esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MPPT Techniques
### On-Line MPPT Techniques
[107] and [PERSON] and [PERSON] [108] presented ESC methods to track the MPP. This method estimates MPP based on changes in the converter circuit's current ripple and output power ripple. The PV system receives the reference current along with sinusoidal current perturbation. The PV system provides the output power ripple and the corresponding current ripple. If the current ripple is in the phase with the output power ripple, the MPP controller raises the ripple reference current to achieve \(I_{MPP}\), and vice versa. [PERSON] and [PERSON] [109] proposed the ripple correlation control (RCC) method for extracting the MPP of the P-V curve of the PV system. The RCC technique is also similar to the ESC technique. To track the MPP, RCC uses the ripple imposed by the power converter on the PV array. It correlates dp/dt to dv/dt or di/dt to make the power gradient to zero, which occurs when the MPP is reached. The RCC and ESC methods require the knowledge of the phase relationship between the dc power ripple and dc voltage or dc current ripple to determine the MPP. These methods are capable of achieving assured MPP. However, the practical implementation of these methods is very complex. The authors have worked hard over the past ten years to improve global MPP monitoring by altering the traditional P&O and INC approaches. However, these modified classical approaches can result in power loss at the local MPP, due to rapidly varying shade conditions. The MPP of the PV system can be extracted using the sliding mode control (SMC) approach, according to the authors. The best choice of sliding surface is crucial to this method's success. The sliding surface's incorrect design produces an undesirable value. As a result, choosing the best sliding surfaces is a challenging task. The advantages of online MPPT techniques are that they are simple and fast-tracking techniques. The disadvantages of online MPPT methods are as follows:  
Undesired oscillations around MPP. May not distinguish between global maxima, cluster head maxima, and local maxima. Under partial shading, the power loss is very high. Accuracy depends on the magnitude of the perturbation. A comparison of various online MPPT techniques is presented in Table 4.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 2 Basic Settings for the Simulations
### Performance Evaluation of the Building Under Wind Loads  
Time-history analysis is a reliable method to evaluate the aerodynamic performance of tall buildings under strong wind conditions. Although it is possible to efficiently carry out the dynamic analysis of tall buildings under wind loads in the frequency domain [31], the time history analysis technique offers a more detailed understanding of their wind-induced responses [32]. This approach not only provides an alternative for calculating statistical measures of wind-induced responses but also gives valuable insights into response distribution, aiding in estimating exceedance probability within performance-based design contexts [33]. In the time history analysis of a wind-excited tall building, the initial task involves generating fluctuating wind forces, which the quasi-steady assumption could link to the wind velocity [34].  
Researchers have recognized the need for random vibration, spectral-based, and peak-estimation approaches in wind engineering since the beginning of tall building research due to the presence of random turbulence in the load and the dynamic vibration of the structure [35]. [PERSON] et al. [36] conducted a study on the generation of time-history wind loads from power spectral density (PSD) functions to evaluate habitability. [PERSON] et al. [37] employed the same approach to perform a performance-based wind design for tall buildings. In the same way, [PERSON] et al. [38] studied high-performance control systems in wind-excited tall buildings by simulating wind loads using the time history method based on the PSD function.  
This paper conducts a wind analysis of the case study building using the time history generation method, which incorporates a quasi-steady approximation. Figure 2 outlines a framework for the overall methodology. The research framework consists of two primary components: dynamic analysis (Part I) and fragility assessment (Part II). The dynamic analysis involves simulating wind loads and evaluating the corresponding acceleration responses of the buildings. By comparing the acceleration responses under different scenarios, this study identifies potential changes in building performance due to evolving wind conditions. The fragility analysis assesses the probability of exceeding performance thresholds related to human occupant comfort using a Monte Carlo simulation framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Return Period** & **10-Year** & **50-Year** & **100-Year** \\ \hline Historical climate & Historical (1979–2015) & 24.9 & 30.6 & 32.2 \\ \hline \multirow{2}{*}{Future climate} & Mid-future (2019–2055) & 25.3 & 31.6 & 33.3 \\  & Late-future (2064–2100) & 26.4 & 32.5 & 34.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Historical and future estimated mean wind speed of Suzhou, m/s.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 3 eXTP
### Expected Performance  
Given the capabilities of the eXTP/WFM (see Figure 4), a detection rate of about 100 GRB/year has been estimated. The WFM will be able to meas  
Figure 3: An artist impression of the eXTP satellite. In the image, we show the 13 nested shell optics of the SFA (9 telescopes) and PFA (4 telescopes), as well as the 6 cameras with coded masks composing the WFM. The LAD is placed on deployable panels equipped with solar shades. There are a total of 40 modules composing the LAD, and each module hosts 16 SDDs.  
