esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Internet of Things and Internet of Everything  
The Internet of Things (IoT) is often perceived in the literature as a supporting element to Industry 4.0 and its successors [35]. This idea is based on the communication between objects using the Internet to send and receive messages from other objects. Its development caused \"smart\" objects to arise, such as machines, houses, factories, etc. Naturally, this communication produces large amounts of data allowing the analysis of people's behavior [36]. Exploiting those datasets is one of the ways for marketing activities to optimize the company's actions to increase the competitive advantage. However, the IoT has changed from 2010, and now the ways of connecting objects are much more developed than at the beginning of the Industry 4.0 era.  
The Internet of Everything (IoE) is an advanced form of IoT. Its idea assumes connections among people, processes, data, and objects [37]. IoE was initially defined by CISCO in 2012 as \"a network of networks that reunites people, processes, data, and things in network connections more significant and valuable than ever\" [38; 39]. So, IoE builds an advanced, intelligent network of objects. IoE supports creating new capacities, both for businesses and society [40]. The IoE paradigm covers more than the IoT concept as follows:* The Industrial Internet (II), concerned with data of interest to industry;
* The Internet of People (IoP), especially social networks and connections among people;
* Internet of Services (IoS) [41].  
Moreover, IoE uses nano-networks to combine nanosensors in various objects. Thus, it offers a new concept that previously seemed impossible as the Internet of Nano-things. Furthermore, IoE allows person-to-person and person-to-machine connections with more valuable connections than machine-to-machine communications [42]. Accordingly, IoE exceeds the relationships of objects and integrates them to build a specific interconnected society. Therefore, intelligent services and things define \"everything\" in the IoE concept [38].  
IoE requires advanced capacities and skills in information sharing. Real-time data are collected from diverse objects and can be extracted and analyzed by IoE tools (usually big data analysis tools). Heterogeneous IoE environments can transform robotic devices and human actors into autonomous service agents. Therefore, the design and implementation of IoE applications have become very complex. To increase the effect, artificial intelligence (AI) can be integrated into smart devices, and then people and things can interrelate appropriately in a multi-user and social context environment [43].
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### The Benefits of Telco Cloud  
There are several benefits for Telco cloud [176; 183; 196] and the Figure 11 showed some benefits of Telco Cloud:  
Figure 10: Telco Cloud Architecture.  
Figure 9: Distributed edge cloud topography.  
1. Capability to reduce CAPEX and OPEX costs despite rapid growth in user numbers and network traffic by introducing cloud computing platforms to connect users and applications.
2. By virtualizing traditional equipment and services, the average utilization of telecom equipment is greatly improved.
3. It offers a versatile infrastructure that is highly scalable in order to provide users with highly scalable services.
4. It offers services with high reliability and availability.
5. Energy efficiency.
6. It enhances resource utilization and management.
7. The telco cloud reduces network offloading by processing data closer to the user.
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 3 Model  
As shown in Figure 1, the process of DST involves several steps, including the preparation of input data, feature extraction, model, and assessment.  
Figure 1: Block diagram for the proposed framework.  
Figure 2 illustrates an overview of the model, which consists of four main parts. Firstly, the context encoder encodes the input dialogue and outputs a semantic vector representation. The second part is the slot attention, which extracts slot-related information from the encoded context. The third part is the external attention layer, which models the relationships between slots, allowing the model to learn important information from multiple domains in the dialogue and exclude interference information from other slots.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Introduction  
Numerous types of defects appear in rail tracks, and multiple parameters affect the prediction of crack growth rates and of the defect size at failure [9]. In principle, the _baseline subtraction_ method could be used to detect the insurgence of a defect over time. Baseline subtraction is based on the idea of feeding a system in a given condition with a known excitation, measuring its response, and storing it as a baseline. Each and every modification in the system reflects in a change in its response to the same excitation. Therefore, in principle, the occurrence of any change potentially due to a defect can be detected by comparing each new response to the baseline. In practice, however, it is not easy to establish if such a change is caused by a defect or by a variation of other parameters affecting the system response to the considered excitation. In fact, it should be always kept in mind that, in harsh environments like rail tracks, any variation observed in the propagation conditions of ultrasonic signals can also originate from changes in _environmental and operating conditions_ (EOCs); these conditions include, for instance, temperature, train passing, or maintenance operations [51].  
