esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Overall Performance Comparison
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Metric** & **Algorithm** & **LIVE** & **TID2013** & **CSIQ** & **MD1** & **MD2** & **WIQCD** \\ \hline \multirow{8}{*}{SROCC} & DIIVINE [12] & 0.941 & 0.824 & 0.895 & 0.932 & 0.876 & 0.561 \\  & BLIINDS-II [13] & 0.885 & 0.793 & 0.822 & 0.665 & 0.015 & 0.496 \\  & BRISQUE [15] & 0.933 & 0.805 & 0.794 & 0.933 & 0.878 & 0.605 \\  & ILNIQE [17] & 0.847 & 0.494 & 0.804 & 0.891 & 0.882 & 0.440 \\  & GMLOG [19] & 0.948 & 0.828 & 0.849 & 0.908 & 0.832 & 0.597 \\  & BIQA-SC-I & 0.948 & 0.837 & 0.881 & 0.899 & 0.921 & 0.632 \\  & BIQA-SC-II & 0.954 & 0.844 & 0.910 & 0.962 & 0.951 & **0.667** \\  & BIQA-SC & 0.956 & 0.906 & 0.928 & 0.957 & **0.964** & **0.682** \\ \hline \multirow{8}{*}{PLCC} & DIIVINE [12] & 0.941 & 0.844 & 0.885 & 0.943 & 0.904 & 0.565 \\  & BLIINDS-II [13] & 0.866 & 0.832 & 0.835 & 0.691 & 0.073 & 0.529 \\  & BRISQUE [15] & 0.934 & 0.833 & 0.826 & 0.953 & 0.909 & 0.621 \\  & ILNIQE [17] & 0.620 & 0.509 & 0.724 & 0.834 & 0.852 & 0.491 \\  & GMLOG [19] & 0.952 & 0.849 & 0.857 & 0.933 & 0.866 & 0.612 \\  & BIQA-SC-I & 0.950 & 0.857 & 0.881 & 0.923 & 0.931 & 0.661 \\  & BIQA-SC-II & **0.955** & **0.869** & **0.904** & **0.971** & **0.959** & **0.682** \\  & BIQA-SC & **0.958** & **0.909** & **0.917** & **0.969** & **0.968** & **0.696** \\ \hline \multirow{8}{*}{RMSE} & DIIVINE [12] & 5.510 & 0.675 & 0.125 & 6.452 & 8.097 & 17.685 \\  & BLIINDS-II [13] & 12.91 & 0.710 & 0.149 & 19.15 & 27.08 & 46.566 \\  & BRISQUE [15] & 5.777 & 0.694 & 0.152 & 5.915 & 7.879 & 16.125 \\  & ILNIQE [17] & 10.802 & 0.956 & 0.143 & 8.169 & 8.248 & 17.664 \\  & GMLOG [19] & 4.945 & 0.660 & 0.138 & 6.974 & 9.484 & 16.165 \\  & BIQA-SC-I & 5.073 & 0.641 & 0.125 & 7.478 & 6.9485 & 15.446 \\  & BIQA-SC-II & **4.778** & **0.623** & **0.115** & **4.668** & **5.361** & **15.020** \\  & BIQA-SC & **4.623** & **0.522** & **0.106** & **4.775** & **4.765** & **14.625** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The median of SROCC, PLCC, and RMSE across the 1000 train-test trials on individual databases. The best two performances in terms of SROCC, PLCC, and RMSE are highlighted in boldface. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & **LIVE** & **TID2013** & **CSIQ** & **MD1** & **MD2** & **WIQCD** \\ \hline DIIVINE [12] & 1 & 1 & 1 & 1 & 1 \\ BLIINDS-II [13] & 1 & 1 & 1 & 1 & 1 \\ BRISQUE [15] & 1 & 1 & 1 & 1 & 1 & 1 \\ ILNIQE [17] & 1 & 1 & 1 & 1 & 1 & 1 \\ GMLOG [19] & 1 & 1 & 1 & 1 & 1 & 1 \\ BIQA-SC-I & 1 & 1 & 1 & 1 & 1 & 1 \\ BIQA-SC-II & 1 & 1 & 1 & \(-1\) & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the \(t\)-test between the SROCC results generated by the competing algorithms across the 1000 train-test trials on benchmark databases. The symbols \(1\), \(-1\), and \(0\) indicate that BIQA-SC is statistically superior, inferior, or indistinguishable to the compared algorithm.
