esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 2 Preliminaries
### Incremental Learning Algorithm Based on 'Clone-and-Branch'  
A deep convolutional neural network (DCNN) denotes a neural network that consists of multiple convolutional layers to extract hierarchical visual features [9; 18]. By tracking the feature projection to these convolutional layers, the earlier layers extract the most basic part of an image, i.e., edges, colors, and textures, while the later layers extract much more detailed and sophisticated structures [19; 20; 21]. For efficient incremental learning, ref. [14] proposed a training methodology called the 'clone-and-branch' technique, depicted in Figure 1. This method leverages general knowledge from previously learned tasks to learn subsequent new tasks by sharing initial convolutional layers of base networks. The basic idea is that the earlier layer of base network can be used for learning new task, especially in a similar domain of input used in new task [8; 9]. Besides, when the branch network is in training, the last convolutional layers of the base network remain disconnected, so that the network can learn new tasks one after another without any performance loss in the knowledge of the base network. In 'clone and branch' technique, there are two steps of training methodology, which consist of both empirical searching method and similarity scoring method.
# Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
###### Abstract
[PERSON] et al. [31] proposed to add an attention mechanism and a feature extraction auxiliary network composed of multiple residual blocks. A smaller scale than the backbone network in the bypass of the original YOLOv3 backbone features extraction networks on the tiny target problem, then the auxiliary network will transfer the extracted location features to the backbone network to learn the detailed features of tiny targets more accurately. The mAP of tiny target detection in the car driving scene reaches 84.76%, [PERSON] et al. [20]. In the tiny target detection for the drone-capture scenario, the TPH-YOLOv5 model is proposed. They add one more prediction head to detect different-scale objects, then use a Transformer Prediction Head (TPH) to replace the original prediction heads to excavate the prediction potential of the network; the results show this model is better than previous SOTA methods by 1.81%. The above models in their respective fields have made corresponding improvements to the structure of the model, according to the characteristics of the tiny targets in their own field datasets, so that the model can more accurately detect the tiny targets existing in the image. In the field of fabric defects, there are few model improvements specifically related to tiny targets. Based on the above research, an improved YOLOv4 network is proposed to achieve accurate detection of tiny objects with fabric defects. The first step uses a new data augmentation method to expand the dataset to enhance the generalization ability of the model, the second step uses a clustering algorithm to cluster the augmented dataset and selects suitable yolo_anchors, the third step is to add a layer of feature detection specifically for tiny targets in the prediction network. The fourth step is to add a convolutional attention module to the backbone network to enhance the ability of the network to extract features. Finally, the loss function of the network is optimized to speed up the network convergence.
## 3 Results and Discussions  
### Homology Modeling  
The amino acid sequence of LAK was used as a reference to model its tertiary structure, leveraging the crystal structure of Lampyridae (PDB ID: 6K4C) to ascertain the relative positioning of LAK residues. Following molecular dynamics (MD) energy minimization, a validation check was performed. A Ramachandran plot analysis revealed that 93.28% of the residues were situated in the most favored regions, with an additional 5.9% in allowed regions, indicating the high quality of the modeled structures. The tertiary structure of the LAK protein comprises a larger N-terminal domain (residues 1-447), a flexible linker peptide (residues 448-454), and a smaller C-terminal domain (residues 455-551) positioned opposite the N-terminal region (Figure 1a). The opposing surfaces of the N- and C-terminal domains form a cleft containing numerous conserved residues, which is presumed to be the active site. Upon substrate-binding, the N- and C-terminal regions converge to clamp the substrate, forming a sandwich-like structure. The primary N-terminal domain featuresa compact architecture with a distorted antiparallel \(\beta\)-barrel and two \(\beta\)-sheets flanked by \(\alpha\)-helices, creating a five-layered \(\alpha\beta\alpha\beta\alpha\) structure. The C-terminal domain adopts an \(\alpha\)+\(\beta\) conformation. The conserved catalytic region of LAK exhibits characteristics typical of the adenylate-forming domain superfamily, commonly involved in binding CoA, Mg\({}^{2+}\), AMP, and ATP. A conserved tri-peptide sequence, serine-lysine-leucine (SKL), is located at the C-terminus, playing a role in peroxidase localization and AMP release (Figure 1b).
