esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sampling Plans and Geospatial Error  
The sampling campaign occurred on 30 July 2020. It involved the use of a high-precision Global Navigation Satellite System (Trimble R10) position device to determine the coordinates of samples along an unbiased 10 m \(\times\) 10 m grid (as displayed in Figure 1). Twice the distance root mean square (2 DRMS-2D) was calculated for the ground-based spectrometer (see Equation (1)). This helped to account for geospatial error and align satellite images to allow for statistical comparison. For the GNSS Trimble R10, a standard deviation of a geodetic latitude and longitude of 50 mm\({}^{2}\) was reported, and HeadWall Photonics Incorporated [22] reported a 1 m\({}^{2}\) circular error probability (CEP). This was converted into 2 DRMS by multiplying it with the conversion value of 2.4022 [33]. The 2 DRMS equation used was as follows:  
\[2 DRMS=2\times\sqrt{\left(\sigma_{x}\right)^{2}+\left(\sigma_{y}\right)^{2}} \tag{1}\]  
Here, \(\sigma_{x}\) represents the standard deviation of geodetic latitude, and \(\sigma_{y}\) represents the standard deviation of geodetic longitude. After the 2 DRMS was calculated, sample locations were plotted in ArcGIS Pro (3.0.2) [34] and converted into circular polygon features. This enabled the extraction of reflectance data overlapping with Wheal Maid's central water body. We tested the Halo handheld spectrometer's on-board GPS, which produced a circle feature with an 18.13 m\({}^{2}\) area. The GNSS Trimble R10 produced circles with a 0.062 m\({}^{2}\) area.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Large Intelligent Surfaces  
A LIS is a revolutionary technique in the fields of wireless communications, signal positioning, and remote signal control. It is made up of a continuous radiating surface that is close to the users and can send and receive information (this is what base stations used to do). A LIS functions as a radio access point, allowing users to communicate directly with it. A LIS, unlike traditional IRS, functions as an adjustable reflector between the base station and the users, occupying a section of the channel. A LIS includes full receivers and baseband processing capability to acquire CSI from pilots sent by users (Figure 2). This makes it possible to accurately calculate the relevant equalization matrix and then find it in the LIS [27].  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Reference** & **Main Contribution** \\ \hline
[5] & Monte Carlo simulations demonstrated that capacity degradation due to phase errors is inversely proportional to SNR, which is more apparent for large L values. \\ \hline
[15] & An energy-efficient design is created to maximize the system’s energy efficiency while considering both transmit power and IRS phase shift limits. \\ \hline
[19] & Because of the uncertainties imposed by environment dynamics and the quick changes in the IRS setup, channel estimate is a vital task of IRS. This research provides a FL framework for simultaneously estimating direct and cascaded channels in IRS-assisted wireless systems. \\ \hline
[21] & This paper aims to characterize the fundamental capacity limit of IRS-aided point-to-point MIMO communication systems with multi-antenna transmitters and receivers. They examined how best to optimize the IRS reflection coefficients and the MIMO transmit covariance matrix. \\ \hline
[22] & This article evaluates the IRS’s capacity limits. It investigates ways to jointly optimize the IRS reflection matrix and wireless resource allocation while limiting the number of IRS reconfiguration times. \\ \hline
[23] & The energy efficiency of the network is maximized in this research by dynamically regulating the on-off status of each RIS and maximizing the reflection coefficients matrix of the RIS. \\ \hline
[24] & This paper presented techniques to minimize the UAV energy consumption by IRS. \\ \hline
[25] & Channel estimation (CE) is somewhat challenging. To solve this problem, this paper designs a CE scheme for large IRS-assisted multi-user wireless communication systems. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representative Overview of Recent Studies on an IRS.  
Figure 2: Block diagram of a LIS serving multiple users simultaneously.
## 3 IoV Enpowered IVI in 3G and 4G  
As aforementioned, tremendous efforts have been made to construct IVI scenarios and realize the connectivity between vehicles, user devices and road infrastructures, e.g., the navigation system. However, the problem of \"information islands\" still exists, which impedes the data flow between vehicles, occupants' equipments, and external environments, and thus restricts IVI functions. The IoV tries to connect each element of smart transports, aiming to deal with the dilemma of \"information islands\" around the vehicle-mounted media scenarios. The concept of IoV includes cellular-based telematics [51] and distributed Vehicular Ad-Hoc Networks (VANETs) [52]. Currently, IEEE DSRC [49; 53] and 3 GPP Cellular-V2X [54] are recognized as popular IoV standards, which can fully support multimedia and multicast IVI services through the Internet access. With the advantages of large-coverage [55], high-density platooning [56], less-delay [57], high-reliability [58], and seamless interoperation [59], the IoV can promisingly accommodate consumer-centric IVI applications via the IoV ecosystem [59] and is vital to smart cities [60; 61].  
