esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Third, it is interfaceable to external physical systems, such as biomedical sensors, imaging scanners, and user interfaces, for perception (e.g., displays, HMD, HHD) and interaction (e.g., smart pointers and haptics) to enhance the viewers' immersion experience. Yet another critical aspect of the system is its independence from any specific platforms and/or operating systems; this provides a key benefit considering the rapid changes in hardware. We utilized standard, secure, and simple communication and data exchange protocols (transmission control protocol (TCP) for data exchange, remote desktop protocol (RDP) for remote management, and secure sockets layer (SSL) for security, all as widely accepted communication and security protocols) to ensure rapid deployment and maximum compatibility with new HMD or HHD hardware with minimum software modification needs to focus users' attention on actual on-the-cloud human immersion instead of dealing with the technical complexity of system deployment. The presented cloud resource was designed and implemented to handle any type of multidimensional and spatio-temporal data we may encounter in the biomedical field, including 3D/4D images and renderings based on them (e.g., cardiac MRI and the 4 Dendocardium segmented). The data can be imported in any major medical imaging and 3D visualization formats, e.g., digital imaging and communications in medicine (DICOM) and universal scene description (USD). As such, it can be used in medical training and diagnosis using virtually any medical image modality as input, such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc. Of particular value, however, is in interventional medicine when used with HMD to support hands-free interventions [31, 42]. The primary contribution of this work is the introduction of a cloud-deployable computational framework and a proof-of-concept demonstration of (i) deploying extended reality applications for human-computer interfacing, (ii) remotely controlling medical devices via XR interfaces, and (iii) enabling interactive synergy of multiple users and at multiple locations with a variety of devices. This resource has a direct impact on addressing current computational and resource limitations, especially for multiuser interactions. The remainder of this manuscript is structured as follows. In Section 2, we explain the methodology, design, and components of the proposed framework. In Section 3, we present the experimental design used in the evaluation of the performance of the framework and results. Lastly, in Section 4, limitations of the framework, possible use cases of the proposed method, and future directions will be discussed.
## 2 Methods and Modeling
### Solid-Electrolyte-Gas Interfaces  
Three different phases (solid, electrolyte, and gas phases) were initially set to zero values at each discretized location (w, z) of the modeled SOFC. These phases had to be unambiguously determined based on two threshold values, which were resolved as described in Section 2.5. The following criteria were considered:  
* **if** (GSV \(<\) Threshold 1) **then** Gas (w, z) = 1;
* **else if** ((GSV \(\geq\) Threshold 1) **and** (GSV \(<\) Threshold 2)) **then** Electrolyte (w, z) = 1;
* **else** Solid (w, z) = 1;  
In the next step, only the connected (percolated) solid and electrolyte phases were determined in between the electric contacts, which were denoted as interfaces A and E in Figure 3. The connected gas phases to interface B and F were determined. For example, delta function \(\delta_{\text{a/c}}\) (w, z) = 1 since TPB was active within the porous anode or cathode if all three phases are percolated and met in a single point (w, z). Otherwise, \(\delta_{\text{a/c}}\) (w, z) = 0 since TPB was inactive. This situation is shown schematically in Figure 5a. The upper part of Figure 5b shows active TPBs within the modeled SOFC. The lower part of Figure 5b shows the actual 2-D profile of the mass fraction (Y) of O\({}_{2}\) and H\({}_{2}\) that corresponds to the percolated gas phase within the porous cathode and anode, respectively, as seen in the upper part. Please note that TPB detection actually needs four neighboring pixels (i.e., a small image of \(2\times 2\) pixels) that represent different phases. It is obvious that TPB detection strongly depends on both threshold levels. Consequently, there is some uncertainty in this kind of detection, as it is impossible to determine whether a TPB at the interface really exists or not. Moreover, the reconstruction of porous material in this paper was based on the 2-D grey-scale image, which may introduce additional uncertainty. Detailed analysis and detection of TPB would require three-dimensional (3-D) tomography techniques, e.g., focused ion beam-scanning electronic microscopy (FIB-SEM) [21] and X-ray computed tomography (CT) [22]. Those provide more accurate SOFC microstructure characterization. These cutting-edge 3-D characterization techniques are very expensive and time-consuming [23]. This is why the detection in our case was restricted to 2-D grey-scale images only.  
