esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 2 Data and Methods
### Study Area and Data Sources
#### 2.1.2 Data Sources  
The data used in this study include Digital Elevation Model (DEM), representing ground elevation, TRMM 3B42 RT and 3B43, representing remote sensing precipitation, and gauged precipitation data by surface ground meteorological and hydrometric stations. The DEM data is from the Geographical Information Monitoring Cloud Platform (GIM Cloud) [59], and the spatial resolution is 1 km \(\times\) 1 km. The TRMM 3B42 RT and 3B43 are from the NASA website [60] with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a time resolution of 3 h. The TRMM products were processed by ArcGIS to fit the Qaidam Basin and the data is from 2001 to 2016. In order to consider the effect of elevation, the 1 km \(\times\) 1 km spatial resolution matched with DEM data was used when resampling the TRMM data. The gauged precipitation data, including 9 meteorological stations and 11 hydrometric stations from 2001 to 2016, was from China Meteorological Science Data Sharing Service Network [61] and Hydrological Red Book of the People's Republic of China [62], respectively.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 3 Evaluation Metrics and Benchmark Datasets  
To ensure fair comparisons across different models, it is essential to establish standardized evaluation metrics and utilize high-quality benchmark datasets. Evaluation metrics quantify the accuracy of keypoint predictions and serve as the foundation for assessing model performance. However, inconsistencies in evaluation protocols can lead to incomparable results, thereby hindering fair comparisons across studies. Dataset quality directly influences training effectiveness and the generalization capability of pose estimation models. Factors such as annotation precision, sample diversity, and scene complexity significantly impact model performance. Therefore, developing well-annotated datasets and unified evaluation frameworks is fundamental to advancing the field of 2D human pose estimation.  
This chapter presents a systematic overview of widely used evaluation standards in 2D human pose estimation, along with a categorized analysis of key performance metrics. It also introduces widely adopted benchmark datasets, such as MPII, with a focus on their annotation protocols, dataset scale, pose categories, and application scenarios. By summarizing mainstream evaluation frameworks and data resources, this chapter aims to offer a clear reference for fair model comparisons, reproducible experiments, and methodological advancements in the field.
## 4 Results and Discussion
### Energy Consumption Per-Bit  
The average energy consumption per-bit, \(E\), may be calculated by dividing the total energy consumption by the multiple of the total number of packets received and the length of each packet. \(E\) is defined by the following Equation (9):  
\[E= \frac{E_{T}}{NP_{PktRx}\times L_{Pkt}} \tag{9}\]  
In this Equation, \(E_{T}\), \(NP_{PktRx}\) and \(L_{Pkt}\) represent the total energy used, the overall packets received by the sink, and length of each packet, respectively.  
Figure 8 shows the average energy consumption per-bit, and the performance of the proposed EQPD-MAC protocol is compared with PMME-MAC, MPQ-MAC, and RMP-MAC. The figure shows that all protocols consume less energy while there are fewer sensor nodes, but as the number of nodes increases, the energy usage increases since the sensor nodes must deal with more packets. It can be seen that the proposed EQPD-MAC gives the best performance, especially when number of sensor nodes increases. When there are 5 sensor nodes, EQPD-MAC consumes 0.0815 mJ/bit whereas PMME-MAC consumes 0.1056 mJ/bit energy per sensor node. When there are 45 sensor nodes, EQPD-MAC consumes 0.2945 mJ/bit whereas PMME-MAC consumes 0.3819 mJ/bit energy per sensor node. Compared to PMME-MAC, MPQ-MAC, and RMP-MAC, the EQPD-MAC provides improvements by up to 29.6%, 24.8%, and 29.5%, respectively. Firstly, the reason is because EQPD-MAC decreases the power consumption at all sensor nodes by using adaptive active/sleep time. Secondly, after receiving the SYNC packet, sensor nodes synchronize with other sensor nodes including the sink and extend their sleep time. On the other hand, PMME-MAC has been seen to consume the most energy among all the protocols. It is because PMME-MAC lacks the ability for the sensor nodes to conserve energy by going to sleep automatically while not participating and prioritizes packet latency above energy conservation.
