esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Stress-Predict Dataset
#### 2.1.1 Study Methodology and Protocol  
The study took 60 min per participant and was completely non-invasive. The protocol followed is illustrated in Figure 1, while Table 1 enlists the inclusion and exclusion criteria for the study. The data were only collected from healthy volunteers, who could quickly recover from the stress state induced by the questionnaire. The age bracket selected for the volunteers was 18 to 75 years old. Furthermore, the volunteers needed to be able to speak English (as all communication and consent forms were in English) and agreed (gave consent) to participate in the study. The exclusion criteria included people who did not consent to participate, unhealthy individuals (as stress might lead them to any acute event), breastfeeding mothers and pregnant women (as stress might harm them and/or their child), and colour-blind people (as one of the stressors was a Colour-Word Stroop test, where the colour name is selected based on the colour seen on the screen).  
The protocol and clinical study were approved by the Clinical Research Ethics Committee, Merlin Park Hospital, Galway, Ireland, as: \"Stress levels monitoring using sensor-derived signals from non-invasive wearable devices and dataset development (Ref: C.A. 2731)\". For sample size calculation, authors followed sensitivity analysis outcomes reported in [22]. The data from all the participants were collected in the daytime (the precise time for data collection was chosen by the participants). Moreover, participants arriving at the study site were asked to sit and relax for 5 min to bring their heart rate and respiratory rate to normal levels.
## 4 Materials and Methods
### RNAi and Infection Assay  
Approximately 500 bp of DNA fragments were amplified by PCR from both _GFP_ and _MgPDI2_. The forward and reverse primers contained T7 promoter sequences at their 5\({}^{\prime}\) end for in vitro RNA synthesis (Table 2). Double-stranded RNA (dsRNA) was synthesized and purified using the T7 RiboMAXTM Express kit (Promega, Madison, WI, USA) according to the manufacturer's instructions. The concentration of RNA was determined by measuring absorbance at 260 nm.  
RNAi soaking was performed using the modified method [37; 38]. 20,000 Freshly hatched J2s of _M. graminicola_ were soaked in the dsRNA solution (2 mg mL\({}^{-1}\) dsRNA, 3 mM spermidine, 50 mM octopamine, and 0.05% gelatin, adjusted with 0.25 \(\times\) M9 buffer) for 36 h at room temperature in the dark on a rotator. Nematodes soaked in solutions with dsRNA targeting GFP were used as controls. Afterward, for each reaction, about 8000 J2s were used by qRT-PCR to evaluate the level of _MgPDI2_ silencing. Approximately 500 J2s were used to determine the ability to survive under H\({}_{2}\)O\({}_{2}\) stress. All remaining J2s were used for plant infection assays.  
For the infection assays, Pluronic F-127 (PF-127) (Sigma-Aldrich, St. Louis, MO, USA) gel was used as previously reported [39; 40], 2-week-old rice seedlings (_Oryzaz sativa_ cv Nipponbare) were individually transplanted in a standard tube (25 mm \(\times\) 95 mm) containing 10 mL Pluronic F-127 medium, inoculated with 80 J2s soaked in dsRNA targeting _MgPDI2_ or _GFP_, and incubated in a growth chamber under 16 h/8 h light/dark photoperiod at 28 \({}^{\circ}\)C/26 \({}^{\circ}\)C for 15 d. Seven replications were included in each experiment and each experiment was repeated at least twice [41].  
At 15 dpi, rice roots were stained with acid fuchsin and the number of eggs was counted after dissecting the stained galls under a microscope [42]. To determine the reproductive potential of _M. graminicola_, the nematode multiplication factor (MF) ((number of egg masses \(\times\) number of eggs per egg mass) \(\div\) nematode inoculum level = MF) was calculated.
