esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposed the WCDPSO-KELM model to predict the performance degradation trend of rolling bearings. Firstly, the particle swarm optimization algorithm was improved by adjusting the inertia weight and linear learning factor, adding the disturbance term, which has more accuracy than the PSO algorithm. Secondly, the penalty coefficient and kernel parameters of KELM model were optimized by an improved particle swarm optimization algorithm (WCDPSO). Subsequently, a training dataset was input to train the WCDPSO-KELM model, the performance degradation trend prediction model was built, then its performance was compared with that of other models. Thirdly, features of the whole life cycle were extracted from the time-frequency domain, wavelet packet space and EEMD space, and major performance degradation indexes were selected from these features spaces, then input to the trained WCDPSO-KELM model to calculate MAPE values in different feature spaces. The experimental result shows the MAPE value in the EEMD space is smaller than in the other two feature spaces. Therefore, compared with other prediction models, the WCDPSO-KELM model has higher accuracy for predicting the performance degradation trend. Compared with the feature extraction methods of wavelet packet, time-frequency algorithms, EEMD can effectively extract the features of vibration signal of rolling bearings. The proposed method based on WCDPSO-KELM and EEMD is an efficient and accurate performance degradation prediction method.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Working Condition** & **MAPE** & & \\ \hline  & phase & EEMD & wavelet \\
1 & 0.0629877 & 0.005971 & 0.141041 \\
2 & 0.1859479 & 0.076398 & 0.182735 \\
3 & 0.1917164 & 0.003721 & 0.007825 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MAPE value of different feature spaces.  
Figure 10: MAPE value in different feature space.  
**Author Contributions:** Formal analysis, H.Z.; methodology, X.L.; software, X.L.; validation, X.L.; writing--original draft, X.L.; writing--review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Liaoning Provincial Natural Science Foundation Guidance Project, grant number 2019-ZD-0099, and Traction Power State Key Laboratory of Southwest Jiaotong University, grant number TPL2203, and Research Foundation for Civil Aviation University of China, grant number 2020 KYQD123.  
We obtained all datasets in GitHub ([[https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset](https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset)]([https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset](https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset)), accessed on 18 February 2022).  
The authors declare no conflict of interest.
###### Abstract
Specifically, the application of AI in the analysis of thermal or infrared images has become essential for detecting drones under low-visibility conditions, such as at night or during adverse weather situations [48; 49; 50]. The use of thermal imaging provides the advantage of identifying objects based on their heat signatures, thus facilitating drone detection regardless of ambient light or unfavorable visual conditions. However, this approach also presents significant challenges, including variability in drone temperatures depending on the time of day or the environment, as well as the necessity to differentiate drones from other hot objects present within the field of view. This work employs advanced AI techniques for the detection of drones in thermal or infrared images. Considering that the threat posed by unidentified civilian flying objects is increasing daily, it is essential to develop surveillance systems capable of accurate detection and optimal performance under extreme conditions, such as low-visibility or nighttime scenarios. The main contribution of this paper is a comprehensive comparative analysis of state-of-the-art convolutional neural network (CNN) and transformer-based neural network architectures, adapted for drone detection in thermal infrared images. This study also evaluates the performance of each of the detectors for each of the nine video types derived from the specific conditions of the Anti-UAV Challenge 2023 [31; 32; 33]. It is important to note that each of these video types is a challenge as can be seen in [PERSON] et al. 's work [51]. In addition, while the most prominent papers in this challenge focus on the performance of Single-Object-Tracking (SOT), this paper focuses on the specific detection problem that is a fundamental part of the tracking systems. This will lay the foundation for future research in this area. This paper is organized as follows. Section 2 reviews the related work on drone detection and thermal imaging using neural networks. Section 3 details the methodology employed in this study, including a description of the dataset and the training process of the selected models. Section 4 presents the results of the experiments, including the performance metrics and a detailed analysis of each model's accuracy and inference speed. Additionally, Section 4 provides an in-depth discussion of the model performance results across different video categories. Finally, the conclusions drawn from this study are presented in Section 5.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 4 Discretization Error Characteristics
### Theoretical Dispersion Error Characteristics  
With the Equation (40), the dispersion error in proposed explicit TD-FEM is evaluated theoretically as the relative error from the exact sound speed \(c_{0}\). The relative error \(e_{\mathrm{dispersion}}\) is defined as shown below.  
