esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.4 Accuracy Assessment  
The confusion matrix is a method that is commonly used to assess the accuracy of image classifications [54]. In this study, the calculation of the confusion matrix was conducted through online programming of the GEE cloud platform, and then the overall accuracy (OA), Kappa coefficient, producer accuracy (PA), and user accuracy (UA) were calculated. Among them, the OA and the Kappa coefficient could fully reflect the comprehensive accuracy of the results, and the PA and UA could be used to assess the classification accuracy of a specific land cover type. The calculation formulas were as follows:  
\[\text{OA}=\frac{\sum_{i=1}^{n}\text{X}_{\text{ii}}}{\text{N}}\times 100\% \tag{12}\]  
\[\text{PA}=\frac{\text{X}_{\text{ii}}}{\text{X}_{+\text{i}}}\times 100\% \tag{13}\]  
\[\text{UA}=\frac{\text{X}_{\text{ii}}}{\text{X}_{\text{i+}}}\times 100\% \tag{14}\]  
where N is the total number of samples used for the accuracy assessment; n is the total number of columns in the confusion matrix; Xii is the number of samples in the i-th row and i-th column in the confusion matrix; and Xi+ and X+i are the total number of samples in the i-th row and i-th column, respectively. The uncertainty analysis of the accuracy of classification results is realized by calculating the error range, which is defined as the z-score multiplied by the standard error [36].  
In this study, all of the data processing and calculations, including data acquisition, data processing, composition of images, construction of feature space, calculation of parameters, implementation of classifiers, and calculations of confusion matrices, were all implemented by the GEE JavaScript API.
# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 4 Methods and Materials
### Evaluating Measures  
We have chosen two types of machine learning tasks: classification-based approach for supervised learning and clustering-based approach for unsupervised learning, to demonstrate the effect of features selection algorithm in machine learning for insurance datasets. We followed the standard methodology for evaluating feature selection methods [42]. To evaluate the clustering performance of insurance datasets before and after feature selection, we utilise the well-known clustering algorithm for clustering mixed data k  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multirow{2}{*}{**Id**} & \multirow{2}{*}{**Dataset Name**} & \multicolumn{3}{c}{**No. of Columns**} & **No. of** & **No. of** \\ \cline{3-6}  & & **Numerical** & **Categorical** & **Total** & **Rows** & **Classes** \\ \hline D1 & Car Insurance Cold Calls & 10 & 8 & 18 & 4000 & 2 \\ \hline D2 & Medical insurance claim fraud & 5 & 7 & 12 & 7000 & 2 \\ \hline D3 & Caravan Insurance Challenge & 76 & 10 & 86 & 9822 & 2 \\ \hline D4 & Health\_Insurance\_Lead\_Prediction & 6 & 6 & 12 & 50,882 & 2 \\ \hline D5 & Insurance company- & 10 & 3 & 13 & 14,017 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Insurance datasets’ description.  
prototypes [50]. In the comparison, two clustering measures are used: clustering accuracy (C-ACC) and normalized mutual information (NMI). The C-ACC is calculated as follows  
\[\text{C-ACC}(\theta)=\sum-i=1^{n}\frac{\delta(p,map(q_{i}))}{n}, \tag{1}\]  
where \(n\) is the total number of observations and \(\delta(a,b)=1\) if \(a=b:\) otherwise \(\delta(a,b)=0\), and \(map(q_{i})\) is a mapping function that permutes clustering labels to get the best match with the true labels based on the Kuhn-Munkres algorithm [51]. The C-ACC values range from 0 to 1; the clustering is better when the C-ACC is higher.  
