esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Threats
### Negotiation
#### 4.2.8 Fake Complaints Regarding the Received QoS  
Spectrum sharing mechanisms operating in DSA networks usually allow nodes to send back either positive or negative feedback messages [50]. Such messages enable evaluating the success and quality of prior resource allocations. Obviously, all users must be honest regarding their achieved satisfaction levels to assist the respective mechanism gain a full and real view of the managed network. Unfortunately, this may not always be the case. A node may on purpose report lower satisfaction levels, or falsely claim that it suffers from significant QoS degradation [28]. Consequently, the misled sharing mechanism might choose to allocate more resources to the complaining nodes aiming at compensating them for their (fake) losses. Lying nodes would gain extra profits in terms of spectrum resources at zero cost, while the overall spectrum allocation would suffer from unfairness. The currently described attack can be effectively eliminated by applying novel cross-checking and validating mechanisms on such feedback-based evaluation methods.
# Optimizing Fundamental Frequencies in Axially Compressed Rotating Laminated Cylindrical Shells
## 2 Vibration Analysis  
For free vibration analysis, the motion of an undamped structure can be written in the following equation [33]:  
\[[\mathrm{M}]\left\{\breve{\mathrm{D}}\right\}+[\mathrm{K}]\left\{\mathrm{D} \right\}=\left\{0\right\} \tag{1}\]  
The \(\left\{0\right\}\) is a zero vector. The displacement vector \(\left\{\mathrm{D}\right\}\) can be expressed as a harmonic motion as  
\[\left\{\mathrm{D}\right\}=\left\{\overline{\mathrm{D}}\right\}\mathrm{sin} \omega\mathrm{t}\mathrm{.};\,\left\{\breve{\mathrm{D}}\right\}=-\omega^{2} \left\{\overline{\mathrm{D}}\right\}\mathrm{sin}\omega\mathrm{t} \tag{2}\]  
Figure 1: Various coordinate systems for a fiber-composite laminated cylindrical shell.  
where \(\omega\) is the nature frequency and \(\{\overline{\mathrm{D}}\}\) is the amplitude of \(\{\mathrm{D}\}\). Then, Equation (1) becomes  
\[([\mathrm{K}]-\omega^{2}[\mathrm{M}])\{\overline{\mathrm{D}}\}=\{0\} \tag{3}\]  
When a laminated cylindrical shell rotates about its axial direction, centrifugal forces develop in the radial direction. Furthermore, axial compression induces axial compressive forces. The centrifugal and axial compressive forces act as initial loads, producing initial stresses in the shell. As a result, the stiffness matrix \([\mathrm{K}]\) in Equation (3) can be decomposed into two distinct matrices as follows:  
\[[\mathrm{K}]\;=\;[\mathrm{K}_{\mathrm{L}}]\;+\;[\mathrm{K}_{\sigma}] \tag{4}\]  
where \([\mathrm{K}_{\mathrm{L}}]\) is the linear stiffness matrix and \([\mathrm{K}_{\sigma}]\) is the geometric stress stiffness matrix generated by the initial stresses. Now, Equation (3) becomes  
\[([\mathrm{K}_{\mathrm{L}}]+[\mathrm{K}_{\sigma}]-\omega^{2}[\mathrm{M}])\{ \overline{\mathrm{D}}\}=\{0\} \tag{5}\]  
In the above equation, if \(\{\overline{\mathrm{D}}\}\) is not a zero vector, we must set  
\[\left|[\mathrm{K}_{\mathrm{L}}]+[\mathrm{K}_{\sigma}]-\omega^{2}[\mathrm{M}] \right|=0 \tag{6}\]  
In this study, the Abaqus program with the subspace iteration procedure [29] was employed to determine \(\omega\) and the corresponding vibration modes. The resulting fundamental frequency was then used as the objective function for maximization.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 2 Morphing Wing Concept
### Actuation Principles  
For a given wing configuration, only three parameters define it: root actuation, kink actuation, and tip actuation setpoints. This means that the inboard rotational servos and the leading-edge linear servo could be actuated independently for a configuration setting: the inboard rotational servos setpoint was interpolated using the root and kink actuation setpoints while the linear servos set points were proportionally related.  
