esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria  
### Model  
The dynamic model has been established by using two segments, including a spherical shell and a cylinder, as depicted as Figure 5a. The nose structure of the submarine can be simplified as a model of a spherical shell with added mass, as illustrated as Figure 5b. The obstacle block in both models has been simulated by solid elements. The shell structure of the submarine nose is modeled using shell elements in LS-DYNA [55]. The material for the obstacle block is set to be the rigid material with \(E=35\) GPa and a density of 2400 kg/m\({}^{3}\). For the cylinder, it is usually reinforced by ring bars, a stiffener, or other measures. It is also considered as a rigid body with a thickness of 0.04 m and a density of 7800 kg/m\({}^{3}\). The spherical shell in both models is assumed to behave as an elastic material with \(E=210\) GPa and a density of 7800 kg/m\({}^{3}\). The added mass is set to 35,800 kg, which can be calculated from the total mass of the cylinder model. In addition, the effect of the strain rate was neglected in this study on the basis of the low velocity impacts. In the modeling, the radius of the shell is set to be 2.0 m, with a thickness of 0.03 m and an external pressure of 20 MPa, which implies that the submarine is considered to be in operations at the depth of 2.0 km under the sea.  
The external pressure can be calculated by Equation (6):  
\[q=\rho g\cdot\mathrm{h}, \tag{6}\]  
where \(\rho\), \(g\), and \(\mathrm{h}\) are the density of water and gravity acceleration and the height under the sea, respectively. The pressure is then applied to the surface of the shell, as illustrated in Figure 5a,b. The initial velocity was set to be in the Z direction, which is normal to the rigid block. Since the shell structure is not a closure surface, there should be a concentrated force applied together with the added mass to resist the external pressure. The total force value was \(q\pi R^{2}\). Figure 6a,b shows the details of the meshes for both models. There are 7935 shell elements and 2600 solid elements after optimal meshing. Since the pressure load exists at all times, the static analysis under the pressure will only be carried out as the precursor prior to the dynamic analysis in LS-DYNA [56; 57; 58].  
Figure 5: The geometry and mesh model for the cylinder and spherical models. (**a**) The plan view of the cylinder model; (**b**) The plan view of the spherical model.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 5 Conclusions  
Satellite images possess several prominent characteristics, including significant scale variations, complex backgrounds filled with distractions, and diverse perspectives, which pose tremendous challenges for general object detectors based on common convolutional networks. In this study, we upgraded the latest natural image detection technology YOLOv8 model to the BA-YOLO model, tailored for satellite images. We improved the YOLOv8 network structure according to the characteristics of remote sensing images. In BA-YOLO, we introduced the BiFPN structure as a feature fusion network and employed learnable weights for fast normalization fusion, effectively reducing the loss of target feature information and enhancing detection performance in satellite images. Additionally, we introduced the C2 fAC module, which combines the advantages of multi-head attention and convolutional networks, enhancing the model's detection accuracy. The BA-YOLO model was tested on the widely used DOTA dataset, achieving an mAP of 0.722, surpassing other models. This fully demonstrates the effectiveness of BA-YOLO for satellite image object detection. In the future, we will strive to improve object detection speed while maximizing the model's detection accuracy to the greatest extent possible. Despite BA-YOLO achieving satisfactory object detection accuracy in satellite images, its performance in detecting small targets is not particularly ideal. In future work, we plan to incorporate image super-resolution techniques to enhance the research on target detection in satellite images.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **mAP\({}_{\textbf{50}}\)** & **mAP\({}_{\textbf{50-95}}\)** \\ \hline YOLOv8 & 0.686 & 0.462 \\ YOLOv8 + C2 fAC & 0.702 & 0.486 \\ YOLOv8 + BiFPN & 0.708 & 0.489 \\ BA-YOLO & 0.722 & 0.499 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effects of the YOLOv8 combining different modules on the DOTA dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Inference Time (Per Picture)** \\ \hline YOLOv5 & 10.6 ms \\ YOLOv8 & 10.4 ms \\ TPH-YOLOv5 & 32.5 ms \\ SPH-YOLOv5 & 19.5 ms \\ BA-YOLO & 14.6 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inference time on the DOTA test dataset for different detection models.  
**Acknowledgments:** We are very grateful for the support and help from [PERSON] of the University of Chinese Academy of Sciences.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 3 Methods
### Segmentation Algorithms
#### 3.2.7 Watershed Segmentation  
The concept of watersheds is based on visualizing an image grayscale distribution in three dimensions (two spatial coordinates versus intensity) similar to a topographic landscape [27; 35]. This approach based on morphological watersheds embodies many of the concepts of the previous segmentation algorithms. The main objective is to find the watershed lines and visualize them creating a connected boundaries [36].  
