esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks
## 2 System Model
### Problem Motivation  
In the considered vehicular VLC/RF networks, handover occurs due to mobility or the interruption and reconnection of VLC links. In the case of LoS VLC link interruption due to blockage from large vehicles or the influence of bad weather, the VTM can perform VHO or early HHO to maintain connectivity and improve reliability, or just wait for link recovery. If the VLC-LoS link is recovered after VHO execution, the switching-back operation will have an increased cost and delay. The early HHO can prevent the extra signaling costs and avoid severe network latency, since VTMs are supposed to be horizontally switched to the next VLC AP when moving. The waiting strategy for VLC link interruption results in increased delays and queue length, though it saves the cost of switching. This raises the handover decision-making problem of choosing an operation from one of VHO, early HHO, and waiting, and thus is called a 'hybrid' handover scheme.  
We aim to find an efficient hybrid handover scheme that makes a handover decision taking the switching cost and delay requirement into consideration. Here, it is assumed that both additional signaling cost and handover delay will be induced during VHO and HHO, but the signaling cost for HHO is set to 0 since will HHO take place sooner or later when the vehicle moves. This hybrid handover problem of vehicular VLC/RF heterogeneous networks can be formulated as an MDP and solved suing a dynamic programming algorithm, as shown in Section 3.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 2 Principles of ROCOF and VVS Relays
### ROCOF Relay  
When the DG unit is running in parallel with the main power grid, the frequency is governed by the main grid. When the main grid is lost, the power imbalance between load and generation could cause the change of frequency. The rate of change of frequency can be expressed as [20]:  
\[\frac{df}{dt}=-\frac{P_{L}-P_{G}}{2H\cdot S_{GN}}f_{r} \tag{1}\]  
where \(P_{L}\) is the total load of DG; \(P_{G}\) is the total active power output of DG; \(S_{GN}\) is the rated capacity of DG; \(H\) is the inertia constant of DG; and \(f_{r}\) is the rated frequency.  
Equation (1) assumes that the machine is running at rated frequency and the time interval is short enough that AVR and governor dynamics can be ignored. If the measured rate of change of frequency after the islanding exceeds the threshold of ROCOF relay, it will operate and the islanding condition is detected. When the DG capacity closely matches the local load, the rate of change of frequency is very small. So inherently there are NDZs for ROCOF relay. Besides, some system events, such as normal heavy load switching, fault clearance or transient voltage disturbance, can cause significant transient frequency fluctuations and on occasions the _df/dt_ value can exceed the ROCOF relay setting. To enhance the reliability, normally a short time delay (such as 0.5 s) is applied for the ROCOF relay. However, it has also reported that ROCOF relay maybe become ineffective when time delay is applied [18].
## 3 Results
### Simulation and Evaluation of Vegetation Photosynthesis Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{_pseudo-\(R^{2}\)_} & **RMSE** & **RMD** & \multicolumn{2}{c}{**GPP\({}_{\text{EC}}\)****a**} & \multicolumn{2}{c}{**GPP\({}_{\text{VPM}}\)****a**} & \multirow{2}{*}{**N**} \\  & & & **(\%)** & & **(\%)** & & **(g C m\({}^{-2}\))** & & **(g C m\({}^{-2}\))** \\ \hline GPP\({}_{\text{VPM}}\) & 2009 & 0.72 *** & 25.09 & \(-1.02\) & 1068.51 & 1057.64 & \(-1\%\) & 26 \\ vs. & 2010 & 0.80 *** & 25.47 & \(-1.00\) & 1102.84 & 1091.76 & \(-1\%\) & 27 \\ GPP\({}_{\text{EC}}\) & 2009–2010 & 0.73 *** & 25.29 & \(-1.01\) & 2171.35 & 2149.39 & \(-1\%\) & 53 \\ \hline \hline \end{tabular}  
*** Significant at probability levels of 0.0001. * Abbreviations: GPP, gross primary production; GPP\({}_{\text{EC}}\), estimated GPP from FLUX tower data; GPP\({}_{\text{VPM}}\), simulated GPP by VPM; RE (relative error) = [(GPP\({}_{\text{VPM}}-\text{GPP}_{\text{EC}})/\text{GPP}_{\text{EC}}\)] \(\times\) 100%.  
