esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Circuit Design
The required inductance (\(L_{s}\)), coupling factor (\(k\)), shunt capacitance (\(C_{s}\)), and coupled capacitance (\(C_{c}\)) are determined using Equations (1)-(4), where the characteristic odd-mode and even-mode impedances (\(Z_{oo}\), \(Z_{oo}\)) and transformation ratio (\(C\)) are given by Equations (5)-(7), [35]:  
\[L_{s}=\frac{Z_{oe}+Z_{oo}}{2\omega} \tag{1}\]  
\[k=\frac{Z_{oe}-Z_{oo}}{2\omega L_{s}} \tag{2}\]  
\[C_{s}=\frac{1}{\omega Z_{oe}} \tag{3}\]  
\[C_{c}=\frac{1}{2\omega Z_{oo}}-0.5C_{s} \tag{4}\]  
\[Z_{oo}=Z_{0}\sqrt{\frac{1-C}{1+C}} \tag{5}\]  
\[Z_{oe}=Z_{0}\frac{Z_{0}}{Z_{oo}} \tag{6}\]  
\[C=\frac{1}{\sqrt{2\frac{Z_{L}}{Z_{0}}+1}} \tag{7}\]  
At the intended carrier frequency of 19 GHz, these equations yield the following component values: \(L_{s}\) = 513 pH, \(k\) = 0.577, \(C_{s}\) = 86.7 fF, and \(C_{c}\) = 118.5 fF. The capacitors can be directly obtained from the process design kit (PDK) models, with MIM-type high-density capacitors of 87 fF and 118 fF used for \(C_{s}\) and \(C_{c}\), respectively. These capacitors offer a compact area and exhibit the lowest process tolerance among the available options in the PDK components. However, the same level of control is not achievable for the required inductances (\(L_{s}\)) and the desired coupling factor (\(k\)), necessitating several electromagnetic  
Figure 3:0 Marchand balun: (**a**) distributed model, (**b**) lumped model, and (**c**) 3-D momentum model. (EM) simulations to achieve the desired combination of these values. Since the implementation of inductors with more than one turn results in a self-resonant frequency (SRF) too close to the intended carrier frequency, a one-turn inductor is used. A 3-D Momentum model of the designed Marchand balun, including the input ground-signal-ground (GSG) pads, is shown in Figure 6c. The Marchand balun results in a total chip size of 440 \(\upmu\)m \(\times\) 410 \(\upmu\)m.
## 3 Synthetic Data Test
### High-Conductivity Model  
In this subsection, we design a high-conductivity model, as listed in Table 1, to test our inversion algorithm for C-response data. The synthetic C-response data are generated at 27 logarithmically spaced periods from 1 day to 114 days, by adding Gaussian distributed random errors with standard deviation of 5% of the corresponding C-responses magnitude.  
In the inversion, one million samples are sampled for each chain. For \(T=1\), the first 100,000 samples are considered as burn-in and not used for the inference to guarantee good chain mixing and stationary sampling. The remaining 900,000 samples are further thinned by a factor of 10 to yield 90,000 samples. The convergence of the remaining samples is examined by visual inspection of the marginal profiles for samples at different sampling periods, which is considered sufficient in the literature [20].  
The inversion result is shown in Figure 2. As shown in Figure 2a, the interface probability profile shows that high probabilities are centered at vicinities of the true interface depths. The first layer interface is well resolved by the data, with a sharp probability peak at the interface of the first layer. Relative to the first layer interface, the uncertainty of the second layer interface is much larger, which is physically reasonable due to the diffuse nature of magnetic fields. The conductivity marginal profile in Figure 2b shows that the three-layer model is generally well recovered with excellent constraint on the first two layers. The uncertainty of the conductivity in the second layer is even smaller than in the first layer due to the high sensitivity of geomagnetic fields to high-conductivity structures (even when buried deeper). As the geomagnetic fields go deeper, they have less resolution power, and the conductivity uncertainty becomes larger.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **1** & **2** & **3** \\ \hline \(\sigma\) (S/m) & 0.01 & 4.0 & 0.1 \\ \(z\) (km) & 300 & 300 & 2090 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conductivity and interface depth information for the high-conductivity model.  
Figure 2: Marginal posterior probability profiles of (**a**) interface depth and (**b**) conductivity for the high-conductivity model.  
Figure 3a,b shows the sampling history and 1D marginal density of the sampled Markov chain at \(T=1\) for data misfit. The sampling history indicates that a well-mixed train is likely obtained. The misfit is bounded at a relative narrow range with peak of 126.8. Figure 3c shows 1D marginal densities at \(T=1\) for the interface number. The result indicates that the interface number is also bounded at a relative narrow range by the data information with a peak of 3, equal to the true interface number.
