esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 4 BlueROV2 Case
### Digital Twin Setup
\(z_{g}\) as the vertical distance of the gravity center from the CO.  
\(I_{XX},\ I_{YY}\), and \(I_{ZZ}\) as the moments of inertia for each axis. \(X_{i\mu},\ Y_{i\
u},\ Z_{i\mu},\ K_{\bar{p}^{\prime}},\ M_{\bar{q}^{\prime}},\ N_{\bar{r}}\) as the added mass coefficients for each degree of freedom (DOF). \[M_{RB}=\left\{ \begin{array}{ccccc}m&0&0&0&mz_{g}&0\\ 0&m&0&-mz_{g}&0&0\\ 0&0&m&0&0&0\\ 0&-mz_{g}&0&I_{XX}&0&0\\ mz_{g}&0&0&0&I_{YY}&0\\ 0&0&0&0&0&I_{ZZ}\end{array} \right\} \tag{3}\]  
\[M_{A}=-diag\Big{\{}X_{i\mu},Y_{\hat{\
u}},\ Z_{i\hat{\
u}},K_{\hat{p}^{\prime }},M_{\hat{q}^{\prime}},N_{\hat{r}}\Big{\}} \tag{4}\]  
The Coriolis matrixes can be expressed as:  
\[C_{RB}(\
u)=\left\{ \begin{array}{ccccc}0&0&0&mw&-mv\\ 0&0&0&-mw&0&mu\\ 0&0&0&mv&-mu&0\\ 0&mw&-mv&0&I_{z}r&-I_{y}q\\ -mw&0&mu&-I_{z}r&0&I_{x}p\\ mv&-mu&0&I_{y}q&-I_{x}p&0\end{array} \right\} \tag{5}\]  
\[C_{A}(v_{w})=\left\{ \begin{array}{ccccc}0&0&0&0&-Z_{\hat{w}}w&Y_{\hat{v}}v \\ 0&0&0&Z_{\hat{w}}w&0&-X_{\hat{u}}u\\ 0&0&0&-Y_{\hat{v}}v&X_{\hat{u}}u&0\\ 0&-Z_{\hat{w}}w&Y_{\hat{v}}v&0&-N_{\hat{r}}r&M_{\hat{q}}q\\ Z_{\hat{w}}w&0&-X_{\hat{u}}u&N_{\hat{r}}r&0&-K_{\hat{p}}p\\ -Y_{\hat{v}}v&X_{\hat{u}}u&0&-M_{\hat{q}}q&K_{\hat{p}}p&0\end{array} \right\} \tag{6}\]  
The damping matrix is the combined expression of a lot of phenomena related to the interaction between the vehicle and the surrounding fluid, mostly dependent on the movement speed. It can be expressed as a second-degree polynomial of the movement speed for each axis, which implies two sets of parameters: one of them linearly coupled with the speed and one with the square of the speed. \[\mathrm{D}(v_{w})=D_{L}+D_{Q}\times|v_{w}| \tag{7}\]  
\[D_{L}=diag\big{\{}X_{u},Y_{v},\ Z_{w},K_{p},M_{q},N_{r}\big{\}} \tag{8}\]  
\[D_{Q}=diag\Big{\{}X_{u|u|},Y_{v|v|},\ Z_{w|w|},K_{p|v|},M_{q|q|},N_{r|r|} \Big{\}} \tag{9}\]  
The restoring forces vector \(\mathrm{g}(\eta)\) depends on \(\eta\), the vector with the position and orientation of the CO relative to the navigation frame. For submersibles where the CO and center of gravity (CG) are in the same vertical line, they can be expressed using \(W\) and \(B\) as the weight and buoyancy of the vehicle, respectively, and \(z_{b}\) as the vertical distance of the buoyancy center from the CO as:  
\[g(\eta)= \begin{cases}\begin{array}{c}(W-B)\sin\theta\\ -(W-B)\cos\theta\sin\varphi\\ -(W-B)\cos\theta\cos\varphi\\ -z_{b}\ B\cos\theta\sin\varphi\\ -z_{b}\ B\sin\theta\\ 0\end{array}\end{cases}  \tag{10}\]  
Regarding the \(\tau\), the external forces and moments are primarily generated by the vehicle's thrusters and can be estimated from their position, orientation, and thrust. These values have been included in the model, and the thrust generated by each propeller is calculated in real time using the thruster curve. \[\tau= \begin{cases}X\\ Y\\ Z\\ K\\ M\\ N\end{cases}  \tag{11}\]
## 4 Constant Fuel Utilization
Voltage transients (dynamics) due to hydrogen concentration changes are an important issue in fuel cell operation and are investigated in References [92; 93], where the constant fuel utilization mode is used to implement a current-based fuel control strategy (changing the fuel flow rate in proportion to the cell current) to keep voltage transients under check. Constant fuel utilization is also shown to be beneficial for control purposes of SOFC systems in Reference [94], where it is reported that a suitably chosen constant fuel utilization operation leads to nearly maximum-efficiency performance for variable loads. Figure 3: Linear dependence of the three partial pressures on the input fuel flow rate (under the constant fuel utilization condition).
