esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 2 Circuit Analysis
### Small Signal Model  
The SSA method is known as a powerful tool, which provides a simple yet precise model for the small signal analysis of inverters [6,7]. Applying small signal perturbation for a given equilibrium point (\(I_{L}\), \(V_{C}\), \(I_{o}\), \(D\), \(M\)) and using the SSA model given by (4) yields (9),  
\[\frac{d}{dt}\left[\begin{array}{c}\dot{t}_{L}\\ \dot{v}_{C}\\ \dot{t}_{o}\end{array}\right]=\left[\begin{array}{ccc}-\frac{r}{L}&\frac{2 (D+d)-1}{L}&0\\ -\frac{2(D+d)-1}{C}&0&-\frac{1-(D+\hat{d})}{C}\\ 0&\frac{2(1-(D+d))}{L_{o}}&-\frac{R_{a}}{L_{o}}\end{array}\right]\left[ \begin{array}{c}I_{L}+\dot{t}_{L}\\ V_{C}+\dot{v}_{C}\\ I_{o}+\dot{t}_{o}\end{array}\right]+\left[\begin{array}{c}\frac{1-(D+\hat{d})} {L}\\ 0\\ -\frac{1-(D+\hat{d})}{L_{o}}\end{array}\right]V_{in} \tag{9}\]where \(\hat{i}_{L}\), \(\hat{v}_{C}\), \(\hat{i}_{o}\) and \(\hat{d}\) are perturbations of state and control variables. Assuming the steady-state and removing second order terms, (9) can be simplified as given by (10).  
\[\frac{d}{dt}\left[\begin{array}{c}\hat{i}_{L}\\ \hat{v}_{C}\\ \hat{i}_{o}\end{array}\right]=\left[\begin{array}{ccc}-\frac{r}{L}&\frac{2D -1}{L}&0\\ -\frac{2D-1}{C}&0&-\frac{1-D}{C}\\ 0&\frac{2(1-D)}{L_{o}}&-\frac{R_{o}}{L_{o}}\end{array}\right]\left[\begin{array} []{c}\hat{i}_{L}\\ \hat{v}_{C}\\ \hat{i}_{o}\end{array}\right]+\left[\begin{array}{c}\frac{2V_{C}-V_{in}}{L _{o}-L_{o}}\\ \frac{2V_{C}-V_{in}}{L_{o}}\end{array}\right]\hat{d} \tag{10}\]  
By solving (10) using Laplace transforms, the transfer functions at any given operating point can be obtained. For instance, the transfer functions of voltage across the capacitors to the shoot through the duty ratio can be obtained as given by (11), where \(K_{1}=I_{o}-2I_{L}\) and \(K_{2}=2V_{C}-V_{in}\).  
\[\frac{\hat{\alpha}_{C}}{\hat{d}}=\frac{K_{1}L_{o}s^{2}+[K_{1}(LR_{o}+L_{o}r)+K_ {2}(1-2D)L_{o}+K_{2}(1-D)]s+k_{1}rR_{o}+K_{2}(1-2D)R_{o}+K_{2}(1-D)r}{CLL_{o}s^ {3}+C(R_{o}+LrL_{o})s^{2}+(CrR_{o}+(2D-1)^{2}L_{o}+2(1-D)^{2}L)s+(2D-1)^{2}R_{o }+2(1-D)^{2}r} \tag{11}\]  
As can be readily seen from (11), by applying the appropriate changes to the shoot-through duty cycle, \(d\), the capacitor voltage can be regulated according to the connected load.
## 4 Conclusions  
This paper introduces a modified version of the BWBN hysteresis model to simulate non-linear hysteretic pinched systems with varying pinching stiffnesses and slip lengths. While the pinching and post-yield stiffness is constant in the original BWBN model, the modified model can simulate the pinching and post-yield stiffness, \(k_{p}\), reduction as a function of absolute maximum deformation, \(D_{max}\). Moreover, the modification applied to \(\xi_{s}\) parameter, as a measure of the total slip in the original BWBN model, helps control the pinching spread more efficiently in the proposed BWBN-VP model.  
