esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalized Electron Emission Theory for One-Dimensional Conducting Materials
###### Abstract  
This research focuses on exploring generalized electron emissions in one-dimensional conducting materials, specifically examining field emission and thermionic emission. Additionally, this study investigates one-dimensional thermal radiation. The current density is derived for both one-dimensional thermionic emission and one-dimensional field emission. A generalized current density in one dimension is devised, taking into account arbitrary accelerating fields and temperature conditions. The formula for the one-dimensional current density is depicted with accelerating field and temperature across various work function materials. The emission current is represented as a function of temperature for various electric fields. The emission current is also represented as a function of the electric field for various temperatures.  
field emission; thermionic emission; electron emission; one-dimensional conductors; low dimensional materials  
quantum mechanical tunneling principles [9]. Comprehensive investigations were carried out on both field emission and field ionization phenomena [10]. Superconducting niobium cavities are extensively utilized for accelerating electrons or heavy ions. However, in high-accelerating electric fields, field emissions originating from micron-sized particles or surface irregularities in these niobium superconducting cavities can lead to performance degradation. Thorough research efforts have been made to comprehend the impacts of field emission on these niobium superconducting cavities [11; 12; 13; 14]. Furthermore, research has explored field emission phenomena in carbon nanotubes and carbon nanotube field emitter arrays [15; 16; 17], with efforts to calculate the generalized current density for field emission across arbitrary dimensions [18]. Carbon nanotubes can have the highest field enhancement, and the field enhancement factor for carbon nanotubes and carbon nanotube displays has been intensively studied [19; 20; 21; 22]. Additionally, a comprehensive electron emission theory has been developed by integrating field emission and thermionic emission principles within two-dimensional conducting materials [23]. Thermoelectric materials were investigated, and the thermoelectric figure of merit was well studied in low-dimensional materials [24; 25; 26].  
In this study, we compute the universal current density encompassing both thermally emitted electrons and field-emitted electrons within one-dimensional materials. We delve into the spectral radiance of one-dimensional entities. The current density is specifically determined for generalized thermionic emission and generalized field emission within the one-dimensional framework. A comprehensive, one-dimensional current density is then calculated to accommodate diverse electric fields and temperatures. Furthermore, the expression of this current density varies with the temperature and electric field for various work functions.
## 9 Conclusions  
The optimal dataset can handle code switching; and is robust to noise; is capable of domain adaptation, transfer learning, and handling multimodal data, scalability, as well as benchmarking and evaluation. These factors contribute to the development of accurate, efficient, and versatile OCR systems capable of handling multiple languages and various real-world scenarios. The most important findings of our experiments can be summarized as follows:  
1. Preparing an optimal dataset directly contributes to improved model performance by providing diverse, high-quality, and representative samples for each language, enabling the OCR systems to learn and generalize better; however, this process needs continuous process control. 2. Preparing a specific dataset that includes noisy samples, such as low-quality images, occlusions, or distortions, ensures that the OCR system is robust and can perform accurately even in challenging real-world conditions. 3. By incorporating multiple languages or hybrid languages, the training dataset enables the OCR system to cater to a broader audience, increasing its applicability and utility in various real-world scenarios. This model could be interesting not only for multilanguage use cases but also for training with technical and/or domain-specific languages. Moreover, by incorporating domain-specific data and contexts, the OCR system can be tailored to different industries or use cases, improving its performance in specialized fields such as the legal, medical, or technical domains. 4. Preparing a dataset that includes multimodal information such as images or audio helps the OCR system leverage additional contextual information to enhance its understanding and interpretation of the text. This process may make the preparation stage more complicated, but the results will compensate for that. 5. The dataset should include variations in writing styles, dialects, and colloquialisms for each language, ensuring that the OCR system is adaptable and can handle various linguistic nuances. 6. Multilingual societies frequently engage in \"code switching\", the act of switching between two or more languages within a single conversation or document. An optimal dataset should account for code-switching scenarios, allowing the OCR system to seamlessly recognize and process text that contains multiple languages. 7. Leveraging pretrained multilingual models or incorporating cross-lingual pretraining enables the OCR system to learn from the shared knowledge of multiple languages, resulting in better generalization and potentially improved performance for low-resource languages.
