esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Importance of Digital Twins in Industry 4.0  
In a conventional smart manufacturing setup, various sensors embedded in machines collect operational and performance data. Engineers leverage these data to pinpoint causes of machine failures and detect early signs of malfunctioning components to eliminate unplanned downtime. But with the rapid growth in Artificial Intelligence (AI), Internet of Things (IoT), cyber-physical systems (CPSs), edge computing and wireless communication technologies, Industry 4.0 has evolved into bringing together a multitude of industrial devices to create a complete digital ecosystem that can monitor, collect, share and analyze data to provide valuable insights with advanced predictive and prescriptive capabilities. Figure 4 outlines the levels of intelligence in Industry 4.0, progressing from descriptive to prescriptive (reactive) intelligence, illustrating how complexity and value increase at each stage. Descriptive intelligence involves analyzing historical data to understand past events. Diagnostic intelligence focuses on identifying the reasons behind events using techniques like root cause analysis. Predictive intelligence anticipates future events through trend analysis and anomaly prediction. Prescriptive (reactive) intelligence uses real-time data and continuous learning to provide actionable recommendations and adaptive responses, crucial for proactive maintenance in Industry 4.0. Central to achieving these advanced levels of intelligence is the concept of a _\"Digital Twin\"_ that allows for real-time decision making through accurate analytics. Digital twins provide comprehensive, real-time digital replicas of physical entities, enhancing decision making and efficiency. The diverse capabilities of digital twins, including system modeling, real-time monitoring, optimizations, decision making and predictive and prescriptive maintenance, make digital twins a helpful tool in various applications of Industry 4.0. In aerospace and automotive applications, digital twins optimize performance, predictive maintenance and customization. In smart manufacturing, digital twins provide virtual design verification, process control and real-time fault prediction and diagnosis using machine learning. In healthcare, digital twins enable personalized medicine and optimized hospital operations. The energy sector utilizes digital twins for optimizing wind farms, power grids and nuclear plants. Smart cities and construction industries leverage digital twins for urban planning, resource management and building lifecycle management [16; 17; 18]. Thus, these digital twins serve as a powerful tool for transforming traditional industrial manufacturing into intelligent, adaptive systems capable of real-time analysis and decision making.  
Figure 3: Digital model, digital shadow and digital twin data flow.
# An Overview of CubeSat Missions and Applications
## 2 The Success of CubeSats and Its Enabling Factors
### Standardization  
A high degree of standardization has played a key role in the success of CubeSats. The 1U CubeSat form factor (10 cm \(\times\) 10 cm \(\times\) 10 cm) set by the CubeSat Design Specification (CDS) serves as a standardized building block, with variations such as 2U, 3U, and 6U configurations allowing for scalability [3; 27]. This standardization extends to multiple aspects of CubeSat development, including structural components, electronic interfaces, and communication protocols. Key standardization aspects include the following:  
* Structural Standardization: The Poly-PicoSatellite Orbital Deployer (P-POD) system, developed by Cal Poly San Luis Obispo and NASA, which allows CubeSats to be deployed safely and reliably into space [28]. This standardization ensures compatibility with a variety of launch vehicles, reducing integration complexity and costs. Figure 5a presents an example of the P-POD system.
* Electronic Standardization: While no strict electronic standard exists, the PC/104 form factor has emerged as the de facto industry standard for CubeSat avionics [29].  
PC/104 components are designed to fit within the limited internal volume of CubeSats while maximizing available space. Their stack-through connectors facilitate the quick and easy assembly of complex electronic stacks that run the length of the satellite. An example of a typical PC/104 electronics board, such as the ones utilized in cubesat manufacturing, is given in Figure 5b.
* Battery Standardization: Similarly, in terms of battery storage, CubeSat designers commonly utilize COTS cylindrical 18650 batteries to create complete battery packs for the satellite's EPS in a cost-effective manner. Common material choices used for the manufacturing of these batteries are Lithium-Ion (Li-Ion), Lithium-polymer (Li-pol), Lithium-Chloride (Li-Cl), Nickel-Cadmium (Ni-Cd), and Nickel-metal hydride (Ni-MH) [30].
* Communication Standardization: CubeSats utilize a range of established communication protocols, including the Serial Peripheral Interface (SPI), Controller Area Network Bus (CAN Bus), Inter-Integrated Circuit (I2C), Universal Asynchronous Receiver-Transmitter (UART), SpaceWire, and Ethernet. These standard protocols enable seamless communication between onboard subsystems and ground stations, improving efficiency and interoperability.
