esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 4 The Proposed Algorithm
### Class Space Compression Sub-Module  
In order to further compress the feature space of known electromagnetic signal targets, the center loss [44] was introduced to optimize our algorithm. The center loss increases the distance between the classes by constraining the inter-class dispersion and intra-class compactness to improve the feature space range of unknown classes. Although it cannot increase the inter-class distance of the class center, the increase in the inter-class edge distance can increase the scope of the unknown class feature space when combined with the definition of reciprocal points to further improve the performance of the method. For each category \(k\), we introduced a central feature \(\mathbf{c}_{k}\) to further define the central loss as:  
\[\mathcal{L}_{cen}:=\sum_{k=1}^{\ell}\sum_{\mathbf{y}_{k}=k}\lVert h(\mathbf{x })-\mathbf{c}_{k}\rVert^{2}, \tag{8}\]  
where \(h(\cdot)\) denotes the feature embedding function based on the above signal representation sub-module. The central feature \(\mathbf{c}_{k}\) of category \(k\) dynamically changes with respect to the signal representation neural network, whose working principle is shown in Figure 2.  
However, due to the large amount of data in the training set, the characteristics of each sample are calculated in every iteration, and the central features of each category are updated at the same time, which leads to a large computational overhead and low computational efficiency. Therefore, in the actual training process, the update of the feature center is not based on the entire training set but based on batch samples. In each batch update process, our algorithm calculated the distance between the sample feature and the feature center, and then the calculation result was used to update the feature center. After the establishment of the clustering centers, although the distance between them did not increase, the intra-class space of each target was compressed. In the open set scenario, the feature spaces of the known and unknown electromagnetic signal targets complemented each other in the global space. So, the inclusion of the central loss could also increase the target feature space of the unknown electromagnetic signals and effectively improve the performance of the algorithm.  
Furthermore, in order to constrain the convergence direction of reciprocal points, the distance loss between the reciprocal points of each electromagnetic signal category \(p^{k}\) and the cluster center of the non-target and known class targets \(\mathcal{T}_{k}\) were introduced, i.e.,  
\[\mathcal{L}_{ac}:=\left\|\mathcal{T}_{k}-p^{k}\right\|^{2}. \tag{9}\]  
The overall loss function of our proposed algorithm is expressed as:  
\[\mathcal{L}_{all}\left(\theta,\left\{p^{k}\right\},\mathcal{R}\right):= \mathcal{L}_{known}+\alpha\mathcal{L}_{o}+\beta\mathcal{L}_{cen}+\eta \mathcal{L}_{ac}, \tag{10}\]  
With \(\alpha\), \(\beta\), and \(\eta\) as the related penalty parameters.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 2 Related Work
### Summary of the Literature  
The survey of the literature shows that the existing work in Arabic pronunciation can be categorized as mispronunciation detection [9; 13], sometimes with a focus on non-native speakers [17; 18], speech error detection [15], correct pronunciation detection [10], and detecting the similarities and differences between pronunciation of vowels and consonants for MSA [30]. As such, there have been a number of approaches from articulatory models [15] and transfer learning [10] to CNN [13] and acoustic models [31]. To the best of our knowledge, we could not identify any work that can detect the correct pronunciation of Arabic alphabets vowels in CA. One possible reason of not undertaking this work previously maybe because it makes a total of 84 cases to be distinguished from one another. This task is not only challenging in terms of data availability and retrieval but also in terms of model development. To carry out such a challenging task will remove many barriers in the correct utterance of CA words, which can benefit millions of people.  
In this research work, we take up this challenging task and propose a deep convolutional neural network algorithm for the classification of Arabic alphabets with vowels. Our research is different from the previous works in terms of the dataset, features engineering, proposed architecture, and evaluation performance.
