarized and shown in Figure 8. Similar to the case of single-date autumn images, there was not a substantial difference among the three classifiers (XGBoost, Random Forests, and SoftVoting) employed for the classification of land cover and vegetation types using both categories of features extraction suite. However, in contrast to the case of single-date autumn images, performance of the RF was slightly lower than that of the XGBoost classifier. In all sites, as described in Figure 8, the ultra-resolution suite showed superior performance over the very high-resolution suite using any machine learning classifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Model** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} & **F1-Score** & **Recall** & **Precision** \\ \hline Hakkoda & XGBoost & 0.715 & 0.690 & 0.715 & 0.715 & 0.715 \\  & RF & 0.720 & 0.696 & 0.720 & 0.720 & 0.720 \\  & SoftVoting & 0.726 & 0.703 & 0.726 & 0.726 & 0.726 \\ \hline Zao & XGBoost & 0.652 & 0.631 & 0.652 & 0.652 & 0.652 \\  & RF & 0.659 & 0.638 & 0.659 & 0.659 & 0.659 \\  & SoftVoting & 0.666 & 0.645 & 0.666 & 0.666 & 0.666 \\ \hline Shiranuka & XGBoost & 0.707 & 0.666 & 0.707 & 0.707 & 0.707 \\  & RF & 0.704 & 0.662 & 0.704 & 0.704 & 0.704 \\  & SoftVoting & 0.712 & 0.672 & 0.712 & 0.712 & 0.712 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the ultra-resolution suite using single-date autumn images.  
Figure 7: Accuracy variation with classifiers using single-date autumn images.  
Figures 9-11, respectively. Though most of the classes were discriminated satisfactorily, weak classification of some classes, for instance Juglans DBF, Fagus DBF, and Alnus DBF in site Hakkoda (Figure 9); Hydrangea Shrub and Pinus ECF in site Zao (Figure 10); and Salix DBF and Wetland Herb in site Shiranuka (Figure 11) was detected.  
Figure 10: Performance of the ultra-resolution suite (**a**) over the very high-resolution suite (**b**) for the differentiation of land cover and vegetation types in the Zao site using bi-seasonal images.
## 1 Introduction
The available information at this stage is often incomplete and inaccurate; furthermore, emergency response activities can be hindered by widespread damage and potentially inhabitable environments following such hazards [31]. Against this backdrop, very recently, researchers have harnessed advances in artificial intelligence (AI) to enhance emergency responses [32; 33; 34]. These efforts focus on rapid damage classification using deep learning and autonomous decision-making driven by (deep) reinforcement learning. Inspired by the prospect of AI-driven, resilience-oriented emergency response of future urban communities, this paper aims to shed light on the progress achieved by reviewing recent developments in this field while also emphasizing the ongoing challenges. To maintain generality, this paper primarily delves into research related to community resilience in the context of earthquake hazards. Accordingly, the subsequent sections are organized as follows: Section 2 traces the historical development of hazard resilience and examines the approaches devised thus far for engineering resilient communities; Section 3 elaborates on the newly established concept of post-shock emergency responses following seismic hazards; Section 4 delves into the latest studies concerning the rapid assessment of earthquake-induced damage and losses. In parallel, it also explores learning-based planning of post-shock emergency responses; Section 5 discusses the pathway forward for enhancing community resilience while addressing the challenges that need to be overcome; Finally, Section 6 summarizes the main findings and draws conclusions.
## 5 Evaluation of Reliability Cost
### Background on Reliability Theory
#### 5.1.4 Definitions  
_Definition 1: Backbone to sensor ratio_ is the ratio of backbone nodes to wireless nodes in a given unit area of a WSN.  
_Definition 2:_ A _subsystem_ is a path between a \(<\)source, destination\(>\) pair in a WSN.  
_Definition 3:_ Sensor nodes in a WSN are the _routing components_ or simply the _components_ of the WSN subsystem.  
_Definition 4:_ Backbone nodes in a WSN are the _backbone routing component_ of the WSN subsystem.  
_Definition 5:_ Reliability of a WSN is the probability of successful transmission of packets between a \(<\)source, destination\(>\) pair.
