esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation of Energy Forecasting and Power Theft Detection Models
### Comparative Analysis Based on Error Indices Calculations  
In order to compare the prediction accuracy of the analyzed forecasting models, error indices can be used. With this aim, in this paper, three error indices are used to compare the efficiency of the analyzed prediction models. The first error index is the MSE [30], which is the mean squared difference between the estimated value and original value of the prediction technique. The second index is the MAE [31], which is the difference between the most similar observations of the model. Finally, the RMSE index [31] is calculated as the standard deviation of the residuals, representing the dispersion of residuals in the series. The comparison of the error values is presented in Figure 12. As is evident, the proposed RNN-GBRT performs better than other forecasting methods.
## 2 Areas and Research Methods
### Gray Relational Model  
Gray system theory was founded by Professor [PERSON] [48] in the 1980s. It has its roots in the three major theories of systems theory, information theory and cybernetics, which were developed in the 1940s. Gray system theory allows us to deeply analyze a given set of data in a \"small sample\" and \"bad information\" system to produce an accurate  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multicolumn{1}{c}{**Item**} & **1949** & **1979** & **1996** & **2003** & **2008** & **2013** & **2016** \\ \hline Population (10\({}^{4}\) person) & 38.35 & 99.28 & 160.38 & 196.38 & 237.06 & 299.25 & 336.25 \\ GDP (CNY10\({}^{6}\)) & 2.87 & 21.38 & 415.77 & 1077.22 & 3300.98 & 7153.13 & 8510.13 \\ The built-up area (H, unit: sq. km) & 6.7 & 51.8 & 104.93 & 96.26 & 209.63 & 393.78 & 476.34 \\  & Residential land (R) & 1.92 & 11.02 & 25.38 & 30.95 & 69.95 & 134.79 & 155.32 \\  & Commercial service land & 1.26 & 13.33 & 26.97 & 37.02 & 43.98 & 73.4 & 66.58 \\ development land (unit: sq. km) & Industrial land (M) & 1.52 & 12.5 & 21.9 & 27.1 & 30.6 & 66.5 & 94.56 \\  & Road and transportation & 0.8 & 2.97 & 9.59 & 20.58 & 31.76 & 37.67 & 52.87 \\  & Lund (S) & 0.6 & 4.8 & 7.94 & 12.29 & 21.42 & 29.13 & 34.13 \\ \hline \hline \end{tabular} Note: the built-up area was meant to express the total area constructed. It contains both the developed land and the non-developed land. The developed land area contains eight categories: R, A, B, M, W, S, U and G, which refer to the GB50137-2011 standard [47] classification of urban land. W (warehouse land) and U (utility land) have little to do with urban carbon emissions. Therefore, classes W and U are not described in the table.  
\end{table}
Table 1: Population, economic and built-up area indices.  
perception of the whole system and formulate predictions. Since the relationship between carbon emissions and the urban spatial expansion index in this study is a typical Gray system, the nonequally spaced stepwise optimization GM (1,1) model method is chosen [49]. This model accurately fits urban carbon emissions. It is an easy-to-model method that has a saturation curve. It is a relatively accurate model for urban carbon emissions prediction with good generalizability.
# Game Theory-Based Interactive Control for Human-Machine Cooperative Driving
## 6 Conclusions  
This work presents HSCV safety control based on game theory and multi-vehicle interaction. First, a comprehensive driver model integrating steering control and speed control was designed based on the BELCM algorithm. Next, a shared control strategy based on the driving risk field was designed to dynamically adjust the driving control authority between the human driver and the HSCV automation control system. Considering that different driving styles of SVs in the driving environment will have different impacts on the HSCV, two critical indicators of driving safety and travel efficiency were used to define the aggressiveness level of SVs. MPC was used to predict the future status of traffic participants. Finally, the interaction problem between HSCV and SVs was transformed into DMPC, which can be solved through the idea of Nash equilibrium. Two different scenarios were designed to evaluate the performance of the HSCV safety control method. The simulation results show that in HSCV lane changing and lane keeping scenarios, the HSCV shared  
Figure 14: Front wheel angle of HSCV in Scenario 2: (**a**) wheel angle in case **e**; (**b**) wheel angle in case f; (**c**) wheel angle in case g; (**d**) wheel angle in case h.  
Figure 15: Driving control authority allocation for HSCV in Scenario 2: (**a**) driving authority allocation in case e; (**b**) driving authority allocation in case f; (**c**) driving authority allocation in case g; (**d**) driving authority allocation in case h.  
controller designed in this study can assist human drivers in safely avoiding neighbor vehicles with dangerous behaviors of sudden acceleration and lane changing.  
