esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Physical Layer
### Spectrum Sensing Techniques
#### 3.1.4 Eigenvalue-Based Detection  
A relatively recent alternative spectrum-sensing method is the eigenvalue-based detection proposed by [PERSON] and [PERSON] in [28]. The algorithm first calculates the covariance matrix of the signal samples. The ratio of the maximum to minimum eigenvalue is then computed as the main metrics and its relationship with the detection threshold yields the sensing results [29]. The detection threshold is key to the performance of this sensing method. The probability of false alarms were employed to deduce the appropriate threshold in a way that its relationship with signal and noise property is eliminated [31].  
The eigenvalue-based detection has several advantages over matched filter and cyclostationary feature detection [30]. Similar to energy detection, it does not require prior signal knowledge or noise patterns. On the other hand, since the metrics and detection threshold is designed to be irrelevant to noise circumstances, the eigenvalue-based measure is not affected by the noise uncertainty unlike the energy detection, which results in superior performances [30]. The enhancement is, however, gained at the expense of computational overhead. According to [29], the complexity of this method is \(M*L\) times that of energy detection, where \(M\) is the oversampling factor or the number of receivers in an multi-antenna scenario and \(L\) denotes the sample size.
# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 2 Literature Review
### Emergency Evacuation Simulation  
The prerequisite for the effective functioning of emergency shelters is that their supply and demand should be balanced in each district of the city [22]. A practical and effective method is to perform an evacuation simulation after the analysis of shelter demand [12]. The key to evacuation simulation is to establish the connection between evacuation activities and the urban environment, then to process complex space, facility, and road network data, and finally to establish a model under objective functions, such as the shortest-path and minimum-cost functions [23; 24]. From the perspective of the evacuation plan, evacuation simulation can be regarded as a two-stage process; the first stage is to find suitable locations, and the second stage is to use an algorithm to determine the best evacuation route in an emergency [13; 25]. The results are related to various traffic scenarios and speed assumptions. [PERSON] and [PERSON] [26] tried to derive an evacuation speed formula based on the slope, which is used to calculate the possible speed and evacuation distance of evacuees when deciding on the shelter location. Due to the different evacuation behaviors and speeds of different age groups, actual evacuation activities are very complicated, and the results will also exhibit diversity. Based on these considerations, agent technology can be used to realize evacuation simulation, and evacuation activities can be determined by various existing behavior models. This method can be used to evaluate the dynamic space allocation of urban emergency shelters, simulate the evacuation process of residents evacating to the shelters, obtain the evacuation times of all evacuees in an area, and employ the flexible combination of existing behavior and decision-making models to generate the best evacuation plan [27; 28]. Based on specific disaster scenarios, [PERSON] [29] assumed the travel time and capacity to be random variables, and focused on the disaster response of the evacuation of residents to safe locations in a two-stage stochastic planning model to describe the urban transportation network during the evacuation process. From the perspective of evacuation decision making, some studies start from the crowds themselves and focus on how evacuees obtain evacuation information as an entry point to evaluate the impact of various social factors (resident psychology, individual differences, social networks, etc.) on residents' evacuation choices [30; 31; 32].
# Smart Clothing Framework for Health Monitoring Applications
## 2 Wearable Technology and Smart Clothing
### Wearable Technology  
Wearable technology comprises the electronic devices that can be attached to the human body for monitoring and delivering intelligent services. Several types of wearable technologies, including smart watches, smart jewellery, fitness trackers, smart shoes, smart belt, brain activity tracker, etc., are currently used for the monitoring of different body parameters (Figure 1).  
Wearable technologies were classified into four different categories, including accessory, textile or fabric, patchable and implantable wearables, by the International Electrotechnical Commission (IEC) [4]. The focus of this paper is on textile-based wearable technology or smart clothing, as shown in Figure 2. Interested readers are advised to consult the recent papers published on wearable technologies [5; 6; 7].  
of smartphones and other devices. Furthermore, clothes are suitable for this purpose since the early stages of textile and flexible electronics can adapt to the direct contact with one's body through clothes [7]. Smart clothing is more natural to wear compared to the other wearables and covers a wider area for monitoring. Biological events including heartbeat, muscle contractions, etc., are sources of various biosignals [8]. Biosignals generated from human body are categorized as shown in Figure 3.  
