esa-sceva/satcom-chunk-collection huggingface dataset:
# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 3 Results
### Model Images  
Figure 7 illustrates the model images generated using the image data from day 14 of the garlic bulb enlargement period and the two-group model consisting of the control group and heat level 2. In the PLS-DA model images, it is challenging to discern clear differences between the control group, heat level 1, and heat level 2 with the naked eye. However, the LS-SVM model images exhibit distinct dissimilarities among the three groups compared to the PLS-DA model images. The LS-SVM model's images show greater separation between the groups due to the nonlinear relationship induced by the stress response to high temperatures in the garlic's multispectral image data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{3-5}  & & & **Control Group** & **Control Group** & **Heat Level 1** \\  & & **vs. Heat Level 1** & **vs. Heat Level 2** & **vs. Heat Level 2** \\ \hline \multirow{5}{*}{Three-group models} & PLS & Calibration & 76.42 & 93.17 & 82.08 \\ \cline{2-5}  & DA & Prediction & 76 & 92.75 & 81 \\ \cline{2-5}  & LS & Calibration & 99.67 & 99.58 & 97.5 \\ \cline{2-5}  & SVM & Prediction & 98 & 97.75 & 89 \\ \cline{2-5}  & DNN & Calibration & 84.92 & 92.08 & 91 \\ \cline{2-5}  & Prediction & 76.25 & 86 & 85.75 \\ \cline{2-5}  & RP & Calibration & 85.17 & 87.5 & 96.67 \\ \cline{2-5}  & CNN & Prediction & 76.25 & 85 & 92.75 \\ \hline \multirow{5}{*}{Two-group models} & PLS & Calibration & 88.83 & 100 & 100 \\ \cline{2-5}  & DA & Prediction & 85 & 100 & 100 \\ \cline{2-5}  & LS & Calibration & 99.92 & 100 & 100 \\ \cline{2-5}  & SVM & Prediction & 96.5 & 100 & 100 \\ \cline{2-5}  & DNN & Calibration & 88 & 98.42 & 99.58 \\ \cline{2-5}  & Prediction & 79.25 & 96 & 98.75 \\ \cline{2-5}  & RP & Calibration & 89.08 & 99.92 & 100 \\ \cline{2-5}  & CNN & Prediction & 79.25 & 98.75 & 99.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of machine learning and deep learning models on day14 of the garlic bulb enlargement period.  
Figure 6: (**a**) The mean of accuracy of three-group models and (**b**) the mean of accuracy of two-group models.  
Regarding the DNN model's images, visible distinctions between the control group and heat level 2 can be observed in most regions, except for certain garlic leaves located near the multispectral camera. In the RP-CNN model's images, we can differentiate between the control group and heat level 2, but distinguishing between the control group and heat level 1 is challenging. This limitation arises from the model's insufficient depth to discern between the control group and heat level 1 in the RP-CNN architecture.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
## 2 CFD Simulations and Energy Loss Model
### CFD Simulations
The wall function in ANSYS CFX, which was first proposed by [PERSON] and [PERSON], establishes the relationship between relevant variables near the wall and wall conditions. In the logarithmic interval, the relative relationship between the near wall tangent velocity and the wall shear stress can be expressed as:  
\[u^{+}=\frac{U_{\mathrm{t}}}{\mu_{\mathrm{t}}}=\frac{1}{\kappa}ln\left(y^{+} \right)+C^{\prime} \tag{7}\]  
where \(u\) is velocity near wall, \(u_{\mathrm{t}}\) is friction velocity, and \(U_{\mathrm{t}}\) is the component of wall tangential velocity at \(y\). In the equations above, \(y\) is the dimensionless distance of wall, C' and \(\kappa\) are constant. Owing to the fact that various flow channels are greatly different in the structure and size, different software has been used to mesh them. The flow channel of turbomachinery such as the inducer, the impeller, and the vaned diffuser are meshed by TurboGrid, and the First Element Offset method is used with 30-80 Y+. The inlet section, the vaneless diffuser, the motor flow passage, and the outlet section are meshed by ICEM. The hub clearance and the flow passage of the bearings are meshed by GAMBIT. The scale of the tip clearance, the hub clearance, and the liquid film thickness of the bearings are regarded as constants in the simulation. The ordinary interpolation method was adopted for the connection of the ordinary stationary area, and the frozen rotor method was used for the connection between the rotating area and the stationary area. The meshes are shown in Figure 2.  
