esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Building Energy Use  
Both climate change and energy retrofit change the energy use of the reference buildings. Their impact is investigated by comparing the average energy use in present and in four future climate scenarios. Figure 9 shows the energy use for space heating in each future projection of Climate zones I and II. The average heating energy use is higher in Climate zone II. However, it should be emphasized that the different characteristics of reference buildings (differences in building function, layout, volume, occupancy, etc.) may prevent a direct comparison of their energy performance. Future climate change can reduce heating energy use, but its impact is only significant at F2 when the building is not retrofitted. In Climate zone I, the average heating demand decrease ranges from 5% to 23% at F1, and between 25% and 58% at F2. In Climate zone II, the reduction in average heating demand due to climate change reaches 4-19% at F1 and 22-48% at F2. On the other hand, retrofit solutions reduce the heating energy use significantly more in both present and future climate. The absolute energy saving through retrofit decreases over time in Climate zones I and II. For instance, it drops from 62.8 kWh/m\({}^{2}\) at present to an average of 55.5 kWh/m\({}^{2}\) at F1 and 39.8 kWh/m\({}^{2}\) at F2 in Climate zone II. However, the effectiveness of retrofit implied from the energy saving ratio is not reduced by global warming. For instance, energy retrofits could save up to 92.2% of the heating energy at the present scenario (P), and 93.4% at near future scenarios (F1) and 95.8% at far future scenarios (F2) in Climate zone I. Therefore, it is important to clarify whether energy retrofit exacerbates overheating, which implies the conflict between climate mitigation and adaptation.  
Figure 7: The changes of the hourly average temperature of seasons in Climate zone I (**a**) and II (**b**). Bars with yellow-toned colour are hourly average temperature increases at F1 compared with present scenario. Bars with red-tone colour are hourly average temperature increases at F2 compared with present scenario.
# Sleep Apnea Classification Using the Mean Euler-Poincare Characteristic and AI Techniques
## Appendix A Results Obtained with Balance Data Using SMOTE  
This study utilized the datasets from the Apnea-ECG database on sleep apnea. However, due to the inherent class distribution, a significant imbalance was observed, with the minority class being underrepresented. To address this issue and improve the model's performance, particularly in detecting the minority class, we employed the synthetic minority over-sampling technique (SMOTE) [52].  
SMOTE generates synthetic samples by interpolating between existing minority class instances, thereby increasing the representation of the minority class in the dataset. This approach helps to balance the class distribution, mitigating the risk of the model becoming biased toward the majority class. A new and balanced dataset was created by incorporating the synthetic samples generated by SMOTE, which was then used for model training and evaluation. This strategy aimed to enhance the model's ability to accurately identify instances of sleep apnea, especially those belonging to the previously underrepresented class. The strategy criteria applied to this dataset are as follows:  
1. **Data preparation:** Load the ECG dataset, separate features and labels, and scale the features using the StandardScaler.
2. **Data balancing:** Apply SMOTE to handle class imbalance, ensuring balanced data for model training. In this step, features from the EPC model are randomly copied to generate new data for the minority class.
3. **Train-test split:** Split the balanced dataset into training and testing sets, resulting in two groups of 52 patients each, with 26 patients from the apnea group and the others from the no-apnea group.
4. **Model building and training:** Create a neural network model, compile it with the Adam optimizer, and train it on the training set with one-hot encoded labels, using the same FNN with parameters described in Table 3.
5. **Model evaluation:** Evaluate the model's performance on the test set and generate predictions.
6. **Performance metrics:** Calculate and visualize confusion matrices, and compute detailed metrics such as accuracy, sensitivity, specificity, precision, and F1 score for both training and test sets.  
To handle class imbalance, the SMOTE algorithm generates 34 new instances for the non-apnea class, each with 101 features. Then, the results are split into two sets (train and test), each containing 52 elements, with 26 apnea and 26 non-apnea patients, achieving a balance ratio of 50.
