esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
\begin{tabular}{l l} \hline \(Q_{1}\) & Main switch \\ \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(C_{5}\) & Current-sharing interleaved capacitors \\ \(L\) & Input inductor \\ \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\), \(D_{5}\), \(D_{6}\) & Diodes \\ \(C_{01}\), \(C_{02}\), \(C_{03}\), \(C_{04}\), \(C_{05}\), \(C_{06}\) & Output capacitors \\ \(LS_{1}\), \(LS_{2}\), \(LS_{3}\), \(LS_{4}\), \(LS_{5}\), \(LS_{6}\) & LED strings \\ \(T_{s}\) & Switching period \\ \(f_{s}\) & Switching frequency \\ \(D\) & Duty cycle \\ \(t_{0}\), \(t_{1}\) & Time instants \\ \(R_{eq}\) & Equivalent output resistance \\ \(v_{gs}\) & Gate driving signal for \(Q_{1}\) \\ \(v_{ds}\), \(v_{a}\) & Voltage across \(Q_{1}\) \\ \(i_{ds}\) & Current in \(Q_{1}\) \\ \(V_{C1}\), \(V_{C2}\), \(V_{C3}\), \(V_{C4}\), \(V_{C5}\) & Voltages across \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(C_{5}\) \\ \(i_{C1}\), \(i_{C2}\), \(i_{C3}\), \(i_{C4}\), \(i_{C5}\) & Currents in \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(C_{5}\) \\ \(i_{C1(on)}\) & Current \(i_{C1}\) during turn-on period of \(Q_{1}\) \\ \(i_{C1(off)}\) & Current \(i_{C1}\) during turn-off period of \(Q_{1}\) \\ \(I_{C1(on)}\) & Constant value of \(i_{C1}\) during turn-on period \\ \(I_{C1(off)}\) & Constant value of \(i_{C1}\) during turn-off period \\ \(v_{L}\) & Voltage across \(L\) \\ \(v_{L(on)}\) & Voltage across \(L\) during turn-on period of \(Q_{1}\) \\ \(v_{L(off)}\) & Voltage across \(L\) during turn-off period of \(Q_{1}\) \\ \(i_{L}\) & Current in \(L\) \\ \(I_{L,min}\) & Minimum DC value of \(i_{L}\) \\ \(\Delta i_{L}\) & Peak-to-peak value of current ripple of \(i_{L}\) \\ \(v_{D1}\), \(v_{D2}\), \(v_{D3}\), \(v_{D4}\), \(v_{D5}\), \(v_{D6}\) & Voltages across \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\), \(D_{5}\), \(D_{6}\) \\ \(i_{D1}\), \(i_{D2}\), \(i_{D3}\), \(i_{D4}\), \(i_{D5}\), \(i_{D6}\) & Currents in \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\), \(D_{5}\), \(D_{6}\) \\ \(i_{LS1}\), \(i_{S2}\), \(i_{LS3}\), \(i_{LS4}\), \(i_{LS5}\), \(i_{LS6}\) & Currents in \(LS_{1}\), \(LS_{2}\), \(LS_{3}\), \(LS_{4}\), \(LS_{5}\), \(LS_{6}\) \\ \(I_{LS1}\), \(l_{LS2}\) & Dc values of \(i_{LS1}\), \(i_{LS2}\) \\ \(V_{in}\) & Input voltage \\ \(V_{in,max}\) & Maximum input voltage \\ \(V_{in,min}\) & Minimum input voltage \\ \(V_{o}\) & Output voltage \\ \(V_{o1}\), \(V_{o2}\), \(V_{o3}\), \(V_{o4}\), \(V_{o5}\), \(V_{o6}\) & Voltages across \(C_{01}\), \(C_{02}\), \(C_{03}\), \(C_{04}\), \(C_{05}\), \(C_{06}\) \\ \(i_{C01}\), \(i_{C02}\), \(i_{C03}\), \(i_{C04}\), \(i_{C05}\), \(i_{C06}\) & Currents in \(C_{01}\), \(C_{02}\), \(C_{03}\), \(C_{04}\), \(C_{05}\)
## 2 Results
### Partial Correlation Analysis between the NPP and Climatic Factors  
The partial correlation of the NPP with temperature, precipitation, solar radiation, and relative humidity during the growing seasons of 2003 and 2012 was analyzed. The temperature over Northeast China is extremely low in winter and vegetation almost stops growing. As the vegetation growth in this period has no linear relationship with temperature, only the relationship between the NPP in the growing season and the temperature was investigated. Over 56% of the NPP was positively correlated with temperature, with Daxing'anling, Xiaoxing'anling, and the Sanjiang Plain being the most obvious. Correlation between the NPP and precipitation over Northeast China indicated spatial variations (Figure 3b). The crop NPP over the Songnen and Liaohe plains correlated positively with precipitation; the other crop NPP had no significant correlation with precipitation caused by irrigation and snow melting. Most of the grass NPP demonstrated a positive correlation with precipitation over Hulunbeier Grassland and Horqin Sandland, where low temperature and high solar radiation appeared frequently. The forest NPP, especially in Daxing'anling and Xiaoxing'anling, was negatively correlated with precipitation.  
