esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The GDIF-3D-CNN classifier method was utilized for classifying the pure pixel and every pixel data set, whose accuracy and performance were enhanced by optimizing the eight parameters of the method. [PERSON] et al. [17] present a fine classifier approach dependent upon multi-feature fusion and DL. During this case, the morphological profiles, GLCM texture, and endmember abundance features were leveraged to exploit the spatial data of HIS. Next, the spatial data were fused with original spectral data to generate a classifier outcome by utilizing a DNN with a conditional random field (DNN + CRF) method. In detail, the DNN is a deep detection method that extracts depth features and mines the potential data. For smaller samples and higher-dimension HSIs, it becomes very complex to learn wide-ranging image features; subsequently, it becomes hard to precisely recognize complex HSI. The UAV-borne HSIs have rich spatial data, and the spatial resolution reaches centimeter level; however, the higher spatial resolution causes serious spatial heterogeneity and spectral variability. Nowadays, the deep learning (DL) method is extensively employed in image processing because of its effective feature learning abilities [9]. Currently, the most common DL-based network framework is the convolution neural network (CNN). CNN has the features of parameter sharing, equivariant mapping, and sparse interaction, which reduce the training parameter size and complexity of the network. Such features permit the algorithm to generate a certain degree of invariance in scaling, shifting, and distortion and also create fault tolerance and stronger robustness [10]. Consequently, CNN has been extensively employed in HSI classification. This article introduces a novel squirrel search optimization with a deep transfer learning-enabled crop classification (SSODTL-CC) model on HSIs. The proposed SSODTL-CC model initially derives a MobileNet with an Adam optimizer for the feature extraction process. The utilization of the Adam optimizer allows for effectual adjustment of the hyperparameters of the MobileNet model. In addition, a bidirectional long-short term memory (BiLSTM) method is employed for crop type classification. To enhance the classifier efficiency of the BiLSTM model, the SSO algorithm is employed for hyperparameter optimization, which shows the novelty of the work. To demonstrate the better performance of the SSODTL-CC model, a wide-ranging experimental analysis is performed on a benchmark dataset.
## 4 Experiments and Analyses
### Result and Discussion
For binary classification tasks, \(p_{t}\) takes its value according to the label; for multi-classification tasks, it is usually the category probability output by the softmax function. \(\alpha_{t}\) is the category weight factor. For a category with a small number of samples, giving a more significant value to \(\alpha_{t}\) increases the weight of the category and improves the attention of the model to the category. \((1-p_{t})^{\gamma}\) is the modulation factor; when the sample is easy to classify (\(p_{t}\) is close to 1), this factor tends to 0, decreasing its loss weight; when the sample is difficult to classify (\(p_{t}\) is small), this factor is more significant, increasing its loss weight. In short, focal loss improves classification performance by adjusting the category weights so that the model can focus more on samples that are difficult to categorize and are in a small number of categories. Table 3 demonstrates the effect of reintroducing visual modalities into the current RVI-LayoutXLM model and changing the loss on the model accuracy. Visual modalities are added to both the SER and RE tasks, which are experimentally found to result in a larger set of model parameters but do not improve mAP performance. In contrast, employing the focal loss proves efficacious in addressing the category imbalance problem. As shown in Figure 14, a comparative analysis based on the two loss functions reveals an uptick in the entity recognition rate across categories. To evaluate the generality of the proposed method, we tested the KIE model on two public datasets, FUNSD and XFUND. A comparison with prior models, as detailed in Table 4, shows that our RVI-LayoutXLM outperformed others, achieving the best F1 scores for both the SER and RE tasks on XFUND\({}_{\text{zh}}\) and FUNSD. This underscores the robust generalizability of our approach. For the associated entity detection, we initially deployed YOLOX [27] for training and testing on the CMERD dataset. However, the results obtained after inference exhibited issues such as missing rows and inaccurate edge localization. In response, we introduced IFAM to the backbone of YOLOX. Table 5 shows the experimental results, demonstrating that our proposed method exhibits excellent performance in the given scenario. This improved model also demonstrates significant advantages in the task of associated entity detection. It improves the accuracy and robustness of the detection by addressing the problems of missing rows and inaccurate edge localization. In addition, the
# A Survey on Hardware Accelerators for Large Language Models
## 5 ASIC Accelerators
### SpAtten  
In 2021, [PERSON] et al. presented a framework for the acceleration of large language models, called SpAtten [28].  
