esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we have analyzed the benefit of demand sharing between an over-demanded hospital (HD) and an under-demanded hospital (HS). Both hospitals set their prices to maximize their revenues from serving delay-sensitive patients. We adopted a cooperative game theoretic framework to characterize the situation where the demand sharing is beneficial. We also obtain the commission fee, which guarantees that the medical alliance is stable.  
We find that collaboration is not always beneficial even if HS has idle capacity. Demand sharing becomes beneficial for both HD and HS only when the service rate of HS is larger than the first threshold (lower one). This threshold depends on the HS's rate of patient flow and the patients' unit waiting cost.  
Furthermore, if the service rate of HS is less than the second threshold (higher one), which depends on the rate of patient flow of both markets and the HD's capacity, then the total revenue gain from demand sharing would be independent of the service rate and potential demand of the HD.  
We also showed the effects of the two hospitals' parameters on the revenue gains from demand sharing. Results show that the medical alliance can be more beneficial with less delay sensitive (or more patient) patients in the moderate service rate case (the HS's service rate is less than the second threshold). However, if the HS's service rate exceeds the second (higher) threshold, then the revenue gains may not be necessarily monotonic with respect to the unit waiting costs of both regions. These insights can help service hospitals to form and manage medical alliances.  
Our study assumes that patients of each region are homogeneous. However, it would also be interesting to consider the heterogeneity of the patients in each market.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### The U-Net Convolutional Neural Network  
In this study, we aimed to classify every pixel in the image as banana or non-banana plantation through semantic segmentation using a convolutional neural network. The structure of the CNN follows the U-Net architecture [33] and is shown in Figure 4. It consists of two parts: (i) An encoding stage that downsamples the resolution of the input images; and, (ii) a decoding stage that upsamples and restores the images back to the original resolution. At each level of the encoding stages, two \(3\times 3\) convolution operations are applied using the rectified linear unit (ReLU) activation and a \(2\times 2\) max pooling operation to downsample the input images. The first level consists of the original satellite image and mask chips (256 pixels in width and height) where \(64\times 3\) filters are applied to each chip. At each subsequent level of the encoding side of the U-Net, the number of filters is doubled, doubling the number of bands of the images and the resolution halved until the bottom level where 1024 filters are applied to images \(16\times 16\) pixels in size.  
The decoding stage also uses two \(3\times 3\) convolution operations but upsamples the data and concatenates the corresponding information in the encoding stage to double the resolution of the images, eventually restoring the original resolution of the input images in the final level. The final step is to conduct a \(1\times 1\) convolution using a sigmoid activation to produce a single band output probability classification with values ranging from 0 to 1. Values closer to 1 are more likely to be banana plantations. Using this configuration of the U-Net allows for the training of 31.4 million parameters overall.  
Figure 3: The training–inference–refinement iteration loop.
## 3 Analysis and Results  
### Effect of Operating Conditions on Sealing Performance  
As can be seen from Figure 5, the maximum stress occurs at the position where the wear ring comes into contact with the chamfer of the cylinder wall, at which time the wear ring is sheared. The maximum stress on the combined seal ring is on the seal ring and is proportional to the working pressure. When the working pressure reaches 25 MPa, the maximum stress reaches 49.836 MPa, but this value is still much less than the allowable stress of the material, which is 280 MPa.  
Figure 4: Combination seal ring convergence analysis.
###### Abstract  
Capacitive deionization (CDI) is a promising desalination technology because of its simple, high energy efficient, and eco-friendly process. Among several factors that can affect the desalination capacitance of CDI, wettability of the electrode is considered one of the important parameters. However, various carbon materials commonly have a hydrophobic behavior that disturbs the ion transfer between the bulk solution and the surface of the electrode. In this study, we fabricated a layered double hydroxide (LDH) coated activated carbon electrode using an in-situ growth method to enhance the wettability of the surface of the carbon electrode. The well-oriented and porous LDH layer resulted in a better wettability of the activated carbon electrode, attributing to an enhanced capacitance compared with that of the uncoated activated carbon electrode. Furthermore, from the desalination tests of the CDI system, the LDH coated carbon electrode showed a higher salt adsorption capacity (13.9 mg/g) than the uncoated carbon electrode (11.7 mg/g). Thus, this enhanced desalination performance suggests that the improvement in the wettability of the carbon electrode by the LDH coating provides facile ion transfer between the electrode and electrolyte.  
