esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast and Reliable Luma Control Scheme for High-Quality HDR/WCG Video
## 3 Linear Approximation of EOTF  
In the linear model of EOTF, like the truncated Taylor series in (6), the accuracy of the model can be significantly enhanced by knowledge of the location of the target(s) to be approximated. The modification of (9) is one example of such an enhancement. In this section, for more precise approximation of the ST.2084 EOTF, we investigate the errors of the fast solution (7), resulting in two, upper and lower, bounds on the location of the optimal luma value.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 3 Trade-Off between Energy Consumption and Data Rate
### Energy Efficiency  
In Figure 6, the two modulation schemes, i.e., the proposed one and TS-OOK, are also directly compared in terms of energy efficiency, which is defined as the number of bits transmitted for every unit of energy and counted in bits per Joule (bits/Joule). It has been shown that the energy needed for the pulses in TS-OOK is 0.1 a) (1 a) = \(10^{-18}\) J) [13], with the same value being adopted for the proposed modulation, for the sake of fair comparison. The energy efficiency, defined as the ration between number of bits per symbol and the energy per symbol and measured in bits/Joule, can be calculated as  
\[EE(k)=\frac{k}{E_{s}}=\frac{k}{E_{pulse}}. \tag{7}\]  
Figure 5: Transmission Data Rate.  
Figure 6: Comparison of Energy Efficiency.  
Interestingly, the proposed scheme is more energy efficient than TS-OOK, i.e., it achieves a better balance between the consumed energy and data rate. Rather than this, the provided gain rapidly increases with the modulation order for \(M>4\), offering a very promising solution for higher energy efficiency in bodycentric THz communications.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 4 Model Overview
### Evaluation Criteria  
Having considered the details of the tackled problem in Section 4.3 and after taking into account the scope of our research in Section 1.1, we dedicate this section to compose an index of system requirements that will serve as the criteria under which the effectiveness and security of our design can be evaluated.  
* **Protection against fabricated reports** A malicious user should not be able to commit fraud against the Advertisers by submitting reports which do not correspond to real consumer activity. Fraud prevention is the primary aim of analogous systems and therefore constitutes our main criterion.
* **Report integrity** After an Ad-Report has been sent, it should not be possible for the Ad-Dealers, Commissioners or Broker to alter its content without being exposed. In contrast to other models, our system utilizes a decentralized architecture over insecure channels and should therefore guarantee the integrity of transmitted data.  
* **User privacy:** At any given moment, the Broker, Advertisers, Ad-Dealers, Agents and Commissioners should not be individually capable of associating a user's advertising interests to his or her identity. User privacy is an aspect that does not necessarily fall within the scope of fraud prevention systems but is a major concern for our research and is therefore taken into consideration.
* **Reporting effectiveness:** Ad-Reports should include all the necessary information as to ensure that all of the participating stakeholders are able to claim their reward. The Broker should be able to ensure that each report is accounted only once and also there should exist a way for the user to confirm that his or her reports were delivered successfully. The effectiveness of submitted reports is taken as a standard requirement by analogous systems but in the case of our model it needs to be examined in more detail as it may be affected by the additional mechanisms that are used to preserve privacy (opportunistic networks and anonymous submission).
## 2 Communication Framework Description
### Human Body Communication  
A unique aspect of the WBAN system is the incorporation of HBC. This technology uses the human body as a conduit for transmitting data, offering a secure and efficient method for data exchange. In contrast to conventional wireless transmission methods, this approach utilizes the skin surface as a quasi-wired connection between individual sensor nodes. As a result, there is minimal emission of radiation, effectively eliminating the presence of radio waves that could be intercepted. Consequently, this method intrinsically constitutes a more secure channel for data transmission. HBC is particularly useful in the secure integration of new sensors into the network and the exchange of critical security information, like public keys. This method significantly enhances the security of the data transmission, making it more resilient to external interference and eavesdropping.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 1 Introduction  
Heuristic, which means \"to find\" or \"to discover by trial and error\", is a way of producing an acceptable solution to a complex problem in a reasonable time [1]. Moreover, metaheuristic algorithms are the further development of heuristic algorithms. In the literature, there is no clear distinction between these algorithms, and the terms are usually interchangeable. Although heuristic methods use randomness to search for the solution, these methods are flexible enough to search through non-smooth space where mathematical methods have difficulty [2].  
