esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multipath Multimedia Dynamic Source Routing (MMDSR)
### Path Classification in MMDSR  
Once the source has selected a set of paths that fulfil the requirements depicted in Equation (1), the classification of those paths is done by checking sequentially the qualifications of the QoS parameters, according to the following list:  
\[RM_{k}^{j}+MM_{k}^{j}\text{, }H_{k}^{j}\text{, }BW_{k}^{j}\text{, }L_{k}^{j}+J_{k}^{j}\text{ and }D_{k}^{j}\text{.}\]  
We first arrange the available paths looking at the metrics RM and MM, since we prefer the most reliable and stable paths to distribute video over MANETs. In case of draw, the decision is taken depending on the hop-count metric (\(H\)) which decides the shortest path. In case of another draw, we consider bandwidth (\(BW\)), then losses (\(L\)) and delay jitter (\(J\)), and finally delay (\(D\)). Nonetheless, other alternative algorithms to arrange the available paths could also be considered. Finally, the source selects the \(k\) paths (with \(k\leq AP\)) required to compose the multipath routing scheme. In our case, \(k\) = 3 paths. Notice that if only two paths were available, we still could differentiate both paths (_i.e._, the best and the medium-quality path), but if only one was available then all the packets would be sent through that single path.
## 3 Results
### Fatty Acid Profile
#### 3.2.1 Fatty Acid Profile in Organic and Conventional Sheep Milk  
Sheep milk was found to contain a large number of FAs, some of which are very important for human health, including PUFA, vaccine acid (VA), and CLA. Palmitic (C16:0), oleic (9-cis C18:1), stearic (C18:0), and myristic (C14:0) acids contributed most to total FAs. Their mean concentrations in the organic farms were 34.03, 24.56, 7.96, and 12.56 g/100 g total FAs, respectively, while in the conventional farms were 33.37, 25.37, 11.68, and 12.32 g/100 g total FAs, respectively.  
The total concentration of short-chain saturated fatty acids, (SCFA) in the organic farms (Caproic C6:0, Caprylic C8:0, and Capric C10:0) was 7.59 g/100 g total FAs, while in the conventional farms was 7.44 g/100 g total FAs.  
Medium-chain saturated fatty acids (MCFA) consist of lauric (C12:0), tridecanoic (C13:0), myristic (C14:0), pentadecanoic acid (C15:0), and palmitic (C16:0). The total concentration in the organic farms of MCFA was 52.01 g/100 g total FAs, while in the conventional farms was 51.01 g/100 g total FAs.  
Long-chain saturated fatty acids (LCFA) consist of stearic acid (C18:0), arachidonic acid (C20:0), and bechic acid (C22:0). In the organic farms, the total concentration of LCFA was 8.21 g/100 g total FAs, while in the conventional farms it was 11.92 g/100 g total FAs.  
The total concentration of saturated fatty acid (SFA) in organic milk (SFA = SCFA + MCFA + LCFA) was 67.80 g/100 g total FAs, and in conventional milk was 67.20 g/100 g total FAs.  
Monousaturated fatty acids (MUFA) consist of myristoleic acid (C14:1), palmitoleic acid (C16:1), heptadecenoic acid (C17:1), oleic (C18:1), and gondoic acid (C20:1). The total concentration of MUFA in organic farms was 28.18 g/100 g total FAs, and in conventional farms, 28.92 g/100 g total FAs. PUFA consist of linoleic (C18:2), cis/trans, \(\alpha\)-linolenic (C18:3), and stearidonic acid (C18:4). The total concentration of PUFA in organic farms was 4.02 g/100 g total FAs, while in conventional farms, 3.81 g/100 g total FAs.  
