esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Examples  
### Single-DOF System: Kelvin Model of Viscoelastic Element  
To illustrate the method's idea, we will first discuss the single-degree-of-freedom system with a Kelvin damper, both fractional and classical, as shown in Figure 4. The equation of motion (15), assuming that the system parameters are described using interval numbers, now takes the following form:  
\[-\lambda^{2}m\overset{\sim I}{q}(\lambda)+k_{s}^{I}\overset{\sim I}{q}( \lambda)+k_{0}^{I}\overset{\sim I}{q}(\lambda)+(\mathrm{i}\lambda)^{a}c_{0}^{ I}\overset{\sim I}{q}(\lambda)=\overset{\sim I}{P}(\lambda) \tag{39}\]  
where \(m\) and \(k\) are understood as the mass and stiffness of the system, respectively, and \(k_{0}\), \(c_{0}\), and \(\alpha\) represent the stiffness, damping coefficient, and the order of the fractional derivative describing the parameters of the Kelvin damper, respectively. \(\overset{\sim I}{P}(\lambda)\) denotes the forcing function, and \(\overset{\sim I}{q}(\lambda)\) represents the displacement of the considered system. The task is to find the system response in the form of displacement values.  
We assumed the following parameters for the analyzed system: \(m=40\) kg, \(k_{s}=1000\) N/m, \(k_{0}=500\) N/m. Various cases of damping coefficients were analyzed, with the data shown in Table 1. It was assumed that the forcing magnitude \(P_{0}=1\) N and is not affected by uncertainties. Only the stiffness and damping parameters of the viscoelastic element are uncertain, with 5% uncertainty. Table 2 presents the parameter values in the form of interval numbers. The response of the structure was calculated using the vertex method (red line), DIM (green line), and the method described in the article (blue line). The  
Figure 4: Single-DOF dynamic system: (**a**) with classical Kelvin damper; (**b**) with fractional Kelvin damper.  
In the first two cases, the classical Kelvin model was considered (Figures 5 and 6), while in the next two, the fractional model was analyzed (Figures 7 and 8).  
The results for the presented method and for the DIM almost overlap, especially when the system's damping is higher. The obtained results are also very close to those of the vertex method, indicating that the presented method can assess the influence of uncertain design parameters on the system's response. In all cases, the results of the presented method lie outside the interval obtained from the vertex method, indicating that they are on the safe side. The example shown demonstrates that the presented method can be applied to determine the response of a system with a viscoelastic element described using both classical and fractional Kelvin models.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 3 A GAN Based Channel Estimation Scheme
### Model Training Stage: Training A GAN Model to Capture the Distribution of Real Channel  
In this subsection, the procedure of generating synthetic channel samples with high similarity to real channel samples by training a GAN model is illustrated in detail. Firstly, the basic concept of GAN is provided as follows.
# An Intrusion Detection System for RPL-Based IoT Networks
## 3 Related Work
### Attacks in RPL-Based Networks  
A great deal of literature work based on attacks in RPL-based networks is presented below. [PERSON] et al. in [24] presented a large study of well-known RPL attacks. Among the studied attacks, the authors explored blackhole and greyhole attacks, sinkhole attack, Hello flood attack, rank decrease attack, version number attack, and DIS attack. In addition to an explanation of the attacks, countermeasures were also presented. Another survey of RPL attacks was presented by [PERSON] et al. in [28]. The authors classified the attacks into three categories: attacks against resources (e.g., Hello flood attack and version number attack), attacks on topology (e.g., sinkhole attack and blackhole attack), and attacks on traffic (e.g., rank decrease attack). Later on, they provided security solutions to mitigate attacks based on the RPL topology. [PERSON] and [PERSON] [27] presented a survey focused on attacks on RPL and 6 LoWPAN. The relevant attacks mentioned in this survey are the selective forwarding attack, sinkhole attack, Hello flood attack, blackhole attack, version number attack, and DIS attack. Moreover, IDS was mentioned as a countermeasure, explaining its limitations and its benefits. Some studies have also been performed on the DIS attack. [PERSON] in [39] presented the spam DIS attack; its main idea was to send a large number of DIS messages in multicast by using multiple fictitious identities to make the victims send back a DIO message to exhaust the affected nodes and the network. [PERSON] et al. in [25] also described the DIS attacks as a powerful weapon against network performance. [PERSON] et al. in [26] introduced the DAO insider attack and showed the big impact caused in network performance, power consumption, and latency. In addition, the authors proposed a new scheme to reduce the effects of such attacks. In terms of the version number attack, a deep study was carried out by [PERSON] et al. in [40], where the authors analysed the attack from multiple points of view. More recent work has been performed in terms of RPL attacks. [PERSON] et al. in [41] presented a study on the possibility of battery-drain DoS attacks on the RPL-based IoT. The authors used the Cooja simulator to implement both the version number attack and the Hello flood attack, and to demonstrate the impact that these caused.
