esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Comparative Analysis
### Training Performance with Comparative Evaluation  
In these experiments, the ASC-RL was compared with other baseline schemes (CAS-IIoT-RL, LSTM-RL, and AEM-RL) in an equal number of episodes with a median success rate. In fixing some hyperparameters like the discount rate and exploration strategy, the proposed scheme achieves high success rates, while the other schemes, with moderate adaptation, exhibit low success rates. In Figure 13, the behavior of these schemes is shown with 90% up to 50 epochs and less noise for ASC-RL. CAS-IIoT-RL with a delay component of up to five epochs exhibits slightly slower convergence. The other two, LSTM-RL and AEM-RL, have more noise and delayed convergence. The success rate is 85-90% and 80-88%, respectively. The statistical values of this experiment are shown in Table 9, in which ASC-RL training performance is shown with the other three baseline schemes. A total of 200 epochs, with different success rates, were used for the main metric. In this table, it is shown that ASC-RL has the highest success rate of 0.997, while the other schemes' highest success rate is 0.862, with the lowest standard deviation of 0.205. In this experiment, it is observed that a success rate greater than 0.8 is observed in just 36 epochs, demonstrating faster convergence. As far as hardware details are concerned, we intend to implement it in hardware in the future. Our published work implemented the off-the-shelf \"CC2420\" module with some APIs to simulate IoT-based communication.  
To verify the overall efficiency and optimization in IoT networks, ASC-RL was thoroughly compared with all known parameters with other baseline schemes, as shown in Table 10. Each scheme has its implementation, creating specific results based on the environ  
Figure 12: Energy efficiency and reliability threshold of ASC-RL with CAS-IIoT-RL, LSTM-RL, and AEM-RL.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Success Rate** & **Mean Success Rate** & **Standard Dev** & **[EMAIL_ADDRESS]+ SR** \\ \hline ASC-RL & 0.997 & 0.865 & 0.207 & 36.21 \\ CAS-IIoT-RL & 0.966 & 0.823 & 0.227 & 43.56 \\ LSTM-RL & 0.945 & 0.782 & 0.241 & 49.12 \\ AEM-RL & 0.936 & 0.778 & 0.256 & 48.34 \\ \hline \end{tabular}
\end{table}
Table 9: Training performance of ASC-RL in 200 epochs with success rate with CAS-IIoT-RL, LSTM-RL, and AEM-RL.  
ment. Most of the parameters in this table are types of algorithms, action spaces, state spaces, confidence intervals, discount factors, the number of episodes, and implementation tools.
## 1 Introduction
In this study, to address the challenge of detecting small objects with complex backgrounds while satisfying the high-speed detection requirements of edge computing, the PDWT-YOLO algorithm was introduced as an improvement to YOLOv7-tiny. Experiments were conducted using the public dataset VisDrone-2019 [25], and the results confirmed that the proposed algorithm enhanced detection accuracy while simultaneously maintaining detection speed. The model shows the following improvements compared with YOLOv7-tiny:  
* A small-object detection layer was added that used a 160 \(\times\) 160 resolution feature map to detect small objects. This adjustment enhanced the detection performance for small objects. * The contradiction between small-object classification and regression was weakened by introducing a decoupled head [26] to replace the detection head IDetect [27] in YOLOv7-tiny, which improved detection accuracy. * The WIoU loss function [28] that expedites network convergence and enhances the regression accuracy was used instead of CIoU thus presenting a balanced regression approach for both high- and low-quality samples. Compared with CIoU [29], WIoU emphasizes anchor boxes of average quality, resulting in an overall performance enhancement for the detector. This function is suited to handling small object boxes and overlapping occluded object boxes, making it beneficial for small-object detection.
## 2 Theory
### The Roofline Model  
To characterize the performance of our GPU implementation, we employ the roofline model [15], which is a visual framework for understanding how kernel performance is limited by both the available memory bandwidth and the peak computational throughput. A key component of this model is the _operational/arithmetic intensity_, defined as  
\[\text{OI}=\frac{\text{FLOPs}}{\text{Bytes transferred}},\]  
where FLOPs is the total number of FLOating-point Operations performed and bytes transferred is the total amount of data read or written from memory. The roofline model states that the maximum achievable performance \(P\) cannot exceed either the GPU's peak floating-point performance \(P_{\text{peak}}\) or the product of the operational intensity and the peak memory bandwidth \(B_{\text{peak}}\):  
\[P\leq\min(P_{\text{peak}},OI\times B_{\text{peak}}) \tag{2}\]  
This formulation highlights whether a given kernel is _compute-bound_ (limited by \(P_{\text{peak}}\)) or _memory-bound_ (limited by \(B_{\text{peak}}\)). By plotting the measured performance against \(\text{OI}\) on the same axes as these theoretical \"roofs\", it is possible to identify performance bottlenecks and optimization patterns; see Figure 1.