Figure 3: Results of the partial correlation analysis between the vegetation NPP and meteorological factors during 2003 to 2012: (**a**) temperature, (**b**) precipitation, (**c**) solar radiation, and (**d**) relative humidity.  
The positive effect of solar radiation on the vegetation NPP was mainly reflected in the central and western parts of Daxing'anling and part of the Changbai Mountain, with a correlation factor over 0.6. However, the grass NPP that was distributed in the areas with a low elevation showed a negative correlation with solar radiation. Relative humidity reflects the moisture content in the air and is substantially affected by precipitation and solar radiation. Relative humidity influences the opening and closing of plant stomata, which, in turn, affect the physiological processes of vegetation, such as transpiration and photosynthesis. Relative humidity that is too high or too low will cause the plant stomata to close, slowing down or even stopping photosynthesis. Overall, relative humidity was positively correlated with the grass and crop NPPs. Since vegetation transpiration was negatively correlated with relative humidity, high relative humidity preserved more water for vegetation growth. Relative humidity was negatively correlated with the forest NPP because the forest accumulated water through its developed root system.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results
### Accuracy of Pose Estimation
Figure 8b,d display changes in absolute pose errors for these two sequences. The results indicate that our method introduces minimal deviation from the original pose, guaranteeing the performance of visual SLAM. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Sequence** & **Original** & **Proposed Method** & **Black** & **Grey** & **White** & **Gaussian** & **Ran25** & **Ran50** \\ \hline Oxford-54 & 0.437 & **0.451** & 0.656 & 0.659 & 0.968 & 0.660 & 0.926 & 0.742 \\ Oxford-58 & 0.636 & **1.223** & 2.837 & 1.958 & 2.046 & 1.344 & 2.950 & 1.302 \\ Oxford-12 & 0.210 & **0.112** & 1.596 & 1.511 & 1.304 & 0.902 & 0.383 & 2.355 \\ Oxford-05 & 0.297 & **0.220** & 0.239 & 0.478 & 0.373 & 0.394 & 0.460 & 0.969 \\ Oxford-15 & 0.190 & **0.310** & 0.683 & 0.673 & 1.345 & 0.534 & 0.985 & 0.853 \\ KITTI-001 & 0.031 & **0.040** & 0.093 & 0.137 & 0.078 & 0.101 & 0.056 & 0.113 \\ KITTI-009 & 0.201 & **0.273** & 0.730 & 0.604 & 0.589 & 1.828 & 0.577 & 0.787 \\ KITTI-051 & 0.135 & **0.170** & 0.324 & 0.191 & 0.380 & 0.787 & 0.306 & 0.307 \\ KITTI-096 & 0.190 & **0.405** & 0.655 & 0.571 & 0.594 & 1.968 & 0.996 & 0.658 \\ KITTI-104 & 0.138 & **0.071** & 0.847 & 0.254 & 0.907 & 0.428 & 0.209 & 0.409 \\ KITTI-019 & 0.214 & **0.189** & 0.421 & 0.519 & 0.325 & 0.521 & 0.503 & 0.565 \\ KITTI-022 & 0.137 & **0.194** & 0.343 & 0.389 & 0.419 & 0.329 & 1.458 & 0.343 \\ KITTI-018 & 0.463 & **0.675** & 2.343 & 2.330 & 1.843 & 1.493 & 2.309 & 1.613 \\ KITTI-020 & 0.312 & **0.261** & 1.013 & 0.945 & 0.792 & 0.737 & 0.651 & 0.745 \\ KITTI-070 & 0.134 & **0.572** & 1.341 & 1.701 & 0.874 & 1.705 & 0.933 & 0.729 \\ KITTI-004 & 0.115 & **0.078** & 0.369 & 0.187 & 0.187 & 0.304 & 0.160 & 0.252 \\ KITTI-016 & 0.062 & **0.110** & 0.304 & 0.256 & 0.674 & 0.376 & 0.978 & 0.399 \\ KITTI-047 & 0.635 & **0.945** & 4.734 & 3.990 & 4.861 & 3.277 & 3.602 & 6.391 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The absolute trajectory error of the trajectory generated by the different privacy-preserved ORB-SLAM3. RMSE/m. Figure 7: Visualization of two consecutive images from sequence 58. Figure 9 presents a comparison of pose estimation results obtained by the original ORB-SLAM3 and the proposed method, alongside the ground truth trajectories for sequences 00 and 05 of the KITTI SLAM