esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions
### Emerging Directions and Practical Considerations for IoE Security  
#### 5.5.1 Split Learning (SL) and Federated Learning (FL) Synergies  
Recent advances in Split Learning (SL) have introduced new possibilities for privacy-preserving machine learning in IoE environments. Unlike FL, where only model updates are shared, SL partitions the model between client and server, transmitting intermediate activations instead of raw data. This approach can mitigate certain privacy risks, especially in environments with constrained devices or intermittent connectivity. Furthermore, combining SL with FL can exploit their complementary strengths, FL's decentralized training and SL's communication efficiency, to create hybrid frameworks for large-scale IoE deployments. For example, recent research on distributed multi-layer learning frameworks for intelligent transportation systems in next-generation aerial-ground integrated networks has demonstrated how such synergies can enhance both privacy and performance [183]. Future research should explore these hybrid designs in diverse IoE domains, particularly healthcare and smart city infrastructures.  
Beyond SL and FL, Transfer Learning (TL) can further enhance learning efficiency in IoE security frameworks by reusing knowledge from pre-trained models. Integrating TL with FL or SL can accelerate convergence, reduce communication overhead, and improve model generalization across heterogeneous IoE nodes. Such integration is particularly promising for dynamic environments, where rapid adaptation to new attack vectors or operational contexts is essential.
## 4 Case Studies: Part I: Simulation Analysis
### Classification Performance Evaluation of the Lightweight L-DenseNet-CBAM  
To verify the performance of the proposed algorithm on the PQD classification task, we use the constructed LT image dataset of PQDs and rely on the same experimental environment. The L-DenseNet-CBAM model, which integrates the CBAM attention mechanism, and its skeleton network, L-DenseNet, which does not incorporate the CBAM module, are trained and tested. The classification accuracy and loss variation curves on the validation set during 50 training epochs are shown in Figure 6. Additionally, we evaluate the classification performance of both models under different SNR scenarios on the test set, as illustrated in Table 4.  
As shown in Figure 6, the L-DenseNet-CBAM model, which integrates the attention mechanism module, exhibits significant improvements in both classification accuracy and convergence speed compared to L-DenseNet during the iterative training process. After reaching the convergent state, the accuracy and loss of the fusion model stabilize around 98.8% and 0.04, respectively, whereas the accuracy and loss of L-DenseNet fluctuate around 95.3% and 0.21. This indicates that while the skeleton model achieves relatively high PQD classification accuracy, the introduction of the CBAM module further enhances the ability of the classification network to extract fine-grained features of LT images corresponding to the PQD signals, thereby capturing the feature information in the input image more accurately.  
In addition, as can be seen in Table 4, L-DenseNet-CBAM exhibits a significant advantage in PQD classification accuracy over L-DenseNet across four noise levels: 0 dB, 60 dB, 40 dB, and 20 dB, with classification accuracy on the test set improving by 3.45%, 3.35%, 3.73%, and 3.92%, respectively. This further demonstrates that integrating the CBAM attention mechanism between the dense connection learning module and the classification  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Overall Classification Accuracy**} \\ \cline{2-5}  & **0 dB** & **60 dB** & **40 dB** & **20 dB** \\ \hline L-DenseNet & 95.38 & 94.70 & 92.45 & 89.94 \\ L-DenseNet-CBAM & 98.83 & 98.05 & 96.18 & 93.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the two proposed models on the test set.  
Figure 6: Training performance of L-DenseNet with/without CBAM on the validation set.  
module enhances the model's ability to capture key feature information, thereby achieving further improvement in the model classification performance.
## 3 The Time Source Techniques
### Pulsar Star
#### 3.3.2 Accuracy  
Since a pulsar is a star far away from Earth, many factors in the propagation path, data processing, and hardware implementation can have an impact on the timing accuracy.  
Through long-term observations of MSPs, pulse arrival times can be measured with a precision better than 1 us [61].  
