esa-sceva/satcom-chunk-collection huggingface dataset:
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Automatic Generation of Streams
#### 4.1.2 Algorithm for Generation of Gradual Drifts  
Detecting and adapting to gradual concept drifts can be difficult due to the smoothness of the changes. Unlike abrupt drifts, gradual drifts occur progressively over time. For this reason, they may go unnoticed until they cause a significant drop in performance. The gradual replacement of the target pattern can result in a gradual shift in the underlying data distribution. Moreover, in some cases, the number and duration of drifts may increase, leading to further challenges in detecting and adapting to changes in the data [22].  
Algorithm 2 is designed to generate streams with gradual drift, and takes as input the variables of duration and amplitude, which are defined by a random number of bits. In addition, the start and end positions of each drift are randomly generated, with the duration variable being used to create a gradual pattern over time.  
```
Input :\(n>0\), \(\mathit{dur}>0\), \(\mathit{div}>0\). Output :Array stream \(\leftarrow[1:n]\); \(\mathit{init}\leftarrow\mathit{random}(1:n)\); \(i\gets 0\); while\(\mathit{init}<n\) and \(i\leq\mathit{ndiv}\)do \(\mathit{duration}\leftarrow\mathit{random}(\mathit{previous\_duration}: \mathit{dur})\); ifprevious_duration\(<\mathit{duration}\)then \(\mathit{interval}\leftarrow\mathit{init}+\mathit{duration}\); \(\mathit{stream}[\mathit{init}:\mathit{interval}]\leftarrow 1\); \(\mathit{init}\leftarrow\mathit{random}(\mathit{interval}:n)\); \(i\gets i+1\); \(\mathit{previous\_duration}\leftarrow\mathit{duration}\);  end while  end while
```  
**Algorithm 2** Gradual drift type
## 1 Introduction
However, this type of method has a limitation, that is, a ratio of canopy and ground reflectance needs to be set; the usual practice is to set a constant value for all GLAS measurements, such as 2.0 [27; 28]. Although it is simple to operate, this strategy can introduce error because the ratio value often varies for different sites or even within sites due to different environmental conditions. In addition, a previous study concluded that the variation in this ratio impacts model performance [18]. At present, no one has tried to develop methods from other perspectives, such as using the transmittedenergy profile (TEP). The TEP is an intuitive representation of the distribution density of forest canopy components along the lidar observation direction, which may be a new perspective for estimating the LAI and VFP. The interaction between the laser and vegetation structure should be understood for better use of lidar measurements in estimating forest structure parameters. This reason drives the development of lidar modules in radiative transfer models (RTMs). Examples include FLIGHT [29], Digital Imaging and Remote Sensing Image Generation (DIRSIG) [30], RAYTRAN [31], LIBRAT [32], Forest Light Environmental Simulator (FLiES) [33], and the Discrete Anisotropic Radiative Transfer (DART) [34; 35]. The lidar module in the DART model has now been extended to enable simulations of lidar sensitivity to different scene elements (e.g., vegetation, urban, and atmosphere) and sensor configuration (e.g., GLAS). Its performance in modeling lidar has been proven in the radiation transfer model intercomparison (RAMI) project [36]. Therefore, the lidar module in the DART model is an ideal tool for evaluating the constructed inversion methods based on the lidar data. In this study, we proposed a new method for retrieving VFP and LAI free from the need for the ratio of canopy and ground reflectance--a major limitation with the existing methods that had not been addressed so far. First, we derived the TEP from the GLAS data based on the radiative transfer theory, which in turn was used to calculate the vertical gap distribution. Then, the obtained vertical gap distribution was utilized as an input to calculate the VFP based on the Beer-Lambert law, and the LAI could be obtained by integrating the VFP information. Next, we comprehensively validated the results with various realistic structure scenes (i.e., DART model) and field measurements (i.e., Tracing Radiation and Architecture of Canopies (TRAC) measurements), and the accuracies and sensitivities of our method were analyzed and discussed. Finally, we implemented our method to create footprint-level LAI over Heilongjiang Province, and comparative analysis was conducted between the GLAS LAI and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## Appendix B Characteristics of Guided Waves in Rails
### Excitation of Guided Modes  
A mode is most efficiently excited when the harmonic force applied by the employed transducer to the considered waveguide is well coupled to the displacement associated with the mode itself. Therefore, a transducer should be possibly placed on the rail surface where the displacement of the selected mode shape is large; the polarization of mode shape and that of the transducer must be equal. These considerations also apply to efficient reception [42]. Mode control is achieved by choosing an appropriate transducer and a suitable excitation signal [7].  
