esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering
## 6 Conclusions  
In this paper, we study the problem of semi-supervised medical image classification with the aim of better utilizing medical images without labels. We propose a new learning framework that combines semi-supervised learning with unsupervised clustering.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Labeled**} & \multirow{2}{*}{\begin{tabular}{c} **Normal** \\ **Tissue** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Benign** \\ **Tissue** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Ductal** \\ **Carcinoma** \\ **in Situ** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Invasive** \\ **Carcinoma** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **AUC** \\ \end{tabular} } \\ \hline supervised & 100\% & 95.61 & 94.69 & 93.16 & 94.36 & 94.46 \\ \hline TCSE & 40\% & 89.36 & 88.12 & 88.07 & 88.71 & 88.57 \\ \hline TE & 40\% & 89.44 & 87.91 & 88.26 & 88.64 & 88.56 \\ \hline MT & 40\% & 91.12 & 90.08 & 89.68 & 90.11 & 90.25 \\ \hline UDA & 40\% & 93.11 & 92.14 & 90.74 & 92.07 & 92.02 \\ \hline FixMatch & 40\% & 94.69 & 93.25 & 91.45 & 93.25 & 93.16 \\ \hline ours & 40\% & 95.44 & 94.36 & 92.89 & 93.77 & 94.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of different methods on the ICIAR BACH 2018 dataset with AUC metric.  
The model alternates between semi-supervised and unsupervised learning. During the semi-supervised learning phase, the model propagates label information to accelerate the convergence speed of unsupervised clustering. In the unsupervised learning phase, the model ignores label information to reduce the risk of overfitting. Our experiments on two datasets have achieved better metrics, with a significant lead in AUC, indicating that our proposed method has stronger classification ability. When reducing the ratio of labeled data, our method has more obvious advantages in all metrics, indicating that it can still perform well with less labeled data. We have tried training the model without pre-training, but the overall effect was not satisfactory. Additionally, the unsupervised phase takes quite a long time, which lead to an increase in training costs. In future studies, we will continue to improve our methods so that they can still perform well under random initialization and lower training costs.  
Conceptualization, B.X.; methodology, B.X. and C.L.; software, B.X. and C.L.; validation, B.X.; writing--original draft preparation, B.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Low-Light Image Enhancement  
To demonstrate the flexibility and extensibility of our method, we applied it to low-light image enhancement and conducted a comparison experiment. Similar to our image combination denoising process, in this experiment, the low-light image is equivalent to the noisy image, and the enhanced image after processing by image enhancement methods is equivalent to the initial denoised image. Using the proposed UWMGN, we set a weight map for each initial enhanced image, and then combined the initial enhanced images to obtain the final combined enhanced image. We used the information content weighted peak signal to noise ratio (IW-PSNR), which is an extension of PSNR and is more suitable for measuring the quality of low-light enhanced images [43], as our image quality evaluation metric to measure the enhancement effect of various methods. The larger the value of IW-PSNR, the better the image enhancement effect. We used four representative image enhancement methods, viz. adaptive gamma correction with weighted distribution (AGCWD) [44], fusion-based enhancing method (Fu) [45], a new image contrast enhancement algorithm proposed by [PERSON] et al. ([PERSON]) [46] and a structure-revealing low-light image enhancement method (SRLLIE) [47], as a comparison. Our method combines the three image enhancement methods, viz. Fu, Ying and SRLLIE. Figure 10 shows the enhancement effect of each method on the typical low-light image selected from [48], with one area enlarged for a clearer view. We can observe from the listed IW-PSNR values that our method ranks first. In terms of visual comparison, our method produces the optimal overall performance in terms of detail protection, color fidelity and avoidance of overexposure.  
Figure 9: Denoising results of a color image with noise level \(\sigma=30\).  
Because of the selection of several complementary images to be combined, the optimal texture details of each image are retained in the final combined image.
