esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Procedure  
Twenty-six passive markers were tracked by a six-infrared-camera optoelectronic motion analysis system (SMART-DX 6000 System, BTS, Milan, Italy) working at a sampling frequency of 340 Hz. A calibration procedure was executed before the first data-capture was performed, where x is the anterior-posterior, y is the vertical, and z is the mediolateral direction. Spatial accuracy was 0.2 mm in the x, y, and z dimensions. A global reference system was adopted by the International Society of Biomechanics [35; 36]. A modified version of [PERSON] protocol [37] was used to place the markers (Figure 4) over the spurious processes of the sacrum and seven cervical verteb's cutaneous projections and bilaterally over the acrom, elbow, wrist, anterior superior iliac spine, great trochanter, lateral femoral condyle, fibula head, lateral malleoli, heel, and metatarsal head. In addition to markers directly applied to the skin, sticks or wands ranging in length from 7 to 10 cm were placed at 1/3 of the length of the body segment (femur and leg). Anthropometric measurements were taken for each patient after the markers were placed. The gait analysis began with a standing position on a platform. The procedure continued by asking the patient to walk at their preferred speed and in their shoes along a 10 m laboratory pathway on level ground without wearing the device (WOE). Following that, the patients were required to wear the suit, and the exospine was randomly inserted, first in one version and then in the other. For these two conditions, with more flexible (WMFE) and less flexible (WLFE) exospines, the patients were also asked to walk at their preferred speed with their shoes. No external cues were provided during the gait tasks. Healthy subjects were asked to walk at their preferred speed and at a slower but comfortable speed to compare the general characteristics of gait between the groups without any potential bias due to speed differences [38]. In particular, we only took into account trials when the walking speed of each healthy subject fell between the ranges determined by the patients' mean walking speed plus SD and mean walking speed minus SD [3]. At least ten trials were recorded for ataxic patients in each WOE, WMFE, and WLFE condition and healthy subjects of the control group at each gait speed.
## 3 Results and Discussion
### Spectroscopic Prediction of Total Polyphenols Content  
A series of dilutions were prepared for each plant extract, as shown in Table 2. Then, the UV-Vis spectrum of each sample and their series of dilutions were recorded separately (Figures 3 and 4). The data's matrix that connects the concentrations and absorption of each plant is then entered for each of the TPC in the plant extracts (dilution series) in the second part of the program, and the following concentrations from the ODMT were then obtained. Table 3 indicates that the results obtained in the reaction method are very close to the results obtained from the ODMT, with a recovery percentage range of 95.3% to 104.3%.  
\begin{table}
\begin{tabular}{c c} \hline
**Plant Extract/Part** & **TPC (mg GAE/g DW)** \\ \hline _Daucus carota_ L. root & 51.10 \(\pm\) 17.56 \\ _Ruta Chalepensis_ L. Leaves & 54.94 \(\pm\) 20.86 \\ _Anisoscadium_ DC. Leaves & 44.67 \(\pm\) 1.10 \\ _Thymus vulgaris_ L. Leaves & 141.8 \(\pm\) 2.86 \\ _Senna alexandrina_ leaves & 45.14 \(\pm\) 2.84 \\ _Myrtus communis_ L. Leaves & 334.7 \(\pm\) 15.17 \\ _Silbum maranum_ L. Flower & 44.23 \(\pm\) 2.89 \\ _Silbum maranum_ L. Leaves & 64.73 \(\pm\) 17.59 \\ _Rosa moschata_ Flower & 258.59 \(\pm\) 8.29 \\ \hline \end{tabular}
\end{table}
