esa-sceva/satcom-chunk-collection huggingface dataset:
# Rosehip Seed Oil-Incorporated Chitosan Films for Potential Fruit Packaging Applications
## 3 Results and Discussion
### Testing the Surface Resistance of Chitosan/RSO Edible Film Samples to Fungi  
All variants were analyzed sequentially after 24, 48 and 72 h of incubation. Due to contact with the surface of the culture medium in the Petri dishes, some of the film discs changed their shape. In the variant with 4% added rosehip oil in one of the replicates, mold growth was observed on the sabouraud medium. No such growth was observed in the rest of the replicates. In general, yeast and mold growth were not observed for all other variants, regardless of the percentage of oil phase content. No halos were formed around the films in the Petri dishes (Figure 7a,b).  
In the second test investigating film surface resistance, zones formed by the films that had changed their shape were observed. Dense growth from the added yeast culture was observed around them. Yeast did not develop on the film surface (Figure 7c). Halos were formed around the films.  
\begin{table}
\begin{tabular}{c c c} \hline
**RSO Emulsion** & **Antioxidant Activity** & **Total Polyphenol Content** \\
**Concentration, \%** & **DPPH, \(\mu\)mOTE/100 g** & **mgGAE/100 g** \\ \hline
0 & 0.717 \(\pm\) 0.012 a  & 10.300 \(\pm\) 0.100 a  \\
1 & 106.453 \(\pm\) 0.595 b  & 65.480 \(\pm\) 0.660 b  \\
2 & 221.577 \(\pm\) 0.611 c  \\
3 & 325.663 \(\pm\) 0.883 d  \\
4 & 428.693 \(\pm\) 0.594 e  \\
5 & 543.720 \(\pm\) 1.357 f  \\ R & 0.9999 & 0.9959 \\ \hline \end{tabular}
\end{table}
Table 5: Antioxidant potential and total phenolic content in chitosan/RSO films.  
Since no holds and yeasts were observed after 72 h of incubation of the samples (with one exception), it was indicative that the film surface did not retain such microorganisms, despite their non-sterile preparation. This is the reason for the absence of introduced and germinated microorganisms in the Petri dishes. The film surface does not retain fungal agents responsible for fruit fermentation or decay.  
The described results were also confirmed in the second design of the film resistance test. The films did not allow the development of the yeast culture on their surface. Clear boundaries of growth were outlined around the films regardless of the presence or absence of rosehip oil. For this reason, it can be concluded that the newly obtained films are resistant to the growth and development of fungal agents responsible for fruit fermentation. This property of maintaining a sterile surface is extremely important when used in the industrial and food and nutrition sectors to increase the shelf life of food, etc. The evaluation of the chitosan film showed significant antimicrobial activity [80]. Some chitosan-based films inhibited the growth of various types of bacteria [81; 82]. Data have been reported for over 99% antifungal activity of a film containing chitosan and cinnamon oil against several fungal representatives [83].
