esa-sceva/satcom-chunk-collection huggingface dataset:
# Multipotent Antiviral Effects of Deacetylated Chitosan
## 3 Results  
Antiviral Activities of Deacetylated Chitosan with GSE against Human Influenza A Virus and Parainfluenza Virus Type 3  
MDCK and A549 cell viability was determined following treatment with deacetylated chitosan and GSE, revealing that the five deacetylated chitosan and GSE combinations had little effect on cytotoxicity (Figure 2). Therefore, MDCK cells infected with H1N1 were treated with five combinations of deacetylated chitosan and GSE, and plaque volumes were measured to evaluate the antiviral efficacy of this treatment. The plaque assay showed that chitosan with an 85% deacetylation degree reduced plaque formation from H1N1 more effectively than with 60% deacetylation degree (Figure 3A). HPIV3-induced plaque formation was induced under similar conditions. Similarly, chitosan with an 85% deacetylation degree reduced plaque formation caused by HPIV3 more than with a 60% deacetylation degree (Figure 3B). Interestingly, although GSE alone induced less H1N1-induced plaque formation, it did not affect HPIV3. These findings substantiate that chitosan with an 80% deacetylation or higher and GSE induce antiviral activity against H1N1 and HPIV3.  
Figure 2: Chitosan with GSE exhibits no cytotoxicity. (**A**,**B**) Dog MDCK cells (**A**) or human A549 cells (**B**) were treated with indicated dilutions of 60% or 85% deacetylated chitosan and GSE for 24 h, followed by MTT cell viability assay. A (60% deacetylated chitosan), B (85% deacetylated chitosan), C (GSE), D (60% deacetylated chitosan + GSE), and E (85% deacetylated chitosan + GSE) are shown in Table 1. The untreated group was compared to the chitosan treatment group. Quantitative data are presented as the mean \(\pm\) SEM from three biological replicates (n = 3). * \(p\) < 0.05, **_p_ < 0.01.  
Figure 3: Antiviral activity of deacetyll-chitosan in combination with GSE against H1N1 and HPTIV3. (**A**) Plaque assay images of H1N1-infected MDCK cells treated with or without deacetyll-chitosan (**upper panel**). Quantitative data for plaque formation (%) are presented as the mean \(\pm\) SEM from three biological replicates (n = 3) (**lower panel**). (**B**) Plaque assay images (**upper panel**) and quantitative data (**lower panel**) for HPTV3 were performed in the same manner. A (60% deacetylated chitosan), B (85% deacetylated chitosan), C (GSE), D (60% deacetylated chitosan + GSE), and E (85% deacetylated chitosan + GSE) are shown in Table 1. Statistical comparison between groups was performed using Student’s \(t\)-test in all figures. Control group was compared to treatment group from three biological replicates (n = 3). * \(p<0.05\), **+ \(p<0.01\).
## 5 Results and Discussion
### Mitigating Adverse Visual Conditions
Although accelerometer-based pothole detection is sensitive to vehicle speed, it is particularly useful for detection in low visibility. For more accurate results, video data were combined with an acceleration sensor for vehicle vibration measurements in public crowdsourcing applications in [65]. As in previous cases, the multi-sensor system can be enhanced with an attention mechanism and enabled to adapt effectively to varying adverse weather conditions [66].
# Determination and Profiling of Human Skin Odors Using Hair Samples
## 2 Materials and Methods
### Data Analysis  
The evaluation of hair from legs as a potential matrix for skin odor collection was based on chromatographic analysis for the detection of the VOCs 6-methylhept-5-en-2-one, octanal, nonanal, decanal and (5_E_)-6,10-dimethylundeca-5,9-dien-2-one (hereafter referred to as geranylacetone). These compounds were selected as quality markers because they are frequently reported in the literature as common VOCs of human skin odor [1].
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Computational Architecture
#### 3.2.4 Runtime and Post-Processing  
The last GUI is the runtime interface, which remains open throughout the whole analysis. It contains two panels: an input panel, with data required before running the analysis, and the output panel, displaying information generated by the solver.  
