esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Device Design and Analysis  
In this paper, a novel approach for developing a highly compact CO\({}_{2}\) gas sensor is proposed by leveraging an SOI-based LT-MZI and PHMB polymer which is deposited onto the sensing arm as shown in Figure 1. The inset shows the E-field distribution at 1550 nm in the cross-sectional view of the waveguide covered with a 300 nm thick PHMB polymer. The height and width of the waveguide are considered as 220 nm and 250 nm, respectively. The LT-MZI configuration incorporates a Sagnac loop at the end of the conventional MZI, effectively halving the interferometer's length. This advancement holds promise for enhanced sensor performance, scalability, and versatility in various applications requiring precise refractive index measurements. The length of the directional coupler waveguide and arms of the MZI segment are designated by L\({}_{\text{DC}}\) and L, respectively. The waveguide maintains a consistent width, denoted as w, across its entirety. Introducing the gap, represented as g, between the two waveguides of the directional coupler segment facilitates efficient coupling. Moreover, the fixed bending radius, denoted as R, set at 2 \(\upmu\)m, ensures the design's compactness, a crucial aspect in crafting effective sensing devices. Table 1 presents the range of the geometric parameters used in the device design.  
In an accurate endeavor, rigorous simulations are conducted to dissect transmittance patterns and field distributions. Employing the 2D-finite element method (2D-FEM) via COMSOL Multi-physics software (v.6.1), the intricacies of light propagation are determined. Leveraging the rich materials library within COMSOL, refractive indices for pivotal materials like silicon, SiO\({}_{2}\), and air are obtained, ensuring alignment with real-world parameters. To maintain precision, the device design undergoes precise segmentation into subdomains, further subdivided into triangular mesh elements. Each element features a grid size precisely set at \(\lambda/100\), striking an optimal balance between simulation accuracy and computational efficiency. In the pursuit of realistic electromagnetic wave phenomena analysis, an open-bounded domain is established as a fundamental imperative. To emulate this environment faithfully, scattering boundary conditions (SBCs) are judiciously implemented along the outer perimeters of the FEM simulation window.  
Through these systematic approaches and the utilization of computing resources--an 8-core processor (3.8 GHz) paired with 128 GB of RAM--a suite of simulations characterizedby their high fidelity and reliability are generated. These simulations offer invaluable insights into transmittance characteristics and field distributions, serving as a cornerstone for the optimization and advancement of photonic device designs.
## 4 Results and Discussion
### Field Measurement Results  
In this paper, measuring equipment is installed to measure the track dynamic response according to train operation on ballasted and concrete track structures. The parameters required to measure the spring constant (load and displacement) and wheel load fluctuation (load and speed) are summarized below in Table 9. As can be seen in the results, the wheel load for the concrete and ballasted structure finite element model share the same load values, as they are both derived based on the [PERSON] theoretical model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Item**} & \multicolumn{4}{c}{**Vertical Rail Displacement (mm)**} \\ \hline \multirow{3}{*}{**Track Types**} & **Speed** & **Finite Element** & \multicolumn{2}{c}{**Field Measurement Data**} \\ \cline{3-5}  & **(km/h)** & **Analysis** & **Min.** & **Max.** \\ \hline \multirow{6}{*}{Ballasted} & 85 & 0.697 & 0.37 & 1.05 \\  & 95 & 0.706 & 0.43 & 1.09 \\ \cline{1-1}  & 155 & 0.749 & 0.41 & 1.07 \\ \cline{1-1}  & 185 & 0.760 & 0.57 & 1.11 \\ \cline{1-1}  & 195 & 0.767 & 0.62 & 1.18 \\ \cline{1-1}  & 205 & 0.772 & 0.51 & 1.13 \\ \hline \multirow{6}{*}{Concrete} & 85 & 1.154 & 0.42 & 0.98 \\ \cline{1-1}  & 95 & 1.162 & 0.49 & 1.01 \\ \cline{1-1} \cline{1-1}  & 155 & 1.215 & 0.58 & 1.28 \\ \cline{1-1} \cline{1-1}  & 185 & 1.238 & 0.62 & 1.24 \\ \cline{1-1}  & 195 & 1.247 & 0.71 & 1.12 \\ \cline{1-1}  & 205 & 1.256 & 0.83 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Field measurement result and finite element method analysis comparison.  