#### Abstract
To the extent that the two phenomena (creep and microcracking/delamination) are mutually reinforcing, this represents a very important aspect. The failure mechanisms, as a consequence, must be studied in order to offer an accurate lifetime prediction. * Scale effects, how microstructural phenomena influence macroscopic creep, are difficult to understand and model. Creep behavior can be greatly influenced by the size, shape, and lay-up arrangement of the composite material, and these effects are frequently hard to generalize across many composite systems. * For the creep phenomenon, environmental factors become important. Fiber-reinforced composites' creep behavior can be altered over time by moisture, UV radiation, or harsh chemicals that weaken the matrix or degrade the fibers. Composite sites may simultaneously experience cyclic loading (fatigue) and continuous loading (creep) in real-world applications. In this sense, the image shown to us is intricate and requires meticulous research and modeling. * Extensive experimental data and sophisticated computational techniques are required for prediction models, which integrate several elements, including material qualities, loading conditions, and environmental parameters, into a single model. * To fully understand the creep behavior of these materials, testing under a range of loading scenarios and orientations is necessary. Because creep behavior is intrinsically time-dependent, it necessitates extensive testing, which can take months or even years. Because of this, collecting enough experimental data for different loading circumstances, temperatures, and habitats is difficult and costly. In conclusion, investigating creep behavior in fiber-reinforced composites entails tackling intricate problems with temperature sensitivity, material heterogeneity, interfacial characteristics, damage evolution, and testing difficulties. Combining theoretical, computational, and experimental methods is necessary to solve these issues. Understanding creep in composites is crucial for their design and use in a variety of sectors, particularly those where materials are exposed to continuous pressures over time. Future investigations into the behavior of creep in fiber-reinforced composites will probably be influenced by developments in material science, testing procedures, computational approaches, and environmental factors. Enhancing modeling skills, integrating real-time monitoring, improving matrix and fiber characteristics, and developing more sustainable composite systems will be the main areas of focus. Fiber-reinforced composites that can tolerate long-term creep deformation in demanding applications will become more robust, effective, and dependable as a result of these developments. This study completes the reviews performed over the five decades of composite material research by introducing new calculation methods that have benefited from the appropriate calculation technique and new mechanical testing tools. It can thus provide a more comprehensive and up-to-date view of the calculation models of the flow phenomenon in composites.
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 5 Conclusions  
1. Lidar can measure and record wind parameters in the field for a long time, and in view of the effective ratio \(\beta\), measurements taken can obtain higher quality data when the weather is clear.
2. The wind field data during the measuring period were selected and analyzed, and it is found that the wind field parameter showed nonuniform characteristics in time and height.
3. The nondimensional wind speed profile was divided into three types (mussy, linear, and piecewise exponential pattern), and the regularity of power exponent is not obvious. The three types are different from those recommended in the specification or those in an open area.
4. The wind direction has a certain consistency at different VMTs, and the phenomenon of wind direction twists with the height could be described by the wind-direction deflection rate \(\alpha\). In addition, below the cutoff point, the wind deflection is more dramatic.
5. According to the distribution pattern of wind speed and direction, wind parameters could be applied to bridge design; for example, wind speed and distribution along the bridge deck or wind-direction change along the pylon, and these could be further discussed.
6. Confined to the performance of the lidar, no analysis has been performed on wind speed power spectrum and spatial coherence, and further updates of equipment or advanced analysis method is needed.  
Figure 18: The law of wind direction changes with height.  
**Author Contributions:** Conceptualization, J.W. and J.L.; methodology, J.W. and J.L.; software, J.W.; validation, J.W., G.H., and S.X.; formal analysis, J.W.; investigation, J.W. and J.L.; resources J.W.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision J.L.; project administration, [PERSON] and F.W.; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (51978077).  
The study did not involve humans or animals.  
The study did not involve humans.  
The availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Terms  
The AD-CR, is based on a classification approach devised from association rule mining called class association rules. In AD-CR, the classification problem's dataset is represented as distinct items along with their corresponding values. Each distinct item is associated with the largest target class label in the dataset with which the item has occurred, and such <item, class> representation is called a 'rule_item'. Below are the main definitions related to the AD-CR using \(D\) as an input classification dataset:  
1. Data Observation: A collection of features with their values plus a target class value represented as \(([(O_{1},v_{1}),(O_{2},v_{2}),(O_{3},v_{3})\ldots,(O_{k},v_{k})],Class_{k})\);
2. Training Dataset \(D\): A combination of data observations, each associated with a target class \(c\);
3. Feature in \(D\): An attribute that relates to the individual undergoing the screening process of dementia, such as age, gender, visit code, etc. The feature can be categorical (linked with a predefined set of values) or continuous (numeric or decimal);
4. Target Class in \(D\): An attribute that represents the progression of dementia stage presented in a multi-class categorical form (0,1,-1). We limit the problem to progression (1) or no progression (0);
5. 1-Rule_Item (1-\(RI_{k}\)) in \(D\): Is represented as \(([(O_{k},v_{k})],Class)\);
6. Support of the \(RI_{k}\), i.e., \(\text{supp}\ (RI_{k})\): Calculated from \(D\) as \(\frac{\text{i}([(O_{1},v_{1}),(O_{2},v_{2}),(O_{1},v_{3})\ldots,(O_{k},v_{k})], Class_{k})]}{[D]}\). When \(\text{Supp}\ (RI_{k})\geq\min\text{supp}\_\text{threshold}\), the \(RI\) is considered frequent;
7. Minimum Support Threshold: Denoted as \(\min\text{supp}\) and is employed to differentiate between frequent and infrequent \(RI_{k}\);
8. Confidence of the rule_item (\(RI_{k}\)), i.e., \(\text{Conf}\ (RI_{k})\): Calculated from \(D\) as \(\frac{[((O_{1},v_{1}),(O_{2},v_{2}),(O_{k},v_{3})\ldots,(O_{k},v_{k})],Class_{ k})]}{[[(O_{1},v_{1}),(O_{2},v_{2}),(O_{3},v_{3})\ldots,(O_{k},v_{k})]]}\). When \(\text{Conf}\ (RI_{k})\geq\min\text{conf}\_\text{threshold}\), the \(RI\) is considered a potential rule;
9. Minimum Confidence Threshold: Denoted as \(\min\text{conf}\) and employed to measure the strength of a rule generated from a \(RI\);
10. Potential Rule: Takes the form (\(\text{I}_{1}\wedge\text{I}_{2}\wedge\ldots\wedge\text{I}_{k}\)) \(\rightarrow\) Class;
11. Test Dataset: A combination of data observations, each is associated with a true class \(c\).
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Implementation Details
#### 3.2.1 Evaluation Indicators  
A total of five performance evaluation indicators were utilized: overall accuracy (OA), which is defined as the percentage of correctly classified pixels relative to the total pixels; precision (P), which measures the proportion of true-positive water body pixels among all predicted water body pixels; recall (R), which is the ratio of predicted water body pixels to the ground truth water body pixels; the F1-score (F1), which calculates the harmonic mean of precision and recall; and the Intersection over Union (IoU), which is defined as the average of the intersection over the union of water bodies and the background, reflecting the overlap degree between the prediction and the ground truth. In this study, the IoU most intuitively and concisely reflects the effect of binary water segmentation. The calculation formulas for the evaluation indicators are shown in Table 1.
## 2 ITS Applications and Communication Technologies, Critical Design Requirements, and Related Works
### Critical Design Requirements for C-DGNSS Applications  
Safety-critical C-DGNSS applications have specific technical requirements regarding latency, reliability, and sampling rate, as exhibited in Table 2, but the networking design parameters such as the minimal and maximal Inter-Vehicle Ranges (IVRs) between the assisted vehicles, the minimum number of participating vehicles, and the interval of GNSS corrections are yet to be standardized. They strongly depend on wireless network connectivity, the mobility of the vehicles, and the local environment. In order to estimate the aforementioned requirements, further deployment, implementation, and several experimental sessions of the proposed MADM algorithm are needed, which are out of this work's scope. In deep urban areas, the streets are narrower, and so the vehicles' distances are much smaller as opposed to suburban and rural areas. Likewise, there is a greater number of candidate neighbors in deep urban environments that require more frequent corrections due to many obstructions. Therefore, the specification of the C-DGNSS is challenging and varies a lot with the local surroundings. Assuming perfect vehicle connectivity, the maximal IVRs must guarantee different multipath conditions and common satellites in view, while at least two vehicles are needed for the MADM module to be useful. Finally, the low-cost onboard receivers and the radio-link equipment must be compatible with the requirements in Table 2.