esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network
## 5 Conclusions  
This study introduces an efficient and lightweight approach for ISFD. We create a lightweight CNN model that gives high accuracy and compares the recent other used methods such Markov features in DCT and DWT domain, PCA, SVM, and C2 RNet. Good  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CASIA 1.0**} & \multicolumn{2}{c}{**CASIA 2.0**} & \multicolumn{2}{c}{**CUISDE**} \\ \cline{2-7}  & **Accuracy \%** & **Parameter** & **Accuracy \%** & **Parameter** & **Accuracy \%** & **Parameter** \\ \hline [PERSON] et al. [10] & 97.0 & 16,458,966 & 97.5 & 16,458,966 & 97.77 & 16,458,966 \\ \hline [PERSON] et al. [35] & 94.55 & - & 96.36 & - & - & - \\ \hline [PERSON] et al. [20] & 98.25 & 18,534,965 & 97.59 & 18,534,965 & 96.66 & 18,534,965 \\ \hline [PERSON] et al. [30] & 64.0 & 23,812,574 & - & - & 64.0 & 23,812,574 \\ \hline [PERSON] et al. [29] & - & - & 97.93 & & 97.27 & - \\ \hline [PERSON] et al. [28] & - & - & 96.45 & - & - & - \\ \hline [PERSON] et al. [22] & - & - & 94.59 & 2,542,144 & - & - \\ \hline [PERSON] et al. [23] & - & - & - & - & 91.46 & - \\ \hline [PERSON] et al. [34] & - & - & - & - & 98.13 & - \\ \hline Proposed method & 99.1 & 97,698 & 99.3 & 97,698 & 100 & 97,698 \\ \hline \hline \end{tabular}
\end{table}
Table 7: A comparison of accuracy and number of parameters in the case of the proposed model and other recently published approaches over the CASIA 1.0, CASIA 1.0, and CUISDE dataset.  
Figure 7: Compares the proposed method and the various approaches in terms of accuracy on the CASIA 1.0, CASIA 2.0, and CUISDE datasets [11, 21, 23, 24, 29, 30, 31, 35, 36].  
results were conducted with appropriate convolutional layers and max-pooling layers. These results revealed that the proposed model is efficient and accurate against the other discovered models. Our experiments were achieved based on benchmark datasets: CASIA 1.0, CASIA 2.0 and CUISDE. The proposed model reached an F1-score of 99.14%, 99.4%, and 100% in CASIA 1.0, CASIA 2.0, and CUISDE, respectively. On the other hand, the presented model achieved an accuracy of 99.1%, 99.3%, and 100% for CASIA 1.0, CASIA 2.0, and CUISDE, respectively. Moreover, our model obtained this accuracy in the number of parameters (97,698) for CASIA 1.0, CASIA 2.0, and CUISDE. Overall, the proposed model can be a strong tool for detecting image splicing forgery in the real world.  
The proposed model is distinguished from previous works in that it achieves high accuracy and uses fewer parameters compared to previous works. Reducing the number of parameters enables us to implement this model in an environment with limited capabilities for memory and processors.