## 4 Numerical Examples
### Case Application and Sensitivity Analysis
However, the \(MTTF\) of \(M_{0}\) is the smallest from Table 3, which is 25.34 a, and it is more close to the actual engineering due to the uncertainties and acceleration factor constant principle compared to other models. In engineering applications, the estimated parameter \(\hat{\Theta}=\left\{\hat{\mu}_{\eta},\hat{\sigma_{\eta}^{2}},\hat{\kappa},\hat{ b},\hat{\sigma_{\varepsilon}^{2}}\right\}\) in \(M_{0}\) would deviate from the true parameter \(\Theta=\left\{\mu_{\eta},\sigma_{\eta}^{2},\kappa,b,\sigma_{\varepsilon}^{2}\right\}\). Thus, it is necessary to do sensitivity analysis because it reflects the degree to which the output value of the model changes with small changes in parameters. Without loss of generality, we assume \(\tau_{1}\), \(\tau_{2}\), and \(\tau_{3}\) represent the bias of parameters \(\mu_{\eta},\sigma_{\eta}^{2},b\), respectively (because the values of \(\kappa\) and \(\sigma_{\varepsilon}^{2}\) are too small, we do not consider their errors). Based on Equation (15), when the bias range of the parameter is \(\pm 10\%\), sensitivity analysis of the reliability of cathode on threshold \(D=13\) is presented in Figure 4. From these results, we can find that the model \(M_{0}\) tends to be robust, and the range of \(\mu_{\eta}\) is more sensitive to the output value than the others. Therefore, more attention of \(\mu_{\eta}\) should be paid to the process of optimization. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \(\hat{\mu}_{\eta}\) & \(\hat{\sigma}_{\eta}\) & \(\hat{b}\) & \(\kappa\) & \(\hat{\sigma}_{B}\) & \(\hat{\sigma}_{\varepsilon}\) & \(\ell\) & **AIC** & **MTTF** \\ \hline \(M_{0}\) & 0.1092 & 0.0387 & 932 & 0.000020 & / & 0.00268 & 2146.9 & \(-\)4283.8 & 25.34 a \\ \hline \(M_{1}\) & 0.0923 & 0.0316 & 1200 & / & 0.034 & 0.00951 & 1976.4 & \(-\)3942.8 & 27.48 a \\ \hline \(M_{2}\) & 0.1281 & 0.03 & 1678 & / & 0.29 & / & 1389.7 & \(-\)2771.4 & 28.28 a \\ \hline \end{tabular} a—annual.  
\end{table} 
Table 3: The parameters of three degradation models with TWT CSADT degradation data. Figure 3: The comparison of the PDF and CDF of the three models for TWT CSADT data (**a**) PDF, (**b**) CDF.
## 3 Results and Discussion
### Antimicrobial Properties of the Plasma-Treated Liquids
This confirms that using this pin-to-liquid plasma device the plasma-treatment of tap water delivers a liquid which has a stronger antimicrobial effect than plasma-treated saline solution. In general, non-buffered aqueous systems such as saline solution are characterized by a decrease of the pH-value in course of the plasma treatment. It was assumed that pH-decrease has an indirect influence on the antimicrobial activity of the plasma-treated liquid [7]. The decrease of the pH-value is hypothesized to be caused by the dissociation of nitric oxides in water. The resulting formation of reactive oxygen and nitrogen species (ROS/RNS) indicated by nitrite and nitrate generation leads to the antimicrobial effect. Thus, the acidic pH plays a rather subordinate role [11; 38]. Such an indirect  
Figure 9: Antimicrobial effect of plasma-treated saline solution (NaCl) on _E. coli_ (boxes) and _S. aureus_ (triangles) as well as plasma-treated tap water (TW) on _E. coli_ (circles) and _S. aureus_ (thombi), \(\mathrm{t_{treat}}\) = 30 min. influence of the pH-value in saline solution cannot be assumed for tap water because the pH remains constant. The origin of the antimicrobial effect in tap water despite constant pH is rather unclear, but [PERSON] et al. described experiments with a DBD regarding the degradation of phenol in plasma-treated deionized and tap water [39]. In these tests, it was confirmed that tap water has a higher degradation efficiency because of the presence of sodium bicarbonate (NaHCO\({}_{3}\)). Sodium bicarbonate maintains the pH-value of the liquid and is a quencher for OH radicals, which is an unwanted effect; however, it leads also to the formation of the CO\({}_{3}\)\({}^{\bullet}\) anion, which oxidizes organic compounds efficiently [39]. Thus, we assume that sodium bicarbonate may also play an important role for antibacterial activity of plasma-treated tap water. The plasma-treatment of tap water resulted in antimicrobial properties which are even stronger than achieved for saline solution. This is very promising because tap water is easy available and inexpensive. No additional means are necessary for decontamination. Thus, deionization or the addition of sodium chloride is not required to generate water with antimicrobial properties. But it is important to note that the chemical composition of tap water strongly depends on the area of use. Therefore, more comprehensive investigations are needed.
