esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture
### Control Logic  
The operation of the PPG readout circuit is orchestrated by three control signals: (1) SI_RST resets the SI by discharging the feedback capacitor and prepares the system for the next sample; (2) SI_EN is utilized to briefly turn the LED on and enable the SI through the switch S1 such that the amplified photocurrent can be captured; and (3) ADC_EN enables the ADC to start the conversion once the output of the integrator has settled. The most critical timing requirement among these signals is the pulse duration of SI_EN, represented as \(T_{int}\), as it has a direct impact on the total gain of the circuit as well as the overall power consumption of the system. This duration is constrained by a lower bound governed by the settling time of the TIA, whereas the active time of the LED should be ideally reduced as much as possible to save energy. For our circuit, we chose an on-time of 20 us (0.4% duty cycle) over a sampling frequency of approximately 200 Hz, as Figure 5a depicts.  
The timer/counter module of the microcontroller is utilized as a timing reference for the control signals, generating an interrupt at every 20 us and toggling the value of SI_RST or SI_EN appropriately (see the inset of Figure 5a). Figure 5b shows the measured output of the SI in response to these control signals. The third control signal, ADC_EN, is generated internally in the microcontroller. The main program loop takes the 8-bit output of the ADC and sends it to the VCO for wireless transmission through one of the IO pins after generating the data frame. The control logic generates the data frame by inserting a predefined header before the 8 bits of the sample, and creates the serial data stream with an effective burst rate of 5 kBd. The complete implementation of the control logic used 52% of the available program memory and less than 10% of the data memory in the microcontroller. The next iteration of the ASIC will replace this microcontroller with our implementation of the ADC and the control logic to lower the power consumption even further.  
Figure 4: Simulation results of the PPG readout circuit. (Blue) Output of the transimpedance amplifier. (Orange) Output of the switched integrator. (Yellow) Control signal for switch S1, which is also used for the LED driver.
# Investigation of High-Sensitivity pH Sensor Based on Au-Gated AlGaN/GaN Heterostructure
###### Abstract  
A high-sensitivity pH sensor based on an AlGaN/GaN high-electron mobility transistor (HEMT) with a 10 nm thick Au-gated sensing membrane was investigated. The Au nanolayer as a sensing membrane was deposited by electron-beam evaporation and patterned onto the GaN cap layer, which provides more surface-active sites and a more robust adsorption capacity for hydrogen ions (H\({}^{+}\)) and hydroxide ions (OH\({}^{-}\)) and thus the sensitivity of the sensor can be significantly enhanced. A quasi-reference electrode was used to minimize the sensing system for the measurement of the microliter solution. The measurement and analysis results demonstrate that the fabricated sensor exhibits a high potential sensitivity of 58.59 mV/pH, which is very close to the Nernstian limit. The current sensitivity is as high as 372.37 uA/pH in the pH range from 4.0 to 9.18, under a 3.5 V drain-source voltage and a 0 V reference-source voltage. Comparison experiments show that the current sensitivity of the Au-gated sensor can reach 3.9 times that of the SiO\({}_{2}\)-gated sensor. Dynamic titration experiments reveal the pH sensor's ability to promptly respond to immediate pH variations. These findings indicate that this pH sensor can meet most application requirements for advanced medical and chemical analysis.  