On the left of GUI 6 (see Figure 9), the user must define the coordinates of the tip of the receiver and the components of the direction of the receiver's motion in the global Cartesian referential used to define the domain's geometry. Other important inputs are the tolerance for considering that a fixed point was found for the shear modulus and the type of correlation to be performed, namely without (zero lag) or with signal normalisation (Pearson).  
Both Sample run and Run buttons, situated in the lower part of the input panel, start the execution of the analysis. The sample run allows the user to foresee the expectable quality of the finite element solution at the lowest computational cost possible. Instead of analysing all spectral problems, GeoHyTE only runs the spectral problem with the largest participation factor in the final solution and writes an output file containing the (real parts of the) displacements in the solid phase, fluid seepage, total stresses, and pore pressures  
Figure 8: Layout of GUI 5.  
Figure 9: Layout of GUI 6.  
in the plotting points defined in GUI 2. The output file can be read using post-processing (visualisation) software like Paraview or Tecplot. If the quality of the sample is satisfactory, a full analysis can be launched. Otherwise, the user may wish to return to the definition of the model and improve the basis and/or the mesh refinement.  
The right panel in GUI 6 is the output panel. On its upper part, two plots are shown after each iteration. The plot on the left shows the numerical (blue) and experimental (black) output signals, overlapped over the correlation window. The cross-correlation plot between the two signals is shown in the plot on the right. The instantaneous and best correlations are identified with blue and red circles, respectively. The bottom part of the right panel contains a log listing information regarding the current and previous iterations. When the analysis converges, the log also registers the final shear modulus estimate.  
The displacement/seepage and stress/pore pressure fields in each (dyadic) time point are saved after each iteration in text files that can be read by external visualisation software.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects  
There have been numerous projects realised in the area of smart grid applications throughout Europe. In this survey we have focused on projects, in which smart metering has been/is a part of their investigation, with the aim of arriving to conclusions about the technologies selected for them. In addition, other projects involving simultaneously many smart grid areas have been included in this survey, such as smart network management, integration of RES and smart home. The purpose has been to make derivations for the role of smart meters and to identify the topics of research that the smart grid scientific community tends to focus. In general, it is noteworthy that the number of smart grid projects is very high, indicating the increased interest in the area. Out of the 50 projects analysed in this paper, 28 and four of them involve collaboration of participants from more than three and more than 10 countries respectively. With respect to the projects' duration, 39 of them have/had duration of more than 2 years, whereas six of them lasted/last for 5 years and more. In the following, we briefly describe the aforementioned projects and refer to their selected technologies, where applicable. The project categorisation is indicative, since many of them have/had multiple topics of investigation.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 3 Methodology
### Image Encoder
#### 3.1.3 Spatially Attended Grid Encoder  
While RAOE focuses on object-centric regions, SAGE captures globally distributed spatial features. This module enhances grid-wise representations and employs a spatial attention mechanism to emphasize salient regions.  
SAGE takes as input the highest-resolution feature map \(F_{4}\) from the backbone network. This feature map is linearly projected into an embedding space to ensure dimensional consistency, resulting in \(G\). The projected features are then refined through \(L_{s}\) stacked layers, each comprising Multi-Head Self-Attention (MHSA) followed by an FFN:  
\[G^{(l)}=\text{FFN}\Big{(}\text{MHSA}\Big{(}G^{(l-1)}\Big{)}\Big{)},\quad\text {for }l=1,\dots,L_{s} \tag{8}\]  
where \(G^{(0)}=G\), ensuring that the initial grid features are enhanced iteratively before applying spatial attention.  
