esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic-Predictive Routing Strategy for Satellite Networks
## 3 Overall Structure of G-AODV Routing Protocol  
To achieve load balancing, this paper improves the original AODV routing protocol, introduces the traffic prediction mechanism, and proposes the G-AODV routing protocol. The protocol is divided into two key stages in route management: route discovery and route maintenance.  
In the route discovery phase, G-AODV incorporates a traffic prediction mechanism to estimate the traffic load of the satellite network at the next moment. Set the network load threshold based on the actual traffic values and predicted values of all neighbor nodes. The transmission control mechanism of the request packet RREQ and the path switching strategy are determined by comparing the prediction value with the threshold. Through this mechanism, the system can detect the load of network nodes in time before the arrival of congested nodes. In this way, the overloaded nodes are avoided, and the routes are updated in time to ensure the load balance of the network. In the stage of route maintenance, the G-AODV routing protocol introduces an intelligent route repair method to solve the problem of path breakage. This mechanism will evaluate the stability of nodes and select different nodes according to the stability of nodes to complete the path repair process. This intelligent route repair method helps to improve the stability of the repaired path, thus enhancing the robustness and performance of the satellite network.  
The overall structure of the G-AODV routing protocol is shown in Figure 1. It combines route discovery and route maintenance to provide a satellite network with better load balancing and route management capabilities, thus enhancing its performance and reliability.
## 5 Experimental Results
### Test Setup and Environment  
The drone was flown manually using a Futaba T10J remote controller inside an indoor laboratory environment. The test area was approximately 4 m wide and 6 m long, with several obstacles including boxes, walls, and furniture placed at different positions to simulate a cluttered inspection environment.  
The sensor module was mounted on top of the drone, providing full 360-degree horizontal coverage. All eight ToF distance sensors were calibrated before the flight to ensure consistent range accuracy. The microcontroller handled sensor communication and SBUS signal modification in real time.  
During the initial verification stage, the drone was mounted on a movable cart and manually pushed around the experimental space toward various types of obstacles, simulating real flight conditions. Although not airborne, the sensor module operated exactly as it would during flight, continuously detecting the surrounding environment. In each test, the drone was approached toward obstacles from different directions (front, side, and diagonal), while sensor data, SBUS control signals, and generated repulsive forces were processed and analyzed in real time.  
The sensor module continuously monitored the drone's horizontal surroundings and transmitted real-time data to the embedded microcontroller; the experimental setup is illustrated in Figure 19a. The 3D point cloud data shown in Figure 19b represent the drone's environment, where the green points indicate that no obstacles were detected within the detection range, while the gray points represent detected obstacles that are outside the critical range and thus not considered threats by the system. This 3D point cloud was then converted into the 2D horizontal data shown as the blue points in Figure 19c. The conversion was achieved by selecting the nearest point in each angular segment (azimuth slice) of the 3D point cloud. Each segment represents a fixed directional slice of the drone'ssurrounding horizontal plane. The closest distance reading within each segment is projected onto the 2D plane and used as a representative value for that direction. This simplification significantly reduces the computational load while preserving essential obstacle proximity information for the collision avoidance algorithm.
## 3 Methods
### Eeg-TCNet  
The first model in this study is EEG-TCNet, which is incorporated from the EEGNet and TCN modules. The EEGNet module in EEG-TCNet is slightly modified, with 2D temporal convolution at the beginning to extract the frequency filters, followed by a depthwise convolution to extract frequency-specific spatial filters. The third part of the EEGNet module is a separable convolution extract temporal summary for each feature map from depthwise convolution and mixes the feature maps before going into the TCN module.  
Regarding the TCN module, based on the idea of the temporal convolutional network (TCN), the TCN module in EEG-TCNet could be described as follows (Figure 5):  
1. [label=()]
2. Causal Convolutions: TCN aims to produce the output of the same size as the input, so TCN uses a 1D fully convolutional network with each hidden layer having the same size as the input layer. To keep the length of subsequent layers equal to the previous ones, zero padding of length (kernel size: 1) is applied. Moreover, to prevent information leakage from a feature to the past in training, only the inputs from time \(t\) and earlier determine the output at time \(t\).
