esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### Post-Processing Routine of the Droplet Measurements  
The in-house post-processing code based on Matlab(r) evaluates each individual droplet in terms of sphericity (which is the ratio of the minimum and maximum distance of the individual droplet in the present study), location within the measurement region, and spacing among neighboring droplets. Only droplets with a sphericity (which is the ratio of major ellipse and minor ellipse) between 1.0 and 1.2 and that were clearly separated from neighboring droplets were considered for further evaluation. This geometric limitation was necessary, since the sphericity of the single droplets had a significant impact on the scattering behavior [45].  
The intensity ratio, as a function of the droplet diameter (\(d\)), was determined by two parameters, which were fitted according to the power-law function, \(I=aJ^{b}\). The fit was performed by a weighted least squares minimization. The weighting factors were determined by the measurement noise, \(\alpha_{I}\), of the signal. The noise was approximately distributed log-normally; thus, the least-squares minimization was performed in log-space. The chosen routine ensured that fitting errors based on the greater weight of high intensities corresponding to large droplets were avoided.
# Initial Estimation of Kinematic Structure of a Robotic Manipulator as an Input for Its Synthesis
## 2 Materials and Methods
### Type B--Joints on Bezier Curve to Achieve a Position
The position vector of \(\mathbf{J}_{i+1}\) on the Bezier curve is given:  
\[\vec{t}_{i+1}=Q_{i+1} \tag{25}\]  
Position vector \(\vec{t}_{i+1}\) of \(\mathbf{J}_{i+1}\) is expressed in the coordinate frame of the currently determining frame \(\mathbf{J}_{i}\) using its inverse matrix:  
\[\vec{t}_{i+1}=\mathbf{J}_{i}^{-1}\vec{t}_{i+1} \tag{26}\]  
A projection of the \(\vec{t}_{i+1}\) vector in the YZ plane of the \(\mathbf{J}_{i}\) frame is calculated:  
\[\vec{t}^{\prime}{}_{i+1}=\vec{t}_{i+1}- \begin{bmatrix}\vec{t}_{i+1,x}\\ 0\\ 0\end{bmatrix}  \tag{27}\]  
The angle \(\alpha_{i}\) is between the unit vector \(\vec{k}\) of the \(\mathbf{J}_{i}\) frame and the projection of the \(\vec{t}_{i+1}\) vector, calculated as the inverse tangent fraction of the cross and dot products of those two vectors:  
\[\alpha_{i}=\tan^{-1}\left(\frac{||\vec{k}\times\vec{t}^{\prime\prime}{}_{i+1} ||}{\vec{k}\cdot\vec{t}^{\prime\prime}{}_{i+1}}\right) \tag{28}\]  
Using a right-hand rule for coordinate frames, if the dot product of the \(Z_{i}\) axis is in the negative direction of the Y\({}_{i+1}\) axis, the angle \(\theta_{i}\) has to be multiplied by \(-1\). Y\({}_{i+1}\) is calculated as a cross product of \(\vec{t}^{\prime\prime}{}_{i+1}\) and \(\vec{i}\) vectors:  
\[\alpha_{i}= \begin{cases}-\alpha_{i},&\mathbf{if}\;(\vec{t}^{\prime\prime}{}_{ i+1}\times\vec{i})\cdot\vec{k}<0\\ \alpha_{i},&\mathbf{otherwise}\end{cases}  \tag{29}\]  
Now, the final form of \(\mathbf{J}_{i}\) that fulfils the DH convention between \((i-1)\)th and \(i\)th can be obtained:  
\[\mathbf{J}_{i}=\mathbf{J}_{i}\text{Rot}(x_{i},\alpha_{i}) \tag{30}\]  
This procedure works smoothly for all joints. However, it will probably not be possible to obtain such DH parameters between the last joint \(\mathbf{J}_{n}\) and the given pose \(\mathbf{J}_{n+1}\) to reach the pose with the right orientation. Therefore, this algorithm is extended as the type D estimation in Section 2.4.
