esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analyzing the Sufficient Water Indicator by Building Type
The international literature provides a broad overview of the multifunctionality of low impact development (LID) techniques in urban environments, particularly in temperate and humid climates [13; 30; 31]. However, these studies tend to generalize the capacity of LID techniques for water management in urban areas without thoroughly addressing the specific parameters required to meet consumption demands that vary according to the population's diverse activities, especially in areas that experience intense but short-duration rainfall. The use of indicators, on the other hand, has facilitated the evaluation of efficiency in water use and security [11], sustainability in water management [32], and the social and environmental impacts of water collection and distribution systems [33]. For instance, implementing an ecological scarcity indicator in China has helped mitigate water shortages in 65% of its provinces, improving conditions in regions with semi-arid and temperate \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Rooftop Type** & **nw (m\({}^{3}\))** & **rws (m\({}^{3}\))** &
\begin{tabular}{c} **SW with** \\ **only _rws_** \\ \end{tabular} & **rw (m\({}^{3}\))** & **vw (m\({}^{3}\))** & **SW** \\ \hline Single family & 3644.1 & 1224.4 & **0.33** & 6028.8 & 7253.2 & **1.9** \\ Duplex & 711.4 & 239.1 & **0.33** & 1653.4 & 1892.5 & **2.6** \\ Triplex & 14.8 & 4.9 & **0.33** & 19.3 & 24.3 & **1.6** \\ Quadruplex & 1816.2 & 610.2 & **0.33** & 1557.9 & 2168.1 & **1.1** \\ Multifamily building 15 & 263.2 & 88.4 & **0.33** & 105.3 & 193.8 & **0.73** \\ Multifamily building 20 & 1449 & 486.8 & **0.33** & 530.7 & 1017.6 & **0.7** \\ Multifamily building 30 & 648 & 217.7 & **0.33** & 283.7 & 501.4 & **0.7** \\ Commercial & 172.9 & 73.5 & **0.42** & 1457.8 & 1531.3 & **8.8** \\ Services & 219.7 & 219.7 & **1.00** & 850.5 & 1070.3 & **4.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: SW indicator behavior for rooftop type. climates [34]. In contrast to these approaches, the indicators proposed in this research allow for adapting LID techniques according to building types, thus maximizing resource use based on the population's specific needs. In a similar water scarcity context, a recent study in Mexico City assessed the social impact of rainwater harvesting systems in promoting water autonomy; however, it did not consider technical and urban elements that would help discern the potential for harvesting at different scales [17]. In contrast, this research identifies building types with the highest potential for directly managing rainwater for self-consumption while also providing alternatives for the collective management of excess volumes in buildings with low demand but high storage potential.
## 1 Introduction
### Significance and Scope of This Study  
After an extensive examination of the existing literature, it became apparent that a majority of research on flyrock predominantly emphasized the estimation of the maximum range or distance that is achieved by a flying fragment. However, this approach of predicting flyrock distance has some inherent limitations as it is largely dependent on the precision of two basic tasks: to identify the concerned rock fragment as a flyrock, and to correctly specify the point of ejection of this identified fragment. The difficulty of these tasks increases, particularly when several benches are consecutively blasted, which is a ususal practice in large surface mines.  
Hence, a modified strategy capable of delimiting the danger zone must be adopted that depends on flyrock launch variables in addition to the existing methodology of distance estimation. The study documented in this research paper was carried out to investigate the factors influencing flyrock launch velocity (\(v_{f}\)) using a high-speed motion analysis of 79 full-scale production bench blasts at five distinct surface mines in India. A dataset containing 231 flyrock instances was anaylzed to develop a model for forecasting \(v_{f}\) by integrating the motion analysis results with robust machine learning regression techniques. A projectile simulation experiment was also conducted to demonstrate the methodology of delimiting the danger zone for bench blasts using the predicted \(v_{f}\) combined with different possible sets of launch circumstances.
