esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 3 Methodology
### Light-Weight Runtime Moving Object Detection in the Video Compressed-Domain
#### 3.2.1 Median Filter and Moving Object Detection
If the object tracking trajectory length is larger than a certain threshold, it indicates that the objects move as a flow and the time is sufficiently long to consider it as a real motion. Figure 8: Moving object detection by the proposed method with two different video test sequences: (**a,e**) MV extraction; (**b,f**) apply median filter; (**c,g**) clustering MVs; (**d,h**) blob detection. Figure 9: Motion vector of a moving object in different video test sequences: (**a,b**) our recorded video; (**c,d**) test video in VIRAT. Moreover, the I Frame does not apply the motion estimation process; therefore, the MV analysis process will be skipped for I frames. In order to overcome the discontinuous object detection process, the object tracking algorithm will also be applied to derive the moving object's bounding box in the I frame based on the last states in the P frames. As mentioned in Section 2.1, the IoU-based object tracking is applied to track motion because it is a light-weight and widely tracking algorithm that calculates the overlap area between two bounding objects. Note that this tracking was implemented and evaluated in some previous studies [40, 41].
## 4 Representative Machine Learning Models for IONM-Related Research
### Neural Networks
#### 4.1.3 Recurrent Neural Networks  
RNNs are a class of neural networks that are specialized in the processing of iterative and sequential data. Unlike feedforward networks, RNNs have feedback connections to deliver information from the past to the future [42]. RNNs have been widely used in natural language and speech processing. In medicine, RNNs are frequently used for the analysis of medical data that require continuous signal reading, such as EMG or fluoroscopic images [19; 43]. An encoder RNN and a decoder RNN can be combined to learn mappings between sequential data of different lengths [9].  
Simple RNNs have the limitation that they cannot learn long-term dependencies from time-series data. LSTM networks [44] and gated recurrent units (GRUs) [45] are advanced RNN architectures that overcome this limitation by applying the gating mechanism. However, all RNNs, including LSTM networks and GRUs, have the drawback that they are hard to parallelize and, therefore, slower than other architectures.
###### Abstract  
Recently, unmanned aerial vehicles (UAVs) have been widely studied in the communication area to work as aerial base stations, due to the high probability of line of sight (LoS) and high flexibility. However, few works consider fairness for the users, which is one of the most important metrics for a network. In this paper, in order to maximize network capacity with the consideration of fairness, trajectory and scheduling of the mobile UAV aerial base station are jointly optimized. Firstly, the problem of maximizing network capacity with the consideration of fairness is formulated. On account of the coupling relationship of trajectory and scheduling, an alternate iteration approach that contains ant colony algorithm and genetic algorithm are then proposed to solve this intractable problem. Finally, the simulation results demonstrate the fairness enhance of the network and the validity and effectiveness of the proposed optimization approach.  
unmanned aerial vehicle (UAV); fairness performance; circular trajectory; optimization; ant colony algorithm; genetic algorithm Article
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Setup
#### 4.1.1 Nsl-Kdd Dataset  
NSL-KDD [27] is an improved version of the KDD99 dataset, designed to address redundancy and imbalance issues. It consists of four major attack categories: Denial of Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Among these, U2R and R2L are significantly underrepresented, making them the primary focus for evaluating the effectiveness of minority-class augmentation.  
**Dataset Splitting:** We directly adopt the original training and testing split provided in NSL-KDD without additional modifications. This ensures that our results remain comparable with prior studies.
## 3 Results  
### Structural Characterization of ZnO, DPOA/ZnO, and DMSA/DPOA/ZnO  
Figure 1 presents the XRD patterns of ZnO, DPOA/ZnO, and DMSA/DPOA/ZnO for 20 ranging from 10 to 80\({}^{\circ}\). All the three ZnO relevant materials exhibited the same XRD pattern, indicating the structure did not change during the coating process. Peaks that were observed at 31.7, 34.4, 36.2, 47.5, 56.5, 62.8, 66.3, 67.9, and 69.0\({}^{\circ}\) were indexed to hexagonal wurtzite (ZnO; JCPDS Data Card no. 36-1451). By using the Williamson-Hall method [28], it was found that the obtained average crystallite size of ZnO was 23, 23, and 25 nm for bare ZnO, PDA/ZnO, and DMSA/PDA/ZnO, respectively. The corresponding strains are 0.88%, 0.88%, and 1.01%.  
