esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Discussion
### Effects of Parameters  
Several parameters are involved in our CNN model design. In this section, we examine how these parameters affect the network performance. Figure 6 shows how the performance varies with respect to the number of convolution (Conv) kernels. The network architecture starts from a 256-kernel in 2-Conv layer to 5-Conv layer and halving parameters. We find that, although many mainstream networks use 256 or more kernels every layer, our model tends to prefer 128 kernels. This may be because the background of the tongue image is single, the position of the tongue is clear, and too many parameters will lead to under-fitting of the model.  
Through observation, we found that the size of the tooth mark region accounts for about 1/8 of the whole picture. Therefore, in theory, the neuron receptive field for detecting tooth marks doesn't need to be too large. For this reason, we explored the influence of different sizes of receptive fields on the classification results. The receptive field was defined as the region in the input space that a particular CNN's feature is looking at [30]. Our choices for kernel size include (3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7), and for convolutional layers include (3, 4, 5, and 6). Based on [30], we compute the size of receptive fields in different network models. The experiment results are shown in Tables 2 and 3. We find that the model with a 3 \(\times\) 3 kernel size is the most effective. As stated in [31], 3 \(\times\) 3 kernel layers have more non-linear rectifications, which makes the decision function more discriminative. A 5-Conv layer whose receptive field is 94 performs better than others. In [9], it was found that the  
Figure 5: Grad-CAM explanations for [PERSON]’s method and our method. We can see that, even though both methods made the right decision, these two models are different in their attention.  
Figure 6: Accuracy with respect to varying the different number of convolutional kernels. The blue, green, and yellow lines represent 128, 256, and 64 kernels, respectively.  
best-looking visualizations are often obtained after the deepest convolutional layer in the network, and localizations get progressively worse at shallower layers. This is because the later convolutional layers capture high-level semantic information and retain spatial information, while the shallower layers have smaller receptive fields and only concentrate on local features. However, in our research, we find that a 5-Conv layer network performs better than a 6-Conv layer network. So, the accuracy of image classification does not depend entirely on the number of network layers, but also on the specific classification tasks. It is necessary to understand the problem deeply and build a suitable network model to solve the specific problem.
## 3 Numerical Modelling
### Mesh Model
#### 3.4.2 Finite Volume Method  
In the liquid sloshing simulation, a k-\(\epsilon\) turbulence model for fluid and the VOF method for the free surface were applied to the water tank model and simulated using ANSYS CFX (ver. 20R2) at full scale. Incompressible flow was assumed for the sloshing simulation. The time step was 0.01 s, which was sufficient to capture the pressure on the wall and to simulate the behaviour of the free surface in the water tank. In the mesh convergence study, the total simulation time was only 5.0 s.  
Five mesh sizes (50, 75, 100, 200, 300 mm) were tested to examine the effect of the mesh size on the free surface profile in the longitudinal direction (i.e., on the _x-axis_) under seismic loads. Figure 10 shows comparisons of the forces acting on the water tank wall in terms of the free surface profile with various mesh sizes. As can be seen, the force converged as the mesh size decreased. Therefore, the proper mesh size for the sloshing simulation was 100 mm. The total number of mesh elements was approximately 3.8 million.  
Figure 10: Results of mesh convergence test for the force acting on the water tank wall.  
Figure 9: Results of the mesh dependency test on the steel frame for total deformation.
###### Abstract  
A new compact left-handed meta-atom for S-, C- and Ku-band applications is presented in this paper. The proposed structure provides a wide bandwidth and exhibits left-handed characteristics at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) (xy-axes) rotations. Besides, the left-handed characteristics and wide bandwidth of 1 \(\times\) 2, 2 \(\times\) 2, 3 \(\times\) 3 and 4 \(\times\) 4 arrays are also investigated at the above-mentioned rotation angles. In this study, the meta-atom is designed by creating splits at the outer and inner square-shaped ring resonators, and a metal arm is placed at the middle of the inner ring resonator. The arm is also connected to the upper and lower portions of the inner ring resonator, and later, the design appears as an I-shaped split ring resonator. The commercially available, finite integration technique (FIT)-based electromagnetic simulator CST Microwave Studio is used for design and simulation purposes. The measured data comply well with the simulated data of the unit cell for 1 \(\times\) 2, 2 \(\times\) 2, 3 \(\times\) 3 and 4 \(\times\) 4 arrays at every rotation angle. Owing to the effective medium ratio (EMR) of 8.50 at 0\({}^{\circ}\) and 180\({}^{\circ}\) rotations, the proposed meta-atom structure is compact in size. Moreover, due to the quality factor of 82, the designed meta-atom is flexible for high-performance antenna, filter and sensor applications. Therefore, the meta-atom integrated antenna shows multi frequency bands with the highest peak gain of 5 dBi, which is used as the long distance radio communication frequency.  