\[e_{\mathrm{dispersion}}=\left|\frac{c_{0}-c_{\mathrm{p}}}{c_{0}}\right|\times 1 00\ [\%] \tag{42}\]  
The dispersion error is well known to show anisotropic behavior in terms of the direction of sound wave propagation in multidimensional analyses. We first evaluate anisotropic characteristics in the dispersion error of the proposed method. We calculated the dispersion error using the parameters of \(c_{0}=343.7\)m/s, \(k=45.7\), \(h=0.02\), and two time interval values \(\Delta t=\Delta t_{\mathrm{limit}}\) and \(0.01\Delta t_{\mathrm{limit}}\). Under these conditions, the spatial resolution, in terms of points per wavelength (PPW), corresponds to 6.87. Figure 2a presents the dispersion error as a function of sound propagation directions \(\theta\) for the two time interval settings. The result indicates that dispersion errors are symmetric with diagonal direction (\(\theta=\pi/4\)) as a center. They take a maximum value at the axial directions (\(\theta=0\), \(\pi/2\)). Furthermore, the result shows the error values as maximal at the critical time interval. Actually, the result becomes lower with a smaller time interval. We present the convergence of the dispersion error with respect to the spatial resolution PPW in Figure 2b, where the dispersion error is an averaged value in terms of propagation directions. Here, the critical time interval value \(\Delta t_{\mathrm{limit}}\) was used. The dispersion error can be found to decrease with fourth-order convergence in terms of spatial resolution. To maintain the error magnitude within 1% or 0.5%, the proposed explicit TD-FEM requires spatial resolution of 5.45 or 6.48 PPW.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.2.4 Education  
The education sector has already met significant improvements with the introduction of digital learning and LLMs but additional improvements arise thanks to the synergistic combination of technologies. Up until now AI was able to create personalized and tailored learning content but it lacks the interactive and engaging elements that VR or AR provide, or the realism that IoT sensors can feed into such environments. In this domain IoT devices are able to capture student interactions, AI personalizes learning experiences by adapting content to individual needs while also assessing performance, and VR offers immersive environments that enhance engagement and retention. Such systems propose a more holistic approach to digital learning and are able to provide students with a deeper understanding of complex concepts, encourage self-paced learning, and bridge the gap between theoretical knowledge and practical application without necessarily rendering the role of a human teacher obsolete just yet. Though several applications exist, not many of them made use of all of the technologies effectively, thus major improvements are expected in the future.
###### Abstract  
A comparative study of glass fiber content (Gc) measurement methods was conducted using actual glass fiber reinforced plastic laminates from the hull plate of a 26-ton yacht. Two prototype side hull plates with the design Gc (40 wt.%) and higher Gc (64 wt.%) were prepared. Four methods were used to study the samples: the calculation method suggested by classification societies' rules; two direct measurement methods using either calipers and scales or a hydrometer; and the burn-off method, wherein the resin matrix is combusted from the laminates. The results were compared and analyzed to identify the accuracy and benefits of each method. The rule calculation method was found to be effective if the quality of the manufacturing process is known. However, fabrication errors in the laminate structures cannot be detected. Additionally, while direct methods are used to measure the density of glass fibers using measurements of the densities of raw materials and laminates, the volume of inner defects occurring during the fabrication of laminates could not be considered. Finally, it was found that the burn-off method measures Gc and considers the defect volume (voids) inside laminates as well as the non-uniformity of the external shape.  
