esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Governing Equation  
The governing equation in three dimensions for the seepage flow through a homogenous porous media is as follows:  
\[\Delta h(x,y,z)=0\text{ in }\Omega, \tag{1}\]  
with  
\[h(x,y,z)=g\text{ on }\Gamma_{D}, \tag{2}\]  
\[\frac{\partial h(x,y,z)}{\partial n}=f\text{ on }\Gamma_{N}, \tag{3}\]  
where \(h\) is the total head, \(\Delta\) is the Laplacian, \(\Omega\) represents the boundary of the problem, \(n\) denotes the normal vector, \(\Gamma\in\Gamma_{D}\cup\Gamma_{N}\) is the boundary of \(\Omega\), \(g\) and \(f\) denote the Dirichlet and Neumann boundary values, \(\Gamma_{D}\) denotes the Dirichlet boundary, and \(\Gamma_{N}\) denotes the Neumann boundary. As demonstrated in Figure 1, the boundary conditions of the free surface flow in three dimensions through a rectangular dam can be presented by \(\Gamma_{abd}\), \(\Gamma_{bcef}\), \(\Gamma_{fegl}\), \(\Gamma_{hgij}\), \(\Gamma_{adjj}\), \(\Gamma_{abij}\) and \(\Gamma_{dcgi}\). The Dirichlet boundary conditions are imposed on the \(\Gamma_{bcef}\) and \(\Gamma_{adjj}\), respectively.  
\[h=\text{H}_{2}\text{ on }\Gamma_{adjj}, \tag{4}\]  
\[h=\text{H}_{1}\text{ on }\Gamma_{bcef}. \tag{5}\]According to the Bernoulli equation, the total head can be expressed as  
\[h=z+\frac{p}{\gamma}, \tag{6}\]  
where \(z\) is the head above the sea level, \(p\) is the pore water pressure, and \(\gamma\) is the water unit weight. On the free surface boundary, \(\Gamma_{hgij}\), the boundary conditions are given as follows:  
\[\frac{\partial h}{\partial n}=0,\ h=z\ \text{on}\ \Gamma_{hgij}. \tag{7}\]  
On the seepage face boundary, \(\Gamma_{fegh}\), the boundary condition is depicted as  
\[h=z\ \text{on}\ \Gamma_{fegh}. \tag{8}\]  
On boundaries, \(\Gamma_{abcd}\), \(\Gamma_{abtij}\) and \(\Gamma_{dcgi}\), the Neumann boundary conditions may be given as follows:  
\[\frac{\partial h}{\partial n}=0\ \text{on}\ \Gamma_{abcd},\ \Gamma_{abtij}\ \text{and}\ \Gamma_{dcgi}. \tag{9}\]  
Since \(h=z\) is unknown and needs to be computed iteratively, we adopt the MFS to solve the governing equation for the nonlinear free surface seepage flow.
## 2 Principles and Methodologies
### Simulation Methods for Cracks, Contraction Joints, and Degradation of Elastic Modulus
#### 2.2.3 Simulation of Degradation of Elastic Modulus  
Due to the influence of factors such as chemistry and mechanics, the performance parameters of concrete materials are not stable and often undergo degradation over time [47]. [PERSON] et al. [48] proposed a mathematical equation for the time-dependent degradation of the elastic modulus of concrete as:  
\[E_{m}(t_{a})=0.0175t_{a}^{3}-3.4054t_{a}^{2}+29.807t_{a}+E_{m} \tag{13}\]  
where \(t_{a}\) represents the degradation duration in years, \(E_{m}\) is the initial elastic modulus of concrete in MPa, and \(E_{m}(t_{a})\) is the degraded elastic modulus of concrete in MPa.
# A Study on the Vertical Bearing Characteristics of Screw Piles in Permafrost Regions
## 3 Numerical Simulation Analysis
### Comparison and Analysis of Screw Pile and Ordinary Pile  
Under winter ground temperature conditions, the bearing performance of cast-in-place piles under the same geological conditions was analyzed using the finite element method. The vertical bearing capacity characteristics of screw piles with a diameter of 0.8 m and a length of 12 m, ordinary cast-in-place piles with a diameter of 0.8 m and a length of 12 m, and ordinary cast-in-place piles with a diameter of 1 m and a length of 20 m were compared and analyzed.  
