esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 4 Proposed Protocol Methodology for E-Commerce
### Implicated Symmetric and Asymmetric Cryptography
#### 4.1.2 Utilizing the AES Algorithm  
The AES algorithm encrypts/decrypts the buy and sell requests upon delivery from the buyer (B), seller (S) or trust authority server (TAS). However, the use of keys as small as 80 and 128 can compromise the customers' sensitive information. Therefore, our protocol relies on a key length of 256, which is enough to block attacks. In addition, our protocol addresses a scalability problem that is inherent in symmetric algorithms, whereby all users use the same secret key. Our protocol uses a different secret key for each user by using the public keys of the ElGamal algorithm. In addition to the importance of key length, fundamental reasons that AES can be vulnerable to attacks are the randomness level of keys and cryptographic randomness. Therefore, our protocol uses strong AES ciphers that provide appropriate key length (256 bit), key randomness (ElGamal), and cryptographic randomness through the use of a random salt with fewer performance expenses.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 2 Materials and Methods
### Improving Firefly Optimization Algorithm
#### 2.5.1 Random Forest  
For the compaction quality evaluation in the core wall region of earth-rock dams, the RF proposed by [PERSON] [16] is used to predict the dry density. This algorithm utilizes the Bootstrap resampling method to generate multiple samples from the original compaction data, each of which is used to construct decision trees. These trees are then aggregated, and the final dry density prediction is obtained by averaging the predictions of the individual trees. The law of large numbers ensures that random forests do not overfit as the number  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Functions**} & \multirow{2}{*}{
\begin{tabular}{c} **Optimal** \\ **Solution** \\ \end{tabular} } & \multicolumn{2}{c}{**FA**} & \multicolumn{2}{c}{**IFA**} \\ \cline{3-6}  & & **Optimal** & **Execution Time (s)** & **Optimal** & **Execution Time (s)** \\ \hline \(f_{1}\) & f(0,0,0,0,0) = 0 & 0.005622466 & 1.47 & 2.04 \(\times\) 10\({}^{-15}\) & 1.52 \\ \(f_{2}\) & f(0,0,0,0,0) = 0 & 4.093030513 & 2.03 & 0.996754151 & 2.11 \\ \(f_{3}\) & f(0,0,0,0,0) = 0 & 0.167418872 & 2.51 & 4.23 \(\times\) 10\({}^{-8}\) & 2.58 \\ \(f_{4}\) & f(0,0,0,0,0) = 0 & 0.001441134 & 2.08 & 0.001130785 & 2.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of benchmark functions on the FA algorithm and IFA algorithm.  
of trees increases, although they do exhibit a finite error value. Extensive research [16; 17; 18] has demonstrated that RF is one of the best algorithms currently available, known for its resistance to overfitting, robustness in handling noise and outliers, low generalization error, and superior prediction accuracy, even in the presence of multicollinearity.  
It is important to note that, if the value of \"_Mtry_\" is too small, overfitting may occur, leading to increased classification error and reduced accuracy. Conversely, if \"_Mtry_\" is too large, the RF construction time increases, which slows down the process. A small \"_Ntree_\" may result in insufficient training, reducing the randomness of RF, while a large value may overly randomize the model, increasing the computation time and reducing the classification precision. However, RF lacks the ability to autonomously determine the optimal \"_Mtry_\" and \"_Ntree_\". To ensure both generalization capability and accuracy, an optimization algorithm is necessary to fine-tune these parameters.