## 2 Background  
### Equalizer and Generalized Partial Response Target  
Figure 1 presents the structure of estimating the coefficients of equalizer \(\mathbf{F}\) and GPR target \(\mathbf{G}\) used in the HDS systems. \(a[j,k]\) can be represented by \(\{-1,\,1\}\) and \(b[j,k]\) denote the modulated signal from the user data and the received signal from the HDS channel, respectively. \(\mathbf{G}\) and \(\mathbf{F}\) have the following matrix forms:  
\[\mathbf{G}=\begin{bmatrix}g_{-1,-1}&g_{-1,0}&g_{-1,1}\\ g_{0,-1}&g_{0,0}&g_{0,1}\\ g_{1,-1}&g_{1,0}&g_{1,1}\end{bmatrix},\,\text{and} \tag{1}\]  
\[\mathbf{F}=\begin{bmatrix}f_{-2,-2}&f_{-2,-1}&f_{-2,0}&f_{-2,1}&f_{-2,2}\\ f_{-1,-2}&f_{-1,-1}&f_{-1,0}&f_{-1,1}&f_{-1,2}\\ f_{0,-2}&f_{0,-1}&f_{0,0}&f_{0,1}&f_{0,2}\\ f_{1,-2}&f_{1,-1}&f_{1,0}&f_{1,1}&f_{1,2}\\ f_{2,-2}&f_{2,-1}&f_{2,0}&f_{2,1}&f_{2,2}\end{bmatrix}, \tag{2}\]Based on the method in [17], we obtained the elements of \(\mathbf{G}\) and \(\mathbf{F}\) using the following expressions:  
\[\delta=\bigg{(}\mathbf{C}^{T}\Big{(}\mathbf{A}-\mathbf{T}^{T}\mathbf{B}^{-1} \mathbf{T}\Big{)}^{-1}\mathbf{C}\bigg{)}^{-1}\alpha, \tag{3}\]  
\[\mathbf{g}=\Big{(}\mathbf{A}-\mathbf{T}^{T}\mathbf{B}^{-1}\mathbf{T}\Big{)}^{- 1}\mathbf{C}\delta,\text{ and } \tag{4}\]  
\[\mathbf{f}=\mathbf{B}^{-1}\mathbf{T}\mathbf{g}. \tag{5}\]  
where \(\mathbf{A}=E\{\mathbf{a}\mathbf{a}^{T}\}\), \(\mathbf{B}=E\{\mathbf{b}\mathbf{b}^{T}\}\), and \(\mathbf{T}=E\{\mathbf{b}\mathbf{a}^{T}\}\). \(\delta\) is a vector containing the Lagrange multipliers. \(\mathbf{a}\) and \(\mathbf{b}\) are vectors of the signal \(a[j,k]\) and \(b[j,k]\), respectively. Finally, \(\mathbf{g}\) and \(\mathbf{f}\) are the vectors of matrices \(\mathbf{G}\) and \(\mathbf{F}\), respectively.  
The matrix \(\mathbf{C}\) and constant \(\alpha\) are defined in the following equations [16]:  
\[\mathbf{C}^{T}=\big{[}0\quad 0\quad 0\quad 0\quad 1\quad 0\quad 0\quad 0\big{]}, \tag{6}\]  
\[\alpha=0.85. \tag{7}\]  
This paper represents the HDS channel using the point spread function that is explained in Section 4.1. Regularly, we choose a size of \(3\times 3\) array pixels for the HDS channel because the channel coefficients outside the \(3\times 3\) array are approximately 0. Therefore, for the equalizer and detection, we set the sizes of the equalizer and target as \(5\times 5\) and \(3\times 3\), respectively.
## 5 Acoustic Emission Characteristics under Different Cyclic Loading and Unloading Conditions
### Acoustic Emission Felicity Ratio  
During the experimental loading process, it was observed that when the applied stress did not exceed the target stress from the previous loading stage, there were few acoustic emission signals. However, when the stress exceeded the target stress, there was a significant increase in acoustic emission signals, which is referred to as the acoustic emission Kaiser effect [21]. According to Equation (8), the Kaiser effect can be objectively expressed through the Felicity ratio. It is stated in reference [21] that the magnitude of the Felicity ratio can indicate the extent of internal damage within the rock. A smaller Felicity ratio implies more severe internal damage during the previous cyclic loading and unloading process.  
Felicity ratio is defined as  
\[R_{F}(i)=\frac{\sigma_{i}}{\sigma_{(i-1)}\text{max}} \tag{8}\]  
In Equation (8): represents the stress corresponding to significant changes in acoustic emission during the cycle process; represents the target stress from the previous loading.  
Calculate the Felicity results for graded cyclic loading-unloading and variable lower limit cyclic loading-unloading using Equation (8) and plot the curve of Felicity ratio against cycle number in Figure 14.  
