esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 3 Voice Spoof Detection and Algorithm Recognition System
### Generalized Voice Spoof Detection
#### 3.1.2 Domain Adaptation Based on Decision Boundary Maximization
The designed loss function is expressed as follows:  
\[\mathrm{L_{oc}}=\frac{1}{\mathrm{M}}\sum\
olimits_{i=1}^{\mathrm{M}}\log(1+ \mathrm{e}^{\mathrm{\delta}(\mathrm{m_{label_i}}-\mathrm{\hat{\omega}}_{0}\ \mathrm{\hat{x}}_{i})(-1)^{\mathrm{label_i}}}) \tag{4}\]  
In OC-Softmax, only one weight \(\omega_{0}\) is used, representing the optimization direction of the target class embedding vector. Both are normalized similarly to AM-Softmax. We then introduced two different angular residuals \(\mathrm{m_{0}}\),\(\mathrm{m_{1}}\)\(\in[-1,\,1]\) for the classification of real voices and spoofing voices, respectively, where \(\mathrm{m_{0}}>\mathrm{m_{1}}\). As shown in Figure 3, there is only one \(\omega_{0}\) weight in OC-Softmax, indicating the convergence direction of the target class of the real voice. The angle between \(\omega_{0}\) and voice feature vector \(\mathrm{x_{i}}\) is denoted by \(\mathrm{\theta_{i}}\). When \(\mathrm{label_i}=0\), the sample is the target class, and the decision boundary is constrained by setting \(\mathrm{m_{0}}\) so that \(\mathrm{\theta_{i}}\) has to be smaller than \(\mathrm{arccosm_{0}}\). When the value of \(\mathrm{arccosm_{0}}\) is small, the target class can be concentrated around the weight vector \(\omega_{0}\), thereby making the feature distribution of the real voice sample more compact and concentrated. When \(\mathrm{label_i}=1\), set \(\mathrm{m_{1}}\) so that \(\mathrm{\theta_{i}}\) is greater than \(\mathrm{arccosm_{1}}\). When the value of \(\mathrm{arccosm_{1}}\) is larger, the data of the non-target class can be moved away from the direction of \(\omega_{0}\). Thus, the purpose of training different decision boundaries for the target class and non-target class samples is achieved.
## 3 Results and Discussion
### Assessment of Cell Line Cytotoxiccial Potential  
Cytotoxicity activity prediction of the PBD compounds against cancer cell lines was performed using the CLC-Pred (Cell Line Cytotoxicitycity Predictor) 2.0 web application. This tool utilizes the PASS (prediction of activity spectra for substances) training set to estimate cytotoxic activity based on known relationship with various cancer cell lines achieving up to 96% accuracy compared to the in vivo results. CLC-Pred results are expressed as Pa (Probability active) and Pi (Probability inactive) values [33; 66].  
The prediction results (Figure 2) show that all PBD compounds exhibit cytotoxic potential against several cancer cells types, including MCF7 (breast carcinoma), HOP-92 (non-small-cell lung carcinoma), RPMI-8226 (multiple myeloma), NCI-H522, (non-small-cell lung carcinoma), SR (adult immunoblastic lymphoma), BT-549 (breast ductal carcinoma), IGROV-1 (ovarian adenocarcinoma), OVCAR-4 (ovarian adenocarcinoma), NCI-H226 (non-small-cell lung carcinoma), OVCAR-5 (ovarian adenocarcinoma), SF-539 (glioblastoma), and UACC-62 (melanoma). Among these, breast carcinoma (MCF7) exhibited the highest probability of inhibition to all PBD compounds. Compounds with Pa > 0.5 are considered to have high cytotoxic potential and are likely to demonstrate similar activity in laboratory testing; those with 0.3 < Pa < 0.5 indicate moderate potential, while Pa < 0.3 suggests a low likelihood of cytotoxic. Notably, all PBD compounds demonstrate Pa > 0.5, against MCF7 cells, suggesting strong potential as anti-breast cancer agents.