SpAtten proposed a novel scheme for the acceleration of NLP using three algorithmic optimizations: cascade token pruning, cascade head pruning, and progressive quantization to reduce computation and memory access.  
Pruning is applied to the tokens and heads, and not to weights (as is usually the case). Cascade means that once a token/head is pruned, it is removed in all following layers, so one layer only needs to process the remaining tokens/heads from previous layers. The deeper the layer, the more tokens/heads are pruned.  
The three techniques are input-dependent since the pruned computation and bit-width are adaptive to input instances. Cascade pruning requires sorting token/head importance scores on the fly. The proposed hardware architecture leverages high parallelism in top-k engines for token and head selections, a specialized memory hierarchy, and a fully pipelined data path to improve performance and reduce energy consumption  
The proposed scheme was implemented on a cycle-accurate design using SpinalHDL and mapped to ASIC using a 40 nm TSMC library. SpAtten achieved speedups of 162\(\times\) and 347\(\times\) over a TITAN Xp GPU and Xeon CPU, respectively. In terms of energy efficiency, SpAtten achieved energy savings of 1193\(\times\) and 4059\(\times\) compared to the GPU and CPU.
# A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem
## 4 Experiments and Results
### Exploratory Experiments
#### 4.2.1 Exploratory Experiment I  
In this experiment, the rationale was to test the performance of HHs on similar instances to those they were trained on. Therefore, we produced 30 HHs with an initial cardinality of 12 heuristics each, as in preliminary experiment 2 (Section 4.1.2). Each HH was trained using set \(\alpha_{1}\) for 200 iterations. Afterward, we tested the resulting HHs on set \(\alpha_{2}\) and we compared the data against those obtained by heuristics applied in isolation.  
Table 4 presents the ranking of the four heuristics as well as the best, median, and worst HHs produced in this experiment and tested on set \(\alpha_{2}\). This table also shows the total profit obtained by each of these methods on the same set. Based on the results obtained (both on ranks and total profit), we observe that the process is forcing the HHs to replicate the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Heuristics**} & \multicolumn{4}{c}{**Hyper-Heuristics**} \\ \cline{2-9}
**Rank** & Def & MaxP & MaxPW & MinW & Best-HH & Median-HH & Worst-HH \\ \hline
1.0 & 1 & 100 & 102 & 78 & 1 & 0 & 0 \\
1.5 & 80 & 0 & 9 & 22 & 12 & 20 & 79 \\
2.0 & 4 & 0 & 111 & 0 & 107 & 1 & 4 \\
2.5 & 6 & 0 & 59 & 0 & 64 & 5 & 6 \\
3.0 & 0 & 13 & 20 & 35 & 83 & 65 & 1 \\
3.5 & 9 & 1 & 82 & 4 & 99 & 21 & 10 \\
4.0 & 0 & 27 & 15 & 90 & 29 & 80 & 0 \\
4.5 & 2 & 0 & 2 & 43 & 3 & 46 & 2 \\
5.0 & 0 & 165 & 0 & 28 & 1 & 103 & 0 \\
5.5 & 17 & 0 & 0 & 34 & 0 & 34 & 17 \\
6.0 & 0 & 12 & 0 & 32 & 1 & 6 & 0 \\
6.5 & 281 & 0 & 0 & 16 & 0 & 16 & 281 \\
7.0 & 0 & 82 & 0 & 18 & 0 & 3 & 0 \\ \hline \hline Profit & 241,081 & 343,872 & **467,145** & 376,148 & 436,393 & 361,284 & 241,025 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ranks and total profit obtained by the four heuristics and the best, median, and worst randomly generated hyper-heuristics when tested on set \(\alpha_{2}\). The best result, measured in terms of total profit, is highlighted in bold.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run** & **SP** & **Run** & **SP** & **Run** & **SP** \\ \hline
1 & 162 & 11 & 46 & 21 & 675 \\
2 & 127 & 12 & 51 & 22 & 41 \\
3 & 743 & 13 & 114 & 23 & 30 \\
4 & 39 & 14 & 38 & 24 & 47 \\
5 & 682 & 15 & 18 & 25 & 40 \\
6 & 38 & 16 & 317 & 26 & 28 \\
7 & 68 & 17 & 56 & 27 & 152 \\
8 & 21 & 18 & 146 & 28 & 78 \\
9 & 405 & 19 & 176 & 29 & 37 \\
10 & 64 & 20 & 30 & 30 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stagnation points for the first 30 runs of the EA-based hyper-heuristic model.  
