esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
For Wi-Fi communication, the ESP32 module and the internal Wi-Fi module are programmed on the TTGO card. Likewise, through Arduino libraries, the Ubidots IoT software is used where the sensor data are stored and displayed through the Ubidots user interface on computers or smartphones. * The DHT22 sensor has an accuracy of 0.5 \({}^{\circ}\)C, which allows for very accurate temperature readings, unlike commercial sensors such as the DHT11 that have an accuracy of 2 \({}^{\circ}\)C. On the other hand, the TTGO card has several modules to be used in different functions, such as ESP32, LORA communication, WI-FI. It incorporates GPS and multiple inputs and outputs to work with analog and digital signals, and a battery power supply; this allows the card to be deployed in any rural environment and to use the most convenient communication. The technologies that merge in this research are IoT and agriculture, since telecommunications, electronics and agriculture sciences are used to improve humps production. * The water was sprayed in the form of a mist using plastic nebulizer sprinklers so that the moisture stayed on the surface and the worms were always at the top, eating the food and depositing the fertilizer at the bottom. If any errors are discovered, the watchdog will reset the system. The recording of the data in reading the variables in real time through a mobile device is evidenced. Figure 6 depicts the project, which is developed with seven worm beds; each bed has an area of 24 m\({}^{2}\) (2 m wide \(\times\) 12 m long). Figure 6: Test of the automated irrigation system for the control of the humidity and temperature variables. Figure 6 depicts how cow excrement is used as worm food. After wetting and drying the manure, it is allowed to rest for two to three weeks so that the gastric acids from the cattle can be removed before the worms use it as food. Worms typically consume 250 kg of food per month, but once the system was installed, this amount climbed to an average of 650 kg per month, which shortened the time it took to produce humus or compost. Step 5: Defining the path of data analysis:  
Construct the instrument to collect the information. For data collection, temperature and humidity sensors and weight scales will be implemented. The measurements will be carried out by the same operator daily and will be recorded in a technical sheet designed by the authors. For the analysis of the data collected, it is intended to execute the following route, depicted in Figure 7, which is aligned with the objectives of the project and allows the expected results to be obtained:
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 3 Methodology
### Simulation of a Battery Pack  
In this sub-section, the output voltage of an electric vehicle battery is simulated. In this case, the input values of a cell will be based on real-life data from an electric vehicle, which corresponds to the vehicle model referenced here is the Nissan Leaf (2019), because it is the most called EV.  
To obtain the 350 V of the car's battery, a battery pack that is composed of 89 sub-models of cells in a series with a \(SOC\) of 80 % was designed. Three different values of \(SOC\) are considered to assess their impact on the final battery voltage \(V_{bat}\): \(SOC=0.1\), \(SOC=0.5\), and \(SOC=0.9\). Figure 5 illustrates the output voltage of the battery when considering these various values of the initial \(SOC\). In this case, to assess the impact of the \(SOC\) in the output battery voltage, the values of \(SOC=0.1\) and \(SOC=0.9\) were selected, since they are critical values in the lowest and highest limits.  
Observe that, with an initial \(SOC\) of 0.1, a significant voltage drop from the expected value and takes almost 0.1 to reach the final value. When the initial \(SOC\) is higher, the final value is reached faster, and there is no significant variation between the initial and final value.  
Figure 5: Output of the battery voltage with: (**a**) \(SOC=0.1\); (**b**) \(SOC=0.5\); and, (**c**) \(SOC=0.9\).
