esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Risk of Spreading COVID-19 in the Room Utilizing Low-Cost Monitoring System
## 2 Components  
Our project was composed of the following components:  
Temperature and humidity sensors--The temperature and humidity in closed spaces (rooms) influence the speed of the spread of the viral disease COVID-19. For this reason, it is necessary to measure these parameters in every room in order to prevent the possible spread of infection. Research [13; 14; 15; 16] shows that the viral disease spreads fastest at a temperature of 5-11 \({}^{\circ}\)C and absolute humidity in the range of 4-7 g/m\({}^{3}\).  
PIR (Passive Infrared) sensor--A passive infrared detector detects objects based on radiated energy. We can measure this energy as the temperature of the human body and find out if the person has one of the symptoms of the disease (fever). The PIR detector was also used in work [17], where the authors created a robot that identified possible infected people.  
CO\({}_{2}\) sensors--These sensors are used to indicate the freshness of the air in the room. With high values of CO\({}_{2}\) in the room, a person can quickly become tired and sleepy, and thus his work performance decreases. At the same time, with high values of CO\({}_{2}\), the virus is spread more.  
VOC (Volatile Organic Compound) sensors--Viruses survive longer in a more polluted environment and become more aggressive for the human body, which is already burdened with harmful substances [18; 19; 20]. It is, therefore, necessary to measure air pollution.  
Microphone--This sensor detects a coughing person in the room. Based on this information, it is possible to war people in the room about the increased risk of developing the respiratory disease COVID-19. The microphone for detecting the frequency of breathing and coughing was also used in research such as [21; 22]. Subsequently, the signal from the microphone needs to be analyzed, as they did in the research [23].  
Smart ring--This sensor measures the contamination of a person's hands. At the same time, this sensor can also measure various biological signals that can be used to identify a potentially dangerous person. A person wears only one ring, so it is possible to determine the number of people in one room using our system.  
Microcontroller ESP32--A simple open-source platform that we can classify as IoT. The development board uses an ESP32 chip that allows connection and communication via Wi-Fi and Bluetooth. The advantage of this board is simple programming within the freely available Arduino IDE program.
## 6 Conclusions  
Based on quasi-distributed fiber Bragg grating (FBG) sensing technology and cloud technology, a remote online real-time monitoring system was established for large volumes of rock pressure data generated during application of the coal pillar-free self-formed roadway construction method.  
(1) The system transfers field monitoring data obtained by FBG sensors from an industrial ring network to the host computer of the above-ground monitoring system and publishes the data on a cloud-hosted web platform, realizing real-time online monitoring and early warnings on the basis of multi-parameter data, such as the force on constant-resistance large-deformation anchor cables, separation in the top layer, relative movements of the top and bottom plate, pressure on the top-cut protection support bracket and lateral pressure from gravel in the lane-formation area.  
Figure 17: Curve of relationship between total footage of working face and roof separation at 30 m from the open-off cut.  
Figure 18: Monitoring curve of roof and floor proximity 90 m from the open-off cut.  
(2) Field application proves that this big data remote online real-time monitoring system can acquire timely and accurate field data and feed back field production information in real time, achieving good monitoring results. This provides a scientific basis for safe mining with the coal pillar-free self-formed roadway method.  
(3) Because cloud servers are superior to traditional servers in many ways such as in elasticity, ease of expansion, stability and security, this big data monitoring system based on cloud servers provides a new technological means of monitoring mine safety and has very good application value.  
Conceptualization, Z.T. and H.Z.; methodology, Z.T.; software, H.Z.; formal analysis, X.Z. ([PERSON]); data curation, H.Z.; writing--original draft preparation, X.Z. ([PERSON]); writing--review and editing, C.Z.; and X.Z. ([PERSON]).  
This research was funded by the Key Research and Development Project of Zhejiang Province, grant number 2019C03104\".  
This work was supported by the Key Research and Development Project of Zhejiang Province (Grant No: 2019C03104).  
