esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AES & Advanced Encryption Standard \\ ANT & Authenticated Network Topology \\ CAN & Controller Area Network (protocol) \\ CAN-FD & Controller Area Network--Full Datarate \\ CAN-XL & Controller Area Network--extended Length \\ CBC & Cipher Block chaining--Mode of operation for block ciphers \\ CBC-MAC & CBC-based MAC \\ CCM & CTR with CBC-MAC \\ CDHP & Computational Diffie-Hellman Problem \\ CTR & Counter Mode--Mode of operation for block ciphers \\ DDHP & Decisional Diffie-Hellman Problem \\ EAP & Extensible Authentication Protocol \\ ECC & Elliptic Curve Cryptography \\ ECDH & Elliptic Curve Diffie-Hellman \\ ECDSA & Elliptic Curve Digital Signature Algorithm \\ ECIES & Elliptic Curve Integrated Encryption Scheme \\ ECTAK & Elliptic Curve Topology Authenticated Key \\ ECTAKS & Elliptic Curve Topology Authenticated Key Scheme \\ ECU & Electronic Control Unit \\ EMERGEE & EMERGE Project--_Veicoll Commerciali Leggeri_ \& Tecnologie EMERGEnti \\  & per operativita di tutti i giorni e di ausilio nelle EMERGEnze \\ GNSS & Global Navigation Satellite System \\ GPU & Graphical Processing Unit \\ HMAC & Hash-based MAC \\ HSM & Hardware Security Module \\ IDS & Intrusion Detection System \\ IRS & Intrusion Response System \\ KDF & Key Derivation Function \\ LIN & Local Interconnect Network (protocol) \\ LKC & ECTAKS Local Key Component \\ MAC & Message Authentication Code \\ MEC & Mobile Edge Computing \\ OBU & On-board Unit \\ PBRDF2 & Public-Key KDF version 2 \\ SBC & Single-Board Computer \\ SHA256 & Secure Hash Algorithm version 2 with 256 bits hash size \\ SIEM & Security Information and Event Management \\ TKC & ECTAKS Transport Key Component \\ TLS & Transport Layer Security \\ TV & ECTAKS Topology Vector \\ TPM & Trusted Platform Module \\ USB & Universal Serial Bus (protocol family) \\ V2I & Vehicle to Infrastructure communications \\ V2V & Vehicle to Vehicle communications \\ V2X & Vehicle to Everything communications \\ \end{tabular}
## 3 Results
### Excess Properties
#### 3.2.4 Partial Molar Volumes  
Partial molar volumes were calculated using the following equations:  
\[\overline{V_{1}}=V^{E}+V_{1}^{0}+(1-x_{1})\Big{(}\partial V^{E}/\partial x_{1} \Big{)}_{p,T} \tag{23}\]  
\[\overline{V_{2}}=V^{E}+V_{2}^{0}-x_{1}\Big{(}\partial V^{E}/\partial x_{1} \Big{)}_{p,T} \tag{24}\]  
where \(V_{1}^{0}\) and \(V_{2}^{0}\) are the molar volumes of pure components. The derivative \(\big{(}\partial V^{E}/\partial x_{1}\big{)}_{p,T}\) in Equations (23) and (24) was obtained by differentiation of Equation (18), which leads to the following equations:  
\[\overline{V_{1}}=V_{1}^{0}+x_{2}^{2}\sum_{k=0}^{3}a_{k}(2x_{1}-1)^{k}-2x_{1}x _{2}^{2}\sum_{k=1}^{3}a_{k}(2x_{1}-1)^{k-1} \tag{25}\]  
\[\overline{V_{2}}=V_{2}^{0}+x_{1}^{2}\sum_{k=0}^{3}a_{k}(2x_{1}-1)^{k}+2x_{1}^ {2}x_{2}\sum_{k=1}^{3}a_{k}(2x_{1}-1)^{k-1} \tag{26}\]  
The calculated values of partial molar volumes are listed in Tables A7 and A8.  
Our results show that for the DEG + DMSO system, the decrease in the DMSO concentration leads to the increase in the values of the apparent molar volumes and partial molar volumes of DEG, and the decrease in the values of the apparent molar volumes and partial molar volumes of DMSO. For the MDEA + DMSO system the decrease in the DMSO concentration leads to the decrease in the values of the apparent molar volumes and partial molar volumes of MDEA and the increase in the values of the apparent molar volumes and partial molar volumes of DMSO.