## 3 Deep-Learning-Based Address Verification Technique
### Model Evaluation  
Among foreign companies exporting food to Korea, Chinese companies are the most common; therefore, we verify the accuracy of the proposed system with addresses in several countries, including China.  
Here, we tested how well the classification model verified addresses of Chinese companies. The dataset consists of 35,373 address components and it is structured as shown in Table 2. Among these, 28,662 components were used for the training dataset and the remaining 7075 components were used as the test dataset. Note that address components corresponding to the country -level 3 have many duplicate values (since the country-level 3 has the same value, but many different addresses occur below level 4). Therefore, the test dataset includes almost all address components in China and the training dataset is sufficient for model training.  
Table 3 lists the results of evaluating the performance of the model using the confusion matrix method and Table 4 lists the precision, recall, and F1 scores for each class.  
Based on the model evaluation results listed in Tables 3 and 4, the predictive model showed a score of 90% or higher in precision, recall, and F1 scores for each administrative district level, with an overall accuracy of 97.70%. Based on these results, the model is judged to be appropriate and accurate for classifying the address components into the corresponding administrative division level. In this research, the ROC curve of model evaluation is also reported in Figure 6.  
The previous step is the process of checking the accuracy of each administrative district level generated by the classification model for a given address. The classification results are not the final objective of our research. The average accuracy of the results of classifying all addresses by administrative district level was obtained and scored. For example, assuming that the address components are classified into four components (Country, Level 1, Level 2, and Level 3) and each has an accuracy of 99%, 90%, 90%, and 95%, respectively, the average accuracy is \((99+90+90+95)/4=93.5\%\), which is determined to be the address quality accuracy. Depending on the domain or country used, the lower limit of acceptable address quality accuracy may be set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Country & 100.00\% & 100.00\% & 100.00\% \\ Level\_1 & 98.79\% & 99.07\% & 98.93\% \\ Level\_2 & 92.85\% & 98.58\% & 95.63\% \\ Level\_3 & 99.56\% & 92.89\% & 96.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Precision, Recall, and F1-Score.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual** & **Predicted** & **Level\_1** & **Level\_2** & **Level\_3** \\ \hline Country & 1788 & 0 & 0 & 0 \\ Level\_1 & 0 & 1802 & 17 & 0 \\ Level\_2 & 0 & 18 & 1740 & 7 \\ Level\_3 & 0 & 4 & 117 & 1582 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.
###### Abstract  
A Cu/SiO2/Pt structure is usually used to study the resistive memory properties of an electrochemical resistive random access memory. It can be reversibly switched between low- and high-resistance states by using DC voltages in the atmosphere. However, its resistive switching behavior disappears in a vaporless environment because no conducting filaments can be formed within the Cu/SiO2/Pt structure. This study inserted a graphene oxide (GO) layer to fabricate a Cu/GO/SiO2/Pt structure that could be resistively switched in a vaporless environment. The X-ray photoelectron spectra depth profile of the Cu/GO/SiO2/Pt structure showed that oxygen-related groups of the GO film reacted with the Cu electrode. The GO film assisted Cu ionization in a vaporless environment, and Cu ions could migrate in an electrical field to the Pt electrode. Cu conducting filaments were formed and ruptured by different polarity voltages, and the resistance of the Cu/GO/SiO2/Pt structure could be reversibly switched in a vaporless environment. A schematic model was proposed to explain the switching mechanisms in the atmosphere and a vaporless environment.  
