esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Graph Convolution-Based Forecasting  
Because of the outstanding performance of graph convolutional networks in dealing with correlation between sequences, some researchers apply graph convolutional networks to time series predictions. Such graph-convolution-based forecasting methods are widely used in the field of traffic flow forecasting. [PERSON] et al. [9] used graph convolution to extract valuable patterns and features in the spatial domain for traffic forecasting. [PERSON] et al. [10] captured spatial dependence by bidirectional random wandering on graphs and temporal dependence by Seq2 Seq. [PERSON] et al. [11] solved the problem by transferring the time series into the spectral domain via the graphical Fourier Transformation. This type of graph-convolution-based forecasting method is largely fixed in structure. It usually consists of a graph convolutional network for extracting spatial correlation and a convolutional neural network [12; 13; 14] or recurrent neural network [15; 16] for extracting temporal correlation. This idea for forecasting also provides advanced theoretical guidance for new types of urban water demand forecasting. Currently, [PERSON] et al. [17] started to implement regional water demand forecasting based on graph structure data through a combination of graph convolution and recurrent neural networks. However, all the aforementioned methods rely on predefined static graphs and thus, lack the ability to model the dynamic correlation of time series. Recently, researchers have been working on dynamic graph generation for two research directions, feature embedding [18; 19; 20; 21] and multi-stage graph training [22], respectively.  
In this paper, we construct a dynamic graph generation module to capture the dynamic correlation of time series.
## 5 Experimental Study
### Comparative Study  
A comparison of the proposed \(MPSA\) scheme with a few well-known existing RDH schemes, reported in [21; 22; 27; 41; 42; 43; 44; 45; 46; 47; 48], is discussed in this section. Table 4 gives a comparative study of the embedding rate considering well-known images such as _airplane_, _baboon_, _boot_, and _peppers_. All three images, except the _baboon_ image, are recovered correctly when \(8\times 8\) pixels is set as the processed block size, leading to an embedding rate of 0.03125 bpp. _Baboon_ is a highly textured image that was restored successfully with \(16\times 16\) pixels or more as the processed block size. Hence, the highest embedding rate on _baboon_ was reduced to 0.0078 bpp. Still, it gives an acceptable rate, superior to many other existing schemes.  
The results listed in Table 4 highlight a _better_ embedding efficiency of the proposed \(MPSA\) scheme in comparison to other well-known existing RDH schemes for encrypted images. It should be noted that the comparison of the schemes is not just based on the embedding rate, but all the compared schemes provide a complete recovery with a PSNR of \(\infty\) and SSIM of 1. It is noteworthy to mention that none of the schemes other than the proposed scheme retains the entropy of the encrypted image and, similarly, the histogram of the encrypted image despite embedding the secret information.
# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 2 Theoretical Background
### IoE Taxonomy  
Taxonomies extend beyond shared vocabularies. Taxonomies classify concepts and objects \"into a hierarchically ordered system that indicates relationships\" [22]. As interpretations of reality, they provide structured frameworks for organizing knowledge into hierarchical relationships between terms [14]. Taxonomies facilitate the exploration of evolving theories, allowing researchers to examine their fundamental elements and interconnections within a research domain [23].  
[PERSON] et al. [14] underscore the pivotal role of IoE paradigms, especially the IoE architecture when examining IoT devices. IoE's orchestration of network intelligence includes the knowledge imparted, types of entities, observation attributes, and technological capabilities [14]. Such architecture is crucial for fostering powerful IoT applications that promote collaboration between humans and devices. Current research in IoT taxonomies mostly focuses on Resilience [24; 25; 26; 27], Security [27; 28; 29; 30; 31; 32; 33], and Industry [34; 35; 36; 37].  
IoE architecture can be comprehensively delineated across four categories [14], as shown in Figure 1. The Knowledge category concentrates on generating knowledge and the conduit of information in IoE applications. Within this, dimensions span from _explicitness_, reflecting the codification degree of knowledge, to _action_, representing the practical application of this knowledge. The Type category restricts the variety of data procured during IoE observations. It emphasizes sensor types, data natures, and abstraction levels, which are fundamental in grasping data attributes and their utility in IoE frameworks. The _Observation_ category refers to the methodologies of data sensing and acquisition, accentuating the spatial, temporal, and contextual dimensions that inform the context and usability of the data in IoE applications. The _Capability_ category underscores the technological prerequisites essential for IoE application design, encompassing communication, computation, and storage faculties, which collectively provide insight into the technical demands of IoE applications and the strategies to address them [14].  
