esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
###### Abstract  
Context: Respiratory morbidity is a leading cause of children's consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children's breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human-artificial intelligence (AI) tool is an essential component for screening and monitoring young children's respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children's breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features--including Mel-Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)--were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted.  
breath sound classification; respiratory health monitoring; digital signal processing; feature extraction; machine learning classifiers; performance assessment
## 1 Introduction
\tag{6}\]  
where \(\rho_{a}\) is the concentration of deposition in the active zone (m\({}^{3}\)/m\({}^{3}\)), \(\rho_{p}\) is the concentration of deposition in the passive zone (m\({}^{3}\)/m\({}^{3}\)), \(c_{0}\) is the inlet suspension concentration, \(\beta_{a}\) is the coefficient characterizing the kinetics in the active zone (1/s), \(\beta_{p0}\) is the kinetic coefficient (1/s) of passive zone and associated with the effect of compaction (aging) of the deposition, \(\rho_{p1}\) is the value of \(\rho_{p}\) at which \"aging\" begins, \(\rho_{a0}\) and \(\rho_{p0}\) are the capacities of the active and passive zones, respectively, the total filter capacity is \(\rho_{0}=\rho_{a0}+\rho_{p0}\). Here, at the beginning of deposition formation, kinetic coefficient of passive zone \(\beta_{p}=\beta_{p0}\). Starting with a certain concentration of \(\rho_{p1}\), the value of \(\beta_{p}\) becomes less than \(\beta_{p0}\), and a further decrease in \(\beta_{p}\) goes inversely with the amount of deposition \(\rho_{p}\). Finally, when the deposition concentration is close to saturation, \(\beta_{p}\) decreases more rapidly. This section is approximated by a step (similar to the approximation of strongly convex isotherms by a rectangular isotherm in the dynamics of sorption) [34]. So the function \(\rho_{p}(x,t)\) is continuous, but its first derivation has first type discontinuity at point \(\rho_{p}=\rho_{p0}\). Most of real suspensions are multicomponent. The solid particles suspended in the suspension can differ in their properties, that is to say, they can have different geometric, physicochemical and electrokinetic characteristics [38; 34; 39]. Many recent works takes into account the polydispersity of suspension [6; 40; 41; 42; 43; 44; 45]. Two-component suspensions consisting of two disperse components with different concentrations and kinetic properties represent the simplest form of a multicomponent suspension. In most cases two component suspension have been studied [41; 42; 43; 44; 45]. As we mentioned above, several varieties of phenomenological relationships have been obtained to describe the flow of various types of disperse systems, but suspension polydispersity and multistage kinetics have not been taken into account together. In this paper, we formulate a problem of two component suspension filtration in a dual-zone porous medium with multistage deposition kinetics. The problem is numerically solved. Influences of suspension polydispersity, dynamical factors and other model parameter on filtration characteristics are studied.
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 6 Discussion
### BN Parameter Issue
#### 6.1.1 Description of EM Algorithm  
**EM** is a classical algorithm used to learn and estimate the parameters (CPT probability) of BN under incomplete observed data. **EM** is an iterative algorithm that works in two steps **step E** et **step M**[40].  
* **Step E (expectation)**: estimate the missing values from the current parameters \(\theta^{r}\). For each BN node V and for each sample of the incomplete database, we compute the conditional probability P(V\({}_{\text{missed}}\mid\text{V}_{\text{measured}}\)) (Equation (5)). The computation of these probabilities is based on the inference process using generally the junction tree algorithm. \[N(V_{i}=v_{k},pa(V_{i})=v_{j}|V^{0},\theta^{r})=\sum_{t=\text{L} \text{-}\text{T}}\sigma^{t}_{i,j,k},\forall j,k.\] (4) \[\sigma^{t}_{i,j,k}=P(V_{i}=v_{k},pa(V_{i})=v_{j}|V^{0},\theta^{r})\] where \(V^{0}\) indicates the evidence of the whole measured data and pa(V\({}_{\text{i}}\)) = v\({}_{\text{j}}\) represent the status of the parent node of V\({}_{\text{i}}\). \(\sigma^{t}_{i,j,k}\) is the conditional probability at time t of V\({}_{\text{i}}\) and its parent knowing the value of the observation about V and \(\theta\). Finally, N represents the sum of the probabilities of all the instances of node V\({}_{\text{i}}\).
* **Step M (maximization)**: re-estimate the parameters from the results obtained from step E by applying the maximum likelihood or the maximum a posteriori approaches. The most used approach is the maximum likelihood. Therefore, the probability at the next time step is equal to the occurrence of V\({}_{\text{i}}\) divided by the sum of all the occurrences of V\({}_{\text{i}}\) across the whole database (Equation (5)). \[\theta^{r+1}_{i,j,k}=\frac{N_{i,j,k}}{\sum_{k}N_{i,j,k}}\] (5)  
The effectiveness of the EM algorithm depends on the rate of missing data in the database. In previous work [39] we evaluate the accuracy of the learned probability with EM in the case of 50% and 80% of missing data in the database. The results show that the learned probability is more accurate when we have a lot of information in the database.
