esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Array Structure
### Transforming the Antenna Array for Ultra-Wideband (UWB) Operation  
The bandwidth of the reference antenna array defined for S11\(\leq\)\(-\)10 dB in Figure 1 is between 5.25 GHz to 10.1 GHz. This partially covers the required UWB spectrum defined by the American Federal Communications Commission (FCC) to be between 3.1 GHz to 10.6 GHz. Therefore, it was necessary to extend the array's bandwidth to fulfill the UWB bandwidth criteria. This was achieved by incorporating CRLH structure in the hexagonal patches of the reference antenna array. This involved etching a hexagonal slot in the radiating patch and inserting a metallic via at the center of the patch to ground it, as shown in Figure 3a-c. Geometrical parameters of the optimized array are listed in Table 2. The hexagonal slot in the radiating patch essentially acts like a series left-handed capacitance, and the metallic via acts like a shunt left-handed inductance. Moreover, surface currents that flow over the antenna establish parasitic right-handed capacitance between the patch and the ground-plane, and series right-handed inductance on its surface. This  
Figure 2: Photo-lithographic fabrication process to implement the antenna.  
modification essentially introduces metamaterial characteristics in the reference antenna. The consequence of this is significant improvement of the impedance bandwidth as well as the radiation gain over the UWB spectrum as is confirmed by the simulation results in Figure 3d,e. The reference antenna's impedance bandwidth for S11\(\leq\)-10 dB is extended from between 5.25 GHz and 10.1 GHz to between 2 GHz and 12 GHz, which corresponds to a fractional bandwidth of 142.85%. Compared to the reference array this constitutes a bandwidth improvement of \(\sim\)80%. The corresponding average gain improved from 1.5 dB to 5 dB.  
Figure 3: Configuration of the proposed metamaterial (MTM) based antenna array: (**a**) top-side view of substrate, (**b**) back-side view of substrate (ground-plane), (**c**) isometric view showing all the structural components and their locations, (**d**) reflection-coefficient response of the reference and MTM antenna array, and (**e**) radiation gain of the reference and MTM antenna array.
## 1 Introduction
A lot of different communication systems have been discussed; nevertheless, there are no proposals for enhancing IoT physical layer security by employing the Colpitts chaos oscillator. This study introduces the Quadrature Chaos Shift Keying (QCSK) communication system based on the Colpitts chaos oscillator for IoT applications. The system employs two chaotic signals: shift keying by a bit sequence, synchronization using the substitution method, and coherent detection [58]. As the QCSK communication system is built using an analog chaos oscillator, as the first step of this study, it is essential to analyze its dynamics' stability. The main characteristics of the chaos oscillator--the ability to synchronize and low signal cross-correlation--depend on the nominal values of individual electronic components--and the system's synchronization relies on the similarity of chaos oscillators. Studying the Colpitts chaos oscillator model in both simulation and hardware is essential to ensuring the oscillator operates in chaotic mode. Depending on the initial conditions, a chaos oscillator can generate even periodic signals, but such signals would not ensure security in the communication system. Chaos oscillators' dynamic stability, synchronization durability, and frequency domain properties significantly impact the performance of the QCSK transmission system. In the current study, extensive synchronization analysis has been performed due to the precise signal detection dependency on QCSK system synchronization. In previous works, frequency-modulated chaos shift keying (FM-CSK) and quadrature chaos phase-shift keying (QCPSK) communication systems were proposed [59; 60] for IoT physical layer security enhancements. Still, the proposed systems had a significant shortcoming in the form of low data transmission rates. In this study, we address this shortcoming by increasing the fundamental frequency of the Colpitts chaos oscillator to increase the data transmission speed for IoT applications. The commonly used IoT protocol, the Narrowband Internet of Things (NB-IoT), can achieve transmission speeds up to 128 Kbps [61]. This manuscript consists of 5 sections. Section 2 explains the concepts and structure of the proposed QCSK communication system. In Section 3, the study on the Colpitts chaos oscillator is presented and examined both in simulation and hardware. Section 4 presents the QCSK communication system performance analysis in the AWGN channel. Section 5 concludes this paper.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Aerial Mobile Sink-Based Frameworks