## 2 Background
### MRAM Technology
#### 2.3.2 Stt MRAM  
STT MRAM represents the second generation of MRAM. STT uses a current passing between the two ferromagnetic layers, separated by a nonmagnetic spacer, to exert a torque on the magnetization of each layer to flip the free layer [23; 25]. STT MRAM is capable of large TMRs, making it excellent for memory [26]. The main advantages of STT MRAM are that it has fast read/write functionality, is easier to scale, and has high reliability.  
STT MRAM represents the second generation of MRAM technology. In STT MRAM, a current passes between two ferromagnetic layers separated by a nonmagnetic spacer.  
This current exerts a torque on the magnetization of each layer, allowing the flipping of the free layer [23; 25].  
STT MRAM is known for its ability to achieve large TMRs, which makes it highly suitable for memory applications, as highlighted in [26]. One of the key advantages of STT MRAM is its fast read/write functionality, enabling quick data access and modification. Additionally, it offers ease of scalability, meaning it can be efficiently implemented with various memory architectures and sizes. Moreover, STT MRAM exhibits high reliability, ensuring data integrity and long-term operation.  
Overall, STT MRAM represents a significant advancement in MRAM technology, combining desirable characteristics, such as fast operation, scalability, and reliability, that make it a promising candidate for memory applications in various computing systems.
## 3 Results
### Effects of Impurities in CO\({}_{2}\) Streams on EOR Efficiency  
#### 3.2.1 Vertical Sweep Efficiency  
The decrease in vertical sweep efficiency due to impurities in CO\({}_{2}\) is explained by the viscosity of the oil. Figure 2 shows the distribution of oil viscosity when 0.22 PV of gas is injected, indicating the decrease in vertical sweep efficiency due to impurities. In Case 2, impurities in the CO\({}_{2}\) stream cause the injected gas to move forward faster since it hinders the role of CO\({}_{2}\) in lowering the viscosity of oil. The blue swept area on the front represents the area where the injected fluid is in maximum contact with the oil, indicating the gas front. The minimum viscosity in Case 1 is 0.48 mPa\(\cdot\)s and in Case 2 is 0.59 mPa\(\cdot\)s. Therefore, the addition of 15% impurities generated a 22.9% decrease in the oil viscosity, indicating less gas acts as a solvent to decrease the oil viscosity after multiple contacts due to the impurities in CO\({}_{2}\).  