Figure 9: Dynamic load of the vehicle on the 3D model concept.
## 2 Preliminaries
### TOPSIS Method Based on Triangular Fuzzy Numbers (TFNs)  
This section covers how to use the TOPSIS technique involving MCDM problems, where the criteria weights are unknown and the decision-makers' (DMs') preference insight, expressed in the form of TFNs, can be estimated by using the Shannon entropy method [42; 43].  
Assume that there exist \(m\) alternatives \(A_{1},A_{2},\ldots,A_{m}\) and \(n\) criterion \(C_{1},C_{2},\ldots,C_{n}\) with a weight vector \(w=\left(w_{1},w_{2},\ldots,w_{n}\right)\), where \(0\leq w_{j}\leq 1\) and \(\sum w_{j}=1\). The characteristics of an alternative \(A_{i}\) with respect to the criteria \(C_{j}\) can be expressed by a TFN \(\widetilde{z}_{ij}=\left(z_{ij}^{1},z_{ji}^{2},z_{ij}^{3}\right)\), \((i=1,2,\ldots,m;j=1,2,\ldots,n)\), and the decision matrix \(\widetilde{Z}=\left(\widetilde{z}_{ij}\right)_{m\times n}\) might be used to convey an alternative's characteristics in relation to the criteria expressed by \(\widetilde{z}_{ij}\), where \(i=1,2,\ldots,m\) and \(j=1,2,\ldots,n\). As a result, the MCDM problem can be expressed using the  
where \(D(\widetilde{z}_{ij},\widetilde{0})\) is the defuzzified value of the triangular fuzzy number \(\widetilde{z}_{ij}\), and \(D(\widetilde{z}_{ij},\widetilde{0})=\frac{1}{4}(z_{ij}^{1}+2z_{ij}^{2}+z_{ij}^{ 3})\), \((i=1,2,\dots,m;j=1,2,\dots,n)\). \(D(\widetilde{z}_{ij},\widetilde{0})\) is derived using the signed distance method [44]. Here, Equation (1) is the required decision matrix of our proposed problem.  
Let \(I^{+}=(1,1,\dots,1)\) and \(I^{-}=(0,0,\dots,0)\) represent, respectively, the positive and negative ideal solution for the \(m\) alternatives \(A_{1},A_{2},\dots,A_{m}\). Therefore, the following formula can be used to compute the separation measures \({sm_{i}}^{+}={sm_{i}}^{+}(I^{+},A_{i})\) and \({sm_{i}}^{-}={sm_{i}}^{-}(I^{-},A_{i})\) of each alternative from positive ideal and negative ideal solutions.  
\[{sm_{i}}^{+}=\sqrt{\sum\limits_{j=1}^{n}\left(w_{j}(1-D(\widetilde{z}_{ij}, \widetilde{0}))\right)^{2}} \tag{2}\]  
and  
\[{sm_{i}}^{-}=\sqrt{\sum\limits_{j=1}^{n}\left(w_{j}D(\widetilde{z}_{ij}, \widetilde{0})\right)^{2}} \tag{3}\]  
where the weight vector \(w=(w_{1},w_{2},\dots,w_{n})\) is calculated using the entropy weighted method described in Section 2.1. Using Equations (2) and (3), we calculate the relative closeness of \(m\) alternatives \(A_{1},A_{2},\dots,A_{m}\) with respect to the positive ideal solution \(I^{+}\) which are as follows:  
\[RC_{i}(A_{i})=\frac{{sm_{i}}^{-}}{{sm_{i}^{-}}+{sm_{i}}^{+}},\ i=1,2,\dots,m \tag{4}\]  
The best alternative among a group of specified possible alternatives can be identified using Equation (4). It also determines the ranking order of all the alternatives. The alternatives may then be ranked according to the closeness coefficient, with the alternative with the greatest rank being considered the best choice.
