esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.2.2 Drugs Affecting Blood Pressure  
[PERSON] et al. performed carvedilol (medication used to treat high blood pressure) precipitation by combined antisolvent-cooling crystallization in the presence of PVP with different molecular weights [70]. A small amount of PVP adsorbed on the crystal surface affected positively the crystal form and induced excellent flowability of the products. [PERSON] et al. investigated the amorphous-to-crystalline transformation of nitrendipine (drug used in the treatment of blood hypertension) in a medium containing PVA and PEG [71].  
[PERSON] et al. proposed a method for producing crystals of poorly water-soluble active pharmaceutical ingredients embedded in a polymer matrix, i.e., crosslinked alginate [72]. They presented a material-based approach to enable control over the crystal size and morphology of a model hydrophobic drug--fenofibrate (drug decreasing the level of cholesterol). The used method avoided excessive energy input and high saturation levels that could lead to amorphous forms or the appearance of metastable crystal phases. Moreover, it enabled control over drug loading. Authors claimed that due to the biofriendly nature of alginate hydrogels, adjustable submicrometer crystal size and high loading capacity of the drug, the proposed composite material could potentially serve as a final drug formulation which could be produced in a continuous manufacturing process. In another paper [PERSON] et al. investigated the usage of biocompatible alginate hydrogels as a material for crystallizing and encapsulating paracetamol and fenofibrate as model active pharmaceutical ingredients [73]. They found that the incorporation of emulsion droplets inside hydrogels enables the high loading of the hydrophobic pharmaceutical ingredients leveraging the high solubility of hydrophobic drug in the dispersed emulsion droplets. [PERSON] et al. described a new bottom-up approach for producing and formulating nanocrystals of fenofibrate using core--shell composite hydrogel beads composed of an alginate core with a PVA shell [74]. The method enables the controlled crystallization and formulation of hydrophobic crystalline nanomaterials that can replace energy intensive top-down processes in traditional manufacturing.  
[PERSON] and [PERSON] investigated the influence of polyethylenimine (PEI) on the particle morphology of crystals during the crystallization of eprosartan (a drug used in high blood pressure treatment) [75]. The presence of PEI during the crystallization induced the self-assembly of small primary crystals into larger aggregates without changing the crystal polymorph, which improved the properties of drug crystals.
# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
###### Abstract  
Centralized/Cloud Radio Access Networks (C-RAN) are deployed in converged fixed-mobile networks to exploit the flexibility coming from joint application of Network Function Virtualization (NFV) and Software Defined Networking (SDN). In this context, optical links connecting C-RAN nodes, possibly based on the Ethernet standards, may carry traffic with different requirements in terms of latency and throughput. This paper considers the problem of traffic aggregation on C-RAN optical Ethernet links with latency control for fronthaul traffic and throughput capability for backhaul traffic. Integrated hybrid network technique is applied to show how time transparency can be enforced for Ethernet encapsulated Common Public Radio Interface (CPRI) traffic while allowing statistical multiplexing of backhaul traffic. Simulation results show the effectiveness of segmentation of backhaul traffic to allow exploitation of the available bandwidth even with high capacity CPRI options.  
hybrid optical networks; Ethernet; fronthaul; backhaul; C-RAN; convergence; CPRI 2018 82279; doi:10.3390/app812279
## 5 Discussion  
The environmental Life Cycle Assessment (LCA) of a cosmetic cream containing gold nanoparticles (AuNPs) offers valuable insights into the environmental impacts associated with incorporating nanomaterials into consumer products. The study identifies the synthesis of AuNPs and electricity consumption as significant contributors to environmental impacts, particularly in categories such as freshwater eutrophication, resource depletion and climate change. These findings align with other studies demonstrating that nanoparticle production processes are resource-intensive and have considerable environmental impacts. For instance, a study on the environmental impacts of using gold nanoparticles in cosmetic highlights the importance of evaluating the entire life cycle to identify critical environmental hotspots [38].  
The contribution of AuNP synthesis to freshwater eutrophication, as noted in the study, is consistent with concerns in the literature regarding the environmental effects of nanomaterials. A review of the impacts of nanoparticles on humans and the environment emphasizes their potential to cause ecological harm, including detrimental effects on aquatic ecosystems [39]. The study's findings on resource depletion due to the use of gold in nanoparticle synthesis reflect broader concerns about the sustainability of utilizing precious metals in consumer products. Evaluating the environmental impact of gold nanoparticles in cosmetics underscores the need for comprehensive life cycle assessments to understand the resource implications [38].  
