esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 1 Introduction
In this research, the proposed controller, fast fractional-order terminal sliding mode control (FFOTSMC), uses the fractional-order derivative on both the sliding surface design and the control/reaching law. The new control law is conducive to reaching the sliding surface faster than the previously studied one (FOSMC), designed using the integer-order derivative only. This fast reaching law is associated with fast convergence. The controller is designed for a seven-degree-of-freedom (7-DOF) robot manipulator. Due to its complex dynamics, only using FFOTSMC is not enough to have an ideal performance. The high frequency switching in the control input of FFOTSMC can ensure the robustness of FFOTSMC; however, it could make the robot oscillate. In this research, we apply the sliding mode perturbation observer (SPO) [13] to estimate the perturbation. The estimated perturbation is used to compensate for the uncertainties and disturbances in the controller design. It reduces the switching gain of the control input of the FFOTSMC, which decreases the amplitude of chattering. SPO is efficient with high estimation accuracy for a low-frequency vibration, which has been demonstrated in electric motors and hydraulic cylinder actuated systems [14; 15]. Through the compensation for the uncertainties and disturbances, the control precision is also improved. The contributions of this paper are summarized as follows. 1. The new proposed controller, FFOTSMC, uses fractional-order derivatives on both sliding surface design and sliding control/reaching law. It has faster convergence and more outstanding tracking accuracy than FOSMC, which has been researched previously. 2. The new controller FFOTSMC was designed to control a real 7-DOF robot manipulator for trajectory tracking. SPO is used to estimate the disturbance and uncertainties for reducing the chattering, ensuring the controller can be implemented in practice. 3. Stability is analyzed using the Lyapunov functions for fractional-order systems [16]. The paper is organized as follows. In Section 2, the 7-DOF robot manipulator modeling, fundamental definitions, two useful properties, and one lemma for proving the stability are presented. Section 3 describes the stability analysis of the new proposed FFOTSMC with SPO (FFOTSMCSPO). In Section 4, simulations using the proposed controller and four different controllers are compared in MATLAB for a single-input and single-output (SISO) system. Then, comparative experiments on the robot manipulator are discussed. The conclusion of this work is shown in Section 5.
## 4 The Proposed Detection and Restoration Framework
### Restorer Design  
The designed AE for restoration also comprises two parts: an encoder and a decoder. The structure of the encoder part follows a standard CNN architecture. The decoder part is primarily responsible for reconstructing the output of the encoder back to the shape of the original input. The design of the decoder is closely related to that of the encoder, but it incorporates batch normalization and dropout layers at each step. Batch normalization accelerates training and enhances model generalization, while dropout prevents overfitting and increases model robustness. The AE structure tailored for restoration is illustrated in Figure 4.  
The primary role of the decoder in the AE designed for restoration is to learn how to recover the original high-dimensional data from the hidden low-dimensional representation. Through this process, the decoder acquires the high-level features of the data and is capable of generating new data that resemble the input data. By utilizing convolutional transpose layers, the decoder can convert the low-resolution features from the encoder output into high-resolution outputs. In this process, the decoder learns how to recover fine-grained details from low-resolution features. With multiple levels of upsampling, convolution, batch normalization, and dropout operations, the decoder can capture features at different scales, enabling it to better restore the original data. Therefore, the design of this decoder allows the AE to effectively learn and generate high-quality data that closely match the input, facilitating the restoration of adversarial signal samples to their original forms.  
Figure 3: Designed detection AE.
## 3 Discussion  
Light-matter interaction at the nanostructures is further examined at three wavelengths: 450 nm, 540 nm and 620 nm where the spectra strongly depend on the incident polarization. Electric field amplitude distribution at the middle of the rods is shown in Figure 3. The incident electric field disturbs electrons' density in the vicinity of aluminum rods. Because of the anisotropic geometry of the rods, generation of this localized surface plasmons depends on both wavelength and polarization. Each rod takes charge of one resonance and forms a dip in the reflection spectrum when the incident polarization is parallel to its dipole direction. In other words, only certain polarization induces localized surface plasmon strongly in a given wavelength despite the cross-talks between nanorods. Since field oscillation leads to high absorption, the resultant reflection has a dip and generates a complementary color.  