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 2 Related Works
### Research Trends and Challenges
Interestingly, the study highlights the non-universality of FHE due to inherent limitations like computational intensity and data volume constraints but illustrates its effectiveness in specific applications like the one presented in this paper. Recent trends in FHE research have focused on optimizing performance and usability [14] to overcome the significant computational challenges inherent in FHE implementations. Innovations in lattice-based cryptography, particularly in constructing more efficient encryption schemes and reducing the noise generated through homomorphic operations, have significantly improved FHE's practicality. Techniques such as bootstrapping and modulus switching have been refined to manage noise and enable deeper computations, thereby extending the range of applications that can benefit from FHE [15]. A good example can be found in [16], where the authors explore the efficiency of comparison operations on homomorphically encrypted data. Their study focuses on the impact of binary and bitmask encoding methods on the performance of these operations within the Toroidal FHE (TFHE) scheme. Their results show that while bitmask encoding simplifies computations and reduces the number of operations, it scales poorly with larger domain sizes due to increased ciphertext size and computational overhead. Conversely, binary encoding offers more scalable performance across varying data sizes but involves more complex operations. The findings suggest that the choice between binary and bitmask encoding should be tailored to the specific requirements of the application in terms of domain size and computational constraints. Moreover, the development of specialized libraries and tools, such as those described in the next section, has played a crucial role in making FHE more accessible to researchers and developers. These tools provide optimized implementations of FHE schemes, abstracting the underlying cryptographic complexities and enabling the integration of FHE into existing software applications. Despite these advancements, several challenges remain in the widespread adoption of fully homomorphic encryption. The primary issue is the computational overhead associated with FHE operations [9; 17; 18; 19]. Even with the latest optimizations, FHE computations are still significantly slower than operations on plaintext data, limiting their applicability in time-sensitive or resource-constrained environments. Another area of ongoing research is the development of more efficient key management and sharing mechanisms for FHE. In multi-party computation scenarios, securely managing encryption keys without compromising data security poses a considerable challenge [20; 21]. Furthermore, the usability of FHE tools and libraries remains a barrier for many potential users. Simplifying the integration of FHE into applications and improving the developer experience are critical steps toward broader adoption.
# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 2 Methods  
The objective of this study was to develop a procedure that conducts image cross-correlation (ICC) autonomously, and which can be implemented for monitoring geophysical processes. The ICC method is briefly described in Section 2.1. As already mentioned, one of the main sources of uncertainty in ICC results relates to the presence of the shadows; therefore, the impact of shadows on the ICC is analysed here to demonstrate how they negatively affect any results (Section 2.2). With respect to this inherent problem, a method that autonomously selects images acquired with diffuse illumination is developed, and to achieve this, a support vector machine (SVM) is trained to distinguish between three classes of images in accordance with the presenting illumination (Section 2.3). Finally, an operative and autonomous procedure is designed to conduct ICC, with the aim of being used in geoscience monitoring applications (Section 2.4).
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 2 Ocean Current Measurement Method Based on GF-3 ATI Data
### Calibration Method of Phase Imbalance between Channels
For example, the change of \(\angle E\{S_{1}^{*}(\tau,f_{a})\cdot S_{2}(\tau,f_{a})\}\) with \(f_{a}\) is shown in Figure 6, which is calculated by real GF-3 ATI data. In addition, due to the existence of high-frequency noise and squint imaging, the Doppler centroid is not zero; the phase is not strictly symmetrical about zero frequency in Doppler frequency domain and loses its linear characteristics at high frequency. In order to avoid high-frequency noise, the curve in the red box is intercepted and fitted linearly. The time delay between channels is \(-0.491\) ms, and the phase imbalance is \(-162.9^{\circ}\) by using the least squares method. Then, the phase imbalance and time delay between the dual channels of the whole SAR image can be obtained by processing each small block of the SAR image along the azimuth and range.
# Comparing the Diagnostic Accuracy of CBCT
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multirow{2}{*}{**t**} & \multicolumn{2}{c}{**Significance**} \\ \cline{3-5}  & & **B** & **Std. Error** & **Beta** & & **(P Value)** \\ \hline \multirow{3}{*}{2} & (Constant) & \(-5.188\) & 0.291 & & \(-17.807\) & **0.000*** \\ \cline{2-5}  & GS Post Max & 0.005 & 0.001 & 0.617 & 7.358 & **0.000*** \\ \cline{2-5}  & GS Ant Man & 0.003 & 0.001 & 0.360 & 4.294 & **0.000*** \\ \hline \hline \end{tabular}  
* GS. Greyscale values; Ant = Anterior; Post = Posterior; Max = Maxilla; Man = mandible; t = t-statistics. R square = 0.924. Excluded variables from the model were GSV values at Ant Max and GSV values at Post Man. CI—confidence interval, *** \(P<0.001\).  