behavior of the best performing heuristic for these types of instances (MaxPW). This also means that the HHs are, most of the time, ignoring the remaining heuristics. Although this may seem like a good choice as MaxPW is the best individual performer, the HHs are sacrificing the opportunity to improve their overall performance and outperform the best individual heuristic.
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 4 Analysis of Segmentation Experiments
### K-Fold  
K-fold cross validation (K-CV) can effectively avoid over-fitting and under-fitting [29]. The data set is randomly divided into K groups to verify the validity of the training model. Each subset of the data set is used as a testing set and the remaining K-1 groups are used as training sets. On the basis of K-CV, we verified the performance of the model by using the mean and variance of PA. K = 3, 5, 7, and 9 are shown in Table 7. With the improvement of PA, the stability of the model would be improved. When K increased to 7, the increase of PA and variance tended to be stable. Here, K was set to 7 in consideration of statistical stability and calculation costs. Therefore, the ratio of the testing set to training set was 1:6, the total data set had 4200 samples, the testing set had 600 samples, and the training set contained 3600 samples.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 3 CU Split Likelihood Modelling Using SVMs
### CU Split Likelihood Modelling
#### 3.1.2 Weighted Support Vector Machines (W-SVMs)  
The empirical observations on the training data collected during the data collection phases in the proposed algorithm (Sections 4.1.1 and 4.2.1) reveal that the dataset is often contaminated by noise. The dynamic nature of the video contents, diversity of texture, and motion complexities of typical video sequences result in certain data points being positioned on the wrong side in the feature space, generating a significant number of outliers. In the standard SVM [43], the classification gets affected when the outliers become support vectors in the decision boundary during the training phase (i.e., outlier sensitivity problem [44; 45]).  
In this context, Weighted Support Vector Machines (W-SVM) is seen as a potential solution which assigns weights to the training samples based on their relative importance [45]. Thus, the effect of less important points is reduced during the training phase. Following a similar approach, the training  
Figure 3: An illustration of a hyperplane that is created using a standard Support Vector Machine (SVM) for a sample dataset. Here, the _green_ coloured hyperplane margin \(M2\) is smaller than margin \(M1\), which corresponds to the hyperplane in colour _blue_.  
dataset utilised in the proposed method is trained with W-SVM using a modified constrained optimisation function given by  
\[\min\frac{1}{2}||w||^{2}+C\cdot W_{ns}\sum_{i=1}^{N_{ns}}\xi_{i}+C\cdot W_{s}\sum _{i=1}^{N_{s}}\zeta_{i}, \tag{6}\]  
where \(y_{i}(w\cdot x_{i}+b)\geq 1-\zeta_{i}\)--where \(\zeta_{i}\geq 0\), \(\forall x_{i}\). Here, \(x_{i}=1,2,3\ldots,N\) is the feature vector of the training set, with \(i\)th sample being represented as \(\{x_{i},y_{i}\}\)--where \(y_{i}\in\{+1(split),-1(non-split)\}\) is the class label. Furthermore, \(w\), \(\zeta\), \(C\), \(W_{ns}\) and \(W_{s}\) are the hyperplane margin, slack variable, and trade-off parameter for hyperplane margin width and misclassification, and weight parameters for CU _non-split_ and _split_ classes, respectively.  