The model efficiency is evaluated for experimental RC column cyclic-loading tests exhibiting different failure modes, including flexural, shear, and flexural-shear. Experimental results show the proposed BWBN-VP model can successfully simulate a wide range of hysteretic behaviors from significantly pinched behavior in shear failure modes to negligibly pinched responses in flexural failure modes.  
Moreover, a real RC-frame building is used to validate the modified model performance and robustness. The force-deformation hysteresis loops obtained for BNZ building stories under the 2013 Seddon earthquake are re-simulated using the BWBN-VP model proposed in this paper. In addition, the elastic and pinching stiffness evolutions re-simulated by the proposed BWBN-VP model match significantly with the measured values. Thus, the strong correlation coefficient between the measured and re-simulated hysteresis loops and the significant match between stiffness evolutions demonstrate the applicability of the proposed BWBN-VP model for real-world complex structures. The model can simulate structural responses and structural earthquake-induced damage simultaneously, making it a reliable choice for developing realistic numerical case studies representing RC structure dynamic behaviors in civil engineering research.  
Conceptualization, M.R., J.G.C. and C.Z.; methodology, M.R. and C.Z.; software, M.R.; validation, M.R.; formal analysis, M.R.; investigation, M.R., J.G.C. and C.Z.; resources, M.R.; data curation, M.R.; writing--original draft preparation, M.R.; writing--review and editing, M.R., J.G.C. and C.Z.; visualization, M.R.; supervision, J.G.C. and C.Z.; project administration, M.R.; funding acquisition, M.R. and J.G.C. All authors have read and agreed to the published version of the manuscript.  
The authors received no financial support for the research, authorship, and/or publication of this article.  
Not applicable.  
Some or all data, models, or code used in this study are available on request from the corresponding author.  
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
## 3 LoMoB Localization Scheme
### System Environment  
LoMoB considers a system that uses an omnidirectional transceiver in addition to a system that uses a directional transceiver [19]. Because the system environment using a directional transceiver was explained in Section 2, we explain here the system environment using an omnidirectional transceiver.  
A mobile beacon moves on the sea surface or underwater. When a mobile beacon has an omnidirectional transceiver, 3D movement of the mobile beacon is possible whereas a mobile beacon that has a directional transceiver is restricted to 2D movement at a fixed depth. The mobile beacon  
Figure 3: Location estimation with projected beacon points based on geometric constraints. (**a**) Location estimation based on two projected beacon points; (**b**) Location decision using two additional projected beacon points.  
is assumed to know its own location and to broadcast a beacon containing its location information at regular distance intervals, which are called the beacon distance \(d\). When the mobile beacon transmits a beacon, the sensor nodes that are located within the communication range of the mobile beacon receive the beacon; e.g., in Figure 4(a), the sensor node in red can receive the beacon \(B_{2}\) and the sensor node in blue can receive the beacons \(B_{1}\) and \(B_{2}\). Here, the communication range \(r\) is assumed to be constant in 3D [11, 12, 20]. In addition, because movement of a mobile beacon in a straight line is more controllable than curved movement, the mobile beacon is assumed to follow the random waypoint (RWP) model [21]; the mobile beacon moves in a series of straight paths to random destinations.
## Materials and Methods
### ARE Reporter Gene Assay  
The SIN-lenti-ARE reporter gene construct [37] was based on the addgene plasmid #24308, in which the luciferase gene is under the control of an upstream minimal promoter driven by multiple TCF (T-cell factor) response elements. The 7 \(\times\) TCF sequences of the 7 TFP plasmid (a kind gift from [PERSON], Department of Developmental Biology, Howard Hughes Medical Institute, Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA [38]) were removed by enzymatic restriction using Pstl and Nhel and replaced with the antioxidant response element (ARE) consensus sequence of the NADPH:quinone oxidoreductaseate 1 promoter (ggtaccagtctagagtccacagtgacttggcaaaatcgcgtcgc) [19; 39]. Test restriction, gel electrophoresis, and DNA sequencing verified the resulting plasmid DNA. The production of lentiviral particles was conducted as described before [40]. Briefly, HEK293 cells were cotransected with a second-generation lentiviral plasmid system containing SIN-lenti-ARE as transfer vector, psPAX2 as a packaging vector,and pMD2.G as the vector encoding for the envelope protein. Supernatants containing lentiviral particles were used for gene delivery.  
