esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Proposed Signature Image Generation Model
This approach is inspired by DenseNet [26], which demonstrates that dense connections between layers alleviate the vanishing gradient problem and facilitate a smooth transfer of information even with small-sized feature maps. Additionally, a self-attention mechanism [27] was incorporated into the residual blocks at deeper levels to address the long-term dependency issues that may arise in deep models. The noise prediction module has a sampling depth of 5, making it a deep and complex neural network model with 60 convolution layers included in the residual blocks. By addressing the long-term dependency problem through the self-attention mechanism, the model can effectively preserve and transmit the detailed information necessary for effective noise prediction. Figures 2 and 3 illustrate the overall structure of the noise prediction module and the detailed structure of the blocks used within the module. As the final strategy, the proposed method incorporates a classifier to distinguish forged signatures, which results in the generation of signature images that are similar to the original images. The classifier is pre-trained to differentiate between original and forged signature images before training the neural network model included in the DDPM. During the training process of the DDPM, when the time step \(t\) is 1, the model generates a noise-free signature image \(\hat{x}_{0}\). Thus, the classifier receives \(x_{0}\) and \(\hat{x}_{0}\) to determine whether the generated signature image possesses the characteristics of the original signature. The classifier is trained to output a value close to zero when it identifies an image as original and a value close to one if otherwise. To incorporate the classifier's output into the signature generation model, we set the model's loss function \(\mathcal{L}\) to the sum of the mean squared error (MSE) between the predicted noise \(\epsilon_{\theta}\) and actual \(\epsilon\) and the classifier's output \(\mathcal{C}(x_{0},\hat{x}_{0})\). The weight \(\lambda\) for the classifier loss was set to one in this study. The details of the classifier are provided in Section 2.3. \[\mathcal{L}=\text{MSE}(\epsilon_{\theta},\epsilon)+\lambda\cdot\mathcal{C}(x_{0}, \tilde{x}_{0}) \tag{12}\]
## 4 Numerical Modelling and Calculation Methodology
### Establishment of Numerical Model of the Surrounding Rock-Initial Support
#### 4.1.3 Calculation Scenarios  
A parametric modelling approach is adopted to construct tunnel excavation models at a burial depth of 400 m, considering three surrounding rock grades (Grade III, IV, and V) and eight different tunnel spacings (20 m, 35 m, 50 m, 65 m, 80 m, 95 m, 110 m, and 125 m).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Bupial Depth (m)** & **Density (kN/m3)** & **Vertical Pressure (MPa)** & \(\mathbf{S_{\mathrm{H}}}\) **(MPa)** & \(\mathbf{S_{\mathrm{h}}}\) **(MPa)** \\ \hline
400 & 27.5 & 11 & 14.588 & 19.193 \\
600 & 28.5 & 17.1 & 19.008 & 27.693 \\
800 & 29.5 & 23.6 & 23.428 & 36.193 \\
1000 & 30.5 & 30.5 & 27.848 & 44.693 \\
1200 & 31.5 & 37.8 & 32.268 & 53.193 \\
1400 & 32.5 & 45.5 & 36.688 & 61.693 \\
1600 & 33.5 & 53.6 & 41.108 & 70.193 \\
1800 & 34.5 & 62.1 & 45.528 & 78.693 \\
2000 & 35.5 & 71 & 49.948 & 87.193 \\  & & & \(S_{\mathrm{H}}=0.0221\;H+5.748\) & \(S_{\mathrm{h}}=0.0425\;H+2.193\) \\ \hline \end{tabular}
\end{table}
Table 2: Ground stress values at different burial depths.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Rock** & **Dry** & **Compressive** & **Tensile** & **Deformation** & **Poisson’s** & **Shear Strength** & **Elasticity** & **Rock** \\
**Grade** & **Density** & **Strength** & **Strength** & **Modulus** & **Ratio** & **Shear Strength** & **Resistance** & **Strength** \\  & **(g/cm3)** & \(R_{\mathrm{w}}\) **(MPa)** & **(MPa)** & \(E_{\mathrm{o}}\) **(GPa)** & \(\mu\) & \(f^{\prime}\) & \(c^{\prime}\) **(MPa)** & \(\mathbf{K_{\mathrm{o}}}\) & \(\mathbf{K_{\mathrm{o}}}\) \\ \hline II & 2.75 & 81 & 8.1 & 16.2 & 0.23 & 1.215 & 1.62 & 49.5 & 4.5 \\ III & 2.72 & 45 & 4.5 & 9 & 0.25 & 1.035 & 1.08 & 40.5 & 3.6 \\ IV & 2.6 & 27 & 2.7 & 3.6 & 0.29 & 0.63 & 0.49
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 3 Beamforming-Based Power Estimation Methods
