esa-sceva/satcom-chunk-collection huggingface dataset:
# A Broadband Ultra High Frequency (UHF) Fat-Dipole Antenna for Digital TV Applications
## 3 Results
The minimum distance (32 cm) is observed when the antenna substrates are touching each other, and in this situation, an isolation level larger than 20 dB was measured across the entire DTV band. The Envelope Correlation Coefficient (\(ECC\)) is a key parameter used to describe the isolation between different communication channels [21]. It can be expressed using either the far-field antenna patterns or the scattering parameters, as shown in Equation (2):  
\[ECC=\frac{|s_{11}^{*}s_{12}+s_{21}^{*}s_{22}|}{(1-|s_{11}|^{2}-|s_{21}|^{2})(1- |s_{22}|^{2}-|s_{12}|^{2})} \tag{2}\]  
Diversity gain (\(DG\)), Equation (3), can be extracted directly from the \(ECC\), as [22]  
\[DG=10\sqrt{1-ECC^{2}} \tag{3}\]  
Another important parameter that describes the concept of return loss applied to MIMO antennas is the Total Active Reflection Coefficient (\(TARC\)). This coefficient takes into account the mutual influence of the other array antennas on the individual antenna input. For a 2-element array, it is expressed according to Equation (4):  
\[TARC=\sqrt{\frac{|s_{11}+s_{12}|^{2}+|s_{21}+s_{22}|^{2}}{2}} \tag{4}\]  
Computed \(ECC\) and \(TARC\) results for the case with orthogonal antennas spaced 32 cm apart are shown in Figure 14. \(ECC\) values below 0.1 are considered adequate for digital transmission [21] and, therefore, suitable for use in DTV. The \(TARC\) results remain below  
Figure 12: Measurement of the coupling (S12) between the two antennas in the collinear and orthogonal arrangements, and in detail, the definition of the distances are shown. Figure 10:0 Coupling (S12) measurement for different element distances in the orthogonal deployment. 10 dB across the DTV frequency range, which are slightly close to the lower band limit of 470 MHz.
# Tightly-Coupled Data Compression for Efficient Face Alignment
## 4 Methodology  
In this section, implementation details about the algorithm proposed in Section 3.1 are described. Algorithm 1 demonstrates the pseudo codes of the approximate algorithm for solving Equation (8).  
```
Input: Projection matrix \(\mathbf{A}_{d}\) at the \(d\)th step; number of elements \(T\) in \(\mathbf{A}_{d}\); number of quantization levels \(Q\). Output: quantization steps bounds \(v_{0}\ \cdots\ v_{Q}\)
1. Sort all the elements of \(\mathbf{A}_{d}\) in ascending order and store them in an array SA with quick sort [42].
2. Calculate \(v_{0}\) and \(v_{Q}\) with Equation (9).
3. For \(k=1\) to \(Q-1\),
4. Calculate \(idx\) with Equation (10).
5. Get \(v_{k}\) with Equation (11). endfor
```  
**Algorithm 1**Approximate algorithm for solving Equation (8).  
Algorithm 2 shows the pseudo codes for the whole procedure of the proposed data quantization algorithm.  
```
Input: Projection matrix \(\mathbf{A}_{d}\) (\(d=1\ \cdots\ D\)) for all \(D\) steps; number of quantization levels \(Q\). Output: Quantized projection matrix \(\mathbf{A}\mathbf{Q}_{d}\) (\(d=1\ \cdots\ D\)) for all \(D\) steps. For \(d=1\) to \(D\),
1. Calculate the optimal quantization steps bounds \(v_{0}\ \cdots\ v_{Q}\) according to \(\mathbf{A}_{d}\) with Algorithm 1.
2. Initialize cluster centers \(\mu_{k}\) (\(k=1\ \cdots\ Q\)) for the K-means algorithm with Equations (12) and (13).
3. Minimize Equation (6) with the K-means algorithm [41] and get the optimal clusters' centers \(q_{k}\) (\(k=1\ \cdots\ Q\)).
