esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Research Directions
### Ship Megaconstellations and Multi-Hop Connectivity  
Figure 13 shows a concept to be studied further in the future, namely ship mega-constellation. This is related to satellite mega-constellations [61] that aim to provide global connectivity, with hundreds of interconnected satellites. Ships could create a mobile ad hoc network with multi-hop features among hundreds of ships in order to communicate using high-data-rate solutions such as 5G or 6G radios. This would also provide resiliency to data transfer. As an example, ship A in the figure could be connected to the remote operations center using the route through ships D and N. The use of multi-hop networks, the selection of suitable radio interfaces and their capacity, coverage, and latency characteristics should be studied in order to understand their feasibility for autonomous shipping and to any use case defined in Figure 1.  
Figure 12: Future maritime connections.
# Candidate Waveforms for ARoF in Beyond 5G
## 5 Comparison of Candidate Waveforms
On the other hand, and for the case of multi-carrier waveforms, FBMC is the best solution since the much better frequency-domain localization for the transmit filter than any other multi-carrier waveform. Therefore, the ICI can be efficiently removed for each subcarrier [17]. * OOB emissions: Because of their configuration, the OOB emissions of multi-carrier waveforms are much lower than those of single-carrier waveforms. At this point, OOB emissions can be reduced via filtering or pulse shaping. The characteristic of these techniques strongly influence the final OOB emissions. FBMC provides the lowest OOB emissions due to its filtering by subcarrier. * Efficient MIMO integration: This factor has a strong relationship with the channel equalization implemented in the system. In addition, it is an indicative of the complexity of MIMO systems. All pure multi-carrier waveforms present highly efficient MIMO integration because they do not use complex channel estimation. Specifically, they use frequency-domain channel estimation through equally spaced pilots. FBMC and GFDM are exceptions in this case, as they require more complex channel equalization. Namely, FBMC needs to eliminate the imaginary interference in each scattered pilot [54], while GFDM requires a channel estimation in each subsymbol. On the other hand, single-carrier waveforms need more complex channel estimation to compensate. A popular estimation technique for this type of waveform is the adaptive decision feedback equalizer (DFE). It is a complex estimation and that is why the single-carrier waveforms are less efficient in terms of MIMO integration. * Enable asynchronous multiple access: Pure multi-carrier waveforms do not allow to implement asynchronous multiple access in the system. This is due to the use of slots distributed in frequency, not in time. Consequently, this was one of the main reasons why SC-FDM was selected to be the waveform in the uplink for LTE. * HW complexity: To implement the waveform in a field-programmable gate array (FPGA), the complexity of the system will determine the needed number of slice registers, look-up tables (LUTs) and random access memory (RAM) blocks [27]. According to Table 1, the single-carrier waveforms present a lower HW complexity as they need a smaller number of operations in order to process the transmitted and received signal. On the other side, GFDM, UFMC, and FBMC are the most complex waveforms due to the additional procedures that they add. Observing Table 2, we can conclude that it is difficult to decide which waveform can be more suitable as they offer different advantages and disadvantages. In fact, any of those waveforms are well-appropriated for a particular type of service or channel due to their capacity to adapt to a subgroup of specific requirements. Indeed, according to IMT-2020, there are three foreseen categories of traffic types in 5G and beyond. These have different characteristics and use cases [55]: enhanced mobile broadband (eMBB) mainly requires high bit rate, massive machine-type communications (mMTC) will
## 3 Public Transport-Assisted Data-Dissemination System
### Multi-Attribute Decision Making
#### 3.1.2 Pre-MADM
All the utility functions fulfill the conditions of being is twice differentiable, monotonic, and concave-convex, as shown in Figure 6.  
Figure 5: The utility function for energy efficiency. * Utility function for Delay Tolerance: Generally, incremental latency values are acceptable in a Delay-Tolerant Networks (DTN). While designing the utility function for network delay tolerance, a larger network delay value will result in a lower utility value. It is a decreasing criterion to measure network delay. Delay varies in both networks as per the data volume. u(d) is defined as a utility function for the delay, as below: \[u(d)=1-u^{\prime}(d)\] (4) \[u^{\prime}(d)= \begin{cases}\frac{(\frac{d}{d_{max}})^{x3}}{1+( \frac{d}{d_{max}})^{x3}}&;d\leq d_{min}\leq d_{med}\\ 1-\frac{(\frac{d_{max}-d}{d_{max}-d})^{x3}}{1+(\frac{d_{max}-d}{d_{max}-d})^{ x3}}&;d_{med}\leq d\leq d_{max}\\ 1&;d>d_{max},\end{cases} \] (5) where \(d_{max}\) is the maximum delay and \(x\) is the sensitivity factor for delay calculation among both networks. The delay utility function is shown in Figure 7.  
