esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 4 DL Use Cases and Validation Datasets
### Regression Analysis for Predicting Numerical Outcome  
For this use case, we tested 120 Multi-Layer Perceptron (MLP) model variants. In brief, MLPs are fully connected feed-forward artificial neural networks. As MLPs are fully connected, each neuron in layer \(D-1\) is connected to every neuron in the subsequent layer (\(D\)). For this use case, the MLP networks' goal is to ingest data points and, through a regression analysis, predict the numerical value of a target variable. A leaky Rectified Linear Unit (ReLU) is embraced for the neuron activation function. These 120 different model configurations differ by network depth (D = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), neurons per layer (W = {32, 64, 128}), batch size (B = {32, 64, 128, 256}), and epochs (E = {1, 3, 5}).  
As our inference workload, we use the open and publicly available California Housing dataset from StatLib provided via Keras ([[https://keras.io/api/datasets/california_housing/](https://keras.io/api/datasets/california_housing/)]([https://keras.io/api/datasets/california_housing/](https://keras.io/api/datasets/california_housing/)) accessed on 7 November 2024). Specifically, the dataset captures 20 Ksamples, with each composed of eight attributes (e.g., house age and number of rooms), and the target variable for reference is the median house value per California district, expressed USD.
# Experimental Study on the Influence of Low Temperature on the Gas Permeability of Granite
## 3 Results and Discussion
### Two-Dimensional Gas Permeability  
#### 3.3.1 Two-Dimensional Intrinsic Gas Permeability  
Tables 3 and 4 present the 2D intrinsic gas permeability measurements of granite hollow specimens with coarse and fine particle sizes. The average 2D intrinsic gas permeability values for coarse-grained and fine-grained granite are 2.40 \(\times\)\(10^{-18}\) and 1.34 \(\times\)\(10^{-19}\) m\({}^{2}\), respectively. These results are consistent with the 1D gas permeability findings, which are also on the order of 10\({}^{-18}\) and 10\({}^{-19}\) m\({}^{2}\), respectively, indicating a difference of approximately one order of magnitude. Similarly, the 2D intrinsic gas permeability of coarse-grained granite exhibits greater stability compared to that of fine-grained granite, further suggesting that the internal pore structure of coarse-grained granite is relatively uniform across specimens.
## 3 Test Results and Discussion
### High-Temperature Permanent Deformation  
Table 6 presents the average results of the wheel tracking rutting resistance tests. It can be seen that the DS values of all mixtures met the minimum acceptance criterion of 3000 passes/mm commonly adopted in China [19]. PAC13 had better high-temperature stability than PAC10-C. For the novel PAC10 mixtures, with the increasing content of the mineral fillers, the rutting depth was significantly reduced, and the DS value was obviously increased. The novel PAC10-I6 mixture showed better high-temperature stability than the conventional PAC10-C mixture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mixtures**} & \multirow{2}{*}{\
\begin{tabular}{c} **Rutting Depth at 45 min** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Rutting Depth at 60 min** \\ **(mm)** \\ \end{tabular} } & \multicolumn{2}{c}{**DS**} \\  & & & **(mm)** & **(mm)** \\ \hline PAC13 & 1.642 & 1.739 & 6495 & 10.7 \\ PAC10-C & 1.821 & 1.943 & 5164 & 8.4 \\ PAC10-I4 & 2.035 & 2.172 & 4599 & 9.5 \\ PAC10-I6 & 1.679 & 1.786 & 5888 & 10.2 \\ PAC10-I8 & 1.424 & 1.519 & 6631 & 5.4 \\ \hline \hline \end{tabular}  
* COV is the abbreviation of coefficient of variation.  
\end{table}
Table 6: Rutting test results.  
Figure 6: Cantabro losses of PAC mixtures.  
