esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Nature-Inspired Algorithms
### Genetic Algorithm  
As already mentioned, the genetic algorithm is integrated into ANSYS APDL via a custom set of routines. The implementation follows the standard steps of a GA: initialization of an initial population, fitness evaluation, selection, crossover, mutation, and reinsertion of the best solutions. Two main termination criteria are adopted: a maximum allowed CPU time and a maximum number of consecutive iterations without improvement, ensuring the process converges in a controlled manner. Figure 7 provides a graphical overview of the workflow for the first phase of the optimization cycle.  
First, a fixed number of chromosomes is randomly generated, each encoding a feasible set of dimensionless design variables, as explained in paragraph 2, which will allow the determination of the dimensional variables of the examined structures, e.g., external diameters and thicknesses. These chromosomes are evaluated according to a specific objective function to be minimized and calculated using the ANSYS solver, here, the weight \(W\) of the structure. Subsequently, the fitness function \(Fc\) of each chromosome is calculated as \(Fc=1/1+W\). This formulation ensures that better (i.e., lighter) solutions have higher fitness values. Each probability of the chromosomes being chosen is proportional to its fitness, which helps ensure that fitter solutions have higher chances of being passed on to subsequent generations. In order to select which chromosomes participate in the creation of offspring, a roulette-wheel method is used (see Figure 8). The algorithm also retains the globally best solution found so far, preventing the loss of superior designs due to random fluctuations.  
Crossover is performed on the selected chromosomes, either at one or two split points (or randomly chosen), to produce new candidate solutions that blend features from the parent chromosomes. A user-defined crossover probability controls how often this genetic exchange occurs. Subsequently, in the mutation phase, a fraction of the newly created genes is altered at random based on a specified mutation rate, thus promoting diversity in the population and reducing the risk of premature convergence.  
The reinsertion step merges the newly generated offspring with the existing population and then re-sorts the extended set of solutions by fitness. To limit the overall population size, only a predefined number of the best-performing chromosomes are retained, effectively discarding inferior designs. This \"fitness-based reinsertion\" technique balances the need to explore new regions of the design space with the requirement to preserve promising individuals.  
At each generation, the algorithm updates the global best solution and logs the iteration, time, and objective function value. The process continues until either the maximum CPU time is reached or the improvement counter exceeds its threshold, indicating that no better solution has been identified for a prescribed number of consecutive cycles. Upon termination, the best chromosome is reported as the final solution, alongside the required computational time.
# A Walkway from Crayfish to Oligochitosan
## 3 Results and Discussion
### Preparation of Oligochitosan Hydrochloride  
As has been shown, the application of oligochitosan instead of chitosan has several advantages due to its lower solution viscosity, higher antimicrobial activity, better mucosal adhesion, and compatibility with neutral and anionic surfactants used in pharmaceutical and cosmetic compositions [20; 35; 43; 44; 45; 46; 47].  
Many methods of chitosan depolymerization have been described so far. Among them, acidic and enzymatic, as well as the depolymerization by nitrous acid or by hydrogen peroxide are the main ones. Although all the methods have advantages and disadvantages, a combination of hydrochloric acid and hydrogen peroxide was found as the more efficient and reproducible for the preparation of oligochitosan hydrochloride samples differing in molecular weights, and that reach the physicochemical characteristics meeting pharmaceutical requirements for commercial chitosan contaminated by residual heavy metals [35]. This method was also chosen since it was found to be well reproducible, and it had minimal impact on the chemical structure of oligochitosan in comparison with the method that used hydrogen peroxide or nitrous acid and did not required the application of expensive enzymes whose residues could contaminate the product [48; 49; 50; 51; 52; 53]. The method of depolymerization of chitosan in hydrochloric acid/hydrogen peroxide solution was specific towards the cleavage of the bonds between N-acetylglucosamine units [54; 52; 55; 56].  
