esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Strategy
### Complexity Analysis  
Note that the closed-form expression of the optimal sensing threshold for the N-out-of-K rule-based CSS scheme under the HCRN situations is unavailable according to the discussion in [24]. The optimal sensing threshold at the FC of the classical N-out-of-K rule-based CSS scheme under the HCRN situation is obtained by iteration and comparison.  
According to Equations (6) and (7), the required multiplication operation is \(O(N^{6})\) while the required addition operation is \(O(N^{4})\) for the mass function probability at the FC on \(D_{i}\). To obtain the optimal sensing threshold for Equations (8) and (9), \(N\) iterations are required. In consequence, the required multiplication operation is \(O(N^{7})\) and the required addition operation is \(O(N^{5})\) for the classical N-out-of-K rule-based CSS scheme.  
For the proposed scheme, Equations (8) and (9) are respectively approximated to the Poisson distribution with the parameter \(\lambda_{F}\) and \(\lambda_{D}\). As a result, the required operation for the proposed scheme is \(O(N)\), which is greatly reduced compared with the classical N-out-of-K rule-based CSS scheme.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 2 Related Work
### DL Methods for DME Detection  
In the literature, various DL-based approaches have been proposed for detecting and grading DME in digital fundus images. In [12], a deep learning-based approach was proposed for grading DME. A deep convolutional neural network (DCNN) was trained on a large dataset of digital fundus images. The DCNN was trained to distinguish between normal, mild, moderate, and severe DME using supervised learning approach, which means that the network was trained to make predictions based on the labeled examples in the training dataset. The training process involved iteratively adjusting the weights of the network to minimize the prediction error on the training set, until the error converged to a minimum. The results showed that the DCNN was able to achieve an accuracy of 90.6% in grading DME.  
The study [13] presents an end-to-end deep learning approach for the detection and grading of DME in digital fundus images. The authors used a convolutional neural network (CNN) to analyze the fundus images and classify them into different stages of DME, ranging from normal to severe. The performance of the system was evaluated on a large dataset of digital fundus images and results showed that the proposed approach achieved an accuracy of 91.5% in detecting and grading DME.  
The study by [PERSON] et al. [14] presents a deep transfer learning-based approach for the detection of DME in digital fundus images. The authors used a pre-trained CNN, which was fine-tuned on a dataset of fundus images with DME. The results of the study showed that the proposed approach was accurate in detecting DME, with an accuracy of 95.7%. This study highlights the potential of transfer learning in medical image analysis, especially in the detection of DME.  
These studies demonstrate the effectiveness of deep learning-based approaches for detecting and grading DME in digital fundus images. However, further research is needed to improve the accuracy and robustness of these methods.
## 3 Methodological Developments and Application to the Greater Geneva Area
### Methodological Improvement on Shadow Casting Algorithms
Finally, we implemented an optional \"masking\" feature that allows to selectively apply the processing to only part of the tile, while still using the excluded areas for determining obstructions. There are many cases for which a mask can be useful, for instance to exclude the padding border of 200 m around each side, to exclude lakes, rivers, forests, fields, etc. from the solar potential analysis, or even for only computing the solar potential on building rooftops.
# Fusing Appearance and Prior Cues for Road Detection
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.2.4 Quantitative Evaluation  
In this section, quantitative evaluations of our proposed algorithm for images in the testing set are provided using five classical metrics known as precision (PRE), recall (REC), maximum F1-measure (MaxF), false positive rate (FPR), and false negative rate (FNR). Where the metric PRE measures what percentage of the road detection results are truly road, the metric REC reflects how much of the overall road region was detected, the metric MaxF gives a tradeoff between PRE and REC, the metric FPR measures how much of the non-road region was improperly detected as road region, and the metric FNR measures how much of the road region was improperly detected as non-road region. The metrics are calculated as follows:  
\[PRE=\frac{TP}{TP+FP}, \tag{16}\]  
\[REC=\frac{TP}{TP+FN}, \tag{17}\]  
\[F1-measure=\frac{PRE\times REC}{(1-\alpha)\times PRE+\alpha\times REC},\text{ where }\alpha=0.5, \tag{18}\]  
Figure 8: Sample visual comparison on the KITTI testing set.  
\[FPR = \frac{FP}{FP+TN}, \tag{19}\] \[FNR = \frac{FN}{TP+FN}. \tag{20}\]  
Note that the metrics are computed after the transformation from an image domain to bird's-eye-view (BEV) space according to the evaluation system of the KITTI road benchmark.  