### Near-Field Focused Beamforming  
In the near-field, the estimated channel transfer function and the weighting vector can be expressed as  
\[\begin{array}{l}\mathbf{\overset{\kappa}{G}}=\frac{e^{-ikr_{m_{0}}}}{r_{m_{0}}} \Bigg{[}\begin{array}{cccc}r_{m_{0}}e^{-ik(r_{1}-r_{m_{0}})}&r_{m_{0}}e^{-ik( r_{2}-r_{m_{0}})}&\dots&r_{m_{0}}e^{-ik(r_{M}-r_{m_{0}})}\\ r_{1}&\dots&r_{m_{0}}e^{-ik(r_{M}-r_{m_{0}})}\\ \mathbf{w}_{NFBF}=\frac{1}{M}\Bigg{[}\begin{array}{cccc}\tau_{1}e^{-ik(r_{1}-r _{m_{0}})}&\tau_{2}e^{-ik(r_{2}-r_{m_{0}})}&\dots&\frac{r_{M}e^{-ik(r_{M}-r_{m _{0}})}}{r_{m_{0}}}\\ \end{array}\Bigg{]}^{\mathsf{T}},\end{array} \tag{10}\]  
where \(r_{m},m=1,2,\dots,M\) is the distance between the \(m\)-th hydrophone and the source. Substituting Equation (10) to Equation (7), the radiated noise power estimate with Near-field Focused BeamForming (NFBF) is given by  
\[\sigma_{s\,NFBF}^{2}=r_{m_{0}}^{2}\mathbf{w}_{NFBF}^{\mathrm{H}}\mathbf{R}_{x}\mathbf{w}_{ NFBF}. \tag{11}\]
## 4 Surface-Enhanced Infrared Absorption (SEIRA) Spectroscopy  
It was the field of surface-enhanced Raman scattering (SERS) that led to the establishment of a novel analytical platform termed as surface-enhanced infrared absorption (SEIRA) spectroscopy [73; 74]. SEIRA is explained as being the result of the enhanced optical fields at the surface of the particles when illuminated at the surface photon resonance frequencies. In analogy with, the interpretation accepted for SERS, it has been suggested that, electromagnetic and chemical contributions are responsible for the observed infrared enhancement. It can be assumed that there is an increase in the rate of absorption per unit volume that is proportional to the energy density of the field at the appropriate density. The enhanced local field augments this energy density at the surface of particles where the adsorbed molecule resides. This local field varies according to several factors, including size, shape, and the dielectric functions, among several others. Further consequences for the observed infrared spectrum from molecules adsorbed at these local fields might be the result of enhanced field polarization. SEIRA can offer the following advantages:  
* The sought sample enrichment along the solid surface reduces the content of water in the observed volume as the strong absorption of liquid water often hampers the IR spectroscopic analysis [75]
* Only those molecules that reside within the optical near-field are probed [73]
* Chip-based technology can be implemented [76]
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results  
We conduct the simulations in a Line-Of-Sight (LOS) environment with the dimensions of 10 m by 10 m. The 100 and 15 candidate mobile (with a 1-m interval) and AP locations are uniformly calibrated in the target area, as shown in Figure 2. In the simulations, we set \(\sigma_{1}\in\left[\begin{array}{cc}0.5\ \mathrm{dB}&6.5\ \mathrm{dB}\end{array}\right]\), \(\sigma_{\alpha}\in\left[\begin{array}{cc}0.5\ \mathrm{deg}&5\ \mathrm{deg} \end{array}\right]\), \(\sigma_{\tau}\in\left[\begin{array}{cc}0.3\ \mathrm{ns}&6\ \mathrm{ns}\end{array}\right]\) and \(\beta=2\), which are also used in [34]. For the sake of investigating the impact of AP number and noise power on the error bound and Fisher information, we calculate the error bound, \(\overline{V}\), for the target area by:  
\[\overline{V}=\frac{1}{s}\sum_{k=1}^{s}\left[\frac{1}{n}\cdot\sum_{i=1}^{n}V_{ \theta_{i}}^{\left\{L_{1}^{k},L_{2}^{k},\cdots,L_{m}^{k}\right\}}\right] \tag{38}\]  
where \(s=C_{15}^{m}\) is the number of sets of candidate AP locations. \(\left\{L_{1}^{k},L_{2}^{k}\cdots,L_{m}^{k}\right\}\) is the \(k\)-th set of candidate AP locations, namely the \(k\)-th AP deployment scheme. \(n=100\) is the number of candidate mobile locations. \(V_{\theta_{i}}^{\left\{L_{1}^{k}L_{2}^{k},\cdots,L_{m}^{k}\right\}}\) is the error bound with respect to \(i\)-th mobile location, \(\theta_{i}\), under the \(k\)-th AP deployment scheme.  
