esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification with Numerical Simulation of a Tank Model
### Volumetric-Drive Gas Reservoir  
Volumetric gas reservoirs exhibit no water encroachment and negligible water production, which cancels out the water influx \(W_{e}\) and water production \(W_{p}B_{w}\) in Equation (4). This type of reservoir is depleted by gas expansion, which is solely controlled by pressure. In addition, it is typical to recover 80-90% of the original gas in place in this type of gas reservoir [5]. To model this case, we set the minimum bottom hole pressure to 500 psi. After 13 years of production, the abandonment pressure is reached, and 87% of the original gas in place is recovered. Next, hydrogen is injected following a 2-year shut-in period until the initial reservoir pressure is reached, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Required Code Input** \\ \hline Grid dimensions [ft] & 300 \(\times\) 300 & Yes (if applicable) \\ Grid thickness [ft] & 50 & Yes (if applicable) \\ Surface pressure, \(p_{\mathrm{sc}}\) [psi] & 14.7 & Yes \\ Surface temperature, \(T_{\mathrm{sc}}\) [\({}^{\circ}\)F] & 60 & Yes \\ Initial reservoir pressure, \(p_{\mathrm{i}}\) [psi] & 3500 & Yes \\ Reservoir temperature, \(T_{\mathrm{i}}\) [\({}^{\circ}\)F] & 100 & Yes \\ Average reservoir porosity [frac] & 0.20 & Yes (if applicable) \\ Average reservoir permeability [md] & 200 & No \\ Original gas in place, \(G_{\mathrm{i}}\) [MMSCF] & \(1.74\times 10^{2}\) & Yes (if applicable) \\ Water saturation, \(S_{\mathrm{w}}\) [frac] & 0.20 & Yes \\ Formation compressibility, \(C_{\mathrm{f}}\) [psi\({}^{-1}\)] & \(1.0\times 10^{-6}\) & Yes \\ Water compressibility, \(C_{\mathrm{w}}\) [psi\({}^{-1}\)] & \(3.0\times 10^{-6}\) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tank model properties.  
Figure 4: Volumetric-drive tank model: average pressure during different stages and the gas recovery factor before hydrogen injection.  
It is challenging to pinpoint the exact source of error since the numerical simulator and the EoS package used in our framework are both black boxes. However, some potential sources for this minor discrepancy may include roundoff errors, the precision used (single vs. double precision), the method for solving the cubic EOS polynomial, and [PERSON]'s method convergence. Nonetheless, this minimal deviation is within an acceptable range for analytical purposes and does not compromise the robustness of the implemented framework.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### YOLOv5 Network Model
#### 3.1.3 Prediction Module  
The prediction module performs the final detection, and an anchor box is applied to the output feature map, generating an output vector with category probability, confidence score, and bounding box. On the anchor, YOLOv5 uses cross-grid matching rules to distinguish the positive and negative samples of the anchor. The loss function uses GIOU_loss, and the confidence loss and category loss use the binary cross-entropy loss function.
## 5 Arabic English Emotion Lexicon (AEELex) Design
### Lexicon Design  
The lexicon-based analysis is one of the main approaches used in sentiment analysis [31]. It includes identifying the sentiments of users based on the semantic orientation of their words [32]. This method relies on a dictionary-based approach comprising a list of words according to their semantic orientation. There are different methods of creating such dictionaries, including automatic [33] and manual [34].  
For the current study, a bilingual emotion dictionary comprising emotions expressed in the English and Arabic languages was created. This dictionary is named \"AEELex\" and is based on eight emotions proposed by [PERSON] [15] including _Fear_ (_), _Anger_ (_), _Sadness_ (_), _Joy_ (_), _Surprise_ (_), _Disgust_ (_), _Trust_ (_) and _Anticipation_ (_). AEELex comprises these eight emotion categories along with two sentiment polarities, i.e., positive (_) and negative (_).  
Due to the complexity of the language used, a manual approach was adopted with the involvement of native Arabic language experts. The emotions fear (_), anger (_), _disgust_ (_), and sadness (_) are associated with negative sentiments, whereas joy (_), trust (_) and anticipation (_) fall in the category of positive sentiments. The category surprise (_) is a mixed sentiment comprising both positive and negative emotion words.  