Figure 4: Detection of two threshold values to differentiate three phases from a grey-scale image [10].
## 3 Proposed Method
### Example  
In this section we describe an example of the FDCNB-MINING algorithm. Suppose the environment has one kernel node, one connection node, one monitor node, and four computing nodes (A, B, C, and D) for distributed computing. After transferring the test packet, the monitor node determines that the network bandwidth of the computing nodes is as follows: A has 6 Mbps, B has 45 Mbps, C has 93 Mbps, and D has 30 Mbps. Therefore, when the kernel node needs to select one of the computing nodes to assist the mining, the monitor node provides the best computing node, i.e., node C, to the kernel node. If the kernel node still needs to select one of the computing nodes to assist the mining again and the bandwidth has not changed, then the monitor node provides the next best node, i.e., node B, to the kernel node and so forth.  
\begin{tabular}{l l} \hline \hline Algorithm FDCNB-MINING \\ Input A transaction database D, minimum support S. \\ Output Frequent Patterns FP \\ \hline
1 & Monitor.work (); \\
2 & HT = getHT(D, S); \\
3 & tree = buildFPtree(D, S); \\
4 & WHILE (isNotEmptyHT) \\
5 & n = Monitor.getBestNode(); \\
6 & transmitTree(tree, n); \\
7 & IF(Monitor.findLowBandwidth(n)) \\
8 & n.stopTransmitting(); \\
9 & CONTINUE; \\
10 & END IF \\
11 & hi = getMiningTask(HT); \\
12 & HT = HT - hi; \\
13 & FLR-Mining.BeginMining(n, hi); \\
14 & END WHILE \\
15 & RETURN FP \\ \hline \hline \end{tabular}
## 2 Datasets
### Case Studies  
To reflect the seasonal meteorological variability and the different type of precipitations, four case studies, summarized in Table 2, have been selected to create an appropriate dataset for this study.  
5 June 2015-Belgium  
The weather in Belgium was influenced in the beginning by a high-pressure zone in central Europe. This system was redirecting tropical continental air masses to western Europe while a low-pressure zone from the Atlantic, accompanied by cold fronts, was slowly approaching the British Isles. Such conditions were favorable for the development  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Time Range** & **Type of Event** & **Country and Boundary** \\ \hline
5 June 2015 & 14:00–23:00 & Thunderstorm & Belgium (48 to 52)\({}^{\circ}\) N-(2 to 8)\({}^{\circ}\) E \\
20 February 2016 & 00:00–22:00 & Rain associated to winter storm & Belgium (48 to 52)\({}^{\circ}\) N-(2 to 8)\({}^{\circ}\) E \\
12 October 2015 & 03:00–12:00 & Thunderstorm & Poland (49 to 55)\({}^{\circ}\) N-(14 to 25)\({}^{\circ}\) E \\
8 July 2015 & 13:00–17:00 & Cyclone over North Sea & Hungary (45 to 50)\({}^{\circ}\) N-(16 to 22)\({}^{\circ}\) E \\ \hline \hline \end{tabular}
\end{table}
Table 2: Case Studies.  
Figure 1: OPERA weather radar network.  
of a thermal trough over Belgium, resulting in thunderstorms and intense rainfall in the afternoon and evening. The zones of highest rainfall accumulation were the central parts of the country and a narrow zone near the coast.  
20 February 2016-Belgium  
This scenario is characterized by a vast zone of very low pressure in the south of Iceland, moving towards Scandinavia. Several fronts and strong winds are accompanying this system. Belgium is under the influence of warm and then cold fronts. During this event, warm air from south-west caused a significant increase in temperatures while precipitation lasted for many hours over several places in Belgium.  
