esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology Description
### Experimental Approach
#### 3.1.1 Damping Ratio, \(\zeta\)  
Following the procedure explained in [28], the Hilbert transform of the acceleration signal is calculated in order to obtain the envelope function of the decay. By denoting the acceleration signal as \(a\), the equation reads as:  
\[\ddot{a}(t)=\mathcal{H}(a)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{a(\tau)} {t-\tau}\quad d\tau \tag{1}\]  
where \(\ddot{a}(t)\) is an imaginary signal that is shifted \(90^{\circ}\) in the complex plane from the real signal \(a(t)\). Defining a function \(A(t)=a(t)+i\ddot{a}(t)\) which is the sum of the real and imaginary parts and considering that the envelope function is the absolute value of this sum:  
\[env(t)=|A|(t)=\sqrt{a^{2}(t)+\dot{a}^{2}(t)} \tag{2}\]  
Then, by assuming a linear decay of the signal, the envelope function is equal to:  
\[env(t)=a_{0}e^{-\zeta a_{0}t} \tag{3}\]  
where \(a_{0}\) represents the initial acceleration, \(\omega_{0}\) is \(2\pi\) times the natural frequency of the experiment and \(\zeta\) is the damping ratio. The natural frequency \(\omega_{0}\) is computed using the mean period, which is obtained as the mean over the number of cycles within the acceleration range \([a_{0},0.1\ a_{0}]\). To calculate the damping ratio \(\zeta\), a linear fitting is performed to the logarithm of the envelope signal in the same acceleration range. An example of this procedure is shown in Figure 3 for the scale \(\lambda=1/5\) reference test (water and glycerine tests at 50% and 39% fill level, respectively; \(a_{0}=10\) g). The damping ratio \(\zeta\) is calculated as the slope of the linear fitting divided by \(\omega_{0}\).  
Figure 3: Logarithm of the envelope function and performed linear fitting for water and glycerine tests at \(f=0.5\) and \(f=0.39\), respectively; \(y_{0}/H=1\) and \(\lambda=1/5\).
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 8 Discussion  
In our upcoming research, we will endeavor to explore the potential for further minimizing measurement errors and enhancing inversion outcomes by investigating diverse factors including electrode material, electrode dimensions, embedding depth, arrangement, coupling degree, and inversion methodologies.  
The measurement of irregularly shaped rock and ore specimens presents a challenge in the field of rock physics, primarily due to decreased coupling between electrodes and rock samples, as well as the inherent difficulty in accurately recording electrode positions. This challenge is particularly pronounced in laboratory-scale experiments, where errors stemming from these factors can significantly impact measurement outcomes. Addressing this issue necessitates specific requirements for both instrument hardware and algorithm design, representing a critical obstacle that our system must overcome.  
Our designed system exhibits excellent scalability. Building upon this system, we intend to conduct further research into near-surface geophysics and hydrogeophysics, among other areas.
# Learning Ad Hoc Cooperation Policies from Limited Priors via Meta-Reinforcement Learning
## 4 Method
### Overview of FEAT Algorithm  
The overall framework of the algorithm is illustrated in Figure 1. Initially, we utilize the prior knowledge of a few known teammate types to derive a Meta-RL policy, which serves as the initial policy for the ad hoc agent. The objective is to enhance the efficiency of generating a diverse policy repository. Subsequently, the agent engages in self-play learning within a series of environments with perturbed reward functions. All policies obtained during this process are collected to form our policy repository, expanding the range of known teammate types for the ad hoc agent. Next, the types within the type repository are clustered based on their similarities, facilitating the identification of the most similar one to the current teammate among a large number of types. At the online stage, the ad hoc agent searches for the most similar type in the type repository based on the observations and actions of current teammates. If a similar type is identified, the most compatible policy from the repository will be directly employed for collaboration. Otherwise, the policy will be updated by imitating teammate behaviors to accomplish the current task within a single episode.  
Figure 1: The framework diagram of the FEAT algorithm. (**a**) Acquisition of Meta-RL policy using a small number of teammate types. (**b**) Self-play generation of a diverse type repository by the Meta-RL policy in a perturbed environment. (**c**) Calculation of diversity metrics and cluster of the diverse type repository. (**d**) Online identification and adaptation of teammates during ad hoc agent cooperation.
