esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Reconstruction of a Single Bubble in Transparent Media Using Three Orthographic Digital Images
## 2 Experimental Apparatus and Visualization
### Image Processing and Camera Calibration  
Images were processed by using MATLAB R2015a to obtain the bubble projection contours during the growth. The key result is shown in Figure 2. Figure 2a is one of the images taken by left view bubble in our experiment in Section 4.2. Figure 2b shows the image after dividing by a background image, which was without bubble, to enhance the contract and then binarizing with a threshold on the gray levels. From Figure 2c, we can notice that the holes are filled and noises are eliminated effectively. Bubble projection contours were detected by edge extraction method, as shown in Figure 2d.  
After image processing, a calibrated chessboard was placed inside the acrylic box to determine the intrinsic parameters of cameras. According to the perspective of [PERSON] et al. and the application  
Figure 1: Experimental set-up.  
Figure 2: Steps of image processing. (**a**) Initial image, (**b**) background division and binarization, (**c**) hole filling, (**d**) edge extraction.  
of their methods [29; 30], the front and side camera parameters could be calculated, whose results were shown in Tables 1 and 2, separately. According to the ideal pinhole model, the magnification factors are determined then for front camera (0.177 mm pixel\({}^{-1}\)) and for side camera (0.171 mm pixel\({}^{-1}\)). Because the top view camera was only used to extract the azimuth angle, which cannot be effected by magnifying, therefore it did not need to be calibrated.  
where, \(f_{ij}\) is the calibrated focal length in pixels, subscript character \(i\) = 1,2 representing horizontal direction and vertical direction of pictures respectively while subscript character \(j\) = 1,2 representing front camera and side camera, respectively. \(\gamma\) is the skew coefficient defining the angle between the \(x\) and \(y\) pixel axes, \(k_{c}\) is the image distortion coefficients (radial and tangential distortions).  
The calibrated focal length was slightly different from the data provided from manufacturer that the camera was equipped with lens of focal distance 12 mm. There are probably two main reasons causing this unexpectedly disparity. On the one hand, ideal pinhole image model was adopted in camera calibration process, assuming focal length was definite. However, the real cameras had much more complex construction. On the other hand, different locations and positions of the calibration chessboard can result in variant calibration results. Thus, calibrating results change with distances and postures. Therefore, in order to receive a more satisfying calibration result, each step of calibration should be done exactly and carefully. Besides, the high quality of calibration chessboard, appropriate illumination conditions, and suitable camera postures should also be taken into consideration.
# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 5 Conclusions  
We proposed a multi-site anti-interference neural network ASD classification method that aims to make the representation learning results reflect the disease differences while being less affected by site confounding factors, and finally realize anti-interference classification. The experimental results show that the MS-AINN achieves the purpose of confounding the site. At the same time, the improvement of various classification indexes shows that reducing the impact of site confounders on representation learning can effectively improve the classification performance of models. Moreover, the MS-AINN can still improve the classification performance after replacing the representation learning module, which proves the universality of the architecture. Therefore, the MS-AINN that takes site confounders into account is of great significance for the design and research of deep learning ASD classification model under multi-site datasets.  
The purpose of our method is to remove the multi-site confounding effect on ASD classification, which is realized by alternating iterative training of site feature regression task and anti-interference classification task under MS-AINN. This training method can limit the feature extraction of the representation learning module, so that the extracted features are less confounded by the site. However, it is found in the experiment that the restriction representation learning module will not only weaken the confounding effect of the sites, but also affect the classification. Neither too large nor too small restriction can achieve the optimal performance. Therefore, hyperparameters are required in the objective function to balance the losses of the two tasks, which also increases the difficulty of parameter adjustment. In addition, we only test classification tasks on ASD datasets, and the next step will be to test other tasks on different multi-site datasets.  
Conceptualization, W.L. and J.X.; methodology, W.L.; software, W.L. and S.L.; validation, W.L., F.L. and S.L.; formal analysis, S.L. and J.X.; writing--original draft preparation, W.L.; writing--review and editing, W.L.; supervision, J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study use of publicly available, previously published data from ABIDE.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results  
### Use Case 1--Results  
We start the analysis with the dataset \"Taiwanese Bankruptcy\". This dataset includes 6819 rows (observations) and 96 features. We remove the \"Bankrupt7\" feature and remain with 95 numerical features. We perform the elbow method; its results are presented in Figure 1.  
Based on the Elbow and Silhouette plots analysis, K = 7 is selected for the K-means clustering procedure. Note that there are a few methods that examine the efficient clustering methods, number of clusters (in K-means), and analysis of the optimum K values in KNN, such as, [2; 42; 43]. These aspects are beyond the scope of this research.  
