esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dual-Port MIMO Antenna
### Validation of S-Parameters: Simulation vs. Measurement (\(S_{xx}\) and \(S_{xy}\))  
Figure 8a illustrates the relation between the measured and simulated S\({}_{\text{XX}}\) parameters. This plot provides a comparative visualization of simulated and experimentally measured S-parameters, allowing for an assessment of their agreement or deviation. The measured S\({}_{\text{XX}}\) values closely match the simulated data, demonstrating fair agreement. The developed 2-port MIMO antenna supports a broad operating bandwidth spanning 2.6 GHz to 10.8 GHz, based on the S\({}_{\text{XX}}<-10\) dB threshold. Figure 8b displays the comparison of transmission coefficients (S\({}_{\text{XY}}\)). The data show that S\({}_{\text{XY}}\) remains below \(-20\) dB at higher frequencies and under \(-15\) dB at lower frequencies, confirming that the 2-port MIMO antenna maintains excellent isolation characteristics. Minor variations in the measured results in both cases may arise from factors such as challenges associated with SMA connectors, manufacturing precision limits, and the relatively narrow ground plane set against the connected cable may affect the current flow pattern [24].  
Figure 6: (**a**) Impedance characteristics measurement setup with Agilent N5247A: A.09.90.02 VNA and (**b**) closer view of fabricated antenna with VNA connectors.  
Figure 7: Radiation characteristics measurement setup using anechoic chamber.
# Electronic and Magnetic Properties of Bulk and Monolayer CrSi\({}_{2}\): A First-Principle Study
###### Abstract  
We investigated the electronic and magnetic properties of bulk and monolayer CrSi\({}_{2}\) using first-principle methods based on spin-polarized density functional theory. The phonon dispersion, electronic structures, and magnetism of bulk and monolayer CrSi\({}_{2}\) were scientifically studied. Calculated phonon dispersion curves indicated that both bulk and monolayer CrSi\({}_{2}\) were structurally stable. Our calculations revealed that bulk CrSi\({}_{2}\) was an indirect gap nonmagnetic semiconductor, with 0.376 eV band gap. However, monolayer CrSi\({}_{2}\) had metallic and ferromagnetic (FM) characters. Both surface and confinement effects played an important role in the metallic behavior of monolayer CrSi\({}_{2}\). In addition, we also calculated the magnetic moment of unit cell of 2D multilayer CrSi\({}_{2}\) nanosheets with different layers. The results showed that magnetism of CrSi\({}_{2}\) nanosheets was attributed to band energy between layers, quantum size, and surface effects.  
electronic property; magnetism; bulk CrSi\({}_{2}\); monolayer CrSi\({}_{2}\); first-principle +
Footnote †: journal: Applied Physics
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Antenna Gain Plots  
The simulated and measured gains of the proposed antenna for a frequency sweep from 2 GHz to 14 GHz are plotted in Figure 16. This comparison shows that the shapes of the simulated and measured antenna gains without and with an FSS follow each other and are in good agreement. It is noticed that the simulated gain with the FSS is enhanced in the overall frequency range, and a peak gain of 14.02 dBi is achieved at 3.5 GHz. An excellent gain enhancement is observed throughout the complete range of frequencies in comparison to the antenna simulated and measured without an FSS.  
Figure 15: _Cont._
## 2 The Proposed RDHEI Method  
This section presents a novel RDHEI method that is highly efficient, reversible, and secure. The method is based on several steps that can be summarized into three main parts, which are the following:  
* RRBE based on pixel prediction, quadtree decomposition, and bit plane reordering. The reserved room will be used for inserting auxiliary information and embedding additional data after image encryption. The content owner is responsible for both the room reservation and image encryption processes,
* data embedding which is performed by the data-hider,
* data extraction and image recovery performed by the receiver.  
Figure 2 illustrates the various stages involved in reserving the embeddable room, encrypting an image, and embedding secret data. A detailed explanation of each of these stages will be provided in the upcoming sections.  