Figure 2: Layout of candidate mobile and AP locations.
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 3 Fragility Curve Based on Incremental Dynamic Analysis
### Scale Factors  
The models were analyzed by gradually increasing the spectral acceleration Sa(T\({}_{1}\)) of the GMRs until the required story drift ratios were obtained. To achieve 1/50 (2%) \(\theta\)max, the Sa(T\({}_{1}\)) increased from 0.01 to 1.5 (g) with an incremental step of 0.05 (g), where g is the gravity acceleration (9.8 m/s\({}^{2}\)).
## 2 Materials and Methods
### Daily Spectral Reflectance Gap-Filling of PlanetScope  
PlanetScope imagery data were used in this study to expand the high-throughput measurement of plant density in a wider coverage based on the actual plant density obtained from the YOLOv3 model (Section 2.3). PlanetScope imagery had a daily temporal resolution that was robust for identifying plant phenology, as we tried to measure the plant density in the later growth stages of soybean. However, due to the frequent observations using many satellites with various illumination geometries and poor inter-sensor calibration, the temporal surface reflectance data of PlanetScope became less stable and noisy [29]. Figure 4 shows that the raw spectral reflectance data of PlanetScope (presented as points) were not stable throughout the growth durations. Moreover, surface reflectance derived from satellite imagery were inherently noisy due to the variable illumination patterns and aerosols in the atmosphere. Smoothing splines can be used to reduce the potential for noise from the spectrum wavelength [44].  
The process was started by extracting the spectral reflectance values of every pixel within the observation field areas using the zonal statistic function in QGIS software. The available raw spectral reflectance data of PlanetScope imagery were then processed using the spline function implemented in the'stats' package in R (R Development Core Team 2019, version 3.5.3). As presented in Figure 4, the distribution of available spectral reflectance data was scattered between the sowing date and 60 DAS. A smoothing spline can also be used to interpolate spectral reflectance values of the missing date due to the presence of thick clouds from the available data. On the other hand, the smoothness of the spline curves can be set to make flexible curves by increasing the degree of polynomial or the number of knots. However, increasing the number of knots can lead to overfitting, while a small number of knots may result in restricted data that have more bias. Therefore, four knots were used in the smoothing spline process to obtain the natural shape of the curve. Our data set had a small range of examples, so a higher number of knots would overfit the data.  
Figure 3: (**a**) Example of the annotation process of established soybean plants using LabelImg; (**b**) the established soybean plants detected by the YOLOv3 model.  
Furthermore, we also calculated the daily normalized difference vegetation index (NDVI) value [45] from the interpolated spectral reflectance of PlanetScope imagery, because it represents the most commonly used vegetation indices that are capable of assessing a particular variable of vegetation, e.g., quantity, quality, and vegetation stage developments [19]. The normalized difference vegetation index was calculated using PlanetScope bands with the equation: \((\text{NIR}-\text{Red})/(\text{NIR}+\text{Red})\). Data for the interpolated spectral reflectance and NDVI value from this process were used as predictor variables in the regression analysis (Section 2.6).
