esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 2 Materials and Methods
### Parametric Numerical Analyses
The arrangement of the spot welds between CFS C-sections and the CW, and between the CW and the shear plates, is determined by the arrangement of the ribs of the CW, as shown in Figure 6a. The spot welds between the C-sections and other steel elements are located 35 mm from the top and bottom edges of the flanges. To create a connection between the steel beam and the concrete slab, the shear connection is secured with a tie connection, which produces a full shear connection, and by positioning the shear connectors in a different arrangement, a partial shear connection is produced. As previously mentioned, structural bolts were used as demountable shear connectors. The chosen height of 84 mm was defined in accordance with EN 1994-1-1 [9]. The diameter of the bolts used was 12 mm; for simplification's sake, they are modelled as beam elements without heads. The bolts were embedded in the concrete slab and their lower end was connected to the upper flange of the C-profile with the wire comprising the MPC Beam connector section; thus, the joint behavior of the C-section and the bolts was ensured. Figure 6b shows how the lower edge of the shear connector and the upper edge of the channel profile were connected. The axial distance between the bolts in the cross-section, within the same rib, was 107 mm. The longitudinal arrangement is defined by the axial distance between the metal sheet ribs, which was 240 mm. Two different arrangements of bolts were analyzed in pairs or in a staggered arrangement. The nomenclature for numerical model names used in this paper is explained in Figure 7.  
Figure 5: Material definition: (**a**) stress-strain relation of C25/30 concrete, in accordance with [13,37,38]; (**b**) tensile stress–strain relationship for steel grade S350 GD; (**c**) tensile stress–strain relation for bolts 8.8. Figure 6: Spot welds and shear connections: (**a**) arrangement of spot-welds; (**b**) model shear connectors.
## 4 Results and Discussion  
### Siewe Analysis of Raw Materials  
The sieve analysis of the coarse aggregates used shows that the size of the material was primarily distributed around 19 mm and 13.2 mm and is consistent with normal practice expected in coarse aggregates. The particle size distribution curve for riverbed sands and the CWG sands are shown in Figure 5. It can be seen that the recycled glass sands peaks just above the upper bound of the recommended grading specifications. This shows that the crushed glass particles are comparatively smaller than the riverbed sand particles. The crushed glass attained a higher fraction of fine particles, with a fineness modulus of 3.29 compared with a fineness modulus of 5 observed for normal sand particles. The median particle size of the recycled glass is 300 \(\upmu\)m, while that for normal sands was over 1.0 mm. Figure 5 shows that nearly 50% of the fresh sand aggregates lies outside the recommended lower bound size, resulting in much coarser sand with a median size of 1.8 mm. Therefore, the mixing of CWG sands and riverbed sands would be expected to provide a combined distribution effect with a particle size range that lies closer to that of the recommended range in Figure 5.  
CWG glass with particle sizes smaller than 140 \(\upmu\)m are known [43] to contribute to pozzolanic activity and result in an increased compressive strength of concrete. In this case, only a fraction of the CWG substitutes falls below the 140 \(\upmu\)m size limit, and there is little evidence that there is a significant pozzolanic contribution because a significant proportion of these CWG particles are too large.  
The SEM image analysis for the riverbed and glass sands is shown in Figure 6. The riverbed sands are more uniform, rounded edges with dimensions ranging from 0.3 to 1.0 mm consistent with particle size distribution curve in Figure 5. On the other hand, the glass sands present a different morphology with irregular sizes and about 90% ranging from 10 \(\upmu\)m to 1.0 mm. The surfaces of glass sands were faceted presenting many sharp edges. This implies that the specific surface of the glass sand on the average is much higher than that of the natural sands because of smaller size distribution and because of the faceted morphology.  
Figure 5: Size distribution curve for the fine aggregates used. Limits according to Australian Standards (AS 1141) indicated.  
Energy-dispersive spectroscopy (EDS) of the glass sands was taken to identify the glass type. The EDS is shown in Figure 7, and the semi-quantitative analysis indicated that the chemical composition of an averagely randomised sample was SiO2, 78.2%; Na\({}_{2}\)O, 9.75%; CaO, 9.98%; and MgO, 2%. This composition is consistent with that of soda lime glass [14, 17], thus suggesting that this CWG comes mostly from glass recycled from food packaging.  
Figure 6: SEM microscopy showing morphology of fine aggregates: (**a**) Riverbed sand, (**b**) Glass sand.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Prediction Models
#### Extra Trees Regressor  
The ETR algorithm develops an ensemble of unpruned regression trees based on the standard top-down process. The difference between ETR and RFR is that the selected cut-points of the split nodes in ETR are extremely random to grow the tree, in addition, ETR uses the whole training sample instead of a bootstrap replica [28].  