This section elaborates on the proposed method in detail. Firstly, Section 2.1 explains how to reserve room for data embedding and outlines the image encryption process. Besides, Section 2.2 details the procedure for embedding additional data. Finally, Section 2.3 provides a thorough explanation of data extraction and image recovery.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things  
[PERSON]  
1 Department of Computer Science, Information Technology Faculty,  
be used while having a route decision. The routing matrices in Low Power and Lossy Networks (LLN) are divided into two types: link metrics and node metrics [7; 11].  
The trickle timer algorithm uses a mechanism for saving power called the duty cycle. It is applied by switching the radio off if no data are received. However, the radio only turns on when packets must be transmitted [7; 11; 12]. Moreover, the trickle algorithm is meant to transmit the packets and the updated packets continually in a flexible manner. This is done via two strategies: the first is used in a network; when an inconsistent state is discovered, the algorithm raises the signaling rate to take control of the problem. The other strategy is called the suppression strategy; it is used once a node realizes that its neighbors have accumulated enough transmission numbers for a packet; this mechanism reduces energy usage by suppressing the transmission [11; 12; 13; 14; 15].  
The trickle timer algorithm has been the subject of diverse previous research that provides positive outcomes, but there are several restrictions to the trickle timer's performance. The fundamental issue with the trickle timer is that the listen-only period is too long, causing the node to hear for an extended amount of time without being able to broadcast before the period ends. Thus, this research introduced a new technique for dealing with the challenges of the traditional algorithm by making the trickle timer method operate dynamically dependent on hop count.  
The remainder of the paper is structured as follows. Section 2 analyzes publications that are closely linked to the Elastic Hop Count Trickle Timer Algorithm on the Internet of Things. Sections 3 and 4 present the trickle timer algorithm and the methodology of the proposed algorithm, respectively. The experimental findings are presented in Section 5. The conclusion and future work are presented in Section 6.
## 3 Overview of the Improved Model Reference Adaptive Control Framework  
Ensuring consistent speed-tracking performance in the longitudinal controller of an HDV is crucial for the vehicle's fuel efficiency and safety. This requires addressing model uncertainties, real-time performance, disturbances, and input regulation. This section introduces the overall concept of MRAC and two extended models: state predictor-based adaptive control and positive \(\mu\)-modification adaptive control. MRAC enhances longitudinal tracking performance by suppressing model uncertainties and disturbances. This state predictor mitigates high-frequency noise in the control inputs, while the \(\mu\)-modification addresses actuator saturation caused by adaptive control inputs.  
Let the nonlinear uncertain dynamical system be given by  
\[\dot{x}(t)=A_{p}x(t)+B_{p}[u(t)+\Delta(x(t))],\;x(0)=x_{0}, \tag{10}\]where \(x(t)\in\mathbb{R}^{n}\) denotes the system state vector available for feedback, \(u(t)\in\mathbb{R}^{m}\) represents the control input, \(A_{p}\in\mathbb{R}^{n\times n}\) and \(B_{p}\in\mathbb{R}^{n\times m}\) are the known matrices of the actual system, and \(\Delta:\mathbb{R}^{n}\rightarrow\mathbb{R}^{m}\) is the matched system uncertainty. Consider the ideal reference system that specifies a desired closed-loop dynamics system given by  
\[\dot{x}_{m}(t)=A_{m}x_{m}(t)+B_{m}r(t),\ x_{m}(0)=x_{m0}, \tag{11}\]  
where \(x_{m}(t)\in\mathbb{R}^{n}\) is the reference state vector, \(r(t)\in\mathbb{R}^{r}\) denotes the given uniformly continuous bounded command, \(A_{m}\in\mathbb{R}^{n\times n}\) is a Hurwitz matrix, and \(B_{m}\in\mathbb{R}^{n\times r}\) is the command input matrix.  
The matched uncertainty in Equation (10) can be linearly parameterized as  
\[\Delta(x)=W^{T}\beta(x(t)), \tag{12}\]  
where \(W\in\mathbb{R}^{s\times m}\) is an unknown constant weighting matrix, and \(\beta:\mathbb{R}^{n}\rightarrow\mathbb{R}^{m}\) denotes the basis function driven by the state vector \(x(t)\).