## 3 Results
### Net Ecosystem Exchange
Similarly, Figure 10 illustrates the spatial pattern of NEE from the resampled CASI HSI mosaics (Table 2) with the 10 years Landsat TM5 model (Equation (5) \(\mathrm{MWI_{S2 Re}}\)) applied (Table 5). The NEE represented in Figure 10 ranges from approximately \(-0.5\) to \(-4.9\) g C m\({}^{-2}\) (high uptake). The general pattern of areas of the bog with higher and lower CO\({}_{2}\) uptake is similar to what is seen in Figure 9. The central section of the bog with the lowest CO\({}_{2}\) uptake throughout the growing season corresponds to both the highest gravimetric water content in hollows (greater than the 1300% maximum for optimal CO\({}_{2}\) uptake [53]) [31], and lower NEE under light saturated conditions (i.e., photosynthetically active photon flux density \(>\)1000 umol photon m\({}^{-2}\) s\({}^{-1}\)) for the vascular plants during the middle of the growing season [31]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor** & **MWI** & **Model** & **Daily NEE** & **3 h NEE** \\ \hline CASI & \(\mathrm{MWI_{S2}}\) & 10 years TM5 & \(-0.11\) & \(0.21\) \\ CASI & \(\mathrm{MWI_{S2 Re}}\) & 10 years TM5 & \(-0.91\) * & \(-0.85\) * \\ CASI & \(\mathrm{MWI_{S2}}\) & 4 years L8 & \(-0.11\) & \(0.21\) \\ CASI & \(\mathrm{MWI_{S2 Re}}\) & 4 years L8 & \(-0.91\) * & \(-0.85\) * \\ CASI & \(\mathrm{MWI_{S2}}\) & Daily L8 & \(-0.11\) & \(0.21\) \\ CASI & \(\mathrm{MWI_{S2 Re}}\) & Daily L8 & \(-0.91\) * & \(-0.85\) * \\ Sentinel 2A & \(\mathrm{MWI_{S2}}\) & 10 years TM5 & \(-0.14\) & \(0.16\) \\ Sentinel 2A & \(\mathrm{MWI_{S2 Re}}\) & 10 years TM5 & \(-0.68\) & \(-0.72\) \\ Sentinel 2A & \(\mathrm{MWI_{S2}}\) & 4 years L8 & \(-0.68\) & \(-0.72\) \\ Sentinel 2A & \(\mathrm{MWI_{S2 Re}}\) & 4 years L8 & \(-0.68\) & \(-0.72\) \\ Sentinel 2A & \(\mathrm{MWI_{S2}}\) & Daily L8 & \(-0.14\) & \(0.16\) \\ Sentinel 2A & \(\mathrm{MWI_{S2 Re}}\) & Daily L8 & \(-0.68\) & \(-0.72\) \\ \hline \end{tabular}
\end{table} 
Table 5: Pearson’s correlation coefficient (\(r\)) between eddy covariance tower measured NEE (daytime daily average and 3 h average surround image acquisition time) and relative NEE determined from the functions in Figure 8 and MWI calculated via Equations (4) (\(\mathrm{MWI_{S2}}\)) and (\(\mathrm{5}\)) (\(\mathrm{MWI_{S2 Re}}\)). The “10 years TM5” model corresponds to Figure 8a, the “4 years L8” model to Figure 8b, and the “Daily L8” model to Figure 8c. * Indicates the correlation was significant at \(\alpha=0.05\) (two-tailed). Figure 9: Spatial estimates of relative NEE from Sentinel-2A imagery for nine dates spanning the growing season in 2016. Gray represents treed areas that have been masked out [31]. * Due to the high-altitude cirrus contamination, the scenes from 24 May and 19 August should be interpreted with caution.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 2 Literature Review
### Dataset  
The success of sign language recognition models mostly depends on the quality and diversity of training data [10,11]. Some of the most commonly used datasets in the literature are the ASL alphabet dataset, Roboflow ASL Dataset, and ASLyset. However, there is currently no publicly available large-scale dataset for Kazakh Sign Language. This gap is a major obstacle to the application and adaptation of modern deep learning methods to gestures specific to the Kazakh language. Creating a labeled Kazakh Sign Language dataset with both static and dynamic gestures is critical to advancing research in this field and providing inclusive communication tools for the Kazakh-speaking deaf community [8].
## 2 Materials and Methods
### Characterization Techniques
#### 2.4.3 Structural Analysis  
The structural and morphological properties of the samples were investigated using a comprehensive suite of analytical techniques. Fourier transform infrared spectroscopy (FTIR) was conducted within a wave number range of 400-4000 cm\({}^{-1}\). X-ray diffraction (XRD) was performed using CuKa radiation (X'Pert PRO MPD PANalytical B.V.) at 40 kV and 40 mA, with a 20 range extending from 5\({}^{\circ}\) to 90\({}^{\circ}\).  
