esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Variable Selection and Research Hypotheses
### Hospital Size  
Large hospitals typically surpass smaller hospitals in terms of service capacity and resource allocation. They not only provide more diverse medical services but also meet patients' demands for high-quality healthcare, thereby attracting a larger number of patients. Studies have shown that, in some countries, patients often bypass smaller local hospitals to seek care at more distant comprehensive or teaching hospitals. This phenomenon highlights the critical role of hospital size in influencing patient choice. Hospital size is generally measured using indicators such as construction area, number of beds, number of medical departments, and number of staff. Numerous studies have demonstrated that differences in hospital size significantly impact patient volume. For instance, [PERSON] [6] found that construction area (\(\lambda_{1}\)) and number of beds (\(\lambda_{2}\)) are key factors influencing hospital choice. Similarly, [PERSON]. [7] focused on the impact of hospital specialties, number of medical departments (\(\lambda_{3}\)), and medical equipment on patient preferences. Additionally, [PERSON], [PERSON] [33] explored the effects of the number of hospital beds and the number of doctors on patients' decisions to seek admission. Based on these findings, the hypothesis is proposed: \(H_{1}\)--hospital size significantly influences traffic attraction.  
\[H_{1}=\mu_{1}\lambda_{1}+\mu_{2}\lambda_{2}+\mu_{3}\lambda_{3}+\varepsilon \tag{1}\]  
In the formula, \(H_{1}\) represents the hospital size level function; \(\lambda_{i}\) denotes the observed variables of hospital size; \(\mu_{i}\) refers to the factor loading of \(H_{1}\) on \(\lambda_{i}\); \(\varepsilon\) represents the error term.
## 3 Dihydroxybenzene-Based Building Blocks (Catechol)
### Dopamine  
Polydopamine was introduced in 2007 and is the first reported synthetic polyphenol-based material [4]. Dopamine is a basic building block that has both catechol and amine groups in its molecular structure and exhibits a self-assembly mechanism similar to that of eumelanin [14; 41]. The oxidation of dopamine results in the production of DHI through a quinone structure (Figure 3). DHI formation mainly contributes to the production of self-assembled polydopamine in two ways, (1) assembly with additional covalent bonds (e.g., DHI-DHI dimer formation) and (2) assembly with non-covalent bonds [42]. In addition to DHI, other intermediates can also be involved in the self-assembly mechanism; however, the complete polydopamine structure has not yet been fully elucidated due to its complexity.  
Polydopamine has been widely adopted for biomaterials, as it can provide catechol functionality to virtually any kind of target substrate upon employing the simple dip-coating process [43; 44]. Moreover, polydopamine has a high potential to contribute to cellular responses. Thus, it has been utilized as a novel tissue scaffold coating material that brings successful transplants. For example, polydopamine can stimulate osteogenic cell differentiation, which consequently promotes osseointegration [45]. This process can be achieved due to the enhanced hydrophilicity of the scaffold materials via polyphenol coatings, which increase the adsorption of biomacromolecules from the extracellular matrix, thereby enhancing cell adhesion, proliferation, and differentiation; recently, in vivo studies on polydopamine-assisted bone regeneration have been actively conducted [46; 47; 48; 49]. Moreover, polydopamine can be beneficial for extending the blood circulation time of benzoic drugs. Here, \(\pi\)-\(\pi\) stacking plays a crucial role in chemically stabilizing drug agents. For example, two antitumor drugs (gossypol and doxorubicin) are highly stabilized by a polydopamine-based nanocarrier, which can significantly extend blood circulation time with two orders of magnitude difference compared to that of free gossypol and/or doxorubicin [50].
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 2 Materials and Methods
### Lw-Yolov7  
Although YOLOv7 performs well in real-time object detection, there are still some issues when we use it to detect maize seedling in images. Firstly, different images have many similarities in features. When the model is training, similar features can be extracted from different images. It will occupy a large amount of memory space for the model to be deployed on mobile devices. Secondly, for detecting small objects, multiple convolutions and up-sampling operations will lead to the loss of location information. Finally, YOLOv7 converges too slowly when calculating loss. To solve the above problems, we propose a lightweight model base on the YOLOv7, which has been improved as follows.  
