esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Plant Materials  
We used two rice varieties in our experiments. IR62 is a highly resistant,'modern' variety that was released by IRRI in 1984. The variety has high resistance to BPH populations from South and Southeast Asia [48]. IR62 is also resistant to WBPH and GLH in the Philippines [47]. However, resistance against WBPH is predominantly expressed after maximum tillering [31]. Resistance in the variety was derived from the Indian landrace PTB33 and is likely associated with the _Bph3_ locus, which contains the _Bph3_ and/or _Bph32_ resistance genes [49; 50]. Although several varieties with PTB33 as a resistance donor were released by IRRI, IR62 appears to have particularly strong resistance to planthoppers and has maintained its resistance for over 30 years [49]. IR64 was used as a susceptible control variety in our experiments. IR64 was released by IRRI in 1985. Since then, the variety has become a mega-variety in South and Southeast Asia, largely due to its high-quality rice grain [51]. Because of its popularity, several beneficial traits, including flood tolerance, have been incorporated into IR64 using marker-assisted selection [51]. IR64 contains the _Bph1_ gene for resistance, as well as quantitative resistance and tolerance [52]. However, widespread exposure of the _Bph1_ gene over three decades throughout Asia has resulted in a high proportion of planthopper populations with virulence against the gene, such that the variety is now susceptible to BPH [48]. Seed of both varieties was acquired through the germplasm bank at IRRI. Recent greenhouse studies have verified that, compared to IR64, the resistant variety IR62 reduces planthopper population or biomass build-up over successive generations after initial infestation [53]. Furthermore, research using field cages indicates that IR62 prevents 'hopperburn' (i.e., planthopper-induced death of rice plants) [54].
## 2 Materials and Methods
### Simulation Models  
Our antennas are implantable in the fat tissue of the skin. Three-layer phantoms, including skin, fat, and muscle with thicknesses of 4 mm, 4 mm, and 8 mm, were chosen for the design. For the human body model, different thicknesses have been reported in the literature depending on the body type and also different body tissues. For example, for arm and chest, skin thicknesses of 2 mm and 5 mm have been reported. Similarly, depending on the body fat index of the human body, fat thicknesses between 2 mm to 15 mm have been measured [24]. In our modeling and simulation, in order to have results applicable to a broader range of cases, we selected average values of 4 mm and 4 mm for skin and fat thicknesses. For muscles thickness, since the simulation time increases with the increase of the model volume, our simulations showed that an 8 mm thickness can be appropriately assumed for the muscle thickness. Further increasing the muscle thickness did not show any significant changes in the simulation results. Therefore, by selecting a muscle thickness of 8 mm, accurate simulation results and lower simulation time have been accomplished. EM characteristics of these tissues are dispersive and thus dispersive models of these tissues in CST were used for the simulation. The dimensions of the phantom have been optimized for reliable results, and they are approximately 5 cm \(\times\) 5 cm. A superstrate is used in addition to the substrate layer to avoid contact with conductive tissues. With the proper selection of feeding points, the input impedances of all designs are 50 Ohm. The substrate and the superstrate used in this paper are Rogers 3003 and Rogers 3210, with relative permittivities of 3 and 10.2 and loss tangents of 0.0013 and 0.0027, respectively [25]. Rogers 3210 is used in the design of Antenna I and III and Rogers 3003 is used in the design of Antenna II. In all designs, the first step is specifying the path for the first resonant frequency. The second and third resonances are added by introducing extra branches. To miniaturize the antennas, all of them benefit from the PIFA idea to reduce the size to half of ordinary designs because of ground via and \(\lambda/4\) length instead of \(\lambda/2\). Other methods used in antenna miniaturization are introduced in the following [26; 27; 28]. Structures and dimensions of the three designs can be seen in Figure 1 and Table 1, and the electrical surface current distribution of each antenna can be viewed in Figure 2.
## 4 Discussion
### Limitations and Future Directions  
A limitation of the current study is that the semi-automated glottic angle extraction algorithm used to identify glottic has only been used in previously published reports on healthy participants [38]. The algorithm estimates the glottic angle in a given video frame using the anterior one-third of the vocal folds (anterior commissure), which is largely outside of the vicinity of phonotraumatic lesions and the effects of bowing due to vocal fold atrophy. Regardless, visual inspection of each VCV production was carried out in this work to verify accurate angle extraction in cases with and without abnormal anatomical deviations (e.g., lesions, atrophy).  
