esa-sceva/satcom-chunk-collection huggingface dataset:
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 5 Implementation and Evaluation
### Sliding Window  
In order to analyze the impact of the statistical window on the detection performance, we have counted the experimental data of the sliding window with 40, 60 and 100 packets under the attack ratio of 0.025. Figure 5 show the comparison of RP, FA, and DR of the detection algorithms under different windows respectively. With the increase of the sliding window, the true positive rate of each algorithm is increasing and the false positive rate is decreasing; the false negative rate of all algorithms is decreasing. It can be seen from the contrast diagram that the performance of the HMM-R algorithm is very good, but only slightly worse on the false negative. This is because as the window increases, a large amount of normal traffic is mixed in the L-DDoS traffic, making the entropy difference between the attack traffic and the normal traffic very small.  
Figure 6 show the comparison of RP, FA, and DR of each order of the HMM-R under different windows respectively. It can be seen from the graph that the best performance of HMM-R at the attack ratio of 0.025 is obtained when the sliding window is 60 and the order of statistical attributes is at \(\alpha=8\). The different sizes of windows and the order of statistical attributes will lead to different performance of HMM-R. Choosing the appropriate detection window and order has a significant impact on detection performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack Ratio** & **RP** & **FA** & **DR** \\ \hline
0.01 & 0.9461 & 0.0181 & 0.0800 \\
0.02 & 0.9692 & 0.0 & 0.0533 \\
1 & 0.9711 & 0.0 & 0.0500 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of HMM-R at different attack ratios (\(\alpha=1\)).  
Figure 5: Comparison diagram of performance Indices of algorithms at attack ratio of 0.025. (**a**) Comparison of RP of algorithms; (**b**) comparison of FA of algorithms; (**c**) comparison of DR of algorithms.
## 3 Results
The mean arterial pressures were similar in the healthy and DCM cardiovascular system models and under HVAD, HM2 and HM3 support. This was because systemic arteriolar resistance was adjusted in the healthy, DCM and heart-pump-supported cardiovascular system models, in consideration of the physiological systemic arteriolar resistance regulation mechanism. The aortic pulse pressure was 43 mmHg in the healthy cardiovascular system model; however, it decreased to 31 mmHg in the DCM model. Aortic pulse pressure decreased further under heart-pump support, whereas it reduced to 6 mmHg under HM3 support. However, HM3 artificial pulse increased aortic pulse pressure to 20 mmHg. The HVAD Lavare cycle also increased aortic pulse pressure by 4 mmHg. Both the HVAD Lavare cycle and HM3 artificial pulse increased left-ventricular end-diastolic volume and diameter. All the devices generated mean pump outputs which were similar to the cardiac output in the healthy cardiovascular system model. Presented in Figure 6 are the static and dynamic continuous-flow VAD pressure head--flow rate curves for the HVAD, HM2 and HM3 devices, at operating speeds of 2600 rpm, 8700 rpm and 5200 rpm, respectively. The steep static H-Q curve at 5200 rpm for the HM3 device resulted in a relatively small dynamic H-Q loop. On the other hand, the flat H-Q curve at 8700 rpm for the HM2 device resulted in a relatively large dynamic H-Q loop. The static H-Q curve of the HVAD device at 2600 rpm remained between the H-Q curves of the HM2 and HM3 devices, resulting in an H-Q loop larger than that of the HM3 device but smaller than that of the HM2 device. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **MAP** & **PP [mmHg]** & **CO/MPO** & **CDV [mL]** & **ESV [mL]** & **EDD [cm]** & **ESD [cm]** \\  & **[mmHg]** & **PP [mmHg]** & **CO/min** & **EDV [mL]** & **ESV [mL]** & **EDD [cm]** & **ESD [cm]** \\ \hline Healthy CVS & 82 & 43 & 4.41 & 92 & 37 & 4.1 & 2.6 \\ DCM CVS & 80 & 31 & 3.06 & 125 & 86 & 4.9 & 4.1 \\ HVAD & 81 & 11 & 4.38 & 91 & 56 & 4.2 & 3.3 \\ HVAD Lavare & 81 & 15 * & 4.37 & 100 * & 53 * & 4.4 * & 3.2 * \\ HM2 & 82 & 14 & 4.40 & 92 & 54 & 4.2 & 3.2 \\ HM3 & 82 & 6 & 4.42 & 89 & 58 & 4.2 & 3.4 \\ HM3 Artificial Pulse & 81 & 20 * & 4.38 & 98 * & 56 * & 4.4 * & 3.3 * \\ \hline \end{tabular}
\end{table} 
Table 2: Haemodynamic variables in the healthy and DCM cardiovascular system models and under HVAD, HM2 and HM3 support. MAP, mean arterial pressure; PP, pulse pressure in aorta; CO, cardiac output; MPO: mean pump output; EDV, end-diastolic volume; ESV, end-systolic volume; EDD, end-diastolic diameter; ESD, end-systolic diameter. * indicates maximal values for PP, EDV and EDD whilst indicating minimal values for ESV and ESD. Figure 5: (**a**) Left-ventricular pressure–volume loops in the healthy and DCM cardiovascular system models, (**b**) left-ventricular pressure–volume loops under HVAD, HM2 and HM3 support, (**c**) left-ventricular pressure–volume loops under HVAD and HM3 support during the Lavare cycle and artificial pulse.
