esa-sceva/satcom-chunk-collection huggingface dataset:
# Criterion-Related Validity and Reliability of the Front Plank Test in Adults: The ADULT-FIT Project
## 3 Results
### Reliability
\begin{table}
\begin{tabular}{l l c c c c c} \hline  & & \multicolumn{4}{c}{**Time in FPT**} \\ \cline{3-6}  & & \multicolumn{3}{c}{**Sex**} & \multicolumn{3}{c}{**Age**} \\ \cline{3-6}  & **Total** & **(n = 84)** & **Male** & **Female** & **Young Adults** & **Adults** \\  & & **(n = 52)** & **(n = 32)** & **(n = 64)** & **(n = 20)** \\ \hline
**FPT and body composition variables and RPE at the end** & & & & & \\ \hline BMI (kg/m\({}^{2}\)) & \(-0.11\) & \(-0.17\) & \(-0.11\) & \(-0.10\) & \(-0.15\) \\ Waist circumference (cm) & \(-0.05\) & \(-0.14\) & \(-0.13\) & \(-0.10\) & \(0.04\) \\ Body fat (\%) & \(-0.32\)**\({}^{\star}\)** & \(-0.35\)**\({}^{\star}\) & \(-0.37\)**\({}^{\star}\) & \(-0.31\)**\({}^{\star}\) & \(-0.55\)**\({}^{\star}\) \\ Lean Mass (kg) & \(0.02\) & \(-0.27\) & \(-0.06\) & \(-0.04\) & \(0.25\) \\ Trunk Fat-free mass (kg) & \(0.02\) & \(-0.20\) & \(-0.04\) & \(-0.05\) & \(0.24\) \\ Trunk Fat Mass (\%) & \(-0.39\)**\({}^{\star\star}\)** & \(-0.35\)**\({}^{\star}\) & \(-0.42\)**\({}^{\star}\) & \(-0.37\)**\({}^{\star}\) & \(-0.59\)**\({}^{\star\star}\) \\ Lean Trunk Mass (kg) & \(0.02\) & \(-0.20\) & \(-0.04\) & \(-0.05\) & \(0.24\) \\ RPE at the end & & \(0.13\) & \(-0.03\) & \(0.29\) & \(0.25\)**\({}^{\star}\) & \(-0.04\) \\ \hline
**FFT and muscle activation** & & & & & \\ \hline URA (\%MVIC) & & \(-0.04\) & \(0.08\) & \(-0.20\) & \(0.04\) & \(-0.30\) \\ LRA (\%MVIC) & & \(-0.17\) & \(-0.18\) & \(-0.22\) & \(-0.18\) & \(-0.13\) \\ EO (\%MVIC) & & \(-0.27\) & \(-0.34\)**\({}^{\star}\) & \(-0.19\) & \(-0.24\) & \(-0.34\) \\ MF (\%MVIC) & & \(0.16\) & \(0.27\) & \(-0.18\) & \(0.25\) & \(-0.48\) \\ ES (\%MVIC) & & \(0.20\) & \(-0.04\) & \(0.59\)**\({}^{\star\star}\)** & \(0.23\) & \(0.03\) \\ \hline
**FTP and force in isokinetic device** & & & & & \\ \hline \multirow{7}{*}{
\begin{tabular}{} \end{tabular} } & Initial Peak Torque (Nm) & 0.17 & 0.12 & 0.11 & 0.10 & 0.39 \\  & Initial Peak Torque per BW (Nm/kg) & \(0.27\)**\({}^{\star}\) & \(0.26\) & \(0.23\) & \(0.21\) & \(0.50\)**\({}^{\star}\) \\ \cline{1-1}  & & Fatigue Index & 0.18 & 0.12 & 0.20 & 0.17 & 0.24 \\ \cline{1-1}  & & Total Work (N/m) & \(0.14\) & \(0.06\) & \(0.08\) & \(0.06\) & 0.38 \\ \cline{1-1}  & & Total Work per BW (N/m/kg) & \(0.25\)**\({}^{\star}\) & \(0.25\) & \(0.19\) & \(0.19\) & \(0.49\)**\({}^{\star}\) \\ \cline{1-1}  & & Average Power per Rep (W/av) & \(0.14\) & \(0.06\) & \(0.05\) & \(0.06\) & 0.39 \\ \cline{1-1}  & & Average Power per Rep per BW (W/av/kg) & \(0.24\)**\({}^{\star}\) & \(0.25\) & \(0.16\) & \(0.18\) & \(0.49\)**\({}^{\star}\) \\ \cline{1-1}  & & Time to Peak (s) & \(-0.11\) & \(-0.16\) & \(0.04\) & \(-0.05\) & \(-0.29\) \\ \hline \end{tabular}
\end{table} 
Table 2: [PERSON]’s bivariate correlation by sex and age between FPT and physical activity, body composition variables, RPE at the end, muscle activation, and force in isokinetic device.
