esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Training Optimizations  
Considering the five different LR-BS combinations tested (see Table 3), the hyperparameter combinations with indexes 1, 2, and 5 clearly provided the best results, with minor [EMAIL_ADDRESS] IoU differences between them. Nevertheless, it was considered that the best and most consistent performance was achieved by combination 5 (LR = 4.862 \(\times\) 10\({}^{-5}\) and BS = 16).
## 4 Experimental Results and Discussions
### Model Training  
For the purpose of training, we used the CASIA WebFace database, which comprises approximately 500 K images of 10,575 different individuals collected from the Internet [19]. For the baseline model, we optimized the 1024-dimensional fully connected layer via fine-tuning using the CASIA WebFace database. After training the baseline model, we added _\"Conv stage 2\"_, which consists of two convolutional layers that extract features from the factor-of-two-downsampled input image. For the extended models, we set the learning rates for the existing layers and the newly added convolutional layers to 0.0001 and 0.001 respectively, encouraging more versatile movements of the newly added weights. The kernel size of each newly-trained convolutional filter was set to \(3\times 3\) in order to maintain consistency with the reference VGG face model. All the model parameters were optimized using the stochastic gradient descent (SGD) method with the weight decay factor set to 0.0005. Further _\"Conv stages\"_ are added and optimized consecutively in a similar manner.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 5 Conclusions  
This study demonstrated the feasibility of using Sentinel-2 imagery and machine learning tools for accurate organ forest mapping and deforestation analysis. Extensive experiments were conducted to identify the best machine-learning algorithms, data configurations, and methods for this task, achieving highly accurate results (OA > 85%). This study underscores the critical role of artificial intelligence and technology in offering precise tools and data for decision-makers. By enabling informed and timely interventions, these tools support effective organ forest conservation efforts and help minimize potential  
Figure 15: Samples of deforestation areas: (**a**) a subset of 2017/2018 image, (**b**) a subset of the image 2022/2023 image, (**c**) a subset of the deforestation map (\(9\times 9\) patch).  
ecological impacts. However, further research is needed to address current limitations, such as spectral confusion and the challenges of single-date classification. Future work will focus on improving data quality and exploring new approaches, such as deep learning models and higher-resolution imagery, to enhance the accuracy of both organ tree and deforestation maps.  
Conceptualization, Y.K. and S.I.; Data curation, Y.K.; Formal analysis, S.I., S.S., A.M. and M.A.; Funding acquisition, S.I.; Investigation, Y.K., S.I., S.S., A.M. and M.A.; Methodology, Y.K., S.I., S.S., M.A. and A.M.; Project administration, S.I.; Resources, S.I.; Software, Y.K. and S.S.; Supervision, S.I. and M.A.; Validation, Y.K.; Writing--original draft, Y.K., S.I. and M.A.; Writing--review and editing, S.I. and M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (ALKHAWARIZMI/2020/29).  
The data presented in this study are available upon request from the first author. The data are not publicly available due to privacy issues.  
We appreciate the valuable feedback from the four anonymous reviewers, which has greatly improved this manuscript.  
The authors declare no conflict of interest.
## 3 System Model
### AMC Problem Formulation  
We assume that the coordinates of a UE are \((lat,lon)\), and the coordinates of a satellite are \((x,y,z)\), and the distance between them can be identified by their coordinates. As cloud thickness and rainfall vary in different locations on Earth, we can use the local real-time weather \(w\) and the position of UE \((lat,lon)\) to indicate the local real-time rain loss \(L_{r}\).  