Figure 6: The utility function for Bandwidth. * Utility function for the Delivery Probability: Delivery probability is to be defined as the volume of data to be sent using any of the networks. We defined the utility function of delivery probability as \(u(dp)\), where \(dpe[0,1]\), in case of successful delivery, is 1, and otherwise, for packet loss, it will be considered as 0. Otherwise, it lies between 0 and 1. \(dp\) is the delivery probability obtained and \(dp_{max}\) is the maximum delivery probability that is acceptable to the user, and is shown in Figure 8. \[u(dp)= \begin{cases}\frac{dp}{dp_{max}}&;0\leq dp\leq dp_{max}\\ 1&;dp>dp_{max}\end{cases} \] (6)  
Figure 8: The utility function for delivery probability. Figure 7: The utility function for delay tolerance.
## 4 Results
### MDPS Algorithm  
The SDPS algorithm is especially effective in heterogeneous, irregular spaces, where the property \"density against the background\" is strongly pronounced. If it is weakly expressed, there may be disadvantages in the work of SDPS caused by the density \(S\).  
**Example 7**.:
1. _Suppose that_ \(X\) _is a uniform finite grid. The nodes at the edge_ \(X\) _have a lower density_ \(S\) _than the central nodes, although space_ \(X\) _looks equally homogenous in both cases._
2. _If in a full-sphere_ \(D_{A}(x,r)\) _all points other than_ \(x\)_, are concentrated on the circle_ \(C_{A}(x,r)\) _and there are many of them, then the density_ \(S_{A}(x)\) _is significant, regardless ther-isolation_ \(x\)_._  
Another construct of the density \(M\), which is also expressing the concentration of space \(X\) at the point \(x\) does not have such disadvantages, It is called solidity and is part of the main DMA-clustering algorithm with the correspondent name [21]. Let us talk about it.  
Fix natural number \(m\in\mathbb{N}\) and construct a uniform grid of nodes \(r_{i}=\frac{ir}{m},i=0,\ldots,m\) in the interval \([0,r]\). Then, we define a concentric in \(x\) semi-open ring \(T_{i}(x,A)\) for each \(i\
eq 0\):  
\[T_{i}(x,A)=\{y\in A:r_{i-1}<d(x,y)\leq r_{i}\}\]  
For each \(T_{i}(x,A)\), we assign the relevant weight \(\psi_{i}:1\geq\psi_{1}\geq\ldots\geq\psi_{m}>0\). Solidity \(M_{A}(x)\) is defined as the ratio of the sum of the weights of non-empty rings to the sum of the weights of all rings:  
\[M_{A}(x)=\frac{\sum_{T_{i}(x)\
eq 0}\psi_{i}}{\sum_{i=1}^{m}\psi_{i}}\]  
The solidity \(M\) is r-local, and the MDPS algorithm is the implementation of the \(M\)-based DPS scheme described in Definition 6-Section 3.3.3: MDPS \(=\) DPS\((M,r,\beta)\).  
**Remark 6**.: _Constructs \(S\) and \(M\) express the density of \(X\) in \(x\) in a different way, and this difference is shown in their names: construct \(S\) is focused on the \"quantity\" \(D_{A}(x,r)\), is concentrated around \(x\), while construct \(M\) is focused on \"uniformity\" \(D_{A}(x,r)\), around \(x\), expressed through the presence in rings \(T_{i}(x)\)._  
**Example 8**.: _The dumb-bell shaped in Figure 10a has a monolithic but weak handle, so MDPS highlights it cleanly (Figure 10c), while SDPS cannot do so (Figure 10b). This example illustrates the independence of the MDPS and SDPS algorithms._  
Figure 9: Illustration from left to right of the quality of the SDPS algorithm at \(\beta=-0.3\); 0.1; 0.3. Clustering in figure (**a**) is clearly better, and in figures (**b,c**) is approximately the same. (As a fuzzy comparison, we used \(n(a,b)=(b-a)/(b+1)\).