During a roll maneuver, the kink and tip servos set points were changed in order to increase camber and twist on one side of the wing while decreasing those parameters  
Figure 5: CAD representation of the “comb-like” parts and inner actuation system supports.  
on the other side, achieving a lift differential between both sides and a corresponding rolling moment. The linear servos at the root maintained their positions while the inboard rotational servos set point were changed to the new interpolated setpoint if the kink setpoint was changed.  
For load alleviation, there was a reduction in the kink and tip actuation set points--with a corresponding camber and twist decrease--and root actuation setpoint increased--with corresponding camber increase and further twist decrease along the span--which should bring the lift distribution towards the center of the wing and reduce its RBM.
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 5 Discussion
As this review shows, many analyses are based on experimental or simulation data, which is not always comparable in terms of boundary conditions, equipment, or measurement characteristics. The development of common test protocols and reference databases could help improve the reliability and replication of results. Such actions are particularly needed in the context of the development of combustion systems with variable fuel composition (e.g., hydrogen-biogas), which require flexible and resilient predictive models. Another important direction for future research is improving the reproducibility and transparency of ML/GA models used in combustion studies. The increasing complexity of hybrid modeling frameworks and the variability in training data sources make it difficult to replicate results and benchmark models across different systems. To address this, greater emphasis should be placed on the publication of open-source code, standardized datasets, and clear documentation of model architecture and hyperparameters. Additionally, the incorporation of explainable AI (XAI) techniques--such as SHAP values or sensitivity analysis--can enhance the interpretability of predictions and increase confidence in the deployment of AI systems in industrial combustion processes. Encouraging open science practices in combustion modeling will foster collaboration, facilitate validation, and accelerate the translation of research into practice. Finally, the development of AI and GA in the context of energy should also take into account environmental and regulatory aspects. The growing importance of the \"zero-emission\" strategy and the pressure to reduce the carbon footprint mean that optimization models must take into account not only technical parameters but also the total environmental cost, plant lifetime, and recyclability. The first studies integrating ML with life cycle analysis (LCA) and external cost analysis are already being developed [81,84], which opens up a new field for interdisciplinary applications of intelligent methods in environmental engineering. While this review presents a broad and detailed overview of ML and GA applications in combustion science, it also reveals critical challenges that must be addressed. The lack of standardized validation frameworks and benchmark datasets limits the comparability of findings across studies. Moreover, the dominance of data-driven approaches with little integration of domain-specific physics raises concerns about model robustness and interpretability. To enhance the scientific value and practical impact of future work, researchers should prioritize transparent reporting, reproducibility, and the integration of explainable AI and hybrid models that incorporate physical laws. These steps will be essential for transitioning ML/GA solutions from academic studies to reliable industrial applications.
## 2 Data and Methods
### Methods
#### 2.2.4 Accuracy Evaluation  
To assess the accuracy of the model inversion, this study employs three evaluation metrics: coefficient of determination (R\({}^{2}\)), mean square error (MSE), and mean absolute error (MAE). Among these, MAE describes the variance between actual and predicted values. R\({}^{2}\) is a metric used to measure the goodness of fit of a regression model [47]. A higher R\({}^{2}\) indicates better predictive performance of the model. The formulas for calculating R\({}^{2}\), MSE, and MAE are as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(Y_{i}-\overline{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i} -\overline{y_{i}})^{2}} \tag{4}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-Y_{i})^{2} \tag{5}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\mid(y_{i}-Y_{i})\mid \tag{6}\]  
In the equations, \(y_{i}\) represents the measured TP concentration value, \(\overline{y_{i}}\) is the mean of the measured TP concentration, \(Y_{i}\) represents the inverse value of the TP concentration, and \(n\) is the number of samples.  
This article will first examine the effectiveness of the multilayer denoising autoencoder. Therefore, this article will compare the differences in water quality inversion performance between models trained with raw data and those trained with data extracted using a multilayer denoising autoencoder. The evaluation metrics will be R\({}^{2}\), MSE, and MAE to determine whether the water quality inversion performance of the model trained with data from the denoising autoencoder has improved, assessing its effectiveness. Subsequently, we will compare the performance of four models--linear regression, decision tree, random forest, and bagging--to identify the optimal water quality inversion model.  
