esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment  
A lab-built fuel cell testing platform was used to carry out the experimental tests, as shown in Figure 1. The platform contained an air supplier with a humidifier and a mass flow controller (MFC), a hydrogen supplier with a pressure regulator, a solenoid valve and a mass flow controller, a nitrogen supplier with a relief valve and a pressure regulator, a water-cooling system, and a control unit. The hydrogen supplier provided reactant gas to the system, while a respective mass flow controller (MFC) regulated the gas flow rates. The nitrogen supplier worked before and after the experiment to remove the air and the residual reactants inside the pipes and the stack in the device. A water-cooling system controlled the operating temperature and the temperature difference between the inlet and outlet of the stack. In addition, pressure and temperature sensors at the inlet and the outlet of the stack monitored the pressures and the temperatures of the anode, the cathode, and the coolant.  
The hydrogen was not humidified in the experiment, while the air was humidified by the wet cathode outlet gas (COG) with a gas-to-gas humidifier to simulate the working condition of the automobile fuel cell system. A constant stoichiometric ratio of \(Stioch_{A}=1.3\) for the anode and \(Stioch_{C}=2.0\) for the cathode was applied to test the voltage. The stoichiometric ratio is the ratio of the reactant supplied to the required for the electrochemical reaction. With load current densities of less than 0.2 A/cm\({}^{2}\), which were equal to those at 0.2 A/cm\({}^{2}\), the constant flow rates were operated for both the anode and the cathode. The cell voltage monitor collected the average voltages in each group of fuel cells in 1 s sampling time, and the recorded results are shown graphically. The operating conditions are listed in Table 1. In the load-changing test, the load current decreased gradually from 730.8 A to 203 A with each change step of 40.6 A. During the test, the stoichiometric ratios of hydrogen and air were kept constant, and the data were sampled at 1 Hz.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Load Current & \(I\) & 203–730.8 A \\ Stoichiometric Ratio of Hydrogen & \(\lambda_{a}\) & 1.3 \\ Stoichiometric Ratio of Air & \(\lambda_{c}\) & 2.0 \\ Assembling Torque & \(p\) & 12 N.m, 14 N.m, 15 N.m \\ Coolant Inlet Temperature & \(T_{int}\) & 72–75.5 \({}^{\circ}\)C \\ Coolant Outlet Temperature & \(T_{out}\) & 74–81 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating conditions.  
Figure 1: (a) Schematic of the fuel cell test system; (b) Photo of the 100-cell fuel stack in operation.
## 2 Configuration of Hybrid Energy Systems
### Solar PV  
Electricity is generated from solar PV by utilizing PV panels and this can be employed to meet essential electrical loads such as residential loads, commercial loads and for healthcare facility loads. The performance of a solar PV system such as the power output and efficiency are determined by solar irradiation, temperature weather conditions and  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**References**}} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Method Utilized**} & \multicolumn{9}{c}{**Technical Parameters**} \\ \cline{4-10}  & & & \multicolumn{4}{c}{**Components**} & & & & & **Remarks** \\ \cline{5-10}  & & & **WTG** & **PV** & **ESS** & **DG** & **CONV** & **BG** & \\ \hline Ref. [2] & Off-grid & HOMER & ✓ & ✓ & ✓ & ✓ & X & X & Rural community \\ Ref. [3] & Off-grid & HOMER & ✓ & ✓ & ✓ & ✓ & X & ✓ & X & Health clinic \\ Ref. [5] & Off-grid & AML & ✓ & ✓ & ✓ & ✓ & X & ✓ & Farming applications \\ Ref. [6] & Grid-iad & HOMER & X & ✓ & ✓ & ✓ & ✓ & X & Remote community \\ Ref. [8] & Off-grid & HOMER & X & ✓ & ✓ & ✓ & X & X & Rural community \\ Ref. [10] & Off-grid & HOMER & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & Urban infrastructural \\  & & & & & & & & & & & & & & & \\ Ref. [11] & Off-grid & MPO \& WCA & X & ✓ & ✓ & X & X & ✓ & Transmitting station \\ Ref. [12] & Off-grid & HOMER & ✓ & ✓ & ✓ & ✓ & X & X & Rural clinic \\ Ref. [14] & Off-grid & HOMER & X & ✓ & ✓ & ✓ & X & X & Healthcare center \\ Ref. [15] & Off-grid \& HOMER & X & ✓ & ✓ & ✓ & X & X & Rural health units \\  & & & & & & & & & & & \\ Ref. [16] & Off-grid & HOMER & X & ✓ & ✓ & ✓ & X & X & Rural village \\ Ref. [17] & Off-grid & HOMER & ✓ & ✓ & ✓ & ✓ & X & X & Rural community \\ Ref. [18] & Off-grid & HOMER & ✓ & ✓ & ✓ & ✓ & ✓ & X & Residential loads \\ Ref. [19] & Grid-iad & HOMER & X & ✓ & ✓ & ✓ & X & ✓ & Official building loads \\ Ref. [20] & Grid-iad & HOMER & ✓ & ✓ & X & X & X & X & Residential loads \\ Ref. [21] & Grid-iad & HOMER & ✓ & ✓ & ✓ & X & ✓ & X & Residential loads \\ Ref. [22] & Off-grid & HOMER & ✓ & ✓ & ✓ & X & ✓ & X & Residential loads \\ Ref. [23] & Grid-iad & HOMER & ✓ & ✓ & X & ✓ & X & ✓ & Local community \\ Ref. [24] & Grid-iad & MPO & X & ✓ & ✓ & X & ✓ & X & Local community \\ Ref. [25] & Grid-iad & MPC & X & ✓ & ✓ & X & ✓ & X & Local community \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of recent studies on hybrid energy system.  