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 4 Distributed Algorithm for Subchannel Allocation
### Computational Complexity Analysis  
At each iteration in MCBR algorithm, the sensing phase complexity is \(O(2T)\) which results while sensing the new subchannel and the subchannel allocated in the previous time slot, where \(T\) is the duration of the sensing time. The complexity for information feedback from the neighboring links is reflected by \(O(|\mathcal{J}_{i}|)\), the upper bound, and \(\Omega(0)\), the lower bound. This range corresponds to the two extreme cases when the sensing subchannel is also allocated by all other neighboring links and none,respectively. Certainly, the overall computational complexity depends on the number of iterations needed for steady-state convergence, which is not unlimited because of a finite number of possible strategy profiles. In contrast, the identical interest game, which is shown to be an EPG [32; 33], with a potential function and utility function equal to Equation (7), has the sensing phase complexity of \(\mathcal{O}(|\mathcal{K}|T)\) and the information feedback complexity of \(\mathcal{O}(|\mathcal{M}|)\) at each iteration. This is not scalable and would result in significant computational complexity in dense SCN scenarios with large numbers of subchannels. Therefore, the computational complexity of the MCBR algorithm for each iteration is comparatively low, scalable, and suitable for practical implementation.
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 5 Problems and Optimization  
### Mapping Algorithm  
In the process of mapping the virtual subgraph \(G^{*^{\prime}}\) back to the graph \(G\), Algorithm 1 employs a strategy that prioritizes connecting the two nodes with the lowest degree. This approach effectively prevents any single node from handling an excessive number of virtual node connections, thus avoiding the node degree from exceeding the acceptable threshold. However, this method does not take into account the weight of the edges, which means that the resulting graph \(G^{\prime}\) may not be the optimal solution in terms of edge weights.  
Let \(f:E^{*}\to E\) represent the mapping of any edge from the virtual graph \(G^{*}\) to an edge in the graph \(G\), and let \(E_{f}=\{e\mid e=f(e^{*}),e^{*}\in E^{*^{\prime}}\}\) be the set of all mapped edges from the virtual subgraph \(G^{*^{\prime}}\) to the graph \(G\). The problem can be formulated as finding a subset \(E_{f}^{\prime}\) of \(E_{f}\) such that  
\[\forall e^{*}\in E^{*^{\prime}},\left|E_{f}^{\prime}\cap\{e\mid e=f(e^{*})\} \right|=1\]  
and  
\[\forall u\in V,\text{degree}(v)\leq 3\]  
while minimizing  
\[\operatorname*{arg\,min}_{E_{f}^{\prime}\subseteq E_{f}}\sum_{e\in E_{f}^{ \prime}}w_{e}\]  
where \(w_{e}\) represents the weight of edge \(e\). This formulation ensures that each edge in the virtual subgraph \(G^{*^{\prime}}\) maps to exactly one edge in the graph \(G\), maintains the degree of each node in \(G\) to be no more than 3, and seeks to find the subset \(E_{f}^{\prime}\) of \(E_{f}\) that minimizes the total weight of the edges included in the mapping.  
Let \(x_{e}=1\) indicate that edge \(e\) is included in the edge set \(E_{f}^{\prime}\). Then, the problem can be transformed into a 0-1 integer programming problem as follows:  
\[\begin{array}{ll}\text{Minimize}&z=\sum_{e\in E_{f}}x_{e}w_{e},\quad w_{e}>0 \\ \text{Subject to}&\sum_{e\in\{e|e=f(e^{*})\}}x_{e}=1,\quad\forall e^{*}\in E^{*^ {\prime}}\\ &\sum_{e\in E_{u}}x_{e}\leq 3,\quad\forall u\in V,E_{u}=\{(u,v)\mid v\in V,(u,v)\in E_{\text{G}}^{*}\}\\ &x_{e}=0\text{ or }1\end{array}\]  
The optimal mapping can be obtained by solving this 0-1 integer programming problem. Since 0-1 integer programming is an NP-complete problem, as the number of nodes in the graph increases significantly, the number of edges also grows exponentially. This makes finding the optimal solution to the problem very challenging.  
