esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalable Dew Computing
## 4 Results
### Hardwareless Computing  
Scalability is the essence of the \"infrastructureless\" or \"hardwareless\" concept, which is to relieve the user from taking care of required hardware resources and focusing on the application. The provider of computing resources is the one that knows better to manage the resources and can scale the system much faster to cope with the demand.  
The _serverless approach_ addresses the use of server instances, such as virtual machines, containers, or storage. The user just specifies the application to be a function that will be executed as a service. This concept is applicable for edge computing in the case when  
Figure 3: Horizontal scalability with centralized approach achieved by offloading to a master device on the same architectural level.  
the \"things\" (IoT devices) communicate with smart devices, or when the smart devices offload computing requests over the Internet to the edge servers, which is realization of the vertical scalability.  
The _deviceless approach_ applies the same \"less\" idea on devices instead of servers, requiring specific functions to be executed on a nearby device, resulting in a seamlessly integrated application execution infrastructure of IoT and smart devices. In this sense, it is an application of horizontal scalability, achieved by offloading functions to nearby smart devices in the same architecture level. Such a concept is also known as **Device as a Service**[30], since a specific device will execute a required function. Examples of smart devices include, at least, those installed in cars, and can be used while the car is in a parking lot using solar energy.  
The _thingless approach_ is a new approach providing a **Thing as a Service**[30], in the case that \"things\" as IoT devices may also be powerful enough to execute functions. The idea is to engage \"things\" to execute functions instead of smart devices or servers. It is a kind of vertical scalability but in the opposite (downwards) direction, instead of upwards.  
All these concepts implement scalable dew computing, especially when they work in the operational mode of an autonomous system without an Internet connection. For example, a dew device may find low battery power and would initiate a request to a nearby device to take care of executing functions. Finally, all these new concepts specify the real scenario of distributed computing resources in an ad hoc computing grid as a new computing trend.
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 4 Numerical Results
### Spectral Efficiency Performance  
As shown in Figure 5, the proposed method has a higher spectral efficiency compared with the CAP method, and the gap is becoming wider with the increase in pilot density. The reasons for this can be explained as follows. In the proposed method, data symbols around pilot symbols were first coded by the power multiplexing matrix in the transmitter, and then, the conventional CAP method was adopted to cancel intrinsic interference completely by sacrificing a few data symbols, whereas these sacrificed data symbols were recovered by the successive interference cancellation scheme in the receiver, contributing to a higher spectral efficiency compared with previous methods. Note that it is obvious that the proposed  
Figure 4: Frame structure adopted in simulations.  
method has a higher computational complexity because of two additional processes, i.e., power multiplexing and successive interference cancellation, but it is meaningful to harvest spectral efficiency improvements.
# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 4 Experiments and Results
### Data Generation Results
#### 4.1.2 SLD Dataset Distribution  
The augmented SLD dataset for the symbol detection model consists of a total of 4600 images with 12,700 samples, which is approximately 6.5 times the size of the original dataset. This dataset includes 690 delta, 560 motor, 689 generator, and 798 air circuit breaker images. The real and synthetic images were divided into three parts in the augmented  
Figure 12: (**a**) DCGAN-generated images of sizes 32 \(\times\) 32 and 64 \(\times\) 64. (**b**) LSGAN-generated images of sizes 32 \(\times\) 32 and 64 \(\times\) 64.  
dataset in a ratio of 6:2:2, which was also used to split the dataset for training, validation, and testing purposes. Figure 13 displays the distribution percentage of the samples.  
After data enhancement, a total of 4000 images were generated using the DCGAN and LSGAN. These images consisted of 610 samples of circuit breakers, 468 samples of switches, 600 samples of voltmeters, 687 instances of inductance, and 569 samples of CTs. Each symbol occurred in a well-balanced manner in the new dataset, and the generated samples were plentiful for training the model and achieving satisfactory results. This was demonstrated by the distribution proportion of original to newly produced samples for different symbol types, as shown in Figure 14.