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods
### Synthetic Data Generation
#### 3.2.3 Addition of Trends  
Linear, quadratic, or cubic tendency was added to the synthesized signal in order to reproduce fluctuations that naturally appear in real PPG signals. Here, \(\mu\) (Equation (2)) corresponds to a linear vector instead of a scalar value. The slope (curve parameters) was randomly selected with a uniform distribution. The results of this procedure are illustrated in Figure 5 for the case of a linear tendency. Figure 5a shows an excerpt of a raw PPG signal (taken from subject #1, UBFC-RPPG dataset). Figure 5b depicts a simulated signal (dotted blue line) with its associated trend (dashed crimson line). The resulting signal is presented in a solid black line.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Coefficient** & **Value** \\ \hline \(a0\) & 0.4402 \\ \(a1\) & \(-\)0.3345 \\ \(b1\) & \(-\)0.1990 \\ \(a2\) & \(-\)0.0502 \\ \(b2\) & 0.0993 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fourier series coefficients computed after data fitting.  
Figure 5: Comparison between signals produced by the synthetic generator and a real PPG signal. **(a)** Excerpt of subject #1 raw PPG signal taken from the UBFC-RPPG dataset. Pixels from the forehead area (green channel) have been spatially averaged to compute the signal. **(b)** Dotted blue line: simulated PPG signal (output of Figure 2b). Dashed crimson line: linear trend. Solid black line: signal combined with the trend. **(c,d)** Signals outputted by the generator with two different noise factors. They were computed using a spatial averaging operation over all the pixels of the synthesized frames. The produced signals are pretty similar to the real PPG signal presented in figure a.
# COX2-Inhibitory and Cytotoxic Activities of Phytoconstituents of _Matricaria chamomilla_ L.
## 3 Results
### Cytotoxicity Assay  
To examine the activity of chamomile extract against cancer cell lines, we have conducted growth inhibition assays. The extract revealed cytotoxicity against different cancer types. The IC\({}_{50}\) values of the two hematopoietic cell lines CCRF-CEM and AMO1 (23.7 \(\pm\) 4.7 \(\mathrm{\SIUnitSymbolMicro g}\)/mL to 28.0 \(\pm\) 2.2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) were lower than those of the colon cancer cell lines. Wildtype HCT116 p53\({}^{+/+}\) had an IC\({}_{50}\) value of 28.0 \(\pm\) 2.2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, and HCT116 p53\({}^{-/-}\) knockout cells had an IC\({}_{50}\) value of 74.3 \(\pm\) 0.4 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Hence, the p53 knockout cells were 2.65-fold more resistant to the chamomile extract than the p53 wild-type cells (Figure 2).  
Figure 1: Typical LC–DAD chromatogram (absorbance vs. retention time) of a tea sample (_Matricaria chamomilla_ L.) at 254 nm (**A**), 280 nm (**B**) and 330 nm (**C**): chlorogenic acid (**1**), _cis_-GMCA [(_Z_)-2-\(\beta\)-D-glucopyranosyloxy-4-methoxycinnamic acid] (**2**), quercetin-7-O-\(\beta\)-D-glucopyranoside (**3**), _trans_-GMCA [(_E_)-2-\(\beta\)-D-glucopyranosyloxy-4-methoxycinnamic acid] (**4**), luteolin-7-O-\(\beta\)-D-glucose (**5**), apigenin-7-O-\(\beta\)-D-glucose (**6**), apigenin 7-O-(6′′-O-acetyl-\(\beta\)-D-glucopyranoside (**7**), apigenin (**8**), and isomers of tonghaosu [2-(2′,4′-hexadiynylidene)-1,6-dioxaspiro [4,4]-non-3-ene] (**9**–10**), esculin (**11**), isoquercitrin (**12**), loligolide/isololololide (**13**–14**), quercetin (**15**), 3,5-di-caffeoylquinic acid (**16**–18**), isorhametin 3-O-\(\beta\)-D-glucopyranoside (**19**), quercetageetin 4′-methyl ether 7-(6-(_E_)-caffeoylglucoside) (**20**), glucuronolactone (**21**), and myricetin (**22**). All compounds were assigned based on mass spectrometry.
## 2 Basic Assumptions and Mechanical Models
### Basic Mechanical Equations  
The plane strain mechanical model shown in Figure 3 is used to calculate the stress and deformation of the frozen wall under nonuniform loading. The basic equations of elastic mechanics used in solving this model are well known. In particular, the basic mechanical equations in polar coordinates given in the classical literature [14; 35] are discussed here.  