In addressing the nuances of the IoE taxonomy, mentioning the Capability category is noteworthy, underscoring the technological requisites for sculpting IoE applications. This category includes communication, storage, and computation. Within computation is the processing capability, referring to the ability of IoE applications to process data and make decisions based on that data (Cloud, Fog, Edge) [14]. Understanding this category is crucial for determining the technical needs of IoE applications and designing them accordingly. All articles were analyzed using this IoE taxonomy to study collaborative aspects, which we discuss in subsequent sections.  
Figure 1: The IoE Taxonomy defined by [PERSON] et al. [14].
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### NLoS Channel Model  
The NLoS channel in VLC generally includes the Channel Attenuation Coefficient \(H_{ij}\), which describes signal attenuation due to multiple reflections and absorption by the surroundings, and the Angular Dispersion \(D(\theta_{ij},\theta_{r})\), which models how the reflected signal is dispersed and affects its propagation direction.  
The angle of incidence at the receiver also affects the received power, as VLC receivers have an efficiency that depends on the angle of incidence of the light, \(\cos(\psi_{r})\), where \(\psi_{r}\) is the incidence angle of the signal at the receiver, which can be represented as follows:  
\[\psi_{r}=\arccos\left(\frac{\mathbf{n}_{r}}{|\mathbf{r}||\mathbf{n}_{r}|} \right), \tag{17}\]  
where \(\mathbf{r}\) is the direction vector of the incoming signal, and \(\mathbf{n}_{r}\) is the normal vector to the receiver plane.  
Incorporating all these factors into the received power equations gives, for a flat mirror,  
\[P_{r,ij}^{\text{flat}}=P_{s}\frac{A_{e}}{d_{ij}^{2}}\cos^{m}(\theta_{ij})R( \theta_{ij})T_{ij}(\lambda)H_{ij}\cos(\psi_{r}). \tag{18}\]  
For a convex mirror,  
\[P_{r,ij}^{\text{convex}}=P_{s}\frac{A_{e}}{d_{ij}^{2}}\cos^{m}(\theta_{ij})F_ {\text{conv}}(R_{ij})R(\theta_{ij})T_{ij}(\lambda)H_{ij}\cos(\psi_{r}). \tag{19}\]
## 2 Beam Shaping in High-Resolution Headlamp Systems  
A modulator-based high-resolution headlamp is composed of the light source, the light modulator, and corresponding optical elements. The light source varies from halogen bulbs, discharge Xenon lamps, LEDs, and laser diodes, emitting light with different specifications and distributions. The light modulators mentioned in Section 1 have rectangular shapes with different aspect ratios. For attaining a good optical efficiency and make full use of the resolution of a modulator, the emitted light from any light source should be pre-shaped to illuminate the light modulator's active area with a small overfill ratio.  
[PERSON] et al. introduce an LCD headlamp design with RGB (red, green, and blue) laser diodes as the light source in [8]. The laser beams from all diodes are collected and shaped to illuminate the entire LCD surface homogeneously. After that, the projection optics project the transmitted light. [PERSON] introduces a concept to supplement a high-resolution module to Mercedes's multi-beam headlamp [11]. The high-resolution light distribution is generated by a DMD with LEDs as the light source. It can realize the near field homogeneous on-road projection, for example, adverse weather notification. Mercedes has brought this headlamp into the market. The idea of complementing a high-resolution module with a smaller angular range to an entire illumination range is also discussed in [12]. An example of this concept is illustrated in Figure 1 from a driver's sight.  
Besides the homogeneous distribution, there are designs to realize a central hotspot in the illumination pattern. The advantage of this inhomogeneous illumination concerning regulations and system efficiency is discussed in [13,14]. For the inhomogeneous illumination pattern, [PERSON] et al. present an approach that uses an ellipsoid reflector with illumination optics to realize the non-uniform distribution pattern on the DMD surface. [PERSON] et al. achieve a similar inhomogeneous pattern using all transparent optics with a white LED [15,16]. [PERSON] and [PERSON] introduce another optical concept of a DMD-based high-resolution headlamp, in which the DMD chip is homogeneously illuminated, but a pincushion distortion projection lens set is used after the DMD chip to centralize the majority of light in the front center [2,17].  
In the above discussed optical concepts of headlamp systems, the light modulators are illuminated by the incident light from the light source either uniformly or non-uniformly. Beam pre-shaping methods before the modulator are requisite in order to achieve the designs of different headlamp systems. It is also necessary to make a good optical efficiency and the lighting performance of the high-resolution headlamps' functionalities with the pre-shaping methods.  
Figure 1: Example of the illumination distribution of a high-resolution headlamp, which includes a general illumination area and a high-resolution area.