## 4 Discussion
(d) AS is a generalization of Cmax/Tmax, and therefore AS can be applied to either equally or unequally spaced (as they usually exist in practice) sampling schemes. (e) Due to the calculation of AS, which relies on many data points, estimation bias that might occur to Cmax/Tmax can be avoided in the case of AS. (f) Finally, the weighted version of AS (i.e., AS\({}_{\text{w}}\)) allows more emphasis to be placed on early time points, thus expressing more purely the absorption process. A limitation of this study is that the machine learning methods were only applied to one actual BE dataset. Even though several simulated datasets were constructed and studied, to circumvent this constraint, the additional use of actual BE datasets is required. Additionally, more research on the performance of the PK measures in more drugs with variable pharmacokinetics, particularly absorption kinetics, is needed. Additional work is needed to explore the sensitivity and specificity of the new metric, AS, under several absorption kinetics scenarios. In this context, it would be necessary to quantify type I (1--specificity) and type II (1--power) errors of AS and AS\({}_{\text{w}}\), using the traditional method of Monte Carlo simulations. Many modeling studies are required to examine all possible scenarios of absorption (e.g., lag time, parallel absorption, Erlang-type, multiple transit compartments, double peaks, etc. ), as has been done in the past for other metrics [20]. In this study, machine learning was applied to reveal the properties of AS in a way that has not been done before. In addition, studies with large sample sizes or merging different studies should be applied in order to increase the data input and facilitate the ML algorithms to get robust estimates. In this study, to address the problem of low data entry, the simulated data consisted of N = 1000 subjects under each scenario.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 1 Introduction
Accordingly, by choosing \(\alpha\) close to or equal to \(1\), we get an iteration that converges faster, but the weights used are far from optimal, which leads to a loss of accuracy. The question arises: _is there a way to modify the Cadzow iterations to obtain both fast convergence and high accuracy?_  
**Proposed approach. ** We propose to consider \"mixed Cadzow iterations\" defined in the following way:  
\[\mathbf{X}_{T}=\Big{(}\Pi_{\mathcal{H}_{t}(\mathbf{L}_{2},\mathbf{R}_{2})}\Pi_ {\mathcal{M}_{r}(\mathbf{L}_{2},\mathbf{R}_{2})}\Big{)}^{T_{2}}\Big{(}\Pi_{ \mathcal{H}_{t}(\mathbf{L}_{1},\mathbf{R}_{1})}\Pi_{\mathcal{M}_{r}(\mathbf{ L}_{1},\mathbf{R}_{1})}\Big{)}^{T_{1}}\mathbf{X}_{0}, \tag{6}\]  
where \(T=T_{1}+T_{2}\) and \(T_{1},T_{2}\geq 0\) set the number of iterations performed with matrix weights \((\mathbf{L}_{1},\mathbf{R}_{1})\) and \((\mathbf{L}_{2},\mathbf{R}_{2})\), respectively. The approach is to use different weights \((\mathbf{L}_{1},\mathbf{R}_{1})\) and \((\mathbf{L}_{2},\mathbf{R}_{2})\) that correspond to different \(\alpha\) to achieve an estimate of the signal that would be both sufficiently accurate and at the same time rapidly computable. **Main results. ** The main result consists of two parts. Theorem 2 contains the limit properties of (6) in the general case of alternating projections. Theorem 6, which is its corollary, is used to justify the proposed algorithm with mixed alternating projections for solving the problem (3). Section 5.2 demonstrates the capabilities of the proposed algorithm. **Paper's structure. ** In this paper, we study general properties of alternating projections (Section 2) and mixed alternating projections (Section 3) and then apply them to Cadzow iterations with a justification of the choice of weights for the projectors participating in the mixed iterations. To do this, we consider a linearized version of the algorithm, where the projection on the set of rank-deficient matrices is replaced by the projection on the tangent subspace to the set of such matrices (Section 4). This allows us to apply the linear operator theory to the mixed iterations (Section 5). Section 5.1 discusses how the algorithm's parameters affect the accuracy and rate of convergence. In Section 5.2, the obtained results are confirmed by a numerical experiment. Sections 6 and 7 conclude the paper.