# Performance Evaluation of Silane in Concrete Bridge Decks Using Transmission X-ray Microscopy
###### Abstract  
The corrosion of steel reinforcement in concrete structures is a most widespread problem. Silane is commonly used as a surface treatment material to reduce the entry of moisture containing harmful chemicals into the concrete matrix, thereby restricting corrosion. There is not a standard test method to evaluate the long-term performance of such sealers when applied on concrete. Moreover, the literature does not have a definitive conclusion on the performance of silanes. In this work, transmission X-ray microscopy (TXM) and optical imaging were used to investigate the mass transport properties and silane thickness of field samples treated with silane to gain insight into the performance of silane coatings. Quantitative measurements using optical staining techniques showed that the average measured silane thickness for all 14 projects was 2.5 mm. The TXM results revealed that, on average, the samples with silane coatings exhibited 8.5 times less penetration of outside chemicals, at a depth of 2 mm, as compared to uncracked samples without silane coatings. This work shows that silanes are a useful and practical tool to significantly reduce the permeability of in-service concrete and resist corrosion.  
silane treatment; hydrophobic coating; concrete; mass transport; durability; chloride; X-ray imaging; bridge deck; pavement +
Footnote †: journal:  
applied sciences
## 3 Experiments and Analysis
### Comparison of the Model in This Paper with Other Models
#### 3.6.2 On Public Datasets Compared with Other Literature  
In order to verify the advancements made using this model, experiments were conducted in this paper using data on grapes from plant trees, which were then compared with the latest literature. The comparison results are shown in Table 5, which shows that the GFCD-YOLOXS model has the highest recognition rate and better generalization and robustness compared with the methods proposed in the literature [6,7,14].
## 3 Establishment and Validation of Finite Element Model
### Establishment of Finite Element Model  
This study investigates the structural response of segmental joints under torsion shear interactions from the perspective of segmental structure. It analyzes the patterns of rotational deformation and structural failure of the segments, without considering the interaction between the segmental structure and the soil. This study first establishes a refined numerical model of segment joints based on the actual design specifications of the circumferential joints, taking into account factors such as joint construction, connecting bolts, and reinforcement. To counteract the torque generated by the TBM cutterhead during excavation, mortise and tenon are incorporated into the circumferential joints, with each segment equipped with two sets of these structures. In the actual segmental structure design, a 7 mm gap is provided between the tenon and the mortise to facilitate assembly during construction, while a 3 mm gap is designed between the bolts and the bolt holes. To enhance computational efficiency, a half-model is constructed, leveraging the symmetry of the segment. The refined segment joint model is illustrated in Figure 3. Due to the structural complexity of the joint configuration, the segments were meshed using a combination of integration eight-node hexahedral linear elements (C3D8R) and ten-node modified quadratic tetrahedron elements (C3D10M). The bolts are simulated using C3D8R elements The rebars are simulated using two-node linear three-dimensional truss elements (T3D2). The joint model with mortise and tenon features consists of 188,451 nodes and145,075 elements, while the joint model without mortise and tenon features consists of 183,295 nodes and 140,784 elements.
# Scale Reduction Techniques for Computing Maximum Induced Bicliques
## 3 Exact Algorithm Based on the Proposed Scale Reduction  
Due to the NP-hardness of the considered problems, solving large-scale instances arising in practice is a formidable challenge. However, many real-life networks are characterized by sparsity, which can be exploited in designing exact, global optimization algorithms. Namely, instead of directly applying standard solution techniques, such as branch-and-bound method, we will aim to reduce large-scale instances to manageable sizes first. Using structural properties of bicliques, we propose a scale reduction technique that attempts to shrink the feasible region of a given instance of the problem while ensuring that the reduced instance contains a global optimal solution to the original problem. The overall structure of the proposed global optimization algorithm consists of the following steps.  
1. _Find a lower bound_. Use a heuristic algorithm to obtain a lower bound on the optimal solution.
2. _Apply scale reduction_. Given a heuristic solution, recursively identify and remove vertices that cannot be included in a globally optimal solution, until no further reduction is possible.
3. _Solve._ Apply a standard exact algorithm to find a globally optimal solution in the residual graph.  
The procedure starts with finding a lower bound solution using a heuristic algorithm described in the next subsection. Given this lower bound, the scale reduction algorithm iteratively identifies and removes edges that cannot be present in the optimal solution (Section 3.2). If no further reduction is possible, the residual graph is extracted and updated. Lastly, an IP model is developed based on residual graph, certain valid inequalities (described below) are added, and the model is solved using a standard solver. Next, we describe the algorithm in detail.
