esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
## 1 Introduction
In the past decade, many scholars have conducted extensive research on the cross-media problem of amphibious multi-rotor UAVs. However, most of these studies remain at the simulation level, and only a few algorithms have been experimentally verified. At present, there is no widely accepted and highly effective algorithm for the cross-media operations of amphibious multi-rotor UAVs. Therefore, the research on cross-media controllers of amphibious multi-rotor UAVs is of great significance. During the water-air cross-media operations, the medium changes, resulting in changes in buoyancy and added mass. Amphibious multi-rotor UAVs are also affected by the \"water surface effect\", making the design of a cross-media controller challenging. Since amphibious multi-rotor UAVs are derived from multi-copters, their controller design can refer to that of a multi-copter. Traditional control algorithms, such as ADRC, SMC, fuzzy control, and PID, as well as their corresponding improved algorithms [24; 25; 26], are often used in the control of quadrotor aircraft. In addition, machine learning methods have recently become very popular in various fields, including quadrotor control [27; 28; 29] where they have also been applied. Many studies have shown that ADRC has good robustness in practical engineering, and its anti-interference ability is often superior to SMC, fuzzy control, and PID control algorithms [30; 31]. Additionally, there is currently little evidence to suggest that the anti-interference ability of machine learning methods is better than that of ADRC. Furthermore, relevant research on machine learning methods is often still at the simulation stage. Therefore, this paper uses the ADRC algorithm to carry out the cross-media control research, as it is expected to improve the robustness of the control process. This paper introduces the ADRC control algorithm into the controller of amphibious multi-rotor UAVs, to improve the robustness of the cross-media process and to carry out the preliminary attempt at cross-media control research. The main work of this paper is based on the research of [PERSON] et al. [32] and [PERSON] et al. [33], focusing on the analysis of the viscous hydrodynamics that plays a major role in influencing the water exit process of the UAV (physically as shown in Figure 1). A dynamic and kinematic model of an amphibious multi-rotor UAV was established, and combined with the ADRC algorithm, a posture and position cascade controller was built for the motion control of the water outlet process. Compared with the control effect of conventional algorithms such as SMC and PID, the superiority of the ADRC controller is proved.
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 2 Eye-Opening Arbiter-PUF  
### Review of Basic EoA-PUF  
The idea of the EoA-PUF is depicted in Figure 1c. The source of entropy is the mismatch of two nominally identical delay lines. As in the arbiter-PUF, an input pulse propagates through both delay lines and an arbiter decides which of the two paths is faster and generates a PUF response bit. In contrast to an arbiter-PUF, the decision in the arbiter is thus only made if the phase difference exceeds a certain deadzone: in case of smaller phase differences \(\Phi_{\text{ab}}\), which are prone to noisy and thus unstable responses \(r\), the intrinsic phase difference of the two delay lines is integrated over time by using RO-PUF features; the propagating pulse is fed back and an oscillation is started until a certain phase difference is exceeded; see Figure 2a. In other words, the conditions under which a decision will be made can be expressed as follows:  
\[decision=\begin{cases}1,&\Phi_{\text{ab}}\geq\text{deadzone}\\ 0,&\text{otherwise}\end{cases} \tag{1}\]  
Thus, the entire EoA-PUF consists of an RO-PUF-body with an arbiter-PUF decision-maker featuring an adjustable deadzone. The principle was first introduced for an eye-opening voltage-controlled oscillator-based comparator in [29] and was adopted for the EoA-PUF [13; 28]. In addition to this core idea, [PERSON]. [28] already noted that at least a single oscillation is necessary in the EoA-PUF since the required feedback paths in Figure 1c add to the statistically mismatched delay of the SDEs. Thus, a single propagation loops once through the delay lines, the feedback paths, and again the delay lines before the arbiter decides if the phase difference exceeds the deadzone. Therefore, the EoA-PUF achieves faster output than an SotA RO-PUF but will be slightly slower than an arbiter-PUF for stabilizing reasons.  
