esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Study on Longitudinal Joints in Quasi-Rectangular Shield Tunnels
## 5 Parametric Study
### Influence of Concrete Strength  
The effect of concrete strength can be investigated by modifying the concrete strength class. In addition to the design concrete strength class of the segments, C50, the classes C70 and C90 were also investigated. The joint rotation curves and bolt stress curves are shown in Figure 24, and only the results for positive cases under the constant 667 kN/m axial force are presented for brevity. There are only slight rotation differences at the first stage, and the differences are gradually noticeable at the second and the third stages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Modulus**} & \multicolumn{2}{c}{**Rotational Stiffness in Positive Cases**} & \multicolumn{2}{c}{**Rotational Stiffness in Negative Cases**} \\ \cline{2-5}  & **Type-BPos** & **Increase Rate** & **Type-BNeg** & **Increase Rate** \\ \hline
200 & 37,404 & - & 32,598 & - \\
372 & 43,187 & 15\% & 38,369 & 18\% \\
661 & 45,882 & 23\% & 42,492 & 30\% \\ \hline \end{tabular}
\end{table}
Table 8: Summary of the rotational stiffnesses during the third stage with different moduli of the bolts (stiffness unit: kN\(\cdot\)m/m/rad, modulus unit: GPa).  
Figure 23: Evolution of the rotations and bolt stresses with different Young’s moduli: (**a**) rotation curves; (**b**) bolt stress curves.  
However, the observed bolt stress curves for C70 and C90 are identical to that of C50. These observations show that there is little difference between the bolt elongations in these cases, and that some differences in the rotation curves occur for the different concrete classes. The classes C70 and C90 have higher Young's moduli and result in smaller deformations of the compression zone. The average rotational stiffness during the third stage for different concrete classes is summarized in Table 9. Compared to the average stiffness at the third stage for the C50 class, the stiffnesses for C70 and C90 increase by 3% and 6%, respectively. The corresponding stiffness increases in the negative cases are 9% and 15%. Within the bending moment range under normal service conditions, the effect of adopting higher concrete classes on the joint deformation is very limited.  
Although an increases of the concrete strength class, the bolt diameter, and the number of bolts have a positive influence on the joint rotation, the effect of bolt repositioning is more efficient. It is found that adapting the joint section to increase the lever arm between the bolts and the compression zone is the most efficient optimizing method. It also increases the joint's bearing capacity for bending moments, as discussed in Section 4. The increase of the bolt diameters or the number of bolts is a relatively effective way to improve the joint rotational behavior under normal service conditions when compared to adopting higher concrete classes.
## 2 Inter-Floor Noise Dataset
### Generating and Collecting Inter-Floor Noise  
The inter-floor noise dataset was collected in building 36 at Seoul National University. The building is reinforced concrete frame structure. From statistics [16; 17], this structure is the most widely used in modern buildings in South Korea. Our experimental building is partitioned  
Figure 1: Five noise types included in the inter-floor noise dataset.  
with concrete walls. The corridor where the noises were generated is a slab covered by concrete terrazzo tiles.  
Figure 2 illustrates the noise source and receiver arrangement for generating and recording inter-floor noise. The 9 solid circles indicate selected positions for noise sources. The noise sources are separated by 6 m along the \(X\) axis. The notation around the noise sources indicate the floor and distance along the \(X\) axis, i.e., 2F0m denotes that position of a noise source is on the second floor and the distance from the origin along the \(X\) axis is 0 m. A single microphone in a smartphone (Samsung Galaxy S6 [18]) was used as a receiver to record inter-floor noises. The receiver was installed on 2 F to receive inter-floor noises from the upper and lower floors. The solid square in Figure 2 indicates the position of the receiver.  
Photo of the corridor and the location of the receiver are shown in Figure 3. Inter-floor noises were sampled at \(f_{s}\) = 44,100 Hz for approximately 5 s. 50 inter-floor noises were generated at each position and for each noise type. Because VC on 1 F and 3 F were barely audible at the receiver position, VC was generated only on 2 F. Table 1 shows the number of data in the inter-floor noise dataset.  
One distinguishing feature of the dataset is that each inter-floor noise can be labeled as a noise type and a position of the noise source. Thus, this dataset can be used to learn the type and position of various noise sources in a building.  
Figure 3: (**a**) Photo of the corridor on 2F. (**b**) The receiver is held 1.5 m above the floor.  
Figure 2: Noise sources and the receiver arrangement for inter-floor noise generation and recording [13].