Table 1: Total polyphenols content (TPC) in the different plant extracts measured using the Folin–Ciocalteu method as a reference method. Data are expressed in gallic acid equivalent (GAE) of dry weight (DW) extract.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{_Daucus carota_**L. (root)**} \\ \hline Concentration (\({}^{1}\)) (mg/mL) & 0.3000 & 0.2700 & 0.2500 & 0.2300 & 0.2140 & 0.2000 & 0.1875 \\ Concentration (\({}^{2}\)) (mg/mL) & 0.0242 & 0.0238 & 0.0231 & 0.0219 & 0.0202 & 0.0190 & 0.0174 \\ Absorbance at \(\mathrm{\Lambda max}\) = 255 nm & 1.9740 & 1.8000 & 1.6530 & 1.5070 & 1.3900 & 1.3010 & 1.2100 \\ \hline \multicolumn{6}{c}{_Ruta Chalepensis_**L. (leaf)**} \\ \hline Concentration (\({}^{1}\)) (mg/mL) & 0.0100 & 0.1000 & 0.0500 & 0.2500 & 0.2083 & 0.1785 & — \\ Concentration (\({}^{2}\)) (mg/mL) & 0.0011 & 0.0110 & 0.0564 & 0.0248 & 0.0219 & 0.0201 & — \\ Absorbance at \(\mathrm{\Lambda max}\) = 261 nm & 0.1290 & 0.9210 & 0.4860 & 2.1950 & 1.8410 & 1.5970 & — \\ \hline \multicolumn{6}{c}{_Anisoscadium_**DC. (leaf)**} \\ \hline Concentration (\({}^{1}\)) (mg/mL) & 0.5000 & 0.4545 & 0.4166 & 0.3846 & 0.3571 & 0.3333 & 0.3125 \\ Concentration (\({}^{2}\)) (mg/mL) & 0.0270 & 0.0268 & 0.0265 & 0.0255 & 0.0240 & 0.0234 & 0.0230 \\ Absorbance at \(\mathrm{\Lambda max}\) = 266 nm & 2.1580 & 2.0060 & 1.8280 & 1.6720 & 1.5670 & 1.4900 & 1.4220 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Two comparison methods for total polyphenol content (TPC) in the indicated plant extract.  
Figure 3: UV-Vis absorbance curve of the plant extracts at different indicated concentrations: (**a**) _Daucus carota_ L. (root), (**b**) _Ruta Chalepensis_ L. (Leaf), (**c**) _Anisosciatium_ DC. (Leaf), and (**d**) _Thymus vulgaris_ L. (Leaf).
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 2 Materials and Methods
### Immunosensor Detection System  
The biosensor (PIC) developed during this study had three main components: a detection ring resonator, a light coupling block using grating couplers, and an optical power distribution block. The lightly coupled section served the crucial function of enabling the input and output of optical signals to and from the PIC. The power distribution block was essential to supply the standard laser source input to all ring resonators within the PIC [44].  
One notable advantage of this biosensor over existing detection systems is the multiplex ring system. The sensor's structure enables simultaneous measurement of several targets within a single analysis because each ring resonator can be equipped with a different probe. This feature makes the system fundamentally different from common applications such as ELISA and qPCR where each of the probes used typically needs separate analysis.  
Although resonant structures such as these are essential elements to develop this technique, they require appropriate interfaces that allow fiber optics to perform applications in real samples and tests on wafers. A key advantage of this sensor pertains to its scheme, which allows light to be coupled. The coupling process is conducted in free space, eliminating the need to attach the collimator directly to the input gratings (Figure 2). This feature removes the need for a direct fiber connection to the PIC for optical laser connection. As outlined by [PERSON] et al. [44], the grating coupler interface meets these requirements.  
Figure 1: Array layout. This figure illustrates the arrangement of the array. It shows surfaces that were both activated and non-activated with primary antibody immobilization (Lines 1 and 4), as well as blank (Line 3) and positive control (Line 2) test configurations. (**a**) Surfaces 1 and 2 had both activated and non-activated surfaces, where primary antibodies were immobilized and then secondary anti-mouse antibodies were immobilized. (**b**) Surfaces 3 and 4 had both activated and non-activated surfaces. They were subjected to immobilization with primary antibodies, which were subsequently bound to fluorophore-linked anti-rabbit secondary antibodies.  
