esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Concrete Environmental Performance Comparison
However, in the control mixture, a high chloride penetration was observed in both 28 and 91 days. These results showed that in 28 days of curing, the concrete with glass powder acquired around three times more resistance. After 91 days, all concretes had low chloride penetration, except for the non-milled waste that was considered a 10% replacement (GP1-10). Furthermore, improvement in the resistance against chloride penetration with the adoption of glass waste was also observed by [PERSON] et al. [16], [PERSON] and [PERSON] [12], and [PERSON] and [PERSON] [18]. Finally, regarding the present results, the concrete with 10% cement replacement without milling can be excluded from the analysis. The options GP1-20, GP2-10, and GP2-20 showed reductions in environmental impact associated with good results in resistance to chlorides. Figure 16 shows the environmental impact in the endpoint category for 1 m\({}^{3}\) of the hollow-core slab. It compares the environmental indicator results with the chloride diffusion coefficient for concrete, measured adopting three laboratory testing methodologies. Notably, the environmental impact tended to reduce with a higher percentage of waste glass powder incorporation, to replace cement, as occurred in GP1-20 and GP2-20. These types of concrete have higher efficiency and lower environmental impact. Additionally, a significant decrease in human health endpoint indicators was also found for the glass powder alternatives. Figure 15: Chloride penetration resistance versus environmental impact. Figure 17 demonstrates the results regarding the relationship between the total impact of endpoint, the compressive strength and the service life of the concretes. For this purpose, a mixture of two or more properties was performed to build a specific functional unit. It was found that there is a correspondence between the environmental impact and the compressive strength, demonstrating that the use of waste glass as a partial substitute for cement can generate environmental benefits even considering the impact per 1 MPa of strength. If the service life of the concrete were also part of the evaluation, the benefit of using the residue would occur in a more accentuated way.
# Dynamic SDN Controller Load Balancing
## 5 DCC Two Phase Algorithm
### Initial Clustering as Input to the Second Phase  
The outcomes of the two types of initialization, namely \"_distance_\" and \"_load_\", presented so far (Section 5.1) are used as an input for the second phase.  
It should be noted that since the \"_maxDistance_\" constraint is an output of the initialization based on the distance (Heuristic 1), the first phase is mandatory in case the distance constraint is tight.  
On the other hand, the initialization based on the load (Heuristic 2) is not essential to performing load balancing in the second phase, but it can accelerate convergence in the second phase.
# Motion Simulation and Human-Computer Interaction System for Lunar Exploration
## 2 Materials and Methods
### Modeling of Lunar Environment and Rover
#### 2.2.1 Lunar Environment Generation  
The lunar environment includes the lunar terrain and the geometric and kinematics model of the manned lunar rover. On the moon, the main obstacles of interest to the lunar rover are rocks and craters. The distribution of obstacles has a significant impact on the simulation effect [11]. We tried to simulate this effect by procedurally placing rocks and craters and using the latest orbital information to simulate the formation of the lunar terrain.  
The topographic data are from the Lunar Reconnaissance orbit satellite LRO, which is located 3 km south of the Apollo 15 landing site [12; 13]. Craters were added through the transformation of the height map. \((x,y,z)\) represents the coordinates of the sampling point. The two-dimensional coordinates \(v\)\((x,y)\) of the height map corresponded to the \((x,y)\) of the sampling point, and the relationship between the brightness value and the \(Z\) of the sampling point is shown in Formula (1):  
\[Z=\frac{L\times h_{max}}{255} \tag{1}\]  
where \(h_{max}\) is the maximum height of the lunar surface. The luminance value of \(v\)\((x,y)\) is \(L\).  
The depth and diameter of the crater met the following formula [14]:  
\[H=0.196D^{1.01} \tag{2}\]  
where \(H\) is the depth of the crater and \(D\) is the diameter of the crater.  
The height map was loaded into Unity3D as a terrain model to generate a simulation environment.  
Figure 3: Diagram of the operating platform.  
The shapes of the lunar rocks included circular, rectangular, concave, and so on. Rocks were generated on the terrain according to the distribution density of rocks with different diameters on the moon. The shape, size, quantity, and spatial distribution of rocks can be adjusted in Unity3D. The final lunar surface model is shown in Figure 4.  
