esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Evaluation
### Simulation and Performance Metrics  
In this work, a Cooja simulator [42] will be used due to its support for the 6 LoWPAN protocol. Moreover, the Cooja works within an operating system called the Contiki [43], designed to control the Internet of Things. Both systems are running on the Oracle Virtual-Box in a machine with a Core i5 processor clocked at 1.8 GHz, 8 GB cache and 12 GB RAM. Table 3 shows the parameters that will be calibrated based on simulation requirements, part of which was taken from [PERSON] et al. [17].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline WSN size & 60 m \(\times\) 120 m \\ AP location & X = 30, Y = 90 \\ \(n\) & 200 \\ Node speed & 5 meter/second \\ \(q\) size & 64 bytes \\ Control message size & 25 bytes \\ \(P\) initial & 0.5 J (joule) \\ \(\beta_{trm}\) & 0.0013 PJ/bit/m\({}^{4}\) \\ \(\beta_{fsm}\) & 10 PJ/bit/m\({}^{2}\) \\ \(P_{elc}\) & 50 nJ/bit \\ \(P_{r}\) & 0.005 J \\ \(P_{b}\) & 0.005 for 16 bits \\ \(d_{T}\) & 87 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters used.  
The simulation system randomly distributes the nodes in the dimensions of 1000 by 1000 of the terrain connected to nine CHs in the initial values as illustrated in Figure 6, then they start to move randomly at a rate of 5 m per second [44]. In our scenario the UDP clients (normal nodes) send Constant Bit Rate (CBR) traffic of around 64 bytes with the periodicity of 0.01 s.  
Related to performance metrics that will be used in this simulation, the network lifetime, node power consumption and packet loss. Each one of these metrics is explained as below:  
* The energy consumed and the remaining energy of the WSN nodes: It depends on the energy difference between different times to know the degree of impact of the proposed algorithm on the energy consumed.
* Network lifetime: The life of the network is based on a measurement of the time period for the first node to reach zero in its amount of remaining power.
* Packet loss: This metric measures the rate at which packets fail to reach their destination when traveling over the network. Packet loss has several factors for its occurrence, but this proposal will refer to link the effect of attacks on its increase and energy consumption.
## 3 Method for Lithology Classification
### Convolutional Neural Networks  
CNNs use convolution operations to capture local features, which is especially useful for image processing as it can effectively identify edges, textures, and other local structures [26]. The weight sharing mechanism in convolution layers significantly reduces the number of parameters, lowering the model's complexity and computational cost. Furthermore, convolution operations allow the model to be invariant to translations in the input data, meaning it can recognize the same features even if the image is shifted spatially. As a consequence, CNNs excel at handling spatial information related to images [27]. The well  
Figure 3: Sample data processing procedure: (**a**) raw data, (**b**) normalization of the individual samples, (**c**) normalization of well logging data. NR, GG, and GR represent normal resistivity (NR, unit: \(\Omega\cdot\)m), natural gamma (GR, unit: 10 pA/kg), and gamma-gamma (GG, unit: CPS) density logging data, respectively.  
logging signals used in this study can be considered as multiple one-dimensional images stacked together, making them suitable for CNNs.  
The CNN model utilized in this study for predicting lithology is depicted in Figure 4. The model's input comprises six normalized well logging curves, each containing 40 continuous sampling points. The input features are processed through three alternating convolutional and pooling layers for effective feature extraction. Following each convolution operation, batch normalization is applied to standardize the data and mitigate the risk of gradient vanishing, while the ReLU activation function defined as \(\text{ReLU(x)}=\text{max(x,0)}\) is employed to enhance the model's non-linearity [28]. Following the convolutional layers, two fully connected networks are employed to perform dimensionality reduction on the extracted features, aligning them with the output categories. The softmax function defined as \(\text{Softmax(x)}=\frac{e^{i}}{\sum_{i=1}^{E}e^{i}}\) is used in the output layer of neural networks, which not only provides the probability for each class but also ensures that these probabilities sum to 1, making it easy to make decisions based on the model's confidence. At last, the model's output utilizes the cross-entropy loss function [26; 29]:  
\[Loss=-\frac{1}{m}\sum_{k=1}^{m}\sum_{i=0}^{n}p^{\prime}_{i,k}\text{log}(p_{i,k})\]  
where \(p^{\prime}_{i,k}\) is the reference catalogue of hot vector, and each element in the vector is a binary value (0 or 1), with exactly one element set to 1 and the rest set to 0. The \(p_{i,k}\) is the prediction of CNN in the range of [0, 1] for \(k\) th sample. Here, \(n=4\) denotes the number of output neurons, \(m\) is the trained number of samples for each training loop.
# Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model
## 3 Stylish Chinese Font Generation
### SE and Semi-Supervised Scheme  
The SE is equivalent to the conditional information of the model, which serves to allow the model to focus more precisely on the local stroke information in the Chinese character structure. The stroke encoding method for Chinese characters uses a simple one-bit encoding method, i.e., the corresponding position in the vector is 1 for whichever stroke exists in a given Chinese character, otherwise, it is 0. As shown in Figure 3, there are 32 kinds of basic strokes in Chinese characters, so the SE of each Chinese character, \(c\), is defined as a vector, \(c\in\{0,1\}^{32}\). Nevertheless, it is worth noting that certain Chinese characters can possess the same one-bit stroke encoding but exhibit vastly different structures, as depicted in Figure 3. As depicted in Figure 1, although StrokeGAN, which only contains stroke encoding as conditional information, alleviates the mode collapse problem that occurs in the original method, it still exhibits a significant number of missing strokes in the generated characters. This indicates that the role of Chinese character stroke encoding as conditional information is limited and insufficient for the model to generate more perfect results. To tackle this challenge, we propose a semi-supervised scheme. Considering the unique structure of Chinese characters and the relatively smaller number of structural categories compared to the total number of strokes, we randomly select a percentage of commonly used Chinese characters from the available data. These paired data are then incorporated as additional supervised data to obtain extra information about the global structure of the entire character image. This approach helps alleviate the challenge of poor model performance when generating different characters with the same stroke encoding. In this paper, we consider the robustness of strokes as only an indicator function, rather than the exact number of strokes for a given Chinese character.
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 2 Related Works
### Analysis of Methodological Constraints in Related Works
#### 2.3.2 Contact Patch Length-Based Methods  
In [6,8], vertical load estimation is achieved by utilizing the geometric relationship between the tire's deformation and the contact patch length (\(L\)). The contact patch length is expressed as follows:  
\[L=2\cdot R\cdot\sin\biggl{(}\frac{180\cdot\Delta t}{t}\biggr{)} \tag{3}\]  
where:  
* \(R\): the free radius of the tire;
* \(t\): the time required for the tire to complete one full rotation;
* \(\Delta t\): the time required for the tire to traverse the contact patch.  
This method relies on the direct relationship between the vertical load and the contact patch length, where \(L\) increases as the load increases. The time \(\Delta t\) is determined as the difference between the local extremum points in the acceleration signal, similar to the approach described in Section 2.3.1. Factors affecting \(\Delta t\), such as noise in the acceleration signal, are also applicable here.  
Unlike displacement-based methods, contact patch length-based approaches are sensitive to variations in tire geometry, such as changes in the free radius (\(R\)) caused by tire wear or pressure fluctuations.  
Despite these limitations, contact patch length-based methods are computationally efficient and provide an intuitive framework for understanding tire-ground interactions. Their simplicity makes them suitable for controlled environments where external variations are minimal, but their dependency on precise geometric parameters necessitates careful calibration and monitoring to ensure accuracy.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 6 Proposed Security Framework
### Block-Chain Based Network  
To keep all the nodes up to date, all Blockchain modules are interconnected and constantly exchange the most updated information in the Blockchain among themselves. Smart contracts will trigger and enforce relevant actions based on the data provided, allowing for verifiable transactions in the Blockchain when such circumstances are met without the requirement for third-party involvement [110; 111]. The Ethereum Blockchain platform serves as the foundation for the blockchain network architecture because it provides a highly flexible and modular design that separates the runtime environment as per theapplication domain, permitting agreements to define the business rules for the application that are not necessary for the core system's fundamental design.  
