esa-sceva/satcom-chunk-collection huggingface dataset:
These are the reasons why this paper chose to study the effect of these factors on the anti-cracking ability of asphalt mixtures. When it comes to the testing method of anti-cracking ability of asphalt mixtures. There are now two modern ways to reveal the cracking resistance of asphalt mixtures. The first one is called IDEAL test [24] and the second test is called Semi-Circle Bending (SCB test) [25]. Some researchers have compared the IDEAL test indexes and the SCB test indexes with the test indexes of other normal test methods such as the four-point trabecular bending test, overlay test, and indirect tensile test, etc., and found that some of the IDEAL test index and SCB test index have a good correlation with the test indexes of other test [26; 27]. However, the study about the correlation between the indexes of IDEAL and SCB test are very limited. The correlation analysis between the test indexes of IDEAL and SCB test could make it more clear when using these two methods together to comprehensively evaluate the anti-cracking ability of asphalt mixtures. Therefore, in this paper, in order to study the effect of basalt fiber on the anti-crack ability of several common used asphalt mixtures, the common used gradations such as SMA-13, SUP-13, SUP-20, and SUP-25 were adopted to study the enhancement effect of basalt fiber on the anti-cracking ability of these asphalt mixtures. With the aim to find the impact of the gradations on the anti-crack ability of asphalt mixtures, two different asphalt mixtures (SMA-13 and SUP-13) without basalt fiber and with the same content of basalt fiber were used to study the effect of gradation type on the cracking resistance of asphalt mixtures. Meanwhile, in order to conclude the effect of maximum nominal bonding stress on the anti-cracking ability of asphalt mixtures, the SUP-13, SUP-20 with SBS modified asphalt and SUP-20, SUP-25 with pure asphalt (non-modified asphalt) were compared separately. SUP-20 mixtures and basalt fiber reinforced SUP-20 mixtures using SBS modified asphalt and pure asphalt were used to study the effect of asphalt type on the cracking resistance. Additionally, the correlation analysis between the test indexes of IDEAL test and SCB test were adopted to find the most relevant test indexes in these two tests. The findings of this work provide a significant reference for the design of asphalt mixtures having good crack resistance.
## 5 Achievable Sum Rate Analysis Using RMT Method  
This section provides the expressions that accurately approximate the SINR and DL achievable sum rate based on the asymptotic RMT approach in [32,33]. In particular, asymptotically tight approximations of \(\gamma_{k}\) for the BF and RZF precoders are obtained when \(N\) and \(K\) grow without bounds while the ratio \(K/N>0\) is kept constant. To this end, the asymptotic expression for BF precoding is given as:  
\[\overline{\gamma}_{k}^{\text{BF}}=\frac{\xi\big{(}\text{tr}(\mathbf{\Phi}_{k}) \big{)}^{2}}{\frac{1}{\rho_{\text{d}}}+\sum_{i=1}^{K}\xi\,\text{tr}(\mathbf{R }_{k}\mathbf{\Phi}_{i})} \tag{11}\]  
where \(\xi=\big{(}\frac{1}{K}\sum_{k=1}^{K}\text{tr}(\mathbf{\Phi}_{k})\big{)}^{-1}\). The SINR approximation for the RZF precoder is given as:  
\[\overline{\gamma}_{k}^{\text{RZF}}=\frac{N\,\xi\,\delta_{k}^{2}}{\frac{(1+ \delta_{k})^{2}}{\rho_{\text{d}}\,K}+\xi\sum_{i=1}^{K}\big{(}\frac{1+\delta_{ k}}{1+\delta_{i}}\big{)}^{2}\theta_{k,i}} \tag{12}\]  
where the term \(\xi\in\mathbb{R}\) is determined later in  
The auxiliary variable \(\bar{\mu}_{k,i}\in\mathbb{R}\) in (12) is obtained from the expressions given in (21)-(23),  
\[\bar{\mu}_{k,i} =\frac{1}{N}\text{tr}\big{(}\mathbf{R}_{k}\mathbf{T}_{i}^{\prime} \big{)} \tag{21}\] \[-\frac{2\text{Re}\big{(}\text{tr}\big{(}\mathbf{\Phi}_{k}\mathbf{T }\big{)}\text{tr}\big{(}\mathbf{\Phi}_{k}\mathbf{T}_{i}^{\prime}\big{)}\big{)} \big{(}1+\delta_{k}\big{)}-\text{tr}\big{(}\mathbf{\[PERSON]}_{k}\mathbf{T}\big{)}^ {2}\delta_{i}^{\prime}}{\big{(}N(1+\delta_{k})\big{)}^{2}}\]  
\[\mathbf{T}_{i}^{\prime}=\mathbf{T}\Bigg{(}\mathbf{\[PERSON]}_{i}+\frac{1}{N}\sum_{k =1}^{K}\frac{\mathbf{\[PERSON]}_{k}\delta_{k}^{\prime}}{(1+\delta_{k})^{2}}\Bigg{)} \mathbf{T} \tag{22}\]  
\[\delta^{\prime}=\big{(}\mathbf{I}_{K}-\mathbf{J}\big{)}^{-1}\mathbf{v}^{\prime} \tag{23}\]  
where \(\mathbf{v}^{\prime}\in\mathbb{C}^{K}\) denotes  
\[[\mathbf{v}^{\prime}]_{k}=\frac{1}{N}\text{tr}\big{(}\mathbf{T}\mathbf{\Phi} _{k}\mathbf{T}\mathbf{\Phi}_{k}\big{)},\qquad 1\leq k\leq K. \tag{24}\]  
The aforementioned analyses of BF and RZF precoding with the RMT method can be very useful for validating our work.