To improve spatial awareness, we apply a spatial attention gate (SAG) [29], which selectively highlights informative regions within the grid representation. The attention map is computed by concatenating average-pooled and max-pooled features along the channel axis, passing them through a 1D convolution followed by batch normalization and a sigmoid activation:  
\[A=\sigma\Big{(}\text{BatchNormNorm}\Big{(}\text{Conv1D}\Big{(}[\text{AvgPool}(G^ {L_{s}});\text{MaxPool}(G^{L_{s}})]\Big{)}\Big{)}\Big{)}\Big{)} \tag{9}\]  
The final grid feature is computed by applying the attention map element-wise to the refined features:  
\[F_{\text{SAGE}}=G^{L_{s}}\odot A \tag{10}\]By dynamically emphasizing spatially relevant regions, SAGE enables the model to capture both global context and fine-grained spatial details. Combined with RAOE, it contributes to a more comprehensive visual understanding, ultimately supporting the generation of numerically grounded and contextually coherent captions.
## 3 Experimental Result Analysis
### Energy Density
Figure 11d illustrates the variation in the energy dissipation ratio in the variable lower limit cycle as a function of the number of cycles. It can be observed that, as  
Figure 10: Variation curves of energy density and dissipation ratio with the number of cycles for constant lower limit-graded cyclic loading and unloading: (**a**) Input energy density. (**b**) Elastic energy density. (**c**) Dissipated energy density. (**d**) Energy dissipation ratio. the number of cycles increases, the energy dissipation ratio of the variable lower limit cycle rises rapidly during the first two cycles before stabilizing. As the specimen approaches failure, the energy dissipation ratio rises again. At this stage, microcracks within the specimen begin to coalesce, forming a macroscopic fracture surface. Consequently, the dissipated energy density surges, leading to a substantial increase in the dissipation ratio. Throughout the loading process, the energy dissipation ratio of the constant lower limit cycle remains below 50% and generally decreases as the number of cycles increases. In contrast, after the second cycle, the energy dissipation ratios of the variable lower limit cycles exceed 50%, indicating that an increase in the lower stress limit promotes crack propagation within the specimen, thereby increasing the dissipation ratio. This effect is particularly evident in the 600 \({}^{\circ}\)C variable lower limit cycle, where the final energy dissipation ratio reaches 74.08%, indicating that nearly three-quarters of the input energy is dissipated (Figure 11d).
## 3 Results and Discussion
### Adsorption of Ammonium Nitrogen on Biochar  
#### 3.2.1 Effect of activation agents on the adsorption capacity of biochar  
Figure 1a shows the NH\({}_{4}\)+-N adsorption capacity and yields of biochar prepared using NaOH and KOH as activators, as well as those of biochar prepared without chemical activation. The NH\({}_{4}\)+-N adsorption capacity of the biochar without activation was about 2.06 mg/g, which was in agreement with the result reported by [PERSON] et al. when they analyzed the ammonium nitrogen adsorption capacity of biochar obtained from corn straw [28]. After activation by NaOH and KOH, the biochar samples exhibited significantly enhanced adsorption capacity of 6.03 and 9.77 mg/g, respectively, indicating that the chemical activation facilitated ammonium nitrogen adsorption. The biochar without activation showed obviously higher yield than the two chemically activated biochars. This result can be attributed to the intensified pyrolysis reactions caused by the base catalysts, which convert the solid carbon substances into gaseous carbon volatiles [29]. Although the two activated biochars had similar yields (31.29% and 30.47%), the adsorption capacity of KOH-activated biochar was higher than that of  
NaOH-activated biochar. Thus, KOH-activated biochar was chosen as the adsorbent for the following adsorption experiments.
