esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The classical approach in IRT is pulsed thermography (PT) in which all the system elements, i.e., the camera, the heat source, and the component to be inspected, are static. The component is thermally stimulated with a fast heat pulse (e.g., using xenon flashes) while the thermal evolution is recorded for as long as necessary so as to allow the component to cool down [11]. An alternative approach is line scan thermography (LST) in which the camera and the heat source move above the surface of the component, which remains static [12]. LST is a very interesting configuration for the inspection of large and complexshaped parts during production given that is fast and can follow geometrical variations when combined for example with a robot, an actuator, or a drone. For the purpose of this study, PT can be considered the gold standard to evaluate the performance of the LST system knowing a priori that defect contrast should be higher for PT than for LST. The first goal of this research work is to explore the use of LST using an uncooled microbolometer camera, i.e., a practical configuration for industrial applications, and compare its performance with respect to PT using a cooled high-definition infrared camera and advanced signal processing, i.e., the gold standard in IRT, for the detection of porosity and BVID. To do this, academic samples containing artificially produced localized porosity and impact damage were manufactured and inspected by both techniques. The contrast-to-noise ratio (CNR) is employed as a quantitative criterion to objectively and quantitatively assess and compare performances. The second objective is to estimate the depth of porosity and BVID samples. There is currently no quantitative approach available for LST. However, depth estimation is possible by PT using advanced signal processing, such as quantitative pulsed phase thermography (PPT) [13], which requires knowledge of thermal diffusivity. Hence, the thermal diffusivity of all samples was measured from PT in transmission mode and used for the estimation of the depths. The content of the paper is as follows: Section 2 deals with the materials and experimental setups employed for this study, and presents as well a description of the techniques. Section 3 offers a discussion of the results, and finally, Section 4 presents the conclusions.
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 5 Discussion, Limitations, and Future Research Trends
### Future Research Trends  
1. Advanced data collection and annotation  
The development of semi-automated and fully automated annotation tools could alleviate the cumbersome data labeling process. Techniques like weak supervision and active learning can substantially reduce human effort while enhancing the quality and diversity of training datasets.  
2. Domain adaptation and generalization Future research could focus on developing models that generalize better across diverse environments. Domain adaptation techniques and unsupervised learning approaches could improve the robustness of AI models, enabling them to perform consistently in varying real-world scenarios.  
Real-time processing enhancements  
Optimizing network architectures to balance accuracy with processing speed will be crucial. Leveraging hardware advancements alongside software optimizations can lead to significant improvements in the real-time application of these models.  
Mitigating AI biases  
Developing techniques to identify, quantify, and mitigate biases within datasets and AI models will be an essential area of future research. Enhancing the transparency and interpretability of AI models can lead to more equitable and trustworthy machine learning systems.  
Integration of Multimodal Sensory Data  
Enhanced sensor fusion techniques that seamlessly integrate data from various sensors (cameras, LiDAR, radar, etc.) are essential for improving contextual understanding and decision-making capabilities. Innovations in multimodal approaches, such as those demonstrated by VLMs, hold significant promise.  
Simulation and virtual environments  
Refining simulation tools like CARLA and integrating them with real-world data can create more effective and versatile training systems for autonomous vehicles. These simulators will be critical for validating algorithms under varied and controlled conditions.  
Human-machine interaction  
Improving the interface between human operators and autonomous systems can enhance safety and trust. Research focusing on intuitive control mechanisms and fail-safe protocols is essential to ensure effective human intervention when necessary.  