Smart clothing is the next generation of clothes, with potential functionality in tracking individual's biometrics, responding to environmental changes, etc. The advantages of wearing smart clothing are presented in Figure 4. Therefore, the focus is shifted to developing smart clothes instead of concentrating only on smart watches and smart bands.  
Wearable 2.0 is proposed in [9] as smart clothing by incorporating multiple connected devices, which can communicate with cloud-based services to offer an improved lifestyle. Smart clothing can ensure comfort, usability, accuracy, product washability, and support for real-time monitoring. Figure 5 presents a comparison between a conventional wearable system and smart clothing.  
Figure 4: Advantages of smart clothing.  
Figure 3: Types of biosignals in the human body (information taken from [8]).  
As this paper focuses on the development of a smart leggings framework, a schematic diagram of smart leggings and other fabric-based accessories for the hip, thigh, knee, ankle and foot is presented in Figure 6.
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 4 Numerical Results
### Performance Improvement  
In this section, the numerical results are discussed to verify the accuracy of the derived PDFs. First, it is observed from Figure 2 that both the first approximation and the histogram PDFs exhibit the same distribution. In addition, the KS test showed a close match between the two distributions, as demonstrated in the previous section. However, Figure 3 did not show a close match between the derived approximation and the histogram plot, which was also proofed by the KS test in Section 3.  
Figures 4 and 5 compared the performance of the turbo-coded system with the proposed LLRs that was derived utilizing both Equation (13) and (14) PDFs and the Gaussian based-LLRs for rates \(1/3\) and \(1/2\), respectively. It was observed that the proposed LLRs with the first approximation improved the performance of the turbo-coded system by more than 2 dB at BER = \(10^{-5}\) for both modulation constellations and for both ratios. On the other hand, the second approximation also improved the performance of the coded system with a small degradation in performance compared to the first approximation, as demonstrated with the red line of the simulation.  
Figure 3: Comparison of distribution of the histogram and the second approximation of the derived PDF.  
Figure 2: Comparison of distribution of the histogram and the first approximation of the derived probability density function (PDF).
# The Blockchain Oracle Problem in Decentralized Finance--A Multivocal Approach
## 5 Risks and Attack Vectors
### Selection Bias (S)  
Selection bias refers to the situation in which a DeFi service selects a data source that is not explicitly meant to serve as an oracle [25]. To give an example, to measure the temperature, we generically employ a thermometer. However, to measure a patient's temperature during surgeries, for instance, special thermometers are needed to rapidly check for any drastic temperature change and warn the surgeon to intervene. In the DeFi space, the role of price oracle should be entrusted to entities specifically made to serve this purpose. Price oracles should, in fact, report price changes avoiding distortions and deviances. Arguably, the higher the value locked into DeFi smart contracts, the higher the accuracy and security of the selected price oracle should be [111]. To better understand the problem of price oracle source selection, the example of Curve Finance fits perfectly. In 2020, Curve Finance was considered responsible for many flash-loan attacks for an amount that exceeds one hundred million dollars [112]. The attackers exploited the fact that many DeFi platforms arbitrarily decided to adopt Curve Finance LP's price feeds. LP price feeds, of course, are characterized by high volatility to rapidly adapt to the size and usage of the selected pool. Exploiting the LP price volatility, flash-loan attacks were incredibly easy and rewarding. Hence, despite the fact that Curve Finance data were publicly available, their LP price feeds were not intended to provide pricing for collaterals in the first place. The Curve Finance team declared in many articles, in fact, that they were not aware that other DeFi projects utilized their LP price feed [112]. Therefore, they publicly suggested relying on other and specialized oracle services.