As for its numerical features, the splitting procedure of ETR has two basic parameters, which are the _number of features_ randomly chosen at each node and the _minimum sample size_ for splitting a node. To obtain the final result, ETR formulates the predictive models of the individual trees, as in RFR, and the predicted models are combined to produce the final prediction result, such as averaging in regression problems. The basic hyperparameters are the _number of features_ to govern the strength of the feature selection procedure, the _minimum sample size_ to strengthen the averaging of the outcome noise, and the _number of trees_ to strengthen the variance reduction of the ensemble model combination.  
In a scikit-learn implementation, the hyperparameters are similar to those of DTR, with additional information about the number of trees (_n_estimators_) in the forest. Usually, a higher number of trees better trains the data. However, adding a lot of trees can slow down the training process considerably, therefore a parametric search to find the optimal configuration is necessary.
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 4 The Secure Auditable Registry Based On Message-Oriented Middleware (SARMOM) Pattern
### Threats  
Apart from the specific threats described in the previous sections, we present a brief threat analysis in the domain of IoT systems, and, in particular, on their messaging systems. Security is always a major concern regarding distributed applications, and this is especially true for IoT systems. Due to their openness and having their components loosely coupled, IoT systems are continuously exposed to vulnerabilities and threats in different layers and scopes. For messaging-based IoT systems, threats cover aspects such as interceptions, modifications or even interruption of the services, but also time altering, evidence elimination, etc. The most common threat categories for these systems are the following:  
* Unauthorized access: the first fear regarding the use of an IoT system (regardless of whether it is centered on a registry or not) is the unauthorized access to the stored data, allowing attackers to read confidential information, possibly also involving data tampering, corruption or destruction attacks. These attacks have evolved from simple data corruption or injection to become more sophisticated and difficult to detect, and for a system relying on a messaging or registering system the consequences can be fatal.
* Denial of Service (DoS): a frequent goal for attackers is to prevent clients from accessing the resources or services of the system. The most common way to achieve this is by flooding the network or the application servers with a huge amount of information and traffic. For instance, it may result in the registry being unable to write new information or access the information already stored.
* Integrity in multi-user access scenarios: most IoT systems operate in a multi-user environment--that is, a scenario involving several users accessing the same information and/or resources (typically a database or registry) simultaneously. Users can be humans, devices or other IT systems. Keeping data integrity in this context (not only resulting from malicious actions but also accidents and errors) is a key concern. In registry-based systems, integrity also affects the timing and ordering of events and messages.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Analysis of Imperceptibility
#### 5.2.5 Comparison of Similar Algorithms
The amplitude range of cropping attack is 10-40%. The algorithm of [PERSON] et al. is more vulnerable to cropping attacks [34], while the algorithm of [PERSON] et al. [27] selects the height value for sorting of point clouds in the 3D model as the watermark embedding feature. Therefore, the impact of cropping attacks on the algorithm of [PERSON] et al. [28], the algorithm of [PERSON] et al., and the algorithm of [PERSON] et al. [35] is relatively small in some areas. In this algorithm, the vertex coordinates are processed through clustering and feature point extraction, which has strong robustness against cropping, and is superior to similar 3D model spatial watermark algorithms. It maintains the same level of robustness as frequency domain algorithms. The embedded features in the texture coordinates establish a multi-to-one mapping relationship with the watermark data, so it is basically not affected by cropping, which is superior to other watermark algorithms. Figure 8: Robustness comparison of noise attacks [28; 34; 35]. Figure 9: Robustness comparison of the clipping attacks [27; 28; 34; 35].
###### Abstract  
SM2 RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2 RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2 RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (\(-\)0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2 RAIN estimation. This study is expected to provide guidance for future applications of SM2 RAIN-derived precipitation. Meanwhile, more reliable SM2 RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.  
Keywords:SM2 RAIN; uncertainty quantification; Tibetan Plateau; SMAP; precipitation +
Footnote †: journal: remote sensing
## 2 Governing Equations and Numerical Methods
### Turbulence Model  
The low-Reynolds number \(k\)-\(\epsilon\) two-equation turbulence model is used as follows [37]:  
\[\begin{array}{l}\rho\frac{dk}{dt}=\frac{\partial}{\partial x_{j}}\Big{[} \Big{(}\mu+\frac{\mu}{\sigma_{k}}\Big{)}\frac{\partial k}{\partial x_{j}} \Big{]}+P_{k}-\rho\epsilon+L_{k}\\ \rho\frac{dk}{dt}=\frac{\partial}{\partial x_{j}}\Big{[}\Big{(}\mu+\frac{\mu} {\sigma_{k}}\Big{)}\frac{\partial\epsilon}{\partial x_{j}}\Big{]}+C_{\epsilon 1}f_{1}P_{k}\frac{\epsilon}{k}-C_{\epsilon 2 }f_{2}\rho\frac{\epsilon^{2}}{k}+L_{\epsilon}\end{array} \tag{8}\]  
where \(C_{\epsilon 1}=1.44\), \(C_{\epsilon 2}=1.92\), \(\sigma_{\epsilon}=1.3\), and \(\sigma_{k}=1.0\).  
