esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Based Supervision Data Security Sharing Framework
## 3 Blockchain-Based Framework for Secure Sharing of Engineering Supervision Data  
A blockchain-based engineering supervision data security sharing framework is designed to address the high pressure of on-chain data storage, data privacy, and security issues in the process of supervision data sharing. As shown in Figure 1, the framework mainly consists of several parts: user end, supervision data-sharing consortium chain, and IPFS cluster. This framework utilizes the immutability, decentralization, and traceability characteristics of blockchain, which utilizes smart contract design and data on-chain storage to achieve decentralized sharing of supervision data. And a fast file retrieval scheme is designed based on the storage characteristics of supervision data. Finally, the proposed sharing framework and smart contract mechanism are combined to solve the problem of difficult information exchange between the supervision party and all participating parties due to high trust costs, effectively improving the security and sharing efficiency of engineering supervision data.  
Client  
After identity authentication, registered users initiate transaction requests from the client to the supervision data-sharing consortium blockchain. Before uploading to the IPFS, the original file is encrypted. To ensure the traceability of the file source, users need to upload a private key signature. Finally, a fully deployed smart contract is used to store and query supervision data.  
The supervision data-sharing consortium blockchain stores supervision data metadata, responding to user upload and query requests, mainly including node networks, consensus mechanisms, and smart contracts. The node network consists of the owner, supervisor, designer, constructor, and government regulator participating in the engineering construction. The supervisor in the engineering supervision department plays the role of a system  
Figure 1: Blockchain-based framework for secure sharing of engineering supervision data.  
administrator, mainly including creating and deploying blockchain networks, managing nodes and ledgers on the chain, registering user identities, and the issuing and managing of certificates and keys. This framework uses the PBFT algorithm to confirm transactions [36]. Nodes participating in the consensus can query data and execute the consensus algorithm, while nodes not participating in the consensus are responsible for supervising the consensus and synchronizing the consensus results to their respective distributed ledgers. Smart contracts are deployed on the blockchain and can be automatically called and perform preset response actions, ensuring consistency and transparency in the data flow process through smart contracts.  
1. IPFS cluster  
As a peer-to-peer distributed file system, the IPFS has high security and storage capabilities. In this framework, it is used for the off-chain storage of encrypted file data. Users can search for corresponding files in the IPFS by uniquely identifying the hash address of the file.
# Performance Evaluation of Bedding Sand and Load Transfer Characteristic of Concrete Block Pavements
## 2 Materials and Experimental Methods
### Experimental Methods  
#### 2.2.1 Bedding Sand Performance  
Depending on the thickness and detailed structure of sand, particle size directly affects the physical and mechanical properties of synthesized sand. The content of small particles, in particular, greatly influences the porosity and moisture content of the synthetic material. The experimental parameters used to evaluate the mechanical performance by sand type and particle size include sand type, 0.6 mm passing ratio, and 0.075 mm content. For the experiments, the direct shear test method as per KS F 2343 was used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Manufactured Sand** & **Quartz Sand** & **River Sand** \\ \hline Density (g/cm\({}^{3}\)) & 2.795 & 2.735 & 2.771 \\ Absorption (\%) & 0.70 & 0.67 & 0.70 \\ Angularity (s) & 33.2 & 33.6 & 34.8 \\ Sand equivalent (\%) & 89 & 68 & 74 \\ Mica content (\%) & 0.4 & 0.4 & 0.5 \\ Hardness (\%) & 3 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of sand.  
Figure 2: Distribution of particle sizes of bedding sand and joint sand.
## 4 Synthesis of Antimicrobial Peptide
### Chemical Synthesis Mechanism  
The synthesis of peptide using chemical reagents to conciliate peptide bonding was performed for the first time 50 years ago. Chemical synthesis of AMPs can be performed in two ways: synthesis in a solution and solid-phase peptide synthesis (SPPS). Synthesis in a solution is implemented by dissolving all the components in the solution [43]. While in SPPS, an insoluble solid is attached to a N-\(\alpha\)-derivative of an amino acid through a linker. Then the protecting group (N-\(\alpha\) group) is removed, followed by washing the complex with the solvent. Coupling of the second amino acid (containing N-\(\alpha\) group) to the complex,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S.No.** & **Type of Synthesis** & **Advantages** & **Challenges** & **References** \\ \hline
1. & Chemical & Easy separation from side products & Toxic byproducts and low yields. & [38; 39] \\  & & & and impure compounds. & It becomes challenging while & \\  & & & Helpful in synthesis of short chain & synthesis of long chain peptides. & \\
2. & Enzymatic & peptides. It also has good & Low productivity and high cost & [39; 40] \\  & & & stereo selectivity. & of catalyst. & \\
3. & Recombinant & Convenient for large & Takes more time due to & \\  & DNA Technology & scale production. & & lengthy process. & [41; 42] \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison between all the three synthesis techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S.No.** & **Criteria for** & & & **Description** & **Examples** & **References** \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\
1. & Structure & Cysteine rich peptide & Peptides with cysteine residues & Defensis & [10] \\  & & Glycine rich peptide & Consists of 14\% to 22\% glycine residues & Attacins & [34] \\  & & Proline rich peptide & Proposed of 14-39 amino acids and contains & Drosocins & [10] \\  & & & & proline residues & \\  & & & & & \\  & & & & & \\  & & & & & \\
2. & Mode of action & Membranolytic & These peptides enter the microbial cell wall & \\  & & & by disruption & & \\
3. & Electrostatic charge & Non-membarolytic & Peptide that enters the cell by endocytosis & Scolopendin 1 & [36] \\  & & & Cationic & Peptide with positive charge & Cecropins & [37] \\  & & & Non- cationic & Peptides with negative charge and isolated & \\  & & & & from mammalian epithelia & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The classification of antimicrobial peptides.  
