esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 1 Introduction
algorithm and the patches of the test images from class-specific dictionaries are recreated to match the test data to the corresponding class. Despite the great progress in human sperm classification tasks, these approaches require the features of sperm heads to be extracted manually and then fed into the classifier for training purposes, making it extremely difficult to classify the cells end-to-end. In the past few years, the emergence of deep learning has dramatically boosted the performance of state-of-art techniques in computer vision tasks [15; 16; 17; 18; 19; 20]. In terms of automatic image classification, deep convolutional neural networks (CNNs) have demonstrated higher classification accuracy than traditional machine learning algorithms. More importantly, the CNNs are able to process raw data without extracting data features manually. In 2019, [PERSON] et al. applied a CNN-based method to the sperm cell classification task for the first time and achieved an average accuracy of 94.1% on the HuSHeM dataset and an average accuracy of 62% on the SCIAN dataset [21]. In their study, the original VGG16 network [22] was optimized for the sperm head classification task. Although the classification results are improved, the high complexity of the network structure requires large computational resources that are not available in typical IVF clinics. In this paper, we propose to use a different deep learning approach to classify sperm heads automatically. Instead of building the deep CNN from scratch, we modify the original AlexNet [23] by adding the Batch Normalization layers for sperm head classification for the HuSHeM dataset. Simultaneously, the pre-training parameters obtained from the training of the ImageNet dataset [24] for the feature extraction are adopted to reduce computational and time costs. The experiment results indicate our approach outperforms the state-of-the-art in terms of average classification accuracy (96.0% vs. 94.0%), average precision (96.4% vs. 94.7%), average recall (96.1% vs. 94.1%) as well as average F-score (96.0% vs. 94.1%) on the HuSHeM dataset. Moreover, compared with the VGG16-based approach presented in [21], the pre-training part our method does not require any fine tuning, and the number of parameters in the feature extraction part is less than one-sixth of those using VGG16. Accordingly, our method is relatively computer-resource-saving and has a low computational cost. The rest of the paper is organized as follows. The model and the dataset used to test the algorithm are introduced in Section 2. Section 3 presents the experimental results, the ablation analysis of the classifier, as well as the influence of data preprocessing on the performance of the model. Finally, Section 4 concludes this work.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 2 Attention Mechanism
### Temporal Attention  
[PERSON] [13] was the first person to propose a video description model based on a temporal attention mechanism. In a video, each frame contains information at a different time, so the visual feature of each frame is a relatively complete semantic information expression. It expresses global time-seriesinformation, such as the order in which objects, actions, scenes, and characters appear in a video. This kind of information may exist throughout a video. A good video description model should focus on important moments in a video sequence. With the help of temporal attention mechanism, we can decide the importance of different frames.
## 3 Results and Discussion
### Reproducibility and Stability of the Electrochemical Aptasensor  
For reproducibility testing, all the experiments were repeated for 20 independent studies detecting three different concentrations of GA (10 mg mL\({}^{-1}\), 1 mg mL\({}^{-1}\) and 0 mg mL\({}^{-1}\)). The relative standard deviations (RSDs) obtained were 2.5%, 2.1% and 1.6%, respectively. The RSD is lower than the acceptable value (less than 10%) [46] and reported RSDs (6.5% and 9.88%) of other aptasensors [12; 14]. This result indicates that the aptasensor can generate highly reproducible electrochemical signals. Moreover, consistency of aptamer immobilization on the electrode surface was observed. The simple layer-by-layer fabrication process enhances the reproducibility of the aptasensor.  
The stability of the proposed aptasensor was investigated by using SWV current responses. The developed aptasensor was kept in the refrigerator at 4 \({}^{\circ}\)C and used for the determination of 500 \(\upmu\)g mL\({}^{-1}\) GA protein. After storage for 7, 15 and 30 days, the aptasensor retained 97%, 92% and 84% of its original response current, respectively (Figure 6B). The proposed aptasensor showed desirable stability towards GA detection. The stability was enhanced compared to that observed with another biosensor [14]. The improved stability and reproducibility of the aptasensor intensify the utility of the aptasensor.