In industry, the IVI evolved continuously to integrate the applications of traffic safety, navigation, audio and video by front-mounted and rear-mounted IVI systems, which can be seen as a landmark for IVI solutions. Onstar is regarded as the earliest IVI system proposed by General Motors in 1996 [62] and then it was applied to Cadillac DeVille, which mainly offered automotive security services. In 1998, Mercedes-Benz COMAND and Honda Internavi system provided basic infotainment services, e.g., music play and navigation. In the following subsections, we present both the characteristics, and the pros and cons of IoV-based IVI solutions in 3G/4G eras, respectively.
## 6 Conclusions  
This paper explores the impact of molten salts, specifically solar salts and MgCl\({}_{2}\)-KCl, on a solar power tower (SPT) integrated with the S-CO\({}_{2}\) recompression Brayton cycle. A simulation model developed in Engineering Equation Solver (EES) is utilised to evaluate the performance of the solar power system with each type of molten salt. A sensitivity analysis is conducted to assess how the pressure ratio influences various key parameters. Finally, the results regarding the pressure ratio that optimizes efficiency are analyzed. The main conclusions of the study are as follows:  
* It has been demonstrated that the power cycle performance is significantly enhanced when MgCl\({}_{2}\)-KCl salts are utilised as the heat transfer fluid. However, this enhancement is accompanied by a reduction in the efficiency of the solar receiver, attributable to its operation at higher temperatures. This, in turn, has a detrimental effect on the overall efficiency of the system.
* The utilisation of chloride salts has been demonstrated to enhance both overall exergy efficiency and the rate of exergy destruction.
* The major irreversibilities occur in the solar system for both types of molten salts. In the power cycle components, the greatest exergy destruction occurs in the precooler for solar salts and the low-temperature recuperator for MgCl\({}_{2}\)-KCl salts, respectively.
* The utilisation of molten salt solutions, such as MgCl\({}_{2}\)-KCl, has been found to mitigate exergy dissipation in various components, with the notable exceptions of high and low-temperature recuperators.
* It is evident that an increased storage volume is necessitated in the case of chloride salts, despite the greater temperature difference. The MgCl\({}_{2}\)-KCl compound has been observed to result in a reduction in the size of the other components.
* Consequently, MgCl\({}_{2}\)-KCl salts enhance the efficiency of the system, notwithstanding the augmented storage capacity necessitated.  
The present study makes the assumption that the pressure drop occurring within heat exchangers and the solar block is negligible. It is recommended that future research efforts concentrate on the enhancement of models of the solar receiver and heat exchangers, with a view to giving due consideration to the effects of this pressure drop and undertaking a comprehensive analysis of its impact on performance and dimensions.  