In a cavitation flow, the standard two-equation models are known to overpredict the turbulent eddy viscosity inside the cavity. In order to resolve the problem, a filter-based model is used [38]. The model decreases the viscosity between the turbulence length scale and the filter size, \(\Delta\), which is determined by the local grid size. The relevant parameters are defined as follows:  
\[\mu_{T}=\rho f_{\mu}C_{\mu}\frac{k^{2}}{\epsilon}f_{FBM},\ f_{FBM}=\min\left( 1,\frac{\Delta\cdot\epsilon}{k^{3/2}}\right) \tag{9}\]  
where \(C_{\mu}=0.09\).
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 1 Introduction
By linking diverse data sources, algorithms can help predict outbreaks, monitor disease progression, and inform comprehensive, integrated management strategies. In this way, they contribute to the protection of ecosystems while supporting agricultural productivity [11]. Phenology and plant diseases have a direct correlation with the plants' ability to develop mycotoxins. These toxic compounds, produced by certain fungal pathogens in the field, contaminate crops and commodities, posing serious risks to human and animal health. Managing mycotoxin contamination requires reliable detection and predictive tools, and algorithms provide strong support in finding valuable solutions [12]. In summary, the aim of this work is to highlight how algorithms effectively contribute to plant monitoring through phenology observation and disease detection, as a valuable support for crop management.
## 4 Discussion
Future studies should incorporate EMG and kinetic assessments to better understand neuromuscular adaptations. Advanced biomechanical modeling tools, such as OpenSim musculoskeletal model or AI-driven algorithms, could provide more precise estimates of knee contact force changes and muscle forces [51]. Moreover, integrating recurrence quantification analysis, EEMD-DFA algorithms, or vibroarthrography may improve the accuracy of knee joint function and cartilage health assessments, leading to better data visualization and greater clinical applicability [52; 53; 54]. Furthermore, the sample size (N = 30) was small, which may affect the reliability and applicability of the results. In addition, the study was limited to middle-aged women with degenerative knee arthritis, restricting the generalizability of the findings to other populations. Differences in sex, age, and health conditions may influence the effects of water inertia-based unstable exercise, and it remains unclear whether similar benefits can be observed in younger individuals, men, or those with different musculoskeletal conditions. To establish more effective exercise therapy strategies for different populations, it is necessary to compare Aqua Vest with other water inertia-based training tools in controlled experiments. Expanding research to include more diverse populations and conducting statistical power analysis will further improve accuracy and confirm the broader applicability of these training methods. Finally, the study lasted 10 weeks but did not measure how long the effects lasted after the program ended. Without follow-up tests, it is unclear if the improvements in movement and balance remain over time. The lasting effects of water inertia-based unstable exercise may differ depending on a person's activity level, muscle strength, and daily habits. Follow-up assessments at different times are needed to understand how long the benefits last. This will also help develop more tailored exercise therapy strategies suited to different individuals and optimize the application of water inertia-based unstable exercise in various clinical and rehabilitation settings. Overall, this study demonstrated that a 10-week Aqua Vest-based unstable load exercise program led to significant improvements in gait performance, postural stability, and knee pain reduction in middle-aged women with DKA. These findings support the hypothesis that unstable exercises leveraging water inertia positively influence postural control, lower extremity function, and pain during stair ambulation. The Aqua Vest represents a promising tool for rehabilitation and fall prevention in individuals with DKA, warranting further research to refine its application in clinical and sports performance settings.
## 2 Materials and Methods
### Design and Implementation of swHPFM
#### 2.2.1 Algorithm and task mapping scheme
Level 2 represents the medium grid. Level 3 stands for the coarse grid. and various physical parameters. Then, the core iteration process of the algorithm, namely, the R-K explicit time marching method, was executed, whose main objective was to solve the equation via iteration, until the results converged. At the same time, multiple nodes were executed in parallel, and the data between nodes communicated through MPI. Correspondingly, the swHPFM simulation computing on each CG of SW26010 processor is illustrated in Figure 7. As previously analyzed in this paper, the MPE is responsible for judging and controlling the whole simulation cycle, including calling the file reading module to read the grid data and communicating the boundary data by using the MPI. The CPE is responsible for data parallel computing. Therefore, kernel functions, such as the original quantity, residual, and source term, are distributed to exploit the computing performance. For the structured grid fluid mechanical simulation algorithm, the main computing characteristic is regular memory access. In detail, the access mode is continuous access and regular stride access. However, in the innermost part of the array, although it is regular, the stride size is so large that the mode is close to discrete access, and the memory access cost is high [28]. To efficiently distribute data to the CPE, the SPM access strategy, which is based on the computing characteristics, serpentine register communication, and SIMD was proposed to improve the computing performance. Figure 6: The algorithm flow chart. This algorithm simulates the rotor solver of the axial compressor. The algorithm flow in the rectangular box represents the tasks to be completed inside each node. Meanwhile, many different rectangular boxes exit, which indicates that multiple nodes are executed in parallel, and the data between nodes communicate through message passing interface (MPI).
