esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Strategies for Sensor Fault Diagnosis
### Centralized Strategy  
The most common solution in agricultural sensor fault diagnosis is a centralized approach. The central node or base station diagnoses each sensor node's condition. The central node's limitless resources (such as batteries and computing power) lengthen the lifespan of agricultural WSNs [78]. A trained fault diagnosis model is stored in the central node that periodically sends requests to the network to detect the state of the entire sensor network and diagnoses and locates faulty sensors after analyzing agricultural sensor data. [PERSON] et al. [65] proposed a centralized fault detection method for WSNs based on the Naive Bayes framework to detect sensor battery issues; the diagnosis process was not performed in each sensor node, thereby reducing battery load. [PERSON] et al. [79] proposed a centralized policy sensor fault scheme based on SVM that performed fault diagnosis at the cluster head and utilized fewer sensor resources.  
This method has minimal hardware requirements for agricultural sensor nodes, requires no additional computing and memory resources, and extends the sensor's service life. However, a large amount of information is sent to the central node, which causes network congestion and slows down detection speed; due to fault diagnosis at the central node, it is difficult to meet real-time requirements; in the process of data transmission to the central node or base station via multiple hops, the pressure on the sink node increases, consuming the sink node's resources.
# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 1 Introduction
The aim of this paper is thus limited to developing a VEN with a low-cost automated DRC that can non-invasively measure current and voltage, calculate real power, directly control load switching, and control home appliances with the function of infrared remote control by emitting infrared remote control codes, and use Power Line Communication (PLC). The main contributions of this paper are summarized as follows. 1. Facilitation of Demand Response Participation: The designed DRC enables existing infrared-controlled air conditioners to participate in automated demand response programs (ADRPs), enhancing demand-side management and significantly reducing demand;
2. Low-Cost Solution: The research focuses on creating a cost-effective DRC that can be easily implemented in homes and buildings, making it accessible to a broader range of users;
3. Non-Invasive Load Power Calculation: The DRC calculates load power non-invasively, allowing it to monitor and manage energy consumption without requiring invasive modifications to existing systems;
4. Power Line Communication (PLC): PLC for communication eliminates the need for additional wiring, simplifying the installation process and making it easier to integrate with existing infrastructure;
5. Energy Savings Demonstrated: The system was tested under various conditions depending on the cooling/fanning duty cycles, showing its effectiveness in energy reduction when participating in the OpenADR program.
## 5 Conclusions, Key Issues, and Concerns  
In conclusion, Gen AI holds significant potential for addressing challenges and barriers in healthcare, offering innovative solutions in various areas. From the review, we found some studies addressing how AI can help solve these issues:  
Data accessibility and integration is addressed by aggregating and harmonizing disparate data sources, creating structured datasets from unstructured medical records and images. AI systems can create frameworks that integrate data from various electronic health record (EHR) systems, medical devices, and other health platforms, improving interoperability between systems [2, 24, 25]. Customized treatment plans are generated based on the genetic information, patient history, and lifestyle data to recommend personalized treatment options and custom therapies for diseases like cancer, diabetes, or rare genetic disorders. Healthcare data privacy and security are addressed by Gen AI models through data synthetization using synthetic datasets that mirror real patient data without exposing actual patient information, enabling research and AI training without compromising privacy. Moreover, applying AI-driven encryption techniques and federated learning models ensures that sensitive data are processed in a decentralized manner, without transferring patient data outside secure environments [17, 19, 23, 26, 31]. Figure 2: Key action points for a Gen AI in personalized patient care framework. Key issues and concerns related to the use of Gen AI in healthcare are summarized in Table 3. Addressing these issues requires a holistic approach involving collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare [2; 5; 22; 26; 29].
