esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 5 Conclusions  
To improve the accuracy and effectiveness of metal corrosion area detection, this paper proposes a novel image segmentation method, MCD-Net, based on an encoder-decoder network structure. First, by introducing visual Transformer sequential encoding into the convolutional encoder-decoder network, the model can better capture long-range dependencies and establish global contextual information, enhancing its performance in detecting irregularly shaped and unevenly distributed corrosion areas, thus improving the accuracy of metal corrosion detection.  
Second, this paper proposes a feature fusion method based on the Squeeze-and-Excitation (SE) module, which explicitly models the interdependencies between feature channels, enhancing the model's ability to represent key features such as corrosion boundaries. At the same time, it suppresses irrelevant feature channels and fuses features from different levels, thereby improving segmentation accuracy and model robustness.  
Finally, a score-based multi-scale feature enhancement module is incorporated, using multi-scale convolution kernels to expand the receptive field, effectively handling variations in scale and shape as well as other environmental factors while enhancing the saliency of important features, further improving model performance.  
The ablation study clearly demonstrates the contribution of each improvement to the overall model performance. Comparative experiments with other models verify the effectiveness of the proposed method. On the public corrosion dataset, the comprehensive F1 score reached 84.53%, and on the self-collected test dataset, the model achieved good inference results, indicating strong generalization ability. The visual analysis highlights the model's deep understanding and reasoning capabilities for shadow and defect features, as well as its resistance to noise interference.  
However, further improvements are needed for real-time applications of this model. Future work will focus on optimizing model complexity through techniques such as model pruning and knowledge distillation, as well as adding post-processing operations to further refine corrosion state evaluation, enabling the model to better perform metal corrosion area detection tasks.  
J.G.: Responsible for conceptualization, methodology, software, draft preparation, and visualization. L.W.: Contributed to writing, reviewing, and editing. L.H.: Provided resources and secured funding. All authors have read and agreed to the published version of the manuscript.  
The research work reported in this paper is supported by the National Natural Science Foundation of China under Grants 62103205.  
Not applicable.  
The data supporting this study are available upon request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
###### Abstract  
Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.  
wireless body area networks (WBANs); wearable sensors; routing protocol; energy efficiency
## 2 Materials and Methods
### Mechanical Test  
The tensile tests were performed using a dynamometric MTS Criterion 43 machine (see Figure 2a). A gap of 70 mm was adopted according to the standard ASTM D638 [39]. The ASTM D883 [42] was consulted to choose the testing speed of 5 mm/min. Five specimens for each combination were tested. The test continued until the specimen broke. The rupture occurred within the gauge, as is shown in Figure 2b. The square and circle pockets created are not the subject of study for this paper, but it should be specified that they were placed at the clamping points, ensuring integrity throughout the test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Low Level (0)** & **(1)** & **(2)** & **High Level (3)** \\ \hline A—Type of infill & Grid & Triangles & Cubic & Zig Zag \\ B—Infill in \% & 20 & 45 & 70 & 100 \\ C—Print speed in mm/s & 40 & 50 & 60 & 80 \\ D—Layer in mm & 0.1 & 0.2 & & \\ E—Fan speed in \% & 50 & 65 & 80 & 100 \\ F—Bed temperature in \({}^{\circ}\)C & 50 & 60 & & \\ G—Extrusion temperature in \({}^{\circ}\)C & 210 & 220 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three-dimensional printing parameters and levels from a fractional factorial DOE.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Run** & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline
3 & 0 & 2 & 2 & 1 & 2 & 0 & 1 \\
4 & 0 & 3 & 3 & 1 & 3
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 5 Enhanced SCAFFOLD Protocol
### Multidimensional Defense-in-Depth Approach  
Using a layered defense-in-depth strategy, the Enhanced SCAFFOLD architecture solves several IoT security threats. This solution incorporates many security methods for comprehensive protection. Basic aspects of this multimodal method include the following:1. The design employs encryption techniques like AES-256 to provide safe communication between IoT devices. Data are protected from unwanted access during network transfers.
2. Integrity: Enhanced SCAFFOLD can be used for secure data transfers between IoT nodes utilizing MACs and digital signatures. Tampering and data modifications during transmission may be fixed.
3. The framework employs robust authentication mechanisms, including mutual authentication and digital certificates to restrict access to the IoT to authorized devices. Impersonation assaults and unauthorized access are reduced.
