esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **FAR (KL)** & **MDR (KL)** & **FAR (BIC)** & **MDR (BIC)** \\ \hline Chroma & 0.2889 & 0.6941 & 0.4911 & 0.4716 \\ Mel-spectrogram & 0.9927 & 0.0062 & 0.5034 & 0.5128 \\ MFCC & 0.5428 & 0.4671 & 0.4837 & 0.4499 \\ Pitch & 0.1470 & 0.8508 & 0.4947 & 0.4894 \\ RMS energy & 0.8809 & 0.1300 & 0.4817 & 0.4720 \\ Spectral bandwidth & 0.5948 & 0.3869 & 0.4938 & 0.4700 \\ Spectral centroid & 0.7606 & 0.2299 & 0.4882 & 0.4690 \\ Spectral contrast & 0.0975 & 0.9009 & 0.4855 & 0.4653 \\ Spectral rolloff & 0.7667 & 0.2318 & 0.4890 & 0.4774 \\ Zero crossing rate & 0.5061 & 0.4827 & 0.4968 & 0.4928 \\ \hline Average & 0.5578 & 0.4380 & 0.4908 & 0.4770 \\ Standard Deviation & 0.2919 & 0.2899 & 0.0110 & 0.0292 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average SCD results for KL-GMM and BIC-GMM across features. Figure 4: Relation between Max\(P(SC|FC)\) with results of BIC-GMM (the lower value is good). BIC-GMM method demonstrates more consistent approx. F1 scores across features, with RMS energy, spectral centroid, Spectral bandwidth, and Chroma all performing reasonably well. Zero crossing rate ranks highest for KL-GMM, with an approx. F1 and a relatively low Max\(P(SC|FC)\) value. However, this feature did not outperform others in BIC-GMM (Figure 4(b)), except for Mel-spectrogram feature that consistently underperforms across both methods, although it shows slightly improved performance in BIC-GMM.
# How Does C-V2X Help Autonomous Driving to Avoid Accidents?
## 2 Solution for C-V2X VRUCW
### C-V2X VRUCW Deployment
#### 2.2.2 Network Topology  
According to live environment, network topology is designed in Figure 3. In order to avoid accidents in blind spot, first, RSU with maximum 1-kilometer reachable radius coverage is connected with PoE switch. Next, IP Cameras 1 & 2 access network by PoE switch 1, and IP Cameras 3 & 4 access network by PoE switch 2. Four IP Cameras make video streaming and transfer all instant data to the central AI server which is placed in the middle of the circular route. The AI server as showed in Figure 4 is responsible for artificial intelligent workload in our solution. The hardware specification of the AI server is listed in Table 3.  
We verified the computation time of the AI program in our lab. Detection latency is the duration between decoding an image data and sending the recognition result from the socket of program, final result is summarized in Table 4. Certainly, the \(\sim\)166 ms latency is too high to meet real-time requirement of advanced C-V2X services, but it is acceptable in our solution because we intend to offer the basic road safety strategy and focus on functional testing as a proof of concept project. We need a tradeoff between real-time constraints and the investment project. Furthermore, we plan to deploy AI workload in the roadside smart sensor level in order to overcome the intelligent transport system (ITS) latency and reliability challenges.  
Under all cameras coverage, a geographic profile with latitude, longitude and elevation information is predefined in AI server, which is measured humanly by handheld Global Positioning System (GPS) tool, an example for one of four cameras is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Step** & **Latency (ms)** \\ \hline \multirow{4}{*}{Detect Program} & Preprocess & 1.3697 \\  & Detect & 165.0450 \\ \cline{1-1}  & Convert result & 0.0071 \\ \cline{1-1}  & Save result & 0.0403 \\ \cline{1-1}  & Total & **166.4621** \\ \hline \hline \end{tabular}
\end{table}
Table 4: AI Program Latency.  
Figure 4: AI Server HW/OS.  
