esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 3 Methods  
### Different Pre-Training Strategies  
**Pre-training with the Hybrid System (HS).** First, we adopted a strategy to initialize the audio-visual encoder part of the baseline system, which is shown in Figure 2. We first employed the conventional hybrid ASR system [5; 6] to generate the corresponding force alignment (FA), then applied them to pre-train the audio-visual encoder, as presented on the left side of Figure 2. After pre-training, we initialize the encoder part of the AVSR system, as shown on the right side of Figure 2, and the gray blocks represent the randomly initialized parts.  
Figure 1: The illustration of the end-to-end AVSR baseline system.  
**Pre-training with the End-to-end System (ES).** As illustrated in Figure 3, we also used the E2E ASR system to initialize the AVSR system. Note that the key difference is whether the visual input is included in the training. Most modules in the AVSR system can directly use the parameters in the pre-training process, and the rest use random initialization (i.e., the parts marked with gray blocks). Through fine-tuning, the AVSR model can gradually absorb visual modal information and avoid training instability.  
**Using Pre-trained Audio-Visual Representation.** Recently, self-supervised speech representation learning has attracted lots of attention. We used the Audio-Visual Hidden Unit BERT (AV-HuBERT) [41] model, which has shown great recognition performance under adverse noise conditions. Specifically, in our previous work, we found that adopting the entire face can achieve better performance on the lipreading task compared with the baseline method using lip as visual input [42]. Therefore, we used it as the feature extractor, as shown in Figure 4.  
Figure 3: Pre-training with the end-to-end ASR system.  
Figure 2: Pre-training with the hybrid system.
###### Abstract  
With the rapid development of computational fluid dynamics (CFD) technology, it has been widely used to study the wind field characteristics of downbursts in mountainous areas. However, there is little guidance on the selection of different turbulence models for simulating downburst wind fields over hills using CFD, and few comparative studies have been conducted. This paper used nine turbulence models to simulate the wind field of a downburst over a 3D quadratic ideal hill. The simulated values of average and transient winds were compared with wind tunnel test data, and the flow characteristics at different moments under a downburst were analyzed. The flow characteristics in the wake region of the downburst over the hill are also quantitatively analyzed using the proper orthogonal decomposition (POD) method. The results show that approximately 85% of the results from the LES and REA models fall within a 30% error range, so the large eddy simulation (LES) model and the realizable \(k\)-\(\varepsilon\) model (REA) are more accurate in simulating the mean wind field, and the transient wind field simulated by the LES model is also in good agreement with the experimental data. In addition, this paper reveals the evolution mechanism of the transient wind field structure over a hill model under a downburst and finds that the first-order mode obtained by POD may be related to the acceleration effect on the hilltop.  
downburst; mountain wind field; CFD simulation; different turbulence models; flow mechanism +
Footnote †: journal: Applied Sciences
###### Abstract  
The present study investigated the impact of came girdling on the 'Hayward' kiwifruit cultivar, both in terms of leaf physiological functions and fruit quality attributes, at harvest and post-storage. Four treatments were conducted: the control cane girdling conducted separately in August (GA), in September (GS), and both in August and September (double girdling) (GAS), using different canes. The results indicated that the carbon assimilation rate was reduced in girdled canes. Nevertheless, girdling resulted in increased fruit dry matter (by 1.7%), weight (by 6.4%), and dimensions without altering fruit shape. Additionally, fruits produced on girdled canes exhibited higher total soluble solids content (by almost 13%) and TSS-to-TA ratio post-storage. There were no significant differences in chlorophyll and carotenoid concentrations, organic acids, and most sugars assessed, both at harvest and post-storage. There were no significant differences among the treatments at harvest regarding total phenolic compounds, except for total flavonoids, which were lowest in the GA treatment. Post-storage, girdling (especially GAS and GS) was found to enhance the fruits' total phenols and total flavanols, as well as its antioxidant capacity (1.88 umol equiv. Trolox g\({}^{-1}\) FW based on DPPH assay under GS versus (0.53 umol equiv. Trolox g\({}^{-1}\) FW under control). Overall, cane girdling can improve the quality of kiwifruit in terms of both fruit size and functional fruit properties.  
Keywords:antioxidant capacity; organic acids; phenolic compounds; photosynthesis; sugars +
Footnote †: journal: Applied
## 5 Simulation Results and Discussion
### Case 2: Total Cost, Penalty Cost, Reserve Cost and Direct Cost vs. Schedule Power  
In this case, the scheduled wind power of two WT connected at bus 5 and 11 are varied from zero to WT rated power; variation of total cost, direct cost, reserve cost and penalty cost are plotted in Figures 8 and 9. The coefficients of direct cost, reserve cost and penalty cost are taken from [8] and provided in Table 3. Simulation results show that reserve cost decreases as scheduled WT power increases, while penalty costs increase as  
Figure 8: Cost vs. Weibull scale parameter (\(c\)) at bus #11.  
