esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 4 Datasets
### Hand Gestures Dataset  
A simple data collection tool is implemented to speed up the collecting process for data consisting of sequences of gestures performed by real human actors. We used the Real Sense SR300 depth camera as the primary tool for recording data since we wanted to input the modality data to our proposed model. The modality data that we recorded are the RGB and depth data. Since we consider the hands as the main part of the gesture, half the body of a user, including the face and hands, was recorded. Later, during the preprocessing step, we extracted the hand from the body to input to the model. Gesture videos were recorded from 20 individuals using 11 dynamic gestures, click, grab, scroll-down, scroll-up, scroll-right, scroll-left, pinch, zoom out, zoom in, backward, and forward, that have been sampled and clearly visualized on the research web page [16]. The user needs to re-perform each gesture six times in a different manner. Each recorded sequence consists of a 3-s gesture with 120 frames. In total there are 2162 sequences (or videos) of gestures in RGB and depth format recorded under five different environments with lighting conditions and variable distances to the camera.
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
###### Abstract  
There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, the space-varying of phase imbalances along the range, caused by antenna phase center position error, attitude error, antenna electronic miss pointing, antenna pattern mismatch, and other reasons, cannot be ignored. Firstly, this paper mainly analyzes the above possible factors by using real GF-3 ATI data and error model simulation results. Secondly, the ocean current has been preliminarily measured by the ATI method and the DCA method, using CDOP model, based on the GF-3 ATI data of the ocean scene near Qingdao, China, which is up to around \(-1.45\) m/s. The results of the two methods are in good agreement with the correlation coefficient of 0.98, the mean difference of \(-0.010\) m/s, and the root mean squared error (RMSE) of 0.062 m/s. Moreover, by comparing with the current measured by high-frequency surface wave radar (HFSWR), the correctness of the analysis is further proved.  
GF-3; ATI-SAR; phase imbalance; ocean current +
Footnote †: journal: remote sensing  
Article
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling
### Lagrange  
The Lagrange polynomial interpolation [7] provides a theoretical solution for the non-uniform case. Perfect reconstruction is possible under two conditions. Firstly, the Nyquist criteria must be fulfilled for the average sampling frequency \(T_{AV}\):  
\[T_{AV}\leq\frac{2}{f_{L}}. \tag{2}\]  
Secondly, the sampling locations \(t_{n}\) are only allowed to deviate less than  
\[\Delta t_{n}<\frac{1}{4}T_{AV} \tag{3}\]  
from uniformly spaced locations according to the Paley-Wiener-Levinson theorem [7]. If the delay \(t_{n}\) between two consecutive samples is larger than \(3/f_{L}\), perfect reconstruction is not feasible.  
If the signal is not truncated, Lagrange interpolation requires the calculation of infinite polynomials. In practice, Lagrange interpolation is limited to a window of about \(\pm 20\) sampling points near the target location. However, this approach results in high deviations at the margins of the window.  
The estimated value \(y^{*}(t)\) for a target point at time \(t\) is calculated as the sum of polynomials \(P_{j}(t)\) for \(n\) input samples at time \(t_{j}\) with value \(y_{j}\)[7]:  
\[y^{*}=\sum_{j=1}^{n}P_{j}(t)y_{j}. \tag{4}\]  
\(P_{j}(t)\) is given by:  
\[P_{j}(t)=\prod_{\begin{subarray}{c}k=1\\ k\
eq j\end{subarray}}^{n}\frac{t-t_{k}}{t_{j}-t_{k}}. \tag{5}\]
of the analytical methods most used in the petroleum industry and is currently applied to energy transition applications evaluating the underground storage of carbon dioxide and hydrogen.  
The material balance equation is one of the applications of the principle of mass conservation. It was first introduced by [PERSON] [1] and has been one of the fundamental tools in the oil and gas reservoir engineering discipline ever since [2]. Despite its name, the MBE introduced by [PERSON] is based on volume conservation and is described as a volumetric material balance by [PERSON] [3]. It has a wide array of applications, such as estimation of original hydrocarbon reserves, forecasting reservoir pressure, estimation of water influx, identification of reservoir drive mechanism, history matching, etc. [4; 5]. In this paper, we explore the application of the material balance equation to estimate hydrogen storage capacity in a depleted dry gas reservoir.  
It is worth mentioning that dry gas reservoirs primarily contain methane and brine with negligible amounts of other hydrocarbons. This high methane content simplifies analytical modeling, as it can be assumed that methane is the only component of natural gas. Furthermore, dry gas reservoirs maintain a single hydrocarbon phase throughout the reservoir's lifespan [6]. Some sources suggest that material balance calculations for dry gas reservoirs can also be applied to wet gas reservoirs since they also remain in a single hydrocarbon phase. However, modeling the phase behavior of wet gas reservoirs requires additional considerations due to the presence of other light hydrocarbons alongside methane [6; 7].  