\end{table} 
Table 1: The evaluation of model simulation results in the typical coastal wetland. Four statistical criteria for the validation tests: pseudo-\(R^{2}\), the coefficient of determination; RMSE, the root of mean square error; RMD, the relative mean deviation; and, RE, the relative error. Figure 7: Comparisons of the seasonal dynamics between the estimated gross primary production from EC tower (GPP\({}_{\text{EC}}\)) and simulated gross primary production by VPM (GPP\({}_{\text{VPM}}\)) during 2009–2010. Spring: March to May; Summer: June to August; Autumn: September to November; and, Winter: December to February.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Convolutional Neural Networks to Spectrum Sensing
#### 3.1.3 Residual Network  
Residual network is a variant of a convolutional neural network, and was proposed by [PERSON] et al. in 2015 [64], aiming to solve the vanishing gradient problem in deep networks due to the inability to pass the data effectively. That is, modules of residual connections and residual blocks are introduced on top of the CNN to allow the network to selectively pass the residuals or bypass some layers, thus alleviating the vanishing gradient problem and also being able to better train the network. The residual block diagram is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Author** & **Year** & **Input** & **Model** & **Detection Probability** & **Advantage** \\ \hline [PERSON] et al. [56] & 2022 & Sample covariance matrix & CM-CNN & 93\% (\(-14\) dB, Pf = 0.1) & The proposed scheme can automatically learn more discriminative features. \\ \hline [PERSON] et al. [57] & 2022 & Covariance matrix of the signal & CNN-SVM & 97\% (\(-14\) dB, Pf = 0.4  
Recently, residual networks have been used by several authors in the field of spectrum sensing and combined with additional techniques to achieve respectable results, as detailed in the next section.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Result and Analysis  
This subsection presents a comprehensive performance analysis of Flame-SeaFormer, encompassing both quantitative and qualitative evaluations. Firstly, the segmentation results of Flame-SeaFormer and the baselines are shown from the global and local perspectives of flame burning. The results reveal that Flame-SeaFormer exhibits superior suitability for the pixel-level flame segmentation task. Secondly, the case study section clearly shows the visualization results of the Flame-SeaFormer model on multi-scale, small-scale, and negative samples. Then, the Flame-SeaFormer method is employed to estimate static flame parameters, such as the flame height, width, and area, in individual fire images. Finally, dynamic flame parameters, the change rates of the flame height, width, and area, are analyzed based on the sequence of flame-burning images to achieve multi-scale flame situation detection and analysis.
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 3 Optisystem Simulation Tool for Indoor VLC System
### LOS Propagation Model  
As illustrated in Figure 1, our simulation of the LOS propagation model considers a direct path from a single LED in an indoor environment.  
From Figure 1, the indoor space is 10 m long, 10 m wide, and 10 m high. A photodiode is used as the detector at the receiving location, with one LED acting as the transmitter. The angles of incidence (\(\theta_{d}\)), irradiance (\(\theta_{s}\)), and transmitter-receiver distance (\(l\)) are defined as follows.  
\[l=11m=\{\theta_{s}=22^{\circ},\theta_{d}=25^{\circ}\} \tag{1}\]  
Figure 2 depicts an Optisystem simulation of the LOS propagation model. The simulation model generates a Pseudorandom data sequence with a bitrate of \(2.5\times 10^{9}\) bits/s. The generated data are converted to NRZ electrical pulses, and these signals directly derive the LED. A connection between the transmitter and the receiver is made through a diffuse channel. At the receiving end, the simulation uses a PIN photodiode as the detector, and a lowpass Bessel filter filters out the detected electrical signal from the photodiode. This filtered signal is regenerated using a 3R regenerator component which analyzes the bit error rate (BER) and Q factor in the BER analysis. An oscilloscope visualizer is used on the transmitter and receiver sides to check the shape of input and out signals. Table 1 shows the components specification used in the Optisystem simulation tool.  
