esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In this paper, we address various challenges, including privacy issues, bandwidth limitations, and real-time streaming complications, which are prevalent in live video analytics on resource-constrained drones. We present a split-model architecture that consists of (i) A Gaussian noise-based injection (noise module) and (ii) a set of feature map selection strategies, to make feature maps sent to the untrusted cloudlet non-invertible. The non-invertible feature maps ensure that the privacy of bystanders encountered during deployment is protected, particularly in reconstructing facial features, while our selection strategy further enhances privacy protection. The combination of our strategies aims to provide strong privacy protection under real-time constraints and the use of feature maps instead of raw images minimizes bandwidth usage. Based on our systematic analysis of design choices and experiments, our noise module provides the best privacy protection when used with the Gaussian annulment feature map selection strategy. Furthermore, encoding feature maps as PNG images significantly improves bandwidth usage with a reasonable latency overhead. In addition to evaluating object detection capabilities, we validate the effectiveness of our method in an object-tracking setting using the canonical MOT16 dataset.  
In recent years, drone technology has proven useful in several promising applications, yet its full potential remains untapped. While there are several valid security and privacy concerns regarding the large-scale adoption of drones, our work proposes an idea that is a step in the right direction toward the safe adoption of drone technology.  
Conceptualization, C.W., Y.S., N.K. and S.Z.; Methodology, P.N., C.W. and Y.S.; Software, P.N.; Writing - original draft, P.N. and S.Z.; Writing--review & editing, P.N., C.W., Y.S., N.K. and Y.H.; Supervision, S.Z.; Project administration, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The datasets used for training our models are all publicly available.  
Author [PERSON] was employed by the company ByteDance Inc. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Degenerate Detection  
In three-dimensional space, the relationship between a ray and a triangular facet can be divided into seven categories as shown in Figure 6: crossing, missing, crossing point, crossing edge, parallel, coplanar nonintersecting, and coplanar intersecting. It is obvious that crossing and missing can be judged and calculated intersection point with the method mentioned above. However, the other situations may not be judged correctly. In this section, all possibilities are analyzed to improve the stability of algorithm.  
As stated above, three orientations \(O_{0}\), \(O_{1}\), \(O_{2}\) are obtained. Crossing point is the relationship of a ray crossing a vertex of a facet. At the same time, because that the geometry is closed, the ray must also cross the vertex of other facets. Under this circumstances, there must be two of orientations equal to 0, and the rest one must not be 0. Intersection judgement should be passed, but the same intersection point can be calculated through more than one facets. As mentioned in Section 2.4, intersectionpoints will be sorted in \(r\) direction, the same point should be reserved only once. Crossing edge is a relationship which a ray crossing an edge of a facet. According to the same principle, there is one of orientations must be 0, and others must be positive or negative at the same time. Two same intersection points can be calculated through different facets and only one will be reserved.  
When a ray is parallel with a facet, three orientations must not be positive or negative at the same time. Therefore, the relationship will be judged as missing, which conform to the fact. When a ray is coplanar but not intersected with a facet, three orientation must be all 0. The relationship will be judged as missing, which also conform to the fact. For the last one, a ray is coplanar with a facet and passes through it. Three orientations must be all 0. In this situation, the facet must be a part of boundary of the geometry where facets are impossible to contain meshes. Therefore, the fact of missing can be judged correctly.  
Generally, the judgment in crossing point and crossing edge situation need to be added to ray casting algorithm, which can guarantee the algorithm correct in all cases. In details, if two orientations are calculated equal to 0, the relationship can be considered as crossing point. Similarly, if one orientation is equal to 0 and the other two have the same sign, the relationship can be considered as crossing edge. In the two additional situation, intersection point need to be calculated in the next step.  
The overall process of parallel Cartesian mesh generation algorithm is shown in Figure 7. The three steps that need large time consumption, including ray generation, intersection calculation and property mapping calculation, are implemented in parallel with GPU through the methods described in the above subsections, as shown in the green part in Figure 7.  
