esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Wrist Angle Prediction Using Machine Learning and a Multi-Camera System (3 DML)  
When comparing the results of the different machine learning algorithms, reasonable accuracy was achieved using several machine learning algorithms (Table 3). The performance of the five-layer neural network achieved the lowest MAE for both FE (5.9\({}^{\circ}\)) and RUD (5.2\({}^{\circ}\)), which occurred from shuffle 1. The MAE values in Table 3 for FE and RUD represent the most accurate individual model results. Statistical analysis showed that cubic regression is a poor choice, while all other models examined exhibited similar performance. The best numbers are associated with both the neural network and support vector machine models for the multi-camera system.  
The global feature importance analysis demonstrated that for both FE and RUD, the coordinates related to mcp2 were the most important, followed by the wrist coordinates, and the elbow coordinates were the least important. The top five features that affect FE angle prediction included the coordinates of the mcp2, the center of the wrist, and the ulna. The top five features that affect RUD angle prediction included the coordinates of mcp2, mcp3, the center of the wrist, and the ulna (Table 4). In general, the elbow markers were less important compared to the mcp and wrist markers.
# Electrocatalysis for the Oxygen Evolution Reaction in Acidic Media: Progress and Challenges
## 3 Tailoring Strategies for Effective OER Electrocatalysts
### Coordination Environment  
Although great efforts have been made to improve the efficiency of OER electrocatalysts, the majority of active sites inside their bulk phases remain inaccessible [23]. In order to maximize the utilization of each active site (approaching 100%) as well as shed light on the effect of the structure of active centers and ligating atoms on the OER activity, catalysts have thus been continuously downsized to the single-atom (SA) level. Single-atom catalysts (SACs) have emerged as a hot new branch of heterogeneous catalysts due to their excellent catalytic performance and financial benefits [28]. Owing to the high requirements for the dispersion, activity, and stability of the atoms, an appropriate support must be selected to optimize the physiochemical properties of the metal atoms anchored [81]. The unique coordination configuration of the dispersed metal atoms and coordinating atoms together create active sites for OER catalysts; therefore, the coordination-activity relationship has a strong impact on the catalytic performance [92]. In fact, targeted synthesis of precious metal single-atom-based OER electrocatalysts remains a bottleneck and has been pursued in the search for better OER catalysis, especially under acid conditions [28]. Generally, carbon supports often suffer severe corrosion problems under acid conditions and oxide supports are not always conductive, which brings about huge trouble for the design of single-atom catalysts [93]. In order to solve these problems, a series of Pt-Cu alloys with an atomically dispersed Ru\({}_{1}\) decoration were studied by [PERSON] et al. [94]. An ultralow overpotential of 170 mV at a current density of 10 mA\(\cdot\)cm\({}^{-2}\) was reached by Ru\({}_{1}\)-Pt\({}_{5}\)Cu in acid media, together with a ten times longer lifetime than a commercial RuO\({}_{2}\) catalyst. Density functional theory calculations suggested that the electronic structure of single Ru sites at the corner or step sites of the Pt-rich shell was modulated by the compressive strain in the Pt skin shell, contributing to the optimized binding of oxygen species and improved resistance to over-oxidation and dissolution. [PERSON] et al. demonstrated that surface-exposed Ir single atom couplings with oxygen vacancies anchored in ultrathin NiCo\({}_{2}\)O\({}_{4}\) porous nanosheets exhibited remarkable OER activity and stability in acid media, with an overpotential of only 240 mV at a current density of 10 mA\(\cdot\)cm\({}^{-2}\) and a long-term durability of 70 h [28].  
Based on density functional theory calculations, high electronic exchange and transfer activities of the surface contributed by Ir atoms anchored at Co sites near oxygen vacancies were determined to be responsible for the prominent OER performance (Figure 5). The synergetic mutual activation between Ir and Co reached the desired H\({}_{2}\)O activation level and stabilized *O to boost OER performance.
