esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Investigation
### Concrete Compression at Head  
In order to study the concrete stress distribution at heads, sectioning of the specimen has been conducted and the concrete stress contour can be seen in Figure 32.  
It can be seen that high compressive stress can be observed in the nearby area of the heads. In addition, a compression stress zone is observed located at the region between the two heads, which generates the diagonal compressive strut. The diagonal compressive strut can be observed in Figure 33, which only displays the element in compressive stress situation with the elements in tensile-stress situation being hidden. The angle \(a\) between the diagonal compression strut and headed bar is proposed and it can be calculated through the following Equation (1):  
\[\tan a=s/l_{lap} \tag{1}\]  
where \(s\) is the spacing of the adjacent headed bar; \(l_{lap}\) is the lap length.  
Figure 31: Crack pattern of experimental and numerical observation.  
Figure 32: Concrete stress contour (**a**) Section of the specimen; (**b**) Maximum principal stress contour at 1-1 section.  
Comparisons are made and shown in Figure 34 between the diagonal cracks in tests and the proposed \(a\) angle. It can be seen that the direction of the \(a\) angle matches with the direction of diagonal cracks. It validates the accuracy of the proposed method to determine the diagonal strut angle \(a\).  
Through the stress vector contours in Figure 35, the diagonal strut can be recognized of the bottle shape, which is the same as the strut in STM model. Again, the direction of the compressive stress vectors matches the angle \(a\). For the diagonal strut, the compression-bearing area near the head is smaller but with a higher compression stress compared to that of the middle height between the two heads.  
Figure 34: Angle \(a\) against the direction of diagonal crack of specimen M4-D12-7.5d.  
Figure 35: Compressive stress vector contours (**a**) Top view (**b**) 1-1 section (**c**) 2-2 section.  
A similar stress distribution can be found in Figure 36. When the section locates at the head (section 1-1 and section 5-5), the high compression stresses can be observed nearby the head. As the section moves to the medium height of the lap length, the compression stresses decrease while the compression region area is increased. Such a trend is consistent with the stress distribution in the diagonal strut of the bottle shape.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 2 Related Work  
### CNN for MIS  
Convolution-based methods are widely used in medical image segmentation due to their outstanding performance. The U-Net architecture, introduced by [PERSON] et al. [1], features a symmetrical encoder-decoder structure. High-resolution features from the encoder are combined with low-resolution features from the decoder via skip connections, designed to retain detailed information. This has established U-Net as a leading framework for MIS due to its superior performance in this area. Building on this, [PERSON] et al. [28] developed UNet++, which enhances the U-Net architecture by adding additional skip connection pathways that link each decoder layer to multiple encoder layers. Additionally, a multilevel feature pyramid is created through the integration of cascading and skip connections. However, this architecture significantly increases the number of parameters. To overcome these drawbacks, [PERSON] et al. [29] proposed Unet3+, which features full-scale skip connections and depth-supervised strategies to reduce the parameter count while effectively capturing both coarse and fine-grained features at multiple scales. With the growing use of attention mechanisms in various computer vision tasks, several researchers have explored their integration into medical segmentation networks. [PERSON] et al. [3] introduced the attention U-Net, which incorporates a gated attention mechanism within the skip connections to better segment small target areas. Similarly, [PERSON] et al. [30] developed the guided attention module, which includes spatial and channel self-attention submodules that help to adaptively integrate features across multiple scales. Drawing inspiration from ResNet [31], the Res-UNet [32] combines weighted attention mechanisms, skip connections, and residual connections within the U-Net architecture. This combination facilitates more effective feature modeling, enhancing the distinction between vascular and non-vascular structures and improving the accuracy of retinal vascular segmentation. Meanwhile, MultiResUNet [33] modifies the standard U-Net by replacing its convolutions with multi-scale convolution kernels and incorporating residual structures into the skip connections, achieving enhanced segmentation performance. Despite these advancements, convolutional neural networks, limited by the size of their convolutional kernels, still struggle to model long-range dependencies and effectively capture global contextual information.