Statistically significant differences between organic and conventional sheep milk were found in all saturated short-chain (C6:0, C8:0, C10:0), medium-chain (C12:0, C13:0, C14:0, C15:0, C16:0), and large-chain (C18:0, C20:0) fatty acids. In addition, significant differences  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Organic (n = 12)**} & \multicolumn{4}{c}{**Conventional (n = 12)**} \\ \hline  & **Mean** & **SD** & **Min** & **Max** & **Mean** & **SD** & **Min** & **Max** & _p-Value_ \\ \hline C14:0 & 11.900 & 0.008 & 11.897 & 11.924 & 11.897 & 0.000 & 11.897 & 11.897 & 0.825 \\ \hline C15:0 & 1.139 & 0.001 & 1.139 & 1.141 & 1.139 & 0.000 & 1.139 & 1.139 & 0.945 \\ \hline C16:0 & 32.241 & 0.021 & 32.235 were also found in the proportion of some MUFA (C10:1, C14:1, C16:1, C20:1) and the PUFA steardonic acid (C18:4n-3) between organic and conventional sheep milk.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 5 Multi-Modality Data Representation and Event Detection
### Matrix Factorization Based Methods  
Matrix factorization based methods aim to learn low-dimensional representation of data for event detection. However, the constraints such as non-negativity, sparsity, low-rank, and so forth, should be considered by observing the data characteristics. The constraints have been widely used and research shows that they are effective in feature extraction and dimension reduction. Matrix decomposition aims to decompose a matrix into two sub-matrices. The idea of matrix factorization can be used in multi-modality representation learning without considering the number of modalities. Recently, some event detection research explored multimodal data representations. Representative works include Singular Value Decomposition (SVD) [85], Non-negative Matrix Factorization (NMF) [86; 87] and Dictionary Learning (DL) [88; 89]. For example, [PERSON] et al. [90] proposed a semi-supervised co-clustering method for multi-modality topic detection. In their work, the non-negative matrices are used to represent multi-modality data, in which the matrix represents the relationship between the central data and the feature modality. Then the clusters of the central data and every feature modality can be obtained by tri-factorizing these matrices simultaneously, and every cluster of the central data can correspond to an event. [PERSON] et al. [86] proposed a shared subspace learning framework, in which a subspace shared between different data sources is learned by a Nonnegative Matrix Factorization. The proposed method learns co-occurrences from the subspaces of the target and auxiliary datasets by explicitly learning a common subset of basis vectors. There are also some studies proposing the multimodal graph regularized sparse representation methods, which are achieved by concatenating the unimodal data representations. For instance, [PERSON] et al. [91] proposed a discriminative coupled dictionary hashing (DCDH) method, in which the side information is used for the learning of coupled dictionary for each modality. The coupled dictionaries can be used to preserve the intra-similarity and inter-correlation among multi-modality data, and it also contain dictionary atoms that are semantically discriminative. [PERSON] et al. [92] proposed a multimodal hypergraph learning-based sparse coding method by concatenating the unimodal data representations. They used the method to predict image click and applied the click data to the reranking of images. [PERSON] et al. [93] presented a multi-view feature extraction approach called Generalized Multiview Analysis (GMA). GMA has various important properties that are required for cross-view classification, which is a supervised model relying on labeled data for training.
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
###### Abstract  
Chloride ions in zinc refining accelerate equipment corrosion and anode and cathode losses, increase lead content, and reduce zinc quality. Therefore, the removal of chloride ions has become a research priority. The existing copper slag dechlorization process has problems such as the solid film barrier leading to impeded mass transfer, product wrapping triggering active site coverage, and incomplete reactions due to insufficient reaction-driving force, leading to low utilization of copper slag, poor dechlorination efficiency, and long reaction times. To address these issues, a new method of deep dechlorination based on the reduction of Cu\({}^{2+}\) by liquid-phase mass transfer is proposed in this paper. The process utilizes ascorbic acid as a reducing agent, establishes a homogeneous aqueous phase reaction system, breaks through the solid membrane barrier, and avoids the encapsulation of the product layer, achieving efficient dechlorination. The end structure of ascorbic acid promotes rapid dechlorination through proton-coupled electron transfer (PCET). Thermodynamic calculations show that compared to the current copper slag dechlorination process, this method increases the reaction-driving force by 18.6%, reduces the Gibbs free energy (\(\Delta\)G\({}^{9}\)) by 59.3%, and increases the equilibrium constant by 6.7 \(\times\) 10\({}^{9}\) times, making the reaction more complete and achieving a higher degree of purification. The experimental results show that under optimized conditions, the chloride ion concentration in the solution decreases from 1 g/L to 0.0917 g/L within 20 min, with a removal rate of 90.8%. The main precipitate is CuCl. This process provides a more efficient solution to the chloride ion contamination problem in the hydrometallurgical zinc refining process.  
