esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Asynchronous Sampled-Data Control Design for Nonlinear Systems with Actuator Failures
## 3 Numerical Experiments  
To show the performance of our control design stated in Theorem 2, consider the undamped torsional pendulum described in Figure 2, where its motion dynamic is described by the following system:  
\[\frac{d\theta}{dt} = w\] \[J\frac{d\omega}{dt} = (u-u_{a})-MgL\text{sin}\theta, \tag{19}\]  
where \(M=\frac{1}{3}\) Kg, \(L=\frac{2}{3}\) m, \(J=\frac{4}{3}ML^{2}\) kg\(\cdot\)m\({}^{2}\), \(g=9.8\) m/s\({}^{2}\) being the gravitational acceleration [17]; and \(\theta\) and \(\omega\) are the angular and angular velocity of the system state. To go on, we use the next actuator failure model [17]:  
\[u_{a}(t)=\left\{\begin{array}{cc}0,&0\leq t<30\text{s}\\ 0.3+0.5\text{sin}(0.478t),&30\leq t<60\text{s}.\\ 0,&t\geq 60\text{s}\end{array}\right. \tag{20}\]  
The next internal control law satisfies the Theorem 1 assumptions:  
\[u(t)=[-0.4\quad-0.8]x(t), \tag{21}\]  
Figure 1: Schematic representation of the closed-loop system by using the Theorem 2 controller system.  
where \(x(t)=[\theta(t)\quad\omega(t)]^{T}\); and hence, the linear model around the origin equilibrium point of the system (19) yields (the un-faulty case) :  
\[\dot{x}=Ax+Bu=\left[\begin{array}{cc}0&1\\ -11.01&0\end{array}\right]x+\left[\begin{array}{cc}0\\ 5.06\end{array}\right]u.\]  
To complete our design by following Theorem 2 demand, we employ:  
\[L=\left[\begin{array}{cc}10&0\\ 0&10\end{array}\right], \tag{22}\]  
\(\alpha=1\), and \(\delta=0.01\). The used  
Figure 4: Numerical experiments: Angular velocity response.  
Figure 5: Numerical experiments: Control law signal.  
Figure 6: Numerical experiments: Fault estimation and the true actuator fault.  
Figure 8: Numerical experiments: torsional angle response for the noisy case.  
Figure 7: Numerical experiments: Noisy control signal.  
Figure 9: Numerical experiment results: sampling-instants for the noisy scenario.  
Finally, and from the modeling point of view, a sample-and-hold device can be modeled by using a filter dynamic with a \"stop-and-go\" programming logic, as follows (see details, for instance, in [21; 22]):  
\[\dot{y}(t)=p(t)\left[\alpha(-y(t)+u(t))\right];\quad x,y,u\in R, \tag{23}\]  
where \(\alpha\) is the time-constant of the filter and \(p(t)\) is a non-uniform square signal taking values from the set \(\{0,1\}\). In this way, when \(p(t)=0\), the solution \(y(t)\) in (23) is hold (the filter dynamic is stopped) to the last value of \(u(t)\) until \(p(t)=1\), where then \(y(t)\) will on time follow the signal \(u(t)\) (the filter dynamic is released). It is clear that if \(\alpha\) is sufficiently large, and the square signal \(p(t)\) is too narrow when \(p(t)=1\), the above dynamic is a kind of an ideal sampled-and-hold mechanism. In fact, and from the circuit point of view, the real sample-and-hold devices use a capacitor which is charged to the sampled value of the signal \(u(t)\), then requiring a constant-time for charging (equivalent to our case). We use the above ZOH-model system in our numerical experiment realization.
# Hybrid-Attention Network for RGB-D Salient Object Detection
## 2 Proposed Method  
The proposed HANet architecture achieves salient object detection and prevents the depth principle error. The processing pipeline of HANet is shown in Figure 1. HANet can be divided into two main parts. The first part extracts features through eight neural network blocks (shown in blue in Figure 1) for the RGB attention map and through two blocks (shown in green) for the depth attention map. The second part consists of six blocks (shown in orange in Figure 1) that fuse the two types of features to generate a hybrid attention map, and one block (shown in pink) that generates the saliency prediction map according to feature filtering based on the hybrid attention map.