According to the [PERSON]'s principle [(39; 40)], the reflection pattern of nanorods is identical to the transmission pattern of nanoslits. Subwavelength-perforated metal film offers a structured transmission spectrum with multiple peaks and dips, which is also known as extraordinary optical transmission [(41)]. Therefore, using the concept of [PERSON]'s principle, we can also design transmission type metasurface composed of three nanoslits on aluminum film as illustrated in Figure 4a,b. Transmission type metasurface follows the same story as the reflection type. Three different nanoslits with different resonance wavelength can be selectively excited by matching incident polarization to the dipole moment direction. Figure 4c-e show the polarization dependent transmission spectra and the corresponding colors. Simulated color shows tunability of transmission type color printed upon polarization of incidence. Note that the transmission efficiency is low whereas the reflection type metasurface shows high reflection spectra originating from metal-insulator-metal structure.  
In conclusion, an active metasurface which produces colors dependent on incident polarization is presented. The reflection type metasurface possessing three different nanorods responds sensitively under rotating incident polarization. We numerically and experimentally demonstrate that the reflection type metasurface covers a wide range of colors by a single design. Furthermore, we present the complementary design of metasurface for transmission type. These two types of active metasurfaces show potential to be integrated with conventional liquid crystal technology thanks to its polarization dependent optical responses. With the compatibility with electrically-driving technologies, this active color generating metasurface may be a promising candidate for future generation display with ultra-high resolution and long-lasting lifetime.  
Figure 3: Calculated electric field distribution at the middle of the rods at (**a**) 620 nm, (**b**) 540 nm and (**c**) 450 nm. White arrows denote polarization angle of incidence.
## 4 Evaluation
### Attack Detection Results  
This section assesses the detection capabilities of our contextual bandit-based reinforcement learning approach through standard evaluation metrics, namely, accuracy, precision, recall, and F1-score. The findings, ordered by F1-score, are presented in Table 3.  
These outcomes show that, within the top five policies employed by our approach on subset A of the CSE-CIC-IDS2018 dataset, the accuracy remains above 86%, while for subset B it surpasses 95%. In particular, when using Thompson sampling on subset A, 0.9 achieves the highest detection effectiveness among all considered reward strategies. Nevertheless, as indicated in Table 2, 0.9's cost-effectiveness lags behind that of the top five policies, showing an approximately 52% increase in cost relative to \(log(k)\). Notably, \(log(k)\) consistently ranks among the top five best-performing policies across all four evaluations (i.e., both subsets combined with both models), regardless of the selection strategy (Thompson, UCB, or UCBE). The Thompson-driven \(\rho^{d_{P,A,t}}\) policy attains the second-best accuracy in three out of the four scenarios. Overall, the experiments demonstrate that detection efficacy remains relatively stable across the two CSE-CIC-IDS2018 subsets for all tested policies, exhibiting only about a 1% fluctuation in accuracy. Lastly, although the Softmax selection approach does not secure a place in the top five, it still provides some degree of adaptability, as evidenced by the relationship between triage rate and false predictions across different parameter settings.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cluster-Based Routing
#### 3.4.3 Comparative Analysis  
There are many similarities between the DSCB and CRPBA protocols. As shown in Table 5, both sinks are used to balance the load and energy consumption of single sink and improve network performance. In DSCB [30], cluster head is pre-designed, considering the residual energy of nodes, transmission power and distance between sink, to choose the next hop. Whereas, the CRPBA [31] uses the residual energy of nodes and distance between sink to select the cluster head. The two-hop transmission also transfers data through a cluster head. Compared with the DSCB protocol, the energy efficiency of the CRPBA cluster head is slightly deficient. In theory, cluster-based protocols can solve network hotpot issues, well, but neither of them can solve the problem, the problem lies in the methods of cluster head selection and cluster size determination. However, the research of this problem in WSN has been quite mature, and can be properly introduced into WBAN research.  
