esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 4 Performance Evaluation
Figure 11 depicts the relation between the number of hops and the message generation interval, where it is clear that with an increase in the message generation interval from 5 to 15 to 45-55, the number of hops required to propagate a message gradually increases. However, this increase is much lower in the case of our proposed CATR, hence performing better than Epidemic, BTRES, and PPHB+ by 30%, 14%, and 9%, respectively. The fact is that when there is an increase in the message generation interval, a node can store data packets in its buffer for a longer duration of time before generating any new packets, and this eventually leads to a decrease in the average hop count for that message. Figure 2:0 Average hop count vs. message generation interval. Figure 2:1 Hop count vs. buffer size. Figure 2:2 Hop count Vs. number of nodes.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 3 Performance Analysis
### Tracking Loop
The frequency-locked loop (FLL) is quite similar to PLL in many ways, while it is dedicating to resolving the presence of external disturbances or frequency drift for retaining a stable and accurate frequency for synchronization. The key differences between are accuracy and complexity. Although PLL is generally more accurate than FLL in maintaining a constant phase relationship, FLL is typically faster and more responsive to tracking changes in the reference frequency, due to the lesser components requirement at a lower filter order. In the meantime, PLL exerts better if the FLL has been tracking the signal frequency steadily, and that signal phase can be further determined through frequency difference reduction. There are several types of discriminators commonly used in FLL design, including the phase detector, the zero-crossing detector, and the costas loop. The error detects an algorithm of a zero-crossing detector, which can be shown as  
\[e_{f}=\frac{\arctan(cross/dot)}{2\pi T_{i}}, \tag{17}\]  
where _cross_ and _dot_ products are determined by control words. The _cross_ product is generated through  
Figure 5:0  
and _dot_ product can be presented in  
Figure 5:1  
where the preceding control words that have been delayed for a single time interval are demanded to be put into the calculation. Since the loop filter order of the FLL is lower than PLL's, the transfer function of applied filter is  
Figure 5:2  
and the diagram of loop filter is shown in Figure 9, where the discrete transfer function through the same mapping relationship can be presented as  
Figure 5:3  
Delay-locked loop (DLL) is erected on previous the PRN code acquisition procedure. Since the code phase delay has been approximately synchronized, where the accuracy is at the level of an entire code chip, it is indispensable to determine whether the delay is somehow early or late within a single chip. Thus, early, prompt, and late codes referring to the phase relationship are demanded, as shown in Figure 7, and either code is a half chip that deviated from the prompt code. The most commonly used kinds of discriminator in DLL are non-coherent early minus late power (NELP), dot product, and normalized early minus late envelope (NEML). Additionally, the normalized NEML discriminator  
Figure 5:4 Structure diagram of digital loop filter of FLL. is implemented, due to its robustness and simple implementation, to remove amplitude sensitivity. Such a discriminator algorithm is defined as  
Figure 5:5  
where \(E=\sqrt{w_{IE}^{2}+w_{QE}^{2}}\) represents the early correlation power, and \(L=\sqrt{w_{IL}^{2}+w_{QL}^{2}}\) represents the late correlation power. For our adopted loop filter of DLL, the transfer function is  
Figure 5:6  
along with its Z-domain formulation as  
Figure 5:7  
Additionally, the diagram of DLL's loop filter is shown in Figure 5:8
## 3 Simulation Analysis and Results  
### Layout Design of LED Array  
The underwater channel model is established using the MC simulation method. A lemniscate-compensated layout of the LED array is proposed, and its performance is compared with two other traditional layouts, an ellipse-compensated layout and a ring-compensated layout. Taking 16-LED light sources as an example, three different layout schemes are shown in Figure 3. For the ellipse-compensated layout as shown in Figure 3a, twelve LED light sources are evenly arranged as an ellipse in the central area, which is simple to implement. For the ring-compensated layout as shown in Figure 3b, eight LED light sources and four LED light sources are distributed in an outer ring and an inner ring, respectively, and the emission power is concentrated in the central area. For the lemniscate-compensated layout, twelve LED light sources are arranged as shown in Figure 3c, with two LED light sources set at the center so that the number of LEDs is consistent with the other two layouts. Additionally, to improve the uniformity of optical signal coverage at the transmitting end, four compensation light sources are arranged at the corners of each LED layout, respectively.
