esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Performance Evaluations  
### Voltage and Current Waveforms  
The parameters and specifications of the circuit design for the high-frequency PDM-controlled soft-switching series load resonant inverter are listed in Table 1. Inductors \(L_{\text{S1}}\) and \(L_{\text{S2}}\) are set to 12 uH, as determined by the IGBT's peak voltage of 350 V. For this circuit, the \(\mathit{di/d_{\text{max}}}\) stress is 12.5 A/uS dynamic switch current and 3.8 uS current overlapping time. Figure 6 shows the IH fixed roller and installed heating coil self-inductance \(L_{1}\). The heating coil, shown in Figure 6, has a diameter of 50 mm and a width of 350 mm.  
The operating waveforms of voltage \(v_{\text{L}}\) and load current \(i_{\text{L}}\) for PDM duty ratios \(D_{\text{PDM}}\) = 0.8 and 0.2 are given in Figure 7. They show that the proposed high-frequency inverter can operate with PDM control.  
Figure 5: Operating conditions. (**a**) Circuit mode transitions. (**b**) Operating waveforms by simulations.  
Figure 8a,b illustrate the operating waveforms of the current and voltage of the power semiconductor switches \(S_{1}\) and \(S_{2}\). These figures show that ZCS soft-switching commutation is achieved in the transitions from turn-on and turn-off. The waveforms of the current and voltage of switches \(S_{1}\) and \(S_{2}\) are shown in Figure 8c,d for the beginning interval of the injection of power. Observing waveforms in Figure 8c,d, the switches \(S_{1}\) and \(S_{2}\) also operate for PDM control implementation using complete ZCS soft-switching commutation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Symbol** & **Value** \\ \hline DC input voltage & \(V_{\text{in}}\) & 280 V \\ Resonant capacitor & \(\text{Cr}\) & 0.49 \(\mu\)F \\ Frequency of switching & \(f\) & 20 kHz \\ Frequency of PDM & \(f\) PDM & 400 Hz \\ Snubber inductor & \(L_{\text{SL}}\), \(L_{\text{S2}}\) & 12 \(\mu\)H \\ Heating coil self-inductance & \(L_{1}\) & 90 \(\mu\)H \\ IH load time constant & \(\tau\) & 9.23 \(\mu\)s \\ Electromagnetic coefficient of coupling & \(k\) & 0.48 \\ IGBT (Mitsubishi: CT75 AM-12) & \(I_{\text{C}}\) & 75 A \\ \(V_{\text{CE}}\) & 600 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: Circuit design specifications.  
Figure 6: IH fixed roller and heating coil. (**a**) Fixed roller. (**b**) Heating coil.  
Figure 7: Load current \(i_{\text{L}}\) and voltage \(v_{\text{L}}\) waveforms under PDM. (**a**) Duty ratio \(D_{\text{PDM}}\) = 0.2. (**b**) Duty ratio \(D_{\text{PDM}}\) = 0.8.
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Contention-Free  
The QoS of the UWA network is enhanced by congestion-free FD-MAC UWAC protocols, which have better channelization, bandwidth efficiency, and high resistivity against frequency selective fading [46]. However, it is unable to handle the high network load and collisions, resulting in low data rates. As a result, the FD contention-free MAC protocolsused various methodologies to protect the network from massive collisions, frequency selective fading, and improved transmission efficiency, and some of which are discussed below.
* **FDMA**. The FDMA based FD-MAC protocol provides an easy and straightforward means of channelization for usable bandwidth, but it is unable to manage the large number of UW nodes, resulting in poor bandwidth utilization and frequency selective fading issues, resulting in significant data loss [46].
* **CDMA**. A CDMA-based FD-MAC protocol has been tested, and it has two major advantages: bandwidth efficiency and the ability to effectively reduce frequency selective fading by using a full-duplex channel. However, it is also unable to protect the network from accidental collisions, which reduce data rate [46].