It is worth noting that rutting development of a mixture is dependent on various factors, such as aggregate gradation, asphalt content, and binder properties. Although PAC13 and PAC10-C have the same fine aggregate content, PAC13 showed better high-temperature stability than PAC10-C, because the former had a larger NMAS. The three novel PAC10s had the same aggregate structure and binder properties. So, a higher mineral filler content mixture led to more asphalt binder and asphalt mastic, which may serve as an \"extender\" lubricating the aggregate structure. But the softening point of SBS modified binder is much more than 60 \({}^{\circ}\)C, and the HVA additive further increases the softening point of binder [12]. The Marshall stability results at 60 \({}^{\circ}\)C also indicated that more asphalt mastic improved the stiffness and strength of the novel PAC10s. So, the stiffening effect of more asphalt mastic was more significant than the increased lubrication effect, which resulted in stiffer mixtures. Therefore, with the increasing content of the mineral fillers, the novel PAC10 mixtures showed better high-temperature stability.
## 2 Materials and Preparation Process
### Preparation of Plate Additional Damping Structures with EMWM  
Vacuum brazing is a metal joining technology that forms a strong connection by introducing a filler metal onto a metal surface and then heating it at a suitable temperature to melt and penetrate the surface of the workpiece. In this paper, BNi-2 is a nickel-based alloy, as shown in Table 2, which can reduce the possible interaction between different materials. BNi-2 is supplied by Shanghai Dahua new brazing materials factory.  
The brazing temperature is determined by the liquid temperature of the solder and the base metal. According to the solidus and liquidus temperatures of BNi-2 and the base layer, 1050 \({}^{\circ}\)C is selected as the brazing temperature. The brazing layer and EMWM are fixed by the designed fixture to guarantee the reliability of the brazing. The heating process can be divided into heating, brazing, and cooling. In the heating stage, the furnace temperature is increased from room temperature to 1050 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min. Maintaining the temperature for 30 min ensures that the specimen is sufficiently and uniformly heated. Brazed joint properties are well formed by holding at 1050 \({}^{\circ}\)C for 20 min. Finally, when the furnace temperature is reduced to 20 \({}^{\circ}\)C, the free damping structure with EMWM and the constrained damping structure with EMWM are obtained, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Chemical Element** & **Si** & **Co** & **Cr** & **B** & **Fe** & **C** & **P** & **Ni** \\ \hline Percentage /\% & 4–5 & 0.1 & 6–8 & 2.75–3.5 & 2.5–3.5 & 0.06 & 0.02 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of BNi-2.  
Figure 2: Brazing process of plate additional damping structure with EMWM: (**a**) brazing fixture of additional damping structure; (**b**) vacuum furnace; (**c**) vacuum brazing heating curve; (**d**) plate additional damping structure with EMWM.  
Insertion damping structures with EMWM employ a simpler preparation process compared to free and constrained damping structures. A more convenient method to achieve the damping properties is provided by the insertion damping structure. The substrate plate, EMWM and restraint plate are tightly coupled by bolt connections. Limiting blocks are introduced into the structure to realize the limitation of the substrate plate and the restraint plate. The insertion damping structure with EMWM is shown in Figure 3.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Intensive Small Area Mapping by UAV
#### 3.1.2 Classification Accuracy  
Having devised the combined unsupervised-supervised classification method that is hypothesised to be robust, time-efficient and simple, the method is now assessed. First, different supervised classification algorithms are compared, then optimal combinations of image bands and indices input to the classifier are compared, and then the accuracy with a range of image resolutions are assessed.  
For brevity, classification performance with different parameters will be compared with the kappa value, computed from the error matrices. An example is given from the 2 July image of the U channel. The selected optimum parameters found in the following sections are used (classification and regression tree (CART) classifier; 0.05 m resolution; and the red, green, blue and near-infrared bands). The error matrix is shown in Table 5. The matrix indicates that all 1000 samples from within the water validation geometry were classified correctly, 23 of the 1000 samples from within the land validation geometry were incorrectly classified as water and 24 were incorrectly classified as weed and so on. This error matrix has an overall accuracy of 0.983 (the sum of the diagonal divided by total observations), a consumers accuracy of [0.978,0.996,0.976] and a producers accuracy of [1,0.953,0.996]. The kappa value for this matrix is 0.974, indicative of excellent classifier accuracy.
