esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Topic 1: Health Status and Needs
#### 3.2.5 Primary Care and Emergency/Trauma Responses  
For oil and gas workers, access to primary care is often required before they can return to their usual healthcare service. Evidence relating to primary care needs of workers in the resource sector is limited, and data primarily relate to utilisation of local health services while on-site. Concerns have been raised that an influx of workers to rural and remote communities could strain already-under-resourced local healthcare services [18]. A study conducted in a remote mining community in Queensland, Australia found that up to 30% of health-service and emergency department presentations were from non-residents, and of those, 50% were living in camps [18]. It was also highlighted that over half of the cases from non-residents were non-urgent and could have been treated by a general practitioner [18]. These data echo concerns raised in an Australian Government report [19], namely, that utilisation of health services by non-residents meant that health staff could not meet the resultant demands. It also aligns with a study of community representatives from rural areas hosting oil and gas workers in the United States, who were concerned about reduced access to healthcare and mental health facilities [20]. While it is unclear whether FIFO workers have insufficient access or they actively choose not to utilise healthcare facilities managed by their employer, strategies need to be in place for companies in the resource sector to offer some primary care to employees or to support local health services in being able to meet the increased demand.  
Although the oil and gas industry is highly regulated in terms of health and safety, strategies must also be in place for dealing with emergency or trauma situations in the workplace. Particularly in a remote setting, there may be lengthy delays until professional care can be provided or evacuation to a higher-order health service can be achieved. These low-probability, high-impact incidents subsequently have considerable consequences [21]. In light of this, it is important to explore healthcare delivery in the resource sector as it relates to both emergency/trauma response and primary care.  
Overall, data relating to the health status and needs of oil and gas workers show that poor health behaviours (e.g., diet, exercise, and excessive alcohol consumption) and the consequences associated with these behaviours (e.g., overweight/obesity), as well as sleep disturbances, are prevalent in oil and gas workers, particularly those on FIFO work patterns. It is also clear that the health-related needs of oil and gas workers relate to appropriate access to both primary healthcare and care in the event of an emergency or trauma situation. Nevertheless, some data suggest that workers may inappropriately access local health services, although the reasons for this remain unclear. Therefore, the subsequent sections of this review will explore available evidence regarding healthcare for oil and gas workers and healthcare models from other industries which may be relevant.
## 3 Results and Discussion
### Influence of Heat Sink Geometry in Flow Distribution
As the vertical configuration has the best flow distribution, an attempt was made to improve it by changing the collector geometry from rectangular to triangular or circular. Figure 15 shows the effect of the shape on the FDU coefficient. The rectangular shape leads to the highest flow uniformity, while the triangular shape leads to the highest flow maldistribution. The flow maldistribution in the triangular case is due to the disturbance of the impinging jet by the collector walls. For small flow rates, the fluid is better distributed at the center. However, the increase in the flow rate leads to a preferential orientation of the flow towards the external channels, as seen in Figure 16. The results obtained in each of the channel sections agree with what was presented in the qualitative analysis (Figure 16). In  
Figure 14: FDU study for the vertical configuration. (**a**) Schematic view of the velocity profile for a 100 mL/min flow and at a scale of 2. (**b**) The standardized flow in each channel of the heat sink. Figure 11.0 Influence of the shape of the collector on the FDU coefficient, S.  
