esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
## 4 CNN for Material Classification
### Preprocessing Using the Windowing Spectrum Dilation Algorithm  
Since the WSD algorithm was shown to be a good preprocessing algorithm for material classification in the preliminary work, [19], it was chosen to determine the optimal frequency range for the imaging applications. The WSD algorithm transforms the series to a 2D array so that each image contains data from the preselected frequency span of the extracted envelope with the dilation factor. Each image row extends the spectrum with a dilation step. At the end of each consecutive row, the original data belongs to the higher or lower measured spectra, which depends on the WSD dilation direction. The dilation direction can proceed from lower to higher frequency or vice versa. Each subsequent row contains the data of the dilated spectra, where the first row represents only starting frequencies, while the last row embraces the whole spectra. The WSD reduces measurement uncertainty and overcomes the problem with spectrum alignment in THz-FDS systems. The WSD transformation for dataset \(D\) is described as:  
\[r_{i}=D_{i}, \tag{11}\] \[n=|\frac{d}{m}|,\] (12) \[D_{i}=\{x|x\in\{D_{j}\}_{j\in N_{i}},j=i\cdot k\},\] (13) \[I_{WSD}=\{r_{i}|r_{i}\in D,1\leq i\leq m\} \tag{14}\]  
where \(r_{i}\) is the \(i\)-th row of the image, \(i=[1,2,\dots,m]\) is the running index and \(k\) is dilation factor. The WSD can also be applied to a down-sampled dataset to lower image resolution and preserve the spectral characteristics of the dataset. Adequately down-sampled data preserves the peak envelope's shape, and WSD preserves the shape of the envelope in all rows of the image. Those mentioned above confirm that WSD transformation should be more resilient to measurement uncertainties and data misalignment. The WSD data structure is presented in Figure 6. In the given experiment, the presented WSD algorithm focuses on lower frequencies, which have a higher amplitude, therefore, a higher impact on classification. To determine the optimal frequency range, the input series to the WSD  
Figure 5: Plastic samples in grey, black and white color. The dimensions of the plastic samples were 30 \(\times\) 45 mm with 5 mm thickness.  
algorithm should be normalized. Transmittance T based on Equation (2) was used to normalize the input series.
## 3 Supporting Block Plane Extraction  
After completing the registration of the point cloud data and the standard track slab design model, the measured values of the characteristics of each detection index are calculated. Based on the geometric size (measured value) of each detected feature of the extracted track slab and the standard size (nominal value) of the standard design model, the deviation values of the two are calculated. The basis for extracting the parameters of each detection index is to fit the point cloud of the geometric parameter. In order to extract the slope of the supporting block, the angle between the supporting block plane and the jaw plane, the features of the height of the convexity and the skew, and the supporting block plane should be extracted first. Therefore, the plane fitting of the point cloud data of the supporting block plane should be carried out first.  
In this paper, the point cloud plane fitting of the supporting block plane is an important part of the point cloud data processing and information extraction process, and it is the basis for the outer dimensions of the track slab. At present, the commonly used point cloud fitting algorithms are least squares method, eigenvalue method, global least squares method, RANSAC algorithm, etc. [36; 37; 38; 39].
