esa-sceva/satcom-chunk-collection huggingface dataset:
techniques, such as the genetic algorithm (GA) and Harmony Search (HS) optimization. The authors in [4] propose the parameter optimization of the flight control system of a single-rotor, medium-scale rotorcraft. A six degrees-of-freedom (DOF) nonlinear mathematical model of a rotorcraft and the design of a closed-loop flight control system for a rotorcraft based on six concurrent PID controllers are developed. A comparative study of computational intelligence optimization algorithms is also performed to find the best PID controller parameters for a given flight regime. These techniques include particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and cuckoo search (CS). It was observed that the optimized PID controllers are effective around the trim point for which they were developed, with ACO-PID performing better for hovering and GA-PID for forward flight. However, the ACO-optimized controller can outperform the other optimization algorithms both in holding the trim state and recovering from external disturbance. In [5], the authors present an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm for the parameter tuning of PID controllers. The accuracy of the algorithm was improved by using an adaptive mechanism adjusting control parameters online and a linear population reduction strategy. The proposed algorithm is compared with the PSO and the standard QUATRE algorithm. It was concluded that the new approach is better than PID parameter tuning using PSO and the original QUATRE algorithm, showing less overshoot and a shorter rise time, which makes the whole control system more stable, thus proving that the PID parameter tuning method based on the improved QUATRE algorithm is more effective. The authors in [6] use two different methodologies to investigate the use of the maximum power point tracking (MPPT) technique in a photovoltaic (PV) system composed of five solar panels in series. The first considered a traditional Perturb and Observe (P&O) algorithm, and the second applied a Fuzzy Logic Controller (FLC) that uses fuzzy logic concepts to improve the traditional P&O. Both methods were implemented in a boost converter. A study is performed to see if the intelligent MPPT method can be more efficient, stable, and adaptable than a traditional MPPT method in varying environment conditions, namely, solar irradiation and/or environmental temperature and also to analyze their behavior in steady-state conditions. It was shown that the proposed fuzzy logic enhanced P&O, which is a P&O algorithm improved with a fuzzy logic algorithm, increases the performance of the P&O algorithm, enabling the system to adapt faster to variable environmental conditions, leading to a faster MPP and an equal performance in steady-state conditions when the MPP is reached, and thus to higher generated energy.  
The guest editors would like to thank the authors and reviewers which contributed to this Special Issue. A special thanks goes to [PERSON] and the staff at the _Algorithms_ Editorial Office for their assistance. The authors declare no conflict of interest.
# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
## 3 Adaptive Modulation for Multi-Beam Massive MIMO  
As discussed in the previous section, multi-beam massive MIMO does not require CSI estimation, thereby eliminating the requirement for a process to obtain channel information at the BS side. As a result, it is not possible to use a modulation and coding scheme (MCS) index to combine the modulation scheme and coding rate from the CQI value, as is done under current wireless communication standards [5; 6]. Therefore, we developed an adaptive modulation scheme based on the received power and amplitude correlation obtained from performing beam selection during uplink.  
Here, we explain the beam-selection method and amplitude correlation in multi-beam massive MIMO. Beam-selection is one of the major challenges in multi-beam massive MIMO. As a directivity peak cannot be directed to a given UT, it is possible for one beam to receive signals from multiple UTs. It has been confirmed that in such situations, signal separation cannot be perfectly performed in digital components, even by a blind algorithm. To address this, the authors previously proposed a beam-selection method that utilizes the power differences between the multi-beams and correlation values obtained from amplitude information [13]. By employing this method, it is possible to achieve beam selection with a high SINR by setting thresholds.  
Under the proposed beam-selection method, amplitude correlation is calculated based on the covariance between the amplitudes of received and adjacent beams. The amplitude of a received signal is defined as a vector, \(\mathbf{x}\), whose number of elements is equivalent to the lengths of all transmission-signal symbols. The correlation coefficient, \(\rho(\mathbf{x},\mathbf{y})\), with respect to the signal amplitudes, \(\mathbf{x}\) and \(\mathbf{y}\), is denoted as  
\[\rho(\mathbf{x},\mathbf{y})=\frac{\sum\limits_{i=1}^{N}(x_{i}-\overline{ \mathbf{x}})(y_{i}-\overline{\mathbf{y}})}{\sqrt{\sum\limits_{i=1}^{N}(x_{i}-\overline {\mathbf{x}})^{2}}\sqrt{\sum\limits_{i=1}^{N}(y_{i}-\overline{\mathbf{y}})^{2}}}, \tag{1}\]  
where \(\overline{\mathbf{x}}\) is the mean of all elements in \(\mathbf{x}\).  
