esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 3 Results and Discussion
### Diagnoser Model Validation  
The effectiveness of the methodology is primarily ensured by the performance of the diagnoser. The accuracy of the forecasting model is critical, with the error needing to be as low as possible for effective anomaly detection.  
The diagnoser model is developed using the following process: 80% of the available data, comprising 350,764 training examples, are used. The model is trained with a batch size of 256 over 300 epochs, utilizing stochastic gradient descent as the optimizer. The \(\delta\) parameter in the Huber loss function is tuned in a cross-validation manner. We keep \(\delta=1\) to limit the influence of some part of our data points which poorly fit the model. As shown in Table 1, the model's performance exceeds the requirements for the anomaly detection task, achieving a mean squared error (MSE) of 0.18. The table also presents the model's mean absolute error (MAE) and mean relative error (MRE) metrics for selected features.  
Using these statistical metrics, thresholds can be proposed for each variable. If these thresholds are exceeded, according to Equation (10), the behavior of the variable is deemed abnormal, indicating potential failures in the related functionalities. The lower the MRE for each variable, the better the system can distinguish between normal and abnormal behaviors.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Component** & **Variable** & **Average Value** & **MAE** & **MRE** \\ \hline Generator & Rotation per Minute & 1121.74 & 95.18 & 8.48\% \\ Generator & Bearing Temperature & 48.46 & 3.63 & 7.5\% \\ Generator & Phase 1 Temperature & 63.10 & 3.41 & 5.4\% \\ Generator & Phase 2 Temperature & 63.59 & 2.96 & 4.66\% \\ Generator & Phase 3 Temperature & 63.31 & 2.89 & 4.57\% \\ Hydraulic group & Oil Temperature & 34.46 & 1.16 & 3.38\% \\ Gear box & Oil Temperature & 49.20 & 1.65 & 3.36\% \\ Gear box & Bearing Temperature & 54.76 & 2.23 & 4.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction accuracy of some of the variables [27].  
Figure 7: Wind turbine systems with their different components.
## 6 Findings and Future Research Scope
### Findings of the Study
* The important keywords are deep learning, crack detection, pavement crack detection, damage detection, convolutional neural network, image processing, feature extraction, recognition, system, and semantic segmentation. In the critical analysis section, the authors categorized the papers into different categories based on the techniques they used and provided an overview of the papers. The results of the critical analysis are listed below. * The deep learning techniques used for crack detection are divided into three categories: classification, detection, and segmentation. Among these, there are relatively few research on the classification technique. * CNN, YOLO, and U-Net are the most commonly used deep learning methods for performing classification, detection and segmentation tasks, respectively. * ResNet is the most commonly used backbone of deep learning methods. * Most methods perform deep learning tasks on the Tensorflow and PyTorch frameworks. * LabelImg is the most widely used annotation tool. * Most researchers use SGD and Adam optimizers to optimize deep learning models. * Using specialized architectures like FPN, FCN, etc., using attention machines like SENet, CoordAtt, and GAU, using a combination of multiple imaging modalities (IR, VIS, 3D), and preprocessing can improve the accuracy of crack detection. * Integrating attention modules (e.g., SENet, CBAM) or feature pyramid networks, can also improve the performance of deep learning methods for road crack detection. * Using architectures such as DenseNet, ResNet, and MobileNet and reduced model size can speed up computation without compromising detection accuracy.
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 3 Results
### Experimental Setup
#### 3.1.5 Tradeoff Parameter  
In the tradeoff process, the intensity images obtained by SCMP and CS were combined by Equation (17) using the tradeoff parameter \(\tau\) (\(0\leq\tau\leq 1\)). The tradeoff parameter is a hyperparameter that should be determined in advance. In general, experimental validation is carried out via cross-validation when there are hyperparameters. Since we used two kinds of satellite images (Ninohmatsu and Yokohama), the value of \(\tau\) was determined using one of these datasets, and the other dataset was processed using the determined value. In our experiment, the tradeoff parameter was determined using the correlation coefficient (CC) and ERGAS ([PERSON] et al., 2017). The results of applying the tradeoff parameter with a step size of 0.1 for image quality evaluation are shown in Table 3. From this result, it was found that the resolution and numerical evaluation were improved when the sum of squares of 1-CC and ERGAS was the smallest. This relationship is shown in Equations (23) and (24).  