## 3 Results and Discussion
### Fragmentation Resistance Test Results
Additionally, literature data presents polymer ceramic composites which were evaluated against fragment simulating projectiles of various calibers to investigate their ballistic impact response. Samples were prepared by mechanically mixing boron carbide (B\({}_{4}\)C) and cubic boron nitride (cBN) over a range of ratios and combinations with either thermosetting phenolic or epoxy resin and aramid pulp. Ballistic tests performed with a 1.1 g FSP fragment for cBN-based armor results in a V50 value of approximately 784 m/s. The obtained value was almost expected as cBN is one of the hardest commercially available materials, second only to industrial diamond. Polymer ceramic composites containing B\({}_{4}\)C tested for a V50 parameter results in a 702 m/s and Kevlar target of the same areal density results with a 680 m/s V50 parameter value [67]. Figure 6: Dependence of V50 with the FSP.22 fragment of mass (1.10 \(\pm\) 0.03) g on the surface mass for traditional ballistic systems and hybrid silicone-ceramic composites. The ballistic limit of the aluminum plate was investigated by the [PERSON]'s et al. [68]. In this study, there were different types of plate arrangement. The first category was a single plate with 3 mm thickness. Meanwhile, the second category was two aluminum plates with distance of 100 mm between them. Those plates were impacted by the cylindrical FSP fragments with 6 mm long, 5 mm in diameter, and a weight of 1.1 g. The ballistic limit V50 for the single plate was equal to 276 m/s and for the double plate about 388 m/s.Much lower V50 ballistic limits were obtained for ballistic materials such as Kevlar(r) (DuPont, Wilmington, United States) and Twaron(r) aramids and Dyneema(r)HPPE (DSM, Heerlen, Netherlands), which were tested in a single-layer configuration. Impact velocity of carbon steel cylinder FSP fragment with 4.5 mm diameter and 0.78 g weight at 50% probability of perforation (V50) through the fabric ranged from about 120 to 250 m/s depending on the type of used material [69]. Another type of hard body armor obtained on the basis of poly (methyl methacrylate) (PMMA) and polycarbonate (PC) laminates was investigated by [PERSON] and co-workers [70]. The ballistic measurements were carried out using the 1.1 g weight 0.22 caliber FSP fragments. Authors showed that the PC-PMMA-PC laminates exhibit a 37% higher value of the V50 (846 m/s) when compared with the PC-PC-PC laminates of equivalent overall layer configuration. The ballistic limit against the FSP.22 increased with increasing plate thickness of PMMA. Comparing the literature data with results obtained for the developed HSC composites concludes that these composites provide higher ballistic protection than various types of laminates, aluminum plates, or polymer ceramic composites with B4C or cBN ceramics.
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 2 Results
### Biomarker Analysis  
Based on the statistical analyses, hexadecanal proved to be the most significant metabolite with a high potential as a biomarker for PE. To further evaluate its diagnostic value, a receiver operating characteristic (ROC) curve was generated (Figure 2), which provides insight into its ability to discriminate between the PE and control groups by balancing sensitivity (true positive ratio) and specificity (true negative ratio). The area under the curve (AUC) for hexadecanal was calculated to be 0.618 (95% CI: 0.492-0.737), indicating a moderate diagnostic ability. The sensitivity was 0.6 (60%) and the specificity was 0.7 (70%), reflecting reasonable diagnostic performance. Although the AUC does not indicate exceptional predictive accuracy, hexadecanal shows promising potential as a biomarker for PE that warrants further investigation.  
Figure 1: Volcano plot highlighting statistically significant metabolites in the preeclampsia vs. control groups.  
