esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Processing and Analysis
### Standard Penetration Data Analysis of Large-Diameter Tubular Piles
Based on the abovementioned analysis, an obvious turning point exists in the penetration rate of Pile #4 around the 500 th blow count. This demonstrated that Pile #4 penetrated through the relatively hard aleurite into the relatively soft silt and further approached the bearing stratum. Figure 9: Schematic diagram of penetration change in first and second pipe piles at pile position 6, displaying the (**a**) spatial diagram of penetration evolution law of pipe pile 1, (**b**) fitting analysis of penetration evolution law of pipe pile 1, (**c**) spatial diagram of penetration evolution law of pipe pile 2, and (**d**) fitting analysis of penetration evolution law of pipe pile 2. Figure 10: Schematic diagram of penetration evolution of pipe pile 3 at pile position 6, showing the (**a**) schematic diagram of pipe pile penetrated the strata in first monitoring stage, (**b**) schematic diagram of pipe pile penetrated the strata in second monitoring stage, (**c**) schematic diagram of pipe pile penetrated the strata in third monitoring stage, (**d**) schematic diagram of pipe pile penetrated the strata in fourth monitoring stage, (**e**) spatial diagram of penetration evolution law, and (**f**) fitting analysis of penetration evolution law. On this basis, the variation curves of the penetration of large-diameter tubular piles were plotted. In Figure 12a, the penetration and the corresponding blow counts evolve as a piecewise function, which is a logarithmic function in the early stage, a gently sloping  
Figure 11: Diagram of penetration evolution of the 4 th pipe pile at pile 6, showing the (**a**) schematic diagram of pipe pile penetrated the strata in first monitoring stage, (**b**) schematic diagram of pipe pile penetrated the strata in second monitoring stage, (**c**) schematic diagram of pipe pile penetrated the strata in third monitoring stage, (**d**) spatial diagram of penetration evolution law, and (**e**) fitting analysis of penetration evolution law. linear function in the middle stage, and a sharply sloping linear function in the late stage. According to the variations in the penetration and penetration rates at different stages, the geological conditions of tubular piles could be assessed intuitively concerning their locations. On this basis, the blow frequency and blow loads of the tubular piling machine could be adjusted promptly. Additionally, Figure 12b shows that monocular visual digital photography can meet the requirements concerning the standard penetration monitoring accuracy of large-diameter tubular piles. It can provide reliable basic data for the planning and design of deep foundations, facilitating the proposal of reasonable suggestions for unstable geological conditions. This can help improve the success rate of field construction significantly.
# On the Optimal Calculation of the Rice Coding Parameter
## 4 Performance on Different Datasets Using Single-Parameter Rice Coding
### Approximation Solutions  
In this subsection, we illustrate whether the conjecture in (20) is valid.  
Equation (20) provides three different potential integer values for the optimal Rice coding parameter: \(\lfloor S\rfloor\), \(\lceil S\rceil\), and \(\lceil S\rceil+1\). In Figure 5 is shown the count of solutions found by approximating the Rice coding parameter as mentioned in (20).  
All minimal bit-length solutions were found by approximating the Rice coding parameter: the optimal Rice coding parameter was found using \(\lfloor S\rfloor\) in 64.8%, \(\lceil S\rceil\) in 71.2%, and \(\lceil S\rceil+1\) in 11.9% of the 601,953 cases.
## 1 Introduction
### Innovative Contributions
#### 1.1.2 Efficient Resource Utilization  
We turn our focus to the imperative aspect of optimizing resource allocation within our proposed model. Efficient resource utilization is paramount for ensuring the overall effectiveness, scalability, and sustainability of the system. Our approach aims to strike a balance between performance and resource consumption, enhancing the model's efficiency.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Visual-Based Modality
#### 3.1.3 Marker-Based  
Marker-based interactions contain all interactions in immersive environments supported by marker tags, such as ARTag markers [102]. [PERSON] et al. [103] performed a reclassification of markers for mixed reality environments based, among other things, on the level of realism, the level of immersion and visibility. However, not all markers are suitable for marker-based interaction. In [104], [PERSON] mentions that Data Matrix, Maxicode, and Quick Response (QR) codes are ideal for conveying information when held in front of a camera and not localized. Thus, they are not suitable to be used as a fiducial marker, as is needed for applications of immersive realities. In contrast, InterSense [105], Reac-TIVision [106], Cyber-code [107], Visual Code [108], Binary Square Marker [109], Siemens Corporate Research (SCR) [110], and BinARyID [111] are several examples of fiducial marker systems that can be used in AR and MR applications. [PERSON] [112] proposed AprilTags3D that improves the accuracy of _fiducial marker recognition_ of AprilTags in field robotics with only an RGB sensor by adding a third dimension to the marker detector. In [113], [PERSON] et al. used the _infrared marker recognition_ method and proposed an AR system for industrial equipment maintenance. As the markers are infrared and invisible to the naked eye, they do not cause any visual disturbance to the user [114]. A camera capable of capturing infrared light can detect information from infrared markers regarding position and rotation and successfully superimpose virtual objects in the real environment.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 2 Corrosion Detection Method of Substation Grounding Grid
### Time Difference Location Method  
Time difference location is a newly proposed method to determine the defect location of grounding grids. This method involves ultrasonic detection and electromagnetic pulse time difference location (D-TDOA).  