## 1 Introduction
The full-field optical measurement technique referred to as amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) is highly effective in determining the resonant frequencies and corresponding vibration mode shapes of piezoelectric materials. [PERSON] and [PERSON] [16; 17] employed the AF-ESPI method to investigate the vibration characteristics of piezoelectric cylinders and rectangular parallelepipeds in order to obtain the resonant frequencies and mode shapes. [PERSON] et al. [18] used three experimental techniques, including AF-ESPI, laser Doppler vibrometry (LDV), and impedance analysis to validate a theoretical analysis model derived for piezoceramic disks under stress-free boundary conditions. They also used theoretical analysis, numerical simulation, and experimental measurements to assess the vibration characteristics for piezoceramic annular disks. [PERSON] and [PERSON] [19] used AF-ESPI and numerical analysis to investigate the out-of-plane resonant characteristics of a cantilevered piezoceramic plate in air and three types of fluid. Both experimental and theoretical measurements revealed that the resonant frequencies decreased with an increase in the viscosity of the fluids. A number of researchers have studied the characteristics of micropumps equipped with a single bimorph; however, there has been relatively little research on the vibration of pumping elements equipped with two bimorphs. In this study, we developed a pumping element that uses two cantilevered piezoelectric bimorphs rather than a single bimorph in order to increase the flow rate. Then, we used AF-ESPI, LDV, and impedance analysis to investigate the vibration characteristics of two piezoelectric plates coupled to bounded compressible inviscid fluids. Finally, we formulated a numerical model using the commercial software ABAQUS v6.13.2 [20] to facilitate a comparison of the results. The gain of flow rate and the flow velocity increment of the pumping element were also investigated through the experiment. Our findings provide a novel perspective for the further development of piezoelectric pumping elements driven by two bimorphs.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 4 Results and Discussion
### Theoretical Solution Error of Laminated Composite T-Beam  
The theoretical and finite element analyses of symmetric and non-symmetric laminated composite T-beams are shown in Figures 4 and 5, respectively. The deflection and stress, calculated using both methods, were compared at different aspect ratios of the beam. The results indicated that the theoretical solution of 1D used the Kirchhoff-Love hypothesis. Therefore, only the axial strain (\(\varepsilon_{x}\)) of the beam was considered in the theoretical derivation for 1D structures, whereas the plane (\(\varepsilon_{x}\), \(\varepsilon_{y}\)) and shear (\(\gamma_{xy}\)) strains were considered in the derivation for the 2D structures. The data suggested that the aspect ratios less than 10 are not sufficiently accurate, owing to the increasing effect of the shear force on the deflection and stress with the decreasing beam length. Therefore, the theoretical formula for composite T-beams derived in this study is not suitable for short beams (L/h \(<\) 10). When the aspect ratio was higher than 10, the error curve tended to be flat and stable values were obtained for the bending stress and deflection. Therefore, the derived composite T-beam formula is accurate and suitable for thin and long beams.  
Figure 3: Finite element model and convergence analysis (a) mesh distribution and boundary conditions (b) element convergence analysis.
## 3 Results and Discussion  
### Compressive Strength  
Figure 4 shows the compressive strengths of 28-day old base concrete containing hybrid fibers. When the vol% of the hybrid fibers was 0.54%, there was no significant correlation on compressive strength with the amount of polyolefin fiber in the hybrid fiber. In the case of 0% polyolefin fibers (100% steel fibers), the compressive strength was 48.99 MPa. The strength reduced to 41.24 MPa in the case of 25% polyolefin fibers (75% steel fibers). However, at 50% polyolefin fibers (steel fibers 50%), polyolefin fibers 75% (steel fiber 25%), and polyolefin fiber 100% (steel fiber 0%), the compressive strengths increased to 48.07 MPa, 48.43 MPa, and 50.69 MPa, respectively. At 0.54% fiber content, the ratio between polyolefin and steel fiber does not significantly affect the compressive strength of base concrete.  
When the vol% of base concrete increased to 1.08%, a clear correlation on compressive strength on the amount of polyolefin fiber in the hybrid fiber was observed. As the amount of polyolefin fiber in the hybrid fiber increased, the 28-day compressive strength of base concrete generally decreased. The compressive strength of base concrete with 0% polyolefin fiber (100% steel fiber) was 56.20 MPa and decreased to 55.53 MPa, 52.18 MPa, and 46.39 MPa, as the volume proportion of polyolefin fiber increased from 25%, 50%, and 75%,  
Figure 4: The 28-day compressive strength of hybrid fiber reinforced base concrete.  
respectively. The compressive strength of the base concrete with 100% polyolefin showed higher compressive strength (51.07 MPa) than the base concrete with 75% polyolefin fiber (46.39 MPa).  
