esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 3 Width-Adaptive Autoencoder
### Proposed Approach
Solving for \(RN(n,l,b)\), we obtain the following closed-form expression:  
\[RN(n,l,b)=\frac{\frac{1}{T(n,l,b)}\frac{1}{q^{-1}}}{\sum_{n=1}^{NN(l,b)}\frac{ 1}{T(n,l,b)}\frac{1}{q^{-1}}} \tag{6}\]  
Figure 3: The block diagram of the proposed system. where \(T(n,l,b)\) represents the accumulated squared deviation of inner product preservation for node \(n\):  
\[T(n,l,b)=\sum_{b}^{NB(b)}\sum_{i\in b}\sum_{j\in b}\bigl{(}x(i).x(j)-x^{\prime}(n,l,i,b).x(n,l,j,b)\bigr{)}^{2} \tag{7}\]  
Thus, nodes with smaller \(T(n,l,b)\) values are those that best preserve input similarity and are assigned larger relevance weights. * Interpretation and Utility  
The learned relevance weights \(RN(n,l,b)\) provide an interpretable measure of each node's utility. In fact, a high weight means that the node meaningfully contributes to preserving structural information in the data. Alternatively, a low weight means that the node contributes little and can be considered for pruning. This process facilitates the dynamic adjustment of the AE's width by pruning less relevant nodes and adding new ones as needed, making the model more efficient and adaptive. The proposed approach is illustrated in Algorithm 1, which illustrates the stages in determining each node's relevance weights. ```
Input: input instances \(\{x_{i}\}\)  Mapping of the instances \(\{x^{\prime}_{i}\}\) Output: Nodes' relevance weights \(\{w_{i}^{b}\}\)  Compute \(T(n,l,b)\) for each node using (7)  Compute the nodes' relevance weights for a single layer \(\mathbf{RN}(n,l,b)\) using (6)
```  
**Algorithm 1**The Nodes' Relevance Weight Learning Algorithm
# Real-Time Multidepth Multiphoton Microscopy Using Pixel-to-Pixel Focus-Switching
## 1 Introduction
However, these systems either require multiple detectors for one imaging modality or are unable to collect signal from scattering specimens. Moreover, it is problematic for these systems to obtain simultaneous TPF and SHG images effectively. Recently, we demonstrated high-speed TPF microscopy systems based on two-dimensional laser scanning [26; 27]. In this paper, we demonstrate a label-free imaging system capable of generating multimodal multiphoton signals at different focal planes simultaneously, by using pixel-to-pixel focus-switching. The major advantages of our imaging system are as follows. * Real-time multidepth microscopy using non-imaging detection enables localization of biological objects in three dimensions within scattering specimens. * As the key technique of the imaging system, the electro-optic modulator (EOM)-based fast-switching light paths are cost-effective and can be easily added to any commercial multiphoton microscope without internal modification. * Simultaneous label-free TPF and SHG imaging can gain functional and structural information together from unstained samples and eliminate possible artifacts from sample motion or laser power fluctuation. We demonstrate the capability of the imaging system by performing real-time multidepth multiphoton imaging of freshly excised mouse lung lobes at 870 nm excitation. Signals are collected by detection channels at two different wavelength ranges. One is between 415 and 455 nm, and the other is between 495 and 635 nm. Multiphoton images of mouse pulmonary alveoli are processed and used to investigate their microenvironment.
## 2 Materials and Methods
### Instrumental Procedure  
IMT and CRZ samples were analyzed with the HPLC-DAD, Agilent 1100 System (Santa Clara, CA, USA) using Kinetex C18 stationary phase (150 \(\times\) 4.6 mm, particle size 3.5 \(\upmu\)m). The mobile phase with a flow rate of 0.5 mL/min consisted of 0.1% formic acid in Mili-Q water (eluent A) and 0.1% formic acid in acetonitrile (eluent B). Gradient elution began with 80% of eluent A. Within the next 6 min, the composition of eluent A decreased to 20%. After holding 20% of A for 1 min, the initial phase composition was returned and maintained for 7 min. The injection volume was 25 \(\upmu\)L. Detection was monitored at 258 nm for IMT and 270 nm for CRZ, respectively.