## 4 Proposed Algorithm
### Main Framework of the Proposed Algorithm
The Effectiveness of the ZS

To validate the effectiveness of the ZS, the ZS-MMBSO and ZS-MMBSO without the ZS strategy (denoted as ZS-MMBSO_1) are used to compare 22 functions in terms of _PSP_. The parameter settings are consistent with Section 5.1. For each function, the optimal value is displayed in bold, and the results are analyzed by [PERSON]'s rank sum test. In Table 3, the mean _PSP_ values of the ZS-MMBSO and its variant are presented. It can be found that ZS-MMBSO performs better than ZS-MMBSO_1 on all test functions. So, the search space segmentation has a significant impact on the proposed algorithm in the decision space. It is because the search complexity is reduced by the ZS, this is important for solving complex problems. In summary, the ZS is effective to assist the proposed algorithm in locating more equivalent PSs. #### 5.3.2 The Effectiveness of the Novel Individual Generation Strategy

To demonstrate the effectiveness of the novel individual generation strategy, the _PSP_ is used to testify the performance of the proposed algorithm and its variant. The ZS-MMBSO and ZS-MMBSO which adopts the Gaussian mutation to generate the offspring individuals (named as ZS-MMBSO_2) are compared on 22 test functions. The parameter settings are shown in Section 5.1. The [PERSON]'s rank sum test is used to analyze the results and the best results are showed in bold. From Table 4, it can be observed that ZS-MMBSO achieves the best results in 19 out of 22 cases. On the MMF14_a and the SYM_PART simple, the ZS-MMBSO_2 obtains the best results. The statistical results indicate the effectiveness of the novel individual generation
# Learning Ad Hoc Cooperation Policies from Limited Priors via Meta-Reinforcement Learning
## 3 Preliminaries
### Ad Hoc Teamwork  
The ad hoc teamwork issue is a sequential decision problem. Sequential decision problems are typically solved based on the Markov Decision Process (MDP). Due to the partial observability of the ad hoc agent in our problem and the presence of multiple agents in the ad hoc team, we adopt the Decentralized Partially Observable Markov DecisionProcess (Dec-POMDP) [27] to comprehensively represent the ad hoc team problem. Based on Dec-POMDP, the model of the AHT problem is as follows:  
\[\langle n,\mathcal{I},\Theta,\mathcal{S},\mathcal{A},P,R,Z,\mathcal{O},\gamma\rangle,\]  
where:  
* \(n\) denotes the quantity of agents within the ad hoc team.
* \(\mathcal{I}\) represents all agents in the ad hoc team, where \(I^{0}\in\mathcal{I}\) denotes the ad hoc agent, which is the agent under our control, and \(I^{-0}\subset\mathcal{I}\) represents all agents in the ad hoc team except the ad hoc agent.
* \(\Theta\) denotes the set of known teammate types, comprising \(m\) types. \(\Theta^{-0}\) denotes the true types of all \(I^{0}\)'s teammates, with no limitation that \(\Theta^{-0}\subseteq\Theta\).
* \(S_{t}\in\mathcal{S}\) represents some feature representation of the environmental state observed by all agents at time \(t\).
* \(A_{t}\in\mathcal{A}\) represents the joint action of all agents at time \(t\).
* \(P:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\to[0,1]\) denotes the transition function of the environment.
* \(R:\mathcal{S}\times\mathcal{A}\to\mathbb{R}\) represents the reward function of the agents.
* \(Z:\mathcal{S}\times\mathcal{A}\to\mathcal{O}\) denotes the observation function of all agents.
* \(O_{t}\in\mathcal{O}\) represents the observation obtained by the agents based on the observation function \(Z(S_{t}=\mathbf{s},A_{t}=\mathbf{a})\).
