esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.4 Performance Metrics  
The following metrics were used to measure the performance of the system for the COVID-19 detection task: sensitivity (1), specificity (2), precision (3), and F1-score (4); since the dataset is unbalanced (see Sections 2.1 and 2.2.3), the balanced accuracy was also used (5). In addition, the Area Under Curves (AUCs) of the Receiver Operating Characteristic (ROC) was calculated.  
\[\text{Sensitivity}=100\times\frac{TP}{TP+FN} \tag{1}\]  
Figure 2: Diagram of COVID-XNet. It consists of five convolutional layers (Conv), four max pooling layers (MaxPool), a GAP layer, and a softmax layer. Conv1, Conv2, and Conv3 use five \(\times\) five kernel size, while Conv4 and Conv5 use three \(\times\) three. All MaxPool layers use \(2\times 2\) kernels.  
\[\text{Specificity} =100\times\frac{TN}{TN+FP} \tag{2}\] \[\text{Precision} =100\times\frac{TP}{TP+FP}\] (3) \[\text{F1-score} =2\times\frac{\text{Precision}\times\text{Sensitivity}}{\text{ Precision}+\text{Sensitivity}}\] (4) \[\text{Balanced accuracy} =\frac{\text{Sensitivity}+\text{Specificity}}{2} \tag{5}\]  
where _TP_ and _FP_ refer to true positive cases (when the system diagnoses a COVID-19 case correctly) and false positive cases (the system detects a normal X-ray image as a COVID-19 case), respectively. On the other hand, _TN_ and _FN_ refer to true negative cases (the system detects a normal case correctly) and false negative cases (the system diagnoses a COVID-19 case as normal), respectively.  
However, we believe that only reporting the metrics of the system is not the best option to validate a model that performs COVID-19 detection in X-ray images, since the system could be learning patterns that are not related to COVID-19. This becomes a greater problem when working with small datasets that do not contemplate the variability of these images that were caused by different factors, such as the X-ray machine used and the patient's constitution, position, or condition. For this reason, we proposed the application of CAMs to visualize what the system is focusing on when performing the prediction. In this way, CAMs can be used by specialists to validate the system's output.
# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
## 2 Materials and Methods
### Mathematical Model
#### 2.1.3 Heating and Cooling Subsystem
The amount of heating or cooling power needed to achieve the desired temperature is calculated from the static equation considering the outside temperature and the sunshine power:  
\[\mathrm{u(1)}=\mathrm{K}\cdot(\mathrm{T_{inside}(1)}-\mathrm{T_{outside}(1 )})-\mathrm{P_{Sun}(1)}, \tag{5}\]In case the calculated power exceeds the limits of heating and cooling, then the actual indoor temperature is calculated from the static equation by the following formula, which is taken from Equation (5):  
\[\mathrm{T_{inside}(1)=(u(1)+P_{Sun}(1))/K+T_{outside}(1)}. \tag{6}\]  
Then, the desired temperature in the first step is considered to be the same as the calculated indoor temperature in order to start the simulation with a control deviation equal to zero. In the following time steps, the indoor temperature is calculated by:  
\[\mathrm{T_{inside}(k)=[(P_{heating}(k-1)+P_{Sun}(k-1)-P_{cooling}(k-1)+K\cdot(T_ {inside}(k-1)-T_{outside}(k-1))]\cdot T_{s}/M+T_{inside}(k-1)}. \tag{7}\]  
The actual heating and cooling power used for indoor temperature calculation is divided by the efficiency value. Each system has its own efficiency, and its range is between 0 and 1, where 0 means 0% efficiency and 1 means 100% efficiency. The resulting forecast, shown in Figure 2, was obtained for the building's thermal loss coefficient of 0.25 kW/\({}^{\circ}\)C, M representing mass and specific heat of 3000, heating maximal power consumption of 3 kW with heating efficiency 90%, maximal power consumption of cooling of 4 kW and the efficiency of cooling 60%. These values were chosen based on experience in terms of the energy consumption values of a real house. The sampling period and the simulation timestep were set to 300 s. The controller's proportional gain \(\mathrm{k_{P}}\) was set to 4 and the parameter of the integration time constant \(\mathrm{T_{i}}\) was set to 12,000 s.  
