esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Analysis  
### Interference Verification Scenario  
As shown in Figure 4, the simulation uses GIS to establish a low-orbit megastar seat interference countermeasure scenario, with a large microwave darkroom as the interference verification scenario. Figure 5 represents the equipment layout. Highly dynamic LEO satellite constellations show different distributions over time. A number of jammers are distributed at a certain height above the area to be disturbed. All distributed jammers cancommunicate with other distributed jammers, and an LEO signal receiving terminal is placed at the centre of the jamming area.  
The distributed interference simulation model is shown in Figure 6. The primary and relative jammers are divided from the airspace. The five primary jammers receive the corresponding LEO satellite signals and then engage in a mixing game. The Nash  
Figure 4: Microwave darkroom scenario.  
Figure 5: Equipment layout.  
equilibrium solution, i.e., the set of sub-interference source strategies, is solved. Broadcast LEO signal information and jamming commands to the corresponding relative jammers. Relative jammers complete the interference coverage of the area.  
In order to verify the feasibility of the method, the simulation is completed to verify the jamming effectiveness of the distributed jammers of the same topology in different scenarios, and of the different topologies in the same scenario. Information such as the BER and interference signal coverage is analysed. The proposed algorithm is compared with the gradient descent algorithm (GD), dynamic programming algorithm (DP), and genetic algorithm (GA) to verify the performance of the proposed algorithm.
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 6 Discussions
### Comparison to the Results from Other Research Studies  
This section compares the results obtained in this study to those obtained by other representative research studies which used the same dataset and the same cross-validation type, namely, fivefold cross-validation. The CS algorithm, the CSA, the GWO, and the PTA ensemble-based methods which also considered the standardization of the data are abbreviated as CS-S, CSA-S, GWO-S, and PTA-S, respectively.  
Table 12 summarizes the comparison of the results.  
Figure 15: Comparison of the PTA Ensemble Average Running Time with the Average Time of Other Nature-Inspired Algorithm-Based Ensembles.  
The comparison to the results from [97], in which methods based on the Tri-Layer Neural Network (TNN) and the Maximum Relevance Minimum Redundancy (MRMR), GPR, Boosted Trees, Medium Tree, Fine Tree, Bagged Trees, LR, SVM, Stepwise Linear Regression (SLR), and Coarse Tree were used, is not accurate even though the same ratio for cross-validation was used because that approach also had a data preprocessing phase in which the irrelevant, noisy, and redundant data was eliminated, and it also had a feature selection phase in which the most representative features were selected. The results are comparable to those obtained by the CS algorithm, CSA, GWO, and PTA methods. The nature-inspired algorithm-based ensemble methods returned better RMSE values than all of the other approaches for the Heating Dataset; however, they were outperformed in the Cooling Dataset by the TNN and MRMR and by the GPR.  
Compared to the method presented in [98], where a Naive Bayes Classifier was applied, the CS-S, CSA-S, GWO-S, and PTA-S methods were better in terms of heating prediction results, but they did not lead to better results in terms of cooling prediction results.  
The authors of [99] considered the application of the GA and the Imperialist Competition Algorithm (ICA) [100] in the optimization of the biases and the weights of an ANN. The results showed that the optimization of the ANN using metaheuristics led to better results. The results obtained by the nature-inspired algorithm-based methods described in this article were better, but the results returned by the ANN methods showed fewer differences between the Cooling Dataset and Heating Dataset results.
## 5 Verification
### Verification Process
#### 5.2.2 Comparison of Control Schemes
Furthermore, From Figure 10d1,f1, after 3.5 s, the reactive power emitted by grid increases from 0 Var to 2000 Var, which causes the voltage amplitude to drop from 311.159 V to 306 V.  
In contrast, for proposed control, the off-board charger I and the grid share the active and reactive power in a 1:1 ratio. Between 1.5 and 2.5 s, it can be observed from Figure 10a2,c2 that the active power of the rectifier reduces from 100 kW to 75 kW, and the power transmitted from the grid increases from 115 kW to 145 kW. The sum of these two adjustments equals the consumed active power 60 kW of the  
Figure 10: Dynamic response waveforms of the grid and off-board charger I (**a1**,**a2**) Active power of the rectifier; (**b1**,**b2**) Reactive power of the rectifier; (**c1**,**c2**) Active power of the grid; (**d1**,**d2**) Reactive power of the grid; (**e1**,**e2**) Frequency of the grid; (**f1**,**f2**) Voltage amplitude of the grid; (**g1**,**g2**) Charging current of the power battery I; (**h1**,**h2**) DC bus voltage. off-board charger II. Compared with traditional control, the grid frequency only declines to 0.17 Hz in Figure 10e\({}_{2}\). In addition, aiming at the access of 2 kVar reactive load at 3.5 s, Figure 10b\({}_{2}\),d\({}_{2}\) display that the rectifier reduces the reactive absorption of 1 kVar and the grid generates 1 kVar of reactive power. According to reactive droop coefficient, the voltage amplitude of the grid is a 3.15 V declines in Figure 10f\({}_{2}\), whose drop degree is less than traditional control. Since VSM-controlled off-board charger I is involved in the regulation of the frequency and voltage, the charging current of the power battery I is lowered in Figure 10g\({}_{2}\) during the access period of the power battery II. However, the charging current remains constant at 150 A in the traditional control of Figure 10g\({}_{1}\). Besides, it can be observed in Figure 10h\({}_{2}\) that the post-stage closed-loop control can stabilize the DC bus voltage at a reference value of 800 V.