## 2 Mathematical Modeling Strategy
### Development of Mathematical Model 1
\begin{array}{c}\left(L^{0}\right)^{2}=\left(x_{0}^{\prime}\right)^{2}+ \left(y_{0}^{\prime}\right)^{2}\\ \left(L_{1,1}^{f}+L_{1,2}^{f}\right)^{2}=\left(x_{b}\right)^{2}+\left(y_{b} \right)^{2}\\ \left(L_{1}^{f}\right)^{2}+L_{1}^{f}\Big{(}L_{2,1}^{f}+L_{2,2}^{f}\Big{)}cos (\theta_{1,2}-\theta_{2,2})+\frac{1}{4}\left(L_{2,1}^{f}+L_{2,2}^{f}\right)^{2 }=\left(x_{d}\right)^{2}+\left(y_{d}\right)^{2}\end{array}  \tag{20}\]  
The corresponding velocities for the fixed sections of the links in the BRD are the following:  
\[ \begin{array}{c}\dot{x}_{1}^{f}=-\frac{1}{2}L_{1}^{f}sin(\theta_{1,2})\dot {\theta}_{1,2}\\ \dot{y}_{1}^{f}=\frac{1}{2}L_{1}^{f}cos(\theta_{1,2})\dot{\theta}_{1,2}\\ \dot{x}_{2}^{f}=-L_{1}^{f}sin(\theta_{1,2})\dot{\theta}_{1,2}+\frac{1}{2}L_{2} ^{f}sin(\theta_{2,2})\dot{\theta}_{2,2}\\ \dot{y}_{2}^{f}=L_{1}^{f}cos(\theta_{1,2})\dot{\theta}_{1,2}-\frac{1}{2}L_{2} ^{f}cos(\theta_{2,2})\dot{\theta}_{2,2}\\ \dot{x}_{3}^{f}=-L_{1}^{f}sin(\theta_{1,2})\dot{\theta}_{1,2}+L_{2}^{f}sin( \theta_{2,2})\dot{\theta}_{2,2}-\frac{1}{2}L_{3}^{f}sin(\theta_{3,2})\dot{ \theta}_{3,2}\\ \dot{y}_{3}^{f}=L_{1}^{f}cos(\theta_{1,2})\dot{\theta}_{1,2}-L_{2}^{f}cos( \theta_{2,2})\dot{\theta}_{1,2}+\frac{1}{2}L_{3}^{f}cos(\theta_{3,2})\dot{ \theta}_{3,2}\end{array}  \tag{21}\]The corresponding velocities of fixed and mobile masses for the linear actuators are given by  
\[ \begin{gathered}\dot{x}_{j\mu}^{a}=\dot{x}_{j,1}^{a,r}+\frac{1}{2}L_{ j,1}^{a}cos(\gamma_{j})\dot{\gamma}_{j}\\ \dot{x}_{jb}^{a}=\dot{x}_{j,1}^{a,r}+L_{j,1}^{a}cos(\gamma_{j}) \dot{\gamma}_{j}+\frac{1}{2}L_{j,2}^{a}sin(\gamma_{j})+\frac{1}{2}L_{j,2}^{a} cos(\gamma_{j})\dot{\gamma}_{j}\\ \dot{y}_{j\mu}^{a}=\dot{y}_{j,1}^{a,r}-\frac{1}{2}L_{j,1}^{a}sin( \gamma_{j})\dot{\gamma}_{j}\\ \dot{y}_{jb}^{a}=\dot{y}_{j,1}^{a,r}-L_{j,1}^{a}sin(\gamma_{j}) \dot{\gamma}_{j}+\frac{1}{2}L_{j,2}^{a}cos(\gamma_{j})-\frac{1}{2}L_{j,2}^{a} sin(\gamma_{j})\dot{\gamma}_{j}\end{gathered}  \tag{24}\]  
Considering the basis of the linear actuators, the following restrictions hold for the relationships between the time-varying lengths of the linear actuators:  
\[ \begin{gathered}\left(\left(\dot{x}_{1,1}^{a,r}\right)^{2}+\left( \dot{y}_{1,1}^{a,r}\right)^{2}\right)=0\\ \left(\left(\dot{x}_{2,1}^{a,r}\right)^{2}+\left(\dot{y}_{2,1}^{a,r}\right)^{2}\right)=\left(L_{1,1}^{f}+L_{1,2}^{f}\right)^{2}\left(\dot{ \theta}_{1,2}\right)^{2}\\ \left(\left(\dot{x}_{3,1}^{a,r}\right)^{2}+\left(\dot{y}_{3,1}^{ a,r}\right)^{2}\right)=\left(L_{1}^{f}\right)^{2}\left(\dot{\theta}_{1,2} \right)^{2}+\left(L_{2,1}^{f}+L_{2,2}^{f}\right)^{2}\left(\dot{\theta}_{2,2} \right)^{2}-\\ 2L_{1}^{f}\Big{(}L_{2,1}^{f}+L_{2,2}^{f}\Big{)}cos(\theta_{1,2} -\theta_{2,2})\dot{\theta}_{1,2}\end{gathered}  \tag{25}\]  
The mathematical expressions of the linear positions and their corresponding velocities are obtained using the configurations in the BRD and considering the relative motions of all the linear actuator devices with respect to the fixed sections of the BRD.