###### Abstract  
To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS's main task is to accurately estimate a battery's available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to its structure, and so must be estimated using indirect measurements. In addition, the methods used to estimate SOC are highly dependent on the battery's available capacity, known as the state of health (SOH), which degrades as the battery is used, resulting in a complex problem. In this paper, a novel adaptive battery health estimation method is proposed. The proposed method uses a dual-filter architecture in conjunction with the interacting multiple model (IMM) algorithm. The dual filter strategy allows for the model's parameters to be updated while the IMM allows access to different degradation rates. The well-known Kalman filter (KF) and relatively new sliding innovation filter (SIF) are implemented to estimate the battery's SOC. The resulting methods are referred to as the dual-KF-IMM and dual-SIF-IMM, respectively. As demonstrated in this paper, both algorithms show accurate estimation of the SOC and SOH of a lithium-ion battery under different cycling conditions. The results of the proposed strategies will be of interest for the safe and reliable operation of electrical systems, with particular focus on electric vehicles.  
lithium batteries; battery monitoring system (BMS); Kalman filters; sliding innovation filter; interacting multiple models; adaptive dual filters; state of health; state of charge; accurate estimation; electric vehicles
## 2 State of the Art
### Visualization  
#### 2.3.1 Visualization Technologies  
The created virtual 3D or 4D models are presented as dynamic and interactive visualizations, denominated extended reality (XR). XR technologies comprise a wide scope of approaches [179] between real and virtual [34]. This ranges from digital-enriched real environments--augmented realities (ARs) or augmented virtuality (AV)--to virtual reality (VR) as fully computer-generated visualizations. According to [PERSON] [180], a blend between real and virtual elements can include adding information that does not exist, or can be subtractive by hiding or deleting parts of the real world. Despite the hype about glasses-based XR, 3D models and visualscapes are mostly accessed via mobile and desktop screens [181]. Various viewers, e.g., Smithsonian3D, 3 DHOP, Potree, and DFG 3D Viewer (Overviews: [182; 183; 184]), are available and used in multiple projects. Other tools come from the gaming industry (Unreal and Unity engines) and are frequently used to visualize heritage content, while Google Earth or Apple Maps are world viewers but mostly lack the 4D history dimension. Some projects already use 4D viewers for city data, e.g., for Venice [158], Nicosia [185], and Trento [166]. To sum up, most open 3D visualization frameworks deal with single objects or specific scenarios (e.g., 3D specific buildings). Since they rarely include temporal changes, world viewers cannot yet visualize time-variant 3D architectural and city data on a large scale.
## 5 Paleomagnetic Mapping
### Paleomagnetic Mapping in Northern Israel
#### 5.1.2 Hula Paleomagnetic Map  
Due to the elaboration of the paleomagnetic mapping methodology in the Sea of Galilee etalon area, it became possible to develop a paleomagnetic map of its northern extension--Hula Valley (Figure 20A). The constructed map also includes areas of Upper Galilee Heights, the northern slope of the Korazim Plateau, the northern part of the Golan Plateau, and the southern side of the Hermon Mts. The Pliocene-Quaternary traps and the sedimentary complexes belonging to the Brunhes-Matuyama zones mainly comprise this area. More ancient Pliocene trap complexes are developed in the south, in the Korazim Plateau and Beteiha Valley, belonging to the Gauss and Gilbert superzones.  
