esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 4 Datasets and Metrics
### Comparisons on Public Defect Datasets  
Table 8 summarizes the comparison results of selected methods on the public defect datasets which have been mentioned in Section 4.1. Specifically, the comparison performance of the AITEX, DAGM, KolektorSDD1, KolektorSDD2, MT, MVTec, NEU-CLS, NEU-DET, NEU-CLS-64, RSDDs, X-SDD, and YDFID-1 datasets are shown in Table 8 (a)-(k).  
Overall, the surface inspection methods based on generative models have made significant achievements since 2022, especially the defect classification tasks. However, the defect detection and segmentation performance still face challenges due to instable generation quality and having to train models in unsupervised learning or weakly-supervised learning manners. It is worth noting that most inspection methods do not open source code for its potential for industrial applications in the future, and thus it is impossible to reproduce them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**(a) ATEX**} \\ \hline
**Method** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** & **mIoU (\%)** & **mAP (\%)** \\ \hline
[60] & \(88.4\) & \(77.2\) & \(82.4\) & - & - \\
[84] & - & - & - & \(89.61\) & \(91.46\) \\ \hline \multicolumn{5}{c}{**(b) DAGM**} \\ \hline
**Method** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** & **mIoU (\%)** & **FID** \\ \hline
[60] & \(94.8\) & \(93.5\) & \(94.1\) & - & - \\
[73] & \(96.4\) & \(98.1\) & \(97.2\) & \(86.9\) & - \\
[94] & - & - & - & \(78.28\) & - \\
[95] & - & - & - & - & \(168\) \\
[99] & - & - & - & - & \(93.83\) \\ \hline \multicolumn{5}{c}{**(c) KolektorSDD1**} \\ \hline
**Method** & **ACC (\%)** & **mIoU (\%)** & **mAP (\%)** & **FID** \\ \hline
[84] & - & \(88.91\) & \(89.55\) & - \\
[95] & \(98.53\) & - & - & \(122\) \\
[99] & \(99.8\) & - & - & \(115\) \\ \hline \multicolumn{5}{c}{**(d) KolektorSDD2**} \\ \hline
**Method** & **ACC (\%)** & **mIoU(\%)** & **AUROC (\%) (image-level)** & **AUROC (\%) (pixel-level)** \\ \hline
[84] & - & \(88.91\) & \(89.55\) & - \\
[95] & \(98.53\) & - & - & \(122\) \\
[99] & \(99.8\) & - & - & \(115\) \\ \hline \multicolumn{5}{c}{**(e) MT**} \\ \hline
**Method** & **ACC (\%)** & **Recall (\%)** & **Precision (\%)** & **F1-score (\%)** & **mIoU (\%)** & **mAP (\%)** & **AUROC (\%)** \\ \hline
[63] & - & - & - & - & - & \(89.1\) \\
[72] & \(99.22\) & - & - & - & - & - \\
[73] & - & \(94.9\) & \(96.3\) & \(95.6\) & \(85.1\) & - & - \\
[74] & - & - & - & - & - & \(95.6\) \\
[84] & - & - & - & - & \(88.36\) & \(90.32\) & - \\
[102] & - & \(81.2\) & \(98\) & - & - & \(82.5\) & - \\
[109] & \(89.32\) & - & - & - & \(83.7\) & - & - \\ \hline \multicolumn{5}{c}{**(f) MVTec**} \\ \hline
**Method** & **ACC (\%)** & **F1-score (\%)** & **mIoU (\%)** & **AUROC (\%) (image-level)** & **AUROC (\%) (pixel-level)** \\ \hline
[58] & - & - & \(66.6\) & - & - & - \\
[59] & - & \(93.1\) & - & - & - \\
[60] & - & \(88.1\) & - & - & - \\
[61] & - & - & \(69\) & \(84\) & - \\
[62] & - & - & - & \(71.7\) & - \\
[63] & - & - & - &
# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 2 Materials
### Data Preprocessing  
The flowchart of the data processing is given in Figure 1. We first downloaded the RRITS signal from the public database. However, we found that RRITS included too many ectopic heartbeat intervals that cover up important physiological information [5]. According to beat annotations files provided from the database, we removed the ectopic intervals of each RRITS signal with manual review and correction. Therefore, the NN interval time series will be obtained by removing all ectopic intervals, such as ventricular ectopic, supraventricular ectopic, or unknown. In order to ensure the reliability of the signal, the ratio of the length of the NN interval time series to the length of the RRITS (NN/RR) was calculated. The ratio was used as a measure of data reliability to exclude the recordings where the ratio was less than the threshold. In general, the ratio is set to 80% because it is a satisfactory compromise between the number of subjects included in the research work and the NN signal quality [5]. Based on the above excluding criterion, one recording was excluded from each of NYHA II, NYHA III, and NYHA IV CHF groups, respectively. Finally, we randomly selected continuous 40,000 NN intervals as one segment for each recording. As shown in Table 2, we obtained 113 samples for building model A, including 72 healthy samples and 44 CHF samples. We obtained 41 samples for building model B, including 4 NYHA I CHF samples, 7 NYHA II CHF samples, 16 NYHA III CHF samples, and 14 NYHA IV CHF samples.  
