esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of CLSM
### Sensitivity Analysis  
The compressive strength, flowability, and bleeding of the CLSM were measured depending on the water-material ratio (W/M). In Figure 5, specimen B is CLSM-10. The W/M was changed by varying the amount of water, whereas the amount of the CLSM was fixed. The compressive strength decreased and the flowability and bleeding increased as the proportion of water increased. In addition, the compressive strength and flowability of specimens A and B were similar, and bleeding was equal to 0%. This indicated that the CLSM could be produced by changing the W/M from 90 to 100% depending on the objectives of construction and the geological properties at the sites of interest.  
Figure 5: The results of sensitivity analysis for the developed CLSM specimens depending on the ratio of water–material; (**a**) compressive strength for each specimen; (**b**) flow for each specimen; (**c**) bleeding for each specimen.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{4}{c}{**Composition (g)**} & \multirow{2}{*}{**Water**} \\  & **Binder** & **Fly Ash** & & **Additive** & \\  & & & **I** & **II** & \\ \hline CLSM-1 & 50 & 950 & - & - & \\ CLSM-2 & 75 & 925 & - & - & \\ CLSM-3 & 100 & 900 & - & - & \\ CLSM-4 & 125 & 875 & - & - & \\ CLSM-5 & 150 & 850 & - & - & \\ CLSM-6 & 98 & 900 & 2 & - & 1000 \\ CLSM-7 & 96 & 900 & 4 & - & \\ CLSM-8 & 98 & 900 & - & 2 & \\ CLSM-9 & 96 & 900 & 2 & 2 & \\ CLSM-10 & 96 & 900 & 3 & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The composition of CLSM specimens.
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 3 Results and Discussion
### Results of Short-Baseline Validation
Moreover, statistical testing results showed that, by applying LMMT individual PCC models in baseline processing, higher short-baseline accuracy was achieved in the height baseline component, horizontal (2D) baseline component, and spatial (3D) baseline component. To summarize, in the short-baseline validation test conducted, the application of LMMT individual receiver antenna PCC models positively transferred into the spatial domain and yielded approximately 2.5 times higher baseline accuracy compared to the use of IGS type-mean antenna PCC models. The developed LMMT robotic calibration system results in higher baseline accuracy compared to IGS type-mean antenna calibration models.
# Real-Time Stress Field Prediction of Umbilical Based on PyEf-U-Net Convolutional Neural Network
## 4 PyEf-U-Net for the Stress Field Prediction  
### PyEf-U-Net Model  
A novel convolutional neural network model is proposed to predict the layout of the umbilical stress field in real-time. The entire architecture of the proposed network model is illustrated in Figure 10. The proposed PyEf-U-Net deep learning model is composed of a symmetric structure with two parts: the encoder part and the decoder part. The overall network consists of eight convolutional units, 4 pooling layers, 4 EffCA modules, 1 Pyra-Con module, and multiple up-sampling blocks. Each convolutional block of the encoder consists of Dropout, batch normalized (BN) layer, and ReLU. The down-sampling operation is performed at a step size of 2, utilizing the maximum pooling operation, which halves the feature map resolution size and doubles the number of channels. The up-sampling operation in the decoder restores the feature map resolution with 2 \(\times\) 2 transposed convolution, which doubles the number of channels, and the skip connection connects the corresponding feature map of the encoder. The Pyra-Con module is adopted to the last transition layer in the encoder and decoder, which adopts multi-branch networks with different convolutional kernels for multi-scale feature fusion to ensure computational efficiency. Additionally, the addition of the EffCA module follows the network's low-level feature maps, which guarantee the network performance while reducing the network complexity with appropriate cross-channel interactions. Additionally, the addition of the EffCA module after the network's low-level feature maps guarantees the network's performance with appropriate cross-channel interactions. The input of the model is the different layout forms of the umbilical components. After a series of convolution, pooling, feature extraction, and merging operations, the output of the model is the corresponding stress field distribution.  