## 2 System Model Network
### Information Transmission (IT)
Outage Probability (OP)  
\[P_{out}=\Pr\Bigg{\{}\varphi_{1}<\frac{c_{1}\varphi_{2}+c_{2}}{c_{3}\varphi_{2} }\Bigg{\}}=\underset{0}{\overset{\infty}{\underset{0}{}}}F_{\varphi_{1}} \Bigg{(}\frac{c_{1}\varphi_{2}+c_{2}}{c_{3}\varphi_{2}}\Bigg{)}f_{\varphi_{2} }(\varphi_{2})d\varphi_{2} \tag{20}\]  
Combining Equation (20) with Equations (14) and (17) we have  
\[P_{out}=1-\underset{0}{\overset{\infty}{\underset{0}{}}}e^{-\lambda_{b}\frac {c_{1}\varphi_{2}+c_{2}}{c_{3}\varphi_{2}}}\times a\underset{l=0}{\overset{ \infty}{\underset{(l!)^{2}}{}}}\frac{(bK)^{l}}{(l!)^{2}}\varphi_{2}^{l}e^{- b\varphi_{2}}d\varphi_{2}=1-ae^{-\frac{1\lambda_{b}\varphi_{1}}{c_{3}}} \underset{l=0}{\overset{\infty}{\underset{(l!)^{2}}{}}}\frac{(bK)^{l}}{(l! )^{2}}\underset{0}{\overset{\infty}{\underset{0}{}}}\sigma_{2}^{l}e^{-b \varphi_{2}}e^{-\frac{\lambda_{b}\varphi_{2}}{c_{3}\varphi_{2}}}d\varphi_{2} \tag{21}\]  
Using Table of Integral Equation [3.471,9] in [24], Equation (21) can be given as  
\[P_{out}=1-2 ae^{-\frac{\lambda_{b}c_{1}}{c_{3}}}\underset{l=0}{\overset{ \infty}{\underset{(l!)^{2}}{}}}\frac{(bK)^{l}}{(l!)^{2}}\bigg{(}\frac{ \lambda_{b}c_{2}}{c_{3}b}\Bigg{)}^{\frac{l+1}{2}}\times K_{l+1}\Bigg{(}2\sqrt {\frac{\lambda_{h}c_{2}b}{c_{3}}}\Bigg{)} \tag{22}\]where \(K_{v}(\bullet)\) is the modified Bessel function of the second kind and \(v^{th}\) order. b. Achievable Throughput (AT)  
Here, the average throughput of the relay network system can be computed regarding the OP as in Equation (23)  
\[\tau=(1-P_{out})\frac{R}{2}=2 ae^{-\frac{\lambda_{h}c_{2}}{c_{3}}}\sum\limits_{l=0 }^{\infty}\frac{(bK)^{l}}{(l!)^{2}}\bigg{(}\frac{\lambda_{h}c_{2}}{c_{3}b} \bigg{)}^{\frac{l+1}{2}}\times K_{l+1}\bigg{(}2\sqrt{\frac{\lambda_{h}c_{2}b}{ c_{3}}}\bigg{)}\times\frac{R}{2} \tag{23}\]  
c. The Bit Error Rate (BER)  
The BER of the proposed system can be formulated from the expression of the OP as the following equation  
\[BER=E\Big{[}\omega Q(\sqrt{2\theta\gamma}\Big{]} \tag{24}\]  
where \(Q(t)=\frac{1}{\sqrt{2\pi}}\int\limits_{0}^{\infty}e^{-x^{2}/2}dx\) is the Gaussian Q-function, \(\omega\) and \(\theta\) are constants which are specific for modulation type. Here, we use \((\omega,\theta)=(1,2)\) for BPSK and \((\omega,\theta)=(1,1)\) for QPSK. Hence, we begin rewriting the BER expression in Equation (24) directly regarding OP at S by using integration as in the equation below  
\[BER=\frac{\omega\sqrt{\theta}}{2\sqrt{\pi}}\int\limits_{0}^{\infty}\frac{e^{- \theta x}}{\sqrt{x}}F_{\gamma_{2}\omega}(x)dx \tag{25}\]
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 5 Conclusions  
This paper proposes a combination of the MTCI and LAI-VIs for univariate linear, binary linear, and random forest regression that fuses leaf area index and chlorophyll information to improve the retrieval of CCC based on the PROSAIL-D model. The validation results based on both field spectra and MERIS satellite data reveal that the vegetation index combinations for all three regression models effectively improved the accuracy, although the VI combination can vary for different types of regression analysis models. The combined multiplicative VI, MTCI \(\times\) MTVI2, showed a better performance for the univariate regression model (field spectra for soybeans and wheat: RMSE = 51.68 \(\upmu\)g cm\({}^{-2}\); MERIS satellite data for soybeans: RMSE = 88.44 \(\upmu\)g cm\({}^{-2}\)) than the MTCI alone (field spectra: RMSE = 77.10 \(\upmu\)g cm\({}^{-2}\); MERIS satellite data: RMSE = 136.54 \(\upmu\)g cm\({}^{-2}\)). Moreover, combining the MTCI and LAI-VIs into the random forest regression models exhibited greater potential for retrieving the CCC than the two linear regression models. The MTCI and MTVI2 combination with random forest regression performed the best, achieving an RMSE of 37.76 \(\upmu\)g cm\({}^{-2}\) for the field spectra data, and an RMSE of 47.96 \(\upmu\)g cm\({}^{-2}\) for the MERIS satellite data. This study indicates that random forest regression models with a combination of the MTCI and LAI-VIs have an advantage in the fusion of LAI and leaf chlorophyll information, and can produce accurate and robust estimates for CCC. Due to its simplicity, the single combined multiplicative VI with an empirical regression model also shows potential for estimating CCC. The vegetation index combination method proposed in this paper can be applied to other chlorophyll-related vegetation indices to improve their performance in CCC estimation. Since the simulation data used in this paper were mainly related to wheat and soybean crops, the above-described model for CCC retrieval based on simulated datasets must be adjusted according to vegetation type.  
Not applicable.  
All data used in this study were from open access satellite products or published field work cited in this article.  
We appreciate the data provided by the Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln.  
The authors declare no conflict of interest.
## 4 Trade-Offs among Performance Parameters  
In this section, we describe several factors that are responsible for maintaining the performance parameter trade-offs in order to enable a low-latency and reliable wireless network for AR and VR applications. In Section 4.1, we discuss the contribution of an innovative and smart wireless network design towards a low-latency and reliable wireless network by balancing the trade-off of the latency measures with other performance factors. Section 4.2 presents a few ways to ensure a balanced trade-off between reliability and latency for the wireless scenario of AR/VR network design. Section 4.3 shows the importance of quality of experience (QoE) in affecting the latency to satisfy the low-latency requirements of AR/VR HMDs.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 1 Introduction  
As nanotechnology continues to develop rapidly, antennas fabricated by transparent conductive films (TCFs) instead of conventionally used metal, i.e., copper, are being fabricated. As a kind of TCF, nano carbon (graphene, carbon nanotubes), transparent conductive oxides, conductive polymer, and metallic nanostructures (metallic nanowires, thin metal films, and patterned metal-grids) are used to design novel antennas. The idea of a transparent antenna was first proposed in order to overlap the antenna with a solar cell on a limited satellite surface area [1]. Since nanomaterial technology processes have progressed, the development of more sophisticated and better performance nano-patterned transparent antennas has been researched over the last decades.  
Transparent antennas that have been sporadically studied using different types of TCFs are analyzed material-analytically in Section 2. In Section 3, we summarize the performance of the published examples of transparent antennas, referring to the figure of merit (FoM) of the TCFs. Finally, the role and promising contribution of the transparent antenna that can be directly integrated within active displays and denoted as Antenna-on-Display (AoD) is discussed.