Figure 6: Intersection cases between ray and triangle in three-dimensional space.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 1 Introduction
### Chaotic Systems  
The behavior of a nonlinear dynamical system that may be extremely sensitive to small changes in initial conditions is known as chaos. This sensitivity to initial conditions means that a slight change in the starting point can lead to different outcomes. For example, the butterfly effect shows how a small change in one state of a deterministic nonlinear system may result in enormous deviations in a subsequent state [1]. The other characteristic of a chaotic system is no periodic behavior. The symmetric property of these nonlinear dynamic systems can play a vital role in producing the systems' chaotic behavior. Due to this fact, various researchers have recently shown much interest in the symmetric properties of chaotic systems. In [2], the authors have proposed a chaotic oscillator with both odd and even symmetries. Similarly, some of the other applications of symmetric properties of chaotic systems lie in image processing, security, and communications [3]. The symmetric and asymmetric behavior has been observed in many natural phenomena. Due to these characteristics, the chaotic motion is difficult to forecast. For instance, predicting the butterfly effect for the long term is impossible [1]. This is because these systems aredeterministic, i.e., the future behavior of these systems is entirely defined by their initial conditions. Hence, these systems are wholly deterministic and unpredictable.  
On the other hand, a chaotic time series is generated when the variable changes with time in a chaotic system. This chaotic time series provides extensive information about the nonlinear system and helps evaluate and analyze the chaotic system's behavior. The phase space reconstruction technique reveals this dynamic information hidden in the chaotic time series and transforms the existing data into a more describable framework [4]. As a result, it is essential to have approaches that can forecast chaotic time series and differentiate chaotic data from stochastic data [5; 6; 7]. The traditional prediction methods for this purpose have failed to produce satisfactory performance. Thus, many advanced techniques using machine learning-based approaches have been proposed recently. Therefore, this paper presents a comprehensive review of the performance of traditional and machine learning-based methods for chaotic time series forecasting and their implementation on nonlinear dynamical systems, such as photovoltaic systems, wind farms, communication signals and systems, oil and gas, hydrological systems, weather, and other systems.
## 2 Mathematical Modeling
### Modeling of a Three-Phase Two-Level Voltage Source Inverter  
A three-phase VSI consisting of six switches and a DC rail, as presented in Figure 1, is commonly used in industrial applications.  
A modulation scheme modulates the DC input voltage \(V_{dc}\) from the DC link to generate a three-phase AC output for driving electric machines by providing appropriate sequences of gate signals to the transistors. To prevent shoot-through, the gate signals for the upper and lower transistors in each inverter leg are complementary, and their transitions are managed with dead time intervals to avoid shoot-through phenomena; see Chapter 10 in [1]. The instantaneous stator voltage of the three-phase inverter is expressed as  
\[\begin{bmatrix}v_{a}(t)\\ v_{b}(t)\\ v_{c}(t)\end{bmatrix}=\frac{1}{3}V_{dc}\begin{bmatrix}2&-1&-1\\ -1&2&-1\\ -1&-1&2\end{bmatrix}\begin{bmatrix}S_{1}(t)\\ S_{3}(t)\\ S_{5}(t)\end{bmatrix}. \tag{1}\]  
Here, \(S_{1}(t)\), \(S_{3}(t)\), and \(S_{5}(t)\) represent the switching states of the upper transistors of the three inverter legs at time \(t\), such that \(S_{i}(t)=1\), with \(i\in\{1,3,5\}\), if the transistor \(i\) is closed and 0 otherwise. The output voltage vectors of a two-level VSI in the \(abc\) coordinate frame are given in Table 1. For ease of representation and analysis, the time dependency in the equations is omitted in further discussions.  