Figure 1: XRD spectra of (**a**) zinc oxide (ZnO), (**b**) polydopamine/ZnO (PDA/ZnO), and (**c**) dimercaptosuccinic acid/Polydopamine/zinc oxide (DMSA/PDA/ZnO).
###### Abstract
Elastic wave transmission through the ground does not require a large antenna, is not subject to electromagnetic interference, and is simple to generate signals. However, there are also two application drawbacks. On the one hand, its transmission attenuation is large, it is susceptible to the influence of formation media, and its elastic wave energy utilization efficiency is poor, making it unsuitable for use in complex geological conditions; on the other hand, this method is prone to secondary collapse, so it cannot be applied to wireless strong penetration information transmission in emergency rescue scenarios for tunnel engineering collapse. The information transmission mechanism of the ground electrode current field uses an extremely low-frequency electrical signal, which is applied to the two electrodes driven into the soil layer or the collapsed body of the tunnel, thereby forming a current field in the rock or soil layer, which is detected by the signal at the receiving end. We create a strong penetration wireless information transmission system. For signals with lower carrier frequencies, the field current is dominated by conduction currents, while for signals with higher carrier frequencies, the field currents are dominated by displacement currents so that the wires, electrodes, and the ground form a closed loop, equivalent to a loop antenna, which is similar to the principle of field electromagnetic wave communication. The characteristics, advantages, and disadvantages of the above three types of wireless strong penetration information transmission technology are compared as shown in Table 1.
## Materials and Methods
### The Rotating Coupon Device and Biofilm Formation Procedure  
A rotating coupon device (RCC-3, Motian Tech., Suzhou, China) was used in this study for biofilm formation. The schematic diagram of the setup is shown in Figure 1.  
The prepared model sewage was contained in a polyethylene beaker, and the beaker was placed in the thermally-controlled water bath of the rotating coupon device. The temperature of the water  
Figure 1: Schematic diagram of the rotating coupon device: (**A**) front view; and (**B**) top view.  
bath was set at 25 \({}^{\circ}\)C, which was the average treated sewage temperature during summer operating conditions. A T thermocouple (GG-T-20, Omega, Shanghai, China) was put at the geometric center of the beaker and was connected to a data logger (34970A, Agilent, Shanghai, China) to monitor the temperature in the beaker. The results showed that the temperature in the beaker reached 25 \({}^{\circ}\)C within 30 min and remained at 25 \(\pm\) 0.5 \({}^{\circ}\)C during the whole process.  
Three stainless steel coupons (AISI 316, 20 mm \(\times\) 20 mm \(\times\) 1 mm) were fixed onto the rotating shaft using screw nuts. Then the coupons were immersed and rotated in the model sewage. The angular velocity was set at 96 rpm, and the linear velocity at the coupon surfaces was 0.4 m/s, which was a common flow velocity in plate heat exchangers in the TSSHPS.  
This biofilm formation process lasted for 120 h, and every 12 h fresh model sewage was prepared to replace the old model sewage in the beaker. A previous study [6] has reported that 120 h were sufficient for the biomass to reach a stable phase.
## 3 Properties of Different STBCs Signals  
The major purpose here is to determine the STBC option used by the \(\mathcal{S}\) node by using the collected signals \(y_{\mathcal{D}}^{(1)}(n)\) and \(y_{\mathcal{D}}^{(2)}(n)\) acquired at the endpoint. To that aim, we investigate the properties of correlation functions of \(y_{\mathcal{D}}^{(1)}(n)\) and \(y_{\mathcal{D}}^{(2)}(n)\) to explore whether we can extract the outstanding properties for STBC categorization. The preceding are the main assumptions used during the research.  
* The information symbols provided by the \(\mathcal{S}\) node are unrelated to one another.
* Neither the sent signals nor the noise are statistically independent of one another.
* Neither the \(\mathcal{R}\) nor the \(\mathcal{D}\) nodes' noise samples are related to one another. The noise samples in the first and second rounds of transmissions are unrelated at the endpoint.
