esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Discussion
### Analysis of Fusion Methods
Although TG2-CAFNet demonstrated an excellent recognition performance, it still produced misclassifications of certain actions. By observing the matrices, it can be seen that misclassification and omission errors mainly occurred in the three groups of actions enclosed in red rectangles. These three groups consisted of similar actions, where differences in amplitude, frequency, and period were not obvious. In fact, when a person performs one action, the characteristics of their movements may resemble those of another person performing a different action, further increasing the difficulty of recognition. Therefore, in order to reduce errors, it is important to enhance the distinction and extraction of features for similar behaviors. Methods that could be applied include performing radar-based temporal analysis, introducing complex radar data, and further integrating other data types. These approaches aim to provide a comprehensive analysis of human behaviors from multiple perspectives, thereby minimizing the model's misclassification rate. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Group 1** & **Group 2** & **Mean Diff** & \(p\)**-adj** & **Confidence Interval** \\ \hline Concatenation & Multiplication & 0.0041 & 0.0003 & [0.0016, 0.0066] \\ Concatenation & Summation & 0.0026 & 0.0418 & [0.0001, 0.0051] \\ Concatenation & TG2-CAFNet & 0.0114 & 0.0 & [0.0089, 0.0139] \\ Multiplication & Summation & \(-\)0.0015 & 0.4044 & [\(-\)0.004, 0.001] \\ Multiplication & TG2-CAFNet & 0.0073 & 0.0 & [0.0048, 0.0098] \\ Summation & TG2-CAFNet & 0.0088 & 0.0 & [0.0063, 0.0113] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tukey HSD test results of fusion methods.
###### Abstract  
Due to the integration of distributed photovoltaic (PV) into distribution networks, significant challenges have affected voltage regulation and power quality maintenance. To improve the flexibility and stability of system operation, a synergetic optimization operation method based on PV and a flexible interconnection device (FID) is proposed. Both PV and FID hold the capability of controlling active power and reactive power. Besides the active power output of PV, three reactive power output schemes of power factor controlling, direct reactive power output, and night static var generator scheme are defined and analyzed. By adopting different schemes during the day or night, five reactive power output modes were built. FID with four-quadrant power control ability was used to coordinate with PV in system power balance. Different port numbers of FIDs are discussed. An optimization model with the aim of reducing voltage deviation, network loss, and the ratio of PV abandonment was constructed. Three algorithms were used for solving the multi-objective optimization model. Simulation results verify that the proposed synergetic optimization method can obviously improve power quality and decrease network loss. The optimal performance is obtained when PV operates in mode 5 and FID holds four ports. The proposed method shows potential in the coordinated operation of various resources and the flexible interconnection of the distribution network.  
synergetic optimization; distribution network; photovoltaic (PV); reactive power output mode; flexible interconnection device (FID) +
Footnote †: journal: Applied sciences
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
## 4 Clutter Modeling from Experimental Data  
RSE OFDM signals were generated in the frequency domain and converted to the time domain as per Equation (1). The transmitted signals contained \(L=16\) subcarriers, each with a uniform amplitude weight of 1. Ten different trials were performed, with \(2\cdot 10^{6}\) collected samples each. These data samples of a random process were composed of cumulative effects of clutter and noise. To collect samples of noise only, the transmitter was turned off while the receiver was on and the same quantity of data was recorded.  
Once the noise and return signals' samples were collected and converted to frequency domain, two sets of PDFs were created for each trial/dataset: (a) noise and \"noise + clutter\" PDFs obtained directly from the collected data by using the function _ksdensity_ in MATLAB(r)(r)(r)(r), and (b) noise and \"noise + clutter\" PDFs recreated from clutter pdf \(p_{C}(x)\) found from (8). Based on the obtained data, we modeled the clutter in the frequency domain using the Arcsine distribution (r)(r)(r).  
Then, we performed mean square error (MSE), normalized mean square error (NMSE), and normalized root mean square error (NRMSE) tests on the two sets of PDFs. The results of these tests are shown in Table 1. Note that 0 is the best value for MSE and 1 is the best value for NMSE and NRMSE. It is evident that the PDFs for same processes obtained in these two ways are well matched, which confirms the validity of our method.  
