esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The UKF strikes a balance between computational complexity and estimation accuracy, making it more suitable for real-time applications. Compared to Moving Window Filters (MWFs), which excel at smoothing but are less effective in dynamic state estimation [14], the UKF provides more stable and precise results in complex dynamic systems. Similarly, while neural networks capture complex nonlinear relationships through data-driven training [1; 2; 13], they require large datasets and are better suited to pattern recognition tasks. UKF, on the other hand, excels in real-time state estimation and noise filtering in systems with strong prior knowledge, offering enhancements in real-time performance and computational efficiency. Given these strengths, the UKF is a superior choice for estimating the aluminum extraction flow rate in the highly nonlinear and noisy aluminum electrolysis system. It significantly improves the accuracy and timeliness of state estimation, making it well suited for practical applications in complex dynamic systems. As highlighted earlier, the accurate calculation of the pre-close wind volume also depends on determining the pre-close wind volume coefficient. Current methods often overlook this critical aspect. Therefore, this study proposes a novel method for adjusting the dynamics of the pre-close wind volume coefficient, using the aluminum extraction deviation as the basis. This deviation, defined as the difference between the planned and actual aluminum output, is filtered using an Average Filter (AF) to enhance the calculation accuracy. The AF is computationally simple, highly stable, and widely applicable, performing exceptionally well in systems with minimal noise [15; 16]. Improving the precision of coefficient calculation has significant implications for enhancing the calculation of the pre-close wind volume and the overall accuracy of control. This paper introduces a precise pre-close wind volume calculation algorithm in order to provide a theoretical foundation for accurate aluminum extraction control in aluminum electrolysis. The remainder of this paper is organized as follows: Section 2 details the calculation of the aluminum extraction flow rate; Section 3 discusses the precise determination of the pre-close wind volume coefficient; Section 4 presents the results and discussion, highlighting the advantages of the UKF algorithm and the effectiveness of the proposed coefficient calculation method, as well as identifying existing shortcomings; and Section 5 concludes the study.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### V2X Application Enabler (VAE) Support
#### 4.2.2 Access Network  
The access network describes the 3 GPP standardization reports and specifications of the radio access network (RAN-TSG). Apart from other supports, band combinations and RAN aspects for 4G & 5G band and V2X are supported by the access network.
## 5 Matching-Iteration-Based Joint User Access and Resource Allocation Algorithm
### Iteration-Based User Access and Resource Allocation Algorithm  
A stable matching between the CUs and the D2D users in the licensed spectrum has been derived by Algorithm 1. Then, the unpaired D2D users and CUs can access to the unlicensed spectrum for communications by using the duty-cycle method. However, many users occupying the unlicensed spectrum to communicate will cause huge interference to the Wi-Fi users. To guarantee the minimum data rate of the Wi-Fi users and avoid severe interference, the maximal value of time slots for D2D users and CUs, i.e., \(\rho_{max}\), can be obtained by:  
\[\rho_{max}=1-\frac{WR_{w}^{t}}{R_{w}^{us}}. \tag{19}\]  
The iteration-based user access and resource allocation algorithm are summarized in Algorithm 2. According to the duty-cycle mechanism, we found that the QoS of Wi-Fi users in the simulated system model will not be guaranteed in the simulated system model, \"20\" represents the maximum number of cellular users and D2D users accessing the unlicensed spectrum, which can guarantee the QoS of Wi-Fi users and make LTE-U and D2D-U be good neighbors for Wi-Fi users. Moreover, \"\(R_{new}-R<0.5*\text{eps}\)\" means that in the extreme problem, \"\(R_{new}-R\) becomes 0\". We first swap the CUs in licensed spectrum and the CUs in unlicensed spectrum orderly, and then swap the D2D users in licensed spectrum and the D2D users in unlicensed spectrum sequentially. The data rate of all users is needed to recalculate after each exchange operation according to (17). If the system rate increases after the exchange operation, we will exchange the accessing modes for these two users. The above steps repeat until the increase of the sum rate is less than a setting value.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network
### Hardware
#### 3.1.2 End Devices  
Each end device or thermometer is composed by (Figure 4a): (i) a platinum resistance temperature detector (RTD), (ii) a resistance-to-digital converter, (iii) a Ra-02 communications module, and (iv) a control unit (microcontroller) for the system management. A lithium battery was used as power supply. In order to protect these elements from the corrosive volcanic environment and to facilitate the transport and installation, end devices were housed in a PVC tube 60 cm long and with a 32-mm inside diameter. Each tube had at one end a 2-dBi antenna and at the other end an aluminium tip with a specific shape favoring heat distribution. For the temperature measurement, commercial sensors were not used, because they do not resist the corrosion of the acid gases that are characteristic of volcanic environments. Instead, PT100 sensors were placed inside the aluminium tip (Figure 4a), providing protection against acid gases. Thermometers were buried 40 cm deep to measure soil temperature and only the antenna was visible, which was raised 20 cm above the ground.