1. To tackle the problem of redundant features during training, we use the GhostNet module to replace the ordinary convolution of the Backbone in the YOLOv7. At the same time, we introduce the CBAM attention mechanism module to improve global attention with channel attention module and spatial attention module.
2. To solve the position information of small objects, we use BiFPN to replace PANet in the Head and enhance the representation ability of features by adding residual links.
3. In terms of loss convergence, the SIOU loss function is used instead of the CIoU loss function to reduce the degree of freedom of the loss function, enhance network robustness, and improve the speed and accuracy of box regression.
# The Evaluation of the Detection of Cr(VI) in Leather
###### Abstract  
The topic of hexavalent chromium (Cr(VI)) in leather has been debated throughout the whole supply chain for years. However, its significance has recently increased due to proposed changes in European legislation concerned with skin-sentising substances suggesting that acceptable Cr(VI) concentrations in leather goods should be lowered from 3 mg kg\({}^{-1}\) to 1 mg kg\({}^{-1}\). The proposition of a stricter limit and current analytical difficulties created the need for a review of current standard test methods. The research presented in this paper investigates both the colorimetric (Part 1) and chromatographic (Part 2) methods under BS EN ISO 17075. The focus of the study was to identify possible sources of interference leading to large statistical variance in results and to define the limit of quantification with respect to the proposed new compliance limit. This study into the colorimetric method has shown that the presence of Cr(III), dyes, and proteins can be significant interferences, becoming critical at low Cr(VI) concentrations. Dilution factors worsen the problem of detecting low concentrations: a reliable quantitative detection of 0.01 mg kg\({}^{-1}\) and 0.003 mg kg\({}^{-1}\) Cr(VI) in solution are required at the 3 mg kg\({}^{-1}\) and 1 mg kg\({}^{-1}\) compliance limits in leather, respectively. BS EN ISO 17075 part 1 was shown to be incapable of reliably resolving to 3 mg kg\({}^{-1}\) or below in leather. Part 2 shows a marked improvement in detection limits and reliability; however, data suggest that 1 mg kg\({}^{-1}\) Cr(VI) is not reliably detectable in leather. Suggested improvements to the established test methods and a possible alternative are discussed.  
chromium (VI); leather; colorimetry; ion chromatography; restricted substances; taming agent +
Footnote †: journal: _Analytica_
## 3 Results and Discussion  
### Chemical Composition of Polysaccharide Fractions  
As illustrated in Table 1, the extraction yield of the five extracted polysaccharides ranged from 0.87% to 31%. The highest percentage was recorded in alginophytes _C. myriophylloides_ and _S. muticum_, representing values of 31% and 23%, respectively. The sulfated arabinolactans AGP and AGB represented 5% and 11% of the dry weight of the green alga _C. decorticatum_, while the fucoidans yield showed the lowest averages (0.87%).  
As far as we know, no previous research has investigated the structural features of _C. myriophylloides_. The ALCM extraction yield was higher than that reported in some of the _Cystoseira_ genus [39; 40; 41; 42; 43; 44]. By comparing the alginate content of _S. muticum_ from previous works [45; 46; 25; 47], the ALSM yield was in accordance with that reported by [PERSON] et al., [45]. The AGB fraction exhibited an important sulfation degree of 22.68%, while lower averages of 13.21% and 12.83% were observed for AGP and FUCCM, respectively. The presence of 7.29% of sulfates in ALCM could be explained by eventual contamination by fucoidans during the co-extraction step. The FUCCM yield was similar to that reported by [PERSON] et al., [48]. However, it was lower than that obtained from other _Cystoseira_ species. Higher averages were recorded in _C. trinodis_ (6.34%), _C. barbata_ (5.45%) and _C. compressa_ (5.2%) [43; 44; 49]. The content of sulfates of FUCCM was within the range of that reported for other _Cystoseira_ species [43; 44; 48; 50]. High averages were found in _Fucus spirals_ (49.53%) and _Bifurcaria bifurcata_ (45.49%) [19] and lower amounts were obtained from _C. schiffneri_ (4.5-10.7%) [48]. These variations in yields and chemical compositions, even within the same species, could be attributed to a variety of factors, including the temporal and spatial conditions of harvesting and extraction procedures [45; 25; 48; 51].
