esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
###### Abstract  
Over the past few years, there has been an increasing fascination with electric unmanned aerial vehicles (UAVs) because of their capacity to undertake demanding and perilous missions while also delivering advantages in terms of flexibility, safety, and expenses. These UAVs are revolutionizing various public services, encompassing real-time surveillance, search and rescue operations, wildlife assessments, delivery services, wireless connectivity, and precise farming. To enhance their efficiency and duration, UAVs typically employ a hybrid power system. This system integrates diverse energy sources, such as fuel cells, batteries, solar cells, and supercapacitors. The selection of an appropriate hybrid power arrangement and the implementation of an effective energy management system are crucial for the successful functioning of advanced UAVs. This article specifically concentrates on UAV platforms powered by batteries, incorporating innovative technologies, like in-flight recharging via laser beams and tethering. It provides an all-encompassing and evaluative examination of the current cutting-edge power supply configurations, with the objective of identifying deficiencies, presenting perspectives, and offering recommendations for future consideration in this domain.  
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# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Architecture  
Network topology of the LoRaWAN is considered as star-of-stars and from the architectural point of view, the system has three main components (i) network servers, (ii) gateways (GWs) and (iii) end nodes. End nodes communicate with the network server (or data server) via GWs and Node-to-GW communication can be either LoRa or FSK modulation with different data rates and channels. Network servers manage the GWs through standard IP technology and data frames sent through end nodes, received by GWs and routed through the network server [7]. An overview of the LoRaWAN architecture is presented in Figure 2.  
LoRaWAN is a MAC layer protocol and aims to solve management issues of the medium and network congestion. Any node using LoRaWAN protocol can benefit the following features provided by the standard;  
* Channel management
* Energy efficiency
* Adaptive data rate
* Security
* GPS-Free geolocation  
Next sections will discuss more details about the protocol.
# Convergence Gain in Compressive Deconvolution: Application to Medical Ultrasound Imaging
## 1 Introduction
For non-orthogonal sparse basis \(\Psi\), approximation algorithms such as [PERSON]'s method are quite time-consuming [15]. In terms of saving such computing time, some accelerating strategies and new iterative schemes need to be investigated and explored. Meanwhile, since the compressive deconvolution (CD) method is an inexact ADMM by solving \(x^{k+1}\) approximately, it is extremely important to present a strict and complete convergence analysis. Motivated by these observations and based on the semi-proximal Peaceman-Rechard splitting method in our previous work [16], in this paper, we propose an improved CD method, referred to as semi-proximal symmetric ADMM. Define \(v=\Psi a\), and the improved compressive deconvolution (ICD) method has the following iterative scheme:\[w^{k+1}=prox_{\pi\|\cdot\|_{1}/\beta}\left(v^{k}-\tau\Psi^{\mathrm{T}}(\Psi w+ \frac{\lambda_{1}^{k}}{\beta}-v^{k})\right), \tag{6a}\] \[x^{k+1}=prox_{\pi\|\cdot\|_{p}^{p}/\beta}\left(x^{k}-\gamma H^{\mathrm{T}}(Hx^{k} +\frac{\lambda_{2}^{k}}{\beta}-v^{k})\right),\] (6b) \[\lambda^{k+\frac{1}{2}}=\lambda^{k}-\rho\beta(A_{2}v^{k}-B_{2}p^{k+1}),\] (6c) \[v^{k+1}=\left(\frac{1}{\mu}\Phi^{\mathrm{T}}\Phi+2\beta I_{N} \right)^{-1}(\frac{1}{\mu}\Phi^{\mathrm{T}}y+\lambda_{1}^{k}+\lambda_{2}^{k}+ \beta\Psi w^{k+1}+\beta Hx^{k+1}),\] (6d) \[\lambda^{k+1}=\lambda^{k+\frac{1}{2}}-\varrho\beta(Av^{k+1}-Bp^{k+1}), \tag{6e}\]  
where \(A=\left( \begin{array}{c}I_{N}\\ I_{N}\end{array} \right)\), \(B=\left( \begin{array}{cc}-\Psi&0\\ 0&-H\end{array} \right)\), \(\rho\in(0,1)\) and \(\varrho\in(0,1]\). Compared with the CD method presented in [13], the contributions of this article can be summarized as follows:  
1. Based on the iterative scheme of the strictly semi-proximal Peaceman-Rechard splitting method, we present an ICD method that will reduce the number of iterations while only involving additional dual update (i.e., \(\lambda^{1+\frac{1}{2}}\)) and requiring almost the same computational effort for each iteration. 2. We prove that the ICD method will converge under mild conditions, while the convergence analysis is not given in the previous CD method. 3. We introduce some elaborate manipulations that can directly generalize the CD method to more general scenarios with a non-orthogonal sparse basis \(\Psi\). The rest of this paper is organized as follows. In Section 2, we first present some preliminaries that are useful for subsequent analysis. Then, we illustrate the ICD method to rebuild the sparse coefficients from the measurements of ultrasound imaging, and the convergence analysis is given. In Section 3, we give extensive ultrasound experiments that can be used to evaluate the performance of the proposed reconstruction algorithm in comparison with CD algorithm. Finally, we make some concluding remarks in Section 4.
