esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Perceptual Audio Features Extraction Techniques
### Cepstral Domain Perceptual Features
#### 5.6.1 Perceptual Filter Banks-Based Cepstral Features
* **Gammatone cepstral coefficients:** also known as GTCC, [PERSON] _et al._ in [229; 230] proposed a filterbank based on Gammatone function that predicts human masking data accurately, while [PERSON] proposed in [231] an efficient implementation of a Gammatone filterbank (using the 4 th-order linear Gammatone filter) for the frequency analysis and resynthesis of audio signals. [PERSON] and [PERSON] derived the Gammatone cepstral coefficients feature by maintaining the effective computation scheme from MFCC but changing the Mel filter bank by a Gammatone filter bank [232]. Gammatone filters were originally designed to model the human auditory spectral response, given their good approximation in terms of impulse response, magnitude response and filter bandwidth, as described by [PERSON] and [PERSON] [212]. Gammatone-like features have been used also in audio processing (see the work of [PERSON] in [233]), in speech recognition applications (see [PERSON] _et al._[234],or [PERSON] _et al._[235]), water sound event detection for tele-monitoring applications ([PERSON] _et al._[236]), for road noise sources classification ([PERSON] _et al._[237]) and computational auditory scene analysis ([PERSON] _et al._[238]). In [206; 207] [PERSON] and [PERSON] describe an axiomatic way of obtaining Gammatone filters as a particular case of a multi-scale spectrogram when the analysis filters are constrained to be causal. They also define a new family of generalized Gammatone filters that allow for additional degrees of freedom in the trade-off between the temporal dynamics and the spectral selectivity of time-causal spectrograms. This approach represents a part of a unified theory for constructing computational models for auditory receptive fields (see a brief description in Section 5.4). * **GammaChirp filterbanks:**[PERSON] and [PERSON] proposed in [239] an extension of the Gammatone filter which was called Gammachirp filter, with the aim of obtaining a more accurate model of the auditory sensitivity, providing an excellent fit to human masking data. Specifically, this approach is able to represent the natural asymmetry of the auditory filter and its dependence on the signal strength. [PERSON] and [PERSON] [240] defined GammaChirp Cepstral coefficients (GC-Cept) substituting the typical Mel filterbank in MFCC by a Gammachirp filterbank of 32 filters over speech signals (within the speech frequency band up to 8 KHz). They showed a better performance of the new Gammachirp filterbanks compared with the MFCC in a text independent speaker recognition system for noisy environments.
## 3 Proposed Smart Route: IoV-Based Congestion Detection and Avoidance (CDA) Using Rerouting  
The proposed IoV-based CDA is designed for a particular area of interest (AOI), i.e., road intersection point. The main goal is to develop a smart transportation system that detects and avoids the congestion in real time using rerouting. At each entry point to AOI, the base station, i.e., RSU, estimates the traffic status by assessing the occupancy of the road segment taking into account the vehicle arrival rate. In any instance, if the arrival rate exceeds the average occupancy level, a congestion state is detected. Once the congestion is identified, the RSU discovers an alternate route using EG-Dijkstra, provided that the placement of those vehicles will not exceed the occupancy level of the alternative routes. The complete design of IoV-based CDA is divided into three parts, i.e., (1) conversion of the road intersection to an abstract routing graph; (2) the IoV-based Congestion Detection (IoV-CD) phase; (3) the IoV-based Congestion Avoidance (IoV-based CA) phase.  
Figure 1: System Model.
## 2 Materials and Methods
### Amazon Blowdown Event Mapping and Data Collection  
To compare the effects of spatial resolution between PlanetScope NICFI and Landsat-8 on estimating blowdown area, we selected blowdown events with similar acquisition times from both satellite datasets. Specifically, we defined threshold values of \(\Delta\)NPV (0.3 and 0.5) to quantify the blowdown areas derived from PlanetScope NICFI and Landsat-8 data, and investigated whether there were significant differences between the two datasets. Such \(\Delta\)NPV thresholds corresponded approximately to minimum and maximum values following disturbance. For GV, the average value pre-disturbance was selected for analysis.  