## 2 Materials and Methods
### Statistical Analysis  
Results were rationalized using multivariate statistical approaches to graphically track the variation of the six analyzed metabolites. Specifically, the PCA (principal component analysis) performs a dimensional reduction with respect to the number of quantitative variables showing, in a 2D graph, the samples as spots (scores) spread in the plot according to the variance of their detected quantitative values (known as loadings). We used Matlab (1994-2021 The MathWorks, Inc., Milan, Italy) and R software for statistical analysis, and the graphs were obtained using these software programs. PCA is the simplest statistical evaluation of the variances working without any given information about the origin of the samples (unsupervised mode); this ensures no artifacts in the performed evaluations.  
We proposed the projection of the variance of several variables (in our case, six) along two common vectors (principal components) on a plane matching the shared variabilities [33]. In order to represent a comprehensive view of the general variance concerning the six measured variables, we used a multivariate statistical approach (MVA) to graph ically track the variation of variables (loadings) over several observables (scores). The modern unsupervised approach called PCA (principal component analysis) [34] performs a dimensional reduction to follow, at a glance, more quantitative variables (in this case, six loadings). 2D plots (called biplots) show just the first two principal components, displaying a pretty clear trend of the variables, to interpret the difference between 56 cheese samples produced in two different seasons (Figure 1 explaining the 92.7% of the total variance) and 28 cheese samples in the same season obtained at the beginning of the study and after two different diet regimes (Figure 2 explains the 87.9% of the total variance for winter variables and Figure A1 explains the 90.4% of the total variance). Scores are spread in the 2D space according to the variance of their detected quantitative values along with the principal components. All the calculations and graphical representations were created in Matlab (1994-2021 The MathWorks, Inc., Milan, Italy) and R software for statistical analysis. PCA is the simplest statistical evaluation of the variances working without any information about the origin of the samples (unsupervised mode); this ensures no artifacts in the visualization and evaluation.  
Figure 1: Biplot 2D\(-\)graph for the principal component analysis (PCA) of all 56 samples of “Ragusano” cheese; the first two principal components represented as PC1 and PC2 explain 92.7% of the total variance. Vectors represent the general variance of the variables (loadings) with respect to the samples represented by spots (scores) distributed in the graph. Even in this unsupervised scattering mode, it is evident the statistical trend of the six antioxidant species, moreover, ellipses clusters samples coming from control (C) or enriched (T) diet; before (0) or after (1) grazing pasture; produced in winter (W) or spring (S).
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Scheduling Algorithms  
In this paragraph, a more detailed explanation will be provided on how the number of slots allocated to each user is calculated.
# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 6 Evaluation
### Inference Quality  
Let us start the discussion of the empirical study with the plots presented in Figure 3. These plots depict the classification accuracy (UC1 and UC2) and MSE (UC3) for the introduced model variants for each use case. First, it is evident that in all use cases, given enough data during training, quality can be improved by increasing the complexity of the model. In the case of the BERT (Figure 3a) and EfficientNet (Figure 3b) models, the classification accuracy increases, while in the case of the MLP regression models (Figure 3c), the mean square error decreases. Hence, scaling the DNN structure's dimensionality (e.g., network depth and width) by creating more computationally complex variants is beneficial for improving the inference quality during runtime inference.  