## 4 Experiments
### Datasets
#### 4.1.1 SEP-28k  
SEP-28k is an open-source English stuttering speech corpus consisting of data from 385 public online podcast collections. These podcast collections come from 8 shows centered around stuttering themes, mainly featuring interviews between people who stutter. The dataset is composed of 28,177 segments, each consisting of 40-250 three-second clips extracted from each podcast episode. The SEP-28k dataset has two different types of annotation: stuttering and non-stuttering. Three annotators label stuttering segments with five types of disfluencies: blocks, prolongations, sound repetitions, word repetitions, and interjections. Additionally, the dataset annotates an additional 4144 stuttering segments from the FluencyBank dataset using the same scheme. All audio segments in SEP-28k are 3 s long and are sampled at 16 kHz. In this study, we mainly focus on stuttering annotations and sample data from the SEP-28k dataset for training our model. We evaluate the model using data from both SEP-28k and FluencyBank and validate the model using data of different sizes. The data distribution used in our experiments is identical to that of Reference [35].  
The current study utilizes the data processing method described in Reference [35] for the SEP-28k dataset. Data points that all annotators agreed upon as unambiguous were chosen for sampling, ensuring that both the training and test sets contained high-quality data, thus increasing the reliability of the experimental results. Table 1 provides definitions of the five different types of disfluencies along with specific examples.
## 3 HIBAS Implementation for Control of a Foraging System with Deviating Motor Speeds
### Comparison Systems  
In order to test how effective the designed hormone systems were, two additional systems were produced for comparison. The first had no adaptive element, keeping all robots at optimal speed (35 cm/s) while foraging. This system was not influenced by 'demand' and should highlight the point at which speed adaptation is required to obtain remaining items required in the collection. In order to keep environmental awareness consistent across the three systems, the return hormone  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\lambda_{r}}\) & \(\mathbf{\gamma_{r}}\) & \(\mathbf{\lambda_{s}}\) & \(\mathbf{\gamma_{s1}}\) & \(\mathbf{\gamma_{s2}}\) \\ \hline
0.9977 & 5 & 0.999 & 9 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values for the Return and Speed Hormones.  
was implemented across all systems, allowing swarm members to return to the nest site should they encounter too many collisions.  
The second comparison system featured an on-line adaptation method similar to reinforcement learning. This engineered adaptation was driven by the same function for demand as featured in the virtual hormone system. This style of online engineered adaptation has been used in the past to modify swarm traits, finding optimal partition lengths in [20] modifying travel distances based on success and failure of swarm individuals.  
The adaptive system, designed for speed control, stepped the robot motor speeds up or down depending on the value of demand upon returning to the nest site. Positive demand values would increase speed, and negative values would decrease it. As with the hormone system, this would allow speed to be increased or decreased (and hence increase or decrease energy expenditure) in relation to collection requirements.  
The increments and decrements made by the engineered system were influenced by demand, providing a variable adaptation to the system. A base change of 1 was applied based on the sign of the demand in addition to a change proportionate to the value of demand itself, increased by a coefficient of 20 to make suitable changes to the speed value. These values were tuned via iterative selection to produce strong rates of collection and energy efficiency across a wide variety of task demands.  
The base change was used so that the swarm can catch up to the required collection rate even when demand is small. If this change was not implemented, increments based solely of demand would be too small to have a perceivable effect on robot speed. The same effect could not be achieved by increasing the coefficient of demand because the system could react too quickly to large disparities in current collection rate versus required rate and overcompensate by a large margin.
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 4 State Feedback Controller for Nonlinear Magnetic Levitation System
### Continuous-Time Nonlinear State Feedback Controller Design for Magnetic Levitation  
In this section, we design the parameters of nonlinear control law (29), developed in Section 3.2, for three variants of closed-loop poles prescribed for a linearized system (25). It should be noted that in this case, a continuous-time system description and the corresponding closed-loop poles are considered. While in previous section, it was sufficient to define only a region for closed-loop poles, here the exact pole position is required, which complicates the design and simulation experiments were used to find the appropriate pole position to receive good quality control law. Three favourite prescribed closed-loop pole sets and the corresponding control law parameters are summarized in Table 7.  
