esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Area, Data and Workflow
### Workflow  
Flood impacts were estimated from the acquired images over five phases as shown in Figure 5. First, all of the satellite images were subjected to radiometric and geometric correction pre-processing. Next, water body information was extracted via the region growing method [49] based on each scene  
Figure 4: Post-Flood Satellite Image Coverage.  
of satellite images after pre-processing for pre- and post-disaster images. Third, all extracted pre-flood water body coverage polygons based on each image frame were geo-merged into one layer as pre-flood water body coverage data. All of the extracted post-flood water body coverage polygons were then geo-merged into one layer as post-flood coverage data. Fourth, the flood extent was determined through a comparative analysis and based on changes in pre- and post-flood water body coverage. Finally, GL30 and MIMU data were used as exposure elements to estimate the impacts of floods on administrative units, cropland, and human settlements using the flood extent information.
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 2 Proposed Weather Station Platform
### Design Architecture  
The platform comprises a portable edge device with an MCU, sensors, and a centralized remote server. The edge device collects sensor measurements, performs data pre-processing, and stores and communicates the collected data to the centralized server for further processing and storage. Pushing the live data to a centralized server also facilitates data storage requirements on each edge device, reducing the cost per unit.  
The backend of the centralized server facilitates data ingestion and monitoring by accepting connections from multiple edge devices, receiving their data stream, and performing post-processing and storage. The frontend provides a web-based dashboard to monitor the live data stream from each edge device. This allows the edge devices to run headless, eliminating the need for a graphical user interface for real-time monitoring, further reducing the per-unit cost of the edge devices.  
This two-tier architecture, consisting of edge devices and a centralized server, provides several key benefits, such as lightweight, cost and power-efficient edge devices, a scalable network of edge devices, and real-time remote monitoring.  
Each edge device MCU coordinates between the sensors and collects and stores measurements. This data is transmitted via the Internet to the remote server using the Message Queuing Telemetry Transport (MQTT) protocol. Internet connectivity is achieved via the MCU's built-in Wi-Fi, which connects to the operator's phone hotspot. The phone effectively acts as a cellular gateway and a real-time frontend that displays the data being received by the centralized server.  
The centralized server is virtualized and hosted on an AMD64-based architecture, a containerized open-source Eclipse Mosquitto [48] message broker and Node-RED [49] server, providing MQTT connectivity and a web-based user interface for live remote monitoring, respectively. The server is further protected by the free open-source pfSense Community edition firewall [50], which only allows MQTT and the Hypertext Transfer Protocol Secure (HTTPS) to reach the centralized server. Finally, a domain name was acquired from a free Dynamic Domain Name System (DDNS) provider, and Let's Encrypt [51] and Certbot [52] were used to automate the process of encrypting server communications by obtaining and updating Secure Sockets Layer (SSL)/Transport Layer Security (TLS) certificates. Figure 1 illustrates the portable weather station (edge device), communication channels, and the real-time monitoring interface.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 2 Architecture
### Relative Navigation Based on Vision and UWB
#### 2.2.2 Calculating the Relative Position  
The camera imaging plane is shown in Figure 5. \(O(u_{0},v_{0})\) is the origin located in the image coordinate system. The target's coordinates in the image coordinate system are denoted by \(T(u,v)\). In the physical coordinate system of the camera, the corresponding coordinate origin is located at \(O(x_{0},y_{0})\), and the target coordinate system is denoted by \(T(x,y)\). \(f\) is the focal length of the camera, and \(d_{x},d_{y}\) are the physical sizes of each pixel on the imaging plane along the axis of \(x,y\).  
\[\begin{cases}\theta=\arctan\Bigl{(}\frac{(u-u_{0})\times d_{x}}{f}\Bigr{)}\\ \phi=\arctan\Bigl{(}\frac{(v-u_{0})\times d_{y}}{f}\Bigr{)}\end{cases} \tag{18}\]  
where \(\bigl{[}\theta\quad\phi\bigr{]}^{T}\) refers to the line-of-sight angle, \(\theta\) refers to the line-of-sight azimuth angle and \(\phi\) refers to the line-of-sight elevation angle.  