Additionally, because there are few available geolocation data for blowdowns from previous research, a visual interpretation of images was conducted where 45 blowdown events were identified between September 2020 and May 2022. These events showed canopy gap openings varying from less than 1 ha and up to 2026 ha. Landsat-8 and PlanetScope NICFI imagery were interpreted to vectorize the blowdown shapes as polygons in ArcGIS Pro 2.8.3 [50]. For each blowdown, the delineated polygons corresponded to the gaps left by the dead trees in the forest. The geographic reference coordinate system of the polygons was set to WGS84, and each polygon's area (i.e., geodesic) was then calculated. The first visible month on the image of each blowdown was added as a field of the attribute table. During vectorization, we qualitatively compared the visualization of Landsat-8 and PlanetScope NICFI imaging of blowdown gaps.  
Figure 2: Spectral reflectance of Landsat-8 OLI and PlanetScope NICFI endmembers.
## 3 Results
### Comparison with Other Methods  
To evaluate the performance and effectiveness of this proposal, the CNN was compared with other popular and widely adopted methods for the detection of acoustic events:  
* Traditional feature-based method (MTBC): this method is based on manually extracting acoustic features such as MFCC, spectrograms, and chromatograms, among others, and using traditional classifiers such as SVM or Random Forest for detection.
* Recurrent Neural Networks (RNN): this approach uses temporal data sequences for detection, especially suitable for temporal signals such as audio.
* 1D Convolutional Neural Network: like our proposal but processes the data only in one dimension.  
The comparison results are shown in Table 11.  
The results show that the use of a Convolutional Neural Network outperforms the other methods in all metrics, demonstrating its effectiveness in detecting acoustic events.
## 3 Federated Learning Solutions in the Medical Domain
### Federated Learning Lifecycle Management  
FL-TAVR operates efficiently through a federated lifecycle management. The FL-TAVR server is key in the deployment and management of the global model and in the real-time monitoring and maintenance of both the clients and the FL server itself. Each client was responsible for data handling and local model building. Lifecycle management is performed using the FedOps platform. Figure 3 illustrates the FL lifecycle management of FedOps.  
The FL server aggregated the parameters of the local models trained by each client to create a global model. Once the global model was generated, it underwent a performance evaluation using the test data stored on the server. After validation, the global model was redistributed to the clients and used for further prediction and learning. Through this process, the FL server manages the entire lifecycle of the global model, including aggregation, building, evaluation, versioning, and deployment. Additionally, the FL server periodically checks the status of both the clients and the server to ensure that each client is in a suitable state to participate in the federated learning process. Regular monitoring is crucial for maintaining the stability and health of federated learning environments.  
Each client manages data processing and model-building tasks, while the participating institutions preprocess and validate their data before local model training. Clients continue to follow the workflow of the centralized post-TAVR complication prediction solution, which includes collecting patient data, storing them in databases, and performing  
Figure 3: Federated learning lifecycle process.  
Figure 2: Structure of the FL-TAVR complication prediction system.  
complication prediction. Newly collected patient data were used to further train the global model, ensuring continuous learning and improvement.  
This proposed solution is meticulously formulated to perpetually improve the efficacy and dependability of the model through the enduring execution of the federated learning lifecycle. By capitalizing on the federated learning lifecycle, it systematically evolves in alignment with the collaboration of diverse institutions and the aggregation of data, thereby establishing a robust framework for the development of a sustainable artificial intelligence solution.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 2 Background on Architectures for Remote Sensing Data Integration
### OODT: A Data Integration Framework  
An FS or DBMS alone are not suited for the storage and management of remote sensing data. In a \"DBMS-FS mixed management mode\", remote sensing images are stored in the file system and their metadata are stored and managed by the DBMS. Typical examples are the European Space Agency (ESA) [19], Tiandi Maps of China, the CCRSDA, the NSMC, the China National Ocean Satellite Application Center (NSOAS), and so on. The mixed management mode both effectively solves the quick retrieval and metadata management problems and maintains the high read/write efficiency of the file system. This has been a longtime issue addressed by NASA, whose the Office for Space Science decided to fund the OODT project in 1998.  