The second insight from the empirical study is that, for all use cases, the learning ratio of simpler models plateaus at some point. An illustrative example is provided by the plots in Figure 3a,b. Scaling the EfficientNet-B0 structure by the first compound scaling coefficient increases the model's classification accuracy by more than 2.2%, but scaling from the B4 variant to the B5 variant introduces minimal gains in accuracy that are less than 0.5%. This can also be confirmed by looking at the MLP-Regression plot (Figure 3c) where performing a further study, one can establish the non-linear nature of the error and the diminishing error reduction through a Ramsey REST test (r-test I-value = 0.000826).  
Third, as is evident in the MLP-Regression plot (Figure 3c) the global minimum is achieved for \(\gamma=0.6\), and afterwards, more complex models present a slight increment in their reported MSE values. This occurs as the DNN model structure scales and becomes too complex, which results in overfitting to the randomized effects that are present only in the specific (California Housing) dataset used for training. In this regard, although the expressive power of a model is (somehow) dependent on model complexity, a mathematical expression for estimating inference quality irrespective of the DL use-case domain does not exist [31]. This is inherently due to the phenomenon introduced in the second (learning plateau) and third (overfitting) insights.  
Figure 5: Inference latency with respect to model complexity. The presented plots include: (**a**) BERT model variants, (**b**) EfficientNet model variants, and (**c**) MLP-Regression model variants.
# Time Domain Analysis of NB-IoT Signals
## 5 Results
### Comparison between Power Measurements of NRS in Time and Code Domains  
A first series of tests has been performed in order to compare what obtained with the two measurement methods for the received power due to the NRS. Figure 12 shows an example of the comparison.  
The typical uncertainty budget of narrow band measurement chains at LTE800 frequency band is over \(\pm\)2 dB. An additional contribution to uncertainty due to in situ measurement must be considered. In view of this, the average values measured in the time domain (Figure 12a,b) show that the acquisitions on two different sub-carriers are in excellent agreement (within 0.5 dB) with what obtained in the code domain (Figure 12c) which, as previously described, represents the average value on the NB-IoT frame. The variability around the mean of the measured values in the time domain is typically within \(\pm\)1 dB.  
These preliminary results are promising from the point of view of using the measurement of the NRS power in the time domain for the extrapolation techniques provided for in CEI 211-7/E technical standard as an alternative or in the absence of that in the code domain, by means of more diffuse and cheaper spectrum analyzers rather than more expensive vector analyzers. For this purpose, however, further tests will be necessary, also analyzing the results varying acquisition parameters such as different types of detectors or RBW filter bandwidth.  
Figure 11: Subcarriers of NB-IoT signals of other network operators.
## 4 Discussion and Future Work
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Chip**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Resolution** (**ps**) & **RMS** (**ps**) & **DNL** (**LSB**) & **INL** (**LSB**) & **Dead** (**ms**) & **Registers** & **LUTs** & **Cost** \\ \hline
[11] & Spartan-6 & \multirow{2}{*}{\
\begin{tabular}{c} TDL+ \\ multi-edge coding \\ \end{tabular} } & 0.9 & \(<\)6 & \(<\)2.91 & \(<\)15.7 & 3 & N/S &
\begin{tabular}{c} 144 \\ SLICES \\ \end{tabular} \\ \hline
[13] & Kintex-7 & \multirow{2}{*}{\
\begin{tabular}{c} TDL+ \\ multi-edge coding \\ \end{tabular} } & 3.9 & \(<\)3 & \(<\)6 & \(<\)18.8 & 3.6 & N/S &
\begin{tabular}{c} 200 \\ SLICES \\ \end{tabular} \\ \hline
[15] & Kintex-7 & \multirow{2}{*}{\
\begin{tabular}{c} TDL+ \\ multi-edge coding \\ \end{tabular} } & 3.0 & \(<\)3 & \(<\)4.5 & \(<\)37.7 & 3.6 & N/S &
\begin{tabular}{c} 144 \\ SLICES \\ \end{tabular} \\ \hline
[21] & Stratix III & Ring Oscillator & 31 & 36.1 & \(<\)0.073 & \(<\)0.091 & 256 & 319 & 104 \\ \hline
[22] & Stratix III & Ring Oscillator & 37 & 39 & \(-\)0.4–0.4 & \(-\)0.7–0.7 & 400 & 319 & 104 \\ \hline
[23] & Stratix III & \begin{tabular}{c} Ring Oscillator \\ \(+\)Bidirectional \\ operating \\ \end{tabular} & 24.5 & 28 & \(-\)0.20–0.25 & 0.03–0.82 & 602 & 986 & 172 \\ \hline This work & Artix-7 & \
\begin{tabular}{c} Ring Oscillator \\ \(+\)Bi-time interpolation \\ \end{tabular} & 20 & 15–21 & \(-\)0.71–0.06 & 0–1.04 & 58 & 35 & 31 \\ \hline This work & Kintex-7 &
\begin{tabular}{c} Ring Oscillator \\ \(+\)Bi-time interpolation \\ \end{tabular} & 20 & 15–20 & \(-\)0.51–0.06 & 0–0.94 & 58 & 35 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison to similar works. the measurement accuracy, we can add a slow RO (compared to the \"fast RO\" in this paper) with a slower oscillation frequency than the reference clock. The oscillation generated by slow RO and fast RO would move in the two opposite directions of the reference clock simultaneously when the rising edge of the hit signal arrives, which can halve the dead time again.