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 3 Data and Methods
### Feature Engineering  
In total, we calculated 18 audience and 6 speaker features for each presentation using the predicted emotions from the recordings. The audience features were created as follows. Firstly, for each emotion, we determined the ratio between the frequency of occurrences of a given emotion by the audience and all the emotions the audience expressed during the presentation. We denote this feature as ratio_audience (E), where E refers to a specific emotion, such as anger, fear, surprise, sadness, neutral, or happiness. For each emotion expressed by the audience, we then calculated the number of times it occurred at least once per second in a given presentation. Subsequently, we put this number in relation to the total recorded time of the same presentation. We denote this audience feature as density (E), with E referring to an emotion. Finally, for each presentation, we normalized the frequency of an emotion expressed by the audience between 0 and 1 and calculated its standard deviation over the entire presentation. This feature is referred to as deviation (E), with E representing the given emotion.  
Since there is only one speaker at a given second in a presentation, we could not compute the same features for the speaker as we could for the audience. More specifically, we only calculated the ratio between the frequency of a specific emotion the speaker expressed and the number of all recorded emotional states the speaker exploited during the presentation. We denote this feature as ratio_speaker (E), where E refers to a given emotion.  
The above described features were used to calculate correlations between audience, speakers, and presentation scores. We also used some of the features for an ordinary least squared regression.
## 3 Results
Tissue-level implants in these studies had mean MBLs (of 0.04 \(\pm\) 1.3 mm, 1.18 \(\pm\) 0.89 mm, 0.22 \(\pm\) 0.35 mm, 0.04 \(\pm\) 0.08 mm, 0.28 \(\pm\) 0.21 mm, 0.38 \(\pm\) 0.46 mm, 1.194 \(\pm\) 0.30 mm, 0.14 \(\pm\) 0.35 mm) over a total of 338 implants. The average mean of each study was 0.67 mm, 1.035 mm, 0.285 mm, 0.06 mm, 0.245 mm, 0.42 mm, 0.747 mm, and 0.245 mm. The analysis results show that both bone-level and tissue-level implants have significantly near the same MBLs. While another study by [PERSON] et al. [40] compared implants placed at the crestal level with a cylindrical and tapered Shiner XT, resulting in a 100% survival rate with mean MBLs of 0.1-0.3 mm. Another study by [PERSON] et al. [38], which compared cylindrical and tapered implants without mentioning the level of placement, had a 100% survival rate and mean MBLs of 0.9 \(\pm\) 0.7 mm. [PERSON] et al. [40] compared cylindrical and conical implants, with a 100% survival rate on both implants but higher bone loss in the cylindrical 0.52 \(\pm\) 0.21 mm than in the conical 0.48 \(\pm\) 0.19 mm. [PERSON] et al. [42] compared tissue-level implants of narrow diameter and regular diameter, showing higher survival for regular-diameter implants, with mean MBLs of 0.06 \(\pm\) 0.09 mm, rather than in narrow-diameter implants, with mean MBLs \(0.14\pm 0.16\) mm. [PERSON] et al. [44], compared 18 tissue-level implants only, where 8 of them received soft tissue grafts, with a control of 12 years, and had no further significant peri-implantitis complication. [PERSON] et al. [45], did not compare tissue-level with bone-level implants either but provided some interesting data; after the placement of 590 internal tapered conical connections, they observed >2 mm MBLs. [PERSON] et al. [46], analyzed 6 mm length bone-level implants, with mean MBLs of 0.04 \(\pm\) 0.14 mm. [PERSON] et al. [47] analyzed dental implants inserted with high-speed drilling with irrigation and low-speed drilling without irrigation, having only high MBL in high-speed drilling (0.83 \(\pm\) 0.73 mm) in comparison with low-speed drilling, 0.70 \(\pm\) 0.62 mm. [PERSON] et al. [48], placed 128 tissue-level only implants and had 2 implants' failure and MBLs of 0.99 \(\pm\) 0.68 mm.
