esa-sceva/satcom-chunk-collection huggingface dataset:
# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 4 Results
### Dataset  
We use a subgraph extracted from the Protein-Protein Interaction (PPI) network [35], specifically within the context of Homo Sapiens. This subgraph was carefully curated to encompass nodes associated with labels derived from hallmark gene sets [36], serving as indicators of distinct biological states. The collection has 50 hallmarks from more than 4000 gene sets. This subgraph is constituted by 3890 nodes, intricately interconnected by 76,584 edges, and characterizes itself with a diverse repertoire of 50 distinct labels.  
As a second PPI dataset in this study, we use model organism _S. cerevisiae_. We source the PPI dataset, specifically _S. cerevisiae_ validated ORFs, from BioGRID [35]. This dataset comprises consecutive edges and features, focusing exclusively on protein-protein physical interactions validated through wet-lab experiments. Repetitive interactions, loops, and unconfirmed ORF node interactions are deliberately excluded. We use the MIPS (Munich Information Center for Protein Sequences) [37] functional catalog version 2.1 (FunCat) third level for functional category tags. This version includes 181 functional categories at the third level.  
BlogCatalog [38] dataset represents a complex social network comprising bloggers registered on the BlogCatalog platform. Each node within the network corresponds to an individual blogger, and the edges interconnecting these nodes denote interpersonal relationships. The assigned labels associated with each node denote inferred blogger interests, which are determined through the analysis of metadata provided by the bloggers themselves. Notably, this network dataset exhibits substantial scale, encompassing a total of 10,312 nodes, connected by 333,983 edges, and categorizes bloggers into 39 distinct interest categories. This resource serves as a valuable asset for the empirical study of social dynamics and content preferences within the blogging community.  
The Wikipedia [39] dataset in question pertains to a cooccurrence network constructed from words found in the initial million bytes of the Wikipedia dump. These words are assigned labels corresponding to their Part-of-Speech (POS) tags, as determined through  
Figure 3: Graphical representation of a depth neural network. \(N\) represents the node size of a target network.  
the Stanford POS-Tagger [40]. The network consists of 4777 nodes, interconnected by 184,812 edges, and encompasses a diverse array of 40 distinct labels.  
It is important to note that for all networks, the evaluation of node and interaction accuracy of original networks falls outside the scope of our study. To represent graph \(G\) with \(n\) nodes, an adjacency matrix with \(n\times n\) elements can be used. The row and column numbers of the matrix represent the nodes of the network, and each column and row intersection point represents the edge presence between these nodes. \(A_{ij}\geq 1\) if there is an edge/edges between nodes \(v_{i}\) and \(v_{j}\), and \(A_{ij}=0\) if there is no edge between nodes.
## 4 Results
### Environmental Impact According to the ISO 14083 Standard  
4.2.1. Tank-to-Wheel Emissions from Operating a Vehicles  
Maximal Tank-to-Wheel emissions from operating a vehicle occur in R + LT = 1 regardless of the WS and demand variability, as visible in Figure 13. For a 5-day WS, the minimal average C\({}_{\text{T}}\), regardless of demand variability, occurs in R + LT = 27 with the value of 1.12 tonnes CO\({}_{\text{2}}\)e/year. The maximal C\({}_{\text{T}}\) in the same WSs has a value of 14.15 tonnes CO\({}_{\text{2}}\)e/year for SD 10 and 13.40 tonnes CO\({}_{\text{2}}\)e/year for SD 30. For a 7-day WS, the minimal C\({}_{\text{T}}\) occurs in R + LT = 30 with a 1.20 tonnes CO\({}_{\text{2}}\)e/year regardless of demand variability. The maximum average C\({}_{\text{T}}\) in the same WSs is 19.86 tonnes CO\({}_{\text{2}}\)e/year for SD 10 and 19.44 tonnes CO\({}_{\text{2}}\)e/year for SD 30. Reducing R + LT from maximal to minimal values increases C\({}_{\text{T}}\) 12.3 times in a 5-day and 16.3 times in a 7-day WS.  
With the increase in R + LT, the average G\({}_{\text{T}}\) decreases. The most significant relative decrease in average G\({}_{\text{T}}\) in a 5-day WS for SD 10 and SD 30 occurs for R + LT = 2/1, with an average reduction of 43.2%. For both demand variability, the most considerable relative decrease in average G\({}_{\text{T}}\), 50.0% in a 7-day WS, happens for R + LT = 2/1. On average, with the increase in R + LT value for a single time unit, average G\({}_{\text{T}}\) decreases 7.5% in a 5-day and 8.4% in a 7-day WS.  
