esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Type** & **P.** & \(k\)**NN** & \multicolumn{2}{c}{**DTC**} & \multicolumn{2}{c}{**RFC**} & \multicolumn{2}{c}{**ETC**} & \multicolumn{2}{c}{**XGBC**} \\  & & **noF** & **Val.** & **noF** & **Val.** & **noF** & **Val.** & **noF** & **Val.** & **noF** & **Val.** \\ \hline RFE [4] & — & 13 & 0.5772 & 13 & **0.7088** & 13 & 0.5717 & 13 & 0.5437 & 13 & 0.5433 \\ \hline GA & 0.3 & 10 & 0.6259 & 25 & 0.5920 & 16 & 0.6435 & 18 & **0.6248** & 14 & 0.6057 \\  & 0.5 & 11 & 0.5540 & 25 & 0.6142 & 9 & 0.6281 & 12 & 0.6033 & 9 & **0.6417** \\  & 0.7 & 8 & 0.5994 & 12 & 0.6049 & 7 & 0.6287 & 9 & 0.5715 & 9 & 0.6404 \\ \hline GA with VarPenalty & 0.3 & 10 & 0.6271 & 25 & 0.6384 & 19 & **0.6505** & 37 & 0.5456 & 34 & 0.6367 \\  & 0.5 & 9 & 0.6273 & 18 & 0.6313 & 9 & 0.6491 & 13 & 0.6168 & 13 & 0.6329 \\  & 0.7 & 4 & **0.6612** & 15 & 0.6747 & 7 & 0.5853 & 11 & 0.5951 & 9 & 0.6214 \\ \hline DE & — & 14 & 0.5148 & 33 & 0.5238 & 72 & 0.5618 & 41 & 0.5630 & 14 & 0.5495 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of feature selection methods for each classifier based on validation results using the F1-score metric.
## 3 Experiment and Analysis  
### Experimental Environment  
An experiment was conducted using a single Nvidia GeForce RTX 3090 24G (GPU) and an Intel(R) Xeon(R) Gold 6248R CPU @ 3.00 GHz (CPU). The training and testing were all implemented on a Windows 10 system. The network model was constructed using the PyTorch1.9 deep learning framework [29], which is widely utilized in the field. PyTorch, an open-source machine learning framework that enjoys extensive adoption, provides a diverse range of pre-trained models and libraries, enabling time and computational resource savings.
## 3 Analysis of Factors Affecting Water Inflow
### Depth of the Tunnel  
Figure 6 illustrates the correlation between the water inflow of the main tunnel and the tunnel burial depth, which is defined as the depth from the seabed to the center of the tunnel. The graph presents this relationship for various rock permeability coefficients. There is a discernible pattern in the water inflow of the main tunnel as the depth of tunnel burial increases. Initially, there is a decrease in water inflow, followed by a subsequent increase. It can be found that there is a critical burial depth \(\mathbf{h_{c}}\), which minimizes the water inflow in the tunnel. Furthermore, the figure indicates that variations in the permeability coefficient of the surrounding rock have no significant impact on the critical burial depth. In other words, the critical burial depth of the tunnel appears to be independent of the permeability coefficient of the surrounding rock.
## 2 Role of AI in Dentistry
### Digital-Dental Perceptions  
The primary use of AI in dental restorations is to create digital impressions of the teeth, gums, and other soft and hard tissues of the patient. Dental prostheses can be designed and manufactured using computer-aided manufacturing (CAM) from three-dimensional data that can be read and communicated. High-precision dental restorations are made using CAD/CAM-based technology. AI is also being used to design and manufacture dental implants and other dental prosthetics. Some contemporary research uses robots with reasonable intelligence and accuracy in the preparation of teeth. Shortening manufacturing time and decreasing errors are two of the benefits of these systems.
