esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 1 Introduction
[14], a cascaded fractional MPC, combined with a fractional-order PID controller, is developed to ensure an efficient response of the power system in the presence of load disruptions and variations in system parameters. The controller is fine-tuned using a Sooty Term Optimization algorithm to identify optimal parameters. Experimental assessments involve testing the controller under scenarios involving power mixing from renewable energy sources such as PV and wind, along with varying load conditions in a multi-area hybrid power system. Controlling a system that generates power from a non-uniform input, such as wind, poses a significant challenge. The constantly changing wind speed, influenced by factors like gusts, adds to the situation's complexity. Efforts have been undertaken to address the wind non-uniformity issue in synchronous wind generator systems by maintaining the rotor speed close to a constant value through blade pitch control. A brushless synchronous generator with a permanent magnet is employed in certain systems, considering the wind turbine as the sole power source. Nevertheless, induction generators offer advantages over synchronous generators, including lower costs, increased reliability, and more straightforward controls. A new advancement in the control of a reactive static power source opens up the possibility of exploring the integration of an induction generator with the control of a reactive static power source as an alternative to the use of synchronous generators. This paper's initial step involves selecting an appropriate mathematical model with detailed considerations for DFIG wind turbines. Selecting an appropriate model is crucial as the electricity generation process in wind turbines involves inherent noise and uncertainties. Therefore, an effective model should encompass these uncertainties to a significant extent. In the subsequent phases of the research, the model predictive control (MPC) controller is employed. The organization of this paper is as follows: the second section outlines the formulation of the DFIG model and describes the proposed MPC method. The third section presents the simulation results of the proposed approach. The concluding part provides an overview of the general findings of the paper.
# The Knudsen Paradox in Micro-Channel Poiseuille Flows with a Symmetric Particle
## 5 Results and Discussion  
We assess the Knudsen paradox in the numerical setup described above by varying the inlet pressure (\(P_{in}\)) and maintaining the inlet to outlet pressure ratio as 3. To compare our DSMC results to the results from [PERSON] et al. [5, 8], the mass flow rate \(Q\) (obtained from the simulations) is represented in a non-dimensional form. This dimensionless flow rate \(Q_{norm}\) used is [34, 41]:  
\[Q_{norm}=\frac{mL\sqrt{2 RT}}{H^{2}w(P_{in}-P_{o})} \tag{1}\]  
where \(L\), \(H\) and \(w\) are the length, height and width of the micro-channel, respectively. Further, \(R\) and \(T\) are the specific gas constant and iso-thermal temperature of the gas. The variable \(\dot{m}\) is the mass flux extracted by the \(dsmcFoam+\) solver (using the mass flux measurement tool described in [34]) and \(P_{in}\) and \(P_{o}\) are the inlet and outlet pressures. Note that \(Q_{norm}\) is independent of the local pressure gradient (depends only on its mean value), but depends mainly on the channel height represented in terms of a rarefaction parameter \(\delta_{m}\) (i.e., on an inverse Knudsen number--\(\lambda/H\)) [41] given by:  
\[\delta_{m}=\frac{\sqrt{\pi}}{2 Kn} \tag{2}\]  
Equations (1) and (2) can be used to describe the rarefied flow with the particle as well under the same constraints, i.e., the local pressure gradient (defined as \(\frac{H}{P}\frac{dP}{dx}\)) is small (or \(\ll 1\)). Correspondingly, we show the comparisons between \(dsmcFoam+\) and the data of [PERSON] et al. [5, 8], along with the effect of particle blockage on the Knudsen paradox. Prior to this discussion, we first establish a hydrodynamic basis for the effect of the stationary particle in the channel. This is done by comparing the corresponding velocity profiles across the micro-channel setups with and without the particle in the transition regime (at \(Kn=0.1\)). We also briefly evaluate the compressibility effects of the particle on the flow system by examining the Mach number developed in in this flow regime.  
