esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
## 3 Results
### Effect of Microwave Irradiation Height on Deicing
When the height of the microwave source trumpet is 40 mm, the deicing area reaches a maximum of 175.24 cm\({}^{2}\), and when the height of the microwave source trumpet is 60 mm, the deicing area is 126.88 cm\({}^{2}\). At a height of 40 mm, the deicing area is 1.38 times that at a height of 60 mm. Therefore, from the point of view of deicing area, the deicing effect is improved when the height of the microwave source trumpet is 40 mm. When the height is 40 mm, the temperature increase curve rises sharply after the formation of a water layer because the deicing area is further expanded at this time, i.e., after the initial formation of a water layer, the absorbed heat is more transversely transmitted to separate the ice sheet from the channel surface rather than longitudinally transmitted to thin the ice sheet. This is consistent with the conclusion that the deicing area at a height of 40 mm is much larger than that at other heights. By observing the relative position of the temperature measuring point (T\({}_{1}\)) and the deicing area of the edge point in the deicing area diagram, it can be seen that the edge line of the deicing area is the isotherm of the temperature at 0 \({}^{\circ}\)C on the surface of the specimen. When the microwave source height is 20 mm, T\({}_{1}\) is located inside the deicing area, which corresponds to a final temperature of 5 \({}^{\circ}\)C at T\({}_{1}\), which is higher than 0 \({}^{\circ}\)C. When the microwave source height is 60 mm, T\({}_{1}\) is located at the edge of the deicing area, which corresponds to a final temperature of exactly 0 \({}^{\circ}\)C. Therefore, the edge line of the deicing area is the isotherm of 0 \({}^{\circ}\)C, which also indicates that the deicing operation can start when the specimen surface reaches 0 \({}^{\circ}\)C.
# Carbon Dioxide Adsorption on Carbon Nanofibers with Different Porous Structures
## 2 Materials and Methods
### CO\({}_{2}\) Adsorption Isotherms  
The CO\({}_{2}\) adsorption isotherms on all the samples were measured using the Micrometric ASAP 2020 system. Prior to measurement, a sample of approximately 0.05 g was outgassed at 350 \({}^{\circ}\)C for 24 h to remove the adsorbed contaminants. The equilibration interval for each pressure point was 45 s, and the CO\({}_{2}\) pressure ranged from 0 to 123 kPa. The temperature during the CO\({}_{2}\) adsorption process was maintained at 25, 40, or 55 \({}^{\circ}\)C using a circulating water bath thermostat. The interaction between the adsorbate molecules and the adsorbent lattice atoms can be predicted by the isosteric heat of adsorption (\(Q_{st}\)), which can be used as a measure of the energetic heterogeneity of a solid surface [33]. The \(Q_{st}\) can be estimated using the Clausius-Clapeyron equation, shown in Equation (1):  
\[-\frac{Q_{st}}{R}=\left(\frac{d\ln P}{d\ \frac{1}{T}}\right), \tag{1}\]  
where \(Q_{st}\) (kJ/mol) is the isosteric heat of adsorption, \(R\) (=8.314 J/mol/K) is the gas constant, \(P\) (kPa) is the CO\({}_{2}\) pressure, and \(T\) (K) is the adsorption temperature. A Pearson correlation analysis was performed to explore the relationships between the CO\({}_{2}\) adsorption capacity and the surface features, and the nitrogen-containing functional groups of the adsorbents.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.2 The 5G Massive MIMO
They conducted a comparative analysis in terms of some performance characteristics, such as peak data rate, energy efficiency, latency, throughput, and more, and concluded that MIMO design played a critical role in 5G throughput enhancement. Uplink and Downlink Transmission of a Massive MIMO Channel  
In the subsequent subsections, uplink and downlink transmissions of a massive MIMO channel are considered. 1. Uplink Transmission of a Massive MIMO Channel  
The massive MIMO uplink channel is used to transport data and the pilot signal from the mobile terminal to the base station. Consider a massive MIMO uplink system with \(M\) base station antennas simultaneously communicating to \(N(M\geq N)\) single-antenna users at the same time. If the channel prediction user signal or deterministic pilot signal is \(x\in C^{N}\), the base station signal received during uplink is given by [87; 88] as (1):  
\[y=Hx+n_{uplink} \tag{1}\]  
where \(y\in\subset^{M}\) denotes the base station received signal, H denotes the channel vector between the base station and the user terminal, and elements of \(H\in\subset^{M\!X\!N}\) are independent and identically distributed with unit variance and zero mean; that is, \(H\sim CN(0,1)\). \(n_{uplink}\in\subset^{M}\) is an additional term denoting the addition of interference from the receiver noise and several transmissions defined by (2). The added interference is independent of the user signal \(x\), but it can be affected by the channel \(H\). \[n_{uplink}=n_{uplink-interference}+n_{noise} \tag{2}\]  
