esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Chitosanan-Based Delivery Systems Loaded**  
**with Gibbenclamide and Lipoic Acid: Formulation, Characterization, and Kinetic Release Studies**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4, [PERSON] 5, [PERSON] 1 and [PERSON] 4,* 1**  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Received: 29 September 2020; Accepted: 22 October 2020; Published: 26 October 2020  
**Featuresd Application: This article presents the development and characterization of new chitosan based polymeric systems containing Gibbenclamide and lipoic acid for diabetes mellitus therapy. The systems were designed as multi-target systems, aiming for optimal glycemic control, as well as the reactive oxygen species generated by hyperglycemia.**  
**Abstract:** Glibenclamide and lipoic acid are two drugs frequently recommended for the management of diabetes mellitus, and so, the development of a new formulation containing both substances has a great benefit in terms of efficiency and compliance, acting also as a multi-target drug system. Accordingly, the aim of this study was the formulation and physicochemical characterization of new polymeric systems based on chitosan (CS) in whose matrix were encapsulated glibenclamide (Gly) and lipoic acid (LA). The polymeric systems were prepared as microparticles (CS-Gly, CS-LA, and CS-Gly-LA) through ionic gelation method, using pentasodium tripolyphosphate (TPP) as crosslinking agent. The polymeric systems obtained were characterized in terms of particle size and morphology, IR spectroscopy, entrapment efficiency and drug loading, swelling degree, and therelease of the active substances from the chitosan matrix. The polymeric systems obtained were stable systems; the presence of glibenclamide and lipoic acid into the polymer matrix were proved by IR spectroscopy. The entrapment efficiency was 94.66% for Gly and 39.68% for LA. The developed polymeric systems proved a favorable swelling degree and drug release profile, the percentage of release being 88.68% for LA and 75.17% for Gly from CS-Gly-LA systems.  
**Keywords:** chitosan; glibenclamide; lipoic acid; drug delivery systems
# Low Discrepancy Sparse Phased Array Antennas
###### Abstract  
Sparse arrays have grating lobes in the far field pattern due to the large spacing of elements residing in a rectangular or triangular grid. Random element spacing removes the grating lobes but produces large variations in element density across the aperture. In fact, some areas are so dense that the elements overlap. This paper introduces a low discrepancy sequence (LDS) for generating the element locations in sparse planar arrays without grating lobes. This nonrandom alternative finds an element layout that reduces the grating lobes while keeping the elements far enough apart for practical construction. Our studies consider uniform sparse LDS arrays with 86% less elements than a fully populated array, and numerical results are presented that show these sampling techniques are capable of completely removing the grating lobes of sparse arrays. We present the mathematical formulation for implementing an LDS generated element lattice for sparse planar arrays, and present numerical results on their performance. Multiple array configurations are studied, and we show that these LDS techniques are not impacted by the type/shape of the planar array. Moreover, in comparison between the LDS techniques, we show that the Poisson disk sampling technique outperforms all other approaches and is the recommended LDS technique for sparse arrays.  
Keywords:phased array; sensor array; sparse array; nonuniform array; planar array; random array; low discrepancy sequence +
Footnote †: journal: Sensors
## 2 Methodology
### Dispersion Curves  
When measuring over a long-range distance with ultrasound guided waves, the wave modes are excited at a low-frequency range between 20 kHz to 100 kHz [18]. Guided waves exist multi-modal orders and dispersed when they propagate along a cylindrical structure [19].  
There are three main families of waves in a pipe, which are longitudinal, torsional and flexural. In these three families of wave modes, only longitudinal and torsional are axisymmetric. [PERSON] and [PERSON] established a nomenclature of \(X(m,n)\) to describe these [20]. The \(X\) represents a letter (L, T, or F, for longitudinal, torsional or flexural, respectively) that relates to characteristics of the individual wave modes. The \(m\) is a positive integer used to identify harmonic changes in displacement around a pipe or the circumference of a pipe-like structure, and \(n\) is an index indicating how it occurs. In field tests, the actual results are normally different from the ideal. Due to the imperfections of equipment and the mode conversion caused by the non-axisymmetric structure on the internal material or geometry, the actual received signal will not be a pure axisymmetric wave but a mixed wave [21]. In addition to the multi-modal nature of guided wave propagation may also exhibit amplitude dispersion, but this situation will lead the energy diffusion signal in space and time at a period of propagation [22]. Unlike other waves, the T(0,1) mode is non-dispersive over the entire frequency range, so the T(0,1) mode is often used for detection in practical applications [21; 23].  
