esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discrete Event-Based Simulator for Dynamic VoD Content Distribution
```
Input: Network status: cache locations, stored content per cache and available bandwidth on links; VoD content request \(r\)(\(t_{s}\),\(D_{r}\),\(m\),\(b_{r}\),\(d_{r}\)); arriving time \(t_{s}\), user \(D_{r}\), requested content \(m\), requested bit-rate \(b_{r}\), chunk duration \(d_{r}\). Output: VoD provisioning (per chunk) \(path=false\); bandwidth = 0;  Allocate list of caches \(C\) storing content \(m\);  path = shortest path between \(D_{r}\) and nearest cache with available bandwidth \(\geq b_{r}\); if path = true then \(bw=b_{r}\); else  path = shortest path between \(D_{r}\) and nearest cache with available bandwidth \(\geq min\). bandwidth; if path = true then \(bw=min\). bandwidth; endif if (\(path=true\)) then  provisionn \(r\) over \(path\) with bandwidth \(bw\);  schedule next video-chunk event at \(t_{s}+d_{r}\);  schedule deprovisioning event at time \(t_{s}+d_{r}\); else  Block \(r\);  endif  End;
```  
**Algorithm 1** Video-on-demand (VoD)-chunk provisioning
## 2 Materials and Methods
### The Behavior of the Doppler Signal in Two-Phase Bubbly Flow  
In two-phase bubbly flow, the Doppler signal is derived from echo signals reflected in a fluid; the echo signals are assessed as either particles or bubbles. Instinctively, the amplitude can indicate its own behavior. Doppler amplitude is considered to be the reflection ratio \(R\) of acoustic intensity from the interface point between two media as described by Equation (7):  
\[R=\left(\frac{Z_{1}-Z_{2}}{Z_{1}+Z_{2}}\right)^{2}=\left(\frac{\rho_{1}c_{1}- \rho_{2}c_{2}}{\rho_{1}c_{1}+\rho_{2}c_{2}}\right)^{2} \tag{7}\]  
where \(Z\) represents acoustic impedance, \(\rho\) is the density, and \(c\) is sound velocity.  
The reflection ratio of the gas-liquid interface (bubble) is greater than the solid-liquid interface (particle). Thus, the Doppler amplitude reflected from a bubble is larger than the amplitude from a particle; moreover, Doppler frequencies are still different due to their velocity. In some situations, when the bubble and particle position concurrently occurs at the same measurement channel in accordance with the number of repetitions (\(N_{REP}\)), multi-frequency and differences in amplitude can be observed at the Doppler signal as shown in Figure 4. Therefore, to measure and separate the velocity profile of the bubble and particle, the integration of time-frequency analysis and Doppler amplitude classification are proposed in this work to analyze the effect of the Doppler signal.  
Figure 4: Multi-frequency and different amplitude effects of the Doppler signal in one measurement channel in accordance with \(N_{REP}\).
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation
### Complexity Analysis  
As shown in Table 2, TSNetIQ exhibits a significantly higher number of floating-point operations (FLOPs) and a higher parameter count than the standard CNN model, with approximately 2.66 \(\times\) 10\({}^{8}\) FLOPs and 6.05 \(\times\) 10\({}^{5}\) parameters compared to CNN's 5.78 \(\times\) 10\({}^{7}\) FLOPs and 6.87 \(\times\) 10\({}^{4}\) parameters. However, the complexity of TSNetIQ remains on the same order of magnitude as ResNetIQ, which has 1.90 \(\times\) 10\({}^{8}\) FLOPs and 2.46 \(\times\) 10\({}^{5}\) parameters. Despite the relatively increased computational cost, TSNetIQ consistently delivers superior angle estimation accuracy, outperforming both CNN and ResNetIQ models. This demonstrates that TSNetIQ achieves a better trade-off between model complexity and predictive precision, making it more suitable for high-resolution DOA estimation tasks.