As indicated in Figure 11(a), the load-settlement curve of an ordinary cast-in-place pile with a length of 20 m demonstrates a steep drop when the bearing capacity reaches 14.24 MN, which belongs to the typical failure mode of a friction pile. The load-settlement curve of an ordinary cast-in-place pile with a length of 12 m indicates a steep drop when the bearing capacity reaches 7.30 MN, which is a typical form of friction pile failure. When the pile length of a screw pile is only half that of an ordinary pile, its ultimate bearing capacity can approach that of an ordinary pile. The lateral frictional resistance of the three piles is depicted in Figure 11(b). For ordinary piles, the frictional resistance gradually increases with depth and loading. In contrast, the effect of depth on the equivalent frictional resistance of the screw pile is relatively small. The screw pile and the soil occlude each other, resulting in a higher equivalent friction resistance of the pile's side at the beginning and the end of the thread than that of the conventional pile type, which is significantly different from the conventional pile type.
# System Architecture Design of IoT-Based Smart Cities
## 7 Discussion  
In this section, we will discuss the key findings of our research and their implications for the design of IoT-based smart cities. First, our proposed architecture for IoT-based smart cities is based on a layered structure, with the physical layer consisting of IoT devices, the network layer comprising communication protocols, and the application layer comprising various smart city services. The architecture also includes a management layer for monitoring and controlling the overall system. We have provided detailed descriptions of each layer, including the challenges and potential solutions for their implementation.  
One of the key challenges in designing an IoT-based smart city is the integration of various technologies and devices. Our proposed architecture addresses this challenge by providing a standardized and modular approach to integrating IoT devices and services. This allows for the seamless integration of new devices and services as they become available without disrupting the overall system.  
Another important aspect of our proposed architecture is the inclusion of a management layer for monitoring and controlling the overall system. This allows for the efficient and effective management of the smart city, including monitoring system performance and identifying and resolving any issues.  
There are several potential threats to the validity of this research. One threat is the limited scope of the case study used to illustrate the proposed architecture. While the case study provides a detailed analysis of a smart city, it may not represent all smart cities, and the proposed architecture may not apply to all smart city scenarios. Another threat is the potential for biases in selecting IoT devices and communication protocols. Therefore, further research is needed to evaluate the proposed architecture in different smart city scenarios and with various IoT devices and communication protocols.  
Another limitation of this research is that it does not consider the potential ethical and privacy concerns that may arise from using IoT devices in smart cities. The collection and use of data from IoT devices in smart cities may raise privacy concerns for citizens. The proposed architecture should be designed to address these concerns through the use of appropriate security and privacy measures.  
In conclusion, our proposed architecture for IoT-based smart cities provides a comprehensive and modular approach to integrating IoT devices and services, allowing for the efficient and effective management of the smart city. It also addresses the key challenges of security and scalability, providing a blueprint for developing smart cities that can benefit citizens and city management.
## 3 System Architecture
### Channel Coding  
Channel coding is an essential part of many digital communication systems. It is most commonly used as a forward error control (FEC) mechanism, which is well known for its ability to identify and minimize bit errors in the digital domain. By using channel coding strategically on both the transmitter and receiver, the communication system's overall reliability is increased. Channel coding appears as an encoding technique on the transmitter side, where additional bits are added to the raw data before they are modulated. By contrast, channel coding facilitates decoding at the receiver end. By using channel coding in a methodical manner, mistakes in the data that are received can be easily found and fixed, which increases the system's resilience. To ensure customized and efficient error-control measures in the context of our experimental setup, the size of the LED matrix determined the best channel-coding scheme. In our works, Hamming (\(11/15\)) code was proposed to improve performance of OCC system.
