esa-sceva/satcom-chunk-collection huggingface dataset:
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 3 Simulation and Experimental Results
### Performance of the Muti-Agents  
The trained model of the aforementioned multi-agent system was tested under the typical conditions shown in Figure 2, tracking the pressure and thrust commands. The conditions were designed as follows:  
* Initial 6 s:
* Pressure: 1;
* Divert control thrust command (\(F_{ycmd}\), \(F_{zcmd}\)): random steps within the upper limit of 0.8;
* Attitude control thrust (\(F_{pitchcmd}\), \(F_{yuncond}\), \(F_{rollcmd}\)): random steps within the upper limit of 1.
* Next 8 s:
* Pressure: 0.22;
* Divert control thrust command (\(F_{ycmd}\), \(F_{zcmd}\)): random steps within the upper limit of 0.1;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **Amplitude Attenuation** & **Phase Lag** \\ \hline
5 hz & 0.016–0.992 & 18\({}^{\circ}\) \\
10 hz & 0.036–0.936 & 36\({}^{\circ}\) \\
15 hz & 0.052–0.968 & 75.6\({}^{\circ}\) \\
20 hz & 0.188–0.828 & 144\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Control performance for sinusoidal valve opening command.  
Figure 11: Control performance of opening closed-loop agent: (**a**) mixing opening, (**b**) step opening, (**c**) 10 Hz Sine, and (**d**) 20 Hz Sine.  
* Attitude control thrust (\(F_{pitchcmd}\), \(F_{yunccmd}\), \(F_{rollcmd}\)): random steps within the upper limit of 0.3.
* Final 6 s:
* Pressure: 1.33;
* Divert control thrust command (\(F_{ycmd}\), \(F_{zcmd}\)): random steps within the upper limit of 1.2;
* Attitude control thrust (\(F_{pitchcmd}\), \(F_{yvacmd}\), \(F_{rollcmd}\)): random steps within the upper limit of 2.  
The control time step was set to 2.5 ms. The control effect of the multi-agent system is shown in Figure 12.  
Figure 12: Performance of the joint agents: (**a**) pressure, (**b**) nozzle opening, (**c**) \(y\)-axis thrust, (**d**) \(z\)-axis thrust, (**e**) angle, (**f**) _yaw-axis_ thrust, (**g**) _pitch-axis_ thrust, and (**h**) _roll-axis_ thrust.  
A statistical analysis was conducted on the joint control effects of multiple intelligent agents. Under these conditions, the hierarchical multi-agent system achieved complete closed-loop control of the solid attitude and orbital control engine, including combustion chamber pressure, thrust, and valve opening.  
The error values are as follows:  
* The impact of thrust distribution on closed-loop pressure was within 5%.
* The divert control thrust error did not exceed 5%.
* The attitude control thrust error did not exceed 10%.  
The adjustment time values are as follows:  
* The closed-loop pressure adjustment time was within 300 ms.
* The thrust adjustment time was within 100 ms.
* The valve opening adjustment time was within 40 ms.
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 6 Results on the Surface Roughness and the Soil Moisture Effect
### Regional Case Studies
The down-left side highlights the effects of h on \(\Gamma_{\rm LHCP,CyGNSS}\) over an area with moderate SMC. Here, the surface roughness was also high h \(>\) 0.13, and thus \(\Gamma_{\rm LHCP,CyGNSS}\) holds quite low \(\approx-\)20 dB despite the increment on SMC from \(\approx\)0.2 m\({}^{3}\)/m\({}^{3}\) to \(\approx\)0.4 m\({}^{3}\)/m\({}^{3}\).
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test was used to assess the normality of data; the mean, along with the standard deviation (SD), was calculated for both BMD and TBS values. The correlation between QCT and DXA data was estimated using a linear regression analysis by calculating the Pearson's correlation coefficient (r). To further evaluate the agreement between QCT and DXA (BMD), as well as between QCT and TBS, we performed a Bland-Altman analysis. For each comparison, we calculated the mean difference (bias) and 95% limits of agreement (mean difference \(\pm\) 1.96 standard deviations). We also calculated the mean absolute difference (MAD) to quantify the average discrepancy, independent of the sign. A Bland-Altman analysis was also performed to assess the agreement between DXA aBMD and DXA TBS.  
The ability of QCT-derived vBMD and DXA-based parameters (aBMD and TBS) to differentiate patients with and without a history of fragility fractures was evaluated using the area under the curve (AUC) of the Receiver Operating Characteristic (ROC). The sensitivity of aBMD, TBS, and vBMD by QCT in identifying fragility fractures was also calculated.  
Statistical analysis was performed using SPSS v24 (SPSS Inc., Chicago, IL, USA). A \(p\)-value \(<\) 0.05 was considered statistically significant [18].