3. Dilated Convolutions: The major disadvantage of regular causal convolutions is that the network must be extremely deep or have large filters to obtain a large receptive  
Figure 4: Segmentation and reconstruction (R&S) augmentation in time domain [30]. Trial 1 cut into A1, B1, and C1 and trial 2 cut into A2, B2, and C2. In the recombination stage, each output trial randomly combines 3 segments of A1, B1, C1, A2, B2, and C2. Artificial trial 1 combines A1, B2, and C1.  
field size. A sequence of dilated convolutions could increase the receptive field exponentially by increasing dilation factors \(d\). 3. Residual Blocks: In EEG-TCNet, the weight normalisation in the original TCN is replaced by batch normalisation. The residual blocks have a stack of two layers of dilated convolution, batch normalisation with ELU, and dropout. The skip connection \(1\times 1\) convolution is applied from the input to the output feature map to ensure the same shape of input and output tensors.  
Figure 6 visualises the architecture of EEG-TCNet. EEG-TCNet has a temporal convolution module, depthwise convolution, separable convolution, TCN, and a fully connected network. Two-dimensional temporal convolution learns the frequency information, and then feature maps are extracted from the frequency-specific spatial information by depthwise convolution before the temporal summary for each feature map is learned by separable convolution. The TCN continues to extract the temporal information from feature maps after separable convolution and, finally, classification by the fully connected network.  
Figure 5: Architecture elements in a TCN. (**a**) Stacking of two residual blocks with dilated causal convolution with dilation factors \(\mathrm{d}=1,2\) and kernel size \(\mathrm{k}=3\). (**b**) Detailed layers in TCN residual block [19].  
Figure 6: Architecture of EEG-TCNet [18].
# Helix-Free Ferroelectric Liquid Crystals: Electro Optics and Possible Applications
## 5 Theoretical Model of the Ferroelectric Liquid Crystal Deformation and Director Reorientation
### Modulation of Light Scattering
It should not exceed the characteristic reorientation time of the director caused by the motion of the orientation inflection [16]. From Equation (21), this is about 150 \(\upmu\)s for the parameters indicated there. There is an optimal relationship between the period of deformation of FLC smectic layers and the electro-optical cell thickness when, at certain electric field strength, the velocity of the soliton wave motion is maximal (the optical response time \(\tau_{0.1-0.9}\) is minimal) and the light modulation frequency is also maximal. Depending on the time of the electric field action (duration of voltage pulses) and the cell thickness, there may occur several maxima of light scattering that may be treated as the scattering efficiency C (or contrast ratio) (Figure 14). The emergence of the second and third maxima of light scattering efficiency occurs with increasing the cell thickness up to 16-20 \(\upmu\)m [16]. The maximum efficiency of light scattering and the maximum light transmittance without scattering are achieved at different durations of control voltage pulses. An increase in the pulse duration results in increasing the domain wall length and leads to irregular scattering structures. As a result, the density of scattering centers reduces; this is a reason for the light scattering efficiency decrease (Figure 14). Figure 14: Dependences of light scattering C (curve 1) and light transmission I without scattering (curve 2) on the duration of bipolar voltage pulses with a fixed amplitude (\(\pm\)50 V). The thickness of an FLC cell with the HF-32 composition is 18 \(\upmu\)m.  
Figure 13: Oscillograms of the control voltage (zero level—digit 3) and optical response (zero level—digit 1) for the bistable switching mode. The upper level of the optical response is the scattering state, the lower level is the nonscattering state. The thickness of the FLC cell with the HF-32 composition is 13 \(\upmu\)m. Control voltage—bipolar pulses of \(\pm\)35 V. The frequency of light modulation is 2 kHz. After switching off the electric field, the transmitted optical radiation does not change in spectral composition, and there is no residual light scattering in the FLC layer (due to the absence of a helix). Since the magnitude of the spontaneous polarization does not exceed 50 nC/cm\({}^{2}\), ferroelectric domains do not arise. Therefore, in the absence of an electric field, scattering and diffraction centers are absent. The saturation voltage is rather low and, consequently, the control voltage of an electro-optical cell is also quite small (less than 50 V). The main possible applications of light-scattering FLC compositions are the following: polarizer-free visible and infrared optical shutters, energetically effective screens of electronic books, 3D visualizers (volumetric screens) of volumetric displays, etc. [16; 27].
