esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods and Means of Eddy Current Testing of Soldered Lap Joints of Electrical Machines
## 4 Discussions  
The novel concept of the application of a two-element eddy current probe with tangentially positioned coils for the non-destructive testing of soldered lap joints has been presented. The design of a two-element eddy current probe with tangentially positioned coils and active shielding was developed. A method for determining the soldering integrity of soldered lap joints of current-carrying copper busbars in the stator windings of electrical machines through the developed probe was suggested. The proposed method ensures the detection of dry solder and cold solder joints and the measurement of the soldering integrity of the soldered lap joints with a relative error of measurement of no more than 5%.  
The proposed method of designing and optimizing the parameters of the finite element model can be used in the development of low-frequency eddy current probes with various designs and to study the influence of stray parameters encountered in practice, which cannot be reliably reproduced in real test objects or reference blocks.  
The developed finite element model of the ECP and TO interaction allows the influence of the test and stray parameters on the ECP signals to be studied for the testing of soldered lap joints of current-carrying busbars in electrical machine windings. The convergence of the measurement results from the finite element model of an ECP and from a real ECP was confirmed with the help of an operational prototype of an ECP. The error in the calculations of the signals from the ECP finite element model does not exceed 1.5%.  
Figure 13: Statistics of measurements of soldering integrity of soldered lap joints in TVF-60-2 turbine generator.  
**Author Contributions:** Conceptualization, P.S.; methodology, V.S. and A.U.; software, A.G. and P.S.; validation, A.G. and P.S.; formal analysis, P.S. and A.U.; investigation, A.G. and P.S.; resources, A.G.; data curation, V.S.; writing--original draft preparation, A.G.; writing--review and editing, V.S. and A.U.; visualization, A.G.; supervision, V.S. and A.U.; project administration, V.S.; funding acquisition, V.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] were employed by the company LLC \"KONSTANTA\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relation-ships that could be construed as a potential conflict of interest.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 2 Literature Review
#### Research Gaps  
* In the real world, data collection and cleaning are often challenging tasks. Ensuring that the data are accurate, consistent, and relevant is crucial for the success of the forecasting models; also, incomplete or noisy data may negatively impact the performance of the models, and the cleaning process might not eliminate all inconsistencies [2].
* Applicability of feature extraction methods directly depends on domain-specific knowledge and the quality selection of techniques. The probabilistic and Apriori approaches may fail to cover all relevant features or introduce biases [5].
* Statistical measures including mean, variance, and skewness, critical for time series feature extraction, are highly susceptible to outliers, potentially distorting extracted features and leading to incorrect predictions. Furthermore, the efficacy of these techniques presupposes the continuation of past trends into the future, a premise vulnerable to disruption by sudden changes or external factors [13].
* The problem of overfitting or underfitting may arise, especially in a limited and unvaried dataset; incorporating relevant external factors into the model is also computationally intensive [16].
###### Abstract
The first stage aims to include appropriate tasks in each satellite. The second stage reaches consensus on the removal impact of tasks among all satellites and removes conflict tasks which may increase the objective value. Using PITO, tasks can be directly offloaded on board without depending on any central server. Its effectiveness and polynomial complexity are demonstrated. * To handle the dynamic arrival of delay-sensitive tasks, a fast re-offloading mechanism is further introduced, which reduces the tasks involved and avoids unnecessary insertion attempts, by pruning. It enables PITO to perform online re-offloading during the computing serving process. The remainder of this paper is organized as follows. Section 2 introduces the related works. Section 3 describes the MILP model. Section 4 details the proposed PITO algorithm. Then, a re-offloading mechanism is further developed for handling the dynamics in SEC. Section 5 shows the simulation results of the proposed PITO, and these are further discussed in Section 6. Finally, the paper is summarized in Section 7.
## 7 Real World-Use Cases
### Public Health Studies  
Gathering data from multiple locations is a usual procedure in healthcare research. ShinyAnonymizer is utilized by health professionals due to its broad anonymization functions and adaptability to different data types. The tool secures users' identities, while helping the fast analysis and critique of the extracted data collection.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Immersive Technologies
#### 6.1.4. Haptic Feedback  
Haptic feedback is a technology that simulates the sense of touch by applying forces, vibrations, or motions to the user [9; 63]. Haptic sensory feedback enhances the immersive experience in virtual environments by allowing users to feel interactions with virtual objects as if they were real. In the context of the metaverse, haptic feedback is delivered through various devices such as gloves, vests, and other wearables, which can replicate sensations like texture, resistance, and impact. For instance, when a user picks up a virtual object or feels a virtual breeze, haptic devices provide physical sensations corresponding to those actions, making the virtual experience more realistic and engaging.  
