esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Methods  
### LA-ICP-MS Zircon U-Pb Dating  
Over 500 zircon crystals were extracted from each sample after being crushed to c. 80-100 mesh for a grain size, using standard gravimetric-magnetic separation techniques at Langfang Technology Service Co., Ltd. of Geoscience Exploration, Hebei Province, China. Then, under a binocular microscope, at least 200 grains larger than 40 um in diameter containing no visible fractures and inclusions were handpicked, mounted in epoxy  
Figure 3: (**a,b**) Representative monzogranite samples for zircon LA-ICP-MS U-Pb dating and element analysis. (**c**) Euhedral to subhedral quartz, K-feldspar, plagioclase and biotite. (**d**) Subhedral K-feldspar with inclusions of biotite, plagioclase and quartz. (**e**) Representative fine-grained dark enclave sample for zircon LA-ICP-MS U-Pb dating and element analysis. (**f**) Fine-grained subhedral plagioclase, hornblende and quartz, K-feldspar and biotite. Mineral abbreviations: Pl: plagioclase, Qtz: quartz, Kfs: K-feldspar, Hbl: hornblende, Bt: biotite.  
resin and polished in order to expose the centre of zircon crystals. To choose the zircon grains without fluid/mineral inclusions and microcracks for U-Pb analyses, zircon internal textures were identified by examining the reflected and transmitted light micrographs in detail, which were obtained by optical microscopy at the geochemical research laboratory at China University of Geosciences (Beijing), as well as the cathodoluminescence (CL) images, which were obtained through a Quanta 200 FEG environmental scanning electron microscope (SEM) at Beijing University.  
Zircon U-Pb dating was performed by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) at the Hefei University of Technology, China, through an Agilent 7500A ICP-Mass furnished with a GeolasPro laser. The laser frequency was 6 Hz, and the spot diameter used for the analyse was 32 um. The external standard used as U-Pb dating was Harvard zircon 91500, and the external standard for calculating the contents of Th, U and Pb was NIST610. ICPMSDataCal 8.3 software was used to calculate the \({}^{206}\)Pb/\({}^{238}\)U and \({}^{207}\)Pb/\({}^{206}\)Pb ratios [53], and on the basis of the method of [54], we corrected the common Pb. The calculation of the weighted average ages and the concordia ages plotting were obtained using the Isoplot 4.16 program [55]. Detailed analytical techniques resembled those documented by [31; 56].
## 2 Fundamentals Revisit  
### Canonical Correlation Analysis  
CCA is a statistical method designed to explore the relationships between two sets of multidimensional variables. Specifically, it identifies the linear transformations of two vectors that maximize the correlation between them. By identifying projections of the original variables, linear CCA determines pairs of canonical variables, each representing a linear combination of the original data. These canonical variables are designed such that the correlation between the corresponding canonical pairs is maximized, enabling the effective discovery of inter-relationships between the datasets.  
Mathematically, consider two random vectors \(X\in\mathbb{R}^{p\times N}\) and \(Y\in\mathbb{R}^{q\times N}\). The goal of linear CCA is to find the linear projections \(J^{T}x\) and \(L^{T}y\), where \(J\) and \(L\) maximize the correlation between them. This objective can be expressed as:  
\[(J^{*},L^{*})=\underset{J,L}{\operatorname{argmax}}J^{T}\sum_{xy}L^{T} \tag{1}\]  
subject to the constraints:  
\[J^{T}\Sigma_{xx}J=L^{T}\Sigma_{yy}L=\mathbf{I} \tag{2}\]  
where \(\mathbf{I}\) denotes an identity matrix. These constraints ensure that the canonical variables have unit variance, which prevents trivial solutions.  