Figure 1: Experiment design for LOS propagation model. The experiment was 10 m long and considered a single LED and photodiode.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**LED** & **Specification** \\ \hline Frequency & 550 nm \\ Electron life time & \(100\times 10^{-12}\) s \\ RC time constant & \(100\times 10^{-12}\) s \\ Slope efficiency & 0.5 W/A \\ Quantum efficiency & 0.65 \\ Spectral line profile & Gaussian \\ Bandwidth & 6 THz \\ \hline
**Bit Sequence Generator** & **Specification** \\ \hline Bit rate & \(2.50\times 10^{9}\) \\ Bit sequence & 100 \\ Non-zero bit locations & 1 \\ Number of leading zeros & (Time window \(\times\) 3/100) \(\times\) Bit rate \\ Number of trailing zeros & (Time window \(\times\) 3/100) \(\times\) Bit rate \\ \hline
**NRZ Pulse generator** & **Specification** \\ \hline Rectangle shape & Exponential \\ Format for pulse range & Min/Max \\ Maximum & 1 a.u \\ Minimum & 0 a.u \\ Amplitude (wrt DC) & 1 a.u \\ DC bias & 0 a.u \\ Rise time & 0.05 bit \\ Fall time & 0.05 bit \\ \hline
**Diffuse Channel** & **Specification** \\ \hline Detection surface area & 1 mm\({}^{2}\) \\ Optical concentration factor & 1 \\ Index concentration factor & 1.5 \\ propagation delay & 0 ps/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components specification used in the Optisystem simulation tool.  
Figure 2: LOS propagation model simulation environment in Optisystem.
## 2 Quasi-Resonance Circuit
### Constraints
During this cycle, energy flows from the source to the inductor while the transistor is conducting. Once the transistor turns off, the energy stored in the inductor is released and subsequently transferred to the load (usually a metallic object) during the oscillation phase. This transfer mechanism is central to achieving efficient induction heating. The effectiveness of this energy transfer is significantly influenced by the coupling between the coil winding (the primary side) and the metallic load (the secondary side). For optimal performance, the system is designed to function as an \"imperfect transformer\", allowing a considerable amount of the magnetic flux generated by the primary coil to interact effectively with the load, maximizing energy transfer. In the above equation, \(T_{on}\) is the same as \(\Delta t\), and \(T_{off}\) is the time taken for a complete energy transfer to load, which, with a good approximation, the oscillation period can be substituted for. Also, as we will show later, during the operation of the system, the energy of the inductor is much higher than the energy of the capacitor, and therefore, by ignoring it, and with the help of Equation (3), the above can be rewritten and simplified as follows:  
\[W=\frac{\frac{1}{2}L\left(\frac{V_{bus}}{L}\Delta t\right)^{2}}{T_{on}+T_{res}} \tag{9}\]  
Equation (9) takes this analysis a step further by simplifying the relationship by neglecting the energy contribution from the capacitor. In many high-frequency applications, the energy stored in the inductor often far exceeds that of the capacitor, making it a dominant factor in the system's energy dynamics. This simplification allows for a clearer understanding of the system's performance without the additional complexity introduced by the capacitor's behavior. Based on Equations (7) and (9), the \(V_{CE,max}\) of the transistor is limited by \(\Delta t\), but on the other hand, the power is directly increased by raising \(\Delta t\), so the maximum power that the device can transfer is limited by the breakdown voltage of the transistor. For instance, if the breakdown voltage is considered to be 1200 V and \(V_{BUS}=220\) V, then by introducing C = 440 nF and L = 80 uH, for the maximum power, the device can roughly handle 3.5 kW [21]. \[ \begin{array}{l}V_{V,max}+220V=1200V\\ \sqrt{\frac{L\left(\frac{V_{bus}}{L}\Delta t\right)^{2}+CV_{bus}^{2}}{C}}=980V \\ \Delta t_{max}=25 ms\to W_{\max}\approx 3.5 kW\end{array}  \tag{10}\]  
The breakdown voltage of the transistor is a critical parameter that dictates the operational limits of the system. If the \(V\)CE, max surpasses this threshold, it may lead to transistor failure. Consequently, effective power management becomes essential. While increasing \(\Delta t\) can enhance the energy transferred to the load, the maximum power that can be handled by the system is constrained by the breakdown voltage of the transistor.