The expressions for the inverse cumulative distribution function (CDF), PDF, and CDF of the Arcsine distribution are shown in Equations (8)-(10), respectively.  
\[F^{-1}(u) =b\sin\biggl{(}\Bigl{(}\frac{u\pi}{2}\Bigr{)}^{2}\biggr{)}, \tag{8}\] \[f(x) =\frac{1}{\pi\sqrt{x(x-b)}},\] (9) \[F(x) =\frac{2}{\pi}\sin^{-1}\biggl{(}\frac{x}{b}\biggr{)}. \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Probability Density** & **Mean Square Error** & \
\begin{tabular}{c} **Normalized Mean** \\ **Square Error** \\ **(NMSE)** \\ \end{tabular} &
\begin{tabular}{c} **Normalized Root** \\ **Mean Square Error** \\ **(NRMSE)** \\ \end{tabular} \\ \hline Directly Obtained & \multirow{2}{*}{6.758 \(\times\) 10\({}^{-6}\)} & \multirow{2}{*}{0.9985} & \multirow{2}{*}{0.9616} \\ (Noise+Clutter) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of clutter and Arcsine distribution.  
Figure 1: Lab setup: (a) Ultra-wideband (UWB) software-defined radar system (SDRS) block diagram; (b) SDRS transceiver in laboratory setting.
## 1 Introduction
### Literature Review
#### 1.1.2 Integrated Optimization of Train Stop Planning and Timetabling  
In the railway transportation system, train stop planning and timetabling are two fundamental problems. As train timetabling is directly influenced by the stop plan, the integrated optimization of the above two problems could provide a high-quality service for both passengers and operators; this is also confirmed in some studies. To minimize both user and operator costs, [PERSON] and [PERSON] [14] established an optimization model integrating line plan and timetable, which was handled by a cross-entropy metaheuristicmethod. It was found that the average journey time of passengers decreased by 20%. Aiming to reduce passenger waiting times, in-vehicle travel times and train travel times in an urban rail transport system, [PERSON], et al. [15] established a 0-1 integer programming model considering the skip-stop operation strategy. They used the tabu search algorithm to address the problem. [PERSON], et al. [16] studied periodic timetabling models integrated with passenger routing, which showed that different modeling methods of route planning had a notable effect on the service quality of the entire transport system. [PERSON], et al. [17] provided an integrated optimization method to decide the train stop pattern and timetable in high-speed railways. The objective function considered is to minimize the total dwell time at intermediate stations and total train delay time at their starting stations; they used the CPLEX optimizer to solve the proposed model. [PERSON], et al. [18] built a coordination-optimization model considering the train stop pattern and train timetable together for the high-speed railway, and designed an algorithm based on the column generation technology to solve a large-scale train timetabling problem. [PERSON], et al. [19] considered passenger robustness in the integration optimization of line plan and train timetable. They built an iterative and interactive framework for the integrated optimization model to generate a robust timetable with a lower cost. [PERSON] [20] proposed an eigenmodel, which could determine line plan, train timetable, and vehicle schedule integrally. An iterative algorithm was designed as heuristics for this integrated problem. To schedule additional trains on the basis of the original timetable, [PERSON], et al. [21] presented a novel double-objective mixed integral programming model with consideration of acceleration/deceleration times and platform assignment at a macroscopic level. A three-phase optimization approach was applied to deal with this problem. Taking the robustness and regularity of the timetable and the passenger travel time into account, [PERSON] and [PERSON] [22] proposed a multi-period model and a multi-frequency model. These two models worked iteratively to achieve more competitive planning in the rail transport system.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Proactive Routing Protocols  
The proactive routing protocols (PRPs) are also called table driven or active routing protocols. In this kind of routing protocol, the routing table is periodically updated and stored on each UAV, which indicates the whole topology of the network. Thus, the routing paths can be promptly available for transmitting data packets when required. The main advantage of proactive routing protocols is that they contain the most recent information about the routes. However, they introduces additional signaling overhead due to their maintaining up-to-date information about the network. As a result, the throughput of the network may be affected, as control messages are transmitted throughout unnecessarily. Consequently, proactive routing protocols are not suitable for high-mobility and large networks. Moreover, when the connection fails or topological change occurs, PRPs show slow reactions. There are various protocols that represent this class [19].
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 2 Seismic Data and Geological Background
### The A Field Real Seismic Dataset  
In this paper, the A Field in the Malay basin is used to test the proposed workflow and model on real seismic data. The A Field is located in the offshore of Peninsula Malaysia, which lies in the southern part of the Malay Basin (Figure 3) [22].  
Figure 1: The velocity model cross section.  
Figure 2: The salt features based on velocity values on the model cross section.  
The field is covered by the seismic survey at the center of Malay Basin, running in the NW to SE direction elongated with anticline structural dips ranging from 1 to 3 degrees. There are E-W trending normal faults that delineate at the main reservoir; additionally, the youngest N-S faults present at the flank of the field occur throughout the reservoirs [23].  