Figure 2: Comparison of the variations in co-axial velocity in the transition regime (\(Kn=0.1\)) along channel height \(H\) at the center of the channel \(x/L=0.5\) across the chosen blockage ratios \(H/h\) of 8, 4 and 2, respectively, with the benchmark case without a particle (represented by the dashed line with squares). In this figure, the square particle is used as an illustrative tool to mask the discontinuities in the region occupied by the particle. The open symbols represent the DSMC data, while the dashed lines represent the weighted least squares fit.
## 4 Proposed Methods
### System Architecture
#### 4.2.3 Features Fusion and Classification  
As described in the previous section, we consider pre-trained models to extract the discriminative features from the spatiotemporal images. We integrate a fully connected layer after the backbone model to reduce the dimensionality of the feature map. Then we apply several fusion mechanisms to compute the better classification accuracy. The fusion techniques allow us to integrate feature maps obtained from different branches of the backbone networks as shown in Figure 5.  
Three different fusion techniques are introduced for comparing the performance of the proposed system. The first method is the straightforward average of the feature maps defined as \(f_{avg}\) and given in Equation (7).  
\[f_{avg}(i)=\frac{1}{3}\sum[f_{xy}(i),\ f_{yz}(i),\ f_{zx}(i)] \tag{7}\]  
where \(i=1,\,2,\,\ldots,\,256\) is the dimensionality of the feature map.  
Each branch of the backbone network along with the fully connected layer generates 256-dimensional features namely as \(f_{xy},\ f_{yz},\) and \(f_{zx}\). These feature maps are averaged to pass a 256-dimensional feature map through a fully connected layer to produce the feature map called \(f_{xyz}\) of size 256. This 256-dimensional feature map is then passed through a fully connected layer and a softmax layer to get the final output.  
One of the most popular fusion methods is the element-wise multiplication of the feature maps defined as \(f_{mul}\) in Equation (8). Since three features with the same dimensionality (256-dimensional) are generated from the front, side, and top views images, the dimension of the resultant feature map is 256.  
\[f_{mul}(i)=\prod[f_{xy}(i),\ f_{yz}(i),\ f_{zx}(i)] \tag{8}\]  
where \(i=1,\,2,\,\ldots,\,256\) is the dimensionality of the feature map.  
The last fusion technique is the element-wise maximization of the feature maps that yields a 256-dimensional feature map defined as \(f_{max}\) in Equation (9).  
\[f_{max}(i)=\max\big{[}f_{xy}(i),\ f_{yz}(i),\ f_{zx}(i)\big{]} \tag{9}\]  
where \(i=1,\,2,\,\ldots,\,256\) is the dimensionality of the feature map.  
Among the above mentioned three fusion techniques, element-wise maximization is more robust compare to straight-forward average and multiplication.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 3 Micro-Supercapacitor Carbon Electrodes
### Activated Carbon  
Activated carbon has a large specific surface area, complex porous structure, good electrical conductivity, high electrochemical stability, and it is relatively inexpensive. The disadvantages are that they have generally poor mechanical properties and need metal current collectors. Activated carbon (fibres) with large specific surface area and high specific capacitance are synthesized by carbonization and activation (physical, chemical, or a combination of both), for example, by KOH chemical activation and fluorination treatment [67], or phosphoric acid pre-impregnation and CO\({}_{2}\) activation [68].  
The conductivity of thin film electrodes can be increased by doping activated carbon with, for example, graphene oxide [69], carbon nanotubes [70], polypyrrole [71], or silver nanowires [72]. Doping activated carbon film electrode with 500 nm silver nanowires, fabricated using ultrasonic spray coating, resulted in an increase in the specific capacitance by 39% to 258 F/g [72]. In a similar study, activated carbon was doped with silver nanowires, with NiO-Co\({}_{3}\)O\({}_{4}\) nanocomposites (produced via a hydrothermal method), which increases the specific capacitance of ultrasonic spray coated thin film supercapacitors to 707 F/g, and 93% of the capacitance remained even after 5000 cycles [73]. Electrodes of activated carbon fibres with well-aligned tin oxynitride nanorod arrays have shown a high specific capacitance of 673 F/g at 1 A/g and a cycling stability of 99% at 5 A/g for 2000 cycles in 1 M H\({}_{2}\)SO\({}_{4}\) electrolyte [74]. Activated carbon was doped with MnO\({}_{2}\) and silver nanowires in order to improve the conductivity of the thin film electrode, resulting in a specific capacitance of 1021 F/g, a 325% increase from the undoped activated carbon [75]. Another recent study presented the direct fabrication of hierarchical porous carbon/silicon carbide (HPC/SiC) composite from bamboo waste raw biomass via a facile method [76]. In this way, hybrid electrodes are made by combining semiconductors in order to overcome the drawback of the limited electrical conductivity of porous carbon. The specific capacitance of the HPC/SiC composite was 234 F/g at a current density of 1 A/g [76].
