esa-sceva/satcom-chunk-collection huggingface dataset:
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 3 One-Class Classifiers
### One-Class Support Vector Machine  
OSVM is an extension of the SVM created by [PERSON] and [PERSON] [PERSON] (2008), which is a traditional supervised ML algorithm used for classification problems. The algorithm works by creating a hyperplane (\(n\)-dimensional plane) that better separates two different classes. SVM tries to optimise the separability of the data, searching for instances that are within the boundaries that split the data. Those instances are called Support Vectors. For the use of SVM, we need to process the data in kernel functions, which are set through a hyperparameter. The kernel functions are responsible for taking non-linearly separable data and implicitly mapping them to a higher dimensional plane that can be linearly separated.  
Unlike the traditional SVM, OSVM uses the hyperplanes to create boundaries around a region that better contains all training data. By doing so, OSVM is capable of identifying if an instance is inside the area or not. To create these hyperplanes, the OSVM algorithm optimises the following objective function:  
\[\min_{w,\xi,\rho}\frac{1}{2}w^{T}w+\frac{1}{vN}\sum_{i=1}^{N}\xi i-\rho \tag{5}\]  
subjected to \(w^{T}\phi(x_{i})\geq\rho-\xi_{i}\) and \(\xi_{i}\geq 0\), where \(x_{i}\in\mathbb{R}^{p}\) is an instance of the training set, \(w\) is the weight vector of the hyperplane in the inner product space, \(\phi(.)\) is a mapping from the original \(p\)-dimensional feature space to an inner product space, \(\xi_{i}\)'s are penalty terms for error, \(\rho\) is a bias term, and \(\
u\in(0,1]\) is a parameter that poses an upper bound on the fraction of outliers in the training set.  
The decision hyperplane can be represented by \(g(x)\equiv w^{T}\phi(x)-\rho=0\). The solution of the convex optimisation problem of the objective function finds the decision hyperplane, and the corresponding decision function \(f(x)\):  
\[f(x)=\begin{cases}w^{T}\phi(x)-\rho\geq 0&\text{if $x$ belongs to the set}\\ w^{T}\phi(x)-\rho<0&\text{if $x$ is an outlier}\end{cases} \tag{6}\]In this work, it is used the radial basis function (RBF), the polynomial and the linear kernel to map the input space to a higher dimensional space. The RBF kernel is described by the following equation:  
\[\phi(x)=\exp(-\gamma||x-x^{\prime}||^{2}),\text{ where }\gamma>0. \tag{7}\]  
The hyperparameter \(\gamma\) is the influence in the training samples. Next, the polynomial kernel is described by the equation:  
\[\phi(x)=(\gamma\langle x,x^{\prime}\rangle)^{d}, \tag{8}\]  
where \(d\) it is the degree function passed as a hyperparameter. Lastly, the linear kernel is represented by the equation:  
\[\phi(x)=\langle x,x^{\prime}\rangle. \tag{9}\]
Accuracy of Trajectory Tracking Based on Nonlinear Guidance Logic for Hydrographic Unmanned Surface Vessels  
[PERSON]  
1 Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11-12, 80-233 Gdansk, Poland; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11-12, 80-233 Gdansk, Poland; [EMAIL_ADDRESS]  
[PERSON]  
2 Marine Technology Ltd., Roszczynialskiego 4/6, 81-521 Gdynia, Poland; [EMAIL_ADDRESS] (K.N.); [EMAIL_ADDRESS] (B.D.-S.)  
Footnote 29: email: {kchallenging task. It requires a relatively frequent change of the watch in the case of the helmsman controlling the hydrographic unit.  
A disadvantage of manual control is also the quite low accuracy of maintaining the measuring unit on the profile and a moment of inattention or distraction may result in deviation from the measuring profile thus requiring repetition of registration on the incorrectly registered profile. For this reason, significant amounts of redundant data are recorded, which hamper the processing of the measurement data. The issue of achieving precise control of the measurement profile on a hydrographic unit can be solved by using an unmanned surface vehicle (USV) with an adaptive autopilot which realizes precise control of both course and speed.  
Generally, unmanned mobile platforms, including surface, aerial, and ground vehicles have been increasingly employed for numerous operations. Currently, the unmanned platforms are widely used around the world, giving rise to many new research opportunities. In this context, various control schemes have been also developed to perform predefined tasks. Unmanned surface vessels suffer from many uncertainties and unknown external disturbances like winds, waves, currents, etc., which inevitably lead to a very challenging task [2] like accurate trajectory tracking within this complex navigation environment [3]. Due to this, various advanced control techniques and schemes for surface vehicles have been developed, and the following groups can be highlighted: backstepping technique [4; 5; 6; 7], chaos control approach [8; 9; 10], fuzzy control [2; 3; 11; 12], neural control [13; 14; 15; 16; 17; 18], and finite-time control [19; 20; 21].  
