esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 1 Introduction
### Contributions  
In this paper, an uncertainty analysis method for line reconstruction is proposed to provide a quantitative evaluation of reconstruction accuracy. The proposed uncertainty analysis is able to distinguish poorly reconstructed lines resulting from line triangulation under insufficient effective ego-motion. Based on the Plucker line representation, a minimal four-vector line representation is proposed. In the proposed representation, each component has a clear physical meaning that describes orientation or position of the line in 3D. The uncertainty of a spatial line can be easily visualized by representing the direction and position with two parameters, respectively. The reliability of an estimated line is interpreted as the confidence interval of every component. Based on the uncertainty analysis of two-view triangulation, the uncertainty analysis can be extended to multi-view line reconstruction. The lines that have a large probability of being inaccurately reconstructed can be rejected by uncertainty rejection. The feasibility of the proposed uncertainty analysis method is validated by experiments on simulation data. The application in map-culling of the proposed uncertainty analysis is tested on synthetic and real-world dataset, experimental results show that a more reliable 3D line map can be obtained by incorporating the reprojection error and the proposed uncertainty analysis in outlier rejection.
## 1 Introduction
### Literature Review
With the advancement of new technologies like artificial intelligence (AI), the hybrid methodology has recently emerged, combining bottom-up models with machine learning (ML) or statistical models [10]. Through ML, a subset of AI that allows machines to learn from data and improve over time without being explicitly programmed for each task, it is possible to analyze patterns of energy behavior that are characterized by the variables of the building [41]. This approach enhances the potential and representativeness of the models, addressing a significant challenge: the treatment, analysis, and communication of massive amounts of data on building energy efficiency [17]. This potential can be highly valuable for creating large-scale models, as the most limiting factor is the lack of data and the complexities in the interoperability of various datasets [18]. The hybrid approach has been combined with the archetype-based one. [PERSON] et al. [10] identify two primary sources of bias in data: the absence or limitation of data at the building scale and the presence of mislabeled data. To enhance model accuracy, they suggest employing hybrid models that integrate machine learning or statistical inference with physics-based models. Various techniques have been studied for both building clustering and energy consumption prediction. [PERSON] et al. [42] conducted a comparative study of ML techniques for building clustering to define archetypes. [PERSON] et al. [43] and [PERSON] et al. [24] use ML to predict energy consumption, testing different ML algorithms to determine the most accurate approach. [PERSON] [44] employs machine learning to predict building age using random forest techniques and incorporating geospatial variables to enrich the model, which is applied to eight cities in Germany. [PERSON] et al. [45] developed a model that compares various machine learning algorithms to forecast energy consumption in renewable energy communities. In this study, a novel approach is proposed by combining a model based on EPCs and other open sources, such as land registries, with machine learning. This enriched model allows for the prediction of the energy performance of buildings, specifically obtaining their GHG emissions and NRPEC, based on their physical and geospatial characteristics. Building upon previous studies, this model will encompass the entire residential building stock of a country as large as Spain, offering an optimal solution to provide insights into residential energy performance while ensuring efficient resource consumption.
# World-Models for Bitrate Streaming
## 5 Results and Discussion
### Sample Complexity Bounds
#### 5.3.2 World-Model Comparisons  
We performed experiments training a model-free agent inside of a world-model trained from lower sample complexities: 1M and 500k actions. Tables 2 and 3 show the results from these experiments. Training with lower sample complexities results in much less stability, but it still allows for performance that is nearly identical to the 5M-sample world-model. During some runs, an agent would learn policies that translate poorly into the real environment. Higher temperatures for the 1M-sample world-model presented in Table 2 result in greater world-model stability, and the performance was very similar between each repeated run.  