(2017) proposed a condition prediction model for bridge components using a convolutional neural network (CNN), which achieved a prediction accuracy of more than 85%. [PERSON] et al. (2018) applied artificial neural networks (ANN) for predicting the condition rating of bridge decks. The ANN model was trained using eight parameters, namely bridge age, average daily traffic, design load, main structure design, approach span design, number of main spans, percentage of daily truck traffic, and the average daily growth rate of traffic, alongside the output values of the bridge deck condition. Then, the ANN model was used to predict future bridge conditions; the results indicate that the established neural network's prediction accuracy has reached 73.6%, and the accuracy of the model can reach 98.5% if the allowable error in the bridge condition rating is \(\pm 1\). Artificial intelligence models aim to explain bridge deterioration by capturing the connections between bridge features from inspection reports. Artificial neural networks are the most commonly used approach in artificial intelligence. [PERSON] et al. [12] proposed an artificial neural network model to predict bridge deterioration performance and, when comparing it with the Markov model and the semi-Markov model, found that the artificial neural network prediction accuracy was higher. [PERSON] et al. [13] summarized the features of ANNs in bridge deterioration prediction, highlighting that they can generate missing inspection data to fill data gaps resulting from unscheduled inspections. Because of the neural networks' generalization ability, they can handle large amounts of data. In summary, bridge deterioration models based on the Markov chain or ANN have been widely used in bridge condition prediction, with significant results achieved. However, the process of selecting bridge features for training neural networks in previous methods was based on the experience of bridge engineers and literature. In this study, a sensitivity analysis of bridge feature parameters is firstly carried out using the Relief algorithm to effectively select bridge features. Then, based on the importance and influence of the feature parameters on the bridge condition, a combination of feature parameters is selected for neural network training, reducing the data dimensionality of training. Finally, a novel prediction model is proposed that combines the Elman neural network (ENN) with the state-based Markov chain method to predict the long-term performance of bridges. To verify the accuracy of the model, the prediction results from the proposed model are compared with actual bridge condition levels. The bridge condition prediction process based on the ENN and Markov chain is illustrated in Figure 1.  
Figure 1: Bridge condition prediction process.
## 3 Experimental Results
### Macroscopic Weathering Indicators
#### 3.1.1 Apparent Morphology
Subsequent changes in environmental conditions lead to the formation of Na\({}_{2}\)SO\({}_{4}\cdot\cdot\)_n_H\({}_{2}\)O crystals, resulting in the appearance of salt frost, powder-like substances, and flocculent detachment on the surface of the rock samples. As the thickness of the weathering layer increases, the damage range gradually extends from the surface toward the interior [24]. Within the experimental scope, the sandstone samples displayed layered peeling, cracking, and other forms of damage.
## 1 Introduction
Related studies have also considered various composites and compared the effectiveness of strategies based on established methods in achieving a specific purpose [41; 42; 43]. However, few studies have employed these approaches for detecting landslides and producing inventories that consider the separation of runout areas from landslide affected regions. The RF method [44] consists of data mining and machine learning algorithms that have displayed excellent performance in the analysis of numerous remote sensing and landslide topics [45; 46]. The RF algorithm is based on the ensembles of various decision tree results and exhibit desirable properties, such as high accuracy, robustness against overfitting the training data, and integrated measures of variable importance [47]. Furthermore, [PERSON] and [PERSON] [48] and [PERSON] et al. [34] have demonstrated that combining the RF algorithm with a cost-sensitive analysis [49] in landslide susceptibility assessments can reduce extreme omission (missing) or commission (false alarm) predictions because it adjusts the decision boundary. The main purpose of this study was to explore the feasibility of separating landslide source and runout areas based on landslide affected extents extracted from remotely sensed images in order to upgrade landslide detections into a landslide inventory. The significance of topographic data in relation to the flow velocity, geomorphology, runoff rate, soil water content, and differences between landslide source and runout areas was demonstrated [34; 50]. The term \"signature\" used in this study represents the patterns of landslide source and runout areas in a feature space. Feature space means that the dimension is the used factors with the samples based on a scatter plot form. Therefore, the developed RF-based data mining models and topographic attributes, such as aspect, curvature, elevation, and slope, constructed for the Typhoon Morakot event in 2009 were combined to compare the results with those obtained using SVM and logistic regression (LR) algorithms. SVM and LR algorithms are commonly applied in the related domains. The performance of cost-sensitive analysis was also evaluated to improve the constructed models by adjusting the decision boundary.
# Creating an Interactive Urban Traffic System for the Simulation of Different Traffic Scenarios
## 1 Introduction  
The ongoing worldwide debates on the consequences of climate change, digitization, and urban migration have led to new political and planning concepts for governing and redesigning cities and their urban systems. An important field of action is inner-city traffic, whose emissions (pollutant emissions and noise) and dynamics have a major impact on the lives of citizens [1; 2; 3]. New approaches to transport systems in the future that pursue smart and climate-neutral goals are leading to significant changes in the urban landscape; for example, the replacement of combustion engines with electric motors, the increase in public transport with a decrease in cars at the same time, the increase in bicycle traffic with more bicycle paths, new approaches in unmanned parcel delivery, more charging stations for electric mobility, more greenery at the roadside, etc.  
