esa-sceva/satcom-chunk-collection huggingface dataset:
### Time-Varying Filters  
Time-varying convolution has been explored in continuous-time systems [9], and in discrete-time systems, both finite impulse response (FIR) and infinite impulse response (IIR) coefficient-modulated filters have been extensively discussed in [10]. Applications are numerous and include, for instance, speech processing [11; 12], equalization of audio signals [13; 14; 15], binaural processing [16; 17], and reverberation [6; 18].  
Digital time-varying filters in music are typically composed of recursive filter structures of lower order and proposed approaches are particularly concerned with stabilization and transient suppression [13; 19]. Strategies reported include intermediate sets of filter coefficients [15; 20; 21], state variable updates [11; 19], and input-switching [12].  
The present paper is concerned with impulse responses live sampled from audio signals. The filter order is also substantially higher than in the approaches listed above, since the audio samples involved may possibly last several seconds. Hence, we are specifically looking at time-varying FIR filters that can be dynamically updated without perceptual artefacts. Virtual acoustic reality is another application area where convolution with time-varying FIR filters find use [16; 17; 22]. One possible approach to avoid perceptual artefacts is to cross-fade between the outputs of several simultaneous convolution processes [23]. However, this gets prohibitively expensive for audio-rate filter updates. [PERSON], [PERSON], and [PERSON] [17] suggests incremental filter switching at high update rates (referred to as commutation) for binaural processing, while [PERSON] et al. [18] suggests a similar approach for artificial late-field reverberation where the rate of change may adapt to characteristics of the input. These strategies minimize the artefacts, but at the cost of computing intermediate filters. [PERSON] [24] presents a number of frequency-domain strategies for time-varying FIR filters. In general, the solutions either violate the constraints of linear convolution and produce artefacts, or they demand extra processing power: typically more than twice the number of real multiplications per output sample compared to the common convolution implementation overlap-add short-time Fourier Transform (OLA STFT).  
Instead, we will present two different techniques that perform dynamic, low-latency convolution of live-sampled impulse responses with negligible computational overhead.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 8 Conclusions  
We have proposed a novel elevation localization model for binaural localization based on the RF algorithm. The algorithm operates on the interaural features--such as ILD and IPD--in the spectral domain. During the training process, the RF algorithm successfully selected the most reliable feature of a spectrum by using information gain, and constructed the elevation-dependent mappings simultaneously based on its selections for each sagittal plane. In the testing, the multi-tree structure showed robustness to additive noise and flexibility to an unfamiliar acoustical environment. Further, a hierarchical localization system was also proposed for 3-D space localization, which achieved outstanding localization performance in the simulation. The proposed method is also tested in a real laboratory environment, and the result justifies the effectness of the model.  
Conceptualization, X.W. and D.S.T.; methodology, X.W., D.S.T. and W.Z.; software, X.W.; validation, X.W., D.S.T., W.Z. and T.D.A.; formal analysis, X.W.; investigation, X.W.; resources, X.W.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, W.Z. and T.D.A.; visualization, X.W.; supervision, W.Z. and T.D.A.; project administration, T.D.A.  
This research received no external funding.  
We thank [PERSON] for assistance with loudspeaker-array application and the laboratory maintenance, and Elite Editing for comments that greatly improved the manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loudspeaker No.** & **True Azimuth** & **True Elevation** & **Estimated Azimuth** & **Estimated Elevation** & **Estimated Error** \\ \hline  
79.19^{\circ}\) & \(-30.00^{\circ}\) & \(-33.75^{\circ}\) & \(39.08^{\circ}\) \\ \hline \hline \end{tabular} \({}^{*}\) indicates the indexes of out-of-range speakers.  
\end{table}
Table 4: To performance of the passive model. The ground truth loudspeaker locations are represented in \(\mathbf{\Theta_{IP}}\).
## 3 Method and Materials
### ERNIE Word Embedding Layer  
The ERNIE model essentially adopts multi-layer Transformer blocks, and uses the bidirectional Transformer encoder structure for text vector representation. It converts the preprocessed data into a digital matrix; each column and row of the matrix represent a recognized feature and a specific review, respectively. The Encoder structure in the Transformer unit of the ERNIE model is shown in Figure 6.  
