esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 1 Introduction  
A considerable proportion of pressure vessels utilized in underwater scenarios rely on an integrated design scheme to ensure the effectiveness of the sealing mechanism. The integration of fasteners, such as latches and bolts, is commonly employed to avoid leakage issues during the storage and transportation phases [1, 2]. Nevertheless, the utilization of fasteners is governed by certain constraints. The research team devised a separable pressure vessel that is capable of being deployed from underwater to the surface autonomously and unfolds automatically. The vessel is anchored to the perimeter trap support using fastening straps, and its primary prototype is illustrated in Figure 1. To ensure that the separable underwater pressure vessel meets the requirements of use under various working conditions, two seals are used: a lip seal and a convex seal. The complete pressing of the two seals into the installation groove is contingent upon the installation pre-load of the fastening band, as well as the two factors of water pressure. A comprehensive analysis of the seal compression process via simulation is thus warranted. For the lip seal, water ingress into the seal cavity occurs, and the higher the water pressure, the closer the lipseal fits the upper and lower ball housing surfaces [3]. Therefore, it represents a pivotal research focus.  
[PERSON] et al. studied the effects of lip curvature, lip height, and lip thickness on sealing performance and optimized the structural parameters of the lip seal [4]. [PERSON] et al. conducted a finite element simulation of the lip seal working in hydraulic oil, analyzed the effect of hydraulic oil temperature on the seal, and derived the changes in the sealing performance of the seal under different working temperatures [5]. [PERSON] et al. established a simulation model based on the lip seal used in a high-pressure flow valve, scrutinized the impact of factors such as medium pressure and surface friction factor on the seal stress condition, and determined the stress condition of the seal boundary conditions [6]. [PERSON] et al. performed a simulation-based analysis of a bespoke non-standard lip seal they had designed. They computed the deformation and stress incurred at varying initial interference and oil pressure conditions and derived the contact stress, contact width, and initial interference variation curves [7].  
This study centers on investigating the performance of a lip seal implemented in a separable pressure vessel. A physical model of the lip seal is established, encompassing the pre-compression stage until the complete compression phase into the seal groove. This study aims to analyze the sealing features and performance of the lip seal throughout its entire working process. The findings of this research could offer guidance for the design of seals in comparable devices.
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
## 1 Introduction  
Large rollers, whose sizes can sometimes reach a diameter of a few meters and a length of several meters, are employed in many heavy industries, e.g., papermaking, automobile, metallurgy, and shipbuilding. Such a roller should be characterized by high form accuracy, because its cylindricity errors would incorporate in the products, such as super calendar papers, roll-to-roll films, piston cylinders, and automobile steel sheets, etc. Using conventional roundness and cylindricity instruments may be impractical due to their large size and mass. Hence, there are increasing needs for accurate measurement of out-of-cylindricity of rollers in order to ensure the surface quality of the products. In situ measurements in production may be a good solution [1; 2]. In general, a spindle supports the measured cylinder to rotate and a carriage slides along the longitudinal direction, which are indispensable in the in situ measuring system for the cylindricity. Raw probe signals representing radial deviations of the cylinder are always superposed by the radial error motions of the cylinder, which come from the spindle radial and tilt error motions, and the carriage straightness error motions [1; 2; 3; 4]. Nevertheless, how to fully eliminate the radial error motions of the cylinder and straightness error motions of the carriage in the in situ measuring system is a great challenge [2; 5]. Two basic issues are involved here: mathematical modeling of cylindrical profile and mathematical modeling of the measuring system.
# Wild Mushroom Classification Based on Improved MobileViT Deep Learning
## 3 Results
### Implementation Details and Performance Evaluation Metrics  
We implemented the M-ViT model with the Python 3.8 and PyTorch 1.7.0 framework. The input image size is \(224\times 224\times 3\). Data enhancements, such as horizontal flipping, rotation, and random cropping, are used on the image to increase the data diversity. The proposed model is trained on a single high-performance NVIDIA GeForce GTX 3090 graphics card. Optimization is performed using the AdamW [42] optimizer with a training epoch of 300, a batch size of 32, a learning rate of \(5\times 10^{-4}\), and a weight decay of \(5\times 10^{-3}\). Our experiments use accuracy, precision, recall, and specificity rubrics to evaluate the model's performance.  