Results of the quantitative evaluation of the proposed algorithm are reported together with the results of the other state-of-the-art algorithms mentioned in Section 3.2.3. Specifically, the comparison of UMM_ROAD, UM_ROAD, and UU_ROAD is shown in Table 1, Table 2, and Table 3 respectively. In addition, a category named URBAN_ROAD which provides the overall performance for all three categories is calculated and the comparison result is shown in Table 4.
# Average Slope vs. Cmax: Which Truly Reflects the Drug-Absorption Rate?
###### Abstract  
Despite ongoing concerns, the primary metric utilized in bioequivalence studies to quantify absorption rate remains the maximum plasma concentration (Cmax). To more accurately depict absorption rate, the concept of \"average slope\" (AS) has been recently introduced. The objective of this study is to elucidate and compare the characteristics of AS and Cmax in their representation of the drug-absorption rate. For this purpose, an investigation was conducted on five drugs (nintedanib, methylphenidate, nitrofurantoin, Isidexamfetamine, and theophylline) with different absorption and disposition kinetics. The properties of AS and Cmax, as well as their correlations with other pharmacokinetic parameters, were assessed using supervised and unsupervised machine-learning algorithms, namely principal component analysis, random forest, hierarchical cluster analysis, and artificial neural networks. This study showed that, regardless of the absorption kinetics and across every ML algorithm, AS was more sensitive in reflecting the absorption rate compared to Cmax. In all drugs and methods of analysis, AS demonstrated significantly superior performance in expressing the absorption rate compared to Cmax. The joint use of different techniques complemented each other and verified the findings. Moreover, AS can be easily calculated and has the appropriate units and properties to be used as a metric to express the absorption rate in bioequivalence studies. The adoption of AS by regulatory authorities, as an absorption-rate metric, could significantly improve the accuracy and reliability of BE assessments. Overall, this study focused on addressing the longstanding problem of finding an appropriate absorption-rate metric by demonstrating the desirable properties of AS.  
average slope; absorption rate; bioequivalence; machine learning Article
## 5 Materials and Methods
### Cost-Benefit Ratio  
The benefit of using IoT sensors to monitor water quality and distribution allows data to be collected remotely and in real time, which facilitates the early detection of problems in the network where they were installed [15]. In addition, IoT sensors are low cost and can make monitoring more accessible in situations where the project does not have a large monetary backing. However, the accuracy and reliability of IoT sensors lie in their cost compared to professional equipment to ensure the data are collected. Its easy access to long-range Wi-Fi connectivity and Bluetooth highlights the ability to visualize data in online databases and suggest action protocols to users in case of any anomaly, as well as the possibility of dynamic monitoring modules [3,10].  
The cost-benefit of using IoT sensors in fog catchers will depend on several factors, such as the scale of the project, specific goals, and available resources. However, we can consider aspects such as accurate monitoring of variables, e.g., wind speed, temperature, humidity, etc. We can also consider operational efficiency by leveraging real-time information provided by the sensors to optimize fog catcher operation. In addition, it will help save resources by adjusting the operation based on real-time data, which facilitates research [1,10].  
Fog collection technology is a low-investment technique that dispenses with electrical power and has significantly lower operating costs compared to conventional water supply systems. Unlike the latter, which requires a large initial investment and generates ongoing expenses for fuel, spare parts, and maintenance, fog water harvesting is relatively cost-effective in areas where it is feasible. As a result, it is possible to supply the needed quantities of water to beneficiary communities efficiently and at a lower cost [10].
## 4 Results and Discussion
### The AE Signal Analysis  
The signal analysis was conducted using the NI Diadem 2023 software developed by National Instruments. Figure 6 displays the analyzed AE signal at both the cutting head and the CFRP workpiece.  
