esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Concrete Preparation and Testing
#### 2.2.2 Properties of Fresh Concrete  
Fresh properties of concrete are investigated by the fresh concrete tests of SCC which includes the V-funnel test, J-Ring test and Slump test [39]. Fresh Properties of mix are observed and results are as follows.  
_Slump Test:_ The standard apparatus for the slump test was used to carry out the examination of the flow if concrete and its workability. The SCC mixes showed efficient results of a slump for the workability. Slump values are used to visualize the performance of SCC during placement [40].  
_V-Funnel Test:_ Standard apparatus for the V-funnel test is employed to perform this test. The test was performed in lab and the time was recorded for the flow using a stopwatch. The v-funnel test enables us to analyze the flow pattern of concrete through the congested bars of steel reinforcement in the structure (Figure 1).  
_J-Ring Test:_ For the analysis of fresh properties of SCC, we performed the J-Ring test for the evaluation of flow ability of concrete with additional replaced percentages of materials [39]. Standard J-Ring apparatus available in the lab made according to BS specifications is used to perform the test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix ID**} & **Cement** & **Silica Fume** & **GGBFS (Slag)** & \begin{tabular}{c} **Marble** \\ **Powder** \\ **kg/m3** \\ \end{tabular} & \begin{tabular}{c} **Coarse** \\ **kg/m3** \\ \end{tabular} & \begin{tabular}{c} **Fine** \\ **Powder** \\ **kg/m3** \\ \end{tabular} & \begin{tabular}{c} **Aggregate** \\ **kg/m3** \\ \end{tabular} & \
\begin{tabular}{c} **Water** \\ **kg/m3** \\ \end{tabular} &
\begin{tabular}{c} **Super** \\ **Plasticizer** \\ **kg/m3** \\ \end{tabular} \\ \hline
**CSSC** & 300 & - & - & - & 900 & 450 & 120 & 4.8 \\
**SF5** & 285 & 15 & - & - & 900 & 450 & 120 & 4.8 \\
**SF10** & 270 & 30 & - & - & 900 & 450 & 120 & 4.8 \\
**SF15** & 255 & 45 & - & - & 900 & 450 & 120 & 4.8 \\
**SF20** & 240 & 60 & - & - & 900 & 450 & 120 & 4.8 \\
**SF25** & 225 & 75 & - & - & 900 & 450 & 120 & 4.8 \\
**GGBFS5** & 285 & - & 15 & - & 900 & 450 & 120 & 4.8 \\
**GGBFS10** & 270 & - & 30 & - & 900 & 450 & 120 & 4.8 \\
**GGBFS15** & 255 & - & 45 & - & 900 & 450 & 120 & 4.8 \\
**GGBFS20** & 240 & - & 60 & - & 900 & 450 & 120 & 4.8 \\
**GGBFS25** & 225 & - & 75 & - & 900 & 450 & 120 & 4.8 \\
**MPS** & 285 & - & - & 15 & 900 & 450 & 120 & 4.8 \\
**MP10** & 270 & - & - & 30 & 900 & 450 & 120 & 4.8 \\
**MP15** & 255 & - & - & 45 & 900 & 450 & 120 & 4.8 \\
**MP20** & 240 & - & - & 60 & 900 & 450 & 120 & 4.8 \\
**MP25** & 225 & - & - & 75 & 900 & 450 & 120 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantities for mixing and development of concrete.  
Distance span is carefully examined. This test enables us to overview the pattern of the concrete inside steel bars of reinforcement. The standard testing equipment is shown in Figure 1.  
