esa-sceva/satcom-chunk-collection huggingface dataset:
# A Projection-Based Augmented Reality System for Medical Applications
## 2 Methodology  
The flowchart of the proposed system is shown below in Figure 1, which is composed of four major components: the separation of blood vessels from the CT images, the alignment of the 3D point cloud to the real-world coordinates, adjusting the projection surfaces with respect to the viewer, and, finally, projecting the adjusted model onto the phantom of a head for augmented reality display. Digital subtraction angiography is used to cut out the vascular portions from each CT image. The spatial position of the optical sensor or RGB-Depth camera is used to align the point cloud virtual coordinate system representing the surfaces of the head with the coordinates of the actual head in the real-world coordinate system. The first part for adjusting the display of the 3D model according to the observer's perspective is to detect the face portion of the observer, which is then combined with the extraction of facial feature points in order to not only obtain the observer's position but also his or her perspective. In order to adjust the display of the 3D model when using a 2D projector the scanlines are first projected onto the phantom following the computed perspective of the observer, and the distortions of the scanlines across the surface of the phantom are then recorded and used to calculate the slopes of changes across the surface of the phantom. The 3D model of the blood vessels is then adjusted according to the surface distortions, and, then, the projector projects the adjusted blood vessels onto the surface of the phantom head.  
The setup of the proposed system is shown in Figure 2. The processing of the data is as follows. First, the areas in the images containing blood vessels and bones are manually marked and, then, Ostu threshold and region growing methods are used to separate them from the images. In order to avoid over-segmentation, the Digital Subtraction Angiograph (DSA) [23] is used, which is an algorithm that augments blood vessel centerlines with their radii, and structures such as bones are subtracted from the image in order to constrain the 3D reconstruction of the blood vessels. Finally, small nodules and noise are removed from the 3D model. An example is shown in Figure 3.  
Figure 1: The flowchart of the proposed projection-based augmented reality system.  
Figure 2: The hardware setup of the proposed system.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 2 Related Works
### 3D Convolutional Networks  
Convolutional neural networks (CNNs) correspond to a particular category of models dedicated to feature extraction from 2D inputs (e.g., images). In CNN, different trainable filters followed by pooling operations are applied on input images [26]. They are quite invariant to pose and lighting variations. Learning models can be trained using supervised or unsupervised approaches. They yield competitive performance in several applications, ranging from object recognition [27], semantic segmentation [28], human action/pose recognition [29; 30], natural language processing [31], and audio classification and speech recognition [32; 33].  
In several applications, like video surveillance, action recognition, and scene analysis, video streams are analyzed instead of simple 2D frames. Thus, 3D CNN models have been developed and employed to extract both spatial and temporal features from video streams by performing 3D convolutions [64; 65]. Motion, by nature present in multiple adjacent frames, is thereby captured by 3D CNNs. More complex architectures (3D CNNs with long-term temporal convolutions) were recently proposed to capture video representations at full temporal scale [66].  
Other neural network architectures dedicated to spatiotemporal data analysis were recently proposed, the majority incorporating recurrent neural networks (RNN). [PERSON] et al. proposed drift neural networks [67], a particular architecture that merges deep CNN with a randomly initialized echo state network (the latter can be assimilated to an unconventional RNN). Convolutional gated recurrent units [68] were employed to ensure temporal reasoning (respect of the temporal order of frames). [PERSON] et al. proposed to observe the relevance of CNN paired with different temporal fusion strategies [29]. Spatiotemporal CNNs [69] and temporal segment networks [70], which combine a sparse sampling strategy and aggregation functions to enhance modeling of long-range information, have recently been proposed. Visual features combined with long short-term memory (LSTM) were also introduced by [PERSON] et al. [71].
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
###### Abstract  
The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the _NDVI_ (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the _NDVI_ values is the main sign of sugarcane harvest. The _NDVI_ time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The _NDVI_ time series trends were used to refine the algorithm. It is supposed that the descending _NDVI_ trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of Sao Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area.  