The results of the current study demonstrate the promise of using physiologically relevant acoustic features to locate the boundary cycle between voiced and unvoiced speech segments, specifically for estimates of RFF. However, additional steps must be undertaken to improve the clinical applicability of the aRFF-APH algorithm. This should include the use of independent training and test sets that span a broad range of vocal function. In doing so, the aRFF-APH algorithm could be modified to include pitch strength-tuned algorithm parameters to account for variations in voice sample characteristics. The aRFF-APH algorithms should also be expanded to neck-surface accelerometer signals, as there has been a growing interest in using the neck-surface vibrations generated during speech for ecological momentary assessment and ambulatory voice monitoring (e.g., [51; 52; 53; 54; 55; 56; 57; 58; 59; 60]). By capturing daily vocal behavior through a neck-surface accelerometer, vocal behaviors associated with excessive or imbalanced laryngeal muscle forces could be identified and monitored via RFF. Although an accelerometer-based RFF algorithm has been developed [61] future work should aim to improve this algorithm by identify physiologically tuned features that can be used to identify the true termination and initiation of vocal fold vibration. Doing so would further improve the clinical relevance of using RFF to assess and track laryngeal muscle tension.
# Investigation on Interference Test for Wells Connected by a Large Fracture
## 3 Model and Solution for the Interference Test
### Extremum Analysis of the Interference Test
From Equation (34), we can get the pressure conductivity coefficient as follows  
\[\eta=\frac{x^{2}t_{p}}{2t_{m}(t_{m}-t_{p})\ln  
In Equation (36), the variable \(r_{D}\) that replaces the variable \(x_{D}\) is the distance between the active well and the observation well along the fracture. The curves of the dimensionless pressure derivative denoted in Equation (36) and the dimensionless pressure denoted in Equation (26) are drawn in one type-curve as Figure 3. This finding can be observed in the double logarithmic coordinate system, when the distance between wells is constant, the pressure and pressure derivative increase over time, and the speed of increase approaches a constant. Both of the two curves become linear with a slope of 0.5.  
It is easy to obtain the cross point of a dimensionless pressure curve and dimensionless pressure derivative curve in an interference test type-curve of WsCLF as follows:  
\[\frac{t_{D}}{r_{D}^{2}}\approx 1.335,\frac{p_{D}}{r_{D}}=\frac{p^{\prime}_{D}}{r _{D}}\approx 0.541 \tag{37}\]  
When \(t_{D}/r_{D}^{2}\rightarrow\infty\), according to Equations (26) and (36), it can be found that  
\[\frac{p_{D}}{r_{D}}\approx\frac{2\sqrt{t_{D}}}{\sqrt{\pi}r_{D}},\text{and} \frac{p^{\prime}_{D}}{r_{D}}\approx\frac{\sqrt{t_{D}}}{\sqrt{\pi}r_{D}} \tag{38}\]  
Therefore, \(p_{D}/r_{D}\sim\sqrt{t_{D}}/r_{D}\) approaches a straight line through the original point. However, in a homogeneous reservoir, \(p_{D}\)-\(\lg t_{D}\) becomes a straight line when \(t_{D}/r_{D}^{2}\rightarrow\infty\). In a double logarithmic coordinate system, it can also be found that  
\[\lg\left(\frac{p_{D}}{r_{D}}\right)-\lg\left(\frac{p^{\prime}_{D}}{r_{D}} \right)\approx\lg\left(\frac{2\sqrt{t_{D}}}{\sqrt{\pi}r_{D}}\right)-\lg\left( \frac{\sqrt{t_{D}}}{\sqrt{\pi}r_{D}}\right)=\lg 2\approx 0.301 \tag{39}\]  
Thus, the distance between \(p_{D}/r_{D}\) and \(p^{\prime}_{D}/r_{D}\) is lg2 log-period in a double-logarithmic coordinate system. If the distance between wells and the width of the fracture are known, the pressure conductivity coefficient and permeability can be obtained by fitting the pressure and pressure derivative with the type-curve of interference test. Because it is difficult to obtain a precise value of the width of a fracture, the fracture conductivity \(Kb\) is usually computed. Figure 3: Interference test type-curve of wells connected by a large fracture.