for increasing safety during fires and explosions while using hydrogen energy. In this regard, the IMO adopted resolution MSC.391(95), the International Code of Safety for Ships using Gases or Low-flashpoint Fuels (IGF Code) such as hydrogen [11]. In accordance with this resolution, the IMO recommends using gas dispersal analysis or a physical smoke test for leak detection equipment best arrangement. Therefore, for hydrogen leakage detection, it is necessary to quantitatively analyze hydrogen dispersion characteristics under various parameter conditions (release pressure, velocity, diameter, etc.) for installing a detector based on these characteristics. Table 1 summarizes various types of hydrogen release scenarios considered in the experimental or numerical methods, including gaseous releases, fuel cell ships, vehicles, semi-closed, and confined environments. [PERSON] et al. [12] numerically studied the release of hydrogen in a hydrogen fuel cell ship and analyzed the effects of different ventilation conditions on hydrogen dispersion behavior. Their results indicated the optimal positions for hydrogen sensors and ventilation. [PERSON] et al. [13] conducted a CFD simulation on high-pressure hydrogen released from storage tanks; furthermore, factors such as wind speed, ambient temperature, leak location, leak hole diameter, and obstructions on hydrogen diffusion were evaluated. [PERSON] et al. [14] summarized their results on the dispersion behavior of hydrogen gas from a hydrogen fuel cell car in an enclosed area and provided safety measures for using hydrogen in ventilation conditions. [PERSON] et al. [15] compared experimental data with CFD computed results for a hydrogen release in a semi-closed ventilation facility and assessed the efficiency of forced ventilation. A simulation was performed using STAR-CCM+. [PERSON] et al. [16] performed large-scale hydrogen release experiments to analyze the formation of flammable clouds inside a confined area with an internal volume of 80 m\({}^{3}\). Earlier studies related to hydrogen leakage and diffusion focused on terrestrial environments. However, the safety risks and accident levels are considerably higher than those of hydrogen vehicles on land because many electrical facilities can act as ignition sources for hydrogen leaks in hydrogen ships, and the hydrogen storage tank's capacity is large. The ship has an airtight structure; therefore, hydrogen cannot be easily discharged outside. The risk is considerably greater at a hydrogen refueling station when an accident such as an explosion occurs [12; 17]. Despite these risks, few studies have focused on hydrogen diffusion behavior for hydrogen ships. Previous studies only considered the structural conditions of the ship, whereas environmental factors, such as the motion conditions of the ship, have not been published. We analyzed the effects of the ship's kinetic characteristics on the hydrogen diffusion behavior and conducted safety analysis using simulations attributed to environmental factors of the ship. We detected the time required by hydrogen to reach each sensor due to the ship's motion conditions. Furthermore, we indicated the
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 2 Preliminaries
### Velocity Design of the Virtual Leader  
According to Theorem 1, one can see that how to design the velocity \(v_{0}(t)\) of the virtual leader is important, since the velocity of the virtual leader has an impact on the movement direction of the multi-robot system. Hence, the velocity of the virtual leader is as:  
\[v_{0}(t)=\lambda(p_{s}^{\prime}(0,t)-x_{0}(t)) \tag{32}\]  
where \(\lambda\) is a positive constant as a step factor. If the virtual leader is installed in the \(i\)-th robot, we have \(p_{s}^{\prime}(0,t)=p_{s}^{\prime}(i,t)\). Therefore, we design Algorithm 1 for signal source localization.  
```
/*Initialization*/  Initialize the parameters of the particle filter \(N\), \(R\) and \(w_{m}(i,t)\), \((m=1,2,\ldots,N)\);  Initialize the parameters \(\alpha\), \(\beta\) and \(\gamma\) of the consensus control (18) and the event-triggered rule (16), the position \(x_{i}(0)\) and the velocity \(v_{i}(0)\) of the \(i\)-th robot; /*Main Body*/ repeat  Receive its neighbors' information;  Detect the new signal strength \(o_{r}(i,t)\) at the position \(x_{i}(t)\);  Calculate the prediction signal strength \(o_{m}(i,t)\) (\(m=1,\ldots,N\)) based on (11);  Give the normalizing weight in (13), and obtain the estimated position of signal source in terms of (15);  Compute the event-triggered condition in (16) and (17); if\(g_{i}(t)>0\)then  Send the estimated position of signal source \(p_{s}^{\prime}(i,t)\), the position of the robot \(x_{i}(t)\) and the velocity of the robot \(v_{i}(t)\) to its neighbors; endif if\(g_{i}(t)\leq 0\)then  Calculate the control input in (18);  According to (5), obtain the force and torque \(I_{i}\), and give the applied torques for the left wheel \(\tau_{l}\) and the right wheel \(\tau_{r}\) in (6) and (7), respectively. endif until The termination condition is satisfied.
