esa-sceva/satcom-chunk-collection huggingface dataset:
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 4 Demystifying the \"Black-Box\" via Visualization
### Heat Map Visualization  
Heat mapping is widely used in computer vision applications [35; 36]. It attempts to visualize the most activated area in an input to reveal how the final classification conclusions are drawn by deep learning models. For example, in a face recognition problem, those image patches corresponding to noses, eyes, and mouths will contribute the most to the final decision. Similarly, we hypothesize that fault-related frequencies in our time-frequency images should be recognized by our ResNet-50 model in the target task.  
The deeper the layer in a model, the more abstract the extracted features. Therefore, we use the feature maps yielded by the last convolutional layer of the ResNet-50 model to construct a heat map. In the ResNet-50 architecture, the output size of the last convolutional layer is \(512\times 7\times 7\), corresponding to channels, heights, and widths, respectively. Taking the average along the first axis produces a \(7\times 7\) matrix. Note this channel-wise average operation is different from the average pooling layer in the ResNet-50 model, as it averages over the whole feature map.  
The magnitude of the above matrix reflects the extent of the activation in the original input but on a much smaller scale. To solve this, we up-sample the matrix to match the input size using bilinear interpolation; this results in an average activation matrix, as shown in Figure 10b. We select bilinear interpolation to make the transition between image blocks smoother. Then, the final heat map can be obtained by overlaying the average activation matrix on the original input. Figure 10 demonstrates an example of heat map visualization taking a time-frequency image of our target sample as input.  
Figure 9: Confusion matrices of the three transfer schemes (**a**) TFS; (**b**) TFI; (**c**) TFC, when the splitting ratio of training/testing samples is 1.99.
## 6 Conclusions  
In this study, we developed a WOE-GA-BP model to predict the occurrence of geological disasters and prevent the damage to pipelines using the GIS at the risk site. The work conducted in this investigation can be summarised as follows:  
1. The area along a certain gas pipeline was considered as the research object. Eight evaluation factors, specifically, the elevation, slope, aspect, ground curvature, lithology, rainfall, NDVI, and laying method, were selected to establish a geological hazard risk assessment system along the pipeline. The risk assessment of the study area was conducted based on GIS and WOE-GA-BP models. The results were consistent with the actual situation. The AUC reached 80.5%, indicating that the WOE-GA-BP model is an effective risk assessment and prediction model.
2. In contrast to the risk assessment of regional landslides, we assessed laying methods closely related to the force of the pipeline. According to the analysis of the WOE model, the WOE for horizontal laying was 1.692, which was the largest. Meanwhile, in the GA-BP model analysis, the weight of the laying method was approximately 1.04, indicating a positive correlation between the stress on the pipeline and risk. This also provides suggestions for pipeline laying. In addition, we observed that during the construction of a new line, it is necessary to pass through landslides and unstable slope areas with vertical or diagonal paving as far as possible to reduce risk.
3. The GA-BP model was used to calculate the weight of each index factor. The results showed that the laying method, stratum lithology, and NDVI were the influencing factors for risk assessment.  
This is consistent with the actual distribution of landslides and unstable slopes, indicating that the GA-BP model can effectively calculate the weights of index factors, avoid subjective effects of previous calculation methods, and utilize the advantages of data mining and machine learning.  
Conceptualization, B.H. and M.B.; methodology, B.H.; software, B.H.; validation, H.S. and M.B.; formal analysis, B.H.; investigation, Y.L. and X.L.; resources, B.H and X.L.; data curation, B.H.; writing--original draft preparation, B.H.; writing--review and editing, B.H.; visualization, Y.Q.; supervision, M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China, the Ministry of Science and Technology of China (grant 2018 YFC1505500).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are available upon reasonable request to the corresponding authors.  
The authors declare no conflict of interest.
## 4 Simulation and Experimental Results
### Simulation  
In the previous text, we analyzed the theoretical acquisition performance of the proposed signals. We verify the acquisition probability of the PCS scheme using Monte Carlo simulation for different \(C/N_{0}\) and Doppler frequency offsets in this section. The simulation experiment process is as follows. We first use a software signal generator to generate a signal stream with a specified Doppler frequency offset. Secondly, a local replica with the closest search frequency to that Doppler is generated. Finally, we perform a large number of PCSs and compare the search results with the threshold to calculate the detection probability. The experimental parameters are set as follows. The signal bandwidth is 10.23 MHz, the sampling rate is 20.46 MHz, the total false alarm probability is \(10^{-3}\), \(N=10\), \(T_{coh}=1\) ms, and the Doppler frequency offset includes 37.25 kHz, 37.5 kHz, and 40 kHz. Moreover, there are \(10^{6}\) Monte Carlo simulations at each \(C/N_{0}\). The simulated detection probability curves are shown in Figure 12.  