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 1 Introduction
[25] propose an improved MobileNetV3 network to address the issue of low accuracy in current gas leakage detection methods. By introducing an improved attention network, the researchers enhance the attention of the network to the crucial areas of the feature images, offering fresh insights into improving the detection accuracy and efficiency of gas leakage detection. Therefore, there are two main challenges in unsupervised ASD tasks in industrial scenarios. First, the machine sound data in the industrial production environments contain a large amount of environmental noise. Existing acoustic feature extraction methods cannot highlight the key machine sound signals, resulting in an insufficient information expression of input features. Secondly, normal machine sound data samples can be so similar that it becomes difficult to establish effective distribution boundaries without capturing efficient normal machine acoustic features. The MobileNetV3 Network includes a Squeeze-and-Excitation (SE) attention module [26], in which the feature matrix is initially dimensioned down and then dimensioned up to obtain the weight channel importance. The dimensionality reduction operation can result in the loss of some sound signal information, leading to subpar detection results for some machines. To address these issues, this paper proposes a machine anomalous sound detection method using the IMS spectrogram and ES-MobileNetV3 network. The main contributions of this paper are as follows:  
1. A novel acoustic feature combination strategy, the IMS spectrogram, is proposed to express machine sound. The IMS spectrogram is obtained by concatenating the log-Mel spectrogram and the SincNet spectrogram. It provides a more comprehensive data description, better capturing the various attributes of machine sound and enhancing the reliability and robustness of feature expression;
2. An improved machine anomalous sound detection network, ES-Mobilenetv3, is proposed. It amplifies the feature information processing ability of the network by incorporating the ECA attention module with the SoftPool method. This network is anticipated to deliver exceptional anomaly detection performance in noisy industrial environments. The rest of this study is organized as follows: Section 2 briefly describes the related work of anomalous sound research. Section 3 introduces the architecture of the proposed method and explains the technical scheme, principle, and function of the method in detail. Section 4 verifies the rationality of the proposed method with experiments. Finally, Section 5 contains the conclusions and future works.
## 3 Results and Discussion  
### Cases Design  
This section will mainly describe the normal operation condition of the satellite in orbit and the damage conditions under different thermal loads. During the normal operation of the satellite in orbit, the satellite's thermal control system makes each component of the satellite work within its own normal temperature range, so the satellite's health will not change significantly in the short term. However, it should be noted that the time-varying and extremely high thermal loads coupled with the full sun/eclipse operating environment yield large temperature changes and large thermal gradients, which will seriously affect the satellite's health and life. So, we designed four groups of thermal load damage conditions that could reflect different time-varying characteristics to study the health changes of satellite typical subsystems.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 5 Results
### PPO-Algorithm  
To evaluate the impact of the choice of the AC method, we also evaluated the PPO algorithm. The AWGN channel was chosen to facilitate the common ground for comparison due to its focus on a single channel impairment. Figure 9 displays the behavior of a PPO approach on an AWGN channel with a static SNR of 15 dB. The agent almost exclusively chooses action 5 (MCS5), resulting in a reward of 15.5 Mbps compared to the theoretical data rate of 18 Mbps.  
The PPO algorithm results in a convergence in approximately 160 episodes, down from the 300 episodes of the NAC approach on the same channel. Furthermore, the variance of the episodic reward diminishes, and the algorithm converges to a single MCS recommendation faster. In contrast with the NAC RL algorithm on the 15 dB AWGN channel, the agent chooses to stick with MCS5 instead of MCS7.
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 7 Conclusions  
This paper proposes an arbitrary sampling Fourier transform method (AS-FT) based on quadratic interpolation of shape function. This method calculates the Fourier transform coefficients in advance, flexibly sets the sampling interval according to the distribution of field and spectrum, and can obtain a semi-analytical solution in the integral interval, taking into account both accuracy and efficiency, and there are no edge effects. The correctness and efficiency of the algorithm were verified by Fourier transform pairs. The AS-FT algorithm was applied to the numerical simulation of magnetic anomalies. Compared with standard-FFT and Gauss-FFT under weak magnetic conditions, we showed that the AS-FT algorithm has higher accuracy and efficiency. Under the condition of weak magnetic field and strong magnetic field, it achieved higher accuracy with fewer sampling points, which shows the high accuracy and efficiency of the AS-FT algorithm. This paper only mentions the application of the AS-FT algorithm to magnetic anomaly simulation. It could also be applied to gravity, electromagnetic, and seismic numerical simulation to further prove the AS-FT algorithm's universality.  