Figure 3 shows the relationship between amplitude correlation and the SINR. The SINR of the received signal tends to be high when the amplitude correlation is high. Based on this finding, we examined a method of determining a simple adaptive modulation approach on the downlink using the amplitude correlation and received power obtained on the uplink.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 2 Related Work
### Spatial Queries in Road Network Distance
#### 2.2.1 K-Nearest Neighbor Queries in Road Network Distance  
The algorithm for kNN on spatial road networks employs two approaches, as aforementioned. The first approach, focused on restriction kNN, is the incremental Euclidean restriction (IER) algorithm. The IER approach involves setting an upper limit and reducing the search area. The second approach, centered on expansion kNN, is called incremental network expansion (INE). The algorithm initiates network expansion from the query point, thus examining entities in the order they are encountered [19].  
[PERSON], [PERSON], and [PERSON] [4] experimented with evaluating kNN query algorithms on road networks, including incremental network expansion (INE) [19], incremental Euclidean restriction (IER) [19], distance browsing [20], route overlay and association directory (ROAD) [21; 22], and G-tree [23; 24].  
In their research paper, [PERSON], [PERSON], and [PERSON] [25] proposed a new approach for handling kNN queries in road networks, thereby catering to stationary and mobile query points. Their approach, named Road Network Embedding (RNE), involves converting a road network into a higher-dimensional space, thus allowing the use of more straightforward distance functions.  
[PERSON] and [PERSON] [26] introduced a novel approach to address the criteria for spatial query in spatial network databases (SNDBs) by transforming the challenge of distance computation in a vast network into the problem of computing distances in numerous smaller networks, which are supplemented by additional table lookups. The central concept behind their approach, Voronoi-based Network Nearest Neighbor (VN3), involves initially partitioning an extensive network into smaller, more manageable regions.  
In the research conducted by [PERSON], [PERSON], [PERSON], and [PERSON] [27], the focus was on continuous k-nearest neighbor (CkNN) queries in transportation networks, where data objects and queries move within a road network. While existing methods have addressed snapshot queries, continuous monitoring of CkNN queries in road networks has not yet been explored. The study introduces incremental monitoring and group monitoring algorithms to compute and update CkNN query results efficiently in real time, thereby considering fluctuations in edge weights due to factors like changing traffic conditions. The proposed algorithms aim to handle dynamic and unpredictable scenarios, such as finding the k closest taxi customers for a free taxi in terms of traveling time.
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 2 Theories and Design
### Design of the Algorithm  
The derivation and calculation process of phase error and the demodulation of the optical signal is shown in Figure 4, which is the theoretical basis for phase detection in this paper.  
As shown in Figure 4, \(E_{11 In}\) and \(E_{12 In}\) are, respectively, input to the external split DPSK demodulation and phase detection structure by two optical fibers to carry out high-precision detection of optical signal phase difference and digital signal recovery. The phase difference meets \(\varphi_{12 In}-\varphi_{11 In}=\varphi_{miss}\).  
Adding a fixed phase difference of +\(\pi/2\) to the optical fiber arm where \(E_{12 In}\) is located. The two optical signals are represented as \(E_{11 Out}\) and \(E_{12 Out}\) after this stage, and there is \(\varphi_{12 Out}-\varphi_{12 In}=+\pi/2\), which equals \(\varphi_{12 Out}-\varphi_{11 Out}=\pi/2+\varphi_{miss}\). The outputs are \(E_{21 In}\) and \(E_{22 In}\) by adding the one-bit delayer to the optical fiber arm where \(E_{11 Out}\) is located. Bringing the phase difference of adjacent symbols, expressed as \(\Delta\varphi\), and the value of \(\Delta\varphi\) can only be \"0\" or \"\(\pi\)\", which is used to demodulate to get the binary \"0\" code or \"1\" code. The phase difference is obtained as \(\varphi_{22 In}-\varphi_{21 In}=\pi/2+\Delta\varphi+\varphi_{miss}\).  
Finally, we obtain \(E_{cont}\) and \(E_{des}\) through a 3 dB coupler. The conjugation of the coupler used in this paper is \(p=1\), The phase difference is obtained as \(\varphi_{cont}-\varphi_{des}=\pi+\Delta\varphi+\varphi_{miss}\). Therefore, the electrical signal of the differential output port of the balance detector is \(i=2A^{2}B\cos(\Delta\varphi+\varphi_{miss})\); that is, \(\mathrm{u}=2A^{2}BR\cos(\Delta\varphi+\varphi_{miss})\).  
