esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Weakly Supervised Method
#### 3.3.1 Incomplete Supervision Method  
Incomplete supervision means that most of the training samples are not labeled and only a small number of the samples are labeled, and this part of the labeled samples is not enough to train a good model. Among the incomplete supervision methods, the semi-supervised method is often used in the surface defect detection of industrial products.  
The semi-supervised methods can automatically develop unlabeled sample data without manual intervention to improve the learning effect. Literature [73] designed a deep convolutional neural network structure based on residual network structure, stacking two layers of residual building modules together to form a 43-layer convolutional neural network, and at the same time, in order to achieve the balance between network depth and network width and improve accuracy, the width of the network was appropriately increased. The network structure shows good performance on DAGM, NEU steel data set, and copper clad plate dataset. Literature [74] proposed a semi-supervised model based on Convolutional Autoencoder (CAE) and Generative Adversarial Network (SGAN), the stacked CAE is trained with unlabeled data, and its encoder network was retained and input into SoftMax layer as GAN discriminator, using GAN to generate false images of steel surface defects to train the discriminator. Literature [75] designed a steel surface defect detection system consisting of sample generation and semi-supervised learning. In the semi-supervised learning part, two classifiers of CDCGAN and ResNet18 were used, and a comparative experiment on the NEU-CLS data set was used. As a result, it is proved that the method is superior to supervised learning and transfer learning. Literature [76] proposed a PCB solder joint defect detection framework. In the classification task of this framework, the concept of active learning based on the \"sample-query-suggestion\" algorithm and the semi-supervised learning based on \"self-training\" are adopted, the proposed framework has been proven to improve the classification performance while greatly reducing the amount of annotations.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
###### Abstract  
Hydraulic fracturing, especially pulse hydraulic fracturing, is an important method for extracting oil and gas from low-permeability reservoirs, improving recovery rates significantly. Pulse hydraulic fracturing, which involves varying injection rates to create pressure waves, outperforms traditional constant-flow fracturing methods significantly. However, during pulse hydraulic fracturing operations, the flow properties of the fluid in the column change from moment to moment. Furthermore, current research on pulse hydraulic fracturing primarily focuses on vertical wells, while horizontal wells have become a common operational strategy. Therefore, a transient flow model of fluid within a horizontal well, considering variable-flow injection and unsteady friction conditions, is established in this paper. The model is solved using both the characteristic line method and the finite difference method. The hydrodynamic properties of the fracturing fluid were analyzed, and the propagation mechanisms of pressure waves within horizontal wells under various fluid injection schemes and well depths are analyzed to provide a reference for selecting appropriate fluid injection schemes in engineering practice. The study highlights the impact of fluid viscosity and injection flow amplitude on bottomhole pressure fluctuations, advancing the efficient development of low-permeability oilfields.  
horizontal wells; pulse hydraulic fracturing; nonconstant molar resistance; transient flow; fluctuation pressure; fluid mechanics
# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 4 Lane Recognition and Prediction  
### Lane Feature Point Extraction  
In order to discern the line between lane and asphalt, it is necessary to extract the edge points of the lane formed lower than the road with respect to the road surface. We have used Equations (4a) and (4b) for extracting these boundary points.  
\[\sqrt{\left(z_{map(i)}-z_{map(i+1)}\right)^{2}}\geq d \tag{4a}\] \[\left|\tan^{-1}\left(\frac{z_{map(i)}-z_{map(i+1)}}{x_{map(i)}-x_{ map(i+1)}}\right)\right|\geq\sigma \tag{4b}\]  
where \(x_{map(i)}\) denotes the road coordinates in the horizontal direction and \(z_{map(i)}\) represents the road height coordinates. Equations (4a) and (4b) are used for calculating the height difference and the gradient between two points, respectively. If the height difference  
Figure 5: Coordinate transformation of map data.  
Figure 6: The 3D road map.  
deviates from a certain range, it implies that there is a topographic change, and the deviation of the gradient between two points from a certain range indicates a feature point of the parts showing large changes, i.e., edges. The edge points building the boundary between the asphalt and the lane can be easily obtained with this equation. In Equation (4a), d denotes the minimum height difference for a feature point, and \(\sigma\) in Equation (4b) represents the minimum angle between two points for a feature point.