# Metabolic Rate Measuring with Indirect Calorimetry for Thermal Comfort Evaluation
## 3 Results and Discussion
### Comparisons of Physiological Parameters in Various Environments  
Table 3 gives the monitored values of environmental parameters in four conditions. The climate chamber controls the ambient temperature, humidity, and air velocity.  
The differences between physiological parameters in various environments are studied with RM-ANOVA. Figure 7 shows the values of MET in cold, cool, warm, and hot environments. For sitting activity, there are no significant differences in MET in four environments (F (3, 87) = 0.359, \(p\) = 0.783). For walking activity, the environments have little impact on MET when subjects walk at 2 km/h and 4 km/h (F (3, 87) = 1.369, \(p\) = 0.258, F (3, 87) = 1.737,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Cold** & **Cool** & **Warm** & **Hot** \\ \hline Temperature (\({}^{\circ}\)C) & 12.1 \(\pm\) 0.3 & 17.9 \(\pm\) 0.4 & 24.0 \(\pm\) 0.2 & 29.8 \(\pm\) 0.3 \\ Humidity (\%) & 29.1 \(\pm\) 3.7 & 30.2 \(\pm\) 3.5 & 29.9 \(\pm\) 2.9 & 28.2 \(\pm\) 3.3 \\ Air velocity (m/s) & 0.07 & 0.09 & 0.10 & 0.09 \\ Atmospheric pressure (kPa) & 100.48 & 100.48 & 100.48 & 100.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environmental parameters.  
Figure 6: The variation of (**a**) MET, (**b**) HR, (**c**) mTsk over time.  
\(p\) = 0.165, respectively). Otherwise, the impacts of environments (F (3, 87) = 5.845, \(p\) = 0.001) on MET are significant for 6 km/h walking activity. In fact, few outliers appear in 30 sample data under each condition, and the reason for the outliers may be related to the subject's diet and physical condition on that day, which was eliminated during the analysis. The results of the LSD test indicate that the MET in the hot environment is significantly higher than that in the cold and warm environments (\(p\) = 0.006 and \(p\) < 0.01). According to the previous study [19], for high activity intensity, there are significant differences in MET in the hot environment due to the rising of subjects' core temperature.  
Figure 8 shows the values of HR in cold, cool, warm, and hot environments. For sitting and walking activities, different environments have significant impacts on HR (\(p\) = 0.037, \(p\) = 0.030, \(p\) = 0.026, and \(p\) = 0.006, respectively). For sitting activity, the LSD test results indicated that HR in the cool environment was significantly lower than that in the hot environment (\(p\) = 0.019). For 2 km/h walking, HR in the cool environment was significantly lower than in the warm and hot environments (\(p\) = 0.035 and \(p\) = 0.023). For 4 km/h walking, HR in the cool environment was significantly lower than that in the cold, warm, and hot environments (\(p\) = 0.045, \(p\) = 0.020, and \(p\) = 0.016). In addition, HR in the cool environment was significantly lower than that in the warm and hot environments (\(p\) = 0.017 and \(p\) = 0.003). In summary, HR in the cool environment was significantly lower than others.
# CFD Analysis of a Tubular Heat Exchanger for the Conditioning of Olive Paste
## 4 Conclusions  
In the olive oil extraction process, the use of a tube-in-tube heat exchanger represents an opportunity to overcome the negative aspects arising during the malaxation phase and improve the process efficiency and the olive oil quality. For this purpose, in the last few years, the tube-in-tube heat exchanger has been increasingly used in the olive oil extraction industry for reasons related to its ease of use and low cost. However, some difficulties in its use have been encountered especially in batch processing due to the variation in the temperature of the inlet olive pastes and the feeding flow rate of the heat exchanger.  
In this research, the CFD simulation allowed us to evaluate the temperature distribution and pressure drop of a tube-in-tube heat exchanger by varying the inlet temperature and mass flow rate of olive paste, which is notoriously a very viscous fluid.  
The analysis of the outlet temperature of the olive paste shows the absence of uniformity of temperature reached by the different portions of pasta in relation to the position occupied in the exchanger. This consequently involves temperature increases on portions of pasta near the inner surface of the pipe wall, with negative consequences on the quality of the oil obtained.  