#### 2.4.2 UAV-Enabled Energy-Efficient WSN Data Collection (UEWDC)  
[PERSON] et al. [65] proposed an energy-efficient UAV-based WSN data collection technique named UEWDC. A joint optimization problem was studied with the UAV's trajectory and the wake-up scheduling of sensor nodes. The mixed-integer non-convex optimization problem was relaxed and solved by obtaining a suboptimal solution. The main goal was to minimize the maximum energy consumption of the overall sensor nodes while simultaneously maintaining the target volume of reliable data collection. The authors considered a general fading channel model for the link between the sensor node and the UAV. It is assumed that the initial location \(q_{o}\) and ending location \(q_{F}\) of the UAV's trajectory are predetermined, and there exists at least one feasible flying path from \(q_{o}\) to \(q_{F}\). Moreover, the sleep and wake-up mechanism of sensor nodes is designed such that a maximum of one sensor node can be in wake-up mode at each time slot for communicating with the UAV. The nodes remain in sleep mode most of the time and wake up only when the UAV is sufficiently close. The relaxed problem with successive convex optimization is solved using an iterative algorithm to determine the suboptimal solution of the UAV trajectory and wake-up schedule.  
Advantages: This work is suitable for sparse network conditions, and when the position of nodes is known. The energy consumption is balanced by maintaining the sensor nodes in sleep mode. For a WSN with periodic data gathering, this scheme balances energy consumption and reliable data delivery.  
Limitations: The proposed method is not compared with any other optimized method. The number of nodes considered for the system is very low, and the manner in which the trajectory and wake-up schedule work in a dense network has not been discussed. This mechanism is not suitable for environments with emergency data collection.
# A Percentile Method to Determine Cold Days and Spells in Bangladesh
###### Abstract  
The 10 th percentiles (10P) of the daily minimum (\(T_{min}\)) and maximum (\(T_{max}\)) during 1971-2000 were determined to estimate a threshold for cold days. This 10P (a standard of extreme climatic condition suggested by the World Meteorological Organization) threshold was applied with the daily \(T_{min}\) and \(T_{max}\) in the winter months (December, January, and February) of 2000 to 2021 to calculate the number of cold days, and consecutively, cold spells, and their trends. A cold day was declared when the daily \(T_{max}\) and/or \(T_{min}\) was lower than that of the 10P threshold, and the average temperature was \(\leq\)17 \({}^{\circ}\)C in a weather station. In this research, the cold days and spells were categorized into five classes, namely extreme (\(\leq\)13 \({}^{\circ}\)C), severe (\(>\)13-14 \({}^{\circ}\)C), very (\(>\)14-15 \({}^{\circ}\)C), moderate (\(>\)15-16 \({}^{\circ}\)C), and Mild (\(>\)16-17 \({}^{\circ}\)C). Moreover, a cold spell was considered when such cold days persisted for \(\geq\)2 consecutive days in at least two nearby stations. The results revealed a higher number of average cold days during winter in the western and northwestern districts of Bangladesh, and it reduced gradually in the south, southeast, and northeast. Dinajpur and Rajshahi districts showed the highest number of extreme and severe categories of cold days, i.e., 4.81 and 3.24 days/year, respectively. Rajshahi division had the highest number of cold spells on average (3.24/year), and Rangpur division had the highest number of extreme-category (the category that carries the lowest temperature range, \(\leq\)13 \({}^{\circ}\)C) cold spells (1.29/year). January was the coldest month, with the maximum number of cold days and spells. The highest average number of cold days (25.54%) was observed during the second ten days of January (i.e., 11-20 January). Significant increasing trends were found in the cold days of 11-20 December (5 stations), 21-31 December (3 stations), and the month of December (13 stations). In contrast, significant decreasing trends were noticed for the 1-10 January period in three weather stations. Our proposed 10P method could be used to determine the cold days and spells in Bangladesh that might be useful for the policy makers in formulating appropriate strategies in minimizing the impact of cold regimes during the winter season.  
