esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals
## 2 Materials and Methods
### Optimization Procedure
#### 2.3.2 Objective Function  
After a simulation run is executed, the objective function is invoked to calculate the fitness for the given parameter configuration. The objective function needs to reflect the fact that the unloading and loading process needs to be fast, while at the same time, resources are only added if they are needed. Therefore, the following objective function was developed (based on [37]):  
\[fitness=\frac{\widetilde{t_{ship}}}{t_{ship}}\cdot\frac{50\cdot\#QCs\cdot util _{QCs}+5\cdot\#RTGs\cdot util_{RTGs}+\#YTs\cdot util_{YTs}}{50\cdot\#QCs+5 \cdot\#RTGs+\#YTs} \tag{1}\]  
The left factor of the multiplication reflects the inverted relative makespan of the ship. \(t_{ship}\) is the time used to unload and load the ship. As an approximation of \(\widetilde{t_{ship}}\), prior to the optimization runs, 100 random samples are drawn from the parameter configuration space, and the makespan for the ship is measured. This normalization process centers the left factor to around 1 and ensures that it remains in proportion to the right factor. The shorter the makespan, the larger the left factor becomes.  
The right factor of the multiplication is the weighted utilization. \(\#QCs\) refers to the number of QCs, \(\#RTGs\) is the number of RTGs and, therefore, also the number of YBs, and \(\#YTs\) is the number of trucks. \(util_{<equipment>}\) refers to the ratio of the time for which the equipment has been working to the overall makespan. When summarizing these utilization values to one factor, weights are assigned according to the investment costs. It is assumed that a QC is 50 times more expensive than a truck, and the cost of an RTG is 10% of a QC [37]. The higher the equipment utilization (with a special focus on expensive equipment), the larger the right factor becomes.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 4 Experimental Results
### Comparison with Related Works  
Although there have been numerous studies on improving point cloud coding algorithms in terms of time consumption, regrettably, there are few methods available to enhance the compression performance of the algorithm while also improving speed. This also reflects that our method can comprehensively consider the time and performance requirements. We compare the proposed method with [25; 26; 35]. As shown in Tables 10 and 11, the data from their respective papers were used for comparison. Among them, refs. [25; 35] compared the overall performance of their algorithms with TMC2-12.0, and ref. [26] compared the overall performance of their algorithms with TMC2-17.0. The method used in [25; 26] improves the refining segmentation. The fast CU decision-making method in [35] enhances the video compression process. The time improvement of [26] in attribute sequence compression is 62.41%. Ref. [25] improved the FGRS time by 47.05% and ref. [35] improved the G-RS by 26.9%. The proposed method reduces the overall point cloud encoding time by 31.86%.  
Figure 13: Example of subjective quality comparison between TMC2 and ours.  
Refs. [25; 35] have different levels of loss in D1 and D2 BD-rate metrics. The D1 BD-rate of [26] improves by \(-0.03\%\), and the D2 BD-rate improves by \(-0.02\%\). The proposed method is improved by \(-1.39\%\) and \(-1.58\%\), respectively. References [25; 35] have different degrees of loss on the Luma BD-rate measure. The Luma BD-rate of [26] only improves by \(-0.09\%\). The proposed method improves by \(-1.35\%\). At the same time, in terms of Cb, Cr BD-rate, the proposed method also has performance improvement.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 3 Coordinated Control Strategy
### Introduced Active Damping Ratio  
In the EMC strategy, the introduced non-dimensional damping is used for the modulation of the control action. In this section, the mathematical expression of the damping introduced by an actuator into a generic vibration mode is derived.  
Taking into account the SNDF control law defined in [10] and assuming the \(i\)-th vibration mode excited (the module of the band-pass compensator corresponding to the compensator frequency is \(0\)\([dB]\)), the dissipated energy related to the electromagnetic force of the actuator \(F_{a}=BII\) evaluated within an oscillation period is:  
\[E_{d,F_{a}}=\int_{0}^{T}B_{a}l_{a}g_{i,j}^{*}\dot{y}^{2}\,dt=\int_{0}^{T}g_{i,j}\dot{y}^{2}dt \tag{5}\]  
where \(\dot{y}\) is the structure velocity and \(g_{i,j}^{*}\) is the control gain. The current absorbed by the actuator is \(I=g_{i,j}^{*}\dot{y}\).  