###### Abstract  
This study investigated the biomechanical effects of varying buccolingual beam widths in maxillary first premolar extraction spaces on canine distal bodily movement during clear aligner treatment. Using finite element analysis, four distinct models were constructed, incorporating beam designs with widths of 0 (edententulous), 1, 2, and 3 mm within the extraction space. Each model underwent a comprehensive 50-stage iterative simulation to evaluate canine displacement patterns, tipping, rotational movements, aligner deformation characteristics, and magnitudes of forces and moments applied to the amines throughout long-term tooth movement. Group 0 (no beam, 0 mm beam width) exhibited the greatest crown displacement and tipping angle. In contrast, Group 2 (2 mm beam width) most effectively reduced the final angulation of the canine, whereas Group 3 (3 mm beam width) was most effective in controlling unwanted rotation and minimizing deformation of the clear aligner. Furthermore, an increase in the beam width was associated with a trend of higher initial force and lower initial moment. Notably, relatively high levels of both force and moment were maintained during the later stages of the simulation, which is advantageous for sustained control of tooth movement. In conclusion, incorporating a beam of 2-3 mm width into the maxillary first premolar extraction space appears to be optimal for managing canine tipping and rotation while promoting bodily movement of the tooth.  
finite element analysis; clear aligner therapy; canine distalization; buccolingual beam width; aligner design optimization Article
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 2 Related Works
### Preliminaries
#### 2.2.1 Bilinear Map  
Pairing is a bilinear map defined for a subgroup of elliptic curves. Assume that \(G_{1}\) and \(G_{2}\) are two multiplicative circular elliptic curve subgroups of the same prime order \(p\). A mapping \(e:G_{1}\times G_{1}\to G_{2}\) is a bilinear map if it satisfies the following [29]:  
1. Bilinearity: For all\(u,v\in G_{1}\), and \(x,y\in Z_{q}^{*}\), we have \(e(u^{x},v^{y})=e(u,v^{xy})\).
2. Non-degeneracy: If \(u\) and \(v\) are generators of\(G_{1},e(u,v)\
eq 1\).
3. Computability: there is an efficient algorithm to compute \(e(u,v)\) for any\(u,v\in G_{1}\).
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
###### Abstract  
To address the issue of low accuracy in insulator object detection within power systems due to a scarcity of image sample data, this paper proposes a method for identifying insulator objects based on improved few-shot object detection through feature reweighting. The approach utilizes a meta-feature transfer model in conjunction with the improved YOLOv5 network to realize insulator recognition under conditions of few-shot. Firstly, the feature extraction module of the model incorporates an improved self-calibrated feature extraction network to extract feature information from multi-scale insulators. Secondly, the reweighting module integrates the SKNet attention mechanism to facilitate precise segmentation of the mask. Finally, the multi-stage non-maximum suppression algorithm is designed in the prediction layer, and the penalty function about confidence is set. The results of multiple prediction boxes are retained to reduce the occurrence of false detection and missing detection. For the poor detection results due to a low diversity of sample space, the transfer learning strategy is applied in the training to transfer the entire trained model to the detection of insulator targets. The experimental results show that the insulator detection mAP reaches 29.6%, 36.0%, and 48.3% at 5-shot, 10-shot, and 30-shot settings, respectively. These findings serve as evidence of improved accuracy levels of the insulator image detection under the condition of few shots. Furthermore, the proposed method enables the recognition of insulators under challenging conditions such as defects, occlusion, and other special circumstances.  
Keywords:insulator; few-shot object detection; feature extraction network; attention mechanism; non-maximum suppression algorithm +
Footnote †: journal: Applied
## 3 Proposed Methodology
### Proposed Minimum Enclosing Ball  
In the proposed method of calculating the minimum enclosing ball, a weighted mean is calculated instead of a mean. Firstly, SVM is applied to compute the classifier attributes, i.e., \(w\) and \(b\). Secondly, the mean of each class is calculated by using the same method adopted by F-SVM, i.e., Euclidean distance. As the time complexity of SVM is \(O(n^{2})\)  
In the proposed method, a weighted mean is calculated instead of a mean. First, F-SVM followed by SVM is applied to get the MEB of SVM. After that, the weighted mean is computed, where the weight of each sample is its Euclidean distance from the mean of class, as shown in (19). The selected square Euclidean distance allows us to get the weighted feature space based on the distance of each sample from mean to samples. Moreover, it is observed that the additional iterations of the weighted feature space lead to the same result due to the choice of weight based on the square of the Euclidean distance. These weights are calculated in kernelized feature space, whereas F-SVM uses these distances as the radius of MEB, as shown in (18).  
\[weightx_{i}=\parallel\overline{x}-x_{i}\parallel_{2}^{2} \tag{19}\]  
Figure 5: Unbounded area for a class.  