For each SC setup, the average G\({}_{\text{T}}\) for a 7-day WS is higher than that for a 5-day schedule. On average, the relative increase in average WWT for SD 10 is 26.3%, whereas for SD 30, it is 26.9%. The relative difference between the 7-day and 5-day WSs in G\({}_{\text{T}}\) shows no trend with the increase in the R + LT. Demand variability almost does not influence the average G\({}_{\text{T}}\). On average, differences between maximal and minimal values of G\({}_{\text{T}}\) are above 62%, regardless of the WS and demand variability. Average G\({}_{\text{T}}\) is higher for an FR of 99.9% compared to 89.9% in 99% of R + LT groups and WSs. In a 5-day WS, they are, on average, 7.0% higher compared to 9.5% in a 7-day WS, regardless of demand variability.  
Figure 13: (**a**) Average G\({}_{\text{T}}\) per year per R + LT group for a 5-day WS and FR 99.9%. (**b**) Average G\({}_{\text{T}}\) per year per R + LT group for a 7-day WS and an FR 99.9%.
## 4 Copper Nanoparticles Synthesis
### Physical Methods  
The most important physical synthesis methods are evaporation-condensation and laser ablation.  
Nanoparticles obtained through physical synthesis present no solvent contamination and have uniform distribution which is an improvement over chemical synthesis. Very small nanoparticles can be obtained using evaporation-condensation (6.2-21.5 nm and 1.23-1.88 nm) but the process requires a lot of energy to increase the operating temperature and it is time-consuming [55].  
The nanoparticles obtained with laser ablation have characteristics depended on the wavelength of the laser, the duration of its pulses, the laser fluence, the ablation time and the liquid medium. One study obtained nanospheres (20-50 nm) in water with femtosecond laser pulses at 800 nm [55].  
Some disadvantages of physical obtaining methods are the requirement of expensive equipment and high use of energy, thus making them less popular than chemical or green methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Used Substances** & **Size** & **Shape** & **UV-VIS** & **References** \\ \hline Na\({}_{2}\)CO\({}_{3}\) & & & & \\ Copper(II) sulphate pentahydrate & 15–30 nm & Nearly spherical & 380 nm & [48] \\ Methanol & & & & \\
2,2 diphenyl-1-picrylhydrazyl hydrate & & & & \\ \hline Copper (II) sulphate pentahydrate & & & & \\ Ascorbic Acid & & & & \\ Chitosan & 35–75 nm & Spherical & 589 nm & [49] \\ Hydrazine & & & & \\ NaOH & & & & \\ \hline Copper(II) sulphate pentahydrate & 45 nm & Spherical & 266 nm—100 \({}^{\circ}\)C & [50] \\ Ascorbic Acid & & & 269 nm—300 \({}^{\circ}\)C & [50] \\ \hline Copper(II) chloride & & & & \\ Sodium dodecyl sulphate & & & & \\ Hydrazine hydrate & & & & \\ Ammonia solution & & & & \\ \hline Copper(II) nitrate & 16 nm & Spherical & 551 nm & [52] \\ Isopropyl alcohol & 23 nm & Hexagonal & 572 nm & [52] \\ Cetyl trimethyl ammonium bromide & 37 nm & Spherical & 553 nm & [52] \\ \hline Copper(II) sulphate pentahydrate & & & & \\ Polyethylene glycol & & & & \\ Sodium borohydride & 4–10 nm & Spherical & 562 nm & [53] \\ Ascorbic acid & & & & \\ \hline Copper(II) sulphate pentahydrate & & & & \\ Sodium borohydride & & & & \\ Ascorbic acid & 10 nm & Cubic & 320 nm & [54] \\ Sodium hydroxide & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical synthesis methods of copper nanoparticles.
# Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation
## 2 Experimental  
In the past, the studies of adsorption equilibrium models were often limited by data on the adsorption equilibrium in different temperature and pressure ranges. Therefore, the experimental data on ethylene's adsorption equilibrium in powder-activated carbon (HHPAC) were obtained in a temperature range from 223.15 to 313.15 K and a pressure range from 0 to 1.5 MPa.