Figure 17, it is possible to verify an alteration in the profile of the flow for a flow rate above 50 mL/min, wherein the accumulation of the flow changes from central to lateral. The flow distribution in the circular collector is very similar to the one observed in the rectangular collector. The impinging jet was also disturbed, but the effect is much smaller than the one obtained with the triangular collector. The results are presented in Figure 11.1  
In order to evaluate the impact of the number of channels on the heat transfer phenomena, a parametric study was performed. As such, the initially considered six-channel configuration was compared to four- and eight-channel configurations. The distance between channels was modified to maintain a uniform distribution over the width of the ABS mold master. Figure 19 presents the FDU coefficient for those cases. Figure 11.2 FDU study for the triangular shape. (**a**) Schematic view of the velocity profile for a 5 mL/min flow and at a scale of 20. (**b**) Schematic view of the speed profile for a 100 mL/min flow and at scale of 2. Figure 11.3 Standardized flow in each channel of the heat sink with triangular-shaped collectors, for different flow rates. Increasing the number of channels improved the flow distribution, as the flow patterns in Figure 20 demonstrate. The increase in the number of channels leads to a more uniform flow in the collector, especially in the channels approaching regions, where the fluid travels through the shortest path to the respective channel. Figure 11.4 Influence of the number of channels of the heat sink on the FDU coefficient. Figure 11.5 FDU study for the circular shape. (**a**) Schematic view of the velocity profile for a 100 mL/min flow and at a scale of 2. (**b**) Standardized flow in each channel of the heat sink.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 2 Methods of Synthesizing Coordinated Polymer-Inorganic Hybrids
### General Synthetic Route for Supramolecular Coordination Complexes (SCCs)  
Supramolecular chemistry, which focuses on non-covalent interactions such as hydrogen bonding, metal coordination, and \(\pi\)-\(\pi\) stacking, plays a significant role in the development of organic-inorganic hybrids (OIHs). The field of SCC-based metallosupramolecular polymers has expanded rapidly in recent years, yielding exciting results and significant progress. These SCCs are characterized by their ability to form complex structures through self-assembly, which, when combined with polymers, has led to the development of two novel types of metallosupramolecular polymers [46]: metallacycle/metallacage-cored star polymers (MSPs) and metallacycle/metallacage-crosslinked polymer networks (MPNs) [47, 48, 49, 50]. More details about supramolecular polymer chemistry are discussed in the last section of this review.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 3 Experimental Results
### Sensor Model Information Generation  
As the sensor-level strip mosaic was completed, the sensor modeling information for the single mosaic strip was generated for the photogrammetric process. A \(7\times 7\times 7\) cubic grid covering the whole mosaic image strips was developed in the ground, and the grid points were projected onto the mosaic strip for the corresponding image coordinates. First, only RPCs of the center strip (Strip #2) were extended to cover the whole mosaic image boundary. Secondly, three RPCs were processed together to generate ground and image coordinate sets for single RPCs generation.  
To check the accuracy of the generated RPCs, we collected 25 GCPs over the mosaic strip from Google Earth, as shown in Figure 12. We used Google Earth Pro to extract the horizontal and vertical coordinates. Though the accuracy of Google Earth may differ depending on the areas, a few meters of positional accuracy was reported over near urban areas in Europe [18]. First, using the 25 GCPs as checkpoints, we estimated the accuracy of the aforementioned two RPCs of the center strip and mosaic strip, as shown in Table 3. RPCs of the center strip showed rather low positional accuracy of 4.02 and 40.07 pixels in RMSE for the line and sample directions, respectively. However, the RPCs of the mosaic showed much better results reported as 2.88 and 21.07 pixels in RMSE for line and sample directions. The accuracy improvement ranged from 18% to 47.4%. The geolocation performance of the resulted mosaic RPCs seemed improved due to the averaging effects of all RPCs of input data. The RPCs of the mosaic should be more accurate than the RPCs of each strip if more image strips are used for the mosaic.  
Figure 11: Sample images showing geometric consistency at a boundary.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Max Errors**} \\ \cline{2-5}  & **Line** & **Sample** & **Line** & **Sample** \\ \hline RPCs of center strip & 4.02 & 40.07 & 6.77 & 45.91 \\ RPCs of Mosaic & 2.88 & 21.07 & 5.51. & 26.78 \\ Accuracy Improvement (\%) & 28.4\% & 47.4\% & 18.6\% & 41.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of mosaic strip RPCs (unit: pixels).  
