esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 3 Methods
### Loss Function  
In deep neural networks, the loss function functions as a metric for measuring the disparity between the model's output and the actual label. It establishes the network's optimization objective and guides the network in learning suitable parameter values. Minimizing the loss function enables the network's predictions to closely match the true labels, thereby improving the accuracy and overall performance of the model. The role of the loss function extends beyond merely assessing the model's performance; it also plays a vital role in the backpropagation algorithm. Calculating the gradient of the loss function with respect to the network parameters enables us to determine the direction in which the parameters should be updated, thereby allowing the network to be adjusted in a more optimal direction.  
Commonly used loss functions include the mean square error and cross-entropy loss. The mean square error is a standard loss function for regression problems. It calculates the squared difference between the predicted value and the true value and then averages these differences to obtain the loss value. The mean square error is expressed as depicted in Equation (5).  
\[\mathrm{MSE}=\frac{1}{\mathrm{N}}{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{5}\]  
where N represents the number of samples, \(y_{i}\) is the actual label of the sample, and \(\hat{y}_{i}\) represents the label predicted by the model.  
Cross-entropy loss is frequently used in classification problems, particularly in multi-category classification tasks. It calculates the loss value by comparing the actual labels with the predicted probability distribution generated by the model. Cross-entropy loss can be expressed as depicted in Equation (6).  
\[\mathrm{CE}=-\frac{1}{\mathrm{N}}{\sum_{i=1}^{N}\sum_{j=1}^{C}y_{ij}}\mathrm{ log}\left(\hat{y}_{ij}\right) \tag{6}\]  
where C represents the number of categories, \(y_{ij}\) represents the actual label of the jth category for the ith sample, and \(\hat{y}_{ij}\) represents the probability distribution predicted by the model.  
Furthermore, in addition to the two common loss functions mentioned above, a variety of loss functions are designed to suit different classification or regression tasks. Moreover, for specific model tasks, customized loss functions can be developed by combining other metrics or incorporating domain-specific expertise.
# On Sharing an FIB Table in Named Data Networking
###### Abstract  
As a new networking paradigm, Named Data Networking (NDN) technology focuses on contents, and content names are used as identifiers for forwarding and routing, as opposed to IP addresses in the current Internet. NDN routers forward packets by looking up a Forwarding Information Base (FIB), each entry of which has a name prefix and output faces. An FIB should have the information to forward Interest packets for any contents. Hence, the size of an FIB would be excessively large in NDN routers, and the traffic for building an FIB would be significant. In order to reduce the traffic associated with building an FIB table and memory requirement for storing an FIB table, this paper proposes a new efficient method which combines the routing of network connectivity and the building of a forwarding engine using Bloom filters. We propose to share the summary of an FIB using a Bloom filter rather than to advertise each name prefix. The forwarding engine of the proposed scheme is a combination of Bloom filters, and hence the memory requirement of the forwarding can be much smaller than the regular FIB. Simulation results using ndnSIM under real network topologies show that the proposed method can achieve nearly the same performance as the conventional link state algorithm with 6-8% of the traffic for distributing the connectivity information and 5-9% of the memory consumption.  
B +
Footnote †: journal: Applied Sciences  
_Article_  
On Sharing an FIB Table in Named Data Networking
# Structure-Guided Image Inpainting Based on Multi-Scale Attention Pyramid Network
## 4 Experimental Results and Datasets
### Ablation Studies  
To substantiate the efficacy of the individual components within the proposed structure-guided multi-scale attention pyramid network, ablation studies were conducted in this section. The baseline was initially established as a standard pyramid inpainting network without incorporating any of the components proposed in this paper. In _Test 1_, the network was modified into a dual-branch structure with edge-assisted repair to improve structural accuracy. _Test 2_ further enhanced this configuration by incorporating the multi-scale attention feature fusion module, allowing for better integration of features across different scales. In _Test 3_, part of the conventional convolutions in the network was replaced with separable masked update convolutions to refine the inpainting process.  
