esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Result and Analysis
### STMD Neuron Features  
To demonstrate the STMD neuron features, we present the output for objects with varying motion velocities, horizontal widths, and vertical heights, as well as Weber contrast values. As seen in Figure 6a, for a small object sized \(w\times h\), the background area is set to \((w+2d)\times(h+2d)\). The initial conditions for Weber contrast, speed, horizontal extension, and vertical extension are defined as 1, 250 pixel/s, 5 pixels, and 5 pixels, respectively. Figure 6b illustrates the STMD responses under different Weber contrasts. We can obtain that the STMD output is increase with increasing the Weber contrast of the target, and the illustration shows that the STMD output becomes maximum when the Weber contrast of the target reaches 1. This indicates that when the moving object's Weber contrast is higher, the moving object is tracked more easily. The STMD response result to the different motion velocities of the moving object is presented in Figure 6c. Notably, the preferred velocity range of STMD neurons is 200 pixel/s-300 pixel/s and the optimal velocity is 250 pixel/s. The STMD responses under different target widths and heights are displayed in Figure 6d,e. The STMD neuron appears to preferentially respond to moving objects with equal widths and heights of \(5\times 5\) pixels. The above four images demonstrate the features of the STMD neuron revealed by biological research, including its sensitivity to Weber contrast and selectivity for velocity, width, and height.
## 4 Experimental Setup
### Evaluation Metrics
#### 4.1.2 Robustness  
The robustness is measured using the normalized correlation (NC) between the inserted watermark and the extracted one as given by the following equation:  
\[NC=\frac{\sum_{i=1}^{M}(w_{i}^{\prime}-\overline{w}^{*})(w_{i}-\overline{w})} {\sqrt{\sum_{i=1}^{M}(w_{i}^{\prime}-\overline{w}^{*})^{2}.\sum_{i=1}^{M}(w_{ i}-\overline{w})^{2}}} \tag{13}\]  
where \(i\in\{1,2,\ldots,M\}\), \(\overline{w}^{*}\) and \(\overline{w}\) are the averages of the watermark bits, respectively.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of IoT in PV Systems  
With the growing adoption of solar energy as a key renewable resource, the need for smarter, more efficient PV systems has become paramount. IoT technologies have paved the way for innovative solutions tailored to the unique challenges faced by PV systems, such as real-time monitoring, maintenance optimization, energy management, solar tracking, and fault detection. This section delves into specific applications of IoT in PV systems, showcasing how these technologies are driving advancements in automation, performance optimization, and system reliability.
## 2 Reliability Analysis Method  
### Failure Probability and Reliability Index  
In the analysis of engineering structure, it is often necessary to evaluate the reliability of the supporting structure. When the supporting structure can function normally according to the predetermined requirements, it can be represented by the reliability probability \(P_{r}\); when the opposite support structure cannot meet the expected functional requirements, it is represented by the failure probability \(P_{f}\), and their relationship is as follows:  
\[P_{r}+P_{f}=1 \tag{1}\]At present, two expressions are usually used to quantify the calculation results of reliability, the failure probability \(P_{f}\) and the reliability index \(\beta\). These two indexes are not isolated but have a clear mathematical relationship and can be transformed into each other. The failure probability \(P_{f}\) directly reflects the possibility of structural failure under given conditions. The reliability index \(\beta\) reflects the reliability of engineering structures, with larger values indicating more reliable structures.  