Important alternatives for the transducers are represented by piezoelectric transducers and _electro-magnetic acoustic transducers_ (EMATs). An EMAT generates a wave in a given structure via the Lorentz force and/or magnetostriction (see Figure A1a). The phase velocity \(c_{p}\) of such a wave is given by \(c_{p}=f\cdot\lambda\), where \(f\) is the frequency of the driving signal and \(\lambda\) is the meander coil spacing (i.e., the wavelength imposed by the EMAT). Direction control can be achieved by employing a second coil overlapping the first one, but displaced from it along the structure by a quarter wavelength. A piezoelectric transducer generates compression waves towards the employed structure via a coupling medium, as shown in Figure A1a (where \(\theta_{t}\) denotes the orientation angle of the transducer with respect to the structure). The wavelength \(\lambda_{p}\) of the waves generated in the structure is related to the wavelength \(\lambda_{c}\) of the compression waves traveling through the coupling medium by \(\lambda_{p}=\lambda_{c}/\sin\theta_{i}\), where \(\theta_{i}\) is the angle of incidence. The phase velocity in the structure is given by \(c_{p}=v/\sin\theta_{i}\), where \(v\) is the velocity of the compression wavesin the coupling medium. Therefore, a mode at a given frequency can be excited by properly orienting the employed transducer.  
The size of the transducer and the generated excitation signal influence the achieved degree of modal selectivity. On the one hand, the effective wavelength bandwidth (the effective phase velocity bandwidth) depends on the transducer size if an EMAT (a piezoelectric transducer) is employed; on the other hand, the frequency bandwidth depends on the excitation signal. If the transducer has a diameter of 3-5 times the selected wavelength, satisfactory mode control is achieved: moreover, an array of point sources has to be preferred in long-range testing with respect to a monolithic transducer. A fundamental contribution to the improvement of the signal to coherent noise ratio is provided by the signal processing techniques employed at the receive side; in fact, these should allow for extracting the desired input mode-received mode combination only, while rejecting the others [7].
# Beyond Spectrograms: Rethinking Audio Classification from EnCodec's Latent Space
## 3 Experiments  
In this paper, we have performed a series of experiments to evaluate the performance of a classification system using EnCodec's latent representation as input. Although the chosen MNIST architecture does not achieve the best results, it is simple and well-known, making the results easier to understand. Furthermore, if a network such as the one used in this work can obtain results comparable to those of more complex networks, it could be concluded that EnCodec's latent representation is adequate for the audio classification  
Figure 3: EnCodec-based versus spectrogram-based audio data processing flowchart.  
task. Any network architecture capable of performing classification tasks could be used to achieve similar results. In contrast, more complex networks would improve the results.  
In each experiment, three different inputs generated from the original audio have been used: the mel spectrogram, the output of the EnCodec encoder at 48 kHz, and the output of the EnCodec encoder at 24 kHz. To ensure that the same results are obtained with data of different natures, these experiments have been performed with three different datasets: the GTZAN music/speech dataset [14], the GTZAN music genres dataset [15], and the ESC-50 environmental sound dataset [16]. These experiments determine whether the proposed system's results are better than those of other audio classification systems. For this purpose, the classification accuracy and the number of epochs needed to reach convergence will be compared. In addition, other characteristics of the result, such as the size of the analyzed samples, will be examined.  
Let us compare the size of 1 s taken from the samples used in this work in three different formats: its mel spectrogram representation, and the 48 kHz and 24 kHz intermediate representation EnCodec's encoder produces. If our hypothesis is correct, although the mel spectrogram representation is the largest, EnCodec's 48 kHz and 24 kHz intermediate latent representations could provide better results, even with a smaller size. There are differences when comparing the size of 1 s taken from the samples in the three formats used in this work. Its mel spectrogram representation consists of 173 items, EnCodec's 48 kHz intermediate representation consists of 150 items (13.29% less), and EnCodec's 24 kHz intermediate representation consists of 75 items (56.65% less).
