esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Linearity Index, f(D)  
Figure 5 illustrates the linearity index, \(f(D)\) of the windshield glass samples from the three brands of motor vehicle, \(f\) (\(D\)) being a measure of deviation from linearity, given by Equation (6):  
\[f(D)\ =\ \frac{\left(M\left(D\right)/D\right)}{\left(M\left(D^{\circ}\right)/D^{ \circ}\right)} \tag{6}\]  
with \(M\) (\(D\)) the TL response at dose \(D\) while \(M\) (\(D^{\circ}\)) is the TL response at the lowest detected dose, \(D^{\circ}\). Linearity is achieved for \(f(D)\) = 1, supra-linearity occuring when \(f(D)\) > 1. The value of  
Figure 4: Dose response for \({}^{60}\)Co exposures: (**a**) 1–10 Gy; (**b**) 1–100 Gy. The error bars are standard error of the mean, with in some cases the error bars smaller than the size of the data points. In (**b**) the linear fittings are biased towards the more elevated doses, (**a**) better showing the situation at doses up to 10 Gy.  
\(f(D)<1\) signifies a saturated or sublinear region as observed when traps trend towards becoming completely filled.  
From Figure 5, the linearity index for Honda and Toyota samples are mainly overlapped at high doses (60 Gy, 90 Gy and 100 Gy) due to the similarity of dose-response behavior; which indicates that the saturation of the signal is likely to be occurred. All of the samples tend towards identical behavior at a practical level, \(f(D)\) manifestly decreasing in approaching saturation, recombination and/or radiation damage effects included [26]. Figure 5 also shows the more linear response at lower doses, trending towards sublinearity with increasing dose.
# A Study on the Application of Laser Cleaning on the Weld of the Gas Fuel Supply Pipe for DF Engine
## 3 Results and Discussions
### Effects of Laser Cleaning Applied to the Fillet Weldment  
The angle of a portable optical head can be adjusted to perform laser cleaning of a fillet weldment uniformly. In this regard, this study conducted an experiment on analyzing the fillet weldment specimen according to different head angles to increase the applicability of the laser cleaning technology based on the shapes of a weldment. Figure 8 illustrates the experimental method. The angle of the laser head was adjusted from 35\({}^{\circ}\) to 55\({}^{\circ}\) at intervals of 5\({}^{\circ}\), and laser irradiation conditions included the average power of 100 W, the energy density of 5 J/cm\({}^{2}\), a laser beam overlap rate of 50%, and the focal position. Figure 9 illustrates the experimental results of laser cleaning according to different laser head angles. It was determined that the remaining contaminants on the surface of the fillet weld, such as an oxidized layer, were completely removed under the entire conditions. Moreover, the influence of laser head angles was insignificant in the process of laser cleaning of the fillet weldment. This result was obtained because laser beams irradiated on each area belonged to the range of depth of focus when the corner of the specimen was laser cleaned.  
These results verify the applicability of laser cleaning to butt and fillet weldments, which were shaped similarly as those of prototypes. In addition, the range of conditions of process parameters for facilitating laser cleaning was identified by a fundamental experiment according to butt and fillet welds.  
Figure 8: Schematic of fillet weld laser cleaning experiment method according to laser head tilt angle.
## 4 Discussion
### Limitations and Future Directions  
Despite these promising findings, current studies are limited by small sample sizes and heterogeneity in MS subtypes. Future research should focus on refining spectral unmixing algorithms, such as multivariate curve resolution, to enhance specificity for myelin-derived signals. Additionally, while discarding the \(<\)1000 nm wavelength range reduces melanin-related variability, and recruiting participants who are regionally homogenous implies similar skin types, we recognize that the absence of quantitative skin tone characterization (e.g., using the Fitzpatrick scale or objective colorimetric measures) may represent a limitation.  
Other limitations are the cross-sectional design, the lack of longitudinal reproducibility data, and the potential influence of disease-modifying therapies (DMTs) on the metabolic status of participants. The cross-sectional nature inherently limits the ability to assess temporal stability and causality of the observed near-infrared spectral changes. Without longitudinal follow-up, we cannot determine whether these spectral biomarkers track disease progression or therapeutic response over time. Furthermore, by altering the inflammatory activity and neurodegeneration, the heterogeneity of DMT regimens may represent a potential confounder that we were unable to control statistically given the limited sample size.  
