esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 2 Measurement Algorithms
### IEC/IEEE 60255-118-1 P-Class Reference Algorithm
[7] adopts the following approximated expression instead of using the exact filter response:  
Figure 1.2  
As mentioned before, the positive sequence synchrophasor estimate has key importance in power systems. Let us obtain its expression under off-nominal frequency conditions; the substitution of (16) into (11) leads to:  
Figure 1.3  
As in (16), also \(\hat{X}_{+}(t_{r})\) is disturbed by the presence of a component which rotates clockwise in the complex plane with angular speed \(\omega+\omega_{0}\). Based on the assumption of weakly unbalanced system, such disturbance has a much lower relative magnitude, since \(W_{tri}(j2\pi\Delta f)\) is close to \(G\), whereas \(X_{-}/X_{+}\) (often called unbalance factor) is well below one. Therefore, the positive sequence synchrophasor estimate is considerably less affected by this phenomenon. Finally, it is worth stressing that \(\hat{X}_{+}\) does not depend on the zero sequence component. The overall algorithm latency is given by \(L=(M_{C}-1)T_{s}+ST_{s}\), which keeps into account the filtering process and the phase-angle derivative computation. It is thus always \(T_{0}\leq L<2T_{0}\), thus \(S\leq M_{C}\) in order to comply with the latency limit for P class algorithms.
## 2 Data and Methodology
### Research Methodology
#### 2.3.1 Drought Event Recognition and Multidimensional Feature Extraction
The composite index addresses single-indicator limitations through an optimized weighting fusion mechanism, enabling comprehensive characterization of drought events' spatiotemporal characteristics. During calculation, Z-score standardization was systematically applied to both normalize measurement scales and ensure dimensional contribution comparability across all input parameters.
# Layer-Level Knowledge Distillation for Deep Neural Network Learning
## 4 Experimental Results
### Results of ASL Framework and LSP
Most cases support the use of Conv5 and FC1 of the teacher network to bind Conv3 and FC1 of the student network, respectively. This validates the suggestion made in Table 3 again. To observe the results of binding three alignment layers, we set the intermediate layers of the student network as Conv3, FC1, and FC2. Different convolutional layers Conv*, FC1, and FC2 were used in the teacher network for layer binding. Table 6 shows that using Conv5, FC1, and FC2 as intermediate layers in the teacher network is the best choice. In our last experiment in Table 7, we fixed Conv5, FC1, and FC2 layers in the teacher network. When Conv3, FC1, and FC2 were selected in the student network, most cases achieved the highest classification accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Configuration** & \multirow{2}{*}{**PPMI**} & \multirow{2}{*}{**Willow**} & \multirow{2}{*}{**UIUC-Sports**} \\ (X, Conv3) & & & & & \\ \hline Student Only & – & 24.1 & – & 41.0 & – & 74.6 \\ Teacher Only & 60.7 & – & 75.5 & – & 94.2 & – \\ \hline Conv2 & 38.7 & 36.1 & 48.0 & 41.4 & 80.2 & 79.9 \\ Conv3 & 39.3 & 35.9 & **49.4** & 42.2 & 80.0 & 79.7 \\ Conv4 & 40.1 & 36.3 & 49.1 & **46.1** & 79.1 & 79.0 \\ Conv5 & **40.8** & **36.7** & 47.7 & 43.4 & **80.5** & **81.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy (%) of the Auxiliary Structure Learning (ASL) network. For each dataset, the first column is for the teacher network and the second column is for the student network. Different layers of the teacher network are bound to the Conv3 layer in the student network. (X, Conv3) indicates that X layer of the teacher network is used to bind Conv3 of the student network. The number marked in bold indicates the layer with the highest accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Teacher Net** & **Student Net** \\ \hline Conv2 & 0.00588 & 0.00193 \\ Conv3 & 0.00322 & **0.00143** \\ Conv4 & 0.00316 & N/A \\ Conv5 & **0.00307** & N/A \\ FC1 & **0.00015** & **0.00028** \\ FC2 & 0.00023 & 0.00037 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LSL loss for teacher network and student network on the People Playing Musical Instruments (PPMI) dataset. The best convolutional layer and the best fully connected layer are marked in bold.
# Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors
###### Abstract
Our method combines QSA with statistical feature analysis and polynomial and Fourier regressions to deliver reliable, practical predictive wear functions for motor maintenance. The proposed method utilizes four vibration signals with 29,999 samples measured from an induction motor operating at steady state and full load (100%): tangential mechanical vibration speed in the housing (\(V_{h}\)), axial mechanical vibration speed on the driven side (\(V_{a}\)), radial mechanical vibration speed on the non-driven side (\(V_{s}\)), and radial mechanical vibration speed on the driven side (\(V_{d}\)). The use of four signals to adapt them to the quaternions allows us to analyze different elements of the motor in various axes. This approach allows us to cover more variables, which may reflect the characteristics of the analyzed failures. However, a disadvantage of using quaternions is that they require four signals, which means that it is necessary to use at least two or more sensors. Not all databases have access to this number of sensors, which can limit the analysis. The method is evaluated across motor conditions ranging from healthy states to one, two, and three BRB faults, demonstrating its effectiveness in fault quantification and progression tracking. This paper is structured into four sections: Section 1 introduces BRB fault detection. Section 2 describes the background of QSA and statistical methodology. Section 3 presents and discusses the results, comparing the results of polynomial and Fourier regressions. Finally, Section 4 presents conclusions and future work.
# Generalized Paxos Made Byzantine
## 3 Model  
We consider an _asynchronous_ system in which a set of \(n\in\mathbb{N}\) processes communicate by _sending_ and _receiving_ messages. Each process executes an algorithm assigned to it, but may stop executing it by _crashing_. If a process does not follow the algorithm assigned to it, then it is _byzantine_. This paper considers the _authenticated_ Byzantine model: every process can produce cryptographic digital signatures [22]. Furthermore, for clarity of exposition, we assume authenticated perfect links [23], where a message that is sent by a non-faulty sender is eventually received and messages cannot be forged (such links can be implemented trivially using retransmission, elimination of duplicates, and point-to-point message authentication codes [23].) A process may be a _learner_, _proposer_ or _acceptor_. Informally, proposers provide input values that must be agreed upon by learners and the acceptors help the learners _agree_ on a value.  
**Problem Statement.** In Generalized Paxos, each learner \(l\) maintains a monotonically increasing sequence of commands _learned\({}_{l}\)_. We define these learned sequences of commands to be equivalent (\(\thicksim\)) if one can be transformed into the other by permuting the elements in a way such that the order of non-commutative pairs is preserved. A sequence \(x\) is defined to be a _eq-prefix_ of another sequence \(y\) (\(x\sqsubseteq y\)), if the subsequence of \(y\) that contains all the elements in \(x\) is equivalent (\(\thicksim\)) to \(x\). We present the requirements for this consensus problem, stated in terms of learned sequences of commands for a learner \(l\), _learned\({}_{l}\)_. To simplify the original specification, instead of using C-structs (as explained in Section 2), we specialize to agreeing on equivalent sequences of commands:  
**Nontriviality.**_learned\({}_{l}\)_ can only contain proposed commands.  
**Stability.** If _learned\({}_{l}=v\)_ then, at all later times, \(v\sqsubseteq\)learned\({}_{l}\)_, for any \(l\) and \(v\).  
**Consistency.** At any time and for any two correct learners \(l_{i}\) and \(l_{j}\), _learned\({}_{l_{i}}\)_ and _learned\({}_{l_{j}}\)_ can subsequently be extended to equivalent sequences.  
**Liveness.** For any proposal \(s\) and correct learner \(l\), eventually _learned\({}_{l}\)_ contains \(s\).
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 3 Results
### Accuracy and Analysis
At least when the number and range of values lower than \(-\)30  
Figure 6: Accuracy comparison of results based on differently preprocessed data. in the VH timeseries were controlled to a certain extent, there could be a certain degree of tolerance for usage of values lower than \(-30\). Generally, for this study, VH polarization data can be considered a reliable or even better option than VV polarization data.
## 1 Introduction
### Quantifying the Surface Solar Irradiance Spectra
This is particularly relevant for models employing rigorous line-by-line calculations across the full solar spectrum. To reduce computational complexity, many radiative transfer models adopt approximations by replacing the vast array of absorption and scattering coefficients for individual atmospheric constituents with a smaller, representative set [28]. Although aerosol distributions vary significantly over short timescales and distances--typically hours and tens of kilometers [29; 30]--most irradiance models use simplified climatologies with fixed annual or monthly values at coarse spatial resolutions [31; 32]. As a result, depending on the atmospheric composition at a given location and time, such models may under or overestimate SSI. Even the more accurate remote sensing SSI products are constrained by limited spatial resolution, limited temporal resolution or both. Many products still operate at coarse spatial scales of several kilometers. These limitations hinder their ability to capture fine-scale variability in irradiance, particularly under rapidly changing atmospheric conditions, such as broken cloud fields or aerosol plumes and land surface heterogeneity due to complex tree canopies or man-made structures. The present study is motivated by the need to address this lack of accurate SSI estimates at sufficiently fine spatial and temporal resolutions to capture and characterize its inherent variability and support detailed atmospheric and surface energy analyses. Here, we introduce a machine learning-based framework that facilitates the quantification of wavelength-resolved SSI at hyperlocal scales by integrating real-time measurements from an ensemble of low-cost spectral sensors with remote sensing-derived data. The sensor ensemble, capable of scalable neighborhood-level deployment due to their relatively lower cost, enables dense spatial sampling of irradiance-related parameters. Together with remotely-sensed surface reflectance data, these measurements serve as inputs to the model, which is trained to effectively reproduce the spectral irradiance observations made by a co-located NIST-calibrated reference instrument. This approach addresses key limitationsof remote sensing SSI products such as coarse resolution and temporal data gaps, and offers a scalable, data-driven solution for high-accuracy solar irradiance estimation in support of solar energy, urban climate, and environmental monitoring applications.
