esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
The Dense model achieved an overall accuracy of 84%, with balanced F1-scores across categories, such as 89% for \"engine idling\" and 93% for \"siren\". Despite performing better than the LSTM model, it did not reach the accuracy level of the CNN model, which achieved 90% accuracy. In comparison, a recent study using the same UrbanSound8K dataset reported a Dense model accuracy of 84%, which is consistent with the performance observed. This study emphasized that Dense models, while effective in capturing complex feature interactions, may face limitations in tasks requiring spatial feature extraction, particularly when spectrogram-based inputs are used [53]. The slight discrepancy in performance compared to the CNN model can be attributed to the Dense model's architecture, which lacks convolutional layers that are critical for capturing local spatial features in spectrograms. Moreover, while the Dense model showed robustness in classifying distinct sounds, it exhibited slightly lower performance in overlapping categories, such as \"children playing\" and \"dog bark\", with F1-scores of 68% and 76%, respectively. These results suggest that deep learning models are reliable and effective tools for urban sound recognition applications. Integrating deep learning models with fog computing, as demonstrated in this research, offers significant advantages by enabling data processing closer to the source. This approach reduces latency and minimizes reliance on centralized data centers, which is essential for urban sound recognition applications requiring real-time data processing [36]. Unlike studies that primarily focus on more specific or limited datasets, this research highlights the adaptability of deep learning models in handling diverse and complex sound data, thereby broadening their applicability across various urban environments [54]. The impact of this integration extends beyond urban sound recognition, offering significant potential for other IoT applications in smart cities, such as environmental monitoring, security systems, and emergency management [55]. By enabling fast and reliable data processing, fog computing enhances the efficiency and sustainability of smart cities, presenting a promising solution to address the increasing data demands of modern urban environments. As fog computing technologies continue to advance, the performance and widespread adoption of these applications are expected to improve, further supporting the development of responsive, AI-driven urban management systems.
## 2 Informative Frequency Band Selectors
### Stability Index  
The class of \(\alpha\)-Stable distributions is known as an extension of the classical Gaussian distribution, see [56]. One of the distribution's parameters is the stability index \(\alpha\in(0,2]\), which indicates the distance from the Gaussian distribution. In other words, this parameter indicates how impulsive is the distribution. For the \(\alpha=2\) the \(\alpha\)-distribution simplifies to the Gaussian distribution with some parameters \(\mu,\sigma\). If the \(\alpha\) tends to \(0\), then the examined distribution becomes more impulsive (the values of the outliers significantly increase). The \(\alpha\)-stable distribution is defined by the characteristic function [56], which is as follows:  
\[\mathbb{E}[\exp i\theta X]=\phi_{X}(\theta)=\begin{cases}e^{-\sigma^{\alpha}| \theta|^{\alpha}\left\{1-i\beta\text{sign}(\theta)\tan(\pi\alpha/2)\right\}+ i\mu\theta},&\alpha\
eq 1,\\ e^{-\sigma|\theta|\left\{1+i\beta\text{sign}(\theta)\,\frac{2}{\alpha}\log(| \theta|)+i\mu\theta\right\}},&\alpha=1.\end{cases}\]  
The parameters \(\sigma>0\), \(\beta\in[-1,1]\), and \(\mu\in\mathbb{R}\) are the scale, skeweness and shift parameters respectively. To estimate the parameter \(\alpha\) from the \(\alpha\)-stable distribution one can use the following definition, adapted from the McCulloch method [57]:  
\[\hat{\alpha}(x)=\psi(\vartheta_{x},\vartheta_{\beta}),\,\vartheta_{\alpha}= \frac{\hat{x}_{.95}-\hat{x}_{.05}}{\hat{x}_{.75}-\hat{x}_{.25}},\,\vartheta_{ \beta}=\frac{\hat{x}_{.95}-\hat{x}_{.05}-2\hat{x}_{.5}}{\hat{x}_{.95}-\hat{x}_{.05}}, \tag{2}\]  
where \(\hat{x}_{q}\) is the sample quantile of order \(q\) based on the vector \(x=(x_{1},\ldots,x_{N})\). The parameter \(\hat{\alpha}\) is obtained by the linear interpolation \(\psi(\cdot,\cdot)\) of the \(v_{\alpha}\) and \(v_{\beta}\) is based on the McCulloch tabulation of the function \(\psi(\cdot,\cdot)\), see Table III in [57].  