## 2 Literature Review on the Personalized Recommendation in the MOOC Context
### Content-Based Recommendation Algorithms  
Content-based recommendation algorithms primarily center on information screening and filtering mechanisms. A direct correlation between items or content was found, and based on user preferences and historical behavior records, relevant items or contents were recommended to users.  
Content-based personalized recommendations on MOOC platforms are based on learners' course preferences embedded in both the relevant content searched and course learning records. Courses with high relevance are then recommended to matched learners. An advantage of this recommendation algorithm is its easy implementation. A content-based personalized recommendation only requires a learner's course information, learning records, and search content. The proposed content-based MOOC recommendation algorithm has a higher accuracy rate than random recommendations. Notably, the richer the course information, the more accurate the recommendations [10; 11].  
However, content-based recommendation algorithms have some obvious disadvantages. This is because the recommendations based on learners' historical behaviors are likely to be too consistent, resulting in a high degree of similarity between the recommended courses and the courses that learners have already studied. Consequently, exploring new interests using this content-based recommendation algorithm is difficult.
## 2 Materials and Methods
### Macroscale Experimental Tests  
Fluorescence and absorbance measurements were conducted using an Avantes AvaSpec-ULS2048x64-EVO spectrometer, which operates over a broad spectral range from 200 nm to 1100 nm. The excitation wavelength was set to 590 nm, selected based on the measured absorption profile of CRNAAD-2 (Figure S1 in Supplementary Materials). The excitation source consisted of a specialized color LED (HLMP-EL10-13K00) configured to deliver a stable and uniform light output. The emitted fluorescence spectrum was collected using an optical fiber. A long-pass optical filter (Thorlabs FGL695) with a 695 nm cut-off wavelength was also integrated into the detection pathway to block the excitation light. To verify the filter's proper performance and ensure accurate fluorescence measurements, e.g., suppressing the excitation light, transmission tests were conducted (Figure S2 in Supplementary Materials) to confirm its expected behavior, as any deviation in filter performance could significantly impact the fluorescence results. Measurements were performed using an Avantes Cuvette support holder (Avantes CUV-ALL-UV/VIS), ensuring consistent sample positioning and minimizing alignment errors between samples. Additionally, the excitation pathway was positioned at a 90\({}^{\circ}\) angle relative to the emission or detection pathway to minimize stray light from the source that could interfere with the fluorescence signal.
## 2 Methodology
### Data Analysis and Processing
#### 2.1.3 Data Processing  
In the data processing section, experimental data need to be subjected to STL seasonal decomposition, dataset division, normalization, and sliding window operations.  
1. STL Seasonal Decomposition:  
In this step, the original data are decomposed into a trend component, a seasonal component, and a residual component, which is discarded directly because it is difficult to predict the residual component. Then, the trend component and seasonal component are integrated into the original dataset to form the trend dataset and seasonal dataset, respectively.  
2. Dataset Division  
The data from the eight groups in the park are divided into training, validation, and test sets at a ratio of 6:2:2. The division ratio has a great impact on the training effect and generalization of the model. To ensure the model is evaluated on completely unseen data, thus preventing data leakage, the datasets should be split before data normalization.  
3. Normalization  
The normalization process stabilizes the scale of the data values, which is particularly beneficial in speeding up the gradient descent during model training. By normalizing the data before prediction, we ensure that the test set and the training set are on a consistent scale. In this paper, we use the maximum-minimum normalization method to scale the original data to between [0, 1], and the calculation formula is as follows:  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{2}\]  
where \(x\) is the original data, \(\min(x)\) and \(\max(x)\) are the minimum and maximum values in each column, and \(x^{\prime}\) is the original data mapped to values in the range [0, 1].  
4. Sliding window  
The learning method of ST-LSTM-RR is supervised learning; each data sample should consist of input X and output y. The data should be transformed into the form of feature label pairs. Therefore, it is necessary to transform the building heat load data into the form of feature-label pair data for the supervised problem first. In this paper, a sliding window is used to transform the heat load data into feature label pair data.  
Figure 6: Residuals for each group.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 1 Introduction  
### Research Background and Significance  
With the continued development of urbanization, intelligent logistics, and emergency response systems, terrestrial transportation networks have become increasingly complex and dynamic. Efficient path planning is essential for improving transportation efficiency and ensuring safe, adaptive navigation across variable environments. Early research has explored foundational approaches including A*, Dijkstra, heuristic search, and optimization-based techniques [1, 2, 3, 4], as well as comprehensive reviews on mobile robot navigation.  