## 5 Recent Technologies Used in Ammonia Production
### New Ammonia Generation Routes
#### 5.3.2 Photocatalytic Ammonia Synthesis  
The use of photocatalysts to create sustainable power and avoid global warming is one promising area of research. Electrochemical approaches for ammonia synthesis frequently require the use of costly and sometimes poisonous electrocatalysts, whereas photocatalysts may employ cheap and abundant materials. The semiconductor catalyst converts light energy into chemical energy, which is then utilized to drive the processes required for ammonia generation. Positively charged (h\({}^{+}\)) holes are left in the valence band by the migration of excited electrons from the valence band to the conduction band, then photoreduction of N\({}_{2}\) and photooxidation of water take place simultaneously in the photochemical cell to produce ammonia. If the valence band of a semiconductor is higher than +1.23 V at pH 0 or +0.82 V at pH 7, surface holes (h\({}^{+}\)) can oxidize water to produce oxygen gas (O\({}_{2}\)) and protons (H\({}^{+}\)) via the oxygen evolution reaction (OER), as described in Equation (7). The H\({}^{+}\) reduces to H\({}_{2}\) on the cathodic side using photogenerated electrons from the conduction band of the semiconductor that contributes to the reduction of N\({}_{2}\) to NH\({}_{3}\). To obtain considerable H\({}_{2}\) production rates, a high-work-potential metal such as Ni, Pd, Pt, or Au must be used as a co-catalyst [100]. Table 11 and Figures 22 and 23 illustrate the catalysts utilized for photocatalytic ammonia production, providing an overview of their properties and effectiveness in facilitating the ammonia synthesis process.  
\[\text{H}_{2}\text{O}+4\text{h}^{+}\text{$\rightarrow$ O}_{2}+4\text{H}^{+} \tag{7}\]  
\[2\text{H}^{+}+2\text{e}^{-}\text{$\rightarrow$ H}_{2} \tag{8}\]  
\[\text{N}_{  
The band gap, VB and CB levels, and photocatalyst stability must all be taken into account. Dynamic constraints such as electron transport may make reducing N\({}_{2}\) to NH\({}_{3}\) challenging.
## 5 Experimental Results
### Results for PI-SMC Controller  
Figure 18 presents the results when the set point of the DC voltage is changed. Initially, it was set in 400 V and a change of 20 V was made, reducing DC voltage from 400 to 380 V, as shown in Figure 18a. Then, the DC voltage set point was increased from 380 to 400 V (Figure 18b). In both cases, the DC voltage presents a stabilization time of 1.3 s with overvoltage around the stabilization point. In addition, the current amplitude slightly changes. Nonetheless, the controller keeps the AC current and voltage in phase, ensuring a \(PF\) close to 1.  
Figure 17: Semi-bridgeless boost converter for experimental tests.  
Figure 18: Experimental results with PI-SMC control for voltage set point changes: (**a**) DC voltage increases 20 V; and (**b**) DC voltage decreases 20 V.  
Figure 19 shows the results when a load disturbance is caused. Figure 19a presents results when load decreases 25%. In this case, an overvoltage of 22 V and a DC voltage stabilization time of 2.9 s are observed. Figure 19b depicts the results when the load increases 25%; SBBC presents a maximum oscillation of 18 V and the DC bus stabilizes at 1.53 s. In both tests, the AC current presents a delay of several grid cycles before reaching the stabilization point.