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.3 Training and Validation Samples Generation for Classification  
The quality of the samples directly determines the reliability of the classification results. On the one hand, the samples must be representative and must be able to represent the characteristics of the features in the study area; on the other hand, the samples must be independent to reduce errors caused by spatial correlation. Google imagery is an important sample source for visual interpretation, where different types of land cover present different shapes, textures, colors, etc. [65]. However, due to the limitation of surface changes and the experience of visual interpreters, the interpretation may be far from accurate. Using existing classification products to select samples has been recognized as a fast and effective method in many articles [52; 66; 67]. Nevertheless, existing classification products may not perform well in hilly areas, and they can only be used as a preliminary reference. Phenological features are the most common and reliable features of vegetation, and the use of high-temporal spectral changes can adequately characterize vegetation, in order to achieve the goal of identification. However, the phenological feature may be deformed to some extent due to cloud disturbance. In this study, Google imagery was used for visual interpretation, and the GFC, JRC, ESA WorldCover 2020 land cover, global ESRI 2020 land cover, CLCD in 2020, and GLC_FCS30 in 2020 datasets were used to select the same land cover. The study area was divided into 3 km \(\times\) 3 km grids, and 1-10 samples were obtained in each grid [68; 69]. Then, the multiple indices of GCVI, NDVI, EVI, LSWI, BSI, NBR, NDWI, VV, and VH timeseries were used to match the phenological characteristics of the land cover by applying the developed timeseries viewer tool of GEE [1]. With the two steps above, a set of samples for pre-classification was obtained.
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface  
The detection and mapping of oil on a water surface is the most common uses of oil spill remote sensing. This is carried out using either active of passive means.
# Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
###### Abstract  
The Internet of Things (IoT) provides technical solutions for monitoring assets in facility layouts, and this is further strengthened by the development of sophisticated software tools for intralagistics operations. The present research provides a taxonomy for the existing tracking technologies and a comparison matrix for supporting decision making when selecting the most suitable technology for real-time tracking in indoor areas. Although numerous tracking technologies exist, ultra-wideband (UWB) technology has gained significant attention in recent years due to its exceptional positioning accuracy and its ability to operate effectively in challenging environments with numerous obstacles, even under non-line-of-sight (NLOS) conditions. Specifically, this research focuses on a real-time location system (RTLS) that is designed and implemented to monitor assets based on UWB technology. Additionally, a new algorithm is introduced to reduce localization errors by attempting to exclude NLOS measurements from the tag's position calculations. The experiments showcased that the proposed algorithm improves the overall positioning error by 24%, reporting an RMSE of 0.124 m in comparison to the 0.163 m of the normal trilateration method. The experimental results highlight the efficiency of the proposed solution for fast and accurate localization and tracking in real-world environments.  
Keywords:real-time locating system; indoor positioning system; ultra-wideband; DWM1000; positioning algorithm +
Footnote †: journal: Applied Sciences
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 4 System and Attack Models
### Attack Model
#### 4.2.1 Single Malicious Entity Attack  
* **Malicious clients**: Clients protect data privacy by encrypting shared parameters using the HE scheme. In the proposed system where all clients have different private keys, even if a malicious client can eavesdrop on all channels between the cloud and the clients, the malicious client cannot access the data because it cannot decrypt the ciphertexts without the corresponding private key. In addition, even if multiple malicious clients cooperate, they cannot access other clients' data without the corresponding private key;
* **Single malicious server**: The CS cannot access decrypted values in the proposed system because it only receives and handles encrypted values. For the CP, local model parameters can be accessed through combined decryption when partially decrypted values are delivered from the CS. However, the CS can prevent the CP from accessing the local model parameters by adding random noise to the encrypted local model parameters.