The effective characteristics of displacement shapes are generated using the semi-analytical finite element method [24]. Figure 1 presents the group velocity dispersion curves for an 8-inch (219.1 mm outer diameter) and schedule 40 (8.18 mm wall thickness) steel pipes. Longitudinal axisymmetric modes of L(0,1) and L(0,2) are also a zeroth circumferential order with first and second mode shapes, respectively. Non-axisymmetric flexural modes (up to \(m=1\)) of F(1,1), F(1,2) and F(1,3) are a first circumferential order with a first, a second and a third mode shapes, respectively.
## 4 Conclusions
### Nomenclature  
\begin{tabular}{l l l} Variable & Description & Value \\ Vehicle dynamics parameters & & \\ \(M_{vch}\) & Effective vehicle mass & 2450 [kg] \\ \(v_{veh}\) & Vehicle speed & [m/s] \\ \(T_{pwt}\) & Powertrain torque & [Nm] \\ \(T_{brake}\) & Brake torque & [Nm] \\ \(R_{wh}\) & Wheel radius & 0.36 [m] \\ \(C_{roll}\) & Rolling resistance & \\ \(g\) & Gravitational acceleration & 9.8 [m/s\({}^{2}\)] \\ \(\delta\) & Road grade angle & [deg] \\ \(\rho_{air}\) & Air density & 1.25 [kg/m\({}^{3}\)] \\ \(A_{f}\) & Vehicle frontal area & \\ \(C_{d}\) & Aerodynamic drag coefficient & \\ \(\omega_{wh}\) & Wheel speed & [rad/s] \\ Transmission parameters & & \\ \(\omega_{t}\) & Transmission speed & [rad/s] \\ \(g_{t}\) & Transmission gear ratio & \\ \(T_{t}\) & Transmission torque & [Nm] \\ \(\eta_{t}\) & Transmission efficiency & \\ \multicolumn{3}{l}{Engine parameters} & \\ \(m_{f}\) & Fuel flow rate & [kg/s] \\ \(f_{FE}\) & Engine FE map & \\ \(\omega_{ice}\) & Engine speed & [rad/s] \\ \(T_{ice}\) & Engine torque & [Nm] \\ Electric motor parameters & & \\ \(P_{elec}\) & Electric motor power & [kW] \\ \(\eta_{rem}\) & REM efficiency & \\ \(\omega_{rem}\) & REM speed & [rad/s] \\ \(T_{rem}\) & REM torque & [Nm] \\ Battery parameters & & \\ \(Q_{nom}\) & Nominal charge capacity & 11.8 [Ah] \\ \(\eta_{coul}\) & Coulombic efficiency & \\ \(P_{batt}\) & Battery power & [\(kW\)] \\ \(V_{L}\) & Load voltage & [\(V\)] \\ \(V_{OC}\) & Open circuit voltage & [\(V\)] \\ \(R_{0}\) & Battery internal resistance & [\(\Omega\)] \\ \(SOC\) & State of Charge & [\%] \\ Optimal Control & & \\ \(x\) & States & \\ \(u\) & Inputs & \\ \((.)_{i}\) & Quantity at time-instance \(i\) & \\ \((.)_{n}\) & Quantity at distance-step \(n\) & \\ \((.)^{*}\) & Optimal solution & \\ \(N_{h}\) & Look-ahead horizon (number of distance steps) & \\ \(G(x)\) & Terminal cost & \\ \(\Delta s_{i}\) & Distance discretization step size & [m] \\ \(\beta\) & Trade-off parameter between FE and travel time & \\ A-ECMS parameters & & \\ \(s\) & Equivalence factor & \\ \(Q_{lhv}\) & Fuel lower heating value & 43.2 [MJ/kg] \\ EDM parameters & & \\ \(a\), \(b\), \(c_{1}\), \(\delta\), \(\theta_{offset}\) & EDM parameters & \\ \end{tabular}
###### Abstract  
Massive machine-type communication (mMTC) is investigated as one of three typical scenes of the 5 th-generation (5G) network. In this paper, we propose a 5G-enabled internet of things (IoT) in which some enhanced mobile broadband devices transmit video stream to a centralized controller and some mMTC devices exchange short packet data with adjacent devices via D2D communication to promote inter-device cooperation. Since massive MTC devices have data transmission requirements in 5G-enabled IoT with limited spectrum resources, the subcarrier allocation problem is investigated to maximize the connectivity of mMTC devices subject to the quality of service (QoS) requirement of enhanced Mobile Broadband (eMBB) devices and mMTC devices. To solve the formulated mixed-integer non-linear programming (MINLP) problem, which is NP-hard, an interference-aware subcarrier allocation algorithm for mMTC communication (IASA) is developed to maximize the number of active mMTC devices. Finally, the performance of the proposed algorithm is evaluated by simulation. Numerical results demonstrate that the proposed algorithm outperforms the three traditional benchmark methods, which significantly improves the utilization of the uplink spectrum. This indicates that the proposed IASA algorithm provides a better solution for IoT application.  