In the case study of reference [62], MSP J1939+2134 with period 1.557 ms, the total process time is 599.5 s, and the sampling period after PFB is 16 us; the theoretically minimum achievable error in PPS signal is 26.69 ns.  
Reference [65] describes the pulsar-based positioning system. Using more than three pulsars, a timing error of 1 us is sufficient to meet the requirements for a pulsar-based positioning system.  
The timing drift of the tested pulsar-calibrated timing source is 1 us over 91 min, as reported in reference [66]. Higher accuracy can be achieved by observing stable pulsars and integrating longer-term pulse data.  
In general, with stable MSPs and enough observation time, the timing accuracy of a pulsar can achieve 1 us or better.
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 1 Introduction
Many of the existing studies employ conventional regression methods to develop the correlations between geotechnical and seismic wave velocity, even though ANN was used in many fields of civil engineering In this study, multi regression analysis and the ANNs approach were used to develop the relationships between seismic wave velocity and geotechnical parameters or, conversely, to predict geotechnical parameters from seismic wave velocity and other geotechnical parameters using data from the literature. The performance of the ANN and regression analysis was compared. Two different ANN approaches with and without validations were also discussed to handle the small size data set.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## Materials
### Data Description
#### 2.2.3 Digital Elevation Model (DEM) Data  
The global 1 arc-second SRTM V3.0 dataset (SRTMGL1) is a joint product of the National Aeronautics and Space Administration (NASA) and the National Geospatial-intelligence Agency (NGA). The SRTMGL1 product can be downloaded at NASA's Earth Observing System Data and Information System (EOSDIS) website ([[http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/)]([http://reverb.echo.nasa.gov/](http://reverb.echo.nasa.gov/)) accessed on 1 July 2021). The World Geodetic System 84 (WGS84) ellipsoid and Earth Gravitational Model 1996 (EGM96) geoid were used in the SRTMGL data products [37]. Several studies have validated the STRMGL1 data and shown that the product has high accuracy in China [38; 39]. The SRTMGL1 data in this study were used to divide small-scale basins.
# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 4 Experiments
### Performance
In addition, for categories containing a large number of small and densely distributed objects, such as small vehicle (SV) and ship, our results are 0.3 and 1 higher than the best results. This indicates that our method can provide a better solution for the problem of densely arranged target objects in remote sensing images. However, there are also some categories with degraded performance because of the small number of objects, such as tennis courts (TC) and large vehicles (LV). On the Love DA dataset, with the backbone of ResNet-50, our method achieves the mIoU of 49.75, which is an improvement over deeplab v3+ by 2.13 and 1.06 over FarSeg. The above multiple sets of experimental data can prove that our algorithm is very effective in dealing with the multi-scale problem on the remote sensing images. The reason is that the proposed GDConv is optimized accordingly for the characteristics of remote sensing images and performs better than the general segmentation methods. The visualization results on the iSAID dataset are shown in Figure 5. We can see that on regular round and square objects, the results of our method are sharper than other methods, where the edges are much smoother. On finer objects, our method is even sharper. To better illustrate our approach, we select two typical examples, as shown in Figure 6. In the first row, we can see that the size of the target plane is small and has an obviously different color between the fuselage and wings. The two wings of the plane are completely disappeared by Deeplab v3+, and only the fuselage part is detected. Thus, it misclassifies the object into the small vehicle class. Meanwhile, our method can completely label the whole object, so that the target can be correctly identified as the plane class. This is because the Gaussian dynamic convolution breaks the limitation of the dilation factors and can randomly produce arbitrary irregularly shaped receptive fields. It has the natural advantage of distinguishing remote sensing image target objects and effectively alleviates the false detection caused by the small size and dense arrangement of remote sensing image target objects. This example fully illustrates the superiority of our method to reduce the probability of false detection and improve the accuracy of segmentation. In the second row, there is a wild space next to the target area, and it is very similar to the soccer ball field class, which makes it misclassify the area into the soccer ball field class by both Deeplab  
Figure 5: Visualization results on the iSAID val set. v3+ and our method. It shows that our method also has some shortcomings to be improved. In the next step, we will continue to optimize our algorithm, for example, deepening the potential of Gaussian dynamic convolution to explore better possibilities in the field of remote sensing image segmentation.