either in the presence of activator or as a preactivated species, takes place. A solvent is used to wash the oligopeptide-linker-support complex, and unreacted matter is discarded. Repetition of withdrawing the protection group and the coupling cycle is performed until the required sequence of amino acids is achieved. Using the cleavage agent, peptide is generated in the form of free acid or amide (Figure 4) [44].
## 2 Literature Review
A recent study suggested a Deep Convolutional Neural Network on point-light source-based shadow features and histogram of oriented pixel positions features [38]. Ensemble learning was used in one of the studies as well [39]. Another recent work suggested a Convolutional Neural Network-based architecture considering a lesser number of trainable parameters, applied their proposed model on Bengali numerals, basic characters, compound characters, modifiers, and the dataset of all 256 characters and outperformed previous studies [40]. Another research that worked with the class-wise performance of 256 classes previously, achieved an overall accuracy of 87.26% using a soft computing paradigm embedded in a two-pass approach, and this work was suggested by the original authors of the CMATERdb datasets [41]. In this research, we worked with the CMATERdb datasets and outperformed all these previous studies. Moreover, there have been some studies where the authors have focused on developing cost-effective Bengali character recognition architectures. One study suggested a multiobjective approach towards cost-effective Bengali handwritten characters recognition [42]. Another research suggested multiobjective optimization for recognition of handwritten Indic scripts [43]. Furthermore, transfer learning has been used by applying various architectures i.e., AlexNet, VGG-16, ResNet-50, etc. beforehand [44; 45]. Our proposed architecture has not only outperformed all these studies in terms of overall accuracy but also in terms of the number of parameters as well. Details on experimental results and comparative analysis can be discovered in the \"Experimental Analysis\" section.
## 2 Analysis and Modeling of Radiation Sources of PWM Fan  
### Analysis of Interference Propagation Paths and Radiation Sources of Fan  
The axial flow fan is driven by a PWM permanent magnet DC brushless motor, which consists of a driving circuit PCB and a three-phase winding. The switching action of the MOSFETs in the circuit results in voltage and current transients. These transients, interacting with the distributed inductance, capacitance, and other parasitic parameters in the PCB, generate high du/dt and di/dt values, leading to significant conducted interference. Conducted interference propagation can be categorized into common mode (CM) and differential mode (DM) conduction paths. As indicated by the red dashed line in Figure 2, CM interference is generated from transient voltage fluctuations occurring during switching operations, with currents flowing in a closed loop path through the winding and ground. Meanwhile, the MOSFET's switching actions lead to sudden fluctuations in the currents within the bridge arms, resulting in DM interference. The conduction path for DM interference is illustrated by the blue dashed line in Figure 2. Assuming that Q1 and Q5 are in the on-state, the DM current flows from the U phase to the DC side, passes through the LISN, and flows through the V phase, and forms a closed loop through the winding.  
Figure 2: Paths of conducted interference.  
According to the interference path and structure of the axial flow fan, the stator winding of the motor and the driving circuit PCB inside the fan have an antenna effect. As depicted in Figure 3, transient voltages and currents produced by power MOSFETs may propagate externally through these elements, forming multiple sources of radiation and causing electromagnetic interference through the apertures between the fan frame and the impeller.
## 3 Materials and Methods
### Dataset Preparation  
The proposed yarn hairiness dataset contributes to the research community by focusing on protruding and loop fibers, filling a gap with specific and comprehensive data on yarn hairiness. This dataset is a valuable tool for textile engineering, distinguished from existing datasets. It consists of images of purple cotton yarn with an average linear mass of 56.4 tex, captured at 22\(\times\) magnification using a 1.6 Mpixel camera at 238 FPS, acquired with a custom-designed mechatronic prototype [18] (Figure 7).  