pH sensor; AlGaN/GaN; Au; HEMT +
Footnote †: journal: Applied Physics Letters
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 4 Experiments
### Original Synthetic Policy
There was no noticeable difference between (e) GWRP and (g) MEX, but the first event was almost missing in (f) AlphaMEX. This explains the low F1 score of AlphaMEX in Table 9 for the SED task. In (h), the proposed GTAP with \(\alpha=0\), the detection results were much more distinctive than the others, and the false predictions almost disappeared. \begin{table}
\begin{tabular}{c l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Pooling** & \multicolumn{3}{c}{**AT Task**} & \multicolumn{3}{c}{**SED Task**} & \multirow{2}{*}{**\#Step/s**} \\  & **Method** & **mPrc** & **mRcl** & **mF1** & **mPrc** & **mRcl** & **mF1** & **mF1** & **mF1** \\ \hline \multirow{4}{*}{CNN} & GWRP & 47.1 & 70.7 & 53.1 & 40.2 & 45.1 & 39.5 & 3.69 \\ \cline{2-10}  & \(\text{GTAP}_{\alpha=0.2}\) & 35.2 & 75.9 & 46.9 & 74.1 & 11.7 & 19.1 & \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=0}\) & 34.3 & 75.1 & 45.8 & 72.6 & 13.3 & 21.2 & 4.54 \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=-0.4}\) & 52.8 & 40.1 & 39.5 & 28.4 & 37.2 & 27.0 & \\ \hline \multirow{4}{*}{U-Net} & GWRP & 53.0 & 80.9 & 62.9 & 40.2 & 66.7 & 49.2 & 8.97 \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=0.2}\) & 48.2 & 79.3 & 58.9 & 56.7 & 49.0 & 50.6 & \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=0}\) & 46.5 & 81.2 & 58.0 & 53.0 & 53.3 & 51.7 & 13.11 \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=-0.4}\) & 68.6 & 70.3 & 68.0 & 38.8 & 66.1 & 47.4 & \\ \hline \multirow{4}{*}{LUU-Net} & AlphaMEX & 58.5 & 73.4 & 64.0 & 62.1 & 32.7 & 40.7 & 21.11 \\ \cline{1-1}  & MEX & 56.7 & 76.6 & 64.1 & 50.1 & 55.6 & 51.5 & 32.25 \\ \cline{1-1}  & GWRP & 56.8 & 77.4 & 64.1 & 45.9 & 60.0 & 50.7 & 28.99 \\ \cline{1-1} \cline{2-10}  & \(\text{GTAP}_{\alpha=0.2}\) & 56.7 & 77.9 & 64.5 & 56.0 & 52.0 & 52.5 & \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=0}\) & 55.7 & 79.0 & 64.4 & 53.2 & 55.6 & 53.1 & 35.68 \\ \cline{1-1}  & \(\text{GTAP}_{\alpha=-0.4}\) & 67.0 & 72.6 & 68.8 & 42.3 & 64.2 & 50.0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Audio tagging (AT) and sound event detection (SED) results on the dataset generated by the original synthetic policy. The neural network models were CNN, U-Net, and the proposed LUU-Net, whose configurations are shown in Tables 4–6, respectively. Various pooling methods were applied to the output of the LUU-Net: AlphaMEX, MEX, GWRP, and the proposed global threshold average pooling (GTAP) explained in Section 3.2 with varying \(\alpha\in\{0.2,0.0,-0.4\}\). GTAP with the same \(\alpha\) values was also applied to the CNN and U-Net to compare the performance variations with LUU-Net. For all the experiments, the mean precision (mPrc), mean recall (mRcl), mean F1 scores (mF1), and the number of steps per unit second were measured.