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 4 Case Studies and Applications
### Case Study 2: Environmental Siting of Hydrogen Production Hubs
One major reason is that the city itself is an industrial region that lacks abundant solar and wind resources, which are more abundant in the rural and regional areas. Another challenge to sighting the location of hydrogen production hubs is the available of water. Hydrogen production requires a significant amount of water, sometimes exceeding the combined capacity of Australia's six major municipal desalination plants in Adelaide, Gold Coast, Melbourne, Perth, and Sydney, which together have an approximate capacity of 480 GL per year. Water scarcity remains a critical issue for farming and communities in regional Australia, with ongoing efforts to improve water risk assessment frameworks. Although Gladstone is not typically considered a water-scarce area, the substantial water demand for hydrogen production could present challenges, especially during droughs or periods of increased industrial activity. Effective water resource management and favorable climatic conditions are crucial for maintaining the sustainability of hydrogen production in the region [72]. One out of the seven hubs identified by the Council of Australian Governments is Pilbara in Western Australia. This pilot project will involve a phased development project aimed at large-scale hydrogen production in the remote region utilizing solar and wind energy [73]. However, producing hydrogen in Pilbara presents substantial challenges due to the region's isolation and arid conditions. The key challenges include the difficulty in transporting hydrogen to markets, as the area lacks the infrastructure necessary for large-scale production and distribution. Water scarcity is another significant issue in Pilbara [74]. Given Pilbara's limited freshwater resources, which complicates the electrolysis process and could make it costly if desalination is required, the high initial capital costs for establishing hydrogen production facilities, coupled with the expenses related to renewable energy, water procurement, and transportation, pose major economic viability concerns. The technical hurdles of integrating intermittent renewable energy sources like solar and wind into a consistent hydrogen production process, along with the challenges of maintaining operations in such a harsh and isolated environment, further complicate the project. Successfully addressing these obstacles will require substantial investment, innovative solutions, and collaboration among stakeholders.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 4 Experimental Details and Analysis
### Ablation Experiments  
The proposed method incorporated two major improvements: (1) angle estimation (AE) and (2) optical flow remapping (OFR). To validate their performance, three variants of RAMC are tested: Baseline (only KCF [10]), Base_AE (add AE to Baseline), and Base_OFR (add OFR to Baseline). Table 3 lists the components of these variants and the experimental results, and Figure 8 shows the success and precision plots. By comparing the Baseline and Base_AE, Base_AE yields a 13.9% and 18% gain in the AUC of the success and precision plots, respectively, after adding the AE. When comparing the proposed RAMC and Base_OFR, we find a 14.5% and 3.6% reduction in the AUC of the success and precision plots, respectively, when removing the AE. Owing to the absence of AE, object rotation can adversely affect the tracking performance. By comparing the Baseline and Base_OFR, Base_OFR obtains 34.4% and 34.2% in the AUC of the success and precision plots, respectively, after adding the OFR. In contrast with Base_AE, the proposed RAMC yields a 35% and 19.8% improvement in the AUC of the success and precision plots, respectively, when adding the OFR. This is because OFR exploits the underlying motion information in adjacent frames to prevent tracking drift. Owing to the synergy of the AE for the rotation branch and OFR for the translation branch, the proposed RAMC yields optimal performance.
