esa-sceva/satcom-chunk-collection huggingface dataset:
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
###### Abstract  
Poly(lactic acid)(PLA) is an aliphatic polyester that can be derived from natural and renewable resources. Owing to favorable features, such as biocompatibility, biodegradability, good thermal and mechanical performance, and processability, PLA has been considered as one of the most promising biopolymers for biomedical applications. Particularly, electrospun PLA nanofibers with distinguishing characteristics, such as similarity to the extracellular matrix, large specific surface area and high porosity with small pore size and tunable mechanical properties for diverse applications, have recently given rise to advanced spillovers in the medical area. A variety of PLA-based nanofibrous structures have been explored for biomedical purposes, such as wound dressing, drug delivery systems, and tissue engineering scaffolds. This review highlights the recent advances in electrospinning of PLA-based structures for biomedical applications. It also gives a comprehensive discussion about the promising approaches suggested for optimizing the electrospun PLA nanofibrous structures towards the design of specific medical devices with appropriate physical, mechanical and biological functions.  
Keywords:electrospinning; fiber morphology; scaffold; PLA stereocomplex; wound healing; tissue engineering; drug delivery; cardiovascular; neural; musculoskeletal +
Footnote †: journal: Applied Sciences
###### Abstract  
In order to enhance the reliability and anti-interference performance of wireless sensor network (WSN) data transmission, this paper designs the low power scheme of the WSN from the angle of error correction coding and proposes the hybrid check matrix construction (HC) algorithm based on iterative coding algorithms with linear coding complexity. The algorithm first improves the traditional iterative coding algorithm, making it suitable for non-binary low-density parity check (LDPC) codes. Then, the algorithm applies the backward iteration method to change the coding scheme and uses the check matrix construction method so that the progressive edge growth (PEG) algorithm has a lower triangular structure, which is used as a base matrix. An improved quasi-cyclic LDPC (QC-LDPC) algorithm, with a lower triangular structure, is used to generate a cyclic shift matrix and a finite domain coefficient matrix. Simultaneously, the short loop is eliminated and the optimal check matrix is selected for use in the channel coding process. The non-binary LDPC-CPM system is modeled and simulated. The simulation results show that the non-binary LDPC code constructed by the HC algorithm not only has linear coding and storage complexity but also has strong error correction capability. The design of non-binary LDPC-CPM system parameters can enhance the reliability, anti-jamming capability and reduce the complexity and reduce the complexity of the WSN.  
wireless sensor network (WSN); non-binary low-density parity check (LDPC); iterative coding algorithm; hybrid check matrix construction (HC) algorithm; LDPC-CPM system +
Footnote †: journal: Sensors
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Fieldwork Procedures  
#### 3.4.1 Flight Safety  
Flint Hall Farm is not subject to any restricted airspace which would impact on our drone operations. The site is not located within any Airport Control Zone (CTR), or Flight Restriction Zone (FRZ) associated with airports, airfields and London airspace. Indeed, the site is south of the Kenley FRZ, Kennel Farm Hang Glider Site and the London Biggin Hill Airport FRZ, and it is north of Gatwick Airport and the Redhill Aerodrome.  
Flight safety procedures outlined in the General Visual Line of Sight (GVC) training course are adhered to; this includes the provision of safety equipment, the completion of extensive risk assessments and conducting a pre-flight inspection of the site and equipment. Whilst the farm itself is private land, a minimum of 50 m separation distance is maintained between the drone and the M25 at all times to avoid impacting uninvolved people. Drone operations are conducted at relatively low altitudes, with a maximum flight altitude of 30 m. These measures ensure that, should the drone fail, it does not fall onto or near the M25. Furthermore, this should prevent drivers being distracted by the drone, which may otherwise create a secondary safety hazard.