Metaheuristic algorithms, at times called intelligent search methods [3], are methods patterned on certain behaviors in our environment [4]: genetic algorithm (GA), which was based on evolution; particle swarm optimization (PSO) and firefly algorithm (FA) are examples of swarm intelligence; gravitational search algorithm (GSA) is a physics-based algorithm; teaching-learning based optimization (TLBO) and harmony search(HS) are based on human behavior and a never-ending list of algorithms that are patterned to different behaviors of organisms.  
The success of a metaheuristic algorithm depends on how it balances the exploration and exploitation capability of each algorithm [5]. Exploration ensures the diversity of the solution by searching on a global scale, while exploitation focuses on the search on the local region where good solutions are. Algorithms provide different weights on these two terms. Another unique characteristic of algorithms is how they perform their random walk that determines a new and, hopefully, a better solution. Certain components of the algorithm that also helps in its movement, such as the inertia weight in PSO or the differential weight in differential evolution (DE) [6], the circling movement around the target in whale algorithm [7], and the use of a quantum bit to store the direction and magnitude of rotation angle in quantum-inspired algorithms (QEA) [8]. Mostly these random walks provide a unique movement that helps in the exploration and exploitation.
## 3 Methodology and Algorithm Descriptions
### FP-Growth Algorithm  
The FP-Growth (Frequent Pattern Growth) algorithm is a highly efficient method for mining frequent itemsets without generating candidate itemsets explicitly. It was developed to overcome the limitations of the Apriori algorithm, particularly the inefficiency caused by multiple database scans and the exponential growth of candidate itemsets. FP-Growth achieves this by using a compact data structure called the FP-tree (Frequent Pattern Tree) and employing a recursive divide-and-conquer strategy.  
The fundamental innovation of FP-Growth lies in its ability to represent the transaction database in a compressed tree structure (FP-tree), which maintains the association of frequent items without needing to scan the database multiple times. Once the FP-tree is built, the algorithm uses a recursive mining approach to extract frequent itemsets directly from the tree, thereby significantly improving computational performance and memory usage.  
The FP-Growth algorithm operates in three main stages, each designed to efficiently discover frequent itemsets without generating candidate itemsets.  
The process begins with an initial scan of the transaction database to determine the support count of each item. Items that fail to meet the minimum support threshold are discarded, while the remaining frequent items are retained and sorted in descending order of frequency. This sorting ensures that the resulting tree structure remains both consistent and compact.  
In the second stage, the algorithm makes a second pass through the database to construct the FP-tree. For each transaction, only the frequent items identified in the first step are considered, and these items are ordered according to their frequency. The ordered items are then inserted into the FP-tree as a path. If part of the path already exists in the tree, the support counts of the corresponding nodes are incremented. Each node in the FP-tree represents an item and includes its support count. To facilitate efficient mining, a header table is maintained to link all occurrences of each item across the tree. This step results in a highly compressed representation of the dataset, which is particularly effective when many transactions share common item subsets.  
The final stage involves recursively mining the FP-tree through the use of conditional pattern bases. For each frequent item, the algorithm extracts its conditional pattern base--the set of prefix paths in the FP-tree that lead to that item. From these paths, a conditional FP-tree is constructed. The mining process then continues recursively on each of these conditional FP-trees until no further frequent itemsets can be found. Unlike the Apriori algorithm, FP-Growth eliminates the need for candidate generation and support counting at each iteration, leading to significant computational efficiency, especially in large and dense datasets.
