esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Algorithm and Validation
#### 3.1.4. Final Algorithm and Validation  
The correlation between the amount of GHG emissions generated and the corrected weighted factor (\(FP_{c}\)) of the MS is shown in Figure 3. Thus, the final equation for GHG emissions estimation (Equation (9)) is established, aiming to estimate the amount of GHG emissions generated (\(Q_{GHG}\)) in an MS (\(i\)), once the corrected weighted factor (\(FP_{c}\)) of the MS is calculated (Equation (9)):  
\[Q_{GHG}=-9939.63\ {FP_{c}}^{2}+4695.51\ {FP_{c}}\ \ \ \mathrm{R}^{2}\ =\ 0.96 \tag{9}\]  
The model shows a strong accuracy in predictions with a high \(\mathrm{R}^{2}\) value of 0.96. Therefore, the developed estimation model can be effective in estimating GHG emissions in an EU MS and could be considered as a cost-effective approach for planning corrective measures. The results obtained with the equation show a mean deviation of around 22% compared to the average amount of GHG emissions generation reported to Eurostat in 2010-2020.  
The maximum deviation is found in Malta, where it reached 59%. If the countries (Sweden, Poland, Malta, Latvia and Croatia) with higher deviations (>35%) were removed from the model, the accuracy would reach 15%. However, in that scenario, there are some limitations that must be taken into account, and the countries that do not meet these values fixed in the following ratios have to be excluded from the model: GHG/population ratio below 6.19; GDP/RES ratio below 601.69 and GHG/population ratio above 920.68.  
Finally, the amounts estimated with the multifactor equation are compared with the data reported to Eurostat for each single year from 2010 to 2020. For this, the relation  
Figure 3: Correlation between the corrected weighting factor (\(FP_{c}\)) and the average amount of GHG emissions generation reported to Eurostat in 2010–2020 for each analysed MS.  
between the amount of GHG emissions generation reported to Eurostat by each EU MS and the amount estimated with the multifactor equation are shown in Figure 4.  
Excluding the countries mentioned before (Sweden, Poland, Malta, Latvia and Croatia), the highest mean deviation is found for the year 2020 (25%) compared to other data for years 2010 (19%); 2011 (16%); 2012 (17%); 2013 (18%); 2014 (17%); 2015 (16%); 2016 (15%); 2017 (15%); 2018 (15%) and 2019 (18%). This is mainly due the lockdowns that in 2020 drastically decreased human activities and overall GHG emissions [35].  
The proposed model shown in Figure 3 has been developed using the data available in the Eurostat database for the dependent and independent variables during the last ten years. The values estimated with the multifactor algorithm have been compared against annual data (2010, 2011, etc.) to ensure robustness and accuracy of the model in case of changes of the adjustment data. The validation results confirm the solidity of the model against sensitivity to changes in data setting (overfitting), as well as accuracy in avoiding systematic errors (underfitting).
## 2 NEOs Environment
### Asteroid Hovering  
According to [69], hovering can be broadly defined as using active (continuous) control to nullify the total acceleration on a spacecraft in order to keep it at a desired position, generally a stable stationary equilibrium or a periodic orbit in a particular reference frame. In the so-called near-inertial hovering, the spacecraft is regulated at relative equilibria in the Sun-NEO frame, either natural or thrust-induced, irrespective of the NEO's spin state. As in this case, the points of equilibrium in the Sun-NEO frame are relatively far from the NEO. This approach may be useful in the initial data-acquisition phase for better characterization of the generally poorly known environment. In the body-fixed hovering approach, the spacecraft is parked at a generally closer fixed location relative to a spinning NEO, implying the spacecraft is synchronously accompanying the NEO's rotation in inertial space. This closer parking is an advantageous option for end-mission approximation tasks such as detailed mappings of the NEO's surface [58], and also an efficient strategy for creating artificial equilibria via continuous thrust in a soft-landing descent maneuver [69]. Whatever the approach, accurate models for the NEO's spin state and gravity will be required to allow, as much as possible, to place the spacecraft on equilibrium points or related periodic orbits such that orbit keeping around the NEO may be accomplished with as low as possible fuel consumption.  