# Naphthoquinone-Based Meroterpenoids from Marine-Derived _Streptomyces_ sp. B9173
###### Abstract  
Naphthoquinone-based meroterpenoids are hybrid polyketide-terpenoid natural products with chemical diversity and a broad range of biological activities. Here, we report the isolation of a group of naphthoquinone-containing compounds from _Streptomyces_ sp. B9173, and their structures were elucidated by using a combination of spectroscopic techniques, including 1D, 2D NMR, and high-resolution mass (HRMS) analysis. Seven flavioregranin congeners or intermediates, three of which were new, have been derived from common naphthoquinone backbone and subsequent oxidation, methylation, prenylation, and amino group incorporation. Both flavioregranin B1 (**1**) and B (**2**) contain an amino group which was incorporated into the C8 of 1,3,6,8-therhydroxynphthalene (THN). Flavioogeranin D (**3**) contains an intact C-geranylgeranyl residue attached to the C2 of THN, while the O-geranylgeranyl group of **2** links with the hydroxyl on the C2 site of THN. Four compounds were selected and tested for antibacterial activity and cytotoxicity, with **3** and flavioregranin C2 (**5**) displaying potent activity against selected bacteria and cancer cell lines. In light of the structure features of isolated compounds and the biosynthetic genes, a biosynthetic pathway of naphthoquinone-based flavioregranins has been proposed. These isolated compounds not only extend the structural diversity but also represent new insights into the biosynthesis of naphthoquinone-based meroterpenoids.  
naphthoquinone; meroterpenoid; flavioregranin family; natural products +
Footnote †: journal: Biomolecules
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Disruptive Health Innovations
#### 3.5.5 Point-Of-Care Testing  
POC testing or bedside testing refers to medical diagnostic testing near the patient in the clinic or in the patient's home instead of a central laboratory. It can be advantageous in emergency situations to avoid delays in sending patients away from the point-of-care. POC testing devices communicate (uni- or bi-directionally) with the POC testing data management system, where the information is used for the decision-making process. There is a myriad of truly portable devices that are being used for POC testing by healthcare practitioner, as well as by patients. For example, dipsticks are being used for the semi-quantitative estimate of a range of clinically useful analytes in urine and whole blood [39]. Similarly, another new entrant for POC teasing called PIMA(r) is being used for the measurement of T-helper cells (also known as CD4 counts). Measurement of these cells is very crucial for antiretroviral HIV therapy and monitoring of immunosuppression [40]. A descriptive list of various existing POC testing devices can be found in [41].
## 3 Proposed Model
### Dynamic Control Architecture for COPD Diseases Based on SLA
#### 3.2.2 Reasoning Layer  
The reasoning engine is mainly based on rules, the KPI_TARGET value extracted from the SLA, and the income data sensor. The reasoning process consists of four phases: the phase of identification of the metric items or KPI target, the detection incident phase result from the comparison of the vital data of the four parameters collected on COPD patients, the implementation phase of the SLA violation control algorithm, and the phase of populate the SLA virtual template. These rules are important for the SLA violation evaluation mechanism. The two metrics of the dynamic SLA violation monitoring mechanism in the context of COPD patient are: the number of incidents from patient data, numberIncidentData, and the number of incidents of unsuccessful patient request, NumBeRequestSend. In effect, if the number of incidents is greater than the metric defined in the KPI-Target, there is a violation of the SLA. The rules for the SLA evaluation mechanisms are important for dynamically triggering the control of the SLA according to the context-aware data and SLA document information in WSA format. * Detection Incident Phase  
The information extracted from the SLA can be grouped into two parts: Content items and KPI-TARGETs. The KPI-TARGETs participate in the dynamic control mechanisms of the SLA. The other content items populate the virtual adaptive SLA template. KPI_IncidentDataValue and KPI_IncidentRequestValue are important items for the mechanism of SLA control violations. To design these rules, we consider that for every vital sign parameter, there is an interval with a minimum and a maximum value, in which the value of the sensor is a data incident. Table 2 describes these incident rules. If IncidentDataNumber \(>\) KPI_IncidentDataValue, then the data quality SLA violation alarm is triggered.