## 3 Results and Discussion
### Infrared Spectroscopy Analysis  
The FTIR spectra of hirudin, CS/PCL MPs, and rH/CS/PCL MPs are shown in Figure 4. Among them, PCL is the most abundant and therefore its characteristic spectrum dominates, producing sharp peaks in the band region in the range of 1720-1740 cm\({}^{-1}\) due to carbonyl stretching vibration; the C-H stretching vibration peak of saturated hydrocarbons appears near 2940 cm\({}^{-1}\)[38]. The broad and absorption peaks at 3390 cm\({}^{-1}\) and 1044 cm\({}^{-1}\) are attributed to N-H and C-O in chitosan [39]. Comparing the IR spectra of hirudin in Figure 4, the two characteristic peaks of rH/CS/PCL MPs at 1678 cm\({}^{-1}\) and 3123-3692 cm\({}^{-1}\) were further enhanced due to the fact that hirudin is also rich in -NH and -C=C. This indicates that hirudin was successfully immobilised in CS/PCL MPs and that the amount of hirudin immobilised increased as the concentration of the hirudin solution increased [40].  
Figure 3: SEM image and diameter distribution of rH/CS/PCL MPs1 (**a–c**), rH/CS/PCL MPs2 (**d–f**), and rH/CS/PCL MPs3 (**g–i**).
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 5 Experimental Data  
In the operative system, the micro-Doppler signals are usually embedded in low signal-to-clutter ratio (SCR) and we should consider how to extract the features from the radar echo [31; 32]. In this section, an experiment was conducted to investigate the effectiveness and physical significance of decomposing micro-Doppler signals based on HHT-AMD.  
Experimental and simulated studies on micro-Doppler signatures of a human walking have been previously reported in literature. For humans observed by radar, micro-Doppler signatures are related to the motion of various parts of the body while a person is walking, running, or performing other movements. We take walking as a typical example of human motion that is comprised of many movements of individual parts.  
The measured radar echoes from a walking man with swinging arms are provided for characteristic analysis of micro-Doppler signals. The setup of the experiment can be seen in Figure 10a. Experimental testing was conducted with a human walking towards the radar to collect the return signals using an X-band CW (continuous wave) radar operating at 10.48 GHz. A sampling rate of 100 kHz was used to collect 100,000 samples in the time interval of 1 s. The SNRs are ranging from 10 dB to 40 dB related to the range between man and radar. The time-frequency signature of the radar echo is given using STFT in Figure 10b. The T-F distribution consists of the trajectories associated with different micro-motion components: the head, torso, arms, hip, legs, and foot from up to down.  
The human data was decomposed into seven IMFs and the residue by EMD. The TFD of the first three IMFs are shown in Figure 11a-c. We can see that, IMF1 signal shows the micro-Doppler signature of foot motion. The IMF2 and IMF3 signals have undistinguishable multiple components.  
Figure 11: Decomposition results of the HHT: (**a–c**) TFD of IMF1–IMF3.  
Figure 10: Walking human: (**a**) the setup of experiment; (**b**) TFD of the micro-Doppler signals.  
Work was done to further decompose the signals of IMF2 and IMF3 by HHT-AMD, and the results are presented in Figures 12 and 13. As expected, the IMF2 signals are decomposed into two signal components which represent the micro-Doppler signatures of the arms and legs motion. Furthermore, the motion directions of the arms and legs are opposite as apparently exhibited in the TFD. The IMF3 signals are decomposed into three signal components which are corresponding to the micro-Doppler signatures of the rest body parts motion of head, torso, and hip, respectively.
## 6 Applications of RF Energy Harvesting
### Space and Satellite Communications  
In space applications, RFEH offers novel approaches to powering satellites, space probes, and communication devices. Satellite systems can harvest energy from Earth-based RF transmissions or cosmic microwave background radiation to supplement their onboard energy storage, extending mission lifespan. A notable advancement includes the development of RF-harvesting modules that are integrated into CubeSats, providing an alternative energy source for low-Earth-orbit operations without increasing payload weight [104]. Furthermore, the concept of space-based solar power involves collecting solar energy in space, converting it into RF signals, and beaming it down to Earth, where ground-based harvesters can capture and convert it into usable electricity. This futuristic application is actively being researched as a solution for global energy sustainability [105]. Such innovations not only support long-term space exploration but also lay the groundwork for a sustainable extraterrestrial energy economy. A significant milestone in this field was achieved by the California Institute of Technology (Caltech) in 2023. Their Space Solar Power Demonstrator (SSPD-1) successfully demonstrated wireless power transfer in space using the Microwave Array for Power-transfer Low-orbit Experiment (MAPLE) technique. This experiment marked the first time that power was wirelessly transmitted in space and detected on Earth, validating the feasibility of space-based solar power systems [106].