## Materials and Methods
### Analysis of Tomato Volatiles by HS-SPME-GC-Ms  
The same GC-TOF-MS instrument was used for tomato analyses. A 1 cm DVB/CAR/PDMS fiber was used for all HS-SPME extractions, with an incubation temperature of 50 \({}^{\circ}\)C, a pre-extraction incubation time of 5 min, and 30 min for HS-SPME extraction. A split/splitless injector was used with a constant temperature of 250 \({}^{\circ}\)C. SPME injections were splitless with a flow rate of 50 mL/min and purge time of 3 min. Helium was used as a carrier gas at a flow rate of 1 mL/min. The GC column was a CP-Sil 8 ms (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent, The Netherlands). The initial GC oven temperature was 45 \({}^{\circ}\)C and held 5 min, then ramped to 180 \({}^{\circ}\)C at 5 \({}^{\circ}\)C per min, then ramped to 280 \({}^{\circ}\)C at 25 \({}^{\circ}\)C per min and held at 280 \({}^{\circ}\)C for 5 min. The TOF-MS was operated in EI mode with an ionization energy of 70 eV. The electron multiplier was set to 1700 V. MS data from \(m/z\) = 41\(-\)250 was stored at 5 Hz. Data processing was carried out by the LECO ChromaTOF software. The qualifier ions were as follows: for 2-octanone, \(m/z\) 43, 58, 71, 128; for nonyl acetate, \(m/z\) 43, 56, 70, 98, 126; for 4-methyl-2-pentanol, \(m/z\) 45, 69, 87; for [2H2]-(\(E\))-2-hexanal, \(m/z\) 57, 71, 85, 100; for [2H3]IPMP, \(m/z\) 127, 140, 155; for [2H4]furfurral, \(m/z\) 42, 70, 98, 100; for [2H8]naphthalene, \(m/z\) 108, 136; for [2H4]4-ethyl phenol, \(m/z\) 111, 126; and for [2H10]benzophenone, \(m/z\) 82, 110, 192. The quantifier ions were as follows: for 2-octanone, \(m/z\) 128; for nonyl acetate, \(m/z\) 43; for 4-methyl-2-pentanol, \(m/z\) 45; for [2H2]-(\(E\))-2-hexanal, \(m/z\) 85; for [2H3]IPMP, \(m/z\) 140; for [2H4]furral, \(m/z\) 100; for [2H8]naphthalene, \(m/z\) 136; for [2H4]4-ethyl phenol, \(m/z\) 111; and for [2H10]benzophenone, \(m/z\) 110.
## 2 Literature Review
### Sensor Fusion and Data Processing in IoT Health Systems
#### 2.4.3 Emerging Trends in Multi-Sensor Data Processing  
Recent advancements in wearable technology and edge computing facilitate real-time data analysis while minimizing network dependency. AI-enhanced data filtering techniques enhance data accuracy and decrease redundant transmissions.  
These advancements in sensor fusion technologies are essential for facilitating medical diagnosis and improving healthcare quality.  
This literature review has examined key advances, challenges, and emerging trends in IoT-enabled healthcare monitoring. By leveraging multi-sensor fusion, AI-driven analytics, and efficient communication protocols, IoT-based health ecosystems have the potential to enhance real-time patient monitoring and personalized healthcare delivery. However, addressing challenges related to data security, interoperability, and energy efficiency remains critical for the widespread adoption of IoT in healthcare.
## 4 Experiments  
Our experiment evaluates the proposed algorithm using the accuracy of solving AWPs, which is the percentage of correctly solved AWPs in a given dataset. The solving accuracy has been widely used in the evaluation of algorithms in previous related studies [14, 17, 19, 28, 32]. Additionally, a case study is conducted to compare and analyze the reliability and error causes of the outputs from the proposed algorithm and the baseline algorithms. To assess solving accuracy, this paper prepared six datasets of AWPs, which are from authoritative sources, and five algorithms were chosen as baseline algorithms.