## 2 Results  
### Identification of Allium Species Volatiles via SPME-GC/MS  
SPME was employed to analyze _Allium_ headspace volatiles and then the trapped volatiles were subsequently analysed via GC/MS [6]. The biological variance within each specimen was assessed using three independent biological replicates, subjected to the same extraction and analysis conditions. Volatiles analysis using SPME led to the detection of 42 volatile components belongingto 30 sulphur containing volatiles, four nitriles, two nitrogenous volatiles, three aromatics and three esters. Representative GC/MS chromatogram of freeze dried _A. sativum_ and _A. cepa_ red cv. is displayed in (Supplementary materials Figure S1). The identity, retention time (r.t.), retention index (RI) and mass-to-charge ratio (\(m/z\)) of these compounds are shown in Table 1. Volatiles belonged to various classes including mostly sulphur and non-sulphur. Sulphur compounds constituted the most dominant volatile class which was found more enriched in _A. sativum_ with a relative percentile of (99.8%) versus (83%) in _A. cepa_. Major identified sulphur volatiles included diallyl disulphide (45-99%) in _A. sativum_ versus allyl methyl trisulphide (13-20%) in _A. cepa_. In the current study, several sulphur rearrangement products i.e., allyl compounds identified in peaks M1, M6, M10, M14, M16, M28, M29, M32, and M36 along with a cyclic, 3-vinyl-1,2-dithiacyclohex-5-ene (M21) were detected in _A. sativum_, and likely to have been formed at the high temperature of the GC/MS injection port. Short chain sulphur molecules exemplified in dimethyl trisulphide (M3), dimethyl tetrasulphide (M23) and dipropyl trisulphide (M30) were identified mostly in _A. cepa_, reported as degradation products in cooked _Allium_. With regard to sulphur compounds abundance, _A. sativum_ was found much more enriched in this volatile class compared to _A. cepa_ in all examined specimens. Next to sulphur containing compounds, esters amounted to the major volatile form especially in the commercially dried garlic product \"Tomex\" represented by diethylphthalate at ca. 99% of its volatile blend. A few aromatics were identifed almost exclusively in _A. sativum_ including cuminaldehyde (M25) and 3-isopropylbenzaldehyde (M26), although at trace levels.
## 5 Discussion
For Case 1, the optimal value of the RMSE of the position estimation is 284.54 m, while in Case 2 and Case 3, the value is respectively 2836.02 and 308.632 m. This proves that under the conditions of the simulation parameters set in this paper, the minimum coordinate estimation error of a single RFI source is 284.54 m, and the minimum coordinate estimation error of multiple RFI sources is 2836.02 m, so the coordinate estimation of a single RFI source is more accurate than that of multiple RFI sources. Likewise, similar to the DOA estimation result, the RMSE of the geographical location estimation in the three cases decreases with ISR increasing. Additionally, this method also has better performance for RFI signals with a narrow bandwidth. It can be proven that the location error is highly consistent with the DOA estimation error in the proposed method. In the evaluation part, the estimated RFI source location and the DBF algorithm are utilized to suppress the RFI signal. The results in Figure 16 indicate that the obtained beamforming pattern will form a notch in a given interference direction to perform interference suppression. Although the location estimation result with errors will deviate the notch direction from the real RFI direction, the beamforming pattern also has a strong weakening effect on incoming signals within a certain error range, which means that these errors will not have a great impact on the use of the DBF algorithm to suppress interference. The results in Figure 17 and Table 3 illustrate that the RFI source location coordinates estimated by the proposed method are useful for interference suppression. They also show that the location estimation error has an impact on the interference suppression effect, but this effect is small and tolerable. In summary, this proposed approach in this paper can estimate the geographic coordinates of the RFI source and the estimation error is acceptable.
