esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 5 Implementation
### Sharing of Monitoring Data  
The monitoring enabler utilizes Apache Kafka [55] as an event broker to distribute information between event producers and event subscribers. Kafka is a publish-and-subscribe system that has been designed to be fast, scalable, and durable. Kafka brokers can be clustered to provide more resources elastically and transparently. A broker keeps messages on disk and replicates them within a cluster to prevent data losses. Each broker should be able to store terabytes of messages and handle megabytes of reads and writes per second from thousands of clients. Event information from a broker in one domain may be subscribed and delivered to other brokers in order to further distribute decision making (to enable further scalability) and to enable domains to share information and detect cross-domain attacks, for example. To control information sharing across domains, Kafka provides authentication and authorization functions.  
The framework is targeted for near-real-time processing of monitored data streams. However, in security inference, history data is also needed, e.g., short-term history for correlation analysis, and a longer history for machine learning. Smaller amounts of history data can be stored by the broker and for longer term history data the inference components must be integrated with a database for storing relevant events. Kafka integrates easily with Hadoop, which is the recommended database for the framework deployments.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 3 Methodology
### System Architecture and Dataset Design for IoT-Supported Blockchain Integration
#### 3.1.2 GPS Logging  
After the coffee is harvested and tagged, GPS tracking modules can be used to monitor the movement of coffee shipments for movement verification and route integrity. Devices such as the Ublox Neo-6M GPS Module [40] or Queclink GV300 [41] industrial trackers can be used to provide continuous location updates. GPS data are associated with the batch's RFID ID and stored on the blockchain, enabling smart contracts to verify that the shipment followed approved routes. Any unauthorized abnormality can trigger blockchain-based alerts or conditional transaction halts, maintaining the security and authenticity of the transportation phase.  
Figure 2: The above architecture illustrates the use of blockchain as a decentralized ledger and smart contracts for automating data validation and storage. Sensor inputs (RFID, GPS, weight, environment, mobile) are captured at key points and submitted via smart contracts, enabling real-time and verifiable traceability throughout the coffee supply chain.
# Mid-State Kalman Filter for Nonlinear Problems
## 4 Nonlinear Filter Performance Evaluation
### Root Mean Square Error  
Root Mean Square Error (RMSE) is widely used in performance comparison and evaluation of nonlinear filtering algorithms. It is based on the estimation error set obtained by the Monte Carlo simulation to evaluate the accuracy of the algorithm [18], defined as follows:  
\[RMSE_{x}=\sqrt{\frac{1}{N_{m}}\sum_{l=1}^{N_{m}}{(\hat{x}_{k|k}^{l}-x_{k})}^{2}} \tag{25}\]  
where \(N_{m}\) is the Monte Carlo simulation times, \(\hat{x}_{k|k}^{l}\) represents the estimation result of the target motion state at time \(k\) in the \(l\)-th Monte Carlo simulation, and \(x_{k}\) is the actual state of the target. Although this metric has a definite physical meaning, it is easily dominated by considerable error values.
