esa-sceva/satcom-chunk-collection huggingface dataset:
# Lattice Design of an Intermediate-Energy Electron Storage Ring Dedicated to Materials Research
## 1 Introduction
MBA is shown in Figure 2. The hybrid MBA structure is characterized by two dispersion bumps, where the sextupole magnets for chromaticity correction are placed only within the dispersion bump, and the sextupole magnets satisfy the -I transformation between them, which means that the phase advance between the sextupole magnets is generally \((3\pi,\pi)\)[21]. This can cancel out the nonlinear effects introduced by the sextupole magnets themselves, facilitating better nonlinear dynamic performance. The HOA MBA structure employs distributed sextupole magnets for chromaticity correction, where each unit cell contains chromaticity sextupole magnets, and each unit cell satisfies a certain phase advance to cancel out the nonlinear effects [22; 23]. The HOA MBA has a very large number of magnets in one cell due to several (M-2) small repeating unit cells and two matching cells. When the HOA MBA structure is used in a high-energy storage ring light source, the length of one cell is much larger than that of one cell of the hybrid MBA, under the restriction of the same magnetic strength. The hybrid MBA structure uses a large number of combined dipole-quadrupole magnets and does not use distributed sextupole magnets, and the number of magnets in one unit is moderate. Therefore, the hybrid MBA structure is generally suitable for high-energy large rings, while the HOA MBA structure is more suitable for low-energy small rings. However, DLSRs are not the only direction of the storage ring light source the community is pursuing. We are also witnessing another emerging direction, pioneered by the NanoTerasu project [24], and followed by the SPS-II project [25], which aims at not the utmost brilliance but rather high stability, reliability, and flux with moderate emittance and more relaxed specifications to related instrumentations. In this paper, we follow this trend, and propose a compact intermediate-energy storage ring light source to fill the gap between the third-generation light sources, SSRF and HLS-II, and the fourth-generation light sources (HEPS and HALF) in China, to meet most of the requests from the materials research users in SZLab. This paper is organized as follows: Section 2 gives the linear optics design, which is followed by its nonlinear optics optimization in Section 3. The intra-beam scattering (IBS) and Touschek scattering effects are evaluated in Section 4. Finally, its photon performance with some typical insertion devices is given in Section 5.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### IQRF Alliance  
The IQRF Alliance is an open community of international IoT professionals that aims to deliver wireless IoT devices and solutions based on the IQRF technology [59]. A few examples of hardware devices developed under the IQRF Alliance include the industrial gateway for IQRF networks, the outdoor industrial gateway (IP68 rugged industrial gateway), and the network traffic analysis modules designed for IoT network analysis, to name but a few. Many IQRF Alliance-based technologies are often built on an integrated mesh networking scheme, which aims to provide low power consumption rates using deep sleep modes, and to provide enhanced security solutions based on the Advance Encryption Standard (AES) 128 encryption scheme [59]. In routing terms, the IQRF Alliance are known to advance the IQMESH routing protocol, which is aimed at providing reliable performance in harsh radio frequency (RF) environments [60].  
Formed in 2011, the Wi-SUN Alliance comprises individuals who came together to form a global organization that drives the adoption of interoperable SUN [61]. The alliance focuses on applications including but not limited to energy conservation, automated metering control, and smart cities [62]. Technically, the Wi-SUN Alliance produces test equipment, network emulators, communication modules, and spectrum analyzers [61]. Some Wi-SUN solutions are based on LoRa using LoRa modulation spread spectrum scheme in the sub-GHz band.
## 4 Conclusions  
In this study, the resistance of recycled aggregate self-compacting concrete to sulfate attack was investigated using the mass loss rate, relative dynamic modulus of elasticity, and corrosion resistance factor. Microscopic tests were used to observe and fen the recycled aggregate self-compacting concrete after erosion. The following are the primary conclusions:  
1. With an increase in RCA, the compressive strength and 7/28 days strength ratio first rise and then fall, reaching their maximums at 50% of RCA. This finding suggests that concrete's compressive strength can be increased by adding the right amount of RCA.
