esa-sceva/satcom-chunk-collection huggingface dataset:
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Near-Infrared Sensors
#### 5.1.2 Temperature
However, these are further classified into various types such as, voltage output, current output, digital output, resistance output silicon or diode temperature sensors [83]. The latest semiconductor temperature sensors are highly accurate, offering high linearity from a \(-55\)\({}^{\circ}\)C to \(+150\)\({}^{\circ}\)C operating range. The output can be measured by internal amplifiers to convenient values, such as 10 mV/\({}^{\circ}\)C. Their application is limited to cold-junction compensation circuits in wide range temperature thermocouples [87]. Silicon temperature sensors simplify the challenges related to broader temperature monitoring. These sensors differ from others in various ways. First of all, silicon temperature sensors offer broader temperature monitoring and can operate over a \(-55\)\({}^{\circ}\)C to \(+150\)\({}^{\circ}\)C temperature range [83]. Secondly, these sensors differ in functionality as they have either analogue circuits or analogue-sensing circuits with digital input/output and control registers to provide alert functions compared to traditional compensation circuits. Temperature is continuously measured and monitored at any time as the digital output sensor usually contains a temperature sensor called an analogue-to-digital converter (ADC). These sensors are usually microprocessor-based systems that offer reliable temperature monitoring over time [88]. Moreover, the high and low temperature threshold can be programmed so that the host can receive a notification when temperature hits the threshold limit. These sensors are highly compatible with digital temperature readings in \({}^{\circ}\)C and therefore are designed to measure temperatures from 0 to 70 \({}^{\circ}\)C with \(\pm 0.5\)\({}^{\circ}\)C accuracy. Digital temperature sensors are simple and advanced equipment as no calibration is required at specific temperatures. The sensor output supports balanced digital reading thus providing a precise reading in \({}^{\circ}\)C. These sensors provide some advanced features such as not needing excess components like an ADC converter and being easier to use compared to a thermistor that provides non-linear resistance with temperature variation [49].
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
###### Abstract  
In energy-constrained wireless sensor networks, low energy utilization and unbalanced energy distribution are seriously affecting the operation of the network. Therefore, efficient and reasonable routing algorithms are needed to achieve higher Quality of Service (QoS). For the Dempster-Shafer (DS) evidence theory, it can fuse multiple attributes of sensor nodes with reasonable theoretical deduction and has low demand for prior knowledge. Based on the above, we propose an energy efficient and reliable routing algorithm based on DS evidence theory (DS-EERA). First, DS-EERA establishes three attribute indexes as the evidence under considering the neighboring nodes' residual energy, traffic, the closeness of its path to the shortest path, etc. Then we adopt the entropy weight method to objectively determine the weight of three indexes. After establishing the basic probability assignment (BPA) function, the fusion rule of DS evidence theory is applied to fuse the BPA function of each index value to select the next hop. Finally, each node in the network transmits data through this routing strategy. Theoretical analysis and simulation results show that DS-EERA is promising, which can effectively prolong the network lifetime. Meanwhile, it can also reach a lower packet loss rate and improve the reliability of data transmission.  
wireless sensor networks; routing; energy efficiency; reliability; DS evidence theory +
Footnote †: journal: Applied Sciences
# Analysis and Implementation of a Hybrid DC Converter with Wide Voltage Variation
## 3 Operation Principle
### Medium Voltage Range Input (\(2V_{in,min}\leq V_{in}<4V_{in,min}\)) under Forward Power Flow  
When a medium voltage range is input to the proposed converter, the half-bridge resonant circuit is operated on the left-hand side (Figure 1c). Since the input voltage of the resonant circuit is \(\pm V_{in}/2\) instead of \(\pm V_{in}\) in Figure 1b, the voltage gain in Figure 1c is only \(1/2\) of the voltage gain in Figure 1b. One can observe in Figure 1c that \(S_{ac1}\) is on and \(S_{3}\) and \(S_{4}\) are off. Only \(S_{1}\) and \(S_{2}\) are operated with the PFM scheme. \(S_{ac2}\) is on and the half-wave diode rectifier or voltage doubler rectifier is operated on the secondary side. Since the half-bridge resonant circuit (Figure 1c) and full-bridge resonant circuit (Figure 1b) have  
Figure 2: PWM signals and state circuits under low voltage range input and forward power flow: (a) PWM signals; (b) State 1; (c) State 2; (d) State 3; (e) State 4; (f) State 5; (g) State 6; (h) State 7; (i) State 8.  
similar operation states, the circuit operations of the proposed converter under a medium voltage range input are neglected in this subsection.  