## 1 Introduction
DSM and Digital Elevation Model (DEM) data evaluated for forestry passability were scanned by LiDAR in 2013. CHM data and field measurements were used for determining the approximate forest structure (tree height, _DBH_, and stem spacing between trees). The described methods were tested on a spruce forest stand composed only from one type of tree--Sitka spruce (Picea abies), situated approximately 300 m south of the village of Brno-Utechov (see Figure 1), where the heights of trees in the Kritny Training Forest Enterprise (TFE) area were detected and measured. This spruce forest was chosen because of the availability of a series of aerial photos and LiDAR data. Additionally, the forest is highly representative, as it contains the tree species most commonly found in many Central European countries. The current age of this forest is about 30 years. The approximate forest structure with the calculated tree density (stem spacing) was used for modeling fire brigade vehicle maneuvers between the trees. _DBH_ data were used in cases where it was easier for the vehicle to override the trees rather than maneuver between them. _DBH_ data were also used for the calculation of the distances between trees. Due to the availability of a DSM with a density of 1 \(\times\) 1 m, it was impossible to precisely determine the locations of individual trees, so a random simulated forest structure was created based on the number of trees per hectare. The article describes the methodology of calculation of the rescue vehicle movement using simulated areas of a burning forest. In the case of a real fire, we can use the above-mentioned LiDAR data (DSM data) or current data from different sensors. The type of sensors and the accuracy of the data obtained will have a significant impact on the terrain analysis and search algorithm for optimal rescue vehicle routes. Only some scattered and low-resolution data of the fire can help. If the optimal route for a special vehicle  
Figure 1: Location and character of TFE Kritny. (fire-resistant rescue vehicle, tank, etc.) that will move through a burning forest is to be calculated, we will need detailed vegetation and elevation data with a meter or decimeter resolution for reconnaissance of fallen trees, boulders, etc.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 3 The Proposed EE-UWSNs Protocol
### Simulation Results
#### 4.3.1 Effect of Changing the Cone Angle  
In this section, the impact of changing the cone angle value on the performance of the underwater sensor network is studied in terms of the average energy consumption of sensor batteries, the number of collisions, end-to-end delay, and jitter. Three cone angles  
Figure 11: Average end-to-end delay.  
Figure 10: Average number of collisions.  
are selected that have different sizes which are 60\({}^{\circ}\) (small angle), 120\({}^{\circ}\) (medium angle), and 180\({}^{\circ}\) (large angle). The cone angle should not be greater than 180\({}^{\circ}\) because this can lead to selecting a relay node that is not located in the direction from sending node to sink and thus the network performance will be influenced negatively.  
Figure 12 shows the values of the average energy consumption per sensor node, using different cone angles and under various simulation times. We found that the amount of energy consumption is high in the case of a small angle (60\({}^{\circ}\)), as opposed to a large angle (180\({}^{\circ}\)). The average improvement from changing the cone angle, in terms of energy consumption of the sensor battery, is estimated at 42.49%. The reason for this is that the larger the cone angle, the greater the percentage of sensors that will enter the inactivation mode, and thus energy will be saved.  
Figure 13 shows the effect of changing the cone angle on the average number of collisions, using different simulation periods. As shown in the figure, there is an inverse relationship between the average number of collisions and the size of the cone angle. Increasing the angle leads to making a percentage of the sensors (50% of them in this scenario) enter the sleep mode, and therefore during this period there is no transmission of packets; consequently, the rate of collisions decreases by an average of 40.61%.  
Figure 12: The effect of changing cone angle on energy consumption.  
Figures 14 and 15 represent the impact of modifying the value of the cone angle on the average of end-to-end delay and packet jitter values. Increasing the value of the cone angle results in more sensors being located within it. The proposed EE-UWSNs protocol considers the distance to the desired destination when calculating score values. Therefore, the closest sensor to the destination is selected as a relay node and thus the propagation delay is enhanced. Furthermore, increasing the cone angle reduces the collision (as we mentioned in the previous figure) and thus decreases the number of retransmissions and improves the delay and jitter. The average percentages of improvements in end-to-end delay and jitter are 67.96% and 56.06%, respectively.