The AE signal is a suitable solution for AWJC process monitoring as it provides accurate and reliable information about the changes occurring during the process.  
Analyzing the AE signal acquired at the cutting head, one can clearly observe the moment the water jet starts (0.12 s), the moment when the abrasive grains are added to the WJ (0.16 s), and the moment when the AWJ stops (0.2 s). A consistent difference between the \(AE_{RMS}\) of 0.7 V for the WJ and 2.63 V measured in the case of the AWJ provides an exact moment when the abrasive is added to the WJ.  
Through investigating the AE signal acquired at the CFRP workpiece, one can see the noise introduced by the AWJ before starting to cut (0.16-0.18 s), the moment when the AWJ starts cutting (0.18 s), and when the cutting finishes (0.2 s).  
Through analyzing the noise introduced by the AWJ before starting the cutting (0.16-0.18 s), when the jet is positioned outside the workpiece, it can be concluded that the \(AE_{RMS}\) amplitude 0.08 V does not have an influence on the signal generated during the cutting \(AE_{RMS}\) 0.75 V.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \  
To extract additional insights from the signal, a frequency domain analysis was conducted. The _Fast Fourier Transform (FFT)_ was employed to obtain the frequency domain, and the Hanning method was utilized to estimate the _Power Spectral Density (PSD)_ of the AE signal.  
The analysis of the frequency domain of the AE signal measured during the experiments revealed frequencies between 50 and 600 kHz at the cutting head (Figure 7a) and 20-400 kHz at the workpiece (Figure 7b).  
Figure 6: The main phases of the AE signal obtained during the experiment.  
Figure 7: The frequency domain of the AE signal analyzed during the experiment (trial no. 3, \(P\) = 350 MPa, \(V\) = 2275 mm/min, \(Ma\) = 0.35 kg/min, and \(T\) = 3 mm): (**a**) the PSD of the AE signal measured at the cutting head, (**b**) the _PSD_ of the AE signal measured at the CFRP workpiece.
## 1 Introduction  
Researchers have always been concerned with risks on busy waterways. From the perspective of the shippers, a vessel collision with a bridge or berth may cause property loss and casualties. The enormous energy of a ship collision can cause great damage to a bridge and even bridge collapse. Those huge potential losses have brought together engineers of transportation and infrastructure systems to conduct risk assessments during the design process for waterway structures. They also take measures to reduce the risk of accidents in operations.  
Normally, the two dimensions for a risk analysis are probability and consequence. Models were developed for calculating ship collision probabilities and predicting consequences [1, 2, 3, 4, 5]. Probabilistic research of ship collisions with structures is a part of the research on risk assessment and the design of structures (such as a bridge span or a bridge pier) [6]. However, the predictions are not always satisfactory due to various uncertainties during validation [7, 8]. Therefore, a better probabilistic prediction model is still needed.  
This paper aims to review the existing probabilistic risk models for ship collisions with structures. The advantages and disadvantages of each model are discussed, which might lead to a better method for future model development. Consequently, improved designs and measurements can be applied for both the infrastructure and waterway structure to mitigate the risks.  
This paper is organized as follows. Section 2 introduces the method of this review. Section 3 introduces statistical models, which are primitive and used to tentatively derive a probability. Section 4 presents statistical models that have been widely used in calculating the probability of ship collision with bridges, and compares the elements considered in each statistical model. Section 5 presents different waterborne traffic simulation models and highlights their advantages and limitations. The paper is summarized in Section 6. The contents of this review have been published in a Ph.D. thesis [9]. However, the authors decided to modify the work and publish it in this journal to gain broader attention.
