esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Experimental Environment  
This paper builds the MA-LSTM model based on Python 3.6 and the keras deep learning library. The experimental equipment is configured with Intel i5-7200u CPU, 8G memory, 500G solid-state hard disk, and the GPU is NVIDIA Geforce 940 MX.
## 3 Results and Discussion
### Measurement of Hydrogen Peroxide  
Figure 7 shows the hydrogen peroxide concentration as a function of the treatment time. Although all gas feedings can provide the hydrogen peroxide content, the oxygen and argon plasmas are the best candidates to generate a high concentration of hydrogen peroxide. The ORP level of PAW generated by argon and oxygen plasmas is much lower than the other DBDs. On the other hand, the hydrogen peroxide concentration is at a higher level in both cases. As shown in OES data of Figure 3a,b, the hydroxyl peak can be observed clearly for both gas phases of oxygen and argon. The hydroxyl radicals are the primary source of hydrogen peroxide production [26]. According to Henry's constant, the value of dissolved gas in water is proportional to the partial pressure above the water. It is known that the hydroxyl has a relatively high Henry's constant [32; 41; 42]. Hence, the hydroxyl can be dissolved easily inside the treated water. It can be the main reason that the hydrogen peroxide concentration is high for the plasmas mentioned above. As shown in Figure 4, the pH value of the PAW generated by oxygen and argon plasmas does not decrease compared to the other DBD plasma systems. Therefore, the PAW generated by oxygen and argon might be a proper choice in our DBD system as a plasma treatment for biological tissue. Because they do not provide an acidic medium, and hydrogen peroxide is supplied as an antibacterial effect.  
Generally, the developed DBD system reported in our investigation has an extremely limited interaction with ambient air, and that is an isolated system related to ambient air. On the other hand, many articles reported PAWs generated by various plasma sources interacting with ambient air, or the working gas is air, as mentioned before. Some reported PAWs generated by argon or oxygen interacting with ambient air, while the pH value of PAW is low, and the medium is acidic [43]. Thus, the varieties of reactive species generated in DBD systems might differ, specifically the generation of nitrite and nitrate responsible for the variation of pH and ORP levels compared to our DBD system. As a result, PAW properties' values and trends might not be the same, and comparison might be difficult, as we already mentioned in the introduction. Nevertheless, we propose our developed DBD system to control pH value, nitrite, nitrate, and hydrogen peroxide contents using different gas feedings.  
Figure 7: Measurement of the concentration of hydrogen peroxide as a function of treatment time with four gas feedings: argon, oxygen, nitrogen, and air.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 2 Composition of Defect Detection System for Mobile Phone Flat Glass  
### Hardware Design and Inspection Process of Glass Defect Detection System  
In cell phone flat glass manufacturing, most manufacturers use manual visual inspection to screen products and eliminate defective ones. In order to realize the production line inspection of cell phone flat glass, this paper designs AOI (automatic optical inspection), an intelligent inspection hardware platform, as shown in Figure 1, and builds a dust-free laboratory. The hardware platform of the cell phone flat glass defect detection system includes a lighting subsystem, a mechanical and control subsystem, an image acquisition and imaging subsystem, and a computer, among other components. The lighting subsystem consists of a main light source, auxiliary light source, light source controller, light source spacer, diffuse film, etc. The mechanical and control subsystem consists of a camera pillar, motion roller, infrared detection module, stepping motor, motion control card, etc. The image acquisition and imaging subsystem consists of two CMOS industrial cameras, an image acquisition card, a fixed-focus lens, etc.  
The inspection flow-chart of this system is shown in Figure 2. The cell phone flat glass is transported to the inspection platform by the conveyor belt of the production line. When the cell phone flat glass reaches the specified position and triggers photoelectric sensing, the roller stops moving. The main light source (TR) is turned on and the camera collects images. Then, the main light source (TR) is turned off and the auxiliary light source (OD) is turned on, and the camera collects images. The collected data are then transmitted to the computer to be processed to determine whether the glass is acceptable. If the detection result shows OK, the inspection is finished and it goes to the next round. If the detection result shows NG, the defect types and their quantity are counted. When the defective glass  
Figure 1: Diagram of inspection platform system’s structure.  
passes through the second round of photoelectric sensing, the rejector is activated to push the NG material into the storage box.
