esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Future Research
### Advantages and Runtime Performance  
Firstly, it is shown that our method is less dependent on context and pretraining than BAAM. This means that for our method, no training data are needed: only successful reconstructions. Secondly, if the reconstruction can be guaranteed (such as in the parking garage application), our method offers very precise pose estimation in low occlusion cases.  
At runtime, our method achieves far faster predictions compared to BAAM. The runtime performance did not change compared to Semantic [15]. OpenPifpaf can run on a RTX 4090 GPU with an Intel i9 core between 5 and 10 Hz on a size 2 batch of 808 \(\times\) 680 images without exerting a load of over 20% on the GPU. BAAM, on the other hand, runs at less than 2 Hz. Our pose estimation pipeline uses Scipy, and its time for a single object is a few milliseconds, depending on the actual and previous poses. This makes our method real-time capable on two sensor stations and a powerful PC. If in the future a faster, more accurate keypoint detector is proposed, our method can integrate it much faster than BAAM.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \(\mu\pm\sigma\) & **Median** & **P95** \\ \hline MM-VSM (ours) & \(\mathbf{1.40\pm 1.48}\) & 1.02 & 4.85 \\ BAAM [13] & \(4.42\pm 2.37\) & 3.97 & 9.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Absolute pose translation error on accepted frames (mean \(\mu\pm\sigma\), median, P95). Lower is better; best mean in bold.  
Figure 17: This histogram shows the comparison between the LiDAR detector [45] and the single-image-based 6 DOF pose estimation using the proposed method with the IP camera’s input images.
# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 1 Introduction
Nonetheless, they compromise the unsupervised nature of AEs. This introduces a fundamental dilemma: while AEs are designed to learn from unlabeled data, many of the methods for selecting their structure assume the availability of labels. This contradicts the core unsupervised learning principle and highlights a significant gap in AE design, motivating our work to address this unresolved challenge. In this paper, we propose to learn dynamically the width of an AE while training it. Specifically, we design a new cost function to optimize the relevance weight of each node and then learn the number of nodes of the hidden layer in the case of a fully connected AE. This optimization is achieved by assessing the inner product between the input vector and the mapped vector at each layer. The proposed approach involves the removal of non-relevant nodes, followed by an update to the AE architecture to reflect these changes. This method provides a fully unsupervised, self-adaptive mechanism that eliminates the need for manual tuning or auxiliary classifiers, thus preserving the integrity of unsupervised learning while improving architectural efficiency. In summary, our contributions are threefold: (i) we propose a novel relevance-weight-based mechanism for dynamically adjusting the width of autoencoders during training; (ii) we formulate a new cost function based on inner-product preservation to guide node importance estimation; and (iii) we validate the method on both benchmark and real-world datasets, showing significant improvements over state-of-the-art fixed-structure and evolving models. The rest of the paper is organized as follows: Section 2 reviews related work and theoretical foundations. Section 3 presents our proposed Width-Adaptive Autoencoder (WAAE) method. Section 4 details the experimental setup, including datasets and evaluation metrics. Moreover, it presents the results and analysis, while Section 5 concludes the paper and discusses limitations and future directions.
## 1 Introduction
### Representation of the Dominant Pulse of Near-Fault Earthquakes  
Earthquakes can have devastating effects on the urban infrastructure of large metropolitan centers in the vicinity of faults such as in California, Japan, Italy, and Turkey [41; 42; 43; 44]. The study of near-fault seismic ground motions and their effects on the performance of engineering structures has been an active research topic for both seismologists and earthquake engineers for the past two decades starting with the works of [PERSON] et al. [45] and [PERSON] et al. [46]. Near-fault ground-motion records exhibit a simple impulsive form with strong single pulses generated by shear dislocation and crack-type ruptures. Ground motions with fling-step characteristics contain a one-sided dominant velocity pulse resulting in a monotonic step in the displacement time-history, whereas, ground motions with forward-directivity characteristics contain a two-sided dominant velocity pulse [47]. [PERSON] et al. [46; 48; 49] have worked on relating the period of the dominant pulse to the magnitude of the earthquake. [PERSON] et al. [50]and [PERSON] et al. [51] have described the cascading and pulse-like ruptures during a recent series of earthquakes. [PERSON] and [PERSON] [52] have reviewed the effects of severe pulse-like ground motions in performance based engineering. [PERSON] et al. [45], [PERSON] and [PERSON] [53], [PERSON] and [PERSON] [47], and [PERSON] et al. [54] have investigated the effects of near-fault ground motions on the response of various building structures showing the particular sensitivity of long period structures.  