# Confidential Smart Contracts and Blockchain to Implement a Watermarking Protocol
## 7 Security Analysis
### Basic Components and Their Impact on Security
In reality, it is necessary to observe that the proposed protocol can correctly complete a content purchase transaction in the presence of at least one honest \(\mathcal{CN}\). In this hypothesis, the validator nodes can certify the correct execution of the transaction by executing a specific algorithm usually present in the \"layer-2\" execution models (see Section 4 and Figure 4). Such an algorithm is executed before reaching consensus in the blockchain and allows comparing the results of the \(\mathcal{CN}\)'s executions in order to identify any malicious behavior [12; 14; 36]. In fact, the presence of at least one honest \(\mathcal{CN}\) allows validator nodes to highlight discrepancies in the different executions and thus to identify the correct execution. However, to minimize the risk of incorrect executions by \(\mathcal{CN}\)s, two useful measures can be adopted. The first is based on implementing an economic model that incentivises honest \(\mathcal{CN}\)s and imposes penalties on malicious ones. In this way, the participation of malicious \(\mathcal{CN}\)s would be discouraged or make them less numerous than honest ones. Such a condition would make it easy to produce correct transactions and identify incorrect ones, thus eliminating malicious \(\mathcal{CN}\)s from the pool of those available to execute the protocol. The second provides a \"permissioned\" approach for \(\mathcal{CN}\)s, which can join the protocol execution pool only after their honesty has been assessed. In this case, the risks of possible attacks on the TEE's enclave would be drastically reduced, even if this approach is more restrictive than the \"permissionless\" one used for blockchain nodes. As it appears from the considerations reported so far, the proposed protocol still manages to be immune to attacks on the TEE's enclave, although the proposed measures can improve its execution and mitigate the consequences of the attacks. Moreover, despite the large attack surfaces, TEEs are still considered the basis for confidential executions in secure enclaves [19; 30; 32], and several important companies, such as INTEL and AMD, are investing in this area. Therefore, the next proposed implementations will likely be characterized by increasingly limited attack surfaces, so as to make such implementations substantially secure [17; 31; 32]. The last critical component is represented by the system of servers responsible for managing the keys used by the smart contract that implements the proposed protocol. By virtue of the importance of the function performed, these servers should be correctly managed \(\mathcal{CN}\)s with a good reputation. However, the protocol is not based on specific assumptions, and only assumes that the servers implement the protocol described in [14], or at least an equivalent one, so as to satisfy the security, confidentiality, and availability requirements that enable the watermarking protocol to be executed in a secure manner.
# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 2 Materials and Methods
### Study Design  
This study was conducted using data collected from a professional soccer club during both the preseason and competitive periods. The observed team competes at the highest level of its national championship and regularly participates in European competitions. The club's medical staff used an internal online platform to collect injury records and monitor the rehabilitation process. To ensure accuracy, each injury and recovery progress was recorded by reaching a consensus among the medical team. Data collection began in February 2021 and continued until February 2023. Muscle injuries were the only injuries included in the analysis for this study.  
The research included 41 unique male soccer players (mean age \(24\pm 4.2\) years) who experienced a total of 84 muscle injuries during the examined period. The players who have not suffered an injury were excluded from the study. The identities of the players were anonymized and not available to the researchers. Of the players involved, one player experienced 7 injuries, two players experienced 5 injuries each, and one, eight, and six players experienced 4, 3, and 2 injuries, respectively. All other players experienced only one injury each.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 1 Introduction  
Electrical impedance tomography (EIT) assesses the human body's internal electrical characteristics by taking measurements from the external surface of the body [1]. Usually, this is done by injecting the current into the object through electrodes located on its surface, and the corresponding voltages are measured. The aim here is to reconstruct the electrical permittivity and conductivity distributions inside the object from the knowledge of the measured electrode voltages and applied current patterns by solving an inverse problem [2]. In EIT, any substantial changes in impedance would only result in a minor change at the surface which makes the solution of the inverse problem a highly challenging task [3].  