To test the influence of grid density on the results, five cases with different grid numbers are simulated, as shown in Figure 1:0 With the increase in grid number, the pump head almost keeps unchanged when the grid number is increased beyond eight million.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 1 Space-Air-Ground Integrated Network
### Motivation and Main Contributions  
An important part of a SAGIN is that the signal transmission between satellites and ground base stations is a kind of wireless signal transmission. These transmissions can be affected by the atmospheric environment. The gas molecules and water vapor condensate in the atmospheric cloud result in electromagnetic wave refraction, reflection, scattering, dispersion, and absorption when propagating. When the cloud thickness is large, it will cause very large signal attenuation. Since the communication path loss between satellites and ground base stations is mainly determined by cloud thickness, rainfall, and the incident angle between the communication link and clouds, the blockage will be calculated according to the above three parameters and divided into six levels for prediction and analysis. Finally, the satellite routing design can be improved according to the predicted blockage, so as to improve the quality of satellite ground communication.  
In this article, we propose to use integration of IoT and satellites for weather data collection and transmission. At the same time, we predict communication blockage by an extended ELM model, and therefore find a communication link with the smallest signal loss so that the ground and satellite can communicate through this communication link to improve communication quality. This article connects IoT networks with SAGINs, because satellite connections for IoT networks are well-suited to delivering service where terrestrial networks are absent. Meanwhile, there are many instances where terrestrial coverage exists, but it must cross many different networks, which makes the transmission efficiency very low. SAGINs make it much easier, as they can transmit data from every network. Therefore, by using integration of IoT and satellites for weather data acquisition, transmission, and analysis undoubtedly becomes a more efficient and reliable scheme.  
The ELM algorithm has small computational complexity and good overall performance, but it also has some limitations. Because of the simple network model, the ELM algorithm can perform well only for small-scale datasets. Once the data scale increases, the performance of the ELM algorithm will decline sharply. Therefore, we propose an extended ELM model. Based on the original ELM model, several full connection layers are added to the model, so that the model overcomes this limitation, successfully processes large-scale data, and shows good performance. The extended ELM model has a fast calculation speed and high accuracy rate in this scenario. As far as we know, this is the first time that the extended ELM model is embedded in the SAGIN framework to improve the communication efficiency of SAGIN.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 5 Outage  
We assume a fixed-rate transmission scheme where all ISNs transmit at a target rate \(R_{target}\). Note that this is the coding rate; the actual rate in bits per second \(s\) needs to be scaled by the probability of a given sensor being scheduled. A sensor is scheduled in each frame based on the selected scheduling algorithm. If the channel of the scheduled sensor can support the rate \(R_{target}\), transmission is successful. Otherwise, transmission errors may occur, and the system is said to be in outage.  
The outage probability \(P_{out,l}\) of a sensor \(l\) during a time frame in which it is scheduled for transmission is written as  
\[P_{out,l}=Pr(R_{l}\leq R_{target}), \tag{35}\]  
where \(R_{l}\) is the rate that can be supported by the channel from ISN \(l\) to the user, which changes according to the channel. Next, we present the system outage probability for the different scheduling schemes.  
The RR scheduling system outage probability can be constructed from \(\bar{P}_{out,l}\) as follows [20]:  
\[\bar{P}_{out,rr}=\frac{1}{L}\sum_{l=1}^{L}P_{out,l} \tag{36}\]  
For OS, we have a smaller system outage probability due to selecting the best ISN to transmit at a given time. The system outage probability of OS is defined as [20]  
\[\bar{P}_{out,os} =Pr(\underset{l=1,\dots,L}{\text{argmax }}R_{l}\leq R_{target}) \tag{37}\] \[=Pr(R_{l}\leq R_{target},\forall l)\] (38) \[=\prod_{l=1}^{L}Pr(R_{l}\leq R_{target})\] (39) \[=\prod_{l=1}^{L}P_{out,l} \tag{40}\]Finally, for the system outage probability of PF scheduling, we have  
\[\bar{P}_{out,pf}=Pr(R_{l^{*}}\leq R_{target}), \tag{41}\]  
where \(l^{*}\) is chosen as in (25).  