12 October 2015-Poland  
On 12 October 2015, a significant weather change took place over Poland. The sunny conditions were replaced by cloudy weather characterized by both solid and liquid precipitation in the southern regions of the country. The change was induced by low weather system directing the advection and thus the precipitating cloud movement. The occluded front located on the Polish/Czech border was the main driver for the rainfall events over Poland on that day. The massive precipitation system was travelling from SE to NW with slight northerly deviation. The stable stratiform precipitation was recorded over southern regions of the country.  
8 July 2015-Hungary  
On 8 July 2015, an extra-tropical cyclone developed over North Sea. The cold front of this cyclone caused lot of convective clouds in Europe. In Hungary (around Budapest) lot of thunderstorms brought hail, storm wind and high precipitation intensity during the afternoon.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 2 Medical Imaging Applications
### Nonlinear Microscopy  
Nonlinear microscopy is an alternative to conventional confocal microscopy and relies on the use of nonlinear optics, where the relation between the polarization and electric fields is non-linear. In confocal microscopy, a pinhole is used to exclude the out-of-focus light coming from the sample, but the illumination light still achieves a large excitation volume. The absorption of the illuminating energy by the tissue surface can cause damage to the tissue surface, denoted as phototoxicity. In nonlinear microscopy, high-energy pulses are emitted by a laser source with a low average energy provided to the sample, which reduces the chances of phototoxicity. In this process, the laser light excites the molecule, and its relaxation to ground state produces the fluorescence emission of light [25].  
Different technologies using nonlinear optics relying on multiphoton processes, higher harmonic generation, and Raman scattering are briefly discussed below. By scanning a light beam using different technologies, it is possible to generate 2-D or 3-D images, as discussed further in this paper. Analogous to a microscopic device, it is possible to design a flexible endoscopic probe. The schematic design of such an endoscopic probe with the scanning mechanism at the distal end is shown in Figure 5.  
Figure 4: In vivo confocal images: (**a**) reflectance image of tissue phantom with cervical cells and collagen (taken with permission of [20] © The Optical Society); (**b**) fluorescence image of an adenoma mucosa (taken with the permission of [24]); (**c**) fluorescence image of a normal mucosa (taken with the permission of [24]).  
Figure 5: Schematic diagram of a nonlinear endoscope.
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 3 Collective Perception Mechanism for Autonomous Vehicles
### Vile Operation
After the selection phase, the chosen CAVs broadcasting CPM contain the position and dynamic state (e.g., velocity, acceleration, and orientation), as well as all or a subset of the perceptual information \(O_{v_{i}i}\), which is known as the perception phase, as shown in Figure 4. Thus, each selected CAV is responsible for sensing a single hexagonal region. The CAVs chosen by the mechanism will send the message to all the neighboring CAVs and the RSU. In this way, the CP services can build the perception of the regions separately and share the objects or obstacles detected with the neighboring regions. In this way, cars might gather information from nearby regions for future use. RSU is also responsible for keeping all areas' perceptions updated after the time window \(W\) has ended. Figure 3: VILE selection phase. Figures 2-4 illustrate each step for establishing a perception in each hexagonal region \(H\) using the VILE mechanism. Assume that vehicles \(v_{1}\), \(v_{2}\), and \(v_{3}\) move on a hexagonal region \(h_{1}\) to form a portion of CAVs \(V\). After the knowledge phase, the RSU has the position information for each vehicle inside region \(h_{1}\). For exemplification, let us assume \(L_{v_{1}}=(50,10)\), \(L_{v_{2}}=(10,20)\), and \(L_{v_{3}}=(0,10)\). Each CAV \(v_{i}\) is aware of its position \(L_{v_{i}}=(X,Y)\) provided by a positioning system, such as GPS. During the selection phase, the RSU applies the VILE algorithm to compute and select based on the distance between each vehicle inside the region \(h_{1}\) and the center position of the hexagonal region \(h_{1}\), defined as \(L_{h_{1}}=(0,0)\). In this example, \(v_{3}\) is selected to share the perception and representative vehicle for region \(h_{1}\) since it has the shortest Euclidean distance, among the set of CAVs \(V\) in the region \(h_{1}\), with distances \(d\left(L_{v_{1}},L_{h_{1}}\right)=50.99\), \(d\left(L_{v_{2}},L_{h_{1}}\right)=22.36\), and \(d\left(L_{v_{3}},L_{h_{1}}\right)=10\).
