esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Kinetics of Fluoride Adsorption on WA  
Kinetic experiments were conducted to determine the optimal contact time and the fluoride removal mechanism of WA. Figure 1 presents the results of experiments conducted to evaluate the efficiency of WA in removing fluoride from a fluoride solution under varying reaction times (0.25-24 h), employing pseudo-first-order (PFO), pseudo-second-order (PSO), Elovich, and intra-particle diffusion (IPD) models. Fluoride adsorption by WA initially proceeded rapidly and reached the equilibrium capacity (112.09 \(\pm\) 3.9 mg/g) within 1 h. Table 1 presents the parameters obtained from the kinetic models, and the coefficient of determination (\(R^{2}\)) was in the order of Elovich (0.998) \(>\) PSO (0.997) \(>\) PFO (0.989). The kinetic adsorption of fluoride by WA was well explained by the Elovich model, which assumes that the activation energy increases during the adsorption reaction, and that the adsorbent surface is heterogeneous [35]. The PSO model also exhibited a high \(R^{2}\), and the maximum fluoride adsorption capacity observed experimentally (115.99 mg/g)closely matched the value predicted through PSO model analysis (113.50 mg/g). This assumes that the adsorption of the contaminant is controlled by chemisorption, which involves electron sharing or exchange between the adsorbent and the contaminant [36]. The PFO rate constant (\(k_{1}\)) of WA for fluoride was faster than those reported for calcined _Mactra veneriformis_ shells (0.55/h), aluminum-impregnated Kenaf biochar (0.57/h), and bottom ash derived from the combustion of cattle manure (6.67/h), as provided in the literature [12; 37]. The IPD results for fluoride adsorption onto WA (Figure 1b) showed a linear relationship in all three plots, none of which passed through the origin. Initially, fluoride was adsorbed onto the surface of WA via the film diffusion process, followed by transfer into the pores of WA through the intra-particle diffusion process, and was gradually adsorbed until equilibrium was reached [38]. This suggests that both surface adsorption and intra-particle diffusion contributed to the rate-determining step in fluoride adsorption by WA [38; 39]. These results indicate that the adsorption of fluorine onto WA occurs rapidly and is primarily driven by chemisorption.  
Figure 1: Fluorine adsorption by wood ash (WA) over time and its comparison with kinetic model fittings. (**a**) pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich model; (**b**) intra-particle diffusion (IPD) model.
# Evaluating Secure Methodology for Photo Sharing in Online Social Networks
## 9 Results and Discussion
### Discussion
First, the photograph is old; that is, different ages are there as shown in Figure 21a. Second, the person wears an accessory such as sunglasses or a mask as shown in Figure 21b. Third, the person uses a filter while taking the photo as shown in Figure 21c. Forth, the face capture angle to the right or left side as shown in Figure 21d,e and face capture angle from down as shown in Figure 21f. Fifth, a person was in dark photos as shown in Figure 21j. Sixth, the image is flipped left or right for example Figure 21h. Seventh, the photo accuracy was also a factor in the model's inability to predict an individual's identity, for example Figure 21i. Eight, when the location of the person is so far when taking a picture, this causes face features to be blurred, for example Figure 21k. Figure 15 illustrates the distribution of these reasons for the photos whose owners are unknown. Face looking right or left photos were the largest in our experiment, accounting for 27% of faces not recognized. 22% were for old photos. 17% for pictures of people wearing an accessory such as glasses or a mask. 9% for medium resolution photos or poor quality. 8% for dark photos. 7% for tilted photos where the app could not identify the face. 6% The location of people on the photos was too far away and the application could not extract the features. 4% of people publish photos using a filter whose characteristics have changed compared to the original. An issue in our application is observed that is the mistakes in prediction process. This has been defined in Section 7. The TP, FN and FP percentage is illustrated in Figure 14. Figure 22 shows sample in which there are errors in the identity of the predicted persons in the model. Figure 13:0 Samples of face not recognized: (**a**) represents the Photograph is old, (**b**) represents wears an accessory, (**c**) represents using a filter, (**d**–**f**) represents face capture angle to the right or left or down, (**j**) represents dark photos, (**h**) represents the image is flipped left or right, (**i**) represents photo accuracy and (**k**) represents the person is so far. Based on our result, we could conclude some registration restrictions. For example, once the face photo is taken the face should be adjusted directly to the camera and the lighting is clear. If the image has clear face features, we can get accurate results, especially with new images.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 3 Methods
### CKSP Convolution Module Based on StripPooling  
In YOLOv11, the C3K2 module serves as the core feature extraction and fusion unit, designed based on the CSP architecture to enhance model expressiveness and computational efficiency. The CSP module splits input feature maps into two branches: one undergoes deep feature extraction via bottleneck layers while the other retains original information via skip connections. This design reduces computational costs, enhances gradient flow, and improves training effectiveness. The C3K2 module leverages the advantages of CSP architecture by combining deep processing of partial features with direct transmission of others, achieving efficient feature fusion. This not only improves detection performance in complex scenarios but also maintains low computational costs, enabling YOLOv11 to balance real-time processing with strong detection capabilities.  