The position among neighboring drones in the swarms can be calculated as  
\[u=\rho\begin{bmatrix}\sin(\phi)\sin(\theta)\\ \sin(\phi)\cos(\theta)\\ \cos(\phi)\end{bmatrix} \tag{19}\]  
where \(u=\begin{bmatrix}u_{x}&u_{y}&u_{z}\end{bmatrix}^{T}\).
## 2 Related Work
The integration of ZT principles with BC and DRL has the potential to create robust and adaptive security frameworks for SC management ([PERSON] et al., 2018). However, the implementation of ZT in SCs is still in its early stages, and further research is needed to develop practical and scalable solutions. Several studies have focused on specific aspects of SC security and optimization. [PERSON] et al. (20) discussed the new challenges in SC management, particularly in the context of cybersecurity across the SC. They emphasized the need for collaborative efforts among stakeholders and the utilization of big data analytics to identify, predict, and mitigate risks. [PERSON] and [PERSON] (20) proposed a DRL and optimization approach for multi-echelon SCs with uncertain demands, demonstrating its effectiveness in handling complex scenarios. [PERSON] et al. (20) applied DRL for capacitated SC optimization under demand uncertainty, showcasing its potential for real-world applications. The integration of BC and ZT principles has been explored to redefine trust in SCs. [PERSON] et al. (20) investigated how BC technology can enhance trust and security in SCs by providing a decentralized and immutable record of transactions. The combination of BC and ZT can help address the challenges of identity-based attacks, data breaches, and physical disruptions in SCs. However, further research is needed to develop practical implementations and address issues, such as scalability and interoperability. Despite the growing interest in applying BC and DRL for SC security and optimization, several research challenges remain unresolved. These include the scalability and performance of BC-based solutions, the interpretability and robustness of DRL models, and the integration of BC and DRL with existing SC systems ([PERSON] et al., 2019). Additionally, the development of standardized frameworks and protocols for BC and ZT implementation in SCs is crucial for widespread adoption and interoperability. In summary, the recent literature highlights the potential of BC and DRL for enhancing SC security and efficiency. The integration of ZT principles with BC and DRL offers a promising direction for creating robust and adaptive security frameworks for SC management. However, further research is needed to address the challenges of scalability, interoperability, and practical implementation. The proposed SAC-rainbow framework aims to address these challenges by leveraging the strengths of BC, DRL, and ZT principles for secure and efficient SC management.
# Sports Video Classification Method Based on Improved Deep Learning
## 2 Methods
### Preprocessing  
Given the inherent nature of sports videos, they are often marred by motion artifacts due to rapid movements. To address this, our study mandates preprocessing steps prior to classification, which encompasses artifact and noise elimination. Specifically, frames are extracted from the sports videos and resized to a uniform resolution of 520 \(\times\) 520 pixels. We leverage the Laplacian second-order differential linear operator (as per Equation (1)) to discern high-frequency components in the images. A paucity of these components suggests potential blurriness and motion artifacts. By computing the image variance using the Laplacian operator filter, and setting a threshold at 0.3, we ascertain that images falling below this variance are likely blurred and artifact-laden.  
\[\begin{split}\
abla f=\frac{\partial^{2}f}{\partial x^{2}}+\frac {\partial^{2}f}{\partial y^{2}}=\\ f(x+1,y)+f(x-1,y)+f(x,y+1)+f(x,y-1)-4f(x,y)\end{split} \tag{1}\]  
Figure 2: Images from self-collected sports video dataset (contains artifacts and low resolution).  
To tackle motion artifacts in images, one can infer that the pristine image has been displaced by \(L\) pixels, either horizontally or vertically. This displacement can be conceptualized as the original image being subjected to a Fourier transform with a blur kernel of magnitude \(L\), oriented either horizontally or vertically. Given a camera exposure time of \(t\), the object's displacement can be delineated into \(x(t)\) and \(y(t)\) for the horizontal and vertical axes, respectively. Thus, the motion-artifacted image can be derived by executing a Fourier transform on the original, as illustrated in Equation (2).  
