esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Use Cases and Applications
### Space Exploration  
There are various methods to observe space objects such as telescopes, spacecraft, landers, or rovers. However, these methods have their own drawbacks that hinder the effective search of space. The use of drones and autonomous ground vehicles for such exploration brings several advantages, such as the ability to map a larger area in less time compared to a rover and at the same time with a much higher resolution than current satellites provide. UAVs and ground vehicles can be used to correct engineering errorsor to gain a comprehensive understanding of the atmosphere and surface/interior of the planet [176].  
As is usual with any technology, it has its flaws and shortcomings. The very implementation of drones and ground vehicles for this purpose poses safety concerns, flight requirements, or data gain. Due to the vast distance between Earth and space, it is not possible to control drones and ground vehicles remotely from a ground station. The aim is to create a communication system that allows such communication. Data transmission would be handled through an intermediary in space, which would receive data from the drone or ground vehicle and send it to Earth. The intermediary is necessary because of the increased weight of the drone or ground vehicle due to the necessary antenna when sending directly to Earth [177]. Overall, the UAV and ground vehicle used must be very light and should consist of only the necessary components. Such a drone or ground vehicle should be powered using LiPo (lithium polymer) batteries combined with solar energy to increase flight time. The navigation and control subsystem also faces challenges, as there is no physical structure to rely on for computation. In other words, there are no clues on alien planets, such as buildings or markers on Earth, by which the drone or ground vehicle can navigate in space [178].
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 1 Introduction
### Power Electronic Applications
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Controller** & **Parameters** & **Comparison** & **System** & **Tuning** & **Measures** & **Simulation/Practical** & **Software** \\ \hline
[27] & 2017 & FLC-PID-PID-PIDD\({}^{2}\) & 4 & FLC PID, PID & \begin{tabular}{c} Unmanned aerial \\ vehicles in indoor \\ terrains \\ \end{tabular} & \
\begin{tabular}{c} Complimentary error \\ minimization \\ algorithms \\ \end{tabular} &
\begin{tabular}{c} response time, control \\ accuracy \\ \end{tabular} & Simulation & MATLAB/Simulink \\ \hline
[0] & 2016 & Feedforward & PIDD\({}^{2}\) & 4 & PID &
\begin{tabular}{c} Hydraultic continuous \\ rotation motor \\ electro-hydraulic \\ servo system \\ \end{tabular} & – & system response & Simulation & AMSElm \\ \hline
[25] & 2015 & 2-DOF-PIDD & 6 & I, PI, PID & \begin{tabular}{c} Multi-area thermal \\ system \\ \end{tabular} & \
\begin{tabular}{c} Cuckoo search \\ algorithm \\ \end{tabular} &
\begin{tabular}{c} setting time, peak \\ overshoot, oscillation \\ rate \\ \end{tabular} & Simulation & MATLAB \\ \hline
[17] & 2015 & PIDD\({}^{2}\) & 4 & \begin{tabular}{c} PID, FOPD with \\ other tuning \\ algorithms \\ \end{tabular} & AVR system & \
\begin{tabular}{c} Particle swarm \\ optimization \\ \end{tabular} &
\begin{tabular}{c} maximum overshoot, \\ rise time, settling time \\ \end{tabular} & Simulation & MATLAB \\ \hline
[33] & 2013 & PIDD\({}^{2}\) & 4 & PID & \begin{tabular}{c} 2 nd-order model with \\ inertia and time delay \\ \end{tabular} & \
\begin{tabular}{c} Maximal stability \\ degree method with \\ iteration \\ \end{tabular} &
\begin{tabular}{c} control time, \\ overshoot \\ \end{tabular} & Simulation & MATLAB \\ \hline
[31] & 2012 & Feedforward & 4 & PID, PID, DDf-PID & \
\begin{tabular}{c} Valve-controlled \\ hydraulic motor \\ system \\ \end{tabular} & – &
\begin{tabular}{c} position tracking \\ capability \\ \end{tabular} & Simulation & AMSElm \\ \hline
[34] & 2003 & PIDD\({}^{2}\) & 4 & PID & \begin{tabular}{c} Analogue model of \\ control plant \\ \end{tabular} & \
\begin{tabular}{c} Identification \\ algorithm REDIC \\ \end{tabular} & transient responses &
\begin{tabular}{c} Simulation and \\ Practical \\ \end{tabular} & ADAPTLAB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Proposed Algorithm
### Proposed Frame Structure and Fi Message Format  
Figure 7 shows the frame structure and FI message format to support the proposed algorithm. Each frame consists of \(C\) control time-slots and \(D\) data time-slots. Each vehicle searches for a control channel when entering the network and occupies one unused control time-slot. Each vehicle transmits an FI message through the occupied control channel. The FI message includes the ID of neighbor vehicles together with that of itself, the hop information of each vehicle, the traveling direction, the location (longitude and latitude), the occupied control time-slot, the current speed, and the acceleration. \"ID Info\" of each vehicle is used as the identification information. \"Hop Info\" expresses itself and the n-hop neighbor vehicle as \"0\" and \"n\", respectively. \"Direction Info\" indicates how far the vehicle has rotated clockwise relative to the north at zero degrees. \"Longitude and Latitude Info\" are converted into x, y coordinates using TM (Transverse Mercator) coordinates. \"Time-slot Info\" represents the occupied time-slot information of a vehicle. \"Speed Info\" is the speed of a vehicle, and \"Acceleration Info\" represents how much the speed of a vehicle has changed during the unit time.  