The Blockchain complete node is made up of the following components:  
1. Node for Validation: This is a key component that verifies batches of transactions before combining them into blocks. It also ensures that candidate blocks are added to the Blockchain version for each node.
2. Blockchain Software: it determines which transactions or operations are permitted on the block-chain and contains the following: * Database model: it defines relevant operations and the description of the transaction's payload.
* Transaction Processor: defines the business logic for different applications, validates batches of transactions, and modifies the blockchain system based on the application's rules.
* Client: it specifies the service client functionality, which the client develops and delivers to the validator. The client also shows blockchain information.
* REST API: This protocol is used to communicate within the clients and the transactions processing system.
3. Digital Signature: To ensure transaction integrity, the data used in the transactions is hashed using SHA-256 and then encrypted using the sender's secret key to produce a signature. The processor then validates the transaction by verifying this signature.
## 3 Results and Discussion  
### Establishment of Screening Scheme for Functional Compounds in Beer  
To establish a functional compound detection scheme for the system, the bioactive ingredients related to the health benefits of beer were identified through literature research and analysis. Based on the existing research data, a systematic classification of functional compounds in beer was conducted. In the industrial beer production process, besides ethanol, beer primarily contains the following functional components: (1) amino acids and derivatives (GABA and GSH); (2) polyphenic compounds (including tannic acid and flavonoids); (3) B vitamins (B1, B2, B3, B5, and B6); (4) polysaccharide substances. The content of functional compounds in industrial beer is generally low, and these functional compounds not only contribute significantly to the flavor characteristics of beer but also exhibit health benefits such as antioxidant, anti-tumor, metabolic promotion, and immune enhancement properties.
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 4 Discussion
### Sensing Technology  
During this project, we tested a new open sensor network designed to collect environmental data, including indoor and outdoor temperature and humidity, to enhance agricultural management. The implementation of IoT sensors in hives allows for the real-time recording and transmission of key variables, providing beekeepers with invaluable data for informed decision-making. Practical applications of this technology include monitoring overheating risks [34; 35], accurately estimating the foraging times of bees [55; 56], anticipating swarming events [57], detecting the formation of winter clusters [58], or the appearance risk of diseases such as chalkbrood [59] caused by _Ascosphaera apis_ (Maassen ex Claussen).  
The developed sensor network effectively monitors both indoor and outdoor temperatures and humidity levels, and it has the potential to integrate additional sensors to monitor a broader range of environmental parameters from a single node. The system used in our study is built on robust platforms previously utilised in river research applications [41]. Designed for low maintenance and high durability, it employs radio base nodes instead of WiFi to ensure reliability and extensive range and features a centralised gateway for efficient data transmission. Both nodes and gateways are based on open-source technology, making the system not only customisable but also fostering community involvement in its improvement.  
During the deployment of this technology, we observed no gaps in the dataset or anomalies. The network is currently undergoing long-term field testing to further assess its performance. Furthermore, we aimed to make this technology affordable, targeting a cost of less than 100 EUR per apiary. This pricing strategy would significantly lower the entry barrier compared to existing technologies, making advanced monitoring accessible to a broader range of beekeepers and providing do-it-yourself opportunities for more technically skilled users.  
Through the improvement of the insulation and the integration of sensor technology, we aim to provide beekeepers with better tools for managing hive health and adapting to environmental changes, thereby enhancing both thermal comfort and the overall wellbeing of the bees.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 5 Simulation Analysis
### Performance Analysis of the Consortium Blockchain Framework  
The results of a comparison of consortium blockchain with traditional blockchain are shown in Figure 7. The consortium blockchain framework uses Hyperledger Fabric to write  
Figure 6: Influence of inconvenient parameter \(\alpha\) on buyer’s benefit and cost.  
smart contracts of blockchain. In this paper, 10 peering points (each peer adopts x64 virtual machine simulation) are compared to 10 RSU to conduct simulation and analysis. Based on Node.js, the load test library completes the scheme test. It is assumed that 200 cars in the covered area perform as data sellers, and they all have a certain willingness to share the data. The number of buyers is 50. The other parameters are exactly consistent with the previous simulation parameters.  