# On Transparency and Accountability of Smart Assistants in Smart Cities
## 1 Introduction
We use description-to-permissions fidelity and functions-to-API-usage fidelity, two traits important from the transparency and accountability perspectives [30; 31]. Further, we use two online user studies to verify how does the transparency of GA affect the privacy protection of its users. We focus on seeking answers to the following questions. 1. _Is GA transparent in its risk communication and implementation?_
2. _How does the transparency of GA affect its accountability?_
3. _How does the transparency of GA affect the privacy protection of its users?_  
To get the answers to the first two questions, we performed a diagnostic analysis of GA, and, to find the answer to the third question, we conducted two online user studies (N = 100 and N = 210) with students of four universities in three countries (China, Italy, and Pakistan). The first user study assessed the ability of potential users of GA to understand data access behavior of GA. This understandability is critical for its risk assessment [32]. The second user study focused on investigating the transparency of risk communication by GA to its potential users. With the purpose to demonstrate the importance of the role of transparency in the design of smart assistants and find its impact on their accountability and privacy protection of smart city citizens, this work makes the following contributions. 1. We performed a diagnostic analysis of GA. We assessed description-to-permissions fidelity and functions-to-API-usage fidelity in GA and compared its discovered traits with those of four leading smart assistants. Our research discovered that GA has unusual permission requirements and sensitive API usage, and its risk communication to potential users and underlying implementation are not transparent, which can affect its potential users and experts in a smart city. 2. We demonstrate that the lack of transparency in GA's implementation makes its risk assessment and validation difficult for experts. It fails many state-of-the-art risk evaluation and malware detection methods and poses threats to check-and-balance and accountability mechanisms in smart cities. 3. We conducted two online user studies (N = 100 and N = 210) with students of four universities in three countries (China, Italy, and Pakistan) to verify whether risk communication in GA is transparent to its potential users, and how it affects their privacy protection. The results of these studies suggest that a lack of transparency in GA's design makes its risk assessment difficult for its potential users. This can adversely affect the privacy protection of these users and may hinder establishing private and secure personal spaces in smart cities. The rest of this paper organizes as follows. Section 2 contains the related research. Section 3 presents the diagnostic analysis of GA. Section 4 provides details of the two user studies. Section 5 provides the results of this research. Section 6 discusses the different implications of this research. Section 7 discusses different limitations of this work. Section 8 concludes this paper.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 4 Numerical Simulations
### Residual CFO Effects  
Before the investigations on the performance of detectors, CFO effects are first analyzed. To verify the CFO compensation results, Figure 4a presents the BER of the ML detector in three cases when CFO = 0.2. Case 1 considers CFO effects but has no CFO compensation step. Case 2 does not incorporate the CFO effects, which perform as the benchmark. Case 3 incorporates CFO effects as well as the compensation step. From Figure 4, we can see the BER of case 3 is very approximated with the one of case 2, which proves that the CFO compensation method in Section 2 is effective, which greatly reduces the performance losses due to the ICI. At BER = \(10^{-2}\), the performance of case 3 is about 7 dB better than case 1.  
\begin{table}
\begin{tabular}{l l l l l l} \hline Bandwidth \(B\) & 5 kHz & No. Subcarrier \(N\) & 1024 & Modulation \(S\) & QPSK \\ Subcarrier spacing \(\Delta f\) & 4.88 Hz & No. Group \(G\) & 256 & Sampling Freq \(f_{s}\) & 100 kHz \\ CP duration \(T_{\mathrm{cp}}\) & 51.2 ms & No. Active subcarrier \(k\) & 2 & Center Freq \(f_{c}\) & 12.5 kHz \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for UWA OFDM-IM system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AMP** & **MMSE-AMP** & **VAMP (SVD/LMMSE)** & **LLR** & **ML** \\ \hline \(\mathcal{O}(n^{2})\) & \(\mathcal{O}(n^{2})\) & \(\mathcal{O}(nR)/\mathcal{O}(n^{3})\) & \(\mathcal{O}(nM)\) & \(\mathcal{O}\left(2^{\left\lfloor\log_{2}\mathcal{C}\left(\begin{array}{ c}n\\ k\end{array}\right)\right\rfloor_{M^{k}}}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity comparison.  
