esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation Analysis
### Parametric Analyses
#### 3.3.3 Axial Compression Ratio  
The axial compression ratio is an important parameter in structural design, which not only affects the ultimate load and ductility, but may also affect the slip. Therefore, it is necessary to determine an appropriate axial compression ratio. The change of axial compression ratio is achieved by modifying the axial pressure, which is applied to the top of the loaded beam (see Table 6). Through the numerical analyses, the force-displacement curves and slip-displacement curves are shown in Figures 23 and 24. It can be seen from Figure 23 that the initial slips of C1-C6 occur at the horizontal displacement of 0.68 mm, 1.03 mm, 1.38 mm, 1.73 mm, and 2.08 mm, 2.34 mm, respectively. This means that the initial slip will be delayed with the increase of the axial compression ratio. In Figure 24, the ultimate loads of C1-C6 are 175.1 kN, 208.6 kN, 237.5 kN, 262.8 kN, 286.4 kN, and 307.1 kN, respectively. It is worth noting that the ultimate loads of the model are obviously enhanced with the increase of the axial compression ratio. However, when the loads reach the peak, the descent of load becomes faster with the increase of the axial compression ratio. In addition, the ductility coefficient of the specimen, calculated by Equation (4), declines with the increase of the axial compression ratio (see Table 7). Therefore, it is concluded that the axial compression ratio has a remarkably significant effect on the force-displacement curve.  
Figure 21: Slip-displacement curves.  
Figure 22: Force-displacement curves.  
It is worth noting that [PERSON] [46] suggests that the ductility coefficient of the shear wall in a reinforced concrete structure should be greater than 4, and the minimum ductility coefficient (the axial compression ratio is 0.5) in this study is also greater than 4. Moreover, as suggested in the Code for Seismic Design of Buildings [53], the limiting value (minimum value) of the ductility coefficient of the shear wall is 0.5 for cast-in place reinforced concrete. Thus, by studying comparatively, this paper recommends that designers set the axial compression ratio to 0.5 for similar shear walls, i.e., a precast reinforced concrete structure. Of course, when the axial compression ratio is less than 0.5, the mechanical behaviors of the PC shear wall can also satisfy the safety requirement.
## 7 Conclusions  
This article has presented a new hardware architecture for performing point multiplication operations using the unified addition law of the BEC model for ECC. The proposed architecture has employed a hybrid approach of Montgomery as well as double-and-add algorithms to improve efficiency. The implementation of a Montgomery radix-2 multiplier has achieved a maximum frequency of 320.584 MHz on Virtex 7 FPGA device. The original mathematical formulations of the PA and PD laws for BECs have been revised, and a two-stage pipelining approach was employed to optimize the throughput over area ratio. Furthermore, the inclusion of a modular multiplier has reduced the total number of clock cycles when compared to alternative parallel multipliers. The implementation results on various platforms have shown that the proposed hardware architecture provides a better throughput/area ratio than the most recent state-of-the-art architectures. The achieved results are important for a variety of applications, such as e-commerce, financial transactions, and government communications. The probable future work may include investigating the use of other optimization techniques, such as operand sharing and operand forwarding, to further improve the performance of the proposed architecture. Similarly, the extension of the proposed architecture to support other ECC curves is another area of interest.  
Conceptualization, A.S. and M.R.; methodology, M.R. and A.S.; validation, O.S.S. and M.Y.I.Z.; formal analysis, M.Y.I.Z. and M.R.; investigation, A.S. and M.R.; resources, O.S.S. and M.R. and M.Y.I.Z.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, M.R.; visualization, O.S.S.; supervision, M.R.; project administration, M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deanship for Research & Innovation, Ministry of Education in Saudi Arabia grant number IFP22 UQU4320199 DSR102. The authors extend their appreciation to the Deanship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number: IFP22 UQU4320199 DSR102. The authors declare no conflict of interest.
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 3 Proposed Scheme
### Construction  
In the distance-encoded Bloom filter, \(2\)\(k\) bits must be set to \(1\) to insert the membership of a node, where \(k\) is the number of hash functions. Each set bit is positioned at a distance \(sd\) or \((ld-sd)\) from the location indexed by each hash function. A total of \(2\)\(k\) bits are set to \(1\) at the following locations: \(h_{1}\left(t\right)-sd,h_{2}\left(t\right)-sd,\ldots,h_{k}\left(t\right)-sd,h_ {1}\left(t\right)+(ld-sd),h_{2}\left(t\right)+(ld-sd),\ldots,h_{k}\left(t \right)+(ld-sd)\). However, if \(sd\) and \(ld\) are equal, all the bits at \(h_{i}(t)+(ld-sd)\) are not set, meaning that only \(k\) bits are set to \(1\).  
