esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this work, monitoring of the transmit power for several base stations operating in a live 5G network (Telstra, Australia) was conducted with the purpose of analyzing the radio frequency (RF) electromagnetic field (EMF) exposure levels. The base stations made use of state-of-the-art massive MIMO antennas utilizing beamforming in order to optimize the signal strength at the user's device. In order to characterize the actual EMF exposure from 5G base stations, knowledge of the amount of power dynamically allocated to each beam is therefore of importance. Experimental data on the spatial distribution of the base stations' transmit power were gathered directly from the network by extracting information on the radio and baseband operations. Out of more than 13 million samples collected over 24 h, the maximum time-averaged power per beam direction was found to be well-below the theoretical maximum and lower than what was predicted by the existing statistical models. The results show that assuming constant peak power transmission in a fixed beam direction leads to an unrealistic EMF exposure assessment. This work provides insights relevant for the standardization of EMF compliance assessment methodologies applicable for 5G base stations.  
mobile communication; EMF exposure; 5G; base stations; RF EMF compliance; massive MIMO; antenna arrays; beamforming
## 3 Results and Discussion
### Recognizing Vulnerable Population Regions  
The degree and severity of the disease can vary when the population from certain demographic groups is affected, and therefore age groups and other characteristics related to the population susceptibility factor are mapped, which can help control risk groups and regions. The most important factors considered for vulnerable regions [23; 24] are related to:  
* presence of virus,
* chronic disease and age, and
* personal protection.
## 3 Theoretical Framework and Hypotheses Development
### Hypotheses Related to Moderating Factors
#### 3.2.2 Hypothesis Related to Gender  
Another moderating factor that may influence information adoption (IA) suggested by UTAUT is gender. Gender was used as a moderator in many studies [43; 44; 47; 48; 49; 50; 53; 64]. As a result, the following hypothesis is proposed.  
**Hypothesis 10 (H10).**_Gender has a significant moderating effect on information adoption (IA) towards YouTube._
## 4 Analysis and Validation  
Sensitivity of BTM system defined as the level of disturbance at the input port of BTM, which leads to the abnormal working status of BTM system.  
To validate the modeling method, the sensitivity of BTM system is measured in a semi-anechoic chamber. Figure 19 shows the measurement set-up. The measurement was performed using two signal generators (RIGOL DG3121A signal generator and RIGOL DG1032Z signal generator) and a spectrum analyzer (ROHDE & SCHWARZ FSH8) to measure signals. The signal is fed from the signal generator to reference loop antenna, and the received terminal disturbance voltage at the BTM input port, which lead to BTM abnormal working status is measured by the spectrum analyzer. Depending on the site  
Figure 17: Field tests for the critical condition T of BTM system.  
Figure 18: The disturbance level for exhaust system.  
installation requirements, the distance between the reference loop antenna and on-board antenna is 350 mm.  
Good agreement between measurements and simulation result of the model is shown in Figure 20.  
The predicted result shows that no matter what function the BTM system carries out, the most sensitive frequency is in the BTM operating frequency range. According to Figure 20a, the maximum error between the results of measurement and our model is only 1.45 dB at 3.4 MHz when BTM system takes the function F2. Between 3.9 MHz and 4.6 MHz, the maximum absolute error is 0.4 dB. The BTM system will report an error when the voltage of disturbance S\({}_{n}\) is larger than 64 dB\(\mu\)V. Figure 20b is the comparison result when the on-board antenna is in the contact zone and the BTM system carries out  
Figure 19: Measurement set-up: (**a**) Block diagram for device connection; (**b**) Experimental setup in semi-anechoic chamber.  
Figure 20: Sensitivity of BTM system: (**a**) Sensitivity of BTM system take the function F2: “Balise Detection Function”; (**b**) The sensitivity of BTM when the on-board antenna is at contact zone, carrying out the function F3: “Data Reception Function” and the useful signal current is I\({}_{U2}\) = 59 mA [7].  
the function F3. Between 3.4 MHz and 4.6 MHz, the maximum absolute error is 2.8 dB. In this band, the sensitivity level got from the simulation is lower than the result of the measurement. That difference may be caused by ignoring the self-error correcting ability of BTM system. Thus, this immunity of the testing on BTM system is better than the theory analysis result. The disturbance \(\mathsf{S_{n}}\) should be smaller than 67 dB\(\mathsf{\mu V}\) to make sure the BTM system performs normal work.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 2 MXenes as Supercapacitor Electrodes
### Advantages of MXenes in Supercapacitors
#### 2.2.2 MXene-Carbon Allotropes: Pioneering Energy Storage Innovation
The adoption of PAM hydrogel electrolytes offered enhanced security and contributed to greater cycling stability for the supercapacitor electrodes when compared to organic electrolytes [35].