Primary murine hepatocytes were transfected with the SIN-lenti-ARE reporter gene construct. Cells were lysed in passive lysis buffer (PLB) as recommended by the manufacturer (Promega). For the luciferase assay, 50 \(\upmu\)L of the lysate was transferred to a white 96-well plate. Luciferase assay reagent I (LAR I) was injected automatically by the luminometer (Glorax 96, Promega). Sulforaphane (SFN: 7.5 \(\upmu\)M) treatment was included as a positive control for Nrf activation. Measurement was conducted with default setting parameters (reagent volume 50 \(\upmu\)L, delay 0.4 ms, integration time 10 s). Because sparingly soluble test substances often need to be dissolved in aqueous DMSO and then added to the test mix, a control with the highest DMSO end concentration but without test substance was performed.
# Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition
## 5 Experiments
### Results on NTU RGB+D Dataset  
The NTU RGB+D dataset [6] contains 56,880 sequences of 60 action classes, collected by three Kinect v2 cameras with 17 different setups. A total of 40 subjects of ages from 10 to 35 years, are involved to performed the action sequences. Two evaluation protocols, including cross-subject (CS) and cross-view (CV) settings, are suggested for evaluation. For the cross-subject setting, the 40 subjects are equally split into training and testing groups. However, the number of samples for different subjects is different, and according to the subject split used in [6], 40,320 and 16,560 samples are used for training and testing, respectively. For the cross-view setting, the samples of camera 1 are used for testing and cameras 2 and 3 are used for training. Each camera is used in several capturing setups (different views), and according to the detailed split used in [6], 37,920 samples and 18,960 samples are used for training and testing, respectively. Additionally, as suggested in [6], 5% of the training data is randomly selected and reserved as evaluation data. Two skeletons (25 joints per skeleton) are used as input; if only one is present in the sample, the second is set as zero. Therefore, the input is of 25 \(\times\) 2 \(\times\) 3 = 150 dimensions, containing the 3D coordinates of the skeleton joints. The batch size is set to 128, and the dropping probabilities of 0.35 and 0.2 are used for the CS and CV settings, respectively.
## 2 Model Development
### Musculoskeletal Model Construction  
The lower limb exoskeleton model was imported into the AnyBody software, just as shown in Figure 2a. It was controlled by the program 'FitnessMachine.any'. The human musculoskeletal model and the lower limb exoskeleton model were connected by the program 'JointsAndDrivers.any'. The human-exoskeleton model is shown in Figure 2b. The human musculoskeletal model was built based on the application repository model library. The motion data captured by the Vicon system. The data were imported into AnyBody software through the \"C3D to Anyscript conversion program.\" The inverse kinetic method was used to calculate muscle force, joint force, and joint moments. The motion of the exoskeleton was constrained by human kinematic data. The ankle and hip joints of the exoskeleton are aligned with the human body, respectively. The knee joint of the exoskeleton is aligned with the human body along the anterior-posterior axis. Compared to the human exoskeleton model, the freedom of flexion and extension of the left knee joint of the SSP subject-exoskeleton model was removed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Material** & **Mass/Kg** \\ \hline Back support & Carbon fiber & 1.2 \\ Hip joint component & High strength aluminum alloy & 1.5 \\ Knee joint component & High strength aluminum alloy & 1.2 \\ Ankle joint component & High strength aluminum alloy & 1.2 \\ Brace of thigh & Carbon fiber & 2.4 \\ Brace of calf & Carbon fiber & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction of each component of the lower limb exoskeleton robot.  
