esa-sceva/satcom-chunk-collection huggingface dataset:
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 5 Numerical Simulation of VVSDSI Bearing
### Comparative Analysis of Test Results and Numerical Simulation Results  
Figure 15 shows the comparison between experimental results and numerical simulation results of the load-displacement curves for VVSDSI bearings under ultimate loading conditions. As can be seen from the figure, the simulated values of VVSDSI bearings exhibit similar overall curve characteristics to the experimental values in terms of the shape. During the seismic isolation phase, the force-displacement curves of the bearings show good agreement between simulation and experimental results. However, after entering  
Figure 14: Finite element model of VVSDSI bearing.  
the protection phase, the deviation between simulation results and experimental values increases, with the peak load error reaching 10.07%. Although local discrepancies exist, the overall comparative results demonstrate that the numerical model can effectively predict the mechanical behavior of the bearings.  
According to the comparison of load-displacement curves for VVSDSI bearings under cyclic loading shown in Figure 16, the numerical simulation results reproduced the typical hysteretic characteristics of the experimental curves well. Both exhibit good agreement in terms of amplitude variation and hysteretic loop morphology. Table 7 compares experimental and numerical simulation results for the equivalent stiffnesses of VVSDSI bearings under cyclic loading. The results demonstrate satisfactory consistency between numerical simulations and experimental data, with a maximum error of 13.34% and an average error of 5.32%.  
Figure 16: Comparison of experiment and simulation values of load-displacement hysteresis curves under cyclic loading. (**a**) Preloading displacement 16 mm; (**b**) preloading displacement 23 mm; (**c**) preloading displacement 30 mm.  
Figure 15: Comparison of the experimental and simulated values of the load-displacement curve under ultimate load.
# Production of Sorbitol via Catalytic Transfer Hydrogenation of Glucose
###### Abstract  
Sorbitol production from glucose was studied through catalytic transfer hydrogenation (CTH) over Raney nickel catalysts in alcohol media, used as solvents and hydrogen donors. It was found that alcohol sugars, sorbitol and mannitol, can be derived from two hydrogen transfer pathways, one produced involving the sacrificing alcohol as a hydrogen donor, and a second one involving glucose disproportionation. Comparison between short-chain alcohols evidenced that ethanol was able to reduce glucose in the presence of Raney nickel under neutral conditions. Side reactions include fructose and mannose production via glucose isomerization, which occur even in the absence of the catalyst. Blank reaction tests allowed evaluating the extension of the isomerization pathway. The influence of several operation parameters, like the temperature or the catalyst loading, as well as the use of metal promoters (Mo and Fe-Cr) over Raney nickel, was examined. This strategy opens new possibilities for the sustainable production of sugar alcohols.  
sorbitol; catalytic transfer hydrogenation; Raney nickel; glucose; biomass; catalysis +
Footnote †: journal: Applied sciences
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Manipulation of Microbots
#### 4.1.4 Trapping Multiple Particles  
The maneuvering of microrobots using light may necessitate multiple particle trapping. Microrobots are frequently designed with multiple handles to provide better control. Multiple traps can be set using different methods, such as the inclusion of scanning mirrors [143], acousto-optic modulators [144], or a spatial light modulator (SLM) [145]. The former two are essentially beam-splitting time-sharing systems in which a single beam is quickly scanned over numerous targeted areas, resulting in multiple traps. SLM essentially divides the incident beam of light into a pattern of multiple traps [146]. A liquid crystal on silicon (LCoS)-based SLM reflects the light beam according to the computer-generated hologram (CGH) displayed on the panel. This not only provides for dexterity in trap position control, but also allows for simultaneous control of multiple traps. Rotation on its own axis is a critical component of many traps. The entrapped sphere can be rotated on its axis by transferring angular momentum. This is often accomplished by rotating an interference pattern [147] or a fixed aperture in the optical line [148]. The spin angular momentum content of several optical traps can be generated and controlled using a single SLM unit [145]. More advanced beam-steering and multiple trap-generating systems use acousto-optic deflectors (AODs) to modulate the optical trap. Multiple traps and beam steering are depicted in Figure 6.