Keywords:compactness; left-handed characteristics; meta-atom; wide bandwidth +
Footnote †: journal: Applied Physics Letters
## 3 System Model, Trust Model and Design Goals
### Adversary Model  
Due to openness and high-speed, vehicular networks are more susceptible to security attacks. In VIoT most of the outside adversary may be tackled by central architecture. The inside malicious attacks are difficult to recognize and need a suitable trust algorithm to evict them. Following malicious attacks are considered in this paper.  
* Simple Attack (SA): In this attack, an attacker manipulates other nodes to interrupt the normal communication going on by altering the route information and data forwarding packets.
* Black Hole Attack (BH): In the Black hole attack, an attacker first convinces the normal node by claiming that it has the shortest path to its desired destination. Once this normal node sends all packet to the attacker and after getting all packets, it drops all packets, and the normal node becomes a victim. The attacker is difficult to recognize and remains active in the system.
* Zigzag (On-and-off) Attack (ZA): In this attack, the malicious nodes may act good and bad with changing alternative behavior. The malicious node on for a few seconds to send some packets and then suddenly gets off and drops the packets due to many selfish reasons.
## 4 Discussion
Our method was evaluated in low-resolution visual systems with the highest considered resolution equaled (\(192\times 192\)). The range of considered resolutions was determined by two factors. The first is the memory limitations of the hardware: the higher the camera's resolution, the larger the input layers of the network (see Figure 2). When camera resolution increases, it becomes necessary to reduce the batch during training, and network learning becomes longer. The second factor was the lack of the dependence of Cumulative Reward value during PPO from the camera's resolution. As shown in Figure 6, the resolution did not have a decisive effect on agent performance--regardless of the camera resolution, each agent completed the task. The camera's high resolution does not critically affect the complication of the obtained explanation using the CART tree. First, the resolution of the analyzed image is not directly the resolution of the input image, but rather the resolution of GradCAM, or the last convolution layer of the feature extractor. In the case of our experiments, for \((48\times 48)\) resolution, GradCam had a resolution of \((4\times 4)\), for \((56\times 56)\)--\((5\times 5)\), for \((64\times 64)\)--\((6\times 6)\), for \((96\times 96)\)--\((10\times 10)\), for \((128\times 128)\)--\((12\times 12)\) and for \((192\times 192)\)--\((20\times 20)\), which is much smaller than the input resolution of the camera. In addition, the heuristics we assumed use a proxy geometry with a maximum resolution of \(3\times 3\) (see Figure 3). In summary, if we consider the image transmitted from the camera, the most significant factor in terms of the complexity of the decision tree is the number of semantic classes visible in the image, an issue we discussed earlier in this section.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 5 Results  
To verify the performance of the proposed MCOTG algorithm, we conducted joint simulations using System Tool Kit (STK) and Visual Studio (VS), with the LEO satellite constellation adopting the Walker constellation for experimentation.