To minimize the number of Bloom filter accesses, it is desirable for both bits at \(sd\) and \((ld-sd)\) positions to reside within the same word. To achieve this, if they fall outside the word boundary, they are wrapped around. Figure 9 illustrates how the bit position of \((ld-sd)\) wraps around when inserting N\({}_{1}\), assuming a word size of \(16\) for convenience. The \(\left\langle sd,ld\right\rangle\) values for N\({}_{1}\) are \(\left\langle 1,6\right\rangle\), meaning \((ld-sd)=5\). In the figure, \(h_{i}\left(\text{N}_{1}\right)+5\) exceeds the word boundary, so the actual bit position becomes \(3\) due to wrapping. The bit position of \(sd\) can be computed in a similar manner. The \(sd\) value is limited to \(w/2-1\), and \((ld-sd)\) is limited to \(w/2\) where \(w\) represents the word size. These values indicate the maximum limits in the FIB rather than their actual values.
## 3 Benchmark Functions  
### Description of Tests  
In this study, we evaluate the performance of the Deephive optimization algorithm in comparison to other algorithms, such as Differential Evolution (DE) [36], the Genetic Algorithm (GA) [37], Particle Swarm Optimization (PSO) [9], Simulated Annealing [38], and other recent algorithms like the Salp Swarm Algorithm (SSA) [39] and Teaching-Learning-Based Optimization (TLBO) [40]. While variants that improve performance (e.g., by using split policies, multiple restarts, and dynamic parameter selection) exist, Deephive was compared to the base algorithms in all these cases. This is because, in the authors' view, this paper proposesa base version of the Deephive algorithm, which can also benefit from such improvement techniques in subsequent studies. Furthermore, these tests are carried out assuming a fixed computational budget; i.e., we impose a maximum of 4000 function evaluations. This fixed budget was imposed to mirror real-world scenarios in cases where function evaluations are computationally expensive or time-consuming, such as in engineering black-box optimizations involving complex experiments or computational fluid dynamics.  
The algorithms are tested on some common benchmark functions, which play a crucial role in assessing the performance of newly proposed optimization algorithms. They serve as standardized tests that enable us to compare the efficacy, robustness, and efficiency of Deephive compared to the other algorithms under varied conditions. These functions are deliberately designed to challenge algorithms with complex surface topologies and characteristics that mimic real-world problems. To facilitate a comprehensive evaluation, we employ the classification used in [41], which groups benchmark functions into four categories depending on their modality, landscape, and dimensions. This categorization is important for understanding how different algorithms perform under varying complexities and problem characteristics.
# Systematic Analysis and Design of Control Systems Based on [PERSON]'s Direct Method
## 3 Stability Analysis
### Input-to-State Stability  
The _Input-to-State Stability_ (ISS) [7; 8] extends the property of global asymptotic stability to systems of the form  
\[\dot{x}=F(x,u) \tag{31}\]  
with a equilibrium \(x=0\) for \(u=0\), which means \(F(0,0)=0\). The input \(u(t)\in\mathbb{R}\) can be taken as both a manipulated variable and a disturbance. Let the input-dependent vector field \(F\) be continuous and locally Lipschitz in the first argument. The input-to-state stability is a generalization of the asymptotic stability, where in addition the influence of the input \(u\) is taken into account. In this paper, the input-to-state stability is introduced directly via the existence of a corresponding ISS-Lyapunov function [7].  