## 3 Metabolic Responses to Different Antibiotic Classes
### Glycolysis and Fermentation: Alternative Fluxes and NAD\({}^{+}\) Homeostasis  
Glycolysis is a core pathway for rapid ATP production and the generation of metabolic intermediates [69].  
Under stress conditions induced by bactericidal antibiotics, many bacteria reprogram the glycolytic metabolism by diverting pyruvate toward fermentative pathways rather than aerobic respiration. This metabolic reconfiguration contributes to reduced production of reactive oxygen species (ROS) associated with the electron transport chain, thereby preserving intracellular redox balance, particularly the NAD\({}^{+}\)/NADH ratio [70; 71].  
In _Staphylococcus aureus_, the inhibition of aerobic respiration during infection prompts a metabolic adaptation toward fermentation, characterized by the production of lactate and acetate [72].  
This process facilitates regeneration of NAD\({}^{+}\) from NADH, maintaining redox homeostasis and supporting ATP synthesis through substrate-level phosphorylation [73].  
Such a bioenergetic adaptation is crucial for the survival of persister cells in hostile environments.  
The transition from aerobic respiration to fermentation is modulated by environmental cues, such as oxygen and nutrient availability, and by specific regulatory systems, including SrrAB and NreBC [74; 75].  
These metabolic adaptations promote NAD\({}^{+}\) regeneration and sustain ATP production via substrate-level phosphorylation, ensuring cellular survival under redox stress or limited oxygen supply [74; 75].  
Table 4 summarizes the main fermentative adaptations and glycolytic reprogramming strategies supporting bacterial survival under antibiotic-induced stress.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 4 Experiment
### Demonstrate Effectiveness of Data Augmentation  
Figure 5 shows the training curves of different CNN models based on the original samples of two datasets. In addition, Table 2 shows the test accuracy of different CNN models trained on the original images and the generated images of two datasets. Specifically, for the original sample, we use the method of inserting repeated frames and randomly discarding frames to unify the frame number of skeleton sequences to 60. Although inserting repeated frames will cause information redundancy, it could retain as much original motion information as possible. Combining the two figures, it can be seen that CNN models have achieved acceptable results in the training stage. However, overfitting appears in the test stage, and as the number of network layers increases, it becomes more serious. Especially for DenseNet121, it reaches an accuracy rate of over 90% in the training stage on two datasets. However, in the testing stage, its accuracy is only 40.91% and 50%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Networks** & **Space Complexity** & **Time Complexity** \\ \hline LeNet & 1.14 MB & 2.19 MFLOPs \\ AlexNet & 213.42 MB & 4.02 GFLOPs \\ VGG19 & 333.88 MB & 11.99 GFLOPs \\ ResNet101 & 259.83 MB & 1.86 GFLOPs \\ DenseNet121 & 41.94 MB & 150.39 MFLOPs \\ Our Model & 31.69 MB & 13.97 MFLOPs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of space complexity and time complexity of CNN models.  
Figure 4: The recognition accuracy of multiply CNN model training on images generated by data augmentation with different frame numbers on the Florence-3D dataset (**a**) and UTkinect-3D dataset (**b**).  
As shown in Table 2, the recognition accuracy of the CNN model training on generated images has significantly improved. Specifically, the test accuracy of our model has improved by 23.86% and 22.48% on two datasets, respectively, and the test results of the other models also have improved to varying degrees. For example, the test results of LeNet separately increased from 77.27% to 82.33% on Florence-3D and improved from 75% to 86.77% onUTkinect-3D. Additionally, DenseNet121 has the biggest improvement, with a boost of 55.83% and 47.32% on two datasets separately. The experimental results show that the overfitting problem caused by a lack of sufficient training samples has been alleviated effectively by training on the enlarged dataset. Besides, the data augmentation methods proposed in this paper are effective and can be widely adopted in skeleton-based action recognition methods.