In this laboratory scale protocol, chitosan was depolymerized with the use of hydrochloric acid and hydrogen peroxide in a borosilicate glass flask, as described earlier in [35]. Following the protocol, the preparation of oligochitosan hydrochloride created a product free of residual proteins (Table 1) with a low content of Fe (2.8 ppm) and the absence of other heavy metals, such as Al and Si residues (Figure 3b).
## 1 Introduction
To maximize the efficacy of UV-C disinfection in confined anatomical structures, such as root canals, the integration of LED light into a thin optical fiber is crucial. The proper integration of UV-C LED into a thin optical fiber not only improves energy transmission but also enables the effective application of photo disinfection in intricate dental structures. Ensuring efficient coupling of UV LED light into these slender fibers is critical for maximizing disinfection efficacy [22,23]. Studies have demonstrated that optimizing the interface between the UV-C LED source and the optical fiber enhances light transmission efficiency, leading to more efficient outcomes in confined spaces [22, 23, 24]. The use of lenses, precise alignment, and coupling techniques can significantly reduce light loss and increase the intensity of UV-C LED light delivered to target areas, but the development and optimization of such devices for practical application still present many challenges to be addressed. The aim of this study was to investigate the bactericidal effect of 270 nm UV-C light-emitting diode (LED) light delivered through a newly designed prototype device with thin optical fiber against _Enterococcus faecalis_ (_E. faecalis_).
## 4 State of Art of the Smart Energy Management Systems
### Peak Load Forecasting Models  
The energy consumption prediction and peak load forecasting is an important feature in the proposed design. After collecting the data and completing the integration of hardware with the SCADA system is the phase to choose the best peak load forecasting model to validate the POC.  
Based on a comparative study proposed in recent work [3], we can see that the quantile regression model has very satisfying results. Table 1 shows the different applications of quantile regression, neural networks, and support vector machines to predict the energy consumption and the peak load. So, we chose to apply fast forest quantile regression (FFQR) [24] to complete the proposed design as a first version of algorithm. In future work we will test more algorithms to enhance the models and improve the prediction while the data are being recorded and first results are analyzed.  
In recent work, a training and testing model was used to predict abnormal energy consumption for residential building [25]. The goals, as shown in Figure 4, were collecting time series data from smart meters, processing the data and extracting the principal features, training the model using support vector machine, and then predicting the abnormal energy consumption behavior of each open-pit equipment [26]. This feature can be proposed after validating the proof of concept of this designed infrastructure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Application** & **Year** & **Accuracy** \\ \hline Quantile regression & Electricity demand & 2021 & 0.99 \\ Neuro-fuzzy inference time series & Campus & 2020 & 0.98 \\ SVM & Hotel & 2020 & 0.94 \\ ANN & Ships in green ports & 2020 & 0.85 \\ Quantile regression & Industry & 2019 & 0.95 \\ Quantile regression & Grid demand load & 2018 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative study of peak load forecasting models.  
Figure 4: Training and testing model to predict the normal and abnormal energy consumption for a residential smart building. Reprinted with permission from Ref. [3]. Copyright 2022 IEEE.
# Influence of Ozone on the Biochemical Composition of Birch Sap
## 5 Conclusions  
Rational use of ozonation on birch sap can reduce the number of microorganisms in the sap. For ozonation of sap using ozone with a concentration of 0.99 \(\pm\) 0.09 mg L\({}^{-1}\), the total reduction in bacterial colony count was up to 6.32-6.48 log CFU mL\({}^{-1}\), for lactic acid bacteria up to 2.46-3.15 log CFU mL\({}^{-1}\) and for yeast up to 3.16-3.76 log CFU mL\({}^{-1}\). The most significant effect of ozone was after ozonating the sap for 25 and 30 min. After seven (at 2 \({}^{\circ}\)C) or five (at 20 \({}^{\circ}\)C) days of storage, the numbers of microorganisms in the ozonated samples were observed to experience a statistically significant decrease compared to the control. Ozone was found to affect the chroma and hue angle of birch sap, although this difference was not visually noticeable. Ozonation was found to have no significant effect on pH, titratable acidity, and \({}^{\circ}\)Brix values. After evaluating the effect of ozonation for 30 min on the monosaccharides, sucrose, total sugars, and ascorbic acid in the juice, it was found that these values varied within the margin of error.
# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 4 Experiments
### Experimental Setup  
In these experiments, we utilized the FF source code ([[https://github.com/loeweX/Forward-Forward](https://github.com/loeweX/Forward-Forward)]([https://github.com/loeweX/Forward-Forward](https://github.com/loeweX/Forward-Forward)) (accessed on 22 July 2025)) implemented in PyTorch (version 2.1.0).  
We employed the Weights and Biases (WandB) [33] hyperparameter tuning tool, known as Sweep, to train our models. Each experiment was repeated 10 times to assess performance variance. The batch size was fixed at 512 for all experiments. For MNIST, the maximum number of epochs was set to 100, and for CIFAR10, it was set to 200. The noise ratio for DAE-LBP was fixed at 0.2. Additionally, the hidden dimension for each model was set to 1024, and in LBP models, the hidden dimension was halved at each cell.  
The hyperparameters explored for the SLP and MLP are as follows:  
* Learning rate: [\(1\times 10^{-3}\), \(1\times 10^{-5}\)];
* Weight decay: [\(1\times 10^{-2}\), \(1\times 10^{-4}\)].  
The FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP models underwent hyperparameter searches. We assigned different ranges of learning rates and weight decay values to the final classifier layer than those used for the preceding FF layers and cells, as the FF layers and cells generally required sufficiently low learning rates.  
* FF layer and cell learning rate: [\(1\times 10^{-4}\), \(1\times 10^{-6}\)];
* FF layer and cell weight decay: [\(1\times 10^{-3}\), \(1\times 10^{-5}\)];
* Classifier layer learning rate: [\(1\times 10^{-3}\), \(1\times 10^{-5}\)];
* Classifier layer weight decay: [\(1\times 10^{-2}\), \(1\times 10^{-4}\)].  
For CNN and CAE-LBP models, the number of output channels in the first convolutional layer was configured so that it doubled with each subsequent convolutional layer.  
* First convolution output channel size: 8, 16, 32, 64.  
The SLP model's hyperparameter search was performed once per dataset, followed by 10 runs to measure performance. For the other models, the number of layers was set to 2, 3, 4, or 5, and hyperparameter searches were conducted for each layer configuration, yielding a total of 40 performance measurements across all datasets.  
When applying the FF learning method, each cell is trained independently. We therefore considered two training strategies. The first, termed sequence training, involves training each cell from the first to the final classifier layer within the same epoch. The second, termed separate training, involves fully training one cell for all epochs before moving on to the next cell. In this experiment, we compared the FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP models under both sequence training and separate training approaches.
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Enabling Mobility through Simple Arduino-Based Mobile Robots  
The experiments reported in the next section relied on simpler, inexpensive mobile robots. The said devices have a three-wheeled configuration, with two driving wheels at the front sides of the chassis, each powered by a continuous rotation servomotor, and a third rear caster/free-wheel (see Figure 10). This configuration is simple but effective, making the vehicles flexible and capable of changing their direction through differential steering techniques with minimum friction. The driving side wheels are equipped with photo interrupters for speed and direction stabilization purposes.  
Each mobile robot is controlled by a dedicated Arduino Uno unit and uses a 2-cell lithium-polymer battery for energy supply. Moreover, the chassis of each robot is equipped with an ultrasound distance sensor on its front, which can sense the robot's approach to an obstacle, e.g., whenever the borders of the testing area are reached. By programming the control unit to respond to triggers from the distance sensor with appropriate changes in the robot's direction, several typical mobility models can be supported (including the Random Direction mobility employed in the experiments of Section 5).  
