esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Time-Varying Environmental Influences  
As discussed above, the quality of the wireless optical signal can deteriorate for two reasons: ambient light (from solar or lunar radiation) that increases optical noise and changing light attenuation due to changes in the concentrations of dissolved and suspended matter.
## 2 Materials and Methods
### Kinetic Experiments  
To determine the adsorption process rate, 5 mg of each adsorbent (GAC, GO, or NaY) was added to individual flasks, each containing 500 mL of RhB or MB with 4 mg/L initial concentrations, at room temperature. The solutions were covered and shaken at 100 rpm at room temperature. Every 24 h, a 3 mL sample was taken from each solution and its concentration was determined until no further change was observed, at which time the equilibrium state was considered to be acheived. Four different kinetic models, including pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion were considered to identify the rate-controlling adsorption process step, according to the experimental data.  
The equation of the pseudo-first-order model is as follows (Equation (3)):  
\[\mathrm{q_{t}}=\mathrm{q_{e}}\Big{(}1-\mathrm{e^{-k_{i}t}}\Big{)} \tag{3}\]  
where \(\mathrm{q_{e}}\) (mg/g) is the adsorption capacity of adsorbent for removal of dyes at equilibrium state, \(\mathrm{q_{t}}\) (mg/g) is the adsorption capacity at time t, \(\mathrm{k_{1}}\) (min\({}^{-1}\)) is the adsorption rate constant for the pseudo-first-order model.  
The pseudo-second-order model equation is outlined as follows (Equation (4)):  
\[\mathrm{q_{t}}=\frac{\mathrm{q_{e}^{2}k_{2}t}}{1+\mathrm{q_{e}^{2}k_{2}t}} \tag{4}\]  
where \(\mathrm{k_{2}}\) (g/mg.min) is the pseudo-second-order rate constant [25].  
The nonlinear equation of [PERSON] is shown as follows (Equation (5)):  
\[\mathrm{q_{t}}=\frac{1}{\mathrm{\beta}}\mathrm{ln}(\mathrm{\alpha\beta})+ \frac{1}{\mathrm{\beta}}\mathrm{Int} \tag{5}\]  
where \(\mathrm{\alpha}\) is the initial adsorption rate (mg/g.min) and the desorption constant is \(\mathrm{\beta}\) (g/mg) [19].  
The intraparticle diffusion model was used to determine the relationship between the adsorption amount at time t (\(\mathrm{q_{t}}\)) and t\({}^{1/2}\). The intra-particle diffusion rate equation is shown as follows (Equation (6)):  
\[\mathrm{q_{t}}=\mathrm{k_{id}}\mathrm{t^{1/2}}+\mathrm{C_{i}} \tag{6}\]  
where \(k_{\mathrm{id}}\) is the rate constant for intra-particle diffusion (mg/g.h\({}^{1/2}\)) and \(\mathrm{C_{i}}\) is the intercept of the plot of \(\mathrm{q_{t}}\) vs. t\({}^{1/2}\) that can be attributed to the boundary thickness. The greater the intercept, the larger the effect of surface sorption on the adsorption rate. If the value of \(\mathrm{C_{i}}\) is zero, it can indicate that rate of the adsorption process of dyes is controlled by the intra-particle diffusion the entire time [21].
# Highly Linear 2.6 GHz Band InGaP/GaAs HBT Power Amplifier IC Using a Dynamic Predistorter
## 1 Introduction
It comprises a diode-connected transistor, a shunt capacitor at the base, and a series-connected inductor and resistor that link the emitter to the inter-stage matching network in parallel. These passive components optimize the RF signal coupling to the diode, which significantly influences the nonlinear characteristics of the predistorter. Furthermore, the nonlinear behavior of the proposed predistorter can be controlled by supplying a dc current to its base collector. Through the integration of these control mechanisms, the predistorter's nonlinearity can dynamically respond to varying input power levels. The performance is evaluated before and after applying predistortion linearization by measuring the fabricated PAIC using both CW and modulated signals.
## 2 Data Preparation
### Data Collection  
For the manned aircraft, the Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data from Shanghai Hongqiao Airport (ZSSS) to Beijing Capital International Airport (ZBAA) were obtained, with flight data for a total of 9752 flights. The ADS-B data of each flight include the flight ID, aircraft type, longitude, latitude, altitude, ground speed, heading angle, monitor time, departure airport, landing airport, etc.  