capative deionization; layered double hydroxide; desalination; activated carbon; wettability +
Footnote †: journal: Applied Physics Letters B
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Verification on Composition of Data  
In order to test the actual effect of the proposed method, we use our calibration result to reconstruct 3D scenes. The 2D image of camera is superimposed to 3D point cloud of flash LiDAR, and the result is shown in Figure 12. As one can see in Figure 12c, the image of the camera and point cloud of LiDAR match quite well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Calibration Method** & **Intrinsic Parameters of Camera** & **Extrinsic Parameters** & **Reprojection Error/(_Pixel_)** \\ \hline Proposed method & ✓ & ✓ & 0.390 \\ [PERSON] et al. method [15] & ✗ & ✓ & 1.392 \\ [PERSON] et al. method [32] & ✓ & ✓ & 2.078 \\ [PERSON] et al. method [28] & ✗ & ✓ & 2.802 \\ [PERSON] et al. method [33] & ✓ & ✓ & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different calibration methods.  
Figure 12: Composition of 2D image and 3D point cloud: (**a**) 2D image captured by camera; (**b**) 3D point cloud captured by flash LiDAR; and (**c**) result of superimposing 2D image to 3D point cloud using calibration parameters through the proposed method.
## 3 Results and Discussion
### Physical Property Evaluation  
The slurry that was poured into the silicone frame via quick binding of CAC completely fell off the frame after 30 min. Subsequently, the uniaxial compressive strength was measured at 1 h intervals to compare the curing rates (Figure 4a). To accurately evaluate the compressive strength, the average value obtained for 20 cube-shaped molded bodies was used for each evaluation.  
Figure 3: (**a**) Droplet absorption time and (**b**) avalanche angle by calcium aluminate cement (CAC) content.  
As shown in Figure 4a, the S + C sample is cured after 4 h and its strength is 6.02 MPa after 4 h. In contrast, the M + C and A + C samples are not completely cured after 4 h. The highest strength of the additive-manufactured commercial holds using the same inorganic binder family, a silicate binder, was 2.5-4 MPa [10,21]. In every sample, the handling strength of the mold was satisfied at strengths of \(\geq\)4 MPa after 4 h of molding [7,15]. Therefore, a mold that can be treated with a curing time of 4 h (in powder bed 3D printing) is obtainable for application in rapid molding.  
As can be seen in Figure 4b, all samples are hydrated even after 6 h of air drying and show slightly higher strengths. All samples show a sharp decrease in strength at 400 \({}^{\circ}\)C. Up to their lowest strengths, the S + C, M + C, and A + C samples show a reduction in strength of 88.9% (6.43 MPa to 0.71 MPa), 37.7% (14.49 MPa to 8.31 MPa), and 35.6% (12.23 MPa to 7.23 MPa), respectively. This affects the characteristics of CAC owing to the different sand types; CAC cement showed the same trend as a result of strength reduction in the temperature range 600-1200 \({}^{\circ}\)C [16,22,23,24]. In comparison, the strength of silica significantly decreased at 600 \({}^{\circ}\)C, and the strength recovery was slow at higher heating temperatures [22]. On the other hand, alumina rapidly recovered its strength after the lowest strength at 800 \({}^{\circ}\)C, whereas mullite showed a decrease in strength after the recovery at 1000 \({}^{\circ}\)C. This is a result of the change in pores due to sintering [25,26,27].  
Figure 4: (**a**) Uniaxial pressing strength by time taken for simple molding, (**b**) Compressive pressing strength by heat treatment temperature.