## 3 Thermal Effects Prediction on Shape Deformation via Infrared Thermogram
### Infrared Temperature Measurement  
In the actual radiation model, in addition to the energy radiated by the object itself, it can also reflect and transmit the environment radiation, so the energy reaching the infrared optical system mainly consists of three parts.  
\[E=E_{1}+E_{2}+E_{3}, \tag{22}\]  
where \(E\) is radiant energy reaching the infrared optical system (kJ), \(E_{1}\) is energy radiated by the object itself (kJ), \(E_{2}\) is environmental radiation energy reflected by the target surface (kJ), \(E_{3}\) is environmental radiation energy transmitted by target surface (kJ).  
Assuming that the target emissivity is \(\varepsilon\) and the reflectivity is \(\rho_{r}\), the transmissivity is \(\tau\), and the total environmental radiation energy is \(E_{a}\) (kJ), and the radiation energy of blackbody at the same temperature with the target surface is \(E_{b}\) (kJ), \(E\) is determined as follows.  
\[E=\varepsilon E_{b}+\rho_{r}E_{a}+\tau E_{a}, \tag{23}\]  
where \(\varepsilon+\rho_{r}+\tau=1\).  
According to [PERSON] law, \(E\) can be obtained as follows.  
\[\sigma_{s}T_{r}^{4}=\varepsilon\sigma_{s}T_{b}^{4}+(1-\varepsilon)\sigma_{s}T _{a}^{4}, \tag{24}\]  
where \(\sigma_{s}=5.67\times 10^{-8}\Big{(}W\cdot m^{-2}\cdot K^{-4}\Big{)}\) is Stefan constant, \(T_{r}\) is radiation temperature of target surface \((K)\), \(T_{a}\) is ambient temperature \((K)\), and \(T_{b}\) is actual temperature of target surface \((K)\).  
Then, actual temperature \(T_{b}\) of target surface can be obtained as follows.  
\[T_{b}=\sqrt[4]{\frac{T_{r}^{4}-(1-\varepsilon)T_{a}^{4}}{\varepsilon}}. \tag{25}\]  
The thermographs using thermal field measurement is shown in Figure 4. The noise equivalent temperature difference (NEDT) of used FLUKE(r) thermography is NEDT (30 \({}^{\circ}\)C) \(\leq\) 0.09 \({}^{\circ}\)C. The instantaneous field of view (IFOV) of thermal infrared imager instrument is IFOV \(<\) 1.25 mRad. The detector resolution of the thermal infrared imager is more than 320 \(\times\) 240 (76,800 pixels). The measurable temperature range is [\(-\)20 \({}^{\circ}\)C, 1200 \({}^{\circ}\)C]. The \(\varepsilon\) = 0.95 and \(\tau\) = 100% for measured target.
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 3 A Framework for Damping Estimation of Passively Controlled Structures
### An Improved Berman-Nagy Method
#### 3.2.1 Problem Description  
Let \(\mathbf{M}_{a}\) and \(\mathbf{K}_{a}\) represent, respectively, the nominal mass and stiffness matrices of a structure with \(n\) degrees of freedom. As discussed in the introduction part, its analytical modal characteristics frequently deviate from those measured from the real structure, with mass and stiffness matrices denoted as \(\mathbf{M}\) and \(\mathbf{K}\). In the traditional Berman-Nagy method, the updated mass matrix, denoted as \(\mathbf{M}_{B}\), and stiffness matrix, denoted as \(\mathbf{K}_{B}\), are  
\[\mathbf{M}_{B}=\mathbf{M}_{a}+\mathbf{M}_{a}\boldsymbol{\Phi}\Big{(}\Phi^{ \text{T}}\mathbf{M}_{a}\boldsymbol{\Phi}\Big{)}^{-1}\Big{(}\mathbf{I}- \boldsymbol{\Phi}^{\text{T}}\mathbf{M}_{a}\boldsymbol{\Phi}\Big{)}\Big{(} \boldsymbol{\Phi}^{\text{T}}\mathbf{M}_{a}\boldsymbol{\Phi}\Big{)}^{-1} \boldsymbol{\Phi}^{\text{T}}\mathbf{M}_{a} \tag{13}\]  