To simplify the control process, three-phase voltages are transformed into two-phase components in both stationary (\(\alpha\beta\)) and synchronous rotating (\(dq\)) reference frames using Clarke and Park transformations, respectively. These transformations are respectively expressed as  
Figure 1: The schematic representation of VSI-fed SPMSM.  
\[\begin{bmatrix}v_{a}\\ v_{\beta}\end{bmatrix} =\frac{2}{3}\begin{bmatrix}1&-1/2&-1/2\\ 0&\sqrt{3}/2&-\sqrt{3}/2\end{bmatrix}\begin{bmatrix}v_{a}\\ v_{b}\\ v_{c}\end{bmatrix}, \tag{2}\] \[\begin{bmatrix}v_{d}\\ v_{q}\end{bmatrix} =\begin{bmatrix}\cos(\theta_{r})&\sin(\theta_{r})\\ -\sin(\theta_{r})&\cos(\theta_{r})\end{bmatrix}\begin{bmatrix}v_{a}\\ v_{\beta}\end{bmatrix} \tag{3}\]  
where \(\theta_{r}\) is the angle between the \(\alpha\beta\) and \(dq\) frames, i.e., in the case of a motor, between the stator and the rotor. The possible switching configurations for a two-level VSI in the \(dq\) frame are shown in Figure 2. In this figure, the \(V_{i}\), with \(i\in\{0,\ldots,7\}\), correspond to the projection of the voltage vectors of Table 1 on the \(dq\) axes.
# Enhancing the Photocatalytic Activity of Immobilized TiO2 Using Laser-Micropatterned Surfaces
## 2 Materials and Methods
### Photocatalytic Degradation Experiments  
Photocatalysis experiments were performed in cylindrical pyrex cells (\(r\) = 2.5 cm and \(h\) = 4.5 cm), open on the upper side. Fortified aqueous solutions of atrazine (30 \(\mu\)g L\({}^{-1}\)) were prepared in water. A volume of 2 mL of the sample solution was added to the cells in each experiment in the presence and absence of the photocatalytic surfaces (1.5 cm \(\times\) 1.5 cm) and it was left in the dark for 15 min before illumination to achieve equilibrium. Photolysis was performed at ambient temperature in the illumination box. The solutions were magnetically stirred throughout the experiment. UV light emission was interrupted at specific time intervals and samples (100 \(\mu\)L) were taken for LC-MS/MS analysis. The samples were spiked with atrazine-d5 (IS) at a final concentration of 30 \(\mu\)g L\({}^{-1}\) (0.139 \(\mu\)mol L\({}^{-1}\)) and analyzed by LC-MS/MS.  
Calculation of Kinetic Rate Constants  
At several time intervals, samples were taken for analysis of atrazine by LC-MS/MS to obtain a plot of atrazine degradation over the time of irradiation. Sampling intervalscovered the whole degradation process (e.g., from 10% to 90% degradation) unless the results indicated that no significant degradation of atrazine was observed or that degradation proceeded very slowly.  
Experiments under irradiation, under dark, and under irradiation in the absence of the test surface were performed.  
Observed degradation rate constants, \(k_{obs}\), were calculated by fitting the plots (natural logarithm (In) of concentrations vs. reaction time) to a first-order kinetic model, as described by Equations (1) and (2) [3; 52].  
\[Rate=-\frac{d[C]}{dt}=k_{obs}[C] \tag{1}\]  
\[In(C)=\ln(C_{0})-k_{obs}t \tag{2}\]  
where the following definitions apply:  
\(C_{0}\): initial concentration of atrazine (\(\mu\)mol L\({}^{-1}\));  
\(C\): concentration of atrazine (\(\mu\)mol L\({}^{-1}\));  
\(t\): reaction time (min);  
\(k_{obs}\): observed rate constant (min\({}^{-1}\)).
###### Abstract  
This paper addresses tracking control problems for autonomous underwater vehicle (AUV) systems with coupled nonlinear functions. For the first time, the radial basis function (RBF) is applied to the model reference adaptive control system, and the vehicle horizontal plane model is proposed. When the AUV movement is affected by the driving force, ocean resistance, and the force generated by the water current, the expected output of the AUV's system is difficult to meet the expectations, making the AUV trajectory tracking problems challenging. There are two main options for finding suitable controllers for AUVs. The first is making the AUV model achieve better stability using a more complex controller. The second is the simpler controller structure, which can ensure faster system feedback. The RBF and model reference adaptive control (MEAC) system are combined to increase the number of hidden layers, increasing the AUV tracking stability. Because the embedded computing module of an AUV is a bit limited, 31 hidden layers are chosen to simplify the controller structures. A couple of Lyapunov functions are designed for the expected surge and sway velocities, and the vehicle tracking error gradually converges to (0.0). The controller design results are imported into the AUV actuator model by software, and after 0.64 s, the AUV tracking error is less than 1%. At last, the vehicle tracking experiments were carried out, showing that after 0.5 s, the AUV tracking error was less than 1%.  