It was necessary to specify that in order to make the HRV signals more stable and homogeneous when extracting the frequency domain features, some previous researchers removed the linear trend of the HRV signal and used the cubic-spline interpolation method to re-sample it at a rate of 4 samples per second [22; 23; 31; 32]. However, we applied the Lomb-Scargle (LS) periodogram proposed in [31; 32] to calculate the power spectrum. In this case, HRV signals did not require resampling in our research [33; 34].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Database** & **DL** & **NR** & **Age** \\ \hline \multirow{2}{*}{Normal} & Nrsdb & Norm & 18 & \(34\pm 8\) \\ \cline{2-5}  & Nrs2 db & Norm & 54 & \(61\pm 11\) \\ \hline \multirow{4}{*}{CHF} & Chfdb & NYHA III-IV & 15 & \(56\pm 11\) \\ \cline{2-5}  & NYHA I & 4 & \\ \cline{1-1} \cline{2-5}  & Chf2 db & NYHA II & 8 & \(55\pm 11\) \\ \cline{1-1} \cline{2-5}  & NYHA III & 17 & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline
**DL: database label; NR: number of recordings; Age: Mean \(\pm\) Std (standard deviation); Nrsdb: Normal Sinus Rhythm Database; Nrs2 db: Normal Sinus Rhythm RR Interval Database; Chfdb: Congestive Heart Failure Database; Chf2 db: Congestive Heart Failure RR Interval Database; NYHA: New York Heart Association.**  
\end{table}
Table 1: RR interval time series (RRITS) database with the normal and congestive heart failure (CHF) subjects.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 3 Noise Modeling and Building the Training Sets
### NM-GAN Structure
Hence, the loss functions in \(G\) and \(D\) become:  
\[\underset{G}{min}V(D,G)=\frac{1}{2}E_{z\sim f_{z}(z),\ x_{c}\sim f(x_{c})} \Big{[}\big{(}D(G(z,x_{c}),x_{c})-1\big{)}^{2}\Big{]}+120\times\|G(z,x_{c})-x \|_{1} \tag{4}\]  
\[\underset{D}{min}V(D,G)=\frac{1}{2}E_{x,\ x_{c}\sim f(x_{c})}\Big{[}\big{(}D( x,x_{c})-1\big{)}^{2}\Big{]}+\frac{1}{2}E_{z\sim f_{z}(z),\ x_{c}\sim f(x_{c})}\Big{[}D(G(z,x_{c}),x_{c})^{2} \Big{]} \tag{5}\]  
where \(f(x_{c})\ \sim N(0,1)\) is the conditional information, and \(z\) and \(x\) are a random input and a target real noise sample, respectively. The inputs of \(G\) are the random signal \(z\) together with the conditional \(x_{c}\) and its outputs are imitative noise samples \(G(z,x_{c})\). The \(l_{1}\) distance between the clean sample \(x\) and the generated sample \(G(z,x_{c})\) is \(\|G(z,x_{c})-x\|_{1}\)  
Figure 3: NM-GAN structure. encouraging \(G\) to generate more fine-grained and realistic results [21; 22; 23]. Additionally, \(D\) learns to classify the \((G(z,x_{c}),x_{c})\) pair as real and the \((x,x_{c})\) pair as fake, while \(G\) tries to fool \(D\) such that \(D\) classifies the \((G(z,x_{c}),x_{c})\) pair as real. Over time, the output samples of G become increasingly like the real noise samples, \(x\).