This paper considers the problems of precise control of the measurement profile on a hydrographic unit using a nonlinear adaptive autopilot originally designed for unmanned aerial vehicles. The results of experiments carried out under real conditions are presented.
## 3 Results
### The Effect of the Treatment Time on Plasma Etching  
As reported, only 1 s exposure to H\({}_{2}\) plasma can etch the surface of polymer film sufficiently to uncover of the metal wires tops. The exposures less than 0.5 s resulted in not completed etching when not all metal wires tops were completely free of the polymer film. The longer etching in hydrogen DCSBD plasma resulted in the cracks in the polymer around the uncovered wires tops and delamination of the polymer as seen in Figure 10. This damage was caused by thermal degradation of the polymer at longer exposures in plasma. The benefit of using 100% hydrogen plasma treatment is also in a significantly lower thermal impact on the surface compared to 100% nitrogen atmospheric plasma or H\({}_{2}\)/N\({}_{2}\) gas mixture due to the absence of any heavy particle. A surface power density of 100% hydrogen plasma was about 2.5 W cm\({}^{-2}\), whereas the electron density and electron temperature were \(1.3\times 10^{16}\) cm\({}^{-3}\) and \(19\times 10^{3}\) K respectively [15].  
Figure 8: The effect of the sample distance from DCSBD plasma layer, and H\({}_{2}\) plasma treatment time on the mechanism of the etching of polymer mesh foil.  
Figure 9: SEM micrographs of free metal wires after 1 s plasma etching in pure H\({}_{2}\) plasma at distance of 0.2 mm (**a**), 0.3 mm (**b**), 0.4 mm (**c**) and 0.6 mm (**d**). Top row—SE image; bottom row—BSE image.  
It was found that the unwanted thermal degradation of polymer foil can be prevented by applying a multiple short-time plasma exposure on the etched surface. This is apparent from Figure 11 comparing the samples etched for 3 s using single 3 s plasma exposure (Figure 11a) and the multiple very short exposures (12 \(\times\) 0.25 s) in hydrogen plasma (Figure 11b).
# A Robust Security Architecture for SDN-Based 5G Networks
## 4 Security Analysis
### MITM Attack  
In the MITM attack, the attacker secretly relays and possibly alters the communication between two parties who believe they are directly communicating with each other. However, this kind of attack can be detected in our scheme. First, the attacker must hijack both uplink and downlink in an authentication process, causing such kind of attack to be conducted. Second, even if the attacker has successfully conducted an MITM attack, the attacker hijacks a device's communication in the authentication process, making the back-end system fail to update the secret stored in this device; the back-end system can find this difference, which indicates a MITM attack, in the next authentication process of this device.
# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Feature Selection
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **WOASAT-2** & **ALO** & **GA** & **PSO** & **FULL** & **EJAYA** \\ \hline Breast cancer & **4.2** & 6.28 & 5.09 & 5.72 & 5.4 & 6 \\ Breast EW & **11.6** & 16.08 & 16.35 & 16.56 & 14 & 14 \\ Congress EW & 6.4 & 6.98 & 6.62 & 6.83 & 7.8 & **6.2** \\ Exactly & **6** & 6.62 & 10.82 & 9.75 & 7 & **6** \\ Exactly2 & 2.8 & 10.7 & 6.18 & 6.18 & 7.2 & **1.6** \\ Heart EW & **5.4** & 10.31 & 9.49 & 7.94 & 6.8 & 7.3 \\ Ionosphere EW & 12.8 & **9.42** & 17.31 & 19.18 & 14.6 & 10.2 \\ Krvskp EW & 18.4 & 24.7 & 22.43 & 20.81 & 19.4 & **11.8** \\ Lymphography & **7.2** & 11.05 & 11.05 & 8.98 & 10.8 & **7.2** \\ M-of-n & **6** & 11.08 & 6.83 & 9.04 & 7.6 & **6** \\ Penglung EW & 127.4 & 164.13 & 177.13 & 178.75 & 141.2 & **124.1** \\ Sonar EW & 26.4 & 37.92 & 33.3 & 31.2 & 29.2 & **19** \\ Spect EW & 9.4 & 16.15 & 11.75 & 12.5 & 11.4 & **8.7** \\ Tic-tac-toe & **6** & 6.99 & 6.85 & 6.61 & 6.6 & **6** \\ Vote & 5.2 & 9.52 & 6.62 & 8.8 & 6.8 & **3.1** \\ Waveform EW & 20.6 & 35.72 & 25.28 & 22.72 & 19 & **14.5** \\ Wine EW & 6.4 & 10.7 & 8.63 & 8.36 & 6.2 & **4.3** \\ Zoo & **5.6** & 13.97 & 10.11 & 9.74 & 7.4 & 6.8 \\ Average & 15.98 & 22.68 & 21.76 & 21.64 & 18.24 & **14.60** \\ \hline \hline \end{tabular}  
* The better results were shown in bold format.  