2. The sulfate resistance of self-compacting concrete under the influence of wet and dry cycles is remarkable in response to the addition of RCA. Due to the high porosity of recycled aggregates, which creates additional channels for SO\({}_{4}\)\({}^{2-}\) erosion and causes the gradual enlargement of primary and secondary fractures, the integration of increasing amounts of RCA significantly speeds up the pace of degradation at later stages.
3. SiO\({}_{2}\), AFat, and CaCO\({}_{3}\) are the byproducts of sulfate erosion that are discovered by XRD, and reliable results are acquired by microstructure SEM and EDS investigations. The old mortar stuck to the surface of RCA, as revealed by the AFM analysis of ITZ and the SEM analysis of RCA, significantly affects the roughness of ITZ inside RCASCC. The erosion products steadily rise with an increase in the RCA replacement rate, the ITZ becomes rougher, and the resistance to sulfate declines.
4. With an increase in the RCA replacement rate, the resistance of RCASCC to dry and wet cycles of sulfate falls in addition to the internal damage mechanism and the fundamental mechanical properties. Concrete's sulfate resistance rapidly declines, especially when the RCA replacement rate exceeds 50%. The RCA replacement rate should therefore not be higher than 50%.
# Weak-Edge Extraction of Nuclear Plate Fuel Neutron Images at Low Lining Degree
## 3 Edge Extraction Algorithm
### Fixed Threshold Based on Otsu  
Since phase consistency has stability and can determine a common threshold for most images, the fixed threshold method is used for image binarization, and the Otsu is one of the most popular methods in common use. It can automatically generate the optimal threshold value based on the grayscale characteristics of the image, and the threshold T divides the image into an object (A) and a background (B). The criterion function is defined as the variance between the two classes, which can be expressed as follows [32]:  
\[\sigma^{2}=w_{A}(t)*w_{B}(t)(\mu_{A}(t)-\mu_{B}(t))^{2}, \tag{11}\]  
where \(w_{A}(t)\) and \(w_{B}(t)\) are the probabilities of t being  
where \(P_{i}\) can be regarded as an estimation of the mixed probability density function of background and object.  
The algorithm steps can be summarized as follows:  
Step 1: Iterate over all integers from 0 to 255 and use Equation (11) to calculate the inter-class variance (\(\sigma^{2}\)) corresponding to that gray level.  
Step 2: Find the \(t\) that maximizes \(\sigma^{2}\), denoted as the optimal threshold \(T\).  
Step 3: Binarize the image according to \(T\); the pixels whose gray value is above \(T\) are marked as edge points, and those whose gray value is below \(T\) are marked as non-edge points.
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 3 Proposed CHEMA and its Implementation
### GEs  
Another layer of environmental concerns or green effects has been introduced in order to relate the residential energy consumption to the Green House Gases (GHGs) emission or carbon footprint. Carbon footprints are defined as \"the total sets of GHG emission caused by an organization, event, product or person\" [27]. GHGs in the earth's atmosphere are water vapor, carbon dioxide, methane, nitrous oxide and ozone. These gases absorb and emit radiations within thermal infrared range and cause the GEs.  
In order to make the calculations simple, [PERSON], [PERSON], and [PERSON] have suggested the definition as: \"the measure of total amount of CO\({}_{2}\) and Methane (CH\({}_{4}\)) emission of a defined population, system or activity, considering all relevant sources, sinks storage within the spatial and temporal boundaries of population, system or activity of interest\" [27].  
The reduction of carbon footprints by developing alternative projects such as solar and wind energy is called carbon offsetting [28]. GHGs can be measured by monitoring the emissions continuously or by estimating the data related to emissions, _i.e._, (amount of fuel) and applying relevant conversion factors. Some of the conversion factors are calorific values, emission factors and oxidation factors. These conversion factors enable us to convert activity data into fuel liters, miles driven, tons of waste into kilograms of carbon dioxide equivalent, (CO\({}_{2}\))e, which is adopted as universal unit of global warming potential. There are two categories of GHG emissions. The first is direct GHG emission, which is the emission at the point of use of a fuel or energy carrier (or in the case of electricity, prior to the point of generation). In other words, the emission from sources which are owned or controlled by reporting entity. The second type is indirect GHG emission, which is the emission emitted by the use of a fuel or energy carrier, _i.e._, as a result of extracting and transforming energy sources. Indirect emission, _i.e._, purchased electricity, extraction and production of purchased fuels and transport related activities in vehicles that are not controlled by a reporting entity. There are various parameters included in the GHG conversion factors for electricity consumption [28]. Calculations of carbon emissions are performed as follows:  
If electricity consumption = 20,000 kWh, and emission factor [29] = 0.000689551 metric tons CO\({}_{2}\)/kWh, then  
\[Amount\ of\ carbon\ dioxide=EC*EF, \tag{11}\]  
where EC shows the electricity consumed in kWh, and EF represents the emission factor in Kg/kWh. Amount of carbon dioxide = 13791 Kg. (for above values of EC and EF).  