graphene oxide; RRAM; vaporless environment +
Footnote †: journal: Applied Physics Letters B
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods  
### Study Area  
The study was conducted in the Pokupsko basin forest complex (lowland deciduous forest, 45\({}^{\circ}\)38\({}^{\prime}\) N, 15\({}^{\circ}\)43\({}^{\prime}\) E, 110 m above sea level) located in Central Croatia, near the city of Jastrebarsko and 35 km southwest of Zagreb. A total of six circular sample plots were set up in an 87-year-old pedunculate oak (_Quercus robur_ L.) forest stand. The pedunculate oak forest is mixed with common hornbeam (_Carpinus betulus_ L.) and black alder (_Almus glutinosa_ (L.) _[PERSON]_), present in a suppressed [8] crown class. The presence of the understory vegetation (_Corylus avellana_ L. and _Crataegus monogyna [PERSON]_) was moderate. The stem density at the plots, for trees with a diameter at breast height (dbh) greater than 10 cm, was 305 stems-ha\({}^{-1}\) (\(\sigma\) = 41). The terrain of the study area was mostly flat with small variations. The last cut was made in 2018, and no further management actions were conducted afterwards; hence, the terrain surface had not been influenced by heavy machinery since 2018. The orthophoto of the study area in the leaf-off and leaf-on condition with the location of the forest plots and data collection schemes for the remote sensing data sources is shown in Figure 1.
# Effect of Cement Content on the Deformation Properties of Cemented Sand and Gravel Material
## 3 Results and Discussion
### Initial Modulus
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cc (kg/m\({}^{3}\))** & **Formula** & **Correlation Coefficient R\({}^{2}\)** & \(E_{\theta}\) & \(n\) \\ \hline
20 & \(E_{i}\) = 65,651(1 + \(\alpha_{3}\)/100)\({}^{0.60}\) & 0.99 & 656.51 & 0.60 \\
40 & \(E_{i}\) = 109,766(1 + \(\alpha_{3}\)/100)\({}^{0.45}\) & 0.99 & 1097.66 & 0.45 \\
60 & \(E_{i}\) = 178,000(1 + \(\alpha_{3}\)/100)\({}^{0.41}\) & 0.99 & 1780.00 & 0.41 \\
80 & \(E_{i}\) = 315,000(1 + \(\alpha_{3}\)/100)\({}^{0.31}\) & 0.99 & 3150.00 & 0.31 \\
100 & \(E_{i}\) = 706,177(1 + \(\alpha_{3}\)/100)\({}^{0.20}\) & 0.98 & 7061.77 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Values of R\({}^{2}\), \(E_{0}\), and \(n\). Figure 7: The relationship between the parameter \(n\) and cement content \(Cc\). In the study, \(E_{0}\) and \(n\) were fitted with the corresponding parameters of CSG material for cement contents of 20, 40, 80, and 100 kg/m\({}^{3}\); we thus get  
\[E_{0}=E_{0}^{\prime}\mathrm{e}^{b^{\prime}(Cc/C_{0})}, \tag{3}\]  
\[n=n_{0}^{\prime}\mathrm{e}^{c^{\prime}(Cc/C_{0})}, \tag{4}\]  
where \(E_{0}^{{}^{\prime}}\) and \(b^{\prime}\) are the fitting coefficients of the relationship between \(E_{0}\) and \(Cc\); \(n_{0}^{\prime}\) and \(c^{\prime}\) are the fitting coefficients of the relationship between \(n\) and \(Cc\); \(Cc_{0}\) is reference cement content with a value of 1 kg/m\({}^{3}\). The curves fitted by Equations (3) and (4) are shown in Figures 6 and 7, respectively. Values of \(\mathrm{R}^{2}\) in these curves that are greater than 0.97 indicate that the calculated results of Equations (3) and (4) can fit the experimental results well. From Equations (2)-(4), we can derive the relationship of the initial modulus of CSG material with the cement content and confining pressure as follows:  
\[E_{\mathrm{i}}=E_{0}^{\prime}\mathrm{e}^{b^{\prime}(Cc/C_{0})}P_{\mathrm{ \textit{a}}}(1+\sigma_{3}/Pa)^{n_{0}^{\prime}\mathrm{e}^{c^{\prime}(Cc/C_{0})}}. \tag{5}\]  
Equation (5) can quantitatively reflect the influence of confining pressure and cement content on the initial modulus \(E_{\mathrm{i}}\): when the cement content \(Cc\) is 0, this equation can be used to describe the relationship between the initial modulus of the rockfill material and confining pressure; with an increase in the cement content, \(E_{\mathrm{i}}\) under a certain confining pressure increases; when the cement content increases to 80 kg/m\({}^{3}\), \(E_{\mathrm{i}}\) is less affected by the confining pressure.