the spectral shape of the detected events and follow the hard to soft evolution of the prompt emission in the 2-50 keV band, where different GRB models are known to make different predictions [25; 26]. This could provide information on the composition and magnetization of the emitting plasma, the geometry of the emission, and the structure of the jet and surrounding material. The combination of sensitivity and soft energy coverage of the WFM provides the opportunity to detect a few high redshift (z \(>\) 6) GRBs per year and many of the so-far elusive absorption features in tens of medium bright GRBs, probing (among others) the surroundings of GRB progenitors (see [27; 28]). The WFM is also expected to detect up to 40 X-ray flashes each year (see [29]).
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 4 Experiments and Analysis
### Security  
In our scheme, the image hash is dependent on the secret key, and different secret keys will produce distinct hashes. Figure 5 tests the security of the proposed PIH scheme based on the average normalized Hamming distance sequences (with a size of 1001), between the hash pairs generated by one correct secret key and those generated by 1000 wrong secret keys. One can observe that only the 500 th normalized Hamming distance (with the correct secret key) is located in the vicinity of 0, and it is very difficult for an unauthorized user to get the same hash without the correct secret key. The proposed PIH scheme is therefore key-dependently secure.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 3 DF Indoor Location Estimation
### Signal Processing  
The array output \(y(t)\) of the \(K\)-element linear array is expressed as:  
\[y(t)=\mathbf{w}^{H}\mathbf{x}(t), \tag{13}\]  
\[\mathbf{x}(t)=\left[x_{1}(t),x_{2}(t),\cdots,x_{K}(t)\right]^{T}, \tag{14}\]  
\[\mathbf{w}=\left[w_{1},w_{2},\cdots,w_{K}\right]^{T}. \tag{15}\]  
where \([\cdot]^{H}\) denotes the complex conjugate transpose and \(\mathbf{x}(t)\) and \(\mathbf{w}\) denote the input signal and the weight of each antenna, respectively. Then, the output power can be expressed as:  
\[P_{out}=\frac{1}{2}E\Big{[}|y(t)|^{2}\Big{]}=\frac{1}{2}\mathbf{w}^{H}\mathbf{R}_{xx} \mathbf{w}. \tag{16}\]  
where \(\mathbf{R}_{xx}\) denotes the correlation matrix of the received signal and is expressed as:  
\[\mathbf{R}_{xx}=E\Big{[}\mathbf{x}(t)\mathbf{x}^{H}(t)\Big{]}. \tag{17}\]  
To direct the main beam of the array antenna to an arbitrary angle \(\phi\), each weight expressed in Equation (15) can be set as:  
\[w_{k}=\exp\left(-\mathrm{j}\frac{2\pi}{\lambda}d_{k}\sin\phi\right), \tag{18}\]  
then the weight vector can be expressed as:  
\[\mathbf{w}=\left[\exp\left(-\mathrm{j}\frac{2\pi}{\lambda}d_{1}\sin\phi\right), \cdots,\exp\left(-\mathrm{j}\frac{2\pi}{\lambda}d_{K}\sin\phi\right)\right]^{T }\equiv\mathbf{a}(\phi). \tag{19}\]  
Here, \(\mathbf{a}(\phi)\) denotes the mode vector, and angle \(\phi\) is a variable. Then, the output power is expressed as:  
\[P_{out}=\frac{1}{2}\mathbf{a}^{H}(\phi)\mathbf{R}_{xx}\mathbf{a}(\phi). \tag{20}\]  
Figure 10: Conference room condition. (**a**) Room model. (**b**) Antenna position. (**c**) Human position.
