esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Design of a Broadband In-Line Coaxial-to-Rectangular Waveguide Transition
A matching waveguide was placed between the converter waveguide and the output waveguide to reduce reflections from a step discontinuity in the waveguide broad wall. Using the powerful capability of a modern electromagnetic simulation tool--CST Studio SuiteTM V2023, a widely used simulation tool in the antenna and RF community--the dimensions of the proposed transition were optimized for a reflection coefficient \(<-20\) dB over a bandwidth approaching the theoretical maximum of 3:1. In many papers, authors present a design with experimental validation. In others, a design method is validated with computer simulation [9; 11; 18; 28; 29; 30; 31; 32; 33]. We believe that the accuracy of the simulation tool used in this Communication is sufficient to prove the proposed design approach. All the dimensions of the design examples are given, and anyone can verify the results presented in this paper. The construction of this Communication is as follows. In Section 2, we review the performance of a representative commercial in-line coaxial-to-rectangular transition to show the state of the art in practice. It is followed by the concept and the equations of the proposed design approach. In Section 3, two design examples are presented: one for a WR75 waveguide with a reduced narrow-wall height and the other for a standard WR75 waveguide. Finally, in Section 4, we draw conclusions and suggest possible future research topics.
## 3 Methodology
### Mitigation Strategies
#### 3.3.1 Device Security Features  
At the device level, on-device security features such as IP or MAC address filtering limit the traffic the device processes, in addition to runtime integrity checks, to ensure that each component operates as intended and resists tampering. This layer of defense is designed so that even with compromised devices that have allowed routing to a device, attacks may still be thwarted. These protections are the simplest to deploy, as they can be configured when the device is being commissioned for a standard deployment. However, there are limitations to the protections this provides, as the device will not have any information other than the packet data.
## 3 Residential Flexible Load Models and DR Strategies
### EV Load  
The model of EV is the relationship between the state of charge (SOC) of the vehicle power battery and the charging power [20]. According to the charging process of the EV, the initial constant power charging time accounts for most of the total time, so it is assumed that the EV is charged with constant power. If the EV is charged with a constant power \(P_{\text{c,i}}\) at time \(t\), the SOC at time \(t\) can be obtained by Equation (4).  
\[\text{SOC}_{i}(t+\Delta t){=}\ P_{\text{c,i}}\eta_{\text{c,i}}\Delta t/C_{i}+ \text{SOC}_{i}(t) \tag{4}\]  
where \(P_{\text{c,i}}\), \(\eta_{\text{c,i}}\), \(C_{i}\) are charging power, charging efficiency and battery capacity of the EV \(i\).  
For residential consumers, the remaining power of the EV at home is mainly determined by the daily mileage. Assuming that the battery power of the EV decreases at a constant rate during the driving process, the relationship between the current SOC of the EV and the mileage of the EV after the last charge is Equation (5).  
\[\text{SOC}_{i}\Big{(}t_{\text{start,j+1}})=\ [\text{SOC}_{i}\Big{(}t_{\text{ end,j}}\Big{)}-d/D]\ \times 100\% \tag{5}\]  
where \(\text{SOC}_{i}\Big{(}t_{\text{start,j+1}}\Big{)}\) is the initial SOC of the EV \(i\) in period \(j+1\); \(\text{SOC}_{i}\Big{(}t_{\text{end,j}}\Big{)}\) is SOC of the EV \(i\) after charging in period \(j\); \(d\) is the driven distance of the EV \(i\); \(D\) is endurance mileage of EV \(i\).  
The EV can participate in DR by transferring charging time. While meeting user travel needs, the charging time can be flexibly adjusted according to the operating characteristics of the grid, as shown in Figure 7. Before and after DR, the EV electricity consumption change value of user \(i\) is \(Q^{\text{DR}}_{\text{EV,i,j}}\) in period \(j\).