# Analysis of Factors Associated with Charcot Neuroarthropathy following Pancreatic Transplantation
## 3 Results  
### Background Characteristics of All Patients  
Table 1 shows the background characteristics of all 61 patients who underwent pancreatic transplantations. The median age was 44 years (range: 29-65), and 23 (37.7%) patients were men. The surgical procedures included 50 cases of simultaneous pancreas and kidney transplantation (SPK), 2 cases of pancreatic transplantation alone (PTA), and 9 cases of pancreatic transplantation after kidney transplantation (PAK). The median DM duration was 28.5 years (16.8-53.1), the median dialysis history of SPK was 3.7 years (0-19.2), and 50 (82.0%) patients had undergone SPK. There was no difference between induction and maintenance immunotherapies. Ten (16.4%) of the sixty-one patients developed rejection. High-dose corticosteroids were administered to 12 (19.7%) patients (rejection, 10 patients; sudden deafness, 1 patient). The remaining patients were administered with high-dose corticosteroids for cardiac arrest during their intensive care unit stay. CN onset during graft survival was observed in six patients.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **All Patients** & **Patients with CN** & **Patients without CN** & **p-Value** \\  & **(n = 61)** & **(n = 6)** & **(n = 55)** & **\(p\)-Value** \\ \hline Age at Tx & 44 [29-65] & 38.5 [29-57] & 44 [31-65] & 0.42 \\ Sex (male, \%) & 23 (37.7) & 1 (16.7) & 22 (44.0) & 0.40 \\ BMI (Kg/m\({}^{2}\)) & 19.9 [15.5-28.4] & 19.8 [18.1-22.8] & 20.2 [15.5-28.4] & 0.98 \\ Duration of DM (years) & 28.5 [16.8–53.1] & 22.7 [18.7–36.6] & 29.2 [16.8–53.1] & 0.20 \\ Duration of ID (years) & 3.7 [0–19.2] & 5.0 [0.4–11.3] & 3.6 [0–19.2] & 0.55 \\ Procedure (\%) & 2 (3.3) & 1 (16.7) & 1 (1.8) & \\ PAK & 9 (14.8) & 0 (0) & 9 (16.4) & \\ SPK & 50 (82.0) & 5 (83.3) & 45 (81.8) & 1.0 \\ Induction dose (\%) & 13 (86.9) & 6 (100) & 47 (85.5) & \\ Insiliximab & 8 (13.1) & 0 (0) & 8 (14.5) & \\ Everolumns (\%) & 13 (21.3) & 1 (16.7) & 12 (21.8) & 1.0 \\ MMF (\%) & 56 (91.8) & 6 (100) & 50 (09.9) & 1.0 \\ Pre-Tx HbA1c (\%) & 7.3 [4.9–9.8] & 88 [7.3–9.8] & 7.1 [4.9–9.8] & \textless{}0.001 \\ HbA1c at IM (\%) & 5.5 [4.3–6.9] & 5.6 [5.0–6.9] & 5.6 [4.3–6.9] & 0.84 \\ Rejection (\%) & 10 (16.4) & 2 (33.3) & 8 (14.5) & 0.25 \\ High-dose steroid dose (\%) & 12 (19.7) & 4 (66.7) & 9 (16.4) & 0.015 \\ Additional steroid dose (mg) & 18928 [8–6000] & 937.5 [0–3075] & 2987.5 [312.5–600] & 0.16 \\ \hline \end{tabular}
\begin{tabular}{c} CN, Charcot neurothermopathy: Tx, transplantation; BMI, body mass index; DM, diabetes mellitus; HD, hemodialysis; PTA, pancreatic transplantation alone; PAK, pancreas after kidney transplantation; SPK, simultaneous pancreas–kidney transplantation; MMF, mycophenolate mofetil; HbA1c, glycated hemoglobin. \\ \end{tabular}
\end{table}
Table 1: Patient background characteristics.
# A Cloud Based Disaster Management System
## 7 Simulation Results and Scenario Modeling
### Cyber-OF Performance Evaluation Results  
The study implemented a Cyber-OF in ContikiOS while utilizing the Cooja simulator to gauge overall performance. The study examined the output from three implemented OFs:  
1. A latency-based OF, which considers the latency.
2. An energy-based OF, which considers the energy.
3. The Cyber-OF, which considers an adaptive behavior, described in Figure 8.  
Simulation experiments were conducted utilizing a 2D-grid surface on a network topology that has 10, 20, or 30 sensors. The DAG architecture consisted of one border router that acts as the data sink while the rest acted as UDP servers producing data. The depth of the resulting DAG is equivalent to 6. For the simulation, a fire alarm is activated by one node, which would then transmit a unicast packet containing the alarm to the sink (Figure 8a). In response, the sink transmits the alarm to all the nodes within the DAG to adjust to the topology consequently (Figure 8b).  