The maximum average score of an agent trained in a world-model with only 500 k samples achieved a score of \(399.79\pm 245.12\), which is higher than the average score of the best Rainbow agent that was trained with 1 M samples. There is no reason to believe that the Rainbow agent would improve performance with a lower sample complexity and, thus, we did not do further training for this sample regime.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & **Dream Score** & **Real Score** & **Test Score** & **Max Average Real Score** \\ \hline
1 & \(-3532.04\pm 2848.57\) & \(-1525.94\pm 88.95\) & 195.52 \(\pm\) 100.36 & 282.45 \(\pm\) 109.67 \\ \hline
1.25 & \(-5666.19\pm 7003.71\) & \(-4158.25\pm 2865.73\) & \(-1205.58\pm 2329.62\) & 132.80 \(\pm\) 38.21 \\ \hline
1.5 & \(-13\),128.57 \(\pm\) 10942.65 & \(-437.6\pm 6462.24\) & \(-3375.84\pm 5514.53\) & 320.92 \(\pm\) 113.37 \\ \hline
2 & \(-235.71\pm 13.72\) & \(369.18\pm 9.25\) & 478.96 \(\pm\) 3.26 & 378.79 \(\pm\) 309.28 \\ \hline
2.25 & \(-1628.17\pm 7.91\) & \(429.08\pm 11.65\) & \(557.84\pm 5.87\) & 441.09 \(\pm\) 316.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature parameter sweep for a world-model trained from 1M recorded actions. The rightmost column contains the maximum mean episode score from an agent trained inside the world-model at the corresponding temperature. Note that the rightmost column contains the standard deviations of the episode scores for that agent, while the other columns contain the standard error of mean episode scores for three repeated runs.  
Figure 5: Training curve for an MDN-RNN with 64 units and five Gaussians.
# A Psychoacoustic Investigation on the Effect of External Shading Devices on Building Facades
## 2 Methodology  
### Experimental Set-Up  
#### 2.1.1 Model of the Shading System  
The model is a portion of a shading device, 4 m \(\times\) 4 m, with louvres measuring 2 m \(\times\) 0.2 m, 0.018 m thick, spaced at 0.2 m from each other. The model was completely built from pine plywood slabs, with mullions measuring 2 m \(\times\) 0.1 m, 0.02 m thick. The model was placed on the floor of the semi-anechoic chamber, simulating a glazed facade. Indeed, the shading devices are usually installed over the glazed surface of the buildings, and glass and concrete have comparable sound absorption properties. The semi-anechoic chamber has a net volume of 796 m\({}^{3}\) (10.1 m \(\times\) 9.5 m, height 8.3 m): The chamber respects the requirements of Annex A of the ISO 3745:2012 [30], with a low cut-off frequency under 50 Hz and a high frequency above 10 kHz. The top of the model was demarcated by the sound absorbing wedges of the chamber, while the other three sides were delimited by a boundary of polyester fibres, 0.25 m thick and 1 m wide. The absorbing boundary was used in order to reduce as much as possible the sound diffraction through the lateral mullions of the structure. The position of the model inside the semi-anechoic chamber and some dimensional data are shown in Figure 1.  
The study was carried out with two different types of shading device: traditional louvres were used for the first set of measurements, while modified sound-absorbing louvres were used for the second set, in order to evaluate the possibility of an enhancement in the acoustic performance of the system. In order to give sound absorption properties to the shading device, a 3 cm thick layer of expanded melamine was used, fixed to the back of each louvre (Figure 2c). The sound absorption coefficient of the used melamine was measured in impedance tubes [31], with diameters of 4.5 cm and 10.0 cm, respectively: Results are shown in Figure 3. The shading system was analysed with three different tilt angles of the louvres (\(0^{\circ},30^{\circ},45^{\circ}\)) toward the sound source. Measurements were carried out also with the blank floor of the semi-anechoic chamber (Figure 2a,d), in order to find out the reference values to be compared with the effects of the shading system. From now on, the floor of the semi-anechoic room will be called \"_facade_\", in order to simplify the reading of the text. Figure 4 shows the three configurations of the facade: without the shading system (Figure 4. Configuration A), with the traditional louvres (Figure 4. Configurations B) and with the sound absorbing ones (Figure 4. Configurations C). The values obtained with configuration A were used as reference in the evaluation of the acoustic effect of the shading system.  