## 2 Materials and Methods
### Clustering Methods  
After decomposing the signals using the CEEMDAN algorithm, the power spectral density (PSD) of each IMF is computed to identify the energy distribution characteristics of each signal across different frequency bands and their differences. The formula for calculating the power spectral density can be presented by Equations (17)-(19):  
The Fourier transform of the deformation time series \(x(t)\) can be represented as:  
\[X(f,T_{r})=\int_{0}^{T_{r}}x(t)e^{-i2\pi ft}dt \tag{17}\]  
where: \(f\) is the frequency, and \(T_{r}\) is the length of the deformation sequence. For a certain discrete frequency, it is represented as \(f_{n}\).  
\[X_{n}=\frac{X(f_{n},T_{r})}{\Delta t} \tag{18}\]  
Figure 3: The configuration of LSTM cell.  
where: \(f_{n}=n/(N\Delta t)\), \(n=1,2,\ldots,N\), represents the time interval, \(\Delta t\) represents the number of sampling points, \(N=T_{r}/\Delta t\). Then the power spectral density can be represented as:  
\[P_{n}=\frac{2\Delta t}{N}|X_{n}|^{2} \tag{19}\]  
Subsequently, IMF signals are clustered based on their energy distribution characteristics within different frequency ranges, along with their frequency peak characteristics. If the energy distribution and signal peak characteristics are similar, the IMF signals are grouped into similarity clusters; otherwise, no clustering is done. Finally, the clustered signals are combined to form a few reconstructed sub-signals of the original time series signal.
## 3 Simulation Results
### Description of the OpenDSS Model
Figure 6.0 Total feeder losses when different control strategies are applied (No DER—C0, No Cur (No curtailment)—C1, PF (C2)—Variable power factor, \(g_{d}=0\) (C3)—drooped positive-sequence control strategy, \(g_{d2}=50\) (C4) damping control strategy VUF\({}_{2}\) only, \(g_{d}=3\),\(g_{d}=10\) and \(g_{d}=50\) (C5–C7) drooped three-phase damping control strategy)  
power exchange. In summary, three-phase damping control strategy considered in case C7 droops 16.6 MWh/a compared to the reference case \(C1\) while the currently employed control strategy with reactive power exchange, curtails about 30 MWh/a. Finally, a comparison between all test cases is listed in Figure 6.1 The control strategies are rated from poor to excellent based on the results obtained in the presented simulations. They are also rated on four key performance indicators such as energy exchange, ability to control and maintain the grid voltage and voltage unbalance, as well as grid losses.
## 4 Description of the Physical System  
### Navier Stokes Equations  
The equations that describe the behavior of the continuous phase correspond to Navier Stokes equations. These equations model the mass and momentum conservation principles. Accordingly, considering that the fluid flow in the cyclone separator is stationary, tridi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Geometry**} & \multirow{2}{*}{**Symbol**} & \multicolumn{5}{c}{**Dimensions (mm)**} \\ \cline{3-6}  & & **Model A** & **Model B** & **Model C** & **Model D** \\ \hline Cylinder length & \(h\) & 435 & 435 & 435 & 435 \\ Conical length & \(hc\) & 725 & 725 & 725 & 725 \\ Inlet height & \(a\) & 145 & 145 & 145 & 145 \\ Inlet width & \(b\) & 58 & 58 & 58 & 58 \\ Cylinder diameter & \(D\) & 290 & 290 & 290 & 290 \\ Cone apex diameter & \(Dd\) & 109 & 109 & 109 & 109 \\ Vortex Finder Diameter & \(De\) & 145 & 145 & 145 & 145 \\ Inlet length & \(Li\) & 400 & 400 & 400 & 400 \\ Vortex Finder Upper Section & \(Le\) & 290 & 290 & 290 & 290 \\ Vortex Finder Lower Section & \(s\) & 145 & 145 & 145 & 145 \\ Pitch & \(t\) & \({}^{\rm a}\) Na & 203 & 30 & 203 \\ Cavity height & \(ac\) & \({}^{\rm a}\) Na & \({}^{\rm a}\) Na & \({}^{\rm a}\) Na & 29 \\ Cavity width & \(bc\) & \({}^{\rm a}\) Na & \({}^{\rm a}\) Na & \({}^{\rm a}\) Na & 29 \\ Cavity diameter & \(Dv\) & \({}^{\rm a}\) Na & 29 & 29 & \({}^{\rm a}\) Na \\ Distance from cyclone roof to cavity & \(c\) & \({}^{\rm a}\) Na & 160 & 160 & 160 \\ \hline \hline \end{tabular} a  
\end{table}
Table 1: Geometrical details of the Stairmand high-efficiency particulate separator cyclone and the new particulate separator cyclone models based on passive flow control techniques.  