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 3 Simulator Software
### Simulation Workflow  
The overall simulation workflow involves:  
* Generating an array of rays, either randomly or uniformly distributed across the light source radius.
* The iterative calculation of the intersection points of each ray with each optical surface.
* Updating the ray's trajectory, based on the calculated intersection and refraction.
* The storing of the traced rays for further analysis.  
Using numerical optimization and efficient computational techniques, this module enables the simulation of complex optical systems, making it suitable for applications in chromatic confocal systems and other optical designs.  
\[\sin(\theta_{2})=\frac{n_{1}}{n_{2}}\text{sin}(\theta_{1}) \tag{6}\]where \(n_{1}\) and \(n_{2}\) are the refractive indices of the incident and refractive media, respectively, and \(\theta_{1}\) and \(\theta_{2}\) are the angles of incidence and refraction. The refracted angle is then used to update the trajectory of the ray as it propagates through the lens.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 4 Implementation and Methodology Overview
### Benchmarking Strategy
The same methodology is applied to both broker and subscriber roles to ensure fair client-to-broker and broker-to-client comparison. For power consumption measurements, external power meters were installed in line with the power supply of each Raspberry Pi device. These meters recorded real-time voltage and current values throughout the entire duration of the experiments. The publisher and subscriber draw 2.369 W (5.15 V, 460 mA), while the broker operates at 1.881 W (4.95 V, 380 mA). The observed values show negligible variance across trials and are treated as constants for energy estimation. Energy consumption per run was computed by multiplying the measured power (in W) by the execution time (in sec) for each device role, yielding the total energy in Joules (J). This method provides a reliable and repeatable approximation of the energy profile for each MQTT entity (publisher, subscriber, and broker) across all experimental configurations.
## 3 WEEE and ELV Management and Circular Economy
### Recovery and Destruction
#### 3.1.2 Plasma Technology  
Plasma technologies, based on the destruction of the compounds by the heat generated by a plasma arc, are currently being used to destroy ozone-depleting substances, and HFCs in some cases. The gaseous by-products of these processes include HCl, HF, CO, and CO\({}_{2}\). These technologies provide high destruction efficiencies, fast decomposition with large throughput, quick start-up and shut-down, greater flexibility of operation and low gas emissions. Moreover, the systems are designed to prevent the formation of dioxins and furans and to neutralize the acids. However, they have high energy consumption.  
Different technologies have been developed differing on the methods for producing plasma: by radio frequency, by microwave energy or by an electric plasma torch in the presence of argon or nitrogen. The most widely used technology is the one using argon gas, in facilities operating commercially worldwide. Utilization for the destruction of HFCs and HFC/HCFC blends was reported in Australia, the United States and Mexico. Technology using nitrogen gas was reported for the destruction of R-23 (GWP 12.400) in China and of the HFC R-134a (GWP 1300) and of the HFC blends R-410A (GWP 2088) and R-407C (GWP 1774) in Japan. The destruction of R-134 (GWP 1300) using microwave plasma has been investigated and a total conversion degree up to 84%, with no interconversion into other HFCs, was observed [46].  
Besides these technologies, others are being investigated and developed specifically for HFCs, aiming to increase efficiency, to improve the flexibility of operation, and to decrease costs of implementation and operation. Such technologies include a water plasma [47] and a dielectric-packed-bed nonthermal plasma reactor with barium titanate beads as the packing material [48], for the decomposition of R-134 (GWP 1300).
# Recent Advances in Brillouin Optical Correlation-Domain Reflectometry
## 4 Development of Slope-Assisted BOCDR  
In this section, we describe the fundamentals, unique features, and recent advances of the high-speed configuration of BOCDR, named slope-assisted BOCDR [59; 60; 61], with an emphasis on the so-called beyond-nominal-resolution effect.