In horizontal gas flow through the cross-sectional 2D model, the vertical sweep efficiency was significantly reduced due to gravity segregation. The steeper the slope of the gas front of the sweep area, the more the injected gas contacts with the reservoir fluid. Since the difference in density and gravity between the injected fluid and reservoir fluid causes an override, the injected gas moves to the upper layer, reducing the vertical sweep efficiency. The change in vertical sweep efficiency due to the addition of impurities can be quantified by the viscous gravitational number (\(N_{gv}\)), which includes the terms of flooding velocity, density, and viscosity of fluid. The viscous gravitational can be expressed by [39]:  
\[N_{gv}=\frac{\text{time for horizontal flow}}{\text{time for vertical flow}}=\frac{k_{v}k_{rs}\Delta ggcos\alpha AL}{q_{s}\mu_{s}h} \tag{3}\]  
where \(k_{v}\) is the vertical permeability, \(k_{rs}\) is the relative permeability, \(\Delta\rho\) is the density difference between injected and reservoir fluids, \(g\) is the gravitational acceleration, \(A\) is the cross-sectional area, \(L\) is the length of the reservoir, \(q_{s}\) is the flow rate of the solvent, \(\mu_{s}\) is the solvent viscosity, and \(h\) is the height of the reservoir. The range that determines whether gravity or viscous forces dominate is as follows [39]:  
\[\begin{array}{l}N_{gv}<\ 0.1:\text{ Viscous forces dominate;}\\ 0.1<\ N_{gv}<\ 10:\text{ Intermediate;}\\ N_{gv}>\ 10:\text{ Gravity dominates.}\end{array}\]  
The decrease in vertical sweep efficiency was quantified from the viscous gravity number, as shown in Table 10. In Case 2, the viscous gravity number increased by as much as 59.6% by adding impurities into the CO\({}_{2}\). Since a larger viscous gravity number prevents the lower layer of the reservoir from being sufficiently swept, impurities in the injecting CO\({}_{2}\) stream lower the vertical sweep efficiency.  
Figure 2: Oil viscosity distribution when 0.22 PV of gas is injected: (**a**) Case 1, (**b**) Case 2.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 5 Conclusions and Future Work  
In this work, massive MIMO transmissions for 5G were studied using experimental measurements in an indoor environment at 38 GHz and 65 GHz. The performance of the HBF and SFBC + HBF techniques were compared in terms of throughput, assuming the same transmitting power. The ratio of SNR\({}_{\rm 38 GHz}\)/SNR\({}_{\rm 65 GHz}\) reached 20 dB in the best case. Therefore, at 38 GHz, with the HBF algorithm and with a BW of 400 MHz, a maximum throughput of 4.30 Gbit/s up to 2.15 m was achieved, while at 65 GHz, the maximum throughput was 0.68 Gbit/s up to 0.95 m.  
It is better to work with aggregated bandwidths than with a single larger one. Results show that at 38 GHz, when the HBF algorithm was applied, along with aggregated bandwidths (4 \(\times\) 100 MHz), a maximum throughput of 4.30 Gbit/s up to 4.1 m was achieved. However, with a single bandwidth of 400 MHz, 4.30 Gbit/s was achieved at up to 2.15 m. At 38 GHz, there was no benefit in using the SFBC + HBF algorithm, but at 65 GHz, as the applied SNR was very low, this was a reasonable solution and it improved the communication distance.  
Future work will include increasing the number of antenna elements, which will allow increasing the number of streams and, therefore, the throughput. In addition, multi-user simulations, and other scenarios and algorithms, will be included in future work.  
Conceptualization, C.S.-B. and J.-M.M.-G.-P.; methodology, J.-M.M.-G.-P.; software, C.S.-B. and M.-T.M.-I; validation, C.S.-B.; formal analysis, C.S.-B. and M.-T.M.-I; investigation, C.S.-B. and M.-T.M.-I; resources, J.-M.M.-G.-P.; data curation, M.-T.M.-I; writing--original draft preparation, [PERSON]-B.; writing--review and editing, [PERSON]-B. and M.-T.M.-I; visualization, [PERSON]-B. and M.-T.M.-I; supervision, J.-M.M.-G.-P.; project administration, J.-M.M.-G.-P.; funding acquisition, J.-M.M.-G.-P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion of the Spanish Government under the National Project PID2019-107885 GB-C33 and funded by MCIN/AEI/10.13039/501100011033.  
The authors declare no conflict of interest.