While Ref. [28] focused on conceptual validation via Monte Carlo simulation on a circuit level, this work focuses on an actual implementation of the EoA-PUF on an FPGA and the challenges that arise in the design process. An FPGA was chosen as it is reprogrammable, allowing for fast prototyping and debugging. An XILINX ZYNQ-7010 on a zynq board (ZYBO) is used as a platform. Next, the FPGA-specific design choices, challenges, and implementation solutions are introduced.
## 3 Methodology
### Obtaining the Local ET,\(LE_{loc}\)  
The \(LE_{loc}\) can be determined by using the energy balance equation. Under the condition of energy balance closure, that is to say, there is no horizontal advection, the energy exchange between the land surface and atmosphere can be described by the modified energy balance equation (Equation (3)). Here, we also ignore the energy consumed by photosynthesis.  
\[R_{n}=H_{loc}+LE_{loc}+G \tag{3}\]  
where \(H_{loc}\) (W/m\({}^{2}\)) and \(LE_{loc}\) (W/m\({}^{2}\)) are the sensible heat flux and the latent heat flux driven by the energy balance system, respectively.  
To calculate \(LE_{loc}\), we need to know the sensible heat flux, \(H_{loc}\), as well. It is given as Equation (4):  
\[H_{loc}=\frac{\rho C_{p}}{r_{a-loc}}\left(T_{0}-T_{a-loc}\right) \tag{4}\]  
where \(\rho\) is the air density, \(C_{p}\) (1004 J/(Kg:K)) is the specific heat at constant pressure of air, \(r_{a-loc}\) (s/m) is the aerodynamic resistance, \(T_{0}\) (\({}^{\circ}\)C) is the aerodynamic temperature that is usually substituted with LST in applications [35; 36], and \(T_{a-loc}\) (\({}^{\circ}\)C) is the surface air temperature when there is no advection. Thus, \(LE_{loc}\) can be derived as:  
\[LE_{loc}=R_{n}-G-\frac{\rho C_{p}}{r_{a-loc}}\left(\text{LST}-T_{a-loc}\right) \tag{5}\]  
Since there is no horizontal advection, the variables in Equation (5) should be acquired under the condition of energy balance closure. Among these variables, \(R_{n}\), \(G\), \(\rho C_{p}\) and LST are thought to remain the same, regardless of whether the energy balance is closed or not on an instantaneous time scale, whereas the other two variables, \(r_{a-loc}\) and \(T_{a-loc}\), are specific and need to be calculated in the case of energy balance closure.  
Figure 2: The principle of the surface energy balance-advection (SEB-A) method.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 3 Mechanism of Chromium and Arsenic Stabilization
### Ion Exchange Mechanism  
Ion exchange is the process of removing chromium or arsenic ions from contaminated sites by exchanging them with ions of stabilization materials. Ion exchange is a reversible and stochastic chemical reaction in which pollutant ions exchange with charged ions  
Figure 10: **(a)** FT-IR spectra of ATP and GATP [83]; **(b)** XPS spectra of element C 1s in soil before and after GATP correction [83]; **(c)** XPS spectra of Cr 2p in soil before and after GATP correction [83]; **(d)** The mechanism of stabilizing Cr(VI) in CM and PC composite materials [84]; **(e)** Q-XRD of As in clay mineral paste [86]; **(f)** TCLP leaching of clay mineral paste containing As [86].  
attached to the surface of a stabilizing material to maintain electrical neutrality. [PERSON] et al. [90] extracted biogenic Mn oxide (BMO) by microbial incubation. The main component of BMO is MnO2, which has poor crystallinity. The hydroxyl group on the surface of BMO undergoes ion exchange with As to form the Mn-As complex. The bioavailable As decreased from 4.56 to 2.04 mg/kg. [PERSON] et al. [91] used a synthetic zeolite with a cation exchange capacity of 188.72 meq/100 g to remove 56% of Cr(VI). The surface of graphene oxide, which contained abundant oxygen-containing functional groups (such as hydroxyl and epoxy groups), was found to electrostatically attract Cr(VI) under acidic conditions and to participate in the redox reaction of reducing Cr(VI) to Cr(III) in the lower valence state, as reported by [PERSON] et al. [92]. However, as the alkalinity increases, these oxygen-containing groups become negatively charged due to ion exchange, resulting in the Cr removal rate decreasing from 99.8% to 10.4%.
