esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Verification
### Experimental Analysis  
#### 5.3.1 Evaluation Metrics  
To evaluate the performance of the model, we use weighted average recall (WAR) and unweighted average recall (UAR) as the evaluation metrics. UAR averages the recall for each class without considering the number of samples per class. WAR, on the other hand, considers the number of samples for each class and calculates a weighted average recall. The difference between UAR and WAR lies in whether they consider the weights of the class sample sizes. UAR treats each class equally, while WAR assigns different weights based on the sample sizes of classes. The formulas for calculating UAR and WAR are as follows:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{8}\]  
\[UAR=\frac{1}{N}\sum_{i=1}^{N}Recall_{i} \tag{9}\]  
\[WAR=\sum_{i=1}^{N}\left(\frac{Recall_{i}\times Class\ Size_{i}}{Total\ Sample\ Size}\right) \tag{10}\]  
where \(TP\) represents True Positives; \(TN\) represents True Negatives; \(FP\) represents False Positives; \(FN\) represents False Negatives; \(N\) is the number of classes; \(Class\ Size_{i}\) is the sample size of the \(i\)-\(th\) class; and Total Sample Size is the total sample size across all classes.  
Figure 10: Training/testing and validation data samples for MELD.
## Materials and Methods
### Accuracy Assessment
#### 2.5.2 Field Assessment  
Six linear transects were selected to ensure full coverage of habitat classes denoted in the full study area. Validation points were created by generating 30 randomly distributed stratified sample points across the nine habitat classes within these transect areas, using the WV 8-band classification to define the strata. To minimize the risk of potential errors from GPS and imagery georectification, a \(-3\)m buffer was put on each habitat class in the image, eliminating border areas where GPS error could have located a point in a separate class, and points were generated in the remaining habitat area only. Additionally, points were created with a minimum distance of 5 m between to accommodate for the resolution of the RE pixel size. With these constraints in place, a total of 214 total sampling points were created.  
Ground observations of habitat classes were taken in February 2018. A Trimble Juno 3B GPS unit was used to locate the approximate location of the validation points. Once the approximate site was reached, the validation point location was taken using a sub-meter accuracy GPS unit (Emlid Reach RS RTK) and the observed habitat type at that validation point was recorded. Classification of the observed habitat type in the field was determined by one individual for all validation points to ensure consistency. The size of a WV pixel was used for determining habitat class. Field-collected validation points were differentially corrected from the local GNSS virtual base station network.  
While not practical in our field validation to do truly random sampling throughout the study site due to time constraints, the possibility of transects being in deep water, and a desired balance between classes, transects were otherwise chosen to best ensure representativeness. Where possible we followed best practices presented in [PERSON] et al. [52]: UAS stratified sample points were chosen at random throughout the entire study site, assessment units were chosen to match input and reference data, and the accuracy assessment accounted for our stratified sampling approach.
# See-Through Near-Eye Display with Built-in Prescription and Two-Dimensional Exit Pupil Expansion
## 2 Design Rules
### Pico Projector  
Figure 3 draws the optical path diagram of the pico projector, of which the microdisplay was self-emissive, i.e., organic light-emitting diode panel on silicon [21], and the projection lens was a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object** & **Parameter** & **Value** \\ \hline  & \(W_{1}\) & 46 mm \\  & \(W_{2}\) & 47.93 mm \\  & \(H\) & 30 mm \\  & \(T\) & 7.47 mm \\ Plano-concave lens & \(\gamma\) & 75.52\({}^{\circ}\) \\  & \(P_{w}\) & \(-3\) m\({}^{-1}\) \\  & \(n\)@\(565\)\(nm\) & 1.5880 1 \\  & \(R\) & 0.1960 m \\ \hline \hline \end{tabular} \({}^{1}\) Polycarbonate is chosen as the lens material.  
\end{table}
Table 1: Parameters for the plano-concave lens.  