\[L_{r}=g(lat,lon,w) \tag{3}\]  
From Section 3.1, SNR can be expressed as a function of \(d\) and \(L_{r}\):  
\[\text{SNR}=f(lat,lon,x,y,z,w) \tag{4}\]  
In a real scenario, we do not know the real-time SNR at this moment due to the delay in channel transmission, and for the estimation method normally used, which we denote as \(S\hat{N}R\), we want the error between the estimated value and the true value to be close to 0:  
\[|SNR_{t}-S\hat{N}R_{t}|\to 0 \tag{5}\]  
Now that we have obtained an estimate of the real-time SNR, the next task is to select a suitable redundancy rate. The communication coding standard proposed by the consultative committee for space data systems (CCSDS) for use in LEO satellites, uses the accumulate-repeat-4-jagged-accumulate (AR4A) [37] code was constructed from the protograph based on LDPC, using three data rates of 50%, 66%, and 80%. As is shown in Figure 2, each data rate will have a specific BER at the corresponding SNR. We should try to maximize the data rate while maintaining the quality of communication to obtain the maximum throughput:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviations** & **Full Name** \\ \hline AMC & adaptive modulation and coding \\ AR4 JA & accumulate-repeat-4-jagged-accumulate \\ BER & bit error rate \\ CCSDS & consultative committee for space data systems \\ DL & deep learning \\ DRL & deep reinforce learning \\ EIRP & equivalent isotropically radiated power \\ FCC & federal communications commission \\ FEC & forward error correction \\ FSL & free-space loss \\ GAE & generalized advantage estimation \\ GDP & gross domestic product \\ G/T & gain-to-noise-temperature \\ LDPC & low-density parity-check \\ LEO & low earth orbit \\ LSTM & long short term memory \\ MAE & mean absolute error \\ MDP & markov decision process \\ PER & package error rate \\ PPO & proximal policy optimization \\ QPSK & quadrature phase shift keying \\ SNR & signal to noise ratio \\ SVR & support vector regression \\ SVM & support vector machine \\ UE & user equipment \\ UNC & ubiquitous networking and computing \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations in the article cross-reference\[\max_{DR}^{*}DR[1-PER(DR,S\hat{N}R)] \tag{6}\]  
where \(DR\) is the data rate and \(PER(\cdot)\) represents the package error rate of the corresponding data rate and SNR.
# Research on Improving Optical Field Distribution of TOSA Based on 3D Defocusing Coupling
## 5 Conclusions  
The factors impacting the distribution of an optical field in fiber is analyzed comprehensively, and the model of 3D defocusing coupling improving the Gaussian distribution of optical filed is established. Two groups of TOSA samples with 3D defocusing coupling and 2D coupling are completed, and the four-direction performance and difference of optical power with different jumpers are tested. Four-direction performance with 3D defocusing coupling is <1 dB, and the difference of optical power with different jumpers is <0.5 dB. Comparing the performance of a TOSA with traditional 2D coupling, the performance of the TOSA with 3D defocusing coupling is better than that with 2D coupling significantly. So, as active coupling, the 3D defocusing coupling model can help to improve the yield and final performance of a TOSA, and it can be used for other optical devices. In the next step, the influences of temperature on and the refractive index of the lens on the optical field will be studied, and a comprehensive factors and model for an optical fiber active device will be established.  
Figure 12: (**a**) is the data of TOSA with 2D coupling tested with different jumpers. (**b**) is the data of TOSA with 3D defocusing coupling tested with different jumpers. The horizontal axis represents the serial number of samples, and the vertical axis represents the power changing.  
**Author Contributions:** Methodology, X.D.; Project administration, S.D.; Validation, J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Scientific Research Projects of Colleges and Universities in Henan Province (Grant No. 23A520054) and International training project of high level talents in Henan Province.  
**Index Availability Statement:** Not applicable.  
**Index Availability Statement:** The authors declare no conflict of interest.
# A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach
## Data  
### Synthetic Aperture Radar Imagery  
The Sentinel-1 mission from Copernicus, the European Union's Earth observation program, provides C-band SAR imaging in a constellation of two polar-orbiting satellites. This constellation offers a 6 day exact repeat cycle at the equator. For this research, the Level-1 Ground Range Detected (GRD) products from the Interferometric Wide Swath (IW) acquisition mode are processed, which consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. Only imagery captured during descending orbits is retained, because different orbits (ascending/descending) result in different radar shadows (among other distortions), generating artefacts upon comparison. For the IW mode, dual polarisation is available, i.e., Vertical transmit-Horizontal receive (VH) and Vertical transmit-Vertical receive (VV). The VV signal shows more consistent results due to the fact that the VV signal is more powerful, implying a higher signal-to-noise ratio, making thresholding easier (a small variation of the threshold will not result in big differences in flood extent) [12].  