Figure 1: Side (**a**) and plan (**b**) views of the model of the shading devices in the semi-anechoic chamber. Dimensions are expressed in cm.
## 3 Building Blocks Designs
### Down-Conversion Mixer  
Figure 6 shows the down-conversion mixer in push-pull configuration. The conversion gain of the mixer can be expressed as \(\mathrm{A}_{v}=\frac{2}{\pi}(\mathrm{g}_{mp}+\mathrm{g}_{mm})\mathrm{R}_{L}\), where \(\mathrm{g}_{mp}\) and \(\mathrm{g}_{mm}\) denote the trans-conductance due to NMOS and PMOS \(\mathrm{g}_{m}\) stage of the mixer, and \(\mathrm{R}_{L}\) is the load impedance. The complementary \(\mathrm{g}_{m}\)-stage input doubles the conversion gain with the given bias current. It also improves the noise and linearity performances. The mixer in the proposed receiver front-end needs to down-convert a 30-40 GHz input signal to a DC-10 GHz IF signal, which gives a strict trade-off between conversion gain and the bandwidth. We employed the peaking inductor at the load as part of the C-L-C LC ladder filter, whose detailed analysis is given in the next section. The down-conversion mixer interfaced with differential outputs of Balun is the double-balanced structure. We employed the double-balanced structure here even though the push-pull configuration is immune to LO-feedthrough effects [16]. Instead, a double-balanced structure is chosen to avoid parasitic oscillation with the single-balanced mixer configuration.  
Figure 7 shows a schema of the single-balanced mixer with parasitic inductance at gate and the capacitance in the switching stages of the mixer due to the parasitic routing effects. As shown in Figure 7  
Figure 5: The simulated frequency response of the LNA with stagger tuning.  
Figure 6: Schema of the double-balanced mixer.  
(right), the impedance seen at the source of the switching stage can have a negative real part due to the parasitic inductance at gate and the parasitic capacitance, C\({}_{gs}\), of the switching stage. This negative resistance causes the undesired oscillation in the single-balanced mixer. On the other hand, because LOP and LON nodes are virtual grounds in the double-balanced structure (Figure 6), the structure is robust to parasitic oscillation.
# Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
## 3 FY-3C POD Results and Analysis
### Assessment of Calibrated BeiDou GEO Orbits  
The daily constant biases in the along-track and cross-track of BeiDou GEO orbit products can be estimated in the LEO POD procedure. Figure 9 shows the calibrated daily biases in GBM BeiDou GEO orbit products during July 2017. All the orbit biases are in several meters and the maximum bias does not exceed 8 m. The orbit biases change every day but hardly show any obvious regularities, and biases vary for different BeiDou GEO satellites. The overall magnitudes of BeiDou GEO orbit biases in the along-track are slightly larger than those in the cross-track except the C04 satellite. The values of estimated daily orbit biases in the BeiDou GEO presented here are only available in the case of GBM products generated by GFZ. The biases in other BeiDou GEO orbit products are vastly different since other ACs compute the BeiDou orbit products generally adopting dissimilar ground stations, observation models, and force modes, etc.  
The calibrated orbits of GEO can be recovered through applying the estimated daily constant biases in along-track and cross-track to the original fitted orbits, the DBDs of the reconstructed orbits are adopted to assess the performance of GEO orbit errors in-flight calibration method. Since there are irregular DBDs with several meters in calibrated orbits, the RMS involving all DBDs and the RMS excluding the DBDs more than 1 m in along-track or cross-track are used to assess the quality of calibrated orbits. The DBDs of the original fitted orbits are also rejected if the corresponding DBDs of the calibrated orbits are excluded. Figure 10 shows the RMS of DBDs of the original and calibrated orbits by excluding anomalous DBDs for each BeiDou GEO satellite. The abnormal DBDs occur synchronously in the original and calibrated orbits especially in along-track of the C04 and C05 orbits. The irregular DBDs are caused by the large DBDs existing in the original GEO orbits. This means that  
Figure 9: Estimated daily systematic orbit biases in along-track and cross-track of GBM BeiDou GEO orbit products published by GFZ during July 2017 in FY-3C POD. The left and right subgraphs denote the daily orbit bias series in the BeiDou GEO orbit along-track and cross-track, respectively. Different color points are prepared for different GEO satellites.  
the estimated GEO orbit biases are not correct when the DBDs of the original GEO orbits are large or irregular.  