# A Study on the Stall Detection of an Axial Compressor through Pressure Analysis
## 1 Introduction
Experiments on inlet-forced surge tests were conducted by adding a distortion simulator to the inlet section of the compressor. There are two kinds of distortion simulators used in these experiments. One simulated 90 degrees of a circumferential sole distortion device, and the other is 90 degrees of circumferential symmetrical distortion device. The fast Fourier transform (FFT) method enabled the comparison of inlet conditions, as well as the analysis of compressor inlet pressure, outlet pressure and blade tip region pressure. The compressor pressure amplitude-frequency characteristics were discussed with the FFT method [20; 21]. At last, a new compressor stall detection is proposed, and blade tip region pressure is applied with the algorithm. In the first section of this paper, the experiment facility and process will be briefly introduced. Then, FFT method principals are mentioned, and the pressures collected in the experiments are analyzed. Finally, a compressor pressure signal analysis algorithm is proposed and applied to compressor stability analysis.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 6 Contemporary Developments with UOC Technology  
Very recent research and successful implementations of AI in UOC techniques with their significance are reviewed in this section. Different protocols of energy harvesting from UOC networks, and their SOTA achievements are also presented.
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 2 Mask and Region-Based Convolutional Neural Network (Mask R-CNN)
### Object Classification and Bounding Box Refinement  
Using the bounding box information and its objectness, Mask R-CNN determines object class and refine the given bounding box one more time. In this step, RoIAlign extracts features with high accuracy in the region of interest (RoI) proposed by RPN. In general, the feature map extracted by CNN tends to have a low resolution owing to the downscaling of the convolutional layers. Because of the low resolution, the spatial information of the objects in the input image is lost, resulting in decreased detection accuracy. RoIAlign is designed to extract features with more accurate spatial information using bilinear interpolation. The features extracted by RoIAlign are input to a fully connected layer first and then transfered to the box-classification layer, which classifies the detected object by the RPN, and the box-regression layer, which corrects the bounding box of the proposed object more accurately. The box classification layer uses softmax function to classify object in the given bounding box. In this study, the box-classification layer is designed to have four outputs of softmax for cracks, efflorescence, spalling, and rebar exposure for multiple concrete damage detection. The box-regression layer has four outputs indicating the position of the bounding box, similar to the regression layer in RPN. More details of RoIAlign are provided in the works of [PERSON] et al. [33]. For more information on the box-classification layer and the box-regression layer, a paper regarding Faster R-CNN [31] can be referred.
## 4 Discussion
### Study Limitations  
A number of limitations in this study must be considered. First, we acknowledge the fact that we did not have the possibility to locate the EEG electrodes directly from the MR image, which would have assured error-free localization [73; 75]. To minimize the effect of any electrode positioning error associated with the GPS technique, we projected the obtained electrode coordinates over the head shape [42; 73] and manually corrected any apparent imprecision in electrode positioning. Additionally, we did not collect real electrode positions with several devices to compare our reference with; we therefore simulated positioning errors for 3D scanning and digitizing techniques, respectively, using error type and magnitude reported in previous studies. Similarly, a manually segmented MR image was not available to be used in our study. The analysis workflow that we used as a benchmark included MR-TIM, which was proven to outperform other commonly-used head tissue segmentation techniques [25]. We would also like to mention that resting-state EEG recordings used in this study were collected for 5 min in each participant [10; 16], and it would be important to test if EEG-RSN reconstruction is stable for different EEG recording lengths. Furthermore, EEG connectivity results were based on time-frequency analyses conducted using short-time Fourier transform with uniform window length for all frequency bands, as in our previous studies [10; 12; 49]. The Wavelet transform could be alternatively used, such that the window length is adaptively defined based on the frequency; this may increase the sensitivity of the connectivity analyses for lower-frequency bands. Lastly, we assessed the impact of different head models on EEG connectivity by calculating the similarity of EEG connectivity results with respect to a standard configuration. This solution permitted us to conduct only relative comparisons among our test datasets; to the best of our knowledge, an absolute threshold level against which assessing the correspondence of two connectivity profiles can hardly be defined.