The resulting nonlinear control law (29) have been implemented in the magnetic levitation simulation model. Simulation results for step changes around the ball position  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Closed-Loop Pole Set \(P\)** & \(K_{1}\) & \(K_{2}\) & \(K_{3}\) & \(K_{4}\) \\ \hline \(P=[-200,-100,-75,-50]\) & \(3.625\times 10^{6}\) & \(6.125\times 10^{4}
# POL-MUX System for Noncoherent Optical Networks
## 5 Conclusions  
There are several ways to extend the bandwidths of optical networks, but many of these approaches are difficult to employ in existing distribution networks. Polarization multiplexes based on dynamic polarization controllers could be a possible method for passive optical networks where coherent detection with digital signal processing is not yet possible. Because mechanical stress and temperature are the factors that influence a POL-MUX system, the temperature dependence of the POL-MUX system was determined, and then a POL-MUX system based on a real academic optical network was tested. Based on the knowledge obtained from previous simulations and practical measurements, a simulation model of a wide-band passive optical network was proposed based on the idea of wavelength and polarization multiplexes. The proposed model demonstrated the functionality of this combination. Simulations verified the negative impact of reducing the PER on signal quality due to polarization crosstalk. Simulations and practical measurements also confirmed that changes in the polarization state do not influence the quality of optical signals for common PON networks. Future work will be focused on the development of DPCs and their implementations in real POL-MUX systems for PON networks.  
Figure 17: State of polarization before polarization demultiplexing ODN2 (**a**), ODN3 (**b**).  
Figure 18: State of polarization after polarization demultiplexing OLT1 (**a**), OLT2 (**b**).  
**Author Contributions:** Conceptualization, R.S., P.M., T.H.; methodology, R.S., M.L., P.M., T.H., validation, P.M., T.H.; formal analysis, R.S., M.L., P.M., T.H.; investigation, R.S., M.L., P.M., T.H., resources, P.M., T.H.; data curation, R.S., M.L., P.M., T.H.; writing--original draft preparation, R.S., M.L., P.M., T.H.; visualization, R.S., T.H.; supervision, P.M.; project administration, T.H.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
The research described in this paper was financed by a grant from the Ministry of the Interior of the Czech Republic, Program of Security Research, VI20192022135, for \"Deep hardware detection of network traffic of next generation passive optical network in critical infrastructures\".  
The authors declare no conflict of interest.
### Simulation Test #4  
Simulation test #4 aims to assess the navigation algorithm's capability to estimate the position of a stationary target within the operational scenario in the presence of communication delays among the swarm of UAVs. The same scenario shown in the previous subsection was considered (see Table 6), where the communication channel among UAVs is not considered ideal but introduces a delay typical of wireless communication.  
Considering the size of UAVs and the typical size of the considered scenarios, three different constant communication delays among aircraft in the formation were considered, 0.1 s, 0.2 s and 0.5 s, to stress the procedure in the worst situations.  
As already shown, results do not depend on the number of consensus steps when \(L\geq 3\), while the communication scheme can give a performance improvement.  
As a representative result, Figure 14 shows the capability of the algorithm to localize the target also in the configuration \(\mathcal{A}=\mathcal{A}_{2}\), considered as the worst possible because it  
Figure 12: Simulation Test #3—3D representation of the estimation error. The true position of the target is depicted by means of the blue marker.  
Figure 13: Top view of the estimation error. The true position of the target is depicted by means of the blue marker.  
shows the slowest convergence in previous results. It is worth noticing that the algorithm is robust to any communication delay until 0.5 s, with a convergence speed that decreases by increasing the length of the delay. Without delays, the estimation of the target position converges to the real value at \(t=43\) s (see Figures 9-11), while with a delay of 0.5 s, the convergence is assured only after \(t=110\) s.