Subsequently, a new feature is incorporated into the dataset, assigning each observation its respective cluster value. Following this, according to step 10 of the algorithm, a feature is systematically removed from the dataset, followed by iterative cycles of K-means clustering, dataset update, and KNN and RF classification, as outlined in the preceding section. The results are summarized in Table 2. It is observed that through this iterative process, the dataset is eventually reduced to 10 features from the original 95, while the performance (for all measurements such as accuracy recall, F1, etc.) of both models (KNN and RF) increases.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**KNN Accuracy**} & \multirow{2}{*}{**KNN Precision**} & \multirow{2}{*}{**KNN Recall**} & \multirow{2}{*}{**KNN F1**} & \multirow{2}{*}{**RF Accuracy**} & \multirow{2}{*}{**RF Precision**} & \multirow{2}{*}{**RF Recall**} & \multirow{2}{*}{**RF F1**} \\ \cline{1-1} \cline{6-11} \cline{As presented in Table 2, all examined measurements improved; for example, KNN accuracy enhanced by more than 2.7%, from 0.925513 on the \"original\" 95 features to 0.953079 with 7 features.
# Vesicles and Reservoirs of Basic Lava Flows in the Laobeishan and Huoshaoshan Volcano, NE China
## 4 Vesicle Distribution
### 4.3. Distal Belt
#### 4.3.5 Hornito  
Hornito is a representative product formed in the distal belt of a lava flow and is distributed near water. The formation mechanism is that lava flows through aquifers or wet areas, and a large amount of gas is rapidly generated at the base of the lava flow. Driven by the gas, the internal volume of the consolidated lava flow under the surface shell expands rapidly, and the internal pressure increases. The liquid lava flow breaks through the weak zone of the consolidated surface or spits out along the fracture channel, and the spilled \"lava cake\" is stacked on the surface in layers until the internal pressure is insufficient to provide the spillage power [37]. When the internal gas generation is less, the pressure is less. The lava is only stacked in a few layers on the surface. At this time, the height is relatively small, and the outflow center \"horn\" is relatively large, called the eruption hole or the eruption dish. When there is more gas generated in the interior and the pressure is high, the lava is stacked in dozens or dozens of layers on the surface to form a tall hornito (tower). The middle of the hornito preserves the gas channel of the hollow structure.  
The formation conditions of hornito in the Huoshaoshan distal belt differ from those in other areas. The lava flow in this facies belt breaks through the semi-consolidated surface and breaks it into slag blocks at the base and front of the lava flow. It then presents a \"caterpillar\" flow form. This mode is not conducive to direct contact between the lava and water and is more manifested as the lava uses its high temperature property to bake and heat water-rich sediments to form water vapor [38].  
In this study, the hornito was located in the distal belt of the northern breached crater of the Huoshaoshan volcano (Figure 1a). Approximately 4500 vesicles were identified in four hornito images. The majority of vesicles were ellipsoidal on the flow surface of each layer and elongated along the flow direction from the center to the edge of the \"drop shape.\" The stacked marginal band vesicles have different shapes; most appear as discrete spheres and ellipsoids, and the arrangement direction is not directional. Although hornito is the product of a distant facies belt, the number of vesicles is substantially greater than that in the distal belt, which is because of its formation mechanism and environment. The apparent diameter of vesicles <10 mm accounted for 75.6%, the apparent diameter >10 mm accounted for 24.4%, and it was concentrated in the range of 4-10 mm. The maximum apparent diameter of the vesicle was 95 mm (Figure 3d). The vesicle surface porosities obtained from analysis of the four images were 15.93%, 16.46%, 18.6%, and 17.75%, respectively, and the average surface porosity was 17.19%, which was higher than that of the distal belt.
## 5 Discussion
### Ablation Studies
#### 5.2.1 Impact of Convolution Blocks  
The first ablation study examines how the convolutional block affects the model's performance by comparing the entire ConvResBiGRU-SE architecture with the convolutional block and a version without it, as revealed in Table 5. The experimental outcomes highlight the convolutional block's significant contribution to the model's overall interpretation of both datasets.  
For the UCI-HAR dataset, adding the convolutional block increased the model's accuracy from 97.29% to 99.18%, an absolute improvement of about 1.89%. Similarly, the F1-score improved from 97.34% to 99.24%. The relatively small standard deviations (\(\pm\)0.20% for accuracy) indicate consistent performance across different runs. This improvement suggests that the convolutional block effectively extracts relevant spatial features from the raw sensor data, providing a better foundation for subsequent layers.  