## 3 Materials and Methods
### Communication Layer
#### 3.7.2 WiMAX  
Despite the diffusion of the GSM/GPRS systems, it is not always present in the areas where the sites may be located. In fact, in rural areas, such as ours, a base station for GPRS communication may not be economical in case of low traffic, making the service not profitable for a provider. Usually, the distance covered by a base station is about a hundred meters and in any case not more than a few kilometers [49].  
Worldwide Interoperability for Microwave Access (WiMAX) technology is a wireless solution based on the IEEE 802.16 standard which allows to overcome this problem reaching distances of tens of kilometers [50; 51] and providing Internet service also in rural areas. By splitting the bandwidth into multiple narrowband channels, WiMAX allows data transport over multiple broad frequency ranges achieving high data rates due to the use of orthogonal frequency division multiplexing (OFDM) [52].  
Our system uses a base station (BS) able to spread a WiMAX single carrier TDD service at 3.4845 GHz center frequency and a bandwidth of 10 MHz. As transmitting antenna, the BS uses an Argus model SPPX310M (65 deg Horizontal cut, 6.5 deg Vertical cut) [53] for a total transmitting power of 55 dBm EIRP. The antenna was placed outside on the roof of the church. To communicate between the heritage site and the BS, a Huawei branded HES-319M outdoor WiMAX Customer-Premises Equipment (CPE) [54] has been used. To reliably send data at high speed, the CPE is equipped with a 3.5 GHz 45 \({}^{\circ}\)Cross-Polarization built-in directional antenna with a maximum power at antenna port of 26 dBm and 14 dBi antenna gain. Furthermore, it avoids any type of interference with the ZigBee standard due to its different operating frequency. The WiMAX module uses 64 QAM5/6 modulation for the downlink when the Carrier to Interference-plus-Noise Ratio (CINR) is higher than 30 dB and 16 QAM3/4 for the uplink. Instead, when the CINR is lower than 10 dB the link is still possible but using QPSK1/2 modulation because it is more robust at higher noise levels. The CPE is Power over Ethernet (PoE) 802.3 a/f compliant and it runs at 48 VDC, so a DC-DC step-up boost has been required to make the CPE working in our system. We used a LTC3863 [55] mounted on its 1286 A demonstration circuit in order to boost up the 12 V from solar panel to the 48 VDC needed at the CPE PoE connector.
# Machine Learning Techniques for Non-Terrestrial Networks
###### Abstract  
Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements and integration in the 5G 3 GPP standards, NTNs have been gaining importance in the last years and will provide further applications and services. 3 GPP standardization is integrating low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs) and unmanned aerial systems (UASs) as non-terrestrial elements (NTEs) in the NTNs within the terrestrial 5G standard. Considering the NTE characteristics (e.g., traffic congestion, processing capacity, oscillation, altitude, pitch), it is difficult to dynamically set the optimal connection based also on the required service to properly steer the antenna beam or to schedule the UE. To this aim, machine learning (ML) can be helpful. In this paper, we present novel services supported by the NTNs and their architectures for the integration in the terrestrial 5G 3 GPP standards. Then, ML techniques are proposed for managing NTN connectivity as well as to improve service performance.  
Keywords:non-terrestrial networks; machine learning; unmanned aerial vehicle; high-altitude platform station; low-Earth orbit satellite; 3 GPP release 17; 3 GPP release 18 +
Footnote †: journal: _Electronics_
## 2 Materials and Methods
### Tower-Based Datasets  
We used both satellite and tower-based data to obtain the relationship between \(\mathit{SIF}_{\mathit{yield}}\) and \(\mathrm{cos}\) (\(\mathrm{SZA}_{0}\)). The SIF inversion was based mainly on the Fraunhofer Line Depth (FLD) and the Spectral Fitting Method (SFM). The tower-based cropland SIF dataset used in this study was acquired at the Huailai, Daman and Aurora sites [(43; 44; 45)]. The canopy SIF data from Huailai and Daman covered the period from July to September in both 2017 and 2018, and from April to October in 2019. The canopy SIF dataset in maize field from Aurora site covered the period from July 2018 to September 2018 (doi: 10.22002/D1.1226), which are available from California Institute of Technology ([[https://data.caltech.edu/records/1226](https://data.caltech.edu/records/1226)]([https://data.caltech.edu/records/1226](https://data.caltech.edu/records/1226))) [(45)]. Another canopy SIF dataset that was used was acquired at the Niwot Ridge site from June 2017 to June 2018; the major species here is evergreen needleleaf. This needle-scale SIF data (doi: 10.22002/D1.1231) are available from the California Institute of Technology ([[https://data.caltech.edu/records/1231](https://data.caltech.edu/records/1231)]([https://data.caltech.edu/records/1231](https://data.caltech.edu/records/1231))) [(9)].  