The detection probability curves in Figure 12 agree well with the theoretical curves in Figure 8, verifying that the theoretical analysis of detection probability is correct. We can equally reach the following conclusions. The detection probability is the same for two pulsed signals as it is for two continuous signals. All signals have the same and highest acquisition probability at 40 kHz. Meanwhile, the Pulse-OTFS and Pulse-BPSK signals at 37.5 kHz and the OTFS and BPSK signals at 37.25 kHz have the worst acquisition probability. Most importantly, the detection probability of the proposed signal at the same Doppler frequency offset is about 1 dB higher than that of OTFS and BPSK. As a result, the proposed signal is robust to significant Doppler shift scenarios and improves the acquisition probability compared to continuous signals.
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 4 Experiments
### 3D Hand Pose Estimation with CFAM
It was more difficult to improve the accuracy when the accuracy was already high, indicating that the attention mechanism in our CFAM was effective, and it not only played a role in channel attention, but also blended the characteristics of the RGB images and 2D score maps; only then could the results be greatly improved. Among the different methods, strategy 3 (CFAM) maximized the AUC. Since the CFAM method had the highest accuracy, we tested it on the STB dataset. Table 7 and the left graph of Figure 5 show the results of our CFAM on the STB dataset from the GT 2D score maps, and our CFAM outperformed [PERSON]'s in terms of both the error and AUC. Table 8 shows the result for each finger; for most hand keypoints, our CFAM outperformed [PERSON]'s framework. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Index** & **Methods** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline \multirow{4}{*}{Mean Error (mm)} & [PERSON] [16] & 0.28 & 24.98 & 27.11 & 22.27 & 20.98 & 22.36 \\  & Strategy 1 & 0.8 & 24.55 & 25.49 & 21.44 & 20.64 & 22.54 \\  & Strategy 2 & 1.39 & 20.09 & 22.46 & 20.63 & 18.56 & 19.77 \\  & Strategy 3 & **0.21** & **18.88** & **20.95** & **18.83** & **17.35** & **19.47** \\ \hline \multirow{4}{*}{Media Error (mm)} & [PERSON] [16] & 0.27 & 20.67 & 22.98 & 18.76 & 17.68 & 18.76 \\  & Strategy 1 & 0.77 & 20.79 & 21.51 & 18.26 & 17.53 & 19.37 \\ \cline{1-1}  & Strategy 2 & 1.32 & 17.3 & 18.91 & 17.8 & 15.87 & 17.21 \\ \cline{1-1}  & Strategy 3 & **0.2** & **16.56** & **17.81** & **16.32** & **15** & **17.47** \\ \hline \multirow{4}{*}{AUC (0–50 mm)} & [PERSON] [16] & 0.97 & 0.55 & 0.52 & 0.58 & 0.6 & 0.58 \\  & Strategy 1 & 0.97 & 0.55 & 0.53 & 0.6 & 0.61 & 0.57 \\ \cline{1-1}  & Strategy 2 & 0.97 & 0.62 & 0.58 & 0.61 & 0.64 & 0.62 \\ \cline{1-1}  & Strategy 3 & 0.97 & **0.64** & **0.6** & **0.64** & **0.66** & **0.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 3D hand pose estimation on the RHD dataset from the GT 2D score map and the GT-cropped RGB image for each finger. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c} \hline \hline
**STB** & **Zimmermann [16]** & **CFAM** \\ \hline AUC (0–50 mm) & 0.83 & **0.837** \\ Median Error (mm) & 7.3 & **7.06** \\ Mean Error (mm) & 8.47 & **8.07** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: 3D hand pose estimation on the STB dataset from the GT 2D score map and GT-cropped RGB image. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Index** & **[PERSON]** & **Strategy 1** & **Strategy 2** & **Strategy 3** \\ \hline AUC (0–50 mm) & 0.585 & 0.591 & 0.629 & **0.648** \\ Median Error (mm) & 18.84 & 18.6 & 16.65 & **15.85** \\ Mean Error (mm) & 22.43 & 21.88 & 19.4 & **18.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 3D hand pose estimation on the RHD dataset from a GT 2D score map and a GT-cropped RGB image. The best results are highlighted in bold.