\end{table} 
Table 7: The number of selected features for different algorithms. Another factor for evaluating the performance of the algorithms is the computational burden. To this end, the time consumption of the algorithms for the 14 th function is represented in Figure 5. As can be seen, GOA and DA have a high time complexity, while JAYA and the proposed method converge in under 1 s. From this, it can be observed that, not only did the performance of EJAYA improve, but it also led to convergence in less time.
## 3 Results
### Out-of-Domain Test
#### 3.2.2 Runtimes of the Different Methods on the Out-of-Domain Test  
Figure 4 shows the running time (in s) required by each of the regression methods for 100 runs of the _Out-of-Domain_ test. It can be seen that the lowest required running times are for _Linear Regression_, with a 50 th percentile runtime of 0.02 s and maximum of 0.158 s, and Lasso Lars, with a 50 th percentile runtime of 0.013 s and maximum of 0.027 s. _XGBoost_ (xg-b) required the longest time (50 th percentile runtime of 55.33 s and maximum 79.05 s); on the other hand, _Random Forest_ and the proposed _Spline Continued Fraction_ regression required very similar times (50 th percentile runtimes of 36.88 s and 41.65 s for rf and Spln-CFR, respectively) on the _Out-of-Domain_ test.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 4 Proposed System Setup
### SP Modulation Aided DSTS Based Transmission Arrangements  
The output \(y_{i}\) of the inner URC code is fed as an input to the SP mapper. SP modulation is used to keep the maximum possible euclidean distance between the modulated symbols. Thereafter, the stream is then modulated into constellation symbols \(S_{i}\), where \(i=1,2,3,\ldots L-1\) having \(L\) modulated signal alphabet. The modulated symbols are then further transmitted by DSTS using two transmitter antennas.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 1 Introduction  
With the continuous improvement of urbanization in China, urban congestion has become more and more serious in recent years. On the one hand, urban rail transit (URT) is an important public means of transportation to alleviate urban congestion, and more and more people choose URT for travel. On the other hand, with the growth of urban residents' travel demand, the line scale of the URT network is expanding. By the end of 2020, 45 cities in mainland China had opened URT systems, with a total of 244 lines and a total mileage of 7969.7 km [1]. This also makes the operation and management of URT more difficult, such as the difficult transportation organization during peak passenger flow, over-saturated passenger flow, and potential safety hazards for passengers. Therefore, the accurate prediction of short-term passenger flow of URT is of great value for maintaining the safety of rail transit, improving efficiency, and avoiding wastage of rail transit capacity.  
The research process of urban traffic passenger flow prediction can be summarized into three stages: statistical methods, traditional machine learning methods, and deep learning methods. Statistical methods are more sensitive to the linear relationship between variables, but they cannot capture the nonlinear relationship in the data. Such methods mainly include the Kalman Filter Model [2], Autoregressive Integrated Moving Average Model (ARIMA) [3], Logistic Regression (LR) [4], and Grey Model (GM) [5]. Traditional machine learning methods can better capture the nonlinear features in time series, and the accuracyfor rail transit passenger flow prediction is higher. Such methods mainly include Support Vector Machine (SVM) [6] and neural network [7; 8]. However, the prediction model using traditional machine learning methods is prone to over-learning or under-learning problems when dealing with massive passenger flow data, which affects the prediction accuracy [9]. With the advancement of related theories and technologies, researchers have begun to use deep learning models to predict rail transit passenger flow [10; 11]. Among them, the most widely used is the long short-term memory (LSTM) model [12]. Based on the Recurrent Neural Network (RNN), related scholars improved the LSTM model [13]. Due to the strong applicability of the LSTM model in processing time series data, it has been widely used in passenger flow forecasting research.  
On the basis of previous studies, this paper proposes a model combining the time convolution network (TCN) with the long short-term memory network (LSTM) to predict the short-term passenger flow of URT based on the inbound and outbound passenger flow data of Chongqing Rail Transit Line 3 in April 2021. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times and provide some guidance for URT operation and management.