###### Abstract  
In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.  
earth-observations; earth system modelling; direct and inverse methods 2018
## 5 Smart Community
### Smart Environment Management
#### 5.1.3 Soil Monitoring  
To measure soil health indicators in urban tree pits, [PERSON] et al. [64] proposed Plant Spike, a low-cost and low-power IoT soil monitoring system. The system's functionality and robustness were demonstrated through testing in New York and Morningside Heights. For soil monitoring, we should collect data including light (e.g., Model OPT3001), temperature, and moisture/capacitive [64]. The model of the light sensor is Model OPT3001 [64].
## 5 Experiments
### Analysis of Parameter Complexity  
While the empirical efficacy of our DAGA framework is evident from the experimental outcomes, an equally significant advantage lies in its computational efficiency, particularly in terms of parameter complexity. The innovative disentanglement design intrinsic to DAGA significantly mitigates the parameter complexity, transitioning from an exponential to a linear growth pattern. This section elucidates the underlying mechanisms and implications of this design choice.  
Consider a conventional single-layer perceptron utilized as an encoder within a group recommendation model. In such a scenario, embedding user representations from an original dimension \(s\) into a hidden dimension \(d\) necessitates a trainable matrix of dimensions \(s\times d\). The complexity and resource requirements for this model scale with the product of the input and output dimensions, potentially leading to computational inefficiency, especially for high-dimensional data.  
In contrast, the disentanglement approach employed by DAGA and its self-attention/MLP encoders restructures this model into \(k\) distinct factors, each responsible for capturing a specific aspect of the user or group preferences. This architectural modification implies that, instead of a single large matrix, the model now comprises \(k\) smaller matrices, each with dimensions \(\frac{s}{k}\times\frac{d}{k}\). Consequently, the total parameter count is significantly reduced to \(s\times\frac{d}{k}\), thereby alleviating the computational burden.  
This reduction in parameter complexity does not compromise the performance; rather, it enhances the DAGA framework's efficiency, making it a compelling choice for scenarioswhere computational resources are limited. The disentangled representation not only streamlines the learning process by focusing on distinct latent factors but also ensures that the model remains scalable and adaptable to various group recommendation contexts.  
In summary, the disentanglement design within DAGA serves a dual purpose: it bolsters the recommendation capabilities of the model while simultaneously ensuring computational parsimony. This approach marks a significant advancement in the design of recommendation methods, marrying effectiveness with efficiency in a manner that sets a new precedent for future research in the field.