## 3 Security Analysis
### Dishonest Participants Attack  
When dishonest participants try to intercept other legitimate participants information to restore the secret by themselves, they can be seen as eavesdroppers. Because participants except legitimate \(t\) participants are illegal user in the large group, it has no effect even through they are dishonest. Next we discuss how to analyze security of the proposed protocol when dishonest participants appear in legitimate \(t\) participants and put error private key into the process of recover secret key. Under the circumstances, it occurs two probability, i.e., the polynomial can be restored and the polynomial can not be restored. For the first case, we assume a dishonest participant \(C_{ij}\) make \(\{x_{j},f(x_{l_{j}})\cdot\}\) as its private key, where \(f(x_{l_{j}})\cdot\
eq S+Z_{1}x_{j}+Z_{2}(x_{j}^{\prime})^{2}+\cdots+Z_{t-2}(x_ {j}^{\prime})^{t-2}+Z_{t-1}(x_{j}^{\prime})^{t-1}\). Using the Lagrange interpolation, the polynomial can be expressed as  
\[\begin{split} f(x)\cdot&=f(x_{l_{j}})\psi_{1}+f(x_{ j_{2}})\psi_{2}+\cdots+f(x_{l_{j}})\cdot\psi_{l}^{\prime}+\cdots+f(x_{t_{j}}) \psi_{t}\\ &=S\cdot+Z_{1}x+Z_{2}x^{2}+\cdots+Z_{l}^{\prime}x^{l}+\cdots+Z_{t -1}^{\prime}x^{t-1},\end{split} \tag{5}\]  
where \(S\) is the recovered secret key. After testing and verifying, \(S\) and \(Z_{\tau}\) do not satisfy the constraint in Equation (1) for \(\tau\in\{1,2,\cdots,t\}\). It seems that the dishonest participant can be found by the legal participants. For the second case, supposing that a dishonest participant substitutes \(\{x_{j},f(x_{l_{j}})\}\) with \(\{x_{j}^{*},f(x_{l_{j}})^{*}\}\), where \(f(x_{l_{j}})^{*}=S+Z_{1}x_{j}^{*}+Z_{2}(x_{j}^{*})^{2}+\cdots+Z_{t-2}(x_{j}^{* })^{t-2}+Z_{t-1}(x_{j}^{*})^{t-1}\). The polynomial can be represented as  
\[\begin{split} f(x)^{*}&=f(x_{l_{j}})\psi_{1}+f(x_{ j_{2}})\psi_{2}+\cdots+f(x_{l_{j}})^{*}\psi_{l}^{*}+\cdots+f(x_{t_{j}})\psi_{t}\\ &=S+Z_{1}x+Z_{2}x^{2}+\cdots+Z_{l}x^{l}+\cdots+Z_{t-1}x^{t-1}. \end{split} \tag{6}\]  
Obviously, the restored polynomial is equal to the primitive polynomial, and \(S\) is the secret key from the dealer. However, it does not satisfy the constraint \(Z_{l}=S\mod x_{j}^{*}\). By the above-mentioned measurements, we can detect the dishonest participant. If the number of dishonest participant is more than one, they can be detected with more possibility [35; 36; 37; 38].
## 5 3D BEM-DMST Code Experimental Validation
### Validation of the Tip Loss Model  
The previous analysis suggests that the tip loss model has a significant impact on the performance prediction, especially for H-shaped rotors. To validate quantitatively the action of the tip loss model, the CFD simulations presented in [16] were considered. As matter of fact, in [16] the authors proposed comparative 2D and 3D CFD simulations of a turbine very similar to the present H-shaped in Table 1, but featuring only one blade and an increased blade height to 1.5 m and operating at \(TSR=3.3\). The model did not include any strut, so the difference between 2D and 3D performance is only due to the tip loss effect. In particular, the overall \(C_{M}\) obtained with the 3D CFD resulted about 10% lower than the one evaluated at midspan or equivalently with a 2D model. The same case set-up with a single blade model with longer span operating at \(TSR=3.3\) has been simulated with the present 3D BEM code to have a fully consistent comparison with CFD data shown in [16].  
The following discussion aims at highlighting differences between low and high fidelity approaches, hence both fully 3D CFD and 3D BEM analyses will be normalised by their respective values at midspan. CFD simulation gives the trend of the torque coefficient along the blade semi-span shown in Figure 10. At the blade tip, due to pressure equalization, forces tend to zero and torque coefficient consequently. The average CFD torque coefficient over the blade span is 92% of the value at midspan. The study also compares the finite aspect ratio model by [PERSON] carried out by [PERSON] et al. to account for tip losses when simple 2D BEM theory is applied at the equatorial plane. 3D BEM average torque coefficient over the blade is 89% respect to the value found at the equatorial plane. The trends are slightly different since CFD predicts that the blade works as the midspan section up to z/H = 0.6, conversely, 3D BEM solution deviates from 2D torque coefficient already at z/H = 0.4. The value of average torque coefficient for CFD and 3D BEM is reached respectively at z/H = 0.8 and z/H = 0.7 suggesting that the tip influence is limited to the tip region when CFD tool is adopted. Moreover \(C_{M}/C_{M_{2D}}\) also assumes negative values in the close vicinity of the blade tip. Whereas the tip loss model entails a slightly more 'distributed' loss over the span and the sections operating in a pure 2D way are concentrated in the first half inboard blade (z/H < 0.4).  