## Appendix A Multiple Read Dominant Pole (MRDP) Controller Tuning
### 2 DoF Controller  
To avoid overshooting of setpoint step responses, the prefilter  
\[F_{p}(s)=\frac{N_{p}(s)}{D_{p}(s)}=\frac{(1-s/s_{o})^{p}}{(1+T_{i}s)(1+T_{1}s+ \cdots+T_{m}s^{m})}=\frac{(1+T_{o}s)^{p}}{(1+T_{1}s)(1+T_{1}s+\cdots+T_{m}s^{m})} \tag{11}\]  
has to be used with \(D_{p}(s)\) canceling the \(F_{nyg}(s)\) (10) zeros [34]. \(N_{p}(s)\) can accelerate the setpoint step responses by canceling \(p\in[0,m+1]\) dominant poles \(s_{o}\), or the dominant time constants \(T_{o}\). Since the success of the pole-zero canceling depends on the compatibility of the used model with the process, the most robust choice is given by \(p=0,N_{p}(s)=1\). Accelerating setpoint step responses by optimizing \(N_{p}(s)\) is also less effective in constrained control and thus is not recommended for uncertain unstable systems.
## 3 Results and Discussion
### Indoor Experimental Results
#### 3.1.2 Environmental Conditions  
Environmental parameters, including temperature, humidity, and light intensity, were monitored (Figure 10). The temperature during the experiment was quite consistent since it was in an indoor environment. Similarly, the humidity level had a slight variation in both growing seasons.  
Figure 9: (**a**) Relationships between moisture content sensor and capacitive soil moisture sensor 1, (**b**) capacitive soil moisture sensor 2, (**c**) capacitive soil moisture sensor 3, (**d**) capacitive soil moisture sensor 4, (**e**) capacitive soil moisture sensor 5, and (**f**) capacitive soil moisture sensor 6.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
### Fully Digital Wideband FIR Beamformers  
Finite impulse response (FIR) filters can be used for transmit beamforming in a fully digital array. Wideband digital FIR realizations can be achieved with a set of narrowband FIR realizations, called sub-band beamformers, where a wideband signal is decomposed into a set of narrowband channels, and each channel is processed with complex-valued weight-and-sum feeding networks [6; 31].  
In [32], a low-complexity wideband transmit beamformer has been proposed using two-dimensional (2-D) sparse finite-extent impulse response (FIR) trapezoidal filters, designed using the windowing method and hard thresholding. This beamformer can achieve wideband operation with 33% fractional bandwidth and provides a path for lower computational complexity compared with comparable discrete Fourier transform (DFT)-based techniques. However, the FIR beamformer in [32] is limited to a single beam (i.e., \(K=1\)), and the 2D sparse FIR trapezoidal filter is suboptimal because the windowing technique has been employed to design the filter. In the context of multibeam beamforming using FIR filters, one key limitation is computational complexity, especially for wideband systems with variously shaped passbands and narrow transition bands. The accuracy of the designed filters for each beam direction in a multibeam system is required to be high, leading to higher-order FIR filters with increased computational complexity. Achieving a wide passband for wideband operation can be challenging while maintaining a sharp transition band for side-lobe suppression. Our work addresses these limitations by designing a 2D sparse FIR trapezoidal filter to be optimal in the minimax sense for multibeam beamforming.
# Research on the Laws of Overlying Rock Fracture and Energy Release under Different Mining Speeds
## 1 Introduction
Overall, current research mainly monitors energy released under different mining speeds through numerical simulations combined with microseismic monitoring data. This research focuses on the correspondence between mining speed and energy release without conducting in-depth research on the correspondence between the number and location of microseismic energy events and the law of overburden fracture. This will lead to inconsistent research conclusions when studying different coal and rock mining conditions. [PERSON] et al. [37] proposed that as the advancing speed of the working face increases, the distribution range of microseismic events generated every day significantly decreases. Increasing the advancing speed to an appropriate degree can improve the conditions of the working face. In summary, to examine the effects of varying mining speeds on the failure modes and fracture energy release of overlying rock blocks at the working face, the author conducted a comprehensive study. By means of theoretical analysis, similarity simulation experiment and numerical analysis, the movement of overlying rock and the size of fracture block under different mining speeds are analyzed, and the strain energy change of overlying rock under different mining speeds is calculated. Based on the laws of overlying rock movement, further research on the energy release in the mining area can more accurately elucidate the relationship between the mining speed of the working face and energy release in the mining area.
