esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 3 Evolution of pavement Image Acquisition Systems
### Image Acquisition During Semi-Automatic Detection Stage  
During the semi-automatic detection phase, pavement image acquisition technology evolved from analog photography to analog videography and finally to digital imaging.  
Developed by France's LCPC Road Administration, the GERPHO system [2] was among the first operational pavement imaging devices. This pioneering system utilized analog-digital photographic technology to record pavement images on photographic film, enabling a fundamental indoor distress assessment. However, its practical application faced major limitations due to the need for manual film development, time-consuming frame-by-frame analysis, and excessive film storage costs, which ultimately restricted its widespread adoption.  
Japan's Komatsu system [3] marked a technological advance by adopting analog videography. It replaced photographic film with high-performance analog cameras and magnetic tape storage, eliminating the need for film development. However, the system's maximum operating speed of 10 km/h proved inadequate for large-scale network inspections. Additionally, the required analog-to-digital conversion of video recordings created significant processing bottlenecks.  
The 1990s witnessed significant progress in CCD (Charge-Coupled Device) technology, enabling the shift to digital image acquisition. Canada's Roadware ARAN system [4] leveraged this advancement to achieve 80 km/h data collection speeds, greatly improving inspection efficiency. However, the system faced limitations in low-light conditions due to digital sensors' short exposure times, requiring artificial lighting that hindered operation in tunnels and at night. These developments laid crucial foundations for modern automated detection methods.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 4 Probabilistic Wheel Re-association for Disassociated Datasets
### Analysis of Re-associated Datasets in Terms of Distance  
How can a re-associated dataset be evaluated in terms of neighborhood generation? A suitable distance provides precise indicators about the similarity between objects. As presented in Section 3, tree edit distance provides a solution to assess the similarity between set-valued datasets. Using this metric, it is easy to assess, first the distance between two re-associated datasets and second the distance between a re-associated dataset and the original dataset. For two datasets to be neighbors the distance between them should be small, in other words, few modifications should be made to make them similar. Neighborhood is generated when multiple re-associated datasets are close to each other. It is interesting to investigate the process of neighborhood initiation by generating re-associated datasets using the same/distinct privacy constraints. However, using the tree edit distance to evaluate the distance between the generated dataset and the original one, can cause a privacy breach in practice. Let us consider a very basic example where the distance is really low, almost zero: in this case, the public can be sure that they have the original dataset. Yet, we stress the idea that with a big dataset and domain, probabilistically it is very rare to regenerate the original dataset. Therefore, the distance between a re-associated dataset and the original one should not be leaked in any sense to the public, increasing the awareness of an attacker to the background data.  
The next section presents experiments evaluating the neighborhood generation and the preservation of the data utility for analysis. Despite our realization of the privacy threat that might occur when calculating the distance with the original dataset, we are going to use the tree edit distance on the original dataset, to prove the efficiency of the probabilistic wheel re-association algorithm in terms of neighborhood generation.
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 2 Establishment of SPH-FEM Model  
SPH is a mesh-free particle method based on the Lagrangian description, which utilizes a set of arbitrarily distributed particles to solve integral equations or partial differential equations with complex boundary conditions, thereby obtaining accurate and stable numerical solutions. This method is particularly adept at addressing problems involving large deformations. Compared to other mesh-free methods, such as the Material Point Method (MPM), SPH offers two notable advantages in simulating the installation mechanism of piles. Firstly, SPH does not rely on a background mesh, allowing for more effective use of techniques like FEM to simulate soil far from the penetration area, thereby reducing computational costs and improving the accuracy of calculations in areas of small deformation. Secondly, as a well-established technique, SPH has been integrated into major commercial software packages like ABAQUS 6.14 and LSDYNA R14.0.0 [12]. The SPH-FEM coupling method proposed in this paper for simulating the installation process of helical anchor foundations is implemented in ABAQUS 6.14/EXPLICIT.
# Neural Network-Based Price Tag Data Analysis
## 3 Materials and Methods
The complete Unet architecture is given in Figure 4.  
