esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Deep Learning CNN--Methodology
Face detection loop: the face detection mechanism is subsequently employed, where the algorithm attempts to identify the face in the acquired video data. This step is crucial for isolating the region of interest (ROI) in the later steps. If a face is successfully detected, the process proceeds to the ROI segmentation. If the algorithm fails in face detection, it enters into a loop, continually attempting to detect the face until successful. ROI segmentation and spatial averaging: upon successful face detection, ROI segmentation and spatial averaging are conducted to localize and focus on the facial region relevant to heart rate and blood pressure estimation. This involves delineating the region where PPG signals can be extracted effectively and then utilizing spatial averaging to enhance the signal quality by reducing noise and irrelevant spatial variations. Color transformation and CNN model application: following the segmentation, color transformation is applied to the ROI, optimizing the visual data for the extraction of hemodynamic signals. The transformed data are then input into the convolutional neural network (CNN) model. The CNN model is designed and optimized using specific parameters, such as utilizing the Adam optimizer, pre-training weights with ImageNet, and gauging performance via accuracy metrics. The training process encompasses 30 epochs, a batch size of 32, a dropout rate of 20%, and utilizing 128 hidden units per layer. Classification and parameter optimization: the CNN model, now processed with the color-transformed ROI, endeavors to classify and estimate the PPG, ABP, and HR of the subject. This step involves the intricate and nuanced utilization of deep learning algorithms to sift through the spatially averaged and color-transformed data, identifying patterns and features relevant to the accurate classification and estimation of the physiological parameters of interest. Output and further analysis: lastly, the classified and estimated PPG, ABP, and heart rate serve as the output, potentially utilized for further analysis or real-time monitoring applications. These data, derived cuffless, present opportunities for non-invasive monitoring in various contexts, offering insights into cardiovascular health without the necessity for traditional, more invasive methods.
# Multizone Leak Detection Method for Metal Hose Based on YOLOv5 and OMD-ViBe Algorithm
## 3 Experiment and Results
### Experimental Results
#### 3.2.4 Leak Location  
The length of the actual measurement tube 1 was 310 mm, and the size of tube 2 was 430 mm. The leakage points of the two were divided into 89.53% and 85.48% of the total tube length. As shown in Table 6, the positioning error was 1.2-9.11% in the application of the corresponding moving target algorithm combined with the foreground point calculation method and the centroid calculation method proposed in this paper, which can meet the actual detection requirements.  
In Table 7, based on the YOLOv5 and OMD-ViBe algorithms proposed in this paper, the algorithm performance was tested at 0.2 Mpa, 0.3 Mpa and 0.4 Mpa. The detection time of YOLOv5, the detection time of OMD-ViBe and the recognition accuracy of YOLOv5 were respectively evaluated. Each experiment used 9000 frames of pictures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pressure** & **No Leak Pressure Drop** & **Combined Pressure Drop** & **No Leak Leakage Rate** & **Combined Leakage Rate** \\ (**Mpa**) & **(Pa)** & **(Pa)** & **(mL/min)** & **(mL/min)** \\ \hline
0.2 & 2 & 2705 & 0.0007 & 0.338 \\
0.3 & 3 & 4653 & 0.0011 & 0.581 \\
0.4 & 6 & 8934 & 0.0021 & 1.116 \\ \hline \hline \end{tabular}
\end{table}
Table 4: No leak and two leaks combined test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Leak 1 Leakage Rate** & **Leak 2 Leakage Rate** & **Leak 1 Error** & **Leak 2 Error** \\  & **(mL/min)** & **(mL/min)** & **Leak 1 Error** & **Leak 2 Error** \\ \hline ViBe & 0.755 & 0.361 & 9.71\% & 9.01\% \\ GMM & 0.711 & 0.405 & 3.35\% & 2.00\% \\ DPPartiMediod & 0.635 & 0.481 & 7.76\% & 21.25\% \\ PBAS & 0.709 & 0.407 & 3.01\% & 2.58\% \\ KNN & 0.701 & 0.415 & 1.89\% & 4.53\% \\ ViBeImp & 0.757 & 0.359 & 9.98\% & 9.48\% \\ SigmaDelta & 0.699 & 0.417 & 1.58\% & 5.07\% \\ Adaptive Background Learning & 0.722 & 0.394 & 5.00\% & 0.85\% \\ Proposed Method & 0.685 & 0.431 & 0.37\% & 3.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Leakage rate comparison between the proposed method and eight traditional methods.  
