esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
An experimental and numerical study is presented in the current work for gas entrainment using an inverted vertical U-tube. Water flows vertically up in an inverted U-tube which creates a low-pressure region in the tube upper portion. This low-pressure region can be used to extract gases by connecting it to a branch pipe. The extracted gases considered in this work are a mixture of air and water vapor. The water vapor from the side branch pipe is mixed with the flowing water under the siphon effect. This results in a progressive water vapor condensation as the mixture proceeds towards the exit due to an increase in vapor partial pressure. The air is drawn by inertia to be released out at the tube lower exit of the inverted U-pipe. The current study deals with these complicated flow behaviors due to the mixing undergoing condensation. A test rig is designed for experimentally studying the behavior of water flow in an inverted U-tube where the air is mixed with the flowing water at the top region of this tube. The CFD computations are accomplished for a side gas mixture with volume fractions up to 0.7 with water vapor mass fractions in this mixture to be 0.1-0.5. The tested water mass flow rates in the main tube are 2, 4, 6, 8 kg/s to account for all possible flow mass ratios. The CFD computations are validated with water and air two phase flow with the measurements of both the experiments of the current research and the literature. The present results reveal that slightly raising the water mass flow rate at a constant side mixture mass ratio produces a reduced generated pressure in the upper tube part. This is attributed to extra water vapor condensation taking place rapidly by increasing the water flow rate in the tube upper part. Furthermore, the turbulence quantities begin to break down at a side mixture volume fraction of 0.55 with water and air mass flow rates of 2 kg/s and 0.002 kg/s, respectively. On the other side, raising the air mass flow rate at the higher values of water vapor and water mass flow rates breaks the generated vacuum pressure and turbulence due to entrainment. Moreover, this proposed framework can produce a lower static pressure, reaching 55.1 kPa, which makes it attractive for gas extraction. This new technique presents innovative usage with less consumable energy for extracting gases in engineering equipment.  
inverted U-tube; two-phase flow; air/water vapor entrainment; steam condenser 2020
## 4 Experiments and Results
### Performance Evaluation for CC-GAN
Compared with the above two denoising methods, the best accuracy of individual identification of 93.16% is achieved by using the CC-GAN, which indicates that the generator of the CC-GAN can better adapt to dynamic noisy signals, effectively eliminating random dynamic noise in the original signals and, consequently, improving the accuracy of individual identification.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### SNR for CS Applications  
In this section, we discuss the theoretical implications of noise on CS reconstruction. For source noise, we generate Gaussian random noise. By varying the standard deviation of the Gaussian kernel, we can obtain noise with different signal-to-noise ratios (SNR). We define SNR as  
\[SNR=10 log10\frac{(P_{x_{o}})}{(P_{x_{on}})} \tag{14}\]  
where \(P_{x_{o}}\) is the power of the observed image, and \(P_{x_{on}}\) is the power of the noisy observed image.
## 5 Disscussion
### Petrogenesis of LG  
#### 5.2.1. _M_agmatic Differentiation  
The LG has high SiO\({}_{2}\) content and a high differentiation index (Di), which indicates its higher degree of differentiation and also explains the differentiation in the data plotted near the region of the highly differentiated I- or S-type granite in Zr-Nb-Ce-Y diagrams (Figure 9). The whole-rock Zr/Hf and Nb/Ta ratios would decrease with increasing magmatic differentiation [49], which makes the two ratios reliable indicators of the degree of granitic magma differentiation [50; 51; 52; 53]. The whole-rock Zr/Hf and Nb/Ta ratios of LG are 13.06-19.93 and 1.89-4.07, respectively, which are distinctly lower and indicate advanced magmatic differentiation.  
The low content of rare earth elements, the small ratio of light and heavy rare earth elements, and the increase of negative Eu anomaly are the characteristics of almost all highly fractionated granites [54; 55; 56]. Moreover, the LG has a distinct feature of REE tetrad effect with an average TE\({}_{1,3}\) value greater than 1.1 [49], which resulted from the interaction between the melt and the fluids that appeared after highly fractional crystallization [49; 50; 57; 58].  