When the outliers in DBDs are removed, the average RMS of DBDs in the calibrated orbits are decreased to 0.29 m and 0.21 m, which give improvements of 78.4% and 83.9% with respect to those in the original fitted orbits in along-track and cross-track, respectively. The results indicate that the GEO orbits at the decimeter level in along-track and cross-track can be recovered by correcting the estimated GEO orbit systematic errors in FY-3C POD. The calibrated GEO orbits can be considered as another form of enhanced orbits which can be used to perform POD for other LEO using BeiDou GEO observations.
## 3 Materials and Methods
### Implementation Limitations and Risks  
Conducting primary research for this research required obtaining ethics approval from a designated academic body. Implementing a survey in addition to interviews, as initially planned, helped gather quantitative primary data efficiently. One key limitation of the survey was its length, which took approximately 10 min to complete. This resulted in a risk of participants losing interest, which led to a high percentage of incomplete responses on the Qualtrics platform.  
The research aimed to interview nine well-established industry experts in the transport and economic development sectors. The small sample size also introduced the possibility of skewed data, as the experts shared similar interests and expertise. A limitation of this study is the presence of self-selection bias in the interview process. The self-selection bias, where individuals who have a particular interest or expertise in the topic are more likely to participate, could skew the findings towards certain viewpoints, as opposed to a more random sampling of industry professionals [50]. This limitation was mitigated by the inclusion of diverse participants across the fields of rail, project management, and infrastructure advisory; future research could expand the participant pool to include a wider array of stakeholders to reduce bias.
###### Abstract
Furthermore, existing studies predominantly concentrate on individual ecological parameters [38; 39; 40; 41], with fewer investigations into the changes in multiple ecological parameters and their responses to climate variations. Therefore, this study introduces the enhanced vegetation index (EVI) and net primary productivity (NPP) in addition to the commonly used ecological parameters to enhance ecosystem quality assessment, enabling a more comprehensive understanding of vegetation dynamics related to growth status and photosynthetic capacity influenced by LULC. It is crucial to acknowledge that the NDVI is influenced by atmospheric conditions, soil characteristics, and the vegetation canopy [42], leading to issues such as saturation. Hence, the inclusion of the EVI addresses the limitations of the NDVI by incorporating soil adjustment parameters and feedback mechanisms to effectively resolve the related challenges [43; 44]. Net primary productivity (NPP), on the other hand, represents the organic carbon fixed by vegetation minus that consumed through respiration, thereby emphasizing the distinction between vegetated and non-vegetated areas [45; 46; 47; 48]. The surge of industrialization and urbanization accompanying global economic expansion has played a significant role in the rise of warming trends worldwide [49; 50; 51; 52; 53; 54]. A global phenomenon is the widespread degradation of vegetation, as many vegetated areas have been converted into urban and industrial zones [55; 56]. However, it is important to highlight that China has initiated a series of ecological initiatives that have helped mitigate degradation and promote greening efforts. As a result of the combined impacts of global warming, industrialization, urbanization, and ecological projects, China has successfully curbed and begun to restore land degradation, with the ecological environment showing positive progress. Nevertheless, there are significant spatial variations in the extent of restoration efforts across different regions. Hence, it is imperative to investigate alterations in ecological parameters across various land use types and their reactions to climate change. The objectives of this research were to (1) analyze the evolution of land use types and ecological parameters in China over the past two decades; (2) investigate the relationship between ecological parameters of various land use types and meteorological variables; and (3) elucidate the influence of LULC on ecological parameters. The outcomes of this study are anticipated to provide valuable data and findings to inform the development of ecological rehabilitation strategies.