Despite playing a very important role in the realm of NEO exploration, the hovering approach may not always be suitable, depending strongly on the targeted body's characteristics. For example, in the case of spacecraft NEAR-Shoemaker orbiting at 50 km distance from the massive NEA (433) EROS (first to be discovered, 30 km diameter, as dense as the Earth) [70], hovering station-keeping for the first 9 months of the mission would have required a total \(\Delta V\sim 4\) km/s, while the actual fuel usage on a classical orbit correction approach was on the order of a few tens of m/s, highlighting the fact that hovering may not be a reasonable approach for massive bodies [42; 70]. On the other hand, the SRM of the large area-to-mass spacecraft Hayabusa (former Muses-C) targeting the low-massasteroid (25143) Itokawa (see Table 1), a mission so highly susceptible to solar radiation disturbances that it could not even be previously assured that an orbital mission would be possible, a successful near-inertial hovering strategy was implemented for orbit maintenance, and body-fixed hovering was used for several descent maneuvers to the asteroid's surface ([69; 71; 72]).
## 3 Data Description  
### Taxi GPS Data  
We obtained the GPS data from the Xi'an Taxi Management Office in Xi'an city of China. The data include location information, vehicle state information, time information, and license plate information. Moreover, the taxi GPS data were recorded every 5 s for 30 days in November 2016 and include 40 million points which are located in Xi'an city of China. The GPS data were cleaned and selected. An instance of taxi GPS data is shown in Table 1.
# Reinforcement-Learning-Based Trajectory Learning in Frenet Frame for Autonomous Driving
## 5 Results
### Evaluation  
To demonstrate the superiority of using the Frenet frame in trajectory learning on curvy roads and the capability of RLTF to overcome drawbacks of the Frenet frame, we implemented and tested two baseline methods: RLTF without road information, which does not include lengths of obstacle vehicles and curvatures of road in the state, and RLTF without Frenet frame, which learns trajectories without using the Frenet frame. To ensure a fair evaluation, all methods share the same reward function. The evaluation was conducted on the same road used for training, with assessments occurring every 20 th episode and totaling 30 evaluations in each episode. All vehicle positions were initialized using the same method as during the training phase. The obstacle vehicles are configured with one in the rear and 0 to 2 in the front, depending on the episode.  
In Figure 7a, RLTF without road information shows a learning curve that closely resembles RLTF, but RLTF achieves a collision-free state at around 1900 th episode, while RLTF without road information continues to result in collisions. This illustrates that not including road information in the state cannot resolve the issues associated with the Frenet frame. RLTF without Frenet frame, on the other hand, learns trajectories in the Cartesian frame instead of the Frenet frame. In Figure 7b, it is evident that methods learning trajectories in the Frenet frame achieve efficient learning, higher average rewards, and higher success rates compared to those learned in the Cartesian frame. Notably, RLTF without road information shows a very low average success rate of 60%.  
To demonstrate that including road information resolves the issues associated with the Frenet frame, we conducted tests by fixing all state components except the road information. If road information indeed resolves Frenet frame issues, the trajectory should vary according to the road's curvature \(\kappa\); the result is in Figure 8. Positive \(\kappa\) corresponds to a left curve, and negative \(\kappa\) to a right curve. The increasing intensity of red indicates higher \(\kappa\), while the increasing intensity of blue indicates lower \(\kappa\). This demonstrates that when learning trajectories for obstacle avoidance on curved roads, using the Frenet frame for  
Figure 6: The roads used for (**a**) training and (**b**) tests. Test roads are not used for training.  
Figure 7: Average reward and success rate. (**a**) Average reward, (**b**) Average success rate.  
training can generate safer trajectories, and it also suggests that our state representation can address the drawbacks associated with the Frenet frame.
## 5 Experiment and Results
### Evaluation and Analysis
#### 5.2.1 Using Only 3D Point Cloud Data as Input  
As mentioned above in the data pre-processing section, we input the acquired 3D point cloud data into the two networks in two different data formats and then train them by 100 epochs separately. After training, we plot the validation set accuracy and loss downloaded from Tensorboard into Figures 10 and 11 and find their maximum and minimum values, respectively, as shown in Table 2.  
