esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections
### Nanoparticles  
#### 4.2.1 Polymeric Nanoparticles  
Polymeric NPs are biocompatible and biodegradable polymer-based delivery systems within the range of 10-1000 nm in which drugs can easily dissolve, encapsulate, or conjugate. Chitosan, a nontoxic, abundant, biodegradable polymer, is a linear amino polysaccharide. [PERSON] et al. determined the antifungal activity of chitosan-based NPs against _Fusarium solani, A niger_, and _C. albicans_. The chitosan NPs with zeta potential from +22 to +55 mV exhibited enhanced inhibitory activity against _F. solani_ (MIC of low molecular weight (LMW) NPs = 0.86-1.2 mg/mL and MIC of high molecular weight (HMW) NPs = 0.5-1.2 mg/mL) and _C. albicans_ (MIC of LMWNPs = 0.25-0.86 mg/mL and MIC of HMWNPs = 0.6-1.0 mg/mL) as compared with the solution (MIC = 3 mg/mL for _F. solani_ and _C. albicans_, as well as LMWNPs and HMWNPs). The inhibitory action was found to be based on the zeta potential and particle size of chitosan NPs [55]. The chitosan NPs exhibited good antifungal activity compared to AmB and reduced the cytotoxicity in L929 fibroblast cells [56]. [PERSON] et al. synthesized a novel nanoplatform containing ITZ (ITZ-NP) for an effective fungal treatment with sustained drug delivery. A slight venous irritation and very mild hemolysis represented much-improved biocompatibility compared to the marketed formulations [57].
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 2 Background
### Orchestra  
Orchestra [2] is an autonomous scheduler for RPL-based TSCH networks. This means that each node in the network computes its schedule based on information collected from RPL, without any additional communication overhead. The resulting schedule of a node is composed of different slotframes, each dedicated to a specific traffic type: one for TSCH EBs, one for RPL control traffic and broadcast traffic, and one for unicast data traffic.  
The transmission and reception cells are allocated with the help of rules. There are four types of cells: common shared (CS), receiver-based shared (RBS), sender-based shared (SBS), and sender-based dedicated (SBD). For CS cells, there is one allocated cell in the slotframe to be used by all the nodes in the network. CS cells are typically used for RPL control messages. In the case of RBS cells, one receive cell and as many transmit cells as the number of RPL neighbors are allocated. Both the time offset and channel offset are computed based on the address of the receiving node with the help of a hashing function. The role of the hashing function is to reduce the chances of a collision, which can occur when two inputs, in our case link layer addresses of nodes, result in the same value, time slot and channel offset. In the case of SBS cells, the time slot and channel offset are computed based on the parameters of the transmitting node. For each node, one cell is allocated for transmitting, and as many cells as the number of RPL neighbors for receiving. If the number of nodes in the network is lower than the number of cells in a slotframe, it is possible to use SBD cells [2]. The allocation of the cells for RBS and SBS Orchestra is visualized in Figure 2.  
Some of the immediate disadvantages in the case of RBS cells are contention, and an increase in delay for bursty traffic. Moreover, in the case of both upstream and downstream  
Figure 2: Example of scheduled Rx and Tx cells for nodes 1 to 6 in the time/channel offset space. The Rx cells are highlighted with red and the Tx cells with green. (**a**) Receiver-based shared (RBS) cell allocation. (**b**) Sender-based shared (SBS) cell allocation.  