# Conceptual Design of a Semi-Dual Polarized Monopulse Antenna by Computer Simulation
## 1 Introduction
Furthermore, it is immaterial in a single-channel monopulse system [18] where a fraction (\(-\)6 to \(-\)10 dB) of the signal received in the difference channel is combined with the sum channel signal to form a single-channel tracking signal, which contains the target tracking information. The single-channel signal is demodulated in the receiver for the target azimuth and elevation angles. The proposed idea is also applicable to antennas for tracking, transmitting, and receiving communication or telemetry signals from moving platforms. In this case, the moving platform is required to transmit a circularly polarized wave, which is often the case since the platform's attitude may change significantly during flight or navigation. Here, again, there will be a maximum polarization loss of 3 dB in the difference channel, which can be tolerated in many cases. The proposed antenna is designed to operate at 14.5-16.0 GHz, part of the Ku-band, which is used in military tracking radars (e.g., KuRFS by Raytheon Missiles & Defense), ASA's Tracking Data Relay Satellite for the International Space Station, and SpaceX Starlink Satellites. To implement the proposed idea, dual-circularly polarized waves for the sum channel are realized using a square waveguide radiator with a septum polarizer, while two rectangular waveguide radiators are employed for linearly polarized difference channels. Both sum and difference channel waveguides are excited by a coaxial probe. Our implementation can be called 'a five-element semi-dual polarized monopulse feed'. This paper has been written to present a new design idea for a monopulse feed, not to describe the outcome of a research project where the fabrication and measurement of the antenna is a natural part of the research process. The proposed antenna, especially the reflector, is costly to fabricate, unlike, for example, simple printed-circuit antennas. Thus, the proposed idea is validated by computer simulation using the widely-used CST Studio SuiteTM, a well-proven tool whose accuracy has been shown in many published papers to be good enough to prove an antenna design idea. The dimensions of the proposed antenna are fully given so that anyone can verify the validity of the proposed design by computer simulation.
# Dissemination of Enterococcuscal Genetic Lineages: A One Health Perspective
## 4 Enterococcal Genetic Traits: Dissemination in the One Health Continuum
### Dissemination of Genetic Traits across One Health Sectors  
Many authors [18; 36; 38; 97] have been focusing on the interplay between human, animal, community, and environmental enterococcal clones, from a One Health perspective. Nevertheless, to the best of our knowledge, no review has provided a roadmap of the clonal spread from human clinical settings to animals, food, the community, and the environment, highlighting the dissemination not only between sectors but also around the globe, as hereby presented. Table 3 summarizes the distribution of specific genetic lineages, virulence features, and antimicrobial resistance of isolates present across the One Health continuum. Only studies comprising MLST analysis with discriminated STs, respective ARGs, and virulence genes were included. To facilitate interpretation and better highlight the dissemination within and between One Health sectors, STs were grouped within the respective CCs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multicolumn{3}{c}{**Clonal Lineages**} & \multirow{2}{*}{ \begin{tabular}{c} **Simulated** \\ **Complex** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **MLST** \\ **Sequence** \\ **Type** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Virulence** \\ **Genes** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Antimicrobial** \\ **Resistance Genes** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Source** \\ **Resistance Genes** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Location** \\ **Ref.** \\ \end{tabular}  } \\ \cline{2-7}  & \multirow{2}{*}{CC2} & ST40 & _GelE_ & _tetM_ & Cheese & Poland & [98] \\ \cline{3-7}  & & ST6 & ND & \begin{tabular}{c} _vanA, tetM,_ \\ _anc6’-le-alpha2''-la_ \\ \end{tabular}  & \begin{tabular}{c} Water in swine \\ facility \\ \end{tabular}  & Portugal & [99] \\ \hline \multirow{3}{*}{CC21} & ST21 & ND & \ \begin{tabular}{c} _tetM, tetL,_ \\ _anc6’-le-alpha2''-la_ \\ \end{tabular}  & \begin{tabular}{c} Air in facility; \\ liquid manure \\ \end{tabular}
# Lightweight NFC Protocol for Privacy Protection in Mobile IoT
## 2 Relative Works  
For any network, identity authentication and identity trust are the key considerations because security threats are diverse and complex. There are many research articles [4; 5; 6; 7; 8; 9] focusing on solving the security issues in IoT, such as intrusion detection in sensor networks and trust models in networks. Even machine learning has been applied to help defeat security threats [10]. In addition, the human factor is also an important subject which attracts the attention of researchers [11].  