## 4 Discussion  
### Fatty Acids Profile  
The distinctive biological properties and the impact of fats on the human body depend on the profile of fatty acids. These compounds are divided according to the type and number of bonds: saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), as well as polyunsaturated fatty acids (PUFA). The specific composition of fatty acids in triacylglycerol molecules is directly related to the type and origin of fats.  
The literature shows that the typical content of unsaturated fatty acids in tomato seed oil is around 80% [35; 36] which is similar to the results obtained in this study (81.55%). In addition, according to [PERSON] et al. [35], the main unsaturated fatty acids in tomato seed oil are linoleic, oleic, and linoleic acids, with contents of 54.52%, 22.36%, and 2.06%, respectively. These results are also comparable to the ones obtained in this study.  
According to the data reported by [PERSON] et al. [37], coconut oil is comprised of around 92% saturated fatty acids and 8% unsaturated fatty acids. These values are in agreement with those reported in this study, in which the saturated fatty acid content was 92.4%, and the unsaturated fatty acid content was 7.6%. According to the authors, the main fatty acids there were lauric (46.03%) and myristic (19.03%) acids, which also largely coincides with the data obtained in this experiment in which the content of lauric acid was 44.5% and myristic acid was 19.95%. In the research by [PERSON] et al. [38] on the fatty acid profile of cocoa oil, coconut oil, and cocoa butter, the lauric acid content in the samples was shown to be 38.4% and 20.2% for myristic acid. The value for lauric acid is lower  
Figure 5: Peroxide value of enzymatically interesterified oil blends and raw oils. Data denoted with the same lowercase letter are not significantly different (\(\alpha=0.05\) with [PERSON]’s post hoc test).  
than that obtained in this work, while the content of myristic acid is very similar to the 19.95% content obtained in this research.
## 3 Basic Design set of the Ideal Class-E PA with FDI Model  
Commonly, the analytical design equations are grouped in a design set \(K=\{K_{L},K_{C},K_{P},K_{X}\}\) that relates the specifications to the PA components [6], as shown in Figure 4. For example, if the designer fixes the values of \(\omega\) and \(R_{L}\) as specifications, the \(K_{L}\) gain can be used to find the \(L_{SH}\) value. Therefore, to find the circuit values referred to the design specifications, the designer could use the links between the PA design variables (e.g., \(V_{DD}\), \(R_{L}\), \(P_{out}\)). According to the model summarized in Section 2, the design set are analytical functions of the variables \(D\) and \(q\)[6; 7], which are  
Figure 4: Design set relations [8].  
\[K_{L}=\frac{\omega L_{SH}}{R_{L}}=\frac{p(D,q)}{2g_{x}(D,q)} \tag{7}\]  
\[K_{C}=\omega C_{SH}R_{L}=\frac{2g_{x}(D,q)}{q^{2}p(D,q)} \tag{8}\]  
\[K_{P}=\frac{P_{out}R_{L}}{V_{DD}^{2}}=2(g_{x}(D,q))^{2} \tag{9}\]  
\[K_{X}=\frac{X_{s}}{R_{L}}=\frac{\frac{1}{\pi}\int_{0}^{T}(v_{S_{W}}(t)cos( \omega t+\varphi))dt}{\frac{1}{\pi}\int_{0}^{T}(v_{S_{W}}(t)sin(\omega t+ \varphi))dt}, \tag{10}\]  
where \(X_{s}\) is a capacitive or inductive reactance that must be added to the resonant network (i.e., \(L_{o}\) and \(C_{o}\)) to guarantee the operation of the PA under ZVS and ZVDS conditions. Additionally and under small ripple approximation, the current gain (\(g_{x}\)) is given by [14]  
\[g_{x}=\frac{\langle i_{S_{W}}(t)\rangle_{2\pi}}{I_{p}}, \tag{11}\]  
where \(\langle m(t)\rangle_{2\pi}\) is the moving average operator over the period of the function \(m(t)\)[31], which is given by  
\[\langle m(t)\rangle_{2\pi}=\frac{1}{T}\int_{0}^{T}m(\tau)d\tau, \tag{12}\]  
the resulting explicit \(g_{x}(D,q)\) function was reported in [8].  