Figure 5: Position Information under Camera Coverage.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 5 Implementation Problems  
Showing how the proposed system is implemented in real time is important. In this section, we aim to present two problems in real-time implementation and address solutions for each. The first is modulation. The proposed system achieves undetectability from Gaussian-distributed codeword samples. However, digital signal processing units do not usually support Gaussian samples. Therefore, in Section 5.1, we discuss how the Gaussian samples can be represented with finite precision, and determine the number of quantization levels required for sufficient precision. The possible adoption of commercially applicable solutions such as analog-to-digital converters and digital signal processing units are discussed as well in order to tackle this finite precision problem. The second problem is computational complexity. The SSR algorithms include the matrix inversion operation in each iteration. In Section 5.2, we aim to show that hardware/software solutions such as parallel processing, field-programmable gate array (FPGAs), and application-specific integrated circuits (ASICs) can achieve high throughput and satisfy power constraints.
## 3 Fuzzy Logic Q-Learning Routing Scheme with Asymmetric Link Aware
### Neighbors Evaluation Criteria  
The proposed model is an opportunistic routing scheme that relies on fuzzy logic and Q-learning for routing decision making. The proposed routing scheme is designed to work in a variety of mobile wireless networks. Several configuration parameters of FQ-AGO are adjustable so that it is adaptive to different network environments. One of the characteristics of the proposed routing scheme is that the routing decisions depend on the proposed metric value, which simplifies network setting-up operations where there is no need for any network layer system management. Consequently, this will alleviate the network's overall end-to-end delay. In this section, we define multiple network metrics concerning mobile ad hoc networks to improve online routing decisions [32].
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 4 Experiments and Analysis
### Experimental Setup  
The point cloud data are restricted to the spatial dimensions of x-axis [0, 70.4] meters, y-axis [\(-\)40, 40] meters, and z-axis [\(-\)3, 1] meters, focusing on the vehicle's front. Voxelization is performed using 0.16 m \(\times\) 0.16 m \(\times\) 4 m dimensions, creating vertical columns with a height of 4 m and a grid resolution of 0.16 m \(\times\) 0.16 m in the xy-plane. Each voxel is limited to 32 points, totaling 40,000 voxels. Experiments are conducted on an Ubuntu 18.04 LTS system with PyTorch 1.7.1, utilizing CUDA 11.0 and cuDNN 8.0.4 for GPU acceleration.  
To enhance model generalization, extensive data augmentation is employed, including global rotation, scaling, and flipping of the point clouds, as well as local adjustments near ground truth targets. Additionally, a positive sample filtering strategy [13] reduces the weights of difficult samples with significant overlap, improving training quality and focusing the model on more discriminative features to boost detection accuracy and minimize false positives.
## 1 Introduction
A capacitor requires a minimum size to store a certain amount of charge and to ensure accurate memory cell operation. As various studies have been conducted on replacing the traditional DRAM with a new one, a DRAM using FBFETs has emerged as promising [43; 44; 45; 46; 47; 48]. The positive feedback mechanism is based on the accumulation of electrons and holes in the potential wall, which leads to a collapse of the potential barriers, resulting in hysteresis. In general, hysteresis is considered as a side effect that is generally handled as being parasitic and unnecessary [49; 50; 51]. However, FBFETs could play the role of memory for a promising new 1T-DRAM with this feature. A new static random-access memory (SRAM) utilizing FBFET unit cells has been proposed. For these SRAM unit cells, a special ability induced by the positive feedback operation allows for high-speed memory operation. Moreover, the big cell area, which has been noted as a limitation of the conventional SRAM, is solved by a simple device structure using FBFETs. The cell achieves a small area at 8F\({}^{2}\), enabling high-density design, while the low switching current dramatically reduces the rapid switching characteristics, simultaneously enabling the performance of low-power memory [52; 53; 54]. The FBFET-based SRAM unit cell also demonstrates the excellent operating performance of the SRAM, including a write speed, fast read speed, and competent retention time because of its positive feedback mechanism [52; 53; 54]. These results demonstrate the great potential of FBFET for next-generation DRAM/SRAM memory applications. FBFETs are also being studied for application to neuromorphic devices to solve the issue for energy efficiency [21; 55; 56]. Based on the mechanisms of biological neurons, artificial neural networks (ANNs) have been proposed as powerful tools for pattern recognition and classification [57; 58]. Beyond ANNs, spiking neural networks (SNNs) have been proposed [59; 60; 61; 62]. However, conventional neuronal circuits in SNNs require large areas as well as high power consumption [21]. PF devices with a split-gate floating body have been proposed as new neuron devices that demonstrate integration-and-fire capabilities [21; 55; 56]. A recent simulation study of those devices used a common magnetic controller in the neuron layer and demonstrated successful operation of a high-density multiple PF neuron system with reset and lateral suppression capabilities. The study proposed a method for SNNs to reduce power consumption by \(\sim\)100 times with FBFET-based circuits [21].