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 3 Our Methods
### Architecture  
The architecture is divided into three modules: (i) a heatmap module that extracts the embedded pose features during the process of regressing a heatmap image, (ii) a SegDepth module that segments the body region from the images, extracting both embedded depth features and convolutional features from the depth map, and (iii) a joint position module that estimates 3D joints based on concatenating the features from the heatmap module, the SegDepth module, and the convolutional features extracted from the depth map.
# Analysis of Potential Markers of Pork Freshness Based on Volatile Organic Compounds
## 2 Materials and Methods
### Analysis of Volatile Organic Compounds  
The method developed by [PERSON] et al. was employed to quantify the VOCs in pork [28]. VOCs were collected via solid-phase microextraction (SPME) fibers and analyzed with an Agilent 7890A gas chromatograph connected to an Agilent 7000B mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Then, 10 \(\upmu\)L of 2-methyl-3-heptanone internal standard solution was added to the headspace vial, which was then sealed. The sample was enriched at 60 \({}^{\circ}\)C for 40 min, followed by adsorption for 20 min under the same conditions, using a 50/30 \(\upmu\)m DVB/CAR/PDMS extraction fiber head (57330-U, Supelco, Bellefonte, PA, USA). Subsequently, the fiber was desorbed at 250 \({}^{\circ}\)C for five minutes.  
Chromatographic conditions involved using an HP-5 MS capillary column (30 mm \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) with a split ratio of 5:1, an inlet temperature of 250 \({}^{\circ}\)C, a flow rate of 1 mL/min, and helium as the carrier gas. Initially, the oven temperature was set to 40 \({}^{\circ}\)C and maintained for 2 min. It was then increased at a rate of 5 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C and held for 5 min, followed by a similar increase to 200 \({}^{\circ}\)C for 2 min, and finally ramped up at 8 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, where it was maintained for 8 min. Mass spectrometry conditions were as follows: the ionization mode was EI, with an electron energy of 70 eV. The mass scan range was set from 45 to 500 \(m/z\). The quadrupole temperature was maintained at 150 \({}^{\circ}\)C, the ion source temperature at 230 \({}^{\circ}\)C, and the GC-MS interface temperature at 250 \({}^{\circ}\)C.  
By comparing EI mass spectra against the NIST database, VOCs with a match quality score exceeding 80% were identified. The linear retention indices (RIs) for the VOCs were calculated using standard alkane mixtures (C\({}_{5}\)-C\({}_{15}\)) from Macklin (Shanghai, China) as reference compounds [29]. The results were then validated against the NIST database ([[https://webbook.nist.gov/chemistry/](https://webbook.nist.gov/chemistry/)]([https://webbook.nist.gov/chemistry/](https://webbook.nist.gov/chemistry/)), (accessed on 2 June 2024)) [30]. Before statistical analysis, peak regions were matched against the internal standard, reported in ng/g, to generate semi-quantitative data.  
\[\mathrm{C(ng/g)}=\left(\frac{\mathrm{C_{0}\times V_{0}\times S_{1}}}{\mathrm{ S_{0}\times M}}\right)\times 1000\]  
where M is the sample mass (g), C is the target volatile flavor compound concentration (ng/g), C\({}_{0}\) is the internal standard concentration (\(\upmu\)g/\(\upmu\)L), V\({}_{0}\) is the volume of the internal standard injected (mL), S\({}_{1}\) is the target volatile flavor compound peak area (AU-min), and S\({}_{0}\) is the internal standard peak area (AU-min).