In order to analyze the effect of near-fault earthquakes on structures the input horizontal ground motion is commonly represented by one or more single pulse in the shape of a simple square, triangular, or sinusoidal waveform [55; 56]. Idealized half or full cycle sinusoidal pulses were suggested by [PERSON] and [PERSON] [16]. [PERSON] and [PERSON] [52] and [PERSON] and [PERSON] [47] proposed the use of one-cycle and 1.5-cycle sinusoidal waves. [PERSON] et al. [21; 57] have simulated the main portion of the pulse-like ground motions using double and triple sinusoidal impulses. [PERSON] and [PERSON] [58] used a corrected sinusoidal pulse for a better representation. [PERSON] et al. [59] used an empirical mode decomposition method to extract high frequency acceleration pulses representing them with simple sinusoidal approximations.  
To improve on simplified waveforms [PERSON] and [PERSON] [60] proposed to use modified Gabor wavelets to represent the dominant impulse of near-fault ground motions. [PERSON] [61] and [PERSON] and [PERSON] [62] also used wavelet analysis to extract the largest velocity pulse from a given ground motion. [PERSON] and [PERSON] [63] used sine, symmetrical Ricker and Asymmetrical Ricker pulses and [PERSON] et al. [64] used a mix of Fourier and wavelet analysis to extract dominant pulses.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 3 Flight Safety Framework
### LoRa Technology  
Several technologies based on the IoT are Radio-frequency Identification (RFID), Near-Field Communication (NFC), and WSNs. These technologies are most often characterized by short-range and low-power communication capabilities that limit their coverage areas to a small range [16]. IoT-based technologies are usually designed for applications that broadcast sporadic short data such as beacons. However, multiple devices in the same application that transmit to the same ground station may become a bottleneck of the application.  
In recent years, we have witnessed a rapid growth in the new technology referred to as Low-Power Wide-Area Network (LPWAN). This technology is characterized by long-range radio communication and utilizes the star network topology such that the data are transmitted from one device to another device or directly to a gateway and finally reach the Internet [16]. Another important feature of this new technology is the ability to place devices in a harsh environment where there is no other type of radio communication. LPWAN technology is operated in several different bands and frequencies depending on a country's regulations. This technology can be broadly divided into two categories depending on the type of modulation: mainly Ultra-Narrow Band (UNB) or wideband, wideband allowing multiple devices to communicate on one channel.  
Long-Range (LoRa) radio communication is based on a modulation called Chirp Spread Spectrum (CSS). This modulation uses its entire allocated bandwidth to broadcast a signal as chirp pulses to encode information [17]. Since it relies on a chirp pulse technique, it does not require the use of an accurate and stable internal clock, as in other transmission modulations, which are more expensive. This technique enables a variable data rate, thus providing the possibility to trade between efficiency of coverage and energy consumption while keeping a constant bandwidth. However, since this technology uses unlicensed bands, it must share radio communication with other devices using the same frequency [16]. Thus, it requires some regulation to ensure fair usage of the channel; hence, LoRa uses a duty-cycled transmission of the range from 0.1% up to 10%, depending on the sub-band [16].
# Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling--An Oil and Gas Sector Case Study
###### Abstract  
As security breaches are increasingly widely reported in today's culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the \"big picture\" of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.  
threat modeling; Operational Technology (OT); Industrial Control Systems (ICS); oil and gas cyber threats; cybersecurity; STRIDE; PASTA; attack trees; CVSS +
Footnote †: journal: Applied Sciences
## Results
### Effects of Extraction and Desorption Times  
Extraction and desorption times are important parameters of VOC quantification by SPME, which have an impact on intensity of analyte responses. Speed of equilibration during the extraction stage depends on a vessel volume, the diffusion coefficient of an analyte and its distribution constant between the coating and the air [35]. The equilibrium between the fiber and air for almost all analytes was reached after 5-10 min of extraction (Figure 1). Extraction time did not affect the responses of methylene chloride, methyl ethyl ketone and 1,2-dichloroethane--their responses varied by 1-13%. The responses for benzene were stabilized after 3 min of extraction. Based on the obtained results, an extraction time of 10 min was chosen as optimal.  