## 4 Robustness of the CATN  
This study investigated four distinct time-stamped versions of the CATN, corresponding to 6:00, 12:00, 18:00, and 24:00. In order to examine the centrality and robustness of the aviation network during these specific time intervals, all flight information from these four time periods in 2018 was combined.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.5 Path Selection Constraints  
In multi-path transmission, the path selection for each task must satisfy certain constraints to ensure efficient data transmission across the network. For each task \(j\), the selected transmission path \(P_{j}\) must adhere to the following constraint:  
\[P_{j}\in\mathcal{P},\quad\forall j=1,2,\ldots,m \tag{7}\]  
where \(\mathcal{P}\) is the set of available paths, and \(P_{j}\) is the transmission path for task \(j\). This constraint ensures that each task selects a valid transmission path, avoiding inconsistencies in path selection and facilitating path optimization and load balancing.
## 3 Results and Discussion
### Investigation of 3D-rGOPFH
#### 3.1.3 Comparison of the Extraction Efficiency of 3D-rGOPFH with Different Sorbents  
For evaluating the extraction ability of the 3D-rGOPFH and elucidating its extraction compatibility for insecticides, four different types of magnetic sorbents, namely Fe\({}_{3}\)O\({}_{4}\), 2D-G-Fe\({}_{3}\)O\({}_{4}\), PNT/Fe\({}_{3}\)O\({}_{4}\), and 3D-G-Fe\({}_{3}\)O\({}_{4}\), were used and compared to extract thirteen insecticides in the same extraction conditions. Figure 3 shows that all materials possess the ability to extract insecticides, while 3D-rGOPFH performs the best. Compared to Fe\({}_{3}\)O\({}_{4}\), the extraction recoveries were improved by using 2D-G-Fe\({}_{3}\)O\({}_{4}\) and PNT/Fe\({}_{3}\)O\({}_{4}\), suggesting the incorporated rGO sheets or PNT possessed higher BET and provided more interaction forces with analytes [34]. When the structure of 2D-G-Fe\({}_{3}\)O\({}_{4}\) was transformed from 2D to 3D, the extraction recoveries were further improved by using 3D-G-Fe\({}_{3}\)O\({}_{4}\). Although the recovery was significantly improved, 3D-G-Fe\({}_{3}\)O\({}_{4}\) showed distinct differences in extraction recovery for different types of insecticides. For triazoles, pyrethroids, and OCPs, the recoveries of 70-85% were satisfactory. However, the recoveries corresponding to some OPPs and carbamates were obviously lower (40-70%). In contrast, the recoveries of 3D-rGOPFH for all thirteen insecticides were between 65-90%. Compared to 3D-G-Fe\({}_{3}\)O\({}_{4}\), the improvement ratios of recovery for 3D-rGOPFH were 21-73% for four OPPs (quinalphos, phorate, chlorpyrifos, and phosalone) and two carbamates (pirimor and carbaryl) insecticides, which were distinctly higher than that (8-24%) for two triazoles (myclobutanil and diniconazole), two pyrethroids (bifenthrin and lambda-cyhalothrin), and one OCPs (2,4\({}^{\prime}\)-DDT). One-way ANOVA analysis was performed to test the significance between 3D-rGOPFH and 3D-G-Fe\({}_{3}\)O\({}_{4}\). For all insecticides, except isocarbophos and mirex, the extraction recovery was statistically significant (\(p<0.05\)). Triazoles, pyrethroids, and OCPs mainly contained aromatic ring group; \(\pi\)-\(\pi\) stacking interaction and hydrophobic force were the main interaction forces between sorbents and analytes in the extraction process [32]. In contrast, most of the OPPs and carbamates insecticides contained more polar (phosphate and amine) groups, and multiple interaction forces may be involved between sorbents and analytes, such as hydrogen bonding, electrostatic force, and \(\pi\)-\(\pi\) stacking interaction. After introducing PNT into graphene, new driving forces emerged from hydrogen bonding and electrostatic interaction, and thus the changed surface property/polarity of magnetic graphene assisted the adsorption process. Because of the 3D architecture and multiple adsorption forces, 3D-rGOPFH exhibited adequate superiority in extraction efficiency and compatibility for different types of insecticides.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 1 Introduction