Figure 2: Optical path diagram for imaging the real object. \(R\) is the radius of curvature of the front surface of lens, \(s_{r}\) the real object distance, and \(s^{\prime}\) the image distance.  
singlet--namely the simple magnifier. The viewing cone, within which the eye must be placed to see the full image, is highlighted. For the beam to be perfectly collimated, the image distance was set to be infinite by equating the virtual object (microdisplay) distance, \(s_{v}\), and the focal length,\(f\), of the projection lens. It would be straightforward to write the FOV of the pico projector as  
\[FOV=2 tan^{-1}\left(\frac{D_{m}}{2f}\right) \tag{5}\]  
where \(D_{m}\) is the size of the microdisplay. Say \(D_{m}=0.165\) inch (4.191 mm) for both horizontal and vertical dimensions and \(s_{v}=f=10\) mm, then FOV = 24\({}^{\circ}\) (horizontal) \(\times\) 24\({}^{\circ}\) (vertical), i.e., 33\({}^{\circ}\) (diagonal). Once the FOV was given, the exit pupil measured at the eye relief (ER)--the distance starting from the last surface of the pico projector to the pupil of eye--could be determined with  
\[EP=A_{p}-2\cdot ER\cdot tan\left(\frac{FOV}{2}\right) \tag{6}\]  
where \(A_{p}\) is the aperture of the projection lens, and also the entrance pupil of our system. If the ER = 12 mm and \(A_{p}=6\) mm, then EP = 1 mm, which is unacceptably small. Since there is not much room to further shorten the eye relief--especially for NEDs without a built-in prescription--the simplest way to expand the exit pupil was to scale up the projection lens. That being said, the wearability was compromised due to the added volume and weight. Table 2 lists the customized specifications necessary for designing the pico projector. Other than the values already mentioned, the resolution of the microdisplay was 640 \(\times\) 640, the pixel size was 6.5 \(\upmu\)m, and the contrast ratio (CR) was 100,000.  
Figure 3: Optical path diagram of the pico projector. \(D_{m}\) is the size of the microdisplay, \(s_{v}\) the virtual object distance,\(f\) the focal length of the projection lens, \(A_{p}\) the aperture of the projection lens, FOV the field of view, ER the eye relief, and EP the exit pupil.
# An Aspect-Oriented Approach to Time-Constrained Strategies in Smart City IoT Applications
## 2 Existing Approaches
### Software Updates in an IoT Context  
There are several existing approaches to the implementation of updating functionality in IoT devices. The Internet Engineering Task Force (IETF) has established a working group on Software Updates for Internet of Things (SUIT) which aims to create a standard for the secure updating of IoT firmware [26]. However, the SUIT architecture is primarily concerned with the secure dissemination process and does not specifically address time constraints arising from the WoV of the decision-making process [26].  
Another approach to updating IoT devices makes use of distributed ledger technologies and smart contracts to run executable code over the blockchain without the intervention of a trusted third party [27; 28; 29]. Smart contracts have the advantage of being able to incorporate a time constraint for the WoV into the execution of the contract; however, they do not provide a standardized software engineering mechanism for managing the associated WoV in the code of the IoT device itself. Another disadvantage of blockchain-based approaches is the relatively high computational overhead which may present a barrier to implementation in constrained IoT devices [27; 30; 31].  
A recent approach which does consider the time constraint of data is the Age of Information (AoI) approach which seeks to optimize the rate at which information is sent from IoT devices with respect to the devices' power and resource usage [32]. AoI is a \"metric that captures how frequently the information status at a destination node \(\ldots\) needs to be updated through status update transmissions from a source node\" [32]. The AoI approach in [32] uses a scheme where different processes in an IoT agent are prioritized according to their importance and AoI values, considered as an optimization problem.  
In [33], the authors approach the AoI from the perspective of an energy-constrained edge-caching node, similar in concept to a fog-computing node in that it processes and caches IoT data close to the edge of the network. The authors propose a time-slotted model which splits the activity of such a node into two phases: a data delivery phase, in which data are transmitted from the edge-caching node to the consumers, and a status updating phase, in which the edge-caching node updates its local copy of the IoT sensor data [33].
## 2 Experimental Test Description  
The French national project (ASIRI, 2012 [24]) studied the soft soil improvement by rigid inclusions technique. However, this research project was mainly concentrated on the system behavior subjected to static loadings. Structures under cyclic loading are commonly encountered in practice. Experiments were done to better understand the soft soil improved by rigid inclusions under monotonic and cyclic loading. A 1:10 scale physical model under normal gravity was set up, in which all the dimensions were reduced 10 times. The detail of the model was presented in [PERSON] (2016) [6]. A total of 16 aluminum cylinder piles were installed in a square box of 1 \(\times\) 1 m. The piles had a diameter of 35 mm and were arranged in a square grid of 0.2 \(\times\) 0.2 m, with an improvement area ratio \(a\) = 2.4%. A 0.4-m-thick layer of soft material was setup, followed by a LTP layer by gravel, as shown in Figure 1. The different testing cases were created by changing the thickness of LTP and with and without a rigid slab: including a mattress of 100 mm with a rigid slab at the surface (test D100); a mattress of 100 mm without rigid slab at the surface (test R100), and a mattress of 50 mm with a rigid slab at the surface (test D50).  