In the design set composed of (7)-(10), the designer must fix the \(D\) and \(q\) values to design the PA from its specifications. The most popular design values are \(D=0.5\) and \(q=1.412\) because a duty cycle of 0.5 reduces the design complexity of the oscillator that controls the switch, and \(q=1.412\), for a fixed \(V_{DD}\) and \(R_{L}\), maximizes the load power [6].  
Using this PA model, the designer can explore the design space defined by the \(D\) and \(q\) variables. The design space of \(q\) was analyzed in [9] with a practical range between 0 and 1.9 for \(D=0.5\). This range allows designing the Class-E PA with lower supply voltages, low complexity impedance matching network of the load, and commercial components, among others. Moreover, the design space of \(D\) was studied in [32; 33] with a practical range between 0.25 and 0.75 because \(D\) values lower than 0.25 and higher than 0.75 can produce high currents and voltages across the switch [33].
## 4 Experiment Design and Comparative Analysis
### VMD Decomposition Results  
In order to enhance the accuracy and stability of the wind power prediction model, it is necessary to solve the issues of significant fluctuations of the original wind power sequence and the large mutual interference of signals at different frequencies. This paper employs VMD decomposition to break down the original sequence into multiple sub-series with different frequencies so that the signals in each sub-series have higher purity and less interference. This approach effectively reduces the noise and interference in the original sequence, thereby simplifying the power sequence. However, selecting the number of decompositions, K, is crucial to the VMD decomposition. A value of K that is too large will generate redundant modes and cause modal mixing. At the same time, a value of K that is too small will lead to modal under-decomposition and reduce the model's prediction accuracy. The central frequency method is based on spectral analysis in signal processing, and the central idea is to determine the decomposition structure of a signal based on its frequency domain characteristics. In the spectrum of a signal, each peak represents a frequency component in the signal, so detecting the peak in the spectrum can determine the number of frequency components with significant energy in the signal. Based on this idea, the central frequency method uses the spectral information of the signal to determine the K value of the VMD decomposition. This article adopts the central frequency method to determine the size of K. The value of \(\alpha\) in VMD can also have an impact on the decomposition. The larger the value of \(\alpha\), the smoother the decomposition result, but it may cause aliasing and information loss between patterns. The smaller the value of \(\alpha\), the closer the decomposition result is to the original signal, but it may cause the decomposition mode to be unsmooth with noise and oscillation. This article uses a grid search to determine the value of \(\alpha\) as 2200.  
Figure 7: The heatmap of the correlation coefficients of input variables.  
\[\text{R}^{2}=1-\frac{\sum\limits_{t=1}^{N}{(\hat{x}_{t}-x_{t})^{2}}}{\sum\limits_ {t=1}^{N}{(\overline{x}_{t}-x_{t})^{2}}} \tag{23}\]  
where \(N\) is the number of prediction series, \(\hat{x}_{t}\) and \(x_{t}\) are the predicted and true values of wind power, respectively, and \(\overline{x}_{t}\) is the average true value of power.
## 5 Discussion  
The simulation and experimental results demonstrate that compared to traditional algorithms, this algorithm improves the effectiveness of anti-jamming threshold settings under conditions of narrow bandwidth and high power. In LEO satellite anti-jamming scenarios, the algorithm significantly improves the frequency domain anti-jamming performance, highlighting its value. However, the SCCI algorithm is essentially a frequency domain anti-jamming algorithm, which cannot effectively handle wideband or complex forms of jamming signals. This limits its further application in the field of UAV navigation. The following aspects are worth further research:  
1. Using synthetic aperture technology, the moving UAV's single antenna is combined with the UAV's motion equation or the formation motion equation of multiple UAVs (which can be regarded as a one-dimensional array) to generate new spatial information. This synthesizes a single-point single antenna into a virtual antenna array, forming a pseudo-spatial anti-jamming technology that can effectively suppress complex interference situations caused by broadband or multiple jamming sources.
2. Establishing a background jamming signal type database and combining artificial intelligence for automatic detection and recognition, reconstructing jamming signals in the background, and then canceling them with the received signals to suppress interference can effectively suppress complex interference or multiple jamming sources. In ideal conditions, this can even achieve zero loss of useful signals.