4. For each element \(\mathbf{A}_{d}(i)\) (\(i=1\ \cdots\ T\)), its corresponding quantized value \(\mathbf{A}\mathbf{Q}_{d}(i)\) (\(i=1\ \cdots\ T\)) can be calculated with Equations (14) and (15). endfor
```  
**Algorithm 2**The proposed data quantization algorithm.  
Figure 6: The diagram of the compressed data storage arrangement.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 5 Numerical Simulation & Results
### Poincare Section for Different Attractors  
In certain cases, a periodic orbit can be confidently identified from a two-dimensional projection of an attractor (phase portraits). Nevertheless, this process typically does not help in yielding a convincing inference between quasi-periodic and strange attractors. The Poincare section, a two-dimensional plane intersecting the steady-state trajectories as seen in Figure 8, offers a better understanding of the periodicity. It transforms a continuous flow into a discrete mapping of time (Poincare map). Usually, only one direction of the crossing of the trajectory is considered. It is also possible to classify the behavior of the system by analyzing the stroboscopic distribution of the distinct points in the Poincare section. A finite number of points on the Poincare section which adequately represent the periodicity of the motion are observed for a periodic motion. For a quasi-periodic motion, a continuous line in the Poincare map is observed. If the motion is chaotic, because of the fractal character of the chaotic attractors, many irregularly points will be seen on the plane.  
For visualization of the Poincare map for the proposed system, the plane \(x_{2}=0\) was taken as a Poincare section and the trajectories that crosses this section from \(x_{2}<0\) or \(x_{2}>0\) were recorded. Figure 7 shows the Poincare section for a different variation of the control parameter \(\beta\). In Figure 7c, the trajectories cut the section in a single line (positive section of the grid), characterized by the quasi period-1 attractor given in Figure 7a. Similarly, the Poincare section for quasi period-2 and quasi period-4 are given in Figure 7f, characterized by the two or four straight lines in the Poincare section. Finally, in Figure 7l, the points are located irregularly on the Poincare section, the behavior of the system can be termed as chaotic as evident from the phase portrait given in Figure 7j.
## 2 Related Work
### Physical Model-Based Synthesis of Deformation Interferograms  
Synthetic datasets derived from physical models offer an effective means of addressing the scarcity of real-world data. Numerical simulation techniques for generating surface deformation fields using physical models are now relatively well established. Most existing approaches are based on [PERSON]'s elastic half-space dislocation theory [35]. By randomly sampling source parameters such as fault strike, dip angle, and slip, researchers can simulate a wide variety of coseismic deformation fields. Because of the complexity and variety of noise, which make precise simulation challenging, synthetic data generation often focuses on accurately modeling different types of noise. For instance, [PERSON] et al. [21] synthesized atmospheric noise with spatial wavelengths ranging from 10 km to 100 km and simulated topographic noise by randomly scaling digital elevation models (DEMs). [PERSON] et al. [24] further modeled atmospheric turbulence delays using Gaussian noise and simulated terrain-related delays with a quadratic polynomial. They also introduced random pixels to simulate decorrelation and phase unwrapping errors. Subsequent studies on InSAR time series employed more refined noise modeling techniques, using various functional models to simulate both temporal and spatial noise components [36, 37]. Numerous studies have shown that physically based interferogram synthesis techniques can effectively produce data resembling real interferograms, thereby partially alleviating the problem of data scarcity.  
The fidelity of the physical model directly affects the quality of the synthetic data. The physical processes underlying seismic deformation are inherently complex. Althoughcurrent physical models are capable of simulating ground deformation to some degree, they fall short in capturing the full range of potential deformation scenarios-especially in regions with irregular topography, complex geological settings, or across varying temporal scales. More importantly, synthesizing noise is substantially more difficult than modeling deformation. Various types of interferometric noise--including atmospheric delays, topographic distortions, decorrelation, orbital errors, and processing-induced artifacts--exhibit highly intricate spatial patterns and temporal variability. Existing physical models face significant limitations in accurately replicating such noise components. Consequently, the overall quality of synthetic interferograms is largely constrained by the fidelity of the underlying physical models.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 4 Experiment Results
### Spectra of SC-FDMA, FBMC, and LP-FBMC  
In a previous study [6], we confirmed that LP-FBMC is robust to signal clipping, because it achieves low PAPR. However, because a HPA in RF circuitry has different nonlinearities, the actual transmit signal may be significantly different from the result assuming a simple nonlinear model, such as signal clipping. In addition, the theoretical PAPR curve has a limitation for quantitatively assessing how the waveform degrades with this practical nonlinearity. Hence, we compare the spectra of SC-FDMA, FBMC, and LP-FBMC when they go through the actual nonlinearity of RF circuits in the SDR device.  