Figure 10: Board of a mobile node attached to an Arduino-based mobile robot.  
Figure 9: (a) An ATLAS mobile node attached to a “FLEXUS” USV; (b) ATLAS static destination node attached to a “PLaDyFeet” USV; (c) ATLAS mobile node attached to Endeavour UGV.  
For more details on the design, the control principles and the implementation of these robotic vehicles, the reader may consult [53; 54].
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 5 Discussions
### Performance Based on Input Shape  
In Section 3.1.1, we described that we conducted several experiments with various input shapes based on the number of packets and bytes in the flow. Through these experiments, we set the optimal shape as 8 packets and 63 bytes. In this section, we compare the performance based on different input shapes. The input shape can be variably defined, and we set the range of packet counts to 4-8 and byte counts to 60-70, taking into account the handshake process and header (IP, TCP/UDP) byte sizes within encrypted communication.  
Figure 8: Test accuracy curve for weight adjustment. (**a**) No weight, (**b**) class weight, (**c**) task weight, and (**d**) class and task weights applied.  
As mentioned earlier, considering BERT's maximum input token limit of 512, we excluded cases where the total token count (packet count \(\times\) byte count) exceeds 512.  
Table 12 in Appendix A indicates the performance of the proposed method based on input shapes. The experiments were conducted for 20 epochs with the same experimental setup, as multiple experiments were required depending on the input shape. We selected the target task as the most challenging task #3 among the three tasks. In Table 12, the highest performance is observed with (8, 63). Therefore, we selected the optimal input shape as 8 packets and 63 bytes.
## 3 Results
### Ensemble Model Application Effect
The distribution area of the fishing grounds in February was about the same as the distribution of the fishing grounds in January, but a small number of albacore fishing grounds started to appear between 25\({}^{\circ}\) S and 35\({}^{\circ}\) S (Figure 7). In March, most of the areas where actual high-yield fishing grounds were incorrectly forecast as low-yield fishing grounds were distributed between 5\({}^{\circ}\) S and 10\({}^{\circ}\) S, and the areas where actual low-yield fishing grounds were incorrectly forecast as high-yield fishing grounds were mainly distributed between 5\({}^{\circ}\) S-10\({}^{\circ}\) S and 150\({}^{\circ}\) E-160\({}^{\circ}\) E waters. The ELM had poor forecasting effect on low-yield albacore fishing grounds in June, among which the overall accuracy (_ACC_), low-yield recall rate (_R_1) and high-yield accuracy (_P_2) of the ELM were the lowest, which were 74.71%, 47.37% and 76.92%, respectively (Table 3). The fishing grounds gradually migrated southward from April to June, with the high-yield fishing grounds clustered between 10\({}^{\circ}\) S and 40\({}^{\circ}\) S and the low-yield fishing grounds showing a tendency to expand southward, which was especially obvious in June (Figure 7). The ELM was more effective in forecasting different grades of fishing grounds for high-yield albacore in September (Figure 7), where both the high-yield recall (_R_2) and low-yield precision (_P_1) were the highest at 98.00% and 94.44% (Table 3), respectively. The highest overall accuracy (_ACC_ = 93.81%) was obtained in October for the albacore fishing ground forecast. As shown in Table 3, the forecast accuracy of the ELM for low-yield fishing grounds of albacore was not as stable as that of high-yield fishing grounds, and its recall for low-yield fishing grounds fluctuated widely in different months in terms of accuracy, while the high-yield recall was above 80% in all months. The effect of the fishing ground forecast was better, with the highest degree of conformity with the real fishing grounds (Figure 7). As can be seen in Figure 7, from July to December, the distribution of high-yield fishing grounds moved further north, with few catches south of 30\({}^{\circ}\) S in January-February and October-December, and most of the high-yield fishing grounds gathered in the 10\({}^{\circ}\) S-25\({}^{\circ}\) S area. In summary, most of the fishing forecast errors occurred at the junction of low-yield fishing grounds and high-yield fishing grounds. This finding may be due to the bias of the fishing grounds divided by tertiles, and the inconsistent delineation of the high-yield and low-yield boundaries in each month, making the model highly susceptible to confusion during training.