* \(\gamma\in(0,1]\) represents the reward discount factor, which captures the uncertainty of the future.  
Each agent \(l\in\mathcal{I}\) has a policy \(\pi:\mathcal{O}\times\mathcal{A}\to[0,1]\) and interacts with the environment to generate a series of action-observation histories \(\tau_{t}=\{O_{0},A_{0},\cdots,O_{t},A_{t}\}\). Here, the policy of the ad hoc agent is denoted by \(\pi^{0}\), and the policy set of all its teammates is denoted by \(\pi^{-0}\). The task of the AHT problem is to enable agent \(I^{0}\) to learn a policy \(\pi^{0}\) that maximizes the collective reward through cooperation with teammates:  
\[J(\pi^{0},\pi^{-0})=\mathbb{E}\left[\sum_{t=0}^{H}\gamma^{t}R_{t}\right], \tag{1}\]  
where \(H\) classifies the maximum length of the ad hoc teamwork environment.
# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 5 Experimental Results
### Main Results  
Figure 1 delineates the mean performance of the model, considering variations in shots and temperature. Interestingly, the few-shots setup consistently outperforms the zero-shot approach. This observation underscores a pivotal insight: when LLMs like ChatGPT are tasked with K-GEC, supplementing the prompt with pertinent explicit examples can effectively harness and augment the inherent capabilities of the model, potentially leading to more accurate error corrections.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**K-NCT** & \multicolumn{2}{c}{**Test**} \\ \hline  & **Error Sentence** & **Correct Sentence** \\ \hline \# of sents & 3000 & 3000 \\ \# of tokens & 129,798 & 129,886 \\ \# of words & 31,183 & 31,700 \\ \hline avg of SL \(\bigtriangleup\) & 43.27 & 43.29 \\ avg of WS & 10.39 & 10.57 \\ avg of SS & 9.39 & 9.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of K-NCT dataset [21]. # of sents/tokens/words: number of sentences/tokens/words; \(\bigtriangleup\) avg of SL/WS/SS: average of sentence length/words/spaces per sentence.  
However, it is imperative to note that there is not a strict linear relationship between the number of shots and the positive influence they exert on performance. To illustrate, the performance differential between 1-shot and 4-shots is as pronounced as a maximum of 12.06 points on the BLEU metric. In contrast, the gap narrows significantly between 4-shots and 8-shots, with a difference of only up to 5.06 points. This indicates diminishing returns as the number of shots increases, suggesting an optimal threshold for shots that strikes a balance between computational efficiency and performance enhancement.  
Additionally, beyond the number of shots, the temperature setting stands as a pivotal parameter influencing ChatGPT's response generation. Intriguingly, performance differentials emerge even under identical shot settings when the temperature varies. For instance, in a zero-shot setting, a temperature of 0.5 yielded optimal results; however, when augmented with a single shot, the model's peak performance was observed at a temperature of 0.8. Moreover, as we increase the number of shots, the relative influence of temperature on performance seems to diminish. Such observations underscore the intricate interplay between explicit example information and temperature settings. It suggests that, by judiciously calibrating these parameters, one can effectively tap into and amplify the latent potential of large language models like ChatGPT, optimizing their capabilities for intricate tasks like K-GEC.
# Regulation of Leaderless mRNA Translation in Bacteria
## 1 Introduction  
The process of translation is of utmost importance to life, as it allows the production of proteins based on the genetic information contained in RNA. Translation can be divided into several steps: usually, a step where the initiation site is recognized by the ribosome and polymerization begins (initiation), a step where amino acids carried by tRNAs are sequentially added to the nascent peptide (elongation), and a step where polymerization stops, releasing the nascent peptide (termination). Translation elongation and termination proceed by similar mechanisms in prokaryotes and eukaryotes. For instance, in all forms of life, the recognition of amino acids in the ribosome depends on two steps: the correct aminoacylation of tRNAs by aminoacyl-tRNA synthetases, followed by proper pairing between three nucleotides in the mRNA (codon) and three nucleotides in a tRNA (anti-codon) that carry amino acids [1; 2; 3]. In contrast to this conservation, the identification of translation initiation sites varies. For instance, the canonical initiation of translation in eukaryotes requires the recognition of 5\({}^{\prime}\) capping by the eukaryotic initiation factor 4F complex (together with additional signals such an initiation codon) [2]. On the other hand, canonical initiation in bacteria is completely independent of the chemical characteristics of the 5\({}^{\prime}\) end of mRNA and commonly begins at sites that are far from this 5\({}^{\prime}\) extreme, allowing the organization of genes in operons [1]. Furthermore, within both eukaryotes and prokaryotes, there are alternative mechanisms that allow the initiation of translation at other, noncanonical, sites [2; 4].  