The increase in desorption time above 1 min had no significant effects on responses of all studied VOCs (Figure S2 in Supplementary Materials). After this time, the responses varied by 0.43-15%. Therefore, the desorption time of 1 min was chosen as the optimal.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multicolumn{4}{c}{**Relative Standard Deviation of a Slope (\%)**} \\ \cline{2-5}  & **Car/PDMS** & **PDMS** & **DVB/Car/PDMS** & **PDMS/DVB** \\ \hline \(o\)-Xylene & 0.60 & 1.3 & 7.5 & 3.4 \\ Chlorobenzene & 3.8 & 1.3 & 1.3 & 1.3 \\
1,3,5-Trimethylbenzene & 0.30 & 0.80 & 2.2 & 3.2 \\
1,2,\(\mathcal{A}\)-Trimethylbenzene & 7.6 & 3.5 & 6.1 & 3.8 \\
3-Picoline & 5.1 & 6.5 & 15 & 6.2 \\ Benzaldehyde & 8.0 & 3.2 & 79 & 3.7 \\ \(n\)-Hexadecane & - & 4.5 & 32 & 8.3 \\ Naphthalene & 5.7 & 1.8 & 20 & 2.2 \\ Phenol & 15 & 1.4 & 24 & 2.5 \\ Acenaphthenene & 4.2 & 5.3 & 4.3 & 2.5 \\ Fluorene & 5.5 & 1.5 & 4.0 & 3.5 \\ \hline \hline \multicolumn{5}{c}{Number of analytes with a relative standard deviation of a slope \(\leq\)} \\ \hline
1\% & 4 & 1 & 2 & 0 \\
2\% & 5 & 8 & 5 & 2 \\
5\% & 13 & 15 & 11 & 17 \\
10\% & 19 & 22 & 20 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 1: _Cont._
## 3 Numerical Results and Discussion  
### Separation of Synthetic Signals  
In Figure 7, statistics are calculated for two signals by choosing \(r\) = 1.11918 and 1.2243 in Equation (8) which is called pair A. We present the dependence of the mean entropy value NNetEn\({}_{\text{av}}\) (with standard deviation \(S\)) based on _Nset_ setting numbers for Dataset 1 (Figure 7a left axis) and Dataset 2 (Figure 7b left axis). Furthermore, F-ratios are shown by blue diagrams based on the right vertical axis. Results for pair B (\(r\) = 1.7161 and \(r\) = 1.7551) are shown in Figure 8.  
Figure 7: Dependences of the average entropy value \(\mathrm{NNetEn_{av}}\) (indicating the standard deviation \(S\)) and F-ratio depending on the _Nset_ setting number for Dataset 1 (**a**) and Dataset 2 (**b**). Pair A signals. The insets in the figure show the entropy ratio for the two settings.  
Several settings have been found that cause one entropy for one signal to exceed the entropy of another signal, while other settings cause the ratio to change. Therefore, Figure 7b for Dataset 2 shows setting where NNetEn\({}_{\text{av}}\) (\(r\) = 1.2243) is greater than NNetEn\({}_{\text{av}}\) (\(r\) = 1.1918) (for example, _Nset_ = 9 see insert 1), and setting where NNetEn\({}_{\text{av}}\) (\(r\) = 1.2243) is less than NNetEn\({}_{\text{av}}\) (\(r\) = 1.1918) (for example, _Nset_ = 12 see insert 2). Similarly, Figure 8a for Dataset 1 shows a change in ratios. A change in the ratios can be beneficial when applying combinations of entropies (see Section 3.2).  
Entropy metrics (Accuracy, Pearson Efficiency, and R2 Efficiency) generally performed similarly, but F-ratios varied depending on the pair of signals. The Pearson Efficiency metric was in the lead for pair A and base D1 (Figure 7a). According to Figure 8a, R2 Efficiency led for pair B and base D2.  
Figure 8: Dependences of the average entropy value NNetEn\({}_{\text{av}}\) (indicating the standard deviation \(S\)) and F-ratio depending on the _Nset_ setting number for Dataset 1 (**a**) and Dataset 2 (**b**). Pair B signals.
## 2 Materials and Methods
### Data Preprocessing Methods
#### 2.2.4 clean_signal  
The clean_signal algorithm (Algorithm 5) is designed to \"clean\" a signal, represented as a structure or array with x and y fields. The algorithm removes the sinusoidal component from the signal \(y\) to investigate the latest signal faults.  