# City Information Modeling: State of the Art
## 4 CIM Applications  
### Typical Applications  
CIM can be applied in the entire life cycle of cities, including the planning and design, construction, operation, and maintenance phases (Table 4). The CIM technique can be applied to different cases (Table 4). For example, the CIM provides a simulation platform for urban planning and design, and helps manage public utilities for improving the operation and maintenance of cities. With the development and use of CIM, the number of related application cases is expected to increase. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Case** & **Feature** \\ \hline \multirow{4}{*}{Visualization platforms} & Simstadt [44; 45] & Visualize multi-level-of-detail CIM models based on CityGML. \\ \cline{2-3}  & NASA World Wind [46; 47; 48] & Create a visual CIM model in an open-source environment. \\ \hline \multirow{4}{*}{VR Application} & Yekaterinburg Center [49] & Visualize CIM by optimizing BIM and GIS models. \\ \cline{2-3}  & Duisburg historic city [50] & Realize interactive visualization of historic city models based on VR. \\ \hline \multirow{4}{*}{AR Application} & Urban CoBuilder [51] & Provide virtual models for urban planning by integrating CIM with AR games. \\ \cline{2-3}  & ARUJI [40] & Realize urban navigation based on POI, AR, and CIM. \\ \hline \end{tabular}
\end{table} 
Table 3: Data visualization applications for CIM. To demonstrate the current degree of application of CIM in detail, two typical cases are provided as follows:  
1. Helsinki 3D city  
The CIM of Helsinki includes two parts: a visually high-quality reality mesh model and a semantic model based on CityGML [58; 59]. To create a visually high-quality reality mesh model, the point clouds and images of the city were first collected by using laser scanning and oblique photogrammetry [60; 61]. Then, a farm of ten computers running Bentley ContextCapture was used to process the 11 TB data of point clouds [62]. The created reality mesh model is good for visualization. Using a new cloud-based optimization and delivery solution, Helsinki can stream a massive 3D model of the entire city to web browsers and mobile devices so that the city can be viewed on any mobile platform supporting AR or VR [63]. Based on the reality mesh model, the semantic model of the city was further created. Based on the semantic model, a variety of analyses [64], including but not limited to energy consumption, greenhouse gases, or the environmental impacts of traffic, can be performed. Except for high-quality visual and semantic models, the main advantage of Helsinki's CIM case is that these models are open to the public. On the one hand, people can help collect the urban data (e.g., mark a tag for a building) for the CIM model; on the other hand, people can also utilize city data from the CIM model for various applications [65]. In this way, the CIM model can be continually updated, and the applications of CIM will
## 6 Numerical study on the error and applications to planar-symmetric problems  
Exact solutions to Stefan problem exist in a very small number of cases such as those of planar symmetry with semi-infinite domain, in which the interface position is proportional to the square root of the time (similarity solutions). These cases are reviewed in this Section, so that the numerical results can be compared to exact values. The accuracy of XFEM is evaluated with respect to space and time discretisations. Penalty and Lagrange multiplier methods to enforce the interface temperature condition are compared, and the treatment of the blending elements is discussed. XFEM and embedded-discontinuity FEM are compared too. Finally, a second planar-symmetric example with finite domain is presented.  
Besides exact solutions and for the cases in which such solutions are not available, we also consider the so-called pseudo steady state (PSS) approximations. These are based on the assumption that the rate of the interface movement is much slower than the rate of the temperature diffusion within the domain, so that the temperature field at a given time instant can be approximated with the steady-state field corresponding to a fixed phase-interface, and the interface position can be, in turn, updated from the resulting steady-state temperature field. It follows that the PSS solution is a suitable approximation for large values of the Stefan number \(\alpha\), defined as  
\[\alpha=\frac{L}{c\Delta T}, \tag{47}\]  
where \(\Delta T\) is some temperature difference, characteristic of the specific problem (e.g. the temperature difference between two thermostats providing essential boundary conditions). A PSS approximation provides a limit behaviour, useful for comparisons where no exact analytical solution is available, such as the cases considered in Sections 6.7 and 7. For further details on the exact and PSS analytical solutions, the Reader is referred to Reference [19].  
From here on, planar-, cylindrical-, and spherical-symmetric problems are considered. These can be treated as spatially one-dimensional, allowing for some simplifications in the interface-position tracking. In particular, with the phase-transition interface reduced to one point in the one-dimensional space (representing a planar, cylindrical, or spherical surface in the original three-dimensional space), one coordinate suffices for individuating the interface position and generating the level set.  
In the examples below, we consider one-dimensional extended finite elements with two-nodes and linear shape functions, enriched as in Eq. (31). The relevant enriched shape functions are shown in Figure 4b. In Section 6.6, we also consider the embedded-discontinuity FEM described in Section 5. Where it is not differently stated, the temperature condition on the phase interface is enforced through Lagrange multiplier method and no enrichment is used in the XFEM blending elements.