Figure 3: An example of the layout of the description segment. Figure 2: Walmart price tag labeling example. The MobileNetV2 architecture is shown in Figure 5. A distinctive feature compared to Unet is the repetitive application of bottlenecks, which allow the accuracy of the final result to be improved [19; 20]. Since this model does not employ the sliding window, shorter training and operation times are possible. VGG16 is a convolutional neural network presented by [PERSON] and [PERSON] [21]. While training on the ImageNet dataset, this model achieved an accuracy rate of 97.2%, thus ranking among the top five solutions for ImageNet dataset. A distinctive feature compared to previous models is the rejection of bottlenecks, which can reduce the neural network training and operating time [22]. This architecture was originally designed for the classification problem, which may raise the question of objectivity in comparing it with other selected architectures. However, if the coordinates of the upper left-hand corner of the segment, as well as its width and height, are chosen as classes, the work will be reduced to a segmentation process, which confirms the objectivity of our comparison. The VGG16 architecture is shown in Figure 6.  
Figure 4: Unet architecture. Figure 5: MobileNetV2 architecture. YOLOv4-tiny is a high-speed, real-time image and video object recognition neural network. The architecture of this model is shown in Figure 7 and similarly features a set of convolutional layers. Its distinctive feature consists in obtaining the final results from several intermediate ones, thus significantly increasing the achieved accuracy [23]. Additionally, YOLOv4-tiny allows multiple objects to be displayed that belong to different classes, which can reduce the application of the neural network to just a single run. The segment detector was trained using Darknet repository [24; 25]. Figure 1:0 VGG16 architecture. Figure 1:1 YOLOv4-tiny architecture. The EasyOCR API by JaidenAI, whose architecture is shown in Figure 8, was used for text recognition from the segments obtained. The CRAFT text recognition model is used as the detector, and the ResNet neural network is used as the recognizer. The advantages of this API are that this product is an open source and supports a large number of languages, including Russian, English, German, Chinese, etc. Additionally, the EasyOCR output is a text string, which simplifies further text processing and analysis. The major disadvantage of this API is the errors in the recognition of special characters such as \"_\", \"#\", \"%\", brackets, etc. ; however, this problem can be solved with additional training. The Python pyzbar [26] library was used to recognize barcodes. This library is able to decode the barcode itself and define its type.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 4 Analysis of Magnetic Field Noise Characteristics  
### Measuring Principle  
Due to the large shaking of the floating body on the water surface, installing a magnetic field sensor (magnetic sensors use three-axis flux gate sensors) on the upper part of the underwater tractor (as shown in Figure 11) will mainly be affected by two aspects of magnetic field interference: first, the tractor steering engine will continuously adjust the rudder angle underwater to maintain the heading, which will generate a magnetic field due to rotation and underwater traction machines generate strong shaking-related magnetic field noise.  
Firstly, place the three-axis flux gate sensor directly below the underwater towing machine. As the movement of the towing machine's steering engine motor must be caused by changes in its attitude angle, after the wave glider is powered on, control the steering engine motor's operation by changing its attitude angle to test the magnetic field generated by the steering engine motor.  
For the induced magnetism generated by the motion of the underwater towing body itself, the testing personnel use the underwater towing body to pass directly above the magnetic sensor and artificially increase the shaking of the towing body during the process, simulating the real motion situation.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 3 Methodology  
The measurement accuracy of the end-to-end delay of video transmission systems in previous work is generally in units of frames. This accuracy is insufficient for interactive video applications with very low latency requirements. To this end, we designed and implemented three methods to accurately measure the end-to-end latency for different application scenarios.
## 2 Requirements Specifications and Measurements Gaps
### Monitoring System: Vessel and Fish Farming Cage Position Tracking  
Norsat-2 and Triton-2 are contributing missions under the Advanced Research in Telecommunications Systems (ARTES) program by ESA. Spaceborne SAR and AIS can also be considered complementary systems to improve the security and surveillance services for maritime navigation.