# A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model
## 4 GPU-Enabled Acceleration Algorithm
### Acceleration Algorithm  
Figure 4 illustrates the domain decomposition in the \(g\)-\(point\) dimension for the RRTMG_LW accelerated on the GPU. The acceleration algorithm in the \(g\)-\(point\) dimension for the RRTMG_LW, is illustrated in Algorithm 4. The algorithm is described as follows:  
1. In the acceleration algorithm, \(inatm\) consists of five kernels (\(inatm\_d1\), \(inatm\_d2\), \(inatm\_d3\), \(inatm\_d4\), and \(inatm\_d5\)). Due to data dependency, a piece of code in \(inatm\) can be parallel only in the horizontal or vertical direction, so the kernel \(inatm\_d4\) uses a 1D decomposition. The kernels \(inatm\_d1\), \(inatm\_d2\), and \(inatm\_d5\) use a 2D decomposition in the horizontal and vertical directions. The kernel \(inatm\_d3\) uses a composite decomposition in the horizontal and vertical directions and \(g\)-\(point\) dimension. Due to the requirement of data synchronization, \(inatm\_d1\) and \(inatm\_d2\) cannot be merged into one kernel. 2. The kernel \(cldprmc\_d\) still uses a 1D decomposition. 3. Similarly, the kernel \(setcoef\_d1\) uses a 2D decomposition, and the kernel \(setcoef\_d2\) uses a 1D decomposition. 4. The kernel \(taumol\_d\) uses a composite decomposition in the horizontal and vertical directions and \(g\)-\(point\) dimension. In \(taumol\_d\), 16 subroutines with the device attribute are invoked. 5. Similarly, \(trtrunc\) consists of 11 kernels (\(trtrunc\_d1\)-\(trtrunc\_d11\)). Here, \(trtrunc\_d1\), \(trtrunc\_d4\), \(trtrunc\_d8\), \(trtrunc\_d10\), and \(trtrunc\_d11\) use a 1D decomposition. Furthermore, \(trtrunc\_d2\) and \(trtrunc\_d9\) use a 2D decomposition in the horizontal and vertical directions. In addition, \(trtrunc\_d5\) and \(trtrunc\_d6\) use a 2D decomposition in the horizontal direction and \(g\)-\(point\) dimension. Finally, \(trtrunc\_d3\) and \(trtrunc\_d7\) use a composite decomposition in the horizontal and vertical directions and \(g\)-\(point\) dimension. ```
attributes(global) subroutine\(trtrunc\_d\)(parameters)
1. iplon=(blockIdx%x-1)\(\times\)blockDim%x+threadIdx%x
2. if(iplon\(\geq\)1.and.iplon\(\leq\)ncol) then
3. Initialize variable arrays
4. do lay = 1, nlayers
5. do ig = 1, ngptlw
6. do some corresponding work
7. enddo
8. enddo //Loop over frequency bands //istart is beginning band of calculation //iend is ending band of calculation
9. do band = istart, iend //Downward radiative transfer loop
10. do lay = nlayers, 1, -1
11. do some corresponding work
12. enddo
13. enddo
14.end if end subroutine
```  
**Algorithm 3**Implementation of 1D \(trtrunc\_d\). Figure 4: Schematic diagram of the decomposition in the \(g\)-\(point\) dimension for the RRTMG_LW in the GPU acceleration. ```
subroutine_rrtmg_lw_d3(parameters)
1.
## 4 Conclusions  
The FTSW is a variable that can help farmers schedule irrigation and reduce water waste. In this study, the propensity of the BiLSTM model to forecast the FTSW for vineyards  
Figure 14: Boxplots showing the dispersion of the observed and predicted FTSW during the testing phase for the RF and BiLSTM models. The box shows the interquartile range (25–75 th from the 5 th to 95 th percentile). The solid line within the box indicates the mean.  
Figure 13: Irrigation event (black triangles), rainfall, and the FTSW (0–80 cm) in 2019 at Colinas do Douro.  
was investigated. The data from two different vineyards, Colinas do Douro and Quinta de Pancas, were used as the training and test datasets, respectively. Using the feature importance method and eliminating the collinearity between variables, the ETo, the month of the year, the VPD (min), the RH (avg), rainfall, and irrigation were selected as the inputs to the model. The model achieved an \(R^{2}\)-score ranging from 87% to 94% on the test set.  
The performance of the BiLSTM model for FTSW prediction was compared with the RF, SVR, and LR models. The degradation of the \(R^{2}\)-score of the Colinas do Douro and Quinta de Pancas test series in RF and SVR was twice that of the BiLSTM model, showing that the BiLSTM could better generalize on the unseen data.  
The results showed that BiLSTM was able to find nonlinear relationships between historical climate and soil moisture data and was able to generalize the results for grapegrowing terroirs. To use the model more broadly for other environments with different conditions, it should be re-trained with the new data.  
Data curation: K.A., P.D.G., and R.M.C.; Formal analysis: K.A., P.D.G., R.M.C., G.C.R., and C.M.L.; Funding acquisition: P.D.G.; Investigation: K.A.; Methodology: K.A., P.D.G., R.M.C., G.C.R., and C.M.L.; Project administration: P.D.G.; Resources: K.A., P.D.G., and C.M.L.; Software: K.A.; Supervision: P.D.G.; Validation: K.A., P.D.G., R.M.C., G.C.R., and C.M.L.; Visualization: K.A.; Writing--original draft: K.A.; Writing--review and editing: K.A., P.D.G., R.M.C., G.C.R., and C.M.L. All authors have read and agreed to the published version of the manuscript.  