The gain of the controller corresponds to the inverse of the system gain, and the integration time constant of the controller is consistent with the dominant time constant of the system. This setting leads approximately to aperiodic control response. The heating power of sunshine is defined by the area affected by the sun during the day and the solar irradiation forecast by the formula:  
\[\mathrm{P_{Sun}=I_{cloud}\cdot A_{exposed}\cdot k_{solar\_heat}}, \tag{8}\]  
where  
\(\mathrm{P_{Sun}}\) is the heating power from the Sun that is transferred indoors, W;  
\(\mathrm{A_{exposed}}\) is the area that is exposed to the Sun, m\({}^{2}\);  
\(\mathrm{k_{solar\_heat}}\) is the coefficient of transformation of the irradiation to heating power. Figure 2: Forecast of heating and cooling power consumptions with regard to the desired and outside temperatures.
## 3 Results
### Trial n.1--Martempering vs. Austempering  
The effects of mattermpering and austempering routes were analyzed by comparing the HRC hardness evolution along the ring's height (d) and in the center of the ring's thickness for the heat-treated EN 100 CrMo7 rings, as summarized in Figure 6. As expected, the highest HRC values are observed for the martempered ring, that exhibits around 66 HRC along the ring's height. The austempeered rings, regardless of the temperature of the salt bath, attain HRC values around 60 with slight differences (about 2 HRC points) between the surfaces and the center of the ring's thickness.  
Such results were substantiated by the SEM analyses, as can be observed by the micrographs reported in Figure 7. For the martempered ring (Figure 7a,b), the microstructure consisted of tetragonal martensite (matrix) and spheroidized carbides (orthorhombic crystal structure and (Fe,Cr)\({}_{3}\)C stoichiometry, as reported elsewhere [12]). The microstructure at the surface of the ring (Figure 7a) showed a completely martensitic matrix with undissolved globulized cementite. Conversely, in the central region of the ring (Figure 7b), where the cooling rate during quenching was lower, some dark regions, ascribed as the incipient formation of upper bainite [34], were detected (see red arrows in Figure 7b). By contrast, the microstructure of the austemped rings mainly consisted of bainite, retained austenite, and undissolved carbides, homogeneously distributed within the matrix and with a fine and uniform size. The representative micrographs of the upper surface and the center region for austempering performed at 240 \({}^{\circ}\)C and 220 \({}^{\circ}\)C, respectively, are depicted in Figure 7c-f. As stated above, also for the austemped rings, the center of the rings revealed some regions ascribed as the incipient formation of upper bainite (see red arrows in Figure 7d,f).  
Figure 5: Micrographs of the EN 100 CrMo7 steel in the as-received condition: (**a**) OM micrograph and (**b**) BSE-SEM micrograph.  
Figure 6: Hardness evolution (HRC) along the ring’s height (d) for the heat-treated EN 100 CrMo7 rings—Trial n.1.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 3 Approaches to Flexible Oxide-Based TFTs
### Flexible TFTs under Mechanical Stress
#### 3.2.1 Bending Test Platforms and Methods  
Some bending test platforms and methods are reported in References [36; 175], which simulate the operation of flexible devices in real-world scenarios. Despite all of the differences in bending behaviors, TFTs suffer no more than the tensile or compressive strain from along with the channel length or the channel width as the mechanical process is applied. Figure 11 shows the four common types of bending test platforms: (A) the substrate between two moving metal splints can be bent to various radius [173], (B) the substrate is wrapped around a rigid cylindrical rods, which makes it easy to contact the electrodes with probes [158], (C) custom-built bending testers for flexible TFTs and displays [162], which includes a programmed counter, a dc motor and a jig. With this kind of testers, the number of bends on devices or displays can be counted conveniently. (D) An extreme bending test device is shown in panel D, where the critical radius of curvature of TFTs can be measured [155].  
Figure 11: Bending test platforms (**a**) Substrate between two moving metal splints [173]. (**b**) Substrate wrapped around the rigid cylindrical rods [158]. (**c**) Custom-built bending testers [162]. (**d**) Extreme bending test [155].
# Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios
## 3 Dynamic Tests
### Measurement Setup  
Figure 6 shows a lateral and plan views of the measurement layout used in both dynamic tests considering the unloaded and loaded configurations. The dynamic tests were performed based on a technique that considers fixed reference points (marked in red in Figure 6) and mobile measuring points (marked in blue in Figure 6). The response was evaluated in terms of the accelerations in the vertical (z) direction, in four setups, totaling 20 measurement points (1 to 20): 14 located on the platform and 6 located on the bogies. The accelerometers located on the bogies allow scaling the movements of the bogies in relation to the movements of the vehicle platform.  