## 4 Experimental and Results Analysis
### Dataset and Baseline Settings
#### 4.1.2 Baseline Setting  
When employing machine learning for regional ENF classification, researchers utilize data primarily characterized by regional disparities, diverse data collection methodologies, and variations in nominal frequencies. To ensure fair and accurate comparisons with our study's findings against existing research outcomes necessitated screening prevailing machine learning models' conclusions based on three prerequisites: a minimum of three classification regions; a nominal frequency set at 50 Hz; and direct acquisitions from power grids.  
In response to the advancements in deep learning techniques, we endeavored to apply deep learning to regional ENF classifications. We selected the Transformer_Hugging Face Time-Series Classification model (THF_TSC) [30], LSTM_TSC model [31], along with the UniTS base model as benchmark models before training them on our preprocessed dataset. Subsequently modifying solely the \(SAF\) led us to name it as 'UniTS_SAF', while incorporating the spectral attention mechanism improvement module resulted in 'UniTS_SAM'. Additionally introducing both modifications concurrently produced an enhanced version termed 'UniTS-SinSpec'.  
The aforementioned benchmark configurations facilitated the selection of baseline models meeting the comparative criteria detailed in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & & **Model** \\
**Category** & & \\ \hline \multirow{6}{*}{Machine learning} & \(\bullet\) & Power line data-based grid identification using signal processing (2016) [32]. \\  & \(\bullet\) & Location identification using power and audio data based on \\  & & temporal variations in electric network frequencies and their \\  & \(\bullet\) & Warner grid estimation using electric network frequency signals (2019) [11]. \\  & \(\bullet\) & Spectrogram analysis in a multiple classifier fusion framework for \\  & & power grid classifications using electric network frequencies (2024) [14]. \\ \hline \multirow{6}{*}{Deep learning} & \(\bullet\) & LSTM_TSC(2016) [31], 1 \\  & \(\bullet\) & THF_TSC (2018) [30]. 2 \\ \cline{1-1}  & \(\bullet\) & UniTS(2024) [15]. \\ \cline{1-1}  & \(\bullet\) & UniTS_SAF. 3 \\ \cline{1-1}  & \(\bullet\) & UniTS\_SAM. 4 \\ \cline{1-1}  & \(\bullet\) & UniTS-SinSpec. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Baseline model.  
\begin{table}
\begin{tabular}{c c|c c} \hline \hline
**Time Span** & **Region and Label** & **Sequence Length** & **Dataset Number** \\ \hline \multirow{3}{*}{1 year} & Baden-Württemberg, Germany—0 & 60 & A1 \\  & London, United Kingdom—1 & 3600 & A2 \\  & Zealand, Denmark—2 & 86,400 & A3 \\ \hline \multirow{3}{*}{41 days} & Karlsruhe, Germany—3 & 60 & B1 \\  & Oldenburg, Germany—4 & 3600 & B2 \\ \cline{1-1}  & Istanbul, Turkey—5 & 86,400 & B3 \\ \cline{1-1}  & Lisbon, Portugal—6 & 86,400 & B3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Preprocessed dataset.
## 3 Results
### Packet Loss Analysis for Satellite Communication Network  
Figure 7a shows the number of successful and failed data packets received by the satellite communication network terminal at \(10^{\circ}\), \(30^{\circ}\), \(50^{\circ}\), and \(75^{\circ}\) elevation angles; Figure 7b shows the number of successful and failed data packets sent by the satellite communication network terminal at \(10^{\circ}\), \(30^{\circ}\), \(50^{\circ}\), and \(75^{\circ}\) elevation angles.  
Analyzing Figure 7a, we find that the packet loss rate when the satellite communication network terminal receives data packets is about 75% to 83%; analyzing Figure 7b, we find that the packet loss rate when the satellite communication network terminal sends data packets is about 77% to 85%. Comparing the two figures, it can be seen that the packet loss rate is generally better when sending data than when receiving data. The network packet loss rate is lowest when the elevation angle of the satellite communication network terminal is about 50\({}^{\circ}\).  