In order to simulate and plan this complex interaction of new approaches in the urban traffic system, modern approaches to visualization in virtual reality (VR) are suitable. VR-based visualizations enable different actors in the planning process to experience the entire construct in an immersive approach. In addition, by assigning rules of motion and behavior to each relevant traffic object, a system can be created (and changed in terms of individual parameters) that simulates the multisensory impact of inner-city traffic.  
This article aims to present an example of a transferable VR-based traffic system in the model (typical traffic situation at an intersection) of a Central European city. After asummary of VR-based approaches to urban traffic simulation (Section 2), the core steps and characteristics of a new approach (Section 3) are presented, and the limitations of the approach (Section 4) are discussed. In terms of methodology, the approach presented in this article is based on the current possibilities of the game engine Unity.
## 2 Materials and Methods
### Quasistatic Tensile Tests  
The quasistatic tensile experiments were conducted on dumbbell-shaped test specimens, prepared as described above. The tests were conducted with a Zwick universal testing machine (Zwick Roell Z001, Test expert, Ulm, Germany) and an optical extensometer. Each material grade was characterized implementing three specimens to ensure reproducibility. Stresses at 50, 100, and 300% of elongation (\(\sigma_{50}\), \(\sigma_{100}\), \(\sigma_{300}\) respectively) were determined, as well as stress at break (\(\sigma_{\rm B}\)), elongation at break (\(\epsilon_{\rm B}\)), and energy at break.
# Effects of a Wearable Carriage Aid on Whole-Body Physiological Measures and Balance
###### Abstract  
Carriage tasks are common and can lead to shoulder and lower back injuries. Wearable carriage aids have shown mixed effects on local physical demand measures. This study examined the impact of a wearable carriage aid on whole-body physiological measures (normalized oxygen consumption, minute ventilation, respiratory rate, and heart rate) to obtain a more comprehensive assessment regarding aid effectiveness. Additionally, this study investigated the effect of wearing the device on perceived balance. The potential moderating effect of carried load mass was considered. The examination was conducted while walking on a treadmill at a constant speed (2 km/h) for 5 min and was completed by 16 participants. Wearing the device reduced normalized oxygen consumption (\(\sim\)14%), minute ventilation (\(\sim\)7%), and heart rate (\(\sim\)3%), while substantially improving perceived balance (\(\sim\)61%). These effects were consistent across examined carried load levels. Although this study highlighted the potential for the developed aid, future studies are required for more diverse and realistic testing conditions.  
assistive device; wearable aid; load carriage; exoskeleton; perceived balance +
Footnote †: journal: Applied Soft
## 3 Features Selection and Preprocessing
### Normalization of Features  
To improve the convergence speed and detection performance of the proposed model, the maximum-difference normalization method is used to transform the original data samples with dimensions into dimensionless expressions so that their values are concentrated between 0 and 1. The normalization formula can be expressed as follows:  
\[x_{i}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{5}\]  
where \(x_{i}\) is the normalized input data, \(x\) is the raw unprocessed data, \(x_{min}\) and \(x_{max}\) are the minimum and maximum values in the raw data, respectively.
### Factors Influencing Water Production  
Various factors, such as the water supply, water content of the coal seam itself, fault structures, coal seam fracturing cracks, and the aquifer of the roof and floor, etc., influence the water production of coalbed methane wells [12; 28]. This paper mainly conducts a comprehensive analysis of factors which include total water content of coal seams, coal seam porosity, groundwater stability index, groundwater sealing coefficient, \(D\) value of the fracture fractal dimension, fault fractal dimension, and sand-mud ratio.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 3 Review of Edge Computing Technologies
### Efficient and Safe Storage  
The limited storage resources of end devices tend to affect user experience. Therefore, efficient and secure storage solutions are worth investigating in the field of edge computing.  
[PERSON] et al. [76] proposed a distributed multi-level storage (DMLS) model with a multiple-factors least frequently used (mLFU) algorithm to address storage limitations. In this model, storage levels are composed of terminal devices at the edge. The mLFU is utilized to upload data from the current device to higher storage levels and subsequently remove this data from the device.  