Figure 5: Dynamic tests of _Sgnss_ wagon: (**a**) loaded configuration, (**b**) detail of the central stowage, (**c**) unloaded configuration.  
Figure 6: Dynamic tests of _Sgnss_ wagon: measurement setup (reference points in red and mobile points in blue).  
In total, 14 high-sensitivity piezoelectric accelerometers were used, PCB model 393B12, with a sensitivity equal to 10 V/g, a measurement range of \(\pm\)0.5 g and a frequency range of 0.15-1000 Hz. The accelerometers were attached to the car body using metallic angles fixed to the vehicle with magnetic disks. The PC, data acquisition system (DAQ) and battery supplying system were located near the centre of the vehicle. The data acquisition was performed through the cDAQ-9172 system using modules NI 9233 for IPEE type accelerometers. The time series were acquired for 10 min periods, with a sampling frequency of 2048 Hz, posteriorly decimated to a frequency of 256 Hz. Figure 7 presents some details of the DAQ and the accelerometer connection to the vehicle platform and bogies.  
The dynamic test was carefully planned to minimize the influence of noise in the measurements, which involved the use of shielded cables, insulated cable connectors, stable DC power supply from electrical batteries, denoise filters in the A/D conversion process and high-sensitivity accelerometers.  
During the dynamic test, the vehicle was operating at slow and intermittent motion, or at rest, and so, a random external excitation, in time and space, was required to increase the vibration levels. The excitation was provided by means of a group of people jumping on the edges of the vehicle platform, as well as impacts induced by impulse hammers. This technique guarantees higher signal-to-noise ratios and consequently, an increase of the coherence between the measured signals.
## 3 Results and Discussion
### Raman Spectra of Reference Biological Macromolecules
#### 3.3.1 Calf Thymus DNA  
The spectra of calf thymus DNA measured using different instruments exhibited evident similarity (Figure 4a). Table S1 summarizes the assignments of Raman bands observed for calf thymus DNA. The spectra of calf thymus DNA are dominated by Raman bands of nucleic bases--purines (A, G) and pyrimidines (C, T). There are three main spectral regions in which these bands are localized [29; 30; 31]: C=O stretching vibrations (1600-1750 cm\({}^{-1}\)); in-plane ring deformations of the ring (1000-1600 cm\({}^{-1}\)); ring breathing and out-of-plane deformations (600-800 cm\({}^{-1}\)). In addition to these vibrations, the bands of phosphate diester (P) and deoxyribose (dRib) residues also contribute to different regions in the spectrum of DNA.  
Figure 4: Raman spectra of reference biological macromolecules collected with laboratory (blue), modular (green), and handheld (red) Raman instruments: (**a**) DNA; (**b**) HSA; (**c**) collagen; (**d**) HA.  
The positions and relative intensities of the most pronounced peaks are very close, although they vary somewhat within a range of up to 5 cm\({}^{-1}\). The band position or intensity deviations observed for portable instruments compared to the DXR SmartRaman could be due to the influence of unresolved nearby bands. The portable instruments, especially Ahura FirstDefender, produced broadened peaks, resulting in a reduced signal resolution and the appearance of shoulders. For example, the spectrum obtained using DXR SmartRaman showed well-resolved weak bands at 1504 and 1462 cm\({}^{-1}\). In the case of portable instruments, these bands were observed as shoulders of the peak at 1482-1485 cm\({}^{-1}\) assigned to the in-plane ring vibrations in purines. The nearest weak band at about 1530 cm\({}^{-1}\) arose from the C=N stretching vibration in C [29; 30; 31]. This band was observed as a shoulder in the spectrum obtained using the Ahura FirstDefender device. DXR SmartRaman and i-Raman Plus devices showed a partially overlapped weak band of the C-O stretching vibration in deoxyribose (dRib) centered at 1066 and 1060 cm\({}^{-1}\), respectively [29; 30; 31], while the Ahura FirstDefender showed only an unresolved low-frequency shoulder of the strong band at 1099 cm\({}^{-1}\), which arose from the symmetric stretching vibration of P. Two intense to medium bands observed at 677, 728, and 783 cm\({}^{-1}\) and a well-resolved shoulder at 744 cm\({}^{-1}\) in the spectrum obtained using the DXR SmartRaman were assigned to the ring breathing vibrations in nucleic bases [29; 30; 31]. For the Ahura FirstDefender, the corresponding bands were observed at higher frequencies, i.e., 670, 733, and 786 cm\({}^{-1}\), due to the adjacent unresolved bands. The shoulder near 744 cm\({}^{-1}\) was unresolved in the spectra obtained by the portable devices. Among the Raman instruments, these bands are the narrowest for the DXR SmartRaman device, as confirmed by the peak fitting procedure described above.