Figure 4b studies the BER performance of detectors with the CFO compensation in the presence of different CFOs, i.e., \(e=[0.05,0.2]\). As shown in Figure 4b, besides the ML and LLR detectors, the proposed detector presents robustness to various CFO effects. However, the IM-AMP fails in both cases even considering CFO mitigation. The proposed detector performs better than the IM-MMSE-AMP over the whole SNR range. The results prove that the VAMP framework in the proposed detector is less sensitive to the noise from the UWA channel, such as the channel estimation and ICI. Moreover, the prior-aided denoising function can exploit symbol sparsity and achieve approximated BER compared with the ML and LLR.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 2 Differential Precise Time and Frequency Transfer
### Double-Difference Fixed Ambiguity Resolution  
In the field of geodesy, the mathematical model for relative positioning [22] based on DD pseudorange and the carrier phase for frequency, \(f\), is expressed as:  
\[\left\{\begin{array}{l}P_{f(m,n)}^{i,j}=\rho_{f(m,n)}^{i,j}+eph_{f(m,n)}^{i,j }+T_{f(m,n)}^{i,j}+I_{f(m,n)}^{i,j}+e_{P}^{i,j}_{f(m,n)}\\ L_{f(m,n)}^{i,j}=\rho_{f(m,n)}^{i,j}+eph_{f(m,n)}^{i,j}+T_{f(m,n)}^{i,j}-I_{f(m, n)}^{i,j}+\lambda_{f}\cdot N_{f(m,n)}^{i,j}+\varepsilon_{Lf(m,n)}^{i,j}\end{array}\right. \tag{1}\]  
where \(P_{f(m,n)}^{i,j}\) and \(L_{f(m,n)}^{i,j}\) represent DD pseudorange and carrier-phase observations from satellites \(i\) and \(j\) to receivers \(m\) and \(n\), respectively. \(\rho_{f(m,n)}^{i,j}\) represents the DD geometric distance, \(eph_{f(m,n)}^{i,j}\) represents the DD ephemeris error residual, \(T_{f(m,n)}^{i,j}\) represents the DD tropospheric delay residual, \(I_{f(m,n)}^{i,j}\) represents the DD carrier-phase integer ambiguity, and \(\lambda_{f}\) is the corresponding wavelength. \(\varepsilon_{P}^{i,j}_{f(m,n)}\) and \(\varepsilon_{P}^{i,j}_{f(m,n)}\) are the sum of the multipath errors and measurement noise of the DD pseudorange and carrier-phase observations, respectively.  
For relative positioning technology, when the distance between two receivers is a short baseline of up to a few kilometers, the DD tropospheric and ionospheric delay residuals can be ignored [23]. The LAMBDA method [16] is used to determine the DD integer carrier-phase ambiguity. In addition, the dual-frequency ionosphere-free (IF) combination can also be used to eliminate the impact of the first-order term of ionospheric delay for longer baselines. DD wide-lane integer ambiguities, \(N_{W(m,n)}^{i,j}\), between two stations are calculated by using the Melbourne-Wubbena combination observation and then obtaining the float DD carrier-phase ambiguity, \(N_{IF(m,n)}^{i,j}\), of the IF combination.  
\[\begin{array}{ll}N_{IF(m,n)}^{i,j}&=\frac{1}{\lambda_{1}}\cdot\left(\frac{f_ {1}^{2}}{f_{1}^{2}-f_{2}^{2}}\lambda_{1}\cdot N_{f_{1}(m,n)}^{i,j}-\frac{f_{2 }^{2}}{f_{1}^{2}-f_{2}^{2}}\cdot\lambda_{2}\cdot N_{f_{2}(m,n)}^{i,j}\right)\\ &=\frac{f_{1}}{f_{1}+f_{2}}N_{f_{1}(m,n)}^{i,j}+\frac{f_{1}f_{2}}{f_{1}^{2}-f_ {2}^{2}}N_{W(m,n)}^{i,j}\end{array} \tag{2}\]  
where \(N_{W(m,n)}^{i,j}=N_{f_{1}(m,n)}^{i,j}-N_{f_{2}(m,n)}^{i,j}\). The DD carrier-phase ambiguity of the IF combination can be estimated from IF combination observation. Furthermore, L1 DD integer ambiguity, \(N_{f_{1}(m,n)}^{i,j}\), can be obtained.  
\[N_{f_{1}(m,n)}^{i,j}=\left[\frac{f_{1}+f_{2}}{f_{1}}N_{IF(m,n)}^{i,j}-\frac{f_ {2}}{f_{1}-f_{2}}N_{W(m,n)}^{i,j}\right] \tag{3}\]  
where \([\cdot]\) denotes rounding to the nearest integer.