Next, the study presents the outcomes of the assessment of Cyber-OF, which also examines the influence of the following parameters:  
* End-to-end delays: Duration from the beginning of packet transmission to its reception at the DAG root.
* Network lifetime: The time required for the initial sensor to expend all its energy.  
Figure 9 compares the average end-to-end delay within the three OFs under investigation: the energy-OF, the latency-OF, and Cyber-OF. It is apparent they displayed similar delay values when the  
Figure 7: Market-based approach using fire scenario with two robots and six tasks.  
network consists of less than 20 nodes. However, a slight difference was noted in the energy-OF, which permitted a higher average delay. This was anticipated because the energy-OF only extends network lifetime and the selection of the best performing parent is subject only to the remaining energy within the node.  
The study also noted that when the network consists of more than 20 nodes, Cyber-OF underwent lower average delay values than the energy-OF. This verifies the predisposition of the Cyber-OF to diminish the holdup when serious events (a fire alarm in this model) are identified. Figure 10 characterizes the energy that the latency-OF and Cyber-OF consume, signifying how Cyber-OF can reduce energy consumption and maximize network lifespan better than the latency-OF during a five-minute demonstration.  
Figure 8: A multicast packet of the whole network simulation. (**a**) Unicast alarm packet sent to sink; (**b**) Sink forward alarm packet by multicast.  
Figure 10: Average end-to-end delay of an energy-based objective function (energy-OF), a latency-based objective function (latency-OF), and the cyber-physical objective function (Cyber-OF), in relation to directed acrylic graph (DAG) size.  
Figure 9: Comparison of the network lifetime of a latency-based objective function (latency-OF) and the cyber-physical objective function (Cyber-OF).
###### Abstract  
Sorption characteristics of nitrogen compounds is necessary in ascertaining their fate in the soil-water environment. This paper presents a laboratory \"batch\" experiment for determining the sorption parameters of ammonium in the soil-water environment. Three agricultural soils (silt loam, loam, and sand) with different contents of the clay fraction, significantly affecting the range of the adsorption of contaminants on the surface of solid particles, were chosen as research material. Considering the composition of ammonium nitrate (a commonly used fertilizer in Poland), ammonium solutions with a concentration of 0-52 mg NH\({}_{4}\)\({}^{+}\)/L were used as markers. Pseudo-first order, pseudo-second order, and intra-particle diffusion kinetic models were used to describe the mechanisms of ammonium adsorption. Experimental data obtained from the equilibrium tests have been analysed using two-parameter ([PERSON], Langmuir, [PERSON]) and three-parameter ([PERSON], Hill, Sips) models. In order to adjust the parameters of the considered isotherms to the experimental data, the method of minimization of the sum of squared errors was used. Additionally, the maximum sorption capacities and reduction ratios of ammonium versus time were assessed. It was observed that the presence of silt loam and loam in the soil profile can increase the possibility of ammonium adsorption up to almost 20%. Taking into consideration the results of the batch tests, it was concluded that ammonium adsorption attains equilibrium within 48 h. Experimental data was best followed by the pseudo-second order equation and the adsorption isotherm performed to the Redlich-Peterson model for loam and sand.  