# Modeling of Distorted Degradation Data Based on Oil Analysis
## 2 Methodology
### Parameter Estimation
#### 2.2.2 Non-Parametric Estimation Approach  
To avoid possible model misspecification in a parametric analysis, we consider an alternative approach, namely, the non-parametric estimation approach.  
Under **Case II**, we use a natural cubic smoothing spline [21] to fit the actual physical degradation curve. The natural cubic smoothing spline is a smooth and continuous twice differentiable curve that minimizes the penalized sum of squares. The penalized sum of squares consists of two parts: the residual sum of squares that quantifies the goodness-of-fit to the data, and the roughness penalty term based on the second derivative. More specifically, we have the following optimization problem:\[\begin{array}{ll}\min_{a,h}&\sum_{i=1}^{n}\left(f_{i}-y_{i}\right)^{2}+ \lambda\int\left(h^{\prime\prime}(t)\right)^{2}\!dt\\ \text{subject to}&h_{i}-h_{i-1}\geq 0,\quad i=2,\ldots,n,\\ &0\leq\alpha\leq 1,\end{array} \tag{5}\]  
where \(\mathbf{h}=(h_{1},\ldots,h_{n})^{T}\), \(\lambda\) is a positive smoothing parameter, controlling the trade-off between fidelity to the data and the roughness of the function estimation, and the constraint condition \(h_{i}-h_{i-1}\geq 0\), to some extent, ensures that the estimation of the actual physical degradation curve is monotonically non-decreasing. Then, the interior-point method is used to solve this problem (see Appendix A for more details).  
Under **Case III**, due to the absence of \(\mathbf{\delta}=(\delta_{1},\ldots,\delta_{n})^{T}\), the three-step explosion search algorithm in the previous subsection is used in the non-parametric estimation approach. Note that its second step is replaced by the non-parametric estimation based on Equation (5). Moreover, similarly to the parametric estimation approach, we can compute the bootstrap confidence interval for non-parametric estimation.
## 3 Experiments And Discussion
### Experiment 1: Reachability  
The aim of this experiment was to determine if potential users could operate the robotic system to position and insert an articulated forceps satisfactorily within the anatomical constraints of the nasal cavity to reach an area of interest. The experimental setup is presented in Figure 12a. We used a human head phantom (M01-SET-TSPS-E1, SurgTrainer Ltd., Ibaraki, Japan), prepared by a surgeon to expose the spinoid sinus, and the area of interest was a pituitary adenoma model representedwith a green mark (see Figure 12b). The head phantom was rigidly attached to a support and slightly tilted to resemble a normal clinical setup. A rigid endoscope (30\({}^{\circ}\), 2.7 mm diameter) was used for visualization inside the head phantom and targeting the area of interest. The participants were asked to introduce a surgical tool from the left nostril and reach the area of interest with the use of conventional surgical instruments and with the proposed robotic system. A magnetic motion capture system (Aurora, Northern Digital Inc., Ontario, Canada) was utilized to measure the motions of the surgical instruments for both the manual and robot task operation. Five subjects between the ages of 20 and 35 participated in this experiment. We defined the optimal trajectory as the line connecting the center of the nostril and the center of the target pituitary region. The root mean squared error (RMSE) of the distance between trajectory performed and its projection over the optimal trajectory unit vector \(u_{opt}=\frac{x_{target}-x_{nostril}}{\left\|x_{target}-x_{nostril}\right\|}\) was used as metric to evaluate the system performance as:  
\[RMSE=\sqrt{\sum_{i=1}^{n_{samples}}\frac{\left\|(x-(x^{\top}u_{opt})u_{opt}) \right\|^{2}}{n_{samples}}}. \tag{24}\]  
A representative trajectory is shown in Figure 12c. The RMSE is shown in Figure 13a. The trajectory obtained with manual operation showed frequent direction changes and oscillations because of hand tremor. With the use of the proposed robotic system, the trajectory became smoother, and the deviation error was reduced by 30%. The time to complete the task is shown in Figure 13b. Although a velocity limitation of 10 mm/s was imposed on the robot, the completion time showed a 20% reduction with the proposed system compared with a manual insertion. The virtual remote-center-of-motion constrained the forceps motion to avoid collision with the surrounded tissue, and the variable damping in the force control provided stable and smooth motion control while approaching the target.  