## 3 Results and Discussion
### HRTEM Analysis and SAED Pattern  
The morphology and size of the particles were determined by the HRTEM images shown in Figure 5a. The particles formed were spherical, hexagonal, and triangular in shape. The NPs formed were in the range of 23 to 46 nm with an average size of 31 \(\pm\) 1.6 nm. The SAED of a single spherical particle confirmed the crystalline nature of AuNPs and the rings corresponding to (111), (200) and (220) planes of FCC crystalline lattices of gold (Figure 5b). The reflection patterns indicated that the surfaces of the gold plates were flat and crystalline in nature. Similar results were found in AuNPs synthesized using ethanolic leaf extract of _Bacopa monnieri_: they were predominantly spherical in shape with a size distribution range of 3-45 nm [39].
## 3 Results and Discussion
### Microscopic Observations
#### 3.2.2 Particles Displacement Vector  
The particle displacement vectors can be accurately and easily captured in the DEM simulation. Figure 11 presents the total particle displacement vectors in the four samples with different fiber orientations at the end of the direct shear test. As shown in Figure 11, the direct shearing results in an arc-shaped narrow zone, which is called the shear zone in this study. Large shear distortion occurs in the shear zone, while the particles in the far field undergo limited shear distortion. [PERSON] and [PERSON] (1985) pointed out that the thickness of the shear zone is an important parameter that influences the fiber-reinforced shear strength. It is interesting to observe that the angle of fiber direction likely influences the shear zone thickness. As shown in Figure 11, the shear zone thickness of FRS with horizontal fibers is the smallest. The specimen with fibers inclined at an angle tends to form a thicker localized zone, and the shear zone thickness of FRS with fibers having an orientation of \(60^{\circ}\) is the largest. This can be explained by fibers inclined at an angle interfering with the development of the shear zone by transferring the shear stresses to zones farther away in which they are entangled. A  
Figure 10: Contour plots of major principal stress: (**a**) fiber orientation = \(0^{\circ}\), (**b**) fiber orientation = \(30^{\circ}\), (**c**) fiber orientation = \(60^{\circ}\), and (**d**) fiber orientation = \(90^{\circ}\).  
larger shear zone thickness means that more particles are mobilized to impede the shearing motion, accounting for a higher strength reinforcement.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 3 Activity-Based Congestion Management (ABC)
### ABC Overview  
ABC features a domain concept which is shown in Figure 1. Ingress nodes leverage activity meters to measure the rate of traffic aggregates that enter the ABC domain. They derive an activity value for each packet and mark that value in its header. Such an aggregate may be, e.g., the traffic of a single user or a user group. Thus, ingress nodes require traffic descriptors for any aggregate that should be tracked.  
Ingress nodes and core nodes of an ABC domain are forwarding nodes. They use an activity AQM on each of their outgoing interfaces within the ABC domain to perform an acceptance decision for every packet. That means, they decide whether to forward or drop a packet depending on its activity. This enforces fair resource sharing among traffic aggregates within an ABC domain.  
Egress nodes just remove the activity information from packets leaving the ABC domain.