\begin{table}
\begin{tabular}{l c c} \hline
**Compound** & \(\mathbf{p}\)**-Value** & **FC (PE/C)** \\ \hline
2-methyl-5-(methylthio)-furan & 0.038 & 0.72 \\ \hline benzenemethanol & 0.042 & 0.68 \\ \hline
1-phenylethylhanol & 0.014 & 0.61 \\ \hline
1,2,3,4-tetrahydro-1,1,6-trimethyl-naphtalene & 0.043 & 0.23 \\ \hline vitispirane & 0.003 & 0.45 \\ \hline
1,2-dihydro-1,1,6-trimethylnaphthalene & 0.029 & 0.66 \\ \hline hexadecanal & 0.042 & 2.75 \\ \hline \end{tabular}
\end{table}
Table 3: Significant volatile metabolites identified by fold change and statistical analysis between the preeclampsia and control groups.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
###### Abstract
For an image with a size of \(\mathrm{w}\times\mathrm{h}\) (\(\mathrm{w}\) represents the width and \(\mathrm{h}\) represents the height), by processing it with CNN, we obtain a new coding feature with a size of \(\mathrm{w}\times\mathrm{h}\times\mathrm{c}\) (\(\mathrm{c}\) represents the new feature obtained by CNN). The convolution kernel can detect different features of an image. In general, the convolution kernel at lower layers can detect information such as edge texture and the convolution kernel at higher layers can detect features with semantic information. Therefore, the feature map obtained from an image through CNN contains spatial and semantic information. However, most existing models only focus on the attention mechanism of time or space [10]. Thus, the substantive feature of the CNN network is not fully utilized. However, our multi-attention video description model introduces the channel attention mechanism on the foundation of a traditional temporal and spatial attention mechanism. Besides, this model makes a stronger combination of visual features and sentence descriptions so that the accuracy of the model is increased. The model is experimented on the datasets Microsoft Video Description (MSVD) and Microsoft Research-Video to Text (MSR-VTT). Further, BLEU@4 [16], CIDEr [17], METEOR [18], and ROUGE_L [19] are adopted as evaluation indexes. Experimental results verified the effectiveness of our model. Then a video visualization model is proposed based on video description. In this video visualization model, we made a visual analysis of our attention mechanism and proved the accuracy of the model intuitively.
## 3 Link Level Parameters and Channel Coefficient Generation
### Propagation Conditions and Pathloss Calculation
In [21], it is suggested to calculate \(PL_{tw}\) by considering 70% of concrete and 30% of glass, while \(PL_{in}\) is a function of the 2-D distance between the user and the internal wall of the building. All the above parameters are listed in [21], along with the shadowing standard deviation \(\sigma_{SF}\) for each scenario.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 5 Lightweight V2V Identity Authentication and Key Agreement Scheme
### Security Architecture  
The C-V2X system includes an OBU (on board unit), RSU (roadside unit), cloud platform, application, etc. To establish a security architecture, it is necessary to set up an equipment (vehicle) registration center (ERC) and a key management center (KMC) in the C-V2X system. The function of the ERC is to generate VID according to the information provided by the user. The ERC can manage the equipment information and monitor the equipment status. The KMC is the most important equipment of the C-V2X security architecture and generates the private key of the equipment according to the equipment public key (uses the equipment VID as the equipment public key) and storage, distribution, and destruction the equipment private key. The ERC and KMC provide services for the whole C-V2X system and are the core equipment of the security scheme. They are generally established in the central node of the system, such as the traffic management department. The KMC implementation based on identity-based cryptographic, has a secure storage area, random number generator, identity-based cryptographic algorithm, symmetric cryptographic algorithm, and so on. The system security architecture is shown in Figure 1.  
The ERC assigns a unique number as a VID to each C-V2X equipment with the equipment information. The VID is composed of organization code, manufacturer code, category, model, serial number, area code, and operator code [17]. The VID is the unique identification of the C-V2X equipment and is also the public key of the equipment. The private key of the equipment is generated by KMC based on the identity-based cryptographic algorithm and the public key of the equipment.  
Figure 1: Security Architecture.
# The Current Development Status of Agricultural Machinery
## 4. Chassis Leveling Systems of Agricultural Machinery
### Mechanisms and Methods for Leveling  
Leveling mechanisms for agricultural machinery can be categorized into five primary types based on their operational principles: hydraulic differential-height type, parallel four-bar linkage type, adjustable center-of-gravity (CoG) type, articulated frame steering type, and omnidirectional leveling type. These mechanisms enable lateral, longitudinal, and full-range adjustments in vehicle posture. Table 3 provides a comparative overview of the key characteristics among these different leveling systems. Interationally developed leveling technologies continue to lead in terms of maturity and application scope compared to domestic Chinese systems, particularly in large-scale agricultural equipment. The VTT Technical Research Centre of Finland has developed a closed-loop leveling system utilizing servo proportional valves. This system achieves synchronized lateral and longitudinal leveling through four-stage linked hydraulic cylinders, with a maximum leveling angle of \(\pm\)15\({}^{\circ}\) and a response time under 0.6 s, resulting in a 12% improvement in operational efficiency. John Deere's 8R-3004 tractor (as shown in Figure 11a) integrates a distributed hydraulic suspension system controlled by load-sensitive pumps and pressure-compensated valves, effectively mitigating header vibration caused by uneven terrain. New Holland's CR9070 mountain tractor (Figure 11b) employs a dynamic lateral leveling system that combines dual rack-and-pinion mechanisms with worm gear self-locking devices, enabling millimeter-level height adjustment during operation. However, its relatively large structural dimensions limit its suitability for hilly and mountainous regions in China. Ferrari's K10s mountain tractor (Figure 11c) features a low-center-of-gravity design (with a center of gravity height \(\leq\) 650 mm), incorporating multi-degree-of-freedom lateral swing mechanisms on the front axle. It achieves \(\pm\) 15\({}^{\circ}\) dynamic tilt compensation through a hydraulic servo system. [PERSON] et al. [122] proposed a self-leveling solution for legged-wheel robots operating in complex terrains using a 22-degree-of-freedom multi-body dynamics model (six degrees of freedom for the chassis and four for each suspension unit). Their approach integrates linear actuators capable of \(\pm\)300 mm vertical wheel adjustment with real-time inclination feedback, laying the foundation for robotic mechanism optimization and control algorithm development. Dettu
## 5 Maintenance
### Diagnostics  
Since diagnostic tasks, most prominently Root cause analysis, investigate hazards by systematically creating problem subsets, classification or clustering based solutions are often required.  