Conceptualization, [PERSON] and [PERSON]; Data curation, P.T.-E. and R.V.-C.; Formal analysis, [PERSON]-[PERSON]; Investigation, [PERSON]-[PERSON], [PERSON] and [PERSON]; Methodology, [PERSON]-[PERSON]; Resources, [PERSON]-A.; Software, [PERSON]-[PERSON] and [PERSON]-[PERSON]; Supervision, [PERSON]-M.; Validation, P.T.-E. and R.V.-C.; Visualization, P.T.-E. and R.V.-C.; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON], [PERSON] and [PERSON]. The authors read and approved the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 4 Research Activities
\begin{table}
\begin{tabular}{c c} \hline \hline
**Country** & **Research Initiatives** \\ \hline _Finland (2018)_ & Finnish 6G research activity is coordinated by the University of Oulu, where a 6G initiative was launched. \\ \hline _United States (2019)_ & \\  & \(\bullet\) & The Federal Communications Commission opened the spectrum between 95 GHz and 3 THz to create a new category of experimental licenses. \\  & \(\bullet\) & IEEE launched IEEE Future Network with the tagline ‘Enabling 5G and beyond’. \\  & \(\bullet\) & ITU-T Study Group 13 also established the ITU-T Focus Group Technologies for Network 2030 intending to understand the service requirements for future networks round 2030. \\ \hline _EUI (2019)_ & \\  & \(\bullet\) & A research group based on the EU’s Terranova project is now working toward the reliable 6G connection with 400 Gbit per second transmission capability in the terahertz spectrum. \\  & \(\bullet\) & An EU-Japan project under Horizon 2020 ICT-09-2017 funding, called ‘Networking Research beyond 5G’, also investigates the possibility of using the terahertz spectrum from 100 GHz to 450 GHz. \\ \hline _South Korea (2019)_ & \\  & \(\bullet\) & LG Electronics established a 6G research centre in collaboration with the Korea Advanced Institute of Science and Technology. \\  & \(\bullet\) & The Electronics and Telecommunications Research Institute has signed a memorandum of understanding with the University of Oulu in Finland to develop 6G network technology. \\  & \(\bullet\) & Samsung Electronics and SK Telecom work together to develop technologies and business models related to 6G. \\  & \(\bullet\) & SK Telecom signed agreements with Finnish firm Nokia and Sweden’s Ericsson to step up collaboration on 6G network research and development. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Research initiatives into 6G communications.
# Latent Dimensions of Auto-Encoder as Robust Features for Inter-Conditional Bearing Fault Diagnosis
###### Abstract  
Condition-based maintenance (CBM) is becoming a necessity in modern manufacturing units. Particular focus is given to predicting bearing conditions as they are known to be the major reason for machine down time. With the open-source availability of different datasets from various sources and certain data-driven models, the research community has achieved good results for diagnosing faults in bearing fault datasets. However, existing data-driven fault diagnosis methods do not focus on the changing conditions of a machine or assume all conditional data are available all the time. In reality, conditions vary over time. This variability can be based on the measurement noise and operating conditions of the monitored machines such as radial load, axial load, rotation speed, etc. Moreover, the availability of the data measured in varying operating conditions is scarce, as it is not always feasible to collect in-process data in every possible condition or setting. Considering such a scenario, it is necessary to develop methodologies that are robust to conditional variability, i.e., methodologies to transfer the learning from one condition to another without prior knowledge of the variability. This paper proposes the usage of latent values of an auto-encoder as robust features for inter-conditional fault classification. The proposed robust classification method MLCAE-KNN is implemented in three steps. First, the time series data are transformed using Fast Fourier Transform. Using the transformed data of any one condition, a Multi-Layer Convolutional Auto-Encoder (MLCAE) is trained. Next, a K-Nearest Neighbors (KNN) classifier is trained based on the latent features of MLCAE. The so-trained MLCAE-KNN is then used to predict the fault class of any new observation from a new condition. The results of using the latent features of the Auto-Encoder show superior inter-conditional classification robustness and superior accuracies compared to the state-of-the-art.  
bearing faults; industry 4.0; robust fault diagnosis; transfer learning; k-nearest neighbors; convolutional auto-encoders +
Footnote †: journal: Applied
## 2 Theoretical Formulation
### Thermo-Elasticity Equations  
Based on a classical linear thermo-elasticity theory, the strain field for an axisymmetric problem reads as follows  
\[[\varepsilon]=\left[\begin{array}{c}\varepsilon_{rr}\\ \varepsilon_{\theta\theta}\\ \varepsilon_{zz}\\ \varepsilon_{r\tau}\end{array}\right]=[d][q] \tag{26}\]  
with  