###### Abstract  
An effective near-field to far-field transformation using a reduced number of near-field measurements collected via a spherical scan over the upper hemisphere, due to the presence of a flat metallic ground, is devised in this paper. Such a transformation relies on the non-redundant sampling representations of electromagnetic fields and exploits the image principle to properly account for the metallic ground, supposed to be of infinite extent and realised by perfectly conducting material. The sampling representation of the probe voltage over the upper hemisphere is developed by modelling the antenna under test and its image by a very adaptable convex surface, which is able to fit as much as possible the geometry of any kind of antenna, thus minimising the volumetric redundancy and, accordingly, the number of required samples as well as the measurement time. Then, the use of a two-dimensional optimal sampling interpolation algorithm allows the reconstruction of the voltage value at each sampling point of the spherical grid required by the classical near-field-to-far-field transformation developed by [PERSON]. Numerical examples proving the effectiveness of the developed sampling representation and related near-field-to-far-field transformation techniques are reported.  
Antenna characterisation; near-field-to-far-field transformation; spherical scanning; infinite metallic ground plane; image principle; non-redundant representations of electromagnetic fields; flexible antenna modelling +
Footnote †: journal: Electronics
## 3 Current Research and Applications
### Policy and Regulations  
Research into policy and regulation for UAVs in ITSs addresses several critical areas to ensure safe and effective integration. It includes developing comprehensive frameworks for airspace integration, allowing UAVs to operate alongside manned aircraft while establishing clear guidelines for flight paths, altitude limits, and no-fly zones [102]. Safety and security standards are paramount, focusing on collision avoidance technologies, data privacy, and cybersecurity measures [103]. Additionally, regulations are being crafted for urban air mobility, managing UAV operations in densely populated areas, and overseeing infrastructure such as vertiports. Compliance and enforcement systems are essential for monitoring UAV operations and ensuring adherence to regulations. Public engagement and international collaboration are also crucial, aiming to align global standards and address public concerns [104]. These efforts collectively aim to balance the benefits of UAV technology with the necessary safety and regulatory safeguards, promoting UAVs' seamless integration into existing transportation systems.
## 5 Conclusions  
In this work, we presented a ground truth dataset of UML diagrams free from duplicates and mislabeled elements, addressing the prevalent issue of repeated elements within existing literature datasets, where it is common to find repeated images based on their content, even if the files are not identical. Our dataset, comprising a total of 2626 images from six UML diagram types (426 activity diagrams, 636 class diagrams, 352 component diagrams, 357 deployment diagrams, 435 sequence diagrams, and 420 use case diagrams), all correctly assigned to their corresponding classes and with no duplicate elements, constitutes a valuable resource for the research community.  
The experiments we carried out to show the utility of our dataset, which consist of training different deep learning models, not only demonstrate that our dataset constitutes a valuable resource for the community but also highlight the potential of ViTs for UML diagram classification, setting our approach apart from others that predominantly used CNNs for similar tasks. Our findings indicate that our dataset, devoid of duplicate elements, outperforms previous studies in terms of the F1-score and achieves remarkable accuracy. Although our accuracy is the second best overall, it is noteworthy that the highest accuracy reported comes from a study that classifies only four types of UML diagrams, includes a fifth class of non-UML images, and uses a dataset with 9% duplicate elements. Finally, it should be noted that the insights gained from this work will serve as a foundation for future research, where we plan to use the best-performing classifiers to automatically expand and enhance our ground truth dataset.  