* **Hybrid-CDMA**. The hybrid CDMA based FD-MAC protocols, such as frequency hopping (FH-CDMA), direct sequence (DS-CDMA), and time hopping (TH-CDMA) have been proposed to address the issues raised by the FD-FDMA based protocols. It was discovered that the majority of these protocols used four HD Desert starRB-1 modems for data transmission and reception to ensure FD communication. The FD-TH-CDMA MAC protocol can prevent collisions, while the FH-CDMA MAC protocol reduces frequency selective fading. We also discovered that certain hybrid MAC protocols provide hopping and substantial bandwidth benefits. Furthermore, the hybrid DS-CDMA and FDMA MAC protocol provides a medium for isolating two concurrently transmitting channels while maintaining FD communication. The FD-DS-CDMA, on the other hand, has some minor hardware problems. According to our findings, all of the above-mentioned protocols perform admirably; however, the FD-FH-CDMA outperforms the other FD-CDMA MAC protocols, particularly in the lack and bucket environments, with a near 90 percent success rate after three to four successful transmissions. Furthermore, it successfully mitigated multipath fading as well as frequency selective fading, resulting in high efficiency and low complexity [46].
# Engineering Applications of Adaptive Kalman Filtering Based on Singular Value Decomposition (SVD)
## 3 Singular Value Decomposition
Thus, the Kalman filter gain can be written as:  
\[K_{k}=P_{k}^{+}\big{(}P_{k}^{+}\big{)}^{-1}P_{k}H_{k}^{T}R_{k}^{-1}R_{k}\Big{(} H_{k}P_{k}H_{k}^{T}+R_{k}\Big{)}^{-1}, \tag{40}\]  
\[K_{k}=P_{k}^{+}\big{(}P_{k}^{+}\big{)}^{-1}P_{k}H_{k}^{T}R_{k}^{-1}\big{(}H_{k} P_{k}H_{k}^{T}R_{k}^{-1}+I\big{)}^{-1}, \tag{41}\]  
where \(I\) denotes the identity matrix. Next, from Equation (26), for \(\big{(}P_{k}^{+}\big{)}^{-1}\) we get:  
\[K_{k}=P_{k}^{+}H_{k}^{T}R_{k}^{-1}, \tag{42}\]  
\[K_{k}=U_{k}^{+}D_{k}^{+2}U_{k}^{+T}H_{k}^{T}L_{k}L_{k}^{T}. \tag{43}\]  
The Kalman gain \(K_{k}\) is fully defined, and the state vector measurement is given by:  
\[\left(U_{k}^{T}\right)^{-1}\!\!\left(D_{k}^{+}\right)^{-2}\!\!\left(U_{k}^{+} \right)^{-1}=\left(U_{k}^{T}\right)^{-1}\!\!V_{k}^{*}D_{k}^{*2}V_{k}^{*T}U_{k} ^{-1}, \tag{35}\]0  
```
Input: Initialization (\(k=0\)). Perform SVD on \(P_{\mathbf{0}}\) to determine the initial \(\mathbf{U_{0}}\) and \(\mathbf{D_{0}}\). The initial \(\mathbf{P_{0}}\in\mathbb{R}^{n\times m}\), where \(\mathbf{U_{0}}\) and \(\mathbf{D_{0}}\) are orthogonal and diagonal matrices, respectively. Matrix \(\mathbf{U_{0}}\) contains the singular values of \(\mathbf{P_{0}}\). Steps (\(k=1\) to n):
1. Update \(\mathbf{U_{k}^{+}}\) and \(\mathbf{D_{k}^{+}}\) by constructing the \((\mathbf{m}+\mathbf{n})\times\mathbf{n}\) matrix using a pre-array (32). Use only the right SVD factor V.
