esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Impact of the Number of Vessels and Voyage Speeds under FAFU  
In case C3, the number of each type of vessel and forward and backward voyage speed factors are decision variables. Even though the optimization model gives the optimal solutions, we investigate the entire range of solution space to obtain insight into the decision factors.  
For the given numbers of each type of vessel, the voyage speed factors may or may not affect the throughput. Such a pattern is illustrated in Figure 5a. It shows the minimum, maximum, and average amounts of the transported products. When the number of vessels is not sufficient: (#LV,#SV) = (0,1), (0,2), (1,0), (1,1), even higher travel speeds cannot transport the target product volume. At the same time, when the number of vessels is sufficient, the travel speed does not affect the throughput. However, when the number of vessels is tight: (#LV,#SV) = (0,3), (2,0), the target transportation volume can be achieved only at high voyage speeds.  
Figure 4: Impact of the number of vessels at the current voyage speed under FAFU (case C2): (**a**) transported volume; (**b**) cost breakdown.  
Figure 5b shows the cost breakdown at the optimal travel speed for the number of each type of vessel. The smallest total cost is acquired when (#LV, #SV) = (3,0), which is the same as case C2. The variation in the port clues is also relatively minor, such as in case C2 (see Figure 4b). However, when (#LV,#SV) = (0,3) and (2,0), the portions of the voyage cost are 0.32 and 0.37, respectively, which are higher than that of other numbers of vessels (0.20-0.27). This outcome is because only high speeds could meet the target throughput when (#LV,#SV) = (0,3) and (2,0), incurring higher fuel consumption than other, lower-speed cases.
## 3 The Pipeline
### Preprocessing and Semantic Enrichment  
After extracting the data, SKATEBOARD undergoes preprocessing to ensure consistency with the source domain and improve the extracted data's quality. An essential aspect of this phase is NER, which identifies entities within sentences in the texts. Subsequently, NEL comes into play, disambiguating and linking these entities to existing databases or ontologies. This semantic enrichment process contributes to making the data more structured and understandable.
## 3 Numerical Simulation Method for Ice Shedding  
### Analysis Method for Ice Shedding on Overhead Transmission Lines under Complex Conditions  
Under natural icing and wind conditions, the longitudinal elastic potential energy and transverse elastic potential energy increased on iced conductors. Under suitable temperatures or external forces, the icing on the overhead conductor suddenly fell off; thus, the elastic energy on the conductor was transformed into kinetic energy and new potential energy, and the conductor underwent a complex swing. Due to air resistance, conductor self-damping, insulator string inertia force and other factors, the conductor vibration gradually weakened and eventually reached a new equilibrium position. The basic kinetic equation of the conductor ice shedding jump is:  
\[Ku+C\overset{\bullet}{u}+M\overset{\bullet\bullet}{u}=F \tag{3}\]  
where \(u\) is the displacement vector; \(\overset{\bullet}{u}\) is the velocity vector; \(\overset{\bullet\bullet}{u}\) is the acceleration vector; \(K\) is the stiffness matrix; \(C\) is the damping matrix; and \(M\) is the mass matrix.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Control Unit  
It consists of two FSMs optimized for the two different cases (without pipelining and with pipelining). In the non-pipelined case, the FSM requires a total of 55 states to execute all control functionalities. By carefully optimizing the state transitions, we minimize the number of required states. Similarly, in the case of pipelining, the FSM requires 93 states to perform all controlling tasks. The increase in the number of states is due to the additional complexity introduced by the pipelining stages. In other words, there is a need to manage data flow and synchronization between the pipeline stages.  
As shown in Figure 3 the control unit starts in an idle state, referred to as State 0. It is triggered by the reset and start control signals. When the start signal is 1, the control unit transitions from the current state to the next state (State 1). In pipelining, the flow from State 1 to State 12 is specifically designed for affine to projective conversions. These control signals facilitate the efficient execution of affine to projective conversions. Furthermore, states 7 to 65 are dedicated to producing signals for the Quad block Itoh-Tsuji inversion operation. State 66 in the FSM is dedicated to counting the number of points on the given BEC using the value of K. If the value of k is 1, State 66 transitions to State 81. On the other hand, if the value of k is not 1, it transitions to State 67.  