\end{table} 
Table 7: Linear regression analysis with Stepwise method for prediction of T value using GS values at different jaw regions as independent variables at 95% CI. devices, exposure protocols and radiation dose voxel sizes [42; 49]. Moreover, other studies concluded that the radiographic densities may be inaccurate and depend on the voxel size [50]. It should be emphasized that the aforementioned studies use different CBCT machines and different voxel sizes [51; 52; 53]. The findings of the present study are restricted to the current model of CBCT used in this study, unless experimental verification and calibration are done, since some manufacturers do not calibrate the gray value scale along with a Hounsfield unit [51]. The findings of this study demonstrated the significant relationships between CBCT GS values on one side and DXA scores and presence of osteoporosis on the other side. CBCT GS values at certain jaw regions were able to predict osteoporosis. Therefore, it would be useful to use CBCT scans obtained for dental implant purposes for predicting osteoporosis and refer suspected patients for further medical investigations. This would help potential patients, because osteoporosis is a silent condition with no symptoms, and patients usually find out that they are osteoporortic, either after fracture or after random screening. In this context, it is suggested to refer patients for the DXA screening of osteoporosis if they showed CBCT GS of 630 or less at anterior mandible and/or 233 or less at posterior maxilla. Limitations: Considering the sample size, the wide variation of CBCT machines, the different nature between DXA and CBCT and the fact that CBCT is used mostly for dental purposes, we call for future research on this topic.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 5 Discussion
### Distribution and Attenuation Patterns of Vibration Energy  
Vibration monitoring data from the Jinan Metro Line 4 project reveal that vibration energy is primarily concentrated within the low-frequency range of 3-4 Hz and follows a distinct exponential attenuation trend during propagation. The data show that vibration displacement gradually decreases outward from the shield machine head (0 m) for both the construction and adjacent tunnels. At a distance of 4.5 m from the vibration source, the acceleration power density diminishes to approximately 15%, and beyond 10 m, vibration signals become nearly undetectable, signifying complete energy dissipation. This behavior aligns with the distance effect principle, where vibration wave intensity inversely correlates with propagation distance. As the distance increases, vibration energy is progressively absorbed by soil and tunnel structures, leading to continuous attenuation of vibration displacement.  
Numerical simulations further highlight this attenuation, showing that, at a 5 m distance, the acceleration power density reduces to below 10%, confirming the localized nature of vibration impact. However, high-frequency vibrations, although they are present, cause relatively weak responses compared to low-frequency waves, due to their rapid attenuation. In the higher frequency range, vibrations dissipate more quickly, resulting in a much smaller impact on the tunnel structure and surrounding areas.  
On-site monitoring data may exhibit additional complexity due to interference from other vibration sources, such as shield cutterhead vibrations or construction machinery noise. These findings provide a robust scientific foundation for evaluating the effects of tunnel construction on surrounding infrastructure and for optimizing vibration monitoring and safety assessment strategies.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Hybrid Routing Protocols  
The hybrid routing protocol (HRP) is a combination of both proactive and reactive routing methods. HRP is designed to take the best features from both routing methods and overcome the limitations of both classes. As mentioned in the literature, the reactive routing protocol generally requires additional time to discover the optimal route, and the proactive routing protocol has a massive overhead of control messages. The problems of the large overheads in proactive routing and long end-to-end delay by reactive routing can be overcome. This routing protocol is basically based on the concept of zones. In HRP, intra-zone routing is executed with the help of proactive routing, and inner-zone routing is achieved by means of reactive routing. Hybrid protocols are especially unsuitable for more than 100 UAVs within the zone, because of overlapping.
## 3 Experiments and Discussion
### Experiment Results and Discussions  
We conduct experiments to validate the effectiveness of the FBGVNP-based temperature controller. Figure 9 shows the response process when the temperature targets for all modules are set to constant. Figure 10 shows the response process with multi-targets or the variational target.  
In Figure 9, it indicates that the different modules' temperatures approach the set target, and the differences between the actual and target temperatures decrease. The cost function value is formulated as the following Equation (1).  
\[\mathrm{J}(n\ )=\sqrt{\frac{1}{2}\sum_{m=1}^{6}e_{m}^{2}(n)} \tag{1}\]  
where the difference between the target and actual temperature is represented by \(e\), the number of the module is represented by \(m\), and the sample number is represented by \(n\).  
Figure 10 shows the results using the FBGVNP-based controller to deal with the temperature control with different targets, which further validates the effectiveness of the temperature control and exhibits its good robustness and adaptability to different scenarios. Figure 10a,b shows the control results when two different targets were set. In the process, the target temperature of module 3 was set to 33 \({}^{\circ}\)C and others were 30 \({}^{\circ}\)C. It can be seen that the cost function gradually reached the minimal and the modules' temperatures approached their specific targets. Figure 10c,d shows the control results when the target temperature for modules 3 to 4 was set to 33 \({}^{\circ}\)C and the target temperature for other modules was 30 \({}^{\circ}\)C. The modules approached both their targets, respectively. Figure 10e,f show the control results if the modules' targets varied, i.e., 33 \({}^{\circ}\)C at the beginning and then turned to 30 \({}^{\circ}\)C. The FBGVNP-based controller modulated the modules' temperature to the set targets successfully and the cost functions got to minimal at last.  