\[\mathbf{K}_{B}=\mathbf{K}_{a}+\mathbf{M}_{B}\boldsymbol{\Phi}\Big{(}\Phi^{ \text{T}}\mathbf{K}_{a}\boldsymbol{\Phi}+\boldsymbol{\Omega}^{2}\Big{)} \boldsymbol{\Phi}^{\text{T}}\mathbf{M}_{B}-\mathbf{K}_{a}\boldsymbol{\Phi} \boldsymbol{\Phi}^{\text{T}}\mathbf{M}_{B}-\mathbf{M}_{B}\boldsymbol{\Phi} \boldsymbol{\Phi}^{\text{T}}\mathbf{K}_{a} \tag{14}\]  
where \(\boldsymbol{\Omega}\) is the \(m\times m\) diagonal matrix of measured natural frequencies, \(m\) is the number of measured modes, \(\boldsymbol{\Phi}\) is the \(n\times m\) matrix of measured mode shapes, and \(\mathbf{I}\) is the \(m\times m\) identity matrix. Notably,such updated mass and stiffness matrices accurately replicate the measured natural frequencies and mode shapes, while they also generate predictions for mode participation factors as  
\[\mathbf{P}_{B}=\mathbf{\Phi}^{\mathrm{T}}\mathbf{M}_{B}\mathbf{I}_{n}=\big{(} \mathbf{\Phi}^{\mathrm{T}}\mathbf{M}_{a}\mathbf{\Phi}\big{)}^{-1}\mathbf{\Phi}^ {\mathrm{T}}\mathbf{M}_{a}\mathbf{I}_{n} \tag{15}\]  
where \(\mathbf{P}_{B}\) denotes the vector of predicted mode participation factors, \(\mathbf{I}_{n}\) is the identity vector of order \(n\). As discussed earlier, these byproduct predictions may differ from those measured from real structures. For applications in earthquake engineering, mode participation factors participate in determining mode response amplitudes, therefore, any error in their predictions will lead to flaws in updated structural models. To address this problem, a new procedure for mass matrix updating is developed as below.
## 2 Materials and Methods
### Nash-Sutcliffe Efficiency Coefficient  
Nash-Sutcliffe Efficiency (_NSE_) has been used to assess the predictive power of hydrological models [46], and it can be also used to quantitatively describe the accuracy of model outputs other than discharge as long as there is observed data to compare  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **Evaluation** & **Abbr.** & \begin{tabular}{c} **Original** \\ **Spatial** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **Temporal** \\ **Resolution** \\ \end{tabular} & **Period** & **Reference** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} climatic \\ dryness \\ indicator \\ \end{tabular} } & GRACE TWS & TWS & 0.50\({}^{\circ}\) & Monthly & 2003–2010 & Humphrey and \\  & ERA-Interim & VPD & 0.125\({}^{\circ}\) & Daily & 2003–2010 & \\  & Terracclimate & \multirow{2}{*}{PDSI} & \multirow{2}{*}{0.50\({}^{\circ}\)} & \multirow{2}{*}{Monthly} & 2003–2010 & \\  & PDSI & & & & 2011 [30; 56] \\  & Climatological & \multirow{2}{*}{CWD} & \multirow{2}{*}{0.50\({}^{\circ}\)} & \multirow{2}{*}{Monthly} & 2003–2010 & [PERSON] et al., 2018 [59] \\  & Water Deficit & & & & & \\ \multirow{4}{*}{
\begin{tabular}{c} canopy \\ photosynthesis \\ proxy \\ \end{tabular} } & MODIS EVI & \multirow{2}{*}{0.05\({}^{\circ}\)} & \multirow{2}{*}{Monthly} & 2003–2010 & [PERSON] et al., 2002 [33] \\  & GOME-2 SIF & & & & 2007–2010 & [PERSON] et al., 2013 [34] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of climatic dryness indicators and canopy photosynthesis.  