```  
**Algorithm 1** Decision-control approach with an event-triggered communication scheme.
## 1 Introduction
Notice that while AC, _atmospheric correction_, includes the atmospheric characterization and the mathematical inversion from Top-Of-Atmosphere (TOA) radiance to surface reflectance, AI, _atmospheric inversion_, refers only to this last step. The paper is structured as follows: Section 2 introduces and assesses the impact of the mathematical assumptions considered in the FLEX AI, as well the steps to couple SF fluorescence retrieval with inverted Top-of-Canopy (TOC) apparent reflectance. Section 3 analyses the errors in the apparent reflectance caused by an inaccurate AC process, essentially by an inaccurate aerosol characterization. Additionally, the predictive power of the apparent reflectance to detect and correct the retrieved aerosols optical properties from the AC is also evaluated. Section 4 discusses the suitability of the mathematical approximations assumed in the AI, highlights the limitations of the presented study and subsequently proposes future in-depth analyses to establish the use of the apparent reflectance as an indicator of inconsistencies in the AC. Finally, Section 5 provides the main conclusions.
## 4 Experiments
### Human Evaluation  
In addition to automatic evaluation, our experiments also performed human judgment evaluation, simulating human needs for summarizing critical information. We chose the Amazon Mechanical Turk (MTurk) platform, a proven and widely used social crowdsourcing platform with many qualified participants worldwide for human evaluation. We randomly selected 20 test instances from the dataset to evaluate the system performance comprehensively. Initial evaluation studies conduct experiments using the question an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** \\ \hline \multicolumn{4}{c}{_Extractive methods_} \\ \hline Lead & 38.22 & 16.85 & 26.89 \\ LexRank & 36.12 & 11.67 & 22.52 \\ GraphSum & 42.63 & 27.70 & 36.97 \\ \hline \multicolumn{4}{c}{_Abstractive models_} \\ \hline T-DMCA & 40.77 & 25.60 & 34.90 \\ FT & 39.55 & 24.63 & 33.99 \\ HT & 40.82 & 25.99 & 35.08 \\ \hline \multicolumn{4}{c}{_Topic-based models_} \\ \hline TG-MultiSum & 42.34 & 26.91 & 35.85 \\ S-sLDA & 37.28 & 15.97 & 26.31 \\ HierSum & 36.27 & 12.84 & 25.79 \\ \hline \multicolumn{4}{c}{_Ours_} \\ \hline Our Model & 38.57 & 18.93 & 28.64 \\ OurModel + & 41.28 & 25.61 & 33.79 \\ OurModel + TA & 39.35 & 20.98 & 30.12 \\ OurModel + & 43.23 & 28.27 & 37.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results.  
swering (QA) paradigm [44, 45], which quantifies how well a summarization model retains critical information in input documents. Under this paradigm, we created a series of questions based on a gold-standard summary, assuming that the summary contains the most essential details from the input passage. We asked questions about important information in the system summary, and the summary is considered good if it answers the questions we set as perfectly as the gold-standard summary. In order to evaluate the system summary answering effect, we set 50 questions and 2-4 questions for each summary. Table 3 shows the evaluation results.  
The Lead model is a summary generated based on the first three sentences and cannot completely cover the core content of multiple documents. The HT model augments the transformer architecture with the ability to encode multiple documents in a hierarchical manner, which improves the ability to obtain cross-document relationships and the ability to control the core content of the full text. TG-MultiSum uses topic models to discover potential topics in multiple documents and select key information fragments under each topic in a targeted manner. Among all the current comparison models, our model is a hierarchical transformer model that adds a discourse parser and a topic attention mechanism and achieves the best results.
## 2 Related Works
### Optimal Power Allocation
Another practical application is in critical infrastructure systems, such as smart grids and industrial control systems, where secure communication is necessary to prevent disruptions or malicious attacks. Wireless sensor networks, commonly used in environmental monitoring and healthcare, also benefit from enhanced security and reliability, particularly in scenarios involving untrusted relays or nodes with varying security clearances. Existing wireless communication standards and security protocols can be adapted to implement the proposed framework in real-world systems. For example, integrating lossy DF relaying and cooperative jamming techniques into current 5G or next-generation wireless networks could significantly improve their resilience against security threats. Software-defined radio (SDR) platforms provide a practical means to prototype and test the proposed methods, allowing for fine-tuning and optimization before deployment in live environments.