The low total REE contents and the small ratios between light and heavy REEs of LG indicate fractional crystallization of REE-rich minerals, such as zircon, monazite, epidote, xenotime, etc. The strong depletion of Eu in chondrite-normalized REEs diagrams and the strong depletion of Ba and Sr in primitive mantle-normalized trace element diagrams reflect the fractional crystallization of plagioclase or K-feldspar.
## 3 Uses of Cold Plasma in Food Industry
### Pesticide's Degradation  
Several studies showed that CP had the potential to degrade pesticide residues in fruits and vegetables. CP's ability to eliminate pesticide residues has been associated with the production of reactive oxygen and nitrogen species. Pesticides are a large variety of chemical substances, widely utilized in agricultural production to protect crops and delay crop deterioration. Nevertheless, pesticide resistance necessitates increased application rates. Pesticide residues are a source of concern in the food business due to their health threats [41; 42].  
After 5 min of plasma treatment at 80 kV, pesticide residues on blueberries satisfactorily deteriorated with degradation efficiencies of 75% and 80% for boscalid and imidacloprid, respectively. Appropriate modifications in the evaluated quality characteristics were noticed for the treatment conditions. These findings imply that CP treatment at 60 kV 5 min and 60 s at 80 kV can sustain the blueberries' nutritional qualities [43].  
Pesticides in water were successfully degraded using atmospheric pressure dielectric barrier discharge plasma in air. The discharge was tested at high voltages in the filamentary regime. It was found to be a quick and effective source of oxygen radicals, excited nitrogen species, and other plasma species. Degradation products are distinguished by simpler chemical groups [44].  
According to studies, CP treatment considerably reduced organophosphorus pesticides without any damaging, hazardous, or undesirable effects on the appearance or texture of many agricultural samples [45; 46]. Figure 3 shows the application of cold plasma in food and water.
# A Load Balancing Routing Mechanism Based on SDWSN in Smart City
## 3 System Framework and Problem Analysis  
We propose a wireless sensor network model based on SDN, as shown in Figure 2. The core idea of this new architecture is to separate the logical control and network forwarding devices in the WSN. The control organization is responsible for abstracting the underlying network device and application logic. It also supports extended deployment through reserved interfaces to achieve flexible control of the entire network. The model adopts a hierarchical network structure, which mainly consists of a data plane, a control plane, and an application plane. The entire network can be divided into multiple sub-areas. Each sub-area may be a particular network formed by the same or different types of terminal sensor nodes. The sensing node is responsible for sensing the collection and forwarding of object information. One or more controllers aggregate, manage, and reconfigure information across multiple network topologies and node behaviors. The data center is mainly responsible for storing and managing the upload information data of each cluster, and providing the terminal application or user with an interface such as a query. The application provides monitoring and other services for different users such as mobile terminals, desktops, and the cloud to provide convenient feedback for each area's information and status.  
There are three basic roles in this model, namely the master node, the central node, and the normal node. The master node acts as a controller for the entire network structure. This is the central execution unit of programmable control, which controls the real-time status of the network, such as network topology, routing, and routing restrictions, and implements the management and control functions of the entire network. The center node is similar to the OpenFlow switch in SDN, the network device maintains a Flow Table and processes the data stream only through the Flow Table. The generation, maintenance, and delivery of the Flow Table are completely implemented by an external controller. As shown in Figure 3, it is responsible for the matching and forwarding of data streams in wireless sensor networks. A normal user node is only responsible for receiving and executing data streams.  
Figure 2: Software defined wireless sensor network architecture.  
A major feature of SDN networks is the separation of the control layer and the data layer. The controller can obtain the topology of the entire network. At the same time, it can obtain real-time network status information from the switch, including link load, delay, etc. We can develop a load-balancing routing strategy for forwarding packets at the control layer.
