esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Model of Second Order Stochastic Dominance
### The Stochastic Dominance Theory  
The stochastic dominance theory originates from discrete stochastic variables optimization theory, which is later developed into generalized stochastic variables optimization theory and widely used in economics and finance. In the stochastic dominance theory, the comparison among the random variables is carried out the point-wise comparison of their k-order distribution function \(F^{(k)}\). Assuming that there are only two portfolios \(X\) and \(Y\), and the utility function of all investors is monotonically increasing. If all investors prefer portfolio \(X\) or believe that only part of the portfolio \(X\) and \(Y\) is no difference, then we can say the portfolio \(X\) stochastically dominates portfolio \(Y\) in the first order.  
From the mathematical point of view, let \(x\) and \(y\) be the decision vectors and \(\xi\) be the random variable. It is said that \(g(x,\xi)\) stochastically dominates \(g(y,\xi)\) in the first order, denoted by \(g(x,\xi)\succeq_{1}g(y,\xi)\), if  
\[F(g(x,\xi);\eta)\leq F(g(y,\xi);\eta),\forall\eta\in\mathbb{R} \tag{3}\]  
where \(g(x,\xi)\) is the concave continuous function both in \(x\) and \(\xi\), \(F(g(x,\xi);\eta)\) and \(F(g(y,\xi);\eta)\) are the cumulative distribution function of \(g(x,\xi)\) and \(g(y,\xi)\). For a random variable \(X\in\mathbb{R}\), the first order distribution function of \(X\) is its right-continuous cumulative distribution function:  
\[F^{(1)}_{x}(\eta)=F_{x}(\eta)=\int_{-\infty}^{\eta}P_{x}(d\xi)=P\{x\leq\eta\},\forall\eta\in\mathbb{R} \tag{4}\]  
The first order of stochastic dominance is applicable to investors with arbitrary risk appetite, while the second order stochastic dominance is only for the risk averse decision makers whose marginal utility of expected utility function decreases. Based on the definition of first order stochastic dominance, similarly, \(g(x,\xi)\) stochastically dominates \(g(y,\xi)\) in the second order, denoted by \(g(x,\xi)\succeq_{2}g(y,\xi)\), if  
\[\int_{-\infty}^{\eta}F(g(x,\xi);\alpha)d\alpha\leq F(g(y,\xi);\alpha)d\alpha, \forall\eta\in\mathbb{R} \tag{5}\]  
The corresponding strict dominance relation \(\succ_{(k)}\) is defined in the usual way:  
\[X\succ_{(k)}Y\Leftrightarrow X\succeq_{(k)}Y\text{ and }Y\
subseteq X \tag{6}\]In this paper, we mainly discuss the second order stochastic dominance. Assuming that the rate of return on \(g(x,\xi)\) and \(g(y,\xi)\) in portfolio \(x\) and \(y\) having a finite expectation. In the portfolio optimization problem, it is said that portfolio \(x\) stochastically dominates portfolio \(y\) in the second order, denoted by \(g(x,\xi)\succeq_{2}g(y,\xi)\), its requirement is the same as model (5).
## 2 Background
### Road-Rubber Friction
#### 2.2.3 Tyre Models as a Function of the Friction Coefficient
\[\mu_{x0}(\lambda) = D_{x}\sin(C_{x}\arctan(B_{x}\lambda-E_{x}(B_{x}\lambda-\arctan(B _{x}\lambda))))\] (16) \[\mu_{y0}(\alpha) = D_{y}\sin(C_{y}\arctan(B_{y}\alpha-E_{y}(B_{y}\alpha-\arctan(B _{y}\alpha))))\] (17) After that, in order to handle combined efforts, the weighting functions \((G_{x\alpha},G_{y\lambda})\) can be defined in the following manner [8]: \[G_{x\alpha} = \cos(C_{x\alpha}\arctan(B_{x\alpha}\alpha-E_{x\alpha}(B_{x\alpha }\alpha-\arctan(\alpha))))\] (18) \[G_{y\lambda} = \frac{\cos(C_{y\lambda}\arctan(B_{y\lambda}(\lambda+S_{Hy\lambda })))}{\cos(C_{y\lambda}\arctan(B_{y\lambda}S_{Hy\lambda}))}\] (19) Finally, the one-directional friction coefficients are obtained from the product of these weighting functions and the normalised forces calculated previously, expression (20). \[\mu_{x}=G_{x\alpha}\mu_{x0},\quad\mu_{y}=G_{y\lambda}\mu_{y0}\] (20) If load sensitivity effects are disregarded, the tyre planar forces can be obtained from the vertical forces as (\(F_{j}=\mu_{j}F_{2}\)), [8]. * Brush model In this formulation the pure longitudinal force is obtained from expressions (21)-(24). During gentle driving conditions, the tyre longitudinal slip \(\lambda\) keeps below the critical slip \(\lambda_{sl}\) and the first expression is used. Under strong braking or acceleration events (\(\lambda>\lambda_{sl}\)) the entire contact patch is sliding, and the second expression is used. \[\mu_{x}(\lambda,\mu_{\max}) = \left\{ \begin{array}{l}3\mu_{\max}\theta_{x}\sigma_{x}\{1-| \theta_{x}\sigma_{x}|+\frac{1}{3}|\theta_{x}\sigma_{x}|^{2}\}\\ \mu_{\max}sign(\lambda)\end{array} \right.