migration; ammonium nitrate; soil; sorption; isotherm; batch tests Article
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 4 Experimental Results
### Comparison with ACO Algorithms
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Instance** & **Algorithm** & **Avg.** & **Std.** & **Best** & **PD\({}_{avg}\) (\%)** & **PD\({}_{best}\) (\%)** & **Time (s)** \\ \hline \multirow{4}{*}{ESC78} & AS\({}_{Rank}\) & 18,609 & 147 & 18,415 & 2.08 & 1.01 & 183 \\  & AS\({}_{Rank}^{ps}\) & 18,470 & 43 & 18,405 & 1.32 & 0.96 & 185 \\  & AS\({}_{ORank}^{ps}\) & 18,584 & 158 & 18,380 & 1.94 & 0.82 & 185 \\  & AS\({}_{ORank}^{ps}\) & **18,460** & 69 & **18,300** & **1.26** & **0.38** & 192 \\ \hline \multirow{4}{*}{ft70.1} & AS\({}_{Rank}\) & 41,403 & 419 & 40,646 & 5.32 & 3.39 & 81 \\  & AS\({}_{Rank}^{ps}\) & 41,082 & 297 & 40,505 & 4.50 & 3.03 & 81 \\  & AS\({}_{ORank}^{ps}\) & 41,053 & 331 & 40,529 & 4.43 & 3.09 & 80 \\  & AS\({}_{ORank}^{ps}\) & **40,768** & 341 & **40,092** & **3.70** & **1.98** & 81 \\ \hline \multirow{4}{*}{p43.1} & AS\({}_{Rank}\) & 28,333 & 107 & **28,220** & 0.69 & **0.28** & 21 \\  & AS\({}_{Rank}^{ps}\) & 28,255 & 65 & **28,220** & 0.41 & **0.28** & 21 \\  & AS\({}_{ORank}^{ps}\) & 28,330 & 107 & **28,220** & 0.63 & **0.28** & 21 \\  & AS\({}_{ORank}^{ps}\) & **28,236** & 34 & **28,220** & **0.34** & **0.28** & 22 \\ \hline \multirow{4}{*}{p43.4} & AS\({}_{Rank}\) & 83,693 & 119 & 83,415 & 0.83 & 0.49 & 67 \\  & AS\({}_{Rank}^{ps}\) & **83,405** & 73 & 83,295 & **0.48** & 0.35 & 68 \\ \cline{1-1}  & AS\({}_{ORank}^{ps}\) & 83,620 & 120 & 83,405 & 0.74 & 0.48 & 67 \\ \cline{1-1}  & AS\({}_{ORank}^{ps}\) & **83,416** & 113 & **83,265** & **0.49** & **0.31** & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for SOP instances obtained by Rank-based ACO variants. The best results for each problem instance are highlighted in bold. Figure 6: Evolutionary curves of ACO algorithms for symmetric and asymmetric TSP instances. To sum up the conclusions from comparative tables and evolutionary graphs, AS\({}_{ORank}^{ps}\) is able to find higher-quality solutions than AS and AS\({}_{Rank}\) in all the problem instances and in all except one instance when compared with ACS. Moreover, AS\({}_{ORank}^{ps}\) showed a much faster convergence than MMAS, while being able to reach better final solutions in most of the problem instances. Figure 7: Evolutionary curves of ACO algorithms for asymmetric TSP instances (\(\hat{\mu}\)\(\hat{\
u}\)64 and \(\hat{\mu}\)\(\hat{\
u}\)70) and four SOP instances. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Instance** & **Algorithm** & **Avg. ** & **Std. ** & **Best** & **PD\({}_{agg}\) (\%)** & **PD\({}_{best}\) (\%)** & **Time (s)** \\ \hline \multirow{4}{*}{brazil58} & AS & 25,930 & 89 & 25,685 & 2.
## 2 Materials and Methods
### Methodology  
The sensitive-indicator approach was used in this research [19] (sensitivity for wind erosion is correlated with the content of erodible fraction, vegetation cover, and climate factor). First, maps of the abovementioned factor were obtained (using interpolation methods, such as kriging, and Inverse Distance Weighting (IDW). Furthermore, the sensitivity to wind erosion for each factor (soil erodibility, climate factor, and vegetation cover) was first calculated separately using fuzzy logic [18; 19; 29], which was developed by [PERSON] [38]. The relationship between the two variables (the first variable was one of the above mentioned factors and the second one was sensitivity to wind erosion) could be described by fuzzy membership functions (Figure 3), where each unit is assigned values between 0 and 1. No possibility of wind erosion was assigned 0, while 1 indicates maximum sensitivity. The relationship between the erodibility fraction and the rate of wind erosion was represented by a linear fuzzy membership function (Figure 3c) [29]. The same function was applied to the relationship between the climate factor and the rate of wind erosion (Figure 3b) [33]. The relationship between the vegetation cover and the rate of wind erosion was represented by a J-shaped monotonically decreased fuzzy membership function (Figure 3a) [19].  
By using the above-described relationship (between the rate of wind erosion and the contributing factors), sensitivity maps were produced for each factor, which showed the spatial distribution of sensitivity for each factor in wind erosion. The individual factor maps were overlapped and an integrated map with zones vulnerable to wind erosion was obtained. In addition, the sensitivity map excluded urban areas, objects such as mine areas, airports, and water bodies. The fuzzy analysis and spatial analysis were carried out by IDRISI Taiga software (Clark Labs, Worcester, MA) and ArcMap 10.3 (ESRI, Redlands, CA). A full overview of the methodological framework is given in the scheme below (Figure 4).  