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Approach and Methods Used** & **Modality** & **Dataset** & **Results** \\ \hline [PERSON] et al. [4] & YOLO detector, Inception ResNet-V2, ResNet-50, and feedforward CNN & Mammography & DDSM, INbreast & Accuracy = 95.32\% \\ \hline [PERSON] et al. [8] & Full Resolution Convolutional Network (FrCN) & Mammography & INbreast & Accuracy = 95.96\% \\ \hline [PERSON] et al. [19] & Diverse Features (DeeBCD), SVM, and ELiEC & Mammography & IRMA & Accuracy = 80.30\% \\ \hline [PERSON] et al. [45] & Lifting Wavelet Transform (LWT) & Mammography & DDSM, MIAS & Accuracy = 98.76\% and 95.80\% \\ \hline [PERSON] et al. [46] & Diversity Analysis, Geostatistics, and Alpha Form & Mammography & DDSM, MIAS & Accuracy = 96.30\% \\ \hline [PERSON] et al. [47] & Intuitionistic fuzzy soft and Multigranulation rough set & Mammography & MIAS, MedPix & Accuracy = 87.22\% \\ \hline [PERSON] et al. [48] & Efficient Adaboost & Mammography & ACRIN & Accuracy = 97.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed summary of recent related works on breast cancer detection and classification. From literature research, previous breast cancer detection and classification approaches have led to better information extraction. However, there are still numerous problems that require serious attention, i.e., (i) the tumor appears in a location with significantly lower contrast, (ii) memory complexity is high, (iii) existing approaches are computationally complex and require more treatment time to identify the accurate tumor, (iv) current deep learning approaches require a large amount of training data to overcome the problems of overfitting and high computational cost, and (v) practical implementation. To resolve the above mentioned problems, we have proposed a new breast cancer detection and classification approach. This will be discussed in more detail in the next section.
## 6 Discussion and Future Research Directions
Prediction systems based on AI techniques, including ML algorithms, hold significant potential to enhance the efficient utilization of EC in power systems and energy management processes.Furthermore, the integration of distributed security solutions, such as blockchain, with EC could play a key role in enhancing data security and integrity. By developing EC-based solutions, the next generation of power distribution networks can be made smarter, more autonomous, and more secure. The use of EC in energy management and power systems will not only improve operational and analytical processes but also contribute to making the overall energy ecosystem more efficient and sustainable. The development of quantum-resistant encryption techniques is becoming increasingly crucial to secure sensitive data processed at the edge of EC systems. As quantum computing evolves, traditional cryptographic methods may become vulnerable, making it essential to integrate quantum-resistant algorithms into EC architectures. This will enhance the resilience of EC systems and protect against emerging cybersecurity threats, particularly in the context of smart grids. In addition, the rise in energy-harvesting edge devices presents a significant opportunity to improve the sustainability and autonomy of EC systems. By harnessing energy from environmental sources such as solar, wind, or vibration, these devices reduce dependence on traditional power supplies. In smart grids, energy-harvesting technologies could enable self-sustaining edge nodes, enhancing the energy efficiency and reliability of the network, especially in remote or off-grid areas. Future research should focus on optimizing energy harvesting mechanisms to improve the scalability and resilience of EC systems in diverse environments.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 3 Cycle Slip Detection Strategy for a UAV
### Doppler-Aided Cycle Slip Detection (DACSD)  
DACSD uses time-differenced single-frequency carrier phase and Doppler measurements as the test metric, calculated as follows:  
\[T_{DACSD}=\delta\phi_{i}^{k}-\Bigg{(}\frac{d_{i}^{k}(t_{2})+d_{i}^{k}(t_{1})}{2} \cdot\delta t\Bigg{)}, \tag{9}\]  
where \(\delta t\) is the time duration between \(t_{1}\) and \(t_{2}\).  
Figure 6 shows the DACSD values without cycle slips and the derived threshold values.  
