esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
###### Abstract  
Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)--has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA's average bias (\(-\)2.1 m/s) was significantly smaller than the average bias in Merged (\(-\)4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed.  
CYGNSS; near-surface ocean winds; tropical cyclones; triple collocation analysis; instrument validation; GNSS-R; coastal winds +
Footnote †: journal: remote sensing  
0000-0002-4607-3999][PERSON]  
1 School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia; [EMAIL_ADDRESS] (A.W.J.; [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (L.H.)
# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
## 5 Conclusions  
For rapidly deploying the 5G network, we designed a placement method of the 5G edge nodes based on hotspots of mobile users. In this method, we first extracted the user trajectory data from the object area and clustered all locations from the user trajectory to get the cluster areas. Further, we calculated the characteristics, such as the number of users and duration time in each cluster. Then, we extracted the hotspots from all clusters based on two thresholds. Then, we defined two parameters, U(i) and B(i), and took the weighted sum of the two parameters as the optimization objective to extract the candidate position of 5G edge nodes. We also conducted a series of experiments to evaluate our proposed methods using the GeoLife dataset. The test results show that when the number of deployed edge nodes is the same, compared with Top-U, the high load utilization rate of our method can be increased up to 7.69%; compared with the Top-T, the bandwidth reduction rate of our method can be improved up to 6.34%. Our work can be used to guide the placement of 5G edge nodes of Telcos and improve the overall efficiency of 5G base station deployment. Further researches will include the analysis of the network applications used by users, identifying the features of each application (such as computing and storage requirements), and the study of the configuration of suitable storage and computing resources for the edge nodes to meet the application requirements of the hotspot areas.  
Conceptualization, X.G.; Methodology, R.G.; Software, R.G.; Validation, X.G.; Resources, X.Z. and J.H.; Writing--original draft, R.G.; Writing--review & editing, X.G.; Visualization, X.G.; Supervision, X.Z. and J.H. All authors have read and agreed to the published version of the manuscript.  
The Science and Technology Project in Shaanxi Province of China under Grant 2023-JC-ZD-38, National Key R&D Projects of China under Grant 2018 YFB1800304.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflict of interest.  
Figure 8: The cluster results of our dataset and the candidate locations of 5G edge nodes.
# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 2 Market and Trends of Memory Technologies  
### Technology Trends of Nonvolatile Memories  
#### 2.1.1 Nonvolatile Memory Market  
The impressive growth of the NVM market was made possible by the advent of flash memories, NOR first and NAND later, and has been fueled by the development of battery-supplied wearable electronics [11]. Thus, successive memory revolutions were supported by a novel application that allowed the market to exponentially increase. Mobile phones, PDAs, MP3 players, and digital cameras were the drivers in the 1990s [11]. With the emergence of smartphones, tablets, USB drives, and SSD (solid state drives), a new driving force led to a NAND market increase in the digital era of the 2000s [12]. More recently, artificial intelligence and other new applications offer a strong opportunity for emerging memories using new switching mechanisms. The next 10 years are expected to provide many growth opportunities for the semiconductor industry, with a continuous  
Figure 1: Main milestones in the history of nonvolatile memories.  
increase in technical and business challenges. In 2026, the NAND Flash market is predicted to represent the current GDP of medium-sized countries. Competitive pressures within many segments of the semiconductor industry will increase significantly in the future, but semiconductor companies that have innovative businesses as well as product strategies are expected to achieve financial success [13].  
The memory market can be divided into two categories, high capacity standalone memories and embedded memories where the memory device is integrated into a core CMOS process flow. These two technologies are presented and described in the following sections.
## 3 Methodology
### Problem Definition  
Due to the critical importance of ore hauling for production, this research aims to determine the minimum amount of hauling equipment necessary to minimize both the costs associated with equipment usage and the completion time of the production target within a defined time horizon. Ore hauling operations are pivotal for continuously supplying essential minerals to production plants. However, technical decisions regarding the required equipment are often made based on experience and under time pressure. While these decisions may be quick, they might only sometimes be optimal, potentially leading to increased costs or delays that impact plant production.  
This research addresses the inefficiency and potential sub-optimality of traditional decision-making processes in determining the minimum amount of hauling equipment needed to realize the production target. This can result in either overestimating or underestimating hauling equipment, leading to unnecessary expenses or bottlenecks during ore transport. By developing a mathematical model that considers the parallel machine scheduling problem with different speeds and release times, we aim to provide a systematic and data-driven approach to optimizing equipment allocation.  
