esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selective Wetting Property
The \(AR\) of the micropillars was also tailored to analyze its influence on the selective wetting performance. The results are summarized in Figure 5b. The water contact angle increased with rising \(AR\) until reaching a plateau at around 150\({}^{\circ}\). On the contrary, the oil contact angle manifested a reducing trend before stabilizing at around 20\({}^{\circ}\). For the aim of achieving both superhydrophobicity and superoleophilicity on the foil, the \(AR\) of the micropillars was expected to be higher than 0.75. Similar to pillar spacing, too high \(AR\) generally requires an elaborated fabrication procedure and is prone to fracturing and buckling during demolding, which might also attenuate the selective wetting performance. Thus, an eligible \(AR\) of 1 was adopted in the following study. This outcome indicated  
Figure 4: Picture of water (left column) and oil (right column) droplets on the PU foil. (**a**) Water and (**b**) oil contact angles on a flat PU surface. (**c**) Water and (**d**) oil contact angles on a micropillar surface. The pillar spacing is 5 \(\upmu\)m. The respective height of the pillars is 5 \(\upmu\)m, resulting in an aspect ratio of 1. (**e**) Water and (**f**) oil contact angles on microporous cross-section of a PU foil. The average pore size of the specific sample used in the experiment is around 126 \(\upmu\)m. The droplet volume is 1 \(\upmu\)L.  
structural parameters of \(s=5\)\(\upmu\)m and \(AR=1\) for the micropillar arrays to achieve strong selective wetting for oil/water separation. As already illustrated in Figure 4, cross-sectional porous structures also enhanced the water repellency of PU. However, the pore distribution differed among samples foaming under different saturation pressures. Such a characteristic might also cause variations in the wetting property. Hence, the water contact angles on the porous cross-section of foils demonstrating varied pore sizes were characterized and are shown in Figure 6. The results of the oil contact angles were not plotted because all of the porous samples revealed a similar value of around 20\({}^{\circ}\). One can observe that the water contact angles fluctuated among porous samples but were all larger than that on the bulk reference. It seems that the pore size did not exhibit a regular dependency on water repellency or oil attraction. The function of pore size might be to tune the oil absorption volume or rate, which will be discussed in the next section. Figure 5: Influence of (**a**) micropillar spacing and (**b**) height/diameter ratio on the selective wetting property of PU foil. In (**a**) the pillar height is kept constant at 5 \(\upmu\)m (i.e., \(AR=1\)). In (**b**) the pillar spacing remains 5 \(\upmu\)m on different samples. For each sample, three measurements were conducted at arbitrary positions. The average values and corresponding statistical error are shown as columns and error bars in the figure.
# GCE: An Audio-Visual Dataset for Group Cohesion and Emotion Analysis
## 3 Construction of the Group Cohesion and Emotion Dataset
### Annotation
(**a**) Annotator training; (**b**) Annotation place; (**c**) Annotation equipment: headset, laptop, explanation document, pen, notepad; (**d**) Annotation process includes three steps: (1) annotator watches a video, (2) annotator takes memos while watching the video, (3) annotator decides labels for the watched video in terms of the group cohesion score and group emotional state. Figure 4 provides an illustration of the distribution of the labels pertaining to group cohesion and group emotion. The depicted data reveal a prevalent inclination towards positive emotional states within group settings across various situations. The analysis indicates that the observed group cohesion tends to exhibit a consistent tendency towards a moderately strong level. Figure 4(a) portrays the association between group cohesiveness and group emotions, revealing distinct patterns. Positive emotions are consistently associated with higher cohesion scores, typically falling within the range of 4 to 7. Conversely, negative emotions tend to coincide with lower cohesion levels, typically below 5. Neutral emotions, on the other hand, tend to exhibit moderate cohesion ratings, typically ranging from 3 to 5. When we consider specific scenarios, the keyword \"debate\" is characterized by predominantly negative emotions, as indicated in Figure 5(a), which correspondingly results in very low cohesion levels, with a median score of 1, as exemplified in Figure 4(b). In contrast, the \"discussion\" scenario exhibits a mix of positive and neutral emotions and displays a moderate level of cohesion. The scenarios of \"casual conversation\", \"meeting\", and \"interview\" consistently exhibit high cohesiveness scores, typically surpassing 5. Figure 4(b),c provides additional insights, suggesting that the number of individuals within a group, ranging from two to five people, does not appear to exert a substantial influence on the expression of cohesion or the emotional dynamics of the group. These findings highlight the nuanced interplay between group cohesiveness and group emotions in various contexts, shedding light on the differential impact of emotions across diverse scenarios and the resilience of group cohesion across different group sizes. Figure 4: Ground-truth distribution of the proposed GCE dataset. (**a**) Cohesion score—(**b**) emotional state. Figure 5: Violet plot of cohesion distribution based on: (**a**) emotional correlation; (**b**) keywords; and (**c**) subjects.