## 3 Results and Discussions
### GalInP Single Junction Solar Cell with Double Back Surface Field
It is worthy to note that increasing the thickness of the BSF layer presents significant advantages for the enhancement of the solar cell performance, which can be ensured by improving the transport of photo-generated carriers and reducing the recombination losses at the rear surface. This improvement in carrier transport primarily results from the reduction in recombination losses, which is effectively compensated by an increased concentration of minority carriers at the rear junction. Indeed, a higher doping concentration strengthens the BSF layer, effectively repelling minority carriers from the rear contact, thereby minimizing recombination. When optimized, these factors significantly contribute to an overall improvement in solar cell performance. The maximum efficiency is achieved with a BSF double-layer thickness of 50 nm, as shown in Table 3.
## 3 Positioning Model Establishment and Solution
### Model Accuracy  
In this paper, one-dimensional, two-dimensional, and three-dimensional RMSE (Root Mean Square Error, root mean square error) are used to evaluate the results and illustrate the validity of the model. The main calculation formulas for each index are as follows [27; 28]:  
\[\text{One}-\text{dimensional}:\ \left\{\begin{array}{l}\text{RMSE x}=\sqrt{ \frac{\sum_{i=1}^{n}\left(\tilde{x}_{i}-\bar{\text{x}}\right)^{2}}{\text{n}}} \\ \text{RMSE y}=\sqrt{\frac{\sum_{i=1}^{n}\left(\tilde{y}_{i}-\bar{\text{y}}_{i} \right)^{2}}{\text{n}}}\\ \text{RMSE z}=\sqrt{\frac{\sum_{i=1}^{n}\left(\tilde{x}_{i}-\bar{\text{z}}_{i} \right)^{2}}{\text{n}}}\\ \end{array}\right. \tag{6}\]  
\[\text{Two}-\text{dimensional}:\ \mathrm{d}_{\text{i}}=\sqrt{\left(\text{x}_{ \text{i}}-\bar{\text{x}}\right)^{2}+\left(\text{y}_{\text{i}}-\bar{\text{y}} \right)^{2}} \tag{7}\]  
\[\text{RMSE}_{\text{xy}}=\sqrt{\frac{\sum_{i=1}^{n}\left(\text{d}_{\text{i}}- \bar{\text{d}}\right)^{2}}{\text{n}}} \tag{8}\]  
\[\text{Three}-\text{dimensional}:\ \mathrm{d}_{\text{i}}=\sqrt{\left(\text{x}_{ \text{i}}-\bar{\text{x}}\right)^{2}+\left(\text{y}_{\text{i}}-\bar{\text{y}} \right)^{2}+\left(\text{z}_{\text{i}}-\bar{\text{z}}\right)^{2}} \tag{9}\]  
\[\text{RMSE}_{\text{xyz}}=\sqrt{\frac{\sum_{i=1}^{n}\left(\text{d}_{\text{i}}- \bar{\text{d}}\right)^{2}}{\text{n}}} \tag{10}\]  
According to the results of the above prediction indicators, we compare the final positioning results of the above two models. The main indicators of the models are compared, as shown in the following Table 3:  
Figure 12: Schematic diagram of abnormal data error.  
The above results show that the position model in this paper has good advantages and effectiveness and can position the position of the target well. In addition, the effectiveness of the model is demonstrated by comparing the theoretical coordinates in each direction with multiple sets of real coordinates, and the results are shown in Figure 13.  
At the same time, its own model characteristics (such as continuously updating and querying other samples, extracting features through many variables, etc.) are also very  
Figure 13: Schematic diagram of XOY and XOZ projections. (**a**) Comparison of theoretical coordinates and actual coordinates of target points in the XOY direction; (**b**) Comparison of theoretical coordinates and actual coordinates of target points in XOZ direction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation Indicators (RMSE)**} \\ \hline  & & **1D** & & **2D** & **3D** \\ \hline  & x & y & z & (x,y) & (x,y,z) \\ \hline Without interference & 7.6168 & 7.4579 & 13.7416 & 10.7321 & 16.6531 \\ \hline With interference & 169.8263 & 97.5270 & 140.5821 & 226.3231 & 334.3860 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main index comparison results.  
suitable for the use of actual projects, and its own model framework has begun to be valued and used in the signal positioning and transmission process.