Recent studies have aimed to overcome limitations in static, single-objective planning by enhancing classical methods. For example, improved variants of the A* algorithm, such as chaos-based or hybridized models, have been shown to improve convergence in known-obstacle scenarios [5, 6]. Deep reinforcement learning (DRL) approaches have further expanded the solution space, enabling agents to optimize trajectories under uncertainty [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16].  
Several domain-specific applications--including UAV patrol [17], agricultural drone routing [12], disaster rescue path estimation [18], and multi-missile or multi-agent tactical planning [14]--demonstrate the flexibility of DRL-enhanced planners across high-risk, multi-objective environments. In parallel, advancements in data-driven frameworks have emphasized the importance of integrating spatiotemporal data and multi-source fusion [19, 20], including graph-based representations of urban road networks.  
Beyond planning, robust localization under real-world constraints has also been explored. CNN-based outdoor positioning using 3D sensing [21] and deep learning models tailored for weather-varying conditions [22] underscore the need for perception-aware path optimization.  
In contrast to most existing studies, which are primarily designed for autonomous platforms, our research focuses on path planning for human-driven urban vehicles. We address the practical need for dynamic route guidance that adapts to real-world constraints--such as congestion, signal delays, and restricted zones--while remaining computationally tractable and interpretable.  
To this end, we propose a hybrid path planning framework that integrates multi-source heterogeneous data--such as satellite elevation, road topology, and real-time traffic--with a global-local algorithmic structure. Specifically, a dynamic heuristic A* module provides efficient global planning, while a Deep Q-Network (DQN) enables local adjustment based on context-aware priorities. This design supports intelligent, multi-objective route optimization in complex urban scenarios and contributes to the development of practical and deployable planning systems.
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 3 Planning and Design of Impedance-Passivity Controller (IPC)
### Impedance Passivity Controller for Haptic Feedback  
The mathematical models of the blocks of Figure 1 are given as follows  
\[G(s)=\frac{1}{ms^{2}+bs},\ G_{z}(s)=\frac{1-e^{-Ts}}{s} \tag{7}\]  
\[E(z)=K+B\frac{1-z^{-1}}{T},\ C(z)=\alpha\frac{1-z^{-1}}{T} \tag{8}\]  
in which \(m\) is the mass, and \(b\) is the damping coefficient of haptic device. For the discrete-time models, \(T\) is the sampling period.  
Let \(H(z)=E(z)+C(z)\). It can be obtained that  
\[H(z)=K+(B+\alpha)\frac{z-1}{Tz} \tag{9}\]  
Based on the passivity theorem addressed in [20], it can be derived that the control scheme of Figure 1 is passive if the following inequality holds  
\[b>\frac{T}{2}\frac{1}{1-coswT}\mathrm{Re}\big{\{}\big{(}1-e^{-jwT}\big{)}H \big{(}e^{jwT}\big{)}\big{\}},\ 0\leq w\leq w_{N} \tag{10}\]  
The last term of (10) can be rewritten as  
\[Re\big{\{}\big{(}1-e^{-jwT}\big{)}\big{(}K+\big{(}B+\alpha\big{)}\frac{e^{jwT }-1}{Te^{jwT}}\big{)}\big{\}}=(1-coswT)K+\frac{(B+\alpha)}{T}(-2)coswT(1-coswT) \tag{11}\]  
Substituting (11) into (10), the stability condition can be obtained as  
\[b>\frac{TK}{2}-(B+\alpha)coswT,\ 0\leq w\leq w_{N} \tag{12}\]  
The (12) is maximized at \(w=wN=\pi/T\), leading to the following sufficient condition:  
\[b>\frac{TK}{2}+(B+\alpha) \tag{13}\]  
To achieve passive stability, this result shows that we can find the stable range of the controller parameter provided with the given values of \(K\) and \(B\).
## 3 Methodology
### Filter Method  
The filter technique, as described by [PERSON] and [PERSON] (2009), is a feature selection method based on the statistical characteristics of data that involves filtering out insignificant features [30]. The primary advantage of the filter method is its high-speed operation, although it tends to demonstrate lower effectiveness in terms of performance impact compared to other techniques. In this paper, we conducted experiments applying the Chi-square test, mutual information, ANOVA F-value, variance threshold, Fisher score, and MultiSURF algorithms to the filter technique.  
Chi-square Test. This method, as detailed by [PERSON] (2005), identifies relationships between categorical variables through the difference between observed frequencies and expected values [31]. Features are ranked based on statistical significance tests, with selection favoring those dependent on the class label [32].  
Mutual Information. As [PERSON] and [PERSON] (2004) explain, this methodology measures the mutual dependence between two probability features [33]. Greater mutual dependence suggests that selecting one feature provides significant information about another, highlighting the importance of the selected feature [34].  