## 5 Experimental Results and Discussion
The spectral signal asymmetry also affected the zero-point determined by fitting and resulted in fluctuation of the measurement results. The phenomena of non-overlapping beams and the fluctuation of atomic number density caused by the temperature fluctuation of Cs atomic vapor due to the absence of temperature stabilization, thereby affecting the accuracy of hyperfine splitting intervals, need to be further improved. Although a \(\mu\)-metal magnetic shield was used to minimize the stray magnetic field, the Zeeman shift also influenced the results. The AC-Stark displacement constitutes the frequency shift of the atomic energy level due to the dipole interaction between the gradient light field and the induced electric dipole moment of atoms. The energy-level frequency shift causes a measurement error which is proportional to the laser intensity. These uncertainties all contribute to the errors of the obtained hyperfine spectra.
# Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
## 4 Result and Discussion
### Analysis in Varied Parameters  
Secondly, we conducted the experiment using the varied parameter as explained in Section 3. The bandwidth parameter was varied according to Figure 4. The experimental objective was to find the appropriate parameters which may perform well during package communications in our real environment. Theoretically, with the increase of spreading factor (SF), the maximum bandwidth that can be set will be reduced. As per the preliminary investigation, we found some anomalies that may affect the implementation in the real world. Then, to find the maximum bandwidth setting for specific SF in our implementation, we tried the bandwidth value ranging from 7800 Hz to 500,000 Hz. Table 2 shows the specific parameter of the spreading factor and the maximum bandwidth that can be used and is workable in our case. The value of SF was selected based on the fact that the default value of SF in Dragino is 7. Therefore, to find the longest distance of the module we used, we need to have the increasing value. Then, 9 was chosen as the median value, since 12 is the maximum workable value supported by the module. Eventually, the findings were regarded as the main consideration to adjust and set the parameters on a second experiment to have the workable solution for our specific agricultural use.
## 3 Architecture Overview
### Network Model  
The network is modeled as a set of N nodes, divided into three groups: I nodes behave as _individually selfish_, S nodes behave as _socially selfish_, and C nodes behave as _non-selfish_ (N = I + S + C). We consider that nodes are not malicious, that is, they do not send false information over the network (information about their own selfishness level). In the evaluations performed, we consider two scenarios: (i) evaluating the impact of increasing the message Time-To-Live (TTL), and (ii) evaluating the impact of increasing the buffer size. In the first scenario (i), we consider that the nodes have unlimited buffer size, that is, they can store all their messages and also the ones addressed to nodes with whom they want to collaborate. When the message TTL expires, it is automatically discarded from the node's buffer. In the second scenario (ii), we consider that the messages have unlimited TTL with limited buffer, that is, the nodes can store a message for as long as they wish.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 3 Proposed Method
### Voice Recovery  
Once the signal has been received, it is possible to recover the original content. The inputs of this module are the secret signal (\(T_{r}(m);1\leq m\leq M\)) and the secret key (seed value). The process starts with the generation of the 2\({}^{b}\) OVSF codes using Equations (1)-(3), and \(SF=2^{b}\), in a similar way to how they were generated in the encryption process. In addition, the OVSF code matrix is scrambled according to Equations (4) and (5), where the seed used to initialize the pseudo-random process is the same as that used in the encryption process.  
On the other hand, the amplitude value of each sample of the received signal (\(T_{r}(m)\)) is converted to a non-negative integer decimal value (\([0\ 2^{w}-1]\)). Subsequently, this value is represented in binary with at least \(w\) bits. The received binary signal will be represented as \(T_{rb}\). This binary signal is divided into words of \(b\) bits. Each of this words, will be obtained as follows:  
\[R_{b}\left(n\right)=\left\{T_{rb}\left(b*n+j\right),\ \ \ 1\leq j\leq b,\ \ \ 0\leq n\leq N-1\right\}, \tag{10}\]  
To extract the corresponding sample value, a search algorithm is used. Each data of \(b\) bits in the vector \(R_{b}\), is compared against each row of the \(SC\) matrix. The output of this process is the index (or row) in \(SC\) that matches \(R_{b}\left(n\right)\). The position (value) of the \(i\)th data of \(R_{b}\) corresponds to the \(i\)th sample value of the recovered signal (\(R\left(n\right)\)).  
