esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Line-Scan Hyperspectral Measurement Techniques
### Spectroscopy Techniques
#### 2.1.2 Fluorescence Spectroscopy  
Fluorescence is the emission of light from a compound when a sample is excited by absorbing radiation energy. In general, the excitation wavelength is fixed, and high-intensity narrowband light (e.g., ultraviolet (UV) radiation) is usually used as a fluorescence excitation source. The emitted light, which normally has lower energy than the absorbed light, is often observed in a broad spectral range towards longer wavelengths, and it carries the composition information of the sample. The fluorescence signals are typically presented as an emission spectrum, which is a plot of the fluorescence intensity versus wavelength. The fluorescence intensity is generally proportional to the concentration of the fluorophore in the sample. When excited by the UV radiation, many plant and animal tissues (e.g., fat, leaves, and fruits) emit fluorescence emissions in the visible spectral region. The fluorescence emission maxima from biological materials typically appear in the blue, green, red, and far-red regions of the spectrum [6], and can be used to analyze the sample compositions. Fluorescence spectroscopy is considered to be a sensitive optical detection technique since it can measure subtle changes of biological samples.
## 4 Conclusions  
In this research article, a quad-band power filler wheel-shaped ENG metamaterial for S-, C- and X-band applications is presented. The size of the proposed MM unit cell has dimensions of 10 \(\times\) 10 \(\times\) 1.524 mm\({}^{3}\) and is based on an RT6002 dielectric substrate. CST microwave studio is used to simulate the unit cell, showing quad-band resonance peaks at 2.35 GHz, 7.72 GHz, 9.23 GHz and 10.68 GHz with amplitudes of \(-\)43.23 dB \(-\)31.05 dB, \(-\)44.58 dB and \(-\)31.71 dB, respectively. The simulated results are also validated by validation processes such as an equivalent electrical circuit model, high-frequency simulation software (HFSS) and various array orientations. The response and contribution of various resonators of the unit cell are inspected by analyzing the E-field, H-field and surface current distribution for propagated electromagnetic radiation. The important features of permittivity, permeability and the refractive index of the metamaterial are extracted using MATLAB. The EMR of the proposed MM is 11.61, which indicates its reliability. The calculated value of \(\frac{3}{4}\) is less than the length (L) of the unit cell, highlighting the compactness of the size of the unit cell. This innovative MM can be deployed to enhance the efficiency of different microwave devices, owing to its NRI and epsilon-negative characteristics. Moreover, the S, C- and X bands are recurrently used for satellite and radar applications.  
Conceptualization, M.S.U.A. and M.R.I.F.; methodology, M.S.U.A. and M.R.I.F.; software, M.S.U.A.; validation, M.S.U.A. and M.R.I.F.; formal analysis, M.S.U.A.; investigation, M.S.U.A, M.R.I.F., S.A., M.T.I, M.U.K. and K.S.A.-M.; data curation, M.S.U.A.; writing--original draft preparation, M.S.U.A.; writing--review and editing, M.R.I.F., S.A., M.T.I, M.U.K. and K.S.A.-M.; visualization, M.S.U.A., M.R.I.F., S.A., M.T.I., M.U.K. and K.S.A.-M.; supervision, M.R.I.F., M.T.I. and S.A.; project administration, M.R.I.F.; funding acquisition, M.R.I.F. and K.S.A.-M. All authors have read and agreed to the published version of the manuscript.  
The autrs acknowledge funding for this work from Universiti Grant, Universiti Kebangsaan Malaysia (TR2022-05). This work was also funded by [PERSON] [PERSON] University Researchers Supporting Project number (PNURSP2023R01), [PERSON] bint Abdulrahman University, Riyadh, Saudi Arabia.  
All data are available within the manuscript.  
The autrs acknowledge funding for this work from Universiti Grant, Universiti Kebangsaan Malaysia (TR2022-05). This work was also funded by Princess [PERSON] University Researchers Supporting Project number (PNURSP2023R01), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
Figure 19: Comparison of transmission coefficients \((S_{21})\) simulated by CST and HFSS.