The sensor comprised eight ring resonators. They were distributed across one pair of channels (or sensing areas), each with four ring resonators (Figure 3). This distribution allows for the inclusion of two detection areas where the antibodies are housed, which allows binding to the _Salmonella_ antigen. This feature aided binding to the _Salmonella_ spp. bacterium. Two additional rings served as negative controls. An alternative probe (anti-fish antibody) was designed to bind to an antigen other than the target bacterium that was not typically found in the food matrices under investigation. Lastly, a reference resonant ring, since no probe is functionalized on it, served as a blank for comparison. This ring resonator offered a baseline for comparison.
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
###### Abstract  
Precise prediction of coal thickness is of the utmost importance in realizing intelligent and unmanned mining. As the channel wave is characterized by an easily recognizable waveform, a long propagation distance, and strong energy, it is widely used for coal thickness inversion. However, most traditional inversion methods are local in nature, and the inversion result is probably not optimal in the global scope. This paper introduces the GA-SIRT hybrid approach, which combines Genetic Algorithms (GA) and Simultaneous Iterative Reconstructive Techniques (SIRT) in order to deal with the above problem and to improve the accuracy of coal thickness inversion. The proposed model takes full advantage of the strong global search capability of GA and of the fast local convergence rate of the SIRT. Moreover, it inhibits the poor local search ability and the local optimal value effect of the GA and the SIRT respectively. The application of the GA-SIRT in the Guoerzhuang coal mine has significantly enhanced its accuracy, stability, and overall computational efficiency. Hence, the introduced novel hybrid model can precisely resolve and identify the coal thickness according to the channel wave. It can also be extended to other geophysical tomographic inversion problems towards the reduction of potential local optimal solutions.  
Keywords:coal thickness; channel wave; tomography; hybrid algorithm; genetic algorithm; simultaneous iterative reconstructive technique algorithm +
Footnote †: journal: Applied
## 2 The Numerical Tool Development
### Mathematical Models: Development of the Numerical Model's Logic  
The development of a numerical model to predict complex distortions relied on established commercial simulation software, specifically MSC PATRAN, MSC NASTRAN, and MSC MARC. To conduct a numerical analysis of distortion in composite material manufacturing, a series of stages were outlined as an initial strategy to evaluate the operational capabilities of a generic model:  
* Developing the geometric configuration for both the test specimen and the manufacturing tool;
* Specifying the thermal and structural material characteristics pertinent to both the specimen and the tool;
* Establishing the universal unit system to be employed throughout the simulation; constructing the finite element model (FEM), including the mesh and stacking sequence for both the coupon and the tool;
* Determining the FEM material properties for each layer, encompassing both thermal and structural attributes;
* Outlining the input parameters necessary for conducting a thermal finite element analysis, which include the initial temperature of the coupon/tool assembly, temperature fluctuations within the oven, convection effects on the outer surface of the assembly, thermal interactions between the coupon and the tool, and specific requirements for the mathematical model utilized in the finite element analysis solver to facilitate the curing calculations;
* Refining the thermal model by data obtained from experimental monitoring and, finally, developing and resolving a coupled thermal-structural finite element model that integrates structural properties such as Young's Modulus, curing shrinkage, and viscosity with respect to temperature and cure rate.
## 2 Materials and Methods
### Full-Field SLDV-Based Mobilities and Receptances from the TEFFMA Project
#### 2.1.1 Direct Characterisation by means of Full-Field _Mobilities_: a Brief Recall  
The _mobility_ matrix \(\mathbf{H}_{\text{p}}(\omega)\) follows the formulation for the H1 estimator--noise only in the outputs--found in [4; 5]: it is estimated as the spectral relation between multi-output velocities and multi-input forces. In this full-field extension, the inputs are just 2 shakers, while _thousands_ (2907) of responses are acquired on the whole surface of interest. The structural domain is represented by the vibrating surface \(S\) of the plate, whose \(q\)-_th_ point has global coordinates \(\mathbf{q}_{q}\), while \(\mathbf{q}\) represents the whole-field's coordinates of the active structural domain. Each element of the _mobility_ matrix \(\mathbf{H}_{\text{p}}(\omega)\) can be expressed, in _complex-valued_ representation, as:  
\[H_{\text{p}_{df}}(\omega)=\frac{\sum_{m=1}^{N}S_{\chi_{q}F_{f}}^{m}(\omega)}{ \sum_{m=1}^{N}S_{F_{f}F_{f}}^{m}(\omega)}\in\mathbb{C} \tag{1}\]  
where \(\hat{X}_{q}\) is the _output velocity_ at \(q\)-th dof induced by the _input force_\(F_{f}\) at \(f\)-th dof, while \(S_{\chi_{q}F_{f}}^{m}(\omega)\) is the \(m\)-th cross-power spectral density between input and output, \(S_{F_{f}F_{f}}^{m}(\omega)\) is the \(m\)-th auto-power spectral density of the input, evaluated in \(N\) repetitions.  