Figure 9: Diagram of umbilical clamping.
# Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection
###### Abstract
While a large amount of real training data is still necessary in order to train GAN models to generate samples with sufficient quality, researchers at NVIDIA have recently proposed StyleGAN2-ADA [18], which makes considerable progress towards the successful training of GANs with limited data. This was made possible by employing an Adaptive Discriminator Augmentation (ADA) mechanism that stabilizes training in limited data regimes, thus avoiding typical issues of overfitting the discriminator. Based on this, we propose a novel approach leveraging these recent advances in GAN training to generate synthetic data as a way to augment scarce training sets in manufacturing quality control tasks, specifically regarding structural adhesive applications. Additionally, we demonstrate that such an approach can improve the viability and performance of automated inspections based on deep learning. We carried out preliminary testing using a pilot application cell to generate real images of defective beads, creating a dataset to train this generative model for data augmentation. Using the augmented training set, we trained a state-of-the-art object detection model to automate the quality inspection task, then compared its performance with the same model trained only on a real training set of limited size. This directly addresses the future research direction delineated in previous work [19], which performed data augmentation using simulation. Shifting to a GAN-based method facilitates the employment of synthetic data generation without requiring the specific modeling of a simulation that closely resembles the real application. This is especially important in cases for which modeling may be too complex or unfeasible. The remainder of this article is organized as follows: Section 2 describes the materials and methods adopted in this work, providing sufficient detail and references for its replication. Section 3 summarizes the preliminary results along with a brief interpretation, followed by a more thorough discussion of their implication and directions for future work on this topic in Section 4. Finally, Section 5 summarizes the conclusions and provides some closing remarks.
## 3 Storage Degradation Belief Reliability Model for AEMR
### Aquired Method of Distribution Parameters  
In practical engineering, for storage reliability assessment of batch AEMRs, traditional methods are often based on probabilistic statistics requiring large test data samples. It is not friendly for products in the early design stage because it often requires multiple rounds of modification and optimization. Therefore, If we want to achieve effective evaluation by large sample data, it will undoubtedly increase the time and development cost. It is difficult to keep the timeliness of product launches in the fierce competition with peers. However, in the belief reliability theory, by considering the uncertainty distribution of parameters, it could estimate uncertain parameters by virtue of a small amount of test data to realize the work of storage reliability assessment. When the parameters obey the normal uncertainty distribution, the parameter estimation can be performed using the UMLE. According to axiom 4 of the uncertainty theory, the likelihood function in the UMLE method is the derivative of the uncertain distribution function taken as minor, which is different from the probability density function product in probability theory. Thus, for a sample satisfying the uncertain normal distribution \(N(e,d)\), the UMLE function is  
\[L=\wedge_{i=1}^{m}\Phi^{\prime}(x_{i}|e,d),i=1,2,\cdots,m \tag{15}\]  
where the symbol \(\wedge\) is taken to mean small, and the specific expression for \(\Phi^{\prime}(x_{i}|e,d)\) is shown below.  
\[\Phi^{\prime}(x_{i}|e,d)=\frac{\frac{\pi}{\sqrt{3}d}\exp\left(\frac{\pi(e-x_{ i})}{\sqrt{3}d}\right)}{\left(1+\exp\left(\frac{\pi(e-x_{i})}{\sqrt{3}d} \right)\right)^{2}} \tag{16}\]  
Figure 3: Normal uncertainty distribution.  
Belief reliability theory also provides solutions for situations where test and measurement data are unavailable. The unknown parameters can be determined by drawing on previous engineering experience in the development of similar products, as well as by expert designation. However, it is difficult to reflect the heterogeneity of existing specific products by the approach using previous engineering experience, while the approach using expert experience is slightly subjective, and it is difficult to ensure the objectivity of the assessment. Therefore, in this paper, we hope to use the manufacturing information of the product to realize the transformation of the initial distribution of process data and performance parameters, based on the functional relationship between the structural characteristics of the relay and the underlying performance parameters. At the same time, with the help of finite element simulation and the circuit simulation technology of relays, the virtual test of storage degradation of batch products is realized by conducting a multi-physical field simulation to obtain a sufficient amount of degradation simulation data. The data obtained will be more realistic and objective by combining the actual manufacturing information of the product and will apply to the case of multiple revisions at the beginning of the product design.