The continuous monitoring of a rail track can provide better performance results in terms of defect detection probability than a single inspection, though it is influenced by the EOC. In this case, the most important technical challenges are: (a) the development of a permanently installed UGW-based monitoring system for rail defect detection at a reasonable cost per kilometer of track; (b) the development of a defect detection technique able to reliably operate in the presence of real EOCs. Of course, if a suitable facility is unavailable, tests can be performed on operative tracks. The problem is that damaged sections of rails are removed as soon as defects are detected; furthermore, rails must not be damaged or altered. Consequently, it is impossible to test the available prototypes on real cracks: their performance is assessed by using welds or glued masses, both acting as reflectors for guided waves [9].  
Different problems must be addressed in order to achieve an effective automatic monitoring system for rail damage detection. In particular, the following specific technical issues should be investigated: (a) the behavior of a defect growing over time; (b) the influence of the selected transducers on the quality of the received signal; (c) the influence of the changes in rail properties on the propagation of guided waves and the parameterization of these changes; (d) the influence of time varying EOCs on wave propagation and the methods for compensating for these variations; and (e) the identification of the defect detection algorithm and the use of proper methods for assessing its performance.
# Forecasting the Distortion in Solar Radiation during Midday
###### Abstract  
Knowing the fluctuation of solar radiation is essential for reliable and safe operation of power systems with a high share of solar PV power plants. This paper introduces a novel method for forecasting the distortion in global solar radiation during the midday time (during the peak of solar radiation at which the generation of the PV plants is maximum), by analyzing the solar radiation during the very early hours. This paper shows that there is a strong relation between the condition of the solar radiation during the very early minutes of the day (when the generation of the PV plants is low) and the condition of the solar radiation during the midday hours (when the output of the PV plants is enormous). This relation can be utilized to help power systems operators in determining the safe share of PV plants that can be fed to power systems. We analyzed real data of one complete year for two sites in Saudi Arabia to approve this approach. The difference between each two readings was calculated for the hours 7 a.m. and 8 a.m. Then, the negative, zero, and amplitude of the difference was used to formulate a distortion index (_DI_) that predicts the distortion/fluctuation in the global radiation. It was concluded that the _DI_ could classify the days into three categories: clean, distorted, and medium level according to the value of the _DI_. The accuracy of this approach was 85.2% and the error was 14.8%.  
forecasting; PV plants; distortion index; real solar radiation data +
Footnote †: journal: _Sciences_
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 5 Results
### Evaluation of Line Feature Extraction Algorithm  
In this section, datasets numbered \"MH_01_easy\" and \"MH_03_medium\" are selected from the industrial factory environment. In the indoor room environment, select datasets V1_01_easy, V1_03_difficult, and V2_01_easy. Then ten groups of adjacent images were randomly selected from the above datasets for line feature extraction experiment.  
Figure 6 (a) shows the scenario of \"MH_01_easy\", \"V1_01_easy\" and \"V2_01_easy\"; (b) is the line segment graph extracted by the traditional LSD algorithm, in which there are a large number of short, crossed and overlapping line segments. In the calculation of  
camera pose and position, a large number of repeated and invalid line segments occupy the computing resources. Figure 6c shows the fixed threshold method (line segment length > 60) adopted in PL-VINS. Compared with traditional LSD, it removes the most useless small line segments. The comparison of Figure 6c-e shows that the method adopted by PL-VINS also removes a large number of useful structural line segment features. As shown in Table 1, compared with the traditional LSD and PL-VINS methods, the extraction quantity of the PL-VINS decreased significantly, and the average running time decreased by 58.5% and 25.6%.  
By combining the data in Table 1 and the effect of Figure 6, it can be observed that many unstable short line segments can be screened out by the length factor, and then the adjacent, overlapping, and other line segments that repeatedly describe the same geometric feature type are merged through line segment merging. There are great improvements in efficient line segment representation in indoor scenes and in reducing algorithm running time.
## 2 Materials and Methods
### Multi-Scale Feature Mixing Preprocessor  
The Multi-Scale Feature Mixing Preprocessor extracts local and high-level semantic information from images through a multi-layer structure, learns features at different scales, exchanges channel features among scales, and increases parameter sharing and model complexity so as to capture the data distribution and adapt to environmental changes. As illustrated in Figure 1, the proposed model utilizes ResNet-18 as the backbone network. ResNet-18 consists primarily of a 7 \(\times\) 7 convolutional layer, four residual blocks, and a fully connected layer. For simplicity, these five components are referred to as Conv1, Res2, Res3, Res4, and fc1. Each residual block includes two standard 3 \(\times\) 3 convolutional layers, a batch normalization layer, and a Rectified Linear Unit (ReLU) function, with optional pooling layers for downsampling. Downsampling is achieved with a stride of 2 in Conv1, Res3, and Res4, resulting in a reduction of the image scale by half.  