## 2 Materials and Methods
### Methods
#### 2.3.4 Data Binning  
To examine the effects of hydrological variables on GPP within the study region, we conducted data binning on the standardized anomalies of all relevant variables. These standardized anomalies were partitioned into eight intervals: \([-2,\,-1.5)\), \([-1.5,\,-1)\), \([-1,\,-0.5)\), \([-0.5,\,0)\), \([0,\,0.5)\), \([0.5,\,1)\), \([1,\,1.5)\), and \([1.5,\,2]\). The primary objective of this research is to assess the responsiveness of RSM and PRE to GPP, as well as to disentangle their individual effects. To reduce confounding influences from E and RUM, the standardized anomalies of \(SA_{E}\) and \(SA_{RIM}\) were restricted to the interval \([-1,1]\). Subsequently, within each \(SA_{RSM}\) bin (\(i=1,2,\,\ldots,8\)), \(SA_{RSM}\) values were ordered in ascending sequence according to corresponding \(SA_{PRE}\) values. Conversely, within each \(SA_{PRE}\) bin (\(j=1,2,\,\ldots,8\)), \(SA_{PRE}\) values were sorted in ascending order based on \(SA_{RSM}\) values [18]. This methodological approach ensures accurate alignment among the standardized anomalies of RSM, PRE, and GPP, thereby enabling a rigorous investigation of the distinct influences of RSM and PRE on GPP. The effects of RSM and PRE on GPP were quantified by calculating bin means. Under conditions where RSM and PRE effects are decoupled, the constraint imposed by RSM on GPP anomalies is represented as \(\Delta SA_{GPP}(SA_{RSM}\,|\,SA_{PRE})\). The average variation of \(SA_{GPP}\) anomalies from high to low \(SA_{RSM}\) within each \(SA_{PRE}\) bin is calculated using the following formula [35]:  
\[\Delta SA_{GPP}(SA_{RSM}|SA_{PRE})=\frac{1}{L}\times(\sum_{i=1}^{L}SA_{GPP_{i, RSM_{i,\max}}}-SA_{GPP_{i,RSM_{i,\min}}}) \tag{4}\]  
where \(L\) represents the total number of \(SA_{RSM}\) bins, \(i\) denotes a specific bin number, and \(SA_{GPP_{i,RSM_{i,\max}}}\) and \(SA_{GPP_{i,RSM_{i,\min}}}\) correspond to the values of \(SA_{GPP}\) at the maximum and minimum \(SA_{RSM}\) values within the \(SA_{PRE}\) bin, respectively. Similarly, under decoupled RSM-PRE conditions, the constraint of PRE on GPP anomalies is denoted as \(\Delta SA_{GPP}(SA_{PRE}\,|\,SA_{RSM})\), representing the average variation of \(SA_{GPP}\) anomalies from high to low \(SA_{PRE}\) within each \(SA_{RSM}\) bin. The calculation formula is  
\[\Delta SA_{GPP}(SA_{PRE}|SA_{RSM})=\frac{1}{L}\times(\sum_{i=1}^{L}SA_{GPP_{i,\max}}-SA_{GPP_{i,\min}}) \tag{5}\]  
where \(L\) is the total number of \(SA_{PRE}\) bins, \(i\) denotes a specific bin index, and \(SA_{GPP_{i,PRE_{i,\max}}}\) and \(SA_{GPP_{i,PRE_{i,\min}}}\) represent the values of \(SA_{GPP}\) corresponding to the maximum and minimum \(SA_{PRE}\) values within each \(SA_{RSM}\) bin, respectively.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 4 Discussions
### TEC Comparisons and Maps
Compared to the recent large number of RO profiles, the sampling from the COSMIC-1 constellation plus a few additions still had many regional data gaps in the 2-hourly maps for August 2008 when they were in a mission prime time (Figure 20). The unsampled regions are clearly more than in the case for October 2021. The regions with missing data appear to be randomly distributed, as the COSMIC-1 constellation had been fully deployed and spread in local time coverage. More \"apparent\" data gaps in the polar region are partly due to the reduced equivalent area from the 4\({}^{\circ}\times 8^{\circ}\) latitude-longitude grid. A few salient features emerge from the 2-hourly _TEC_ maps from October 2021 (Figure 19). First, there appears to be a trough in the NH mid-latitude that evolves with time. October 2021 was generally a quiet month, but this trough feature is absent in the background _TEC_ model [82]. Second, an auroral contribution to _TEC_ is evident in the nighttime SH during UTC = 11Z-17Z. The auroral contribution is generally too weak to observe in _TEC_ except from the enhancement during substorms [83]. Third, there is UTC-dependent symmetry in the subtropical _TEC_ about the magnetic equator. Asymmetric distributions are more pronounced for the UTC time before 07Z. Figure 19: _TEC_ maps from multiple GNSS-RO satellites/constellations (Spire, Metop-A, COSMIC-2, FY3-C/D, Kompsat-5, TSX, and PAZ) for October 2021. Satellite data with the high-rate RO acquisition top < 100 km are excluded in this _TEC_ climatology. The monthly data are aggregated into a 4\({}^{\circ}\times 8^{\circ}\) latitude–longitude and 2-hourly local time bins. The grid boxes without any measurements are colored in white. Current global _TEC_ nowcast and forecast rely primarily on the data analyses from ground-based measurements at the International GNSS Service for Geodynamics (IGS) stations. Because the ground stations are mostly over lands, the IGS-associated analysis centers, including GFZ (GeoForschungsZentrum), JPL (Jet Propulsion Laboratory), and USNO (U.S. Naval Observatory), use various interpolation-filtering methods to fill the data-sparse regions in producing a global _TEC_ map [82; 84; 85]. Thus, a large _TEC_ uncertainty is expected over the data-sparse regions. Over oceans, satellite altimeters may fill sampling voids with a better sensitivity to _TEC_ measurements [86]. Nevertheless, dense spatiotemporal sampling is needed to provide globally uniform quality for the _TEC_ measurement. As demonstrated in this study, the high-rate RO data can provide such sampling and contribute to the Global Ionosphere Map (GIM) analysis for improved accuracy over data-sparse regions.