Evaluation 2012 dataset. Figure 9a,c compare our method with ORB-SLAM3, and the ground truth trajectory on the xz plane for sequences 00 and 05, respectively. Figure 9b,d display trajectory changes along the xyz axes. As shown, the proposed method maintains high positioning accuracy. Notably, in certain corners, such as those on the left side of Figure 9a,c, our method produces a trajectory even closer to the ground truth. This demonstrates that privacy-preserving visual SLAM can achieve excellent positioning accuracy while protecting privacy-sensitive information.
## 2 Application to Flexible Structures
### Structural Vibration Control
This change of control strategy is closely related to real applications in practical environments where reliability of the control system and cost effectiveness are critical issues to be seriously considered.
## 1 Introduction
We have evaluated this architecture in two domains: pipeline leak detection and patient-ventilator asynchrony detection, finding that the proposed algorithm performs well for both datasets in comparison to the literature. Our contribution in this paper is to design and evaluate a semi-supervised AD algorithm for time series data. To the best of our knowledge, this is the first time a deep neural network forecaster has been combined with a shallow one-class classifier and synthetic-example oversampling for this task. Our oversampling technique is also unique in the literature, as it produces synthetic forecasts, rather than a synthetic point in embedding space or an entire synthetic time series. This new architecture is able to achieve very high performance in the leak-detection and PVA-detection problems. The remainder of this paper is organized as follows. We first review essential background in Section 2 and related work in Section 3. We discuss the theoretical development of our model in Section 4 and our design and evaluation methodology in Section 5. We present our experimental results and compare them with the existing literature in Section 6. We offer a summary and discussion of future work in Section 7.
## 2 Materials and Methods
### GNSS Instruments  
In this study, we used the multi-frequency ZED-F9P GNSS chip integrated into a simpleRTK2B V1 board. The ZED-F9P chip is supplied by u-blox and can receive satellite signals in L1 and L2 frequencies from GPS, GLONASS, Galileo, and BeiDou satellite constellations [27]. However, in the case of GPS satellites, the L2 signal is tracked only from Blocks IIR-M, IIF, and III. Further details and specifications related to the SimpleRTK2B V1 board and the u-blox ZED-F9P module are listed in Table 1.  
The board used is compatible with different GNSS antennas, and three low-cost antennas were selected. Survey and Survey Calibrated are supplied by the manufacturer Ardusimple, where calibration parameters from National Geodetic Survey [28] are known for the Survey Calibrated antenna. Both antennas are reported to have small phase center errors that are in the range of \(\pm\)3 mm for Survey and \(\pm\)1 mm for Survey Calibrated, and they have low energy consumption [29]. The multi-band Tallysman TW3882 antenna can also track all of the same satellite signals as the first two, and it is light and has low phase  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **SimpleRTK2B V1** \\ \hline GNSS chip & ZED-F9P \\ Constellations & GPS, GLONASS, Galileo, BeiDou \\ Frequencies & L1/L2 \\ Signals & LIC/A, LIOF, E1, B1I, L2C, L2 OF, E5b, B2I \\ Channels & 184 \\ Weight & 19.5 g \\ Size & 69 mm x 53 mm \\ Ports & 5 \\ Messages & UBX, NMEA, RTCM3 \\ Supply voltage range & 4.5–5.5 V \\ Supply current & 80 mA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications for GNSS devices used.  
