esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Framework
### Localization with Combined Dictionary
#### 3.4.2 Bhattacharyya Distance to Match Observed Tracks  
Whenever a new observed track \(\mathbf{z}_{t}^{\text{obs}}\) appears in the LiDAR data, we must determine which track cluster it corresponds to in the combined dictionary \(\mathcal{I}_{\text{combined}}\). We use the Bhattacharyya distance \(d_{B}\):  
\[d_{B}\Big{(}\mathbf{z}_{t}^{\text{obs}},\,\mathbf{z}_{t}^{\text{cluster}} \Big{)}=\frac{1}{8}\Big{(}\mathbf{z}_{t}^{\text{obs}}-\mathbf{z}_{t}^{\text{ cluster}}\Big{)}^{\top}\mathbf{\Sigma}^{-1}\Big{(}\mathbf{z}_{t}^{\text{ obs}}-\mathbf{z}_{t}^{\text{cluster}}\Big{)}\ +\ \frac{1}{2}\ \ln\!\left(\frac{\det(\mathbf{\Sigma})}{\sqrt{\det( \mathbf{\Sigma}_{\text{obs}})\,\det(\mathbf{\Sigma}_{\text{cluster}})}} \right), \tag{2}\]  
where  
\[\mathbf{\Sigma}\ =\ \frac{1}{2}\Big{(}\mathbf{\Sigma}_{\text{obs}}+\mathbf{ \Sigma}_{\text{cluster}}\Big{)}.\]  
Here, \(\mathbf{z}_{t}^{\text{obs}}\) denotes the observed track position vector at time \(t\), while \(\mathbf{z}_{t}^{\text{cluster}}\) represents the mean position of a cluster in the combined dictionary. Similarly, \(\mathbf{\Sigma}_{\text{obs}}\) and \(\mathbf{\Sigma}_{\text{cluster}}\) are the covariance matrices corresponding to the observed track and the cluster, respectively. The matrix \(\mathbf{\Sigma}\) is the average covariance, balancing the uncertainty between the two.  
We assign the observed track to the cluster \(S_{vt}\) that yields the minimum Bhattacharyya distance:  
\[S_{vt}\ =\ \arg\min_{S\in\mathcal{I}_{\text{combined}}}\ d_{B}\Big{(}\mathbf{z}_{t }^{\text{obs}},\ \mathbf{z}_{t}^{\text{cluster}}\Big{)}. \tag{3}\]  
Thus, the combined dictionary still uses the same track-cluster matching but across all possible track entries from \(1\) to \(K\).
## 3 Results
### Upregulation of Phase II Enzymes by Herb-Derived Essential Oils in DPCs  
Since all essential oils had the potential to activate NRF2, further experiments were performed to test whether they could upregulate the expression of downstream target antioxidant proteins. As representative antioxidant proteins of phase II enzymes, we focused on HO-1, NQO-1, and GSTpi expression in DPCs. In RT-PCR experiments followed by densitometric analysis, the mRNA expression levels of our phase II enzyme genes were found to be upregulated in a dose-dependent manner (Figure 4a,b). Consistently, Western immunoblots demonstrated that essential oils upregulated HO-1, NQO-1, and GSTpi protein levels in a dose-dependent manner (Figure 5a,b). These results show that a set of phase II enzymes expressed in human DPCs were upregulated by essential oils, suggesting that DPCs express sufficient NRF2 and downstream target proteins to cope with the oxidative stress.  
Figure 4: Dose-dependent induction of the mRNA expression of phase II enzymes in essential oils-treated DPC. (**a**) The DPCs were treated with lavender oil (0.025–0.01%), lemongrass oil (0.0012–0.005%), rosemary oil (0.005–0.02%), and chamomile oil (0.005–0.02%). The figure shows representative results from triplicate RT-PCR experiments. (**b**) Phase II enzymes mRNA level are quantified by densitometric analysis * \(p<0.05\).