Now, the dissipated energy can be expressed in terms of modal coordinates. Considering an harmonic disturbing force \(Q_{i}\cos(\omega_{i}t)\) which affect the structure, whose frequency is the same of the structure \(i\)-th eigenfrequency, the formulation of the dissipated energy becomes:  
\[E_{d,F_{a},i}=\pi g_{i,j}\dot{\phi}_{i,j}^{2}\,Q_{i}^{2}\omega_{i} \tag{6}\]  
where \(\dot{\phi}_{i,j}\) is the modal amplitude. Then, the non-dimensional damping ratio can be defined as a function of \(E_{d,F_{a},i}\):  
\[h_{i,j}=\frac{E_{d,j}}{2\,\pi\,m_{i}\,Q_{i}^{2}\,\omega_{i}^{2}}=\frac{E_{d, j}}{4\,\pi\,E_{k,i}} \tag{7}\]  
where \(E_{k,j}\) is the maximum kinetic energy related to the \(i\)-th vibration mode. Finally, by substituting Equation (6) into Equation (7):  
\[h_{i,j}=\frac{g_{i,j}\,\dot{\phi}_{i,j}^{2}}{2\,m_{i}\,\omega_{i}}=\frac{g_{i, j}\,R_{i,j}}{2\,\omega_{i}} \tag{8}\]  
As shown in Equation (8), the introduced damping ratio \(h_{i,j}\) depends also on the control gain \(g_{i,j}\) applied on the \(i\)-th mode, which should be considered as a known parameter. Since the aim is to increase as much as possible the damping introduced, the gain must be the highest possible. However, the maximum feasible gain is limited by instability or saturation phenomena which affect the PMA. As a result, the gain \(g_{i,j}\) in Equation (8) is the minimum between the maximum gain which ensure the stability of the closed - loopsystem (related to the actuator dynamics) and the saturation one. The derivation of these limiting gains will be presented in Section 4.
## 4 Experimental Work and Data Analysis
### Experiment 1: Using Seed of Words Related to Learning Analytics  
For our first approach, a seed of keywords (provided in Table 1) was applied to analyse the publications' metadata and find the core publications in LA. First, we performed semantic matching between the publication metadata and the seed keywords. Each publication was labelled either as relevant (yes) or nonrelevant (no) with reference to the percentage of seed keywords that matched the metadata. Table 2 shows the publication metadata of a researcher as an example, along with the labels (yes/no) assigned by matching the metadata and seed keywords. We also calculated the number of relevant publications by each author (see Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Author ID** & **Publication ID** & \begin{tabular}{c} **Publication Title** \\ **and Description** \\ \end{tabular} & \begin{tabular}{c} **Method 1: Is It a Core LA Paper** \\ **with Seed Keywords?** \\ \end{tabular} \\ \hline mjmJ16 YAAAAJ & cFHS6 HbyZ2 cC & \begin{tabular}{c} Predicting student \\ performance using advanced \\ learning analytics \\ \end{tabular} & Yes \\ \hline mjmJ16 YAAAAJ & ZfRJV9d4-WMC & \begin{tabular}{c} Predicting academic \\ performance of students from \\ VLE big data using deep \\ learning models \\ \end{tabular} & yes \\ \hline mjmJ16 YAAAAJ & LPZeul\_q3 PIC & \begin{tabular}{c} CC-GA: A clustering \\ coefficient based genetic \\ algorithm for detecting \\ communities in social \\ networks \\ \end{tabular} & No \\ \hline mjmJ16 YAAAAJ & yD5 IFk8b50 cC & \
\begin{tabular}{c} Measuring social media \\ activity of scientific literature: \\ An exhaustive comparison of \\ Scopus and novel altmetrics \\ big data \\ \end{tabular} & No \\ \hline mjmJ16 YAAAAJ & 70 eg2 SAEIzsC &
\begin{tabular}{c} Modelling to identify \\ influential bloggers in the \\ blogosphere: A survey \\ \end{tabular} & No \\ \hline \end{tabular}
\end{table}
Table 2: Sample results from experiment 1 for authors and publications mapped by labelling yes or no, using the publication metadata and seed keywords.
