esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 2 Related Work
Assuming that the depth of all sensor nodes is known, the algorithm uses projection technology to transform the 3D space into the 2D underwater localization problem, which does not require the time synchronization. The scheme includes three phases: (1) sea surface anchor localization, (2) iterative localization, and (3) complementary phase. With just three surface anchor nodes, in the first phase, three surface anchor nodes send messages in the order described in basic time-synchronization-free localization (BSFL) [28], and then the ordinary sensor node that receives all three beacon messages calculates their positions independently. When an unknown node is located, in the second stage, it can be used as a new reference node to assist the localization process of other unknown nodes. If the unknown node fails to locate in the above stage, in the third stage, it sends a location request, and a new set of anchors is then selected to locate the unknown node. In this section, some excellent underwater localization algorithm papers are discussed. The abovementioned localization algorithm papers are summarized from ten aspects, including network scale, computation algorithm, auxiliary equipment, anchor requirement, and message exchange. As shown in Table 2, the computation algorithm can be divided into two categories, respectively, centralized or distributed. The centralized localization method estimates the position of the sensor node at a sink node. The distributed localization method requires sensor nodes to have autonomous computing capability and be able to locate themselves. According to the utilization of the reference node, the algorithm can be divided into two classes, respectively, single-stage and multistage. The single-stage localization scheme is that all the ordinary nodes exchange the message directly with reference nodes. The located node is not used as a new reference node to assist other nodes in positioning. As for the multistage localization scheme, the located node can be used as a new reference node to assist other nodes in positioning. Moreover, the localization algorithm can be divided into two classes, namely, silent and active. Silent messaging means that only anchor nodes can send localization messages, and ordinary nodes can only receive localization messages but cannot send them. In active messaging, ordinary nodes can both receive and transmit localization messages. In Table 2, we use the abbreviations to express all aspects, namely, Reference (Ref. ), network scale (NS), lager-scare network (L-SN) or small-scale network (S-SN), computation algorithm (CA), centralized or distributed (C or D), range measurement (RM), range-free or range-based (Rf or Rb), anchor requirement (AR), anchor-free or anchor-based (Af or Ab), synchronization requirement (SR), synchronization or asynchronization (Sy or As), communication between nodes (CBNs), single-stage or multistage (Ss and Ms), message exchange (ME), and active or silent (A or Si).
## 2 Proposed Method
### Gradient-Oriented Prioritization in Meta-Learning (GOPML)
Model Evaluation: The model, with updated parameters \(\theta\), is evaluated against new tasks, iterating until a satisfactory performance is achieved. GOPML's strategic framework for task selection and sequencing enhances the model's ability to adapt and generalize. By focusing on the most impactful tasks, determined by gradient information, GOPML aims for more efficient learning and improved performance across diverse tasks. This approach, outlined in Algorithm 1, marks a shift towards a  
Figure 1: Conceptual overview of GOPML for intelligent few-shot fault diagnosis. refined meta-learning paradigm, where the nuanced interplay of task characteristics is leveraged to amplify the learning potential, paving the way for enhanced generalization and mastery of new categories. ```
0:\(p(T)\): distribution over tasks