For clustering results (P) and true labels (Q), NMI [40] is defined as:  
\[\text{NMI}(\theta)=\frac{I(P,Q)}{max(H(P),H(Q))}, \tag{2}\]  
where \(H(P)\) and \(H(Q)\) are the entropies of \(P\) and \(Q\), respectively, and \(I(P,Q)\) is the mutual information [52] between \(P\) and \(Q\), which is defined as:  
\[I(P,Q)=\sum_{p_{i}\in P\wedge q_{j}\in Q}p(p_{i},q_{j})\ log_{2}\frac{p(p_{i}, q_{j})}{p(p_{i})\ p(q_{j})}. \tag{3}\]  
In the classification evaluation-based approach, two classification techniques, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN), are used to evaluate classification accuracy: the ratio between the correctly classified objects and the total number of objects.
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 2 Channel Model  
### Atmospheric Model  
The theoretical model constructed in this work compromises link propagation under four kinds of weather conditions--clear, rainfall, fog and haze, and wind. In clear weather, gaseous absorption plays a main role, apart from the free space path loss (FSPL) due to diffraction. There is no multipath or misalignment caused by weather particles and wind-induced turbulence bubbles. The gaseous absorption is often caused by water vapor and oxygen in the air and can lead to serious link interference when the operating frequency is close to absorption peaks. It can be modeled by the international telecommunications union (ITU) model [15] and can be expressed by equation \(\gamma=0.1820\,f(N^{\prime\prime}\,\alpha_{ oxygen}(f)+N^{\prime\prime}\,\psi_{ Water Vapor}(f))\)[15] with parameter \(f\) as the operating frequency [GHz], and \(N^{\prime\prime}\,\alpha_{ oxygen}\), \(N^{\prime\prime}\,\psi_{ Water Vapor}\) as the imaginary part of the associated negative refractive index of oxygen and water vapor, respectively. Values of these parameters depend on atmospheric pressure, water vapor density and temperature. In this work, this model is considered under all the weather conditions with the proposed water vapor density set to 12.3, 12.3, 7.7 and 7.7 g/m\({}^{3}\) for rain, fog and haze, clear weather and wind, respectively. The corresponding atmospheric pressure and temperature are chosen to be \(1.0037\times 10^{5}\) Pa and 287.63 K, respectively. Environmental parameters such as link distance are shown in Table 1. The attenuation caused by these parameters is considered to be static, due to the spatial- and temporal-average algorithms on the receiver side in typical wireless communication and sensing systems.
## Reduction in AOG Events
### Financial Impacts of Forecasting Models  
The financial benefits of using RAIDO, OASES, and ROAM are clear in SmartLynx's cost optimization efforts. This will be clearly highlighted in the following two fields:  
Cost Reductions through Predictive Maintenance: RAIDO and OASES allow operators to transition from reactive to predictive maintenance. For instance, predictive modeling flagged potential failures in the avionics systems of the A320 fleet, allowing repairs to be scheduled during downtime. This approach avoided emergency repair costs of EUR 2 million annually.  
Lean Inventory Management with ROAM: By leveraging ROAM's predictive tools to keep all sorted records, air operators minimize excess inventory while ensuring critical parts are readily available and recorded when redelivery is required or during long-planned maintenance, reducing storage costs by 10%.
## 3 Experimental Techniques
### Particle Image Velocimetry  
The PIV measurements were done using a LaVision PIV system. Illumination was handled by a Quantel Evergreen 200 double-pulse Nd:YAG laser, which offers a 200 mJ light pulse at a wavelength of 532 nm. The lens was adjusted such that the light sheet formed was parallel with the freestream flow and covered the entire chord of the airfoil, with a thickness of 1.2 mm. The images were acquired using a LaVision Imager SX 4M camera, equipped with a Nikon lens with a focal length of 28 mm. The time separation was set to 20 \(\upmu\)s for both velocities tested. Finally, the flow was seeded using non-toxic DEHS (Di-2-Ethylhexyl-Sebacat) using an aerosol generator to produce particles with an average diameter of 1.6 \(\upmu\)m. The choice of the chemical composition of the seeding droplets in presence of a discharge plasma requires a careful choice to avoid charging of the aerosol, in which case the images would be biased. Several products were pre-tested and evaluated for their respective charging, DEHS providing an optimal solution.  