## 3 Results and Discussion
### Factors Affecting the Bacterial Capture  
Since PEI-MNPs binds to bacteria mainly through electrostatic interactions, the pH and ionic strength of the sample solution have significant effects on the combination of PEI-MNPs with bacteria. The PEI-MNPs exhibited a CE higher than 70% for _L. monocytogenes_ in the pH range of 4 to 10 (Figure 3c). The decrease in CE at pH 9 and 10 was due to the weakening of electrostatic interactions by negatively charged PEI-MNPs, where the binding of PEI-MNPs to _L. monocytogenes_ was mainly driven by hydrogen bonding or hydrophobic interaction between functional groups [32]. In terms of ionic strength, PEI-MNPs still had a high CE (over 95%) for _L. monocytogenes_ when the concentration of NaCl was between 100 mM and 400 mM (Figure 3d). When the concentration of NaCl reached 500 mM, the CE of _L. monocytogenes_ by PEI-MNPs decreased due to the charge neutralization caused by excess NaCl but remained above 74% (Figure 3d). Considering the salt concentration in food samples and the dilution times during sample pretreatment, the above results indicated that the ionic strength has little influence on the separation of bacteria in food samples by PEI-MNPs. These results revealed that PEI-MNPs could capture _L. monocytogenes_ at a wide pH range and ionic strength and achieved a relatively ideal CE (more than 70%).
# Classification of EEG Signals for Prediction of Epileptic Seizures
## 5 Feature Extraction  
Statistical moments have been extracted from all 23 channels as handcrafted features and include mean [77], standard deviation, and skewness [78], and are given by Equations (4)-(6), respectively.  
\[\beta=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{3} \tag{4}\]  
\[\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{2}} \tag{5}\]  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}(x_{i}) \tag{6}\]  
where \(x_{i}\) denotes the selected window of the EEG signal, and the total number of samples is given by \(N\). After extraction of these handcrafted features, a custom CNN architecture was also used for feature extraction. CNNs are widely used for automated extraction of features and to classify time series and image data. Typical CNNs have multiple convolutional layers with different numbers of filters. Afterwards, the size of the layer is reduced by using a pooling layer that is then fed to fully connected layers used for classification. The last layer of such systems has neurons equal to the number of classes. Weight updating was as follows:  
\[\Delta W_{l}(t+1)=-\frac{x\lambda}{r}W_{l}-\frac{x}{n}(\frac{\partial C}{ \partial W_{l}})+m\Delta W_{l}(t)) \tag{7}\]  
\[\Delta B_{l}(t+1)=-\frac{x}{n}(\frac{\partial C}{\partial B_{l}})+m\Delta B_{ l}(t). \tag{8}\]  
Weight values are denoted by \(W\), \(l\) represents the layer number, bias is denoted by \(B\), and \(x\) and \(m\) are regularization parameters. After convolution, an activation function is used; some of the commonly used activation functions are rectified linear unit, sigmoid activation function, and the softmax activation function, and is computed using following equations.  
\[y=\frac{1}{1+e+^{-x}} \tag{9}\]\[\sigma(z)=\frac{e^{z}}{\sum_{i=1}^{k}e^{zj}} \tag{10}\]  
\[f(x)=max(0,x). \tag{11}\]  
A custom, three-layered CNN was proposed to extract machine-learned features. The first convolutional layer consists of 16 filters of \(3\times 3\), the second layer has 32 filters of \(3\times 3\), and the final convolutional layer is comprised of 64 filters of \(3\times 3\). All these layers are followed by ReLU activation function and batch normalization. A flattened layer is then applied to get the machine-learned features to a size of 7192. Figure 4 presents the proposed architecture of the CNN, which consists of three layers of convolution, and the number of parameters to be trained in each layer is listed in Table 2.