Apache OODT [20] is an open-source data system framework that is managed by the Apache Software Foundation. OODT focuses on two canonical use cases: big data processing [21] and information integration [22]. It provides three core services: (1) a file manager is responsible for tracking file locations and transferring files from a staging area to controlled access storage, and for transferring their metadata to Lucene or Solr; (2) a workflow manager captures the control flow and data flow for complex processes, and allows for reproducibility and the construction of scientific pipelines; and (3) a resource manager handles allocation of workflow tasks and other jobs to underlying resources, based on the resource monitoring information from Ganglia or other monitoring software.  
In addition to the three core services, OODT provides three client-oriented frameworks that build on these services: (1) a file crawler automatically extracts metadata and uses Apache Tika or other self-defined toolkits to identify file types and ingest the associated information into the file manager; (2) a push-pull framework acquires remote files and makes them available to the system; (3) a scientific algorithm wrapper (called the Catalog and Archive Service Production Generation Executive, CAS-PCE) encapsulates scientific codes and allows for their execution, regardless of the environment, while capturing provenance, making the algorithms easily integrated into a production system (Figure 1).
# The Effects of Stress on Second Harmonics in Plate-Like Structures
## 3 Analytical Method of Second Harmonics Under Stress Change
### Mode Shape Calculation  
SAFE is a kind of calculation method to solve the mode shapes of ultrasonic guided waves for waveguide with arbitrary cross-section. Through the finite element discretization of the cross-section of the waveguide, the displacement along the wave propagation direction of waveguide is represented by the simple harmonic waves. The cross-section of the waveguide was defined as the y-z plane, and the waves propagate along the x direction. When the cross-section of the waveguide is discretized by finite elements, the displacement of any node in the element can be expressed in the form of shape function, as follows:  
\[u_{i}{}^{(e)}(x,y,z,t)=\left[\begin{array}{c}\sum\limits_{k=1}^{m}N_{k}(y,z) U_{ik}\\ \sum\limits_{k=1}^{m}N_{k}(y,z)U_{jk}\\ \sum\limits_{k=1}^{m}N_{k}(y,z)U_{jk}\end{array}\right]^{(e)} \tag{24}\]  
where \(m\) is the number of nodes of element, \(k\) is the wavenumber, \(\omega\) is the angular frequency, \(N(y,z)\) is the shape function matrix, and \(q^{(e)}\) is the nodal displacements of element. According to Hamiltonian principle, the wave equation of ultrasonic guided waves propagating in the structure can be obtained, which can be transformed into the standard characteristic equation:  
\[\Big{[}K_{1}+ikK_{2}+k^{2}K_{3}-\omega^{2}M\Big{]}u=0 \tag{25}\]  
where \(K_{1}\), \(K_{2}\), and \(K_{3}\) are the element stiffness matrix, and \(M\) is the mass matrix. Further introduce the stress variable \(K_{0}\), and the expression [15,38] is as follows:  
\[\Big{[}K_{1}+ikK_{2}+k^{2}(K_{3}+K_{0})-\omega^{2}M\Big{]}u=0 \tag{26}\]  
where \(K_{0}=\frac{\sigma}{\rho}M\), and \(\sigma\) is the axial stress on waveguide. It should be noted that only the axial load on the waveguide is considered in this article. For each given wave number, the eigenvalue and eigenvector are computed by solving Equation (26). The eigenvalue is the angular frequency, and the eigenvector is the displacement vector of each node in the cross-section, which represents the mode shape of the ultrasonic wave mode under certain stress.
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 5 Full-System Load Test and Validation of the Hub Drive System
### Output Characteristics Analysis of the Hub Transmission Reduction System  
Based on the validated dynamic model and the relevant parameter analysis, this section investigates the dynamic parameters influencing the system's stability and smoothness. Focus is placed on the transmission and output characteristics under the given inputs, including the stability of the angular velocity of the planetary carriers and the fluctuations in meshing forces.  
Meanwhile, based on the time-domain and frequency-domain characteristics of the numerical simulation results, a detailed discussion is provided on the system's dynamic output characteristics and the influencing factors during operation, offering insights for the smoothness design of the wheel hub transmission reduction system.