## 3 DNN-Based Demodulation Evaluation
### Two Level Supervised Model Analysis  
From the results presented in Table 4, it is clear that, for the most part, the VGG16 model performed best on 2 Level, full-color data, followed by the 3 layer convolution model. The results for the VGG16 model show that the accuracy was nearing 99% consistently and was well above 95% with the different frame types and activation function for the output layer. In terms of which activation function performed best in the output layer, it is clear that the Softmax function performed better than the Sigmoid function, across all model types, although technically in a binary case, Sigmoid is just a special case of Softmax. In terms of what type of frame performed better between full frames and cropped frames, the full frames evaluated better. The reason why this might be the case is that the full frames distinguished the LED clearly, whereas the cropped frames were completely polarized by the LED. Therefore, the LED was more clearly segregated in the image, and its feature was, therefore, more easily learnable. However, further exploration into what the models are actually learning in each layer is needed to verify this intuition. Considering that the common bit error rates (BER) for data communication systems are 99 \(\times\) 10\({}^{-13}\)% or greater, it would be more appealing to observe results closer to 99.999% and above. However, considering that the models achieved this level of accuracy with only 10 epochs of training and less than 10,000 frames of training data, this begs the question of what the potential could be. By tweaking the hyperparameters and other parameters and perhaps training the model for a longer duration than 10 epochs, as well as training on a larger and more pristine dataset, it might allow the BER to be closer to the ideal case.
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
###### Abstract  
The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.  
split delivery; vehicle routing problem; heuristic algorithm; ant colony algorithm +
Footnote †: journal: Applied Sciences
## 2 Experimental Methods  
The YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-8}\) single crystals investigated in this study were grown by the melt solution method [1; 3; 5]. The crystals were subjected to an oxygen atmosphere at 430 \({}^{\circ}\)C for four days and this led to their saturation. The crystals contained twins, whose planes of which had a block structure. Resistivity measurements were taken with the standard 4-pin method. The measured crystal sizes were (1.5..2) \(\times\) (0.2..0.3) \(\times\) (0.01..0.02) mm\({}^{3}\), where the third dimension was the c axis. To prepare crystals with partial replacement of Y by Pr, Y\({}_{1-x}\)Pr\({}_{x}\)Ba\({}_{2}\)Cu\({}_{3}\)O\({}_{7-8}\), PrS\({}_{011}\) was introduced to the initial mixture in the appropriate percentage.  
The modes of growth and oxygen saturation of Y\({}_{1-x}\)Pr\({}_{x}\)Ba\({}_{2}\)Cu\({}_{3}\)O\({}_{7-8}\) crystals were the same as for undoped single crystals [5; 20]. Y\({}_{2}\)O\({}_{3}\), BaCO\({}_{3}\), CuO and Pr\({}_{5}\)O\({}_{11}\) compounds were used in the crystal growth. A transport current of up to 10 mA passed through the largest sample size, where the typical distance of the potential contacts is 1 mm.  
The grown single crystals exhibited characteristics of normal and superconducting states corresponding to a given oxygen and/or praseodymium content. The technological and experimental details for the synthesis of experimental samples, resistivity measurements and analysis of the transport properties were previously described [1; 3; 5; 19; 20].  
Electron irradiation with energies in the range of 0.5 to 2.5 MeV was employed at temperatures below 10 K. The irradiation fluence \(\Phi\) = 10\({}^{18}\) e/cm\({}^{2}\) by electrons with an energy of 2.5 MeV corresponded to a defect concentration averaged over all sublattices of \(10^{-4}\) displacement per atom [26]. The measurement technique was the following: first, the temperature dependences of the resistivity of the sample were measured. The sample was then cooled to 5 K and irradiated with electrons. The beam intensity was chosen such that the temperature of the sample during irradiation did not exceed 10 K. After irradiation, the sample was heated to a temperature of 300 K and, gradually lowering the temperature, the resistivity of the sample was measured. The same sample was successively irradiated at different fluences. To measure electrical resistance after irradiation in the temperature range of 4.2 \(<\) T \(<\) 300 K, a specially designed helium cryostat was used. All electrical resistance measurements were carried out at a fixed temperature; temperature stability was about 5 mK. Temperature was measured with a platinum resistance thermometer.