Compared with other routing methods, the research on cluster-based routing methods is not very mature. Some existing studies usually refer to the methods of WSN, which are not suitable for WBAN. The research on WBAN is still insufficient and is not perfect, there is still a lot of research space for cluster-based routing in the future. The problem that needs to be solved is how to choose the appropriate cluster head and determine the size of cluster, to realize the real balance of network energy consumption, improve the success rate of data transmission, and solve the reliability transmission of the edge nodes and network hotpot issues.
## 3 New Hybrid Optimization Algorithm
### Hybrid IC-GWA Optimization Approach  
Hybrid algorithms are created to increase the performance of an optimization algorithm. They combine the advantages of two or more algorithms. The HIC-GWA is a combination of two evolutionary algorithms where the GWO is used to enhance the exploration ability of the ICA as shown in Figure 1.  
In this proposed approach, ICA is initialized first to solve the OPF optimization problem. The assimilation and revolution of colonies, imperialist competition, elimination, and uniting empires are performed. The best solution of ICA is calculated as an initial condition of the GWA. The solution of the GWA is saved as the best value if it is less than the ICA's solution. The simulation continue until the stop condition is satisfied. The converged answer is achieved after termination of the algorithm.  
The following steps show how to use the HIC-GWA to solve the OPF problem:  
1. The power system data is specified. The HIC-GWA parameters are determined.
2. Initialize the countries randomly, calculate their costs, and use assimilation.
3. Revolution.
4. Exchange positions between imperialist and colony if it has a lower cost.
5. Unite similar empires.
6. Calculate the total cost of all empires.
7. Imperialist competition.
8. Discard powerless empires.
9. Use solution obtained by ICA as initial condition for GWA.
10. The lower solution between ICA and GWA is saved as best solution.
11. Go to step (ii) if the stop condition is not satisfied, otherwise, finish simulation.  
Figure 1: Flowchart of the proposed hybrid imperialist competitive and grey wolf algorithm (HIC-GWA).
# True Equalization of Polarization-Dependent Loss in Presence of Fast Rotation of SOP +
###### Abstract  
In an optical fiber communication system, polarization-dependent loss (PDL), referring to polarization-dependent optical power attenuation, might be a main system performance limiting factor. PDL causes a difference in signal power and imbalance of optical signal-to-noise ratio (OSNR) between the two polarization branches. The OSNR asymmetry will eventually deteriorate the overall system performance. At present, most of the equalization methods of PDL in the literature can only accomplish power imbalance equalization and are, thus, not the true equalization of PDL. Besides, in some extreme environments, like lightning strikes, Kerr and Faraday effects would generate ultra-fast rotation of state of polarization (RSOP), as fast as mega-radian per second. The fast RSOP in combination with PDL makes the eigenmodes of the PDL randomly time-varying, which will put a heavy burden on the equalization module of the receiver. Faced with this dilemma, in this paper, we propose a scheme for the true equalization of PDL in the presence of fast RSOP through an approach based on the combination of the polarization-time code and Kalman filter. The proposed approach is proved to be very effective and provides significant performance. With a 1.2 dB OSNR penalty, it can jointly equalize large PDL (7 dB) and fast RSOP (up to 1 Mrad/s).  