## 3 Results and Discussions
Since the results of intercalibration provided by the collocation method and rainforest are comparable with NOC, almost the same curves of wind speed bias are expected for scatterometer winds using the calibrations provided by these methods. While the presented  
Figure 10: Wind speed bias between HY-2 scatterometer and ECMWF model winds as a function of average wind speed. The bin size is 0.5 m/s, and it is masked if the number of samples are less than 100 in a bin. three methods for achieving intercalibration of scatterometer backscatter measurements are all effective, the rainforest and NOC methods depend on geophysical models of the rainforest or the ocean, which essentially ignore spatial and temporal variability effects. The collocation method proves very useful to obtain a more direct assessment of relative instrument calibration, more independent of geophysical variability effects, and to obtain an estimate of the uncertainty in the rainforest and NOC methods.
## 4 Time-Frequency-Space Resource Allocation Method in Sharing Network  
In information theory, [PERSON]'s theorem defines the air interface performance for a single link. Over the past decades, spanning from 2G to 5G and from convolutional codes to Polar codes, physical layer channel coding techniques have continuously advanced. They have progressively approached the Shannon limit in terms of single-user, link-level performance. Indeed, practical communication networks are complex systems comprised of multiple users and links. As illustrated in Figure 5, within the entire network, multiple users coexist. The overarching goal is to optimize the collective experience of all users to achieve network-level experience maximization.
## 3 Materials and Methods
### Methods
#### 3.1.1 Image Acquisition  
The images were taken with a DJI Mavic 2 Pro drone with a weight of 907 g, a battery autonomy of 32 min and a maximum speed of 72 km/h. The images were taken with GPS + GLONASS positioning, whose accuracy was \(\pm\)0.5 m vertically and \(\pm\)1.5 m horizontally. The camera carried by the drone was a Hasselblad RGB (Red, Green, Blue) with a 1\" CMOS sensor and 20 MP. The camera's lens had a FOV of 77\({}^{\circ}\) and an aperture of 2.8-11.  
Different flight plans were made to obtain images depending on the characteristics of the element to be mapped. The design and execution of the flight plans were carried out with the drone-link software. The different flight plans were:  
* _North City-Wall Bastion_: A set of two flights were made, the first flight in orbit 12 m above the ground to obtain a 3D modelling (Figure S1) with maximum resolution of the north bastion complemented with a second ellipse 13 m above the ground to map the junction of the bastion to the wall. Finally, 221 images were taken (Figure 4A).
* _Marble dish tsunami_: The drone was flown several times in orbit at different heights from the ground (0.5, 0.75 and 1 m) and at a distance from the object ranging from 0.5 to 2 m in order to obtain both the tsunamite in high resolution and the inclination,  
Figure 3: Aerial oblique view of the _Baelo Claudia_ archaeological site highlighting the main buildings cited in the text with enlarged images of the 3D models analyzed in this paper.  
direction and modelling of the arrays (Figure S2). Finally, a total of 295 photographs were taken (Figure 4B).
* _Folded Decumanus maximus_: A grid flight plan was made at a flight altitude of 30 m, with an overlap of 80% front and 70% side, finally taking 350 photos (Figure 4C).
## 3 Discrete Event Simulation Model
### Data Collection and Processing  
Based on the previously confirmed patient flow diagram (Figure 2), our simulation model was modeled by a DES system using the Arena simulation tool. The system requires three kinds of inputs: arrival rates of patients, the frequency of each pathway, and the processing times of services.  