## 3 Result
### Assessment with AHP for Generating Landslide Susceptibility Mapping
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Weight**} & \multicolumn{3}{c}{**Sub-Layer**} \\ \cline{3-6}  & & **Class** & **Weight** \\ \hline \multirow{3}{*}{Earthquake} & \multirow{3}{*}{0.151} & Low & 0.106 & 1 \\  & & Moderate & 0.260 & 3 \\  & & High & 0.633 & 5 \\ \hline \multirow{3}{*}{Lineament Density} & \multirow{3}{*}{0.109} & Low & 0.083 & 1 \\  & & Moderate & 0.193 & 3 \\  & & High & 0.723 & 7 \\ \hline \multirow{3}{*}{Drainage Density} & \multirow{3}{*}{0.078} & Low & 0.083 & 1 \\  & & Moderate & 0.193 & 3 \\  & & High & 0.723 & 7 \\ \hline \multirow{3}{*}{Geology} & \multirow{3}{*}{0.056} & Chungthang Formation & 0.750 & 1 \\  & & Darjeeling Gneiss & 0.249 & 3 \\ \hline \multirow{3}{*}{Geomorphology} & \multirow{3}{*}{0.040} & Highly Dissertated Hill Slope & 0.750 & 1 \\  & & Folded Ridge & 0.249 & 3 \\ \hline \multirow{3}{*}{Soil type} & \multirow{3}{*}{0.015} & Coarse Loamy & 0.103 & 1 \\  & & Fine Loamy & 0.174 & 2 \\  & & Loamy Skeletal & 0.722 & 6 \\ \hline \multirow{3}{*}{Aspect} & \multirow{3}{*}{0.028} & Flat & 0.028 & 1 \\  & & North & 0.071 & 3 \\  & & North-east & 0.189 & 5 \\  & & East & 0.071 & 3 \\  & & Southeast & 0.071 & 3 \\  & & South & 0.071 & 3 \\  & & South-west & 0.353 & 7 \\  & & West & 0.071 & 3 \\  & & North-west & 0.071 & 7 \\ \hline \multirow{3}{*}{Land use and Land Cover} & \multirow{3}{*}{0.020} & Agriculture & 0.138 & 7 \\  & & Barren Land & 0.138 & 7 \\  & & Rural & 0.030 & 2 \\  & & Sparse Forest & 0.072 & 5 \\  & & Tea Plantation & 0.298 & 9 \\  & & Urban & 0.298 & 9 \\  & & waterbody & 0.022 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._From the outcome of the AHP model, the results were utilized to generate the final LSM and classified into low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility zones (Figure 5). Susceptibility mapping for the study area shows that the very high susceptibility zone covers 14.40% (19.312 km\({}^{2}\)), the high susceptibility zone covers 32.20% (43.180 km\({}^{2}\)), and the moderate susceptibility zone covers 36.77% (49.321 km\({}^{2}\)). The low susceptibility zone covers 16.63% (22.303 km\({}^{2}\)) of the total area. So, we observed that 83.37% of the total area comes under the moderate to very high zone.
## 4 Conclusions  
The application of membrane filtration to the management of waste water streams from a dairy manufacturing operation has been completed. The waste water stream had a pH of 11.32 \(\pm\) 0.55 and was heterogeneous in terms of COD, protein and total solids. A Dissolved Air Flotation (DAF) system removed large particles, total solids and proteins. The DAF system was not effective in reducing the COD of the waste stream to below a target COD of 1200 ppm on a consistent basis.  
All six membranes (200, 20, 8, 4, 0.083, 0.058 kDa) reduced the permeate COD to less than 1200 ppm. These reductions were independent of the COD of the feed stream. The permeate COD from the membranes was not influenced significantly by pressure. The reduction in permeate COD improved as the membrane pore size decreased.  
Two membranes with operating pressures of 100 psi (200 and 20 kDa) and two with operating pressures of 350 psi (8 and 4 kDa) were selected for extended fouling studies. The permeate flux from the four membranes decreased with time due to fouling, and the decrease in flux was described by a three-parameter expression, including a fouling rate constant. The 20 kDa pore size membrane had the lowest fouling rate in the extended fouling study compared to the other three membranes. In addition, the 20 kDa pore size membrane operated at a lower pressure than the 8 or 4 kDa pore size membranes.  
[PERSON], [PERSON] and [PERSON] designed the experiments and goals of the research study; [PERSON] and [PERSON]. Executed pilot trails and analyzed data; [PERSON] wrote the paper; [PERSON] is responsible for overall content of the research.  
Funding for this research was received by the Center for Innovative Food Technology (Toledo, OH, USA) Project number 4703.  
The authors wish to acknowledge the generous donations from [PERSON]. And also would like to thank OARDC (Ohio Agricultural Research and Development Center). This work was supported by the Centre for Innovative Food Technology (CIFT) in Ohio.  