## 2 IL-1 Family Cytokines, Receptors and Co-Receptors  
The IL-1 family of cytokines is composed of 11 cytokine members, with seven agonists (IL-1\(\alpha\), IL-1\(\beta\), IL-18, IL-33, IL-36\(\alpha\), IL-36\(\beta\), and IL-36\(\gamma\)) and four antagonists (IL-1 receptor antagonist (Ra), IL-36 Ra, IL-37, and IL-38) [1]. According to their structural and functional characteristics, these cytokines are further classified into four subfamilies (IL-1, IL-18, IL-33, and IL-36), each one having a cognate receptor (IL-1R1, IL-18R\(\alpha\), IL-33R (suppression of tumorigenicity 2 or ST2), and IL-36R, respectively). Furthermore, IL-1 RAcP is an accessory protein shared by all these cytokines, with the exception of IL-18 (IL-18 RAcP or IL-18R\(\beta\) chain) (Table 1) [2]. To produce their pro-inflammatory functions, IL-1 cytokines form complexes with their respective receptor and a co-receptor (Figure 1). All of them except IL-1 Ra are synthesized as precursors and require N-terminal processing in order to acquire their full function [3,4]. They can be activated both extracellularly by proteolytic cleavage and intracellularly via inflammasome-mediated cleavage [5]. Binding of cleaved IL-1\(\alpha\)/\(\beta\) to the extracellular domain of IL-1R1 leads to recruitment of IL-1 RAcP, resulting in the initiation of a signaling cascade with the recruitment of the myeloid differentiation primary response 88 (MyD88) accessory protein and Interleukin 1 receptor-associated kinases (IRAKs). This in turn results in activation of the nuclear factor kB (NF-kB) and mitogen-activated protein kinases (MAPKs), ultimately resulting in pro-inflammatory gene expression [6]. Furthermore, IL-1 signaling also induces activation of defense mechanisms (antigen recognition, phagocytosis, degranulation, and nitric oxide production) and activates lymphocyte functions implicated in adaptive immunity, thus acting as a link between innate and adaptive immune responses [7]. The other IL-1 family cytokines IL-33, IL-18, and IL-36\(\alpha\)/\(\beta\)/\(\gamma\) form similar ternary complexes with their respective receptors and co-receptors and also act through Myd88 to induce pro-inflammatory gene expression. Regulatory mechanisms are necessary to maintain homeostasis; they include decoy receptors, receptor antagonists, and anti-inflammatory cytokines (Figure 1). IL-1R2 is a cytoplasmic soluble receptor without a functional TIR domain that binds to IL-1\(\alpha\)/\(\beta\) precursors, preventing their processing and secretion. Under proinflammatory conditions, IL-1R2 is cleaved by an inflammasome-dependent mechanism [8,9]. Likewise, the soluble ST2 receptor (sST2) and the soluble protein IL-18 binding protein (IL-18 BP) bind to IL-33 and IL-18, respectively, neutralizing their activities [10,11]. Furthermore, receptor antagonists IL-1 Ra, IL-36 Ra, and IL-38 compete with IL-1\(\alpha\)/\(\beta\) and IL-36\(\alpha\)/\(\beta\)/\(\gamma\)[4]. Lastly, IL-37 binding to IL-18R\(\alpha\) leads to recruitment of the IL-1R8 co-receptor (also called single immunoglobulin IL-1R-related molecule (SIGIRR), with activation of the inhibitory STAT3 signaling pathway [12].
## 2 Materials and Methods
### EEG Recording and Analysis  
The EEG was recorded with a passive 32-channel EEG recording system using AgAg electrodes and the software Brain Vision Recorder, both from Brain Products GmbH, Gilching/Germany. Data were digitized at a sampling rate of 1 kHz using an Easy Cap where electrodes were placed in an extended 10-20 system, including 31 electrodes for recording brain activity (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, Pz, FC1, FC2, CP1, CP2, FC5, FC6, CP5, CP6, FT9, FT10, TP9, TP10) with a reference at FCz and ground at AFz. An additional electrode was used to record a lower vertical electrooculogram to monitor eye movements.  
We analyzed EEG data with the software Brain Vision Analyzer 2 (Brain Products GmbH, Gilching/Germany) from the picture learning condition in the following steps.  
For preprocessing, common average re-referencing was performed, and data were filtered from 0.5 to 48 Hz with zero-phase-shift Butterworth filters in order to exclude line noise (50 Hz) and muscle artefacts above that range. Next, an independent component analysis (ICA) and back-transform with the infomax restricted algorithm was applied to automatically remove eye-blink artefacts. The vertical lower oculogram was used as a vertical activity channel. As a last preprocessing step, an automated raw data inspection was carried out to identify and exclude artefacts using standard thresholds as implemented in Brain Vision Analyzer:  
* Check gradient: maximal allowed voltage step: 50 microvolts/ms;
* Check difference: maximal allowed difference in values in intervals of 200 ms: 200 microvolts;
* Lowest activity allowed in 100 ms intervals: 0.5 microvolts.  