###### Abstract  
To address the limitations of poor environmental adaptability, unsatisfactory bit-error performance, and high complexity of conventional antenna selection algorithms applied to a multi-array visible-light optical generalized spatial multiplexing-multiple-input multiple-output (OGSMP-MIMO) system, an OGSMP-MIMO system based on Pearson coefficient antenna selection is proposed. The algorithm adopts the correlation of Pearson coefficients among photoelectric detector (PD) terminals at different positions and active transmit antennas to select the optimal antenna combination without relying on the accuracy of channel estimation, for realizing the multiplexing of the time and space domains, and to improve the bit-error performance. Finally, experiments were conducted to verify the feasibility of the antenna selection algorithm, based on the Pearson coefficients. The results indicated that when the bit-error rate reached \(10^{-6}\), using the antenna selection algorithm based on the Pearson coefficient, the signal-to-noise ratio was improved by 2.7 dB and 3.7 dB when compared with the norm-based antenna and random selection algorithms, respectively. In addition, increasing the number of active transmitting antennas can improve the transmission rate; however, the bit-error performance will be compromised. In the same modulation mode, increasing the number of transmitting antennas will reduce the bit-error performance.  
O 2024, 11, 67. [[https://doi.org/10.3390/photonics11010067](https://doi.org/10.3390/photonics11010067)]([https://doi.org/10.3390/photonics11010067](https://doi.org/10.3390/photonics11010067))
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 4 Experimental Setup and Datasets
### Statistical Tests
#### 4.4.1 Friedman Test  
Parametric tests require validation of assumptions regarding data distributions while non-parametric tests are distribution-free [11]. Friedman test is a non-parametric counterpart of ANOVA (Analysis of Variance). Since the assumptions of parametric tests cannot be guaranteed on our datasets, Friedman Test is used in this study. The purpose of performing the Friedman test is to analyze that whether there are any significant differences in the performance of different deep learning methods in ALSC. Friedman test is applied for testing the following statistical hypothesis.  
**Hypothesis 1 (H1).**_The performance of deep learning methods is not significantly different with respect to of Accuracy, Macro-F1 score and Training Time, i.e., all deep learning methods perform alike in terms of these evaluation metrics._  
vs.  
**Hypothesis 2 (H2).**_At least two of the investigated deep learning methods have significant differences in their performance with respect to Accuracy, Macro-F1 score, and Training Time._  
Friedman test is explained with the help of Equations (6) and (7). Let '\(m\)' be the number of deep learning methods and '\(n\)' be the number of datasets, then the test statistic of the Friedman test is calculated as:  
\[F_{f}=\frac{(n-1)\chi_{f}^{2}}{n(m-1)-\chi_{f}^{2}} \tag{6}\]  
\[\text{where}\ \chi_{f}^{2}=\frac{12n}{m(m+1)}[\sum R_{j}^{2}-\frac{m(m+1)^{2}}{4}] \tag{7}\]  
\(F_{f}\) follows the F-distribution with \((m-1)\) and \((m-1)(n-1)\) degrees of freedom with critical value as available in F- distribution table [54]. In case the value of \(F_{f}\) is more than the critical value, the null hypothesis is rejected leading to the conclusion that the performance of at least two deep learning methods is significantly different.
## 1 Introduction
* Although it is able to accurately track the nonlinear behavior of the converter, the model provides modularity and allows for the application of (well-known and highly experienced) linear control theories at a low computational cost [26]. * Unlike impedance or transfer function models, the developed model explicitly integrates as state variables (impedance or transfer function models only deal with input-output relationships) the main electrical variables associated with the converter's non-idealities. This allows for the effect and impact of non-idealities on the dynamic response of the converter to be evaluated individually. It is general and flexible, and can easily be adapted to consider variations in the values of the converter's non-idealities (linear or non-linear), such as the variation in the magnetizing inductance, the dependence of certain parameters on temperature, etc. Finally, the impedance or transfer function models are lineal all the time, from the initial condition to steady state, if this is reached. Moreover, this is the only time information that they contain. This is not the case with the developed model, which constantly adapts (at each sampling period) its operation to accurately follow the nonlinear behavior of the real converter, while also providing explicit time information all the time. To highlight the novelty of this research, Table 1 summarizes its main characteristics in comparison with the analyzed literature. The rest of this article is organized as follows. Section 2 studies the operating modes of the converter and develops its dynamic equations to obtain the average and small-signal model expressions. In Section 3, an experimental validation of the developed model is carried out, together with a comparison with the most complete models (used as a reference) found in the literature. The results are discussed in Section 4. Finally, Section 5 concludes the paper by discussing future work.