\[\underset{\tau}{\operatorname{argmin}}S(\tau)\ s.t.\ 0\leq S(\tau)\leq 2 \tag{23}\]  
\[S(\tau)=\left(\frac{1-CC(\tau)}{\max(1-CC)}\right)^{2}+\left(\frac{ERGAS(\tau )}{\maxERGAS}\right)^{2} \tag{24}\]  
The tradeoff parameter was determined as the value that minimizes Equation (24). Figure 4 displays the results of evaluation of the intensity of RGB images with various tradeoff parameters. The red line shows Nihonmatsu, the blue line shows Yokohama, the solid line is the result of CC, and the dotted line is the result of ERGAS. Figure 5 shows three images with some tradeoff parameter values. The lower the evaluation index S, the better the visual appearance. From these results, the value that minimized \(S(\tau)\) was \(\tau=0.4\) for Nihonmatsu and \(\tau=0.3\) for Yokohama. Therefore, in the following experimetns, \(\tau=0.3\) was used for Nihonmatsu and \(\tau=0.4\) was used for Yokohama.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficient** & **Nihonmatsu** & **Yokohama** \\ \hline \(c_{1}\) (NIR) & 0.3857 & 0.3789 \\ \(c_{2}\) (Blue) & 0.2199 & 0.2549 \\ \(c_{3}\) (Green) & 0.1980 & 0.1123 \\ \(c_{4}\) (Red) & 0.0486 & 0.1099 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The correction coefficient estimated by SCMP.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 5 Discussion  
In order to reduce the positioning errors caused by NLOS, this paper has proposed a LOS/NLOS identification model which is suitable for complex indoor environments, and the parameters of the model have been adaptively determined. After identifying and discarding NLOS ranging results, the LS-based single point positioning algorithm was used. It has been deduced that the average precision of the model was 97.08% and the average recall was 74.97%. The average error of the positioning results was 0.935 m. The accuracy found here is better than that found without identification and discarded in the case of a different number of NLOS FTs. In conclusion, the proposed method can eliminate some NLOS ranging results and effectively improve the positioning accuracy. However, the recall of the model is not high, which may cause some LOS ranging results to be eliminated during positioning and affect the positioning accuracy. In order to solve this problem, map matching [44; 45] can be added to the proposed method to reduce misjudgment.  
Conceptualization, M.S.; Formal analysis, M.S. and S.X.; Funding acquisition, Y.W.; Methodology, M.S.; Project administration, Y.W.; Data curation, H.C. and M.S.; Writing--original draft preparation, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under grant number 2016 YFB0502102.  
In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
The authors declare no conflict of interest.
## 5 Discussion
This trend was probably due to the nature of the different treadmill walking activities. In particular, high-speed walking activities had a low error profile. This activity type showed high peaks during the heel-strike and toe-off gait phases, allowing the model to predict it more easily. However, even for high-speed walking activities, their error percentage increased when larger rotations were applied (\(\approx\)90 degree on the frontal axis). A possible reason for this could be that large rotations along the frontal and sagittal axis (X- and Z-axis) implied switching the acceleration component parallel to gravity that usually carries most part of the information. Figure 9 shows an example of walking activities of 0.8 km/h and 4.0 km/h and their corresponding applied rotations of twenty and sixty degrees. It was visible how slower walking had a smaller acceleration range. On the other hand, faster walking acceleration range had more dynamism and the information spanned a wider acceleration range. \begin{table}
\begin{tabular}{c c c c c} \hline
**Author** & **Sensors\({}^{*}\) Position** & **Applied Augmentation** & **Augmented Sensors** & **Recognized Activities** \\ \hline
[17] & Forearm & Rotations around X-axis & Accelerometer Gyroscope EMG \({}^{*}\) & Holding, Twisting, Folding \\ \hline
[18] & Left wrist & Averaging, combining, shuffling & Spectral features of accelerometer and gyroscope & Sitting, Standing, Walking \\ \hline
[19] & Wrist & Rotation, Permutation, Time-warping, Magnitude-warping & Accelerometer & Motor state of Parkinson sbj: Bradykinesia, Dyskinesia \\ \hline
[20] & Mobile phone pockets & Resampling for & Accelerometer Gyroscope Magnetometer & UCI-HAR [37], MotionSense [38], USC-HAD [39] \\ \hline Proposed model & Body trunk & Rotations around the three axis separately & Accelerometer & Stairs up, Stairs down, Static, Walking, Wheelchair \\ \hline \end{tabular}
*: augmented sensors that undergo different kind of augmentation than the one reported in the table.  