## 3 Results
### Re-Dislocation Rates  
Starting with a comparison of re-dislocation rates, the conservative treatment group had an average rate of 33.14% (standard deviation (SD) 13.44%), whereas the surgical treatment group had an average rate of 17.16% (SD 15.19%). The result of the \(t\)-test was 2.49 with a \(p\)-value of 0.023. The \(p\)-value was less than the standard significance level (\(p=0.05\)), indicating a statistically significant difference between the re-dislocation rates of the two groups. This demonstrates that surgical treatment is much more effective in reducing re-dislocation rates than conservative treatment. These findings are illustrated in Figure 2, whereas in Figure 3, there is a chart that displays the re-dislocation rates for both conservative and surgical treatments.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Studies** & **N\({}^{\circ}\) of Patients** & **Conservative Treatment** & **Surgical Treatment** & **Re-Dislocation in Conservative** & **Re-Dislocation in Surgical** & **Kujala in Conservative** & **Kujala in Surgical** \\ \hline [PERSON] et al., 2018 & 74 & 36 & 38 & 16 (44.4\%) & 8 (21.1\%) & 95.9 & 90.9 \\ [PERSON] et al., 2008 & 77 & 35 & 42 & 7 (20\%) & 7 (16.7\%) & 78 & 85 \\ [PERSON] et al., 2011 & 37 & 23 & 14 & 3 (13\%) & 5 (35.7\%) & NI & NI \\ [PERSON] et al., 2017 & 62 & 30 & 32 & 7 (23.3\%) & 4 (12.5\%) & 80.2 & 93.6 \\ [PERSON] et al., 2016 & 36 & 20 & 16 & 11 (55\%) & 5 (31.3\%) & NI & NI \\ [PERSON] et al., 1997 & 125 & 55 & 70 & 15 (27.3\%) & 12 (17.1\%) & NI & NI \\ \hline Total & 543 & 265 & 278 & 83 (33.1\%) & 43 (17.1\%) & 80.7 & 89.8 \\ \hline \hline \end{tabular}  
NI: not informed.  
\end{table}
Table 2: _Cont._  
Figure 2: Comparison of re-dislocation rates between conservative treatment and surgical treatment.
## 5 Conclusions  
Compared to the traditional scanning spectral imaging system, the benefits of the LED-based multispectral imaging system are its cost-effectiveness, fast switching, and easy implementation. It is mostly used for color identification. In this paper, we introduced an efficient LED multispectral imaging system for human tongue detection. It only captures 2 frames in order to reconstruct 31 multispectral images. A simulation and experiment verified its effectiveness.  
1. Spatial similarity and segment linear calibration were used to improve the reconstruction process. The spatial pixels were merged based on cosine similarity, which greatly decreased the computation time. A following linear calibration was used to improve the reconstruction accuracy. Our simulation results showed that the reconstruction time was only 5.5% of that of the point-by-point optimization method when the threshold value was 0.5. Further, the mean PSNR and SAM of the reconstructed images were better than those of the traditional point-by-point method.
2. A portable prototype was built to detect the human tongue. As this system is not affected by environmental light, it can obtain an accurate tongue color. Further, it has a fast detection and reconstruction speed for a friendly human experience. The  
Figure 9: SAM images of fur region (the middle area of the tongue within red lines). (**a**) Tongue image of Sample 2. (**b**) SAM image of the middle region compared to the tongue body spectra. (**c**) SAM image divided into 4 parts by the radian value—the white region has larger SAM values which can be regarded as the tongue fur.  
experiment showed that multispectral images are useful for quantitatively analyzing tongue color and fur thickness.  
Although our proposed method can effectively reconstruct spectral images, it still exhibited some errors when comparing reference images. In the future, much more attention should be paid to the development of better reconstruction algorithms and optimal LED combinations. As for tongue feature detection, it is a complex problem that is being studied by various people, and more samples are necessary to determine the exact quantitative values.  
[PERSON] contributed to the methodology, simulation, and writing of the original draft preparation; [PERSON] helped to build the prototype, software, and validation; [PERSON] helped to design the prototype and experiment; [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shenzhen Fundamental Research Program, grant number: JCYJ20170818163928953, and the National Natural Science Foundation of China, grant number: U1713210.  