fiber optics communications; polarization; Kalman filter
# Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning
## 3 Literature Review of GANS for SSL
### Manifold Regularization-Based Methods  
Many recent GANs for SSL used manifold regularization. For example, [PERSON] et al. [41] presented a methodology involving using the ability of GANs to model the manifold of natural images to perform manifold regularization by leveraging the Monte Carlo approximation of the Laplacian norm. They claimed that this regularization would encourage classifier invariance to local perturbations on the image as points close to the manifold would be assigned similar labels. For their work, the authors made use of the feature matching semi-supervised GAN presented in [32] as the base GAN. The primary challenge in this approach is the estimation of the Laplacian norm, for which they present an approach on the basis of the assumptions that GANs can model the distribution as well as the manifold of images. Based on these assumptions, their technique involved training the GAN on a large number of unlabeled images, after which they inferred that the GAN approximated the marginal distribution over images that could then be used to estimate the Laplacian norm over a classifier using Monte Carlo integrations with samples drawn from the space of latent representations of the generator. Furthermore, the second assumption allowed the manifold on the image space to be utilized to compute the gradient in the form of a Jacobian matrix with respect to the latent representations. Based on this, the classifier loss is shown in Equation (5)  
\[\parallel f\parallel_{L}^{2}=\int_{x\in M}\parallel\
abla_{M}f(x)\parallel^{2 }\mathrm{d}P_{X}(x)\approx\frac{1}{n}\sum_{i=1}^{n}\left\|\
abla_{M}f\Big{(}g \Big{(}z^{(i)}\Big{)}\Big{)}\right\|^{2}\approx\frac{1}{n}\sum_{i=1}^{n}\left\| \left|f_{z}f\Big{(}g\Big{(}z^{(i)}\Big{)}\Big{)}\right\|_{F}^{2} \tag{5}\]  
A similar approach was taken by [PERSON] et al. [41; 42], where the Monte Carlo integrations were used to estimate a variant of the Laplacian norm seen in Equation (6).  
\[\Omega(f)=\int_{x\in M}\|\
abla_{M}f\|_{F}\mathrm{d}P_{X} \tag{6}\]  
More recently, the SelfAttentionGAN [43] made use of manifold regularization as part of a self-attention mechanism for a semi-supervised GAN. A variable attention unit was used as part of the attention-based GAN architecture, while manifold regularization based on [42] was added as an additional regularization term to the loss function to make full use of unlabeled samples using a Monte Carlo approximation.  
An interesting technique was proposed by SVMGAN [44], that tried to solve the issue of GAN-based SSL models being sensitive to local perturbations by introducing a  
Figure 5: The EC-GAN Architecture.  
discriminator using a scalable support vector machine (SVM) classifier with manifold regularization, while SVM was used due to its nature of performing well in situations with small datasets, which fit the semi-supervised problem well. Furthermore, the use of manifold regularization was reported to force the discriminator to be resistant to local perturbations.
## 4 Experiment Details and Results
### Dataset Overview and Preprocessing  
The dataset that was used in the context of this publication is a dataset for movie recommendations, and it was taken from MovieLens [32]. The initial dataset of ratings consists of 9724 items (movies) and 610 users. As the dataset is very sparse and there were limited computational resources, the experiments were executed for a subset of items. More specifically, movies that had 10 or less ratings were filtered out, and as a result only 2121 movies were kept.
## 2 Materials and Methods  
### Control Strategy for ESS  
To control voltage in a LV distribution line, a shunt-connected compensator is connected as shown in Figure 1. The distribution network is modeled as a lumped parameter model, characterized by source voltage _Us_ and short circuit impedance \(Z_{k}\). The compensator is a 3-phase Voltage Source Inverter (VSI) equipped with any type of energy storage on the DC side. It can provide flexible active and reactive current injection to the grid and is represented by a controlled current source \(l_{c}\). Together with the load \(Z_{l}\), it is connected at the Point of Common Coupling (PCC).  
Figure 1: Simplified equivalent circuit of shunt active compensator installed in distribution line.  
With appropriate control of injected current, the compensator can induce a voltage drop \(\Delta U_{s}\) along the line impedance and regulate the \(U_{PCC}\) voltage. Conventional shunt-voltage-regulator methods use reactive power to induce a voltage drop across the line reactance \(X_{k}\), as shown in Figure 2a. However, in LV grids, especially in rural areas, line resistance \(R_{k}\) is a dominant component of the line complex impedance. In this case, reactive power induces voltage drop orthogonal to the PCC voltage, which results in additional losses along the line.  