This article mainly focuses on the safety control of HSCVs, and the test of the experimental scenario is based on reasonable assumptions about the driver's intention to change lanes and deceleration intention. A more realistic integrated driver model that considers steering control and speed control requires us to conduct further research in the future, especially on the impact of driver intention on shared control, to improve shared control technology in HSCVs.  
Writing--original draft, Y.Z.; Writing--review & editing, C.H.; Supervision, C.H. and P.H. All authors have read and agreed to the published version of the manuscript.  
This research was fund by the Departmental General Research Fund (DCRF: P0040253).  
Not applicable.  
Data Availability Statement: Data supporting this study are included within the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest regarding the publication of this paper.  
The following abbreviations are used in this manuscript:
# A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
## 4 The Intrinsic Limit Determination Algorithm  
Let us introduce the predictor vector \(\mathbf{p}_{S}\equiv(1,1,\ldots,1)\), for which it clearly holds  
\[\begin{cases}TPR|_{\mathbf{p}_{S}}=&1\\ FPR|_{\mathbf{p}_{S}}=&1\end{cases} \tag{21}\]  
\(TPR|_{\mathbf{p}_{S}}\) and \(FPR|_{\mathbf{p}_{S}}\) indicate \(TPR\) and \(FPR\) evaluated for \(\mathbf{p}_{S}\), respectively.  
Let us indicate as a flip the change of a component of a prediction vector from the value of 1 to the value of 0. Any possible prediction vector can thus be obtained by a finite series of flips starting from \(\mathbf{p}_{S}\), where a flip is done only on components with a value equal to 1. Let us denote with \(\mathbf{p}_{1}\) the prediction vector obtained after the first flip, \(\mathbf{p}_{2}\) after the second, and so on. After \(N_{B}\) flips, the prediction vector will be \(\mathbf{p}_{N_{B}}\equiv(0,0,\ldots,0)\). The \(TPR\) and \(FPR\) evaluated for a prediction vector \(\mathbf{p}_{i}\) (with \(i=1,\ldots,N_{B}\)) are indicated as \(TPR|_{\mathbf{p}_{i}}\) and \(FPR|_{\mathbf{p}_{i}}\). The set of tuples of points' coordinates is indicated with \(\mathcal{P}\):  
\[\mathcal{P}=\{(FPR|_{\mathbf{p}_{S}},TPR|_{\mathbf{p}_{S}}),\ldots,(FPR|_{ \mathbf{p}_{N_{B}}},TPR|_{\mathbf{p}_{N_{B}}})\}. \tag{22}\]  
A curve can be constructed by joining the points contained in \(\mathcal{P}\) in _ordered segments_, where ordered segments means that the point \((FPR|_{\mathbf{p}_{S}},TPR|_{\mathbf{p}_{S}})\) will be connected to \((FPR|_{\mathbf{p}_{1}},TPR|_{\mathbf{p}_{1}})\); \((FPR|_{\mathbf{p}_{1}},TPR|_{\mathbf{p}_{1}})\) with \((FPR|_{\mathbf{p}_{2}},TPR|_{\mathbf{p}_{2}})\); and so on. The segment that joins the point \((FPR|_{\mathbf{p}_{S}},TPR|_{\mathbf{p}_{S}})\) with \((FPR|_{\mathbf{p}_{1}},TPR|_{\mathbf{p}_{1}})\) is denoted as \(s^{[0]}\); the one that joins the points \((FPR|_{\mathbf{p}_{1}},TPR|_{\mathbf{p}_{1}})\) and \((FPR|_{\mathbf{p}_{2}},TPR|_{\mathbf{p}_{2}})\) is denoted as \(s^{[1]}\), and so on. In Figure 2, a curve obtained by joining the tuples given by the respective prediction vectors obtained with three flips is visualized to give an intuitive understanding of the process.  
The ILD algorithm provides a constructive process to select the order of the components to be flipped to obtain the curve \(\mathcal{C}\) characterized by the theoretical maximum AUC that can be obtained from the considered dataset, regardless of the predictive model used.  
To be able to describe the ILD algorithm effectively and prove its validity, some additional concepts are needed and described in the following paragraphs.  