Identity authentication is also very important in radio frequency communication and can be used against complex security threats [12]. There are some protocols proposed to solve the identity  
Figure 1: The communication components of the radio frequency identification system.  
authentication problem in the IoT. To make the protocol safer, these protocols use strong encryption operations excessively, such as symmetric encryption and the hash function [13; 14; 15].  
Later, researchers found that the abuse of encryption often results in a high communication overhead and requires high hardware resources [16; 17; 18]. On the other hand, with the development of society, people are increasingly concerned about their privacy and security, and the requirements for privacy protection are getting higher. Some researchers are working on privacy protection and tag anonymity in authentication [19; 20; 21].  
In 2015, [PERSON] and [PERSON] proposed a secure and lightweight authentication protocol for NFC tag-based services [22]. Tags require less memory storage and computational overhead in the protocol. Moreover, they announced that the protocol effectively achieves security by preventing spoofing, DoS, data modification, and phishing attacks. However, the protocol still cannot resist the de-synchronization attack.  
As we can see, all these lightweight protocols above still have many problems. In 2016, [PERSON] and [PERSON] offered us some recommendations in order to avoid some typical mistakes. This work, as a sanity check, can help designers of RFID, NFC, and sensor networks-based security solutions to improve the security, reliability, and longevity of lightweight authentication protocols [23].  
When it comes to mobile IoT, especially when an authentication system contains phones, these protocols become less effective. Firstly, the mobile IoT prefers NFC technology for authentication. Secondly, the system model is also complex. An NFC phone can work in a tag mode and a reader mode, the role of the device in mobile IoT is changeable and uncertain. Thirdly, people have higher requirements for the anonymity and privacy of the IoT in which mobile devices participate in. Lastly, battery power limitations, communication overhead, and hardware costs are also important considerations for mobile IoT.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 2 Materials and Methods
### Drought Assessment  
Daily temperature and precipitation were used to calculate monthly SPEI values following [PERSON] et al. [48]. Monthly potential evapotranspiration (PET) was computed using the Hargreaves-Sanani formula [71]. The monthly climatic water balance at different time scales was calculated as the difference between precipitation and PET and fitted with a three-parameter log-logistic probability distribution function used to calculate the standardized precipitation index (SPI) [49].  
The cumulative probability distribution \(F(x)\) of a water balance time series is given as:  
\[F(x)=\left[1+\left(\frac{\alpha}{x-\gamma}\right)^{\beta}\right]^{-1} \tag{6}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are scale, shape, and origin parameters, respectively.  
Using the approximation of [PERSON] and [PERSON] [72], SPEI is calculated as:  
\[\text{SPEI}=-\left(W-\frac{C_{0}+C_{1}W+C_{2}W^{2}}{1+d_{1}W+d_{2}W^{2}+d_{3}W ^{3}}\right),\text{ for }0<F(x)\leq 0.5 \tag{7}\]  
\[\text{SPEI}=+\left(W-\frac{C_{0}+C_{1}W+C_{2}W^{2}}{1+d_{1}W+d_{2}W^{2}+d_{3}W ^{3}}\right),\text{ for }0.5<F(x)<1 \tag{8}\]  
where \(C_{0}=2.515517\), \(C_{1}=0.802853,C_{2}=0.010328,d_{1}=1.432788,d_{2}=0.189269\), and \(d_{3}=0.001308\); \(W\) is determined as:  
\[W=\sqrt{-2\ln(F(x))}\quad\text{for }0<F(x)\leq 0.5 \tag{9}\]  
\[W=\sqrt{-2\ln(1-F(x))}\quad\text{for }0.5<F(x)<1 \tag{10}\]  
In this study, SPEI was computed for each pixel at time scales from 1 to 24 months to capture the accumulated effects of water balance anomalies on terrestrial carbon fluxes. Positive SPEI values denote wet conditions while negative values indicate drought conditions.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 9 IoT and Smart Contracts on Distributed Ledgers