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 4 Conclusions  
To improve the accuracy of heat load prediction in DHSs, a novel AE-GWO-GRU model is proposed, integrating an AE network, the GWO algorithm, and a GRU network. By optimizing GRU model parameters and establishing the prediction model, along with a comprehensive case study, the following conclusions are drawn:  
1. The AE model augments data derived from a limited number of original samples, ensuring an adequate volume of samples for training the GRU model. This augmentation process significantly enhances the model's prediction accuracy and stability.
2. The GWO algorithm tunes the GRU model parameters, addressing issues such as inadequate model fitting, low prediction accuracy, and prolonged parameter adjustment times associated with manual selection. Compared to BP, AE-BP, GRU, and AE-GRU models, the AE-GWO-GRU model demonstrates superior prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** & **SDAPE** \\ \hline BP & 57.86 & 42.75 & 2.51\% & 2.45\% \\ AE-BP & 54.83 & 41.46 & 2.45\% & 2.18\% \\ GRU & 56.69 & 41.77 & 2.45\% & 2.38\% \\ AE-GRU & 53.98 & 41.18 & 2.41\% & 2.07\% \\ AE-GWO-GRU & 47.90 & 36.90 & 2.17\% & 1.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of different models.  
Figure 18: The prediction results of different models.  
In summary, heat load prediction enables the estimation of future heat load values, serving as a fundamental component for optimal heat control strategy research. This predictive capability aids control systems in adjusting the water supply flow or temperature based on anticipated changes in the heat load, facilitating on-demand heating, and promoting energy efficiency and emission reduction efforts.  
Conceptualization, Y.Y. and J.Y.; methodology, Y.Y.; software, Y.Y.; validation, Y.Y.; formal analysis, X.Z. and Y.Y.; investigation, Y.Y. and J.Y.; resources, J.Y.; data curation, Y.Y. and J.Y.; writing--original draft preparation, J.Y., X.Z. and Y.Y.; writing--review and editing, X.Z. and Y.Y.; visualization, X.Z. and Y.Y.; supervision, J.Y. and X.Z.; project administration, J.Y.; funding acquisition, J.Y. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Guangdong Province, grant number 2022A1515011128.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality requirements of the project, the research data cannot be disclosed.  
The authors declare no conflicts of interest.
## 2 Study Area and Data
### Observation Data  
Ground-based observations were downloaded from the Central Tibetan Plateau Data Center ([[https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)]([https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)), accessed on 27 January 2022) and originated from the Integrated Observation Network for Surface Processes in the Heihe River Basin. The comprehensive observation network of surface processes in the Heihe River Basin utilizes a variety of advanced observation equipment, including automatic weather stations, moisture monitors, and eddy covariance systems, and combines the monitoring means of many disciplines, such as meteorology, hydrology, ecology, and geography, so as to achieve all-round observation of surface processes in the basin. The observations include meteorological parameters such as temperature, humidity, wind speed, and precipitation, along with remotely sensed parameters such as vegetation index and surface temperature. The observation network covers the whole basin of the Heihe River, and in this study, the Arou site located in the upstream alpine meadows (100.46\({}^{\circ}\)E, 38.05\({}^{\circ}\)N), the Daman site in the middle reaches of the irrigation area (100.37\({}^{\circ}\)E, 38.86\({}^{\circ}\)N), the Huazhaizi desert site (100.32\({}^{\circ}\)E, 38.77\({}^{\circ}\)N), and the Sidaoqiao site (101.14\({}^{\circ}\)E, 42.00\({}^{\circ}\)N) located in the downstream riparian forest were selected. The observations of the meteorological stations included air temperature, air humidity, barometric pressure, soil moisture, and other parameters. The three soil heat flux panels were mounted two meters due south of the meteorological tower to observe the soil heat fluxes. The sensible and latent heat fluxes were observed by the eddy covariance systems, with a sampling frequency of 10 Hz. Typically, eddy covariance systems underestimate sensible and latent heat fluxes due to site characteristics [20], which may partially affect the research findings. As such, the energy balance closure had values ranging from 0.80 to 1.05, which were forced using the Bowen ratio approach.