The usage of the parameter \(\alpha\) has been presented in [46; 58], where the Alpha selector has been defined as 2-\(\hat{\alpha}\) and applied to the spectral frequency \(f_{i}\): \(\hat{\alpha}(f_{i})\) after time-frequency signal decomposition performed by the short-time Fourier transform (STFT). The Alpha selector was found as an effective tool for the IFB selection in the bearing fault diagnostics. As it was mentioned, if the amplitude of the impulses in the examined data distribution increase then the \(\hat{\alpha}\) parameter tends to \(0\) and Alpha selector increases.
# A Construction Method for the Random Factor-Based G Function
## 5 Simulation Results
### Uniformity  
In terms of mathematical probability theory, uniformity can be defined as the condition in which the number of occurrences of each frequency point is approximately equal. This indicates that each frequency point can be selected with equal probability. It has been demonstrated that enhancing the uniformity of the sequence can lead to an improvement in the anti-interference performance of communication systems [32]. In this paper, the frequency statistics results of the \(\chi^{2}\) test method and the equal distribution test in statistics are employed to assess the uniformity of the sequence. The \(\chi^{2}\) test method is employed to ascertain the extent of discrepancy between the actual observed value of the statistical sample and the theoretical estimated value. For the purposes of this analysis, the variable \"ki\" will be used to represent the number of frequency points present in the frequency-hopping sequence, while the variable \"M\" will be used to represent the total number of samples. The test equation for frequency uniformity can be expressed as follows:  
\[X^{2}(N-1)=\sum_{i=0}^{N-1}\frac{(k_{i}-M/N)^{2}}{M/N} \tag{12}\]  
When the calculated value of \(\chi^{2}(N-1)\) from the actual measured data is less than the theoretical value, the original hypothesis is confirmed, indicating that the current frequency-hopping sequence exhibits good uniformity. For N = 64, the theoretical value is 82.2447 [18]. The mean results of the frequency-hopping sequences generated by the G function of the four methods following multiple \(\chi^{2}\) tests are presented in Table 4.  
As illustrated in the table, the outcomes of the \(\chi^{2}\) test for the frequency-hopping sequences generated by the G function of the four methods are all below the theoretical value and all satisfy the uniform distribution assumption. Amongst the methods presented, the RFGF method, as proposed in this paper, yielded the lowest result. In order to ascertain the uniformity of the frequency-hopping sequence, the results of the equal distribution test of the frequency-hopping sequence generated by the G function of the four methods are subjected to statistical analysis. The theoretical value (TV) is calculated to be 1/64. Frequency statistics results of the equal distribution test are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RFGF** & **TVID-GF** & **SA-GF** & **MRCS-GF** \\ \hline Value & 51.9453 & 52.6886 & 22.1937 & 58.5664 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of Chi-square test results.  
Figure 8: Frequency statistics results of the equal distribution test.  
A more intuitive representation of the frequency distribution can be observed in Table 5. The RFGF method has the smallest range, 0.00157, indicating that the maximum and minimum probabilities of each frequency point are closer together. This suggests a more uniform overall distribution, demonstrating that the RFGF method achieves the best performance in frequency distribution uniformity.
## 3 Results and Discussion  
### Characterization of the Diatomaceous Fiber  
The diatomaceous earth samples used for the production of SPME fibers belong to the class _Bacillusariohypercea centricae_ and their color may vary from white to gray. The material consists mainly of silica, SiO\({}_{2}\) (87-91%), alumina and ferric oxide [21].  