Figure 12: (**a**) Reachability experiment setup. (**b**) Adenoma model in the pituitary region represented with a green mark in the human head phantom. (**c**) Forceps tip trajectory comparison between manual and robot operation.
## 3 Results
### Linking UAV LAI Retrieval Accuracies to Spatial Scale  
There was evidence of substantial LAI heterogeneity from comparing UAV retrievals of LAI at the scales of in-situ data (2 m). The impact of spatial scale on LAI retrievals was investigated by  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Sentinel-2 \(\text{CI}_{\text{red-edge}}\) (20 m)**} & \multicolumn{4}{c}{**UAV \(\text{CI}_{\text{red-edge}}\) (0.05 m)**} & \multicolumn{4}{c}{**Sentinel-2/UAV**} \\  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**Farm**} & \multirow{2}{*}{**GS**} & **Number** & \multirow{2}{*}{**Mean**} & **SD** & **CV (\%)** & \begin{tabular}{c} **Number** \\ **of Pixels** \\ \end{tabular} & **Mean** & **SD** & **CV (\%)** & \begin{tabular}{c} **Mean** \\ **of Pixels** \\ \end{tabular} & **Mean** & **SD** & **CV (\%)** & \
\begin{tabular}{c} **R\({}^{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **NRMSE** \\ **(\%)** \\ \end{tabular} \\ \hline Farm 1 & 39 & 80 & 7.46 & 0.81 & 11 & 5,491,483 & 8.88 & 1.18 & 13 & 0.37 & 28 \\ Farm 1 & 51 & 80 & 7.07 & 0.92 & 13 & 5,491,483 & 9.62 & 1.10 &  
comparing the UAV-estimated LAI at 2-m and at 20-m spatial resolutions to in-situ measurements recorded on the ground (Figure 5). From a linear fit, the correlation with ground measurements of the LAI retrieval model calibrated with the 20-m resolution data was poor (R\({}^{2}\) = 0.56) when compared to that of calibration with the 2-m spectral data (R\({}^{2}\) = 0.80). The overall error between the measured and retrieved LAI was more similar for the two spatial resolutions; the NRMSEs were 11% and 13% for the 2-m and 20-m calibrations, respectively. However, bias was clearly greater when using the 20-m resolution data (particularly for LAI measurements \(>\) 3 m\({}^{2}\) m\({}^{-2}\)) with a regression slope of 0.57, compared to 0.80 for the 2-m calibration.
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 3 Insulator Damage Detection Based on Zernike Moment
### Hu Moment and Zernike Moment  
In the process of image acquisition, it is difficult to collect a large number of damaged insulator samples, which is not conducive to the judgment of damage through deep learning and other methods. If the detailed features of the insulator pieces can be directly described in some way, it will directly reduce the need for the number of samples.  
At present, the methods used to directly describe the invariant features of images mainly include Harris corner detection, Speed-Up Robust Features algorithm (SURF) [29], Scale-Invariant Feature Transform algorithm (SIFT) [30], and invariant moment. The invariant moment can better describe the geometric features of the object, and has translation, scale, rotation invariance, and uniqueness [31], which can well distinguish the damage forms of catenary insulators. The characteristic moments include complex moments, rotational moments, orthogonal moments, and geometric moments. The Hu moment and Zernike moment commonly used in pattern recognition belong to geometric moments and orthogonal moments, respectively.  
Figure 5: Dynamic adjustment of threshold value of image binarization.  
Figure 6: Results of image preprocessing. (**a**) original image; (**b**) binarized image; (**c**) the whole contour; (**d**) the contour of the insulator pieces.
# Detection of Thermal Strain in Steel Rails with BOTDA
## 3 Results and Discussion  
### Thermal Effect of Fiber Jacket  
First, the Brillouin frequency shift of the loose optical fiber under different temperatures is shown in Figure 4. According to the figure, Fiber C had a linear response for the entire temperature range in the test, and the measured Brillouin frequency shift with the temperature was calculated to be 1.04 MHz/\({}^{\circ}\)C, which was slightly larger than that of the standard single-mode fiber without any coating (1.01 MHz/\({}^{\circ}\)C) [7]. This slightly larger shift of the Brillouin frequency with temperature was caused by the extra stain transfer from the coating layer to the cladding due to the elastic-modulus difference between the carbon/polymide coating and the inside silica cladding. Linear Brillouin frequency change within such a large temperature range demonstrates the stable thermomechanical property of the coating material.  
