esa-sceva/satcom-chunk-collection huggingface dataset:
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 1 Introduction
At the same time, the influence of channel noise and recognition performance under high error conditions must be further improved. Reference [21] constructed a rank difference matrix, and when different scrambling methods were used, there was a periodic loss of rank at the integer multiples of the code length, which was used as a basis to judge the scrambling method. Reference [27] proposed to apply neural networks in order to extract rank features for the purpose of classification. However, the structure and parameters of the neural network were not given. Meanwhile, when the BER is greater than 0.01, the matrix tends to be a random matrix, the rank feature is not obvious, and the recognition rate tends to be 0. To summarize, due to the need for accurate scrambling-type information, the existing algorithms cannot achieve the full blind identification of linear block code scrambling. In recent years, deep learning methods have achieved good results in signal recognition [28; 29], modulation recognition [30; 31], malicious file detection [32; 33], and other fields [34; 35]. At the same time, deep-learning-based methods avoid manual feature extraction and have high practical value. To solve the above-mentioned problems and the difficulty of manual feature extraction, under the conditions of a high bit error rate (BER) and a low signal-to-noise ratio (SNR), in this paper, we propose a linear block code scrambling-type identification based on correlation feature extraction. The contributions of this paper are summarized as follows:  
(1) We deduce the symbol cross-correlation of the scrambling sequence and construct the partial autocorrelation function, both of which can reflect the correlation characteristics of different scrambling methods. (2) We construct a shallow network consisting of convolutional neural network (CNN) and long short-term memory (LSTM) neural network models, which can accomplish the identification of scrambled linear block codes under a high BER and a low SNR. The remainder of this paper is organized as follows: In Section 2, we summarize the principle and mathematical model of the scrambler. In Section 3, we analyze the cross-correlation characteristics of the symbols and construct a partial autocorrelation function. A correlation feature extraction network model is built in Section 4, and we analyze the training process of the network. In Section 5, the proposed method is evaluated using Monte Carlo simulations, and we compare its performance with that of the multi-fractal spectrum method. Finally, a discussion is provided in Section 6.
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
## 3 Results
### Stiffness Tensors for 3D Fractured Model  
In the 3D case, as shown in Figure 5, we build four models, each being a cube of 0.1 m on all sides for validating the 3D numerical oscillatory relaxation tests. These models include penny-shaped ellipsoidal cracks along the horizontal direction, and \(x\)-, \(y\)-, and \(z\)-semiaxes of 0.02 m, 0.02 m, and 0.003 m, respectively, with different fracture densities.  
As with 2D, the four fracture densities are set to 0.04, 0.112, 0.192, and 0.416, respectively, according to the basic definition,  
\[\zeta=\frac{Na^{3}}{v} \tag{30}\]  
where \(v\) is the volume of the cube. The same physical parameters in Table 1 are applied to the 3D models, and the relaxation tests are conducted at a frequency of 20 Hz.  
Equations (1) and (11) are also solved by the relaxation experiments with FEM. Once the stress and strain fields are obtained, the stiffness tensor is calculated by the least-squares method. Figure 6 shows the resulting equivalent stiffness tensors \(M_{12}\) and \(M_{44}\). With increasing fracture density, the equivalent stiffness tensors \(M_{12}\) and \(M_{44}\) tend to decrease.  
Figure 4: Effective stiffness tensors \(M_{22}\) (**a**) and \(M_{12}\) (**b**) for 2D fracture model.  
Figure 5: Four 3D models, each being a cube with 0.1 m on its sides, contain penny-shaped spheroid cracks with different fracture densities: 0.04 (**a**), 0.112 (**b**), 0.192 (**c**), and 0.416 (**d**).
## 2 Materials and Methods
### Vegetation Index Selection  
Vegetation indices are quantitative indicators derived from remote sensing imagery, widely utilized to characterize the growth status and physiological attributes of surface vegetation. These indices are typically computed by linearly or non-linearly combining reflectance values from multiple spectral bands, serving as a compact semantic representation of spectral information [33]. In disease identification tasks, vegetation indices provide an effective means of assessing crop health and capturing the physiological changes induced by disease stress [34]. The spectral characteristics of plants are influenced by the chlorophyll content, cellular structure, and water status [35]. Red and blue light absorption is primarily controlled by chlorophyll, while near-infrared (NIR) reflectance depends on the integrity of the cellular structure. Disease infection can induce symptoms such as leaf yellow, necrosis, spotting, or wilting, leading to a diminished chlorophyll content, cellular damage, and water loss. Consequently, diseased plants exhibit increased red and blue light reflectance, decreased NIR reflectance, and altered spectral features in the green and red-edge bands [36]. These spectral changes can be quantitatively characterized by vegetation indices, which serve as critical features for identifying wheat disease areas. By computing appropriate vegetation indices from multispectral data, the detection accuracy of disease stress signals can be significantly enhanced [37]. Based on these principles, 20 vegetation indices with high sensitivity to wheat health and disease variations were selected and incorporated into the model as features derived from multispectral remote sensing data. Table 2 presents the calculation formulas of various \begin{table}
\begin{tabular}{l c c c c} \hline
**Diseases** & **Training** & **Validation** & **Test** & **Total** \\ \hline Healthy & 1577 & 198 & 197 & 1972 \\ Others & 1569 & 198 & 197 & 1964 \\ Stripre rust & 1647 & 207 & 206 & 2060 \\ Yellow dwarf & 1288 & 162 & 161 & 1611 \\ \hline \end{tabular}
\end{table} 
Table 1: The distribution of the MSWDD2024 dataset. Figure 2: The data processing workflow. vegetation indices and outlines their relevance in disease monitoring, providing essential feature support for subsequent classification tasks.