Accuracy: This indicates the accuracy of the prediction result, the number of correctly predicted samples divided by the total number of samples, as in the following equation: \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\] (12)
2. Precision: This indicates the probability of correctly predicting a positive sample among the predicted to be positive in the prediction result, as in the following equation: \[Precision=\frac{TP}{TP+FP}\] (13)
3. Recall: Also known as the sensitivity, this indicates the probability that a positive sample of the original sample will be correctly predicted as a positive sample in the end, as in the follwing equation: \[Recall=\frac{TP}{TP+FN}\] (14)
4. F1-Score: This represents the harmonic mean evaluation metric of precision and recall. The F1-score is a weighted average of the model's precision and recall, with a maximum value of 1 and a minimum value of 0, as in the follwing equation: \[F1\text{-}Score=2*\frac{Precision*Recall}{Precision+Recall}\] (15)
5. Specificity: This describes the proportion of identified negative samples to all negative samples, as in the following equation: \[Specificity=\frac{TN}{FP+TN}\] (16)  
where \(TP\) (true positive) indicates the number of samples judged to be positive and are positive; \(FP\) (false positive) indicates the number of samples judged to be positive but are negative; \(TN\) (true negative) indicates the number of samples judged to be negative and are negative; and \(FN\) (false negative) indicates the number of samples judged to be negative but are positive. Negative indicates the number of samples judged to be negative but are positive.
## 5 Interpretation of the Results and Future Works
It must be stressed that the model has been tested in a simulation environment, which can be perceived as an incomplete scientific pathway. Therefore, in the future, some works will be carried out towards validation of the model, using available measures and current real data.
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 5 Analysis and Discussion of Experimental Results
### Comparison with Existing Work  
The comparison of the proposed work is done with the existing work [29]. For comparison, we have selected some parameters such as PSNR, MSE, SSIM, and CC (Concealing Capacity). These parameters help to find the trade-offs between concealing capacity, robustness, and imperceptibility.  
For the proposed work, values of evaluation, parameters are found to be somewhat higher than the existing work as shown in Figure 8. PSNR for the proposed work is calculated as 61.3 and for the existing work is calculated as 54.89. The MSE of the proposed work is almost half of the existing work. SSIM is calculated as 0.98. Concealing capacity is found to be 18.79% which is very high compared to the existing work.  
Figure 8: Comparison Chart.  
In Figure 8, comparison of total time taken to conceal and retrieve the secret message is also done. Along with that size of secret message hidden is also compared with work in paper [29]. We have embedded and retrieved a secret message of size 20 KB in 26 s. Form the comparison it has been concluded that the proposed work achieved good concealing capacity in less time. It also proved the cost effectiveness of the proposed work.