## 3 NTNs for IIoT Applications
### Overview of Airborne Platforms for IIoT
Moreover, Facebook [56] has attempted to use solar-powered drones to serve Internet access to underdeveloped or sparsely populated countries. Additionally, Microsoft has deployed balloons equipped with cameras and sensors that wirelessly communicate with the Azure IoT platform in order to send the telemetry data to the field gateway through the constrained application protocol (CoAP) [57]. Qualcomm has also investigated the use of UAVs for fourth-generation (4G) communications and beyond [58]. Table 1 provides a perspective on available communication technologies that are capable of extending the coverage for the IIoT. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Terrential Networks**}} & \multicolumn{6}{c}{**Non-Terrestrial Networks**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Spacebeme**} & \multicolumn{3}{c}{**Alibome**} \\ \cline{2-10}  & \multicolumn{1}{c}{**Collular**} & \multicolumn{1}{c}{**Low-Power Wide Area**} & \multicolumn{1}{c}{**Geostationary Earth**} & \multicolumn{1}{c}{**Medium Earth Orbit**} & \multicolumn{1}{c}{**Low Earth Orbit**} & \multicolumn{1}{c}{**Cube-Sets**} & \multicolumn{1}{c}{**High-Altitude Platform**} & \multicolumn{1}{c}{**Low-Altitude Platform**} \\  & \multicolumn{1}{c}{**Network (LPWAN)**} & \multicolumn{1}{c}{**Ohtie (GEO)**} & \multicolumn{1}{c}{**(MED)**} & \multicolumn{1}{c}{**(LED)**} & \multicolumn{1}{c}{**(LED)**} & \multicolumn{1}{c}{**(PAR)**} & \multicolumn{1}{c}{**Pattern (LAP)**} \\ \hline
**Altitude (Max)** & - & 35,786 & 3000 & \textless{}000 & \textless{}000 & \textless{}1000 & 17.22 & \textless{}15 \\ \hline
**Modility** & Static & Static & Static to Earth & Medium & High & High & Quasi-Stationary & Varying Specels \\
**Round-Trip Time (ms)** & Lowest & Lowest & 500 & \textless{}100 & \textless{}100 & \textless{}10 & Low & Low \\ \hline
**Traspospipt** & Medium to High & Low & Low to High & Low to High & Low to High & Low to High & Low to High \\
**Role Coverage** & Urban and Suburban & Urban & Global & Global & Global & Global & Global & Global \\ \hline
**Propagation Loss** & Least & Least & Highest & High & Medium & Medium & Low & Low \\ \hline
**Network Complexity** & Complex & Complex & Simple & Medium & Complex & Complex & Medium & Medium \\ \hline
**Resources** & Rich & Rich & Limited & Limited & Limited & Limited & Limited & Limited \\ \hline
**Cost** & Medium & Medium to Low & High & High & High & Medium & Medium & Medium to Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of long-range terrestrial and non-terrestrial communication technologies.
## 3 The Proposed Methodology
### DNN Construction-Based Deep Learning
#### 3.1.1 Raw Data Pre-Processing  
First, significant and minor errors are pre-processed from the acquired vibration signals. In this study, a new method of overlapping sampling by sliding windows is proposed. The sampling point is 2048, the step length of deviation (S) is 28, the standard deviation normalises the data, and then the data is coded in one pass. With this method, the dataset gets N 620,544 data, the number of training samples is N-(L-S) and the dataset is divided into training set, verification set and test set in the ratio of 7:2:1 after processing.  
Second, training dataset \(X_{s}\) with many significant fault samples and training dataset \(X_{i}\) with only a few minor fault samples are obtained.  
To eliminate the negative effects caused by large differences in the dimensions of the characteristic variables. Standardisation of the data is particularly important. Min-max normalisation is used, which can be described as follows:  
\[x^{*}=\frac{x-min}{max-min} \tag{2}\]  
where \(x\), \(x^{*}\) is pre-conversion and converted value. \(max\), \(min\) is the maximum and minimum value of the original data. If the sample size is uneven, the model may perform well in the training dataset but not in the test dataset. For this purpose, Synthetic Minority Oversampling Technique (SMOTE) is used, which is a synthesis of some classes of oversampling techniques to make better use of the data [40,41]. Figure 2 shows the principle of SMOTE, by assuming that some classes are oversampled four times.  
Figure 1: Flowchart of the DTLCL approach.
## 4 Description of the Proposed Topology Construction
### Communication Abstraction of the Proposed Topology  
This section describes the communication abstraction for the proposed multiple chain oriented logical topology. Communication is fundamental to any logical topology of WSNs. The power of a WSN comes not from the capabilities of the individual devices, but from the collective capabilities achievable through wireless communication.  