**Definition 1** (ISS-Lyapunov function).: _A continuously differentiable function \(V:\mathbb{R}^{n}\to[0,\infty)\) is called ISS-Lyapunov function for a system (31) if it satisfies the following conditions._  
\[\bar{\alpha}(|x|)\leq V(x) \leq\bar{\alpha}(|x|), \tag{32}\] \[|x|\geq\gamma(|u|) \implies\dot{V}(x,u) \leq-\alpha(|x|) \tag{33}\]  
_with_  
\[\dot{V}(x,u)=L_{F}V(x,u)=\frac{\partial V}{\partial x}(x)\,F(x,u)\]  
_for all \(x\in\mathbb{R}^{n}\) and \(u\in\mathbb{R}\) with \(\bar{\alpha},\bar{\alpha}\in\mathcal{K}_{\infty}\) and \(\alpha,\gamma\in\mathcal{K}\) is satisfied._  
For \(u(t)\equiv 0\) we obtain the asymptotic stability of the equilibrium at the origin. Alternatively, ISS-Lyapunov functions can also be defined by a so-called \"dissipation\" characterization ([7] Remark 2.4):  
**Lemma 1** (Alternative characterization).: _A continuously differentiable function \(V:\mathbb{R}^{n}\to[0,\infty)\) is an ISS-Lyapunov function for a system (31) if and only if it satisfies conditions_  
\[\bar{\alpha}(|x|)\leq V(x)\leq\bar{\alpha}(|x|) \tag{34}\] \[\dot{V}(x,u)\leq\gamma(|u|)-\alpha(|x|) \tag{35}\]_for all \(x\in\mathbb{R}^{n}\) and \(u\in\mathbb{R}\) with \(\varrho,\bar{\alpha},\alpha,\gamma\in\mathcal{K}_{\infty}\) fulfilled._
# Optical Fiber Sensor with Stable Operating Point for AC Magnetic Field Measurement
## 3 Sensor Fabrication and Characterization
### AC Magnetic Field Sensing and Stable Operating-Point Performance
**(d)** Variation in peak-to-peak amplitude of waveform with driving currents when the frequencies of AC magnetic field are 50, 100, 150, 200, and 250 Hz, respectively. Figure 6: Operating principle and process of maintaining a stable operating point: **(a)** a recorded mean below \(V_{m}\) prompts a reduction in the heating laser power, and **(b)** a recorded mean larger than \(V_{m}\) prompts an increase. (The red line represents the original voltage signal with initial heating power; the orange line represents the changed voltage signal with changed heating power). To verify the performance of the proposed stable operating point algorithm based on PID control, the sample is placed in an incubator, which is used to simulate a varying ambient temperature. Figure 7 shows measured voltage signals when the driving current and frequency of the coil are set to 9 A and 50 Hz, respectively, and temperature is varied in the range 22.2-41.6 \({}^{\circ}\)C. It should be noted that the received signals were recorded at several random temperatures, and the operating point was kept stable during the temperature rising process. The whole heating process took about 40 min and the temperature rising rate is calculated as about 0.48 \({}^{\circ}\)C/min. Figure 7a shows results when the PID control algorithm was not used; the received signal drifts significantly with the variation in ambient temperature, and is even distorted. Figure 7b shows results when the PID is used to control the heating laser power, and it can be clearly seen that the signals are well stabilized at the operating point while the temperature is changed by more than 15 \({}^{\circ}\)C. The PID control parameters for this test were \(K_{P}\) = 12, \(K_{l}\) = 0.05, and \(K_{\text{D}}\) = 0.02. As can be seen in Figure 2d, the maximum wavelength shift \(\Delta\lambda_{\text{max}}\) caused by the tunable heating laser power \(\Delta P_{980}\) can be calculated as:  
\[\Delta\lambda_{\text{max}}=\Delta P_{980}\times S_{980}=120.8\,\text{pm}, \tag{8}\]  
Thus, the theoretical maximum temperature range \(\Delta T_{\text{max}}\) that can be compensated by adjusting the heating laser power is:  
\[\Delta T_{\text{max}}=\Delta\lambda_{\text{max}}/S_{T}=15.36\,^{\circ}\text{C}. \tag{9}\]  
which is consistent with the experimental results seen in Figure 7. The initial power of the heating laser used in the experiment is 100 mW, and the adjustable range is 0-200 mW. The adjustable temperature range can be expanded by increasing the laser power; however, the laser power should not be too large, otherwise the loss and the bandwidth of the spectrum will increase due to non-uniform heating of the HAF.