## 3 Analysis of Simulation Data Based on D-S Evidence Theory
### Data Analysis Based on D-S Evidence Theory
The data collection of the rate of change of the cross-sectional area of each node of the cable in the field is shown in Figure 7.  
The rate of change of the cross-sectional area of each node on the cables was calculated according to Equation (16). To make the comparison of the results clearer, this study adjusted the sum of the detection result values to 1 for each point. The rate of change of the cross-sectional area of each node on the cable was corrected according to Equation (17), and the results of the detection of the structural safety performance expressed by the corrected rate of change of the cross-sectional area of each node on the cable were used for comparison with the results of the D-S evidence theory. The discrimination of the sensitivity of each node of the cable is presented in Table 3.  
\[D_{mi}^{*}=\frac{\left|A_{mi}-A_{mi}^{d}\right|}{A_{mi}} \tag{16}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & **Discrimination Based** & **Discrimination** & **Discrimination** & **Discriminative** & **Detected Results of** \\  & **on Cable Force** & **Based on Strain** & **Based on Strain** & **Based on** & **Results of D-S** & **Structural Safety** \\  & & & & **Deflection** & **Evidence Theory** & **Performance** \\ \hline Node 1 & 12.5\% & 12.3\% & 11.2\% & 8.21\% & 8.69\% \\ Node 2 & 13.1\% & 12.8\% & 12.3\% & 9.84\% & 9.24\% \\ Node 3 & 13.7\% & 13.7\% & 12.7\% & 11.37\% & 10.72\% \\ Node 4 & 15.6\% & 15.9\% & 14.9\% & 17.63\% & 18.67\% \\ Node 5 & 16.4\% & 15.6\% & 15.6\% & 19.04\% & 20.18\% \\ Node 6 & 14.9\% & 15.3\% & 16.9\% & 18.38\% & 19.43\% \\ Node 7 & 13.8\% & 14.4\% & 16.4\% & 15.53\% & 14.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Judgment of the sensitivity of each node of the cable. Figure 7: Data acquisition of the rate of change of cross-sectional area. In Equation (16), \(D_{mi}^{*}\) denotes the rate of change of the cross-sectional area of each node of the cable in a realistic structure, and \(A_{mi}\) denotes the cross-sectional area of each node on the cable before the effect of temperature. \(A_{mi}^{d}\) denotes the cross-sectional area of the nodes on the cable after a temperature increase of 55 \({}^{\circ}\)C.  
\[D_{mi}=\frac{D_{mi}^{*}}{k} \tag{17}\]  
In Equation (17), \(D_{mi}\) denotes the corrected rate of change of the cross-sectional area of each node on the cable, and \(\mu\) indicates the coefficient of correction of the rate of change, \(k=\sum_{i=1}^{7}D_{mi}^{*}\). In Table 3, by comparing the change rate of mechanical parameters of each point based on D-S evidence theory analysis and the change rate of area based on field acquisition, it can be obtained that when the temperature increases by 55 \({}^{\circ}\)C, the order of sensitivity of each point is consistent, and the sensitivity of node 5 is the highest, so it is necessary to focus on monitoring the node.
# A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles
## 3 Model Frame Construction  
### Modeling Framework  
The proposed method, outlined in Figure 1, integrates preprocessed data from shared bicycles (in the following text, \"bike-sharing\" specifically refers to \"dockless bike-sharing\") and POI classification data from Gaode Maps (known as Google Maps in China). Initially, a correlation between travel purposes and POI categories is established. POIs are ranked from 1 to 9 based on their frequency in each category, as displayed in Table 1. Using bicycle data, the start and end points of trips are identified, and a maximum walking radius is defined to determine candidate POI destination areas. A list of potential POIs within each area is compiled. The model, termed GMOD (gravity model considering origin and destination), adapts the basic gravity model by considering distance, time, and activity type proportions. The specific attraction of each POI is assessed at both trip endpoints, refined using Bayes' rule, to accurately infer the purpose of each trip.