Referring to the Apollo manned lunar rover, we designed a symmetrical springless suspension model, including two single-sided rocker arms and four bogies. The rocker arms on both sides had rotational freedom and were connected by a differential device. The pitch angle of the body was the average of the pitch angles of the rocker arms. The four wheels were independently driven and combined for steering [15,16]. The model of the rover is shown in Figure 5.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Results Validity  
#### 4.1.1 Effects of the Orbit Configuration in the Elevation Angle Distribution  
The Kepler orbital elements were chosen to describe the orbits, those parameters include the semi-major axis, \(a\), eccentricity, \(e\), orbit inclination, \(i\), right ascension of the ascending node, \(\Omega\), argument of perigee, \(\omega\), and true anomaly, \(\
u\). The values of the right ascension of the ascending node, \(\Omega\), argument of perigee, \(\omega\), and true anomaly, \(\
u\), do not affect the statistical properties of the elevation angle, then, those values are not further discussed. Figure 11(a),b show the PDF and CDF elevation angle curves observed from the same location for two simulated satellites with different orbit configurations which share the same initial semi-major axis, \(a\), eccentricity, \(e\), and orbit inclination, \(i\); but, differ in the initial values of the right ascension of the ascending node, \(\Omega\), argument of perigee, \(\omega\), and of the true anomaly, \(\
u\).
## 3 Results of the Simulation
In the simulation, the maximum value of the spectrogram in each time portion was extracted, so moments in which signal amplitude is near to zero, the maximum energy value was found in the noise, which caused significant errors. In order to present the advantages of the proposed method, an extended scenario of the simulation was carried out. In this situation two objects are considered, however, one of them approaches the baseline from a different angle in comparison to the first one, and with slightly higher velocity. The simulation parameters are presented in Figure 8.  
A spectrogram presenting the energy distribution of the reflected signal from the two targets is depicted in Figure 9. The additional target introduces a second curve with a different Doppler rate depending on the velocity and trajectory. Thus, based on the previous results it should be possible to differentiate considered targets on the accelerogram because of the individual Doppler rate seen during the time of analysis. In the simulated case, it was assumed that both targets cross the baseline approximately the same time in order to verify the possibility of extracting particular targets in PPSR geometry. An accelerogram of the simulated data is presented in Figures 9 and 10. Figure 5.0 Radar scene for the 2 nd simulation case. Figure 5.1 Accelerogram of the 2 nd simulation case. Figure 5.2 Spectrogram of the 2 nd simulation case. Based on the simulation carried out it was proved that CR estimation may be an effective technique, allowing additional information in PFSR to be extracted. Even if the Doppler rate in the vicinity of \(t=0\) (s) is similar for both targets, the Doppler rate history from the entire observation time provides additional information about the trajectory of the objects. Such data can be used as an extension for PFSR systems. In the next section, the method is tested using real-life data to verify the adaptability and usability of the proposed approach.
# Practical Approach for Developing Lateral Motion Control of Autonomous Lane Change System
### Lateral Motion Model in Cylinder Domain  
The main objective behind removing the state jump is to develop a lateral motion model in the cylinder domain. When the vehicle changes lanes, the reference lane for the travelling car changes to another lane. To eliminate this phenomenon, we glue the sides of the plane road to construct a cylindrical domain. Let us consider the lane change situation on a two lane road, as shown in Figure 3.  
Figure 3: Lane change on the cylinder domain.  
In (9), both vehicle states \(\xi_{1}\) and \(\xi_{2}\) are available because \(x_{1}\) is measured using the vision sensor. In addition, \(x_{3}\) is also obtained using the vision sensor and \(x_{4}\) from the in-vehicle sensor.  
The gluing function \(\rho(x)\) and its inverse, which transform system (5) into (9), are described as follows:  
\[\xi:=\rho(x) =\begin{bmatrix}\sin\left(\dfrac{2\pi}{L_{w}}x_{1}\right)\\ \cos\left(\dfrac{2\pi}{L_{w}}x_{1}\right)\\ x_{2}\\ x_{3}\\ x_{4}\end{bmatrix}, \tag{10}\] \[\rho^{-1}\left(\xi\right) =\begin{bmatrix}\overline{\text{sgn}}\left(\sin^{-1}\left(\xi_{1 }\right)\dfrac{L_{w}}{2\pi}\cos^{-1}(\xi_{2})\right)\\ \xi_{3}\\ \xi_{4}\\ \xi_{5}\end{bmatrix}.\]  
Using Equation (10), one can obtain the original state \(x_{i}\) of lateral dynamics.  
