esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 3 Methodology
### Classification of COVID-19, Normal and Pneumonia Using Deep Convolutional Neural Networks
#### 3.3.5 Approach 5: Deep Learning with Hybrid Sampling on an Unbalanced Dataset  
The undersampling approach may discard valuable images and lose invaluable information while resampling. On the other hand, oversampling may cause overfitting and could increase the computation effort if a higher oversampling rate is applied. The combination of both undersampling and oversampling can improve the overall performance and avoid overfitting and information loss. A hybrid approach resamples the training set with a modest amount of oversampling to the minority class and a modest amount of undersampling to the majority class. This can result in improved overall performance compared to performing one or another of the techniques in isolation [44, 45]. In our case, our dataset comprised an approximate 1:3:10 ratio of pneumonia, COVID-19 and normal images. Pneumonia was the most common minor class, whereas normal was the most common majority  
Figure 11: Deep learning with undersampling on the unbalanced dataset. Normal and COVID-19 CXRs were randomly removed to ensure a better balance with the number of pneumonia CXRs.  
Figure 12: Deep learning with oversampling on the unbalanced dataset. COVID-19 and pneumonia CXRs were randomly duplicated to ensure a better balance with the number of normal CXRs.  
class. We oversampled pneumonia CXRs and undersampled the normal CXRs until they reached a quantity nearly equal to the number of COVID-19 CXRs. The hybrid sampling approach is depicted in Figure 13. Using the hybrid-sampled training dataset, 11 CNNs are fine-tuned using categorical cross entropy loss as described in Section 3.3.
## 3 Results  
### In Situ Spectra  
#### 3.1.1 MODIS In Situ Spectra  
For a reliable assessment of the errors in the remote sensing reflectance values, identification of a day on which the atmosphere was clean and a day on which dust transport occurred, with no phytoplankton seasonal blooming, over the Black Sea is necessary. On 12 September 2017, a case of dust aerosol transport was recorded over the Black Sea region, as confirmed by in situ measurements (AERONET, SPM), satellite measurements (MODIS, VIIRS), and the 7-day AERONET (BTA) and HYSPLIT backward trajectories. The date meets the abovementioned criteria, namely: no sea blooms were present and this day was free of clouds, which allows evaluation of the standard values of Rrs(\(\lambda\)). It should be noted that in this paper, all time is presented in Greenwich Mean Time.  
For comparison, 8 September 2017 was selected as the day with a clean atmosphere in the same month and in the same year. On this day, very low (less than 0.1) values of AOT were observed, which corresponds to a very clean atmosphere, and the satellite images did not contain clouds (Figure 1).  
Thus, the first stage of the study was to consider the eastern part of the Black Sea (analysis of data of the coordinates of in situ measurements for the corresponding pixels).
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 5 Experimental Results: Test Scenarios  
### Lab Test  
Before the system was installed in any given location, some experiments were implemented in the lab, where it is easier to check the system's functionality. The nodes were placed in separated labs to have the walls limit the radio range. In particular, we implemented connection and reconnection tests for the nodes and control of the lamps.  
We executed a global operation test that saw the whole system as a black box. The global test consisted of programming some lamps in the SCADA system with a given regulation and watching the power consumed by each lamp. We implemented tests with different lamps to check that the dimming by the designed tlemanagement module was working correctly. A high-pressure 100 W OSRAM VIALOX NAV(SON)-E sodium vapor lamp and a LED luminaire were used. The tests implemented with the LED luminaire will be just commented upon as an example. In particular, the BANDEJA FCO model by LED & POLES S.L.L. with STREETLED ASW/X-50/740 IP65 46 W module with adjustable Xitanium 75 W 0.7 A Prog + GL-Z sXt module by Philips was tested. Figure 17 shows an image of the system consisting of the LED luminaire, the Xitanium driver and the UCODALI board.  
Figure 16: CC2531 USB Dongle [Source: TL.com].  
As a result, these data show that the power dimming of the lamp controlled by the remote management module works properly. Thus, it also proves the good communication between both through the DALI bus.  