According to the above curves and tables, adding the LSTM network to the pure convolution network improves the accuracy of the network, reduces the loss, and improves the convergence speed. This verifies that the point cloud data has obvious timing characteristics between frames. To better evaluate the precision, we visualize the confusion matrix, as shown in Figure 12. Each column of the confusion matrix represents the predicted category, and the total number of each column represents the number of the data predicted as that category. While each row represents the true ascribed category of the data, the total number of the data for each row represents the number of data instances of that category. We find that the prediction difficulty of these activities, except circling, which is slightly more difficult, is all close.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 3 Methodology
### Evaluation Metrics  
In this study, overall accuracy (OA), Kappa, producer accuracy (PA), and user accuracy (UA) were chosen as evaluation metrics. OA can be used as a standard that the proportion of non-collapsed and collapsed buildings were correctly identified. However, there is no thresholds of OA for image classification. In addition, it cannot tell the exact identified proportion of non-collapsed and collapsed buildings, respectively. Kappa has threshold provided by [PERSON] and [PERSON] [28], which values between 0.01 and 0.20 are slight, between 0.21 and 0.40 are fair, between 0.41 and 0.60 are moderate, between 0.61 and 0.80 are substantial, and between 0.81 and 1.00 are almost perfect. PA is the probability that collapsed or non-collapsed buildings are classified correctly. UA is the probability of predicting that it is indeed of this type in collapsed or non-collapsed buildings.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning
### Mathematical Model
#### 4.5.2 Train Rescheduling  
In fact, the train rescheduling deals with the elaboration of decision variables in the form of a new route plan \(\mathbf{r}^{\text{T}}=[r^{\text{T}}_{p}]_{p\in\mathbf{TR}}\), \(\mathbf{r}^{\text{T}}_{p}=[r^{\text{T}}_{p,i,j,k}]_{i,j\in\mathbf{V},\mathbf{k} \in\mathbf{K}}\), where \(r^{\text{T}}_{p,i,j,k}=1(0)\) if train \(p\) is planned to run by track \(k\) between stations \(i\) and \(j\) (otherwise). Moreover, arrival time shifts \(\Delta t^{\text{ARR}}_{i,p}\) and departure time shifts \(\Delta t^{\text{DEP}}_{i,p}\) constitute the respective matrices of decision variables \(\mathbf{\Delta t}^{\text{ARR}}=[\Delta t^{\text{ARR}}_{i,p}]_{i\in\mathbf{V},p\in \mathbf{TR}}\) and \(\mathbf{\Delta t}^{\text{DEP}}=[\Delta t^{\text{DEP}}_{i,p}]_{i\in\mathbf{V},p \in\mathbf{TR}}\), as well as a new timetable \(\mathbf{TT}=[TT_{p}]_{p\in\mathbf{TR}}\), where \(TT_{p}=<r^{\text{T}}_{p,i}(t^{\text{ARR}}_{i,p}+\Delta t^{\text{ARR}}_{i,p},t^ {\text{DEP}}_{i,p}+\Delta t^{\text{DEP}}_{i,p})_{i\in\mathbf{V}}>\) is a new timetable for train run \(p\). Both time shifts, together with the arrival and departure times from the original timetable \(\mathbf{TT}^{\text{G}}\), define new arrival and departure times. The existence of some planned closures, unknown while preparing the original timetable \(\mathbf{TT}^{\text{G}}\), is the only reason for rescheduling that is for the elaboration of a new timetable \(\mathbf{TT}\). No other unexpected events justifying possible rescheduling are taken into account in this paper. The considered track closures may directly affect train runs by their delaying, accelerating, or even canceling. The latter possibility is expressed in the model by an auxiliary decision variable \(\mathbf{x}=[x_{p}]_{p\in\mathbf{TR}}\) influencing \(\mathbf{TT}\) by constraint (see Formula (9), where \(x_{p}=1(0)\)
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Field Measurement Techniques
#### 3.1.2 Step and Touch Voltage Analysis Using High-Precision Meters  
Analysis of step and touch voltages is essential to assess the safety of the personnel under fault conditions [13; 14]. Step voltage is the voltage between a person's feet when standing on the ground, and touch voltage is the voltage between a person's hand and the ground. Precision voltmeters and safety analyzers are employed to measure these voltages during fault simulations. The touch voltage \(V_{touch}\) and step voltage \(V_{step}\) can be verified for IEEE 80-2013 compliance [3], which recommends that these voltages should not exceed 430 V for 10 kV systems under fault conditions.
