esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of the research was to examine how the nest selection preference of laying hens with different genotypes differed, the proportion of eggs laid in the litter, eggs laid in the upper and bottom nests, and the number and duration of nest visits. The experiment was conducted with laying hen genotypes provided by Babolna TETRA Ltd. (Babolna, Hungary) (Commercial hybrid (C); pure-line maternal (Maternal); pure-line paternal offspring group (Paternal)). N = 318; \(n\) = 106 hen/genotype; and 53 hens/pen. We placed 53 19-week-old, non-beak-trimmed hens in each of the six 5.52 m\({}^{2}\) alternative pens. We provided 14 nests for the hens at two levels per pen (3.8 hens/nest). We recorded the number of eggs laid in the nests on the bottom and upper levels, as well as within the litter. Infrared cameras were installed above the pens, and we conducted recordings on a test day at the beginning of the third production month. In our results, we found a significant difference in the proportion of litter eggs overall during the 12 months of production (C. hybrid: 30.7%; Paternal: 41.1%; Maternal: 10.2%). A significant difference was found between all genotypes in the proportions of eggs laid at the bottom (B) and upper (U) level during the 12 months of production (C. hybrid: B: 72.2%, U: 27.8%; Paternal: B: 88.0%, U: 12.0%; Maternal: B: 71.4%, U: 28.6%). The evaluation of the video recordings revealed that the C. hybrid and Paternal genotypes visited the bottom nests in 97.2% and 96.0% of the cases, respectively, and the Maternal genotype individuals in a significantly lower proportion, 72.5% of the cases; the Paternal genotype spent significantly more time (13.4 min) on average in the bottom nests compared with the C. hybrid (7.9 min) and the Maternal genotypes (8.6 min). Our conclusion is that it is not enough to ensure the desired ratio of egg nests in egg production, as laying hens may not use nests in certain positions at all. This generates a relative shortage of egg nests and can increase the ratio of eggs laid in the litter, which in turn involves human health risks.  
laying hen; non-caged; non-trimmed; litter eggs; nesting behavior +
Footnote †: journal: Applied Sciences
## 4 Generation Scheme  
One of the advantages of the ACE-FHBOC baseband signal is that it can be digitally implemented because its waveform is multilevel, which greatly reduces the hardware complexity. We will discuss two-generation schemes that can be implemented based on that of ACE-BOC with minor modifications. One is the direct generation, and the other is the phase rotation generation.
# Experiments on Longitudinal and Transverse Bedload Transport in Sine-Generated Meandering Channels
## 5 Summary and Conclusions  
This study conducted two experiments in sine-generated flumes with deflection angles of 30\({}^{\circ}\) and 110\({}^{\circ}\), which represent the mildly and highly curved channels, respectively. Bed grains were dyed with four different colors and paved along two consecutive bends in order to quantitively trace the same- and opposite-side sediment transport. The experiments recorded the change in flow patterns, bed deformation and the gain-loss distribution of colored grains in the pool-bar complexes. The longitudinal and transverse transport of bedload were evaluated in the examined bends of the two meandering flumes. The following conclusions can be drawn:  
1. Two types of erosion zone occurred during the process of the bed deformation, i.e., Zone 1 in the foreside of the point bars and Zone 2 near the concave bank downstream of the bend apexes.
2. Most grains eroded from both Zone 1 and Zone 2 are observed moving longitudinally as opposed to crossing the channel centerline. The ratios of eroded D\({}_{1}\) grains from the B\({}_{1}\) bend who were longitudinally transported to the next bend B\({}_{2}\) were 71% for the 30\({}^{\circ}\) flume and 72% for the 110\({}^{\circ}\) flume. The ratios of the D\({}_{2}\) grains eroded from the B\({}_{1}\) bend who were longitudinally transported to the next bend B\({}_{2}\) were 90% for the 30\({}^{\circ}\) flume and 86% for the 110\({}^{\circ}\) flume.
3. Most building material of the point bars comes from the upstream bends, although low- and highly curved channels behave differently. For building material of the B\({}_{2}\) bend point bars, the contribution ratios of grains coming from upstream bend were 87% for the 30\({}^{\circ}\) flume and 73% for the 110\({}^{\circ}\) flume.  
The results of this study are appliable to most free meanders which may develop from low-sinusosity to high-sinusosity meander.  