The crystallinity of cellulose was determined using XRD spectrum deconvolution to identify latent reflections. This is a crucial step in distinguishing crystalline structures from amorphous ones. The process enables the identification of reflections related to the crystalline structure, providing a more comprehensive understanding of the material's structural characteristics. Following deconvolution, we applied a method to calculate crystallinity by integrating the area of the identified reflections. This involved analyzing the areas under the intensity curves for both crystalline and amorphous peaks in the X-ray images. This approach provides a reliable assessment of crystallinity and allows for a more accurate evaluation of the impact of chemical heat treatment on the pulp structure. This methodology enables the quantification of crystalline and amorphous regions within the sample through the application of the following equation:  
\[X_{c}=\frac{A_{c}-A_{am}}{Al_{c}}\times 100\% \tag{1}\]  
where \(X_{c}\)--crystallinity level, \(A_{c}\)--the total area under the curves of crystalline peaks, \(Al_{c}\)--the intensity of the maximum peak of the crystalline phase, and \(A_{am}\)--the area under the curve of the amorphous peak.
###### Abstract  
End-to-end text-to-speech (TTS) models that directly generate waveforms from text are gaining popularity. However, existing end-to-end models are still not natural enough in their prosodic expressiveness. Additionally, previous studies on improving the expressiveness of TTS have mainly focused on acoustic models. There is a lack of research on enhancing expressiveness in an end-to-end framework. Therefore, we propose HierTTS, a highly expressive end-to-end text-to-waveform generation model. It deeply couples the hierarchical properties of speech with hierarchical variational auto-encoders and models multi-scale latent variables, at the frame, phone, subword, word, and sentence levels. The hierarchical encoder encodes the speech signal from fine-grained features into coarse-grained latent variables. In contrast, the hierarchical decoder generates fine-grained features conditioned on the coarse-grained latent variables. We propose a staged KL-weighted annealing strategy to prevent hierarchical posterior collapse. Furthermore, we employ a hierarchical text encoder to extract linguistic information at different levels and act on both the encoder and the decoder. Experiments show that our model performs closer to natural speech in prosody expressiveness and has better generative diversity.  
text-to-speech; hierarchical VAE; expressive TTS; multi-scale; end-to-end Article
# Low-Cost _Ka_-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies
## 2 Materials and Methods  
### Experimental Setup  
At the University of Vigo campus in Vigo, Spain, with coordinates 42.170\({}^{\circ}\) N, 8.688\({}^{\circ}\) W and 447 m a.m.s.l., a _Ka_-band beacon receiver pointing toward KaSAT, 9\({}^{\circ}\) E, was set up. This experiment has been running from the beginning of 2018.  
The receiver was tuned to the 19.68 GHz beacon. The geometry of the link is as follows: azimuth 154.6\({}^{\circ}\), elevation approximately 38.1\({}^{\circ}\), and linear polarization with an angle with respect to the local horizontal axes, \(\tau\), of 18.5\({}^{\circ}\). The receive antenna is a dual-reflector parabolic dish. The measurement bandwidth is approximately 50 Hz with a data rate of over 10 Hz. The measurement dynamic range exceeds 20 dB.  
Our setup also includes a meteorological station co-located with the beacon receiver that measures the following variables: rain rate derived from two optical disrometers [25] and three standard tipping-bucket rain gauges [26], wind direction and speed, relative humidity, ambient temperature, and atmospheric pressure from their corresponding sensors (all with a 1 min time resolution). Furthermore, from the METAR files provided by the OGIMET service [27], it is possible to access the meteorological data gathered at Vigo Airport, less than 10 km from the beacon receiver, with a half-hour time resolution: mainly the cloud cover (in oktas) and cloud heights (in hundreds of feet).  
The beacon receiver was built using off-the-shelf elements, which were grouped into two main parts, as follows.