Conceptualization, V.M., J.L. and A.G.; methodology, J.T.; software, J.T.; validation, J.T., V.M., J.L. and A.G.; formal analysis, J.T.; investigation, J.T.; resources, J.T. and J.L.; data curation, J.T.; writing--original draft preparation, J.T. and A.G.; writing--review and editing, J.T., V.M., J.L. and A.G.; visualization, J.T. and A.G.; supervision, V.M. and A.G.; project administration, J.T., V.M. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset presented in the study is openly available in Zenodo. [[https://doi.org/10.5281/zenodo.13831663](https://doi.org/10.5281/zenodo.13831663)]([https://doi.org/10.5281/zenodo.13831663](https://doi.org/10.5281/zenodo.13831663)), accessed on 22 November 2024.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNNs & Convolutional Neural Networks \\ DenseNets & Densely Connected Neural Networks \\ MLP & Multi-Layer Perceptron \\ ReLU & Rectified Linear Unit \\ RAM & Random Access Memory \\ STTL & Semi-Trainable Transfer Learning \\ UML & Unified Modeling Language \\ ViTs & Vision Transformers \\ \end{tabular}
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 3 Path Planning Problem
### Objectives
#### 3.1.3 Path Safety  
Firstly, the secure interval is defined. It is the minimum distance of the path from the nearest obstacle. The barrier set (\(O=O_{1},O_{2},...,O_{m}\)) contains all the obstacles in the space; the number is \(m\). \(MinDis(\overline{p_{i}p_{i+1}},O_{j})\) is the minimum length between the line segment (\(\overline{p_{i}p_{i+1}}\)) and the obstacle (\(O_{j}\)). Thus, \(\underset{0\leq i\leq n\leq l\leq m}{Min}\left\{MinDis(\overline{p_{i}p_{i+1} },O_{j})\right\}\) is the safe interval. For convenience, the maximum safety interval problem is converted to a minimum problem. The general operation is to add a negative sign  
Figure 1: The representation of a path is given.  
before the safe distance to convert the function to a negative number. The safety objective of the path can be expressed by Equation (3):  
\[Safety(p)=-\underset{0\leq i\leq n1}{\underset{1\leq j\leq m}{\text{Min}}}\{ MinDis(\overline{p_{i}p_{i+1}},O_{j})\}. \tag{3}\]  
At this time, a model of path planning is presented below:  
\[\left\{\begin{array}{l}p=[S=p_{0},p_{1},p_{2},...,p_{n},p_{n+1}=T]\\ \text{Minimize }f_{1}(p)=Length(p)\\ \text{Minimize }f_{2}(p)=Smoothness(p)\\ \text{Minimize }f_{3}(p)=Safety(p).\end{array}\right. \tag{4}\]  
In the above model, the only constraint is that the path does not collide with the obstacle, but it is allowed to reach the edge of the obstacle. If the path intersects the obstacle, then it is not feasible. The edge of the workspace is also regarded as an obstacle.  
In general, at least two goals are contradictory. That is to say, one or more other objectives have to be sacrificed while increasing one objective. This problem is a typical multi-objective optimization problem. There is no longer one best solution, but a set of solutions. Moreover, these solutions are non-dominated. In other words, when one or more objectives of one solution are better than the other, there must be at least one or more poor objectives. In recent years, some surpassing evolutionary algorithms have emerged for solving path planning problems. However, due to the complexity and practicality of this issue, many scholars remain interested.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Optimization Based Methods
#### 3.3.2 [PERSON]'s Method  
In Reference [132], the differential evolution particle swarm optimization (DEPSO) algorithm combined by differential evolution and PSO is proposed to design the FIR filter. In the DEPSO algorithm, the offspring is generated by the nutation of the parent, the Gaussian distribution is considered and the parent can be represented by the _gbest_. For nutation, there are four random particles chosen from the population to produce an offspring. A mutation operator is expressed as below  
\[if\ (rand<RR\ or\ d==m) \tag{57}\] \[T_{i,d}=gb_{d}+\delta_{2,d},\] \[\delta_{2,d}=\frac{(pb_{1,d}+pb_{2,d})+(pb_{3,d}+pb_{4,d})}{2}, \tag{58}\]  
where \(RR\) is the reproduction rate, \(rand\) is a random number in the range of \([0,1]\), \(m\) means a dimension which is randomly chosen, \(T_{i,d}\) is the offspring and \(\delta_{2,d}\) is the weighted error in different dimensions.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 6 Performance Evaluations
### Simulation Setup
#### 6.1.3 Comparing Algorithms  
Based on the aforementioned review and discussion, we select four comparing methods depending on related works to evaluate their performance:  
* Linear regression: Based on the confidence interval (\(CI=\pm 10\%\)), linear regression analyzes the distribution of the observed data in each time interval and uses the linear relation to express and smooth the curve, which predicts the traffic flow's trend. The algorithm will shorten the time interval once the prediction leaks the \(CI\) area.