## 1 Introduction
One such research project is the Euratom Evaluation and Viability of Liquid Fuel Fast Reactor System (EVOL) project. In their recent work, a cylindrical core with the diameter equal to the height, surrounded by steel reflectors axially and a fertile blanket, a boron carbide layer, and a reflector radially is studied. The fuel salt used in their study is a fluoride-based salt and is a mixture of lithium fluoride, thorium fluoride, and actinide fluoride salts. Though there are optimized core geometry designs available, rigorous studies have not yet been performed in this regard. The optimized core geometry available in the literature [8; 9] is only based on one factor, i.e., hot spots inside the core. These hot spots occur mainly due to the formation of recirculation and stagnation zones which increase the salt temperature considerably. This undesirable phenomenon needs to be avoided or minimized as it leads to higher temperature gradients inside the core. There are also other factors that need to be considered when optimizing the design. Extensive studies need to be performed to optimize the capital cost and performance of major systems. These analyses must consider the multi-physics design impacts on plant economic performance such as pumping requirements, sizing of heat exchangers, sizing of system pressure boundaries, and coolant inventory requirements. The objective of this work is to develop a framework for multi-physics modeling, design optimization, and uncertainty propagation of fast-spectrum liquid-fuel molten-salt reactors. GeN-Foam is capable of performing coupled fine-/coarse-mesh thermal-hydraulics calculations, multi-group neutron diffusion calculations, and thermomechanics calculations using both structured and unstructured meshes. This multi-fidelity approach, together with DAKOTA, will address the need for a new structure for advanced reactor analysis. This article next presents the materials and methods used in its associated GeN-Foam simulations, optimization, and uncertainty analysis which are loosely based on a work by [PERSON] et al. [10]. The results are divided into five main sections: primary loop optimization, steady-state results, transient results, uncertainty propagation, and sensitivity analysis. Core design, heat exchanger placement, and pump position are included in the primary loop optimization results. The uncertainty propagation includes scalar analysis, functional analysis, and three-dimensional analysis.
## 3 System Model
### Adaptive Thermal-Energy-Aware Routing Protocol in WBAN
#### 3.2.1 Initialization Phase  
The initialization process is initiated by the destination node to obtain the distance of each node from destination in terms of the number of hops. This phase involves the organization of the network into levels. At the end of this phase, each node is assigned a ring level value; this value of the level indicates its distance from the sink. At the beginning of this phase, a packet is broadcast by the sink with a ring level value of 0 in the packet, and this packet is received by all of its neighbours. The nodes that receive this packet will update the ring level field by incrementing 1 in the ring level value and rebroadcast the packet. This operation of rebroadcasting and updating of the ring level value continues until the end of this phase until each node acquires its ring level. The format of the setup packet in initialization phase includes sourceID, destinationID, temperature, ringlevel, and energy. The sourceID indicates the unique ID of the node which broadcasts the setup packet. Destination ID represents the destination address, energy and temperature field indicates the energy and temperature of the sender node.  
In the initialization phase, the primary goal is to generate the ring levels for all nodes--every node broadcast this setup packet to explore its neighbours. On receiving the setup packet, a sensor node considers itself in level n if the hop count is n from the sink. If a node receives a packet with a smaller value of ring level, then it will update according to the new ring level. The algorithm of the initialization phase is given below, and the flow chart is shown in Figure 3.  
This phase is responsible for acquiring available sensors nodes distance from sink with the current statistics of temperature and energy of every node. The appropriate route selection is based on the stability of route, which depends on the node energy and temperature. The multiple forwarding nodes  
Figure 3: Initialization phase.  
are available, the cost is evaluated, and selection of node depends upon this calculated cost value. The algorithm of the initialization phase is given below, and the flow chart is shown in Algorithm 1.  
```
Input:\(Interger\)\(nodes[9]\) \(Interger\)\(sinklevel=0\) \(Interger\)\(nodeslevel[i]=-1\) \(Interger\)\(RLevel=-1\)\(\triangleright\) RLevel represent the ring level \(Interger\)\(relayLevelvalue\) \(Interger\)\(SLevelvalue\)\(\triangleright\) SLevelvalue represent the Source level value Output: Organized network into ring levels \(\text{Sink broadcast setup packet to its neighbors }\) \(\text{All neighboring nodes will receive packet if}(RLevel==-1\mid RLevelvalue>SLevelvalue)\)then \(\text{SLevelvalue++}\); \(\text{relayLevelvalue=SLevelvalue}\); \(\text{rebroadcast}\); else \(\text{retransmit the packet to its acquaintance;}\)  end if The process of retransmission continues till each node is aware of its level within the network.