# IW-NeRF: Using Implicit Watermarks to Protect the Copyright of Neural Radiation Fields
## 2 Related Work
### Model Watermarking Algorithm  
With the burgeoning advancements in deep learning technology, a plethora of sophisticated deep learning models have surfaced, prompting the development of diverse watermarking strategies tailored to safeguard these models. These approaches can be broadly categorized into two groups: watermark embedding based on network weights and watermark insertion based on the classification labels of trigger sets. [PERSON] et al. [12] pioneered a method for embedding watermarks through network weights, utilizing a parameter regularizer to infuse watermarks into model parameters. Building on [PERSON] et al's work, [PERSON] et al. [30] enhanced this technique by introducing an additional neural network that maps network weights to the watermark space, although this is susceptible to ambiguity attacks. In response to these vulnerabilities, [PERSON] et al. [31] devised a novel passport-based ownership verification scheme for deep neural networks (DNNs), showcasing resilience against network modifications and ambiguity attacks. [PERSON] et al. [32] proposed DeepSigns, an end-to-end protective framework empowering developers to embed digital watermarks into pertinent deep learning models prior to dissemination. On the other hand, employing trigger set classification labels for watermark incorporation aims to introduce a backdoor into the network architecture, delivering exclusive activation rights to the copyright verifier. [PERSON] et al. [33] introduced a framework integrating author signatures during DNN training to establish a backdoor-based watermarking mechanism, enabling verification of author identity through predefined signature patterns in watermarked DNNs. Additionally, [PERSON] et al. [34] proposed a backdoor-inspired approach for watermarking DNNs. While these methodologies are tailored for specific network models and not directly applicable to neural radiance field (NeRF) copyright protection, insights from model watermarking can inform the design of watermark algorithms tailored for NeRF security.
## 1 Introduction
In general, increasing the number of the local linear models will improve the model accuracy; however, it also intensifies the complexity of the model structure, which can lead to a significant growth in the computation time of the model predictions and thus make the multi-linear model lose its advantage. Therefore, the accuracy and complexity of the multi-linear model must be balanced by choosing appropriate local linear models. In order to determine the best local linear models for the construction of a multi-linear model, the nonlinearity distribution of the process must be investigated. 2. Proper mathematical expression. For multi-input multi-output (MIMO) nonlinear process, the model with an appropriate mathematical expression can greatly reduce the complexity of the advanced controller design and the computational cost of the control law. Considering these requirements for multi-model system development and the flexible operation of the CO\({}_{2}\) capture plant, this paper investigates the nonlinearity distribution of the process to select proper local linear models and studies the construction of the multi-linear model. The local linear models at different CO\({}_{2}\) capture rates and flue gas mass flow rates are firstly obtained via a subspace identification approach such that the model with the state-space form can be directly obtained, which facilitates the derivation of advanced controllers. Then the nonlinearity distribution of the process is analyzed using the gap metric as the tool for quantifying the magnitude of the process nonlinearity [34; 35]. As mentioned above, the CO\({}_{2}\) capture plant should operate flexibly under large variations of the flue gas and CO\({}_{2}\) capture rate. Thus, in this study the process nonlinearity distribution is analyzed for these two operating situations. Finally, based on the nonlinearity distribution, the identified linear state-space models are combined to construct the multi-linear model. As the proposed multi-linear model has a mathematical expression similar to the linear model, the linear control techniques can be modified to handle the nonlinearity of the CO\({}_{2}\) capture process. This work has two major contributions: (1) the nonlinearity distribution of the MEA-based post-combustion CO\({}_{2}\) capture process is systematically investigated using the gap metric; (2) a multi-linear model of the CO\({}_{2}\) capture process is developed, which can provide a foundation for the advanced control system design. The main content of this paper is organized as follows: Section 2 presents the dynamic model of the MEA-based post-combustion CO\({}_{2}\) capture plant model developed using the go-carbon capture and storage (gCCS) toolkit. Section 3 explores the nonlinearity distribution of the CO\({}_{2}\) capture process and establishes the multi-linear model. Section 4 presents the simulation results. Section 5 introduces the proposed multi-linear model for advanced control system design. Section 6 summarizes the results and draws the conclusions.