the model results. Therefore, it was widely used for optimizing parameter values of geophysical models [71], hydrology about water quality constituents such as sediment, nitrogen, phosphorus loading [72], climate variables [73; 74], land-atmosphere heat flux [75] and Gross primary production (GPP) [76]. To only compare the phase of seasonality correcting for the impacts of different seasonal amplitudes, we used the Z-transformed variables to calculate the \(NSE\) between modeled and observation-based data.  
\[NSE\ =\ 1-\frac{\sum_{t=1}^{T}(MTVDI_{t}-Obs_{t})^{2}}{\sum_{t=1}^{T} \left(Obs_{t}-\overline{Obs}\right)^{2}} \tag{13}\]  
where \(MTVDI_{t}\) is the normalized MTVDI data at time \(t\), and \(Obs_{t}\) is the normalized photosynthesis proxies (EVI and SIF) or drought indicators (TWS, VPD, PDSI and CWD) at time t (month). \(NSE\) is sensitive to both absolute values and seasonal variations [77]. The range of \(NSE\) is (\(-\infty\), 1].  
An efficiency of 1 (\(NSE\) = 1) corresponds to a perfect match of modeled outputs to the observed data. An efficiency of 0 (\(NSE\) = 0) indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency less than zero (\(NSE\) < 0) occurs when the model outputs perform poorly in comparison with the observed data.  
Particularly, the MTVDI has a negative correlation with CWD, PDSI, TWS, EVI, and SIF, therefore, we used minus MTVDI to calculate \(NSE\) between MTVDI and those indicators.
## 4 Method
### Transformers
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Model** & **DA** & B & **f1** & **Precision** & **Recall** & **Accuracy** \\ \hline bert-base-multilingual-cased & & & 0.83 & 0.80 & 0.87 & 0.76 \\ bert-base-multilingual-cased & & ✓ & 0.83 & 0.76 & **0.91** & 0.73 \\ bert-base-multilingual-cased & ✓ & & 0.83 & 0.80 & 0.87 & 0.76 \\ bert-base-multilingual-cased & ✓ & ✓ & **0.84** & 0.78 & **0.91** & 0.76 \\ distilbert-base-multilingual-cased & & & **0.84** & **0.81** & 0.88 & 0.77 \\ distilbert-base-multilingual-cased & & ✓ & 0.80 & 0.76 & 0.86 & 0.70 \\ distilbert-base-multilingual-cased & ✓ & & 0.82 & 0.77 & 0.88 & 0.84 \\ distilbert-base-multilingual-cased & ✓ & ✓ & 0.80 & 0.73 & 0.88 & 0.81 \\ xlm-roberta-large & & & 0.83 & **0.81** & 0.86 & 0.76 \\ xlm-roberta-large & & & ✓ & 0.81 & 0.78 & 0.84 & 0.72 \\ xlm-roberta-large & ✓ & & 0.77 & 0.69 & 0.88 & 0.79 \\ xlm-roberta-large & ✓ & ✓ & 0.75 & 0.69 & 0.83 & **0.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performance for the “offshorable” indicator with/without domain adaptation (DA) and layer freeze (LF). The best results are indicated in bold. \begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Model** & **DA** & **LF** & **f1** & **Precision** & **Recall** & **Accuracy** \\ \hline bert-base-multilingual-cased & & & 0.71 & 0.73 & 0.69 & 0.69 \\ bert-base-multilingual-cased & & ✓ & 0.73 & 0.72 & **0.79** & 0.68 \\ bert-base-multilingual-cased & ✓ & & **0.75** & **0.76** & 0.74 & 0.73 \\ bert-base-multilingual-cased & ✓ & ✓ & 0.73 & 0.72 & 0.74 & 0.69 \\ distilbert-base-multilingual-cased & & & 0.69 & 0.70 & 0.69 & 0.66 \\ distilbert-base-multilingual-cased & & ✓ & 0.69 & 0.68 & 0.72 & 0.65 \\ distilbert-base-multilingual-cased & ✓ & & 0.67 & 0.58 & 0.78 & 0.75 \\ distilbert-base-multilingual-cased & ✓ & ✓ & 0.68 & 0.60 & **0.79** & **0.76** \\ xlm-roberta-large & & & 0.74 & 0.75 & 0.75 & 0.72 \\ xlm-roberta-large & & & ✓ & 0.69 & 0.66 & 0.74 & 0.65 \\ xlm-roberta-large & ✓ & & 0.67 & 0.62 & 0.74 & 0.73 \\ xlm-roberta-large & ✓ & ✓ & 0.67 & 0.64 & 0.71 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performance for the “automatable” indicator with/without domain adaptation (DA) and layer freeze (LF). The best results are indicated in bold. For hyperparameter optimization, we used Optuna framework [45], one of the earliest tools that offered streamlined sampling and pruning algorithms. Adding Optuna improves the efficiency of our pipeline. Table 4 provides a summary of the Transformer hyperparameters used in the experimental section. The final experiments drew upon the DistilBERT classifier (without domain adaptation and layer freeze), which provided the best results for the offshorable indicator, a decent performance for the automatable label, and required the least resources for training, therefore making it the most efficient choice for our specific task.