Figure 2: Example of a curve constructed by joining 3 segments obtained after 3 flips.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis  
In this section, we perform an informal security analysis of our proposed scheme in which we consider the Dolev-Yao threat model. In this model we assume that the attackercan attack our scheme in an open channel and in response our scheme provides important security attributes such as data privacy, authentication, prevention of data tampering attack, data integrity, resilience against replay attack, and nonrepudiation.
## 4 Investigation of Scalable Configurations Using Containerization
### Results Analysis  
#### 4.4.1 Performance of Different Set-Ups  
Performance measured as the number of successfully processed messages within the test time frame is shown in Figure 10. X axis values represent growing load defined as the number of IoT devices and values on the Y axis represent the number of successfully processed messages. Firstly, we observe that the versions with only one node, with and without a load balancer, have similar results. This indicates that, in such a scenario, the impact of the load balancer is really low and has practically no effect on total results. The results for the configuration with 2 nodes and 10 cores each are visibly better and configuration of 5 nodes with 4 cores each obtained the best results. It shows that it is beneficial to use more nodes with fewer cores assigned. Figure 11 provides more detailed view on differences between the configurations. It should be noted that increasing the number of Docker nodes might potentially benefit from more resources allocated this way (numbers of threads or preallocated memory buffers, depending on actual middleware code) but is limited by the system resources. For instance, we failed to end a test with the configuration of 10 nodes 2 cores each because of lack of RAM. It was possible to begin the test but a growing size of data exceeded available memory before the test ended. We also measured 7 nodes with 3 cores each and this configuration provided us with worse results than 5 nodes with 4 cores. In Table 1 we present exact results of each configuration at optimal load point (2030 clients) and also RAM usage of each configuration at the beginning of the test--when the middleware was ready to receive data from IoT clients. It should be noted that memory consumption is growing over the time of test. Table 2 contains the percentage difference compared to the architecture with the best performance.
## 3 Evaluation Methods of the Performance of GSP
### Computationand Simulation Method
#### 3.3.2 Compressive Strength Prediction Model  
Compressive strength of GSP is assessed by Cube compressive strength test. The values have a linear relationship with that of grouted hydrated cement mortar [136]. The expression is shown as below:  
\[y=1.3619x^{0.4736},\ R^{2}=0.8525 \tag{4}\]  
\(x\) is the compressive strength of cement mortar; \(y\) is the compressive strength of GSP.  
And the relationship can be illustrated in Figure 4:  
In addition, [PERSON] et al., indicated that void rates of OMA and compressive strength of GSP had an empirical regression relationship as follows [137; 138]:  
\[y=8.3x^{2}-319.7x+5181.4 \tag{5}\]  
\(x\) was the void rate of OMA (20%-30%); \(y\) was the resilient modulus of GSP.  
Figure 4: Relationship of compressive strength between cement mortar and GSP [136].
## 3 Theoretical Model  
In order to precisely calculate fog and rain attenuation, rational mathematical models are required. This section presents Rayleigh approximation theory and Mie theory, which were adopted in this study. The detailed calculation methods of the related parameters, including the attenuation coefficient, extinction efficiency factor, and scattering coefficient, are provided. Additionally, the dominant extinction efficiency factor in Mie theory is simulated and analyzed.
## 3 Methodology
### Approaches for Data Collection and Analysis
#### 3.8.1 Temperature Field Distribution  
Once the simulation process was completed and the predefined convergence criteria were met, the analysis focused on the temperature distribution across the facade surface. To examine thermal variations within the cavity, cross-sectional views were generated at key locations of the model--namely, the inlet, the central region, and the outlet. Figure 1 presents the passive facade model, configured within the ANSYS Fluent 17 environment, upon which the numerical simulations were performed. The visual representations generated by the software illustrate the temperature differences shown in the XY plane (as presented in Figure 2) in the graphs of Figures 3-9, and in the _ZY_ plane (as depicted in Figure 6).  
Figure 1: Passive facade simulation in ANSYS Fluent 17.  
Figure 3: Temperature chart in the XY plane for the inlet section.  
Figure 2: The three cross-sections analyzed in the XY plane.  
Figure 4: Temperature chart in the XY plane for the middle section.  
Figure 5: Temperature chart in the XY plane for the outlet section.  
Figure 6: The three sections analyzed in the ZY plane.  
Figure 8: Temperature chart in the ZY plane for the middle section.  
Figure 7: Temperature chart in the ZY plane for the inlet section.  
Figure 9: Temperature chart in the ZY plane for the outlet section.  