Addressing the intricacies of wireless communication can be a difficult and error-prone task. This is especially true of WSN applications, where the number of participating devices can be large, the communication patterns can be complex, and the network links are ad-hoc and unreliable. However, the proposed topology restricts the communications of a sensor node to only its successive nodes in its chain. Thus, the burdens of multicasting and broadcasting are removed from the sensor nodes. The communication abstraction of the proposed topology can be divided into two parts, namely (i) communications within a chain; and (ii) communications between chains.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 5 Biomolecules Produced by Halophilic Fungi
### Fungal Halophilic Enzymes
#### 5.1.3 Other Classes of Enzymes  
Marine fungi _Aspergillus oryzae_ isolated from the brown alga _Dictyota dichotomous_ has been reported to produce an extracellular alginate lyase (EC 4.2.2). This enzyme specifically cleaves at the \(\beta\)-1.4 glycosidic linkages between polymers consisting of 1,4-linked \(\beta\)-D-mannuronic acid (M) and \(\alpha\)-L-galuronic acid (G) blocks of sodium alginate that produce homopolymeric blocks of polyM and polyG. Fungi were grown on medium containing 3% NaCl and 0,2% KCl, and the addition of NaCl (up to 150 mM) to assay medium increased the enzyme activity. The polyM and polyG blocks as obtained due to enzyme lyase from sodium alginate may have potential use for example in the biomedical industry [86].
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 5 Conclusions  
An in-house DSMC solver is validated and extended to incorporate the moving wall boundary condition. A series of 3D DSMC simulations were carried out to study  
Figure 14: Moment coefficients of the sphere at different rotation parameters. Symbols: DSMC results; line: free-molecular solution.  
Figure 13: Lift coefficients of the sphere at different rotation parameters. Symbols: DSMC results; line: free-molecular solution.  
the hypersonic rarefied flow past a rotating sphere. Flow phenomena and aerodynamic characteristics under the conditions of \(M_{\infty}=10\), \(Kt_{\infty}=0.51\), \(\alpha=1\), and \(T_{\rm w}/T_{\infty}=0.65\) are discussed in detail, with conclusions as follows:  
1. A lift, called the inverse Magnus force, is exerted on the rotating sphere. Its direction is opposite to that observed in the Magnus effect for continuum flows.
2. The rotation of the sphere leads to a distortion of the shock wave, an increase in the shock stand-off distance, and changes to other flow features, e.g., the position of the front stagnation point.
3. Velocity slip is very significant on the wall surface, and fascinating flow patterns can be formed on the sphere under different rotation rates.
4. The symmetry of the wall pressure distribution is not affected by the rotation of the sphere, and thus the pressure distribution makes no contribution to the formation of the inverse Magnus force.
5. The formation of the inverse Magnus force is attributed to skin friction, which is caused by the relative motion between the gas and the wall surface. The strong-friction region is on the upper half of the sphere's windward side.
6. The microscopic mechanism of the inverse Magnus effect is the transfer of tangential momentum from the incident gas molecules to the windward surface of the rotating sphere.
7. As the rotation rate increases, the lift and the moment on the sphere increase in proportion to the rotation rate, while the drag remains almost unchanged. In addition, the aerodynamic heating on the sphere is greatly enhanced with the increase in rotation rate.  
Future work includes extending the current DSMC database for more comprehensive investigations of the inverse Magnus effect. Parametric studies for key flow conditions (e.g., \(M_{\infty}\) and \(Kt_{\infty}\)) are necessary to reveal their influences on the inverse Magnus effect.  
Not applicable.  
The raw data supporting the conclusions of this article will be available by the authors on request.  
The authors acknowledge [PERSON] for his efforts in visualizing the numerical data.  
The authors declare no conflict of interest.
## 3 Controller Design  
### Disturbance Observer  
The proposed disturbance observer is used to estimate the disturbance \(d\), which is given by:  
\[\begin{split}\dot{\tilde{x}}&=h\big{(}x_{p}\big{)} \big{(}-A_{1}\dot{x}_{p}+Q_{in,d}+\hat{d}\big{)}+k_{1}\widetilde{x}_{o}\\ \dot{\tilde{d}}&=k_{2}\widetilde{x}_{o}\end{split} \tag{12}\]  
where \(\hat{x}\) and \(\hat{d}\) are the estimates of \(x\) and \(d\), respectively, \(\widetilde{x}_{o}\) is the estimation error, \(Q_{in,d}\) is the virtual control flow of the system, and \(k_{1}\) and \(k_{2}\) are control gains, respectively. The dynamics of \(\widetilde{x}_{o}\) can be given by:  
\[\dot{\widetilde{x}}_{o}=-k_{1}\widetilde{x}_{o}+h\big{(}x_{p}\big{)}\big{(} \widetilde{d}+\widetilde{Q}_{in}\big{)} \tag{13}\]  
where \(\widetilde{Q}_{in}\) is the control flow error, i.e., \(\widetilde{Q}_{in}=Q_{in}-Q_{in,d}\).  