chloride removal; ascorbic acid; liquid phase reduction 2025155673
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
## 3 Results and Discussion
### Simulations for Two Photon Absorption
Figure 4a-c show the details of dynamics for the states at the end face of the Al thin film with \(\sigma_{2}=10^{-50}\,\mathrm{cm}^{4}\mathrm{s}\) for fluences of 1.5, 15 and 150 J/cm\({}^{2}\), respectively. The conditions of the calculations are the same as in Figure 2a-c. For 1 and 10 J/cm\({}^{2}\) in Figure 4a,b, respectively, the results are similar to the previous saturation model (ratio \(N_{1}\):\(N_{2}\)). For high fluence in Figure 4c, the ratio of the occupation numbers of the states approaches \(N_{1}\):\(N_{2}\):\(N_{3}=\) 1/3:1/3:1/3 in steady conditions (which corresponds to the highest entropy in the sense of the equilibrium statistical mechanics); therefore, the population inversion is not obtained. Figure 2:1 Time dependence of the density of occupation numbers (normalized by the total number N) at the end surface of the Al thin film (53 nm), using the two photon absorption (TPA) contribution at 92 eV photon energy (FEL pulse). The fluences of the incident pulse are 1.5, 15 and 150 J / cm\({}^{2}\) from the top to the bottom, respectively. The TPA cross-section \(\sigma_{2}\) is set to \(10^{-50}\) cm\({}^{4}\)s.  
Figure 2:2 Transmission for Al thin film (53 nm) with two photon absorption (TPA) model at 92 eV photon energy of X-ray pulse, compared with experimental data (Expt.). Four different TPA cross sections—0, \(10^{-50}\), \(10^{-49}\) and \(10^{-48}\) cm\({}^{4}\)s—are used to demonstrate a non-linear optical effect.
# VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application
## 2 Related Work
### Deep-Learning-Based Method  
Using deep learning to detect source code vulnerabilities has become hot research. Most research is for C and C++ programs [5; 9; 35], but it's rare for PHP code vulnerability detection. TAP [6; 36] presents a static analysis method for PHP vulnerability detection based on code tokens and deep learning. The tool proposed a custom tokenizer to generate PHP code tokens and use data-flow analysis to find the relevant function calls. TAP implements a sequence-based deep learning model that uses Word2 Vec to generate token embedding and LSTM (Long Short-Term Memory) to train and classify vulnerabilities. Compared with RIPS and WIRECAML, TAP achieves the best effects regarding accuracy, F1 score, and AUC (Area Under the Curve) on both safe and unsafe test cases. Vulhunter [18] proposed a different way to represent vulnerabilities using bytecode features. It generates potentially suspicious code fragments by analyzing the CFG (Control Flow Graph) and DFG (Data Flow Graph) and then converts the code snippet to a bytecode. Vulhunter vectorizes the bytecode fragment by Word2 Vec and passes it along with the tag into a BILSTM (Bi-directional Long Short-Term Memory) model. The evaluation results indicate that Vulhunter can detect SQLI and XSS vulnerabilities and achieves a higher effect than RIPS regarding the recall and F1 scores. DeepTective [21] is the first approach that uses Graph Neural Network techniques to identify vulnerabilities in PHP source code. It combines GRU (Gated Recurrent Unit) and GCN (Graph Convolutional Network) to detect cross-site scripting, SQL injection, and OS command injection vulnerabilities. DeepTective transforms source code into a sequence of tokens for GRU to analyze syntactic structural properties and extracts the CFG for GCN to learn the semantic properties. The paper claims that this hybrid architecture achieves a 99.92% F1 score on synthetic data from SARD.
## 2 System Description and Modeling of Proposed Nonlinear Interconnected Power System
### Wind Turbine Model  
A basic equation relating to wind speed and the mechanical power generated by awind turbine is as follows [16]:  
\[P_{o}=C_{PC}(\lambda,\beta)\frac{1}{2}\rho AV_{w}^{3} \tag{1}\]  
where \(P_{0}\) is the output mechanical power of the turbine (W), \(\lambda\) is the tip speed ratio, \(\beta\) is the blade pitch angle (\({}^{\circ}\)), \(C_{PC}\) is the performance coefficient of the turbine, \(\rho\) is air density (kg (m\({}^{3}\))\({}^{-1}\)), \(V_{w}\) is wind speed (ms\({}^{-1}\)) and \(A\) is turbine swept area (m\({}^{2}\))  
Normalization and simplification can be done in Equation (1) for particular values of \(\rho\) and \(A\). The new equation of the system in per unit (p.u.) is as follows:  
\[P_{o-pu}=k_{p}C_{PC-pu}V_{w-pu}^{3} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Rating of each area & 0.1 MW \\ Base power & 0.1 MW \\ K\({}_{\rm P1}\), K\({}_{\rm P2}\), K\({}_{\rm P3}\) & 120 Hz/p.u. MW \\ T\({}_{\rm P1}\), T\({}_{\rm P2}\), T\({}_{\rm P3}\) & 20 sec \\ T\({}_{\rm 12}\), T\({}_{\rm 13}\), T\({}_{\rm 23}\) & 0.545 p.u. \\ a\({}_{\rm 12}\), a\({}_{\rm 31}\), a\({}_{\rm 23}\) & \(-1\) \\ R\({}_{\rm 1}\), R\({}_{\rm 2}\), R\({}_{\rm 3}\) & 2.4 Hz/p.u. MW \\ T\({}_{\rm W}\) & 1 sec \\ K\({}_{\rm d}\) & 4.0 \\ K\({}_{\rm i}\) & 5.0 \\ K\({}_{\rm p}\) & 1.0 \\ F & 50 Hz \\ Base Power & 0.1 MW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data for interconnected, three-area power system considering RES.  