* The channel parameters stay constant during the observational window. We define a correlation \(U^{(\alpha)}(\tau)\) as follows \[U^{(\alpha)}(\tau)=\frac{1}{u_{\alpha}}\sum_{n=0}^{u_{n}-1}\mathrm{E}\Big{[}y_ {\mathcal{D}}^{(1)}(n)y_{\mathcal{D}}^{(2)}(n+\tau)\Big{]},\] (6) where \(\tau\) is the time lag, and \(\mathrm{E}[\cdot]\) is the statistical mean operator. The accompanying formulations are written for the codes of \(\alpha=c_{0},c_{1},c_{2}\), and \(c_{3}\). The analysis is flexible enough to be applied to various coding systems, and these signals are used as descriptors.
## 3 Monitoring Methods  
### Digital Camera Calibration  
Due to inherent imperfections in the digital camera lens, captured images will exhibit various forms of distortion. The purpose of the digital camera calibration is to obtain internal, external, and distortion parameters of the camera for correction. Four coordinate systems are involved in the camera imaging system, namely, the pixel coordinate system, image coordinate system, camera coordinate system, and world coordinate system.  
The following formula can be used to express the relationship between these coordinate systems [22]:  
\[s\widetilde{m}=A\left[\begin{array}{cc}R&t\end{array}\right]\widetilde{M} \tag{1}\]  
where \(\widetilde{m}=\left[\begin{array}{ccc}u&v&1\end{array}\right]^{T}\) represents the pixel coordinate in the pixel coordinate system; \(\widetilde{M}=\left[\begin{array}{ccc}X&Y&Z&1\end{array}\right]^{T}\) is the physical coordinate of a point in the world coordinate system; \(s\) is the scale factor; \(A=\left[\begin{array}{ccc}\alpha&\gamma&u_{0}\\ 0&\beta&v_{0}\\ 0&0&1\end{array}\right]\) is the internal parameter matrix, with \((u_{0},v_{0})\) being the coordinates of the principal point; \(\alpha\) and \(\beta\) are the scale factors in the image's \(u\) and \(v\) axes, and \(\gamma\) is the parameter describing the skewness of the two image axes; and \(\left[\begin{array}{cc}R&t\end{array}\right]\) is the external parameter matrix, where \(R\) is the rotation matrix and \(t\) is the translation vector.  
The distortion model is divided into two types: radial and tangential distortion. The radial distortion formula (of the third order) is as follows:  
\[\begin{array}{l}\hat{x}=x(1+k_{1}r^{2}+k_{2}r^{4}+k_{3}r^{6})\\ \hat{y}=y(1+k_{1}r^{2}+k_{2}r^{4}+k_{3}r^{6})\end{array} \tag{2}\]  
The tangential distortion formula is as follows:  
\[\begin{array}{l}\hat{x}=x+(2p_{1}y+p_{2}(r^{2}+2x^{2}))\\ \hat{y}=y+(p_{1}(r^{2}+2y^{2})+2p_{2}x)\end{array} \tag{3}\]  
where \((x,y)\) is the ideal normalized image coordinate without distortion; \((\hat{x},\hat{y})\) is the normalized image coordinate after distortion; \(k_{1},k_{2},\) and \(k_{3}\) are the radial distortion parameters; and \(p_{1},p_{2}\) are the tangential distortion parameters.  
The calibration board used in this experiment is a square with an edge length of 18 mm and a total of 9 \(\times\) 6 corners. Using a digital camera with focal length of 55 mm, the calibration board was imaged from multiple angles, capturing a total of 20 photographs. The camera calibration was conducted in MATLAB software. The obtained camera calibration parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Content** & **Value** \\ \hline Model & EOS 1500D \\ Sensor type & CMOS \\ Focal length (mm) & 55 \\ Sensor size (mm) & 22.3 \(\times\) 14.9 \\ Photo resolution (pixels) & 6000 \(\times\) 4000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the camera.
