esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Approaches
#### 2.2.2 Double Differences  
Because the GDAS data is only an estimate of the true environmental conditions and the \(\pm\)2-h collocation time window is relatively large for environmental parameter stability, it is desirable to use another well calibrated instrument with relatively high temporal resolution for validation and calibration. The GMI instrument has been carefully vetted and found to be an extremely well calibrated microwave radiometer. As a result, the radiometric intercalibration of TMI to GMI serves to provide a common radiometric calibration for other radiometers that existed previously during the TRMM era. Therefore, the intercalibration between TMI and GMI was performed using the three versions of TMI products: V6, V7 and the new V8.  
Because of differences in frequency and EIA between the two radiometers, as shown in Table 1, the direct comparison between their observed brightness temperatures does not reflect the true instrument Tb bias of one to the other. However, using the difference between the SD's of the two instruments, which is the DD, is a robust technique for establishing the calibration bias [21]:  
\[\mathrm{DD}=\mathrm{SD_{target}}-\mathrm{SD_{reference}} \tag{2}\]  
where the reference is the well calibrated instrument (GMI) and the target is TMI.  
Since the resolution of the SD is a 1\({}^{\circ}\) box in latitude and longitude, the DD resolution will be the same. To improve the quality of the intercalibration, any match-ups with time difference more than \(\pm\)1 h were removed. Moreover, the standard deviation of the DD values is indicative of the radiometric stability, and the mean DD will be used to calibrate TMI to match GMI. Ideally, the DD biases should be a constant; however, results show some correlation with the yaw orientation, orbital latitude, solar coordinates and scene temperature, which will be discussed next.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & TMI & \multicolumn{2}{c}{GMI} \\ \hline
**Freq (GHz)** & **EIA (deg)** & **Freq (GHz)** & **EIA (deg)** \\ \hline
10.65 V/H & 53.5/53.6 & 10.65 V/H & 52.8 \\
19.35 V/H & 53.4 & 18.70 V/H & 52.8 \\
21.30 V & 53.4 & 23.80 V & 52.8 \\
37.00 V/H & 53.4 & 36.64 V/H & 52.8 \\
85.50 V/H & 53.4 & 89.00 V/H & 52.8 \\ \hline \end{tabular}
\end{table}
Table 1: Center frequency and earth incidence angle (EIA) of TMI and GMI channels.
## 2 System and Channel Model
### System Model
#### 2.1.1 System Model for Scenario 1  
The signal from S is transmitted to D through the RIS consisting of N reflecting elements. Each component of the RIS receives the incident light and adjusts the phase and amplitude under program control, and then transmits it to the receiver. It is assumed that the signal transmitted through the RIS is fully radiated and undergoes full phase compensation. The received signal is given as  
Figure 1: The proposed system model. (**a**) Jamming at the destination, (**b**) Jamming at RIS.  
\[y_{1}=R\sqrt{P_{s}}h_{i}s+R\sqrt{N_{I}}h_{J}\Lambda s_{J}+n \tag{1}\]  
where \(R\) is related to the responsivity of the photodetector, whose value is considered as 1; \(s\) represents the modulated information symbol; \(h_{i}=h_{p}\partial e^{\left|\overline{r}_{P}\right|}h_{q}\), \(h_{p}\), \(h_{q}\) and \(h_{J}\) are the S-RIS, RIS-D and jammer channel fading coefficient, respectively; \(\partial e^{\overline{r}_{P}}\) characterizes the IRS element at a position \(P\), with \(\partial\) representing the amplitude reflection coefficient and \(\Psi_{p}\) denoting the induced phase; \(s_{I}\) is the injected jamming signal, whose value is considered as unity; \(P_{s}\) represents the peak transmit energy of the legitimate transmitter; \(n\) is the additive white Gaussian noise (AWGN) with zero mean and variance \(N_{0}\); \(\Lambda\) represents the state of jamming, modeled using a Bernoulli distributed random variable, Thus, the probability distribution of \(\Lambda\) is given by [24]  
\[\left\{\begin{array}{l}P(\Lambda=1)=\rho\\ P(\Lambda=0)=1-\rho\end{array}\right. \tag{2}\]  
where \(P(\Lambda=1)\) represents the probability of the occurrence of a jamming event; \(P(\Lambda=0)\) represents the probability of the jamming event being idle. we define \(P_{I}\) as the average power of jamming. When the jamming event is in an active state and starts to jam legitimate information, the energy it emits is denoted as \(N_{I}=P_{I}/\rho\).  
