esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.  
Keywords:b bond graph modeling; model-based control; rehabilitation; exoskeleton; robust reference tracking +
Footnote †: journal:
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 1 Introduction  
It is known that in today's printed circuit board laminates, there is a plate-like material made of electronic glass fiber cloth that is impregnated with epoxy resin, covered with copper foil of a certain thickness on one or both sides, and then hot-pressed. The fiber weave and resin have relatively dielectric constant (Dk) of \(\sim\)6 and \(\sim\)3, respectively, presenting a nonhomogeneous medium for signal transmission. The performance of differential transmission traces is dependent upon the underlying material properties of the laminates used [1; 2; 3; 4; 5; 6; 7]. At high edge rates, especially transmission rates exceeding 25 Gbps, the impact of the fiber weave effect on high-speed signal transmission becomes non-negligible. For 25 Gbps single channel transmission rate or coming 56 Gbps, the fiber weave effect continues to challenge the PCB (short for Printed circuit board) industry to provide novel solutions. Research on the impact of fiber weave effect on \(\geq\)25 Gbps high-speed materials has become more meaningful. In this work, the dielectric constant fluctuations of different types of fiber weaves are studied using a vector network analyzer. Then the impact of the dielectric constant fluctuation on the impedance of single-ended lines and the signal propagation delay of differential lines are discussed. Furthermore, we also discuss the effect of trace direction on impedance fluctuation. Finally, we investigate different methods to improve the fiber weave effect. This paper allows high-speed digital designers to have a more in-depth assessment of the fiber weave effect.
## 4 Experiments and Analysis
### Visualized Comparative Analysis of Experimental Results  
After data preprocessing, two typical trajectories are selected for experiments. This paper selects AIS data with MMSI values of 413826669 (ship-1) and 413997528 (ship-2) and compares the experiments with the LSTM model and the SVM model. The original trajectories of the two ships are shown in Figure 6.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 2 Introduction of Measurement Procedure and Method
### Data Processing Method
#### 2.2.2 Turbulence Intensity  
Turbulence intensity is a scale characterizing the strength of atmospheric turbulence. This scale is calculated by dividing the standard deviation of variable wind speed by the longitudinal wind speed averaged during a time interval, i.e., [35]:  
\[I_{i}=\frac{\sigma_{i}}{U}(i=u,v) \tag{5}\]  
where \(\sigma_{i}\) is the root mean square of the sum of fluctuating wind speeds \(\widehat{u(t)}\),\(\widehat{v(t)}\); \(\widehat{u(t)}\) and \(\widehat{v(t)}\) are the longitudinal and lateral components of the fluctuating wind speed, respectively.  
The fluctuating wind speed is calculated using Formulas (6) and (7) [36]:  
\[\widehat{u(t)}=u_{x}(t)\cos\varphi+u_{y}(t)\sin\varphi-U \tag{6}\]  
\[\widehat{v(t)}=-u_{x}(t)\sin\varphi+u_{y}(t)\cos\varphi \tag{7}\]  
The longitudinal turbulence intensity is influenced by the time interval, and the longitudinal turbulence intensity over a certain time interval is [37]:  
\[SD_{u}(T,t)=\sqrt{\sum_{i=1}^{n}u_{i}^{2}(t)/(N-1)}\Bigg{/}U(T) \tag{8}\]  
where \(u_{i}\) is the longitudinal fluctuating wind speed, \(T\) is 1 h, and \(U(T)\) is the one-hour averaged wind speed; t is the specific time interval variable, which is 1-3600 s in this paper; \(N=T/t\).
## 4 Example Analysis
### Comparison with the Delaunay Method  
Based on the initial Delaunay triangles in Figure 9, the Delaunay method randomly adds point sets to the interior. It uses the incremental inserting algorithm to make the Delaunay triangle mesh. Figure 11 shows a comparison between the Delaunay triangular mesh made by the combined Delaunay-TCOGIM algorithm and the Delaunay triangular mesh made by the Delaunay method after cycling with 1.52 times the mean value of the boundary triangles.
## 4 Simulation Results
### Hovercraft Model and Test Conditions  
The purpose of the numerical simulations was to verify the effectiveness of the control algorithm under near-real conditions for two vehicles with different dynamics. This meant that the operating conditions had to be chosen so as not to exceed the maximum forces and torques that could be obtained from the thrusters. In addition, the velocity values could not exceed those that are achievable by each vehicle.  
The parameters of the hovercraft adopted for testing are similar to those contained in [21]. They are collected in Table 1. The simulations were performed using the software from [40] (Matlab/Simulink source codes) but modified for an algorithm containing an asymmetric inertia matrix and for quasi-velocities. In this program, in order to avoid discontinuities in the system, the _sign_ function was approximated by _tanh_ functions as used in the literature, e.g., [34; 41].  