Three experiments were conducted under different pressures. The detection results of YOLOv5 can met the actual detection requirements, and the detection speed of the algorithm satisfied the purpose of application.
# An Interference Cancellation Scheme for High Reliability Based on MIMO Systems
## 3 Proposed Scheme
However, the complexity for QRD-\(M\) is increased exponentially as modulation order and the number of transmit antennas is large because unnecessary survival paths at each layer are also increased exponentially. In [13], thresholds are calculated at each layer to eliminate unnecessary survival paths. For path eliminations, ASED is compared with a calculated threshold at each layer and paths which are larger than a threshold are eliminated. After applying low-complexity QRD-\(M\), IRI is fully removed. Then, R\({}_{1}\) and R\({}_{2}\) transmit decoded symbols by using CDD and D decodes received symbols by using MRC. Also, transmit symbols at other time slots are similarly processed.
## 4 RSCR
### Overview  
As early as 2003, [PERSON] et al. [33] proposed the addition of a knowledge plane to achieve intelligence in networks. Subsequently, [PERSON] et al. [34] built on this concept and introduced the idea of a Knowledge-Defined Network (KDN). RSCR, on the foundation of SD-CFN, incorporates a knowledge plane where RL is integrated to achieve intelligent computing routing.  
RSCR utilizes available bandwidth, delay, and packet loss rate as features for the RL process. Combined with the optimal computing node identified in Section 3, it computes the route from the computing request source to the optimal computing node. RSCR, leveraging the global view of SDN, can dynamically adjust routes based on changing traffic patterns. Furthermore, unlike traditional routing that relies on protocols such as RIP, OSPF, and BGP [35], RSCR, as described in paper [36; 37; 38], integrates with SDN using new routing protocols. In SD-CFN, timely and reliable communication is also crucial. Reference [39] evaluates the timeliness of drone-assisted networks, and we can introduce protocols centered on the Age of Information (AoI) for further research in the future. Regarding SD-CFN architecture communication security, we can try to introduce blockchain technology to ensure data security and privacy. [PERSON] et al. [40] proposed a blockchain-assisted distributed access control scheme for drone computing networks,which can enable drones to autonomously manage their identities, attributes, and access policies. Referring to this solution, SD-CFN can be provided with higher efficiency and fault tolerance, making reliable computing services possible.  
As shown in Figure 1, a detailed explanation of RSCR is provided below: (1) The Computing Resource Perception Unit periodically perceives the computing resources of the computing devices on the computing resource end in the data plane. Computing resources include CPU, memory, and storage resources. It is important to note that the computing resource end is composed of a Kubernetes cluster responsible for providing computing resource data. (2) The Network Resource Perception Unit periodically queries the data plane to collect raw data on the state of the network topology. (3) The Management Plane retrieves computing resource data to segment computing nodes. (4) The Management Plane retrieves network resource data to calculate and store link state information. (5) The Knowledge Plane retrieves information about the overall computing-network resources from the management plane. (6) RL Agent explores all possible routes between each source-destination node pair and computes the optimal route based on link states. (7) The Knowledge Plane stores all routing information. (8) The Control Plane retrieves routing information before the computing task arrives and proactively installs the optimal path between the computing request source and the optimal computing node in the form of flow tables on the switches.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 6 Research Challenges and Future Directions
### Strengthening the Research on Adaptive Modulation Identification  
At present, most researchers only study the modulation identification module, and there are few studies on the joint design of the entire modulation and demodulation system. However, with the development of intelligent communication, it is necessary to jointly study the modulation identification and demodulation of the physical layer and to design an adaptive modulation identification and demodulation system. It is also an important development direction.
## 4 Discussion
### Evaluation Metrics  
In evaluating the performance of models across various tasks, a combination of qualitative and quantitative metrics is utilized. Quantitative metrics provide objective and numerical measures to assess different aspects of model performance. In image-based tasks like text-based image synthesis or generation, metrics such as inception score (IS), Frechet inception distance (FID), PSNR, and SSIM quantify the quality, fidelity, and similarity of generated images. Text-based tasks, such as language translation, rely on metrics like BLEU, accuracy, and F1 score to measure the accuracy, fluency, and alignment of translations. Speech-related tasks, like text-to-speech synthesis, utilize metrics such as MOS, PESQ, and RTF to evaluate the quality, intelligibility, and real-time performance of synthesizedspeech. For code generation and understanding tasks, metrics like BLEU, CodeBLEW, pass@k, and code evaluation benchmarks quantitatively assess the correctness, similarity, and performance of generated code. Additionally, qualitative metrics play a crucial role by capturing subjective assessments and human judgments. These metrics involve human evaluation, expert reviews, and user feedback to evaluate factors such as visual appeal, coherence, naturalness, readability, and user satisfaction. By considering both quantitative and qualitative metrics, a comprehensive evaluation approach is achieved, ensuring that the models perform effectively and meet both objective standards and subjective expectations across a wide range of tasks.