The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
However, various dielectric layers deposited on top of a GaN-based HEMT cause an interface state trap and mechanical stresses, resulting in changes in threshold voltage and carrier concentration, respectively. Therefore, appropriate dielectric material and thickness selections are required. The Si\({}_{3}\)N\({}_{4}\) dielectric layer causes compressive stress, but the Al\({}_{2}\)O\({}_{3}\) dielectric layer deposited on the Si\({}_{3}\)N\({}_{4}\) dielectric layer generates tensile stress. Since proper tensile stress helps to increase the carrier concentration compared to compressive stress, the combination of Si\({}_{3}\)N\({}_{4}\) and Al\({}_{2}\)O\({}_{3}\) layer thicknesses is very important [21]. This study investigated transfer I-V characteristics, gate leakage current, lattice temperature across two-dimensional electron gas (2-DEG) channel layer, and MIS-HEMT drain current variation by changing the dielectric layer compositions and thicknesses with the help of a two-dimensional simulator [22]. To begin with, we compared electrical properties, including threshold voltage, transconductance, and gate leakage current with the self-heating effect (SHE) by applying a single Si\({}_{3}\)N\({}_{4}\) dielectric layer. We then employed different Al\({}_{2}\)O\({}_{3}\) dielectric layer thicknesses on top of the Si\({}_{3}\)N\({}_{4}\) and analyzed the lattice temperature across the 2-DEG channel layer to verify the thermal effect on the gate leakage current. Finally, we discuss an optimized MIS-HEMT structure as a normally-on device.
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
## 3 Results
### Analysis of RSA Reactivity
#### 3.2.4 Mechanical Studies about Mortars
Moreover, the \(\beta\) parameter increased in relation to the control, which indicates a significant contribution of the pozzolanic reaction with curing time, especially for the system with the 30% replacement. This confirms the mid- to long-term effect of RSA as pozzolanic material.
## 2 Health Condition Monitoring
### Data Acquisition
#### 2.1.1 Data Collection  
The data collected on-board involve navigation, MSAE, ship status, and environment data, which are used as condition parameters to realize the PHM process of HCM, FD, HP, and MD. Some researchers have attempted to research marine machinery performance using the measurement parameters [9].  
(1) The navigation (bridge) data affecting the health condition of MSAE mainly include speed, drafts, rudder angle, and wind speed. Some of these data can be obtained directly from sensors, such as wind speed, draft, and so forth. Some of them come from the received external information, such as meteorological data, GPS data, and so forth. These navigation data are usually used as the condition or identification mark of MSAE health mode judgment, and some of them can also be used as the input of the model for more accurate decision-making [10];  
(2) MSAE parameters include sensor measurement data, recorded manual data, experimental data, mooring test data, sea trial data, and so forth. The measuring sensors mainly include the sensors provided by the equipment and the sensors configured by the system integration. Due to different equipment manufacturers, the equipment usually has different data interfaces, and some data are not output. The manually recorded data are directly read from the instrument, recorded, and stored by the engineers. At present, various test data are mainly used to facilitate comparison and query so that engineers can further understand the performance and use of equipment [7]. These data can provide training samples for FD and failure prognostics to improve the efficiency of PHM.  
The application of intelligent sensors and sensor networks will maximize the acquisition of MSAE condition information and provide effective support for intelligent O&M [11]. ABS issued a guidance note to help guide intelligent technology applications in the maritime domain [12]. Through the implementation of intelligent monitoring, more operation and status information can be leveraged to support and improve daily operations and be the foundation of a PHM program [13].