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 2 Image Sampling Methods
### Super-Resolution Microscopy  
Several super-resolution microscopy technologies are developed to break the diffraction limit of optical microscopes during recent years. Figure 4a illustrates the configuration of a structured illumination microscope (SIM), whose spatially patterned excitation light can be generated by multiple laser beams; as such, SIM can be used for super-resolution observation [108]. Specifically, the resolution of SIM can reach approximately less than 100 nm laterally and 300 nm axially [109]. The optical section can be sampled by generating either a grating at the conjugate plane of specimens or a fringe pattern onto them [110]. Figure 4b shows the sampled images of cytoskeletal and nuclear components in a uterine endometrial epithelial cell [109]. The images obtained with a SIM have better contract and resolution than the images sampled via a WFFM. However, the high-resolution sampling of SIM is prone to small position movement and intensity changes, Therefore, in case of a poor signal-to-noise ratio, the specimen should be fixed during SIM measurement [111], which will reduce the application range of SIM in intracellular microscopy.  
Another exciting phenomenon for super-resolution microscopy is stimulated emission through which PSFs are scaled down to a subdiffraction level [112]. Figure 4c shows a typical configuration of a stimulated emission depletion microscope (STEDM); here, excited-state electrons can return to their ground state by interacting with a depletion laser beam of a certain frequency [113]. The lateral resolution of STEDM can reach 30-80 nm for most commercial systems, and the axial resolution of less than 100 nm of STEDM can be achieved with specialized depletion schemes [114]. Figure 4d illustrates the STEDM-sampled images of living hippocampal primary neurons; the enlarged view proves its ability to provide a nanoscale resolution that is superior to that of CFM [115]. However, stimulated emission is obtained by using a high-powered depletion laser. The higher the laser density, the higher the sampling resolution, but the photobleaching due to high depletion laser intensities is a problem that cannot be ignored [116].  
Super-resolution microscope technology is advanced in observing small-scale intracellular components (such as protein [117; 118], lysosomal [119; 120], bacteria [121; 122]). However, there are few attempts to use SIM and STEDM for micromanipulation because the current robot-aided manipulation tools can only handle objects at as low as sub-micrometers. With the rapid development of microfabrication and information technology, super-resolution microscope will play a more important role in the future.  
Figure 4: _Cont._
## 2 Optimization Model for Reference Trajectory in ARP
### Principles of ARP Coordinate System Conversion  
During the automatic parking process, the raw sensor data and system calculations need to be effectively connected through coordinate system alignment. Although the real-world coordinate system constructed from sensor data contains complete parking scene information, it suffers from baseline shift due to differences in sensor installation posture  
Figure 2: Principal schematic diagram of ARP.  
and distortion effects. Therefore, it needs to be converted into a unified standardized coordinate system: Taking the far corner of the parking space as the coordinate origin, extending the x-axis along the bottom line of the parking space, and determining the y-axis through the right-hand rule. This standardized system eliminates sensor deviations through the following three steps (scene extraction, coordinate system rotation, and origin translation), not only establishing a unified spatial reference for multi-source data but also significantly improving the computational efficiency of parking trajectory planning and tracking control. Additionally, it provides a coordinate reference that aligns with human spatial cognition for the visualization interface.  
In the standardized Cartesian coordinate system for inbound parking planes, the coordinates of a point are determined to be \((x_{s},y_{s})\). It is straightforward to derive the angle \(\angle B=\arcsin\left(\frac{y_{s}}{\sqrt{x_{s}^{2}+y_{s}^{2}}}\right)\) between this point and the x-axis.  
\[\begin{array}{l}x_{r}=\sqrt{x_{s}^{2}+y_{s}^{2}}\cdot\cos(\angle B-\angle A )+xf\\ y_{r}=\sqrt{x_{s}^{2}+y_{s}^{2}}\cdot\sin(\angle B-\angle A)+y_{f}\end{array} \tag{2}\]  
To further obtain the coordinates \((x_{r},y_{r})\) of this point in the real-world coordinate system, the real-world projection rotation angle \(\angle A\) detected by the sensor system and the coordinates \((x_{r},y_{r})\) of the far corner point of the garage bottom in the real-world coordinate system can be substituted in the above reverse-engineer equation.  