A detailed analysis of how costs relate to completion time could reveal valuable scenarios, enabling more informed and appropriate decisions in the event of any contingency in the transportation operation. This model will allow decision-makers to balance the trade-offs between equipment costs and operational efficiency, ensuring that the right amount of equipment is deployed to meet production targets without incurring unnecessary expenses.  
The problem can be conceptualized as a parallel machine scheduling problem with different speeds and release times. While the primary focus is a tactical decision to determine the minimum amount of hauling equipment and analyze costs related to completion time, an adaptation to this scheduling problem under an integer programming model is executed. According to the notation established by [PERSON] [52], an operations scheduling problem is defined by three fields (\(\alpha|\beta|\gamma\)): \(\alpha\) represents the system configuration, \(\beta\) the constraints and characteristics of the system, and \(\gamma\) the objective function. Using this framework and following the nomenclature described by [PERSON] and [PERSON] [53], the system configuration corresponds to parallel machines with different speeds (\(Q_{m}\)), where these machines are the loading equipment with varying processing times. A significant system characteristic includes the release times (\(r_{i}\)) incorporated within the hauling time. The objective function aims to minimize both costs and completion time. Figure 2 illustrates the schedule of the ore hauling operation conceptualized as the problem previously mentioned.
Sensors_**2012**, _12_, 1612-1624; doi:10.3390/s120201612  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Fast Decision Algorithms in Low-Power Embedded Processors for Quality-of-Service Based Connectivity of Mobile Sensors in Heterogeneous Wireless Sensor Networks  
**Mar a D. [PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Department of Technologies of Computers and Communications, Polytechnic School, University of Extremadura, Campus Universitario s/n, 10003 C c eeres, Spain;  
E-Mails: [PERSON]ESS] (M.D.J.-S.); [PERSON]_ADDRESS] (M.A.V.-R.);  
[PERSON]DDRESS] (J.M.S.-P.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-927-257-264; Fax: +34-927-257-187.  
_Received: 6 December 2011; in revised form: 13 January 2012 / Accepted: 2 February 2012 / Published: 7 February 2012_  
**Abstract:** When a mobile wireless sensor is moving along heterogeneous wireless sensor networks, it can be under the coverage of more than one network many times. In these situations, the Vertical Handoff process can happen, where the mobile sensor decides to change its connection from a network to the best network among the available ones according to their quality of service characteristics. A fitness function is used for the handoff decision, being desirable to minimize it. This is an optimization problem which consists of the adjustment of a set of weights for the quality of service. Solving this problem efficiently is relevant to heterogeneous wireless sensor networks in many advanced applications. Numerous works can be found in the literature dealing with the vertical handoff decision, although they all suffer from the same shortfall: a non-comparable efficiency. Therefore, the aim of this work is twofold: first, to develop a fast decision algorithm that explores the entire space of possible combinations of weights, searching that one that minimizes the fitness function; and second, to design and implement a system on chip architecture based on reconfigurable hardware and embedded processors to achieve several goals necessary for competitive mobile terminals: good performance, low power consumption, low economic cost, and small area integration.  
keywords: heterogeneous wireless sensor networks; mobility; vertical handoff; quality of service; fitness; embedded processors; FPGA +
Footnote †: journal: Journal of Power and Energy
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion  
In meeting our objective, our study proposes a storage security solution that consolidates different types of spatial files with an easy-to-use encryption solution with reasonable encryption times for protection during storage. For a practical deployment of such a security solution, we evaluate its security and usability (including performance).
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## Nomenclature  
\(w\)Fauted phases  
\(v\)and \(u\)Fauted phases for generalized expression of line-to-line (LL) faults  
\((p)\)Indicator of the prefault measurements  
\((f)\)Indicator of the fault measurements  
\((+)\)Indicator of positive sequence componentes  
\((-)\)Indicator of negative sequence componentes  
\(\Im\)Imaginary part  
RReal part  
\(V_{F_{w}}\)Fault point voltage on phase w  
\(I_{F_{w}}\)Fault current on phase w  
\(V_{k_{w}}\)Voltage on phase w and terminal k.  
\(I_{k,F}\)Three phase currents of fault from k to the fault point (F).  