## 3 Results
### Experimental Design
The experimental environment included a workstation configured with Ubuntu 18.04 LTS, Intel Xeon Gold 6154 CPU, NVIDIA Tesla V100 GPU, and 512 GB RAM. PyTorch 1.8.1 was used as the deep learning framework for model training, and PySyft 0.2.9 for federated learning simulation [29]. Attack-related experimental code, such as Byzantine behavior injection, was internally implemented, and seed values were fixed for reproducibility. This experimental setup was designed to verify the effectiveness of the proposed technique in realistic non-IID conditions and Byzantine failure scenarios based on diverse EV data configurations. Client selection, batch configuration, and learning parameter settings followed conventional practices from previous studies [19; 20], but Byzantine attack patterns were more finely controlled. Additionally, comparisons between stepwise/single prediction and centralized/federated learning were conducted based on the same dataset and model structure to objectively demonstrate the superiority of the proposed technique. By detailing specific model training conditions and Byzantine behavior injection methods, the experiment design enhances its reproducibility by third parties and ensures the reliability of the results by specifying the computing resource specs, representing a significant advancement over previous studies. However, further persuasive validation could be achieved by diversifying the range and intensity of non-IID and Byzantine settings and incorporating more real data limitations such as noise and missing values. Moreover, conducting comparisons between heterogeneous models and sensitivity analyses based on client-scale changes could help gauge the general effectiveness of the proposed technique. If FL-QLMS can demonstrate consistent Byzantine robustness across complex model structures and diverse datasets, it could establish a new paradigm applicable to federated learning in general, beyond the EV mobility sector. Despite this potential, further research is needed to optimize FL-QLMS for on-device learning, introduce differential compression techniques to improve transmission efficiency, and consider heterogeneous hardware performance for practical application in the industry. Planning demonstration projects with EV-related companies through the business modelization of the proposed ideas and exploring connections with the open-source ecosystem could maximize the impact of the research outcomes.
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 4 Results
### Comparative Results  
The development of object detection models consistently faces dual challenges in balancing efficiency and accuracy: while lightweight methods reduce computational costs at the expense of compromised detection accuracy, high-precision models encounter real-time deployment limitations stemming from parameter redundancy. To validate the effectiveness of the Dab-Deformable architecture in addressing this trade off, we systematically compared six categories of mainstream algorithms including DETR variants, YOLO series models, and two-stage frameworks on a custom dataset. In Table 6, the evaluation covers three critical dimensions: overall detection performance, computational efficiency, and target coverage capability. Due to the architectural similarities between Dab-Deformable and Deformable-DETR, this paper extends the improvements described herein to the Deformable-DETR architecture, as shown in Table 6 as Our-Deformable.  
As shown in Table 5, Our-Dab (based on the full optimization of Dab-Deformable) is significantly ahead in accuracy: both mAP\({}_{50-95}\) (0.338) and mAP\({}_{50}\) (0.867) lead the way, with improvements of 3.6 percentage points and 4.2 percentage points, respectively, over the baseline Dab-Deformable, and only a slight increase in parameter count of 1.5 percentage points. Although Our-Dab recall is slightly lower than that of YOLOv8I, the model's overall performance improvement compensates for this shortfall in recall. In terms of efficiency, RT-DETR achieves an mAP\({}_{50}\) of 0.802 with the smallest parameter count, while Our-Dab improves mAP\({}_{50}\) by 4.2 percentage points with a marginal increase in parameters through modular synergistic optimization, proving that structural improvements outperform mere parameter expansion. Finally, Our-Dab achieves the optimal balance between accuracy and model parameters through hierarchical feature fusion and dynamic modeling.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 5 Discussion