## 5 Evaluation
### Setup
#### 5.1.2 Comparison Scheme  
1. MPC: We used MPC as the default ABR to train the algorithm for the fair bandwidth allocation, so MPC was used as the benchmark algorithm for evaluation;* Bola: A fixed heuristic algorithm that uses Lyapunov optimization to select the bitrate taking buffer occupancy only into account. Although it is not directly designed for QoE fairness, it achieves relatively good results;
* Pensieve [20]: a relatively advanced adaptive algorithm based on deep reinforcement learning.
## 5 Results and Discussion
### Results and Discussion
It is worth noting that the significant performance Anomaly Transformer in terms of precision is due to its small number of true positive samples, which is confirmed by the low recall value (0.342). Anomaly Transformer could only detect a small proportion of anomalous patterns in the ground truth. Conversely, our network exhibits superior performance with respect to the recall evaluation metric, with a value of 0.8316, followed by the two GAN models, which achieved values of 0.787 and 0.813, respectively. The deep autoencoder model still performs poorly, with a recall of 0.226. The above comparison verifies the feasibility and effectiveness of the proposed U-FFT-based anomaly detection network for energy consumption auditing for robotic manipulators.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Targeted Use Cases
#### 3.3.3 Multimedia  
In the last decade, multimedia applications with high-throughput and low-latency requirements have become common in mobile devices. Examples are 4K/8K video and audio streaming, online gaming, virtual reality, augmented reality, etc. This traffic surge goes beyond the capacity of legacy Wi-Fi access points [27]. Wi-Fi 6 increases the capacity of access points by higher-order modulation, OFDMA, and MU MIMO, which leads to a better user experience [28].
# Therapeutic Applications of Halloysite
## 3 Halloysite Structure and Applications
### HNTs in Drug Delivery  
HNTs have been used as a drug delivery carrier for many clinically meaningful drugs [56; 57]. HNT can be loaded with different drugs, including anticancer drugs, antibiotics, analgesics, antihypertension, anti-inflammatory drugs, and therapeutic nucleic acids [57]. HNTs have also been used for the controlled release of antibiotics, including tetracycline, ofloxacin, nonfloxacin, amoxicillin, and ciprofloxacin [57]. Amoxicillin (AMX) loaded HNT is incorporated into a polylactic acid-glycolic acid copolymer (PLGA) solution, which is electrospun with water-soluble chitosan nanofibers in two different syringes simultaneously, thereby making a composite material [58]. Compared to loading the drug directly into the polymer matrix, HNT extends the release time of AMX and reduces the initial burst release [58].  
Analgesic drugs and anti-inflammatory, such as ibuprofen (IBU), diclofenac sodium, and aspirin, have low water solubility and bioavailability [59]. Therefore, developing an efficient drug delivery system by encapsulating drugs in a nanoparticle system to enhance their bioavailability is urgently needed [59]. 3-aminopropyltriethoxysilane (APTES) functionalized surface HNT as a carrier for IBU could promote IBU loading [60]. By restricting the APTES oligomerization in the lumen, free lumen space was preserved, resulting in a 25.4% greater loading rate than that in unmodified halloysite. In order to sustain a more significant release of IBU, an ideal hydrophobic sustained-release drug delivery system was designed [60]. The HNT lumen (EHNT) was enlarged, and hydrophobic modification of the external surface by organosilane (OS) was done prior to loading IBU [60]. The OS composite of EHNT demonstrated a sustained-release performance for IBU (100 h) [60].  
Halloysite has been used in other drug delivery systems such as anti-hypertension and gene therapeutic agent-delivery systems. Polydopamine was used to cap HNT for a controlled drug release [61]. After dispersion in a sodium alginate matrix and crosslinking via Fe\({}^{3+}\), HNTs were used to deliver diltiazem hydrochloride, widely used in high blood pressure therapy [61]. In gene therapeutic agent-delivery systems, HNTs were surface-modified with \(\gamma\)-aminopropylriethoxysilane and assembled with antisense oligodeoxynucleotides (ASODNs) [62]. These functional HNT complexes showed improved intracellular delivery efficiency and inhibited the tumor growth activity of ASDONs [62].