Commercially available equipment, such as the USTER TESTER, is characterized by the following:  
* A high cost--exceeding EUR 100k;
* A high weight, limiting portability;
* Destructive sample analysis;
* A limited capability to analyze yarn parameters.  
While this article highlights the high efficiency and cost-effectiveness of the developed system, it acknowledges that a more explicit analysis of cost savings was not initially included. The total cost of the developed mechatronic system (hardware and software) is approximately EUR 1300, compared to the commercial USTER TESTER, which costs over EUR 100k. This means that the developed prototype is about 10 times less expensive, representing substantial savings in acquisition costs alone, without compromising the effectiveness in yarn quality monitoring.  
The dataset [57; 58], available in Mendeley Data, comprises 684 original images, which underwent augmentation to yield 1644 images. These images were accompanied by 11,037 annotations extracted from videos showcasing 100 m of purple cotton yarn. The videos were captured and hosted on Roboflow [59].  
Figure 7: Prototype mechanical system with camera [18].  
This dataset facilitates the development of machine learning models for yarn hairiness detection and advances AI in textile science. It was categorized into two annotation classes: protruding fibers and loop fibers. Using this dataset, tests and training were conducted to build a robust model for real-time hairiness identification and classification. The annotation was carried out in a polygonal mode [58; 59; 60; 61] using LabelME [60] (Figure 8).  
Of the 684 images in the dataset, 70% were used for training (480 images), 20% for validation (138 images), and the remaining 10% for testing (66 images). This dataset was generated using augmentation techniques [18; 58]. An average of approximately 16 annotations per image was obtained, resulting in a total of 6733 annotations for loop fibers and 4304 annotations for protruding fibers (Table 2).  
In the used dataset, specifically for training, Data Augmentation was performed through the Roboflow and the following techniques were selected:  
* Flip: horizontal, vertical;
* Saturation: between \(-25\%\) and \(+25\%\);
* Blur: up to 5% of pixels;
* Noise: up to 5% of pixels.
## 7 Stop Condition and Algorithm Parameters  
For the sake of the algorithm description be self-contained, in this Section it is described when the algorithm stop and several parameters that can be set to tune the algorithm behavior.  
The most common method to stop the algorithm is to specify a maximum number of generations. In addition, if the best average fitness of a generation, does not improve within (specified) number of generations then the algorithm is stopped and the best element so far is returned as the near optimal control.  
There are some general parameters that can be modified in order to conduct the search process. All of them are constant during the algorithm execution. Some of the most important parameters are:  
* Algorithm parameters
* Generation size: It indicates the number of elements of each generation, these are the search agents in the search field of potential solutions.
* Range of the constants: It establishes the range of the random numeric constants.
* Depth: It determines the minimum and maximum number of sub-trees that a controller can have.
* Elite-Size: The number of the best elements that are copied directly to the next generation.
* Genetic Operation probability: The relative probability of an element to be operated by replication, mutation or crossover.
* Stop condition parameters
* Maximum number of generations without average fitness improvement
* Simulation parameters
* Simulation time
* Max integration step size
* Integration method
## 6 Results and Discussion
### Outlier Detection  
A Williams plot of the MLP-LMA model is depicted in Figure 6. It can be seen from the figure that the majority of data points are situated in the range of \(-3\leq R\leq 3\) and \(0\leq H\leq 0.03\), which validated and verified the implemented MLP-LMA model statistically, where 13 points are considered suspect. In summary, the leverage approach states that the developed model is reliable and can be used with high accuracy to predict CO\({}_{2}\) solubility in ILs.
# Towards Domain-Specific Knowledge Graph Construction for Flight Control Aided Maintenance
## 7 Conclusions  
In this paper, we propose a knowledge-graph-based system prototype in order to facilitate achieving knowledge from unstructured data for daily flight control maintenance. We first build ontology and then perform entity-relation extraction by leveraging natural language processing techniques. Finally, we design and implement a flight control maintenance system prototype based on the knowledge graph constructed whose duty is to return maintenance suggestions for grass-roots engineers.  
Our work is a start-up of promoting flight control maintenance by leveraging information technologies. In our prototype system, a natural language-processing-based method plays a key role. However, in the vertical area of flight control, the lack of sufficient corpus restrains the performance of a machine learning model. Hence, we will improve our system from three aspects in the future. First, finding more corpus in this area, such as textbooks, research papers or even related technical web pages. Second, adopting more techniques to improve the extraction model, such as joint extraction, Pointer Network, pretrained word embedding model such as BERT (bidirectional encoder representation from Transformers), and generative methods. Finally, we will expand the system to adopt all structured data to replace the current relation-database-based system.  