## 5 Discussion
Experimental results demonstrate that the model exhibits robust generalization performance across varied real-world contexts. However, performance limitations persist when handling challenges such as significant illumination variations, occlusions, and pose changes. To address these domain adaptation issues, subsequent research will focus on collecting and incorporating multimodal training data covering broader illumination conditions and scene complexities, thereby further enhancing the model's robustness and generalization performance. While our method achieves robust performance in general face segmentation, it currently lacks refinement in subtle facial feature segmentation (e.g., eyebrows, lips, or fine textures), which is critical for high-end applications like virtual makeup or forensic analysis. This limitation stems from the pseudo-label generation process, which prioritizes boundary accuracy over intra-face detail. Future work could address this by integrating domain-specific fine-tuning or multi-task learning to explicitly model facial landmarks. Additionally, while the twin model architecture improves stability, its computational overhead may hinder real-time deployment. Exploring lightweight network designs (e.g., MobileUNet variants) or edge computing optimizations could expand applicability to on-set production environments. Another area for improvement lies in handling extreme variations in lighting or occlusion (e.g., low-light conditions or partial face coverage). Incorporating domain adaptation techniques or contrast enhancement modules may enhance robustness, as observed in recent studies on cross-domain segmentation).
## 4 Discussion
### Limitations of Research  
In this study, time series NDVI data were used as predictors to evaluate SOC content and map its spatial distribution. Overall, the results were relatively satisfactory but can still be improved. First, many reasonable sampling methods, such as sampling location and number of samples settings, were recommended. This process ensures that the measured data are reliable and close to real value. Second, for the time series approach, continuous and high-quality images of true time series may lead to better accuracy. Moreover, the relationship between VI and soil based on time series characteristics should be determined to improve the mapping accuracy. Future studies should combine the time series characteristics of multiple important auxiliary variables to predict the target soil properties given the complexity of environmental factors.
## 3 Proposed Analog RF Beamforming
### Relay Analog RF Beamforming  
It has already been mentioned in Section 2 that end-to-end SNR maximization can be achieved under the following condition  
\[\max(\beta_{1k})\max(\beta_{2k})\Rightarrow\max(\beta_{k}), \tag{32}\]  
This condition makes it possible to decompose the original complicated optimization problem into two sub-problems. One sub-problem leads to the solution of relay analog combiner \(\mathbf{F}_{1k}\) and the other facilitates in evaluating the relay analog precoder \(\mathbf{F}_{2k}\).
# A Study of Emergency Aircraft Control During Landing
## 5 Aircraft's Model Identification
### State-Space Model Identification
#### 5.4.2 Estimation of Aircraft's Dynamic Models  
The identification of longitudinal and lateral motion state-space models was performed using the \"ssest\" function, as a part of the System Identification Toolbox. During the aircraft's dynamics identification, special attention was paid to ensure a close correspondence between computed models and their mathematical description from Section 5.2. For this reason, after the first execution of the _ssest()_ function, specific state matrix coefficients were fixed with their known, theoretical values, which were entered manually. For such corrected models, the function was executed again. The percentage fitness of state responses to measured data, computed for the four models, are shown in Table 3.  
A quantitative assessment of the estimated model, returned by the _ssest()_ function, includes the percentage fitness expressed as in (14).  
\[\mathrm{fit}=100\cdot(1-\mathrm{NRMSE}) \tag{14}\]
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
###### Abstract  
To ensure that the cementitious grouting material (CGM) layer and existing concrete work together, the interfacial bond strength between CGM and normal concrete substrate was experimentally investigated by conducting direct and slant shear tests. The effects of connection interface form, concrete strength, interface shear key, and shear angle on the bond strength of CGM and normal concrete were discussed in detail. The results showed that for composite specimens, the interfacial bond strength was the highest for the triangular interface form, followed by the trapezoidal interface and smooth interface, which had the lowest. The interfacial bond strength increased as the existing concrete's strength increased; the interfacial bond strength could be greatly improved under shear load in the slant section or when the shear key was set at the interface. Moreover, the interfacial bond strength increased with the increase in the shear angle. The interfacial bond-slip curves were analyzed, and a bond-slip model of the interface between CGM and concrete was proposed. A certain value of interfacial bond stiffness was also recommended.  
CGM; concrete; interfacial bond behavior; interface form; shear key; shear angle +
Footnote †: journal: Applied  
Acticle Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete  
2022
# A Review on Styrene Substitutes in Thermosets and Their Composites
###### Abstract  
In recent decades, tremendous interest and technological development have been poured into thermosets and their composites. The thermosets and composites with unsaturated double bonds curing system are especially concerned due to their versatility. To further exploit such resins, reactive diluents (RDs) with unsaturated sites are usually incorporated to improve their processability and mechanical properties. Traditional RD, styrene, is a toxic volatile organic compound and one of the anticipated carcinogens warned by the National Institute of Health, USA. Most efforts have been conducted on reducing the usage of styrene in the production of thermosets and their composites, while very few works have systematically summarized these literatures. Herein, recent developments regarding styrene substitutes in thermosets and their composites are reviewed. Potential styrene alternatives, such as vinyl derivatives of benzene and (methyl)acrylates are discussed in details. Emphasis is focused on the strategies on developing novel RD monomers through grafting unsaturated functional groups on renewable feedstocks such as carbohydrates, lignin, and fatty acids. This review also highlights the development and characteristics of RD monomers and their influence on processability and mechanical performance of the resulting thermosets and composites.  