The main flight stages of manned aircraft can be divided into the take-off stage, cruise stage, and landing stage. Since drones can only collide with manned aircraft in the take-off and landing stages, the trajectory data of the manned aircraft in the two phases around the airport terminal area are selected for further analysis, which usually has an altitude of below 1000 m. Table 1 is an example of ADS-B trajectory data for manned aircraft, including aircraft type, longitude, latitude, altitude, ground speed, and monitoring time, as follows:  
Because the ADS-B data transmit various information to ground equipment through data link broadcasting, and are affected by various factors such as signals, the collected data might be inaccurate or missing. It is necessary to preprocess the data to improve the quality of the original data and delete the flight data with crucial missing or inaccurate information.  
Toward the research on the trajectory distribution prediction of drones, this paper collected a total of 34 h of historical trajectory data of three types of quadrotor drones over 30 days. Each drone automatically records relevant information every 0.1 s. Table 2 is an example of part of the trajectory data of the drone, including timestamp, longitude, latitude, x-speed, y-speed, z-speed, heading angle, pitch angle, roll angle, etc. It can be seen that the trajectory is flexible and changeable. Table 3 is a summary of the collected data for drones, including the number of trajectory records, as well as the statistical information about the ground speed and vertical speed. The ground speed for the drone ranges from 0 to 20.60 m/s and the vertical speed ranges from \(-\)9.86 to 11.60 m/s.
# Online Learning Algorithms for the Real-Time Set-Point Tracking Problem
###### Abstract
The available literature lacks in terms of investigating the capability of online decision-making to provide residential consumers with daily consumption optimization within known DR programs, such as set-point tracking problems. In this study, we investigate a set-point tracking problem utilizing DR at the residential level. We consider an aggregator that is in charge of a large population of thermostatically controllable loads (TCLs) that, by increasing or decreasing their consumption at each step, attempts to track the reference signal. The general decision-making framework is that a decision is made without relying on any prediction and information about future parameters. Thus, the aggregator decides the increase or decrease in consumption of each load at time \(t\). After committing to a decision, the signal to be tracked and the uncertain response of each load is realized; therefore, the aggregator suffers a loss, and decisions can be updated accordingly. These decisions are required to be made fast. Consequently, in this work, two types of online algorithms based on classical optimization algorithms are modified and applied to the aforementioned problem, namely online gradient descent (OGD) and online Frank-Wolfe (OFW). The first involves a projection at each step, and the second is a projection-free algorithm. In this work, the aim is to investigate not only the general performance in terms of the loss function optimization but also the computational time and burden. As mentioned above, in this set of real-world problems, the solution needs to be found within seconds otherwise those approaches, even with a good general performance, become impotent. In summary, the contributions of this study are as follows:  
1. We propose two algorithms to address autonomous decision-making for the set-point tracking problem of residential TCLs. The offered approaches are easy to implement for the residential end-users and could output decisions very fast, thus, matching the required real-time settings. 2. We analyze and evaluate the aptitudes of the proposed algorithms while giving a fully comparative set of numerical studies. The remainder of the paper is organized as follows. Section 2 explains the mathematical framework and the structure of each online algorithm. The set-point tacking optimization problem is introduced in Section 3. Numerical results are illustrated in Section 5. Finally, Section 7 concludes the paper.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Theoretical Limits