traffic, the children contend with their parent for cells. Considering node 3 in Figure 2a, one can observe that there is only one receive cell for all its children. If at least two children send messages to their parent simultaneously, they compete for the same receive cell. The back-off mechanism of CSMA is used to try to avoid future collisions. This leads to an increase in delay. The more children a node has, the more severe the problem can become. In the case of SBS cells, a node has only one allocated cell per slotframe for transmitting packets (Figure 2b node 3). The packets received from its children are queued until they can be forwarded. In this situation, the latency increases, and depending on the traffic load, the queues might overflow. These drawbacks appear when traffic load increases above a certain value.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 6 Conclusions  
This paper contributes to the literature with an adaptive blind equalization proposal using a convex optimization technique based on genetic algorithms. The proposed technique is a refinement of earlier work concerning global convergent cost function based on linear programming. It uses genetic algorithms as an optimization technique, which is robust to the stochastic characteristics of the signal to be equalized. An analysis of the BLE-GA performance has been presented, and it shows that the proposed method is more likely to perform better than CMA in greater modulations. However, due to the GA's complexity, the results of the simulations performed were only possible because of the block architecture of the equalizer, running the GA much fewer times than CMA. The experimental analysis discloses the robustness of the proposed technique as it equalizes channels with proper performance. Thus, the obtained results indicate a significant advancement in the development of efficient algorithms for blind equalization.  
All the authors have contributed in various degrees to ensure the quality of this work (e.g., [PERSON] and [PERSON] conceived the idea and experiments; [PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; and [PERSON] coordinated the project). All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(CNPq) and Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)--Finance Code 001.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors wish to acknowledge the financial support of the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) for their financial support.  
The authors declare no conflicts of interest.
## 2 Proposed PMSM-SCS Based on DTC Strategy
### PMSM-SCS Based on DTC Strategy Using Constant Flux  
In this section, as shown in Figure 2, the classic PI speed controller is replaced by optimally tuned PI controllers using an EOA-type algorithm and FO-type speed controllers: FOPI, TID, and FO-Lead-Lag. In all these cases, the stator reference flux is constant. Throughout this article, it is useful to present the PMSM operating equations in both the \(d\)-\(q\) rotating reference-frame coordinate system and the \(\alpha\)-\(\beta\) stationary reference-frame coordinate system. For PI-type controller tuning, in addition to heuristic or classical Ziegler-Nichols tuning [42], superior control system results can be achieved by using computational intelligence algorithms, of which the EOA-type algorithm is presented in [30; 31]. As a result of implementing this algorithm, the optimal parameter values for tuning the PI controller are achieved. EOA-type optimization algorithms have a high efficiency in obtaining optimal or near-optimal solutions in a small computational time for most of the studied problems, with high algorithmic robustness and low parametric sensitivity [30]. In Appendix A, some details about EOA are presented.  
Figure 2: Schema of the proposed PMSM-SCS based on DTC strategy using PI-EOA, FOPI, TID, and FO-Lead-Lag speed controllers at constant flux.
# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
## 3 Proposed Method
### Data Preprocessing  
Data preprocessing is divided into three parts: effective signal screening, data normalization, and dual-channel data generation. First, each 100 ms data is segmented, and the signals in each data file are divided into effective signals, reference signals, and time slots, among which the effective signals are selected. Second, the individual fingerprint features of the emitter are independent of the transmitted power of the signal. In order to avoid the influence of the signal power difference, the data samples are normalized. Finally, in view of the particularity that the OFDM signal contains 108 subcarriers, each subcarrier will overlap with the image input, resulting in a poor effect. However, deep learning is mostly based on real number operation, which to some extent loses the intrinsic connection between IQ signals. As shown in Figure 4, in view of the OFDM signal, we first draw it  
Figure 3: OFDM signal introduction.  
into a spectrum diagram which is overlapped then save the data value. The amplitude of IQ data is calculated as the first channel, and the phase is used as the second channel to generate single-channel dataset and two-channel dataset. New datasets generated in this way comprehensively utilize the amplitude and phase information of the IQ signal, and make full use of the internal connection between the two IQ channels.