The work is supported by by the R&D Project BioDAgro - Sistema operacional inteligente de informacao e supporte a decisao em AgroBiodiversidade, project PD20-00011, promoted by Fundacao La Caixa and Fundacao para a Ciencia e a Tecnologia, taking place at the C-MAST - Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilha, Portugal.  
Not applicable.  
P.D.G. acknowledges Fundacao para a Ciencia e a Tecnologia (FCT--MCTES) for its financial support via the project UIDB/00151/2020 (C-MAST).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Photocatalytic Experiments  
The photocatalytic activity of TiO\({}_{2}\)/Leca was evaluated in a glass reactor with 2 L, equipped with 3 Philips TL 6W BLB lamps (tube diameter 16 mm), with the maximum emission peak at 365 nm (UVA radiation). The experiments were performed at 25 \({}^{\circ}\)C, controlling the temperature through a thermal bath. The solution in the reactor was homogenized using a magnetic stirrer at 750 rpm and 0.2 L/min of oxygen flow rate. The reactor was covered with aluminum foil to avoid external light interference.  
The kinetic studies involved irradiating the solution containing 1 mg/L of SMX. The experiments took place for 2 to 6 h with different loads of TiO\({}_{2}\) powder (to compare the activity with the supported) and supported catalysts (TiO\({}_{2}\)/Leca). The liquid samples were taken from the reactor at different time intervals (0, 5, 10, 15, 30, 45, 60, 90, 120, 180, 240, 300, 360 min) and were filtered using cellulose acetate filters with 0.45 um pores.  
The direct photolysis of SMX with UVA was studied using the procedures described before. Additionally, the SMX adsorption onto the catalyst surface under dark conditions was evaluated by contacting the SMX solution with the solid materials without the radiation sources.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 5 Modelling of DCSS Protocol  
In this section, the proposed protocol is modelled using entities; \(UE\), \(SPAAA\) and \(SS\) leveraging on system architecture shown in Figure 2. We have omitted \(SMF\) as it has no significant role in the protocol execution. The protocol applies cryptographic primitives such as hash function, symmetric/asymmetric encryption, digital signature, and Message Authentication Code (MAC), access, and cache tokens to provide message authentication, confidentiality, integrity, and non-repudiation. Its security assumptions are based on the 5G specifications [1], while the Universal Subscriber Identity Module (USIM) in the UE has cryptographic capabilities such as key agreement, Key Derivation function (KDF), encryption facilitated by Elliptic Curve Integrated Encryption Scheme (ECIES) [50].
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up
### Active Shape Model (ASM)
#### 3.3.3 Fitting the Model  
To perform the adjustment of the model, the best shape and pose parameters to match a shape in the model coordinate frame, \(x\) and \(y\), with a new shape in the image coordinate frame are located, while minimizing the error function as shown in Equation (17).  
\[E=(y-\mathbf{Tx})^{T}\mathbf{W}(y-\mathbf{Tx}) \tag{17}\]  
where \(\mathbf{W}\) is a diagonal matrix whose elements are weight factors for each landmark, and \(\mathbf{T}\) represents the geometric transformation of rotation, translation, and scaling. The weight factors are set relative to the offset between the computed positions of the old and new landmarks in the profile. If the offset is large, then the corresponding weight factor in the array is set to low, and if the offset is small, the weight is set to high. Given a single point denoted by \([x_{0},y_{0}]^{T}\), the geometric transformation matrix is defined by Equation (18).  
\[\mathbf{M}=\begin{bmatrix}x_{0}\\ y_{0}\end{bmatrix}=s\begin{bmatrix}cos(\theta)&sin(\theta)\\ -sin(\theta)&cos(\theta)\end{bmatrix}\begin{bmatrix}x_{0}\\ y_{0}\end{bmatrix}+\begin{bmatrix}x_{t}\\ y_{t}\end{bmatrix} \tag{18}\]  
After computing the set of pose parameters (\(\theta\), rotation; \(\mathbf{t}\), translation; and \(s\), scaling), the projection of \(y\) onto the model's coordinate frame is obtained from Equation (19).  