# Forecasting Students Dropout: A UTAD University Study
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision 1** & **Recall 1** & **F1-Score 1** & **AUROC 1** & **Accuracy 1** \\ \hline
**CatBoost** & 0.78 \(\pm\) 0.26 & 0.82 \(\pm\) 0.25 & 0.79 \(\pm\) 0.19 & 0.90 \(\pm\) 0.12 & 0.84 \(\pm\) 0.14 \\ \hline
**Random Forest** & 0.81 \(\pm\) 0.21 & 0.81 \(\pm\) 0.29 & **0.81**\(\pm\) 0.22 & 0.91 \(\pm\) 0.10 & **0.86**\(\pm\) 0.15 \\ \hline
**XGBoost** & 0.78 \(\pm\) 0.25 & **0.83**\(\pm\) 0.30 & 0.80 \(\pm\) 0.23 & 0.91 \(\pm\) 0.11 & 0.85 \(\pm\) 0.16 \\ \hline
**ANN** & **0.85**\(\pm\) 0.28 & 0.71 \(\pm\) 0.38 & 0.75 \(\pm\) 0.23 & **0.92**\(\pm\) 0.09 & 0.83 \(\pm\) 0.14 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 4: Comparison between the different models results of a stratified 10-fold cross validation test, on training data. Figure 8: Different ROC curves obtained on 10-fold cross validation test, for Random Forest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **AUROC** & **Accuracy** \\ \hline
**CatBoost** & **0.84** & 0.84 & 0.84 & 0.95 & 0.88 \\ \hline
**Random Forest** & 0.81 & 0.88 & 0.85 & **0.96** & 0.88 \\ \hline
**XGBoost** & 0.82 & **0.92** & **0.87** & 0.95 & **0.90** \\ \hline
**ANN** & 0.77 & 0.80 & 0.78 & 0.94 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the different models’ predictions on the final test set. papers reviewed showed that non-academic data, in particular socioeconomic data, also influence the academic results. As the proposed model presents promising results, it can be concluded that the grades obtained by the students somehow also incorporate their social extract and way of life.
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 6 Experimental Results
### Effectiveness of the Proposed Approach versus Benchmark Models
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Stock Name**} & \multirow{2}{*}{**Our Approach**} & \multicolumn{4}{c}{**Baselines**} \\ \cline{3-6}  & & **SVM** & **RF** & **KNN** & **Fisher and Krauss [20]** \\ \hline Samsung Electronics Co. & 59.74 & 52.07 & 48.25 & 50.80 & 54.95 \\ SK Hynix Inc & 60.38 & 55.28 & 51.43 & 51.44 & 55.27 \\ Celltrion & 61.02 & 56.55 & 53.67 & 54.97 & 59.74 \\ Hyundai Motor Co. & 64.54 & 54.96 & 57.51 & 58.47 & 57.83 \\ LG Chem & 60.05 & 50.47 & 49.98 & 50.15 & 58.78 \\ \hline Boeing Company & 60.19 & 48.78 & 52.78 & 50.00 & 58.64 \\ Apple Inc. & 60.49 & 43.83 & 46.91 & 50.62 & 57.72 \\ Amazon.com Inc. & 62.65 & 54.01 & 59.95 & 50.93 & 58.02 \\ Johnson \& Johnson & 59.26 & 48.77 & 50.62 & 50.31 & 56.48 \\ JPMorgan Chase \& Co. & 58.64 & 50.94 & 52.16 & 50.00 & 56.17 \\ \hline Average & 60.70 & 51.57 & 51.49 & 51.77 & 57.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of our proposed approach and baselines over all study periods. Support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN). factors [1]. Unlike [20], related stocks are considered in our study. Results prove the effectiveness of using returns of related stocks to form the model input. Overall, the proposed approach outperforms the model in [20] for all stocks, particularly, in the case of Hyundai Motor Co. company, with only 57.83% average accuracy from the existing model, compared to 64.54% average accuracy of our proposed approach (a broad difference of 6.71 percentage points). Additionally, 5.11% and 4.63% higher average accuracy are achieved for SK Hynix Inc and Amazon.com Inc., respectively. However, Table 6 illustrates similar average accuracy for the proposed approach and the existing model in predicting stock price movements of LG Chem, at 60.05% and 58.78%, respectively.