Table 3 explains the results from the tests performed on fresh concrete.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 3 Improved Penzien Model Validation
### Validation of the Soil-Structure Interaction Simplified Model  
#### 3.2.1 Model Overview  
The superstructure was a single-span steel-framed structure with six stories, each with a uniform height of 2 m, with both the span and depth measuring 2 m. The construction material selected was Q235 steel. The pile consisted of circular concrete piles that penetrated  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Peak** \\ **Responses** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Story** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Literature** \\ **Data** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MATLAB** \\ **Newmark** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Relative** \\ **Difference** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MATLAB** \\ **Difference** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Relative** \\ **Difference** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **ANSYS** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Relative** \\ **Difference** \\ **(\%)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} Peak displacements \\ (m) \\ \end{tabular} } & 1 & 0.158 & 0.161 & \(-\)2.14 & 0.164 & \(-\)3.75 & 0.166 & \(-\)4.76 \\  & 2 & 0.200 & 0.195 & 2.60 & 0.200 & \(-\)0.18 & 0.210 & \(-\)5.24 \\  & 3 & 0.338 & 0.374 & \(-\)9.55 & 0.345 & \(-\)2.12 & 0.345 & \(-\)1.95 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Peak velocities \\ (m/s) \\ \end{tabular} } & 1 & 1.090 & 1.073 & 1.59 & 1.075 & 1.36 & 1.051 & 3.56 \\  & 2 & 1.180 & 1.159 & 1.77 & 1.164 & 1.35 & 1.113 & 5.65 \\  & 3 & 0.980 & 0.976 & 0.39 & 0.970 & 1.04 & 0.923 & 5.80 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Peak accelerations \\ (m/s\({}^{2}\)) \\ \end{tabular} } & 1 & 7.450 & 7.451 & \(-\)0.02 & 7.475 & \(-\)0.33 & 7.075 & 5.04 \\  & 2 & 9.870 & 9.696 & 1.76 & 9.698 & 1.75 & 9.618 & 2.55 \\ \cline{1-1}  & 3 & 8.890 & 8.448 & 5.23 & 8.887 & 0.03 & 8.795 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of peak responses with the literature data (based on [39]).  
Figure 2: The first story structure responses concerning: (**a**) hysteresis curve, (**b**) displacement time–history curve, (**c**) velocity time–history curve, (**d**) acceleration time–history curve.  
the soil to reach the bedrock. The diameter of the piles was 0.45 m, and the concrete used for the piles was C30, with a platform area of 5 m\({}^{2}\). The site soil was classified as type II, comprising three layers of varying materials, starting from the surface and extending towards the bedrock: 4 m of clay, 4 m of silty clay, and 8 m of silty soil. The parameters of various soils were calculated after fitting, according to the test data of [PERSON] et al. [40]. The detailed material parameters for each soil layer are provided in Table 2.
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 1 Introduction  
In recent years, various industries, including oil and gas, chemical manufacturing, food and beverages, and pharmaceuticals, have prioritised automating their process plants by incorporating advanced technology and software to manage and oversee industrial processes, aiming to minimise human intervention and enhance efficiency and safety. Automation is typically achieved by regulating and supervising process variables, such asflow, temperature, level, and pressure within the process plant. PID controllers have gained popularity in automatic control systems due to their simplicity, good performance, and cost-effectiveness [1; 2; 3; 4]. However, their effectiveness must be improved to cater to more complex and higher-order systems [5]. Recent research suggests the integration of more complex PID controller configurations, such as fractional order calculus and higher-order derivative terms, such as PIDD2 and proportional integral derivative acceleration controllers, to regulate more complex systems [1; 3; 6; 7; 8]. The PIDD2 controller has found extensive usage in power systems, power electronics, and control systems, and its recent developments and applications are discussed in the following sections. The list of abbreviations used in the following sections is available at the end of the manuscript.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 3 Results
### Evacuations in Historical District
#### 3.4.4 Exit Usage Patterns  
Evacuation behavior within the district influences subsequent evacuation outside the district. In both scenarios, the total number of evacuees utilizing the western passage increases rapidly, potentially reaching 3 times or more that of the eastern passage at the same moment (3.52:1 to 3.60:1). This pattern is attributed both to the higher number of exits on the western side and to the fact that the W4 exit serves as the shortest evacuation route. However, the number of evacuees using each exit is also influenced by fire ignition. In 16-600, the number of evacuees using eastern exits (2189) is slightly higher than in Scenario 2-600 (2171), possibly due to early stage accessibility before fire spread.  
This phenomenon underscores the critical role of the fire ignition location, the distance between the ignition point and the shortest evacuation route, and wind direction in determining evacuation patterns within historical districts.