\(\bullet\)**Service times distribution:** The input analyzer of Arena software is used to generate the distributions of service times. The statistical distributions of service times are displayed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Operation** & **Time (minutes)** \\ \hline Administrative registration & TRIANG (0.5, 15, 2) \\ \hline Triage & From 8 a.m. to 10 p.m.: NORMAL (5, 2) \\  & From 10 p.m to 8 a.m.: NORMAL (10, 2) \\ \hline Transportation time & Short circuit: UNIFORM (5, 10) \\  & Long circuit: UNIFORM (5, 15) \\ \hline Consultation (short circuit) & UNIFORM (3, 10) \\ Consultation (long circuit) & UNIFORM (5, 20) \\ \hline Radiology & TRIANG (5, 15, 25) \\ Scanner & TRIANG (15, 20, 25) \\ Echography & TRIANG (10, 15, 30) \\ Specialist advice & UNIFORM (60, 240) \\ \hline Final Exam (short circuit) & NORMAL (5, 3) \\ Final Exam (long circuit) & TRIANG (5, 10, 15) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthesis of the processing time distribution.  
\(\bullet\)**Patient arrival distribution:** The nature of ED arrivals is strongly correlated with the hour of the day, as it is impacted by human behavior. In fact, we can see on the graph in Figure 3 that the hourly rate of patient arrivals (number of arrivals per hour/total) in the ED follows a repetitive pattern from one day to another. We notice that it is low during the late night period (from 12 p.m. to 6 a.m.) and increases during the day (from 8 a.m. to 10 p.m.), with peak at 10 a.m., 2 p.m., and 6 p.m. For our purposes, we generated a parametric distribution dependent on the number of daily arrivals for each time slot in the day.  
Let \(T_{i}\) be the random variable that models the inter-arrival time, \(t_{h}\) the average arrival rate observed for the hour \(h\) of the day, and \(Y_{j}\) the number of daily arrivals for the day \(j\). Following the procedure introduced in the previous section, we find that the distribution that models the time of arrival of patients is the exponential distribution of the average \(Y_{i}\times t_{h}\), i.e., \(Ti\sim Exp(\frac{1}{Y_{i}\times Ti})\).  
\(\bullet\)**Queue priority:** In our case study, the priority of patients for a given department is subject to two conditions: the time of arrival \(T_{arr}\) and the patient's state \(EP\). We will model our queues with an additional attribute \(P_{FdA}=\frac{EP}{T_{arr}}\) that will be assigned to a patient and will serve as a priority indicator in queues [30]. The higher the \(P_{FdA}\) index is, the higher the patient's priority over others.
## 2 Materials and Methods
### Reliability Assessment Method Based on Bayes Minimum Risk Decision
#### 2.2.2 Bayes Decision  
Reliability assessment was used to verify whether the software reliability satisfies the pre-defined reliability requirement, and the test result was assumed to be binary--acceptable or rejected. This transforms it into a decision-making problem (hypothesis testing) [23]. Let \(H_{0}\) and \(H_{1}\) denote the two decision states. \(H_{0}\) denotes the test result meets reliability requirement, \(H_{1}\) denotes the test result does not meet the reliability requirement. The result of the software reliability assessment can be described by  
\[H_{0}:\theta\leq\theta_{0},\,H_{1}:\theta>\theta_{0},\]  
where \(\theta\) denotes the actual failure rate, \(\theta_{0}\) denotes the failure rate of the reliability requirement, and the test significance level is taken to be \(c\) (\(0<c<1\)). In this decision-making problem, the authors utilized the Bayesian decision function to describe the satisfaction of the reliability requirement by the assessed software in the presence of a certain number of failures. The decision function is given by  
\[\delta(x)=P(H_{0}\big{|}X=x)\]  
where \(x\) denotes the number of failures during reliability assessment based on [PERSON] concept of assessment. The actual failure rate, \(\theta\), of the web service composition is taken to be a random variable,in this paper, assuming the prior distribution of parameter \(\theta\) to be \(G(\theta)\); therefore, the Bayesian decision function is given by  
\[\delta_{G}(x)=\left\{\begin{array}{ll}1,&\theta\leq\theta_{0}\\ 0,&\theta>\theta_{0}\end{array}\right.\]  
according to (8), assuming the hyperparameter \(a\) and \(b\) are known, after computing. When the number of failures allowed by reliability requirement are 0, 1, and 2, \(N_{0},N_{1}\), and \(N_{2}\) denote the upper limit number of test cases that need to be running, respectively. \(\delta_{G}(x)\) can be transformed into the following (11):  
\[\delta_{G}(x)=\left\{\begin{array}{ll}1,&x=0\;and\;n<N_{0},x=1\;and\;n<N_{1},x=2\;and\;n<N_{2}\ldots\\ 0,&\theta>\theta_{0}\end{array}\right. \tag{11}\]  
The authors considered the result of reliability testing as Bayes decision problem; once the test result is wrong, which is bias from truth, it will cause the loss. In this paper, the authors utilized the Bayes decision idea in reliability testing of web service composition. In order to reduce the loss and improve accuracy of reliability testing, and control the bias of prior distribution \(G(\theta)\) of parameter \(\theta\), in the next chapter, the authors propose the method of minimum risk (MMR) to produce the result with the lowest risk of decision.