High Voltage Range Input (4\(V_{in,min}\leq V_{in}\) < 8\(V_{in,min}\)) under Forward Power Flow  
When the input voltage is in the high voltage range, the half-bridge resonant circuit is operated on the left-hand side and the full-wave diode rectifier (\(S_{ac2}\) is off) is operated on the right-hand side, as shown in Figure 1d. Comparing Figure 1c,d, the leg voltage \(v_{de}\) is \(\pm V_{o}\) in Figure 1d instead of \(\pm V_{o}/2\) in Figure 1c with voltage doubler rectifier structure. Therefore, the converter in the Figure 1d operation has less voltage gain than the Figure 1c operation. From Figure 1b-d, it can be concluded that the Figure 1b circuit is selected to control the load voltage with the maximum voltage gain under a low voltage range input condition, and the Figure 1d circuit is selected to regulate the load voltage with the minimum voltage gain under high voltage range input conditions. The circuit operation in Figure 1d is similar to the circuit operation in Figure 1b,c. Thus, the operation principle of the Figure 1d circuit is neglected in this subsection.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 1 Introduction
At first, French space agency researchers used CHAMP's accelerometer data to retrieve the atmospheric density. The accuracies of the DTM (Drag Temperature Model) series and the MSIS (Multiple Sclerosis Impact Scale) series were evaluated using the inversion results. The DTM-STAR model was obtained after improving the DTM-2000 model [18]. [PERSON] and other scholars used the CHAMP satellite's accelerometer data to study the atmospheric density from October to November 2003[PERSON] They also studied atmospheric density distribution differences between the northern and southern hemispheres. Using the inversion results, they evaluated the MSIS00 atmospheric model's accuracy and HWM93 (Horizontal Wind Model 93) [12; 19][PERSON] [PERSON] et al. used the MAVEN spacecraft's accelerometer data to detect the upper atmosphere's structure on Mars, estimated the vertical distribution of Martian atmospheric temperature, and reconstructed the profile to 170 km [17]. [PERSON] et al. proposed an iterative algorithm for measuring atmospheric density and wind field using satellite accelerometer data [20][PERSON] [PERSON] et al. calculated the mean error between the measured results and the empirical model [21]. [PERSON] et al. obtained the latest atmospheric density data set by re-scaling Sutton's mass density datasets of CHAMP and GRACE satellites [22]. [PERSON] et al. used TDW (Thermospheric density and wind), and Ghost software provided by NASA to retrieve the thermosphere and horizontal wind fields of the GRACE and CHAMP satellites. They analyzed the feasibility of retrieving atmospheric density from the accelerometer data Swarm satellite. Unfortunately, the author did not conduct specific experiments and research on Swarm satellites [23]. Densities from accelerometers on Swarm-C were retrieved by [PERSON] et al. [24]. The main points of this paper are as follows: the Swarm-C accelerometer data were corrected by referring to the correction method of the GRACE-A satellite accelerometer [25; 26]. The correction results were compared with the data correction results published by ESA([[https://earth.esa.int/eogateway/missions/swarm/data](https://earth.esa.int/eogateway/missions/swarm/data)]([https://earth.esa.int/eogateway/missions/swarm/data](https://earth.esa.int/eogateway/missions/swarm/data))) (accessed on 18 November 2022). Then, the satellite surface shape model was established, and the simplified conical shadow model replaced the traditional cylindrical shadow model for light radiation pressure modeling [27; 28]. The similarities and differences between the modeling effects of the two models were compared. Finally, the atmospheric drag coefficient of the Swarm-C satellite was calculated [29; 30], and the atmospheric density was retrieved.
## 5 Conclusions  
In order to investigate the water-weakening effects on mechanical behavior and reveal the weakening mechanisms for different rock types, a series of uniaxial compression tests was carried out on three kinds of rocks (i.e., sandstone, granite and marble) in oven-dried and water-saturated states. Mechanical parameters, such as UCS, elastic modulus and critical strain, were obtained and compared. AE technique was also used to monitor and record the evolution of ring counts during loading process. The failure pattern was identified by the correlation between RA and AF values.  