To obtain a more accurate picture of the flow field structures caused by the actuation, phase-locked PIV measurements were performed. The timing was handled by a LaVision Programmable Timing Unit, which received its triggering signal by the same function generator used for the DBD actuator. Since the maximum trigger rate of the laser system was limited to 15 Hz, it was not possible to capture an image-pair at every actuation of the DBD. Therefore, an image-pair was acquired after a certain number of DBD actuations, depending on the frequency of actuation of the test case in question. Overall, 500 image-pairs were utilised to provide instantaneous velocity fields as well as time-averaged quantities. After the image acquisition, each image pair was processed using DaVis 8.2.0 with a decreasing window size (64 \(\times\) 64-32 \(\times\) 32) multi-pass processing with a 50% overlap for the 64 \(\times\) 64 window size and 75% overlap for 32 \(\times\) 32.
## 4 Design and Implementation
### Recovery  
Failures can occur when a record is not fully written to the hash table. Instances like power failures or system crashes during the modification of the hash table can lead to such failures. For time-sensitive applications, it is crucial to have a recovery system that can swiftly recover from such failures and resume services. In ESH, records are stored as soon as they are received, ensuring that keys are also stored promptly. If a record is only partially written and the valid key values are not stored in the valid segment, it will be ignored. During startup, the hash directory is reconstructed following the principles of extendible hashing, and the directory and local depth are recovered by loading the records into memory. To evaluate the recovery performance, we conducted tests and comparisons with the previous scheme. We ran the hashing scheme for insertions, intentionally killed the process while it was loading records, and then restarted the scheme to measure the time it took to accept requests for various data sizes [18]. The results demonstrated that our scheme performed competitively in terms of recovery time. For instance, the recovery time required for our scheme to handle 1 million records was 103 ms, while it took 101.7 ms for CCEH. This result holds significance even for smaller data sizes, as the recovery time is nearly comparable to other schemes.
## 4 Materials and Methods
### Volatile Analysis Using SPME-GC-MS  
Volatile compounds in thawed watermelon samples were analyzed using solid-phase microextraction-gas chromatography (SPME-GC-MS) as described in our previous publication [29]. To begin, 3 g of each sample was added into 20 mL glass headspace vials (prefilled with 1 g of NaCl). The vials were sealed with 18 mm magnetic screw craps with a PTFE blue silicone septum. The sample vial was incubated for 15 min at 40 \({}^{\circ}\)C to reach equilibrium. After equilibration, a SPME fiber was injected into the vial headspace for volatile extraction for 20 min, with agitation at 250 rpm. The SPME fiber was coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, 50/30 \(\upmu\)m film thickness; Supelco, Bellefonte, PA, USA). An automated AOC-6000 sampler (Shimadzu, Columbia, MD, USA) was used for sample vial incubation, headspace volatile equilibration, and volatile extraction with SPME fiber.  
The SPME fiber was desorbed for 3 min at the splitless injection port (250 \({}^{\circ}\)C) of the GC unit (GC-2010 Plus coupled with a QP2020 mass spectrometer; Shimadzu, Columbia, MD, USA). A helium carrier gas flowed at 1 mL/min. The oven temperature was held for 1 min at 40 \({}^{\circ}\)C, ramped up at 5 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, and held for 5 min at 230 \({}^{\circ}\)C. A 30.0 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness ZB-Wax column (Phenomenex, Torrance, CA, USA) was used to separate compounds. The mass spectrometer used an ionization source at 200 \({}^{\circ}\)C, an interface temperature of 230 \({}^{\circ}\)C, and a fragment range of 35 to 350 \(m\)/z [29].  
To calculate the linear retention index (LRI), alkane standards were analyzed on the SPME-GC-MS. A straight-chain, C6-C26 alkane mixture was prepared by adding the alkane stock solution to pentane in a 1:100 ratio. SPME parameters included a 1 min equilibration time and 5 min extraction time. GC-MS parameters were identical to those used for thewatermelon samples. The linear retention indices of the identified watermelon blend volatile compounds were calculated.  