Sentinel-1; Sentinel-2; _NDVI_; SLC; harvest; sugarcane; coherence; Brazil; monitoring 2020 February 2020; Accepted: 10 December 2020; Published: 13 December 2020
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.7 UAV  
In [121], the authors propose a two-stage DRL algorithm for joint content placement and trajectory design. The two stages of the proposed scheme include offline content placement and online user tracking. In the first stage, the authors maximize users hit rate while constraining cache capacity. In the second stage, a Double Deep Q-Network (DDQN) is developed for online tracking mobile users, while maintaining energy constrains. Simulation results show that the proposed algorithm can easily adapt to dynamic conditions, predict trajectory and provide enhanced achievable throughput.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 6 Results
### Description of the Pedestrian Step-Out Scenario  
We show the efficacy of the optimisation method on the functional scenario of a pedestrian stepping onto the road in front of the ego vehicle. In this functional scenario, the ego vehicle is driving along a straight road, when suddenly a pedestrian crosses in front of the vehicle from the near side of the road. Furthermore, the pedestrian is occluded behind a parked car, and only becomes visible once they step onto the driving lane, as illustrated in Figure 3. The ego vehicle is similarly fitted with a non-pivoting radar sensor, and equipped with longitudinal control and emergency braking systems. The radar sensor was set to recognise obstructions such as pedestrians in the driving lane of the ego vehicle and pass these on to the vehicle control.  
The scenario can be described in its logical form using the following 5 input parameters:  
1. Speed of ego vehicle (\(S_{1}\));
2. Speed of pedestrian stepping onto street (\(S_{2}\));
3. Distance between pedestrian and ego vehicle at the time the pedestrian steps out (\(d\));
4. Sensor Range (blue cone) (\(SR\));
5. Horizontal field-of-view of sensor (opening angle of blue cone) (\(H\)).  
In addition to the initial speed of the ego vehicle, the speed of the pedestrian, which in this case moves in a straight line orthogonal to the ego vehicle, is parametrised. The radar sensor is parametrised using two input parameters: its horizontal field-of-view and its range. Furthermore, we add a parametrisation of the distance between the ego vehicle and the pedestrian, controlling when the pedestrian starts their movement to step onto the road. This spans up a five-dimensional logical scenario space, from where possible concrete values are sampled. The ranges of these five input parameters are listed in Table 1.  
Figure 3: Pedestrian step-out scenario with the yellow ego vehicle, the pedestrian and a static blue vehicle, which acts as an obstruction so that the pedestrian is only seen once they step onto the driving lane.
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 3 Brightness Modelling
### Correlates of Brightness
#### 3.1.1 Metric Parameter Values  
Four frequency scales for calculating the \(SC\) metrics were considered: Hertz, mel, ERB, and cents (with a 27.5 Hz reference). For the crossover frequencies used to calculate the \(SC_{fc}\) and ratio metrics, all values reported in previous research were tested: 500, 750, 900, 1000, 1250, 1500, 2000, and 3000 Hz. The additional value of 6 kHz was also included. Thus, nine crossover frequencies in total were considered.  
Each metric was tested with three audio weighting options: unweighted and with A- and C-weighting applied. In terms of spectral smoothing, seven options were tested: no smoothing, octave-band, half-octave, \(\frac{1}{3}\)-octave, \(\frac{1}{12}\)-octave, \(\frac{1}{24}\)-octave, and \(\frac{1}{64}\)-octave smoothing.  
It may also be beneficial to consider the interaction between the various metrics. This can be shown by including the product of the various \(SC\) and ratio metrics. Additionally, logarithmic transformations may also improve the correlation of all suggested metrics,since human perception of frequency and amplitude is roughly logarithmic; all metrics were tested both untransformed and with logarithmic transformation.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Granulosa Cell Tumor  
Granulosa cell tumors are sex cord-stromal ovarian neoplasms showing differentiation toward follicular granulosa cells. The two types of granulosa cell tumors are known as adult and juvenile types. Macroscopically, adult granulosa cell tumors appear as unilateral, usually solid or solid-cystic masses, with an area of hemorrhage or necrosis following torsion. In the adult type granulosa cell tumors, the tumor cells resemble normal granulosa cells. They are small and round, cuboidal, or spindle shaped, with pale cytoplasm and ill-defined cell borders. The nuclei are round or oval, with fine chromatin and a single small nucleolus. An important diagnostic feature is the presence of longitudinal folds or grooves in the nuclei with a 'coffee-bean' appearance [91]. Occasionally, mitotic figures, nuclear pleomorphism and bizarre nuclei are seen, but do not appear to affect the prognosis adversely [92; 93; 94]. The tumor cells are rarely extensively (>50% of cells) luteinized with abundant eosinophil cytoplasm, well-defined cell borders and central nuclei resembling the luteinized granulosa cells of the corpus luteum. Cells are arranged in different patterns, including diffuse, trabecular, micro-follicular, macro-follicular, insular and pseudopapillary. Macroscopically juvenile granulosa cell tumors are solid-cystic masses, rarely exclusively solid. They are variably composed of a double component, granulosa and theca component. The former shows polygonal cells with abundant cytoplasm, from eosinophilic to vacuolated, and clear and hyperchromatic nuclei without grooves. The second is composed of oval- to spindle-shaped cells with pale cytoplasm. Several growth patterns are observed, from solid to follicular or pseudo-papillary. In the atypical forms, brisk mitotic activity is seen. Bizarre nuclei may occur. WT1 is a marker that has not been widely studied in granulosa cell tumors. It is expressed in 65% to 88% of adult granulosa cell tumors (Figure 4) [95; 96; 97; 98]. Although several different non-sex cord-stromal tumors can express WT1 [99], most of them are not in the differential diagnosis of ovarian sex cord-stromal tumors, and tumors that are in the differential diagnosis are typically negative or limited for WT1 expression.