Scanning electron microscopy (SEM) was carried out to characterize the surface morphology of the recycled diatomaceous earth. The images obtained at magnifications of 2000 and 4000\(\times\) for the surface evaluation are shown in Figure 1 (A and B, respectively). An image of a cross-section of the proposed fiber was obtained at a magnification of 100\(\times\) (Figure 1C). According to the SEM results, the morphology of the material shows a high porosity which facilitated the physical processes involving the sorption of the analytes.  
Figure 1: SEM micrographs obtained with the biosorbent fiber at magnifications of **(A)** 2000\(\times\) and **(B)** 4000\(\times\), and a cross-section of the proposed fiber **(C)** at a magnification of 100\(\times\).  
FTIR spectroscopy was carried out to identify the functional groups in the sorbent. The FTIR spectrum obtained from the material previously conditioned at 240 \({}^{\circ}\)C is illustrated in Figure 2. A broad peak at \(\sim\)3400 cm\({}^{-1}\) corresponds to the O-H bonds of silanol groups. Two intense peaks between \(\sim\)1200 and 1080 cm\({}^{-1}\) were assigned to the asymmetric stretching of the Si-O-Si siloxane groups and one at \(\sim\)790 cm\({}^{-1}\) is related to the Si-O-Si vibrations attributed to mesoporous silicas. At \(\sim\)475 cm\({}^{-1}\), a peak related to O-Si-O vibration was present. Lastly, the peak at \(\sim\)1600 cm\({}^{-1}\) refers to the angular deformation of the adsorbed water molecules.  
Thermogravimetric analysis was conducted to identify if there was any organic material present in the sample and since no mass loss was observed the material can be characterized as thermally stable (data not shown). This result was already expected, since the sorbent comes from inorganic material.
## 4 Results
### Internal Defects
An Independent Samples \(T\)-test analysis comparing the manual and robotic configurations for the 60\({}^{\circ}\) and 70\({}^{\circ}\) probes separately indicated that there was no significant difference for this parameter in the 2.5 mm and 3 mm defect categories, as well as for the readings obtained by the 60\({}^{\circ}\) probe for the 6 mm hole (Figure 9). The only significant difference observed was for the 70\({}^{\circ}\) probe in the 6 mm defect category. These results further confirm the comparability between the manual and the robotic system. In conclusion, the overall results of the study show the high potential of the robotic system coupled with the ultrasonic flaw detector system for rail defect detection and localisation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Mean Defect Size** & **STD. Deviation** & \(p\)**-Value** \\ \hline \multicolumn{4}{c}{For the 2.5 mm defect} \\ \hline Manual 60\({}^{\circ}\) & 2.613 & 0.118 & 0.124 \\ \hline Manual 70\({}^{\circ}\) & 2.515 & 0.082 & 0.124 \\ \hline Robotic 60\({}^{\circ}\) & 2.257 & 0.176 & 0.101 \\ \hline Robotic 70\({}^{\circ}\) & 2.423 & 0.141 & 0.101 \\ \hline \multicolumn{4}{c}{For the 3 mm defect} \\ \hline Manual 60\({}^{\circ}\) & 3.028 & 0.046 & 0.066 \\ \hline Manual 70\({}^{\circ}\) & 2.995 & 0.035 & 0.066 \\ \hline Robotic 60\({}^{\circ}\) & 2.893 & 0.175 & 0.952 \\ \hline Robotic 70\({}^{\circ}\) & 2.884 & 0.494 & 0.952 \\ \hline \multicolumn{4}{c}{For the 6 mm defect} \\ \hline Manual 60\({}^{\circ}\) & 6.323 & 0.147 & 0.008 \\ \hline Manual 70\({}^{\circ}\) & 5.847 & 0.081 & 0.008 \\ \hline Robotic 60\({}^{\circ}\) & 4.740 & 0.316 & 0.001 \\ \hline Robotic 70\({}^{\circ}\) & 6.557 & 0.235 & 0.001 \\ \hline \end{tabular}
\end{table} 
Table 2: Independent T-test data for 2.5 mm, 3 mm and 6 mm. Figure 8: Comparison of the mean DGS reading from all experiments (blue—Manual 60\({}^{\circ}\); green—Robotic 60\({}^{\circ}\); orange—Manual 70\({}^{\circ}\); purple—Robotic 70\({}^{\circ}\); dots—2.5 mm; vertical lines—3 mm; horizontal lines—6 mm).