# 5G/B5G Internet of Things MIMO Antenna Design
## 4 Comparison with State-of-the-Art Designs  
The proposed MIMO antenna design is compared with the recent state-of-the-art shared aperture MIMO antenna designs in Table 1. The MIMO design [13] only covers the mm-wave band and is not a shared aperture antenna, while other designs [14, 16, 17] covered both Sub-6 GHz and mm-wave bands. However, [17] does not have MIMO configurations and others have somewhat larger-sized and comparatively complex structures. The proposed design, which operates at Sub-6 GHz and mm-wave bands using a single tapered slot structure and provides MIMO characteristics at both bands, has a compact size and a simple structure.  
Figure 10: The simulated and measured realized gain at (**a**) Sub-6 GHz and (**b**) mm-wave bands.  
Figure 9: The simulated and measured 2D radiation patterns in terms of gain at 28 GHz: (**a**) Ant. 1, (**b**) Ant. 4, (**c**) Ant. 2, and (**d**) Ant. 3. Simulated: dashed red curves; measured: solid blue curves.
## 3 Results and Discussion  
Based on the S2I and S2S measurements above, we analyze the measurement data to understand the characteristics of the wireless propagation over the sea deeply. Note that, in this paper, we mainly focus on the key characteristics, including power delay profile, delay spread and propagation path loss, which are essential for the research of maritime wireless channels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **S2I** & **S2S** \\ \hline Center frequency [GHz] & 5.9 & 5.9 \\ Bandwidth [MHz] & 100 & 100 \\ Delay resolution [ns] & 10 & 10 \\ Tx power [dBm] & 16 & 16 \\ Tx gain [dBi] & 16 & 2 \\ Rx gain [dBi] & 10 & 10 \\ Tx height [m] & 3.13 & 3 \\ Rx height [m] & 3.12 & 3.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Measurement Campaigns.
## 3 Results
### Impact of Forest Fragmentation on Amazon Rainforest at Forest Stand Level  
Each GEDI shot was assigned to forest edge or forest core areas using the TanDEM-X forest/non-forest map. A forest edge is located up to 100 m away from non-forest areas and a forest core is located more than 100 m away from non-forest areas (Figure 3a). In this study, edge and core areas are distributed as follows: 6.5% of the Amazon is edge area and 93.5% is core area. Lidar waveform matching with a forest model provides important forest attributes (aboveground biomass AGB, net primary productivity NPP, gross primary productivity GPP and leaf area index LAI) for all GEDI shots (Table 1, Figures 3 and S2).  
Figure 3: The density distribution of aboveground biomass (**b**) and net primary productivity (**c**) for edge and core areas. Core and edge areas are defined as in the scheme shown in (**a**). Non-forest is at 0 m, edge ranges from 0 to 100 m distance from non-forest and core areas are at >100 m distance from non-forest areas. In the density plots, the mean value of aboveground biomass and net primary productivity is shown accordingly in the coloring of edge and core. The relative frequency was plotted for all 110 million GEDI shots for the respective forest attributes at footprint level.  
The mean value for AGB in the forest edge area is 172 \(\pm\) 86 Mg odm ha\({}^{-1}\) compared to 235 \(\pm\) 85 Mg odm ha\({}^{-1}\) in the core area. This means AGB is 37% higher in the forest core area than in the edge area. The coefficient of variation (CV) is 50% for the edge area and 36% for the core area, meaning higher AGB variability in the forest edge area than in the forest core area. (Table 1 and Figure 3a).  
The NPP also shows different values for forest edge (mean: 5.4 \(\pm\) 1.5 Mg C ha\({}^{-1}\) a\({}^{-1}\)) and core (mean: 4.7 \(\pm\) 1.3 Mg C ha\({}^{-1}\) a\({}^{-1}\)) areas which differ by 13%. The distribution of NPP values in forest core areas is slightly narrower and thus less dispersed compared to NPP values in forest edge areas. It is noticeable that the CV of the two forest areas is similar (Figure 2c, Table 1).  