* Kernel regression: Depending on the core of the support vector machine, kernel regression lies at the Gaussian process to label the observed and historical data. The Laplacian matrix is used for predicting and recovering the results.
* Fourier series: The intervals and prediction intervals of the observation process are divided evenly. The traffic flow trend in the observation process is fixed and predicted based on the Fourier series' parameter determination. The Fourier method improves the prediction performance via the increment of the Fourier series degree and the adjustment of parameters.
* LSTM (long short-term memory): Besides the above three comparing algorithms, LSTM sets up a single-input-and-single-output recurrent neural network with 96 neural cells. During the training ratio determination and 250 iterations, LSTM can output the predicted results. Because the selected LSTM model needs some extent of observations as the training data, comparison with our proposed algorithm is separated.
# Local Ecological Knowledge (LEK) Can Guide Decision-Making in Inland Fisheries Management
## 4 Discussion
### Demographic Data  
The fact that almost all interviewed fishers were born in the broader area of the reservoir's basin indicates a strong connection with the region and its resources. The gradual aging trend observed in the fishers' population in other European marine and freshwater fisheries [18; 25] was not evident here, indicating that the fishery in the reservoir had significant untapped economic potential. However, the historical trend of passing down the fishing profession from generation to generation [26] was observed, as more than 43.2% of the fishers had fathers who were either exclusively or partially involved in fishing. The fact that fishers with children preferred their offspring not to pursue a fishing career indicates their concerns about the sustainability or economic viability of the profession and sets the stage for a potential upcoming inter-generational discontinuity in the fishery. Similar trends have also been noted in other Greek freshwater ecosystems [18; 24], as well as throughout Europe, where inland fisheries, in general, are in decline [27]. This decline results from decreasing fisheries' productivity due to overfishing, habitat degradation, low demand for freshwater fish, and, subsequently, low economic productivity leading to an increased marginalization of freshwater fisheries [16; 18; 27].  
Given that the research targeted professional fishers, fishing activity was expected to be the sole or primary source of income. However, the findings revealed a notable diversity among fishers in terms of their reliance on fisheries. Specifically, fishers here could be categorized as full-time, part-time, or even subsistence fishers. This diversification is a common characteristic of small-scale European inland fisheries [18; 27], driven by the need for supplementary income that motivates individuals to engage in multiple activities.  
Participants exhibited a varied range of educational backgrounds, from elementary school to university degree holders. This diversity underscored the heterogeneous educational experiences within the fishing community. From a socio-economic standpoint, it also highlighted the challenges associated with securing employment in rural areas of Greece. In these regions, individuals with higher education degrees are often compelled to engage in agriculture or fisheries, due to a scarcity of job opportunities commensurate with their qualifications [28]. Collectively, these factors create an environment where flexible arrangements can be more easily established and maintained.
## 5 Simulation
### Experimental Setup
#### 5.1.3 Implementation and Hyperparameter  
To facilitate efficient communication between IoT devices, edge nodes, and the cloud server, we implemented WebSocket as the communication protocol. WebSocket provides a full-duplex communication channel over a single TCP connection, enabling low latency and efficient data exchange. This choice ensures that the asynchronous nature of updates from heterogeneous devices is effectively handled, which is critical in federated-learning scenarios.  
The local model training on each IoT device was implemented using TensorFlow, an open-source machine-learning library. Encryption of model updates during transmission was achieved using the SALSA20 stream cipher, which ensures privacy without introducing significant computational overhead. Figure 2 illustrates the experimental setup, showing four Raspberry Pi units simulating IoT devices alongside a control interface reflecting their real-time operation in tandem with edge and cloud environments. This setup mimics real-world IoT conditions, encompassing resource heterogeneity and network instability.  
The parameters in Table 2 are designed to balance the scalability, robustness, and efficiency of the hierarchical federated-learning framework. Key parameters such as \(T=50\) and \(E=1\) ensure a balance between computational efficiency and the framework's ability to achieve convergence under realistic conditions. The learning rate (\(\eta=0.01\)) and batch size (\(B=32\)) are empirically selected, adhering to best practices for IoT environments to ensure consistent local model training across heterogeneous devices. Parameters, such as \(\alpha_{\text{edge}}\), \(\beta_{\text{edge}}\), \(\beta_{\text{cloud}}\), \(\eta\), and \(B\) align with practices commonly employed in FL setups and have been fine-tuned for hierarchical IoT environments. The dynamic parameters, including \(p_{e}\) and \(w_{window}\), adapt in real time during training, ensuring responsiveness to system variations while maintaining overall stability.  
