esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Results and Analysis
#### 5.3.4 The Impact of the Round-Trip  
When the user selects remote cloud servers to deploy services, the round-trip latency between the user and remote cloud servers should be taken into consideration. In Figure 7, we show how edge servers' resources, services' number and users' number impacted the total waiting time using the MESP algorithm. From Figure 7a, we can see that with the edge server's resources increasing, the total waiting time decreased. Under the same conditions, less round-trip latency would result in less total waiting time. When the resources of the edge server were small, it would make many users connect to the remote cloud servers, so that the impact of the round-trip latency was obvious. With the edge servers' resources increasing, more users will connect to the edge servers, so that the impact of the round-trip latency will be less obvious.  
From Figure 7b, we can see that with the increasing number of services invoked by each user, the total waiting time also increased. Because the edge server's resources and users' number stayed unchanged, the users connected to remote cloud servers would not change. Therefore, the effect of different round-trip latencies was not obvious.  
As shown in Figure 7c, the total waiting time increased with the increasing user number. Because the edge server's resources did not change, with increasing the user number, more users would connect to the remote cloud server. Therefore, the impact of different round-trip latency was more and more obvious with the number of users increasing.
## 4 Picture Fuzzy Petri Nets (PFNs)
### Reasoning Algorithm Based on PFPNs  
In this section, based on the basic matrix operators introduced in [16], a concurrent inference algorithm of PFPNs is proposed.  
_Input_: \(I\) and \(O\) are \(m\times n\)-dimensional matrices, \(\widetilde{U}\) and \(GW\) are two \(n\)-dimensional vectors, and \(LW\), \(Th\), and \(\widetilde{M}_{0}\) are \(m\)-dimensional vectors.  
_Output_: \(\widetilde{M}_{k}\) is an \(m\)-dimensional vector, signifying the final truth degree of all the propositions.  
1. Let \(k=1\). The parameter \(k\) denotes the time of iterations.
2. Calculate the enabled place vector \(D^{(k)}\). \(D^{(k)}\) can be obtained by comparing the truth values of the places with their threshold values, which indicates the enabled input places of transitions. \[D^{(k)}=\widetilde{M}_{k-1}\rhd\widetilde{Th}.\] (25)
3. Calculate the token value vector of input places \(\widetilde{\Gamma}^{(k)}\) with respect to the local weight of each input place. If \(D^{(k)}\) is a nonzero matrix, \(\widetilde{\Gamma}^{(k)}\) can be computed by Equation (26); otherwise, go to Step 7. \[\widetilde{\Gamma}^{(k)}=PFWG\Big{(}(I\circ W)^{T},\widetilde{M}_{k-1}\Big{)},\] (26) where \(W=\left[LW,LW,\ldots,LW\right]_{m\times n}\).
4. Calculate the enabled transition vector \(F^{(k)}\) by Equation (27), which indicates the enabled transitions of output places. \[F^{(k)}=(E\times I)\rhd\left(\left(D^{(k)}\right)^{T}\times I\right),\] (27) where \(E=(1)_{1\times m}=[1,1,\ldots,1]\).
5. Calculate the output truth degree vector \(\Theta^{(k)}\). If \(F^{(k)}\) is a nonzero matrix, \(\Theta^{(k)}\) can be calculated by Equation (28); otherwise, go to Step 7. \[\Theta^{(k)}=\left(F^{(k)}\circ\Gamma^{(k)}\right)\circ\widetilde{U}.\] (28)
6. Compute the new marking vector \(\widetilde{M}_{k}\). \[\widetilde{M}_{k}=\widetilde{M}_{k-1}\oplus PFWG\Big{(}(O\circ W_{gw}),\Theta^ {(k)}\Big{)}.\] (29) where \(W_{gw}=\left[GW,GW,\ldots,GW\right]_{m\times n}^{T}\). If \(\widetilde{M}_{k}=\widetilde{M}_{k-1}\), then go to Step 7; otherwise let \(k=k+1\), go back to Step 2.  The reasoning is over.