Bad events were marked \(\pm\)200 ms around the identified artefacts. Then, data were band-pass filtered in the alpha (8-12 Hz [26]) and gamma ranges (35-45 Hz [39]). Finally, time-averaged rectified activity was exported for two time-windows: 50-150 ms post-stimulus-onset and 300-400 ms post-stimulus-onset, separately for the two frequency bands and the three stimulus categories. Segments overlapping with bad events were excluded. The electrodes used for statistical analysis were F3, F4, F7, F8, T7, T8, O1, and O2 to cover core electrodes over the regions of interest (frontal, temporal, and occipital). This data can be found in the supplementary data file.  
For the purpose of illustrating the appropriateness of the choice of the time-window and frequency range, for each picture category (negative, neutral, and positive), data were segmented from \(-500\) ms to \(+1000\) ms around stimulus presentation and submitted to a wavelet transform. We conducted wavelet analysis with Morlet complex wavelet (Morlet parameter c = 5) for the frequency range 1-48 Hz in 1 Hz linear frequency steps. Wavelet normalization was performed using instantaneous amplitude (Gabor normalization). We averaged the results for each picture category over the participants with elevated seasonality and low seasonality scores.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### The Effect of System Errors
#### 2.3.5 Total System Errors  
If the above system errors exist in total, the mean of \(\boldsymbol{n}\) is \(E(\boldsymbol{n})=\boldsymbol{\gamma}\) and \(G(\theta_{1})=\delta\). The localization accuracy is obtained from (48) as  
\[\begin{split}\text{MSE}(\theta_{1})=4A_{11}J_{1}^{T}C^{-1}J_{1}A_{ 11}^{T}+4A_{11}J_{1}^{T}C^{-1}(\boldsymbol{\gamma}\boldsymbol{\gamma}^{T})C^{ -1}J_{1}A_{11}^{T}\\ +A_{12}\delta\delta^{T}A_{12}^{T}-4A_{11}J_{1}^{T}C^{-1}\boldsymbol{\gamma} \delta^{T}A_{12}^{T}\end{split} \tag{56}\]
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 3 Results
### Ablation Experiment  
For demonstrating the effectiveness of the improvements proposed in this study, the ablation experiments were carried out on the CP15 dataset. It can be seen from the experimental results in Table 4 that the number of parameters and computations of YOLOLite are only approximately one-fifth of that of YOLOv3, while the precision is only reduced by 1.5%, which is more suitable for practical deployment. The mAP of YOLOLite was improved by 0.2% after replacing k-means with k-means++ for the prior box generation, indicating that the k-means++ method can generate the prior box with a higher quality to enhance the effectiveness of pest detection. Moreover, the addition of the lightweight sandglass block improves the mAP by 0.5% and further reduces the number of parameters and computations, which demonstrates that the lightweight sandglass block outperforms the classic residual block. Finally, the mAP was improved by 1.3% by introducing the coordinate attention into the classic residual block and the lightweight sandglass block, while only slightly increasing the number of parameters and computations, and demonstrating the importance of the attention mechanism in the crop pest detection task. The above results show that the improvements we adopted effectively improve the performance of our method in terms of accuracy, number of parameters and number of computations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**YOLOv3** & **YOLOLite** & **K-Means++** & **LightSandGlass** & **Coordinate Attention** & **mAP (\%)** & **Params (M)** & **FLOPs (G)** \\ \hline ✓ & & & & & 82.4 & 61.6 & 65.5 \\ ✓ & ✓ & & & & 80.9 & 13.1 & 13.4 \\ ✓ & ✓ & ✓ & & & 81.1 & 13.1 & 13.4 \\ ✓ & ✓ & ✓ & ✓ & & 81.6 & 4.8 & 9.6 \\ ✓ & ✓ & ✓ & ✓ & ✓ & 82.9 & 5 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the ablation experiment for the proposed improved methods.  
Figure 11: The visualization of the size distribution of six groups of a priori boxes: (**a**) Group 1; (**b**) Group 2; (**c**) Group 3; (**d**) Group 4; (**e**) Group 5; and (**f**) Group 6.