## 5 Experimental Results
### Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**without Clustering**} & \multicolumn{4}{c}{**with Clustering**} \\ \hline  & & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Average** \\ \hline BP & 18.22 & 14.73 & 34.41 & 8.24 & 19.13 \\ \hline RBF & 46.93 & 12.78 & 34.68 & 16.28 & 21.25 \\ \hline ELM & 30.53 & 21.16 & 43.30 & 11.85 & 25.44 \\ \hline LSTM & 147.43 & 26.23 & 32.21 & 26.00 & 27.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: MAE of solar radiation forecast errors in winter. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**without Clustering**} & \multicolumn{4}{c}{**with Clustering**} \\ \hline  & & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Average** \\ \hline BP & 47.11 & 39.61 & 64.62 & 27.65 & 43.96 \\ \hline RBF & 105.10 & 45.05 & 62.96 & 9.81 & 39.27 \\ \hline ELM & 64.77 & 66.09 & 13.92 & 73.06 & 51.02 \\ \hline LSTM & 195.22 & 73.35 & 76.66 & 122.46 & 90.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE of solar radiation forecast errors in autumn. Due to the added cluster analysis, the four data sets of spring, summer, autumn and winter are divided into three clusters with different irradiation intensities. At the same time, the similarity of samples in each cluster is relatively high in general. It can be seen from the aforementioned experimental results that the clustering strategy does improve the prediction accuracy. This observation can be explained by the reasoning that data preprocessing and sample partitions have a favorable impact on short-term solar radiation prediction. Ultimately, through analyzing the prediction results of various artificial neural networks, the proposed methods have indeed improved the prediction accuracy on the whole. These experimental results mean that the hybrid models of machine learning have advantages to some extent.
# Activated Carbons as Effective Adsorbents of Non-Steroidal Anti-Inflammatory Drugs
## 2 Materials and Methods  
### Chemicals  
Sodium salts of therapeutic agents, such as ibuprofen sodium (IBP; Fluka, Mumbai, India), diclofenac sodium (D; Sigma, Beijing, China), and naproxen sodium (NPX, Sigma, Beijing, China), were used as adsorbates for testing. The basic physicochemical properties of these substances are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbate**} & **Chemical** & **Molar Mass** & **Solubility in** & **Ionization** & **Melting Point** & **Boiling** & **Chemical** \\  & **Formula** & **[g/mol]** & **H\({}_{2}\)O [mg/L at 25 \({}^{\circ}\)C]** & **Constant pKa** & [\({}^{\circ}\)C] & **Ratio Point** & **Safety** \\ \hline \hline \multirow{4}{*}{Ibuprofen Sodium} & \multirow{4}{*}{\(H_{\text{G}}\)} & \multirow{4}{*}{228.26} & \multirow{4}{*}{100} & \multirow{4}{*}{4.91} & \multirow{4}{*}{75–77} & \multirow{4}{*}{319.6} & Corrosive \\  & & & & & & &  
GAC 1240W activated carbon (GAC), purchased from Norit (Utrecht, The Netherlands), was selected as the adsorbent for the experiment. The reason for this is that this adsorbent is widely used in water treatment plants [8; 18; 19]. Its textural properties were studied using low-temperature nitrogen adsorption/desorption isotherms. Based on the results of these tests, the basic structure parameters were estimated--specific surface area \(S_{\text{BET}}=900\) m\({}^{2}\)g\({}^{-1}\), external surface area \(S_{\text{EXT}}=523\) m\({}^{2}\)g\({}^{-1}\), total pore volume \(V_{\text{t}}=0.52\) cm\({}^{3}\)g\({}^{-1}\) including micropore volume \(V_{\text{mic}}=0.20\) cm\({}^{3}\)g\({}^{-1}\) using the t-plot method and the average hydraulic pore size \(d_{\text{h}}=2.31\) nm from the relationship \(d_{\text{h}}=4V/S\). Potentiometric titration was used to estimate the acid-base properties of GAC. Based on the obtained test results, the zero charge point pH\({}_{\text{PZC}}\) was determined to be 10.8 [8].