# Study of Self-Heating and High-Power Microwave Effects for Enhancement-Mode p-Gate GaN HEMT
## 3 Results and Discussion
### Electrical Characteristics
With the accumulation of heat, the temperature rise leads to the enhancement of the irregular thermal movement of the carriers and the scattering is further improved. Under the influence of the above-mentioned negative effects, the carrier mobility of the two-dimensional electron gas layer, AlGaN buffer layer, has dropped by 13.85% and 41.48%, respectively, as shown in Figure 5. The difference can be explained as  
Figure 4: Simulated band diagram (red and grey line, right Y-axis) of the HEMT and electron distribution (green and blue zone, left Y-axis) among different layers. Figure 3: (**a**) Off-state carrier density distribution diagram; (**b**) current density distribution of the device when Vg = 0 V, Vds = 18 V.  
follows: the carriers in the buffer layer are scattered in the horizontal and vertical directions, while the carriers in the 2 DEG layer are only scattered in the horizontal direction. It can be seen from Figure 6 that the decrease in mobility results in a drop of current which leads to an evident negative differential conductance (NDC) phenomenon [35]. When the device is just turned on, the current has no obvious downward trend. With the continuous increase in the inject voltage, the current decreasing trend caused by the temperature rise effect increases due to the negative effects discussed above. Besides, the current decrease caused by the temperature rising effect could neutralize the current increase by the channel length modulation effect in output characteristics. The channel length modulation effect is further weakened with the increase in gate voltage that makes the overall output characteristics no longer show the features of the channel length modulation effect. This trend aggravates as the drain voltage enlarges.
# A Novel Approach to Data Extraction on Hyperlinked Webpages
## 2 Related Work
### Schema Extraction and Matching
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sr\#** & **Paper Title** & **Published In** & **Table Detection** & **Worked On** & **Corpus Size** & **Schema Extraction** & **Linked \textgreater{}Tables** \\ \hline \multirow{2}{*}{1} & Mining tables & \multirow{2}{*}{
\begin{tabular}{c} Computational \\ HTML \\ \end{tabular} } & \multirow{2}{*}{Yes} & \multirow{2}{*}{HTML} & \multirow{2}{*}{-} & \multirow{2}{*}{No} & \multirow{2}{*}{No} \\  & & & & & & & \\ \hline \multirow{3}{*}{2} & Detecting tables & \multirow{3}{*}{Springer, 2002} & \multirow{3}{*}{Yes} & \multirow{2}{*}{HTML} & \multirow{2}{*}{1393} & \multirow{2}{*}{No} & \multirow{2}{*}{No} \\  & in HTML & & & & & & \\ \cline{1-1}  & documents & & & & & & \\ \hline \multirow{3}{*}{3} & Uncovering the & \multirow{3}{*}{Web DB, 2008} & \multirow{3}{*}{Yes} & \multirow{2}{*}{HTML} & \multirow{2}{*}{14.1B} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} \\  & relational web & & & & & & \\ \hline \multirow{3}{*}{4} & Factoring web & \multirow{3}{*}{Springer, 2011} & \multirow{3}{*}{No} & Spread & \multirow{3}{*}{200} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & tables & & & & & & \\ \hline \multirow{3}{*}{5} & Table header & \multirow{3}{*}{AAAI, 2012} & \multirow{3}{*}{Yes} & \multirow{3}{*}{PDF} & \multirow{3}{*}{200} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & detection and & & & & & & \\ \cline{1-1}  & classification & & & & & & \\ \hline \multirow{3}{*}{6} & Schema & \multirow{3}{*}{VLDB, 2013} & \multirow{3}{*}{Yes} & HTML, & \multirow{3}{*}{7883, 14669} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & extraction from & & & & & & \\ \cline{1-1}  & tabular data & & & & & & \\ \hline \multirow{3}{*}{7} & Web table & \multirow{3}{*}{SIGMOD, 2013} & \multirow{3}{*}{Yes} & HTML & \multirow{3}{*}{3000} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & taxonomy and & & & & & & \\ \cline{1-1}  & formalization & & & & & & \\ \hline \multirow{3}{*}{8} & Ten years & \multirow{3}{*}{VLDB, 2018} & \multirow{3}{*}{Yes} & HTML & - & Yes & \\ \cline{1-1}  & of webtables & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of main papers.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 5 Comparison of Online SOC Estimation before and after Model Parameter Optimization  
The verification of the AUKF based on the optimization of the model parameters is divided into three aspects. First, the estimation accuracy of the AUKF is compared to that of the normal UKF. Second, the superiority of the AUKF estimation accuracy before and after the optimization of the model parameters is verified. Finally, the convergence of the AUKF to the initial value error of the SOC before and after the optimization of the model parameters is verified.  
In the process of setting the battery charge and discharge state, the UDDS (Urban Dynamometer Driving Schedule) waveform [27] is used as a reference. To ensure correspondence with the experimental object (SANYO 18650 Li-ion battery) in the process of OCV-SOC calibration, the current signal in Figure 11 is adopted to describe the increase or decrease of the current in the discharging or charging process of the power battery. In one period, the average output current is 1.77 A, the maximum discharging current is 5.28 A, and the maximum charging current is 2.42 A. Each period is 1367 s, and the condition lasts for two periods.
# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
## 2 Materials and Methods
### Gaussian Processes  
Here, we give a brief overview of Gaussian processes. A more comprehensive treatment can be found in [9]. The authors define a Gaussian process (GP) as a continuous collection of random variables, any finite subset of which is normally distributed as a multivariate distribution.  
We denote a dataset of \(n\) observations as \(\{(\mathbf{x}_{i},y_{i})\}_{i=1}^{n}\), where \(\mathbf{x}\) is an input vector of dimension \(d\), and \(y\) is a scalar-valued observation. In regression, the objective is to find a function \(f:\mathbb{R}^{d}\rightarrow\mathbb{R}\),  
\[y=f(\mathbf{x})+\epsilon,\quad\epsilon\sim\mathcal{N}(0,\sigma_{\epsilon}^{2}), \tag{1}\]  
with \(\epsilon\) being identically distributed observation noise. This function can be drawn from a \(\mathcal{GP}\), which is fully defined by its mean \(m(\mathbf{x})\) and _covariance function_\(k(\mathbf{x},\mathbf{x}^{\prime})\), also denoted as  
\[f(\mathbf{x})\sim\mathcal{GP}(m(\mathbf{x}),k(\mathbf{x},\mathbf{x}^{\prime} )). \tag{2}\]  
The covariance function is parametrized by a set of hyperparameters \(\boldsymbol{\theta}\) that can be learned by maximizing the log marginal likelihood. In our experiments, we use BFGS, a quasi-Newton method described in [14]. The squared exponential kernel (SE), also called the radial basis function kernel, is applicable in a wide variety of situations because it generates smooth (infinitely differentiable) functions. It has the form  
\[k_{SE}(\mathbf{x},\mathbf{x}^{\prime})=\sigma_{f}^{2}\exp\left(-\frac{\left| \mathbf{x}-\mathbf{x}^{\prime}\right|^{2}}{2l^{2}}\right), \tag{3}\]  
in which \(\sigma_{f}^{2}\) is a height-scale factor and \(l\) is the length scale that determines the radius of influence of the training points. Since our data are both very smooth and stationary (covariances only depend on the distance between two data points, not their location), the squared exponential kernel is a more than reasonable choice. We do, however, implement a different length-scale parameter for every input dimension. This technique is called automatic relevance determination (_ARD_) and allows for functions that vary differently in each input dimension [15]. The kernel used in this work has the form  
\[k_{SEARD}(\mathbf{x},\mathbf{x}^{\prime})=\sigma_{f}^{2}\exp\left(-\frac{1}{2 }\sum_{j=1}^{d}\left(\frac{\left|\mathbf{x}_{j}-\mathbf{x}_{j}^{\prime} \right|}{l_{j}}\right)^{2}\right). \tag{4}\]
###### Abstract  
Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPP\({}_{\text{EC}}\)). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R\({}^{2}\) = 0.13-0.88, \(p\) < 0.001; OCO-2 SIF: R\({}^{2}\) = 0.11-0.99, \(p\) < 0.001; MODIS GPP: R\({}^{2}\) = 0.15-0.79, \(p\) < 0.001). MODIS GPP demonstrates a high correlation with GPP\({}_{\text{EC}}\) in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gcm\({}^{-2}\)day\({}^{-1}\) for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPP\({}_{\text{EC}}\) for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.  