After converting text to word embedding, enter it into the self-attention layer in Transformer to get context information. Then the multi-head self-attention layer mixes word vectors and context information and sends the results to the Add & Normalize layer for residual connection and normalization. Subsequently, the processed text vector is linear changed through the feedforward neural network. Finally, the residual connection and normalization are performed again, and the dynamic word embedding representation of the reviews is obtained by combining the prior semantic information obtained by training and masking strategies in the heterogeneous corpus.  
The calculation process of multi-head self-attention layer in ERNIE is as follows:  
First, the input vector matrix is subjected to position encoding to obtain the vector matrix Y. Then, the matrix Y is multiplied by the three weighted matrices, respectively, and the word correlation degree is calculated. Finally, the output of the single self-attention layer is obtained by normalization. The calculation formula is given by Equations (2) and (3):  
\[Q=\text{Linear(Y)}=\text{YW}^{q}\ \ \text{K}=\text{Linear(Y)}=\text{XW}^{k}\ \ V=\text{Linear(Y)}=\text{XW}^{p} \tag{2}\]  
\[S_{\text{h}}=\textit{softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V \tag{3}\]  
where represent \(\text{W}^{q},\ \text{W}^{k},\ \text{W}^{p}\) weighted matrices, \(d_{k}\) is the dimension of the input vector, and the _softmax_ function normalizes the output result to obtain the weight sum of the word vector.  
Figure 6: Encoder structure in the Transformer unit of the ERNIE model.  
Next, the output of the self-attention layer is combined to obtain the output of the multi-head attention mechanism. The calculation formula is given by Equation (4):  
\[S_{\text{mh}}=\text{Concat}(S_{\text{h1}},S_{\text{h2}},\cdots,S_{\text{ht}}) \tag{4}\]  
Finally, the operation of residual connection-linear change-residual connection is performed and combined with prior semantic information to obtain the dynamic representation of the review:  
\[w_{2}=(x_{1},x_{2},x_{3},x_{4},\ldots,x_{t}) \tag{5}\]
## 3 GB-RAR Deformation Information Estimation Method Considering the Influence of Colored Noise
Then, the settlement model of the monitoring point can be expressed as follows:  
\[y(t_{i})=t_{i}x+v_{t_{i}} \tag{5}\]  
where \(y(t_{i})\) represents the settlement deformation time series of monitoring piers; \(x\) refers to the settlement rate; and \(v_{t_{i}}\) is the linear combination of white noise \(\alpha\) and colored noise \(\beta\), and its manifestations are as follows:  
\[v_{i}=\sigma_{w}\alpha_{t_{i}}+\sigma_{K}\beta_{t_{i}} \tag{6}\]  
where \(\sigma_{w}\) and \(\sigma_{K}\) are the amplitudes of the white and colored noises with spectral index \(K\), respectively. The matrix form and stochastic model of Equation (5) can be expressed as follows:  
\[y=Ax+\varepsilon \tag{7}\]  
\[D(y)=Q_{y}=\sigma_{w}^{2}I+\sigma_{K}^{2}Q_{K} \tag{8}\]  
where \(A\) denotes the design matrix, \(\varepsilon\) is the error, \(Q_{y}=D(y)\) represents the covariance matrix, \(I\) refers to the identity matrix, and \(Q_{K}\) is the covariance matrix of colored noise. Under the combination model of white noise and power law noise, the MLE criterion was used to analyze the settlement deformation time series and determine the estimation of each noise amplitude in the sequence, which can maximize the joint probability density of the residual and covariance of the deformation time series. The joint probability density of the residual \(v\) series and covariance matrix of the settlement deformation time series is expressed as follows:  
\[\text{lik}(v,Q_{y})= \frac{1}{(2\pi)^{N/2}\big{(}\text{det}Q_{y}\big{)}^{1/2}}\exp\Big{(} -0.5v^{\text{T}}Q_{y}^{-1}v\Big{)} \tag{9}\]  
where det denotes the determinant of the matrix, and \(N\) is the amount of data in the time series. After taking logarithms on both sides of Equation (9), it is equivalent to:  
\[\ln\big{[}\,\text{lik}(v,Q_{y})\big{]}=-0.5\Big{[}\ln(\text{det}Q_{y})+v^{ \text{T}}Q_{y}^{-1}v+N\ln(2\pi)\Big{]} \tag{10}\]  
When the spectral index \(K\) of colored noise is adjusted, the covariance matrix \(Q_{y}\) continuously changes until the maximum likelihood function value of Equation (10) reaches the maximum. The parameter estimation \(\hat{x}=\left(A^{\text{T}}Q_{y}^{-1}A\right)A^{\text{T}}Q_{y}^{-1}y\) is obtained by weighted least squares estimation, and the covariance matrix \(Q_{\hat{x}}=\left(A^{\text{T}}Q_{y}^{-1}A\right)^{-1}\) of the parameter can be finally obtained.