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.1 \(\rho(\text{SO}_{2})\) Product Data  
We collected SO\({}_{2}\) data on a 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) (55.55 km \(\times\) 69.38 km) grid from the monthly time series dataset of the Modern-Era Retrospective Analysis for Research (MERRA)-2 satellite, known as M2 TMNXAER (V5.12.4), which is available online ([[https://disc.gsfc.nasa.gov/datasets/M2](https://disc.gsfc.nasa.gov/datasets/M2) TMNXAER_5.12.4/summary]([https://disc.gsfc.nasa.gov/datasets/M2](https://disc.gsfc.nasa.gov/datasets/M2) TMNXAER_5.12.4/summary), accessed on 8 July 2021). This dataset is the latest atmospheric reanalysis system in modern satellites built by the Global Modeling and Assimilation Office, and is generated by the Goddard Earth Observing System Atmospheric Data Assimilation System version 5.12.4. For more detailed information on the MERRA-2 dataset, please refer to [PERSON] et al. [45] and [PERSON] et al. [46] Considering the bias between observations and background predictions, the three-dimensional variation (3 DVAR) algorithm was used for bias correction, thus generating a time series dataset from 1980 to the present, including the target variable (SO\({}_{2}\) surface mass concentration) in this study [47]. To date, SO\({}_{2}\) data from the MERRA-2 dataset have been widely used in global studies, such as for monitoring the temporal and spatial variability of SO\({}_{2}\)[48], and the association of SO\({}_{2}\) with environmental factors and transport [17]. \(\rho\)(SO\({}_{2}\)) was classified into five classes based on the average value of \(\rho\)(SO\({}_{2}\)) from 2001 to 2020, namely very low (2-5.2), low (5.2-8.4), moderate (8.4-11.6), high (11.6-14.8), and very high (14.8-18). In order to make a correlation with vegetation characteristics values, the remote sensing images were resampled using ArcGIS 10.6 software with a resolution of 500 m.
###### Abstract  
Transdermal delivery of insulin is a great challenge due to its poor permeability through the skin. The aim of the current investigation was to evaluate the prospective of insulin loaded niosome emulgel as a noninvasive delivery system for its transdermal therapy. A 2\({}^{3}\) full-factorial design was used to optimize the insulin niosome emulgel by assessing the effect of independent variables (concentration of paraffin oil, Tween 80 and sodium carboxymethyl cellulose) on dependent variables (in vitro release, viscosity and in vitro permeation). The physical characteristics of the prepared formulations were carried out by determining viscosity, particle size, entrapment efficiency, drug loading, drug release and kinetics. In vitro permeation studies were carried out using rat skin membrane. Hypoglycemic activity of prepared formulations was assessed in diabetic-induced rats. It was observed that the independent variables influenced the dependent variables. A significant difference (\(p<0.05\)) in viscosity was noticed between the prepared gels, which in turn influenced the insulin release. The order of permeation is: insulin niosome emulgel \(>\) insulin niosome gel \(>\) insulin gel \(>\) insulin niosomes \(>\) insulin solution. The enhancement in transdermal flux in insulin niosome emulgel was 10-fold higher than the control (insulin solution). In vivo data significantly demonstrated reduction (\(p<0.05\)) of plasma glucose level (at six hours) by insulin niosome emulgel than other formulations tested. The results suggest that the developed insulin niosome emulgel could be an efficient carrier for the transdermal delivery of insulin.  