Figure 3: Overall architecture of the proposed model.  
To further enhance YOLOv11's ability to detect occluded targets in complex scenes, this paper integrates the StripPooling [34] module into the C3K2 component. StripPooling captures global contextual information along the horizontal and vertical directions using elongated pooling kernels (1 \(\times\) N and N \(\times\) 1), effectively aggregating long-range dependencies and local context. For input feature map X, StripPooling operates as follows: (1) Extract horizontal and vertical features to capture long-range relationships and local context. (2) Apply 1D convolution to pooled features to extract adjacent feature information. (3) Fuse horizontal and vertical features via summation to generate a feature map enriched with global priors. (4) Refine the fused features using 1 \(\times\) 1 convolution and a sigmoid function to balance feature weights. (5) Multiply the refined features with the original input to produce the final output. The structure diagram of CKSP is shown in Figure 4.  
After integrating StripPooling, the C3K2 module adjusts input channels via standard convolution, then splits features into a main branch (with StripPooling-enhanced bottlenecks for global context) and an auxiliary branch (preserving original details). The two branches are fused along the channel dimension, followed by 1 \(\times\) 1 convolution for further integration. This significantly enhances YOLOv11's robustness and accuracy for small and occluded targets.
## 3 Results and Discussion

Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Predictive** & \multicolumn{2}{c}{**Input**} & \multicolumn{1}{c}{**Stages**} & **RMSE** & **MAE** & **LMI** & **CC** & **WI** & **SRMSE** \\
**Models** & **Combination** & **Stages** & **RMSE** & **MAE** & **LMI** & **CC** & **WI** & **SRMSE** \\ \hline ANFIS-GA & Model-1 & Train & 0.291 & 0.222 & 0.636 & 0.922 & 0.958 & 16.416 \\ ANFIS-GA & Model-1 & Test & 0.368 & 0.271 & 0.562 & 0.889 & 0.939 & 22.172 \\ ANFIS-GA & Model-2 & Train & 0.313 & 0.235 & 0.614 & 0.908 & 0.951 & 17.673 \\ ANFIS-GA & Model-2 & Test & 0.433 & 0.297 & 0.520 & 0.844 & 0.915 & 26.069 \\ ANFIS-GA & Model-3 & Train & 0.383 & 0.270 & 0.556 & 0.859 & 0.921 & 21.571 \\ ANFIS-GA & Model-3 & Test & 0.457 & 0.345 & 0.443 & 0.822 & 0.896 & 27.497 \\ ANFIS-GA & Model-4 & Train & 0.301 & 0.215 & 0.647 & 0.915 & 0.953 & 16.976 \\ ANFIS-GA & Model-4 & Test & 0.356 & 0.262 & 0.578 & 0.897 & 0.943 & 21.442 \\ ANFIS-GA & Model-5 & Train & 0.325 & 0.245 & 0.598 & 0.900 & 0.944 & 18.343 \\ ANFIS-GA & Model-5 & Test & 0.392 & 0.282 & 0.544 & 0.874 & 0.926 & 23.577 \\ ANFIS-GA & Model-6 & Train & 0.479 & 0.385 & 0.367 & 0.766 & 0.858 & 27.024 \\ ANFIS-GA & Model-6 & Test & 0.571 & 0.427 & 0.311 & 0.711 & 0.828 & 34.367 \\ ANFIS-GA & Model-7 & Train & 0.347 & 0.256 & 0.579 & 0.887 & 0.932 & 19.838 \\ ANFIS-GA & Model-7 & Test & 0.385 & 0.284 & 0.544 & 0.881 & 0.923 & 24.027 \\ ANFIS-GA & Model-8 & Train & 0.300 & 0.231 & 0.620 & 0.918 & 0.957 & 17.127 \\ ANFIS-GA & Model-8 & Test & 0.328 & 0.237 & 0.619 & 0.913 & 0.955 & 20.444 \\ ANFIS-GA & Model-9 & Train & 0.352 & 0.249 & 0.591 & 0.883 & 0.932 & 20.101 \\ ANFIS-GA & Model-9 & Test & 0.448 & 0.336 & 0.461 & 0.828 & 0.896 & 27.925 \\ ANFIS-GA & Model-10 & Train & 0.352 & 0.259 & 0.574 & 0.884 & 0.930 & 20.111 \\ ANFIS-GA & Model-10 & Test & 0.389 & 0.285 & 0.542 & 0.876 & 0.923 & 24.277 \\ ANFIS-GA & Model-11 & Train & 0.336 & 0.258 & 0.576 & 0.894 & 0.940 & 19.167 \\ ANFIS-GA & Model-11 & Test & 0.376 & 0.280 & 0.551 & 0.882 & 0.933 & 23.466 \\ ANFIS-GA & Model-12 & Train & 0.457 & 0.350 & 0.424 & 0.791 & 0.870 & 26.120 \\ ANFIS-GA & Model-12 & Test & 0.431 & 0.344 & 0.447 & 0.842 & 0.905 & 26.884 \\ ANFIS-GA & Model-13 & Train & 0.300 & 0.232 & 0.618 & 0.916 & 0.955 & 17.119 \\ ANFIS-GA & Model-13 & Test & 0.331 & 0.262 & 0.579 & 0.912 & 0.951 & 20.650 \\ ANFIS-GA & Model-14 & Train & 0.295 & 0.237 & 0.610 & 0.922 & 0.959 & 16.836 \\ ANFIS-GA & Model-14 & Test & 0.370 & 0.279 & 0.552 & 0.897 &The best performing hybrid model (i.e., ANFIS-PSO) used input combination Model-7 configured with pre-processed variables (_d, a, a, g, fc_, and \(\rho\)) (Table 6). The best inputs combination (i.e., Model-7) was generated good prediction results over both modeling phases with statistical results (_RMSE_ = 0.206, 0.283; _MAE_ = 0.157, 0.213; _LMI_ = 0.742, 0.659; _CC_ = 0.961, 0.935; _WI_ = 0.980, 0.965; _SRMSE_ = 11.791, 17.671). The uncertainties arise in model, variables, and data are reported in Table 7 based on the interquartile range (IQR) indices. The assessment metrics attained in investigating model and variable
## 3 Results and Discussion
### Influence on the Hysteresis Loops  
The hysteresis loops of the viscoelastic material are shown in Figure 4. The first subplot shows the hysteresis loops before the viscoelastic tapes were submerged into seawater. Subplot 2 to Subplot 10 correspond to the tapes being submerged for 1-9 months. Subplot 11 shows the hysteresis loops when the samples was submerged for 5 years. Subplot 12 compares all the hysteresis loops.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Initial** & **1 m** & **2 m** & **3 m** & **4 m** & **5 m** & **6 m** & **7 m** & **8 m** & **9 m** & **5 Year** \\ \hline
48,258 & 52,854 & 45,657 & 52,776 & 45,619 & 47,462 & 53,313 & 53,943 & 47,848 & 50,754 & 50,793 \\ \hline
51,335 & 53,221 & 46,887 & 52,493 & 50,100 & 53,910 & 46,269 & 49,303 & 46,506 & 53,625 & 51,562 \\ \hline
53,417 & 46,997 & 50,629 & 47,807 & 51,063 & 51,356 & 51,846 & 49,369 & 46,316 & 47,525 & 48,739 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimated bounding stiffness (unit N/m\({}^{1.5}\)).  
Remark 1: As illustrated from these figures, the shapes of the hysteresis loops are very similar, which indicates that the seawater has little influence on the mechanical properties of the viscoelastic material.  
Remark 2: Although all the hysteresis loops in Figure 4 are very similar, minor differences are still observed among these loops. The cause of these minute differences may be the different impact points during the impacts although these locations are very close to each other.  
Remark 3: From the subplots in Figure 4, we also notice that there is a slight decrease in the stiffness measured during the 5 th year as compared to previous years. It is possible that the seawater has a \"softening\" effect on the viscoelastic material over a long period, which should be further studied.
# Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination +
## 2 Data and Method
It is widely acknowledged that how effectively remove outliers plays a decisive role in the accuracy of power curve estimation since they prominently influence either the fit of the model or the estimation of parameters [14]. The causes of outliers are multifaceted, which may include the operational abnormality because of outage, curtailment, and maintenance, the average-out effect from the lack of temporal resolution, and measurement errors. No  
Figure 1: Traditional Power Curve of Wind Turbines. matter what the causes of outliers are, the most important aspect of outlier issues is the technique to identify them. It is suggested that outliers are divided into three different types in this study as shown in Figure 2. Data points that little power is generated even at a wind speed higher than the cut-in speed are labeled as Type I outliers. The main probable causes of Type I outliers are the outage and curtailment due to failure and maintenance. Type II outliers designate data points that unusually higher outputs are generated at a given wind speed, and their most probable causes are measurement errors [10]. The so-called average-out effects may cause Type III outliers that the generation output can be less than the expected output when a turbine is stopped or the output is curtailed for a shorter period of time than the data collection interval, i.e., 1-h in this case. The incorrect load of wind rotor as well as measurement errors may also be the source of Type III outliers. It may seem simple and intuitive to screen out these outliers, but it will be highly inefficient to revise the power curves better representing the operational characteristics of individual turbines as more data become available over time. This study postulates that a rigorous and scientifically sound procedure for outlier detection may facilitate the periodic revision of power curve modeling. Machine learning algorithms combined with traditional statistical approaches can enhance the ability to cope with variations in wind power production while ensuring the accuracy of power curves, especially when a larger amount of training and testing data is being collected over the long-term time period. Figure 2: Observations of Power Output against Wind Speed. (**a**) 1.5 MW Wind Turbine; (**b**) 3.0 MW Wind Turbine.
## 2 Models and Principles
### LAC Block
#### 2.2.2 Depthwise Separable Convolution  
Depthwise separable convolution is a lightweight enhancement to traditional convolutional neural networks. Using this structure can reduce the number of parameters during model training, reduce the amount of computation, and boost the model-trained speed. Feature acquisition and feature composition for the ordinary convolutional layer are performed in one step, while depthwise separable convolution decomposes this into two steps. First, the depthwise convolution [33] uses the convolution kernel with input channel 1 to extract features. Additionally, then, the pointwise convolution [33] uses the convolution kernel of size 1 to combine features. This process is shown in Figure 3.  
Figure 3: Diagram of the depthwise separable convolution process.  
Figure 2: Schematic diagram of backbone-enhanced convolution kernel.  
Supposing the feature map has a length and width equal to \(D_{in}\), its size is \(D_{in}\times D_{in}\times M\), where \(M\) refers to the number of channels in the input feature map. Similarly, it is assumed that the feature map has a height and width equal to \(D_{out}\). However, the output feature map's size is \(D_{out}\times D_{out}\times N\), where \(N\) refers to the number of channels of the output feature map. If the convolution kernel size is \(D_{K}\times D_{K}\), after passing the standard convolution, the parameter quantity of the standard convolution layer is \(P_{standard}\); see Formula (1).  
\[P_{standard}=D_{K}\times D_{K}\times M\times N \tag{1}\]  
The parameter quantity of the depthwise convolution is \(P_{depthwise}\); see Formula (2).  
\[P_{depthwise}=D_{K}\times D_{K}\times 1\times M \tag{2}\]  
The parameter quantity of the pointwise convolution is \(P_{pointwise}\); see Formula (3).  
\[P_{pointwise}=1\times 1\times M\times N \tag{3}\]  
The parameter quantity of the depthwise separable convolution and the standard convolution part is compared as in Formula (4).  
\[\frac{P_{standard}}{P_{depthwise}+P_{pointwise}}=\frac{D_{K}\times D_{K} \times M\times N}{D_{K}\times D_{K}\times 1\times M+1\times 1\times M \times N}=\frac{1}{N}+\frac{1}{{D_{K}}^{2}} \tag{4}\]  
Previous experiments [33] demonstrated that the amount of parameters is 8- to 9-fold fewer than the amount of standard convolution when using a 3 \(\times\) 3 depthwise separable convolution, and the accuracy is only slightly less.
# Comparing the Quality and Speed of Sentence Classification with Modern Language Models
## 4 Results and Discussion
### Results of the Main Study  
A comparison of selected LMs in the previously described framework was carried out on two data sets. The results obtained with the TREC6 data set are presented in Figure 5. The results obtained with the preliminary data set are shown in Figure 6.  