## 4 Evaluation Method
### Weight Calculation Method
#### 4.1.6 Criteria Importance Through Intercriteria Correlation (CRITIC)  
The CRITIC method, introduced by [PERSON] et al. [124], serves as an impartial technique for assessing the relative importance of various indicators within a multi-index evaluation framework. This method allocates objective weights to factors depending on their interrelations and the degree of information they provide, which in turn highlights the relevance of decision-making criteria. Within a multi-index evaluation framework, the impact of each index on the overall evaluation outcomes differs, making it essential to ascertain the weight assigned to each index. The CRITIC method effectively determines these weights by thoroughly analyzing both the strength of contrasts and conflicts present among the indicators, thereby enhancing the reliability of the evaluation outcomes. To begin with, the CRITIC method mandates the standardization of data to enable comparisons on a common scale. Following this, it computes the standard deviation for each indicator to evaluate contrast strength and establishes the correlation coefficients between indicators to indicate conflict. The method subsequently combines the intensity of contrast with the information capacity concerning conflicts to comprehensively derive the weight for each indicator. Ultimately, the overall score for every evaluation entity is calculated using these weights along with the standardized data.  
The CRITIC method provides the benefit of thoroughly evaluating the strength of contrasts among assessment indicators as well as identifying potential conflicts between them. This facilitates an objective and precise assignment of weights to each indicator, reducing the subjectivity and unpredictability linked to subjective weighting. On the other hand, one significant drawback of this method is its comparatively intricate calculation process, which requires considerable amounts of data and computational power. Furthermore, the method has strict criteria for choosing indicators and processing data; any mismanagement of these factors could negatively influence the accuracy of the evaluation outcomes (Figure 5).  
In response to the growing demands of big data and the necessity for detailed planning, the application examples of CRITIC have begun to increase. A combination of the CRITIC method with AHP, the EWM, and genetic algorithms (GA) was employed to objectively evaluate the relative strength of the assessment indicators and their interrelations in Wuhan [7]. This methodology facilitated the calculation of the weight assigned to each indicator. By normalizing every evaluation metric, determining the standard deviation and correlation coefficient for each, and assessing the capacity of information conveyed, the weights alongside the standardized data were derived, leading to a quantitative GSEUUS (Figure 6c,e and Table 1).
# Power Management Strategy for an Autonomous DC Microgrid
## 2 Autonomous DC Microgrid Configuration and Component Description
### Electrochemical Battery Bank Modelling  
As an essential component of the autonomous microgrid, the electrochemical battery bank is used to guarantee the stable operation of the whole system by supporting the intermittent renewable energy source and absorbing excess power not used by the load. Like the inherent properties of the battery, the recharging and discharging power of the battery bank should still be constrained to slow down battery aging, as in the following equation.  
\[p_{B\_MIN}\leq p_{B}(t)\leq p_{B\_MAX} \tag{5}\]  
where \(p_{B}\) is the power drawn from or injected into the battery bank. Since battery bank discharging power is defined as positive, and recharging power as negative, in this work, the discharging limit \(p_{B\_MAX}\) and recharging limit \(p_{B\_MIN}\) are positive and negative, respectively. Moreover, to protect the battery bank from over-charging and over-discharging, the state of charge of the battery bank, noted as \(SOC_{B}\), should be kept within a certain range. It can be expressed as given in Equation (6):  
\[SOC_{B\_MIN}\leq SOC_{B}(t)\leq SOC_{B\_MAX} \tag{6}\]  
where \(SOC_{B\_MAX}\) and \(SOC_{B\_MIN}\) are the upper and lower limitation, respectively. The \(SOC_{B}\) value at time \(t+\Delta t\) depends on the \(SOC_{B}\) value at time \(t\), and the energy absorbed or released by the battery bank during the time period \(\Delta t\). It can be expressed as follows:  
\[SOC_{B}(t+\Delta t)=SOC_{B}(t)+\frac{1}{C_{B}\times 3600\times v_{B}(t)} \int_{t}^{t+\Delta t}p_{B}(t)dt \tag{7}\]  
where \(C_{B}\) is the total capacity in Ah of the electrochemical battery bank and \(v_{B}\) is the voltage of the electrochemical battery bank.