Figure 9: Cost vs. lognormal mean (\(\mu\)) at bus #13.  
scheduled power increases. There is a linear relationship between direct cost and wind schedule power. The total cost increased as schedule power increased, as it is the sum of direct cost, reserve cost and penalty cost.  
Similarly, SPV total cost, direct cost, reserve cost and penalty costs are plotted against SPV scheduled power in Figure 10. The SPV different parameters and cost coefficients are provided in Table 4. The total minimum cost occurs somewhere between 15 MW and 20 MW in the case of solar power, while in the case of WT sources there is a linear relation between cost and wind schedule power, which can be observed from Figures 10-12.  
Figure 11: Cost vs. wind schedule power (MW) (bus#11).  
Figure 10: Cost vs. wind schedule power (MW) (bus#5).
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security  
The concept of network slicing has existed since the 1960s [13], when the concept of network virtualization emerged. This allowed virtual entities to be created from physical ones by virtualizing systems through network resources, computing infrastructures, and storage devices [14]. Essentially, this meant running multiple virtual machines (VMs) on a single physical machine, each VM acting as if it were a separate physical entity. During the 1970s and the beginning of the 1980s, the concept of network virtualization was commonly implemented in data centres [15]. Near the end of the 1980s, surface overlay networks were the primordial embodiment of the network slicing concept. However, they did not have the automation and programmability needed in the network controls, which proposal appeared and happened in the following two decades [14]. Over time, the idea of network virtualization has contributed to the evolution of the definition of SDN [14] and was one of its first successful use cases [16]. However, it was not until 2009 that SDN first experimented to apply programmability capabilities in a network slice using open interfaces [14]. Although it seems that SDN appears suddenly, it has been around for the past 20 years, and it is revolutionising network design and management with two key features. Firstly, it decouples the control plane, which makes traffic decisions, from the data plane, which executes these decisions by forwarding traffic. Secondly, it centralises the control plane, allowing a single software program to manage multiple data plane elements [16]. Nowadays, it is becoming more evident that the complementary relationship between SDN and NFV enables and leads to the softwarization of the network [13] and is the principle of separating network functions from the hardware they run on using virtual hardware abstraction [17].  
In the following subsections, we provide a concise overview of SDN and its significance in contemporary networks, examine NFV's pivotal role in transforming network architecture, and discuss the critical importance of network slicing in future networks while exploring their security dynamics, concerns and implications considering how policy-based network security interacts with these concepts, as well as the approach related to AI/ML associated with network security and its challenges.
## 3 Stability Analysis for Upper Limit
### BT Margin  
In system theory, the gain margin is the ratio of the maximum loop gain with stability to the loop gain at the design point and is used to determine the region of stability. Typically, the gain margin is obtained using the ratio of the minimum damping parameter that produces instability to the minimum damping parameter at the design point because the loop gain is proportional to the damping parameter. A similar concept is utilized to determine the region of stability of a system that has a unity gain for different values of \(BT\).  
Defining the normalized bandwidth margin as the ratio of the maximum \(BT\) value with stability to the \(BT\) value at the design point, the margin can be calculated as follows:  
\[BTM=\frac{BT_{\text{osc}}}{BT} \tag{10}\]  
Note that the normalized bandwidth margin is inversely proportional to \(BT\) and that for the same value of \(B\) within the stable region (i.e., \(BT<BT_{\text{osc}}\)), a larger loop update interval makes the system reduce the capacity of the sampled output to accurately measure the true output.
## 5 Semi-Active Structural Control  
It is well known that semi-active control strategies can be approached by both active and passive control techniques. Instead of directly applying a force to the structure, as in the case of active control strategies, the control actuator in a semi-active approach modifies the parameters of a passive energy device--in this case, a TMD--whose parameters are adjusted based on specific performance criteria. This semi-active control strategy is simpler than an intelligent or active control approach, as it allows for easier optimization of the damper in situ [24; 25]. It enhances the performance of passive TMDs, which have fixed coefficients.  
In this study, the TMD parameters are defined as functions of the vibration amplitude and input frequency. Consequently, two semi-active control approaches are proposed:  
* TMD optimized based on the amplitude of tower oscillations;
* TMD adjusted by adaptive control as a function of the oscillation frequency.