EIT has a lower imaging resolution in comparison to magnetic resonance imaging (MRI) and computed tomography (CT). EIT is entirely non-invasive and carries out a radiation-free imaging modality. EIT has an advantage over the other imaging techniques such as its portability, low-cost, and faster imaging capabilities. EIT proves to be efficient when producing an impedance map of the inside of a human skull even for areas where ultra-sonic techniques do not prove to be sufficient. The initial usage of EIT was reported 100 years ago in the study of geological surveys [4]. Others have used a similar technique for the detection of air bubbles in process pipes or for monitoring the pipe flows, which today is known as industrial process tomography. In 1978 for the first time, [PERSON] and [PERSON] published impedance images of human tissues [5]. They created a transmission map from current-voltage data with 100 electrodes formed into a rectangular array by fixing it on one side of the human chest while earthing the opposite side of the body using a single large electrode. On the object's surface, using electrical measurements, EIT seeks to reconstruct a resistivity distribution. In the resulting image, the low conductivity areas would correspond to the lungs. Unlike X-ray tomography [6], in EIT the current paths and the equipotential surfaces are the functions of the unknown resistivity distribution.
# Chemical Characterization of Cider Produced in Hardanger--From Juice to Finished Cider
## 3 Results and Discussion
### Chemical Composition of Apple Jices and End Fermented Cider of Different Apple Fruit Cultivars
#### 3.1.2 Total Phenols and Individual Polypphenols  
Polyphenols are important for cider colour, stability, taste (bitterness and astringency), and aroma. Processing apple fruits to juice impacts the phenolic content due to the presence of enzymes. This is explained by differences in the extraction of phenolic compounds [20] and the degradation or transformation of phenols through oxidation [21; 22; 23; 24]. Table apples have lower levels of phenols than cider apples [25; 26]. The apple fruits produced in Hardanger are mostly table apples, and we already know that ciders from Hardanger have lower amounts of total phenols than French ciders [2]. As evident from Table 3, the single-cultivar juices had total phenol content ranges on average from 198 mg L\({}^{-1}\) ('Summerred') to 789 mg L\({}^{-1}\) ('Discovery'), determined in gallic acid equivalents using the _Folin-Circulate_ method. These results agree with earlier reports of other Norwegian groups of scientists [27], who found the concentration of total phenols in the juice of cv. 'Summerred' to be 272 mg L\({}^{-1}\). Table apples have lower levels of phenols than cider apples [25; 26]. In the juice from mixtures of Gravenstein and cider apples (Table S4), the total phenol reached 1772 mg of gallic acid L\({}^{-1}\). The contents of phenolic compounds analysed using the _Folin-Cioclteu_ method showed quantitative changes during fermentation but not the same pattern for all cultivars. The juice with the highest total phenols was from 'Discovery' and 'Gravenstein', where we observed a similar decrease during fermentation, reaching 496 and 306 mg of gallic acid L\({}^{-1}\) in the final ciders, while in 'Aroma' and 'Summerred', the total phenols did not change. The literature presents various findings regarding changes in phenolic compounds during fermentation. Some studies indicate a decrease in these compounds during fermentation [1; 28], while others indicate an increase [16; 29]. Our data are in the range of previous findings about Norwegian ciders [6; 27] and from the other ciders made from table apples, containing total phenols from 110 mg L\({}^{-1}\) up to 706 mg L\({}^{-1}\) (expressed as gallic acid equivalents [16; 28; 30].