By looking at Figure 15, it can be observed that both types of cycles show a decreasing trend in Felicity ratio. In the graded cyclic loading-unloading, from the first to the sixth cycle, the Felicity ratio reduces from 1.8 to 1, exhibiting a significant Kaiser effect. After the sixth cycle, the Felicity ratio remains below 1 and slowly decreases, indicating the failure of the [PERSON] effect. On the other hand, in the variable lower limit cyclic loading-unloading, from the first to the fifth cycle, the Felicity ratio decreases from 1.83 to 1.17, showing a noticeable Kaiser effect. After the fifth cycle, the Felicity ratio drops below 1, indicating the failure of the [PERSON] effect. Through nine cycles of loading-unloading, it is observed that  
Figure 14: Energy Counting and Time Curve during Cycle Loading and Unloading with Variable Lower Limit.  
the rock undergoes severe damage in the variable lower limit cyclic loading-unloading, which aligns with the damage calculation using the energy dissipation method.
# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 2 Methodology Background
### Long Short-Term Memory  
In the Recurrent Neural Network (RNN), information is allowed to be transmitted in different time steps, which is conducive to learning order dependency in input data, making it more suitable for long-time series. Gradient descent is often used in the training of neural networks. However, with the increase of time step, the RNN suffers more and more from gradient disappearance and explosion, which aggravates the difficulty of model training. In order to solve this problem, the LSTM is developed based on the RNN model. The LSTM model structure is shown in Figure 4, including a core unit (memory part) and three gating structures (forget, input and output gates), which guide the information flow inside the LSTM unit.  
The calculation steps of LSTM are as follows:  
1. Determine the information to discard in the cell state. Input the output \(h_{t-1}\) at the last moment and the input \(x_{t}\) at the current moment, through the forget gate, add the Sigmoid function, and output a vector whose element values are between 0 and 1, which represents the stored proportion of the cell state \(C_{t-1}\) at the last moment. The calculation formula is: \[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)}\] (12)
2. Determine the new information stored in the cell state. There are two steps: the input gate determines the updated value, and the candidate value vector \(i_{t}\) is then added to the cell state. The calculation formula of the input gate value and the new candidate value vector \(\widetilde{C}_{t}\) are as follows: \[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i})\] (13) \[\widetilde{C}_{t}=tanh(W_{C}[h_{t-1},x_{t}]+b_{C})\] (14)
3. Update the states of old cells. \(C_{t-1}\) is updated to \(C_{t}\) by discarding some of the information and adding new candidate values. The calculation formula of cell state value at the current moment is: \[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t}\] (15)
4. Determine the value of the output. It includes two steps: determine the value \(O_{t}\) of the output gate, and then determine the final output result value \(h_{t}\) at the current moment. The calculation formula is as follows: \[O_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})\] (16) \[h_{t}=O_{t}\odot\tan\h(C_{t})\] (17) where \(W_{f}\), \(W_{i}\), \(W_{C}\) and \(W_{o}\) are the weight terms to be learned, \(b_{f}\), \(b_{i}\), \(b_{C}\) and \(b_{o}\) are the offset terms to be learned, \(\sigma\) is the Sigmoid activation function, \(\odot\) is the Hadamard product, where the components of vectors are multiplied one by one.  
Figure 4: Schematic diagram of LSTM structure.  
The key advantage of using the LSTM unit is that the cell state will be updated dynamically with the passage of time, which increases the processable sequence length of time-series neural network and has better effect on prediction tasks with long-term regularity.
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Renewable Energy Systems
#### 4.2.2 Photovoltaic (PV) System Fault Detection  
Photovoltaic (PV) systems convert sunlight into electricity, but they are prone to faults such as shading, panel degradation, and inverter failures. Prompt fault detection ensures that the system operates at peak efficiency [70].  
Sensors are positioned at key locations such as the solar panels, inverters, and grid connections to gather data on voltage, current, and temperature. These data are analyzed to identify anomalies like underperforming panels or malfunctioning inverters that could lead to energy losses.  
Before feeding data into fault detection models, dimensionality reduction techniques such as principal component analysis (PCA) and kernel PCA (KPCA) are commonly applied. These methods minimize complexity while retaining crucial information that may indicate faults, particularly amid environmental noise like shading or temperature fluctuations [71].  
Machine learning methods play a crucial role in photovoltaic (PV) fault detection. Convolutional neural networks (CNNs) are effective at identifying spatial anomalies in sensor data, such as irregular current distributions that indicate partial shading. Random forests (RFs) are also widely used for fault classification, providing robust and interpretable models for identifying issues based on sensor readings.  