The optimization problem can be effectively solved using Singular Value Decomposition (SVD) on the matrix:  
\[\gamma=\sum_{xx}^{-\frac{1}{2}}\sum_{xy}\sum_{yy}^{-\frac{1}{2}} \tag{3}\]  
where \(\sum_{xx}\) and \(\sum_{yy}\) are the individual covariance matrices, and \(\sum_{xy}\) is the cross-covariance matrix between them. The rank of \(\gamma\) is denoted as \(rank(\gamma)=\kappa\). Performing an SVD of this matrix yields:  
\[\gamma=\Gamma\Lambda\Delta^{T} \tag{4}\]  
where \(\Gamma=\left(\gamma_{1},\cdots,\gamma_{p}\right)\) and \(\Delta=\left(\delta_{1},\cdots,\delta_{q}\right)\) are column orthogonal matrices, and \(\Lambda=\left(\begin{matrix}\Lambda_{\kappa}&0\\ 0&0\end{matrix}\right)\) is a diagonal matrix. \(\gamma_{i}\) and \(\delta_{j}\) are the corresponding singular vectors \(\Lambda_{\kappa}=diag\{\lambda_{1},\cdots,\lambda_{\kappa}\}\).  
Then, the canonical vectors \(J\) and \(L\) can be defined as:  
\[J=\sum_{xx}^{-\frac{1}{2}}\Gamma(:,1:\kappa) \tag{5}\]  
\[L=\sum_{yy}^{-\frac{1}{2}}\Delta(:,1:\kappa) \tag{6}\]  
where \(\Gamma(:,1:\kappa)\) and \(\Delta(:,1:\kappa)\) are the first \(\kappa\) columns in the corresponding matrices.
## 6 Discussion and Limitations  
The experimental results presented in this study demonstrate the significant potential of integrating wavelet transforms with artificial neural networks for accurately classifying  
Figure 18: Visualization of the predicted classes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Classification Accuracy (\%)\(\sim\)**} \\ \cline{3-5}  & & FTP & Video & Low-Latency \\ \hline [PERSON] et al. [4] & k-NN & 97.5 & 97.9 & 98.2 \\ [PERSON] et al. [50] & CNN & 94.5 & 96.5 & 84.5 \\ Deep Packet [39] & CNN & 98.0 & 98.0 & 98.0 \\ [PERSON] et al. [51] & ANN & NA & 59.0 & 92.0 \\ FlowPic [40] & LSTM & 98.8 & **99.9** & 99.6 \\
**This Paper** & **ANN** & **99.1** & 99.3 & **99.6** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification performance comparison among six methods. Results are in the format of Avg.  
low-latency Internet traffic. The high classification accuracy across both basic and complex traffic scenarios highlights the robustness of the proposed approach, particularly in identifying real-time traffic amidst heterogeneous traffic environments. Furthermore, the use of frequency-domain features derived through wavelet transforms proved especially effective in isolating the subtle, noise-like characteristics of low-latency communication.  
Despite these encouraging results, several limitations must be acknowledged. First, the dataset used for training and evaluation was generated under controlled conditions on a local Wi-Fi network. While this setup provides a consistent and manageable environment for analysis, it may not fully reflect the complexities and variability of large-scale, real-world networks. Thus, generalization of the model to broader deployments requires further validation using diverse and publicly available datasets. Second, the current implementation employs a relatively simple MLP architecture. Although effective, more sophisticated deep learning models--such as CNNs or transformers--could potentially further enhance classification performance, particularly in highly congested or encrypted network environments. Third, while wavelet-transformed features significantly improve performance, they also introduce computational overhead. This could be a limiting factor for deployment in edge devices or latency-sensitive network components without optimization or hardware acceleration. Finally, the current model does not dynamically adapt to evolving traffic patterns or new application behaviors over time. Future research could explore online learning or adaptive classification mechanisms that evolve with the network environment. Finally, while comparative analysis with other wavelets (e.g., Haar, Daubechies) could further validate our choice, this was beyond the scope of this study. Future work will explore wavelet function adaptability for broader traffic classification scenarios.
# Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
## 5 Discussion
In this work, the generated data was subsampled at equidistance time points, resulting in less frequent but equispaced voltage observations; future work may consider the effects of measurement times as well as frequency on the resulting time-varying applied current estimates in order to further explore the importance of data availability in obtaining accurate time-varying parameter estimates. Additional numerical experiments contained in Appendix A demonstrate the capability of the EnKF with parameter tracking in estimating constant currents of increasing amplitude, as well as the capability of the algorithm in distinguishing a higher-amplitude constant current from a time-varying sinusoidal current with similar multiple-spiking voltage data. The results in Figure 10 show that the parameter tracking algorithm can well capture the behavior of the applied currentfor increasing magnitude using a relatively small choice of parameter drift standard deviation \(\sigma_{\xi}\). However, the results in Figure A3 further demonstrate the importance of choosing an appropriate value for \(\sigma_{\xi}\) in the parameter tracking algorithm, as this choice also has a significant impact on the filter's ability to distinguish between a constant and time-varying current. While the focus of this work is on estimating different deterministic forms of the time-varying applied current parameter, in future work we aim to estimate stochastic forms of the current, as well as estimating applied currents relating to networks of neurons. In addition to the applied current, we are also interested in applying parameter tracking methodology to estimate the \(\alpha(V)\) and \(\beta(V)\) rate functions as additional unknown parameters that vary with voltage, which would be particularly useful in applications of the Hodgkin-Huxley model where these rate functions do not necessarily share the same parameterized forms as in (8)-(11) and (15)-(16). Future work also includes comparing our results for the Hodgkin-Huxley model with results using other single neuron models, such as the FitzHugh-Nagumo and Hindmarsh-Rose models [29; 30; 31]. We further aim to apply our results to biomedical applications utilizing Hodgkin-Huxley dynamics to model various neurodegenerative diseases affecting the function of neurons and ionic channels, such as Alzheimer's disease and amyotrophic lateral sclerosis [32; 33; 34]. The methodology used in this work can be employed in developing patient-specific models for personalized medicine applications, with the potential of identifying possible differences in time-varying input stimuli between healthy and disease states.
# Passive Harmonic RFID System for Buried Assets Localization
## 5 Discussion
Both UHF [29] and High Frequency (HF) RFID [28] are often proposed for smart floor applications. However, in the work of [27], passive reflectors buried under the tiles and semipassive ultra-wide-band (UWB) tags are detected with a low-power Impulse Radio-UWB (IR-UWB) radar using a time-domain analysis similar to the one used in GPR systems. In smart floor applications, the tag is often under tiles in indoor environments and the depth of the buried tag is from a few millimeters to 1 cm. Therefore, the attenuation introduced by these materials is lower than that due to tags underground, especially if the soil is not dry. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Cost (\$)** \\ \hline RTL–SDR & 20 \\ Reader Antennas & 100 \\ Filters & 10 \\ Amplifiers & 10 \\ Power Supply & 20 \\ Connectors and box & 20 \\ Trolley & 50 \\ Tag & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimated cost of different parts of the system. SDR—software defined radio.
# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
###### Abstract  
Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.  
Keywords:convolutional neural network; image classification; transfer learning; medical images; deep learning; fine-tuning +
Footnote †: journal: Medical Image classification
# Formation of ZnO/CuO Heterostructures Based on Quasi-One-Dimensional Nanomaterials
## 3 Applications and Peculiarities of Synthesis of ZnO-Based Quasi-One-Dimensional Nanostructures
Therefore, the improvement of this area of their application, as well as others listed, directly depends on the development of methods for creating zinc oxide nanostructures with controlled predetermined properties. That is, there is a need for the controlled modification of the properties of quasi-one-dimensional zinc oxide nanostructures for specific purposes. In [106; 107], the synthesis of ZnO nanowires and nanow whiskers by direct current arc discharge between graphite cathode and graphite anode filled with ZnO in a pre-vacuum chamber was described. Zinc oxides of diverse morphologies (nanowires, whiskers and particle agglomerates) has been synthesized on a silicon plate and a glass substrate. A modified arc process for the synthesis of a hybrid composite based not only on graphene and zinc oxide but also on copper was presented in [108]. The composite material, which combines the synergistic properties of graphene with nanostructured metals or semiconducting materials was obtained. Arrays of ZnO nanorods/nanowires oriented vertically to the substrate have been fabricated by a direct current electrochemical method [109; 110], and it has been shown that the morphology of ZnO is extremely sensitive to the synthesis conditions and is difficult to control. The morphology control of synthesised ZnO nanostructures formed on metal substrates has been discussed in [111; 112]. It was shown that the performance properties of such materials tend to improve with an increase in the dispersion and uniformity of the oxide phase distribution on the metal surface. Field emission scanning electron microscopy (FE-SEM) images of ZnO nanowires grown on ITO-coated unannealed and annealed glass substrates are presented in Figure 5[112]. In [113], ZnO nanowires were created by the hydrothermal method and the need to control their morphology was conditioned. However, studies focused on the morphology control of quasi-one-dimensional materials are not numerous, so the consideration of this issue is still relevant.