\[[q]=\left\{\begin{array}{c}u\\ v\end{array}\right\},\qquad[d]=\left[\begin{array}{cc}\frac{\partial}{ \partial r}&0\\ \frac{1}{r}&0\\ 0&\frac{\partial}{\partial z}\\ \frac{1}{2}\frac{\partial}{\partial z}&\frac{1}{2}\frac{\partial}{\partial r} \end{array}\right] \tag{27}\]  
and \(u\) and \(v\) refer to the displacement components across the \(r\) and \(z\) directions, respectively. Moreover, the kinematic relations read as follows [59]  
\[[\sigma]=\left[\begin{array}{c}\sigma_{rr}\\ \sigma_{\theta\theta}\\ \sigma_{zz}\\ \tau_{rz}\end{array}\right]=\left[D^{d}\right]([\varepsilon]-[\varepsilon_{T}]),\qquad\quad[\varepsilon_{T}]=\alpha(x)\Delta T\left[\begin{array}{c}1\\ 1\\ 1\\ 0\end{array}\right] \tag{28}\]where \(\Delta T=T-T_{0}\) is the temperature estimated from the heat conduction equation. In addition, \([\varepsilon]\) stands for the elastic strain and \([\varepsilon_{T}]\) denotes the thermal strain field stemming from the temperature variation. The elastic stiffness matrix \(\left[D^{d}\right]\) is defined as [68]  
\[\left[D^{d}\right]=\frac{E(x)}{(1+v)(1-2v)}\ \left[\begin{array}{cccc}1-v&v&v&0\\ v&1-v&v&0\\ v&v&1-v&0\\ 0&0&0&\frac{1-2v}{2}\end{array}\right] \tag{29}\]  
The FG-GPL porous truncated cone is simply supported on its two bases, such that the following kinematic boundary conditions are enforced  
\[u(r,0)=u(r,L)=0 \tag{30}\]
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
###### Abstract  
The study of global phenomena requires the combination of a considerable amount of data coming from different sources, acquired by different observation platforms and managed by institutions working in different scientific fields. Merging this data to provide extensive and complete data sets to monitor the long-term, global changes of our oceans is a major challenge. The data acquisition and data archival procedures usually vary significantly depending on the acquisition platform. This lack of standardization ultimately leads to information silos, preventing the data to be effectively shared across different scientific communities. In the past years, important steps have been taken in order to improve both standardization and interoperability, such as the Open Geospatial Consortium's Sensor Web Enablement (SWE) framework. Within this framework, standardized models and interfaces to archive, access and visualize the data from heterogeneous sensor resources have been proposed. However, due to the wide variety of software and hardware architectures presented by marine sensors and marine observation platforms, there is still a lack of uniform procedures to integrate sensors into existing SWE-based data infrastructures. In this work, a framework aimed to enable sensor plug and play integration into existing SWE-based data infrastructures is presented. First, an analysis of the operations required to automatically identify, configure and operate a sensor are analysed. Then, the metadata required for these operations is structured in a standard way. Afterwards, a modular, plug and play, SWE-based acquisition chain is proposed. Finally different use cases for this framework are presented.  
Sensor Web Enablement; plug and play; interoperability; SensorML; Open Geospatial Consortium; sensor integration; OGC PUCK protocol 2017
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Study of FL-LoRaMAC  
In the following, we present results from simulations on the performance of FL-LoRaMAC to capture its general performance characteristics.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
###### Abstract  
Supported by the advances in rocket technology, companies like SpaceX and Amazon competitively have entered the satellite Internet business. These companies said that they could provide Internet service sufficiently to users using their communication resources. However, the Internet service might not be provided in densely populated areas, as the satellites coverage is broad but its resource capacity is limited. To offload the traffic of the densely populated area, we present an adaptable aerial access network (AAN), composed of low-Earth orbit (LEO) satellites and federated reinforcement learning (FRL)-enabled unmanned aerial vehicles (UAVs). Using the proposed system, UAVs could operate with relatively low computation resources than centralized coverage management systems. Furthermore, by utilizing FRL, the system could continuously learn from various environments and perform better with the longer operation times. Based on our proposed design, we implemented FRL, constructed the UAV-aided AAN simulator, and evaluated the proposed system. Base on the evaluation result, we validated that the FRL enabled UAV-aided AAN could operate efficiently in densely populated areas where the satellites cannot provide sufficient Internet services, which improves network performances. In the evaluations, our proposed AAN system provided about 3.25 times more communication resources and had 5.1% lower latency than the satellite-only AAN.  
a 2021 21 8111 1  
Federated Reinforcement Learning Based AANs  
with LEO Satellites and UAVs.  