composite ship; composite structure; glass fiber content; void volume; burn-off test; calcination test +
Footnote †: journal: Applied Sciences
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 5 Discussion on the Small-Scale Blade Results
### Power Consumption  
The power consumption of the system for the de-icing of the blade setup was calculated using the following formula:  
\[P_{m}=C*f*U_{h}^{2} \tag{1}\]  
with \(P_{m}\) being the average electrical power, \(C\) the actuator electrical capacitance, \(f\) the frequency of excitation and \(U_{h}\) the voltage swing. The electrical capacitance of the actuators was 45 nF. The average frequency of excitation obtained during the de-icing test was 2495 Hz, and the average voltage swing was 668 Vpp. At those conditions, the average power consumption of an actuator was 50 W, meaning a total average power for the six actuators of 300 W. To compare with thermal de-icing systems, the power density is calculated. The common practice is to consider the first 30% of the chord of the profile to calculate the de-iced area [8], corresponding to approximately 15% of its surface area. For this blade setup, an iced zone of 22.9 cm (9 in) spanwise by 4.1 cm (1.6 in) chordwise was considered, for a total of 93.9 cm\({}^{2}\) (14.5 in\({}^{2}\)), which results in an average power density of 32 kW/m\({}^{2}\) (21 W/in\({}^{2}\)). When compared to 39 to 43 kW/m\({}^{2}\) (25-28 W/in\({}^{2}\)) [27] for a thermal de-icing system, this results in a power reduction of up to 25%. While interesting, this power reduction is small enough to justify the implementation of such a complicated system. The blade profile was very small, especially compared to the actuator size. With a longer profile spanwise, it is believed that the blade should have higher flexibility, and vibration would be favored. Moreover, the actuators used in this study would be more fit to be anti-nodes of the first vibration modes for a longer blade, since the ideal length of an actuator corresponds to the size of the anti-node, and larger actuators diminish the efficiency of the system [17], resulting in a more efficient excitation of the structure. Finally, the very low angular speed at which the de-icing tests were performed, as well as the small radius of rotation, did not yield tangential speeds representative of the reality. With higher tangential speeds, higher centrifugal forces would have facilitated de-icing. All these factors would contribute to reduce the power consumption of the system.
## 3 Results
### In-Silico Binding Mechanism Analysis  
Molecular docking analysis of Compound **1** with EGFR kinase domain showed several important interactions with ATP binding site residues as well as DFG motif residues, which is important for inhibition of EGFR kinase. Interactions involved hydrogen bonding interaction with ATP binding site and DFG motif residue Phe856 and \(\pi\)-anion interaction with Asp855 (Figure 4A,B). Binding analysis of Compound **2** with kinase domain of EGFR kinase revealed that, it formed hydrogen bond with Phe856, \(\pi\)-anion interaction with Asp855, \(\pi\)-sulfur interaction with Met790, \(\pi\)-alkyl interactions with Met766, Leu777, Leu747 and Leu858 (Figure 4C,D). Among them, Asp855 and Phe856 involved in the ATP binding site and DFG motif.  
Docking analysis of Compound **1** with VEGFR-2 kinase domain revealed 2 hydrogen bond interactions with Cys919 and Glu917, \(\pi\)-\(\pi\) T shaped interactions with Leu840, Val848, Ala866, Lys868, Val899, Val916, Leu1035, Cys1045 and Phe1047 as well as van der Walls interaction with Phe918 (Figure 5A,B). Compound **2** similarly made 2 hydrogen bond interactions with the gate keeper residue Cys919 and \(\pi\)-\(\pi\) T shaped interaction with DFG  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Entry**} & \multicolumn{2}{c}{**IC\({}_{\text{50}}\) (\(\mu\)M)**} & \multicolumn{3}{c}{**Kinase IC\({}_{\text{50}}\) (\(\mu\)M) 1 **} \\ \cline{2-6}  & **MCF7 [5]** & **CDK2 [5]** & **EGFR** & **VEGF-2** & **FLT-3** \\ \hline \(1\) & 1.51 \(\pm\) 0.09 & 0.246 \(\pm\) 0.05 & 0.269 \(\pm\) 0.08 & 0.232 \(\pm\) 0.01 & 1.535 \(\pm\) 0.03 \\ \hline \(2\) & 3.56 \(\pm\) 0.31 & 0.301 \(\pm\) 0.02 & 0.369 \(\pm\) 0.32 & 0.266 \(\pm\) 0.04 & 0.546 \(\pm\) 0.28 \\ \hline Control b-f & 3.10 \(\pm\) 0.29 b & 0.131 \(\pm\) 0.24 c & 0.056 \(\pm\) 0.02 d & 0.091 \(\pm\) 0.03 e & 0.262 \(\pm\) 0.01 f  \\ \hline \end{tabular}
\end{table}
Table 1: Inhibitory activities of Compounds **1** and **2** against MCF7 cell lines, CDK2, EGFR, VEGFR and FLT-3 protein kinase.  
motif residue Phe1047. Additionally, it formed several \(\pi\)-alkyl interactions as Compound **1** with the similar residues except Lys868 (Figure 5C,D).  