Figure 4: Brillouin frequency-shift variation with the temperature of three loose optical fibers.  
Figure 3: Demonstration of the two-layer structure of the optical fiber with a cladding diameter of \(2r_{1}=125\,\upmu\)m and a jacket diameter of \(2r_{2}\).  
However, the Brillouin frequency shift of Fibers A and B had a nonlinear response with temperature variation in a broad temperature range. Quadratic curve fitting was used, and perfect matching with the measured data was obtained. Based on the fitting function, the ratio of Brillouin frequency shift with temperature variation can be given as:  
\[(dv_{B}/dT)_{i}=2*\beta_{2i}T+\beta_{1i}, \tag{5}\]  
where \(i=A,B\) indicate the results for Fiber A and Fiber B, separately, and \(\beta_{2i}\) and \(\beta_{1i}\) is the coefficient of the quadratic term and linear term in the fitting equation displayed on Figure 5. As was previously mentioned, the temperature coefficient of the bare fiber was constant, and, after extraction, this constant from Equation (2), the thermal strain caused by the optical jacket material, was temperature-dependent. This, we believe, is a consequence of a mechanical-property change of the fiber jacket material with temperature, i.e., the Young's modulus and thermal-expansion coefficient of the jacket material being temperature-dependent. Both of the tight buffer fibers have larger thermal strain when the temperature is lower than 0 \({}^{\circ}\)C, since \(\beta_{2i}\) has a negative value. The reason behind that is the Young's modulus of jacket material, which is the polyvinyl chloride, increases with decreasing temperature in a low temperature region; hence, the thermal strain between bare fiber and the coating material increases. Instead of taking advantage of this excess strain, when used in distributed sensing, this nonlinear response of a tight buffer fiber should be calibrated.
## 1 Introduction
However, the above-mentioned methods combining TF and distance measure are only used to detect the occurrence of damage, but not damage location. In addition, TF is calculated by acceleration and displacement response in the previous studies, while there is little research on TF based on strain response. Structural strain response is a local characteristic of the structure. When the structure near the strain gauge is damaged, the strain response recorded by the strain gauge will change significantly. Since strain TF is more sensitive to damage than traditional TF [37], it is applied to structural damage identification in this paper. To overcome the above limitations, an improved method for identifying the structural damage location under unknown seismic excitation using TF and MSD is proposed in this paper. Firstly, strain TF is used to eliminate the influence of unknown ambient excitation and unknown seismic excitation, which is more sensitive to local damage than traditional TF based on acceleration and displacement data. Then, the strain TF observation matrix in the intact structure under ambient excitations is obtained, and the MSD threshold is determined to judge outliers. Finally, the strain TF test vector in the test structure under unknown seismic excitation is obtained to calculate MSD between the test structure and the baseline, which is compared with the threshold to identify the structural damage location. To verify the effectiveness of this method, numerical simulations of a simply supported beam and a cable-stayed bridge were carried out. All simulation results show that the proposed method reveals quite a significant capacity for accurately locating structural damage, identifying the degree of damage to a certain extent, and showing good noise resistance to small damage. The remainder of this paper is as follows. In Section 2, the strain TFs of unknown ambient excitation under normal operating conditions and unknown seismic excitation under damage conditions are analyzed. In Section 3, the damage localization based on MSD of strain TFs is discussed. In Section 4, the numerical examples of a simply supported beam and a cable-stayed bridge are provided to demonstrate the accuracy of the method. In Section 5, there are some conclusions and future work directions.
# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 3 Results
### Validation Results of Fog Detection Algorithm
#### 3.2.2 Quantitative Validation Results
: \(-\)0.18--0.33) between the detection skill and number of fogs was not only weak but also dependent on the time of the day, indicating that the detection skill of GK2A_FDA was clearly dependent on the characteristics of fog, such as fog area and intensity. Figure 11: Validation results for the training (T) and validation cases (V) with ground-observed visibility data according to the time ((**a**) day, (**b**) night, (**c**) dawn/twilight, and (**d**) total). Red, blue, and green lines with dots indicate POD, FAR, and ETS for each fog case, respectively. The bars represent the total number of fog points used for validation. The gray and blue bar graphs are used to distinguish between training and validation cases.