During firefighting operations in a forest, the firetruck will not always be in the same position, i.e., it will not always work at a fixed angle. However, it is found that even if a firetruck equipped with a satellite communication network terminal is tilted at a certain angle, it can still ensure that the packet loss rate in the whole process is not less than 75%.
## 3 Development of Retaining Wall Displacement Measurement System
### Hardware and Control Software Development  
From the design presented in Section 3.2, the displacement measurement hardware was developed, as shown in Figure 6. The hardware is operated by an external power supply of 220 V, while the rotation speed and number of rotations of the servomotor is manually controlled using the toggle switch inside the control panel. Additionally, the operation status of the hardware can be checked using LED.  
When the laptop-type displacement analysis device is used, it is possible to control the hardware wirelessly using the software (as shown in Figure 7) after remotely accessing the control computer inside the hardware. The software can operate the servomotor after setting the parameters of the 2D LiDAR sensor as well as the rotation speed and number of rotations of the servomotor. Additionally, the software can check the scan pattern of the 2D LiDAR sensor in the 2D LiDAR sensor scan view during hardware operations and can monitor the current hardware status. When hardware operation is completed, the collected 3D point cloud data are automatically stored in the control computer and wirelessly transmitted to the displacement analysis device.  
The 3D point cloud data collected using the displacement analysis device were saved in a comma-separated value (CSV) format, since they were stored in a data structure, as shown in Figure 8, by measuring the number of times the 2D LiDAR sensor performed line scanning. The data packet collected using the 2D LiDAR sensor comprised the distance data (\(d\)) and the corresponding vertical angle (\(\alpha\)) for each resolution set for the 2D LiDAR sensor. Since the servomotor rotates at a constant speed, the equation for converting the measurement time (\(t\)) and data packet of the 2D LiDAR sensor obtained using the Cartesian coordinate system (\(x,y,z\)) is expressed by Equation (1).  
\[\begin{split} x=d\ sin\ \alpha\ cos\ \beta\,\ y=d\ sin\ \alpha\ sin\ \beta\,\ z=d\ cos\ \alpha\\ d=distance\ data,\alpha=vertical\ angle\\ \beta=horizontal\ angle=Rotation\ speed(degree/s)\times t \end{split} \tag{1}\]  
Figure 6: Retaining wall displacement measurement hardware. (**a**) Perspective view; (**b**) inside the hardware; (**c**) 2D LiDAR sensor and slip ring; (**d**) control panel.
###### Abstract  
We aimed to evaluate the impact of Virtual Reality Dental Simulators (VRDS) on preclinical training in endodontics for undergraduate dental students. Purposive sampling technique was used to target undergraduate dental students at two dental schools: in Qatar and Turkey. After training on endodontic access cavity preparation on upper anterior teeth using acrylic teeth on dental mannequins and virtual reality haptic dental simulator, a questionnaire based on a combination of open- and closed-ended items was distributed to the participants. The sample included 60 dental undergraduate students. The participants reported positive experiences with VRDS and 76% considered it to be helpful in improving their fine motor skills. Endodontic access cavity preparation on VRDS was perceived to be similar to natural and acrylic teeth by 73.34% and 53% of participants, respectively. Overall, 85% of participants supported the use of VRDS training to supplement conventional training on dental mannequins but also recommended the need for improvements in VRDS training in endodontics. The findings of this study underscore the benefits of VRDS in endodontics. Future research involving larger samples from multiple institutions may help to optimize VRDS in undergraduate dental education.  
dental; endodontics; simulation; students; virtual reality Article
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 8 Impact on Climate Change Monitoring
### Impact of OLCI Uncertainties on Chlorophyll Trend Detection Capabilities  
The tandem data has been used to extract measurement noise for OLCI L3 chlorophyll concentration products and evaluate its impact on trend detection capabilities. For this purpose, a synthetic time series is generated as follows:  
* A baseline time series representative of seasonal effects is created by regridding and de-trending ESA OC-CCI v3.1 products;
* \"Observational\" noise is added by sampling a zero-mean distribution whose variance matches the one computed for each grid cell in the tandem data;
* Finally, a global trend in the range of \(\pm 2\) %yr\({}^{-1}\) is added to the time series.  