This paper will characterize the data quality of remote sensing images by calculating the signal-to-noise ratio (SNR). The higher the SNR, the better the data quality of the remote sensing images. For the SNR, this paper [48] will calculate it using the variance method, and the calculation formula is as follows.  
\[SNR=\mu/\sigma \tag{7}\]  
In the equations, \(\mu\) represents the mean of the remote sensing image, and \(\sigma\) represents the variance of the remote sensing image.
## 4 Analysis of Optimization Calculation and Results
### Optimization Scheme and Result Analysis
#### 4.2.2 Optimal Results  
The optimal decision results of Scheme 1 are listed in Table 3. It can be seen from the table that, under the optimal decision, the three optimization objectives of MCS life cycle planning optimization are the life cycle cost of CNY 2.4214 million, life cycle carbon emissions of 1.51 thousand tons, and life cycle primary energy efficiency of 8.97. Using this optimal solution to determine the rated capacity of each piece of equipment, it can be seen that the capacity of the boiler is moderate. The solar panels with very few solar collectors are mainly photovoltaic power generation panels. By combining the energy storage capacity, energy storage, and energy conversion, the energy storage device could be made full use of to adopt renewable energy for power generation. When focusing on planning optimization in practical engineering, the system design capacity can be reasonably determined by referring to the decision results obtained via the optimization method.  
The Pareto optimal solution obtained by Scheme 2 is listed in Table 4. It can be seen that, under the optimal decision, the three objective values obtained by the upper level programming optimization of Scheme 2 are the life cycle cost of CNY 2.3492 million, life cycle carbon emissions of 1.44 thousand tons, and life cycle primary energy efficiency of 8.74. In the lower model, the total operating cost of the target value obtained by optimization is CNY 2.3966 million when the carbon transaction costs are taken into account. In addition, among the decision variables of Scheme 2, the capacity of the SC, HE, and HS varies greatly. The SC optimization capacity is 58.6 times that of Scheme 1, the HE capacity is of 77% less than that of Scheme 1, and the HS optimization capacity is 1.9 times than that of Scheme 1. Analysis shows that after the optimization under Scheme 2, the utilization of solar energy in terms of thermal energy is increased, reducing the use of heat exchanger equipment with high primary energy consumption, and the multi-energy cascade utilization of the MCS through the adjustment of the heat storage equipment is achieved.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Life Cycle** & **Life Cycle** & **Energy** & **CB** & **PV** & **SC** & \(GSH_{\textbf{b}}\) & \(GSH_{\textbf{c}}\) & \(ASH_{\textbf{b}}\) & \(ASH_{\textbf{c}}\) & **HE** & **ES** & **HS** \\
**Cost** & **Carbon** & **Emissions** (kg) & **Efficiency** & **(kW)** & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(kW)** & **(kW)** & **(kW)** & **(kW)** & **(kW)** & **(kW)** & **(kWh)** \\ \hline
2,421,400 & 1,510,000 & 8.97 & 178.73 & 371.45 & 0.55 & 199.77 & 144.24 & 33.23 & 5.76 & 534.91 & 150.68 & 19.43 \\ \hline \end{tabular}
\end{table}
Table 3: Optimal compromise solution of single-level programming multi-objective optimization of the MCS.
# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
## 4 Case Study  
### DC Distribution Network Modeling  
Based on the PSCAD simulation platform, the simulation model of the double-ended DC distribution network shown in Figure 8 is established.  
Figure 8: The topological structure of the simulation model.  
Figure 7: Traveling wave fault location process based on EMD-SVD.  
The length of the line is 20 km using an XLPE cable, and the line voltage is \(\pm 10\) kV. MMC (modular multilevel converter) is used for both the sender-end rectifier and the receiver-end inverter. The fault type is set as DC arc fault. The simulation duration is 3.5 s, and the fault occurs at a moment of 3 s with a duration of 0.1 s. The simulation sampling frequency is 1 MHz.  