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
###### Abstract  
The pile foundations below approach slab in a semi-integral abutment jointless bridge (SAJB) that requires high flexibility to accommodate the horizontal cyclic deformation of approach slab generated by the girder's thermal expansion and contraction as well as earthquake action. In this paper, reactive powder concrete (RPC) and reinforce concrete (RC) micro piles were designed and fabricated. The shaking table tests on dynamic response of micro piles-soil interaction were conducted to investigate the dynamic response characteristics such as the strain time history of pile-soil system, the bending moment, and the deformation of piles. The maximum strain response of piles was observed at the buried depth of 4.2 D (D is the diameter of pile). Meanwhile, the maximum bending moments of RPC and RC piles appear at the depth of 0.64 D and 0.42 D, respectively, under the dynamic load excitation, and the peak horizontal deformation of piles were observed at pile head. It is found that the bending moment and the strain response of the RPC pile are larger than that of the RC micro pile, and increased by 40% and 98%, respectively. The RPC micro pile has better crack resistance, higher ductility, and flexural rigidity than that of the RC pile, and it can be widely used as pile foundations in SAJBs for the earthquake area.  
Keywords:semi-integral abutment jointless bridge (SAJB); dynamic response characteristics; RPC micro pile; RC micro pile; shaking table tests; pile-soil interaction +
Footnote †: journal: Applied Soft. 2019, 9, 2644; doi:10.3390/appr9132644
## 3 Related Work and Literature Review
### Machine Learning in Watermarking: A Cautious Approach for Forensics  
ML-based watermarking techniques have shown notable success in multimedia applications [14; 47]; however, their application in forensic contexts involves additional complexity and requires nuanced evaluation. Similar to blockchain, and initially proposed as a digital evidence management solution but later found to conflict with GDPR deletion rights [55], machine learning introduces challenges that require careful consideration before forensic watermarking application.  
Forensic investigations require transparency and reproducibility. Machine learning models often function as black boxes that may introduce uncertainty into watermarking processes. This lack of transparency may raise questions about the admissibility of evidence in court proceedings. Furthermore, machine learning models operate effectively only within the parameters of the training dataset [56]. The incorporation of evidence watermarking raises questions about training data types, and the use of evidential material without consent can raise ethical and legal concerns.  
Although machine learning offers promising adaptive watermarking improvements, forensic applications remain limited by privacy, auditability, and transparency requirements. Forensic watermarking requires techniques that are amenable to court audit and verification. Future research might develop explainable AI models that balance adaptability with the requirements of transparency of forensic applications.  
The cautious application of machine learning in forensic contexts reflects the broader concerns surrounding the implementation of watermarking techniques, as highlighted in recent research on forensic watermarking applications.