The vehicle models under consideration have vastly different dynamics, which makes it possible to verify the suitability of the control algorithm for different vehicles.  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Value** & **Unit** \\ \hline \(m\) & 12 & kg \\ \(\int\) & 1.65 & kgm\({}^{2}\) \\ \(d_{u0}\) & 1.0 & kg/s \\ \(d_{u}\) & 4.5 & kg/s \\ \(d_{v0}\) & 1.0 & kg/s \\ \(d_{v}\) & 4.5 & kg/s \\ \(d_{r0}\) & 0.5 & kg/s \\ \(d_{r}\) & 0.8 & kg/s \\ \(d_{ur}\) & 1.0 & kg/s \\ \(d_{vr}\) & 0.5 & kg/s \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of hovercraft.  
Moreover, the center of mass is shifted, i.e., \(x_{g}=0.1\) m, \(y_{g}=0.1\) m, and the desired trajectory position profiles \(p_{d}=[x_{d},y_{d}]^{T}\) were selected as follows:  
\[p_{d1} =[0.2\,t,\,7\sin(0.05\,t)]^{T}, \tag{40}\] \[p_{d2} =[0.2\,t,\,0.5\,t\sin(0.02\,t)+0.3t\cos(0.12\,t)+0.22\,t]^{T}. \tag{41}\]  
Other parameters applied are as follows: \(a=0.5\) m, \(b_{T}=0.5\) m and \(\delta_{x}=0.005\) m. The first is the sine trajectory, whereas the second is a complex trajectory. The initial points for the hovercraft were \(p_{0}=[0,\,1.0]^{T}\). The saturation of the control signal was \(|T\cos\psi|\leq 30\) N, \(|T\sin\psi|\leq 30\) N. A comparison of the simulation results for two different set trajectories can be useful at the stage of trajectory design to perform the set task. The simulations were performed using Matlab/Simulink, the method ODE 3 (Bogacki-Shampine), the time of motion \(t=30\) s and the time step \(\Delta t=0.05\) s.
## 2 Related Work
### Related Work of Dynamic Characteristics in USNet  
The development of USNet has become increasingly mature, and users' expectations for the service quality provided by USNet are also increasing. There are also corresponding studies on the communication problems of USNet, but they mainly focus on the communication of the underwater acoustic channel, media access control mechanism, and routing strategy. In contrast, there are few research studies on efficient computation in USNet.  
USNet plays an important role in marine environment monitoring. Some existing research is devoted to deployment optimization. For the dynamic characteristics of mobile sensors, [PERSON] et al. studied the scheduling problem in multi-sensor networks composed of a small number of mobile sensors and a large number of fixed sensors in [31]. The problem is expressed as a multi-objective dynamic scheduling model, in which mobile sensors are taken as cluster heads. Then, a dynamic multi-objective collaborative optimization algorithm is designed by decomposing decision variables, designing strategies to respond to environmental changes, and generating cooperation strategies that approximate Pareto optimal sets.  
Due to the expensive system deployment cost, the existing USNet is usually deployed as a sparse network. Therefore, in a sparse USNet, AUVs are necessary to realize underwater surveillance and target detection [32]. With the increasing difficulty and complexity of underwater tasks, the cooperative positioning of Underwater Unmanned Aerial Vehicles (UUVs) in the form of clusters has become an inevitable trend. Therefore, due to the weak underwater communication conditions and the complexity of the meteorological environment, UUVs have problems such as observation limitations and time delays. Therefore, in [33], a distributed cooperative localization method for UUVs under the condition of weak underwater communication was proposed. In addition, [PERSON] et al. proposed a location-free path routing scheme based on Q-learning, in which the Q value is obtained by considering the data rate, energy, and depth of underwater wireless sensor nodes. This scheme extends the life cycle of underwater wireless sensor nodes and reduces the communication time [34; 35].
# Treatment Strategies in Neutrophil Dermatoses: A Comprehensive Review
## 5 Generalized Pustular Psoriasis
### Pathogenesis  
The new advances in the pathophysiology of this disease have broadened the horizons of possible therapeutic targets for GPP:  
* Mutations in IL36 RN (gene that encodes the interleukin-36 receptor antagonist) have been identified in cases of GPP. These loss-of-function mutations result in the hyperactivation of IL-36 signaling. This induces neutrophil epidermal accumulation and the formation of pustules mediated by the production of inflammatory cytokines;
* Proinflammatory functions of IL-36 can be potentiated by a positive feedback loop with the IL-17/IL-23 axis. The sustained activation of IL-1 and IL-36 in GPP suggests that the IL-1/IL-36 inflammatory axis is the main physiopathological mechanism in GPP. IL-1 inhibition produces a partial response in patients with this entity, suggesting that IL-1 by itself may not play a central role in GPP; instead, IL-1 may act in a positive loop with IL-36;
* Mutations in caspase recruitment domain family member 14 (CARD14) and mutations in the adapter protein family 1 (AP1S3) have also been associated with pustular psoriasis [88].