## 3 Thermal Controller Design
### FLC with LSTMNN for Hydrogen Production Thermal Control System  
In the thermal control system for hydrogen production of this study, the output from the FLC controller, once defuzzified, is passed to the electric three-way valve. However, due to delays in adjusting the chilled water flow rate to the electrolyte, discrepancies arise between expected and actual temperature differences in the hydrogen production system. To address this challenge caused by the operational delay of the electric three-way valve, it became necessary to predict the temperature of the electrolysis stack when the suitable flow rate was achieved after the electric three-way valve was turned on. To overcome this, this study employed the thermal controller combining FLC and LSTMNN strategies in the hydrogen production system, as illustrated in Figure 3. The robust capability of the LSTMNN in learning from time series data enabled the hydrogen production system to forecast temperature trends post adjustment of the electric three-way valve. Moreover, integrating the real-time adjustment capability of the FLC controller allows the hydrogen production system to proactively respond to anticipated temperature variations. Through comprehensive performance evaluation during actual application testing, the effectiveness of the control strategy in enhancing prediction accuracy and system stability was validated.  
Figure 4 illustrates the thermal control flowchart of the hydrogen production system. Utilizing an FLC controller alone maintained the desired temperature within a range of \(\pm\)2 \({}^{\circ}\)C. However, integrating the LSTMNN algorithm for temperature prediction enhanced the control effectiveness, achieving the desired temperature within a narrower range of \(\pm\)1 \({}^{\circ}\)C, thus enhancing the existing framework.  
Figure 3: Thermal controller with FLC and LSTMNN strategies in the hydrogen production system, illustrating the integration of these strategies within the thermal control system to enhance temperature regulation and overall performance.
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Neighbor Cooperative Name Resolution  
Through the aforementioned latency neighbor discovery method, each name resolution node can maintain several latency neighbors with cooperative name resolution capabilities. The ACNR approach also uses ILN as the basic metric for selecting the latency neighbor nodes for cooperative name resolution when receiving a name resolution request. The choice of cooperation neighbor using ILN is also reasonable because the larger the value of ILN means that the node is more important, indicating that the value of the information implied by the node is relatively higher, which means that the username's name resolution request has a higher probability to be successfully responded at this name resolution node. The specific algorithm of cooperative name resolution is as follows: when a name resolution node receives a name resolution request from a user, if this node is overloaded or if the locator mapped by the identifier is not stored at local storage, it first calculates the remaining response time based on the timestamp carried by the request, then filters out the name resolution nodes from its latency neighbors whose communication latency are less than the remaining response time. Then, it selects several name resolution nodes from which the ILN is maximum to forward this name resolution request. The earliest successful resolution response will be returned to the user as the final name resolution result, thus completing the cooperative process of this name resolution request. The specific algorithm is described in Algorithm 3.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 3 Experimental Work
### Experimental Design  
Information transmission experiment is a complex operation, and it is easy to introduce errors, especially for the detection of weak signals. Nonstandard experimental operation is easy to have a negative impact on the experimental data. Therefore, in order to obtain high-quality experimental data, we must design a set of careful and effective experimental procedures. The system experiment is divided into two parts: the overall test experiment and the test experiment about influence factors of information download quality. During the experiment, the transmitter continuously sends data 170 (i.e., binary 10101010). The purpose of sending continuously the same data is to facilitate the statistics of BER and reduce the statistical error. The error rate of the evaluation index used in the experiment is the proportion of error data in every 12,600 data received. The experimental parameters and experimental methods are as follows:distance d = 5 cm, room temperature condition t = 25 \({}^{\circ}\)C. The BER data can be obtained and analyzed by changing the drilling depth.  