## 5 Conclusions  
A new method based on the DRSMs is proposed for the underwater sound radiation of a cylindrical shell. When the cylindrical shell vibrates at low frequencies, some DRSMs exist in the corresponding frequency bands. The radiated sound power of these DRSMs can be superposed to obtain the prediction results of the total sound power. The modal decomposition can use two different methods, both of which reduce the number of measuring points. Through the modal analysis of a fixed cylindrical shell, the DRSMs exist and contribute largely to the sound radiation in their frequency band, and high-efficiency modes are more likely to become the DRSM. A further study on the modal decomposition of the same cylindrical shell shows that different DRSMs can apply different methods--the orthogonal decomposition method or the solving equations method--to solve modal coefficients, which depends on the modal participant degree in the vibration displacement. These two methods show their superiority to the BEM in terms of the number of measuring points. Moreover, the cases of the free cylindrical shell and the cylindrical shell with mass rings demonstrate the correctness and the quickness of this new prediction method. This method can efficiently lower the requirement on the number of measuring points, which is beneficial to engineering applications. Furthermore, structural modal information of the underwater structure can be precomputed and saved in the database for the next forecast, which makes it easier to meet the real-time requirements of online prediction.  
When this method is used to predict the radiated sound power by superposing the sound power of the DRSMs, the mutual coupling between each order is ignored at low frequencies. This assumption is valid, because the circumferential modal coupling of the cylindrical shell is relatively weak. For other  
Figure 14: Comparison between the superposed curve of the radiated sound power from the DRSMs and the reference prediction result.  
Figure 13: Cylindrical shell model with mass rings.  
models, such as flat plate, where the mutual coupling between modes cannot be ignored, the modal coupling effect needs to be considered during modal superposition. Otherwise, it will cause a large prediction error.  
All authors have read and agreed to the published version of the manuscript. Conceptualization, C.Z. and D.S.; Data curation, S.L.; Formal analysis, C.Z. and S.L.; Funding acquisition, C.Z. and D.S.; Investigation, S.L.; Methodology, C.Z. and S.L.; Project administration, D.S.; Resources, D.S.; Software, C.Z. and S.L.; Supervision, C.Z. and D.S.; Validation, C.Z., S.L., Y.H. and Y.S.; Visualization, S.L., Y.H. and Y.S.; Writing--original draft, S.L.; Writing--review & editing, C.Z.  
This research was funded by the Stable Supporting Fund of Acoustic Science and Technology Laboratory (Grant No. JCKYS2019604 SSJS009) and National Natural Science Foundation of China (Grant No. 11974089).  
The authors declare no conflict of interest.
## 2 OAM Light and Ocean Turbulence Channel Model
The random phase spectrum of seawater on any slice perpendicular to the optical axis \(z\) is  
\[f_{\Phi_{n}}\big{(}\kappa_{x},\kappa_{y}\big{)}=2\pi k^{2}\Delta z\Phi_{n} \big{(}\kappa_{x},\kappa_{y}\big{)} \tag{9}\]  
Furthermore, the spectral variance of the ocean turbulence stochastic phase screen can be obtained as  
\[\sigma_{\phi}^{2}\big{(}\kappa_{x},\kappa_{y  
The intensity and phase distribution of a typical LG beam is shown in Figure 2, which describes the intensity and phase distribution of the LG beam with modal values at the source. As can be seen from the figure, LG beam has annular intensity distribution and spiral phase distribution. When the mode value \(\ell\) increases, the annular distribution increases and the phase changes around the optical axis.Because the phase distribution of two LG beams with different topologies and values has obvious orthogonal characteristics, OAM light can often be used as a dimension resource for encoding in UWOC. Figure 3 shows the spatial distribution of the LG beam (\(\ell=1,3,5\)) at different values of \(C_{n}^{2}\) (\(1.0\times 10^{-15}\) K\({}^{2}\)m\({}^{-2/3}\), \(1.0\times 10^{-14}\) K\({}^{2}\)m\({}^{-2/3}\), and \(1.0\times 10^{-13}\) K\({}^{2}\)m\({}^{-2/3}\)) when \(\omega=-2.5\). When \(C_{n}^{2}=1.0\times 10^{-15}\) K\({}^{2}\)m\({}^{-2/3}\), it is obvious that both the intensity and phase distribution maintain quite good integrity and the phase dividing line of the phase distribution is obvious. When \(C_{n}^{2}=1.0\times 10^{-14}\) K\({}^{2}\)m\({}^{-2/3}\), the integrity damage of LG beam intensity and phase distribution is very obvious and the helical boundary of the phase distribution is no longer smooth. When \(C_{n}^{2}=1.0\times 10^{-13}\) K\({}^{2}\)m\({}^{-2/3}\), the intensity distribution of