air temperature; cold severity; cold-related mortality; trend analysis; cold days and spells; weather station +
Footnote †: journal: selected topics
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 2 ETC Systems: Historical Development and Current Applications
### Current Application Status of ETC in Urban Transportation
As technology continues to mature and applications continue to expand, the role of ETC in urban traffic management will become more prominent, and its data value will be more fully explored and utilized.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 3 Possible Attack Types on Physical and MAC Layer  
Since the 5G NR networks support various types of devices from low-power IoT devices, MTC, and smartphones working in a variety of frequency ranges and bandwidths, the NR technology has to support various types of protocol technologies and also inherit some technologies from their predecessor. Thus, 5G is susceptible to various types of attacks, such a Radio Frequency (RF) jamming, spoofing, and sniffing [19, 56], with some of them being similar to the case of LTE. Several case studies on PHY layer security are presented in [64, 65, 66], along with several attacks and proposed countermeasures that have been realized in real-world scenarios, as published in [56, 67]. An interesting study focused on higher-layer security and False Base Station (FBS), with examples of several attacks, and is available in [68]. The authors provide an example of a novel FBS attack including clock information injection and baseband fuzzing, which are manual and automated traffic injection methods that result in continuous DoS attacks.  
The PHY vulnerabilities can be categorized into several groups, depending on which technology is used as the input point. Table 2 summarizes the selected attacks on the PHY and MAC layers. The table is sorted from less sophisticated attacks to more sophisticated ones. It is important to note that even smart jamming attacks need to be synchronized in most cases with the targeted gNB. The column \"Effectiveness\" represents the ratio between the attack's efficiency and its complexity.
## 4 Materials and Methods
### Data Engineering and Preparation Framework
#### 4.1.3 Feature Extraction and Selection  
In developing an effective ML model, proper pre-processing of data should be conducted. This pre-processing includes feature selection and extraction, which, in turn, transforms raw network data into structured representations that are suitable for classification algorithms. There are several network attributes that can be utilized in DDoS detection such as packet size distributions, flow duration metrics, protocol type indicators, TCP flag configurations, and network addressing parameters (source/destination IP addresses and ports). The selection method uses both a statistical technique as well as domain knowledge to extract the most informative attributes and eliminate redundant features. This approach enhances computational efficiency without compromising classification performance, as demonstrated by feature selection improving RF training time from 21.028 s to 16.455 s while increasing accuracy from 93.20% to 99.99%, and reducing SVM training time from 334.456 s to 85.322 s while improving accuracy from 75.06% to 99.26% (24.20% improvement). In addition to computational efficiency, this approach improves detection time and other performance criteria. Thorough analysis and sufficient quantitative evidence supporting these improvements are presented in Section 5. In this research, principal component analysis (PCA) was applied to minimize dimensionality and transform potentially correlated features into linearly uncorrelated variables ordered by explained variance. This method ensures the preserve of essential information while reducing computational requirements.
# Observations on the Computation of Eigenvalue and Eigenvector Jacobians +
## 8 Comparison of Performance  
The primary goal of the derivations presented in this paper was to decrease the computational complexity of those presented in [39]. An examination of the two formulations indicates that both techniques are \(\mathcal{O}(n^{4})\) due to the \(n\times n\) by \(n\times n^{2}\) multiplication in Equation (40) and the \(\operatorname{Tr}\left[\mathbf{A}^{n}\right]\) term in Equation (25) (assuming that the technique used to calculate the determinant is faster than \(\mathcal{O}(n!)\), as this is the case in most modern linear algebra libraries). Despite the fact that both these formulations have the same upper limit on their computational complexity, it should be clear that the new formulation is much simpler, both in terms of operations performed (the formulation from [39] has two operations that are of order \(\mathcal{O}(n^{4})\) as opposed to one for the formulations proposed here) and in terms of memory use.  