## 3 How to Calculate Accurate Airtime at the AP Side  
Paper [17] introduces the concept of responsible airtime fairness, suggesting that airtime fairness should encompass not only data transmission time but also all related overhead. However, paper [17] does not precisely determine the responsible airtime for each data frame. Its scheduling algorithm calculates based on the throughput of each node, while a more accurate approach should consider the rate and wireless transmission logic of each node. Additionally, paper [17] does not address how to handle the airtime issues caused by TCP ACKs.  
This section first analyzes the airtime composition of UDP and TCP. UDP's airtime is simpler as it does not include transport layer acknowledgment packets. It then examines the wireless queuing issues caused by TCP ACKs in TCP upload data and provides solutions. For ease of derivation and explanation, the calculation analysis in this paper excludes the RTS and CTS mechanisms of DCF. Including RTS and CTS would only increase the complexity of the derivation without affecting the paper's conclusions.
## 1 Introduction  
Sign Language is the mode of communication employed by specially abled people who are unable to hear or speak or both. It comprises a series of hand gestures mainly concentrated around the upper portion of the body, including occasionally the face, to convey sentences and emotions. There are various sign language systems based on the diverse alphabet systems as well as regional variants. For instance, Indian Sign Language addresses both the Hindi alphabet and the English alphabet, varying with respect to the number of hands utilized compared to American Sign Language. Despite the contrasts exhibited by such systems, their main goal is to facilitate human interaction. However, not everyone is well versed with sign language which can a be real hassle when a normal speaker is trying to understand a sign user. Hence, it is necessary to devise a system capable of recognizing sign language to foster better communication with maximum possible accuracy. With regards to this, the proposed system aims to tackle a small subset of sign language recognition known as static sign image classification by extracting features most representative of the input image.  
The proposed system makes use of ensemble methods such as random forest [1] and XGBoost [2] to augment overall classification accuracy at the expense of an initial feature extraction phase which reduces the input of the system to numerical features. The extracted features are mostly derived from a convolutional neural network [3] on the original images, images with edge detection [4] for the hand and non-hand region and images infused with keypoints that define spatial regions of interest, as well as hand coordinates. In addition to the ensemble methods, artificial neural networks [5] are also employed alongside the genesis of a hybrid artificial neural network that takes both original image and edge image as input. Due to the complexity of some benchmark datasets, they are trained directly on a convolutional neural network without a feature extraction stage.  
Overall, the experiments performed in this paper have led to the following research contributions:  
* The usage of numerical image features obtainable from a convolutional neural network, with classifiers other than artificial neural networks for acquiring stellar accuracy values, in this case, ensemble models such as random forest and XGBoost.
* A neoteric method to obtain features from an image with binary robust invariant scalable keypoints on hand edges.
* The inception of a hybrid artificial neural network architecture that exploits both original image data and edge image data to augment classification performance on sign gestures compared to a single artificial neural network.  
With the objective of the research now adequately defined, the following section discusses some notable works for the problem domain under consideration. Next, Section 3 elaborates on the dataset and implementation details followed by Section 4 that displays the results with the significant inferences. Lastly, a conclusion is drawn on the entire research and any scope for future improvement is also highlighted.
# Releasing the Full Potential of Cannabis through Biotechnology
## 4 Overcoming Limitations in Cannabis Gene Editing  
### Transformation Strategies  
Particle bombardment is a conventional tool to deliver reagents to somatic tissue, which has transformed a wide range of species without biological limitations. Besides, it enabled the production of DNA-free gene editing through the direct delivery of RNAs and proteins into plants [56]. However, the biolistic delivery is often inefficient, and the gold particles are expensive with specific protocols, making it less widely used as a valuable tool. Poly-ethylenimine cationic polymer-modified silicon dioxide-coated gold nanoparticles have been demonstrated as an efficient alternative to gold particles for passively infiltrating DNA and RNA into the Cannabis cells [55]. Since grafting DNA onto the surface of the nanoparticles is difficult, it is still a skill demanded process and far from routine in most laboratories. Thus, most of the transient and stable transformations reported in Cannabis were developed using _Agrobacteriumium_-mediated methods. Among them, sonication or vacuum-assisted _Agrobacteriumium_-mediated transformations were widely attempted in mature leaves, male and female flowers, proving to be an easy and low-cost method to for non-susceptible plant species, such as Cannabis [57].  
