esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c} \hline
**Network** & **Inference Time** \\
**Architecture** & 0.174 s \\ \hline Hourglass (Baseline) & 0.230 s \\ YOLOv9c & 0.039 s \\ Hourglass + Depthwise Separable & 0.139 s \\ Hourglass + Depthwise Separable + Attention & 0.157 s \\ Hourglass + Depthwise Separable + Attention + Find Peaks & 0.225 s \\ Hourglass + Depthwise Separable + Attention + Find Peaks + Post-Process & 0.267 s \\ \hline \end{tabular}
\end{table} 
Table 4: Validation set inference times for the various models. Our model provides better inference speed compared to the YOLOv3 model. \begin{table}
\begin{tabular}{c c c} \hline
**Network** & **\# Params** & **[EMAIL_ADDRESS]** \\
**Architecture** & **(Mean)** \\ \hline Hourglass (Baseline) & 192.63 M & 0.783 \\ YOLOv3 & 62.49 M & 0.723 \\ YOLOv9e & 58.1 M & 0.833 \\ Hourglass + Depthwise Separable & 28.20 M & 0.778 \\ Hourglass + Depthwise Separable + Attention & 28.25 M & 0.819 \\ \hline \end{tabular}
\end{table} 
Table 2: Validation set mAP for the various models. (# Params is the total number of parameters). \begin{table}
\begin{tabular}{c c c} \hline
**Object Class** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline Japanese Place Names & 0.851 & 0.695 \\ English Place Names & 0.822 & 0.670 \\ Distance Numbers & 0.813 & 0.645 \\ Expressway Numbers & 0.808 & 0.621 \\ General Information & 0.78 & 0.617 \\ \hline \end{tabular}
\end{table} 
Table 3: Validation set AP for top object classes. Figure 27 illustrates the loss curves for the text recognition model. The validation loss improved until epoch 50, then the training was stopped at 60 epochs to avoid overfitting, and the best weights for the validation loss were saved. Around 500 k samples were used to train the OCR model, hence the faster convergence. However, the images while predicting are different from the training dataset but have similar fonts.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 1 Introduction
This sampling scheme increases the robustness of the solutions against degenerate configurations and local optima without requiring additional computation or prior information about the matches. Third, the objective function of robust estimation is not defined based on the support cardinality, but the robust least trimmed sum  
Figure 1: Summary of outlier detection techniques in stereo sparse matching based on robust estimation of epipolar geometry. of squared residuals is used instead. Therefore, there is no need to successively set a threshold at any iteration for detecting the support of the estimated model or to assume any specific probability distribution for outlier/inlier residuals. Finally, to identify all of the inliers, a detection method based on adaptive thresholding is proposed, as opposed to using a fixed threshold. In this approach, the uncertainty of the estimated model is taken into account to identify all of the inliers correctly. The rest of the paper is organized as follows. In Section 2, a review of the techniques of robust epipolar-geometry estimation is presented. The main problem of simultaneous inlier detection and epipolar-geometry estimation is formulated in Section 3. Section 4 describes the solution using the genetic algorithm, which is followed by the method of detecting all of the inliers. The experimental results are discussed in Section 5, and the conclusion is presented in Section 6.
###### Abstract  
Phosphate is an important component for the growth of plants and microorganisms; however, excess phosphate causes serious eutrophication in natural waters. New potential low-loss adsorbents from natural biomass for phosphate removal are desired. Bamboo is one of the most abundant renewable cellulose resources; however, the pure bamboo cellulose is poor to adsorb phosphate. To enhance the adsorption capacity, in this work, bamboo sawdust (BS) was chemically modified by two kinds of amines. First, glycolidyl methacrylate (GMA) was grafted on BS using radiation induced graft polymerization. Then, the GMA-grafted BS was further modified by a ring-opening reaction with amines, including ethylenediamine (EDA) and pentaethylene hexamine (PEHA). The amine groups were then quaternized to prepare the BS-GMA-EDA-Q and BS-GMA-PEHA-Q adsorbents. The adsorbents were characterized by FTIR, SEM, TG, and XPS analysis. The adsorption performances of the adsorbents for phosphate were evaluated through batch experiments. The adsorption by BS-GMA-EDA-Q and BS-GMA-PEHA-Q both well obeyed the pseudo-second-order kinetic model and the Langmuir isotherm model, indicating that the adsorption process was chemical monomolecular layer adsorption. The maximum adsorption capacities for BS-GMA-EDA-Q and BS-GMA-PEHA-Q calculated by the Langmuir model were 85.25 and 152.21 mg/g, respectively. A total of 1 mol/L HCl was used to elute the saturated adsorbents. A negligible decrease in adsorption capacity was found after five adsorption-desorption cycles.  