In [152], the authors proposed a Decision Tree and Principal Component Analysis (PCA)-based RCA solution for rotating machinery, where PCA can eliminate the redundant features, while Decision Tree can classify data with high precision. Ref. [153] also showedthat Decision Tree and Ensemble algorithms perform the best for large amounts of data with uncertain problems. For the specific rotating machinery use-case, [154] applied Random Forest and KNN as classifiers within their proposed methodological framework for time-series data, while [155] also used Random Forest for time-domain classification.  
RCA is not the only important diagnostic task, as diagnostics also includes modelling that serves as the basis for prognostics. Such tasks usually requires feature extraction to build models. In [156], the authors proposed a solution for rotating machinery fault diagnosis based on auto-encoder to perform feature extraction and a fish swarm algorithm to optimize its key parameters. Ref. [157] also proposes a similar method for rolling bearings, but with an enhanced Deep Wavelet Auto-encoder and Extreme Learning Machine. For bearings, different CNN-based solutions were introduced in [158; 159] to process time and frequency series data from sensors. Another solution is presented in [157] using RNN and GRU for high accuracy and robust performance.
## 2 Nutritional Value  
Various studies have been conducted to evaluate the nutritional value of _A. bisporus_[8; 18; 19; 20; 21; 22; 23; 24; 25].  
Figure 1: A summary of nutritional, medicinal, and cosmetic value of _Agaricus bisporus_.  
Tables 1 and 2 represent the various nutritional components of _A. bisporus_ found in different studies. _A. bisporus_ is relatively rich in carbohydrates, proteins, and fats as compared to some other widely consumed species [26]. According to [PERSON] et al. [27], mushrooms exhibit different nutritional value when grown on different substrates.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Raw Nutritional Value/100 g**} \\ \hline Energy & 94 KJ (22 kcal) \\ Water & 92.43 g \\ Fats & 0.34 g \\ Proteins & 3.09 g \\ Carbohydrates & 3.26 g \\ Dietary fibers & 1 g \\ Sugar & 1.65 g \\ Iron & 0.50 mg \\ Vitamin C & 2.1 mg \\ Niacin (Vit B3) & 3.607 mg \\ Riboflavin (Vit B2) & 0.402 mg \\ Pantothenic acid (B5) & 1.497 mg \\ \hline \end{tabular} Source: USDA Nutrient database.  