Binding mechanism of Compound **1** with FLT-3 kinase domain involved hydrogen bond interaction with Cys694, \(\pi\)-\(\pi\) stacked binding with Tyr693, several \(\pi\)-alkyl interactions with Leu616, Val624, Ala642, Lys644, Val675 and Leu818. One \(\pi\)-sulfur interaction was observed with Cys828 (Figure 6A,B).  
Figure 2: Overall folding of EGFR protein kinase (**A**); VEGFR kinase (**B**) and FLT-3 protein kinase (**C**).  
Figure 5: Docking pose of **1** and **2** within the active site of VEGFR-2 protein kinase: (**A**) 2D of **1**; (**B**) 3D of **1**; (**C**) 2D of **2**; (**D**) 3D of **2**.  
Figure 6: Docking pose of **1** and **2** within the active site of FLT-3 protein kinase: (**A**) 2D of **1**; (**B**) 3D of **1**; (**C**) 2D of **2**; (**D**) 3D of **2**.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design
### CMOS-Based Switch Design and Parameter Extraction
#### 3.1.2 Switch Test and Calibration  
Probe test is employed, combined with TRL calibration test, to obtain the accurate S-parameters of the switch in the THz frequency band. Figure 3 presents chip photographs of the switch area and the TRL area. The figure illustrates two fabricated switches, one incorporating an inductive loop and the other without. By comparing the S-parameters of switches with and without an inductive loop, the accurate inductance value \(L\) can be obtained.  
Excluding the probe pads, the switch measures 30 \(\upmu\)m \(\times\) 40 \(\upmu\)m. The chip probe testing is conducted using an R&S 24-GHz vector network analyzer (VNA), an R&S extension  
Figure 3: Switch test and TRL calibration.  
module operating in the range of 220 to 330 GHz, and SUNV WR-3 waveguide-type ground-signal-ground (GSG) probes [42]. The calibration process is performed using a self-designed TRL calibration module on a CMOS chip.
# A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm
## 4 The Proposed Multi-Objective Five-Elements Cycle Optimization Algorithm
### Expression of Solutions and Population Initialization  
Taking the MOO problem in Equation (1) as an example, each solution of \(Fun(X)\) corresponds to an element in the FECM. In the proposed algorithm MOFECO, each element represents an individual in the population and the population are divided into \(q\) cycles. There are \(L\) elements in each cycle, so the population size is \(L\times q\). We use \(X_{ij}(k)\) to denote the \(i\)-th element of the \(j\)-th cycle at the \(k\)-th iteration and in the population of MOFECO, \(X_{ij}(k)\) represents one individual. Since MOFECO is designed for multi-objective problems, one individual corresponds to multiple objective function values. Therefore, we use \(M_{r,ij}(k)\) to represent the mass of \(X_{ij}(k)\), corresponding to the values of objective function \(Fun(X)\), \(r\in\{1,2,\ \ldots,m\}\) that represents \(m\) objective functions. In FECM, the force exerted on \(X_{ij}(k)\) by other elements in the \(j\)-th cycle is related to the element mass. Therefore, in MOFECO, the \(X_{ij}(k)\) has multiple forces from the other elements in the \(j\)-th cycle, denoted as \(F_{ij,r}(k)\), which is computed by Equation (9).  
\[\begin{split} F_{ij,r}(k)&=w_{gp}\cdot ln[\frac{M_{ r,(i-1)j}(k)}{M_{r,ij}(k)}]-w_{rp}\cdot ln[\frac{M_{r,(i-2)j}(k)}{M_{r,ij}(k)}]\\ &-w_{ga}\cdot ln[\frac{M_{r,ij}(k)}{M_{r,(i+1)j}(k)}]-w_{ra}\cdot ln [\frac{M_{r,ij}(k)}{M_{r,(i+2)j}(k)}]\end{split} \tag{9}\]  
When iteration number \(k=0\), we generate \(L\times q\) initial individuals \(X_{ij}(0)\) (\(i\in\{1,2,\ \ldots,L\}\), \(j\in\{1,2,\ \ldots,q\}\)) within the feasible range of search space randomly. Then we use FECM to calculate their masses and forces expressed as \(M_{r,ij}(0)\), \(F_{ij,r}(0)\), respectively. In the \(k\)-th iteration, we firstly use objective functions \(M_{r,ij}(k)\) to evaluate \(F_{ij,r}(k)\) by Equation (9). According to FECM, it can be noted that the value of \(F_{ij,r}(k)\) measures the pros and cons of element \(X_{ij}(k)\), therefore \(F_{ij,r}(k)\) determines the update pattern of \(X_{ij}(k)\). The specific update pattern will be described in detail in Section 4.3.