# Valley-Selective Response of Nanostructures Coupled to 2D Transition-Metal Dichalcogenides
###### Abstract  
Monolayer (IL) transition-metal dichalcogenides (TMDCs) are attractive materials for several optoelectronic applications because of their strong excitonic resonances and valley-selective response. Valley excitons in 1L-TMDCs are formed at opposite points of the Brillouin zone boundary, giving rise to a valley degree of freedom that can be treated as a pseudospin, and may be used as a platform for information transport and processing. However, short valley depolarization times and relatively short exciton lifetimes at room temperature prevent using valley pseudospins in on-chip integrated valley devices. Recently, it was demonstrated how coupling these materials to optical nanoantennas and metasurfaces can overcome this obstacle. Here, we review the state-of-the-art advances in valley-selective directional emission and exciton sorting in 1L-TMDC mediated by nanostructures and nanoantennas. We briefly discuss the optical properties of 1L-TMDCs paying special attention to their photoluminescence/absorption spectra, dynamics of valley depolarization, and the valley Hall effect. Then, we review recent works on nanostructures for valley-selective directional emission from 1L-TMDCs.  
monolayer transition-metal dichalcogenides; valley-selective response; valley excitons; valley Hall effect +
Footnote †: journal: Applied Physics Letters
## 4 Interfacing with External Control Platforms
### Hardware-in-the-Loop Setup  
Figure 7 shows a photo of the FPGA-based real-time simulator in the HIL configuration. The real-time simulator is realized based on Altera's DE3-150 Development and Education board [47], where the mathematical model of the converter system of Figure 6 is implemented inside the simulator Stratix III FPGA. The simulator operates in real-time with a fixed simulation timestep of 500 ns and the inputs to the simulator are 6 kHz PWM signals, _i.e._, the simulation timestep is approximately 340 times less than the switching period. Thus, there is no need for any corrective measures to account for the ITS or MITS as described in Section II. The VHDL language is used to describe the behavior of the designed hardware [48; 49; 50]. Altera Quartus II version 8 software tool is used for (i) design entry, (ii) design synthesis, (iii) fitting the design on the target FPGA, and (iv) programming the FPGA. The developed FPGA-based simulator is also very cost effective.  
The controller and the sinusoidal pulse width modulator (SPWM) are implemented on the National Instrument CRIO platform comprised of an 800 MHz real-time PowerPC processor and a Virtex-5  
Figure 7: Photo of the experimental setup: (**A**) the FPGA-based real-time simulator; (**B**) the CRIO controller platform; and (**C**) the oscilloscope.  
LX110 FPGA. The control algorithm is expressed in C programming language and executed on the PowerPC processor. On the other hand, both the SPWM strategy and the internal oscillator are implemented on the CRIO FPGA. Labview software is used to program both the powerPC processor and the Virtex-5 FPGA comprising the CRIO platform. The sampling frequency of the controller is 6 kHz.  
For the HIL operation, the FPGA-based real-time simulator sends two voltage signals, _i.e._, phase a and phase b voltage at the load terminals, to the controller, and the controller sends the gating pulses to the FPGA-based real-time simulator.
## 2 Methods
### Meteorological Data  
Gridded potential evapotranspiration and precipitation data provided by CRUat the University of East Anglia (CRU TS 3.24) have been used in this study as the global SPEI database is based on the CRU dataset. The data for monthly time series of 10 climatic variables from 1901 to 2015 are contained in the CRU TS 3.24 dataset at a spatial resolution of 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\), which is roughly 55.5 km near the equator. The climatic variables included in the dataset are precipitation, potential evapotranspiration, monthly average daily maximum temperature, daily mean temperature, monthly average daily minimum temperature, diurnal temperature range, cloud cover, vapor pressure, frost day frequency, and wet day frequency. The CRU database is created from over 10,000 stations across the globe [31]. The principal sources include World Weather Records decadal data publications, World Meteorological Organization, and monthly climatic data for the world. Due to the number of stations contributing to the dataset varying throughout time, the dataset is not necessarily homogeneous; however, previous studies on trend analysis using CRU dataset have shown promising results [32]. A recent study on the Indus Basin by [PERSON] et al. [27] concluded that, despite poor representation of the spatial distribution of precipitation over the Indus Basin using the CRU dataset, the temporal trend of precipitation compared well with that calculated using more stations.  