## 4 Discussion
### Model Accuracy and Robustness  
The random forest (RF) model achieved high classification accuracy, especially in identifying Functionally Weak and Strength-Deficient profiles. Importantly, it never confused unrelated profiles (e.g., Stability vs. Strength), suggesting robust pattern recognition. Misclassifications occurred primarily at classification boundaries, e.g., borderline strength  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Soccer** & **Handball** & \\  & **(_n_ = 37_)** & **(_n_ = 9_)** & **\(p\)** \\ \hline \multicolumn{4}{c}{Performance} \\ Mean Strength (kg/\(\%\)BW) & 0.58 \(\pm\) 0.11 & 0.58 \(\pm\) 0.08 & 1.0 \\ Mean YBT (\%) & 89.15 \(\pm\) 4.76 & 94.11 \(\pm\) 5.90 & 0.035 * \\ FMS Total Score (0–21) & 16.89 \(\pm\) 2.48 & 15.22 \(\pm\) 2.68 & 0.118 \\ Profiles & & & \\ Functionally Weak (\%) & 8 (21.6\%) & 2 (22.2\%) & 1.0 \\ Strength-Deficient (\%) & 5 (13.5\%) & 1 (11.1\%) & 1.0 \\ Stability-Deficient (\%) & 6 (16.2\%) & 1 (11.1\%) & 1.0 \\ No Clear Dysfunction (\%) & 18 (48.6\%) & 5 (55.6\%) & 1.0 \\ FMS & & & \\ FMS Low (\%) & 8 (21.6\%) & 2 (22.2\%) & 1.0 \\ FMS Medium (\%) & 23 (62.2\%) & 4 (44.4\%) & 0.456 \\ FMS High (\%) & 6 (16.2\%) & 3 (33.3\%) & 0.348 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of functional movement and performance metrics between soccer and handball athletes.  
values, and never contradicted the expert logic. In contrast, the decision tree (DT) underperformed, frequently labeling mild deficits as No-Dysfunction. These findings underscore the RF model's superior fit to expert logic and its potential as a reliable decision-support tool.
# Deep Learning-Based Sound Source Localization: A Review
## 2 Classification of Sound Source Localization
### Unmanned Aerial Vehicle Localization
proposed a linear shrinkage-subspace projection (LSP) method to reconstruct covariance matrices (see Figure 4), suppressing interference and enhancing localization accuracy by 15.12% [43]. [PERSON] et al. achieved 98.87% accuracy using STFT-based CNN features for outdoor UAV detection, demonstrating robust spectral-temporal feature extraction [44]. Collectively, these approaches leverage lightweight architectures, spatiotemporal Transformer fusion, and data augmentation to address UAV noise non-stationarity, enabling real-time edge deployment with detection errors reduced by 20-45% compared to traditional acoustic methods. Figure 4: In the experiments, the distance between the unmanned aerial vehicle (UAV) and the array was adjusted using a laser rangefinder, and mixed signals containing UAV-generated acoustic sources and voice interference were recorded [43]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Methods** & **Performance** & **Dataset** & **Scene** \\ \hline
[36] & MUSIC + DP & \
\begin{tabular}{c} Localization error \(\leq\) 20 m at \\ SNR = \(-\)8 dB \\ \end{tabular} & Simulated data + field data &
\begin{tabular}{c} Low-SNR \\ environments \\ \end{tabular} \\
[37] & MFCC-1 DCNN & Precision 96.57\%, recall 92.53\% & Field data & Outdoor UAV detection \\
[38] & Log-Mel + MFCC & Accuracy 94.5\%, effective & Field data & Outdoor UAV detection \\
[39] & Fusion & detection range \(\leq\) 50 m & Field data & Complex environments \\
[39] & Multi-scale + Attention & Accuracy 98.11\% & Field data &
\begin{tabular}{c} High-noise \\ environments \\ \end{tabular} \\
[40] & LWCNN + SVM & Accuracy 95.61\% & Field data &
\begin{tabular}{c} High-noise \\ environments \\ \end{tabular} \\
[41] & GAN & Accuracy 96.38\% & Synthetic data + field data &
\begin{tabular}{c} Low-SNR \\ environments \\ \end{tabular} \\
[42] & BF + CNN & Detection range \(\leq\) 135 m & Field data & Outdoor UAV detection \\
[43] & LSP & Detection accuracy improved &
\begin{tabular}{c} Complex background \\ environments \\ \end{tabular} \\
[44] & STFT+CNN & Accuracy 98.87\% & Field data & Outdoor UAV detection \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of deep learning methods in UAV localization.
# DevEmo--Software Developers' Facial Expression Dataset
## 5 DevEmo Dataset
### Validation  
In order to validate the results, the videos that had been the subject of the annotation process were analyzed in Noldus FaceReader 8.0. This program provides emotion recognition based on facial expression analysis, relying primarily on a subset of Action Units (AUs) provided by the Facial Action Coding System (FACS) [26]. FaceReader is a well-established system in the research community that is known for its relatively high level of accuracy in recognizing emotions [27]. However, it is also known for its vulnerability to poor lighting conditions and for overestimating the emotion of anger.  