physical features of the study location. The configuration of a PV system can be based on parallel and series connections to meet the required electrical load demand at any time [27]. Therefore, the hourly power output for one solar PV panel can be estimated as [28]:  
\[P_{pv}(t)=A_{pv}*I_{pv}*\eta_{pv} \tag{1}\]  
The total power produced by the solar PV system can be expressed as:  
\[P_{pv}^{total}(t)=n_{pv}*P_{pv}(t) \tag{2}\]  
where \(A_{pv}\), \(I_{pv}\) and \(\eta_{pv}\) are the area, solar irradiation and efficiency of the solar PV module, and \(n_{pv}\) is the number of solar PV modules.
## 2 Materials and Methods
### GNSS Double-Differenced
\[\
abla\Delta\dot{\Phi}\,=\,\
abla\Delta\Phi(t_{2})\,-\,\
abla\Delta\Phi(t_{1}) \tag{5}\]  
These techniques eliminate receiver and satellite clock errors and mitigate atmospheric effects. Among them, the double-differencing method is most commonly used for high-precision baseline processing in GNSS and serves as a key component in geodetic network adjustments using software such as GAMIT/GLOBK and Bernese (5.2) [10; 30; 31].
## 2 Methodology
### Design of the Enhanced Error-Adaptive ESKF-MPC
After numerous adjustments, the values of 1.7 for the amplification factor and 0.8 for the smoothing factor were identified as an effective parameter combination, resulting in the enhanced EA-ESKF-MPC framework used in this paper. This configuration maintains strong adaptability to dynamic operating conditions, such as load variations in coal-fired power plants, while significantly improving the smoothness of the control actions, as evidenced by reduced oscillations in the control signal. Figure 3 is referenced as the control system schematic. The supercritical power plant is modeled as a multiple-input multiple-output (MIMO) system, with key control inputs including throttle valve opening, coal feed rate, and water feed rate, and corresponding outputs such as steam pressure, unit load, and header temperature. This MIMO structure inherently exhibits strong coupling and pronounced nonlinearity, thereby demanding a control strategy capable of effectively managing these interdependencies. Although the derivation of the filter parameters in this study adopts a single-input single-output (SISO) formulation for the sake of clarity and conciseness, the proposed EEA-ESKF-MPC framework is inherently designed to accommodate MIMO systems. The SISO formulation serves to elucidate the core mechanisms--adaptive error correction and exponential smoothing--in a more accessible manner. However, the actual controller implementation is based on a MIMO model, as reflected in both the architecture of the neural network and the multivariable optimization within the MPC scheme. The methodology scales naturally across all input-output channels. It can tolerate weak coupling due to the robustness of the predictive model, while stronger coupling can be mitigated through existing decoupling strategies or appropriate tuning. This scalability  
\[Y=[y(k+1|k),\ldots,y(k+Np|k)]^{T}=F\overline{x}(k)+\Phi\Delta U \tag{23}\]4 Schematic of the control system. enables the EEA-ESKF-MPC to coordinate all control inputs and outputs simultaneously, ensuring optimized performance across the complex dynamics of the supercritical unit.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 7 Conclusions  
In this study, we proposed a lightweight and robust authentication protocol for securing fog-based VANETs. Considering the features of fog-based VANETs, we used the ECC system and fuzzy verifier to establish a session key securely and efficiently for vehicle-to-infrastructure communication. The proposed protocol provides perfect forward secrecy and resists various attacks, such as trace and ephemeral secret leakage attacks. Furthermore, we conducted informal and formal security analyses to demonstrate the efficiency and security robustness of our protocol. The informal security analysis demonstrated that our protocol satisfies security requirements and the formal security analysis using BAN logic, the AVISPA simulation tool, and the RoR model, demonstrating that our protocol offers mutual authentication and session key security. Finally, we compared the performance of our protocol with that of other related protocols to evaluate its efficiency. The results demonstrated that our protocol outperformed the compared protocols in computational and communication cost. In the future, we plan to assess our protocol's practical issues through simulations that consider actual VANET conditions. Moreover, future research will aim to expand this study to enable secure communication under various network conditions. Additionally, we plan to introduce outsourcing computing methods to lower the computational costs for vehicles, and these improvements will expand the potential applications of intelligent vehicle systems.  
