esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Preprocessing  
#### 4.1.1 Masking  
Two masking steps precede the workflow. In a first step, a land cover mask is applied to each Sentinel-1A and Sentinel-1B scene, as this study focuses on soil moisture estimation within agricultural areas. Using a binary mask created from the CORINE Land Cover Data, where non-agricultural areas are set to zero and agricultural areas are set to one, masked pixels are excluded from further processing, reducing the amount of computational resources in the first place. In this regard, the method is not applied to areas, where C-band is not able to penetrate through the vegetation cover and would give unreasonable results, e.g., forest areas [65]. In the next step, a soil freeze and snow cover filter were applied, as both events influence the retrieved dielectric constant and lead to wrong soil moisture estimations using a short-term change detection method. The snow filtering was done using the MODIS Terra Snow Cover Daily Global dataset [66], while the freeze filtering was done using the COM-C Land Surface Temperature (V2) dataset [67], filtering out scenes, where the air temperature at 2 m height is below 3 \({}^{\circ}\)C, as soil freezing is possible [68]. The filtered pixels are replaced by calculating the temporal mean between 15 days before and 15 days after the snow or freeze event, to ensure a continuous time series also during the winter period. Since heavy convective precipitation events could also potentially affect the backscattering signal, there could be theoretical possibility that a change in backscatter is related to such specific meteorological conditions. As these effects usually only occur under heavy convective storm events (with over 50 mm/h), these conditions only prevail in approximately 0.1% of areas on a global scale, with the majority of these areas located in the tropics [69]. Since the likelihood of such events occurring during SAR observations in both study areas is even lower, it is not considered in this study. However, if the method is applied on a global scale, it could become a crucial factor, particularly within the tropics.  
Figure 4: Workflow of the soil moisture estimation algorithm, which can be divided into two main blocks: Preprocessing (1) and soil moisture estimation (2). Especially, the steps of Normalize Incidence Angle, Fourier Series Transformation, Convolution and Vegetational Correction are introduced and discussed in detail, as they are crucial for the assimilation of multi-orbit SAR observations for obtaining a vegetation adapted, temporally high-resolution time series for the subsequent short-term change detection approach.
## 3 Results and Discussion
### Evaluation of Novel HDES Solvent Combinations for R-32 Absorption  
Table 1 contains ln activity coefficients for the top preforming DES combinations and R32 systems. The entire data from the study may be found as a heatmap with values in Figure S1 of the Supplementary Files. Interestingly, all DES combinations provide ln activity coefficients below 1, suggesting non-ideal attractive forces. All compositions are kept at 1:1 molar ratio with a temperature of 25 \({}^{\circ}\)C and a pressure of 1 bar. The top four HBA in rank of lowest ln activity coefficients to highest were found to be tetrabutylammonium chloride  
Figure 1: Experimental literature solubility data fordifluoromethane in ionic liquids at varying pressures between 0.1 and 2.5 bar compared with computed values. The resulting error analysis produces an AARD% value of 25.7%, and a regressed R-squared value of 0.86. The plotted points are experimental vs computed values, the line is an overlay with a slope of 1 for reference of how well the experimental and computational agree with each other. Closer proximity to the line of slope one indicates lower discrepancies between the experimental and literature values.  
(N4 Br) (\(-1.39\)), tetraoctylammonium chloride (N8 Br) (\(-1.39\)), benzyltriethylammonium (BTACI) (\(-1.27\)), and methyltrioctylammonium bromide (MTOA) (\(-1.03\)). These HBA outperformed the terpen, shorter chain quaternary ammonium salts, imidazoles, and phosphonium-based HBAs. These four HBA share a common trait of being the largest molecules. The results are logical as they contain significant amounts of carbon, which offers non-polar interaction sites. The results are in line with the sigma analysis in Section 3.3. Other works have shown that longer chain quaternary ammonium salts have higher absorption capacities for non-polar gases vs. their shorter chain counterparts [12].  
The accompanied HBDs that produced the lowest In activity coefficient with HBA are polyethylene glycol (PEG), camphor, eucalyptrol, and lidocaine. Three of these are terpen and one is a glycol. These four HBD share the property of lacking polarity, which is favorable for a relatively non-polar molecule such asdifluorethane. The results are reasonable due to the electronic signatures of difluoromethane and the DES components, discussed in detail in Section 3.3. These types of DES components are utilized in non-polar absorption scenarios and aqueous extractions. The rest of the studies are performed with the 16 HBA-HBD combinations of these solvents.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 2 Overview of the Technologies and Comparative Discussion
### Related Work
With these limitations in mind, in the following, we provide results from large scale simulations performed in different scenarios and with different MCSs; in addition, we consider an ideally modified IEEE 802.11p with a PHY layer providing the same performance as that of C-V2X.