2. Downlink Transmission of a Massive MIMO Channel  
The downlink channel is used to communicate between the user and the base station as well as to estimate the channel. The base station estimates the channel by employing training pilots. Consider a massive MIMO downlink system in which the base station has \(M\) antennas and serves \(N\) users with a single antenna. At the same time, the base station delivers independent data to multiple customers [87; 88]. The signal received by user \(k\), \(y_{k}\in C^{M\times 1}\), is given by (3)  
\[y_{k}=h_{k}x_{k}+n_{download} \tag{3}\]  
where \(h_{k}\) denotes a channel vector between the \(k\)-th user and the base station, with independent and identically distributed elements with a zero mean and unit variance; that is, \(h\sim CN(0,1)\). \(x_{k}\in C^{\rm M}\) is the signal transmitted by the base station for user \(k\), and \(n_{download}\) is the additional noise that is made up of receiver noise \(n_{noise}\sim CN\big{(}0,\sigma^{2}I\big{)}\). The interference during the downlink is caused by transmitting to other users at the same time and is denoted as (4):  
\[n_{downlink}=n_{downlink-interference}+n_{noise} \tag{4}\]
# Electrical and Thermal Behaviour of Crystalline Photovoltaic Solar Modules in Shading Conditions
## 1 Introduction
The modular and easily scalable nature of PV systems makes it necessary to adopt different maintenance strategies according to each case [20]. In large PV Plants, IR evaluation at ground level can be a waste of time and inefficient, due to the great number of PV modules installed. In order to solve this problem, the use of aerial IR thermography performed by a drone was recently proposed in [14; 15]. The objective of this study is to know in detail the driving process of each substring of a PV module under different conditions of shading of cells and the performance of the corresponding bypass diode. The study was carried out both in the monocrystalline type PV module and in the polycrystalline type.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
###### Abstract  
The hybridization of cellular networks and GNSS systems has gained increasing attention, especially in urban canyons and indoor environments where GNSS performance degrades significantly. Hybrid localization is part of the 3 rd Generation Partnership Project (3 GPP) standard, offering an effective solution when satellite visibility is limited. Additional cellular measurements can enhance the accuracy and reliability of standalone UE. Hybrid methods offer multiple benefits: an improved availability, continuity, and integrity; better signal penetration due to proximity; a lower power consumption; and, in harsh environments, potentially more accurate positioning than a GNSS. Moreover, GNSS chipsets in mobile phones or smartwatches are typically power-intensive. This work presents a user-level hybridization method that enables UE to receive both GNSS and 4G/5G data and autonomously determine whether to apply hybrid positioning. The developed algorithms improve the precision and reliability, allowing user-driven decisions based on data quality. The system was tested under static conditions across various scenarios: outdoors, in urban canyons, and indoors. The results show that, while hybridization enhances positioning, the 4G-only solution often performs in terms of vertical accuracy. Standard deviation metrics help guide the selection of the most precise option in real time.  
GNSS positioning; GNSS-4G/5G hybridization; 4G/5G positioning; 3 GPP; least squared; mobile communication technology; standard point positioning (SPP); user-level positioning +
Footnote †: journal: Applied sciences  
Article
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 4 Experimental Results and Analysis
### Evaluation Criteria  
To conduct a comprehensive assessment of the proposed method, we employed qualitative and quantitative analyses using Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) values. The Receiver Operating Characteristic (ROC) curve is a graphical representation that illustrates the performance of a binary classifier across various discrimination thresholds. It shows the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR) as the threshold for classification is adjusted. The FPR and TPR are defined as follows:  
\[\text{TPR}=\frac{\text{TP}}{\text{TP+FN}} \tag{8}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP+TN}} \tag{9}\]  
where TP represents true positive, TN is true negative, FP means false positive, and FN refers to false negative. The AUC is a widely used metric derived from the ROC curve. It quantifies the overall performance of the classifier. A higher AUC value indicates better classification accuracy, with a value of \(1\) indicating perfect classification.