# V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
## 3 SOVANET+ Scheduling Algorithm
Regarding the critical services, the number of bytes per LC is determined using the throughput formula:  
\[x_{i}=alpha\times load\times(TBS/activeLC) \tag{2}\]  
where \(x_{i}\) is the user's throughput, alpha is the weight factor, load is the network load, TBS is the Traffic Block Size and activeLC is the active Logical Channel used. The values chosen  
Figure 1: SOVANET+ scheduling algorithm. for this factor have been determined through extensive simulations. The alpha factor decreases while the network load increases, as the maximum allocated resources should not surpass the available resources. The beta weight factor is calculated and appointed to all non-critical services in all load scenarios, as 0.5% of the overall network load. The throughput requirements for the UL and DL of the non-critical RTSA are modest (8 kbps). Regarding the LCs with very low MCS, a radical approach was adopted: temporarily non-providing resources. Extensive simulations proved that despite the resources wasted on UEs under poor channel conditions, critical V2N UCs would still be unfeasible in terms of throughput and delay. At this point, the fast-changing conditions of vehicular networks should be highlighted. As the channel conditions may rapidly and drastically improve, the denial of resources is temporary. Thus, the wastage of resources is avoided.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 4 Evaluation and Analysis of NSCC with Representative Parameters  
### Spectral Separation Coefficient  
The SSCs for typical modulation schemes are listed in Table 2. The NSCC modulation adopts flat power distribution for sub-carriers, and the number of mutually orthogonal sub-carriers is 19. The pre-filtering bandwidth used in the simulation is 20 MHz.  
The comparison data in the Table 2 demonstrate that the NSCCs possesses better SSCs than P, MBOC(6,1,1/11), BOC(1, 1), MSK, C/A, except for NSCC monomodal. This is because the NSCC signals consist of spectrally overlapping and mutually orthogonal sub-carriers resulting in very efficient spectrum utilization. The NSCC monomodal signal concentrates its main power within a 10 MHz bandwidth centered on the carrier frequency, yielding the largest overlap with the legacy navigation signals, while the NSCC bimodal dodges these legacy signals and imposes the least interference on the desired signals. Furthermore, with the increasing chip rate, the compatibility between NSCC modulation and C/A, MSK, BOC(1,1) MBOC(6,1,1/11), is becoming worse, but for larger bandwidth signals, such as QPSK(10), P, the compatibility is improving. It is not surprising that the number of subcarriers associated with the chip rate under the limitation of finite bandwidth, influences the power lever for NSCC with equipower distribution.
###### Abstract  
The present study aimed to investigate the ability of _Lactobacillus plantarum_ TA4 in tolerating Ag\({}^{+}\) and its ability to produce silver nanoparticles (AgNPs). The biosynthesized AgNPs were characterized using UV-Visible spectroscopy (UV-Vis), dynamic light scattering (DLS), Fourier-transform infrared (FTIR), and high-resolution transmission electron microscope (HR-TEM). The cell biomass of \(L\). _plantarum_ TA4 demonstrated the ability to tolerate Ag\({}^{+}\) at a concentration of 2 mM, followed by the formation of AgNPs. This was confirmed by the visual observation of color changes and a presence of maximum UV-Vis absorption centered at 429 nm. HR-TEM analysis revealed that the AgNPs were spherical with an average size of 14.0 \(\pm\) 4.7 nm, while the SEM-EDX analysis detected that the particles were primarily located on the cell membrane of \(L\). _plantarum_ TA4. Further, DLS analysis revealed that the polydispersity index (PDI) value of biosynthesized AgNPs was 0.193, implying the monodispersed characteristic of NPs. Meanwhile, the FTIR study confirmed the involvement of functional groups from the cell biomass that involved in the reduction process. Moreover, biosynthesized AgNPs exhibited antibacterial activity against Gram-positive and Gram-negative pathogens in a concentration-dependent manner. Furthermore, the antioxidant property of biosynthesized AgNPs that was evaluated using the DPPH assay showed considerable antioxidant potential. Results from this study provide a sustainable and inexpensive method for the production of AgNPs.  