In summary, this section outlines popular edge computing technologies and state-of-the-art research. Computational offloading and traffic offloading are two topics that leverage edge computing architecture to extend computing resources and bandwidth. The resource placement subsection discusses how to strategically deploy edge nodes. Caching, storage, and energy consumption pertain to hardware in edge computing. Table 2 lists the references in this section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technologies** & **References** \\ \hline Computational Offloading & [58; 59; 60; 61] \\ Resource Placement & [63; 64; 65; 66] \\ Traffic Offloading & [67; 68; 69] \\ Caching & [70; 71; 72] \\ Energy Efficiency & [73; 74; 75] \\ Efficient and Safe Storage & [76] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reference Table of Edge Computing Technologies.
## 2 Materials and Methods
### Procedures  
Data were collected during soccer matches using WIMU Pro 18 Hz GPS tracking system technology (Real-Track Systems, Almeria, Spain). The use of this device for time-motion analysis in soccer has proven to be a valid and reliable system with good inter-unit and intra-unit reliability (bias in mean velocity measurement 1.18\(-\)1.32 km/h; bias in mean distance was 2.32-4.32 m; and \(>\)0.93 intraclass correlation coefficients) [17]. Moreover, this device has been certified according to the FIFA quality standard for time-motion analysis [20] after rigorous testing procedures [22]. Importantly, during monitoring, 13.1 \(\pm\) 2.4 satellites were connected and horizontal geometric dilution of precision was 0.55 \(\pm\) 0.15, which is considered ideal and guarantees the quality of the recorded data by the GPS [23; 24].  
All devices were fully charged and calibrated before the start of the match to ensure an optimal quality data record. The recording button was then pressed, and the devices were placed in the vest pocket located on the player's upper back (figure showing the placement of a GPS device in a player's vest is added as Supplementary Material--Figure S1).  
The devices were collected at the end of the match to extract the raw data of acceleration and speed from each player using SPro (version 980) software (Real-Track Systems, Almeria, Spain). Note that the raw acceleration data were directly derived from raw speed data. The time window used to calculate acceleration raw data was 0.1 s. Approximately 52.000 and 200.000 AS points for each player were taken per match and period, respectively, to establish their AS profiles. Only positive accelerations were selected to exactly replicate the AS profile method proposed by [PERSON] et al. (2021) [16].
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 3 Proposed Methods
### Multi-Objective Tabu Search
#### 3.2.1 Initial Solution  
The initial solution is created by randomly shuffling the VMs and assigning them to PMs in a random order. If a VM cannot be placed on a PM due to insufficient capacity, the VM is rejected.
## 1 Introduction
### Hypotheses  
Against this backdrop, our primary objective is to conduct a comparative analysis of several ML techniques--both ensemble-based and individual models--to predict innovation outcomes from the CIS2014 Croatian dataset. Our overarching hypothesis (H1) posits that different ML models will exhibit varying levels of predictive performance, with ensemble methods generally outperforming single-model counterparts. Building upon this framework, we further hypothesize (H2) that tree-based ensemble learning methods (Random Forest, XGBoost, CatBoost, and LightGBM) will outperform individual models (Linear Regression, SVM, and ANN). We also anticipate (H3) that CatBoost, due to its specialized handling of categorical features, may outperform other boosting algorithms. However, questions persist regarding whether ANNs (H4) can outperform simpler methods such as Logistic Regression in relatively small, binary datasets. Finally, we consider the computational efficiency of each approach (H5), hypothesizing that Logistic Regression, given its structural simplicity, will have the least computational overhead.  
By systematically investigating these hypotheses, this work aims to contribute empirical evidence that clarifies performance trade-offs among various ML techniques in the context of firm-level innovation. In doing so, we aspire to offer actionable insights for policymakers, industry practitioners, and researchers seeking to harness the power of ML to drive effective innovation strategies. While our focus is on Croatian data, the methodological framework and findings are broadly relevant to comparable datasets and innovation contexts in other regions. Ultimately, we hope to refine and extend the body of knowledge on how best to leverage ML for predicting--indeed, shaping--innovative success.  
Primary Hypothesis:  
**H1:**: _Machine learning models applied to predict innovation outcomes based on company innovation activities will exhibit differential performance across key predictive metrics, largely driven by each model's inherent algorithmic properties and alignment with the dataset._  
Secondary Hypotheses:  
**H2:**: _Ensemble learning methods (Random Forest, XGBoost, CatBoost, and LightGBM) will yield superior predictive performance compared to individual models (Linear Regression, SVM, and ANN) in forecasting innovation outcomes from firm-level innovation activities._  
**H3:**: _CatBoost will outperform other gradient boosting algorithms (XGBoost and LightGBM) owing to its efficient handling of categorical variables, resulting in improved innovation outcome predictions._  
**H4:**: _Artificial Neural Networks (ANNs) will not significantly surpass simpler models (e.g., Linear Regression and SVM) in predicting innovation outcomes, primarily due to the limited size and binary nature of the dataset._  
**H5:**: _Logistic Regression (LR) will be the most computationally efficient model among those examined, given its relatively simple structure and reduced computational demands compared to more complex algorithms._