# Reality Check: Experimental Studies on Microplastics Lack Realism
## 2 Shape (Spheres vs. Fibers)
### Aquatic Effects
Lower concentrations of MF had more severe effects on the amphiopod _G. fossarum_ than higher concentrations of MP particles. [PERSON] et al. (2017) reported that elevated concentrations of PE particles decreased the mobility of _Daphnia magna_, while irregular shaped fragments (10-75 um) had greater effects than beads (10-106 um) [14]. _D. magna_ egested regular-shaped PE faster than irregular ones, indicating that \"spiky\" particles are retained longer and have more severe effects. Polyethyleneerephthalate granular particles (_p_-PET, approximately 150 um diam) and fibers (_f_-PET, approximately 3-5 mm L and 20 um diam) were compared for effects on development of zebrafish (_Danio rerio_) embryos and their joint effects with cadmium [15]. Both types of MP accelerated blood flow and heart rate and inhibited hatching. Both forms decreased the toxicity of Cd. The detoxification effect of _f_-PET was greater than that of _p_-PET. [PERSON] et al. (2021) found that MP fibers, but not spheres, reduced photosynthesis of algal symbionts of _Acropora_ sp. corals, with a 41% decrease in photochemical efficiency after 12 days [16]. Grass shrimp (_Palaemontetes pugio_) were exposed to either sediment, polyethylene spheres, polypropylene fragments, tire fragments, polyester fibers, or clean-water for 96 h at a nominal concentration of 50,000 particles/L before a bacterial challenge with _V. campbellii_[17]. Mortality was not observed in any of the exposures, and survival following the bacterial challenge was similar among shrimp exposed to particle-free water, sediment, polypropylene fragments, polyethylene spheres, tire fragments, and polyester fibers. The grass shrimp cleared most of the ingested particles and all of the ventilated particles within 48 h.
## 3 Improved LEACH Protocol
### Parallel Transmission Based on Scheduling for Cluster Head Nodes and Sensor Nodes
#### 3.3.2 Scheduling for Sensor Nodes
A data conflict is induced at the \(i\)th cluster head node if the completion time of data reception from the \(j\)th sensor node within the same \(i\)th cluster exceeds the arrival time of the data transmitted by the \(l\)th sensor node from the \(n\)th cluster. Correspondingly, a data conflict is also induced if the completion of data reception from the \(l\)th sensor node in the \(n\)th cluster is delayed beyond the arrival of data from the \(j\)th sensor node in the \(i\)th cluster at the respective cluster head node. Then, \(t_{j}^{i}\) that will cause conflict with the transmission of the \(l\)th sensor node in the \(n\)th adjacent cluster at the \(i\)th cluster head node satisfies the following inequalities:  
\[t_{j}^{i}+d_{ij}^{i}+t_{guard}+t^{p}\geq t_{l}^{n}+d_{il}^{n}\text{ and }t_{j}^{i}+d_{ij}^{i}\leq t_{l}^{n}+d_{il}^{n}+t_{guard}+t^{p} \tag{19}\]  
So, \(t_{j}^{i}\) that will cause conflicts with sensor nodes in adjacent clusters at the \(i\)th cluster head node belongs to the set of:  