While the inclusion of AuNPs in cosmetics offers potential benefits, it raises questions about environmental sustainability. The study's findings support the need for the cosmetics industry to adopt more environmentally friendly production practices and consider the environmental consequences of using nanomaterials carefully. This perspective is echoed in discussions about the environmental impacts of nanomaterials, which call for thorough life cycle assessments [40].  
The study employs the EF 3.1 methodology for impact assessment, which aligns with European regulatory frameworks. This approach ensures relevance to regional policies but may limit global applicability. By comparison, other studies have applied different methodologies, such as the SPOT methodology, which integrates strategic life cycle assessment for product sustainability [41].  
The study acknowledges uncertainties in certain impact categories, emphasizing the need for improved inventory data and standardized methodologies, a common challenge in LCA studies. To mitigate environmental burdens, future research should explore eco-friendly or bio-based nanoparticles, to reduce dependence on precious metals. Expanding the life cycle scope to include downstream impacts, such as consumer use and disposal, will be essential for a more comprehensive environmental assessment.
## 5 Conclusions  
1. The magnesium matrix composites reinforced by stainless steel (Fe-18 Cr-9 Ni), titanium alloy (Ti-6 Al-4V), and aluminum alloy (Al-5 Mg-3 Zn), were prepared by the pressure infiltration technology. The reinforcements of the composites are interwoven with the matrix and have an integrated interface.  
2. The grain size and distribution in the as-cast magnesium matrix composites have the same characteristics, and their sizes are mainly concentrated at 200-300 \(\upmu\)m, and the size distribution is random and does not obey normal distribution. However, during the extrusion process, the grain size of magnesium matrix composite is more refined than that of the as-cast state, because of the dynamic recrystallization and the superposition of shear strain, and its size is mostly concentrated at 10-30 \(\upmu\)m. In addition, the grain size distributions of MISC and MIAC follow the normal distribution and the Wien distribution, respectively.  
3. Different reinforcement materials result in different microstructure characterization of magnesium matrix during extrusion. The average grain sizes of magnesium matrix composites with the reinforcements of MISC, MITC, and MIAC are 14, 20, and 24 \(\upmu\)m, respectively.  
4. Compared with AZ31, the mechanical properties of metal composites interpenetrated by metal reinforcement have a significant improvement. Different MIMCs with different reinforcements exhibit different mechanical properties. Among the three composite materials, the tensile strength, yield strength, and elongation of MISC are 355 MPa, 241 MPa, and 13%, with an increase of 47.9%, 60.7%, and 85.7%, respectively; while for MITC, they are 340 MPa, 220 MPa, and 12%, with an increase of 41.7%, 46.7%, and 71.4%, respectively; and for MIAC, are 280 MPa, 175 MPa, and 8%, an increase of 16.7%, 16.7%, and 14.3%, respectively.
## 4 Discussion
### Future Works  
Future research could increase the numbers and diversify the types of vibration sensors [60] in different parts of the HBM at the construction site, as well as on auxiliary equipment. This could provide more comprehensive data and potentially improve the accuracy of state recognition.  
In addition to acceleration sensors, other sensor types such as cameras [61] and jacking cylinder stroke and pressure sensors could be integrated to complement the vibration signals. These would provide a more comprehensive dataset for the ML model, thereby improving recognition accuracy. This could also help to identify the more nuanced operating states of the HBM, such as formwork opening/closing and rebar tying states.  
Improved kNN classifiers [62, 63] or other types of classifiers such as ANN and SVM could be considered. The integration of deep learning algorithms [64], which can provide higher classification accuracy, is also a promising direction.  
The applicability and improvements of the method can be extended to HBMs with different dynamical systems and structural types, and the analysis of the effect of larger wind loads on HBM vibration can also be carried out to increase the versatility of the method. These initiatives could lead to significant advances in the monitoring of HBM working states.
# Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures
## 5 Analysis and Comparison Based on Application Scenarios
### Differences in Applying Game Theory in Different Networks  
Different types of networks have different topologies and therefore face different problems. The three networks discussed in this paper are members of complex networks, and their network characteristics determine the different applications of game theory in networks. The characteristics of centralized data management in a centralized network cause problems such as being prone to network congestion and slow processing speed. The application of game theory in centralized networks can ensure convenient data processing in the network while improving the efficiency of the network (that is, maximizing the network utility in terms of transmission overhead and effectiveness). The combination of trust and game theory can better ensure the security and reliability of information storage. In a distributed network (such as a typical wireless sensor network), a large number of nodes are randomly distributed in the network, and the structure of the network is complex and changeable. The application of game theory and trust in distributed networks can help the network form reliable transmission links. While ensuring the reliability of the network, it also ensures the security of information transmission, resists malicious attacks, and maximizes the utility of the network. In social networks, the relationships between players are complex and dynamically changing, especially the trust relationships between players. The introduction of game theory and trust helps players in social networks to better cooperation and maximize network utility.