\(I\)Third-order identity matrix  
\(x\)Calculated fault distance  
\begin{tabular}{l l} \(d\) & Distance from the substation to the fault point \\ \(nt\) & Number of sections analyzed by the algorithm \\ \(L_{i}\) & Length of the \(i\)-th distribution feeder \\ \(L_{nt}\) & Length of the last distribution line analyzed \\ \(Z_{F_{ab,c,g}}\) & Fault impedance \\ \(\mathbf{V}_{k}\) & Three-phase voltages vector in the \(k\)-th terminal. \\ \(\mathbf{Y}_{abc}\) & Line shunt impedance matrix \\ \(\mathbf{Z}_{abc}\) & Line series impedance matrix \\ \(\mathbf{V}_{DER_{k}}\) & Three-phase voltages vector in the \(k\)-th terminal of the DER \\ \(I_{DER_{k}}\) & Three-phase current vector in the \(k\)-th terminal of the DER \\ \(I_{DER_{k+1}}\) & Three-phase current vector in the \(k+1\) terminal of the DER \\ \(\mathbf{Z}_{k,k+1}\) & Line series impedance matrix between terminals \(k\) and \(k+1\). \\ \(\mathbf{Y}_{k}\) & Shunt admittance matrix of line between terminals \(k\) and \(k+1\) \\ \(I_{L_{k}}\) & Three-phase current vector flowing through \(k\)-th lateral. \\ \(\mathbf{E}\) & Internal voltage vector of the DER \\ \(\mathbf{V}_{G}\) & Three-phase terminal \(G\) voltage vector of the DER \\ \(I_{G}\) & Three-phase terminal \(G\) current vector of the DER \\ \(\mathbf{Z}_{DER}\) & Impedance matrix of the DER \\ \(\mathbf{y}_{DER}\) & Admittance matrix of the DER \\ \(\mathbf{I}_{sat}\) & Saturation current of the inverter \\ \(I_{L}\) & Reference current that crosses the filter inductor \\ \(c_{f}\) & Capacitance of the filter inverter \\ \(L_{f}\) & Inductance of the filter inverter \\ \(\mathbf{V}_{Th}\) & Downstream system Thevenin's voltage vector from the fault point \\ \(\mathbf{Y}_{Th}\) & Downstream system Thevenin's admittance matrix from the fault point \\ \(\mathbf{Y}_{Ig_{g}}\) & Diagonal elements of the self-admittance matrix of the DER lateral \\ \(\mathbf{Y}_{Ig_{g}}\) & Off-diagonal elements of the admittance matrix of the DER lateral \\ \(\mathbf{V}^{\prime}_{L_{G_{k}}}\) & Fault voltage vector in the \(k\)-th terminal of the DER radial \\ \(\mathbf{f}^{\prime}_{g,k+1}\) & Fault current vector between terminals \(k\) and \(k+1\) of the DER radial \\ \(\mathbf{\Omega}\mathbf{i}\) & Set of connected elements in the \(i\)-th node including loads and shunt elements \\ \end{tabular}
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Latency in Satellite Networks  
Communication latency is a crucial and fundamental performance indicator for wireless communication that ensures service quality. The goal of communication network development, from 1G to 5G, has always been to cut down on communication latency. In general, delays in transmission, propagation, processing, and queuing make up communnication latency. While the other three types of delays may be equivalent for satellite and terrestrial networks, satellite networks have substantially longer propagation delays than terrestrial networks because of their high-altitude orbits.  
In satellites, there are two types of latency: fixed, owing to propagation delay; and dynamic, due to transmission, processing, and queuing delays [31]. Due to their high altitude and mobility, the inherent latency of GEO and non-GEO satellites may impact overall system performance. Furthermore, each link in the infrastructure may have its own processing time, causing additional processing delays. Designing an effective HSTN architecture that does not compromise system performance while also taking into account the satellite's and associated systems' inherent latency is a difficult task.