In this review, we focus on one such mechanism, leaderless mRNA (lmRNA) translation, by which bacterial ribosomes recognize an initiation codon that is very near to the 5\({}^{\prime}\) end of an mRNA to begin translation. It must be noted that eukaryotes also presentnon-canonical initiation mechanisms, explanations of which can be found elsewhere [2]. Nevertheless, our focus is mainly on lmRNA translation in _Escherichia coli_, which has been an important model for studying the mechanisms and regulation of bacterial lmRNA translation. Other bacteria are also considered, although this is limited because there is far less information available on them.
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 2 Single-Element Antenna
### Design Methodology  
The schematic of the suggested dual-band antenna is given in Figure 1. The antenna design makes use of a flexible polyimide substrate with a dissipation factor of 0.008 and a dielectric value of 3.5. The overall physical values of the suggested antenna are 20 \(\times\) 20 \(\times\) 0.1 mm\({}^{3}\). The values of the design parameters are tabulated in Table 1. The table provides information about the single-element antenna dimensions including feedline width and length, stub length and width, ground length, and dimensions of slit incorporated in the ground plane. In terms of impedance bandwidth, the suggested antenna has a dual-band response that spans 3.68-3.85 GHz (resonance at 3.75 GHz) and 5.84-6.06 GHz (resonance at 5.9 GHz), respectively.  
The suggested antenna has progressively evolved in four stages to achieve the prescribed dual bands. The antenna modeling starts with a rectangular patch antenna, which provides scattering parameters below \(-\)10 dB. The second stage incorporates two rectangular slots in the radiator. The antenna operates at 5.62 GHz in this stage with a very low bandwidth of 5.6-5.64 GHz. To provide a dual-band response, the third stage is introduced with two L-shaped strips in the radiator. This stage showed two bands below \(-\)6 dB from 3.09-3.19 GHz and 5.9-6.04 GHz. Although the antenna gives a two-band response, the bandwidth is very low. To incrementally increase the antenna bandwidth with a dual-band response below \(-\)10 dB, the antenna ground is made partial with a rectangular cut towards its center. Reducing the antenna ground is a well-known technique for improving the bandwidth by reducing the antenna quality factor. The rectangular cut, on the other hand, provides better impedance matching. The suggested last stage exhibits a dual-band operation with resonating at 3.75 GHz and 5.9 GHz, respectively, and spans from 3.68-3.85 GHz to 5.84-6.06 GHz. Figure 2 depicts the suggested single-element antenna's  
Figure 1: Single-element design configuration.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Parameter** & **Ws** & **Ls** & **W** & **L** & **Lg** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **h** & **i** \\ \hline Value (in mm) & 20 & 20 & 0.8 & 8 & 6.8 & 4 & 7 & 1 & 7 & 5.5 & 4 & 1 & 1 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Single-element antenna parameter dimensions.  
reflection properties at various stages. The antenna offers gain and efficiency of 3 dBi/90% and 3.6 dBi/93% for the first and second bands, as illustrated in Figure 3.
## 4 Conclusions  
In the present study, the dissimilar Ti-6 Al-4V/Nitinol welds were prepared using the linear friction welding technique. The influence of macrostructure and microstructure on hardness and tensile properties were investigated for dissimilar Ti-6 Al-4V/Nitinol as-welded and PWHT conditions. The following conclusions were drawn.  
* A defect-free dissimilar Ti-6 Al-4V/Nitinol weld (no cracks, solidification cracking, incomplete bonding) could be obtained by a linear friction welding technique.
* The macrostructure of dissimilar Ti-6 Al-4V/Nitinol welds exhibited more flash on the Ti-6 Al-4V side, and little flash on Nitinol side can be attributed to drop-in flow stresses of Ti-6 Al-4V at high temperatures.