# Compact Dual-Band Tapered Open-Ended Slot-Loop Antenna For Energy Harvesting Systems
## 2 Antenna Design Architecture
### Description of the Antenna Structure  
The antenna is composed of a notched rectangular loop which contributes to the OEL configuration (identified by ABCDEFA structure), slotted parallel transmission line that handles the operation of OES (identified by CDEFGMIC structure), and an ACS strip feed line. As shown in Figure 1, the OEL is divided into two segments with small gap separation. Normally, the loop antenna resonates (with a purely real impedance) when the perimeter of the loop approaches one wavelength is comparable to 920 MHz. The first segment has an L shape and it represents a quarter wavelength defined by (\(L_{p1}+L_{p2}\)). The other segment is composed of trapezoidal shape defined by (\(L_{p4}+L_{p5}+L_{p6}\)) that is connected with a curved transmission line (\(L_{p3}\)) to tune the input impedance. The total length of the trapezoidal shape is a half wavelength. Each segment is loaded with an open slot, so the total length of the loop antenna can be reduced and resonance frequency is tuned to the desired frequency of operation.  
On the other hand, the OES is created by extending the ground plane along with side-by-side to the trapezoidal shape. The higher resonance is obtained by adjusting the length of the slotted transmission line \(L_{p6}\).  
The antenna is fed by using ACS that has a single lateral ground. The ACS fed method is used to reduce the size of the antenna and improve its performance. The ACS configuration provides similar benefits of CPW fed antenna in addition to more compactness. In case of ground width more than the width of the feed line, the characteristic impedance of ACS that achieves \(50\Omega\) is calculated according to the following equations [24]:  
\[Z_{0}=\frac{60\pi}{\sqrt{\epsilon_{\text{eff}}}}\frac{K(k)}{K(k^{\prime})} \tag{1}\]  
\[k=\frac{0.5W_{f}}{g+W_{f}},k^{\prime}=\sqrt{1-k^{2}},\epsilon_{\text{eff}} \approx 1+\left(\epsilon_{\text{r}}+1\right)/2 \tag{2}\]  
and \(\frac{K(k)}{K(k^{\prime})}\) is called an elliptical integral, and equal to  
\[\frac{K(k)}{K(k^{\prime})}=\left\{\begin{array}{ll}\frac{\pi}{\ln\frac{2(1+ \sqrt{k})}{(1-\sqrt{k})}}&0\leq k\leq\frac{1}{\sqrt{2}}\\ \\ \frac{\pi}{\ln\frac{2(1+\sqrt{k})}{(1-\sqrt{k})}}&\frac{1}{\sqrt{2}}\leq k\leq 1 \end{array}\right. \tag{3}\]
# Blind Image Quality Assessment with Deep Learning:
## 2 KonCept512 Model  
The original BIQA manuscript [9] presented several approaches followed in order to find a better end-to-end deep BIQA architecture. These approaches took into consideration three main factors: CNN base architectures proposed for image classification (ImageNet [14]), several loss functions, and different input sizes and combinations of the above factors. The authors found that the best performing model (named KonCept512) was obtained by applying the InceptionResNetV2 base architecture [15], with a Mean Square Error (MSE) loss, that was trained and tested on images of input size of \(512\times 384\).  
We explored KonCept512 model in this work. Figure 1 shows the architecture proposed by [PERSON] et al. [9], in which the KonCept512 model was trained. The input image is passed through the InceptionResNetV2 backbone (without the final fully-connected layers) pre-trained on ImageNet dataset in order to extract features from the image, followed by a Global Average Pooling (GAP) layer. These layers are connected to four Fully-Connected (FC) layers. The first three layers have 2048, 1024, and 256 units, respectively. Each one of these FC layers are followed by an activation function, the Rectified Linear Unit (ReLU), and a dropout layer, each with rates of 0.25, 0.25, and 0.5 in order to avoid over-fitting, followed by a final linear FC layer. This output layer has one output unit to predict MOS.