The A Field was interpreted as the meandering fluvial channel environment reservoir [24]. A significant amount of commercial hydrocarbon can be found on the channel sand-body reservoir. Hence, it is crucial to interpret and predict the structure and geometry of the channel features. All the identified channel features in this seismic field are classified based on fourth-order meandering fluvial hierarchical architecture, which consisted of a point bar, an abandoned channel, crevasse splays, and a flood plain [25]. By integrating the seismic result to the well log information, the previous study interpreted six channel features in the data, which consist of a flood plain, channel, point bar, crevasse splay, crevasse channel, and mud-filled channel, as shown in Figure 4[24].  
Figure 3: The location of A Field.
## 3 The OPTCON Algorithm
### The OPTCON3 Algorithm
Initialize the following auxiliary matrices for backward recursion: \[H_{T+1}^{xx}=O_{n\times n},h_{T+1}^{x}=O_{n},\] \[H_{T+1}^{x\theta}=O_{n\times p},H_{T+1}^{\theta\theta}=O_{p\times p}.\] Calculate the Riccati matrices \(K_{t}^{xx}\), \(K_{t}^{\theta x}\), and \(K_{t}^{\theta\theta}\) and the auxiliary matrices \(\Lambda_{t}^{xx}\), \(\Lambda_{t}^{xu}\), \(\Lambda_{t}^{uu}\), \(\lambda_{t}^{x}\), \(\lambda_{t}^{u}\), \(H_{t}^{xx}\), \(H_{t}^{\theta x}\), \(H_{t}^{\theta\theta}\), and \(h_{t}^{x}\) backwards in time from \(T\) to \(S+1\). \[ \begin{array}{l}K_{t}^{xx}=W_{t}^{xx}+H_{t+1}^{xx},K_{t}^{\theta x}=H_{t+1}^ {\theta x},K_{t}^{\theta\theta}=H_{t+1}^{\theta\theta},\\ k_{t}^{x}=h_{t+1}^{x}+x^{\prime}W_{t}^{xx}+W_{t}^{xu}u+w_{t}^{x}.\end{array} \] (7) \[ \begin{array}{l}\Lambda_{t}^{xx}=(A_{t})^{\prime}K_{t}^{xx}A_{t},\\ \Lambda_{t}^{ux}=(B_{t})^{\prime}K_{t}^{xx}A_{t}+W_{t}^{ux}A_{t},\\ \Lambda_{t}^{xu}=(\Lambda_{t}^{ux})^{\prime},\\ \Lambda_{t}^{uu}=(B_{t})^{\prime}K_{t}^{xx}B_{t}+2(B_{t})^{\prime}W_{t}^{xu}+ W_{t}^{uu}.\end{array} \] (8) \[ \begin{array}{l}\lambda_{t}^{x}=k_{t}^{x}A_{t},\\ \lambda_{t}^{u}=k_{t}^{x}B_{t}+x^{\prime}W_{t}^{xu}+u^{\prime}W_{t}^{uu}+(w_{t} ^{u})^{\prime}.\end{array} \] (9)  
\[ \begin{array}{l}H_{t}^{xx}=\Lambda_{t}^{xx}-\Lambda_{t}^{xu}(\Lambda_{t}^{uu })^{-1}\Lambda_{t}^{ux},\\ H_{t}^{\theta x}=[D^{\prime}K_{t}^{xx}+K_{t}^{\theta x}]A_{t}-[[D^{\prime}K_{t} ^{xx}+K_{t}^{\theta x}]B_{t}+DW^{xu}](\Lambda_{t}^{uu})^{-1}\Lambda_{t}^{ux}, \\ H_{t}^{\theta\theta}=D^{\prime}(K_{t}^{xx}D+K_{t}^{x\theta})+K_{t}^{\theta x }D+K_{t}^{\theta\theta}-[[D^{\prime}K_{t}^{xx}+K_{t}^{\theta x}]B_{t}+DW^{xu}] \\ \times(\Lambda_{t}^{uu})^{-1}[B_{t}[K_{t}^{xx}D+K_{t}^{\theta}]+W^{ux}D], \end{array}  \tag{10}\]  
\[ \begin{array}{l}h_{t}^{x}=\Lambda_{t}^{x}-\Lambda_{t}^{xu}(\Lambda_{t}^{uu })^{-1}\lambda_{t}^{u},\end{array} \]  
where \(D=(I-F_{x_{t}}^{x})^{-1}F_{\theta}^{x}\). \(F_{\theta}^{x}\) is the derivative of the system function with respect to \(\theta\). **Step I-2C:** Calculate the deterministic component of the approximate objective function \(J_{D,S}\) and the cautionary component \(J_{C,S}\):  
\[ \begin{array}{l}J_{D,S}=\frac{1}{2}[x_{S}^{s\pi}-\bar{x}_{S}]^{\prime}W^{xx} [x_{S}^{s\pi}-\bar{x}_{S}]+[x_{S}^{s\pi}-\bar{x}_{S}]^{\prime}W^{xu}[u_{S}^{s \pi}-\bar{u}_{S}]\\ \hskip 14.226378 pt+\frac{1}{2}[u_{S}^{s\pi}-\bar{u}_{S}]^{\prime}W^{uu}[u_{S}^{ s\pi}-\bar{u}_{S}]\\ \mbox{and}\\ J_{C,S}=\frac{1}{2}tr(H_{S+1}^{xx}\Sigma_{S+1/S}^{xx})+tr(H_{S+1}^{\theta x} \Sigma_{S+1/S}^{x\theta})+\frac{1}{2}tr(H_{S+1}^{\theta\theta}\Sigma_{S+1/S}^ {\theta\theta}).\end{array}