The LTP was made from crushed gravel material, in which the content of the particle diameters ranging from 2 to 5 mm accounted for more than 90% of the total material. Corresponding to respectively void ratios in the range from \(e_{min}\) = 0.52 to \(e_{max}\) = 0.78. The measured unit weights were 16.5 and 14 kN/m\({}^{3}\). Monotonic and cyclic triaxial tests on gravel samples allowed determining the geotechnical properties, listed in Table 1. The compressible soil was a mixture of fine-grained Fontainebleau sand and expanded polystyrene balls with a water content of \(w\) = 10%. Its geotechnical properties were defined by using oedometer tests. The geotechnical parameters of LTP and compressible soil are shown in Table 1.  
Figure 1: Geometry of the physic model: (**a**) Plan view; (**b**) View A-A [6].  
[PERSON] et al. [9] also carried out numerical simulations to validate the above experimental tests, in which, the LTP and compressible soil were respectively modeled by the CYsoil and MCC models. The finite difference method using the code FLAC3D software was used. Their numerical results were compared with the experimental data. However, as the work considered a simple constitutive model for the LTP, it was not able to rigorously consider the soil behavior under cyclic loading. In this study, a 3D numerical modeling of the rigid inclusion-reinforced compressible soil tests under monotonic and cyclic loading is presented, in which the HYP model is considered to model the effect of cyclic loading.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 5 Results and Discussion
### Wafer Warpage under Different Processing Conditions
#### 5.3.3 The Influence of Sawing Process Parameters on Wafer Warpage  
By changing the wire speed \(\mathrm{vc}\) in Equation (3) to calculate the correspondent heat flux density, the relationship between wire speed and wafer warpage is established based on simulation calculation of the finite element model. Figure 15 shows the effect of wire speed on wafer warpage at different feed rates. In simulation calculations, the wafer thickness is 500 \(\upmu\)m and the diamond wire diameter is 0.28 mm. It can be seen that as the wire speed increases, the wafer warpage gradually increases. This is because the higher the wire speed, the greater the cutting power of the wire saw, and the more heat is generated during cutting. However, the cutting heat loss coefficient remains constant during this process, the amount of heat entering the wafer per unit time increases, the heat dissipation capacity of the wafer remains unchanged, and the wafer warpage increases. Previous studies have shown that increasing the wire saw speed can improve the surface quality of slices [2; 3], but a higher wire saw speed leads to an increase in wafer warpage, from the perspective of wafer geometry quality.  
Figure 14: The effect of diamond wire diameter on wafer warpage.  
By changing the wire speed vw in Equation (3) to calculate the correspondent heat flux density, the relationship between feed rate and wafer warpage is established based on simulation calculation of the finite element model. Figure 16 shows the effect of feed rate on wafer warpage at different wire speeds. It can be seen that as the feed rate increases, the wafer warpage gradually increases. This is because as the feed rate increases, the amount of material cut per unit time increases, resulting in an increase in heat generation. At the same time, the amount of heat entering the wafer per unit time increases. However, the heat dissipation capacity of the wafer remains basically unchanged, and the wafer warpage increases.  
From the above research results, it can be seen that in the current pursuit of high wire speed and large feed rate sawing processes to achieve efficient cutting, how to achieve effective lubrication and cooling of narrow saw ker  
Figure 16: The effect of feed rate on wafer warpage.  
Figure 15: The effect of wire speed on wafer warpage.