## 3 Interpretation of Experimental Results  
### Relation of Dynamic Elastic Modulus and Dynamic Strain  
For this analysis, data from average value to three cycles of each dynamic loading level were employed. The relationship curves depicting the dynamic strain (\(\epsilon_{d}\)) against dynamic elastic modulus (\(E_{d}\)) for samples S1 to S3 are illustrated in Figure 5. The graph indicates that under a constant dynamic load, the decrease in relative density results in an increase in the dynamic strain experienced by specimens and a reduction in the rate at which the dynamic elastic modulus decreases. Specifically, it can be seen that \(\varepsilon_{d}\) of the sample with the same relative density (\(D_{r}\)) of 0.3 is larger. Under the same dynamic strain conditions, the sample with \(D_{r}\) = 0.9 showed the most significant decrease in the dynamic modulus. This variation is attributed to the transition from tight to loose contact among coarse- and fine-grained particles as \(Dr\) decreases, thereby diminishing the structural capacity to resist deformation. Consequently, under identical dynamic loads, a higher dynamic strain is observed. While the contact remains relatively firm at \(D_{r}\) = 0.7, resulting in a minor deviation from the curve with \(D_{r}\) = 0.9, the contact becomes considerably looser at \(D_{r}\) = 0.5 and 0.3, significantly reducing structural resistance, hence the notable disparity from the curve with \(D_{r}\) = 0.9.  
A further comparative analysis of Figure 5a-c indicates, under identical density conditions, an increase in maximum particle size correlates with increasing dynamic elastic modulus and decreasing dynamic strain. This trend can be attributed to the denser packing of coarse- and fine-grained particles and enhanced inter-particle contact in samples with larger maximum particle sizes, leading to increased resistance to deformation.
## 4 Conclusions  
By considering the moisture content (\(Mc\)), air velocity (\(v_{air}\)), and grain bed layer depth (\(L\)) as the key factors affecting the ventilation resistance of the air, one must regard the pressure drop (\(\Delta p\)) as the key index characterizing the ventilation resistance of the air. The present work investigated the influences of each factor and the interactions of the factors on the pressure drop; a second-order polynomial model was also obtained based on the RSM. The detailed conclusions depending on the main results are listed as follows:  
1. The relations among the \(Mc\), \(\varepsilon\), and \(d_{g}\) were revealed and the mathematical expressions were also given in the present work.
2. The \(\Delta p\) increases with the increase of \(Mc\), \(v_{air}\), and \(L\) and the contributors to the \(\Delta p\), in ascending order of significance, are as follows: \(Mc<L<v_{air}\).
3. The interaction analysis indicates that the interactions of the \(v_{air}\) and \(L\) has the most significant influence on the \(\Delta p\), and the match principle between the \(v_{air}\) and \(L\) should be firstly considered when optimizing the drying process.
4. A second-order polynomial \(\Delta p\) model based on the RSM was obtained, and its fitting performance was verified to be better than that of the traditional Ergun model by evaluating the \(R^{2}\), \(MSE\), \(MAE\), and \(RMSE\).
5. The developed \(\Delta p\) model based on the RSM shows a better fitting performance than the Ergun model when the \(v_{air}\geq 0.2\) m/s, while the Ergun model shows better when the \(v_{air}\) is under 0.2 m/s.  
The present work revealed the influences of the variables that can be directly measured in an engineering drying application on the pressure drop and established a prediction model of the pressure drop. Thus, the main results would be helpful for further optimizing the drying process. Further study is recommended to investigate the pressure drop variation characteristics by taking the air temperature as well as the paddy grain flow velocity into consideration.  
Conceptualization, X.W. and C.H.; methodology, X.W. and C.H.; software, X.W, T.H. and C.H.; validation, X.W., T.H. and C.H.; formal analysis, X.W. and J.X.; investigation, X.W., Z.Z. ([PERSON]) and T.T.; resources, X.W. and Z.Z. ([PERSON]); data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W.; visualization, X.W, Z.Z. ([PERSON]) and T.T.; supervision, W.W.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Realm R&D Program of Guangdong Province (2019B020214001, 2019B020223001), Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams (No. 2020 KJ120-CJXG), and China Agriculture Research System (CARS-26).  
Not applicable.  
The authors would like to thank the editors and reviewers for their valuable and constructive comments.  