###### Abstract  
In plant factories, light is fully controllable for crop production but involves a cost. For efficient lighting, light use efficiency (LUE) should be considered as part of light environment design. The objectives of this study were to evaluate and interpret the light interception, photosynthetic rate, and LUE of lettuce under electrical lights using ray-tracing simulation. The crop architecture model was constructed by 3D scanning, and ray-tracing simulation was used to interpret light interception and photosynthesis. For evaluation of simulation reliability, measured light intensities and photosynthetic rates in a growth chamber were compared with those obtained by simulation at different planting densities. Under several scenarios modeling various factors affecting light environments, changes in light interception and LUE were interpreted. The light intensities and photosynthetic rates obtained by simulation showed good agreement with the measured values, with R\({}^{2}>0.86\). With decreasing planting density, the light interception of the central plant increased by approximately 18.7%, but that of neighboring plants decreased by approximately 5.5%. Under the various scenarios, shorter lighting distances induced more heterogeneity light distribution on plants and caused lower light interception. Under a homogenous light distribution, the light intensity was optimal at approximately 360 umol m\({}^{-2}\) s\({}^{-1}\) with an LUE of 6.5 g M\({}^{-1}\). The results of this study can provide conceptual insights into the design of light environments in plant factories.  
c 2020, 10, 1545; doi:10.3390/agronomy10101545
# Deep Learning-Enhanced Spectrogram Analysis for
## 1 Introduction  
Biomedical signal processing is critical in modern healthcare, particularly in non-invasive diagnostics. Traditional physical examination methods, such as palpation and percussion, provide essential clinical insights; however, their interpretation remains subjective and heavily dependent on practitioner expertise. The integration of machine learning (ML) and deep learning (DL) techniques has revolutionized biomedical signal classification, enabling automated, consistent, and reproducible diagnostic outcomes, particularly in respiratory sound analysis and smart stethoscope applications [1, 2, 3].  
Historically, the classification of biomedical signals has predominantly focused on detecting illness-related anomalies, including chronic obstructive pulmonary disease (COPD)and cardiovascular disorders [4; 5]. Although these approaches have demonstrated clinical utility, limited research has been conducted on anatomical region-based classification, which is critical for improving clinical relevance and interpretability [6]. Most prior studies emphasized participant-based classification, overlooking the need for anatomical localization, which is essential for precise medical applications [7].  
This study introduces a deep learning-driven approach for classifying biomedical spectrograms into anatomical regions by leveraging a fine-tuned ResNet50 model. Unlike conventional classification models, the proposed method ensures that signal mapping aligns with distinct anatomical regions, thereby improving clinical interpretability and application [8; 9]. Furthermore, this framework enhances diagnostic capabilities by incorporating transfer learning and optimizing feature extraction for biomedical signal classification [10; 11].  
Figure 1 depicts the comprehensive workflow of the proposed deep learning-based biomedical signal classification pipeline. The procedure commences with the collection of biological data using touch and percussion, followed by the creation of spectrograms utilizing Short-Time Fourier Transform (STFT). The spectrograms underwent preprocessing, including grayscale conversion, resizing, and data augmentation, to ensure optimal feature extraction. These processed spectrograms served as inputs to the fine-tuned ResNet50 model, which classifies signals into anatomical regions. The classification results provide interpretable outputs for non-invasive diagnostics, contributing to improved clinical decision-making. Unlike prior studies that relied solely on conventional auscultation or disease classification, this work presents a diagnostic approach focused on anatomical region-based classification of palpation and percussion signals using spectrogram analysis. To the best of current knowledge, this study is the first to integrate region-specific labeling of palpation- and percussion-derived signals with a fine-tuned ResNet50 model, emphasizing both spatial context and deep feature learning in non-invasive diagnostics.