## 3 Results and Discussion  
### Risk Distribution  
The risk assessment indicator system comprised six subsystems for spatial location, land attributes, population density, plot ratio, GDP density, and fire incident density. According to the method of calculation for each factor, the scores of all research units could be obtained. Spatial location, population density, and GDP density were obtained from the Shanghai Statistical Yearbook [29]. Land attributes and building volume ratios were derived from the publicly available National Spatial Geographical Information Database [30]. Incident density was provided by the Command Center of Shanghai Fire and Rescue Department. The research object considered the entire administrative division space of Shanghai. The area units were divided based on the urban road network, up to 19,140 units.  
Figure 5a illustrates the scores of the factors across the entire spatial units; the detailed scores of all factors can be found in Appendix A. Taking fire incident density as an example, the risk score exhibited distinct spatial characteristics. The highest scores, up to 100 points, were concentrated in the core urban area within the inner ring road. This area also corresponded to the highest GDP and population density risk scores. In contrast, the risk scores were much lower along the outer ring and suburban areas, with the minimum score being only 20 points. This also resulted in the risk classification following a similar trend. Figure 5b further shows the specified risk classification of all the research units in Shanghai, divided into levels A, B, C, or D according to the composite score based on the preset proportions (Table 1) of all parameters. Level A was qualified for scoring over 73.62 points, with only three units in the core area of Shanghai. Most of the areas within the outer ring region were classified as level B, with scores ranging from 46.23 to 73.62 points. The other suburban areas outside the outer ring were all below 46.23 or 36.27 points, categorizing them as level C or D.  
Figure 4: Impact of different response time requirements on coverage area for different risk zones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Risk Classifications**} & **Travel Time** & \multirow{2}{*}{**Area Proportion**} & \multirow{2}{*}{**Risk Classifications**} \\  & **(min)** & & \\ \hline A & 4 & 16 & 22.56 \\ B & 7 & 49 & 7.37 \\ C & 14 & 196 & 1.84 \\ D & 19 & 361 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Area correction coefficients for different risk classifications.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### NSL-KDD Results  
#### 4.2.1 Performance on Minority Class  
Table 2 presents the precision, recall, and F1-score comparisons for the minority classes (U2R and R2L) across traditional oversampling techniques (SMOTE, ADASYN, ROS) and several GAN variants (WGAN, CWGAN, WGAN-GP) along with their SNNL-enhanced counterparts. The best results are highlighted in **bold**, and the second-best are underlined.  
Traditional oversampling methods (SMOTE, ADASYN, ROS) generally yield lower performance in minority-class detection compared to GAN-based methods. For R2L, SMOTE achieves the best performance among traditional baselines with an F1-score of 0.150, while ADASYN and ROS follow with 0.137 and 0.107, respectively. A similar pattern is observed for U2R, where SMOTE and ADASYN reach 0.150 and 0.137, and ROS again performs the worst at 0.107.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Temp** & **Accuracy** & **Macro-F1** & **FPR (\%)** & **Detection Rate (\%)** \\ \hline Random Forest & 0.01 & 1.00 & 0.94 & 0.14 & 93.74 \\ XGBoost & 0.01 & 1.00 & 0.98 & 0.08 & 99.82 \\ Random Forest & 0.1 & 1.00 & 0.94 & 0.14 & 93.96 \\ XGBoost & 0.1 & 1.00 & **0.97** & **0.03** & **99.97** \\ Random Forest & 0.3 & 1.00 & 0.94 & 0.15 & 93.87 \\ XGBoost & 0.3 & 1.00 & 0.97 & 0.07 & 99.92 \\ Random Forest & 0.5 & 1.00 & 0.94 & 0.17 & 93.61 \\ XGBoost & 0.5 & 1.00 & 0.98 & 0.05 & 99.93 \\ Random Forest & 1.0 & 1.00 & 0.94 & 0.16 & 93.81 \\ XGBoost & 1.0 & 1.00 & 0.97 & 0.06 & 98.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SNNL temperature sensitivity on NSL-KDD dataset (validation set, averaged over 3 runs).  
