esa-sceva/satcom-chunk-collection huggingface dataset:
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation 3: Performance Evaluation under High-Density Nodes Scenario (100 Nodes)
#### 5.3.3 Jitter Performance Analysis  
The results of the scenarios in Figure 20 show a high jitter margin for the malicious scenario as compared to the other scenarios. It was eight times higher than that of the framework. The jitter levels rise from 309,406.55 to a constant 796,236.23 microseconds and this result is less desirable from the malicious scenario because it has an even lower throughput to that effect. In the sinkhole attack scenario, the first 28 nodes show a constant level of 796,236.267 microseconds. Node 29 is on 309,406.55 microseconds. There is a continuous drop of jitter levels from 796,236.267 microseconds for three other nodes in the network. The drop fluctuates between 297,602.65 and 305,562.99 microseconds. This is a relatively high level of jitter, but it is not surprising as the network is under an active sinkhole attack. The projected result from the malicious scenario was caused by the late access to enough bandwidth as it is shared among the different nodes. The density of the nodes in the network further affected this result but the framework has an even lower margin because there was no malicious behavior. The sinkhole attack in the other scenario maximizes bandwidth usage unnecessarily in certain nodes and thus starving others.  
Figure 18: Throughput for sinkhole and framework scenarios.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 2 Related Works
[25] proposed an improved BP algorithm called sojourn-time-based back-pressure routing (STBP) by introducing the sojourn time backlog (STB). STB takes queue length and cumulative packet delay into account, which improves the delay performance of BP. [PERSON] et al. [26] proposed the shortest-path-aided BP routing (SBR) by introducing short-path information. Nevertheless, the scalability of SBR is limited due to centralized strategy. [PERSON] et al. [27] proposed differential BP routing (DBPR) with single queue management, which simplifies data queue management and improves packet delay performance. [PERSON] et al. [28] proposed a throughput-optimal biased BP algorithm (OBBP). The bias is learned through GNN, which seeks to minimize end-to-end delay. [PERSON] et al. [29] developed a throughput-optimal scheme utilizing the utility maximization framework for data collection through satellite networks. The shortest path is determined by the distance between adjacent nodes and sink nodes. A comparison of load-balancing routing algorithms is presented in Table 1.  
Due to the regular topology of the LSN, a large number of available paths may lead to large delays and overhead. The above strategies based on BP routing cannot be adopted \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Load-balancing Method** & **Scheme** & **Cost** & **Overhead Reduction Method** \\ \hline \multirow{4}{*}{Global load-balancing} & LBRA-CP [9] (2019) & Queue delay, geographic location & Fixed/mobile agent \\  & SLSR [14] (2014) & Propagation delay, queuing delay & Improved flooding algorithm \\  & SALB [8] (2017) & Queue occupancy & Shortest path tree \\  & NCMCR [16] (2019) & Propagation delay, queuing delay & No-Stop-Wait ACK \\  & SIDA [7] (2020) & Link Load & Improved shortest path algorithm \\ \hline \multirow{4}{*}{Local load-balancing} & TLR [21] (2014) & Propagation delay, queuing delay & Local adjustment \\  & HGL [23] (2017) & Propagation delay, queuing delay & Local adjustment \\ \cline{1-1}  & HLBR [30] (2018) & Propagation delay, queuing delay & Local adjustment \\ \cline{1-1}  & LSP [31] (2019) & Propagation delay, queuing delay & Local adjustment \\ \cline{1-1}  & DBPR [27] (2023) & Link distance & Restrict rectangular forwarding area \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of load-balancing routing schemes in the LEO satellite network. to the LSN directly. In this work, we introduce end-to-end hops in the LSN into weight determination, and transmission is also restricted to a rectangular region, which is different from other studies.
###### Abstract  
In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.  
5G; cloud radio access network; computing resource allocation; quality-of-experience; profit maximization +
Footnote †: journal: _Sustainability_
## 2 Results and Discussion
### Phytochemical Characterisation
#### 2.2.2 Physicochemical Characterisation by UHPLC  
The different extracts were analysed by ultra-high-performance liquid chromatography at the Institute of Polymers, Composites and Biomaterials (IPCB-CNR), Italy. The details of the main compounds are presented in Table 6.  