# A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments
## 2 Highly Prominent and Extensible Categories of Cloud Simulators
### Energy-Aware Provisioning  
For the supplier, the energy supply is the most important because it can produce substantial savings. Initially, energy efficiency can be achieved both at the level of the network devices and at the level of the host. The normal parameter for estimating energy usage is primarily the processor fee. As this aspect is a significant contributor to the energy bill, more advanced simulators provide savings because they concentrate on modeling energy usage [10]. [PERSON] [8] reported that CloudReports are a powerful and flexible simulation tool for computing DCs and resource energy using a GUI in a CC environment; with high-level data, it produces reports and organizes simulation results. The central enterprises of CloudReports are composed of clients, DCs, physical computers, VMs, network and network area space, and so on. These organizations work to run a simulation environment with the simulation administrator. [PERSON] et al. [23] proposed several changes to the top of the architecture of CloudSim. The proposal introduces a GUI that offers several features. First, it enables the simultaneous running of several simulations. With detailed information and the ability to export simulation data, it can produce reports. This data is linked to the usage costs of services, energy usage, ET, and so on. Table 1 lists all of the key questions associated with the review's subtopics, while Table 2[24] shows the clear benefits and drawbacks of the reviewed simulators. Table 3 summarizes the key models of cloud simulation presented in this section I-A (CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports) for various parameters such as simulator type, simulation time, availability, hardware/software associated, programming language, basic policy, repetition of analysis, GUI, user expectations, and dynamic run. The energy-consuming parameters in the selected cloud simulators are listed in Table 4[15]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Subtopics** & **Questions** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Prominent Cloud Simulators} & What is the motivation behind the simulator’s development? \\  & & What are the features or characteristics of cloud simulators? \\  & & What is the scope of extension or application of the targeted \\  & & cloud simulators? \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Discussion} & What parameters would be used for the comparison of simulation \\  & & tools vs. non-simulation tools? \\ \hline \multirow{4}{*}{3} & \multirow{2}{*}{Energy-Aware Provisioning} & What metrics are covered in the cloud simulators that are targeted? \\  & & What are the energy-consuming parameters found in the selected \\  & & cloud simulators? \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A list of key questions.
## 4 Numerical Modeling: Mass-Spring Waveguides
### Single Chain Mass-Spring Waveguides  
For a single mass-spring chain with a total of \(N_{m}\) identical masses, the discrete elastic equations of motion are:  
\[m\ddot{u}_{n}-k_{nm}(u_{n+1}-2u_{n}+u_{n-1})+\eta\dot{u}_{n}=0 \tag{18}\]  
In Equation (18), \(u_{n}\) is the displacement of \(n^{th}\) mass of the chain, \(m\) is the mass, and the viscous damping coefficient \(\eta\) models the dissipation. The term \(k_{nm}\) describes the coupling constant of the nearest-neighbor interaction. To model the excitation applied by the transducer, the prescribed base periodic excitation \(F^{u}(t)=F^{u}_{0}\sum_{\omega_{i}}\sin(\omega_{i}t)\) is applied to the first mass of the chain, where \(F^{u}_{0}\) and \(\omega_{i}\) are the amplitude and frequency of the applied force. We use free boundary conditions; hence, the equations of motion for the first mass and last mass in the chain are:  
\[m\ddot{u}_{1}-k_{nm}(u_{2}-u_{1})+\eta\dot{u}_{1}=F^{u}(t), \tag{19}\]  
\[m\ddot{u}_{N}-k_{nm}(-u_{N}+u_{N-1})+\eta\dot{u}_{N}=0 \tag{20}\]  
To mimic the physical experiment, in the numerical model we have used \(m=m_{R}/N_{m}\) and \(k_{nm}=EA/a\), where \(a(=L/N_{m})\) is the inter-mass spacing, and \(m_{R}(=\rho AL)\), \(E(=60\) GPa) and \(A\Big{(}=\pi d^{2}/4\Big{)}\) are the mass, Young's modulus and cross-sectional area of the aluminum rod, respectively.  