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
###### Abstract  
Currently, the Global Navigation Satellite System (GNSS) mainly uses the satellites in Medium Earth Orbit (MEO) to provide position, navigation, and timing (PNT) service. The weak navigation signals limit its usage in deep attenuation environments, and make it easy to interference and counterfeit by jammers or spoofers. Moreover, being far away to the Earth results in relatively slow motion of the satellites in the sky and geometric change, making long time needed for achieved centimeter positioning accuracy. By using the satellites in Lower Earth Orbit (LEO) as the navigation satellites, these disadvantages can be addressed. In this contribution, the advantages of navigation from LEO constellation has been investigated and analyzed theoretically. The space segment of global Chinese BeiDou Navigation Satellite System consisting of three GEO, three IGSO, and 24 MEO satellites has been simulated with a LEO constellation with 120 satellites in 10 orbit planes with inclination of 55 degrees in a nearly circular orbit (eccentricity about 0.000001) at an approximate altitude of 975 km. With simulated data, the performance of LEO constellation to augment the global Chinese BeiDou Navigation Satellite System (BeiDou-3) has been assessed, as one of the example to show the promising of using LEO as navigation system. The results demonstrate that the satellite visibility and position dilution of precision have been significantly improved, particularly in mid-latitude region of Asia-Pacific region, once the LEO data were combined with BeiDou-3 for navigation. Most importantly, the convergence time for Precise Point Positioning (PPP) can be shorted from about 30 min to 1 min, which is essential and promising for real-time PPP application. Considering there are a plenty of commercial LEO communication constellation with hundreds or thousands of satellites, navigation from LEO will be an economic and promising way to change the heavily relay on GNSS systems.  
navigation; LEO; PPP; ambiguity convergence time +
Footnote †: journal: Sensors
## 2 Methodology
### Simulation of the Engine-Generator Set  
The simulation of downdraft gasifiers fueled with biomass wastes from the olive oil industry has already been addressed and validated in previous works of the authors [8; 12; 13; 17; 18]. However, given the unavailability of commercial engines in Cycle-Tempo, the engine-generator set was modeled separately in a different simulation environment [13]. For simulation of the engine-generator set, Thermoflex(r) was used, which is a product of Thermoflow Inc. (Jacksonville, FL, USA) [38]. This software provides simulation and modeling tools for a variety of power plants, including combined cycles, conventional steam cycles, and repowering, as well as a wide range of power generation plants and renewable energy systems. In addition, it includes a rich library of market products [38; 39].  
The producer gas composition from the simulation in Cycle-Tempo was used as input data to model the engine-generator set in Thermoflex, for both olive cake and pruning. The model of the engine-generator enables the determination of the required flow rate of producer gas to generate the electric power demanded by the olive oil mill, as well as the thermal power from the exhaust gas and jacket cooling water circuit.  
Furthermore, the mass flow rate of water in the jacket cooling circuit was determined through a heat exchanger model. This heat exchanger was designed to increase the temperature of the supply water required by the olive oil extraction process from 25 \({}^{\circ}\)C to 45 \({}^{\circ}\)C. To ensure a conservative scenario, we assumed key parameters for the heat exchanger, including a conservative heat loss of 15% to the environment and a pressure drop of 2% [40; 41]. The jacket cooling water circuit operated within a temperature range of 70 \({}^{\circ}\)C to 90 \({}^{\circ}\)C.  
The net electrical efficiency (\(\eta_{e}\)) and the CHP efficiency (\(\eta_{CHP}\)) of the power generation unit were determined as given by the expressions shown below in Equations (2) and (3), respectively [16; 20].  
\[\eta_{e}=\frac{P_{e}}{\bar{v}_{pg}\,\mathrm{LHV}_{pg}} \tag{2}\]  
\[\eta_{CHP}=\frac{P_{e}+P_{th}}{\bar{v}_{pg}\,\mathrm{LHV}_{pg}}, \tag{3}\]  
where \(P_{e}\) is the net electric power developed by the engine-generator set, \(P_{th}\) is the heat flow required to raise the temperature of the water in the olive oil extraction process from 25 \({}^{\circ}\)C to 45 \({}^{\circ}\)C, and \(\bar{v}_{pg}\) and \(\mathrm{LHV}_{pg}\) are the volume flow rate and LHV of the producer gas, respectively.  
Figure 5 shows the process simulation flowsheet of the engine-generator set in Thermoflex for a rated electric power of 80 kW. In this example, the engine-generator set was fueled with the producer gas from gasification of olive cake pellets with a 10% moisture content (wet basis).  
Figure 4: Process simulation flowsheet of the downdraft gasifier and producer gas conditioning unit in the Cycle-Tempo environment.