## 4 Methodology
### Activation Fine-Tuning
#### 4.2.2 Optimization Problem for Activation Fine-Tuning  
For a training image \(x\), let us denote the target CNN for which we want to create input attribution as \(f(x;w)\) and the layer as the \(\ell\)-th layer, where we obtain activations for creating activation-based input attribution and also attach our tuner network. We consider the learning weights \(w\) in three parts, namely \(w=(w_{0},w_{1},w_{2})\) where \(w_{0}\) corresponds to the first to \((\ell-1)\)-th layers, \(w_{1}\) corresponds to the \(\ell\)-th layer, and \(w_{2}\) to the remaining layers. Note that we freeze the weights in \(w_{0}\) to their pre-trained version to prevent the original CNN from fluctuating too much from its optimal weights, losing its best prediction performance. We also denote the activations at the \(\ell\)-th layer as \(A(x;w_{1})\in\mathbb{R}^{d_{h}\times d_{w}\times K}\): \(A\) depends on both \(w_{0}\) and \(w_{1}\), but only \(w_{1}\) is used for fine-tuning.  
For the tuner network \(f_{tuner}(A(x;w_{1});w^{\prime})\) and the optimal mask \(M_{\tau^{+}}(x)\) generated by TFT, we define activation fine-tuning as the following optimization problem:  
\[\min_{w_{1},w_{2},w^{\prime}}\ \sum_{i=1}^{n_{\text{FFT}}}\mathcal{L}_{CE}(f(x_{ i};w_{1},w_{2}),y_{i})+\lambda\|f_{tuner}(A(x_{i};w_{1});w^{\prime})-M_{ \tau^{+}}(x)\|_{2}^{2}, \tag{8}\]  
where the input-label pairs \((x_{i},y_{i})\) are from training data, \(\mathcal{L}_{CE}\) is the cross-entropy loss, and \(\lambda>0\) is a hyper-parameter balancing the strength of the tuner network.  
Figure 2: An overview of our activation fine-tuning strategy.
## 3 Resources and Demand Assessment of the Case Study
### Economic Assignment Criteria
#### 3.5.5 Grid-Extension Cost  
In this research, the grid extension cost is determined through the computation of breakeven grid extension distance (BGED). The BGED is the distance away from the grid at which the NPC of expanding the grid equals the NPC of a minigrid (stand-alone) system. When the grid extension distance exceeds the BGED, the minigrid system is more cost-effective [19; 32]. The BGED is computed using Equation (7).  
\[BGED=\frac{NPC\times CRF(i,N)-c_{power}\times E_{demand}}{c_{cap}\times CRF(i,N)+ c_{om}} \tag{7}\]  
where total \(NPC\) of the minigrid system is in USD, \(CRF\) is capital recovery factor, \(i\) real discount rate in %, \(N\) is project lifetime in years, \(E_{demand}\) is total annual electrical demand including primary and deferrable in kWh/yr, \(c_{power}\) is cost of power from gird in USD/kWh, \(c_{cap}\) is capital cost of grid extension in USD/km, \(c_{om}\) is O&M cost of grid extension in USD/yr/km.
## 4 Conclusions  
To aid in orthodontics or the diagnostic and treatment planning process in dental caries, a 3-D tooth model reconstruction is one of the important parts. In this paper, we introduce a registration process using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to determine the optimal affine transform between images in 3-D. Because there were several consecutive images scanned from a commercial tooth model, we implemented a hierarchical registration process to produce the final registration image. We also compared the results with those from the particle swarm optimization (PSO) method with the ICP method. The best results from the SR-PSO algorithm with the ICP method for the regular-tooth model and the orthodontic-tooth model yielded the mean-squared error (MSE) of 7.3666 micrometer\({}^{2}\) and 7.4130 micrometer\({}^{2}\), respectively. For the sake of comparison, the MSEs from the PSO algorithm with the ICP method for the two tooth models were 17.115 micrometer\({}^{2}\) and 7.4672 micrometer\({}^{2}\), respectively. The SR-PSO algorithm with the ICP method provided better results than that of the PSO algorithm with the ICP method. Moreover, the resulting 3-D images from the SR-PSO algorithm with the ICP method are also viewable by human eyes and are useful for experts. In future work, we will implement this algorithm for the tooth models with defections in order to simulate dental caries in real situations.  