## 6 Discussion
### Usability  
Although the results of the CSUQ score showed that there is no significant difference among the three interaction methods, the results of CSUQ for the three interaction methods were positive for all four aspects: System Usefulness, Information Quality, Interface Quality, and Overall. In addition, Head Interaction got the highest average of the CSUQ score among the three interaction methods.  
For Hand Interaction, some participants gave positive feedback that they could manipulate the virtual object by moving their hands. On the other hand, some participants gave negative feedback that during the manipulation, the virtual object was stopped in the middle. This is because the participants performed hand gestures outside the camera's FOV. This feedback was because the user did not know the recognition range of the camera that could recognize the hand gesture when manipulating the virtual object using Hand Interaction.  
We found that the participants preferred Head Interaction over Hand Interaction or Finger Interaction when repeatedly manipulating the virtual object. Since Hand Interaction requires the hand to be raised to manipulate the virtual object, this interaction increases the fatigue of the arm when manipulating the virtual object for a long time. In contrast, Finger Interaction does not have to interact with the virtual object with the hand raised, but has a problem that the virtual object is occasionally out of sight due to the problem of high CD ratio. In Head Interaction, on the other hand, the user can manipulate the virtual object without raising his/her hand at the center of his/her FOV.  
Moreover, the participants did not prefer to use Hand Interaction in public because the manipulation of the virtual object requires the users to raise their hands. These results can also be found in other studies [10]. On the other hand, Finger Interaction and Head Interaction, which can manipulate the virtual objects without the user needing to raise his/her hands, received high scores from participants.
### Literature Review  
DH systems have been widely studied, with several review papers addressing their technological evolution, market trends, and environmental benefits. However, while previous reviews have provided valuable insights into various aspects of DH, none have specifically focused on a system-level overview with an emphasis on return temperature reduction. This review aims to fill that gap by synthesizing existing research on system-level components and operational strategies that influence return temperatures, thereby improving DH network efficiency.  
[PERSON] [32] presents a global overview of DH (and district cooling), structured around market dynamics, technical advancements, supply characteristics, environmental impacts, institutional frameworks, and future outlooks. The review highlights key challenges, including the limited uptake of DH in buildings, country-specific variations in implementation rates, and a general lack of recognition of DH potential for carbon dioxide reduction. These findings emphasize the importance of optimizing system-level components, such as thermal storage and hydraulic balancing, to maximize the environmental and economic benefits of DH systems.  
[PERSON] et al. [33] provide a historical perspective on district energy systems, examining their development and current applications. The authors underscore the role of government policies and incentives in shaping the efficiency and adoption of DH systems. They also discuss the environmental advantages of DH, including reduced greenhouse gas emissions and enhanced energy security. However, the review focuses on broader aspects of DH and does not delve into the specific system-level measures required for return temperature reduction.  
[PERSON] et al. [34] explore the evolution of DH systems through four generations, emphasizing the integration of low-temperature renewable heat sources and decentralized heating technologies. The study highlights operational optimizations and policy support as critical factors for the transition to fourth-generation DH systems. Although the review provides an excellent overview of these developments, it lacks a detailed discussion of system-level strategies, such as thermal balancing and advanced control methods, for reducing return temperatures.  
[PERSON] et al. [35] focus on fourth-generation DH technologies, including the incorporation of renewable energy sources like solar and geothermal systems. The study highlights the importance of thermal storage for balancing supply and demand and reviews early implementations of smart DH and cooling systems. Despite its comprehensive coverage of modern technologies, the review predominantly addresses supply-side innovations, leaving system-level considerations, such as hydraulic balancing and network design, less explored.
## 3 Results
### Impact of the Compression
The average size \(s_{\varnothing}\) was uncorrelated for the 20% quantile level, and the largest area \(s_{\text{max}}\) was not correlated in any case. The transfer of the detailed information from transport simulations in the GDL to larger scales of cell and stack simulations requires additional investigations of the channel and flow field modeling. Simulation domains of GDLs were typically much smaller than any flow field of real fuel cells, which is illustrated in Figure 1. Furthermore, the GDL/channel interface connected a region of deterministic geometry with an irregular microstructure. As a consequence, many simulations on stochastic equivalent representations of the microstructure are required to consider the results from GDL transport simulations in the operating or boundary conditions of the cell-level models. Transport simulations on the microstructure can be evaluated statistically to characterize material or surface properties.