First, the equilibrium differential equation for the plane strain mechanical model can be expressed as follows:  
\[\begin{cases}\frac{\partial\sigma_{r}}{\partial r}+\frac{1}{r}\frac{\partial \tau_{r\theta}}{\partial\theta}+\frac{\sigma_{r}-\sigma_{\theta}}{r}=0\\ \frac{\partial\tau_{r\theta}}{\partial r}+\frac{1}{r}\frac{\partial\sigma_{ \theta}}{\partial\theta}+\frac{2\tau_{r\theta}}{r}=0\end{cases} \tag{3}\]  
where \(\sigma_{r}\) and \(\sigma_{\theta}\) represent the normal stress components in the radial and hoop directions, respectively, while \(\tau_{r\theta}\) represents the shear stress component.  
Second, the geometric equation for the plane strain mechanical model can be expressed as follows:  
\[\begin{cases}\begin{array}{c}\varepsilon_{r}=\frac{\partial u}{\partial r} \\ \varepsilon_{\theta}=\frac{1}{r}\frac{\partial\tau}{\partial\theta}+\frac{u}{ \partial r}\\ \gamma_{r\theta}=\frac{1}{r}\frac{\partial u}{\partial\theta}+\frac{3u}{ \partial r}-\frac{v}{r}\end{array}\end{cases} \tag{4}\]  
where \(u\) and \(v\) represent the displacement components in the radial and hoop directions, respectively. In addition, \(\varepsilon_{r}\) and \(\varepsilon_{\theta}\) represent the normal strain components in the radial and hoop directions, respectively, while \(\gamma_{r\theta}\) represents the shear strain component.  
Third, the physical equation for the plane strain mechanical model can be expressed as follows:  
\[\begin{cases}\begin{array}{c}\varepsilon_{r}=\frac{1+v}{\varepsilon}[(1-v) \sigma_{r}-\
u\sigma_{\theta}]\\ \varepsilon_{\theta}=\frac{1+v}{\varepsilon}[(1-v)\sigma_{\theta}-\
u\sigma_{ r}]\\ \gamma_{r\theta}=\frac{2(1+v)}{\varepsilon}\tau_{r\theta}\end{array}\end{cases} \tag{5}\]  
where \(E\) and \(\
u\) are the elastic modulus and Poisson's ratio of the stratigraphic material, respectively.  
The above basic mechanical equations are applicable to both unfrozen soil as well as frozen wall.
## 4 Experimental Validation
### Classifier Selection  
The selection of classifiers for our analysis was based on several factors, including the size of the dataset, the time taken for model training, and the shape of the data. After initial analysis, we observed that the features were overlapping, which required a careful selection of classifiers. We aimed to use simple, lightweight, effective, quick-to-converge, and accurate classifiers for our analysis. Therefore, we chose several state-of-the-art machine learning classifiers for our analysis, including Gaussian Naive Bayes (GN), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Multilayer Perceptron (MLP), Local Discriminant Analysis (LD), Logistic Regression (LR) and Random Forest (RF). These classifiers were implemented in python using the scikit ([[https://scikit-learn.org/stable/accessed](https://scikit-learn.org/stable/accessed)]([https://scikit-learn.org/stable/accessed](https://scikit-learn.org/stable/accessed)) on 15 February 2023) learn library. We applied these chosen classifiers in their default settings in both the malware detection and categorization scenarios.