2. Compute the Kalman gain \(\mathbf{K_{k}}\) from Equation (43). 3. Update the estimate \(\hat{x}_{k}^{+}\) from Equation (44). Notice that \(\mathbf{L_{k}^{T}}\) is triangular, and the computation of \(\mathbf{H_{k}^{T}L_{k}}=\big{(}\mathbf{L_{k}^{T}H_{k}}\big{)}^{T}\) can be obtained from step 2. 4. Compute the extrapolation \(\hat{x}_{k+1}^{-}\); from Equation (3), \(\mathbf{U_{k+1}}\) and \(D_{k+1}\) can be obtained by the SVD from the Equation (21) pre-array matrix without forming the left orthogonal factor (the one associated with Equation (13) of the SVD). 5. Compute the measured noise time-variant matrix using Equations (29) and (30). Return: Compute \(\mathbf{L_{k}}\) using Equation (31) as \(\mathbf{L_{k}}=\big{(}\mathbf{R_{k}^{1/2T}}\big{)}^{-1}\). ```  
**Algorithm 1** Singular value decomposition-based Kalman filtering:  
The Algorithm 1 requires the Cholesky decomposition of noise covariance matrices \(R\); for time-invariant matrices, the Cholesky decomposition is performed once during the initial step--i.e., before the KF recursion. Note that the algorithm also requires the m \(\times\) m matrix inversion for the terms \(R^{\frac{1}{2}}\) and \(R^{\frac{1}{2}T}\) in Equation (31). Again, if the process noise covariance is constant over time, the matrix calculation is pre-computed while the filter is initialized.
## 5 Detection of the Robot Mounting Release from the Base Using Machine Learning Algorithms  
Detecting loose robot fasteners is key to ensuring the reliability and safety of the entire automated process. Traditional methods for detecting loose fasteners tend to rely on manual inspection or fixed rule-based systems that are error-prone and limited in adaptability. In contrast, machine learning algorithms can use large data sets to learn complex patterns related to mounting anomalies, enabling more robust and scalable detection solutions.  
As mentioned, choosing key signal features reduced the total number of training parameters from 81 to 36 for the whole dataset. However, the fact that the selected metrics are the same for each sensor must be considered. Therefore, it may be irrational to design and train machine learning models using data from all three sensors.  
Correct placement of the minimum required number of sensors also plays an essential role in condition monitoring. The manufacturer of the Balluff BCM0002 sensors suggests methods for sensor placement, shown in Figure 9. These methods are common for monitoring rotating components.  
However, we investigate the release detection of a sequential machine. For this reason, we decided to place one sensor in accordance with the manufacturer's recommendation (sensor 1 in Figure 3). The two remaining sensors were placed at the locations where the most variation in attachment release is expected to occur. For the above reasons, we decided to divide experiment into two steps:  
* Firstly, the selected models are trained and tested separately on data from each sensor.
* Next, for the final analysis, data from sensors that enable the models to demonstrate the highest accuracy will be utilized.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & \begin{tabular}{c} **Number of Fully** \\ **Connected Layers** \\ \end{tabular} & \
\begin{tabular}{c} **First Fully Connected** \\ **Layer Size** \\ \end{tabular} &
\begin{tabular}{c} **Next Fully Connected** \\ **Layers Size** \\ \end{tabular} & **Activation** \\ \hline Narrow Neural & 1 & 10 & - & ReLU \\ Network & 1 & 25 & - & ReLU \\ Wide Neural & 1 & 100 & - & ReLU \\ Network & 2 & 10 & 20 & ReLU \\ Trilayered Neural & 3 & 10 & 20, 30 & ReLU \\ Network & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Neural network models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Number \\ of splits: 20 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ensemble models.  
In the next section, we test designed machine learning techniques for detecting loose assembly elements in robotic assembly processes. We will use the signal features selected in the previous section for training and verification of the selected models. Our goal is to prove the main hypothesis of the current research and to establish basic concepts for future research in the field of technical diagnosis of sequential mechanisms using machine learning algorithms.