# Time-Domain Dynamic Modeling and Analysis of Complex Heavy-Duty Gearbox Considering Floating Effect
## 5 Conclusions
**Nomenclature**  
**Symbol/Abbreviation** **Description**  
LTC & Lateral-torsional coupling (model/modeling)  
MW & Megawatt  
WTG & Wind turbine gearbox  
DOF & Degrees of freedom  
LSP & Low-speed planetary gear set  
HSP & High-speed planetary gear set  
PSH & Parallel-shaft helical gear set  
FEA & Finite element analysis  
\(x_{R},y_{R}\) & Translational displacements of the ring gear  
\(x_{S},y_{S}\) & Translational displacements of the sun gear  
\(x_{C},y_{C}\) & Translational displacements of the carrier  
\(\theta_{R},\theta_{S},\theta_{C}\) & Rotational angles of the ring gear, sun gear and carrier respectively  
\(x^{i}_{p},y^{i}_{p},\theta^{i}_{p}\) & Translational displacements and rotational angle of the \(i\)th planet gear  
\(N^{i}_{RP},f^{i}_{RP}\) & Normal pressure force and tangential force exerted on the \(i\)th planet gear  
\(N^{i}_{SP},f^{i}_{SP}\) & Normal pressure force and tangential force exerted on the \(i\)th planet gear  
\(F^{i}_{CP},T^{i}_{CP}\) & Reaction force and torque exerted on the \(i\)th planet gear by the bearing  
\(\mathcal{O}_{S},\mathcal{O}_{P},\mathcal{O}_{R}\) & Center points of the sun gear, the \(i\)th planet gear, and the ring gear  
respectively  
\(\gamma^{i}_{PS}\) & Time-varying position angle between the center connection line \(\mathcal{O}^{i}_{P}\mathcal{O}_{S}\)  
and the X axis  
\(\gamma^{i}_{PS0}\) & Inital position angle between the center connection line \(\mathcal{O}^{i}_{P}\mathcal{O}_{S}\)  
and the X axis  
\(\Delta\gamma^{i}_{PS}\) & Time-varying changing value of position angle \(\gamma^{i}_{PS}\)  
\(Z_{S},Z_{P},Z_{R}\) & Tooth number of the sun gear, planet gear, and ring gear respectively  
\(\theta^{i}_{P-PS}\) & Ideal rotational angle of the ith planet gear under \(\theta_{P}\)  
\(\Delta\theta^{i}_{P-PS}\) & Difference between the ideal and actual rotational angles of the ith  
planet gear  
\(\delta^{i}_{\theta\theta-PS}\) & Linear deformation caused by angle difference \(\Delta\theta^{i}_{P-PS}\)  
\(d^{i}_{PS}\) & Time-varying center distance between the ith planet gear and the sun gear  
\(d^{i}_{PS0}\) & Initial center distance between the ith planet gear and the sun gear  
\(\Delta d^{i}_{PS}\) & Time-varying changing value of center distance \(d^{i}_{PS}\)  
\(r^{i}_{P-PS}\) & Pitch radius of the ith planet gear while engaged with sun gear  
\(\alpha^{i}_{PS}\) & Engagement angle between the ith planet gear and sun gear  
\(r_{Pb},r_{Sb},r_{Cb}\) & Radius of base circle of the planet gear, sun gear and ring gear respectively  
\(\delta^{i}_{dn-PS}\) & Linear deformation caused by variation of center distance \(\Delta d^{i}_{PS}\)  
\(\delta^{i}_{n-PS}\) & Normal composite deformation between the ith planet and sun gear  
\(\delta^{i}_{t-PS}\) & Tangent composite deformation between the ith planet and sun gear
## 3 Results and Discussion
### 31P-NMR Determination  
Phosphorus nuclear magnetic resonance spectroscopy (\({}^{31}\)P-NMR) was employed to quantitatively analyse the presence of various hydroxyl and carboxyl groups in lignin (Figure 2). This was achieved by replacing the unstable hydrogen atoms with a phosphorylation reagent (TMDP), to generate lignin-TMDP derivatives. Signal peaks corresponding to different types of hydroxyl groups were observed at specific regions, including aliphatic hydroxyl (149.1-146.3 ppm), condensed phenolic hydroxyl (144.5-143.4 ppm), (142.0-141.3 ppm), syringyl phenolic hydroxyl (143.5-142.1 ppm), guiaacyl phenolic hydroxyl (140.1-138.8 ppm), p-hydroxyphenyl phenolic hydroxyl (138.3-137.1 ppm), and carboxyl (135.3-134.0 ppm) [27; 28]. The quantitative calculations of the NMR phosphorus spectra for each lignin sample are shown in Table 2. Following ozone pretreatment, the total phenolic hydroxyl content in MWL exhibited a significant decrease, while the carboxyl content showed an increase. This can be attributed to the oxidation of ozone molecules leading to the conversion of hydroxyl groups on the benzene ring into carboxyl or carbonyl groups [29; 30]; this conclusion is consistent with the results of our previous study [16]. Previous literature suggested that the phenolic hydroxyl groups present in lignin can form hydrogen bonds with hydroxyl groups in cellulase. Additionally, lignin has the ability to bind with nucleophilic sites in the molecular structure of cellulase, resulting in the formation of quinone methylation intermediates, which adversely affect enzymatic digestion [10]. Consequently, during the process of enzymatic hydrolysis, lignin may undergo non-productive adsorption with cellulase through the formation of hydrogen bonds. This can lead to the occupation of some of the adsorption sites within the cellulase catalytic channel by lignin, ultimately hindering the enzymatic hydrolysis of cellulose. Therefore, the decrease in phenolic hydroxyl content in MWL is advantageous for mitigating ineffective adsorption between lignin and cellulase, as the presence of phenolic hydroxyl groups positively correlates with the formation of hydrogen bonds between cellulase and lignin [31]. Consequently, the reduction in phenolic hydroxyl content also signifies a decrease in the strength of the hydrogen-bonding interaction between the two entities. Conversely, the augmentation of carboxyl content in MWL not only amplifies the hydrophilicity of lignin but also augments the negative charge density on the surface of lignin, thereby enhancing its electrostatic repulsion towards cellulase.  