Figure 7: Frame structure and frame information (FI) message format.
## 3 Performance Analysis of System
### BER Performance Analysis of GCI-DCSK SWIPT
#### 3.2.3 BER of System  
The BER of the GCI-DCSK SWIPT over AWGN channel is given by  
\[P^{BER}=\frac{p_{1}}{p}P_{i}^{BER}+\frac{p_{2}}{p}P_{m}^{BER}. \tag{27}\]  
An \(L\)-path independent and identically distributed (i.i.d.) Rayleigh fading channel is considered [20], and the PDF of \(\gamma_{b}\) can be expressed as [21]  
\[f(\gamma_{b})=\frac{\gamma_{b}^{L-1}}{(L-1)!\overline{\gamma_{c}}^{L}}\exp \left(-\frac{\gamma_{b}}{\overline{\gamma_{c}}}\right), \tag{28}\]  
where \(\overline{\gamma_{c}}=\frac{E_{b}}{N_{0}}E[\alpha_{2}^{2}]=\frac{E_{b}}{N_{0} }E[\alpha_{1}^{2}](j\
eq l)\) is the average bit-SNR per channel, and \(\gamma_{b}=\frac{E_{b}}{N_{0}}\sum_{l=1}^{L}\alpha_{l}^{2}\) with \(\sum_{l=1}^{L}E[\alpha_{l}^{2}]=1\).  
Finally, the BER of the system over multipath Rayleigh fading channel is given by  
\[P_{multi}^{BER}=\int_{0}^{+\infty}P^{BER}\cdot f(\gamma_{b})\mathrm{d}\gamma_{b}. \tag{29}\]
## 2 Materials and Methods
### Determination of Functional and Sensory Properties
#### 2.6.3 Odor-Active Volatile Compounds  
Volatile compounds were determined using HS-SPME-GC-MS/O. First, three samples (CONC3, HYDR1, and HYDR2) chosen by pretesting were analyzed with GC-MS-O with four trained assessors using detection frequency method for the identification of odor-active compounds. Then, relative concentration (RC) was determined for all samples by adding 1-butanol (1048 ng/vial) as an internal standard (ISTD), whose RC was set to 100, and comparing the compound area to ISTD's compound area using the following equation:  
\[\text{RC}=\text{(Compound area)}/\text{(ISTD's compound area)}\times\text{ISTD RC} \tag{2}\]  
The odor-active compounds were collected from all identified volatile compounds with two methods: (1) collecting all odor-active compounds identified with GC-MS-O analysis and (2) additional compounds found in GC-MS analysis that have been determined as odor-active in prior literature [24; 25; 26; 27].  
The GC-MS/O analysis was performed using an Agilent 6890N gas chromatograph equipped with an Agilent 5973 mass spectrometer (Agilent Technologies, Santa Clara, CA, USA), with olfactory ODP\({}_{4}\) sniffing port, (Gerstel, Linthicum, Maryland, USA) and PAL3 autosampler system (RTC, CTC Analytics AG, Swingen, Switzerland). Samples were analyzed with a capillary column Vf-Wax (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.5 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). High-purity helium was applied as the carrier gas. GC eluent was split 1:1 with the detectors. With MS, the ion detection range was 25-450 m/z, the temperatures were 250 \({}^{\circ}\)C for the ion source and 150 \({}^{\circ}\)C for the quadrupole. The injector temperature was 250 \({}^{\circ}\)C. The temperature program for GC was modified based on [PERSON] et al. [28]: 40 \({}^{\circ}\)C held for 3 min, 40-150 \({}^{\circ}\)C at a rate of 12 \({}^{\circ}\)C/min, 150-240 \({}^{\circ}\)C at the rate of 10 \({}^{\circ}\)C/min and held at 240 \({}^{\circ}\)C for 5.8 min. The temperature for olfactory and the transfer line was 220 \({}^{\circ}\)C. Samples were incubated (60 \({}^{\circ}\)C, 20 min) in 25% (_w/v_) water solution, and the volatile compounds were collected from the headspace of the 20 mL vial to SPME fiber (2 cm, DVB/CAR/PDMS, phase thickness 50/30 \(\upmu\)m, Supelco Inc., Saint Loius, MO, USA). Samples were injected in splitless mode with a purge time of 8 min. Analyses were performed in triplicate.  