Figure 11: Example of querying “F/CTL SEC 1 FAULT”.  
Figure 10: Example of querying “F/CTL L + R ELEV FAULT”.  
**Author Contributions:** Conceptualization, C.L., M.S. and W.L.; formal analysis, M.S. and W.L.; funding acquisition, C.L.; investigation, S.L.; methodology, X.Y., M.S. and W.L.; project administration, C.L. and M.S.; resources, W.L.; software, X.Y. and S.L.; supervision, W.L.; validation, W.L.; writing--original draft, C.L.; writing--review and editing, C.L., X.Y., S.L. and W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China under Grant 61902063, by the Provincial Natural Science Foundation of Jiangsu, China under Grant BK20190342, and by the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing by Grant 2020A04.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Unlike off-line measurements in which the samples are manually collected (subject to sampling errors) and studied in a viscometer, in-situ measurements have the additional advantage of data collection on a continuous basis without any time delays, leading to better quality control of the emulsion product. This is especially significant in processes where emulsions  
Figure 1: A schematic diagram of surfactant-stabilized and nanoparticles-stabilized oil droplets in an oil-in-water (O/W) emulsion. are prone to phase inversion. When phase inversion of an emulsion takes place, an abrupt change in viscosity occurs resulting in processing problems of emulsion-based fluids. A number of on-line techniques are available for the measurement of fluid viscosity [10]. They can be broadly classified into three groups: (1) Rotational-type devices such as co-axial cylinder visometers; (2) tube or capillary viscometers; and (3) vibrational-type devices. The rotational-type on-line viscometers available commercially are generally suitable for viscous fluids. The tube or capillary devices and vibrational devices are generally single-point (one flow or shear rate) devices suitable for viscous Newtonian fluids. The objectives of this work are as follows: (a) To develop an in-situ method of measuring the viscosity of emulsions over a broad range on a continuous basis under agitation conditions; (b) to study the viscous behavior of surfactant-stabilized O/W emulsions using the in-situ measuring device; (c) to study the viscous behavior of nanoparticle-stabilized (Pickering) O/W emulsions using the in-situ measuring device; and (d) to investigate the stability of surfactant-stabilized and nanoparticles-stabilized O/W emulsions.
## 4 System Model  
In this section, our proposed system model is presented in Figure 1. The system model consists of source nodes, relay nodes and sonobuoys. Source node forwards data packets toward the destined sonobuoys during transmission. The proposed protocol follows multi-hoping feature for packets transmission. Source and relay nodes only use acoustic signals while radio waves are used for communication among sink node, submarine, satellite, base station and the main processing unit.  
In the proposed system model, sensor nodes are randomly deployed in an underwater medium. Nevertheless, sink nodes are deployed at the sea surface. The same transmission range and energy are assigned to each sensor node. Moreover, each sensor node has also the ability to adjust their depth from the lower layer to the upper layer. During depth adjustment, nodes only move in a vertical direction. The process of depth adjustment occurs in the case when a node cannot find its next forwarder even by increasing the transmission range. There are three different cases that are elaborated through the proposed system model.  
* Successful transmission
* Occurrence of a void hole
* Void hole recovery
## 2 Results and Discussion
### TiO2 Primary Particles and Their Nanoclusters
In Figure 1b, the transmission FT-IR spectra of the TiO\({}_{2}\) NCs with and without MPS modification are shown. It is seen that with MPS modification, peaks appear at 1697 cm\({}^{-1}\), 1630 cm\({}^{-1}\) and 1173 cm\({}^{-1}\), corresponding to C=O, C=C, and C-O-C bonds, respectively, assigned to the MPS molecule. More importantly, a peak has been observed around 1010 cm\({}^{-1}\), which specifically represents the Si-O-Ti bond, confirming the successful MPS modification of the TiO\({}_{2}\) NC surface. It should be noted that the diameter of the TiO\({}_{2}\) NCs after surface modification increases from the initial 30.2 nm to 46.9 nm, as shown in Figure 3a, based on DLS measurements. Two factors may contribute to this size increase: First, since each NC is constituted by several TiO\({}_{2}\) primary particles, the MPS grafting occurs on the surface of all the TiO\({}_{2}\) primary particles, thus separating them from each other and leading to certain \"expansion\" of the NC size. Second, since several grafting sites are present on each MPS molecule, they may lead to certain bridging among the NCs. However, such bridging should not occur significantly under acidic conditions [46]. The MPS-modified TiO\({}_{2}\) NCs, after separation from the solvent and washing, were mixed with styrene and DVB monomers to form a TiO\({}_{2}\) NC/monomer mixture, which will be dispersed in water to form mini-emulsions in the next step.