Figure 1: Three-area AGC model considering RES.  
where \(P_{o-Pu}\) is the nominal power for particular values of \(\rho\) and \(A\) (p.u.), \(K_{p}\) is power gain for \(C_{Pc-pu}=1\) and \(V_{w-pu}=1\) p.u. (\(k_{p}\leq 1\)), \(C_{Pc-pu}\) is per unit (p.u.) value of the performance coefficient \(C_{Pc}\) and \(V_{w-pu}\) is the p.u. value of base wind speed.  
Figure 2 shows a modified Simulink model of the wind turbine in which wind speed was the input and mechanical torque was the output that was applied to the induction generator. Corresponding to various values of wind speed, turbine speed and turbine output power characteristics are shown in Figure 3. Turbine output power remained zero from 0-0.2 p.u. turbine speed due to inertia in the turbine.
## 2 Materials and Methods  
The binder-free In\({}_{2}\)S\({}_{3}\)-SWCNT heterostructure-based electrodes were grown on NF by utilizing a two-step cyclic SILAR deposition method. All the chemicals utilized were of analytical grade and were used without any further treatment or purification. The chemicals indium chloride pentahydrate and thiourea were purchased from Sigma Aldrich (St. Louis, MI, USA). The single-walled carbon nanotube solutions (SWCNT) were procured from KH Chemicals Inc. The average diameter and length of SWCNT were 1.1-1.4 nm and 5 \(\upmu\)m, respectively, with a G/D ratio of more than 100 [6]. The detailed method for electrode synthesis is as follows: A two-step cyclic SILAR process is employed for the deposition of electrode material. As a typical procedure for a one-cycle SILAR process, in which the nickel foam substrate (NF) was first immersed in an anion-rich solution (0.05 M Na\({}_{2}\)S) and then immersed in a solution containing metal precursors (0.05 M) as the cationic source. This was followed by a dip in DI water in a beaker (25 mL volume) to remove any loosely bound corpuscles from the substrate. In each beaker, an NF substrate was dipped for 30 s after the previous beaker, and 20 cycles with the same methods were repeated consecutively for homogeneous, binder-free deposition on the surfaces of the substrates. Following that, the samples were dried in an oven at 60 \({}^{\circ}\)C for 12 h. For composite material preparation, one more beaker (1 mL SWCNT in 25 mL water) is kept between each SILAR cycle (after DI water).
## 2 Theoretical Background
### Continuity Conditions
#### 2.5.3 Flexible Joint Model  
The flexible joint model (\(m=3\)) was developed to explain the deformation appearing at the interface of each sub-beam. This deformation is, in general, nonlinear as explained in [8]. A proportional relationship between the interface stress and the deformation was considered in [15; 31]. This assumption requires including additional terms in the continuity conditions. In particular, these terms express the contribution from the interface stress to the total displacement and are mentioned as \"interface compliance coefficients\" \(C_{i}^{(s)}\) (\(i=1\): normal, \(i=2\): shear) in [32]. Considering the above, the continuity conditions read:  
\[w^{(31)}-C_{1}^{(1)}\sigma = w^{(32)}+C_{1}^{(2)}\sigma,\ \ u^{(31)}-\frac{h^{(1)}}{2}\phi^{(31)}-C_{2}^{(1)} \tau=u^{(32)}+\frac{h^{(2)}}{2}\phi^{(32)}+C_{2}^{(2)}\tau \tag{13}\]  
with \(C_{1}^{(s)}=h^{(s)}/10E_{33}^{(s)}\) and \(C_{2}^{(s)}=h^{(s)}/15G_{13}^{(s)}\).  
The interface compliance coefficients are estimated by a semi-analytical and semi-numerical calibrating process [33]. \(E_{33}^{(s)}\) and \(G_{13}^{(s)}\) are the Young's and shear moduli of the sub-beam \(s\) along its thickness. Note that for zero compliance coefficients, Equation (13) reduces to the one presented for the semi-rigid joint model.  