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 3 Hybrid Methodology EMD-VMD
### Advantages of the Hybrid Approach  
By integrating EMD and VMD with adaptive gain calculation, this hybrid methodology enhances the precision and efficiency of signal decomposition:* Enhanced Precision: The method identifies and corrects mode mixing, ensuring accurate separation of vibratory components.
* Adaptive Decomposition: It dynamically adjusts the number of modes, tailoring the decomposition to the signal's characteristics.
* Computational Efficiency: By utilizing VMD selectively based on EMD results, it reduces unnecessary computation, improving efficiency. This comprehensive and technical approach provides a robust solution for the estimation of vibratory components in complex signals.
## 2 Related Work
As a result, the study argues that the coordination of the components should be handled separately and should be implemented outside of the components. The formal semantics of Reo have been presented in [17] using timed data streams. The study presented in [18] also draws attention to exogenous communication. They argue against components calling methods from each other. The study argues that the components can be fully decoupled from each other only if connectors possess the whole control flow in the system. Therefore, they indicate that objects are not good candidates for components because they call methods from each other. Their work excludes n-ary connectors. The work presented in [19] proposes an architectural definition based on components, ports, and connectors; however, they deliberately exclude n-ary relations. They introduce FIFO queues on component ports to aid asynchronous communications. The study in [20] presents a formal framework to support the rigorous design of software architecture focusing on the communication aspects. They use UML class diagrams to describe the high-level architecture model, where classes represent the software components, and associations represent the relationships between them. They propose using Alloy [21] to formalize the communication styles and to verify the conformance of the communication styles at the model level. As a reusable library of connectors, they provide four basic connector behaviors: message passing, message passing with FIFO ordering, remote procedure call, and distributed shared memory. The proposed approach supports the validation of communication behavior at the software architecture design level of a distributed system. The study presented in [22] uses the \(\pi\)-ADL [23] to provide a formal semantics for the SysADL [24] models. The study maps SysADL architecture descriptions to \(\pi\)-ADL using model transformations defined in the ATLAS Transformation Language (ATL) [25]. The behaviors of connectors in SysADL are described as compositions of ports on either side of the binary connectors. SysADL also enables using ALF statements to define details of component behaviors and to instantiate elements in the model. However, since SysADL is an architecture description language based on SysML modeling language, it inherits the same omissions from UML regarding the n-ary connectors.
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
## 3 Indoor Simulation Experiment under Inhomogeneous Sea Fog Particle Environment  
### Experimental Setup  
Figure 5 shows the diagram of an indoor simulation experiment device which can create an inhomogeneous sea fog particle environment. Figure 6 is a physical diagram of the device. The laser is used as a light source to emit laser light, and the attenuator is used to adjust the power of the incident light. The polarizer and 1/4 wave plate are used as a lens group to generate linearly the polarized light and circularly polarized light. The inhomogeneous sea fog particle environment simulation device is connected to an ultrasonic atomizer to generate salt fog. It can achieve the effect of simulating sea fog. The hygrometer in the simulation unit is used to measure and record the humidity of the simulated sea fog. The beam-splitting prism divides the outgoing light into two ways. One way is received by the polarization state meter, and the other is received by the optical power meter. The DOP and light intensity of the emitted light is measured. The computer is used for real-time monitoring and data processing.  
Figure 4: Flowchart of inhomogeneous particle scattering model.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### Ground Control Station Node  
The ground control station allows the monitoring of both the forest and autonomous systems via a custom dashboard. It also facilitates communication between MQTT-based and ROS-based devices employing specific flows that act as a communication bridge. The system also processes data series and provides real-time insights, allowing the operator to make decisions and take appropriate action as required. In the next section, we will be discussing the primary setup required to allow for MQTT communication between IoT devices.
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 2 Preliminaries
### Domain Adaptation  
In the practical application of transfer learning, the model trained by the source domain can help the target domain train in the absence of sample labels ([PERSON] et al., 2018). Domain shift resulting from discrepancies in data distribution across source and target domains becomes difficult in this scenario. However, domain adaptation techniques assist in alleviating this problem.  