## 3 Datasets for the Attack and Anomaly Detection
### HAI Dataset Analysis
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**File** & **Normal** & **Attack** & **Total** & **Class Balance, \%** & **Features** \\ \hline hai-20.07/train1.csv.gz & 309,600 & 0 & 309,600 & 100/0 & 59 \\ hai-20.07/train2.csv.gz & 240,424 & 776 & 241,200 & 99.7/0.3 & 59 \\ hai-20.07/test1.csv.gz & 280,062 & 11,538 & 291,600 & 96/4 & 59 \\ hai-20.07/test2.csv.gz & 147,011 & 5989 & 51,106 & 96.1/3.9 & 59 \\ hai-21.03/train1.csv.gz & 216,001 & 0 & 216,001 & 100/0 & 79 \\ hai-21.03/train2.csv.gz & 226,801 & 0 & 226,801 & 100/0 & 79 \\ hai-21.03/train3.csv.gz & 478,801 & 0 & 478,801 & 100/0 & 79 \\ hai-21.03/test1.csv.gz & 42,572 & 629 & 43,201 & 98.5/1.5 & 79 \\ hai-21.03/test2.csv.gz & 115,352 & 3449 & 118,801 & 97.1/2.9 & 79 \\ hai-21.03/test3.csv.gz & 106,466 & 1535 & 108,001 & 98.6/1.4 & 79 \\ hai-21.03/test4.csv.gz & 38,444 & 1157 & 39,601 & 97.1/2.9 & 79 \\ hai-21.03/test5.csv.gz & 90,224 & 2177 & 92,401 & 97.6/2.4 & 79 \\ hai-22.04/train1.csv & 93,601 & 0 & 93,601 & 100/0 & 86 \\ hai-22.04/train2.csv & 201,600 & 0 & 201,600 & 100/0 & 86 \\ hai-22.04/train3.csv & 126,000 & 0 & 126,000 & 100/0 & 86 \\ hai-22.04/train4.csv & 86,401 & 0 & 86,401 & 100/0 & 86 \\ hai-22.04/train5.csv & 237,600 & 0 & 237,600 & 100/0 & 86 \\ hai-22.04/train6.csv & 259,200 & 0 & 259,200 & 100/0 & 86 \\ hai-22.04/test1.csv & 85,515 & 885 & 86,400 & 99/1 & 86 \\ hai-22.04/test2.csv & 79,919 & 2881 & 82,800 & 96.5/3.5 & 86 \\ hai-22.04/test3.csv & 58,559 & 3841 & 62,400 & 93.8/6.2 & 86 \\ hai-22.04/test4.csv & 125,177 & 4423 & 129,600 & 96.6/3.4 & 86 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical data on the HAI dataset class balance by version. Figure 3: Features with the highest correlation value with class label.
# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 3 Prediction Models
### One-Dimensional Convolutional Neural Network (CNN)-Based Model
#### 3.2.2 Model Architecture  
The ability of 1D CNNs to learn sequential dependencies makes them suitable for seismic response modeling. One-dimensional CNNs can learn the relationships between the input earthquake motion and the corresponding ground response. We design the model to take the bedrock spectral motion as a sequential input, and let it extract relevant features and patterns contributing to the seismic ground surface spectral response. Through stacks of CNN and pooling layers, the model learns the nonlinear and complex relationships between the input earthquake motion and the resulting ground surface response hierarchically, enabling the accurate prediction of the seismic response.  
Figure 4: Convolutional neural networks (CNNs) with pooling layer.  
Figure 3: Normalized shear modulus reduction curves and damping ratio curves (reproduced from [PERSON] et al. [32]).  