### When to Use Blockchains for IIoT?  
Use-cases where even per-item micro-transactions should be noted and acknowledged, or where various parties of the supply chain should access validated data on the smart product (footprint), the data storage on a distributed, replicated ledger makes sense. Furthermore, for transactions (either sensor data or actual physical equipment) on a highly changing market with various vendors who are not always trusted, blockchains powered with smart service contracts can enforce contractual steps. These are potential applications of blockchains in an IIoT environment.  
On the other hand, in many cases, it is not justifiable to use distributed ledgers or blockchains for industrial IoT scenarios. When specifically taking the data storage, access, security, and privacy issues, Figure 10 provides a decision support flowchart whether it is worth considering the usage of blockchain technologies for the given IIoT scenario. This flowchart was strongly motivated by the work in [97; 98].  
Current blockchain systems suffer from several drawbacks when considered for IIoT scenarios: the currently achieved number of transactions (less than 100 per second) can be slow for many applications; the mixed use of private & public blockchains is not clearly solved; vulnerabilities such as the infamous 51% attack, race attacks, finney attacks, and the fact that bugs in smart contracts are not patchable, are a few of the more severe ones.  
Advantages and drawbacks of using blockchain technologies for IoT are discussed by [98; 99; 100]. We summarized the main findings in Table 9.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Area to Consider** & **Advantage** & **Drawback** \\ \hline \multirow{2}{*}{Ledgers} & \multirow{2}{*}{Ledgers as distributed and trusted databases} & Mixed usage of private and public blockchains are not clearly solved in practice \\ \cline{3-3}  & & Accelerated transactions when compared to industrial bulk commissioning & Number of transactions are still less than 100 per second \\ \hline \multirow{2}{*}{Transaction speed} & \multirow{2}{*}{Possibility of micropayments} & Limitations for resource constrained devices \\ \cline{3-3}  & & Advanced, inherited security & Security vulnerabilities, such as \\ \cline{3-3} Security & Trust Data Privacy & 51\% attack, fencey attack, \\ \cline{3-3}  & Confidentiality, Integrity, Availability & bugs in Smart Contracts are not patchable \\ \hline \multirow{2}{*}{Leaving out third parties} & \multirow{2}{*}{Anonymity Cost reductions by removing middemen} & Legal issues are not easy to solve \\ \cline{3-3}  & & \\ \end{tabular}
\end{table}
Table 9: Some advantages and drawbacks of blockchain usage when compared to traditional data handling.  
Figure 10: Decision support on considering the use of blockchains in the given IIoT scenario.
###### Abstract  
The Co-frequency Co-time Full Duplex (CCFD) is a key concept in 5G wireless communication networks. The biggest challenge for CCFD wireless communication is the strong self-interference (SI) from near-end transceivers. Aiming at cancelling the SI of near-end transceivers in CCFD systems in the radio frequency (RF) domain, a novel time-varying Least Mean Square (LMS) adaptive filtering algorithm which is based on step-size parameters gradually decrease with time varying called the DTV-LMS algorithm is proposed in this paper. The proposed DTV-LMS algorithm in this paper establishes the non-linear relationship between step factor and the evolved arct-angent function, and using the relationship between the time parameter and error signal correlation value to coordinately control the step factor to be updated. This algorithm maintains a low computational complexity. Simultaneously, the DTV-LMS algorithm can also attain the ideal characteristics, including the interference cancellation ratio (ICR), convergence speed, and channel tracking, so that the SI signal in the RF domain of a full duplex system can be effectively cancelled. The analysis and simulation results show that the ICR in the RF domain of the proposed algorithm is higher than that in the compared algorithms and have a faster convergence speed. At the same time, the channel tracking capability has also been significantly enhanced in CCFD systems.  