# An Observer-Based Active Fault Tolerant Controller for Vehicle Suspension System
## 1 Introduction
In [25], a specific actuator failure for the three-tank system was first detected and confirmed by a fault diagnosis unit, the control law was then reconfigured based on the information of the detected fault. Both the stability and the acceptable \(H_{\infty}\) disturbance attenuation level were guaranteed for the closed-loop system using the remaining reliable actuators. Besides, a new FTC algorithm for an automotive air suspension control system has been developed in [26], where the fault detection, diagnosis, and management of a closed-loop air suspension control system are included to enhance the robustness of the fault detection and isolation against uncertainties. However, only the height sensor faults are considered and it is of high cost in practical engineering applications. The authors in [27] developed an FTC algorithm for vehicle active suspension systems in finite-frequency with a sine wave input fault and a new \(H_{\infty}\) FTC controller was developed based on a GIMC (Generalized Internal Mode Control) structure by ignoring the design of the fault diagnosing system. However, the fault model is less convincing and hard to be applied in reality. Thus, it is still a challenging issue to make the related FTC scheme in the active suspension system. Motivated by the aforementioned research and discussion, this paper investigates the problem of observer-based active fault-tolerant control design for a half-vehicle active suspension system subjected to the actuator fault as the nonzero offset fault. The system augmentation technique and Lyapunov stability theory were introduced to design the fault tolerant controller. The key contributions of this work are summarized as follows. First, a comprehensive control scheme of active fault tolerant controller was constructed for an active suspension system, which can not only deal with the desirable performance control for active suspension system without the actuator faults, but also achieve a way for ensuring the system performance when actuator faults occur. Second, a robust observer was designed to make an accurate estimation of the fault information with the auxiliary diagnosis system and to further develop an \(H_{\infty}\) fault compensation controller, such that the closed-loop control system can eliminate the negative effects of the actuator faults on vehicle suspension performances. Finally, some illustrative examples were provided to demonstrate the validity of the control scheme proposed in this paper. The rest of this paper is organized as follows: Section 2 presents system modeling problem formulation for active suspension system with the general faults. The proposed robust observer-based active fault-tolerant controller design is presented in Section 3. In Section 4, the simulation investigation is provided to demonstrate the effectiveness of the designed controller. The conclusions are given in Section 5.
## 4 Discussion
### Climatic Sensitivity of Vegetation Indices  
The sensitivity of monthly vegetation indices in April-September to monthly temperature and precipitation is heterogeneous for both the meadow and the steppe (Figure 5). Generally, the temperature played a key climatic role in driving the \"greenness\" (indicated by the vegetation indices) anomalies in the early growing season (before July). However, the contribution of precipitation increased and even a dominant role in the later growing season (July and August) (Figure 5). Temperature is commonly and widely considered as the key role to trigger the green-up of vegetation on the TP [29; 65; 66; 5]. Meanwhile, the sensitivity of green-up dates to preseason precipitation increases in drier areas and periods [34; 66]. Thus, the \"greenness\" during the early growing season for both the meadow and the steppe is highly correlated with temperature. However, during the middle and later growing season, high radiation and temperature may weaken the heat limitation, meanwhile high radiation and temperature-driven higher evapotranspiration directly increase the \"water stress\" [23; 67], which prompted temperature sensitivity to decrease but precipitation sensitivity to increase.  
Precipitation sensitivity of monthly vegetation indices nearly peaked in July for both the meadow and the steppe (Figure 5). Generally, July is the warmest month on the TP [68]; thus, precipitation-related water availability dominates the variations in vegetation growth and plant productivity [49]. Specifically, variations in the steppe vegetation indices in July were sensitive to precipitation and the contributions of temperature were quite limited (Figure 5), which may be the result of high temperature and radiation-induced water stress, and the coarser soil texture induced poor water-holding capacity [18; 50]. Moreover, we also found the relationship between July precipitation and the 28-100 cm depth soil moisture in August is significant (\(p<0.05\)) for the steppe (Figure 7g). It may be caused by the infiltration-induced lagged effect, and July precipitation may partly contribute to water availability in August, leading to the increase in temperature sensitivity of the vegetation indices in August for the steppe (Figure 5).  