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System  
Figure 1 shows the structural diagram of the proposed system. The proposed system can measure the 3-axis acceleration of each vehicle in 10 ms cycle through IMU. Four-layer CNN-2 stacked LSTM is performed to predict the behavior of the vehicle using measured information from time series data as input. The vehicle's behavior is predicted to be defensive, normal or aggressive behavior. After that, the predicted behavior of the driving vehicle is inserted into the BSM. The BSM containing the driving vehicle's behavior information is transmitted to nearby vehicles through the V2X module. Nearby vehicles can drive more safely by sharing the driving behavior of other vehicles.
## 2 Results and Discussion
### Effects of Fining Treatments on Wine Stability and Protein Content  
The three unfined white wines under analysis were treated with dicarboxymethyl cellulose (DCMC) or bentonite at four different dosage rates (0.5, 1, 1.5, and 2 g/L) commonly employed on commercial wines, even if the lower effective dose was the objective [26]. The wines were treated for 48 h and the remaining soluble protein was quantified using a Macro-Bradford assay. The haze forming potential of the wines was subsequently assessed by a HST (Figures 1 and 2).  
As expected, the use of increasing concentrations of both DCMC and bentonite in wines promotes the reduction in protein concentration, with DCMC more effective than bentonite at lower doses of finding agent used (0.5 g/L), but less effective at all the higher doses used (1.0, 1.5, and 2.0 g/L) on the three wines, except in the case of Moscatel de Setubal at 1.0 g/L of finding agent, where the varietal wine contained a very high protein concentration (near 200 mg/L) before the treatments. Since both agents presented negative electrical charge at the wine pH, the results obtained may vary based on the different surfaces exposed by DCMC and bentonite, since the latter has the ability to accommodate harder proteins, usually less prone to absorb, but also more likely to promote haze formation [26].  
Figure 1: Protein concentration (mg/L; quantified by the Bradford method) in wine samples treated with increasing amounts of bentonite or DCMC. Different letters represent statistically significant differences among different homogeneous subsets for \(p=0.05\). (**a**) Encruzado, (**b**) Viosinho, and (**c**) Moscatel de Setubal monovarietal wines.  
Figure 2: Turbidity (following a HST) of wines treated with increasing amounts of bentonite or DCMC. Different letters represent statistically significative differences among different homogeneous subsets for \(p=0.05\). (**a**) Encruzado, (**b**) Viosinho, and (**c**) Moscatel de Setübal monovarietal wines.  
Concerning the HST results, while the samples from the varietal wines Encruzado and Viosinho were stabilized by bentonite and DCMC at all tested dosages, the wine Moscatel de Setubal required doses higher than 1.5 g/L of bentonite or, even with a very low turbidity formation after the HST (Figure 3), it never achieved full stability (the difference in A540 values before and after HST were greater than 0.02) after DCMC treatment.
## 3 Results
### Glycan-Dependent Recognition of MSPL by DC-SIGN  
It has been reported that MSPL/TMPRSS13 retains its protease activity when it is phosphorylated, shed, and released from the cell surface [24]. Next, to explore the functional aspects of the molecular interaction between DC-SIGN and MSPL/TMPRSS13, we constructed a plasmid containing the MSPL extracellular domain (MSPL-ECD). A stable transfectant of MSPL-ECD showed multiple bands after CBB staining (Figure 3a), as previously reported [24], due to self-cleavage and glycosylation variants. When we conducted the DC-SIGN-ECD lectin blot assay, we found that DC-SIGN-ECD binds to MSPL-ECD in a Ca\({}^{2+}\)-dependent manner (Figure 3b) and that all self-digested peptide fragments contained DC-SIGN-bindable glycans. Next, to test for direct involvement of N-glycans on MSPL-ECD in this molecular association, MSPL-ECD was treated with PNGase F. In CBB staining, most bands were shifted to lower molecular weights (Figure 3c, left), indicating the presence of several N-glycans in MSPL-ECD. Moreover, the DC-SIGN lectin blot showed that DC-SIGN binding was markedly attenuated with PNGase F treatment (Figure 3c, right). Together, these results demonstrate that MSPL recognition by DC-SIGN occurs in an MSPL N-glycan-dependent manner.