Regarding the network tests, nodes have been connected and reconnected to check if the network was correctly allocating the addresses and if the SCADA system informed the user about the connection and reconnection in an automatic way. The lighting network has also been tested regarding turning on/off without fulfilling the communication with all of the nodes to check whether the lamps begin the previously established operation curve when there is no communication with the SCADA system.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### Field Measurements  
#### 2.2.1. Canopy Reflectance Measurements  
The canopy reflectance at the XTS site was measured using an ASD FieldSpec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA) with a spectral range of 350-2500 nm and spectral resolutions of 3 nm (350-1050 nm) and 10 nm (1050-2500 nm). The measurements were obtained between 10:00 and 14:00 local time during clear and cloudless conditions, at a height of 1.3 m above the wheat canopy, with a 25\({}^{\circ}\) field of view. The average canopy reflectance for each plot was obtained via 20 individual measurements [30]. The canopy spectral reflectance at the US-Ne2 and US-Ne3 sites was obtained using two inter-calibrated Ocean Optics USB2000 radiometers (Ocean Optics Inc., Dunedin, FL, USA) ranging from 400 to 1100 nm in spectral range, with a spectral resolution of 1.5 nm [44]. One radiometer upwardly measures the upwelling radiance of the crop at a height of about 5.5 m above the canopy with a 25\({}^{\circ}\) field of view. The other downwardly measured the incident irradiance with a hemispherical field of view. The measurements were taken under clear sky conditions between 11:00 and 14:00 local time, and the reflectance was then calculated using methods described by [PERSON] [1].
## 3 ML Model Development
### Input Data Selection  
As mentioned in Section 2.2, both 0-D and 2D input data structures are tested for each of the model, except for the CNN because it takes only 2D image as an input. In addition to pixel selection, we will also test the impact of predictor variable selection. As aforementioned, the feature importance analysis of the RF method provides us with a useful estimate of the usefulness of each predictor variable in Table 2 for dust detection. Using this analysis, we are able to rank the feature importance of all 23 predictor variables and selected nine variables (highlighted in bold in Table 2) with feature importance higher than the mean value. These selected variables include 4 M-bands (M09, M10, M11, M12 and M16) and two solar-viewing angles (SZA and SAA), as well as two pieces of geolocation information (Lat and Lon). The selected M-bands are in either the shortwave infrared (i.e., M09 to M12) or thermal infrared (i.e., M16). These bands have also been used, either separately or in combinations, in the previous physics-based dust detection algorithms. However, it is interesting and somewhat surprising to see that not a single visible band (e.g., M05) is selected, even though visible bands are frequently used in the literature for dust detection [20; 25]. The selection of two solar-viewing angles is, on the other hand, not surprising because all the selected M-bands are solar-reflective bands. In addition, as the occurrence of dust events is very dependent on the region, the selection of two pieces of geolocation information of Lat and Lon is not unexpected. In addition, we used permutation importance from FFNN to compare the importance of features with RF. The top nine features from FFNN and RF was mostly common, but, in FFNN, latitude and longitude were excluded form the top 9 features, but M08 and M04 were included.  
Based on different combinations of pixel selection and predictor variable selection, we prepared four sets of input data structures, 0D-allvar (23 predictor variables), 0D-selectvar (9 predictor variables), 2D-allvar (\(5\times 5\times 23\) predictor variables), and 2D-selectvar (\(5\times 5\times 9\) predictor variables) for each of the cases. A comparison between the 0-D and 2D results would help us understand to what extent the pixels adjacent to the CALIOP track provides additional information for dust detection on the CALIOP track. A comparison between \"allVar\" and \"selectVar\" results will help us understand if the same level of accuracy can be achieved by using a reduced number of predictor variables.
# Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario
## 4 Implementation
### Software Architecture
#### 4.2.3 The QoS Metrics Provider Agent  
The QoS Metrics Provider agent computes the QoS metrics explained in Section 3 using the context information monitored from the DSR. As Figure 7 shows, this contextual information will be sequentially processed by four modules:  
* The Context Monitor module reads the contextual data from the DSR and sends contexts to the Complex Event Processor (e.g., changes in the battery level).