## 2 Materials and Methods
### Instrumentation  
A total of 16 T-type thermocouples were used to instrument the setup; all temperature sensors had an uncertainty of \(\pm 0.5\,^{\circ}\)C. The five surface thermocouples had preinstalled adhesive pads (SA1-T, Omega Engineering Inc., Norwalk, CT, USA) and were attached to the display of the mock tablet. Inside the tablet and on the back cover, traditional thermocouples, (5 TC-TT-T-30-36, Omega Engineering Inc., Norwalk, CT, USA) with a diameter of 0.25 mm, were used. They were more robust than the surface sensors and did not have adhesive pads. These thermocouples were secured in place with a dab of cyanoacrylate glue and a small piece of polyimide tape. Using this method, sensors had a low profile, were securely attached, and could be removed/relocated by peeling off the tape and softening the glue with acetone. Three arrays, each with five sensors, were placed inside the tablet computer (\(T_{in-1}\,\dots\,T_{in-5}\)), on the back cover (\(T_{B-1}\,\dots\,T_{B-5}\)), and on the display (\(T_{F-1}\,\dots\,T_{F-5}\)) in the same positions. The locations and designations of thermocouples are shown in Figure 4. One additional thermocouple was suspended from the plastic frame to record the ambient air temperature (\(T_{\mathrm{amb}}\)). All of the measurements were recorded using a Compact DAQ data acquisition system and a 16-channel thermocouple module (NI 9213). Data were collected at 1 Hz using a program written in LabVIEW 2014.  
Figure 4: Rendering of the mock tablet showing the location and designation of the thermocouples attached to the (**a**) internal components, (**b**) back cover, and (**c**) front display.  
The DC power supply for the polyimide heater (Statco Energy 3 PN501B) could supply up to 30 V and was controlled via an analogue voltage signal produced by a National Instruments Compact DAQ system with an NI-9263 voltage output module. Using this system, the voltage to the heater could be controlled from a PC workstation [22].  
The voltage across the heater (\(V_{in}\)) was measured using an analog input NI-9201 module. This module was limited to measuring voltages in the range of 0-10 V; therefore, a voltage divider was used to reduce the voltage read by the module. The current through the heater (\(I_{in}\)) was determined by measuring the voltage drop (\(V_{Shunt}\)) over a shunt resistor (HSA25R05J, 0.05 \(\Omega\)) that was placed in series with the heater, as Equation (1)  
\[I_{in}=\frac{V_{shunt}}{R_{shunt}} \tag{1}\]  
However, the voltage drop across the shunt resistor was small; therefore, an amplifier (type INA122P) with an internal resistor \(R_{3}\) (1 k\(\Omega\)) was used. The voltage across the amplifier (\(V_{Gain}\)) was measured with an analog input model NI-9201. The voltage drop (\(V_{Shunt}\)) across the shunt resistor could be calculated using Equation (2) and the measured \(V_{Gain}\).  
\[V_{shunt}=\frac{V_{gain}}{5+\frac{200\text{k}\Omega}{R_{3}}} \tag{2}\]  
The heat dissipation rate was then calculated with Equation (3).  
\[P=I_{in}V_{in} \tag{3}\]  
The uncertainty in the power measurements was \(\pm\)7.3%.