Conceptualization, L.H. and D.C.; methodology, L.H. and D.C.; validation, L.H. and S.Z.; data curation, Z.Z.; writing--original draft preparation, L.H.; writing--review and editing, D.C. and D.T.; supervision, D.C. and D.T.; funding acquisition, L.H. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (1) the National Key R&D Program of China (2017 YFC0405203) and (2) the National Natural Science Foundation of China (51779242 and 51979264). This paper does not necessarily reflect the views of the funding agencies.  
Not applicable.  
The authors declare no conflict of interest.
## Appendix A  
The formulas used to calculate the GMD parameters using the VB algorithm are as follows:  
\[q\left(\mathbf{Z}\right)=\prod_{k=1}^{K}\prod_{n=1}^{N}r_{kn}^{z_{kn}}\] (A1) \[r_{kn}=\frac{\rho_{kn}}{\sum_{j=1}^{N}\rho_{kj}}\] (A2) \[ln\;\rho_{kn}=E\left[ln\;\pi_{n}\right]+\frac{1}{2}E\left[ln\; \left|\mathbf{\Lambda}_{n}\right|\right]-\frac{1}{2}ln\;(2\pi)-\frac{1}{2}E_{\mu_{ n},\mathbf{\Lambda}_{n}}\left[\left(u_{n}-\mathbf{\mu}_{
## 6 Results and Discussion
### Estimation  
For the RFR(F) model, the training and testing phases are visualised in Figure 6, while the estimation outcome--including the training error (MSE,RMSE), testing error (MSE,RMSE), training time and testing time, are detailed in Table 2. The training MSE of 3.16 dBm, along with the testing MSE of 3.20 dBm, indicates the higher accuracy of this model. The training time of 0.0134 s and the testing time of 0.0010 s signifies the model's efficiency. In an RFR(S) model, we utilised a variety of feature combinations such as \([3,0,1]\) and \([3,1,1]\). These combinations show the LoS and NLoS conditions with a fixed distance of 3 and varying receiver locations. Similarly, the combinations from \([2,0,2]\) to \([0.5,1,2]\) show the LoS and NLoS conditions with varying receiver distances and locations. The RFR(S) model's outcome using these feature combinations demonstrates the model's viability in handling the different RSSI series. Figures 7-14 illustrate the training and testing phases for the selected sequences of feature combinations. We evaluated the model's performance by obtaining the MSE, Root Mean Squared Error (RMSE), training time, and testing time across these selected sequences. The estimation outcome of the selected RSSI series can be visualised in Table 3. Additionally, it is to be noted that the RFR(S) model (in Table 3) demonstrates varying levels of MSE across different sequences during the training and testing phases. For instance, the sequence using features \([3,0,1]\) shows the training MSE of 1.66 dBm during the training phase. During the testing phase, there is a slight increment in the MSE of 5.65 dBm. Despite these variations, the RFR(S) model demonstrates relatively lower MSE, RMSE, training, and testing times across all sequences, which indicates high computational efficiency.  
Figure 6: Visual results of RFR(F) model for LP-IoT wireless channel estimation.  
Figure 7: The results of selected sequence using the features [3, 0, 1].  
Figure 11: The results of selected sequence using the features [1, 0, 2].  
Figure 8: The results of selected sequence using the features [3, 1, 1].  
Figure 10: The results of selected sequence using the features [2, 1, 2].  
Figure 9: The results of selected sequence using the features [2, 0, 2].
## 3 Radar Intensity Images  
The intensity of each pixel in a single radar image represents the proportion of microwave backscattered from that area on the ground, giving information of the type, shape, roughness, orientation, and moisture content of the target area. At Pacaya, radar intensity images can be used  
Figure 1: Location and major topographical features of Pacaya Volcano. **(a)** Pacaya volcano (red triangle) is located in the Central American Volcanic Arc (CAVA), with other CAVA volcanoes marked with black triangles; **(b)** Map showing the location of topographic features, including the summit vent, ancestral collapse scarp, and the linear collapse and lava flow as a result of the May 2010 eruptions. The colored boxes show the extent of Figures 2–5.  