\end{table}
Table 1: Nutritional status of _A. bisporus_.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nutritional Group** & **Components** & **Amount** & **Unit** & **References** \\ \hline \multirow{8}{*}{Proteins (amino acids)} & Alanine & 5.8 & \multirow{8}{*}{g 100 g\({}^{-1}\) total protein} \\  & Cysteine & 1.1 & & \\ \cline{1-1}  & Proline & 6.1 & & in FW \\ \cline{1-1}  & Tyrosine & 4.2 & \\ \cline{1-1} \cline{2-3}  & Methionine & 0.8 & \multirow{8}{*}{mg g\({}^{-1}\) DW} \\  & Threonine & 1.3 & \\ \cline{1-1}  & Asparagine acid & 3.4 & \\ \cline{1-1}  & Serine & 3.1 & \\ \cline{1-1}  & Histidine & 14.1 & [28; 29; 30] \\ \cline{1-1}  & Leucine & 0.8 & \\ \cline{1-1}  & Arginine & 2.2 & \\ \cline{1-1}  & Lysine & 3.5 & \\ \cline{1-1}  & Phenylalanine & 2.1 & \\ \cline{1-1}  & Glycine & 2.0 & \\ \cline{1-1}  & Valine & 2.3 & \\ \cline{1-1}  & Isoleucine & 1.0 & \\ \cline{1-1}  & Total amount of amino acids & 44.2 & \\ \hline \multirow{8}{*}{Lipids (fatty acids)} & Palmitic acid & 13.35 & \multirow{8}{*}{mg 100 g\({}^{-1}\) DW} \\  & Palmitoleic acid & 4.84 & \\ \cline{1-1}  & Caprylic acid & 1.08 & \\ \cline{1-1}  & Caprinic acid & 0.85 & \\ \cline{1-1}  & Oleic acid & 6.07 & \\ \cline{1-1}  & Linoleic acid & 67.29 & \\ \cline{1-1}  & Linoleic acid & 1.52 & \\ \cline{1-1}  & Liurmic acid & 0.11 & \\ \cline{1-1}  & Myristic acid & 0.94 & \\ \cline{1-1}  & Stearic acid & 3.72 & \\ \cline{1-1}  & Arachidic acid & 0.92 & \\ \cline{1-1}  & Pentadecanoic acid & 0.23 & \\ \cline{1-1}  & Total unsaturated fatty acids & 79.72 & \\ \cline{1-1}  & Total saturated fatty acids & 20.28 & \\ \hline \multicolumn{2}{c}{Total lipids} & 2.7 & \% DW \\ \hline \end{tabular}
\end{table}
Table 2: Nutritional components present in the basidiocarp of _A. bisporus_.
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
###### Abstract  
In this paper, we propose a channel assignment method that can mitigate the inter-WBAN interference when the density of WBANs is high. To achieve the goal, we group the coexisting WBANs into a set of clusters by using the Louvain algorithm and allocate different channels to the WBANs in the same cluster by using a graph coloring method. By increasing the distance between the WBANs using the same channel, our method reduces the inter-WBAN interference. As a result, compared with the conventional centralized channel allocation method, our method increases the average data rate of a WBAN more than twice even when the number of coexisting WBANs is larger than the number of available channels. Compared with a distributed method involving an iterative process, our method reduces the channel decision time by 94.6%. Furthermore, since our method self-configures the algorithm parameters dynamically according to the topology changes, it can be used without human intervention even when the topology changes.  
wireless body area network; interference management; channel allocation; graph clustering; graph coloring +
Footnote †: journal: Applied  
doi:10.3390/APpl
## 3 Proposed Algorithm
### Proposed Algorithm  
In this subsection, we discuss the proposed Algorithm 1. First, Q-values for every state-action pair are initialized. From steps 2 to 8, state-action pairs are evaluated iteratively to select actions that can maximize the Q-function. During steps 4 to 7, stochastic decisions are performed that are influenced by the random probability \(p\) to exploit the exploration and exploitation of actions depending on \(\epsilon\). In this manner, optimal caching actions are performed. Furthermore, in steps 9 and 10, the rewards are observed based on the executed action \(a_{ct}^{m}\) and system transition to the next state \(s_{c(t+1)}^{m}\), respectively. The system's long-term rewards are improved according to Equation (19). Finally, in Step 11, the Q-table is updated based on the reward values.  
```
1:Initializing \(Q_{c}(s_{ct}^{m},a_{ct}^{m})\) for every state-action pair.
2:for\(\mathcal{T}=1,2,\ldots,T\)do
3: Select random probability \(p\).
4: if \(p\geq\epsilon\)
5:\(a_{ct}^{m}=\arg\max_{a_{ct}^{m}\in\mathcal{A}}Q_{c}(s_{ct}^{m},a_{ct}^{m})\),
6: otherwise,
7: randomly select an action \(a_{ct}^{m}\).
8: Execute action \(a_{ct}^{m}\) in the system.
9: Obtain reward \(R_{ct}^{m}\).
10:\(s_{ct}^{m}\) shift to the new state \(s_{c(t+1)}^{m}\) and the subsequent interval.
11: Update, \(Q_{c}^{t+1}(s_{ct}^{m},a_{ct}^{m})=(1-\psi)Q_{c}^{t}(s_{ct}^{m},a_{ct}^{m})+\) \(\psi(R_{ct}^{m}(s_{ct}^{m},a_{ct}^{m})+\alpha V_{c}(s_{ct}^{m}+a_{ct}^{m}))\),
```  
**Algorithm 1** RL-based dynamic data caching decision algorithm.