the LG beam only retains partial annular distribution, the helical phase boundary of phase distribution shows obvious irregular jitter, and the distortion degree around the central singularity is significantly increased. In conclusion, OAM with a smaller \(\ell\) value has stronger resistance to turbulence interference, which has also been verified by simulation through the support vector machine in a previous paper [26]. According to the spatial distribution of OAM and the performance of different modes of OAM in ocean turbulence, we determined the system block diagram, as shown in Figure 4. BPSK modulation technology is used as the modulation module, because, compared with QPSK and 8 PSK, BPSK has a lower transmission efficiency but stronger anti-noise performance. The modulated signal cannot be transmitted directly in the ocean turbulence, so the OAM mapping module is used to map the modulated signal to the OAM mode. In the text, the OAM of \(\ell=1\) represents the modulated symbol \"1\" and the OAM of \(\ell=-1\) represents the modulated symbol \"\(-1\)\". The OAM de-mapping module is used to recover the signal from the received OAM light. Figure 2: Intensity and phase distribution of the LG beam (at the source). Figure 3: Intensity and phase distribution of the LG beam in the ocean turbulence channel.
###### Abstract
Combined with the PIV (Particle Image Velocimetry) technology that is originally developed in the field of experimental fluid mechanics [17], the microscopic kinematic information of the particles can be obtained directly, which can serve as an important supplement to the commonly-observed macroscopic quantities. The PIV technique that can measure the displacement/velocity field of the soil particles has been widely used in geotechnical problems, such as the pile-soil interaction [18], the ground deformation induced by tunneling [19], the interaction of foundation structure and soil [20], etc. As for the vacuum consolidation of dredged slurry, the PIV analysis has been recently adopted by [PERSON] et al. [21] and by [PERSON]. [22] to obtain the displacement field of the slurry, based on which the formation mechanism of the soil column has been revealed. As a non-intrusive testing method, the PIV technique fundamentally relies on the tracer particles to accomplish the image correlation analysis, i.e., the actual displacement/velocity field of the soil is represented by that of the tracer particles. However, the commonly-used and commercially available tracers, e.g., the carbon powder, could be of different diameters of mass density when compared to the measured objects, i.e., slurry particles in the present paper. Questions may arise over the representativeness of the tracers on the kinematic information of the slurry particles during the vacuum consolidation process, which is monitored by using the PIV technique. To this aim, a microscopic model on vacuum consolidation of the slurry has been built using the CFD-DEM method. The slurry particles and the pore water are, respectively, described by the DEM and the CFD methods. Moreover, the PVD membrane is included in the model through fixed-in-space particles that are modeled by the DEM. The tracer particles that are also modeled by the DEM are distributed evenly over the slurry surface, which is the same as the tracer configuration in the practical PIV technique. By using the established model, the representativeness of the tracers on displacements of the slurry particles has been investigated for different combinations of the tracer diameter, the trace density, and the initial void ratio of the slurry soil.
## 2 Methodology
### Determination of Failure Assessment Diagram  
The fundamental fracture mechanics correlation collaborates three factors: The length of defect a, the constraint enforced \(\sigma_{app}\), and the fracture resistance R. R can represent the crucial stress intensity factor \(K_{lc}\), the crucial value of the J integral \(J_{c}\), or the critical crack opening displacement \(\delta_{c}\). In this survey, the utilisation of the FAD concept requires the replacement of these parameters by the calculation of two parameters, i.e., the load ratio \(S_{r}\) (or equivalently \(L_{r}\)) and the toughness ratio \(K_{r}\). The failure curve is defined by:  
\[f(S_{r})=K_{r} \tag{4}\]  
where \(K_{r}\) is the non-dimensional critical crack driving force and \(S_{r}\) (or equivalently \(L_{r}\)) the non-dimensional load. Structures that are subjected to loads and present a surface defect are represented by a control point in the plane \(K_{r},S_{r}\):  
\[K_{r}\ =\ \frac{k}{K_{lc}},\ S_{r}\ =\ \ \frac{\sigma_{app}}{R_{m}} \tag{5}\]  
where K and \(K_{lc}\) are the stress intensity factor and fracture toughness, respectively, and \(\sigma_{app}\) and \(R_{m}\) are the applied stress and ultimate strength, respectively.  
