esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Nanoemulsions
#### 3.1.2 Zeta Potential of Emulsions  
Zeta potential was measured in order to assess emulsion stability. Zeta potential analysis is used as an indicator of the surface charge of particles. The value of the zeta potential provides an indication of the potential stability of the colloidal system. When the particles in the suspension have a high negative or positive zeta potential, they tend to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Size (\(\upmu\)m)** & **PDI** & **Zeta Potential (mV)** \\ \hline L3 & 0.11 \(\pm\) 0.02 \({}^{\text{c}}\) & 0.17 \(\pm\) 0.02 \({}^{\text{c}}\) & \(-\)39.96 \(\pm\) 1.16 \({}^{\text{c}}\) \\ L7 & 0.09 \(\pm\) 0.01 \({}^{\text{d}}\) & 0.21 \(\pm\) 0.02 \({}^{\text{bc}}\) & \(-\)47.83 \(\pm\) 1.36 \({}^{\text{d}}\) \\ P3 & 0.22 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.25 \(\pm\) 0.04 \({}^{\text{b}}\) & 28.93 \(\pm\) 1.13 \({}^{\text{a}}\) \\ P7 & 0.62 \(\pm\) 0.06 \({}^{\text{a}}\) & 0.61 \(\pm\) 0.03 \({}^{\text{a}}\) & \(-\)29.73 \(\pm\) 0.58 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of nanoemulsions. For results with the same letter, the difference between the means is not statistically significant.  
repel each other, which prevents the aggregation of the droplets and the flocculation of the emulsion. It is generally considered that emulsions having zeta potential values higher than 30 mV (absolute value) are stable [26]. The zeta potential values of the tested emulsions are presented in Table 2, and results show that the average zeta potential (absolute value) measured for all emulsions is high enough to induce emulsion stability. The emulsions stabilized using lecithin had a zeta potential of \(-39.96\pm 1.16\) and \(-47.83\pm 1.36\) mV at pH 3 and 7, respectively, and tended to be more stable than those emulsified using pea protein with a zeta potential of \(+28.93\pm 1.13\) and \(-29.73\pm 0.58\) mV at pH 3 and 7, respectively. The emulsion prepared at pH 7 had a higher value of zeta potential, indicating that this pH is more appropriate for the preparation of emulsions. This result is in accordance with those of the study by [PERSON] et al. [14] that measured zeta potential values comprised between \(-33.7\) and \(-58.6\) mV for different formulations of emulsions prepared with 2% soybean lecithin and 10% essential oils.
## 3 Results  
### Noise-Free Basis Data  
To conduct an initial analysis, we undertook a comparative study of the Support Vector Machine (SVM) and Random Forest (RF) models. Our investigation utilized a meticulously curated dataset, featuring samples devoid of noise and encompassing tumor radii spanning the range of 6 mm to 12 mm. This preliminary assessment aimed to verify the integrity of the data and validate the robustness of our approach. The outcomes of this evaluation, illustrated in Figure 4, unveiled a notable trend: as the tumor radius increased, the F1-Score exhibited a corresponding upward trajectory. Specifically, when considering a tumor radius of 6 mm, the RF model yielded an F1-Score of 0.52, while for a radius of 12 mm, this score ascended to 0.97. In comparison, the SVM model achieved an F1-Score of 0.73 for a 6 mm radius, which soared to 0.98 for a 12 mm radius. Consequently, it became apparent that as the tumor size expanded, the discernible disparities between the performances of the SVM and RF models diminished.