In the sonication method, when tensile stress is superimposed on plant tissues with the ambient pressure, cavitations created thousands of micro-wounds, allowing _Agrobacteriumium_ to penetrate into or even throughout the tissue completely, increasing the probability to infecting Cannabis cells compared to conventional wounds by cutting. Tests were performed to determine the best sonication time, and it was reported that thirty seconds of sonication led to 20% higher beta-glucuronidase (GUS) expression than that without sonication [57]. For vacuum treatment, explant tissues were submerged into the suspension of _Agrobacteriumium_ harboring binary vectors. Vacuum resulted in air evacuation from interstitial space of the submerged tissues, and then _Agrobacteriumium_ suspension entered plant cells to replace the evacuated air when breaking the vacuum [42]. Excessive vacuum time damages plant tissues, while short vacuum time results in insufficient evacuation with low efficiency. In [PERSON]'s study, both 10 min and 15 min of the vacuum treatment resulted in seven times higher GUS expression compared to non-vacuum control, also higher than those after shorter vacuum application [57].
# A Simple Water Retention Model Based on Grain Size Distribution
## 3 Proposed Model
### Pore Size Distribution to Water Retention Curve
For materials that do not contain coal particles, or if a simplified estimate of the contact angle is required, a constant contact angle should be used, as opposed to Equation (14). Each subpore within a pore space is assigned a bulk soil volumetric water content when the subpore is filled with water (\(\theta_{\rm ik}\)), such that the summation of every \(\theta_{\rm ik}\) equals the soil's porosity. This water content is based on the weighted average of the individual pore area (A\({}_{\rm ik}\)) to total pore area (A\({}_{\rm i}\)) as shown in Equation (15), which can determine the volumetric water content \(\theta_{\rm ik}\) of the k-th subpore within the i-th pore space. \[\theta_{\rm ik} =\theta_{\rm i}\frac{\Delta_{\rm ik}}{\Delta_{\rm i}} \tag{15}\]  
where A\({}_{\rm ik}\) is the area of the k-th subpore in the i-th particle size bin, and A\({}_{\rm i}\) is the total pore area for the pore space for the i-th size fraction, i.e., the sum of the k A\({}_{\rm ik}\) values in the i-th particle size bin, \(\theta_{\rm i}\) is the volumetric water content associated with the i-th particle size bin given by Equation (4). The volumetric water content at a particular suction \(\theta\)(s) is determined by Equation (16), which sums all \(\theta_{\rm ik}\) values that have a corresponding drainage suction s\({}_{\rm ik}\) greater than the suction s.  
\[\theta(\rm s)=\sum_{s<\theta_{\rm ik}}\theta_{\rm ik} \tag{16}\]  
Equation (16) allows a WRC to be constructed by considering a number of suction values and determining their corresponding volumetric water contents.
## 2 Materials and Methods
### Costs
#### 2.2.2 PV Cost  
The pricing of PVs has been in constant decline, currently at USD 0.2/Wp [38]. For the purposes of this study, a price of USD 1000/kWp has been utilized, in line with current commercial pricing in Greece. This is a turn-key price, including the PV panels, cabling, mounting, inverters, and other needed consumables for connection to the main grid or a microgrid.
## 3 Results
### Percentile Values Based on Maturity Status and Gender  
Table 7 presents the 5 th, 10 th, 25 th, 50 th, 75 th, 90 th, and 95 th percentile values for physical and physiological performance categorized by gender and maturity status for Tunisian basketball players.  
\begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**MANOVA Age \(\times\) Maturity**} & \multicolumn{4}{c}{**MANOVA Age**} & \multicolumn{4}{c}{**MANOVA Maturity**} \\ \hline  & & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) \\ \hline
5m sprint time (s) & Boys & 0.342 (0.914) & 0.009 & 1.633 (0.091) & 0.076 & 0.876 (0.418) & 0.008 \\ Girls & 0.517 (0.763) & 0.012 & 0.519 (0.889) & 0.026 & 0.344 (0.709) & 0.003 \\
10 m sprint time(s) & Boys & 0.243 (0.961) & 0.007 & 1.501 (0.133) & 0.071 & 1.358 (0.259) & 0.012 \\ Girls & 0.843 (0.520) & 0.020 & 1.517 (0.127) & 0.074 & 0.722 (0.487) & 0.007 \\
20 m sprint time without the ball (s) & Boys & 3.567 (0.002) \({}^{\star}\) & 0.090 & 4.600 (0.001) \({}^{\star}\) & 0.189 & 1.335 (0.265) & 0.012 \\ Girls & 0.318 (0.902) & 0.008 & 0.162 (0.999) & 0.008 & 0.072 (0.931) & 0.001 \\
20 m sprint time with the ball (s) & Boys & 0.378 (0.892) & 0.010 & 1.098 (0.364) & 0.053 & 0.182 (0.834) & 0.002 \\ Girls & 3.122 (0.010) \({}^{\star}\) & 0.069 & 0.941 (0.502) & 0.047 & 0.213 (0.808) & 0.002 \\ Agility test (s) & Boys & 1.266 (0.274) & 0.034 & 4.021 (0.000) \({}^{\star}\) & 0.169 & 0.293 (0.746) & 0.003 \\ Girls & 0.638 (0.671) & 0.015 & 1.955 (0.034) \({}^{\star}\) & 0.093 & 0.074 (0.929) & 0.001 \\ Flexibility (cm) & Boys & 0.963 (0.452) & 0.026 & 2.277 (0.012) \({}^{\star}\) & 0.103 & 0.555 (0.575) & 0.005 \\ Girls & 0.514 (0.766) & 0.012 & 0.668 (0.768) & 0.034 & 0.729 (0.484) & 0.007 \\ \hline \hline \end{tabular} Note: multivariate F statistics. \({}^{\star}\) indicates significant results (\(p<0.05\)) VO2 max: maximum oxygen uptake; MAV: maximum aerobic velocity; S]: squat jump; CM]: counter movement jump; height-S: counter movement jump with arm swing; Power-S: relative power with arm swing.  
\end{table}
Table 7: Percentile values for physical performance based on maturity status and gender.
# Leveraging Explainable AI to Support Cryptocurrency Investors
## 4 SHapley Additive Explanation Values
### Additive Feature Attribution Methods  
Given a training dataset consisting of a set of features \(\mathcal{F}=\{F_{1},F_{2},\ldots,F_{n}\}\), each value of an individual feature \(F_{i}\) acts as a player in a coalition. The number \(n\) of considered features can be interpreted as maximum coalition size.  
Let \(f\) be a complex prediction model, trained on \(\mathcal{F}\) instances. For the sake of simplicity, we assume the financial forecasting model \(f\) predicts the next-day closing price direction (i.e., _Uptrend_ or _Downtrend_) of a specific cryptocurrency based on the past samples observed in the last \(W\) days (Hereafter, we will disregard the _Stationary_ class (neither uptrend nor downtrend)).  
We seek explanations of \(f\) clarifying the effects of features in \(\mathcal{F}\). Specifically, we aim at explaining the prediction \(f(x)\) of an instance \(x\) of \(\mathcal{F}\) by computing the contribution of each individual feature.  
Within this scope, the Shapley value of feature \(F_{i}\) indicates how to fairly distribute the _payout_ among the features; i.e., it quantifies the effect of the individual feature \(F_{i}\) on the outcome of the prediction task. To generalize players as sets of feature values, we exploit the additive feature attribution method to linearly combine the individual Shapley values.  
The explanation model \(g\) is defined as a linear combination of binary features associated with each feature \(F_{i}\):  
\[g(z^{\prime})=\phi_{0}+\sum_{i=1}^{n}\phi_{i}\cdot z^{\prime}_{i}\,,\,\,z^{ \prime}\in 0,1^{n}\]  
where \(z^{\prime}_{i}\) is a binary variable denoting either the presence of a feature (\(z^{\prime}_{i}=1\)) or its absence (\(z^{\prime}_{i}=0\)). \(\phi_{i}\) is the \(F_{i}\)'s attribution value, which quantifies the effect of \(F_{i}\) on \(f(x)\). The explanation model sums the effect of all individual feature attributions approximating the output.