## 3 Applications in Earthquake Emergency Response
### 2017 M7.0 Juzhaigou Earthquake  
The seismic damage assessment of the 2017 Juzhaigou earthquake is a typical application case [31]. After the earthquake, several sets of ground motion records were obtained from the seismic network, and the seismic damage prediction of the target region was completed in 2 h (including the time for the data checking and report editing/publishing) by using the method proposed. The predicted damage of a typical town and country in the Aba region under the ground motion of the Juzhaigou Baihe station is shown in Figure 10. The predicted results show that the buildings in the disaster area may be damaged to some extent, but the ratio of collapse is very small, which is consistent with the actual post-earthquake site investigations [32]. The results provide a useful reference for the earthquake emergency response and scientific decision-making of earthquake disaster relief.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Earthquake Name** & **ID** & **Earthquake Name** \\ \hline
1 & 12/08/2016 M6.2 Xinjiang Hutubi earthquake & 18 & 11/26/2018 M6.2 Taiwan Strait earthquake \\
2 & 12/18/2016 M4.3 Shanxi Qingxu earthquake & 19 & 12/08/2018 M4.5 Xinjiang Changji earthquake \\
3 & 03/27/2017 M5.1 Yunnan Yangbi earthquake & 20 & 12/16/2018 M5.7 Sichuan Yibin earthquake \\
4 & 08/08/2017 M7.0 Sichuan Jiuzhaigou earthquake & 21 & 12/20/2018 M5.2 Xinjiang Kizilsu earthquake \\
5 & 09/30/2017 M5.4 Sichuan Qingchuan earthquake & 22 & 01/03/2019 M5.3 Sichuan Yibin earthquake \\
6 & 02/06/2018 M6.5 Taiwan Hualien earthquake & 23 & 01/07/2019 M4.8 Xinjiang Jiashi earthquake \\
7 & 02/12/2018 M4.3 Hebei Yongqing earthquake & 24 & 04/16/2016 M7.3 Japan Kumamoto earthquake \\
8 & 05/28/2018 M5.7 Jilin Songyuan earthquake & 25 & 08/24/2016 M6.2 Italy earthquake \\
9 & 08/13/2018 M5.0 Yunnan Tonghai earthquake & 26 & 11/13/2016 M8.0 New Zealand earthquake \\
10 & 08/14/2018 M5.0 Yunnan Tonghai earthquake & 27 & 09/20/2017 M7.1 Mexico earthquake \\
11 & 09/04/2018 M5.5 Xinjiang Jiashi earthquake & 28 & 11/23/2017 M7.8 J Iraq earthquake \\
12 & 09/08/2018 M5.9 Yunnan Mojiang earthquake & 29 & 06/18/2018 M6.1 Japan Osaka earthquake \\
13 & 09/12/2018 M5.3 Shanxi Ningqiang earthquake & 30 & 09/06/2018 M6.9 Japan Hokkaido earthquake \\
14 & 10/16/2018 M5.4 Xinjiang Jinghe earthquake & 31 & 10/26/2018 M5.4 Japan Hokkaido earthquake \\
15 & 10/31/2018 M5.1 Sichuan Xichang earthquake & 32 & 12/01/2018 M7.0 Alaska earthquake \\
16 & 11/04/2018 M5.1 Xinjiang Atushi earthquake & 33 & 01/03/2019 M6.2 Japan Kumamoto earthquake \\
17 & 11/25/2018 M5.1 Xinjiang Bole earthquake & & \\ \hline \end{tabular}
\end{table}
Table 2: Applications of the RED-ACT system.  
Figure 10: Seismic results of a (**a**) typical town and (**b**) country in the Aba region subjected the ground motion from the Juzhaigou Baihe station.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 3 Materials and Methods  
Figure 2 illustrates the structure of the proposed SCP optical flow odometry method, which contains three parts. First, the raw panoramic image is converted into a three-dimensional spherical structure through spherical mapping, and the pixels in the corresponding spherical image are evenly segmented. Then, the spherical image is divided into six subregions of equal area according to the pixel distribution. Eventually, the gradient of pixels within each subregion is calculated, followed by optical flow tracking based on these gradients.
# An Improved Blind Recognition Algorithm of Frame Parameters Based on Self-Correlation
## 1 Introduction  
In digital communication systems, frame synchronization is a prerequisite for subsequent signal processing such as error correction and information extraction [1; 2; 3; 4]. Frame synchronization words can mark the place for mapping receiving binary stream and eliminating redundant information. However, the manner of frame synchronization is different under different communication conditions. This paper focuses on the frame synchronization methods in the background of the non-cooperative communication. The frame parameters of the received binary signals must be blindly recognized in the context of non-cooperative communication. The cumulative filtering algorithm is the most commonly used blind identification method, the core of which is to use the randomness and synchronization features of asynchronous codes to identify the frame parameters by traversing frame lengths [5]. However, there are some disadvantages of this method, including weak resistance against bit errors, low correctness rate, too many memory matrix lines and also more consumption of the calculation resources. Based on the existed cumulative filtering algorithm, this paper proposes an improved frame parameter blind identification method based on self-correlation, and its performance is greatly improved.  
The rest of the paper is arranged as follows: in Section 2, the cumulative filtering algorithm is briefly described. In Section 3, algorithm based on self-correlation is demonstrated detailly. In Section 4, the improved algorithm based on self-correlation is explored. Section 5 demonstrates the simulation results and Section 6 concludes the whole paper.