## 2 Materials and Methods
### Total Phenolic Content (TPC) Measurement  
The TPC was measured based on the methods described in the previous study, with some modifications [43]. In the corresponding wells of a 96-well microplate, 30 \(\upmu\)L of the extract solution was added to 150 \(\upmu\)L of 10% 2 N Folin-Ciocalteu reagent. After thoroughly shaking for 5 min, 160 \(\upmu\)L of 7.5% (\(w/v\)) sodium carbonate solution was added. The mixture was then placed away from the light for 30 min. Eventually, the absorbance was measured at 715 nm. The total phenolic content was calculated from a standard curve using gallic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gene** & **Primer Sequence (5/-3/)** \\ \hline \multirow{4}{*}{Tyrosinase} & F: TTG CCA CTT CATGCTC ATC ATA GAA \\  & R: TTT ATC AAA GGT GTG ACT GCT ATA \\  & F: GCT GCA GGA GCC TTC TTT CTC \\  & R: AAG ACG CTG CAC TGC TGG TCT \\  & F: GGA TGA CCG TGA GCA ATG GCC \\  & R: CGG TTG TGA CCA ATG GGT GCC \\  & F: CGC CTG ATC TGG TGA ATC G \\  & R: CCT GGC TGC AGT TCT CAA GAA \\  & F: ATT CTA CTG GGG GTT TGA \\  & R: ATG TCC TTG GGG TAT CGG TGT AG \\  & F: GCT GGT GATCC CAC TTA CT \\  & R: AGA GTA CTG CTT GCC CAG GA \\  & F: TGA CGA GAC CAA GAA CTG \\  & R: TAC CAG GGT TTG AGC TCA GC \\  & F: CAA GGT CAT CCA TGA CAA CTT TG \\  & R: GTC CAC CAC CCT GTT GCT GCTA G \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sequences of primers used for mRNA gene expression analysis by qRT-PCR.  
acid as the standard. The results were expressed as \(\upmu\)g gallic acid equivalent per mg of extract (\(\upmu\)g GAE/mg extract).
# A Comparative Study of Weighting Methods for Local Reference Frame
## 4 Experimental Results and Analysis
### Comparison of Weights  
In order to further compare the five weighting methods, we depict the weight values of the five weighting methods, which are shown in Figure 5. Because the weights are relative quantities, we need to normalize the weights, i.e., all the weights are divided by the maximum weight for each method.  
From Figure 5b, we can easily know the contributions of the neighboring points with different distances to the covariance matrix in each method. The normalized weights of SHOT are absolutely the same as that of BSC. This explains why the two methods always get the same performance in the experiments. Therefore, we can also infer that the weighting method (\(w_{q_{i}}=(\frac{R-\|q_{i}-p\|}{R})^{2}\)) is equivalent to TOLDI. We can also see that EM and TOLDI are two relatively extreme methods. This explains why the two methods always achieve relatively poor performance on some datasets. Certainly, GF(\(\sigma=0.2\)) is also an extreme method, so the method always obtains the poorest performance.
###### Abstract  
The objective of this paper is the comparison of two dedicated subcooling methods, after the gas cooler, in a CO\({}_{2}\) transcritical refrigeration system. The use of vapor compression refrigeration with R134a for subcooling is the first method, and the second is the use of an absorption chiller that operates with a LiBr-H\({}_{2}\)O working pair. The examined systems are compared energetically and exegetically with the reference transcritical CO\({}_{2}\) refrigeration cycle without subcooling. The analysis is conducted for different operating scenarios and in every case, the system is optimized by selecting the proper temperature and pressure levels. The analysis is performed with a developed and validated model in Engineering Equation Solver. According to the final results, the use of the absorption chiller is able to decrease the system electricity consumption by about 54% compared to the simple transcritical cycle, while the decrease with the mechanical subcooling is 41%. Both systems with dedicated subcooling are found to have an important increase in the system exergy performance compared to the simple transcritical cycle. However, the system with the mechanical subcooling is found to be the best choice exegetically, with a small difference from the system with the absorption chiller.  
Keywords:refrigeration subcooling; energy savings; exergy analysis; coefficient of performance; comparative study +
Footnote †: journal: Applied Soft. 2019, 9, 1605; doi:10.3390/app9081605
## 2 Modification of the Timoshenko Beam Model
### Equation of Motion for Modified Timoshenko Beam  
For a beam, it is assumed as follows for the purpose of simplicity:  
1. The motion of the beam is only restricted in the x-y plane with a small displacement in the y dimension.
2. During the deformation, the cross section still sticks to the planer assumption with a symmetric axis in the y direction.