Figure 1: Study area: roof of UL FGG building.  
center variation, but the calibration parameters are not known [30]. Further details of the antennas used are shown in Table 2 and also presented graphically in Figure 2.
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.5 Communication Bit Error Ratio  
In this subsection, the communication bit error ratio (BER) performance is evaluated. The BER performance proof is the same as for the traditional OFDM signal in communication. Therefore, this paper will not provide a detailed discussion of the bit error rate  
Figure 10: Simulation results for multiple targets (scenario 3). (**a**) Multiple-target echo distribution, (**b**) integration result on velocity–range plane, (**c**) integration result on range–acceleration plane, (**d**) integration result on velocity–acceleration plane.  
Figure 9: Simulation results for multiple targets (scenario 2). (**a**) Multiple-target echo distribution, (**b**) integration result on velocity–range plane, (**c**) integration result on range–acceleration plane, (**d**) integration result on velocity–acceleration plane.  
(BER) process in OFDM communication [23]. Figure 11a presents the varying error rates corresponding to different modulation methods in OFDM IRCS as the SNR improves, resulting in a lower BER. Figure 11b illustrates a higher BER in complex modulation under noise-free channel conditions. Figure 11c demonstrates the relationship between different demodulation methods and the BER: soft verdict outperforms hard verdict and uncoded BPSK at a high SNR, whereas, at a low SNR, uncoded BPSK performs better than soft verdict and hard verdict.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 3 Results
### EEX and MEX Chemical Composition  
The GC-MS analysis of the extracts allowed us to identify twelve compounds whose relative percentages are summarized in Table 2. Derivatives of furan were the main class of detected compounds, among which, 5-HMF was the main exponent with relative percentages of 14.7% and 24.8% in the EEX and in the MEX, respectively. 2-Furanmethanol (3.9%; 6.7%) and furaneol (3.1%; 3.6%) were also present in both extracts. Coumarin, 5,7-dimethoxy (50.6%) was the most abundant compound in the EEX followed by 2-pyrone (23.4%). On the contrary, 2-pyrone (33.1%) and 2,3-butanediol (23.7%) were the main component non-furanoic derivatives, present in MEX.
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
###### Abstract  
Thermal comfort and sensation are important aspects of building design and indoor climate control, as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. To overcome the disadvantages of static models, adaptive thermal comfort models aim to provide opportunity for personalized climate control and thermal comfort enhancement. Recent advances in wearable technologies contributed to new possibilities in controlling and monitoring health conditions and human wellbeing in daily life. The generated streaming data generated from wearable sensors are providing a unique opportunity to develop a real-time monitor of an individual's thermal state. The main goal of this work is to introduce a personalized adaptive model to predict individual's thermal sensation based on non-intrusive and easily measured variables, which could be obtained from already available wearable sensors. In this paper, a personalized classification model for individual thermal sensation with a reduced-dimension input-space, including 12 features extracted from easily measured variables, which are obtained from wearable sensors, was developed using least-squares support vector machine algorithm. The developed classification model predicted the individual's thermal sensation with an overall average accuracy of 86%. Additionally, we introduced the main framework of streaming algorithm for personalized classification model to predict an individual's thermal sensation based on streaming data obtained from wearable sensors.  
Keywords:thermal sensation; adaptive model; personalized model; machine leaning; support-vector-machine; adaptive control; streaming algorithm +
Footnote †: journal: Applied Soft. 2019, 9, 3303; doi:10.3390/app9163303
## 3 Results and Discussion
### Assessment of the PCB Concentration in the Water of Lake Baikal
#### 3.3.2 Statistical Analysis of Biomonitoring Results
The third group of vectors included dl-congeners 156, 105, and 123, whose concentrations were associated with each other by a strong positive correlation and a weaker positive correlation with the vectors of the first group. The vectors of the third and second groups were almost perpendicular to each other. Therefore, the concentrations of PCBs in the specimens from these groups were not correlated and interconnected with each other. The evaluation of _C. migratorius_ as a bioindicator of PCBs in Baikal water by PCA based on the analysis of the results of the detection of PCBs in its tissues revealed that (i) theage of the fish determined the concentrations of indicator congeners with a high degree of chlorination (101, 138, and 153) and dioxin-like congeners 114 and 118, including the most toxic congener 126; (ii) the concentrations of 'light' congeners 28 and 52 did not depend on the age of the fish and were not associated with the accumulation of other congeners.