\end{table} 
Table 4: Description of the most relevant studies related to wearable sensor data augmentation in the context of HAR. Each study is described by its sensors’ positioning, applied augmentation, type of sensor, and recognized activities. As shown in Figure 4, the static activities were the least affected by rotations on all orientation axes, probably due to their low acceleration values. Considering the models' performance differences among activities, another possible approach could be a tailored augmentation to the activity itself. Future work might consider transformations applied only to the classes that are majorly influenced by rotation, i.e., the classes with a high dynamic range, in terms, for example, of acceleration magnitude. This way, redundancy would be avoided and data would be augmented more efficiently.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 3 Framework Design
### Contextual Information
#### 3.1.3 Quality of Context Information
multi-factor authentication attributes (a combination of static credentials and contextual information), this work includes many authentication attributes for decision making, with an assigned confidence level for each. The confidence level with an access threshold is assigned to users in an easy manner, giving homeowners the choice of including the available factors according to their situation. In the designed framework, different contextual attributes and properties can be utilized to enhance the determination of a legitimate user. This can be achieved by capturing long-term contextual information (e.g., power consumption, access patterns); short-term contextual information (e.g., location, calendar, profile); sensor data (e.g., pressure, temperature); and dependent data (e.g., power consumption, speed). Additionally, it is important that the collected contextual information is transmitted to and stored securely on the server (Home Gateway), and has backups in case of data loss, so models and profiles do not need to be retrained. Historical information and resources of this information will be considered as factors for measuring the quality of contextual information. Long-term contextual information, sensor data, and dependent data will be utilized for future work using machine learning techniques. The contextual information that is applied in this work includes user profile (e.g., name, ID, age, and other information), location (IP and Bluetooth), user's calendar, and historical information (access patterns and logs). Based on the categorization of IoT applications as presented in [17], the implementation and evaluation of the proposed framework is based on the first category (smart home scenario), as Figure 1 shows. Regarding data collection, various scenarios are possible in an effort to achieve contextual information that is related to the device, environment, user, and the network.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method
### Critic Architecture  
The discriminator is designed similarly to the discriminator in generative adversarial networks (GAN). Its primary function is to assess the authenticity of input images, determining whether they are original cover images or stego images. The architecture mirrors the decoder, comprising three convolutional blocks that include a convolutional layer, a LeakyReLU activation function, and Batch Normalization layers. However, the discriminator differs in its final layers: the last convolutional layer reduces the channel count of the feature map to one, followed by a global average pooling (GAP) layer that aggregates the feature map into a scalar score. This score represents the probability that the input is either a cover or stego image. This design choice strikes an effective balance between computational efficiency and adversarial learning performance. By adopting an architecture similar to the decoder, the discriminator reduces computational costs and parameter complexity, avoiding the introduction of excessive hyperparameters that could increase resource consumption during training and inference. At the same time, this streamlined design ensures that the discriminator can effectively distinguish between cover and stego images without succumbing to issues such as overfitting or gradient instability. This balance is critical for maintaining both robust adversarial evaluation and practical computational efficiency. By providing precise and reliable decisions, the discriminator plays a crucial role in supporting adversarial training, ensuring robust evaluation of the steganographic process.