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 1 Introduction
The interrelation and influence of these soil properties on the \(C_{c}\) value are fundamental in geotechnical investigations and form the basis of effective soil behavior modeling and predictive analysis in civil engineering. From the 1950s to the present, research on consolidation processes has prompted the development of several important equations. These equations are critical in geotechnical engineering for understanding and predicting the compression behavior of fine-grained soils. These mathematical models are utilized to forecast how soils will respond under a load, offering precise settlement predictions for engineering applications. Table 1 summarizes the evolution of these equations over time. \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Empirical Equation** & **Soil Type** & **Reference** \\ \hline (1) & \(C_{c}=0.75(e_{0}-0.5)\) & Low-plasticity claye soil & Sowers and Sowers [12] \\ \hline (2) & \(C_{c}=1.15(e-0.35)\) & (from Rio de Janeiro, Santos, Sao Paulo, Minas Gerais, and Mexico City) & Nishida [13] \\ \hline (3) & \(C_{c}=0.013(\mathrm{LL}-13.5)\) & Clay & Yamagutshi [14] \\ \hline (4) & \(C_{c}=0.017(\mathrm{LL}-20)\) & Clay & Shouka [15] \\ \hline \end{tabular}
\end{table} 
Table 1: Evolution of the prediction equations for the compression index (\(C_{c}\)) over history. The use of computational intelligence methods, such as artificial neural networks (ANNs), is extensively combined with traditional statistical techniques like regressions [28; 29; 30]. These techniques have been widely employed in various geotechnical problems, such as estimating the \(C_{c}\) parameter [31; 32; 33]. However, different solution approaches for these problems yield varying results. The goal is to identify the algorithms that yield the most accurate predictions. Deep learning and machine learning algorithms, which are sub-branches of artificial intelligence, offer predictive equations. An area of data science and artificial intelligence called machine learning (ML) makes use of algorithms and statistical models to facilitate the automated acquisition of knowledge by computers via experience so they can perform certain tasks without being explicitly programmed. This process provides the ability to recognize patterns from data and make decisions, allowing machines to solve complex problems and make predictions [34]. In fields such as geotechnical engineering and consolidation, machine learning methods are increasingly used to more accurately predict soil properties and behavior, model soil compaction and consolidation, and optimize engineering designs. These techniques can
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Classification Algorithms
#### 4.2.3 Random Forest  
The Random Forest is a machine-learning algorithm based on combining numerous decision trees. Each tree is constructed from a random subset of the training data and a random subset of the features. Introduced by [PERSON], this method quickly became a benchmark in machine learning thanks to its simplicity, robustness, and performance.  
The operation of the Random Forest is based on the following principles:  
Training trees: Many decision trees are built independently, each trained on a random sample of the data (bootstrap) and a random subset of the features.  
Model diversity: Random selection of data and features ensures tree diversity, which improves generalization ability and reduces the risk of overfitting.  
Prediction by vote: Each tree casts a vote when a prediction is required. The final decision is made by a majority vote for classification tasks or by averaging predictions for regression tasks [23].  
In geology, Random Forests are widely used to analyze complex data from various sources, such as satellite images or geophysical measurements. Their ability to capture complex relationships between variables makes them a valuable tool for classifying geological formations and predicting geophysical properties [24].
## 5 Conclusions  
In this paper, we propose an automatic filter-level DCNN pruning method FPRL based on the DDPG algorithm. FPRL can achieve over 10\(\times\) parameter compression and 3\(\times\) floating point operations (FLOPs) reduction while maintaining similar accuracy to the original network. The accuracy of pruned VGG-19, ResNet-18, and ResNet-34 is greater than 90% on CIFAR10. In the proposed FPRL method, the DDPG algorithm optimizes the layer-wise filter pruning rate, and the Taylor-expansion-based criterion is developed for filter importance sorting. To illustrate the effectiveness of the proposed FPRL method, extensive numerical studies are conducted with state-of-the-art DCNNs on large public datasets. The results demonstrate that the Taylor-expansion-based filter sorting criterion is more efficient than the widely used minimum-weight-based criterion. Moreover, the proposed FPRL method achieves a much higher compression rate while reserving higher network accuracy compared to the DRL-based benchmark method.  