# Detection of Malicious Websites Using Symbolic Classifier
## 2 Materials and Methods
### Dataset Description and Preparation
#### 2.2.1 Dataset Transformation  
To obtain the dataset which could be used in GP symbolic classifier the data must be in numeric format. However, the initial dataset consists of numbers and strings. To transform the dataset into numeric format following modifications were made:  
* The url variable was omitted from further analysis instead url_len is used which is the length of each url;
* The ip_add was replaced with net_type variable that is created as a classification process of IP addresses to classes A, B, and C, and later transformed into values 0, 1, and 2;
* The geo_location is transformed into numeric format using ISO 3166-1 numeric code format [26];
* The tId was transformed from string to number format using LabelEncoder [27]. The LabelEncoder encodes the labels with values between 0 and n_classes \(-\) 1. In this case, the tId variable has 1247 different types of tId-s, i.e., the range of possible numeric format values are 0-1246.
* The who_is was transformed from complete/incomplete to binary values 0 and 1.
* The initial https column values \"yes\" and \"no\" were transformed into binary values 1 and 0.
* The js_len represents the total length of JavaScript code embedded in HTML code of a website.
* The js_len and js_obf_len variables are already in numeric format and will remain unchanged
* The content variable will be used to develop two additional variables, i.e., content_len and special_char. The content_len is the length of the content variable value. The special_char represents the number of special characters in a string.
* Labels (output of symbolic classifier) were replaced with 1 and 0; 1 for a malicious website and 0 for a benign website.  
The geo_location was initially a string (name of the country) which was later transformed into a numeric value using ISO 3166-1 country codes in numeric format. According to [28], the IP address can be categorized into one of three classes, A, B, and C, which will be eventually transformed into 0, 1, and 2, respectively. Each class of IP address has its range, network address, and host address. It should be noted that each byte of the IP address is represented in xxx form. The range of IP address is specified by decimal values range of the first byte of the network number. The number of bytes of the IP address dedicated to the network part of the address is labeled as a network address. The number of bytes dedicated to the host part of the address is labeled as the host address. The division of IP Address Spaces is shown in Table 3.
## 3 Preliminary Knowledge  
### Payment Channel Network  
To overcome this scalability issue, the PCN proposed by [PERSON] et al. [13] can take most of the transactions off-chain. Users only need to enter the initial and final balances for each channel during the transaction. The typical PCN transaction is shown in Figure 1. In PCN, a set quantity of electronic money is deposited into a shared account by both parties to the transaction. Through several hops, the sender can transfer money to the recipient. The intermediary node in the payment channel path will assess the transfer fee when the payment travels through its payment channel. The blockchain is updated with the details of both parties' balances when the transaction is completed [19].  
Figure 2 depicts a particular payment procedure. In the hash time lock contract (HTLC), the maximum level of user tolerance for transaction time is specified. Hash lock and time lock make up HTLC. The sender creates a random number R and receives its hash value H through the hash lock from the receiver. The hash value H in the transaction contract is stored in the sender or each intermediate node. The transfer fund can only be withdrawn after the forwarder has given the secret number R, which guarantees that the payment receipt cannot be disputed. Each transaction under time locking has a deadline for completion. The sent monies will be returned to the sender if they do not obtain the random number R within the allotted time.  
Figure 1: An example of off-chain payment channel transaction.  
Figure 2: A demonstration of payment channel capacity. [PERSON] sends 2 bitcoin to [PERSON] through 3 nodes.
# Sigma Routing Metric for RPL Protocol
## 5 Design and Simulation of the Network
### Parameterization of Scenarios  
In this section, each experimental test that was performed with the Cooja simulator is briefly described in order to evaluate the multi-hop wireless sensor network. First, tests were performed to evaluate the losses caused in the transmission and later the latency experienced by the shipments of packets in the network. Following this, different scenarios were simulated to evaluate the efficiency of the multi-hop wireless sensor network; the results are discussed.  
In order to comply with the above, the simulations were implemented in an experimental environment, with different number of nodes (20, 40, 50 and 100) as scenarios, which were deployed in a 500 m \(\times\) 500 m zone, with a number of 200 packets delivered, and transmission range of 150 m.  