# Spectrum Demand Forecasting for IoT Services
## 5 Study Case Implementation: Colombia
### Market Study: Technologies and Services  
A list of technologies was studied according to all criteria described in Section 3.1 (proximity, ease of use, business model, bands, etc.). The list of technologies can be found in Table 1. After finding a complete list of technologies, a thorough list of services was created, researching through all types of IoT services around the world. These services were focused on Colombia's economic activity. A key important step is to relate technologies with services. Some of these services (depending on their nature or the carrier technology) will not only need an access link, but also a transport link that could be wireless. If this is the case, some spectrum should also be reserved for this traffic.  
Figure 1: Flow diagram for IoT spectrum calculation methodology.  
The list of all services related to their possible carrier technologies, their transport link, and the main direction of communications per scenario is presented in Table 1.
## 3 Materials and Methods
### Absolute Coordinate Estimation of the Target Combined with Camera Attitude
The conversion process between point \(P_{ECEF}(X_{ECEF},Y_{ECEF},Z_{ECEF})\) in the ECEF coordinate system and point \(P_{\text{G}}(B,L,H)\) in the WGS-84 coordinate system can be obtained through iterative calculations. The conversion relationship here is the following:  
\[\left\{ \begin{array}{l}L=\arctan\frac{Y_{ECEF}}{X_{ECEF}}\\ B=\arctan\frac{Z+Ne^{2}\sin B}{\sqrt{X_{ECEF}^{2}+Y_{ECEF}^{2}}}\\ H=\frac{Z_{ECEF}}{\sin B}-N(1-e^{2})\end{array} \right. \tag{16}\]  
In the equation, \(N\) represents the radius of curvature in prime vertical, \(e\) is the first eccentricity of the reference ellipsoid, and \(B\) is the iterative quantity. Initially, the geodetic latitude \(B\) is computed based on the initial latitude \(B_{1}\). Subsequently, \(B\) is updated iteratively until the convergence condition is met, resulting in the desired latitude and longitude coordinates. These steps will provide us with accurate positional information for the target on the Earth. \[B_{1}=\arctan\frac{Z}{\sqrt{X^{2}+Y^{2}}} \tag{17}\]
###### Abstract
[PERSON] [17] proposed a marker-free displacement measurement method based on background segmentation and DIC technology, and results showed that the measurement was accurate and strongly illumination-invariant. [PERSON] [18] proposed a marker-free dynamic displacement measurement method to measure the large-span beam vibration displacements and modals, and the results were compared and verified with Linear Variable Displacement Transducer (LVDT) measurements. [PERSON] [19] proposed a multi-point structural dynamic monitoring framework combining marker-free feature tracking and optical flow tracking methods and conducted displacement extraction and modal identification through large-scale indoor and outdoor structural experiments. The ability to measure and extract structural displacements is closely related to the density of marker placement. Sparse markers on large structures can result in insufficient density of extracted feature points, leading to incomplete spatial field displacement information and inadequate precision in structural displacement measurement. Additionally, feature mismatches can also affect measurement results. Insufficient feature point quantity and feature mismatches are two core challenges in machine-vision-based dynamic displacement measurement of structures. The accuracy of structural displacement measurement is closely related to the density of marker placement, so sparse markers on large structures will result in insufficient density of feature points and cannot ensure accurate full-field displacement measurement. In addition, feature mismatches inducing measurement error can also affect measurement accuracy. Insufficient feature point quantity and feature mismatches are two core challenges in machine-vision-based full-field structural dynamic displacement measurement. To address these issues, this paper proposes a full-field cantilever beam dynamic deflection measurement method by dense feature extraction and matching with dual-step mismatch removal (DFEM-DSMR). The structure of this paper is outlined as follows: Section 2 introduces the theory of dense feature extraction and matching by Scale-Invariant Feature Transform (SIFT) and the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm, and the analysis of the causes of feature mismatch is presented. Section 3 elaborates on the full-field dynamic displacement measurement of structures by dense feature extraction, matching, and a dual-step mismatch removal method combining Random Sample Consensus (RANSAC) and Structural Displacement Continuity Constraints (SDCCs). Section 4 presents the indoor cantilever beam dynamic deflection measurement experiments by the proposed method and the validation with commercial DIC software. Finally, the paper is the concluded and summarized.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 4 Evaluation Framework
### Datasets
#### 4.1.3 MS COCO--Common Objects in Context  
COCO [57] is an extensive dataset suitable for tasks such as object detection and semantic image segmentation. It contains 328,000 images. From the total amount of images, more than 82,873 are specified for training, around 41,000 for validation and over 80,000 for testing.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 3 Simulation Validation
### Structural Model and Damage Simulation
The first 100 sets were used as training samples, and the rest of them were used as test samples. For four damage cases, a total of 100 \(\times\) 4 = 400 measured data sets were created for training, along with the same numbers measured data sets for the test.