## 3 Results
### Bioactivity Response  
The ability of the coatings to promote apatite formation in SBF is a key indicator of their bioactivity and potential for osseointegration in biomedical applications [55]. To assess this, SBF immersion tests were conducted on the uncoated Ti-6 Al-4V substrate, the TiO\({}_{2}\) pre-coated substrate, and the HAp-coated substrate. Figure 12 presents SEM images of each sample before and after a 15-day incubation period in SBF at 37 \({}^{\circ}\)C.  
As observed in Figure 12a,b, the uncoated substrate maintained uniform and smooth individual grain surfaces, with no evident signs of apatite formation after SBF exposure, indicating negligible bioactivity. The TiO\({}_{2}\) pre-coated sample (Figure 12c,d) exhibited a slight increase in surface porosity after immersion, possibly due to minor surface reactions; however, the absence of distinguishable apatite crystals suggests limited bioactivity. In contrast, the HAp-coated sample (Figure 12e,f) showed a notable transformation. Prior to immersion, the surface presented the typical rough and porous morphology associated with HAp coatings. After 15 days in SBF, the formation of a dense layer of spherical,  
Figure 12: SEM micrographs of uncoated substrate (**a**,**b**), TiO\({}_{2}\) pre-coating (**c**,**d**) and HAp coating (**e**,**f**) before and after 15 days of incubation in SBF at 37 \({}^{\circ}\)C.  
globular deposits was evident--an indication of extensive apatite (Ca+P) precipitation. This morphological change is a strong indicator of the coating's bioactivity tendency. The EDS spectrum of the post-immersion surface, present in Figure 13, confirmed the composition of the deposited layer as apatite, with dominant peaks of calcium (Ca), phosphorus (P), and oxygen (O). Trace amounts of sodium (Na), magnesium (Mg), and chlorine (Cl), originating from the SBF solution, were also detected, while titanium (Ti) signals are attributed to the underlying substrate.  
Before immersion in SBF, the HAp coating exhibited a Ca/P atomic ratio of 1.90, which is slightly higher than the stoichiometric value of 1.67 for pure hydroxyapatite (Table 3). This deviation suggests a calcium-enriched surface, likely resulting from the synthesis conditions, particularly the highly alkaline pH [44]. Following 15 days of immersion in SBF, the Ca/P ratio increased further to 2.10. This rise can be attributed to the selective adsorption and incorporation of calcium ions (Ca\({}^{2+}\)) from the SBF solution onto the HAp surface during the nucleation and growth of new apatite phases. Although SBF contains both calcium and phosphate ions, its ionic exchange dynamics tend to favor the accumulation of calcium--especially on already calcium-rich surfaces--resulting in the formation of a calcium-rich apatite layer.  
These results confirm the superior bioactivity tendency of the HAp coating, in stark contrast to the inert behavior observed for the uncoated and TiO\({}_{2}\)-coated substrates. This clearly demonstrates the effectiveness of the electrodeposited HAp layer in promoting in vitro bone-like mineralization and its potential for enhancing osseointegration in biomedical implants.