In the case of the WISDM dataset, including the convolutional block led to an accuracy increase from 97.91% to 98.78% and an F1-score increase from 97.11% to 98.09%. Although the performance gain is smaller than on UCI-HAR, it remains significant and confirms the block's effectiveness across different datasets. The slightly more significant standard deviations on WISDM (\(\pm\)0.24% for accuracy) suggest more variability in the model's effectiveness, possibly due to the different characteristics and complexity of datasets.
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 2 Methods
### Data Processing Strategies  
The single-frequency and dual-frequency observations were processed in PPP mode using the above algorithm and RTKLIB, and the parameters to be estimated included station coordinates, receiver clock offsets, zenith tropospheric delays, ISBs and the ambiguities. Orbit and clock errors were mitigated by using WUM (Wuhan University) final products, and the DCB files were from CAS. The data processing strategies are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Strategies** \\ \hline Observations & Dual-frequency: GPS L1/L5, Galileo E1/E5a ionosphere-free combination of code and carrier phase \\  & Single-frequency: GPS L1, GLONASS L1, Galileo E1 code and carrier phase \\  & and carrier phase \\ Sampling rate & 1 s \\ Elevation cutoff & 10\({}^{\circ}\) \\ Observation weight & Elevation dependent weight \\ Orbits & WUM final orbits \\ Satellite clocks & WUM final clocks, 30 s interval \\ Tidal loadings & IERS conventions (2010) and FES2004 model [26] \\ Ionospheric delay & Dual-frequency: ionosphere-free combination mode \\  & Single-frequency: ionospheric broadcast model \\ Satellite antenna phase center correction & IGS14.4 tx \\ Receiver antenna phase center correction & IGS14.4 tx \\ Antenna phase wind-up correction & IGS model \\ DCB & CAS DCB file \\ Parameter estimation & Extended Kalman Filter (EKF) \\ Receiver coordinates & Estimate \\ Receiver clock & Estimate \\ Inter-system bias & Estimate \\ Ambiguities & Estimate, float \\ Tropospheric delay & Estimate ZTD and horizontal gradients \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data processing strategies.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol
### Results
#### 6.3.2 Energy Saving Protoc--Second Method: \(L\) Variable
As explained before, the reason for this fact is that the design conditions are not optimal, that is, the performance index is not directly associated with the increase of the probabilities. Figure 6b shows the values of the mathematical expectation of the global number of transmissions \(\mathcal{E}(M)\) by route computed according with Lemma 4. Since the greater the number of transmissions, the greater the energy consumption, observe that the route associated with the highest resource consumption is \(R_{4}\), that also contains the critical network node. Finally, Figure 6c exhibits the energy consumption \(E_{GE}\) for each exchange interval \(I_{L_{m}}\), that is monotonic decreasing, meaning that the greater the number of devices operating in the resource savings working mode (finite \(L\)), the greater are the savings in the global energy consumption of the network. As conclusion, note that the main advantage of this method is that, at first, the performance is optimized (minimum in terms of \(\mathcal{H}_{\infty}\) norm, that is equivalent to the classical case since \(L=\infty\)) and over time, although the \(\mathcal{H}_{\infty}\) performance is deteriorated, the energy saving is gradually increased, augmenting the equipment life span.
# Special Issue on IberSPEECH 2022: Speech and Language Technologies for Iberian Languages
## Contributions
### Affective Computing and Applications  
Speech Emotion Recognition (SER) plays a crucial role in applications involving human-machine interaction. These systems still struggle to accurately discern the emotional state of their users, which is a fundamental aspect of human communication. However, the scarcity of suitable emotional speech datasets presents a major challenge for accurate SER systems. Most datasets include artificially simulated emotions and feature a small number of audio samples and speakers. To enhance the overall performance and generalization capabilities of subsequent systems, researchers have employed a technique known as cross-corpus or joint training. This approach involves amalgamating multiple datasets during the training phase. Cross-corpus or joint training has been utilized in certain SER systems as a means to overcome the challenges posed by the aforementioned dataset limitations, for example see [7].  
In the paper by [PERSON] et al. (contribution 9), authors explore the cross-corpus strategy as an extension of the training set and investigates the cross-corpus strategy in greater detail. They explore additional language variability by incorporating a dataset in a different language, and they evaluate the system's performance by gradually increasing the amount of matched data in the training set. Furthermore, another self-supervised (SS) representation, WavLM [8], is assessed, considering its favorable performance in previous studies. The obtained results show that combining databases, even when they encompass different languages, can enhance the system's performance. Moreover, The study presented in this paper reveals that the WavLM representation outperforms other representations in the SER task.  
Prosody is one of the essential characteristics of human voice communication, with prosody we can communicate everything from emotions to the purpose of a sentence. Through automatic speech processing, it is possible to analyze prosody and extract prosody characteristics that can be used in a variety of speech-related applications. In particular, by analyzing prosody characteristics, it is possible to identify differences between the voices of typical speakers and individuals with Down syndrome (DS) [9].  