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 8 Conclusions
\begin{tabular}{l l} FEC & Forward Error Correction \\ BER & Bit Error Rate \\ BCH & Bose, Ray-Chaudhuri and Hocquenghem \\ RS & Reed-Solomon \\ CC & Convolutional Code \\ BPSK & Binary Phase-Shift Keying \\ QPSK & Quadrature Phase-Shift Keying \\ AWGN & Additive White Gaussian Noise \\ PSK & Phase-Shift Keying \\ SNR & Signal-Noise-Ratio \\ ISI & Inter-Symbol Interference \\ LDPC & Low-Density Parity-Check \\ LoS & Line of Sight \\ NLoS & Non-Line of Sight \\ WCDMA & Wideband Code Division Multiple Access \\ CWMC & Column Weight Multipath Combiner \\
**Nomenclature**  
\(K\) & Length of uncodedword \\ \(N\) & Length of codedword \\ \(K\)-\(N\) & Number of redundant bits/symbols \\ \(x\) & Data \\ \(mi(x)\) & Uncodeword polynomial \\ \(g(x)\) & Generator polynomial \\ \(r(x)\) & Remainder polynomial \\ \(C(x)\) & Codeword polynomial \\ \(m_{1}\) & Any positive integer greater than or equal to 3 \\ \(t\) & Number or errors that can be corrected in a codeword \\ \(d\) & Hamming distance \\ \(m_{2}\) & Number of bits per symbol \\ \(q(x)\) & Quotient polynomial \\ \(TL\) & Path length (the travelling distance needs between paths) \\ \(\tau\) & Delay time for NLoS \\ \(L\) & Number of selective NLoS \\ \(L_{\rm c}\) & Represents the matrix of selective NLoS with \(L\) rows and \(N\) columns \\ \(c_{\rm ij}\) & Represents the bits of each codeword received from NLoS and LoS paths \\ \(w_{h}\) & Hamming weight \\ \(y_{\rm ij}\) & Represents the combiner output for each column \\ \(n_{1}\) & Codeword length for BCH code \\ \(n_{2}\) & Codeword length for RS code \\ \(k_{11},k_{12}\) & Number of uncoded bits used for BCH codes \\ \(k_{21},k_{22}\) & Number of uncoded symbols used for RS codes \\ \end{tabular}
# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 4 Related Work  
A widely used meta-learning algorithm is model-agnostic meta-learning (MAML) [15], which is applied to a range of different models and shows good performance in practice. MAML learns an initial set of parameters for the model--typically, a neural network. It achieves good performance with a few gradient steps. A recent work interprets MAML as a special case of hierarchical Bayes [7]. Hierarchical Bayes obtains distributions of the priors, whereas MAML learns point estimates of the priors with maximum a posteriori probability (MAP) algorithm. Other works extend MAML to learn distributions rather than point estimates--Bayesian MAML [6; 16; 17].  
Another popular state of the art meta-learning approach is ALPaCA, which performs Bayesian linear regression in the feature space. Interestingly, it learns the feature space as well as prior distributions for the linear regression coefficients as offline meta-learning. It generates predictive posteriors analytically with a computationally efficient recursive method. Compared to the MAML algorithm, the ALPaCA algorithm is computationally and sample efficient, whereas Bayesian MAML can capture wider distribution families. CORAL is close to ALPaCA in terms of structure. However, ALPaCA does not support the non-stationary target scenario, and its meta-learning happens in an offline manner. In contrast, CORAL explicitly tackles non-stationary targets using online meta-learning with a computationally efficient algorithm. Online meta-learning is useful, especially when there are no prior training data available of the target or only very general data are available.  
It is also worth mentioning the connection with the few-shot learning literature. Some of the approaches use Bayesian linear regression on a learned feature space [9; 10]. They also use (offline) meta-learning to learn the feature space and the prior distributions of the linear regression coefficients. Although the structure is similar to CORAL, they target discrete (abrupt) task switching using offline meta-learning.  
Regarding algorithms for non-stationary tasks with meta-learning, this has been addressed in the reinforcement learning literature [4]. There, the authors use MAML to learn a set of initial values of the policy parameters and train the policy parameters for every new task starting from the learned initial value. Although it deals with similar tasks to ours, it re-trains the model for every new task, whereas CORAL incrementally updates the model to adapt to the new task and hence is more computationally efficient.  
In what follows, we focus on three concepts that are present across the literature, namely sliding windows, forgetting methods and filtering, and discuss their links to our approach.
# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
## 2 Problem Description
### Description of Controlled System  
The transfer function and state equation of the controlled system are generally described as following a transfer function or state equation:  
\[G(s)=\frac{K}{\prod{(Ts+1)}}\cdot e^{-Ts}, \tag{3}\]  
\[\begin{array}{c}\dot{X}=AX+BU(t-\tau)\\ Y=CX\end{array} \tag{4}\]  
In the transfer function shown in Equation (3), \(K\) is the system gain, which is generally affected by the driving power and load. It is related to the \(B\) in the state Equation (4). The \(T\) in transfer function is the inertia time constant of controlled system, which is related to the parameters of \(A\) in the state equation. \(\tau\) is the delay of control system, which is related to the action process of actuator. The matrix \(C\) in the state equation is the parameters related with system output.  
In the application of ILC, the parameters in Equation (3) or (4) must be the same in all iterations. However, sometimes, one of the parameters in the system might change due to the replacement of a unit in the system, such as a motor, a sensor, a harder material, a lighter load, etc. Although one parameter changes in the system, the structure and the order of the transfer function or state equation remain unchanged. This paper focuses on the above situations and manages to transfer the experience obtained in the ILC of the former system into the new system. In this paper, only the following changes are considered: the change of transfer function on time constant \(T\) of dominant pole due to the change of controlled system's damping, the change of state equation's parameters in matrix \(A\) due to parts' replacement, the change of parameters in the matrix \(B\) due to the change of power intensity in the driving unit, the change of the parameters in the matrix \(C\) due to change of the sensor's proportional coefficient in the sampling unit, and the change of the lag time in the system input and feedback units due to the replacement of actuators or sensors.
## 2 Materials and Methods
### Flux Partitioning  
The CO\({}_{2}\) flux data after u* threshold correction were partitioned into gross primary productivity (GPP) and ecosystem respiration. The gross primary production (GPP) was calculated using Equation (1):  
\[\text{GPP}=-\text{NEE}+\text{Reco} \tag{1}\]  
NEE is the net ecosystem exchange, and Reco is the ecosystem respiration. Night time NEE is generally used as a proxy for Reco. In mangroves, Reco is representative of NEE only during emersion periods [40].  
The reliable night time data (i.e., after the removal of low turbulent flux data) were regressed with air temperature using the Lloyd-Taylor regression model [41]. The air temperature was used as a proxy for ecosystem respiration to estimate daily respiration rates over a 14-day period. Reco was calculated using Equation (2):  
\[\text{Reco}\ =\ \text{Ref}\times\left[\frac{\text{Ea}}{\text{R}}\bigg{(} \frac{1}{\text{Tref}}-\frac{1}{\text{T}}\bigg{)}\right] \tag{2}\]  
Reco is the ecosystem respiration, \(\text{Ref}\) is the ecosystem respiration at 20 \({}^{\circ}\)C, \(\text{Ea}\) is the apparent activation energy (J mol\({}^{-1}\)), \(\text{R}\) is the universal gas constant (J mol\({}^{-1}\) K\({}^{-1}\)), \(\text{Tref}\) is the reference temperature (20 \({}^{\circ}\)C, in K), and \(\text{T}\) is the absolute temperature (K).
## 3 Proposed PBFT Architecture and Model
### Roles of Nodes and Network Architecture  
The proposed method is designed to operate on a private blockchain, wherein institutions and operators manage the nodes and hierarchies. Due to the intrinsic nature of private blockchains, the locations of participating nodes tend to remain stable. Furthermore, gathering node information becomes straightforward since only verified nodes can participate. Capitalizing on these features, our method clusters all participating nodes based on their location data as shown in Figure 2, and establishes a hierarchical structure for the nodes within each cluster.  
In the proposed PBFT, nodes participating in the blockchain network for consensus are categorized into three main roles:  
* CPN (Cluster Primary Node): CPNs are responsible for managing the cluster.
* GPN (Group Primary Node): GPNs manage and lead consensus within the group.
* NN (Normal Node): NNs participate in consensus and verify consensus messages.  
Figure 2: An example of proposed PBFT architecture.  
Detailed explanations of each node's role can be found in Sections 3.1.1-3.1.3. Each node participating in the blockchain where the proposed method is applied performs one of the three roles described earlier.  