## 2 Materials and Methods
### Mathematical Modeling and Implementation
Turbulent or eddy viscosity (\(\mu_{T}\)), which is defined by the \(k\)-\(\epsilon\) turbulence model, is given as follows:  
\[\mu_{T}=\frac{\rho C_{\mu}k^{2}}{\epsilon} \tag{5}\]  
in which \(\mu_{T}\) is the turbulent viscosity (Pa s), \(\epsilon\) is the turbulent dissipation rate (m\({}^{2}\)/s\({}^{3}\)), \(k\) represents the turbulent kinetic energy (m\({}^{2}\)/s\({}^{2}\)), \(\rho\) is the air density (kg/m\({}^{3}\)), and \(C_{\mu}\) is the model constant. The present study selected several parameters (air duct hole diameter, air duct hole shape, bed slanting angle, and air inlet velocities) to analyze their influence on separation process because of their significant effect on the air ballistic trajectory of the density separator [26; 27]. All the geometrical dimensions (air duct hole diameter and air duct hole shape) and boundary conditions (bed slanting angle and air inlet velocities) in this study were selected based on the actual flow conditions of the air-based density separator. Moreover, this study used arbitrary values of these parameters to analyze their effect on the flow characteristics of the density separator. This approach helps to select the suitable values of these parameters to improve the air ballistic trajectory and consequently enhance the separation process. The air inlet was assigned with an initial inlet velocity of 35 m/s, and the trash outlet boundaries were maintained under atmospheric pressure conditions. A duct was installed on the upper boundary of the basic separator model to release the extra pressure and improve the ballistic trajectory of the air stream. Duct hole size or diameter was varied according to the air inlet channel diameter (\(D=0.08\) m). The positive and negative signs present with the duct hole diameters represent the respective location of duct hole from the reference position (Figure 1). Positive signs show the increase in duct hole diameter toward the right side of the reference position and vice versa. Furthermore, two different duct shapes (i.e., round and triangular) were used to see the effects of duct shape on ballistic trajectory for separation improvement. The vibrating bed was assigned with slanting angles (\(\theta\)) from 0\({}^{\circ}\) to 8\({}^{\circ}\). Inlet velocities were varied from 30 to 45 m/s to observe the effect on the flow characteristics in the density separator. Two points in the radial direction (point 1 and 2) were selected on the vibrating bed for the computation of flow properties (Figure 1). A commercial code COMSOL-Multiphysics (V 5.3a) was used to optimize the separation process by computing flow properties in the air-based density separator. A steady state solver is employed to simulate the \(k\)-\(\epsilon\) turbulence model. All computations were performed on an Intel Core i7-3370 3.90 GHz processor with a 16 GB RAM operating system. The air-based density separator models are discretized by using a free triangular mesh type with 147,547 interior elements and 1717 boundary elements (extra fine mesh), respectively.
## 1 Introduction  
The Kenyan livestock farming is mainly for food and income generation. However, it also has more significance as a symbol of wealth and status among the pastoralist communities. Therefore, there are two distinct livestock producers in Kenya, the pastoralists and the farmers. The farmers are either in subsistence or commercial cattle production. One of the main challenges facing the pastoralists is cattle rustling. The commercial cattle farmers are also faced with rampant cases of cattle theft. Cattle raiding and cattle rustling have a long history as an aspect of the traditional pastoralist culture. Cross-border cattle rustling violence is common among the Karamojong community of Uganda and the Turkana people of Kenya. In the semi-arid northern Kenya, armed cattle rustling violence is common among the pastoralist communities of Samburu, Turkana, and the Pokot. In November 2012 more than 40 policemen were killed by Turkana cattle rustlers as they sought to recover animals stolen in a violent cattle rustling conflict involving Turkana and Samburu pastoralists [1]. Trying to follow animal tracks without knowing their exact location always puts the lives of the recovery personnel at risk which in most cases has resulted in a high number of military and civilian casualties. Considering the benefits and features of mobile communication, wireless sensor networks, and advancements in animal identification, we propose a framework that provides a secure and convenient way to identify and track stolen animals in areas faced with regular cattle rustling and livestock theft.  