The authors declare no conflict of interest.  
Figure 11: Protein concentration in the feed and the output/permeate of different treatments: A—DAF; B—membrane filtration; C—post-DAF membrane filtration.
# Haskap Juicing Method Effects on Haskap Juice Quality
## 3 Results and Discussion
### Total Anthocyanin Content in Haskap Juices  
Dark fruits derive their color from anthocyanins and photoprotective agents, with the color often correlated with fruit maturity and quality [55].  
Prior reports on ultrasound-assisted extraction of anthocyanins from haskap berries identified five anthocyanins in the extract of haskap berries, namely, cyanidin 3,5-diglucoside, cyanidin 3-glucosidase, cyanidin 3-rutinoside, pelargonidin 3-glucose, and penoidin 3-glucose [56]. The total anthocyanin content in fresh haskap berries was from about 4 to 7 mg cyd-3-glu/g FW by using pH differential methods [57].  
Here, we used the pH differential method to calculate the monomeric anthocyanin content. The results indicate that the juicer and press juicing methods yielded an approximately equal amount of anthocyanin (about 35 mg/100 mL). However, the steam juicer produced juice with 32.5% lower anthocyanin concentrations than the other methods (Figure 1). In our research, although steam juicing could pasteurize food with a higher temperature and extract haskap juice at the same time in principle, it extracted less anthocyanins compared to the other two methods. Furthermore, due to the increases in processing temperature, anthocyanin profiles might also be changed [47].
## 3 Results
### Performance Analysis of Test Functions
Even against the quantum-inspired **QPSO**, QSA trims the average error by **\(\approx\)15%**. QSA's consistency is equally compelling: it exhibits the tightest dispersion (\(\sigma=0.72\)), and its worst-case run (3.96) still lies far below the means of all competing solvers, underscoring a narrow, dependable convergence profile. Statistical analysis corroborates these observations. A one-way ANOVA across the seven algorithms yields \(F=704.59\) with \(p<1\times 10^{-15}\), confirming that their mean errors differ significantly. Taken together with the pair-wise test results reported earlier, these figures demonstrate that QSA not only approaches the global optimum but does so with a stability and repeatability unmatched by any of the six reference algorithms on this rugged search landscape. The findings demonstrate that QSA surpasses competing algorithms in both unimodal (HyperSphere) and multimodal (Levy) test scenarios. The superior performance of QSA can be attributed to its hybrid search strategy, which integrates local exploitation (via elite individual perturbations and superposition moves) with global exploration (through collision and tunnelling mechanisms). The early stopping criterion implemented in QSA, which terminates the iterative process when no improvement is observed for 20% of the maximum iterations (i.e., 10 out of 50 iterations), prevents unnecessary computational effort once convergence is reached. This enhances computational efficiency and contributes to the consistent quality of the solutions obtained by QSA. In summary, the empirical evidence substantiates the conclusion that QSA is a highly effective and robust algorithm for continuous optimization problems. Its capacity to attain lower average fitness values with reduced variability across independent runs signifies its superiority over traditional algorithms such as ABC, GA, PSO, SA, CS, and QPSO when applied to both simple convex functions and more complex multimodal landscapes.
## 2 Study Area and Data
### Data  
The data used in the study include SRTM DSM and AW3D30 DSM. The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained digital elevation models on a near-global scale from 56\({}^{\circ}\) S to 60\({}^{\circ}\) N during the 11-day STS-99 mission in February 2000. SRTM DEM data was released by NASA in 2003, with a resolution of one arcsec, an EGM96 (gravity model 1996) vertical datum, and a WGS84 horizontal datum, which is generated by interferometric synthetic aperture radar (InSAR) technology [52; 53; 54; 55; 56; 57]. AW3D30 is a global digital surface model (DSM) with a horizontal resolution of approximately 30 m (basically one arcsec) created by the Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM), which was an optical sensor onboard the Advanced Land Observing Satellite \"ALOS\". AW3D30 DSM uses 3 million scene archives acquired by the PRISM panchromatic optical sensor on the Advanced Land Observing Satellite \"DAICHI\" (ALOS), which operated from 2006 to 2011. In this study area, by comparing the height