Figure 2: Montgomery radix-4 multiplier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _Adder_A[1:0]_ & **00** & **01** & **10** & **11** \\ \hline \(PP_{l_{2}}\) & 0 & \(p\) & 2\(P\) & 3\(P\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: For selection of prime numbers.  
States 81 to 92 are responsible for executing the \"if\" portion of Algorithm 1. Similarly, states 67 to 80 in the FSM generate signals related to the \"else\" portion of the target algorithm. Moreover, states 80 and 92 check the value of m, for the corresponding value of k (either 0 or 1). These states accurately determine the next steps based on the specific conditions and requirements of the algorithm. If the value of m is equal to 233, State 93 will be the next state. If the value of m is not equal to 233, State 66 will be the next state. By carefully optimizing the control signals and state transitions within these sections of the FSM, we streamlined the execution of Algorithm 1 within the cryptographic accelerator. These optimizations enhance the overall efficiency and performance of the accelerator, enabling it to handle cryptographic computations with precision and speed.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Actuation Mechanism
#### 2.1.2 Lift-Hop-Drop Actuation  
A novel actuation mechanism, where the switch rails are actuated following lift-hop-drop movement, has recently been developed such as in the REPOINT light switch shown in Figure 6[21; 41]. The switch rails are actuated in a semicircular path to allow redundant actuation. A recent simulation study by [PERSON] et al. [40] proposed the use of a closed-loop controller to improve the performance of  
Figure 5: Schematic of actuators: (**a**) traditional electromechanical switch actuator, (**b**) hydraulic actuator.  
HPSS to secure movement of the switch rails and to include self-adjusting capability. As can be seen in Figure 6, two electrical motors are connected to two individual gearboxes, which act as speed-reduction units as well. The output shaft of an individual gearbox is attached to a cam-hopper assembly. The switch rails are attached to the hopper, which moves in a semicircular way with the rotation of the electrical motor. This lift-hop-drop actuation technique has been followed in another innovative track switch research project [42].
# Collision Study on New Aluminum Alloy W-Beam Guardrail
## 6 Discussion  
The structural optimization method discussed in Section 2 is equally applicable to traditional three-wave guardrails. By altering the shape of the three-wave guardrail, the cross-sectional moment of inertia of the new three-wave guardrail beams increased by 42% under the same thickness, surpassing the 28% increment of the new two-wave guardrail. Therefore, applying this optimization method to optimize three-wave guardrails is expected to yield superior results compared to two-wave guardrails. The primary focus of this paper is to introduce the conceptual framework and theoretical derivation of this optimization method, and specific performance enhancements resulting from the optimization of three-wave guardrails will not be demonstrated herein.  
Wave guardrails absorb energy through plastic deformation during collisions. Plastic deformation serves as an energy absorption mechanism, where energy is utilized to overcome interatomic forces within the material, resulting in permanent changes in shape. Plastic deformation plays a crucial role in energy absorption, as the energy-absorbing capability of crash barriers essentially depends on the extent of plastic deformation after the collision.  
In the theoretical analysis of Section 2.3, it was assumed that the momentum of the impacting object is entirely converted into the deformation of the beam, without considering the influence of the shell structure of the wave beam. This assumption resulted in an overestimation of the theoretical calculation results, although it does not affect the subsequent conclusions. The research findings indicate that during the collision process, the shell structure of the W-beam guardrail can absorb over 50% of the impact energy, demonstrating a significant effect in terms of energy absorption. Additionally, the revised theoretical formula aligns well with the finite element results, allowing for the calculation of the maximum dynamic deformation of the wave beam after collision.  