# Robust Backstepping Control Applied to UAVs for Pest Recognition in Maize Crops
## 4 Control Strategy Applied to the Quadrotor
### Controller for Subsystem \(x-\theta\)
x_{8}, \tag{27}\] \[\dot{x}_{8}=\tau_{\theta a}+\delta(\vartheta,t)=u_{3x}+\delta( \vartheta,t).\]  
The proposed Lyapunov function for subsystem (27) is  
\[V_{3}=V_{3}(x_{5},x_{6},x_{7})=\frac{1}{2}x_{5}^{2}+\frac{\beta_{1}}{2}(x_{6} +\alpha_{5}x_{5})^{2}+\frac{\beta_{2}}{2}\Big{(}\sin x_{7}-\frac{\cos x_{7} \cos x_{11}}{p}(d_{1}x_{5}+d_{2}x_{6})\Big{)}^{2},\]  
and the virtual input \(\mu_{2}\) is associated with \(u_{2x}\), given in (25), as  
\[\mu_{2}=\Big{(}\frac{k_{6}d_{1}\cos x_{11}}{p}+\frac{\beta_{1}\alpha_{5}p}{ \cos^{2}x_{7}\cos x_{11}}\Big{)}x_{5}+\Big{(}\frac{(d_{1}+k_{6}d_{2})\cos x_{ 11}}{p}+\frac{\beta_{1}p}{\cos^{2}x_{7}\cos x_{11}}\Big{)}x_{6}-(d_{2}+k_{6}) \tan x_{7}.\]  
According to Theorem 1, the controller is given by  
\[u_{3}x = -k_{7}z_{3}-\frac{\partial V_{3}(\vartheta)}{\partial\vartheta}f _{1}(\vartheta)-sgn(z_{3})\Delta_{3}+\frac{\partial\mu_{2}(\vartheta)}{ \partial\vartheta}[f_{0}(\vartheta)+f_{1}(\vartheta)\xi],\]  
where \(z_{3}=x_{8}-\mu_{2}\), \(k_{7}>0\), \(\vartheta=(x_{5},x_{6},x_{7})^{T}\), \(f_{1}(\vartheta)=[0,0,1]^{T}\), and  
\[ \begin{array}{l}\frac{\partial\mu_{2}}{\partial x_{5}}=\Big{(}\frac{k_{6}d_ {1}\cos x_{11}}{p}+\frac{\beta_{1}\alpha_{5}p}{\cos^{2}x_{7}\cos x_{11}}\Big{)} \\ \frac{\partial\mu_{2}}{\partial x_{6}}=\Big{(}\frac{d_{1}\cos x_{11}}{p}+\frac{ k_{6}d_{2}\cos x_{11}}{p}+\frac{\beta_{1}p}{\cos^{2}x_{7}\cos x_{11}}\Big{)}\\ \frac{\partial\mu_{2}}{\partial x_{7}}=\Big{(}\frac{2\beta_{1}\alpha_{5}p\sin x _{7}}{\cos^{3}x_{7}\cos x_{11}}\Big{)}x_{5}+\Big{(}\frac{2\beta_{1}p\sin x_{ 7}}{\cos^{3}x_{7}\cos x_{11}}\Big{)}x_{6}-(d_{2}+k_{6})\sec^{2}x_{7}\\ \frac{\partial V_{3}}{\partial x_{7}}=\beta_{2}\sin x_{7}\cos x_{7}-\Big{[} \frac{\beta_{2}\cos x_{11}(d_{1}x_{5}+d_{2}x_{6})}{p}\Big{]}\big{(}2\cos^{2}x_ {7}-1\big{)}.\end{array} \]  
Therefore, the input \(u_{3x}\) is  
\[u_{3x} = \frac{\partial\mu_{2}}{\partial x_{5}}x_{6}-\frac{\partial\mu_{2}}{ \partial x_{6}}\frac{p\sin x_{7}}{\cos x_{7}\cos x_{11}}+\frac{\partial\mu_{2}}{ \partial x_{7}}x_{8}-\frac{\partial V_{3}}{\partial x_{7}}-sgn(x_{8}-\mu_{2}) \Delta_{3}-k_{7}(x_{8}-\mu_{2}).\]
## 4 Finite Element Simulation Results
### Study on the Influence of Horizontal Spacing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Horizontal Spacing/Temperature** & **Ambient Temperature** & **400 \({}^{\circ}\)C** & **600 \({}^{\circ}\)C** \\ \hline
300 m (uniform distribution)) & 27 m/s & 26 m/s & 12 m/s \\
250 m (non-uniform distribution) & 25 m/s & 24 m/s & 10 m/s \\