## 2 Methods  
### Principle of WECM with Pulsating Radial Electrolyte Supply  
Figure 1a shows the principle of WECM with pulsating radial electrolyte supply. Fresh electrolyte flows into the two ends of the tube electrode, flows out from the holes on surface, then through the machining gap, and finally out from behind the tube electrode. During processing, the holes on the tube electrode are always facing the machining area along with the axial vibration of electrode or workpiece. Thus, fresh electrolyte directly enters the machining area after being ejected from the holes. The proposed method shortens the flow path greatly, decreases the flow resistance of electrolyte, and strengthens its flushing ability. For every point in the machining gap, the electrolyte pressure and flow rate fluctuate periodically, keeping a dynamic uniform distribution. By means of separating micro-holes on the tube electrode, the workpiece is separated into several regions in its thickness direction. Fresh electrolyte can directly reach the electrode surface in the whole thickness range of the workpiece at the same time, and quickly participate in electrochemical reaction, which improves the ability of cutting thick workpiece. Therefore, pulsating radial supply of electrolyte in WECM could enhance the mass transfer, thereby greatly ameliorating the situation in which transmission of electrolyte is limited in the narrow machining gap for thick workpieces. However, since the electrolyte enters from both ends of the tube electrode, it is squeezed at the intermediate position, causing the electrolyte pressure in the intermediate portion to be higher than both sides. If the diameters of the holes are uniform, there will be a difference in flow rate of each hole. The electrolyte supply on both sides is likely to be insufficient, which may affect the process stability. Increasing the flow rate of the holes on both sides by increasing the diameter should be an effective method to reduce the difference in flow rates of holes at different locations. Thus, a tube electrode with varying micro-holes shown in Figure 1b is in need. In this paper, the tube electrode has a wall thickness of 0.1 mm and an outer diameter of 0.7 mm.
## 5 Simulation and Discussion
### Comparative Experiment  
In the experiment, compare the methods used in this study with the velocity obstacle method. Table 5 contains the initial parameters of the OS and TS. Figure 15 depicts the initial scene of the experiment. The ship trajectory in Figure 16a is obtained using the method proposed in this study. The ship trajectory in Figure 16b is obtained by the velocity obstacle method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ship List** & **Initial Position (m)** & **Initial Course (\%)** & **Diversion Angle (\%)** & **Speed (kn)** \\ \hline OS & (41,000,8000) & 000 & – & 12 \\ TS1 & (40,950,10,000) & 000 & 0 & 9 \\ TS2 & (59,800, 37,365) & 240 & 30 & 12 \\ TS3 & (24,000, 58,500) & 120 & 60 & 11 \\ TS4 & (20,000, 52,000) & 120 & 60 & 11 \\ TS5 & (25,500, 500) & 000 & \(-\)30 & 11 \\ TS6 & (5500, 46,000) & 150 & 30 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial parameters of OS and TSs in simulation experiment 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ship List** & **Initial Position (m)** & **Initial Course (\%)** & **Initial Speed (kn)** & **Length (m)** \\ \hline OS & (25,400,18,000) & 000 & 12 & 225 \\ TS & (34,000, 39,000) & 235 & 12.5 & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial parameters of OS and TS in comparative experiment.  
Figure 14: OS’s Navigation trajectory when the TSs variable course. (**a**) The initial scene of the experiment. (**b**) Simulation experiment process from 1 s to 1810 s. (**c**) Simulation experiment process from 1811 s to 4201 s. (**d**) Simulation experiment process from 4202 s to 5823 s. (**e**) The whole process of the simulation experiment.  
In Figure 16a, when T = 292 s, the OS identifies the presence of a collision risk and alters course by 18\({}^{\circ}\) to the starboard to avoid collision. When T = 1435 s, the OS begins to resume the planned route. In Figure 16b, when T = 1356 s, the OS identifies the presence of a collision risk and alters course by 23\({}^{\circ}\) to the starboard to avoid collision. When T = 2089 s, the OS begins to resume the planned route. It can be seen that the method proposed in this study can identify the collision risk earlier and make corresponding avoidance measures when the OS alters course along the traffic flow.  
Figure 16: Contrast experiment: (**a**) the trajectory of the OS when using the method proposed in this study; (**b**) the trajectory of the OS when using the velocity obstacle method.  
Figure 15: The initial scene of the experiment.