The study was approved by the Institutional Review Board (or Ethics Committee) of Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (protocol code SIAT-IRB-220215-H0588, 29 March 2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### Alarming Approach  
This section introduces the alarming mechanism of BDS-3. BDS-3 is designed with a hybrid alarming mechanism of the ground monitoring and alarm approach and the satellite monitoring and alarm approach, to make up for the limitations for BDS's failure to deploy ground monitoring stations globally.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 6 Numerical Results
### Numeric Validation
#### 6.3.3 Fraction of Cylinder  
A quarter of cylinder, with different radius to thickness \((r/h)\) and length to radius \((L/r)\) ratios, is analyzed. The geometry is in cantilever, where the edge with the maximum thickness is clamped. The natural frequencies are presented using the dimensionless parameter \(\Theta\). Table 20 shows the results for different \(r/h\) ratios. It is observed that the highest error is presented for relatively thick shells \((r/h=5)\) with a variation of 10.02%, when the present formulation is compared with solid elements.
# Idle Vibration Reduction of a Diesel Sport Utility Vehicle
## 5 Design Optimization for the Idle Vibration Reduction
Accordingly, a design, wherein the natural frequencies for the bounce modes of the radiator and intercooler are proximate to the idling frequency, is recommended to reduce the idle vibration on the driver's seat of a diesel SUV. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Mode**} & \multicolumn{4}{c}{**Natural Frequency (Hz)**} \\ \cline{3-5}  & & **Present Design** & **Optimal Design** & **Difference** \\ \hline \multirow{3}{*}{Powertrain} & Bounce mode & 6.7 & 6.6 & \(-\)0.1 \\  & Pitch mode & 15.3 & 15.3 & 0 \\  & Roll mode & 21.1 & 20.9 & \(-\)0.2 \\ \hline \multirow{3}{*}{Radiator} & Bounce mode & 19.4 & 26.7 & 7.3 \\  & Roll mode & 30.3 & 34.1 & 3.8 \\ \hline Intercooler & Bounce mode & 31.0 & 28.9 & \(-\)2.1 \\ \hline \multirow{3}{*}{Body floor} & Bounce mode & 1.2 & 1.2 & 0 \\  & Pitch mode & 1.7 & 1.7 & 0 \\ \cline{1-1}  & Bending mode & 25.2 & 24.1 & \(-\)1.1 \\ \cline{1-1}  & Torsional mode & 28.0 & 28.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the natural frequencies for the present and optimal designs. Figure 22: Comparison of the frequency response functions for the dynamic models with the present and optimal designs. The reduction of idle vibration presented in this paper is differentiated and innovative from the methods of previous studies in that it is based on a dynamic model that considers the deformation of the body floor. The previous studies tried to reduce idle vibration by considering only the vibration transmission path from the powertrain to the driver's seat. However, this study presents a new method to reduce idle vibration method, based on the dynamic model of the diesel SUV, which considers not only the rigid-body motions of the powertrain, radiator, and intercooler but also the bending and torsional deformation motions of the body floor. In addition, it is novel that the radiator and intercooler act as a vibration absorber so that the vibration energy of the powertrain is not transmitted to the driver's seat, but the radiator and the intercooler.
# Optimal Siting and Sizing of FACTS in Distribution Networks Using the Black Widow Algorithm
## 1 Introduction
### Motivation  
Energy losses in distribution networks are high in comparison with transmission systems, as distribution grids operating at medium- and low-voltage levels are constructed considering a radial topology (a connected tree with only one path between the substation and each load node) [7]. These grids are built with a radial structure to reduce investment costs and the complexities of coordinating protection devices [8]. To minimize energy loss indices in radial distribution networks, the following strategies are typically employed: (i) optimal grid reconfiguration using available tie-lines [9]; (ii) the optimal placement of dispersed generators and energy storage systems [10; 11], and (iii) the optimal siting and sizing of shunt reactive power compensators [12]. This research aims at reducing the expected energy losses costs by proposing the efficient integration of FACTS (flexible alternating current transmission systems) devices in the grid. The FACTS analyzed correspond to the unified power flow controller (UPFC), the thyristor-controlled shunt compensator (TCSC), and the static var compensator (SVC) [13].  