The signal-to-jamming-and-noise ratio (SJNR) can be obtained from (1) when the jamming is active.  
\[\gamma_{1}=\frac{(h_{i})^{2}P_{s}}{N_{I}h_{J}^{2}+N_{0}} \tag{3}\]  
When the jamming is idle, the signal-to-noise ratio (SNR) can be calculated as follows:  
\[\gamma_{2}=\frac{(h_{i})^{2}P_{s}}{N_{0}}=\overline{\gamma}_{D}(h_{i})^{2} \tag{4}\]  
where \(\overline{\gamma}_{D}=\frac{P_{s}}{N_{0}}\) represents the average signal-to-noise ratio (SNR).
## 2 Passive Wake-Up Radio
### Review of the Existing Passive Wake-Up Radio Hardware Design  
In this section, we discuss the prototypes that power the wake-up circuitry using the harvested energy from the RF signal. Figure 3 classifies the existing designs. We categorize and present the literature based on the passive wake-up radio addressing type: ID-based and the broadcast-based. These architectures are achieved using RFID (Radio Frequency ID) or CMOS (complementary metal oxide semiconductor) technologies either by simulation, prototype, or printed circuit boards (PCB). The classification of the existing passive wake-up radio designs is shown in Figure 3. In some passive wake-up radios, the wake-up signals contain a bit sequence for selective node addressing. This is referred to as the ID-based addressing. Although the ID-based increased the size of the signal packet, it reduces false wake-up and thus affecting the overall system energy consumption. In the other addressing types, which are broadcast-based, the wake-up signals are received by the entire neighboring nodes. Broadcast-based wake-up has less data latency as compared to ID-based system, since the receiving node does not need to decode the wake-up packet to check the recipient ID. We summarize the prototypes and their corresponding features in Table 1.
# Augmented Reality in Cultural Heritage: An Overview of the Last Decade of Applications
## 3 Top Trending Applications of AR in Cultural Heritage
### Three-Dimensional Reconstruction
It can be used to identify the most appropriate restoration approach to produce a realistic replica, which reduces costs and speeds up the process. Using 3D reconstruction technologies, a digital model of the damaged object can be created [49], which is then used to generate an AR application [50] that helps to determine the best restoration approach [42]. The main drawback of AR technology is related to the lack of proper guidelines on how to use this technology. The limited experience with AR applications is another significant issue that limits the adoption of this technology in the reconstruction process.
## 1 Introduction
The seed mass is 11-18% of the date fruit mass [22] and has an oil content of 4-13% [23; 24]. These figures indicate that it is theoretically possible to extract an annual 127 kt of date-seed oil (DSO) to produce an identical quantity of DSO biodiesel, a substantial quantity relative to the total annual biodiesel production in the Middle East and Africa of 38.7 kt in 2015 [2]. To date, however, there have been no significant implementations of this byproduct stream and it is, in most cases, disposed of in landfills [25]. However, there have been some unscalable applications, including animal feed [26; 27], caffeine-free coffee [28; 29], and pharmaceutical and cosmetic products [30]. A limited number of studies [19; 30; 31] have examined date seeds as a potential feedstock for biodiesel production with a restricted focus on DSO extraction and biodiesel transesterification. Although the parameters and conditions that govern the extraction and production processes were identified and their optimum values specified, none of these studies tested the final biodiesel product in an actual automotive engine to assess the performance and tailpipe emissions. In addition, there has been no research into whether DSO biodiesel meets the entire range of specifications set out under the standards, such as ASTM D6751 and EN14214. To fill this gap in the research, in this study, biodiesel was produced from desert date palm seeds following a systematic procedure that adjusted the product specifications to match those set out under international fuel standards. The four-resultingstandard-compatible blends (B5, B10, B15, and B20) were prepared and tested in a compression engine to evaluate their tailpipe emissions of CO2, CO, HC, and NOx and to assess their relative cost viabilities relative to fossil-based diesel fuel.
## 3 Proposed Method
### Ensuring Authorization Security
#### 3.2.4 Encoding and Querying Clauses  
To satisfy as many clauses as possible, soft clauses are encoded and queried in the following steps:  
**Step 1.** For given set of soft clauses \(SC=\{sc_{1}s_{2},\ldots,sc_{n}\}\), introduce \(n\) new blocking variables \(b_{i}\)(1 \(\leq i\leq n\));  
**Step 2.** Construct new clause set \(SC^{b}=\{sc_{1}\lor b_{1},sc_{2}\lor b_{2},\ldots\,sc_{n}\lor b_{n}\}\), where clause \(sc_{i}\lor b_{i}\) is considered as \(b_{i}\) copies of \(sc_{i}\);  
**Step 3.** Search for minimal integer \(k\), such that \(SC^{b}\) is true and \(\sum\limits_{i=1}^{n}b_{i}\leq k\);  
**Step 4.** Consider \(HC\cup SC^{b}\) as parameters, take them into the QMaxSAT [39], and output all true clauses from \(SC\).  