Figure 7a shows the normalized power spectral density of the last mass displacement time series. Figure 7a shows resonance frequencies of the standing waves of the finite mass-spring waveguides. The peak positions define the eigen frequencies (i.e., the resonance modes) of the system with uniform frequency spacing. After calculating the eigen frequencies from Figure 7a, we drive the first mass of the chain with a single driving frequency of 3884 kHz (Figure 7b) and 7.767 kHz (Figure 7c) that corresponds to the first and second resonance mode of the system. In Figure 7b,c, we present snapshots of the displacements of all masses. Figure 7b,c depicts the spatial distributions of the displacement components at two different time instants, \(t_{1}\) and \(t_{2}\). We can clearly note the spatially extended response of the chain with only a single node (Figure 7b) or two nodes (Figure 7c). Therefore, the wavelength and the wave number are \(\lambda=2N_{m}a/n\); \(n\) being the number of nodes, and \(k=1/\lambda\). Figure 7d shows the resultant dispersion relation obtained from Figure 7a-c. The speed of sound equals 4706 m/sec, which is slightly higher than the experimental value of 4571 m/sec for single rod, however, close to the model value of \(\beta=\sqrt{k_{nm}a^{2}/m}=\sqrt{E/\rho}=4750\) m/s. In Figure 7, we limited the analysis to frequencies below 60 kHz, since above that value the resonant modes begin to deviate from  
the linear dispersion relation (cf. Figure 7d), i.e., is dispersive, indicating that at high frequency regime the discretized mass spring model does not represent the 1D continuous elastic waveguides.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Early Testing  
The study undertook preliminary testing to determine whether such predictions could be established for this study. The initial testing experiments involved linear regression, random forest, multilayer perceptron, and voting ensemble. High error rates of around 50% were observed, indicating random predictive capability, but they pointed to areas where preprocessing, feature selection, and model selection may be improved. The second phase of experiments utilized LSTM, support vector regression, random forest, and XGBoost models for their ability to handle non-linear data. This resulted in a lower prediction error rate and increased accuracy.  
The early testing experiments were executed without any preprocessing, other than combining the separate datasets and power plant information. As expected, the results were unsatisfactory, highlighting the need for preprocessing techniques, such as feature scaling and train and test splitting.  
The results of the initial runs are shown in Table 3, and the visual results are shown in Figure 8. Four ML models in Table 3 are presented. Of the four ML models, linear regression had the worst performance, with the mean average error (MAE) and mean square error (MSE) slightly higher than the other models. The other three models produced similar results, with RF having the lowest error rate and residual standard error, making it the best model in this first cycle of the tests.  
Figure 7: Early testing random forest models’ feature importance using all features for a single power plant (Alabama) dataset.
## 1 Introduction
(2013) conducted soil erosion studies in the Vamsadhara Basin and discovered that the region with minimal vegetation and shifting cultivation, combined with heavy precipitation on steep slopes, resulted in the loss of fine fertile topsoil with runoff, leading to the sedimentation and silting up of the drainage course in the lower reach of the Vamsadhara watershed. [PERSON] et al. (2020) [PERSON] et al. (2020) conducted spatio-temporal assessment of rainfall extremes over the Nagavali and Vamsadhara watersheds. According to [PERSON] et al. (2020); [PERSON] et al. (2020), there has been an increase in annual rainfall for these two river basins at a rate of 2 mm and 8 mm per decade, respectively, and an increase in monsoon rainfall at a rate of 4 mm and 9 mm per decade. Increased rainfall is expected to increase soil erosion and sediment yield in the future. In addition, according to [PERSON] et al. (2021) [PERSON] et al. (2021) [PERSON] et al. (2021), future monsoon rainfall forecasts from CMIP3 to CMIP6 models indicate that the Asian region will experience intensification and an increase in precipitation extremes. This will have significant impacts on the socioeconomic sectors of these regions. As of now, climate change analysis using a multimodel ensemble of CMIP5 data under RCP scenarios has been conducted for the Mahanadi and Godavari basins. The aforementioned previous studies have already highlighted the adverse effects of climate change on precipitation patterns, with increased rainfall rates observed in the Godavari and Mahanadi basins. The Nagavali and Vamsadhara watersheds are located between the Godavari and Mahanadi basins. However, a comprehensive regional assessment of streamflow and sediment response to climate change is lacking in these specific watersheds. Thus, the motivation for our study is to understand the potential changesin streamflow and sediment dynamics resulting from climate change, which will provide valuable insights into the future water availability, soil erosion, and sediment yield in the Nagavali and Vamsadhara watersheds. The state government of Andhra Pradesh has planning to interlink Nagavali and Vamsadhara watersheds, which is aimed at improving the water resources management for agricultural as well as domestic purposes. Hence, this study can be impactful in making decisions regarding the interlinking project. The novelty of this study lies in considering the Warm-Wet, Cold-Wet, Cold-Dry, and Warm-Dry analyses over the Nagavali and Vamsadhara river basins under various Shared Socio-economic Pathway scenarios (SSPs). The results of this research will be useful for developing adaptive strategies for managing soil and water resources in the face of climate change. Additionally, understanding how streamflow and sediment load may be affected by climate change can provide policymakers with the scientific evidence needed to create sustainable watershed management plans.