The UCS, elastic modulus and critical strain of rocks decrease after water saturation. The weakening degree varies with rock type: the mechanical properties of the sandstone are the most susceptible to water, followed by the granite and the marble.  
In crack closure stage, the saturated sandstone specimen cumulates less ringing counts than the dry one, while more ringing counts are generated in the saturated state for the granite and the marble. This depends on which condition the specimen is tested in (drained or un-drained). The pore-water  
Figure 10: Water-induced reduction in friction coefficient for different minerals (data from [43; 44; 45; 69]; the data indicate the corresponding minerals comprising the tested rocks).  
lowers the crack damage threshold of rocks. Additionally, in post-peak stage, the ringing counts of dry specimens are much greater than those of saturated specimens.  
According to the RA-AF relationship, the failure of the three rocks is dominated by tensile cracks in a dry state. When tested under saturated conditions, more shear cracks occur in the sandstone and the granite, probably due to the water-induced frictional reduction. However, water has no effect on the failure pattern of the marble.  
The coupled effects of quartz hydrolysis, clay mineral swelling, calcite dissolution and frictional reduction are responsible for the notable strength drop of the sandstone. The mild strength decrease of granite is mainly caused by the quartz hydrolysis, pore pressure increase and friction reduction. The pore pressure and calcite dissolution only cause slight reduction in the UCS of the saturated marble.  
[PERSON] and [PERSON] conceived and designed the tests; [PERSON] and [PERSON] proposed and participated in the mechanical experiments and processed the data; [PERSON] conducted the XRD tests; [PERSON] wrote the paper; [PERSON] and [PERSON] revised the paper.  
This research was funded by the National Basic Research Program of China, grant number 2015 CB060200, the National Natural Science Foundation of China, grant number 41772313, the Natural Science Foundation of Hunan, grant number 2015J4067 and the Graduated Students' Research and Innovation Fund Project of Central South University grant number 20182 zts210.  
The authors wish to thank [PERSON] from the Advanced Research Center in Central South University, and [PERSON] from the School of Resources and Safety Engineering in Central South University for their help in experiments.  
The authors declare no conflict of interest.
## 3 Decoupling Mechanism  
The isolation enhancement process is analyzed by plotting the surface current distribution of the projected antenna, as portrayed in Figure 10. Current distribution graphs are obtained by stimulating one port and terminating the other port with matched conditions. The projected antenna without a neutralization line has a significant coupling effect on the neighboring element, as depicted in Figure 10 ant_1. Positioning the neutralization line (NL) onto the radiator is the most challenging step in using the neutralization line as a decoupling element. NL is originally placed near the middle of the antenna where the largest current concentration is present, based on the current distribution in Figure 10 ant_1. As in Figure 10 ant_2 ant_6, the position of NL is shifted upwards by observing the current distribution plots and their S-parameter curve at a specific placement referred to as ant_1 to ant_6. The S-parameter curve for each arrangement is illustrated in Figure 11. The ant_6 arrangement provides the out-of-phase current to that of the coupling current, resulting in improved isolation, as demonstrated in Figure 12.  
Figure 8: Projected Two-port dual band-notch UWB antenna.  
Figure 9: Simulated S-parameters of the projected band-notch UWB MIMO antenna.  
Figure 11: S-parameter curves of the NL evolution.  
Figure 12: Demonstration of the operation of NL as a decoupling element (**a**) no decoupling (**b**) NL as decoupling structure.  
Figure 10: The projected antenna’s current distribution plots of various configurations.  
It is clear from Figure 11 that ant_1 to ant_5 have poor isolation and reflection coefficient values, particularly at lower frequencies. The ant_6 arrangement provides isolation greater than 16 dB throughout the impedance bandwidth from 3.4-12.1 GHz.