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 5 Cryptanalysis  
### Randomness Tests  
One of the requirements of a secure cryptosystem is that the output sequence should be undistinguishable from the output of a truly random function for any key used [30]. In order to verify that our stream cipher meets this condition, several sequences generated by different initial conditions were subjected to the NIST tests. Furthermore, in order to see how the inclusion of the LFSR to increase the period length affects the randomness of the sequences, some sequences generated only by the STM were subjected to the same randomness tests. After analyzing several sequences, we concluded that our algorithm was able to generate sequences with good randomness properties and that the presence of the LFSR dramatically increases the randomness properties of the digitized STM.  
Figure 11: (**a**) Histogram of the test image; (**b**) histogram of the encrypted image.  
Figure 10: (**a**) Test image; (**b**) Encrypted image.  
As an example, Figure 12 shows the NIST results for a particular sequence generated by the STM and the STM-LFSR algorithms respectively. It can be seen that the sequence generated by the STM-LFSR algorithm passes all of them, whereas the sequence generated by the STM only passes seven out of 17 tests.
## 4 Experiment and Comparison
### Comparison  
We compare the proposed SC generation tool with other state-of-the-art related tools. First, the models and methodologies used in each tool are summarized. The expression of business interaction processes allows for the alignment of SCs with a broader spectrum of business processes, thereby providing enhanced functionality, so we use _Business Processes_ as a comparative feature. The articulation of the interactive relationship with SCs facilitates the understanding and communication of complex contract logic and interaction processes. This contributes to collaboration and consensus building among the involved parties. Therefore, we use _Interactive Relationship_ as a comparative feature. Code-reusing libraries can reduce the development costs and support developers in rapidly building new SCs, thereby fostering collaboration and sharing within the blockchain development community. In light of these benefits, the comparative feature _Code Reusing_ has been established. _Security Checking_ refers to whether attention is given to conducting security checks on the generated  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Contract**} & **Total Code** & **Reused** & **Number of** & **Number of** & **Number of** & **Number of** & **Reused** \\  & **Lines** & **Code Lines** & **Variables** & **Reused** & **Functions** & **Function** & **Code Rate** \\ \hline Crowdfunding & 130 & 55 & 15 & 6 & 11 & 4 & 40.74\% \\ Voting Result Notification & 80 & 39 & 9 & 6 & 8 & 4 & 48.75\% \\ Election & 58 & 58 & 7 & 7 & 5 & 5 & 100\% \\ \hline Supply Chain & 150 & 65 & 19 & 8 & 11 & 6 & 43.30\% \\ Buying and Selling & 87 & 65 & 11 & 8 & 7 & 6 & 74.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reusability evaluation on the five contracts.  
SCs. SCs with vulnerabilities can potentially cause significant losses. _Complete Smart Contract_ refers to whether a fully functional SC code has been generated, which can be directly compiled and deployed. Some methods may generate a template that developers need to fill in with their specific details.  