# The Prediction of the Performance of a Twisted Rudder
## 3 Numerical Approaches  
### Reynolds-Averaged Navier-Stokes Equations  
The RANS equations with turbulence models were solved to simulate wetted and cavitating flows around the test ship model, since the RANS models have been shown to be still successful in the estimation of the hydrodynamic performances of ships and offshore structures. A single set of the mass conservation and RANS momentum conservation equations in Cartesian coordinates for two-phase (vapor and liquid) incompressible flows is given by:  
\[\frac{\partial U_{i}}{\partial x_{j}}=0 \tag{7}\]  
\[\frac{\partial(\rho U_{i})}{\partial t}+U_{j}\frac{\partial(\rho U_{i})}{ \partial x_{j}}=-\frac{\partial P}{\partial x_{j}}+\frac{\partial}{\partial x _{j}}\left(\mu\frac{\partial U_{i}}{\partial x_{j}}\right)+\frac{\partial\left( -\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\right)}{\partial x_{j}} \tag{8}\]  
where, \(\rho\) is the fluid density, \(U_{i}\) are the time-averaged velocity components corresponding to the Cartesian coordinates \(x_{i}\), \(t\) is time, \(u_{i}^{\prime}\) are the velocity fluctuations, \(P\) is the time-averaged pressure, \(\mu\) is the fluid dynamic viscosity and \(-\rho\overline{u_{i}u_{j}}\) are the Reynolds stresses. The Reynolds stress term can be defined in terms of known quantities. In turbulence modeling, the problem is to develop a suitable closure model to predict the Reynolds stresses. As one of the various types of turbulence closure models, the Boussinesq hypothesis provides a simple relationship between the Reynolds stresses and velocity gradients through the eddy viscosity \(\mu_{t}\) as follows:  
\[-\rho\overline{u_{i}u_{j}}=\mu_{t}\left(\frac{\partial U_{i}}{\partial x_{j}} +\frac{\partial U_{j}}{\partial x_{i}}\right) \tag{9}\]  
Among the two-equation turbulence models, the realizable \(k-\varepsilon\) model based on the suggestions by [PERSON] et al. (2010) was used. This model uses the transport equations for the turbulent kinetic energy \(k\) and turbulence dissipation rate as the standard \(k-\varepsilon\) model. Better predictions of the distribution of the dissipation rate and boundary layer  
Figure 1: Experimental setup in the LCT: **(a)** full appended ship model mounted in the test section; **(b)** close-up view of the stern region; **(c)** pressure measurement holes on the inboard and outboard surfaces of the port flat rudder.  
characteristics in a large pressure gradient, separated, and recirculating flows can be expected compared to the standard \(k-\varepsilon\) model.
## 3 Results and Discussion  
### Pollen Analysis  
Monofloral honey originated predominantly from a single floral source to at least 45%, however, as previously referred, for this specific honey the pollen composition from _P. undulatum_ is over 30% [36]. The six honey samples under study were submitted to pollen analysis to guarantee they could be considered monofloral honey from _P. undulatum_. In Table 1, the values registered are the predominant, secondary, and minority pollen percentages per sample.  
The samples showed several pollen types, highlighting the _P. undulatum_, _C. sativa_, _Eucalyptus_ spp., and _Acacia_ spp. among them. All honey samples are monofloral and had origin in _P. undulatum_. The sample with a higher percentage of Victoria box tree was sample 6, with a presence of 69%, followed by sample 4, with 65%, with a secondary predominance pollen type, _Eucalyptus_ spp. On the other hand, samples 2, 3, and 5, besides their higher percentage in Victoria box tree pollen, had as a secondary pollen predominance pollen from _C. sativa_. In contrast, sample 1 revealed being the sample with the most variety of pollen type percentages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Predominant Pollen**} & \multicolumn{2}{c}{**Secondary Pollen**} & \multicolumn{2}{c}{**Minority Pollen**} \\  & \multicolumn{2}{c}{**(\textgreater{}30\%)**} & \multicolumn{2}{c}{**(16–30\%)**} & \multicolumn{2}{c}{**(3–15\%)**} & \\ \cline{2-6}  & \multirow{2}{*}{**Species**} & \multicolumn{2}{c}{**Percentage**} & \multirow{2}{*}{**Species**} & \multicolumn{2}{c}{**Percentage**} & \multirow{2}{*}{**Species**} & \multicolumn{2}{c}{**Percentage**} \\  & & & & & & **(\%)** \\ \hline \multirow{4}{*}{1} & _Pittsporum_ & & & & _Eucalyptus_ spp. & 12 \\  & _undulatum_ Vent. & & & & _Trifolium_ spp. & 8 \\  & _Pittosporum_ & & & _Castana sativa_ Mill. & 7 \\  & _undulatum_ Vent. & & & & _Acacia_ spp. & 5 \\  & & & & _Scrophularia_ spp. & 5 \\  & & & & _Lathyrus_ spp. & 3 \\ \hline \multirow{4}{*}{2} & _Pittsporum_ & & & _Acacia_ spp. & 9 \\  & _undulatum_ Vent. & & & Mill. & 19 \\  & _undulatum_ Vent. & & & _Eucalyptus_ spp. & 3 \\ \hline \multirow{4}{*}{3} & _Pittsporum_ & & & _Acacia_ spp. & 14 \\  & _undulatum_ Vent. & & & Mill. & 21 \\  & _undulatum_ Vent. & & & _Eucalyptus_ spp. & 3 \\ \hline \multirow{4}{*}{4} & _Pittsporum_ & & & _Acacia_ spp. & 14 \\  & _undulatum_ Vent. & & & _Eucalyptus_ spp. & 3 \\ \hline \multirow{4}{*}{5} & _Pittsporum_ & & & _Trifolium_ spp. & 8 \\  & _undulatum_ Vent. & & & Mill. & 24 \\  & _undulatum_ Vent. & & & _Eucalyptus_ spp. & 7 \\ \hline \multirow{4}{*}{6} & _Pittsporum_ & & & _Acacia_ spp. & 11 \\  & _undulatum_ Vent. & & & _Eucalyptus_ spp. & Labill. & 10 \\ \cline{1-1}  & _undulatum_ Vent. & & & _Castana sativa_ Mill. & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Pollen spectrum of types of honey studied. Data presented are about predominant, secondary, and minority pollen percentages per sample.
## 4 Analytical Results
### Unit Traffic Patterns Extracted by One/Two-Stage Clustering
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clustering**} & \multicolumn{3}{c}{**Distance from Cluster Feature**} & \multicolumn{3}{c}{**Distance between Cluster Features**} \\  & \multicolumn{3}{c}{**(Unit of Time: 120 s)**} & \multicolumn{3}{c}{**(All Settings of Units of Time)**} \\ \cline{2-9}  & **Ave** & **Max** & **Min** & **Var** & **Ave** & **Max** & **Min** & **Var** \\ \hline One-stage & 2.70 & 31.9 & 0.84 & 1.76 & 2.93 & 9.99 & 0.55 & 2.41 \\ Two-stage & 0.82 & 18.5 & 0.00 & 0.81 & 3.84 & 11.4 & 0.15 & 2.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical data regarding the distances of the feature values of divided traffic data pieces from the cluster features of the corresponding unit traffic pattern. The left column represents the statistical metrics when the unit of time was 120 s, and the right column shows the same metrics at all settings of the units of time. The proposed two-stage clustering method can classify divided traffic data pieces with more similar characteristics. Figure 8: Frequency distributions of the Euclidean distance of each traffic data piece from the corresponding cluster features derived from one-stage clustering.
## 3 Experiments and Results
### Ablation Studies
#### 3.5.1 Learning from Scratch vs. Sim2 real  
The inclusion of transfer learning from the simulation to the real robot for the proposed learning framework was evaluated. We compared the learning performance of training the agent in the real robot from scratch versus learning starting from a policy learned in a simulation. Training from scratch was performed for 50,000 steps, while retraining from the simulation lasted 15,000 steps. Figure 10 shows the learning curve for both training sessions. Learning from scratch required at least 50,000 steps to succeed at the tasks most of the time. In contrast, learning from the pretrained policy in the simulation achieved the same performance in under 5000 steps. The policy from the simulation still  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Success Rate** & **Insertion Force** \\ \hline
**Ring** & 80\% & 5N \\
**Electric Outlet (x)** & 75\% & 10N \\
**Electric Outlet (y)** & 75\% & 10N \\
**LAN port (x)** & 55\% & 5N \\
**LAN port (y)** & 60\% & 5N \\
**USB** & 80\% & 8N \\ \hline \hline \end{tabular}
\end{table}
Table 5: Success rate of learned policy in several insertion tasks.  