## 4 Discussion  
This study suggests that ALS provides a highly accurate alternative to the currently applied field-based methodologies for aquatic reed bed monitoring. In contrast to classical monitoring, ALS involves no direct entry of the habitat and no disturbance of the species living there, and therefore, only samples of the study area are needed for field control. In addition, ALS can be carried out in a very standardised way that will help increase accuracy at a reduced cost. In line with studies focused on reed bed or wetland habitat mapping [23; 25; 26; 27], ALS is an innovative technical solution compared to classical approaches based on analysis of satellite imagery or of airborne optical data sources [6; 44; 45]. Compared to optical sensors in which recorded information on every image is only of top structures observable to an imaging sensor, ALS delivered valuable information in the horizontal as well as the vertical gradient. This technology allowed for characterisation of structural parameters in vegetation stands, and the usage of a Green-LiDAR provided relevant information about the water column and lake bottom in the areas of shallow waters, which was relevant to mapping the shoreline independent of the water surface level. In order to develop environmental protection measures, the material and methodologies implemented for monitoring aquatic reed beds should deliver reliable information on vegetation extents and status. LiDAR data, in combination with a decision tree algorithm, was developed to estimate the reproducibility of an ALS processing chain. The suitability of the applied system was assessed based on (1) an accurate estimation of aquatic reed extents; (2) density; (3) the accuracy of DSMs derived from LiDAR data.
## 3 Results and Discussion
### Adsorption Thermodynamics  
Thermodynamic parameters, i.e., standard free energy (\(\Delta G^{0}\)), enthalpy (\(\Delta H^{0}\)) and entropy (\(\Delta S^{0}\)) provide in-depth information of the heat change, feasibility and spon  
Figure 18: The kinetics for adsorption of MG onto nZVZ (initial pH = 5, temperature = 25 \({}^{\circ}\)C, initial concentration = 700.00 mg/L).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Equations** & **Parameters** & **Values** \\ \hline The pseudo-first-order kinetics & \(\log(q_{e}-q_{t})=\log q_{e}-\frac{k_{1}t}{2.303}\) & \(k_{1}\) (1/min) & \(1.4\times 10^{-2}\) \\  & & \(q_{e}\) (mg/g) & 990.19 \\  & & \(R^{2}\) & 0.9344 \\ \hline The pseudo-second-order kinetics & \(\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}}\) & \(k_{2}\) (g/mg\(\cdot\)min) & \(8.0\times 10^{-6}\) \\  & & \(q_{e}\) (mg/g) & 1428.57 \\  & & \(R^{2}\) & 0.9463 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The kinetic parameters for the adsorption of MG onto nZVZ.  
process [63; 64]. The effect of temperature at 293, 298, 303 and 308 K was investigated by the experiments with initial pH of 5, initial MG concentration of 700.00 mg/L and nZVZ dosage of 30 mg. The values of \(\Delta H^{0}\), \(\Delta S^{0}\) and \(\Delta G^{0}\) were calculated from the following Van't Hoff equation [65]:  
\[Kc=\frac{q_{e}}{c_{e}} \tag{18}\]  
\[\Delta G^{0}=-\mathrm{RTInKc} \tag{19}\]  
\[\mathrm{lnKc}=\frac{\Delta S_{0}}{R}-\frac{\Delta H_{0}}{RT} \tag{20}\]  
where \(q_{e}\) is the adsorption capacity at equilibrium (mg/g), \(c_{e}\) is the equilibrium concentration (mg/g), \(Kc\) is the equilibrium constant, the universal gas constant value (\(R\)) is \(8.314\) J/mol/K and \(T\) (K) is the absolute solution temperature.  