Apart from the words belonging to the eight mood categories, additional words were manually added under the category of positive and negative emotions. We developed our dictionary based on the Moodbook and NRC Word-Emotion Lexicon (Emolex). Both Emolex and Moodbook also tag words according to [PERSON]'s eight emotions along with the two categories of semantic polarities--positive and negative. However, neither dictionary categorized the moods according to our context. Moreover, these dictionaries are based on words in the English language only. Therefore, we extended these dictionaries to the Arabic language, along with new words, to form our bilingual dictionary, \"AEELex\". This dictionary was developed using more than 19,000 tweets written in the Arabic language. Table 5 shows an example of a few mood words in the AEELex dictionary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Quantity** \\ \hline Total number of tweets & 35,383 \\ Tweets collected from Riyadh & 16,726 \\ Tweets collected from Mecca & 18,657 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics on the data after preprocessing.  
\begin{table}
\begin{tabular}{c c} \hline \hline Unique User ID & Aggregated Unique Tweets & Region (Mecca/Riyadh) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Schema Used in Experiments.
## 2 Control Algorithm  
### Conventional Sliding Mode Control  
The theory of SMC has been extensively applied in linear and nonlinear systems, and different practical engineering control problems. Studies in the past few decades have demonstrated that the SMC methodology provides a systematic approach to solving control engineering problems. By introducing a sliding mode to the control of a system, one can achieve both stabilization and disturbance rejection.  
The conventional PSMC is considered as a proportional type which forces system states to slide into the predetermined sliding surface, and to reach the origin of the phase plane eventually. When the state-trajectory is sliding on the sliding surface, the corresponding dynamic performance of the system is governed by the equations of the sliding surface. Therefore, the PSMC has a strong robustness against external uncertainties and disturbances.  
There are two major requirements for the PSMC. The first one is to design a sliding surface. Then the states are enforced to slide into the sliding surface by the switching the control law. Secondly, each of the tracking points on the sliding surface must satisfy the sliding conditions. The switch of the control law is determined by the predefined switching conditions. With the help of the control switch, the trajectory can be guided into the region bounded by boundary layers. Governed by the sliding conditions, the trajectory gradually moves onto the sliding surface and steps forward to the target point.  
Consider a desired signal \(\tilde{x}_{d}(t)\) and the system output \(\tilde{x}(t)\), where both of \(\tilde{x}_{d}(t)\) and \(\tilde{x}(t)\) are in \(\mathbb{R}\). The objective of the PSMC is to make the system's output approach the desired signal when \(t\to\infty\); i.e.,  
\[\lim_{t\to\infty}\left[\tilde{x}(t)-\tilde{x}_{d}(t)\right] = 0, \tag{2}\]  
Here, we define the tracking error as  
\[e(t) = \tilde{x}(t)-\tilde{x}_{d}(t), \tag{3}\]  
where \(e(t)\in\mathbb{R}\). Before the PSMC is applied, a sliding surface must be designed in advance. The goal of the PSMC is to guide the system trajectory toward the sliding surface, which naturally leads the trajectory to the target point in the end. Based on the magnitude of errors, we define a time-varying linear differential function in \(\mathbb{R}^{n}\), \(s(\tilde{x},t)\), named the condition space [27], given by  
\[s(\tilde{x},t) = \left(\frac{d}{dt}+\lambda\right)^{n-1}e(t), \tag{4}\]  
where \(\lambda\) is a positive constant. The equation \(s(\tilde{x},t)=0\) represents a sliding surface passing through the origin of the phase plane. According to the above equation, \(s=0\) is an \((n-1)\)th-order linear differential equation, and all the eigenvalues of the characteristic equation are located in the left complex plane. Once the sliding surfaces are determined, we may start to design the controller and define the sliding conditions as follows [27].  
\[s\dot{s} \leq -\eta|s|, \tag{5}\]  
where \(\eta>0\), and \(\dot{s}\) denotes the time derivative of \(s\).