tracking system; uncertain nonlinear systems; AUV; RBF; MRAC
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 4 Discussion of Key Concepts
### Issue with Reduction-by-Specialisation  
For the example application involving a quantum integer divider, an important part of the quantum arithmetic operations perform controlled subtractions during each of the long-division steps. Assuming an example 4-qubit divisor is represented in 2's complement representation by the qubits \(|q4d^{\prime}|q3d^{\prime}|q2d^{\prime}|q1d^{\prime}|q0d^{\prime}\rangle\), the subtraction is assumed to be carried out by a 5-qubit ripple-carry modulo adder, as shown in the top half of Figure 6. To achieve quantum circuit width reduction, the proposed transformation techniques aim to eliminate one or more of the divisor qubits (in 2's complement representation). The qubits defining the dividend are to be left unchanged, as explained later. As an illustration of a reduced circuit, the bottom half of Figure 6 shows the transformed ripple-carry modulo adder when the three most-significant divisor qubits, i.e., \(|q4d^{\prime}|q3d^{\prime}|q2d^{\prime}\rangle\), specialised to a state of \(|101\rangle\). This specialisation followed similar steps to those outlined previously for the 3-qubit modulo adder in Figure 5. However, this _reduction-by-specialisation_ as shown here is challenging to automate by a quantum circuit transformation tool. A key part of this challenge is that the states of the 'constant' qubits (i.e., those for which circuit is specialised) can temporarily change during the quantum computation. This challenge to automating the reduction-by-specialisation transformations as illustrated in Figures 5 and 6 extends to more general quantum arithmetic circuit implementations where similar ripple-carry based addition or subtraction is performed, e.g., circuits where controlled additions are used in multiplications.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 4 Results and Discussion  
### Structure and Performance Test for the STE  
#### 4.1.1 Introduction and Impedance Evaluation for the STE  
During exercise, the human body produces a substantial amount of sweat. The commercial Ag/AgCl gel electrodes are poorly ventilated, making them unsuitable for perspiration. Prolonged use can lead to skin irritation and allergic reactions. To address this issue, the STE shown in Figure 9b was fabricated using machine weaving, with silver-coated polyamide yarn (a commercially available conductive thread composed of polyamide fibers uniformly coated with metallic silver, with approximately 18 wt% silver content, manufactured by THINKER, Qingdao, China; see Figure 9a) as the conductive element and common denim fabric (cotton fiber) as the substrate. The conductive yarn is a ready-to-use, off-the-shelf product and is readily available via standard laboratory procurement channels. This textile-like bioelectrode offers excellent breathability and sweat absorption, providing high wearing comfort for daily ECG monitoring.  
To evaluate the performance of the STE as a bioelectrode, impedance measurements were conducted using an impedance analyzer to compare the electrode-skin contact impedance of the Ag/AgCl gel electrodes and the STE under various conditions. Two identical electrodes were placed 10 cm apart on the lateral side of the subject's right arm and connected to the impedance analyzer to form a loop [48]. Detailed experimental procedures are described in the Experimental section. To assess the long-term stability and  
\(R=0.98\) (Figure 11), indicating a high degree of similarity between the signals obtained from the two electrode types. This result highlights the effectiveness of the STE in accurately capturing physiological signals and suggests its potential as a viable alternative to commercial Ag/AgCl gel electrodes.