## 2 Materials and Methods
### Compressive Strength Test  
To determine the influence of the mixtures of different proportions on compressive strength, specimens of all mix proportions were mixed according to Section 2.2 and formed into 15 cm \(\times\) 30 cm cylinders, which were hardened via standard curing. Compressive  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mix** & & & & & & & & **Unit: kg/m\({}^{3}\)** & \\
**Designation** & **Water** & **Cement** & **FA** & **FGCBA** & **Fine** & **Coarse** & **Coarse** & **Coarse** \\  & & & & & & **Aggregate** & **Aggregate (3/8\({}^{\prime\prime}\))** & **Aggregate (6/8\({}^{\prime\prime}\))** & **SP** \\ \hline OPC4 & 210.4 & 526 & 0 & 0 & 853 & 652 & 115 & 0 \\ OPC45 & 236.7 & 526 & 0 & 0 & 853 & 652 & 115 & 0 \\ OPC5 & 263 & 526 & 0 & 0 & 853 & 652 & 115 & 0 \\ \hline OPC4-20F & 210.4 & 420.8 & 105.2 & 0 & 853 & 652 & 115 & 0 \\ OPC45-20F & 236.7 & 420.8 & 105.2 & 0 & 853 & 652 & 115 & 0 \\ OPC5-20F & 263 & 420.8 & 105.2 & 0 & 853 & 652 & 115 & 0 \\ \hline OPC4-20B & 210.4 & 420.8 & 0 & 105.2 & 853 & 652 & 115 & 5.26 \\ OPC45-20B & 236.7 & 420.8 & 0 & 105.2 & 853 & 652 & 115 & 3.682 \\ OPC5-20B & 263 & 420.8 & 0 & 105.2 & 853 & 652 & 115 & 2.104 \\ \hline OPC4-40F & 210.4 & 315.6 & 210.4 & 0 & 853 & 652 & 115 & 0 \\ OPC45-40F & 236.7 & 315.6 & 210.4 & 0 & 853 & 652 & 115 & 0 \\ OPC5-40F & 263 & 315.6 & 210.4 & 0 & 853 & 652 & 115 & 0 \\ \hline OPC4-40B & 210.4 & 315.6 & 0 & 210.4 & 853 & 652 & 115 & 7.364 \\ OPC45-40B & 236.7 & 315.6 & 0 & 210.4 & 853 & 652 & 115 & 5.786 \\ OPC5-40B & 263 & 315.6 & 0 & 210.4 & 853 & 652 & 115 & 4.208 \\ \hline OPC4-60F & 210.4 & 210.4 & 315.6 & 0 & 853 & 652 & 115 & 0 \\ OPC5-60F & 263 & 210.4 & 315.6 & 0 & 853 & 652 & 115 & 0 \\ OPC45-60B & 210.4 & 210.4 & 0 & 315.6 & 853 & 652 & 115 & 0 \\ OPC5-60B & 263 & 210.4 & 0 & 315.6 & 853 & 652 & 115 & 0 \\ \hline OPC4-60B & 210.4 & 210.4 & 0 & 315.6 & 853 & 652 & 115 & 9.468 \\ OPC45-60B & 236.7 & 210.4 & 0 & 315.6 & 853 & 652 & 115 & 7.890 \\ OPC5-60B & 263 & 210.4 & 0 & 315.6 & 853 & 652 & 115 & 6.312 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The concrete proportions in this study.  
strength tests were carried out with reference to ASTM C39 [27]. The cylinders were placed into a compression tester according to curing ages of 3, 7, 14, 28, 56, 91, and 180 days for compressive strength tests, and the strength was measured and recorded. Three samples were taken for each test, and the test results were discussed on the basis of the average of these three samples.