The proposed methodology is validated through numerical simulation of a tank model representing a depletion drive and a water drive gas reservoir. Next, the methodology is illustrated through a theoretical case study of a complex heterogeneous reservoir, demonstrating its practical application in hydrogen storage assessment.
## 4 Comparison of Classical and Computer-Aided Reconstruction Results in Case 1
These conditions include, for example, a homogeneous background and a neutral expression but primarily focus on well-defined frontal upright captures of faces [72; 73]. A quantitative comparison using computer vision, especially through machine learning, is often not constrained by specific conditions and can yield robust results, even with faces of varying perspectives [74]. Facial recognition can be used in many applications, including surveillance, security, crime prevention, and access control. One possibility for performing face recognition is the use of the Dlib software library (Dlib version 19.3.0 was used in this work) [75]. Dlib is an open-source toolkit written in C++ that provides machine learning and computer vision algorithms. It enables automatic face detection, comparison, and identification using artificial intelligence techniques. The facial features are not explicitly defined but are learned from large datasets such that the resulting numerical representations (embeddings) are nearly identical for different images of the same individual. In this work, Dlib serves as one of the core systems for face recognition. To compare two faces, the framework requires an input image of the target person and an image of the 3D model's face. The comparison process involves multiple preprocessing steps, including face detection and localization, facial landmark detection for alignment, and feature vector extraction using Dlib's CNN-based pipeline [75]. The implementation is based on the open-source project by [76]. In addition to Dlib, two modern deep learning-based face recognition models, VGG-Face [77] and GhostFaceNet [78], were also employed. These models offer high recognition accuracy and robust feature extraction capabilities. They are integrated via the DeepFace framework and utilize YOLOv8 (Version 8.0.0) [79] for initial face detection. Unlike Dlib, they do not rely on explicit facial landmark detection for alignment but instead process the cropped and scaled face images directly. The individual preprocessing steps and model differences are explained in more detail in the following sections.
# Study on Mechanical Properties of Simply-Supported Composite Beams Considering Creep and Slip
## 4 Analysis of the Result
### Comparison of Slip under Different Load Situations
The change rate is 7.09%, 11.05%, and 12.50%. In Table 5, the span deflections of the combined beam at day 7 and day 407 of loading are presented for different stud stiffnesses and different calculation methods. Table 5 shows that regardless of the stud stiffness, the maximum error between the calculated and numerical simulation values is 5.65%. With the increase in stud stiffness, the deflection increases more in the same creep time under the formula calculation results, which are 11.92 mm, 13.01 mm, and 13.57 mm, respectively. The change rate is 31.97%, 41.30%, and 46.60%. In Table 6, the beam end slips are listed for different stud stiffnesses and different calculation methods at 7 d and 407 d of loading are presented. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time/d**} & \multicolumn{3}{c}{**Simulated Calculation Value**} & \multicolumn{3}{c}{**Values Calculated**} & \multirow{2}{*}{**Error 1**} & \multirow{2}{*}{**Error 2**} & \multirow{2}{*}{**Error 3**} \\ \cline{2-2} \cline{6-9}  & \(K_{1}=2\times 10^{4}\) & \(K_{2}=5\times 10^{4}\) & \(K_{3}=1\times 10^{5}\) & \(K_{1}=2\times 10^{4}\) & \(K_{2}=5\times 10^{4}\) & \(K_{3}=1\times 10^{5}\) & \\  & **N/mm** & **N/mm** & **N/mm** & **N/mm** & **N/mm** & **N/mm** & & \\ \hline
7 & 1023.35 & 1189.13 & 1254.24 & 1024.12 & 1189.85 & 1254.84 & 0.08\% & 0.06\% & 0.05\% \\
407 & 950.79 & 1057.76 & 1097.51 & 949.62 & 1063.19 & 1105.25 & 0.12\% & 0.51\% & 0.71\% \\ value & \(-\)72.56 & \(-\)131.37 & \(-\)156.73 & \(-\)74.5 & \(-\)126.66 & \(-\)149.59 & & \\ \hline \hline \end{tabular} Note: Error 1: between numerical solution and formula solution under stiffness of studs \(K_{1}\); Error 2: between numerical solution and formula solution under stiffness of studs \(K_{2}\); Error 3: between numerical solution and formula solution under stiffness of studs \(K_{3}\).  