## 3 Results
### Changes of Abdominal Fat According to Exercise Intensity  
The changes in abdominal fat after eight weeks of exercise were compared between the MICT and HIIT groups. There were significant differences in the abdominal fat thickness, abdominal circumference, and hip circumference (abdominal fat thickness: z = \(-\)2.004, \(p\) = 0.050, abdominal circumference: z = \(-\)2.223, \(p\) = 0.026, hip circumference: z = \(-\)2.111, \(p\) = 0.035) (Table 4). Within the groups, both groups showed significant changes in abdominal fat thickness, abdominal circumference, and hip circumference (MICT: abdominal fat thickness: z = \(-\)2.184, \(p\) = 0.029, abdominal circumference: z = \(-\)2.555, \(p\) = 0.011, hip circumference: z = \(-\)2.555, \(p\) = 0.011, hip circumference: z = \(-\)2.555, \(p\) = 0.011, hip circumference: z = \(-\)2.527, \(p\) = 0.012, abdominal circumference: z = \(-\)2.536, \(p\) = 0.011, hip circumference: z = \(-\)2.552, \(p\) = 0.011) (Table 4, Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MICT (\(n\) = 8)**} & \multicolumn{2}{c}{**HIIT (\(n\) = 8)**} & \multicolumn{2}{c}{**Diff**} \\ \cline{2-7}  & **Pre** & **Post** & **Pre** & **Post** & **z** & **\(p\)** & **Cohen’s d** \\ \hline
**BW (kg)** & 66.88 \(\pm\) 4.57 & 65.01 \(\pm\) 4.29 * & 65.51 \(\pm\) 4.77 & 59.92 \(\pm\) 4.68 * & \(-\)1.785 & 0.074 & 1.133 \\
**BFM (kg)** & 24.92 \(\pm\) 2.77 & 23.86 \(\pm\) 2.50 * & 25.38 \(\pm\) 4.60 & 19.86 \(\pm\) 4.40 * & \(-\)2.102 & 0.036 & 1.117 \\
**SMM (kg)** & 22.66 \(\pm\) 1.86 & 22.37 \(\pm\) 1.67 & 21.58 \(\pm\) 2.18 & 21.66 \(\pm\) 2.07 & \(-\)1.261 & 0.210 & 0.213 \\
**BMI (kg/m\({}^{2}\))** & 25.36 \(\pm\) 2.33 & 24.63 \(\pm\) 2.01 * & 25.82 \(\pm\) 1.98 & 22.41 \(\pm\) 1.44 * & \(-\)2.312 & 0.021 & 1.269 \\
**VFL +** & 12.75 \(\pm\) 1.83 & 11.87 \(\pm\) 1.72 * & 12.12 \(\pm\) 1.88 & 8.00 \(\pm\) 1.85 * & \(-\)3.018 & 0.003 & 2.166 \\
**PBF (\%)** & 37.51 \(\pm\) 2.29 & 35.56 \(\pm\) 2.57 * & 38.56 \(\pm\) 5.00 & 34.50 \(\pm\) 5.17 * & \(-\)0.579 & 0.563 & 0.259 \\ \hline \hline \end{tabular} Note: Values are presented as mean \(\pm\)SD (\(n\) = 8 per group). * \(p\) \(<\) 0.05 from Pre and Post. * \(p\) \(<\) 0.05 between groups. HIIT: high intensity interval training, MICT: moderate intensity continuous training, BW: body weight, BFM: body fat mass, SMM: skeletal muscle mass, BMI: body mass index, VFL: visceral fat level, FBF: percentage of body fat.  
\end{table}
Table 3: Responses on body composition for MICT and HIIT groups.
# Responses of Water Temperature and Level to Large Earthquakes in Tibet
## 5 Conclusions  
Although it is currently a single well, as the world's rare highest altitude (>3500 m) and well-equipped well water level and temperature observation system, and as the frontier of geodynamics research on the Tibetan Plateau, our data from the Lhasa station will undoubtedly be of great reference value to other research colleagues (both within China and worldwide). By analyzing the responses of the water temperature and water level at the Lhasa geomagnetic station to large earthquakes, the following conclusions can be obtained.  