Keywords:Co-time Co-frequency Full Duplex (CCFD); self-interference (SI) signal cancellation; RF domain; optimize Least Mean Square (LMS) algorithm; interference cancellation ratio (ICR) +
Footnote †: journal: Applied
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods  
### Study Area  
The study area is located in South Tyrol, Northern Italy, and includes the subalpine and alpine altitudinal zones of the Sarntal Alps (Figure 1). It covers an area of 583 km\({}^{2}\) and includes the entire Sarntal Valley with its side valleys (Pens Valley and Durnholz Valley). The orography of the Sarntal Alps is characterized by mostly gentle, moderately steep slopes, with elevations ranging from 923 m a.s.l. to 2781 m a.s.l. Due to the location at the southern edge of the Alps, the climate is characterized by moderate precipitation and a high number of sunny days [31]. At the valley floor (Sarnthein 970 m a.s.l.), the annual mean temperature is 8.4 C, while annual mean precipitation sums up to 908.7 mm/year [13]. With increasing altitude, annual mean temperature decreases to 1.1 C at 2260 m a.s.l. (Rittner Horn), and annual mean precipitation increases to 1016 mm/year [13].  
The study area is dominated by conifers, which grow up to the treeline between 1800 and 2000 m a.s.l., while deciduous woods are rare [5]. While spruce-fire forests occur at the valley flood, subalpine spruce forests cover most slopes below the treeline. Mountain pines dominate the treeline, especially on the south-facing slopes of the Durnholz Valley and around the Rittner Horn, where they form large contiguous patches. Where mountain pines are less abundant, larch-pine forests characterize the treeline with the sporadic growing of green alder (_Alnus virids_). Overall, mountain pines occur in combination with green alder (_Alnus virids_), spruce (_Picea abies_), larch (_Larix decidua_), swiss pine (_Pinus cembra_) and alpine dwarf shrubs (e.g., alpine rose (_Rhododend ferrugineum_)) in our study area [5].  
The use of mountain pine stands in the Sarntal Valley has intensified in the last years to serve the rising demand for wellness products in South Tyrol [32]. As a result, large contiguous mountain pine areas on the southeast-facing slopes of the Durnholz Valley have been clear-cut for the oil production by the three distilleries in the Sarntal Valley (Figure 1c,d).
## Appendix B
As such, they are significantly more understandable from an engineering or physical perspective and also correspond to the use of first principles, which are the basis of every science. Note that in such a way, the determined smoothing parameter values amount to \(w_{min}=0.05\) and \(w_{max}=0.1\). Validation with the \"leave-one-out\" technique with the above chosen values for the smoothness parameter is presented in Figure A5. From Figure A5, it can be observed that predicted results according to the \"leave-one-out\" validation technique are scattered around the line _prediction = measurement_. Small values of \(w\) (Figure A5, left) reveal bad generalization capability, while large values of \(w\) (Figure A5, right) reveal over-generalization capability. Smoothness parameters close to optimal value (minimal error), rounded up, exhibit the best behavior. Large scatter indicates relatively low prediction accuracy, but it should be noted that only two input parameters were used. A larger number of input parameters reduces the scatter, which we can see from Figure A6. However, the remaining scatter results from epistemic and aleatory uncertainties and the lack of data in some cases. The lack of data can be estimated (and possibly excluded from validation) by the density of data (Equation (8)). From Figure A7 (two input parameters) and Figure A8 (four input parameters), it can be observed that the predicted results, according to the test technique (which corresponds to the learning procedure of CAE ANN), are scattered around the _prediction = measurement_ line. Compared to the validation technique (Figures A5 and A6), the results are closer to the _prediction = measurement_ line, although they are still scattered. This scattering is, once again, partly due to epistemic and aleatory uncertainties, as well as a lack of data in certain cases and regions. Figure A7: Test of the proposed CAE ANN model in the case of two input parameters for output parameter “height of cross-section”. Again, keep in mind that the design of bridges is an iterative process; thus, \"precise\" values are not the most important factor at this stage. What matters most is that the user, engineer, or designer develops an understanding of the relationships between individual parameters and their mutual influences. Design, after all, is a creative process that currently surpasses the capabilities of AI. However, the results from CAE ANN provide valuable learning opportunities and significant assistance throughout the design process.