\[P_{f}=1-P_{r}=1-\varnothing\beta=\varnothing(-\beta) \tag{2}\]
## 2 Microseismic Monitoring Method under Tunnel Construction
### Microseismic Location Detection Technology
[28], the wave velocity is set as an unknown quantity and is used to solve the source coordinates, which is verified by AE testing and field monitoring. This method only requires the sensor coordinates and time difference, does not require pre-measuring the wave velocity, and significantly improves the applicability of positioning. However, for the straightening of the propaga  
Figure 4: Schematic of locating the source based on the arrival time difference approach. tion path and the simplification of the propagation speed of the actual signal in the rock mass, the velocity error is high, which is different from the actual situation. [PERSON] et al. [29] designed a piecewise velocity model along the tunnel axis and inverted the source location and cross-sectional velocity simultaneously, which significantly improved the positioning accuracy. The velocity model obtained by inversion has a good corresponding relationship with geological conditions and rockburst location. [PERSON] et al. [30] considered the influence of the gap of the excavated tunnel on the wave propagation path and located the residual between the theoretical arrival time and the observed arrival time calculated using the fast-marching method (FMM). The test results showed that this method can locate microseismic events with a higher accuracy than that based on the single velocity model. [PERSON] et al. [31] proposed a method to locate microseismic events based on interference imaging and cross-wavelet transform. This method does not require accurately picking the arrival time, but uses the cross-wavelet power function to obtain the difference in the arrival times of different sensors. Different methods have their own applicable conditions. To improve the positioning accuracy, it is necessary to choose the appropriate positioning method based on the monitoring conditions, speed model, and picking accuracy. For example, if the calibration source can be generated at a specific location, such as the vault and floor by blasting, the P-wave velocity structure can be inverted, and the dynamic velocity model can be selected to determine the location. When the energy of the microseismic signal is low, or the monitoring environment noise is high, resulting in a low signal-to-noise ratio, it may not be able to ensure reliable picking time and the inversion accuracy of the velocity model. At this time, we can consider using a method that does not require the first break time or reduce the dependence on the velocity model as appropriate.
## 3 Results and Discussion
### Determination of Relevant Visual Information
#### 3.2.4 Effect of Operators' Experience on Information Gained  
The years of operating experience of the participants did not influence their responses towards the level of importance of each video clip or with respect to replacing the clips with a graphical display; however, it did influence the responses to the open-ended questions in the questionnaire. Participants with intermediate or extensive experience operating a sprayer were able to articulate more useful or relevant information from the video clips than their peers with less operating experience (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clip** & **General Description** & **\textless{} 5 Years Experience** & **5-15 Years Experience** & **\textgreater{}15 Years Experience** \\ \hline
1 & Front view from & Turning, speed, track path & Turning, track path, windy. & Turning, windy, obstacle. \\ \hline
2 & Right boom & Nozzle status, & Nozzle status, boom height, distance from field & Sprayer is operating, crop condition, boom height, obstacles, travel speed, distance from field edge. \\ \hline
3 & Left side of sprayer. & Leak, how deep you can & Topography, leaks, teaching moment, machine integrity, not much. & Boom is still attached, travel speed, boom stability, not much. \\ \hline
4 & Underneath of sprayer. & Leaks, crop respond, crop height. & Field condition, leaks, machine integrity, not much. & Crop conditions, leaks, crop height, crop damage, travel speed, clearance, nothing much. \\ \hline
5 & Distance sprayer & Field variability, size of & Wind condition, amount of drift, sprayer location, & Weather, sprayer is in the desired field, windy, sprayer is still moving, not much. \\ \hline
6 & View of spray/rinse & Tank is full, sprayer is & Machine integrity, weather, nothing. & Leaks, field conditions, sprayer is operating, making turns, obstacles, nothing. \\ \hline
7 & Left side of sprayer & Side view of & Beneficial for off-field operation, nothing. & Travel speed, crop condition, weather, field conditions, obstacles, nothing. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information gained as a function of a participant’s experience.  
Figure 8: Information gained based on from the operator’s look zone during spraying operation.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 2 5G Testbed Overview
### ADC/DAC Sample Rate Synchronisation  
With both ADC/DAC fully integrated in VC707 FPGA, considerations on their data sampling process was studied and addressed. Such process at the receiver might be subject to severe timing imperfections such as erroneous information acquisition or transmitted samples loss depending on both DAC/ADC sampling rates difference [5]. The erroneous information acquisition can occur when ADC rate is higher than DAC, whereas loss of transmitted samples might happen if ADC rate is lower than DAC. That said, sampling rate of both converter boards considered should match.  