The use of different weights for \(y_{i}\in\{+1(split),-1(non-split)\}\) samples (i.e., \(W_{s}\) and \(W_{ns}\), respectively) reduce the effect of less important data points (such as outliers and noise) when building the hyperplanes during the training phase. Figure 4 depicts an illustration of two hyperplanes generated for two SVM models by applying different weights for _split_ and _non-split_ samples in the training dataset (refer Section 4).
## 3 Results and Discussion
### Validation of the Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Matrix** & **Mycotoxin** & \
\begin{tabular}{c} **Linear Range** \\ **(\(\upmu\)g/L)** \\ \end{tabular} & **Slope** & **Intercept** & \(R^{2}\) &
\begin{tabular}{c} **LLOQ** \\ **(\(\upmu\)g/L)** \\ \end{tabular} & **LLOQ** & **SSE** \\ \hline Solvent & ZEN & 0.1–200 & 76,190 & 11,438 & 0.999 & / & / & / \\  & ZEN-14G & 0.1–200 & 8063 & 639 & 0.999 & / & / & / \\ \hline Plasma & ZEN & 0.5–200 & 57,817 & 22,503 & 0.999 & 0.2 & 0.5 & 76 \\  & ZEN-14G & 0.5–200 & 6322 & 2427 & 0.999 & 0.2 & 0.5 & 79 \\ \hline Liver & ZEN & 1–200 & 43,990 & 43,727 & 0.999 & 0.3 & 1 & 58 \\  & ZEN-14G & 1–200 & 6792 & 8478 & 0.998 & 0.3 & 1 & 84 \\ \hline Kidney & ZEN & 0.1–200 & 60,238 & 11,066 & 0.999 & 0.03 & 0.1 & 79 \\  & ZEN-14G & 0.1–200 & 6828 & 1006 & 0.999 & 0.03 & 0.1 & 85 \\ \hline Stomach & ZEN & 0.2–200 & 39,823 & 52,550 & 0.999 & 0.06 & 0.2 & 52 \\  & ZEN-14G & 0.5–200 & 6567 & 1994 & 0.999 & 0.2 & 0.5 & 31 \\ \hline Small & ZEN & 0.5–200 & 23,466 & 48,872 & 0.999 & 0.2 & 0.5 & 31 \\ intestine & ZEN-14G & 1–200 & 5674 & 13,574 & 0.999 & 0.3 & 1 & 70 \\ \hline Large & ZEN & 0.5–200 & 23,850 & 55,944 & 0.999 & 0.2 & 0.5 & 31 \\ intestine & ZEN-14G & 0.1–200 & 6111 & 8753 & 0.996 & 0.03 & 0.1 & 76 \\ \hline Ovary & ZEN & 0.5–200 & 60,129 & 48,669 & 0.999 & 0.2 & 0.5 & 79 \\  & ZEN-14G & 0.5–200 & 7009 & 2030 & 0.999 & 0.2 & 0.5 & 87 \\ \hline Urine & ZEN & 0.2–200 & 52,337 & 8821 & 0.999 & 0.06 & 0.2 & 69 \\  & ZEN-14G & 0.1–200 & 6865 & 906 & 0.999 & 0.03 & 0.1 & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Linearity, lower limit of detection (LLOD), lower limit of quantification (LLOQ), and matrix effect (SSE) of zearalenone (ZEN) and zearalenone-14-glucoside (ZEN-14G).
# An Integration Model for Flux Density Distribution Formed by a Heliostat
## Appendix A Location Determination
### Meshing Process and Coordinate System Rotation  
The next step is to grid the heliostat and the receiver plane into a 100-by-100 grid, and the center point of each grid, namely the grid point, is used to represent the grid. The unit grid of a heliostat is expressed by \(\Delta\)hg, and the unit grid of a heliostat is expressed by \(\Delta\)ng. Then, we calculated the coordinates of the heliostat and the receiver plane in the coordinate system.  
In the receiver plane coordinate system, the coordinate of the receiver plane is \((x_{r},y_{r},z_{r})_{receiver}\), where \(x_{r}\) and \(y_{r}\) were obtained by meshing the length and width of the receiver plane. Since the receiver plane is flat, the \(z_{r}\) coordinate is always 0.  