## 4 Results and Discussion
### Establishment and Application of Evaluation Models
#### 4.1.2 Modelling
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unstandardised**} & \multicolumn{2}{c}{**Standardised**} & & & & & \\ \multicolumn{2}{c}{**Coefficient**} & \multicolumn{2}{c}{**Coefficient**} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(\mathbf{R^{2}}\)} & & & & \\ \cline{2-2} \cline{5-8}  & \multicolumn{2}{c}{**\(B\)**} & \multicolumn{2}{c}{**Standard**} & & & & & & \\ \multicolumn{2}{c}{**Error**} & \multicolumn{2}{c}{**\(Beta\)**} & & & & & & \\ \hline a constant & \multirow{2}{*}{\(-0.104\)} & \multirow{2}{*}{0.023} & \multirow{2}{*}{-} & \multirow{2}{*}{\(-4.632\)} & \multirow{2}{*}{0.000} & & & & \\ \cline{2-2} \cline{5-8}  & & & & & & 0.939 & 0.938 & \(F(1,50)=771.383\), \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Probit \\ value \\ \end{tabular} } & \multirow{2}{*}{0.122} & \multirow{2}{*}{0.004} & \multirow{2}{*}{0.969} & \multirow{2}{*}{27.774} & \multirow{2}{*}{0.000} & & & \(p=0.000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Regression models. Figure 3: Contour plot of relative proximity using the TOPSIS method. Based on the fitting order of WRSR values and the corresponding common number grading method within the rational grading framework, the grouting effect was classified into three levels: relatively weak zone, qualified zone, and excellent zone. This classification was achieved using the Delphi method, as outlined in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grade** & **Percentile Thresholds** & **Probit Threshold** & **RSR Threshold (Fitted Value)** \\ \hline Relatively weak zone—1 & \textless{}15.866 & \textless{}4 & \textless{}0.382 \\ \hline Eligible area—2 & 15.866–84.134 & 4–6 & 0.382– \\ \hline Distinguished Area—3 & \textgreater{}84.134 & \textgreater{}6 & 0.625– \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Thresholds for slotting and ranking. Figure 4: Contour plots of fitted values by RSR method of binning. Figure 5: RSR method fitting curve.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Autonomous Boat Development
#### 2.1.2 Propulsion and Steering  
An off-the-shelf electric trolling motor (Figure 8a, Minn Kota Powerdrive 45, Johnson Outdoors, Racine, WI, 200.2 N thrust rating) was selected for propulsion, owing to its relatively high efficiency and thrust capability and its quiet operation. For remote steering, the factory-installed steering motor was interfaced to a motor controller with remote control (RC) input and feedback capabilities (Pololu Jrk 12v12, Pololu Corporation, Las Vegas, NV, USA). Measurement of the shaft position for closed-loop control was implemented through a custom-mounted potentiometer rotationally coupled to the shaft via mechanical gears on each of these components (Figure 8b). A translating, spring-loaded frame and oversized gear teeth accommodated bow-to-stern shaft wobble, inherent in the system, to maintain gear meshing and avoid damage to the potentiometer. An airboat propulsion system, consisting of a direct current (DC) motor-driven propeller on a RC-servo-actuated rotating platform, was also evaluated. Despite its impressive steering capabilities (0.2 m turning radius) and low entanglement risk, this system was thrust-limited (22.7 N max thrust), inefficient with regards to power consumption, noisy, and highly sensitive to wind and wave disturbances.  
Figure 8: Propulsion and steering systems: (**a**) Minn Kota marine propulsion unit and (**b**) rotational potentiometer used for shaft position feedback.  
Figure 7: Aluminum struts bolted within the pontoons for enhanced lateral support: (**a**) before foam filling); (**b**) urethane foam filling process.
# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 3 Results
### HVAC Upgrade ROI vs. System Age Relationship
Despite improving thermal comfort and performance, it has a high cost and relatively moderate energy savings limit its economic competitiveness, making it less suitable as a first-line intervention. Similarly, Solar Panel Installation, while often associated with environmental benefits, demonstrates ROI values between 1.15 and 1.27 and payback periods ranging from 13 to over 15 years, suggesting that, under current market conditions and without incentives, it is less financially attractive compared to energy efficiency measures. Overall, Table 1 reinforces the necessity of tailoring retrofit strategies to the specific characteristics of building clusters. Interventions should be selected not only based on potential energy savings but also on their economic performance within the context of building typologies. This ensures optimal resource allocation and maximizes both environmental and financial impact. Smart Home Systems, HVAC System Upgrades, and Wall Insulation emerge as the most cost-effective solutions in their respective clusters, offering the best ROI and the shortest payback periods. Cost-effectiveness is evaluated excluding subsidies or incentives; values may shift under incentive regimes.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### ML Techniques  
Generally, ML techniques are used in many estimation scenarios. Readers are referred to [11] for a basic review of ML methods. The ML technique requires both noise and signal models. These models depend on the parameters estimated by the ML technique. Finding the parameters of the models that will mostly explain the observed data is the problem in ML estimation. This paper applies two earlier developed ML techniques in the subspace domain to SAA processing.