## 1 Introduction
### Related Works
The authors thoroughly examined widely discussed proposed techniques, assessing their effectiveness, assumptions, and computing requirements; a side-by-side view of different solutions enlightened on the feasibility of the approach and its shortcomings that could be a determining factor for the system design, improving the safety and efficiency of self-driving vehicles.
# Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs +
## 6 Experiments and Results
### Performance Comparison of Proposed Scheme with Torch Framework  
All these experiments are performed on three classification networks ([PERSON]'s CIFAR-10, OverFeat and ResNet-34). These networks are trained in Torch framework and trainable parameters are imported to utilize by the proposed scheme. Table 11 presents a comparison of Torch-based networks and proposed scheme utilizing trained parameters from former networks. Jetson TX1 embedded board is used to perform this comparative analysis. Jetson TX1 board has Maxwell GPU with 256 CUDA cores and 4 GB of RAM. It provides 16 GB of embedded MultiMediaCard (eMMC) to store the operating system, required frameworks, packages, and files.  
Performance of cuDNN torch library is evaluated for the same three classification networks. cuDNN is a wrapper of Nvidia's cuDNN library for optimized GPU implementations of Neural Networks.  
It can be noted that for [PERSON]'s CIFAR-10 and OverFeat models, the proposed flow shows the comparable performance to the Torch-based library while the proposed flow is slower for ResNet-34 model having small filters. However, proposed flow can be accelerated for networks having small filters using Winograd's minimal filtering algorithms.  
Because embedded devices have limited memory storage, so one constraint to consider is the size of the trained model and installation of required packages on an embedded platform. Table 12 lists the sizes of trained models imported from torch and converted trained parameters for proposed optimized models.  
It can be noted that a complete trained model with installation of main framework (in this case torch, consuming 828 MB of memory on Jetson TX1 board) require a significant amount of memory storage. While just using trainable parameters, a comparable optimized scheme can be realized on an embedded platform.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Layers** & \begin{tabular}{c} **Sequential** \\ **ConvMM** \\ \end{tabular} & \begin{tabular}{c} **Hetero** \\ **ConvMM** \\ \end{tabular} & \begin{tabular}{c} **Unified** \\ **ConvSAM** \\ \end{tabular} & \
\begin{tabular}{c} **Efficiency Over** \\ **Sequential** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency Over** \\ **Over Hetero** \\ \end{tabular} \\ \hline Alex’s CIFAR-10 & 5 & 16 & 0.410 & **0.202** & 79\(\times\) & 2\(\times\) \\ OverFeat & 8 & 850.8 & 3.2 & **0.529** & 1608\(\times\) & 6\(\times\) \\ ResNet-34 & 34 & 480 & 1.8 & **0.432** & 1111\(\times\) & 4\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of power consumption on Nvidia Shield K1 tablet (joule); best results are written in bold.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 4 Conclusions  
A miniaturized meandered loop slot-line antenna that is suitable for IoT applications was proposed in this work. The presented antenna was optimized using bending, meandering, and reactively loading the slot methods to operate in the sub-GHz bands. Recently, sub-GHz IoT technology is becoming more popular in order to enable devices to achieve long-range communications with low power consumption. A very wideband tuning, 758 to 1034 MHz, was achieved. The antenna's reconfigurability was also investigated in this work. A good understanding of the general antenna design guidelines for this antenna system type was developed. The proposed antenna design was fabricated on a RO4350 substrate with dimensions of 60 mm \(\times\) 27 mm. The compact planar structure of the antenna, its simple biasing circuitry, its ability to operate over a wide band of sub-GHz, and its narrow-band operation are unique features of this design. **Author Contributions:** Conceptualization, R.H.; software, R.H., A.M.A. and S.I.A. ; validation, R.H. and A.M.A. ; writing--original draft preparation, R.H.; writing--review and editing, R.H., N.H., S.I.A. and K.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** The authors would like to acknowledge the support provided by Researchers Supporting Project number (RSP2022R474), King Saud University, Riyadh, Saudi Arabia. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Experiment
### Calibration Setup  
The calibration process begins with the alignment of the solar simulator outlet surface and the diffuser's reflective surface to be parallel. The spatial and spectral dimensions of the UVS detector are 512 \(\times\) 1024. To improve the inversion accuracy, the BRDF is altered  
Figure 4: Schematic diagram of calibration in the earth observation mode.  