mensional, highly turbulent, isothermal, and incompressible, the Navier Stokes equations could be expressed as:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\frac{\partial u_{i}}{\partial x_{i}}+u_{j}\frac{\partial u_{i}}{\partial x_{i} }=-\frac{1}{\rho}\frac{P}{\partial x_{i}}+v\frac{\partial^{2}u_{i}}{\partial x _{i}\partial x_{j}}-\frac{\delta}{\partial x_{j}}\Big{(}\overline{u^{\prime}_ {i}u^{\prime}_{j}}\Big{)} \tag{2}\]  
where \(u_{i}\) is the average velocity of the fluid, \(P\) is the average pressure of the fluid, \(x_{i}\) is the spatial direction, \(v\) is the kinematic viscosity, \(\rho\) is the fluid density, and \(\overline{u^{\prime}_{i}u^{\prime}_{j}}\) is the Reynolds stress tensor. In this study, Navier Stokes equations are solved by an Eulerian approach.
## 3 Case Study  
### Building Geometry and Structures  
Simulations were carried out in a room with a floor area of 100 m\({}^{2}\) (Figure 2). The windows are east and west oriented and their total surface is equal to 12.5 m\({}^{2}\). The other two walls are adjacent to flats at the same temperature of the simulated one, as well as the ceiling and the floor. The case study represents a flat in an intermediate floor of a multi-story building. No internal walls were considered, therefore the entire space was simulated as a single room. Point P, situated in the middle of the room at 1.10 m height from the floor, was considered for the evaluation of the operative temperature.  
The simulations were performed on an hourly basis, with the climatic TRY _De Giorgio_[35] of the city of Venice. In particular, the following hourly mean data were considered: air temperature, direct solar radiation, diffuse solar radiation and relative humidity, while the effect of wind on the infiltration was not taken into account. The official heating period defined by law for Venice has been considered, i.e. from mid-October to mid-April.  
Eight types of building structures have been considered (Table 1): one type of stock building with a U-value of the walls equal to 1.3 Wm\({}^{-2}\) K\({}^{-1}\), three types of insulated buildings with a U-value of about 0.5 Wm\({}^{-2}\) K\({}^{-1}\) and four types of well-insulated buildings with a U-value ranging from 0.15 to 0.20 Wm\({}^{-2}\) K\({}^{-1}\). The stratigraphies of the structures of each type of building, along with the thermal properties of each layer, are listed in Appendix A. Two kinds of windows were considered in this work, one for the buildings which are not insulated and a more performant one for all the other cases. The U-value of the window, the g-factor of the glass and the ratio between frame area and the entire window area are listed in Table 2. In order to have an overview of the room thermal characteristics, in  
Figure 2: The simulated room considered as case study.  
Table 1 also the time constant \(\tau\) of the external walls and the heat capacity Cm calculated according to EN ISO 13786 [36] are listed.