Overall, changes in the intra-annual climate regimes are expected to change the supply of heat or water for plants and alter the climatic sensitivity of alpine grasslands. For example, in a temperate steppe in Mongolia, both advanced and delayed precipitation peaks suppressed annual soil respiration, plant growth, and soil microbial activities in the middle growing season [21]. The suppression effects were attributed to the lack of water supply in the middle growing season when large water is in demand. The response of GPP to precipitation monthly distribution is biome-specific, and the mismatch between water demand and supply generally leads to the different responses [23].
## 2 System Model of RSST-SR-DCSK
### Chaotic Signal Generator  
Chaos signals, due to their noise-like characteristics, are widely studied in the field of communication research as they offer excellent confidentiality when used as carrier signals. One popular method for generating chaotic signals in chaotic digital modulation systems is by utilizing chaotic system mappings. In this paper, we will focus on the statistical characteristics of chaotic signals using the second-order Chebyshev map as an example. The expression for the second-order Chebyshev map is as follows:  
\[x_{k+1}=1-2x_{k}^{2},\hskip 28.452756 ptx_{k}\in(-1,+1) \tag{1}\]  
Secondly, the probability density function is  
\[\rho(x)=\left\{\begin{array}{ll}\frac{1}{\pi\sqrt{1-x^{2}}},&x\in(-1,+1)\\ 0&\mbox{otherwise}\end{array}\right. \tag{2}\]  
Since \(\rho(x)\) is an even function, the mean \(x_{k}\) is  
\[E[x_{k}]=\int_{-\infty}^{\infty}x\rho(x)dx=\int_{-1}^{1}x\rho(x)dx=0 \tag{3}\]  
Similarly, it is easy to obtain  
\[E\Big{[}x_{k}^{2}\Big{]}=\int_{-\infty}^{\infty}x^{2}\rho(x)dx=\int_{-1}^{1} \frac{1}{\pi\sqrt{1-x^{2}}}x^{2}dx=\frac{1}{2} \tag{4}\]  
\[Var\Big{[}x_{k}^{2}\Big{]}=\int_{-1}^{1}\frac{1}{\pi\sqrt{1-x^{2}}}x^{4}dx-E^{ 2}\Big{[}x_{k}^{2}\Big{]}=\frac{1}{8} \tag{5}\]  
where \(E[\cdot]\) represents the mathematical expectation operator, and \(Var[\cdot]\) represents the variance operator.  
The second-order Chebyshev map displays different states in different parameter regimes, which correspond to the various ranges of chaotic state parameters in the map. When iterated with initial values of 0.4 and 0.4001, the time-domain plot of the second-order Chebyshev map is depicted in Figure 1. It is evident that even a slight change in the initial chaotic value results in a significant modification of the entire chaotic sequence.  
Figure 1: Time\(-\)domain diagram. (**a**) Second\(-\)order Chebyshev map \((x_{0}=0.4)\); (**b**) second\(-\)order Chebyshev map \((x_{0}=0.4001)\).
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 3 Results  
### Flooding  
A total of 197 changes attributed to the \"Flooding\" (F) group were highlighted, for a total of 32.10 km\({}^{2}\). These changes are distributed throughout the whole extent of the riverbed of the Rimac River and, as expected, also include the areas that have been affected by the flooding events occurred in the years 1997-1998 and 2017-2018.  
The 197 changes identified in the study area were delimited by the use of polygons, which were divided into three categories described in Table 5. Their spatial distribution is shown in Figure 7.  