## 4 Materials and Methods
### Database Construction  
To identify coding regions within the transcriptomic data, we used TransDecoder v5.7.1 ([[https://github.com/TransDecoder/TransDecoder](https://github.com/TransDecoder/TransDecoder)]([https://github.com/TransDecoder/TransDecoder](https://github.com/TransDecoder/TransDecoder)), accessed on 8 January 2024) with a minimum open reading frame of 50 AA. We then constructed 7 protein databases (db) categorized by species and tissue type (whole body/non-specific, tentacles and nematocysts) for both Anthozoa and Medusozoa: Db1--6 proteomes derived from sequenced genomes of Anthozoa; Db2--2 proteomes derived from sequenced genomes of Medusozoa; Db3--46 whole-body/non-specific transcriptomes of Anthozoa; Db4--24 whole-body/non-specific transcriptomes of Medusozoa; Db5--25 transcriptomes specific to the tentacles of Anthozoa; Db6--7 transcriptomes specific to the tentacles of Medusozoa; Db7--2 transcriptomes specific to the nematocysts of Anthozoa. Duplicate sequences within each db were removed using Seqkit tool v2.6.1 [64]. Since AMPs often originate from larger precursor proteins, we screened all dbs for potential antimicrobial proteins using the AMPir (antimicrobial peptide prediction in r) precursor model [49]. Following this, the Seqkit tool v2.6.1 [64] was again employed to evaluate sequence similarities within and between the dbs before and after AMP screening. The Jaccard Index, expressed as percentages, served as the metric for all-vs-all pairwise similarity comparisons among databases [65] to evaluate their overall compositional similarities in amino acid content.
## 5 Discussion
### Variation of Operational Losses  
If initially, an immobilization period of 2 h was considered, basing the calculation of operational losses, we also consider the possibility of a variation in this time between 1 h and 5 h (1 h, 3 h, 4 h and 5 h) for the same hourly cost. Quite logically, a shorter--less expensive--downtime of one hour makes inspections less frequent, going from an optimal number of 10 to 9 in the linear case and remaining at 23 by mobilizing Prospect Theory. In this new context, the minimum cost including the risks goes from \(\ell\)595 to \(\ell\)567 and from \(\ell\)1332 to \(\ell\)1324, respectively. Similarly, a longer immobilization makes inspections more frequent, going for 3 h of immobilization from an optimal number of 10 to 11 and remaining at 23 by mobilizing Prospect Theory. With such an evolution, the minimum cost including risks goes from \(\ell\)595 to \(\ell\)622 and from \(\ell\)1332 to \(\ell\)1341, respectively. Similarly, a 4 h immobilization leads to, respectively, 11 and 23 inspections at the optimum. The minimum cost including the risks goes from \(\ell\)595 to \(\ell\)645 and from \(\ell\)1332 to \(\ell\)1349, respectively. Finally, an immobilization period of up to 5 h can still change the situation with an optimal number of inspections being, respectively, 12 and 23 according to the linear approach or that of Prospect Theory. Compared to the initial case, the minimum cost including the risks goes, respectively, from \(\ell\)595 to \(\ell\)668 and from \(\ell\)1332 to \(\ell\)1357 (Figure 7).  
The stability of the inspections carried out when the optimization is based on the second method actually comes from the fact that the amounts associated with operational losses are low compared to the assessment of the risks, in particular human risks, for a high number of inspections. Thus, the trade-offs and the optimal number of inspections are not modified. The variations are quite small even with linear approach. However, the slightly higher number of operational losses for fewer inspections (in the case of a linear approach) as well as the lower risk assessment can change slightly the situation and the trade-offs, thus modifying the number of inspections (Figure 7).