## 3 Vision System for Therapeutic Adaptation: Analysis of Functional Requirements
### Software Architecture and Compatibility  
The software architecture represents a crucial element in ensuring robustness, scalability, and interoperability within the system and is designed to be used in assisted rehabilitation, and for this reason, the software infrastructure must be optimized to ensure accurate synchronization between the different modules.  
The system's software implementation was developed in the ROS2 environment (Figure 6). ROS2 offers modularity and scalability, allowing modules to be distributed across multiple devices, reducing the computational load and allowing the addition of new features without modifying the system's structure. Furthermore, it ensures fast and reliable data transmission between modules, reducing latency, optimizing motor feedback thanks to real-time communication, and improving security and resource management, ensuring more efficient data transmission, thanks to the support for DDS middleware.  
The system architecture is organized through an ROS2 node structure (Figure 7), which allows modular and extensible management:  
* ROS2 Nodes: The vision module acquires biomechanical data, while the suggestion module processes the information received to generate personalized recommendations.
* ROS2 Topic Biomechanical data is published on a ROS2 topic, to which the suggestion module subscribes to receive the parameters needed for analysis in real time.
* ROS2 Services and Actions: Services manage specific requests between modules, while actions allow the coordination of long-term operations, such as the progressive adaptation of motor assistance.  
To ensure reliable and scalable data transmission, the system uses ROS2 as its main infrastructure, supported by advanced protocols:  
* MQTT (Message Queuing Telemetry Transport): optimizes data transmission by reducing latency and bandwidth consumption.
* ZeroMQ: ensures fast and reliable communication between modules, ideal for distributed architectures.
* REST APIs: allow integration with therapeutic dashboards, offering therapists an intuitive interface to monitor patient progress.  
Figure 6: ROS2 architecture.
## 5 Results and Discussion
### Cost Comparison of Different Sensor Combinations  
The technical route dispute of sensor combination runs through all levels of intelligent driving. The cost comparison of different sensor combinations at different levels under single-vehicle intelligence is shown by the red arrow in Figure 4. Table 8 shows the TCO  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TCO Composition** & **M1-FSV** & **M1-SSV** & **M2-SSV** & **M1-FSM** & **M1-SSM** & **M2-SSM** \\ \hline Hardware & \$2361 & \$2083 & \$3371 & \$5027 & \$4530 & \$7737 \\ Software & \$670 & \$462 & \$765 & \$1884 & \$1353 & \$2337 \\ Data Traffic & \$143 & \$143 & \$143 & \$143 & \$143 & \$143 \\ Power Consumption & \$437 & \$323 & \$423 & \$752 & \$420 & \$609 \\ Maintenance & \$455 & \$382 & \$620 & \$1037 & \$883 & \$1511 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cost comparison of different development strategies. All costs are shown in USD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TCO Composition** & **H-SSV** & **H-SCV** & **H-SSM** & **H-SCM** \\ \hline Hardware & \$3970 & \$3642 & \$6679 & \$4559 \\ Software & \$923 & \$1004 & \$2017 & \$1570 \\ Data Traffic & \$714 & \$714 & \$714 & \$714 \\ Power Consumption & \$596 & \$541 & \$985 & \$641 \\ Maintenance & \$979 & \$929 & \$1739 & \$1226 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cost comparison of different intelligence allocations. All costs are listed in USD.  