* The Context Event Processor module searches for event patterns that were specified in the model (Listing 3). If there is a match, it will produce observations (e.g., battery is draining too fast) that will be sent to the Probabilistic Reasoner. Specifically, this Complex Event Processor: (1) supports the creation of derived context variables, i.e, if a user defines a derived context, the Complex Event Processor comes into play to calculate its value each time the primitive contexts on which it depends are updated; (2) evaluates observations as event-typed expressions, which produces an event whenever the pattern is satisfied; and (3) allows the computation of aggregate functions, such as, sum, average, maximum and minimum and the use of timers.
* The Probabilistic Reasoner module implements the mathematics described in Section 3. It reinforces or undermines the properties according to the observation definitions available with the model. As stated before, it computes a numeric estimation for each metric (i.e., the degree of fulfillment of each non-functional property) returning a value between 0 and 1.
* The QoS metric server module reads estimations sent by the probabilistic reasoner and forwards them to any external software in charge of interpreting these estimations (e.g., for benchmarking or for robot's self-adaptation, among others).
# Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
## 4 Dynamics Modeling and Simulation Analysis
### Controller Design  
In order to verify the basic motion performance of the proposed VJ, a PID controller was designed for the main drive chain and the VSM drive chain, as shown in Figure 15. The position information of the output end disk and the input end disk is provided by sensors, which are installed at the corresponding positions, and is used to calculate the elastic deviation angle of the entire VSJ. The angle information of the stiffness adjustment disk is provided by the sensor at the end of the stiffness adjustment motor. Based on the stiffness model established using Equation (9), the effective length of the leaf spring can be calculated. Furthermore, based on Equation (16), the rotational position information of the stiffness adjustment transmission chain can be determined.  
Figure 14: Simulink model of the VSJ: (**a**) main drive module; (**b**) stiffness adjustment module.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
###### Abstract  
Autonomous driving technology, as a key component of intelligent transportation systems, has gained considerable attention in recent years. While significant progress has been made in areas such as path planning, obstacle detection, and navigation, relatively less focus has been placed on vehicle speed control, which plays a critical role in ensuring safe and efficient operation, especially in complex and dynamic road environments. This paper addresses the challenge of speed tracking by proposing a genetic algorithm-based optimization method for PID controller parameters. Traditional PID controllers often struggle with maintaining accuracy and response time in highly variable conditions, but by optimizing these parameters through the genetic algorithm, substantial improvements in speed control precision and adaptability can be achieved, enhancing the vehicle's ability to navigate real-world driving scenarios with greater stability. The experimental results clearly indicate that the autonomous vehicle, after PID parameter optimization using a genetic algorithm, demonstrated the following speed tracking errors: when the road surface adhesion coefficient was 0.5, the maximum speed tracking error was 0.22, the average value was 0.063, and the standard deviation was 0.124; when the adhesion coefficient was 0.6, the maximum speed tracking error was 0.180, the average value was 0.056, and the standard deviation was 0.099; when the adhesion coefficient was 0.8, the maximum speed tracking error was 0.179, the average value was 0.056, and the standard deviation was 0.098. This method significantly improved the controller's performance in maintaining the desired speed, even under challenging conditions. These findings highlight the potential of genetic algorithms in supporting the future development of autonomous driving technology, ensuring its successful integration into intelligent transportation systems.  
Autonomous driving; speed control; genetic algorithm; parameter optimization
## 2 Methods
### Robustness of the Results  
Unlike wide-angle crystallographic data, SAXS data are not amenable to direct structure determination. Hence, the structures are usually derived from SAXS data using one of the many modeling methods. While a structure gives a unique SAXS pattern, a SAXS pattern can be fitted to many structures. This ambiguity is usually resolved by using results from alternative sources, such as direct imaging by TEM or AFM, molecular simulations, or by comparison with similar structures that have been validated. In many instances, rather than insisting on a definitive model, it is sufficient to parameterize the scattering pattern so that the changes due to the environment, processing, and disease states can be monitored. This can be carried out quite reliably by using parameters that are derived from Bragg, Guinier, and Porod and Scherrer equations, or variations of these [41; 42]. The uncertainty in the parameters derived from these is typically \(<\)1% and is sufficient to discern the effect of environmental conditions such as temperature, humidity, and stress, and, more importantly, disease conditions. These will be illustrated in Sections 3-5.