Figure 7 shows the received power spectra of the three waveforms when the carrier frequency is 600 MHz and 3.5 GHz in the LoS environment of Figure 5, and the uplink power control is performed so that the power of each user is the same in the BS. The number of subcarriers \(N\) is 64, the subcarrier spacing \(\Delta f\) is 15 kHz, and the distances between the transmitter and receiver are 10 m and 20 m. In accordance with the well known drawback of OFDM-based waveforms, it is shown that SC-FDMA has the highest OOB emission among the three waveforms. Whereas, since the FBMC-based waveforms are filtered for each carrier, the its OOB emission is quite suppressed as compared to SC-FBMC. However, the field test results shown in Figure 7 also confirmed that the OOB emission of FBMC increases significantly as the distance increases from 10 m to 20 m. This is because, as the distance increases, the transmit power is accordingly increased to compensate path loss and, thus, the FBMC undergoes signal distortion due to its high PAPR characteristics. This issue is even more critical in millimeter band (i.e., 3.5 GHz in our field test), where the path loss is more severe when compared to the lower frequency bands (i.e., 600 MHz in our field test). On the other hand, the OOB emission of the LP-FBMC is maintained almost the same, irrespective the frequency band and the distances. Based on these results, the LP-FBMC system maintains the superb OOB suppression, not only in theoretical signal clipping model, but also in real RF circuits and wireless channels.  
It is also shown in Figure 7 that the indoor wireless channel in the 600 MHz band has higher frequency selectivity than in the 3.5 GHz band. This confirms a well known property that the multipath delay spread gets smaller as the frequency increases.
## 3 Advanced Techniques in Fault Injection and Error Rate Estimation
### Fault Injection Method
#### 3.1.3 Statistical Fault Injection Test  
Injecting faults into all cells can ensure accurate test results but requires a significantly extended time period [31]. Additionally, if a fault is applied to components other than the DUT (e.g., an external module introduced to inject faults, etc.), it may lead to inaccurate  
Figure 5: Generating fault injection commands through Linear Frame Address.  
failure results (injection side effects). To address this issue, this study restricts fault injection to the DUT and presents a framework for statistical fault injection.  
First, SLICE information is essential to limit fault injection to the DUT. However, when implementation is conducted after general synthesis, the module (test module) for fault injection by the user is distributed across various slices and undergoes synthesis. This presents the challenge of having to run the FPGA Fault Injection Tool using SLICE multiple times, which can be a cumbersome and time-consuming process.  
To overcome this, after synthesis, the p-block function must be employed to specify a particular SLICE range for synthesis enablement. P-block is a function provided by Xilinx, allowing the netlist for a specific sub-block to be placed at a designated location during the floor planning stage. Figure 6 depicts the floor planning results after configuring the p-block for the PRINCE circuit and performing the implementation. We can observe that the blue color was properly synthesized in the area where the p-block was placed as the test module.  
To determine the number of statistical fault injection tests for a limited fault injection location, we can use the following formula to select the number of tests based on the confidence interval and error margin. In the equation below, n represents the total number of tests, and e denotes the error margin value, typically set at 1%. \(N\) represents the fault injection space (memory coverage), which can be obtained in the previous step. The \(t\) is a confidence level factor, and its value varies depending on the desired confidence interval. For a 90% confidence interval, The \(t\) is 1.645; for a 95% confidence interval, \(t\) is 1.96; and for a 99% confidence interval, The \(t\) is 2.58. Lastly, \(p\) is a statistical parameter, assumed to be the worst-case value, and is typically set to 0.5.  
\[n=\frac{N}{1+e^{2}.\frac{N-1}{I^{2}\cdot p(1-p)}} \tag{1}\]  
\(n\) = _total number of tests_, \(N\) = _Fault Injection Space_,  
\(e\) = _error margin_ (1%), \(t\) = _confidence level factor_ (CI 95% = 1.96),  
\(p\) = _statistical parameter_ (_worst \(-\) case value_ = 0.5).  
Figure 6: P-block Floor Planning.