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 2 Materials and Methods
### Design of DC-DC Boost Converter
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Input Voltage (\(V_{in}\) ) & 346.71 \(\mathrm{V}\) \\ Output Voltage (\(V_{out}\)) & 540 \(\mathrm{V}\) \\ Input Current (\(I_{in}\)) & 33.92 \(\mathrm{A}\) \\ Output Current (\(I_{out}\)) & 21.78 \(\mathrm{A}\) \\ Voltage Ripple (\(\Delta V_{out}\)) & 5.4 \(\mathrm{V}\) \\ Current Ripple (\(\Delta I_{out}\)) & 2.18 \(\mathrm{A}\) \\ Inductor Value (\(L\)) & 0.018 \(\mathrm{H}\) \\ Capacitor Value (\(C\)) & 72.18 \(\mathrm{\mu F}\) \\ Load Resistor Value (\(R_{L}\)) & 24.79 \(\Omega\) \\ Inductor Current Ripple (\(\Delta I_{L}\)) & 0.34 \(\mathrm{A}\) \\ Switching Frequency (\(f_{sw}\)) & 20 \(\mathrm{kHz}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters of the DC-DC boost converter. from the MATLAB function block that runs the MPPT algorithm. Then an error is generated from this reference voltage with the measured voltage of the PV array. This error reaches the Proportional-Integral (PI) controller. The signal from the PI controller output is compared with the 20 kHz triangle wave. As a result, the Pulse Width Modulation (PWM) signal that will trigger the switching element is generated. Figure 3 shows an example DC-DC boost converter control circuit.
## 3 Evaluation Results and Discussion
### Data Recorded and Visualized Directly on Platform  
The feedback from users regarding the attractiveness and user-friendliness of the functional features of the smart system is very important to improve the system. The feedback may be on utilization of functional features to perform specific actions and/or visualize the results.  
Samples of visualizations of recorded data are presented in Figures 6-9, which describe information gathered with smart box 2015085119 (see Figure 6) at a test site in Germany during transport of pruning products registered as Field3, lot number 151008B03R1.  
Figure 8: Delivery route based on GPS coordinates recorded by the smart box.  
Figure 7: Visualization of measured parameter values (temperature and humidity as recorded on 21 December 2015, from 08:29:14 to 11:32:02 and displayed as a table).  
Figure 9: Example of smart box reading visualization: graphic presentation of measured parameters (along with displayed values of temperature, humidity, and GPS coordinates as recorded on 21 December 2015, from 08:29:14 to 11:32:02).  
The relative humidity data retrieved from the testing activity in Germany are presented in Table 6, together with the moisture content of the biomass at the storage site. It must be noted that relative humidity indicates the humidity in the air inside the truck box after loading the woodchips. In the initial moment, the moisture measured was just the air moisture at the site. Once the probe (see Figure 5a) was inserted into the truck box, the air in the internal void volume among the woodchips increased its moisture content until it reached equilibrium with the moisture of the woodchips.
## 4 Evaluation of One-Hop Neighbor Wireless Link Status Based on Fuzzy Logic Model
### Rule-Based and Inference Procedure  
The intelligence of evaluating wireless link status in this work comes from the developed fuzzy inference engine [25]. After mapping the numerical values of the input metrics into a fuzzy set variable,  
Figure 5: Fuzzy membership functions plots: (**a**) distance progress Fuzzy membership function; (**b**) transmission power Fuzzy membership function; (**c**) ATE Fuzzy membership function; and (**d**) link quality Fuzzy membership function.  