# Voice Communication Systems Impact on Navigating Officers
## 2 Research Methods  
To collect reliable data on the workload analysis, two sources were used. The first and the main one was the questionnaire survey on active deck officers and the second one was a series of personal interviews with active deck officers. As an additional data source, the official communication log from a Liquefied Natural Gas (LNG) carrier was used.  
A total of 104 seafarers participated voluntarily in the survey, out of which 39% were masters, 26% first officers, 25% second officers and 10% third officers. It was emphasized in the survey that the answers should be based on the experience during navigation in a high traffic or coastal area, conducted by one OOW. The questionnaire was structured by using open-ended questions designed to investigate several data sets (Figure 1). The first data set included the frequency of incoming voice calls during coastal navigation through: satellite phone, official mobile phone, ship's internal phone and internal UHF radio calls. The second data set included process time referring to the time spent in conversation through a certain communication system. The third data set included the subjective estimation on how many incoming calls could be classified as non-important for the ship's essential safety or security, i.e., as distracting for the OOW in the moment of occurrence. The participants were asked to state the predominant examples of such distracting calls and/or poor communication habits by the crew or the Company.  
Collected data were used for quantitative data analysis, except the data from the 3 rd question, which were used for qualitative data analysis.  
The second source of data was a series of interviews done with selected participants after the survey. The authors themselves conducted the interviews individually, by using open questions (asking for an explanation) and taking selective notes. The interviews involved 20 officers, navigating actively on ships with modern integrated navigational bridges. Half of them were the first officers and the other half were second officers who represented the most experienced personnel engaged in navigational bridge watches. The objective was to gather their experience on the most frequent nature of calls through every communication system, on the approximate duration of calls, and to clarify the most frequent examples of \"distracting\" calls.  
An additional source to collect the necessary data objectively was the official communication log from an LNG carrier in international trade. The log file contained 62 incoming calls for the period of 3 months during the ship's extensive exploitation. The communication log file was used to estimate the average process time of incoming satellite and ship's mobile phone calls.
###### Abstract
With the development of powerful rare earth magnets, there is the possibility of using the MFL or MEC sensors as permanently installed sensors due to considerably reduced probe size and low power requirements. The magnetic eddy current method, which is an adaptation of magnetic flux leakage and eddy current technique, requires low magnetization levels and has good sensitivity for detecting internal corrosion in ferromagnetic structures [22; 23]. Additionally, the technique has the capability to distinguish between internal and external corrosion, which is not possible with the traditional MFL [24]. Moreover, the MFL method relies on the detection of the leakage magnetic field using Hall effect sensors, which are manufactured from semiconductors. The performance of semiconductors is highly affected by temperature variations, whereas coil sensors in MEC, being passive in nature, are more tolerant to changes in temperature. MEC sensors have been found to be promising for the detection of narrow-surface and buried defects such as cracks by measuring the perturbations in the permeability of the test sample [25; 26; 27]. Most of the research on MEC sensors has focused on the effect of the magnetization level and defect quantification using different signal parameters [22; 23; 24]. A wide range of detection frequencies are used for MEC testing. Apart from the magnetization level in the test sample, the sensitivity of the sensor is predominantly affected by the AC characteristics such as excitation frequency and voltage [28]. A wide range of inspection frequencies (1 kHz-500 kHz) are used in the above-mentioned studies, but the reasons for selecting a particular frequency are not explained. The effectiveness of the MEC sensors is well established in the literature, but the detection mechanism and the frequency-related sensitivity of the technique are still unclear. The research work presented in this paper has two novelty aspects: (1) the study on the feasibility of developing a low-power wireless MEC sensor for permanent installation to monitor internal corrosion and (2) a comprehensive description of the relationship between the sensitivity of the sensor and the AC excitation frequency. The rest of the paper is arranged as follows: Section 2 covers the working principle of the MEC sensor, Section 3 presents the studies on sensitivity optimization using FEA and experiments, Section 4 illustrates the design of the wireless sensor and results of the accelerated corrosion test, Section 5 includes the discussions, Section 6 concludes the research work, and Section 6 suggests future research.