**Keywords:** antibacterial; biosynthesis; FTIR; HR-TEM; _Lactobacillus_; silver nanoparticles; sustainable +
Footnote †: journal: Applied Physics Letters
## 3 Wireless Communication Technologies
### Short-Range Communication Technologies
#### 3.1.1 Wi-Fi (IEEE 802.11)  
Wi-Fi, or Wireless Fidelity, is a short-range communication technology that consists of a set of standards for designing WLAN (Wireless Local Area Network) in the following radio bands: 2.4 GHz, 3.6 GHz, 5 GHz, and 60 GHz. To provide the throughput required for large-sized data transmissions like videos and images, variants of IEEE 802.11a/b/g/n/ac, can be the top choice for many FANET applications. The transmission range of a traditional Wi-Fi system is around 100 m. However, the transmission range can be extended up to several kilometers with the help of ad-hoc networking between the UAV. In a typical 802.11 network, clients, on the basis of announcement by access points (APs), discover and associate with wireless local area networks (WLANs) [7]. In this manner, a device behaves either as a client or as an AP. Moreover, these roles are assigned dynamically and could even be executed simultaneously by the same device. In [8], the authors investigated the performance measurement in terms of throughput, RSSI, and distance of a wireless link from UAV to GS. It is also suggested that applying an 802.11a wireless link for UAV and GS is useful for a UAV-based networking.
## 4 Performance Metrics  
This section is dedicated to the analysis of various performance metrics, such as spectral efficiency, energy efficiency, and capacity. The proposed eOFDM scheme is compared based on these parameters with TDS-OFDM and CP-OFDM.
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
## 3 Method and Implementation
### The Use of NSGA-II  
In the method proposed in this paper, defect localization is first used to determine the first part b of x, and the values in the vector b are assigned based on the degree of suspiciousness. Positions with a higher suspiciousness are more likely to be assigned a value of 1, while positions with a lower suspiciousness are more likely to be assigned a value of 0. Then, the selection of values in v is determined based on the first part, and a random repair statement is selected from the repair material set for each suspicious location. After the first and second parts are determined, an initial variant can be obtained. This process continues until N variants are selected to form the initial population of the first generation.  
This paper formulates the problem as a multi-objective search problem. To evaluate the fitness of each variant in the population, the objective functions \(F_{1}\) and \(F_{2}\) are used to assess the variants. \(F_{1}\) and \(F_{2}\) are functions related to the number of test cases passed and the patch size, respectively, with the goal of minimizing both objective functions to achieve optimal results.  
In Formula (4), \(T_{N}\) and \(T_{S}\) represent the failed test cases and successful test cases in the test case set, respectively. \(T_{n}\) is the set of failed test cases in \(T_{N}\), \(T_{s}\) is the set of failed test cases in \(T_{S}\), and \(\omega\in(0,1]\) is a global parameter that can introduce bias for failed tests. Formula (5) represents the size of the patch x, which actually refers to the number of patch operations.  
\[F_{1}(x)=\frac{T_{n}}{T_{N}}+\omega\times\frac{T_{s}}{T_{S}} \tag{4}\]  
\[F_{2}(x)=\sum_{i=1}^{n}b_{i} \tag{5}\]  
Based on the proposed solution, NSGA-II [19] is used as the solution for the multi-objective genetic search algorithm. Firstly, the population is initialized according to the  
Figure 4: Explanation and Illustration of Repair Material Set v.  
Figure 3: Explanation and Illustration of Defect Location b.  
initialization strategy mentioned in the beginning of this section. Then, the algorithm enters a loop state until the maximum number of iterations is reached or the maximum time for repair is exceeded. In each generation, a child population is generated using binary tournament selection, and N best solutions are selected using fast non-dominated sorting and crowding distance comparison. These N best solutions constitute the next generation of population variants. Finally, the non-dominated solutions for \(F_{1}=0\) are output, as they can only be considered as correct patches when \(F_{1}=0\). The results need to be discarded when \(F_{2}=0\), because \(F_{2}=0\) indicates that the patch makes no changes to the source code, which is meaningless.