## 3 Experiments and Discussions
### Measure Respiratory Variations for Human Subjects by the Proposed Method  
The authors have collected data from 12 subjects and their respiratory splits are shown in Figure 11. Each recording has 3 min to 5 min consisting of several hundred cardiac cycles. A pair of respiratory  
Figure 10: Validation by computer simulation. (**a**) Simulated S2 signals with varying split, (**b**) Extracted aortic components, (**c**) Extracted pulmonary components, (**d**) Comparison between the true splits and measured splits.  
phase and split can be obtained from each cardiac cycle based on the proposed method, which can be represented by a red star in the joined domain. Hence, the respiratory variations in splitting of the second heart sound can be clearly illustrated by the stars, where the measured splits are re-sorted in respiratory phase in ascending order, seen in Figure 11. We can see that the split gradually increases with the phase increasing from 0 to \(\pi\) (inspiration) or decreases with the phase from \(-\pi\) to 0 (expiration). The minimum split generally occurs at phase zero and the split value is about 20 ms. Meanwhile, the maximum split is about 50 ms at phase \(\pi\) or \(-\pi\). These values are comparable to the physiological results [11]. The experimental results imply that the proposed method is reasonable to monitor the respiratory variations in the split of the second heart sound. The authors wish to declare that each step of the proposed method can run automatically without any human intervention.
## 4 Tools for WebAssembly Development  
Several tools exist that can be used for Wasm programming and system development. This section discusses compilers, runtimes, build tools/frameworks, and the Wasm Binary Toolkit (WABT). Table 3 shows a comparison of Wasm compilers, Table 4 presents a comparison of Wasm runtimes, Table 5 presents a comparison of build tools/frameworks with Wasm support, and Table 6 presents a comparison of tools belonging to the WABT. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**IoT Application** & **Performance Efficiency** & **Security** & **Portability** & **Noteworthy Use Case** \\ \hline Edge Computing & Wasm’s near-native performance enables real-time data processing reducing latency. & The sandboxed execution model isolates applications, enhancing security on edge devices that are often exposed to the network. & The same Wasm codebase can run across a variety of edge devices regardless of their hardware or operating system. & Wasm allows sophisticated computation close to the data source, minimizing bandwidth usage and latency. \\ \hline Smart Home Devices & Efficient use of resources, enabling high-speed operation on devices with limited resources. & Sandboxed execution enhances the security personal data often handled by these devices. & Cross-platform nature allows for a variety of smart devices to run the same Wasm applications. & Powers applications on devices like smart thermostats, home security systems, etc., enabling quick responses and reliable operation. \\ \hline Industrial IoT & Ability to process complex tasks quickly is critical in industrial scenarios, e.g., real-time monitoring of industrial processes. & Strong isolation between applications minimizes the impact of security breaches in an industry setting. & The same code can run on a variety of industrial devices, reducing development efforts. & Wasm can be used for predictive maintenance, automation of tasks, etc., improving operational efficiency. \\ \hline Wearable Devices & Wasm’s efficient use of resources enables applications to run smoothly even wearables with limited resources. & The sandboxed environment ensures user data on the devices remains secure. & The device-agnostic nature of Wasm means applications can run on various types of wearables. & Powers applications on devices like smartwatches, fitness bands, enabling efficient operation and real-time responses. \\ \hline IoT Gateway & Enhanced due to edge computing, protocol translation, and efficient data processing using Wasm. & Fortified with Wasm’s sandboxed execution for untrusted code and robust access control policies. & High, thanks to Wasm’s compatibility across various hardware architectures and operating systems. & Protocol translation layers for seamless integration of diverse IoT devices and performing advanced analytics at the edge using pre-trained models. \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of practical applications in the context of WebAssembly in IoT.