4. Enhanced SCAFFOLD provides precise access control using attribute- and role-based models. This limits users' and IoT devices' responsibilities, characteristics, actions, and resource access.
5. Detecting Anomalies: The system uses machine-learning methods like ensemble classifiers and correlation analysis to identify security vulnerabilities. Proactive assault detection and reaction are possible.  
Enhanced SCAFFOLD can quickly respond to attacks with SDN. SDN controllers may swiftly rebuild networks to separate affected devices and decrease attack damage. Enhanced SCAFFOLD integrates many security capabilities for a complete defense-in-depth approach. This comprehensive IoT security plan addresses network-based assaults, data manipulation, unlawful access, device impersonation, and data breaches.  
SDN-based network reconfiguration, real-time threat detection, and response capabilities make the framework nimble, protecting IoT devices from new attacks. Encryption, access control, anomaly detection, and adaptive response can safeguard IoT settings.
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
###### Abstract
In 2010-2011, still within the SAFELAND project, the lower part of the landslide was monitored using a Ground-Based SAR (GBSAR). In this sense, the Valleebre landslide can be considered to be a real-scale laboratory where the performance of different monitoring techniques can be assessed and compared. For instance, Laser Scanning (both Terrestrial or Aerial) have been disregarded because the ground displacements, mainly parallel to the topography, would be difficult to detect; also the vegetation would mask the results. In the paper, Section 2 describes the measuring systems. In Section 3, some results are presented and compared in terms of precision and repeatability. Finally, some conclusions are outlined regarding the advantages and drawbacks of the systems tested in Valleebre. The aforementioned comparison was made in terms of cost, ease of use, precision and continuity of the results.
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 4 Comparative Experiment on Deep Learning Model Performance
### Determination of the NMS IOU Threshold  
A sensitivity analysis with the NMS IOU threshold, which is a sensitive hyperparameter in OD tasks involving overlapped objects, is undertaken. For this purpose, a series of deep learning models are trained across a spectrum of epochs and training conditions, as ascertained in prior sections. The prescribed datasets, ORG and TRANS, are enlisted to facilitate the training procedure. Notably, six distinctive IOU threshold values (0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) are selected for comprehensive analysis, and these thresholds subsequently underpin the training regime for the deep learning models. As a result, the trajectory of the loss function dynamics and the intricate interplay of average precision (AP) values are scrutinized [51], focusing on the diverse facets of the NMS IOU threshold.  
Figure 8: Evolution of loss values over epoch in case ORG: (**a**) classification loss; (**b**) regression loss.  
Figure 9: Evolution of loss values over epoch in case TRANS: (**a**) classification loss; (**b**) regression loss.  
Figures 10 and 11 affords a comprehensive view of the classification and regression loss trends, with emphasis on the ORG dataset. A notable observation is the discernible divergence in the validation dataset's classification loss curve, depicted in Figure 10b, as the NMS IOU threshold escalates, attaining its zenith at a threshold value of 0.9. Similarly, the regression loss curves depicted in Figure 11b reveal elevated values for NMS IOU thresholds ranging from 0.7 to 0.9, thereby suggesting the prudent selection of thresholds between 0.4 and 0.6 based on loss metrics and their trends.  
Further elaboration is carried out for the TRANS dataset, as disclosed by Figures 12 and 13. The training dataset manifests convergence in all loss values, aptly depicted in Figure 12a, while the classification loss curves within the validation dataset demonstrate a proclivity for divergence at NMS IOU thresholds between 0.7 and 0.9, prompting the prudent selection of thresholds within the 0.4 to 0.6 range for the TRANS case.  
Subsequently, a study of AP values at the culminating training epoch, as exhibited in Figure 14, follows the initial loss function assessment within NMS IOU thresholds ranging from 0.4 to 0.6. An assessment of AP values within this range reinforces this notion, leading to the selection of 0.6 as the optimal NMS IOU threshold for the ORG case. In concurrence, the TRANS case aligns with a comparable viewpoint, endorsing an NMS IOU threshold of 0.6. This choice is substantiated by the marginal variation in AP values within the 0.4 to 0.6 range, guided by the advantageous aspects of elevated threshold values, a principle elaborated upon in the introduction.  
Figure 14: Cont.  