## Abbreviations  
EV: extracellular vesicle  
miRNA: microRNA  
mRNA: messenger RNA  
TEX: Tumor-derived exosomes  
PD-L1: programmed death-ligand 1  
IFN-\(\gamma\): interferon-\(\gamma\)  
MVEs: multivesicular endosomes  
MVB: multivesicular body  
ILVs: intraluminal vesicles  
TEMs: tetraspanin-enriched microdomains  
HGF: hepatocyte growth factor  
MSCs: mesenchymal stem cells  
TGF-B1: transforming growth factor-\(\beta\)1  
TGF4: human T-cell factor 4  
IL-8: interleukin-8  
ECs: endothelial cells  
NF-\(\kappa\)B: nuclear factor-kappa B  
CAD: coronary artery disease  
ox-LDL: oxidized low-density lipoprotein  
NO: nitric oxide  
KLF5: kruppel-like factor 5  
SMCs: smooth muscle cells  
PNI: Peripheral nerve injury  
SCs: Schwann cells  
NGF: nerve growth factor  
FGF-1: fibroblast growth factor-1  
GDNF: glial cell-derived neurotrophic factor  
IGF-1: insulin-like growth factor-1  
BDNF: brain-derived neurotrophic factor  
WD: Wallerian degeneration
\begin{tabular}{p{128.0 pt} p{284.5 pt}} TGF-\(\beta\)3: & transforming growth factor-beta 3 \\ TIMP1: & tissue inhibitor metalloproteinases 1 \\ WAT: & white adipose tissue; IFN: interferon \\ B AFF: & B cell-activating factor \\ SCECs: & salivary gland epithelial cells \\ RNPs: & ribonucleoproteins \\ TLRs: & Toll-like receptors \\ STIM1: & Stromal interacting molecule 1 \\ AQP5: & aquaporin 5 \\ hUC-MSC-Exos: & human umbilical cord-derived MSC-Exos \\ IRBP: & interphotoreceptor retinoid-binding protein \\ \end{tabular}
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Comparisons of Different Calibration Methods  
In this section, we use the reprojection error to compare different calibration methods. The work of [15] applies two chessboards and one auxiliary calibration object to calibrate the extrinsic parameters of the camera and LiDAR system. They combine ten pairs of 3D-2D and 3D-3D point correspondences. The work of [32] employs more than three diamond planar boards and extracts four vertexes of each board to obtain the intrinsic and extrinsic parameters. The work of [28] is a targetless method and maximizes the mutual information between the sensor-measured surface intensities to calibrate the extrinsic parameters. The work of [33] proposes a method based on supervised learning to calibrate the extrinsic parameters.  
As shown in Table 4, the reprojection error of our method is 0.390, much smaller than other methods. Moreover, our proposed method can calibrate both the intrinsic parameters of camera and extrinsic parameters of the camera and LiDAR system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **Before Translation** & **After Translation** & **Increment/mm** \\ \hline \multirow{3}{*}{\(T\)} & \(\left[\begin{array}{c}146.752\\ 26.527\\ -3.574\end{array}\right]\) & \(\left[\begin{array}{c}224.763\\ 11.835\\ -3.267\end{array}\right]\) & & 79.383 \\ \cline{1-1}  & \(\left[\begin{array}{c}0.976\\ 0.060
# Dispersion of the Retardation of a Photoelastic Modulator
###### Abstract  
Recently, ellipsometry and polarization imaging using photoelastic modulators (PEMs) have been applied to a wide spectral range, from vacuum ultraviolet to the mid-infrared wavelengths. To ensure high accuracy polarization performance, the accurate calibration of the retardation of PEM is crucial. In this report, the dispersion of the retardation of the PEM is studied. According to the operational principle of PEM, their retardation can be separated into independent dispersion and driving terms. The effect attributed to the dispersion on PEM retardation calibration is experimentally explored. These experiments indicate that the dispersion term can be defined in advance using the refractive index of the photoelastic crystal under incident light, and that the driving term is directly proportional to the amplitude of the driving voltage. The calibration method  
To ensure the highly accurate polarization performance of the PEM, the user is required to accurately calibrate its retardation. Recently, ellipsometry and polarization imaging using PEM have been applied to a wide spectral range, from the vacuum visible to the mid-IR wavelengths [7; 9; 10]. Due to the dispersion of the retardation of the PEM, its accurate calibration at a number of wavelengths is a complex problem. In 1999, [PERSON] concluded that voltages applied on the half-wave retardation amplitudes of the PEM were a variable in the wavelengths, which revealed the dispersion phenomenon of the PEM [11]. Until now, there has been no detailed PEM calibration method considering the differences induced by different wavelengths of the incident light, which has been reported. For single wavelength light, the observation oscilloscope waveform and the application of Bessel functions are the two typical PEM calibration methods [12]. The oscilloscope waveform observation method can be used to calibrate the PEM retardation amplitude of a half-wave or radians, when a flattop waveform is observed on the oscilloscope. For the method using Bessel function zeros, the retardation amplitudes of the PEM are set to 2.405, 3.872 and 5.136 rad, respectively, when the zeroth, first, and second Bessel functions are zero, and the corresponding harmonic components are nulled. The two methods can only calibrate the PEM retardation for the special values mentioned above. The method that uses the ratio of multiple-harmonic components is developed to calibrate large numbers of retardation amplitudes of the PEM [13; 14]. The calibration of the PEM retardation at any amplitude for incident light with different wavelengths combined with this method is a promising area for further investigation.  