## 2 Literature Review
### Related Work  
In [26], [PERSON] et al., who are researchers at the Aalborg University department of Development and Planning, recorded a vehicle dataset during an intelligent speed adaptation project called INFATI. The dataset was generated in February and March 2001 in Aalborg, Denmark. It is non-commercial and is available free of charge for researchers. Each vehicle was equipped with a Global Positioning System (GPS) receiver in addition to a small computer. When vehicles were moved, their GPS location was sampled every second. When vehicles were parked, no sampling was generated. The process of collecting vehicle information took more than a month. The generated datasets were saved in Universal Transverse Mercator (UTM) format. Figure 2 shows the vehicle samples on the INFATI dataset. In [27], the authors found that the resolution of the INFATI dataset was low and inconsistent.  
In [28], [PERSON] and [PERSON] introduced a vehicle dataset which is based on real data that were recorded on 13 February 2017 in the city of Los Angeles. It was created for research purposes to investigate the movement of vehicles in a real-world environment. The database includes 128,199 samples, distributed over 64 comma-separated values (CSV) files. Figure 3 depicts a snapshot from one of these cvs files and Figure 4 shows the locations of the vehicle samples on the LA map. Five kinds of sensors have been used: GPS, orientation, acceleration, gyroscope, and magnetic field sensors. A platform called MediaQ was utilized to achieve vehicle sample collection, organization and sharing of the recorded dataset. The MediaQ platform includes a server and an application for smartphones. It can be used to record videos in MP4 format. Figure 5 shows how a smartphone was mounted during the data recording process using the MediaQ application. The driving time to collect the data took about 22.4 h and the driving distance was 1177.4 km [23].  
Figure 2: Illustration of vehicle samples of the INFATI dataset.  
Figure 4: Illustration on the LA map of vehicle samples collected by [PERSON] and [PERSON].  
Figure 3: The vehicle dataset introduced by [PERSON] and [PERSON] in LA.  
In [27], [PERSON] et al. proposed a real vehicle dataset in Jordan in 2019. An Android application called Ultra GPS Logger (UGL) was used to collect the samples, using a Samsung Galaxy S Duos 2 S7582 smartphone, as shown in Figure 6. The vehicle sample generation process took about eight months (from January to August). Once per second, vehicle information was collected, recording GPS position, speed, direction, and distance between successive positions. The dataset covered a distance of around 6600 kilometers.
## 2 Materials and Methods
### Analysis  
Descriptive data were expressed as proportions or averages \(\pm\) standard deviations. Multivariate linear regression analyses were performed to evaluate the associations of the frequency of exercise, change in exercise status, MNA scores, and the number of diseases with baseline ADL and IADL scores, as well as subsequent 4- and 8-year ADL and IADL scores, after adjusting for confounding variables. In the cross-sectional analysis, frequency of exercise (q 2, 3-5, \(\geq\)6/week) was compared with none of the exercises per week, a reference level. In the longitudinal analysis, being a past, current, or consistent exerciser was compared with being not an exerciser, a reference level. MNA scores were analyzed as a continuous variable. Multivariate logistic regression analyses were performed to evaluate the associations of changes in exercise status, MNA scores, and the number of diseases with 4- and 8-year ADL and IADL decline, adjusted for confounding variables. ADL and IADL decline was defined as subsequent 4- and 8-year positive net ADL/IADL changes scores (>0 points), which meant that participants had worse ADL/IADL functional ability over 4 and 8 years. Confounding factors included sex, age, years of education, and ADL scores at baseline. The beta (\(\beta\)) coefficients, adjusted odds ratios and 95% confidence intervals (AOR (95% CI)) were reported. The SAS software package (SAS Institute Inc., Cary, NC, USA) version 9.1 was used to perform all statistical analyses. Statistical significance was set at \(p<0.05\).
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 3 Preliminary  
The term _Heterogeneous Graph_ is used uniformly in the descriptions in this paper. Because the graph structure means that it contains more comprehensive information and rich semantics, it has been widely used in many data mining tasks. In this section, we first introduce several definitions used in this article, including the HG and the meta-path of the HG. Then, we illustrate the working process of performance prediction using meta-path embedding. Several important notations are listed in Table 1, and we present a more detailed explanation of their role in this context.  