In the system, the block diagram of the converter control strategy is shown in Figure 9. The inverter at the receiving-end operates in a constant DC voltage and constant reactive power control mode, controlling the DC voltage of the system based on the voltage value provided by the DC distribution system, providing a voltage reference value for the entire DC distribution network and providing reactive power support to the AC side when required. The sending-end rectifier uses constant active and reactive power control to control the flow of power. This control strategy regulates both the distribution system power and the DC voltage.  
In the locating algorithm, the traveling wave velocity is fixed using XLPE cable as the reference wave velocity \(v_{0}(v_{0}=C_{0}/\sqrt{(\epsilon_{r}\mu_{r})})=1.9781\times 10^{8}\) m/s, where \(C_{0}\) is the speed of light. \(\epsilon_{r}\) and \(\mu_{r}\) are the relative dielectric constant and relative magnetic conductivity of XLPE, the insulation material for DC cables, \(\epsilon_{r}=2.3\), \(\mu_{r}=1\)).
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Data Processing
#### 3.2.1 Trajectory Determination  
In order to determine coordinates for each laser return event (to georeference the return), in addition to the range and scan angle, it is necessary to determine the aircraft's position and orientation (trajectory). This is achieved by an integrated navigation system (INS) that processes and combines observations of an IMU and GNSS receiver into what is called a smoothed best estimate trajectory or SBET (Figure 2). The IMU is comprised of triads of accelerometers and gyroscopes that record the linear accelerations and angular rotation rates of the aircraft to accurately determine its attitude. The GNSS observations are carrier phase measurements collected from both the aircraft and fixed ground reference stations that are used to determine the aircraft position [48].  
The first step towards obtaining an SBET consists of determining the 3D position of the ALS system using the GNSS observations. The aircraft position is determined with respect to a base station or group of base stations using one of several differential post-processing positioning algorithms. The result of this process is a 3D trajectory in which the position of the aircraft is determined once, or a few times, per second with an accuracy of a couple of decimeters in the horizvertical, depending upon such factors as distance to reference GNSS stations, atmospheric activity, number of GNSS satellites observed and the satellite constellation geometry. With the exception of the aircraft heading and ground speed, no other aircraft attitude parameter can be determined with the required accuracy from the GNSS observations.  
Because the laser source is pulsed at a rate of several tens of thousands to a few hundred thousand times per second, and because there is a significant displacement of the aircraft position between the GNSS trajectory epochs, it is necessary to increase the temporal resolution of GNSS trajectory and complement it with the attitude information. This is why the next step consists of blending the GNSS trajectory with information from the IMU using a Kalman Filter [49]. As mentioned before the IMU accurately records the aircraft's attitude, usually at a rate of several hundred times per second. The angular rotation rates and linear accelerations estimates can be integrated to derive the aircraft's positions between GNSS observations, increasing the trajectory temporal resolution and providing the additional attitude (roll, pitch and yaw) information required to produce one of several SBETs. Different SBETs can be produced by using different sets of base stations or by processing the observations forwards and backwards in time. These different SBETs can be combined, or one can be selected for use in the following steps.
# Meta Surface-Based Multiband MIMO Antenna for UAV Communications at mm-Wave and Sub-THz Bands
###### Abstract  
Unmanned aerial vehicles (UAVs) need high data rate connectivity, which is achievable through mm-waves and sub-THz bands. The proposed two-port leaky wave MIMO antenna, employing a coplanar proximity technique that combines capacitive and inductive loading, addresses this need. Featuring mesh-like slots and a vertical slot to mitigate open-stopband (OSB) issues, the antenna radiates broadside and bidirectionally. H-shaped slots on a strip enhance port isolation, and a coffee bean metasurface (MTS) boosts radiation efficiency and gain. Simulations and experiments considering various realistic scenarios, each at varying vertical and horizontal distances, show steered beam patterns, circular polarization (CP), and high-gain properties, with a maximum gain of 13.8 dBi, an axial ratio (AR) <2.9, a diversity gain (DG) >9.98 dB, and an envelope correlation coefficient (ECC) <0.003. This design supports drones-to-ground (D2G), drone-to-drone (D2D), and drone-to-satellite (D2S) communications.  