# Utilization of a Mobile Application for Motor Skill Evaluation in Children +
## 5 Conclusions  
We developed an application for the quantification of fine motor coordination in children based on movement precision. We have not found similar tools in recent works; therefore, it is not possible to compare the obtained performance results with those of other approximations. This mobile app provides objective data on the accuracy of the movement required by the exercises, which is not taken into account with other evaluation tools.  
The proposed app has been revealed as an important instrument for the assessment of the evolution in the motor development of children without motor difficulties, allowing us to establish reference values that can be used in the following phase of clinical trials with children affected by some disorder in their motor development.  
Conceptualization, J.V.C. and L.G.D.; methodology, J.V.C. and L.G.D.; software, F.J.V.-A.; validation, A.D. and P.M.C.; experiments, D.M.N.; formal analysis, D.M.N. and P.M.C.; writing--original draft preparation, D.M.N., F.J.V.- and A.D.; writing--review and editing, P.M.C. and J.V.C.; supervision, A.D. and L.G.D. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Xunta de Galicia (ED431G2019/01), the Agencia Estatal de Investigacion of Spain (TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Clinical Research Ethics Committee of Galicia-CEIC. (protocol code 2019/477 and date of approval 30 October 2019).  
**Informed Consent Statement:** Informed consent was obtained from parents and legal guardians of all subjects involved in the study.  
**Data Availability Statement:** Data available on request due to ethical restrictions. The data presented in this study are available from the corresponding author upon reasonable request. The data are not publicly available due to them containing information that could compromise research participant privacy/consent.  
**Acknowledgments:** We are grateful for the award given by CITIC to the original conference paper presented in XoveTIC entitled \"Mobile application for analyzing the development of motor skills in children\" [36]. We also thank the management of the educational center CEIP Francisco Vales Vilamarin (Betanzos, A Coruna) for their collaboration in the development of this study.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 2 Materials and Methods
### Sample Preparation  
In mortar proportioning, the alkaline activator was chosen based on Na\({}_{2}\)O percentages of 4%, 6%, 8%, and 10% by slag weight (abbreviated as AAS 4%, AAS 6%, AAS 8%, and AAS 10%, respectively). The ratios of fine aggregate to GGBFS and water to solid were maintained at 2.75 and 0.45, respectively. The raw materials and activator were mixed at a stirring rate of 400 rpm for 15 min to obtain the slurry. Subsequently, the mixed slurry was poured into steel triple prismatic molds (4 \(\times\) 4 \(\times\) 16 cm) for flexural and compressive strength testing. The AAS mortar mixture was prepared using a universal mixer. After casting into the molds, the fresh mortar was left to cure for 24 h before demolding. The mortars were then stored under ambient conditions (at a temperature of 25 \(\pm\) 2 \({}^{\circ}\)C and a relative humidity of 90%) for the next 27 d until testing. The AAS preparation procedure is shown in Scheme 1.