All authors conceived and designed the experiments; [PERSON] performed the experiments; and all authors contributed to the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Royal Golden Jubilee Ph.D. Program (Grant No. PHD-0170-2558).  
Not applicable.  
The authors would like to thank the Royal Golden Jubilee PhD Program (RGJ-PhD) for support under grant No. PHD-0170-2558 for financial funding.  
Figure 14: The final registration result of six consecutive views of the orthodontic-tooth model from the SR-PSO with the ICP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**SR-PSO with ICP**} & \multicolumn{2}{c}{**PSO with ICP**} \\ \hline \(\alpha=0.5\) & \(\alpha=1.0\) & \(\alpha=1.5\) & \(\alpha=2.0\) & \\
**7.4130** & 7.4141 & 7.4141 & 7.4131 & 7.4672 \\ \hline \hline \end{tabular}
\end{table}
Table 10: MSE of the final registration of six consecutive views (micrometer\({}^{2}\)) for the orthodontic-tooth model (the best value is in bold).  
**Conflicts of Interest:** The authors of the paper do not have any conflict of interest with any companies or institutions. This work was supported by the Thailand Research Fund under the Royal Golden Jubilee Ph.D. Program (Grant No. PHD-0170-2558). This article does not contain any studies with human participants or animals performed by any of the authors.
## 1 Introduction
Furthermore, in some cases where the feasibility of biopsy has been confirmed, histological and molecular analyses may not be possible due to inadequate samples that may be insufficient for molecular and histological analyses, causing biopsy failure rates to be 8-43% [9; 10; 11]. Additionally, substantial time and costs are associated with biopsy and NGS. Radiomics is a method in which features are systematically extracted from digital medical images and then analyzed for the purpose of constructing comprehensive databases that aid in diagnosis and treatment. This approach utilizes advanced algorithms, often in combination with computer assistance, to quantitatively analyze radiological images and extract numerous features for the detection of tumor phenotypes. Radiogenomics incorporates specific features obtained from radiological images, along with genomic information. The quantitative data generated through radiomics and radiogenomics analyses can be used to predict patient risk, individualize treatment strategies, and facilitate non-invasive biopsy. Computed tomography (CT) is a technique that is commonly utilized for the diagnosis and staging of cancer patients. Numerous studies have explored whether there is an association between CT imaging features and \(EGFR\) mutation status in NSCLC; however, these investigations have produced inconsistent results. The analysis of CT-based radiomic features for the identification of \(EGFR\) mutation status has the potential to act as a crucial clinical determinant in the development of effective treatment plans for lung adenocarcinoma and NSCLC-NOS patients [17; 18; 19; 20]. Therefore, we evaluated the performance of pretreatment CT radiomics features in terms of their ability to predict \(EGFR\) mutation status in lung adenocarcinoma and NSCLC-NOS patients. While researchers have previously concentrated on biopsy samples in their studies, the inclusion of a large number of surgical cases in the present study ensured the collection of adequate tumor tissue to minimize false negative results and enhance the robustness of our findings. Hence, the current study's aim was to explore the potential for pretreatment chest computed tomography (CT) radiomics features to act as a predictor of epidermal growth factor receptor (\(EGFR\)) mutation status in NSCLC.
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 3 Experiment and Result
### Defect Recognition  
#### 3.3.1 Building the Dataset  
The arc sound signals for the three weld states were obtained according to the experimental scheme in Section 3.2.1. The arc sound signal collected at the head and tail of each welding experiment was clipped due to the instability of the arc at the head and tail. The cropped arc sound signal was divided into small segments. The length of each segment was calculated according to Equation (7).  
\[\mathrm{L}=\mathrm{c}\frac{F_{\mathrm{s}}}{F_{\mathrm{CMT}}} \tag{7}\]  
where \(F_{\mathrm{s}}\) denotes the sampling frequency of the arc sound signal, \(F_{\mathrm{CMT}}\) denotes the frequency of the CMT arc, and \(\mathrm{c}\) denotes the number of CMT cycles contained in a segment  
Figure 8: Time–frequency spectrum of different welding states. (**a**) Normal. (**b**) Burn-through. (**c**) Lack of fusion.  