The authors declare no conflict of interest.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
###### Abstract  
Animal location technologies have evolved considerably in the last 60 years. Nowadays, animal tracking solutions based on global positioning systems (GPS) are commercially available. However, existing devices have several constraints, mostly related to wireless data transmission and financial cost, which make impractical the monitorization of all the animals in a herd. The main objective of this work is to develop a low-cost solution to enable the monitorization of a whole herd. An IoT-based system, which requires some animals of the herd being fitted with GPS collars connected to a Sigfox network and the rest with low-cost Bluetooth tags, has been developed. Its performance has been tested in two commercial farms, raising sheep and beef cattle, through the monitorization of 50 females in each case. Several collar/tag ratios, which define the cost per animal of the solution, have been simulated. Results demonstrate that a low collar/tag ratio enable the monitorization of a whole sheep herd. A larger ratio is needed for beef cows because of their grazing behavior. Nevertheless, the optimal ratio depends on the purpose of location data. Large variability has been observed for the number of hourly and daily messages from collars and tags. The system effectiveness for the monitorization of all the animals in a herd has been certainly proved.  
precision livestock farming; global positioning system; bluetooth low energy; low-power wide-area network; wireless sensor network +
Footnote †: journal: Sensors
## 2 WSNs Data Acquisition Model
### Compressive Sensing Theory
#### 2.1.1 Signal Sparsity  
Various common transforms can be used in the sparse signal model, such as Haar wavelets, the Fourier transform, principal component analysis (PCA) [15, 16, 17], and the discrete cosine transform (DCT). In the following, considering a real world signal [18], we focus on the latter two transforms, _i.e._, PCA and DCT, which are used in the later model. Based on matrix algebra, in order to identify patterns in data and express the data in such a way as to highlight their similarities and differences in \(\mathbf{X}\), we apply PCA to find patterns in the data and compress them, _i.e._, by reducing the number of dimensions, without much loss of information. First, we calculate the eigenvectors and eigenvalues of the covariance matrix. Then, we choose components to form a feature vector. DCT is another transform [19,20], defined as \(\mathbf{\Psi}\in R^{N\times N}\), \(\mathbf{\Psi}^{T}\mathbf{\Psi}=E\). In our previous work, we used the observed chlorophyll-A data from the sensor nodes located in the Taihu Lake in China (longitude: 120.296, latitude: 31.387) as the original signal and applied DCT as a transform. From the results given in [2], almost 99% of the energy of size \(N=92\) is contained in only six important coefficients, _i.e._, \(K=6\).
## 5 Decentralized Algorithm
### Decentralized RB Scheduling Algorithm  
A decentralized RB scheduling algorithm based on the CE method is proposed in this subsection. The same as the centralized one proposed in Subsection 4.1, the decentralized scheduling algorithm initializes the probability distribution \(\mathbf{q}^{m}\) at the first place. Then, the iteration procedure is processed to obtain RB scheduling results. It should be noticed that the decentralized strategy cannot accurately estimate data rates of ongoing transmissions due to the lack of information about the scheduling results of other TPs. Therefore, the decentralized scheduling algorithm deletes the procedure of removing unqualified transmissions (as described in Section 4.1.3). In the simulation, we consider unqualified transmissions as failures, which waste energy and contribute nothing to the data rates of the system. Algorithm 3 summarizes the decentralized RB scheduling based on the CE method.  
```
1:Calculate \(R_{k}^{n}\) according to Equation (2).
2:Initialize probability distribution \(\mathbf{q}^{m}(m=1,\cdots,M)\) according to Equation (10).
3:for\(t=1:t_{\text{max}}\)do
4: Process Algorithm 1, and output \(\mathbf{X}_{\text{out}}^{m}\), \((m=1,\cdots,M)\)
5:if All of the elements in \(\mathbf{q}^{m}=[q^{m,n},\ \forall n]\) converge then
6: Output \(\mathbf{X}_{\text{out}}\)
7:BREAK
8:endif
9:endfor
10:Map the obtained \(\mathbf{X}_{\text{out}}^{m}\) into \(\{\beta_{m,k}^{n}\}\).
```  
**Algorithm 3** Decentralized RB scheduling algorithm.