Here, we look into two extreme cases: as the time window \(t_{w}\) approaches infinity, we obtain ultimate fairness, with each sensor being scheduled uniformly on average; thus, we have the same expression as RR (36). On the other hand, for \(t_{w}=1\), we take into account only the current SNR values in the system and reverting back to OS (40). Finally, for the range of \(t_{w}\) values, we obtain a weighted sum of the two Equations (36) and (40), which can be considered as a trade-off between OS and RR. Defined below is an approximation of the PF system outage probability, in which \(\lambda\in[0,1]\) and \(\lambda\) can be some monotonic inverse function of \(t_{w}\), provided as follows:  
\[\bar{P}_{out,pf}\approx\lambda\bar{P}_{out,as}+(1-\lambda)\bar{P}_{out,rr}= \lambda\prod_{I=1}^{L}P_{out,I}+(1-\lambda)\frac{1}{L}\sum_{I=1}^{L}P_{out,I}. \tag{42}\]
## 2 Digital Twin Architecture
### Data Acquisition System  
The initial phase of data-driven damage diagnostics involves collecting and safeguarding essential information, categorized into event data and condition data. Event data include historical records of faults, failures, repairs, and their causes, while condition data offer insights into asset health through measurements like vibration, oil quality, temperature, pressure, and humidity. This research used the PrismaSenseTM data acquisition system [21], featuring a hybrid data fusion architecture for distributed processing and storage (Figure 4).  
A comprehensive wired network was implemented at the power plant to facilitate wireless data transmission to the gateway. This setup incorporated signal collectors and power analyzers, while the wireless communication was based on the IEEE 802.15.4 Zigbee protocol, specifically designed for maritime applications. The gateways interfaced with the Prisma server through Ethernet and raw TCP, employing WebSocket protocols to ensure data security before transmitting information to the cloud for processing and visualization. The power analyzer was capable of monitoring a three-phase, four-conductor system with precision of 10 mA at a sampling frequency of 20 kHz, calculating effective values and other metrics every five minutes, with adjustable rates for users. Although Prisma Sense was utilized on an older, mostly intact vessel for two years, the validation of the algorithms was based on data obtained from a system simulator linked to the smart collectors via TCP/IP.
## 2 Material and Method  
Alumina (Al\({}_{2}\)O\({}_{3}\), 99.7%) samples of dimensions \(50\times 50\times 10\) mm from Ceram Tec(r) Germany were used as the workpiece material. The experimental setup used in this work was lasertec-40 from Deckel Maho Gildemeister, Germany. It is equipped with a Flashlamp pumped Nd:YAG laser with a wavelength of 1064 nm and maximum power of 30 W. The laser beam is operated in fundamental Gaussian mode (TEM\({}_{00}\)) and focused on the workpiece with the help of a galvanosanner as shown in the schematic in Figure 1. The specifications and laser process parameters used in this study were as shown in Table 1.  
Microchannels of dimension 200 \(\upmu\)m width and 5 mm length were machined by varying the laser fluence at three different pulse overlaps 58%, 67%, and 75%.  
Pulse overlap is the percentage of overlap between successive laser pulses and is given by the following Equation (1).  
\[\textit{Pulse overlap}=\left(1-\frac{V}{f*D}\right)\times 100 \tag{1}\]  
\(V\) is the laser scan speed which was varied at 75, 60, 45 mm/s, \(f\) is the pulse frequency and \(D\) is the laser spot size which were fixed at 6000 Hz and 0.03 mm respectively based on previous experimental findings.  
In this study laser fluence was varied by varying the Flashlamp current as shown in Figure 2. A laser power meter (Ophir Nova-II) in combination with a sensor (Ophir L50) was utilized to measure the average power input for each of the lamp intensity settings. Average power and pulse frequency together were used to calculate the laser peak fluence.  