From the results of the video processing with FaceReader, excerpts corresponding to manually annotated clips from the DevEmo dataset were analyzed. Among the data obtained from FaceReader, the confidence of occurrence of discrete emotions was selected for further analysis. It should be noted that this program recognizes a different set of emotions that do not completely overlap with the labels assigned in the DevEmo set: _Happy, Sad, Angry, Surprised, Scared, Disgusted_ and _Neutral_.  
For each excerpt, the maximum confidence of each emotion occurrence was calculated. Figure 3 shows two confusion matrices of annotated labels and FaceReader analysis results. The Y-axis shows labels from the DevEmo dataset, and the X-axis shows labels from FaceReader. The left chart presents a summary for the dominant emotion. For each excerpt, the emotion with the highest maximum confidence value was selected and matched with the emotion selected during the manual annotation process. A similar summary, but for the average of the maximum confidence in recognizing each emotion, is shown in the right chart.  
The results of FaceReader only partially match the assigned labels. The figure clearly shows the over-recognition of the emotion of anger and the neutral state. When these two emotions are ignored, the automatic and manual annotated results become more consistent. Looking at the averages of maximum confidence, _happiness_ has the lowest accuracy, being recognized as _sad_ actually as often as _happy_. Confusion, which is not an emotion recognized by FaceReader, was most often recognized as _angry_ and _disgusted_, which is most reasonable since recognition is mostly based on AU.  
Manual analysis of the videos showed that the fragments whose FaceReader results differed the most from manual annotation were mostly recorded in poor lighting conditions  
Figure 3: Confusion matrices of annotated labels and FaceReader analysis results.  
or low resolution. In addition, analysis by a fourth author not involved in the annotation process confirmed the validity of the manual annotation in all cases. This confirms the shortcomings of existing tools and the rationale for sharing datasets of emotion-annotated video clips, such as DevEmo, to develop new emotion recognition tools.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 2 Methodology
### Modeling
#### 2.1.5 Obtaining the Desired Correlations
Therefore, it is only necessary to compute the elements above (or below) the main diagonal to find the spatial correlations and the number of calculated values is \(\frac{m^{2}-m}{2}\). * Temporal correlation: Autocorrelation  
Autocorrelation, \(acorr_{X}\), measures the similarity of a variable \(X\) with a time-shifted version of itself, given by Equation (14) [21]. \[acorr_{X}=\frac{\sum_{i=1}^{n}X_{i}X_{i-k}}{n} \tag{14}\]  
where \(k\) is the time lag and \(X_{i-k}\) is the variable \(X\) at position \(i-k\). Figure 4 shows the example of a short sequence, where different situations can be seen that can occur when considering a time-shifted version series over itself. As expressed before, the autocorrelation takes into account the correlation between a series and a shifted version of it. Consequently, the order of calculations to be determined is given on one hand by the number of locations and on the other by the number of time delays that are considered. Therefore, the number of operations is \(mk\), so when a series is related to itself, it is only necessary to calculate the elements of the main diagonal, being necessary to multiply this number by the number of delays considered in the calculations. Temporal correlation: Cross-correlation  
Cross-correlation, \(xcorr_{XY}\), measures the similarity of a variable \(X\) with a time-shifted version of the variable \(Y\), given by Equation (15) [21]:  
\[xcorr_{XY}=\frac{\sum_{i=1}^{n}X_{i}Y_{i-k}}{n} \tag{15}\]  
where \(Y_{i-k}\) is the variable \(Y\) at position \(i-k\). Figure 5 presents an example of a short sequence, where different situations can be seen that can occur when considering a time-shifted version series over another series are shown. As stated before, the cross-correlation expresses the correlation of a sequence with a displaced version of another one. As a consequence, the order of calculations is influenced on one hand by the number of locations and on the other by the number of temporary delays that are taken into account. Therefore, the number of operations is \(\left(m^{2}-m\right)\)\(k\). When relating one series with another, it is necessary to calculate the elements outside of the main diagonal. In contrast to the spatial correlations, there is no symmetry when considering the displacement of one series over another, and this makes it necessary to multiply this number by the number of lags being considered in the calculations. Figure 4: Autocorrelation between the series \(X\) (continuous line) and a shifted version of the series \(X\) (dotted line): (**a**) lag 0, (**b**) lag 1, (**c**) lag 2, (**d**) lag 3.
## 4 Discussion
### Data and Method Limitations
Camera data hence face the limitations similar as optical satellite images, such as reduced visibility due to bad weather conditions and the presence of gasses and ash from the volcano [40; 43; 44]. These limitations also hindered the interpretation of the data in our kymographs. This is the case for the daytime images, which had more cloud cover and haze; thus, the activity of the lava fountains was not visible; only the time-lapse photography from 20h00 to 08h00 UTC was used. However, the combination of SAR images and camera data complement each other well, resulting in a whole dataset with good spatial coverage and nearly daily temporal resolution.