## 4 Application
### Discussion  
In fact, when the water table inside stabilizes, the total pumping rate of all operative pumping wells will be approximately equivalent to the leakage of jet-grounded bottom sealing barriers in case of neglecting the leakage of the underground enclosures. From the results of the field pumping test, the daily water inflow towards the excavation is about 550 m\({}^{3}\) when the water table inside stabilizes. To some extent, this proves the truth that, although jet-grounded bottom sealing barriers can greatly reduce the water inflow, it still has permeability and it is not able to completely cut off the groundwater below. Therefore, it is necessary and practical to propose a design method of jet-grounded bottom sealing barriers when considering seepage failure.  
Furthermore, the variation of groundwater drawdown measured outside the excavation is less than 0.2 m. This suggests that the jet-grounded bottom sealing barriers play a crucial role for cutting off  
Figure 11: Variation of the drawdown measured in observation wells: (**a**) inside the excavation; and, (**b**) outside the excavation.  
Figure 12: Time-history curves of pumping rate of wells in different excavation zone.  
groundwater, resulting in dewatering inside having little impact outside the excavation. This paper proposes the calculation method of jet-grouted bottom sealing barriers considering seepage failure mechanism, which is based on the hypotheses without considering the fluctuation of groundwater level during pumping inside the excavation. The results of groundwater drawdown measured outside the excavation during pumping inside also prove the validity of this hypothesis to some extent.  
The area of jet-grouted bottom sealing barriers that were applied for the excavation case is about 3600 m\({}^{2}\), that is, the seepage flow rate per unit area of jet-grouted bottom sealing barriers (1 m\({}^{2}\)) is 0.15 m\({}^{3}\)/d, which is less than the maximum allowable seepage flow rate per unit area of jet-grouted bottom sealing barriers (0.25 m\({}^{3}\)/d). This implies that the performance of jet-grouted bottom sealing barriers applied for the excavation case is good, which meets the requirements of design and safety.
## 5 Comparison of Experimental and Numerical Results and Discussions  
Our observations in Sections 3 and 4 show that while the distributions of the measurement and numerical results are analogous, there is a notable difference in their absolute values. Specifically, the measured saturation throughput in the experiments ranges from 1.4 to 1.5 Gbps, whereas the theoretical analyses project values around 4.5 to 4.6 Gbps. This discrepancy was due to many factors but the main reason is that the theoretical throughput is computed at the PHY layer while the hardware's output throughput was evaluated at the MAC layer. At the same time, our numerical analyses target the PHY layer throughput using [PERSON]'s formula as a basis. Factors like the beam alignment process and the limitations of available modulation and coding schemes introduce uncertainties that theoretically make it challenging to gauge the interlayer's inefficiency. Recognizing this complexity, we have chosen an empirical methodology in this study to bridge the gap between the experimentally measured and theoretically computed saturation rates.  
\[\overset{\sim}{T}=\alpha\times\min(C_{0},C) \tag{12}\]  
In other words, the numerical throughput is scaled by a factor of \(\alpha\), which is a spectral efficiency coefficient that is responsible for all protocol-dependent loss (e.g., MAC portion over the whole physical frame, bandwidth, and time usage efficiency, etc.). In this paper, \(\alpha\) is estimated from the difference between the saturated throughput of the experiment and theoretical results. The result was \(\alpha=1/3.2636\). After this modification, the results of experimental and numerical analyses are redrawn and depicted on a same graph again for comparison and discussion in the following sections.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Rule/Logical Pattern Mining
#### 6.5.1 Technical and Methodological Insights
OTARL uniquely embeds temporal granularity, allowing analysts to read drift not just in which variables change, but in how their timing relationships evolve. X-Fuzz advances explanation fidelity by rigorously verifying LIME's local interpretations through monotonicity and correlation checks--ensuring explanations reflect actual model behavior rather than approximations. DAEMON introduces platform agnosticism, using raw-byte N-grams tagged with distinguishing family pairs to explain cross-platform drift (e.g., Windows-to-Android malware shifts) as the emergence or disappearance of human-readable code signatures. Later papers like ERulesD2S and FGRI address scalability: by capping rule-set size (e.g., five rules per class) and employing stratified sampling or GPU parallelism, they ensure that explanations remain concise and traceable even under high-frequency drift, preventing the \"rule explosion\" that plagues less constrained systems. The