Next, the bias compensation of the mosaic RPCs was carried out with the GCPs, and the improved accuracy was presented in Table 4. The bias compensation is a process to improve the input sensor modeling using ground controls. The biases are estimated in image coordinates using the rules and compensated for better accuracy [7]. The errors of the mosaic RPCs were compensated for line and sample directions with constant values estimated from the GCPs. Table 4 shows the RPCs' accuracy after the compensation process. The compensated RPCs showed adequate accuracies ranging from 1.4 to 3.3 pixels in RMSE compared to the ones shown in Table 3.
# Monitoring of the Variation in Pore Sizes of Woven Geotextiles with Uniaxial Tensile Strain
## 3 Variation in POAs and Characteristic Pore Size
### Variation in Characteristic Pore Sizes  
The pore-opening size is equal to the square of the pore area obtained by using image analysis. Figure 5 shows the variation in \(\mathrm{O_{n}}\) (with \(n\) ranging from 10 to 95) with uniaxial tensile strain for geotextiles W120\({}^{c}\) and W150\({}^{c}\), where \(\mathrm{O_{n}}\) is the pore-opening size for which \(\mathrm{n\%}\) of the remaining pores are smaller. The differences between variations of \(\mathrm{O_{95}}\), \(\mathrm{O_{80}}\), \(\mathrm{O_{50}}\), \(\mathrm{O_{30}}\), and \(\mathrm{O_{10}}\) with tensile strain can be noted. For example, the largest characteristic pore size \(\mathrm{O_{95}}\) of geotextile W120\({}^{c}\) increases by 15.2% as uniaxial tensile strain increases to 14%. The other characteristic pore sizes \(\mathrm{O_{80}}\), \(\mathrm{O_{50}}\), \(\mathrm{O_{30}}\), and \(\mathrm{O_{10}}\) increase by 17.9%, 21.6%, 31.3%, and 144.5%, respectively.  
For geotextile W150\({}^{c}\), the largest characteristic pore size \(\mathrm{O_{95}}\) increases by 58.7% with uniaxial tensile strain ranging from 0 to 12%. The other characteristic pore sizes \(\mathrm{O_{80}}\), \(\mathrm{O_{50}}\), \(\mathrm{O_{30}}\), and \(\mathrm{O_{10}}\) increase by 75.1%, 92.5%, 140.9%, and 201.9%, respectively. W150\({}^{c}\) indicates relatively higher growth rates of characteristic pore sizes \(\mathrm{O_{95}}\), \(\mathrm{O_{80}}\), \(\mathrm{O_{50}}\), \(\mathrm{O_{30}}\), and \(\mathrm{O_{10}}\) than the growth rates of corresponding pore sizes of the other four tested woven geotextiles. The main reason is that W150\({}^{c}\) has the smallest unstrained pore sizes among five tested woven geotextiles. As a consequence, the characteristic pore sizes of geotextile W150\({}^{c}\) have a more significant growth rate with uniaxial tensile strain.  
Considering the different growth rates of five characteristic pore sizes, the large pore size indicates a lower growth rate of pore size for woven geotextiles W120\({}^{c}\) and W150\({}^{c}\). The medium pore size suggests a higher growth rate of pore size than that of large pore size. The small pore size illustrates the highest growth rate of pore size among three different typical pore sizes. The latter findings corroborate previous pore size results obtained from Figure 3, where the initially small pores indicate more obvious enlargements in size than large pores. The results exhibit less difference among the five tested woven geotextiles. Therefore, uncertainties exist in predicting the distribution frequency of various pore sizes and the variation of equivalent pore sizes with uniaxial tensile strain. For example, the small pore size under low tensile deformation perhaps increases to become the medium or even large pore size as uniaxial tensile strain increases.  
Figure 4: Variation in \(\mathrm{POAs}\) of five tested woven geotextiles with uniaxial tensile strain.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Supervised Method
#### 3.1.1 Siamese Network  
Siamese network can be used to judge the similarity between two samples; the core idea of its loss function is to make the input distance of similar categories as small as possible and the input distance of different categories as large as possible [40].  