# The Application of Fine Sand in Subgrades: A Review
## 3 Treatment Technology for Sand-Filled Subgrades
### Mechanisms and Effects of Fiber Treatment for Fine Sand Subgrade
The observations reveal that, among the planar and honeycomb reinforcement configurations, the honeycomb reinforcement is more effective in enhancing strength, considering the low tensile strength of joints. The stress-strain curves of geocells remain almost flat after reaching the peak value under all confining pressures, unlike other configurations where strength loss is observed after the peak. Compared to geotextiles, polyester geocells exhibit high efficiency in increasing sand strength due to the formation of indentations. However, the use of discrete fiber forms of steel reinforcement in the study performs worse than planar or honeycomb forms, which may be attributed to the reduction in overall confinement effect caused by small-sized fiber elements. [PERSON] [39] conducted tests on Oil Palm Empty Fruit Bunch (OPEFB) fibers coated with a polyacrylonitrile-butadiene-styrene thermoplastic material to determine the influence of coating on the reinforcement effect. The coated fibers increased the frictional contact between the fibers and the soil by increasing the surface area. Compared to unreinforced fine sand, under undrained loading conditions, the friction angle and cohesion of reinforced fine sand containing 0.5% coated fibers (30 mm long) increased by approximately 25% and 35%, respectively. [PERSON] [40] performed conventional triaxial compression and tensile tests on polypropylene short-fiber-reinforced sand. The contribution of fibers to strength is significant during compression but limited during tension. The stiffness matrix of the sand soil is based on the Mohr-Coulomb model, and the constitutive model of the fiber-reinforced sand composite material was calibrated based on the results of drained triaxial compression and tensile tests. Figure 5: Schematic diagram of triaxial samples of geosynthetic-reinforced sand.
## 3 Results and Analysis  
### Spectral Feature Analysis of Soil Samples with Different Moisture Content  
Figure 4a shows the average spectral reflectance characteristic curves of soil samples given different moisture levels. It can be seen clearly from this figure that soil spectral reflectance increased with the reduction of soil moisture content within the visible light range. Within the near-infrared spectrum range, soil spectral reflectance continued to decline with the rise of moisture level (including dried soil). When the moisture level in soil dropped from the saturation level (when specular reflection occurred) to 22.83%, soil spectral reflectance still increased but at a slower pace. However, it began to increase substantially when the moisture level in soil further declined from 22.83% to 18.95%. Although moisture exerted a significant effect on the variation of soil spectral reflectance when the moisture level in soil fell below 22.83%, such impact ceased to be significant when the moisture level in soil reached or exceeded 22.83%. Additionally, soil spectral reflectance decreased at a slower rate. In particular, the spectral reflectances within the spectrum range of 400-800 nm basically overlapped each other. As the level of soil moisture rose, there was an increasing trend shown by both the depth and area of the spectral absorption near the wavelengths of 1435 nm and 1935 nm.  
Figure 4b shows the differences in average spectral reflectance between the soil samples obtained at varying moisture levels and the dried soil sample. It can be seen from this figure that the spectral absorption trough near the wavelength of 1940 nm was the largest one across the waveband range. According to Figure 4a,b, the effect of soil moisture content on soil spectral reflectance in the near-infrared band was clearly more significant compared  
Figure 4: Spectral characteristic curve of soil samples with different moisture content: (**a**) spectral characteristic curve of soil samples under different moisture gradients, and (**b**) dried soil samples.  
to the visible light band. This is largely attributed to the apparent absorption of the -OH bond in soil moisture by near-infrared spectroscopy [28].