Conceptualization, S.L.; methodology, S.L., S.S. and D.K.; software, S.S. and D.K.; validation, S.S., D.K., Y.P. ([PERSON]) and Y.P. ([PERSON]); investigation, S.L. and D.K.; formal analysis, S.L., S.S., D.K. and Y.P. ([PERSON]); writing--original draft, S.L.; writing--review and editing, S.S., D.K., Y.P. ([PERSON]) and Y.P. ([PERSON]); supervision, Y.P. ([PERSON]); project administration, Y.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2024-00450915).  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 3 Use Cases for the Internet-Connected Vehicle Ecosystem
### IoV Use Cases in the Smart City  
Issues addressing traffic control, surveillance, natural disasters, and the monitoring of the environment are addressed by the concept of smart cities. In order to implement these solutions, urban data need to be gathered and shared over communication infrastructures. As a consequence, these smart city ecosystems call for more integrated, diverse, and intelligent wireless communication. In these environments, the IoT has a key role as it allows for the cost-effective collection, transmission, and processing of huge amounts of data from smart devices [57]. IoV-based applications for smart cities offer many advantages for urban environments by utilizing connectivity, data exchange, and intelligent systems [58]. Examples of the applications of smart city-related IoV are traffic management, parking management, public transportation management, emergency service, pollution monitoring, energy management, waste management, intelligent street lighting, pedestrian safety, and environmental monitoring.  
There are four functions performed by a vehicle in a smart city system:  
* It enables the moving of the data from one node to another and speeds up the data exchange between several nodes in the network. It helps keep network security and reliability by preserving connections between nodes. Additionally, it plays a crucial role in upholding network security and reliability by preserving connections between nodes.
* Vehicle objects function as clients consuming IoV and Internet services. Mutual authentication and data validation are essential roles for the nodes that exist within this network. These functions are very important for ensuring data transmission integrity and generally for data transmission security and trustworthiness in order to achieve the security and reliability of the IoV services network.
* It represents a vehicle object that will collect data from smart devices and send it to data centers inside a smart city. Data are encrypted and a digital signature is added to the vehicle object before transmitting it to the network. This process enables the data center to verify the authenticity and validity of the data.
* Vehicle objects augment the limited information processing capabilities of smart devices by playing the role of providing distributed computing resources. It offers more complicated data processing and analysis with the addition of extra computational capacity to the network.  
IoV applications for smart cities include efficient traffic management, integrated public transportation, optimized parking, improved safety and security, energy efficiency, data-driven urban planning, and better quality of life. In the future, these applications lead to even more connected, sustainable, and livable cities.
## 11 Stability  
### Building Level  
In high-rise buildings, lateral loads from winds and earthquakes are counteracted by an independent lateral stability system, such as a bracing truss or shear wall core. The lateral stability system can be constructed from steel, concrete, and hybrid steel-concrete structures using either traditional or modular construction methods [104]. While timber modular structures are primarily confined to low-rise applications, steel and precast modular units have established themselves as the industry standard for high-rise projects [109].  
In contrast to the bracing truss, the shear wall core has the flexibility to accommodate elevators, stairs, and service risers. In current practice, four out of five of the world's tallest modular buildings constructed by the 3D volumetric method are based on concrete shear wall cores. Concrete cores can be constructed using either conventional methods, such as slipform and jumpform, or through the modular approach employing precast concrete systems. The composite steel-concrete-steel sandwich system is another option for the shear wall core [83]. Due to the discontinuity in the structural components among modules, the modules are designed to resist the gravity loads only whereas the core wall resists all the lateral loads. The result is a laterally stable building if the inter-module connection is designed correctly to transfer the horizontal load via axial and shear forces. The floor slab design in each module needs to be stiff enough so that each can act as a rigid plate [113].  
While the use of pure timber structures for medium- and high-rise buildings remains a challenge, it is undoubtedly feasible up to a certain height. The Jiosstraet tower in Norway, standing at 18 stories, exemplifies current possibilities and challenges. Additional mass, such as screed or concrete slabs without composite action, has been introduced to control sway and vibration. Although visible, the stabilizing system's diagonals sometimes impose limitations on functionality [114].  
A typical 50-60 m tall timber high-rise building usually consists of a concrete core (to meet fire protection, acoustic, and structural requirements) in combination with timber or timber-concrete composite floors and possibly timber columns along the facade (at relatively short intervals). For taller buildings, additional bracing for sway vibration and comfort requirements or additional stability systems are usually also required [115].