In the CRU dataset, the Correlation Decay Distance (CDD) method [33] is implemented to interpolate the data, such that those stations within the CDD range of a grid cell are used to calculate the value for that grid cell [32]. The gridded values that are produced are thus relatively robust to  
Figure 2: Methodological framework.  
fluctuations in the density of stations. The Penman-Monteith equation is used to calculate PET for each grid cell as this method has been proven better than other simpler methods such as Thornthwaite equation [34], which only makes use of temperature data to estimate PET [35]. Moreover, sensitivity analysis of Penman-Monteith shows that the influence of meteorological parameters on evaporation is almost linear with temperature having the greatest influence on annual evaporation, while the relationship between sunshine duration and evaporation on a monthly scale is not constant. During the winter months, increase in sunshine duration leads to a decrease of evaporation [36]. Both surface energy balance and atmospheric water demand are considered when estimating PET using the Penman-Monteith equation [37].
All of the related research works we investigated in the literature review are associated with the optimal bidding strategies of GenCos and the impacts of the P2G facility on the integrated electricity and natural gas systems, which are proposed with various objectives and solution approaches. However, all of those researches are mainly about the optimal scheduling and bidding frameworks without extra wind-power utilization. Thus, according to this issue, there is no focus on the P2G conversion facility's bidding strategy in the integrated electricity and natural gas networks. This topic is the research gap of the analyzed works. To this end, in this paper, a coordinated optimal bidding strategy of hybrid energy system coupled with GenCos and P2G conversion technology and also with wind power unit (WPU) is proposed to participate in the day-ahead energy market. Applying the P2G facility in the combined manner has a significant impact on producing electric power of GFUs with considering gas-consumption limitations in order to participate in the energy market. In addition to obtaining a realistic and accurate optimal bidding strategy, the electricity market price's uncertainty is handled via the robust optimization technique. A review of the exitance work portfolio is provided in Table 1. Briefly, the main contributions of this paper are summarized as follows:  
1. Introducing a large-scale P2G conversion technology to handle the gas-consumption limitation of GFUs. 2. A risk-based method is considered to handle the day-ahead electricity-market-price uncertainty in the scheduling problem. 3. An optimal bidding strategy for a hybrid energy system coupled with GFU-P2G-WPU facilities is proposed to participate in the day-ahead energy market. The rest of this paper is organized as follows. Section 2 describes the mechanism and energy contribution of permanent energy storage, i.e., a P2G conversion facility. The proposed problem formulation is indicated in Section 3. The numerical results and analysis of two case studies are introduced in Section 4, to represent the capability and effectiveness of the considered optimal bidding strategy for a hybrid energy system. Finally, Section 5 concludes and reports noticeable outcomes.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion
### Metrics  
To validate the performance of the automatic point of interest recognition and tracking, we define several custom metrics that provide an individual assessment of each POI regarding the recognition correctness and timing. The metrics include the following:  
* **Predicted**: Determines whether a POI is predicted during the ground truth timeframe;
* **Correct**: Assesses whether the POI prediction is correct;
* **Start offset**: Measures how early or late the initial recognition occurs;
* **End offset**: Evaluates how early or late the tracking stops;
* **Completeness**: Quantifies the extent to which the ground truth POI shot is covered by tracking.  
Figure 10 illustrates these metrics by presenting a ground truth POI time window (in green) that the automatic methodology aims to predict. In this example, both the \"predicted\" and \"correct\" metrics are true because of a correct POI prediction (in blue) within the timeframe. The \"start offset\" is a positive number, indicating that the prediction was delayed. Ideally, the offset should be negative, indicating that recognition occurred before the director switched to the helicopter view. Conversely, the \"end offset\" is positive, meaning that the tracking continued beyond the director's switch, which is desirable in this context. The \"Completeness\" metric is approximately 75%, as the entire helicopter shot was not fully encompassed by POI recognition and tracking information.  
Subsequently, these individual validation results are aggregated into metrics such as precision, recall, mean completeness, mean start offset, and mean end offset to provide a global overview of the system's performance.  