composition of related technical routes. Regardless of level, the vision-only route has a lower cost than the multi-source fusion route because of its simple hardware design and software complexity. However, the cost advantage of the vision-only route is most obvious at the medium- and high-level stages of single-vehicle intelligence. At the low-level stage, the cost advantage of vision-only is 11.2% because only a low-cost MMW radar is used in the multi-source fusion route. At the medium-level stage, the cost advantage of the vision-only route is expanded to 51.6% due to the use of high-cost hardware such as lidar in multi-source fusion. At the high-level stage, on the one hand, it is expected that the cost of the lidar and 4D-imaging MMW radar will be greatly reduced; on the other hand, the use cost will increase, resulting in a reduction in the proportion of hardware cost. Therefore, the absolute difference between the vision-only route and the multi-source fusion route is widened, but the relative difference is slightly reduced to 38.8%. An important finding is that when collaborative intelligence is adopted, the relative cost difference between the vision-only and multi-source fusion routes is further reduced to 17.7%, and the absolute difference is no more than USD 2000, which means that for some cost-insensitive vehicle models, the cost difference between vision-only and multi-source fusion options can already be accepted by OEMs and consumers [43].
# Camera-Sourced Heart Rate Synchronicity: A Measure of Immersion in Audiovisual Experiences
## 2 Background  
### Audio Technologies  
There are various technological factors that influence whether an audio presentation may be considered high-end or basic. Many of these, such as audio compression bitrate, loudspeaker placement, and frequency response, are numerically described in Section 3.2. However, the difference in audio formats is more complex to characterise.  
_Dolby Atmos_ is an audio format that premiered with the release of _Brave (2012)_[14]. Unlike traditional channel-based audio systems, _Dolby Atmos_ utilises an object-based approach. In channel-based systems, audio assets are confined to a fixed number of output channels, with each being delivered to a specific loudspeaker or set of loudspeakers in a predefined configuration. In contrast, _Dolby Atmos_ encodes each sound asset as an audio object, complete with metadata that describes its position within a three-dimensional digital environment. During playback, object-based systems decode the audio objects to the available loudspeakers based on their locations in the configuration.  
In a domestic context, _Dolby_ has created guidelines for loudspeaker locations associated with thirty-four different configurations, with a minimum of three and a maximum of twenty loudspeakers [15]. The 7.1.4 loudspeaker configuration used in this study is composed of seven loudspeakers placed around the listener, one subwoofer, and four loudspeakers placed above the listener.
###### Abstract  
Sun-induced chlorophyll fluorescence (SIF) provides a new method for monitoring vegetation photosynthesis from space and has been widely used to estimate gross primary productivity (GPP). However, the ability of SIF obtained from the Orbital Carbon Observatory 2 (OCO-2 SIF) and Global Ozone Monitoring Experiment-2 (GOME-2) to estimate GPP in the cold and arid region of Heihe River Basin remains unclear because previous comparisons were insufficient. Here, we choose maize and alpine meadow to evaluate the performance of SIF obtained by OCO-2 and GOME-2 in GPP estimations. The results of this study show that daily SIF\({}_{757}\) had stronger correlations with daily tower GPP than daily SIF\({}_{771}\), and the correlation between daily SIF\({}_{757}\) and daily tower GPP was stronger than the correlation between 16-d averaged SIF\({}_{740}\) and 16-d averaged tower GPP. The 16-d averaged absorbed photosynthetically active radiation (APAR) and reconstructed sun-induced fluorescence (RSIF) had the strongest linear correlations with 16-d averaged tower GPP. GPP_VPM and GPP_RSIF exhibited the best performance in GPP estimation, closely followed by GPP_SIF\({}_{757}\), then GPP_SIF\({}_{771}\) and GPP_SIF\({}_{740}\). We also found that the robustness of the correlation coefficients of OCO-2 SIF with GOME-2 SIF was highly dependent on the size of their spatial footprint overlaps, indicating that the spatial differences between OCO-2 and GOME-2 footprints contribute to the differences in GPP estimates between OCO-2 and GOME-2. In addition, the differences of viewing zenith angle (VZA), cloud contamination, scale effects, and environmental scalars (T\({}_{\text{scalar}}\times\text{W}_{\text{scalar}}\)) can result in differences between OCO-2 SIF and GOME-2 SIF.  
vegetation photosynthesis model; sun-induced fluorescence; gross primary productivity; eddy covariance; carbon cycle +
Footnote †: journal: Accepted: 12 December 2018; Published: 14 December 2018