The overall network was optimized with hyperparameters set to \(\lambda_{mul}=10\), \(\lambda_{r}=10\), \(\lambda_{adv}=1\), and \(\lambda_{fm}=90\) to better generate detailed textures and structures. These parameters were particularly effective on datasets with complex structures, such as the Paris Street View dataset, where they demonstrated superior performance.  
Experimental results, as depicted in Table 3, indicate that the addition of each new module led to improved performance metrics over the previous baseline. This progression underscores the significant contribution of each component to the enhancement of image inpainting outcomes. Notably, the introduction of separable masked update convolutions in the proposed network resulted in a significant reduction in network parameters, with a decrease of approximately 28%. This reduction highlights the critical contribution of the separable masked update convolutions in optimizing large-scale image inpainting networks. The gated mechanism within these convolutions effectively mitigates the impact of erroneous information during the inpainting process, leading to improved restoration accuracy. Moreover, this mechanism not only enhances the overall performance but also substantially reduces computational resource consumption, making it a valuable advancement in the field of image inpainting.  
Figure 6: Inpainting results of different image inpainting methods on the CelebA dataset. The first column presents the real image, the second column shows the damaged image, and the subsequent columns display the inpainting results of FFTI, SWT, FMGDN, MFMAM, and the proposed method, respectively.
## 4 Optimal Design of Passive Energy Dissipation Systems
### Tuned Mass Damper
After focusing on the margin between the two proposed mass ratio for each of the three investigated buildings, it is clear that an increase in the mass ratio brings a relatively small additional damping for Artificial 4 ground motion. This is why for economic concerns, 0.5% is adopted for the low-rise building as a value of mass ratio, and 3% for the mid and high-rise building. Optimized TMD design parameter values are listed in Table 6. However, the optimal position for irregular buildings is not necessarily on the upper story due to the different stiffness values for each story in elevation. Furthermore, the damper location in plan is considered a primary design variable; in this case, it depends essentially on the eccentricity between the center of mass and rigidity, but is always near the geometric center of the plan [41]. It is important to note that buildings optimal design is evaluated for Artificial 4 ground acceleration by applying a nonlinear time history analysis because its spectral response is close to the EC8 elastic response spectrum [31]. An alternative solution is using a PTMD configuration, which can provide additional damping, as it can act in all directions. Table 6 shows that the cable length required for the low and mid-rise building are too small, and thus, unrealistically small values. So, the PTMD configuration is only evaluated for the high-rise building. The cable that relates the additional mass to structure is composed of steel rods, with a circular section offering high axial rigidity. In this optimization section, a comparison between TMD and PTMD configuration is established. It is clear that the values obtained for the three parameters studied are relatively close. This can be explained by the fact that the building is not too flexible, which limits the performance of TMD and PTMD as well (Figure 6). However, the PTMD configuration is adopted in the high-rise building because it offers more reduction than the translational configuration, and its frequency can be easily retuned (Table 7). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Building Type** & \(\mathbf{\overline{m}}\) & \(\mathbf{\omega_{d}}\) **[rad/s]** & \(\mathbf{L}\) **[m]** \\ \hline Low-rise & 0.50\% & 28.893 & 0.012 \\ Mid-rise & 3.00\% & 6.143 & 0.259 \\ High-rise & 3.00\% & 2.747 & 1.300 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cable length required for the three investigated buildings. Figure 6: Modeling configuration of (**a**) TMD for low-rise and mid-rise buildings, (**b**) PTMD for high-rise building.
# Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
## 4 SERS-Based MNPs Detection
### Detection of MNP Using SERS-Based Techniques
#### 4.2.2 SERS Detection Employing Nanostructured Devices
Besides the detection of molecules of biological interest, polyethylene microplastics in water were successfully identified at a concentration of 0.1%, but no other tests were performed to determine the LOD. Despite that, these first results show that the device is robust for microplastics detection both in dry and wet conditions with a very high potential for in situ monitoring. Another interesting method that exploits the SERS effect for nanoplastics detection was proposed by [PERSON] et al. : a single gold nanopore was fabricated on the tip of a glass nanopipette as the supporter [69]. To fabricate this interesting device, first, a cap-like gold structure was achieved by in situ reduction to cover the glass tip. Then, they employed a high voltage electric pulse to create a single nanopore in the gold cap by optimizing the drilling condition to obtain a diameter ranging from 30 to 50 nm and employing a 0.1 mM KCl solution as the nanopore drilling solution. To evaluate the SERS activity of this device, 4-mercaptoethanol-benzothitrile (4 Mbn) was employed as the Raman probe obtaining an enhancement factor of 1.87 \(\times\) 10\({}^{5}\). The results obtained by testing the 20 nm PS nanoplastic solutions indicate that PS nanoplastics randomly pass through the nanopore, but with high variability on the Raman intensity. In addition, 10 nm PMMA nanoplastics were also identified. This gold nanopore device shows promising results for the qualitative and quantitative determination of nanoplastics, but a deeper study is needed to determine the limits of detection and the viability of the method on real samples.
## 3 Simulation Results  
To verify the proposed signal-processing approach, numerical simulations were performed using MATLAB. As shown in Figure 4a, three point sound sources were located at (0.7 m, 1.4 m), (0.6 m, 0.4 m), and (1.2 m, 0.4 m), and an annular simulative array with eight receivers was adopted around the sources. The directivity pattern of the microphone array is shown in Figure 4b by assuming a single frequency point source signal in the geometric center of the scanning area.  
Figure 4: Geometry of the simulation performed in MATLAB (**a**) and the directivity pattern of microphone array (**b**).  
Figure 3: Signal denoised by DNNs. (**a**) Original signal; (**b**) mixed signal; (**c**) estimated signal.  
The eight-channel measured mixed signal, which is shown in Figure 5, consists of three randomly selected signals from the ST-CMDS data testing set that do not contain any additional noise.
# Comparison of Fitness and Physical Activity Levels of Obese People with Hypertension
## 3 Results
### Physical Activity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Physical Activity** & **MET-min/week** & **Female** & **Male** & **Total** & **_p_** & **(Age \textless{} 60/\textgreater{})** & \(p\) \\  & **min/week** & **_n_ = 48_** & \(n\) = 34_ & \(n\) = 82_ & **(F/M)** & **(Age \textless{} 60/\textgreater{})** & **(DM)** \\ \hline TOTAL, & Mean (SD) & \(\begin{array}{c}1961.25\\ (2862.99)\end{array}\) & \(\begin{array}{c}3194.25\\ (3403.80)\end{array}\) & \(\begin{array}{c}2472.49\\ (3138.44)\end{array}\) & \(\begin{array}{c}0.048\\ 0.048\end{array}\) & \(\begin{array}{c}0.781\\ 0.781\end{array}\) & \(\begin{array}{c}0.677\\ 0.677\end{array}\) \\ \hline VIGOROUS, & Mean (SD) & \(\begin{array}{c}611.67\\ (2023.29)\end{array}\) & \(\begin{array}{c}1625.88\\ (2676.57)\end{array}\) & \(\begin{array}{c}1032.19\\ (2355.16)\end{array}\) & \(\begin{array}{c}0.006\\ 0.006\end{array}\) & \(\begin{array}{c}0.172\\ 0.254\end{array}\) \\ \hline MODERATE, & Mean (SD) & \(\begin{array}{c}730.83\\ (1116.67)\end{array}\) & \(\begin{array}{c}523.53\\ (806.06)\end{array}\) & \(\begin{array}{c}644.88\\ (999.40)\end{array}\) & \(\begin{array}{c}0.381\\ 0.381\end{array}\) & \(\begin{array}{c}0.481\\ 0.172\end{array}\) \\ \hline WALKING, & Mean (SD) & \(\begin{array}{c}618.75\\ (841.46)\end{array}\) & \(\begin{array}{c}1044.84\\ (1311.87)\end{array}\) & \(\begin{array}{c}795.42\\ (1075.45)\end{array}\) & \(\begin{array}{c}0.223\\ 0.223\end{array}\) & \(\begin{array}{c}0.391\\ 0.853\end{array}\) \\ \hline \hline \end{tabular} Note: MET-min/week, metabolic equivalent of work—minutes for the week. One MET corresponds to the amount of oxygen consumed at rest and is equal to 3.5 mL of oxygen per kg of body weight per minute; SD, standard deviation. Statistical significance was set at \(p\) < 0.05.  