```  
**Algorithm 1**Initialization Phase
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 3 Problem Description and Formulation
### Constraints
#### 3.3.4 Resource Constraints  
Resources allocated to each subtask must not exceed the total resources of the satellite:  
\[\sum r^{p}(T_{ij})\leq R_{s}^{p},\quad\sum r^{b}(T_{ij})\leq R_{s}^{b},\quad \sum c(T_{ij})\leq C_{s}. \tag{16}\]
## 3 Results and Discussion
### Structural Variation Analysis through Boundaries between IR and SC Regions  
Although plastome sequences are generally well conserved, structural variations such as boundary shifts have been reported to be a major cause of evolution [44]. Using six plastome sequences, we investigated boundary shifting such as contraction and expansion events in the IR boundaries. In all boundary regions, gene variations exhibited highly variable trends at the boundary junction parts (Figure 3). At the LSC/IRb (JLB) boundary junction, six genes (_ycf2_, _rpl22_, _rps19_, _trmL_, _rpl2_, and _rm16_) were found. The _ycf2_ hypothetical  
Figure 2: The codon distribution of synonymous codons and stop codons. The histograms show each encoded amino acid with colored boxes indicating individual codons. Amino acids are shown on the x-axis, and RSCU values are presented on the y-axis.  
gene was located 51 base pairs to the left of the JLB boundary, while structural variations were observed in other species. Recently, _ycf2_ has been implicated as a potential molecular marker [45], and its phylogenetic classification shows high polymorphism across family species [46]. At the SSC/IRA (JSA) boundary, four genes (_ndhE_, _ndhF_, _ndhG_, and _psaC_) were discovered. Generally, a plastome contains many _ndh_ family genes, but only three _ndh_ genes were found in the JSA boundary. These _ndh_ genes cause variations in SSC size through gene loss and retention [47]. Notably, two copies of _ndhE_ genes were located separately at the two boundaries. A complete _ndhE_ gene (305 bp) was situated at the IRb/SSC (JSB) boundary, while a pseudogene (165 bp) shifted by four base pairs from the IRa to the SSC region. The results of boundary shifting analysis indicate that many evolutionary events have occurred between _A. japonicum_ and Campanulaceae species.
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 3 Design Parameters
### Determining the Output Capacitance: \(C_{o}\)  
A small output ripple requires a large output capacitance; however, the higher output capacitance, the bigger the capacitor size. Therefore, a proper percent flicker specification without an output ripple eliminator should be considered to determine the output capacitance while considering voltage stress and size. The output capacitance \(C_{O}\) has the following relationship [5; 24]:  
\[C_{O}\approx\frac{I_{C\_PP}}{4\pi f_{L}\cdot V_{C\_PP}}\approx\frac{2\cdot I_{ LED}}{4\pi f_{L}\cdot N_{S}\cdot\Delta V_{LED\_Device}} \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**10 W LED Converter**} \\ \hline
**Item** & & **MIN** & **TYP** & **MAX** \\ \hline \multirow{2}{*}{Input Voltage (VAC)} & 100 & 90 & 100 & - \\ \cline{2-5}  & 240 & - & 240 & 264 \\ \hline Input Current (mA) & 34.1 (@90 VAC) & - & 122.2 (@264 VAC) \\ \hline Input Frequency (Hz) & 50 & - & 60 \\ \hline Input Power (W) & 9.0 & 10.0 & 11.0 \\ \hline Output Voltage (V) & 102 & 106 & 112 \\ \hline Output Current (mA) & 81.8 & 87.0 & 92.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the proposed LED converter.  
where \(I_{C\_PP}\) is the peak-to-peak current of the output capacitor, which is approximately twice the average LED current \(I_{LED}\). Here, \(\Delta V_{LED\_Device}\) is the one LED peak-to-peak, which varies with the LED current and \(N_{S}\) is the number of LEDs in series. Referring to Equation (15), the minimum output capacitance can be estimated with \(I_{LED\_MAX}\) of 92.2 mA and \(f_{L\_MIN}\) of 50 Hz. In addition, to keep the light flicker around 10% without output ripple eliminator, \(\Delta V_{LED\_Device}\) of 95 mV should be used according to V-I characteristics [26].  
\[C_{O}>\frac{2\times 92.2\ m}{4\times 3.14\times 50\times 35\times 95\ m}=88.3\ \mu\text{F} \tag{16}\]  
For practical design for a T8 LED tube, \(C_{O}\) should be chosen as 160 V/100 \(\mu\)F with dimensions of 12 \(\times\) 25 mm\({}^{2}\) (D \(\times\) H).