# Novel MIMO Antenna System for Ultra Wideband Applications
## 2 Proposed MIMO Configuration
### 4 x 4 MIMO Design  
The proposed MIMO antenna configuration is shown in Figure 5. From the figure, one can observe that four antenna elements are arranged in a plus-shaped configuration, which tends to achieve both pattern and polarization diversity. Antenna-1 and antenna-2 are placed at a 90\({}^{\circ}\) angle, which is the typical configuration of polarization diversity, while antenna-1 and antenna-3 offer pattern diversity (see Figure 5). As a whole, each antenna element is able to provide both pattern and polarization diversity characteristics. It is also worth mentioning that no decoupling network is used to enhance the isolation between antenna elements. The dimensions of the antenna elements are the same as those of a single element (see Figure 1). As shown in Figure 5a, there is an extra space created between the antenna elements, which is represented by \(W_{S1}\times L_{S1}\) = 30 \(\times\) 30 mm\({}^{2}\). Therefore, the overall dimensions of the proposed MIMO antenna are 90 \(\times\) 90 mm\({}^{2}\) (consider both sides of the plus shape).  
Figure 4: Simulated realized gain and radiation efficiency of single antenna element.  
Figure 3: Reflection coefficients (S\({}_{11}\)) of different design stages.  
Computer Simulation Technology (CST) Microwave Studio 2021 was used to develop and simulate the proposed MIMO antenna. The simulated S-parameters of the designed MIMO are shown in Figure 6. In the figure, the reflection coefficients are represented by \(S_{ii}\) (\(i\in 1\), 2, 3, 4), while the isolation between adjacent elements is denoted by \(S_{ij}\) (\(i,j\)\(\in\) 1, 2, 3, 4 and \(i\
eq j\)), and for parallel elements, it is represented by \(S_{ki}\) (\(k,i\)\(\in\) 1, 2, 3, 4 and \(|k-i|=2\)). The MIMO antenna's impedance bandwidth is 9.27 GHz in the range of 2.73-12 GHz. Furthermore, the isolation between adjacent antenna elements, denoted by \(S_{ji}\), \(s>\)17 dB whereas the isolation between parallel elements, denoted by \(S_{ki}\), is \(\geq\)14 dB.  
To better understand the isolation characteristics, the surface current distribution (see Figure 7) is plotted by exciting only port-1 of the array, while the rest of the ports are terminated with a 50 \(\Omega\) matched load. Three different frequencies, such as 3 GHz, 6 GHz, and 9 GHz, are used to depict the surface current. It can be observed from Figure 7 that the surface current generated by antenna-1 does not influence adjacent or parallel placed elements.  
Figure 5: Configuration of the proposed UWB MIMO antenna (a) front side (b) back side.  
Figure 6: Simulated S-parameters of the proposed UWB MIMO antenna.
## 3 Results and Discussion  
### Functionalization of Aliphatic Amine  
#### 3.1.1 Effect of Amine Concentration on Amine Density  
The amine functionalization process was carried out at different concentrations of amine and isopropanol solution to investigate effect onto AD. The concentration of amine: isopropanol solution used was 50:50%, 60:40% and 70:30% by volume. The results attained exhibited in Table 1. As the concentration of aliphatic amine increases in the amine solution, from 50% to 70% (vol.), the AD had demonstrated a descending pattern. The density reduces from 5.12 to 3.80 mmol/g\({}^{-\text{ad}}\) as the EDA concentration increases. Similar trends observed with DETA, TETA, and TEPA as well. As the concentration of aliphatic amine increases, the portion of isopropanol which had used as solvent reduces. It is known that solvent swells the polymer backbone, and therefore helps the functional group additive to reach the active sites [22]. Thus, the reduction of solvent fraction volume has a definite influence in the amine functionalization. This leads to a deceleration in the functionalization process which had shown lower AD yield.  