Figure 6: Class bounded by its mean.  
After calculating the weight for each sample. Weighted features are calculated by using (20). The weight of each sample is added to all the samples of the class.  
\[\overline{x}^{(w)}=\frac{\sum_{i=1}^{n}x_{i}.\mathit{weight}x_{i}}{\sum_{i=1}^{n }\mathit{weight}x_{i}} \tag{20}\]  
After assigning weights to each sample, all the class points are shifted from kernelized feature space to weighted kernelized feature space. By approaching the weighted feature space again, the mean of the weighted feature space is calculated. The mean of weighted feature minimizes the MEB radius of each class as shown in Figure 7.  
After calculating weight for each sample from (19) and then weighted samples from (20), the mean of the class shift towards scattered points of the class, as a result, our radius of MEB is minimized.  
The weighted mean for the multiclass problem using one vs all technique is illustrated in Figure 7(a)-c. Furthermore, the MEB crosses the separating line; these points of MEB are excluded by calculating the circumference of the MEB. All the points which do not satisfy the constraint given in (4) are excluded from MEB.
## 3 State-of-the-Art MIMO-SDR Systems
### MIMO-OFDM Block Enhancements  
In the literature, interesting studies were presented to enhance the MIMO-OFDM transceiver performance. Notably, the studies related to the channel estimation and equalization process. In the following, the relative studies and notions will be further expanded.
# Investigation of Spatial Chirp Induced by Misalignments in a Parallel Grating Pair Pulse Stretcher
###### Abstract  
Spatial chirp induced by the misaligned gratings and mirrors in a parallel grating pair pulse stretcher can significantly affect the performance of the output pulses. Firstly, a detailed analysis about the spatial chirp of the stretched pulses caused by the misalignments has been carried out using the ray tracing simulation method. According to the simulation results, an adjustment procedure has been summarized to accurately calibrate these misalignments. The proposed method has been successfully applied in a home-made chirped pulse stretcher. By measuring the output pulse with an imaging spectrometer, the results show the stretched pulse has a good linear temporal chirp and little spatial chirp, which demonstrates the good adjustment of the stretcher.  
parallel grating pair pulse stretcher; misalignments; spatial chirp; adjustment method +
Footnote †: journal: Applied sciences  
Article
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes
### Theoretical Solution
#### 2.3.2 Wear Length \(l\)  
Owing to the tangential impact effect of moving ores, these ores have a slight sliding relative to the shaft wall, resulting in wear to the shaft wall. Based on the kinetic energy theorem, the change in tangential kinetic energy when the ore collides with the shaft wall is transformed into friction work, thereby satisfying the following equation:  
\[-\mu F_{mm}^{*}l=\frac{1}{2}mv_{t}{}^{2}(e_{t}^{2}-1) \tag{14}\]  
where \(v_{t}\) is the ore tangential velocity before collision; \(\mu\) is the sliding friction factor; and \(e_{n}\) and \(e_{t}\) are, respectively, the normal and tangential collision recovery coefficients. Based on the quasi-static contact mechanics theory, the collision recovery coefficients are expressed as follows [28; 29; 30]:  
\[\left\{\begin{array}{l}e_{n}=(\frac{\frac{9}{3}\pi\frac{5}{2}}{5E^{*}})^{\frac{ 1}{2}}(\frac{8R^{3}\chi_{q}^{5}}{m})^{\frac{5}{8}}v_{n}-\frac{1}{4}\\ e_{t}=1-\mu(e_{n}+1)(\frac{v_{n}}{v_{t}})\end{array}\right. \tag{15}\]  
Combining Equations (14) and (15), the wear length \(l\) can be written as  
\[l=\frac{mv_{n}(e_{n}+1)[2v_{t}-\mu v_{n}(e_{n}+1)]}{2F_{nm}^{*}} \tag{16}\]
###### Abstract  
A novel ultra-wideband (UWB) KAYI-shaped and common KITE-shaped ground plane tri-port antenna is proposed. The proposed research work has a small size of (30 \(\times\) 30 \(\times\) 1.6 mm3). The MIMO antenna elements are placed in a KAYI-shaped (Y-shaped) with a symmetric phase shift of 120\({}^{\circ}\) between the nearby MIMO antennas element improving the isolation. The antenna's gain is more than 5 dBi for the entire bands of WiMax, WLAN, and X-band satellite communication. The suggested work includes notches at 3.2 GHz, 5.2 GHz, and 8.9 GHz, respectively. The notching characteristics are made possible by L-shaped slits for the WiMax band, the inverted U-shaped slot for WLAN, while the third is created by the interaction between the L-shaped and U-shaped notching elements. Results were measured after making the prototype antenna on the FR-4 substrate. The proposed antenna has good impedance matching for 2-20 GHz and three notching characteristics with high isolation among the MIMO elements. Mean effective gain (MEG), envelope correlation coefficient (ECC), and total active reflection coefficient (TARC) are the diversity metrics of MIMO antennas which are in good comparison to the proposed antenna. The antenna is a good candidate for deployment in wireless communication and MIMO applications.  
