esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Tests and Methods
### Model Test Design and Operation Monitoring  
According to the water pipeline section scale 1:10 design, the test device and the site layout is shown in Figure 3. The device is made of transparent plexiglass, and the gas sampling hole (c) and the reflux hole (d) are set on both sides of the left and right walls. The gas sampling pipe (e) and gas reflux pipe (f) of the online gas detector (g, Model NF-GAS4-200F) are respectively connected, and the multi-purpose hole (b) is used for adding and cleaning Limnoperna fortunei (a) before and after the test. The resolutions of NH3, H2S, and CH4 sensors are 0.1 ppm, 0.01 ppm, and 0.1% LEL, respectively. In order to ensure that the temperature, humidity, and other environmental conditions in the device are consistent with the on-site situation, the test is carried out at 30-35 \({}^{\circ}\)C. Before the test, the device was filled with water, and then the water was drained to simulate the pipeline maintenance process; finally, the humidity in the device was maintained at 63%-65%RH. In the formal experiment, we put the Limnoperna fortunei into the test device and made them die of natural dehydration. The three harmful gases of NH3, H2S, and CH4, as well as environmental factors such as temperature, humidity, and oxygen content during the putrefraction of Limnoperna fortunei, were continuously monitored.  
According to previous survey results on the distribution law of _Limnoperna fortunei_ in the water conveyance tunnel [27], The adhesion density of _Limnoperna fortunei_ at the inlet of the water conveyance tunnel is as high as 6.0 \(\times\) 10\({}^{4}\) mussels/m\({}^{2}\), and the density of _Limnoperna fortunei_ in the downstream gradually decreases as the distance from the water inlet increases. Considering extreme factors, set up experiments on _Limnoperna fortunei_ samples of 0-7.0 \(\times\) 10\({}^{4}\) mussels/m\({}^{2}\) and divide the samples with 0.5 \(\times\) 10\({}^{4}\) mussels/m\({}^{2}\) as the gradient interval (Table 1). That is, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550,  
Figure 3: Schematic diagram of test device (**a**) and site layout (**b**); (**a—**mussel cluster; b—**multi-purpose hole; c—**gas sampling hole; d—**gas reflux hole; e—**gas sampling pipe; f—**gas reflux pipe; g—**online gas detector).  
Figure 2: _Limnoperna fortunei_ sample collection.  
600, 650, and 700 _Limnoperna fortunei_ were placed on a 0.01 m\({}^{2}\) plane at the bottom of the device for subsequent tests.  
The test was carried out at Taiyuan Pumping Station, Qiaotou Town, Dongguan City, Guangdong Province, China, from July 2020 to September 2020. The concentration of the three harmful gases NH3, H2S, and CH4 released during the spoilage of the 14 groups of _Limnoperna fortunei_ and the changes in the three environmental factors of temperature, humidity, and O2 in the test device were continuously monitored for a period of 5 days. The data recording frequency of the online gas detector is 1 s/time; that is, the cumulative amount of data for a single indicator in a single set of tests is about 432,000.
## 3 Findings
### Techniques  
Among the techniques incorporated for the data analysis, it is highly noticeable that most of these articles were investigated by using manual coding techniques to carry out their content analysis. Sentiment analysis too has been employed by five research articles [23; 26; 35; 39; 41] where LDA and Mallet implementation of LDA were used to perform topic modeling by [PERSON], et al. [35] and [PERSON], et al. [33] respectively. Statistical analysis has been conducted by four articles [22; 25; 34; 40] of which two considered regression analysis techniques. Although discourse analysis and co-occurrence analysis have been widely discussed techniques for hate speech detection, we find that only two studies have applied discourse analysis while another two applied word co-occurrence analysis amongst the selected literature.  
Apart from these commonly used techniques, some studies employed specific techniques to analyze social media data. [PERSON], et al. [24] used ERGM (exponential random graph modeling) which is a widely used technique to analyze social network structures. ERGM assisted them in estimating the probability of a tie between any 2 Twitter users based on their characteristics and network structure. TF-IDF (term frequency inverse document frequency) and word embedding were among the few analytical techniques used by [PERSON], et al. [35]. Using TF-IDF, the study collected data from two social media domains that were then used in word embeddings to extract the context of words used in these collected datasets. Social media clustering has been used by [PERSON] and [PERSON] [36] to analyze relationships such as friends, followers, etc., to acquire details of obesity-related social network characteristics. [PERSON], et al. [26] have used bivariate frequency analysis, along with sentiment analysis and manual coding techniques, to statistically analyze the number  
Figure 4: Analytical methods used: Qualitative (darkest blue), quantitative (blue), experimental (light blue) and mixed methods (lightest blue).  
of occurrences of obesity and cancer in social media platforms. The application of MMMA (moderated moderated mediation analysis) to explore complex media effects by enabling multiple individual differences was studied by [PERSON] and [PERSON] [38]. The authors applied MMMA with appearance comparisons as the mediator and further with BMI and perceived weight as the moderators to identify the effects of fitspiration and thinspiration on women's self-esteem.