\end{table} 
Table 2: Various levels of PA of the participants by gender, age, and diagnosed DM (with the Mann–Whitney U test).
## 4 Experiment Arrangement and Results
### Experiment II: Comparison of Different Optimization Algorithms  
In Experiment II, the prediction performance of the four models, BPNN, GA-BPNN, PSO-BPNN, and MPSO-BPNN, is compared by combining the R\({}^{2}\), MAPE, RMSE, MAE, and U1 and U2 evaluation indexes. The research found that the BPNN had six indicator values, which were 0.876, 9.52, 6635, 4787, 0.057, and 0.243. In contrast, the GA-BPNN demonstrated indicator values of 0.892, 9.13, 6228, 4292, 0.052, and 0.183. For the PSO-BPNN, the respective indicator values were found to be 0.895, 8.85, 6140, 3962, 0.049, and 0.179. Meanwhile, the MPSO-BPNN presented a distinct set of indicator values, which were 0.912, 8.23, 5735, 3677, 0.047, and 0.173. Taking the two dimensionless metrics of R\({}^{2}\) and MAPE as an example, Figure 5 visualizes the prediction performance of the four  
Figure 4: The diagrams of MAPE and R\({}^{2}\) for the two benchmark models.  
models. The comparison of the four models reveals that the BPNN has the worst prediction performance and that all three optimization algorithms (GA, PSO, and MPSO) significantly improve the prediction performance of the base BPNN model. In addition, among all the optimization algorithms, the combination of MPSO and the BPNN is better than the other compared models and the MPSO-BPNN can achieve the best prediction results. Therefore, MPSO is selected as the optimal optimization algorithm for its ability to significantly enhance the predictive accuracy of the BPNN model.
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 3 Policy-Dividing and Combination Mechanism in SUDC  
In this section, we describe the policy-dividing and combination mechanism, which aims to improve the efficiency of policy distribution, therefore improving the problem of unsynchronized policy updates across routers and maintaining network consistency. SR technology is key to two points. The first point is to segment the forwarding path, and each segment is identified by SID. The second point is to sort and combine SIDs at the start node to form a SID list and determine the forwarding path of data packets. Data packets can then be transmitted through the network along a forwarding path that has been planned in advance. In SRv6, SID is a 128-bit value in the form of an IPv6 address. Considering that an SRv6 policy entry is a sequence formed by multiple SIDs, we propose to divide each SRv6 policy into several SID blocks and transmit these blocks in multicast form with the aim of improving policy distribution efficiency.  
Based on the above ideas, the mechanism contains three functional modules: SRv6 policy dividing, SRv6 policy pushing, and SRv6 policy combination. First, the policy-dividing function module is responsible for dividing the policy entries into multiple policy distribution SID blocks. Then, the policy-pushing function module is responsible for distributing these SID blocks in a multicast manner to the corresponding target nodes of the policy to which the SID blocks belong. Finally, the SRv6 policy combination function module is responsible for combining the SID blocks to generate complete SRv6 policies. Figure 1 shows the architecture of SUDC.  
We will describe the three functional modules in detail and use a specific example throughout the entire process to make SUDC easier to understand. Figure 2 shows an example of SUDC.  
Figure 1: The architecture of SUDC.