## 4 The Test Phenomena and the Result Analysis  
### The Test Phenomena  
When the vertical load on the beam end was less than 80 kN, no parts of specimen S1 demonstrated significant phenomena, and the measured strain values did not reach the yield strain of the steel material. At the same time, the load-displacement curve showed an approximative linear relationship, which indicated that the specimen was in an elastic state. When the vertical load reached 80 kN and the curve radian increased, the loading was changed by 5 kN per level. When the loading at the end of the beam reached 100 kN, the strain value of measurement points B1, B2 and B9 had also reached the yield strain value of the steel (\(1.75\times 10^{3}\)), which showed that some parts of specimen S1 had reached the stage of plasticity. Specim S2 could only reach the yield strain when the load reached approximately 110 kN. As the load on the beam end increased gradually to 105 kN, with a loud bang, a weld failure occurred at the join between the right beam and the column of specimen S1, and the load on the beam end fell back. Then, a weld failure also occurred at the join between the column and the diagonal stiffeners, as shown in Figure 13. As judged, the early breakdown occurred due to the poor quality  
Figure 12: Strain gauge arrangement.  
of the welding line. The undamaged left end beam of S1 continued to be loaded, and a weld failure occurred when the load increased to 120 kN. As for to specimen S2, the diagonal stiffeners had been strengthened and the weld quality had also been inspected. Thus, loading could continue, and the beam warped up obviously, as shown in Figure 14. When the loading reached 150 KN, the left side of the beam end was buckling and its displacement increased suddenly. A large inward deformation occurred at the beam end near the joint domain, as shown in Figure 15, and the loading came to an end.  
Figure 14: Loading procedure  
Figure 13: Crack in the welding seam.  
Figure 15: Inward deformation of the beam.  
It was concluded from the experimental phenomena that two kinds of damage occurred--namely weld failure and beam buckling. The former happened when the diagonal stiffeners were small and the welding line was of poor quality. When the diagonal stiffeners were large, the failure mode was beam buckling. The damages happened to the beam or the beam--column joints in the module. The connection between modules remained undamaged, which can be seen from Figure 14 and the experimental phenomenon.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 5 Performance Evaluation  
### Simulation Setup  
We implement PPUR on the Opportunistic Networking Environment simulator [23]. There are 40 mobile nodes in the simulation, which are divided into four groups on average. The node mobility model is a shortest path map-based movement model. In this mobile model, after choosing a destination, a node will select the path of shortest distance by which to move on the map through the Dijkstra single-source shortest-path algorithm. By default, the message generation interval is 4-8 s, message size is 100-200 KB, buffer size is 10 MB, TTL is 2 h, node movement speed is 2-5 m/s, data transmission speed is 250 KB/s, communication radius is 10 m, simulation area is 4500 \(\times\) 3400 m\({}^{2}\), simulation time is 54,000 s, and warm-up time is 4000 s, assuming that there are a total of 4 APs with the locations (1795,1265), (1135,1545), (2573,1104), and (2957,2266), respectively.
## 2 Materials and Methods
### Evaluation of Thermal Comfort and Energy Consumption  
The primary objective of a heating system is to provide occupants with an adequate level of thermal comfort while simultaneously minimizing energy consumption to ensure efficient building energy management. Individual thermal sensations can vary depending on personal characteristics and environmental conditions. However, the Predicted Mean Vote (PMV) index is commonly used to assess thermal comfort. PMV quantifies the average thermal sensation of occupants, and it is calculated using Equation (2) [44]:  
\[\text{PMV}=f(Met,Clo,T_{a},RH,MRT,V_{a}) \tag{2}\]  
where \(Met\) and \(Clo\), which have been previously defined, represent personal factors. The remaining variables, \(RH\), \(MRT\), \(T_{a}\), and \(V_{a}\), denote the relative humidity (%), mean radiant temperature (\({}^{\circ}\)C), indoor air temperature (\({}^{\circ}\)C), and air velocity (m/s), respectively.  
