esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, we investigate the electrical transport properties of back-gated field-effect transistors in which the channel is realized with two-dimensional transition metal dichalcogenide nanosheets, namely palladium diselenide (PdSe\({}_{2}\)) and molybdenum disulfide (MoS\({}_{2}\)). The effects of the environment (pressure, gas type, electron beam irradiation) on the electrical properties are the subject of an intense experimental study that evidences how PdSe\({}_{2}\)-based devices can be reversibly tuned from a predominantly \(n\)-type conduction (under high vacuum) to a \(p\)-type conduction (at atmospheric pressure) by simply modifying the pressure. Similarly, we report that, in MoS\({}_{2}\)-based devices, the transport properties are affected by pressure and gas type. In particular, the observed hysteresis in the transfer characteristics is explained in terms of gas absorption on the MoS\({}_{2}\) surface due to the presence of a large number of defects. Moreover, we demonstrate the monotonic (increasing) dependence of the width of the hysteresis on decreasing the gas adsorption energy. We also report the effects of electron beam irradiation on the transport properties of two-dimensional field-effect transistors, showing that low fluences of the order of few e/nm\({}^{2}\) are sufficient to cause appreciable modifications to the transport characteristics. Finally, we profit from our experimental setup, realized inside a scanning electron microscope and equipped with piezo-driven nanoprobes, to perform a field emission characterization of PdSe\({}_{2}\) and MoS\({}_{2}\) nanosheets at cathode-anode separation distances as small as 200 nm.  
Keywords:2D materials; field-effect transistor; electrical transport; hysteresis; field emission; gas adsorption +
Footnote †: journal: Applied Physics Letters
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.5 Video Compression  
In order to transmit large volumes of medical data over a telemedicine network, there is a need to compress data for bandwidth optimization and effective management of data traffic. Due to the significance of this issue, researchers have attempted to develop a variety  
Figure 13: Research Challenges in Telemedicine.  
of video compression techniques, including lossless and lossy compression, as discussed in [3].
## 2 Background and Methodology
### Methodology  
To answer the research question, this work follows [PERSON]'s six phases for developing a maturity assessment [14]. The approach typically begins with a systematic literature review to establish a clearer understanding of how to structure the maturity model. In this work, the development process starts with an existing holistic data management maturity model used as a reference model. The results and implications of this iteration will inform future research aimed at developing a maturity model with a stronger focus on people and organizations and aligned with the principles of Industry 5.0, to guide the transition to data-driven organizations. Figure 1 illustrates the phases that were followed. The subsequent sections provide a detailed description of the development model, presenting results for each phase.  
The aim of the scope phase was to define the model. Using an existing reference model, the focus was placed on an industry-specific and practice-oriented model that addresses the specific challenges and requirements of the respective domain.  
The structure of the model was developed in the design phase. The structure and maturity levels were already defined by the existing reference model and were only adapted based on findings from the literature review and gaps to existing research.  
In this work, the populate phase goes hand in hand with the design phase. This is where the relevant content of the model was finally defined, which captures the maturity level of the domain.  
The model was then validated in the test phase using results from an industry survey. In addition, experts from the field of data management validated possible correlations between the dimensions.  
In the subsequent deploy phase, interviews and pilot tests were conducted for relevance, validity, and comprehensibility. As part of the short assessment, which contains a small number of questions per dimension from the questionnaire, further input was obtained. The participants in the industry survey and the short assessment were different. Nevertheless, care was taken to ensure that different industry sectors were represented. For the participants in the short assessment in particular, care was also taken to ensure that they had in-depth expertise in the field of data and analytics and that their answers were representative of the respective industry.  