niosome; optimization; emulgel; insulin; skin permeation +
Footnote †: journal: Applied Physics Letters  
1
# Application of Bonded-Block Models to Rock Failure Analysis
## 4 Analysis of Underground Structures  
### Example Problem  
The case of a tunnel in a rock mass was selected to exemplify the application of bonded-block models at the scale of field problems. Because the aim is discuss the essential features and difficulties of using these models, a number of simplifying assumptions were made regarding unrelated issues. In particular, rock mass jointing was not explicitly represented, and thus, the only discontinuities in the numerical model are fictitious joints that are potentially involved in the fracture process. Therefore, it is possible to simplify the geometry by considering a vertical symmetry plane, as shown in Figure 3. The tunnel diameter is 8 m, and the domain around the opening, with an area of \(8\times 16\) m, is represented by the bonded-block assembly of rigid blocks in a Voronoi pattern with an average dimension of 0.3 m, also shown in detail in the figure. An alternative triangular-block pattern was also considered, as described in the following sections. The outer domain, with the overall dimensions \(20\times 40\) m, is assumed to remain elastic and is represented by deformable blocks with an internal triangular finite element mesh with the target elastic properties. These outer blocks are connected to each other and to the bonded blocks by numerical joints, which are prescribed to remain elastic. The nonlinear behavior is thus confined to the rectangular domain of randomly generated inner blocks surrounding the tunnel. Boundary conditions enforce the symmetry plane on the left-hand side, preventing horizontal displacements, while the lower boundary is fixed in the vertical direction. At the top boundary, vertical stress is applied, representing the overburden load. On the right-hand side, horizontal stress is applied, with a value equal to a given fraction of the vertical stress at the tunnel axis depth and a gradient in the vertical direction to account for the self-weight of the material. The main focus of the analysis is to simulate the effects of high compressive hoop stresses at the tunnel sidewalls in order to examine the development of spalling failure modes.  
Figure 3: (**a**) Domain for tunnel analysis; (**b**) randomly generated blocks around tunnel.
## 3 Results and Discussion
The results of statistical tests are presented in Table 6. As can be seen, the values of the determination coefficient R\({}^{2}\) ranged from 0.9662 to 0.9895 for mass gain, from 0.7042 to 0.9881 for dry matter loss, from 0.9732 to 0.9913 for water gain, from 0.9770 to 0.9929 for moisture content increase, and from 0.9374 to 0.9847 for volume increase. The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Extreme** & \(A_{1}\) & \(A_{2}\) \\  & **Solid-to-Liquid Ratio** & & \\ \hline Mass grain & \(M_{w}\)\({}^{(1)}\)/\(m_{d}\)\({}^{(2)}\)\(\rightarrow\)\(\infty\) & 1.4239 & 0.0183 1/g \\ \hline Dry matter loss & \(m_{d}/M_{w}\)\(\rightarrow\)\(\infty\) & 1.5546 & - \\  & \(M_{w}/m_{d}\)\(\rightarrow\)\(\infty\) & - & 0.1305 1/g \\ \hline Water gain & \(M_{w}/m_{d}\)\(\rightarrow\)\(\infty\) & 0.6382 & 0.0171 1/g \\ \hline Moisture content & \(M_{w}/m_{d}\)\(\rightarrow\)\(\infty\) & 6.8909 & 0.0617 1/d.b. \\ increase & \(M_{w}/m_{d}\)\(\rightarrow\)\(\infty\) & 0.6257 & 0.0205 1/cm\({}^{3}\) \\ \hline \hline \end{tabular} \({}^{(1)}\)\(M_{w}\) is the mass of rehydrating water at the beginning of the rehydration. \({}^{(2)}\)\(m_{d}\) is the mass of dried red beets at the beginning of the rehydration.  
\end{table} 
Table 5: Values of the Peleg model parameters at extreme values of the solid-to-liquid ratio according to Equations (5)-(7). values of the root mean square error (RMSE) were within 1.0075 to 2.2519 g for mass gain, 0.1681 to 0.3707 g for dry matter loss, 1.2462 to 2.3298 g for water gain, 0.3295-0.6443 d.b. for moisture content increase, and 1.3127 to 2.6472 cm\({}^{3}\) for volume increase. The obtained results allowed for the statement that the Peleg model with its parameters \(A_{1}\) and \(A_{2}\) expressed with Equations (5)-(7) and constants from Table 3 can be applied to simulate dried red beets rehydration curves (mass gain, dry matter loss, water gain, moisture content increase, and volume increase) at all considered values of the solid-to-liquid ratio. Although the determination coefficient values at the solid-to-liquid ratios of 1:3 and 1:1 for dry matter loss were not high enough (0.7042 and 0.7253, respectively), the root mean square error values were low enough (0.3616 g and 0.1681 g, respectively).