## 2 Basis of the QuEChERS Method
### Modifications in the Clean-Up Sorbents
In this sense, they can be specifically designed to display different chemical properties in adsorption processes, such as the clean-up step in the QuEChERS procedure. In this work, two ordered mesostructured silicas were prepared, one of them without surface modification (LP-MS) and other with its surface modified with amino groups (LP-MS-NH\({}_{2}\)\({}^{+}\)) [28]. Both materials were evaluated as clean-up sorbents and compared with PSA under same conditions. The results revealed that in general matrix effects were stronger with PSA than with the ordered mesostructured silicas, which suggested that the clean-up efficiency of these materials was higher than the one provided by PSA. Among the two ordered mesostructured silicas, LP-MS-NH\({}_{2}\)\({}^{+}\) seemed to be the most effective clean-up sorbent, as fewer analytes presented matrix interferences when using this material [28]. Conversely, several authors decided to omit the clean-up step of the QuEChERs procedure to reduce the operating time or to avoid losses of analytes as observed in previous findings with SPE cartridges (Table 1). However, this may not be suitable, as this step is important to reduce matrix interferences and enhance the sensitivity and detection of the analytes. Moreover, in the QuEChERS procedure, a salting-out extraction is carried out with different salts, which are convenient to remove before chromatographic analysis and mass spectrometry detection. In this sense, when mass spectrometry is used for detection, it is not convenient to inject sample extracts without a previous clean-up or purification procedure, especially if there are matrix interferences or salts, as they can foul the ionization source and decrease the sensitivity of the equipment, leading to more frequent and thorough expensive maintenance of the detector. Likewise, the presence of salts can produce precipitation phenomena in the chromatographic column, reducing its lifetime. Moreover, the recovery values achieved in these works were in general lower than the ones achieved in the works that included the clean-up step (Table 1).
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 1 Introduction
However, these STA controllers have some disadvantages: the methods in [24; 25; 26; 29; 32] require the upper bounds of disturbances derivatives to select the filter constants; and the gains of methods in [27; 28; 30; 31] keep increasing throughout the progress, and cannot follow disturbances when they are decreasing, which may lead to large output, or even instability. In order to overcome these drawbacks, references [33; 34; 35] proposed barrier function-based adaptive super-twisting controllers. The algorithm based on barrier function can drive the output variable in a predefined neighborhood of zero and then keep on it. However, the missile guidance system with terminal angle constraint is a multivariable problem, the existing barrier function-based method cannot be applied directly. Inspired by the above works, this study presents a multivariable barrier function-based super-twisting controller and applied the algorithm to guidance law design with impact angle consideration for a maneuvering target. Compared with other sliding mode control or super-twisting guidance laws, the distinguishing properties of the guidance law suggested in this paper can be concluded as follows:  
1. The guidance law does not require the information of disturbance derivatives, nor utilize a filter to obtain the value of equivalent control;
2. The super-twisting gains of the guidance law are small but sufficient to counteract the disturbances, and consequently, the output variable (acceleration command) is small but adequate to achieve performance goals;
3. The guidance law can converge to a predefined neighborhood of zero and maintain the sliding mode, whether the disturbances increase or decrease;
4. The acceleration commands have no chatterings. The rest of the paper is organized as follows: Section 2 presents the kinematics of missile-target engagement and preliminaries; Section 3 designs a multivariable adaptive super-twisting guidance law based on barrier function and analyzes the stability, and Section 4 gives three simulation examples to demonstrate the effectiveness and robustness of the proposed guidance law.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 5 Discussion
### Accuracies, Errors, and Uncertainties  
In the first experiment, different resolution discrepancies, where the ratio of original image to down-sampled image was 1:3 and 1:6, were selected for the matching tests. Comparative analysis revealed that our method proves that images with a higher resolution difference are more difficult to match. In the second experiment, 20 checkpoint pairs were manually selected for image matching accuracy analysis. The original positioning precision, which was around 100 m, was enhanced to within eight meters by the image matching results. Our method is more precise and efficient than conventional SIFT, SURF, and A-SIFT.  
The matching error in this study is mainly attributed to four factors. First, the error may be generated because of the different acquisition dates of the two images. Second, the error may be caused by bad results of error estimation. The estimation based on local statistics may be insufficient when the matching points contain numerous errors above 20%. Third, the error may be caused by image resampling. The bilinear interpolation adopted in our approach may also produce errors. Fourth, the differences in the levels of image rectification and the effects of projection error of the terrain may lead to mismatches.  
In the proposed method, the matching accuracy is significantly determined by checkpoints. The maximum resolution discrepancy in our test datasets was approximately three times. The first experiment proved that such a discrepancy is uncertain if the proposed approach can ensure accuracy with high differences in resolution. We are also uncertain about the georeference accuracy of DOM of ETM+, which uses uncertain DEM for geometric correction.