## 1 Introduction  
Carbon fiber reinforced plastics (CFRP) are widely used in the aerospace industry with the combination of high hardness, high strength, and being lightweight [1; 2]. Drilling is one of the most important operations in the CFRP aerospace structures manufacturing due to the need for component assembly [3]. However, mechanical drilling of CFRP is a difficult and challenging task because of their specific material properties. The main problems encountered in CFRP drilling are rapid tool wear and insufficient hole quality, including inter-layer damage, fiber pullout, and exit burrs. This can be attributed to the abrasive nature of the fiber reinforcement, and the anisotropic and heterogeneous nature of the CFRP structure. During the interaction of tool and workpiece, the abrasive actions of the fiber dulls the cutting edges and the low thermal conductivity of CFRP causes heat accumulation on the tool surface, which aggravates the tool wear. Looking for materials with excellent performance in CFRP drilling is a very important task to improve CFRP machining.  
Tool materials (substrate) and type of coatings have become crucial factors in the final tool characteristics for machining CFRP [4; 5; 6; 7]. Chemical vapor deposition (CVD) diamond coatings are famous for their unique characteristics such as high hardness, good wear resistance, low friction coefficient, and high thermal conductivity. The mechanical properties of diamond coated tools depend mainly on the coating structure, such as its thickness and surface morphology. The microcrystalline diamond (MCD) coatings exhibit higher hardness and better adhesion characteristics than that of nanocrystalline diamond (NCD) coatings [8], while the NCD coatings present a lower friction coefficient with a smoother surface. The composite coatings of MCD and NCD exhibit more advantages with the superiority of two coatings simultaneously [9; 10; 11]. In the case of cutting composite materials, diamond coatings have a positive effect, due to their excellent mechanical and physical properties. However, many studies focused only on the cutting performance of diamond coated tools with single layer coating [12].  
In the present work, MCD, NCD, and MCD/NCD coated drills are fabricated by depositing different diamond films on the WC-Co drills. The coating characterizations are examined using scanning electron microscopy (SEM) and Raman spectra. The dry drilling tests are carried out on CFRP in a CNC machine. Comparisons of the drilling performance are made among the three types of diamond coated drills. The drilling force and tool wear are investigated employing dynamometer and SEM.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 8 Case Study-FOS-LW for Gaoqin Landslides  
To verify the feasibility of the FOS-LW, the system was installed to monitor the GaoQin landslides, located in the Huangshan Mountains, southern China. The slope of the ladder-shaped GaoQin landslides is about 18 to 25 degrees. According to preliminary reconnaissance, the landslide is covered with residual loose soil, and the underlying bedrock is badly weathered. As a result, rainwater can easily penetrate into the slope body, causing instability of slope. The landslide is at creep deformation stage at the time of this study. Around the slope body, a lot of cracks can be identified around the boundary of the landslide. Figure 9 shows the installation of the event sensors. The event sensors are installed in the slot with the fixture to guarantee the simultaneous deformation between the sensor and slope body.  
Figure 10 is the sensor layout of the FOS-LW system. Twelve event sensors were placed along the main sliding direction, which are used for the monitoring of horizontal and vertical displacements respectively. For comparison, an inclinometer was installed in a borehole extending to the bed rock beneath the slip surface in the central part of the landslide body to monitor the deformation of the landslide. After nearly a year of monitoring, this system is at the green level. According to the data acquired through the inclinometer (0.5 m per point), as shown in Figure 11, the sliding displacement is less than 1 mm, which is consistent with the warning status of the all fiber warning system. Figure 11 shows the horizontal displacements monitored throughout the study period. The maximum displacement of the whole slope is very small; which corresponds to the creep stage in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Alarm Level** & **Horizontal Displacement** & **Vertical Displacement** & **Crack Width** \\ \hline Green level & \textless{}5.00 mm & \textless{}3.00 mm & \textless{}3.00 mm \\ Yellow level & 5.00 mm\textasci{}10.00 mm & 3.00 mm\textasci{}10.00 mm & 3.00 mm\textasci{}7.00 mm \\ Red level & \textgreater{}10.00 mm & \textgreater{}10.00 mm & \textgreater{}7.00 mm \\ \hline \end{tabular}
\end{table}
Table 1: The alarm class of absolute thresholds.  
Figure 9: Site pictures: (**a**) slotting; (**b**) installation of fixture; (**c**) sensors with patch cord; (**d**) splicing of the fiber.