Conceptualization and methodology, Y.F. and C.H.; software and writing --original draft preparation, Y.F.; writing--review and editing, L.W., X.L. and Q.L.; funding acquisition, C.H. and L.W. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Natural Science Foundation of China under Grant 62002016, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515110431, in part by the Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) under Grant FRF-IDRY-GD21-001, and in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Architecture** & **Dataset** & **Method** & **Acc (Base)** & **Acc (Pruned)** & \(\Delta\)**Acc** & **Params.(\(\times\))** \\ \hline \multirow{2}{*}{VGG-19} & \multirow{2}{*}{CIFAR10} & N2N & 91.97\% & 91.64\% & \(-\)0.33\% & 20.53\(\times\) \\  & & FPRL & 92.58\% & 92.31\% & \(-\)0.27\% & 22.94\(\times\) \\ \hline \multirow{2}{*}{ResNet-18} & \multirow{2}{*}{CIFAR10} & N2N & 92.01\% & 91.81\% & 0.18\% & 11.10\(\times\) \\ \cline{3-6}  & & FPRL & 95.28\% & 94.76\% & \(-\)0.52\% & 11.43\(\times\) \\ \hline \multirow{3}{*}{ResNet-18} & \multirow{2}{*}{CIFAR100} & N2N & 72.22\% & 68.01\% & \(-\)4.21\% & 4.64\(\times\) \\ \cline{3-6}  & & FPRL & 74.71\% & 72.67\% & \(-\)2.04\% & 10.93\(\times\) \\ \hline \multirow{2}{*}{ResNet-34} & \multirow{2}{*}{CIFAR10} & N2N & 92.05\% & 92.35\% & 0.30\% & 10.2\(\times\) \\ \cline{3-6}  & & FPRL & 94.46\% & 94.07\% & \(-\)0.39\% & 14.10\(\times\) \\ \hline \multirow{3}{*}{CIFAR100} & N2N & 72.86\% & 70.11\% & \(-\)2.75\% & 5.02\(\times\) \\ \cline{3-6}  & & FPRL & 74.51\% & 72.24\% & \(-\)2.27\% & 16.18\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: N2N learning vs. our approach.  
part by the Scientific and Technological Innovation Foundation of Foshan under Grant BK20 BF010, Grant BK21 BF001, and Grant BK22 BF009.
# Examining the Amount of Particulate Matter (PM) Emissions in Urban Areas
## 4 Assessment of Dependence between Monitored Data  
For research purposes, we measured the dependence between the size of particulate matter (PM\({}_{10}\) and PM\({}_{2.5}\)) and the number of vehicles that passed each measurement point. We found that the amount of particulate matter air pollution depends on the number of vehicles that pass the measuring point. We evaluated the dependence between monitored data within the framework of correlation, which is a measure of dependence between two or more variables.  
Figure 7 shows a linear relationship between particulate matter emissions concentration and the number of vehicles that passed the measuring point. We monitored the highest PM\({}_{10}\) emissions values at the bus station measuring point; the values are comparable to those at the railway crossing measuring point, despite the number of vehicles at the railway crossing being several times higher. This may be because the emissions at the bus station were mainly created by buses, in contrast to the measuring point at the railway crossing where the emissions were mainly produced by passenger vehicles. The exact values of the correlation coefficients can be found in Table 6.  
Figure 6: Particulate matter concentrations at selected location—primary school.  
Based on the results of our correlation analysis, we observed a high dependence between the number of vehicles passing a measuring point and the particulate matter emissions concentration values. The highest degree of dependence for PM\({}_{10}\) particulate matter is at the bus station. The correlation coefficient is 0.895, which means that the observed data have great dependence, and we observed a high dependency in all assessments. Table 7 shows there is a high degree of dependence for the monitored data, with a correlation coefficient value of 0.7.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Correlation Coefficient Values of Monitored Data**} \\ \hline
**Measuring Point** & **PM\({}_{2.5}\)** & **PM\({}_{10}\)** \\ \hline Bus station & 0.907 & 0.895 \\ \hline Primary school & 0.904 & 0.727 \\ \hline Railway crossing & 0.818 & 0.882 \\ \hline \end{tabular}
\end{table}
Table 6: Evaluation of monitored data dependence.  
Figure 7: Relationship between the number of vehicles and concentration of particulate emissions.  