The 3D _rectance map_\(\mathbf{H}_{\text{d}}(x,y,\omega)\in\mathbb{C}\) is easily obtained in the frequency domain, with \(i\) as the _imaginary unit_, as:  
\[\mathbf{H}_{\text{d}}(\omega)=-i/\omega\mathbf{H}_{\text{p}}(\omega)\in\mathbb{C}. \tag{2}\]
## 3 Biosynthetic Pathways and Key Enzymes of Mushroom Polysaccharides  
### Biosynthetic Pathways  
Fungal PSs are involved in a wide variety of cellular functions, including energy storage, cell wall structure, cell-cell interactions and signaling, host-pathogen interaction, and protein glycosylation [5]. The PSs are synthesized using intracellular nucleotide sugars at the membrane level to construct the main structure of cell walls or form a gel-like matrix on the hyphal surface. In submerged fermentation, gel-like PSs may be further excreted into the growth medium and are referred to as EPSs [63]. Currently, the biosynthetic pathways of mushroom PSs remain unclear because of the inadequate knowledge of relative enzymes and its functions in pathway, which should be proved by gene cloning and genetic transformation.  
However, a simplified biosynthetic pathway of mushroom PSs has been constructed based on identification of intermediate compounds and activities of synthesis-related enzymes by various researchers [11; 16; 64]. Approaches such as gene expression [13; 14] and RNAi-mediated gene silencing [64] have also been applied for study of the biosynthetic pathway. In Figure 1, we propose a more detailed biosynthetic pathway based on the previous publications. In this model, the biosynthetic pathway of nucleotide sugar precursors in mushroom PSs is similar to that in plants [65], providing a basis for analysis of other metabolic pathways in mushroom PSs. The genomes of _Ganoderma lucidum_[66], _Antrodia cinnamonea_[67], _Lentinual eddes_[68], and _Lignosus rhinocerotis_[69] have been published. These genomic studies provide insights into the genetic basis of reported medicinal properties of PSs, and a platform for further characterization of putative bioactive PSs and pathway enzymes.
## 7 IoMT Healthcare Services and Applications
### Heart Rate Monitoring  
Heart rate monitoring helps measure the intensity of physical activity and determine the effectiveness of a workout. It provides insight into a person's overall health, as changes in heart rate indicate potential health issues. By tracking a person's heart rate during physical activity, it determines the effort exerted and the required rest between sets. Moreover, changes in heart rate can indicate if someone is overexerting themselves, which may lead to injury or illness. Monitoring heart rate also helps detect underlying health issues [100]. Overall, tracking heart rate during exercise offers valuable information for ensuring a safe and beneficial workout routine. However, some individuals may feel uncomfortable with their heart rate being monitored during exercise. Additionally, privacy concerns may arise if heart rate data is collected and stored.
# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 3 Proposed Approach  
For direction prediction and simulation trading of the rebar futures, a multiclassification method, which combines XGBoost, SMOTE, and NSGA-II, is proposed in this research.  
The main structure of the proposed approach is presented in Figure 1, with its specific procedures summarized in the following steps:  
Step 1: Data collection and pre-processing  
At first, 5 min historical trading data of the rebar futures are collected from databases. After timeframe transformation, the 1-h interval trading data are generated and normalized. Then, the entire dataset is divided into four consecutive experimental datasets. Each subdataset consists of a training period and a testing period.  