(1) The co-seismic changes in water temperature and water level differ: the water level changes are of oscillation type, while the water temperatures exhibit first heating and then recovery. For the M8.0 Wenchuan earthquake, the water level exhibited a water shock wave, and the co-seismic temperature first increased by 0.0066 \({}^{\circ}\)C and then decreased to 0.0079 \({}^{\circ}\)C.  
(2) The co-seismic changes are related to the epicentral distance, magnitude, and focal depth of the earthquake. The closer and larger magnitude earthquakes triggered greater water temperature changes, such as the M8.1 Nepal earthquake (0.0072 \({}^{\circ}\)C) and the M8.0 Wenchuan earthquake (0.0065 \({}^{\circ}\)C). The smaller the epicentral distance is, the earlier the co-seismic responses of the well water level and the well water temperature occur; however, the time interval between the co-seismic changes in the well water level and the well water temperature differ.  
(3) The co-seismic ratio of the water temperature is higher than that of the water level; this may be related to faulty water level instrumentation or paragraph recordings, although the specific reasons need to be further studied.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122211584/s1](https://www.mdpi.com/article/10.3390/app122211584/s1)]([https://www.mdpi.com/article/10.3390/app122211584/s1](https://www.mdpi.com/article/10.3390/app122211584/s1)). [PERSON] performed the observation analysis and interpreted the results. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] elaborated the study and wrote the manuscript. [PERSON] and [PERSON] provided comments to improve the manuscript. All authors examined the results and interpretations and participated in manuscript writing. All authors have read and agreed to the published version of the manuscript.  
This study benefited from financial support provided by the Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program and the Second Tibetan Plateau Scientific Expedition and Research Program (2019 QZKK0708).  
Not applicable.  
Figure 10: Long-term observation curve of the static water level (unit: m) at the Lhasa geomagnetic station.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 5 Discussion
### Technical Challenges and Scalability Considerations  
Deploying this large-scale infrastructure presented several common technical challenges in big data studies, including the following.  
* Normalization of domains with spelling variants, such as login.institution.edu and www.institution.edu/login.
* Real-time processing of Tshark data streams, which presented latency-related difficulties.
* A balance between accuracy and performance in machine learning models is critical in educational environments with limited computational resources.  
While BotSoul effectively emulates user behavior to capture dynamic cookie attributes, the simulation may still differ from actual human interaction in subtle ways, such as mouse movement, scrolling behavior, or response to pop-ups. These behavioral discrepancies could influence the creation or modification of cookies on certain websites. Future work could incorporate more sophisticated human-like interaction patterns--potentially using reinforcement learning or human behavior modeling--to increase fidelity and improve data completeness.
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 6 Conclusions  
Feature selection is a crucial step in text classification in order to overcome the high dimensionality of the feature space and improve the classification accuracy. Thus, many feature selection methods have been proposed in the literature. Those methods could be ranking methods, which rank the features and select the top ranked ones, or meta-heuristic-based methods that work as a wrapper around certain classification algorithms. However, the ranking methods ignore the correlation between features, while the wrappers are classifier-dependent. To overcome those limitations, this paper introduced the cooperative binary Bat algorithm (BBA\({}_{\text{CO}}\)) and investigated its performance for the text feature selection. The quality of the final solution and the number of selected features are also compared with those of BBA\({}_{\text{LHS}}\); in order to test the impact of cooperative BBA.  
The text classification was performed on two slandered English datasets, namely Reuters and WebKB, to evaluate the discriminative ability of the produced feature set using BBA\({}_{\text{CO}}\). The best classification results obtained with BBA\({}_{\text{CO}}\) were 94% and 92.8% in terms of Micro Average for the Reuters and WebKB datasets, respectively. In comparison, the best results obtained by the other methods were 90.5% and 89.3% for the Reuters and WebKB datasets, respectively, and those results were obtained by IG. The text classification was also performed for two Malay and one Arabic datasets. The statistical test demonstrated that the improvement of the classification performance was significant. The experimental results in this work have shown the ability of the proposed method to improve the final solution, control the population's diversity and improve the text classification accuracy. In addition, the proposed method was approved to be general in terms of dataset language.  