* X-ray diffraction studies revealed a formation of titanium-rich intermetallics (Ti\({}_{2}\)Ni) on the fracture surface of linear friction welds in the as-welded and PWHT conditions. This could be attributed to the diffusion of Ti being higher at the weld interface than Ni.
* The dissimilar Ti-6 Al-4V/Nitinol linear friction welded joint exhibited a slightly higher ultimate tensile strength (UTS) and low ductility (UTS = 282 MPa, 3.9% elongation) compared with PWHT condition (UTS = 250 MPa, 5.9% elongation), and this may be attributed to high residual stresses in the as-welded condition and Ti\({}_{2}\)Ni brittle intermetallics present at the weld interface.
* The tensile fracture surfaces of the dissimilar Ti-6 Al-4V/Nitinol linear friction welds exhibited cleavage (brittle) mode of fracture due to the formation of Ti\({}_{2}\)Ni intermetallics at the weld interface.  
Figure 13: Fracture surfaces of the dissimilar Ti-6 Al-4V/Nitinol welded joints in the (**a**) as-welded and (**b**) PWHT conditions.  
**Author Contributions:** Conceptualisation, A.U.R., Y.U. and H.A.; methodology, A.U.R., N.K.B., M.K.T. and Y.U.; formal analysis, A.U.R., N.K.B., M.K.T., Y.U. and H.A.; investigation, A.U.R., N.K.B., M.K.T. and Y.U.; resources, A.U.R., Y.U and H.A.; data curation, A.U.R., N.K.B. and M.K.T.; writing--original draft preparation, A.U.R., N.K.B. and M.K.T.; writing--review and editing, A.U.R., N.K.B., M.K.T., Y.U. and H.A.; visualisation, A.U.R., N.K.B. and M.K.T.; supervision, A.U.R., N.K.B. and M.K.T.; project administration, A.U.R. and Y.U.; funding acquisition, A.U.R., Y.U. and H.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (14-ADV110-02).  
**Institutional Review Board Statement:** Not Applicable.  
**Infrared Consent Statement:** Not Applicable.  
**Data Availability Statement:** Data are contained within this article.  
**Acknowledgments:** This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (14-ADV110-02).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Induction Heating-Assisted Compaction in Porous Asphalt Pavements: A Computational Study
## 2 Objectives  
The IH compaction process is complicated, and involves three coupled physics: (i) Electromagnetic induction, (ii) heat conduction, and (iii) viscoelastic mechanics. Due to the complex composition of the asphalt mix, the nonlinear mechanical behavior, and the interaction between different physics, this compaction of pavement structures can be optimized through experiments. However, the experimental studies are both expensive and time-consuming. Numerical simulations can avoid the shortcomings faced by experimental studies and provide an efficient way for this kind of research.  
In the past, the finite element method (FEM) has been used to analyze the heating performance of inductive asphalt mortar under static [15] and moving [16] electromagnetic sources, but the compaction characteristics of asphalt mixtures have not been discussed. Considering that asphalt mix is a kind of granular material, and the FEM faces many difficulties in solving large deformation problems, some scholars have used the discrete element method (DEM) to simulate the indoor asphalt compaction process, but without electromagnetic heating [18; 19].  
The highlight of this paper is to combine these three physics together and study the on-site compaction process of asphalt mix through continuous and discontinuous mechanical computation approaches. Specifically, the COMSOL Multiphysics (V5.3, COMSOL Inc., Stockholm, Sweden, 2017) FEM and PFC3D (V5.0, Itasca Consulting Ltd., Minneapolis, MN, USA, 2017) DEM were employed in this study to simulate the IH compaction process. FEM performs well in simulating electromagnetic heating and the evolution of temperature fields, whereas DEM has the advantage in analyzing the large deformation and complex mechanical behavior of asphalt pavement caused by particle reorganization.  
The analyses have been divided into three sequential steps: (i) The prediction of temperature field during IH, (ii) the adjustment of the material parameters of asphalt mortar, and (iii) the execution of the corresponding compaction analysis. The main objectives were to (i) demonstrate the impact of induction technology on the heat generation on the pavement surface by coupling and solving electromagnetic and heat transfer phenomena under a continuously moving induction coil, and (ii) simulate the compaction process using the discrete element method and predict the effect of induction technology on this process in different compaction environments.  
Figure 1: Induction heating-assisted pavement compaction process [17].