## 3 Results and Discussion
### Thermal Stability of COOH-APTMS SAM
This was owing to the generation of the Cu-Si phase with a high resistance, indicating the failure of the APTMS diffusion barrier. However, the sheet resistance of the Cu/COOH-APTMS/SiO\({}_{2}\)/Si sample kept stable up to 500 \({}^{\circ}\)C, which meant that the failure temperature of the COOH-APTMS was 100 \({}^{\circ}\)C higher than that of the APTMS. This improvement in thermal stability was due to the following reasons. On the one hand, benzene with large steric hindrance was introduced into the APTMS diffusion barrier after carboxyl modification, thereby making a significant contribution to the barrier performance of the APTMS diffusion barrier. On the other hand, the Cu-O bonds between the carboxyl groups and the copper film enabled the copper atoms to be firmly fixed to the COOH-APTMS,  
Figure 8: AFM images of samples after annealing at different temperatures: (**a**) 350 \({}^{\circ}\)C; (**b**) 400 \({}^{\circ}\)C; (**c**) 450 \({}^{\circ}\)C; (**d**) 500 \({}^{\circ}\)C; (**e**) 550 \({}^{\circ}\)C.  
which could block the diffusion of Cu atoms to the Si substrate. These variations of sheet resistance were in accordance with the XRD data in Figure 6.
# Independent Double-Boost Interleaved Converter with Three-Level Output
## 3 Simulation and Practical Implementation
Moreover, based on an application with 50 V and 100 V input voltages and a 400 V output DC voltage in symmetric operation mode, the efficiency measurements carried out with the Tektronix PA3000 power analyzer are illustrated in Figure 15. The laboratory test setup is presented in Figure 16. For the prototype, the components and some general test specifications are presented in Table 1. In addition, Table 2 comprises the generalized information regarding the main characteristics of the proposed converter, directly compared with actual non-isolated topologies usually found targeting the same applications. Figure 13: The generic output voltage control loop and PWM generator. Figure 16: Laboratory test setup. Figure 14: Practical measurements of the proposed converter in symmetrical and asymmetrical operation modes. Figure 11:0 Laboratory practical efficiency measurements. For analyses in wide voltage range applications, the laboratory prototype testing setup from Figure 16 was developed using high-voltage transistors, which exhibit quite high internal resistances (0.23 \(\Omega\)). As one can see in Figure 15, at a 50 V input voltage, the efficiency decreases at high powers because of the transistors' conduction losses that become predominant.
# Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems
Therefore, this paper also considers the limited detection and communication capabilities of the agent itself, forming a multi-level, distributed task allocation method and ultimately, successfully hunting various targets. Driven by the aforementioned issues outlined previously and based on the original work, this paper introduces a novel approach to self-organization task allocation and hunting control strategy that integrates fuzzy logic and heuristic optimization algorithms, considering the practical engineering application of multi-target hunting in multi-agent systems. By establishing appropriate target models and collaborative strategies, multi-agent systems can achieve the coordinated hunting of targets during the hunting process. The main contributions of this article are as follows:  
In this paper, we first focus on the self-organizing allocation of tasks for multi-agent and multi-target hunting and propose a multi-agent multi-target task self-organization allocation algorithm for a dynamic environment. The algorithm combines fuzzy logic and heuristic optimization algorithm, determines the evaluation factor of task allocation, and then implements a globally distributed task assignment based on an improved particle swarm optimization algorithm, with practical application scenario constraints to achieve optimal global system performance. In the hunting control strategy section, an attraction/repulsive force model, based on potential field function, was designed and introduced to achieve the hunting control strategy of predicting the target's motion trajectory. Without knowing the target's motion state, only the relative position and velocity information between the agent and the target can be used for hunting control of the target. This can ensure that various agents can collaborate to reach the target area, hunt the target, and ultimately form an encirclement based on the set hunting radius for hunting. The organizational structure of this article is as follows: Section 2 lays some preliminary groundwork; Section 3 establishes a dynamic, multi-target, self-organizing task allocation model; Section 4 introduces the design of the hunting strategy and the proof of stability; Section 5 verifies the effectiveness of the theoretical results through simulation experiments; and finally, in Section 6, a brief summary is provided.