Combined with the spatial wavenumber domain theory, the AS-FT algorithm has advantages in solving partial differential equations. The combination can perfectly solve the boundary effect, realize arbitrary sampling in spatial and wavenumber domains, and achieve efficient and high-precision solution of partial differential equations. Therefore, the AS-FT algorithm proposed in this paper provides a new means for solving partial differential equations with high efficiency and accuracy. It has contributed to geophysical forward and inversion research and the improvement of exploration geophysics theory.  
Conceptualization, S.D. and Y.Z.; methodology, S.D. and Y.Z.; validation, S.D., Y.Z., K.L., Q.C. and J.L.; formal analysis, Y.Z.; investigation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, S.D.; project administration S.D., Y.Z. and J.L.; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 41574127).  
Not applicable.  
All contributors to this work have been included as authors. We would like to thank the editor and the reviewers for helping us improve the quality of the manuscript.  
The authors declare no conflict of interest.
## 5 Conclusions  
This paper introduces a technical solution to the due diligence obligations on importers and exporters to the EU single market under the EU Deforestation Regulation. It provides routine insights into forest cover loss around the areas of source farms in countries from which commodities are imported.  
The analysis of the ForestMind reports from satellite imagery presented here demonstrates that automated forest loss detection algorithms can be customised by training a random forest model for the transition between land cover classes, or groups of classes, of interest and applied with very good accuracy.  
The ForestMind farm-scale company reports on estimated deforestation rates offer a solution to suppliers of produce subject to the EU Deforestation Regulation and similar laws. While the data processing and visualisation are entirely automated with the help of the PyEO package, the farm-scale reporting does require human interpretation by a Forest Analyst to write a short narrative text about the data. However, AI solutions may in the future offer a means of automating this interpretation step.  
Conceptualisation, H.B. and C.M.; methodology, H.B. and C.M.; software, H.B., I.R., S.C. and J.B.; validation, K.B. and S.C.; formal analysis, H.B., T.D., I.R. and K.B.; writing--original draft preparation, H.B.; writing--review and editing, all authors; visualisation, I.R., S.C. and K.B.; supervision, H.B. and C.M.; project administration, H.B. and C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Space Agency and UK Space Agency, ForestMind contract AO/1-9305/18/NL/CLP, and the Natural Environment Research Council (NERC) under the National Centre for Earth Observation (NCEO). The APC was funded by UK Research and Innovation's Open Access Fund.  
The datasets presented in this article are not readily available because the data are commercially sensitive to the data owners. Requests to access the datasets should be directed to the authors.  
We are grateful to the entire ForestMind project team for its administrative and technical support, and to the stakeholder organisations for their user feedback on the outputs of the analysis.  
Authors [PERSON] and [PERSON] were employed by Satellite Applications Catapult. Author [PERSON] is still an employee at the Satellite Applications Catapult. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The Satellite Applications Catapult had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Painting Path Planning for a Painting Robot with a RealSense Depth Sensor
## 4 Wall Extraction Algorithm  
The task related to the painting robot and the depth camera can be divided into the following steps:  
1. Capturing images for part of the building;
2. Detecting the window and obstacles in the wall that must not be painted;
3. Forwarding the information about the coordinates of the wall to the robot for painting;
4. Painting the wall by bypassing the detected window and the obstacles.  
The corresponding painting robot is shown in Figure 1. Since the process of developing and testing the painting equipment is still in its building phase, the experiments were operated with the embedded robot operating system (ROS) simulator.  
In the simulation phase, a laser pointer was placed in the robot's tool center point. Later, a spray gun was attached here using a tool holder.  
Further, because in robotics a high degree of accuracy and precision is required, the depth cameras needed to be calibrated in the appropriate manner, so that the obstacle was clearly visible and detected in a complex image. There were numerous parameters and conditions that affected the quality of the depth image, such as noise and light [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. Finally, when the camera and the robot parameters were tuned appropriately, the experiments began.  