Two microchannels were machined at each experimental condition and the averages were utilized for the analysis. Channels were cut through the cross-section using an Isomet(r) 1000 precision saw from Bueler. The cross-sections were viewed under JEOL JSM-6610 LV Scanning Electron Microscope (SEM). The channels were analyzed with respect to material removal rate (MRR) and width error, that is the difference between the actual width and the target width. At the top of the channel, referred to as top width error (TWE), and at the bottom, referred as bottom width error (BWE) and depth per scan (D/S), is the total depth obtained over the number of laser scans. The obtained channels were uniform throughout the 5 mm length. The cross-sections of a few channels were analyzed at three points along the length. The variation within the individual channel measurements was found to be within 1%. The process was found to have good repeatability, the average variation in the width and depth within the repetitions was found to be around 2%, and 3% respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process Parameter** & **Process Condition** \\ \hline Wavelength & 1064 nm \\ Power & 30 W \\ Pulse width & 10 \(\upmu\)s \\ Spot size & 30 \(\upmu\)m \\ Pulse frequency & 6 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser specifications.  
Figure 1: Schematic diagram of laser setup.
## 2 Materials and Methods
### Incentive Score Mechanism
When the collaborative trust evaluation task \(\text{T}_{\{\text{user}\}}\) is completed, the total incentive score awarded to the collaborative edge devices are calculated as follows:  
\[R=R_{b}+R_{q}+R_{e} \tag{4}\]  
where \(R_{b}\) is basic cooperation reward score, \(R_{q}\) is quality reward score and \(R_{e}\) is efficiency reward score. \(R_{q}\) is calculated from the quality of completion of the trust evaluation tasks assigned to the edge devices. We set the quality threshold \(\varphi_{1}\) and \(\varphi_{2}\) (\(0\leq\varphi_{1}<\varphi_{2}<1\)), and the completion quality is divided into excellent (collaborative evaluation quality is \(\varphi_{2}\) or above), medium (collaborative evaluation quality is between \(\varphi_{1}\) and \(\varphi_{2}\)) and low (collaborative evaluation quality is below \(\varphi_{1}\)). In order to motivate the edge devices to complete the cooperative trust evaluation task with high quality, we give the edge devices with excellent completion quality a higher increase in quality reward scores. For collaborative edge devices with moderate completion quality, we give moderate quality reward scores to maintain their base motivation. For collaborative edge devices with low completion quality, quality reward scores will not be given in this paper. The calculation of \(R_{q}\) is as follows:  
\[R_{q}=\left\{\begin{array}{cc  
where \(\vartheta_{q}\) is the baseline quality reward score, \(\varphi_{1}\) and \(\varphi_{2}\) are the quality threshold of the collaborative trust evaluation task and \(\varphi_{q}\) is the completion quality of the collaborative trust evaluation task.  
\(\varphi_{q}\) is calculated from the accuracy of collaborative evaluation:  
\[\varphi_{q}=\frac{\delta_{r}}{\delta_{r}+\delta_{e}} \tag{6}\]  
where \(\delta_{r}\) is the number of times that the collaboration evaluation of the edge device group is correct and \(\delta_{e}\) is the number of times that the collaboration evaluation of the edge device group is wrong. \(R_{e}\) is calculated from the completion time of the evaluation task assigned to the edge device:  
\[R_{e}=\left\{ \begin{array}{ccc}\frac{\theta_{e}\cdot(\tau_{max}-\tau_{e})}{ \tau_{e}}&,&\tau_{e}<\tau_{max}\\ 0&,&\tau_{e}\geq\tau_{max}\end{array} \right. \tag{7}\]  
where \(\vartheta_{e}\) is the baseline efficiency reward score and \(\tau_{e}\) is the actual time for the cooperative edge device to complete the collaboration trust evaluation task. When the actual completion time of the collaborative edge device is shorter, the efficiency reward score will be higher. Conversely, when the actual completion time of the cooperative edge device exceeds \(\tau_{max}\), it will not receive the efficiency reward score. Through this incentive reward mechanism, CEEC will reward collaborative edge devices that can accomplish the task of collaborative trust evaluation quickly and with high quality. The reward score is mainly used as a short-term incentive to quickly motivate cooperating edge devices.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 5 Performance Evaluation