\[\begin{array}{l}G(x,y)=\int_{0}^{T}f(x-x(t),y-y(t))dt\\ g(x,y)=\int_{0}^{T}f(x-x(t),y-y(t))dt\end{array} \tag{2}\]  
By applying the Fourier transform to \(g(x,y)\) we obtain the formula:  
\[\begin{array}{l}G(u,v)=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}g(x,y) e^{-j2\pi(ux+vy)}dxdy\\ =\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}(\int_{0}^{t}f(x-x(t),y-y(t) ))dte^{-j2\pi(ux+vy)}dxdy\\ =\int_{0}^{t}\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}f(x,y)e^{-j2\pi(ux +vy)}dxdy\end{array} \tag{3}\]  
Assuming:  
\[\begin{array}{l}J(u,v)=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}f(x,y )e^{-j2\pi(ux+vy)}dxdy\end{array} \tag{4}\]  
\[\begin{array}{l}k(u,v)=\int_{0}^{t}e^{-j2\pi(ux(t)+vy(t))}dt\end{array} \tag{5}\]  
Then, we can derive that  
\[\begin{array}{l}G(u,v)=J(u,v)k(u,v)\end{array} \tag{6}\]  
Here \(G(u,v)\) represents the spectral amplitude of the transform. Utilizing the Radon transform, we ascertain the frequency offset angle, aligning with the blur kernel's orientation. The offset scale is derived from the spacing between consecutive wave patterns evident in the blurred image. Conclusively, executing the inverse Fourier transform facilitates the retrieval of a pristine image devoid of motion artifacts.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 3 Hybrid RF-FSO Systems Techniques and Their Advanced Achievements
### FSOC with WDM and SCM Techniques
FSO-SCM performance was assessed in 2009 for the transmission of CDMA signals underneath the Gamma-Gamma situation FSO channel [137]. In 2016, Petr et al. combined the technique of PDM with the FSO-SCM method and showed that the system performance was improved by using PDM in terms of launch power to reach the required error vector magnitude (EVM) limit [138]. In Ref. [139], the BER performance of an SCM-based FSO was calculated using dual-drive MZM for the sideband signals of optic single under the atmospheric turbulent channel. The performance improvement attained by the combination of SCM and spectral amplitude coding optical code division multiple access (SAC-OCDMA) in the FSO system has been discussed [140; 141].
## 3 Parametric Numerical Analysis  
### Modelling  
The numerical analysis was carried out in ABAQUS/Standard for all the examined configurations in dynamic Implicit simulations. To this aim, the reference model for structural floors and pedestrians was assembled in accordance with Figure 1b, that is, in the form of shell elements for the structure and a coupled SMD model representative of the pedestrian. The lumped mass \(m\) was positioned at a given height of 1.1 m from the walking surface, which is in line with the CoM of examined volunteer (based on [16; 17; 18; 19; 20]). A spring/dashpot element was also used to introduce the equivalent spring \(k\) and damping coefficient \(c\) terms.  
To this aim, the input SMD parameters for pedestrians were taken from Section 2 and Figure 4 as a function of the imposed \(f_{p}\). Moreover, comparative calculations were carried out towards the deterministic, equivalent force loading approach as in Equation (14), based on a rigid uncoupled SDOF assumption, with \(m=M\) (\"RU\" model, in the following).  
In terms of damping, a conventional Rayleigh approach was taken into account to define the mass-proportional and stiffness-proportional damping ratio terms of each floor configuration [23]. More specifically, they were calculated as:  
\[\alpha=\xi_{s}\frac{2\omega_{1}\omega_{2}}{\omega_{1}+\omega_{2}} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Coefficient**} & \multicolumn{2}{c}{\(f_{p}\) (Hz)} \\ \cline{2-3}  & \(\leq\)**1.75** & **1.75 \(\div\) 2** \\ \hline \(K_{1}\) & \(-8f_{p}+38\) & \(24f_{p}-18\) \\ \(K_{2}\) & \(376f_{p}-844\) & \(-404f_{p}+521\) \\ \(K_{3}\) & \(-2804f_{p}+6025\) & \(4224f_{p}-6274\) \\ \(K_{4}\) & \(6308f_{p}-16.573\) & \(-294,144f_{p}+45,468\) \\ \(K_{5}\) & \(1732f_{p}-13,619\) & \(109,976f_{p}-175,808\) \\ \(K_{6}\) & \(-24,638f_{p}+16,045\) & \(-217,424f_{p}+353,403\) \\ \(K_{7}\) & \(31,836f_{p}-33,614\) & \(212,776f_{p}-350,259\) \\ \(K_{8}\) & \(-12,948f_{p}+15,532\) & \(-81,572f_{p}+135,624\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of input coefficients \(K_{i}\) for Equation (14), based on walking frequency \(f_{p}\).  