Figure 9: Time–frequency spectrum of different welding states.  
of the arc sound. In the experiment, \(F_{s}\) is 32,000 Hz, \(F_{\rm CMT}\) is 70 Hz, and c is 6. The value of L is calculated as 2742. A segment of the arc sound signal contains more than enough information. The samples of the three different weld states were mixed sufficiently to finally obtain the original dataset containing 1810 samples.  
Each sample in the original dataset is transformed to the time-frequency spectrum by STFT. The first-order differential and first-order differential of the time-frequency spectrums are computed by Equations (2)-(5). Finally, the datasets of the i-channel time-frequency spectrum (i-CTF dataset) are obtained as shown in the schemes of Table 1, including the 1-CTF dataset, 2-CTF dataset, 3-CTF dataset, 4-CTF dataset, and 5-CTF dataset. Each dataset was divided into a training set (70%) and a test set (30%). The proportion of samples with different welding states in the training and test sets was the same as that in the original dataset.
## 5 Testing Plans
### Vibration Test  
To ensure the CubeSat can withstand the cyclic loading of the launch condition and its effect afterward, the CubeSat will be tested in a simulated launch environment. Vibration tests include a pre-sine sweep followed by random vibration and then a post-sine sweep vibration test on all three axes (\(X,Y\) and \(Z\)) of the CubeSat. The shaker machine setup, which will be used to perform the vibration test, is a low-force shaker LDS V780. The system used is an air-cooled based system, which uses an amplifier to provide amplification for the accelerometer signals.  
The same setup can be used to test Light-1 by inserting the model into a 3U test pod, which will be attached to a head expander before mounting it on the shaker plate. For CubeSat sizes greater than 1U, a head expander is needed to mount the test pod with the CubeSat on the shaker due to clearance requirements between the shaker plate and the vibrator structure. Figure 19 below illustrates the head expander and the 3U test pod mounted on it that are manufactured at Khalifa University and will be used to perform the vibration testing on Light-1.  
As per Figure 20, the test pod is mounted on one of the axes, which in the figure, is the \(X-\) axis. Similarly, the test pod will be mounted on the other two axes and a series of sine sweep and random vibrations will be performed. Figure 20 presents the sequence in which the tests will be conducted on Light-1.  
After performing the initial functional testing, the sine sweep and random vibration tests will be conducted on the head expander alone, followed by the head expander mounted with the test pod with the Light-1 model on all three axes. The sine sweep vibration profile obtained from QB 50 is defined in Table 16 [14]. The low-level sine sweep will help acquire the CubeSat's modes of vibration, natural frequencies, damping and mode shapes.  
Figure 19: Test pod mounted on the head expander.  
Next, a random vibration will be conducted on the CubeSat using the applicable testing profile based on mission requirements from JAXA. Possible random vibration profiles from the satellite launchers are illustrated in Table 17. The random vibration test on Light-1 will be performed using the HTV testing profile. After the random vibration, another low-level sine sweep will be conducted, and the results obtained from the post-sine sweep will be compared with the pre-sine sweep to observe for any frequency/mode shifts. Finally, after completing all the necessary testing protocols, a functional test will be performed to determine the health status of the CubeSat. If the health check is satisfactory and the required vibration levels have been reached without damaging the CubeSat, the vibration test will be deemed successful.
## 2 Materials and Methods
### pH and Salinity Stress Tolerance  
The following experimental conditions were employed to evaluate pH and salt tolerance. Both pH and salt tolerance were assessed using 100 mL of BG-11 medium in 250 mL flasks. To evaluate the effects of pH, the BG-11 medium was first prepared and adjusted to target pH values of 4, 6, 8, 10, and 12 using either 1 N hydrochloric acid (HCl) or 1 N sodium hydroxide (NaOH). After pH adjustment, the medium was autoclaved to maintain sterile conditions throughout the cultivation. For salinity stress experiments, BG-11 medium was prepared by directly adding NaCl to reach final concentrations of 5, 10, 20, and 30 g L\({}^{-1}\) in 100 mL volumes. The salt-containing media were also autoclaved prior to use to maintain sterility. Cultures were maintained under the following conditions: 25 \({}^{\circ}\)C, 200 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) light intensity, a 16:8 light/dark cycle, and 160 rpm. The pH and OD at 680 nm were monitored over 12 days period.