Figure 9: Several insertion tasks with different degrees of complexity. **(A)** Metal ring (high stiffness) with 0.2 mm of clearance. **(B)** Electric outlet requiring high insertion force. **(C)** Local-area-network (LAN) port, delicate with complex shape. **(D)** Universal serial bus (USB).  
required some training to fine-tune the controller to real-world physics dynamics, which are difficult to simulate, as can be seen from the slow start and the drops in cumulative reward.
## Appendix A Fluid attributes.  
Currently, the main rheological models studied in rheology are the Newtonian fluids model, the Bingham fluids model, the Power-law fluids model, the Casson fluids model, and the Herschel-Bulkley fluids model [30]. Among them, The Herschel-Bulkley fluids model is commonly used to describe the non-Newtonian rheological properties of complex fluids with high viscosity slurries, such as sludge, drilling fluids, sand slurry, and fresh concrete. This model combines the plastic characteristics of Bingham fluids and the pseudoplastic characteristics of Power-law fluids, so it is more accurate than other models in describing high-viscosity metal slurry fluids. The flow characteristics of Herschel-Bulkley fluids are: after the force is not immediately flowing, the yield stress to flow needs to be overcome, resulting in shear thinning characteristics. The relationship between the shear stress (\(\tau\) in Pa) and shear rate (\(\dot{\gamma}\) in s\({}^{-1}\)) can be expressed as:  
\[\tau=\tau_{0}+K\dot{\gamma}^{n} \tag{1}\]  
From the definition of Power-Law fluids model, we know that:  
\[\dot{\gamma}=\frac{du}{dr}=\left(\frac{\tau-\tau_{0}}{K}\right)^{1/n} \tag{2}\]  
Equation (1) contains three parameters, which are the yield stress (\(\tau_{0}\) in Pa), consistency (\(K\) in Pa\(\cdot\)s\({}^{n}\)), and the Power-law index (\(n\)). The numerical magnitude of \(n\) indicates the strength of the non-Newtonian fluid characteristics: \(n=1\) for Newtonian fluids, \(n<1\) for shear thinning fluids, and \(n>1\) for pseudoplastic fluids.  
When \(n=1\), Equation (1) becomes a Bingham fluids model equation:  
\[\tau=\tau_{0}+K\dot{\gamma} \tag{3}\]  
When \(\tau_{0}=0\), Equation (1) becomes a Power-law fluids model equation:  
\[\tau=K\dot{\gamma}^{n} \tag{4}\]  
When \(\tau_{0}=0\) and \(n=1\), Equation (1) becomes a Newtonian fluids equation:  
\[\tau=K\dot{\gamma} \tag{5}\]
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 4 Analog-Digital Receiver Design
### Complexity Comparison  
In this section, we compare the complexity of the proposed algorithm with the two-step approach proposed in [31]. This analysis may be divided in two parts, one for the computation of the analog, and the other for the digital component of the equalizer. For Algorithm 1 both the digital and analog components are computed in a given iteration \(i\). On the other hand, for two-step only the digital component is updated in iteration \(i\). The analog part is only computed once. The update of the digital component needs the inversion of a \(U\times U\) matrix, and therefore has complexity \(O(U^{3})\). The computation of the analog component requires the evaluation of the metric in (27) for all elements of the codebook \(\mathcal{F}_{a}\). The complexity of a matrix-vector product of sizes \(U\times N_{rx}\) and \(N_{rx}\), respectively, is \(O(N_{rx}U)\). As codebook \(\mathcal{F}_{a}\) has \(N_{CB}=N_{cl}N_{ny}U\) elements, then the complexity of the metric evaluation is \(O(N_{CB}N_{rx}U)\). As previously mentioned both the analog and digital components are updated in each iteration of Algorithm 1, which means that its computational complexity is \(O(i_{max}(U^{3}+N_{CB}N_{rx}U))\). Note that \(i_{\max}\) denotes the maximum number of iterations. For two-step only the digital part is updated in all \(i_{\max}\) iterations, then the computational complexity of this algorithm is \(O(i_{max}U^{3}+N_{CB}N_{rx}U)\). As the number of receive antennas is larger than the number of user and \(N_{CB}=N_{cl}N_{ray}U\geq U\) follows that \(N_{CB}N_{rx}U\geq U^{3}\). Therefore, the complexity of Algorithm 1 and two-step simplify to \(O(i_{max}N_{CB}N_{rx}U)\) and \(O(N_{CB}N_{rx}U)\), respectively. Hence, the two-step is approximately \(i_{\max}\) times faster than Algorithm 1. However as shown in the next section, the proposed fully iterative analog-digital equalizer clearly outperforms the two-step approach.  