Due to the large noise level of Doppler measurements from UAVs, the threshold was set to have a false alarm rate of \(3\times 10^{-7}\). Here, the thresholds of DACSD were found to be four to six cycles, depending on the elevation angle. Therefore, cycle slip combinations listed in Table 2 can be reliably detected, except for the (1,1) cycle combination. Other monitoring approaches could be used for the (1,1) cycle slip case, including range residual tests, which require further investigation.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
###### Abstract  
The detection of the formation of pore pressure while drilling is of great importance to ensure safe drilling operations. At present, the dc-exponent concept is mainly used to detect pore pressure while drilling. The dc-exponent concept is based on the theory of shale compaction, which is limited when used in carbonate rocks. A mechanical specific energy (MSE)-based method is proposed to detect pore pressure in deep, complex intervals. The method is based on the theory that the energy consumed by the bit to break and remove a unit volume of rock can reflect the effective stress and pore pressure of the rock in situ. In this paper, a torque and weight on bit (WOB) transfer model is proposed for estimating the downhole torque and WOB using drill string mechanics. Meanwhile, the rotary speed and torque of the positive displacement motors under compound drilling are considered, and the model of total MSE under compound drilling is modified. The MSE-based method was used to estimate the pore pressure in a region in western Sichuan, and there is a good agreement between the detected and measured pore pressure. The results demonstrate that the accurate computed MSE-based method is useful in detecting pore pressure in deep complex intervals.  
formation pore pressure; mechanical specific energy; drill string mechanics; effective stress; compound drilling +
Footnote †: journal: Physics Letters B  
1481  
[[https://doi.org/10.3390/pr10081481](https://doi.org/10.3390/pr10081481)]([https://doi.org/10.3390/pr10081481](https://doi.org/10.3390/pr10081481))
## 5 Discussion
### Analysis of Typical Landslides
#### 5.4.2 Lianfeng Landslide  
After the time series of the upper part (P1 point) and lower part (P2 point) of the Lianfeng landslide was extracted (Figure 17a), the ALOS/PALSAR\(-2\) time-series results of P1 and P2 showed severe deformation during the period 26 September\(-22\) October 2021. During this period, the cumulative deformation of P1 reached \(-71.39\) mm, and the sequential deformation has been fluctuating since then. The cumulative amount of deformation  
Figure 16: InSAR time-series results for the Hujiayuan landslide; (**a**) is the optical image of the landslide, and the positions of P1 and P2 points are marked on the image; (**b**,**c**) are the time-series results from ALOS/PALSAR\(-2\) and Sentinel\(-1\)A at P1 and P2, respectively; (**d**,**e**) are comparisons of time series between P1 and P2 after the time difference was removed.  
reached \(-\)80.93 mm on 2 May 2022 (Figure 17). P2 shows similar trends to P1. After the time difference between the time-series results of the two data sources was removed, the two were in good agreement, indicating that the deformation of the landslide was concentrated before 22 October and Sentinel\(-\)1A missed monitoring due to the low coherence of summer data. The summer rainfall in the Qinling-Daba Mountains is concentrated, and excessive rainfall will directly lead to large displacement or failure of the slope, causing sudden seasonal landslides [70]. The time-series results further confirm the landslide characteristics of the Qinling-Daba Mountains. The reason for non-detection of landslides by Sentinel\(-\)1A is also clarified.