Figure 5: Example of analytical description of single football vertical load, based on the equivalent-force deterministic model as in Equation (14). Vertical load \(F\) (in Newton) is normalized to the mass of the pedestrian (\(M=80\) kg).  
\[\beta=\xi_{s}\frac{2}{\omega_{1}+\omega_{2}} \tag{18}\]  
with \(\omega_{1}\), \(\omega_{2}\) are the natural circular frequencies corresponding to the first and second vibration modes of the F#1-to-#4 floor systems with input features summarized in Table 1.
###### Abstract  
Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this  
PV systems perform in real time so that they are capable of being optimized, managed, and modeled [2]. The single-diode model (SDM), double-diode model (DDM), and PV module model (PVMM) are typically used despite the existence of many mathematical models for PV nonlinearity. These models must include parameters that can change with environmental changes, faults, and aging [3]. Thus, regardless of the model used, it is essential to accurately determine unknown parameters as early as possible by using a robust optimization algorithm. Therefore, developing an optimization algorithm capable of accurately estimating the properties of PV models using the current-voltage measurements of the PV cell and module is imperative [4].  
An optimization problem can be established to extract PV cell and module parameters, which involves the formulation of an objective function and the establishment of a set of constraints. There is noise in the measured current-voltage data. There are, therefore, several local optima in the search space, resulting in a nonlinear and multimodal search space [5; 6]. Deterministic and metaheuristic algorithms are commonly used to solve this challenging optimization problem. The former method makes use of gradient information as well as the initial points. As a result, classical techniques are ineffective in identifying the parameters of photovoltaic models due to their nonlinear and non-convex nature [7; 8; 9]. There is a consensus that metaheuristic algorithms are more modern and easier to use than deterministic algorithms. Since then, there has been an increase in interest in metaheuristic algorithms for optimizing PV systems more efficiently and flexibly.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 1 Introduction
Additionally, many works related to the calculation of natural frequencies for plates and shells can be found in the literature; these use different numerical approaches, than the finite element method, and considered structures; see [PERSON] & [PERSON] [30] and [PERSON] & [PERSON] [31]. Both papers reported a semi-analytic finite element analysis of free vibration for thin circular isotropic cylindrical shells with variable thickness based on [PERSON]'s shell theory, but [PERSON] & [PERSON] [30] reported analysis of cantilevered cylindrical shells. [PERSON] & [PERSON] [32] presented a three-dimensional vibration analysis to determine natural frequencies of thick spherical shell panels with variable thickness using the Ritz method. [PERSON] & [PERSON] [33] determined natural frequencies and vibration modes of cylindrical shells with variable thickness based on the assumptions of [PERSON], [PERSON], and [PERSON] shell theories; the natural frequencies were found using the Wittrick-Williams algorithm. [PERSON] et al. [34] presented an exact series solution for the vibration analysis of circular cylindrical shells by means of the Fluge shell theory. [PERSON] et al. [35] performed free vibration analysis of variable thickness plates and shells using the Generalized Differential Quadrature method. The aim of this work is to implement a seven-parameter finite element formulation (with thickness stretch as the seventh degree of freedom) to determine natural frequencies and mode shapes of arbitrary isotropic shell structures with uniform and nonuniform thicknesses. In this formulation, a continuum element with the _hp_ spectral interpolation functions (see the text of [PERSON] & [PERSON] [36]) is used. The description of shell kinematics with seven parameters, higher-order spectral interpolation of all variables, and the use of three-dimensional constitutive equations avoids all types of locking. By means of _hp_ spectral interpolation functions, the errors in the numerical interpolation are reduced, achieving great accuracy and avoiding the Runge's effect (more details about this effect can be found in the works of [PERSON] [15] and [PERSON] [37]). The seven-parameter formulation was presented by [PERSON] & [PERSON] [14] for static nonlinear analyses of isotropic, FGM, and laminated composite shells, and [PERSON] & [PERSON] [38] extended the work to thermo-mechanical nonlinear analysis of FGM plates and shells. Numerical results obtained were compared with those available in the literature for constant thickness formulations and with those obtained by means of the commercial code ANSYS.