The PMV model is a widely adopted design tool in the field of thermal comfort standards. ASHRAE Standard 55 recommends a PMV range of \(-\)0.5 to 0.5, indicating a zone of thermal neutrality [36]. ISO 7730 offers a more nuanced approach, specifying different PMV ranges for various building types [35]. For example, existing buildings are often associated with a PMV range of \(-\)0.7 to +0.7, while new buildings are typically designed to achieve a PMV range of \(-\)0.5 to +0.5.  
Under conditions where heat loss through an exterior wall and heat gain from internal heat sources and solar irradiance occur continuously, the heating energy supplied to the interior space to satisfy the indoor temperature set by occupants or to ensure thermal comfort can be calculated using Equation (3).  
\[Q=\dot{m}C_{p}(T_{in}-T_{out}) \tag{3}\]  
where \(Q\) is heating power (W), and \(\dot{m}\), \(C_{p}\), \(T_{in}\), and \(T_{out}\) denote the water mass flow rate (kg/s), specific heat (J/kg-\({}^{\circ}\)C), supply water temperature (\({}^{\circ}\)C), and return water temperature (\({}^{\circ}\)C), respectively.
## 7 Using CAMINO for Real-Life CCAM Experimentation
### Use Case 2: Interoperability Testing  
This use case aimed to evaluate the interoperability of V2X communications and services between the Belgian and the Dutch pilot sites, using commercial V2X communication equipment. Among others, the interoperability tests included compatibility of data transmissions at the radio level of short-range technologies (ITS-G5 and C-V2X PC5), compatibility of facility layer messages (CAM, DENM, IVIM) at the service level over short- and long-range communication paths, security compatibility, etc. In that direction, firstly, vehicles from the Dutch pilot performed tests at the Belgian Smart Highway testbed (E313 highway) in order to evaluate their interoperability with the Belgian RSUs and vehicles. In a second phase, a Belgian vehicle performed tests at the Metropol Region Amsterdam (A16 highway) to evaluate its interoperability with RSUs and vehicles form the Dutch pilot.  
The CAMINO framework was used by the OBU of the Belgian vehicle and the RSUs at the E313 highway to manage the V2X wireless technologies and services running on top of them, as well as to provide the necessary logging of the transmitted and received data. As it has been described in Section 6, the CAMINO-Core supports logging according to the InterCor logging format, which is also used by the Dutch pilot. Hence, the consistency of the logged data was very valuable for the post-processing analysis, based on the selected KPIs.  
Figure 7 shows the packet loss observed by the vehicle of the Belgian pilot for CAM messages transmitted by a specific RSU of the Amsterdam Pilot site, using ITS-G5. The packet loss was calculated for both driving directions of the highway for CAM messages transmitted at a frequency of 1 Hz. As shown in this figure, the packet loss was zero when the vehicle was close to the RSU and it increased as the vehicle drove away. In the direction towards the North, the measured transmission range was around 600 m, towards the South it was around 500 m. This was potentially a result of the placement of the RSU on the gantry and the curvature of the highway. When the vehicle entered the tunnel that was part of the highway, the GNSS signal was lost and the transmissions of ITS-G5 were interrupted, as a GPS signal was required by the ITS-G5 modules in order to transmit. Further discussion on the interoperability testing is not in the scope of this article and it will be addressed in our future work.
# A Deep Learning Approach for Kidney Disease Recognition and Prediction through Image Processing
## 3 Proposed System
### Dataset  
The Changhua Christian Hospital in Taichung, Taiwan, is the source of the dataset. With patients' identifying information deleted, a total of 5617 records from 1 January 2000 to 27 July 2017 were obtained. Patients' conditions were monitored throughout this time or until they pass away. Patients' monitoring times varied from less than a month to over five years, with an average of about two and a half years. A flat  
Figure 1: Proposed Model for Kidney Disease Prediction.  
Figure 2: Stages of Renal Kidney Disease.  
hold the dataset. The intended variable, \"survival or not\", is among the 35 properties that are listed in columns. Other factors can be broken down into two categories: general information about the body or the CKD itself, such as the ID, diagnosis date, age, gender, height, weight, BMI, stage, haemodialysis and its date; and whether or not the person was taking special medications or had comorbid conditions, such as high blood pressure, heart disease, chronic liver disease, or other conditions. Table 1 contains statistics regarding a number of significant characteristics [30].  
As the dataset is mostly clean and comprehensive, minimal preparation was required. The ID element and two date attributes were simply dropped, since the former was not important and the latter were not full. Moreover, the absence of a haemodialysis data might reflect one of two very different things: either the patient was receiving haemodialysis but the date was not recorded, or the patient did not yet need it. As there is no easy method to discern between the two diametrically opposed circumstances, we decide to combine them all so that the remaining data would better fit the prediction algorithm. Moreover, we tested our models using 5-fold cross-validation on the dataset.  