**Example 2**.: _Given set \(P\) of permissions, set \(R\) of roles, role-hierarchy relationships \(RH\), role-permission assignment relationships PA, and set \(\mathcal{C}\) of constraints, where \(P=\{p_{0},p_{1},p_{2},p_{3},p_{4},p_{5},p_{6},p_{7}\}\), \(R=\{r_{0},r_{1},r_{2}\}\), \(RH=\{r_{0}\geq r_{2}\}\), PA = {(r_{0},p_{0}),(r_{0},p_{1}),(r_{0},p_{2}),(r_{0},p_{4}),(r_{0},p_{5}),(r_{0},p_{6}),(r_{1},p_{3}),(r_{1},p_{7}),(r_{2},p_{2}),(r_{2},p_{6})}\), and \(C=\{dmer<\{r_{0},r_{1}\},2,s\geq\}\). In the preprocessing stage, hard clauses are constructed by the transforming rules, and results are listed in Table 3._  
Suppose there are three authorization requests: (1) \(P_{lb}=\Phi\), \(P_{ub}=\{p_{2},p_{3},p_{6}\}\), \(match=max\); (2) \(P_{lb}\) = \(\{p_{2},p_{3},p_{6}\}\), \(P_{ub}=P\), \(match=min\); (3) \(P_{lb}=\{p_{2},p_{3},p_{6}\}\), \(match=exact\). Then, \(HC\) and \(SC\) are, respectively, updated in Algorithms 5 and 6; the results are listed in Table 4, and different sets of activated roles are listed in Table 5.
## 4 Bio-Phytoremediation by Plants  
### Halophyte Plants  
Phytodesalination is a plant-based method for removing excess salt from contaminated areas and reclaiming land for farming [3]. It is a low-cost, environmentally friendly way to clean salt-affected places utilizing a variety of plants, depending on the degree of the pollution [16]. Halophytes are salt-loving plants with special traits that enable them to grow in severe environments [15]. However, halophytes are a diverse group of plants that make up just 1% of the world's flora. They demonstrate habitat diversity, abiotic stress responses, and dispersion across flowering plant groups [17]. According to [PERSON] [48], plants can be classified as halophytes if they can complete their life cycle at 200 mM NaCl, and based on this, 350 species are classified as halophytes, divided into 20 orders and 256 families [49].  
There have been various studies on salt-tolerant crops [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Halophytes have been widely studied in order to obtain better knowledge of their salt adaptability from physiological, morphological, and biochemical perspectives [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]. However, the management and compartmentalization of ions, the manufacture of suitable solutes, the activation of antioxidative enzymes, the stimulation of plant hormones, and alterations in photosynthetic systems are all processes that most plants possess to mitigate the deleterious impacts of salinity [51; 52].  
Numerous stress-related genes were discovered to be constitutively transcribed at greater levels in _T. halophila_ than in _A. thaliana_ in a comparative transcriptome investigation conducted by [PERSON] et al. [51], in which they suggested an effective transcriptional regulatory pathway for salt stress response in halophytes. In addition, halophytes have a multitude of cis-regulatory components and stress-inducible motifs that were discovered in many stress-sensitive genes [53].
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 2 VMD Parameter Optimization and Wavelet Noise Reduction  
### Variational Mode Decomposition  
VMD is an adaptive and robust signal processing method specifically developed to extract inherent modal components with varying frequency and time-frequency localization characteristics from non-stationary signals. The entire VMD process can be conceptualized as the construction and solution of a constrained variational problem, as illustrated in Equation (1).  
\[\underset{\{u_{k},\omega_{k}\}}{min}\left\{\sum\limits_{k}\left\|\partial_{t} \left[\left(\delta(t)+\frac{j}{\pi t}\right)*u_{k}(t)\right]e^{-j\omega_{k}t} \right\|^{2}\right\}s.t.\underset{k}{\sum}u_{k}=f \tag{1}\]  
where \(u_{k}\) represents the Intrinsic Mode Function (IMF), \(\omega_{k}\) denotes the central frequency of the IMF, and \(f\) represents the input signal.  