###### Abstract  
The present research investigates the application of a cardan suspension making use of permanent magnet (PM) bearings employed to obtain high reliable/low-cost solutions for the permanent alignment of directional payloads such as laser reflectors for the Next Generation Lunar Retroreflector (NGLR) experiment or antennas to be deployed on the moon's surface. According to Earnshaw's Theorem, it is not possible to fully stabilize an object using only a stationary magnetic field. It is also necessary to provide axial control of the shaft since the PM bearings support the radial load but, they produce an unstable axial force when losing alignment between the stator and rotor magnets stack. In this work, the use of commercial off-the-shelf (COTS) sapphire as axial bearings in the cardan suspension has been investigated by testing their behavior in response to some of the dynamic loads experienced during the qualification tests for space missions. The work is innovative in the sense that COTS sapphire assembly has never been investigated for space mission qualification. As Artemis mission loads have not been yet provided for NGLR, test loads for this study are those used for the proto-qualification of the INFN INRRI payload for the ESA ExoMars EDM mission. Tests showed that, along the x and y directions, no damages were produced on the sapphire, while, unfortunately, on the z direction both sapphires were badly damaged at nominal loads.  
magnetic suspension; jewel bearings; synthetic sapphire; alignment by gravity; dynamic loads; COTS +
Footnote †: journal: Physics Letters B  
article
## 4 Machine Learning for Wireless 6G Communication Systems
### Types of Machine Learning Techniques
#### 4.1.3 Reinforcement Learning (RL)  
In RL, the model aims to correlate the input data to an output by interacting in a cause-and-effect or trial-by-error approach and learning from the mistakes or feedback in this interaction [6]. It functions as a system that seeks to optimize a given state. Examples of RL algorithms include Q-Learning (QL), Fuzzy Q-Learning (FQL), R-Learning, actor-critic, etc. [6; 131]. RL is mostly applicable in optimization scenarios and will play a critical role in the future 6G network.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 3 Intelligent Tensioning System Design
### Pushing and Receiving
#### 3.3.1 Transport Mechanism  
Tensioning jobs will generate multiple packets, including two flag packets, four conclusion packets, and \(n\) process packets. The flag packets appear at the head and tail of the entire packet sequence. Flag packet 1 marks the beginning of the job, and flag packet 2 indicates the end of the job. The process packets contain data of several time points, which are arranged in sequence between the flag packets according to collection time. The packet arrangement order is shown in Figure 6.  
In the process of packet pushing and receiving, network anomaly is an important affecting factor of data integrity. In this study, a packet queue response mechanism is adopted to ensure data integrity. First, the packet queue is initialized when the job is started, and the packet is enqueued during the operation. Second, the data-pushing service reads the header packet and pushes it. Third, after receiving the confirmation message returned by the remote server, the data-pushing service performs dequeueing operations. The flow of the packet queue control mechanism is shown in Figure 7.  
During the data push process, the exception handing service is activated when an acknowledgment message from the remote server is not received. The service writes error information to the log file and select the operation according to the cause of the error. When it is not a network problem, the data is pushed again. When the cause of the error is a network problem and the tensioning operation has ended, the data in the packet queue is recorded, and the data-pushing service is ended. Otherwise, continue the current data-pushing service.