\[U_{i,j}^{iteh}=\mathop{\cup}\limits_{n=1}^{N}\mathop{\cup}\limits_{l=1}^{J_{ n}}\left[t_{l}^{n}+d_{il}^{n}-d_{ij}^{i}-t_{guard}-t^{p},t_{l}^{n}+d_{il}^{n}-d_{ ij}^{i}+t_{guard}+t^{p}\;\right] \tag{20}\]  
where \(J_{n}\) is the number of sensor nodes that will cause conflicts at the \(i\)th cluster head nodes in the \(n\)th cluster. Let \(\overline{U}_{i,j}=\left(U_{i,j}^{ita}\cup U_{i,j}^{iten}\cup U_{i,j}^{iteh}\right)\); then, the conflict-free transmission time of the \(j\)th node in the \(i\)th cluster can be given by  
\[t_{j}^{i}=\mathop{\underset{t_{j}\in\overline{U}_{i,j}}{\min}}_{t_{j}\subset \overline{U}_{i,j}}\left(max\left(t_{j}^{i},t_{k}\right)-min\left(t_{j}^{i},t_ {k}\right)\right) \tag{21}\]  
where \(t_{k}\) denotes the transmission time of scheduled nodes in the network. Finally, all transmission times are aligned to avoid negatives when the scheduling of all sensor nodes is complemented as:  
\[t_{k}^{\prime}=t_{k}-min(t_{k}) \tag{22}\]  
The proposed scheduling for sensor nodes consists of the following steps:  
1. Schedule the transmission times of all sensor nodes in the first cluster. 2. Find out the nodes in the \(i\)th cluster that will interfere with the reception of the first to the (\(i-1\))th cluster head nodes and the nodes in the first to the (\(i-1\))th clusters that will interfere with the reception of the \(i\)th cluster head node according to their locations. 3. Obtain the transmission schedules of the first to the (\(i-1\))th clusters. 4. For the \(j\)th node in the \(i\)th cluster, calculate its transmission time that does not cause conflicts at the \(i\)th and the scheduled adjacent cluster head nodes while minimizing the total transmission time. 5. Repeat step 4 until all the transmission times of the nodes in the \(i\)th cluster are scheduled. Then, schedule the transmission time of the next cluster. 6. Repeat steps 2-5 until all the nodes in the network are scheduled.
## 3 Result and Discussion Section
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Data Sources** & **H (/m)** & **D-Value (/m)** & **D (/m)** & **D-Value (/m)** & **Crown Diameter (/m)** & **D-Value (/m)** \\ \hline \multirow{2}{*}{9} & Manual measurement & 16.46 & 0 & 0.331 & 0 & 8.54 & 0 \\  & SLAM & 16.39 & 0.09 & 0.337 & 0.005 & 8.65 & 0.11 \\ \hline \multirow{2}{*}{13} & Manual measurement & 7.81 & 0 & 0.174 & 0 & 5.63 & 0 \\  & SLAM & 7.87 & 0.06 & 0.175 & 0.001 & 5.80 & 0.17 \\ \hline \multirow{2}{*}{15} & Manual measurement & 7.63 & 0 & 0.299 & 0 & 6.22 & 0 \\  & SLAM & 7.71 & 0.08 & 0.304 & 0.005 & 6.15 & \(-\)0.07 \\ \hline \multirow{2}{*}{23} & Manual measurement & 5.61 & 0 & 0.149 & 0 & 6.05 & 0.02 \\  & SLAM & 5.64 & 0.03 & 0.145 & 0.04 & 6.02 & 0.009 \\ \hline \multirow{2}{*}{36} & Manual measurement & 15.93 & 0 & 0.332 & 0 & 12.54 & 0 \\  & SLAM & 16.01 & 0.08 & 0.335 & 0.003 & 12.50 & \(-\)0.04 \\ \hline \hline \end{tabular}  
Notes: D: DBH of trees, H: Tree height.  