### Estimation of m from Standard Deviation or from Coefficient of Variation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Materials** & **\textless{}\textless{}\textgreater{}** & **S** & **CV** & **M\({}_{\text{abs}}\)** & **M\({}_{\text{est}}\)** & **N** & **Note** & **Ref** \\ \hline Copper & 150.00 & 27 & & 6.11 & 24 & As received & [112] \\ Copper & 413.00 & 18 & & 25.24 & 24 & Cold rolled & [112] \\ Cu-44 Ni alloy & 300.00 & 28 & & 11.79 & 24 & As received & [112] \\ Cu-44 Ni alloy & 722.00 & 50 & & 15.88 & 24 & Cold rolled & [112] \\ Al 2030 & 490.00 & 1.46 & & 369.18 & 15 & Laboratory practice & [113] \\ Al 2030 & 487.00 & 3.64 & & 147.17 & 15 & Automated-industrial & [113] \\ Dental wires & 1845.80 & 142.3 & & 14.27 & NA & 316 S cold drawn 7.4 & [114] \\ Dental wires & 874.10 & 275.9 & & 3.48 & NA & Ti-Mo alloy & [114] \\ Dental wires & 1449.80 & 156.6 & & 10.18 & NA & Co-C alloy & [114] \\ AerMet100\({}^{\otimes}\)steel & 1966.60 & 50.9 & & 42.50 & 5 & Tensile strength & [115] \\ AerMet100\({}^{\otimes}\)steel & 142.50 & 37.5 & 2.96 & 4.17 & 6 & K\({}_{\text{k}}\) & [115] \\ AerMet100\({}^{\otimes}\)steel & 101.18 & 52.75 & & 2.11 & 6 & J\({}_{\text{k}}\) & [115] \\ Battle solids & & & 4.00 & & NA & theory & [116] \\ CNT fibers & 1241.00 & 261 & & 5.23 & 10 & reference & [117] \\ CNT fibers & 1375.00 & 187 & & 8.09 & 10 & coating 1 & [117] \\ CNT fibers & 972.00 & 160 & & 6.68 & 10 & coating 2 & [117] \\ CNT fibers & 1240.00 & 246 & & 5.54 & 10 & coating 3 & [117] \\ CNT fibers & 1073.00 & 162 & & 7.29 & 10 & reference & [117] \\ CNT fibers & 1360.00 & 119 & & 12.35 & 10 & coating 1 & [117] \\ CNT fibers & 1455.00 & 173 & & 9.25 & 10 & coating 2 & [117] \\ CNT fibers & 1214.00 & 134 & & 9.97 & 10 & coating 3 & [117] \\ CNT fibers & 714.00 & 26 & & 30.21 & 10 & reference & [117] \\ CNT fibers & 616.00 & 86 & & 7.88 & 10 & coating 1 & [117] \\ CNT fibers & 700.00 & 48 & & 16.04 & 10 & coating 2 & [117] \\ CNT fibers & 826.00 & 80 & & 11.36 & 10 & coating 3 & [117] \\ CNT & & & 1.70 & & 26 & Multi wall & [118] \\ CNT & & & 2.40 & & NA & Multi wall 7.5 & [118] \\ CNT bundles & & & 2.70 & NA & & [118] \\ CNT fibers & 300.00 & & 4.30 & & 60 & Low strain rate & [119] \\ CNT fibers & 650.00 & & 6.80 & & 85 & High strain rate & [119] \\ CNT & 31,200 & 11.839 & & 2.23 & 2.90 & 19 & Single CNT & [120] \\ CNT & & & & 2.48 & & 9 & Multiwall CNT & [121] \\ Mid-Hudson Bridge & 1609.07 & 51.66 & 32.10 & 34.26 & \(>\)10 & Location: IN-2N & [43] \\ Mid-Hudson Bridge & 1608.26 & 644.49 & & 27.43 & \(>\)10 & 428 43N & [43] \\ Mid-Hudson Bridge & 1609.18 & 67.67 & & 26.16 & \(>\)10 & 89N 9N & [43] \\ Mid-Hudson Bridge & 1613.44 & 53.71 & & 33.04 & \(>\)10 & 133 134 & [43] \\ Mid-Hudson Bridge & 1634.38 & 66.98 & & 26.84 & \(>\)10 & 348s & [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 3 Challenge: Data Quality
### Transfer Learning
#### 3.4.3 Parameter-Based Transfer Learning  
Parameter-based is the most common approach for deep transfer learning [89]. The common practice is to first train the model purely in \(\mathbf{D_{s}}\) and then transfer their parameters to the target model(s) for initialization, followed by fine-tuning the models with samples from \(\mathbf{D_{t}}\). A toy example is illustrated in Figure 10. The source and target networks do not need to be identical; layers that share the parameters should merely be of the same size. Parameter-based TL is widely adapted for CNN-based computer vision tasks. It is proven that the early layers of the pretrained CNN models are capable of capturing low-level features of images, which are relatively independent of the datasets [90]. CNN layers from _AlexNet_, _VGG_, or _ResNet_ trained on large-scale image recognition datasets, such as _CIFAR_ and _ImageNet_, are popular sources for pretraining [91]. [PERSON] et al. develop a CNN-based metal crack recognition system with pretrained parameters borrowed from the publicly released trained GoogleNet and achieved an accuracy of 99.8% [9].  
Transfer learning has drawn more and more attention in recent years since data scarcity is one of the most commonly seen obstacles for deep learning applications. Compared with other techniques introduced in Section 3, TL is the most widely applied. However, the methods that choose the contents to transfer and thus prevent a negative transfer is case-dependent.  
Figure 10: In parameter-based TL, the model is firstly trained using relevant datasets (left) and the layers of corresponding model parameters (in red dashed box) are transferred to a target model (in purple dashed box) followed by fine-tuning.