Although the spectral analysis identified wavelength-specific patterns potentially related to tissue composition and physiological status, the present study relied on in vivo spectra without direct biochemical validation. Spectral assignments were based on the established NIR literature, yet no ex vivo tissue phantoms or reference liquids (e.g., lipid emulsions or water) were employed to confirm molecular attributions. Incorporating such validation in future work will be essential to strengthen the biochemical interpretation of key spectral features identified by VIP analysis. A final limitation of the present approach is that spectra were treated as independent observations, despite multiple spectra being acquired from each subject. This approach is common in NIR chemometrics but may introduce pseudo-replication, potentially inflating statistical power. Nonetheless, future studies should explore the use of mixed-effects models that treat subject identity as a random factor, which would allow for a more accurate generalization of findings at the participant level.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Performance Evaluation  
To assess the recognition performance of the DCC, four evaluation metrics have been selected: accuracy, recall, precision, and F1 score. The definitions of these metrics are outlined below.  
Accuracy is defined as the ratio of the number of samples that are correctly predicted by the model to the total number of samples. This relationship is expressed mathematically in Equation (6).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{6}\]  
Recall is defined as the ratio of actual positive samples that are accurately identified as positive by the model. It serves as an indicator of the model's ability to correctly classify instances belonging to the positive class. A high recall value signifies that the model is proficient in recognizing positive class samples. The corresponding formula is presented in Equation (7).  
\[Recall=\frac{TP}{TP+FN}, \tag{7}\]  
Precision is defined as the ratio of samples that the model predicts as positive and are, in fact, true positives. This metric provides insight into the accuracy of the positive predictions made by the model. A high precision value suggests that the model has a lower incidence of false positives in its predictions. The formula for calculating precision is presented in Equation (8).  
\[Precision=\frac{TP}{TP+FP}, \tag{8}\]  
F1 score represents the harmonic mean of precision and recall, effectively integrating the performance of both metrics. This score is particularly advantageous in situations characterized by class imbalance, as it provides a balanced assessment of precision and recall. The F1 score reaches its maximum value when precision and recall are equal. The mathematical representation of the F1 score is provided in Equation (9).  
\[F1=\frac{2 Precision\times Recall}{Precision+Recall} \tag{9}\]  
where _TP_ (True Positive) refers to a correctly identified positive instance of the true class, _TN_ (True Negative) refers to a correctly identified negative instance of the true class, _FP_ (False Positive) refers to an incorrectly identified positive instance, and _FN_ (False Negative) refers to an incorrectly identified negative instance.
## 3 The Proposed Method
### Dynamic Cross-Feature Fusion Model
It significantly improves the model's capacity to adapt to heterogeneous information sources and enhances matching accuracy, demonstrating strong robustness and generalization in complex multimodal tasks such as fragmented bone-stick image alignment. The fusion process begins by feeding the input feature vectors into the Dynamic Weight Generation (DWG) layer, where each modality is assigned a learnable weight based on its importance. The weight for modality i is computed using a sigmoid activation function applied to a linear transformation of its feature vector, as expressed in (18), where \(\mathrm{W_{i}}\) and \(\mathrm{b_{i}}\) are the trainable weight matrix and bias vector, respectively, and \(\sigma(\cdot)\) denotes the sigmoid activation function. The resulting raw weights \(\mathrm{w_{i}}\) are subsequently normalized using softmax operation to ensure that their sum equals 1, thereby producing a stable and dynamic distribution of modal weights. This normalization is given in (19), where \(\alpha_{\mathrm{i}}\) represents the normalized importance of modality i, and the summation runs across the three modalities (e.g., visual features and two textual representations). Following weight generation, the Nonlinear Transformation of In-modal Features (NTIF) module enhances the expressiveness of each modality through dedicated non-linear transformations. For each modality i, its feature vector \(\mathrm{h_{x}^{\prime}}\) is passed through a fully connected layer followed by a ReLU activation, as shown in (20). This transformation allows each modality to build a modality-specific encoding path, particularly beneficial for sparse or low-dimensional modalities such as archaeological metadata. To model