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 4 Simulation Results
### Robustness Analysis  
The challenge in data-driven control methods is ensuring robustness and reliability with discrete data points. On the one hand, to achieve the optimal control in a complex environment, the agent must visit each state-action pair enough times, which may result in over-fitting, i.e., poor control performance under the unseen traffic conditions. On the other hand, to perform robustly on unseen states, the solution is either to train the agent with a large dataset including as many state-action variations as possible or to simplify the state and action space, but both approaches may compromise convergence and optimality of the controller. As stated in previous sections, our solution tackles this challenge by using the CAE to reduce the dimensionality of the state space while increasing the action space to enhance the control performance.  
The previous simulations were conducted with a constant traffic flow rate of 480 vehicles per hour per lane. To examine the robustness of the proposed DRL-based method, the controller was evaluated under different traffic volumes with varying traffic flow rates. The results of the previously trained controller with volumes of 400, 600,  
Figure 14: Traffic flow simulated by the referenced method in each lane.  
Figure 13: Traffic flow simulated by the proposed method in each lane.  
720, and 800 vehicles per hour per lane are shown in the green box-plots in Figure 16. The orange box-plots represent the performance of controllers specifically trained with the corresponding traffic flow rates. For instance, the orange box-plots of 400 vehicles per hour per lane show the simulation data generated by a DRL-based controller trained with a constant traffic flow of 400 vehicles per hour per lane. The fixed-time and actuated controllers are also included for comparison. The comparison shows that the proposed DRL-based controller performs well in unseen traffic scenarios. However, retraining the controller with specific volumes does result in improved performance, but at a higher training cost and without guaranteed results. The control policy depends on the traffic volume, but the agent cannot determine the traffic volume from a single control cycle's traffic state. To overcome this limitation, future investigation could focus on incorporating temporal information into the traffic state inputs to achieve optimal performance in various traffic scenarios.  
Figure 15: Simulation comparisons with the DRL-based controller trained using symmetric SPaT.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Platform**} & \multirow{2}{*}{
\begin{tabular}{c} **Spatial** \\ **Resolution (m)** \\ \end{tabular} } & \multirow{2}{*}{\(n\)} & \multicolumn{2}{c}{**MBE** **(\%)**} & \multicolumn{2}{c}{**RMSE (mm/d)**} & \multirow{2}{*}{**NRMSE (\%)**} & \multirow{2}{*}{**R\({}^{2}\)**} \\ \hline \multirow{6}{*}{LIRF} & Landsat-8 TIRS & 30 & 14 & \(-\)0.07 & \(-\)13\% & 0.14 & 27\% & 0.48 \\ \cline{2-8}  & Landsat-8 IRT & 30 & 14 & \(-\)0.07 & \(-\)13\% & 0.10 & 20\% & 0.81 \\ \cline{2-8}  & Sentinel-2 & 10 & 35 & \(-\)0.01 & \(-\)2\% & 0.08 & 15\% & 0.83 \\ \cline{2-8}  & CubeSat & 3 & 63 & \(-\)0.03 & \(-\)6\% & 0.09 & 16\% & 0.79 \\ \cline{2-8}  & MSR5 & 1 & 17 & 0 & 0\% & 0.08 & 14\% & 0.82 \\ \cline{2-8}  & UAS & 0.03 & 11 & \(-\)0.04 & \(-\)8\% & 0.08 & 14\% & 0.78 \\ \hline \multirow{6}{*}{IIIC} & Landsat-8 TIRS & 30 & 12 & \(-\)0.07 & \(-\)13\% & 0.16 & 30\% & 0.41 \\ \cline{2-8}  & Landsat-8 IRT & 30 & 12 & \(-\)0.01 & \(-\)2\% & 0.12 & 21\% & 0.71 \\ \cline{1-1} \cline{2-8}  & Sentinel-2 & 10 & 32 & \(-\)0.05 & \(-\)8\% & 0.11 & 17\% & 0.75 \\ \cline{1-1} \cline{2-8}  & CubeSat & 3 & 62 & 0.01 & 1\% & 0.08 & 13\% & 0.78 \\ \cline{1-1} \cline{2-8}  & MSR5 & 1 & 28 & \(-\)0.04 & \(-\)6\% & 0.09 & 14\% & 0.75 \\ \cline{1-1} \cline{2-8}  & UAS & 0.03 & 13 & \(-\)0.09 & \(-\)13\% & 0.11 & 17\% & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Error analysis from the TSEB\({}_{\text{ser}}\) hourly maize ET\({}_{\text{a}}\) evaluation for the LIRF and IIC 2020–2021 data. that integrates multiscale RS imagery from different platforms to generate RS data on a daily basis with similar radiometric quality to be readily available for water stakeholders (e.g., water management authorities, engineers, and agronomists). This integrated multiscale approach can provide consistent daily RS data for calculating crop ET\({}_{\text{a}}\) and supporting water management decisions at local and large scales in agricultural settings. Beyond the RS platforms' spectral resolution, revisit frequency, and sensor-specific limitations and calibration, other factors merit consideration for a comprehensive understanding and effective adoption of RS technologies in agriculture. Atmospheric variables such as humidity, temperature, concentration of gases, dust, and wind speed can significantly affect the accuracy of RS data, particularly for spaceborne sensors. Nonetheless, a multi-source RS data analysis can compensate for the limitations of individual RS sensors/platforms, offering an expanded perspective of crop water requirements that can lead to more sustainable irrigation practices in cropland.