Keywords:Gross primary production (GPP); sun-induced chlorophyll fluorescence (SIF); MODIS GPP (MYD17A2H); GOSIF; OCO-2 +
Footnote †: journal: Remote Sensing, 2020, 12, 258; doi:10.3390/rs12020258
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 3 Fundamental Dispersion Theorems
### Pulse Boosting and Decaying  
Let \(A(t,0)\) be an initial pulse whose dispersive dynamics is known, i.e., at the end of the dispersive medium its profile is also given as \(A(t,z)\). Then, if the initial pulse is boosted by shifting the pulse's spectrum, i.e., by multiplying the profile by a harmonic function:  
\[A^{\prime}(t,z=0)=\exp(i\omega_{0}t)A(t,0) \tag{5}\]  
then the pulse's profile at the end of the medium (after a distance \(z\)) is (see Appendix B)  
\[A^{\prime}(t,z>0)=\exp\left(i\frac{\beta_{2}}{2}\omega_{0}^{2}z+i\omega_{0}t \right)A(t+\beta_{2}\omega_{0}z,z) \tag{6}\]  
Clearly, this theorem can be generalized to the case where the initial pulse is multiplied by an exponent:  
\[A^{\prime}(t,z=0)=\exp(at)A(t,0) \tag{7}\]  
where \(a\) can be any real, imaginary, or complex number. In which case, if the initial function does not diverge, the solution at the end of the medium is  
\[A^{\prime}(t,z>0)=\exp\left(-i\frac{\beta_{2}}{2}a^{2}z+at\right)A(t-i\beta_{ 2}az,z) \tag{8}\]
## 3 Development and Verification of the Inspection Device for Field Application
### Cross-Assessment of Measurements Data through MCNP Simulation  
#### 3.2.1 Computer Simulation Evaluation Methods  
FLUKA (FLUtiaerende KAskade) code, a particle transport simulation tool, was used to assess radiation leakage. Table 6 outlines the setup wherein \"Precisio\" was adjusted to validate simulation results against measured radiation leakage values. The ISOTOPE parameter reflected Se-75's radiation emission characteristics.  
The energy radiated per decay was absorbed by the measuring instrument. Effective dose rates were calculated using AMB74 and AUXSCORE (FLUKA Software, Joint development by CERN and INFN, [[http://www.fluka.org](http://www.fluka.org)]([http://www.fluka.org](http://www.fluka.org))) to compute dose equivalents. Secondary radiation, emitted from primary gamma rays, was measured up to 50 keV. The simulation involved 3.0 \(\times\) 10\({}^{10}\) particle histories to achieve a dose rate of 1 \(\upmu\)Sv/h. Dose distribution values were obtained using a dose map scored by Region Type [20; 21; 22].  
\[\text{E}=\frac{GeV\cdot Volume}{g\cdot decay}\]  
Depending on the distance and survey location  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Division** & \multicolumn{2}{c}{**Details**} \\ \hline
**Input Parameter** & **Input Card** & **Input Condition** \\ \hline Physical setting & Default & Precisio \\  & Beam & Type: Isotope/Momentum \\ Source term & Hi-Prope & Type: Region \\  & & Part: DOSE-EQ \\  & \multirow{2}{*}{Usrbin} & Type: Region \\ Scoring & & Part: DOSE-EQ \\  & \multirow{2}{*}{Auxscore} & Set: AMB74 \\ Transport & & \\  & & \\  NPS * & Start & No: \(3.0\times 10^{10}\) \\ \hline \hline \end{tabular} \({}^{*}\)Number of particle history.  
\end{table}
Table 6: Input parameters of FLUKA modeling and radiation energy calculation formula.  
Figure 6: Results of radiation dose measurements at the horizontal piping location.  
Energy per decay in the ion chamber (E).  
\[\mathrm{D(Gy/h)}=\frac{\mathit{GeV\cdot Volume}}{\mathit{g\cdot decay}}\times \frac{1.6\times 10^{-10}\mathrm{J}}{\mathit{GeV}}\times\frac{10^{3}\mathrm{g}}{ \mathrm{kg}}\times\mathrm{S(Ci)}\times\frac{3.7\times 10^{10}\mathit{ decay}}{\mathit{Ci\cdot s}}\div\frac{3600\mathrm{s}}{\mathrm{h}}\times\frac{1}{\mathit{ ion Chamber Volume}}\times\frac{\mathrm{Gy}}{\mathrm{J/kg}}\]  
S = Source Intensity  
Absorbed dose in the ion chamber (D).  
\[\mathrm{H}=\mathrm{D(Gy/h)}\times\frac{1.2\mathit{Sv}}{\mathit{Gy}}\]  
Ambient dose equivalent (\(H\)) formula.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 2 PFAS Applications and Related Concerns
### Analytical Techniques for PFAS Detection in Water
#### 2.4.3 Total Fluorine Analysis (TF)  
Typical methods for organic and inorganic TF determination include particle-induced gamma-ray emission (PIGE), absorbable organic fluorine (AOF), and combustion ion chromatography (CIC). Recently, fluorine-19 nuclear magnetic resonance spectroscopy (19F-NMR), inductively coupled plasma-mass spectrometry (ICP-MS/MS), and X-ray photoelectron spectroscopy (XPS) were also reported on for fluorine detection [49; 50; 51].