Multiple headspace solid-phase microextraction (MHS-SPME), which was developed by multiple headspace extraction (MHE) and firstly proposed by [PERSON] and [PERSON], along with [PERSON] [29; 30; 31], is one of the applications of exhaustive extraction by HS-SPME. The principle of the MHS-SPME procedure is based on the consecutive extraction of the same sample several times, while the total theoretical peak areas of the analyte corresponding to exhaustive extraction can be estimated from each individual extraction. The \"matrix effect\" can be eliminated by \"exhaustive extraction\". [PERSON] et al. firstly applied MHS-SPME to quantify volatile organic compounds (VOCs) in multilayer food packaging materials in 2003 compared with the SA [32]. The results showed that MHS-SPME is a powerful method for the direct quantification of VOCs in solid samples. The comparison of the total peak areas of the calibration solution prepared with water and \(n\)-hexadecane showed that the peak area obtained by MHS-SPME were statistically equal, which proved that MHS-SPME could effectively eliminate the matrix effect [26]. Moreover, the use of MHS-SPME in wine flavor analysis proved that MHS-SPME could overcome the low repeatability when SPME was applied to the complex matrix [33]. To our knowledge, MHS-SPME combined with GC-MS has been successfully used to quantify volatile compounds in food products such as sausage, wine, herbal teas, roasted coffee, and so forth [34; 35; 33]. However, its application has not been performed in flavor-enhanced edible oils. The aim of this study is to set up a novel and more accurate MHS-SPME-arrow-GC-MS method to quantify pyrazines in flavor-enhanced edible oils. This is the first time applying MHS-SPME-arrow to quantify flavor compounds in oils combined with internal standard (ISTD) while carried out in organic solvent for quantitative calibrations. There are three steps needed to develop the quantitative method based on MHS-SPME-arrow: (1) Optimizing the HS-SPME conditions for the maximum extraction efficiency and sensitivity improvement; (2) finding the appropriate sample loading suitable for MHS-SPME and clarifying the feasibility of SPME-arrow fiber in replacement of the traditional SPME fiber; and (3) validating the MHS-SPME-arrow method combined with the internal standard in solvent by selected conditions, in which the MHS-SPME-arrow and SIDA were compared using [\({}^{2}\)H\({}_{6}\)]-2-methyl-pyrazine.
## 2 Results
### Effect of (2'S)-Columbianetin on the UVA-Induced MAPK Activation  
The mitogen-activated kinase (MAPK) signaling cascade was investigated to determine whether (2'S)-columbianetin could intervene with the UVA-induced transcriptional activities of AP-1, MMP-1 expression inducing transcription factor. In this context, effects of (2'S)-columbianetin on the total and phosphorylated (p-) levels of Jun-amino-terminal kinase (JNK), extracellular signal-related kinase (ERK1/2) and p38 MAPKs were investigated. The UVA-irradiated HDFs expressed significantly increased phosphorylation of JNK, ERK andp38 (Figure 5A). Treating irradiated HDFs with 10 \(\upmu\)M (2'S)-columbianetin effectively suppressed the phosphorylation of ERK and JNK but failed to change the p-p38 levels significantly.
## 4 Data Fusion  
In this section, the data fusion process is examined, with particular emphasis on the ray-casting technique and the multiple thermograms handling. It is assumed that all the calibration parameters have already been computed, following the steps described in the previous sections.
## 3 Methodology
### Fuzzy Delphi Method  
The Delphi method is widely used as a group decision-making approach, where expert opinions in specific fields are gathered through questionnaires. If experts do not reach a consensus or share ideas, a negotiator will integrate their views and use either the average or 50% of the results as the collective opinion. If the outcome does not meet the convergence standard, the survey is repeated until the criteria are satisfied [31]. In this study, a modified Fuzzy Delphi method based on Triangular Fuzzy Numbers is used. This approach addresses the limitations of the traditional Delphi method and reduces the influence of extreme opinions, making it suitable for confirming the managerial competencies and characteristics of the NAP function.  
A structured methodology was followed, as illustrated in Figure 1, which represents the following key stages of the study:  
Fuzzy Delphi Method: The Triangular Fuzzy Number (TFN) for expert consensus was computed using the following:  
\[\overset{\sim}{A}=(a,b,c)=\left(\frac{1}{n}\sum_{i=1}^{n}a_{i}\,\frac{1}{n} \sum_{i=1}^{n}b_{i}\frac{1}{n}\sum_{i=1}^{n}c_{i}\right)\]  
Figure 1: Flow chat of the methodology.  
where \(a,b,c\) represent the minimum, mean, and maximum values of expert judgments.  
FAHP Calculation: The relative weight of the criteria was determined using the geometric mean method, as follows:  
\[W_{i} =\frac{\prod_{j=1}^{n}x_{ij}^{1/n}}{\sum_{i=1}^{n}\prod_{j=1}^{n}x_{ ij}^{1/n}}\]  
where \(x_{ij}\) represents pairwise comparisons from expert input.