The relative displacements (\(\Delta u^{(3)},\ \Delta w^{(3)}\)) and the relative rotation (\(\Delta\phi^{(3)}\)) for the flexible joint model (\(m=3\)) can be expressed as:  
\[\Delta u^{(3)}=u^{(32)}-u^{(31)}\
eq 0,\ \ \Delta w^{(3)}=w^{(32)}-w^{(31)}\
eq 0, \ \ \Delta\phi^{(3)}=\phi^{(32)}-\phi^{(31)}\
eq 0 \tag{14}\]
## 3 Discussion
### Comparison of Results Obtained with Literature  
It is known from the previous studies that the expansion pressure of SCA ranged from 20-70 MPa [7; 23; 24; 27; 28]. Similar to the results of previous studies, the axial and  
Figure 8: Sensitivity analysis for elastic modulus and axial expansive pressure.  
Figure 7: Variation of elastic modulus with axial pressure.  
radial expansion pressure of SCA reached about 48 MPa in this study. Meanwhile, both model experiments and numerical experimental results show that the expansion pressure gradually increases with increasing diameter, which is consistent with the results of [23,24]. By increasing the diameter of SCA, a higher expansion pressure can be obtained. However, it should be noted that the relationship between the expansion pressure and diameter of SCA is not linear.  
The elastic modulus of SCA is greatly affected by the constraint conditions. Under unconstrained conditions, the elastic modulus of SCA after hydration is small, about 20 MPa [28,29]. Under constrained conditions, the elastic modulus of SCA can reach dozens of GPa. This shows that constraint condition, i.e., the application environment, is important factors that affect the mechanical parameters of SCA.  
Due to the limitation of measurement methods, there are few studies on the evolution law of mechanical parameters in the hydration process of SCA. In this study, based on the experimental results, the evolution law of mechanical parameters is inversed by using the finite element model, which is helpful to understand the hydration process of SCA.
## 2 Materials and Methods
### Data Resources  
#### 2.2.1 Index Data  
The index data used in this paper include Sun-induced Chlorophyll fluorescence (SIF), Normalized Differential Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Near-infrared Reflectance (NIR\({}_{\rm{T}}\)) data. SIF data from the Global Ecological Panel ([[https://globalecology.unh.edu](https://globalecology.unh.edu)]([https://globalecology.unh.edu](https://globalecology.unh.edu)) accessed on 18 May 2021) is a new high spatial and temporal resolution (0.05\({}^{\circ}\), 8 d) global \"OCO-2\" SIF (GOSIF) dataset based on discrete OCO-2 SIF, MODIS and meteorological reanalysis data [17], which has shown good performance in estimating GPP [18]. NDVI, EVI and NIR\({}_{\rm{T}}\) data are taken from the MOD13C2 dataset of the Land Processes Distributed Active Archive Center (LP DAAC) website ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) accessed on 21 May 2021), with temporal resolution of days and spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). The period chosen for this paper is from 2003 to 2018.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 2 Design Constraints and Security Issues in WSNs
### Security Issues in WSNs  
In addition to the above characteristics of wireless sensor networks, security is also an important part of the Internet of things. Since WSNs use a wireless medium for data transmission, sensors are more vulnerable to various malicious attacks based on wireless channels. The typical malicious attacks in WSNs include eavesdropping, data modification, sink hole, spoofing attacks, denial of service attacks, sybil attacks, and node capture. For example, in node capture, the attacker accesses the hardware and software of one or more sensors through the network [29]. After successful intrusion into the sensor, the attacker steals all cryptographic keys and algorithms. Thus, it is possible for the attackers to eavesdrop and tamper with messages, as well as pretend to be legal terminals to forward data to hackers.  
In recent years, a lot of research work has focused on security problems in WSNs. An asymmetric key pre-distribution scheme called AP was first proposed for hierarchical sensor networks in [30]. The famous \"probabilistic\" schemes had low computational complexity and communication loads. However, this scheme cannot guarantee accurate sharing of pairwise keys between any two sensors. Based on the Blom matrix, a key management scheme is proposed by [PERSON] in [31] to improve the resilience against node capture. However, complex matrix operation leads to that high resource consumption by ordinary sensors. [PERSON] presented a key renewal approach for authentication based on modular exponentiation in clustered WSNs [32]. Although this scheme improved the connectivity of the network, public-key encryption brought about a large amount of computation. [PERSON] presented a blockchain-based trusted key management approach [33], which realized key management in WSNs through a secure cluster formation algorithm and a node mobility algorithm. In the literature [34], a novel key management model for hierarchical sensor networks based on public key infrastructure (PKI) was proposed. However, the key distribution issues in case of movement were not investigated.