The easiest solution, found in this work, to meet the above requirement is to configure the clock tree of the FMC230 to drive a 1.25 GHz clock signal into both AD9129 cores. This would be a very straightforward task if the internal VCO of the AD9517-1 could generate a 1.25 GHz operating frequency (maximum rate achievable by FMC126 without phase interleaving) or integer multiples of this frequency. In Figure 7, it is seen that the DAC sample clock, driven by the \(fDAC\_CLK\) path, is provided by the \(\mathit{OUT2}\) output of AD9517-1. This port provides clock frequencies from 2457.6 MHz down to 12.8 MHz, which are the result of configuring either both block divisors \(\mathit{VCO\_div}\) and \(\mathit{div1}\) or, alternatively, bypassing \(\mathit{VCO\_div}\) and using \(\mathit{div1}\), or vice versa. However, only integer values are acceptable in such block divides, and, thus, is not possible to obtain a divisor combination where \(\mathit{OUT2}\) port is at 1.25 GHz. Therefore, meeting the ADC sample clock in the FMC230 is only possible considering an external signal as sample clock reference.  
As a workaround, the clock tree of the FMC126 has been configured to enable a Clock Output (CO) signal, with Low-Voltage Positive-referenced Emitter Coupled Logic (LVPECL) waveform type, at 1.25 GHz, to be connected to the Clock Input (CI) port of FMC230. Although the AD9517-3 circuit can be easily configured to enable \(\mathit{OUT1}\) as CO, this port is not connected to any external connector in the FMC126 board. To overcome this, an additional SSMC connector has been soldered to the CO port pad in the reverse side of the device PCB.
## 9 Specific RID Algorithm Examples
### LibCorNID  
Any library correlation or template method requires the measured spectrum, standard energy, peak shape, and peak efficiency calibrations. The LibCorNID approach is somewhat unusual in that it includes both peak fitting and template matching with tolerances for variations in the calibrations [29]. Its key steps are:  
1. Estimate the continuum background by peak erosion after background compensation to isolate the peaks. If available, a scaled background spectrum is subtracted from the measured spectrum.
2. Apply nuclide filtering in which candidate nuclide peak spectra are constructed in accordance with line abundances and energy, shape, and efficiency calculations. Nuclides that cannot meet the identification criteria for amplitude do not need to be made candidates, thus reducing the search space during the shape matching step. The percent gain shift tolerance is the maximum gain shift considered during analysis, expressed as the percentage change in the energy calibration slope. The sensitivity threshold is the minimum nuclide spectral area required for identification, as a percentage of the measured peak spectrum. The percent gain shift tolerance and sensitivity thresholds are used along with the residual peak spectrum after baseline estimation to filter out non-candidates from the library.
3. Simultaneously fit constructed spectra to the isolated measured peak data while allowing tolerance for calibration errors. Calibration compensation includes fitting gain shift and an effective thickness of absorbers to account for shielding not considered in the default efficiency. Only iron shielding is considered to modify the peak efficiency calibration.
4. Match shapes by calculating the normalized correlation coefficient between the theoretical shape and the interference corrected measured shape and applying a threshold.
# COVID-19 Lung Injury: Unique and Familiar Aspects of Pathophysiology
## 5 Therapeutic and Supportive Strategies for COVID-19
### Pharmacotherapy Targeting SARS-CoV-2  
Significant research in recent years has focused on identifying compounds with antiviral activity against SARS-CoV-2. However, translating this research to effective, well-tolerated antiviral effects in humans remains challenging. Remdesivir, nirmatrelvir, and molnupiravir have been among the most effective antivirals to date.  
Remdesivir, a nucleoside analog prodrug, inhibits RNA viruses by competing with ATP, hindering viral RNA replication [179]. Three clinical trials (ACTT-1, PINETREE, Solidarity) have demonstrated benefit with remdesivir treatment compared to placebo and may point towards the importance of appropriate timing and patient criteria in treatment effectiveness. In these studies, remdesivir was shown to shorten the time to recovery in hospitalized patients (10 days vs. 15 days), reduce hospitalization or death in high-risk outpatients (1.6% vs. 8.3%), and reduce mortality in non-ventilated (at entry) hospitalized patients (11.9% vs. 13.5%) [180; 181; 182]. Interestingly, in the latter study, no benefit was observed in patients who were already ventilated at entry. This could suggest immunopathology, and not viral replication, as the main contributing factor to disease progression once mechanical ventilation is required. The 87% lower risk of hospitalization or death in the outpatient cohort is consistent with a more beneficial effect in early stages of COVID-19 [181].  