In the following sections, change patterns are discussed and interpreted according to the above categorization. For convenience and to avoid redundancies in figure legends, the same color coding is used for all the RGB composites and ratio maps presented hereinafter, according to the representation scheme described in Figure 5. Each figure is aimed to show a specific pattern, and not to be comprehensive of all the SAR backscatter change patterns displayed in the whole extent of the figure. Therefore, it should not be assumed that the neighbouring patterns were not interpreted, were excluded arbitrarily, or were not considered as relevant changes despite their similarity of appearance with the pattern that is highlighted in the figure and is commented in the respective caption.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category Code** & **Description** & **Numbers of Changes** & **Area (km\({}^{2}\))** \\
**Flooded areas (F1)** & Areas that have been affected by the flood events in the years 1997–1998 and 2017–2018. & 15 & 18.90 \\ Riverbed flooded by increased water flow (F2) & Areas of the riverbed of the Rimac River which, due to an increase in its flow rate, have been submerged totally or partially. & 175 & 12.70 \\ Riverbed areas of the Rimac River (bordering & & & \\ Riverbank collapses and damage (F3) & the banks) which, as a consequence of the increase in its flow rate, have been totally or partially removed, causing damage to infrastructure or houses. & 7 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Description of the categories of changes related to the studied flood events.
## 4 Results
### Comparison to a Map-Based Navigation Approach  
We further compare the result of our learning approach to a full perception and navigation pipeline provided by NVIDIA Isaac SDKTM [21], which is deemed as a baseline approach. This baseline application is specifically designed for the load carrier docking task. In contrast to our solution, the baseline uses a global map for multi-LiDAR Localization of the robot. The target pose for the robot is determined by object detection followed by 3D pose estimation of the dolly. In the baseline application the camera resolution is 720 \(\times\) 1280 and the number of the LiDAR beams used for localization is 577 per sensor, which have a maximum detectable range of 20 m. The used mobile robot except for the camera and the LiDAR resolution, remains the same.  
To find the goal position for the robot, the pose of the dolly is inferred from the frontal facing camera of the robot. This poses a constraint that the dolly has to be detected in the input image. For the baseline approach, this is done by using DetectNetv2 [69], which was pre-trained on real images and then fine-tuned for dolly detection by using randomized images of the dolly, created with IsaacSim Unity3D [21]. DetectNetv2 generates a 2D axis-aligned bounding box for a detected dolly. The detected bounding box is used to create a cropped image of the dolly, which forms the input into a pose estimation model. In this case, the pose estimation model is based on PoseCNN [70]. The output of the PoseCNN network is an estimated orientation and translation of the dolly. Given an image of the dolly, the perception pipeline estimates the 3D pose of the dolly. This pose is transformed into the global coordinate-frame and serves as a target pose. Then, a local planner based on the linear quadratic regulator navigates the robot under the dolly.  
We also conduct the same grid testing as mentioned in Section 4.2 for the baseline approach. Since the baseline approach enforces that the target dolly must be captured with a RGB camera, it is only possible to show the result with the initial orientation angle of 0\({}^{\circ}\) and \(\pm\)45\({}^{\circ}\). The baseline method achieves the success rate of 100% in the 0\({}^{\circ}\) orientation case under the condition that the displacement on x-axis remains below 1 m, and the distance in y-direction remains below 5 m, which is illustrated in Figure 10. However, the baseline approach proves incapable of performing the docking maneuver once the distance on the x- or y-axis surpasses the mentioned limitations. In the \(\pm\)45\({}^{\circ}\) case, only few positions are solved successfully, all of which provide full visibility of the dolly. The \(\pm\)45\({}^{\circ}\) cases require y-displacements between \(-\)2 m and \(-\)4 m for successful docking maneuvers. From Table 3, it can also be witnessed that the baseline approach achieves overall much worse performance compared to all other DRL variants in all initial orientations. As a conclusion, our learning approach definitely outperforms the baseline with larger initial orientation and distances to the target.
# An Improved Blind Recognition Algorithm of Frame Parameters Based on Self-Correlation
## 5 Simulation  
In the algorithm simulation experiments, the frame length is 1024 bits while the frame synchronization word sequence is 0 xf628. The length of sliding window and frame initial values are 0 and 500 respectively. When the BER is 0, the result of the frame parameters recognition algorithm based on the self-correlation is shown in Figure 4. When the frame estimation is right, apparent self-correlation peaks appear at the head of the frame (Figure 4a). When the frame estimation has a fault, the sum of each column of the storage matrix \(Y_{sum}^{*}\) is distributed randomly and there is no apparent peak (Figure 4b).  