# Biomechanical Task-Based Gait Analysis Suggests ReWalk Gait Resembles Crutch Gait
## 4 Discussion  
In this study, we present an in-depth investigation of lower-limb EMG and joint angle patterns while walking with ReWalk. Analysis of EMG obtained from four muscles involved with controlling the hip, knee, and ankle joints was executed hand in hand with the joint angles. Because gait is a series of reciprocal events that require specific biomechanical tasks in order to maintain balance and forward progression, we investigated the gait patterns by segmenting the signals into WA, SS, and LA phases. We find that with ReWalk, significant deviation from natural gait can be observed in the WA and LA phases. By further examining crutch- and AFO-gait experiments collectively, our results suggest that ReWalk gait patterns closely follow crutch gait rather than that of natural gait. Throughout this paper, we have painted the fact that ReWalk gait resembles that of crutch gait rather than a natural gait with a rather negative brush. However, this is not always the case, as with some patients, it might be too optimistic to expect full recovery and achieve a bipedal, natural gait. These patients need a means to increase quality of life, which is not confined to rehabilitative gains but also includes feasibility, costs, and assistance with activities of everyday life [37; 38; 39; 40]. It is our belief that conventional comparisons of average EMG levels during stance and swing phases, or sometimes even cruder entire gait cycles, may be sufficient for measuring volitional intent but do not come close when describing gait patterns. For instance, while the average BF EMG WL during the WA phase from ReWalk, crutch, and natural gait are comparable, the elicited time trace patterns and the underlying functions are significantly different. Thus, a thorough comparison of the time traces is warranted. However, precaution must be taken when comparing time traces, especially with unconventional gait patterns, as the relative timing of each biomechanical task within the gait cycle may be significantly different. This is most evident with the different relative LO positions within a single gait cycle that can be clearly observed from our gait sessions. Taken together, we would like to emphasize that our data analysis within the corresponding gait-related biomechanical phases played an important role in elucidating gait patterns elicited via ReWalk. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**Natural**} & \multirow{2}{*}{**ReWalk**} & \multirow{2}{*}{**Crutch**} & \multicolumn{1}{c}{**ReWalk-**} & \multicolumn{1}{c}{**Crutch-**} \\  & & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Difference** & **Difference** \\  & **(μV)** & **(μV)** & **(μV)** & **(%)** & **(\%)** & **(\%)** \\ \hline \hline \end{tabular}  
* Statistical significance was assessed by the [PERSON] signed rank test (* \(p<0.01\), ** \(p<0.001\), ** \(p<0.0001\)).  
\end{table} 
Table 4: Comparison of mean SL EMG WL during the WA phase.
# Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors
## 1 Introduction
In the second section, the required features to design the proposed antennas are presented, and in Section 3, the simulation and measured results of both antennas built are shown. Then, the fourth section presents a light discussion on the results obtained, where mainly a comparison between both antennas is highlighted. Lastly, in section five, the main conclusions are reported.
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 3 Effect of Cage Guiding Clearance  
### Radial Motions of the Bearing Cage  
In order to find out the effect of the law of clearance, the cage motion had to be normalized by the corresponding clearances. The vibration waveforms, spectra of the cage motion (\(z_{c}\)) in vertical direction, and the normalizations of trajectories of the cage mass center for the tested bearing with constant preload (axial load 1000 N and radial load 500 N) and three guiding clearances at a rotating speed 6000 rpm are illustrated in Figure 5.  
Figure 4: Cage with different pocket clearances.  
Figure 3: Cage with different guiding clearances.  
The cage rotating frequency \(f_{c}\) can be expressed as follows:  
\[f_{c}=\frac{f_{i}(1-D_{b}\cos\alpha^{0}/d_{m})}{2} \tag{1}\]  
where \(f_{i}\) = \"\(\%\)e is the rotating frequency of the inner ring (Hz), \(\omega_{i}\) is the inner ring rotating speed, \(\alpha^{0}\) is the original contact angle, and \(d_{m}\) is the bearing pitch diameter.  
As shown in Figure 5, the motions of the cage are obviously periodic with the cage rotating frequency. Its two times, i.e.,\(f_{c}\) and \(2f_{c}\); the inner ring rotating frequency \(f_{i}\); and the combination frequencies, namely \(f_{i}\)-\(2f_{c}\), are deeply affected by the guiding clearances. The beat vibration of the cage motion when guiding clearance is relatively small appears in the time domain.  