## 5 Annotation-Free Transcription Alignment
### Performance Measures  
For assessing the quality of the character detection, we consider the standard intersection over union (IoU) measure  
\[IoU(A,B) = \frac{|A\cap B|}{|A\cup B|} \tag{3}\]  
with respect to ground truth \(A\) and detection result \(B\). Since the IoU is highly sensitive to small changes in the width and height of the bounding boxes, we apply the box shrinking procedure detailed in Algorithm 1 to the detection results, in order to obtain tight boxes for performance evaluation. This holds true for all reported results in this article if not stated otherwise.  
Because the number of ground truth boxes may be different from the number of detected boxes, we first establish an optimal assignment between the ground truth and the detection results. For this purpose, we solve a linear sum assignment problem with the IoU as the underlying matching cost. This results in the following three types of boxes:  
* Matching pairs \(\mathcal{M}=\{(A,B)\}\)
* Deletions \(\mathcal{D}=\{(A,\epsilon)\}\), i.e., unassigned ground truth boxes
* Insertions \(\mathcal{I}=\{(\epsilon,B)\}\), i.e., unassigned detection results  
Furthermore, we distinguish successful assignments \(\mathcal{M}^{+}\) and failed assignments \(\mathcal{M}^{-}\)  
\[\mathcal{M}^{+} = \{(A,B):IoU(A,B)\geq 0.5\} \tag{4}\] \[\mathcal{M}^{-} = \{(A,B):IoU(A,B)<0.5\} \tag{5}\]  
with \(\mathcal{M}^{+}\cup\mathcal{M}^{-}=\mathcal{M}\). Based on these distinctions, and with the total number of assignments \(N=|\mathcal{M}|+|\mathcal{D}|+|\mathcal{I}|\), we calculate the mean IoU (IoU), precision (P), recall (R), F\({}_{1}\) score (F1), and character detection accuracy (Acc) as follows:  
\[IoU = \frac{\sum_{(A,B)\in\mathcal{M}}IoU(A,B)}{N} \tag{6}\] \[P = \frac{|\mathcal{M}^{+}|}{|\mathcal{M}|+|\mathcal{I}|}\] (7) \[R = \frac{|\mathcal{M}^{+}|}{|\mathcal{M}|+|\mathcal{D}|}\] (8) \[F1 = \frac{2 PR}{P+R}\] (9) \[Acc = \frac{N-|\mathcal{M}^{-}|-|\mathcal{D}|-|\mathcal{I}|}{N} \tag{10}\]  
Please note that our definition of character detection accuracy is closely related to the character recognition accuracy commonly used for handwriting recognition. It takes into account substitution errors (\(\mathcal{M}^{-}\)), deletion errors (\(|\mathcal{D}|\)), and insertion errors (\(|\mathcal{I}|\)). Because the number of insertions errors is not limited, the accuracy can also be negative.
## 4 Results
### Scientometric Analysis
#### 4.4.4 Latency Evaluation  
Figure 11 presents latency metrics across the hydrogen aviation patent lifecycle. Figure 11a shows an average delay of 9.6 months from disclosure to filing, with many filings occurring immediately. Figure 11b indicates an average of 29.1 months from filing to grant, while Figure 11c shows a total average of 38.7 months from disclosure to grant. Figure 11d reveals a downward trend in grant time since 2020, dropping remarkably from 46 months to 25 months by 2024. This suggests accelerating urgency in examination and growing efficiency in processing hydrogen aviation patents.  