\] (21) \[\theta_{x} = 2\frac{c_{p}l^{2}}{(3\mu_{\max}F_{z})}\] (22) \[\sigma_{x} = \frac{\lambda}{(\lambda+1)}\] (23) \[\lambda_{sl} = \frac{1}{(\theta_{x}-1)}\] (24)In this case the normalised tyre forces are presented (\(\mu_{j}=F_{j}/F_{z}\)), the tyre tread stiffness is denoted by \(c_{p}\), and the longitudinal slip at which the full-sliding condition starts is \(\lambda_{sl}\). The derivation of the friction model in the lateral direction is straightforward from expressions (25)-(28) [17]. \[\mu_{y}(\alpha,\mu_{\max}) = \left\{ \begin{array}{l}-3\mu_{\max}\theta_{y}\sigma_{y}\{1-| \theta_{y}\sigma_{y}|+\frac{1}{3}|\theta_{y}\sigma_{y}|^{2}\}\\ -\mu_{\max}sign(\alpha)\end{array} \right. \tag{25}\] \[\theta_{y} = 2\frac{c_{p}l^{2}}{(3\mu_{\max}F_{z})}\] (26) \[\sigma_{y} = \tan(\alpha)\] (27) \[\alpha_{sl} = \frac{1}{\tan(1/\theta_{y})} \tag{28}\]  
Once again, during gentle cornering, the lateral slip (\(\alpha\)) remains far below the nonlinear region (\(\alpha<\alpha_{sl}\)), and the lateral friction is approximated by the first expression. Otherwise, during limit cornering and full sliding conditions, the second expression is used. Finally, the tyre self-alignment torque (SAT) \(\tau_{a}\) can be calculated with the expression (29) [17].
## 2 Materials and Methods
### Manganese Determination Using the Formaloxime Method  
The soil sample at a concentration of 2% (_w/v_) was added to 250-mL Erlenmeyer flasks containing 100 mL of K medium (0.7 g L\({}^{-1}\) yeast extract, 2 g L\({}^{-1}\) dextrose, 2.2 g L\({}^{-1}\) peptone, pH 6.5). In order to verify soil sterility, the mixtures were incubated in a shaker at 28 \({}^{\circ}\)C at 120 rpm. After 24 h, 1 mL of the log-phase cells of isolated strains was introduced to the mixtures. Each set containing soil and a specific strain was prepared in triplicate. Uninoculated medium with the 2% soil (_w/v_) in triplicate was used as the control to determine the abiotic influences on Mn removal. The flasks were incubated at 28 \({}^{\circ}\)C for 14 days with shaking at 120 rpm. Mn concentration in the medium was determined using the formaldehyde method according to the ISO PN-C-04590-03:1992 protocol. Briefly, to 50 mL of each sample, 1 mL of ammonium iron(II) sulphate hexahydrate, and 2 mL of EDTA (Sigma-Aldrich) were added. After mixing the samples, 1 mL of formaldehyde (prepared by dissolving 10 g of hydroxylamine hydrochloride solid in 50 mL of water and 5 mL of 37% formaldehyde solution was then added, and the volume was increased to 100 mL with water) and 1 mL of sodium hydroxide were added followed by incubation for 10 min. Subsequently, 3 mL of ammonia solution of hydrogen chloride was added. The samples were incubated 2 h at 22 \({}^{\circ}\)C. The manganese-formaldehyde complex produced in the colorimetric reaction was detected at \(\lambda\) = 450 nm, and the Mn concentration in the media was calculated based on a standard curve. The standard curve was prepared by adding to 0, 0.5, 1, 2.5, 5, 10, and 20 mL of standard solution (287 mg potassium permanganate in 3 mL sulphuric acid, some drops of 5% sodium sulphite was then added until the pink colour disappeared and the volume was increased to 1000 mL with water; 1 mL of this solution contained 0.01 mg of Mn) to the same compounds as for the tested samples. The analyses were performed before and after the 14 days incubation period. The Mn concentration (mg L\({}^{-1}\)) was calculated based on the following formula:  
\[\text{Mn concentration }=\ \frac{(A\,\times\,100)}{V}\]  
where \(A\) was Mn content in the sample calculated from the standard curve while \(V\) was the volume of analyzed sample.  