The voltage signal displayed by the oscilloscope is the maximum value. Thus, the value of amplitude can be obtained when the phase difference is zero. The power value collected by the power meter is used as the compensation factor. The relationship is \(P=U^{2}/2R\) between the power and voltage of the pure resistance circuit, and \(R\) takes 1 in the simulation. Therefore, the mathematical relationship with \(P\) is established as \(\varphi_{miss}=\frac{1}{2}\mathrm{arccos}\Big{(}\frac{P}{2A^{2}}-1\Big{)}\).
## 3 Materials and Methods
### Coseismic Deformation  
Theoseismic deformation displacement fields based on the ascending and descending tracks of Sentinel-1A observations are shown in Figure 2, respectively. The shapes of the two coseismic deformation fields are elliptical and extend in the NW direction of the long axis, which is consistent with the surface fissure zone found in the field survey results of the Institute of Geology, China Earthquake Administration [30]. The deformation field of the long axis modification shows a significant difference, and the sign of the deformation field between the ascending and descending track is opposite. A positive value indicates movement close to the satellite, and the surface shows uplift or westward movement; a negative value indicates movement away from the satellite, and the surface shows subsidence or eastward movement. The ascending orbit coseismic deformation field was along the line of sight to the east disk with a maximum deformation value of \(-\)7.3 cm, indicating that it was dominated by subsidence and far away from the satellite, and the noise was obvious,. The reason may be that the study area is located in the Sichuan-Yunnan area with high vegetation coverage, with large terrain undulation and atmospheric influence. The coseismic deformation field of the descending orbit was dominated by subsidence along the line of sight to the west, with a maximum deformation value of \(-\)6.5 cm, and the east disk was dominated by uplift, with a maximum deformation value of 8.9 cm. Combining with the ascending and descending tracks' imaging mode and the deformation field showing the main deformation trend with horizontal deformation, it was preliminarily inferred that the deformation field of the Yangbi earthquake conformed to the characteristics of the dextral strike-slip earthquake deformation. Specifically, it needs to be further determined whether the seismogenic fault is the Weixi-Qiaohou fault in the NW direction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Satellite** & **Master** & **Slave** & **Mode** & **Polar** & **t/d** & **Orbit** \\ \hline Sentinel-1A & 8 May 2021 & 1 June 2021 & IW & VV & 24 & Ascending \\ Sentinel-1A & 10 May 2021 & 22 May 2021 & IW & VV & 12 & Desending \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-1A data parameters.
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 4 Nanomaterials for RFID-Based Sensing System  
RFID-based sensing devices achieve wireless detection systems capable of effectively sensing various external stimuli, relying on a variety of nanomaterials. Polymers are predominantly used as substrates due to their cost-effectiveness and ease of processing. Conducting polymers, in particular, garner attention as sensing materials because they can indicate activity for specific substances. Metal nanoparticles and carbon-based materials are used as conductive inks, applied in patterns to RFID components such as antennas and sensing materials. Furthermore, various materials with different compositions and structures are being researched for the advancement of RFID sensors.
## 5 Simulation and Analysis
### Parameters Setting and Simulation  
According to the achieved mathematical model of the system, the model (Figure 5) was built in the Simulink software. In order to validate the superiority of the proposed method, the conventional tuning method Z-N, PSO, and improved PSO were applied in the PID controller to compare the simulation results. The improved PSO algorithm referred to three types: the inertia weight of PSO is linearly changed (WPSO); a hybrid algorithm combining GA, PSO and the cross probability is stable (SPSO) and a hybrid algorithm combining GA, PSO and the cross probability is adaptive (APSO). In the conventional PID controller, three parameters were determined: \(K_{p}=40,\;K_{i}=0.4,\;K_{d}=4.5\), by constant adjustments and examinations,  
Under the same parameters, simulations by the above methods were performed to compare the convergence characteristics. In the PSO algorithm, the parameters were set as follows:  
Inertia weight \(\omega=0.6\), learning factors \(c_{1}\;=\;c_{2}\;=\;2\), search dimension \(D=3\), swarm size \(N=100\), maximum iteration \(t_{max}=100\).  
In the WPSO, the bound of inertia weight was set as \(\omega_{max}=0.9,\;\omega_{min}=0.4\).; the inertia value was calculated with Equation (7) at each iteration. Other parameters were the same with the PSO algorithm.  
In the SPSO, the stable crossover probability was set as \(P=0.6\), the selection probability \(\delta=0.3\).; other parameters were the same with the WPSO algorithm.  
In the APSO, the bound of crossover probability was set as \(P_{1}=0.9,P_{2}=0.6\).; other parameters were the same with the SPSO algorithm.  