Step 2: Data oversampling and model training  
In the second step, the proposed approach uses the SMOTE technique to generate the class-balanced training datasets. Then, XGBoost is adopted to construct a multiclassification forecasting model using the balanced datasets. Meanwhile, NSGA-II is incorporated for parameters optimization of the pre-designed trading rule. The multiobjective optimization is performed to achieve the Pareto optimal solution for two objectives, including the maximization of accumulated return and minimization of the maximum drawdown.  
Step 3: Model testing  
In the third step, the optimized XGBoost model and trading rule are selected for price-movement-direction prediction and trading simulation of rebar futures in four consecutive testing periods.  
Step 4: Model explanation  
In the fourth step, LIME is adopted to explain how the XGBoost made multiclassification predictions for rebar futures price direction.  
Step 5: Performance evaluation  
In the final step, evaluation measures are employed to judge the prediction and trading performances of all methods from the perspective of direction prediction accuracy, profitability, and trading risk.  
Figure 1: The structure of the proposed approach XGBoost-SMOTE-NSGA-II.
## 4 Validation and Applications  
In this section, two test cases are presented to validate the robustness and accuracy of the developed time-marching throughflow method in the prediction of the off-design performances and the flow details. The two test cases include the low-speed centrifugal compressor (LSCC) and the high-performance centrifugal compressor (HPCC).
## 4 Test Results and Discussions
### Dynamic Compression Tests  
#### 4.2.1 Failure Patterns  
Figure 8 provides the failure patterns of the FCHC specimens under dynamic compression. Only failure modes of specimens with one-layer and three-layer FRP jackets are presented in Figure 8 to limit the space of the paper. From Figure 8a, it can be seen that with an increase in the strain rate, the failure mode of the unconfined high-strength concrete experienced cracking, crushing, and smashing failures, which showed that concrete is a strain rate-sensitive material. The effect of the FRP type on the failure pattern is negligible, namely, the failure modes of all FCHC specimens with different FRPs are similar. In fact, the failure mode is related to the strain rate (see Figure 8b-j). The FCHC specimens with smaller impact loading energies (i.e., smaller strain rates) experienced dispersed microcracking failure and the FRP jacket was intact. With an increase in the impact loading energy, the core concrete experienced crushing failure, while the FRP jacket experienced fracture failure. The failure patterns of the specimens under impact loadings are summarized in Table 4. For a given impact energy (e.g., the impact pressure is 0.6 MPa), the crack propagations of FCHC with the same layer of FRP (AFRP, BFRP, and CFRP) are slightly different. The surface cracks of the AFRP-confined concrete were distributed on the entire surface of the specimens, while relatively less cracks were found in the BFRP and CFRP-confined concrete specimens. The cracks in the latter specimens were mainly concentrated at the edge of the specimens, as seen in Figure 8b-j. This phenomenon implies that the AFRP-confined concrete specimens have a better impact resistance compared to the CFRP and BFRP-confined concrete, provided that the impact strain rate and number of FRP layers are identical.  
Figure 8: \(Cont\).  
Figure 8: Typical failure modes of FRP-confined and unconfined high-strength concrete specimens under impact loading. (**a**) Typical failure mode of unconfined high-strength concrete specimens under dynamic compression loadings; (**b**) Specimens with a one-layer BFRP jacket; (**c**) Specimens with a three-layer BFRP jacket; (**d**) Specimens with a one-layer AFRP jacket; (**e**) Specimens with a three-layer AFRP jacket; (**f**) Specimens with a one-layer CFRP jacket; and (**g**) Specimens with a three-layer CFRP jacket.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & **FRP** & **Launch** & **Projectile** & **Strain** & & & & \\
**Thickness** & **Pressure** & **Velocity** & **Rate** & \(f_{ec,D}\)(MPa)** & \(\varepsilon_{ec}\) & \(f^{\prime}_{rad,a}\) & **Failure** \\  & **(MPa)** & **(m\(\cdot s^{-1}\))** & **(\(s^{-1}\))** & **(\(s^{-1}\))** & **(\(s^{-1}\))** & **(\(s^{-1}\))** & **(\(s^{-1}\))** \\ \hline D-0-1