## 4 Applications
### Security Inspection and Target Tracking  
In fields such as the military, border protection, and public safety, drones can carry out tasks such as border surveillance, reconnaissance, patrolling, and target tracking with the support of an integrated space-air-ground network. They collect and transmit real-time image data, sensor data, and GPS information, feeding the situation back to the command center. This enables commanders to stay updated on frontline developments and respond swiftly to emergencies. Furthermore, drones can cooperate with ground forces and satellite imagery for multi-layered collaborative monitoring, providing comprehensive coverage of borderlines or high-risk areas and establishing a dynamic three-dimensional defense network. This efficient inspection and tracking system significantly enhances emergency response capabilities, thereby safeguarding national and public security. The literature [153] refers to the use of UAVs for surveillance and reconnaissance in military areas, and by analyzing the case of the Ecuadorian armed forces, it is concluded that the use of UAVs in terms of national security has a positive impact.
# Impacts of Micro-Deviations of Aperture on the Characteristics of Collision Atomization Field
## 1 Introduction
The existing attempts mostly assume the apertures as fixed, the micro-deviation of the nozzle structure created by machining error or abrasion in the machining process is therefore entirely neglected.. As one of the minor deviations caused by machining errors, the diameter deviation of orifice seriously affects the quality of collisional atomization. Moreover, there are few available perspective holes to show the flow field in the impact atomizer in the current research. It is necessary to develop a visual orifice so that we can better obtain the real-time state of the internal flow field. In this paper, the influence of the small deviation of pore size on hydraulic parameters and atomization characteristics, and the sensitivity of atomization field to its response are studied in a targeted manner. A transparent nozzle was applied to visualize the internal flow field to study the effect of cavitation phenomena on the atomization field parameters. The nozzle diameter is ultra-precisely measured by the self-developed optical fiber probe based on orthogonal micro-focus alignment to ensure the accuracy of the nozzle diameter. In this paper, by building a collision atomization and laser interference particle measurement device, the flow coefficient, droplet diameter and atomization cone angle were measured, and the influence of the nozzle hole processing deviation on the above parameters was clarified.
## 2 QPN Model
### Mathematical Model of QPN  
In [23], the general model of QPN was defined and some symbols were introduced. This is briefly summarized in this subsection.  
Queueing Petri nets have the advantages of QNs (e.g., evaluation of the system performance, the network efficiency) and PNs (e.g., logical assessment of the system correctness). The main idea of QPNs is to add queueing and timing aspects to the net places. QPN is a tuple (1).  
\[QPN=(PL,TR,CO,IN,MA,QU,WE) \tag{1}\]  
where:  
* \(PL=\{p_{1},p_{2},\ldots,p_{k}\}\) is a finite and non-empty set of places;
* \(TR=\{t_{1},t_{2},\ldots,t_{l}\}\) is a finite and non-empty set of transitions;
* \(PL\cap TR=\varnothing\);
* \(CO\) is a color function defined from \(PL\cup TR\) into finite and non-empty sets (specify the types of tokens that can reside in the place and allow transitions to fire in different modes);
* \(IN(p,t)\) are the backward and forward incidence functions defined in \(PL\times TR\), such that \(IN(p,t)\in[CO(t)\longrightarrow CO(p)_{MS}],\forall(p,t)\in PL\times TR\) (specify the interconnections between places and transitions; the subscript \(MS\) denotes multisets. \(CO(p)_{MS}\) denotes the set of all finite multisets of \(CO(p)\));
* \(MA(p)\) is an initial marking defined on \(PL\), such that \(MA(p)\in CO(p),\forall p\in PL\) (specify how many tokens are contained in each place).
* \(QU=(QU_{1},QU_{2},(q_{1},\ldots,q_{|PL|}))\), where:
* \(QU_{1}\subseteq PL\) is a set of timed queueing places;
* \(QU_{2}\subseteq PL\) is a set of immediate queueing places;
* \(QU_{1}\cap QU_{2}=\varnothing\);
* \((q_{1},\ldots,q_{|PL|})\) is an array with a description of places (if \(p_{i}\) is a queueing place, \(q_{i}\) denotes the description of a queue with all colors of \(CO(p_{i})\) into consideration, or if \(p_{i}\) is the ordinary place (\(p_{i}\)) equals \(null\)).