## 3 Results  
### XRD Analysis  
Figure 1 shows XRD results from the precursor material treated at 250 \({}^{\circ}\)C. As expected, at that temperature, the ZnAl\({}_{2}\)O\({}_{4}\) showed high-intensity peaks that suggested its high purity and crystallinity. The peaks' height and width were an indication that the material was made up of nanometric-sized particles [21]. The compound's pure phase was identified through PDF # 65-3104, indicating that the ZnAl\({}_{2}\)O\({}_{4}\) belongs to the family of spinel-type materials, with a cubic crystalline structure (a = 8.087 A) and a spatial group Fd-3m (227) [2,3,5]. Our results were consistent with those reported by other research groups, which synthesized the same compound by different processes [5,6,8,9,10].  
Considering Figure 1, the crystal size \(t\) was calculated with [PERSON]'s equation [22]:  
\[t=\frac{0.9\lambda}{\beta cos\theta} \tag{1}\]  
where \(\lambda\) is the wavelength of the radiation (\(\lambda\) = 1518 nm), \(\theta\) is the Bragg angle, and \(\beta\) is the full width at half maximum (FWHM) of the diffraction peak. In our case, all the diffractograms' reflections were considered, obtaining an average crystal size of \(\sim\)14.63 nm.
# Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
###### Abstract  
The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.  
association rules; frequent itemset mining; big data; multi-objective optimization; maximal frequent itemset
## 5 Simulation and Experiment  
### Simulation  
To ensure that agricultural production is not delayed, a large number of simulation experiments are designed in this paper to verify the effectiveness and reliability of the DABAA. The following are the specific steps of the simulation.  
1. The agricultural machines in the rental service are numbered 1 to 6, and the number of scheduling tasks for each agricultural machine is determined to be 10;
2. The 600 rental orders received by the cloud platform are numbered, and then, according to the QoS evaluation model, data processing is performed on the _UT_, _WC_, _SE_, _DT_ and other indicators of each order. This paper sets \(q_{i}^{+}\in[0.8,0.95]\) and \(q_{i}^{-}\in[0.6,0.75]\), and the corresponding \(c_{1}\), \(c_{2}\), \(c_{3}\) and \(c_{4}\) are 0.35, 0.3, 0.2 and 0.15, respectively [34];
3. Table 4 shows the initial parameters [17; 35; 36]. All experiments are performed using a PC with an Intel core i5-6500 CPU at 3.20 GHz, 8 GB of memory, a Windows 10 operating system, and MATLAB 2015b software;
4. Figure 3 shows the optimization process of the agricultural machinery rental service. It shows that this algorithm tends to converge after 100 iterations, and this experimental result corresponds to the previous convergence analysis. The unit of the vertical axis is %;
5. Tasks assigned for each agricultural machine. After completing a scheduling sequence that has 10 rental tasks, each agricultural machine will receive a new scheduling sequence from the task pool. The smaller the number of tasks scheduled for each deployment is, the less time it takes to execute the tasks and the higher the real-time performance. Figure 4 shows the result of task assignment, where the axes indicate the virtual plane coordinates, each color line indicates a complete scheduling plan for each agricultural machine, and each point represents a coordinate given by the rental order.  