Overall, despite the inevitable local differences, and also considering the inherent complexity of the phenomenon, the tip loss model appears capable of estimating the overall effect of the finite blade length on the turbine performance, providing quantitatively comparable effects on the total torque of a VAWT blade preserving also the qualitative decrease towards the tip.  
Figure 9: Tangential force coefficient \(C_{T}\) distribution at the upstream disc (**left**) and downstream disc (**right**) along semi-span for micro H-shaped (**top**) and micro Troposkein rotor (**bottom**) at \(TSR\) = 2.4. View from upwind.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 2 Backgrounds and Security Problems
### Analysis of Attacks in WSNs
#### 2.2.1 Active and Passive Attacks  
Passive attacks: Passive attacks are carried out by malicious nodes to perceive the nature of the activities and illegally collect data transmitted in the network without disrupting network operations (such as eavesdropping, interference, tampering, replay...). The collected packets are analyzed to determine the traffic and exchange process of the packets, thereby identifying the server, and determining the location.  
Active attacks: Attacks that attempt to change, insert, delete, or destroy data transmitted in the network. Attacks can intercept data and attempt to modify and drop packets, which can be achieved internally or externally. On the other hand, some attacks may come from compromised nodes, which are part of the network. Based on the purpose of the attack, active attacks can be classified as node-compromising attacks; routing attacks; and DoS attacks [34; 35].
## 2 Fundamental Methods  
### Signal Integrity and S-Parameters  
As transmission rates increase, the inefficiencies of traditional parallel signal transmission methods are becoming apparent and high-speed serial digital transmission is becoming widely used. Figure 1 shows the widely used SerDes structure in a high-speed serial link system. It comprises a transmitter chip with a serializer and a feedforward equalizer (FFE), a channel, and a receiver chip with a decision feedback equalizer (DFE) and a de-serializer. The FFE at the sending end mainly performs pre-emphasis on the signal to improve its high-frequency component. The composition of a wireline channel is complex, usually including traces, vias and connectors, etc. These physical structures exhibit low-pass filter characteristics and can enlarge the high-frequency loss of a signal, severely worsening the signal transmission quality and leading to SI problems. The continuous-time linear equalizer (CTLE) at the receiving end is mainly used to compensate for the high-frequency loss of the channel, eliminate the pre-cursors, and suppress the trailing of the pulse response. The DFE is mainly used to eliminate the post-cursors of the pulse response. The combination and parameter settings of the FFE, CTLE, and DFE have a significant impact on the SI of ultra-high-speed wireline serial communication links.  
The main goal of SI analysis is to detect and reduce the factors that cause losses, such as dithering, reflection, and crosstalk. On the one hand, the SI can be analyzed from the frequency domain, as shown in Figure 2a. This mainly includes IL, RL, insertion loss deviation (ILD), and the insertion loss-to-crosstalk ratio (ICR), which are subsequently placed into a template for evaluation. On the other hand, the SI can be evaluated in the time domain, such as through eye diagrams, transient simulations, bathtub curves, and bit error rates, as shown in Figure 2b.  
Figure 1: Common structure of a SerDes circuit.  
The critical method of traditional SI analysis involves the acquisition of S-parameters. These parameters contain comprehensive Frequency Domain (FD) characteristics of the transmission channel and offer a large amount of information on aspects such as reflection, crosstalk, and loss. Moreover, S-parameters can be employed in time-domain simulations to generate data such as eye diagrams and bathtub curves.
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 4 Results and Discussion
### THz Raman Spectra of Nucleoases  
RNA is the intermediate carrier of biological genetic information and participates in protein synthesis and gene expression. The chain molecule is formed by the condensation of nucleotides with phosphate diester bonds. A nucleotide is composed of pentose, phosphoric acid, and a nucleobase. There are four RNA nucleobases: cytosine, uracil, guanine, and adenine. Figure 8 shows the THz Raman spectra of the four nucleobases under a laser power of 50 mW and integration time of 80 s. The measured results are consistent with the theory. The measurement of RNA nucleobases is of great significance for structural research on and identification of biomacromolecules.