## 1 Introduction
### Contributions and Limitations of the Work  
By conducting a comparative analysis of SARIMA, FB Prophet, Holt-Winters, and TBATS, this research seeks to determine their respective strengths and weaknesses by investigating the complex dynamics of energy demand in Brazil, along with an estimation of energy demand in the country in 2033. It also contributes by updating the energy consumption forecasting in Brazil, with better-assessed accuracy than previous recent studies, as seen in Section 2.3.  
This study aims to offer practical recommendations for enhancing energy demand forecasting since this will lead to more efficient resource management within the energy sector and, thus, assist in achieving sustainable development goals.  
Additionally, this research contributes to the understanding of time series model biases, such as overestimation or underestimation, providing a nuanced perspective on model selection. This work, thus, serves as a robust framework for researchers aiming to improve forecasting accuracy in diverse fields. We also make available the forecasted energy consumption values for Brazil up to 10 years ahead using the four time series models, providing an important benchmark for future validation as actual data become available.  
This work also proposes a statistically robust methodology that rigorously evaluates time series, patterns, and anomalies. This methodology is not limited to energy consumption forecasting but is versatile and can be applied to any time series application, enhancing its utility across various domains.  
As limitations, this study uses solely economic, industrial, and climatic indexes as exogenous variables. Other categories of variables could also be useful in describing energy patterns, such as demographic data. Such a study could explore the relationship between energy consumption and population growth rate, age distribution of the population, and urbanization rate.  
Another limitation refers to the restricted scope of the data and of the time series models evaluated. This work forecasts exclusively the energy consumption, not production. It also assesses only the models SARIMA/X, FB Prophet, Holt-Winters, and TBATS, not encompassing promising models such as Temporal Fusion Transformers (TFTs), N-BEATS, DeepAR, and Gated Recurrent Units.
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 3 Risk Assessment Methodology
### Nonlinear Fuzzy Comprehensive Evaluation  
The uncertainty in the risk assessment of drilling and blasting construction leads to nonlinearity in the evaluation. Linear weighted methods may not fully reflect the significant impact of certain evaluation indicators, and the evaluation results may deviate from the actual situation. Therefore, a nonlinear fuzzy comprehensive evaluation method [29; 31] is introduced to compensate for the shortcomings of linear weighted methods. The specific form is as follows:  
\[f(m_{1},m_{2},\cdots,m_{n};q_{1},q_{2},\cdots,q_{n};\mathbf{\Lambda})=\left(m_{1}q_ {1}^{\gamma_{1}}+m_{2}q_{2}^{\gamma_{2}}+\cdots+m_{n}q_{n}^{\gamma_{n}}\right) ^{\frac{1}{\gamma}},\gamma_{i}\geq 1,i=1,2,\cdots,n \tag{23}\]  
where \(m_{1},m_{2},\cdots,m_{n}\) are the weights of risk indicators, \(m_{i}\geq 0\), and \(\sum\limits_{i=1}^{n}m_{i}=1,q_{1},q_{2},\cdots,q_{n}\) are the values of a column in the fuzzy evaluation matrix, \(q_{i}\geq 1\); \(\Lambda\) is the vector of prominent influence coefficients, denoted as \(\mathbf{\Lambda}=[\gamma_{1},\gamma_{2},\cdots\gamma_{n}]\), \(\gamma=\max(\gamma_{1},\gamma_{2},\cdots\gamma_{n})\) The prominent influence coefficient \(\gamma_{i}\) is determined based on the 9-scale method and the principle of \(\gamma_{i}\) values, depending on the degree of influence of risk factors on the evaluation results. The specific values are shown in Table 4. When a risk factor has a significant impact on the evaluation results, its prominent influence coefficient \(\gamma_{i}\) increases accordingly; otherwise, if the impact is negligible, \(\gamma_{i}\) is set to 1, resulting in linear fuzzy matrix calculation.  
According to the principle of prominent influence coefficient \(\gamma_{i}\), the influence degree of each risk factor on drilling and blasting construction is evaluated, and the following formula is used for fuzzy transformation to meet the requirements of nonlinear operator for fuzzy matrix synthesis.  
\[d^{\prime}{}_{ij}=10\times d_{ij} \tag{24}\]  
where \(d_{ij}\) is the value of the original fuzzy evaluation matrix; \(d^{\prime}{}_{ij}\) is the corresponding value in the nonlinear fuzzy evaluation matrix.  
During the fuzzy transformation of the original matrix function, it is necessary to ensure that the proportional relationship between each value in the nonlinear fuzzy evaluation matrix and the original matrix remains consistent. Therefore, when \(d_{ij}\leq 0.05\), \(d^{\prime}{}_{ij}=0\); when \(0.05\leq d_{ij}\leq 1\), \(d^{\prime}{}_{ij}=1\).