The three samples that were first analyzed with the GC-MS-O were also analyzed with two assessors with an Agilent 6890 GC-FID/O (Agilent Technologies, Santa Clara, CA, USA) equipped with DB-5 (60 m \(\times\) 0.25 mm i.d. \(\times\) 1.0 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA) column to confirm the compound candidates. GC-FID/O used the same SPME- and GC parameters as GC-MS-O. FID temperature was 220 \({}^{\circ}\)C.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 3 Methodology  
Airborne LiDAR point cloud data cover vast areas and contain large volumes of data, while the targets to be detected are notably small and sparsely scattered throughout the scene, occupying a minimal fraction of the overall area. To enhance detection efficiency without sacrificing the accuracy of TT detection, this paper proposes a fast TT detection method based on virtual views. Initially, the method computes the flight direction by analyzing the spatial extent of the LiDAR point cloud data. With the flight direction established as the x-axis and the elevation direction as the y-axis, a parallel projection matrix is created. This matrix projects the entire LiDAR point cloud onto a vertical plane in real-world coordinates, thereby generating a comprehensive virtual view. To overcome the challenge of detecting the small-sized TTs and potential omissions, this paper puts forth a local-global dual-path feature fusion network. This network extracts the two-dimensional coordinates of all TTs from the virtual view. Ultimately, the extracted two-dimensional positions are converted back into three-dimensional spatial coordinates within the LiDAR point cloud through the application of the projection matrix. The overall pipeline is illustrated in Figure 1.
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
###### Abstract  
A method is proposed for the derivation of new classes of staggered compact derivative and interpolation operators. The algorithm has its roots in an implicit interpolation theory consistent with compact schemes and reduces to the computation of the required staggered derivatives and interpolated quantities as a combination of nodal values and collocated compact derivatives. The new approach is cost-effective, simplifies the imposition of boundary conditions, and has improved spectral resolution properties, on equal order of accuracy, with respect to classical schemes. The method is applied to incompressible Navier-Stokes equations through the implementation into a staggered flow solver with a fractional step procedure, and tested on classical benchmarks.  
compact schemes; Navier-Stokes equations; turbulence +
Footnote †: journal: Applied Sciences
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 5 Simulation Testbed
### Test Setup  
The 3D scenario constructed inside Gazebo contains the model of a power plant obtained from the Gazebo model library ([[http://models.gazebosim.org/](http://models.gazebosim.org/)]([http://models.gazebosim.org/](http://models.gazebosim.org/))), as illustrated in Figure 9a. The operator sets a subset of the model as the region of interest. The UAV starts the flight between the pillars and the building. This region includes various types of obstacles: Two thin rails suspended in mid-air, a beam structure, and an indoor portion. Not all parts of the structure can be accessed because of the minimum distance to obstacles, as shown in Figure 9b.  
The parameters used in this setup are specified in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(v_{linear}\) & 1 m/s & \(path\_planning\_time_{max\_execution}\) & 4 min \\ \hline \(v_{angular}\) & 0.1745 rad/s & local space minimum & \(10\times 10\times 10\) m \\ \hline Octree resolution & 0.5 m & Operator-defined region & \(70\times 38\times 31\) m \\ \hline \(d_{safe}\) & 2.5 m & Sampling distance & 5 m \\ \hline Frontiers amount & 35 & Flyby options amount & 6 \\ \hline \multicolumn{3}{c}{Flyby length} & 4 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the HitL data collection.  
Figure 8: The diagram of the Hardware-in-the-Loop (HitL) environment details how the load balancing between the Upboard and the support laptop reflect the resources available in a real flight.  
Figure 9: The model used to simulate the inspection scenario. (**a**) The factory to inspect with the UAV and a rendering of the sensor range. (**b**) A part of the structure impossible to cover safely.  