Even considering the pressure head at the inlet of the inner tube, in all conditions, the authors point out that the use of a tube-in-tube heat exchanger, as it is, appears to be inappropriate.  
Considering the results of CFD analyses, the authors suggest the desirability of using a scraped surface heat exchanger on olive pastes that comprises a rotor coaxially inserted in the internal tube to clean the inner surface of the stator avoiding particle deposition and, consequently, the occurrence of backmixing phenomena improving the heat transfer coefficient. The use of a device inside the exchanger should avoid deposition and cooking of olive paste particles, preserving the oil quality. In addition, it would facilitate the transport of the olive pastes, standardizing the supply pressures.  
Definitely, CFD analysis has represented a valid tool to identify critical issues to be solved in order to facilitate the introduction of a new technology in the olive oil extraction industry.  
Conceptualization, C.P. and A.T.; methodology, C.P., R.R. and P.C.; software, C.P. and P.C.; validation, A.T., R.R. and A.L.; formal analysis, C.P., R.R. and P.C.; investigation, C.P., R.R. and A.L.; data curation, C.P.; writing--original draft preparation, C.P., A.L. and A.T.; writing--review and editing, A.T., C.P. and R.R.; supervision, A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 6 Experiment and Results  
In the first part of the experiment, we collected approximately 12 min of Doppler measurements from the NOAA-15 satellite at five different passes to analyze the Doppler error measurements. From the analysis, we were able to gain an overview of the Doppler error measurements, thus helping us to accurately model the Doppler measurements. We then emulated both the Doppler and AoA measurements based on the previously discussed models. The simulated measurements were then used to localize the ground IoT device. We present the simulation results of the localization performance under multiple conditions in the upcoming subsection.
## 4 Discussion
Therefore, we suggest in order to obtain a reliable indication of the stability of the target gene, a comprehensive ranking should be made based on the results of different methods. In this study, in order to overcome the differences in the results of different algorithms and obtain the final ranking, the web-based analysis tool RefFinder was used. RefFinder obtained comprehensive overall ranking based on the Geomean of the ranking values from the above-mentioned algorithms [33]. However, one disadvantage of RefFinder is that the results of these algorithms are not weighted according to the unavailability of their cut-offs and appropriate weights [56]. Based on the results from the comprehensive analysis using the five algorithms, _UBCE_ and _EF1\(\gamma\)_, _EF1\(\gamma\)_ and _Actin_ are pairs of the reliable combination. These findings will facilitate the molecular mechanisms conferring functional and development genomics studies of the nematode in future studies. Furthermore, according to the results of gene expression studies, we found that traditional reference genes are not always stable and consistent with earlier studies. Many studies have shown that some common reference genes have different expression stabilities under different experimental conditions [48; 52; 57; 58; 59]. Our current study further emphasizes the shortcomings of simple use of common reference genes without a comprehensive evaluation (Table 2; Figure 4). For example, the commonly used internal reference gene _ACT_ has unstable expression under different developmental stages and was especially unstable in different temperature treatments. Similarly, _UBCE_ gene expression is stable under different temperature treatments, but unstable at different developmental stages (Table 2; Figure 2). These data indicate that under different conditions, the expression levels of traditional reference genes may vary significantly, highlighting that all reference genes should be validated.
# Performance Comparison of Machine Learning Disruption Predictors at JET
## 3 Models Implementation and Results
### MLP-NN Disruption Prediction Model
Figure 5: (a) GTM map of JET operational space with trajectory of the disrupted discharge #94218; (b) disruptive likelihood of the disrupted discharge #94218. The dashed black line identifies the alarm time. Figure 6: Multiple conditions alarm scheme of the GTM disruption predictor (\(T_{0}\) is the starting time of the flattop). Despite the lower performance with respect to the MLP-NN, the GTM model has had a considerable appreciation for its remarkable capabilities of visualizing the plasma operational space and the trajectories of the discharges on the map. This allows one to perform disruption prevention actions by monitoring the proximity of the discharge to the safe operational boundary.