thermosteting resin; styrene-free; reactive diluent; composites +
Footnote †: journal:
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 3 Proposed Method
### Relevance Calculation  
As mentioned before, the value of MIC ranges from 0 to 1. FF-MIC denotes a matrix which can measure the relevance between features and features. The closer to 0 each element in the FF-MIC matrix approaches, the stronger the irrelevance between the features corresponding to the row and column of the element is. On the contrary, the closer to 1 each element in the FC-MIC matrix approaches, the stronger the relevance between the feature and category is. On this basis, a feature can be judged whether it is a weak relevance or strong relevance through the value in the FF-MIC matrix.  
In each column of the FF-MIC matrix, each minimum value is found to form a set \(FFmin=\{min_{1},\ min_{2},\ldots,min_{m}\}\), and then the maximum value \(FF\_threshold\) is found in the set \(FFmin\). Therefore, the features, of which corresponding FF-MIC values are smaller than \(FF\_threshold\) are, are called strong irrelevance features. The reason of setting up \(FFmin\) is that the condition of the strong irrelevance between a certain feature and each one in the rest can be observed. Through the set \(FFmin\), the maximum of \(FFmin\) is utilized to judge whether a feature is strong irrelevant with other features.  
At the same time, in each row of FC-MIC, each maximum value is found to form a set \(FCmax=\{max_{1},\ max_{2},\ldots,max_{m}\}\), and then the minimum value \(FC\_threshold\) is found in the set \(FFmax\). The features, of which corresponding FC-MIC values are bigger than the \(FC\_threshold\), are called strong relevance features, which are being selected soon. The reason of setting up \(FCmax\) is that the condition of the strongest relevance between a certain feature and each category can be observed. Through the set \(FCmax\), the minimum of \(FCmax\) is utilized to judge whether a feature is strong relevant with categories.  
The details of relevance calculation are shown in Algorithm 3.  
```
0:\(FS=\{f_{1},f_{2},\ldots f_{m}\}\), FF-MIC, and FC-MIC.
0:\(FF\_threshold\), and \(FC\_threshold\).
1. for each column in the FF-MIC matrix
2. for elements in each column
3. find each minimum value to form a set \(FFmin=\{min_{1},\ min_{2},\ldots,min_{m}\}\)
4. end for
5. for each \(min_{i}\) in \(FFmin\)
6. find the maximum \(FF\_threshold\) in \(FFmin\)
7. end for
8. for each row in FC-MIC
9. for elements in each row
10. find each maximum value to form a set \(FCmax=\{max_{1},\ max_{2},\ldots,max_{m}\}\)
11. end for
12. for each \(max_{i}\) in \(FCmin\)
13. find the minimum \(FC\_threshold\) in \(FCmax\)
14. end for
```  
**Algorithm 3** Relevance calculation
## 3 Results
### Comparative Analysis
#### 3.2.4 Match Point Precision Analysis
One reason is that in the experimental data, triple-matching points typically occur only in configurations of \"two-left-one-right\" or \"two-right-one-left,\" resulting in a relatively small number of such tie points. Table 4 demonstrates that Sentinel-1 data does not perform well in SIFT, SURF, and AKAZE algorithms, especially in the case of the SIFT algorithm, which fails to obtain accurate matching points. From Table 4, it can be observed that the STSU-SAR-SIFT algorithm produces more matching points, and the accuracy of these points is slightly higher than that of the SAR-SIFT algorithm. Within the same  
Figure 9: Matching results of GF-3. STSU-SAR-SIFT algorithm is slightly lower than that of the SAR-SIFT algorithm. This is because Sentinel-1 data has less overlap, and the STSU-SAR-SIFT algorithm requires more detailed descriptors. Additionally, there are more black edges in the images, resulting in more repeated regions in the descriptors. On the other hand, SAR-SIFT relies less on descriptors for peripheral regions, resulting in higher accuracy of connection points in the same orbit for SAR-SIFT compared to STSU-SAR-SIFT. However, as the feature descriptions of the SAR-SIFT algorithm are less detailed than those of the STSU-SAR-SIFT algorithm, the latter achieves higher accuracy in extracting tie points on vertical orbits.