## 3 Results
### Research Farm Trials
\begin{table}
\begin{tabular}{c c c} \hline
**Treatment** & **Leaf N** & **Leaf C/N Ratio** \\ \hline ORG \(-\) & 1.84\% c & 26.2 a \\ ORG o & 1.84\% c & 26.1 a \\ ORG + & 1.84\% c & 26.4 a \\ CON \(-\) & 2.11\% abc & 22.7 bc \\ CON o & 2.33\% a & 20.6 c \\ CON + & 2.30\% a & 20.9 c \\ LOC \(-\) & 1.96\% bc & 24.8 ab \\ LOC o & 2.21\% ab & 21.6 bc \\ LOC + & 2.26\% a & 21.3 c \\ \hline \end{tabular} Values followed by different letters within a column are significantly different at \(p<\) 0.05 using [PERSON]’s HSD.  
\end{table} 
Table 3: Mean values for leaf nitrogen (N), and carbon/nitrogen (C/N) ratio among strawberries grown under nine different management regimes. ORG = organic inputs, CON = conventional inputs, LOC = low carbon inputs; (\(-\)) = low nitrogen fertilizer, (o) = recommended rate of N fertilizer, (+) = recommended rate of N fertilizer plus supplemental inputs. In 2020, average individual fruit weight decreased by 1.5 g between the first and second harvest (7.45 vs. 5.95 g). There were no significant differences in firmness (10.3 + / - 0.27 N) between treatments at any single date in 2019, although there was a tendency of firmness to decrease with higher N rates in the form of urea. Firmness significantly decreased over the harvest season, with most fruits decreasing by approximately 3.17 N each week (\(r\) = -0.81, \(p<0.0001\)). There were no significant differences between treatments in SSC (5.95 + / - 0.12%), nor was there a change in SSC over the course of the 2019 season. Again, in 2020, no significant differences were observed in SSC among treatments (8.01 +/ - 0.16%), although SSC was much higher than in 2019. In 2019, there were no significant differences in TA between treatments (1.41 +/ - 0.05%), but TA increased by approximately 0.12% with each consecutive harvest. Total phenolic content was not significantly different between samples from CON +, ORG +, LOC +, and LOC - treatments, with a mean gallic acid equivalent measurement of 1.06 +/ - 0.04 mg\(\cdot\)mL\({}^{-1}\). Volatile content amongst field treatments exhibited extremely high variation (CV ranged from 90 to 176%), but this could not be associated with any imposed management practice. Panelists were unable to distinguish differences between selected contrasting treatments by either smell or taste (Table 4). They were as likely to say that two samples were the same when they were different as to say samples were different when they were the same. Correct responses never exceeded 50% for any set of comparisons, indicating a random pattern of discernment.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 4 Modelling the Noise
### Ornstein-Uhlenbeck Model
However, [PERSON] also gives explicit estimates for the asymptotic form of these estimators, e.g.,  
\[\mathbb{E}\Big{(}e^{-\widehat{\theta}\Delta t}\Big{)}=e^{-\theta\Delta t}- \frac{4-3\theta\Delta t}{n}+O\bigg{(}\frac{1}{n^{2}}\bigg{)}\.\]  
Thus, one needs only a moderate number of observations, \(n\), or short sampling time \(\Delta t\) to estimate the \(\theta\) parameter, especially compared with the estimator of the mean for an uncorrelated process, which would have \(O(1/\sqrt{n})\) (much slower) convergence to the true mean. Parameter estimation for SDEs is a rapidly developing field, especially the sub-field of online estimators that update the parameter estimates as newer observations become available. Such estimators are usually associated with constant computational cost, as their performance is independent of \(n\). A method of online estimators for a sampled, continuous-time Ornstein-Uhlenbeck is the subject of future publication. After estimating \(\mu\), \(\theta\), and \(\sigma\), we simulated an ensemble of Ornstein-Uhlenbeck processes using the Euler-Maruyama method [54]. We calculated and plotted the autocorrelation function for each run (see Figure 6) as well as the range covered by these functions. Clearly, the OU noise model represents the autocorrelation in the data up to at least two hours. However, it is unable to capture the undulation of \(\widehat{p}(h)\). Therefore, an Ornstein-Uhlenbeck process should be complemented with some other process for longer-term predictability. We believe that a more complicated noise model is unwarranted unless the sampling period exceeds an hour. Figure 6: The thick black solid line depicts \(\widehat{p}(h)\) for the GPS recordings on 15 August, 2014 in Dunedin, as in Figure 3. All grey lines represent the autocorrelation function of a simulated Ornstein–Uhlenbeck process with input parameters \(\mu=\widehat{\mu}\cong 45.864\), \(\theta=\widehat{\theta}\cong 1.329\times 10^{-4}\), and \(\sigma=\widehat{\sigma}\cong 2.745\times 10^{-7}\). The lines labelled as ‘maximal’ and ‘minimal’ are connecting the extremal values of simulated autocorrelation functions at all lags.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 3 Adversarial Attack Taxonomy