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 5 Conclusions  
In this work, we propose a medical segmentation model VLSM-Net, which combines the structural features of the V-Net and the LSM. After our experiment, compared to the V-Net, the segmentation results of our model are finer. Compared to the LSM, our model can more accurately segment the ROIs. We not only prove the effectiveness of the VLSM-Net model in 3D CT image segmentation, but also show that it is feasible to improve performance by adding the LSM into the V-Net model.  
In future work, we will concentrate on the improvement of the \(DSC\), and adding the LSM into more CNN models to assess their segmentation performances.  
[PERSON], [PERSON] and [PERSON] designed the research and wrote the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the National Natural Science Foundation of China (Grant Nos. 71601030, 62276049) and the Science and Technology Department of Sichuan Province (Grant No.2020Y0266).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions which helped improve the quality of this manuscript.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **DSC/\%** & **Ref. Name** \\ \hline \multirow{3}{*}{LiTS} & SegNet & 90.03 & [PERSON] et al. [27] \\  & U-Net & 91.24 & [PERSON] [28] \\  & Net01 & 91.4 & [PERSON] [29] \\  & VLSM-Net & 92.73 & - \\ \hline \multirow{3}{*}{LUNA} & U-Det & 75.77 & [PERSON] [30] \\  & U-Net & 79.5 & [PERSON] [31] \\ \cline{1-1}  & InceptionV3 & 80.63 & [PERSON] [32] \\ \cline{1-1}  & RUNet & 81.98 & [PERSON] [33] \\ \cline{1-1}  & VLSM-Net & 82.27 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of models in LiTS17 and LUNA16.
## 6 Performance and Discussion
### Comparison with State-of-the-Art Performance  
Table 6 shows a brief comparison with other state-of-the-art performances. [PERSON] et al. [46] used a multimodal imaging technique combining MRI, CSF, and PET data for classification and reported up to 86.2% accuracy. [PERSON] et al. [47] combined MRI and CSF measurements to classify AD with NC using Freesurfer-extracted cortical thickness and subcortical volumes. [PERSON] et al. [48] used the same features for classification based on the clinical dementia rating (CDR) score. [PERSON] et al. [7] used the VBM extracted VOI features of the voxel and PDF for feature selection, which was classified using the SVM classifier. Subsequently, [PERSON] et al. [8] reported a higher accuracy of 92.48% using feature-ranking techniques to select the discriminative top feature instead of a feature selection technique. The most used dataset is ADNI, whereas few researchers have used OASIS [49] to report the accuracy. Most of the stated accuracy is from cross-validation (CV). Conversely, we have reported average accuracy results from the test set. We preferred a completely untouched test set so that the neural network model is trained only using training features (Figure 2). Besides, it will also report a generalized cum unbiased performance parameter. However, for verification we also tested 5-fold CV on the whole feature set of ADNI and GARD datasets, to be accuracy equivalent to 98% and 97% respectively, however, the most important accuracy is in other untouched datasets, which cannot be calculated using k-fold. Besides, in contrast to single-step feature extraction from voxels of \(t\)-map, we have implemented multistep AAL based masking (ROI_AAL) and cluster-based selection of ROI (ROI_CL), then used the selected voxels as features (results in Supplementary Data). This mask creation steps from AAL and cluster helps to identify the atrophic anatomic regions of the brain, signifying the probable area of interest for AD/ADD detection. All these methods are still progressively being updated and modified  
for better classification performance. Our reported accuracy of 98.22% for CN vs. AD is the highest hitherto using VBM features on the ADNI database. Further, the accuracy of 90.48% in a completely different dataset, i.e., GARD indicated that it has generalized well.