In summary, while substantial progress has been made in leveraging AI for image analysis in autonomous vehicles, ongoing research and innovation are required to overcome existing limitations and pave the way for more advanced, reliable, and safe autonomous driving systems.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 4 Applications of Small Satellite SAR Data
### Novel Topics in Biosphere-Anthroosphere InterActions  
Global forest/non-forest mapping and forest area/biomass estimation could be made both accurate and cost-efficient in remote, underdeveloped areas if multi-band information is combined, which is already the case in agricultural applications [136; 137; 138; 139]. Another potential research area where data fusion may be useful is vulnerability arising from biosphere-anthroosphere interactions. Previous disaster monitoring projects concentrated on anthropogenic interactions mainly with geosphere (e.g., earthquakes or volcanoes) or hydrosphere (e.g., floods), but even without these catastrophic events, public health is being threatened by global climate change and associated infectious diseases [140]. For example, malaria transmission might be facilitated in tropical highlands in the future [141]. Development of early-warning systems against malaria and other vector-borne diseases was attempted using satellite imagery such as Landsat's Thematic Mapper sensor in visible and infrared wavelengths [30; 31; 32; 142], Soil moisture in croplands, wetlands, and flooded areas can be measured through clouds and vegetation using SAR [143; 144; 145; 146]. The idea of disease modeling and surveillance using satellites has been around since the late 1980s, whose implementation in multiple wavebands would be timely and worthwhile considering increased pandemic risks today [147; 148]. Minisatellites, microsatellites, and picosatellites  
Figure 23: Formation flight for 3-D SAR techniques: (**left**) SAR tomography. (**middle**) Holographic SAR tomography. (**right**) Teardrop pattern in the Cobra array of satellites.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Resolution (Global/Local)** & **Accuracy** \\ \hline Upper canopy height and change & 50 m/30 m & 10\% error for height \(<\) 2 m \\
3D forest structure and change & 50 m/30 m & - \\ Forest biomass and change & 100 m/50 m & 20\% error for biomass \(<\) 100 ton/ha \\ Agricultural SAR products & 40 m & - \\ \hline \hline \end{tabular}
\end{table}
Table 9: Tandem-L requirements in biosphere monitoring.  
introduced in Section 3 may have their own contributions ranging from surface monitoring to precipitation mapping.
## 2 Methods
### Step 1  
#### 2.3.1 Reference Measurement System  
Turn 7 was equipped with a video-based reference system, which was used to determine the GNSS antenna trajectory independently from the GNSS methods. The reference system consisted of six panned, tilted and zoomed HDV cameras (Sony, PMW-EX3), which captured the skier at 50 Hz. The camera images were time-synchronized using an electronic gen-lock signal. The GNSS antenna was manually digitized in the camera images. Reference points were mounted along the course for turns 6, 7 and 8 and their positions were determined using a geodetic tachymeter in a local coordinate system (LCS). The reference points and the camera positions were used along with a DLT-based panning algorithm by [PERSON] (1994) to reconstruct the GNSS antenna trajectory (\(\mathbf{P_{REF}}\)) during turn 7. The start and end of turn 7 was determined using the definition of [39]. \(\mathbf{P_{REF}}\) was low-pass filtered using a second-order Butterworth filter with a cut-off frequency determined according to the Jackson Knee method [40]. The mean resultant photogrammetric error is known to be 23 mm with a standard deviation of 10 mm [41].
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 2 Theory  
There are many factors that affect the electro-optical performance of a compact VCDM, such as the electromagnetic force of micro VCA, power dissipation and efficiency of micro VCA, response time of VCDM, and fitting error. DM with only one or two of them with excellent values as reported in references could not really lead to a high electro-optical performance for a compact VCDM. Therefore, it is necessary to analyze systematically the requirements from the application's point of view and the relationships among them according to electromagnetic theory.
## 4 Experiments and Results
### Performance of Individual ML Models  
After data preprocessing, the performance of individual models was compared. To evaluate the performance of individual ML models, it is necessary to find appropriate hyperparameters for each model. First, the raw dataset is randomly partitioned into two subsets: a training set (80%) and a testing set (20%). The grid search provided by the scikit learn package was performed. In this study, the scikit learn package and some libraries of Python were utilized. Table 2 shows the initial ML models and their hyperparameter values searched for in the hyper-parameter space by the best cross-validation score. The hyperparameter of the DT model represents the value applied to the BDT model.  