# A Study of 5G Edge-Central Core Network Split Options
## 6 Split Models Assessment
This includes most of the Non-Public Network (NPN) deployments for example for factory shop floors in Industry 4.0 use cases, hospital networks, construction sites or logistics. The rest of NPNs where the backhaul availability cannot be guaranteed, it is better to use autonomous edge nodes no matter of the extra-resource consumption at the edge. This includes Public Protection and Disaster Relief (PPDR) use cases like ambulances, firefighters, technical support, or police as well as logistics and maritime networks, railway and in-aircraft communication. Based on the quality of the backhaul, the appropriate network management solution should be chosen. Specifically, for nomadic or mobile use cases where high reliability is needed and autonomous edge nodes are deployed, a distributed solution is highly appropriate. For the other situations, the centralized split of the network management can be considered if the delay of the procedures and the potential loss of communication are considered. However, the distributed network management would be the best alternative in most of the cases as it can immediately and locally respond to the different requirements provided that a properly dimensioned solution is developed addressing such edge network deployments and not directly porting the wide area network operators' ones. Furthermore, with the more functionality added at the edge of the network, the network management operations needed to be executed is increased. Each of the edge nodes includes more network functions which must be installed, configured, and upgraded. Additionally, the complexity of the runtime network management is also significantly increased in terms of fault management, performance, optimization management and security management. This increase in complexity should be traded off against the end-to-end communication delay requirements quantified in this article.
## 2 Results
### Analysis of Sesquierpene Biosynthetic Genes in A. sinensis  
The gene expression profiles of the MVA and MEP pathways involved in terpenoid backbone biosynthesis were analyzed to characterize the sesquiterpene biosynthetic pathway across different tissue sites (Figure 8). A total of 28 unigenes associated with the biosynthetic pathway of the sesquiterpene precursor FPP were identified. Among them, 16 unigenes were identified as being associated with 6 enzymes in the MVA pathway, while 10 unigenes were identified as being associated with 4 enzymes in the MEP pathway responsible for synthesizing the key terpen precursors IPP and DMAPP. In addition, the annotation identified two unigenes as IDI, responsible for the reversible conversion of isomers IPP and DMAPP. Two unigenes were annotated as FPS, involved in the synthesis of FPP, a crucial precursor for sesquiterpenes. Furthermore, 46 unigenes were annotated as TPS, contributing to the synthesis of terpenoids with diverse skeletal structures. A total of 28 TPS were annotated related to sesquiterpene synthesis, comprising 10 germarane synthase genes, 8 guaiane synthase genes, 4 chain synthase genes, 3 cadinane synthase genes, 1 agaroospirane synthase gene, and 2 others. The gene with the highest expression level was AS_ACAT_236 (ACAT) in leaf callus, seeds, and roots; AS_HMGS_319 (HMGS) in stems and leaves; and AS_HDR_446 (HDR) in husks. The expression patterns of TPS in roots and stems exhibited significant similarity. Within the callus, 60% of TPS contained sesquiterpene synthase genes, while 24% harbored monoterpene synthase genes and 16% possessediterpene synthase genes.
## 3 Results and Discussion
### Separation of RB8 from Real Wastewater
Figure 11: Comparison of sorption rate of RB8 (7 g L\({}^{-1}\)) from real wastewater on PAC (280 g L\({}^{-1}\)) and BC (42 g L\({}^{-1}\)) in co-action with BAC (42 g L\({}^{-1}\)) or BT (28 g L\({}^{-1}\)) in co-action with BAC (20 g L\({}^{-1}\)). Figure 10: Optimization of co-action of BAC (50%) and BT within the separation of RB8 from real wastewater (containing 7 g L\({}^{-1}\) of RB8 dye among others) after 5 h of action. The economic costs of the respective methods are presented in Table S1 in the Supplementary Materials. Although RB8 separation using BAC alone is the economically acceptable method, the co-action of BAC with BT applied for RB8 removal from model and real wastewater offers the most effective decolorization and COD removal; see Table S1 in the Supplementary Materials. Moreover, the direct combination of BT and BAC enables the cheapest separation of RB8 dye or ion pairs, respectively. The new approach of the circular economy and the requirements of the price decrease of treatment processes aim at the facile and effective recycling of materials applied, for example, in treating wastewater. Therefore, our previous article [45] proposed new innovative methods of recycling ion pairs formed in dye separation processes. We also designed a process for prolonging the life cycle of spent carbonaceous sorbents using BAC [44]. Based on these findings, it can be assumed that the exhausted sorbents applied in co-action with BAC have great potential for regeneration and recycling too. However, the direct and simultaneous recycling of both materials--BAC and respective alternative sorbents--is a challenge for our future research. Finally, for a comparison of the above-described results of the separation of RB8 dye from wastewater (calculated per one gram of removed dye for the demonstration of practical application) with the available literature [9; 15; 16; 17; 18; 19], see Table 2. Only photocatalytic oxidation using a high dosage of a photocatalyst [16] or adsorption on expensive ZnO nanoparticles used in huge excess [19] reaches the removal efficiencies of the RB8 dye comparable with the method developed in this article. The combination of an alternative cheap sorbent such as BT with an available ionic liquid BAC offers effective, rapid and smooth removal of the RB8 dye from the model as well as from real wastewater.
# Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs
###### Abstract  
Computation Offloading (CO) can accelerate application running through parallel processing. This paper proposes a method called _vehicular adaptive offloading_ (VAO) in which vehicles in vehicular ad-hoc networks (VANETs) offload computationally intensive tasks to nearby vehicles by taking into account the dynamic topology change of VANETs. In CO, task scheduling has a huge impact on overall performance. After representing the precedence relationship between tasks with a directed acyclic graph (DAG), VAO in the CO requesting vehicle assigns tasks to neighbors so that it can minimize the probability of task reallocations caused by connection disruption between vehicles in VANETs. The simulation results showed that the proposed method decreases the number of reallocations by 45.4%, as compared with the well-known task scheduling algorithms HEFT and Max-Min. Accordingly, the schedule length of entire tasks belonging to one application is shortened by 14.4% on average.  
computation offloading; directed acyclic graph; task scheduling; vehicular ad hoc network Article
## 2 Materials and Methods
### Comparative Evaluation with Traditional Feature Selection Methods and Classifiers
conducted a review on the use of machine learning in human microbiome studies, summarizing its applications in feature selection, biomarker discovery, disease prediction, and treatment strategies [53]. The review identified Random Forest, Support Vector Machines (SVM), and logistic regression as the most commonly employed classification algorithms for microbiome analysis [54]. The use of different feature selection methods on metagenomic data analysis is reviewed in [55]. IG, CMIM, XGBoost, MRMR, FCBF, and SKB feature selection methods have been previously utilized for type 2 diabetes [56], inflammatory bowel disease [45], and CRC-associated metagenomic datasets to identify disease-associated taxonomic biomarkers [57]. In this study, we have performed several experiments to define the effectiveness of different feature selection techniques in combination with various classification algorithms. To conduct a comparative performance analysis, traditional feature selection (TFS) methods, such as XGBoost (eXtreme Gradient Boosting), IG (Information Gain), SKB (Select K Best), and FCBF (Fast Correlation-Based Filter), have been applied to CRC-associated metagenomic relative enzyme abundance. RF is an ensemble learning algorithm that aggregates multiple decision trees (DT) to enhance predictive accuracy. By constructing decision trees on random subsets of training data and features, RF can handle high-dimensional data effectively. Adaptive Boosting (Adaboost), another ensemble method, iteratively combines weak classifiers to create a robust classifier, focusing on misclassified samples to boost overall prediction accuracy. LogitBoost, tailored for binary classification tasks, utilizes logistic regression as the base learner to iteratively adjust instance weights for model refinement. In this study, while evaluating TFS, we have employed different classifiers, such as RF, LogitBoost, DT, SVM_opt (Support Vector Machines with Optimization), Support Vector Machines (SVM), Stack_Logitboost_Kmeans, Stack_SVM_Kmeans, and XGBoost. We have aimed to evaluate the impact of feature selection approaches and classifiers on model performance by performing a consistent experiment repeated in 10 iterations of Monte Carlo Cross-Validation.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 4 Fast Time Simulation Results
### LECMTLU Sector  
#### 4.2.1 Capacity  
To compute the capacity in the LECMTLU sector, the days of the busy week (1-7 July 2019) were simulated in RAMS, determining the WL for each hour of each of the days. The capacity value determined for the Baseline Scenario is 46 aircraft per hour (46.81 aircraft/hour). The declared capacity for the LECMTLU sector is slightly lower, 45 aircraft per hour (the same as the LECMZMU sector). Consequently, the capacity value derived using the CAPAN methodology is deemed acceptable.  