* \(WE=(WE_{1},WE_{2},(w_{1},\ldots,w_{|TR|}))\), where:
* \(WE_{1}\subseteq TR\) is a set of timed transitions;
* \(WE_{2}\subseteq TR\) is a set of immediate transitions;
* \(WE_{1}\cap WE_{2}=\varnothing\), \(WE_{1}\cup WE_{2}=TR\);
* \(W=(w_{1},\ldots,w_{|TR|})\) is an array (entry \(w_{j}\in[CO(t_{j})\longmapsto\mathbb{R}^{+}]\), such that \(\forall c\in CO(t_{j}):w_{j}(c)\in\mathbb{R}^{+}\)) of:
* rate of a negative exponential distribution specifying the firing delay due to color \(c\in CO(t_{j})\), if \(t_{j}\in WE_{1}\);
* firing weight specifying the relative firing frequency due to color \(c\in CO(t_{j})\), if \(t_{j}\in WE_{2}\).
# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 6 Conclusions and Future Studies  
In this study, a vision-based finite element model updating using the genetic algorithm was proposed. The proposed system automatically measured the displacement of the structure from a video and updated the parameters of the FE model by using the genetic algorithm. To validate the performance of the proposed method, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one of the columns. The genetic algorithm was used to automatically optimize a non-damaged state of the FE model to a damaged state. Seven different object tracking algorithms (KLT, CSRT, KCF, MedianFlow, MIL, MOSSE, and TLD) were used and the FE model updating results were compared. As a result, proposed method using KLT, KCF, MIL, and MOSSE algorithms successfully updated the FE model to the as-is damaged condition, whereas CSRT, MedianFlow, and TLD were not able to update the FE model appropriately. Proposed method using KLT tracker showed the most reliable result with 89% accuracy for estimating the member stiffness. While some limitations are remaining as a future study, the proposed method is expected to reduce the time and effort for FE model updating by using computer vision.  
Conceptualization, H.Y.; methodology, H.Y.; software, G.P.; validation, G.P.; formal analysis, G.P.; investigation, G.P.; resources, H.Y.; data curation, G.P.; writing--original draft preparation, G.P.; writing--review and editing, H.Y., K.-N.H.; visualization, G.P.; supervision, H.Y., K.-N.H.; project administration, H.Y., K.-N.H.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C153021-02).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Fourier-Transform Infrared Spectroscopy (FT-IR) Analysis of EGCG-Modified Magnetic Beads  
To confirm the chemical bonding and surface modification of the magnetic beads with EGCG, Fourier-transform infrared (FT-IR) spectroscopy was performed on T-Fe3O4 (bead only, blue peak), pure EGCG (red peak), and two different EGCG-coated bead preparations (EGCG@T beads_1 (green peak) and EGCG@T beads_2 (purple peak)) (Figure 2).  
Figure 1: Results from morphological analysis using SEM and Normalized Atomic Percent values obtained from EDS.  
In the FT-IR spectrum between 3000 and 3500 cm\({}^{-1}\), the three magnetic bead samples showed similar patterns, while EGCG (red) showed a different pattern. The FT-IR spectrum of pure EGCG displayed characteristic absorption peaks corresponding to carbonyl stretching (C=O) near 1691 cm\({}^{-1}\), alkene C=C stretching at 1545 cm\({}^{-1}\), and methyl group (CH\({}_{3}\)) deformation at 1375 cm\({}^{-1}\). These signals are consistent with the polyphenolic structure of EGCG (red peak). In contrast, the T-Fe\({}_{3}\)O\({}_{4}\) sample lacked these distinct organic peaks, exhibiting only the typical metal-oxide-related bands.  
Upon modification with EGCG, both EGCG@T beads_1 and EGCG@T beads_2 exhibited new peaks in the 1300-1700 cm\({}^{-1}\) region that correspond to those observed in the pure EGCG spectrum. The appearance of the C=O-, C=C-, and CH\({}_{3}\)-related peaks in the coated beads indicates successful surface functionalization of the magnetic beads with EGCG molecules.  
These findings confirm the chemical incorporation of EGCG onto the bead surface, which may contribute to enhanced binding interactions with exosomal components in downstream applications.  
The T-Fe\({}_{3}\)O\({}_{4}\), EGCG, and EGCG@T complexes were analyzed using FT-IR at the Korea Basic Science Institute (KBSI). As a result, the C=O peak of EGCG in the EGCG@T mixture was confirmed at the peak position of 1691 [cm\({}^{-1}\)]. The C = C peak at 1545 [cm\({}^{-1}\)], and the CH3 peak at 1375 [cm\({}^{-1}\)] were also confirmed. Thus, EGCG was combined with T-Fe\({}_{3}\)O\({}_{4}\).