# The Use of Asymmetric Polynomial Profiles for Planning a Smooth Trajectory
## 2 Materials and Methods
### Asymmetric Acceleration Profile
The form of the polynomial determining the acceleration profile meets the above requirements as follows:  
\[s_{m_{1}m_{2}m_{3}}^{(2)}(t)=p\cdot t^{m_{1}}\cdot(t-k\cdot t_{\text{end}})^{ m_{2}}\cdot(t-t_{\text{end}})^{m_{3}} \tag{3}\]  
The first requirement refers to planning the PTP (point-to-point) trajectory. In the case of concatenation during planning the trajectory with intermediate points, the shift in time of all profiles which determine the courses of kinematic quantities will be necessary. The second requirement enables obtaining various values of velocity at the beginning and end of motion. The third requirement confines the number of zero places up to three and determines the position of the second zero place. For the change of the acceleration phase into braking (or inversely) the multiplicity of the second root t = k\cdot t_{\text{end}}\) should be an odd number. The fourth requirement causes elimination of the unfavourable phenomenon which is jerk in the initial and final periods of motion. This will allow for better positioning accuracy and will constrain the system vibrations at the beginning and end of the motion. For meeting this requirement, the multiplicity of the first and third roots must follow the inequality m \(\geq\) 2. Considering the above requirements, the smallest degree of the polynomial determining the acceleration function is n = 5. In this case, the successive roots have the multiplicities m\({}_{1}\) = 2, m\({}_{2}\) = 1 and m\({}_{3}\) = 2. In the case of the polynomial of degree 6, there are two satisfying polynomials whose roots have the multiplicities m\({}_{1}\) = 3, m\({}_{2}\) = 1, m\({}_{3}\) = 2 and m\({}_{1}\) = 2, m\({}_{2}\) = 1, m\({}_{3}\) = 3. Table 2 presents the roots multiplicity for the 5 th-9 th degree polynomials. The considerations were limited to the analysis of the polynomials whose roots multiplicity m\({}_{i}\)\(\leq\) 3. This reduces the polynomial degree and prevents formation of the Runge phenomenon [43]. The roots multiplicity included in Table 2 refers to the polynomials subjected to the analysis. The acceleration profiles were denoted with the symbols: s\({}_{212}\)(t), s\({}_{312}\)(t), s\({}_{213}\)(t), s\({}_{213}\)(t), s\({}_{313}\)(t), s\({}_{322}\)(t), s\({}_{332}\)(t), s\({}_{333}\)(t) and s\({}_{333}\)(t). The notation of the roots multiplicity recorded in the bottom index of the acceleration function is preserved in the denotations of the displacement, velocity, jerk and snap profiles. Thus, these profiles for the exemplary 5 th degree polynomial will be denoted: s\({}_{212}\)(t), s\({}_{212}\)(t), s\({}_{212}\)(t), s\({}_{212}\)(3), s\({}_{212}\)(4)(t). Formulas for the displacement, velocity, acceleration and jerk functions of the analyzed polynomials are included in the Appendix A.
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 5 Application of the Proposed SHM Methodology to Numerical Data
### Computational Processing Times  
To ensure reproducibility, result accuracy, and feasibility verification for model application, each stage of the experiment--from data loading to the final damage assessment using  
Figure 19: Box-plots of \(T^{2}\) values from the VAE model. (**a**) Accelerometer 1; (**b**) accelerometer 2; (**c**) accelerometer 3; (**d**) accelerometer 4.  
the \(T^{2}\) index--was timed. The systematic repetition of the training, validation, and testing stages, along with the application of \(T^{2}\), the generation of box-plots, and damage index quantification, guaranteed the reproducibility of the results. For each AE model, procedures were executed six times using the available accelerometers. The average execution times obtained for each model are presented in Table 6.  
The evaluation of execution times across different methodologies revealed a clear hierarchy of computational complexity. Variational AutoEncoder models exhibited the longest processing time, averaging 7 h 01 min 21 s. This characteristic is intrinsic to the probabilistic nature of VAEs, which require inference of complex latent distributions. In contrast, Sparse AutoEncoder models demonstrated an average execution time of 5 h 28 min 48 s, indicating a considerable reduction compared to VAEs. Convolutional AutoEncoders emerged as the most efficient approach, with an average execution time of 3 h 31 min 9 s, representing a 35.78% reduction compared to SAE and 50.11% compared to VAE. This computational performance advantage can be attributed to the convolutional architecture, which weights the hierarchical structure of the data and reduces the number of parameters to be estimated.  