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 4 Numerical Simulation
It is revealed in Table 3 that when the detection requirements are specified as follows: continuous replay attack detection rate \(\beta_{1}\) between 88% and 92%, discontinuous replay attack detection rate \(\beta_{2}\) between 87% and 91%, continuous replay attack false alarm rate \(\alpha_{1}\) between 4% and 6%, and discontinuous replay attack false alarm rate \(\alpha_{2}\) between 6% and 10%, the lowest control cost of 24 is achieved by the proposed method. Figure 11: The \(G(k)\) of the UMVs during discontinuous replay attacks in **Case 2**. Figure 10: The \(G(k)\) of the UMVs during continuous replay attacks in **Case 1**. It is shown in Table 4 that when the control cost \(J_{ave}\) for various methods is fixed at 80, other methods are surpassed by the proposed method in both continuous replay attack detection rate \(\beta_{1}\) and discontinuous replay attack detection rate \(\beta_{2}\). Additionally, the continuous replay attack false alarm rate \(\alpha_{1}\) and discontinuous replay attack false alarm rate \(\alpha_{2}\) are both lower than those reported in previously published methods. The superiority of the proposed method is evident from the data and analysis presented. When the detection accuracy requirement is held constant, the proposed method results in reduced control costs. Conversely, when the control cost requirement is fixed, the proposed method achieves higher detection accuracy and a lower false alarm rate.
## 3 Results and Discussions
### Optimum Mixing Ratio
#### 3.2.2 Mixing Ratio of GGBFS  
RC manufactured using CWP as a replacement material for OPC could show some defects, such as a relatively lower Ca content as a result of the mingling of fine aggregate constituents and a decrease in strength. Compared with OPC, CWP experienced the burning process, and the RC using it exhibited remarkably lower contents of hydration-provoking materials, such as \(\beta\)-C\({}_{2}\)S. Therefore, industrial by-products containing a relatively higher Ca content, such as FA and GGBFS, were added to supplement the insufficient CaO content of CWP and improve the quality of RC. In this study, the effect of GGBFS on the compressive strength of SRC was investigated, and the results are presented in Figure 4. Overall, the compressive strength of the SRC increased with increasing GGBFS content. Specifically, the disposal criterion was met with GGBFS contents of over 15% after 28 days of curing, as shown in the cases of WC-3 and WC-4 in Figure 4. Therefore, the optimal GGBFS substitution ratio for CWP was determined to be 15%.
## 7 Impact Assessment of EVs
### Economic Impacts  
EVs have witnessed a significant increase in their popularity owing to their environmentally friendly characteristics and higher efficiency in comparison to conventional gasoline-powered vehicles. EVs have gained significant traction worldwide due to recent advancements in battery technology, the expansion of charging infrastructure, and the growing global demand from customers. As a result, EVs have emerged as a practical and viable choice for a substantial number of drivers. The vehicles in question are powered by batteries and utilize electric motors to drive the wheels, as opposed to relying on gasoline. The nascent and emerging technology also provides economic advantages on a global level. Both EV owners and the utility power grid hold unique perspectives that offer valuable insights into the economic impacts of EVs [128].  
EVs place a substantial burden on the electrical grid due to their ongoing power requirements for meeting daily charging demands. The projected increase in electricity demand will result in higher fuel and capacity expenses for electricity generation. Moreover, the maintenance of the grid becomes increasingly challenging during periods of peak demand. One of the advantages, both from an economic and environmental perspective, is the ability to charge EVs using renewable energy sources.  
EVs offer a multitude of advantages to their owners, primarily attributed to the remarkable efficiency of electric motors and the relatively cost-effective power source. Consequently, these vehicles experience decreased operational expenses, rendering them a financially efficient option for consumers. The efficiency of EV technology is significantly higher, typically ranging from 60 to 70 percent, when compared to internal combustion engine vehicles. EVs exhibit a disadvantage compared to conventional ICE vehicles, namely, their elevated initial investment cost. The primary reason for this is the utilization of costly battery technology in EVs [59].  