\end{table} 
Table 2: Comparison of different parameters extracted by different methods from the same wood. Figure 17: Single wood information diagram of different methods. (**(a)** shows laser slam single wood point clouds; **(b)** shows manual measurement diagram). dominant tree species in the \"Guideline for Measurement and Monitoring of Carbon Sink in Afforestation Projects\", the allometric growth equation was used to calculate the above-ground and underground biomass of camphor and cypress (refers to different equations for camphor and cypress calculation shown in Tables 3 and 4). After the completion of single wood segmentation, the biomass of a single tree was calculated by the equation of allometric growth equation of the dominant species of camphor and cypress, and the carbon stock was derived from the biomass. The conversion of biomass into carbon storage was calculated according to the proportion of carbon in the dry-weight organic matter of trees. The conversion rate varies among different tree species, but the overall
## 3 Current State of Object Detection Algorithms
### Region-Based CNN  
The original R-CNN model [22] was introduced in 2014, and it has been a crucial development in object detection systems, providing higher accuracy than other approaches. The R-CNN model proposes several RoIs in an image and performs convolution on these regions, which is a departure from the CNN approach of handling multiple regions. R-CNN uses a heuristic search technique to identify congested locations and the convolutional network receives these scaled regions of interest and generates a feature matrix. The presence of an object is then assessed via SVM using this feature matrix. In the final stage, the offset region is estimated to fine-tune and correct the position of the bounding box.  
The main drawback of the R-CNN model is its time-consuming nature. Learning a neural network can be very time consuming, as each image may require the classification  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Year** & **Enhancement Techniques** & **Model** & **Image Resolution** & **Inference Speed [FPS]** & **mAP@** **[\%]** & **mAP@** **[0.50.95]** [\%]** \\ \hline [PERSON] & 2022 & ESRGAN for image quality improvement & EfficientDet & – & 39.00 & 26.00 \\ \cline{3-6}  & & & YOLOv5 & – & 46.00 & 32.00 \\ \hline [PERSON] & 2022 & DA: flipping, 90\({}^{\circ}\) rotation, 20\% crop & YOLOv5 & – & – & 93.70 & – \\ \hline [PERSON] & 2022 & & EfficientDet & – & 63.00 & – \\ \cline{3-6}  & & DA: flipping, warping, blurring, zooming & YOLOv5x & – & – & 69.00 & – \\ \cline{3-6}  & & YOLOv5l & – & – & 74.00 & – \\ \hline [PERSON] et al. [7] & 2023 & Multi-scale detection network proposed as a combination of SPP and FPN with & YOLOv3 & – & – & 72.70 & – \\ \hline  & & CSPDarknet53-tiny (SPFPN) & & – & 74.80 & – \\ \cline{3-6}  & & DA: gamma regulation, flipping and scaling & SPFPN- & & & \\ \cline{3-6}  & & YOLOv4 & – & – & 79.60 & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._of up to 2000 distinct regions of interest, making real-time recognition virtually impossible. Additionally, the algorithm for selective search does not undergo any form of learning and is, hence, immutable, not learning to provide more appropriate options over time. This section will further provide an overview of the successors of R-CNN.
# Evaluation of Manual Skill Degradation Due to Automation in Apparel Manufacturing
### Focus of Study  
There is a research gap in apparel manufacturing related to the effects of highly automated sewing machines on operators' performance when exposed to manual machines. Therefore, the present study focuses on examining the effects of automation exposure on the retention of manual skills of apparel sewing operators. In addition, this study aims to provide an outlook on how much compensation efforts are needed to recover the depreciated manual skills and how refresher training drills help retain manual skills. Sewing machine operators were randomly divided into three groups: a manual machine group (MMG), a refresher training group (RTG), and an automation group (AG). Based on the same initial training and complex skill development among randomly divided sewing operators, it was hypothesized that  
**Hypothesis 1 (H1)**.: _There is no significant difference between the manual skill levels of sewing operators after initial training._  
The skill retention period is positively related to the amount of skill loss [21; 30]. Therefore, as the retention interval increases, the skill loss increases. This leads to the development of a second hypothesis that  
**Hypothesis 2 (H2)**.: _The use of automation for an extended period will result in more skill degradation in the automation group than the manual machine and refresher training group._  
A mix of machines or machine cells with both manual and automated machines allows the operator to practice his manual skills from time to time and helps the operator retain the skill set for a more extended period. This working style leads to the development of the third hypothesis that  
**Hypothesis 3 (H3).**_Refresher training after suitable intervals helps in minimizing the manual skill loss._