Integrating haptic feedback into the metaverse has significant implications for various applications, including gaming, training, and remote collaboration [174]. In gaming, haptic feedback enhances immersion by allowing players to physically feel in-game actions, such as holding equipment or surface texture. In training simulations, particularly in fields like medicine and engineering, haptic technology can provide realistic practice scenarios, helping users develop practical skills without real-world risks. For remote collaboration, haptic feedback can bridge the gap between digital and physical interactions, enabling more effective and intuitive communication. Overall, haptic feedback enriches the user experience in the metaverse by adding a critical layer of sensory interaction that deepens engagement and realism.
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 5 Validation of Classification Accuracy  
In this section, the classification accuracy of the proposed encryption algorithm is tested through deep learning classification using transfer learning. Figure 12 shows the Original, Encrypted, and Decrypted buttercup and iris images, which are further used to test the classification accuracy. The AlexNet transfer learning model on the deep learning designer of MATLAB 2021 is used here for the simulation. Figure 13 shows the classification of buttercup images before and after encryption. Furthermore, it is advent from the results that the images encrypted through the proposed method were classified accurately.  
Figures 14 and 15 show the accuracy versus iteration graph, and the loss versus iteration graph for the buttercup image, respectively.  
Figure 16 shows the classification of iris images before and after encryption. Furthermore, it is advent from the results that the images encrypted through the proposed method were classified accurately.  
Figure 17 shows the accuracy versus iteration graph, and Figure 18 shows the loss versus iteration graph for the iris image. The validation accuracy of buttercup and iris image is 90.62% and 91.35%, respectively.  
It is advent from the results of this section that the method not only works well with color images but also has decent classification accuracy too.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Values** & **Results** \\ \hline Frequency & 0.8315 & Pass \\ Block Frequency & 0.2888 & Pass \\ Cumulative Sums Forward & 0.5120 & Pass \\ Cumulative Sums Reverse & 1.0000 & Pass \\ Runs & 0.6572 & Pass \\ Longest Run & 0.1339 & Pass \\ Rank & 0.1885 & Pass \\ FFT & 0.6250 & Pass \\ Overlapping Template & 0.3525 & Pass \\ Approximate Entropy & 0.9875 & Pass \\ Linear Complexity & 0.1045 & Pass \\ Serial & 0.1514 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 6: NIST test results for the 5D Guass Map.  
Figure 12: Original, encrypted, and decrypted Buttercup and Iris Images.  
Figure 14: Accuracy versus iteration graph for buttercup image.  
Figure 13: Classification of Buttercup image before and after encryption.  
Figure 15: Loss versus iteration graph for Buttercup image.
# MIRA-ChatGLM: A Fine-Tuned Large Language Model for Intelligent Risk Assessment in Coal Mining
###### Abstract  
Intelligent mining risk assessment (MIRA) is a vital approach for enhancing safety and operational efficiency in mining. In this study, we introduce MIRA-ChatGLM, which leverages pre-trained large language models (LLMs) for the domain of gas risk assessment in coal mines. We meticulously constructed a dataset specifically designed for mining risk analysis and performed parameter-efficient fine-tuning on the locally deployed GLM-4-9B-chat base model to develop MIRA-ChatGLM. By utilizing consumer-grade GPUs and employing LoRA and various levels of quantization algorithms such as QLoRA, we investigated the impact of different data scales and instruction settings on model performance. The evaluation results show that MIRA-ChatGLM achieved excellent performance with BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L scores of 84.47, 90.63, 86.88, and 90.63, respectively, highlighting its outstanding performance in coal mine gas risk assessment. Through comparative experiments with other large language models of similar size and manual evaluation, MIRA-ChatGLM demonstrated superior performance across multiple key metrics, fully demonstrating its tremendous potential in intelligent mine risk assessment and decision support.  
intelligent assessment; large language models; fine-tuning; coal mine; gas risk; quantization algorithm +
Footnote †: journal: applied sciences  
of these alarms and the corresponding on-site response measures. Leveraging its powerful data analysis capabilities, this system can identify risk signals from sensors with over 90% accuracy and, based on historical handling experiences, make preliminary judgments and propose appropriate response measures to alarms. Compared to traditional rule-based alarm systems, MIRA-ChatGLM not only automates data analysis but also possesses rapid response and dynamic adjustment capabilities, adapting to the complex and variable conditions in the mining environment.  