For Scenario 1, the implementation of the Ad Hoc separation concept results in a new TC WL in the LECMTLU sector, which yields a new capacity value of 47 aircraft per flight hour (47.24 aircraft/hour). In Scenario 2, the capacity computation considers ADSM implementation, with anticipated decreases in ATCO detection and decision making times due to the CD&R functionalities of the ASMT tool. The resulting capacity value for this scenario is 47 aircraft per flight hour (47.74 aircraft per flight hour).  
Table 3 compares the capacity outcomes attained in the LECMTLU sector, which are slightly lower than those of the LECMZMU sector. However, in relative terms, there is an increase from 46 aircraft per flight hour to 47 aircraft per flight hour. In absolute terms, this represents nearly 1 aircraft per hour of increased capacity, signifying a growth of 2% in the sector's capacity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Scenario** \\ **LECMTLU** \\ \end{tabular} } & \multicolumn{3}{c}{**Capacity**} & \multirow{2}{*}{\
\begin{tabular}{c} **No. of** \\ **Aircraft/hour** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Difference** \\ **Capacity Increase** \\ **(\%)** \\ \end{tabular} } & \multicolumn{3}{c}{**Percentage of**} \\  & & & **Capacity Increase** \\ \hline \multicolumn{2}{c}{**Baseline scenario**} & 46.81 & 46 & - & - & - \\ \hline \multicolumn{2}{c}{**Scenario 1**} & \multirow{2}{*}{47.24} & \multirow{2}{*}{47} & \multirow{2}{*}{0.43} & \multirow{2}{*}{0.9\%} \\  & & & & & & & & & & & \\ ASMT (no CD&R) & & & & & & & & & & \\ \hline \multicolumn{2}{c}{**Scenario 2**} & \multirow{2}{*}{47.74} & \multirow{2}{*}{47} & \multirow{2}{*}{0.93} & \multirow{2}{*}{2\%} \\  & & & & & & & & & & & & \\ \end{tabular}
\end{table}
Table 3: Capacity results in LECMTLU.  
Figure 8: Environment and cost-efficiency results for the LECMZMU sector.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 4 The Proposed Scheme
### Scheme Overview
#### 4.1.2 Design of Mechanism for Resisting Report Disruption Attacks  
The adversary might launch the report disruption attacks by controlling the compromised nodes to contribute invalid partial signatures. When a participating node provides an invalid partial signature, it will result in an invalid cluster signature being generated for the event report. Thus, the event report will be dropped by some forwarding node and unable to reach the BS. The existing PKC-based schemes do not provide any measure to verify the partial signatures. To defend against such attacks, the authors propose a verification mechanism to detect the invalid partial signatures, thereby avoiding generating an invalid cluster signature for the event report.  
The adversary might control a compromised node to refuse to participate in report generation, in an attempt to disturb the generation of a cluster signature. Once the CH detects that a participating node refuses to participate in report generation, or provides an invalid partial signature, it needs to request a trusted non-participating cluster node to join in report generation. Therefore, a substitution scheme is designed to ensure that enough cluster nodes are maintained to participate in report generation. Furthermore, if a CH node is compromised, it might refuse to send out the signed event report or send out an event report with an illegitimate signature, in an attempt to make the event report unable to reach the BS. The existing PKC-based schemes (DSEDA and PDF) do not provide any solution to such attacks. To resist such attacks, the authors propose an effective solution. Once the CH node has any malicious behavior, a new CH will substitute it and regenerate a signed event report.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 3 YOLOv8-TLC Algorithm
### New Detection Layer  
To address the issue of missed detections caused by certain defects occupying a large proportion of the image in steel datasets, this paper introduces a new large-scale detection head into the network. The original YOLOv8n detection head includes three different detection layers with scales of 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20. The YOLO model partitions targets based on receptive field size: large-scale feature maps are used for detecting small objects, while small-scale feature maps are used for detecting large objects [34]. Since the sizes of various defects on steel surfaces differ, their scales when mapped onto images also vary. To improve YOLOv8n's detection accuracy for steel surface defects, this paper adds an extra-large scale detection layer, specifically targeting defects that occupy a large proportion of the image. This modification aims to reduce missed detections while enhancing the network's generalization ability. The size of the new detection layer is shown in Table 1.  