## Empirical Analysis  
### Demographic Characteristics of the Sample  
The demographic characteristics of the surveyed sample included sex, age, occupation, and educational background. The analysis of Table 1 reveals a balanced sex distribution, with 175 males (53.0%) and 155 females (47.0%). In terms of age, 37 respondents (11.2%) were below 20 years old, 98 (29.7%) were in their 20s, 91 (27.6%) were in their 30s, 59 (17.8%) were in their 40s, and 45 (13.6%) were over 50 years old, indicating a relatively higher proportion of respondents in their 20s and 30s. Regarding education, 179 respondents (54.2%) held a bachelor's degree, 97 (29.4%) had high school education or below, and 54 (16.4%) had postgraduate degrees. Regarding occupation, 145 respondents (43.9%) were employed, 45 (13.6%) were self-employed, 107 (32.4%) were students, and 33 (10.0%) reported other occupations. The high proportion of students indicates that many South  
Figure 1: Research model.  
Korean students actively use entertainment platforms. The other category reflects the reality that the proportion of full-time homemakers and unemployed individuals is relatively high.  
Classifying respondents into four primary occupational groups--employed, self-employed, student, and others--effectively captures diverse social dynamics. These groups collectively represent a significant segment of the population, providing a nuanced reflection of societal diversity. Across these occupational categories, users of entertainment platforms demonstrate distinct perspectives and experiences. Employed individuals utilize these platforms for relaxation and social interaction, while self-employed individuals integrate them into their flexible schedules for networking and inspiration. Students leverage platforms to balance academic commitments with leisure activities, and other groups such as full-time homemakers and non-employed individuals engage for leisure and social connectivity. This classification approach not only ensures a balanced and comprehensive dataset but also facilitates insightful exploration of varied viewpoints within each group. Such categorization establishes a solid foundation for future analyses, enabling researchers to uncover diverse behaviors and preferences within the sample, thereby offering insights that can better reflect broader societal trends.  
Specifically, the distribution of occupations helps pinpoint which groups exerted significant influence on the research outcomes. Students, in particular, prioritize content that is diverse, innovative, and fits their flexible schedules. Due to their substantial presence in the sample, the unique preferences of students can significantly impact perceived value, customer satisfaction, trust, and loyalty.
# Demonstration of a Hybrid B5G System Integrating VLC and RF-Based Technologies with Access Networks
###### Abstract  
Visible-light communication (VLC) has emerged as a promising technology to provide the very high-throughput wireless communications demanded by beyond-fifth-generation (B5G) applications. However, few works are found in the literature regarding the integration of VLC systems with other wireless communications technologies and with access networks. In this context, and as a proof of concept, we implement and experimentally evaluate a hybrid network architecture based on VLC, radio-over-fiber (RoF), free space optics (FSO), fiber-wireless (FiWi), and millimeter-waves (mm-waves) for B5G applications. Such optical networks make use of fiber-optic links based on RoF technology as backhauls, whereas their fronthauls might be either by FSO or RoF. Finally, a triple-wireless-access network is ensured by VLC, FiWi, and mm-wave links. The latter use a real 5G new radio (5G NR) signal. The system performance is evaluated in terms of a root mean square error vector magnitude (EVM\({}_{\text{RMS}}\)) parameter in accordance with the 3 rd-Generation Partnership Project (3 GPP) requirements. The experimental results demonstrate a total maximal theoretical throughput of approximately 1.66 Gbps, aligning with the digital performance requirements set by 3 GPP.  
B5G; FiWi; FSO; mm-wave; OWC; RoF; VLC +
Footnote †: journal: Applied sciences  
Article
## 4 Hierarchical Incremental Maps
### Prominence Condition  
As explained in Section 4.1, the Node Level Loop Closure process selects \(N^{*}\) possible nodes. Among them, the Image Level Loop Closure process finds the most similar image (\(I_{i}\)) which closes the loop. The node to which \(I_{i}\) belongs, is the finally selected node. Prior to selecting this node \(N_{i}\), the image detected as the most similar to \(I_{i}\) should meet a prominence condition.  
The prominence measures how much the peak of the similarity curve (Figure 6c) stands out due to its intrinsic height and its location relative to other peaks. This condition is taken into account because not only a high peak should occur to close the loop, but also that peak should be very distinct compared to its neighbours. The image selected from the Image Level Loop Closure should fulfil the condition presented in Equation (8). In this equation, \(P_{I_{i}}\) is the prominence value of the candidate image, \(\mu(P_{I_{k^{*}}})\) is the mean prominence of the candidate images contained in \(N^{*}\). \(\gamma\) is a threshold, which is empirically tuned from the experiments as \(\gamma=5\).  
\[P_{I_{i}}\geq\gamma*\mu(P_{I_{k^{*}}}) \tag{8}\]