Literature [41] proposed a two-stage multi-scale feature similarity measurement model. After using the Siamese network as the backbone architecture to complete the feature extraction of pairwise image, the spatial pyramid pooling network was incorporated into the feature maps of each convolutional module to fuse the multi-scale feature vectors, and then the discriminative feature embedding and similarity metric were obtained by using the contrastive loss during the training process. The test was performed on the PCB data set with 6 types of defects including short circuit, open circuit, mouse bite, burr, leak, and copper, and the area under the ROC curve of all types was above 0.92. Literature [42] proposed a two-layer neural network for cross-category defect detection (SSIM layer: generating the function of analog SSIM components; SNN layer: composed of Siamese network connected to SSIM layer) without re-training. This method learns the differential features from image pairs containing some structural similarities, and it is assumed that different classification objects can share some structural similarities caused by the differences of these learned image pairs. Experiments in the actual factory dataset show that: this method has the ability of cross-class defect detection.
# Feasibility of a Helium Closed-Cycle Gas Turbine for UAV Propulsion
## 4 Discussion
### Component Design Constraints
#### 4.2.3 Precooler  
The challenge with closed-cycle cycle system is the high heat sink requirement. This is an addition to drag and get worse at the climb phase. The design of the heat sink is critical to the success of the helium closed-cycle gas turbine. An involute spiral multi-pass counter-cross flow heat exchanger can be used to achieve efficient cooling. The cold side inlet Mach number and the number of tubes are essential factors to consider when designing the precooler. A longer tube design will result in additional weight, and a smaller tube diameter will perform better thermally. The heat rejection effectiveness improves for the closed-cycle propulsion system with altitude because of increasing temperature differentials.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 3 Earth Observation  
A significant portion of the research on remote sensing through nanosatellites revolves around utilizing photographs captured by PS and Doves from Planet Labs, the sole private nanosatellite constellation that consistently furnishes data. For instance, nanosatellites like PlanetScope have been extensively used for vegetation monitoring, enabling detailed crop mapping and land-cover analysis [12, 13]. Most research papers are related to vegetation, inland waters, oceanography, and natural disasters such as earthquakes, volcano monitoring, and wildfires. Other research articles cover diverse topics, including geology, topography estimation, and carbon emission mapping.
## 3 Application
### Canopy Level
#### 3.2.2 Seasonal Changes  
Only a few studies reported correlations between PRI and \(\Delta\)Fv/Fm' (n = 2), EPS (n = 1) and the chlorophyll/carotenoid ratio (n = 1) at long-term (seasonal to inter-annual) timescales, with \(R^{2}\) between 0.44 and 0.78 (Figure 7a-c). Twenty-four articles, however, linked seasonal or inter-annual changes in PRI with RUE at individual-plant to stand levels. Median \(R^{2}\) (0.79 and 0.65, respectively; Figure 7d) was 7.0% and 4.0% higher for broadleaved and coniferous trees, respectively, than those (\(R^{2}\) = 0.72 and 0.61, respectively) in Figure 5 in [PERSON] et al. [20]. PRI accounted for 66% of the variability of RUE over an entire growing season for a mixed forest [107]. Four articles published after 2010 illustrated that PRI was strongly correlated with changes in net CO\({}_{2}\) uptake for broadleaved (\(R^{2}\) = 0.59 and 0.75) and herbaceous/crop (\(R^{2}\) = 0.54 and 0.81) plants (Figure 7e).  
Figure 6: The mean values of coefficients of determination of the relationships between PRI and several physiological variables at the canopy level and short daily timescale. All the correlations were significant (\(p\) < 0.05) except for one relationship between PRI and carotenoid in _Pinus sylvestris_[117] and one between PRI and steady-state chlorophyll fluorescence (Ft) [73]. Error bars indicate standard error.  