**Remark 3**.: _In the cylinder domain, the vehicle position can be represented as \(\left\{x,\xi_{1},\xi_{2}\right\}^{l}\) rather than \(\left\{x,y\right\}^{l}\), where the superscript \(l\) denotes the local coordinate of vehicle. When the vehicle changes the lane, the trajectory of vehicle changes as follows:_  
* _Driving on the current lane center_ \(\left\{x_{t_{1}},0,1\right\}^{l}\)__
* _The moment the vehicle crosses the lane_ \(\left\{x_{t_{2}},0,-1\right\}^{l}\)__
* _Driving on the target lane center_ \(\left\{x_{t_{3}},0,1\right\}^{l}\)__  
_where \(x_{t_{i}}\) denotes the longitudinal position at time \(t_{i}\). As compared to the lateral model (5) in the plane domain, the lateral offset jump does not occur during the lane change in the cylinder domain lateral model (9)._  
Designing the trajectory of a vehicle for lane change is simple in cylinder domain. Let us consider the lane change to the right. The lateral offset with respect to the road increases until half of the lane width. After the moment of crossing the lane, the lateral offset decreases to zero. Further, using Equation (10), one can obtain the following reference trajectory for the autonomous lane change system.  
\[\xi_{1}^{r}(t),\ \xi_{2}^{r}(t):\begin{cases}\text{RLC}&=\quad\sin\left( \dfrac{2\pi t}{t_{lc}}\right)\,,\ \cos\left(\dfrac{2\pi t}{t_{lc}}\right)\,,\\ \text{LLC}&=\quad-\sin\left(\dfrac{2\pi t}{t_{lc}}\right)\,,\ \cos\left(\dfrac{2\pi t}{t_{lc}}\right)\,,\end{cases} \tag{11}\]  
where RLC and LLC denote right and left lane changes, respectively. The desired lane change time \(t_{lc}\) is a user-defined parameter for the lane change system. In the simulation section, we can see the example of reference trajectories.
# Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm
## 4 Test Problems and Optimization Results  
### Statement of the Optimization Problem  
The sizing optimization problem for a truss structure with \(NOD\) nodes (k = 1, 2,..., _NOD_) and \(NEL\) elements (j = 1, 2,..., _NEL_), subject to \(NLC\) independent loading conditions (_ilc_ = 1, 2,..., _NLC_), can be stated as a weight minimization problem:  
\[\text{Minimize }W(\mathbf{X})=\rho g\sum_{j=1}^{NEL}l_{j}x_{j}\text{:Subject to}\left\{\begin{array}{c}u_{(\text{x},y,z),k}^{ \text{L}}\leq u_{(\text{x},y,z),k,\text{ilc}}\leq u_{(\text{x},y,z),k}^{\text{U}} \\ \sigma_{j}^{\text{L}}\leq\sigma_{j,\text{ilc}}\leq\sigma_{j}^{\text{U}}\\ x_{j}^{\text{L}}\leq x_{j}\leq x_{j}^{\text{U}}\end{array}\right. \tag{21}\]  
where:* \(x_{\rm i}\) is the cross-sectional area of the jth element of the truss, included as a sizing design variable, ranging between its lower bound \(x_{\rm i}^{\rm L}\) and upper bound \(x_{\rm i}^{\rm U}\);
* \(l_{\rm j}\) is the length of the jth element of the structure;
* \(g\) is the gravity acceleration (\(9.81\) m/s\({}^{2}\)) and \(\rho\) is the material density. The g term must not be considered if structural weight is expressed in kg as it was done in the present study;
* \(u_{(\rm x,y,z),k,ik}\) are the displacements of the kth node in the coordinate directions, varying between the lower limit \(u_{(\rm x,y,z),k}^{\rm L}\) and the upper limit \(u_{(\rm x,y,z),k}^{\rm U}\);
* \(\sigma_{\rm j,ik}\) is the stress in the jth element, varying between \(\sigma_{\rm j}^{\rm L}\) (compressive stress limit accounting also for buckling strength) and \(\sigma_{\rm j}^{\rm U}\) (allowable tension limit);
* \(\rm{i}\)ic indicates the litch loading condition acting on the structure. Constraints on nodal displacements, element stresses and buckling strengths are normalized with respect to their corresponding limits.  