## 3 Experiments and Data Analysis  
The experimental platform used in this study mainly comprised a three-axis turntable and a camera. The SGT320E three-axis turntable had an accuracy of 0.0001 degrees and a mechanical structure in which a U-shaped outer frame rotated around the azimuth axis, an O-shaped middle frame rotated around the pitch axis, and an O-shaped inner frame rotated around the transverse roller axis. The outer frame, middle frame, and inner frame can all be considered rigid bodies, as shown in Figure 3. The three axes simultaneously had speed, position, and sinusoidal oscillation modes and did not interfere with each other, enabling the high-precision measurement of the pose. A Phantom M310 high-speed camera, produced by Vision Research in the United States, was used. The camera had adjustable resolution, shooting speed, and exposure time. The camera sensor size was 25.6 mm \(\times\) 16.0 mm, and the camera resolution was 1280 \(\times\) 800, giving a pixel size of 20 \(\upmu\)m \(\times\) 20 \(\upmu\)m. The focal length of the lens was adjustable within the range of 24-85 mm, the shooting speed was 1000 frames/s, and the f-stop was f/2.8. Two 300 W shooting lights with illumination up to 39,800 lux were used to ensure the brightness of shooting at any time.
## 4 Experimental Setup  
The performance of the proposed watermarked method in terms of imperceptibility and robustness is tested on several 3D objects: Bunny (\(34,835\) vertices, \(69,666\) facets), Horse (\(112,642\) vertices, \(225,280\) facets), Venus (\(100,759\) vertices, \(201,514\) facets), Armadillo (\(26,002\) vertices, \(52,000\) facets), Rabbit (\(70,658\) vertices, \(141,312\) facets), Flower (\(2523\) vertices, \(4895\) facets), Vase (\(2527\) vertices, \(5004\) facets), Cup (\(9076\) vertices, \(18,152\) facets), Ant (\(7654\) vertices, \(15,304\) facets), Bimba (\(8857\) vertices, \(17,710\) facets), and Cat (\(3534\) vertices, \(6975\) facets). LIRIS/EPFL General-Purpose [20] database was created in Switzerland and contains \(88\) models in total. It contains \(4\) reference models which are Armadillo, Dyno, venus and RockerArm. The subjective evaluation was done by \(12\) observers. The distortions that the reference mesh undergoes are smoothing and noise addition. We note that the scores are between \(0\) referring to good quality and \(10\) representing a bad quality. For each mesh, the average of the scores given by the \(12\) observers is computed in order to have a normalized mean opinion score (MOS). The LIRIS Masking database was created at the University of Lyon in France and contains \(26\) models, and the subjective evaluation was done by \(11\) observers [21]. Only some objects are taken from the two mentioned databases. The model sizes are provided in Table 1.  
Figure 5(a,c,e) show an example of three original 3D objects. The quantization step is tuned experimentally in such a way that ensures the best tradeoff between imperceptibility and robustness. To do so, extensive experiments have been conducted (see Figures 7-9) using several empirically different values of \(\Delta\). We note that these experiments have been carried out for all the 3D meshes. For the brevity of space we have given only the results for 3D meshes Bimba, Horse, and Bunny. According to these results, the best found value is \(\Delta=0.10\). The parameter \(T_{r}\), which represents the threshold used to choose the candidate wavelet coefficients, is kept at the \(70\%\) maximum value of the saliency vector. We note that the size of the used watermark in the simulations is \(64\) bits.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Object** & **Mean Opinion Score (MOS)** \\ \hline Armadillo & 2.5 \\ Venus & 2.917 \\ Bimba & 3.091 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean opinion score (MOS) for Armadillo, Venus, and Bimba objects taken from LIRIS-Mask [20] and LIRIS-EFPL-Gen-Purpose [21] databases.  
Figure 8: The imperceptibility performance in terms of MSDM using several quantization steps for Bimba, Horse, and Bunny models.  
Figure 6: (**a**) Flower; (**b**) Watermarked Flower; (**c**) Vase; (**d**) Watermarked Vase; (**e**) Cup; (**f**) Watermarked Cup; (**g**) Ant; (**h**) Watermarked Ant; (**i**) Cat; (**j**) Watermarked Cat.  
Figure 7: The robustness performance in terms of correlation using several quantization steps for Bimba, Horse, and Bunny models.