## 2 Research Significance  
This research carries significant implications in the field of soil engineering and artificial intelligence applications. It provides a robust foundation for understanding and predicting soil compaction parameters across a variety of soil classifications. Firstly, this study is rooted in comprehensive literature-based data collection, which has amassed an extensive database of different soil types, including gravel, sand, silt, and clay. This broad spectrum of data offers a detailed and diverse basis for analysis and prediction. Secondly, we employed the MARS model to predict soil compaction parameters. To enhance the accuracy and reliability of predictions, we further fine-tuned the MARS model's performance via hyperparameter tuning. Thirdly, we conducted a comparative study to evaluate the performance of the MARS model against other existing empirical and artificial intelligence models. This comparison seeks to identify the most effective and accurate model for predicting soil compaction parameters, contributing to the optimization of methodologies in this area of research. Lastly, we carried out a sensitivity analysis to determine the relative influence of each input variable on the scour depth. Through identifying the most impactful variables, this study aids in refining the prediction model and enhances the overall understanding of the relationships between different factors.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 2 Research Methodology
### Phase 1: Planning the Systematic Literature Review (SLR)
#### 2.1.3 Review Protocol  
This step of the systematic literature review (SLR) is the most critical, since it specifies and evaluates the actions before the conducting phase [14]. When exploring various sources for articles, the review process supports the creation and authentication of pertinent keywords. Moreover, the review protocol is refined throughout the entire selection of studies. Furthermore, to choose the appropriate keywords and key phrases, we followed the guidelines of [13; 14], and a number of sources were searched using Boolean operators. The following are the ways by which we have extracted a number of papers from different repositories:  
* When searching for journal articles, we used the \"AND operator\" and \"OR operator\" to combine keywords.
* Extracted keywords and key phrases from questions.
* Used different terms related to the targeted topic.
* Included various keywords from the number of publications from different repositories.  
We have added keywords that describe the specific methods and techniques, which are shown in Table 2, which presents key terms and keywords used to select various publications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Research Question (RQ)** & **Research Objective (RO)** \\ \hline RQ1. What are the existing deep learning techniques used in short-text semantic similarity (STSS)? & RO1. The objective of this study is to summarize current deep learning techniques used in short-text semantic similarity. \\ RQ2. Which existing deep learning techniques are most appropriate for generating high-level contextual representations? & RO2. This research question aims to uncover the most appropriate existing studies that extract key features from short sentences based on contextual information. \\ RQ3. What are the available datasets used for the short-text semantic similarity? & RO3. The purpose of this research question is to determine the available datasets for STSS. However, we are interested in discovering such datasets that support short sentences. \\ RQ4. What are the current challenges and suggested improvements for short question–answering systems (QAS)? & RO4. The aim of this research question is to discuss challenges and provide future recommendations for short question–answering systems. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research questions and objectives of this research study.
## 3 Temperature Characteristics Simulation
### Simulation Models  
A three-way flameproof cable box for coal mines was the research object. The rated current and rated voltage were 60 A and 1140 V, respectively. The box consisted of an explosion-resistant enclosure, wiring terminals, an insulating base, and cable entry. By using computer-assisted design software, a 3D model was developed on a 1:1 scale for the box and subsequently imported into thermodynamic simulation software for numerical solution [18; 19]. Figure 2 details the 3D model of such flameproof cable boxes for coal mines.  
The numerical solutions are as follows. Because the computational domain is replaced with a series of discrete points, the relationship of variables at these points was established by a governing equation. The differential equation was discretized into an algebraic equation. Variables at the discrete points could be solved [20]. Figure 3 depicts the flow diagram of numerical calculation. The governing equations of the computational domain are generally mathematical formulations of physical laws, and various conservation laws can be expressed using partial differential equations. However, the analytical solutions of a partial differential equation cannot be obtained in many cases. The partial differential equation should be first discretized and subsequently solved by a numerical method to acquire its approximate solution.  