## 4 Multilevel Inverters (MLIs)
### Classification of Multilevel Inverters  
MLIs are important for medium and high voltage usage due to their capability of synthesizing a sinusoidal voltage at different \(DC\) levels [115]. Although there are many  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Conventional Inverter** & **Multilevel Inverter** \\ \hline \multirow{2}{*}{Harmonic} & The output has high & The output has low \\  & harmonics & harmonics. \\  & It is not allowed for use in & It can be used in high-voltage \\  & high-voltage applications. & applications. \\  & It is not possible to generate & It is possible to generate high \\  & high voltage levels. & voltage levels. \\  & The voltage stresses on & The voltage stresses on \\  & switching are greater. & switching are less. \\  & Increasing switching losses. & Decreasing switching losses. \\  & Switching frequency is high. & Switching frequency is low. \\  & The rate of voltage change & The rate of voltage change \\  & is high. & is low. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between multilevel and traditional converters.  
Figure 28: Two-level waveform without _PWM_[112].  
Figure 29: (a) Three-, (b) five-, and (c) seven-level (multilevel) waveforms [112].  
_MLI_ topologies, they are generally classified into neutral-point-clamped (diode-clamped), cascaded, and flying capacitor (capacitor-clamped) types, as shown in Figure 30 [116; 117; 118; 119].
###### Abstract  
This article presents an advanced dual chaotic watermarking scheme to improve information security. To ensure confidentiality in digital image transmission, a secure dual watermarking scheme is proposed by applying the chaotic logistic system and hyper-chaotic dynamical system. Chaotic watermarking was conducted in the spatial domain, where suboptimal secure hashing with a variable length was selected in preprocessing stages. The secret key was generated by the chaotic sequence for pixel shuffling using a chaotic logistic map, so that a controlled amount of distortion was inserted into the host digital image. Watermarking was proceeded after the chaotic watermark had been embedded into the shuffled image. To strengthen the security, the hyper-chaotic system was used to generate chaotic blocks for block scrambling in order to achieve dual chaotic watermarking. Characteristics analysis was conducted for multiple examples in both spatial and frequency domains. Potential effects induced by the chaotic driving parameter on processing time and integrity authentication of chaotic dual watermarking were also analyzed in detail.  
watermarking; information security; logistic map; hyper-chaotic system; hashing; shuffling; scrambling +
Footnote †: journal:
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 3 Proposed FH Sequence Recovery Method  
### Overall Framework  
In an intelligent anti-jamming frequency-hopping communication system, the transmitter intelligently generates the optimal frequency-hopping sequence during the confrontation with the jammer. Due to unknowing it in advance, the receiver needs to intelligently estimate the frequency-hopping sequence according to the received signal to complete the de-hopping processing. The problem of estimating frequency-hopping sequences can be transformed into the problem of frequency point classification by using deep learning. A CNN-GRU module is designed for an intelligent estimation of frequency-hopping sequences in this paper. The strong learning ability of the neural network is used to learn the characteristics of the frequency-hopping signals in the presence of jamming and complete the estimation of the frequency-hopping sequence, and thus realize the intelligent reception of frequency-hopping signals.  
The structure diagram of the proposed method is shown in Figure 2. The input data are the time-frequency diagrams of the frequency-hopping signals. The output is the frequency-hopping sequence, which is obtained from the coordinates and frequency-hopping patterns of the signal pixels detected on the time-frequency diagram. The basic network structure is composed of an SE (squeeze and extraction) ResNet network and a GRU network, and the output is the frequency-hopping sequence. SE ResNet is the basic module of the CNN-GRU network, which is composed of deep residual networks [30] (ResNet) followed by the SE module, which is used to extract high-dimensional features of the time-frequency graph of received signals. The GRU [31] module jointly deduces the frequency-hopping sequence value of the current frequency-hopping cycle according to the currently extracted features and the previous memory.  
Figure 1: Structure diagram of intelligent anti-jamming frequency-hopping communication system.