Next, the principles and methods for the four main steps of the wall extraction procedure are briefly described. The first sub-section introduces the RTAB-Map, the second describes the statistical filter, the third sub-section depicts the RANSAC algorithm, and the fourth sub-section describes the segmentation algorithm with the corresponding clustering procedure. Figure 2 shows a block diagram of the wall extraction algorithm.  
Figure 1: The painting robot in the testing process.
# New Insights into Fuzzy Genetic Algorithms for Optimization Problems
## 4 Experiments and Discussion
### A Real-World Problem: The Case of Rice Production  
Rice is a staple crop in Asia, with China and India as the major producers in the world [26]. While rice is not a staple crop in most of Europe, there are countries, such as Italy, that have developed specific traditions around rice cultivation and cuisine. Italy is the main rice producer in Europe [26], with many well-known rice-based recipes. One important stage in the production of rice from paddy (rice with husk) is milling, which is the process of removing the husk and a portion of the bran using techniques like drying, winnowing, and whiting. This process is energy-demanding, requiring proper optimization. Here, we rely on a model discussed in [20], dealing with a case study from Iran, which is solved by GA. The optimization of converting paddy to white rice foresees three goals, i.e., maximize the output energy, minimize the global warming potential (GWP), and maximize the net profit. The GWP contribution represents the cost of CO2 emission. It takes into account climate change damages, and it is one of the most important outputs of the Life Cycle Assessment (LCA) analysis, especially when dealing with crop production  
Figure 10: \(r\) vs. \(p\).  
Figure 9: TSP: shortest path length by each approach.  
and supply chains [27; 28; 29]. The multi-objective optimization (MOO) problem above can be tackled as a single-objective optimization (SOO) problem (e.g., see [30]). This means that the benefits to be maximized are converted into costs to be minimized. The SOO problem can be tackled by summing up all the single contributions, and the resulting function to be minimized can be written as follows:  
\[F(x_{1},\ldots,x_{6})=\sum_{i=1}^{3}\sum_{j=1}^{6}\alpha_{ij}x_{j}=\mathbf{A} \mathbf{x},\qquad x_{j}\in X_{j}, \tag{14}\]  
where \(x_{1}\) is the energy equivalent of human labor, \(x_{2}\) is the energy equivalent of machinery, \(x_{3}\) is the energy equivalent of natural gas, \(x_{4}\) is the energy equivalent of nylon, and \(x_{5}\) is the energy equivalent of transportation. These independent variables can be arranged into a vector \(\mathbf{x}\). The respective domains (in MJ \(\text{TIP}^{-1}\)) are the following: \(X_{1}=[44,67]\), \(X_{2}=[92,346]\), \(X_{3}=[675,2005]\), \(X_{4}=[45784,81580]\), \(X_{5}=[934,2240]\), \(X_{6}=[44,133]\). The matrix \(\mathbf{A}\) of the coefficients \(\alpha_{ij}\) is the following:  
\[\mathbf{A}=\left(\begin{array}{cccc}-0.99&-2.70&-1.35&-2.02&0.18&-0.19\\ -0.28&-0.01&-0.30&3.15&2.21&0.21\\ -2.64&-0.44&-1.58&2.85&0.42&0.65\end{array}\right) \tag{15}\]  
All the numerical values above have been retrieved from [20]. In [20], the best solution was found after 26 generations. No information about the runtime was reported in [20]. The best solutions by EFGA-NNC and GFGA-NNC were retrieved after 43 generations. They are all depicted in Figure 11. The difference between the solutions by EFGA-NNC and the baseline [20] is around 10% on average. Same for GFGA-NNC.
## 3 Materials and Methods
### Evaluation Metrics
#### 3.5.4 Mean Absolute Percentage Error (MAPE)  
MAPE expresses the prediction error as a percentage of actual values, making it easier to interpret in relative terms. As it provides a percentage-based error measure, MAPE is particularly useful for comparing errors across datasets with different scales.  
Mean Absolute Percentage Error is given by [37]:  
\[MAPE=\frac{1}{n}\ \sum_{i=1}^{n}\left|\frac{A_{i}-F_{i}}{A_{i}}\right|\]  
where n is the number of observations, F\({}_{i}\) is the forecasted value for observation i, and A\({}_{i}\) is the actual value for observation i.