To reduce the model parameters, a \(1\times 1\) convolutional layer is used to unify the feature maps of different scales obtained from the residual blocks into a 32-dimensional space. These feature maps are then evenly divided into four segments along the channel  
Figure 1: Overview of the proposed model. This model adopts ResNet-18 as the backbone network to extract multi-scale features. These features are then fused using a Multi-Scale Feature Mixer, enabling each output feature to contain multi-level semantic information. Subsequently, the features are processed by Cross-Channel Spatial Transformers in separate branches, capturing spatial dependencies across channels over long distances. Finally, a Multi-Scale Attention Decoder further extracts and integrates multi-scale features to enhance the performance of change detection.  
dimension, with each feature map obtaining data from eight channels. To ensure the thorough mixing of features across different channels and scales while maintaining image smoothness and continuity and effectively suppressing artifacts, trilinear interpolation is employed. For interpolation at the target pixel location \((x,y)\), the mathematical formula for bilinear interpolation is given as follows:  
\[(x,y)=\sum_{i=-1}^{2}\sum_{j=-1}^{2}w(i,j)\cdot g(x+i,y+j) \tag{1}\]  
Let f(x,y) denote the interpolated value at the target pixel location (x,y), and let g(x,y) represent the values of the known data points in the original image. Bicubic interpolation calculates f(x,y) by performing a weighted average of the 16 known data points surrounding the target pixel. The specific formula for bicubic interpolation is as follows:  
\[w(i,j)=h(i)\cdot h(j) \tag{2}\]  
where h(t) is the cubic interpolation weight function, defined as follows:  
\[h(t)=\begin{cases}1+|t|^{3}-2|t|^{2},&\text{if }|t|<1,\\ 4-8|t|+5|t|^{2}-|t|^{3},&\text{if }1\leq|t|<2,\\ 0,&\text{otherwise}.\end{cases} \tag{3}\]
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
## 1 Introduction
In this paper, we redesign the \(L_{1}\) adaptive control architecture (L1 AC) with anti-windup (AW) compensator for vehicle, and input saturation, coupling disturbances and fluctuations of vehicle states are all considered. The purpose of this paper is to combine the effective \(L_{1}\) adaptive control architecture with a modern AW compensator to improve the response of vehicle control via exploiting the full potential of actuators with constraints. Apart from model parameter uncertainties, input saturation brought by actuators as well as control instructions should be considered in controller design. AW compensator has been proposed to address the problem of input nonlinearities, and it also has been used in conjunction with adaptive control approach to tackle the adaptive control problem of a class of nonlinear systems with input saturation [13]. MRAC is extended with AW compensator to control the pitch angle in the presence of input saturation [4; 15; 25]. In contrast with MRAC sacrificing the performance of response speed during adaption, L1 AC method accelerates the process of adaption and update reference model simultaneously. L1 AC surpasses MRAC in control performance and parameters adjustment [26; 21; 27]. It is valuable to augment L1 AC architecture using an AW compensator in autonomous vehicle control with input constraints, where Riccati equation is used to determine the gain vector for compensator's state feedback equation. The output of the compensator is devoted to modifying and improving the output value of the control law. In this paper, L1 AC with Riccati-based compensator is proposed to control the pitch mode of the six DOF REMUS vehicle model with actuator nonlinearities and coupling disturbances. REMUS vehicle model is an accurate model extracted from the pool experimental data. The controller designed is executed in REMUS vehicle and compares results with L1 AC controller [28; 29]. The hypothetical linear plant model of the vehicle and the realistic nonlinear six DOF system models are compared and investigated comprehensively. Moreover, the pitch channel control for the six DOF unsteady vehicle system is also implemented using the proposed method with coupling dynamics and hydrostatics disturbances. Simulation results can demonstrate the effectiveness of pitch channel control of the REMUS vehicle. This article is structured as follows. Section 2 describes kinematics and dynamic model of the REMUS vehicle, and simplifies the nonlinear six degrees of freedom model in pitch channel for the vehicle to obtain the linear one. Section 3 presents the control strategy where \(L_{1}\) adaptive control approach is combined with a Riccati-based AW compensator. In Section 4, the introduced approach is studied using different vehicle plants, illustrating the performance of controller developed when taking into account model uncertainties, input nonlinearities, measurement noise, etc. Section 5 provides some conclusions to close the paper.