The maximum possible data rate for MCS8 (specified data rate is 7800 kbps) and 32 TCP segments of 2144 bytes (resulting in full channel utilization) per cycle is thus 5505 kbps according to:  
\[DR_{p}^{max}=\frac{N_{TX_{max}}^{b}\cdot(l_{p}+90B)}{102.4\ \text{ms}}, \tag{11}\]  
where \(l_{p}\) denotes the payload size. However, since RAW restricts channel access time for each station, the maximal effective data rate per station is:  
\[DR_{eff}^{max}=\frac{DR_{p}^{max}\cdot T_{slot}}{N_{TIM}\cdot 102.4\ \text{ms}}. \tag{12}\]  
Thus, for 5-slot RAW, four TIM groups and a 2144 bytes TCP segment, the maximum effective data rate per station is 274.44 kbps. Due to stochastic channel access and downlink data scheduling, the experimentally achieved data rate is 41.7% lower (160 kbps) in this scenario. Existence of DL \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**TCP Segment Size** & \multicolumn{2}{c}{**536**} & \multicolumn{2}{c}{**1072**} & \multicolumn{2}{c}{**1608**} & \multicolumn{2}{c}{**2144**} \\ \hline
**MCS** & \(\mathbf{t_{TX}^{total}\) **(ms)**} & \(\mathbf{N_{TX_{max}}^{b}}\) & \(\mathbf{t_{TX}^{total}\) **(ms)**} & \(\mathbf{N_{TX_{max}}^{b}}\) & \(\mathbf{t_{TX}^{total}\) **(ms)**} & \(\mathbf{N_{TX_{max}}^{b}}\) & \(\mathbf{t_{TX}^{total}\) **(ms)**} & \(\mathbf{N_{TX_{max}}^{b}}\) \\ \hline MCS0 & 8.64 & 11 & 15.24 & 6 & 21.84 & 4 & 28.44 & 3 \\ MCS1 & 4.76 & 21 & 8.08 & 12 & 11.36 & 9 & 14.68 & 6 \\ MCS2 & 3.48 & 29 & 5.68 & 18 & 7.88 & 12 & 10.08 & 10 \\ MCS3 & 2.84 & 36 & 4.48 & 22 & 6.12 & 16 & 7.8 & 13 \\ MCS4 & 2.2 & 46 & 3.28 & 31 & 4.4 & 23 & 5.48 & 18 \\ MCS5 & 1.88 & 54 & 2.68 & 38 & 3.52 & 29 & 4.36 & 23 \\ MCS6 & 1.76 & 58 & 2.48 & 41 & 3.24 & 31 & 3.96 & 25 \\ MCS7 & 1.68 & 60 & 2.32 & 44 & 3 & 34 & 3.64 & 28 \\ MCS8 & 1.56 & 65 & 2.08 & 49 & 2.64 & 38 & 3.2 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transmission durations \(t_{TX}^{total}\) of various-sized TCP segments determine maximum possible number of segments to be transmitted in one beacon interval \(N_{TX_{max}}^{b}\) (\(N_{TIM}=1\), \(T_{TIM}=102.4\) ms) for various MCSs and using a 2 MHz channel. data can largely reduce the available channel time for UL since for every RAW group with at least one paged station, and full RAW is used only for DL unless DL data is immediate AP response. Evidently, network density, TCP, TIM and RAW configurations must be appropriately chosen in order to achieve a targeted throughput.
## 2 Data and Methods
### Modelling Algorithms  
Numerous SDM algorithms have been developed to predict species distribution based on environmental factors [80]. In this study, SDM was employed using six commonly used algorithms, which can be categorized into two regression methods, namely, generalized linear models (GLMs) [81] and multivariate adaptive regression spline (MARS) [82]; three machine learning methods, namely, boosted regression trees (BRT) [83], random forest (RF) [84], and support vector machine (SVM) [85]; and one classification and regression method, namely, classification and regression trees (CART) [86]. Furthermore, the prediction results of individual algorithms were combined based on their weighted mean of TSS to create an ensemble model [87], which is widely recognized as a powerful, well-referenced, and stable method for tree species prediction [88]. The weights assigned to the ensemble model were determined proportionally to the computed TSS derived from the cross-validation runs of the best performing models. This methodology ensures a nuanced and optimized weighting scheme for the ensemble model within the context of each model's performance across multiple cross-validation iterations [89], and significantly enhances the accuracy of SDM [47].