The results presented in Figure 5 indicated that the baseline provided by static embeddings created by Glove and FastText was high and reached a \(0.933\pm 0.005\) MCC score. Recent transformer-based models outperformed this baseline and have achieved up to a \(0.96\pm 0.006\) MCC score for the BERT large uncased scalar mix version. Simultaneously, the MCC scores achieved by RoBERTa, XLNet, and variants of these models were unexpectedly worse than those of BERT for several possible reasons, such as incorrect training regimens for these LMs and data set properties. Generally, these results may indicate that, given the small differences in quality and the small data set size, to distinguish among very good models, a similar benchmark analysis must be used on an n-times-larger data set. Simultaneously, the results achieved on the MPD (see Figure 6) revealed a different picture. A large and statistically significant discrepancy was observed between the MCC scores of the baseline LMs providing static word embeddings, such as Glove (\(0.402\pm 0.051\)) or FastText (\(0.47\pm 0.047\)), and state-of-the-art context-aware LMs, such as RoBERTa (\(0.59\pm 0.026\)). Leveraging the scalar mix technique allowed us to use the output from all available layers of these complex models and thus achieve the best results in all tested model pairs. The default layer of the XLNet achieved a very poor result of \(0.377\pm 0.063\), whereas the scalar mix version achieved \(0.521\pm 0.038\); the straightforward BERT scored \(0.532\pm 0.043\) and its scalar mix counterpart scored \(0.596\pm 0.027\); and the RoBERTa and RoBERTa scalar mix version achieved \(0.59\pm 0.026\) and \(0.603\pm 0.023\), respectively. Therefore, the scalar mix technique appeared to be highly useful, in agreement with findings from [PERSON] et al. [32]. However, the increase in quality came at the expense of both TT and IT. As expected, the IT was the lowest for LMs providing static embeddings, reaching as low as \(0.111\pm 0.011\) s per 100 sentences for the FastText LM. The highest inference time of \(3.822\pm 0.015\) was obtained with the XLNet scalar mix LM. Additionally, the TTs of the contextualized LMs in their scalar mix versions were the highest, reaching \(1.444\pm 0.175\) h for the XLNet scalar mix LM, whereas the baseline Glove-based framework required only \(0.069\pm 0.011\) h to train.
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 5 Experiment Results  
The present study was conducted using the historical price data of three high-volume stocks, namely, STC, Mobily, and Zain KSA, over a period of 1462 days, specifically, from 1 January 2017 to 8 November 2022. Only the closing prices of the aforementioned stocks were used to univariately forecast their closing prices for the subsequent seven days. Meanwhile, four different prices, namely closing, opening, highest, and lowest, were used to multivariate forecast their closing prices for the subsequent seven days.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 4 QMTSF Algorithm
### Uqrl Algorithm  
Reinforcement learning is an experiential machine learning technique where the agent acquires experience through a trial-and-error process to improve future choices. In this case, the problem to be solved is represented as a discrete-time stochastic control process known as a Markov Decision Process (MDP). Typically, a finite MDP is defined by a tuple \((S,A,p,r)\), where \(S\) is the finite state space, \(A\) is the finite action space for each state \(a\in A\), \(p\) is the state transition probability from state \(s\) to state \(s^{{}^{\prime}}\in S\) when taking action \(a\in A\), and \(r\in R\) represents the immediate reward obtained after performing action \(a\). The main objective of the agent is to interact with its environment at each time step \(t=0,1,2,\dots\) to find the optimal policy \(\pi^{*}\) that achieves the goal while maximizing the long-term cumulative reward. The policy \(\pi\) is a function that represents the strategy used by the RL agent. It takes a state \(s\) as input and returns the action \(a\) to be executed. The policy can be stochastic as well, in which case it returns a probability distribution over all possible actions instead of a single action \(a\). A Q-Value table is constructed in the server to store the accumulated rewards obtained, and is iteratively updated using the Bellmanequation, allowing the decision-maker to always choose the best action at each decision time, as shown in the following formula:  
\[Q_{(s,a)}\gets Q_{(s,a)}+\alpha\left[R+\gamma\max_{a^{\prime}\in A(s^{\prime })}Q_{(s^{\prime},a^{\prime})}-Q_{(s,a)}\right]\]  
where \(\alpha\) is the learning rate and \(\gamma\in[0,1)\) the discount factor, which controls the importance of future rewards relative to the current reward. A larger \(\gamma\) places more emphasis on expected future returns. If \(\gamma=0\), the agent is \"short-sighted\" and only concerned with maximizing immediate rewards.