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Satellite Missions for Inland Water Level Monitoring
#### 3.1.3 Other Satellite Missions  
Except for radar and laser altimetry missions, there are a few different techniques that can also perform altimetry with quite good coverage, such as Gravimetry [105] and GNSS-R [106]. Figure 9 depicts the annual number of applications per these satellite missions. Initially, the GNSS-R field has gained exponential growth thanks to its increasing relevance across a wide range of applications [107]. A research investigation conducted on Lake Qinghai highlighted the altimetry potential of space-based GNSS-R, specifically of the Cyclone GNSS mission (CYGNSS) [106]. The CYGNSS mission consists of eight satellites, whose primary objective is to measure wind speed within and around the inner core of tropical cyclones over the ocean [108]. Using CryoSat-2 InSAR mode products for the validation of inland water level estimates is shown that water level time series derived by GNSS-R follows the CryoSat-2 one quite well. The root-mean-square deviation between CYGNSS and CryoSat-2 water levels is 0.70 m, while between CYGSS measurements and In situ data is 0.68 m [106]. Also, the GNSS Interferometric Reflectometry (GNSS-IR) technique is used to estimate ten years of lake surface heights, assessing its reliability by comparisons with gauges and satellite radar altimetry water level heights [109]. The relative RMS differences between gauges and GNSS-IR water level heights range from \(\pm\)0.027 m to \(\pm\)0.028 m, while between radar altimetry and GNSS-IR range from \(\pm\)0.069 m and \(\pm\)0.124 m showing that GNSS-IR technique can provide reliable water level estimates. Although a continuously operating GNSS station within a lake is uncommon, GNSS-IR presents an opportunity for satellite altimetry calibration/validation. Moreover, a method is proposed for the water level estimates in reservoirs using Sentinel-1 SAR data and Shuttle Radar Topography Mission (SRTM) surface models. Sentinel-1A/-1B satellites, with a 12-day return period, generate high-resolution backscatter images. This research marks a significant advancement in the worldwide assessment of inland water bodies, regardless of their size or geographical location, as long as a DEM is available. Also, while radar altimeters are more accurate, the combination of Sentinel-1 and SRTM missions
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 4 Cross-Section Design and Case Analysis for Minimum Bending Moment Shield Tunnel in Soft Soil Area
### Cross-Section Design for the Minimum Bending Moment Shield Tunnel  
The above analysis indicates that the cross-section of the zero bending moment shield tunnel is related to the assumed earth pressure. However, usually one metro line only adopts one tunnel cross-section in light of practical construction, and tunnel buried depth and soil properties of the ground are changed along the metro line. Therefore, it is necessary to evaluate the earth pressure mode and corresponding length for the shield tunnel. Suppose the shield tunnel has \(n\) kinds of engineering conditions, which are shown in Table 4. Taking every condition into account, the cross-section axis of the minimum bending moment shield tunnel can be calculated as Equation (21).  
\[y=\frac{y_{1}l_{1}+y_{2}l_{2}+y_{3}l_{3}+\ldots\ldots+y_{n}l_{n}}{L} \tag{21}\]  
in which \(L\) is the total length of the metro shield tunnel and is calculated as Equation (22).  
\[L=l_{1}+l_{2}+l_{3}+\ldots\ldots+l_{n} \tag{22}\]  
where \(y_{i}\) refers to Equation (8) and can be calculated as Equation (23).  
\[y_{i}=f(P_{1-i},P_{2-i},P_{3-i},l_{i}) \tag{23}\]  
In order to apply the cross-section design theory of the minimum bending moment shield tunnel, this proposed design flow chart is presented as Figure 14.
# Chromaticity Recognition Technology of Colored Noise and Operational Modal Analysis
## 3 Numerical Simulations  
### Chromaticity Recognition Simulation  
This section consists of two parts. The first part identifies the chromaticity of noise. Four different colored noise excitations of cantilever beam responses are selected for chromaticity identification, and the error of using CRF to identify chromaticity is analyzed. In the second part, the identified chromaticity was incorporated into the frequency domain fitting algorithm for modal parameter identification. We then analyzed the impact of using CRT before and after on the accuracy of modal parameter identification.  
Figure 3 shows the aluminum cantilever beam that we used in the simulation, which also shows the size, locations of the excitation, and response points of the beam. Points 1 to 5 are excitations. Points 6 to 10 are responses. The elastic modulus \(E\) of the beam is set as 71 GPa. The density is 2700 kg/m\({}^{3}\). The damping ratio is set as 0.002 for each mode. The analysis bandwidth is set from 0 to 800 Hz. The number of spectral lines is 1600.  
We applied colored noise excitation at excitation points 1-5 in the simulation. The acceleration responses of points 6-10 were obtained using the finite element method of plane beam elements. We calculated the PSD of points 6-10, and then analyzed the points near the first two modes to identify noise chromaticity using CRT.  