2021 21 8111. [[https://doi.org/10.3390/s2128111](https://doi.org/10.3390/s2128111)]([https://doi.org/10.3390/s2128111](https://doi.org/10.3390/s2128111))
## 4 Results and Discussion
### Energy Consumption and Economic Analysis in Conventional HVAC
#### 4.4.2 Energy Consumption in PSA Oxygen Generation for Oxygen Supplementation  
According to the national standard [48], the oxygen consumption of an adult man in a light physical work state is 0.03 m\({}^{3}\)/h, and the oxygen consumption of two adult men in an air-conditioned room is 0.06 m\({}^{3}\)/h. Using the PSA oxygen production system to produce 1 m\({}^{3}\)/h of oxygen, the power consumption is about 0.35 kWh, and the oxygen consumption of two individuals per hour requires the power consumption of the oxygen production system to be 0.021 kWh. With the electricity fee set to 0.5 RMB/(kWh), the annual operating cost of the oxygen generation system is \(0.021\times 24\times 365\times 0.5=91.98\) RMB. The PSA oxygen supplementing air-conditioning system consumes \(5.05\times 10^{-4}\) kW in summer and \(1.1\times 10^{-3}\) kW in winter, so the annual load cost is 3.47 RMB. The PSA oxygen-making air-conditioning system can save 1632.55 RMB per year compared with the traditional air-conditioning system with additional fresh air. According to the current oxygen production level of the PSA oxygen generator, the average cost of oxygen production for 1 m\({}^{3}\)/h is about 2500 RMB [47], and the equipment investment and the installation costs required to meet the oxygen enrichment for two people is about 150 RMB. The payback period of the equipment is 0.1 year, which is 33 days.  
The combination of PSA oxygen production technology and air-conditioning systems can deliver a certain concentration of oxygen to air-conditioned rooms, which can not only improve indoor air quality, but also reduce building energy consumption. Compared with the traditional increase of fresh air volume to supplement oxygen in air-conditioned rooms, PSA oxygen generation technology has certain advantages in terms of operating costs and energy consumption.
# Dynamic Stiffness Effect of Mechanical Components on Gear Mesh Misalignment
## 3 Gear Train Modeling Process with Dynamic Stiffness
 \text{for $i$ and $j=1,2,\ldots,N_{B}$} \tag{20}\]  
As an example, when the gearbox housing has four bearings (\(N_{B}=4\)), the size of the reduced matrices in Equation (20) are 24 by 24, and then the block diagonal matrices, \(\overline{\mathbf{M}}_{B_{1}B_{1}}\) and \(\overline{\mathbf{K}}_{B_{1}B_{1}}\), which are the mass and stiffness matrices of the \(B_{1}\) bearing, are 6 by 6 matrices. Thus, the condensed matrices, \(\overline{\mathbf{M}}\) and \(\overline{\mathbf{K}}\), may be much smaller than the mass and stiffness matrices, \(\bar{\mathbf{M}}\) and \(\bar{\mathbf{K}}\). Using the reduced matrices, the dynamic stiffness in Equation (8) can be approximated as:  
\[\overline{\mathbf{K}}_{DS}(\omega)\overline{\mathbf{u}}=\overline{\mathbf{f}},\overline{\mathbf{K}}_{DS}(\omega)=\overline{\mathbf{K}}-\omega^{2}\overline {\mathbf{M}}\pm j\omega\overline{\mathbf{C}} \tag{21}\]  
In this work, we present the reduced-order FE modeling process with the dynamic stiffness for only the housing. However, the same process can be applied to other gear train components, such as the shafts and bearings. Hence, the general form of the dynamic stiffness can be defined as:  
\[\overline{\mathbf{K}}_{DS}= \begin{bmatrix}\overline{\mathbf{K}}_{DS}\end{bmatrix}  _{H}+ \begin{bmatrix}\overline{\mathbf{K}}_{DS}\end{bmatrix} _{Sh}+ \begin{bmatrix} \overline{\mathbf{K}}_{DS}\end{bmatrix} _{B} \tag{22}\]  
where the subscripts \(H\) and \(Sh\) represent terms associated with the housing and shaft, respectively. In the typical gear design process, only the static stiffness, \(\overline{\mathbf{K}}\), is considered to compute the gear mesh misalignment [5]. However, the gear train is a rotary machine, and its housing is under dynamic loading in the operating condition, which means that precise predictions of the gear train behaviors require consideration of the system's kinetic energy along with its potential energy. Therefore, using the dynamic stiffness, \(\overline{\mathbf{K}}_{DS}(\omega)\), which contains both potential and kinetic energies, is a favorable approach for modeling the gearbox housing despite the imposed quasi-static condition. In particular, because the dynamic stiffness is a function of \(\omega\), its effect becomes more significant in high-speed gear trains, such as those used in electric vehicles.