The ultrasonic detection method utilizes the uniform velocity propagation of ultrasonic waves to determine the reflection characteristics of defects, determines the defect location by calculating the time difference of the received reflected wave, estimates the defect magnitude according to the reflected wave amplitude, and detects the non-electromagnetic parameter by ultrasonic method [41; 42]. This method is not sensitive to strong electromagnetic interference in power systems, which is its main merit. The overall response of the whole network is measured by the electric network method, but the transmission speed of the guided wave is slow. Early signal response conductor near-end corrosion information can eliminate the influence of near-grid conductor. However, the underground network is deeply buried. In addition, the directional problem caused by the mesh structure, influence of the flat steel solder joint, down-line and electrical equipment connection points, and influence of soil non-uniformity are difficult to solve for the ultrasonic guided wave detection method. A time difference location method (D-TDOA) was proposed to inject the Gauss pulse current and detect pulse transmission time with a broadband measurement sensor (1 kHz-1 GHz) [43]. The fault location accuracy is 10 cm. This method has not been tested on site. Moreover, the output power is a huge problem because the excitation source frequency is very high.
## 3 Methodology
### Problem Definition and Our Approach  
In our study, we extracted a separate matching dataset from a battery that was excluded from the training and validation sets. This subset was then used to calculate the FL gradients by combining discrete signal components. Other techniques could also be explored, such as training a separate DL model to reconstruct the input signal from the FL model updates or using an analytical approach. This could be further explored in future work. Figure 2 below shows the process of signal recovery from the DL model.  
We integrate the process described above into a federated learning (FL) framework, both with and without differential privacy (DP). To assess the privacy risks, we perform a model inversion auditing scheme [4], which evaluates how much information about individual data records can be inferred from the model's outputs--e.g., confidence scores or gradients. In this way, the extent of privacy leakage in the system can be quantified. For privacy auditing, we employ a signal reconstruction algorithm. The corresponding pseudo-algorithm is listed in Algorithm 1.  
The algorithm encapsulates the process and begins with the generation of a random signal, step 1. Then a loop begins, which is continued until the gradients are close enough to each other. This involves generating a signal update step 2 and checking whether the update is valid for step 3. If it is not valid, a new update is generated until a valid one is found. The signal is then updated with the valid update (4). A check is made to see whether the gradients are close enough to each other; if not, the process is repeated. As soon as the gradients are close enough to each other, the loop is exited and the reconstructed signal is returned. It is an iterative process consisting of different stages that lead back to previous stages depending on the conditions met. This solves the gradient matching problem and restores the unknown signal. In the signal update stage, the various techniques can be used to generate updates, including training a deep learning (DL) model that leads to reinforcement learning.  
In our experiments, we used the following: (1) a simple technique of signal recovery with the generation of random signal updates to show that gradient inversion can also be solved, and (2) a discrete set of probing signals with parameter change. The second approach could solve gradient inversion in specific cases, where the probe set consisting of dedicated signals for gradient matching is representative.  
Figure 2: Process of signal recovery from DL model updates.