# Stand Structure Impacts on Forest Modelling
## 5 Height
The model's good performance could be explained by stand structure: balanced diameter distribution with _Plenter_ equilibrium, attained using periodical selective cuttings where trees of all diameter classes were cut; increment was centred in the trees with large dimensions (diameter and height); and the period of regeneration was long and the recruited regeneration growth rates were low. Regarding natural stands of shortleaf pines in USA, Ref. [81] used inventory field data with linear and nonlinear mixed models. The latter outperformed the former, due to the incorporation of the variability of the tree allometry that resulted from artificial (thinning) and natural (storms) disturbances. The mixed models had better predictive ability than those of the existing ones; and the inclusion of mean quadratic diameter and dominant height improved the performance of the nonlinear mixed h-d model. Ref. [82] modelled the height for _Pinus ponderosa_ in seven states of the USA with nonlinear mixed effect models. The authors demonstrated that the fitted model was able to accommodate the variability in tree allometry of site: higher temperature and precipitation promoted height growth, while drought restrained it. Regarding competition, the increase of competition increased the _ld_ ratio, and in terms of species mingling, the lower the mingling of species, the lower the _hd_ ratio. The generalised mixed effects models have also been used to model h-d in young (3-19 years) _Betula pendula_ stands of natural regeneration in Poland [83]. From the fitted models, those that included the largest diameters of the existing range as random effects created an improvement in the predictive ability of the model. The variability of stand structure and tree allometry is continuously bringing challenges to modelling height. This includes the selection of the independent variables with the highest predictive ability, low collinearity and regression methods that enable the accommodation of the differences and variability between a wide range of stand structures, sites and species.
## 3 Results
### Zeta Potential (ZP) of SBO-NLCs  
SBO-NLCs with the optimum ZP were predicted (\(-\)17.07 mV) in STD run 9, whereas the actual optimum ZP (\(-\)15.63 mV) was observed in STD 14. The predicted ZP value of the center point STDs was \(-\)27.07 mV, but the actual values were different (Table 2). The reduced cubic model was employed for the prediction of the ZP of the SBO-NLCs. The model was significant (\(p\) = 0.0085) and the lack-of-fit value was non-significant (\(p\) = 0.1503). Acceptable ranges of adjusted R\({}^{2}\) (0.7196) and predicted R\({}^{2}\) (0.5654) values were detected in the reduced cubic model (Table 3). The results recommended that the reduced cubic model was appropriate for predicting the ZP of SBO-NLCs.  
The CCD-generated reduced cubic model equation for the ZP of SBO-NLCs was as follows (Formula (5)):  
\[\mathrm{ZP}=-65.2627+7.1499\mathrm{A}+16.1094\mathrm{B}+0.4764\mathrm{C}-3.66 81\mathrm{AB}+0.0155\mathrm{BC}-1.6616\mathrm{B}^{2}+0.3525\mathrm{AB}^{2} \tag{5}\]  
Figure 2: The response surface plot presents the effects of wax, surfactant, and PEG in the surfactant on the polydispersity index of SBO-NLCs. (**a**) Interaction of wax and surfactant. (**b**) Interaction of surfactant and PEG400 percentage in the surfactant.  
where A = the percentage of wax in the formulation (2.5 to 7.5%), B = the percentage of surfactant in the formulation (2.5 to 7.5%), and C = the percentage of PEG400 in the surfactant (10 to 30%).  
The concentration of wax and the percentage of PEG400 in the surfactant significantly influenced the ZP of the SBO-NLCs. The high content of wax and low percentage of PEG400 in the surfactant generated SBO-NLCs with a lower ZP. The surfactant concentration did not significantly affect the ZP of the SBO-NLCs (Figure 3).
# Optimization of Pile Driver Frame Based on Sensitivity Analysis
## 2 Body Model Structure and Analysis
### Static Analysis  
#### 2.2.1 Typical Working Condition Selection  
As shown in Figure 3, a hydraulic oil tank and cab, used to power the pile driver for pile extraction and pile pressing, are also mounted on the large body skeleton. The static pile driver works on the pile by means of a pile pressing mechanism. Hydraulic cylinders in the chassis platform provide the power for the pile pressing and pile pulling action.  
Figure 3: Hydraulic pile driving mechanism and cab.  
Figure 2: Large body structure finite element model.  