**Definition 1** (Heterogeneous Graph).: _(1) Given a graph \(G=\{V,E\}\), where \(V\) and \(E\) are the node set and relation set, respectively, if there is a mapping function \(\phi(\cdot)\) of node type and a mapping function \(\theta(\cdot)\) of edge type which map the nodes \(v\in V\) and edges \(e\in E\) to the specific types \(A\) and \(C\), respectively, then this type graph is a heterogeneous graph. (2) If \(|A|+|C|>1\), then  
Figure 1: Network schemas of heterogeneous information networks for the two course datasets.  
is a heterogeneous graph and \(G=\{A,C\}\) is called heterogeneous graph (abbr. HG) schema. The schematic of a heterogeneous graph of a course is shown in Figure 1.  
**Definition 2** (Meta-path of HG).: _A meta-path is defined as a sub-path that links two different nodes in a heterogeneous graph. In this study, a meta-path is denoted as a path in the form of \(B_{1}\xrightarrow{C_{1}}B_{2}\xrightarrow{C_{2}}\dots\xrightarrow{C_{1}}B_{l}\) which describes a composite relation \(C=C_{1}\circ C_{2}\circ\dots\circ C_{l}\) between entity \(B_{1}\) and \(B_{i+1}\), where \(\circ\) denotes the composition operator on relations._  
**Definition 3** (Reciprocal Relationship of HG Embedding).: _Given the HG embedding of a course, denoted by \(\vec{c}\), and a student, denoted by \(\vec{s}\), the reciprocal relationship between them is an interactive module \(\Theta\), e.g., \(\mathcal{R}=\Theta(\vec{c},\vec{s})\), where \(\mathcal{R}\) is the representation of the interactive result._
## 2 Numerical Calculation Method for Telecommunication Towers
### Computational Domain and Mesh Division Strategy  
The accuracy of numerical simulation results is directly influenced by the selection of the computational domain. The scale independence of the domain was verified by choosing domains of different sizes. As shown in Figure 2, the computational domain's inlet is located \(6H\) away from the telecommunication tower, the outlet of the flow domain is at \(12H\), and the left and right boundaries are \(3H\) from the tower, with the domain's height set to \(6H\), where \(H\) represents the height of the telecommunication tower. The mesh division of the flow domain is shown in Figure 3. To enhance the accuracy of the numerical simulation, an unstructured mesh was used to refine the mesh around the tower cylinder and antennas. This involved the addition of an inflation layer near the walls of the tower cylinder and antennas to address areas of high-stress concentration. The mesh of the inflation layer, which was achieved by stretching along the boundary normals, improved the mesh accuracy and consisted of a combination of triangular and quadrilateral meshes. The distance of the first layer of mesh nodes to the wall surfaces satisfies \(\mathbf{y}^{+}\leq 5\). The mesh around the telecommunication tower is shown in Figure 4.  
To ensure the simulation results were not affected by the quality of the mesh, while also considering computational costs, this paper conducted a grid independence verification. For both the bare tower condition without antennas and the condition with a single layer of six antennas arranged, three different mesh schemes of varying densities and numbers of elements were divided for each condition, as shown in Tables 1 and 2, which present the calculated average drag coefficient and lateral force coefficient for the telecommunication tower. With the increase in the number of elements, the average lateral force coefficient of the tower is very small, approximately zero; the drag coefficient gradually increases. In Tables 1 and 2, the relative errors for the drag coefficient between mesh scheme 1 and scheme 2 are 3.8% and 5.4%, respectively. Furthermore, the differences in the drag coefficient between scheme 2 and scheme 3 are even smaller, with relative errors of only  
Figure 4: Mesh around the tower cylinder and antennas.  
Figure 3: Mesh map of the flow domain.  
Figure 2: Diagram of the computational domain dimensions.  
0.8% and 1.1%, respectively. Therefore, to obtain accurate simulation results within the constraints of limited computational resources and time, the second mesh scheme was chosen for calculations. The total number of elements used in this scheme ranges from 4.34 to 10.7 million.