the inter-modal interactions, the Weighted Feature Interaction (WFI) module computes pairwise cross-modal feature relationships. Given two modalities i and j, their cross-interaction is computed as the element-wise product of their weighted features, formulated in (21), where \(\alpha_{\mathrm{i}}\odot\mathrm{h_{i}}\) and \(\alpha_{\mathrm{j}}\odot\mathrm{h_{j}}\) denote the element-wise scaled features, and \(\mathrm{h_{ij}^{\mathrm{cross}}}\) captures the semantic synergy between the two modalities. Finally, the Generation of Fusion Features (GFF) module aggregates the transformed intra-modal features and all cross-modal interaction features. These are combined through a final deep nonlinear transformation to form the fused multimodal representation \(\mathrm{h_{fusion}}\), as defined in (22). This unified representation \(\mathrm{h_{fusion}}\) incorporates both deep intra-modal semantics and cross-modal relationships, resulting in a rich and discriminative multimodal embedding suitable for downstream matching tasks. \[\mathrm{w_{i}}=\sigma_{\mathrm{(}}\mathrm{W_{i}}\mathrm{h^{\prime}}+\mathrm{b_ {i}}\mathrm{)},\mathrm{w_{i}}\in\mathbb{R}^{1024} \tag{18}\]  
\[\alpha_{\mathrm{i}}=\frac{\exp(\mathrm{w_{i}})}{\sum\limits_{j=1}^{3}\exp( \mathrm{w_{j}})},\alpha_{\mathrm{i}}\in\mathbb{R}
## 2 Results  
### Validation of the BEPS Model  
Figure 1 shows a comparison between the annual average NPP for 2003-2012 that was obtained from the BEPS model and that from observations at forest sites over Northeast China. Generally, the BEPS model performed well at simulating the annual NPP, with \(R^{2}\) = 0.84, \(RMSE\) = 42.73 gC/m\({}^{2}\cdot\)a, and \(p\) < 0.01. The mean bias of the simulated NPP was 55.82 gC/m\({}^{2}\cdot\)a, indicating that the BEPS model slightly overestimated the NPP at forest sites. The scale mismatch between the forest plots and the resolution of the input data could be a major reason for the discrepancy. However, the overall satisfactory performance of \(R^{2}\) and \(RMSE\) suggested the potential of the BEPS model regarding simulating the regional NPP and analyzing the NPP patterns.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 3 Methodology
### Fully Convolutional Network (FCN)  
The fully convolutional network has been applied successfully in many domains such as semantic segmentation [31; 32; 33], breast cancer detection using historical image data [34], time series classification [35; 36; 37], model-free prediction of multilane traffic scenes [38], etc. We were inspired by CNN models used for time series prediction and based on the literature review. We applied a fully convolutional network to the traffic prediction problem. The model's architecture consisted of a Conv1D, also known as temporal convolution, used for spatial/temporal input. We used temporal convolutional networks (Conv1D) in the FCN branch, which means we applied a 1D filter. A convolutional block consisted of a convolution layer, batch normalization (BN), and a sigmoid activation function.  
\[y=W*x+b \tag{16}\]  
\[\hat{y}=BN+(y) \tag{17}\]  
\[h=Sigmoid+(\hat{y}) \tag{18}\]  
Figure 2: AttBDLSTM architecture.  
where \(*\) is the convolutional operator. We applied causal convolutional architecture provided by the TensorFlow implementation by adding the padding parameter as 'causal' results in causal convolutions, which means no information leakage from the future to the past in the network. The causal shift of the filter in the right direction did not depend on future inputs, as shown in Figure 3. Finally, after the convolutional block, learned features fed into the global average pooling layer rather than a fully connected layer, which vastly decreased the model's number of parameters and weights.
## 2 Materials and Methods
### Multiple Regression Analysis  
The data were used for multiple regression by stepwise multiple regression to find a representation of the data, which is the profit equation.  
A stepwise multiple regression analysis was carried out to investigate the relationship between independent variables and the dependent variable and use the regression tool in SPSS software to fit the sample data, and the values of coefficients of regression equations were computed. In the next step, the results obtained from the multiple regression analysis were applied to the optimization model for clarifying the model by the case study.  
Therefore, this model can be applied to other projects, which may have conditions for use: the model is suitable for large or densely populated cities, such as Beijing, New Delhi, and others. Multiple regression analysis is used to forecast the relationship between independent and dependent variables in linear form, as shown in Equation (2) [15; 16; 17; 18; 19].  
\[\hat{y}=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{k}x_{k}+\varepsilon \tag{2}\]  
where \(\hat{y}=\) dependent variable, \(x_{k}=\) independent variable, \(\beta_{0}=\) intercept point, \(\beta_{k}=\) regression coefficient of independent variables, and \(\varepsilon=\) random error.  