The innovation of this study lies in the deep integration of LLMs with coal mine gas sensor data, creating an intelligent assessment model that features adaptive decision-making and anti-interference capabilities. MIRA-ChatGLM enhances the efficiency and accuracy of mining risk assessments and provides intelligent support for workers' decision-making. The application of this system offers new ideas and tools for coal mine risk assessment and demonstrates its broad application prospects in complex industrial environments.
## 3 Results and Discussion
### Fracture and Failure Modes  
An analysis of the fractured surfaces of the joints was undertaken aiming to correlate the adhesion performance of each joint with the resulting failure mode.  
The aluminium-PA single lap joins with the substrate abraded by SP, and this showed a fractured surface characterized by poor adhesion between both adherents. An adhesive failure mode is clearly defined, indicating poor wetting of the substrate surface, as shown in Figure 7a. Contrary to this, the aluminium-PA + CF single lap joints with the substrate abraded by SP displays a cohesive failure mode, where the PA + CF coating is strongly adhered to the aluminium substrate, and is shown in Figure 7b. These observations corroborate the previously described mechanical results that indicate that in joints where the substrate was abraded by SP, the PA + CF specimens perform better than the PA type specimens. The better adhesion of aluminium-PA + CF single lap joints can be explained by a positive effect of the presence of fibers in the adhesion between aluminium and PA and by the effect of remelting the primer, enhancing the mechanical interlocking between the aluminium and PA.  
The aluminium-ABS single-lap joints with the substrate abraded by GB displays a critical adhesive failure mode between the ABS and the aluminium substrate that is probably due to the poor wetting of its surface, shown in Figure 7c. The same adhesive failure mode on aluminium-ABS single-lap joints, with the substrate abraded by GB, was identified in 3D printed samples [83]. On aluminium-PA single-lap joints with the substrate abraded by GB, the fractured surfaces reveal a mixed adhesive/cohesive failure mode (Figure 7d), while in the aluminium-PA + CF single-lap joints with the substrate abraded by GB, the fracture occurred in the PA + CF region outside the zone of the joint, as shown in Figure 7e. These observations are in agreement with the results from the mechanical tests, which assign the highest ULSS values to joints made with PA and PA + CF, and can be justified by a good adhesion between aluminium and PA or PA + CF, due to higher values of the surface roughness of aluminium, and due to the remelt of the primer that enabled the flow of the polymer-based materials into the aluminium surface cavities, enhancing the mechanical interlocking between the two materials.
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 5 Conclusions  
The DAA algorithm proposed in this paper provides a new way of thinking to solve the access problem of large-scale satellite networks. First, we dynamically update satellites in the communication domain of the ES in real-time. Then, the set of access satellites is determined based on the relationship between the \(t_{u}\) values and the satellite coverage time in the communication domain. Finally, to solve the problem of insufficient satellite resources, access satellite failure, or ESLs interruption, we propose solutions for different scenarios. Through theoretical analysis, the use of the virtual destination address in the DAA algorithm and the strategy of selecting access satellites in the original path can reduce the rerouting overhead. Experimental results show that the DAA algorithm achieves maximum \(P_{u}\) compared to traditional access algorithms. As the probability of insufficient satellite resources increases, the DAA algorithm has the least switching times compared to traditional access algorithms. Therefore, the DAA algorithm is more suitable for large-scale satellite networks than traditional access algorithms.  
With the development of satellite Internet, the design of routing algorithms for large-scale satellite networks will be promising research. Based on this work, we will study routing algorithms for large-scale satellite networks suitable for the DAA algorithm in the future. where not only the \(P_{u}\) value and switching times but also other aspects will be considered. For example, the impact of the DAA algorithm on end-to-end delay, throughput, and robustness of satellite networks.  
Authors contributed as follows: Conceptualization, [PERSON] ([PERSON]), X.J. and H.L.; methodology, [PERSON] ([PERSON]), S.S. and X.J.; investigation, [PERSON] ([PERSON]) and Z.Z.; supervision, [PERSON] ([PERSON]), X.J. and S.S.; writing--original draft preparation, [PERSON] ([PERSON]) and X.J.; writing--review and editing, S.S. and Z.Z.; project administration, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (grant number U21A20443), in part by the by the Youth Innovation Promotion Association CAS (grant number 2020294), and in part by the Shanghai Industrial Collaborative Innovation Project (grant number 2021-CYTX2-KJ03).  