In this report, based on the fundamental principle of photoelastic modulation, we present the principle analysis of the dispersion of the PEM and experimentally explore the effect attributed to the dispersion on the calibration of the PEM retardation. Furthermore, we also demonstrate an optimized calibration method for the PEM retardation, which considers the dispersion.
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 6 Analysis and Conclusion
Regarding the installation cost, it depends largely on the environment in which the sensor system is deployed. Sensors and cables are easy to install in flat and open areas, and labor and time costs are small, while it is difficult and costly to deploy in mountains or tall buildings. Taking the application of a seismic exploration scenario as an example, a seismic acquisition system with 3000 channels has a cost of 80%-85% of the cost of a wireless system, and in a 12,500-channel seismic acquisition system with a higher density, the datais 50%-60% [32]. Of course, in different applications, the ratio of the installation cost of the power line to the total cost is also different, and the user can make a professional evaluation according to the actual situation. In our proposal, the cables are installed only once during system deployment and can be used throughout the lifetime of the system. Moreover, the fact that few nodes exhaust energy in advance means that the number of nodes can be reduced and the cost can be saved. In some application scenarios, it is cost-effective to obtain longer lifetime at the cost of connecting the power line. This method can effectively improve working hours in wireless systems of different scales, and may provide reference for other similar systems. Conceptualization, J.W. ; Data curation, X.L. ; Project administration, J.W. ; Software, X.L. ; Writing--original draft, X.L. ; Writing--review & editing, J.W. This research was funded by NSFC (Grant No. 41574106), Major National Science and Technology Special Program of China (Grant No. 2017 ZX05008-008-041). The authors declare no conflict of interest.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 2 SCA: Basic and Proposed Forms
### SCA with Staggered Steering (SCASS)  
In the proposed variant of the SCA, the basic SCA structure, as described above, is employed with staggered beam-steering of SA1 and SA2. To be specific, if the desired steering angle of the whole array is \(\theta_{0}\), then the SAs 1, 2 and 3 are beam-steered towards \(\theta_{1}=\theta_{0}+\Delta\), \(\theta_{2}=\theta_{0}-\Delta\) and \(\theta_{3}=\theta_{0}\), respectively. Here, \(\Delta\) is the deviation of the steering angle from \(\theta_{0}\) and is a configurable parameter. On the contrary, in CSA and SCA, all the SAs are beam-steered towards \(\theta_{0}\). SCASS has been investigated here in two forms, namely (i) SCASS with uniform weights (SCASS-U) and (ii) SCASS with Chebyshev weights (SCASS-C). In the first form, uniform weights are employed in all the SAs; while in the second form, Chebyshev weights are employed in SAs 1 and 2 and uniform weights are  
Figure 1: A typical arrangement of an SCA with \(M=3\), \(N=2\), \(P=2\) and \(Q=2\).  
employed in SA3. Array factors of the three SAs cannot be expressed in a closed-form for Chebyshev weights; however, for uniform weights the array factors are:  
\[AF_{1}(\theta)=\frac{1}{PM}\left[\frac{\sin\Bigl{(}\frac{PMQN\pi}{2}\bigl{(}\sin \theta-\sin\left(\theta_{0}+\Delta\right)\bigr{)}\Bigr{)}}{\sin\bigl{(}\frac{1} {2}QN\pi(\sin\theta-\sin\left(\theta_{0}+\Delta\right))\bigr{)}}\right] \tag{3}\]  
\[AF_{2}(\theta)=\frac{1}{PN}\left[\frac{\sin\Bigl{(}\frac{PMQN\pi}{2}\bigl{(}\sin \theta-\sin\left(\theta_{0}-\Delta\right)\bigr{)}\Bigr{)}}{\sin\bigl{(}\frac{1} {2}QM\pi(\sin\theta-\sin\left(\theta_{0}-\Delta\right))\bigr{)}}\right] \tag{4}\]  
\[AF_{3}(\theta)=\frac{1}{Q}\left[\frac{\sin\bigl{(}\frac{Q}{2}\pi(\sin\theta- \sin\theta_{0})\bigr{)}}{\sin\bigl{(}\frac{1}{2}\pi(\sin\theta-\sin\theta_{0}) \bigr{)}}\right] \tag{5}\]  
Both forms have been investigated for different steering angles. The value of \(\Delta\) is selected for each steering angle \(\theta_{0}\), such that HPBW and PSLR remain either the same or degrade minimally when the main beam is steered away from the broadside direction. This contrasts with the existing ULAs and NULAs in which the HPBW and PSLR degrade considerably when the beam is steered away from the broadside.