In general, the process of agricultural machinery rental is as follows. The number of tasks is determined for each schedule according to the maintenance time of the machine and the real-time performance of the scheduling tasks (i.e., 10 above), and the optimal scheduling sequence is generated for each machine based on the evaluation model through the DABAA. Since the optimal scheduling sequence makes the execution distance of the machine the shortest, the machine executes tasks in sequence according to the order and typically moves to the location of task 2 after executing task 1. This paper assumes that there are no emergencies during the movement; that is, the machine can reach the destination smoothly within the specified time. After the machine completes the current scheduling sequence, it is returned to the factory for maintenance and inspection, and then, a new scheduling sequence is generated for it until all tasks are completed.
## 4 Test Results
### AC Losses Tests of an Assembled Phase
Before measurements, the level of hysteresis losses in the yoke with taking into account the steel real magnetization curve using the ANSYS software [34] was calculated. For electromagnetic analysis, ANSYS implements the magnetic vector potential formulation. As a result, the hysteresis losses per cycle at a current amplitude of 60 A are less than 2\(\cdot\)10\({}^{-3}\) J/cycle, which is much less than the losses in the winding. The magnetic field distribution at 60 A amplitude of the current in the steel core was shown in Figure 9b. Figure 8: Losses for BLAC current case versus computation period number. The phase was tested on a sinusoidal current, and the frequency of the main harmonic was 50, 100, and 200 Hz (Figure 10a). Figure 10a shows that the losses increased more than the sum of the losses in the eight single coils (compare with Figure 6a). One of the reasons for this effect is the changed configuration of the magnetic field on the HTS coils. It is also possible that additional electromagnetic vibrations occur due to complexity of the assembly. Thus, for the phase, in terms of one coil, the losses increase by approximately 1.2 times compared to a solitary coil. Just as in the case of a single coil, the winding was tested on the same bench with modulated currents (Figure 10b). An important feature was a significant reduction in the amplitude of high-frequency fluctuations on the winding (Figure 11) in comparison with a single coil. To achieve the same current amplitudes, the supply voltage was raised to 100 V (from previously 15 V) due to the increased inductance. However, despite the improved operating conditions of HTS coils with respect to the high-frequency component, the losses in the winding increased significantly in comparison with the sinusoidal current (compare Figures 6b and 10b). This is due to both increased  
Figure 10: (**a**) The losses in the assembled phase for sinusoidal current; (**b**) stator losses without rotor on modulated currents. Figure 9: (**a**) The figure shows how the coils are connected inside the phase. Note that the voltage taps are soldered directly to the superconductor on the first and last phase coils to eliminate the influence of the current leads; (**b**) magnetic field distribution in the steel core at 60 A amplitude. losses in HTS coils operating at such a current and being exposed to an external magnetic field produced by the coils [35] and losses in the steel of the magnetic circuit.
###### Abstract  
Non-destructive testing (NDT) of materials and structures is a very important industrial issue in the fields of transport, aeronautics and space as well as in the medical domain. Active infrared thermography is an NDT method that consists of providing an external excitation to cause an elevation of the temperature field in the material, consequently allowing evaluation of the resulting temperature field at the surface. However, thermal exciters that are used (flash lamps, halogen, lasers) act only on the surface of the sample. On the other hand, several energy conversion systems can lead to the generation of volumetric sources; the phenomena of thermo-acoustics, thermo-induction, thermomechanics or thermochemistry can be cited. For instance, ultrasonic waves can generate volumetric heat sources if the material is viscoelastic or if there is a defect. The reconstruction of these sources is the initial process for the quantification of parameters responsible for the heating. Characterizing a heat source means reconstructing its geometry and the supplied power. Identification of volumetric heat sources from surface temperature fields is a mathematically ill-posed problem. The main cause of the issue is the diffusive nature of the temperature. In this work, 3D reconstruction of the volumetric heat sources from the resulting surface temperature field, measured by infrared thermography, is studied. An analysis of the physical problem enables specifying the limits of the reconstruction. In particular, a criterion on the achievable spatial resolution is defined, and a reconstruction limitation for in-depth sources is highlighted.  
inverse problem; tomography; infrared thermography; non-destructive testing; 3D reconstruction +
Footnote †: journal: Applied Physics Letters