Unsupervised domain adaptation is an important research field in domain adaptation ([PERSON] et al., 2018). In order to illustrate this problem clearly, the basic standard notation of domain adaptation is introduced. A domain \(D=\{\chi,P(X)\}\) is set, where \(\chi\) is a feature space, and \(P(X)\) is the marginal distribution of data in the feature space. Given a labeled source domain \(D_{s}=\big{\{}\chi_{s},P_{s}(X)\big{\}}\) and an unlabeled target domain \(D_{t}=\big{\{}\chi_{t},P_{t}(X)\big{\}}\), the feature space \(\chi\) and label space \(Y\) of the two domains are the same, \(\chi_{s}=\chi_{t}\), \(Y_{s}=Y_{t}\), while the prob ability distribution is different, \(P_{s}(x,y)\
eq P_{t}(x,y)\). In this case, the potential representation of the source domain data can be learned through feature extraction and representation learning of the source domain data. These potential representations are then applied to the target domain data.  
For unsupervised domain adaptation, one of the most popular techniques is the domain adversarial neural network (DANN) [23]. The core idea of the domain adversarial adaptation method is to learn a good enough representation to reduce the difference in edge distribution between the source domain and the target domain at the representation level. Inspired by Generative Adversarial Networks (GANs) [24], a DANN uses domain discriminators to distinguish features generated by different data domains, and the adversarial loss of domain discriminators is as follows:  
\[L_{DANN}(\psi)=\max_{D}\mathbb{E}_{x^{s}\sim\hat{s}}\log[D(z^{s})]+\mathbb{E}_ {x^{s}\sim\hat{t}}\log[1-D\big{(}z^{t}\big{)}] \tag{8}\]  
where \(\psi\) is a feature extractor, and \(z\) represents the extracted features. The accuracy of the domain discriminator depicts the difference of the edge distribution of the two data domains. The DANN tries to deceive the domain discriminator by using the feature extractor, thereby reducing the difference in the edge distribution. The overall loss is expressed as follows:  
\[L=\min_{\psi,h}\mathbb{E}_{(x^{s},y^{s})\sim\hat{s}}L_{CE}(h(z^{s}),y^{s})+ \lambda L_{DANN}(\psi) \tag{9}\]  
With this adversarial learning strategy, domain-invariant features can be learned when domain discriminators cannot distinguish correctly. In order to realize knowledge transfer effectively, a DANN is adopted as the domain adaptation method.
## 3 Results  
### NPCs Expanded Inside the Nichoid Show Differences in Non-Coding RNAs Expression  
This work aims at characterizing the deregulation of non-coding RNAs expression in NPCs expanded inside the Nichoid with respect to standard floating conditions. NPCs were grown for seven days both in standard floating conditions [39; 41; 64] and inside the Nichoid [32]. Nichoid-grown NPCs expanded themselves in the niches showing different morphology to that of the typical spheroids' conformation (Figure 1A). In particular, cells expanded in standard floating conditions autonomously form neurospheres, while in the scaffold they create a more uniform carpet of cells. This is even more evident from the ESEM images which allow to see in detail the interactions between the 3D scaffold structure and NPCs (Figure 1A). Cells interact directly with the structure and geometry of the scaffold, as can be seen from the presence of cell extensions [32]. Indeed, we demonstrated that over 50% of NPCs grown inside the Nichoid present protrusion in contrast with NPCs grown in standard floating conditions, where the cytoskeleton surrounds cells' nuclei without protruding from the cell core [32]. This highlights the impact of the Nichoid on cellular morphology and mechanotransduction processes [32]. Thus, from these first qualitative evidences we decided to investigate how the Nichoid exerts its effects on the non-coding transcriptome and we performed a whole transcriptome analysis of NPCs grown in standard floating conditions or inside the Nichoid [32]. Through RNA-Seq we identified 1934 differentially expressed coding and non-coding RNAs (DE RNAs), 1577 were coding genes, whereas 357 were non-coding genes [32]. Heatmap and PCA analysis of the differentially expressed non-coding RNAs (ncDE RNAs) in NPCs expanded inside the Nichoid are shown in Figure 1B and different expression profiles can be visibly distinguished (Figure 1B). The volcano plot here reported highlights the specific non-coding DE RNAs deregulation (Figure 1C). Among the 357 non-coding RNAs that emerged as differentially expressed, 147 were up-regulated and 210 down-regulated. A detailed classification of the functionally present ncDE RNAs is reported in Figure 1D with detailed bar graphs: besides the \"unannotated class\" represented in orange, which refers to regions that require further experimental validations for the presence of protein coding genes, the most represented functional class is that of lncRNAs represented in violet, with 102 deregulated genes (subdivided in 27 antisense, 3 bidirectional promoter lncRNAs, 60 lncRNAs, and 12 sense intronic), followed by the 91 pseudogenes in green, and lastly 46 small ncRNAs in light blue (Figure 1D).