For example, dynamic parameters \(p_{e}\) and \(w_{window}\) are initialized with reasonable defaults but adapt over time. \(p_{e}\) captures actual device participation rates and updates in each round, while \(w_{window}\) adjusts based on the gap between the desired (\(p_{\text{desired}}=0.75\)) and actual participation rates. These mechanisms provide flexibility and responsiveness in real-world IoT scenarios with varying device conditions. Edge-level and cloud-level aggregation weights (\(\alpha_{\text{edge}}=0.7\), \(\beta_{\text{edge}}=0.3\), \(\beta_{\text{cloud}}=0.5\)) were determined based on initial experiments. These values emphasize historical stability, incorporate current updates effectively, and balance participation frequency with model quality at the cloud level. The fixed model quality metric (\(q_{e}=1.0\)) simplifies this study, focusing on participation rates (\(p_{e}\)) as a primary indicator of aggregation dynamics.
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 3 Methods
### Statistical Analysis
#### 3.2.2 Assumption and Diagnostic Test  
Assumption and diagnostic tests ensure the reliability and accuracy of regression model predictions. Figure 4 presents the diagnostic procedure.  
Homoscedasticity and normality of residuals are key assumptions tested. The Breusch-Pagan test diagnoses heteroscedasticity by regressing squared residuals against predicted values to detect variance patterns [42]. A residual vs. fitted values plot is also generated to visually assess homoscedasticity. Randomly scattered residuals indicate homoscedasticity, whereas fan-shaped distributions suggest heteroscedasticity, necessitating corrective measures such as data transformation or robust standard errors.  
The Shapiro-Wilk test evaluates residual normality, producing a W-statistic that assesses the correlation between ordered residuals and their expected normal distribution values [43]. A Q-Q plot visualizes residual normality, where a straight diagonal line confirms normality, and deviations suggest non-normality. In response to potential assumption violations, additional remedial strategies are implemented to evaluate and improve model robustness. A logarithmic transformation and Box-Cox transformation are applied to the dependent variable to stabilize variance and improve residual normality. The Box-Cox method determines the optimal transformation parameter (\(\lambda\)), with values close to 1 indicating minimal need for transformation. In parallel, a robust regression model using the Huber loss function is applied to mitigate the influence of outliers and non-constant variance. To complement these parametric adjustments, residual bootstrapping is performed, generating resampled distributions of residual means to evaluate model stability without relying on strict normality assumptions. These diagnostic and corrective procedures are conducted in anticipation of assumption violations and are further supported by visualizations in Section 4. These tests ensure that violations are detected and mitigated to enhance model reliability.  
Figure 4: Flowchart of assumptions and diagnostic tests.
# Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer
###### Abstract  
In this paper, we investigate a design method for a cable-driven snake arm maintainer (SAM) and its dynamics modelling. A SAM can provide redundant degrees of freedom and high structural stiffness, as well as high load capacity and a simplified structure ideal for various narrow and extreme working environments, such as nuclear power plants. However, their serial-parallel configuration and cable drive system make the dynamics of a SAM strongly coupled, which is not conducive to accurate control. In this paper, we propose an equivalent dynamics modelling method for the strongly coupled dynamic characteristics of each joint cable. The cable traction dynamics are forcibly decoupled using force analysis and joint torque equivalent transformation. Then, the forcibly equivalent dynamic model is obtained based on traditional series robot dynamic modelling methods (Lagrangian method, etc.). To verify the correctness of the equivalent dynamics, a simple model-based controller is established. In addition, a SAM prototype is produced to collect joint angles and cable forces at different trajectories. Finally, the results of the equivalent dynamics control simulation and the prototype tests demonstrate the validity of the SAM structural design and the equivalent dynamics model.  
snake arm maintainer; cable-driven; equivalent dynamic; special environment application