the forward node employs the predefined combination of **IF-THEN** rules to convert the fuzzy values into fuzzy output, which rank the wireless link between the forwarder node and each potential candidate. The linguistic variables of the rank are defined as {Perfect, Good, Acceptable, Not Preferred, Bad, Very Bad}. The fuzzy rule-base construction requires a clear and profound understanding of the nodes' behavior in dynamic environments and the ways input metrics values can affect the ranking output. The fuzzy rule-base consists of a set of **IF-THEN** rules applied to infer output ranking values. The fuzzy rule-base of FQ-AGO composes of 54 rules. A few sample rules are shown in Table 2. The first four columns show the four input variables considered in FQ-AGO fuzzy analysis system, and the last column contains the output fuzzy ranking values. Each rule consists of an IF component and a THEN component. The IF component is applied to constructs conditions by employing predicates and logical connections, while the THEN component gives the degree of membership that the fuzzy variable corresponds to. For example, in Table 2, the first rule defines the best candidate for relaying the forwarder packet as follows:  
**IF** ATM is High, LQ is High, DP is VeryClose, and TP is Long **THEN**, the candidate rank is Perfect. In contrast, the worst candidate node for forwarding can be defined using the following rule:  
**IF** ATM is Low, LQ is Low, DP is Far, and TP is Short **THEN** Rank is Very Bad.
## 3 Results and Discussion
### Aerodynamic Performance  
Firstly, we calculated the aerodynamic force of a seagull in an untrimmed state, which means the total drag and pitching moment are not balanced to zero. Figure 7 gives the variation of the coefficients of lift, drag, and pitching moments within one flapping period. The angle of attack is 5 degrees, cruise velocity is 10 m/s, flapping frequency is 3 Hz, and the center of mass is set at half root chord, as the data collected by [PERSON] and [PERSON] suggests [1]. It can be seen from this figure that the course of downstroke mainly produces the lift and thrust, while less lift and the majority of drag is generated during the upstroke. These results are very normal and can be easily explained.  
Figure 6: Validation of grid, time steps, and method.  
According to the average values of lift, drag, and pitching moment given in Table 3, the average lift can be used to support 0.5 kg of weight. The drag and pitching moment are unbalanced and should be trimmed by wash-out and forward-sweep methods, while neglecting the effect of the tail as seagulls usually keep their tails retracted [2].
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
## 3 Data Processing
If only the CYGNSS data were fused to observe TCs, the maximum wind speed in the region marked by the black circle was obtained directly from the Level 2 data products derived by the YSLF GMF. Figure 2:4 The final fusion image of Hurricane Florence observed from 8 to 12 September 2018 using the SMAP radiometer data and the CYGNSS data. (**a1**)–(**a4**), (**b1**)–(**b4**), (**c1**)–(**c4**), (**d1**)–(**d4**), and (**e1**)–(**e4**) show the daily fusion results from 8 to 12 September 2018, respectively. A black circle with a radius of 150 km was used to mark the location of Hurricane Florence in the fusion image.
## 5 The Query Management Scheme
### The Query Processing Phase (QPP)  
The QPP is the core of our management scheme. In this phase, each sensor node receives its query set and merges the query results to generate the response message.
## 1 Introduction
Based on the foregoing background, this paper proposes a topology-based solution approach to the identification of suitable locations for DG placement in order to optimize system operational efficiency. The major contributions offered by the proposed scheme presented in this study are as follows:  
* The network topological-based approach proposed in this study eliminates the problems caused due to iterative procedures such as the refactorization of large-sized matrices and the divergence of solutions and multiple solutions that could be misleading. * Another important issue eliminated by this method is the challenges associated with slack bus selection, which is associated with iterative-based methodologies. * To the best knowledge of the authors, this is the first time a non-iterative-based approach has been explored to identify suitable DG locations in radial distribution networks. * Consequently, the proposed approach serves as an alternative framework that is fast, provides accurate information and avoids issues that could generate misleading information. The remaining sections of this paper are arranged as follows: Section 2 presents the relevant mathematical formulations including that of the suggested scheme. The results and a discussion of the results obtained are presented in Section 3, while Section 4 concludes the paper.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 4 Multiple Scattering Effects