## 2 Related Work
### Anchor-Free One-Stage Detection  
The one-stage object detector of anchor-free, which abandoned a large number of anchor boxes with preset sizes and aspect ratios, directly predicted the object category and generated bounding boxes on the feature maps. DenseBox [14] uses Fully Convolutional Network (FCN) to predict the bounding boxes and confidence scores, which belongs to dense prediction, and uses FCN structure to realize landmark localization. YOLO [15] divides the image into several grids and predicts on each grid. Through the full connection layer after the last feature map, YOLO directly obtains the confidence scores of categories and the object positions. CornerNet [16] converts object detection into keypoint detection, which inputs heatmap into two brunches with Corner Pooling to obtain Top-Left Corners and Bottom-Right Corners respectively. Then, the location of the object bounding box is obtained by the embedding vectors of a pairs of corners. Grid R-CNN [17] detects (3 \(\times\) 3) 9 keypoints of the object on the feature map to get the boundary of the object. FCOS [18] outputs the category of each point, the coordinates of \((I,t,r,b)\) four points, and center-ness through three branches of the network. The center-ness proposed in this paper can effectively filter some boxes with high scores and low IoU. Centernet [19] based on Cornernet detects the center point in addition to the two corners of the object, which further improves the false detection rate.  
In the field of pedestrian detection, the advanced CSP [20], a full FCN structure, which fuses features directly with concatenating feature maps for prediction, achieved excellent results as well as state-of-the-art.
## 4 Case Study  
As of December 2023, the Shanghai rail transit system had a total of 20 operating lines and 508 stations, with a total operating mileage of 831 km and a maximum daily passenger volume of 13.294 million. Since January 2020, to better formulate a maintenance strategy for the platform screen door systems, operators have begun to monitor faults in electromechanical equipment, such as station screen doors, through a command center in combination with manual inspection to better understand and prevent faults in the platform screen door systems.  
Operators and managers seek to gain a precise understanding of the prospective fault trends associated with PSD systems. The application and verification processes employed in this study are illustrated in Figure 7. The processes encompassed three primary components: data preprocessing, data augmentation, and the prediction of fault rates.  
Figure 6: The operating mechanism of the SSA-CNN-LSTM model.
## 5 Discussion and Future Research Directions
### Advancements in Machine Learning for Water Contaminants and Soil Erosion  
The topic of water contaminants and soil erosion presents promising avenues for future research, with machine learning acting as a catalyst for advancements in this field. A focal point of such research endeavors is predicting groundwater contamination using machine learning, calling for the development of a unified framework. This framework should merge multiple models for improved accuracy, leverage cutting-edge feature selection techniques to identify pivotal factors and evaluate the impact of climate change on contamination distribution.  
Moreover, another integral aspect is the integration of remote sensing, and hyperspectral imagery data, aimed at enhancing monitoring capabilities. Developing robust uncertainty assessment methods is equally vital, as well as examining the applicability and transferability of models across diverse regions and contaminant types. An area of critical importance is assessing how effectively these predictive models inform policy-making,  
Figure 15: Mind map grouping the main challenges to address in the different identified topics.  
conservation efforts, and sustainable management plans. An additional line of exploration lies in harnessing sensor data and IoT technologies to enable real-time monitoring and prediction, thus fostering proactive water management.  
One innovative approach lies in employing reinforcement learning--a type of machine learning where an agent learns to make decisions by interacting with its environment, earning rewards or penalties. This technique could lead to optimal policies for managing water resources and minimizing soil erosion under a wide array of conditions. The resultant policies could then guide decisions about water use, irrigation practices, land use planning, among other factors influencing soil erosion and water contamination.  
Further, understanding and predicting the socio-economic impacts of water contamination and soil erosion using machine learning should also be considered. These impacts inform policy decisions and help prioritize mitigation efforts, which could involve creating models that predict the impact of different contamination and erosion levels on agricultural productivity, public health, and economic activity.  
Lastly, the potential of using machine learning algorithms to automate real-time detection of water contaminants, directly from sensor data, is another promising research direction. This entails using machine learning techniques to process and interpret data from sensors that detect contaminants in water, ultimately enabling faster and more accurate responses to contamination events.
# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 2 Enf Extraction and Database Formation Based on Location Specific Recordings
### Separation of Power and Audio Recordings  
The sequential approaches to separate the digital recordings as power and audio signals are described as follows:  
**Step-1:** Initially a raw power or audio recording is segmented into a number of time frames. The window length of each time frame is empirically taken as 5 min. The dominant or center frequency \(F_{c}\) of each signal component is determined using short time Fourier Transform (STFT).  