Compounds were identified by matching their ion spectra chromatogram with those from the National Institute of Standards and Technology (NIST) library. Identity confirmation was completed by comparing their LRI with values from the webbook.nist.gov database (accessed on 5 April 2022). Relative compound quantities (percent of peak area) were calculated.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods
### Spikelet Instance Segmentation Method
#### 2.2.3 Image Optimization  
The sizes of the wheat spike images were 2400 \(\times\) 2400 pixels after the image perspective transformation. The perspective image had many ineffective background pixels for wheat varieties with shorter awns, such as Z618 and Y19. If the whole images were used as the input data, the calculation amount of the model would be increased and the operation efficiency would be reduced considerably. Therefore, this study preprocessed the perspective image based on the following method to reduce the size of the input images and improve the quality of the input dataset. First, the perspective image Figure 4a was converted from RGB to the HSV color space. Second, the pixels with HSV values ranging from (15, 43, 46) to (77, 255, 255) were selected, where the color of the wheat spike (green to yellow) was mainly distributed. Then, we cropped the perspective image along the bounding rectangle of selected pixels (shown in Figure 4b), and determined the remained region as the input image (shown in Figure 4c). According to the specific shape of each wheat spike, the spike images were reduced from the uniform sizes of 2400\(\times\)2400 pixels to  
The input datasets for spikelet segmentation consisted of 1800 spike images in total, including 600 spike images of Z618, 600 spike images of Y19, and 600 spike images of S8. The training and test datasets were divided by 8:2 when segmenting the wheat spike images. The YOLOv8-seg model was trained using Python 3.8.18, PyTorch 1.13.1, and Cuda 11.6 in an NVIDIA GeForce RTX 1080 Ti GPU device with 11 GB RAM.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
###### Abstract  
Although theoretical investigation has demonstrated that fewer strain transfer layers imply a greater strain transfer ratio, as well as increased accuracy, most researchers are still focused on investigating encapsulated Fiber Bragg gratings (FBGs) in surface strain measurements. This is because, in a traditional view, bare FBGs are too fragile to be mounted on the substrate for measuring surface strain. Polyimide FBGs may provide a better balance point between accuracy and protection. A new method to measure surface strain with polyimide fiber Bragg gratings is proposed. Bare polyimide FBGs have a polyimide coating, but like regular non-coated FBGs. This gives polyimide FBGs a higher strain transfer ratio and response frequency. Bare polyimide FBGs can be considered as uncoated FBGs. The coupling of the matrix material of polyimide FBGs is improved as compared to FBGs without coating. In order to verify the capacity for surface strain measurement, polyimide FBGs are mounted to obtain the surface strain of a concrete specimen with SM130-700 interrogator from Micron Optics Incorporation (MOI) with a sampling frequency maximum of 2000 Hz. The experiment demonstrates that polyimide FBGs work well even in dynamic surface strain measurements such as explosion measurement. Validation experiment in this paper also proposed that fewer strain transfer layers can increase dynamic response frequency and coupling between FBG and substrate.  
Keywords:polyimide FBGs; bare FBGs; strain transfer investigation; surface strain measurement; practicality and validation +
Footnote †: journal: Applied Sciences
## 2 System Model
### LEO-SMC-ICN-IoT Scenario  
The LEO-SMC-ICN-IoT scenario consists of a space segment and a ground segment. The space segment includes the LEO satellite mega-constellation network. And the ground segment includes the User Equipment (UE), the Base Station (BS), the Ground Station (GS), and the MU, etc. The contents can be cached at the satellite nodes, at the UE, at the BS, at the GS, or at the MU. The content caching scenario in LEO-SMC-ICN-IoT is shown in Figure 1. In Figure 1, the dashed line denotes the coverage of the satellite. Moreover, a solid line represents the IoT content caching and forwarding processes. The processes are briefly described as follows: The IoT content A is cached at GS, the IoT content B is cached at the satellite nodes, BS, and MU, and then the IoT content C is cached at UE. MU sends an interest A to BS. Since the IoT content A does not exist at BS, BS sends the interest A to GS and obtains IoT content A to return to MU. Compared with terrestrial communications, satellite communications have wide coverage, can realize the Internet of Everything, and can reduce the load on the ground segment. Here, to minimize user terminal content access delay, we consider the caching policy of satellite nodes for spatial segments, and the caching policy is applied to all satellite nodes. We present this in detail in Section 3.