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 8 Identifying Obfuscation Techniques: The Starting Point
### Dynamic Analysis: Gaining Insights from Runtime Behavior  
Complementing static analysis is dynamic analysis, which involves observing the code's behavior during execution. This approach can uncover runtime decryption of strings or code segments, reveal hidden functionalities, and provide insights that may not be apparent from static analysis alone. Dynamic analysis often breaks through obfuscation layers that resist static methods, offering a more complete picture of the code's true nature.  
Dynamic analysis can find suspicious calls to the operating system even if they are well hidden in the source code, but may miss rare events (like strange conditions clearly visible in the source code while rarely materializing in execution).
# The Application of Satellite Image Analysis in Oil Spill Detection
## 1 Introduction
According to a 2008 study by Chinese scientists [24], the spectral characteristics of oil points are clear at wavelengths of 550 nm and 645 nm, and these wavelengths are the best wavelengths to monitor marine oil and estimate oil thickness. They also developed hyperspectral data that found that very fine oil spots change reflection at 440 nm and are useful for leakage tests. [PERSON] [25] in his paper described the colour of the oil as silvery and stated that it reflects light in a wide spectral range. Furthermore, he found that brown heavy oil is most preferably detected at a wavelength of about 650 nm and red-brown impurities at a wavelength of 700 nm. [PERSON] et al. [26] conducted research on the hyperspectral characteristics of the polarized reflection of an oil spot on the sea surface. They found that seawater polarization was higher than oil spectra and observed changes in oil spectra thickness in near-infrared 785 and 880 nm. However, Greek scientists [27] observed that wavelengths ranging from 660 to 760 nm of multispectral sensors are best suited for oil spill detection in coastal areas. The longer the wavelength, the more difficult it is to identify the water and the image becomes unreadable. Another study of 2021 [28] was devoted to the creation of an index to map oil spills using various combinations of the spectral bands of the Sentinel-2 mission and decorrelation stretching methods. Furthermore, scientists were able to distinguish between different thicknesses of the oil. Most satellite sensors have bands that extend over the area that allow oil spills to be observed. This paper focuses on analysing the environmental characteristics associated with oil spills using satellite data from various missions. Snow, ice, water, and vegetation were examined immediately before and after the accident. The results have been extended to longer-term analysis, helping to understand the origin of the problem and facilitate the choice of appropriate strategies for subsequent actions. Such practical approaches have not been seen in the literature. Therefore, the purpose of this article is to articulate the following research objectives clearly:  
* conduct an analysis of oil spills in rivers,group data for long- and short-term analysis,
* select the appropriate classes according to the remote sensing device,
* propose a practical method for monitoring oil spills. The above research tasks will enable the practical aspects of evaluating natural disasters such as oil spills in rivers. In the past, they were primarily studied in the sea and oceans. The method of grouping long- and short-term analyses enables us to understand the origins of problems and select appropriate satellite products for analysis. The analysis of satellite images from the point of view of changes in water levels proposed in this paper will provide a practical indication of potential observation sites where oil can enter the soil directly, resulting in soil pollution.
## 2 Modified Glasgow Coupled Model (MGCM) Overview
### Effective Degree of Saturation Considering the Influence of the Microscopic Pore Structure  
The \(S_{\mathrm{e}}\) can be defined as follows to consider the effect of the microscopic pore structure [27]:  
\[S_{\mathrm{e}}=\frac{S_{\mathrm{r}}-S_{\mathrm{r}}^{\mathrm{m}}}{1-S_{ \mathrm{r}}^{\mathrm{m}}}=\frac{S_{\mathrm{r}}-S_{\mathrm{res}}}{1-S_{\mathrm{ res}}} \tag{1}\]  
where \(S_{\mathrm{r}}^{\mathrm{m}}\) is the \(S_{\mathrm{r}}\) of the intra-aggregate pores, which is numerically equal to the residual degree of saturation \(S_{\mathrm{res}}\) of the unsaturated soil. The \(S_{\mathrm{e}}\) essentially reflects the degree to which the macropores are filled by water, and its value range is (0,1). For a soil with a single pore structure, there is no difference in the saturations between the large and small pores--that is, \(S_{\mathrm{r}}^{\mathrm{m}}=0\), \(S_{\mathrm{e}}=S_{\mathrm{r}}^{\mathrm{m}}\). For soil with two pore structures, the pores at each scale account for a certain proportion of the total pore volume. With the increase in clay content, the degree of micro-pore saturation increases gradually, the large pores are gradually replaced by small ones, and the saturation limit is reached. At this time, \(S_{\mathrm{r}}^{\mathrm{m}}=S_{\mathrm{r}}\), \(S_{\mathrm{e}}\)= 0.  