to discern features such as the summit crater area, 2010 collapse, ancestral scarp, and 2010 lava flow (Figure 2). The 2010 lava flow appears brighter than its surroundings due to its rough surface and its emplacement on the vegetated slope outside of the ancestral collapse scarp, which has different backscattering properties. The southern part of the lava flow becomes more difficult to distinguish as it moves into the lava flow fields within the ancestral scarp, suggesting that past and current flows have similar roughness (Figure 2b). The different orientations of the sloping walls of the 2010 collapse and ancestral collapse scarp make these features easily distinguishable. Additionally, the change in crater size can be seen after the eruption, which increases nearly six times to \(\sim\)600 m in diameter (Figure 2b). Single SAR images from the ALOS-1 satellite are very noisy due to speckle, a phenomenon in which the resolution of the SAR sensor is not sufficient to resolve individual scatterers. This speckle noise can often be reduced by averaging several SAR images. However, it is clear by comparing the averaged ALOS-1 scene (Figure 2c) to the UAVSAR images (Figure 2a,b) that a satellite sensor with a finer resolution is more desirable for distinguishing geological features (pixel size of \(\sim\)0.6 m in azimuth and \(\sim\)2.2 m in ground range for the original UAVSAR imagery, _vs._\(\sim\)3.2 m in azimuth and \(\sim\)6.5 m in ground range for ALOS-1 SLC imagery over Pacaya volcano).
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 4 Real-Time ZTD Grid Product Evaluations
### Accuracy Assessments  
GPS Data at 260 CMONOC GPS stations (Figure 5) in four months (Jan., Apr., Jul. and Oct.) during year 2015 were processed, among which 16 stations were selected as test stations, and the other stations were taken as base stations for real-time ZTD grid product generation.  
Figure 4: Power Spectral Density (PSD) of \(1/H\) time series at all grid points over China.  
Taking post-processing ZTD as references, the bias and error RMS of ZTD calculated from real-time grid product at 16 test stations are summarized in Table 5. We can find smaller RMS of about 1.09 cm in winter (Jan.) with insignificant bias, but larger RMS of 2.07 cm in summer (Jul.) with bias of 0.66 cm. The bias and error RMS at each test station are presented in Figure 6. The largest bias (-2.2 cm) is found at station XJBC in summer, which may be due to the fact that station XJBC locates at the edge of the CMONOC network with sparse stations around. RMS in summer are usually larger than RMS in other seasons mainly due to larger variations of water vapor content in summer. Overall, according to Table 5 and Figure 6, the generated real-time ZTD grid product based on CMONOC can provide real-time ZTD with bias of 0.39 cm and RMS of 1.56 cm, and the accuracy is not significantly affected by the station altitude.  
Figure 7 presents the comparisons of performances for RtZTD and four widely used empirical ZTD models, including Global Pressure and Temperature model (GPT), GPT2w, UNB3m and EGNOS. ZTD derived from GPT means that the GPT and standard atmosphere parameters were used as inputs in Saastamoinen model. For GPT2w, ZTD is the sum of GPT2w-derived ZWD and ZHD estimated from Saastamoinen model based on GPT2w-derived pressures. From Figure 7 we find that GPT2w is evidently better than GPT, especially for stations with high elevation. The mean bias of UNB3m is about 3.74 cm, which is larger than values in both [5] (0.5 cm) and [8] (1.46 cm). The reason might be that only 16 stations in China were used in Figure 7 while [5,8] used global distributed stations for the assessment. Compared to the empirical models, the improvement by using RtZTD is significant, with bias of 0.39 cm and RMS of only 1.56 cm.
# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
###### Abstract
One of the most popular methods is the random forest (RF) classification algorithm, which consists of many decision trees [25]. This algorithm has demonstrated reliable results for digital mapping of soil properties at different scales [26; 27; 28]. For example, [PERSON] et al. [29] applied the RF approach for the spatial prediction of soil organic carbon (SOC) stocks in Inner Mongolia, Northern China. The authors showed that land use, soils and geology were the most important variables influencing SOC storage. Similarly, this method was applied to digital mapping of SOC and calcium carbonate equivalent contents, where it was found that the key factors for the distribution of properties were remote sensing data [30]. In the context of climate change, proper soil irrigation is even more critical as it can help to mitigate the impacts of drought and extreme weather events, which are becoming more frequent and severe due to climate change. Efficient use of water through soil irrigation can help to conserve water resources, reduce greenhouse gas emissions associated with water pumping and transportation and enhance soil carbon sequestration, which can help to mitigate climate change. Climate change equally occurs in forest-steppe zones, including in the study region [31; 32]. Such conditions make it necessary to increase the share of irrigated land and design new irrigation systems. Hence, detailed land cover studies and mapping are needed in planning irrigation system construction and operations to avoid degradation processes (water and wind erosion, accumulation of toxic salts, erosion of soil structure and loss of SOC and nutrients). The study's primary purpose is to assess the soils for use in irrigated agriculture in the forest-steppe zone of the Republic of Bashkortostan (Russia). Therefore, the objectives of this study are the following: (i) study the physicochemical properties of soils, (ii) assess the geochemical condition of soils, and (iii) conduct the digital mapping of soil properties using the RF approach in combination with remote sensing and topography data.