The results of \(\Delta G^{0}\) for the adsorption of MG onto nZVZ in this study were \(-1.2512\), \(-2.8320\), \(-4.1823\) and \(-5.7419\) kJ/mol at 293, 298, 303 and 308 K, respectively (Table 10). The negative values of \(\Delta G^{0}\) illustrate that the spontaneous nature of this adsorption process was observed in the range of temperature studied (293 K-308 K). In addition, the values of \(\Delta H^{0}\) and \(\Delta S^{0}\) were calculated from the slope and intercept of the plot between ln\(Kc\) and \(1/T\) (Figure 19) [66]. The positive value of \(\Delta H^{0}\) implies that the adsorption of MG onto nZVZ is an endothermic process. The value of \(\Delta S^{0}\) is positive reflecting the degree randomness at the solid/liquid interface during the adsorption process. The positive values of \(\Delta S^{0}\) and \(\Delta H^{0}\) show that the interaction of nZVZ with MG is basically entropy driven [67].
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 2 Fundamental Quantum Computing Concepts  
### Qubits  
The core distinction between classical and quantum computing lies in their fundamental units of information: the classical bit and the quantum bit (qubit), respectively. In classical computing, a bit is a binary unit that can exist in one of two states, either 0 or 1. This binary nature underpins all classical computing processes, with data storage and computations executed through combinations of these binary states.  
In contrast, the qubit, the fundamental unit of quantum computing, transcends this binary limitation. A qubit differs from a classical bit in its ability to exist in a superposition of states. Rather than being limited to a strict 0 or 1, a qubit can represent both 0 and 1 simultaneously, a phenomenon that is central to quantum computing's potential for processing complexity.  
This unique property of qubits arises from the principles of quantum mechanics. Unlike classical bits, whose state is definite and observable, a qubit's state is described probabilistically. The actual state of a qubit is not determined until it is measured. Before measurement, a qubit exists in a superposition of the states \(|0\rangle\) and \(|1\rangle\), where the probabilities of these states are determined by quantum amplitudes. These amplitudes, which are complex numbers, dictate the likelihood of the qubit collapsing into either the \(|0\rangle\) or \(|1\rangle\) state upon measurement.  
The concept of superposition enables qubits to perform computations on a scale and at a speed unattainable by classical bits. This capability forms the basis for quantum computing's potential to solve problems that are currently intractable for classical computers, which will be introduced in the following section.
# Study on Overturn Proof Monitoring System of Mobile Crane
## 1 Introduction  
While up-right build structures are under construction, an over-hung crane has a major role in efficient lifting and transporting heavy materials from one point to another. There are several types of crane in various construction sites, such as bridge/overhead, barge lift, tower crane, etc. The mobile crane is one of the most widely used types of construction equipment due to its mobility. The mobile crane is a system equipped with a crane on a commercial vehicle for general crane functions. Recently, large-sized and high-powered cranes are required according to the size of the construction site [1]. The increase in architectural scale occurs in high altitude operations with heavy materials. Moreover, lifting and holding materials in harsh conditions such as strong winds, heavy rain and ground vibration has catastrophic consequences on the safety of operators [2]. [PERSON] et al. (2016) reported that mobile cranes account for approximately 72% of accidents during the entire construction period [3]. Mobile cranes are prescribed to prevent accidents as a type of heavy lifter in \"Article 132 of the Rules for Occupational Safety and Health\" in South Korea. Nevertheless, the number of crane accidents including casualties and death have increased over the last decade [4]. Much research on the safety of tower [5,6] and bridge cranes [7,8] have been performed worldwide [9]. The major causes of mobile crane accidents are overturning (45%) due to structural failure [10] of the outrigger, collapse of ground and overloading. Overloading could be well addressed through obeying procedure and following regulations [11].  