This drawback can be eliminated, if the compensator injects proper values of active and reactive current components. This situation is shown in Figure 2b. Here, the voltage drop induced by the injected current is parallel to the source voltage. This way, voltage regulation is achieved with minimum compensating current and, as a result, with minimum compensator apparent power, reduced line losses and no phase shift. This also helps to minimize storage capacity compared to voltage-control methods, which use only active power.  
The goal of minimizing the compensating current requires information on grid-impedance parameters to calculate current set points for the compensator. Therefore, a grid-impedance-estimation method implemented in the converter control system is adopted [31]. It is a type of perturb and observe method: prior to the operation, the converter injects the fundamental components of active and reactive current and, having measured voltage response, calculates the complex grid impedance seen from the compensator terminals. It is further utilized for tuning of controllers and splitting the current into active and reactive components.
## 3 Results
### Optimisation Problem Formulation  
The scheduling problem is formulated as a combinatorial optimisation task that assigns events to feasible time-resource slots. At the same time, it minimises scheduling conflicts and disruptions.  
Let  
\(E=\{e_{1},e_{2},\ldots,e_{n}\}\) denotes the set of events (e.g., classes or exams),  
\(T=\{t_{1},t_{2},\ldots,t_{m}\}\) denotes the set of possible time slots,  
\(R=\{r_{1},r_{2},\ldots,r_{k}\}\) denotes the set of resources (e.g., rooms, instructors),  
\(x_{e,t,r}\in\{0,1\}\) be a binary decision variable defined as:  
\[x_{e,t,r}=\begin{cases}1&\text{if event $e$ is assigned to time slot $t$ with resource $r$,}\\ 0&\text{otherwise.}\end{cases}\]
## 4 Discussion  
In this paper we build on the current knowledge of DPOAE serial measurements by exploring DPOAE measurements at conventional frequencies (1-8 kHz) and high-frequencies (>8 kHz) to capture the earliest changes to cochlear function in individuals undergoing plain-based treatments. Platinum derivatives, such as cisplatin, oxaliplatin, and carboplatin, are often used as treatment for cancer, and are known to cause damage to the auditory system impacting the outer hair cells (OHCs) in the basal end of the cochlea first. Several studies have examined the repeatability of high frequency DPOAEs as well as the sensitivity of behavioral and physiologic measures of auditory function in response to suchotoxic damage. The clinical utility of high-frequency DPOAEs using suitable hardware, calibration, and signal delivery have been accomplished (e.g., [39]) and are further highlighted here through clinical case studies. Most notably, here we explore the influence of platinum derivatives on high frequency DPOAE paradigms in 12 individuals undergoing chemotherapy. DPOAE paradigms included gross (f\({}_{2}\) = 16-2 kHz; f\({}_{2}\)f\({}_{1}\) = 1.2; L\({}_{1}\)/L\({}_{2}\) = 62/52 dB FPL) and concentrated (f\({}_{2}\) = highest frequency; f\({}_{2}\)/f\({}_{1}\) = 1.2; L\({}_{1}\)/L\({}_{2}\) = 62/52 and L\({}_{1}\)/L\({}_{2}\) = 72/72 dB FPL) frequency sweeps, level sweeps (L\({}_{2}\) = 57, L\({}_{1}\) = 27-67 dB FPL and L\({}_{1}\) = 57, L\({}_{2}\) = 12-67 dB FPL), ratio sweeps (f\({}_{2}\)/f\({}_{1}\) = 1.1-1.25; L\({}_{1}\)/L\({}_{2}\) = 62/52 dB FPL).  
Moreover, this paper aims to explore how clinical practice for ototoxic monitoring may be enhanced by emerging DPOAE applications, including use of high-frequency stimuli to assess basal cochlear function, advanced calibration techniques to ensure stability of serial measurements, and targeted monitoring protocols to gauge OHC function. These DPOAE applications permit visualization and quantification of the earliest signs of underlying cochlear damage which may go undetected with conventional methods (behavioral hearing thresholds as well as standard DPOAE measures up to 8 kHz).