## 2 Concept
### Fundamentals
Obviously, the final receiving signal is a 1-dimensional signal, which does not directly provide information on the investigated 2-dimensional scene. Thus, the 2-dimensional signal separation in range and cross-range direction must be performed in signal processing. While a first separation is performed in a range direction, which is equal to a time gating operation, identified targets carry the information of the unique RX-coding filter transfer functions. After obtaining this information, the detected targets are allocated to the corresponding arrival directions. As a result, the AOI is subdivided into a 2D grid, which is scanned instantaneously by a single radar measurement. The corresponding resolution cells depend on the utilized bandwidth in the range direction and the number and beamwidth of the RX antennas in the cross-range direction. For enhancing the cross-range resolution, the RX-antennas are arranged in a way so that the AOI is perfectly subdivided by their patterns while neighboring beams are overlayed by 1/3 of their 3 dB beamwidth. Targets, which are located in the overlapping areas, are carrying the transfer functions of both corresponding RX-coding filters. This results in an increase of cross-range resolution cells to \(N_{\mathrm{x}}\) resolution cells, depending on a closed or non-closed AOI:  
\[N_{\mathrm{x}}= \begin{cases}2N&\text{360 degree closed AOI}\\ 2N-1&\text{non-closed AOI}\end{cases}  \tag{6}\]
## 4 Discussion  
In this paper, we performed logo detection using attention based mechanisms with an anchor-free detector for the logo datasets containing real-world images. The performance of our approach is evaluated with anchorfree detector CenterNet and anchorbox based detectors like SSD and Faster R-CNN. The experiments show that the CenterNet method is robust and faster with an 80.7% mAP. We propose a feature extractor network with spatial and channel attention modules to effectively capture information from complex logo images to fuse visual and semantic features. Our proposed approaches, LogoNet  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **mAP** \\ \hline LogoNet [15] & 63.2 \\ (w/o domain adaptation) & \\ \hline LogoNet + Domain Adaptation & 59.6 \\ (Class-wise heatmaps) & \\ \hline LogoNet + Domain Adaptation & 64.5 \\ (Proposed method - Mid-level feature maps) & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effectiveness of the domain adaptation on Logos-32 plus dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **mAP** \\ \hline [PERSON] et al. [37] & 59.4 \\ (Entropy Minimization Loss) & \\ \hline [PERSON] et al. [37] & 59.6 \\ (Maximum Square Loss) & \\ \hline [PERSON] et. al. [38] & 59.7 \\ \hline Proposed method & 64.5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with existing domain adaptation methods.  
and Dual-Attention LogoNet, provide significant detection capability with a considerable detection time and achieve better performance with 82.2% and 82.5% mAP, respectively. The proposed architecture can be learned to detect new sets of logo classes. Logo images include diverse context, illumination, resolutions that make logo detection a challenging task. A robust feature extractor that can emphasize and discriminate various logo regions would be more suitable. More attention-based methods can be used to generate refined feature maps. A logo detector can be trained by considering all logo classes as a single logo (in a class-agnostic way). In this case the logo detector will be able to detect and classify the logo regions as a general logo class [1]. We also proposed lightweight CNNs architecture to improve the real-time performance of network. We apply different lightweight modules with the proposed backbone and compared the networks. According to the experiments, LogoNet-Lightweight network achieves 79.7% accuracy, with a smaller number of parameters and reduced detection time. The proposed methods improve the focus on logos and detect logos more precisely than conventional algorithms. To bridge the gap between different domains we exploit the adversarial-domain adaptation learning. We propose a pragmatic way of dealing with the domain-shift problem using an anchorfree object detector. We make use of mid-level output feature maps to align the domains and to train a robust detector model. This approach can be easily be adapted to other anchorfree detectors. Training in adversarial manner is a difficult task for detection we will consider more approaches for better performance and stability.