## 3 Results
### Sevooflurane AP Increases Blood Levels of Plasma Cytokines Implicated in Chemotaxis of MSC
#### 3.2.1 Estimation of Myocardial Apoptosis  
Apoptosis was assessed by the TUNNEL technique (Figure 3. AP was associated with reduced apoptosis expressed as number of apoptotic nuclei/1000 nuclei, both in the early group (37.83 \(\pm\) 2.56, \(p\) = 0.006) and in the late group (38.16 \(\pm\) 2.48, \(p\) = 0.007) compared to the control group (53.16 \(\pm\) 10.55) (Figure 4). Qualitative analysis also revealed extension of the apoptosis area to the interventricular septum, which was not seen in from the preconditioned groups.  
Figure 2: The evolution of the plasma level of several cytokines in the first 48 h after the AP stimulus: VEGF (**A**), G-CSF (**B**), IL-6 (**C**), IL-1 \(\alpha\) (**D**), \(n\) = 3 for every subgroup. * marks statistically significant results.  
Figure 4: Sevooflurane preconditioning is associated with reduced mean number of apoptotic nuclei/1000 myocardial cells in both early (green) and late (purple) windows of anesthetic preconditioning compared to the control group (blue). * marks statistically significant results.  
Figure 3: Immunofluorescentence, positive TUNEL cells (green) superimposed on the image of DAPI-labeled nuclei (blue) on the same microscopic field, \(\times\)600. Image obtained on myocardial sections harvested 24 h after I/R.  
A more homogeneous distribution of the values obtained for the late preconditioning group was observed, compared to that of the control group (see Figure 5).  
.2.2 Late AP Increases the Number of MSC in the Myocardiium Subjected to Ischemia/Reperfusion Injury  
We hypothesized that MSC may be involved in the late AP window. To study this hypothesis, we performed a quantitative analysis of c-kit+flk-1\({}^{+}\) cells on hearts harvested 24 h after the I/R injury (Figure 6). We found that c-kit\({}^{+}\) cells were significantly increased in the heart following late AP (3.2 \(\pm\) 0.48 vs. 0.85 \(\pm\) 0.1, \(p\) \(<\) 0.0001, Figures 6 and 7).  
The flk1+ staining was found to be non-specific, with confounders due to endothelial labeling, thus it was not quantified.
# Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations
## 4 Results and Analysis
### Validation by Radiosonde and ERA5
From the distribution of stations on land and sea, no significant changes are found, or the feature is not easy to detect (possibly due to the fact that there are not enough stations with similar latitudes on the same continent). Applying the multi-system combined PPP processing strategy, even in low-latitude areas with frequent changes in tropospheric water vapor, the fluctuations in tropospheric delay can be well captured and corrected by the added multi-system observations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution** & **RMS (mm)** & **Bias (mm)** \\ \hline GPS & 8.7 & 5.1 \\ BDS & 9.5 & 5.6 \\ Galileo & 9.0 & 5.3 \\ GLONASS & 8.8 & 5.1 \\ GPS/BDS & 8.4 & 4.9 \\ GPS/Galileo & 8.3 & 4.8 \\ GPS/GLONASS & 8.3 & 4.9 \\ GPS/BDS/Galileo & 7.8 & 4.6 \\ GPS/BDS/GLONASS & 7.8 & 4.6 \\ GPS/Galileo/GLONASS & 7.8 & 4.5 \\ GPS/BDS/Galileo/GLONASS & 7.6 & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average RMS and absolute bias of ZTD differences of single-system and multi-system solutions with respect to the radiosonde solutions at 9 stations. Figure 12: ZTD time series from the four-system solution and ERA5 data for a period of 40 days (**top panel**: RGDG; **bottom panel**: STJ3).
# Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor
## 4 Experimental Results and Analysis  
To verify the feasibility and effectiveness of the controller, we configure two experimental platforms in the laboratory (Figure 3). The proposed method is compared with the traditional PI control.  
Figure 2: Tracking curve of the motor angular speed in the simulation.  