A simulation was run in an attempt to detail the increase in computational efficiency from the technique in [39] to the technique presented in this paper. The simulation was performed by applying each technique in turn to 50 randomly generated matrices (the same 50 matrices for each technique) at matrix sizes varying from 2 to 50. For each run, the computation time of each method was recorded. Finally, the minimum computation time for each matrix size was chosen for each method, and the results are shown in Figure 4. As can be seen in the figure, the new method is at minimum an order of magnitude faster and the distance between the performance of the two methods increases as the matrix size increases. In addition, note that the new method is less susceptible to numerical precision issues, as is evidenced by the cut-off of the results for the method from [39].  
Figure 3: Histograms of percent difference between analytic derivatives computed using Equations (36) and (40) (eigenvalue derivatives top and eigenvector derivatives bottom) and finite forward differencing for 5000 randomly generated matrices of each size. The histograms are of the percent difference for each element of the eigenvalue and eigenvector derivatives (for example, for each \(n\times n\) matrix there are \(n^{2}\) eigenvalue derivative elements and \(n\times n^{2}\) eigenvector derivative elements). Similar histograms are presented in [39] for the method discussed in that paper.  
These simulations were performed by the authors on the campus of West Virginia University (WVU) in Morgantown, WV in 2016, using an Intel Core i7-3770 processor at 3.4 GHz,and executed within the Matlab programming language (version R2015b).
## 2 Materials and Methods  
### Chemicals and Culture Media  
4-Nitrophenyl-N-acetyl-\(\beta\)-D-gluco/galactopyranoside (4 NP-\(\beta\)-Glc/GalNAc), 2-nitrophenyl-\(N\)-acetyl-\(\beta\)-D-gluco/galactopyranoside (2 NP-\(\beta\)-Glc/GalNAc), 4-methylumbelliferyl-\(N\)-acetyl-\(\beta\)-D-galactopyranoside (4 MU-\(\beta\)-GalNAc), 2-chloro-4-nitrophenyl-\(\beta\)-D-galactopyranoside (2 Cl-4 NP-\(\beta\)-Gal), and 5-bromo-4-chloro-3-indolyl-N-acetyl-\(\beta\)-D-galactoside (X-\(\beta\)-GalNAc) were purchased from Carbosynth, Ltd. (Compton, UK). The anomeric mixture of 4-methylumbelliferyl-\(N\)-acetyl-\(\alpha\)/\(\beta\)-D-galactopyranoside (\(\alpha\):\(\beta\) anomer ratio = 4:3) was kindly provided by SynthCluster Co., Ltd. (Bratislava, Slovakia). All other chemicals were of analytical grade.  
The cultivation medium contained the following constituents (g/L; pH 6.0 \(\pm\) 0.2): KH\({}_{2}\)PO\({}_{4}\), 3.0; NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\), 5.0; (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 2.0; yeast extract, 0.5; NaCl, 15.0; MgSO\({}_{4}\), 0.5; \(N\)-acetylglucosamine, 5.0. MgSO\({}_{4}\) and \(N\)-acetylglucosamine were added after sterilization. Malt extract agar (MEA) contained the following constituents (g/L; pH 5.3 \(\pm\) 0.2): malt extract, 20.0; peptone, 1.0; glucose, 20.0; agar, 20.0; stock solution of trace elements, 1 mL/L (0.5 g of CuSO\({}_{4}\) 5H\({}_{2}\)O, 0.1 g of ZnSO\({}_{4}\) 7H\({}_{2}\)O in 100 mL of dH\({}_{2}\)O). Czapek yeast autolyaste agar (CYA) contained the following constituents (g/L; pH 7.3 \(\pm\) 0.2): sucrose, 30.0; NaNO\({}_{3}\), 3.0; KH\({}_{2}\)PO\({}_{4}\), 1.0; KCl, 0.5; MgSO\({}_{4}\) 7H\({}_{2}\)O, 0.5; FeSO\({}_{4}\) 7H\({}_{2}\)O, 0.01; agar, 15.0. These media were sterilized in an autoclave at 121 \({}^{\circ}\)C for 20 min.