## 3 Numerical Applications  
The proposed numerical model has been conceived to analyze macroscopic crack propagation in concrete structures and not to analyze the cracking behavior of laboratory specimens subjected to simple loadings (uniaxial tension or compression, bending on prismatic specimens, etc.). The problem is to find, in the literature, experimental tests on structural elements without any reinforcement (the actual state of the proposed model does not considers rebars or fibers) devoted to the study of strain rate effect. Experimental data from L-shaped specimens [38] were selected in order to validate the model proposed here. These specimens were tested for several loading rates, ranging from quasi-static (0.25 mm/s) to very fast loading (4500 mm/s). Due the specimen geometry and boundary conditions, numerical simulations were performed in the plane stress condition. For each loading rate, 20 specimens were numerically simulated. The specimen's geometry is shown in Figure 5. The mesh and boundary conditions are in Figure 6. The physical and modeling data are in Table 1. Displacements are applied and read 30 mm away from the free left edge (see Figure 6). The mesh was generated in the Gmsh open-source package [39] and crack patterns are visualized through the Paraview toolkit [40].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Value** \\ \hline Maximum aggregate size, mm & 12.00 \\ \hline Comp. strength, MPa & 46.25 \\ \hline Elastic modulus, GPa & 32.20 \\ \hline Poisson coefficient & 0.18 \\ \hline Density, kg.m\({}^{-3}\) & 2200.00 \\ \hline Grav. acc., m.s\({}^{-2}\) & 10.00 \\ \hline Tensile strength & Probabilistic/rate-dependent \\ \hline Tensile strength rate effect & \(f_{t,dyn}=f_{t,stat}+2.8+0.3\ln(\sigma)\) \\ \hline Fracture energy & Probabilistic/rate-dependent \\ \hline \(E_{G_{lc},t}\), N.m\({}^{-1}\) & 130.00 \\ \hline Fracture energy rate effect & \(G_{lc,dyn}=G_{lc,stat}\Big{(}f_{t,dyn}\big{/}f_{t,stat}\Big{)}\) \\ \hline Softening law & Linear \\ \hline Equivalent strain & \(\varepsilon_{eq}=max\langle\sigma_{p}\rangle/E\) \\ \hline Damage formulation & Scalar isotropic \\ \hline Crack band width & \(\sqrt{A_{e}}\) \\ \hline Element type & Constant Strain Triangle \\ \hline Time increment, s & \(10^{-6}\) \\ \hline \(\beta\) (Newmark algorithm) & 0.25 \\ \hline \(\gamma\) (Newmark algorithm) & 0.50 \\ \hline Struct. damp. (Rayleigh) & \(C=\alpha_{1}K+\alpha_{2}M\) \\ \hline \(\alpha_{1}\) (Rayleigh damp.) & \(10^{-4}\) \\ \hline \(\alpha_{2}\) (Rayleigh damp.) & \(10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and modeling parameters for L-shaped specimen.