### Performance Evaluation of SDNPS
#### 5.1.2 Delay and Loss Rate for 1:m Transmission  
This set of experiments studies the delay and loss rate characteristics of the aforementioned testbed when faced with one publisher and multi-subscribers. The same experimental methods and evaluation methods are adopted. Table 2 presents the results. Compared to Table 1, the number of subscriptions does not impact delay significantly, as the average delays of these three tests increased (albeit slightly). This also validates our design. If unicast routing is adopted, there will be four-times the packets injected into the network for 1:4 transmission. In this case, the performance worsens greatly. Thus, we can draw a conclusion: SDNPS reaps benefits from its multicast routing.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 3 Numerical Model Description
### Model Construction  
#### 3.3.1 Model Assumption  
This paper aims to investigate the influence of the nonhomogeneity of the hydrate deposit on the gas-driven water process and the role played by the wettability and the interfacial mobility velocity during the flow process. The reservoir microstructure is simplified to a homogeneous porous medium consisting of rounded particles and pores, and the following assumptions are made for the model:  
1. The porous media structure is assumed to be a uniformly distributed circular media structure, without considering the nonhomogeneity of the geometry (e.g., rectangular structure, rhombic structure, etc.);
2. It is assumed that no dissolution, phase change, etc., occurs between the liquid and gas phases during two-phase displacement;
3. It is assumed that, in a porous medium, both phases flow at low velocities and fall within the scope of laminar flow;
4. It is assumed that the gas phase being studied satisfies the ideal gas equation of state and that both phases of the fluid are incompressible;
5. The two-phase fluid flow is driven by differential pressure while ignoring the effects of gravity.
## 1 Introduction
In addition to the systematic bias due to sport-specific movements, the EE during rest (no change of position) represents another systematic error between \(\text{EE}_{\text{LPM}}\) and \(\text{EE}_{\text{Spiro}}\) due to the different considerations of internal or organic processes. It was suggested to subtract the resting energy expenditure (REE) [20] from the gross \(\text{EE}_{\text{Spiro}}\); the alleged [21] lack of this subtraction in a previous study [12] was already criticized [22]. However, as the \(\text{EE}_{\text{LPM}}\) considers only locomotion data, the subtraction of EE in standing position in a pre-activated rest might be appropriate for a comparison between \(\text{EE}_{\text{LPM}}\) and \(\text{EE}_{\text{Spiro}}\). Such EE in a pre-activated, motionless rest may be affected by the excess post-exercise oxygen consumption (EPOC) [23]. Thus, in addition to uncertainties about the precision of EPOC determination and the uncertainty of whether to subtract it or not [24], the use of such EE in a pre-activated, motionless rest is unclear. Consequently, the EE in a pre-activated, motionless rest in team handball should be determined separately to demonstrate if a subtraction of EE (REE or pre-activated, motionless EE) from the gross \(\text{EE}_{\text{Spiro}}\) is necessary to determine the \(\text{EE}_{\text{LPM}}\) accurately. To our best knowledge, neither the EE in team handball in competition-like conditions nor the comparison of \(\text{EE}_{\text{LPM}}\) and \(\text{EE}_{\text{Spiro}}\) in team handball have been investigated. Therefore, the objectives of the study were (1) to analyze the difference between \(\text{EE}_{\text{Spiro}}\) and \(\text{EE}_{\text{LPM}}\) in the team handball GBPT, (2) to investigate the correlation between \(\text{EE}_{\text{Spiro}}\) and \(\text{EE}_{\text{LPM}}\), (3) to develop a regression model that predicts \(\text{EE}_{\text{Spiro}}\) based on \(\text{EE}_{\text{LPM}}\), and (4) to compare REE with EE during a pre-activated motionless rest in team handball. We hypothesized that (1) \(\text{EE}_{\text{Spiro}}\) would be higher compared to \(\text{EE}_{\text{LPM}}\), (2) that \(\text{EE}_{\text{Spiro}}\) and \(\text{EE}_{\text{LPM}}\) show a systematic correlation, (3) which allows for developing a regression model to predict \(\text{EE}_{\text{Spiro}}\) based on \(\text{EE}_{\text{LPM}}\) and (4) that REE underestimates the relevant EE, whereas EE in a pre-activated, motionless rest will be overestimated.