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 4 Results and Discussion  
This section presents and discusses the effect of using UAVs in the evacuation process, presenting measurements of the average evacuation time and mortality resulting from the use of partial and full guidance models with different crowd densities. When the hall is partially full, CD \(\leq\) 0.5; when the hall is full, CD = 1; and when the hall is crowded, CD = 2; the speed of pedestrians in our model was varied accordingly. In addition, we examined two positions for the exit doors (opposite and adjacent), and three positions of UAVs (middle, random, and near exit) with partial guidance, and two positions of UAVs (middle and random) with full guidance.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### Spatiotemporal Data Refinement and Categorization
#### 4.1.2 General Outliers  
In addition to addressing GPS outliers, we also tackled general outliers present in the dataset. These outliers encompassed various aspects, such as the number of passengers, pickup and drop-off times, and distance traveled. To identify and handle these outliers, we employed statistical methods and visual inspection. By applying filters and carefully reviewing the data, we distinguished genuine outliers from data errors, ensuring the accuracy and reliability of our refined dataset.
# Stereo-DIC Measurements of a Vibrating Bladed Disk: In-Depth Analysis of Full-Field Deformed Shapes
###### Abstract  
Vibration measurements of turbomachinery components are of utmost importance to characterize the dynamic behavior of rotating machines, thus preventing undesired operating conditions. Local techniques such as strain gauges or laser Doppler vibrometers are usually adopted to collect vibration data. However, these approaches provide single-point and generally 1D measurements. The present work proposes an optical technique, which uses two low-speed cameras, a multimedia projector, and three-dimensional digital image correlation (3D-DIC) to provide full-field measurements of a bladed disk undergoing harmonic response analysis (i.e., pure sinusoidal excitation) in the kHz range. The proposed approach exploits a downsampling strategy to overcome the limitations introduced by low-speed cameras. The developed experimental setup was used to measure the response of a bladed disk subjected to an excitation frequency above 6 kHz, providing a deep insight in the deformed shapes, in terms of amplitude and phase distributions, which could not be feasible with single-point sensors. Results demonstrated the system's effectiveness in measuring amplitudes of few microns, also evidencing blade mistuning effects. A deeper insight into the deformed shape analysis was provided by considering the phase maps on the entire blisk geometry, and phase variation lines were observed on the blades for high excitation frequency.  
bladed disk vibration; digital image correlation; low-speed cameras; downsampling; mistuning +
Footnote †: journal: Applied Sciences
# A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions
## 2 Topology, Operation Principle and Optimization Strategy of Proposed PV/ES System  
### Topology Configuration  
The topology of the proposed cascaded modular PV/ES system is presented in Figure 2. Each PV/ES module consists of a PV module and a bidirectional DC/DC converter supplied by an ES module. The PV/ES modules are connected in series to satisfy the voltage level of DC bus, and the DC bus is connected to a power grid via a DC/AC converter. The control scheme of the proposed system is divided into two layers, including a converter control in the bottom layer and an energy management system (EMS) in the top layer. In the converter control layer, the bidirectional DC/DC converter is controlled in order to achieve the MPPT for an individual PV module and to compensate the current difference between the PV module and DC bus. As each PV module is regulated by an ES module through a DC/DC converter, the hot spots and hot strings phenomena can be avoided. The DC/AC converter controls the grid-connected power according to the reference value obtained by the EMS. In the EMS layer, the power reference is generated by the proposed energy optimization strategy according to the voltages and currents of the PV modules and the voltages and states of charge (SOC) of the ES modules.  
Compared with the conventional topology shown in Figure 1a, the multi-peak characteristic of the PV power curve in the proposed PV/ES system under partial shading conditions is eliminated, and all PV modules can operate at respective maximum power points. Compared with the conventional topology shown in Figure 1b, only a part of power from the PV module flows through the DC/DC converter in the proposed PV/ES system, reducing the power loss and construction costs. Compared with the conventional topology shown in Figure 1c, the DC/DC converters in the proposed PV/ES system are not directly connected to the DC bus, thus the voltage stresses of the power switches and devices can be greatly reduced.
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 3 Deep Neural Network and Entropy to Multiple PUE Detection in Cooperative MCRN
### Cooperative Spectrum Sensing Model  
The designed model has a malicious _PUE attack_, some SUs and PUs in the network, and an FC for the centralized CSS. There is one radio frequency channel of the PU detected by the SU, another one of the PUE attacks detected by the SU, and one common channel where the SUs communicate with the FC; these signals can be seen in Figure 1.  
For cooperative spectrum sensing, each SU detects the _PUE attack_ based on the entropy and DNN techniques described in the following subsections and informs the FC of the results. The FC takes an OR decision that helps the system detect any attack within the network coverage and the assigned frequencies. The FC manages the frequencies for detection and the state of each frequency in the MCRN.