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 3 N-Element Array Design  
Given the specification in Table 1, we now calculate the number of elements needed to meet those specifications. The total field of an array is a vector superposition of the fields  
Figure 3: Linear phased antenna array.  
Figure 2: Simulated 2-element linear array radiation patterns.  
radiated by the individual elements. To provide a directive pattern, it is necessary that the partial fields (generated by the individual elements) interfere constructively in the desired direction and interfere destructively in the remaining space.  
The estimated far-field patterns for different N values from 1 to 6 are calculated using MATLAB [8] and are plotted in Figure 4. From Figure 4, it can be seen that at least 3 elements would be needed to get gain to meet the specification; however, more elements are needed to meet the beamwidth specification. So, we have selected N = 5 and the element spacing is chosen to be \(\lambda/2\) to meet the gain specification of 10 dBi and 21\({}^{\circ}\) as the 3 dB beamwidth. These are now arranged along a straight line to from our ULA (Figure 5). The values of progressive phase \(\beta\) for different beam scan angles is calculated using Equation (14). The progressive phase shift \(\beta\) needed to steer the beam to angle \(\theta\) is shown in Table 2.  
Figure 4: Calculated radiation patterns for an array with 1 to 6 elements.  
Figure 5: A 5-element dipole ULA: (**a**) Fabricated prototype and (**b**) Return Loss measured.
## 2 Modeling of the Unified Analysis Model of Vibro-acoustic Characteristics
### Stress-Strain and Displacement Relations
Integrating the stresses on the plane yields the forces and moments experienced by the laminated plate and the laminated curved beam. From one layer of the laminated plate and laminated curved beam to the other layer, by integrating the thickness, we can obtain:  
\[ \begin{bmatrix}N_{r}^{\mathrm{P}}\\ N_{\theta}^{\mathrm{P}}\\ N_{r}^{\mathrm{P}}\\ M_{r}^{\mathrm{P}}\\ M_{\theta}^{\mathrm{P}}\\ M_{r\theta}^{\mathrm{P}}\end{bmatrix} = \begin{bmatrix}A_{11}&A_{12}&A_{16}&B _{11}&B_{12}&B_{16}\\ A_{12}&A_{22}&A_{26}&B_{12}&B_{22}&B_{26}\\ A_{16}&A_{26}&A_{66}&B_{16}&B_{26}&B_{66}\\ B_{11}&B_{12}&B_{16}&D_{11}&D_{12}&D_{16}\\ B_{12}&B_{22}&B_{26}&D_{12}&D_{22}&D_{26}\\ B_{16}&B_{26}&B_{16}&D_{26}&D_{66}\end{bmatrix}  \tag{21}\]  
\[ \begin{bmatrix}Q_{\theta}^{\mathrm{P}}\\ Q_{r}^{\mathrm{P}}\end{bmatrix} =\overline{\kappa_{s}} \begin{bmatrix}A_{44}&A_{45} \\ A_{45}&A_{55}\end{bmatrix}\begin{bmatrix}\gamma_{\theta x}^{\mathrm{p}0}\\ \gamma_{\theta z}^{\mathrm{p}0}\end{bmatrix}  \tag{22}\]  
\[ \begin{bmatrix}N_{\theta}^{\mathrm{b}n}\\ N_{\mathrm{ex}}^{\mathrm{b}n}\\ M_{\theta}^{\mathrm{b}n}\\ M_{\theta x}^{\mathrm{b}n}\end{bmatrix} = \begin{bmatrix}A_{22}&A_{26}&B_{22}&B_{2 6}\\ A_{26}&A_{66}&B_{26}&B_{66}\\ B_{22}&B_{26}&D_{22}&D_{26}\\ B_{26}&B_{66}&D_{26}&D_{66}\end{bmatrix}  \tag{23}\]  
\[ \begin{bmatrix}Q_{x}^{\mathrm{b}n}\\ Q_{\theta}^{\mathrm{b}n}\end{bmatrix} =\overline{\kappa_{s}} \begin{bmatrix}A_{44}&A_{45} \\ A_{45}&A_{55}\end{bmatrix}\begin{bmatrix}\gamma_{xz}^{\mathrm{p}0}\\ \gamma_{\theta z}^{\mathrm{p}0}\end{bmatrix}  \tag{24}\]  