The selection of these temperature sections was intentional, with the primary objective of highlighting the thermal contrasts in the five analyzed scenarios. Subsequently, specific details will be presented for the cases with cavity widths of 4 cm, 6 cm, 8 cm, 10 cm, and 12 cm.  
A detailed examination of the graphical representation presented in Figure 2 reveals a remarkable uniformity of the thermal values recorded at the inlet section across all five analyzed configurations. This homogeneity in the temperature distribution within the inlet zone makes it difficult to visually distinguish any significant variations between the different studied cases.
## 4 Conclusions  
This paper proposes a double-light positioning system based on image sensor and set up relevant experiments to test the performance of the proposed system. We utilize the rolling shutter mechanism of the CMOS image sensor and combine machine learning to identify the LED-ID. It is worth mentioning that the proposed double-light positioning system has satisfactory performance in real-time capability and positioning accuracy. In addition, the proposed double-light positioning algorithm has low complexity and takes the symmetry problem of angle into consideration, which is never not considered by other studies.  
Compared with the existing work, the proposed double-light positioning algorithm is innovative and achieves high precision. A relevant experiment was conducted to demonstrate the positioning performance. Experiments showed that the proposed double-light positioning algorithm could provide an accuracy of 3.85 cm. Meanwhile, in terms of the real-time ability, the computational time of the proposed double-light positioning algorithm was reduced to 56.28 ms and could realize positioning 17 times in 1 s, which represents good real-time performance. Therefore, it can be concluded that the proposed double-light positioning algorithm delivers satisfactory performance in terms of real-time ability and positioning accuracy, which makes it a promising candidate for future indoor positioning applications.  
**Author Contributions:** Conceptualization, W.G.; Data curation, X.Z. and J.L.; Formal analysis, X.Z. and J.L.; Investigation, X.Z. and Z.X.; Methodology, W.G.; Project administration, J.Z.; Resources, Y.W.; Software, J.Z. and X.Z.; Supervision, J.L.; Validation, W.G.; Visualization, J.Z.; Writing--original draft, W.G.; Writing--review & editing, W.G., X.Z. and Z.X.  
**Funding:** This research was funded by National Undergraduate Innovative and Entrepreneurial Training Program grant numbers 201510561003, 201610561065, 201610561068, 201710561006, 201710561054, 201710561057, 201710561058, 201710561199, 201710561202, 201810561217, 201810561195, 201810561218, and 201810561219; Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (\"Climbing Program\" Special Funds) grant numbers pdjh2017b0040 and pdjha0028; and Guangdong science and technology project grant number 2017B010114001.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Modeling Framework
The features of the MODFLOW-2005 are to model the three-dimensional  
Figure 2: The three regions for the GSFLOW model, including the plant canopy and the soil zone, the streams and lakes, and the unsaturated zone and the aquifer system (Modified from [11]). steady or transient flow of groundwater through porous media. The unsaturated zone flow (UZF) package [24] used in MODFLOW-2005 was available for simulating the flow through the unsaturated zone. The package, associated with the PRMS module, can adequately account for the complex water interactions between the land surface and unsaturated zones above the groundwater levels. In the MODFLOW-2005, the movement of the groundwater flow with the constant density can be described by the following formula [14]:  
\[S\frac{\partial h}{\partial t}=\frac{\partial}{\partial x}(K_{x}\frac{ \partial h}{\partial x})+\frac{\partial}{\partial y}(K_{y}\frac{\partial h}{ \partial y})+\frac{\partial}{\partial z}(K_{z}\frac{\partial h}{\partial z})+W \tag{1}\]  
where notations \(K_{x}\), \(K_{y}\), \(K_{z}\) [L/T] are the hydraulic conductivity in the x-, y-, and z-directions, \(h=h(x,y,z,t)\) [L] is the hydraulic head. Notation \(W=W(x,y,z,t)\) [1/T] is a volumetric flux per unit volume, representing sources and sinks of an aquifer system. The parameter \(S\) is the storage coefficient of the aquifer, and \(t\) [T] is time. In MODFLOW-2005, based on solving Equation (1) the boundary conditions can be the constant hydraulic head, the specified hydraulic head, or the default no-flow boundary conditions surrounding the simulation domain. The unsaturated zone flow package considers the effects of flow, evapotranspiration, and storage in the unsaturated zone. The package plays an important role in coupling MODFLOW-2005 with many precipitation-runoff models [24]. In the UZF package, the equation used to model unsaturated flow is the one-dimensional Richards' equation, which has the following formula:  
\[\frac{\partial\theta}{\partial t}+\frac{\partial K(\theta)}{\partial z}+i=0 \tag{2}\]  
where \(\theta\) [L\({}^{3}\)/L\({}^{3}\)] is the volumetric water content and \(z\) [L] is the altitude in the vertical direction. The notation \(K(\theta)\) [L/T] is the unsaturated hydraulic conductivity, determined by the unsaturated water content \(\theta\). Notation \(i\) [L\({}^{2}\)/T] is the evapotranspiration rate per unit depth beneath the soil zone. In the MODFLOW model, the analytical solution to Equation (2) was developed by using the method of characteristics (MOC) to solve for hyperbolic types of partial differential equations in which the solution can be integrated along the characteristic line from a given initial state. Previous investigations had extended the solution to account for the evapotranspiration loss for site-specific conditions [24].