###### Abstract  
This study aimed to examine the sensitivity of the isometric knee extension (IKE) test to detect changes in the lower-limb strength of institutionalized older adults after exercise and inactivity periods. Thirty-four and fifteen institutionalized older adults completed the training and inactive periods, respectively. At each time point, the participants completed two testing sessions. In the first session, they performed the IKE test. As a complement to this evaluation, the second testing session was used to assess their functional capacity and handgrip strength. The sensitivity of the IKE test was examined by comparing the changes generated in this test against the repeatability of the protocol. A 4-week multicomponent Vivifrail program was implemented. After that, a subsample of the participants was re-evaluated after a 14-week inactivity period. Significant changes (\(p<0.01\); ES \(\geq 0.27\)) in the IKE strength for both the dominant (+0.27 N/kg) and non-dominant legs (+0.25 N/kg) were produced after the training intervention. Likewise, significant decrements (\(p<0.01\); ES \(\geq 0.31\)) were detected after the inactive period for the dominant (\(-0.29\) N/kg) and non-dominant legs (\(-0.32\) N/kg). All mean changes were found to be superior to the variability threshold of the IKE test for both legs, with superior sensitivity for the non-dominant leg (\(\geq\)73%). Thus, the IKE test is a sensitive and practical tool for detecting changes in the lower-limb strength of institutionalized older adults after exercise and inactivity periods. Because of its applicability, it seems pertinent to implement the IKE test in a geriatric context.  
physical evaluation; multicomponent exercise; detraining; COVID-19 confinement; aging 2023
# Nitrogen-Related Defects in Crystalline Silicon
## 2 Background Information of N-Related Defects in Si
### The Nitrogen Di-Interstitial (N\({}_{\mathrm{i}}\)N\({}_{\mathrm{i}}\)) Defect  
The N\({}_{\mathrm{i}}\) defect is mobile at around room temperature [50; 63]. The diffusion of N\({}_{\mathrm{i}}\) in the Si lattice can lead to its association with other defects and impurities to form defect clusters. Among others, it can be readily trapped by another N\({}_{\mathrm{i}}\) impurity and form the nitrogen di-interstitial pair labeled as N\({}_{\mathrm{i}}\)N\({}_{\mathrm{i}}\). It has been found that the N\({}_{\mathrm{i}}\)N\({}_{\mathrm{i}}\) pair, also labeled as the N\({}_{\mathrm{2}}\) defect, is the most dominant defect of N in Si [9; 64; 65; 66; 67; 68; 69]. [PERSON] et al. [64] used DFT calculations to predict that the pair consists of two neighboring <100> oriented N-Si split interstitials, arranged in an antiparallel configuration, and with the N-Si bonds forming a square lying on <011> (refer to Figure 3b). This configuration is the more stable one due to chemical and geometrical reasons [64; 65; 22]. Indeed, since the N atom has three valence orbitals, and the Si atom has four it is geometrically impossible to make perfect bonds between them. One valence bond will be left over. Evidently, in order to have perfect bonds two N atoms should form a pair in Si. The defect anneals out [64] at a temperature around 800 \({}^{\circ}\)C. Two IR active modes at 772.9 and 967.8 cm\({}^{-1}\) and two Raman active modes at 743.1 and 1070.0 cm\({}^{-1}\) were calculated and attributed to the N\({}_{\mathrm{2}}\) defect in agreement with the experimentally [9; 67] observed IR bands at 766 and 963 cm\({}^{-1}\).
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 4 SDN Based Architecture
### Performance Parameters
#### 4.3.1 Bandwidth Utilization  
Connection-oriented TCP takes the same route from source to destination for every flow, increasing the possibility of one link being overloaded and bottlenecked, which may be demonstrated by carefully monitoring bandwidth. The possibilities of a congested path are minimal with connectionless UDP because packets are spread over numerous pathways,which allows them to reach their destinations. Here, the amount of time it takes for the flow to entirely reach its destination, where jitter is a significant factor, is what matters. Therefore, bandwidth and jitter are the best characteristics to watch for UDP traffic.  
The iperf tool has been utilized in order to perform measurements on the data flow in both directions. Iperf also provides information regarding the utilization of available bandwidth in terms of the speed at which data is sent between the client and the server. In each and every one of the trials, the Mininet-Wi-Fi default TCP window size of 85.3 kb was utilized [43].