Figure 2: \({}^{31}\)P-NMR spectra of MWL samples.
###### Abstract  
This work deals with the installation of two smart in-situ sensors (for underwater radioactivity and underwater sound monitoring) on the Western 1-Mediterranean Moored Multisensor Array (W1-M3A) ocean observing system that is equipped with all appropriate modules for continuous, long-term and real-time operation. All necessary tasks for their integration are described such as, the upgrade of the sensors for interoperable and power-efficient operation, the conversion of data in homogeneous and standard format, the automated pre-process of the raw data, the real-time integration of data and metadata (related to data processing and calibration procedure) into the controller of the observing system, the test and debugging of the developed algorithms in the laboratory, and the obtained quality-controlled data. The integration allowed the transmission of the acquired data in near-real time along with a complete set of typical ocean and atmospheric parameters. Preliminary analysis of the data is presented, providing qualitative information during rainfall periods, and combine gamma-ray detection rates with passive acoustic data. The analysis exhibits a satisfactory identification of rainfall events by both sensors according to the estimates obtained by the rain gauge operating on the observatory and the remote observations collected by meteorological radars.  
Keywords:rainfall; marine technology; underwater spectroscopy; passive acoustic +
Footnote †: journal: Sensors
## 2 Multi-Static Receiver PCL System  
Figure 1 shows elliptical trajectories for location estimation using the multi-static PCL system when three illuminators with one receiver are used for a single target. In Figure 1, the distance from the illuminator to the target is \(R_{1}\), while the distance from the target to the receiver is \(R_{2}\). The elliptical trajectory then comprises \(R_{1}+R_{2}\), indicating the potential location of the target. If multiple ellipses are created by two or more illuminators, the point at which they intersect is the target's possible location. However, there exist \"Ghost\" points at the intersections that are not the correct location, and thus at least three illuminators are needed to accurately estimate the target position in the multi-static PCL system [11].  
Figure 2 illustrates the FM and T-DMB broadcast stations in the Seoul-Gyunggi urban area and the potential coverage region for the multi-static PCL system when those stations are used as illuminators. There are two FM stations, sixteen T-DMB stations, and two stations transmitting both FM and T-DMB. Assuming a maximum reception distance of about 100 km for typical FM and DMB broadcasts [11; 15], the black, green, and purple circles represent the radius of 100 km within which the PCL system can estimate the target position using those FM stations. The overlapping area colored in yellow is the region of potential coverage by the PCL system where all three FM station signals can be used. Note that the south area of Seoul-Gyunggi cannot be covered by FM stations alone. If additional south T-DMB broadcast stations are added to the multi-static PCL system in this outer area, the PCL system's coverage can be extended to the entire urban area of Seoul-Gyunggi.  
Figure 1: Elliptic trajectories corresponding to the multi-static passive coherent location (PCL) system (3 illuminators, 1 target, and 1 receiver).