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 5 Artificial Intelligence (AI) for Healthcare Delivery
### Prognosis and spread control  
Recently, the IoT or IoMT has been regarded as an emergent technology in the field of vital signs prognosis and disease spread control. Healthcare is a significant field, and the recent integration of A-IoT has created more opportunities for proper health monitoring. The IoT has been used by researchers all over the world to develop systems for monitoring, detecting, preventing, and controlling the spread of various diseases. In another recent study, the IoT was deployed in active and assisted living (AAL) research and development to support elderly people suffering from memory impairment in their daily activities [82]. According to the report, \"the model is implemented as an extension of the human behaviour monitoring and Support (HBMS) approach that provides a conceptual \"human cognitive model\" for representing the user's behaviour and its context in her/his living environment. The prevention of the occurrence of a pandemic outbreak involves prompt diagnosis. Pulmonary infections like the influenza virus have a long history of quickly turning into pandemics, with the most recent worldwide pandemic also being one that affects the respiratory system--the COVID-19 pandemic. Therefore, it is required that there is a system in place to identify and predict the occurrence of such diseases that might cause outbreaks before they take effect since it is easier to control these diseases at an early stage than when they start spreading rapidly.
# Design and Validation of an Adjustable Large-Scale Solar Simulator
## 3 Results
### I-V Curve  
Further investigation was conducted by characterizing the I-V curve of a PV panel. The experimental analysis was carried out with tests performed under the artificial radiation and with tests outside with natural solar radiation. The panel was connected to a variable resistor, and the curve was obtained by varying the value of the resistance from zero to infinity [24] (Figure 10). In each step, the voltage and the current were simultaneously measured. A 40 W 12 V panel with monocrystalline cells was chosen, with dimensions of 650 \(\times\) 505 \(\times\) 30 mm. The module voltage (Vmp) was 17.8 V, the nominal current (Imp) was 2.3 A, the short-circuit current (Isc) was 2.7 A, and the open-circuit voltage (Voc) was 21.3 V. The panel was composed of 36 cells.  
To validate the experimental results, a numerical method was implemented on the software MATLAB. The relationship between voltage and current is as follows [25]:  
\[I(T,G)=\ I_{s_{c},TG}\bigg{(}1-\bigg{(}\frac{V(T,G)}{V_{oc,TG}}\bigg{)}^{s} \bigg{)} \tag{4}\]  
where \(I_{s_{c},TG}\) and \(V_{oc,TG}\) are the short-circuit current and the open-circuit voltage at a given temperature (\(T\)) and solar irradiance (\(G\)), respectively, and \(s\) is the shape parameter.  
The temperature and voltage dependence of Isc and Voc parameters are the following:  
\[I_{s_{c},T}=\ I_{sc}(1+\ \mu_{Isc}(T-T_{STC})) \tag{5}\]  
\[V_{oc,T}=V_{oc}+\mu_{Voc}(T-T_{STC}) \tag{6}\]  
\[I_{sc,G}=I_{sc}\bigg{(}\frac{G}{G_{STC}}\bigg{)} \tag{7}\]  
\[V_{oc,G}=V_{oc}+N_{S}V_{t,T}\log\bigg{(}\frac{G}{G_{STC}}\bigg{)} \tag{8}\]  
where \(STC\) indicates standard test conditions (cell temperature equal to 25 \({}^{\circ}\)C, solar irradiance equal to 1000 W/m\({}^{2}\) and an average solar spectrum at air mass AM1.5), \(\mu_{Isc}\) and \(\mu_{Voc}\) are the current-temperature and voltage-temperature coefficients, respectively; Ns is the number of solar cells of the PV panel; and \(V_{t,T}\) is the thermal voltage given by Equation (9) as a function of the cell temperature, the Boltzmann constant, and the value of the electron charge [26]:  
\[V_{t,T}=\frac{kT}{q} \tag{9}\]  
Iterations are required to determine the shape parameter s. The value is initially set to 1, and it is increased by a fixed amount after each iteration. The procedure ends when the difference (called error) between the fill factor and the normalized electric power  
Figure 10: Variable resistor scheme [24].