## 4 Experimental Settings
### Metrics  
As already proposed in earlier works, the primary objective metrics used were related to privacy and utility [18; 74]. As such, for the speaker identification/verification, the Equal Error Rate (EER) is used as the classic metric, which is defined as the error rate corresponding to a certain threshold for which the false alarm rate is equal to the the false miss rate.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Corpus** & \multicolumn{2}{c}{**LibriTTS**} & \multicolumn{2}{c}{**LibriSpeech**} & **VCTK** \\ \hline
**Set** & **Train** & **Dev-Clean** & **Test-Clean** & **Test-Other** & **Train** \\ \hline Speakers & 2311 & 40 & 40 & 33 & 109 \\ Utterances & 354,780 & 5736 & 2620 & 2939 & 44,242 \\ Size & 555 h & 9 h & 5.4 h & 5.3 h & 44 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets used for training and evaluation. The present work considers LibriSpeech [69], LibriTTS [70], and the voice cloning toolkit dataset (VCTK) [71].  
For privacy preservation purposes, higher values indicates better anonymization. The EER is measured by employing the NVIDIA speaker recognition baseline using TitaNet [60].  
Similarly, for utility, the classic Word Error Rate (WER) was adopted. In this case, lower values are preferred as they would indicate there are lower speech distortions introduced in the generated waveforms. As the present work aims to measure the differences in performance, a Kaldi standard recipe was used as the back-end for ASR ([[https://kaldi-asr.org/models/m13](https://kaldi-asr.org/models/m13)]([https://kaldi-asr.org/models/m13](https://kaldi-asr.org/models/m13)) (accessed on 1 March 2024)). Decoding was performed with the conventional factorized time delay neural network (TDNN) acoustic model and the associated (_small_) trigram language model [69].  
Other measures can be used to evaluate the perceptual quality of the generated speech such as, for example, the Perceptual Evaluation of Speech Quality (PESQ) metric [75] or the Short-Time Objective Intelligibility (STOI) metric [76]. However, these metrics often do not correlate well with ASR performance. Additionally, since the acoustic similarity was evaluated against a reference clean signal, these metrics were found to not be suitable in our anonymization context (i.e., changing the speaker properties inevitably compromises the acoustic similarity).
## 2 Sector-Specific Applications
### Equipment Manufacturing  
Equipment manufacturers face unique challenges, including legacy system modernization and the need for rapid prototyping [41; 42]. Low-code platforms address these by offering drag-and-drop process design and prebuilt templates for industrial automation. For example, the vf-OS platform enables manufacturers to model production line workflows visually, integrating CAD designs with real-time machine data to simulate and validate automation sequences before deployment. This reduces development time compared to traditional PLC programming methods [17; 43]. Opto 22's case study demonstrates that Node-RED's low-code platform has enhanced the development efficiency of industrial automation projects by over 50%, significantly reducing project delivery time and enabling rapid ROI through real-time data integration and protocol conversion, as reported by the company.  