A five-fold cross-validation technique is employed to compare the performance of individual models, as it is a well-known technique for cross-validation. The hyperparameters required for individual models were applied as values searched by the grid search applied in the previous step. Table 3 shows the five-fold mean values of RMSE and \(R^{2}\) calculated for the training dataset and the testing dataset according to individual models. As can be seen in Table 2, the individual models showed relatively similar performances, with an \(R^{2}\) value of 0.89 for the testing data. The exception is the BDT model. Most ML models do not have much of a difference between the \(R^{2}\) value for the training dataset and the \(R^{2}\) value for the test dataset, indicating that they have a relatively even generalization ability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Models** & **Hyperparameters** & **Values** \\ \hline \multirow{4}{*}{ANN} & activation & relu \\  & batch\_size & 16 \\  & epochs & 100 \\  & initializer & normal \\  & optimizer & adam \\ \hline \multirow{4}{*}{SVM} & Regularization parameter (C) & 400 \\  & epsilon & 0.005 \\  & Kernel type & RBF \\  & RBF gamma & 0.010 \\ \hline \multirow{4}{*}{RF} & n\_estimators & 715 \\  & max\_depth & 5 \\  & min\_samples\_leaf & 4 \\ \hline \multirow{4}{*}{DT} & min\_samples\_split & 25 \\  & max\_depth & 6 \\  & min\_samples\_leaf & 10 \\  & max\_leaf\_nodes & 30 \\ \hline \multirow{4}{*}{Adaboost} & n\_estimators & 114 \\  & learning\_rate & 0.089 \\ \hline \multirow{4}{*}{XGBoost} & max\_depth & 200 \\  & max\_depth & 3 \\  & learning\_rate & 0.023 \\ \hline \multirow{4}{*}{LightGBM} & n\_estimators & 100 \\  & max\_depth & 4 \\  & learning\_rate & 0.056 \\ \hline \multirow{4}{*}{CatBoost} & iterations & 250 \\  & learning\_rate & 0.050 \\ \cline{1-1}  & depth & 2 \\ \cline{1-1}  & l2\_leaf\_reg & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial ML models and their hyperparameter settings.
## 3 Mitigation Scheme for SAR
### Decomposition and Reconstruction  
#### 3.4.1 Decomposition  
The decomposition method manages to extract the latent components or subspaces corresponding to RFI according to specific difference between RFI and SAR echoes [70], including the power, statistical difference, etc. The eigensubspace projection method uses singular value decomposition (SVD) to separate the large and small singular values into two groups, and then constructs the RFI subspace and signal subspace via the singular vectors corresponding to different singular value groups [71]. [PERSON] et al. extends the eigensubspace projection into the instantaneous spectrum for removing the wide band interference [8]. By making use of the statistical distribution difference, independent component analysis [72] and independent subspace analysis [73] are proposed to extract latent components corresponding to RFI.  
[PERSON] et al. proposed using complex empirical mode decomposition (CEMD) to distinguish the RFI and radar echoes [74]. The essence of the CEMD is to decompose different trends or fluctuations contained in the signal gradually to obtain a series of data sequence with different intrinsic time scales. [PERSON] et al. further incorporates the empirical mode decomposition with the fractional Fourier Transform [75]. These methods are data driven and adaptive under specific assumption, but has relatively large computation burden due to iterative optimization. The performance relies on certain prior knowledge of the RFI source.
## 3 Experiments and Data Analysis
### Experiment on Measuring the Pose of a Rigid Body with a Camera
#### 3.3.1 Attitude Measurement and Analysis  
We used the high-speed camera to record video frames of dynamic changes in the middle and outer frames of the turntable. To ensure that the pixel coordinate measurement did not affect the pose measurements, the pixel coordinates of the control points were determined using TEMA, an advanced motion analysis tool developed by Image Systems AB in Sweden. We adopted the DLT method mentioned earlier for measurements and then used Equations (4) and (6) to obtain the attitude of the turntable. Dynamic changes in the attitude of the turntable are shown in Figure 7.  