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 5 Classification
### Training  
The \(128\times 128\) px windows created during data collection are randomly grouped into batches of size 128 and fed into the network. For the entire training set, this results in 250 batches. These batches are iterated over during every training epoch. Due to its fast convergence, the Adam optimizer with a learning rate of 0.001 was chosen for training the network [16]. The optimizer applies gradient updates to the network weights to lower a binary cross-entropy loss function. The graph in Figure 5 displays training and validation loss of the network during training. A 20% subset of the training data is withheld from training and used for validation. Since this validation data also consists of unrealistic simulated defects, it is not wholly representative of the network's final generalization error on real  
Figure 4: Comparison between a decoder activation map with zero-padding (**a**) and reflection padding (**b**). Reflection padding reduces artefacts at the edge of the activation map.  
data. Therefore, a smaller validation dataset of real defect data was generated. These samples were manually labeled to measure the network's generalization error to real defects. After every epoch during training, the errors for both validation sets are recorded. Every epoch of training took around one minute to complete on a Nvidia RTX 2070 Super GPU.  
As apparent in Figure 5, the blue validation curve reaches its minimum after the ninth epoch, which took around nine minutes to train. The continuing downward trend in the other two curves indicates that further training would reduce their respective errors. However, the network would then over-fit on simulated defects and perform worse on real defects. This fast over-fitting on simulated defects can be attributed to their dissimilar appearance to real defects. Stopping the training when the validation error on real data is smallest is necessary as a regularizing measure. This so-called early stopping allows generalization from defect simulations to real defects. In repeated trials, the lowest generalization error on real defects reliably lay between the fifth and tenth epoch. When the system is retrained and no validation data is available, the above-mentioned early stopping point can be used to avoid overfitting. The complete data collection and training process is summarized in Figure 6.
## 3 Non-Thermal Technologies in Food Processing
### High-Pressure Processing
#### 3.4.1 Application of HPP in Fruits and Vegetables  
The extension of shelf life of fresh and minimally processed fruits and vegetables is a key priority at their post-harvest storage [202]. Due to their very high vulnerability, many fruits and vegetables show a relatively short post-harvest life and degraded quality over time [203]. The food industry has turned its attention to the production of minimally processed fruits and vegetables through techniques aimed at maintaining safety and reducing  
Figure 6: Schematic display of a high-pressure processing (HPP) vessel. Adapted from [192].  
their contamination from foodborne pathogens, which are inevitable microorganisms. The HPP technique can help significantly in the processing of minimally processed fruits and vegetables, causing minimal losses in natural aromas and colors and maintaining their high quality [204]. Actually, the effect of HPP treatment on the covalent bonds of low molecular weight compounds of fruits and vegetables is limited, and therefore, the nutrients and sensory properties are well preserved. Since the success of the technique is due not only to the parameters of the treatment but also to factors related to the integrity and texture of the fruit, there may be conflicting results for the same product [205].  
The wounding stress caused by the various changes that occur in the membranes of fruits and vegetables is the main effect of the treatment with the HPP technique [206]. The exposure of whole fruits and vegetables to treatment with the HPP technique has caused an increase in their bioactive components [207], such as polyphenols, isothi cyanates, fatty acids, and essential oils [208]. For example, HPP enhanced the extraction of carotenoids, phenolics, and ascorbic acid in whole mangoes [209], free and bound phenolics in whole carrots [206], and total phenolics in strawberries [210]. To date, the accumulation of nutraceuticals caused by the application of the HPP technique has not been fully elucidated, and it is not known whether it is a result of the time of application of the pressure or increases as the pressure increases [206].  
The HPP technique has also been used for the extraction of valuable compounds from plant sources, such as anthocyanins from grapes [211], pectin from tomato peel [199; 212], carotenoid from tomato peel [213], and pectin from potato waste peel [214].