## 2 Materials and Methods
### Wavelength Selection Based on BFWA
#### 2.3.2 BFWA  
As a swarm intelligence algorithm, FWA is inspired by actual firework explosions that illuminate the surrounding area, and has the advantages of a search, explosiveness, and diversity in parallel. Conventional FWA is suitable for searching for an optimal value of a continuous system, but not suitable for discrete problems, because of the coding mode. To overcome the disadvantage of conventional FWA, a binary firework algorithm (BFWA) was proposed. The BFWA was achieved by FWA combined with binary coding in solving a discretization problem, and can be applied for the selection of optimal wavelengths. In this study, the location of fireworks in FWA was encoded. Similarly to the FWA implementation process, population initialization, evaluation, explosion, generation of \"sparks\", and selection operators were used based on fitness in the BFWA.  
Population Initialization  
The obvious difference between the BFWA and original FWA is that the firework location is defined as a binary string. In the BFWA method, binary numbers \"1\" and \"0\" are used to indicate whether the hyperspectral image information at a certain wavelength is selected (\"1\" indicates that these wavelengths are selected in the evolutionary process, and \"0\" indicates that these wavelengths are excluded) [14; 17]. The remaining number of wavelengths after selection is \(m\), and \(N\) firework locations are distributed in the analytic domain to randomly combine the initial population in initializing the original firework population. First, a number of (0, 1) is randomly generated at each location of the 94 wavelength locations. Then, the obtained random array is sorted in order from large to small, and the previous \(m\) values are selected to record the corresponding wavelength location. The wavelength location that corresponds to the largest number of the first \(m\) is placed in \"1,\" and the remaining \((94-m)\) wavelength locations are \"0.\" This process is repeated \(N\) times, in which an initial population that contains \(N\) fireworks can be obtained. In this study, the number of fireworks (\(N\)) in the population was set to 20.
## 1 Introduction
In terms of segmentation algorithms, U-Net-based architectures stand out with their simple structure and high performance, providing precise segmentation at the pixel level [12; 13; 14; 15; 16]. In addition, Mask R-CNN-based architectures are effective in discriminating different regions of meniscal tissue (e.g., medial and lateral) and injury types (e.g., tear and degeneration), and excel in instance segmentation tasks [11; 17; 18]. The objectives of the studies also determine the choice of these methods. For example, some studies in the literature focus only on segmenting meniscal tissue [12; 15; 18; 19], while others use segmentation as a preprocessing step to detect meniscal injuries [11; 14; 16; 17]. This study used the YOLOv8, YOLOv9, and YOLOv11 series of YOLO algorithms for meniscus segmentation. Segmentation masks were created with five models (YOLOv8x-seg, YOLOv8x-seg-p6, YOLOv9c-seg, YOLOv9e-seg, YOLOv11-seg) that can perform the segmentation task. By utilizing the different structural properties of these models, the strengths of each model are combined through ensemble methods. The masks obtained using the YOLO series are combined with innovative methods such as pixel-based multiple voting and dynamic weight optimization to improve the performance of the YOLO series on meniscus segmentation (Figure 1). Despite the significant advances in the literature, studies using ensemble methods for meniscus segmentation and injury classification are limited. Therefore, ensemble methods aim to fill an important gap in this field. Furthermore, this is the first known study using the YOLO series for meniscus segmentation. The proposed method is validated with a test set (internal dataset) and an external set (external dataset). The contributions of this study to the literature are as follows:  
* Using YOLO series models, the proposed ensemble methods (pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting optimized by grid search) improved the meniscus segmentation performance. * The proposed ensemble-based approach sheds light on the development of studies using ensemble methods as a preprocess in the detection or classification of meniscal tears. * By improving the accuracy and reliability of the results of meniscus segmentation, the proposed method has a significant impact on the planning of surgical interventions and the improvement of the quality of life of those affected. * The experiments on the internal and external sets demonstrated the generalization ability of the proposed method and its applicability in different clinical settings. In the following sections of the study, the datasets, segmentation models, and ensemble methods are described in the materials and methods section. In the results section, the performance of the proposed method is evaluated using several metrics and is compared with other methods. In the discussion section, the strengths and weaknesses of the proposed method are discussed and compared with the literature. In the conclusion section, the general findings are summarized, and the potential impacts of the method in the health field are emphasized.