0:\(\alpha,\beta,\gamma,\delta\): step size and scoring hyperparameters
1: Randomly initialize \(\theta\)
2:while convergence not achieved do
3: Sample batch of tasks \(T_{i}\sim p(T)\)
4:for all \(T_{i}\)do
5: Generate task embedding \(E_{T_{i}}\) using deep learning model
6: Sample \(N\times K\) datapoints \(D=\{x^{(i)},y^{(i)}\}\) from \(T_{i}\)
7: Compute task-specific loss \(L_{T_{i}}\) and gradient \(\
abla_{\theta}L_{T_{i}}\)
8: Compute gradient magnitude: \(G_{\text{mag}}(T_{i})=\|\
abla_{\theta}L_{T_{i}}\|\)
9:endfor
10: Perform clustering on \(E_{T_{i}}\) to identify groups of similar tasks
11:for each cluster do
12:for all \(T_{i}\), \(T_{j}\) in the cluster do
13: Compute gradient alignment: \(G_{\text{align}}(T_{i},T_{j})=\frac{\
abla_{\theta}L_{T_{i}}\cdot\
abla_{ \theta}L_{T_{j}}}{\|\
abla_{\theta}L_{T_{i}}\|\
abla_{\theta}L_{T_{j}}\|}\)
14:endfor
15:for all \(T_{i}\) in the cluster do
16: Compute task score: \(T_{\text{score}}(T_{i})=\gamma G_{\text{mag}}(T_{i})+\delta\sum_{j\
eq i}G_{ \text{align}}(T_{i},T_{j})\)
17:endfor
18: Aggregate gradients within the cluster based on \(T_{\text{score}}\)
19: Compute parameters with gradient descent: \(\theta^{\prime}=\theta-\alpha\sum_{T_{i}\in\text{cluster}}\
abla_{\theta}L_{T _{i}}\)
20:endfor
21: Update \(\theta\leftarrow\theta-\beta\
abla_{\theta}\sum_{T_{i}\sim p(T)}L_{T_{i}}\)
22: Evaluate model on unseen tasks and adjust \(\theta\) if necessary
23:endwhile
```  
**Algorithm 1** Gradient-oriented prioritization meta-learning (GOPML)
## 2 Related Work
### Nonphysical Model-Based Methods  
Nonphysical model-based methods apply white balance techniques to sand-dust images first, followed by weight maps to fuse multiple enhancement results. For instance, [PERSON] et al. [3] utilized gamma correction to generate two inputs with varying brightness, calculated three weight maps (sharpness, chromaticity, and saliency), and fused them to produce an enhanced image. While effective in enhancing image details, this approach also results in localized darkening and color artifacts. [PERSON] et al. [15] proposed a rapid color balancing technique and an effective fusion strategy to enhance sandstorm images. [PERSON] et al. [4] introduced a contrast-limited adaptive histogram equalization (CLAHE) method based on normalized gamma transformation, which performed color correction and compensation in the Lab color space. However, this method does not substantially enhance the edges of the dust storm images. [PERSON] and [PERSON] [5] devised a color balance algorithm for sandstorm image enhancement, using the mean and standard deviation of RGB channels in the color histogram for adaptive adjustment, followed by the CLAHE method to enhance the contrast. Although this technique produces high-quality results, it does not eliminate color artifacts. [PERSON] et al. [16] introduced a Lab color space-based algorithm for color balancing and sandstorm image enhancement. [PERSON] et al. [17] noted the similarity in contours across the RGB channels of natural scenes and proposed an image enhancement algorithm using a tensor least squares optimization model. However, this method does not restore the original colors of the dust storm images. While nonphysical model-based methods can deliver rapid and effective enhancement results, they remain limited in their effectiveness for complex sandstorm scenes.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Initial Parameter Variation
#### 3.2.1 Initial Vector Decomposition  
In the proposed algorithm, to calculate the coefficients in a series of forward matrices \(F^{k},k=1,...,t\), the Initial Vector \(\Gamma\) is divided into \(\Gamma\) and \(\widehat{\Gamma}\) that are independent of and dependent on \(\theta_{\gamma}\), respectively, for mathematical convenience. The matrices \(\bar{\Gamma}\) and \(\widehat{\Gamma}\) are computed based on the proportional co-variation assumption shown in Equation (4).  