200 m (non-uniform distribution) & 24 m/s & 22 m/s & 9 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Collapse-resisting wind speed under different horizontal spacing conditions. Figure 14: Collapse-resisting wind speed under different horizontal spacing conditions. occurs earlier at 3.10 s, and within the next 0.15 s, observed members 2, 3, and 4 fail in sequence. Observed members 5, 6, 7, and 8 remain intact. The tower loses its load-bearing capacity and collapses. When the horizontal spacing is 200 m (uneven distribution), the instability process accelerates further. Observed member 1 fails at 3.00 s, and within the next 0.10 s, observed members 2, 3, and 4 fail in sequence, ultimately causing the entire transmission tower to collapse. The stress-time histories of the most critical members of the transmission tower-line system with horizontal spacings of 300 m (uniform distribution), 250 m (uneven distribution), and 2
## 2 Materials and Methods
### Selecting Dust Events
#### 2.2.3 Determination of Dust Storm Origin Using HYSPLIT  
The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was designed by [55] and has been upgraded over the past few decades. This model is in fact a dual model for calculating dust motion, scattering, and particle simulation. This model is used to simulate dust trajectories and identify areas of origin as well as areas affected by dust storms. HYSPLIT is one of the most widely used models for atmospheric trajectory and dispersion calculations [56; 57]. In this study, HYSPLIT calculations were performed online for the identified two events for 24 h integration periods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Statistical** & **Longitude** & **Latitude** & **Station Name** & **Statistical** & **Longitude** & **Latitude** & **Station Name** \\
**Period** & **(Degree)** & **(Degree)** & & **Period** & **(Degree)** & **(Degree)** & **Station Name** \\ \hline
2006–2018 & 53.08 & 32.58 & [PERSON] & 2006–2018 & 60.47 & 29.47 & [PERSON] \\
2006–2018 & 51.90 & 33.53 & [PERSON] & 2006–2018 & 54.28 & 31.90 & Yazd \\
2006–2018 & 51.83 & 31.98 & Shahr Reza & 2006–2018 & 54.25 & 30.50 & Marvast \\
2006–2018 & 55.25 & 30.88 & Anar & 2006–2018 & 53.62 & 32.43 & Aoda \\
2006–2018 & 58.35 & 29.10 & Bam & 2006–2018 & 53.42 & 35.59 & Semnan \\
2006–2018 & 57.67 & 28.73 & Jiroft & 2006–2018 & 52.28 & 32.89 & Birjand \\
2006–2018 & 57.70 & 27.97 & Kahnood & 2006–2018 & 56.90 & 33.67 & Tabas \\
2006–2018 & 56.97 & 30.25 & Kerman & 2006–2018 & 58.18 & 34.03 & Ferdous \\
2006–2018 & 57.70 & 30.42 & Shahdad & 2006–2018 & 50.85 & 34.70 & Qom \\
2006–2018 & 55.13 & 30.10 & Shahr Babak & 2006–2018 & 51.18 & 34.78 & Salafchegan \\
2006–2018 & 55.68 & 29.55 & Sirjan & 2006–2018 & 49.83 & 34.13 & Arak \\
2006–2018 & 50.90 & 35.92 & Karaj & 2006–2018 & 50.48 & 33.88 & Mahalat \\
2006–2018 & 50.75 & 36.00 & Hasthgred & 2006–2018 & 50.33 & 35.05 & Saoh \\
2006–2018 & 50.00 & 36.25 & Qazvin & 2006–2018 & 57.65 & 36.18 & Sabzevar \\
2006–2018 & 49.70 & 36.05 & Takestan & 2006–2018 & 52.38 & 33.38 & Ardestan \\
2006–2018 & 51.32 & 35.75 & Abali & 2006–2018 & 51.71 & 32.52 & Isfahan \\
2006–2018 & 52.83 & 35.92 & Firozkoh & 2006–2018 & 51.85 & 32.52 & Kabootar-khan \\
2006–2018 & 5132 & 35.68 & Mehrabad & 2006–2018 & 55.08 & 33.78 & Khoor va bibanak \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on synoptic stations in the study area.  