Figure 7 shows the change in the measured attitude angle of the rigid body when the camera is at a distance of 2.21 m. Figure 7a-c shows the real-time change in the camera's attitude angle \(Q_{x}\) of the middle frame of the turntable measured for different camera positions. Table 1 compares the mean absolute error and standard deviation of the camera's attitude angle measured for the three camera positions. Figure 7a-c and Table 1 reveal the minimum error in the attitude angle \(Q_{x}\) measured by the camera at the second camera position. The mean absolute error difference of the attitude angle measured at three positions is not significant (not more than 0.2 degrees). Figure 7d reflects the error of the camera in measuring the attitude angle \(Q_{x}\) at the first position. The error increases with the tilt of the rigid body relative to the camera lens. The mean absolute error is 0.8092 degrees, the standard deviation is 0.6623 degrees, and there are individual errors as large as 2.5 degrees. Figure 7e,f shows that although only one attitude angle \(Q_{x}\) is changing in theory, the angles \(Q_{y}\) and \(Q_{z}\) measured by the camera still include errors. The mean absolute error is 0.186 degrees, and the standard deviation is 0.641 degrees for \(Q_{y}\), and the mean absolute error is \(-0.032\) degrees and the standard deviation is 0.222 degrees for \(Q_{z}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **The First Camera** & **The Second Camera** & **The Third Camera** \\  & **Position** & **Position** & **Position** \\  & \(Q_{x}\)\(I^{c}\) & \(Q_{x}\)\(I^{c}\) & \(Q_{x}\)\(I^{c}\) \\ \hline Mean absolute error & 0.8092 & 0.6287 & 0.7359 \\ Standard deviation & 0.6623 & 0.5671 & 0.6297 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control point parameters measured by the total station instrument.  
Figure 7: Attitude angle in turntable swing mode. (**a**) Attitude angle \(Q_{x}\) in the first position; (**b**) Attitude angle \(Q_{x}\) in the second position; (**c**) Attitude angle \(Q_{x}\) in the third position; (**d**) Error of \(Q_{x}\) in the first position; (**e**) Error of \(Q_{y}\) in the first position; (**f**) Error of \(Q_{z}\) in the first position.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 4 The Node Placement Algorithm
### Dynamic Node Placement (Locally-Significant Paths)  
The first algorithm proposed is the Dynamic Node Placement algorithm, referred to as Dynamic MobileCC. This algorithm places a mobile node in a carefully computed position so to alleviate the problematic area and assist the affected nodes. This mobile node can forward the packets either directly to the sink if the sink is in its transmission range, or it can serve as a relay node and forward the received packets to other upstream nodes.  
_High-level idea._ Initially, the Dynamic MobileCC algorithm calculates the average number of packets per time unit that the defective node receives and cannot forward due to lack of buffer space. Then, it discovers the nodes that transmit their packets to the defective nodes and calculates the best position that the mobile node(s) should move to in order to receive data from a number of them. Ideally, the best position is the position where the minimum number of nodes can divert their traffic through the mobile node(s), whereas at the same time, their total sending rate should be equally or more than the amount of the excess traffic of the defective node (see Algorithm 1 and Appendix A.1)).  
Its operation is based on the following functions:  
* Identification of defective and \"defecting\" nodes;
* Calculation of extra resources;
* Calculation of the position that the mobile node should be placed.  
We now provide a detailed description of these functions below.
# Blind Recognition of Frame Synchronization Based on Deep Learning
###### Abstract  
In this paper, a deep-learning-based frame synchronization blind recognition algorithm is proposed to improve the detection performance in non-cooperative communication systems. Current methods face challenges in accurately detecting frames under high bit error rates (BER). Our approach begins with flat-top interpolation of binary data and converting it into a series of grayscale images, enabling the application of image processing techniques. By incorporating a scaling factor, we generate RGB images. Based on the matching radius, frame length, and frame synchronization code, RGB images with distinct stripe features are classified as positive samples for each category, while the remaining images are classified as negative samples. Finally, the neural network is trained on these sets to classify test data effectively. Simulation results demonstrate that the proposed algorithm achieves a 100% probability in frame recognition when BER is below 0.2. Even with a BER of 0.25, the recognition probability remains above 90%, which exhibits a performance improvement of over 60% compared with traditional algorithms. This work addresses the shortcomings of existing methods under high error conditions, and the idea of converting sequences into RGB images also provides a reliable solution for frame synchronization in challenging communication environments.  