In scenario I, the network was simulated with 20 nodes using the OF0, MRHOF, PH-ETX and SIGMA-ETX metrics, all of them with the same configuration (see Figure 7). Figure 8 shows scenario II with 40 nodes; scenario III with 50 nodes and scenario IV with 100 nodes are presented in Figures 9 and 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Area & 500 m \(\times\) 500 m \\ Boot delay per node & 1 gg \\ Number of nodes & 20, 40, 50 and 100 \\ TX Range & 150 m \\ Network Protocol & RPL \\ Standardized Objective Functions & OF0, MRHOF \\ Objective Functions proposed by other authors & PER HOP ETX \\ Standard for PHY and MAC layer & IEEE 802.15.4 \\ Transmission Mode & Storing Mode \\ Topology & Random \\ Number of packets & 200 \\ Model of radius & UDG \\ ContikiOS Note Type & Cooja Mote \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of simulation parameters.  
Figure 7: Scenario I with 20 nodes.  
Figure 8: Scenario II with 40 nodes.  
Figure 10: Scenario IV with 100 nodes.  
Figure 9: Scenario III with 50 nodes.
## 2 Study Sites and Methods
### Screening Method for Macro-Litter on the Bottom of Marinas  
For the sea-bottom screening a fast mobile system was developed, tested and improved in several iteration cycles. An underwater camera and an underwater light were installed on a 3 m carbon telescope-stick. The camera was connected to a smartphone via a coax cable. The smartphone App GoPro Quick, GoPro, Inc., San Mateo, CA, USA was used to control the underwater camera and to transfer, store and view the photos and videos on the smartphone above the water. During each sampling start- and end-time, cloud cover, wind speed [m/s], estimated wave height [m], water depth [m], water transparency/Secchi depth [m] camera depth and spatial coverage of the underwater photos were protocelled. The system was applied from bridges, catwalks, quay walls and piers. Depending on the structure and size of the marina, between 2 and more than 20 different spots in a marina were investigated, and the surveyed area ranged from 7-8 m\({}^{2}\) in Montazah and Ludewigsbecken to 756 m\({}^{2}\) in Hohe Dune, Rostock. A total of 1938 m\({}^{2}\) of harbor bottom was sampled in Germany and 596 m\({}^{2}\) in North Africa. Data collection took place between March and July 2022 [36].  
Before the sea-floor pictures were taken, the suitable height of the camera above the sea bottom was determined. The acceptable distances between camera and sea floor were between 0.25 m and 1.6 m and depended on water transparency (Secchi depth) and water depth. On average, the distance was about 40% of the Secchi depth and about 35% of the water depth. The present system was suitable for a maximum water depth up to 4 m, which was sufficient for all harbors.  
The underwater photos were analyzed visually, with bare-eye observation of the photos on a large computer monitor, according to [PERSON] et al. [37]. The identified litter items were documented and categorized according to the OSPAR (Convention for the Protection of the Marine Environment of the North-East Atlantic) guidelines for monitoring marine litter on beaches [38]. A video-based analysis was tested but not applied for field-data collection.  
Figure 1: General overview of the sample sites in Tunisia, Egypt and Germany as well as all North African harbors including name, investigated sea-bottom area (m\({}^{2}\)) and concrete sampling locations within the harbors (numbered red areas). Satellite images by GoogleMaps.  
The total costs for equipment were 666 EUR, including the underwater camera (GoPro Hero 8, GoPro, Inc., San Mateo, CA, USA, 225 EUR) and the smartphone (Huawei P8 Lite, Shenzhen, Guangdong, China,167 EUR), an additional underwater light (60 EUR) and the telescope stick (60 EUR). The time required to cover one square meter of harbor bottom ranged from 0.3 to 10 min (median about 1 min), depending on the degree of pollution and water transparency (Figure 3). The total time for analyzing the photos was about 50 h or 1 min/m\({}^{2}\) sea floor.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 4 Simulation Experiment and Analysis
### Analysis of Complexity  
As can be seen from Algorithm 1, the GA requires several iterations of computation under the condition that the population size is \(NP\), so the time complexity of it is approximated as \(O(n^{3})\) and the space complexity is approximated as \(O(n^{2})\). Compared with GA, GA-AS differs mainly in its initial population generation method, which is shown in Algorithm 2, so its time and space complexity can be approximated as \(O(n^{3})\) and \(O(n^{2})\), respectively. Since the complexity of the improved A* strategy is smaller than that of the GA, the time and space complexity of GA-AS remains the same as the GA.  
The running times of GA and GA-AS with the same computer configuration are shown in Table 3. As can be seen from Table 3, the running time of GA-AS is almost always larger than that of GA for the same population size, and this extra time can be considered as the running time of the improved A* strategy.