## 1 Introduction
[24] investigated the microscale stress-strain variability in pegmatite specimens during microwave heating by a fully coupled thermomechanical model. [PERSON] et al. [25; 26] studied the effect of microwave radiation on rock fracture strength by a damage viscoelasticity model. [PERSON] et al. [27] proposed a electromagnetic-thermomechanical damage coupled model to investigate damage development in rock specimens caused by non-uniform microwave heating. These simulation studies contributed to the study of rock damage, but the effect of mineral and confining pressures on coal fracturing is still unclear. Pyrite, a typical associated mineral in coal, has abundant presence and uneven distribution within the coal matrix [28]. It has a higher microwave energy absorption capacity than the coal matrix, meaning that is easier to induce thermal stress and coal fracturing in this mineral. Therefore, it is important to investigate the effect of pyrite on the damage mechanisms of coal samples. As widely known, the coal seam is always affected by ground stress. It is important to study the effect of confining pressure on coal fracturing during microwave irradiation. Therefore, this manuscript establishes a coupled electromagnetic-thermomechanical damage model (ETMD) using COMSOL link with MATLAB2021b to study the effect of pyrite and confining pressure on coal fracturing. The goal of this study is to acquire a comprehensive understanding of the interaction between coal and microwave irradiation. It offers a theoretical foundation for the engineering applications of microwave-assisted coal seam gas extraction through heat injection. In addition, this study compares the differences between static and rotating radiation modes in terms of electric field distribution, temperature distribution, and damage distribution. The result can provide theoretical references for practical engineering applications to improve permeability enhancement efficiency and save energy.
## 4 Fluid Dynamic Performance
### Comparison of Experimental Techniques  
The experimental techniques mentioned above have been widely used in the study of the flow dynamics in micro channels; however, they have inherent advantages and disadvantages that are worthy of mentioning and analyzing.  
Flow visualization is able to provide a quick and qualitative assessment of a flow field, that is, it allows identification of the flow distribution and flow regimes by the analysis of the streamlines without any post processing and time consumption. It is also an excellent tool to support the quantitative results provided by other techniques like \(\mu\)-PIV. \(\mu\)-PIV is used for the assessment of the velocity field. There are also other techniques such as micro Laser Doppler Velocimetry (\(\mu\)-LDV) and hot-wire anemometry to measure flows; however, they only provide the velocity at a point and not the two-dimensional or even three-dimensional vector fields as \(\mu\)-PIV does. Therefore, it has several advantages as the capability of measuring flows in 3D or the high degree of accuracy. On the other hand, disadvantages worth mentioning are that it requires a proper selection of seeding particles and the high cost of its setup. For these two techniques (flow visualization and \(\mu\)-PIV), the capture of fast transient phenomena on a microscale requires the use of a high speed camera.  
Pressure drop measurements provide valuable information between different points along the micro channel. However, very accurate measurements are still a challenge. The problems are mainly related to the design of the micro channels where the pressure taps are located, and the presence of air and the surface tension play an important roles in complicating the measurement process.  
Flow birefringence is generally considered as a very convenient method for measuring the stress distribution in polymer melts, and one advantage is that it can be applied without disturbing the flow field. However, it is worthy to address that, in spite of being a valuable experimental technique, it requires the use of birefringent materials, and this fact introduces a restriction to the fluids being used.