Next, we compare the tools in terms of some main functional features, as shown in Table 2. The comparison shows that the proposed tool has more functional support for SC generation, especially the automatic code generation and reuse techniques, improving the development efficiency.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 2 Multi-Template Strategy
### Influencing Factors
#### 2.3.2 Functional Monomer  
The characteristics of functional monomers affect the success of creating MIP. Functional monomers are an important factor for binding interactions in molecular imprinting technology. The strength and type of template-monomer interactions are taken into consideration while choosing the optimal monomers for the synthesis of imprinted materials to create a particular donor-receptor interaction because the success of molecular recognition depends on the creation of a stable template-monomer complex [77; 78]. To maximise their interactions and to create highly specialised holes created for the template molecule, it is imperative to match the activity of the functional monomer to that of the imprinted molecule (template). In addition, the choice of monomer must consider its interactionswith the solvent: the monomer must be stable and soluble in the chosen solvent [79]. The best monomers can be chosen that will likely exhibit cage effects or strong interactions in the specified solvent. This will boost the capacity of binding cavities and have an effect on their homogeneity. The monomer also needs to be adapted to a certain polymerisation method [80]. Moreover, the monomers used for MIPs must withstand thermal and chemical changes and are not easily affected by environmental influences [81; 82]. The most commonly used monomers are MAA, 2-VP, 4-VP, 1-vinylimidazole, AM, trifluoromethyl acrylic acid (TFMAA), 2-acrylamido-2-methylpropane sulphonic acid, and 2-hydroxyethylmetacrylate (HEMA) [77].
## 5 Simulation Experiments and Result Analysis
### Simulation Experiments on Intelligent Power Distribution and Utilization Dataset (I)
According to the data shown in Table 6, life prediction results could be greatly improve by the data restoration of missing information in this case. It also demonstrated that the newly proposed GANs-HDI algorithm can effectively deal with small sample sized life prediction problems, which cannot be handled by the combinations of traditional algorithms, as caused by the disunity on cable test categories of different manufacturers.
## 2 Background and Related Works
### Related Work
However, this work relies primarily on fine-tuning large instruction-tuned LLMs on a comprehensive dataset, without utilizing semantic features aligned with Cypher query structures to enhance performance. Ref. [17] introduces SyntheT2C, a framework for generating synthetic datasets to fine-tune large language models for the Text2 Cypher task. Auto-Cypher in Ref. [18] presents an automated pipeline that uses LLMs to generate and verify Cypher queries, enhancing the quality of training data for Text2 Cypher tasks. In Ref. [19], a pretrained Text2 Cypher LLM is adapted into an AI agent to retrieve knowledge from a graph database based on user requests. A common approach in Text2 Cypher is to incorporate the database schema into prompts. However, complex schemas can introduce noise, increase hallucinations, and raise computational costs. The schema filtering proposed in Ref. [20] addresses these challenges by including only relevant schema elements, improving query generation while reducing token costs. CoBGT at Ref. [21] combines semantic value extraction with a transformer-based model to improve performance on the Text2 Cypher task. However, this approach may struggle with unseen schemas, as the underlying transformer model lacks the broad pre-training and generalization capabilities of LLMs. All these efforts do not propose any more effective training methods so as to let trained LMs deal with the semantic gap between natural text and cypher query more satisfactorily, so that they can enhance diverse complex query conversion across various domains.
## 2 PSO Algorithm
### Improved PSOA (IPSOA)  
During each generation of the search process, the particle randomly adjusts its velocity using its own experience and that of its neighbors. The mathematical model of velocity and position update for the basic PSO algorithm was introduced in Section 2.1, as shown in Equations (1) and (2). The two random weights \((r_{1},r_{2})\) for social learning and individual cognition in the velocity update Equation (1) are randomly generated. Therefore, these two random parameters may be too large or too small at the same time. If the two random weights are too small at the same time, the convergence speed is reduced, and the social learning and individual cognition are not fully utilized, leading to the premature convergence of the algorithm. Conversely, when the two random weights are too large at the same time, the social learning and the individual cognition are over-utilized to the extent that the particles may gradually move away from the global optimal solution. To this end, the IPSOARW is proposed, which can balance local search ability and global search ability in the search process by randomly weighing the weights.  