Test experiment on the factors influencing the quality of information downlink communication: The single variable method is used to test the factors affecting the quality of information downlink communication. The transmission voltage, the distance between electrodes, and the working temperature are tested one by one, and the BER is obtained and analyzed.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 2 Analysis of Robot Structure and Steering Radius
### Analysis of the Skid and Slip Rates  
When the tracked robot is running, the speed performance of each point on the track is inconsistent. Through the speed analysis of each point on the track, the relationship between \(v_{j}\), \(v_{q}\), and \(v_{r}\) can be obtained as shown in Figure 2. The expressions of \(v_{j}\), \(v_{q}\), and \(v_{r}\) are established as follows:  
\[v_{j}=\sqrt{v_{r}^{2}+v_{q}^{2}+2v_{r}v_{q}\cos\beta} \tag{1}\]  
where \(v_{r}\) is the winding velocity, that is, is the belt linear speed. \(v_{q}\) is the traction velocity. \(v_{j}\) is the absolute speed on the track and \(\beta\) is the angle between \(v_{r}\) and \(v_{q}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Body weight (G) & 1200 N \\ \hline Body width (B) & 790 mm \\ \hline Body height (h) & 360 mm \\ \hline Wheelbase (L) & 650 mm \\ \hline Radius of driving wheel (R\({}_{w}\)) & 102 mm \\ \hline Motor torque (T) & 28.2 N.m \\ \hline Inspection speed (\(v\)) & 2 km/h \\ \hline The width of swing arm (B\({}_{0}\)) & 123 mm \\ \hline The length of swing arm (L\({}_{b}\)) & 312 mm \\ \hline Track width (b) & 100 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of tracked robot [12].  
Figure 1: Tracked robot with four swing arms and six tracks.  
When there was no skid or slip between the robot track and the ground, the velocities of the two points A and B were analyzed. At point A, \(\beta=0\) and \(|v_{r}|=|v_{q}|\), and \(v_{JA}=2v_{r}\). At point B, \(\beta=180^{\circ}\) and \(v_{r}=-v_{q}\), and \(v_{JB}=0\), and the track remains stationary with the ground. When \(|v_{r}|\
eq|v_{q}|\), \(v_{JC}=v_{q}-v_{r}\
eq 0\). During \(v_{j}<0\), the absolute velocity at point A is in the same direction as the winding velocity, and the robot produces a slipping phenomenon. During \(v_{j}>0\), the absolute velocity at point B is opposite to the direction of the winding velocity, and the robot produces a skidding phenomenon.  
According to the terramechanical models [19; 20; 21; 22], the skid and slip rates of the robot track can be expressed as follows:  
\[i=\left\{\begin{array}{ll}\frac{v_{r}-v_{j}}{v_{r}}=\frac{v_{q}}{v_{r}}\times 1 00\%=i_{s},\;if\;v_{r}\geq v_{j}\\ \frac{v_{j}-v_{r}}{v_{r}}=-\frac{v_{q}}{v_{r}}\times 100\%=i_{t},\;if\;v_{j}>v_{r} \end{array}\right. \tag{2}\]  
where \(i_{s}\) is the slip rate, \(i_{t}\) is the skid rate, and \(v_{j}\) is the actual velocity at the center of the robot's tracked wheel.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Food Products** & **Pulse Energy (J/cm\({}^{3}\))** & **Treatment Time (sys)** & **Log\({}_{10}\) Reductions** & **Reference** \\ \hline \hline Dry, non-creamed octage & 16 & 2 pulses; 0.5 us Applied at 3 s & PVR * of 96.7\% of _Pseudomonas_ & \\ cheese cards & 16 & interval & PVR * of 97.2\% of mold spores & \\ \cline{2-4}  & 16 & 0.5 us & PVR of 97.2\% of mold spores & \\ \cline{2-4}  & 16 & 2 pulses; 0.5 us; Applied at 5 s & PVR of 99.8\% of mold spores & \\ \cline{2-4}  & 16 & interval & & \\ \hline Cakes packaged in plastic containers & 16 & 3 pulses; 0.5 us; Applied at 5 s & No mold growth in treated samples during storage at room temperature for 10 days, & [30] \\ \cline{2-4}  & 16 & interval & Untreated samples-moldy in 3 days & \\ \hline Whole fish, treated on the dark side & 10 & 3 flashes; 0.5 us & 1.93 of chlororms & \\ \cline{2-4}  & & & 2 of psychotrophs & \\ \hline Shrimp & 1–2 & - & Reduction by 10–1000 factors \({}^{\text{b}}\) of _Listeria, Salmonella_ & \\ \cline{2-4}  & & & Shelf-like increased by 1 week compared to untreated samples & \\ \hline White bread slices & Broad Spectrum PL & - & Shelf life more than 2 weeks, & [73] \\ \cline{2-4}  & & & & no surface modals & \\ \hline Most & Broad Spectrum PL & - & Reduction of _Listeria_ and _Salmonella_ & [73] \\ \hline Chicken wings & Broad Spectrum PL & - & 2 for _Salmonella_ (inoculated) & [6] \\ \hline Frankfurters & 30 & - & 2 of _L. imocaux_ (inoculated) & [6] \\ \hline Retail meat & Broad Spectrum PL & - & 1–3 of the lactic, total aerobic, enteric bacteria and _Pseudomonas_ & [6] \\ \hline Commercial eggs & 4 & 8 & Up to 8 or S. Enterfields (inoculated) & [7] \\ \hline Strawberries & 0.5 & 0.3 ms duration for pulses 4 s & No mold growth after 2 weeks storage & [74] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effects of PL on some food products based on several published researches.