## 5 Test Results and Discussion
### The Variation of Elasticity Modules after Traffic Loading
On the other hand, as the number of repetitions increased in the repeated plate loading tests, the increase in the elasticity  
Figure 10: Definition of modulus from the repeated PLT. modulus improvement ratio (MIR) was even greater. As a result of the fifth repeated plate loading test (N\({}_{\text{PLT}}\) = 5), the MIR value was approximately 85% for the section improved with rock filling, while this rate was 48% for the section reinforced with geocell. Additionally, in the test sections reinforced with geogrid, the MIR value was obtained at approximately 12% for single geogrid and 28% for double geogrid. Figure 11: The variation of elasticity modules for different repeated plate loading (**a**) N\({}_{\text{PLT}}\) = 1 (**b**) N\({}_{\text{PLT}}\) = 1–2–5. Figure 12: _Cont._  
In this study, the reinforcement of the base course with different geosynthetics was investigated in order to evaluate it as an alternative to quality rock fill. The test results obtained showed that rock fill showed the best performance. However, quality materials, such as those used for rock filling, are rapidly depleting around the world. In addition, the cost of this material is constantly increasing. Although geocells were initially designed to support base layers in flexible pavements, their use was limited to unpaved low-volume roads due to concerns about stiffness, durability, and a lack of design methodologies [40]. With the confinement of the materials, the geocells significantly increased the filler materials' strength and the pavement layer's elastic modules. Reduced layer thickness for the asphalt, base, and subbase layers might also be possible thanks to geocells' increased strength and bearing capacity. This circumstance creates a sustainable construction method with clear environmental and economic benefits suitable for all types of transportation infrastructure. Also, the results of the tests showed that geocell reinforcement performs better than geogrid reinforcement, followed by cases with double- and single-row geogrid reinforcement. Especially in cases with geogrid reinforcement, although the degree of improvement remained low after the first plate loading, the degree of improvement increased rapidly as the number of plate loadings increased. This situation is thought to be due to the membrane and interlocking effects that occur after the increasing displacement of the geogrid. The mobilization of rock fill into a very soft subgrade has been shown to be effective in improving the weak subgrade and preparing a fairly stable working platform for the construction of the layers being placed above [41].
## 2 Materials and Methods
### Detection of Lignin Peroxidase Expression and Enzyme Activity  
The recombinant ZHMX4 and _P. chrysosyoporin_ were each cultured separately in 10 mL optimized enzyme production medium (10 g Glucose, 1 g Yeast extract, 0.1 g (NH4)2 SO4, 0.3 g KH2 PO4, 0.4 g MgSO4.7H2O, 0.035 g MnSO4, 0.007 g CuSO4.5H2O) at 8% inoculum, pH 3, 30 \({}^{\circ}\)C and 200 rpm/min shaking until the culture achieved the logarithmic phase. The cultures were sampled at 24, 48, 72, 96, 120, 144, 168 and 192 h, centrifuged for 10 min at 5000\(\times\)\(g\), and the supernatant was used to determine the enzyme activity. The peroxidase enzyme activity was determined using veratryl alcohol (VA) as the substrate [30]. One milliliter of 50 mmol/L tartrate buffer (pH 3), 500 \(\mu\)L of 10 mmol/L veratryl alcohol and 1 mL crude enzyme solution were mixed and warmed up for 10 min in a warm bath at 30 \({}^{\circ}\)C after which, 20 \(\mu\)L of 20 mmol/L H2O2 solution was added to start the reaction. The change in absorbance at 310 nm in the first 3 min of the reaction was measured. One unit (U) of enzyme activity was defined as the amount of enzyme needed to oxidise 1 \(\mu\)M of veratryl alcohol to veratraldehyde per minute. The experiment was carried out with the enzyme solution but without H2O2 as the control.  
Enzyme activity was calculated based on the following formula:  
\[U(U/L)=\frac{OD}{t}\times\frac{V}{\varepsilon\times 10^{-6}\times Va} \tag{2}\]  
\(OD\) = absorption  
\(V\) = reaction volume (ml)  
\(t\) = reaction time (min)  
\(Va\) = enzyme volume (ml)  
\(\varepsilon\) = extinction coefficient  
LiP: \(\varepsilon\) = 3.6 \(\times\) 10\({}^{4}\) [(mol/L)\({}^{-1}\)cm\({}^{-1}\)].