###### Abstract  
The Fourth Industrial Revolution (4 IR) has significantly impacted healthcare, including sexually transmitted infection (STI) management in Sub-Saharan Africa (SSA), particularly among key populations (KPs) with limited access to health services. This review investigates 4 IR technologies, including artificial intelligence (AI) and machine learning (ML), that assist in diagnosing, treating, and managing STIs across SSA. By leveraging affordable and accessible solutions, 4 IR tools support KPs who are disproportionately affected by STIs. Following systematic review guidelines using Covidence, this study examined 20 relevant studies conducted across 20 SSA countries, with Ethiopia, South Africa, and Zimbabwe emerging as the most researched nations. All the studies reviewed used secondary data and favored supervised ML models, with random forest and XGBost frequently demonstrating high performance. These tools assist in tracking access to services, predicting risks of STI/HIV, and developing models for community HIV clusters. While AI has enhanced the accuracy of diagnostics and the efficiency of management, several challenges persist, including ethical concerns, issues with data quality, and a lack of expertise in implementation. There are few real-world applications or pilot projects in SSA. Notably, most of the studies primarily focus on the development, validation, or technical evaluation of the ML methods rather than their practical application or implementation. As a result, the actual impact of these approaches on the point of care remains unclear. This review highlights the effectiveness of various AI and ML methods in managing HIV and STIs through detection, diagnosis, treatment, and monitoring. The study strengthens knowledge on the practical application of 4 IR technologies in diagnosing, treating, and managing STIs across SSA. Understanding this has potential to improve sexual health outcomes, address gaps in STI diagnosis, and surpass the limitations of traditional syndromic management approaches.  
Sub-Saharan Africa; sexually transmitted infections; HIV; key populations; general population; fourth industrial revolution; artificial intelligence technologies; algorithms +
Footnote †: journal: _algorithms_  
_Systematic Review_
# A Comprehensive Safety Analysis Study for Concrete Core Dams
## 3 Seepage Analysis  
For the three-dimensional finite element model, the characteristics of the different materials are listed in Table 2.  
The phenomenon of water flowing into soil pores is called seepage. The existing states of water in soil include gaseous water, adherent water, membrane water, capillary water, and gravity water, of which gravity water is the object of seepage theory research.  
Seepage mainly refers to the flow of fluid in porous media. For groundwater, the flow of water in pores and fractures is a specific seepage example. The space area occupied by seepage flow is called the seepage field. Seepage is divided into saturated seepage and unsaturated seepage; seepage can be divided into stable seepage and unstable seepage according to whether the water head, hydraulic gradient, and other indicators change with time. Saturated seepage refers to seepage in a saturated zone, and seepage in an unsaturated zone is called unsaturated seepage. As long as one of these characterization parameters changes with time, it is called unstable seepage. Equation (10) is used for stable seepage, while Equation (11) is for unstable seepage. On the contrary, when each basic element is independent of the time factor in the seepage process, it is called stable seepage. In the earth rock dam project, seepage mainly refers to the flow of water in the soil pores, but the size and shape of the soil pores are very irregular, so the flow of water in the soil pores is a very complex phenomenon.  
The calculation was completed based on the above-proposed method. A checking water level of 125.12 m was chosen. The water pressure at the upstream surface of the dam was defined as a certain value, and that at the downstream surface was taken as zero. The base of the dam and the downstream surface were considered permeable boundaries. Dam foundation seepage is a typical form. A certain thickness of the permeable base of the dam was considered to truly reflect the actual project. The seepage results were also embodied at the base of the dam in Figures 8 and 9.  