## 3 Results and Discussion  
### ESP Analysis  
The distribution of surface ESP in Figure 3a is relatively uniform. The ESP near to the H-atom is positive, while that above the C-atom is negative, reflecting a rich \(\pi\) electron cloud. Comparing Figure 3a-c, it becomes evident that the absolute value of surface ESP on the plane of the activated carbon molecule without functional groups surpasses that with a pyridine functional group and is lower compared to that with a pyrrole functional group. This disparity has implications for the adsorption behavior of VOCs on activated carbon planes. In Figure 3b, incorporating a pyridine functional group alters the ESP distribution of the activated carbon model, with negative ESP being present around the doped N-atom as an electron donor [39; 40]. In Figure 3c, adding a pyrrole functional group causes an increase in ESP around the H-atom connected to the N-atom, where the highest ESP functions as an electron acceptor [39; 40]. In summary, introducing nitrogen-containing groups can modify both the surface ESP distribution and molecular polarity characteristics of the activated carbons.  
Figure 4a-d illustrate the surface ESP distributions of the CH\({}_{3}\)Cl, CS\({}_{2}\), C\({}_{2}\)H\({}_{6}\)S, and C\({}_{6}\)H\({}_{6}\) molecular models. For CH\({}_{3}\)Cl, the ESP surrounding the three H-atoms exhibits a positive charge, rendering it an electron acceptor that can be adsorbed onto activated carbon planes and around pyridine nitrogen sites. Conversely, the ESP around the Cl-atom  
Figure 3: The surface electrostatic potential (ESP) of activated carbon models on the van der Waals (vdW) surface: (**a**) NF, (**b**) PD, (**c**) PR, and (**d**) a color scale bar.  
Figure 2: Common interpretation of coloring method of mapped function sign(\(\lambda_{2}\))\(\rho\) in the independent gradient model based on the Hirshfeld partition (IGMH) maps [38].  
displays a negative charge, signifying its role as an electron donor. The ESP distribution on the surface of the CS\({}_{2}\) molecular model exhibits a relatively homogeneous pattern, characterized by small absolute values. Notably, the ESP maximums are situated at both ends and in the middle region. The presence of negative ESP surrounding the S atom in C\({}_{2}\)H\({}_{6}\)S indicates its electron-donating properties and ability to form hydrogen bonds with the H atom at the edge of the activated carbon model. Conversely, the positive ESP near the H atom in C\({}_{2}\)H\({}_{6}\)S suggests its electron-accepting characteristics and capacity to engage in hydrogen bonding with the C atom and pyridine N atom of the activated carbon model. These predictions are subsequently validated. The ESP of C\({}_{6}\)H\({}_{6}\) is similar to that of NF in that its \(\pi\)-electron cloud is rich, allowing it to perform \(\pi\)-\(\pi\) stacking interactions with other benzene rings.
## 2 Materials and Methods
### Statistical Method  
Accuracy of earthquake prediction is the basis of practical application, because wrong predictions may cause serious economic losses and public panic. The PPV of infrared abnormal signals can evaluate the accuracy of its application in earthquake prediction, which is defined as the proportion of abnormal signals related to an earthquake in total anomalies, as shown in Equation (2) [28].  
\[\text{PPV}=\frac{N_{0}}{N_{a}}, \tag{2}\]  
where \(N_{0}\) is the number of abnormal signals related to an earthquake, and \(N_{a}\) is the number of total abnormal signals in the research area. To calculate the PPV, we propose a method based on the connected domain to identify the spatiotemporal occupancy range of abnormal signals. We deem the point where the relative power spectrum is more than five as abnormal. The connected region of abnormal points is regarded as the spatial-temporal range of a single abnormal phenomenon, and abnormal regions with relatively small areas or short-term are ignored to eliminate the infrared radiation increase caused by human activities. In this paper, an anomaly that covers less than 500 pixels or lasts less than 3 days is ignored. In previous studies, anomalies appeared on average one to three weeks before the earthquake [38]. As a result, the anomaly is regarded to be related to the earthquake, if the distance between the abnormal region and the epicenter is less than the distance threshold (Td) and the anomaly appears within one month before the earthquake.  