During the optimization of the cross-section as depicted in Figure 1, the guardrail gradually loses its shell structure characteristics, leading to an increase in moment of inertia and deformation. This negatively impacts the guardrail's resistance to collisions. Through  
Figure 14: Acceleration of body center of mass during collision.  
the analysis of the results in Figure 4, an optimized new W-beam guardrail cross-section, as shown in Figure 5, was selected. Compared to the traditional cross-section, this new design enhances the moment of inertia while effectively absorbing 67% of the collision energy through its shell structure features, as illustrated in Figure 6. Consequently, it significantly reduces dynamic deformation during collisions, thus enhancing the guardrail's protective performance.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 5 Improving Animal Growth through Biochar  
Scientists and a growing number of farmers conducted experiments where biochar, as non-toxic and edible material, was included into animal farming systems (2010). Since 2010, this practice has begun to be adopted by agricultural practitioners, who found good results in terms of improving productivity. Since 2012, the biochar market in Europe has grown considerably with the use of biochar as an additive to animal feed in Germany and Switzerland (2011). To frame this market, the European Biochar Foundation introduced in 2016 a new certification standard particularly for biochar used in animal feeding (212).  
[PERSON] et al. (2013), in their interesting review, evaluated more than 100 scientific publications on the use of biochar for animal feeding to various animal groups. Most of the reported results were promising and exhibited for all investigated animal groups positive (but not always significant) effects on parameters such as digestion, toxin adsorption, blood values and cell numbers in milk. The authors also claimed the positive effect of biochar on livestock weight gain by increasing the pH, since an alkaline pH in the rumen could prevent acidosis, which is known to affect livestock weight gain. Similarly, [PERSON] et al. (2014) and [PERSON] (2015) reported that the oral ingestion of biochar improves animal health by improving nutrient intake and toxins adsorption. [PERSON] et al. (2016) reported an enhancement in growth rate of catfish following the addition of biochar at 2% in animal feed ingredients. [PERSON] et al. (2017) also recorded an enhancement in piglet growth after dietary biochar powder use as a feed additive.  
In other studies, the addition of bamboo biochar enhanced the feed conversion efficiency of flounder (112) and wheat straw-derived biochar reduced abdominal fat deposition and enhanced the growth of poultry (218). Biochar used as a feed additive can also provide resistance to various diseases and anti-bacterial effects (219) and can enhance the immune function and anti-stress properties of piglets (220). Despite the large quantity of investigations demonstrating that biochar used as animal fodder has no negative effect, there are still open questions on its long-term impacts, for instance its effect on the resorption of liposoluble feed ingredients and potential interaction with themycotoxin fumonisin that should be considered before the unrestricted recommendation of its adoption.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.5 Cooperative Energy-Efficient Routing Protocol (CO-E2 RP)  
In [151] the author presented a cooperative and energy efficient routing protocol (CO-E2 RP) for UWSNs using both the data location and the sensor Rx nodes depth. In this routing protocol the sensor Rx node with the smallest location values and depth is chosen by the Tx anchor node. In terms of location values, the communicating node in the network which is closest to the sink node will have a smallest location value. As opposed to, the nearby node selected as a repeater node for forwarding communicating data packets, which is closest to sensor Rx node. However, this protocol increased the end-to-end delay of the network and unnecessary energy consumption occurs due to a lot of hearing.
## 2 Materials and Methods
### Atmospheric Dispersion Modeling  
The calculations of the pollutant spread in the ambient air were made using the CALMET/CALPUFF modeling system. CALMET is a diagnostic meteorological model, which generates variables, in terms of time and location, a three-dimensional field representing the wind and temperature. Additionally, on a two-dimensional scale, it calculates the microclimatic parameters determining the description of air pollution dispersion. On the basis of spatial data, this model determines the initial wind field (including such factors as kinematic effect of terrain, slope flows and blocking effects) and carries out a three-dimensional procedure minimizing the discrepancies, and then, using the observational data, enables an objective analysis of the final wind field [48]. CALPUFF is a multi-layer, multi-species non-steady-state puff dispersion model that simulates the concentration of air pollution. It has the ability to calculate dispersion of contaminants for point, surface and volumetric emitters, taking account of the variation in time of the level of air pollutant emissions and other considered parameters, such as: temperature, outlet speed of flue gases, the initial dispersion rates, etc. This model has the possibility to calculate in the area of a few dozen meters to several hundred kilometers [49]. It enables taking topography into consideration, along with dry and wet deposition of dust and gas substances and chemical changes of pollutants in the atmosphere. The CALMET/CALPUFF modeling system is recommended by the U.S. Environmental Protection Agency (U.S. EPA) for the calculations of air pollution dispersion to great distances and in a complex area [50; 51]. It is also possible to use it for near field calculation, when a diverse wind field is of critical importance for the expected concentrations [52].  