## 4 Results and Discussion: Finite Element Validation  
This section shows the results obtained by applying the analytical method described in Section 3 for both four-pole and six-pole SynRMs. In order to provide the results in a clear fashion, this section is divided into four subsections. The first presents the machine evaluations without considering any type of skew, hereby called skewless machines; and the following three sections address machines with different slide number (\(N=2\), \(N=3\) and \(N=4\)). All the results are obtained by means of 2D and 3D FEA simulations carried out in the commercial package ANSYS Electronic Desktop. The simulation time was chosen to evaluate a whole period of the machine's torque ripple.
# Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces
## 2 The Proposed Method
### Workstation Building and Force Control
#### 2.1.2 Force Control in Surface Polishing
It uses motion supervision, which is a highly sensitive feature in robot for the supervision of load, jam, and collision. If it is kept very sensitive, the movement may trip for a large force acts on the robot. Depending on the polishing process force, the sensitivity level should be raised until no trigger occurs. If any collision detected under the sensitivity level, the robot will quickly stop and move back a short distance to remove residual forces as shown in Figure 6.  
Another separate setup is for ACF, which controls the normal contact force. The block diagram of the ACF setup is shown in Figure 7. It is a hybrid position/force control system. It consists of a high priority control and a core force feedback regulation unit. Its Ethernet-based interface allows the user to set the desired force and constantly provides the status of the actual force and position displacement. Figure 5: Different workpiece motions in polishing: (**a**) workpiece moving against wheel rotation direction and (**b**) workpiece moving in wheel rotation direction. ACF does not interfere in the predefined robot movement and uses the sensor-based feedback to measure output force and position. Any error (\(F_{error}\)) between set force (\(F_{set}\)) and actual force (\(F_{actual}\)) can be found by comparing these forces as in Equation (5). The corrected force and its corresponding position are asserted by an autonomous pneumatic actuator, which quickly compensates any abrupt change in the force and provides dexterity and sensitivity for high-speed precision surface treatment. Additionally, it offers gravitation compensation and provides constant force even with changing orientations if it is used at the robot end effector. \[F_{error}=F_{set}-F_{actual}. \tag{5}\]  
It is important to note that ACF is a one-dimensional system. When polishing a freeform surface as illustrated in Figure 8, the workpiece surface must be constantly moved by the robot against the polishing tool so that the polishing force remains normal to the tool. The width of the polishing wheel (\(W_{Tool}\)) covers the workpiece surface strip by strip. This proposed setup separates the control of the workpiece position (through the robot) and the control of the polishing force (through the polishing wheel). It has two advantages: first, the passive nature of the end effector avoids mechanical vibrations generated due to the motor rotation. Second, the force control does not affect the payload of the robot and hence, it is easy to implement. Figure 6: Robot movement for the removal of residual forces in case of collision. Figure 7: The block diagram of the active contact flange (ACF).
## 1 Introduction
[27] also confirmed the optimal circular track in a shallow sea environment. [PERSON] et al. [28] comprehensively considered the effects of varying the seafloor transponder array size and the survey line radius on the positioning accuracy and required observation time. The positioning of the underwater stations is subject to the complex ocean environment, and the spatiotemporally varying oceanic physical phenomena, such as the variable sound speed, ocean currents, internal waves, etc., are investigated in positioning [9; 29; 30]. An underwater positioning with system time synchronization and speed uncertainties has also been studied [31]. To construct an UGN, the network configuration and dimensions should be designed appropriately. One possible configuration is to combine various types of acoustic platforms, including seafloor base stations, underwater moored buoys, and sea surface platforms. However, the impact of the floating height of the underwater acoustic equipment on the positioning performance of combined UGNs has not been investigated. In addition, the network dimension is closely related to the separation of seafloor base stations. The impact of changing network dimensions on the positioning performance of an UGN also needs thorough investigation. In this study, we propose an underwater geodetic network that combines three seafloor base stations, one underwater subsurface moored buoy, and a sea surface buoy (\"3+1+1\" configuration). The main purpose of the proposed UGN is to provide kinematic positioning for underwater submersibles applications. To further optimize the UGN, we evaluated the kinematic positioning range and accuracy of the network by simulation tests. By analyzing the separation of the seafloor base stations and the height of subsurface buoys, we evaluated the impact of the network dimensions on the positioning performance. Compared with the network configurations of fewer or more stations, the proposed \"3+1+1\" configuration with the underwater subsurface moored buoy and the surface buoy improved the kinematic positioning performance and cost-efficiency of the UGN. Additionally, the impact of the acoustic transmission characteristics, e.g., acoustic ray bending, multipath effect, transmission loss, etc., on the acoustic signal propagation of UGN was investigated.