###### Abstract  
The purpose of this paper was to study the influence of cartographic scale and flight design on data acquisition using unmanned aerial systems (UASs) to create augmented reality 3D geovisualization of geosites. The relationship between geographical and cartographic scales, the spatial resolution of UAS-acquired images, along with their relationship with the produced 3D models of geosites, were investigated. Additionally, the lighting of the produced 3D models was examined as a key visual variable in the 3D space. Furthermore, the adaptation of the 360\({}^{\circ}\) panoramas as environmental lighting parameters was considered. The geosite selected as a case study was the gorge of the river Voulgaris in the western part of the island of Lesvos, which is located in the northeastern part of the Aegean Sea in Greece. The methodology applied consisted of four pillars: (i) scale-variant flight planning, (ii) data acquisition, (iii) data processing, (iv) AR, 3D geovisualization. Based on the geographic and cartographic scales, the flight design calculates the most appropriate flight parameters (height, speed, and image overlaps) to achieve the desired spatial resolution (3 cm) capable of illustrating all the scale-variant details of the geosite when mapped in 3D. High-resolution oblique aerial images and 360\({}^{\circ}\) panoramic aerial images were acquired using scale-variant flight plans. The data were processed using image processing algorithms to produce 3D models and create mosaic panoramas. The 3D geovisualization of the geosite selected was created using the textured 3D model produced from the aerial images. The panoramic images were converted to high-dynamic-range image (HDRI) panoramas and used as a background to the 3D model. The geovisualization was transferred and displayed in the virtual space where the panoramas were used as a light source, thus enlightening the model. Data acquisition and flight planning were crucial scale-variant steps in the 3D geovisualization. These two processes comprised the most important factors in 3D geovisualization creation embedded in the virtual space as they designated the geometry of the 3D model. The use of panoramas as the illumination parameter of an outdoor 3D scene of a geosite contributed significantly to its photorealistic performance into the 3D augmented reality and virtual space.  
UAS; scales; flight planning; 3D mapping; 3D geovisualization; AR; geosites; panorama +
Footnote †: journal: Applied Sciences
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Regression
#### 3.6.1 Noise Figure vs. Input Power  
From the formula shown in Equation (9), it is clear that the noise figure is mainly influenced by three variables: \(P_{ASE_{AMP}}\), \(G\), and \(f\). The input power primarily impacts the noise levels, including ASE and SSE values, while channel gain is less affected by this parameter. SSE represents the noise impacting the input signal before it is amplified. As shown in Figure 5a, the SSE value, measured for a commercial EDFA in dBm, exhibits a linear relationship with the input power. This linearity is expected, given that all measurements were performed using a test signal with a consistent SNR, which remains stable throughout the measurements. In contrast, ASE behaves differently. At higher input powers, it follows a linear trend similar to SSE. However, at lower input powers it levels off to a minimum floor. This behavior is due to additional noise contributions that are independent of the input power, as well as the RBW of the OSA used for the measurements. This characteristic pattern is illustrated in Figure 5b.  
The behavior of the parameter \(P_{ASE_{AMP}}\), which integrates the other two noise contributions, is depicted in Figure 6. According to Equation (13), at low input powers, the amplified SSE noise is roughly 12 dB lower than ASE, making \(P_{ASE_{AMP}}\) nearly equal to the ASE level. As the input power increases, the gap between the two parameters narrows, causing the SSE contribution to become more significant in the formula. The curve of \(P_{ASE_{AMP}}\) remains remarkably stable with respect to input power, exhibiting a maximum variation of around 0.5 dB across the entire input power range. Figure 7 shows the noise figure results as a function of input power for various values of target gain, tilt, and frequency.  
It is clear that the noise figure tends to increase slightly with input power when a low target gain is set, but this trend reverses under high-gain settings. This shift can be attributed to the greater influence of SSE on \(P_{ASE_{AMP}}\) at higher gain, resulting in a negative trend due to the steeper slope of SSE compared to ASE. Conversely, at low gain, \(P_{ASE_{AMP}}\) closely follows the positive trend of ASE because the SSE contribution is minimal. A forth-degree polynomial is deemed appropriate for modeling the variations of the noise figure with respect to input power.  