## 2 Theoretical Background
### Elbow Method  
The elbow method is a classical heuristic approach for determining the optimal number of clusters \(K\) in clustering problems [24]. The core idea is to compute the within-cluster sum of squared errors (SSE) for various values of \(K\). As the number of clusters increases, data points within each cluster become more tightly grouped, leading to a gradual decrease in clustering error. However, after a certain critical point, the reduction in error begins to level off, indicating diminishing marginal returns. This critical point, where the decrease in SSE significantly slows, is referred to as the \"elbow\", representing a balance between clustering performance and computational complexity. The within-cluster SSE [25] is calculated as follows:  
\[J_{k}=\sum_{i\in\mathcal{C}_{k}}|p_{i}-p_{k}|^{2} \tag{2}\]  
\[J=\sum_{k=1}^{K}\sum_{i\in\mathcal{C}_{k}}|p_{i}-u_{k}|^{2} \tag{3}\]  
where \(K\) denotes the number of clusters, \(J\) is the total sum of squared errors when the data are partitioned into \(K\) clusters, and \(J_{k}\) represents the SSE for the \(k\)-th cluster. \(C_{k}\) denotes the set of data points in the k-th cluster, \(p_{i}\) is the position vector of the i-th point in the cluster, and \(p_{k}\) is the position of the cluster center. Unlike other clustering algorithms, hierarchical clustering does not explicitly define the centroid of a cluster; instead, it adopts a single representative data point (medoid) as the cluster center.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 3 Topic Overview
### Understanding Jamming Attacks  
Jamming attacks are a type of Denial of Service (DoS) attack that aims at disrupting the physical communication between two or more devices. This is achieved by transmitting a signal on the same frequency as the one used by the devices to communicate. If the jamming signal power level is high enough, it is able to overwhelm the legitimate signal, effectively blocking the communication between the devices [19]. Since the ability to communicate is affected, jamming attacks falls under the umbrella of attacks that target the _availability_ of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Data Source** & **Method** & **Metrics** \\ \hline
[9] & Simulation & Random Forest & BPR, PDR, RSS, CCA \\
[12] & Real data & CNN & RSS \\
[9] & Simulation & Linear SVM & BPR, PDR, RSS, CCA \\
[15] & Real data & DT + IF & PDR, BPR, SNR, RSSI \\
[11] & Real + Simulated data & DT + MLP & Throughput, PDR, \\  & & & RSSI \\ Our solution & Real data & IF & RSS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing approaches for jamming detection in the scientific literature.  
the service in the CIA triad [20]. Jamming attacks can be classified into five main categories, based on the attack pattern of the jammer:  
* **Constant Jamming:** The jammer continuously transmits a strong signal on the same frequency used by the devices it wants to disrupt to communicate.
* **Periodic Jamming:** The jammer transmits a strong signal for a certain period of time \(t_{on}\), then stops transmitting for another period of time \(t_{off}\). This cycle is repeated until the attack ends.
* **Random Jamming:** In random jamming, the jammer is active at random intervals, jamming each transmitted packet with a probability \(p\) based on a random pattern [5].
* **Reactive Jamming:** A reactive jammer starts transmitting its jamming signal only when it senses energy in the communication channel, indicating that a legitimate transmission is taking place. This type of jamming attack is more power-efficient compared to other attack patterns, as it only transmits its signal when it knows that it can actually disrupt the communication [21].
* **Smart Jamming:** A smart jammer is a more advanced and computationally intensive type of jammer that is able to adapt its jamming signal to maximize the disruption of the communication between the devices. Smart jammers employ a traffic analysis module, meaning they are able to modify their attack pattern based on the transmission specifics of the devices they are targeting and adapt to changes in the communication channel [22].
## 4 Review: Slant Links Rain Fade Models
### Fade Slope Model
#### 4.4.1 Japan Model  
In the Japan model [96], signal-level variations on Ku-band low-elevation Earth-space paths are examined. The significant signal fades due to rain, and tropospheric scintillations, sometimes coincide [132]. If the effect of scintillation becomes dominant, it needs to filter out, especially for low-margin systems operating at low elevation angles. To obtain the low-frequency components (Xlow), a moving average filter (MAF) was used (53) to filter out the high-frequency components (Xhigh) that results from scintillation effects, according to the Equation (54):  
\[X_{\text{low}}\left(t\right)=\tfrac{1}{M}\sum_{j=-m}^{m}X(t+j\Delta t) \tag{53}\]  
where \(M\) is equal to \(2m+1\) and \(\Delta\) is equal to 1 s.  
\[X_{\text{high}}\left(t\right)=X(t)-X_{\text{low}}\left(t\right) \tag{54}\]  
The detailed steps of the Japan model are given in Algorithm 19.  
```
/* Choose proper sampling time to avoid aliasing effect on X(\(t\)) */
1 Find Xlow /* Apply Equation (53) */
2 Find Xhigh /* Apply Equation (54) */
3 Apply FFT /* Choose number of FFT points for better visualization */
4 Generate time domain diagram /* To observe the fluctuations */
```  
**Algorithm 19**Japan Model [96]  
Advantages  
It provides a mechanism to identify the attenuation due to rain and the scintillation effect. The results show that rain attenuation and signal fade could be treated independently.  
Limitations  
The technique has been tested only at low elevation angle Earth stations and for the Ku-band only.