progression within the class reveals a maturation toward sustainable, granular, and integrated explainability. While early works (e.g., [PERSON] et al.) established hybrid rule-scoring systems to flag novel patterns, later frameworks like OTARL and DMI-LS enhance temporal or feature-hierarchy dynamics for finer-grained insights. DMI-LS further streamlines the workflow by using SHAP-driven active learning to focus annotation efforts precisely where explanatory uncertainty is highest. Collectively, these approaches demonstrate that drift interpretability is most potent when explanation is not an add-on, but the mechanism of adaptation itself. By maintaining a rule-based, feature-centric, or clause-oriented interface--free of latent embeddings or opaque thresholds--these models ensure that every adjustment to the concept is simultaneously a step in its explanation, fulfilling the mandate for intrinsically interpretable drift adaptation. As such, this class reflects the paradigm of white-box modeling aimed at human-understandable representations, situating the class in the Interpretable AI category.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 2 Multi-Wavelength QKD with a PUF  
Like the protocols mentioned previously in Section 1.3.2, we propose a unique QKD setup that could be applied to different QKD protocols. Our work includes adding more than one wavelength and including a physical unclonable function (PUF). Multi-wavelength QKD has not been used with a PUF to create the encoding schemes in our work, to our knowledge. The additional wavelength enables different protocols using ternary and quaternary data. In a ternary protocol, [PERSON] and [PERSON] no longer need to use a subsection of their key to detect eavesdroppers, enabling longer key lengths. Similar to previous work, we also address the security issues introduced by practical limitations. Our method includes adding a PUF as an additional layer of security, since adversaries must have access to the PUF to intercept the data transmission. This is useful against QKD attacks since an eavesdropper cannot obtain the final key without access to the PUF or PUF data.  
In Section 2.1, we describe the encoding schemes allowed by multi-wavelength QKD. In Section 2.2, we discuss the entire multi-wavelength protocol with a PUF included.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Protocol Architecture  
The role of the protocol architecture given in Figure 6 is to standardize communication between network components so that different vendors can develop compatible products (software or hardware). This protocol stack combines energy routing and management, and integrates the data with the network. It communicates efficiently (in terms of energy) through the wireless medium and promotes co-operative efforts among sensor nodes [45]. Depending on detection tasks, different types of application software can be designed and used in the transport layer. A WSN can be seen as a series of connections between sensors that enable them to communicate. The content, scope, size, speed, and connectivity of the network depend on the set of protocols and their implementation. This includes application, transport, network, link, and physical layers, and energy-, mobility-, and task-management plans.  
* _Application layer._ Depending on the functionality of the sensors, different applications can be used and built on this layer.
* _Transport layer._ It is used to maintain the flow of data when needed in the applications used, especially when connected to the Internet.
* _Network layer._ This deals with the routing of data provided by the transport layer.
* _Data-link layer._ As the sensor network environment is noisy and nodes can be mobile, the MAC layer must ensure low power consumption in the data broadcast of neighboring nodes.
* _Physical layer._ This deals with data-transmission, -reception, -modulation, and -encryption techniques.
* _Energy-, mobility-, and task-management levels_ are responsible for controlling the consumed energy, node movements, and task distribution across the entire protocol stacks. These levels allow sensors to co-ordinate their tasks and minimize energy consumption.
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### Yolo NAS  
YOLO-NAS is an object detection model developed by Deci that achieves SOTA performances compared to YOLOv5, v7, and v8. Unveiled in May 2023, YOLO-NAS represents a significant advancement in real-time object detection by integrating the \"You Only Look Once\" (YOLO) architecture with a Neural Architecture Search (NAS). This development is particularly vital for applications demanding rapid and precise object identification, such as autonomous driving, underscoring the importance of real-time capabilities for safety and efficiency [27; 28; 29].  
Employing NAS, YOLO-NAS automates the optimization of neural network architecture, focusing on achieving the optimal balance between speed and accuracy. This process includes selecting stage sizes, configurations, and the number of blocks and channels based on a comprehensive search across extensive datasets.  