## 4 Conclusions  
Graphene nanoplatelet and nanosilver were utilized to enhance water-based drilling mud properties. According to density, rheology, and filtration studies, both graphene nanoplatelet and nanosilver are potential candidates as a chemical additive in the formulation of water-based mud. In this study, the results showed that the presence of graphene nanoplatelet and nanosilver gave insignificant effect on mud weight (density). The drilling mud with added graphene nanoplatelet exhibits an increment of its plastic viscosity by up to 89.2%. Unlike the nanographene platelet, the nanosilver increased the mud plastic viscosity by only 64.2%. Both graphene nanoplatelet and nanosilver reduced the yield point by 13.1% and 58.3%, respectively; in the form of 3 rd order polynomial correlation. A similar effect is observed with the fluid loss measurement (volume of filtrate) where the graphene nanoplatelet and nanosilver reduced the fluid loss by 89.0% and 77.7%, respectively. We also found that both the mud filter cake without the presence of nanoparticles and the mud filter cake with added graphene nanoplatelet or nanosilver are similar in which the texture is thin and smooth. In the future, these nanoparticles will be utilized at elevated temperatures and pressures for water-based drilling mud's performance enhancement.  
Conceptualization, H.H., K.A.E. and Z.A.; Investigation, H.H.; Validation, K.A.E., Z.A. and H.J.C.; Formal Analysis, H.H., Z.A. and H.J.C.; Resources, K.A.E. and H.J.C.; Project administration, H.H.; Writing-Original Draft Preparation, H.H.; Writing-Review & Editing, Z.A. and H.J.C.; Visualization, H.H. This research received no external funding.  
We acknowledge the YUTP grant (015 LC0-064) Universiti Teknologi PETRONAS for funding the publication of this work and we wish to thank the reviewers for making this a better paper. The authors declare no conflict of interest.
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 3 Problem Statement & System Model  
The most critical considerations in combating forest fires include the detection of a fire at its early stages, proper categorization of fire, and rapid response from fire prevention units. A vast variety of well-studied strategies are out there to solve this issue, but there is still some improvement needed because those systems are time-consuming. Established structures depend on semi-formal approaches, simulation, and checking, which are only appropriate for evaluating quantitative performance but do not guarantee correctness. Hence, several innovations have been developed, such as formal methods that can make the systems more efficient. The motivation of this paper is that none of the studies exist in this area using IoT and drones by integrating the approaches of graph theory and formal methods. Most importantly, there is a lack of system maintenance for the failure recovery of the network.  
The major objectives of this paper are to overcome these issues by constructing an optimized network using IoT devices for forest fire detection and counteraction, to develop a formal model of the system by integrating approaches to perform timely actions precisely, and to identify network failures, develop recovery procedures, and validate the formal model by utilizing the different resources available in the VDM-SL toolbox for validation and verification of the system.
# Predictive Modeling of Light-Matter Interaction in One Dimension: A Dynamic Deep Learning Approach
## 2 Methods
### FDTD-Based LMI Model Based on Three Energy Levels  
If there are three energy levels involved in the electron/photon dynamics, then the interaction medium can have six different electron transitions (\(1\leftrightarrow 2\), \(1\leftrightarrow 3\), \(2\leftrightarrow 3\)) and three electron transition frequencies (\(v_{3}>v_{2}>v_{1}\), \(\omega_{1}=2\pi(v_{2}-v_{1})\), \(\omega_{2}=2\pi(v_{3}-v_{1})\), and \(\omega_{3}=2\pi(v_{3}-v_{2})\)). In this case, the associated LMI process is governed by modified versions of Equations (1)-(4), which are given as follows:  