Figure 19: (**A**) Combined paleomagnetic–magnetic–radiometric scheme of the Sea of Galilee (modified and supplemented after [49]). (1) Outcropped Cenozoic basalts, (2) points with the radiometric age of basalts (in m.y.), (3) wells, (4) faults, (5) general direction of the discovered buried basaltic plate dipping in the southern part of the Sea of Galilee, (6) counterclockwise (a) and clockwise (b) rotation of faults and tectonic blocks, (7) pull-apart basin of the Sea of Galilee, (8) suggested boundaries of the paleomagnetic zones in the sea, data of land paleomagnetic measurements: (9 and 10) (9) reverse magnetization, (10) normal magnetization; (11 and 12) results of magnetic anomalies analysis: (11) normal magnetization, (12) reverse magnetization; (13) reversely magnetized basalts; (14) normal magnetized basalts; (15) Miocene basalts and sediments with the complicated paleomagnetic characteristics; (16) Pliocene–Pleistocene basalts and sediments with the complicated paleomagnetic characteristics; (17) index of paleomagnetic zonation. The tectonic setting is derived from [54; 78; 79; 80]. The indicators 1n, 2n, 3n, 1 Ar, 2 Ar, and 3 Ar are the indexes of paleomagnetic subchrons (see Figure 18). Radiometric data (K-Ar and Ar-Ar) are compiled from [43; 78; 81; 82; 83]. Paleomagnetic data are generalized after [43; 76; 78; 81; 82; 84; 85; 86; 87; 88]. \(\rm{H_{TB}}\) and \(\rm{H_{HCC}}\) designate the calculated depths of the basaltic bodies in the basin: \(\rm{H_{TB}}\) is the upper edge for the model of the thin bed, \(\rm{H_{THP}}\) is the upper edge for the model of the thin horizontal plate, and \(\rm{H_{HCC}}\) is the center for the model of the horizontal circular cylinder. (**B**) Paleomagnetic scale for the integrated paleomagnetic scheme for the Sea of Galilee.  
In the Upper Galilee Heights, at the extreme west of the district, are mainly Upper Cretaceous and Paleogene sedimentary formations related to the Khorezm-Tuarkyr and Jalal paleomagnetic superzones. In the northeast, on the slope of Hermon Mt. are Jurassic and Cretaceous formations penetrated by pre-collisional, collisional, and post-collisional traps of the Gissar and Jalal superzones (Figure 20A,B). This peculiarity is reflected in more detail in a separate paleomagnetic map of this uplift (see Section 5.1.3).
# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 2 Related Research
### Anomaly Detection Integrator  
An integrated anomaly detector is a sophisticated approach that combines multiple anomaly detection techniques or models to improve the accuracy and effectiveness of anomaly detection in various domains, including software systems, industrial processes, cybersecurity, and more. Instead of relying on a single detector, an integrated anomaly detector leverages the collective strength and diversity of multiple detectors to enhance the overall detection performance [27].  
The integration process involves combining the outputs or predictions of individual anomaly detectors to generate a consolidated result. This integration can be achieved through different methods, such as voting, weighted averaging, stacking, or clustering [27; 28; 29]. The choice of integration method depends on factors like the nature of the data, the characteristics of the anomaly detection models, and the specific objectives of the detection task.  
The integrated anomaly detector offers several advantages over using a single detector. By combining multiple detectors it can leverage their complementary strengths and mitigate their individual weaknesses. This leads to improved detection accuracy, robustness, and the ability to handle a wider range of anomaly types and patterns. Moreover, the integrated detector can provide more reliable and confident anomaly alerts or predictions, as it takes into account multiple perspectives and evidence [29].  
Two recent outstanding representative anomaly detectors are LSCP [30] and SUOD [31]. LSCP uses the consistency of the nearest neighbors in randomly selected feature subspaces to define the local area around the test instance, thereby selecting the best detector and optimizing the detection result to a certain extent. SUOD focuses on solving the efficiency problems caused by using multiple detectors. SUOD designed three acceleration modules to optimize the anomaly detection process from three levels of data, model, and execution and achieved better experimental results.  
However, existing studies rarely incorporate the cumulative properties of anomaly data over time-varying anomalies into anomaly detection [4; 5; 6]. Both weighting-based and clustering-based identify anomalies from the characteristics of the data itself. For sudden anomalies, these methods play a good supervisory role. But for cumulative anomalies, these methods have some limitations.
## 6 Conclusions  
This research conducted DEM simulations of the vibratory compaction process in S-RM with varying degrees of mixing homogeneity. The study analyzed the relationships between void ratio changes, particle movements, and energy dissipations during compaction. Additionally, the influences of different excitation forces and frequencies on energy dissipation trends were examined. The key conclusions are summarized as follows:  
(1) The mixing uniformity of S-RM significantly affects the compaction characteristics of the filler material. More homogeneous specimens exhibit smaller void ratios after compaction.  