# Super-Wideband Monopole Printed Antenna with Half-Elliptical-Shaped Patch
## 3 Results and Discussion
### Gain and Radiation Characteristics  
The proposed SWB antenna is simulated using CST Microwave Studio and produces peak gains of 11.2 dBi. At 0.5 GHz, the proposed antenna exhibits an omnidirectional radiation pattern due to the excitation of the dominant mode. However, as the frequency increases, higher-order modes begin to excite, and the presence of a partial ground plane disrupts the ideal radiation pattern from the ground plane towards the patch, resulting in the emergence of increased minor lobes (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Design** & **Operating Frequency, GHz** & **Bandwidth** & **Bandwidth Ratio** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{L1 = 5 mm} & 0.536–1.03 & 0.494 & 1.92 \\  & & 1.18–40 & 38.82 & 33.89 \\ \hline
2 & L1 = 10 mm & 0.53–40 & 39.47 & 75.47 \\ \hline
3 & L1 = 15 mm & 0.52–40 & 39.475 & 76.2 \\ \hline
4 & L1 = 20 mm & 0.52–40 & 39.48 & 76.92 \\ \hline
5 & L1 = 25 mm & 0.514–40 & 39.486 & 77.82 \\ \hline
6 & L Figure 5 presents the normalized radiation pattern of the antenna in a polar plot. As the frequency increases, the minor lobes become more distinct, and the antenna exhibits a more directional radiation pattern with the main beam oriented towards the top semicircle. Figure 6 depicts the simulated gain and the radiation efficiency of the proposed antenna across a frequency range of 0.5 GHz to 40 GHz. The antenna exhibits two peak gains: 8 dBi at 10 GHz and 10 dBi at 38 GHz, while the radiation efficiency is more than 80% at all frequency range.  
Figure 4: Simulated 3D far-field characteristic of proposed antenna: (**a**) 0.5 GHz; (**b**) 2.4 GHz; (**c**) 6 GHz; (**d**) 15 GHz; (**e**) 30 GHz; (**f**) 40 GHz.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## 1 Introduction
However, the focus of that existing work was on novel discriminator and loop filter designs, while our paper focuses on a PLL design and _performance analysis tool_ based on the tracking error. The augmented linear model allows for more accurate calculation of tracking error standard deviation in the presence of AWGN as compared to the traditional linear model. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, thus time-consuming direct simulations and costly experimental testing can be reserved for designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered. Section 2 briefly explains GPS PLL operation, and the augmented linear model is derived in Section 3 from first principles. The effects of AWGN and reference oscillator phase-noise are quantified. Section 4 explains the clock phase-noise models, and the popular phase jitter metric is defined in Section 5. In Section 6, we gain some insight into the effects of PLL bandwidth and coherent averaging time on the phase jitter and tracking error metrics. The effect of AWGN on the augmented linear model is validated in Section 7, and the new PLL performance metric based on the tracking error is explained in Section 8. Insight is gained into which performance improvement techniques will be the most effective at preventing PLL cycle slips in the presence of wideband interference. Tightening the PLL bandwidth is shown to be of no benefit in rejecting noise on the tracking error, while increasing the coherent averaging time provides the most benefit. Finally, the effect of oscillator phase-noise modeling on the tracking error metric is discussed in Section 9, and the theory developed in this paper is compared with experimental results.
## 2 Materials and Methods  
### Pinhole Camera Model  
The camera model used in this paper is the standard pinhole camera model [8], where the image projection \(x\) of a 3D point \(X\) can be written as  
\[\lambda x=MX \tag{1}\]  
Here \(M\) is a 3 \(\times\) 4 camera projection matrix and \(\lambda\) is a scale value. The camera projection matrix \(M\) can be written as  
\[M=K\big{[}\begin{array}{cc}R&t\end{array}\big{]} \tag{2}\]  
In this equation, \(\big{[}\begin{array}{cc}R&t\end{array}\big{]}\), namely the extrinsic parameter matrix, contains the information about the camera pose where \(R\) is a 3 \(\times\) 3 rotation matrix and \(t\) is a 3 \(\times\) 1 translation matrix. \(K\) is a 3 \(\times\) 3 intrinsic parameter matrix of the camera and can be written as  
\[K=\left[\begin{array}{ccc}f&s&u_{0}\\ 0&\gamma f&v_{0}\\ 0&0&1\end{array}\right] \tag{3}\]  
In this matrix \(f\), \(\gamma\), \(s\), \(\big{(}\begin{array}{cc}u_{0}&v_{0}\end{array}\big{)}\) represent the focal length, aspect ratio, skew and principal point, respectively. For the all existing P3P solvers, the camera is fully calibrated, which means the \(K\) is known. However, the camera is uncalibrated in this paper. For most digital cameras, skew is zero and the aspect ratio of the pixels is very close to one.  