Metrics  
The behaviour of the system will be analysed over ten runs. In each run, data is collected to evaluate the following metrics:  
1. The exploration time;
2. The volume explored;
3. The resulting map contextualised with the flight path;
4. The path length of the flight path;
5. The evolution of occupied space during the mission;
6. The time spent in path planning;
7. The rate of success of path planner;
8. The average execution time per view;
9. Entropy of the map in the final iteration.  
The first eight aspects are the most commonly used among the following works [19; 20; 27; 29; 31; 39; 51; 52; 53; 54; 55; 56].  
Entropy [57] is also analysed to provide an insight about the information gain when the exploration finishes. The entropy \(H_{i}\) of the \(i\)-th voxel with occupancy probability \(p_{i}\) is computed using Equation (2).  
\[H_{i}=-(p_{i}\log p_{i}+(1-p_{i})\log(1-p_{i})) \tag{2}\]
# Pareto Effect of LMI for Ship Propulsion
## 1 Introduction
In [6; 7; 8], during the synthesis of the LMI controller, the fulfillment of the \(H_{\infty}\) norm was taken into account to achieve vibration damping in a dynamic damper, which could level the vibration of a building during an earthquake. The multi-criteria optimization problem is solved by computing Pareto-optimal solutions. The authors prove that it is possible to synthesize an LMI controller to solve a multi-criteria optimization problem together with the \(H_{\infty}\) norm. In addition, they show that the controller can be successfully applied to a dynamic building damper. Interesting research is presented in [9] where a multi-criteria optimization problem is formulated for a low-noise amplifier (LNA) of an NE3511S02 \((HJ-FET)\) transistor. For this purpose, the authors use the multi-criteria evolutionary algorithm NSGA-III (Non-dominated Sorting Genetic Algorithm III) to obtain Pareto characteristics of the optimal solutions for transistor performance. Multiple transistor control solutions are presented for different values of frequency \(f\) (from 10 GHz to 16 GHz) and current values (from 5 mA to 15 mA) to obtain possible LNA solutions. The authors conclude their work by stating that any challenging LNA design can be achieved by following this work along with the application of novel algorithms and technological innovations. In [10], the authors first formulate a multi-objective optimization problem (MOP) with two objective functions capturing the contradicting targets, then find the Pareto optimal solutions for the pilot signals by using a weighted sum scalarization technique. Finally, they introduce an alternating optimization approach to cast each of the resulting distributed optimization problems into a convex linear matrix inequality (LMI) form. Of course, there are many articles related to LMI and Pareto, but the authors of this paper have selected what they believe to be the the most interesting ones, where the results were applied to specific real objects or their simulations and not only to formulas and definitions. The main objective of this work was to optimize \(H_{\infty}\) and \(H_{2}\) norms according to LMI conditions and, in this particular case, the motivation was to verify how much the choice of \(\gamma_{\infty}\) would affect the operation of a ship's propulsion and steering equipment. Section 2 of this paper presents essential parameters and state-space equations of the \"Blue Lady\" training ship model. Section 3 briefly shows the method of creating conditions for the \(H_{\infty}\) norm for LMI followed by steps for creating conditions for the \(H_{2}\) norm. It is vital to find a compromise between both norms, which is described in Section 5. Section 6 shows values of LMI controller signals for given longitudinal, lateral, and rotational velocities followed by key simulation results showing the operation of ship model thrusters for selected values of \(\gamma_{\infty}\) and a set value of \(\gamma_{2}\). The final Section 7 focuses on highlighting the advantages and disadvantages of the proposed method for the Pareto curve.
## 4 Implementation
### Benchmarks  
We implemented user- and kernel-level micro-benchmarks to measure the performance of the synchronization schemes on sv6 variant. While the execution spaces of the two benchmarks are different, the operation procedure and algorithm are identical. The micro-benchmark performs multiple operations on a single linked list and a chained hash table, which are synchronized using the RCU, RLU, and MV-RLU techniques. A chained hash table comprises fixed-size buckets, each of which contains a single linked list sorted by key value. Since each bucket has its own lock, different buckets can be accessed at the same time. To insert, remove, or search for nodes in a hash table, a thread generates a random integer, hashes the random integer to get a bucket number, and then traverses the linked list of the appropriate bucket using the random integer as a key. Each node's access is uniformly random. The main implementation for the synchronization of node deletion in a linked list is referred to in Section 4.1. It follows the same algorithm as the benchmark of existing synchronization studies [24; 25; 39]. The benchmark implementation is split into user and kernel, with an identical operation algorithm.