# An Integrated Decision Support System for Low-Disturbance Surface Mining
## 3 Overview of Results
### Analysis of the Results of the F-AHP Study
It was analyzed that CE1, CE3, and CE4 were all ranked less realistically than desirability, so these indicators need more attention and improvement, with CE4 needing to be focused on. As shown in Figure 8c, the realism weights of sub-indicators under the leading indicator \"Control of pollution emission (CP)\" are: \"Dust emission intensity (CP1)\" > \"Emission intensity of gaseous pollutants (CP3)\" > \"Emission intensity of water pollutants (CP2)\" > \"Noise pollution (CP4)\". The desirability weights are: \"Emission intensity of gaseous pollutants (CP3)\" > \"Dust emission intensity (CP1)\" > \"Emission intensity of water pollutants (CP2)\" > \"Noise pollution (CP4)\". The realism weight of CP3 was analyzed to be less than the desirability weight, so the indicator needs more attention and improvement. As shown in Figure 8d, the realism weights of sub-indicators under the leading indicator \"Control of drainage (CD)\" are: \"Groundwater discharge intensity (CD1)\" > \"Intensity of in-pit water discharge (CD2)\". The desirability weights are: \"Intensity of in-pit water discharge (CD2)\" > \"Groundwater discharge intensity (CD1)\" > \"Emission intensity of water pollutants (CP2)\". Therefore, CD2 indicators need more attention and improvement. After determining the relative weights of the main indicators and sub-indicators, the final step of the F-AHP is to calculate the total weights of all sub-indicators. This can be quantified by taking the relative weights of the sub-indicators and their corresponding leading indicators and multiplying them together. The weights and ordering of the sub-indicators are shown in Figure 9.  
In summary, the relative weights of the leading indicators were determined using F-AHP. Based on an evaluation of the actual situation, the indicators \"Control of stripping works (CS)\" and \"Control of emission rock (CE)\" are of the highest importance, both in terms of reality and desirability, and should require the immediate attention of mining companies. In particular, the indicator \"Control of stripping works (CS)\" has a lower realism weight than desirability weight and, therefore, needs much more improvement. The sub-indicators \"Stripping intensity (CS2)\", \"Utilisation of inner dump (CE3)\" and \"Emission intensity of gaseous pollutants (CP3)\" need focused improvement. Figure 9: Overall weighting and ranking of sub-indicators.
## 3 System Model and Problem Modeling
### System Model  
We consider the uplink transmission in the wireless communication system depicted in Figure 1. The system comprises a single LEO satellite, and \(N\) GPFs serve as users and \(M\) auxiliary communication airborne platforms serve as APs. In this configuration, the GPFs initiate power data transmission tasks, and the LEO satellite \(s\), functioning as a space-based cloud server base station, replaces ground stations to receive signals.  
Some GPFs periodically generate a substantial volume of real-time power business data, which is time sensitive and needs to be immediately transmitted to the cloud server within the 5G base station for processing. However, in power emergency scenarios where public network communication facilities are damaged or malfunctioning, the 5G base station signals cannot cover the area. So, LEO satellites are employed as substitutes for ground stations to fulfill the corresponding communication services. Due to the considerable distance between LEO satellites and the Earth's surface, the direct connection speed of the GPF is relatively low, and the communication delay may not meet the needs of power businesses. To address this limitation, APs are utilized to relay and amplify certain signals, thereby enhancing communication transmission rates.  
Additionally, some GPFs generate data involving non-real-time business operations that require large amounts of data transmission. Since this type of business operation is insensitive to time constraints, and considering efficiency and communication resource optimization, the data can be temporarily offloaded on APs. After completing communication tasks, the APs can fly to the nearest 5G ground station cloud server for data processing. In  
Figure 1: System model.  
the system model tailored to the data transfer requirements of various business types, APs serve a dual role. They act as relay nodes for ground-to-satellite communication, facilitating signal forwarding [9]. Simultaneously, APs can function as short-term data storage devices, providing diversified services to meet the data transfer needs of GPFs [8].  
\(N\) GPFs are represented by the set \(\mathcal{N}=\{1,\cdots,n,\cdots,N\}\), while \(M\) APs are denoted by the set \(\mathcal{M}=\{1,\cdots,m,\cdots,M\}\). The system employs a quasi-static model, dividing a coverage cycle into \(T\) time slots of length \(\Delta t\), with \(\mathcal{T}=\{1,\cdots,t,\cdots,T\}\). It is assumed that the positions of the GPFs are known and remain approximately constant throughout the task duration. The location of GPF \(n\) is represented as \(q_{n}=[x_{n},y_{n}]^{T}\). The APs fly at a fixed height of approximately \(H_{a}\) above the ground, with negligible height differences between them to prevent collisions. Within each time slot, the horizontal position of AP \(m\) is considered constant and denoted as \(q_{m}(t)=\left[x_{m}(t),y_{m}(t)\right]^{T}\).