In total we acquired 372 samples from the Huailai and Daman sites, 209 samples from the Niwot Ridge site and 57 samples from the Aurora site. Table 1 shows the detailed information of these sites.
## 3 Results
### Evaluation Criterion and Temporal Sampling
Figure 6: Averaged emissions [g h\({}^{-1}\) LU\({}^{-1}\)] predicted by gradient boosting, multilinear regression with reduced number of input features, a hyperparameter-tuned artificial neural network and support vector regression. The box plots show the mean values and the variabilities based on 30 random realizations of each scenario, where the associated length of the measurement period in the scenario is indicated by the colors cyan (1 day), blue (7 days) and black (14 days). For comparison the mean observed emission value (11.6 g h\({}^{-1}\) LU\({}^{-1}\), calculated from the full experimental dataset) is indicated by a red line. Please note that the \(y\)-axes have different scaling. This can be also seen in Table 3. The three best performing models in terms of MAE, RMSE and R\({}^{2}\) were all scenarios with six times 14 days. The lowest TAE (i.e., the best prediction of the 10-month average emission value), on the other hand, was obtained in a scenario with six times 7 days, followed by scenarios with four times 14 days and four times 7 days. Most of the best performing methods focused on data from transition periods. Particularly, winter measurements were under-represented in the best performing scenarios. Figure 7: Ranking of the explored scenarios: In the upper diagram, scenario indices are sorted based on the mean predicted emission values, starting with the largest value. In the lower diagram, scenario indices are sorted based on the mean absolute extrapolation errors (MAE), starting with the smallest error. All means were calculated over 30 realizations per scenario and are represented by the border between brown and orange bars. The width of the one-sigma interval is indicated by the orange and brown bars. The red line in the upper panel corresponds to experimental emissions averaged over the 10 month measuring period (11.6 g h\({}^{-1}\) LU\({}^{-1}\)).
## 2 The Wear Data Analysis for PCCS and Problem Statement
### Problem Statement  
PCCS is a critical component of railway vehicles. It is desirable to know its RUL after one inspection is carried out. As stated in Section 2.3, the wear trend for each PCCS is distinctive. Therefore, RUL prediction is necessary for determining the maintenance and also inventory control, which motivates the work in this paper.  
The Wiener process is widely used for the modeling of linear degradation process with random noise. A major advantage of the method is its sound mathematical characteristics. The standard BM-driven stochastic model can not only model the non-monotonic degradation process but also model the monotonic degradation process approximately, which has great flexibility. The method proposed in [8] is chosen because of its several attractive features: the degradation coefficient is update  
Figure 5: Average LM histogram of the three types of PCCS.  
Figure 6: The wear trend of the three types of PCCS.  
recursively by the KF algorithm; the EM algorithm is adopted to update the model parameters, and the exact PDF of the RUL is deduced based on FHT.  
The standard method in [8] uses equal-interval data and the degradation process of the gyros has the non-monotonic. Because of the flexibility of the standard BM-driven stochastic model, the Wiener process can be used to model the monotonic degradation process of PCCS approximately. However, the wear inspection data is usually unequal interval data. Therefore, the problem to be solved is how to use the unequal interval data with the method in [8] to predict the RUL. This issue will be addressed in the following section first.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 5 Experiment Design
### Metrics  
Several well-established metrics were used to evaluate the accuracy, intelligibility, and quality of the reconstructed speech. 2D Pearson's correlation coefficient (Corr2D) [67] was used to measure the correlation between the reconstructed and main spectrograms (ranging from \(-1\) to \(+1\)). To assess the intelligibility, the Short-time Objective Intelligibility measure (STOI) was utilised, which has a range between 0 and 1. For evaluating the overall quality, Perceptual Evaluation of Speech Quality (PESQ) [68] was employed. PESQ returns a score between \(-0.5\) and \(+4.5\). Additionally, the Objective Overall Quality (OOQ) metric was utilised, introduced by [PERSON] and [PERSON] [69], which combines individual objective measures such as PESQ, Log-Likelihood Ratio (LLR) [70], and Weighted-Slope Spectral Distance (WSS) [69], as defined in Equation (3):  
\[C_{Q}=1.594+0.80 PESQ-0.512 LLR-0.07 WSS \tag{3}\]