# Position Detection of Doors and Windows Based on DSPP-YOLO
###### Abstract
put forward the SSD algorithm [17], which improves the detection accuracy of single-stage detector by multireference and multiresolution detection technology. The YOLO algorithm proposed by [PERSON] et al. makes YOLO detection network have the functions of classification, localization, and detection simultaneously by dividing the image into multigrid mosaics [18]. In 2017, [PERSON] et al. proposed the DSSD algorithm to replace the VGG16 backbone network with ResNet101 to optimize the SDD algorithm. The YOLO algorithm was optimized to generate the YOLOV2 algorithm [19], and the anchor box mechanism of the fast R-CNN algorithm was used to make the network have fine granularity. In 2018, a new algorithm called YOLOV3 was proposed in the backbone network-Darknet53 [20], which abandoned the idea of full connection layer and fused residual error. However, the YOLOV3 algorithm still has the following three problems when the autonomous mobile robot detects the position of doors and windows: (1) Since windows with different backgrounds have different features, the features learned by CNN will change with the background, and it is difficult to find general features. (2) Certain types of windows and doors have common features that will confuse the network when learning labels. (3) The size and area of different windows vary greatly, even some remote windows have the feature of small targets. Therefore, in this paper, we propose the DSSP-YOLO framework based on YOLOV3 that can help to address these three challenges in the position detection of doors and windows. Our contribution can be summarized as below:  
1. We propose the DSPP-YOLO algorithm to detect doors and windows in an unknown environment and optimize the SLAM algorithm to realize the classification of an unknown environment. 2. We add down the sampled layer to deepen the Darknet53 network structure to enhance the learning of target semantic features and integrate the YOLOV3 network framework with the idea of DenseNet and spatial pooling pyramids to achieve optimization. 3. We use K-means++ clustering method to generate anchor boxes to improve the training accuracy. After training, according to the experimental results of the improved network, we can know that the detection accuracy of doors is improved by 3.3%, the detection accuracy of windows is improved by 8.8%, and the average accuracy of various types is improved by 6.05%.
## 3 Experimental Verification
### Discussion and Analysis  
In the testing results, three images failed to extract geometric feature points due to minor changes in lighting, resulting in poor binary image quality and subsequently affecting the extraction of geometric feature points. To avoid this situation, it is recommended to install a darkroom on the production line to provide sufficient and stable lighting. Many researchers have studied coaxiality detection, and our proposed method was compared with those of other researchers, as shown in Table 2. Currently, the main methods involve using laser displacement sensors and laser alignment. However, most of the methods of other researchers are only suitable for the detection of a certain part. In this paper, only the coaxiality measurement of automobile brake piston components in a certain braking system was carried out. The proposed measurement method, by modifying the algorithm parameters and extracting the required geometric feature points, can be fully extended to the coaxiality measurement of other stepped shaft parts, such as the motor shaft, gear shaft and guide shaft, which will be the focus the author' future research work. Therefore, the novelty of this measurement method lies in its flexibility and accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Image Number** & **Angle (\({}^{\circ}\))** & **Image Number** & **Angle (\({}^{\circ}\))** & **Image Number** & **Angle (\({}^{\circ}\))** \\ \hline
1 & 0.2665 & 11 & 0.5877 & 21 & 0.9111 & 31 & 0.8944 \\
2 & 0.3155 & 12 & 0.5954 & 22 & 0.9470 & 32 & 0.8313 \\
3 & 0.3551 & 13 & 0.6186 & 23 & 0.9827 & 33 & 0.7747 \\
4 & 0.4052 & 14 & 0.6563 & 24 & 1.0071 & 34 & 0.6863 \\
5 & 0.4265 & 15 & 0.6877 & 25 & 1.0254 & 35 & 0.5115 \\
6 & 0.4655 & 16 & 0.7254 & 26 & 1.0204 & 36 & 0.4313 \\
7 & 0.4751 & 17 & 0.7586 & 27 & 1.0004 & 37 & 0.3947 \\
8 & 0.4952 & 18 & 0.7763 & 28 & 0.9827 & 38 & 0.3136 \\
9 & 0.5177 & 19 & 0.8517 & 29 & 0.9570 & 39 & 0.2907 \\
10 & 0.5377 & 20 & 0.8937 & 30 & 0.9344 & 40 & 0.2717 \\ \hline \end{tabular}
\end{table}
Table 1: Angle \(\theta\) between spring axis and datum axis of 40 images.  
Figure 16: Schematic diagram of evaluation coaxiality.