The chemical composition analysis of the two varieties' extracts shows a quantitative and qualitative variation in the chemical profile, depending on the part and variety studied. The results of the chromatographic analyses show that caffeic acid, geraniol, and deca-2E,4E-dienoic acid N-Me IBA were detected only in the variety _A.P_ var. _pyrethrum_, with the presence of caffeic acid just in the empty capitula, and deca-2_E_AE_-dienoic acid N-Me IBA only in the seeds. On the other hand, catechchin, chlorogenic acid, p-cumaric acid, ferulic acid, trans-ferulic acid, hesperetin, quercetin, and ((2_E_AE_)-N-(2-methylpropyl)tetradeca-2_A_-diene-8,10-diynamide) were limited to the variety _A.P_ var. _depressus_, with the presence of catechchin, chlorogenic acid, and p-cumaric acid only in the roots, while myricetin and ferulic acid and ((2_E_AE_)-N-(2-methylpropyl)tetradeca-2,4-diene-8,10-diynamide) were detected only in the leaves, and trans-ferulic acid in the seeds. However, examination of the extracts revealed the presence of pellitorine, coumarin, and L-arginine in all parts of the two varieties. According to the results of the high-performance liquid chromatography analysis of the extracts, 21 compounds were detected: 16 are new compounds that have never been reported in _A. pyrethrum_, such as caffeic acid, hydroxytyrosol, L-arginine, catechin, vanillic acid, chlorogenic acid, coumarin, cinnamic acid, P-cumaric acid, oleupreetin, naringin, quercetin, geraniol, hesperetin, transferrin acid, and ferulic acid. The difference between
## 4 Experiments
### Case Western Reserve University (CWRU) Bearing Experiment Data Analysis
#### 4.1.1 Effects of Individual Modules on Experimental Test Results  
In order to demonstrate the performance of the fault diagnostic model in this paper, the test results of methods lacking adversarial mechanisms and channel reconstruction units and this paper's method were visualized using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The visualization results of the migration task from Domain B to Domain A are depicted in Figure 5. In each category, there was a notable distribution of samples. However, as shown in Figure 5a, when the adversarial mechanism was absent, a more severe cross-mixing phenomenon occurred between categories due to differences in the data distribution. The model that lacked the adversarial mechanism performed poorly in recognizing the test samples in the target domain. As shown in Figure 5b, each category of samples had a large distance between them, and confounding occurred between sample class 7 and sample class 0. However, when deep features were extracted using this paper's method, the method was better able to aggregate the samples in each health state compared to the previous two methods.  
As illustrated in the confusion matrix in Figure 6, it can be seen that due to the lack of an adversarial mechanism, the accuracy of each category shown in Figure 6a was relatively low, especially for category 4, which had an accuracy of only 45%. Figure 6b,c depict that the adversarial module could effectively extract consistent features from the two domains and improve the accuracy of bearing fault recognition. Additionally, comparing Figure 6b,c, the accuracy of nearly every category reached 100% after introducing the channel reconstruction unit in the feature extractor. This indicates that deeper features associated with faults can be effectively learned after the introduction of the channel unit, reflecting the model's superiority. The results of other migration tasks using the three methods are shown in Table 4.  
Figure 4: Time-domain and frequency-domain images of each health state of the Skf6205 bearing.
# Design of a Bionic Spider Robot with a Two-Degrees-of-Freedom Leg Structure and Body Frame
## 2 Analysis of Spider Locomotion Behavior
### Analysis of Spider Physiological Structure and Leg Drive Mechanism  
The structure of a spider's body and legs is shown in Figure 1a,b.The extension of the spider's legs primarily relies on its hydraulic mechanism (the spider's leg joints contain a unique muscle structure capable of storing and releasing hydraulic pressure). When a spider wishes to extend a leg, it uses this muscle to pressurize the fluid into the joint, thereby straightening the leg. Spiders flex their legs by contracting their leg muscles. During the leg contraction movement, the neural network transmits signals to control the contraction of the leg muscles and drive the vertical rotation of the femur. Due to the rotation, the patella, tibia, basal bone, femur end and paw are driven to achieve vertical contraction and complete the leg lift action. Thus, the rotation of the tibia, basal femur, and distal femur allows for the spider to achieve a leg-bending motion. At the same time, the rotation of the paw can adjust the degree of contact pressure with the ground.  
In addition to vertical movement, spiders can also move their legs horizontally. The horizontal movement of spiders' legs mainly relies on the horizontal rotation of the base segment, pushing the femur, patella, tibia, base segment, distal femur and claw to move in the horizontal direction so that the legs can complete the forward and backward movement. This unique mode of movement ensures that the spider can adapt to a variety of complex environments, not only helping to capture prey, but also helping the spider to escape from predators.
## Materials and Methods
### Pre-Processing and LEPA-Processing
For this reason, it is important to use the constraints of variance to increase the reliability of LEPA. According to the above introduction, the flow of the algorithm is shown in Figure 11.  