## 3 The Proposed Intelligent Mixed Approach
### The Mixed Frequency Hopping Spread Spectrum and Smart Clustering Algorithm
#### 3.2.1 The Proposed Frequency Hopping Spread Spectrum Approach
\] (5) The obtained RSSI values are then sorted as follows, \[x_{n,(0\leq n<j)}=RSSI_{n,(0\leq n<j)}.\] (6)  
The initial seed is distributed as part of the downlink broadcast packet to all the NB-IoT nodes. The initial seed is used to form the MLFG sequence that is used by the selected CHs for dynamic frequency hoping and channel selection within the available 200 kHz narrowband bandwidth either deployed in standalone, in-band or guard-band mode. This dynamic frequency hoping and channel selection is performed by CHs as they relay aggregated data packets received from the respective member nodes (MNs) within their clusters to the BS. The proposed dynamic frequency hopping spread spectrum approach is used to mitigate the interlink interference within the cell as the various nodes attempt to reach the BS. It is further supplemented by the clustering approach as described and boosts the overall network security, energy efficiency and robustness of the network but most important, enhances the possibility for more nodes to join the network without compromising its overall network performance.
# A Survey on Change Detection and Time Series Analysis with Applications
## 5 Other Methods and Applications  
There are many other time-frequency decomposition methods for analyzing non-stationary time series, such as the Wigner-Ville distribution [79; 80; 81], Empirical Wavelet Transform (EWT) [82], tunable-Q wavelet transform [83], and ensemble empirical mode decomposition [50; 84]. [PERSON] et al. [85] used a wavelet-based Hilbert space reproducing kernel method and showed its advantages in terms of the mean squared error. Furthermore, [PERSON] et al. [86] proposed a least-squares-based technique to deal with undesirable side effects of nonuniform sampling in the presence of constant offsets. There are other methods of analyzing two unequally spaced time series together, such as the least-squares self-coherency analysis that splits the series into shorter and independent segments depending on the study to be conducted. Each segment is then analyzed via LSSA, and the resulting spectra are multiplied to derive the product spectrum that strengthens common spectral peaks, particularly if the phenomenon contains signals with signal-to-noise ratios in the vicinity of \(-40\) dB [87; 88]. The least-squares self-coherency method may be considered the precursor of the LSCWA mentioned herein. There are also other change detection techniques, such as Detecting Breakpoints and Estimating Segments in Trend (DBEST) [89], Exponentially Weighted Moving Average Change Detection (EWMACD) [90; 91], and others [92; 93]. Table 1 lists some of the applications of the change detection and time-frequency analyses mentioned herein.
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 6 Conclusions  
A small-scale model car could be trained to perform a lane-following task.  
The stage built with chromas allowed us to segment the lines of the road from the rest of the elements in the scene and correctly identify them in a single step. This replicates the advantages that usual simulators provide, avoiding either time-consuming labeling tasks or the need to combine other conventional techniques to segment and identify the elements of the image in a target scenario.  
The use of augmented images made it possible to train an Autoencoder capable of replicating the segmentation without having to resort to simulators. Furthermore, a noise addition in the input data helps to generate Autoencoders with a higher tolerance to environmental and illumination variations.  
In the validation tests, the driving model was found to be robust to changes in lighting on the stage, as well as to shadows or to the presence of obstacles on the track. In the presence of an occlusion on one of the lines, the driving model is able to follow the other line. In addition, it was found that the car was able to follow a different circuit than the one used for training.  
The approach discussed in this project shows an interesting and alternative method to automate the training of vehicle steering without the use of driving simulators.  
Tasks for future work include developing a more elaborate proposal based on a mixed reality system, using generative methods to induce variations in lighting and perspective, and including more elements (road signs, for example) in the semantic segmentation. This would allow the merging of the training process in both simulators and real environments, retaining the advantages of both.  