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 4 Discussion
More specifically, copious amounts of shrubs and trees were established to prevent and control water and soil erosion, especially in the central region with serious desertification. The identification of a planted forest in a sparsely vegetated area was easily disturbed by background signals (bare soil or understory vegetation), which leads to confusion with grassland or cultivated land. [PERSON] et al. [72] accurately mapped rubber and palm oil plantations using a combination of Sentinel-2, Landsat-8, and Sentinel-1 data; [PERSON] et al. [73] capitalized Corona and Landsat data for forest change mapping (OA = 92%). A combination of multi-sensor data, therefore, seems promising to be able to produce a more accurate basic map as the input layer for mapping a planted forest, which helps to reveal the authentic change process of the forest canopy. Furthermore, the integrated OASB method has the potential to map (1) planted forests in other years, (2) planted forests in other regions, and (3) other planted forest events. In this study, the natural vs. planted forest classification map was used as an example to illustrate the practicality of the method. Similarly, it can be applied for mapping planted forests in other years and tracing their planting years, as long as the forest masks for the corresponding years are prepared and the corresponding annual time-series data are constructed. For the other regions, the OASB can be used directly without changing the values of the parameters (min and max) or threshold (\(\alpha\)) if it is an arid and semi-arid region; however, in terms of the identification of planted forests in tropical or subtropical regions where there is an inevitable effect of cloud noise on optical images, image datasets excluding cloud shadows can be constructed with the help of auxiliary data such as LiDAR [74; 75]. In addition, local expert opinions need to be ascertained to set the min when extracting the shapelets of forest objects for detecting temporal characteristics caused by planting activities and mitigating the effects of other disturbances; and the threshold (\(\chi^{2}\,^{*}\)) for the m-test might be adjusted by users after measuring a few alternative settings (e.g., \(\alpha=\,0.1\); 0.05; 0.025). The third application is based on the interpretability of a shapelet-based approach. [PERSON] et al. [20] used the shapelet length to determine the land-cover type before rubber plantations, but this was not applicable to regions with strong landscape heterogeneity. This means that valuable information embedded in the shapelet segment deserves mining, which is of great interest to us but has not yet been addressed. A logical-shapelets (several interrelated shapelets) algorithm [76], in cooperation with the trend feature symbolization [77], is worth exploring in characterizing pre-conversion land use or other events.
###### Abstract  
This study investigates bidirectionally sinusoidal corrugated steel shells using finite element analysis (FEA) software, primarily ABAQUS 2017, supplemented by RFEM 6.05 for an initial result comparison. The research aims to establish a robust numerical solution to understand the structural behaviour of these shells under static and dynamic loading. Starting with meticulous calculations for a selected structural element, the paper emphasises a comparative analysis between ABAQUS and RFEM, offering valuable insights into simulating the responses of corrugated steel shells. A distinctive contribution of this research lies in its novel and comprehensive parametric analysis of these shells under static and dynamic loading, an area that has not been explored in previous studies. The study systematically explores various model parameters, including geometrical and mechanical properties, with detailed analyses revealing their influences on deformation, stress distribution, dynamic properties, and behaviour. Another key feature is the development of a coded script that systematically generates diverse numerical models, allowing for a thorough exploration of the structural system's response. This study advances our understanding of the structural behaviour of bidirectionally sinusoidal corrugated steel shells. These findings are set to enhance structural analysis and design practices by optimising parameters for improved performances in various engineering applications.  
bidirectional sinusoidal corrugation; steel shell; finite element analysis; static analysis; dynamic analysis +
Footnote †: journal: Applied sciences
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 2 Materials and Methods  
In this section, we provide an in-depth mathematical treatment for accurate 3D object rendering in AR, encompassing coordinate mapping between a virtual object coordinate system and a GPS coordinate frame through three-point matching, camera-pose estimation and rectification, and transformations between the object coordinate space and image plane coordinate system.