Figure 1: Lower limb exoskeleton. (**a**) (1) Back support; (2) Driving source of hip joint; (3) Hip joint component; (4) Brace of thigh; (5) Driving source of knee joint; (6) Brace of calf; (7) Ankle joint component; (8) Flexible belt of waist; (9) Waist component; (10) Thigh component; (11) Flexible belt of thigh; (12) Knee joint component; (13) Calf component; (14) Flexible belt of calf; (15) Pedal. (**b**) Lower limb exoskeleton prototype.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Power Components** & **Specification** & **Hip Joint** & **Knee Joint** \\ \hline \multirow{3}{*}{Motor} & Rated output (W) & 750 & 400 \\  & Rated torque (N-m) & 2.4 & 1.3 \\ \cline{1-1}  & Stroke range (mm) & 100 & 100 \\ \cline{1-1}  & Maximum dynamic load (N) & 4000 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values of joint power elements.
# Data Driven Leakage Detection and Classification of a Boiler Tube
###### Abstract  
Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.  
Acoustic emissions; boiler tube; deep learning; deep neural network; leakage detection; wavelet packet transform +
Footnote †: journal: Applied Physics Letters  
_Applied_  
_Article_  
Data Driven Leakage Detection and Classification of a Boiler Tube
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 2 Governing Equations  
Unsteady supersonic flows of inviscid compressible gas are considered. These flows are described by Euler equations. Finite volume method and time-marching scheme are applied to discretization of Euler equations. Euler equations are written for three independent variables, time, and two Cartesian coordinates. Generalization of governing equations for three Cartesian coordinates is straightforward.  
Governing equations are written in conservative form as  
\[\frac{\partial\mathbf{U}}{\partial t}+\frac{\partial\mathbf{F}}{\partial x}+\frac{ \partial\mathbf{G}}{\partial y}=0, \tag{1}\]  
where \(t\) is the time, \(x\) and \(y\) are the Cartesian coordinates. Equation of state of perfect gas is  
\[p=(\gamma-1)\rho\varepsilon.\]  
Total energy per unit volume is found as  
\[e=\rho\varepsilon+\frac{1}{2}\rho(u^{2}+v^{2}).\]  
The vector of conservative variables, \(\mathbf{U}\), and the vectors of fluxes, \(\mathbf{F}\) and \(\mathbf{G}\), have the form  
\[\mathbf{U}=\left(\begin{array}{c}\rho\\ \rho u\\ \rho v\\ e\end{array}\right),\quad\mathbf{F}=\left(\begin{array}{c}\rho u\\ \rho uu+p\\ \rho uv\\ (e+p)u\end{array}\right),\quad\mathbf{G}=\left(\begin{array}{c}\rho v\\ \rho uv\\ \rho vv+p\\ (e+p)v\end{array}\right).\]  
Here, \(\rho\) is the density, \(u\) and \(v\) are the velocity components in Cartesian directions \(x\) and \(y\), \(p\) is the pressure, \(e\) is the specific total energy and \(\varepsilon\) is the specific internal energy, and \(\gamma\) is the ratio of specific heat capacities at constant pressure and constant volume. Euler equations and relevant initial and boundary conditions do not contain any non-dimensional parameters. Euler equations written in dimensional and non-dimensional variables have the same form.  
Steady state flows are described with Euler Equation (1). In this case, it is assumed that flow is hyperbolic (velocity magnitude is larger than speed of sound, \(u^{2}+v^{2}>c^{2}\)) in one of the Cartesian directions. For example, hyperbolicity in \(x\) direction takes place if \(u^{2}>c^{2}\).
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 1 Introduction
A simulation model with fault injection and performance evaluation using queries is also provided to validate our approach. The rest of the paper is organized as follows: Section 2 presents some relevant works. Then, the WSN topology used in this paper is explored in Section 3. In Section 4, the theory for our WSN fault-tolerant model is provided. In Section 5, a fault detection and recovery scheme for our WSN is proposed. Then, a formal conception and verification of the proposed scheme is provided in Section 6 using the UPPAAL model checker. In Section 7, complexity estimation is introduced and a comparative analysis against some recent works is provided. Finally, Section 8 concludes the paper with future research direction.