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
###### Abstract  
Building a new tunnel adjacent to an existing tunnel has become a common means of transformation in engineering. Existing tunnels are prone to some deterioration, such as cavities and cracks under long-term traffic load. This kind of deterioration tunnel is prone to collapsing under the action of blasting. Therefore, the vibration caused by blasting should be strictly controlled. Based on the reconstruction project of the Bo Jiling Tunnel, this paper puts forward the method of mechanical cutting in a central position combined with an ordinary detonator to reduce blasting vibrations. ANSYS/LS-DYNA version 19.2, was used to simulate two conditions of full-section blasting and central mechanical cutting blasting. By comparing the stress and velocity of the existing tunnel, the damping effect of mechanical cutting blasting is analyzed. Via field experiments, the superiority of the mechanical cutting method in reducing blasting vibration is further discussed. At the same time, the relationship between the main vibration frequency and the peak velocity of the existing deterioration tunnel is obtained by wavelet packet analysis of the field experimental data. The frequency band energy distribution in each direction of vibration velocity is also obtained. The results show that the central mechanical cutting increases the blasting free surface, and the mechanical cutting method reduces the vibration velocity by 36.3%. The third frequency band (31.25-46.875 Hz) is the most concentrated, which is the dominant frequency band of the signal. The novelty of this paper is to propose mechanical cutting of the central hole instead of traditional blasting for existing deterioration tunnels. The feasibility of this method is verified by numerical simulation and field tests. The relationship between peak vibration velocity, band energy, and tunnel frequency is clarified, which can better control blasting vibration and ensure the safety of existing deterioration tunnels.  
deterioration tunnel; vibration control; mechanical cutting; ANSYS/LS-DYNA; wavelet packet analysis Article  
Blasting Vibration Control and Signal Analysis  
Existing Deterioration Tunnels  
[PERSON], [PERSON], [PERSON], H.
# pH Measurement of Cement-Based Materials: The Effect of Particle Size
## 3 Results and Discussion  
### Effect of Particle Size on pH  
When a solid particle breaks into smaller particles, the total surface area increases. A large surface area allows more chemical reactions than a small surface area. Therefore, CBMs with smaller particle sizes have a higher total surface area in contact with solvent for the leaching of the OH\({}^{-}\) ion [33]. Figure 5 shows that larger particle sizes have a lower pH value, as evident in particle sizes of 2.36-4.75 mm to 0.60-1.18 mm. For particle sizes of 2.36-4.75 mm, the pH is about 0.26 units lower than very fine particles (<150 \(\upmu\)m using ball mill). Fine particles with sizes below 600 \(\upmu\)m have a pH differential of less than 0.05 units. For pH measurement, the sample particle sizes could be divided into two zones, namely, large particle size (particle sizes larger than 600 \(\upmu\)m) and fine particle size (particle sizes smaller than 600 \(\upmu\)m). Figure 5 also shows that the proposed pH measurement method has good repeatability, as the standard deviations are 0.05 or less for each category of particle size, as can be seen in Table 3. The standard deviation for each particle size denotes that the electrode probe has high accuracy during the measurement.  
Figure 4: Benchmark methodology for effect of particle size.  
The results for cement mortar are consistent with the findings from other research (for similar particle size) but differ for CBMs, such as cement paste and concrete (Table 3). The pH value in the reference paper is higher than that in the present study, possibly due to the different calibration buffers used; this discrepancy requires further study for clarification. [PERSON] et al. [25] determined the pH of cement paste by using calibration buffer solutions of 4, 7 and 10. [PERSON] and [PERSON] [19] studied the pH of concrete by using calibration buffers of 10, 11, 12 and 13. The table also shows the similar trends of pH values for large and fine particle sizes.
## 4 Discussion
When comparing these findings to previous studies, it becomes evident that there is a growing trend toward updating traditional classifications through increasingly advanced statistical analyses to better define contemporary player roles [4, 33]. Limitations and Practical Applications  
The findings of this study have significant implications for both practice and theory in women's basketball. From a practical standpoint, the identification of specific performance-based roles, such as perimeter specialists, defensive specialists, and primary scorers and rebounders, enables coaches and analysts to assign tasks more effectively, plan strategies tailored to each player's strengths and optimize talent selection and development processes. These insights support the construction of rosters that maximize individual contributions and enhance overall team performance. Theoretically, this study contributes to the evolving framework of position classification by applying advanced statistical techniques, such as PCA and k-means clustering, to reflect modern gameplay more accurately. It moves beyond traditional static roles, offering a dynamic, data-driven model for evaluating player impact based on actual performance. Moreover, it addresses a critical gap in the literature by focusing specifically on women's basketball, a context often overlooked in previous analyses. However, the main limitation of this study lies in its exclusive focus on the Women's Euroleague, which, despite its high level of competition, may limit the generalizability of findings to other leagues or competitive contexts. Additionally, the absence of qualitative variables, such as tactical roles, coaching strategies, or game context, means that some dimensions of player behavior may not be fully captured by statistical outputs alone.