The Indian subcontinent, after Germany and Japan, is the third-largest vehicle market in the world in terms of sales. There is currently a drive for manufacturers and governments to work together to shift demand towards greener options. With 7.1 percent of India's GDP and a sizeable employment base, the automotive industry is a key economic force in the country. The Economic Survey 2023 projects a 49% compound annual growth rate (CAGR) in India's domestic electric car market between 2022 and 2030, with 10 million annual sales by 2030. By 2030, it is expected that the electric car industry would provide over 50 million direct and indirect jobs. The Indian government has implemented many incentives and policies to assist the growth of the EV industry and has set a goal of 30% vehicle fleet electrification by 2030. The industry received a significant financial boost in the FY24 Union Budget for the development of EVs, the use of hydrogen fuel, and the acceptance of new technology. This effort offers additional advantages from an economic and environmental standpoint [129].
# Minimizing Travel Time and Latency in Multi-Capacity Ride-Sharing Problems
## 4 The Case \(c=2\): Algorithms and Their Analysis
### The Match-and-Assign Algorithm and Its Analysis
Observe that an optimal solution is \(\{(k_{1},\{1,3\}),(k_{2},\{2,4\})\}\) with \(\text{cost}(M^{*}(I))=2\). Note that \(\text{MR}^{*}=\{\{1,3\},\{2,4\}\}\). Let us now analyze the performance of \(\text{MA}(1,u)\) on this instance. Based on the \(u\) values as defined in (20), \(\text{MA}(1,u)\) can find, in the first step, matching \(M_{3}=\{\{1,2\},\{3,4\}\}\) because \(v_{3}(M_{3})=v_{3}(\text{MR}^{*})=2\). Then, no matter how the second step assigns the request pairs to cars, the total waiting time of \(\text{MA}(1,u)\) will be three. \[\leq\frac{1}{2}\sum_{c\in\mathbb{C}}\left(\sum_{(k,\{i,j\})\in M^ {c}_{\ell}}v_{4}(k,\{i,j\})+\sum_{(k,\{i,j\})\in M^{c}_{r}}v_{4}(k,\{i,j\})\right)\]7 A worst-case instance for the \(\text{CA}_{sum}\) of \(\text{CS}_{sum}\). **Theorem 5**. : _The \(\mathrm{MA}(2,\mu)\) is a two-approximation algorithm for \(\mathrm{CS}_{lat}\). Moreover, there exists an instance \(I\) of \(\mathrm{CS}_{lat,s=t}\) for which \(\mathrm{wait}(\mathrm{MA}(I))=2\mathrm{wait}(M^{*}(I))\)._  
Proof. : \[\leq\frac{1}{2}\sum_{c\in\mathbb{C}}\left(\sum_{(k,\{i,j\})\in M^ {c}_{\ell}}v_{4}(k,\{i,j\})+\sum_{(k,\{i,j\})\in M^{c}_{r}}v_{4}(k,\{i,j\})\right)\]8 (40) \[\leq\frac{1}{2}\sum_{c\in\mathbb{C}}\left(\sum_{(k,\{i,j\})\in M^ {c}_{\ell}}v_{4}(k,\{i,j\})+\sum_{(k,\{i,j\})\in M^{c}_{r}}v_{4}(k,\{i,j\})\right)\]9 (41) \[\leq\sum_{(k,\{i,j\})\in M}\frac{\alpha w(d_{k},s_{i})+\alpha w(d _{k},s_{j})}{2}.\]0 (42) \[\leq\sum_{(k,\{i,j\})\in M}\frac{\alpha w(d_{k},s_{i})+\alpha w(d _{k},s_{j})}{2}.\]1 (43) \[\leq\sum_{(k,\{i,j\})\in M}\frac{\alpha w(d_{k},s_{i})+\alpha w(d _{k},s_{j})}{2}.\]2 (44) \[\leq\sum_{(k,\{i,j\})\in M}\frac{\alpha w(d_{k},s_{i})+\alpha w(d _{k},s_{j})}{2}.\]3 (45)  
Inequality (41) follows from Lemmas 5 and 6. Inequality (42) follows from the triangle inequality. Inequality (43) follows from \(w(s_{i},s_{j})\leq\min\{\mu_{ij},\mu_{ji}\}\). To see that the equality may hold in \(\mathrm{wait}(\mathrm{MA}(I))\leq 2\mathrm{wait}(M^{*}(I))\), consider the instance \(I\) depicted in \[\leq\sum_{(k,\{i,j\})\in M}\frac{\alpha w(d_{k},s_{i})+\alpha w(d _{k},s_{j})}{2}.\]4 This instance has two cars \(D=\{k_{1},k_{2}\}\) with car locations \(\{d_{1},d_{2}\}\) and four requests \(R=\{1,2,3,4\}\). If two points are not connected by an edge, their travel time equals five. Observe that an optimal solution is \(\{(k_{1},\{1,3\}),(k_{2},\{2,4\})\}\) with \(\mathrm{wait}(M^{*}(I))=4\). Note that \(M^{*}_{R}=\{\{1,3\},\{2,4\}\}\). Let us now analyze the performance of the \(\mathrm{MA}(2,\mu)\) on instance \(I\). Based on the \(\mu\) values as defined in (22), the \(\mathrm{MA}(2,\mu)\) can find, in the first step, matching \(M_{3}=\{\{1,2\},\{3,4\}\}\) because \(v_{3}(M_{3})=v_{3}(M^{*}_{R})=8\). Then, no matter how the second step assigns the request pairs to cars, the total waiting time of the \(\mathrm{MA}(2,\mu)\) will be eight.