Additionally, in the next section, we also made a performance comparison with the algorithm of [14], extended here to broadband SC-FDMA systems. In the Gram-Schmidt orthogonalization is done \(N_{rx}U^{2}\) multiplications and \(U(U-1)/2\) divisions, therefore, we have \(O(N_{rx}U^{2}+U(U-1)/2)\). Then, the analog equalizer matrix is computed by \(N_{rx}U\) multiplications, \(2N_{rx}U\) divisions and \(N_{rx}U\) square roots, which means that \(O(4N_{rx}U)\). Finally, in digital part of the equalizer it is used the linear MMSE, which is equivalent to iteration 1 of Algorithm 1, and then \(O(U^{3})\). The total complexity is \(O(U^{3}+(N_{rx}+0.5)U^{2}+(4N_{rx}-0.5)U)\), where for \(N_{rx}\gg 0.5\), it is approximately \(O((U^{2}N_{rx}^{-1}+U+4)N_{rx}U)\). Hence, the algorithm of [14] is approximately \(i_{\max}N_{cl}N_{ray}U/(U^{2}N_{rx}^{-1}+U+4)\) times faster than Algorithm 1.
## 1 Introduction
The GHSL is a framework of data and tools that was developed from EO, census data, and volunteered geographic information that produces global maps of built-up areas, resident population, and settlement typologies for four epochs (1975, 1990, 2000, and 2015). GHSL layers have global coverage and they are released as open and free data. GHSL data and derived scientific information to inform policy decisions were released in 2016 at the Habitat III conference with the aim of supporting the 2030 Development Agenda and its thematic agreements (Sendai Framework for Disaster Risk Reduction, United Nations Framework Convention on Climate Change, and the New Urban Agenda). GHSL information is particularly salient in the contexts of disaster risk reduction, urbanization, and human settlement dynamics, where fine scale information on the presence of people and built-up areas are of high importance [15]. GHSL data served to quantify the process of urbanization [16], observe population density [17], over 40 years of human settlements development [18], and the exposure to natural hazards [19]. In this paper, the GHSL contributes to estimating SDG 11.3.1, which aims to measure the \"ratio of land consumption rate to population growth rate\". This indicator requires data on the spatial extent of the settlement and its population. Furthermore, information is needed with a fine scale level of detail, and it must be consistent across the world and over time in order to provide a comprehensive global overview. The article focuses on the Tier II nature of SDG 11.3.1, for which an agreed methodology exists. However, it is not implemented, as the necessary data are not yet established. With the use of GHSL data, we provide evidence to suggest that SDG 11.3.1 could be raised from its Tier II classification. First, we present the principles of the GHSL and its data layers; second, we explain how this information is used to feed the SDG 11.3.1 formulation. The results of data analysis present the process of spatial expansion and demographic change in urban centers across the globe between 1990 and 2015, the Land Use Efficiency (LUE) value of urban centers (LUE value or LUE is used in this research to indicate the _ratio of land consumption rate to population growth rate -LCRPGR_, as per the United Nations SDG 11.3.1 Metadata), and the change in built-up areas per capita by region of the world.