Base concrete with a higher amount of fiber showed a higher compressive strength up to 50% of replacement with polyolefin fiber. When the volume proportion of polyolefin fiber was 75%, the compressive strength did not show any difference as the volume of fiber increased. This indicates that polyolefin fibers contribute much less to the compressive strength than steel fibers. Taking this finding into consideration, in terms of compressive strength, the amount of polyolefin fiber should not exceed 50% for successful hybridization of steel and polyolefin fibers.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 4 Performance Analysis of the UAV-BS
### Experiment Results of the Coverage Area Decision Model for the UAVs-BS
#### 4.1.2 Throughput Coverage Analysis with Path Loss Exponent and Users in Macro Cell  
In this section, the second experiment is performed to check the performance of the proposed model CADM-UAVs compared to without UAV deployment to check the throughput coverage percentage. Figure 5 shows the throughput analysis.  
In throughput analysis, the throughput coverage percentage is defined. The coverage percentage of users is defined with a high SINR from the threshold, which is around 65%. As shown in Figure 5 the proposed model throughput coverage is better compared with non-UAV networks. The path loss exponent refers to the distance between the transmitter and the receiver and is measured in meters. Figure 6 shows the throughput analysis in the presence of users in a single macrocell.  
In this experiment, the throughput analysis is performed in the presence of several users in a single macrocell. Figure 6 indicates the better throughput percentage of the proposed model CADM-UAVs compared to the non-UAV network. This result is performed to set the threshold value, which is 65%. The placement of UAVs based on user demand improves the model performance, as depicted in the above experiment. It is indicated that when users are around 800 in a single macrocell, the performance of the proposed models is better, and at 70%, throughput converges. These results proved that the proposed CADM-UAVs model is one of the feasible solutions for cellular networks.  
Figure 4: Delay analysis in the presence of user at cellular networks.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 1 Introduction
### Organization  
The rest of the paper is organized as follows. In Section 2, the considered system model is presented. Section 3 theoretically derives the closed-form expressions of the outage probability and reliable throughput of the system. Simulation results are presented in Section 4 to validates our derived theoretical results. Finally, Section 5 draws the conclusions.
# Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
## 4 Method
### Evaluation Metrics  
The metrics of accuracy, precision, recall, and F1-score were used to evaluate all the algorithms above, and the exact calculation methods are described below.  
The special values were explained as follows:  
* TH (True Harsh): number of correctly detected \"Harsh\".
* TB (True Blowing): number of correctly detected \"Blowing\".
* FH (False Harsh): number of incorrectly detected \"Harsh\".
* FB (False Blowing): number of incorrectly detected \"Blowing\".  
In addition, H is for \"Harsh\" and B is for \"Blowing\".  
The evaluation metrics were calculated as follows:  
1. Accuracy: percentage of the number of correctly detected segments (patients) to the total number of segments (patients). \[Accuracy=\frac{TH+TB}{TH+TB+FH+FB}\] (6)
2. Precision: percentage of the number of correctly detected \"Harsh\" (\"Blowing\") segments (patients) to the total detected \"Harsh\" (\"Blowing\") segments (patients). \[Precision_{H}=\frac{TH}{TH+FH},Precision_{B}=\frac{TB}{TB+FB}.\] (7)
3. Recall: percentage of the number of correctly detected \"Harsh\" (\"Blowing\") segments (patients) to the total real \"Harsh\" (\"Blowing\") segments (patients). \[Recall_{H}=\frac{TH}{TH+FB},Recall_{B}=\frac{TB}{TB+FH}.\] (8)
4. F1-score: weighted average of precision and recall. \[F1_{H}=2*\frac{Precision_{H}*Recall_{H}}{Precision_{H}+Recall_{H}},F1_{B}=2* \frac{Precision_{B}*Recall_{B}}{Precision_{B}+Recall_{B}}.\] (9)  
A comprehensive and thorough understanding of the model's performance is given by these metrics, as they indicate the model's ability to correctly detect the murmur quality (accuracy), the extent to which it produces misdetections (precision) as well as misses of the correct class (recall), and the different performances in detecting the two classes (F1-score).
# Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
## 3 Kinematics and Dynamics  
An an inverted pendulum is a highly unstable, open-loop system [33], the present system is analogous to that of an inverted pendulum. It is implemented as an inverted pendulum pivoted on a cart that can move horizontally under the control of an electronic system, as shown in Figure 2.  
The cart is analogous to the monowheel itself, while the mounted pendulum is analogous to the rider. All model parameters are mentioned in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** \\ \hline m & Mass of the pendulum rod & 75 kg \\ M & Mass of the cart & 35 kg \\
1 & Pendulum length up to COG & 0.6 m \\ J\({}_{m}\) & Motor rotor moment of inertia & \(3.26\times 10^{-2}\) kg\(\cdot\)m\({}^{2}\) \\ R\({}_{m}\) & Motor armature resistance & 6.5 \(\Omega\) \\ k\({}_{b}\) & Motor back EMF constant & 0.013 V/rad/s \\ k\({}_{t}\) & Motor torque constant & 0.58 N\(\cdot\)m/A \\ R & Motor pinion radius & 0.04 m \\ C & Friction coefficient for cart & 0.04 N\(\cdot\)m/s \\ I\({}_{p}\) & Inertia of pendulum rod & 6.75 kg\(\cdot\)m\({}^{2}\) \\ G & Gravitational constant & 9.81 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nominal values of model parameters.  
Figure 2: Pendulum cart system—analogous to the monowheel system.  
The kinematics of the system are defined as  
\[\begin{bmatrix}x_{p}\\ y_{p}\end{bmatrix}=\begin{bmatrix}x+l\ sin\ \theta\\ -l\ cos\ \theta\end{bmatrix}. \tag{1}\]  
The Lagrangian for a system of particles is defined by  
\[\mathcal{L}=T-U, \tag{2}\]  
where \(U=-mglcos\ \theta\) is the total potential energy of the system, and \(T\) is the total kinetic energy, which is the sum of the translational (\(T_{t}\)) and rotational kinetic energy (\(T_{t}\)). The total translational (\(T_{t}\)) kinetic energy includes the kinetic energy of both the cart and pendulum; i.e., \(T_{t}=T_{tc}+T_{tp}\). The translational kinetic energy \(T_{tc}\) cart is given by  
\[T_{tc}=\frac{1}{2}M_{c}\dot{x}^{2}. \tag{3}\]  
The rotational kinetic energy (\(T_{tp}\)) cart is given by  
\[T_{tp}=\frac{1}{2}m\bigg{[}\bigg{(}\frac{dx_{p}}{dt}\bigg{)}^{2}+\bigg{(} \frac{dy_{p}}{dt}\bigg{)}^{2}\bigg{]}. \tag{4}\]  
From (1)-(4), we get  
\[\mathcal{L}=\frac{1}{2}M_{c}\dot{x}^{2}+\frac{1}{2}m\dot{x}^{2}+\frac{1}{2}ml^ {2}\dot{\theta}^{2}+ml\dot{x}\dot{\phi}cos\ \theta+\frac{1}{2}l\dot{\theta}^{2}+mglcos\ \theta. \tag{5}\]  
Since the system has 2 DOF, it possesses two Lagrangian equations of motion, as given below:  
\[\frac{d}{dt}\frac{\partial\mathcal{L}}{\partial\dot{x}}-\frac{\partial \mathcal{L}}{\partial x}=F-c\dot{x}, \tag{6}\]  
\[\frac{d}{dt}\frac{\partial\mathcal{L}}{\partial\dot{\theta}}-\frac{\partial \mathcal{L}}{\partial\dot{\theta}}=-b\dot{\theta}. \tag{7}\]  
From (5)-(7), we get  
\[(M+m)\ddot{x}+ml\ddot{\theta}cos\ \theta+ml\dot{\theta}^{2}sin\ \theta=F-c\dot{x}, \tag{8}\]  
\[(I_{p}+ml^{2})\ddot{\theta}+ml\dot{x}cos\ \theta+mglsin\ \theta=-b\dot{\theta}. \tag{9}\]
###### Abstract  
The developed laboratory platform and associated algorithms can be applied in educational and research settings for real-time simulation, detection, and isolation of multiplicative faults in electromechanical systems. Its remote-access capability allows engineering students and researchers to conduct fault diagnosis experiments on DC motor systems without physical presence, supporting distance learning and development of robust FDII methods for safety-critical applications.  