Accordingly, in this paper, we assume \(\gamma=1\), \(s=0\) and it will be shown that in practice, these assumptions yield good results even though they are not strictly true. Now the intrinsic parameter matrix \(K\) contains three unknown parameters \(f\), \(\left(\begin{array}{cc}u_{0}&v_{0}\end{array}\right)\) and therefore in this paper, we assume  
\[K=\left[\begin{array}{ccc}f&0&u_{0}\\ 0&f&v_{0}\\ 0&0&1\end{array}\right] \tag{4}\]  
We can see that the unknown parameters from the intrinsic parameter matrix \(K\) are the focal length, principal point and we will propose a novel method for intrinsic and extrinsic parameter estimation based on three 2D-3D point correspondences with known camera position, which is different from the existing P3P solvers. As with many P3P solvers, distortions are not considered in this paper.
# Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
## 3 Results & Discussions
### Optimization Performance
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Function Name**} & \multirow{2}{*}{**3D Plot**} & \multirow{2}{*}{**Function Formula**} & \multirow{2}{*}{**Minimum Value**} & \multirow{2}{*}{**Search Domain**} & \multicolumn{2}{c}{\begin{tabular}{c} **Optimum Value of** \\ **the Cost Function** \\ \end{tabular} } \\ \cline{5-8}  & & & & & **GA** & **TEA** \\ \hline \multirow{3}{*}{\begin{tabular}{l} Rosenbrock function \\ constrained with a \\ cubic and a line \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} \(f_{21}(x,y)=100(y-x^{2})^{2}\) \\ subjected to \(x\) - \((x-1)^{3}-y+1\leq 0\) \\ and \(x+y-2\leq 0\) \\ \end{tabular} } & \multirow{3}{*}{\(f_{21}(1,1)=0\)} & \multirow{3}{*}{\(-1.5<x<1.5\)} & \multirow{3}{*}{3.4817} & \multirow{3}{*}{0} \\ \cline{5-8}  & & & & \(-0.5<y<25\) & & \\ \cline{5-8}  & & & & \(-0.5<y<25\) & & \\ \hline \multirow{3}{*}{\begin{tabular}{l} Rosenbrock function \\ constrained to a disk \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} \(f_{22}(x,y)=100(y-x^{2})^{2}\) \\ subjected to \(x\) - \((x-1)^{3}-y+1\leq 0\) \\ and \(x+y-2\leq 0\) \\ \end{tabular} } & \multirow{3}{*}{\(f_{22}(1,1)=0\)} & \multirow{3}{*}{\(-1.5<x,y<\)} & \multirow{3}{*}{2.8727} & \multirow{3}{*}{0} \\ \cline{5-8}  & & & & \(-1.5\) & \\ \cline{5-8}  & & & & \(-0.5<y<25\) & & \\ \cline{5-8}  & & & & & \\ \cline{5-8}  & & & & & \(-0.5<y<25\) & & \\ \hline \multirow{3}{*}{\begin{tabular}{l} Townsend function \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{l} \(f_{34}(x,y)=-\cos^{2}((x-1-2\cos^{2}(x-1)-2\cos^{2}(x-1)-1.1944509)-\) \\ where: \(r=Attai(2x,y)\) \\ \end{tabular} } & \multirow{3}{*}{\(f_{34}(2,0.052938,\)} & \multirow{3}{*}{\(-1.1944509)-\)} & \multirow{3}{*}{\(-2.25<x<2.5\)} & \multirow{3}{*}{\(-1.1486\)} & \multirow{3}{*}{\(-2.0195\)} \\ \cline{5-8}  & & & & \(-0.2039884\) & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{l} [PERSON] function \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{l} \(f_{25}(x,y)=0.13\) \\ subject to \(x\) - \(y^{2}\leq[1+y-0.048425813)-\) \\ \(0.2\cos(8 tan^{-1}\frac{x}{2})^{2}\) \\ \end{tabular} } & \multirow{3}{*}{\(-1.25<x,y<1.25\)} & \multirow{3}{*}{\(-0.01562\)} & \multirow{3}{*}{\(-0.0717\)} \\ \cline{5-8}  & & & & \(-0.072\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of the functions [54]. Figure 9: Minimum costs of TEA, GA, and PSO versus iteration are compared: (a) \(f_{1}\), (b) \(f_{2}\), and (c) \(f_{3}\) (convergence speed). in the local minima and required a much larger number of iterations to achieve an acceptable performance. Additionally, in complex constrained function such as \(f_{24}\), GA showed higher tendency to converge towards local optima instead of the global minimum of the function. In contrast, by checking the second law of thermodynamics, TEA ensured that most of the systems were able to obtain the optimal answer and avoided local minima convergency.