```
1:Input:\(signal\)--structure/array with fields \(x\) and \(y\)
2:Output: New one-dimensional array \(y\)_clean_
3:\((vals,\ counts)\leftarrow\text{unique}(signal[``y^{\prime}|],\ \text{return\_counts}=True)\)
4:\(Ampl\leftarrow\text{vals}[\text{arg\,max}_{i\in counts}(counts)]\)\(\triangleright\) Find the most frequent value
5:\(y\gets signal[``y^{\prime}|^{\prime}]-|Ampl|\cdot\sin(100\cdot\pi\cdot signal[``x^{\prime\prime}])\)
6:return\(y\)
```  
**Algorithm 5** clean_signal  
Main Steps of the Algorithm  
Input data: The algorithm takes as input a structure or array signal, which is assumed to contain the fields x and y. The x field may represent the time axis or other independent variables, and the y field may represent the signal values.  
Find the most frequent value. The algorithm uses the unique function to find unique values in the array signal[''y''] and count their frequency. The result is the arrays vals (unique values) and counts (the number of occurrences of each unique value).  
Determination of the amplitude of Ampl:. The algorithm finds the value of Ampl, which is the most frequent value in signal[''y'']. This is achieved by finding the index of the maximum value in the counts array using the argmax function and then extracting the corresponding value from the vals array.  
Sinusoidal component subtraction: The algorithm creates a new array y by subtracting the sine function from the original signal signal[''y'']. The sine function is of the form \(|Ampl|\cdot\sin(100\cdot\pi\cdot signal[``x^{\prime\prime}])\). The amplitude of the sinusoid is given as the absolute value of Ampl, the frequency is fixed as \(100\pi\) (which corresponds to a frequency of 50 Hz if x is in seconds, or 100 cycles per unit of x), and the phase is zero.  
Output: The algorithm returns a new one-dimensional array y_clean, which is the result of subtracting the sine component from the original signal signal[\"y\"].  
Remarks  
The clean_signal algorithm is designed to remove a specific type of noise: a sine wave interference with a fixed frequency (or frequency proportional to x). The method of determining the amplitude of the noise (by searching for the most common value) is heuristic and may not always be efficient or accurate. The choice of frequency (\(100\pi\)) and the shape of the sine function assumes that the type of interference is known in advance. More general noise reduction may require more sophisticated techniques, such as wavelet noise reduction or filtering in the frequency domain.
## 2 Results and Discussion
### Effect of Administration
#### 2.2.2 Influence on Serum Parameters  
Seaweed supplementation can lower blood levels of triglycerides in diabetes patients [27]. This effect may occur via the inhibition of fatty acid synthesis by fermentation products or by changes in intestinal motility [28]. Increased levels of high-density lipoprotein and decreased levels of triglycerides are positive effects that may reduce the incidence of cardiovascular diseases.  
In this respect, the reduction in lipid peroxidation induced by the ingestion of the _Sargassum_ extract may reduce the deleterious effects due to free radicals. No significant differences in the levels of the other serum parameters analyzed were observed in relation to administration of _S. muticum_ extract (Table 2).  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Trophic Balance** & **Control** & **0.5 g kg\({}^{-1}\)** & **1.0 g kg\({}^{-1}\)** & **2.0 g kg\({}^{-1}\)** \\ \hline Weight gain (g day\({}^{-1}\)) & 6.79 \(\pm\) 0.15 & 7.02 \(\pm\) 0.11 & 6.93 \(\pm\) 0.11 & 6.79 \(\pm\) 0.22 \\ Liver/body weight (\%) & 3.30 \(\pm\) 0.04 & 3.43 \(\pm\) 0.07 & 3.35 \(\pm\) 0.08 & 3.42 \(\pm\) 0.04 \\ Kidney/body weight (\%) & 0.86 \(\pm\) 0.00 & 0.79 \(\pm\) 0.02 \({}^{*}\) & 0.79 \(\pm\) 0.02 \({}^{*}\) & 0.81 \(\pm\) 0.01 \({}^{*}\) \\ Intestinal mucosa/body weight (\%) & 0.85 \(\pm\) 0.01 & 0.87 \(\pm\) 0.03 & 0.74 \(\pm\) 0.02 & 0.80 \(\pm\) 0.05 \\ Heart/body weight (\%) & 0.41 \(\pm\) 0.00 & 0.41 \(\pm\) 0.00 & 0.42 \(\pm\) 0.01 & 0.39 \(\pm\) 0.01 \\ Lung/body weight (\%) & 0.55 \(\pm\) 0.01 & 0.48 \(\pm\) 0.03 & 0.59 \(\pm\) 0.02 \({}^{*}\) & 0.53 \(\pm\) 0.07 \\ Stomach/body weight (\%) & 0.62 \(\pm\) 0.03 & 0.59 \(\pm\) 0.02 & 0.57 \(\pm\) 0.02 & 0.64 \(\pm\) 0.02 \\ \hline \end{tabular} \({}^{*}\) Significant difference from the control group at \(p<0.05\).  