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 2 Analysis of Video Transmission Latency
### Video Codec Latency
The latest method uses machine learning to reduce the complexity of codec slicing and motion search operations and reduces the coding complexity by 20-70% under the premise of 5% or less compression performance loss [32, 33, 34, 35]. However, the time required for entropy encoding and related data access is unavoidable. Similarly, the decoding process also brings some latency due to the large amount of computation.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Energy Consumption for DRFTSA Using Large Dataset by Fixed Number of VMs  
In this section, we present the next scenario of generated energy consumption for DRFTSA with a large dataset and by using a fixed number of VMs. For the large dataset, we considered 700, 800, 900, 1000 tasks and a fixed number of VMs in this scenario. Generated energy consumption for PSO with 700, 800, 900, 1000 tasks is 82.56, 84.39, 93.57, 112.68, respectively. Generated energy consumption for GA with 700, 800, 900, 1000 tasks is 86.16,  
Figure 9: Calculation of energy consumption for regular dataset by varying number of VMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Tasks** & **PSO** & **GA** & **ACO** & **DRFTSA** \\ \hline
100 & 63.25 & 67.03 & 69.47 & 54.99 \\
200 & 65.72 & 68.87 & 70.58 & 57.18 \\
300 & 68.16 & 71.46 & 73.84 & 62.18 \\
400 & 72.76 & 72.52 & 79.73 & 64.86 \\
500 & 77.95 & 76.88 & 82.81 & 66.71 \\
600 & 82.56 & 81.64 & 83.88 & 72.13 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Energy consumption for DRFTSA using regular dataset by varying number of VMs.  
87.99, 97.17, 108.99, respectively. Generated energy consumption for ACO with 700, 800, 900, 1000 tasks is 93.42, 97.19, 98.42, 134.77, respectively. Generated energy consumption for DRFTSA with 700, 800, 900, 1000 tasks is 48.89, 59.18, 68.74, 100.67, respectively.  
From Table 10 and Figure 10, it is evident that energy consumption of DRFTSA compared to various approaches is greatly minimized with the considered dataset and VMs.
## 2 Materials and Methods
### Participants  
A minimum sample size of 24 was calculated based on a repeated-measures analysis of variance design that incorporated (i) a within-between interaction, (ii) a moderate effect size (f = 0.25), (iii) a type-I error rate of 5% (\(\alpha\) = 0.05), (iv) a type-II error rate of 20% (\(\beta\) = 0.20), (v) 2 groups (control and PFP), (vi) 2 repeated measurements, and (vii) a minimum expected correlation of 0.50 among the repeated measurements [19; 20]. A total of 35 participants volunteered for the study, of which 17 were part of the control group (female [n = 10] and male [n = 7]) and 18 were part of the PFP group (female [n = 15] and male [n = 3]). Accountingfor a potential drop-out of 20%, the minimum sample size for adequate statistical power was 29 participants. Participants were recruited using a combination of information flyers and patient referrals from various rehabilitation and performance clinics throughout the region where the study was conducted. Two participants were lost to follow-up, both in the PFP group (female [n = 1] and male [n = 1]), due to incomplete data, resulting in a final sample size of 33 participants.  
The inclusion criteria for the PFP group consisted of the following: (i) aged between 18 and 35 years, (ii) could be male or female, (iii) had to exhibit retropatellar and/or peripatellar pain aggravated by at least one activity that loads the patellofemoral joint during weight bearing on a flexed knee, such as squatting, stair climbing, jogging/running, and hopping/jumping, and (iv) had to participate in some form of rehabilitation program with any practitioner of choice. The inclusion criteria for the control group were the same, with the exception of points (iii) and (iv).  
The specific exclusion criteria for both groups consisted of the following: participants should not (i) have had previous patellar dislocation or subluxation, (ii) have had previous injury to or surgery on the knee, and (iii) have had recent (within the last 6 months) injury to the lower limbs (e.g., Achilles tendinopathies, ankle sprain, etc.).
# A Context-Aware Edge Computing Framework for Smart Internet of Things
## 1 Introduction
Among the numerous advantages it promises are real-time processing, scalable and resilient sensing systems, energy saving and more. However, most existing selective sensing implementations are designed to satisfy a specific set of requirements for certain applications. They fall into one of two major categories, namely passive, user request-based selection or single-goal application-tailored selection. Thus, the need for a general-purpose adaptive selective sensing scheme compatible with all IoT applications is the next significant and urgent step [13]. Context-aware systems offer a great deal of insights and understanding of data which can be exploited to develop a general-purpose selective sensing framework that is aware of its surroundings, giving it the ability to refine the selection criterion and adapt to every situation [10]. In this work, we propose a context-aware selective sensing framework (CONTESS) for IoT to tackle the data explosion issue and alleviate the impact of the huge sensed data on the limited sensing resources, while providing a real-time and scalable services. The main contribution of this work can be summarized as follows. * A novel general-purpose context-aware selective sensing framework is proposed. This framework takes advantage of the available context information to improve the selection of important sensed data to reduce the impact of data explosion. * We demonstrate the applicability of our framework with a parking management system in a smart city using linked data and semantic web technologies. The rest of this document is organized as follows. Related works are presented in Section 2. The proposed platform is explained in Section 3. A use case scenario is given in Section 4. Implementation and testing results are discussed in Sections 5 and 6, respectively. Finally, this paper is concluded in Section 7.