## 4 Discussion  
This study shows that species-specific or multi-site parameters can be derived and evaluated even for a complex forest growth module such as PnET. In order to address the optimization of plant physiological processes that describe C exchange processes, it is necessary to keep parameters from other model parts such as those describing soil organic matter decomposition. Therefore, the possibility of adjusting respiration fluxes at the ecosystem scale, which originate from plant (autotrophic) as well as soil (heterotrophic) respiratory processes, was limited. With respect to those processes, we thus relied on a previous parameter calibration study of the soil biogeochemistry sub-module [43]. In the current investigation, the parameter derivation of the plant physiology module reveals that: (a) parameters are specific to particular ecosystem properties (see Section 4.1); and (b) generally defined (species-specific) model parameters can still describe forest gas exchange across a multitude of sites (see Sections 4.2 and 4.3).  
Figure 2: Comparison of daily simulated and measured Net Ecosystem Exchange (NEE) for all investigated sites. The dotted line separates simulations with calibrated (left side) and non-calibrated (right side) parameters. For DE-Hoeg, no evaluation run with a non-calibrated parameter set has been carried out.
## 3 Overview Communication Technology for Smart Grid
### ZigBee  
ZigBee is based on the Standard IEEE 802.15 and is used in applications that require a low data rate, long battery life, low cost and network security. Its applications include smart meters and industrial equipment, which require a short range of wireless data transmission and a relatively low cost. ZigBee has the capability to connect up to 60,000 devices in one network [15; 16]. This technology has been developed to be simpler, easier to implement and more cost-effective than other wireless networks. The ZigBee network provides a 128-Bit symmetric encryption key. The use of this application allows for the integration of smart meters in the ZigBee network with other devices. These intelligent measurement devices can collect information from integrated devices and act as monitoring systems [17; 18].
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 6 Conclusions  
In this paper, we focus on the FANET networking for high-mobility scenarios and propose a novel network clustering algorithm based on the Kalman-filter-predicted position and speed information. First, we focus on node clustering and use the Silhouette coefficient to determine the number of clusters. On this basis, the \(k\)-means++ method is utilized to group those nodes with similar properties into a cluster. To cope with the disturbance in high mobility scenarios, we propose a location and velocity prediction-assisted networking method, which uses the Kalman filter to predict this information. Furthermore, we use the cluster maintenance metric and the ratio of changed edges to evaluate the networking performance. The simulation results reveal that the Kalman filter presents a good prediction result event at high mobility with strong external disturbance, and our proposal can reduce the communication overheads evidently. Our findings provide insights into the future high-mobility FANET networking.  
Nonetheless, the improvements of our proposal is still marginal comparing with the existing algorithms in highly mobile scenarios considering the inteference of external turbulences. In the future, it can be improved from two aspects. (1) Accurate and precise node trajectory prediction under the case of external disturbances can be realized by algorithms such as the extended Kalman filter, deep learning, Q-learning, etc. (2) Another aspect refers to optimizing clustering algorithms, such as taking the topology variation into the cost function, fuzzy C-means algorithms, etc.  
Conceptualization, J.Z. and L.L.; methodology, J.Z. and X.W.; validation, J.Z.; investigation, J.Z. and L.L.; writing--original draft preparation, J.Z.; writing--review and editing, L.L. and X.W.; supervision, L.L.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Science and Technology on Metrology and Calibration Laboratory (Grant No. JLKG2022001A001).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 4 Proposed Detection Scheme  
To reduce the complexity of nonlinear detection scheme, the proposed detection scheme is composed of three stages, i.e., partial ZF (slightly different to the conventional ZF in Section 3.1), DFE, and QRD-M. For easy understanding and detailed explanation, the proposed detection scheme is explained in 4 \(\times\) 4 MIMO-OFDM system without noise vector as follows,  