## 4 Early Warning of Rockburst Based on Microseismic Indices  
### Commonly Used Microseismic Indices  
The microfractures of different scales in the rock mass correspond to the microseismic events of different intensities. For a single microseismic event, many source parameters can be solved using different principles. Most of these parameters have a functional relationship, which is the derivation of two basic intensity parameters: the seismic moment and energy. The value of the individual source parameter is random, but overall, it shows a certain trend, such as accumulation in space, frequent occurrence in a short time, and gradual increase or decrease in energy. Therefore, microseismic indices can be used to reflect the evolution law of source parameters in a certain space-time range. In addition, affected by the ground stress, the integrity of the surrounding rock, the propagation path of the microseismic waves, the installation quality of the sensor, and the consistency of the channel, the amplitude and frequency attenuation of the microseismic signal vary to a certain extent, and the calculated source parameters also have some deviation. Even in the same project, the microseismicity rules of different cave sections are different; in other words, the source parameters are relative and can be compared only under similar conditions. Therefore, what type of microseismic statistical index is used to reflect the precursory law of rockbursts is a basic problem to be considered in the early warning of rockbursts. The commonly used early warning indices for microseismicity are the apparent stress, apparent volume, \(EI\), and \(b\) value.
# Estimation of Cyclic Demand in Metallic Yielding Dampers Installed on Frame Structures
## 1 Introduction
These observations led them to conclude that the relative importance of the cyclic demand grows at higher distances from the fault, while structural response is governed by peak demand in the near-fault. Using this equation requires calibration of the regression parameters \(C\), \(\chi\), \(\varphi\), and \(\kappa\). \[N=1+C\cdot\tau^{\chi}\cdot(R-1)^{\varphi}\cdot I_{d}^{\kappa} \tag{9}\]  
It can be deduced from Equations (6)-(9) that (i) accurate predictions of the number of inelastic cycles requires calibration of parameters for each specific structural system or hysteretic behaviour, (ii) all equations recognise the influence of the yield level (either via \(R\) or \(\mu\)), and (iii) impulsive near-fault ground motions has been found to significantly reduce the number of inelastic cycles. This last point is of paramount importance in the design of structures with hysteretic dampers in the near-fault. Because the response of hysteric dampers is displacement dependent, the decrease in the number of yield cycles necessarily reduces the efficacy of the dampers. The purpose of this investigation is to highlight the relevant parameters to predict the equivalent number of yield cycles (\(\eta\)/\(\mu\)) of hysteretic dampers installed in reinforced frames within the framework of performance-based seismic design, with explicit consideration of near-fault effects. To this end, this paper analyses the seismic response of twelve representative reinforced concrete (RC) frames equipped with hysteretic dampers in a region with medium to high seismicity in Europe. The numerical models account for three building heights (3, 6, 9 stories), the collapse pattern of the bare RC frames (with or without capacity design), and two setups of hysteretic dampers (designed for near-fault or far-field seismicity).
# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 4 Methods and Evaluation
### Evaluation Indices  
After obtaining every hourly forecast value of one-day-ahead PV power with the proposed model, it was necessary to validate the applicability of the model through certain indices. In machine learning, the most common indices used to evaluate the performance of models are MAE, RMSE, and MAPE [40].  
1. Mean Absolute Error (MAE). MAE can be used to measure the error between predicted values and actual values. It depends on the scale of continuous variables. The lower the value, the higher the accuracy of the predicted model. The equation is given below: \[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|\hat{X}_{i}-X_{i}\right|\] (1) where \(\hat{X}_{i}\) and \(X_{i}\) represent the \(i^{th}\) forecasted and actual values, respectively, and \(N\) is the size of the test dataset (\(N=168\) for one week in this study).
2. Root Mean Squared Error (RMSE). RMSE can be used to measure the deviation between predicted values and actual values. The difference between RMSE and MAE is that RMSE is sensitive to outliers. That means that RMSE is easily influenced by large deviations. Hence, a smaller error indicates better performance. The equation is given below: \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\hat{X}_{i}-X_{i}\right)^{2}}\] (2) where \(\hat{X}_{i}\) and \(X_{i}\) represent the \(i^{th}\) forecasted and actual values, respectively, and \(N\) is the size of the test dataset (\(N=168\) for one week in this study).  
Mean Absolute Percentage Error (MAPE). MAPE measures the accuracy as a percentage. It can be used to judge the quality of the predicted result. The definition is: \[\text{MAPE}=\left[\frac{1}{N}\sum_{i=1}^{N}\left|\frac{X_{i}-\hat{X}_{i}}{X_{i}} \right|\right]\times 100\%\] (3) where \(\hat{X}_{i}\) and \(X_{i}\) represent the \(i\)th forecasted and actual values, respectively, and \(N\) is the size of the test dataset (\(N=168\) for one week in this study).