bamboo sawdust; radiation induced graft polymerization; phosphate adsorption +
Footnote †: journal: Applied Sciences
# The Concept of Segmented Wind Turbine Blades: A Review
## 6 Adhesive Joints in Segmented Blades  
### Cost of Energy  
Adhesive joints can be structurally efficient, light and inexpensive. They have low stress concentrations and good damage tolerance. However, when used in segmented blades, they result in high installation costs due to the the need for specialized equipment and the number of added time consuming steps during on-site assembly. Various improvements have proposed approachesto alleviate these issues. An overview can be found in Table 3. One problem is the lack of inherent self-alignment of adhesive joints. This increases the complexity and time required to assemble the blade from its segments [82]. [PERSON] [83] presented a system to align blade segments on different carriers using laser positioning. Alternatively, [PERSON] [84] suggested using brackets attached to the spar caps to ease alignment, after which the adhesive bond can be formed. [PERSON] [85] proposed using alignment pins. Additionally, [PERSON] [86] and [PERSON] [87] suggested using different types of overlapping portions to ease alignment. Further, [PERSON] [88] proposed using joint portions that are offset in the span-wise direction to create an overlap.  
A second issue is the difficulty of producing a high quality bond on-site compared to under controlled conditions [57]. Surface preparation, temperature and humidity affect the quality of adhesive joints [16]. Good control over the bond thickness is important to avoid stress concentrations. In [PERSON] [89], the use of a bonding grid is proposed. This grid is incorporated into the joint to obtain a very accurate bond thickness. [PERSON] [84] suggested using shims to ensure a constant minimum distance between the parts to be adhered. To ensure a perfect fit between two segments, [PERSON] [87] advocated producing the segments in a single mold. By folding in a vacuum bag with release agent, the two adjacent segments can be manufactured while in contact with each other. Afterwards, they can be separated easily and will have a very good fit at the interface. Further, air entrapment can drastically reduce the strength of adhesive joints. [PERSON] [90] suggested to put the connecting surfaces in place first, creating a cavity, which can subsequently be flooded or infused to create the joint while avoiding air entrapment. Similarly, [PERSON] [86] suggests a segmented blade with overlapping spar caps, which cause the formation of a spar cap cavity, which is subsequently filled with adhesive. Another issue is the assembly time and requirement of specialized equipment, such as ovens, heat tents and heater blankets to cure the bonds [91]. Up to ten hours at elevated temperature may be required to fully cure the adhesive [90]. [PERSON] [91] suggested the use of resistance heated bonds to alleviate these issues. Furthermore, the O&M costs are lower for adhesive joints compared to mechanical connections.
# Case Study of an Underpinning Pile Foundation for an Interval Tunnel Crossing an Existing Bridge
###### Abstract  
When urban subway tunnels cross existing bridge pile foundations, having a pile foundation underpinning that ensures the safe operation of existing bridges while enabling the safe construction of subway tunnels is the focus of attention. This paper takes the running tunnel project from Huaguoyuan West Station to Huaguoyuan East Station of Rail Transit Line 3 in Guiyang City, Guizhou Province as the background. The reasonableness and feasibility of the passive underpinning construction scheme for the Guihuang Viaduct was studied. The construction plan includes the following steps: underpinning pile construction, foundation pit excavation, the concreting of the underpinning bearing platform, and existing pile truncation. In order to ensure the structural safety of the existing viaduct during the construction of the pile foundation underpinning, a 3D numerical model of the construction of pile foundation underpinning and the whole process of tunnel construction was established. The settlement calculation results of the foundation pit and bridge pier were compared and analyzed with the field monitoring data to verify the accuracy of the numerical model. Further detailed analysis of the settlement of the bridge deck, the deformation of the existing piles, the axial forces of the existing piles, and the forces on the underpinning bearing platform was carried out. The results show that the bridge superstructure load can be transferred to the underpinning bearing platform smoothly after the existing pile truncation construction. The removal of obstacle piles during tunnel excavation has a very limited impact on the superstructure of the bridge, proving the reasonableness and feasibility of the construction plan.  
passive underpinning; interval tunnel crossing excavation; numerical simulation; field monitoring
## 6 Conclusions  
An automated tool based on an AI camera, a microbit board, and Lego Mindstorms EV3 elements has been presented. This tool estimates the polarization angle and refractive index of semi-transparent objects by detecting the color of the reflected laser spot using the Huskylens camera feature. This implementation significantly improves upon traditional techniques, which often require the manual detection of beam extinction or mechanical mounts to position the detector in front of the reflected beam. Additionally, with certain modifications, this tool can be improved to be used as a remote laboratory. This alternative would involve the use of a Wi-Fi module (considering that the microbit board already includes a Bluetooth module) and a mobile or web application. A suggestion could be the use of app inventor, which is a mobile application development platform created by MIT (Massachusetts Institute of Technology). It allows anyone, even without previous programming experience, to create applications for Android devices in a simple and visual way.  