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Comparative Scenario Analysis
In terms of resource use, the high-sensitivity setting performed more frequent analyses and generated more alerts, leading to a modest increase in energy consumption (we estimate \(\sim\)10% higher average CPU usage and power draw than low-sensitivity mode, due to the extra processing and communication for those additional alerts). From a user standpoint, these differences are significant: the high-sensitivity configuration might overwhelm users with alarms (some of which are false), while the low-sensitivity configuration might be too quiet, potentially missing early warnings. In practice, a balanced sensitivity setting is preferable--one that achieves a middle ground (as seen in our default configuration results earlier, \(\sim\)95% recall with \(\sim\)5% false alarms). This experiment highlights how tuning the DIKWP agent's parameters can shift its consciousness from a \"vigilant\" mode to a \"conservative\" mode, and the impacts of each on performance. This comparison illustrates the classic sensitivity-specificity trade-off. An overly sensitive system may lead to alarm fatigue, whereas an overly insensitive system risks missing critical early warnings. Tuning is therefore essential for optimal performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Daytime (Active)** & **Nighttime (Rest)** \\ \hline Event detection rate (recall) & 95\% & 96\% \\ False alarm rate & 5\% & 2\% \\ Avg. detection latency & 2.0 s & 1.8 s \\ Avg. power consumption & 55 mW & 50 mW \\ Alert frequency (per 8 h) & 4 alerts (including 1 false) & 2 alerts (nearly 0 false) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison between daytime and nighttime monitoring conditions.
## 4 Conclusions  
In this research, the anti-corrosion efficiency of multi-component corrosion inhibitors were comparatively studied with other inhibitors. The OCP, EIS, and CV methods were experimentally determined and compared. The mechanism of passive film enhancement was discussed. Based on the experimental results, the following conclusions can be drawn:  
1. Multi-component corrosion inhibitors significantly improved the corrosion resistances of steel bars in an SCP solution over a long period (60 days). The open circuit potential reached \(-0.37\) V after 5 days and remained above \(-0.45\) V at 60 days, and its electrochemical impedance spectroscopy reached 730.51 k\(\Omega\cdot\)cm\({}^{2}\) and remained 932.19 k\(\Omega\cdot\)cm\({}^{2}\). As a contrast, the OCP and Rct values of calcium nitrite after 5 days of immersion merely reached \(-0.28\) V and 69.54 k\(\Omega\cdot\)cm\({}^{2}\).
2. The adsorption and oxidation-reduction reactions that occurred on the steel bar's surface were comparatively analyzed and experimentally determined by the cathodic potential and the reduction peaks in cyclic voltammetry for the first time. The organic compounds could adhere on steel bar surface, thereby reducing the corrosion current densities in the wide passivation interval of the voltammograms. Additional protective precipitates (FePO\({}_{4}\) and Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\)) formed by the inorganic substance were detected, which contributed to corrosion resistance.
3. A \"multi-functional protective film\" was generated by the synergy of organic and inorganic substances, possessing a superiority inhibition effect and serving as a thick barrier. Considering their ideal inhibition efficiency and eco-friendly characteristics, multi-component component corrosion inhibitors could be an ideal substitute and have great application potential for the reinforcement of concrete in a chloride environment.  
Figure 13: Microstructure of steel bars. (**a**) Polished steel bar, (**b**) Steel bar suffered chloride attack, (**c**) Calcium nitrite, (**d**) Multi-component corrosion inhibitor, (**e**) Organic corrosion inhibitor, (**f**) Inorganic corrosion inhibitor.  
**Author Contributions:** Conceptualization, L.C.; Methodology, M.H.; Validation, L.C. and T.C.; Formal analysis, M.H.; Investigation, L.C.; Resources, M.H.; Data curation, T.C.; Writing--original draft, L.C.; Writing--review & editing, X.G.; Visualization, T.C.; Supervision, X.G.; Funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. U2106220) and the Major Project of Ningbo Science and Technology Innovation 2025 (No. 2020Z056).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 4 Results and Discussion
### Broader Implications and Future Prospects  
While our study demonstrated the effectiveness of the TCN model in projecting carbon tax prices across 30 countries, it is crucial to consider its broader implications and potential limitations in real-world applications. The complex interplay of economic, political, and environmental factors that influence carbon pricing policies presents ongoing challenges for accurate long-term forecasting. For instance, sudden geopolitical events or technological breakthroughs could significantly alter the trajectory of carbon prices in ways that historical data may not fully capture. Moreover, as more countries implement carbon pricing mechanisms, the global landscape becomes increasingly interconnected, potentially leading to spillover effects that our current model may not fully account for.  