Several studies have tried to implement wireless sensor networks in animal tracking, behavior monitoring, and activity control. An implementation of wireless sensor devices to monitor the state of the animals and the landscape within a paddock has been implemented by [2], while [3] implemented virtual fences for controlling the movement of cows relative to the fence boundary for automatically herding the cows in the absence of physical fences. The studies in [2,3] concluded that new wireless sensor devices could be developed with different engineering trade-offs to existing devices and meet the needs of particular areas of applications. One usage of wireless sensor networks (WSN) in animal management that has not been explored is the application of wireless sensor networks systems in alleviating cattle rustling. The contribution of this paper is a framework for alleviating cattle rustling through animal identification and tracking in harsh terrain using mobile communication assisted wireless sensor networks. The paper is organized as follows: Section 2 discusses existing work in electronic animal identification and tracking. Section 3 defines the requirements for the proposed conceptual framework system and gives an overview of the proposed framework. Finally, Section 4 gives conclusions and discusses future work.
# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Space Exploration
The regression task is executed by a long short-term memory (LSTM) recurrent neural network, while a CNN is used to extract features and process them. A CNN is trained with a dataset of synthetic images of the landing area at different relative poses and illumination conditions. Indeed, in a no-space application, the matched exploitation of CNN-LSTM has already been proven to have excellent performance in providing image processing and model prediction. To summarize our analysis, we highlight that AI techniques, such as deep and reinforcement learning, are widely used in the field of spacecraft guidance dynamics and control to improve the autonomous functionalities of space equipment, spacecraft, and space robots. The main goal of such systems is also to enable constructive cooperation between multi-robot systems and humans. As far as deep space exploration, approaches based on neural networks make possible the automation of the detection of astronomical bodies and the accessibility to better maps of planets' surfaces.
## 4 Results
### Human Attention Maps  
#### 4.3.1. Dice Score  
Before considering the statistical analysis, an inspection of Figure 5A reveals that the overlaps between human tasks (grey bars, \(M\) = 0.45) were consistently higher than the overlaps between humans and CNN (red bars, \(M\) = 0.27). This was the case even for landscapes, which had produced very low human-CNN overlap, whereas the two eye movement tasks still overlapped more with each other than Grad-CAM had overlapped with any eye movement task for any image type. The 3 (human-human comparison: spontaneous gaze vs. gaze-pointing, spontaneous gaze vs. drawing, gaze-pointing vs. drawing) \(\times\) 3 (image type: objects, indoor scenes, landscapes) ANOVA yielded a main effect of human-human comparison, \(F\)(1.8,103.0) = 50.139, \(p\) < 0.001, \(\eta\)p\({}^{2}\) = 0.468, a main effect of image type, \(F\)(2,57) = 54.290, \(p\) < 0.001, \(\eta\)p\({}^{2}\) = 0.656 and an interaction, \(F\)(4,114) = 4.318, \(p\) = 0.004, \(\eta\)p\({}^{2}\) = 0.132. The main effect of human-human comparison indicated that all comparisons between human tasks yielded different degrees of overlap, all \(p\)s < 0.001. That is, the two eye movement tasks had the highest overlap (0.55), followed by gaze-pointing vs. drawing (0.44) and then spontaneous gaze vs. drawing (0.37). The main effect of image type indicated that the overlap between human attention maps was highest for objects (0.61), followed by indoor scenes (0.44) and landscapes (0.30), all \(p\)s < 0.005. Finally, the interaction indicated that the differences between human-human comparisons depended on image type. However, the direction of these dependencies was opposite to what we had observed for the human-CNN comparison: for objects, the overlap was _least_ (instead of most) dependent on human tasks because all of them were highly similar. Accordingly, the only difference that just passed the significance threshold indicated that spontaneous gaze overlapped more with gaze-pointing (0.66) than with drawing (0.57), \(p\) = 0.034. No other differences were found, all \(p\)s > 0.2. For indoor scenes, the two eye movement tasks still had a high overlap with each other (0.54), while the overlap between drawing and either spontaneous gaze or gaze pointing (0.36 and 0.42, respectively) was considerably lower, both \(p\)s < 0.002. These two latter comparisons did not differ from each other, \(p\) = 0.100, indicating that drawing maps were generally quite different from eye movement maps for indoor scenes. Finally, for landscapes, all comparisons between human tasks were significant, all \(p\)s < 0.001, with the highest overlap between the two eye movement tasks (0.44), medium overlap between drawing and gaze-pointing (0.30) and very low overlap between drawing and spontaneous gaze (0.17).