Figure 10: Production measures by (**a**) inventors spread by category and (**b**) claims spread by category.  
Figure 9: Innovation thrust measures by (**a**) patent category and (**b**) annual category diffusion.  
Figure 12 compares the top 15 patenting organizations in hydrogen aviation by awards and grant speed. Figure 12a ranks Airbus, RTX Corporation, and ZeroAvia as the top three awardees, highlighting strong engagement from both traditional aerospace firms and emerging players. Figure 12b shows the average time from filing to grant. RTX, ZeroAvia, and Universal Hydrogen experienced faster grants (under 25 months), while GE Aviation had the longest average delay (over 45 months). A low Pearson correlation coefficient of \(-0.31\) suggests that the patent award level for each organization is not related to their average grant latency. These trends reflect both organizational maturity and patent prosecution efficiency.  
Figure 11: Latency measures: (**a**) from disclosure to filing, (**b**) from filing to award, (**c**) disclosure to grant distribution, and (**d**) average latency from filing to grant by award year.  
Figure 13 compares patent volume and grant latency across hydrogen aviation categories. Figure 13a shows that fuel cell integration leads in patent awards, followed by hybrid propulsion and fuel storage. Figure 13b indicates that fuel cell integration also experiences the longest average time from filing to grant, suggesting greater complexity or examination scrutiny. In contrast, turbine enhancement and fuel management show shorter grant times, reflecting potentially clearer claims or streamlined reviews. A low Pearson correlation coefficient of 0.19 suggests that the patent award level in each category is not related to their average grant latency. Nevertheless, these patterns highlight how a few category-specific dynamics may influence innovation pace.
## 5 Results and Discussion  
### Visual Inspection  
The detection of defects inside a structure requires lighting and image acquisition conditions that allow obtaining the highest possible contrast between the material and defects. In this case, the color and the type of lighting play the most significant roles. Inspection under various lighting conditions was possible thanks to a ring multi-spectrum light.  
After determination of the best lighting conditions and the method for finding defects, the whole liner was inspected to find air bubbles, inclusions, and other defects.  
In the cylindrical part and domes, a few individual and small (<1 mm) air bubbles were found. No agglomerations of air bubbles were observed. In close proximity to the boss, there were numerous air bubble chains with diameters of about 2 mm. The defect is shown in Figure 10. For automating the process of detecting production defects, the automatic detection methods available in Keyence CV-X Machine Vision System were used.  
Figure 9: Liner model used in FEA with zoomed mesh around the porosity.  
Experience related to pressure container tests suggests that this defect may lead to container faults during cyclic hydraulic loading (fatigue defects, leaks near the boss/HDPE interface) or to hydrogen accumulation during exploitation of a container. To investigate the influence of this defect on the strain field around the boss, FBG sensors were attached close to the boss (one near the defect and one near a non-defective area) and FEA analysis of this point was performed.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments
### Ablation Studies
#### 4.4.2 Channel and Spatial Attention  
To evaluate the effectiveness of channel and spatial attention mechanisms used in the VSFM, we conduct a quantitative analysis through an ablation study (see Table 3). The experimental results indicate that while each mechanism yields only slight improvements when used independently, their combination results in a 1.4% increase in the HmIoU metric on the ScanNet dataset. This underscores the cumulative enhancement effect of the dual attention mechanism: spatial attention improves the representation of critical areas by analyzing spatial relationships between modalities, while channel attention fosters a joint focus on key channels across them. The collaboration between these two mechanisms effectively balances the need for detailed recognition and generalization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**VSFM**} & \multirow{2}{*}{**BCM**} & \multicolumn{4}{c}{**mIoU**} & \multirow{2}{*}{**HmIoU**} \\ \cline{3-3} \cline{5-6}  & & & \(C^{\textbf{S}}\) & \(C^{\textbf{U}}\) & \(All\) & \\ \hline
**Fixed Calibration** & \(\bigtimes\) & \(\bigtimes\) & 34.6 & 14.2 & 30.5 & 20.1 \\ _w/o_ **BCM** & \(\bigvee\) & \(\bigtimes\) & 34.9 & 13.9 & 30.8 & 19.9 \\ _w/o_ **VSFM** & \(\bigtimes\) & \(\bigvee\) & 32.4 & 14.0 & 28.7 & 19.6 \\
**Ours** & \(\bigvee\) & \(\bigvee\) & **34.9** & **14.9** & **30.9** & **20.9** \\ \hline \hline \end{tabular} \(\bigtimes\) symbolizes the absence of corresponding modules, \(\bigvee\) symbolizes the presence of corresponding modules.  