## 3 Monitoring Data Collection and Preparation
### Climatic Variables  
Climatic variables are strongly influencing any landslide behavior. For that purpose, temperature, precipitation and insolation data from the El Hierro airport [52] were downloaded. The location of this station is not directly on the SAL. However, it is in its vicinity and provides daily undisturbed data for the whole monitoring period. Precipitation can play a principal role in landslide activation. For that purpose, the daily rainfall amounts were analyzed. Additionally, the 30-day antecedent precipitation index (API\({}_{30}\)) [53,54] was calculated to better depict the influence of retrospective precipitation history and its influence on soil moisture [55]:  
\[API_{n}=\sum_{i=0}^{n}\Bigl{(}c^{i}\cdot P_{n-i}\Bigr{)} \tag{1}\]  
where \(n\) is the number of antecedent days; \(c\) is decay constant and it depends on watershed and seasonal parameters. The value of \(c\) ranges between 0.80 and 0.98 [56]. In this study, a \(c\) of 0.95 was used; \(i\) is the number of days counting backwards from the date on which the API is determined and \(P_{n}\) is the amount of precipitation \(i\) days before the causal rainfall (mm).  
Mean and maximum daily temperatures can influence both the monitoring instrument and the rock on which it is attached by thermal dilation [57]. For that purpose, the temperature data were used to observe influence on the monitoring records and assess seasonal trends.
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Licensed Shared Access  
In LSA, other than only between MNOs, non-MNOs can also share their spectrums with one or more MNOs. LSA differs mainly from the Authorized Shared Access (ASA) by the fact that ASA addresses mainly spectrum sharing between MNOs only. In this paper, we consider a satellite system license holder also known as the incumbent as a non-MNO, which shares its spectrum with MNO 1 as shown in Figure 7. Due to high external wall penetration loss of any building, we consider sharing the entire satellite spectrum to one transceiver and the spectrum of MNO 1 to the other transceiver of small cells of MNO 1 deployed in each building. Both the satellite and mobile spectrums are allocated orthogonally to all small cells. We assume that the multiband enabled SBSs can serve satellite UEs only at the satellite spectrum. However, small cell UEs can be served at both the satellite and mobile spectrums by SBSs within a building. The presence of any satellite UE inside a building causes co-channel interference with the SBSs, which is avoided according to the following interference management strategies as shown in Figure 5:  
* Multiband enabled SBSs serve small cell UEs at the satellite spectrum as follows. If a satellite UE is present inside a building, during non-ABSs \(t_{\text{non-ABS}}^{\text{SPS}}\in T\big{|}T_{\text{ABS}}^{\text{SPS}}\) of an APP, small cell UEs can be served and during ABSs \(t_{\text{non-ABS}}^{\text{SPS}}\in T_{\text{ABS}}^{\text{SPS}}\), satellite UEs can be served. However, if no satellite UE is present inside a building, in any TTI of an APP, small cell UEs can be served (Figure 5).
* Similarly, small cells UEs can be served by multiband enabled SBSs at the mobile spectrum as follows. At the presence of an indoor macro UE, during non-ABSs \(t_{\text{non-ABS}}^{\text{MNO},1}\in T\big{|}T_{\text{ABS}}^{\text{MNO},1}\) of an APP, small cell UEs can be served and during ABSs \(t_{\text{ABS}}^{\text{MNO},1}\in T_{\text{ABS}}^{\text{MNO},1}\), indoor macro UEs can be served. In the absence of an indoor macro UE within a building, small cell UEs can be served at the mobile spectrum in any TTI of an APP (Figure 6).  
Figure 6: Intra-operator spectrum sharing technique and co-channel interference management using the ABS based eICIC technique for sharing mobile spectrums of MNO 1 with the transceiver 1 of its multiband enabled SBSs.
# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
## 3 Analysis of RS Overhead
If \(N_{t}\) is more than 273/150, the RS takes more than the data signal, and it is not acceptable from the system performance point of view. Table 1 shows the maximum number of transmitter (TX) antennas in a given overhead of RS. As expected, the WOCR case takes more RS overhead than the WCR case. However, in the case of WCR, we need a fine uplink/downlink channel calibration technique, which is not an easy problem to solve. Figure 2: reference signa (RS) overhead (%) versus the number of transmitter (TX) antennas, \(N_{t}\) for 5 ms coherence time.