While selecting cases for upscaling, the following criteria were considered: (1) The flood area should be about 100 times larger than in the previous prove of concept [9]; (2) the images should fall completely within single Sentinel-1 captures in order to avoid stitching; (3) at least one external flood extent map should be available for comparison; (4) the cases should be different in nature to detect possible different behaviors. With these criteria, two recent floods were chosen, namely a flood in Mozambique and a flood in India, which both took place in the year 2019.  
The first case is a flood caused by a tropical cyclone, Idai, which made landfall during the night of 14 to 15 March 2019 near Beira City, Sofala Province, in central Mozambique (Figure 1). This is a coastal area with mostly cropland and grassland areas, yet also some built up area (city of Beira). Sentinel-1 imagery (SAR IW GRD) is available (via Sentinel Hub [13]) on seven timesteps in the period between 2 March and 19 April 2019.  
The second case is a flood which occurred between the 27 and 30 September 2019, caused by heavy rain in the East of India, in the State of Bihar, near the city of Patna (Figure 2). While India's monsoon season normally starts to retreat by early September, heavy rainfall has continued here, triggering floods in many low-lying areas, resulting in waist-deep floodwaters [15]. For this case, Sentinel-1 imagery is available on eight timesteps in the period between 6 September and 30 October 2019.  
Figure 1: Case I: ROI near the city Beira in the Province of Sofala, Mozambique (Spatial reference system EPSG:4326 [14]).
# Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging
###### Abstract  
Fractured imaging is an important target for oil and gas exploration, as these images are heterogeneous and have contain low-impedance contrast, which indicate the complexity in a geological structure. These small-scale discontinuities, such as fractures and faults, present themselves in seismic data in the form of diffracted waves. Generally, seismic data contain both reflected and diffracted events because of the physical phenomena in the subsurface and due to the recording system. Seismic diffractions are produced once the acoustic impedance contrast appears, including faults, fractures, channels, rough edges of structures, and karst sections. In this study, a double square root (DSR) equation is used for modeling of the diffraction hyperbola with different velocities and depths of point diffraction to elaborate the diffraction hyperbolic pattern. Further, we study the diffraction separation methods and the effects of the velocity analysis methods (semblance vs. hybrid travel time) for velocity model building for imaging. As a proof of concept, we apply our research work on a steep dipping fault model, which demonstrates the possibility of separating seismic diffractions using dip frequency filtering (DFF) in the frequency-wavenumber (F-K) domain. The imaging is performed using two different velocity models, namely the semblance and hybrid travel time (HTT) analysis methods. The HTT method provides the optimum results for imaging of complex structures and imaging below shadow zones.  
Keywords:diffraction modelling; double square root equation; travel time; dip frequency filtering; migration +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Steps of Training Dataset Generation  
Generating synthetic training data for a multilanguage OCR system involves creating synthetic images that simulate the real-world conditions under which the OCR system is expected to operate. This can be done by manipulating various parameters of the image, such as the font type, size, color, background texture, and noise levels. The generated images should be representative of the various languages and writing systems that the OCR system is expected to recognize. Here are some methods and mathematical techniques commonly used for generating synthetic training datasets for a multilanguage OCR system:  
1. Text Generation Process: The first step is to generate the text that will be used in the synthetic images. This can be done by using natural language processing techniques such as language models or by collecting and curating text datasets for each language.
2. Font Generation Process: To generate realistic synthetic images, it is important to use fonts that are representative of the languages and writing systems. There are several methods for creating fonts, including the use of generative adversarial networks (GANs) [12; 13] or variational autoencoders (VAEs) [14] or simply choosing fonts from libraries that already exist.
3. Image Generation Process: Once the text and fonts have been generated, the synthetic images can be created. This can be done using image manipulation techniques such as rotation, scaling, and translation and by adding various types of noise, such as blur, salt-and-pepper noise [15; 16; 17; 18], or Gaussian noise.