The measurement errors are primarily due to the mechanical vibration of the shaft connected to the test object on the servomotor and illumination variations affecting the detection of the reflected beam. These factors impact the estimation of the critical angle. The precision and accuracy of the proposed method can be enhanced by using mechanical elements with less hysteresis or reduced vibration effects, while maintaining the implementation principle outlined in this manuscript. Another source of error is the minimum angular increment of the EV3 motor, which is \(1^{\circ}\). This can be mitigated by implementing a gear train to connect the motor shaft with the test object, though this may introduce additional vibrations. However, variations in environmental conditions such as temperature and humidity do not affect the experiment results.  
Finally, it is worth noting that the functions integrated into some artificial intelligence cameras, such as the one used in this implementation, can be used in applications related to the measurement and detection of physical parameters such as the wavelength of a laser source and the refractive index of liquids [15; 19].  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
###### Abstract  
Blockchain technology, as a distributed ledger technology, is becoming increasingly popular in various fields. However, the performance limitations of blockchain networks hinder their further development. Existing research on optimizing blockchain communication mechanisms based on P2P networks is constrained by the end-to-end transmission principles of TCP/IP networks, which lead to network congestion and bandwidth wastage during large-scale blockchain content distribution. Meanwhile, studies on ICN-based blockchain systems primarily focus on blockchain communication protocol implementation and compatibility within ICN/NDN networks. However, research on blockchain communication mechanisms in hybrid IP/ICN networking environments remains limited, failing to fully leverage ICN's advantages to enhance the communication efficiency of existing blockchain P2P networks. To address this issue, this paper proposes BLOCK-ICN, an ICN-based blockchain network communication architecture compatible with IP networks. BLOCK-ICN enables ICN nodes with computing and storage capabilities to deploy blockchain applications, while maintaining compatibility with P2P networks. By leveraging ICN multicast technology, the architecture provides relay acceleration services for blockchain data dissemination. Specifically, in terms of network topology, BLOCK-ICN classifies network domains based on delay information provided by an enhanced resolution system and establishes select domain gateways based on data flow forwarding dependencies, thereby constructing a hierarchical and structured relay network topology. Regarding the broadcast protocol, ICN nodes perform parallel broadcasting via ICN multicast, and upon receiving messages, they further disseminate them to P2P nodes, reducing the overall network broadcast latency and bandwidth consumption. We extended SimBlock to implement and evaluate BLOCK-ICN. Simulation results demonstrated that, in a Bitcoin network with 16,000 nodes and an ICN node ratio of 1%, the broadcast delays for propagating blockchain data to 90% and 50% of the network were reduced by 25% and 33.2%, respectively, compared to Bitcoin.  
blockchain; information-centric networking; P2P network; network partition; broadcast algorithm +
Footnote †: journal: Applied sciences
# The Impact of the Stringent Response on TRAF GTPases and Prokaryotic Ribosome Assembly
## 4 (p)ppGpp-Binding RA-GTPases Involved in 30S Assembly
### Era
16S rRNA processing remains a crucial aspect of 30S assembly and deletion of the highly conserved, multifunctional endoribonuclease YbeY (Figure 1) in _E. coli_ imparts a striking defect in 16S processing at both the 3\({}^{\prime}\) and 5\({}^{\prime}\) ends [84]. \(\Delta y\underline{b}\)_y_e_Y strains of _E. coli_ accumulate 17S and truncated 16S rRNA (16S\({}^{\prime}\)) lacking the 3\({}^{\prime}\) terminus [85]. While mature 16S rRNA is present within these cells, the excess of immature rRNA leads to the production of defective 30S subunits resulting in defects in 70S ribosome profile [85]. The _ybeXYZ_ and _era_ genes are located within the same operon in many bacteria, indicating a functional relationship, and indeed YbeY has been shown to interact directly with Era and the r-protein uS11 adjacent to the Era binding site [86; 87]. The YbeY-Era interaction is mediated by the G domain of Era, while the opposite side of the YbeY endonucleasedomain contacts uS11 directly. This has led to the proposition of a model where Era is responsible for recruiting YbeY to the immature 16S rRNA to undergo processing [76; 85]. However, overexpression of Era in \(\Delta ybeY\) strains improves the 16S processing defect, 70S assembly and partially rescues growth in a GTPase-dependent manner [86]. This indicates that other exoribonucleases such as RNase R, RNase PH and RNase II that are likely involved in 16S processing can compensate for a deletion of \(ybeY\), however, the mechanism behind this is not clear [86]. Evidence for Era functioning as a hub protein also come from the fact that Era can interact with the DEAD-box RNA helicase CshA in _S. aureus_[76], which had previously only been shown to be involved in 50S biogenesis through direct subunit binding [88]. A deletion of _cshA_ in _S. aureus_ showed a defect in 16S maturation at 25 \({}^{\circ}\)C, supporting the suggestion that Era acts as a scaffold protein for enzymes involved in 16S rRNA maturation and processing [76]. However, evidence for CshA directly processing 16S rRNA is lacking, meaning that indirect effects cannot be discounted.
# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
## 4 The Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F-score** \\ \hline VGG-16 & 0.7694 & 0.7955 & 0.7694 & 0.7743 \\ ResNet-50 & 0.8444 & 0.8520 & 0.8444 & 0.8443 \\ DenseNet201 & 0.8777 & 0.8883 & 0.8777 & 0.8798 \\ EfficientNetB0 & 0.9027 & 0.9078 & 0.9027 & 0.9021 \\ ViT & 0.9055 & 0.9116 & 0.9055 & 0.9067 \\ ConvViT & 0.9361 & 0.9396 & 0.9361 & 0.9355 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative analysis of experimental results. better generalization performance than VGG-16. The model's overall performance was consistent since there was a balanced decrease between the training and test losses of EfficientNetB0. The training loss of the ViT decreased rapidly, while the test loss decreased more slowly. The difference between ConvViT's training and test losses was the lowest. Since the loss graph decreased steadily for both training and test data, it can be seen that the model has an excellent generalization capacity. Figure 13: Loss graphs of the compared models. Table 10 provides a comparative analysis between the proposed ConvViT model and prior studies on pest classification. The results demonstrate that ConvViT achieved superior accuracy compared to traditional CNN-based and transformer-based approaches. For instance, the Xception-based method by [PERSON] et al. achieved an accuracy of 83.99% [8], while the custom CNN-based approach by [PERSON] et al. reached an accuracy of 92% [10]. Our proposed ConvViT model demonstrated an accuracy of 93.61%, outperforming these previous approaches. Other methods in the literature typically focus either solely on local features (CNN-based approaches) or only on global relationships (transformer-based approaches), leading to limitations in distinguishing species with similar morphological traits. The ConvViT model combines CNN and vision transformer components, effectively learning local details and global context. Additionally, previous approaches often struggled with overfitting and class imbalance, leading to inconsistent generalization across datasets. The ConvViT model, through its hybrid structure and enhanced learning capacity, mitigates these issues and achieves a more stable performance across multiple pest classes. This hybrid approach not only enhances model accuracy but also strengthens generalization capacity. The success of ConvViT represents a significant advancement in the early and accurate detection of pests, offering a sustainable and efficient artificial intelligence solution to the literature for agricultural applications.
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 4 Discussion
### Perspectives  
Some of the trends in biomechanics analysis is the implementation of Big Data approaches. A very detailed review on different machine learning and big data implementations on biomechanics of human gait is provided in [105]. This work remarks the benefits of performing such techniques, such as multivariate analysis of complex variables, automatic relevant feature selection, obtaining insight on different populations and accounting for inter-subject variability. The 3D GAIT system was developed by a research group in the University of Calgary, as an automatic biomechanical data collection system that can be used by different research groups, and therefore contribute to Big Data approaches in biomechanics [105]. This system allows to obtain high-dimensional (74 variables) measurements of gait cycles at 200 Hz, and then all the data from each recording is transferred to a research database with demographic information (e.g., mass, height, age, etc.). All this massive amount of data can be used to obtain high-quality models after implementing carefully designed machine or deep learning methodologies.  
Other authors have pointed out that artificial intelligence and the Big Data approaches are offering emergent solutions with the proposal of large scale data analysis, such as the development of automated pattern recognition for injury identification in joints [106], and individualized sport teaching between coaches and students, using the historical information available from the users [107]. In a similar context, the transmission of such data will need powerful methods to be transferred in real-time and perform analysis; hence, another important technology that will take importance in Sports Biomechanics research is that of Internet of Things (IoT) to alleviate this challenge [108].