###### Abstract  
Soil aquifer treatment (SAT) is a nature-inspired solution for improving the water quality through soil percolation. The biodegradation of organic matter typically occurs in the shallowest soil layer and it depends on the contaminant's characteristics (water solubility, molecular structure) and specific soil properties (pore size distribution). The present study aims at identifying which grain size fraction of typically used sandy soils in the shallowest layer of SAT systems can provide the optimal conditions for microbiological growth that can be reached by a trade-off between soil moisture as well as nutrients and oxygen supply. For this, soil columns were used at a laboratory scale to determine the relationship between the pore size distribution of four different grain size fractions and biodegradation rates of organic matter from synthetic wastewater. The results obtained from this experimental setup indicate that bacterial colonies reached optimum growth when about 60% of the available pore space was filled with water. For the selected soil, this was achieved by the fraction with grain sizes in the range of 630 \(\upmu\)m to 1000 \(\upmu\)m, having pore diameters between 87 \(\upmu\)m and 320 \(\upmu\)m and a mean pore diameter of 230 \(\upmu\)m.  
Keywords:managed aquifer recharge; column experiments; soil properties; water filled pore space; pore size distribution; organic substances; degradation rate +
Footnote †: journal: Applied Physics Letters B
## 2 Materials and Methods
### Wavelet Cross-Correlation  
To extract the spectra degree of correlation, a Wavelet Cross-Correlation (XWT) approach was employed. More specifically, the Wavelet Cross-Correlation Coefficient (XWTC) was evaluated using the following equation:  
\[XWTC=\frac{\int W_{1}(a,\tau)W_{2}^{*}(a,\tau)d\tau}{\sqrt{WS_{1}(a)WS_{2}(a)}} \tag{1}\]  
where \(W_{1}(a,\tau)\) is the spectrum Wavelet Transform (WT), \(a\) is the scale parameter and \(\tau\) is the shift parameter, while \(W_{2}^{*}(a,\tau)\) is the second spectrum WT, and \(WS_{1}\) and \(WS_{2}\) are the first and the second wavelet spectra, respectively. When there is no correlation, the \(XWTC\) value is zero, while it is close to 1 for strongly correlated spectra [12; 13; 14; 15]. For such analysis, MATLAB 2016a software (MathWorks, Natick, MA, USA) was employed.
###### Abstract  
According to the IHO (International Hydrographic Organization) S-44 standard, hydrographic surveys can be carried out in four categories, the so-called orders--special, 1a, 1b, and 2--for which minimum accuracy requirements for the applied positioning system have been set out. These amount to, respectively: 2 m, 5 m, 5 m, and 20 m at a confidence level of 0.95. It is widely assumed that GNSS (Global Navigation Satellite System) network solutions with an accuracy of 2-5 cm (\(p\) = 0.95) and maritime DGPS (Differential Global Positioning System) systems with an error of 1-2 m (\(p\) = 0.95) are currently the two main positioning methods in hydrography. Other positioning systems whose positioning accuracy increases from year to year (and which may serve as alternative solutions) have been omitted. The article proposes a method that enables an assessment of any given navigation positioning system in terms of its compliance (or non-compliance) with the minimum accuracy requirements specified for hydrographic surveys. The method concerned clearly assesses whether a particular positioning system meets the accuracy requirements set out for a particular IHO order. The model was verified, taking into account both past and present research results (stationary and dynamic) derived from tests on the following systems: DGPS, EGNOS (European Geostationary Navigation Overlay Service), and multi-GNSS receivers (GPS/GLONASS/BDS/Galileo). The study confirmed that the DGPS system meets the requirements for all IHO orders and proved that the EGNOS system can currently be applied in measurements in the orders 1a, 1b, and 2. On the other hand, multi-GNSS receivers meet the requirements for order 2, while some of them meet the requirements for orders 1a and 1b as well.  