## 2 Materials and Experiments
### Fourier Transform Infrared Spectroscopy (FTIR) Test  
Infrared spectral analysis is one of the most widely used methods in the study of chemical structure of polymers. In this study, by comparing with the unpretreated GF particles and observing the changes in the absorption peaks of the main functional groups, characteristics of the functional groups in silane coupling agent pretreated GF particles were expected to be obtained and the surface treatment process of GF particles was expected to be further understood based on chemical changes. The FTIR (Thermo Scientific Nicolet 6700, Waltham, MA, USA) parameters were revolution 4 cm\({}^{-1}\) with a scan number of 32 times. The sample was blended with potassium bromide (KBr) with ratio of about 1:10, grinded fully, and finally tabletted to thin circular chip. Immediately, the prepared circular specimen was put into FTIR and scanned by infrared light. To avoid the wrong operation or a sample preparation deviation, for each experimental run using the FTIR test, two parallel specimens were measured.
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Experimental Results for Computing Time Analysis  
According to the experimental results, the I-PSO-Jaya algorithm required more computation time (given in seconds) than the other five algorithms (I-PSONSO, I-GWO, GWO, I-JMSO, and Java) for all tests in both datasets, as demonstrated in Tables 11 and 12. The I-PSO-Jaya algorithm required a time of around 6.2458 to 542.7532 s for the Academia Stack Exchange dataset and 2.9176 to 236.111 s for the ACM dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exp. No.**} & \multirow{2}{*}{\
\begin{tabular}{c} **No. of** \\ **Skill** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Iterat-** \\ **ions** \\ \end{tabular} } & \multirow{2}{*}{**JAYA**} & \multirow{2}{*}{**I-JMSO**} & \multirow{2}{*}{**GWO**} & \multirow{2}{*}{**I-GWO**} & \multirow{2}{*}{**I-PSONSO**} & \multicolumn{2}{c}{**I-PSO-Jaya**} \\ \hline
1 & 2 & 5 & 0.0361 & 0.0328 & 0.0363 & 0.0318 & 0.0565 & 0.0546 \\ \hline
2 & 3 & 10 & 0.085 & 0.0813 & 0.0671 & 0.0823 & 0.0956 & 0.0855 \\ \hline
3 & 4 & 15 & 0.1151 & 0.1395 & 0.1324 & 0.1564 & 0.1545 & 0.1483 \\ \hline
4 & 5 & 20 & 0.2134 & 0.2234 & 0.2074 & 0.2364 & 0.1243 & 0.1234 \\ \hline
5 & 6 & 25 & 0.415 & 0.455 & 0.3751 & 0.4364 & 0.0826 & 0.0796 \\ \hline
6 & 7 & 30 & 0.6669 & 0.6721 & 0.6316 & 0.4684 & 0.0795 & 0.0697 \\ \hline
7 & 8 & 30 & 0.6877 & 0.6921 & 0.7586 & 0.7841 & 0.0882 & 0.0577 \\ \hline
8 & 9 & 30 & 0.8772 & 0.8821 & 0.8018 & 0.8702 & 0.0473 & 0.0401 \\ \hline
9 & 10 & 30 & 0.8943 & 0.9014 & 0.9441 & 0.8774 & 0.0691 & 0.0605 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Standard deviation of the communication cost of I-PSO-Jaya and existing methods for the ACM dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exp. No.**} & \multirow{2}{*}{\
\begin{tabular}{c} **No. of** \\ **skill** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Iterat-** \\ **ions** \\ \end{tabular} } & \multirow{2}{*}{**JAYA**} & \multirow{2}{*}{**I-JMSO**} & \multirow{2}{*}{**GWO**} & \multirow{2}{*}{**I-GWO**} & \multirow{2}{*}{**I-PSONSO**} & \multicolumn{2}{c}{**I-PSO-Jaya**} \\ \hline
1 & 2 & 5 & 0.02 & 0.07 & 0.10 & 3.73 & 4.01 & 6.24 \\ \hline
2 & 3 & 10 & 0.03 & 0.24 & 0.01 & 7.32 & 10.99 & 27.90 \\  
In the Stack Exchange dataset, specifically in skill 10, the minimum communication cost of I-PSO-Jaya is 0.37, with a computing time of 542.753 s. In comparison, the other algorithms have minimum communication costs ranging from 0.48 to 5.23, with a computing time of 234.074 s to 287.941 s (as shown in Tables 5 and 11). Similarly, when the communication cost of I-PSO-Jaya is 0.04 with a computing time of 236.11 s, the minimum cost obtained by the other algorithms ranges from 0.05 to 1.62, with a computing time of 0.1167 s to 115.23 s in the ACM dataset; see Tables 6 and 12.  