# Phase Calibrated Ring Oscillator PUF Design and Application
## 7 Conclusions  
In this paper, we demonstrated a practical design and implementation of RO PUFs on FPGAs. Our main contribution was the phase calibration process, which focused on improving the frequency measurement techniques in the crossing clock domain area. By using a phase calibration process, the 7.81% bit error rate was reduced to less than 1%. Another contribution was the application of instability in PCPUFs. By using the unstable RO pairs as stable responses and obfuscation, we improved the stability and security of PCPUF-based authentication.  
**Author Contributions:** Conceptualization, Y.W. and C.J. Methodology, Y.W. Software, Y.W. Validation, Y.W. and C.J. Formal analysis, Y.W. Investigation, Y.W. Resources, Y.W. Data curation, Y.W. Writing, original draft preparation, Y.W. Writing, review and editing, C.J. Visualization, C.J. Supervision, C.J. Project administration, C.J. Funding acquisition, C.J.  
**Funding:** This research was funded by National Science Foundation grant number DGE-1318964. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**FPGA1** & **BER** & \(t\) & **False Positive** & **False Negative** \\ \hline PCPUF1 & 0.30\% & 20 & \(4.30\times 10^{-16}\) & \(5.38\times 10^{-22}\)\% \\ PCPUF2 & 0.38\% & 20 & \(4.30\times 10^{-16}\) & \(1.41\times 10^{-19}\)\% \\ PCPUF3 & 0.31\% & 20 & \(4.30\times 10^{-16}\) & \(8.82\times 10^{-22}\)\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: False positives and false negatives of USR-based authentication.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**PUF Type** & **ECC Type** & **Overhead** & **False Negative** \\ \hline RO PUF1 & Hamming(255,247) & 8 & 1 \\ RO PUF2 & Hamming(255,247) & 8 & 1 \\ RO PUF3 & Hamming(255,247) & 8 & 1 \\ RO PUF1 & BCH(255,128,15) & 116 & \(3.82\times 10^{-2}\) \\ RO PUF2 & BCH(255,128,15) & 116 & \(4.84\times 10^{-2}\) \\ PCPUF3 & BCH(255,128,15) & 116 & \(2.21\times 10^{-2}\) \\ PCPUF1 & Hamming(255,247) & 8 & \(1.21\times 10^{-2}\) \\ PCPUF2 & Hamming(255,247) & 8 & \(2.10\times 10^{-2}\) \\ PCPUF3 & Hamming(255,247) & 8 & \(1.59\times 10^{-2}\) \\ PCPUF1 & BCH(255,128,15) & 116 & \(<\)10\({}^{-6}\) \\ PCPUF2 & BCH(255,128,15) & 116 & \(<\)10\({}^{-6}\) \\ PCPUF3 & BCH(255,128,15) & 116 & \(<\)10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of ECC-based authentications.
# The Ultimate Strength of Fully Transparent Pressure-Resistant Spherical Cabins
## 3 Ultimate Strength of PMMA Spherical Cabin
### Spherical Cabin with Double Access Openings
#### 3.3.2 Effect of Hatch Stiffness  
In Section 3.2, it was observed that an increase in the thickness of the hatch cover can improve the structural strength within a certain range, but blindly increasing the thickness can actually reduce the structural strength. To verify whether the PMMA spherical cabin with double access openings conforms to the above conclusion, the analysis for the present case was conduced with hatch cover thicknesses at 9.6 mm, 14.4 mm, and 19.2 mm, and the inner and outer diameters consistent with Section 3.2. The calculation results are shown in Table 6. It can be clearly seen from the data that when the thickness of the hatch cover increases from 9.6 mm to 14.4 mm, the critical pressure increases from 3.06 MPa to 3.21 MPa, with an increase in strength of about 4.9%. Continuing to increase the thickness of the hatch cover to 14.4 mm, the structural strength does not further improve, but decreases to 3.08 MPa, which better verifies the correctness of the previous conclusion. In the future's study, further work should be done to explore the coordinated design between the PMMA shell and the metal hatch cover.  