Figure 13: Evolution of regression loss over epoch with NMS IOU thresholds in case TRANS: (**a**) training dataset; (**b**) validation dataset.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 5 Experiment
### Visual Interpretability
#### 5.2.2 LJSpeech-Based Dataset  
Although interpretation of the model based on the ASVspoof dataset showed the possibility of interpretation, a near-real-world dataset without paired transcripts over labels obstructed thorough interpretation. For better interpretation by XAI methods and model structures, the constrained dataset LJSpeech, which featured a single speaker and pre-defined transcript, was used.  
In Figure 5, the CNN-only model trained with LJSpeech showed no significant difference compared to the model trained with ASVspoof. Similar to ASVspoof dataset, generally, the model tended to focus on formants 1, 2, and 3 around areas \(F1\), \(F2\), and \(F3\), and unvoiced frames around area \(U\) when analyzed with [PERSON].  
Figure 4: ASVspoof attribution score with CNN-LSTM model. (**a**) [PERSON] Deepfake voice. (**b**) Integrated gradients deepfake voice. (**c**) LRP deepfake voice. (**d**) [PERSON] real voice. (**e**) Integrated gradients real voice. (**f**) LRP real voice.  
Similarly, in Figure 6 (CNN-LSTM) and Figure 7 (CNN-LSTM-perm), the formant dependency for [PERSON] and LRP was diminished, as the previous ASVspoof-trained model has demonstrated. For [PERSON] in Figure 6a,d, an identical model tendency on the frequency band found on ASVspoof was observed as well.  
Figure 5: LJSpeech attribution score with CNN model. (**a**) [PERSON] deepfake voice. (**b**) Integrated gradients deepfake voice. (**c**) LRP deepfake voice. (**d**) [PERSON] real voice. (**e**) Integrated gradients real voice. (**f**) LRP real voice.  
Figure 6: LJSpeech attribution score with CNN-LSTM model. (**a**) [PERSON] deepfake voice. (**b**) Integrated gradients deepfake voice. (**c**) LRP deepfake voice. (**d**) [PERSON] real voice. (**e**) Integrated gradients real voice. (**f**) LRP real voice.  
Using the corresponding transcript of LJSpeech with both a human voice and deepfake voice, a detailed comparative analysis can be conducted, which assists the user in interpretation for decision-making. As can be seen in Figures 6 and 7a,d, [PERSON] maintained highlights distinctively for each real human voice and deepfake voice at a frequency around 2 kHz, which is marked as area \(C\). Inspecting the spectrogram in Figure 8 area \(D\) based on the assistance of the attribution score, the deepfake voice showed a flatter waveform around 2 kHz, whereas the human voice showed more variance.
## 5 Simulation Settings
### Performance Metrics  
To evaluate the performance of different algorithms, this study employs two commonly used metrics in multi-objective optimization: Hypervolume (HV) and Spacing (SP).  
Hypervolume (HV) is a widely adopted metric that measures both the convergence and diversity of the solution set in the objective space [57]. A higher HV value indicates a more optimal and comprehensive set of solutions. In this study, HV is calculated using the following formula:  
\[HV(S)=\frac{1}{n}{\sum_{i=1}^{n}VOL\left(\bigcup_{x\in S}[f_{1}(x),z_{1}^{r}] \times[f_{m}(x),z_{m}^{r}]\right)} \tag{14}\]  
In the HV calculation, \(VOL(\cdot)\) represents the Lebesgue measure, which is used to quantify the volume in the objective space formed between each solution and the reference point. Here, \(z_{m}^{r}\) denotes the reference point defined by the user in the objective space. It is important to note that in this study, the HV is calculated as the mean value rather than the cumulative sum of the HV across all solution sets. This adjustment is necessary because different algorithms may produce varying numbers of solutions, and the use of the mean ensures a fair comparison across algorithms.  
Since the problem under investigation is a real-world scenario, there is no known true Pareto front available. Therefore, the choice of the reference point becomes critical for comparing the performance of different algorithms. To address this, the reference point is determined through preliminary experiments. Specifically, the maximum values across all objectives achieved by the solution sets of all algorithms are identified, and an offset (e.g., 1.1 times these maximum values) is added to ensure the reference point is sufficiently large to encompass all solutions. This approach guarantees that the HV is computed under the same reference point for all algorithms, thereby ensuring the fairness and reliability of the comparison.  