Numerical model for wave dynamics:  
\[\
abla^{2}E-\mu_{0}\varepsilon_{\infty}\frac{\partial^{2}E}{\partial t^{2}}= \mu_{0}\sigma\frac{\partial E}{\partial t}+\mu_{0}\frac{d^{2}(P_{1}+P_{2}+P_{3 })}{dt^{2}} \tag{5}\]Numerical model for polarization dynamics:  
\[\frac{d^{2}P_{1}}{dt^{2}}+\Gamma_{1}\frac{dP_{1}}{dt}+\omega_{1}{}^{2}P_{1}- \frac{\omega_{1}{}^{2}{P_{1}}^{2}}{2(N_{1}-N_{2})ed}+\frac{\omega_{1}{}^{2}{P_{ 1}}^{3}}{6(N_{1}-N_{2})^{2}e^{2}d^{2}}=\frac{(N_{1}-N_{2})e^{2}E}{m_{e}} \tag{6}\]  
\[\frac{d^{2}P_{2}}{dt^{2}}+\Gamma_{2}\frac{dP_{2}}{dt}+\omega_{2}{}^{2}P_{2}- \frac{\omega_{2}{}^{2}{P_{2}}^{2}}{2(N_{1}-N_{3})ed}+\frac{\omega_{2}{}^{2}{P_{ 2}}^{3}}{6(N_{1}-N_{3})^{2}e^{2}d^{2}}=\frac{(N_{1}-N_{3})e^{2}E}{m_{e}} \tag{7}\]  
\[\frac{d^{2}P_{3}}{dt^{2}}+\Gamma_{3}\frac{dP_{3}}{dt}+\omega_{3}{}^{2}P_{3}- \frac{\omega_{3}{}^{2}{P_{3}}^{2}}{2(N_{2}-N_{3})ed}+\frac{\omega_{3}{}^{2}{P_ {3}}^{3}}{6(N_{2}-N_{3})^{2}e^{2}d^{2}}=\frac{(N_{2}-N_{3})e^{2}E}{m_{e}} \tag{8}\]  
Numerical model for electron dynamics:  
\[\frac{dN_{1}}{dt}=-\Gamma N_{1}+\frac{N_{2}}{\tau_{21}}+\frac{N_{3}}{\tau_{31 }}-\frac{1}{(h/2\pi)\omega_{1}}E\frac{dP_{1}}{dt}-\frac{1}{(h/2\pi)\omega_{2}} E\frac{dP_{2}}{dt},\quad(\Gamma=\Gamma_{1}+\Gamma_{2}) \tag{9}\]  
\[\frac{dN_{2}}{dt}=\Gamma_{1}N_{1}+\frac{1}{(h/2\pi)\omega_{1}}E\frac{dP_{1}}{ dt}-\frac{N_{2}}{\tau_{21}}+\frac{N_{3}}{\tau_{32}} \tag{10}\]  
\[\frac{dN_{3}}{dt}=\Gamma_{2}N_{1}+\frac{1}{(h/2\pi)\omega_{2}}E\frac{dP_{2}}{ dt}-\frac{N_{3}}{\tau_{31}}-\frac{N_{3}}{\tau_{32}} \tag{11}\]  
Notice that in this case, the electric field wave equation (Equation (5)) is stimulated by three different polarization terms (\(P_{1},~{}P_{2},~{}P_{3}\)) which are governed by Equations (6)-(8), representing the dynamics of bound charge polarization densities as induced by each electronic transition. The electron populations at each level are modeled via Equations (9)-(11), which are coupled to the Lorentz dispersion equations (Equations (6)-(8)) through the source term and the nonlinear polarization terms. Hence, to solve for the electric field intensity, one must solve all seven equations (Equations (5)-(11)).
## SNR Wall Effect Alleviation by Generalized Detector Employed in Cognitive Radio Networks  
**[PERSON] \({}^{\dagger}\) and [PERSON] \({}^{\dagger,\star}\)**  
School of Electronics Engineering, College of IT Engineering, Kyungpook National University,  
1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-53-950-5509; Fax: +82-53-950-5506.  
Academic Editor: [PERSON]  
_Received: 8 April 2015 / Accepted: 18 June 2015 / Published: 3 July 2015_  
**Abstract:** The most commonly used spectrum sensing techniques in cognitive radio (CR) networks, such as the energy detector (ED), matched filter (MF), and others, suffer from the noise uncertainty and signal-to-noise ratio (_SNR_) wall phenomenon. These detectors cannot achieve the required signal detection performance regardless of the sensing time. In this paper, we explore a signal processing scheme, namely, the generalized detector (GD) constructed based on the generalized approach to signal processing (GASP) in noise, in spectrum sensing of CR network based on antenna array with the purpose to alleviate the _SNR_ wall problem and improve the signal detection robustness under the low _SNR_. The simulation results confirm our theoretical issues and effectiveness of GD implementation in CR networks based on antenna array.  