# Electromagnetic Analysis of Vertical Resistive Memory with a Sub-nm Thick Electrode
## Materials and Methods  
### Fabrication  
Fabrication of the graphene-based VRRAM can be understood as serial stages of graphene transfer, dry etching to form a trench, deposition of active memory layer, and a pillar electrode. Graphene (monolayer on copper foil, Graphene Supermarket) was transferred to the top of the Al\({}_{2}\)O\({}_{3}\) (5 nm) dielectric (atomic layer deposition) of the deposited SiO\({}_{2}\)(1000 A)/Si substrate. Metal contact pads Ti/Pt were deposited and patterned by evaporation and lift-off processes. The SiO\({}_{2}\) (60 nm) isolation layer was deposited using the Low Pressure Chemical Vapor Deposition (LPCVD) method, followed by a deep trench forming using dry etching. Next, an active memory layer, HfO\({}_{2}\) (5 nm), and a pillar electrode, TiN (200 nm), were sequentially fabricated using Atomic Layer Deposition (ALD) and DC sputtering, respectively. Finally, a metal contact opening was conducted by dry etching. More detailed information regarding graphene transfer and fabrication of multiple layers of plane electrodes in 3D VRRAMs can be found in previous works [20; 22; 23]. The active device area, i.e., the contact area of the metal oxide and the edge of the plane electrode, was found to be 0.5 \(\upmu\)m\({}^{2}\) and 0.03 \(\upmu\)m\({}^{2}\) for the VRRAM with platinum and graphene electrodes, respectively.
# Interactive Airfoil Optimization Using Parsec Parametrization and Adjoint Method
## 5 Results and Verification
### Xfoil Verification Computations: Compressible and Viscous Flow  
The results presented in this section were obtained using the well-established Xfoil 6.99 software. The Reynolds and Mach numbers were set to \(Re\) = 750,000 and \(Ma\) = 0.02, allowing a direct comparison between the obtained results and those published in [13]. The nominal AoA for the adjoint optimization was zero; the resulting values of the design variables can be found in Table 3, Col. 4, and the original and optimized shapes can be seen in Figure 6. The dependency of the optimal shape on AoA is, at least in this case, very weak, as can be seen in Table 3. This means that the value of AoA is not of great importance in this case and that it has negligible effects on the optimization result. The result's lack of dependence on AoA, however, cannot be generalized and has to be assessed on each optimized airfoil separately. The adjoint optimization results presented in Table 4 were compared to those acquired by [PERSON] and [PERSON] [13], as shown in Table 5. For details regarding the acquisition of the data in Table 5 please see the original paper by [PERSON] and [PERSON] [13]. The CFD computation model, grid, BCs, and other necessary information used for generating the CFD data in Table 5 are described in great detail in [13]. A comparison of the different computation methods used during the research presented in this paper can be seen in Table 6 which directly compares those simulation methods to the available experimental data. For the viscous Xfoil 6.99 simulation, \(Re\) = 750,000 and \(Ma\) = 0.02 were used to match the experimental data. From this comparison, it can be concluded that the inviscid computation methods consistently overestimate the lift coefficient, which is an expected aspect of their behavior. Also, the accuracy of the inviscid methods can be considered sufficient only when there is no large flow separation occurring since they are unable to predict and account for this phenomenon. If a large separation occurs, their results can qualitatively differ from reality. Those methods cannot be used for computing the drag coefficient; thus, they cannot compute the L/D ratio either. However, as their results are consistent, the MATLAB (R2023b) version can be used as the highly efficient core for adjoint optimization, as long as no large flow separation is expected to occur. It can generally be said that a significant separation starts occurring when the lift coefficient curve starts deviating from the linear progression seen near zero AoA (roughly 6-8 deg AoA for NREL S809 airfoil at the presented values of \(Re\) and \(Ma\)). As for the compressible and viscous simulation, it can be concluded that this Xfoil 6.99 computation is accurate enough to serve as a verification computation for the optimization in this case. Thus, the benefit of the optimization can be assessed by the compressible and viscous simulation in Xfoil 6.99. When assessed in this way, the benefits should be maintained in the real world.