## 2 Materials and Methods
### FE Model Design  
The FE mesh was constructed based on the CT scans of the 3D UCLP and BCLP models. The UCLP FE mesh consisted of 238243 4-node tetrahedral elements with 43822 nodes, whereas the BCLP FE mesh consisted of 409027 4-node tetrahedral elements with 74551 nodes. The position of the centroid of each element was calculated and a gray scale was associated with it based on spatial interpolation of the gray scale values of the nearest voxels. The gray scale values from the CT scans were correlated with the material density and the material stiffness. Relevant material properties were assigned for the elements representing bone, arithmetic relationships between gray scale values, apparent densities and Young's moduli [46; 47; 48; 49]. Depending on its apparent density and the associated gray scale values, the bone tissue was stratified into ten different material property groups, ranging from very low-density cancellous bone (bone 1) to dense cortical bone (bone 10). The material properties of these ten types of bone (apparent density, Young's modulus, Poisson's ratio and yield strength) were same as used by [PERSON] et al. [44].To highlight the compositional difference between the UCLP and BCLP model each type of bone density (bone 1 to bone 10) has been cumulatively compared between both models (Figure 2).  
Figure 2: The cumulative percentage of the ten material property groups for the unilateral cleft lip and palate (UCLP) and bilateral cleft lip and palate (BCLP) models, respectively. The material properties of the ten different bone types are retrieved from [PERSON] et al. [44].
## 2 Related Work
### Real-Time Routing Protocols
[33] presented a new real-time routing protocol for a (_m,k_)-firm model by introducing multiple attributes to evaluate the path. In addition to this, [PERSON] et al. [34] proposed a new tree-based broadcast protocol to meet the requirements of the (_m,k_)-firm stream in WSNs. They focused on energy efficiency by selecting as few as nodes on a tree as possible and a fast recovery scheme based on (_m,k_)-firm real-time conditions and local states monitoring. However, these existing schemes focus on the real-time routing based on networks environments without the considering the application requirements. This implies that a path is selected by depending on Distance Based Priority (DBP) value of (_m,k_)-firm stream without the quality of application. The analysis we provide above indicates that most previous schemes would need to be extended to enable real-time delivery. Furthermore, a multimedia traffic model for streaming application would need to be introduced. Additionally, the severely constrained capabilities of sensor nodes in terms of their energy, memory, and processing power demand an adaptive approach to meet the requirements of varying network parameters.
## 3 Data Analysis and Results
### The Classona Aftershock Sequence  
Here we focus on the aftershock sequence of the main earthquake that took place near the capital city of Larissa in Thessaly on 3 March 2021. The mainshock had a magnitude of \(M_{w}6.0\) (from the Geophysical Laboratory of the Aristotle University of Thessaloniki  
Figure 3: (**a**) The interevent times CDF of the 2021 \(M_{w}6.0\) Arkalochori earthquake, for the aftershocks with \(M>M_{c}\). The scarlet solid line is the \(Q\)-exponential operation with \(q=1.62\). The change of colors indicates the crossover between the NESP (blue circles) and BG statistics (green triangles). (**b**) The \(ln_{Q}P(>T)\) as a function of interevent times \(T\), where the scarlet line presents the fitting with \(q=1.62\) and correlation coefficient 0.9953 up to \(T_{c}\). \(T_{c}\) value close to 7750 s is suggested by the deviation from linearity. (**c**) Interevent time \(T\) evolution with time \(t\) since the main shock. The red line illustrates the \(T_{c}\) value.  