## 2 Smart Grid Background
### Security of Smart Grid  
Smart grids offer a wealth of opportunities and advantages, but they also come with several security risks. The creation of a highly secure information system is vital for maximizing the benefits of smart grids. It is believed that automation and control systems like SCADA were not meant for use in an unsecure and open environment, and neither was the Modbus communication protocol, which shares SCADA information to operate in industrial processes [39; 40]. The security of a smart grid information system must be given top importance since electricity assets are important parts of a country's infrastructure that may attract terrorists. Damaging a power grid due to security intrusions might destabilize large areas or entire cities [41].  
Additionally, there are numerous reasons why IoT-based smart grids are more prone to security issues and threats. Many threats occur due to their online accessibility, which makes data tampering by attackers possible. The smart grid is more susceptible to attack because of the growing number of IoT devices being employed in it, and here are some of the reasons that make it fragile to breaches [42; 43; 44]:  
* The devices in the smart grid exchange private and sensitive data between consumers and utility corporations utilizing an IP-based communication network. These networks can be affected by many security risks, including denial of service, man in the middle, replay attacks, and eavesdropping.
* The two-way information flow itself may make the smart grid vulnerable to several threats by permitting unauthorized access.
* It has been asserted that wireless sensor networks, which are used by smart grids to connect smart meters, are vulnerable.
* Employing IoT components could result in the smart grid inheriting their security issues, which might be related to controlling and monitoring these IoT devices.
* A smart grid contains various components with diverse technologies that require an instant interaction between them for the smooth functionality of smart grid. This interaction will mandate creating access points between components, which increases the vulnerability to security threats.
## 5 Numerical Results
It can be observed that the system employing eOFDM has a higher capacity than CP-OFDM and TDS-OFDM for the given range of SNR. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Value** \\ \hline Number of channel realizations (h) & 10,000 \\ Path delays (s) & [0, 200, 800, 1200, 2300, 3700] \(10^{-9}\) \\ Average path gains (dB) & \([0,-0.9,-4.9,-8,-7.8,-23.9]\) \\ Doppler frequency (\(f_{D}\)) & 50 Hz \\ Number of OFDM blocks & 10,000 \\ IDFT/DFT size (\(N\)) & 32 \\ Ratio of CP/PN to \(N\) (\(v\)) & 1/4 \\ Modulation order (M) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters for OFDM block and ITU pedestrian B channel model. Figure 5: BER comparison of CP-OFDM, TDS-OFDM and eOFDM in AWGN channel.
## 5 Experiments and Results
### Comparing the Results of CBHIR with a Classifier  
CBHIR and classification are two different CAD tools for pathologists. CBHIR provides similar patches for pathologists based on the content features of the query and the database. However, the main objective of classification is categorizing images into pre-defined labels. In CBHIR, in addition to the corresponding label of the query, the top K similar patches are shown to the pathologists. This enhances the reliability of CBHIR for pathologists as it avoids being a completely black box. CBHIR gives pathologists an opportunity to compare the histological patterns of the query with similar patches in addition to knowing the corresponding label.  
Table 3 reports the area under the curve (AUC) of the unsupervised CBHIR as a result of applying all CN techniques. These results were compared with the results of applying VGG19 on CAM17 as a classifier, which is reported in [36]. Following [46], the main objective of this comparison with the supervised classifier is to evaluate the unsupervised CBHIR performance in terms of retrieving images belonging to the same cancer grade [46]. According to Table 3, BKSVD-VGG19, with a 98.17% AUC, had the highest performance among (Vah, Mac, Original)-VGG19. The unsupervised BKSVD-CBHIR method, with a 96.31% AUC, has a comparable performance to the fully supervised method.