MIMO; UWB; high isolation; triple notches; compact size; mutual interaction; KAYI shaped; Tachism 2022104491
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 2 Related Work
### iForest Algorithm  
The iForest algorithm is an anomaly detection algorithm proposed by Mr. [PERSON] in 2008 [18]. The algorithm first uses a random hyperplane to cut the data space, and each time it is cut, two subspaces are generated. The subspace is repeatedly cut using the random hyperplane until there is only one data point remaining in each subspace. Since a data set with high density requires numerous cuts to stop, the point with a low density will stay in a subspace very early after cutting [19], so the low density point is the abnormal point.  
There are multiple trees in the iForest and the tree in the algorithm is called isolated tree(iTree) [20]. The following is the process of establishing the iTree.  
1. Randomly select sub samples from training data as root nodes in trees.
2. Arbitrarily select a dimension to generate a cut point \(p\) in the current node data.
3. The cut point \(p\) separates the current node data space into two subspaces. The data that are less than the cut point are placed in the left subtree of the current node, and the data greater than or equal to the cut point are placed in the right subtree of the current node.
4. Perform steps 2 and 3 continuously to generate new sub nodes until the iTree reaches a finite height or there is only one data on the child node.  
Then, the training data are detected by the generated iForest. \(h(x)\) is the number of edges that the sample point \(x\) traverses from the root node of the iTree to the leaf nodes. Given a dataset with n samples, the tree's average path length is:  
\[c(n)=2H(n-1)-2(n-1)/n \tag{1}\]  
\(H(i)\) is the harmonic function and it can be estimated by \(In(i)+0.5772\). \(c(n)\) is the average of the path lengths for a given number of samples \(n\), used to normalize the path length \(h(x)\). The anomaly score of the sample \(x\) is calculated as:  
\[s(x,n)=2^{\frac{E(h(x))}{c(n)}} \tag{2}\]  
\(E(h(x))\) is the mathematical expectation of the path length of the sample \(x\) in a batch of iTree, from which the following inferences can be drawn:  
When \(E(h(x))\)\(\rightarrow\)0, \(s\)\(\rightarrow\)1, the probability of \(x\) being an outlier is high.  
When \(E(h(x))\)\(\rightarrow\)\(c(n)\), \(s\)\(\rightarrow\)0.5, whether \(x\) is an outlier is uncertain.  
When \(E(h(x))\)\(\rightarrow\)\(n-1\), \(s\)\(\rightarrow\)0, the probability of \(x\) being normal is high.