## 5 Corrections for Off-Reference Conditions
### Correction of Orientation Parameter  
Some extra CFD calculations were carried out to correct the basic correlations for orientation parameter. The ranges of the different parameters used for the correction are listed in Table 6, which synthetically include the parameters of inlet metal angle, camber angle, solidity, and CR. Figure 13 presents the correction curves with blade inlet metal angle as the only independent variable. It was found that all the other geometry parameters have a slight influence on the correction curves based on the results of the database. Through the averaged modifications, it is plausible that the correction curves can be treated as approximately linear curves.  
Figure 12: Comparisons between the results predicted by using basic correlations and the CFD database for (**a**) incidence and (**b**) deviation angles.  
Figure 14 compares the results obtained by using basic correlations and the modified correlations. It is shown that the modified correlations have higher accuracy to match the CFD results compared to basic correlations. In addition, the scatter points almost concentrate within a certain error band of \(\pm 1^{\circ}\) for the predicted results from the modified incidence and deviation correlations.
# New Insights into Fuzzy Genetic Algorithms for Optimization Problems
## 3 Nearest-Neighbor Caching  
Here, we refer to caching as a technique to avoid repetitive computations thanks to some values stored in memory. Such values are retrieved when a function call with the same or similar inputs is needed. This may significantly reduce the runtime. Formally, there exists a metric space \((X,d)\) over the presented vector \(\mathbf{x}\), and a threshold radius \(R\). When a new vector \(\overline{\mathbf{x}}\) (query) arrives, and if there is a stored vector \(\mathbf{x}\) in the cache with \(d(\mathbf{x},\overline{\mathbf{x}})\leq R\), the request can be satisfied by the cache without any new computations. Let \(k\) be the size of the cache. It is expected that \(k\) increases as \(R\) tends to zero [21].  
Operationally, the vector space is stored in a matrix. Distances from a query vector to the matrix rows are computed. A certain number of closest neighbors are selected, and their distances are stored. After finding \(k\) nearest neighbors, a weighted average of them is computed as an approximation of a certain function value:  
\[\overline{g_{i}}=\sum_{i=1}^{k}w_{i}g_{i}, \tag{6}\]  
where \(g_{i}\) is a value of a function at the \(i\)th nearest point and \(w_{i}\in[0,1]\) is a weight such that \(\sum_{i=1}^{k}w_{i}=1\).  
The weights of the nearest neighbors \(w_{i}\) can be computed as normalized \(L_{2}\) distances to the nearest neighbors \(d_{i}\):  
\[w_{i}=\frac{d_{i}}{\sum_{j=1}^{N}d_{j}}. \tag{7}\]  
For the remainder of this work, EFGA-NNC and GFGA-NNC will denote the implementation of the respective methods with NNC.  
Figure 4: Diversity linguistic variable with trapezoidal MFs (\(N_{diversity}=5\)).
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Classification Algorithms
#### 4.2.2 XGBoost  
XGBoost (Extreme Gradient Boosting) is a robust machine learning algorithm based on the concept of Gradient Boosting. It improves this algorithm by optimizing calculations and introducing advanced features, making it faster and more efficient. XGBoost uses a set of decision trees (weak learners) to predict the residuals of previous models and correct errors. Its uniqueness lies in how decision trees are constructed and optimized, making the algorithm powerful and resource-efficient [21].  
This algorithm follows a structured process:  
* Initial model: A simple basic model is built to make an initial prediction.
* Calculation of residuals: The difference between predictions and actual values is calculated to identify errors.
* New trees: A new decision tree is trained to predict these residuals and reduce errors.  
Figure 4: Distribution of geological formation types.  
Model update: The predictions from the new tree are integrated into the existing model with a weighting factor.
* Repetition: This process is repeated until performance reaches a satisfactory level or a stopping criterion is reached.  
XGBoost is particularly effective for applications requiring heterogeneous data processing, including numeric, categorical, and textual variables. In geology, where the relationships between geological formations are often complex and non-linear, XGBoost is particularly effective. It excels at capturing these relationships thanks to its ability to process data from various sources, such as satellite images or seismic measurements [22].