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Single Shooting Pattern
#### 4.1.1 Multiple Regression
In the Surface pattern, ensuring image overlap plays a decisive role in improving result quality, while in the Circular pattern, the shooting radius and camera tilt angle can have opposite effects on reprojection error and point count, respectively, and the potential for interaction between these variables must be considered. Furthermore, the relatively low adjusted R\({}^{2}\) values observed in some result metrics (e.g., Reprojection Error in the Surface pattern model) suggest that these result metrics cannot be perfectly explained by simple linear relationships with the shooting variables considered in this study.
## 5 Experimental Evaluation
### Evaluation and Benchmarking of AQM Algorithms
#### 5.1.3 FQ-CoDel  
For our experiments (12)-(15), the internal parameters of FQ-CoDel were configured with target = 5 ms, interval = 100 ms, quantum = 1514 bytes, limit = 10,240 packets, and flows = 1024 queues, tested with both ECN enabled and disabled.  
**Scenario 1 (Bandwidth = 10 Mbps, Delay = 20 ms)** : We evaluated the performance of FQ-CoDel in two cases: Case 1 with enabled ECN and Case 2 with disabled ECN for comparison. Figures 12 and 13 present the results for the two cases, respectively.  
**Scenario 2 (Bandwidth = 1 Mbps, Delay = 20 ms)**: We evaluated the performance of FQ-CoDel in two cases, Case 1 with ECN enabled and Case 2 with ECN disabled, for comparison. Figures 14 and 15 present the results for the two cases, respectively. FQ-CoDel shows similar fairness in throughput regardless of ECN, even though it shows high variation in throughput in Figure 14a when ECN is enabled. FQ-CoDel shows similar _cvnd_ values to Scenario 1, except it shows higher variations in _cvnd_ when ECN is disabled, as depicted in Figure 15b, indicating high packet loss. When ECN is disabled, FQ-CoDel drops packets during congestion, thus ensuring RTT is low for all successfully transferred packets, as illustrated in Figure 15c.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 5 ECM-Derived Peptide Sequences and Their Use in Biomimetic Hydrogel Functionalisation
### Elastin: GRKRK Motif  
Tropoelastin contains a C-terminal pentapeptide sequence GRKRK [123], between residues 782-786. The adhesive properties of the synthetic peptide GRKRK were confirmed by adding GRKRK to cells already adhered to tropoelastin, which reduced cell adhesion from 50% to 16% [123]. Cation dependency was also used to determine if GRKRK adhesion was integrin mediated. Without metal ions, the GRKRK motif supported only 5% adhesion; however, in the presence of Mn\({}^{2+}\), 58% cell adhesion was observed [123]. Although GRKRK has been identified as a key integrin-binding adhesive site within tropoelastin, to the best of our knowledge, there have been no studies which incorporate this adhesion motif within hydrogels.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 3 Proposed Schemes
### Network Architecture  
In the network architecture, \(N=n_{1},n_{2},n_{3},...,n_{n}\) sensor nodes are deployed randomly in a three-dimensional area. Sensor nodes could be of different types in terms of size, battery, processing capabilities, etc. [19; 20]. This network consists of homogeneous sensor nodes and a sink deployed at the ocean surface as shown in Figure 1. All of these sensor nodes are equipped with sensing devices and an acoustic modem for communication purposes. Sensor nodes are deployed at different depths, and data are relayed towards the destination node using forwarding mechanisms. Data are reported to the sink, which is equipped with an acoustic modem and a radio modem. Acoustic links enable it to connect with sensor nodes, and radio links are used for the land communication. Received data at sink node are further transmitted towards satellites using radio links. At the end of this forwarding process, data reach the data center onshore.