## 3 Case Studies and Analyses  
To assess the performance of the proposed WATEBI method, three case studies are presented in this paper. These are vibration of a SDOF system and the ground motions of El Centro earthquake and Kaikoura earthquake. In each case study, the WATEBI method is compared with time-domain trapezoidal integral by Labview [36], MATLAB Ode45 (Ordinary Differential Equation) function with tolerance of \(10^{-6}\)[37], low-frequency cutoff [18], and low-frequency attenuation with the target accuracy \(\alpha_{T}=0.97\)[16; 17]. For a fair comparison among all methods, both the linear trend in velocity and the nonlinear trend in displacement have been removed, respectively by linear fitting and nonlinear fitting. The only difference is that the WATEBI method employs EMD while the benchmark methods use the common least-squares method.  
Please note that the attenuation algorithm in reference [16] is only given for displacement. The accuracy of the frequency domain methods (low-frequency cutoff [18] and low-frequency attenuation [16; 17]) heavily depends upon parameter selection. In this paper, the parameters in the selected frequency domain methods for all three case studies are tuned to obtain the ideal results. In Case 1, the cut-off frequency in the cutoff method and the target frequency in the attenuation method  
Figure 3: The flowchart of the Walsh transform and Empirical Mode Decomposition-based integral (WATEBI) algorithm.  
are 0.1 Hz, coincidently. In Case 2, they are 0.07 Hz and 0.1 Hz respectively. In Case 3, they coincide again at 0.15 Hz.  
In each case study, the following three indicators are used to evaluate the deviation of the reconstructed signal away from the reference velocity/displacement.  
(1) Average peak error \(Er_{1}\), which denotes the average deviation at both positive and negative extremes of the time series, is defined as  
\[Er_{peak}{}^{+}=\left|\frac{max[X(t)]-max[x(t)]}{max[x(t)]}\right|,\ \ \ Er_{peak}{}^{-}=\left|\frac{\min[X(t)]-\min[x(t)]}{\min[x(t)]}\right| \tag{9}\]  
where \(x(t)\) is the reference signal and \(X(t)\) is the reconstructed signal.  
\[Er_{1}=\frac{1}{2}\Big{(}Er_{peak}{}^{+}+Er_{peak}{}^{-}\Big{)} \tag{10}\]  
(2) Maximum peak error \(Er_{2}\), denoting the larger deviation at positive and negative extremes of the time series, which is often the most concerned and useful information for comparison.  
\[Er_{2}=max(Er_{peak}{}^{+},Er_{peak}{}^{-}) \tag{11}\]  
(3) Root-mean-square error \(Er_{3}\) represents the overall deviation of the whole time series at every time instants.  
\[Er_{3}=\sqrt{\frac{\sum_{i=0}^{N-1}\left(x(t_{i})-X(t_{i})\right)^{2}}{N}} \tag{12}\]
## 2 Materials and Methods
### Satellite Imagery  
The spectral radiance reflected from the Earth's surface was recorded in five spectral bands within the wavelength range of visible/red-edge/near-infrared (440-850 nm) using the RapidEye five-satellite constellation. Multispectral satellite imageries were provided via the Planet Labs, Inc. archive for the years 2014-2016. In every year, only scenes exhibiting 100% cloud free pixels were selected over a whole phenological vegetation period. Whereas in 2014 and 2015, four scenes could be recorded (2014: 09.03,/28.04,/06.08,/04.09. and 2015: 18.03,/04.07,/03.08,/12.10), the year 2016 held five cloud free scenes (04.03,/01.04,/05.05,/24.06,/10.09.), resulting in 65 wavebands for the final time-stack. Subsequently, the recorded sensor radiance values were atmospherically corrected using Atmospheric & Topographic Correction for Satellite Images ATCOR [53] in order to calculate surface reflectance values for every scene individually. Geometric correction was performed at Planet Labs, Inc., followed by an in-house co-registration procedure to geometrically align all scenes towards one master image with a final spatial resolution of 5 m [54].
## 1 Introduction
In this paper we focused on identification and understanding of forest linear response to drought conditions [29; 50], and analyzed resultant shifts in forest phenology and green biomass production. The latter topic is still rarely investigated despite its high relevance [51]. The study addressed drought variability of the alpine mountain forest in South Tyrol (2001-2012) and comprised three steps: (i) presentation of regional meteorological drought conditions recognized using scPDSI (self-calibrated Palmer Drought Severity Index; [52]) synthesized over a selection of meteorological stations; (ii) temporal and spatial assessment of drought impact on forest status expressed through changes in canopy photosynthetic activity and water content, approximated, respectively, using MODISderived 16-day NDVI and NDII/7 time series at 250 m spatial resolution; (iii) analysis of drought induced changes in forest phenology and green biomass production as a function of local biophysical conditions. The study employed PCA (Principal Component Analysis; [53]) and concentrated on short- to medium-term drought events of moderate to severe intensity.