A study to obtain prosody features to detect problems in individuals with Down syndrome is carried out in contribution 11. By means of these features it is possible to help therapists to design training therapies adapted to the particular problems of each user. These prosodic features have been extracted from a speech corpus from individuals with Down syndrome. The corpus was obtained through the use of a tool (a video game) designed to train prosody and pragmatics in individuals with Down syndrome.
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms
### Recoverable Semi-Fragile Watermarking Algorithms
#### 5.2.1 DCT Domain
At the same time, the algorithm also utilizes the tamper detection method based on the multi-neighborhood feature and multi-threshold optimization to improve the performance of the algorithm. Therefore, the algorithm performs excellently in terms of invisibility, security, and the ability of tampering detection and recovery.
## 3 Procedure for Estimation of Optimal Analysis Parameters
### Design of Experiments  
The design of experiments (DOE) is used to efficiently create a regression model using analysis data. The DOE is to select the optimal analysis or experimental cases for the problem to be solved and to plan the experiment. The DOE is a procedure for choosing a set of samples in the design space, with the general goal of maximizing the amount of information gained from a limited number of samples [33].  
Factors and levels affecting the results must be determined before designing an experiment with the DOE. In this study, as mentioned in Section 2, factors affecting the thermal analysis results are determined, and the parameter ranges and levels are determined considering the assumptions and physical conditions applied in previous studies. Considering the number of analyses and region of interest, the Box-Behnken design, one of the DOE, is used to construct parameter cases, and then, thermal analysis is conducted for the parameter cases. Figure 6 illustrates the concept of the Box-Behnken design with three factors.
## 3 Results
### Simulation of Dust Storm on 21 February 2015
#### 3.4.2 Spatial Pattern of Vertical Dust Flux Using GOCART Wind Erosion Scheme  
The GOCART scheme simulated the vertical flux (\(\upmu\)g/m\({}^{2}\)s) of the dust storm on 21 February 2015, with results as shown in Figure 12.  
Storm dust emission flux results on 21 February 2015 by GOCART scheme using WRF-Chem model show that Saudi Arabian deserts (to the south-west of the study area), Qareqoom desert (to the north-east) and Hirmand Basin (to the south-east) formed the primary core of the storm; additionally, emission flux simulations showed that the south-eastern regions of Iran as well as northeast Iran have the potential for dust emission. There is high VDF in the Loot Desert and in the Central Plateau; hence, the elevated ground levels in these areas and favorable climatic conditions can cause severe dust emission. The average spatial pattern in Figure 13 also shows that the Jazmoorian basin in southeastern Iran may be identified as a critical hotspot. The GOCART scheme illustrates this phenomenon well.  
Figure 11: Mean Spatial Pattern of Storm Dust Flux (\(\upmu\)g/m\({}^{2}\)s) on 21 February 2015 with AFWA Scheme.  
Figure 12: Spatial pattern of dust flux (\(\mu\)g/m\({}^{2}\)s) with 3 h interval by GOCART scheme.  
GOCART for 21 February 2015 shows internal dust sources in the Jazmourian and Loot Desert and Hirmand Basin. In the Jazmourian Basin, the VDF simulated by the model is up to 14,000 \(\upmu\)g/m\({}^{2}\)s.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Error Analysis  
To evaluate and compare the forecasting results of each model, the errors are calculated by root to mean square error (RMSE) and percentage deviation (PD), and correlation coefficient (\(\rho\)). Among them, RMSE and PD stand for forecasting performance, while \(\rho\) presents the correlation between forecasting values and the observed values [14; 16].  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{(f_{obsi}-f_{forei})^{2}}} \tag{3}\]  
\[PD=\frac{1}{N}\sum\limits_{i=1}^{N}{\frac{\left|{f_{obsi}-f_{forei}}\right|}{ f_{obsi}}} \tag{4}\]  
\[\rho=\frac{\sum\limits_{i=1}^{N}{(f_{forei}-\overline{f_{fore}})(f_{obsi}- \overline{f_{obs}})}}{\sqrt{\sum\limits_{i=1}^{N}{(f_{forei}-\overline{f_{fore }})^{2}}}\sqrt{\sum\limits_{i=1}^{N}{(f_{obsi}-\overline{f_{obs}})^{2}}}} \tag{5}\]  
where \(N\) is the total number of data samples, \(f_{obsi}\) and \(f_{forei}\) are the observed value and forecasting value, respectively. \(\overline{f_{obs}}\) and \(\overline{f_{pre}}\) are the average of the observed and forecasting values, respectively.