The specific process of LEPA is as follows:  
1. We assume that the original image has M \(\times\) N pixels, we use each pixel as the initial target region \(R_{i}\), where \(0\leq\) i \(\leq\) M \(\times\) N;
2. Set a variable SCOPE to determine if the target region needs to be grown: if the size of \(R_{i}\) is larger than SCOPE, the growth of the \(R_{i}\) is terminated;
3. If the size of \(R_{i}\) is smaller than SCOPE, we use the function _DIFF_ to calculate the difference between \(R_{j}\) and every adjacent region \(R_{j}\). \[DIFF(R_{i},R_{j})=FD(R_{i},R_{j})\times W_{FD}+VAR(R_{i},R_{j})\times W_{VAR}\] (3) where \(W_{FD}\) and \(W_{VAR}\) are the weight for \(FD\) and \(VAR\), respectively, with \(0\leq W_{FD}\leq 1\), \(0\leq W_{VAR}\leq 1\), \(W_{FD}+W_{VAR}=1\);
4. Get the smallest _DIFF_ value \(DIFF_{k}\); for all \(DIFF_{k}\), we compare the value of \(DIFF_{k}\) and MD. If \(DIFF_{k}\) is larger than MD, \(R_{i}\) is considered a small target, and it will be adjusted to the background region around it. If \(DIFF_{k}\) is smaller than MD, \(R_{i}\) will be merged into the most similar region \(R_{k}\);
5. Perform the above calculations for all regions \(R_{i}\) and then the method continues to the next cycle until all target regions are larger than SCOPE, then the LEPA ends. Figure 1: Flow chart for local edge preservation algorithm (LEPA). A typical remote-sensing image containing the harbor as a test image of the proposed method, the image processing results and other smoothing algorithms processing results are compared as shown in Figure 27. In this experiment, the value of SCOPE is 150, and the value of MD is 10. Figure 2: _Cont._  
As can be seen, the processing result of our algorithm is better than the general smoothing algorithm and other edge-preserving filtering algorithms. The useless edges in the bilateral filter and guided filter are larger than our method, and the useful edges are less than our method. These results show that our method meets the expected requirements and does not lose too much useful edge information.
## 3 Methodology
### Solar System Simulation  
The solar system was sized based on a theoretical desorption heat load. An Apricus model AP-30 solar collector was experimentally tested, and its thermal efficiency (\(\eta_{i}\)) curve was calculated using ASHRAE 93-1986 standard [31] as follows:  
\[\eta_{i}\ =0.485-1.579\left(\frac{\Delta T}{G_{b}}\right) \tag{1}\]  
where \(\Delta T\) is the outlet and inlet temperature difference (\({}^{\circ}\)C), and \(G_{b}\) is the solar radiation (W/m\({}^{2}\)).  
Based on this thermal efficiency, the solar system was simulated and sized for the desorber energy requirements. An insulated storage tank was considered, and serial configuration was assumed for the solar system simulation. The energy balance inside the storage tank is defined as follows:  
\[\left(mC_{p}\right)_{t}\frac{dT}{dt}=Q_{u}-Q_{loss}-Q_{Des} \tag{2}\]  
where \(Q_{u}\) is the useful heat load, \(Q_{loss}\) is the storage tank heat loss, and \(Q_{Des}\) is the heat load required by the membrane desorber. The temperature inside the storage tank (\(T^{+}{}_{t}\)) was calculated using Equation (3) solved by finite differences, as follows:  
\[T_{t}^{+}=T_{t}+\frac{\Delta t}{\left(mC_{p}\right)}[Q_{u}-Q_{loss}-Q_{Des}]. \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Sensor/Instrument** & **Operation Range** & **Uncertainty** \\ \hline Temperature (\(T\)) & RTD PT100 & \(\sim\)30 to 350 \({}^{\circ}\)C & \(\pm\) 0.2 \({}^{\circ}\)C \\ Volumetric flow (\(V_{cw}\)) & Volumetric flowmeter & 0 to 7 L/min & \(\pm\) 5.0\% f.s. \({}^{*}\) \\ Volumetric flow (\(V_{hIb}\)) & Volumetric flowmeter & 0 to 1.2 L/min & \(\pm\) 4.0\% f.s. \({}^{*}\) \\ Mass flow (\(m_{LiBr}\)) & Coriolis mass flowmeter & 0 to \(4.0\times 10^{-2}\) kg/s & \(\pm\) 0.1\% \\ Distillate water weight (\(w_{dis}\)) & Electronic balance & 0 to 600 g & \(\pm\) 0.01 g \\ Refractive index (\(IR\)) & Electronic Refractometer & 1.3000 to 1.7200 & \(\pm\) 0.0001 \\ \hline \hline \end{tabular} Note: \({}^{*}\) f.s., full scale.  