The proposed algorithm computes the gradient magnitude as a first step. The gradient (gradient is a directional change in the intensity or colour in an image) is high at the borders of the objects and low inside the objects. Next, it creates a two-dimensional filter of the specified type returning a correlation kernel, which is the appropriate form to use using an image filter function. A variety of procedures could be applied to find the foreground markers, which must be connected blobs of pixels inside each of the foreground objects. In this sense, morphological techniques called \"opening-by-reconstruction\" and \"closing-by-reconstruction\" are used to \"clean\" up the image. These operations will create flat maxima inside each object. Opening is an erosion followed by a dilation, while opening-by-reconstruction is an erosion followed by a morphological reconstruction. Following the opening with a closing can remove the dark spots and stem marks.
## 4 Conclusions  
1. After monitoring the growth of microorganisms for 72 h, a linear correlation between OD\({}_{600}\) and conductivity was detected. There was a linear increase within 48 h after the inoculation of the solid medium into the liquid medium, after which there was a decline. At 48 h, the OD\({}_{600}\) was 2.585, and the conductivity was 110 \(\upmu\)s/cm/min. The bacterial solution obtained at 48 h was used in the mineralization test.
2. The application of microbial mineralization for repairing 500 \({}^{\circ}\)C damaged cement mortar specimens resulted in an improvement in both the compressive strength and chloride ion penetration resistance. The repairing rate of the specimen damaged at 500 \({}^{\circ}\)C was found to be 78.2%. This percentage was calculated by comparing the compressive strength of the repaired specimen to that of the undamaged specimen.  
Figure 4: Microbial mineralization repair cement glue test parts high-temperature damage product.  
Figure 5: Mineral analysis of Microbial mineralization repair cement glue test parts high-temperature damage product.  
3. The analysis of the repair products indicated that CaCO\({}_{3}\) was mainly responsible for the restoration of high-temperature, damaged cement mortar specimens. The CaCO\({}_{3}\) exhibited moderate strength, and a portion of the pores was filled with calcium silicate hydrate. The high-temperature alkaline environment somehow affected the cracks, resulting in a reduced mineralization effect by microorganisms. Several factors influenced the crystal morphology of the repaired CaCO\({}_{3}\) product, for example, temperature, pH, order of introduction of calcium sources, etc.
4. Through the study of microbial repair of high temperature damage to cementitious sand specimens, further microbial repair methods and effects after high temperature damage to concrete specimens can be explored, and after a reasonable construction method, this will potentially make the MICP technique one of the repair methods after fire damage to concrete buildings.  
Conceptualization, H.D.; methodology and writing--original draft preparation, T.Z.; funding acquisition, H.D. and L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the key research project of Shanxi Province: \"Research on Mechanism and multi-scale properties of microbial mineralization repair of concrete damaged by fire\", 201903D321113 and the Natural Science Foundation of Shanxi Province, 201901D211019.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 3 Performance of Broadband Power Line Communication--Related Works  
### BB-PLC Standardization  
TIA-1113 was the first BB-PLC standard adopted by the Telecommunications Industry Association (ITA) and released in 2001. This standard was based on HomePlug specifications 1.0. HomePlug 1.0  
Figure 2: Smart Metering communication network architecture with major technologies used for pilots, future deployments, or roll-outs in Europe.  
allowed a PHY data rate of 14 Mbps based on OFDM [16]. After that the HomePlug AV specification was available with an 85 Mbps PHDYA data rate [17].  
In 2010, IEEE 1901 and ITU-T G.hn standards were released. Both specify the physical and data link layers, coexistence mechanisms, and PSD masks. Similarly, HomePlug published the HomePlug AV2.  
HomePlug AV2 specification delivers gigabit rates due to the extended frequency range (from 30 to 86 MHz) [18].  
The IEEE 1901 standard uses the 2-30 MHz frequency band with an optional extended band of up to 50 MHz. The IEEE 1901 is defined for in-home and access networks. This standard defines two physical layer specifications, FFT-OFDM [19] and Wavelet-OFDM [20]. Wavelet OFDM PHY specification employs 512 subcarriers with 360 active subcarriers used for data modulation in the range from 1.8 MHz to 28 MHz and a frequency spacing of 61.03515625 kHz. FFT OFDM PHY specification uses 4096 subcarriers with up to 1974 usable subcarriers in the range of 1.8-50 MHz, and the carrier spacing is approximately 24.414 kHz. Support for carriers above 30 MHz is optional. Of the subcarriers below 30 MHz, 917 are active.  