**Step-2:** Then, the signal to noise ratios (SNRs) are evaluated considering [\(F_{c}-F_{b}\), \(F_{c}+F_{b}\)] as the power band of each signal. Here \(F_{b}=0.5\) Hz is the step size of variation from the base frequency. Except this one, other bands are considered as noisy segments. The band power values are estimated using Welch power spectrum method. From the obtained SNR values, it is decided whether the recording is a power signal or an audio one. The underlying consideration is that at the nominal frequency or harmonics, the SNR values of power signals are greater than those of the audio signals.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 3 Results and Discussion
### Automatic Slide Sequential Scanning  
In our microscope, a sequential scanning process was implemented following a row-by-row path--to standardize backlash effects--depending on the FOVs' array size (i.e., X rows and Y columns) adjusted by the user (see Figure 9b). Moreover, this task implies several image processing steps such as: denoising, background correction, contrast enhancement, autofocus, focal stacking, and multifocus fusion.  
In addition to sequential scanning, a mode of random scanning was included to allow aleatory subsampling of slides in order to reduce scanning times, especially if a uniform distribution of diatoms in the sample may be assumed. In random scanning, a certain number of cells' FOVs are selected aleatory from the array that cover the inspected region on the slide. Random scanning is selected by selecting the random fields button on the GUI control panel (see Figure 9b).  
Table 3 summarizes the time--in seconds--for image acquisition (Acq. time), image processing (Proc. time), and the total time (Acq. time + Proc. time) obtained with different scanning sizes (i.e., FOV array size). Note that the image resolution is obtained by stitching individual patches of 1280 \(\times\) 1024 pixels--with overlap among adjacent patches.  
Figure 7: Effect on a microscopy image captured (at 60\(\times\), res. 1280 \(\times\) 1024 pix.) with different filters masks: (**a**) circular, (**b**) concentric, and (**c**) eccentric.  
For the sequential scanning, the whole scanned area ranged from 0.17 mm\({}^{2}\) (for a 3 \(\times\) 3 FOV array size) to 2.07 mm\({}^{2}\) (for a 10 \(\times\) 10 FOV array size) with the respective total \(w\times h\) resolution from 3660 \(\times\) 2928 to 12,800 \(\times\) 10,240 pixels and total time of operation (i.e., acquisition image plus image processing) from around 15 s to less than 3 min.  
The acquisition time is strongly dependent on the speed of the stepper motors and the time to settle at each stop position to avoid blurry images. In order to reduce the required time, it is possible to investigate future improvements for the stepper motors' control strategy to achieve: (**A**) higher speeds without losing steps and shorter stopping times and (**B**) simultaneous X-Y motion to follow the optimum paths to mitigate the backlash effects.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 2 Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
#### 2.1.2 Calculate Gradient Magnitude and Direction of Gradient  
The edge of the image is the part where the brightness changes significantly in the local area of the image, and the modulus maximum point of the gradient corresponds to  
Figure 2: NLM denoising process diagram.  