Figure 3: Applied fuzzy membership functions for each affecting factor (**a**) vegetation cover; (**b**) climate factor; (**c**) soil erodibility fraction.
_applied_  
_sciences_  
_Article_  
Turbine Blade Temperature Field Prediction Using the  
Numerical Methods  
**[PERSON]**\({}^{\copyright}\), [PERSON]**\({}^{\copyright}\) and [PERSON]**\({}^{\copyright}\)  
Faculty of Aeronautics, Technical University of Kosice, Rampova 7, 041 21 Kosice, Slovakia; [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (K.D.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +421-55-602-6158  
**Featured Application: The results of the research presented in the proposed article in terms of a novel quick and precise methodology for temperature prediction using numerical methods can be applied to research, development and monitoring engine parts. The methodology can be applied in aviation to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only maintenance but also research and development costs due to the significantly lower computational time demands. The advantage of this methodology is also that it can be applied to any engine parts without the need for artificial neural network modification; the optimization process must only be performed on the CFD model.**  
Nowadays, material science and stress characteristics are crucial in the field of jet engines. There are methods for fatigue life, stress, and temperature prediction; however, the conventional methods are ineffective and time-consuming. The article is devoted to the research in the field of application of the numerical methods in order to develop an innovative methodology for the temperature fields prediction based on the integration of the finite element methods and artificial neural networks, which leads to the creation of the novel methodology for the temperature field prediction. The proposed methodology was applied to the temperature field prediction on the surface blades of the experimental iSTC-21v jet engine turbine. The results confirmed the correctness of the new methodology, which is able to predict temperatures at the specific points on the surface of a turbine blade immediately. Moreover, the proposed methodology is able to predict temperatures at specific points on the turbine blade during the engine runs, even for the multiple operational regimes of the jet engine. Thanks to this new unique methodology, it is possible to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only the maintenance but also the research and development costs due to the significantly lower time demands. The main advantage is to predict temperature fields much faster in comparison to the methods available today (computational fluid dynamics (CFD), etc.), and the major aim of the proposed article is to predict temperatures using a neural network. Apart from the above-mentioned advantages, the article's main purpose is devoted to the artificial neural networks, which have been until now used for many applications, but in our case, the neural network was for the first time applied for the temperature field prediction on the turbine blade.  
**Keywords:** temperature field; turbine blade; jet engine; artificial neural network; numerical analysis; prediction model
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Document Pre-Processing in Short Text Clustering
#### 3.1.1 Tokenization and Normalization  
Tokenization is defined as a standard text representation that divides a flow of natural language text into distinct significant elements called tokens as part of the pre-processing [58]. Tokenization transforms the text from a document into data that can be analysed by machine learning methods. Generally, these algorithms segment the text into separate units by adding a space or some other kind of distinctive marker so that each team may be mapped to a different word in the text [55].  
The normalization step aims to clean the data by removing unnecessary and noisy data, such as numbers, symbols, code tags and special characters. Whilst clustering search results, noise filtering is an essential task of the tokenizer. Likewise, the retrieved results, such as the contextual snippets supplied as input, include file names; URLs; characters that demarcate portions of whole documents, such as ellipsis characters (@, %, &, etc.) and other symbols whose meanings are not readily apparent. A reliable tokenizer needs to be able to recognize and get rid of this type of noise whilst it creates a token sequence. This step is necessary to carry out acceptable data representation and pre-processing.  
As explained above, text tokenization and normalization transform the text into words or phrases by deleting extraneous strings of characters, such as punctuation marks, numerals and other strings of characters [59]. In essence, the white space is utilized to distinguish the collection of tokens that may be differentiated from one another. A text tokenization sample is illustrated in Figure 3. The text is displayed as a collection of tokens, with all of the characters written in lowercase, and white space is utilized to differentiate between each token. The commas, periods and other punctuation marks, along with any other special characters, are deleted.  
The first step of the text pre-processing is describing the tokenization and normalization, which are as follows:  
1. Remove numbers (2, 1... ).
2. Remove punctuation marks (!', ', -, \", :?, [L \(\backslash\),... ).
3. Remove special characters (-, @, #, #, $, %, &, =, +).
4. Remove symbols (e.g., ).
5. Remove non-English words, such as.
6. Remove words with less than three letters.  
Figure 4 shows the tokenization and normalization steps.  
Figure 3: Example of output of tokenization.