Key to YOLO-NAS's efficiency is its use of quantization-aware blocks and selective quantization, which minimize the computational and memory requirements during inference, maintaining high model accuracy. The training process incorporates Object365 and COCO 2017 datasets, enhanced by Knowledge Distillation (KD) and Distribution Focal Loss (DFL) techniques, leading to a model that excels in speed, accuracy, and applicability across various settings [30].  
YOLO-NAS is distinguished as below:  
High Accuracy: Achieving leading results on diverse object detection benchmarks.  
Real-Time Inference: Capable of instantaneous object detection.  
Figure 6: YOLOv8 architecture [26].  
Single-Stage Model: Enhancing efficiency with a one-pass detection approach.  
NAS Optimization: Ensuring a finely tuned model for object detection tasks.  
YOLO-NAS demonstrates successful performance in downstream tasks. By fine-tuning the Roboflow-100, our YOLO-NAS model can achieve higher mAP than our closest competitors [31]. Figure 7 shows a comparison table with other Yolo models and Figure 8 shows the model architecture. Successful results can be achieved by finely adjusting the given YOLO-NAS codes.
## 3 Results and Discussion
### Performance of 2L-piRNAPred  
One way to prove the superiority of the new model is to compare its prediction performance with that obtained by other existing methods. The compared results are listed in Table 4. For the first layer, our model achieved the best values of \(Sn\) and \(Sp\) among the four methods. While for the second layer, our model also achieved the best \(Sn\) and \(Sp\) values if our model piRNAPred was compared with the first two-layer model named 2L-piRNA. We noted that the increase in \(Sp\) value was more significant than that in \(Sn\) value both for the first and the second layers. Next, we further implemented the jackknife validation test. From the last line in Tables 2 and 3, we showed that our model piRNAPred obtained the \(MCC\) value of 0.784 for the first layer and the \(MCC\) value of 0.683 for the second layer. To further rank the classification methods for the first layer, a Friedman or a Friedman Aligned Ranks test (number of datasets: <20) with the Holm post-hoc test [34] was performed using [R]. Among all predictors, piRNAPred was significantly better than other approaches according to the MCC measurement. These comparisons illustrate the stability and robustness of our model piRNAPred. The reason why the methodology works well is the comprehensive features we extracted. The features reflected the global and local information of the samples in the dataset. Moreover, the running time for predicting one sample was about one second.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methodology** & _Sn (\%)_ & _Sp (\%)_ & _Acc (\%)_ & _MCC_ \\ \hline \hline
**First Layer** & & & & \\ piRNAPred & 90.4 & 87.5 & 89 & 0.779 \\
2L-piRNA & 88.3 & 83.9 & 86.1 & 0.723 \\ Accurate piRNA prediction & 83.1 & 82.1 & 82.6 & 0.651 \\ GA-WE & 90.6 & 78.3 & 84.4 & 0.694 \\ \hline \hline
**Second Layer** & & & & \\ piRNAPred & 84.3 & 83.6 & 84 & 0.68 \\
2L-piRNA & 79.1 & 76 & 77.6 & 0.552 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The best prediction achieved by single type of features for the first layer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cross-Validation** & **Features** & **Dimension** & _Sn (\%)_ & _Sp (\%)_ & _Acc (\%)_ & _MCC_ \\ \hline \hline \multirow{8}{*}{5-fold} & BPB & 15 & 73.0 & 73.0 & 72.9 & 0.459 \\  & SNC & 4 & 69.2 & 73.6 & 71.4 & 0.427 \\  & DNC & 10 & 75.2 & 73.6 & 74.3 & 0.488 \\  & TNC & 48 & 77.8 & 77.6 & 77.7 & 0.554 \\  & BPB + SNC + DNC + TNC + PP & 24 & 74.7 & 74.2 & 74.3 & 0.489 \\  & BPB + SNC + DNC + TNC + PP & 101 & 80.0 & 77.3 & 78.7 & 0.573 \\  & BPB + SNC + DNC + TNC + PP + KNN (N-terminal) & 111 & 80.1 & 79.3 & 79.8 & 0.598 \\  & BPB + SNC + DNC+ PP + KNN (N- and C-terminals) & 121 & 84.3 & 83.6 & 84.0 & 0.68 \\ \hline \hline Jackknife & BPB + SNC + DNC + PP + KNN (N- and C-terminals) & 121 & 85.1 & 83.2 & 84.1 & 0.683 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The best prediction achieved by single type of features for the second layer.