## 4 Experiments and Results
### Validation Experiments
#### 4.2.3 Comparison of Graph Shift Operators  
To verify the advantages of the graph shift operator designed in this paper, the weighted link contraction matrix, Laplace matrix, and Adjacency matrix are respectively used as the graph shift operator to train and test the strategy. The other parameters are kept the same in this process. The experimental results are shown in Table 3.  
From Table 3, it can be seen that after using the weighted link contraction matrix as the graph shift operator, the values of the evaluation metrics are significantly improved. The increased values of velocity reward and distance reward and the less steady-state time represent the better and faster consistency on the velocity and one-hop distance. Furthermore, the improvement of graph importance entropy and success rate especially indicates that the swarm robustness is increased. In summary, the weighted link contraction matrix can significantly help to achieve better swarm consistency and improve the swarm robustness.
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 5 System Model
### Vehicle to Base Station (V2B) Communication Model  
If we assume that MBS serves the vehicle, then the transmission rate \(R_{B:i}(t)\) is calculated with the help of Equation (1).  
\[R_{B,\;i}\;(t)=B_{i}\;(t)\cdot log_{2}\;(1+(P_{T}\cdot L(D_{i}(x))/\sigma^{2})) \tag{1}\]  
where \(B_{i}\;(t)\) denotes the bandwidth of the vehicle at time \(t\), \(P_{T}\) expresses the transmission capacity of the base station (BS), \(L\) shows the average height of the antenna in meters, and \(D_{i}(x)\) denotes the distance between the vehicle and MBS in kilometers.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 3 Methods
### Channel-Spatial Lattice Block (CSLB)
Again, a sigmoid function is used to generate combination coefficients ranging from 0 to 1. The CSAIM is an efficient module designed to enhance the flow of feature signals, with minimal network complexity introduced by the learning of weight coefficients. By employing the CSAIM along the spatial and channel dimensions, the upper and lower branches effectively capture feature signals with varying levels of attention, resulting in more diverse and enriched information integration.
# A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge
## 2 Methodology
### Newmark-Beta Method  
The Newmark-beta method is a numerical method for solving the dynamic response of a structure [31; 32]. The basic procedure of the Newmark-beta method is as follows. Given the hypothesis that the acceleration varies linearly during the time interval \(\Delta t\), the velocity and displacement at time \(t+\Delta t\) can be presented as follows:  
\[\dot{y}(t+\Delta t)=\dot{y}(t)+\frac{1}{2}\big{(}\ddot{y}(t)+\ddot{y}(t+ \Delta t)\big{)}\Delta t \tag{3}\]  
\[y(t+\Delta t)=y(t)+\dot{y}(t)\Delta t+\bigg{(}\frac{1}{3}\ddot{y}(t)+\frac{1} {6}\ddot{y}(t+\Delta t)\bigg{)}(\Delta t)^{2} \tag{4}\]  
According to the aforementioned formulas, the relationship among increments of acceleration, velocity and displacement can be presented as follows:\[\Delta\ddot{y}(t)=\frac{6}{{{{(\Delta t)}^{2}}}}\Delta y(t)-\frac{6}{{{\Delta t}}} \dot{y}(t)-3\ddot{y}(t) \tag{5}\]  
\[\Delta\dot{y}(t)=\frac{3}{{{\Delta t}}}\Delta y(t)-3\dot{y}(t)-\frac{1}{2}\ddot{ y}(t)\Delta t \tag{6}\]  
With the increment form of the single-degree-of-freedom dynamic equilibrium equation being  
\[\Delta\ddot{y}(t)+2\xi\omega\Delta\dot{y}(t)+\omega^{2}\Delta y(t)=\Delta f \tag{7}\]  
the displacement of time \(t+\Delta t\) can be calculated as follows:  
\[\Delta y(t)=\frac{{{f_{eff}}}}{{{k_{eff}}}} \tag{8}\]  
\[{{f_{eff}}}=\Delta f+\left(\frac{6}{{{\Delta t}}}+6\xi\omega\right)\dot{y}(t) +(3+\xi\omega\Delta t)\ddot{y}(t) \tag{9}\]  
\[{{k_{eff}}}=\frac{6}{{{(\Delta t)}^{2}}}+\frac{6\xi\omega}{\Delta t}+\omega^{2} \tag{10}\]  
Then, the velocity and acceleration at time \(t+\Delta t\) can be calculated accordingly.