In this paper, a dynamic simulator of mobile cranes based on analyzed overturn limit data from zero moment point (ZMP) theory [12,13] has been developed and analysis results were applied to site tests. The test bench was composed of an actual construction field and the practical experiments were performed in a realistic manner of operation. Moreover,wireless network communication systems [14] were applied for monitoring the status of the crane from a distance where visibility was not secure. Consequently, the applicability to the field from operating the simulator and actual vehicle tests, confirmed the feasibility of application to the construction site.
###### Abstract
Landsat and Sentinel represent the two flagships for medium-resolution land imaging [22]. Since L8/OLI and S2/MSI data share many technical characteristics [23], they represent cost-effective tools to describe landscape dynamics at broad scales. Literature shows the importance of L8/OLI and S2/MSI data application for producing timely and accurate maps [24; 25]. However, the results have not yet been synthesized to provide coherent guidance on their effect in LULC classification processes, and to know limitations and promising approaches. To do so, it is necessary to synthesize the collective knowledge on this topic, as opposed to using individual experience and expertise [26]. Recently, systematic reviews of remote sensing applications have provided reliable synthesis and scientific guidance [19; 27; 28]. However, none focused on L8/OLI and S2/MSI data for LULC classification and LULCC detection. Therefore, the main objective of this work is to conduct a systematic review of peer-reviewed papers published in Journal volumes from 2015, the year of the S2/MSI launch, to 2020 related to the use of L8/OLI and S2/MSI spectral bands (bands) and spectral vegetation indices (VIs) to map LULC and detect and monitor LULC changes. Our goal is specifically to: (1) synthesize the current state of knowledge, aiming to identify relevant developments and applications of classification methods concerning sensors, LULC types, classifiers, geographical regions, training sample sizes, segmentation algorithms, and other variables; and (2) summarize scientific advances for LULC classification and LULCC detection, and existing issues and limitations, providing scientific guidelines and new insights for future research regarding their use.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 4 Formulation for Optimal Solution
### Optimal Path Planning Problem
To analyze the validity of the selected trend for the zone priority function curve in Figure 2, we consider that, to find the best path for a mobile sink, MS, it is necessary to move among three cluster heads, as an example. Assume zone priorities of 10, 5, and 0 for the three cluster heads, respectively. The minimum and maximum zone priority values are 1 and 10, and the value of \(\varepsilon\) is set to 0.1. The sink node should shift to the highest priority cluster head. Applying the zone priority function yields the values 1, 0.5, and 0. Cluster heads with the greatest priority have the highest value, while those with no importance have zero. This suggested function moves the mobile sink to the highest priority cluster head. Figure 2: Function curve for zone priority. Urgent, real-time data must be sent to the mobile sink within predefined deadlines; performance is assessed by how many packets are received before the deadline. Therefore, Urgent messages must be transmitted with the proper latency, reducing packet loss. Equation (14) defines the required message delivery latency for cluster head \(x\)[31]:  
\[Dd_{x}=\frac{Deadline}{Hc_{(x,MS)}(t)}. \tag{14}\]  
According to Equation (14), latency is inversely proportional to sink node distance. As the sink's distance grows, the required delay lowers. Therefore, critical messages will not arrive in time. Consequently, the increase in lost packets affects real-time performance. We estimate distance using hops. Path planning must minimize the distance from the sink node to cluster heads with urgent messages to boost real-time performance. The cluster head with the shortest distance should be the new mobile sink location, according to this article's analysis of the average distance between cluster heads carrying urgent messages. The average distance measure is provided by Equation (15) as follows for a certain cluster head \(x\):  
\[Ad_{x}=\frac{\sum\limits_{l\in CHH^{l}}ED_{xl}}{|CHU^{l}|} \tag{15}\]  
where \(1.1\) denotes the size of a set. The created method sought to concurrently delay the mobile sink path and decrease movement cost (measured in terms of movement energy).