Figure 2: Distribution of projected carbon tax prices for 2025. The green dotted line indicates the 5 th and 95 th percentile. The blue histogram indicates the frequency distribution.  
The application of our findings to countries beyond the 30 examined in this study requires careful consideration. Developing economies or countries with nascent carbon pricing systems may exhibit different dynamics compared to the more established markets we analyzed. Future research could explore the model's adaptability to these diverse contexts, potentially incorporating additional variables such as governance indicators or technological readiness indices to enhance the predictive power.  
Our approach builds upon and extends previous work in this field, such as the studies by [PERSON] and [PERSON] [21] on multi-factor forecasting models and [PERSON] et al. [22] on hybrid EMD-LSTM models for carbon price prediction. While these studies focused primarily on emissions trading systems, our work demonstrates the efficacy of TCNs in the specific context of carbon taxation. This novel application opens up new avenues for research at the intersection of deep learning and environmental economics.  
Looking ahead, integrating our TCN model with broader macroeconomic models could provide more comprehensive insights into the economy-wide impacts of carbon pricing policies. Additionally, exploring the potential of transfer learning techniques could enable the model to better generalize across different countries and policy contexts, addressing some of the current limitations in cross-country applicability.  
As global efforts to combat climate change intensify, the need for accurate and robust carbon price forecasting tools will only grow. Our study contributes to this crucial area, offering a promising approach that can aid policymakers, businesses, and researchers in navigating the complex landscape of carbon pricing. However, it is important to view these projections as one tool among many within the broader context of climate policy analysis and decision-making.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 3 Method
### CNN-Mamba Parallel Structure  
By combining convolutional neural networks (CNNs) and Transformers, the complementary advantages of both can be utilized. For example, CNN can introduce inductive bias into the Transformer, thereby improving its generalization ability. Furthermore, the local feature extraction capability of CNN, combined with the global feature extraction ability of Transformer, enables more detailed and comprehensive feature extraction from defocused images. However, traditional Vision Transformers (ViTs), despite having global receptive fields, suffer from high computational costs. To address this, many ViT variants reduce the initial global receptive field and use local attention and other techniques to reduce computational burden. Although these methods effectively lower the computational complexity to some extent, they also sacrifice the ability to capture global information initially.  
Mamba, a current research hotspot, achieves global feature extraction with linear complexity and serves as a good alternative structure. This approach not only retains the ability to extract global features but also significantly reduces the computational overhead, thus balancing performance and efficiency.  
Based on this, we propose a lightweight feature extractor called the CNN-Mamba parallel structure block. This method considers both extensive global information and specific local information when processing input data, enabling better feature extraction.  
Figure 3: Architecture of CMDDNet for image defocus deblurring. CMDDNet adopts the classic U-Net framework. The encoder, latent layer, and decoder are constructed by stacking two CNN–Mamba blocks as illustrated in (**a**). The structure of each CNN–Mamba block is depicted in (**b**).  
The overall workflow of the CNN-Mamba parallel structure is shown in Figure 3. Specifically, for a feature map \(F\in\mathbb{R}^{H\times W\times C}\), it is processed through both the Mamba branch and the CNN branch. Prior to the two branches, the dimension order is adjusted, followed by channel normalization. It is important to note that the CNN branch undergoes two adjustments in dimension order to facilitate convolution operations. The fusion module combines the results of the two branches using addition fusion, followed by a learnable residual connection with the original input to obtain the final output. The entire processing flow can be summarized as  
\[F_{\text{out}}=\text{Skip}(F)+\text{Fusion}(\text{Mamba}(F_{\text{mamba}}), \text{Res}(F_{\text{cnn}})) \tag{1}\]  
where \(\text{Mamba}(F_{\text{mamba}})\) and \(\text{Res}(F_{\text{cnn}})\) represent global perception and local capture operations, respectively. Fusion denotes the feature fusion operation, and \(\text{Skip}(F)\) represents the learnable skip connection operation.