Figure 4 compares the proposed method and the traditional PI control and shows the effect of suppression under the two methods. In particular, Figure 3(a) shows the tracking situation of the motor angular speed. Relative to the speed closed-loop system, the periodic speed ripple exhibit relatively strong nonlinearity, and it is very challenging to completely respond only depending on the speed PI controller; moreover, the speed PI controller has some bandwidth limitations, which makes it susceptible to relatively high-order speed ripple, whereas the proposed method can satisfactorily suppress the ripple. The tracking error in Figure 3(b) reinforces this point.
# Laser Welding on Cerclage Wire Joining in Femur Surgery
## 4 Experimented Results and Discussion
### Effects of Laser Parameters and Length of Welding Based on Mechanical Testing  
Regarding the 5 mm welding length, the mechanical testing results of the ultimate load at failure on weld joint are shown in Figure 14 for different pulse frequency at 6 Hz, 9 Hz, and 12 Hz. The charge voltage was adjusted stepwise at 80, 90, 100, and 110 V while the pulse width was kept constant at 3 ms. Figure 15 depicts the results of 10 mm welding length. Figures 14 and 15 show that the ultimate load at failure is affected by the increment of charge voltage and pulse frequency. Particularly, the increment of charge voltage results in the increment of sound bead as well as the joint strength. The experimental results of mechanical testing indicated that at the charge voltage of 110 V, the highest ultimate load at failure could be obtained. Comparing to the case of charge voltage being at 80 V, for the 5 mm welding length, the ultimate load at failure was increased from 800 N to 1200 N when the charge voltage was adjusted to 110 V, as shown in Figure 14. For the 5 mm welding length, the ultimate load at failure was  
Figure 12: Effect of charge voltage on area of fusion and throat size.  
Figure 13: Effect of pulse frequency on area of fusion and throat size.  
increased from 1400 N to 1700 N when the charge voltage was adjusted stepwise from 80 V to 110 V, as shown in Figure 15.  
Regarding the 15 mm welding length, welding failure occurred at base metal. This phenomena indicated that the weld beads are stronger than the base metal. The experimental results of mechanical testing showed that the range of ultimate load at failure is approximately 1700-1800 N in the case of 15 mm welding length. Figure 16 indicated that an increase in charge voltage and pulse frequency did  
Figure 14: Effect of laser parameters on ultimate load at failure \(\pm\) standard deviation (SD) (length of welding 5 mm).  
Figure 15: Effect of laser parameters on ultimate load at failure \(\pm\)SD (length of welding 10 mm).  
not affect the ultimate load at failure significantly in comparison to the situation where the length of welding is 15 mm.
# Optimizing Mobile Vision Transformers for Land Cover Classification
## 3 Materials and Methods
### Vision Transformers
A stack of \(L\) transformer blocks, where \(L\) represents the number of transformer blocks, is applied to the non-overlapping flattened patches to generate a new sequence of non-overlapping flattened patches with the same dimensions \(d\), \(N\), and \(P\). However, unlike ViT, MViT can remember the patch order and spatial order of pixels within each patch. The new sequence of flattened patches is then folded and projected to a high-dimensional space to make a tensor with dimensions \(d\), \(H\), and \(W\), where \(d\) represents the size of the fixed dimensional space. Then, a \(1\times 1\) convolutional layer is applied to the tensor to project it to a low-dimensional space with the dimensions \(C\), \(H\), and \(W\). The newly formed tensor is concatenated with the input tensor to produce a new tensor with dimensions \(2C\), \(H\), and \(W\). Finally, an \(n\times n\) convolutional layer is applied to fuse the concatenated features and generate the output tensor with dimensions \(C\), \(H\), and \(W\). MViT is a powerful model because it can achieve high performance with a reduced number of parameters relative to heavyweight ViTs and CNNs. It leverages the inherent advantages of both architectures. Figure 2: (**a**) Overall architecture of mobile-friendly vision transformer (MViT), and (**b**) shows the architecture of one MViT block in (**a**). While MViT can utilize the dependable aspects of both CNNs and transformers, there are still potential improvements that can further optimize MViT to boost its performance on geospatial datasets and reduce its training parameter count. In this research, we incrementally explore these parameters to further optimize this architecture by strategically replacing blocks that are used to extract higher-order features.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 7 Discussion
### Discussion on European Option Pricing
#### 7.1.2 Convergence  
The convergence behavior of Finance-Informed Neural Networks (FINNs) during the training process is critical for understanding their efficacy in financial applications. As illustrated in Figure 5, the trajectories of the loss functions reveal distinct patterns across different models. Notably, the KAFIN model exhibits rapid convergence, with a stabilizing trend across all monitored loss functions. This rapid stabilization reflects the KAFIN approach's efficiency in integrating multi-modal data and advanced training techniques, contributing to its superior performance in financial contexts.  