For future work, the proposed coevolutionary method could be tested on a different population-based meta-heuristic algorithms to evaluate its performance. Additionally, the proposed coevolutionary method could be adapted to different high dimensional optimization problems. Moreover, designing a feature selection method based on parallel BBA could also be a better option for very high-dimensional datasets.  
Conceptualization, S.A.; Funding acquisition, N.O. and S.A.; Investigation, A.A.; Project administration, N.O.; Resources, A.A.-S.; Software, A.A. and A.A.-S.; Supervision, N.O. and S.A.; Writing--original draft, A.A.; Writing--review & editing, N.O. and S.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the grant FRGS/1/2020/ICT02/UKM/02/6 and TAP-K007009.  
Not applicable.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effects of Varying Reaction Times in Absolute Alcohol on SnO\({}_{2}\) Morphology  
When absolute alcohol was used as the solvent and PVP was not added, the reaction temperature was 200 \({}^{\circ}\)C, and the reaction periods were 8, 12, and 48 h.  
As observed in Figure 1, the morphology evolution of the products indicated that small particles with smooth edges aggregate into large particle clusters after reacting for 8 h. Furthermore, irregular flake morphology appears in the vicinity of aggregated particle clusters after reacting for 12 h, whereas the product morphology is dominated by lamelliform structures without any 1D structures after reacting for 48 h. Because of the absence of templates, surfactant, and aqueous solution under this condition, the chemical potential in the solution was insufficient to trigger the nucleation and growth of 1D structures.  
The effects of varying the volume ratios (V) between absolute alcohol and deionised water on SnO\({}_{2}\) morphology were investigated at a reaction temperature of 200 \({}^{\circ}\)C for 48 h without adding PVP.  
Figure 1: SnO\({}_{2}\) morphology at 200 \({}^{\circ}\)C, with 80 mL absolute alcohol, without polyvinylpyrrolidone (PVP), and reaction period of (**a**) 8, (**b**) 12, and (**c**) 48 h.  
As observed in Figure 2, with a decreased content of absolute alcohol and an increased content of deionised water, the SnO\({}_{2}\) morphology evolves from a lamelliform into a microsphere structure via self-assembled 1 DSnO\({}_{2}\). Eventually, these microspheres aggregate into a quasi-microsphere structure. Note that 1D structures appear in final products only if the value of V approaches one. Based on the aggregation degree of 1D structures self-assembled into quasi-microspheres, the edges of the quasi-microspheres in Figure 2e are more prominent at V = 1, which is distinct from the quasi-microsphere aggregation indicated in Figure 2d,f. The formed microstructure is relatively clear in Figure 2e for V = 1:1.
## 6 Discussion and Conclusions  
### Discussion  
We proposed a real-time prediction method of online taxi-hailing demand and studied the impacts of forecasting taxi demand on the accuracy of online taxi-hailing demand. Then, we obtained the findings below:  
1. The demand for taxi and online taxi-hailing is regular in the Bell Tower area, and taxi demand decreases while the online taxi-hailing demand increases in peak hours. This is because online taxi-hailing is more convenient to obtain than taxis in peak hours;
2. Based on the BPNN methods, including information \"E\" leads to an MAPE reduction from 0.224 to 0.190 while it decreases RMSE from 28.576 to 23.921, and it increases the R\({}^{2}\) from 0.819 to 0.845. Likewise, including information \"TX\" leads to an MAPE reduction from 0.190 to 0.132, and it increases the R\({}^{2}\) from 0.845 to 0.866. Figure 7a shows that model \"BPNN + T + E + TX\" obtains the lowest MAPE among three predictions except at 6 a.m., 8 a.m. and 9 p.m., and from Figure 8, we know that model \"BPNN + T + E + TX\" obtains the lowest RMSE among three predictions except at 4, 7, and 9 p.m.;
3. Based on the XGB methods, including information \"E\" leads to an MAPE reduction from 0.333 to 0.197 while it reduces RMSE from 26.296 to 21.206, and it increases the R\({}^{2}\) from 0.833 to 0.857. And including information \"TX\" leads to an MAPE reduction from 0.197 to 0.139, and it increases the R\({}^{2}\) from 0.857 to 0.865. Figure 7b indicates that the performance of the model \"XGB + T + E + TX\" is the best, except at 11 a.m. and 5 p.m., and from Figure 8 we know that performance of model \"XGB + T + E + TX\" is the best except at 11 a.m., 12 a.m., 4 p.m. and 5 p.m.;
4. We found that the model \"BPNN + T + E + TX\" is available for forecasting online taxi-hailing demand. The performance of the model, which includes information of \"PTX\", is better than the model \"BPNN + T + E\", but its accuracy is lower than the model \"BPNN + T + E + TX\". Compared with the model \"BPNN + T + E\", including the information of \"PTX\" leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R\({}^{2}\) from 0.845 to 0.853. This indicates that considering the information of \"PTX\" improves the predictability of online taxi-hailing demand. However, due to a reduction in the accuracy of \"PTX\", the performance of the model \"BPNN + T + E + TX\" is better than the model \"BPNN + T + E + PTX\".  