\[\left[\begin{array}{c}Y_{1}\\ Y_{2}\\ Y_{3}\\ Y_{4}\end{array}\right]=\left[\begin{array}{cccc}H_{11}&H_{12}&H_{13}&H_{14} \\ H_{21}&H_{22}&H_{23}&H_{24}\\ H_{31}&H_{32}&H_{33}&H_{34}\\ H_{41}&H_{42}&H_{43}&H_{44}\end{array}\right]\left[\begin{array}{c}X_{1}\\ X_{2}\\ X_{3}\\ X_{4}\end{array}\right]. \tag{15}\]
## 2 Materials and Methodology
### RGB Benchmark Experiment  
The advantage of HSI compared to standard RGB is deeper tissue penetration and wider spectral coverage. Aiming at a comparison with standard RGB, we synthesize and transform the HS radiance data to RGB color images. The color images are then used to detect skin features. In order to render and visualize HS images in a uniform RGB color space, we adopt the approach of [PERSON] et al. [42]. The methodology introduces image transformation to the application of color metric representations and color rendering. A color space for rendering is needed to visualize a spectral image [42; 43]. With the RGB color space, the radiance values are converted to CIE XYZr tristimulus values \(X\), \(Y\), \(Z\)[43]. Then, the RGB values are obtained by a linear transformation formulated by  
\[\begin{bmatrix}R\\ G\\ B\end{bmatrix}=\begin{bmatrix}3.2406&-1.5372&-0.4986\\ -0.9689&1.8758&0.0415\\ 0.0557&-0.2040&1.0570\end{bmatrix}\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}. \tag{4}\]  
Figure 6 shows an example RGB rendering of an HS image. As suggested by [PERSON] et al. [44], to improve appearance, the RGB levels of the image can be clipped to the level of a less specular--but still bright--region and then scaled [45]. The result is shown in Figure 6. Clipping can lead to artifacts  
Figure 5: Architectures for feature extraction and matching adapted from [PERSON] et al. [39]. The left panel shows the input HS data corresponding to the two different breathing phases. The middle panel depicts the feature extraction, indicating the attention scores assigned to relevant features and used for feature selection. The matched features are shown as the final output of the system in the right panel. KNN: K nearest neigbor.  
(such as specular glare), which can be reduced by adopting a further algorithm that preserves the relationship between different colors [46]. The clipped image is used to detect and match the 2D skin features with the method described in Section 2.5. The results are then evaluated and compared with the results found by using the HS images.
## 2 Materials and Methods  
### LIBS Experimental Setup  
The schematic of the experimental setup for LIBS-based penicillin sample detection is shown in Figure 1. We used a He-Ne laser (\(\lambda\) = 632.8 nm) to calibrate the optical path. Laser pulses were generated by using a Q-switched Nd:YAG laser (\(\lambda\) = 1064 nm, pulse frequency \(f\) = 1 Hz, beam diameter \(\phi\) = 6 mm, pulse duration \(\tau\) = 5 ns, and pulse energy \(E\) = 40 mJ/pulse). We used three plane mirrors (M1, M2 and M3) to modulate the propagation direction of the laser beam, and the laser beam was focused on the sample surface by using a convex lens with a 100 mm focal length. The penicillin sample was fixed on a three-dimensional motorized stage. The laser interacted with the sample to produce plasma radiation, which was collected in a 600 \(\upmu\)m-diameter optical fiber using a lens with a 36 mm focal length. The optical fiber was connected to a dual-channel spectrometer (AvaSpec 2048-2-USB2, Avantes, Apeldoorn, The Netherlands). The resolution of the spectrometer was 0.2-0.3 nm. The wavelength range was 190-1100 nm. The spectrometer was integrated with a charge-coupled device (CCD) camera. A detector was used to detect the laser pulse and trigger the delay generator (DC535). DC535 triggered the CCD after a preset time, and the spectral acquisition delay time was set to 1.28 \(\upmu\)s to reduce continuous radiation. The integration time was 2 ms. The Avasoft 7.6 software was used to control the collection of the penicillin LIBS spectra by the spectrometer.
# Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant-Oil-Water Systems
## 2 Methodology and Materials
### Data Generation for Machine Learning  
After fine-tuning the modified HLD-NAC model, we used it to generate a comprehensive dataset consisting of 5000 data points. These data points included variations in system parameters, such as surfactant concentration, oil composition, and salinity, representing a wide range of possible conditions in surfactant-oil-water systems. Each data point included corresponding phase behavior outcomes, including the number of phases and compositional details for each phase, see Figure 1. This dataset ensured a balanced representation of the compositional space across all phases, from single-phase regions to complex three-phase microemulsions.