## 1 Introduction
The highest and marginal thinning of \(-\)1.69 \(\pm\) 0.60 ma\({}^{-1}\) and \(-\)0.11 \(\pm\) 0.32 ma\({}^{-1}\) were observed in Pir Panjal and Karakoram glaciers, respectively. This thinning of glaciers is also influenced by topographic parameters [6]. In the western Himalaya, the total glacier cover of 77 glaciers in the Dras basin has dropped by 5.31 \(\pm\) 0.33 km\({}^{2}\) with a mean snout retreat of 155 \(\pm\) 9.58 m (30 m to 430 m range) from 2000 to 2020 [7]. In the Chandra Basin, the glacier area dropped off to 608 \(\pm\) 10.3 km\({}^{2}\) in 2016 from 639.4 \(\pm\) 5.8 km\({}^{2}\) in 1971 at a rate of 0.7 \(\pm\) 0.3 km\({}^{2}\) per year, resulting in a total loss of 4.9 \(\pm\) 1.9%, equivalent to 31.3 \(\pm\) 11.8 km\({}^{2}\)[10]. Reports show a 2.5% loss in glacier cover from 377.6 km\({}^{2}\) to 368.2 km\({}^{2}\) from 1980 to 2010 [11]. The average glacier snoutretreated by 465.5 \(\pm\) 169.1 m at an average rate of 15.5 \(\pm\) 5.6 m per year during this period. Such extensive mass loss of Himalayan glaciers will have significant direct consequences for freshwater availability, irrigation, and hydropower generation, and can also increase the risk of glacial lake outburst floods (GLOFs) throughout Asia. The large-scale glacial retreat observed worldwide since the latter half of the 1900s is widely attributed to global warming and climate change. Climatic elements such as monsoon circulation, wind patterns, temperature, and precipitation exhibit distinct characteristics between the western and eastern Himalayas, with the Tibetan Plateau also influencing their glacier retreat patterns. According to the projections of the Intergovernmental Panel on Climate Change (IPCC), the Asian land mass is expected to experience an average annual mean warming of approximately 3 \({}^{\circ}\)K by the 2050s and around 5 \({}^{\circ}\)K by the 2080s. However, it is anticipated that the Tibetan Plateau will undergo even more significant temperature increases [12]. The tropospheric warming trends between the western and eastern Himalayas and the Indo-Gangetic (IG) plains exhibit inequality in the magnitude of tropospheric warming trends in these regions [13]. Ice core studies from the Himalaya-Tibet region have identified the role of the South Asian monsoon and the mid-latitude westerlies in influencing the Himalayan glacier mass balance over millennial timescales and the intensified heating caused by dust and anthropogenic pollution, including black carbon, in the Indo-Gangetic (IG) plains, Himalayas, and Tibetan Plateau, affecting the temperature gradient and monsoon circulation [14; 15; 16]. Understanding the intricacies of climate change and their interaction with glacial dynamics is essential for devising effective policies and strategies to safeguard the Himalayan region and its dependent communities.
## 3 Results
### Translation (Figure 6, Table 5)
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline
**Resorption (mm)** & & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Translation & Structure & & **504.40** & **505.10** & **505.80** & **506.50** & **507.30** & **582.67** & **658.00** & **733.40** & **808.80** \\
0.6 N/60 gf & \multirow{2}{*}{Tooth + bracket} & \multirow{2}{*}{a} & 100.90 & 117.95 & 135.00 & 152.05 & 169.10 & 267.70 & 366.30 & 464.90 & 563.61 \\ Stress & & & m & 100.90 & 191.92 & 282.90 & 373.90 & 465.00 & 536.58 & 608.10 & 679.70 & 751.35 \\ Amount & & & c & 202.41 & 268.00 & 333.69 & 399.30 & 465.00 & 489.60 & 415.30 & 538.90 & 563.61 \\ In each & & & C & 504.40 & 505.10 & 505.80 & 506.50 & 507.30 & 582.67 & 658.00 & 733.40 & 808.80 \\ Component & & & a & 