However, the research still has some limitations. In the future, these limitations should be studied. For example, we did not use linear regression models to predict online taxi-hailing demand. Moreover, we should propose a method to forecast multiple trip demands simultaneously. Additionally, we will set projected environmental data as factors of real-time demand prediction for real-time forecasting.
## 2 Materials and Methods
### Concentration Measurement of \(NO_{3}{}^{-}\), \(NH_{4}{}^{+}\), \(PO_{4}{}^{3-}\), \(DOC\)  
\(NO_{3}{}^{-}\) and NH\({}_{4}{}^{+}\) concentrations were measured in the field by portable concentration measuring probes (Vernier Go Direct, Beaverton, OR, USA). The PO\({}_{4}{}^{3-}\) concentration was measured by ion chromatography (IC, Dionex Aquion, Thermo Fisher Scientific, Waltham, MA, USA). The DOC concentration was measured by a total organic carbon (TOC) analyzer (TOC-L, SHIMADZU, Kyoto, Japan). A series of concentration gradient standards were prepared to periodically calibrate the instrument. Water samples were passed through 0.45 \(\upmu\)m membrane filters before IC and TOC measurements.
## 2 6G Developments  
In addition to current applications, 6G is being designed to support disruptive futuristic applications, like extended reality, the Internet of Things, machine-to-machine communication and Industry 4.0 [1; 2], connected and autonomous vehicles, smart transportation [8], the metaverse [11], holographic telepresence [12], Tactile Internet [13], digital twins [14], full immersiveness [15], and blockchain, ultra-high-capacity wireless backhaul connections, wireless connectivity within data centers, terahertz transceivers for innovative applications, and others [16; 17].  
While designing the IMT-2020 standard (5G), the primary three types of usage scenarios were considered as enhanced mobile broadband (eMBB) for streaming applications, ultra-reliable low-latency communications (URLLC) for mission-critical applications, and massive machine-type communications (mMTC) [18] for low-power and low-data-rate applications of IoT in industrial and other sectors. The WP 5D came up with a recommendation in 2023 for IMT-2030 for upgraded scenarios and applications. The three scenarios of IMT-2020 are now called immersive communication, hyper-reliable and low-latency communication, and massive communication, respectively [9]. Additionally, three new paradigms are recommended: integrated sensing and communication, ubiquitous connectivity, and integrated AI and communication, as shown in Figure 1[19]. The framework also focuses on sustainability, security, and resilience, connecting the unconnected and ubiquitous intelligence as overarching aspects that act as design principles commonly applicable to all usage scenarios.  
The nine enhanced and six new capabilities for IMT-2030 are also proposed to support future usage scenarios, applications, and their requirements, as shown in Figure 2[19]. These capabilities are peak data rates per device in ideal conditions; user-experienced data rate per device in the throughout the coverage area; more than three times the spectrum efficiency compared to IMT-2020; total traffic throughput capacity per geographic area; connection density with a target of 10\({}^{6}\)-10\({}^{8}\) devices/km\({}^{2}\); mobility with a target of 1000 km/h; end to end latency in the range of 0.1-1 ms; reliability over the air interface in the range of 1-10\({}^{-5}\) to 1-10\({}^{-7}\); coverage to provide connectivity [7] for usage of desired applications and positioning of localization of the devices with an accuracy of 1-10 cm; sensing-related capabilities for a radio interface with information, such as range, velocity, angle estimation, object detection, localization, imaging, and mapping; applicable AI-related capabilities for AI-enabled applications; security and resilience for robust data security and privacy as well as resilient network operation; and sustainability with networks and devices having higher energy efficiency, optimized designs for equipment, improved lifetimes, and reuse and recycling [20].