## 6 Conclusions  
In this study, we propose a new topic in air quality prediction that forecasts the fine-grained air quality in regions without monitoring stations. The challenge to this objective is the lack of historical air quality data, which is crucial to precise long-term prediction. Hence, we propose a hybrid predictor structure that consists of two data flows to predict time-independent and time-dependent values respectively. We then combine the two complementary results with the dynamic weights sum, which effectively increases the accuracy of long-term prediction. Additionally, we propose inversed distance attention based on the MLP attention mechanism that further improves the overall performance. Our attention networks can provide crucial correlation information of all monitoring stations, as well as any region in the city for an accurate fine-grained air quality prediction. According to the experiment results, our model outperforms all baselines significantly for the next 19-24 h in MAE and RMSE. As a result, our fine-grained air quality prediction not only performs well in the current time but satisfies the demand for long-term prediction. In our future plan, we aim to devise a score specific to the air quality prediction problem that quantifies the human health improvement induced by the model to measure the model prediction performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Labeled Grids (k)** & **Metrics** & \(\mathbf{T_{0}}\) & \(\mathbf{T_{1}-T_{6}}\) & \(\mathbf{T_{7}-T_{12}}\) & \(\mathbf{T_{13}-T_{18}}\) & \(\mathbf{T_{19}-T_{24}}\) \\ \hline k = 10 & MAE & 20.89 & 23.22 & 27.94 & 27.76 & 32.22 \\  & RMSE & 36.71 & 40.73 & 47.36 & 47.34 & 56.61 \\ k = 15 & MAE & 19.98 & 22.62 & 27.46 & 26.63 & 30.43 \\  & RMSE & 35.86 & 39.74 & 45.67 & 44.94 & 52.54 \\  & MAE & 19.43 & 21.83 & 25.72 & 25.33 & 28.8 \\  & RMSE & 35.01 & 39.05 & 43.63 & 42.59 & 50.33 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results for our proposed model with the different number k of labeled grids. The values in the table are the MAE and RMSE of AQI values.  
**Author Contributions:** Supervision, H.-P.H.; methodology, H.-P.H., S.W., Y.-W.C. and Z.-T.Y.; validation, C.-C.K. and Z.-T.Y.; investigation, H.-P.H., S.W. and Z.-T.Y.; writing--original draft preparation, Y.-W.C., S.W. and Z.-T.Y.; writing--review and editing, H.-P.H., C.-C.K. and C.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology (MOST) of Taiwan under grants MOST 109-2636-E-006-025 and MOST 110-2636-E-006-011.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was partially supported by Ministry of Science and Technology (MOST) of Taiwan under grants MOST 109-2636-E-006-025 and MOST 110-2636-E-006-011(MOST Young Scholar Fellowship).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 System Model  
In this section, we first introduce OTFS transmitters and receivers and give mathematical representations of the transmitted and received signals. Then, we analyze the equivalent channel matrix of OTFS in the time, delay, and Doppler domains for the V2X scenario, which is the basis for the subsequent channel estimation. Finally, the sparse channel-based OTFS relation is reformulated in vector form and modeled as a sparse basis coefficient recovery problem. The base-band signal transmission model of the OTFS system is shown in Figure 1.
## 1 Introduction
This paper aims to propose a novel approach for epileptic seizure detection by integrating the Synthetic Minority Over-Sampling Technique (SMOTE) for data balancing and a hybrid feature selection method combining Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). Unlike previous models that typically use either temporal or spectral features in isolation, our approach fuses both domains to enhance discriminative power. Also, existing models do not reflect real-world clinical data distributions. This leads to overfitting and poor generalization. Moreover, existing approaches often overlook feature redundancies and noise. Therefore, using either single-step feature extraction or limited dimensionality reduction is effective. Additionally, unlike traditional methods, our approach addresses the issue of data imbalance and leverages advanced feature extraction techniques to enhance the accuracy and reliability of seizure detection systems. This layered pipeline is designed to improve both robustness and minority class sensitivity. Compared to existing literature, our method demonstrates significant performance improvements, achieving higher accuracy and robustness in detecting seizures from electroencephalogram (EEG) signals. The primary contributions outlined in this paper include the following:  
* Proposing a novel approach consisting of a holistic pipeline for epileptic seizure detection using ML techniques;
* Addressing data imbalance through the integration of SMOTE to improve model performance;
* Proposing a hybrid feature selection technique combining PCA and DWT to extract discriminative features from EEG signals;
* Providing a comprehensive evaluation of the proposed approach, demonstrating significant performance gains and validating its effectiveness in enhancing seizure detection systems. The rest of this paper is organized as follows: Section 2 provides the necessary background about the topic, Section 3 presents related work, Section 4 introduces the proposed epileptic seizure detection model followed by experimental results in Section 5, Section 6 presents the discussion and evaluation, and finally, Section 7 concludes the paper.