On the other hand, the AD reduces as the concentration of EDA, DETA, TETA, and TEPA increases. At the same concentration of 50% of amine; 50% of isopropanol, EDA, DETA, TETA, and TEPA had attained AD around 5.12, 4.06, 3.04, and 2.56 mmol/g-ad, respectively. The functionalization process takes place by fusion of amine group onto poly-GMA chains upon the opening of the epoxy group of GMA [23]. Among all four amine EDA had shown a higher yield of AD and reduced further with an increase in the number of the amine group. The molecular weight and structure grow more significant as the number amine group increases as well as the aliphatic chain. This may attribute to the steric hindrance effect which prevents the fusion of the amine group onto poly-GMA chains [24]. Thus, it leads to lower AD yield as the EDA, DETA, TETA, and TEPA, molecular weight, and structure progress further.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 4 Data and Methodology
### Data Source and Data Preprocessing  
In this paper, electricity load data in Queensland Australia from 2 May to 4 July (2019-2021) were collected as our data. Data of each year are considered as an independent time series and we will evaluate our model on each time series. Data interval is half an hour, and every point in this dataset represents electricity demand at that moment.  
Figure 4: The structure of the NODE block.  
Figure 3: The latent trajectory created by forward propagation when inputting an initial value into NODE. These arrows represent the time point that is created by the adaptive mechanism of ODE solver to approach the real solution during the integration process.  
In order to achieve a better effect, data normalization is adopted to map the data to interval between 0 and 1. It can be represented as:  
\[X_{s}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{12}\]  
where \(X_{s}\) is the normalized value. \(X_{min}\) and \(X_{max}\) represent the minimum value and maximum value of the time series, respectively.  
In the experiments, we divided annual data into three parts: training set, validation set, and test set. Training set accounts for 60% of the whole dataset, validation dataset accounts for 20% of the dataset after training set, and test dataset takes the last 20% of the dataset. Models are trained on the training dataset and evaluated on the validation dataset.
## 2 Related Work  
In [8], the energy and delay performances of cooperative virtual MIMO system with Alamouti coding for WSNs were investigated and compared with SISO system for the same throughput and BER. The performance was also compared over different transmission distances with the contemplation of circuit and transmission energy consumption. Alamouti coding is an STC technique in which space and time (two-dimensional coding) with multiple antenna setups can be used to attain coding gain and diversity gain for the same bit rate, transmission power and bandwidth as compared single antenna system. In STC techniques, information bits are transmitted according to some pre-defined transmission sequence. At the receiver, the received signals are combined by using optimal combining scheme followed by a decision rule for maximum likelihood detection [10].  
A V-BLAST based virtual MIMO WSN with QAM was proposed in [14], which does not require spatial encoding on transmitting side nodes, thus eradicating the local communication and corresponding synchronization requirement on transmitting side nodes as previously involved in [8]. To make the system more energy efficient without any information loss, the use of WOFDM with V-BLAST based WSN was proposed in [15] and evaluated under a co-located true MIMO receiver architecture. In [16], the BER performance of such systems was also observed using different V-BLAST detection algorithms.  
With the advent of smart antennas for WSNs [17, 18], a non-cooperative STC technique based MIMO system was recently proposed in [19]. By using 2-element switched antenna array, there is no requirement for local communication at transmitter and receiver side which makes the system more energy efficient. To simplify the structure of MIMO WSN for energy consumption reduction, a nonlinear MIMO technique was proposed in [20], where real or imaginary part of the complex-valued received signal was considered for further processing which results in simpler receiver architecture at the cost of some information loss.  
Based on existing studies, cooperative virtual MIMO with V-BLAST detection can be a promising communication architecture for WSNs. Furthermore, the choice of the modulation scheme for use with the architecture is also critical for reliable communication in WSNs. As discussed in [1], the modulation technique should be simple and low-power, and whose characteristics preferably can be tailored according to the channel conditions. To our knowledge, the performance of multi-carrier modulation techniques have not been studied in V-BLAST based virtual MIMO system for WSNs.  
Multi-carrier modulation techniques such as WOFDM is promising for enabling high data-rate WSNs and which can be implemented with low complexity [21]. Unlike its counterpart, FOFDM, whose bases are static sine/cosine, wavelet bases of WOFDM can also be optimized according to system or channel requirement.