# Review of Automatic Estimation of Emotions in Speech
## 3 Acoustic Analysis of Speech
### Basic Spectral Measures  
The time waveform (or shape) of speech signals varies significantly, in part due to unintended phase variations, which derive from complex airflow variations, but may be affected by emotion. However, most classical speech analysis has focused instead on its spectral amplitude, as that contains information useful to estimate the intent of speech. The elementary discrete Fourier transform (DFT) is the simplest analysis method commonly used, as it displays energy as a function of frequency [36]. A DFT transforms \(N\) consecutive time samples (during a local time window, which is shifted periodically in time, to handle the dynamic speech signal) into \(N\) samples of spectral amplitude and phase (\(N\) is usually 256-512). In theory, there is no data reduction or information loss by this invertible transform (however, for digital purposes, the quantization necessary causes inevitable loss, to be minimized with more bits of representation). While DFT amplitudes furnish more pertinent information for speech classification than time samples, the entire DFT has excessive detail for most applications, and thus needs data reduction to be efficient.  
One common variant of the DFT is filter bank energies (FBEs), as they smooth DFT amplitudes over a broad series of restricted frequency spans (e.g., bandpass filtering). As useful speech information is distributed non-uniformly in frequency, the nonlinear Mel scale is often used in frequency [45]; it has filters of equal bandwidth up to 1 kHz, and then logarithmic spacing; this corresponds to the reduced resolution of audition at higher frequencies (effects that derive from the tapered width of the basilar membrane inside the inner ear). ASR typically uses 20-100 of these filters to cover the full frequency range, which reduces data well below a 512-sample DFT, while nonetheless keeping adequate information about the spectra for applications of speech.
# Methods of Pre-Identification of TITO Systems
## 2 Materials and Methods
### Least Squares Method with Exponential Forgetting  
The estimation of model parameters is computed as  
\[\hat{\theta}(k)=\hat{\theta}(k-1)+K(k)\hat{e}(k) \tag{3}\]  
The gain vector is calculated as  
\[K(k)=\frac{C(k-1)\phi(k)}{1+\phi^{T}(k)C(k-1)\phi(k)} \tag{4}\]  
and covariance matrix  
\[C(k)=C(k-1)-\frac{C(k-1)\phi(k)\phi^{T}(k)C(k-1)}{1+\phi^{T}(k)C(k-1)\phi(k)} \tag{5}\]  
The following applies to the calculation of the prediction error  
\[\hat{e}(k)=y(k)-\phi^{T}(k)\hat{\theta}(k-1) \tag{6}\]  
In the case of exponential forgetting, the criterion of identification is  
\[J=\sum_{i=k_{0}}^{k}\left(\phi^{k-i}e(i)\right)^{2} \tag{7}\]  
where the exponential forgetting factor is chosen in the range of 0 to 1, the most common near 1.  
If  
\[\phi^{T}(k)C(k-1)\phi(k)>0 \tag{8}\]  
Figure 2: Identification scheme for continuous-time systems.  