\end{table} 
Table 4: Comparison of axial force of composite beams with different stud stiffnesses (kN). Figure 23: (**a**) Comparison of beam end slip under uniform loads. (**b**) Comparison of beam end slip under point loads. As can be seen from Table 6, the maximum error between the calculated and numerically simulated values is 4.90%, regardless of the stud stiffness. With the increase in the stud stiffness, the decrease in the slip of the equation calculated for the same creep time is smaller, at 0.0808 mm, 0.0571 mm, and 0.0249 mm, respectively, and the rate of change is 4.85%, 7.32%, and 10.47%. In summary, the existence of slip and creep in composite beams significantly affects the axial force, deflection, and slip of composite beams. The formula solution derived in this paper is basically consistent with the finite element simulation value and is suitable for the solution under different creep and slip effects.
# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 1 Introduction
In this respect, many recent works on object detection have explored the visual attention mechanism in the deep network design for guiding the network to focus on target specific details [20; 21; 22; 23]. Along the same lines, our approach incorporates carefully placed text attention blocks in the detector network design for enhancing the representative power of extracted features. The common design of attention mechanisms includes convolutional blocks, which increase the overall model complexity and computational requirements. The text attention blocks in the proposed detector are based on efficient channel attention (ECA) [24], which has demonstrated excellent performance on object detection tasks with marginal computational cost. The following are the major contributions in this paper. * A novel U-shaped network for scene text detection for real-time applications is presented, which applies the depthwise separable convolution operation. The network predicts a quadrilateral bounding box around text instances in scene images. The incorporation of skip connections enables the detector to efficiently capture arbitrarily shaped text instances at multiple scales. The network is incorporated with ECA-based text attention blocks for robust and efficient text feature extraction, and the training is performed using a novel multitasking formulation. Next, a novel post-processing method using non-maximal suppression is applied for final prediction, which accounts for the text expectation in candidate quadrilateral bounding boxes. * The different components of the proposed detector have been extensively validated on _ICDAR2013, ICDAR2015, COCOText_, and _MSRA-TD500_ datasets. With thorough experiments under different settings, the results demonstrate that the proposed scene text detector presents an efficient solution for the detection of arbitrary shaped, multi-oriented text instances in different real-life settings. As shown later in the results, the proposed detector outperforms many prominent deep neural network-based methods, and it achieves on par performance in comparison with others. The paper structure is as follows. Section 2 presents relevant works pertaining to the deep learning methods for scene text detection. The proposed scene text detector is discussed in Section 3. The experimental validation of the proposed methodology and relevant discussions are available in Sections 4 and 5. The final section summarizes our contribution and discusses the direction of future work.
## 3 Results
Analyzing jointly the entire profiles generated by the configurations 1 and 2, and those obtained with the new combination, 1 ws and 2 ws, demonstrates how the values resulting from Equation (6) show an upper limit (calculated with the relation \(P/2k\)) lower when the new yield plane is considered and, therefore, it provides a more accurate adjustment of the upper bound value necessary to make the material flows (Tables 5 and 6, Figure 13a,b). \begin{table}
\begin{tabular}{c c c c c c} \hline
**h1** & **Module 1** & **Module 2** & **Module 3** & **Module 4** & **Total** \\ \hline
6 & 7.748 & 1.734 & 1.618 & 2.388 & 2.259 \\
5.8 & 7.492 & 1.692 & 1.578 & 2.333 & 2.199 \\
5.6 & 7.236 & 1.650 & 1.537 & 2.278 & 2.141 \\
5.4 & 6.980 & 1.609 & 1.498 & 2.225 & 2.083 \\
5.2 & 6.725 & 1.570 & 1.460 & 2.173 & 2.027 \\
5 & 6.470 & 1.531 & 1.423 & 2.123 & 1.971 \\
4.8 & 6.214 & 1.493 & 1.386 & 2.074 & 1.917 \\
4.6 & 5.959 & 1.456 & 1.351 & 2.027 & 1.863 \\
4.4 & 5.704 & 1.420 & 1.318 & 1.983 & 1.812 \\
4.2 & 5.450 & 1.386 & 1.287 & 1.942 & 1.762 \\
4 & 5.915 & 1.355 & 1.257 & 1.904 & 1.715 \\ \hline \end{tabular}
\end{table} 
Table 4: \(P/2k\) modules values and total values: configuration 2 ws. \begin{table}
\begin{tabular}{c c c c} \hline
**h1** & **Total Conf 1** & **Total Conf 2** & **Total Profile** \\ \hline
6 & 2.166 & 2.346 & 2.252 \\
5.8 & 2.124 & 2.300 & 2.208 \\
5.6 & 2.082 & 2.254 & 2.164 \\
5.4 & 2.042 & 2.208 & 2.121 \\
5.2 & 2.001 & 2.163 & 2.079 \\
5 & 1.962 & 2.120 & 2.037 \\
4.8 & 1.923 & 2.076 & 1.996 \\
4
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## 3 Results