HYSPLIT was drives by Global Data Assimilation System (GDAS) meteorological fields, available globally at 0.5-degree resolution for 2007-2019.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 4 Experimental Results and Settings  
The proposed fundus image enhancement strategy was tested on 1240 images comprised of two publicly available datasets, i.e., DRIVE [30] and MESSIDOR [31]. The details of these images are given in Table 1. DRIVE is an online available dataset of 40 lossless fundus images in an RGB format with clinical expert annotations. These 40 images are equally classified into training and testing sections. The resolution of each image is 768 \(\times\) 584 pixels with a field of view (FOV) of 45\({}^{\circ}\). MESSIDOR is also a publicly available dataset that contains 1200 lossless images with ophthalmologist labels. These images were captured with a FOV of 45\({}^{\circ}\) at various dimensions. To evaluate the suitability of the proposed scheme with other histogram-oriented methods, a statistical analysis was conducted using entropy, peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), histogram, and structure similarity index measure (SSIM) parameters. These parameters have been employed by the contrast enhancement arts in the measurement of image quality. All experiments were executed using MATLAB R2017b on a Core i5 64-bit Intel processor system with 16 GB DDR3 RAM, running an operating system (Windows 10). An average time of 7 s was computed for each enhanced image by the presented enhancement art. The subsequent section discusses the mathematical foundation of the applied quality parameters with their results.  
Figure 6: A sample of fundus images: (**a**) An input image; (**b**) a \(J\) component grayscale image; and (**c**) the proposed enhanced image.
## 4 Numerical Results
### BER Performance  
Figure 4 shows the BER performance with respect to the signal-to-noise ratio (SNR) for different modulation orders when \(\xi=0.5\), which demonstrates a significant BER performance improvement introduced by the IRS. Given a fixed PD location with a certain direct channel, the BER performance is improved due to the total channel gain increase shown in Figure 3. Furthermore, the SNR in the received PD depends on many factors in practical VLC systems, such as the transmitter power, the distance of the communication channel (or the locations of the transceivers and the IRS units), the noise power, and the interference of the background light.  
In Figure 5, the BER performance of the tunable IRS-aided SISO VLC systems with the optimized constellation and reflectances are compared for different dimming coefficient \(\xi\). The BER performance improvement shows a strong correlation with the dimming coefficient. Specifically, the BER performance of \(\xi=0.5\) outperforms that of \(\xi=0.7\) which outperforms that of \(\xi=0.3\) for 2-PAM. While for 8-PAM and 16-PAM, the BER curves for \(\xi=0.5\) and \(\xi=0.7\) are exactly the same and exceed that of \(\xi=0.3\). This observation indicates an extra BER performance enhancement for \(\xi=0.7\) because the BER  
Figure 4: Comparison of the BER performance with and without the IRS for \(\xi=0.5\).  
Figure 3: Channel gain distribution for IRS-aided VLC system.  
for \(\xi=0.3\) is supposed to be the same as that for \(\xi=0.7\) for VLC systems with no IRS or with mirror-based IRS [18].  