\end{table}
Table 2: Effects of the VSFM and BCM on the ScanNet dataset.
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 3 Fast Algorithms for Small-Size DHT-II
### Algorithm for 7-Point DHT-II
,\] \[\mathbf{W}_{11\times 9} =1\oplus(\mathbf{T}_{4\times 3}\mathbf{T}_{3}^{(0)})\oplus( \mathbf{T}_{4\times 3}\mathbf{T}_{3}^{(0)})\oplus\mathbf{I}_{2},\;\;\mathbf{W}_{9 \times 11} =1\oplus(\mathbf{T}_{3}^{(1)}\mathbf{T}_{3\times 4})\oplus(\mathbf{T}_{3}^{(1)} \mathbf{T}_{3\times 4})\oplus\mathbf{I}_{2},\] \[\mathbf{W}_{9\times 14} =1\oplus(\mathbf{H}_{2}\otimes\mathbf{I}_{3})\oplus\mathbf{P}_{2 \times 7},\;\mathbf{W}_{9}\;=\;1\oplus(\mathbf{I}_{2}\otimes\mathbf{I}_{3})( \mathbf{H}_{2}\otimes\mathbf{I}_{3})\oplus\mathbf{I}_{2},\;\mathbf{W}_{14 \times 7} =1_{2\times 1}\otimes\mathbf{I}_{7},\] \[\mathbf{D}_{11} =\text{diag}(1,\;s_{0}^{(7)},\;s_{1}^{(7)},\;s_{2}^{(7)},\;s_{3}^{ (7)},\;s_{4}^{(7)},\;s_{5}^{(7)},\;s_{6}^{(7)},\;s_{7}^{(7)},\;1,\;1),\] \[\mathbf{P}_{2\times 7}\;=\; \begin{bmatrix}1&&\\ 1&&&&1\\ &1&&&1\\ &&1&&&1\\ &&&1&&&1\\ &&&1&&&1\\ &&&1&&1\\ &&&1&&1\\ &&&1&&1\\ \end{bmatrix} .\]  
Figure 6 shows a data flow graph of the synthesized algorithm for the seven-point DHT-II. As can be seen, we are able to reduce the number of multiplications from 36 to 8, but the number of addition operations is increased from 42 to 46.
###### Abstract  
Transferring of a real quantum state in a long-distance channel is an important task in the development of quantum information networks. For greatly suppressing the relative phase fluctuations between the signal beam and the corresponding local oscillator beam, the usual method is to transfer them with time-division and polarization-division multiplexing through the same fiber. But the nonclassical states of light are very sensitive to the channel loss and extra noise, this multiplexing method must bring the extra loss to the quantum state, which may result in the vanishing of its quantum property. Here, we propose and realize a suitable time multiplexing method for the transferring and measurement of nonclassical states. Only the local oscillator beam is chopped into a sequence of light pulses and transmitted through fiber with continuous orthogonal-polarized signal beam. Finally, when the local oscillator pulses are properly time delayed compared to the signal beam, the quantum state can be measured in the time sequences without the influence of extra noise in the fiber. Our work provides a feasible scheme to transfer a quantum state in relative long distance and construct a practical quantum information network in metropolitan region.  
**Keywords:** squeezed states of light; transferring; fiber channel