the edge of the image. Let \(\theta(x,y)\) denote a two-dimensional Gaussian smooth function, with a scale factor \(s=2^{j}\), and introduce:  
\[\theta_{s}(x,y)=\frac{1}{s^{2}}\theta(\frac{x}{s},\frac{y}{s}), \tag{5}\]  
Compute the partial derivatives of \(\theta_{s}(x,y)\) along the \(x\) and \(y\) directions as a wavelet function:  
\[\phi_{s}^{1}(x,y)=\frac{\partial\theta_{s}(x,y)}{\partial x}, \tag{6}\]  
\[\phi_{s}^{2}(x,y)=\frac{\partial\theta_{s}(x,y)}{\partial y}, \tag{7}\]  
Convolve Equations (6) and (7) on the denoised image \(f\) to obtain the two wavelet components of the image in the \(x\) and \(y\) directions:  
\[W_{s}^{1}(x,y)=f*\phi_{s}^{1}(x,y), \tag{8}\]  
\[W_{s}^{2}(x,y)=f*\phi_{s}^{2}(x,y), \tag{9}\]  
Then the magnitude of the gradient is:  
\[M_{s}(x,y)=\sqrt{\left|W_{s}^{1}(x,y)\right|^{2}+\left|W_{s}^{2}(x,y)\right|^ {2}}, \tag{10}\]  
The direction of the gradient is:  
\[A_{s}(x,y)=\arctan\left[\frac{W_{s}^{2}(x,y)}{W_{s}^{1}(x,y)}\right]. \tag{11}\]
# Allosteric Activation of GDP-Bound Ras Isoforms by Bisphenol Derivative Plasticisers
## 4 Materials and Methods
### Low Molecular Weight Compounds and Solvents  
The following compounds were purchased from Sigma-Aldrich (St. Louis, MO, USA):  
* AFX (4-(Trifluoromethyl)phenol, CAS 402-45-9),
* Bisphenol A (BPA, 2,2-Bis(4-hydroxyphenyl)propane, CAS 80-05-7),
* Bisphenol AF (BPAF, 2,2-Bis(4-hydroxyphenyl)hexafluoropropane, CAS 1478-61-1),
* Bisphenol AP (BPAP, 4,4\({}^{\prime}\)-(1-Phenylethylidene)bisphenol, CAS 571-75-1),
* Bisphenol BP (BPBP, Bis(4-hydroxyphenyl)diphenylmethane, CAS 1844-01-5),
* Bisphenol C (BPC, 2,2-Bis(4-hydroxy-3-methylphenyl)propane, CAS 79-97-0),
* Bisphenol FL (BPFL, 4,4\({}^{\prime}\)-(9-Fluorenylidene)diphenol, CAS 3236-71-3),
* Bisphenol M (BPM, 4,4\({}^{\prime}\)-(1,3-Phenylenediisopropylidene)bisphenol, CAS 13595-25-0),
* Bisphenol NH2 (BPNH\({}_{2}\), 2,2-Bis(3-amino-4-hydroxyphenyl)hexafluoropropane, CAS 83558-87-6),
* Bisphenol P (BPP, 4,4\({}^{\prime}\)-(1,4-Phenylenediisopropylidene)bisphenol, CAS 2167-51-3),
* Bisphenol S (BPS, 4,4\({}^{\prime}\)-Sulfonyldiphenol, CAS 80-09-1),
* Bisphenol Z (BPZ, 4,4\({}^{\prime}\)-Cyclohexylidenebispenol, CAS 843-55-0)
* Deuterated solvents for NMR measurements were obtained from Deutero GmbH (Kastellaun, Germany) and the following compounds were purchased from TCI Germany (Eschborn, Germany):
* Bisphenol B (BPB, 2,2-Bis(4-hydroxyphenyl)butane, CAS 77-40-7),
* Bisphenol E (BPE, 1,1-Bis(4-hydroxyphenyl)ethane, CAS 2081 08 5),
* Bisphenol F (BPF, 4,4\({}^{\prime}\)-Methylenediphenol, CAS 620-92-8)
* 4-(Trifluoromethyl)phenol (AFX) was used as a control compound that bears a single phenolic ring system. Although AFX exhibits a rather low affinity towards K-Ras4B, it causes chemical shift perturbations for L6, I55, L56, I74, and G75, similar to bisphenols BPA and BPS. We designate these amino acids as \"first-ring\"-residues as they cluster on the surface of K-Ras4B and constitute a defined ligand binding site for bisphenols (Figure 1).
## 4 Discussion
### Varying Performances of the Eight Satellite-Based GPP Models in the Temperate Steppe
The GPP reduction caused by drought might have been slightly underestimated at the XL site in this study due to ignorance of the possible vegetation recoveries after grazing exclusion [(115)] (Table 2). It should be noted that although the current water stress indicators, such as EVI, cannot fully capture the GPP responses to drought, they can still be used to monitor the drought effects, especially over large areas where no other information is available [(116)]. Meanwhile, there is room for future studies to improve the water stress indicators of GPP in the area with extensive data coverage.