## 4 Experiments
### ElGamal Digital Signature  
The original image was first digitally signed by ElGamal Digital Signature; the details are in Table 1. Afterwards, we use Equation (1) to verify the result.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **The Signature (r, s)** \\ \hline  & (49403562386972889101239812641985822426261568600929413555373108317189108566 \\  & 302658182904229360129949456643308318348548341680203213984974337247242125011 \\ Sample 1 & 69538, 5415969039856386258794620465278847222275499179163913546847561390577 \\  & 7408393254427228816112672556061791465948092490707649457244495662029914180 \\  & 4364454722) \\ \hline  & (61001817069789903834733532799590115645066963165036129235083962521897558133 \\  & 265020005042140403856630849184326379561282504819727116596265888384423692910 \\ Sample 2 & 71429, 3920960075631645675970373440968561770676083376398210402795962699171 \\  & 07235033674879731516758177943346676224530786017623396755496443988115606378 \\  & 0318669776) \\ \hline  & (83345755023986145287656082248492284463108343079487467735995468215457225650 \\  & 19305273097636835099686340988791157567088860769682632792587357903568157370 \\ Sample 3 & 8395, 511673388020738351588653584915939255539347817096131862841806899426477 \\  & 759446320893879827883901827816611580792330510167512291999660365620098141184 \\  & 53637218) \\ \hline  & (32770927389310396880229248324450790460037678547426721009761838561579359893 \\  & 047631045241429231475954642673069715296770005380574566961047385390808713462 \\ Sample 4 & 55119, 74378416046657919345607984326818428988558066082148176690139071795 \\  & 934032129165396259197221939388538484146940794569126383972922057870323588 \\  & 664349163) \\ \hline  & (275786521174049911145951637187248527380432772143931481984376629899945229197 \\  & 277066959839502656689013352925824270332348874587097539131050747438050569635 \\ Sample 5 & 85944, 1132589294074062584634985931707101277883682299089533597056761749248 \\  & 49722036969802956260725697193195194604413515981547873313147142543260952175 \\  & 0571054637) \\ \hline  & (25612173890034996243941155180559197217428638497892432771341656992282011218 \\  & 1574286792179799487812604411056992561024889531461698026902559077836620456859 \\ Sample 6 & 11367, 283753769518796641690897877767440003236302035449590642481861906095254 \\  & 839050909511039686729548034278165885300358266188831922658822951813221290332 \\  & 2132958647) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The digital signature (r, s) of all samples.  
Figure 5: Samples of privacy-preserving biometric images that require encryption.  
In this process, the signature part is completed, such that this model has a complete authentication, encryption, and decryption process, which is the innovation point of this method.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 3 Theoretical Framework  
This section presents the theoretical foundation for the proposed framework, integrating financial ontologies, stochastic models, and machine learning techniques. The mathematical principles and structures underpinning each component are outlined, establishing a cohesive context for the methodology. The integration of these methodologies is visually summarized in Figure 1, which provides an overview of the key components and their interconnections.  
Figure 1: The framework combines ontology-based stock selection with market state prediction. Selected stocks are processed through NMC and BERT models, providing outputs that, along with market volatility (VIX) and stock volatility (GARCH), serve as inputs to the LSTM. The LSTM’s hyperparameters are tuned to enhance performance, enabling the prediction of market states, which are then used to inform trading strategies.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 3 Network and System Model  
We consider a single cell network scenario with one evolved node B(eNb) in the center of the cell, a road that runs in the middle of the cell, vehicular users on that road, and cellular users distributed randomly throughout the cell. Let c = {1......, C} by the CUE involved in direct communication with the infrastructure and let v = {1......, V} be the V2P users (a pair consisting of a cellular and a vehicular user).