Hypothesis testing used to determine whether the model is adequate for estimating dependent variable was developed. Null hypothesis (H\({}_{0}\)): \(\beta_{1}=\beta_{2}=\ldots=\beta_{k}=0\) (no model). Alternative hypothesis (H\({}_{1}\)): At least one \(\beta_{k}\
eq 0\) (model exist).
## 1 Introduction
[46] conducted optimizations of curved and graded AFSPs, respectively, using NSGA-II with ANN and Kriging models as metamodels. [PERSON] and [PERSON] et al. [47; 48] optimized re-entrant ASVs using the radial basis function and NSGA-II. The optimum design produces better performance by reducing maximum deflection by 33% and increasing the areal specific energy absorption by 158.6%. [PERSON] et al. [49] and [PERSON] et al. [50] further optimized 3D double-arrow sandwich panels. Previous studies have primarily focused on re-entrant and double-arrow structures as cores for ASVs. However, other fundamental auxetic structures, such as star honeycomb and chiral honeycomb, remain largely unexplored. This study investigates ASVs incorporating four basic auxetic geometries: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and chiral honeycomb (CH), specifically for air-blast-loading scenarios. Numerical simulations were employed to evaluate the blastworthiness performance of these ASP configurations. Subsequently, a metamodel was developed to establish relationships between the design variables of ASVs and their blastworthiness performance. Global sensitivity analysis was conducted to highlight the influence of each design variable on blastworthiness. Following this, multi-objective optimization was performed to generate a Pareto-optimal front. Finally, the optimal design from the Pareto front was identified as a protective structure suitable for ASVs subjected to air-blast loading.
## 3 Experimental Results
Table 5 shows the concentration ranges, estimated in this study, for these two standardly identified VOCs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **RT (min) a** & **Common Compound Name** & **Match** & **Probability** & **Standard Identity** \\  & & **(\%)** & **(\%)** & **Confirmation b** \\ \hline
37 & 16.61 \(\pm\) 0.05 & 2-Pentanone & 903 & 89 & \\ \hline
38 & 16.70 \(\pm\) 0.03 & Butanoic acid & 933 & 97 & yes \\ \hline
39 & 17.42 \(\pm\) 0.06 & Cyclotrisiloxane hexamethyl & 807 & 58 & \\ \hline
40 & 17.51 \(\pm\) 0.04 & Methyl benzene & 938 & 97 & yes \\ \hline
41 & 18.27 \(\pm\) 0.06 & Octine & 907 & 70 & yes \\ \hline
42 & 18.40 \(\pm\) 0.05 & 2-Hexanone & 881 & 68 & \\ \hline
43 & 19.66 \(\pm\) 0.05 & Hexanal & & & \\ \hline
44 & 19.80 \(\pm\) 0.05 & Methyl isobutyl ketone & 902 & 76 & \\ \hline
45 & 20.00 \(\pm\) 0.07 & Hexanoic acid, methyl ester & 874 & 83 & \\ \hline
46 & 20.16 \(\pm\) 0.04 & Nonane & 934 & 54 & yes \\ \hline
47 & 20.32 \(\pm\) 0.08 & Pentanoic acid, methyl ester & 879 & 79 & \\ \hline
48 & 20.53 \(\pm\) 0.05 & Pentanoic acid & 809 & 54 & yes \\ \hline
49 & 22.00 \(\pm\) 0.07 & Di(isobutyl)acetone & 815 & 58 & \\ \hline
50 & 22.49 \(\pm\) 0.07 & Hexanoic acid & 879 & 79 & yes \\ \hline
51 & 22.95 \(\pm\) 0.04 & 3-Heptanone & 918 & 82 & yes \\ \hline
52 & 23.02 \(\pm\) 0.06 & Heptanoic acid, methyl ester & 988 & 83 & \\ \hline
53 & 23.55 \(\pm\) 0.03 & Epitane, 2,2,4,6,6-pentamethyl & 888 & 55 & \\ \hline
54 & 23.99 \(\pm\) 0.06 & Tetrasiloxane, decamethyl & 848 & 51 & \\ \hline
55 & 25.10 \(\pm\) 0.03 & Butanoic acid, dimethyl ester & 855 & 74 & \\ \hline
56 & 25.52 \(\pm\) 0.05 & Benzaldehyde & 933 & 95 & yes \\ \hline
57 & 25.93 \(\pm\) 0.06 & Octanoic acid, methyl ester & 832 & 68 & \\ \hline
58 & 26.26 \(\pm\) 0.07 & Decane & 932 & 55 & yes \\ \hline
59 & 26.88 \(\pm\) 0.06 & Benzoic acid, methyl ester & 815 & 54 & \\ \hline
60 & 27.54 \(\pm\) 0.08 & 1-Decanol-2-esil & 877 & 53 & \\ \hline
61 & 28.33 \(\pm\) 0.06 & Dodecane & 928 & 54 & yes \\ \hline
62 & 29.00 \(\pm\) 0.08 & Unidentified & & & \\ \hline
63 & 29.65 \(\pm\) 0.06 & Silane, ethyl-dimethyl-phenyl & 813 & 62 & \\ \hline
64 & 29.83 \(\pm\) 0.07 & 4-Phenyl benzofurane & 822 & 56 & \\ \hline
65 & 30.51 \(\pm\) 0.06 & Tri-tetra-contane & 812 & 56 & \\ \hline
66 & 31.53 \(\pm\) 0.04 & Pentacosane & 811 & 58 & \\ \hline \end{tabular} a
[FOOTNOTE:a] a footnotetext: Values expressed as mean (s.d.); b