The ITU-T G.hn standard is specified for home networking and HAN capable of operating over all types of in-home wiring (power line, phone lines, and coaxial cables) and provides bit rates up to 1 Gbps [21].  
The IEEE 1901 and ITU-T G.hn standards are noninteroperable. However, coexistence is enabled through the ISP specified in IEEE 1901 and ITU-T G.9972 [22].  
A comprehensive review of PLC standards can be found in previous studies [23; 24].
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
## 3 Proposed Method for One-Dimensional Image Reconstruction
Phase shifts caused by the dispersion in the observed sample are smaller than the wave shift resulting from a frequency sweep. Nevertheless, the latter will always be similar in value, since it depends on the frequency sweep and distance between the PCA emitter and the PCA detector. Phase compression can then be carried out as phase difference detection, and can be estimated as:  
\[\overline{\Delta\phi}=\phi(v)-\phi(v_{0})=\phi(v) \tag{10}\]  
where \(\phi(v_{0})=0\) is the unwrapped phase at the start frequency in the sweep, and \(\phi(v)\) is the unwrapped phase at the last frequency in the sweep. The proposed algorithm's results are intensity image and phase image.
## 2 Offline Handwritten Text Recognition  
Offline Handwritten Text Recognition (HTR) has evolved in the past few decades for two reasons: (i) the use of training and recognition concepts and techniques previously developed in the field of Automatic Speech Recognition (ASR); and (ii) the growing number of publicly available datasets for training and testing. In this way, the optical models in HTR systems are generally associated with language models, usually at the character or word level, to make the text recognition plausible [2, 41].  
The most traditional approaches to HTR are based on N-gram language models (statistical approach) and Hidden Markov Model (HMM) optical modeling with gaussian mixture emission distributions [42], most recently improved with emission probabilities by multilayer perceptrons [21]. However, notable improvements in HTR recognition accuracy have been achieved through artificial neural networks as optical models, specifically the Convolutional and Recurrent Neural Network (CRNN), becoming the current state-of-the-art [2, 15, 40].  
This current approach, detailed in Figure 1, uses images as input data and has final recognized texts as output data. Then, the defined steps are: (i) image preprocessing and normalization; (ii) image features extraction through Convolutional Neural Network (CNN); (iii) selected features propagation over the sequence with a Recurrent Neural Network (RNN); and (iv) the loss function calculation (to conduct model training) through the Connectionist Temporal Classification (CTC) [43], combined to the traditional language model (dictionary included) and HMM to achieve even better accuracy through the text decoding with linguistic knowledge built from dataset [2, 44, 45].
## 4 Modeling and Validation
### Individual Modeling
#### 4.1.2 k-Nearest Neighbor (kNN)  
kNN classification is an instance-based learning algorithm. The kNN algorithm calculates the distance between the test data point and all the training data points.The correct class for the test data is then predicted by selecting the \(k\) number of training points that have the smallest distances to the test data based on a simple majority vote [25]. Optimum classification results for the sklearn KNeighborsClassifier model were achieved by setting the number of nearest neighbors to \(k=5\).  
Figure 5: Scree plot for a factor analysis of the RF top features dataset.
## 2 Materials and Methods
### Industry 4.0 Hub  
In summation, SMEs are being supported by European initiatives in order to improve KT in regard to Industry 4.0. Moreover, there are even Industry 4.0 KT platforms, clusters, and hubs for SMEs, like the DIHs or _Platform Industrie 4.0_, which will increase their influence and support SMEs in the following years through European investment plans.  
In the case of DIHs, the European Commission is creating a complex, regional, multilayered, and heterogeneous innovation ecosystem that will promote its expansion in the following years. Some studies have suggested the importance of addressing Industry 4.0 at a regional level to foster innovation locally [33; 34; 35]. However, given the number, the different focus, and the specialization of DIHs at the regional, national, and EU levels, new strategies to coordinate, control, and optimize communication through those levels are necessary. DIHs are regionally based, supporting the local industry. Nonetheless, collaboration and networking between the DIHs are essential to ensure that knowledge related to the best practices can be transferred.  
Industry 4.0 technologies are evolving fast, and knowledge must be collected in real time. Moreover, the inclusion of new technologies, and especially the advent of Industry 4.0, are facilitating collaboration while at the same time accelerating the development of innovation outcomes and setting new challenges for SMEs [25; 28; 31; 36]. New KT strategies must be developed considering the dynamics resulting from the implementation of Industry 4.0. Consequently, promoting a collaborative atmosphere by boosting the synergies between SMEs and promoting networking through the different levels previously mentioned will enhance a culture of innovation.  