## 1 Introduction
### Necessary Elements for QoS Multicast  
The functionality of the various protocols can be isolated to a few common elements, namely QoS routing, call admission, resource estimation, resource reservation, and preemption. We will explain in detail these elements in Section 2. Although, the survey describes them sequentially, these elements cannot be organized into a fixed sequence. Different protocols will order them differently or combineseveral of them into one operation. For example, many of the protocols based on reactive multicast routing combine QoS routing, resource reservation, and call admission.  
All the schemes need to identify potential segments of a distribution tree that may have resources or capabilities sufficient to meet the requirements (QoS routing). The segments will be used to search for the best path that can admit the session or admit the combination of session and source. If there is at least one potential segment, admission control determines whether source, relaying node or receiver can be connected to the distribution mechanism. Relaying nodes may occupy part of the resources in neighboring nodes, therefore the impact which relaying nodes make on other relay nodes, sources or groups must be assessed.  
Depending on the forwarding model, resources may be reserved on a per source or per multicast group. We make the distinction between admission control and resource reservation although these elements are combined in several protocols; admission control is implicit when resource reservation is successful. However, the resource reservation is not necessary. It is sufficient to assess whether a new source or relay does not have a decremental impact on existing sources and/or relays. Admission control and resource reservation are not alternative elements; the mesh based protocols tend to separate the two [11, 15, 16, 18, 37, 42]. They will admit sources and relays, but the resource reservation will only be done for a subset of the relay nodes [11, 15, 16, 18, 37, 42].  
Both admission control and resource reservation rely on the estimation of available resources and the estimation of required resources (consumed resource estimation). The complexity of the estimation depends on the capabilities of underlying MAC and physical layer.  
The last necessary element is preemption. In a wireless ad hoc network all resources are stochastic entities. Random changes in the conditions of the radio channel or movement and changes in topology may affect the capacity. Earlier admission decisions may therefore result in oversubscription. The oversubscribed traffic must then be rejected or their reserved resources must be released. An explicit part of preemption is monitoring QoS or resource availability. Few protocols have implemented explicit preemption. Instead, preemption is performed as periodic admission control and resource reservation.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 3 The Data Storage and Exchange Method Based on Superscalar Pipeline
### Superscalar Pipeline Ping-Pong Buffer
#### 3.2.1 Data Exchange in Range Direction Based on Superscalar Pipeline  
When the range access is performed, as shown in Figure 9, the eight data points in green will be accessed from DDR3A, which belongs to two consecutive range directions, and is stored in the buffer RAM0 and RAM1 corresponding to DD3A. The eight data points in yellow will be accessed from DDR3B, which also belongs to two consecutive range directions, and is stored in the buffer RAM0 and RAM1 corresponding to DD3B. After the 16 data points in the four range directions are accessed, other data in the four range directions will be accessed by the operation in the range direction. After the data access of the four range directions in the sub-matrix is completed, the adjacent range direction sub-matrix is accessed by range direction. Repeat the operation until all the data of these four range directions are accessed.  
The following four range directions' data will be accessed when DDR3A and DDR3B keep working simultaneously after the first four range directions' data are accessed and stored in the Ping RAM buffers corresponding to DDR3A and DDR3B. As shown in Figure 9, the eight data points in blue will be accessed from DDR3A, which belongs to two consecutive range directions, while the eight data points in orange will be accessed from DDR3B, which also belongs to two consecutive range directions. The four types of range data will be stored in the Pong RAMs corresponding to DDR3A and DDR3B, and form a pipeline design mode with the Ping RAMs corresponding to DDR3A and DDR3B. At this time, while the Pong RAM corresponding to DDR3A and DDR3B is buffering the four types of range data, the Ping RAM corresponding to DDR3A and DDR3B transfers the four types of range data that have been buffered to the compute core for processing.