The MJ Health Research Foundation gave permission for and provided all of the datasets that were utilised. The MJ Health Research Foundation and the Far Eastern Memorial Hospital's Research Ethics Review Committee assessed and approved the study's strategy with regard to ethical concerns about the use of database data (FEMH-IRB-107126-E, Protocol Version 7, 18 June 2020). (Approval No.: MJHRF-2016005A).  
The dataset was then preprocessed using the normalising technique known as min-max normalisation. One of the most popular methods for normalising data is the min-max method. For each feature, the lowest value is converted to a 0, the highest value is converted to a 1, and all other values are converted to a decimal between 0 and 1.
## 2 Materials and Methods
### Reverse Engineering Techniques and Technologies
Stereo vision (Shape from Stereo, SfS) using dual sensing: * Stereo vision with two optical sensors outside the head of the device; * Stereo vision with two optical sensors located on the head of the device. 3. Stereo vision based on dual optics and single optical sensor sensing: * channel with split optics; * dual aperture optics; * dual optical channel with prismatic lens; * dual aperture optics with interlaced image; * dual aperture optics with Complimentary Multiband Band-pass Filters (CMBF); * variable optical path system; * an off-axis static aperture system and a rotating disc. 4. Digitization based on the use of structured light: * structured light technique with optical light guiding device; * the structured light technique with a projection device on the external side of the endoscope; * structured light technique based on phase shift analysis; * structured light technique with spectrally encoded light pattern; * a stereoscopic sensor pair using structured light; * structured light technique with a multi-component coupled optical system sensor. 5. Digitization using endoscopic equipment with one optical channel: * a technique based on differential focusing of the image and astigmatic projection of the light pattern; * ToF reflected light Time of Flight calculation technique; * holographic optics technique with a sensor on the endoscope head; * holographic optics technique, light guided through an optical channel and a sensor outside the endoscope head; * Shape from Shading (SfSh) technique; * Shape from Defocus (SfD) technique of the sensor optics; * SfD based technique for projected light patterns, 3D measurement technique using laser beams [20]. In the selection of a reverse engineering technique for 3D surface digitization that does not involve the use of external references or repositioning of the technology or the object, it was imperative to evaluate the capabilities of each technique and select the most suitable one. The employment of techniques founded on structured light projection necessitates the utilization of a projection device. However, the intricate and complex projected pattern may not be well suited for utilization in challenging industrial environments, which encompass hard-to-reach locations. Consequently, the SfS stereovision technique employing dual imaging has been selected.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 4 Discussion  
Our study is compared to two recent global studies by [PERSON] et al. [4] and [PERSON] et al. [38] that investigated the drivers of global vegetation change. [PERSON] et al. employed a 10-model ensemble and performed change attribution by running models with and without different drivers, then comparing the results. Meanwhile, [PERSON] et al. used AVHRR-derived vegetation fractions to analyze changes in vegetation types and used high-resolution imagery at 1500 locations worldwide to attribute the change. They identified visible signs of human activity at each location and attributed the observed change to land use if present, or to indirect drivers such as climate change if not. Both [PERSON] et al. and [PERSON] et al. have demonstrated the inconsistency that exists within the published literature, as they reveal large differences in attribution, while also highlighting the consistency in broad trends in vegetation change. While our study only focuses on the dryland biomes of China and America, it is worth noting that these global studies have used different spatial domains, which makes direct comparison challenging.  
[PERSON] et al. found that nitrogen deposition was the second largest global driver, but this finding was uncertain since only two of their models could be run with and without nitrogen deposition. However, it is widely accepted that nitrogen deposition is not a significant driver of vegetation change in drylands. Recent research on the effects of nitrogen deposition on plant species showed that drylands are not responsive to increased nitrogen loads due to their overwhelming water limitation, and are predominantly located in regions with very low nitrogen deposition, except for a small part of the southwestern United States [39].
## 2 Prior Works
### Diagnostic Applications  
Several studies [27; 28; 29; 30] monitored chest X-ray and CT image changes prior to COVID-19 symptoms and found that such techniques play an important role in early diagnosis and treatment of COVID-19. [PERSON] [31] suggested a machine learning-based sensor for facial thermal scans to detect COVID-19 patients. [PERSON] [32] developed a machine learning, speech processing method to detect COVID-19 patients from coughs. Machine learning and AI-based smartphone sensors were used by [PERSON] [33] to detect COVID-19 symptoms. [PERSON] [34] attempted to predict patients' risk of a COVID-19 diagnosis using a machine learning model trained on routinely collected data from emergency care admissions. The work most related to our study was presented by [PERSON] [35], which suggested a machine learning model to predict patients at risk for developing acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Their models achieved 70-80% prediction accuracy. It should be noted that their model focused on only one level of severity and did not include any explainability methods.