###### Abstract  
We investigate the structural, electronic, optical, and thermoelectric properties of three compositions of Mo\({}_{2}\)C-MXenes (Mo\({}_{2}\)CF\({}_{2}\), Mo\({}_{2}\)C(OH)\({}_{2}\), and Mo\({}_{2}\)CO\({}_{2}\)) from monolayer to multilayer by first principles calculation within Density Functional Theory (DFT) and Boltzmann transport theory. Firstly, the atomic structures of Mo\({}_{2}\)C-MXenes are optimized, and their respective structures are created with comparative research. Secondly, their electronic band structures and optical properties are studied in detail. The estimation of the bandgap energy of Mo\({}_{2}\)C-MXenes with its functionalization reveal that most Mo\({}_{2}\)CF\({}_{2}\) and Mo\({}_{2}\)C(OH)\({}_{2}\) layers are semiconductors, while Mo\({}_{2}\)CO\({}_{2}\) behaves as a metal. The electrical and optical properties can be altered by controlling the on-surface functional groups and the number of layers. Computation of the thermoelectric (TE) properties of Mo\({}_{2}\)C-MXenes reveals that, upon heating to 600 K, Mo\({}_{2}\)CF\({}_{2}\) and Mo\({}_{2}\)C(OH)\({}_{2}\) exhibit a high Seebeck coefficient and a relatively high electrical conductivity. The Seebeck coefficient reaches \(\sim\)400 \(\mu\)V K\({}^{-1}\) at room temperature for all layers of Mo\({}_{2}\)CF\({}_{2}\) MXenes. Our results prove that Mo\({}_{2}\)CF\({}_{2}\) is considered a promising material for thermoelectric devices, while Mo\({}_{2}\)CO\({}_{2}\) does not possess better thermoelectric performance. Mo\({}_{2}\)C-MXenes from monolayer to multilayer have outstanding properties, such as flexible bandgap energy and high thermal stability, making them promising candidates for many applications, including energy storage and electrode applications.  
Footnote †: journal:  
Article  
Electronic, Optical and Thermoelectric Properties of Two-Dimensional Molybdenum Carbon Mo\({}_{2}\)C-MXenes
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
## 2 Rationale and Theory
### MPI Parallel Inversion Algorithm
(5) The update direction is calculated, and the step size is determined. (6) The conductivity model is updated. (7) The iteration termination criteria are checked. If the criteria are met, the iteration terminates; if not, the iteration continues. (8) The termination criteria of inversion are when the data misfit is below a predefined threshold or the number of iterations reaches a set limit. The formula for calculating the misfit (root mean square, RMS) is as follows:  
\[RMS=\sqrt{\frac{\big{[}D_{\rho,obs}-F(M_{\sigma})\big{]}^{T}V_{\sigma}^{-1} \big{[}D_{\rho,obs}-F(M_{\sigma})\big{]}}{N}}, \tag{15}\]  
where \(N\) is the number of data points. In this study, the termination condition is set such that RMS is less than 1 or the number of iterations is greater than 100.
# Cold-Water Immersion Promotes Antioxidant Enzyme Activation in Elite Taekwondo Athletes
## 4 Discussion  
This study was conducted on 12 male elite Taekwondo athletes with the aim to assist with the recovery of the athletes, who need to compete in all their respective matches of a tournament within a single day. To this end, the athletes' HR was measured to investigate the intensity of a Taekwondo match, and CWI was applied to the below-knee area after a match to investigate its effects on the athletes' lipid peroxide and antioxidant enzymes  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time**Group** & **CONG** & **CWIG** & **F** \\ \hline \({}^{\text{a}}\) Pre-match & 112.4 \(\pm\) 14.37 cd & 99.2 \(\pm\) 37.30 bed & Group & 6.778 \({}^{\star}\) \\ \({}^{\text{b}}\) Post-match & 119.1 \(\pm\) 15.84 cd & 65.7 \(\pm\) 14.35 ad & Time & 13.317 \({}^{\star}\) \\ \({}^{\text{c}}\) Post-treatment & 94.3 \(\pm\) 14.38 ab & 70.8 \(\pm\) 9.78 ad & Group \(\times\) Time & 26.588 \({}^{\star}\) \\ \({}^{\text{d}}\) Post-rest & 93.4 \(\pm\) 13.74 ab & 134.7 \(\pm\) 24.98 abc & \multicolumn{1}{c}{Group \(\times\) Time} & 26.588 \({}^{\star}\) \\ \hline \hline \end{tabular}  
Mean \(\pm\) standard deviation, \({}^{\text{a}}\): Pre-match, \({}^{\text{b}}\): Post-match, \({}^{\text{c}}\): Post-treatment, \({}^{\text{d}}\): Post-rest; \({}^{\text{*}}\)\(p\) < 0.05, \({}^{\star}\)\(p\) < 0.001; significant differences in \(p\)-values of the Bonferroni post hoc test are indicated with letters.  