\end{table}
Table 2: Uncertainty of the measured variables.
## 3 System Design
### Security Protection Mechanisms
Concurrently, during link breaks, automatic reconstruction, or fallback forwarding, all newly established sessions automatically inherit the original encryption parameters, ensuring that data security is not compromised throughout the re-connection process. Within the entire system, the server-side signaling communication utilizes WebSocket long connections. While TLS is not enabled by default for these connections, all critical control messages do not contain unencrypted audio or video data and can be further upgraded to encrypted transmission (e.g., using \(\mathtt{ws}\)://) based on specific deployment environment requirements. The physical decoupling of the control plane and data plane means that even if the signaling plane were to be compromised, an attacker would be unable to directly access or manipulate the actual video stream content.
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Gases Concentrations**} \\ \cline{2-4}  & **O\({}_{2}\)** & **CO\({}_{2}\)** & **Rest Gases (N\({}_{2}\))** \\ \hline steak & 0.30 \(\pm\) 0.19 & 35.27 \(\pm\) 1.06 a  & 64.43 \(\pm\) 0.94 b  \\ noodles & 0.07 \(\pm\) 0.01 & 37.70 \(\pm\) 0.26 ab  & 62.24 \(\pm\) 0.25 c  \\ filet & 0.45 \(\pm\) 0.19 & 35.23 \(\pm\) 1.36 a  & 64.34 \(\pm\) 1.22 b  \\ burger & 0.22 \(\pm\) 0.07 & 34.63 \(\pm\) 0.63 ac  & 65.14 \(\pm\) 0.59 b  \\ minced & 0.08 \(\pm\) 0.06 & 33.97 \(\pm\) 0.74 c  & 65.97 \(\pm\) 0.70 a  \\ \hline \hline \end{tabular}  
* Different superscripts a, b, c indicate significant differences (\(p<0.05\)) among analogue meat products.  
\end{table} 
Table 2: Gas concentrations (35% CO\({}_{2}\) and 65% N\({}_{2}\)) (ratio in %, mean \(\pm\) SD) in modified atmosphere packaging of analogue meat products. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **Steak** & **Noodles** & **Filet** & **Burger** & **Minced** \\ \hline TBARS (mg MDA-kg\({}^{-1}\)) & 6.67 \(\pm\) 1.08 b  & 5.22 \(\pm\) 0.58 c  & 9.71 \(\pm\) 1.73 a  & 4.81 \(\pm\) 1.27 c  \\ FFA (\% total fat as oleic acid) & 2.31 \(\pm\) 0.17 c  & 3.77 \(\pm\) 0.30 a  & 3.52 \(\pm\) 0.30 b  & 1.48 \(\pm\) 0.31 a  & 1.12 \(\pm\) 0.20 b  \\ polyphenols (mg gallic acid/g) & 2.63 \(\pm\) 0.03 a  & 1.08 \(\pm\) 0.03 a  & 1.01 \(\pm\) 0.01 a  & 1.19 \(\pm\) 0.03 b  & 1.15 \(\pm\) 0.03 c  \\ DPPH (\% inhibition) & 18.78 \(\pm\) 1.82 b  & 18.91 \(\pm\) 3.30 b  & 1.53 \(\pm\) 2.49 c  & 13.83 \(\pm\) 3.71 c  & 32.26 \(\pm\) 10.09 a  \\ ABTS (\% inhibition) & 0.82 \(\pm\) 0.32 & 1.25 \(Correlations between \(\mathrm{O_{2}}\) (in the steak samples) and two of the measured chemical parameters, the polyphenol content (R\({}^{2}\) = 0.6313) and CUPRAC (R\({}^{2}\) = 0.7347), were found to be statistically significant (\(p\) < 0.05); in the burger samples, a significant (\(p\) < 0.05) correlation was found with ABTS (R\({}^{2}\) = 0.9221); in the file samples, with FRAP (R\({}^{2}\) = 0.8518) and CUPRAC (R\({}^{2}\) = 0.9452; the highest found positive correlation); in the noodles samples, with polyphenols (R\({}^{2}\) = 0.9286) and DPPH (R\({}^{2}\) = 0.6357); and in the minced samples, with FFA (R\({}^{2}\) = 0.8866) and DPPH (R\({}^{2}\) = \(-\)0.689). Correlations between \(\mathrm{CO_{2}}\) (in minced samples) and two of the measured chemical parameters, FFA (R\({}^{2}\) = \(-\)0.8738; the highest found negative correlations) and DPPH (R\({}^{2}\) = 0.7345), were found to be statistically significant (\(p\) < 0.05).