5G; internet of things; mMTC; eMBB Article
# Machine Vision--Moving from Industry 4.0 to Industry 5.0
## 5 Discussion
### Contribution of Machine Vision in Industry 5.0
#### 5.3.2 Supporting Technologies  
All aforementioned foreseen contributions of machine vision in Industry 5.0 are based on the capabilities of current developments in both hardware and software accompanying machine vision systems. Key hardware developments include camera technology, AI, and chipsets. The latter developments increase the typical machine vision benefits of cost saving, competitiveness increase, and product quality improvements. Notable technologies that are expected to affect the landscape of machine vision applications in Industry 5.0 towards boosting its adaptation are listed as follows. (1) _High-resolution cameras_ in some cases are superior to the human eye, at 45 megapixels and more, and are able to track objects of high speed without distortions. (2) Newly introduced _event-based vision sensors_ (EVSs) are capable of high-speed data output with low latency by restraining the data output to changes of luminance from each pixel, along with data on coordinates and time. Focusing on movements, they can be applied in a wide variety of fields, ideally for gesture tracking, e.g., hand movements, promoting the machine-human collaboration as defined by Industry 5.0. (3) Newly introduced _sensor technologies_ that can enhance the capabilities of conventional cameras include point cloud machine vision and hyperspectral machine vision, to provide details such as depth and what cannot be seen in the optical spectrum. (4) _AI_ is a key enabling technology towards efficient decision making. Nowadays, generative AI is at the forefront, presenting advancements in natural language processing (NPL) that have not yet been fully employed in the industrial sector. Large language models (LLMs) are expected to provide new opportunities in Industry 5.0, by taking advantage of large volumes of data to cover contextual needs within the industry, e.g., for customer service, provision of guidance to workers, supervision, and problem solving by domain-specific models, offering a powerful tool for human workers to elevate their knowledge. (5) Advanced powerful _chipsets_, e.g., the AM69A flagship AI vision processor from Texas Instruments, are able to control up to eight cameras.
## 3 Results
### DPPH Radical Scavenging Activity in SF and SSF Extracts in Different Solvents  
The IC\({}_{50}\) of DPPH radical scavenging activity ranged 5-69 \(\upmu\)g/\(\upmu\)L for SF and 8-26 \(\upmu\)L for SSF extracted in different solvents. The SF and SSF methanol extracts showed the highest activity, followed by ethanol and water extracts. The highest IC\({}_{50}\) value of activity for SF and SSF methanol extracts were 5.8 \(\upmu\)g/\(\upmu\)L and 8.4 \(\upmu\)g/\(\upmu\)L, respectively (Table 2). The IC\({}_{50}\) values of activity for SSF ethanol extract was 19.8 \(\upmu\)g/\(\upmu\)L, which was 1.76-fold higher than that for SF ethanol extract. In addition, the lowest activity was shown by water extracts, with SSF (IC\({}_{50}\) = 25.4 \(\upmu\)g/\(\upmu\)L) showing 2.7-fold higher activity than SF (IC\({}_{50}\) = 68.5 \(\upmu\)g/\(\upmu\)L).  