**Theorem 1**.: _According to the disturbance observer (12), if \(d\) is bounded, the disturbance estimation error \(\widetilde{\text{as}}\) bounded and will converge exponentially to a sphere centered at the origin._  
Proof.: By combining (12) and (13), one obtains  
\[\begin{bmatrix}\dot{\widetilde{x}}_{o}\\ \dot{\tilde{d}}\end{bmatrix}=\begin{bmatrix}-k_{1}&h\big{(}x_{p}\big{)}\\ -k_{2}&0\end{bmatrix}\begin{bmatrix}\widetilde{x}_{o}\\ \dot{\tilde{d}}\end{bmatrix}+\begin{bmatrix}h\big{(}x_{p}\big{)}\widetilde{Q} _{in}\\ \dot{d}\end{bmatrix} \tag{14}\]  
If \(k_{1}>0\) and \(k_{2}>0\), then the disturbance observer is stable. Furthermore, if the actuator constraints are respected, then \(\widetilde{Q}_{in}\) is bounded; therefore, the disturbance estimation error \(\widetilde{d}\) is bounded. \(\square\)
## 3 Results and Discussion
### Adsorption Studies  
#### 3.2.1 Effect of pH  
The acidity of the environment is a critical parameter in the biosorption process. It influences the chemical state of the active centers of the biosorbent, the form and concentration of metal ions in the solution, the surface charge of the biosorbent, and the tendency to form complexes [29; 30]. The effect of initial pH on copper(II) ion adsorption was studied for the four plant materials over a pH range of 1.8-5.0 at a copper(II) ion concentration of 200 mg L\({}^{-1}\) (see Figure 5). For all samples, adsorption increased with pH, reaching a maximum around pH 4.0. Beyond this point, adsorption remains constant or even decreases slightly for the investigated materials. This decline is attributed to the formation of dissolved hydroxyl complexes of copper ions, which prevent further binding to the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Biosorbent** & \begin{tabular}{c} **BET Surface Area** \\ **m\({}^{2}\) g\({}^{-1}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Pore Volume** \\ **cm\({}^{3}\) g\({}^{-1}\)** \\ \end{tabular} &
\begin{tabular}{c} **Average Pore** \\ **Diameter, nm** \\ \end{tabular} & **pH** \\ \hline MO & 0.8 & 0.001 & 6 & 5.8 \\ M & 0.6 & 0.001 & 8 & 6.0 \\ LA & 0.8 & 0.003 & 14 & 5.8 \\ L & 1.0 & 0.002 & 6 & 6.3 \\ \hline \end{tabular}
\end{table}
Table 1: Texture characteristics and pH of water leachates for potential sorbents MO, M, LA, and L.  
Figure 4: SEM images of the surface of LA (**a**) and L (**b**) at 10 or 20.00 kV and SE detector.  
biosorbent's active sites. These findings are consistent with previous studies on copper biosorption [28; 31; 32].  
In a strongly acidic environment, the reduced adsorption of copper ions is due to competition with protons, which dominate the solution and interact with the biosorbent's active sites. At low pH, the biosorbent surface is saturated with H\({}^{+}\) ions, creating a repulsive force that hinders Cu(II) ions from approaching. As the pH increases, the concentration of H\({}^{+}\) ions decreases, and the surface charge of the biosorbents becomes more negative, facilitating Cu(II) ion adsorption. Furthermore, the degree of deprotonation of functional groups, such as carboxyl groups, increases, enhancing their ability to bind metal ions [33; 34; 35].  
At low pH, carboxyl groups remain undissociated and cannot bind copper ions, though they may participate in complexation reactions [36; 37]. As pH rises, the deprotonation of active centers improves, promoting attraction and coordination bond formation with copper ions, thereby increasing the biosorption rate. These observations suggest that copper ion binding to the surface of the four biosorbents primarily occurs through an ion-exchange mechanism [28; 32]. All further experiments were conducted at a pH of 4.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 1 Introduction  
Telecommunication network infrastructures can be described by three main network segments, i.e., core or backbone, metro/regional, and access networks, as depicted Figure 1. Long-distance communications with high aggregate traffic are attained with core and metro networks, while access networks entail service provider to end-user fiber-optic telecommunications technology. These systems are subject to significant growth in capacity requirements with operators looking for feasible cost-effective solutions. The potentiation of cost-effective solutions can be reached with the improvement of current network technologies, as they become widely used, e.g., coherent systems in future optical access networks [1, 2].  