The modeling of atmospheric dispersion of NO\({}_{2}\) was performed for four variants characterized previously, differing in the resolution of the computational grid. The completed numeric simulations used the method of Simple CALPUFF of Terrain Adjustment, prepared on the basis of the assumptions of the CTDM model (Complex Terrain Dispersion Model). This method enables determination of the highest values of pollution concentrations in the air, as compared to other available options. For reasons of simplification, the calculations did not take the wet scrubbing mechanisms and chemical transformations into account [53]. In each grid receptor, the calculations of NO\({}_{2}\) concentrations in the air were made at area surface each hour. On the basis of the results of these calculations, the following values, among others, were defined: maximum 1-h (C\({}_{1\_{\_{\_{\_{\_{\_}{\)\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\  
results from various calculation variants, non-gridded discrete receptors were additionally defined in grids, in the number of 676, regularly distributed in the adopted calculation area (26 km \(\times\) 26 km), precisely coinciding with the grid with the resolution of 1000 m.
# Thermal Management Systems for Civil Aircraft Engines: Review, Challenges and Exploring the Future
## 2 Pioneering Work on Thermal Management Systems Design  
First, in 1958 an invention presented on oil cooling and drag reducing system for high speed vehicles such as aircraft, sleds, or boats. It is the first publicly available publication that presents thermal management system architecture for aircraft engine systematically [18]. Moreover, another invention related to lubricating oil cooling systems for turbojet engines presented in 1963 [19]. The main idea of these two patents were to utilize two heat exchangers, Air/Oil Heat Exchanger (AOHE) and Fuel/Oil Heat Exchanger (FOHE), to dissipate extra heat generated by engine components. These two heat exchangers could be mounted in the cooling loop in series or parallel architecture.  
In 1969, a patent was registered by [PERSON] et al. in which a cooling system for engine and airframe is proposed where fuel is used as the heat sink to absorb frictional heat produced by airstream passing over wing and fuselage surface [20]. They showed that by using a catalyst an endothermic chemical reaction would be promoted, allowing fuel to absorb more heat. The main idea is to break down the fuel into smaller molecules and to alter the fuel's characteristics as a propellant. However, this embodiment requires temperature of 700 to 1000 \({}^{\circ}\)F for operation and is consequently limited in high supersonic or hypersonic applications.  
Finally, fuel delivery control system and its effects in oil cooling system of gas turbine aero engines was discussed at last years of the pioneering work phase [21; 22]. The main idea of these embodiments was to use a position control valve to tune the oil flowrate in FOHE in order to protect the engine from fuel over temperature and malfunctions.  
Consequently, the main presented ideas of the pioneering work phase could be summarized as follow:  
1. Using two heat exchangers to manage the excess heat of the engine: FOHE and AOHE
2. Using a catalyst to increase the fuel heat absorption capacity
3. Tuning the oil flow rate through the FOHE to control the engine fuel temperature  
These interesting ideas were the main motivations for the next phase.