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## 2 Materials and Methods
### Varying Thickness of Hollow Electrodes  
For this method, hollow electrodes with different thicknesses were used. The outer shape, size, and depth of all the electrodes were the same, but the thickness of the perimeter of hollow electrodes was different in different sets of experiments. The thicknesses of the hollow electrodes were 0.04\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.12\({}^{\prime\prime}\). The shape, size, and depth of hollow electrodes were square, 1\({}^{\prime\prime}\)\(\times\) 1\({}^{\prime\prime}\), and 0.2\({}^{\prime\prime}\), respectively. Furthermore, the performance of a block electrode of the same size and shape was also compared with hollow electrodes. The hypothesis for this method is that the thickness of the perimeter should be as small as possible, as the sparking will occur only on the surface area of the wall thickness that is facing the silicon workpiece, and that area will be eroded. As a result, the energy per area will be increased for the smaller thickness of the EDM power unit and will have a better machining speed or higher MRR.
## 3 Workspace Analysis
### Feasibility Check and Feedforward of Wrist Angle  
Utilizing the techniques elaborated in Section 3, in which collection and establishment of the database are conducted, the feasibility check is performed when the inverse kinematics request is assigned. The value obtained from Equation (4) is compared to the heuristic threshold value to decide whether the motion command should be dropped out or not. The heuristic threshold value and the fitness value of termination are the sum of the distances between target points and end-effectors of the two fingers. Basically, the heuristic threshold value is set to be a bit bigger than the fitness value of termination. Provided that the heuristic threshold value is as low as termination value, the feasibility check will frequently fail. On condition that the heuristic threshold value is much larger than the fitness value of termination, the fitness value will not converge to termination 1. In this research, the fitness value of termination is set to be 1 and the heuristic threshold value is set to be 5.  
On the assumption that the motion command passes the feasibility check, Equation (5) yields an optimal wrist angle set \(\vec{q_{w}}^{*}\) according to the aforementioned workspace database. The new wrist angle range provided to the IGA is, therefore, generated in the form of \(\vec{q_{w}}^{*}\pm\theta_{w}\). The value \(\theta_{w}\) is associated with the intervals of the wrist angle used in creating the workspace database. A wider interval denotes a larger value of \(\theta_{w}\). In this paper, \(\theta_{w}\) was set to be \(10^{\circ}\) to cover the adjacent workspace. For example, the original range of \(\theta_{w1}\) was \(-60^{\circ}\) to \(+60^{\circ}\) and that of \(\theta_{w2}\) was \(-60^{\circ}\) to \(+90^{\circ}\). Supposing that the \(\vec{q_{w}}^{*}\) provided by the feasibility estimation function is \([0^{\circ},\,12^{\circ}]\), then the new range of \(\theta_{w1}\) becomes \(-10^{\circ}\) to \(+10^{\circ}\) and that of \(\theta_{w2}\) becomes \(+2^{\circ}\) to \(22^{\circ}\). Note that, if the new angle range exceeds the original angle limit, the boundary shall be set to the original one.