The 2 De strain showing the highest ability for Mn adsorption was chosen for further analysis.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 4 Case Studies, Results, and Discussion
### Balanced Load (Scenario I)  
In this scenario, the distribution network was assumed to be fully balanced. A balanced inductive load was used. The details of the distribution network parameters for this scenario are summarized in Table 1.  
Figure 5 shows the measured three-phase current at the sending end for a line-to-ground fault. The fault is initiated at time t = 0.0167 s (equivalent to one cycle). Before the fault, the current signals look alike for all phases, as they are seen more clearly in Figure 5. The phase currents after the fault were generally higher than the pre-fault current signals. The fault currents go under transients and settle down after a cycle or so if the fault is not cleared.  
As the fault clearance process usually takes more than one cycle (0.0167 s for a 60 Hz system) and the fault currents settles after one cycle, taking one cycle before the fault and one cycle after the fault will give sufficient information about the nature of the fault. Decompose, and extract seven details and an approximate coefficient. Examples of these coefficients for the current in phase A under a SLG (single line to ground) fault are shown Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Values** \\ \hline Transformer I & 12 MVA, 120 kV/25 kV, \(Y_{g}-\) \\  & D, Resistance 0.02 pu, Leakage inductance 0.08 pu \\  & 12 MVA, 25 kV/575 V, \(Y_{g}-\) D \\   Load & 5 MW, 2.5 Mvar \\   Frequency & 60 Hz \\   Load variations & \(\pm\)5\% \\  & 30 km \\   Distribution line & Positive sequence: 0.1153 Ohms/km, \(1.05\times 10^{-3}\) H/km \\  & Negative sequence: 0.41 Ohms/km, \(3.32\times 10^{-3}\) H/km \\   Fault impedance & 0–15 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the distribution network for scenario I.  
Figure 5: Useful features’ extraction from three-phase current signals and DWT: Three-phase AG (phase A to ground) fault currents measured at the sending end.
## 3 Results
### Impacts of DSPPs on Adipocyte Differentiation and PPAR\(\gamma\) Protein Manifestation  
We used the AdipoRed test and Oil Red O staining to determine whether DSPPs prevent lipid buildup during differentiation. DSPPs reduced fat buildup in representative pictures of Oil Red O staining (Figure 4) that was beyond substantiated by quantifiable results from the AdipoRed analysis. When compared to the control, the three concentrations of DSPPs that were employed reduced the fat content by approximately 55-78% (\(p<0.05\)). The effects of commercially produced DSPPs on GPDH activity, a marker of 3T3-L1 adipocyte development, were next investigated. In parallel to the control, the GPDH activity of adipocytes cured with 50 and 100 \(\upmu\)g/mL of DSPPs produced industrially was considerably reduced (\(p<0.05\)). Nevertheless, there was no difference in GPDH activity between the control and adipocytes given 25 \(\upmu\)g/mL of DSPPs (Figure 5A). We investigated the impacts of DSPPs on the utterance of PPAR\(\gamma\), a key transcription factor controlling adipogenesis, to pinpoint a potential mechanism by which adipogenesis is decreased. DSPPs reduced the levels of PPAR\(\gamma\)1 and PPAR\(\gamma\)2 in 3T3-L1 cells by 22 and 18%, respectively, at a dosage of 100 \(\upmu\)g/mL (Figure 5B). The interaction between the GPDH enzyme and epicatechin, the main polyphenol in DSPPs, is shown in Figure 6. In a summary, epicatechin conjugated with the GPDH by forming two H-forces and \(\pi\)-\(\pi\) stacking with Arg340 and Arg50. The impacts of DSPPs on GPDH may be caused by this binding, but further research is needed to determine the exact underlying mechanism.  