Different from GPS/GLONASS/GALILEO satellites, two types of attitude modes were adopted for BDS-2 satellites: yaw-steering mode and orbit normal mode. Al GEO (Geostationary Orbits) satellites will always use the orbit normal mode while the IGSO (Inclined Geosynchronous Orbit) and MEO (Medium Earth Orbit) satellites will experience the switch of attitude mode when the sun angle is below the threshold of about 4\({}^{\circ}\)[11;12]. Compared with BDS-2 satellites, the BDS-3 experimental satellites (5 IGSO/MEO satellites) did not use orbit normal mode anymore and adopted another threshold of about 3\({}^{\circ}\) for noon and midnight maneuver [13]. Since few stations can track the signals of BDS-3 satellites, only BDS-2 satellites are considered in the following experiment and analysis. However, the previous studies mainly focus on the POD and the establishment of the attitude control model. At the same time, PPP has been widely used for scientific research and civilian applications [14; 15; 16; 17]. To obtain high precise and reliable positioning performance, the impact of eclipsing satellites on PPP should also be understood. The positioning errors resulting from different GPS satellite types during eclipsing periods have been analyzed [18]. The results showed that the positioning accuracy could be improved by 20-30% with the modeled attitude, especially when more GPS IIA satellites were in eclipse. However, the GPS IIA satellites had been removed from GPS constellation in early 2016 [19]. Therefore, the new analysis for current GPS constellation should be also necessary. Without doubt, the introduction of multi-GNSS will significantly improve the convergence time and the positioning reliability [20; 21; 22]. Therefore, the impact of GNSS eclipsing satellites on the GNSS PPP needs further analysis, which is also the purpose of this paper. This paper is divided into four sections. Following the Introduction, the materials and methods are given in Section 2, including the attitude model for GNSS satellites and the GNSS PPP model. Subsequently, Section 3 compares the positioning performance of three eclipsing strategies for the single-system and multi-GNSS PPP. Finally, the main points and the conclusions are summarized.
## 2 Materials and Methods
### Target Section and Field Survey
The image quality, data, camera optimization, and matching quality used to verify the quality of the generated point cloud produced satisfactory results, and the georeferencing mean RMS error, which is a standard for location accuracy, was 0.0487 m. A 3D point cloud was generated using the SFM method from the overlapped results of the images taken by the drone. The analysis was conducted on an area that, while exhibiting vigorous vegetation growth, also had high image overlap and low light reflection, making it suitable for representing the vegetation patch (red circle in Figure 3a).
## 5 Conclusions  
As a result of the conducted research, the following conclusions can be stated:  
* The density of the infill has little effect on the strength of the element. Increasing the infill density improves the strength, but not so much: it is suggested to use 90% infill density.
* The number of contour layers significantly influences the strength, and increasing them directly leads to an increase in mechanical strength. However, this parameter has its limits, and we must distinguish the cross-section geometry of the samples printed with 100% infill from those printed without the infill. The travel path of the printing nozzle was similar to that of the contour layers in the first example, and in the other example, layers were printed from lines parallel to the Y and X axes.
* The arrangement of the element on the table is of great importance as it influences the strength in a specific direction. The weakest parts were printed in the \(Z\)-axis orientation (vertical). It is best to print samples horizontally, most often printing in a flat orientation. The direction of forces causing the highest stresses must always be considered when designing elements and placing the design on the printer table for printing.
* The height of the layer in the \(Z\)-axis determines its strength. The low height of the layer produces very durable parts. However, they influence the economic factor of printing time.  
In further research, it will be necessary to determine the influence of different geometries of cross sections on the FDM printed parts. The obtained results will be used in the near future to compare ABS composites filled with graphene and carbon nanotubes.  
Conceptualization, A.S. and M.S.; methodology, B.P., A.S. and W.R.; validation, A.S. and M.S.; formal analysis, B.P., W.R. and A.S.; investigation, W.R. and A.S.; resources, A.S. and M.S.; writing--original draft preparation, B.P. and W.R.; writing--review and editing, M.S.; visualization, W.R. and M.S.; supervision, A.S. and M.S.; project administration, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Foundation for Polish Science (project nr First TEAM/2016-1/7).  
This research was supported by the Institute of Metrology and Biomedical Engineering (Faculty of Mechatronics at the Warsaw University of Technology).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.