## 5 Conclusions  
Study of application of the developed algorithm based on fuzzy logic methods and subsequent statistical modeling for post-processing of the components extracted by SSA showed that the proposed approach is quite effective when working with the generated data. However, when solving the economic task of modeling and forecasting the GDP of the Russian Federation, it was found that more complex rules are required when evaluating the components extracted by SSA, as well as use of special preprocessing of these components. The algorithm resulted in an effective descriptive model, which can be used both for analytical purposes and for forecasting purposes. However, to increase the efficiency of this algorithm in solving various economic problems, its further development, tuning, and modifications are required.  
The models created on the basis of application of the proposed algorithm confirmed that innovation processes are associated with a higher level of qualitative uncertainty compared to other economic processes. The models built to describe the dynamics of a number of researchers are more complex and require addition of fuzzy component recognition rules. Moreover, both the constructed models and ARIMA models (as well as other models of dynamics that were used for comparison) turned out to be much less accurate than when forecasting the dynamics of GDP of the Russian Federation. All this proves the high degree of complexity of the tasks of analysis of innovation activity. Nevertheless, the proposed approach of combining SSA and fuzzy estimation has coped quite effectively with this difficult task as well.  
Although the SSA method is widely used for solving problems in many fields, its application in economics, and, especially, innovativeness, is not wide enough. In our opinion, this is due to considerable complexity of economic processes and regularities, which increases the role of the expert in application of SSA (as well as most other mathematical methods) in economics. However, use of an expert, particularly for recognition of the results of the SSA method, raises a number of problems related to its qualification, subjectivity, impartiality, and so on. We hope that the above algorithm, at the expense of the built-in base of fuzzy rules and possible explanation of the economic content of the selected components, which includes some kind of expert system, will help to solve this problem and enable wider application of the promising SSA method in further economic research.
## 3 Results and Discussion
### Hardness  
The hardness measurements were carried out a day after welding to prevent age-hardening of the specimens. Figures 2 and 3 show the hardness test results of the welds obtained using the as-received and abraded electrodes at 400 kgf and 600 kgf. As shown in Figure 2, at the electrode force of 400 kgf, the average hardness values of the fusion zones (FZs) formed with the as-received and abraded electrodes were 55 HV and 59 HV, respectively. The hardness of the base material was about 95 HV. In addition, the hardness of the heat-affected zone (HAZ) produced with both the electrodes was about 80 HV. On the other hand, at the electrode force of 600 kgf, the hardness of the FZs produced using the as-received and abraded electrodes were 59 HV and 61 HV, respectively, excluding the defect in the nugget center, as shown in Figure 3. Moreover, the HAZ hardness values were similar to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Material** & \multicolumn{6}{c}{**Chemical Composition (wt. \%)**} & \multicolumn{6}{c}{**Mechanical Properties**} \\ \hline Al & Si & Fe & Cu & Mn & Mg & Cr & Ti & Al & \begin{tabular}{c} yield \\ strength \\ (MPa) \\ \end{tabular} & \
\begin{tabular}{c} tensile \\ strength \\ (MPa) \\ \end{tabular} &
\begin{tabular}{c} Elongation \\ (\%) \\ \end{tabular} \\  & 0.59 & 0.39 & 0.25 & 0.07 & 1.12 & 0.19 & 0.03 & Bal. & 298 & 338 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and mechanical properties of aluminum alloy 6061-T6 used in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrode** & **Electrode** & **Welding** & **Welding** & **Tensile Shear** & **Nugget Size** \\
**Surface Type** & **Force (kgf)** & **Current (kA)** & **Time (ms)** & **Strength (kN)** & **(mm)** \\ \hline as-received & 400 & 32 & 83.5 & 4.99 \(\pm\) 0.08 & 7.20 \(\pm\) 0.32 \\  & 600 & 36 & 83.5 & 4.89 \(\pm\) 0.24 & 8.20 \(\pm\) 0.04 \\ \hline abraded & 400 & 32 & 83.5 & 4.37 \(\pm\) 0.02 & 7.04 \(\pm\) 0.01 \\  & 600 & 36 & 83.5 & 4.23 \(\pm\) 0.02 & 6.90 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tensile shear strength and nugget size of welds produced using electrodes with different surface roughness and at different electrode forces.  
those measured at the electrode force of 400 kgf. These results show that the weld hardness was not significantly affected by the electrode force or surface roughness.