Figure 4: Result of system identification.  
Figure 5: PID controller model.  
After setting the parameters, the steady state time, overshoot, fitness value, convergence iteration, running time and the PID parameters for the five optimization methods were illustrated in Table 1. Figure 6 showed the fitness curves of simulations and Figure 7 was the partial enlarged view of the response curves derived from the step input. From Table 1 and Figure 7, it was easy to find that the steady state time of APSO was the shortest. In addition, the system overshoots of APSO decreased 164% while the steady state time reduced 23.4% compared with the Z-N tuning method. The dynamic characteristic was improved by APSO. From Figure 6, the response curve of APSO declined most quickly. Thus, the convergence velocity of APSO was obviously the fastest compared with other methods. Furthermore, the convergence iteration of APSO was at 40 iteration and far smaller than the 82 iteration in the case of PSO. From the response and convergence curves, the proposed method not only avoided the premature convergence but also accelerated the convergence velocity. Due to the complexity of the hybrid algorithm, the running time was 38% longer than PSO. However, with the improvement of computer configuration, the running time can be further reduced. In conclusion, the simulation results showed that the APSO algorithm conducted efficient performance on the parameters' optimization of the PID controller compared with Z-N, PSO, WPSO and SPSO.
## Hardware and Measurement System  
### Concept  
Since conventional measurement systems in the context of SHM are costly and often bound to specific hardware, the main goal of the present work is to provide two alternatives based on consumer grade and mobile hardware. The following two approaches have been previously presented in [21].  
The first approach uses off-the-shelf single-board computers (Raspberry Pi 3 Model B+), which are equipped with an interface board developed by the authors that provides connections for different digital sensor interfaces and analog-to-digital converters (ADC) for connecting analog sensors, as well as a battery-buffered realtime clock. Note however, that the bare RPi can also be used alone as long as the standard GPIO connectors can accommodate the sensor setup. The RPi hardware setup is used as a node within an established wireless mesh network that is dynamically extendable with additional nodes during runtime. Each node has the ability to function as sensor node collecting data employing the attached sensors and to act as an access point (AP) simultaneously. Configured network ports serve as a communication channel where all commands and requests are transmitted from a central node to each available WSN node. Due to the automatic synchronization of the sensor nodes prior to measurements, simultaneous measurements are also possible.  
The software framework is designed to be platform-independent, so hardware systems other than the RPi are applicable. The only platform requirements are that a Linux operating system is used and that WiFi is available for automatized meshing. At the time of writing, the presented implementation, however, has only been extensively tested on the RPi platform.  
The second approach comprises an Android application (App) customized for vibration measurements using the built-in accelerometer of smartphones and tablets. In addition to the data collection, the developed application features several tools for data processing, allowing interactive signal analysis. Thus, the app enables the user to perform further calculations, e.g., the determination of tendon forces or the integration of accelerations to determine velocity and displacement time histories. Besides the measurement data, the application can store a few different types of information about the conducted measurement or inspection such as photos, notes, and sketches directly on the device. This second approach does not include the meshing functions like the RPi system does. Therefore, the smartphone in connection to the app developed is not able to be used as a network node without further changes to the system architecture.  
In order to exploit the full potential of the showcased software framework, the feasibility of the combination of both approaches is investigated. The authors present a way to utilize smartphones as user facing control units as well as regular sensor nodes in the WSN.
# Analysis and Optimization of Output Low-Pass Filter
## 2 Filter Resonance and Damping
### Filter Variables  
The capacitance, inductance, and damping resistance are the three circuit parameters that comprise the filter's four variables; the filter configuration is the fourth variable. Table 2 summarizes these filter variables together with the main filter parameters they impact.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 3 Background  
The design of UWSNs routing protocol must be on the basis of its unique characteristics of underwater environment. So we present the channel model and the network model in this part.  
Moreover, as we need to design routing protocol and transport protocol jointly, we also present a review of NCHARQ in this section.
# Cascaded Nanorod Arrays for Ultrabroadband, Omnidirectional and Polarization-Insensitive Absorption
## 4 Results and Discussions
For the TE-polarized incident wave, its tangential component of the electric field is always equal to the total electric field for different incident angles, and it has a stronger contribution to match the tangential fields at the interfaces of the CNA-based absorber compared with the TM polarization, which may be responsible for the better angular response. Since any oblique incident waves can be decomposed into TM and TE modes, the results indicate that the proposed structure can function as an omnidirectional absorber due to its high angular tolerance for both TM and TE modes in the range of 0-60\({}^{\circ}\).