Figure 5: Noise levels vs. input power for a commercial EDFA. (**a**) SSE; (**b**) ASE.
# Evaluation of the Large-Length Indoor Standard Installation Based on Single Point Scanning
## 4 Results and Analysis
### Comparative Analysis of Single Measurement Results  
The constant speed stage refers to the time from the first acceleration to the nominal speed, to the last; the local average speed is the mean value of the speed in the constant speed stage; and the total length of the movement period is the summation of three stages, i.e., acceleration, constant speed and deceleration. The global average speed is the mean value of speed collected during the whole movement period. In the comparative experiment of the single measurement, the results of the two methods are shown in Figure 2, both of which were divided into acceleration, constant speed and deceleration stages. The acceleration and deceleration stages were instantaneous changes. In the constant speed stage, there were slight fluctuations in the measurement results of the two methods, with the data of the laser interferometer having more obvious oscillation. In terms of the number of measurement points with a speed over 150 mm/s, the laser tracker and the laser interferometer had 313 and 314 points, respectively. In addition, the local average speeds of the two were 149.9985 mm/s and 150.0052 mm/s, respectively. Thus, by comparing the single measurement results of the two methods, the laser tracker was more stable in the submillimeter-level and had strong resistance to environmental interferences, which is more suitable for the measurement of LLISI.
# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
## 1 Introduction
The effectiveness of mining Wi-Fi access log data to extract activity patterns in various types of space functions using K-means clustering has been demonstrated, which can reveal a deeper understanding of the spatio-temporal behaviors of students [26]. By incorporating a space function, it is possible to uncover more nuanced groups of individuals with similar spatio-temporal activity patterns. Incorporating space functions into human clustering has shown promising results within campus environments [4]. By analyzing stay points within these spaces, researchers can gain valuable insights into campus inhabitants' behavior, optimize space utilization, and improve the effectiveness of infrastructure services. Specifically, in the context of a school campus, clustering students into groups based on their presence in different spaces can help identify spatio-temporal patterns of their study habits, social interactions, and space usages. The basic objective of this research is to investigate what human clustering patterns are in view of space functions. In this paper, we present a methodology on human clustering based on space function data of human movement using graph embedding techniques. Human movement data were collected from Wi-Fi networks. Based on our previous efforts in geo-visualization of spatial occupancy on campus [4], this work extends available research on human clustering based on the use of spatio-temporal similarities of trajectories to address human clustering based on space function similarities of stay points in movements. After the introduction in this section, we first describe the collection of human mobility data and space data with functions. Then, we present our methodology for building a network of human individuals, employ graph embedding algorithm Node2 Vec to representation learning, and generate feature vectors of individual nodes of the graph. Afterwards, the clustering algorithm and the method to determine an optimal number of clusters are elaborated. In Section 3, we present the results and conduct corresponding analysis of the clustering patterns in spaces under various scenarios. Then, discussions about our methodology and experimental results are extended. Conclusions and future research directions are summarized in the last section.
## 6 Conclusions  
In recent years, the number of studies on estimating and measuring the water requirements in plum orchards has cumulatively grown. The results obtained from the literature show that the full-year water requirements for well-watered full-bearing Japanese orchards ranged between 835 and 1211 mm a\({}^{-1}\). Canopy growth and pruning appeared to be the most common causes of differences in the water requirement estimates. Growing season length also plays a role, with late-season maturing orchards having higher water requirements than their early and mid-maturing counterparts. Additional causes of variation include differences in growing conditions (combination of climate and orchard management practices) and the chosen regulated deficit regime (where applicable). Extensive research on the water-saving potential of regulated deficit irrigation (RDI) was performed in various studies from the literature, with the bulk of the work originating from Spain. Similar to South Africa, increasing water scarcity and climate change appear to be significant challenges in Spanish agriculture. Approximately 67% of the reviewed studies adopted the use of varying RDI treatments (particularly in stage II and post-harvest phenological growth stages) to reduce water use and improve fruit quality whilst maintaining commercially acceptable crop yields. The adoption of the FAO-56 coefficient approach, together with varying RDI treatments, was common in most orchards. The FAO-56 coefficient approach is widely used due to its ability to estimate ETc using environmental and crop-specific conditions. In the wake of a recent drought in the Western Cape, this methodology could assist in developing a drought resiliency framework to protect the plum industry from the effects of climate change. However, further research is required on the response of plums to water stress in the context of South Africa. Additionally, there is a need to quantify the water use of plum orchards from planting to full-bearing age for holistic, long-term water planning and the sustainability of the industry. Lastly, extensive validation of remote-sensing-based crop ET estimates against ground-based measurements is required to evaluate the performance of models. The continual validation and calibration of the models aid in improving the model accuracy.  