**Keywords:** cognitive radio (CR); spectrum sensing; generalized detector (GD); energy detector (ED); noise uncertainty; sample complexity; antenna array; _SNR_ wall
# Barley Malt as a Binder for Moulding Sands--Gas Evolution and Surface Quality of Iron Castings
## 3 Results and Discussion  
### Volume and Rate of Gas Emission  
Figure 7 shows the volume and rate of emitted gases over time for moulding sand with barley malt as a binder (D1: sample No. 1; D2: sample No. 2). Table 1 shows the results of the determination of compounds from the BTEX group, calculated per 1 kg of moulding sand.  
Figure 7: Gas volume and gas evolution rate emitted from moulding sand: (**a**) volume of gas evolved for samples No. 1 (D1) and No. 2 (D2); (**b**) gas evolution rate for evolved for samples No. 1 (D1) and No. 2 (D2).  
The volume of gases generated when pouring a standard shape made of moulding sand with dried barley malt is less than 18 dm\({}^{3}\)/kg of mass and should not have a negative impact on the surface quality of the castings. The gas emission rate is the highest in the initial heating phase (immediately after pouring liquid cast iron into the sample) and decreases over time. There are no intense exothermic effects resulting in a significant increase in the gas emission rate over time, which should be considered satisfactory. The highest emission among the compounds from the BTEX group was recorded for benzene and amounts on average to approximately 271 mg/kg of moulding sand. Emissions of toluene, xylenes, and ethylbenzene are low for the sample. Xylene emissions represent the combined content of m-, p-, and o-xylene, hence these values are higher than those for ethylbenzene. The samples exhibit emissions of BTEX compounds, with benzene being the most prominent, which is correlated with the temperature of gas extraction. Based on the literature review Table 2, it can be concluded that for the composition used in the research, the barley malt pulp shows a similar emission of BTEX compounds as the popular binder used in the Alfaset technology and the pulp with a phenolic binder (Table 2). It is also less harmful to the environment than bentonite moulding sand with a shiny carbon carrier. Due to the fact that it belongs to organic binders, it has a higher emission level than inorganic binders. It should be noted that sands with barley malt emit a similar volume of gases when sand samples are poured with liquid casting alloy (grey cast iron at a temperature of 1350 \({}^{\circ}\)C); therefore, they do not have a greater tendency to create defects in gaseous castings. It should be noted that inorganic binders have disadvantages such as poor knock-out properties, incompatibility with bentonite moulding sands (green sands), and poor susceptibility to mechanical regeneration, which is not the case with organic binders.  
Based on the results obtained, it can be concluded that moulding sands with barley malt as a binder do not cause excessive emission of harmful gas products when pouring moulds with liquid casting alloy. The analysis of gas emissions allows us to conclude that their gas formation is lower than that of commonly used moulding sands with other binders.
## 3 Results & Discussions
### Hydrological Thresholds  
Figure 10 illustrated the correlations of factor of safety with soil moisture content, matric suction, and daily rainfall. The results indicated that the thresholds of volumetric water content and matric suction for triggering landslides were about 0.16 and 2 kPa respectively. However, the correlation of factor of safety with daily rainfall exhibited a rather poor relationship. The hydrological processes and slope stabilities were affected by infiltration, evaporation, drainage, soil hydraulic properties, and the antecedent rainfall, all these factors brings uncertainty in the statistical rainfall threshold. The rainfall threshold shall be calculated by including long-term historical monitoring information [11]. By means of the in situ big data analysis and slope stability analysis of partial saturation in response to rainfall infiltration, the hydrological parameter criteria of moisture content and matric suction for early warning of rainfall-induced shallow landslides in the Wenchuan earthquake regions can be established by coupling the factor of safety with these parameters.