Another oral prodrug, molnupiravir, incorporates into newly synthesized viral RNA as N4-hydroxyctidine and induces transcription errors [183]. These errors cause non-functional proteins which accumulate and worsen with each replication cycle, leading to non-functional virions. Molnupiravir was trialed in 1433 outpatients with mild-to-moderate COVID-19 and significantly reduced the primary outcome of hospitalization or death compared with placebo (6.8% vs. 9.7%) [184]. Notably, a point of concern has been raised regarding the mechanism of action of molnupiravir, since partially functional proteins could confer resistance over time. However, this has not been observed to date [178].  
One of the most recently studied antivirals for COVID-19, nirmatrelvir, specifically inhibits the SARS-CoV-2 main protease (NSP5). NSP5 is responsible for the formation of other NSPs via cleavage of viral polyproteins; thus, loss of NSP5 function attenuates viral replication [185]. Most recently, nirmatrelvir was trialed as a twice-daily combination therapy with ritonavir, a repurposed viral protease inhibitor which improves the drug's pharmacokinetic profile, for 5 days in 2246 symptomatic unvaccinated outpatients. Nirmatrelvir-ritonavir was associated with a lower risk of hospitalization or death compared to placebo (0.77% vs. 7.01%) as well as reduced viral loads [186]. The effects of nirmatrelvir-ritonavir thus appear to mirror those in the outpatient remdesivir study, and its potential efficacy in hospitalized cohorts remains to be seen.
# Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation
## 3 Results and Discussion
### Modulation Mechanism of the Microring Resonator  
As shown in Figure 4a, the working principle of the lumped electro-optic modulator is that when the radio frequency field incidents into the microring resonator of LNOI, the refractive index of the LN crystal varies linearly with the electric field intensity due to the Pockels effect, resulting in the drift of the microring transmission spectrum, and finally changing the output light intensity. Figure 4b shows that when the optical carrier with the frequency of \(f_{opt}\) is modulated by the RF electric field, two sidebands of different frequencies will be generated near the optical carrier with the frequency of \(f_{opt}-f_{RF}\) and \(f_{opt}+f_{RF}\). Free Spectral Range (FSR) is the distance between adjacent resonant peaks, which is one of the key parameters of microring resonator. To observe the change of the output light intensity, the two sidebands should fall within the microring resonance peak; that is, conditions \(f_{RF}=\) m\(\cdot f_{FSR}\) (m = 1,2,3... ) or \(f_{RF}<f_{FWHM}\), where \(f_{FWHM}\) is the full width of the resonant peak. The second condition (\(f_{RF}<f_{FWHM}\)) is suitable for low-frequency signal modulation, but the target RF signal of the modulator designed in this paper is 35 GHz, so the condition \(f_{RF}=\) m\(\cdot f_{FSR}\) should be satisfied. It is noteworthy that the input light needs to be placed at the maximum slope of the resonant peak. In this case, even if the received RF signal Is very weak, the modulation light intensity will change greatly.  
The relationship between FSR and the length of the microring can be expressed as in Equation (10);  
\[f_{FSR}=\frac{\lambda^{2}}{L\cdot n_{g}} \tag{10}\]  
where \(L\) is the total length of microring, \(n_{g}\) is group refractive index, and \(\lambda\) is optical carrier wavelength. Given the values of \(n_{g}\) and \(\lambda\), the length \(L\) can be derived by Equation (10), which yields a result of 3846.6 \(\upmu\)m. This corresponds to the microring length for a 35 GHz RF modulator.  
Figure 5 shows that when transmission loss is not taken into account, the maximum Q-factor is reached when the length of the outer ring is twice that of the inner ring. Considering the factors of the integration of the chip and the improvement of sensitivity, the parameters will be discussed and analyzed below when the outer ring length as twice the inner ring.  
Figure 4: (**a**) Intensity modulation of the microring resonator. The green, black and blue lines show the drift of the resonant wavelength of the material in the microring modulator due to the pockets effect due to the change in RF signal, and the red line shows the change in light intensity as the RF signal is modulated onto the light. (**b**) Mechanisms for setting the parameters of the microring resonator. The red line is the local oscillation light and the yellow line is the sideband of the modulated finished RF signal, which carries the RF information.
# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 3 Methodology
### BRDF Correction
#### 3.2.1 Accumulation  
Normalizing surface reflectance time-series to a standard Sun-sensor geometry requires a knowledge of the surface BRDF. This is an inverse problem, and its solution involves the finding of wavelength-dependent kernels. The quality of the inversion procedure and further reliability of BRDF kernels strongly depends on the number and diversity of angular observations. Persistent cloud coverage can seriously jeopardize the collection of surface reflectance observations, thereby shrinking the ensemble of observations to invert. Gathering observations over a long period of time (e.g., \(>\)30 days) would provide enough data to constrain the inversion, but vegetation properties cannot be longer assumed stationary and temporal trend would contaminate the BRDF retrieval. On the other hand, if a too short composite period is retained, the risk exists to lack of sufficient observations to ensure a successful inversion.  
Based on numerical experiments, an accumulation period of 16 days appeared as a fair trade-off between the objective to gather enough clear surface reflectance observations and to get the stationary properties of the surface. Please note that such strategy is consistent with MODIS MCD43A4 product [27; 29] for which a period of 16 days was deemed appropriate to prevent smoothing out short timescale variations. Since the cloud/shadow mask product of VEGFATION could still be improved, accumulated clean observations may incorporate outliers resulting from some undetected clouds and shadows in the scene. Therefore, we performed a simple screening to remove these outliers using a modified z-score following the guidelines proposed by [30].
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 3 Energy Deposition of Electrons in Materials
### The Monte Carlo Calculation Method of Energy Deposition
\[E_{n+1}=kE_{n},k=1/2^{m} \tag{12}\]  
\[\Delta s_{n}=s_{n+1}-s_{n}=\int_{s_{n+1}}^{s_{n}}\left|\frac{\mathrm{d}E}{ \mathrm{d}s}\right|^{-1}\mathrm{d}E \tag{13}\]  
In the formula, the constant \(m\) is selected as required and is generally set at 16. Only considering collision loss and radiation loss, the energy loss of incident electrons in unit mass thickness is:  
\[-\left(\frac{\mathrm{d}E}{\mathrm{d}s}\right)_{\mathrm{t}}=-\left(\frac{ \mathrm{d}E}{\rho\mathrm{d}s}\right)_{\mathrm{col}}-\left(\frac{\mathrm{d}E}{ \rho\mathrm{d}s}\right)_{\mathrm{rad}} \tag{14}\]  
\(\left(\frac{\mathrm{d}E}{\mathrm{d}s}\right)_{\mathrm{col}}\) is the collision loss and \(\left(\frac{\mathrm{d}E}{\mathrm{d}s}\right)_{\mathrm{rad}}\) is the radiation loss. From the nth collision to the (\(n+1\))th collision, the coordinate change is determined by the following equation:  
\[x_{n+1}=x_{n}+\Delta s_{n+1}\frac{\cos\theta_{n+1}}{\rho} \tag{15}\]  
where \(\Delta s_{n+1}\) is the \(n+1\) step range of electrons; \(\omega\) is the determined scattering angle sampling of the \(n+1\) step; the azimuth angle \(\chi\) is evenly distributed between \([0,2\pi]\). 4. Assume that the energy deposited by each incident electron on the interval is \(\Delta E_{i}\), and the energy deposited by \(N\) electrons on this interval is:  
\[E_{z}(J)=\sum_{i=1}^{N}\Delta E_{i} \tag{16}\]  
This method of calculating energy deposition causes an error, which, however, gets smaller and smaller as the incident electrons increase.
# An Accelerated Slicing Algorithm for Frep Models
## 2 Materials and Methods
### Quadtrees  
A quadtree can be defined as a tree-type data structure where each node either represents a leaf or contains four other subtrees. The term \"quadtree\" was introduced as an extension of B-trees for indexing and searching points in two-dimensional space (point quadtree) [35]. The region quadtree (or Q-tree) is a hierarchical data structure often used for analysing and processing binary images [36]. The quadtree data structure was employed in [37] for slicing implicit surfaces and FRep models.  
In this work, we use quadtrees with a K-d tree and an R-tree as supplemental data structures to accelerate the slicing algorithm. The elements of an FRep model are arranged into a selected tree structure using spatial queries during the process of evaluating the defining function of FRep.