pixel by pixel during ground calibration. At the ground port, one irradiation can cover only 6-7 rows of pixels in the spatial dimension. To ensure that the spatial dimension of the FOV is entirely covered, the rotary table must be used to adjust the UVS multiple times. The specific adjustment steps are as follows.  
1. The centers of these two surfaces must be perpendicular to their respective planes, with a consistent distance of approximately L, in accordance with the standard irradiance calibration.
2. This involves transitioning the UVS operational mode to \"earth mode\" to initiate observations of a designated point beneath the Earth's surface. Adjustments are made to align the line connecting the center of the Earth port with the center of the diffuser to create a 30\({}^{\circ}\) angle with the normal of the diffuse reflector plate. Before commencing the calibration process, the light source is activated and allowed to stabilize for ten minutes.
3. To capture comprehensive data, the rotation of the payload turntable begins, starting from the edge of the payload field of view (FOV) and progressing in 0.5\({}^{\circ}\) intervals until the instrument completes a full FOV scan. Ten observations are collected for each rotation position.
4. Following this, the payload's operational mode is transitioned to \"solar mode\", and the onboard diffuse reflective plate is configured to \"calibration 1\" mode. The test apparatus is setup according to the provided diagram, ensuring that the front surface of the light source outlet aligns parallel to the sun port's surface. Equal distances must be maintained between the optical stimulus and the onboard diffuser, both in the irradiance mode and the radiance mode, through careful adjustments.
5. The angles are set to \(\beta\) = 26.45\({}^{\circ}\) and \(\alpha\) = 0, and 50 data frames are acquired. \(\alpha\) measurements start from \(-4^{\circ}\), adjusting in 1\({}^{\circ}\) increments up to +4\({}^{\circ}\), with images captured at each \(\alpha\) angle. Starting at 14.95\({}^{\circ}\), \(\beta\) is measured in 1\({}^{\circ}\) increments up to 37.95\({}^{\circ}\), and \(\alpha\) is recorded at each \(\beta\) angle. Finally, the onboard diffuse reflector calibration board is transitioned to calibration 2 mode, and the test procedure is repeated to conclude the BRDF calibration for both onboard diffuse reflector boards.
# Mechanical Properties of Sandstone Cement-Stabilized Macadam
## 3 Test Results
### Indirect Tensile Strength
#### 3.2.2 Water Stability  
Sandstone, a type of sedimentary rock, is usually affected by water action, so ITS tests were conducted in both dry and saturated states. The calculated softening coefficients are summarized in Table 6. It can be seen from the table that the softening coefficient is less than 1, which means that the water saturation has a weakening effect on the splitting tensile strengths of the SCSM and CSM. Apparently, for the same cement content, the softening coefficient of the CSM is higher than that of the SCSM. For example, the CSM value is 5% higher than the SCSM value at a curing age of 7 days. This result may be related to their different water absorption rates and porosities, as mentioned above. Meanwhile, as the curing age increased, the softening coefficient is gradually increased at a decreasing rate. With the curing age ranging from 7 to 60 days, the softening coefficients of the SCSM and CSM increased by 5% and 6%, respectively, while the coefficients were almost unchanged from 60 to 180 days. This finding might be mainly due to the increase in the curing age, which caused the transformation of the hydration products into a hydrophobic gel [33]. In addition, it is clear that the softening coefficient of the SCSM increases with the increase in the amount of cement. It is well known that the water resistance of a material is tightly related to the pore structure of the material and the method of adhesion between the particles. Therefore, effectively reducing the porosity of the sandstone is an important means to improve the performance of CSM with sandstone as the aggregate.  
Figure 6: Relationship between the ITS and curing time. (**a**) Dry state (**b**) Water-saturated stat.  
Figure 7: Relationship between the ITS and aggregate type.