The main idea of integrating FACTS in distribution networks is to determine the expected reductions that can be reached with different shunt reactive power compensators based on power electronic interfaces regarding energy loss reductions. This is an alternative to classical fixed-step capacitor banks [14]. Therefore, in this research, our efforts are focused on locating and sizing UPFCs, TCSCs, and SVCs in distribution networks with radial or meshed topologies, because the current literature mainly deals with SVCs (or distribution static compensators, D-STATCOMs) [15; 16] and conventional fixed-step capacitor banks [17].
## 5 Results and Discussion
### RQ1: What State-of-the-Art Methods Exist for Optimizing LoRaWAN Energy Efficiency?
#### 5.1.4 State-of-the-Art Datasets and Simulators
The integration of Python-based tools like TensorFlow and Keras for implementing DL algorithms highlighted the shift towards more sophisticated, AI-driven approaches in network management and device allocation in LoRaWAN, as shown in Figure 6. These tools enable researchers to construct and train complex models efficiently by using large datasets for enhanced predictive accuracy. RQ2: How can ML and AI Algorithms Be Used to Optimize the Resource Constraints to Enhance Energy Efficiency and LoRaWAN's Performance? Machine learning and AI significantly enhanced LoRaWAN efficiency through adaptive algorithms such as K-Means clustering, Dynamic Priority Scheduling, and NB classifiers for predictive transmission modes, as explained in [46]. Advanced strategies such as the LP-MAB [50] further optimized the performance with a RL-based approach to minimize energy consumption and improve packet delivery. Studies [31; 33; 38; 39; 41; 43; 52] used DL techniques to reduce collisions and power consumption to boost network efficiency and device performance. Techniques like multi-agent systems dynamically allocate resources based on real-time conditions, as discussed in [31; 43; 47; 48] improving scalability and network efficiency. Algorithms such as ANNs and PSO predict and fine-tune transmission parameters that have significantly enhanced network performance metrics such as SNR and BER. Methods like SVR and DNN [52] adjust transmission power and duty cycles based on data-driven predictions that extend device battery life. Ensemble methods and predictive modeling have also been discussed [48] to dynamically adjust network parameters. This reduced the need for power-intensive methods such as GPS by conserving energy and enhancing the precision of node positioning within the network. Key findings from the literature can be summarized in Table 8.  
Figure 6: Types of simulations used in the study. A visual representation of the most effective techniques discussed in literature can be seen in Figure 7.  
RL emerged as the most frequently adopted ML technique for optimizing resource constraints and enhancing EE in LoRaWAN networks, followed by DL and Ensemble Learning. Supervised and Unsupervised Learning techniques, while effective, are mentioned less frequently in this specific context.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### RADAR Techniques
The results obtained had a correlation coefficient of 0.9 compared to the reference BP values measured by a cuff-based monitor. Although this study only included two drivers, the results indicate that future work in this direction has the potential to continuously measure systolic and diastolic BP values in DMSs. SFCW radar emits a series of discrete tones in a stepped frequency sequence [89]. An advantage of SFCW over FMCW signals is the use of a step-frequency variation of the emitted signal. This gives a possibility to apply compression of the signal in monitoring with this type of radar. By significantly reducing the number of measurements, the time to obtain a result can be reduced. The acceleration of the measurement process can be achieved by dividing the entire frequency band of the emitted signal into sub-bands, which are emitted simultaneously and in parallel instead of sequentially. Despite the advantages mentioned, there is still insufficient information about the application of this type of radar in DMS. UWB-IR radars are based on time-of-flight measurements and often have separate transmit and receive antennas. They emit pulses in a wide frequency band. The interval between the emitted pulses is used to receive the incoming pulses reflected from the object under study. UWB-IR radars can detect fine