\end{table}
Table 4: Change in glutathione peroxidase (GPX) concentration after CWI (unit/\(\upmu\) mol).  
Figure 5: Change in GPX concentration after CWI. (Data are expressed as mean \(\pm\) SD; A main effect was evident for time and group \(\times\) time—\(p\) < 0.001; differences between each group were statistically significant—\(p\) < 0.05: difference from before, \({}^{\star}\)\(p\) < 0.5, \({}^{\star}\)\(p\) < 0.5; difference from after game, \(\#p\) < 0.01, \(\#\#p\) < 0.001; difference from before, \(\ddagger p\) < 0.05, \(\ddagger p\) < 0.01, \(\ddagger\)\(p\) < 0.001. CWI: cold-water immersion.).  
levels. Our study's findings regarding recovery after a match of elite Taekwondo athletes showed that the mean HR after a match was about 91% HR\({}_{\text{max}}\). CWI treatment after a match neither decreased lipid peroxide concentrations, nor did it confer positive effects on the concentration of SOD. However, it did improve GPX concentrations (Group \(\times\) Time, \(p<0.001\); Group, \(p<0.05\)).
###### Abstract  
In order to develop a new type of prefabricated bridge structure system with green, efficient and recoverable function, which complies with the new requirements of rapid repair of pier function after earthquake, and improves the applicable performance of prefabricated assembled pier in medium and high-intensity seismic areas, a precast segmental concrete-filled steel tubular (PSCFST) pier with an external energy dissipation ring is proposed. Based on ABAQUS analysis software, a four-segment PSCFST pier model is established, and the pseudo-static comparative analysis is carried out between the traditional PSCFST pier and the PSCFST pier with the external energy dissipation ring. The results show that compared with the traditional PSCFST pier, the lateral bearing capacity of the PSCFST pier with an external energy dissipation ring is increased by 60%, the energy dissipation capacity is increased by about 20 times, and the damage is concentrated in the energy dissipation ring, the damage is controllable, and the rapid repair after the earthquake can be realized by replacing energy dissipation devices and other measures. At the same time, the seismic performance of pier models with three different control parameters (initial prestress, material strength of energy dissipation ring and section width of energy dissipation ring) under reciprocating loading is analyzed. The results show that the initial prestress does not affect the cumulative energy consumption of the pier; the increase in the material strength of the energy dissipation ring improves the overall stiffness and improves the energy dissipation capacity of the PSCFST pier; the reduction in section width will affect the overall equivalent stiffness and unloading stiffness of the segmental pier, and the energy dissipation capacity will be significantly reduced.  
bridge engineering; precast segmental concrete-filled steel tubular bridge pier; energy dissipation ring; post-earthquake repair; reciprocating loading; seismic performance
# Road Segmentation and Environment Labeling for Autonomous Vehicles
## 2 Proposed Method
### Feature Learning and Preprocessing  
Feature learning is a part of the preprocessing step for understanding the dataset before it can be applied to train the network for object detection, semantic segmentation, and classification tasks. Feature learning [6] of the point cloud data can be challenging, as it is very unorganized. The multidimensional point cloud data take more computational time to classify the object on the road. In the case of road scenes, targets such as vehicles, buildings, and vegetation have varied heights compared to the flat road surface. The reflectance value of these objects is much higher than the road surfaces. Thus, filtering out the point cloud based on the height and reflectance value is the preliminary criteria for separating the non-ground points from the ground points [18].  
Recent studies have shown that separating ground and non-ground points makes the segmentation easier for extracting the region of interest (ROI). [PERSON] et al. [19] proposed various techniques to identify the ground points and have used them as data separators to segment the objects in the road scene. [PERSON] et al. [20] proposed a CNN-based method to learn features for scene and object recognition tasks. The neural network uses the scene-centric database with over 7 million labeled pictures of scenes called places to train the model and establish a new state-of-the-art technology for learning the diversity of the scene objects.  