From the result shown in Figures 10 and 11, the maximum penetration ratio of the gravel is 0.12 (the allowed penetration is 0.15), the maximum penetration ratio of the dam core wall is 0.38 (the allowable penetration is 0.4) [24], the maximum penetration ratio of the cut-off wall is 16.2 (the allowed penetration ratio is 50); these were all less than the allowable penetration of the dam. Based on the above conclusion, there will not be seepage failure. The single wide seepage flow of the main dam is 0.52 m\({}^{3}\)/(m\(\cdot\)d), which belongs to a small flow. The arrow of the velocity vector at the right side upper layer of the dam existed for direction guidance for the combing of vertical and horizontal water pressure for Figure 12.  
Figure 11: Seepage pressure contour map of the checking flood level by the proposed method.  
Figure 9: Isograms of equipotential line for the main dam using the common method.
## 4 Mobile Mapping  
For rapid _in situ_ image data capturing, a mobile mapping system (GFZ-MOMA) has been developed and extensively tested [25,26]. The system is composed of a Ladybug3 omnidirectional camera from Point Grey Research Ltd., a data capturing and storage unit, a navigation unit, and an external battery pack that supplies the energy for up to 6 h of autonomous operation (Figure 6).  
The _Ladybug3_ (See [[http://www.ptgrey.com/](http://www.ptgrey.com/)]([http://www.ptgrey.com/](http://www.ptgrey.com/)) for full technical specifications) _camera_ is made up of six color Complementary Metal Oxide Semiconductor (CMOS) sensors that capture concurrent image sequences with an acquisition rate of up to 15 fps (frames per second). The six single-camera image streams are synchronized and automatically stitched into an omnidirectional (panoramic) high resolution (5700 \(\times\) 2700 px) format with JPEG compression. The camera system is operated from inside the car and is mounted on the vehicle's roof with a simple mounting system composed of a light-weight aluminum frame and four high-power suction cups.  
Figure 6: GFZ-MOMA omnidirectional mobile mapping system with data capturing and storage unit, and battery pack.  
The data capturing and storage unit has been developed with a specific focus on ease of use and ruggedness for robust outdoor applications, even under rough conditions (e.g., unpaved roads, dust). The main component of the unit is a standard notebook with a 750 GB Serial ATA hard drive. A commercial-grade GPS receiver provides geo-localization with a 1 Hz epoch rate. An optional Inertial Measurement Unit (IMU) can be used to record additional data about the 3D camera pose. The notebook and all the other components are fixed into a rugged hard plastic case. A custom-designed software application captures, synchronizes, and saves the different data streams coming from the camera, the GPS, and the IMU. The synchronization of the data, within 125 ms, is based on the timer embedded in the ieee-1394B hardware controller. Location is associated with each omnidirectional image by b-spline interpolation of GPS positioning.  
The _navigation unit_ uses QGIS, a free, open-source GIS environment, as the main software component for location tracking and car navigation. Its map interface is able to combine various background maps of the study area and to display pre-calculated sample areas and routes. The position can be tracked and displayed in real time with the GPS live tracking functionality. This allows an operator to not only navigate the car along pre-calculated routes, but also to reschedule the path on-the-fly to cope with unexpected environmental conditions (e.g., traffic jams or road blockages).
## 5 Challenges, Open Research Problems, and Future of WBANs
### Challenges and Open Research Problems of WBANs  
Recent technological advances in sensors and low-power integrated circuits and wireless communications have allowed the design of low cost, miniature, lightweight, and intelligent physiological sensor nodes. These nodes are capable of sensing, processing and communication of one or more vital signs can be easily integrated into wireless personal area networks or the authority to monitor the health condition. These networks promise to revolutionize health care by allowing non-expensive, non-invasive, continuous, ambulatory health monitoring with almost real-time updates of medical records via the Internet. Although the number of research efforts is focusing on various technical, economic, and social issues, many technical hurdles still remain to decide whether to have a flexible, reliable, secure, and energy efficient WBAN suited of medical applications. In this subsection, an overview of hardware and software platforms for the problem of medical monitoring using WBANs is presented. As well as, we will introduce some open problems, and introduce the authors' suggested solutions for these problems.