Figure 11: Relationship between the probability of insufficient resources for access satellites and switching times.
## 3 Experimental Setup
Thereby, the excitation pressure on the sample surface was measured by a microphone (CUI Inc., Tualatin, OR, USA) and the generated mechanical vibration by a laser vibrometer. Since the acoustic wavelengths are in the range of 0.686 m to 6.86 m and the radius of the both pipettes are in a few millimeter range, the pressure is constant in all the pipette volume. Therefore, the excitation pressure on the surface of the sample is equal to the pressure measured by the microphone that is placed in the pipette wall in front of the loudspeaker. The sound pressure received by the microphone is assigned to the pressure \(\Delta P\) in Equation (1) to calculate the modulus of elasticity. The amplitude of the surface vibration is in the range of a few micrometers, which demands a very accurate measurement method. Therefore, the OVF-5000 Xtra (Polytec GmbH, Waldbronn, Germany) is the newest Polytec modular laser vibrometer employed. A helium-neon laser exploits the Doppler Effect [46] to derive the velocity and the displacement obtained by integrating over the velocity, which is done by a LabVIEW automation program (LabVIEW NXG 3.1, National Instruments, Austin, TX, USA). A sketch of the whole setup is depicted in Figure 3a. To minimize the external influences,  
Figure 2: Pipette aspiration method; (**a**) the pipette placed at the sample surface; (**b**) a schematic of the sample deformation under the pipette. the experimental setup was placed on a vibration isolation desk for damping the external vibrations. Moreover, the other environmental factors like room vibrations, temperature, noise and humidity should be controlled to get precise results. The measurement in this study had been done at normal room temperature and humidity and the ground vibration and room noises had been kept as small as possible. The laser reflectability of the sample surface is low, making the measurement difficult, therefore, a titanium dioxide spray Ardrox(r) 9 VF2 (Chematall GmbH, Frankfurt am Main, Germany) was sprayed on the samples to reflect the laser light better. Since the coating is very fine, the elastic modulus is not influenced by it.
## 2 Materials and Methods
### Sequence Analysis  
The latest version of high accuracy basecalling of the fast5 files was conducted with Guppy basecalling software (Oxford Nanopore Technologies, Limited) v5.0.11 + 2b6 dbf using the dna_r9.4.1_450 bps_hac model. The acquired reads were filtered to obtain Q scores > 9 and to remove adapter sequences (120 bases trimmed at 5\({}^{\prime}\) and 3\({}^{\prime}\) ends). The shortest 5% of all reads were also removed from the analysis. After filtering, the median quality scores were between 13.1 and 13.5, representing a mean error rate of 4.9%. To compensate for the remaining error rate, classification was performed with a more conservative value for the minimum length of partial hits (50).  
Taxonomic classification was carried out using Centrifuge v1.0.4 (Johns Hopkins University CCB, Baltimore, MD, USA) with a custom database containing reference genomes for Bacteria, Archaea and Fungi retrieved from NCBI. For Bacteria and Archaea, genomes with complete genome status, and for Fungi, genomes with the complete and draft genome indication were chosen. Addition of draft genomes for Fungi was selected due to their somewhat larger and more complex genomes resulting in a low number of complete genome entries. In addition to the default parameters, the minimum length of partial hits was set to 50, and k, the number of distinct primary assignments per read, was set to 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Petroglyph Site** & **Brief Description of the Crust** \\ \hline AV1 & Avdat & Very dark crust covering a reddish layer \\ AV2 & Avdat & Very dark crust covering a reddish layer \\ AV3 & Avdat & Thinner black crust, intermixed with reddish layer \\ EZ1 & Ezuz & Very dark crust covering a reddish layer \\ EZ2 & Ezuz & Very dark crust covering a reddish layer \\ EZ3 & Ezuz & Dark crust covering a reddish layer \\ EZ4 & Ezuz & Orange thick crust \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of samples collected at the two petroglyph sites with brief description.  
Relative abundances were calculated for all genera with a read count abundance of more than 0.5% in at least one sample and used for the clustered heatmap, which was generated with the R package pheatmap. All data are available at the NCBI BioProject PRJNA847191.