A large-scale truss structure is comprised of hundreds or thousands of elements, which may be categorized into groups in order to reduce the number of sizing variables. Hence, a large-scale optimization problem usually counts at least 200 design variables. Since the structure must withstand several independent loading conditions, the optimization problem has several thousands of nonlinear constraints on nodal displacements, element stresses (including buckling strength). Here, we optimize a planar 200-bar truss subject to five independent loading conditions (200 sizing variables and 3500 nonlinear constraints), a spatial 1938-bar tower subject to three independent loading conditions (204 sizing variables and 20,700 nonlinear constraints), and a spatial 3586-bar tower subject to three independent loading conditions (280 sizing variables and 37,374 nonlinear constraints).
## 5 Results and Discussion  
### Pareto Solution Set  
The Pareto solution sets under different inflow frequencies were obtained by solving the above model through NSGA-II, as shown in Figure 7. The Pareto solution sets under different inflow frequencies had different shapes, but the whole was smooth and continuous. There was an obvious negative correlation between total power generation and total water diversion, which further explained the competitive relationship between the two objectives. With the continuous increase in natural inflow, the total power generation and total water diversion were also increasing. For example, the maximum of total power generation and total water diversion in extraordinarily dry year were 0.775 \(\times\) 108 kW\(\cdot\)h and 5.27 \(\times\) 108 m\({}^{3}\), the maximum of total power generation and total water diversion in extraordinarily wet  
Figure 6: General framework of multi-objective optimization and multi-attribute decision making method for reservoir operation.  
year were 5.75 \(\times\) 10\({}^{8}\) kW\(\cdot\)h and 14.45 \(\times\) 10\({}^{8}\) m\({}^{3}\), with an increase of 4.975 \(\times\) 10\({}^{8}\) kW\(\cdot\)h and 9.18 \(\times\) 10\({}^{8}\) m\({}^{3}\), respectively. It showed that the amount of natural inflow had a significant impact on the benefits of the reservoir. For the Pareto solution sets under different inflow frequencies, the increasing range of power generation was less than that of water diversion, indicating that the water diversion was more sensitive to the amount of natural inflow. In other words, power generation objective was more sensitive to water diversion objective.
# CFD Analysis of a Tubular Heat Exchanger for the Conditioning of Olive Paste
## 2 Materials and Methods
### Governing Equation and Boundary  
The Navier-Stokes equations are formulations of mass, momentum and energy conservation laws for fluid flows. Since it is assumed that the heat exchanger is applied in the continuous extraction process of the olive oil, the analysis was performed at regime and in steady-state conditions. In this case, the Navier-Stokes equations could be expressed as follows:  
\[\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{1}\]  
\[\frac{\partial}{\partial x_{i}}(\rho u_{i}u_{k})=-\frac{\partial P}{\partial x _{i}}+\frac{\partial}{\partial x_{i}}\left(\mu\frac{\partial u_{k}}{\partial x _{i}}\right) \tag{2}\]  
\[\frac{\partial}{\partial x_{i}}(\rho u_{i}T)=\frac{\partial}{\partial x_{i}} \left(\frac{\partial T}{\partial x_{i}}\frac{k}{c_{p}}\right) \tag{3}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Description** & **Unit** & **Value** \\ \hline Inner tube diameter & mm & 88.9 \\ Inner tube thickness & mm & 2.0 \\ Inner tube length & m & 1.6 \\ External jacket inner diameter & mm & 101.6 \\ External jacket thickness & mm & 1.5 \\ External jacket inlet/outlet diameter & mm & 16.4 \\ External jacket inlet/outlet length & mm & 90.7 \\ Number of elbows & - & 2.0 \\ Elbows r/D & - & 1.75 \\ Elbows inner diameter & mm & 88.9 \\ Elbows thickness & mm & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical data of the tube-in-tube heat exchanger.  
Figure 1: Three-dimensional model—longitudinal section.  
where \(u\) is the fluid velocity [m/s], \(\rho\) is the fluid density [kg/m\({}^{3}\)], \(P\) is the static pressure [Pa], \(T\) is the temperature [K] and \(k\) [W/mK], \(\mu\) [Pa s] and \(c_{p}\) [J/kgK] are the thermal conductivity, dynamic viscosity and constant pressure specific heat capacity, respectively.