Figure 1: YOLOv8n model structure.
# Covert Channel Based on Quasi-Orthogonal Coding
## 4 Simulation Tests
### Simulation No. 5  
Simulation conditions are the same as in the previous example, except that we increase the value of modulation of the covert information to 8 PSK. The results are presented in Figure 10.  
Multi-valued 8 PSK modulation requires a higher ratio of energy per bit of information. For this reason (with an assumed SNR = 45 dB), even for zero estimation error, the BER is different from zero (Figure 10f) and there is less immunity to channel estimation errors.
## 3 Mathematical Formulation for Stochastic SPV and WT Power  
The adopted IEEE 30 bus system is changed at bus number 5 and 11 to WT, and at bus number 13 a SPV power source is integrated. Weibull PDF and wind speed distribution possess a similar shape [50]. A Weibull PDF with shape factor (\(k\)) and scale factor (\(c\)) can be used to obtain the probability of wind speed (m/s), as follows:  
\[f_{v}(w)=(\frac{k}{c})(\frac{v}{c})^{k-1}e^{(-v/c)^{k}}\ \ \ \text{for}\ 0<v<\infty \tag{26}\]The output power of WT, which is a function of wind speed, can be modeled as follows:  
\[P_{w}(v)=\left\{\begin{array}{ll}0,&\text{for $v<v_{in}$ and $v<v_{out}$}\\ P_{wr},&\text{for $v_{in}\leq v\leq v_{r}$}\\ P_{wr},&\text{for $v_{r}\leq v\leq v_{out}$}\end{array}\right. \tag{27}\]  
The values for the shape factor (\(k\)) and scale factor (\(c\)) are taken from reference [8] and are mentioned in Table 3. A Weibull fitting curve and wind speed frequency distribution curve are illustrated in Figures 2 and 3, which are obtained after simulation of 8000 Monte Carlo scenarios.  
Output power from SPV sources relies on solar irradiance that follows the lognormal PDF [51]. The lognormal PDF, having a mean (\(\mu\)) and standard deviation (\(\sigma\)), may be used to obtain the probability for solar irradiance (\(G\)) by utilizing the following expression:  
\[f_{G}(S)=\frac{1}{G\sigma\sqrt{2\pi}}\exp\left\{\frac{-(\ln x-\mu)^{2}}{2\sigma ^{2}}\right\}\text{ for $G$ }>0 \tag{28}\]  
Figure 3: Wind speed distribution at bus #11.  
Figure 2: Wind speed distribution at bus #5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**WT** & **Bus** & **P\({}_{rw}\)** & **Scale Factor** & **Scale Factor** & **Weight Mean** & **Direct Cost** & **Reserve Cost** & **Penalty Cost** \\
**No.** & **No.** & **(MW)** & **(\(c\))** & **(\(k\))** & **(\(k\))** & **Coefficient** & **Coefficient** & **Coefficient** & **Coefficient** \\ \hline
1 & 5 & 75 & 9 & 2 & \(v=7.976\) m/s & \(g_{w,5}=1.6\) & \(K_{kw,5}=3\) & \(K_{pw,5}=1.5\) \\
2 & 11 & 60 & 10 & 2 & \(v=8.862\) m/s & \(g_{w,11}=1.75\) & \(K_{kw,11}=3\) & \(K_{pw,11}=1.5\) \\ \hline \end{tabular}
\end{table}
Table 3: WT PDF parameters and cost coefficients.  
The output power of the SPV source, which is a function of solar irradiance (\(G\)), can be mathematically modeled as follows:  
\[P_{SPV}(G)=\begin{cases}P_{sr}(\frac{G^{2}}{C_{\text{s}td}^{2}Rc})&\text{for }0<G<Rc\\ P_{sr}(\frac{G^{2}}{C_{\text{s}td}Rc})&\text{for }G\geq Rc\end{cases} \tag{29}\]  
The parameters for the lognormal PDF are taken