Figure 5 shows our CNN-based model for seismic ground response estimation. The model maps the input sequence, which is the acceleration, velocity, and displacement spectra, to the output sequence, which is the ground surface acceleration spectrum. The model consists of three consecutive sets of 1D-CNN and max-pooling layers. Each convolution layer is configured with the kernel size of 24, 12, and 6, and output channel size of 16, 32, and 16, respectively. The pooling size is all set to 2, which downsamples the sequence by a factor of two. After the last CNN layer, a dense layer is placed to map the output of the CNN layers back to the original sequence length. This structure follows the typical sequence learning of 1D-CNNs, which is also used by [PERSON] et al. [33].
## 5 Simulation Results and Analysis
### Data Rate Performance  
The communication data rate is  
\[R=N_{b}\times N_{s}\times f_{PRF}, \tag{37}\]  
where \(N_{b}\) is the number of bits per symbol, \(N_{s}\) is the number of symbols in one pulse, and \(f_{PRF}\) is the pulse repetition frequency.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 2 Related Work  
Recently, single-image dehazing has attracted widespread attention in the field of computer vision. Due to the unknown global atmospheric light and transmission map, single-image dehazing is an inherently ill-posed problem. Many different methods have been proposed to address the issue. These methods can be roughly divided into prior-based and learning-based methods. The main difference between these two methods is that the prior-based methods mainly utilize prior statistical knowledge and hand-crafted features to process the hazy images, while the learning-based methods can automatically learn from the training set through a neural network and save it in the network's weights.  
Single-image dehazing methods that extensively utilize prior knowledge have emerged. A patch-based dark channel prior (DCP) [11] method proposed by [PERSON] et al. is one of the representative prior methods. Based on the assumption that hazy images may have extremely low intensity in at least one color channel, DCP uses an atmospheric scattering model for haze removal. Pixel-based dehazing methods [37; 38] provide another solution to estimate the transmission map; however, pixel-based dehazing methods may result in insufficient information and an inability to estimate transmission maps. In addition, a method for establishing a linear model based on local prior images was proposed by [PERSON] et al. [18] to restore depth information. Although prior-based methods have achieved good results, the existence of priors is conditional. These hand-crafted priors are only applicable to specific cases and may not be applicable in changing scenarios.  
The human brain is able to quickly distinguish hazy regions in natural images without other information, and convolutional neural networks have been inspired by this to be applied in image dehazing. These learning-based methods demonstrate extremely strong capabilities in dehazing. For example, [PERSON] et al. [31] proposed Dehaze-Net, which is a trainable end-to-end network consisting of four parts: feature extraction; multiscale mapping; local extremum; and nonlinear regression. It is used to estimate the transmission map, and then the output transmission map is restored to a clean image through an atmospheric scattering model. [PERSON] et al. [39] further proposed a multiscale convolutional neural network (MSCNN) for estimating scene transmission maps. [PERSON] et al. [36] proposed an end-to-end feature fusion attention network (FFA-Net) to directly recover clean images, taking into account different weighted information. Due to the difficulty in obtaining paired clean images and hazy images in nature, [PERSON] et al. [40] studied the implementation of image dehazing without training on real clean image sets on the ground. These learning based methods have achieved good performance in dehazing and are more widely used in image dehazing.