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### Experimental Results: O-AODV Multipath Topology with Six Nodes  
In this section, we show the results of the experiments with the six-node AODV topology proposed by [6].  
The single source transmits data to the sink via two hops through the mesh, which has four different possible routes.  
The routing protocols in Figures 13 and 14 had less traffic load than the mesh (flooding) protocol, which had very heavy traffic loads due to uncontrolled traffic flow, as depicted in Figure 15. Furthermore, the routing protocols created less overhead traffic with route selection mechanisms, resulting in better results with O-AODV in light of the optimizations discussed in the preceding sections.  
Furthermore, as shown in Figure 16, O-AODV had a better route request to route reply setup time of 5100 ms compared to 6100 ms for AODV.  
Furthermore, the proposed optimized protocol had a lower average end-to-end delay of 4000 ms compared to 4500 ms for AODV and 6500 ms for the mesh (flooding) protocols, as shown in Figure 17. In addition, O-AODV achieved lower overheads of 20% compared to 40% for the AODV and 79% for mesh (flooding) protocols, as depicted in Figure 18. However, in terms of the PDR, O-AODV (84%) outperformed AODV (72%), but not at the mesh level (flooding) of 95%, as shown in Figure 19.  
Figure 14: Traffic load: AODV.  
Figure 13: Traffic load: O-AODV.  
Figure 15: Traffic load mesh (flooding)-based protocol.
## 1 Introduction
Then, Fe (II) is transported across the plasma membrane of roots via the divalent metal transporter, IRT [33]. However, the Fe acquisition mechanisms of the strategy I plants, such as the rhizosphere acidification and FCR activity, are largely hampered by high pH conditions [34]. Importantly, inoculation with certain PGPR strains, such as _Paenibacillus polynyva_ BFKC01 [14] and _B. subtilis_ GB03 [22], significantly enhances Fe uptake in _Arabidopsis_ plants with upregulation of the Fe deficiency-induced transcription factor 1 (FIT1), which is responsible for inducing the expression of _IRT1_ and _FRO2_[35]. Moreover, GB03 VOCs have been demonstrated to regulate plant Fe acquisition through FIT1-dependent pathways [22]. However, it remains unclear how GB03 VOCs activates the FIT1-mediated Fe acquisition in _Arabidopsis_. In the past decade, Fe deficiency in plants has become one of the most crucial issues for human health. Over 50% of the world's population is anemic, primarily due to Fe deficiency [36]. Therefore, understating mVOCs-induced plant uptake of Fe is beneficial to increase the source of Fe in foods for humans. Besides Fe, improved selenium (Se) absorption by plants has recently attracted considerable attention. Although Se is not essential for plant growth, it can enhance the tolerance of plants to various abiotic stresses by activation of antioxidant defense systems [37]. For both animals and humans, Se is an essential mineral element require for growth, immune functions, and metabolism [38]. Se in soil can be directly taken up by plants and enter into the food chain. The Se content of plant foods is closely related to soil Se levels, and a low availability of Se in soils has been reported in many countries such as China, Korea, Japan, the USA, and Australia [39]. Thus, it is imperative to develop efficient strategies to improve plant Se absorption. It has increasingly been recognized that several _Bacillus strains_ such as _B. cereus_ YAP6, _B. licheniformis_ YAP7, and _B. pichinotyi_ YAM2 exhibit great potential to increase Se uptake in wheat plants [40; 41]. Nevertheless, whether mVOCs affect plant Se assimilation and its underlying mechanisms has not been determined to date. In the present study, a novel strain of _Bacillus amploliguefaciens_ (strain BF06) was isolated from calcareous soils. A blend of VOCs was released by BF06 to trigger plant growth without physical contact with the plant roots. BF06 VOCs not only induced a marked increase of biomass, but also augmented the content of Fe in plants with promoted synthesis of photosynthetic machinery and activities. Our results indicated that microbial-induced accumulation of Fe depended on VOCs-mediated cellular NO synthesis in _Arabidopsis_. Furthermore, the production of VOCs by BF06 remarkably enhanced plant Se uptake with upregulation of some putative sulfate transporter genes. Our results indicated that BF06 VOCs could improve plant nutrient status by invoking various metabolic pathways such as response to Fe, Fe homeostasis, and sulfate transport.