3. Flexural deformation, shear deformation, and the rotary inertia they caused are all concerned.
4. Lateral shear stress is distributed uniformly along the height of the beam.  
Figure 1 shows deformation of the modified Timoshenko beam and the corresponding cross section.  
Figure 2 shows the lateral vibrations of a beam in the x-y plane due to lateral load. As is shown in the figure, the force equilibrium diagram for a differential element, including the internal force and the inertial force of bending moment direction and vertical direction, is presented. In this paper, shear force Q and bending moment M applies to the sign conventions in a beam. The inertia forces are \(-\rho A\frac{\partial^{2}y}{\partial t^{2}}\) in the y direction and \(-\rho I\frac{\partial^{3}y}{\partial x\partial t^{2}}\) in rotational direction. The total slope \(\theta\) consists of two parts: \(\varphi\) represents the bending slope, and \(\beta\) represents the slope induced by shear deformation.  
Figure 1: Deformation of the modified Timoshenko beam and a differential element diagram: (**a**) the lateral deformation under lateral load and (**b**) the element deformation.  
The equation of motion for the Timoshenko beam is expressed as follows:  
\[\begin{array}{c}EI\frac{\partial^{4}y}{\partial x^{4}}-\left(q-\rho A\frac{ \partial^{2}y}{\partial t^{2}}\right)-\rho I\frac{\partial^{4}y}{\partial x^{2} \partial t^{2}}+\frac{EI}{AG\mu}\frac{\partial^{2}}{\partial x^{2}}\left(q- \rho A\frac{\partial^{2}y}{\partial t^{2}}\right)\\ -\frac{\rho I}{AG\mu}\frac{\partial^{2}}{\partial t^{2}}\left(q-\rho A\frac{ \partial^{2}y}{\partial t^{2}}\right)=0\end{array} \tag{1}\]  
where \(A\) represents the area of section, \(I\) represents the moment of inertia, \(G\) represents the shear modulus, \(\rho\) represents the material density, \(x\) represents the horizontal coordinates, \(y\) represents the transverse displacement, and \(t\) represents the time.  
When the shear deformation-induced rotary inertia is considered, the equation of motion for the modified Timoshenko beam can be expressed as follows [21]:  
\[EI\frac{\partial^{4}y}{\partial x^{4}}-\left(q-\rho A\frac{\partial^{2}y}{ \partial t^{2}}\right)-\rho I\frac{\partial^{4}y}{\partial x^{2}\partial t^{2 }}+\frac{EI}{AG\mu}\frac{\partial^{2}}{\partial x^{2}}\left(q-\rho A\frac{ \partial^{2}y}{\partial t^{2}}\right)=0 \tag{2}\]  
Compare Equations (1) and (2), we will note that the last term on the left side of Equation (1), \(-\frac{\rho I}{AG\mu}\frac{\partial^{2}}{\partial t^{2}}\left(q-\rho A\frac{ \partial^{2}y}{\partial t^{2}}\right)\), is due to the neglect of rotary inertia caused by the shear deformation.
# Spatiotemporal Dynamics and Obstacles of the Multi-Functionality of Land Use in Xiangxi, China
###### Abstract  
The multi-functionality of land as the basis of land use and utilization is under increasing investigation. This study assesses the spatiotemporal dynamic multi-functionality of land use and analyzes the obstacle indicators in Xiangxi using two methods, i.e., the analytic hierarchy process (AHP) and the hierarchical weighting method (HWM). First, we found that the total function level of land use in Xiangxi was constantly optimized. Spatial heterogeneity was clearer. Land use had a trend toward diversification, with a focus on production or living function. The coordination of multifunctional land use has undergone certain changes. It was more apparent in the south than in central and northern regions. Second, we discovered that production function of land use in Xiangxi grew slowly and spatial differentiation was enhanced. The living function fluctuated with the trend of spatial equilibrium. Changes in ecological function and any spatial differentiation were not clear. Third, land use can be divided into living-ecological, production-living, and production obstacle types. Lastly, we state that, to narrow the gap between urban-rural areas and reduce the non-point pollution from agriculture in living-ecological barrier areas, we need to develop production and social public utilities in production-living barrier areas, and develop production and eliminate poverty in production barrier areas.  