ANOVA F-value. One statistical method for evaluating differences between data groups is ANOVA, which, according to [PERSON] (2011), assesses the significance of differences by analyzing the variance levels within groups [35]. This approach measures feature similarity and identifies significant features, thereby reducing the high dimensionality of the feature space [36].  
Variance Threshold. This feature selection technique, described by [PERSON] and [PERSON] (2021), eliminates characteristics that do not meet a certain feature percentile, thereby focusing on more significant features ([PERSON] et al., 2020).  
Fisher Score. This method, as [PERSON] (2001) outlines, identifies the optimal feature set that maximizes the distance between data points of different classes and minimizes the distance within the same class in the data space generated by the selected feature ([PERSON] et al., 2020; [PERSON] et al., 2020).  
MultiSURF. Based on the Relief algorithm, this methodology, as presented by [PERSON] and [PERSON] (2020), generates a threshold defining the average pairwise distance between the target instance and all other instances ([PERSON], 2001). Implementing this threshold aids in selecting features across all instance pairs in the dataset, increasing the probability of choosing optimal features.
Among these options, fiber ring architecture with the interferometer filter is particularly favored for shaping multi-wavelength lasers because of its ability to easily produce different numbers of wavelengths, as well as its adjustable wavelength spacing and positions. In the pursuit of improved multi-wavelength laser performance, it has been found that a single interferometer filter may not fully meet the requirements. This limitation arises from the potential for reduced effectiveness in suppressing mode competition due to the large FSR of the interferometric spectrum. As a solution, researchers have proposed the use of cascaded interferometer-based filter architectures [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60]. This approach involves the superposition of two interferometric spectra, which can lead to the compression of the FSR or the creation of a vernier effect. These effects are beneficial, as they can more effectively suppress mode competition or mode hopping, enabling the generation of more wavelength outputs, narrower laser line width, or facilitating the conversion of wavelength spacing and position. However, the issues with the architecture of a single interferometer filter or a cascaded interferometer filter stem from irregularities in the wavelength distribution positions of constructive and destructive interferences. These irregularities can occur within the interferometric spectrum itself or when superimposing two interferometric spectra. As a result, inconsistencies in the spacings between multi-wavelength lasers or in the spacings between wavelength tuning positions can arise. Additionally, there are instances where the FSR of the interference is restrictively tuned or is non-tunable, leading to irregular spacing changes between multi-wavelength lasers or non-tunable spacing between wavelength-tuning positions. This study aims to demonstrate a novel multi-wavelength laser system using EDFA within a fiber ring architecture. The objectives include enhancing the stability and tunability of laser wavelengths through the integration of a TDLII and a TBF, thereby addressing the challenges posed by homogeneous broadening in EDFA. The TDLI can offer two phase-complementary interferences, a strong extinction ratio, and high tunability in terms of the interference's FSR [64]. Two power-different complementary interferences can form a switchable standing wave interference by tuning FSR. This leads to a spatial hole-burning effect, causing the laser to be excited at the half-upper-waist of the lobe in the interferometric pattern. Moreover, the built-in BS of TDLI creates weak FP interferences, thus shaping a deeper layer of filtering to deepen the spatial hole-burning effect. Hence, by changing the FSR of the TDLI to create various interference superposition effects, it is feasible to generate multi-format, multi-wavelength lasers with switchable wavelength positions and spacings.
## 1 Introduction
Due to the unique characteristics of underwater environments, the effects of unevenness, diffusion, reflection, and attenuation of light sources may destabilize image-processing methods [17]. Consequently, detection and recognition may become unstable, complicating the image processing. Nevertheless, in cases of close-range object recognition and distance detection underwater, vision-based systems remain more cost effective than acoustic-based systems. [PERSON] et al. [18] developed a vision-based system for automatic docking. The docking device was fixed with the predefined lamp at the front end. Image information was acquired by the AUV front camera, and image processing was performed to identify the center coordinates of the docking device. [PERSON] and [PERSON] [19] used a charge-coupled device camera to conduct an optical flow method for target tracking. [PERSON] et al. [20] proposed a vision-based method for target-recognition evaluations by using objects that were clearly different from the color of the environmental background. In the present study, a series of object-tracking tests for the developed AUV, Bigeye Barracuda, were conducted to verify underwater visual recognition and tracking system (UVRTS) application in the towing tank of National Cheng Kung University. A light source towed by the towing carriage in the water was selected as the target object. Data for the body coordinates (3D) were mapped using the image coordinates (2D) to define the correspondence of the distance and orientation between the target and the AUV. Two fuzzy controllers were adopted for the distance and orientation of the AUV by using the object marker as the factor determining the routing strategy. The results and discussion are introduced in the subsequent sections.