Figure 14 shows the comparison of strength reduction tendency for three cases with initial geometrical imperfections. Access openings will change the strength reduction tendency. The sensitivity of initial geometrical imperfections can be reduced. Under the maximum initial geometrical imperfection level of 0.5%\(R\) required by the China Ship Classification Society (CCS, 2018), the effects of normalized critical pressure is reduced by at least 2.56%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(t/R\) & & \(0.04\) \\ \hline Cover thickness (mm) & 9.6 & 14.4 & 19.2 \\ Numerical results of buckling & 3.06 & 3.21 & 3.08 \\ pressure (MPa) & & \\ Failure Mode & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of hatch thickness on ultimate strength of the PMMA spherical cabin with double access openings.  
Figure 13: Load Proportional Factor versus Arc Length curve of a PMMA spherical cabin with double access openings.
## 3 Methodology
### Medium and High Band Speech Enhancement Network  
MB and HB have wider frequency bands and fewer speech features. We designed MB-Net and HB-Net as the speech enhancement networks of MB and HB, respectively. When using them for speech enhancement, we only process the magnitude, while keeping the phase of noisy speech unchanged. This can reduce the computational burden and avoid the compensation effect.  
MB-Net and HB-Net have identical structures, as depicted in Figure 7. They comprise two encoders, a sub-band interaction module, a stacked gated temporal convolutional module, and a decoder. The two encoders process the main spectrum feature of the current frequency band and the guided spectrum feature of the previous model output, respectively. For MB-Net, the guided feature is the speech enhanced by LB-Net, \(\hat{X}^{lb}\). For HB-Net, the guided feature is the speech concatenated on the frequency axis by the speech enhanced by LB-Net and the speech enhanced by MB-Net, \(\big{[}\hat{X}^{lb};\hat{X}^{mb}\big{]}\). After concatenating the spectrum feature of the current frequency band and the spectrum feature of the guided speech, they are passed to the sub-band interaction module for processing. The sub-band interaction module retains the important spectral features of the guided speech and suppresses irrelevant features. Then, the processed feature map is outputted to the stacked gated time convolution module for feature extraction, and the output is sent through the decoder. After applying HB-Net, the enhanced speech of the three sub-networks is concatenated on the frequency axis to obtain the enhanced full-band speech.  
The encoder comprises three frequency down-sampling (FD) layers, illustrated in Figure 8a. FD is built using gated convolution (GConv) [28; 29]. Cumulative layer normalization (cLN) [13] is applied to normalize the features, thereby enabling streaming inference and updating of frame-wise statistics. PReLU is used for activation. The kernel size for the time and frequency axes is (1, 7). To gradually reduce the frequency dimension, we set the stride to (1, 4). The decoder is made up of three frequency up-sampling (FU) layers, whose structure is depicted in Figure 8b. FU is similar to FD, but transposed gating convolution (TrGConv) replaces GConv [30].  
The stacked gated temporal convolutional module (S-GTCM) is shown in Figure 9 and consists of a stack of GTCM layers. Following [31], we employed four groups of S-GTCM for sequence modeling, with each group comprising four GTCMs having increasing dilation rates, i.e., d = {1, 2, 5, 9}, to achieve a large temporal receptive field. Each GTCM layer comprises two pointwise convolutions (PConv) and two dilated convolutions (DConv) with a kernel size of 5. We interpolate PReLU and cLN between adjacent convolutions. To facilitate the training of deeper networks, residual connections are established between the input and output.  
Figure 8: (a) Frequency down-sampling layer; (b) frequency up-sampling layer.  
Figure 7: The architecture of MB-Net and HB-Net.  
Figure 9: Details of GTCM.