reliability model; Navigation Satellite System (NSS); positioning accuracy; positioning availability; hydrography +
Footnote †: journal: Sensors
# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
## 3 Experiments and Results
### Analysis and Comparison of Experimental Results
#### 3.2.3 PSNR  
Peak Signal-to-Noise Ratio (PSNR) is a widely used metric to measure the quality of reconstructed images. PSNR measures the difference between the original image and the processed image by quantifying their Mean Squared Error (MSE). To compute PSNR in image processing, the original image is compared pixel by pixel with the processed image to calculate the MSE, which represents the average squared difference between corresponding pixels. The MSE is then compared to the maximum possible pixel value squared, and the result is converted to decibels (dB). The formula for PSNR is as follows:  
\[PSNR=10\cdot\log_{10}\left(\frac{\left(MAX\right)^{2}}{MSE}\right) \tag{6}\]  
In Formula (6), MAX represents the maximum possible pixel value in the image. For an 8-bit image, MAX would be 255. MSE is the Mean Squared Error between the original and processed images calculated as the average of the squared pixel differences. A higher PSNR value indicates a smaller distortion between the original and processed images, indicating better quality. Therefore, in image processing, a high PSNR value is often considered indicative of a better preservation of the original information and higher quality.  
We computed the PSNR between our reconstruction results and the ground truth images, as shown in Table 3:  
A PSNR value within the confines of 20 to 30 delineates a favorable echelon of reconstruction quality, thus signifying the efficacy of the restoration process. Our devised methodology engenders a robust and commendable performance envelope, substantiated by the comprehensive panorama of scene archetypes as tabulated in Table 4. The method's conspicuous adaptability and augmented resilience vis a vis conventional paradigms serve to underscore its versatile utility, traversing a broad spectrum of reconstruction exigencies that span disparate contextual settings.  
An inherent rationale behind the attenuated metrics recorded within Datasets 2 and 3 can be ascribed to the dominant luminosity characterizing the image acquisition phase. Our in-depth examination posits that the excessive luminance permeating the acquisition milieu could have fundamentally precipitated the observed outcomes. It is plausible that mitigating the deleterious influence arising from heightened luminous conditions during the data collection process holds significant potential in effecting a palpable enhancement in the caliber of the reconstruction outcomes.
# A Novel Real-Time Virtual 3D Object Composition Method for 360\({}^{\circ}\) Video
###### Abstract  
As highly immersive virtual reality (VR) content, 360\({}^{\circ}\) video allows users to observe all viewpoints within the desired direction from the position where the video is recorded. In 360\({}^{\circ}\) video content, virtual objects are inserted into recorded real scenes to provide a higher sense of immersion. These techniques are called 3D composition. For a realistic 3D composition in a 360\({}^{\circ}\) video, it is important to obtain the internal (focal length) and external (position and rotation) parameters from a 360\({}^{\circ}\) camera. Traditional methods estimate the trajectory of a camera by extracting the feature point from the recorded video. However, incorrect results may occur owing to stitching errors from a 360\({}^{\circ}\) camera attached to several high-resolution cameras for the stitching process, and a large amount of time is spent on feature tracking owing to the high-resolution of the video. We propose a new method for pre-visualization and 3D composition that overcomes the limitations of existing methods. This system achieves real-time position tracking of the attached camera using a ZED camera and a stereo-vision sensor, and real-time stabilization using a Kalman filter. The proposed system shows high time efficiency and accurate 3D composition.  
virtual reality; 3D composition; pre-visualization; stereo vision; 360\({}^{\circ}\) video 26 October 2020; Accepted: 30 November 2020; Published: 4 December 2020