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 2 Oc-Smart  
### Overview  
To garner meaningful information about water properties from satellite ocean color measurements, atmospheric and water surface effects must be accounted for. This task has historically been accomplished by taking the total radiance at the top of the atmosphere (TOA), \(L_{t}\) and then subtracting the contributions from Rayleigh scattering, aerosol scattering and absorption, surface whitecaps, and sunlight to arrive at only the contributions transmitted upward through the air-water interface due to scattering by pure water and embedded constituents in the water column. This approach works reasonably well in open ocean areas where aerosol and water properties are well characterized, but breaks down in the presence of complex coastal water and aerosol conditions and frequently lead to unreliable results including negative water-leaving radiances in up to 30% of pixels [14].  
To overcome these problems, a new approach, focused on coastal areas, was invented [13] and this methodology was further developed to construct global AC and ocean property retrieval algorithms [14]. Instead of using a traditional AC approach, the radiative transfer Equation (RTE) pertinent for the coupled atmosphere-water system was solved to obtain the required radiances [13; 14]. Then a machine learning neural network implicit AC algorithm that takes advantage of the similarity in spectral shape of the Rayleigh-corrected TOA radiance, \(L_{rc}\), and the water-leaving radiance, \(L_{w}\), was developed to derive \(L_{w}\) directly from the \(L_{rc}\) without an explicit AC step. This approach is advantageous because it does not rely on subtraction of aerosol radiance contributions like in typical AC algorithms. Global application of this spectral matching approach requires RT simulation datasets for a significant variety of atmospheric and marine conditions. To this end, six atmospheric profiles were used including the US standard model, mid-latitude summer and winter models, sub-arctic summer and winter models, and a tropical model. A variety of aerosol model packages [31; 32] were used to develop the implicit machine learning global AC algorithm. In addition, inherent optical properties (IOPs) of the water were derived from a combination of the three bio-optical models, those being the modified GSM model [33], the modified CCRR model [34], and the MAG model [35]. Details of the modifications made to the original models are described elsewhere [14].
## 3 Results and Discussion
### Changes in Selected Film Properties as a Result of Plasma Modification
When analyzing the ageing of the PLA films studied, we limited ourselves to only selected studies, choosing them based on the influence of a given parameter on the application aspects of the material and its relevance to wettability. However, the scope of material ageing studies could be significantly extended to include, for example, XRD, FTIR, XPS, or DSC analysis, which would be particularly relevant for, for example, medical applications of PLA, while this work focuses on applications related to further processing (printing, bonding, etc.). A particularly important parameter seems to be the change in PLA crystallinity and the chemical changes that occur as a result of plasma activation, both of which have a significant impact on degradation. Lower crystallinity, a more hydrophilic monomer, more hydrophilic end groups, and a more reactive hydrolytic group in the backbone will lead to an acceleration in polymer degradation [65]. Research in this area can be continued in future work on PLA ageing.
# Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny
## 2 Materials and Methods
### Evaluation Metrics  
To ensure the empirical analysis was both rigorous and objective, we adopted three principal metrics: precision, recall, and mAP. Precision is meticulously defined as the proportion of accurately detected eye states relative to the aggregate number of eye states identified by the model. Conversely, recall is determined by the ratio of detected eye states to the aggregate actual eye states present. These metrics are mathematically represented in Equations (1) and (2), where TP (True Positive) symbolizes the accurately identified eye states, FP (False Positive) denotes instances erroneously classified as eye states, and FN (False Negative) signifies eye states that were not correctly detected.  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
The formula for calculating mAP is outlined in Equations (3) and (4). The area under the precision-recall curve (PR curve) is known as AP (average precision), and mAP represents the mean of AP values across different categories. In this study, with the detection of drivers' eyes being open or closed, the N value, indicating the total number of categories tested, is 2.  
\[\text{AP}=\int_{0}^{1}P(R)dR \tag{3}\]  
\[\text{mAP}=\frac{\sum_{1}^{N}\int_{0}^{1}P(R)dR}{N} \tag{4}\]  
Additionally, to provide a comprehensive evaluation of the model's efficiency and effectiveness, we included the following metrics: GFLOPS (Billion Floating-Point Operations Per Second), number of parameters, and FPS.  
GFLOPS measures the computational complexity of the model, indicating the number of billion floating-point operations required to process a single input. It is crucial for understanding the model's demands on computational resources. The number of parameters quantifies the total number of trainable parameters within the model. A lower number of parameters typically implies a more lightweight and potentially faster model. FPS assesses the model's processing speed, specifically how many frames it can analyze per second. Higher FPS rates are indicative of better suitability for real-time applications.