Hybrid models are becoming increasingly common as they combine the strengths of various algorithms. For instance, CNNs can extract features, while SVMs manage fault classification. This integration leads to more accurate, real-world-ready diagnostic systems.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 1 Introduction
We apply stable RoI detection technology to automate the entire respiration measurement process. 3. A breathing signal that is strong against noise elements, such as movement and occlusion noise, can be obtained. The proposed method shows a state-of-the-art accuracy of 0.1 bpm even in the noise environment, which is a noticeable improvement, considering that the existing methods have an error of 4-8 bpm in the noise environment. These improvements can be confirmed through the experimental results in Section 4. 4. The proposed method can operate in real time to immediately detect changes in patients' states. In addition, even in a CPU environment, if there is an RGB camera, it has a measurement speed of 30 FPS or more and is a robust measurement method against motion and occlusion noise, so it can be applied to various fields, such as medicine, home healthcare, emotional marketing, forensic investigation, and fitness. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Pros** & **Cons** \\ \hline Manual measurement [18; 19] & No device required & High possibility of human error when monitoring for long periods \\ \hline Impedance change & Various physiological signals can be measured & Sensitive to posture and movement \\ measurement [20; 21; 22; 23] & Long-term monitoring possible & Special device required \\ \hline Pressure change & Allows for more freedom of movement & Discomfort caused by physical pressure \\ measurement [24; 25; 26] & Relatively strong in movement & Special device required \\ \hline Airflow measurement [27; 28; 29] & Accurate measurement & Restricted environment \\  & Long-term monitoring possible & Special device required \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pros and cons of existing respiration measurement approaches.
## 2 State-of-Art Review
### General Parameters Affecting the Performance of RFA in Concrete
#### 2.1.2 Particle Size  
The recycling process though as explained in Section 2.1.1 could be very thorough, it does not detach the old mortar from the aggregates. Hence, as seen in Figure 1, NA have only one transition zone between the aggregate and the cement matrix, and it is a porous narrow band which forms at the cement past/aggregate interface called the interfacial transition zone (ITZ). For RFA there are two ITZ phases: a new ITZ between the new cement paste and the old mortar from the RFA and an old ITZ between the old cement paste and the original aggregate NA [46]. Moreover, the crushing process itself results in micro cracks in the adhered mortar/original aggregate. These pores and cracks in the RFA absorb water and lead to high water content, which then leads to weaker concrete.  
According to study of [PERSON] et al. [47], the higher the minimum particle size of the RFA is, the higher it would contain old adhered cement binder. Fineness modulus is a standard index that defines how fine or coarse a sample of sand is. A fineness modulus value smaller than 2.8 indicates fine sand, while a larger number indicates coarse sand. Evidence shows that the fineness modulus of the RFA incorporated in concrete has a significant effect on several functional parameters. First, for RFA with similar oven-dried density and lower fineness modulus (<2.8), the following studies [15; 47; 48; 49; 50; 51; 52] obtained higher water absorption compared to the results of the studies, which used RFA with higher fineness modulus (>2.8) [42; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66]. Similarly, [PERSON] and [PERSON] [67] studied the carbonation behaviour of RFA concrete with different minimum particle sizes and incorporation level of RFA with two design compositions. In the first design, the water content was kept constant and in the second design the workability was kept constant. The authors reported that as the minim  
Figure 1: Structure of full replacement of fine and coarse naturally sourced aggregates (NA) with fine and coarse recycled aggregate (RA) and their transition zones.  
decreases, the carbonation depth of RFA based concrete increase. Contrary to the previous statement, [PERSON] and [PERSON] [67] studied the effect of RFA's particle size and reported that, as seen in Figure 2, the compressive strength of concrete for the same incorporation levels of RFA decreased with increasing content of smaller fine RFA particles. The researchers cited the fact that larger RFA particles have higher adhesion properties due to the presence of a higher % of old adhered binder.