The construction of this curve was taken from a plasticity correction introduced by [PERSON]'s model [33]. This methodology considers that any sort of failure from a fragile crack to plastic collapse is obtained from the brittle fracture by a plasticity correction. Several methods are used to determine this curve: Structural INtegrity Assessment Procedure (SINTAP) in Europe [34], Electric Power Research Institute (EPRI) in the United States [35], and R6 in the UK [36]. The mathematical expression of this curve determined from the SINTAP method is presented by:  
\[f\left(S_{r}\right)\ =\ \left[1+\frac{S_{r}^{2}}{2}\right]^{\frac{-1}{2}}\left[0. 3+0.7*e^{\left(-0.6S_{r}\right)}\right]\ \text{for}\ 0\leq S_{r}\leq 1 \tag{6}\]  
In the FAD procedure, the failure region was estimated by using a failure curve, the acceptable region, and the unacceptable region. A conventional FAD is represented [21] in Figure 2.  
Figure 1: The distribution of elastic–plastic stress along defect [32].  
If the operational condition is situated as shown in Figure 2, we can ensure the safety of our design; if it is situated on the failure curve, failure will arise by crack propagation. From the assessment point A, we can specify the safety factors:  
\[f_{s,\sigma}=\ \frac{O^{\prime\prime}B}{O^{\prime\prime}A},\ f_{s,\mu}=\ \frac{OD}{OA},\ f_{s,K}=\ \frac{O^{\prime}C}{O^{\prime}A} \tag{7}\]  
where, \(f_{s,\sigma}\), \(f_{s,\sigma}\), \(f_{s,K}\) represent the safety factor on the load, on the size of the defect, and on the stress intensity factor, respectively.
## 1 Introduction  
Maritime container traffic is the backbone of international trade and has reached an impressive size and number in recent decades. Dry containers are used to move 75 percent of global goods, and in 2021, according to UN trade statistics, 851.1 million twenty-foot equivalent units (TEUs) of containers were handled in ports worldwide [1]. Container shipping is by far the most economical and effective way to move goods over long distances. It is made possible through collaboration among a range of players (shipping lines, freight forwarders, shipping and customs agents, terminal operators, public institutions and ancillary service providers) and the use of shared tools, practices and standards. Digital technologies are spreading rapidly in this sector, bringing significant economic and environmental benefits. Major shipping companies have identified continuous container tracking and monitoring as key tools for increasing the reliability of shipments and generating important economiesof scale. Equipping all containers with an Internet-connected tracking device is necessary in the global shipping market that is still waiting to be met.  
The \"5G Smart Edge Node and Smart Objects enabling Reliable Services Extended All over the seas\" (5G SENSOR@SEA) project [2], funded by the Artes 4.0 program of the European Space Agency, addressed this need and aimed to realize a low-cost tracking and monitoring system for ISO 668 dry containers that integrates 5G cellular technologies for massive Internet of Things (IoT) deployment with GEO satellite link in a Ku-band-optimized backhauling function.
## 3 Study Area and Data
### Explanatory Variables
#### 3.1.1 Land Use  
[PERSON] et al. (2017) [30] believe that in large and medium-sized cities, friendly walking distance is generally 500 m. Therefore, we determine the PCA for each Shenzhen Metro Station as a circular buffer with a radius of 500 m. Then, with the help of Baidu Map API, we can collect the relevant data of land use variables. Land use variables include surrounding residence, recreational facilities, commercial districts, educational institutions, office areas, and so on. To be more detailed, land use variables involve the number of residential units, restaurants, schools, offices, hospitals, banks, shops, and hotels within the PCA with a radius of 500 m.