Using the \(S_{\mathrm{e}}\) instead of the \(S_{\mathrm{r}}\) to establish the model can distinguish the contribution of different types of pores to the soil deformation.
## 2 Methodology and Site Description
### Sea Water Elevation Prediction
Therefore, the simulated sea level incorporating both tidal oscillations and barometric pressure-induced changes is updated from Equation (2) for the value of \(h_{at}\), as shown in Equation (3):  
\[h_{sea}=h_{t}+h_{at}+\overline{h} \tag{4}\]  
where the third right hand term (\(\overline{h}\)) represents mean sea level values as calculated from the observed signal, \(h_{t}\) represents tidally induced sea level oscillations and \(h_{at}\) stands for barometric pressure-induced sea level change. To assess wind-generated wave height in front of the coastline, we start with a determination of deep water wave parameters. For the location of interest and relevant incident wave directions, fetch length has been assessed by applying the Saville method [26]. In order to determine deep water wave parameter values, data sets from the Section 2.3 climatological station have been used. Fully developed sea conditions have been checked by applying the Wilson criteria [27] prior to the determination of wave parameters. After defining relevant fetch length for a given wind duration and wind velocity, the Groen\(-\)Dorrestein nomogram is used to determine deep water wave parameters [28]. Wave transform analysis has been performed by applying SMS CGWAVE software [29]. The wave phenomena that can be simulated by CGWAVE are: bathymmetric refraction, diffraction by structures (e.g., breakwaters) and bathymetry, reflection (from structures, natural boundaries (seawalls, coastlines, etc.) and bed slopes), friction, wave breaking and floating (fixed) docks influence the wave field. The model is based on the use of a triangular finite element formulation to solve the two-dimensional elliptic mild slope equation, with grid sizes varying throughout the domain based on the local wavelength [30]. The grid can be efficiently generated using the SMS graphical interface when a bathymetry file is provided. The model allows one to specify the desired reflection properties along the coastline and other internal boundaries. While the basic equation is intended for monochromatic waves, irregular (i.e., spectral) wave conditions are simulated in CGWAVE through a linear superposition of monochromatic simulations [31; 32]. The procedure or the algorithm for the absolute sea water elevation assessment is based on three steps which are presented above and summarized as: (i) sea level change caused by tidal forcing prediction, (ii) sea level change caused by a barotrophic pressure forcing assessment and (iii) assessment of the wind-generated wave height in front of the coastline.
###### Abstract  
In this paper, a method for automatically separating the mixed circular fringe patterns based on the fractional Fourier transform (FrFT) analysis is proposed. Considering the mixed two-dimensional (2-D) Gaussian-based circular fringe patterns, detected by using an image sensor, we propose a method that can efficiently determine the number and parameters of each separated fringe patterns by using the FrFT due to the observed higher sparsity in the frequency domain than that in the spatial domain. First, we review the theory of FrFT and the properties of the 2-D circular fringe patterns. By searching the spectral intensities of the various fractional orders in the FrFT projected along both the frequency axes, the proposed method can automatically determine the total fringe number, the central position, binary phase, and the maximum fringe width of each 2-D circular fringe pattern. In the experimental results, both the computer-simulated and optically mixed fringe patterns are used to verify the proposed method. In addition, the additive Gaussian noise effects on the proposed method are investigated. The proposed method can still successfully separate the mixed fringe pattern when the signal-to-noise ratio is higher than 7 dB.  
fractional Fourier transform; signal separation; 2-D Gaussian function; circular fringe pattern; spectral projection Article