## 4 ADMM Formulation and Solution
### Update of \(\overline{\mathbf{r}}_{r}\)  
To obtain the \((m+1)\)th update of \(\overline{\mathbf{r}}_{r}\), we take the gradient of Equation (23a) with respect to \(\overline{\mathbf{r}}_{r}\) and equate the result to \(0\), i.e.,  
\[\
abla_{\overline{\mathbf{r}}_{r}}\mathcal{L}(\overline{\mathbf{r}}_{r}, \overline{\mathbf{s}}_{r}^{m},\mathbf{u}^{m},\mathbf{v}^{m},\mathbf{w}^{m})= \mathbf{0}. \tag{24}\]  
The solution to Equation (24) is given by  
\[\overline{\mathbf{r}}_{r}^{m+1}=\Xi_{1}^{-1}\xi_{1} \tag{25}\]  
where  
\[\Xi_{1}=\rho_{1}\mathbf{A}_{r}(\Theta)\mathbf{A}_{r}^{T}(\Theta)+\rho_{2} \mathbf{T}^{T}(\overline{\mathbf{s}}_{r})\mathbf{T}(\overline{\mathbf{s}}_{ r})+\rho_{3}\mathbf{I} \tag{26}\]  
and  
\[\begin{split}&\xi_{1}=\rho_{1}\mathbf{A}_{r}(\Theta)\Big{(}2 \mathbf{x}_{r}-\mathbf{u}-\mathbf{A}_{r}^{T}(\Theta)\overline{\mathbf{s}}_{r} \Big{)}\\ &\rho_{2}\mathbf{T}^{T}(\overline{\mathbf{s}}_{r})(\mathbf{1}- \mathbf{v})\rho_{3}(\overline{\mathbf{s}}_{r}-\mathbf{w})\\ &-\mathbf{A}_{r}\Big{(}\widetilde{\Theta}\Big{)}\mathbf{A}_{r}^{ T}\Big{(}\widetilde{\Theta}\Big{)}\overline{\mathbf{s}}_{r}.\end{split} \tag{27}\]
# An End-to-End Mutually Interactive Emotion-Cause Pair Extractor via Soft Sharing
## 5 Ablation Study
### Mutual Transfer of Information  
We selected the most comprehensive metric F1 score and the primary task emotion-cause pair extraction to analyze the effectiveness of the mutual transfer of information by varying the soft-sharing setting: no soft-sharing, sharing only the first layer of the emotion and cause encoders, and sharing all layers of both encoders. Figure 6a shows that sharing only the first layer of the encoder achieves better performance compared to the other two cases, indicating that the intrinsic connection between emotion and cause is closer to the lexico-syntactic level [29]. Due to the long training time of model Emiece-BERT, only model Emiece-LSTM is selected here.  
In Figure 6b, the results illustrate that the number of parameters shared in the encoder is not linearly correlated with the effect of the prediction results. This is consistent with the results of previous experiments in which the weight of soft sharing was altered by changing the value of \(\lambda_{sf}\), but the higher the weight is not better. Ref. [52] found that in the seq2 seq machine translation model, the low-level layer of the RNNs unit (i.e., the first layer in the encoder) represents the word structure, while the high-level layer focuses on the semantic meaning. Since the semantic information of the emotion clause and the cause clause is quite different, sharing the high-level layer alone will blur the features of the text, not to mention that sharing all layers will confuse the word structure information with the semantic information, resulting in a reduced effect.
# Correlation Analysis and Its Application on an Asymmetry
###### Abstract  
Overhung rotors are widely used in the industrial field. However, compared with normal structure rotors, the prediction and control of overhung rotors cannot achieve good performance. The work aims to investigate the dynamical behaviours of an overhung rotor by means of correlation analysis, and find its possible application. In this work, based on a real type of rotor, the dynamic model of the rotor with overhang is established by means of the finite element method. Simulation of the dynamic model with different input positions and support stiffnesses is conducted. Based on the methodology of correlation analysis, by introducing a correlation parameter of a proportion of amplitude of measured signal and imbalance mass, the position which has most effect on the vibration is found. Meanwhile, an experiment on the same type of overhung rotor is carried out to validate the results. The numerical results and corresponding experimental results prove that the overhung node has the most effect on the vibration amplitudes of the measured points. Choosing the overhung node to add trial weight, the overhung rotor can be easily balanced. The theory provides an alternative approach to modal analysis which needs more knowledge of the system.  
overhang; asymmetry rotor; correlation analysis; rotor balancing Article