The constrained variational problem described in Equation (1) is solved by introducing an augmented Lagrange multiplier \(\lambda\) and a quadratic penalty factor \(\alpha\), as illustrated in Equation (2).  
\[\begin{split}& L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\underset{k}{ \sum}\left\|\partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t}\right)*u_{k}(t) \right]e^{-j\omega_{k}t}\right\|^{2}_{2}\\ &+\left\|f(t)-\underset{k}{\sum}u_{k}(t)\right\|^{2}_{2}+\left\langle \lambda(t),f(t)-\underset{k}{\sum}u_{k}(t)\right\rangle\end{split} \tag{2}\]  
The alternating direction multiplier method is utilized to determine the values of \(u_{k}^{n+1}\), \(\omega_{k}^{n+1}\), and \(\lambda^{n+1}\), which are continuously updated throughout the process. Once certain conditions \(\underset{k}{\sum}\left\|\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\right\|^{2}_{2}/ \left\|\hat{u}_{k}^{n}\right\|^{2}_{2}<\epsilon\) are met, the iteration is halted, and \(K\) IMF components are output.  
In summary, one of the most critical parameters in the VMD algorithm is the number of decomposition layers \(K\), which is predetermined without prior knowledge of the signal under analysis. Another crucial parameter is the penalty factor \(\alpha\), which influences how noise interference is suppressed and thus requires careful selection. The primary focus of applying the VMD method is to identify the best parameter combination tailored to the specific signal being analyzed.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 3 Methodology
### Threshold Selection
#### 3.3.1 Method One  
In this method, a static constant is defined by the inspector. If the comparisonValue is less than this defined threshold, the sample would be marked as one, which means that a feature is detected; otherwise, it would be marked as zero:  
\[\text{Result}[n]=\left\{\begin{array}{rl}1,\ \text{comparisonValue}[n]&\leq \ \text{Threshold}_{\text{One}}\\ 0,\ \text{else}\end{array}\right. \tag{6}\]  
For small signals, this formula would not be accurate, since inherently, their sum would be bigger than the threshold; thus, a compensation factor, 0.001, is added to prevent outliers from being detected in the noise region. This is an acceptable level, since the defect signals smaller than 0.001 would not be detected by the analog to digital converters of the device due to their limited discretization range. If the amplitude of the signal in the temporal domain is greater than this compensation factor, the algorithm would work as normal, else it would be zero:  
\[\text{Result}^{\prime}[n]=\left\{\begin{array}{rl}\text{Result}[n],\ \text{|temporal}[n]|>\ \text{compensation}\\ 0,\ \text{else}\end{array}\right. \tag{7}\]  
The disadvantages of this method are that firstly, a user input is required, and secondly, the value is fixed for all of the features, disregarding the existing characteristics in each sample.
## 4 Experimental Environment and Preliminaries
### Datasets and Preprocessing  
The dataset used for the classification experiments is publicly available and was acquired from Kaggle ([[https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset](https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset)]([https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset](https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset)), accessed on 28 June 2023.). The data consist of 10 relevant features available in AIS data, including vessel Maritime Mobile Service Identities (MMSIs) numbers, navigational status, speed over ground, course over ground, vessel heading, width, length, and draft. These features were leveraged to determine the type of maritime vessel. Furthermore, the navigation status feature, given as a string in the original dataset, needed further processing. Originally, this string was encoded as an integer, due to the ability of the decision tree model to handle categorical features encoded as integers. However, compared to one-hot endorsing, this approach yielded less favorable outcomes. Therefore, the ship navigation status was encoded using one-hot encoding. The vessel class distributions in the classification dataset can be seen in Figure 1.  
The dataset used for the second experiment of forecasting the vessel trajectory was acquired from the public U.S. marine cadastre ([[https://marinecadastre.gov/ais/](https://marinecadastre.gov/ais/)]([https://marinecadastre.gov/ais/](https://marinecadastre.gov/ais/)), accessed on 28 June 2023.). These data on the location and properties of ships in both U.S. and international waters were gathered by the U.S. Coast Guard using a navigation safety device installed onboard, which transmits and tracks this information. The AIS data for 31.03.2022. were used for this experiment. However, due to the large number of vessels available in the dataset, a single vessel was selected for experimentation. The selected vessels were chosen due to their possession of the largest amount of available data. Nevertheless, the trajectory data needed further reprocessing before being used for training and testing.  
Figure 1: Class distribution bar chart and pie diagram.  