The real-world coordinates \((x_{r},y_{r})\) of this point can be obtained from the above reverse-engineer equation. To further obtain the conversion formula from \((x_{r},y_{r})\) to \((x_{s},y_{s})\), the real-world projection rotation angle \(\angle A\) detected by the sensor system and the coordinates \(\left(x_{f},y_{f}\right)\) of the far corner point of the garage bottom in the real-world coordinate system can be substituted in the above reverse-engineer equation.
## 3 Related Theory  
In the second section, the methods for dealing with outliers and noises are presented, and the evaluation index is given. In this section, the GRU network model is introduced.  
The biggest difference between RNN and other neural networks is that, by introducing the circular structure, the information is allowed to persist. The network retains the characteristics of the processed information, ensures the memory of the neural network and can connect the previous information with the current task. Therefore, it is widely used in time-series data analysis. However, the classical RNN network has the risk of gradient explosion and gradient disappearance. In fact, it does not have the ability to deal with long-time dependent data.  
Figure 2: Comparison between the original stack voltage and the stack voltage after MAF.  
In order to solve this problem, LSTM is proposed. The novelty of LSTM is that it introduces the concept of cell. Specifically, there are three gates in a cell: input gate, forget gate and output gate, and the structure of LSTM can be found in Reference [31]. Through these three gates, LSTM can make information pass selectively when processing information, that is, memorize part of information and forget part of information.  
GRU is improved on the basis of LSTM; it combines the forget gate and input gate into a single update gate. Moreover, the cell state and hidden state are mixed, etc. The final model is simpler than the standard LSTM model, and the parameters are less than 33%, but the effect is close. The cell structure of GRU is shown in Figure 3  
At first, the reset gate and the update gate are obtained by the previous output of the hidden layer and the current input of the cell.  
\[\begin{array}{l}r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}])\\ z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}])\end{array} \tag{5}\]  
where \(r_{t}\) means the reset gate, \(z_{t}\) represents the update gate, \(h_{t-1}\) represents the previous output of hidden layer, \(x_{t}\) is the current input of the cell and \(\sigma\) is the sigmoid function.  
Secondly, after getting the gate signal, the reset gate is used to process data, and the memory of data is realized in this step. The function of the \(\tanh\) function is to scale the data to 0 and 1 directly for later use. The calculation formula is as follows:  
\[\widetilde{h}_{t}=\tanh(W\cdot[r_{t}*h_{t-1},x_{t}]) \tag{6}\]  
At last, the same gate is used for data forgetting and selective memory. Moreover, \((1-z_{t})*h_{t-1}\) realizes the selective forgetting of the original hidden state. \(z_{t}*\widetilde{h}_{t}\) means selective memory of current cell. The formula is shown as follows:  
\[h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\widetilde{h}_{t} \tag{7}\]  
where \(z_{t}\) belongs to [0, 1] and represents the weight of forgetting.  
As with LSTM, GRU has the ability of long time-series information processing, but its structural unit is more compact than LSTM, so the convergence speed of GRU is faster.
## 2 Test Equipment and Material Properties
### Test Parameters  
The contact state of the interlayer interface is related to the type and gradation of upper- and lower-layer materials, the type and number of adhesive materials, test temperature, specimen size, shear rate, and other factors. The test parameters were selected as follows.  
Test temperature: According to the climatic characteristics, \(-\)10, 0, 25, and 50 \({}^{\circ}\)C were selected as the test temperatures which reflect the asphalt mixture temperature of pavement under the conditions of low temperature, 0 degrees, room temperature, and high temperature.  
Mixture type: According to the characteristics of mixture types commonly used for asphalt pavement and cement-stabilized base, three kinds of asphalt mixture (AC-13, AC-20, and AC-25) and one kind of cement-stabilized base mixture were adopted.  
Specimen size: Referring to relevant data, the stability of the shear test data has a certain relationship with the size of the specimen. The data obtained from specimens with larger size indicate more stability than smaller size specimens. According to existing research results [15], a specimen diameter of 100 mm is relatively stable. Therefore, the 100 mm diameter was taken as the basic size of the specimen used in this study, and the thickness of the asphalt mixture specimen was 50 mm.  