To reveal the extra BER performance enhancement for \(\xi=0.7\), Figure 6 shows the BER curve of 8-PAM modulation with a varying dimming coefficient employing the tunable IRS and the mirror-based IRS, respectively. For comparison purposes, we assume that the channel gains reflected by the tunable IRS and mirror-based IRS are the same. It is shown that the best BER is achieved at \(\xi=0.5\), and the BER incorporating the mirror-based IRS is symmetric about \(\xi=0.5\). This is not surprising because dimming usually degrades communication performance and the dimming related capacity has been shown to be symmetrical with respect to \(\xi=0.5\)[15; 18]. But the BER performance for \(\xi>0.5\) is improved by the tunable IRS and can be enhanced to its best as \(\xi=0.5\) with the optimized constellations and reflectances, which means the tunable IRS units not only improve the BER performance due to larger channel gains but also relax the dimming constraint. Thus, the BER performance for \(\xi=0.7\) exceeds that for \(\xi=0.3\) and may reach \(\xi=0.5\) when employing sufficient tunable IRS units. The results are consistent with the BER curves in Figure 5.
## 3 Results
### IOD of Observations of Space Debris Related to the COSMOS 1408 Satellite  
#### 3.3.1 Background  
Space events are happening with increasing frequency, such as the breakup events of the YunHai 1-02(2019-063A, NORADID 44547), the NOAA 17(2002-032A, NORAID 27453), the ASAT 1408 in 2021, and the American GEO satellite Galaxy 11 (1999-071A, NORADID 26038) in 2022 (from ISON). The antisatellite incident had the biggest impact. On 15 November 2021, at 10:47 p.m. Beijing time, Russia conducted an antisatellite test (ASAT) that destroyed the COSMOS 1408 satellite. The satellite, a former Soviet satellite, was launched in 1982 with NORAD ID 13552. The orbit of Cosmos 1408 prior to the ASAT test had an altitude of 490 \(\times\) 465 km. It is estimated that more than 1500 trackable pieces of space debris, as well as countless smaller pieces, were generated from the ASAT event. This posed a threat to the security of space assets of countries around the world. The debris number evolution and the occurred operations are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**SMA Error** **(km)** & **Rate (\%)** & **Eccentricity** **Errors** & **Rate (\%)** & **Inclination** **Errors (Deg)** & **Rate (\%)** \\ \hline \textless{}5 & 4.12 & \textless{}0.0001 & 21.70 & \textless{}0.01 & 11.26 \\ \textless{}10 & 8.24 & \textless{}0.0005 & 47.80 & \textless{}0.03 & 31.32 \\ \textless{}30 & 21.15 & \textless{}0.001 & 53.02 & \textless{}0.05 & 40.66 \\ \textless{}50 & 33.24 & \textless{}0.005 & 82.42 & \textless{}0.1 & 64.29 \\ \textless{}100 & 55.49 & \textless{}0.01 & 97.53 & \textless{}0.2 & 83.79 \\ \hline \end{tabular}
\end{table}
Table 7: The statistical errors of IOD SMA, eccentricity, and inclination of observations observed by FocusGEO.  
Figure 4: The error distribution of IOD SMA, eccentricity, and inclination of observations observed by FocusGEO. (**a**) SMA; (**b**) eccentricity; (**c**) inclination.  
As of 26 May 2022, 900 pieces of debris from this antisatellite event, with a cataloged target of 1740, have fallen, and 818 will fall in the next 3 years (SATEVO). The estimated in-orbit times of debris of different sizes generated by antisatellite events in NASA's Space Debris Quarterly Report are shown in Figure 6. Based on the TLE from [[http://celestrak.org](http://celestrak.org)]([http://celestrak.org](http://celestrak.org)), (accessed on 7 February 2023), the number of pieces of space debris created in this ASAT event is 260 on 12 February 2023.  
Figure 5: The Russian ASAT conjunction squall predications vs. SDC operational results (from COMPSOC). (**a**) The COSMOS 1408 debris fragment tracking and decay evolution. (**b**) Daily Encounters between Planet’s 233 spacecraft and ASAT debris.  
Figure 6: The predicted orbital decay of the Cosmos 1408 fragment cloud. The three curves are, from top to bottom, \(\geq\)10 cm fragments, \(\geq\)1 cm fragments, and \(\geq\)1 cm fragments, respectively [11].