The static pile driver operates on the pile through a pile pressing mechanism, powered by hydraulic cylinders in the chassis platform to enable a pile pressing and pile pulling action. The pile pressing mechanism is connected to the large body in the upper part of the massive body skeleton structure. The entire pile driver travels and moves through a travel mechanism and a traverse mechanism in the lower part of the large body platform. The outriggers are bolted to the bulkhead structure, and the outriggers have hydraulic cylinders attached to the long boat by hinges. The ball head of the ball hinge is attached to the slewing mechanism and the short boat. The large body platform structure will link the static pile drivers into a single unit that will directly bear the reaction force when the pile is pressed. The maximum deformation of the bulk structure occurs when the static pile driver implements the pile pressing action. In this paper, we present a hydrostatic analysis of the pile pressing operation and its hydrostatic properties under the most dangerous operating conditions.
## 6 Limitations and Future Scope  
In future, we would like to verify the performance of the proposed model on other datasets and experiments with more novel features and their influence. A major drawback of our model is that it cannot identify whether the URL is active or not; therefore, it is essential to verify whether the URL is active or not before detection for ensuring the detection performance. At the same time, the computational complexity of the proposed model can be analyzed in future. Additionally, few attackers utilize URLs that are not impersonations of other websites, and such URLs will not be identified. In addition, the robust nature of the proposed model can be tested against adversarial attacks which are commonly utilized by malicious parties. In the future, we plan to exploit novel models for automatic extraction of other features to detect phishing sites, such as web code features, web text features, and web icon features.  
Conceptualization, H.A.; Investigation, S.S.A. and M.M.; Methodology, H.A. and S.S.A.; Project administration, M.A.D.; Resources, F.S.A.; Software, F.S.A., I.A.-T. and M.M.; Supervision, I.A.-T.; Validation, K.A.-A. and A.S.A.A.; Writing--original draft, H.A., K.A.A. and A.S.A.A.; Writing--review & editing, K.A.A., A.S.A.A. and M.A.D. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (61/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R319), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4210118 DSR09).  
Not applicable.  
Data sharing not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written with contributions of all authors. All authors have approved the final version of the manuscript.  
This article does not contain any studies with human participants performed by any of the authors.
###### Abstract
In actual engineering, the soil not only exhibits elasticity but also viscosity after being subjected to stress. The Kelvin-Voigt, Maxwell, and Merchant models have been used to illustrate the consolidation characteristics of viscoelastic soils [22]. [PERSON] [23] analyzed the consolidation characteristics of saturated soils by employing the viscoelastic constitutive model in the 1950s. Afterward, [PERSON] [24] and [PERSON] and [PERSON] [25] extended the consolidation of viscoelastic saturated soils. However, they all have a common shortcoming in that they are limited to saturated soils only [26; 27; 28]. In the aspect of unsaturated soil, the consolidation properties of viscoelastic unsaturated soils under instantaneous uniform loading and assuming constant permeability coefficient have been investigated only by [PERSON] et al. [29] in the current published literature [30; 31]. In most previous studies of unsaturated soil consolidation, the permeability coefficient is normally supposed to be constant during the solving process. Although this assumption has been accepted in preliminary studies of unsaturated soil consolidation, it is undeniable that the supposition of the permeability coefficient being approximated to be the same during the consolidation of unsaturated soils is a special case in practical engineering. During the consolidation process, the permeability coefficients of the air and water phases in the soil exhibit nonlinear variations in response to alterations of saturation and porosity. Some scholars, for example, [PERSON] et al. [32], [PERSON] and [PERSON] [33], and [PERSON] et al. [34], have studied the effect of permeability coefficient variation on consolidation [35; 36]. However, their studies were limited to saturated or linearly elastic unsaturated soils. Therefore, studying the impact of permeability coefficient variation on the consolidation of viscoelastic unsaturated soils is significant and imperative. In this paper, the nonlinear variation of the permeability coefficient was considered, and the semi-analytical solution for 1D consolidation of viscoelastic unsaturated soil under exponential loading was acquired. Firstly, based on the Merchant model, the 1D consolidation controlling equations for viscoelastic unsaturated soil were obtained, employing [PERSON]'s correspondence principle. Then, the solution was obtained via the Laplace transform. Moreover, the correlation between the permeability coefficient and matric suction was applied to derive the final solution, considering the alteration of the permeability coefficient. The numerical inverse Laplace transform was applied using [PERSON]'s method to acquire the semi-analytical solution in the time domain. Finally, the consolidation behavior of viscoelastic unsaturated soils was analyzed using the examples of the air permeability ratio, elastic modulus ratio, and varying permeability coefficient.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 2 Related Work
### Software-Defined UAV Networks  
SDN allows for network programmability by separating the control layer from data-forwarding network elements. SDN architecture can be used to reduce network configuration time and cost, which is critical for rapidly changing UAV networks [22]. It can also handle offloading requests and associated routing management based on network traffic [23].  