## 2 Materials and Methods
### Implementation of PID, MPC, and AMPC
#### 2.3.3 Energy Analysis  
In the experiments, the tomato basil sauce was heated to 90 \({}^{\circ}\)C when PID, MPC, and AMPC controllers were independently deployed on the Continuous Flow Ohmic Heater. During the heating process, the flowrate of the tomato basil sauce was kept constant at 1 L/min. The measured infeed temperature of the sauce was 24 \({}^{\circ}\)C and the measured infeed electrical conductivity was 0.85 S/m. The energy analysis can be expressed as Equations (7)-(9).  
\[\sum Energy_{in}=\sum Energy_{out} \tag{7}\]  
\[\sum Energy_{in}=\dot{m}C_{p}(T_{in}-T_{room})_{in}+E_{electrical} \tag{8}\]  
\[\sum Energy_{out}=\dot{m}C_{p}(T_{out}-T_{in})_{out}+E_{loss} \tag{9}\]  
where \(\sum Energy_{in}\) represents the total energy input to the CFOH, while \(\sum Energy_{out}\) corresponds to the energy output due to the ohmic heating effect and the thermophysical properties of the food product. \(E_{electrical}\) denotes the electrical energy supplied to the ohmic heater. The term \(\dot{m}\) represents the mass flow rate, \(C_{p}\) is the heat capacity of the food product being heated, \(T_{in}\) is the inlet temperature of the food product entering from the infeed tank, \(T_{room}\) is the ambient room temperature, and \(T_{out}\) is the recorded outlet temperature. The energy losses, \(E_{loss}\), considered in the system are due to thermal conduction to the applicators and heat dissipation at the titanium electrodes. These losses are quantified by Equation (10).  
\[E_{loss}=\
abla\cdot(\mathrm{k(T)}\cdot\
abla\mathrm{T})+\dot{m}C_{Ti}(T_{out }-T_{electrode})_{loss} \tag{10}\]  
In Equation (10), \(C_{Ti}\) represents the heat capacity of titanium and \(T_{electrode}\) denotes the initial temperature of the electrodes. The latent heat of vaporization is not taken into account since the only point of release is through the applicator outlet. The energy efficiency \(\eta\) is detailed in [13].
# Micromachining in Powder-Mixed Micro Electrical Discharge Machining
## 4 Application of Power-mixed \(\upmu\)-EDM (PM \(\upmu\)-EDM)
### Application of PM\(\mu\)-EDM in Finish Machining  
In \(\mu\)-EDM, the surface of the machined workpiece requires further treatment to obtain the desired surface quality. However, it is arduous to have good surface quality on a complex microstructure, therefore micromachining in \(\mu\)-EDM utilizes suspended powder to attain higher surface quality.  
[PERSON] et al. [36] was able to improve the surface quality of Inconel 706 up to 25.9% by adding Al powder into dielectric fluid at a concentration of 10 g/L. The introduction of Al powder into dielectric fluid generates the extension of the machining gap during the process, resulting in a less irregular deposition of debris, microholes and crater marks.  
Finish machining at a higher material-removal rate can also be achieved by mixing boron carbide into de-ionized water [57]. The presence of carbide powder aids in building uniform and intense discharge in the machining gap, which significantly augments the unit-removal rate with less amount of recast layer on the surface machined.  
[PERSON] et al. [53] added SiC and Al nanopowders into dielectric fluid and had an average of 14%-24% reduction of the surface roughness. The additional advantage of introducing the powder is keeping the geometrical accuracy shape produced. [PERSON] et al. [58] utilized SiC nanopowder in order to have a better surface quality. The presence of nanopowder in dielectric fluid promotes a reduction of thermal energy generation; hence smaller diameter of craters and a larger amount of re-solidified materials are produced.  
[PERSON] et al. [59] fabricated a 3D microstructure by using deionized water containing B\({}_{4}\)C powder. Reciprocating powder-mixed \(\mu\)-EDM with a laminated 3D microelectrode method was suitable for the fabrication of a 3D microstructure with high accuracy. The polishing properties of B\({}_{4}\)C on the microelectrode and microstructure provides smooth material removal and guarantees an efficient and stable machining process. [PERSON] et al. [60] investigated the feasibility of improving the surface properties of carbide in fine-finish and milling \(\mu\)-EDM graphite-nanopowder-mixed dielectric fluid. The introduction of semi-conductive graphite nanopowder within dielectric fluid leads to a uniform distribution of sparking among powder particles to produce shallower craters for better distribution, resulting in a smooth and glossy surface finish.