### Attacker's Capability
Hence, the attack's success during deployment time relies on two main capabilities, the presumption of transferability or the feedback to inquiries. Consequently, the attacker's capability during the deployment phase can be categorized into two distinct groups, namely transfer-based attack and query-based attack. * Transfer-based Attack: The fundamental concept underlying transfer-based attack revolves around the creation of adversarial examples on local surrogate models in such a way that these adversarial examples can effectively deceive the remote target model as well. The transferability propriety encompasses two types: task-specific transferability which applies to scenarios where both the remote victim model and the local model are concerned with the same task, for instance, classification. Cross-task transferability arises when the remote victim model and the local model are engaged in diverse tasks, such as classification and detection. * Query-based Attack: The core idea behind query-based attacks lies in the direct querying of the target model and leveraging the outputs to optimize adversarial samples. To do this, the attacker queries the target model's output by providing inputs and observing the corresponding results, which can take the form of class labels or score values. Consequently, query-based attacks can be further categorized into two distinct types: decision-based and score-based.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Search Method** & **Fitness Function** & **Means of Hybridization** \\ \hline \hline P3 [66] & HS, RTEA & \(Fitness=\omega\zeta\left(F^{\prime}\right)+(1-\omega)^{\times}\frac{|F^{\prime} |}{|F|}\), where F\({}^{\prime}\): array of chosen attributes, \(\zeta\left(F^{\prime}\right)\): error rate with reduced feature string, \(F\): original feature array and \(\omega\in[0,1]\) & HS and RTEA have been hybridised by following the pipeline model. \\ \hline P4 [67] & BSO and FA & \(Fitness=\alpha E_{R}(D)+\beta\frac{|\mathbb{E}|}{|C|}\), where \(E_{R}(D)\): classifier’s error rate, \(|\mathbb{R}|\): size of the chosen attribute string, \(|\mathbb{C}|\): count of total features and \(\alpha\) lies in between 0 and 1 and \(\beta=(1-\alpha)\). & To reduce the drawbacks of the conventional BSO, this new architecture combines the best elements of BSO’s great exploration and FA’s exceptional exploitation where if the cycle counter is odd, the FA search mechanism is used for a location change; otherwise, the original BSO is used for solution improvement. \\ \hline P5 [68] & GWO and HBO & \(Fitness=\frac{|N_{I}|}{|N_{I}+N_{I}|}\times 100\%\), where NT: \#truly predicted instances, NF: \#instances that are falsely predicted. & The best solution obtained from GWO is stored as a record. If the new solution generated by the HBO is more than 90\% similar to the above record, then crossover is used. After crossover, if the new solution is the same as the record then mutation is performed. \\ \hline P6 [69] & CSA and HHO & \(cost(x_{i}(t))=(E_{x_{i}(t)}\times(1+0.5\times\frac{FS}{N}))^{2}\), where \(cost(x_{i}(t))\): fitness value of \(x_{i}(t)\), \(E_{x_{i}(t)}\): performance of classifier. FS/N: \#features selected/\#features. \\ \hline P7 [70] & GWO and PSO & \(Fitness=h_{1}E(D)+h_{2}\frac{|\mathbb{E}|}{|f|}\), where E(D): CE, s: \#selected features, f: \#features and \(h\in[0,1]\), \(h_{2}=1-h_{1}\) are constants. \\ \hline \hline P8 [71] & GWO-PSO & \(Fitness=\alpha\rho_{R}(D)+\beta\frac{|S|}{|T|}\), where \(\alpha=[0,1]\) and \(\beta=(1-\alpha)\), \(\rho_{R}(D)\): KNN’s error rate, \(|\mathbb{S}|\): length of chosen feature vector and \(|\mathbb{T}|\): length of actual feature vector. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._