## 3 On the Prospect of TinyML/DL
### Knowledge Distillation--KD
#### 3.2.2 On-Device Real-Time Learning (Reformable TinyML)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Devices/Boards** & **Power** & **Instruction Set** & **SRAM** & **Flash Memory** & **CPU Clock** \\ \hline _Raspberry Pi family_ ([106–108]) & & & & \\ Raspberry Pi 3B+ & Low & ARM (Cortex-A53) & \(1\) GB SDRAM & – & \(1.4\) GHz \\ \hline Raspberry Pi 4B & Low & ARM (Cortex-A72) & 256 KB & – & \(1.5\) GHz \\ \hline Raspberry Pi Pico & Ultra low & ARM (Dual core Cortex-M0+) & 264 KB & 2 MB & \(133\) MHz \\ \hline _Arduino family_ [74,108,109] & & & & \\ Arduino Nano 33 BLE Sense (FF52840 SoC) & Ultra low & ARM (Cortex-M4) & 256 KB & 1 MB & \(64\) MHz \\ \hline Arduino Portenta & Low & ARM (Cortex-M7–M4) & 8 MB SDRAM & 16 MB & \(240\)–\(480\) MHz \\ \hline _STM Micromcontrollers_ (_MCD_) [70,106,110] & & & & \\ STM32F/ board & Ultra low (_high-performance MCU_) & ARM (Cortex-M7) & 512 KB & 2 MB & \(216\) MHz \\ \hline STM32H743 & Ultra low (_high-performance MCU_) & ARM (Cortex-M7) & \(1\) MB & \(1\)–\(2\) MB & \(480\) MHz \\ \hline STM32F401 & Ultra low (_general-purpose MCU_) & ARM (Cortex-M4) & 96 KB & \(\begin{array}{c}128\\ 512 KB\end{array}\) & \(84\) MHz \\ \hline STM NUCLEO & Ultra low (_general-purpose MCU_) & ARM (Cortex-M4) & 320 KB & 1 MB & \(80\) MHz \\ \hline STM NUCLEO & Ultra low (_high-performance MCU_) & ARM (Cortex-M7) & 512 KB & 2 MB & \(216\) MHz \\ \hline _Adraft Feather Family_ [108,111] & & & & \\ Bluertu Sense board (FF52840 SoC) & Ultra low & ARM (Cortex-M4F) & 256 KB & 1 MB & \(64\) MHz \\ \hline M4 express & Ultra low & ARM (Cortex –M7) & 192 KB & 2 MB & \(120\) MHz \\ \hline _Microprocessors_ & & & & \\ GAP8 [98,112] & Parallel ultra-low-power processing platform (PULP) & RISC-V (FC) & \(\begin{array}{c}80\text{ KB}+\\ 8\text{ MB SDRAM}\end{array}\) & 512 KB & 250 MHz \\ \hline VEGA [98] (22 nm technology) & Parallel ultra-low-power processing platform (PULP) & RISC-V (FC) & \(\begin{array}{c}\text{L2 (interleaved)}\\ 1.5\text{ MB}+64\text{ KB}\end{array}\) & 64 MB & 250 MHz \\ \hline Mr.Wolf [113] (40 nm LP CMOS technology) & Parallel ultra-low-power processing platform (PULP) & RISC-V (RVC32 IMF) & \(\begin{array}{c}\text{latch-based}\\ memory instead of SRAM (25 Gbit/s at 100 MHz) \\ \end{array}\) & – & \(\begin{array}{c}32\text{ KHz}\\ 450\text{ MHz}\end{array}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Devices/boards used for the experiments.
## 5 Results and Discussion
### Discussion and Result
#### 5.2.2 Issues and Blockchain Type  
This section sheds light on the different blockchain types adopted in previous research and the security issues addressed by each type. This assists in understanding the potential solutions that can be addressed by particular blockchain types or technology.  
The majority of the reviewed studies adopted access control and IDM to find solutions for system issues by using the Ethereum blockchain type. In [27], the IDMS adopted by cloud users relies too much on third-party services. Studies published in [24] and [18] suffered from third-party issues, especially trackability, and both used Ethereum in their solutions. In [20], authors used Ethereum-based IDM Protocol as a solution for the U.S. beef cattle supply chain. By utilizing Ethereum blockchain, the authors in [19] provided a solution for identifying the root cause of system problems. An Ethereum-based food  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Written English & Studies written in other languages \\ \hline Studies From 2018 until now & Studies before 2018 \\ \hline Original research paper & Survey, systematic review papers \\ \hline Proposed solution implemented & Proposed solutions not implemented \\ \hline \hline \end{tabular}
\end{table}
Table 3: Criteria selection.  
supply chain system was proposed in [17]. Other studies have also used the Ethereum blockchain type to improve their systems, such as [14; 23; 29; 31; 34; 36].  
Other types of blockchain have also been used in some of the studies reviewed. A permissioned blockchain was used in [15] as a solution for the same third-party issue in a different domain. Trust relationships between SPs, users, and IDPs in ABC systems have many privacy concerns, and the authors in [26] tried to improve this by using Hyperledger technology. The later blockchain type was used by [16] to solve three main issues: (1) single failure point; (2) privacy information leak; (3) Distributed Denial of Service (DDoS) attack of the delegate node. In addition, [30] preserved a vehicle's identity privacy by using blockchain to prevent fake message distribution. Communication and computational overheads in healthcare systems were discussed by [33], using a permissioned blockchain to improve them. The reviewed studies proposed solutions to enhance and improve centralized systems by using blockchain technology in a different way, but there are still open issues that need to be addressed and enhanced, such as enhancing the scalability of blockchain-based IDMS platforms, system usability, and privacy enhancement.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
###### Abstract  
Pretrained language models have achieved great success in various natural language understanding (NLU) tasks due to their capacity to capture deep contextualized information in text using pretraining on large-scale corpora. Tokenization plays a significant role in the process of lexical analysis. Tokens become the input for other natural language processing (NLP) tasks, like semantic parsing and language modeling. However, there is a lack of research on the evaluation of the impact of tokenization on the Arabic language model. Therefore, this study aims to address this gap in the literature by evaluating the performance of various tokenizers on Arabic large language models (LLMs). In this paper, we analyze the differences between WordPiece, SentencePiece, and BBPE tokenizers by pretraining three BERT models using each tokenizer while measuring the performance of each model on seven different NLP tasks using 29 different datasets. Overall, the model pretrained with text tokenized using the SentencePiece tokenizer significantly outperforms the other two models that utilize WordPiece and BBPE tokenizers. The results of this paper will assist researchers in developing better models, making better decisions in selecting the best tokenizers, improving feature engineering, and making models more efficient, thus ultimately leading to advancements in various NLP applications.  