\[ \begin{array}{c}A_{ij}=\sum\limits_{k=1}^{N_{t}}\overline{Q_{ij}^{k}}(Z_{k+ 1}-Z_{k})\ \ B_{ij}=\frac{1}{2}\sum\limits_{k=1}^{N_{t}}\overline{Q_{ij}^{k}}(Z_{k+1}^{2}-Z _{k}^{2})\\ D_{ij}=\frac{1}{2}\sum\limits_{k=1}^{N_{t}}\overline{Q_{ij}^{k}}(Z_{k+1}^{3}-Z _{k}^{3})\end{array}  \tag{25}\]In which \(N_{r}^{\rm P}\), \(N_{\theta}^{\rm P}\), and \(N_{r\theta}^{\rm P}\) represent the resultant force in the plane of the laminated plate; \(M_{r}^{\rm P}\), \(M_{\theta}^{\rm P}\), and \(M_{r\theta}^{\rm P}\) represent the bending and torsional moments in the plane of the laminated plate; and \(Q_{\theta}^{\rm P}\) and \(Q_{r}^{\rm P}\) are the resultant force of the horizontal shear force of the laminated plate. \(N_{\theta}^{\rm bm}\) and \(N_{\theta x}^{\rm bm}\) represent the resultant force in the plane of the laminated curved beam, \(M_{\theta}^{\rm bm}\) and \(M_{\theta x}^{\rm bm}\) represent the bending and torsional moments in the plane of the laminated curved beam, and \(Q_{x}^{\rm bm}\) and \(Q_{\theta}^{\rm bm}\) are the resultant force of the horizontal shear force in the plane of the laminated curved beam. \(\overline{\kappa_{s}}\) is the shear correction coefficient, \(N_{L}\) indicates the layer number of the laminated plate or laminated curved beam, \(Z_{k}\) is the thickness coordinate value of the bottom surface of the \(k\) layer, and \(Z_{k+1}\) is the thickness coordinate value of the upper surface.
## 4 Experimental Methods and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**0 cm**} & \multicolumn{4}{c}{**5 cm**} & \multicolumn{4}{c}{**10 cm**} \\ \cline{2-13}  & \multicolumn{4}{c}{**0 deg**} & \multicolumn{4}{c}{**0 deg**} & \multicolumn{4}{c}{**0 deg**} & \multicolumn{4}{c}{**0 deg**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} \\ \cline{2-13}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline X & 0.332 & 0.152 & 0.127 & 0.081 & 0.413 & 0.294 & 0.135 & 0.105 & 0.226 & 0.232 & 0.217 & 0.163 \\ Y & 0.13 & 0.105 & 0.032 & 0.025 & 0.402 & 0.264 & 0.179 & 0.126 & 0.527 & 0.296 & 0.233 & 0.179 \\ Z & 0.163 & 0.122 & 0.095 & 0.074 & 0.361 & 0.245 & 0.19 & 0.123 & 0.475 & 0.269 & 0.247 & 0.18 \\ d & 0.413 & 0.18 & 0.178 & 0.084 & 0.755 & 0.331 & 0.32 & 0.162 & 0.785 & 0.389 & 0.314 & 0.207 \\ \hline \multicolumn{13}{c}{} \\ \hline  & \multicolumn{4}{c}{**0 cm**} & \multicolumn{4}{c}{**0 cm**} & \multicolumn{4}{c}{**0 cm**} & \multicolumn{4}{c}{**0 cm**} \\ \cline{2-13}  & \multicolumn{4}{c}{**30 deg**} & \multicolumn{4}{c}{**45 deg**} & \multicolumn{4}{c}{**60 deg**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} \\ \cline{2-13}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline X & 0.258 & 0.128 & 0.11 & 0.008 & 0.169 & 0.176 & 0.126 & 0.104 & 0.435 & 0.338 & 0.331 & 0.167 \\ Y & 0.325 & 0.423 & 0.198 & 0.169 & 0.207 & 0.17 & 0.112 & 0.096 & 0.197 & 0.175 & 0.09 Figure 7 presents the standard deviations of pose estimation taken over 60 s hovering periods using the drone-mounted camera. Red lines indicate the estimation results using a single marker, while blue lines represent the estimation results using multiple markers. Figure 7a shows standard deviations in position estimates along each axis and Euclidean distances, while Figure 7b presents standard deviations in roll, pitch, and yaw angles.