In the coordinate system of the heliostat, the coordinate of the heliostat is \((x_{r},y_{r},z_{r})_{heliostat}\), where \(x_{h}\) and \(y_{h}\) were obtained by meshing the length and width of the heliostat. Since the heliostat is spherical, the \(z_{h}\) coordinate is:  
\[z_{h}=\sqrt{4f_{0}^{2}-x_{h}^{2}-y_{h}^{2}} \tag{12}\]  
After completing the meshwork of the heliostat and receiver plane, the next step is to transform the heliostat coordinate system and receiver plane coordinate system into the global coordinate system. The total rotation matrix from the global coordinate system to the heliostat coordinate system is:  
\[AH=\begin{bmatrix}cos\gamma_{n}&-sin\gamma_{n}&0\\ sin\gamma_{n}sin\alpha_{n}&cos\gamma_{n}sin\alpha_{n}&cos\alpha_{n}\\ sin\gamma_{n}cos\alpha_{n}&-cos\gamma_{n}cos\alpha_{n}&sin\alpha_{n}\\ \end{bmatrix} \tag{13}\]  
Thus, the coordinate of the heliostat grid point in the global coordinate system is:  
\[(x_{h},y_{h},z_{h})_{global}=(x_{heliostat},x_{heliostat},x_{heliostat})_{ global}+(x_{h},y_{h},z_{h})_{heliostat}\times AH^{-1} \tag{14}\]  
Similarly, the rotation matrix from the global coordinate system to the receiver plane coordinate system is:  
\[AR=\begin{bmatrix}cos\gamma_{m}&-sin\gamma_{rm}&0\\ sin\gamma_{rm}sin\alpha_{rn}&cos\gamma_{rn}sin\alpha_{rn}&cos\alpha_{rn}\\ sin\gamma_{rm}cos\alpha_{rn}&-cos\gamma_{rm}cos\alpha_{rn}&sin\alpha_{rn}\\ \end{bmatrix} \tag{15}\]  
After gridding the heliostat, we can further determine the coordinates of the heliostat spherical center:  
\[x_{spherical}=x_{heliostat}+2f_{0}(-cos\alpha_{n}sin\gamma_{n}) \tag{16}\]  
\[y_{spherical}=y_{heliostat}+2f_{0}(-cos\alpha_{n}cos\gamma_{n}) \tag{17}\]  
\[z_{spherical}=z_{heliostat}+2f_{0}sin\alpha_{n} \tag{18}\]  
Further determining the normal vector of each point on the heliostat:  
\[u_{lm}=\frac{x_{spherical-x_{h}}}{l_{so}} \tag{19}\]\[v_{lm}=\frac{y_{spherical}-y_{h}}{l_{so}} \tag{14}\] \[w_{ht}=\frac{z_{spherical}-z_{h}}{l_{so}} \tag{15}\]  
where \(l_{so}\) is the distance between the heliostat grid point and the heliostat spherical center, calculated as follows:  
\[l_{so}=sqrt(x_{spherical}-x_{h})^{2}+(y_{spherical-y_{h}})^{2}+(z_{spherical}-z_{h}) ^{2} \tag{16}\]
## 3 Results
### The Medical Outcomes and Related Factors
#### 3.1.2 The Inferential Statistical Outcomes  
The differences in the patients' medical outcomes distribution resulting from the \(X^{2}\) tests are illustrated in Table 2. The results of the univariate analysis showed that the variables that reached statistically significant difference in the distribution of medical outcomes were BMI level (\(p\) = 0.006), source of admission (\(p\) = 0.011), APACHE II scores classification (\(p\) = 0.028), and diabetes (\(p\) = 0.023). When comparing the differences in medical outcomes, a higher percentage of successful weaning was found in patients with a normal BMI level (51.4%), those being transported from a long-term care institution (56.1%), and those with APACHE II score of less than 15 (56.3%). Conversely, 47.1% of the patients with BMI > 30 and 33.3% of the patients received from home resulted in death or were deemed critical. A higher APACHE II score also resulted in a lower percentage of weaning. No statistically significant differences between groups were found regarding gender, age, and other diseases (Table 2).