The validation of the tracking is conducted using the intersection over union (IoU) metric, comparing the predictions against the annotated bounding boxes at the start, middle, and end of the POI time window. By calculating the IoU at these specific points, the accuracy and consistency of the tracking can be assessed throughout the duration of the POI.
# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 6 Experimental Validation
It is important to evaluate the performance of our model under these angular constraints. We investigated a 60\({}^{\circ}\) angular window, striving to achieve effective recognition with a limited range of angles, as shown in Figure 5. We also tried offsetting the angular window relative to the direction of specular reflection (with an offset angle \(\Delta\)). The results are shown in Table 4. We enforced a constraint of 20 scattering data points within each angle, which aligns with urban vehicle cruising speeds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Idx.** & **Offset Angle \(\Delta\) (\({}^{\circ}\))** & **Sequence Length** & **F1 (\%)** \\ \hline
1 & +15 & 20 & 83 \\
2 & +5 & 20 & 85 \\
3 & \(-5\) & 20 & 89 \\
4 & \(-15\) & 20 & 90 \\
5 & \(-25\) & 20 & 89 \\
6 & \(-35\) & 20 & 85 \\
7 & \(-45\) & 20 & 78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Narrow-angle window observations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Train Data** & **Test Data** & **F1 (\%)** & **Note** \\ \hline Low speed & High speed & 91 & Zero shot \\ High speed & High speed & 96 & Control group \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Emulated high speed from low-speed data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equivalent Speed (km/h)** & **Resampling Length** & **F1 (\%)** & **Note** \\ \hline \(\approx\)40 & 30 & 91 & Vehicular cruising speed \\ \(\approx\)60 & 20 & 84 & Urban speed limit \\ \(\approx\)80 & 10 & 71 & UAV flight speed \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Emulated higher-speed scenarios. It should be noted that the best classification \(F1\) scores were achieved when the angular window enveloped the angles of mirror reflection and was slightly shifted towards the direction of richer features along the normal. Nevertheless, our method consistently achieved \(F1\) scores around 80%, even in the most angularly restrictive scenarios. This experiment demonstrates that our method can maintain high recognition accuracy at low angles, even under medium to low urban scanning speeds, further attesting to the validity of this approach.
# Spatial Evolution Analysis of Tailings Flow from Tailings Dam Failure Based on MacCormack-TVD
This method demonstrates higher accuracy in solving the shallow water equations associated with the dynamics of mountain hazards in natural terrain. The novelty of the MacCormack-TVD scheme lies in its adaptive hybridization of the classical MacCormack predictor-corrector framework with TVD-enforced flux limiting, uniquely balancing the second-order accuracy in smooth regions and oscillation-free stability near discontinuities, while retaining computational efficiency--a significant advancement over traditional linear high-order methods like Lax-Wendroff (prone to spurious oscillations) and computationally intensive high-resolution approaches like WENO or Godunov-type solvers [17; 18]. Unlike purely Riemann-solver-based methods, which prioritize physical fidelity at higher costs, or first-order upwind schemes sacrificing accuracy for robustness, MacCormack-TVD achieves a pragmatic compromise for shock-dominated flows by integrating nonlinear stabilization via limiters without abandoning its explicit, low-storage algorithmic structure [19; 20]. However, the MacCormack-TVD method has been observed to generate artificial numerical fluctuations in cases where the bottom topography undergoes significant changes [21]. To mitigate or eliminate these errors, special treatment of the source terms is necessary [22; 23]. This paper presents a simulation and analysis of the spatial evolution of the tailings flow following the dam failure of the Guangdong Xiagoa Tailings Dam. The analysis was conducted using Massflow software in the MacCormack-TVD format. The plant is located in close proximity to the tailings dam, situated only 580 m away in a straight line, making it a significant case study for the analysis of the spatial evolution of the tailings flow post-dam failure. The study evaluated hazard indicators, encompassing the inundation depth, tailings flow rate, and inundation extent. The findings of the study are designed to furnish technical support for the prevention and control of tailings dam failure accidents, thereby ensuring the safety of production in mining operations.