As an inner-ring-guided bearing, the inner ring rotating frequency \(f_{i}\) dominates in the spectrum of the cage motion, and two times cage rotating frequency takes the second place, when guiding clearance is at \(C_{g}\) = 0.1 mm. The smaller the guiding clearance, the easier it is to contact the cage and to transmit the inner ring motion to the cage due to the impact, so the dominant frequency component is \(f_{i}\).  
Figure 5: Radial motions of the cage with different guiding clearances (\(\omega_{i}\) = 6000 rpm). (**a**) Waveform, spectra and normalized trajectory of cage motion when \(C_{g}\) = 0.1 mm; (**b**) waveform, spectra and normalized trajectory of cage motion when \(C_{g}\) = 0.2 mm; (**c**) waveform, spectra and normalized trajectory of cage motion when \(C_{g}\) = 0.4 mm.  
In the operating condition of \(C_{g}=0.2\) mm, the amplitude of \(f_{i}\) decreases, while that of \(2f_{c}\) increases and becomes the dominant frequency component. This is because larger guiding clearance will increase the free space between the cage-guiding face and the inner ring and impact with the ball. This will weaken the impact of the inner ring; it is easy to bring out smaller contact force applied to the cage from the inner ring but a greater one from balls.  
As the guiding clearance increases, the impact of cage with balls and inner ring will gradually decrease and the unbalance forces will play a more important role, so the regular cage motion with the main frequency component \(f_{c}\) will appear.
## 3 Results
### Antibiotic Resistance of Enterococcus spp
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **n** & **ECH** & **IDH** & **ECHC** & **Chi-Square Test** \\  & & **(N1 = 48)** & **(N2 = 20)** & **(N3 = 18)** & **(\(p\))** \\ \hline \multirow{2}{*}{Gender} & Male & 44 & 22 & 11 & 11 & 0.502 \\ \cline{2-6}  & Female & 42 & 26 & 9 & 7 & \\ \hline \multirow{2}{*}{Living area} & Urban & 50 & 30 & 14 & 6 & 0.047 \\ \cline{2-6}  & Rural & 36 & 18 & 6 & 12 & \\ \hline \multirow{2}{*}{Year of isolation} & 2019 & 25 & 5 & 8 & 12 & \\ \cline{2-6}  & 2020 & 20 & 12 & 5 & 3 & \textless{}0.001 \\ \cline{2-6}  & 2021 & 41 & 31 & 7 & 3 & \\ \hline \multirow{2}{*}{COVID-19} & Positive & 8 & 5 & 3 & 0 & 0.261 \\ \cline{2-6}  & Negative & 78 & 43 & 17 & 18 & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Biological sample \\ \end{tabular} } & Hemoculture & 29 & 26 & 2 & 1 & \\ \cline{2-6}  & Uroculture & 33 & 11 & 11 & 11 & 0.005 \\ \cline{2-6}  & SSTI & 18 & 8 & 6 & 4 & \\ \cline{2-6}  & Other & 6 & 3 & 1 & 2 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} _Enterococcus_ \\ spp. \\ \end{tabular} } & _E. faccium_ & 41 & 28 & 3 & 10 & \\ \cline{2-6}  & _E. facalis_ & 41 & 17 & 17 & 7 & 0.018 \\ \cline{2-6}  & Other & 4 & 3 & 0 & 1 & \\ \hline \multirow{2}{*}{MDR} & Yes & 43 & 34 & 2 & 7 & \\ \cline{2-6}  & No & 43 & 14 & 18 & 11 & \\ \hline \multirow{2}{*}{Van-R} & Yes & 20 & 16 & 1 & 3 & \\ \cline{2-6}  & No & 66 & 32 & 19 & 15 & 0.031 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of _Enterococcus_ spp. in three hospitals of southeast Romania. Considering the isolates from homocultures, the prevalence of MDR was 75%, though the _E. faecalis/facetium_ isolates preserved sensitivity to vancomycin, teicoplanin, and linezolid.
## 3 Research Questions and Method  
This study aims to investigate state-of-the-art studies, gather their findings, and recapitulate their empirical evidence associated with the application of ML and DL mechanisms for identifying suspicious behaviors in RPL-based 6 LoWPAN networks. Furthermore, this study presents the existing researchers-generated datasets and benchmarked datasets. To conduct the aspirated goals, we define sets of research questions as follows:  
* **RQ1:** What is the distribution of the selected studies according to the year of publication, digital library, publication type and topic, and country of origin?