## 3 Results
### Effect of Printing Parameters on 2D Printability
#### 3.3.3 Offset Test  
For this section of studies, the offset was modulated to study its influence on printability. Figure 7 shows that offset can significantly affect strand diameter. All groups printed with a 200 \(\upmu\)m needle, but with a wide range of strand diameters (between 0.1 to 0.6 mm), were observed as modulating the offset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **Pressure (bar)** & **Speed** & **Strand Printability** & **Pore Printability** \\ \hline \multirow{6}{*}{**1**} & 0.2 & 4 & \(0.97\pm 0.060\) & \(1.01\pm 0.002\) \\  & 0.2 & 6 & \(1.10\pm 0.180\) & \(1.01\pm 0.012\) \\  & 0.2 & 8 & \(1.18\pm 0.090\) & \(1.01\pm 0.009\) \\  & 0.2 & 10 & \(1.12\pm 0.140\) & \(1.01\pm 0.003\) \\  & 0.2 & 12 & \(1.32\pm 0.150\) & \(1.00\pm 0.003\) \\  & 0.2 & 14 & \(1.36\pm 0.150\) & \(0.98\pm 0.022\) \\ \hline \multirow{6}{*}{**2**} & 0.2 & 6 & \(0.81\pm 0.066\) & \(1.139\pm 0.086\) \\  & 0.2 & 8 & \(0.89\pm 0.069\) & \(1.050\pm 0.042\) \\  & 0.2 & 10 & \(0.91\pm 0.083\) & \(1.066\pm 0.070\) \\  & 0.2 & 12 & \(0.95\pm 0.062\) & \(1.010\pm 0.006\) \\  & 0.2 & 14 & \(1.09\pm 0.049\) & \(1.048\pm 0.054\) \\  & 0.2 & 18 & \(1.22\pm 0.077\) & \(1.002\pm 0.015\) \\  & 0.2 & 22 & \(1.26\pm 0.111\) & \(1.04\pm 0.027\) \\ \hline \multirow{6}{*}{**3**} & 0.1 & 20 & \(1.203\pm 0.136\) & \(1.152\pm 0.135\) \\  & 0.1 & 30 & \(0.997\pm 0.067\) & \(1.012\pm 0.019\) \\  & 0.1 & 35 & \(0.871\pm 0.059\) & \(1.010\pm 0.010\) \\  & 0.1 & 40 & \(0.897\pm 0.106\) & \(1.010\pm 0.018\) \\  & 0.1 & 45 & \(0.925\pm 0.056\) & \(1.045\pm 0.065\) \\  & 0.1 & 50 & \(0.975\pm 0.059\) & \(1.033\pm 0.041\) \\  & 0.1 & 55 & \(0.923\pm 0.052\) & \(0.937\pm 0.122\) \\ \hline \multirow{6}{*}{**4**} & 0.2 & 18 & \(1.075\pm 0.110\) & \(1.338\pm 0.488\) \\  & 0.2 & 20 & \(0.931\pm 0.138\) & \(0.974\pm 0.036\) \\ \cline{1-1}  & 0.2 & 22 & \(0.939\pm 0.097\) & \(0.982\pm 0.056\) \\ \cline{1-1}  & 0.2 & 24 & \(0.947\pm 0.116\) & \(1.039\pm 0.029\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of nozzle speed on strand and pore printability.  
Figure 7: Effect of offset on the strand diameter of scaffolds made of Groups 1 to 4.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 4 Experimental Setup
### Experiment Procedure  
To answer RQ1, we analyzed the PDR performance of routing protocols AODV, DSDV, and OLSR. For the simulation of each protocol, we created the trace file of each mobility model. This is to set up the same experimental condition to evaluate each protocol based on making the same movements of nodes. In the simulation, we changed the number of nodes to observe the changes in the protocol performance as the density of nodes increases.  
To answer RQ2, we first measured the NCR value. The NCR value represents network connectivity. Therefore, we increased the number of nodes in the experiment and measured the change in network connectivity by calculating the NCR. Here, we already measured the PDR of each protocol for RQ1. By using these two performance metrics, NCR and PRD, we analyzed how changes in network connectivity affect the performance of routing protocols.  
To answer RQ3, we found the mobility model that showed the highest PDR performance in the combination of mobility models and FANET protocols (AODV, DSDV, and OLSR).  
To answer RQ4, we measured the reconnaissance rate on our own mobility model simulator. We calculated the reconnaissance packet delivery ratio (RPDR) for each mobility model. This analysis helps identify mobility models suitable for reconnaissance scenarios.  
Finally, to answer RQ5, we changed the GCS location. Whenever we changed the GCS location, we measured the PDR performance of the mobility model to analyze how the change in the GCS location affected the performance of the mobility model.