When SMEs start the transition to Industry 4.0, first of all, they do not want to start from scratch, and secondly, it can be easy to get lost in the multitude of technologies and tools that are available in today's market. There is a lack of an essential Industry 4.0 collaborative knowledge platform for every SME at every level, as shown in Figure 5. There is a gap for providing a collaborative web application Industry 4.0 KT hub without borders and without the level of granularity that initiatives like DIHs have created (European countries, European regions, and specialized DIHs).  
The KT of Industry 4.0 technologies and projects, as well as the networking of SMEs, can be facilitated via the creation of a collaborative hub. This research aims to fill this gap and has its focus on developing a collaborative, web-based KT hub for the implementation of Industry 4.0 components in the supply chains of SMEs.  
Figure 4: Interest over the last four years in DIH, generated via Google Trends.
## 3 Proposed System Model
### Performance Measurement of Machine Learning Classification Algorithms
#### 3.4.1 Confusion Matrix  
The confusion matrix evaluates classification model performance by comparing predicted values with actual ones, allowing the calculation of metrics such as the accuracy, precision, recall, and F1 score. It identifies four outcomes: True Positive (TP), where the model correctly predicts the positive class; False Positive (FP), where a negative instance is incorrectly predicted as positive; True Negative (TN), where the model correctly predicts the negative class; and False Negative (FN), where a positive instance is wrongly predicted as negative. These metrics are especially useful in imbalanced datasets for assessing model strengths and weaknesses [77].
## 4 Experimentation and Results
### Comparison of Index Construction and Data Query Efficiency  
Figure 9 illustrates the range query process based on the quadtree. The three images from top to bottom represent queries from the first to the third level. The red dashed box indicates the query area, while the solid boxes in different colors indicate the selected areas that meet the query criteria. The highlighted parts represent the returned query results. Test 1, Test 2, and Test 3 represent the query processes under different error control conditions.  
Figure 7: Comparison of File Sizes with Lossless Compression.  
Figure 8: PSNR compared with different compression methods.  
Figure 9: Range query process.  
When the error control is set to \"loose\", it means that the PSNR range for reconstructed data is 25-32 dB. For \"moderate\" error control, the PSNR range is 32-39 dB, and for \"strict\" error control, the PSNR range is 39-46 dB. For Test 1, the error control is relatively loose, so the desired result can be queried at the first level. However, for the moderate error control query (Test 2), the data at the first level are insufficient to meet the user's data needs, so further querying is required at the second level. In contrast, Test 3 represents a range query with the strictest error control. Neither the data at the first level nor the second level can fully satisfy the data requirements. Therefore, recursive querying is needed to reach the lowest level of the data to provide the desired data result to the user.  
To further demonstrate the superiority of our indexing design, we calculated the amount of data returned for different request scopes and under different error controls. The amount of data will intuitively demonstrate query efficiency.  
Our test results are shown in Figure 10. Figure 10a-c display the data volume returned under small, medium, and large range requests, respectively, with different error control settings. From the three images, it can be observed that as the data request range increases, the returned data volume also gradually increases. Regarding the different error control settings, a looser error control setting can return a smaller amount of data, greatly ensuring the efficiency of data queries. Overall, the quadtree-based indexing strategy excels in query efficiency.
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 2 Single-Element Antenna  
This section elaborates on the design of the single-element dual-band Sub-6 GHz antenna. The design procedure of the antenna is discussed in detail, highlighting the evolution stages followed by the parametric analysis of different design parameters.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 5 Implementation Based on Hyperledger Fabric
### Peer Node  
As mentioned in Section 3, a peer node of HLF performs the following operations:  
* _Endorsing_: performing the calculations defined in transactions;
* _Committing_: preserving blocks to the file system of the peer and preserving some block-related information to the peer's local databases;
* _Responding query requests_: searching blocks and local databases to find information that is requested by users and answering the requests;
* _Generating snapshot_: stop committing and then exporting information from local databases to snapshot files.  
Figure 5: Two phases of our advanced solution. In the transaction-processing phase, the nodes process the transactions as vanilla blockchain nodes do and save the blocks locally. In the data-reduction phase, the nodes generate commitments for the blocks, transmit the blocks to the remote DB server, and delete the blocks locally.  
The \"Generating snapshot\" operation is modified to transmit the blocks to the remote DB server and delete the blocks from its local file system. The \"Responding query requests\" operation is also modified by adding verification process after the blocks and transactions are retrieved from the remote DB server. The communications between peers and the remote DB server are realized by way of a GRPC protocol [44]. The \"Endorsing\" and \"Committing\" operations remain unmodified.