For long-term use, the model will tested as part of the maintain phase with the comprehensively compiled questionnaire. The model will thus be regularly reviewed and adapted to new requirements to maintain its relevance and ensure comparability. Continuous further development is part of future research, as is the creation of a guide for applying the model in practice.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 3 Methods
### HVSR and VSR Calculation Method
#### 3.2.1 Calculation of HVSR  
The Fourier amplitude spectrum of the processed acceleration time history was calculated using FFT, and the spectrum was then smoothed with a Parzen window, applying a bandwidth of 0.5 Hz. The HVSR was calculated according to Equation (6), which represents the ratio of the horizontal-to-vertical Fourier amplitude spectra. The horizontal Fourier amplitude spectrum was obtained by calculating the geometric mean of the Fourier amplitude spectra in the EW and NS directions.  
\[HVSR=\frac{F_{H}(T)}{F_{V}(T)}=\frac{\sqrt{F_{\text{EW}}(T)\times F_{NS}(T)}}{ F_{\text{HD}}(T)}, \tag{6}\]  
where \(T\) denotes the period and \(F_{\text{EW}}(T)\), \(F_{NS}(T)\), and \(F_{\text{HD}}(T)\) denote the Fourier amplitude spectra in the EW, NS, and UD directions, respectively.
## 5 (N >> 1) SOA-Based Fiber Compound-Ring Laser Structure  
Passive coherent beam combining of two SOAs based fiber compound-ring laser has been demonstrated above with high beam combining efficiency without using any active phase modulators. The proposed fiber compound-ring cavity has great potential to achieve an efficient, stable, simple and inexpensive compact highly power-scalable fiber system by passively combining a [49; 50] low power (N >> 1) number of SOAs fiber compound-ring lasers by using N \(\times\) N fiber couplers with high beam combining efficiency. Estimation studies of beam combining efficiency have shown that it degrades as the number of individual combined Y-shaped linear cavity-based fiber lasers increases (i.e., N > 8) [72]. However, a large number of combined individual fiber lasers has been demonstrated in Y-shape coupled arrays [73] with high efficient coherent beam combining from 16 channels made by cascading two fiber laser arrays using EDFAs as gain medium. Thus, in addition to combining several arrays of compound-ring laser cavities, semiconductor optical amplifiers are the right candidates for the proposed (N >> 1) fiber laser compound-ring laser because they possess wide bandwidth to allow enough lasing modes within the actual gain bandwidth. Yet, we anticipate that the changes in the behavior of mode competition among the oscillating modes might lead to diminishing likelihood of obtaining the same spectral lasing mode [74; 75]. Therefore, the beam combining efficiency is expected to degrade at very large number of combined arrays of merged compound-ring resonators. In addition, nonlinearities that manifest in semiconductor optical amplifier [76] as well as the optical damage and nonlinear effects, such as stimulated Raman scattering, stimulated Brillouin scattering and four-wave mixing in all-single-mode fiber output couplers, will become dominant at very high optical power and limit the maximum amount of optical power that can be achieved from the proposed fiber compound-ring laser.  
Unlike previous work [51], the expensive high power combiner with a multimode fiber output port has been eliminated. The proposed SOA-based fiber compound-ring laser structure is a dual-output port all-single-mode-fiber laser structure that can be used in single or dual-output operation with adjustable output power. Figure 11. illustrates the proposed fiber laser structure with N \(\times\) N fiber couplers and two Sagnac loop mirrors, SLM\({}_{1}\) and SLM\({}_{2}\), which form the output couplers, OUT1 and OUT1.  
Figure 10: Shows the output power short-term fluctuations of the proposed fiber compound-ring laser at a 1550-nm wavelength.
# Photonics-Based Microwave Image-Reject Mixer
## 3 Photonic IRM Based on Phase-Cancellation Techniques  
Phase-cancellation technique is another way to realize photonics-based microwave IRMs [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30], also known as Hartley architecture. Figure 5 shows the principle method. A pair of quadrature LO (or RF) signals are introduced to generate two quadrature IF outputs, which are then combined by a low-frequency 90-degree hybrid, introducing an additional 90\({}^{\circ}\) phase difference, to make the downconverted image out of phase, and those downconverted from the wanted RF signals in phase. In this way, the wanted IF signals are enhanced, while the downconverted image at the same band is cancelled. Since this kind of photonics-based microwave IRM uses the phase differences of the signals to realize the image rejection, any requirement of filtering or multi-stage frequency conversion is avoided. In addition, when the RF signal frequency changes, the IRM based on phase cancellation can still suppress the corresponding image, which enables broadband applications. Three typical IRM schemes based on phase cancellation were proposed, using 90\({}^{\circ}\) electrical hybrid, microwave photonic phase shifters, and optical 90\({}^{\circ}\) hybrid.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 3 Results
### Rule and Surface Viewer  
The rule viewer is used in the fuzzy inference diagram. It is used as a diagnostic tool to see, for instance, which rules are active, or how individual membership function influences the results.  