71.50 & 90.10 & 108.80 & 127.40 & 146.10 & 250.47 & 354.84 & 459.20 & 563.61 \\  & & & m & 71.50 & 163.20 & 254.90 & 346.60 & 438.31 & 516.50 & 594.80 & 673.00 & 751.35 \\  & & & c & 202.41 & 261.37 & 320.34 & 379.30 & 438.31 & 469.60 & 500.90 & 532.20 & 563.61 \\  & & & C & 357.90 & 359.70 & 361.60 & 363.40 & 365.30 & 383.55 & 401.80 & 420.00 & 438.30 \\  & & & c & 67.94 & 78.67 & 89.40 & 100.12 & 110.85 & 208.50 & 306.16 & 403.81 & 501.46 \\  & & & m & 67.94 & 160.53 & 253.13 & 345.72 & 438.31 & 516.57 & 594.83 & 673.09 & 751.35 \\  & & & c & 200.11 & 250.38 & 300.65 & 350.91 & 401.18 & 441.88 & 482.57 & 523.27 & 563.96 \\  & & & C & 67.94 & 69.55 & 71.16 & 727.77 & 74.38 & 87.45 & 100.51 & 113.58 & 126.64 \\ Bone & & & a & 74.17 & 85.75 & 97.33 & 108.92 & 120.51 & 130.76 & 141.04 & 151.30 & 161.57 \\  & & & m & 74.17 & 85.75 & 97.33 & 108.92 & 120.51 & 130.76 & 141.04 & 151.30 & 161.57 \\  & & & c & 185.28 & 191.68 & 198.08 & 204.49 & 210.90 & 220.91 & 230.93 & 240.95 & 250.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum stress average values (KPa) produced by orthodontic forces in tooth structure and dentine–cement. Figure 6: Translation—Tresca stress display in dental tissues subjected to 0.6 N for 0, 4, and 8 mm bone loss: (**A**)—dental tissues’ structure, (**B**)—tooth with bracket, (**C**)—tooth without bracket, (**D**)—dentine component, (**E**)—dental pulp and NVB, (**F**)—PDL, (**G**)—bone with alveolar socket.
## 3 Multi-Source Information Fusion Method
### Leakage Detection Based on Kalman Filter and SPRT
#### 3.1.1 Kalman Filtering  
Since the relay valve pressure signal is one-dimensional, the Kalman filter theory of the scalar signal is employed. For the characteristics of the relay valve pressure signal sequence collected by the BC pressure sensor in real time, the following mathematical model is established:  
\[s(k)=s(k-1)+w\ (k-1) \tag{1}\]  
\[x(k)=s(k)+n\ (k) \tag{2}\]  
where \(s(k)\) represents the sequence of state signal, i.e., the signal model; \(w(\text{k})\) is a white noise sequence with zero mean, often referred to as system noise; \(x(k)\) is the observation sequence, i.e., the observation model; and \(n(k)\) is also a white noise sequence with zero mean, often referred to as measurement noise.  
According to the linear least mean square criterion, the recursive algorithm of the [PERSON] filtering model is as follows:  
Prediction error variance:  
\[p_{1}(k)=p(k-1)+\sigma_{w}^{2} \tag{3}\]  
Optimal filtering gain:  
\[b(k)=p_{1}(k)\Big{[}p_{1}(k)+\sigma_{n}^{2}\Big{]}^{-1} \tag{4}\]  
Filtering error variance:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Leakage Mode** & **Gas Flow Direction** & **Impact** & **Causes** \\ \hline \multirow{4}{*}{Internal leakage} & \multirow{4}{*}{MR to BC} & The output pressure is & Supply valve assembly \\  & & higher, the braking & vulcanizing parts aging; \\  & & release is slow, and even & return spring aging; \\  & & braking does not release & supply valve stuck \\ \hline \multirow{4}{*}{External leakage} & \multirow{4}{*}{BC to BC-EX (external atmosphere)} & The output pressure is & \\  & & lower; too low will & Supply valve assembly \\ \cline{1-1}  & & trigger abnormal & vulcanizing parts aging; \\ \cline{1-1}  & & emergency braking & supply valve stuck \\ \hline \end{tabular}
\end{table}
Table 1: Relay valve leakage mode.  
\[p(k)=\mathrm{p_{1}(k)}-b(k)p_{1}(k)=b(k)\sigma_{n}^{2} \tag{5}\]  
Filtering equation:  
\[\hat{s}(k)=\hat{s}(k-1)+b(k)[x(k)-\hat{s}(k-1)] \tag{6}\]  
Innovation sequence:  
\[e(k)=x(k)-\hat{s}(k-1) \tag{7}\]  
where \(\sigma_{w}^{2}\) is the system noise and \(\sigma_{n}^{2}\) is the measurement noise.  