Despite the differences observed in execution times, it is interesting to highlight that, in a practical context, the average values obtained for all methodologies were quite reasonable. Furthermore, although the computational cost is relevant, it does not represent a hindering barrier to the application of any of these techniques. Consequently, the choice between VAE, SAE, and CAE models should be guided mainly by the quality of the results produced and the suitability of each model to the specific problem under investigation.
## 2 Synthesis and Characterization of MIP
### Polymerization Technique
#### 2.2.2 In Situ Polymerization  
In situ polymerization is a particularly effective technique for the production of MIPs for high-performance liquid chromatography (HPLC) or solid phase extraction (SPE) separation. In a typical method, the reaction mixture, including the functional monomers, template molecules, porogenic agents, crosslinking agents, and initiators is placed in a stainless-steel tube, sealed at one end, and ultrasonically degassed. The process is then allowed to proceed by heating the other end to initiate polymerization. Once the template molecules have been removed, the MIP monolith column can be directly connected to the HPLC system for online SPE analyte quantification [55]. The general scheme for the in situ polymerization is shown in Figure 2.  
The rod-shaped MIP separation medium is synthesized by direct polymerization in an empty column or capillary via in situ polymerization. The mixed solution for pre-polymerization is directly injected into a column or capillary to develop a continuous  
Figure 3: Scanning electron micrographs images of MIP; (**A**): bulk polymerization [58], (**B**): in situ polymerization [59]], (C): precipitation polymerization [60], (**D**): suspension polymerization [61].  
rod-shaped polymer. It provides consistency and homogeneity properties, as well as the capacity to provide excellent separation efficiency [55].  
In situ molecularly imprinted SPE of matrine from radix _Sophorea tonkinensis_ was developed by [PERSON] et al. [62] in 2011. Matrine was made in the lab using melamine-urea-formaldehyde (MUF) as the functional monomer and matrine as the template in this study. The surface binding sites of the MIP could selectively recognize matrine when the template/functional monomer ratio (T/M) was 5 mg g\({}^{-1}\). The MIPs had a high adsorption and elution ability towards the target molecule, indicating its excellent adsorption and elution ability. The SEM images of the polymer synthesized using the in situ polymerization technique is illustrated in Figure 3B.
## 3 Data and Methodology
### Modeling Methods
#### 3.4.5 Ensemble Modeling  
Once all base learning algorithms are prepared, they are integrated into a whole framework using the stacking method. Suppose the initial dataset \(D\) consists of examples \(d_{i}=(x_{i},y_{i})\), where \(x_{i}\) indicate landslide conditioning factors, and \(y_{i}\) indicates corresponding classifications (landslide or non-landslide). \(\mathrm{i}\in[1,\mathrm{N}]\), where \(\mathrm{N}\) represents the total number of the modeling dataset. Base learning algorithms such as SVM, ANN, LR, and NB are denoted as \(L_{t}\) (\(\mathrm{t}=1,2,3,4\)). Firstly, the dataset \(D\) is repeatedly divided into two disjoint subsets; one is used to train base learning algorithms to generate level-0 classifiers, noted as \(h_{t}\):  
\[h_{t}^{i}=L_{t}(D-d_{i})\ \forall\mathrm{i}=1,2,\cdots,\mathrm{N};\ \forall\mathrm{t}=1,2,3,4 \tag{7}\]  
The remaining examples are used to make predictions (\(z_{it}\)) through trained classifiers:  
\[z_{it}=h_{t}^{i}(x_{i}) \tag{8}\]  
These outputs from level-0 classifiers along with their true classification comprise a new dataset \(D^{\prime}=((z_{it},z_{it},\cdots,z_{it}),y_{i})\), and are then fed to level-1 to train the meta-learner (LR). Thus, LR can assemble the classification results of base learners to produce the final prediction for new cases:  
\[Y_{x}=LR(h_{1}(x),h_{2}(x),h_{3}(x),h_{4}(x)) \tag{9}\]  
Based on four single candidate algorithms and the stacking technique, the initial ensemble model was constructed, denoted as the SVM-ANN-NB-LR (SANL).