In agreement with the findings of the present study, [PERSON] et al. [53] reported that the DPPH radical scavenging activity for solvent-extracted _Sargasasum corrifolium_ and _Hypnea pannosa_ was in the order of methanol > ethanol > water. In another study by [PERSON] et al. [54], the DPPH radical scavenging activity of the red algae _Gracilaria_ sp. was high in 100% ethanol and methanol extracts at IC\({}_{50}\) = 13.60 \(\upmu\)g/mL and IC\({}_{50}\) = 38.03 \(\upmu\)g/mL, respectively, whereas the lowest activity was detected in water extract (IC\({}_{50}\) = 622.30 \(\upmu\)g/mL). [PERSON] et al. [55] found that the DPPH radical scavenging activity of the _H. elongate_ methanol extract at 100 \(\upmu\)g/mL according to the pretreatment method was 75% for the fresh sample and 52.5% for the sample steamed for 45 min, indicating the possibility that heat treatments, such as steaming, boiling, and microwaving, destroy the thermally unstable antioxidants. In contrast to these reports, the DPPH radical scavenging activity was higher in steamed  
Figure 1: Total polyphenol (**a**) and flavonoid (**b**) contents in ethanol, methanol, and water extracts of fresh and steamed _Sargasasum fusiforme_. SF: _S. fusiforme_ extract, SSF: Steamed _S. fusiforme_ extract, TAN: Tannic acid, QUE: Quercetin. The different lowercase letters represent significant differences between solvents within the same sample (\(p<0.05\)).  
SF than that of fresh SF in the present study, implying that conditions such as the steaming duration and extraction solvent influence the DPPH radical scavenging activity in SF and SSF. In this study, the DPPH radical scavenging activity was determined as IC\({}_{50}\) using a general method based on expressing the discoloration percentage of the solution after the reaction. It was reported that the initial absorbance of the colored radical is reduced when a reaction with a potential antioxidant occurs and a change of absorbance depends on the reaction time and conditions, reactivity, solvent used, and concentration of the tested phenol [56, 57]. Since a single point was measured here, it is necessary to analyze the DPPH radical scavenging activity of SF and SSF extracts by monitoring the entire reaction time.
# Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
## 3 Results and Analysis
### Discussion  
The identification of codes on trolleys takes place in a complex environment, primarily due to the presence of significant interference levels. We conducted an analysis of the primary factors that impact the performance of the proposed method within an empirical setting.
# Influence of Noble Metals on the Microstructure and Properties of Biodegradable Mg-Nd-Zr Alloy
It regulates renal, intestinal and skeletal homeostasis;
* Zirconium possesses low toxicity and acceptable biocompatibility;
* Rare earth elements have anticancer properties. It has been established that the most suitable alloying system is Mg-Zr-Nd, which ensures the non-toxicity of alloying elements and the possibility of structural strengthening of the alloy [24; 25]. The optimal chemical composition of a biodegradable Mg-based alloy has been determined as follows: 0.8-1.1 wt.% Zr, 2.4-2.8 wt.% Nd, 0.5-0.7 wt.% Zn. This composition ensures the following mechanical properties: UTS = 266-271 MPa; TE= 4.3-4.6%. After three months of immersion and biodegradation in artificial blood substitutes, the alloy retained mechanical properties close to the properties of bone tissue: UTS of 150 MPa and TE of 3.2% [26]. Installation of various fixing screws during osteosynthesis involves certain preparations. Using a drill and a guide, a canal is formed in the bone. In the formed channel, using a threaded tap, a thread is cut and a screw is fixed. Reliable fixation of bone tissue fragments without prior preparation with a tap is impossible because of the high possibility of damage to the implant. Thus, the preparatory formation of the canal and threading complicates the process of surgical intervention and lengthens the operation time. Therefore, an important task is to increase the mechanical properties of magnesium alloy to eliminate the specified preparatory operations. One of the methods for increasing the mechanical properties of an alloy is modification of the microstructure. There are a number of requirements for modifying elements, one of which is non-toxicity. Noble metals, particularly silver and gold, are promising modifiers for Mg-based alloys. Gold has been known since ancient times