Keywords:land use; multi-functionality; production-living-ecology function; spatiotemporal dynamics; obstacle factors; Xiangxi +
Footnote †: journal: Applied Sciences
# Domain Adaptation for Arabic Machine Translation: Financial Texts as a Case Study
## 3 Methodology
### Approach
#### 3.1.1 Synthetic Data Generation  
Synthetic data generation for data augmentation has been used in domain translation due to the scarcity of domain-specific datasets that are suitable for training large models. Ref. [7] proposed the use of state-of-the-art large language models to general unlimited new sentences in the source language and then back-translating in the target language. Recent studies explored the use of ChatGPT for generating new parallel sentences. However, in this study [50], the authors showed that the performance of ChatGPT for Arabic shows inferior performance compared to the finetuned AraT5. In our case, we leverage a pipeline of different models. We start with AraGPT2 [51] and gpt2 [52] as text generation models for Arabic and English to create synthetic pairs for (AR-EN) and (EN-AR), respectively. For Arabic, we use titles only from the collected authentic dataset as text prompts to generate corresponding long-form text using AraGTP2 hosted on HuggingFace ([[https://huggingface.co/aubmindlab/aragpt2-base](https://huggingface.co/aubmindlab/aragpt2-base)]([https://huggingface.co/aubmindlab/aragpt2-base](https://huggingface.co/aubmindlab/aragpt2-base)), accessed on 13 March 2024). Then, we use mT5 [53] a summarization model hosted on HuggingFace ([[https://huggingface.co/eslamxm/mt5-base-arabic](https://huggingface.co/eslamxm/mt5-base-arabic)]([https://huggingface.co/eslamxm/mt5-base-arabic](https://huggingface.co/eslamxm/mt5-base-arabic)), accessed on 20 March 2024) to summarize the generated bunches of texts to obtain short summaries that will serve as generated titles. After that, we apply forward translation from Arabic to English using the OPUS-MT model published on HuggingFace ([[https://huggingface.co/Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en)]([https://huggingface.co/Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en)), accessed on 20 March 2024). Figure 1 shows the case of augmenting the authentic dataset with AR-EN synthetic pairs using this method. The same pipeline applies to English to obtain EN-AR synthetic pairs.  
Figure 1: Data augmentation pipeline using (**a**) Arabic text generation and (**b**) Arabic text summarization.
## 3 Conversion Efficiency
### Discussion  
Figure 2(a) shows that for a fixed thickness \(e=280\) nm, the smaller the radius, the larger the conversion efficiency. We can also observe that \(\eta\) increases when the width decreases in the range \(800\leq w\leq 1000\) nm. The evolution of \(\eta\) with the thickness is shown in Figure 2(b) where the width is fixed to \(w=800\) nm. Even if small radii give better efficiency, we chose to represent here the points of Figure 2(b) corresponding to \(m_{f}=400\) (dashed line), since they are the points that will be discussed in the next section. For smaller radii, the numerical values would be different, but the shape of the curve would be similar, which makes the following discussion general. To get rid of the variation of \(\eta\) due to the variation of the radius, we plotted another set of points where, this time, the radius is kept as close as possible to \(R_{in}=60\)\(\upmu\)m while varying \(e\) (solid line). Indeed, it is not possible to fix the value of the radius for given \(w\) and \(e\), since it must correspond to an integer for the azimuthal numbers. We observe that the variation of \(\eta\) with \(e\) is not monotonous for these geometries. \(\eta\) is maximum for \(e=360\) nm.  
The SHG simulations allow choosing the optimal geometry by compromising between the experimental, fabrication, and theoretical requirements. For instance, Figure 2(a) shows that the best conversion efficiencies are obtained with small radii, but Figure 2 shows that to avoid the two-photon absorption of the fundamental mode, it is required either to choose \(w\geq 900\) nm for \(e=280\) nm, or a thick microring if the width is kept equal to 800 nm.