Photonic integrated circuits (PIC) are considered an evolving technology offering a sustainable (efficient and cost-effective) alternative to data transmission interfaces [4]. The implementation of integrated photonics can thus enable savings and new functionalities, increasing the transmission capacity [5]. In the access network context, PIC can offer compelling performance improvements compared to traditional bulk technology [6], like bi-directional optical sub assembly (BOSA). Upgrades in terms of weight and volume, reduced power consumption, high mechanical and thermal stability, and assembly simplification for high subsystem integration are expected under PIC solutions [6; 7; 8; 9].  
By combining the need of providing enhanced passive optical network (PON) technologies and taking advantage of photonic integrated solutions benefits, a comprehensive description of PIC design challenges for the optical network unit (ONU) side of next-generation PON is delivered in this study (see optical access network branch in Figure 1). The paper is organized into four additional sections. The historical PIC evolution addressing current developments and expectations are described in Section 2. Section 3 addresses the PON standards and next-generation PON timeline. The implementation of PIC solutions for PON is introduced in Section 4, discussing the technology impact on reach (Section 4.1), the adopted band and wavelength selection criteria (Section 4.2), photonic packaging (Section 4.3), and control complexity, power dissipation and form factor issues (Section 4.4). The study is concluded in Section 5.
## 3 Voltage Transfer Gain Calculation
### Voltage Transfer Gain in DCM  
\[\left.\begin{array}{l}\frac{1}{Ts}(\int_{0}^{DTs}(2 Vg)dt+\int_{D}^{Ts}(Vs+ Vc_{1}-Vc_{2})dt=0\\ \frac{1}{Ts}(\int_{0}^{DTs}(Vc_{2}+Vc_{3})dt+\int_{0}^{DTs}(Vc_{4})dt+\\ \int_{D}^{DTs}(Vc_{4}-V_{o})dt=0\end{array}\right\} \tag{16}\]  
\[\left.\begin{array}{l}\frac{Vc_{2}}{Vg}=\frac{2}{(1-D)}\end{array}\right\} \tag{17}\]\[D1=\frac{4 Vg(1+D)}{Vo(1-D)} \tag{18}\]  
Equations (1) and (4) lay the groundwork, and applying the volt-second balance of Equation (7) to L\({}_{1}\), L\({}_{2}\), L\({}_{3}\), and L\({}_{4}\) leads to the derivation of Equation (16). Solving this equation produces the results presented in Equations (17) and (18). Notably, Equation (16) can be solved by leveraging the fact that the average Vc\({}_{1}\) equals Vg, as indicated in Equation (12). The parameter (D1), denoting the discharging time of L\({}_{3}\) and L\({}_{4}\), can be determined from Equation (18) after solving Equation (16). Equations (19) and (20) provide the average inductor current in L\({}_{3}\) and L\({}_{4}\), respectively.  
\[<I3>=\frac{2 VgD(D+D1)}{fsL_{3}(1-D)} \tag{19}\]  
\[<I4>=\frac{2 VgD(D+D1)}{fsL_{3}(1-D)} \tag{20}\]  
\[I_{o}=\frac{(Vc_{2}+Vc_{3})DD1}{2 fzL_{3}} \tag{21}\]  
\[Mdc(DCM)=\frac{8 VgDRL(1+D)}{fsL_{3}V_{o}(1-D)^{2}} \tag{22}\]  
\[Mdc(DCM)=\frac{4\sqrt{D(1+D)}}{(1-D)\sqrt{K}} \tag{23}\]  
\[k_{crit}=\frac{D(1-D)^{2}}{(D+1)} \tag{24}\]  
\[Kcrit=\left\{\begin{array}{l}if\;\text{Kcrit}>\;\text{(K)}\;\text{The Proposed Converter work in DCM}\\ if\;\text{Kcrit}<\text{(K)}\;\text{The Proposed Converter work in CCM}\end{array}\right. \tag{25}\]  
Equation (21) yields the average Io. Equation (22) represents the relationship between output voltage and input voltage source when the converter is operating in DCM. It is worth noting that, from Equation (22), it can be seen that voltage gain Mdc is contingent on (RL, D, Fs). The voltage gain of the proposed converter as a function of the load loss factor (K) can be found in Equation (23). The critical value of K (Kcrit) can be determined using Equation (23), leading to the formulation of Equation (24). The boundary condition between the DCM and CCM operation modes is defined by the condition in Equation (25).