Author Contributions: J.M.A.-W. and A.S.; methodology, J.C. and J.M.A.-W.; software, M.P.S., J.C. and J.M.A.-W.; validation, J.M.A.-W.; formal analysis, M.P.S., J.M.A.-W. and A.S.; investigation, J.C., J.M.A.-W. and A.S.; resources, A.S.; data curation, J.C. and J.M.A.-W.; writing--original draft preparation, J.C., J.M.A.-W. and A.S.; writing--review and editing, A.S. and J.M.A.-W.; visualization, J.M.A.-W. and M.P.S.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors contributed to manuscript revision, read, and approved the submitted version. All authors have read and agreed to the published version of the manuscript.  
Funding: This work was supported by the Spanish Government under projects PID2019-104793 RB-C31/AEI/10.13039/501100011033, RTI2018-096036-B-C22/AEI/10.13039/501100011033, TRA2016-78886-C3-1-R/AEI/10.13039/501100011033, and PEAAUTO-CM-UC3M and by the Region of Madrid's Excellence Program (EPUC3M17).  
Data Availability Statement: Several videos and datasets are available at the repository of this project [39].  
Acknowledgments: We want to thank those people from the Universidad Carlos III de Madrid who participated in the experiments and for their contributions to the driving tasks.  
Conflicts of Interest: The authors declare no conflict of interest.
## 5 Evaluation of Results
### Comparison between Train on Real Test on Real (TTRR) and Train on Augmented Test on Real (TATR)  
Alternatively, research conducted by [PERSON] et al. [43], [PERSON] et al. [39], and [PERSON] et al. [44] evaluated synthetic data by training models on an augmented dataset with both real and synthetic data, subsequently comparing the performance to models trained solely on real data.  
Figure 9 illustrates the experimental design for validating ballast segmentation models with augmented data. In addition to the datasets utilized in the prior experiment, a new augmented dataset combining real and synthetic training images was developed for this study. The experimental results reveal that the model trained on the augmented dataset (\(mAP_{max}\) = 32.9%, \(F1_{max}\) = 34.6%) surpasses the performance of the model trained exclusively on real data (\(mAP_{max}\) = 32.1%, \(F1_{max}\) = 34.3%). This enhancement indicates that augmenting the real ballast dataset with synthetic ballast data in training improves the model's segmentation performance, thereby confirming the utility of the generated synthetic ballast images.  
When comparing the segmentation results of models trained on the real dataset to those trained on the augmented dataset (Figure 10), it is evident that the segmentation kernel trained with the augmented datasets demonstrates superior performance. This enhancement is particularly pronounced when ballast particles are partially obscured by fine material, likely attributed to the adaptive texture-mixing technique applied during the synthetic scene rendering.  
Table 3 summarizes the evaluation metrics on the real ballast test dataset for all three models trained among the experiments. The augmented ballast dataset combining both real and synthetic ballast data could clearly enhance the segmentation performance.  
Figure 9: Comparison between TRTR and TATR. (**a**) Experimental flowchart. (**b**) Mean average precision (\(mAP\)) comparison. (**c**) F1-Score calculated from \(mAP\) and mean average recall (\(mAR\)) comparison [23].
## 2 Aerial Robot Design and Development
### Diverter Installation Tool
#### 2.2.1 Tool Design and Mechanical Components  
The tool developed has three main components as shown in Figure 3: the electric linear actuator, the clamps, and the pulling cables.  
The electric linear actuator is a Firgelli FA-PO-240-12-4. This actuator provides a 200 lb (90 kg) dynamic force with a 4-inch (10 cm) stroke and a 7.5 mm/s speed. It is equipped with a built-in 10 K\(\Omega\) potentiometer to provide position feedback, and it is operated using a 12 VDC input. The actuator housing is fixed to an aluminum disc that supports the clamps as shown in Figures 3 and 4. The rod end of the linear actuator holds the pusher. This is a square-shaped metallic part with two 6 mm pins that support the diverter and two ends of the pulling cables (see Figure 4).  
Two plastic clamps are attached to the aluminum disc as shown in Figure 4. Each clamp is made of two jaws, a pivot joint, an innerspring between the jaws (K2 in Figure 4), and an outer spring (K1 in Figures 3 and 4). The outer spring stiffness (K1) is higher than the innerspring stiffness (K2) by design, and their equilibrium length keeps the clamp open. Moreover, a cable guide (red part in Figure 3) allows for centering the power line cable in the tool and supporting the diverter's flaps to avoid obstructions during the insertion process.  