Object detection and segmentation tasks in many real-world applications require multi-instance and multiview labeled training samples. In most cases, the data are weakly labeled or unlabeled for changing and unpredictable environments. Many machine learning techniques have been studied to train the classifier with unlabeled training samples. [PERSON] al. [21] proposed a co-labeling technique to classify the view from a set of pseudo labels generated by training another view.
## 2 Trajectory Design and Optimization
### Thrust Vector Model of the Gradient-Index Sail with a Sun-Facing Attitude
Second, since the sail nominal plane \(\mathcal{P}\) is assumed perpendicular to the Sun-spacecraft line at each instant, the normal unit vector \(\hat{n}\) coincides with the (radial) Sun-spacecraft unit vector \(\hat{p}\). Therefore, Equation (1) indicates that the component of \(a_{p}\) along (perpendicular to) the Sun-spacecraft direction, that is, the radial (or horizontal) component of the propulsive acceleration \(a_{p_{\mathrm{R}}}\) (or \(a_{p_{\mathrm{H}}}\)) depends only on the distance of the spacecraft from the Sun, viz.,  
\[a_{p_{\mathrm{R}}}=a_{p}\cdot\hat{n}=a_{c}\left(\frac{r_{\oplus}}{r}\right)^{2 }\eta_{n} \tag{4}\]  
\[a_{p_{\mathrm{H}}}=a_{p}\cdot\hat{m}=a_{c}\left(\frac{r_{\oplus}}{r}\right)^{2 }\eta_{m} \tag{5}\]  
with the ratio \(a_{p_{\mathrm{H}}}/a_{p_{\mathrm{R}}}=\eta_{m}/\eta_{n}\simeq 1.233\) remaining fixed during flight.
## 3 Results and Discussion
### Method Validation
#### 3.2.7 Robustness  
To evaluate the robustness of the chromatographic system, the effect of small changes (\(\pm 1\)) on its operating parameters at the tailing factor (Tf) and the AUC value (area under the curve) were investigated for each API separately (Table 5).  
According to the results, the system is robust to small changes in \(\lambda_{\text{max}}\) (nm) and temperature, while the mobile phase flow rate significantly affects the AUC values (areas under the curve).
## 5 WebGIS-Based Violation Monitoring Network Information Management System
### System Functions and Design  
According to the actual demands of illegal construction management, the system is divided into the following four functional modules, as shown in Figure 8.  
(1) Comprehensive demonstration module  
Based on remote sensing data of UAV, every illegal construction project can be processed into the products of orthophoto images, digital surface models, 3D models, and panoramic images. These products can display the inspection area in a more comprehensive display mode and show the development status of illegal buildings in a more vivid and flexible way. It is one way of mixing management and supervision for the phenomenon of illegal buildings.  
(2) Image database and management module  
The main task of this module is to manage and schedule the preprocessed UAV remote sensing images and it can provide basic services for the entire management system. It mainly includes the establishment of the image database, the import of multifformat image data, the browsing and displaying of image data, and the location and scope of image data, and other information management.  
(3) Vector database and management module  
This module is mainly used for the management and scheduling of the relevant vector data generating from the processing of original data and vector data of buildings' planning provided by relevant departments. By superimposing the vector data on the images and comparing them with the planning approval maps, they can accurately identify the illegal buildings.  
(4) GIS analysis and monitoring module of illegal buildings  
By using the accurate positioning and analysis capability of spatial information of the GIS system, the information such as the location, area, and center point of the illegal buildings can be calculated. Then, we can exclude and screen out the initially extracted illegal buildings' information, and we can determine the geographical locations of the illegal buildings, the types of the illegal buildings, and other information to prepare for the next step of monitoring. The monitoring work of illegal buildings is mainly divided into six steps: segmentation grid, polygon establishment, polygon confirmation,  
Figure 8: Functional modules of designed system.  
task distribution, field verification, and archive confirmation. The monitoring area is gridded and divided, and the management polygon of illegal buildings is created (a polygon represents one household of illegal building). After confirming that there is no error in the polygon information, the task is dispatched to the mobile station's grid members, and the grid members can verify the illegal construction in the field, confirm the relevant information, and then return the data through the mobile terminal to archive.