Then  
\[C(k)=C(k-1)-\frac{C(k-1)\phi(k)\phi^{T}(k)C(k-1)}{\eta^{-1}+\phi^{T}(k)C(k-1)\phi( k)} \tag{9}\]  
where  
\[\eta(k)=\varphi(k)-\frac{1-\varphi(k)}{\xi(k)} \tag{10}\]  
If  
\[\phi^{T}(k)C(k-1)\phi(k)=0 \tag{11}\]  
Then  
\[C(k)=C(k-1) \tag{12}\]  
Furthermore  
\[\varphi(k)=\left\{1+(1+\rho)[\ln(1+\xi(k-1))]+\left[\frac{(\varphi(k-1)+1) \eta(k-1)}{1+\xi(k-1)+\eta(k-1)}-1\right]\frac{\xi(k-1)}{1+\xi(k-1)}\right\}^{-1} \tag{13}\]  
\[\eta(k)=\frac{e^{2}(k)}{\lambda(k)} \tag{14}\]  
\[v(k)=\varphi(k)[v(k-1)+1] \tag{15}\]  
\[\lambda(k)=\varphi(k)\left[\lambda(k-1)+\frac{e^{2}(k)}{1+\xi(k-1)}\right] \tag{16}\]  
\[\xi(k)=\phi^{T}(k)C(k-1)\phi(k) \tag{17}\]  
The parameters estimation vector is in the form  
\[\hat{\theta}^{T}(k)=\left(\hat{a}_{0},\hat{a}_{1},\;\ldots,\;\hat{a}_{\deg(a)},\;\hat{b}_{0},\hat{b}_{1},\;\ldots\;,\;\hat{b}_{\deg(b)},\;d\right) \tag{18}\]  
and regressor  
\[\phi^{T}(k)=\left(-y(t_{k}),\;\ldots,-y_{L}^{(n-1)}(t_{k}),\;u(t_{k}),\; \ldots,\;u_{L}^{(m)}(t_{k}),\;1\right) \tag{19}\]
## 3 Wireless Power Transfer System in Seawater
### Power Loss of Coreless Coupler
In addition, due to the skin effect and proximity effect, the change in the distance between the turns will also cause the change in the coils' AC impedance. Therefore, on the basis of the previous simulation model, we have studied the influence of distance change in power loss and mutual inductance at 200 kHz operating frequency. Figure 8 shows the simulation results. From Figure 8a, we can see that as the distance increases, the copper loss of the coils and the eddy current loss in the seawater gradually decrease. What we expect is that the copper loss and eddy current loss are as small as possible. From Figure 8b, we can obtain the changing trend of the mutual inductance between the transmitter and receiver; the mutual inductance decreases with the increase in the distance. In wireless power transfer, we hoped that the mutual inductance between the transmitter and receiver was as large as possible so that greater transmission power could be achieved, as expressed in Equation (4). However, the requirements of power loss and mutual inductance on the changing trend of the distance between the turns are opposite. In order to reduce the loss, the distance needs to be increased, but this will cause the mutual inductance between the transmitter and receiver to decrease. Therefore, when designing the coils, how to reduce the power loss as much as possible while satisfying the mutual inductance is a problem worthy of being considered. Figure 7: Simulation results of power loss vary with frequency.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 2 Materials and Methods
### Extraction of Reflectance Data  
To avoid discrepancies between field measurements and radiometric information, a threshold of 3 days of difference between imaging date and sampling date was applied, which means that if the time difference between the available cloud-free satellite imagery and field sampling was more than 3 days, then the observation would be discarded. Reflectance information for each subsample from each band was extracted using the \"_extract_\" function from package \"_raster_\" in R environment [37].  
The extracted reflectance values and total _DMY_ values of the three subsamples were averaged for regression analysis. The spatial and temporal distribution of collected datasets are listed in Table 3. There are less observations (_n_) compared to those listed in Table 2 due to the exclusion of samples based on the difference between dates of available Sentinel-2 data and field sampling data. Since the datasets were from different locations, years and sampling days, there was no autocorrelation issue for all of the datasets, which was determined by using the \"_aef_\" function from the package \"forecast\" in R environment [37].
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 6 Results and Discussion  
To verify the effectiveness of the proposed method, we present several numerical simulations. The simulation is divided into the following two parts. The first part is to verify the effectiveness of the guaranteed cost strategy proposed in this paper. The second part is to verify the effectiveness of the compensation control strategy by comparing the orbital pursuit-evasion problem under three scenarios: no time delay, with compensation for time delay, and without compensation for time delay.  
In these simulations, the initial altitude of the reference orbit is 500 km, the control boundary of the pursuer is \(\rho_{P}=0.2\) m/s\({}^{2}\), the control boundary of the evader is \(\rho_{E}~{}=~{}0.1\) m/s\({}^{2}\), the safe distance between the players is \(m=1\) m, and the sampling step is 0.01 s. The initial state of the pursuer and the evader is shown in Table 2.