## 3 Related Work
Various scenarios are studied to assess the ability of EVs to store wind-generated electricity, and a smart energy management system is developed, offering a more economical and efficient scheme for integrating wind resources into the generation mix. To tackle more complex problems, several authors have opted to merge different algorithms in order to provide better solutions. In the study conducted by [PERSON] et al. [20], a hybridization of the binary PSO algorithm (BPSO) and the ant colony optimization (ACO) method is proposed to optimize a implemented V2G system for the short-term unit commitment problem. To ensure reliable solutions, a reliability constraint is incorporated. The BPSO schedules the units based on logical operators, while the ACO allocates appropriate power to all committed generators. Two scenarios are considered: one with renewable sources, and another without them. Numerical results demonstrate the method's efficiency in providing near-optimal solutions. Additionally, the results indicate that as the minimum reliability level decreases, the total operation cost of the system increases. Therefore, a trade-off between the required reliability level and the total cost of the system should be considered. Even though evolutionary algorithms provide a relatively simple and robust way to produce solutions, the use of other heuristics and metaheuristics has proved to be an excellent alternative. Thus, in the last years, authors have developed a variety of heuristic algorithms to tackle these problems. The paper by [PERSON] et al. [21] presents a novel heuristic technique known as the quantum lightning search algorithm, which is an extension of the binary lightning search algorithm designed to determine the optimal placement of charging stations in a distribution network. In this study, a multi-objective function was employed, wherein the goals were to minimize line loading, voltage deviation, and circuit power loss. Computational experiments were conducted to demonstrate the effectiveness of the proposed algorithm compared to both the binary lightning search algorithm and BPSO. The results highlight its superior performance in optimizing charging station placement within the distribution network. Another example is the paper presented by [PERSON] et al. [22], where a V2G framework is proposed. This problem integrates two renewable energy sources: a photovoltaic system and a wind turbine, along with a backup battery, connected to an EV charging system (EVCS). The study is based on real data and aims to study the feasibility of implementing an EVCS in the Libyan city of Tripoli. To determine the sizing of the system components, an antilo optimization algorithm is employed, subsequently validated against a PSO algorithm and a cuckoo search algorithm. The optimization results indicate that the sizing proposed meets the load demand for the on-grid system, and thus the proposed system can be effectively applied in Tripoli, especially considering its high renewable energy factors. Furthermore, the results demonstrate that the proposed method outperforms other validated algorithms.
## 4 Proposed Scheme
### Security Comparison  
In this section, a security comparison is made between the existing related schemes and our work. The outcome of the comparison is tabulated in Table 2, where src1, src2, src3, src4, src5, src6, src7, src8, and src9 indicate pseudonym identity, authentication, and integrity, traceability, unlinkability, replay attack, modify attack, forgery attack, Man-In-The-Middle (MITM) attack, and side-channel attack, respectively.  
As is concluded in Table 2, the privacy and security requirements could be achieved in our work; nevertheless, side-channel attack could not be achieved in the schemes of [PERSON] et al. (2015), [PERSON] et al. (2016), [PERSON] et al. (2018) and [PERSON] et al. (2019).
## 4 Conclusions  
The Ag/PFB/p-Si heterojunction is fabricated by using PFB as an active semiconducting layer with Ag as ohmic contact. The microelectronic properties of the Ag/PFB/p-Si device are studied by two different characterization methods, i.e., conventional \(I\)-\(V\) and Norde's functions. It is observed that the measured device parameters (smaller value of \(n\) and \(I_{0}\)) confirms better quality of the PFB/p-Si heterojunction. The lower ratio of \(R_{s}\) to \(R_{sh}\) demonstrates relatively stable junction of PFB/p-Si. The microelectronic parameters obtained from Norde's functions are in closer agreement with those obtained from the conventional \(I\)-\(V\) method. From the SEM and AFM analysis, the surface features of PFB film are nonuniform, which results in a higher value of \(R_{s}\) that subsequently decreases the current across the PFB/p-Si interface. XRD reveals the amorphous nature of PFB, whereas UV-vis spectrum exhibits its optical bandgap as 2.88 eV in the visible range. On the basis of this study, PFB is proposed to be used as an active material for its potential applications in electronics and optoelectronics.  
Conceptualization, M.T. and M.I.; methodology, M.I., M.T., and M.Z.; software, M.I. and M.T.; validation F.A., M.R.S., and M.T.; formal analysis, M.T. and M.R.S.; investigation, M.T. and M.Z.; resources, M.I. and M.T.; data curation, M.Z. and M.R.S.; writing--M.I. and M.T.; writing--review and editing, M.T.; M.I., F.A., and M.R.S.; supervision, M.T.; project administration, M.T.; funding acquisition, M.A.I. and R.M. All authors have read and agreed to the published version of the manuscript.  
Universiti Kebangsaan Malaysia (UKM) for funding the research under Grant Code GUP-2017-093.  
All the authors are grateful to the Abdul Wali Khan University Mardan, Pakistan, Higher Education Commission (HEC) of Pakistan, for providing the research facilities and Project No. 10170/KPK/NRPU/R&D/HEC.  
The authors declare no conflict of interest.