(GL-AUTH), [[http://geophysics.geo.auth.gr/](http://geophysics.geo.auth.gr/)]([http://geophysics.geo.auth.gr/](http://geophysics.geo.auth.gr/)), accessed on 27 September 2022) (Table 1), and generated a prolonged aftershock sequence in a general SE-NW direction [34]. The aftershock area, for a 1-month time interval from 3 March 2021 to 4 April 2021, covers the region between longitudes 21.47\({}^{\circ}\) E-23.13\({}^{\circ}\) E and latitudes 39.01\({}^{\circ}\) N-40.56\({}^{\circ}\) N. The aftershocks' catalogue includes 676 aftershocks characterized by magnitudes \(2.5\leq M_{w}\leq 5.8\), with a completeness magnitude of \(M_{c}=2.5\)[33,35].  
The CDF of the interevent times for the Elassona 2021 aftershock sequence, based on the fitting of the \(Q\)-exponential function (Equations (7) and (8)) to the values of T, up to a value approaching \(T_{c}\), reaches to \(q=1.62\) (see Figure 4). Next, in Figure 4b we present the \(ln_{Q}P\)(\(>\)\(T\)) as an operation of interevent times \(T\) for \(q=1.62\). From its deviation from the expected linearity, the approximated value of \(T_{c}\approx 4587\) s is extracted. A graph of the evolution of interevent time \(T\) over time t since the main shock is shown in Figure 4c.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 4 Review on Energy Consumption and Efficiency Model  
Energy efficiency is one of the key issues in WSN. Several models and many techniques have been used to analyse energy consumption and efficiency of WSN in the literature. In this section, we will discuss an overview of the energy consumption model and available energy efficiency model. The total energy consumption on any sensor node constitutes several components including the energy s spent by the electronics, start-up process, RF transmission, encoding and decoding. A radio model [60] is used to represent the energy consumption in a typical transceiver of a sensor node and is illustrated in Figure 10.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 3 Glycosylated Proteins of Hosts Act as Barriers to Defense Pathogens  
Animals and plants are exposed to an array of pathogens, but only a few can actually cause severe diseases [73; 74]. This is because the attack of most pathogens is blocked by glycosylated proteins covering the host cell surface as barriers. Such barriers may act in two manners in host defense, (I) physically separate pathogens and host cells [75; 76]; (II) chemically inhibit or kill pathogens [77; 78; 16; 79].
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 2 The Principle of the Fuzzy-FTA Analysis
### Application of the Fuzzy-FTA Method
#### 2.3.2 Basic Operators  
(1) AND gate fuzzy operator  
If the probability of occurrence of the \(i\)-th basic event is a fuzzy number, which is denoted as \(P_{i}\), the AND gate fuzzy operator can be denoted as:  
\[(P_{AND})(z)=\prod_{i=1}^{n}P_{i}=\left(P_{AND}^{\alpha},P_{AND},P_{AND}^{\beta}\right) \tag{10}\]  
where \(P_{AND}^{\alpha}=\prod\limits_{i=1}^{n}(m_{i}-\alpha_{i})\), \(P_{AND}=\prod\limits_{i=1}^{n}m_{i}\), \(P_{AND}^{\beta}=\prod\limits_{i=1}^{n}(m_{i}+\beta_{i})\).  
In Formula (10), if the probability of the occurrence of \(n\) events is an AND relationship, then the total probability \(P_{AND}\) of the simultaneous occurrence of n events is the multiplication of the probability of every occurrence of n events alone. \(P_{AND}\) is the total probability, \(P_{i}\) is the probability of a single event occurring alone. \(m_{i}\) is the mean value of the fuzzy numbers, and is specified as a real number, while \(\alpha_{i},\beta_{i}\) are the left and right extensions of the mean, respectively [19].  
(2) OR gate fuzzy operator  
If the probability of occurrence of basic events is a fuzzy number, then the OR gate fuzzy operator can be written as:  
\[(P_{OR})(z)=1-\prod_{i=1}^{n}(1-P_{i})=\left(P_{OR}^{\alpha},P_{OR},P_{OR}^{ \beta}\right) \tag{11}\]  
where  
\[P_{OR}^{\alpha}=1-\prod_{i=1}^{n}[1-(m_{i}-\alpha_{i})],\ P_{OR}=1-\prod_{i=1} ^{n}[1-m_{i}],\ P_{OR}^{\beta}=1-\prod_{i=1}^{n}[1-(m_{i}+\beta_{i})]\]  
In formula (11), if the probability of occurrence of n events is an OR relationship, then the total probability \(P_{OR}\) of n events occurring at the same time is equal to the result of the probability of 1 minus the multiplication of n events not occurring [20].  
After the fuzzy operator of fault tree analysis is established, the fuzzy analysis of the fault tree can be carried out. Moreover, the resulting probability of the top event is a fuzzy number. With different confidence levels, different confidence intervals for the probability of the top event can be obtained.