To provide a comprehensive evaluation of the algorithm performance and mitigate the limitations of relying solely on a single metric, this study introduces an additional metric: Spacing
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 2 Proposed Methodology
### Preprocessing  
In the initial phase of the research study, data is collected, typically captured with mobile device cameras or other vision sensors that record information crucial to influencing the output of subsequent deep learning (DL) and machine learning (ML) models. Following this data collection, the preprocessing stage becomes paramount, involving various techniques such as resizing, noise reduction, and data augmentation to enhance the quality of images or videos. As a foundational step in our investigation, we employ data normalization, a process illustrated mathematically in Equation (1), where each pixel value is standardized to the range of [\(-\)1, 1].  
\[R_{i}=\frac{I-I_{min}}{I_{max}-I_{min}} \tag{1}\]  
In this context, the representation of the initial data is denoted as \(I\), where \(I_{min}\) and \(I_{max}\) represent the minimum and maximum values within the input data, respectively. To standardize the data input, \"\(R_{i}\)\" is introduced. Before the training, all images underwent resizing to dimensions of 224 \(\times\) 224 \(\times\) 3. Additionally, various data transformations were applied to the input images, encompassing operations such as flipping, 15\({}^{\circ}\) rotation, and 0.2 zooming. Introducing data augmentation, as depicted in Figure 2, played a pivotal role in enhancing dataset variability, effectively mitigating overfitting and bolstering the model's accuracy and generalization capabilities.
## 2 Related Work
### Joint Learning of Multiple Driver Behaviors  
Due to its complexity, learning multiple driver behaviors under a single model remains one of the most understudied areas of driver monotioring. In 2016 [PERSON] et al. [32] proposed a framework operating over visual, audio and physiological features to tackle both driver fatigue and distraction. The authors suggested a method based on two different Bayesian networks, each dedicated to a single condition, while both networks operated on the same input features. In 2017, [PERSON] et al [33], proposed a multi-class approach based on inertial and physiological measurements to monitor stress, fatigue and drowsiness at the same time. In spite of being one of the very first approaches to address multiple driver conditions under the same framework, the vague distinction of the classes and the relatively simplistic simulation setup make their overall findings hard to generalize. In 2019, [PERSON] et al. [34], proposed a single framework to jointly learn multiple user states. In particular, the authors tried to quantify cognitive load and user's expertise using a deep-multitask-learning pipeline. Even though the method was evaluated on a physical trauma treatment scenario and not in a driving setup, their analysis suggested its potential to generalize across tasks. Finally, as referenced in Section 2.1, in 2020, [PERSON] et al. [26] studied alertness and distraction together by formulating their problem as a multi-class classification task, similarly to [33]. However, their limited dataset and evaluations also narrow down the generalizability of their findings.  
In contrast to most past research works, in this study we compile a relatively larger dataset of 45 male and female subjects with multiple recordings each, so to account for richer alertness and distraction variations within and across participants. We focus our analysis exclusively on physiological signals and their corresponding features in order to explore the strength of different bio-markers to capture the two conditions under the context of driving. Eventually, we evaluate different machine-learning classification techniques as we explore further how modern deep-learning pipelines can be applied to jointly monitor multiple driver states.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Application of Fine-Tuned Model to Architectural Space: 360\({}^{\circ}\) VR Experience
#### 4.2.4 Implications and Future Directions  
The results indicate individual variations in emotional classification, suggesting that EEG responses are highly influenced by personal characteristics. Even within the same environment, participants exhibited differing emotional classifications, highlighting the subject-specific nature of EEG-based affective responses.  
The study also provides preliminary insights into the influence of environmental characteristics. By examining differences in brain-region activation and emotion classification across DDP and Long Island City, the findings suggest a potential relationship between artificial versus natural spatial elements and emotional responses.  
However, as this study was conducted with a small sample size (n = 4), the results should be interpreted as a pilot test rather than a statistically generalisable conclusion. Both groups (DDP (A) and Long Island (B)) exhibited variability in emotion classification depending on the EEG regions observed, suggesting that the classification model may be influenced by measurement location bias across the frontal, parietal, temporal, and occipital lobes.  
The fine-tuned Model Version 8 primarily produced classification distributions within the 40-50% range, with some cases falling in the 30% or 50-60% range, indicating the need for further improvement in classification performance. Future research should focus on detailed analyses of specific lobe combinations, enhanced data-preprocessing techniques, and hybrid approaches integrating CNN-LSTM and LLM-based inference, which are expected to improve EEG-based emotion classification accuracy.