# Quantum Biotech and Internet of Virus Things: Towards a Theoretical Framework
## 2 Literature Review and Synthesis
### Relationships between Related Topics and Insights
#### 2.2.2 QmRNA  
Albeit one distinct gene requires to do its work, it makes a copy of itself, which is called mRNA. For the past three decades, vaccine scholars have been fascinated and discouraged by the potential of mRNA. Currently, researchers are testing vaccine candidates that use mRNA, without using actual bits of the virus, to generate the immune system to create defensive antibodies. mRNA is a fragile molecule, which means it must be coated in a protective, fatty covering to keep it stable, and the refrigeration conditions have to do with how the mRNA was manufactured and stabilized [23].  
A fundamental aspect of human physiology and vital to unleashing the immune system is the tiny particles of genetic code that are key to influencing cells to build proteins. mRNA guides the body's protein production in a much more fixate manner. The experimental vaccines have received approval (emergency authorization of use), for the first time, from regulator bodies, such as Food and Drug Administration (FDA, USA) [23] (p. 3) and European Medicines Agency (EMA) [23] (pp. 3-9) that use mRNA technology. Such approval is a windfall for the mRNA technology, a brand-new platform, which was thought to be wishful thinking for a long time. Researchers at companies like Moderna [24] and Pfizer (BioNTech) [25] vaccine used synthetic mRNA that contains information about the COVID-19 signature spike protein and demonstrated that the vaccine based on mRNA technology platform bestows more than 90% efficacy at preventing symptomatic COVID-19 [23; 24; 25]--Pfizer/BioNTech and Moderna hence, the distinction between Moderna and Pfizer/BioNTech vaccine being how the vaccines' synthetic mRNA is manufactured and packaged.  
Recently, quantum-inspired genetic algorithms (QGAs) were introduced for the prediction of RNA secondary structures, demonstrating superiority over the existing strategies. Researchers have introduced a new QGA named multi-population assisted quantum genetic algorithm (MAQGA) [26], demonstrating the performances of genetic algorithms with substantial enhancement. The holy grail end-to-end in silico vaccine discovery involves evaluating and breaking down the entire chemical structures of the virus and the cure. Quantum computers, if commercially successful as fault-tolerant systems, will enable end-to-end in silico vaccine discovery. By 2030, with the QmRNA technology, the entire process transforms into a quantum simulation that could eliminate 99.9% of false leads in a fraction of the time empowering QmRNA vaccine discovery in a few days [27].
## 2 Proposed System  
### Simulation Configuration  
To analyze the performance of the proposed solution, a typical empty room (5 m \(\times\) 5 m \(\times\) 2.5 m) was assumed, as depicted in Figure 1[22]. We assumed that there were four LED bulbs suspended from the ceiling at a height of 2.5 m. The total transmitted optical power per LED bulb was fixed at 25 W. The semi-angle of each LED group and the field-of-view (FOV) angle of the photo-detector (PD) were both set at 70\({}^{\circ}\). The four walls were made from plaster that had a reflective rate of 0.7-0.85 [23]. Other important parameters are represented in detail in Table 1.  
Figure 1: Typical room and channel model.  
The technique of RSS-based positioning is often used for indoor positioning because of its simplicity [24]. For this work, we develop our solution based on the RSS and fingerprints. We gathered the input data from each reference fingerprint. The distance between adjacent fingerprints was 20 cm, and a 26 \(\times\) 26 fingerprint grid was assigned to the floor surface corresponding to 676 reference points. The original coordinates and the fingerprints distribution are shown in Figure 2.
## 3 Mitigation Scheme for SAR
### Adaptive Filtering
#### 3.3.3 Spatial Filtering  
Aforementioned techniques are applicable for single channel SAR systems. For modern SAR system, multi antennas or channels are basic configurations, which provides additional degree of freedom to suppress the RFI from spatial directions. Its basic idea is using array beam-forming techniques to orient pattern nulls towards sources of RFI. [PERSON] et al. proposed constrained fast-time space time adaptive processing (STAP) techniques which exploit adaptive beamforming to suppress the interference with minimal distortion to the final image [64; 65; 66]. [PERSON] et al. proposed using digital beamforming techniques for the 32-channel EcoSAR system [67; 68; 69].  
The merit of spatial filtering is that it is applicable to the cases that the RFI and SAR signals come from different spatial directions, independent of the RFI signal types. This means that it also works forthe most complex broadband CW RFI with the existence of angular difference challenges include the difficulty of accurately estimating the spatial properties of interference, which limits the achieved null depth. In general, an adaptive system using a beam-forming algorithm requires a high INR and is limited to a small number of RFI targets to be tracked during an observation. The RFI sources also need to remain stable and predictable through an observation. These multichannel-based methods are effective to remove interference, but will also increase the complexity of SAR system and cannot be applied to existing mono-channel SARs without an update of the hardware structure.