The TPR is the proportion of earthquakes with pre-seismic infrared abnormalities in total earthquakes, which is defined as Equation (3) [28].  
\[\text{TPR}=\frac{N_{1}}{N_{e}}, \tag{3}\]  
where \(N_{1}\) is the number of earthquakes with any pre-seismic anomaly, and \(N_{e}\) is the number of total earthquakes in the research area. It is used to assess the universality of infrared anomalies before earthquakes.  
It provides higher PPV and TPR to increase the distance threshold, but the spatial accuracy of prediction would decrease. Therefore, earthquake prediction needs to predict more earthquakes successfully with lower spatiotemporal occupancy. The probability gain is the ratio of TPR to spatiotemporal occupancy, shown as Equation (4) [39; 40]. It can be regarded as a criterion for selecting the optimal distance threshold.  
\[\text{Gain}=\frac{\text{TPR}}{\tau}, \tag{4}\]  
where TPR is the true positive rate, \(\tau\) is the fraction of space-time occupied by the predicted range, and is associated with the distance threshold. Matlab platform was used for statistical analysis and results display.
## 3 System Model
### Computation Model
#### 3.2.1 Local Execution  
For the local execution method, the computation task of mobile device \(j\) will be executed locally and the total time execution and consumption of energy can be expressed as, respectively:  
\[T_{i,j}^{l}=\frac{c_{i,j}}{f_{i,j}^{l}} \tag{2}\]  
\[E_{i,j}^{l}=\zeta_{i,j}c_{i,j}, \tag{3}\]  
where \(f_{i,j}^{l}\) indicates the capability of computation (CPU/cycles per second) of the mobile device, and \(\zeta_{i,j}\) is a coefficient that indicates the CPU cycle's energy consumed for mobile device \(j\), which is connected with UAV \(i\). We set \(\zeta_{i,j}=10^{-11}(f_{i,j}^{l})^{2}\), in which the consumption of energy is a superlinear function of the mobile device's frequency [40,44].
## 4 Discussion
### Significance of the Research  
This study is important at both the theoretical and practical levels.  
At the Theoretical Level:  
Association Between Peer Relationships and Academic Performance: This study explored the link between peer relationships and academic performance and found different effects of various types of peer relationships on academic performance. This provides new evidence for understanding the impact of peer relationships on academic performance.  
Complexity of Peer Relationship Networks: The careful differentiation of students' social types reveals the complexity of the composition of peer relationships, and comparisons and analyses across semesters reveal the complexity of the evolution of peer relationships. This analysis has important theoretical value for a deeper understanding of the mechanisms of formation and evolution of peer relationship networks.  
Construction and Application of Graph Networks: This study extracts social relationships among students through spatio-temporal co-occurrence data and constructs a graph network model. This method effectively maps the students on campus and their various behavioral characteristics in the graph network model. It establishes a new research paradigm for behavioral big data in the field of higher education research and provides new research ideas for future smart campus-related studies.  
At the Practical Level:  
Peer Mentoring: Research has indicated that strong peer relationships tend to help students achieve better academic performance. This finding suggests that schools can implement interventions such as peer mentoring to promote better academic performers to assist students who are struggling academically.  
Prediction of Academic Difficulties: It was found that academic difficulties can be identified using a prediction model based on social characteristics. This suggests that the occurrence of academic difficulties can be prevented and mitigated in advance.  
Optimization of Educational Resources: Instructional administrators can utilize big data analytics to identify students in need of additional support, optimize the allocation of educational resources, and ensure effective resource usage.