Conceptualization, M.M., N.M., N.J., T.D., U.M. and Y.N.; methodology, M.M., N.J., U.M. and Y.N.; formal analysis, M.M.; writing--original draft preparation, M.M., N.M., N.J., T.D., U.M. and Y.N.; writing--review and editing, M.M.; supervision, N.J. and T.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Water Research Commission and HORTGRO, grant number C2019.2020-00093.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of the data; in the writing of the manuscript or in the decision to publish the results.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 3 State of the Art on Visualisation and Modelling of River Flooding
### Critical Analysis of ML and DL Applications in Flood Modelling  
[PERSON] et al. [18] reviewed several recent published studies on classical ML and deep learning (DL) applications for flood modelling. They explained how the ML/DL models suffer from the lack of using expert knowledge in modelling flood events. They explained how the traditional fixed-grid 2D model is not always sufficient to reproduce river conveyance. They explained how the model's grid is not aligned with the riverbanks, and in many cases, the lowest points in the river are not adequately represented in the model. They stated that the grid size must be small enough to actually model the hydraulic effects producing the hydraulic gradient through the floodway for sharp bends and narrow channels.  
One solution that solves the problem that small and big grids have is to use flexible mesh models as they allow for complicated floodway geometries to be modelled with precision without losing efficiency. The review then detailed the use of machine learning for two important tasks: classification and regression. The authors presented a study that used 70% of the data for the training models and 30% for validation, and also used DEMs of resolution sizes ranging from 12.5 to 30 m from ALOS PALSAR. They concluded by explaining that the major limitations of ML/DL models are their generalisation, the fact that they are oriented in a specific case and not uniform for approaches like river basins. They also showed the lack of benchmark data to evaluate model performance as a limiting factor for developing efficient ML/DL models for flood inundation simulation/prevention.
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
## 4 Conclusions  
This communication presents the design and validation through prototyping of a microstrip phase shifter at 18 GHz for K band applications, with electronically controllable phase shift. The design is based on the use of loaded 3 dB/90\({}^{\circ}\) couplers. The performance of the reflective loads is improved due to the use of non-sequential impedance transformers. This design is particularly useful for reconfigurable phased arrays such as transmitarrays or reflectarrays, being fully integrable in such designs because of the reduced footprint. Furthermore, the shifter architecture is suitable for miniaturization, increasing the permittivity of the substrate. The dynamic phase range of the phase shifter is higher than 600\({}^{\circ}\) for the entire shifter bandwidth (>1.5 GHz). This phase range is higher than a complete phase turn of 360\({}^{\circ}\), which allows the introduction of phase wrapping. The device losses are lower than 8 dB.  
[PERSON], [PERSON], and [PERSON] designed and simulated the phase shifter. [PERSON], [PERSON], and [PERSON] manufactured and measured the prototype. [PERSON] and [PERSON] wrote the document. [PERSON] and [PERSON] supervised the whole study. All the authors participated in revising the article. This work has been partially supported by the TIN2016-75097-P, RTI2018-102002-A-I00, and EQC2018-004988-P projects of the Spanish National Program of Research, Development, and Innovation and project B-TIC-402-UGR18 of Junta de Andalucia. The authors declare no conflict of interest.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 3 Virtual Network Embedding Problem Formulation
### Network Model
#### 3.1.2 Virtual Network  
The virtual network is also modeled as a weighted undirected graph \(G_{v}=(N_{v},L_{v},C_{u}^{v},C_{l}^{v})\), where \(N_{v}\) represents the sets of virtual nodes, \(L_{v}\) represents the sets of virtual links, \(C_{n}^{v}\) represents the sets of virtual nodes constraints, which mainly refers to the computing and storage requirements of virtual nodes, and \(C_{l}^{v}\) represents the sets of virtual link constraints, which mainly refers to the bandwidth requirements of virtual links. The virtual network link constraints with delay requirements should also include delay constraints.