## 5 Multivariate Statistical Analysis
### Principal Component Analysis (PCA)  
A factor analysis reduces a large number of variables to a few different types of components. The PCA results processing approach seeks to uncover any simple underlying structure within a multivariate results collection. For values greater than 0.7, loadings that demonstrate the relevance of the changeable for the elements are bolded. Characterizing  
Figure 6: Frequency distribution analysis and Q-Q plot of \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K in the Wadi sediments in Wadi Rod Elsayalla.  
each item reveals a few high loadings and a lot of near-zero loadings, achieving the rotation's goals. According to [PERSON] (1986), a maximizing variance involves raising the range of loadings, which drives to the extreme with negative or positive or near-zero loadings. In the current study, the PCA proceeded on the matrix correlation among various factors based on varimax rotations. The components PC1 and PC2 are listed in Table 6 and plotted in Figure 8. The \({}^{238}\)U activity concentration possesses a high positive loading in the PC1 loading associated with all radiological parameters. The variance explained is 80.72%. This suggests the \({}^{232}\)Th activity concentration as the main contribution of natural radioactivity in the sediments, followed by the \({}^{238}\)U activity concentration at the area under study. On the other hand, 40 K has a weak negative loading in the PC2 load. The variance explained represents the percentage of 12.14. As can be observed, the loading variance is positive as explained by the fact that potassium that potassium does not contribute to the grade of the radiation exposure. The PC analysis shows that the total explained variance is 92.86%; thus, the radioactive data appears good [2].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Radiological Parameters**} & \multicolumn{3}{c}{**Principal Component Analysis**} \\  & **PC1** & **PC2** & **Communalities** \\ \hline U-238 & 0.62 & 0.74 & 0.38 \\ Th-232 & **0.90** & \(-\)0.28 & **0.81** \\ K-40 & 0.56 & \(-\)0.01 & 0.31 \\ Raeq & **0.99** & 0.12 & **0.98** \\ Hin & **0.94** & 0.32 & **0.89** \\ Hex & **0.99** & 0.12 & **0.98** \\ Ig & **0.99** & 0.10 & **0.99** \\ Dair & **0.99** & 0.14 & **0.98** \\ AEDout & **0.99** & 0.14 & **0.98** \\ AEDin & **0.99** & 0.14 & **0.98** \\ AGED & **0.99** & 0.12 & **0.98** \\ ELCR & **0.99** & 0.14 & **0.98** \\ Th-232/U-238 & 0.11 & \(-\)0.97 & 0.01 \\ Eigen value & 10.49 & 1.58 & \\ \% Total variance & 80.72 & 12.14 & \\ Communalities \% & 80.72 & 92.86 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Principal component loadings and explained variances in the Wadi sediments.  
Figure 7: Dendrogram for the Wadi sediments in Wadi Rod Elsayallal.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 5 The Development of Theoretical/Practical Contributions
### Functionalities of the Application  
In the pursuit of optimizing academic scheduling, an effective Automatic Timetable Generator for Faculty encapsulates multifaceted functionalities. The application allows administrators to define and manage courses and various academic activities such as seminars, laboratories, and exams. It provides options to specify activity durations, resource requirements, and any constraints associated with each activity. Furthermore, the application supports the management of faculty members and staff by allowing administrators to input their availability, preferences, and workload constraints. This data are essential for generating optimized timetables that take individual preferences into account.  
Additionally, the application facilitates the management of rooms and resources available within the faculty. It allows administrators to define room capacities, availability, and any specific requirements for activities, ensuring that appropriate rooms and resources are allocated during the timetable generation process. The core functionality of the application is to generate automated timetables based on the provided input data. To achieve this, it employs optimization algorithms, such as Tabu Search algorithms or other suitable techniques, to create conflict-free and optimized timetables while considering various constraints and preferences.  
Moreover, the application has mechanisms to identify and resolve conflicts that may arise during the timetable generation process. Conflicts include overlapping activities, resource constraints, faculty availability issues, or student preferences that cannot be accommodated. The system provides options for resolving these conflicts based on predefined rules or user-defined priorities. Finally, the generated timetables are presented in a user-friendly and intuitive manner. The application provides visualization tools to display timetables, allowing users to easily view and navigate through the schedules.