Figure 3: Pre-confluent preadipocytes (**A**), post-confluent preadipocytes through mitotic clonal growth (**B**), and matured adipocytes (**C**) all showed 3T3-L1 cell survival. Then, 24 h after cell plating, pre-confluent cells were treated with commercially produced DSPPs or a vehicle. Industrially produced DSPPs were introduced to the MDI differentiation media at D\({}_{0}\) in post-confluent cells. The MTS colorimetric test was used to determine the cell viability that was then represented as a ratio of the optical intensity at 0 h. Matured adipocytes were hatched with DSPPs, and their percentage of the control was recorded after each incubation. The numbers depict the means \(\pm\) SD (\(n=3\)) for each treatment. Mean values with different letters are significantly different (\(p<0.05\)).
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 6 Discussion
The node with high residual energy and nearest distance have a greater probability as the final CH, thereby avoiding the node with low remaining energy to become the final CH, and further reducing the energy consumption deviation of the network. For FIGWO, the GWO is used to select the final CH within each initial cluster, and the selected CH is only optimal in the cluster; however, the set of final CHs may not be optimal throughout the whole network. The FIGWO algorithm only solves the local optimal problem of CHs but does not consider the optimal problem of CHs in the whole network. However, the HMGWO adds a new algorithm to the outer layer of MGWO. The outer layer algorithm selects the bad set of CHs with a certain probability as the initial cluster set of the next iteration, so that the selected CH set is not only the best in the cluster, but also, the final CH set is the best in the whole network. HMGWO makes rational use of the heterogeneity of nodes, solves the randomness of CHs, and considers the global optimality of CHs. Therefore, HMGWO is superior to the SEP, DEEC, M-SEP, and FIGWO protocols in terms of the energy consumption, network life cycle, and throughput.
## 4 Implementation  
One critical WSN design challenge involves providing reliable and performing solutions while coping with constrained resources. Memory, energy and bandwidth represent the resource constraints to meet. Thereby, the design process at any level has to be focused on optimizing their use.  
In this paper, we develop a CoAP library for TinyOS to be embedded in highly constrained WSNs. TinyCoAP implements all the features of CoAP [11] except for the blockwise transfer. The goal of our implementation is to minimize the resource consumption by developing a lightweight and efficient code optimized for the OS in use. Because of its popularity and diffusion, TinyOS has been chosen as the OS target.  
TinyOS [39] is an embedded OS for WSNs designed to meet the requirements of constrained networks and devices. It is composed by a set of reusable components that can be used to build specific applications. TinyOS is implemented in the NesC language [40]. NesC is a C dialect designed to improve code efficiency and robustness in embedded software applications [39]. Through its simplicity, NesC is able to reduce RAM occupation, code size, and prevents low-level bugs. The programming model of TinyOS is also based on this language.  
Besides NesC, TinyOS allows using more complex languages such as Java, Python or C. In particular, C code can be embedded in NesC programs or it can be used to build libraries that TinyOS components can use. As we will explain later in this section, a TinyOS based WSN can achieve better performance and be more reliable when using exclusively NesC.  
The design philosophy of TinyCoAP follows the principles of the TinyOS programming model. The code is structured in TinyOS components and the use of external libraries is avoided. TinyCoAP is completely written in the NesC language. The rest of this section focuses on the memory allocation system, library and the data structures of TinyCoAP.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 2 BitTorrent-Like Systems and Network Coding Background
### Network Coding Challenges  
The use of linear algebra in encoding and decoding incurs additional computation overhead that is not present in the baseline BitTorrent. In BitTorrent, the file is split into pieces, and the peers are only concerned with completing the file by exchanging pieces with each other. A piece is considered useful if it is received by a peer for the first time. With network coding, the coded pieces are distributed rather than the original pieces. As the coefficients are received with a piece, they must be tested against all previous sets of coefficients that the peer has received to examine if the new piece contains any previously unknown information. The received encoded piece is said to be innovative if it contains beneficial information; otherwise, it will be discarded. Additionally, decoding the file after receiving enough pieces is more computationally intensive using network coding. The file decoding within BitTorrent is very trivial and simple. Each piece is checked and verified separately; then, it is directly stored on the hard disk. Conversely, the decoding process in network coding cannot be done until all the needed pieces are received. Consequently, decoding the file requires solving many linear equations. As the size of the file increases, the cost of solving these equations increases [32]. For example, for downloading a 3 GB file that is segmented into 1000 pieces, the required disk read operations are 3000 GB on each receiving node. Meanwhile, the BitTorrent system only needs to write 3 GB of data [33].