## 4 Tire Force Distribution and Actuator Control
### Cost Function Design
#### 4.1.2 Energy-Saving Distribution Method of Longitudinal Tire Force  
The drive efficiency diagram of in-wheel motor is shown in Figure 7. There is a relatively high efficiency area in the operation of the motor. At low rotate speed and small torque, the efficiency of motor is low. At the same rotate speed, the efficiency of motor increases first and then decreases with the increase of motor torque in the low and medium rotate speed range. Therefore, the distribution of longitudinal tire force should make the motor work in the high efficiency area as far as possible, which can effectively achieve the goal of energy saving.  
Since the motor efficiency is a function of the motor torque and rotate speed, that is:  
\[\eta=\eta(T_{i},n) \tag{69}\]  
Defining the cost function \(J_{2}\):  
\[\text{min}J_{2}=\sum_{i=1}^{4}\frac{1}{\eta(F_{xi}\cdot r,n_{i})} \tag{70}\]  
Figure 7: Drive efficiency diagram of in wheel motor.  
s.1.  
\[\left\{\begin{array}{l}F_{x1}+F_{x2}+F_{x3}+F_{x4}=\sum F_{x}\\ n_{i}\leq n\quad\text{max}\\ \\ F_{xi}\leq\min\Bigl{[}\sqrt{\mu^{2}F_{2 zi}-F^{2}y_{i}},\frac{T_{\max}(n)}{r} \Bigr{]}\quad(i=1,2,3,4)\\ F_{x1}+F_{x3}=F_{x2}+F_{x4}\\ |F_{x1}-F_{x3}|\frac{B}{2}\leq\Bigl{|}\delta_{\text{max}}\cdot\frac{\partial M_ {\text{w}}}{\partial\delta}\Bigr{|}\\ |F_{x2}-F_{x4}|\frac{B}{2}\leq\Bigl{|}\delta_{\text{max}}\cdot\frac{\partial M _{\text{w}}}{\partial\delta}\Bigr{|}\end{array}\right. \tag{71}\]  
Constraint 1 means that the sum of the longitudinal tire force of four wheels is the resultant longitudinal force required; the constraint 2 means that the motor speed \(n_{i}\) is not higher than the maximum speed \(n_{max}\) that can be provided; the constraint 3 means that the longitudinal force of the wheels is less than the maximum longitudinal force provided by the in-wheel motor, and the adhesion condition is also satisfied; the constraint 4 means that the longitudinal force of the wheels does not generate additional yaw moment to the vehicle; in constraint 5 and 6, where \(\frac{\partial M_{\text{w}}}{\partial\delta}\) is deformation steering coefficient, \(M_{\text{w}}\) is the torque acting on the suspension, \(\delta_{amax}\) is maximum allowable steering angle due to deformation steering. In theory, as long as constraint 4 is satisfied, the vehicle can keep running in a straight line, but the torque of the left and right wheels on the same axis is not distributed. The torque difference between the left and right wheels could generate additional yawing moment to the suspension, which could result in the deformation steering effect. Therefore, the torque difference should not be too large.
## A Novel Contrast Enhancement Technique on Palm Bone Images  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 1**  
1 Department of Information Networking Technology, Hsiuping University of Science and Technology, No. 11, Gongye Rd, Dali Dist., Taichung 412, Taiwan;  
E-Mails: [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (W.Y.)  
2 Department of Information Management, Hsiuping University of Science and Technology, No. 11, Gongye Rd, Dali Dist., Taichung 412, Taiwan  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] ; Tel.: +886-9-6321-0280; Fax: +886-4-2496-1178.  
_Received: 1 July 2014; in revised form: 17 August2014 / Accepted: 22 August 2014 / Published: 5 September 2014_  
**Abstract:** Contrast enhancement plays a fundamental role in image processing. Many histogram-based techniques are widely used for contrast enhancement of given images, due to their simple function and effectiveness. However, the conventional histogram equalization (HE) methods result in excessive contrast enhancement, which causes natural looking and satisfactory results for a variety of low contrast images. To solve such problems, a novel multi-histogram equalization technique is proposed to enhance the contrast of the palm bone X-ray radiographs in this paper. For images, the mean-variance analysis method is employed to partition the histogram of the original grey scale image into multiple sub-histograms. These histograms are independently equalized. By using this mean-variance partition method, a proposed multi-histogram equalization technique is employed to achieve the contrast enhancement of the palm bone X-ray radiographs. Experimental results show that the multi-histogram equalization technique achieves a lower average absolute mean brightness error (AMBE) value. The multi-histogram equalization technique simultaneously preserved the mean brightness and enhanced the local contrast of the original image.  
**Keywords:** contrast enhancement; multi-histogram equalization; X-ray radiograph; mean-variance analysis method; absolute mean brightness error (AMBE)