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 6 Conclusions  
This paper presented a formal model for route availability (RA) as a metric of route nonuniformity; this is a property unique to MANETs, in which nodes move about in the service area, causing routes to be disconnected and re-established repeatedly. In addition, we proposed a method of measuring RA in a network that operates with either of the two representative routing protocols: AODV and OLSR. We examined how useful RA is as a metric of network QoS and video streaming QoE by building a MANET emulation environment capable of sending streaming videos. We have confirmed that RA has a linear correlation with the two conventional network QoS metrics: packet loss rate and throughput. In addition, by conducting a subjective quality assessment test, we have confirmed that DMOS changes with RA and that these have a positive correlation in a way that DMOS rises sharply when RA is around 80. From the way RA and packet loss rate relate to their respective DMOS values, we have found that RA is more sensitive to changes in video quality.  
Looking forward, it is necessary to examine the usefulness of RA for routing protocols other than AODV and OLSR and for multipath routing protocols. It is also necessary to study how RA relates to metrics other than packet loss rate and throughput. It is necessary to extend the emulation time in cases where the steady state values of the mobility model are used as initial values for emulation. In addition, it will be necessary to examine how RA relates to QoS metrics and QoE metrics in cases where the video transmission bit rate changes dynamically or where the frame rate of the key frame is changed. It will be also necessary to study how to use observed RA to control the quality of video communication in a way such that the video coding rate can be changed as necessary.  
Figure 15: Relationship between RA and DMOS (for two different routing protocols).  
**Author Contributions:** [PERSON] designed the proposed method, developed the software prototype of the evaluation system, collected the evaluation data, and wrote the initial draft of the paper. [PERSON] provided the direction for his research activities and refined the proposed method, the analysis of the evaluation results, and the writing of the paper.  
**Acknowledgments:** We would like to thank our laboratory's members who worked on the evaluation experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Numerical Results and Discussion  
### YOLO Training and Reasoning Results  
Figure 7 shows the loss graph of YOLO learning with a batch size set to 320 \(\times\) 320, 15 classes and 10,000 iterations. The model was trained with 800 input data for training and 200 for validation. The mAP result indicates a 35% learning accuracy (1).  
\[mAP = \frac{1}{15}\sum_{i=1}^{15}AP_{i}, \tag{1}\]  
where \(i\) represents the number of classes used in the training.  
AP is the abbreviation for Average Precision, which is a metric indicating the precision of an object-detection model as  
\[AP = \sum_{i}\left(r_{i+1}-r_{i}\right)\rho_{interp}(r_{i+1}), \tag{2}\]  
and \(r_{i}\) represents the \(i\)-th recall, which is a metric indicating how well an object-detection model detects all actual accurate objects that exist.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Specification Item** & **Details** \\ \hline Video Format & MP4 (H.264), 1080P@30 FPS \\ \hline Lens Type & 140 wide angle \\ \hline Input & Type-c usb, TRRS port \\ \hline Bluetooth & Build-in, remote control compatible \\ \hline Size (L \(\times\) W \(\times\) H) & 47 mm \(\times\) 92 mm \(\times\) 35 mm \\ \hline Photo model & 4, 8, 12 Mega pixels \\ \hline Battery & 3000 mAh rechargeable \\ \hline memory & Micro sd, SDHC, SDXC, up to 256 gb \\ \hline Waterproof & IP \(\times\) 7 waterproof \\ \hline Weight & 97 g \\ \hline Sensor type & SONY 12 MP \\ \hline Microphone & Build-in \\ \hline Wi-Fi & 2.4/5.8 G \\ \hline \hline \end{tabular}
\end{table}
Table 2: Body cam information.  
Figure 6: Model name: Drift X3.  