I-PSO-Jaya is a more intricate algorithm than the other five heuristics. Thus, by performing more computations, I-PSO-Jaya reaches a better solution than that achieved by the other methods.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
###### Abstract  
Laser class is an Olympic sport in which technical and tactical variables are very important in the performance of the sailor. However, the variables that determine performance in a regatta have not been studied, and less so with Olympic sailors. Therefore, the main objectives of this study are to analyze the technical and tactical variables that differentiate sailors based on their level of performance and sex and determine the most important courses in a regatta. The sample consists of 159 Olympic sailors (67 females) of the Laser class, who participated in a World Cup. Velocity made good (VMG), distance, and maneuvers were evaluated using Global Navigation Satellite System (GNSS) devices in the upwind, downwind, and broad reach courses. VMG in upwind and downwind is the technical variable that determines performance in the Laser class. The VMG is decisive in the performance of elite female sailors in the upwind, downwind, and broad reach courses, while in elite male sailors, performance is mainly influenced by speed in upwind and downwind and the distance covered in upwind. The maneuvers do not determine sailing performance in any of the courses of a regatta.  
GNSS; performance; dinghy sailing; Olympic sailors Article GNSS Applications to Assess Performance in Olympic Sailors: Laser Class April 2021, 11, 264. [[https://doi.org/10.3390/app11010264](https://doi.org/10.3390/app11010264)]([https://doi.org/10.3390/app11010264](https://doi.org/10.3390/app11010264))  
1
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
## 3 Theoretical Methods
### Analysis Using the Criteria of Maximum Tensile Stress  
In this section, according to Equations (1) and (2), the stress on the inner wall of the borehole is analyzed and the influence of the confining pressure of the anchorage section on the mechanical properties of the rock around the borehole (\(r=R_{1}\)) is discussed. According to the boundary conditions, since the radial stress on the borehole wall is always in equilibrium with \(P_{1}\), and \(\sigma_{r}=P_{1}\) at the borehole wall (\(r=R_{1}\)) is always unchanged.  
(1) Assuming \(P_{2}=0\), that is to say, the inner wall of the borehole is only affected by the radial stress \(P_{1}\) caused by dilation. According to Equations (1) and (2), the following Equation can be obtained:  
\[\sigma_{\theta}=-P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}}. \tag{3}\]  
The radial and tangential stresses around the borehole are shown in Figure 9. As can be seen from the Figure, when the effect of confining pressure \(P_{2}\) is not considered around the anchorage section and the inner wall of the borehole is only subjected to the radial stress, the tangential stress \(\sigma_{\theta}<0\). That is to say, the stress on the rock of the borehole wall is the tensile stress. When the tangential stress caused by dilatation on the borehole wall exceeds the tensile strength \(\sigma_{t}\) of the rock, i.e., \(P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}}>R_{t}\), the rock will be fractured.  
(2) When the confining pressure of anchorage section is \(P_{2}\
eq 0\), the tangential stress at the borehole wall can be obtained according to Equation (1):  
\[\sigma_{\theta}=-P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}}+\frac{2 P_{2}R_{2}^{2}}{R_{2}^{2}-R_{1}^{2}}. \tag{4}\]  
Compared with Equations (3) and (4), when \(P_{2}\
eq 0\), the tangential stress on the rock of borehole wall increases \(\frac{2P_{2}R_{2}^{2}}{R_{2}^{2}-R_{1}^{2}}\). In this case, the tensile stress of the rock is reduced, which is undoubtedly beneficial for rock materials with tensile strength far less than compressive strength. When the confining pressure \(P_{2}\) increases to a certain value and reaches \(\sigma_{\theta}>0\), the tangential stress of the rock around the borehole changes from tensile stress to compressive stress. Obviously, this will greatly improve the  
Figure 9: Elastic analytical solution of stress distribution in thick-walled cylinders.  
mechanical properties of the rock, the probability of rock splitting failure is greatly reduced, and the bearing capacity of anchorage structure will be greatly improved.