[FOOTNOTE:a]Footnote a: Values expressed as mean (s.d.); b  
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Compounds** & **pg*mL-1 in Exhaled Breath** \\ \hline Benzaldehyde & n.d.-LOD \\ Methylbenzene & \textgreater{}50 \\ \hline \end{tabular}
\end{table} 
Table 5: Concentration ranges of VOCs exhaled by seven selected patients affected by adenomatous colonic polypoid lesion, before curative surgery.
## 4 Simulation Results and Discussion
### Comparison between Constant and Tuning Parameters of VSG  
As mentioned in Section 3.2, adapting the parameters (_J,D_) of VSG is the key superior to the conventional synchronous generator to enhance the system dynamical response. Therefore, an online adapting based on BSA is investigated in this section. It is worth to mention that the increase in number of population and epoch could provide the better result by reducing the overshoot and settling time; at the same time, it increases the computation burden and simulation time. The parameter of BSA such as population size and epoch are set to 25 and 5, respectively, by considering the trade-off between computation burden and performance. The virtual inertia (_J_) and damping factor (_D_) which are computed by BSA is shown in Figure 18. To investigate the perfomance of adapting VSG's parameters, two case studies as discussed in Sections 4.1 and 4.2 are conducted.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method  
### General Architecture  
The proposed watermarking framework integrates perceptual modeling, adaptive modulation, and temporal redundancy in a unified, computationally efficient pipeline. This design addresses key challenges in video watermarking: robustness against attacks, imperceptibility to the human visual system, and scalability in processing. The pipeline is divided into six stages: perceptual map computation, robust block selection, calculating DCT coefficients directly from spatial domain, JND-modulated embedding using Quantization Index Modulation (QIM), redundancy encoding with temporal distribution, and final extraction with majority decoding. Each stage is described below.
## 1 Introduction
If the relay function that constitutes the UAV route is activated on all nodes, the number of control messages for the route construction increases, and the message forwarding load of each node increases. To maintain connectivity for any node pair, the relay functions should be operated in as few nodes as possible. End nodes that have no relay function will connect to the nearest relay node. In this paper, we propose methods for selecting nodes that operate the relay functions called relayed nodes. The first simple method randomly selects the nodes. Our original second and third methods select them based on the number of adjacent nodes. The fourth method is our innovative idea based on the Multipoint Relay (MPR) of the Optimized Link State Routing Protocol (OLSR). For each method, we evaluate the route construction success rate, the number of selected relay nodes, the total distance of constructed routes, and the distance in population-dense regions. We will find the best method for constructing UAV navigation routes and the minimum relay node density to reduce the number of relay nodes suppressing route conflicts. The rest of this paper is organized as follows:Section 2 shows the safe and efficient route construction method for UAV navigation in a wireless multi-hop network. Section 3 introduces the issue related to the route construction method discussed in this paper. Section 4 discusses related works on this issue. Section 5 presents four methods that have been proposed to solve this issue. Section 6 shows the evaluation results of the proposed methods and discusses their effectiveness and characteristics. Section 7 concludes the paper with a summary and future works.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 1 Introduction
### Benefits of Multi-Media/Multi-Band Communication Techniques Underwater  
1. Increase the lifetime of sensor nodes
2. Increase the reliability of data transmission
3. Improve the faster discovery of neighbor nodes
4. Reduce the transmission delay between nodes
5. Long-term connectivity between nodes
6. Faster medium selection mechanism to transfer data