The interpolated precision value e for the threshold \(r_{i+1}\) is defined as  
\[\rho_{interp}(r_{i}+1) = \frac{max}{r_{i}r_{j}\geq r_{i+1}}\rho(\widetilde{r}), \tag{3}\]  
where \(\frac{max}{r_{i}r_{j}\geq r_{i+1}}\rho(\widetilde{r})\) represents obtaining the maximum precision value for all recall values \(\widetilde{r}\) that are greater than or equal to the threshold \(r_{i+1}\).  
Figure 8 shows the performance-analysis results by class. The AP figure for each class varies significantly, with a minimum value of 37% and a maximum value of 100%. Although there are 15 classes, only 9 classes were observed in the video used for testing. This is because the labeling process did not evenly distribute the learning data, resulting in a low mAP. The results indicate that evenly distributing the data per class during labeling is crucial to achieving a high mAP. Additional labeling work could lead to better performance.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation  
In order to evaluate empirically the proposed framework, a fully distributed mobile application was implemented using MASU. The framework provides a number of services for automatic collection and collaborative distribution of the data gathered from the device sensors. Such services facilitate the access to the sensors available on the device, enabling data collection and sharing between devices according to the specifications of the Collaborative Sensing Mechanism (CoSM) of the framework.
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 1 Introduction
Subsequently, the routing algorithm selects the optimal path by considering the battery levels of the satellites and their eclipse states. The proposed routing algorithm ensures both accuracy and efficiency while balancing energy consumption across the network. By accounting for eclipse conditions, it helps distribute energy usage more evenly, thereby extending the network's operational lifespan and improving the overall efficiency of satellite operations. To simulate the operation in an actual satellite environment, the routing algorithm was implemented on an OnBoard Computer (OBC). While it efficiently determined routing paths at relatively small node scales, real-time applications became challenging for large-scale nodes due to computationally intensive operations. To address this, parallel processing was performed using a GPU to enable real-time routing. Rather than redesigning the routing algorithm for reduced complexity, a parallel acceleration strategy was adopted due to the high computational cost of path search. GPU-based execution allows massive parallelism with predictable memory access and minimal synchronization, making it suitable for onboard environments with strict timing requirements and limited CPU resources. In satellite environments, real-time data processing is crucial, and relying solely on traditional CPU-based computation may not ensure high performance [13; 14]. This paper enhances real-time routing performance by leveraging GPU-based parallel computation to mitigate bottlenecks in the algorithm, thereby improving the overall performance of the satellite network. Finally, an experiment was conducted by integrating the routing algorithm with the SpaceWire Brick Mk4, a SpaceWire [15] device for spacecraft communication networks. The experiment involved modifying the routing tables of remote devices according to the paths derived from the algorithm's results [16]. This demonstrated the feasibility of networking in a more realistic satellite environment. The main contributions of this work are as follows:  
* Proposing an Eclipse-Aware Routing (EAR) algorithm that considers both satellite battery levels and eclipse conditions for energy-efficient path selection in LEO satellite networks. * Implementing a BFS-based K-Shortest Path (KSP) algorithm optimized for GPU-based parallel processing to enable real-time routing. * Demonstrating the feasibility of the proposed method through integration with a SpaceWire-based onboard system and dynamic routing table updates.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 3 Modeling for Entanglement Distribution Network  
### Network Components  
As shown in Figure 1, the components of the entanglement distribution network can be abstracted into three types: Quantum server, wavelength routing network, and users.  
* Quantum server. A quantum server provides and controls the wavelength switching of entangled photons.
* Wavelength routing network. A wavelength routing network is responsible for dividing the entangled bandwidth into multiple wavelength channels and routing them to distant users with OS, WSS, and other wavelength switching devices.
* Users. Users perform various protocols to generate secure keys, synchronize their clocks and so on.  
The entanglement distribution network can be described as a graph \(G=\langle V,E\rangle\), where the node set \(V\) consists of the quantum server, wavelength routing devices, and users, and the edge set \(E\) includes the fiber or free-space links among the nodes.