According to the analysis of Figure 7, the average transaction confirmation time of a block in the BCB scheme is basically not more than 10 to 15 min, while this index in the traditional blockchain is about 60 min [20]. The proposed scheme can confirm the block more efficiently. The reason is that from the perspective of BCB, it only completes the blockchain block consensus between the RSUs, avoiding the consensus of the traditional blockchain on all of the connected nodes. Moreover, in the BCB scheme, the number of consensus nodes is strictly limited, so the network throughput has been significantly improved. Block can be confirmed in a shorter time. Based on the proposed mode, the nodes can reach a consensus more efficiently and effectively shorten the waiting time. The efficiency of the consortium blockchain framework is ensured.
## 2 Multi-Scale Moisture Diffusion Coefficient Model of Concrete
### Model Validation  
In order to verify the reliability of the multi-scale moisture diffusion coefficient model of concrete in this paper, the data obtained from the empirical equations in the CEB-FIP (2010) code [35] and some experimental data in the references [16; 36; 37; 38; 39] were chosen to compare with the model calculation result. Considering the influence of the concrete mix ratio difference on the moisture diffusion coefficient in the reference, a similar mix ratio (in which the water-cement ratio is around 0.5) is selected as far as possible, and the dimensionless moisture diffusion coefficient \(D_{h}/D_{max}\) is used for comparative analysis and verification.  
According to the empirical Equation (19) in the CEB-FIP (2010) code [35], the moisture diffusion coefficient of concrete can be expressed as follows:  
\[D_{h}=D_{max}\bigg{\{}m+\frac{1-m}{1+\left[\left(1-H\right)/\left(1+H_{c} \right)\right]^{n}}\bigg{\}} \tag{19}\]  
where \(D_{max}\) is the maximum value of \(D_{h}\) when RH = 1, m\({}^{2}\)/s; \(H_{c}\) is the relative humidity when \(D_{h}\) = 0.5 \(D_{max}\), and the code recommendation \(H_{c}\) = 0.8; \(m\) and \(n\) are the empirical coefficients; and it is recommended that \(m\) = 0.05, \(n\) = 15.  
The pore size distribution curve required in the multi-scale moisture diffusion coefficient model was obtained via the data in reference [39], and the temperature is selected at room temperature (20 \({}^{\circ}\)C). The comparison results are shown in Figure 2, which indicate that the predicted data of the moisture diffusion coefficient model is in good agreement with the empirical and reference date, and the model can effectively describe the moisture diffusion coefficient of the multi-scale of concrete.
## 3 Results
### Distance and Angle between MSP and Each Mandibular Midsagittal Plane before and after Surgery
In contrast, the SI calculated using MSP as the reference plane 1 year after surgery was \(0.80\pm 0.05\), had no significant difference with the SI calculated using cmAMP as the reference plane before surgery, which was \(0.83\pm 0.04\). This indicates that the symmetry of the mandible anterior segment, as calculated based on cmAMP before surgery, does not significantly differ from the symmetry of the mandible anterior segment, as calculated based on postsurgical MSP. This implies that the symmetry of the mandible can be maximized after surgery only when surgery is performed in the direction where the cmAMP overlaps the MSP. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Between MSP andAMP**} & \multicolumn{2}{c}{**Between MSP and mAMP**} & \multicolumn{2}{c}{**Between MSP and cmAMP**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \cline{2-7}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & \\ \hline Before surgery & & & & & & \\ Distance (mm) & 3.64 b  & 2.22 & 3.16 b  & 2.07 & 1.77 a  & 1.19 & 0.001 \\ Angle (degree) & 5.21 b  & 3.61 & 3.98 ab  & 2.35 & 2.87 a  & 1.49 & 0.005 \\ After surgery & & & & & & \\ Distance (mm) & 2.46 b  & 1.84 & 2.34 b  & 1.65 & 1.15 a  & 0.74 & 0.002 \\ Angle (degree) & 4.91 b  & 3.15 & 2.90 a  & 1.63 & 2.02 a  & 0.82 & \textless{}0.001 \\ \hline \hline \end{tabular} MSP, facial midsagittal plane; AMP, absolute mandibular midsagittal plane; mAMP, modified absolute mandibular midsagittal plane; cmAMP, computed modified absolute mandibular midsagittal plane. \(p\) values were calculated by one-way ANOVA. Different letters indicate significant differences between groups.  