\[\mathbf{x}_{m}=\mathbf{M}^{-1}y \tag{19}\]  
Finally, the model parameters are updated as depicted in Equation (20).  
\[b_{m}=\Phi^{T}(\mathbf{x}_{m}-\bar{x}) \tag{20}\]The optimal displacement of a landmark is obtained from the search procedure through the detected contours. The combination of optimally updated landmarks generates a new shape in the image coordinate frame. This new form is now used to find the closest form using Equation (17). After computing the best pose, denoted by \(M\), this new shape is projected onto \(\Phi\), which contains the principal components of the given training set. This process updates the \(\mathbf{b}\) parameter of the model. As a result, only a similar variation corresponding to the principal components can affect the model parameters. After computing the model parameters, the new form, denoted by \(\mathbf{x}_{m}\), can be generated by Equation (15), and this new form is used for subsequent iterations, as in Equation (17). After a suitable number of iterations, the final form obtained is \(\mathbf{x}_{new}\).  
In this work, experimental parameters M = 50, L = 50, and the segment length of 40 pixels were set according to state of the art and the frame resolution [9; 36; 37]; additionally, they are based on the scale of the person's contour. A programming function could be used to analyze the relationship of those parameters and estimate an optimal value; nevertheless, we considered that out of this study's scope, which was focused on the analysis of MDBS and ASM methods for pedestrian localization in a video sequence.
## 2 PV Model Modeling and Problem Formulation
### Solar Cells
#### 2.1.1 SDM Modelling  
The SDM is widely regarded as a fundamental circuit model that is frequently employed and constructed based on physical principles [26]. The SDM is widely utilized due to its simplicity in integration and comprehensiveness in characterizing the inherent features of fixed solar cells. The SDM consists of a solitary current source, a solitary diode, a solitary shunt resistor \(R_{sh}\), and a solitary series resistor \(R_{s}\), as depicted in Figure 1.  
A series resistor is used to offset the voltage loss across the solar cell's gearbox resistors. The shunt resistor is used to ascertain the influence of leakage current through the p-n diode interface and restrictions, as well as the shunt resistor that develops within the solar cell [27]. The current mathematical expression of the SDM cell is [28]  
\[I=I_{ph}-I_{d}-I_{sh}, \tag{1}\]  
\[I_{d}=I_{sd}\bigg{[}exp\bigg{(}\frac{V+R_{s}\cdot I}{\gamma\cdot V_{t}}\bigg{)} -1\bigg{]}, \tag{2}\]  
\[V_{t}=\frac{K\cdot T}{Q}, \tag{3}\]  
\[I_{sh}=\frac{V+R_{s}\cdot I}{R_{sh}}, \tag{4}\]  
where \(I_{ph}\) is the symbol for the photo-current, \(I_{d}\) is the symbol for the diode current, and \(I_{sh}\) is the symbol for the shunt resistor current. Many internal parameters can be altered to improve the output characteristics of the diode, which is fabricated using semiconductor components. The utilization of the SDM equation and Kirchhoff's law of voltage enables the manipulation of the fundamental parameters of a diode. Symbol \(I_{sd}\) indicates the diode's reverse overload current. Variable \(V\) represents the cell's output voltage. Parameter \(\gamma\) represents the intrinsic typical operator of the diode. Lastly, \(V_{t}\) refers to heat conduction voltage. The calculation can be performed using Equation (3). Symbol \(K\) represents the Boltzmann constant, which has a value of \(1.3806503\times 10^{-23}\) J/K. Variable \(T\) represents the temperature of the junction, measured in Kelvin. Additionally, Symbol \(Q\) represents the charge of an electron, which is equal to \(1.60217646\times 10^{-19}\)\({}^{\circ}\)C. Consequently, the present value of the cell's output, denoted as \(I\), may be reformulated utilizing Equations (1)-(4) as stated in work [29].  
\[I=I_{ph}-I_{sd}\left[exp\left(\frac{V+R_{s}\cdot I}{\gamma\cdot V_{t}}\right)-1 \right]-\frac{V+R_{s}\cdot I}{R_{sh}}. \tag{5}\]  
The determination of the five unknown parameters (\(I_{ph}\), \(I_{sd}\), \(\gamma\), \(R_{s}\), \(R_{sh}\)) from the solar cell I-V data is necessary, as given in Equation (5). The choice of suitable parameters has a significant impact on the output and efficacy of a solar cell.