\[R\left(n\right)=i\ \ \ \textit{where}\ \ \ \ R_{b}\left(n\right)=SC_{i,*}\ . \tag{11}\]  
Samples of the recovered signal \(R(n)\) can be converted to a floating point value, whose amplitude is normalized to a standard value (for example [\(-\)1 1]). The content of the recovered signal, (\(R(n);1\leq n\leq N\)), must be identical to the original signal \(S(n)\).
The success of the zero-emission system can also be seen in the aquaculture industry where the composition of bacterial predators such as _Bdellovibrio_ and like organisms (BALOs) decreased and thus the balanced functional and structural microbial community in the receiving seawater was re-instated [12]. The positive impacts of zero-emission implementations in numerous industries have been so insightful that it is worth exploring the possibility of also restoring the original conditions of the receiving water polluted by POME final discharge through the application of a similar system. Proven as the unique bacteria within the shifted community due to POME introduction which were carried over from the POME final discharge, _Alcaligenaceae_ and _Chromatiaceae_ were proposed as the pollution bioindicators in the receiving river [13]. The reliability of these bioindicators was then proven when they were detected in different POME final discharges from different palm oil mills despite the use of a non-identical layout of POME biotereatment processes [14]. The detection of these bacteria in only the POME polluted rivers but not in others [15] added more credibility to the claim that _Alcaligenaceae_ and _Chromatiaceae_ are the POME pollution bioindicators. Given the uniqueness of these bioindicators, they could also be used to indicate the success of the zero-emission of POME final discharge in the receiving river. Figure 1: Flow diagram of the zero-emission system for palm oil mill effluent (POME) treatment [3,4]. Nowadays, the palm oil industry is no less important than other industries due to the increasing demand for palm oil supplies throughout the year. Despite the growing implementation of zero-emission in other industries, to the best of our knowledge, there has not been any study conducted on the effect of the implementation of the zero-emission system on the bacterial community in the POME final discharge-receiving water so far. Therefore, this study was conducted to assess the shift in the bacterial community in an artificial river water system before and after the implementation of zero-emission of POME final discharge. The bacterial community in the receiving river carried over by this effluent, represented by _Chromatiaceae_ and _Alcaligenaceae_, was hypothesized to rebound following the implementation of zero-emission of POME final discharge, restoring the original ecosystem in the river water. To test this hypothesis, an artificial river water system was constructed to mimic the usual practice of discharging POME final discharge, which later was followed by the implementation of a zero-emission system. To assess the bacterial community composition, 165 rRNA sequencing on Illumina MiSeq platform was utilized, which then was correlated statistically with the physicochemical properties and nutrient analyses. The functional status of the bacterial cells was analyzed using flow cytometry.
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
## 1 Introduction
The IR femtosecond laser-based FBG sensors showed higher reflected power with excellent temperature sensitivity compared with the UV femtosecond laser-based FBG sensors. In addition, we compared the performance of the F-doped silica fiber-based FBG sensors with the Ge-doped silica fiber-based FBG sensors. A significant reduction in the wavelength shift was noticed in the F-doped FBG sensors fabricated using optimized laser parameters. The wavelength shift was constant over a wide range of radiation doses (0-10\({}^{5}\) gray), indicating outstanding radiation resistance. On the contrary, F-doped FBG sensors showed higher reflected signal power as compared with the Ge-doped FBG sensors. The F-doped FBG sensors show promise as a candidate for a stable radiation-resistant sensor in the atomic energy industry.
# Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation
###### Abstract
While the utilization of training parameters in the activation function enhances the accuracy and trainability of PINNs, the trade-off between enhanced performance and increased training costs must be carefully considered when designing PINNs. The strategy to decompose the computational domain into discrete sub-domains has been proposed to enhance the capability of PINNs for resolving high-dimensional complex systems of (differential) equations [18; 34; 35; 36]. Domain decomposition enables the construction of smooth solution curves using shallow neural networks for each subdomain [34]. The additional constraints on interfacial flux between sub-domains ensure that the solution curve satisfies conservative equations [34]. An additional merit of the domain decomposition strategy is that the computations of each sub-domain can be parallelized to reduce the computational time and enhance the accuracy of the solution curve over a complex computational domain [18; 35]. An alternative architecture construction of PINN, such as the addition of gating networks [36], is currently being actively investigated to elaborate the domain decomposition strategy. However, these studies have not been extended to provide robust guidelines in order to construct PINNs for structural dynamics problems. This study aims to extend the application of PINNs within the domain of structural dynamics. The dynamics of cantilevered beams under the description of Euler-Bernoulli beam theory is considered, which involves the balance between fourth-order spatial differential of elastic force and second-order temporal differential of inertial force. Therefore, the optimal strategies to construct PINNs for resolving high-order differentials in structural dynamics are explored. In the following, the bottlenecks that hinder the construction of reliable PINN solutions for high-order differential equations are discussed, and potential remedies are proposed. These can be summarized as follows: (1) scaling analysis for loss configuration, (2) variants in adaptive activation, and (3) the elaboration of neural network architectures. Demonstrations of these remedies are provided through structural dynamics problems. Initially, the free vibration of a cantilevered beam is considered, subsequently extending the analysis to a more challenging problem involving the coupled interaction between a fluid and a structure.
# Energy Management in Microgrids with Renewable Energy Sources: A Literature Review
## 3 Microgrid Energy Management with Renewable Energy Generation
### Energy Management in Microgrids Based on Stochastic Methods and Robust Programming
The energy management is divided in two sub-problems: The first involves scheduling within the defined energy boundaries for system protection, while the second evaluates the real time energy capacity deviation limit for frequency regulation. The presented approach was found to be more cost effective. Kuztnesova et al. [86] proposed a decentralized energy management system for an interconnected MG using agent-based modeling and robust optimization. The MG performance was evaluated in terms of the cost from the power imbalances associated with the uncertainty of renewable generation and load power demand. Zachar and Daoutidis [87] proposed a hierarchic control mechanism to regulate and supervise the loads and dispatchable energy inside a MG. Stochastic optimization was used on a low scale to avoid errors in the forecast of renewable energies. Deterministic optimization was realized on a fast scale to update the optimal dispatch conditions. Battistelli et al. [88] proposed an energy management system for a remote hybrid AC/DC MG, which ensures economical dispatch in spite of the uncertainties associated with the use renewable energy sources. A load control is determined (thermic and electric vehicles) based on the demand, while taking the limits of the generators, controllable loads, and charge and discharge of batteries into consideration. Lujano et al. [89] developed an optimal load management method for hybrid systems composed by the wind tubine, battery bank, and diesel generator. The autoregressive moving average (ARMA) was used to predict the wind speed. The results showed that the load management strategy improved wind power usage by shifting the controllable loads to the wind power peaks, thus increasing the charge in the battery bank. This research contributed strategies for the energy management of hybrid MGs.
## 2 The Baseline ECU Design and Numerical Models
### The Beta Model  
The Beta model uses Mathworks(r) Simulink(r)/SimscapeTM to model the same system described in Section 2.2, while including electrical components, such as a three-phase source and a VSD, a piping system, and other built-in Simscape models which simulate the PCS as well as the centrifugal pump. The Pelton turbine was modelled using an amalgamation of mathematical functions via Simulink(r) while also applying some built-in components from the SimscapeTM library [31; 33]. Due to computational challenges, the ECU hydraulic machinery and PCS modelling was split into two models, (i) a charging model including the variable speed, multi-stage centrifugal pump and PCS and (ii) a discharging model including the mathematical model of the Pelton turbine and PCS. The following Sections (Sections 2.3.1 and 2.3.2) will describe the two individual models making up the Beta model.