4. Augmentation Process: The generated images can be enhanced by using a variety of ways to further diversify the training dataset. This includes techniques such as random cropping, flipping, and color jittering.
5. Labeling Process: Finally, the synthetic images must be labeled with the correct character or text sequence that they represent. This can be accomplished by either manually labeling the photos or by utilizing optical character recognition techniques to extract the text from the artificial images.  
Mathematical techniques such as _probability distributions_, _random number generation_, and _matrix transformations_ are used extensively in generating synthetic images with realistic noise and other image characteristics, adjusted specifically in the ways required by specific use cases. For example, a Gaussian distribution can be used to add random Gaussian noise to an image, while affine transformations can be used to rotate, scale, or translate an image.  
In summary, generating synthetic training datasets for a multilanguage OCR system involves a combination of text and font generation, image manipulation, image augmentation, and labeling techniques. Various mathematical techniques are used to manipulate image characteristics and generate realistic noise, which helps train the OCR system to recognize characters and text sequences in real-world scenarios.
# _photonics_
## 4 Simulation Results
### Practical Exploitation of Distilled Key Bits
#### 4.4.1 Satellite as a Trusted Node  
In the security architecture where the satellite can be used as a trusted node, one key that is shared between a satellite and an OGS can be also securely transmitted to another OGS as depicted in Figure 8[61]. It is assumed that the satellite has already established quantum links between two different OGSs and two key strings of 256 bits distilled by these QKD links have been stored in its memory. By using the method of one-time pad (OTP), the satellite can send the 256-bit string result of the exclusive OR (XOR) operation between Key1 and Key2 (Key1 \(\oplus\) Key2) to the OGS2. Given that Key1 and Key2 are only known by the satellite and each of the OGSs, the transmission of Key1 \(\oplus\) Key2 through a classical channel is proven to be completely secure by the theory of OTP. Finally, OGS2 performs (Key1 \(\oplus\) Key2) \(\oplus\) Key2 to obtain Key1. Similarly, OGS1 obtains Key2; therefore, no quantum-distributed key bits are sacrificed in this chain.
# Automatic Refactoring Approach for Asynchronous Mechanisms with CompletableFuture
## 4 Empirical Research
### Refactoring Templates  
We have summarized four refactoring patterns based on four methods of traditional asynchronous constructs, illustrated with templates. As shown in Table 5, the Source Code column represents the code before refactoring, and the After Refactoring column represents the code after refactoring, where the syntax elements that remain unchanged before and after refactoring of an expression are shown in bold. These include the execute method declared by the Executor interface and the three overloaded submit methods declared by the ExecutorService interface.  
* Pattern 1: Refactoring of execute(Runnable) Converts execute(Runnable) from the Executor interface to CompletableFuture.runAsync, facilitating asynchronous task submission with Runnable.
* Pattern 2: Refactoring of submit(Runnable) Transforms submit(Runnable) from an ExecutorService interface instance into a CompletableFuture, using runAsync to handle tasks returning Future.
* Pattern 3: Refactoring of submit(Runnable, Value)  
The refactoring is divided into two parts. The first part draws from the source code of the FutureTask constructor in JUC. By calling the callable method from the Executors class in JUC, Runnable and Value are converted to Callable. This thus converts to the situation of Refactoring Pattern 4. To simplify the code in Table 5, the first part is shown in the third line of Table 5, and the second part is left for the reader to refer to in Pattern 4.  