The performance of the model, which was trained on the imbalanced training dataset, achieved an ACC of 89.50%, TPR of 87.81%, and TNR of 91.18%. The examination results obtained on both EyePaCS and DDR test data are higher than those reported previously [13; 15]. Test results of the Messidor-2 [38] dataset significantly outperformed the state-of-the-art model results [14], with an ACC of 90.68%, a TPR of 86.0%, and a TNR of 92.33%. The models trained by adopting a wrapper algorithm that leveraged the unlabeled DR data used in self-supervised or semi-supervised learning are 4-5% higher in accuracy than the ones trained on the labeled data used in supervised learning only. It can be observed that the performance of the model trained on a relatively small but balanced training set is not worse than that of the model trained by a relatively large but imbalanced dataset. This reveals that it is not necessary to use a large-scale dataset to feed deep-learning-based CNN models to obtain better performance. Under the circumstances, where it is very difficult to collect clean, labeled medical data, it is crucial to use a small amount of data to ensure that the model works well. The proposed method can be applied to any deep learning model. The main contributions of this study can be outlined as follows:  
1. We present a method in the form of a wrapper algorithm to help improve DR detection using supervised learning. It uses semi-supervised or self-supervised learning to first learn and gain features from unlabeled data, and then transfers these learned features to a model using supervised learning to optimize learning. 2. A combination of self-supervised and semi-supervised learning is used to perform DR detection, and this combined approach can significantly reduce the learning time while providing a viable solution for training models when labeled data are particularly scarce. 3. To analyze the impact of data imbalance on learning, this study draws two different training datasets, one larger and imbalanced, and the other a smaller and balanced, using down-sampling. All experiments are conducted on these two datasets, and the results show that the small balanced dataset is more advantageous for training models than the larger and imbalanced data. 4. To examine the proposed method, we conduct experimental tests on three different DR datasets: EyePaCS, DDR, and Messidor-2. The remainder of this paper is organized as follows: Section 2 describes the proposed methods for DR detection. Section 3 presents experiments, result analyses, and comparisons with the previous results. Finally, the discussion and conclusion are given in Sections 4 and 5, respectively.
## 4 Results and Discussion
### DQ1: Most Active Researchers  
A total of 243 researchers appear as authors in the 90 primary published research studies selected by our SMS study. We only report the top 10 active researchers, based on their number of published works, however, the full list can be accessed online as complementary material [44].  
It is worth mentioning that the data allowed us to identify groups of researchers who often published collaborative works (e.g., [PERSON] and [PERSON], [PERSON], [PERSON] and [PERSON], and [PERSON] and [PERSON]). In addition, most of the active researchers are affiliated with academic organizations (80%). It is interesting to note that the top six active researchers, those with more than three publications, are based in Europe. Most of the identified researchers are currently active, except for the research group composed by [PERSON], [PERSON] and [PERSON], inactive since 2012. The top ten active researchers and their affiliations are shown in Table 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author’s Name** & **Organization** & **Support Level** \\ \hline [PERSON] & AIRBUS, U.K. & 4 \\ \hline \multirow{2}{*}{[PERSON]} & Software Engineering Research Group, University of the West of & \\  & England, U.K. & 4 \\ \hline \multirow{2}{*}{[PERSON]} & Institute of Software Technology and Interactive Systems, & \\  & Vienna University of Technology, Austria & 4 \\ \hline \multirow{2}{*}{[PERSON]} & Department of Computer and Information Science, Norwegian & \\  & University of Science and Technology, Norway & 4 \\ \hline \multirow{2}{*}{[PERSON]} & Department of Computer and Information Science, Norwegian & \\  & University of Science and Technology, Norway & 4 \\ \hline \multirow{2}{*}{[PERSON]} & Novosibirsk State Technical University, Russia & 4 \\ \hline \multirow{2}{*}{[PERSON]} & Department of Multimedia and Mobile Commerce, Kainan & \\  & University, Taiwan & 3 \\ \hline [PERSON] & Novosibirsk State Technical University, Russia & 3 \\ \hline \multirow{2}{*}{[PERSON]} & Department of Computer Science, North Carolina State & 2 \\  & University, USA & \\ \cline{1-1} [PERSON] & University of the West of England, U.K. & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Active researchers based on number of papers.
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
###### Abstract  
To address the limitations of traditional early warning methods in open-pit slope displacement monitoring--particularly their neglect of spatiotemporal correlations and their difficulty in analyzing multi-scale non-stationary sequences--this study proposes an early warning framework that integrates spatiotemporal clustering with multi-scale decomposition. Taking the southern slope of the Bayan Obo Main Pit as a case study, high-risk deformation zones were identified using DBSCAN-based spatiotemporal clustering applied to slope radar monitoring data. The displacement time series were decomposed using Variational Mode Decomposition (VMD) into trend and periodic components, for which Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models were respectively developed. The results indicate that (1) DBSCAN effectively detects clusters characterized by high average cumulative displacement and broad spatial distribution, while filtering out isolated outliers. (2) The trend component prediction achieved a coefficient of determination (R\({}^{2}\)) of 0.99755, while the periodic component prediction yielded a root mean square error (RMSE) of just 0.0978 mm. The reconstructed total displacement achieved an R\({}^{2}\) of 0.9973, verifying the proposed multi-scale decomposition and hybrid modeling framework's high accuracy and robustness in slope deformation modeling and early warning.  
open-pit slope; DBSCAN; variational mode decomposition; landslide prediction Article