Figure 9: Experimental results of temperature control response when the target temperature is set to constant. **(a,c)** Temperature responses of different modules (target temperature is 30 \({}^{\circ}\)C and 32 \({}^{\circ}\)C, respectively); **(b,d)** changes of cost function values corresponding to the temperature responses shown in **(a,c)**.
## 3 Experimental Results
### Comparison of Location Results in Different Scenarios
#### 3.1.2 Semi-Occluded Scene  
The positioning result after setting the azimuth angle and mask angle of the semi-occlusion scene in Section 2.2 is shown in the figure below.  
It can be seen from Figure 11 that in the Semi-occluded scene, because the azimuth range is reduced by half compared with the Open scene, the positioning accuracy of LEO/INS has obvious influence, and the position error in the north direction is significantly larger than that of GNSS/INS. It can be seen from Figure 12 that the geometric configuration of the LEO constellation in the Semi-occluded scene is obviously worse than that of GNSS in three-dimensional position and horizontal position, which further affects the positioning accuracy of LEO. Due to the influence of high orbit height, the positioning accuracy in the semi-occluded scene has little influence on the GNSS constellation. Due to the better fault tolerance of the federated filtering algorithm and the visible feature of the 5G base station, the 5G ranging data used in this paper is not occluded, so 5G/INS positioning is used as a supplement, which makes the positioning accuracy of the whole federated filtering about 2 m. The results of velocity error and attitude error of the Semi-occluded scene can be seen from Figures A3 and A4 in Appendix A. The fluctuation of velocity error and attitude error of the Semi-occluded is larger than the value in the Open scene.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Time interval (s)—At & 1 \\ Receiver clock offset (m)—\(\delta\hat{p}_{ct-5G}\) & 10,000 \\ Receiver Clock drift (m/s)—\(\delta\hat{p}_{ct-5G}\) & 100 \\ The number of base stations & 4 \\ Base station signal coverage radius (m)— r & 400 \\ Signal number tracking error (m)—\(\delta\hat{p}_{ct-5G}\) & 0.2 \\ Range rate tracking error (m/s)—\(\delta\hat{p}_{ct-5G}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: 5G navigation model parameters.  
Figure 10: DOP value of the Open scene.  
Figure 9: Position error of the Open scene.
## 4 Dataset and Data Augmentation
### Data Augmentation and Division of Data  
The EEG dataset contains EEG signals of 84 subjects; each signal is of a one-minute duration. To keep the complexity of the backbone CNN model low, we segmented each EEG signal into trials (epochs) of small duration (e.g., 3 s). If the length of the signal is too long (128 \(\times\) 60 \(\times\) 60 = 460,800 samples), the complexity of the 1D-CNN model will be too high, 84 EEG signals are not enough to train the model, and overfitting is unavoidable.  
Using the cross-subject approach [33], we divided the signals into 90% for training and 10% for testing. We created EEG trials for training the 1D-CNN model by cropping an EEG signal with windows of fixed temporal length (e.g., 1 s, 2 s,..., etc.). A CNN model's complexity and performance depend on the size of an input EEG trial. We kept the length as small as possible so that the EEG trial was discriminative, and the parameter complexity of the CNN model was low to avoid overfitting. Let us assume that the window size was 3 s; in this case, we obtained 20 trials from each EEG record (as the duration of one EEG record is one minute) and the total number of trials from the training signals (about 1500) was not enough to train even a simplest lightweight 1D-CNN model (e.g., Model-1 in Table 1 contains 7358 learnable parameters). Motivated by the cropping approach adopted in [34; 35], we cropped each EEG signal with overlapping windows to overcome the scarcity of the data. To find the best choice for the overlapping amount, we tested different strides (the amount of movement of windows). In a case where the stride is 0.125 s (i.e.,128 \(\times\) 0.125 \(\approx\) 16 samples with a sampling rate of 128 Hz), the number of crops from one EEG record is  
\[\frac{128\times 60-\,128\times 3}{16}+1=457\,\text{patterns} \tag{6}\]  
The number of crops from 75 subjects (90%) is 457 \(\times\) 75 = 34,275, which is enough to train a CNN model, avoiding overfitting. Figure 3 illustrates the creation of training crops with overlapping windows of size 3 s and a stride of 0.1 s.  
The above-described data augmentation is used for the training set. The test length is 6 s, which is further cropped into crops of 3 s for prediction using majority vote, as described in Section 3.3.  
Figure 3: Creation of training trials. An EEG signal used for training is split into EEG trials using three-second windows.