Figure 14: Energy dissipation pathway under different excitation frequencies: (**a**) dashpot energy; (**b**) sliding energy; (**c**) rolling slip energy; and (**d**) strain energy.  
(2) Particle motions during vibratory compaction do not necessarily promote densification, although rotations are found to be more closely associated with the compaction process than displacements.  
(3) The cumulative external load input energy corresponds to the total energy dissipated by the specimen, indicating that the compaction mechanism can be effectively studied through energy dissipation pathways. The consistent correlations observed between input energy, dissipated energy, and void ratio changes over time and mixing degree support this premise.  
(4) Dashpot energy does not directly cause variations in void ratio, sliding provides ambiguous effects on densification, rolling predominantly facilitates compaction during changes in void ratio, and strain energy passively reflects void ratio trends.  
In summary, this work provides insights into the underlying energy dissipation mechanisms that govern the densification of S-RM under vibratory loads. These findings contribute to more effective filler selection, improved vibratory compaction procedures, and enhanced predictions of subgrade quality and performance. Further efforts may involve experimental validations through instrumented laboratory vibratory compaction tests on S-RM specimens.  
Conceptualization, S.L.; methodology, S.L.; software, W.W.; validation, W.W. and W.H.; data curation, S.L.; writing--original draft preparation, W.W.; writing--review and editing, S.L.; supervision, W.H.; project administration, W.H.; funding acquisition, W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52178332; and the Scientific Research Project of the Hunan Provincial Education Department, grant number 22C250.  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflict of interest.
)1 NASA's Jet Propulsion Laboratory (JPL), California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA; [EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (C.Z. ); [PERSON]Z. ); [PERSON]Z. ); [PERSON]E.); [PERSON]S.O. )1 NASA's Jet Propulsion Laboratory (JPL), California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA; [EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (C.Z. ); [PERSON]E.); [PERSON]S.O. )1 NASA's Jet Propulsion Laboratory (JPL), California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA; [EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (C.Z. ); [PERSON]Z. ); [PERSON]E.); shadi.Oveisgharan@jpl. scatterometry [13; 14], and sea ice detection [15]. More recently, the 6U cubesat \({}^{3}\)Cat-2 [16] developed by the Universitat Politecnica de Catalunya (UPC) was launched on 15 August 2016, and NASA's Cyclone Global Navigation Satellite System (CyGNSS) GPS L1 C/A code-based constellation was recently launched on 15 December 2016 [17], providing an unprecedented sampling of the Earth's surface. In 2000, the use of polarimetric GNSS-R was first proposed by [PERSON] for soil moisture determination [18]. At present, there are two main approaches for soil moisture determination with GNSS-R: (i) the first uses tower-based geodetic GNSS receivers and the multipath Signal-to-Noise Ratio (SNR) to retrieve ground moisture [4; 19; 20] in a small region around the tower; (ii) the second one uses specifically designed receivers and two antennas either at Left and Right Hand Circular Polarizations (LHCP and RHCP) as in [21; 22; 23], or at horizontal H and vertical V polarizations as in [24]. The second technique using an horizontal H-Pol and V-Pol antenna is used in this work. Theoretical [25; 26] and experimental [27] assessments have shown a sensitivity to forest biomass. Recent airborne [21] and stratospheric balloon [28] flight experiments have been performed using new instruments and circular polarization antennas (RHCP and LHCP) for geophysical parameter retrieval over land surfaces, and wetlands. These experiments have shown the capability of GNSS-R techniques for wetlands monitoring even from high altitude platforms (\(\sim\)27 km) [28] because of the high spatial resolution provided by the coherent component of the scattered field. This study presents results from a serendipitous GNSS-R experiment performed on-board the SMAP mission [29] over continental areas. This unique data set, which provides very high-gain GPS L2C code dual-polarization (H and V) sampling of the Earth's surface up to high latitudes, is evaluated to assess the behaviour of different parameters of the so-called reflected waveforms corresponding to Soil Water Index (SWI), Normalized Difference Vegetation Index (NDVI), Above Ground Biomass (AGB), and topography. Section 2 describes the methodology and the experiment set-up. Section 3 presents the results, Section 4 provides final discussions, and the main conclusions are highlighted in Section 5.