# DentalArch: AI-Based Arch Shape Detection in Orthodontics
###### Abstract  
Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercamine, 2 interpremolar, and 1 intercmid, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community.  
arch dental form; orthodontics; artificial intelligence; DentalArch software; AutoScan-DS-EX; machine learning; open source
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 5 Results Simulation and Analysis
### Convergence Analysis  
Figure 12 shows that the proposed method has obvious advantages in convergence speed, which shows that the model can learn data well.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 5 Possibilistic Task Allocation
### Possibilistic Markov Chains: Theory  
As was proved in [10], possibilistic Markov chains provide a lot of advantages and outperform their probabilistic counterpart when they are applied to task allocation problems. This section summarizes the main theoretical concepts of possibilistic (fuzzy) Markov chains.  
Following [20], we can define a possibility Markov (memoryless) process as follows: let \(S=\{s_{1},\ldots,s_{m}\}\) (\(m\in\mathbb{N}\)) denote a finite set of states. If the system is in the state \(s_{i}\) at time \(\tau\) (\(\tau\in\mathbb{N}\)), then the system will move to the state \(s_{j}\) with possibility \(p_{ij}\) at time \(\tau+1\). Let \(x(\tau)=(x_{1}(\tau),...,x_{m}(\tau))\) be a fuzzy state set, where \(x_{i}(\tau)\) is defined as the possibility that the state \(s_{i}\) will occur at time \(\tau\) for all \(i=1,\ldots,m\). Notice that \(\bigvee_{i=1}^{m}x_{i}(\tau)\leq 1\) where \(\vee\) stands for the maximum operator on \([0,1]\). In the light of the preceding facts, the evolution of the fuzzy Markov chain in time is given by  
\[x_{i}(\tau)=\bigvee_{j=1}^{m}p_{ji}\wedge x_{j}(\tau-1),\]  
where \(\wedge\) stands for the minimum operator on \([0,1]\). The preceding expression admits a matrix formulated as follows.  
\[x(\tau)=x(\tau-1)\circ M=x(0)\circ M^{\tau}, \tag{9}\]  
where \(M=\{p_{ij}\}_{i,j=1}^{m}\) is the fuzzy transition matrix, \(\circ\) is the matrix product in the max-min algebra \(([0,1],\vee,\wedge)\), and \(x(\tau)=(x_{1}(\tau),\ldots,x_{m}(\tau))\) is the possibility distribution at time \(\tau\).  
Taking into account the preceding matrix notation and following [20], a possibility distribution \(x(\tau)\) of the system states at time \(\tau\) is said to be stationary, or stable, whenever \(x(\tau)=x(\tau)\circ M\). During experiments, explained in Section 6, each state will be a task to execute and, therefore, \(m\) will stand for the number of tasks.  
One of the main advantages of possibilistic Markov chains with respect to their probabilistic counterparts is given by the fact that under certain conditions, provided in [21] by [PERSON], the system converges to a stationary state in, at most, \(m-1\) steps.
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 2 Materials and Methods
### Deep-Learning Algorithm for PAI Segmentation  
Deep learning plays an important role in medical image segmentation by combining the powerful feature learning capability of deep neural networks and the rich information of medical images to achieve automatic and accurate segmentation of lesions, tissues, and organs, which provides important assistance and support for medical diagnosis and treatment [40; 41]. DeepLabV3+ [30], as a deep-learning algorithm that focuses on semantic segmentation tasks, is able to accurately identify blood vessels, tissues, and other structures in PAI to provide accurate segmentation results for medical diagnosis, and its architecture is shown in Figure 3. Its core feature lies in the combination of deep separable convolution, null convolution, and multi-scale feature fusion, which can effectively capture the subtle structures and complex information in medical images to achieve accurate segmentation of organs and lesions. The model employs advanced backbone networks (e.g., ResNet, Xception, or MobileNetV2) to extract image features, and effectively captures contextual information at different scales through the cavity convolution module and feature pyramid pooling operations. Meanwhile, DeepLabV3+ also introduces a decoder module that fuses low-level and high-level features by transposing convolutional layers and hopping connections, which improves the accuracy and detail of the segmentation results, and provides powerful support for the development of medical imaging and clinical practice.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 4 Colloidal Semiconductor  
As mentioned before, materials nanostructuring offers the possibility to enhance material properties and devices performances. In the case of PDs, bottom up and top down fabrication of nanocrystals (NCs), nanoparticles (NPs), nanowires (NWs) etc. have been largely experienced to improve resistivity, work function (WF), bandgap or dielectric constant (\(\epsilon\)). At the same time, dopant percentage, nanocomposition and several kind of deposition methods have been exploited to realize PD with low dark current, high EQE and wide band absorption.