In contrast, GAN-based methods demonstrate noticeable improvements over traditional baselines. For R2L, WGAN and CWGAN achieve F1-scores of 0.168 and 0.200, while WGAN-GP achieves 0.167. Notably, integrating SNNL into WGAN-GP raises the F1-score to **0.217**, the highest among all evaluated methods. For U2R, CWGAN+SNNL outperforms all baselines with an F1-score of **0.175**, followed by WGAN+SNNL (0.170) and WGAN-GP+SNNL (0.154), each exceeding their corresponding base models.  
These improvements may be attributed to SNNL's effect on improving either precision, recall, or both. For instance, WGAN-GP+SNNL raises recall from 0.090 to 0.120 for R2L while maintaining competitive precision. Although a slight decrease in recall is observed for CWGAN on U2R after SNNL integration, the overall F1-score still improves.  
The observed performance gains across different GAN variants suggest the potential effectiveness of the proposed SNNL approach in addressing class imbalance and improving detection of underrepresented attack categories.
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 5 Discussions
### Future Directions  
Future research in the field of cardiac biometrics and human identification can explore several exciting avenues:  
1. Conduct research on more extensive and diverse datasets to evaluate the system's performance under various conditions and demographics. Access to larger datasets can help improve the generalization and robustness of the proposed system.
2. Investigate the integration of multiple biometric modalities (e.g., ECG, PPG, PCG, fingerprint, and facial recognition) for even more reliable and accurate human identification. Combining multiple modalities can enhance security and reduce the risk of spoofing attacks.  
Explore the feasibility of continuous authentication using cardiac biometrics. Investigate how the system can continuously monitor and verify individuals' identities over extended periods, ensuring a seamless and secure user experience.
4. Experiment with more advanced transfer learning techniques, including fine-tuning the LSTM model on a related dataset or employing other pre-trained models that are specifically designed for sequential data analysis.
5. Enhance the interpretability of the system's decision-making process. Investigate methods to understand which features or patterns contribute most to the identification process, making it easier to diagnose and address potential issues.
6. Conduct rigorous adversarial testing to assess the system's robustness against potential attacks, ensuring that it remains secure in real-world scenarios.
7. Investigate ways to improve the user experience while maintaining privacy. Examine user consent, data anonymization, and secure storage methods to protect individuals' sensitive biometric data.
8. Study the long-term reliability and stability of cardiac biometrics as individuals age or undergo physiological changes. Ensure that the system maintains its accuracy and effectiveness over time.
9. The field of biometrics involves authenticating and recognizing individuals using their behavioral and physical characteristics [61]. The latest progress in multimodal biometrics around physiological attributes. In the future, we will focus on more techniques utilizing finger veins, palm veins, fingerprints, facial, lip, iris, and retinal patterns to identify humans.
10. At the forefront of this framework lies the utilization of spectrogram images for biometric identification. The explainable nature of AI ensures [62] that the identification process is transparent and decisions are justifiable. This is crucial in scenarios where patient data security, accuracy, and ethical considerations are of paramount importance.  
By focusing on these future research directions, advancements can be made in cardiac biometrics and human identification, leading to more secure and reliable authentication systems with broad practical applications.
###### Abstract
This receiver was installed in October 2023 and has been invaluable from the outset for very long baseline interferometry (VLBI) observations, as well as for various scientific projects, with the data still being processed. The SRT is situated in the Pranu Sanguini area, approximately 35 km northeast of Cagliari (latitude 39.493072\({}^{\circ}\) N, longitude 9.245151\({}^{\circ}\) E) at an elevation of about 650 m above sea level. Detailed descriptions of the telescope's technical specifications, including its radio receiver features, are provided in [17; 18]. This paper presents the methodology and results of an RFI measurement campaign conducted in the range of 4-5.8 GHz. The measurements were carried out in the area surrounding the site of the SRT during October and November of 2023. Thanks to the results of this measurement campaign, it is possible to map the RFI scenario in the C-band portion used by the SRT's cryogenic receiver. This facilitates the processing of data collected during scientific observations by filtering out spurious signals from the surrounding environment. Additionally, based on the campaign's findings, preventive measures can be implemented to enhance the observation experience. For example, adjusting the local oscillator to a specific frequency can help filter out unwanted portions of the band. Section 2 provides a detailed description of the measurement setup. The results of the RFI measurement campaign are presented in Section 3. Lastly, Section 4 offers a discussion and conclusions based on the findings.
# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **10 Epochs (s)** & **20 Epochs (s)** & **30 Epochs (s)** & **40 Epochs (s)** & **50 Epochs (s)** \\ \hline CNN + TextCNN + MLP & 96.63 & 94.12 & 94.69 & 92.81 & 93.84 \\ VIT + TextCNN + MLP & 93.55 & 94.22 & 94.31 & 94.41 & 94.50 \\ Ensembles + TextCNN + MLP & 93.55 & 94.12 & 94.60 & 94.50 & 94.79 \\ Ensembles + TextCNN + MLP & 92.0 & 95.99 & 94.66 & 94.06 & 94.22 \\ CNN + TextCNN + MLP + Transformer & 89.10 & 98.76 & 98.76 & 91.75 & 92.88 \\ VIT + TextCNN + MLP + Transformer & 94.12 & 94.31 & 94.69 & 95.77 & 95.17 \\ Ensembles + TextCNN + MLP + Transformer & 96.68 & 96.76 & 96.07 & 96.07 & 97.16 \\ Ensembles + TextCNN + MLP + Transformer & 96.97 & 96.16 & 97.06 & 97.16 & 97.16 \\ UIT + TextCNN + MLP + Mamba & 90.23 & 92.80 & 93.18 & 93.36 & 94.50 \\ Ensembles + TextCNN + MLP + Mamba & 96.88 & 96.97 & 97.16 & 97.16 & 97.23 \\ Ensembles + TextCNN + MLP + Mamba & 97.44 & 97.54 & 97.82 & 97.82 & 97.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy comparison across 10, 20, 30, 40, and 50 epochs for different models integrated with TextCNN, MLP, Transformer, and Mamba. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **10 Epochs (s)** & **20 Epochs (s)** & **30 Epochs (s)** & **40 Epochs (s)** & **50 Epochs (s)** \\ \hline CNN + TextCNN + MLP & 1330.80 & 2633.33 & 3939.43 & 5154.41 & 6532.31 \\ VIT + TextCNN + MLP & 2426.06 & 4841.36 & 7314.91 & 9726.87 & 12138.96 \\ Resembles + TextCNN + MLP & 1349.23 & 2697.17 & 4024.09 & 53283.33 & 6737.37 \\ EfficientNet + TextCNN + MLP & 1537.36 & 3029.55 & 4604.01 & 6412.67 & 4778.36 \\ CNN + TextCNN + MLP + Transformer & 1337.27 & 2619.55 & 3592.58 & 5177.68 & 6540.05 \\ VIT + TextCNN + MLP + Transformer & 2485.81 & 5002.86 & 7481.98 & 9750.50 & 12473.33 \\ Resnets + TextCNN + MLP + Transformer & 1392.34 & 2776.66 & 4164.51 & 5590.57 & 6958.78 \\ EfficientNet + TextCNN + MLP + Transformer & 1500.34 & 2998.56 & 4464.44 & 5936.96 & 7808.73 \\ CNN + TextCNN + MLP + Mamba & 1314.78 & 2624.52 & 3983.90 & 5313.03 & 6677.78 \\ VIT + TextCNN + MLP + Mamba & 2484.70 & 5002.63 & 7519.70 & 10022.49 & 1685.56 \\ Resnets + TextCNN + MLP + Mamba & 1489.99 & 2964.93 & 4279.99 & 5659.92 & 6972.15 \\ EfficientNet + TextCNN + MLP + Mamba & 1634.76 & 3203.93 & 4686.35 & 5935.88 & 7434.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of training time (in seconds) for different models with TextCNN, MLP, Transformer, and Mamba, evaluated across 10, 20, 30, 40, and 50 epochs. while improving the inference efficiency, introduces additional computational complexity during training due to its recursive and dynamic selection mechanisms. Although ResNet18 + TextCNN + MLP + Mamba is 6.64% faster than EfficientNet + TextCNN + MLP + Mamba, in real-world rescue operations, the accuracy of scene recognition is more critical. Incorrect decisions can further waste valuable time and resources, making EfficientNet + TextCNN + MLP + Mamba the preferred choice for rescue scenario analysis.