# Training a Minesweeper Agent Using a Convolutional Neural Network
## 2 Algorithmic Principles
### Training a Minesweeper Agent with DQN  
By designing an appropriate reward mechanism, the agent's goal can be formulated as maximizing the cumulative reward in a game round. Define the function \(Q^{*}(s,a)\) as the maximum expected cumulative reward the agent can achieve by taking action \(a\) in state \(s\):  
\[Q^{*}(s,a)=\max_{\pi}E[R_{t}|s_{t}=s,a_{t}=a,\pi\,] \tag{1}\]  
where \(\pi\) represents the policy for selecting action \(a\) in state \(s\), \(R_{t}\) is the cumulative reward starting from time \(t\), and \(E\) denotes the mathematical expectation. The \(Q^{*}(s,a)\) function reflects the quality of taking action \(a\) in state \(s\), and is therefore referred to as the optimal Q-function. Since this method uses a deep neural network to map states to actions, the network is called the Q-network, which is the basis for the name \"Deep Q Network\". The Q-network is the Minesweeper agent that needs to be trained. Based on the agent's goal and the definition of the optimal Q-function, the optimal policy \(\pi^{*}(s)\) is  
\[\pi^{*}(s)=\operatorname*{argmax}_{a}Q^{*}(s,a) \tag{2}\]  
Therefore, the key to finding the optimal policy is accurately estimating the optimal Q-function. A commonly used method for this is estimation via the Bellman equation:\[Q^{*}(s,a)=E\bigg{[}r+\gamma\underset{a^{\prime}}{\max}Q_{\theta}\big{(}s^{\prime},a ^{\prime}\big{)}|s,a\bigg{]} \tag{3}\]  
where \(s^{\prime}\) represents the next state, \(r\) is the current reward, and \(\gamma\) is the discount factor. The Bellman equation is a recursive estimation method, which states that the maximum cumulative reward for a given state is the sum of the immediate reward and the maximum cumulative reward for the next state, a process that aligns with intuition. The DQN model uses the Q-network to approximate the optimal Q-function, i.e., \(Q_{\theta}(s,a)\approx Q^{*}(s,a)\). By having the Q-network repeatedly play Minesweeper and explore different states, the action-value function \(Q_{\theta}(s,a)\) for all actions is computed through forward propagation for the current state \(s\), and the action with the highest function value is selected. After executing this action, a reward and the next state \(s\prime\) are obtained. The Q-network's parameters are updated using back-propagation to minimize the loss function, with the learning objective being to reduce this loss. After sufficient training, the Q-network will be able to effectively perform the Minesweeper task. The loss function is defined as the squared error (abbreviated as SE):  
\[L_{SE}(\theta)=\bigg{[}r+\gamma\underset{a^{\prime}}{\max}Q_{\theta}\big{(}s ^{\prime},a^{\prime}\big{)}-Q_{\theta}(s,a)\bigg{]}^{2} \tag{4}\]  
To enhance training stability, the practical DQN algorithm typically incorporates mechanisms such as experience replay with random sampling and periodic target network updates. Additionally, to balance exploration and exploitation, the widely used \(\varepsilon\)-greedy strategy is employed in reinforcement learning. The DQN algorithm used in this paper is shown in Algorithm 1.
## 6 Application of the Optimization Techniques: Routing Protocols
### Attribute-based or Data-centric Routing Protocols
#### 6.1.2 Directed Diffusion  
As a data-centric protocol, applications in sensors label the data using attribute-value pairs. A node that demands the data generates a request where an interest is specified according to the attribute-value based scheme defined by the application. The sink usually injects an interest in the network for each application task [10]. The nodes update an internal interest cache with the interest messages received. The nodes also keep a data cache where the recent data messages are stored. This structure helps on determining the data rate. On receiving this message, the nodes establish a reply link to the originator of the interest. This link is called gradient and it is characterized by the data rate, duration and expiration time. Additionally, the node activates its sensors to collect the intended data. The reception of an interest message makes the node establish multiple gradients (or first hop in a route) to the sink. In order to identify the optimum gradient, positive and negative reinforcements are used. There algorithm works with two types of gradients: exploratory and data gradients. Exploratory gradients are intended for route set-up and repair whereas data gradients are used for sending real data.