Figure 7: Boxplots of the coefficients of determination of the relationships between PRI and physiological variables at the canopy level and seasonal timescale: **(a)** Actual photochemical efficiency (\(\Delta\)F/Fm’); **(b)** Epixodation state of xanthophylls (EPS); **(c)** Carotenoid/chlorophyll ratio (Car/Chl); **(d)** Radiation-use efficiency (RUE); and **(e)** Net CO\({}_{2}\) uptake. Central lines represent medians, boxes represent 50% of the data, whiskers represent minima and maxima and circles represent outliers. The numbers of correlations reported in the literature are shown in brackets.  
Some studies also analyzed the correlations of PRI with water content (RWC, \(R^{2}\) = 0.64) [150] and stomatal conductance (gS, \(R^{2}\) = 0.46) [125]. One study reported that PRI varied with the difference between foliar and air temperature for maize (\(R^{2}\) = 0.82) [150]. Another study illustrated that PRI obtained from a multi-angular spectroradiometer varied with the change in the fraction of the canopy shadow (\(\alpha_{\mathrm{s}}\)) [4] under constant RUE (0.45 g CMJ\({}^{-1}\)) for Douglas fir (\(R^{2}\) = 0.45) and Aspen (\(R^{2}\) = 0.83).
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 3 Image Feature Extraction Method Based on Invariant Learning  
### Dataset Construction  
This study utilizes the iWildCam 2019 dataset for training and testing the relevant models. The dataset comprises images of coyotes and raccoons captured in the wild by cameras at 143 different locations [28]. Due to differences in landscape, seasons, vegetation, and other environmental factors, the animal images collected at different locations exhibit significant background variations. In the classification task of coyotes and raccoons, these environmental backgrounds may create spurious correlations with the animal species, leading to the lower classification accuracy of traditional ERM models on images taken at other locations.  
Images of animals from two distinct places in the dataset were chosen to serve as the test set and the training set, respectively. Sample images from the dataset are shown in Figure 4, and the composition of the training and test sets is detailed in Table 1. These images vary in lighting conditions, with animals sometimes not being prominent, and the animals' perspectives, areas, and sizes in the images differing. The complex and diverse environmental features in the images pose significant challenges for animal classification tasks. To accurately identify animal species in different environments, this section combines invariant learning theory with the ResNet18 network, proposing an IRM-ResNet18 model-based image feature extraction method, enabling the model to generalize to new and complex environments.  
Figure 4: The image examples of training and test set: (**a**) First training set environment; (**b**) second training set environment; (**c**) test set environment.
# PID Control Assessment Using L-Moment Ratio Diagrams
## 2 Applied methods
### CPA Integral Indexes  
The main integral indexes used for control loop assessment are the integral square error and integral absolute error. They are evaluated using a selected norm for the control error signal, which is the difference between the setpoint (STP) and process variable (PV),often also denoted as a CV--controlled variable. The ISE we evaluate is the mean integral of the squared control errors \(\epsilon(k)\) (norm \(\ell_{2}\))  
\[\text{ISE}=\frac{1}{N}\sum_{k=1}^{N}e^{2}(k). \tag{1}\]  
In practical implementations, we exchange an integral with the summation of control errors collected in sampled time moments \(k=1,\ldots,N\). Integral square measure penalizes those errors, which exhibit large values, neglecting the ones with small values. As large control errors frequently occur as a result of large disturbances or rapid setpoint changes, the ISE favors control aggressiveness and disregards small but continuing imbalances like oscillations. Statistically, the ISE measure is seriously affected by outlying observations and exhibits a 0% breakdown point [31].  
The IAE index sums the errors' absolute values over a given time period, i.e., it uses the \(\ell_{1}\) norm of the control error  
\[\text{IAE}=\frac{1}{N}\sum_{k=1}^{N}|\epsilon(k)|. \tag{2}\]  
This measure is much less conservative than ISE, and it allows penalizing continuing oscillations, which are comparable to large error incidents. Although the breakdown point of IAE also equals 0%, it is shown that this measure exhibits robustness against some kinds of outliers, i.e., the outliers in OX. This is much more important in the case of time series, as those anomalies reflect outlying time observations.