### Results of the Advanced RST-OIL Configuration
In any case, as already mentioned in Section 1, oil can exhibit a negative contrast, even under sun glint conditions. To test the behavior of the proposed index (Equation (3)), in this second case, we also analyzed the Aqua image from 27 May 2010 at 18:55 (Figure 12a, totally acquired under high glint conditions) in which the oil appears in both positive (offshore area, NE corner) and negative (near shoreline, SW corner) contrast. Figure 12b shows the corresponding \(\otimes_{\texttt{R}^{\texttt{T}}0.8}\) index output: almost all of the area covered by oil in a positive contrast (violet area) has been detected, which is in line with the results already presented in Figures 9-11. Regarding the negative contrast zone, it has also been detected with negative \(\otimes_{\texttt{R}^{\texttt{T}}0.8}\) values lower than \(-1\) (yellow pixels). As already mentioned, this is an expected output, considering the very low NIR signal (close to 0) of water for unperturbed conditions, which reduces the \(\otimes_{\texttt{R}^{\texttt{T}}0.8}\) dynamic range. Figure 10: The same as in Figure 8 for the MODIS Terra image of Figure 4b. Figure 11: _Cont._  
Figure 11: New approach results for all images available in the period April–May 2010 together with the corresponding true-color RGB. Such a behavior is clearly observed in Figure 13, showing the 0.8 \(\upmu\)m reflectance variability (red line) along the Y-Y' transect shown in Figure 13a. In the graph, the temporal mean (black line) along the segment as well as the \(\pm 1\) interval range of variability (gray lines) have been plotted. Looking at the figure, the oil affected areas (at the beginning and the end of the red line) are clearly characterized by \"anomalous\" reflectance with respect to the expected (i.e., mean) value, even considering its normal fluctuation (i.e., \(\pm 1\) sigma). However, in case of a positive contrast, the reflectance may increase significantly, and, when negative contrast occurs, observed values decrease but do not move much lower from the expected signal. Since the reflectance is limited to positive values, it cannot allow for higher negative \(\otimes_{\mathbb{R}^{*}0.8}\) values, which results, in some cases, in sensitivity issues or missing detections (i.e., false negatives). This is what has been observed for the Terra image acquired on 10 May 2010 at 16:35 (Figure 11) where the proposed approach was not able to detect any spill and showed a negative contrast. Figure 12: (**a**) RGB of the MODIS Aqua image from 27 May 2010 at 18:55, (**b**) ALICE\({}_{\mathbb{R}^{*}0.8}\) (\(\otimes_{\mathbb{R}^{*}0.8}\)) map relative to the same image. Figure 13: _Cont._
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Distinguishing Attack Traffic from Benign Traffic
#### 5.1.2 Detection Rate vs. Threshold Value  
As can be seen from Figure 5, the size of the threshold \(\delta\) will affect the detection rate. The threshold \(\delta\) is not proportional to the detection rate. If the threshold \(\delta\) is too small, the attack traffic will be misjudged as benign traffic. If the threshold \(\delta\) is too high, the benign traffic will be misjudged as attack traffic. Due to the network traffic variability, we have done a large number of experiments, and the value of the threshold \(\delta\) ultimately ranges from 0.5 to 2 in this section. As shown in Figure 5, when the thresholds \(\delta\) range from 0.5 to 2, the detection rate is close to 1. In Figure 5, when the threshold value is 1.1, the detection rate reaches 1.  
In summary, we choose a threshold value \(\delta\) of 1.1 because it can clearly distinguish between abnormal and benign traffic and the detection rate reaches 1.
###### Abstract  
With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.  
event triggered; binary observations; FIR systems; parameter identification; communication uncertainty +
Footnote †: journal:  
_Applied_  
_sciences_
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 1 Introduction
### Commercial Solutions  
There exist commercial network management platforms that, in addition to fulfilling their primary objectives, are capable of delivering quality of service (QoS)-related statistics of the networks, such as Dynatrace [1] dedicated to virtual environments, or those providedby hardware vendors, such as Huawei's NCE [2] or Nokia's NSP [3] provided as the extension of vendor-specific technology solutions. From the perspective of the TV service, Witbe Workbench [4] and Enensys StreamProbe [5] allow the behavior of clients on the set-top box (STB) to be simulated, as well as the quality of experience (QoE) associated with the video stream displayed on the STB to be evaluated. As a result, they allow numerous maintenance and operational tasks to be optimized, which in turn can improve operational efficiency and reduce field work. It should also be noted that commercial solutions often focus on delivering the concept of Intent-Based Networking [6; 7]. Theoretically, this should bridge the gap between the network and business goals, using artificial intelligence and machine learning. Such an Intent-Based Network should enhance preventive maintenance and increase the reliability of the network. Existing commercial solutions, however, are only the first steps to achieve the described goals and do not focus on QoE estimates, which was our primary goal.