Let us explain the Initial Vector \(\Gamma\) decomposition considering the HMM presented in Section 3.1 where the Initial Vector \(\Gamma\) is \([\gamma_{1},\gamma_{2},\cdots,\gamma_{n}]\) and the initial parameter variation \(\theta_{\gamma}\) is \(\gamma_{2}=p(\mathbf{x}_{2}^{1})\). This makes the Initial Vector \(\Gamma\) a function of \(\theta_{\gamma}\), and, with the proportional co-variation assumption shown in Equation (4), it becomes  
\[\Gamma(\theta_{\gamma})=\left[\begin{array}{cccc}\frac{\gamma_{1}(1-\theta _{\gamma})}{1-\gamma_{2}}&\theta_{\gamma}&\cdots&\frac{\gamma_{n}(1-\theta_ {\gamma})}{1-\gamma_{2}}\end{array}\right]\]  
This matrix is divided into \(\Gamma\) and \(\widehat{\Gamma}\) that are independent of and dependent on \(\theta_{\gamma}\), respectively, as follows, and such that \(\Gamma(\theta_{\gamma})=\Gamma+\widehat{\Gamma}(\theta_{\gamma})\).  
\[\bar{\Gamma}=\left[\begin{array}{cccc}\frac{\gamma_{1}}{1-\gamma_{2}}&0& \cdots&\frac{\gamma_{n}}{1-\gamma_{2}}\end{array}\right]\quad\text{and}\quad \widehat{\Gamma}(\theta_{\gamma})=\left[\begin{array}{cccc}\frac{-\gamma_{1 }}{1-\gamma_{2}}&1&\cdots&\frac{-\gamma_{n}}{1-\gamma_{2}}\end{array} \right]\cdot\theta_{\gamma}\]In short, this decomposition can be represented simply as \(\bar{\Gamma}\) and \(\bar{\Gamma}\) that are independent of and dependent on \(\theta_{\gamma}\), respectively.  
Once the Initial Vector \(\Gamma\) is decomposed as shown above, to compute the coefficients of the sensitivity function \(p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{\gamma})\) using the recursive Equation (8), the Observation Matrix \(O\) is represented as an \(n\times n\) diagonal matrices \(O^{t}\).
## 4 Experimental Results
### Baselines
#### 4.3.1 Bidirectional Encoder Representations from Transformers (BERT)  
BERT is a deep stack of bidirectional Transformer-based encoders that uses the attention mechanism [27] to learn the relationship between words. It uses WordPiece tokenization to generate a fixed-size vocabulary encompassing single characters, common words, and subwords from the training corpus. BERT accepts input data in a specific format with limited input sequences of 512 tokens. In our study, we pre-trained a custom code-text BERT model using malware scripts and subsequently fine-tuned it on our dataset. During fine-tuning, we added a BiLSTM layer with an input dimension of 256.
## 2 System Design
### Data Center
#### 2.4.1 Working Flow  
C-S structure is adopted for the server data center, as shown in the flow chart in Figure 6. The center also makes use of TCP-IP socket protocol in a communication protocol to achieve multi-block simultaneous communication in a multi-threaded manner.  
The C-S (Client-Server) structure of the workflow is as follows:  
C: a single module's connections links to the server through a fixed IP external network and a specified port number.  
S: each terminal is assigned with a separate thread to receive and send data at terminals from the server, which is always in the monitoring state.  
C: the module sends login information.  
S: the server tests login information.  
C: the module sends data  
S: the server interprets the data through the specified analytical method call (class file) in accordance with the logging information.
###### Abstract  
A real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by actual experimental values was used to verify the proposed method. To obtain a realistic vehicle distribution for the bridge, vehicle type and actual headways of moving vehicles were taken, and the measured vehicle distribution was generalized using Pearson Type III theory. Twenty-five load scenarios were created with assumed vehicle speeds of 40 km/h, 60 km/h, and 80 km/h. The results indicate that the model can reasonably predict the overall displacements of the bridge (which is difficult to measure) from the strain (which is relatively easy to measure) in the field in real time.  
vertical displacements; longitudinal strains; artificial neural network; moving vehicle; Pearson Type III +
Footnote †: journal: Applied Mechanics and Physics Letters
## 3 Predicting Missing Soil Heavy-Metal Values Based on SA-FOA-BP
### Adaptive Step-Size Fruit Fly Optimization Algorithm Based on Individual Differences
#### 3.1.1 Steps and Problems of FOA  
Fruit flies search for food by following a scent; their position is related to the odor concentration. Individual fruit flies fly toward areas with a higher concentration of food odor. When one fruit fly finds the best position for food, other fruit flies converge toward it and start the next search. The FOA simulates this process, iterating a certain number of times to find the global optimal solution [17]. The steps of the FOA are presented as follows.  