To prepare the trajectory dataset for further experimentation, features irrelevant for the trajectory needed to be removed. These included the ship's MMSI, vessel type, transceiver class, navigational status, length, and height. Thus, the remaining features included the latitude and longitude, speed over ground, course over ground, and heading. Due to the inconsistent nature of the transmissions, the data were resampled into 60-second intervals, and the missing values were interpolated using polynomial interpolation techniques. Finally, two additional values were derived from the vessel heading and speed over the ground. These represented the vessel velocities in the X and Y directions. The introduction of these features helped the model account for the complex relationship between the heading and trajectory and improved the prediction accuracy.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 3 Materials and Methods
### Jigsaw Transform and Cellular Automata
#### 3.2.1 Jigsaw Transform  
The Jigsaw transform is a popular scrambling technique to hide visual information in digital images. Its name is reminiscent of an image cut into pieces of different sizes that must be joined correctly to form the picture again. It is considered a nonlinear operator, which rearranges sections of an image following a random ordering [43]. The direct JST breaks a grayscale image into \(\mathcal{M}\) non-overlapping blocks of \(s_{1}\times s_{2}\) pixels, each one of which is moved to a location following a random order. In the same way as the direct JST (\(J_{\mathcal{M}}<>\)), the inverse Jigsaw transform (\(J_{\mathcal{M}}^{-1}<>\)) uses the initial order of the sections to recover the original image. The JST holds the energy of a grayscale image (\(I(x,y)\)) and is, therefore, considered a unitary transform (Equation (1)).  
\[I(x,y)=J_{\mathcal{M}}^{-1}\bigg{\langle}J_{\mathcal{M}}\bigg{\langle}I(x,y) \bigg{\rangle}\bigg{\rangle}. \tag{1}\]  
Figure 3 shows a grayscale image of \(512\times 512\) px and the corresponding results for the JST, giving \(\mathcal{M}\) non-overlapping blocks of \(64\times 64\) px for \(\mathcal{M}=64\), \(32\times 32\) px for \(\mathcal{M}=256\), and \(8\times 8\) px for \(\mathcal{M}=4096\).  
Figure 2: (a) Watermark image LOGO. (b) Image MetaData of the _Barbara_ image in plaintext.  
The set of each possible combination of the security keys used in the encryption of a digital image is called the key space. Thus, for the JST, the key space is related to the number of blocks \(\mathcal{M}\), i.e., \(K_{I}=\mathcal{M}\)!.
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
## 4 Results Analysis
### TCs Making Landfall in Hainan  
Given the focus of subsequent research on Hainan Province, this section specifically analyzes TCs that made landfall in Hainan. The genesis locations of these TCs are visualized in Figure 10. The yellow and red lines denote the 24 h and 48 h warning lines, respectively, while the blue box indicates the SCS domain, providing geographic context for classification outcomes.  
As shown in Figure 10, TCs that made landfall in Hainan and originated from the Northwest Pacific were mostly classified into Cluster 0, while those originating in the SCS were exclusively classified into Cluster 3. Only two TCs generated in the Northwest Pacific were assigned to Cluster 0. Further analysis of the clustering results and associated tracks is presented in Figure 11. Meanwhile, Figure 12 visualizes the center average trade of each cluster.  
According to Table 4, all TCs making landfall in Hainan belonged to either Cluster 0 or Cluster 3. As illustrated in Figure 11, the two TCs generated in the Northwest Pacific but classified into Cluster 0 were relatively weak before entering the SCS, where they intensified to TS strength or higher. In general, TCs that originated in the NWP and later made landfall in Hainan had longer development periods, greater intensities, and longer durations of high intensity compared with those formed within the SCS.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Category** & **Liaoning** & **Shandong** & **Jiangsu** & **Shanghai** & **Zhejiang** & **Fujian** & **Guangdong** & **Guangxi** & **Hong Kong** & **Hainan** & **Taiwan** \\ \hline Cluster 0 & 1 & 80 & 1 & 1 & 65 & 69.4 & 60.7 & 57.1 & 1 & 50 & 70.7 \\ Cluster 1 & 0 & 20 & 0 & 0 & 10 & 8.3 & 4.9 & 0 & 0 & 0 & 12.2 \\ Cluster 2 & 0 & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 0 & 0 \\ Cluster 3 & 0 & 0 & 0 & 0 & 20 & 22.2 & 34.4 & 42.9 & 0 & 50 & 17.1 \\ Total & 2 & 5 & 1 & 4 & 20 & 36 & 61 & 7 & 3 & 32 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proportion of TCs making landfall in China’s coastal provinces (%).  
Figure 9: Yearly distribution of genesis locations for landfalling TCs in each cluster.  
Figure 11: Clustering results of TCs that made landfall in Hainan.  
Figure 12: Representative central tracks of Hainan landfalling TCs. Red lines represent central tracks, and gray lines represent the tracks of other TCs in each cluster.  
Figure 10: Distribution of the locations of various TCs that made landfall in Hainan.