Due to the delay of the state estimation, the innovation sequence can extract the inflection point of the BC pressure signal sequence, and the abnormal point can be identified.
# A Survey on Hardware Accelerators for Large Language Models
## 7 Quantitative Comparison  
Table 1 shows all of the hardware-based accelerators that have been proposed, along with the main features of each accelerator. Each row lists the name of the accelerator, its type (FPGA/GPU/ASIC/In-memory), the speedup, the energy efficiency, and the reference platform for comparison. In some cases, the proposed scheme is compared against both a CPU and a GPU. In these cases, both speedup and energy efficiencies are shown. In this section, we present a quantitative and qualitative comparison of the proposed schemes.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Year** & **Framework** & **Technology** & **Speedup** & **Energy Efficiency** & **Baseline** \\ \hline
2019 & MNNFast [9] & FPGA 7020 & 5.4\(\times\) & 6.5\(\times\) & CPU (Xeon 24-core) \\
2020 & FTRANS [10] & FPGA VCU118 & 27\(\times\)-81\(\times\) & 8.8\(\times\) & CPU/GPU RTX5000 \\
2020 & Multi-Head [11] & FPGA XCVUPI3 & 14\(\times\) & — & GPU V100 \\
2021 & NPE [12] & FPGA Zynq7100 & 35\(\times\) & 4\(\times\)–6\(\times\) & CPU/GPU RTX5000 \\
2021 & Pruning[13] & FPGA U200 & 11\(\times\)/2\(\times\) & — & CPU (i5)/GPU TX2 \\
2022 & DFX [14] & FPGA U280 & 3.8\(\times\) & 4\(\times\) & GPU V100 \\
2022 & Transformer [16] & FPGA U200 & 2.3\(\times\) & — & CPU (80-threads) \\
2023 & OPU [15] & FPGA & 15\(\times\)/2.9\(\times\) & — & CPU (Gold)/GPU RTX3090 \\
2023 & FlexRun [17] & FPGA S10 & 2.7\(\times\) & — & GPU V100 \\
2024 & ODE [18] & FPGA & 12.8\(\times\) & 9.2\(\times\) & ARM A53 \\
2022 & SoftMax [19] & GPU A100 & 2.5\(\times\) & — & GPU A100 \\
2022 & LightSeq2 [20] & GPU A100 & 3\(\times\) & — & GPU A100 \\
2023 & LLMA [22] & GPU V100 & 2\(\times\) & — & GPU V100 \\
2023 & UltraFastBERT [25] & CPU & 78\(\times\) & — & CPU \\
2020 & A3 [26] & ASIC 40 nm & 7\(\times\) & 11\(\times\) & CPU (Gold)/GPU Volta \\
2021 & ELSA [27] & ASIC 40 nm & 157\(\times\) & 1265\(\times\) & GPU V100/TPU \\
2021 & SpAtten [28] & ASIC 40 nm & 347\(\times\)/162\(\times\) & 4059\(\times\)/1093\(\times\) & CPU (Xeon)/GPU Titan \\
2021 & Sanger [29] & ASIC 55 nm & 22.7\(\times\)/4.64\(\times\) & — & CPU (3970)/GPU V100 \\
2023 & Energon [30] & ASIC 45 nm & 168\(\times\)/8.7\(\times\) & 10,000\(\times\)/1000\(\times\) & CPU/GPU V100 \\
2024 & H3D Transformer & ASIC 22 nm & – & 3.1\(\times\) & TPU (7 nm) \\
2020 & ATT [32] & In-memory & 202\(\times\) & 11\(\times\) & CPU (Gold)/GPU Volta \\
2020 & ReTransformer [33] & In-memory & 23\(\times\) & 1086\(\times\) & GPU \\ \hline \hline \end{tabular}
\end{table}
Table 1: LLM Transformer accelerators.