Revised: 9 June 2024  
4 August 2024  
1  
Footnote 1: [[https://www.mdpi.com/journal/drones](https://www.mdpi.com/journal/drones)]([https://www.mdpi.com/journal/drones](https://www.mdpi.com/journal/drones))  
2024, 8, 403. [[https://doi.org/10.3390/drones8080403](https://doi.org/10.3390/drones8080403)]([https://doi.org/10.3390/drones8080403](https://doi.org/10.3390/drones8080403))
# GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
## 3 Results
### Tachymetric Determination of Tree Positions  
Figure 6 shows the positions of the deciduous trees, consisting of the predominant winter oak (_Quercus petraea_), with mixed white birch (_Betula pendula_) and aspen (_Populus tremula_), that were targeted by tachymetry. Although the accuracy of the tachymetric method for determining the positions of regular objects is in the order of millimeters, the positions of trees were measured with an accuracy of centimeters because some tree trunks were tilted; and from some tachymeter locations, the more distant trees could not be seen well because they were obscured by branches of the vegetation. Nevertheless, these determined positions of trees can be considered sufficient with respect to the GNNS method of determining the position of a moving vehicle, which oscillated in decimeters to meters depending on the position of the satellites, the structure of the vegetation, and meteorological conditions.  
Figure 7 shows the positions of the trees, consisting mostly of conifers, predominantly with Scots pine (_Pinus silvestris_) and Norway spruce (_Pica abies_), mixed winter oak (_Quercus petraea_), places with aspen (_Populus tremula_), white birch (_Betula pendula_) or beech (Fagus sylvatica), and larch (_Larix decidua_). The tachymetric determination of the positions of coniferous trees was more accurate than that of deciduous trees, primarily because spruces and pines form a sparser stand with upright trunks. Predominantly coniferous stands allowed better conditions for targeting the positions of trees, as there was usually no undergrowth that hindered the targets for the determined trees.
## 2 Materials and Methods  
### Diagram of a Water Supply Network  
The water supply network of a city has a complex structure, consisting of water tanks, pumping stations, water treatment plants and the network pipeline system. The water, by its flow through pipes, leads to a hydrodynamic loading of the network and consequently, the water pipes are subjected to mechanical stresses. In the case of the pipeline network, there are points where the requests can become large, leading to the damage of the pipe, by ductile rupture or by breakage. These points are generally found at changes of direction, at connections or at pipes where the water pressure is very high, generally due to level differences. The behavior of pipes in water supply systems is studied in a number of works [25]. Thus, water companies frequently encounter problems related to the rehabilitation and extension of water supply systems due to the aging of the existing water network and housing development. An important parameter in the planning of these interventions in the water supply networks is the quality of the material used for the pipes, so as to ensure a long life as long as possible and the maintenance interventions to be made as little as possible. A model for the reliability of such a network is presented in [26]. It is interesting to note that a failure-repair process of such a network system can be considered an asymptotically homogenous Markov process. Another approach uses the Bayes theorem to simulate the failure rate of the water supply network [27]. This type of analysis is based on the failure rate index. In [28], he proposes a new concept of water treatment and of the water station. Using the topological diagram of the water supply network, it is possible to design a refined water resource allocation.  
Figure 1 shows the diagram of a water supply network of a medium-sized city, with the highlight of the technological scheme.  
Figure 1: The water supply network of a medium-sized city.  
The technological way of the water supply network is presented, as an example, in the following:  
1. The water taken from the T. storage lake is treated in the Water Plant, and then, by gravity, it is sent to the storage tanks in D and to the pumping stations Z, H and, partially to R. From storage tank D, through gravitational pipelines, the water is transported to the city's distribution network (Figure 1).
2. The water from the storage tank S, through a gravitational pipe, supplies the high-pressure area of the city's distribution network.
3. The water from water sources (wells) ST, passed through pumping stations R, is charged into the storage tank L. and from there, through arteries in the distribution network of the municipality.  
From storage tank L, through the tanks of PS and W, the water is introduced into the distribution network.