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 2 Materials and Methods
### Mission Planner  
A mission planner tool was developed to prepare and automate image capturing by drones, with the following requirements:  
* It should be able to process a polygon consisting of GPS coordinates delimiting the designated area for each use case, allowing drawing and editing of the polygon directly on a WebGIS interface.
* It should be able to calculate the GPS locations (waypoints) and the optimal path to capture the mosaic of images for the designated area, according to the sensor characteristics and bundle requirements concerning image overlapping and represent it on a WebGIS interface.
* It should be able to export the waypoints as a Waypoint Markup Language (WPML) file (KMZ file), which could then be used with drones compatible with WPML, such  
Figure 5: Aerial view of the two use cases: Castanheira (**left**) and Oleiros (**right**).  
as some DJI drones. The use of a KMZ file allows fully automatic flight and image capture along the path.  
The module was successfully developed and tested, and all the requirements were met. Initially, the flight pattern was based on south-north patterns, but after some testing, it was discovered that east-west patterns with a gimbal pitch of a few degrees deviated from nadir (\(-90^{\circ}\)) were more effective for point cloud generation.  
Figure 6 shows the interface of the mission planner module, where the user can define several parameters related to the drone's camera, namely the horizontal and vertical resolution, its field of view (FOV), and the gimbal pitch to use. The user can also define the parameters related to the bundle's recommendations regarding the captured images, specifically resolution in cm/pixel, forward overlapping, and lateral overlapping.  
The module calculates and shows the waypoints (represented as yellow dots in Figure 6) where images must be captured to comply with the required parameters. The path is initially calculated as a simple east-west linear sweep that embraces the designated area, but it can be further optimized using a nearest neighbour algorithm, followed by a 2-opt optimization algorithm. Our tests have shown that the linear sweep solution is good enough for most cases. Optimisation steps can slightly reduce the number of points and the total distance travelled, but these marginal gains hardly justify the need for optimisation.  
These waypoints can then be saved in CSV, PDF, or KMZ format (WPML) so that they can be exported to KMZ-compatible drones (such as some DJI drones), allowing automated mapping operations.
# Experimental Evaluation of the Durability of Concrete Repair Materials
## 3 Results and Discussion
### Chloride  
Table 4 and Figures 8 and 9 show the results for the chloride depth and diffusion coefficient. The chloride depths in the intact specimens with target strengths of 20 and 40 MPa were 30.5 and 23.0 mm, respectively. The unrepaired 2 and 5 mm notched specimens with a target strength of 20 MPa had chloride depths of 21.8 and 19.3 mm, respectively. In the corresponding impregnant and epoxy/impregnant specimens, the chloride depths decreased to 9.5 and 7.7 mm, and 10.0 and 9.4 mm, respectively, approximately 3-3.5 times lower than the epoxy specimens. The unrepaired 2 and 5 mm notched specimens with a target strength of 40 MPa had chloride depths of 13.4 and 13.7 mm, respectively. The chloride depths in the corresponding impregnant and epoxy/impregnant specimens were 6.5 and 7.9 mm, and 8.1 and 9.5 mm, respectively, approximately 2-3 times lower than the epoxy specimens. The chloride diffusion coefficient showed a similar tendency as the chloride depth.  
These results show that the repair methods that included impregnant had more obvious repair effects than the epoxy repair method in terms of the chloride depth. This shows that the use of epoxy alone would not be effective for repairs in chloride-rich environments.  
As such, the epoxy effect of chloride is insignificant, but the impregnant effect is pronounced. Impregnant repair will be mainly applicable to areas such as apartments, roads, breakwaters, and offshore structures in coastal areas. If chloride penetrates along the crack, it reaches the reinforcing bars of the structures and causes corrosion of the reinforcing bars, resulting in a decrease in structural performance.  
Figure 9: Chloride diffusion coefficients for specimens with target compressive strength of (**a**) 20 MPa and (**b**) 40 MPa.