## 5 Conclusions  
For ultrasonic inspection of back-surface micro-cracks, the energy intensity changes in the ultrasonic wave are mainly caused by losses of transmitted and reflected waves. The simulation results agreed with the experimental measurements on the relationship between surface roughness and pulse-echo signal of back-surface micro-crack. It was found that surface roughness influenced  
Figure 14: (**a**) Normalized mean signal amplitudes of notches reduce as back-surface roughness \(R_{a}\) increases; T’ represents CNC turning, ’M’ represents vertical CNC milling. (**b**) The signal amplitude ratio factor (SARF) reduces as back-surface roughness \(R_{a}\) increases.  
crack signals, which was reflected in two aspects. On one hand, the mean amplitude of the received pulse-echo signal of the micro-crack decreased obviously with the growth of \(\mathbf{R_{a}}\). The horizontal direction parameter \(\mathbf{R_{sm}}\) had a negligible effect on change in crack signal. On the other hand, the mean amplitude of the noise signal caused by multiple scattering nearly in all directions on rough surface increased slightly with an increase in \(\mathbf{R_{a}}\).  
The SARF value was applied to investigate the detection limits for back-surface micro-crack with rough front surface and rough back surface. For the rough front surface and smooth back surface, the signal of micro-crack with depth lower than 400 \(\upmu\)m was difficult to distinguish from the noise signal when \(\mathbf{R_{a}}\) exceeded 15 \(\upmu\)m. Compared to the rough front surface, attenuation of the energy intensity of micro-crack pulse-echo caused by the rough back surface was slighter. The micro-crack with depth over 200 \(\upmu\)m was accurately identified when the \(\mathbf{R_{a}}\) of back surface was less than 24 \(\upmu\)m. The surface roughness leads to substantial changes in the results of ultrasonic inspection for micro-cracks. This research provides a valuable reference for obtaining detection limits of micro-cracks under different rough surfaces, which is beneficial for improving detection reliability and accuracy for micro-cracks. Further work will focus on the effect of surface roughness on the detection of front-surface micro-cracks.  
[PERSON] and [PERSON] conceived the paper structure; [PERSON] and [PERSON] performed the simulations and analyzed the data; [PERSON] and [PERSON] designed and conducted the experiments; [PERSON], [PERSON] and [PERSON] prepared the manuscript and checked the writing.  
This paper was financially supported by the National Natural Science Foundation of China (NNSFC) (No. 51275193, No. 61401415).  
The authors declare no conflict of interest.
## Appendix A
### Polarization of Light
The parameter \(S_{1}\) describes the dominance of linear horizontal polarized (LHP) light over linear vertical polarized (LVP) light; \(S_{2}\) describes the preponderance of linear \(+45^{\circ}\) polarized (L + 45P) light over linear \(-45^{\circ}\) polarized (L - 45P) light and \(S_{3}\) describes the dominance of right circular polarized (RCP) light over left circular polarized (LCP) light [79]. For pure polarization states, the normalized vector is situated on the Poincare sphere with unity-radius, while in case of partially polarized states the normalized vector will be placed inside the unity radius Poincare sphere at a distance of \(p\) from the origin. The orientation and ellipticity angles, \(\psi\) and \(\chi\), associated with the polarization ellipse can be related to the Stokes parameters associated with the Poincare sphere as follows [80]:  
\[ \begin{array}{rcl}\psi\ =\ \frac{1}{2}\tan^{-1}\!\left(\frac{S_{2}}{S_{1}} \right),&0\leq\psi\leq\pi\\ \chi\ =\ \frac{1}{2}\sin^{-1}\!\left(\frac{S_{3}}{S_{0}}\right),&-\frac{\pi}{4} \leq\chi\leq\frac{\pi}{4}\end{array}  \tag{11}\]Following the Mueller formalism, the Stokes components at the output of a given optical sample can be modelled as \(S^{\prime}=M_{\text{sample}}\ S\), where \(S\) and \(S^{\prime}\) are the input and output Stokes vectors, respectively, and \(M\) is the \(4\times 4\) Mueller matrix of the sample,  
\[\left( \begin{array}{c}S^{\prime}_{0}\\ S^{\prime}_{1}\\ S^{\prime}_{2}\\ S^{\prime}_{3}\end{array} \right)=\left( \begin{array}{cccc}M_{11}&M_{12}&M_{13 }&M_{14}\\ M_{21}&M_{22}&M_{23}&M_{24}\\ M_{31}&M_{32}&M_{33}&M_{34}\\ M_{41}&M_{42}&M_{43}&M_{44}\end{array} \right)\left( \begin{array}{c}S_{0}\\ S_{1}\\ S_{2}\\ S_{3}\end{array} \right) \tag{10}\]  
Similarly to the Jones calculus, when a polarized light wave passes through several optical objects, the polarization of the outgoing beam can be evaluated by knowing the Stokes vector of the input wave and applying Mueller calculus that needs a Muller matrix for each crossed optical object; the resulting vector contains the Stokes parameters of the light outgoing the system [80].