micromotions, which makes them suitable for precise monitoring of the driver's breathing and heart rate, but they have a more complex design and are more expensive compared to CW radars. In 2017, [PERSON] et al. [90] used UWB-IR (NVA6201) radar to measure HR and RR, and detect drowsiness and smartphone use while driving. The significant contribution of this study was the successful application of the radar method in real-world driving conditions. The presented results for measuring vital parameters in a \"moving\" driver showed a maximum error of \(\pm\)1.2 bprpm in RR values and \(\pm\)2.5 bpm in HR values. In 2018, [PERSON] et al. [91] demonstrated the possibility of monitoring HR and RR parameters using a novel design of UWB-IR radar embedded in the backrest of a car seat. To increase the penetration depth of the electromagnetic pulses, the authors chose a carrier frequency of 3.9 GHz, using pulses with an approximate bandwidth of 2 GHz. In the same year, [PERSON] et al. [92] demonstrated 16 different positions of the UWB-IR (Xethru X2M200) radar sensor in a car to monitor the driver's breathing and its immunity to engine noise. In urban driving, with the optimal placement of the radar next to the rearview mirror, the authors reported an error of 1.06 bprm in RR values.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 4 Structural Transformations of Edge Couplers in Vertical Direction
### Edge Couplers with Index-Matching Cladding
Taking the utilization of only one single taper and its narrow tip width into consideration, the spot size is usually much smaller (approximately 3 um) than that of the standard SMF. Thus, edge couplers with index-matching cladding are particularly feasible for coupling with lensed fibers or high-numerical-aperture (HNA) fibers whose spot size is also compressed.
## 4 Model Verifications  
### Degradation Test of Non-Uniform Sediment  
The capacity of the model to simulate non-uniform sediment deposition was validated by comparing the simulated results with experimental data obtained by [PERSON] [40].  
The experimental flume used by [PERSON] was 100 m long and 0.23 m wide. The inflow condition was obtained by injecting fine sediment slurry into a steady flow. The water depth at the outlet of flume was maintained at 15.2 cm by a tailgate. A summary of sediment experimental conditions is given in Table 1. Kaolinite was used in experiments W4 to W10, and Atafalaaya Bay mud was used in experiment W11. The grain size distributions of the kaolinite and Atafalaaya Bay mud are shown in Figure 1. Median diameters were approximately 1 \(\upmu\)m for the kaolinite and 15 \(\upmu\)m for the Atafalaaya Bay mud. The water used in the flume experiment had a pH of 7.8, 18.3 ppm of chloride, 13.1 ppm of sodium, and 1.9 ppm of calcium.  
Figure 2 shows the sediment size distribution at the beginning of the experiment and the calculated sediment size distribution after 30 min, when a water-sediment parcel  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & & **Inflow** & & **Slurry** & & **Manning’s** \\
**Number** & **Sediment** & **Concentration** & **Kg/m3** & **Temperature** & **Duration h** & **Roughness** \\  & & **kg/m3** & & **deg C** & & **Coefficient** \\ \hline W4 & Kaolinite & 10 & 0.0052 & 27 & 1.5 & 0.020 \\ W5 & Kaolinite & 10 & 0.0070 & 27 & 1.0 & 0.021 \\ W6 & Kaolinite & 10 & 0.0034 & 29 & 1.0 & 0.021 \\ W9 & Kaolinite & 2 & 0.0034 & 27 & 1.25 & 0.022 \\ W10 & Kaolinite & 5 & 0.0034 & 23 & 1.25 & 0.023 \\ W11 & Atafalaaya & 10 & 0.0017 & 13 & 1.25 & 0.031 \\  & Bay Mud & & & & Kaolinite & **KaoInit** \\  & & & & & & **Attafalaaya** \\  & & & &  
would have neared the end of the flume in experiment W11. The size distribution calculated by present model is similar to the results obtained by [PERSON] [40]; the distribution is bimodal, with the peak remaining at approximately 75 \(\upmu\)m and the secondary peak moving toward larger sizes as ongoing aggregation of the smallest particles occurred.  
Figure 3 shows [PERSON] observed and calculated depth-averaged sediment concentration profiles over the length of the flume, with laboratory observations depicted as solid points and calculated depth-mean profiles as lines. Figure 3 demonstrates that the computed sediment concentrations generally agree with the measured concentrations. These results indicate that the critical bed-shear stress for deposition used in this model, Formula (6), reasonably well represents the flocculation of fine particles.