## 4 Modeling of Acoustic Target Tracking
### Results of Geometric Solution for Acoustic Target Tracking  
**Lemma 3**.: Two sensing cones in a pair intersect with each other on a degree two curve in 3-dimensional space.  
Proof.: We proved in Lemma 1 that the intersection of each pair of sensing cones is a plane. Except special cases, when a plane passes from the apex point of a cone or when a plane lies on the surface of a cone, the intersection of a plane and a cone can produce four different degree two curves [37]. This is proved in many geometric text books like [38] and these curves are known as conic sections.  
**Definition 4**.: We call a degree two curve that is generated from the intersection of two sensing cones as the _intersection curve_ and denote the intersection curve of two sensing cones \(i,j\) with \(\sigma_{ij}\).  
**Theorem 1**.: Solving simultaneous equations of three different sensing nodes can generate incorrect answer.  
Proof.: As proved in Lemma 1, the intersection curve of two circular right vertical cones in a pair with equal aperture angles reside on a plane. In Lemma 2 we proved that the intersection planes of three sensing cones that are constructed from sensed information of motes make a pencil that intersect on a common line. This is obvious that a line can meet the target tracking cone of Figure 4 in more than one point. One of these two points is the real position of the target. Another point is also mathematically  
Figure 6: Sensing cones, constructed pencil and axis of pencil.  
true, but in reality a moving target object cannot be at two different places at once. This is because our basic equations are degree two. So one of the answers is feasible and the other one is incorrect.  
Figure 7 shows the intersection planes, curves, and axis line of a pencil that is constructed by these intersection planes. As the figure shows, the intersection curve passes from two points that axis plane passes from them too. One of these two points (\(R_{2}\)) is the real spatio-temporal information of the target object and another one (\(R_{I}\)) is its dual and is not the correct result. The real spatio-temporal information of the target object is shown with point \(T\) in this figure.
## 5 Validation Experiments
### Experimental Setups  
In the pre-processing module, according to the sampling frequency (256 Hz) of the sensors used in the shaking table experiments, the segmentation window size was taken as 200 data points. Thus, the duration of each segment was about 0.78 s (\(1/256\times 200\approx 0.78\)), which was considered sufficient in this study. To further investigate the influence of overlapping in the sliding window, both Non-OL and OL patterns were considered for each model. From the selected segmentation window size of 200, the OL size was assigned as 100 for convenience.  
Figure 7: Structural idealization of the test steel structure.  
To evaluate the classification performance in ML, the collected data were separated into training and test datasets. The TS data collected after the arbitrarily selected shaking table runs EQ4, EQ7, EQ9, and EQ12 (Table 1) were used for testing, which covers the scenarios of FOE, DBE, and MCE level earthquakes, and the remaining data were used for training.  
As mentioned above, there were three accelerometers installed on each floor; thus, a total of 117 (13 records \(\times\) 3 floors \(\times\) 3 sensor locations) TS signals and 36 (4 records \(\times\) 3 floors \(\times\) 3 sensor locations) TS signals were used for training and testing, respectively. To indicate the generalization of the predictive classifiers, only the test accuracy is presented in the following sections. For the feature importance analysis, the range of \(F\)_I_ scores for each feature, \(F\)_I_ (\(i\in\{1,2,\ldots,(p+q)\}\)) was normalized to be 0.0 \(\leq\bar{F}\bar{I}_{i}\leq\) 1.0, where \(\bar{F}\bar{I}_{i}=F\mathbf{I}_{i}/\sum_{j=1}^{p+q}F\mathbf{I}_{j}\), and all \(\bar{F}\bar{I}_{i}\) values sum up to 1.0.  
To avoid loss of generality, \(k\)-fold cross-validation was applied and used for tuning the hyper-parameters of the classifiers. In this cross-validation, all training data were randomly and equally split into \(k\) folds; at each time, \(k-1\) folds with a new combination of folds were used as a training set; and the remaining \(k\)-th fold was assigned as a validation set until each fold had been validated once. In the following experiments, \(k=5\) was used. Multiple runs in cross-validation determined the relatively optimal setting for each classifier, Table 2.