Figure 9 shows how air pollutants NO\({}_{2}\), SO\({}_{2}\) and PM\({}_{2.5}\) concentration levels greatly affect the Kampala Air Quality Index in the rule view. For example, the figure demonstrates the resultant Air Quality Index, given the concentration values of the air pollutants NO\({}_{2}\), SO\({}_{2}\) and PM\({}_{2.5}\); if NO\({}_{2}\) = 30 ppb, SO\({}_{2}\) = 50 ppb and PM\({}_{2.5}\) = 20.9 \(\mu\)g/m\({}^{3}\), then the predicted KAGI = 27.  
As indicated in Figure 10, the surface viewer is used to view the dependency of one of the outputs on any one or two of the inputs, that is, it generates and plots an output surface map for the model. In this case, the surface view shows how the output KAGI is dependent on the air pollutant, NO\({}_{2}\), SO\({}_{2}\) and PM\({}_{2.5}\), concentrations. A change in any of the air pollutant concentrations affects the KAGI. The plot also shows the relation between the air pollutants and the Air Quality Index and it is seen that when there is an increase in any of the air pollutant input concentrations, there is an increase in the KAGI, irrespective of the other values of the air pollutant values.  
Figure 8: MATLAB evaluation inference rules editor view design for NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\) vs. KAQI.  
Figure 9: Active rules and Membership functions of input NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\) vs. output of KAQI.
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
###### Abstract
The designed system employed an intensity modulation and direct detection (IM/DD) with on-off keying (OOK) modulation for evaluating the BER performance. In this paper, unlike previous works related to this topic, we propose using the APD receiver for the UAV-based FSO system considering subcarrier intensity quadrature amplitude modulation (SI-QAM). We derived and analyzed the system's SER performance, taking into account the combined channel parameters such as transmitted power, link loss, various atmospheric turbulence conditions, pointing error loss and the total noise of the APD. The rest of this paper is structured as follows. Section 2 describes the system and channel modeling. The SER analysis of the considered system over different atmospheric turbulence conditions and pointing error is presented in Section 3. Section 4 presents the numerical results to analyze the derived mathematical expressions of the system performance. Section 5 concludes this paper.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Anticancer Properties  
#### 3.5.1 Overview  
As established, pioglitazone has been associated with elevated risk of bladder cancer, especially in the Caucasian population, to such extend that it has catalyzed its withdrawal from the French, German, and Indian markets, and led the Food and Drug Administration (FDA) to introduce a mandate for the providing of information regarding potential risk [41; 42]. However, there have been several reports and studies that point out the anti-neoplastic properties of pioglitazone and the related biochemical pathways that PPAR-\(\gamma\) is implicated in. Given the pleiotropic effects of PPAR-\(\gamma\) and its agonists, we consider that investigating the exact molecular interactions will contribute to shedding some light on the mechanism of action and toxicity, as well as the interactions and effects of pioglitazone, and potentially suggest new applications and optimalization/personalization of interventions involving TZDs.
## 3 Results
### Genetic Analysis of Sandbodies
#### 3.3.4 Provenance Supply  
Detrital zircon LA-ICP-MS U-Pb dating was conducted on thin sandstone layers of the Dongying Formation to compare zircon age discrepancies in target strata across the study area [49], delineate sediment provenance zonation, and clarify sediment supply sources for beach-bar deposits in the central uplift zone. Integrated analysis of zircon age distributions from five wells (E\({}_{3}\)d\({}_{2}\)\({}^{1}\), E\({}_{3}\)d\({}_{3}\)) reveals similar age spectra, with U-Pb ages ranging from 65 to 3850 Ma, predominantly clustered in the following three intervals: 65-135 Ma, 500-1000 Ma, and 1800-3100 Ma. Weighted mean ages for these intervals are 137.3 Ma, 689 Ma, and 2339 Ma, respectively (Table 5; Figure 9).  