The material attribute was defined based on the material of flameproof cable boxes for coal mines. In this study, the enclosure was fabricated from cast iron. The specific heat capacity and heat conductivity coefficient were 460 J/(kg\(\cdot\)K) and 66 W/(m\(\cdot\)K), respectively. The insulating base was ceramic and had a specific heat capacity of 850 J/(kg\(\cdot\)K) and a heat conductivity coefficient of 28 W/(m\(\cdot\)K). The wiring terminals and connecting wires were prepared from copper, with a specific heat capacity of 381 J/(kg\(\cdot\)K) and a heat conductivity coefficient of 388 W/(m\(\cdot\)K). The simulation utilizes material homogeneity assumptions, isotropy assumptions, temperature boundary condition assumptions, electromagnetic boundary condition assumptions, and ideal geometry assumptions. Hybrid meshing was conducted to generate the meshes of the flameproof cable box model. Considering simulation requirements and the arithmetic speed of the computer, high mesh generation precision was selected for guidelines and wiring terminals, whereas mesh generation precision was set to be low for other parts [21; 22; 23; 24]. Figure 4 depicts a schematic of mesh  
Figure 2: Temperature characteristics simulation models for the flameproof cable box for coal mines.  
generation. The changes in key electromagnetic and thermal physical quantities after each mesh refinement of the coal mine blast box model were compared. The relative errors of magnetic field strength, eddy current density, and thermal physical quantities were calculated. The relative errors of the key physical quantities are less than the pre-set threshold (3%), and this meshing meets the above requirements and is considered to be mesh-independent.
## 5 Simulation Results
### Suspension Control of the AMBs Rigid Rotor System  
In the AMB rigid rotor system, the analysis of the suspension waveform of the rotor reflects the dynamic response and control effect of the system. The impacts of different control strategies on systemic stability are observed by comparing PID control and AFC. As shown in Figure 7, under the PID control, the maximum vibration displacement of the rotor is 3.2 \(\times\) 10\({}^{-4}\) m, and the system enters a stable state after approximately 0.17 s. When using the AFC, the maximum vibration displacement is reduced to 2.3 \(\times\) 10\({}^{-4}\) m, and the stability of the system is significantly better than that when using the PID control, quickly stabilizing after about 0.06 s.  
In addition to the performance of rotor vibration displacement, the rotor center of mass displacement is also an important indicator for evaluating system performance. With the PID control, the maximum displacement of the rotor center of mass is within a circular range of 7.12 \(\times\) 10\({}^{-6}\) m, as shown in Figure 8a, and this shows the significant displacement fluctuations and slower response time of the system. With the AFC, as shown in Figure 8b, the maximum displacement of the rotor center of mass is significantly reduced to a circular range of 1.69 \(\times\) 10\({}^{-6}\) m, indicating that the AFC not only reduces rotor vibration but also significantly improves the stability of the center of mass position, optimizing the dynamic performance of the system.  
From the perspective of the control effect, the AFC has significant advantages in reducing rotor vibration and accelerating systemic stability compared to traditional PID control. The AFC makes the system respond more accurately by adjusting the nonlinear and dynamic characteristics of the system in real time, which significantly improves the anti-interference ability and stability of the system. Therefore, the AFC is a more effective control strategy, especially for the AMB rigid rotor systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Meaning** & **Value** \\ \hline \(m\) & Rotor mass (kg) & 18.09 \\ \hline \multirow{2}{*}{\(J\)} & Moment of inertia about \(x\) & \\  & and \(y\) axes (kg\(\cdot\)m\({}^{2}\)) & 0.2 \\ \hline \multirow{2}{*}{\(l_{a}\) and \(l_{b}\)} & Distance from ends A and & \\  & B to the central plane (m) & 0.13 \\ \hline \multirow{2}{*}{\(k_{i}\)} & Current stiffness & \\  & coefficient (\(N/\)A) & 577.96 \\ \hline \multirow{2}{*}{\(k_{h}\)} & Displacement stiffness & \\  & coefficient (\(N/\)m) & 2.75 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the AMB (active magnetic bearing) rigid rotor system.  
Figure 7: Suspension waveform of the rotor. (**a**) Rotor vibration of \(x\)-direction. (**b**) Rotor vibration of \(y\)-direction.