# A Comparative Study of Storage Batteries for Electrical Energy Produced by Photovoltaic Panels
## 2 Materials and Methods
### Model in MATLAB-Simulink for Controlling Electric Energy Storage in Batteries
#### 2.1.2 Functioning of Model Developed in MATLAB-Simulink  
The electric energy produced by the photovoltaic panels is controlled using a step-down buck-type DC-DC convertor. The MPPT algorithm for incremental conductance is implemented in the control subsystem of the convertor as per Figure 4, with the help of a MATLAB function. In order to implement this algorithm, the author developed a program in the C++ programming language. The MPPT algorithm enables maximal transfer of energy from the photovoltaic panels to the battery. The design of the relations of DC-DC buck-type convertor elements is detailed in ref. [26]. The output of the buck convertor is connected to the storage battery by means of a power electronic circuit built with two IGBT-type transistors. Between the buck convertor and the power electronic circuit, a filter is inserted, based on an RC series circuit. The battery is connected to the two transistors, between the mass and the middle point of the circuit, by means of a group of resistors and inductors connected in series (RL). The transistor that receives the PWM control pulses of the type s_P will command the battery charging mode, whilst the transistor that receives the PWM control pulses of the type s_N will command the battery discharging mode. The two PWM signals of types s_P and s_N are interlocked in the control block of the battery through the module NOT. The error signal between the reference voltage value, equal to 24 V, and the voltage measured at the output of the buck convertor, Vbus, is processed through the discrete regulator PI_b1, which supplies the reference signal, Ib_ref, to the current regulator of the battery, PI_b. The current regulator, PI_b, processes the error signal between the reference value, Ib_ref, and the value of the current in the battery, Ib, in accordance with the adjustment law, PI, and provides, at the output, the control signal Duty_B for controlling the PWM signal generator block. The PWM signals at the output of the generator block (PWM Generator) are transmitted to the two transistors in the power electronic circuit. Variations in the photovoltaic panel parameters and battery parameters are measured with the help of oscilloscopes PV1 and PV2. The types and parameters of the batteries used in this model are presented in Figure 5.  
Figure 5: Types of batteries used in model developed in MATLAB-Simulink.  
In this model, two types of batteries are used: Lead-Acid and Lithium-Ion batteries. The parameters of these batteries are as follows: rated voltage = 12 V; rated capacity = 100 Ah; initial charging status = 45%; response time of battery = 1 s.
## 3 Results
### Themes Covered Across the Included Articles
#### 3.3.4 Air Pollution  
Six articles specifically addressed air pollution concerns at bus shelters due to their proximity to main roads with potentially high concentrations of air pollutants.  
[PERSON] et al. [36] focused on resuspended road dust dispersed by vehicles in Seoul, South Korea, and its impact on users of roadside bus shelters. Simulation models identified the most effective bus-stop design for reducing fine dust entry, with a combination of a curved fence and a canopy-type air-blast system showing an 88% reduction in particulate matter compared to a fence-free model.  
[PERSON] and [PERSON] [27] investigated air quality at transportation stops in Ethiopia, revealing unhealthy levels of air quality, particularly at busy intersections. [PERSON] et al. [37] researched short-term exposure to ultrafine particle pollution at bus shelters in street canyon environments in Guangzhou, China, with higher pollution levels recorded near 2-3 floor buildings compared to open spaces.  
In 2024, [PERSON] and [PERSON] [12] evaluated the Airbitat Oasis Bus Stop's ability to provide clean and cool air in Singapore. Despite the prototype's integrated cooling and filtering systems, the study showed only slight reductions in fine particles, air temperature, and increased relative humidity.  
[PERSON] et al. [41] reported on a design plan for air purification systems at two bus stops in an industrial South Korean city, utilizing computational fluid dynamics simulation to optimize system placement and analyse pollutant concentrations.  
Lastly, [PERSON] et al., examined the potential of vegetation in reducing airborne pollution in Nanjing City, China, with 1.5 m-high hedges proving most successful in filtering particulate matter [40].  
These studies collectively highlight the significant concern of air pollution in urban environments and the need for effective strategies to minimize exposure of bus users to fine particulate matter (PM\({}_{2.5}\)).