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 2 System Model
### User Satisfaction Ratio Model  
In such a model, the user can get the data from: (1) locally associated BS cache storage unit, (2) remote BS cache storage unit but within the same tier, (3), remote BS cache storage unit but in another tier, and (4) content server. The remote content unit is the other BS that caches the user's required contents but is not associated to the user. It means there is cooperation among the BS not only within the tier but also across the tiers. Also, within the tier there will be intra-cluster and cross-cluster cooperation which will be discuss in Section 6 in detail.  
The average user satisfaction can be modelled as:  
\[N(\text{y})\ =\ \frac{1}{R_{\text{t}}}\sum\
olimits_{re\text{R}_{\text{t}}}Y_{r }=\text{Average request satisfaction ratio} \tag{3}\]  
where:  
\(Y=\{\text{y}_{1},\text{y}_{2},\text{y}_{3},\text{y}_{4}\ \ldots\ \text{y}_{\text{RT}}\}\)  
\(\text{R}_{\text{t}}=\sum\
olimits_{t\in\mathcal{T}}\sum\
olimits_{m\in \mathcal{M}}\left|R(m_{t})\right|\)  
\(y_{r}=\left\{\begin{array}{c}1\text{ if request }r\text{ is served}\\ \text{ o otherwise}\end{array}\right.\)  
In order to serve the user content request, two methods are proposed which will be discussed in Section 5.
## 3 Delay Inversion and Jitter Optimizations
### Adaptive Jitter for Non-Trivial Metrics  
The window jitter principle can be naturally extended to non-trivial link metrics, for instance based on the probability of successful transmission (Expected Transmission Count [24]) or the available bandwidth in the link. This extension of window jitter to link metrics other than hop count is denominated adaptive jitter.  
Given a link quality indicator \(\text{LQ}\in(0,1)\) (\(\text{LQ}\longrightarrow 1\) for high quality links), jitter values are selected uniformly within the interval \([(1-\text{LQ})J_{m},J_{m}]\). This reduces the probability of delay inversion or, equivalently, increases the probability that an RREQ packet is forwarded faster by routers receiving it on better links.  
Note that the window jitter distribution presented in Section 3.2 corresponds to the particular case of \(\text{LQ}=1-\alpha\) for all available links.
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 3 Application of 2D Nanofluids
### Transformers
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Authors/Year** & **Nanomaterial/Concentation** & **Base Fluid** & **Main Findings** & **Remarks** \\ \hline
[152] & Amiri et al., 2015 & CNTs functionalized with hexylamine at 0.001\% and 0.005\% wt. & Transformer oil & Increase of 10\% in the TC & Increases in the natural \\  & & & compared with that of the & convection and forced convection \\  & & & transformer oil alone at & HTC of 23\% and 28\%, \\  & & & 0.005\% wt. & respectively, at 0.005\% wt. \\ \hline
[153] & Amiri et al., 2017 & Gr quantum dots at 0.001\% wt. & Transformer oil & Increase in the HTC of 23.9\%. & Negligible increase of 1.3\% in the viscosity. \\ \hline
[154] & Subaimi et al., 2020 & MWCNTs at up to 0.02 g/L & Disposed Transformer oil & Increased of approximately 21.26\% and 40\% in the breakdown strength and lightning impulse pattern indicated at 0.05 g/L & The presence of the MWCNTs resulted in a decrease in the threshold at 0.05 g/L & resulted in a decrease in the sensitivity and resistivity of the transformer oil alone. \\ \hline
[155] & Alizadeh et al., 2022 & MWCNTs functionalized with OH at 0.001\% and 0.01\% wt. & Transformer oil & Increased of approximately 26.2\% and 30.1\% in the HTC at 0.01\% wt. and 0.001\% wt., respectively. \\ \hline
[156] & Subaimi et al., 2022 & CNTs from 0.01 g/L to 0.2 g/L & Mineral oil & Increases of nearly 118.3\% in the breakdown voltage at 1\%. & At 0.01 g/L, the nanofluid & strongly impacted on the storage modulus, viscosity, and thermal \\  & & & probability and at 0.01 g/L & behavior, which contributes to the breakdown performance. \\ \hline \end{tabular}
\end{table} 
Table 10: Recent studies on the use of 2D nanofluids in electrical transformers.