* Pattern 4: Refactoring of submit(Callable)Unlike Patterns 1 and 2, which use Runnable type asynchronous tasks, Callable type tasks have return values, requiring the use of CompletableFuture's supplyAsync method. It is necessary to refactor the Callable type task into a Supplier type. In the first line of the code on the right side of Table 5, a new Callable type variable is defined because the Callable type asynchronous task might not be a simple variable but an expression, and placing it directly into the newly defined task could lead to data races. Since the Callable interface's call method declares that it throws exceptions, whereas the Supplier interface's get method does not, to maintain consistency in exception handling before and after refactoring, as shown on the right side of Table 5, in lines 2-6, a Supplier type asynchronous task object is defined using a lambda expression. By wrapping the exceptions thrown by the call method in a RuntimeException using a try...catch statement, in lines 8-9, the CompletableFuture's handle method is used to process the exception and convert it to a normal result. Then, through the thenCompose method, in lines 11-13, a new CompletableFuture object is instantiated; if the result is an exception, it is unwrapped and set as the exception result of the new CompletableFuture object. In lines 15-17, if it is a normal result, it is directly set as the normal result of the new CompletableFuture object, which is then returned.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 4 Quantitative Model
### Transition Probability Matrix Model  
According to the speech generation model and the statistical law, the process of selecting the optimal code word can be regarded as a discrete time random process. Assuming that each code word is related to its previous code word. The relationship is defined as follows [3].  
\[P\Big{(}S_{j}/S_{1},S_{2},\cdots,S_{j-1}\Big{)}=P\Big{(}S_{j}/S_{j-1}\Big{)} \tag{8}\]  
The speech signal has short-term stability in a certain period of time (10-30 ms). There is also a certain correlation between adjacent sub-frames. The QIS can be regarded as a first-order Markov chain, and the correlation is quantized by using the Markov transition probability [3].  
The Markov transition probability \(m_{ij}\) is defined as  
\[m_{ij}=P\Big{(}S_{j}/S_{i}\Big{)}=P\Big{(}S_{j},S_{i}\Big{)}/P(S_{i}) \tag{9}\]  
where \(S_{i},S_{j}\in[0,255]\). After the transition probability is obtained, the Markov transition probability matrix \(\mathbf{M}\) can be expressed as  
\[\mathbf{M}=\left(\begin{array}{cccc}m_{1,1}&m_{1,2}&\cdots&m_{1,256}\\ m_{2,1}&m_{2,2}&\cdots&m_{2,256}\\ \vdots&\vdots&\vdots&\vdots\\ m_{256,1}&m_{256,2}&\cdots&m_{256,256}\end{array}\right) \tag{10}\]  
\(\mathbf{M}\) characterizes the correlation between indexes, and the dimensionality of the \(\mathbf{M}\) is \(256\times 256\).
## 3 Colloidal Silica Transport Through Soil Stratum
### Advancement for CS Transport  
As stated above, CS grouting is conventionally implemented by injecting a stabilizer through a vertical injection well and consequent horizontal transport, where the underground water flow is motivated by the extraction well (Figure 1). The gel time of CS should be controlled to be sufficiently long, such that the CS viscosity stays below 4 cP during delivery. A low viscosity status should be sustained to facilitate CS horizontal transport. However, once CS arrives at the targeted location, sinking and deposition will occur on the condition of low viscosity [66; 67; 68; 69; 70]. Therefore, gel time is difficult to control in the conventional CS grouting method.  
To settle this problem, [PERSON] et al. (2016) reported a method where the delivering injection nozzle to the designated area uses a controlled curved drilling (CCD) machine [37], as shown in Figure 14. The new CCD machine used for CS grouting originates from the curved drilling machine that has been adopted in the oil industry [71; 72]. The critical technology of a CCD machine is the curved drilling rod. To make such a curvature, the drilling head and water jet should firstly advance slantwise. At the location where the bore needs to be curved, the drilling head is inclined downwards and water is injected from the nozzle under a high pressure to push the drilling rod and the earth. As a consequence, an upward pressure is produced and applied on the inclined surface of drill head, finally creating a curvature along the boring path, as shown in Figure 15. Here, a CCD machine has  
Figure 13: Setup of the pilot-scale model test to investigate the influence of injection rate (modified after [PERSON] and [PERSON], 2015) [69].  
been adopted to transport CS to reduce the liquefaction potential of runways at Fukuoka International Airport in a seismic-retrofitting project. By using the CCD method, a CS grout with short gelation time is required. Post-treatment measurements show that the CCD method could be applied to inject CS to the loose sand layer to mitigate the liquefaction risk without disturbing the surface of the runways. In addition, the new grouting method could transport CS to the desired location directly, avoiding unnecessary attachment and deposition during the delivery process.