## 2 Challenges in the Market Structure
OneNet proposes new, standardized products and services and makes them available through an open, interoperable, scalable, and standardized IT infrastructure. The development of GRIFOn platform (GRId FOrum), for the stakeholders that will be utilized to foster ongoing communication between the project and all other stakeholders, is a crucial component of this project's organizational structure. Regarding the Greek demo, the \"TSO-DSO Flexibility Channel\" (F-Channel) is an online platform that may show how to set up a flexibility market with a variety of common items for TSO-DSO coordination (Figure 4). The coordination module, which will take into account data, and the functionality of IPTO-HEDNO key systems, will develop a flexibility platform, which will provide grid services for the balancing and congestion management challenges. Modules on forecasting and coordination will take into account all the various market participants in Greece who are currently or will be in the near future. Additionally, such modules will include \"prosumers\", \"aggregators\", and their participation in the energy mix and flexibility provision, flexibility providers/storage owners/EV charging station operators, and others (RAE is currently formulates EV regulation and grid storage). Two scenarios will be examined in the Greek demo: (i) the current situation, dry-run testing of forecasting in various timeframes, balancing and congestion management processes, linkage with the present processes in IPTO and HEDNO, and (ii) examining near-term market reform scenarios and business prospects for flexibility services as a result of DERs, prosumers, storage owners, owners of EV charging stations, and new aggregator profiles, as well as extensive scenarios, such as markets in a local area, plans for new investments and interconnections, and reforms in regulation (Figure 5). It is important to note that all of the aforementioned events will occur near the Peloponnees-Crete AC link. The latter presents considerable operational challenges: the HVDC interconnection will be in operation in 2023, while the transmission network of Crete (owned by HEDNO) was linked with the mainland transmission backbone (run by IPTO) in 2021. High-RES penetration is already present in the area and is predicted to increase. Figure 4: F-channel architecture. Platforms, Specifications, and Scenarios for the Pilot Projects for Flexibility Services to the Greek Transmission and Distribution System
## 2 Related Works
### Available Transitional HAR Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Model** &
\begin{tabular}{c} **Learning** \\ **Algorithm** \\ \end{tabular} & **Accuracy (\%)** & **Dataset** & \multicolumn{1}{c}{**Weaknesses**} \\ \hline
2016 & TAHAR [31] & ML & 96.44 & HAPT &
\begin{tabular}{c} An SVM with a heuristic filtering approach needs a feature mapping \\ layer to achieved with a high accuracy. \\ \end{tabular} \\ \hline
2018 & SVM-TED [34] & ML & 81.62 & own dataset &
\begin{tabular}{c} A conventional SVM with a transition occurrence detection module \\ can detect postural transitions but lacks the precision essential for \\ effective movement recognition. \\ \end{tabular} \\ \hline
2018 & DBN [35] & DL & 95.80 & HAPT &
\begin{tabular}{c} DBN causes it more challenging to train the network design, and [PERSON] \\ has substituted it, a more suitable lesson for the vanishing \\ gradient issue. \\ \end{tabular} \\ \hline
2018 & GBDT [36] & ML & 94.90 & HAPT &
\begin{tabular}{c} Provides the most excellent outcomes with smaller datasets, \\ but accuracy declines as the size of the dataset increases. \\ \end{tabular} \\ \hline
2020 & SVM + SFFS [37] & ML & 96.81 & HAPT &
\begin{tabular}{c} Increased accuracy with fewer datasets and increased data volume \\ results in decreased accuracy. \\ \end{tabular} \\ \hline
2020 & STD-TA [38] & ML & 80.0 & own dataset &
\begin{tabular}{c} A standard SVM with moderate accuracy extracts statistical information to \\ distinguish between transitional and fundamental actions. \\ \end{tabular} \\ \hline
2020 & Multi-LSTM [39] & DL & 89.00 & own dataset &
\begin{tabular}{c} Multiple pipelined LSTM units are employed in this strategy, which \\ slows the network’s training and increases the model’s sophistication. \\ Any flaw or inconsistency in a single LSTM unit affects the pipeline of \\ LSTM units as a whole. \\ \end{tabular} \\ \hline
2020 & CNN+LSTM [38] & DL & 95.80 & HAPT &
\begin{tabular}{c} The model is complicated, and the CNN utilized is a standard CNN with \\ a simple three-layered construction that is not enhanced. No \\ consideration was given to complex tasks and their transitions. \\ \end{tabular} \\ \hline
2021 & Multiple DL + Fusion & DL & 96.11 & HAPT &
\begin{tabular}{c} The model contains many hyperparameters and spend lots of \\ computation time. \\ \end{tabular} \\ \hline
2021 & HiHAR [40] & DL & 97.98 & HAPT &
\begin{tabular}{c} The transitional activities were grouped into two classes to avoid \\ the class imbalanced problem. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the related literature.