## 4 Proof-of-Concept Background and Setup
### Development Setup  
The P4 target used was the Intel Tofino (Available online: [[https://www.intel.com/content/www/us/en/products/network-io/programmable-etherent-switch/tofino-series.html](https://www.intel.com/content/www/us/en/products/network-io/programmable-etherent-switch/tofino-series.html)]([https://www.intel.com/content/www/us/en/products/network-io/programmable-etherent-switch/tofino-series.html](https://www.intel.com/content/www/us/en/products/network-io/programmable-etherent-switch/tofino-series.html)), accessed on 1 September 2022) programmable Ethernet switch application-specific integrated circuit (ASIC). The Tofino ASIC follows the PISA architecture discussed in Appendix A and is designed to deliver up to 100 GbE speeds. The target used was the first version of the Tofino ASIC, which is contained inside the Aurora 710 programmable switch from Netberg (Available online: [[https://netbergtw.com/products/aurora-710/](https://netbergtw.com/products/aurora-710/)]([https://netbergtw.com/products/aurora-710/](https://netbergtw.com/products/aurora-710/)), accessed on 1 September 2022). The Aurora switch utilized Intel's P4 Studio SDE version 9.7.0 to compile the program. This target was chosen because, according to our experiences, it was a less restrictive subset of the P4 language and was easier to use compared to other hardware targets studied. In addition, the Aurora switch receives regular updates from Intel as updates are released.  
The configuration involved two endpoint computers, named Rho and Eta, connected to hardware ports on the Aurora 710 switch. Rho and Eta acted as DTN nodes generating or receiving bundles to be sent to its counterpart over UDP, IP, and Ethernet. Version 4.1.1 of the ION software implementation of DTN was installed on both endpoint nodes, which supports both BPv6 and BPv7 (configured respectively in installation). ION was chosen for development as it offers an application called bpchat, a command-line interface (CLI) chat function, which enabled quick confirmation of different bundle payloads. Figure 7 shows the setup, where the two DTN nodes, each running different versions of BP, are connected to the programmable switch.
# Impacts of Material Orthotropy on Mechanical Behaviors of Asphal Pavements
## 4 Analysis Results
### Impact of Anisotropy on Compressive Strain  
Compressive strain can be used to show permanent deformation performance [20]. The compressive strain on the top of the subgrade is an essential mechanical index for permanent deformation. The results of the compressive strain on the subgrade top in the transverse direction are shown in Figure 10.  
The results of the ANOVA of compressive strain on top of the subgrade are shown in Table 7. As noted, the significant \(p\)-value of the compression strain at the subgrade top was less than 0.05, and the value of F was 8.346, which is larger than the critical value of 1.999, indicating that the material anisotropy had a significant effect on the compression strain at the subgrade top.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of Squares** & **Degree of Freedom** & **Mean Square** & **F** & \(p\)**-Value** & \(\mathbf{F_{crit}}\) \\ \hline Between group & 2722.112 & 8 & 340.264 & 8.346 & 2.3 \(\times\) 10\({}^{-9}\) & 1.999 \\ \hline Within group & 6238.086 & 153 & 40.772 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of the ANOVA of the compressive strain on top of the subgrade.  
Figure 10: The effects of anisotropy of different materials on the compressive strain on the top of the subgrade: (**a**) Anisotropy in all layers. (**b**) Anisotropy in the HMA layer. (**c**) Anisotropy in the semi-rigid base layer. (**d**) Anisotropy in the subgrade layer.  
Figure 10a shows that the absolute compressive strain on the subgrade top increased at a transverse distance of 0 to 0.15 m and decreased after that. As noted, the compressive strain on the subgrade top was remarkably affected by the orthotropy and transverse isotropy. In addition, the changing rate peak values for the all-layer orthotropy and transverse isotropy were 19.0% and 6.7%, respectively. Figure 10b,d show that the orthotropy in the HMA and subgrade layers had a larger effect on the compressive strain on top of the subgrade, with changing rates of 5.6% and 8.6%, respectively; while those of the transverse isotropy were relatively slight, 2.8% and 0.3%. In addition, the effects of the two anisotropies in the base layer are close, showing a changing rate of around 4% in Figure 10c.  
A larger compressive strain was generated when the orthotropy was taken into consideration. Moreover, the subgrade layer orthotropy made the most significant contribution to the compressive strain under all of the investigated scenarios.