## 3 Analysis and Optimization of HRM_CS Model
### Measurement Matrix Analysis  
We consider two different schemes to build the measurement matrix, _i.e._, random sampling (RS) and the hierarchical routing method (HRM). The RS scheme is used to determine in a fully distributed way which sensors randomly transmit their data to the sink node at any given time \(k\). In the HRM scheme every sensor node first sends its data to local cluster base stations and then these data are transferred to the sink node by the local clusters.
## 3 Time-Frequency Representation
### Short-Time Fourier Transform  
The basic idea of STFT is to use a window function for signal interception, and assume that the signal is stable within the window. Fourier transform is used to analyze the intercepted signal, and then move the window function along the signal time direction to obtain the time-frequency distribution relationship. The STFT of signal \(x(t)\) is expressed as:  
\[\text{STFT}(\tau,\ f)=\int_{-\infty}^{\infty}x(t)g(t-\tau)e^{-j2\pi ft}dt \tag{1}\]  
where \(x(t)\) is the target signal and \(g(t)\) is the window function [28].  
In the process of STFT, the length of the window determines the time and frequency resolution of the time-frequency image. The longer the window length, the higher the frequency resolution after Fourier transform and the worse the time resolution. The length of the window needs to be adjusted according to the specific situation.
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Application** & **Main Input Datasets or Causative Factors** & **Algorithms Used** & **Algorithm with the Best Accuracy** \\ \hline
[70] & Reference evapotranspiration time series forecasting & Maximum and minimum temperature, wind speed at 2 m high, average relative humidity, and the insolation & Three CNN models CNN1, CNN2, CNN3, ensemble-CNN1, ensemble-CNN2, ensemble-CNN3, hybrid ensemble & Ensemble models Ensemble models \\ \hline
[71] & Short term electricity consumption forecasting & Electricity consumption & ANN, RF, GMRT, LR, DL, DT, evolutionary algorithms for regression trees, ARMA, ARIMA, stacking, & \\ \hline
[72] & Forest biomass estimation & ALS data and Landsat 8 imagery & ELM, BPNN, RT, RF, SVR, kNN, CNN, MLP, stacking & Stacking \\ \hline
[73] & Prediction of eucalyptus stand volume & SPOT-5 raw spectral features, spectral vegetation indices, rainfall data, and stand age & SGB, RF, stepwise MLR & SGB \\ \hline
[74] & Alfalfa yield prediction & Narrow-band indices (e.g., simple ratio index, NDVI, chlorophyll absorption ratio index, modified versions of these indices) derived from UAV-based hyperspectral images & RF, SVR, GP, RR, stacking & Stacking \\ \hline
[75] & Wheat grain yield prediction & Vegetation indices derived from UAV-based multispectral images & RF, SVR, GP, RR, stacking & Stacking \\ \hline
[76] & Estimation of forest variables & Statistics extracted from LiDAR data & MLR, MLP, SVR, kNN, GP, RT, RF & SVR \\ \hline
[77] & Forest biomass estimation & Multi-temporal Sentinel-1 and 2 data-derived variables (vegetation indices, SAR backscatter) & RF, GBM, XGBoost, ensemble model based on weighted averaging & Ensemble model \\ \hline
[78] & Land subsidence susceptibility mapping & Elevation, slope, aspect, profile curvature, plan curvature, TWI, distance to road, distance to river, distance to fault, precipitation, land use, lithology, drainage density, and groundwater drawdown & Logistic regression, MLP, AdaBoost and LogitBoost & AdaBoost \\ \hline
[79] & Forest disturbance detection & Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM +) imagery & Eight automated change detection algorithms, stacking & Stacking \\ \hline
[80] & Downscaling LST & topographic variables derived from SRTM DEM data, land cover, and surface reflectance in visible red and near-infrared bands & RF, TsHARP & RF \\ \hline
[81] & Landslite susceptibility mapping & Altitude, slope, aspect, cross sectional curvature, profile curvature, plan curvature, longitudinal curvature, channel network base, convergence index, distance to fault, distance to river, valley depth, and lithology map & FDA, GLM, GBM, RF ensemble based on weighted average & Ensemble \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._