In this chromaticity recognition simulation example, four types of colored noise are used for excitation. The first one uses chromaticity of \(-1.2\), the second one uses chromaticity of \(-0.8\), the third one uses chromaticity of 0.4, and the fourth one uses chromaticity of 0.6. For each test, the chromaticity of each excitation point is same, but the magnitude is different. Table 1 shows the magnitude of each excitation point, and \(R_{11}\)-\(R_{55}\) represent the excitation PSD information from points 1 to 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(R_{\mathbf{11}}\) & \(R_{\mathbf{22}}\) & \(R_{\mathbf{33}}\) & \(R_{\mathbf{44}}\) & \(R_{\mathbf{  
Figure 6: Time domain response information of point 10 under different chromatic excitations. (**a**) Time history when \(\rho=-1.2\); (**b**) Time history when \(\rho=-0.8\); (**c**) Time history when \(\rho=0.4\); (**d**) Time history when \(\rho=0.6\).  
Figure 7: Frequency domain response information of point 10 under different chromatic excitations. (**a**) PSD when \(\rho=-1.2\); (**b**) PSD when \(\rho=-0.8\); (**c**) PSD when \(\rho=0.4\); (**d**) PSD when \(\rho=0.6\).  
The analysis of point 10 is similar to the analysis of other response points. So, the analysis results of other signal points are not detailed. Table 2 shows that the error of the chromaticity is within 1%. Furthermore, there is no significant difference between the theoretical and recognition results, which proves the feasibility of the theory proposed in this study.
## 3 High Level Architectural Approach  
Based on the aforementioned key enablers for NPN networks, a conceptual approach of the system is depicted in Figure 1. Firstly, an identification of the involved architectural layers can be observed. At the lowest level, the infrastructure layer includes the components directly related to the actual network deployment, such as edge components, cloud infrastructure and transport network. Since individual operators need to have access to this common infrastructure in order to achieve cost-efficient network deployments, neutral hosting procedures are placed on top of these components. In the same context, as our goal is to provide various services to the end users simultaneously, different slices should be supported by the considered 5G Network. To this end, each slice has access to resources of both the 5G Core and O-RAN components of our architectural approach. The core and RAN network functions constitute the network function layer of the proposed system.  
Finally, the upper architectural layer includes the proper procedures for resource and service management and orchestration, as well as network automation. In addition, as mentioned in the previous section, AI/ML algorithms target at a holistic network optimization. These algorithms gather inputs from network telemetry modules and apply dynamic network reconfigurations, depending on the network state. This functionality belongs to the management, orchestration, and automation (MOA) lay  
Figure 1: Conceptual approach of our proposed approach.
## 3 Algorithms
### Inter-Resonator Coupling Formulation  
The \(Q\)-factor method can be applied from full-wave or eigen-mode solutions [16]. The inter-resonator coupling coefficient can be determined through the full-wave simulation of two identical resonators in a shared housing, as specified in (9). For isolated resonators with identical natural frequencies \(f_{0}\), coupling produces frequency splitting into two distinct eigen-frequencies, \(f_{1}\) and \(f_{2}\). The coupling coefficient \(k_{\text{full-wave}}\) is then derived from this characteristic frequency separation.  
\[k_{\text{full-wave}}=\frac{f_{2}-f_{1}}{f_{2}} \tag{9}\]  
This work implements an alternative symmetric approach for determining inter-resonator coupling using identical resonator pairs with the \(Q\)-factor method. Leveraging structural symmetry, only half the geometry requires simulation, substantially reducing computational cost. Perfect Electric Conductor (PEC) and Perfect Magnetic Conductor (PMC) boundary conditions are applied at the symmetry plane to extract the odd-mode and even-mode resonant frequencies, respectively. These characteristic frequencies enable the computation of the coupling coefficient \(k_{\text{even-odd}}\) through the relationship given in (10), derived from [5, 16].  
\[k_{\text{even-odd}}=\frac{f_{\text{odd}}^{2}-f_{\text{even}}^{2}}{f_{\text{ odd}}^{2}+f_{\text{even}}^{2}} \tag{10}\]  
Figure 7 shows the equivalent circuit representation used for analyzing even-odd modes with respect to (10).  
Section 4 incorporates all presented algorithms to fully characterize and validate the filter designs, including the determination of the unloaded quality factor, input-output coupling coefficients, and inter-resonator coupling values through even-odd mode analysis, along with group delay evaluation and complete frequency response modeling. The Generalized Chebyshev Matrix (GCM) approach [29] provides the theoretical foundationfor this validation process, with its effectiveness demonstrated throughout the design methodology and experimentally confirmed in Section 5.