The two straight lines paralleling to the X-axis are the upper and lower threshold. From Figure 4a, the frame synchronization word can be recognized, which is 0 xf628.  
Assume that the bit stream is transmitted by QPSK system with BER [13]. The recognition accuracy rate is defined as the ratio of the times of recognizing correctly to the total times. 100 simulations are performed at each BER level. After 1000 algorithm simulations, the recognition accuracy rates of algorithms in Sections 3 and 4 are demonstrated in Figure 5.  
Figure 4: This is the result of \(Y_{sum}^{*}\) when BER is 0. (**a**) The peak appears when the frame estimation is right. (**b**) No peak appears when the frame estimation is fault.  
As the result shows, when the BER is over 0.03, the recognition accuracy rate of the unimproved algorithm is lower than 0.8, while that of the improved algorithm is 1. When the BER increases to 0.1, the unimproved algorithm can hardly recognize the frame parameters. However, the improved algorithm in Section 4 still works at the recognition accuracy rate of 0.43.
## 2 Solar PV Modeling and Characteristics  
### PV Cell Model  
PV cell is a fundamental unit that uses the photovoltaic effect to turn solar energy into electrical energy. When exposed to sunlight, the \(PV\) cell generates electrical energy. The equivalent circuit of a typical \(PV\) cell contains series and shunt resistances, as presented in Figure 1. The single diode model is the most accurate of the many PV cell modeling methods. The _KCL_ can be used to obtain the \(PV\) cell output current, \(I_{PVC}\)[78] as follows:  
\[I_{PVC}=I_{LC}-I_{d}-\frac{V_{PVC}+R_{S}*I_{PVC}}{R_{SH}} \tag{1}\]\(I_{LC}\) stands for light generated current (\(A\)), I\({}_{d}\) for diode current (\(A\)), \(V_{PVC}\) for photovoltaic cell voltage (V) and \(R_{S}\) and \(R_{SH}\) are the series and shunt resistances of the \(PV\) cell (\(\Omega\)).  
When a PV cell is exposed to insolation and ambient temperature, the light-current, \(I_{LC}\), is expressed as follows:  
\[I_{LC}=\frac{G}{G_{ref}}\Big{[}I_{LCref}+\mu_{sc}\Big{(}T_{cell}-T_{ref}\Big{)} \Big{]} \tag{2}\]  
\(T_{cell}\) and \(T_{ref}\) are the cell and standard temperatures in Kelvin (K), \(G\) for irradiance (w/m\({}^{2}\)), and \(\mu_{sc}\) for the temperature coefficient.  
Additionally, the diode current \(I_{d}\) is  
\[I_{d}=I_{o}\Big{(}e^{\frac{V_{PVC}+R_{S}+I_{PVC}}{V_{t}}}-1\Big{)} \tag{3}\]  
where \(V_{t}\) denotes thermal voltage (V).  
The Io diode saturation dark current is proportional to temperature can be expressed as  
\[I_{o}=I_{o,ref}\left(\frac{T_{cell}}{T_{ref}}\right)^{3}\exp\left[\left(\frac{ q\epsilon_{G}}{A.K}\right)\left(\frac{1}{T_{ref}}-\frac{1}{T_{cell}}\right)\right] \tag{4}\]  
A standard PV cell only produces 0.6 V, which is extremely low, so many cells are usually connected in series and parallel to form a PV module (panel) to raise the voltage to a level appropriate for the application, as shown in Figure 2. A typical rooftop solar panel has 60 cells, resulting in a 36 V open circuit voltage. To increase the system voltage, multiple panels (or modules) are connected in series for larger systems [79]. Parallel connections of modules can be present in an array of multiple solar panels, but parallel connections make sense only if the voltage is small. A series connection raises the voltage while holding the current constant, while a parallel connection raises, the current while keeping the voltage constant. Series connection is more effective than parallel connection because losses increase as the current increases.  
Figure 1: PV cell model.  
Figure 2: From solar cell to array.