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{6}{c}{**Medical Outcome**} \\ \hline  & \multicolumn{2}{c}{\(n\) (\%)} & & \\ \hline  & **Critical or Deceased** & **RCW** & **Weaning** & **Total** & \\ \hline Gender & & & & 0.486 \\ Female & 30 (29.7) & 26 (25.7) & 45 (44.6) & 101 (100) & \\ Male & 31 (23.0) & 36 (26.7) & 68 (50.4) & 135 (100) & \\ Age & & & & 0.102 \\ \(<\)64 & 10 (20.0) & 11 (22.0) & 29 (58.0) & 50 (100) & \\ 65–74 & 13 (27.7) & 6 (12.8) & 28 (59.6) & 47 (100) & \\ 75–84 & 21 (26.9) & 26 (33.3) & 31 (39.7) & 78 (100) & \\ \(\geq\)85 & 17 (27.9) & 19 (31.1) & 25 (41.0) & 61 (100) & \\ BMI * & & & & 0.006 \\ Under weight & 13 (26.5) & 12 (24.5) & 24 (49.0) & 49 (100) & \\ Normal weight & 17 (15.3) & 37 (33.3) & 57 (51.4) & 11 (100) & \\ Over weight & 15 (36.6) & 8 (19.5) & 18 (43.9) & 41 (100) & \\ Fatty & 16 (47.1) & 5 (14.7) & 13 (38.2) & 34 (100) & \\ Source of admission * & & & & 0.011 \\ Home & 43 (33.3) & 33 (25.6) & 53 (41.1) & 129 (100) & \\ Long-term care institution & 18 (16.8) & 29 (27.1) & 60 (56.1) & 107 (100) & \\ APACHE II scores * & & & & 0.028 \\ \(<\)15 & 11 (17.2) & 17 (26.6) & 36 (56.3) & 64 (100) & \\ \(15\)-24 & 24 (20.9) & 33 (28.7) & 58 (50.4) & 115 (100) & \\ \(\geq\)25 & 21 (41.2) & 12 (23.5) & 18 (35.3) & 51 (100) & \\ Cancer  
Table 3 shows the results from the ordinal logistic regression analysis. The results illustrate that BMI, source of admission, and APACHE II scores can be considered as predictors for medical outcomes. Obese patients were 2.426 times (95% C.I. = 1.106 to 5.318) more likely than normal-weight patients to be critical or deceased (\(p\) = 0.027). Patients who were transported from home were 2.104 times (95% C.I. = 1.257 to 3.523) more so than those from the long-term care institution (\(p\) = 0.005), and patients with APACHE II scores \(\geq\) 25 were 2.640 times (95% C.I. = 1.283 to 5.433) were similarly found to be so (\(p\) = 0.008) compared with patients whose APACHE II score \(<\) 15.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 5 Conclusions  
This paper presented a privacy-preserving scheme for data generated by IoMT devices and stored for further encryption and computation using homomorphic encryption and homomorphic secret sharing. Data transmitted from IoT devices to the base station are protected using a clock synchronization scheme. Timestamps and nonce values prevent replay and delay attacks on the network. Cloud services support edge-based base stations and virtual nodes with extensive computational resources for secure and near device encryption and data computation. Virtual edge nodes operate as multi-party computation nodes combined with homomorphic secret sharing that prevent untrusted cloud services from eavesdropping on circuit operations. A detailed comparative analysis between the proposed scheme and recent research studies are discussed. Future research focus is to secure IoT-edge data communication using a lightweight encryption mechanism.  
Conceptualization and methodology, M.M.S.; software, M.M.S. and U.D.; formal analysis and investigation, M.M.S. and I.K.; writing--original draft preparation, M.M.S., I.K. and U.D.; writing--review and editing, J.H.P., C.L. and M.M.S.; supervision, J.H.P.; funding acquisition, J.H.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Energy Cloud R&D Program (2019M3F2A1073386) through the NRF (National Research Foundation of Korea), both funded by the Ministry of Science and ICT.  
The authors declare no conflict of interest.