The southern gentle slope zone (Well KL1-1-a; Figure 9c) and northern steep slope zone (Wells B8-2-a, B8-2s-b; Figure 9b) exhibit pronounced age peaks across all three intervals. In contrast, the central uplift zone (Well C3-1-a; Figure 9a) displays dominant peaks at 500-1000 Ma and 1800-3100 Ma. Comparative analysis of zircon age distributions among the central uplift zone, southern provenance (Well KL1-1-a), and northern provenance (Wells B8-2-a, B8-2s-b) demonstrates consistent age trends for Mesozoic, Paleozoic, Neoproterozoic (Pt\({}_{3}\)), and Mesoproterozoiczoic/Paleoproterozoiczoic/Neoarchean (Pt\({}_{2}\)/Pt\({}_{1}\)/Ar\({}_{2}\)) zircon populations.  
Zircon age signatures in the E\({}_{3}\)d\({}_{2}\)\({}^{1}\) suggest that mixed provenance contributed from northern and southern sources to beach-bar deposits in the intra-depression uplift zones (central uplift and strike-slip zones). Paleogeomorphic analysis further indicates (Figure 9d)the following: The western segment of the northern steep-slope zone features abrupt topography with limited sediment transport and minor wave reworking. The eastern segment of the northern steep-slope zone, connected to the central strike-slip fault zone, exhibits gentler gradients and enhanced sediment delivery to beach-bar systems. The southern gentle-slope zone hosts large-scale braided river deltas, providing extensive and stable sediment supply for beach-bar development.  
Figure 9: Detrital zircon age spectrum of the Dongying Formation in the HHK Depression. (**a-1**), C3-1-a, 3450; (**a-2**) C3-1-a, 3515; (**b-1**) B8-2-a, 3165; (**b-2**) B8-2s-b, 3190; (**c-1**) KL1-1-a, 2670; (**c-2**) KL1-1-a, 2775; (**d**) histogram and moving average trendline of zircon age distribution.
# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 4 Optimization
### Solution Method  
Genetic Algorithm (GA) is utilized in optimizations. GA simply focuses on minimizing the OF with the constraints of the input parameters [39]. GA optimization has a process flow as shown in Figure 5. The first step is the generation of initial populations. Initial populations (chromosomes) are selected randomly depending on the constraints. The second step is fitness evaluation where the OF is evaluated using each chromosome, and results are scaled. Then, the evaluated chromosomes are selected to create the new generation regarding their fitness performance. There are a few selection methods available, and the stochastic uniform selection is utilized in this study. It provides diversity in the population and so it prevents inadequate preterm convergence [40]. Next, the crossover process begins following the selection of fittest parent chromosomes. The crossover is employed to interchange some of chromosomes to create a new-generation offspring. Finally, mutation starts to change chromosomes randomly. Both the crossover and the mutation are executed with regards to the constraints of the parameters. All processes are executed repeatedly until the targeted termination criteria. In the optimization process, MATLAB 2020a Global Optimization Tool is used. GA optimization parameters are presented in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Range** \\ \hline Population size & 50 \\ Creation function & Constraint-dependent \\ Selection function & Stochastic-dependent \\ Crossover function & Constraint-dependent \\ Mutation function & Constraint-dependent \\ Function tolerance & \(1\times 10^{-8}\) \\ Constraint tolerance & \(1\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Genetic Algorithm (GA) Optimization Parameters.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 8 Experiment Setup  
To validate the claim that the synchronization accuracy of two transmitters working in an SFN can be calculated using only passive radar receivers, an experiment was conducted. Four surveillance stations imitating passive radar receivers were placed in different locations in Warsaw and Pruszkow, Poland (Figure 6). Each of the stations consisted of a receiver based on a software defined radio (SDR), a wide-angle antenna, and a portable personal computer (PC) used as a communication and storage device (Figure 7). Due to the researchers having previous experience [15] with it, the B210 Universal Software Radio Peripheral (USRP) platform was chosen to fulfill the role of a receiver. It is capable of collecting a signal with 56 MHz of instantaneous bandwidth, covering frequencies from 70 MHz up to 6 GHz.  