One pulling cable is needed to close or open each clamp moving the jaws. One end of the cable is attached to the rod end of the linear actuator, then the cable goes through the clamp innerspring K2 (see Figure 4), and the other end is attached to the outer spring K1 (see Figures 3 and 4). When the linear actuator stretches out, the cable is pulled so the clamp closes (compressing both springs). When the linear actuator retracts, the springs recover their equilibrium length, so the clamp opens.  
Figure 4: Detailed view of the attachment of the clamps to the linear actuator.  
Figure 3: (**a**) Design of the diverter installation tool and (**b**) the current prototype developed.
## 2 Model and Methodology
### Model Structure
#### 2.2.3. II Step: Calculation of Daily Charging Events on Publicly Available Infrastructure
The overall range of variation of \(E_{a}\) spans from zero to the maximum value of battery capacity \(\left(batt_{BEV}\right)_{2030}\); in order to simplify the model structure, this range has been divided in 10 equally spaced classes, each one with a constant energy value \(E_{j}\), with:  
\[\left\{ \begin{array}{l}E_{j}=j\star\frac{\left(E_{a}\right)_{\max}}{10}\\ E_{0}=0\end{array} \right.,1\leq j\leq 10. \tag{10}\]  
Then, every \(E_{a}\) value has been compared with \(E_{j}\), so that for every \(E_{j-1}\leq E_{a}\leq E_{j}\) the model apply the substitution \(E_{a}=E_{j}\). This way, several different values of energy requests \(E_{a}\) are reduced to only ten values of \(E_{j}\); this assumption is safe since it always overestimates the energy requests. The couples \(\left(BEV_{a},E_{a}\right)\) and \(\left(PHEV_{b},E_{b}\right)\) are transformed into \(\left(\left(BEV_{j}+PHEV_{j}\right),E_{j}\right)\), thus inscribing BEV and PHEV energy requests into the same framework and allowing us to use the additive and modular architecture that was one of the basic choices for the model. Given the modular architecture of the model, infrastructure size is calculated for every j-th class of energy requests and, after the cost optimization phase, the total value is obtained by summation. Since \(\left(r_{k,j}\right)_{y}\) already takes into account charging energy requests, all the public infrastructure compositions that are able to satisfy the total number of daily charging events \(R_{j}=\left(BEV_{j}+PHEV_{j}\right)\) are considered as suitable. The space of the solutions, for every j-th class, is a triangular portion of a plane, as Figure 7 shows, described by the following equations:  
\[\left\{ \begin{array}{l}R_{j}=r_{s,j}\ast s_{j}+r_{m,j}\ast m_{j}+r_{f,j}\ast f _{j}\\ s_{j}\geq 0\\ m_{j}\geq 0\\ f_{j}\geq 0\end{array} \right., \tag{11}\]  
Figure 6: Sample comparison of total energy recharged by infrastructure vs. the average energy consumed by xEV circulating fleet. where the triplets \(\left(s_{j},m_{j},f_{j}\right)_{n}\) define all the \(n\) possible combinations of CS that satisfy total daily charging requests \(R_{j}\). After all the technically possible solutions are found, CS costs are applied, in order to optimize the system and find the least-cost solution for each energy level; this is done by searching the minimum value of the \(\left(C_{TOT,j}\right)_{n}\) term of the equation:  
\[\left(C_{TOT,j}\right)_{n}=C_{s}*s_{j,n}+C_{m}*m_{j,n}+C_{f}*f_{j,n},\ \ \forall\left(s_{j},m_{j},f_{j}\right)_{n}. \tag{12}\]  
The outputs of this step are the minimum cost of a charging infrastructure suitable for the j-th class charging request and its composition in terms of CS:  
\[\left\{ \begin{array}{c}\left(C_{MIN}\right)_{j}\\ \left(s_{MIN},m_{MIN},f_{MIN}\right)_{y}\end{array} ,j\in[1,10].\right. \tag{13}\]