1. Randomly initialize the starting positions of the fruit fly population, and set the maximum number of iterations maxgen and the population size.
2. Determine the position of each fruit fly based on its flight direction and search step length.
3. Calculate the concentration judgement value Si, which is the reciprocal of the distance between the position and origin coordinates.
4. Substitute Si into the fitness function, which is also the odor concentration judgement function, to obtain the odor concentration for the individual fruit fly.
5. Integrate the fruit fly population; the individual with the highest odor concentration is selected to represent the optimal fruit fly position.
6. Save the best odor concentration value; find the coordinates of the individual with the best odor concentration value, and let the other fruit flies fly in that direction.
7. The model begins iterative optimization, repeating Steps 2-6 and assessing whether the odor concentration is better than that of the previous iteration. If it is better, continue to search; otherwise, maintain the position of the previous generation of fruit flies and end the algorithm.  
The FOA has some shortcomings in optimization problems, indicated in Step 2. The optimization step size set by the FOA during the fly search for food is a fixed value. This leads to weak information exchange between individual flies in the algorithm and limits the search ability of the fly to some extent. This results in individuals with small search iteration steps converging slowly in the fly population, whereas those with large search iteration steps oscillate repeatedly in later iterations, leading to a decrease in the local optimization ability of the model. Thus, differences between individual flies and their positions should be fully considered to improve the search ability of the FOA.
## 3 Optimization and Analysis
### Corner Optimization Theory
Second, the error caused by a single small corner is not big; its main influence lies in the error accumulation of all corners in the whole circle. According to the above characteristics, the following optimization method is proposed: extend the original vertex B by d along the direction of AB, and the new point B' obtained will replace point B. According to Figure 6c, the fiber section is simplified to a point and the nozzle radius is reduced to d accordingly, to meet the requirements of the original motion mechanism. A mathematical model is established for the actual motion trajectory of the fiber at the corner, which meets the following requirements:  
\[ \begin{array}{l}(x-vt)^{2}+y^{2}=R^{2}\\ \frac{vt^{\prime}}{x^{\prime}}=\frac{y}{(x-vt)}\\ x(0)=n1\\ y(0)=n2\end{array} \]  
\(x\) is the \(x\) coordinate at time \(t\) of the fiber, \(y\) is the \(y\) coordinate at time \(t\) of the fiber, \(x\) is the \(x\) coordinate at time \(t\) of the nozzle center, \(y\) is the \(y\) coordinate at time \(t\) of the nozzle center,  
Figure 6: (**a**) Acute angle. (**b**) Optimize the effect of the acute angle. (**c**) Schematic diagram of a general corner fiber track. the R-value is d, \(v\) is the nozzle moving speed, and \(n1\) and \(n2\) are determined by corner angle and are known values that can be calculated. According to the above formula, the moving direction of the nozzle at each inflection point should be the tangent direction of the point on the arc. The position should be ahead of the original vertex d in that direction. The tangent direction should be the average of the direction vectors of the front and rear edges of the vertex. Since the corners of the small arc are obtuse angles, the difference between the direction vectors of the front and rear sides of the vertex is small, so the direction of the front edge of the vertex can be selected as the compensation direction. There are still errors in this optimization method, but the error is far less than d when the corner is obtuse, which belongs to the acceptable range. The flowchart for optimizing different printing angles by using the corner optimization algorithm is shown in Figure 7.