In the IPSOARW, \(\omega\) is set to a random number varying between 0 and 1, thus compensating for the linearly decreasing disadvantage of \(\omega\). First, if the optimal solution is approached early in the evolution, the randomly generated value of \(\omega\) may be relatively small, resulting in faster convergence. In addition, if the optimal solution is not found early in the algorithm's operation, the linearly decreasing nature of \(\omega\) allows the algorithm results to eventually converge to the optimal solution. Therefore, the \(\omega\) generated by random weights can effectively solve the problem of premature convergence. The equation for generating \(\omega\) by random weights is as follows.  
\[\left\{\begin{aligned} \omega=\mu+\sigma*N(0,1)\\ \mu=\mu_{\text{min}}+(\mu_{\text{max}}-\mu_{\text{min}})*rand(0,1) \end{aligned}\right. \tag{3}\]  
where \(N(0,1)\) is a random number of standard normal distribution and \(rand(0,1)\) is a random number between 0 and 1.  
The calculation steps of IPSOARW are as follows:  
* Initialize the velocity and position of each particle in the PSOA.
* Evaluate the fitness of each particle, store the current fitness value and position of each particle in the \(P_{best}\) of each particle, and store the fitness value and position of the individual with the best fitness value in all \(P_{best}\) in \(g_{best}\).
* Update the velocity and disarrangement of the particle using the following equation: \[v_{i,j}(t+1)=\omega\;v_{i,j}(t)+c_{1}r_{1}[p_{i,j}-x_{i,j}(t)]+c_{2}r_{2}[p_{ g,j}-x_{i,j}(t)]\] (4) \[x_{i,j}(t+1)=x_{i,j}(t)+v_{i,j}(t+1),j=1,2\ldots,d\] (5)
* Update the weights according to Equation (3). \[\left\{\begin{aligned} \omega=\mu+\sigma*N(0,1)\\ \mu=\mu_{\text{min
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
## 4 Analysis of Horizontal Optical Fiber Strain Monitoring in Slope
### Analysis of Overlying Rock Collapse Displacement  
To better determine the overlying rock collapse displacement, the optical fiber strain values obtained in the previous section were substituted into the established relationship between collapse displacement and fiber strain. Additionally, Get-Data software was used for point extraction analysis to obtain the vertical displacement of the overlying rock layer during working face excavation.  
As shown in Figure 11, the computed collapse displacement was compared with the fiber strain-derived displacement (hereafter referred to as 'fiber displacement') for further analysis.  
As shown in Figure 11, the vertical displacement curves of the overlying rock at 10 m (H1) and 30 m (H2) above the coal seam exhibit a strong correlation with the displacement curves derived from optical fiber strain conversion. This consistency demonstrates that the optical fiber strain monitoring method effectively captures the actual displacement of collapsing rock layers.  
With the advancement of the working face, the collapse displacement of the overlying rock gradually increases. In the early stage, rock deformation is relatively small, but as the goaf expands, the roof stress intensifies, leading to a significant increase in collapse displacement. During the excavation process, the rock displacement pattern transitions from a 'V-shape' to a 'U-shape', indicating that initial collapse is localized, whereas, in later stages, collapse extends across the entire roof, eventually forming a large-scale subsidence zone.  
Between 30 m and 75 m of the model length, the collapse displacement increases sharply, which coincides with the sudden changes in the H1 and H2 optical fiber strain curves. This indicates that the rock mass in this region has undergone significant fracturing and collapse. As excavation continues, at approximately 50 m from the advancing face, a sharp drop in collapse displacement is observed, corresponding to another sudden change in optical fiber strain. This further confirms that the peak fiber strain values are closely related to the fracture positions in the rock mass.  
Overall, the collapse displacement values calculated from fiber strain closely match the actual measured data, validating the feasibility and accuracy of this monitoring method. The use of optical fiber strain data to compute displacement enables a more detailed understanding of overlying rock collapse mechanisms, providing reliable data support for roof stability assessments in coal seam mining. Furthermore, this study reinforces the scientific validity and engineering applicability of optical fiber strain theory in mining monitoring, aiding in the prediction of goaf collapse evolution trends and enhancing mine safety management.