\end{table} 
Table 4: Distance and angle between MSP and each plane before and after surgery. Figure 4: Comparison of the similarity index (SI) between the facial midsagittal plane (MSP) 1 year after surgery and each mandibular midsagittal plane before surgery. MSP, facial midsagittal plane;  
AMP, absolute mandibular midsagittal plane; mAMP, modified absolute mandibular midsagittal plane; cmAMP, computed modified absolute mandibular midsagittal plane. The \(p\) values were calculated using one-way ANOVA. Different letters indicate significant differences between groups. ***\(p<0.001\).
# Characteristics and Mechanisms of the Zigzag and Spiral Movement of Rising Bubbles in Still Water
## 3 Results and Discussion
### Spiral Movement
#### 3.3.2 Characteristics of Spiral Movement  
Figure 9 shows the change curve and the frequency spectrum of displacement, velocity, and acceleration in \(x\)-, \(y\)-, and \(z\)-directions during the process of a bubble's trajectory developing from vertical to zigzag and finally to spiral. After 0.1 s, the bubble's trajectory changes from vertical to zigzag. After 0.35 s, it develops into a relatively standard spiral. In the spiral movement stage, the trajectory in the \(x\)- and \(y\)-directions differs by \(1/4\) cycle. The frequencies of the displacement and velocity oscillation in the \(x\)- and \(y\)-directions are close to each other. The velocity in the \(z\)-direction does not oscillate, and the frequency of acceleration in the \(z\)-direction is about 4 times the frequencies in the \(x\)- and \(y\)-directions.  
Figure 8: Several trajectories of bubbles. (**a**) Transition from a zigzag to an elliptical trajectory. (**b**) Elliptical trajectory. (**c**) Elliptical trajectory with a change in the major axis direction. (**d**) Transition from an elliptical to a spiral trajectory.  
According to the zigzag movement mechanism, the change in the bubble trajectory is due to the shedding of the trailing vortices and the change in the tractive force at the tail of the bubble. The asymmetric change in the trailing vortices on the left and right sides of a plane causes an asymmetric force on the left and right sides of the bubble, resulting in zigzag movement. The velocity of the spiral movement is in the range of the velocity oscillation during zigzag movement, so the asymmetric shedding mode of the wake vortices does not change. A disturbance in the direction perpendicular to the zigzag movement plane of the bubble will also cause zigzag movement in this plane, and then this will develop in the third direction (\(y\)-direction) to form three-dimensional spiral movement.  
At the same time, the trailing vortices of zigzag movement fall off once in a half cycle, while the oscillating movement in the \(x\)- and \(y\)-directions differs by 1/4 cycle, which proves that the trailing vortices of spiral movement fall off once per 1/4 cycle, that is, the vortex shedding point rotates along the tail of the bubble. At the same time, the staggered shedding of trailing vortices in the orthogonal directions causes the oscillation frequency of acceleration in the \(z\)-direction to be 4 times that in the \(x\)- and \(y\)-directions in a movement cycle.  
In the process of bubble movement, due to various factors, such as the bubble shape, the changes in the trailing vortices in the \(x\) and \(y\) planes are not completely identical, which leads to a variety of bubble trajectories, as shown in Figure 8.  
Figure 9: Spiral movement law of bubbles. (**a**) Changes in displacement and velocity over time. (**b**) Displacement and velocity spectrum analysis diagram. (**c**) Changes in displacement and acceleration over time. (**d**) Displacement and acceleration spectrum analysis diagram.