## 6 Solution Types/Algorithms
### Non Dominated Sorting Genetic Algorithm II  
Inherently, multi-objective formulations do not result in a single solution which simultaneously optimizes all objectives. Therefore, contrary to the single objective optimization, multi-objective optimization gives a large number of alternative solutions located on or near the Pareto-optimal front. Non dominated sorting algorithm II (NSGA II) has the ability to find multiple Pareto-optimal solutions in one single run [172]. NSGA II has been opted by many researchers to solve various multi-objective optimization formulations relating to different problems of wireless sensor network. For example in [173], NSGA II has been used for the topology control. The authors argued that the Pareto-optimal front can be achieved in order to obtain low power consumption, higher robust structure and lower contention among the nodes. A compatible control algorithm has been proposed in [174] for greenhouse environment by using NSGA II. The technique focuses on finding a trade-off between the minimum energy consumption and higher control precision. In [175], the authors have used NSGA II to solve  
Figure 5: Different Types of Solution Algorithms.  
a multi-objective optimization problem relating to distributed detection in wireless sensor networks. The proposed scheme was analyzed and the simulation results showed that significant energy savings at the cost of slightly increasing the best achievable decision error probability. A multi-objective optimization approach has been suggested in [148] for the optimal deployment of wireless sensor networks. The authors have used NSGA II algorithm to solve the problem and verified the results through simulation that their proposed scheme could maintain coverage and connectivity in the given sensing area with relatively small number of sensors.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN
### ARIMA-Based IMF Forecasting
#### 4.1.2 Order Determination and Parameter Estimation for the ARIMA
**Step 15:**: Perform the \(d\)-order differential reduction of \(IMF_{i,d}(t)\) and assign the result of reduction to \(IMF_{i}^{Fit}(t)\). **Step 16:**: Calculate the fitted residual sequence \(\xi_{i}(t)=IMF_{i}^{Fit}(t)-IMF_{i}(t)\). **Step 17:**: Judge whether \(i<1\). If yes, \(i=i+1\) and perform step 2, otherwise, end the modeling step. Specifically, the modeling steps of AFOS (Step 5-Step 13) are shown in Figure 5.  
According to Lemma 1, the IMFs obtained via EMD have an SRD property. Thus, the fixed-order optimization search operator can identify the truncation property of the ACFs. Meanwhile, the \(p\) and \(q\) of ARIMA\((p,d,q)\) can also be limited within the third order, which reduces the computational complexity.
# Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation
## 3 Experiments
### Flutter of a Slender Cantliever in Axial Flow  
#### 3.2.1 Modeling  
The fluid flow-induced flutter of a cantilever was considered, where the transverse load \(q(x,t)\) in Equation (5) originated from the coupled interaction with fluid flow. When the cantilevered beam is assumed to be slender (\(L\gg l\)), the aerodynamic force exerted on the cantilevered beam can be modeled using [PERSON]'s elongated body theory [41]. The transverse load is approximated as a reaction force to the rate of change of the fluid momentum, \(-m_{a}(Dv/Dt)\), where \(m_{a}\) is the added mass, and \(v\) is the transverse fluid velocity. The added mass of the slender plate can be modeled as \(\pi\rho_{f}l^{2}/4\), where \(\rho_{f}\) is the fluid density [42].  
The transverse fluid velocity, \(v\), can be described by the kinematic boundary condition on the cantilevered beam, which is expressed as \(v=\partial\omega/\partial t+U\partial\omega/\partial x\), based on the small-perturbation assumption with respect to the incoming fluid velocity, \(U\). When all the equations are integrated, the transverse load \(q(x,t)\) is expressed as a second-order differential equation of the deflection of the beam \(\omega\):  
\[q(x,t)=-\frac{\pi\rho_{f}l^{2}}{4}\bigg{(}\frac{\partial^{2}\omega}{\partial t ^{2}}+2U\frac{\partial^{2}\omega}{\partial x}\frac{\partial t}{\partial t}+U^ {2}\frac{\partial^{2}\omega}{\partial x^{2}}\bigg{)}. \tag{7}\]  
Figure 2: (**a**) Displacement profiles of the free vibration of a cantilevered beam over a half-cycle of the first bending mode from the truncated solution (dots) and STD-PINN (solid lines). (**b**) The trend in training loss over the number of epochs for model 1 (black) and model 2 (red). (**c**) Trajectories of the beam ends as predicted using Model 1 (black) and Model 2 (red), with the truncated mode solution shown as a blue dotted line.  
Thus, the entire system is described by the coupled equations of fluid and structure, which correspond to the fourth-order differential equation from the dynamic beam-bending Equation (5) and the second-order differential equation from the elongated body theory (7).