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 3 AE Impact on Network Traffic Classifiers  
We begin by developing well-trained ML-based models for network traffic classification to study the impact of adversarial attacks on these models. For development, we employed the Python programming language, as well as the popular ML libraries Keras [48] and scikitlearn [49]. Additionally, Adversarial Robustness Toolbox (ART) [50] is used to guide the AE generation process.
## 3 Experiments and Results
### Improvement of Working Stability by Electrical Feedback Control
By calculation the improvements of stability in terms of wavelength-shift and power-shift respectively reach 64.2% (\(=(1-\frac{0.029}{\varepsilon_{00}})\times 100\%\)) and 69.7% (\(=(1-\frac{0.182}{\varepsilon_{00}})\times 100\%\)) in our dual-ring configuration.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.2. Keccak  
Keccak [56] is considered a family of Sponge-based hash functions, which are renowned for their flexibility and security. This family includes various instances and configurations to suit different requirements. Keccak functions are based on the Keccak-f permutation, which is the fundamental building block of these hash functions. The Keccak-f permutation is comprised of a series of elementary rounds that entail logical operations and bit permutations. Keccak-f[200] and Keccak-f[400], along with their lightweight implementation permutations reported by [PERSON] and [PERSON] [57], are derivatives of the SHA-3 hash function. The introductory element or component is the Keccak-f permutation, which comprises a multitude of elementary rounds comprising logical operations and bit permutations. Keccak-f[\(b\)] involves a permutation chosen from a collection of seven permutations, and therefore \(b\) denotes the width of the permutation (ranging from 25 to 1600) and the width of the state within the Sponge construction.  
The permutation Keccak-f[\(b\)] operates on a state \(a\), which is structured as a three-dimensional array of elements in \(GF(2)\), denoted as \(a[5][5][w]\), where \(w=2^{\ell}\), where \(w\) represents the lane size, and the state consists of five rows, five columns, and \(w\) bits per lane. The bit at position \((x,y,z)\) in the state a is indexed as \(a[x][y][z]\), where \(x,y\in\mathbb{Z}_{5}\) (modulo 5) and \(z\in\mathbb{Z}_{w}\) (modulo \(w\)). The mapping between the linear bitstring \(s\), used in the Sponge construction, and the three-dimensional state \(a\) is expressed as \(s[w(5y+x)+z]=a[x][y][z]\). This mapping ensures that the state is interpreted correctly in its 3D form during the permutation process. Indices \(x,y\), and \(z\) are taken modulo their respective dimensions, ensuring cyclic behavior as required by the design. In cases where indices \([z],[y][z]\), or all three indices are omitted, the statement applies universally to all valid values of the omitted indices. This notation allows for compact expressions in describing operations that affect the entire state or specific subsets of it [56].
# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 4 Experimental Conditions  
### Dataset  
This section is divided into two categories: Benchmark Dataset HateXplain [13] and New Dataset Creation.  
_Benchmark Dataset--HateXplain:_ This dataset was obtained from platforms like Twitter and Gab, which were previously explored in hate speech studies. The corpus, comprising tweets and Gab posts, was annotated with labels (hatespeech/offensive/normal), target communities, and text snippets marked by annotators supporting the labels. Each data point was evaluated by three independent annotators, and if there was a consensus among two or more annotators on a specific category, the data were officially categorized accordingly.  
_New Dataset Creation:_ From the benchmark dataset, HateXplain (which is publicly available in online), 3199 data points were randomly selected and each underwent manual scrutiny to determine its sarcastic nature, illustrated in Figure 4a. Three independent annotators meticulously labeled each data offering a nuanced perspective on the presence or absence of sarcasm, complemented by a detailed rationale for their labeling decisions, illustrated in Figure 4b.  
About 12 anonymous annotators, along with recruited volunteers, were engaged in the labeling task for this project. Exclusively provided with textual data (posts) from the selected entries in the HateXplain dataset, these annotators focused on identifying sarcasm. Drawing inspiration from the use of rationales in detecting hate/offensive comments, as observed in the benchmark dataset, our approach involved creating rationales [23] for sarcasm in comments. When an annotator labeled a data point as sarcasm, they highlighted a specific text span they believed conveyed the sarcastic nature of the comment/post. Given the subjective nature of sarcasm perception, even if just one annotator categorized a data point as sarcastic, it was officially placed into the sarcastic category.