###### Abstract  
During geomagnetic storm events, the highly variable solar wind energy input in the magnetosphere significantly alters the structure of the Earth's upper atmosphere through the interaction of the ionospheric plasma with atmospheric neutrals. A key element of the ionospheric storm-time response is considered to be the large-scale increases and decreases in the peak electron density that are observed globally to formulate the so-called positive and negative ionospheric storms, respectively. Mainly due to their significant impact on the reliable performance of technological systems, ionospheric storms have been extensively studied in recent decades, and cumulated knowledge and experience have been assigned to their understanding. Nevertheless, ionospheric storms constitute an important link in the complex chain of solar-terrestrial relations. In this respect, any new challenge introduced in the field by a better understanding of the geospace environment, new modeling and monitoring capabilities and/or new technologies and requirements also introduces new challenges for the interpretation of ionospheric storms. This paper attempts a brief survey of present knowledge on the fundamental aspects of large-scale ionospheric storm time response at middle latitudes. Further attention is paid to the results obtained regarding the critical role that solar wind conditions which trigger disturbances may play on the morphology and the occurrence of ionospheric storm effects.  
space weather; ionosphere; ionospheric storms +
Footnote †: journal: 2022, 13, 346. [[https://doi.org/10.3390/atmos13020346](https://doi.org/10.3390/atmos13020346)]([https://doi.org/10.3390/atmos13020346](https://doi.org/10.3390/atmos13020346))
## 1 Introduction
### Organization  
The remainder of this paper is organized as follows. Section 2 discusses some of the recent literature that applied FL and FRL techniques to achieve performance improvement in UAV-assisted vehicular communications. The section also identifies the challenges and opportunities for the performance enhancement of UAV-assisted C-V2X communications using FRL [8]. Section 3 illustrates our system model and discusses the UAV vehicle communication architecture used in this work. Section 4 presents our problem formulation where we formulate the problem of latency minimization, power optimization, and UAV trajectory control in UAV-assisted C-V2X as a Markov decision process (MDP). Here, a mixed-integer non-convex UAV trajectory optimization problem is proposed and is divided into four optimization sub-problems using Lagrangian dual decomposition. Section 5 outlines our proposed proposed solution approach, where Q-learning and policy gradient learning are applied at each vehicle to generate the local model parameters. Here, federated-DDPG using the LSTM model with temporal aggregation is proposed to enhance the UAV trajectory prediction. Additionally, federated reinforcement learning (FRL) is investigated to minimize the UAV energy consumption for time-varying data size and channel conditions. Section 6 discusses the findings of this work and investigates the impact of a control parameter on optimal UAV trajectory prediction. Here, we also compare the latency observed for packets of varying byte sizes for a varying number of vehicles and available UAV power. Section 7 concludes the paper and discusses some avenues for future research. The main abbreviations used in this paper are described in Table 1.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 3 Data Sets
### ESA Climate Change Initiative (CCI) Sea Level Data  
Global altimetry sea level data sets are regularly produced by different groups worldwide. Five of them are solely based on the TOPEX-Poseidon/Jason-1/2/3 satellites (reference missions): AVISO (Archivage, Validation et Interpretation des donnees des Satellites Oceanographiques) in France, NOAA (National Oceanic and Atmospheric Administration), GSFC (Goddard Space Flight Center) and CU (University of Colorado) in the United States, and CSIRO (Commonwealth Scientific and Industrial Research Organization) in Australia. The Climate Change Initiative (CCI) sea level (sl_CCI) project of the European Space Agency (ESA) has been developed in the recent years to realize the full potential of multi-mission altimetry as a significant contribution to climate research [20; 21; 22]). In the context of the CCI, altimetry missions have been reprocessed using improved geophysical corrections and improved links between missions, providing reduced errors of sea level products. In this study, we use the sl_CCI (v 2.0) data from July 2008 to December 2014 to estimate sea level trends in the open ocean away from the HK coast. The sl_CCI v 2.0 data set is a gridded product at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) resolution. The Sl_CCI project was not dedicated to estimate sea level near the coast. However, it can be used as a good independent reference to link open ocean sea level variations to coastal altimetry sea level, thus providing a regional context to interpret the coastal results obtained in this study.