Privacy and protection against external cyber threats are both possible by exploiting the capabilities of SDN architecture. In that regard, important application domains are analyzed from a security standpoint in [24]. The description and classification of performance assessment metrics in drone design are discussed for test-bed based approaches,most widely used simulation platforms, and hybrid methods. In addition, potential vulnerabilities for drone-enabled SDN-based societies are presented.  
An SDN routing framework for UAV networks is developed in [25]. A monitoring platform to obtain network statistics for SDN controllers and a load balancing algorithm based on that platform's analytical results are introduced. Routing decisions are controlled by considering the power limit of UAVs and the priority of flows.  
For UAV-assisted infrastructure-less vehicular networks, [PERSON] et al. [26] propose a distributed SDN-based architecture. They also propose a road safety scenario as a use-case for their design. UAVs are used in this case to help emergency rescue vehicles in exploring and investigating inaccessible affected zones. Lastly, they formulate an offloading decision problem based on a two-player sequential game and prove the existence of Nash equilibrium.  
[PERSON] et al. [27] consider an SDN-based cellular network with a UAV and BSs. UAV serves as a computational server in the system, calculating users' tasks, or as a relay node, forwarding users' jobs to MEC-enabled BSs, where they can be calculated in MEC servers. The objective function is formulated as minimization of the total weighted delay and energy consumption of the UAV and all users. They propose a joint mode selection and resource allocation optimization algorithm to solve this optimization problem. The proposed algorithm decomposes the optimization problem into two subproblems: task mode selection and resource allocation, which are performed in alternating iterations.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 4 Future Research Challenges
### Protection of Data Property in Data Fusion  
Today, block chain [72] is the main solution for multi-party sharing of files and data. We need to ensure that data ownership is always the data provider, so we also need to pay attention to the privacy protection of data fusion. By the data fusion of multi-source material data and knowledge discovery using machine learning, it is highly likely that the original data cannot be included in the results, but data protection for the data provider is still required. Therefore, in the future, it is necessary to record the flow changes of the data throughout the fusion process in detail.  
When the final result is obtained, the data can be traced to obtain the source of the data. Using techniques such as block chain and Ethereum [73] to solve the problem of smart contract and technology sharing [74], data providers can confidently contribute their important data.
# Changes in Biochemical and Volatile Flavor Compounds of Shine Muscat at Different Ripening Stages
## 2 Materials and Methods
### Volatile Flavor Compounds  
An SPME fiber coated with divinylbenzene/carboxen/polydimethylsiloxane (50/30 \(\upmu\)m DVB/CAR/PDMS; Supelco, Bellefonte, PA, USA) was used for the analysis of volatile flavor compounds. Frozen grape berries were quickly sliced using a razor blade and ground in an electric blade grinder. Then, 20 g of the homogenized sample was centrifuged at 4500 rpm for 10 min. The resulting supernatant was filtered under vacuum. For SPME fiber adsorption, 2 mL of the grape juice was transferred into a headspace vial with NaCl to improve the volatility of flavor compounds. The sample vial was heated to 70 \({}^{\circ}\)C for 30 min on a heating block (HB-48P; DAIHAN Scientific, Wonju, Korea) to bring the sample solution to temperature equilibrium. Then, the SPME fiber was introduced into the sample vial headspace. After adsorption for 30 min, the fiber was removed and injected into the GC injection port for desorption. A gas chromatograph (6890N; Agilent Technologies, Santa Clara, CA, USA) equipped with an Agilent 5975 series mass selective detector was used for the analysis of volatile flavor compounds. The volatile compounds were separated on an HP-INOWAX capillary column (30 mm \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies). Purified helium was used as a carrier gas at a constant flow rate of 2 mL/min. The analytes were thermally desorbed at 250 \({}^{\circ}\)C for 10 min in splitless mode. The column temperature was initially held at 40 \({}^{\circ}\)C for 5 min, increased to 220 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and lastly held at 250 \({}^{\circ}\)C for 5 min. The injector and source temperatures were set at 250 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. The mass detector was operated in positive ion electron impact ionization mode at 70 eV with a scan range of 50-700 \(\upmu\)z. Volatile compounds were identified by comparing the retention times with those of authentic standards. The volatile compounds without their corresponding standards were tentatively identified by comparing their mass spectra with those from NIST 11 library and retention indices with those from the literature [16]. The concentrations of volatile flavor compounds were estimated using a 4-nonanol calibration curve. The calibration curve was constructed in the concentration range 1-4 \(\upmu\)g/mL.