frame synchronization; blind recognition; deep learning; ResNet; non-cooperative communication +
Footnote †: journal: _Sensors_
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 3 Data  
The performance of ASR systems does not rely only on the type of algorithms used; it also depends on the quality and quantity of the available data. Some languages, such as English, are widely present in the majority of the ASR applications of today's world, e.g., smart assistants, with large amounts of research in the area of ASR, e.g., [28]. To build an ASR system, data may be acquired through private entities, open-access datasets, crowdsourcing, or by creating the dataset from scratch, e.g., collecting audiobooks and their transcriptions. Although scarce, some sources of audio and their transcriptions are available in Portuguese. Nevertheless, the quantity and quality of publicly available datasets are not usually good enough to create high-performance ASR systems. These sources usually lack audio or transcription quality, data quantity, and structure standardization. The following sections describe the data sources used in this project. Section 3.1 describes the LibriSpeech dataset, used to evaluate the effectiveness of the transfer learning technique, and the Multilingual LibriSpeech dataset. Section 3.2 describes the SpeechDataset dataset. These datasets were used in the current work to perform the data-driven experiments focused on transfer learning and domain adaptation.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### Jakarta's Urban Green Belt  
Jakarta's urban green belt refers to a network of connected green spaces spanning from South Jakarta, through Central Jakarta, and up to North Jakarta. This expansive belt encompasses various types of urban green areas, ranging from agricultural zones and forests to grasslands, shrubbery, mixed-use areas, and wetlands. As depicted in Figure 14, Jakarta's urban green belt is quite extensive, covering approximately 124.34 km\({}^{2}\) or 19.11% of Jakarta's total land area.  
Figure 12: The portion of urban green space in South Jakarta.  
Figure 14: Map of Jakarta’s urban green belt. The base map is CartoDB Dark Matter. QGIS version 3.22.8-Bialowiěza was used to create the map.  
Figure 13: Urban green space of South Jakarta. The base map is CartoDB Dark Matter. QGIS version 3.22.8-Bialowiěza was used to create the map.  
Some parts of Jakarta's urban green belt are located in exclusive areas, which means limited access to the public or even closed to the public, such as elite residential green areas and exclusive golf courses. However, a large part is still open to the public such as Ragunan Zoo and City Forest within the University of Indonesia in South Jakarta; the Gelora Bung Karno Complex, Gelora Bung Karno City Forest, and the Monas Complex in Central Jakarta; and Angke Kapok mangrove forest, Kemayoran City Forest, and Penjaringan City Forest Park in North Jakarta. Jakarta's urban green belt areas are essential components of the city's urban planning and environmental sustainability efforts. These areas play a crucial role in preserving natural ecosystems, enhancing the city's resilience to environmental challenges, and improving the overall quality of life for its residents. Some benefits of these areas are summarized in Table 5.  
However, despite the benefits, Jakarta's urban green belt areas face challenges such as land use conflicts and inadequate enforcement of zoning regulations. These challenges can lead to the degradation of these critical natural spaces. It is suggested that the Jakarta government collaborates with various stakeholders and implements policies and programs aimed at protecting and enhancing green belt areas. This may include reforestation efforts, public awareness campaigns, and stricter land use regulations. Jakarta's green belt areas are likely to become even more critical in the face of climate change and urbanization. Urban green space has an important role to play in creating a culture of health and well-being [33]. However, the availability of urban green space can also increase the land price [34]. Future planning should focus on strengthening conservation efforts, expanding green belts where possible, and integrating them into a broader sustainable urban development strategy.