Table 7 shows a high dependence between the number of vehicles passing each measuring point and the amount of particulate matter emissions concentration. Based on these results, we attribute particulate matter air pollution with traffic activity in the monitored areas.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AERPAW & Aerial Experimentation and Research Platform for Advanced Wireless \\ ANN & Artificial Neural Network \\ B5G & beyond 5G \\ CR & Cognitive Radio \\ DSA & Dynamic Spectrum Access \\ ETSI & European Telecommunications Standards Institute \\ FANETs & Flying Ad hoc Networks \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ HMM & Hidden Markov Model \\ IoFT & Internet of Flying Things \\ IoT & Internet of Things \\ ITU & International Telecommunications Union \\ MAC & Medium Access Control \\ ML & Machine Learning \\ PU & Primary User \\ QoS & Quality of Service \\ SCF & Spectral Correlation Function \\ SDR & Software-Defined Radio \\ SNR & Ratio of Signal to Noise \\ SpecPSO & Spectrum Particle Swarm Optimization \\ SU & Secondary User \\ UAS & Unmanned Aerial Systems \\ UAV & Unmanned Aerial Vehicle \\ URLLC & Ultra-Reliable and Low Latency \\ USRP & Universal Software Radio Peripheral \\ WARP & The Wireless Open-Access Research Platform \\ Wi-FI & Wireless Network \\ \end{tabular}
# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 2 Related Work  
Deep neural network models are highly complex and prone to overfitting on training data, especially on small sample datasets like sonar data. To address this, inductive transfer learning (often referred to simply as transfer learning) is commonly used to transfer generalizable knowledge to the target task to improve the generalization capability of deep neural network models [21, 22, 23].  
In this research context, most current deep learning-based sonar image object detectors also adopt a transfer learning approach, utilizing the feature extraction portion of convolutional neural networks (CNNs) pre-trained on large optical image datasets such as ImageNet [24] as the backbone network for the detector. These transferred features are then used to localize and classify objects in sonar images, as illustrated in Figure 1.  
However, compared to above-water scenarios where deep learning recognition algorithms are widely applied, underwater object detection typically relies on sonar for image acquisition. Sonar images have lower resolution and do not provide color information compared to optical cameras [25]. Furthermore, highlights and associated shadows on objects are often among the features that algorithms pay special attention to in recognition tasks [26]. However, during sonar image acquisition, acoustic shadows can vary significantly depending on the sonar's perspective, and multipath reflections can produce artifacts, which is markedly different from optical images. This means that the features learned by the feature extraction portion of pre-trained models based on optical RGB images are not well suited for sonar images.  
Nevertheless, due to the complexity of underwater object types, numerous factors affecting sonar imaging and issues such as small sample sizes, imbalanced class distributions, and insufficient quality of target sonar images in sonar image datasets, even pre-trained models trained on sonar images exhibit poor generalization performance and high false alarm rates on actual sonar test data. False alarm rate, a commonly used metric in object detection tasks, reflects the detector's tendency to misclassify backgrounds as targets. Consequently, the high false alarm rate of current deep learning detection models on actual sonar test data is fundamentally attributed to the inadequate ability of deep learning networks to distinguish between backgrounds and targets. Therefore, this paper considers  
Figure 1: Pre-trained detection paradigm for sonar image target detection based on large-scale optical image datasets.  
constructing a proxy task to learn target features in sonar images, thereby simplifying the learning of target and background features by deep learning networks in detection tasks.
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 4 Experiment and Result
### Ablation Experiment  
To gain a deeper understanding of the contribution of PRF and IF-Fusion modules to the performance of the 3D object detection model in this paper, we design a series of ablation experiments, as shown in Table 3. The purpose is to evaluate the effect of each module individually and their combined influence when integrated. After incorporating the PRF module, we observe significant improvements in AP\({}_{\text{3D}}\) and AP\({}_{\text{BEV}}\), indicating that PRF plays a crucial role in enhancing the model's capability to capture local features. When applying only the IF-Fusion module, the model performs better in handling small and distant objects. This validates the effectiveness of IF-Fusion in multi-scale fusion and information preservation. In the configuration that uses both PRF and IF-Fusion modules, we achieve the highest AP\({}_{\text{3D}}\) and AP\({}_{\text{BEV}}\) among all setups. This demonstrates that the combination of the two modules can complement each other, providing a more comprehensive feature representation than when used individually.