## 4 Discussion
The wrought material showed almost no grain growth from the two additional hours of ordering with a final grain size of 51.48 \(\pm\) 5.05 \(\upmu\)m and the AM material showed significant grain growth with a final grain size of 8.91 \(\pm\) 0.22 \(\upmu\)m. As can be seen in Figure 9, all wrought and AM samples showed an unexpectedly high coercivity. When compared to commonly reported coercivity trends for wrought material, both the wrought and AM samples in this work, which were ordered for two hours, had similar deviations from the common trend line with an approximately 250 A/m higher coercivity than would be expected from their grain sizes. Wrought material that was exposed to a second ordering treatment showed a slight decrease in coercivity, which corresponded to the slight increase in grain size. These changes qualitatively matched the generally expected trend, though still did not align with previously reported values. The AM material exposed to a second ordering treatment showed an increase in coercivity that corresponded to the significant grain growth observed. This result is counter to the expected trend and could have several possible causes such as higher oxygen content, remnants of the AM solidification structure, or other sources of domain wall motion barriers. Figure 7: Hall-Petch fit of yield strength for both ordered AM and wrought material. In order to investigate the possibility of a second phase formation and its impact on the magnetic properties, XRD was performed on the samples using a Cu K\(\alpha\) source. Review of both AM and wrought material after four hours of ordering showed no significant presence of a second phase (Figure 10). Residual stress, texture, atypical grain boundary character, or nanometer-scale precipitates that might be present and might not be captured by XRD analysis are possible sources of the high coercivity. It is not expected that material aged at elevated temperature for 2-4 h and furnace -cooled would retain any significant residual stresses; however, it is possible that small stress gradients in the AM material could remain and contribute to the observed values. Transmission electron microscopy (TEM) could provide further insight into the structure of the material and its influence on coercivity, as well as identify any possible nanometer-scale precipitates. Such precipitates are commonly observed in AM microstructures and could be a source for both the slightly increased strength and higher coercivity observed in AM materials when compared to expected wrought material trends [20]. Figure 8: Magnetic saturation at various applied magnetic fields for AM and wrought samples. Figure 9: Coercivity data for wrought and AM materials after ordering for two or four hours compared to a previously reported grain size relationship for Fe-Co-2V.
# Edge Computing Based on Federated Learning for Machine Monitoring
## 2 The Background  
### Research Field  
In this research, the IoT system was implemented in a SMSF with plastic injection machines. Because the factory gradually purchased new machines over time, it resulted in different types, panels, and control methods. It is impossible to widely distribute various sensors in production equipment, processes, and factory environment through IoT architecture, and we expect to collect and analyze data automatically. Four kinds of plastic injection machines of different ages in the factory are shown in Figure 1. Therefore, this research study adopts the method of computer vision technology to deploy IoT to assist business operators in understanding production lines and making further decisions.  
Figure 1: Plastic injection machines.  
For a plastic injection molding machine, it integrates clamping, injection, dwelling, cooling, mold opening, and the removal of products to produce a plastic product. Parameter settings and the control of each step needs to be strictly controlled. Otherwise, it is easy to produce defective products and cause damage to the machine. Programmable Logic Controller (PLC) has been widely used in various control systems and plays the core control task in factory automation control. The PLC display screens of machines of different generations or models will be very different. Six types of PLC display screens are shown in Figures 2 and 3. Figure 2 shows the control panel of the traditional machine equipment, which includes the knob switch status, pointer meter, status lights, LED digital lights, mechanical counters, and explanatory texts, etc. Figure 3 shows the new control panel of LCD screen. Light signals, numbers, pointers, bar graphs, dynamic code tables, flow charts, and related texts are included on the screen. It is obvious that two kinds of panels are completely different.  
The information on the PLC panels generally corresponds to parameters about time, speed, temperature, pressure, and measurement. They are represented by the original threshold setting and the current condition of the machine. If the difference between the current situation and the threshold parameter is too large, it means that an abnormal event occurs. Combining panel information captured by cameras and IoT monitoring mechanism can prevent abnormal events and increase productivity. For example, when the pressure is too high, the plastic material is easy to overflow from the mold and produce deformed products. If the system can immediately detect this situation from the PLC screen and find that the temperature is too low, the system can reduce the feeding pressure and increase mold temperature in time. Increasing the rate of melting plastic materials can effectively prevent the situation of overflow and reduce the probability of defective products.  
Figure 3: LCD screen of PLC panels.  
Figure 2: Traditional PLC panels.