The quantitative statistics presented in Table 5 provide further insights into the performance of the models. KAFIN achieves the lowest mean total loss of 28.8352, significantly outperforming other models such as FINN REG, FINN FFN, FINN DNN, and FINN MLP, which show mean losses exceeding 8000. The low mean loss indicates that KAFIN effectively balances various loss principles, resulting in a more harmonious training process. This balance is essential for addressing the complexities inherent in financial data, where capturing intricate relationships and dependencies is paramount.  
The standard deviation (STD) values further elucidate the stability of KAFIN during training. With an STD of 151.5633 for total loss, KAFIN demonstrates a consistent training trajectory, whereas other models display significantly higher standard deviations, suggesting greater volatility in their training processes. Such volatility can lead to overfitting or underfitting, making KAFIN's ability to maintain stability a noteworthy advantage.  
Additionally, the minimum and maximum loss values highlight KAFIN's robustness. The model's minimum total loss of 0.9411 and maximum of 5934.0322 indicate that it effectively minimizes errors, even under challenging conditions. In contrast, models such as FINN REG and FINN FFN exhibit much higher minimum losses, suggesting they struggle more significantly to capture the underlying dynamic patterns.  
The performance metrics indicate that KAFIN not only converges rapidly but also maintains a low total loss across different loss components, including financial and boundary losses. This performance can be attributed to KAFIN's architecture, which likely incorporates mechanisms that enhance the learning of critical features from financial patterns while preventing excessive variance in predictions.  
The analysis of loss function trajectories and statistical metrics reveals that KAFIN outperforms other FINN models in terms of convergence speed, stability, and overall loss minimization. These characteristics are crucial for practical applications in finance, where accuracy and reliability are paramount. The insights gained from this training analysis underscore the importance of employing advanced techniques, such as KAFIN, to address the challenges posed by financial patterns, ultimately leading to better-informed decision-making.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 6 Conclusions  
The intent of this research within the wider internet community is not to declare an ultimate solution to the neighbor spoofing issues present in normative NDP, though it may perhaps be a suitable baseline. Rather, it is to draw attention to a longstanding security issue and to spur thought for future research to build upon. Future developments based upon the concepts herein might continue to focus on how they could be refined or better implemented, which vulnerabilities they introduce, any gaps in their theory or practice, and how the cross-application of each technique could be applied otherwise. Researchers are encouraged to continually explore more modern, alternative approaches to securing the NDP address resolution process from neighbor spoofing threats.  
To conclude, the broad goal of introducing VBA is to define an alternative to SEND, CGAs, and other monolithic approaches to solving the neighbor spoofing problem. This research aims to pragmatically harmonize three important attributes: privacy, flexibility, and simplicity. It has set out to molt the Neighbor Discovery security paradigm that has continued to depend upon sophisticated Public Key Infrastructure, limiting infrastructure-only protocols, superfluous new data constructs, unwieldy asymmetric cryptography, and centralized address registration authorities. This research has defined a decentralized and empirical approach that mostly avoids the aforementioned tar pits and creates a new perspective. The problem of neighbor spoofing in IPv6 can indeed be solved in a way that is comprehensible, privacy-focused, wholly transparent to end users, and not at all disruptive to incompatible neighbors.  
Conceptualization, Z.T.P.; investigation, Z.T.P.; methodology, Z.T.P.; project administration, J.G. and Z.T.P.; software, Z.T.P.; supervision, J.G. and Z.T.P.; validation, J.G. and Z.T.P.; writing--original draft, Z.T.P.; writing--review, J.G. and Z.T.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data sharing is not available as no new data were created as a result of this research. The authors declare no conflicts of interest.