A notable application is in legacy system integration. Manufacturers often operate with outdated ERP or MES systems that lack IoT connectivity. Low-code tools like Apache Activiti [44] provide connectors to harmonize data flows between legacy infrastructure and modern IIoT devices, enabling predictive analytics for equipment health monitoring. Case studies in collaborative manufacturing environments demonstrate that such integrations improve production yield through real-time anomaly detection [28; 43]. Additionally, low-code fosters cross-departmental collaboration. Business analysts and shop-floor operators can co-develop applications using platforms like OutSystems [45; 46; 47; 48], aligning IT solutions with operational needs.  
In summary, low-code platforms streamline the integration of legacy systems with modern IIoT devices, enhancing real-time data processing and predictive analytics. By facilitating cross-departmental collaboration, these tools not only reduce development time but also improve production yields and operational efficiency, making them indispensable for equipment manufacturers aiming to modernize and optimize their processes.
## 2 Results
### Effect of qSCM4 on the Traits Related to Lodging and Panicle and Anatomical Structure Analysis  
Comparing the lodging resistance related traits and panicle yield traits of NILs revealed that NIL carrying _qSCM4_ exhibited bigger basal culm diameter (Figure 3b,c), thicker basal culm thickness (Figure 3d,e), and larger breaking strength of the basal internode  
Figure 2: Fine mapping and candidate gene analysis of the strong culm and lodging resistance QTL _qSCM4_: (**a**) genetic localization of _qSCM4_ in recombinant inbred lines population; (**b**) the genotypes of residual heterozygous line 08-3-2-1, The grey parts indicate the genotypes of the LTH, and the white indicate the genotypes of SH265; (**c**) fine mapping of _qSCM4_ in remaining heterozygous-derived populations; (**d**) fine mapping of _qSCM4_ in BC3F2 segregating populations, The grey parts indicate the genotypes of the LTH, and the white indicate the genotypes of SH265; (**e**) candidate gene prediction for _qSCM4_ region; and (**f**) candidate gene DNA sequence variation of _qSCM4_.  
than SN265 (Figure 3g). Measurement of anatomical structural changes in the basal culm internode (Figure 3b-e) showed that NIL carrying _qSCM4_ showed a 25.3% increase in basal culm diameter (Figure 3b,c), an average increase of 45.6% in the thickness of thin basal culm (Figure 3b,c) and an average increase of 13.6% in thick basal culm (Figure 3e). The plant type analysis of two NILs showed that NIL-_qSCM4_ had no change in the plant type and height (Figure 3a,f). Analysis of yield-related traits showed that NIL-_qSCM4_ had 14.7% more grains per panicle than SN265 (Figure 3h), 16.7% more primary branch (Figure 3i) and 9.9% more secondary branch (Figure 3j). The culm lodging resistance of NIL-_qSCM4_ also increased by 20.3% (Figure 3g).
# Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques
## 4 Conclusions  
It has been demonstrated that the ACU method is a powerful tool for automated defect detection in solid timber panels. The most challenging problem is that the signal captured by the receiver is very weak because of the significant impedance mismatch between the air and solids. Applying phase-coded pulse compression technology is a promising method to improve measurement accuracy without increasing the instantaneous peak power of the transmitter. So far, measurements in the proposed testbed are limited to samples not thicker than 40 mm, because signal attenuation increases dramatically for thicker samples, making it impossible to distinguish between noise interference and actual signals. In future research, thicker samples should be examined, applying more sensitive and powerful transducers and more precise signal processing circuits. Furthermore, samples used in this paper were solid timber panels made of Chinese fir, with a density of 450 kg/m\({}^{3}\). In future work, experiments should focus on timber panels made with other materials, since density is a key parameter for ACU methods. Nevertheless, common defects, such as knots, cracks, and wormholes, can be detected, together with their size and location. Thanks to its non-contact nature, the measuring process of the ACU method is more efficient than the conventional contact methods. Moreover, using the composite images (called C-scan and D-scan), combined with the pattern recognition technology, it may be possible to identify the types of defects.  
Since the ACU methods allow for regular, fast reliable, and comprehensive inspections, the possibility to detect defects in timber and engineered wood products may improve  
Figure 13: Drilled holes (**a**) and amplitude image (**b**) of simulated wormhole defects. A–D represent 5 drilled holes in which B represents an two holes area with diameters less than 3 mm.  
the quality of the products and reduce the costs of their production and maintenance. The ACU technology can help to perform timber product quality inspection and maintenance.  
Methodology, X.J. and S.J.; Software, X.J.; Validation, J.W. and Y.Z.; Investigation, Y.Z.; Writing--original draft, X.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.