## 2 Miniaturization of the Microwave System
### Microwave Transmission System Miniaturization Design
We used a 50 \(\Omega\) coaxial line instead of the magnetron antenna for microwave feed in the simulation. We used the water load instead of plasma to match the microwave transmission section impedance for the calculation [15]. For plasmas, the water load corresponds to plasma at a steady state. Water has a dielectric constant of 78.5, comparable to plasma at 890 G with a density of \(1\times 10^{17}\) m\({}^{-3}\). Eventually, since the complete microwave transmission system lacks a circulator and three-pin tuner, the matching waveguide's design is crucial to the system's performance. Right now, transitional waveguide and ridge waveguide are used for microwave system matching. The waveguide system is connected to the source body through a matching waveguide. Electromagnetic simulations are conducted on the entire microwave transmission segment, which extends from the microwave source to the discharge chamber. The effectiveness of the system matching is evaluated based on the results obtained from the electromagnetic simulations. There are three matching strategies in the simulation: 1. Standard length excitation cavity, high voltage isolation waveguide, and transition waveguide. 2. Standard length excitation cavity, high voltage isolation waveguide, and miniaturized ridge waveguide. 3. Shortened length excitation cavity, high voltage isolation waveguide, and miniaturized ridge waveguide. The results of these three schemes shown in Figures 4 and 5, and the electric field, S11 parameters, and power transfer efficiency at the entry to the discharge chamber are used to assess the performance of the microwave system. Figure 4: The electric field distribution at 2.45 GHz for the microwave transmission system of the three schemes is as follows: (**a**) is a regular-length waveguide plus transition waveguide; (**b**) is a regular-length waveguide plus ridge waveguide; (**c**) is a reduced-length waveguide plus ridge waveguide.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
###### Abstract  
The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3 rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method.  
uplink prediction; energy saving; NB-IoT +
Footnote †: journal: Sensors
# Fatigue Damage Assessment of Turbine Runner Blades Considering Sediment Wear
###### Abstract  
The wear phenomenon that occurs on the blades during operation has a significant impact on the fatigue life of the blades. To address the issue of fatigue life assessment for turbine runner blades subjected to increased dynamic stress due to sediment wear, taking a specific high-head hydropower unit's mixed-flow turbine as the research subject, a hydraulic model of the turbine was established. The wear zones of the runner blades are determined based on the distribution of the flow field's velocity and the sediment volume fraction. According to the wear rate formula for runner blade material, the amount of wear on the blades is determined, and the dynamic stress data for the dangerous areas of the blades under different degrees of wear are calculated using a unidirectional fluid-structure coupling method. The load spectrum of the time-stress history data for the dangerous area at different levels of wear was compiled using the rain-flow counting statistical method. The operating time ratios for the flood season and the non-flood season are combined. Based on the fatigue cumulative damage theory, the total fatigue damage at the maximum stress part of the runner blade was calculated for different stages of wear, providing a reference for the life calculation of mixed-flow hydraulic turbines.  
Francis hydraulic turbine; fluid-solid coupling; wear; fatigue damage
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters
### Energous--WattUp  
Energous offers a few solutions that create the WattUp family. Those consists of modules for different applications. Apart from RF energy harvesting over larger distances, the company's products allow for conventional charging. Energous cooperates with a number of different companies that create IoT devices or use e-peas modules. Energous offers the EN22223 module, which is a wireless power receiver that converts RF energy into DC (direct current) [17]. It can be used to power IoT devices.  
Energous provides development kits. The company's solutions use the WattUp Power-Bridge transmitter, which provides enough energy for the RF energy harvester to charge the end device.
## 7 Security Analysis and Cryptographic Guarantees
### Mutual Authentication Guarantees
#### 7.2.2 Key Compromise Impersonation Vs. Forward Secrecy  
It is important to distinguish between forward secrecy (FS) and key compromise impersonation (KCI) resilience, as they address different attack scenarios.  
Forward secrecy guarantees that even if a party's long-term private key is later compromised, previous session keys remain secure due to their derivation from ephemeral, session-specific scalars. In contrast, KCI resilience ensures that compromise of a party's private key does not enable an adversary to impersonate _other_ entities to the compromised party. This means that even if an attacker learns the private key of party \(A\), they cannot pretend to be \(B\) towards \(A\) without also knowing ephemeral secrets.  
Our protocol ensures both properties: FS is achieved through the use of fresh \(\kappa\) values for each session, while KCI resilience follows from the symmetric dependence on both parties' pseudorandom contributions in the session key computation.