### Monte-Carlo Simulation Results  
In principle, the multiple-scattering gain depends not only on path length and on rain properties (primarily rain rate and DSD shape), but also on the transmitter and receiver characteristics, and on the beam type. For the sake of simplicity, the following parameters were fixed for all simulations:  
* Beam type: collimated (i.e., zero divergence), transmitted beam width = 2.5 cm
* Receiver aperture: 10 cm radius (circular)
* Receiver field of view: 90\({}^{\circ}\) (open detector)  
On the other side, different path lengths, rain rates, and DSDs were considered. As an example, the multiple scattering gain for propagation through a homogeneous rain layer is shown in Figure 9 as a function of the path length in the case \(\mu\) = 1. The solid lines are power-law best fits of the simulation results for seven different rain rates. Finally, Table 2 reports the values of the power-law best fit coefficients for \(\mu\) = 1, 2, and 3, and the above seven rain rates.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rain Rate**} & \multicolumn{2}{c}{\(\mu\) = 1} & \multicolumn{2}{c}{\(\mu\) = 2} & \multicolumn{2}{c}{\(\mu\) = 3} \\ \cline{2-7}  & \(a\) & \(b\) & \(a\) & \(b\) & \(a\) & \(b\) \\ \hline
1 & 0.011 & 0.484 & 0.010 & 0.522 & 0.008 & 0.553 \\
2 & 0.016 & 0.521 & 0.015 & 0.547 & 0.013  
The multiple-scattering gain best-fit coefficients \(a\) and \(b\) are dependent on the rain rate \(R\). The following polynomial best-fit is a good approximation of the measured coefficients (see Table 3):  
\[a = p_{0}+p_{1}\ln(R)+p_{2}[\ln(R)]^{2}, \tag{20}\] \[b = p_{0}+p_{1}\ln(R)+p_{2}[\ln(R)]^{2}. \tag{21}\]  
The difference between the multiple-scattering gain obtained from the simulations and the best-fit calculated through (18), (20), and (21) is within 15% (on a dB scale), except for the case \(R=1\) mm/h, where rain attenuation has a negligible impact on the link performance. The \(P(A)\) curve including multiple-scattering for Hyderabad and the one previously shown in Figure 6 are compared in Figure 10 (\(L=1\) km and \(\mu=1\)). Multiple-scattering effects produce a reduction of path attenuation in the order of 50% (on a dB scale) when a laser beam propagates through heavy rain. Similar conclusions were achieved in [14] from propagation measurements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{\(\mu\) = 1} & \multicolumn{2}{c}{\(\mu\) = 2} & \multicolumn{2}{c}{\(\mu\) = 3} \\ \cline{2-7}  & \(a\) & \(b\) & \(a\) & \(b\) & \(a\) & \(b\) \\ \hline \(p_{0}\) & 0.0112 & 0.4965 & 0.0103 & 0.5194 & 0.0092 & 0.5504 \\ \(p_{1}\) & 0.0060 & 0.0390 & 0.0040 & 0.0389 & 0.0019 & 0.0372 \\ \(p_{2}\) & 0.0019 & 0.0053 & 0.0025 & 0.0052 & 0.0031 & 0.0047 \\ \hline \end{tabular}
\end{table}
Table 3: Polynomial best fit coefficients for \(a\)(R) and \(b\)(R) in (20) and (21), respectively.  
Figure 9: Multiple scattering gain (see (18)) as a function of the path length \(L\), for optical propagation through rain. DSD: Gamma type, \(\mu=1\).
###### Abstract  
Unmanned aerial vehicle (UAVs) is capable of adding significant potential to the internet of thing (IoT) devices and hence smart UAV-IoT collaborative system has attracted the attention of many researchers. This system has to be energy efficient for its nature and functionalities. Optimized trajectory planning is a significant area of research for any automatic movable device. In this article, we propose a technique, called EETO-GA for energy-efficient trajectory optimization of UAV-IoT using a genetic algorithm (GA). This technique prescribes each device of: (i) the next timestamp of arrival on the present cluster of IoT devices, at which the task queue of its header contains the highest possible number of tasks, and (ii) the minimum amount of energy that requires to complete all the tasks present in task queue of the IoT device. This technique uses a GA for optimization where the fitness function is designed by optimizing objectives: (i) the total number of tasks that can be completed, (ii) minimization of consumed energy, and (iii) the number of devices that could be served. A GA is applied here to accommodate a large number of IoT devices. A binary method of encoding is applied and methods like cross-over and mutation are used to arrive at the optimal solution. Through a simulation study, the proposed technique shows significant improvement in terms of UAV energy saved (UAVE), energy saving in IoT devices (IoTDEC), the average delay in execution of the task (ADET), and the percentage of tasks that could be completed (PTSK). Proposed EETO-GA improved average UAVE: 43%, IoTDEC: 56%, PTSK: 7.5%, and ADET: 38% over the state of the art.  
Keywords:ARMA model; clustered model; delay aware; energy efficient; internet of thing; path planning; trajectory optimization; unmanned aerial vehicles +
Footnote †: journal: Applied