\end{table}
Table 1: Trophic balance in rats control and _Sargassum muticum_ extract treated groups. Values are mean \(\pm\) SEM. Number of animals per group = 6.
## 2 Problem Formulation and Theoretical Methods
### Kriging Surrogate Model  
The Kriging surrogate model is a stochastic interpolation algorithm that assumes that the model output is a realization of a Gaussian process indexed by \(x\in D_{X}\subset\mathbb{R}^{M}\). A Kriging model is described by the following equation [20; 21]:  
\[y\approx M^{K}(x)=\beta^{T}f(x)+\sigma^{2}Z(x,w) \tag{1}\]  
where \(y=M(x)\) is the output data and \(\beta^{T}f(x)\) and \(\sigma^{2}\) are the  
\[\beta=\left(F^{T}R^{-1}F\right)^{-1}F^{T}R^{-1}y. \tag{5}\]  
The generalized least-square estimate of the underlying regression problem can be expressed as [24]:  
\[u(x)=F^{T}R^{-1}r(x)-f(x). \tag{6}\]  
Another useful corollary of the Gaussian assumption is that:  
\[\overset{\wedge}{\mathbf{Y}}(x)\sim N(\mu_{\overset{\wedge}{\mathbf{Y}}}(x), \sigma_{\overset{\wedge}{\mathbf{Y}}}^{2}(x)). \tag{7}\]  
Thus,  
\[P\bigg{[}\overset{\wedge}{\mathbf{Y}}(x)\leq t\bigg{]}=\phi\Bigg{(}\frac{t- \mu_{\overset{\wedge}{\mathbf{Y}}}(x)}{\sigma_{\overset{\wedge}{\mathbf{Y}}}( x)}\Bigg{)} \tag{8}\]  
where \(\phi(\cdot)\) denotes the Gaussian cumulative density function. Based on the above equation, the confidence intervals on the predictor can be calculated by Equation (9) with probability \(1-\alpha\).  
\[\overset{\wedge}{\mathbf{Y}}(x)\in\bigg{[}\mu_{\overset{\wedge}{\mathbf{Y}}}( x)-\phi^{-1}\bigg{(}1-\frac{\alpha}{2}\bigg{)}_{\overset{\wedge}{\mathbf{Y}}}(x), \mu_{\overset{\wedge}{\mathbf{Y}}}(x)+\phi^{-1}\bigg{(}1-\frac{\alpha}{2} \bigg{)}\sigma_{\overset{\wedge}{\mathbf{Y}}}(x)\bigg{]}. \tag{9}\]  
In order to create the Kriging surrogate model and perform it to provide precise prediction, the functional basis of the Kriging trend should be selected first, and then the appropriate correlation function \(R(x,x^{\prime};\theta)\) is chosen. If the hyper-parameters \(\theta\) and the Gaussian process variance \(\sigma^{2}\) are unknown, then this involves setting up an optimization problem and solving it. Using the optimal value of \(\theta\), the rest of the unknown Kriging parameters \((\sigma^{2},\beta)\) can be calculated.
## 2 Example Bridge
### Uncertainty of the Structural Parameters Considering Chloride Ion Erosion  
#### 2.3.1 Chloride Ion Erosion  
The example bridge is located in an offshore tidal environment and is prone to corrosion degradation caused by chloride ion erosion. The chloride ion erosion process consists of three steps [24]: (1) diffusion, in which chloride ions diffuse to the reinforcement surface through the concrete protective layer until the reinforcement begins to corrode; (2) corrosion, in which the reinforcement corrodes and then the concrete cover begins to crack; and (3) degeneration, in which cracks occur in the concrete cover. The corrosion caused by chloride ion erosion has a negative impact on the overall seismic performance of the entire bridge. Therefore, the element model with consideration of chloride ion erosion should be employed to calculate the precise structural parameters of the example bridge at the current service time. The process of chloride ion corrosion in reinforced concrete structures is shown in Figure 5.