* **RQ2:** What are the existing ML-based approaches to detect attacks in RPL-based 6 LoWPAN?
* **RQ3:** What are the prevailing DL approaches contributed by existing studies to detect RPL-based 6 LoWPAN attacks?
* **RQ4:** What state-of-the-arts combined ML and DL approaches have been used to detect attacks in RPL-based 6 LoWPAN?
* **RQ5:** What are the recent applications based on ML and DL approaches proposed for detecting attacks in RPL-based 6 LoWPAN?
* **RQ6:** What are the existing threats in RPL-based 6 LoWPAN that the existing studies had addressed?
* **RQ7:** What tools and network simulators are used in the existing studies, and what are the occupied evaluation metrics and parameters in the reviewed studies?
* **RQ8:** What are the datasets utilized to evaluate the existing studies, and are there any available datasets designed specifically for RPL-based 6 LoWPAN?  
Regarding the research method used in this study, we followed the guidelines presented by PRISMA [41].
## 3 Results and Discussion  
### Foaming Process  
The course of the foaming process depends on the raw materials used [36]. Development of the correct composition of the polyol mixture of RPU/PIR foam allows obtaining a product with the desired properties [37]. The foaming agent used in the foaming process has a great influence on the thermal insulation properties of the foams. A suitable catalytic system and surfactant give a cellular structure that ensures the stability of the performance-usable parameters. Two different series of foams were synthesized according to the formulations presented in Table 1. The basic technological parameters of the foaming process of modified PU premixes are characteristic times. Synthesis of RPU/PIR foams was monitored by measuring the appropriate processing times by using an electronic stopwatch. Result of this measurement were shown in Table 3. Addition of a modifier in the form of cinnamon extract contributed to the prolongation of the processing parameters of RPU / PIR foams. Cream times for both of foams series were elongated from 8 s for foam W_0t and WU_0t to 10 s for foams with content of cinnamon extract (foams C series and CU15_0t). Free rise times were also increased from 34 s (for reference foams) to 54 s (C15_0t, foam with 15 wt% of cinnamon extract) and to 44 s (CU15_0t, foam with 15 wt% of cinnamon extract without flame retardant). String gel times were extended about 7 s (for C foams) and about 10 s (for CU foams) in compared with W 0t and WU_0t foam. Modifier cinnamon extract (CE: 5% wt., 10% wt., 15% wt.) influenced the viscosity of the initial mixture. The foaming process was partially disrupted, because the processing parameters slightly increased. There were probably additional reactions between -OH and -COOH groups (from CE) with the isocyanate, thus creating an imbalance in the NCO/OH ratio. This also manifested in the increase in Tmax-modified foams according to the reference foams (Table 3) [38]. Maximum reaction temperatures were increased by about 17-25 \({}^{\circ}\)C for modified foams. The process of free growth of the foam and its gelation was disturbed by the addition of cinnamon extract. The free rise time and string gel time increased (respectively, from 34 s to 54 s and from 23 s to 33 s). Elongation of these times is advantageous when obtaining foams in complicated shapes, in which the polyol mixture must thoroughly fill the entire mold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Foam**} & \multirow{2}{*}{**Cream Time (s)**} & **Free Rise Time** & **String Gel** & **Tack Free Time** & \multirow{2}{*}{**Tmax (\({}^{\circ}\)C)**} \\  & & **(s)** & **Time (s)** & **(s)** & \\ \hline W\_0t & 8 & 34 & 23 & 21 & 126 \\ WU\_0t & 8 & 34 & 23 & 21 & 125 \\ C5\_0t & 10 & 46 & 28 & 24 & 151 \\ C10\_0t & 10 & 48 & 30 & 25 & 146 \\ C15\_0t & 10 & 54 & 30 & 26 & 148 \\ CU5\_0t & 9 & 36 & 25 & 24 & 144 \\ CU10\_0t & 9 & 40 & 26 & 25 & 144 \\ CU15\_0t & 10 & 44 & 33 & 27 & 142 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Processing times of RPU/PIR foams (0t series).