Representative synthetic bootstrap times series is thus created for OLCI-A vicariously adjusted, OLCI-B and OLCI-A aligned to OLCI-B using the harmonization methodology described in Section 6.2. For each time bootstrap, the trend is then estimated using generalized least squares regression. The error between the input trend and the estimated trend reveals the impact of observational noise on trend detection capability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GMSL Bias Uncertainties (2-Signas) (mm)** & \multicolumn{2}{c}{**Tandem Phase**} & \multicolumn{2}{c}{**Final Orbits**} \\  & \multicolumn{2}{c}{**(5 Cycles)**} & \multicolumn{2}{c}{**(5 Cycles)**} \\
**Sources** & **Estimation Method** & **SAR** & **PLRM** & **SAR** & **PLRM** \\ \hline Ocean variability (orbit) & CMEMS L4 extraction & 0.06 & & 1.1 \\ Instrument acquisition modes & GMSL corrections & 2.32 & 1.60 & - & - \\ SSH measurements & Monte Carlo & 0.30 & 0.65 & 0.40 & 0.60 \\ Total uncertainty & 2.34 & 1.72 & 1.16 & 1.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GMSL bias uncertainty budget, comparisons between estimations on the tandem and final orbits.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maximum GMSL Trend Uncertainty (2-Sigma) (mm/Year)** & **SAR** & **PLRM** \\ \hline Ideal tandem (5 cycles in SAR OL) & 0.13 & 0.26 \\ Tandem with SAR OL/CL switch & 0.70 & - \\ Tandem with LRM and SAR OL/CL switches (actual S3B tandem) & 0.94 & 0.70 \\ Final orbits & 0.45 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum GMSL trend uncertainty (mm/yr) resulting from the S3A/B bias uncertainty for various scenarios.  
The analysis reveals that the trends are retrieved for all simulated sets without any significant bias and a standard deviation of 0.27 % yr\({}^{-1}\). No significant difference in trend detection capabilities is found between the three data sets OLCI-B, OLCI-A vicariously adjusted, and OLCI-A aligned. The OLCI-A-aligned and OLCI-B data sets are found to be most similar. In addition, the analysis identifies spatial patterns in the differences, see Figure 10. The larger differences are found in the high latitudes, possibly because of higher values of the chlorophyll concentration and associated dispersion.
## 7 Conclusions  
In conclusion, this work presents a method for pre-processing data and a model for accurately predicting energy consumption in commercial buildings, specifically focusing on buildings on the Hawthorn and Wantirna campuses. The proposed pre-processing method effectively improves the accuracy of energy consumption prediction, even when training data are limited. The results demonstrate the applicability of the proposed method and model for accurately predicting energy consumption in various commercial buildings.  
The proposed model, denoted as M-LSTM, achieved the lowest \(MPAE\) values of 0.159, 0.139, and 0.072 for _DatasetS1_, _DatasetS2_, and _DatasetS3_, respectively. This achievement is crucial for effective energy management and conservation in commercial buildings. The practicality of this approach extends to other commercial buildings with similar energy consumption patterns, making it a viable solution for energy management in the commercial building sector. Visualizations were also provided to aid in understanding the data patterns and trends in the model predictions. Additionally, further research can explore the effectiveness of the proposed pre-processing method and models in predicting energy consumption for different types of buildings or larger datasets. Exploring alternative techniques, such as seasonal decomposition or time series analysis, for incorporating time information into the models could also yield valuable insights. These advancements in energy consumption forecasting contribute to significant cost savings and environmental benefits in commercial buildings.  
Individual Contribution: Conceptualization, T.N.D., G.S.T., M.S., S.M. and A.S.; Methodology, T.N.D., G.S.T. and M.S.; Software, T.N.D. and G.S.T.; Validation, M.S., A.S. and S.M.; Formal analysis, G.S.T., M.S., A.S. and S.M.; Investigation,G.S.T., M.S., S.M. and A.S.; Resources, M.S. and S.M.; Data curation, G.S.T., T.N.D. and M.S.; Writing--original draft preparation, T.N.D. and G.S.T.; Writing--review and Editing, M.S., S.M. and A.S.; Visualization, G.S.T., T.N.D. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 11: Bart chart in the comparison of M-LSTM with labelled time (M-LSTM\({}_{t}\)) with M-LSTM without labelled time, and other models with and without labelled time in case of lack of training data (_DatasetS3_ in 2019).