## 2 Materials and Methods
### Goal and Scope
#### 2.1.1 Goal Definition  
The goal of this study is to evaluate the environmental sustainability of implementing RFID technology in the fresh milk supply chain within the European Union following the LCA approach. Specifically, the analysis compares the environmental burdens introduced by RFID (e.g., tag production, use, and disposal) with the potential environmental savings derived from its ability to reduce product waste--particularly waste resulting from expiry due to inventory mismanagement. This dual consideration ensures a more holistic evaluation of the overall environmental benefits and trade-offs associated with the adoption of RFID technology in perishable goods management.  
This work builds directly on field observations from the _RLP2 project_ as reported by [PERSON] et al. [13]. In that real-world retail implementation, RFID systems enabled strict First-In-First-Out (FIFO) replenishment by allowing the precise tracking of receiving dates of milk cases. The study found that 27% of fresh milk shrinkage at retail was caused by improper FIFO policy, which resulted in older stock being left unsold in the backroom and eventually expiring. Simultaneously, the overall share of expired products at retail was measured at 9.6% of the total tagged product volume. Therefore, the fraction of product waste that can be directly avoided through RFID-enabled FIFO enforcement was estimated to be 9.6% \(\times\) 27% \(\approx\) 2.6%. From a LCA perspective, this means that the environmental burdens associated with producing and disposing of this 2.6% of fresh milk can be considered as \"avoided impacts\" attributable to RFID intervention. Accordingly, the LCA conducted in this study quantifies the net environmental benefit of RFID deployment by comparing its embedded impacts with the avoided environmental loads from 2.6% waste prevention. While conservative in nature, this assumption aligns with field-based RFID trials and represents a realistic estimate of achievable benefits within the defined system boundaries (farm to retail).  
The functional unit (FU) is defined as the environmental footprint reduction per 1 L of fresh milk packaged in a carton and tracked using RFID technology throughout its supply chain in the EU. Each RFID tag is assigned to product cases containing 12 items of fresh milk, meaning that approximately 8.3% of the tag's life cycle is allocated to each FU. This FU forms the basis for assessing the environmental impacts and sustainability of RFID technology when applied to the identification and tracking of perishable products like fresh milk.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 3 Estimation Methods for DRR, CDC Error, and Phase Inconsistency Errors
### Estimation of the DRR Error  
Based on the analysis on the effects of DRR error, a novel estimation method is proposed in this section, which contributes to the imaging processing of multi-channel BS-SAR. The central positions of signals coming from different point targets in azimuth after pre-processing will overlap without DRR error, while they will shift when DRR error occurs (see (27)). Assume there are two point targets located at (r,\(vt_{1}\)) and (r,\(vt_{2}\)), and the time interval between them is  
\[\Delta_{t}=\left(t_{2}-t_{1}\right)/\alpha\left(r\right). \tag{30}\]  
Then the time shift between the targets after pre-processing is  
\[\Delta t_{s1}=\frac{\delta}{\left(1+\delta\right)}\frac{\left(t_{2}-t_{1} \right)}{\alpha\left(r\right)}. \tag{31}\]  
Therefore, the DRR error can be obtained as  
\[\delta=\frac{\Delta t_{s1}/\Delta t}{1-\Delta t_{s1}/\Delta t},\ \ \Delta k_{ \omega}=\frac{\delta}{1+\delta}k_{\omega}^{\varepsilon}\left(1+\delta\right). \tag{32}\]  
Since the raw data are a collection of echoes reflected by all scatterers on the ground, we cannot separate the echoes of two point targets from the raw data. If we split the raw data into two equal parts and extend them to the same size as the whole data by zero-padding, we obtain two images after the pre-processing. Although the envelopes of different targets in each image do not perfectly overlap due to their different locations, the whole envelope will also shift some pixels from the center of the image after pre-processing. So, the formula (32) derived for the point target echo will also apply to the raw data.  
The raw data is split into two equal parts in azimuth. To hold the timing positions of sub-data, zero-padding is performed as shown in Figure 6. The zero-padded sub-data are then pre-processed. The time shift between the products of pre-processing can be obtained using image registration. Finally, the DRR error can be determined by (32). Since the time shift is estimated from image amplitude, the last phase multiplication in pre-processing can be ignored.