During the consensus process, the CPN broadcasts the consensus message it receives from the client to the GPNs in the first layer of the cluster under its management. Subsequently, each GPN forwards the consensus message from the CPN to the NNs in its group and to the GPNs in its subgroup of the sub-layer. NNs within the group execute PBFT and participate in transaction verification under the management of the GPN. The role of each node is not fixed and can change during the repeated consensus process.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 3 The Mathematical Optimization Model and Two Selected Optimization Problems  
Previous studies of LS were mainly on the application of LS's mechanical performances, and few studies used LS in turbine blades to improve both thermal and mechanical performances of blades. We simplified the application of LS on turbine blades as an LS channel with variable aspect ratio and developed a corresponding optimization method. As shown in Figure 1, to set more LS on thin walls of regions \"\(1\)\" and \"\(2\)\", bearing load capacity, vibration, and heat transfer performances of turbine blade would be better. However, it would enlarge weight of turbine blades and pressure drop of the channel. Therefore, the optimization method aimed to seek a reasonable topology of LS which can improve both heat transfer and mechanical performances. Herein, based on the given functions which represented relevance between heat transfer and mechanical performances and geometric parameters of the LS channel, a mathematical optimization model was established; two selected optimization problems were proposed; NSGA-II algorithm was used to solve these problems, and optimal structural parameters were obtained.
# Gaussian Quantum Trajectories for the Variational Simulation of Open Quantum-Optical Systems
## 3 Kerr Bistability and the _Xp_-Gaussian Methods  
As a first example, we look at a driven-dissipative cavity with Kerr non-linearity, described by the Hamiltonian:  
\[\hat{\mathrm{H}}=-\Delta\,\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}+\frac {U}{2}\,\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a }}\,\hat{\mathrm{a}}+Fa^{\dagger}+F^{*}\,\hat{\mathrm{a}} \tag{9}\]  
where \(\Delta\) is the cavity-laser detuning, \(U\) the photon-photon interaction and \(F\) the classical laser amplitude. Furthermore, photons leak to the vacuum with rate \(\gamma\) as described in Section 2. Exact solutions for the expectation values \(\left\langle\hat{\mathrm{a}}^{\dagger m}\,\hat{\mathrm{a}}^{n}\right\rangle\) of the stationary state, as well as a semi-classical model were calculated in [59]. Interestingly, at the classical level, this system features a bistability regime where, given all other parameters, two stable solutions for the density exist. Such a Kerr cavity provides a model for polariton condensates [48], for which it has been shown that trajectory methods can provide an adequate description [62; 63]. Furthermore, coupled arrays of these cavities are an object of current interest [64], and it is in these systems that the TWA method has been proven insufficient [58]. As it is the most straightforward ansatz corresponding to the Gaussian approximation of Section 2, we assume the state to be Gaussian in the quadrature operators \(\hat{\mathrm{X}}\) and \(\hat{\mathrm{P}}\) or, equivalently, \(\hat{\mathrm{a}}\) and \(\hat{\mathrm{a}}^{\dagger}\)i.e., the states that are colloquially known as Gaussian states [60]. Such an \(XP\)-Gaussian state is characterized entirely by its mean-field value \(\alpha=\left\langle\hat{\mathtt{a}}\right\rangle\) and its two-point correlation functions \(\left\langle\hat{\mathtt{a}}\,\hat{\mathtt{a}}\right\rangle\) and \(\left\langle\hat{\mathtt{a}}^{\dagger}\,\hat{\mathtt{a}}\right\rangle\) or, equivalently, \(\alpha,\;\left\langle\hat{\delta}\hat{\delta}\right\rangle\) and \(\left\langle\hat{\mathtt{a}}^{\dagger}\,\hat{\delta}\right\rangle\) where \(\hat{\delta}=\hat{\delta}_{\delta}=\hat{\mathtt{a}}-\alpha\). The connected correlation functions equal \(\left\langle\hat{\delta}\hat{\delta}\right\rangle=\left\langle\hat{\mathtt{a} }\,\hat{\mathtt{a}}\right\rangle-\alpha^{2}\) and \(\left\langle\hat{\mathtt{a}}^{\dagger}\,\hat{\delta}\right\rangle=\left\langle \hat{\mathtt{a}}^{\dagger}\,\hat{\mathtt{a}}\right\rangle-\left|\alpha\right|^ {2}\).