Figure 5: Proposed method of calculating the desynchronization of transmitters working in an SFN.  
The receivers used a GPS signal to achieve 50 ns (RMS 1-Sigma) synchronization accuracy and 10-m position accuracy. All were set to collect data on the 690 MHz central frequency and 10 MHz bandwidth, which covers the entire 48 th DVB-T channel.  
The stations were controlled remotely via an internet connection provided by a PC. All the data collected by the receivers were transferred to a central processing station.  
Two DVB-T transmitters operating in an SFN were chosen as illuminators. Both transmitters were located in Warsaw, Poland. The main parameters of both transmitters are shown in Table 1.  
Figure 6: Map showing positions of used transmitters of opportunity (Raszyn—green marker, PKiN—blue marker) and surveillance stations (red markers).  
Figure 7: A simplified scheme of two surveillance stations. In the described experiment, four stations were used.  
It is important to mention here that the precise localization of both radio transmitter towers is ambiguous. Several sources from on-line databases provided different information (see Table 2). Taking the PKiN transmitter as an example, these differences varied over 40 m. As mentioned before, such an error will influence any passive radar localization attempts.  
Choosing Wikipedia.org as a source of the transmitters' positions, and gathered GPS data for the receivers' positions, all distances were calculated. All previous calculations (5) were expanded by a z-factor to match a three-dimensional scenario. The results of these calculations, recalculated from time units into distance units and rounded to one meter, are presented in Table 3.
## 3 Results  
In this section, we conduct a numerical analysis of the intensity distribution and M\({}^{2}\) factor of partially coherent flat-topped beam rectangular arrays during bidirectional transmission through turbulent atmospheres and plasma links, as shown in Figure 2. Forward transmission involves the propagation of these beams in a turbulent atmosphere before traversing through plasma. Conversely, reverse transmission occurs in the direction opposite to that of forward transmission. In this setup, we assume horizontal beam propagation, with the transmitter and receiver remaining relatively stationary. Within the ABCD optical system for both turbulent atmosphere and plasma links, the transmission matrices for forward and reverse transmissions without turbulence are presented as follows [42]:  
\[\begin{bmatrix}A&B\\ C&D\end{bmatrix}=\begin{bmatrix}1&L_{z}\\ 0&1/n_{z}\end{bmatrix}\begin{bmatrix}1&z_{1}\\ 0&1\end{bmatrix}=\begin{bmatrix}1&z_{1}+L_{z}\\ 0&1/n_{z}\end{bmatrix} \tag{22a}\] \[\begin{bmatrix}A^{\prime}&B^{\prime}\\ C^{\prime}&D^{\prime}\end{bmatrix}=\begin{bmatrix}1&z_{2}\\ 0&1\end{bmatrix}\begin{bmatrix}1&L_{z}\\ 0&1/n_{z}\end{bmatrix}=\begin{bmatrix}1&z_{1}/n_{z}+L_{z}\\ 0&1/n_{z}\end{bmatrix} \tag{22b}\]  
where \(n_{z}\) denotes the refractive index of the graded plasma surrounding the high-speed vehicle, and the optical path function, \(L_{z}=\int 1/n(z)dz\), is determined by this refractive index.  
In the numerically simulated sections, the spectral density of the turbulent atmosphere is characterized using the Tatarskii spectrum, which is expressed as \(\Phi_{n}(\kappa)=0.033C_{n}^{2}\kappa^{-11/3}\times\exp(-\kappa^{2}/\kappa_{m} ^{2})\); then, the parameter induced by the turbulent atmosphere in Equation (4b) can be obtained as \(T=0.1661C_{n}^{2}l_{0}^{-1/3}\), where \(C_{n}^{2}\) is the structure constant of the turbulent atmosphere \(\left(C_{n}^{2}=1\times 10^{-15}m^{-2/3}\right)\), and l\({}_{0}\) (l\({}_{0}\) = 0.01 m) is the inner scale.