large language models; BERT; Arabic language; natural language processing; tokenizer; distributed computing; NLP applications
## 2 Materials and Methods
### MISR Data and Processing
\[M(\theta_{S},\;\theta,\;k)=\frac{\cos^{k-1}\;\theta_{S}\;\cos^{k-1}\theta}{ \left(\cos\;\theta_{S}+\cos\theta\right)^{1-k}} \tag{2}\]  
\(M\) involves the power function of the zenith angle cosines governed by the \(k\) parameter which describe the overall shape of the angular field, and  
\[F_{HG}(\Theta_{HG},\;g)=\frac{1-\Theta^{2}\;_{HG}}{\left[1+2\Theta_{HG}\cos g +\Theta^{2}{}_{HG}\right]^{3/2}} \tag{3}\]  
\(F_{HG}\) is the Henyey-Greenstein phase function, which uses the single parameter \(\Theta_{HG}\) to characterize preferentially forward or backward scattering depending on its sign, and where  
\[\cos g=\cos\theta\;\cos\;\theta_{S}+\sin\theta\;\sin\theta_{S}\;\cos\phi \tag{4}\]  
Detailed descriptions of the RPV model and its parameters are available in the literature [40; 42; 45; 46; 55]. The MISR-HR BRF and RPV products were converted from the native HDF-EOS format to GeoTTFF and re-projected from the original Space Oblique Mercator to UTM Zone (WGS 84 datum) projection to match the LiDAR CHM data. Two MISR acquisitions were processed: The first one took place on the 10 April 2012 (Path 168, Orbit 65487, Block 110, covering the Lowveld site), and the second on the 6 April 2013 (Path 167, Orbit 70744, Block 113, covering the iSimangaliso St-Lucia site). These MISR acquisition dates were chosen primarily to correspond as closely as possible with the LiDAR data acquisition dates. In addition, images at the end of the wet season (autumn) have additional possible benefits. First, since grasses senesce earlier than trees, this period would produce a larger spectral contrast between dry grasses and green woody canopies, compared to images taken in the middle of the season when both grasses and woody canopies are green. Second, this period may also be more suitable than the summer, because the sun zenith angle is larger and this condition produces a wider range of projected shadows depending on vegetation structure and height, a condition which may enhance surface reflectance anisotropy.
## 2 Methodology
### Summary of Reliability Assessments  
For one of the speakers, the W&B reliability data (i.e., nine duplicate samples) were lost due to technical factors. However, good-to-excellent (agreement \(>\) 80%) reliability data for this speaker was found for the other two-word lists suggesting that consistency of listener judgments was good. Inter- and intrajudge agreement for the other lists produced by all other speakers were all above 80% agreement when second judgments of duplicated stimuli were compared to first judgments of the same sample. For the NIT, the intrajudge reliability ranged between 82% and 90% and the interjudge reliability ranged between 80% and 84%. The CRT revealed intrajudge reliability which ranged from 83% to 88% and interjudge reliability between 89% and 92%. Finally, the intrajudge reliability for the W&B list across four speakers ranged from 82% to 96%, while the interjudge reliability ranged between 81% and 94%.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 4 Experimental Results
### Training Details  
We set \(\lambda\) to 0.01, 0.02, 0.05, 0.09 and 0.14 to obtain the model with different bitrates. The model is trained on the dataset for 100 epochs, with the learning rate decreased from \(2\times 10^{-4}\) to \(1\times 10^{-5}\). The batch size is set to 8 and the optimizer is Adam [30] with parameter \(\beta_{1}\) and parameter \(\beta_{2}\) set to 0.9 and 0.999, respectively. The GPU device is Nvidia GeForce RTX 3090.