for its bioinertness towards living organisms [27]. Silver, according to many literary sources, has an antibacterial effect [28]. An analysis of the phase diagrams of binary systems of silver and gold with magnesium showed that they can form solid solutions, as well as new intermetallic phases. These phases have higher melting points compared to magnesium, therefore they act as additional crystallization centers during the initial period of crystallization, thus refining the grain and positively affecting the properties of the metal [29]. Therefore, the development of new Mg-based alloys modified with noble metals with an improved structure and increased mechanical properties is a challenging task of biomaterials science. In this study, the separate and combined effects of silver and gold on the structure formation and properties of a biodegradable Mg-based magnesium alloy were studied.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 2 Geometry of Stripmap SAR Observation
### Characteristics of Stripmap SAR  
The most conventional imaging geometry of a spaceborne SAR operating in stripmap mode is referred to as boresight, with the antenna beam point being perpendicular to the flight path [2], as shown in Figure 1. The antenna beam illuminates the Earth with a fixed look angle and remains unchanged relative to the satellite body. In addition, a spaceborne SAR is usually equipped with an electrically scanned or mechanically scanned antenna. When the electrically scanned antenna beam is squinted, it causes beam broadening, grating lobe effects, and quantization effects, which lead to a decrease in imaging performance. The mechanically scanned antenna beam points in the same direction as the normal direction of the antenna, which avoids the beam broadening effect and the grating effect [19]. In addition, most SAR processing algorithms set the ground target at the zero Doppler time; that is, the antenna direction is perpendicular to the satellite flight path [20]. Therefore, in this article, a spaceborne SAR operating in stripmap mode is modeled for observing the Earth in boresight geometry; i.e., the antenna beam point is located within the zero Doppler plane, as shown in Figure 1.
## 5 Proposed Method
### Characteristics of the Fuzzy Controller Used for the Robot  
Below the characteristics of the fuzzy controller used in this case study are presented. We chose to optimize a fuzzy controller of the path for a mobile unicycle robot to test the proposed method in a more complex problem. The controller is of Mamdani type, so that the input and output parameters are represented by linguistic variables. The input variables are the error in the linear velocity (\(\mathrm{e_{v}}\)) and angular velocity (\(\mathrm{e_{w}}\)), and the output variables are the right (T1) and left (T2) torques [9; 10], which are represented in Figure 11.  
The membership functions of the input variables are of trapezoidal type in the negative (N) and positive (P) linguistic terms and triangular for the linguistic term for zero (Z). For the output variables we have three membership functions, negative (N), zero (Z), and positive (P) of triangular type. In the range of values of [\(-\)1, 1] is used for each variable because we have normalized all the variables.  
Figure 11: (**a**) Lineal velocity error (\(\mathrm{e_{v}}\)); (**b**) angular velocity error (\(\mathrm{e_{w}}\)); (**c**) left torque (T1); (**d**) right torque (T2).  
The fuzzy rules that are used are shown below  
1. If (e\({}_{\rm v}\) is N) and (e\({}_{\rm w}\) is N) then (T1 is N) (T2 is N).
2. If (e\({}_{\rm v}\) is N) and (e\({}_{\rm w}\) is Z) then (T1 is N) (T2 is Z).
3. If (e\({}_{\rm v}\) is N) and (e\({}_{\rm w}\) is P) then (T1 is N) (T2 is P).
4. If (e\({}_{\rm v}\) is Z) and (e\({}_{\rm w}\) is N) then (T1 is Z) (T2 is N).
5. If (e\({}_{\rm v}\) is Z) and (e\({}_{\rm w}\) is Z) then (T1 is Z) (T2 is Z).
6. If (e\({}_{\rm v}\) is Z) and (e\({}_{\rm w}\) is P) then (T1 is Z) (T2 is P).
7. If (e\({}_{\rm v}\) is P) and (e\({}_{\rm w}\) is N) then (T1 is P) (T2 is N).
8. If (e\({}_{\rm v}\) is P) and (e\({}_{\rm w}\) is Z) then (T1 is P) (T2 is Z).
9. If (e\({}_{\rm v}\) is P) and (e\({}_{\rm w}\) is P) then (T1 is P) (T2 is P).  
The trajectory to be traversed by the robot is shown in Figure 12.  
The path given to the robot is constant (does not change), and in the course of iterations to the algorithm in combination of Type-2 FLS are responsible for adapting the parameters to direct the robot in that trajectory.