The amount of viscosity-level oil spread: According to the amount and type of viscosity-level oil used in asphalt pavement construction, two different viscosity-level oil types were considered in this study, namely, PC-3 fast-cracking cationic emulsified asphalt and high viscosity fast-cracking cationic emulsified asphalt. Three different densities of emulsified asphalt were selected, namely, 0.5, 1.0, 1.5 kg/m\({}^{2}\).  
Vertical compressive stress: Combined with mechanical calculation, 0.7 MPa was selected as the vertical compressive stress between the AC-13 and AC-20 layers; 0.16 MPa was selected as the vertical compressive stress between the AC-20 and AC-25 layers; and 0.07 MPa was selected as the vertical compressive stress between AC-25 and the cement-stabilized base layer, striving to be closer to the actual stress of each structural layer on the road surface.  
Shear rate: Based on existing research results [16], a shear rate of 5 mm/min was selected for the test.
###### Abstract  
Background: Enchondroma is the most common benign bone tumor of the hand. Surgical excision of the tumor using curettage is the treatment of choice. The management of the resulting defects is still a controversial topic in the literature. Methods: This retrospective study includes 15 patients diagnosed with solitary enchondroma in the hand bones: eight cases with type A, three cases with type B and four cases with type D according to Takigawa classification. The aim of this study was to compare the course and outcome in the three patient groups treated by curettage associated with natural consolidation of the bone defect, autologous bone graft or injectable synthetic bone substitute in association with plate and screw osteosynthesis. Results: Outcomes were assessed using the DASH score (mean score 2.5) and TAM score (excellent in all patients) with no significant functional differences between the three groups. Defects managed with k-IBS\({}^{\circled{\otimes}}\) injectable bone substitute were associated with shorter operating time, simpler surgical technique and less postoperative pain assessed by VAS score. Conclusion: The use of k-IBS\({}^{\circled{\otimes}}\) bone substitute is efficient and less technically demanding than autologous bone grafting. The Takigawa classification could be a good indicator for treatment choice.  
Keywords:enchondroma; bone defect; autologous bone graft; injectable bone substitute; hydroxyapatite;tricalcium phosphate; chitosan +
Footnote †: journal:  
Article Treatment of Bone Defects Resulted after Excision of Enchondroma of the Hand in 15 Patients, Comparing the Techniques of Autologous Bone Graft, Injectable Bone Substitute and Spontaneous Healing
## 4 Experiments
### Analysis of Experimental Results
#### 4.4.2 Improved Model Comparison Experiments  
To better demonstrate the benefits of the improved model on the IGBT dataset, each improvement point will be validated separately below:  
* To verify the superiority of the C3 STR module, the following comparison experiments were carried out between it and the classical attention module. To ensure fairness in the experiments, all experiments were carried out at the same location based on the addition of a small target detection layer and the results are shown in Table 3. The results show that the CSTR module proposed in this paper improves the detection accuracy by 7.4%, 5.7%, 6.2% and 9.9% over the CA [43] module, SE module [44], CBAM [45] module and C3 CBAM module, respectively, which is because C3 STR combines the advantages of Swin Transformer Blocks and retains the maximum features based on Improved detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision\%** & **mAP\%** & **GFLOPs/M** & **Weight/M** \\ \hline YOLOv5n & 81.3 & 57.9 & 4.2 & 3.9 \\ YOLOv5n\_SEST & 85.4 & 63.5 & 17.7 & 5.3 \\ YOLOv5s & 81.7 & 58.2 & 15.8 & 14.5 \\ YOLOv5s\_SEST & 74.1 & 63.2 & 69.8 & 17.5 \\ YOLOv5n & 83.5 & 59.9 & 48.0 & 42.2 \\ YOLOv5m\_SEST & 77.8 & 62.9 & 272.5 & 52.7 \\ YOLOv5l & 75.9 & 60.7 & 107.8 & 92.9 \\ YOLOv5l\_SEST & 80.0 & 62.7 & 690.6 & 118.7 \\ YOLOv5x & 75.1 & 61.3 & 204.0 & 173.2 \\ YOLOv5x\_SEST & 84.8 & 63.5 & 1401.6 & 225.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of YOLOv5 basic network model and improved model results.  
* Performance comparison of the number of C3 STR settings was carried out to compare the effect of different numbers on the performance of the IGBT solder layer defect detection model, and the experimental design gradually replaced the C3 module when the detection results were compared, and the results are shown in Table 4. Experimental results show that replacing a C3 module gives the greatest accuracy gain at a minimal cost.
* In order to verify the effectiveness of the EIoU loss function for the dataset of this paper, the following experiments will be compared with the loss functions of DIoU, GIoU, CIoU and AlphaIoU, and the results are shown in Table 5. The experimental results show that the EIoU loss function improves the detection accuracy by 3%, 2.3%, 1% and 2.8%, respectively, compared with other loss functions, proving its indispensable advantage for sample imbalanced data sets.