In addition to AGB and NPP, the forest attributes GPP and NPP were also examined (Figure S2, Table 1). The obtained mean value for GPP in the forest edge area is 22 \(\pm\) 7 Mg C ha\({}^{-1}\) a\({}^{-1}\) and 24 \(\pm\) 6 Mg C ha\({}^{-1}\) a\({}^{-1}\) in the core area (Figure S2a, Table 1). For LAI, the mean value in the core is 18% larger than in the edge (Figure S2b, Table 1).  
In addition to the analysis of forest stands, forest core areas were also investigated (Figures S3 and S4). Since no edge effects occur in the core area of forests, these AGB and NPP values should only depend on the site factors and not necessarily on fragmentation. We observe that low values of AGB dominate particularly in the areas of the Arc of Deforestation, on the edge of the Andes and on the Amazon River. Biomass values range from approx. 30-150 Mg odm ha\({}^{-1}\) and the net primary productivity values are between 1 and 3 Mg C ha\({}^{-1}\) a\({}^{-1}\).
## 2 Materials and Methods
### Model Selection
#### 2.2.2 Fractional Order Grey Accumulation Model  
In the case that the regularity of the original sequence is not strong, and the fluctuation is large, the result of the combination of first-order accumulation and least squares to obtain parameters is poor with the actual data, and it needs to be optimized. The emergence of fractional-order accumulation operators can solve the disadvantages of poor fitting, but its operation process tends to be average, and it is easy to overfit. However, through the analysis of the original time series, it can be seen that individual data can affect the accuracy of the ARIMA model's predictions. For those individual data, it may be more reasonable to treat them separately, and the grey model plays a good role in correcting these. In addition, in the face of annual forecasts that cannot be trend-decomposed, the ARIMA model is no longer suitable, and the introduction of a fractional grey accumulation model for the rolling prediction can solve the problem that the result is accidental due to too little sample data.  
The steps to build the model are as follows:  
First, let the \(r\)-order accumulation generation sequence be \(X^{(r)}=\{x^{(r)}(1)\), \(x^{(r)}(2)\),..., \(x^{(r)}(n)\}\). \(x^{(r)}(k)=x^{(0)}(i)\), \(k=1\), \(2\),..., \(n\). \(x^{(r)}(k)\) means the original sequence \(X^{(0)}\)'s grey accumulation of \(r\)-order generated operator which is defined by the gamma function. Update the mean value of its adjacent items and order \(Y^{(r)}(k)=\frac{X^{(r)}(k)+X^{(r)}(k-1)}{2}\). Thus establish the grey differential equation, i.e., the GM\({}^{r}\)(1,1) model [23]:  
\[X^{(r)}(k)-X^{(r)}(k-1)+a\ Y^{(r)}(k)=b \tag{2}\]  
\(X^{(r)}(k)\): grey accumulation of \(r\)-order generated sequence;  
\(Y^{(r)}(k)\): mean of grey accumulation of adjacent \(r\)-order generated sequence;  
The parameters \(a\) and \(b\) in this model satisfy the following matrix relationships:  
\[(a,b)^{T}=(A^{T}A)^{-1}A^{T}B \tag{3}\]\[A=\begin{pmatrix}-Y^{(r)}(2)&-Y^{(r)}(3)&\dots&-Y^{(r)}(n)\\ 1&1&\dots&1\end{pmatrix}^{T}B=\begin{pmatrix}X^{(r)}(2)-X^{(r)}(1)\\ X^{(r)}(3)-X^{(r)}(2)\\ \dots\\ X^{(r)}(n)-X^{(r)}(n-1)\end{pmatrix}\]  
Since least squares estimation minimizes the sum of squared errors, the least squares are used to obtain the parameters. From this, the whitening differential equation for the GMr(1,1) model can be obtained:  
\[\frac{dX^{(r)}(t)}{dt}+aX^{(r)}(t)=b \tag{4}\]  
There are two parameters \(a\), \(b\), and to solve these two parameters, the whitening equation is now discretized. To solve the differential equations to obtain the response of the sequence and the reduction value [24]:  
\[\hat{X}^{(r)}(k)=(X^{(r)}(1)-\ \frac{a}{b})e^{-a(k-1)}+\frac{a}{b} \tag{5}\]  
\[\hat{X}^{(0)}(k)=\sum_{i=1}^{k}C_{k-i-r}^{k-i}\hat{X}^{(r)}(k)-\sum_{i=1}^{k-1 }C_{k-1-i-r}^{k-1-i}\hat{X}^{(r)}(k-1) \tag{6}\]  
\(\hat{X}^{(r)}(k):\) predictive values before restoration;  
\(\hat{X}^{(0)}(k):\) predictive values after restoration.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 5 Numerical Simulation & Results
### Lyapunov Exponent Test  
Lyapunov Exponent test is commonly used for checking the existence of chaos in dynamical systems. If for any dynamical system, one of the Lyapunov exponents is positive, it indicates chaos. There are various algorithms available to find the Maximum Lyapunov Exponent (MLE) spectrum of a system described by differential (or difference) equations [38; 39]. The basic equation for finding the Lyapunov exponents is given below [40].  