_applied sciences_  
_Article_  
**Trajectory Tracking Control of a Skid-Steer Mobile Robot Based on Nonlinear Model Predictive Control with a Hydraulic Motor Velocity Mapping**  
[PERSON] 1,2 [PERSON] 1,2 [PERSON] 1,2 [PERSON] 1,2 [PERSON] 3,4 [PERSON] 4  
1 School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China; [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (Y.Z)
2 State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Changsha 410083, China
3 College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
4 Sunward In Intelligent Equipment Co., Ltd., Changsha 410100, China; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** In this study, we address the trajectory tracking control problem of a hydraulic-driven skid-steer mobile robot. A hierarchical control strategy is proposed to simultaneously consider the robot's position control and the velocity control of the hydraulic motors. At the upper level, a nonlinear model predictive control (NMPC) method is employed to control the position and heading of the mobile robot. The NMPC controller takes into account the robot's physical constraints and generates the desired robot motion velocity. Then, to control the hydraulic drive system, a current-velocity mapping-based control method is introduced. By establishing the mapping relationship between the control current applied to the hydraulic motor and its corresponding output velocity, the dynamics of the hydraulic motors are characterized. Consequently, the lower-level controller can directly obtain the control signal for the hydraulic actuator through lookup mappings. Additionally, PID controllers are adopted to compensate for velocity tracking errors. The proposed hierarchical control strategy decouples the robot's position control and the hydraulic system control, simplifying the overall controller design, leading to improved control performance. To validate the effectiveness of the proposed control strategy, several experiments were conducted on a hydraulic-driven skid-steer mobile robot, and the results demonstrate the effectiveness of the proposed approach.  
**Keywords:** skid-steer mobile robot; hydraulic-driven system; nonlinear model predictive control; nonlinear mapping; trajectory tracking control
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 4 Validation of the Sonic Tomographic SAR Results
### Tomographic Validation
It is clear that the temporal correspondence of the SAR acquisition which consists of a few seconds is not comparable with the large temporal duration of the measurements made through the \"in situ\" sensors, but we find a good correlation between the red seismic events that coincide very much in space with the magmatic consistency estimated through the SAR seismic tomography. The depth also coincides greatly. Figure 25: Overlay of SAR Doppler tomography with magnetotelluric tomography (particulars). (**a,d,g**) Radar tomographic results. (**b,e,h**) Radar tomographic results 50% overlapped to magnetotelluric tomographic results. (**c,f,i**) Magnetotelluric tomographic results. The symbol - represents the scalar multiplication.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
###### Abstract  
Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edge and masks all arithmetic operations, preventing untrusted cloud servers from learning the tasks performed on the encrypted patient data. Virtual edge nodes benefit from cloud computing resources to accomplish computing-intensive mathematical functions and reduce latency in device-edge node data transmission. A comparative analysis with existing studies demonstrates that homomorphically encrypted data stored at the edge preserves data privacy and integrity. Furthermore, secret sharing-based multi-node computation using virtual nodes ensures data confidentiality from untrusted cloud networks.  
homomorphic encryption; privacy; secret sharing; IoMT +
Footnote †: journal: Applied sciences
## 5 Discussion
Such examples of these applications are as follows: 1. Telemetry and commands--these rates can range from a few kilobits per second (Kbps) to several tens of Kbps. 2. Science and imaging data--these data rates can range from several Kbps to multiple megabits per second (Mbps), depending on the complexity and volume of the data being collected. 3. Real-time communication--this includes live video real-time communication of critical events in which the data rates can reach multiple Mbps or more. 4. Laser communications--this applied emerging technology provides significantly higher data rates than traditional radio frequencies, with data rates in the tens to hundreds of gigabits per second (Gbps) or more. In summary, the data rate used for a lunar mission is determined by mission needs, available technology, and financial constraints. It is critical to match data rate requirements with available power capacity because higher data rates may necessitate more energy for transmission. On another hand, communication capabilities are projected to improve as technology progresses and additional lunar missions are planning to achieve better data rates and more efficient use of power in lunar communications. In addition, to avoid interference with other satellite systems, regulatory authorities such as the International Telecommunication Union (ITU) assign certain frequency bands and impose power limitations. These standards require that power levels be adhered to. We firmly believe that this comprehensive review, encompassing multiple articles and their comparative analysis, will offer a valuable contribution to the lunar scientific community. Furthermore, this review serves as the foundation of our ongoing research efforts. Our research group is dedicated to exploring the intricate aspects of lunar antenna behavior in the lunar environment and developing models for the corresponding propagation channel in preparation for the upcoming Rashid rover mission. These findings will not only advance our understanding of lunar antenna systems but also play a vital role in optimizing the selection and placement of antennas for future missions, including the Emirates Lunar Mission (ELM).