## 3 Inverse Kinematics
### Closed-Form Solution for Multiple-GSJ Section
#### 3.3.1 Locations of the GSJs
Take a five-UJ manipulator as an example to describe the above collision detection analysis process, as shown in Figure 5.  
By combining the boundary distribution, and taking the avoidance of collision between the links and the boundary as the criterion, the distances \(l_{v,i+1}\) and \(l_{\mathbf{O}_{i}}\) can be used to describe the manipulator's overall configuration in a narrow space scene more intuitively. This is the reason why the two distances are chosen as the parameters of the inverse kinematics solution process. Take Figure 6 as an example, the environment has four boundaries, and the coordinate system origins \(\mathbf{O}_{i+1,\mathbf{O}}_{i+2}\) and the link lengths \(l_{i+1}\), \(l_{i+2}\) are known. Considering that Link \(i+1\) whose posture is to be determined cannot collide with Boundaries 3 and 4, it is easy to select a \(l_{v,i+1}\) based on the position information of the two boundaries. At this time, the choice of \(\mathbf{O}_{i}\) is the intersection (only part of the intersection is made in the figure) of two spheres whose sphere centers and radii are \(\mathbf{O}_{i+1}\), \(\mathbf{O}_{i+2}\) and \(l_{i+1}\), \(l_{v,i+1}\) respectively. Some points (collision points 1 and 2) in the intersection may cause the link to  
Figure 5: Collision detection for a five-general spherical joint (GS) manipulator. collide with Boundaries 1 and 2. By introducing the distance \(l_{O_{i}}\), these points that cause collision can be eliminated. Therefore, \(l_{v,i+1}\) and \(l_{O_{i}}\) jointly determine the collision-free posture (collision-free point) of Link \(i+1\). Combining Equations (10) and (11), the determination algorithm of \(l_{v,i+1}\) and \(l_{O_{i}}\) can be summarized, as shown in Figure 7. The algorithm successively uses Equations (11)-(16) to gradually narrow the value range. In this process, according to Equations (14) and (15), only finite number of calculations of explicit equations and conditional judgments of some calculation results are performed, which satisfying the closed-form definition in [24]. Therefore, this will not affect the closed-form nature of the inverse kinematics algorithm proposed in this paper. Figure 6: Under a certain boundary distribution, the posture of Link \(i+1\) determined by the distances \(l_{v,i+1}\) and \(l_{O_{i}}\). Figure 7: Determination algorithm for the distances \(l_{v,i+1}\) and \(l_{O_{i}}\).
## 4 Numerical Algorithm and Results
### Example
Thus, the switch between different optimal paths occurs when \(x_{f}\) is close to a point \(x_{f}^{*}\) satisfying \(2U(x_{f}^{*})-2U(x_{sl})=2U(x_{u})-2U(x_{0})\). For our choice of \(x_{0}=-0.8\), we get \(x_{f}^{\ell}=-0.2589\). The PPD and its peak trajectories for \(x_{f}=-0.24\), \(-0.25\), \(-0.26\) are presented in Figure 9, confirming the above analysis. Figure 1:0 (**a**) The time required for a Hamiltonian trajectory to hit the line \(x=x_{f}\) from the initial point \((x_{0},p_{0})\). There exist five cases with their Hamiltonian orbits drawn in (**b**). Here, \(x_{0}=-0.8\) and \(x_{f}=-0.3\). Figure 1:1 The emergence of multiple peaks of the dispersion parameter \(\kappa\) for distinct time gaps \(T\). (**a**) The variations of the dispersion parameter \(\kappa\) with respect to time \(t\). (**b**) The variations of the dispersion parameter \(\kappa\) with respect to position \(x\). Here, \(x_{f}=1\) and \(\varepsilon=0.001\).