\[\lambda(x_{i})=\underset{n\rightarrow\infty}{lim}\frac{1}{n}\sum_{i=0}^{n-1} ln\left(f^{\prime}(x_{i})\right| \tag{14}\]  
where, \(x_{i}\) is the time series for the \(i\)th ODE.  
The dynamic Lyapunov spectrum of the proposed system, calculated using [PERSON]'s algorithm, for the control variable '\(\beta=5.535\)' (chaotic case) is shown in Figure 4.  
From Figure 4, it can be concluded that one of the Lyapunov spectra is positive. Since the positive Lyapunov exponent implies chaos, the system trajectory will diverge only in one direction, thereby confirming the single scroll attractor shown in Figure 3.  
Figure 3: Sensitivity dependence on the initial conditions: Two sets of initial conditions (0.1, 0.1, 0.5), & (0.5, 0.5, 0) with \(\beta=5.535\).
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 3 Results  
### Model Comparison  
To evaluate the detection performance of the XGBoost model, the following algorithms were selected for comparison: (1) Linear Regression [29] (LR), a classical machine learning algorithm for solving regression problems; (2) Stochastic Gradient Descent [30] (SGD), an optimization algorithm for minimizing the loss function (it is a variant of gradient descent algorithms, which gradually reduce the value of the loss function by iteratively updating the model parameters); (3) Multilayer Perceptron [31] (MLP), a neural network-based machine learning model that is often used to solve classification and regression problems; (4) Gradient Boosted Regression Tree [32] (GBRT), an integrated tree-based learning algorithm that accumulates the results of all regression tree outputs; (5) Random Forest [33] (RF), an integrated learning algorithm for solving classification and regression problems based on an integrated approach of decision trees, which constructs multiple decision trees and combines their predictions to perform classification or regression.  
Table 4 presents the results of the performance analysis of the same dataset using the stated models. The XGBoost model achieved a better learning rate and lower error than the other machine learning algorithms, as well as accurate estimates of the protein and fat in the milk samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{3}{c}{**Protein**} & \multicolumn{3}{c}{**Fat**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & \(MAE\) & \(MSE\) & \(\mathbf{R^{2}}\) & \(MAE\) & \(MSE\) \\ \hline Fold-1 & 0.8641 & 0.0325 & 0.0084 & 0.9831 & 0.0354 & 0.0127 \\ Fold-2 & 0.9021 & 0.0238 & 0.0052 & 0.9209 & 0.0413 &
## 4 Discussion
A short (\(\tau_{p}<\)\(f_{ce}\)\({}^{-1}\)) electromagnetic pulse impacts the plasma electrons and transfers a part of its energy to them. As the EMP passes along the GCL, the electrons begin to simultaneously rotate at the cyclotron frequency \(f_{c}\) in each of GCL cross sections. This phased rotation of electrons represents the EMP polarization trace in the plasma, which results in the observed EMP tail. A similar physical picture underlies demonstrations of the cyclotron echo effect, in which a short microwave pulse spins plasma electrons at a cyclotron resonance. In experiment, an increase in the duration of the initial pulse at the electron cyclotron frequency can be observed, see, for example [29]. Finally, the issue of efficiency of transferring the EMP energy to plasma is significant. In particular, it is of interest due to the proposal of heating the ionosphere with periodic sequences of UWB EMPs [5]. First, the performed experiments demonstrated that the EMP energy is transferred to the plasma electrons in accordance with the cyclotron absorption mechanism. Second, the effects of energy absorption under the plasma resonance conditions (\(f\)-\(f_{p}\)) do not manifest them explicitly in the experiment, since the plasma inside the GCL is distributed nonuniformly, both over the length and radius of the line. At the same time, wideband energy absorption is observed in the experiment in the plasma evanescence region, i.e., between the electron cyclotron frequency \(f_{\rm cr}\) and the maximum plasma frequency corresponding to the maximum of the radial distribution of the density \(N_{\rm e}\). This result is of significance for planning of the experiments similar to that proposed in [6], since in the future it will allow synthesizing a pulsed wideband signal whose parameters are optimized from the absorption viewpoint.