These results are compared with emission of carbon from different sources in Table 1[29]. Relative reduction of carbon emission with different cases of CHEMA is provided in the results section.
## 5 Conclusions  
The present research clearly shows that statistical uncertainty related to soil properties may be significant and that it can only be minimized by performing targeted field investigation; the latter is defined by the number and location of sampling points. As samples are taken from a material field (i.e., the ground), which simultaneously is a stress field (stresses caused by the self-weight of the soil and any external load), the location of the optimal sampling points is affected by the coexistence of these two fields. Two main sampling strategies were investigated--namely, sampling from a single point and sampling from a domain--through an extensive parametric analysis.  
One of the main findings of the present analysis is that the optimal horizontal sampling location in the active state is at x/\(H\) = 0--that is, immediately adjacent to the wall face. Regarding the depth of  
Figure 23: \(F_{predicted}/F_{``actual''}\) vs. \(x/H\) curves using both mean and characteristic values (dashed and solid lines respectively) for \(FS=1\) and 1.3. Figure referring to the case of a sliding wall and to two sampling domain cases (\(d_{d}/H=1\) (figure (**a**)) and to \(d_{d}/H=0.25\) (figure (**b**))). The reference wall was used. Soil characteristics as shown in Table 1 (Example #3).  
the _optimal sampling point_, it was found that this lies at depth greater than two-thirds or one-half of the wall height for the sliding and rotational mode of failure respectively; the exact depth depends on the spatial correlation length of the soil. For the _optimal sampling domain length_, it is advisable that the entire wall height be considered.  
In addition, it was observed that the benefit from a targeted field investigation is much greater as compared to the benefit gained using characteristic soil property values. Moreover, despite the conservatism which is inserted in the analysis using the characteristic value concept, the characteristic values alone, as shown, cannot guaranty a conservative enough engineering study. The safety level can be increased by applying a statistical uncertainty partial factor (similar to the model factor \(\gamma_{R}\) used by Eurocode 7) or a unified and more conservative model factor to the resistances, which will absorb the statistical uncertainties related to the soil.  
Conceptualization, L.P. and P.C; methodology, P.C. and L.P.; software, P.C.; validation, E.G.; formal analysis, P.C.; writing--original draft preparation, P.C.; writing--review and editing, P.C., L.P. and E.G.; visualization, P.C.; supervision, L.P. and E.G.  
This research was funded by the Cyprus University of Technology, grant number EX-20081.  
The authors declare  
\begin{tabular}{l l} \(\
u\) & Poisson's ratio of soil \\ \(\tau\) & Lag distance or separation distance between two measurements \\ \(\phi^{\prime}\) & drained friction angle \\ \end{tabular}
## 4 Implementation
### Proposed Scheme
#### 4.4.2 Undersampling  
When the frame rate of the received camera is lower than the transmitter light source, undersampling occurs. In this case, the DS is missed, confusing the packet merge function. By applying an eligible SN to each packet, all the missing payload can be recovered easily. The SN value of n is represented by the DS received from the packet n. Similarly, the SN value of n + 1 is represented by the DS received from the packet n + 1. By comparing two continuous SN values, the proposed system can verify that the packet n is dropped, and the payload could be missed if the actual DS possesses an SN value of n + 2. Moreover, the  
Figure 12: The spatial frame format in an LED array.  