## 4 Experiments
### Comparison between FastSLAM and FastSLAM-PSO
#### 4.2.4 Time Analysis  
As shown in Table 1 the experimental results indicate that while FastSLAM achieves the fastest mapping times across all scenarios, its performance is compromised in terms of accuracy and robustness. In contrast, FastSLAM-PSO and FastSLAM-DE, despite having longer mapping times, offer significantly better performance. Notably, FastSLAM-PSO outperforms FastSLAM-DE, suggesting that the PSO algorithm is more effective in optimizing the SLAM process, particularly in complex environments. The superior performance of FastSLAM-PSO can be attributed to its ability to refine particle distribution and mitigate degradation, leading to more accurate and reliable mapping outcomes.  
These findings underscore the importance of selecting an appropriate optimization strategy for SLAM algorithms. FastSLAM-PSO emerges as a balanced choice, providing a commendable trade-off between mapping time and performance. Its enhanced capabilities make it a suitable option for applications where accurate and robust mapping is critical for safe and effective autonomous navigation.  
In summary, the comprehensive experimental evaluation across varying scales and complexities has conclusively demonstrated the superior performance of the FastSLAM-PSO algorithm in comparison to the conventional FastSLAM and its DE counterpart. The FastSLAM-PSO algorithm, with its integration of Particle Swarm Optimization, has exhibited a heightened level of accuracy and robustness in mapping and trajectory tracking. This enhancement is attributed to the ability to effectively manage the optimization of particles, thereby mitigating the degradation typically associated with traditional SLAM approaches. The global search capabilities of PSO have been instrumental in refining the particle distribution, leading to more reliable and precise estimation of the robot pose and the surrounding environmental landmarks. The experimental outcomes, which include comparisons across small-scale, large-scale, and square scenarios, consistently show that FastSLAM-PSO maintains its superiority in terms of mapping quality and localization accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **FastSLAM** & **FastSLAM-DE** & **FastSLAM-PSO** \\ \hline Scene\_1 & 4101.973994 & 7867.448255 & 7140.355758 \\ Scene\_2 & 7334.884456 & 16,145.49226 & 15,157.19925 \\ Scene\_3 & 6159.537662 & 12,651.76538 & 11,456.88336 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The time analysis results.  
Figure 7: Comparative results of FastSLAM, FastSLAM-PSO and FastSLAM-DE in the large-scale scenario scene_3.
## 4 Data Diversity of UAV
### Variability of UAV Databases
#### 4.2.1 Data Types  
UAV datasets include RGB images, infrared images, depth maps, and multispectral and hyperspectral images [50]. To capture complex scenarios for human behavior analysis, the UAV-Human dataset, for example, combines RGB videos, depth maps, infrared sequences, and skeleton data.
## 2 The ProbPlot via IR-AVPCA Technique for Bearing Fault Detection and Diagnosis (BFDD)
### Discussion
#### 3.3.2 Under Variable Speed Environment  
Under variable speed environment, which is a more challenging though more realistic situation, the AVPCA was capable of detecting the bearing fault with 68% and 98.67% success rate for the different features generating methods (image of raw vibration data, signal, and image of the ProbPlot of the vibration signal), respectively, and could diagnose those detected fault with a 66.67% and 95.56% average success rate. In contrast, the classical PCA did detect the bearing fault with only a 49.33% and 81.33% success rate, respectively, for the two features generating methods and did diagnose the detected faults with only 37.78% and 76% average success rate, respectively.  
Furthermore, these results show clearly the advantage of generating the features (images) using the proposed ProbPlot technique (98.67% success rate for fault detection and 95.56% average success rate for fault diagnosis) compared to directly considering the image of the raw vibration signal (68% success rate for fault detection and 66.67% average success rate for fault diagnosis) under variable speed environment.  
In addition, the proposed ProbPlot technique is more suitable for online bearing fault detection and diagnosis and for being adapted to real industrial applications, since it does not require any computation or space compared to the use of the FFT of the vibration signals, and since it can detect and diagnose the different bearing faults under nonstationary conditions, unlike the FFT.