The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. The study focuses on the FDII of multiplicative faults in a DC motor and its electronic amplifier. To simulate such scenarios, a complete laboratory platform was developed for real-time FDII, using relay-based switching and custom LabVIEW software 2009. This platform enables real-time experimentation and represents an important component of the study. Two estimation-based fault detection (FD) algorithms were implemented: the Sliding Window Algorithm (SWA) for discrete-time models and a modified Sliding Integral Algorithm (SIA) for continuous-time models. The modification introduced to the SIA limits the data length used in least squares estimation, thereby reducing the impact of transient effects on parameter accuracy. Both algorithms achieved high model output-to-measured signal agreement, up to 98.6% under nominal conditions and above 95% during almost all fault scenarios. Moreover, the proposed fault isolation and identification methods, including a decision algorithm and an indirect estimation approach, successfully isolated and identified faults in key components such as amplifier resistors (R\({}_{1}\), R\({}_{9}\), R\({}_{12}\)), capacitor (C\({}_{8}\)), and motor parameters, including armature resistance (R\({}_{\text{a}}\)), inertia (\(J\)), and friction coefficient (\(B\)). The decision algorithm, based on continuous-time model coefficients, demonstrated reliable fault isolation and identification, while the reduced Jacobian-based approach in the discrete model enhanced fault magnitude estimation, with deviations typically below 10%. Additionally, the platform supports remote experimentation, offering a valuable resource for advancing model-based FDII research and engineering education.  
fault detection, isolation, and identification; multiplicative faults; DC motor; electronic amplifier; Sliding Window Algorithm; Sliding Integral Algorithm; remote laboratory
## 3 Proposed Fast Multichannel Parallel Acoustic Score Computation
As shown in Table 4, the transmission sizes are \(\mathcal{N}_{bat}\times k\) for \(k\) speech feature vectors from CPU to GPU and \(\mathcal{N}_{node}\times k\) for acoustic scores from GPU to CPU, respectively, when the worker thread is ready and \(\mathcal{M}^{CPU(W)}_{feat\_vec\_f}\) contains \(k\) speech feature vectors. In addition, the frequency is varied according to the size of \(k\) from \(\mathcal{N}_{channel}/\mathcal{N}^{GPU}_{parallel}\) to \(\mathcal{N}_{channel}\). Assuming that the number of concurrent clients is \(\mathcal{N}_{channel}\) and the number of decoded speech feature vectors obtained by the proposed method is \(1\leq k\leq\mathcal{N}^{GPU}_{parallel}\), the main differences between the baseline and proposed acoustic score computation methods are as follows:  
**Decoding subject(s)** The decoder thread of each client calculates acoustic scores in the baseline method, whereas the additionally used worker thread does so in the proposed method. Transmission frequencyThe transmission occurs \(2\times\mathcal{N}_{channel}\) times in the baseline method and \(2\times k\) times in the proposed method. Therefore, the proposed method reduces the transfer frequency by \(k\). Transmission sizeFor a transmission from CPU to GPU, the baseline method transmits \(\mathcal{N}_{win}\times\mathcal{N}_{seg}\) for each client, whereas the proposed method \(\mathcal{N}_{bat}\times k\) does so for each decoding turn. The total transmission data size to GPU is reduced by the proposed method. On the other hand, for the transmission from GPU to CPU, the baseline method transmits \(\mathcal{N}_{node}\) for each client, whereas the proposed method transmits \(\mathcal{N}_{node}\times k\) for each decoding turn. The total transmission size to CPU is equal. Decoding size at a timeThe baseline method decodes one speech feature vector, whereas the proposed method decodes \(k\) vectors. This leads to more GPU parallelization.