length of the SN is considered to calculate the number of missed payloads. For instance, if the SN length is 4 bits, the system can detect a maximum of seven missed payloads. If the system detects the two non-adjacent of the SN value within two adjacent payloads (e.g., n and n + 2), the missed payload n + 1 is found.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
###### Abstract  
Transitioning to sustainable energy systems is crucial for reducing greenhouse gas (GHG) emissions, especially in remote industrial operations where diesel generators remain the dominant power source. This study examines the feasibility of integrating a redox flow battery (RFB) storage system to optimize wind energy utilization at the Raglan mining site in northern Canada, with the goal of reducing diesel dependency, enhancing grid stability, and improving energy security. To evaluate the effectiveness of this hybrid system, a MATLAB R2024b-based simulation model was developed, incorporating wind energy forecasting, load demand analysis, and economic feasibility assessments across multiple storage and wind penetration scenarios. Results indicate that deploying 12 additional E-115 wind turbines combined with a 20 MW/160 MWh redox flow battery system could lead to diesel savings of up to 63.98%, reducing CO\({}_{2}\) emissions by 68,000 tonnes annually. However, the study also highlights a key economic challenge: the high Levelized Cost of Storage (LCOS) of CAD (Canadian dollars) 7831/MWh, which remains a barrier to large-scale implementation. For the scenario with high diesel economy, the LCOS was found to be CAD 6110/MWh, and the corresponding LCOE was CAD 590/MWh. While RFB integration improves system reliability, its economic viability depends on key factors, including reductions in electrolyte costs, advancements in operational efficiency, and supportive policy frameworks. This study presents a comprehensive methodology for evaluating energy storage in off-grid industrial sites and identifies key challenges in scaling up renewable energy adoption for remote mining operations.  
decarbonization; renewable energies; energy storage; redox flow batteries; wind energy; economic analysis Article
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
###### Abstract  
One of the technologies used for localization is GNSS (Global Navigation Satellite Systems), which is exposed to many phenomena, i.e., the occurrence of terrain covers, reflections of the radio waves, and the multipath nature of the radio waves in the propagation environment. To increase the resistance to environmental phenomena, special types of antennas are used, which include, among others, choke ring antennas. The article describes the design and construction of the choke ring antenna and the impact of the mentioned device on the quality of GNSS positioning. The accuracy of the constructed antenna (based on selected accuracy measures: DRMS, 2 DRMS, SEP, MRSE, SAE) is calculated together with positioning errors determined for two types of antennas: choke ring antenna and the dedicated antenna supplied by the manufacturer in RTK mode. The results confirm that the designed choke ring antenna can be used for positioning without significantly losing accuracy in the RTK mode.  
GNSS; choke ring antenna; GNSS accuracy; DOP; ECEF +
Footnote †: journal: Accepted: 9 January 2023
# Bit Streaming Processing Algorithms for Intelligent Hardware Converters
###### Abstract  
The need to transfer the primary data conversions close to the sensors, to the endpoints of monitoring systems, as well as in IoT terminal devices makes the development of new approaches to computing and the design of appropriate algorithms relevant. The article shows stream processing algorithms that provide functional transformations of signals presented in bit stream form (single pulse streams, PWM signal streams) and binary codes at the same time. In such algorithms, the computational process is based on discretization, pulse frequency sweep and pulse-width sweep of codes as well as organization of parallel-serial processing. The suggested principles of algorithm organization are based on the fact that the computation is considered not as an event associated with calculation but as a continuous process of a result formation. The transition to algorithmic representations proposed by the authors makes it possible to obtain universal behavioral descriptions, independently of the specific hardware on which their implementation is performed.  