## 3 Results
### Oxidative Stability  
The results of PDSC measurements are shown in Table 3. The PDSC tests for boar, badger and wild goose fats were performed at constant temperature of 100 \({}^{\circ}\)C, 110 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, 130 \({}^{\circ}\)C and 140 \({}^{\circ}\)C. Generally, samples with higher induction times are more stable than those for which the induction time obtained at the same temperature is lower. Analyzing the results obtained, it can be concluded that boar fat was the most oxidatively stable, as it was characterized by the longest induction time at all temperatures (~118 min at 100 \({}^{\circ}\)C, ~55 min at 110 \({}^{\circ}\)C, ~26 min at 120 \({}^{\circ}\)C, ~12 min at 130 \({}^{\circ}\)C and ~6 min at 140 \({}^{\circ}\)C). Badger fat was slightly less stable. The determined induction times for badger fat ranged  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Positions of**} & \multirow{2}{*}{**Fat**} & \multicolumn{5}{c}{**Fatty Acid (wt%)**} \\ \cline{3-8}  & & **C16\(\mathbf{0}\)** & **C16\(\mathbf{1}\)** & **C18\(\mathbf{0}\)** & **C18\(\mathbf{1}\)** & **C18\(\mathbf{2}\)** & **C18\(\mathbf{3}\)** & **C18\(\mathbf{3}\)** \\ \hline \multirow{3}{*}{sn-2} & boar & 43.24 \(\pm\) 0.085 b  & 3.03 \(\pm\) 0.141 a  & 7.68 \(\pm\) 0.049 b  & 26.55 \(\pm\) 0.191 a  & 9.33 \(\pm\) 0.099 a  & 1.78 \(\pm\) 0.021 ab  \\  & badger & 24.17 \(\pm\) 0.445 a  & 5.67 \(\pm\) 0.297 a  & 2.36 \(\pm\) 0.014 a  & 34.50 \(\pm\) 0.212 b  & 21.19 \(\pm\) 0.064 b  & 2.98 \(\pm\) 0.021 b  \\  & goose & 38.85 \(\pm\) 0.163 a  & 3.17 \(\pm\) 0.368 a  & 8.80 \(\pm\) 0.007 b  & 29.56 \(\pm\) 0.474 ab  & 8.51 \(\pm\) 0.099 a  & 1.63 \(\pm\) 0.078 a  \\ \hline \multirow{3}{*}{H} & H & 10.057 ** & 7.543 NS  & 10.057 ** & 10.057 ** & 10.057 ** & 10.057 ** \\ \hline \multirow{3}{*}{sn-1,3} & boar & 7.22 \(\pm\) 0.042 a  & 13.79 \(\pm\) 0.071 b  & 6.59 \(\pm\) 0.025 a  & 43.70 \(\pm\) 0.095 a  & 13.03 \(\pm\) 0.049 ab  & 2.15 \(\pm\) 0.011 b  \\  & badger & 19.46 \(\pm\) 0.223 b  & 0.53 \(\pm\) 0.148 a  & 17.11 \(\pm\) 0.007 b  & 46.55 \(\pm\) 0.106 ab  & 11.54 \(\pm\) 0.032 a  & 0.46 \(\pm\) 0.011 a  \\  & goose & 12.81 \(\pm\) 0.081 ab  & 2.44 \(\pm\) 0.184 a  & 14.32 \(\pm\) 0.004 b  & 51.19 \(\pm\) 0.237 b  & 14.53 \(\pm\) 0.049 b  & 0.95 \(\pm\) 0.039 ab  \\ \hline \multicolumn{3}{c}{H} & 10.057 ** & 10.057 ** & 10.057 ** & 10.057 ** & 10.057 ** & 10.057 ** & 10.057 ** \\ \hline \hline \end{tabular} Data expressed as means \(\pm\) SD. a  
The different lower case letters in the same column (for the position sn-2 and sn-1,3 separately) indicate significantly different values (\(p\leq\) 0.05). H–Kruskal-Wallis statistics. **  
from \(\sim\)4 min. at the temperature of 140 \({}^{\circ}\)C to \(\sim\)99 min at the temperature of 100 \({}^{\circ}\)C. The least oxidatively stable of the analysed fats was goose fat, because its induction time was the shortest at each temperature (\(\sim\)57 min in 100 \({}^{\circ}\)C, \(\sim\)30 min in 110 \({}^{\circ}\)C, \(\sim\)16 min in 120 \({}^{\circ}\)C, \(\sim\)8 min in 130 \({}^{\circ}\)C and \(\sim\)4 min in 140 \({}^{\circ}\)C).