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.2 Semantic Segmentation  
This research focused on collecting the data from a conference room and performing a semantic segmentation using a feature-enhanced point transformer. The algorithmic segmentation results are shown in Figure 14a, while the manually annotated results are shown in Figure 14b. In some regions where certain categories were closely related, such as windows, wooden boards, and walls, the algorithmic segmentation often misclassified windows as wooden boards or walls. Additionally, in areas where chairs and walls were close to each other, the model indirectly segmented a portion of the chair's point cloud as part of the wall. These issues indicate that there is still room for improvement in achieving accurate local semantic segmentation results for point clouds. Overall, the results of the automatic segmentation can be substituted for manual segmentation results; although, improvements in local semantic segmentation's accuracy are needed.  
The accuracy of the conference room point cloud data collected in this study was noticeably lower than that of the data in the S3 DIS dataset. This was because the conference room point cloud data collected in this study were more complex compared to the data in the S3 DIS dataset. The scene included elements such as lantern  
Figure 14: Comparison of experimental results and ground truth values: (**a**) algorithmic segmentation; (**b**) manual segmentation.  
Figure 12: Schematic diagram of the scanning stations.  
Figure 13: Complete point clouds for the conference room: (**a**) raw data; (**b**) internal structural data.  
screens, central air conditioning, curtains, and more. The presence of these elements had an impact on the accuracy of the semantic segmentation in the conference room. The semantic segmentation of the interior of the conference room point cloud data is shown in Figure 15.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption  
To achieve end-to-end voice encryption and mixing, we used the Paillier homomorphic encryption algorithm, a mature and easily implemented method available today [1]. By utilizing its additive homomorphic properties in combination with mixing algorithms [2], we performed ciphertext mixing. The plaintext for homomorphic encryption must be a positive integer, and there are limitations on the maximum length of plaintext encrypted. Therefore, we employed a value-shifting method to convert the voice data to positive integers and utilized the big integer library for preprocessing the voice data. This simplified complex bit operations and enabled reaching the maximum encryption plaintext length. We successfully solved the issue of signed operations encountered during the mixing process, thereby completing the homomorphic encryption-based end-to-end encrypted mixing function.
# Correlation Technologies for Emerging Wireless Applications
###### Abstract  
In this article, we introduce correlation technologies both at RF/mmWave and baseband frequencies. At RF and mmWave frequencies, power-spectra and energy-spectra metrics are introduced for measuring the power-density of mobile devices and systems. New _ASIC-embedded smart connectors_ are developed for bringing correlation-based signal processing close to antenna modules. At baseband frequencies, DSP-based convolutional accelerators are proposed for fast and accurate measurement of EVM (error vector magnitude) using correlation technologies. Porting of the DSP-based convolutional accelerators into advanced fully depleted silicon-on-insulator (FDSOI)-based ASIC platforms for co-integration with adaptive RF/mmWave front-end modules will enable real-time extraction of _auto-correlation_ and _cross-correlation_ functions of stochastic signals. Perspectives for optically synchronized interferometric-correlation technologies are drawn for accurate measurements in noisy environments of stochastic EM fields using power-spectra and energy-spectra metrics. Adoption of correlation technologies will foster new paradigms relative to interactions of humans with smart devices and systems in randomly fluctuating environments. The resulting new paradigms will open new possibilities in communication theory for properly combining and reconciling information signal theory (_Shannon information-based entropy_) and physical information theory (_statistical-physics-based entropy_) into a unified framework.  
c 2022  
power (TRP), which are of relevance to standardization bodies for establishing maximum permissible transmitted power of wireless mobile devices in the mmWave frequency range. The requirements and challenges of OTA tests, including the need for unified measurement and modeling platforms, are highlighted in several contributions [2; 4]. New _ASIC-embedded smart connectors_ are developed for bringing correlation-based signal processing close to antenna modules. At baseband frequencies, perspectives are drawn for DSP-based convolutional accelerators toward fast and accurate measurement of EVM using correlation technologies.  
The originality of the proposed technology solutions includes the following attributes:  
* Use of correlation functions for energy and power-density-based metrics testing of wireless devices.
* Minimally invasive electro-optical probes for very near-field sensing of EM fields in noisy environments.
* Cognition-ready signal processing solutions using SinC [Cardinal-Sine] convolutional algorithms for OTA testing.  
The proposed correlation technologies for overcoming the limitations of classical interferometers offer new solutions for measuring arbitrary correlations functions of stochastic fields. Perspectives for optically synchronized interferometric-correlation technologies are drawn for accurate measurements in noisy environments of stochastic electromagnetic fields using power-spectra and energy-spectra metrics.