In light of this issue, an alternative heuristic approach has been proposed. The method involves sorting all the mapped edges and sequentially selecting the edge with the minimum weight to be included in \(G^{\prime}\) while eliminating other edges that share the same mapping. For details of the algorithm, refer to Algorithm 2.  
```
Input:\(E_{f}\),\(W\) Output:\(newE\) \(E\)=sort \(E_{f}\) with \(W\),\(newE=\{\}\) for\(edge=(u,v)\) in \(E\).\(pop()\)do if\(degree(u)<3\) and \(degree(v)<3\)then \(newE=newE\cup\{edge\}\) \(E=E-f(f^{-1}(edge))\) endif endfor
```  
**Algorithm 2** Node Mapping Algorithm
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## Discussion  
### Performance Improvement Using the Corrected Trajectory Data  
To evaluate performance improvements using the corrected trajectory data over the original data, a simulation was conducted with Epidemic using different numbers of pairs for sparse periods. Epidemic disseminates packets through broadcasting to identify the most transmission paths in VANET. Figures 44-46 illustrate Epidemic's average packet delivery ratio, end-to-end delay, and overhead ratio with four and eight pairs of source and destination vehicles. These results show that, when using the corrected trajectory data, Epidemic achieves a 20% higher average packet delivery ratio, a 0.9 s lower average end-to-end delay, and a lower average overhead ratio (by 265) than the values achieved using the original trajectory data. Without a loss of generality, other routing schemes also perform better when using the corrected trajectory data.  
Figure 43: The average overhead ratio with different transmission ranges for congested periods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sparse Period** & \begin{tabular}{c} **IDRF over** \\ **TDRL-RP** \\ \end{tabular} & \begin{tabular}{c} **IDRF over** \\ **VRDRT** \\ \end{tabular} & \begin{tabular}{c} **Congested Period** \\ \end{tabular} & \
\begin{tabular}{c} **IDRF over** \\ **TDRL-RP** \\ \end{tabular} &
\begin{tabular}{c} **IDRF over** \\ **VRDRT** \\ \end{tabular} \\ \hline Average packet delivery ratio & +19.54\% & +9.62\% & Average packet delivery ratio & +7.85\% & +6.06\% \\ \hline Average end-to-end delay & \(-\)41.36\% & \(-\)11.84\% & Average end-to-end delay & \(-\)21.42\% & \(-\)14.77\% \\ \hline Average overhead ratio & \(-\)7.08\% & \(-\)16.77\% & Average overhead ratio & \(-\)7.60\% & \(-\)22.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average performance improvements with the IDRF over TDRL-RP and VRDRT with different transmission ranges for sparse and congested periods.  
Figure 44: The average packet delivery ratio with the original and corrected trajectory data for sparse periods.  
Figure 45: The average end-to-end delay with the original and corrected trajectory data for sparse periods.
## 7 RF-EH Circuit
### Power Management Module  
A power management unit (PMU) ensures that the load receives appropriate power conditions based on the energy harvested. For instance, if the load necessitates a regulated or precise voltage, or if the load necessitates precise running times, a commercial or tailored power management system can be used, depending on the design constraints. Finally, supercapacitors are preferred in EH Wi-Fi systems as a result of their high power density, high loading and unloading speed, long-life cycle, small size, and low cost compared to batteries [155]. The primary goals of the PMU are to maximize the amount of energy transferred from the antenna to the reservoir in real-time and regulate the sensor node's supply voltage using the harvested energy properly and efficiently [156]. To investigate the performance of different integrated designs of low energy harvesting, power management technologies, and energy storage, the majority of studies have relied on theoretical and lab experiment research methods [157].