bit stream algorithms; digital converters; intelligent equipment; fault-tolerant; computational process; programmable logic device; VerilogHDL
# Design and Material Optimization of Oil Plant Piping Structure for Mitigating Erosion Wear
## 2 CFD Modeling
### Discrete Phase Model  
The DPM was used in CFD simulations to track the motion of bitumen and CO\({}_{2}\) in a fluid flow. The DPM takes a Lagrangian approach, which means that it tracks the motion of individual particles as they go through the fluid. The particle trajectories were acquired by integrating the motion equation of the particles under Lagrangian coordinates [29; 32; 33]. The governing equation for linear and angular particle motion is proposed according to [PERSON]'s second law, considering drag force, pressure gradient force, virtual mass force, and buoyancy force, as follows in Equation (8) [11]:  
\[\frac{d\overrightarrow{U}_{p}}{dt}=\overrightarrow{F_{D}}+\overrightarrow{F _{P}}+\overrightarrow{F_{G}}+\overrightarrow{F_{VM}} \tag{8}\]  
The drag force, denoted as \(\overrightarrow{F_{D}}\), is the force produced by the contact between the solid particles and adjacent fluid. Among all the forces operating on particles as shown in Equation (9), the drag force \(\overrightarrow{F_{D}}\) is the most significant force acting on particles. The equation of \(\overrightarrow{F_{D}}\) is shown in Equation (9):  
\[F_{D}=\frac{18\mu}{{\rho_{pd}}_{p}^{2}}\frac{C_{d}Re_{p}}{24}\Big{(} \overrightarrow{u}-\overrightarrow{u_{p}}\Big{)} \tag{9}\]  
where \(\overrightarrow{u_{p}}\) signifies the velocity vector of the particle, \(\rm{d_{p}}\) represents the particle diameter, \(\rho_{p}\) is the particles density, and \(Re_{p}\) is the Reynolds number of particles, as follows in Equation (10):  
\[Re_{p}=\frac{\rho d_{p}\Big{|}\overrightarrow{u_{p}}-\overrightarrow{u}\Big{|}} {\mu} \tag{10}\]  
Whereas in Equation (9), \(C_{d}\) signifies the drag coefficient, as follows in Equation (11):  
\[C_{d}=a_{1}+\frac{a_{2}}{Re_{p}}+\frac{a_{3}}{Re^{2}p} \tag{11}\]  
where \(a_{1}\), \(a_{2}\), and \(a_{3}\) are the particles constants. The particle's pressure gradient force \(\overrightarrow{F_{P}}\), as follows in Equation (8), measures the pressure changes across the particles, as follows in Equation (12):\[\vec{F}_{P}=\left(\frac{\rho}{\rho_{P}}\right)\
abla P \tag{12}\]  
The buoyancy force exerted on a particle, denoted as \(F_{G}\), follows below in Equation (13):  
\[F_{G}=\left(\frac{\rho_{p}-\rho_{f}}{\rho_{p}}\right) \tag{13}\]
## 3 The Problem
In order to solve the above problem, this paper proposes a routing decision model, named ANCR, which can sense network coding opportunities and use high-quality links to create coding opportunities to enhance the overall performance of a network. This approach not only passively applies the existing network coding opportunities as the traditional approaches [19], but also uses the high-quality links to create more coding opportunities. Its main idea is as following: When the quality of a link between two nodes is not good enough (as the link between D and E in Figure 3b), that is to say, in the case of different-success-rate links, we will use another node (as the node C in Figure 3b) which is linked with the two nodes and respectively has the better link quality as the forwarding node. In this way, the new forwarding node can have a coding opportunity to realize cooperative  
Figure 3: Illustration of routing with network coding. (**a**) Network with the same transmission success rate links; (**b**) Network with different transmission success rate links. data forwarding. If there is a decrease in the number of transmission to bring an increase in the network throughput, then the whole network performance would be improved. Since the quality of the link CD is as good as that of the link CE, we take C as the intermediate forwarding node. So the initial path \(A\to B\to D\to E\) will be changed to \(A\to B\to D\to C\to E\), and the other initial path \(F\to E\to C\to B\) will be changed to \(F\to E\to C\to D\to B\). Then there are the coding opportunities at D and C. The total number of transmission is only six (the statistical result for multiple transmission tasks [12]). Compared with the previous strategy that uses D as the forwarding node, the total number of transmission is reduced by three. The direct effect is that the transmission time and the energy consumption of the nodes are all saved. The indirect effect is that the network throughput is improved.
## 2 Satellite Systems and Errors in Position Determination  
Satellite systems are an integral part of human civilization nowadays. They allow positioning as well as navigation in real time. Thanks to even the simplest smart device, it is possible to reach a specific destination by the shortest and the fastest route, and this is just the outset.
## 3 Experimental Results  
To evaluate the performance of the proposed NSSRD framework, various tests were conducted by varying the drone altitude and the drone distance from sensors to generate diverse datasets. This approach aimed to validate the superiority of the proposed small drone detection model through comparisons with existing state-of-the-art models with similar structures.  
Figure 6: Example of resolution enhancement for small drones using SRCNN: (**a**) original image; (**b**) enhanced-resolution image.