esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structures, Synthesis, and Magnetic Properties of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
### Other Geometries  
The isotropic spherical geometry and anisotropic cubic geometry are two morphologies of Fe\({}_{3}\)O\({}_{4}\) nanoparticles that have been extensively studied in synthesis and applications. Moreover, various anisotropic or special geometries of Fe\({}_{3}\)O\({}_{4}\) nanoparticles have been synthesized, such as the 1D structures of nanorods and nanotubes and the 2D structures of nanoprisms, hexagonal nanoplates, multiarmed nanostars, tetrapods, and hollow nanopar  
Figure 5: (**a**) Schematics showing the synthesis of Fe\({}_{3}\)O\({}_{4}\) nanocubes with edge lengths in the 9–80 nm range and (**b**) the growth mechanism of Fe\({}_{3}\)O\({}_{4}\) nanocubes. Reproduced with permission from ref. [74]. Copyright 2019 American Chemical Society. ticles [17]. Detailed descriptions of the sizes, morphologies, and related magnetic properties of these special geometries of Fe\({}_{3}\)O\({}_{4}\) NPs are presented in Table 4.  
For simple one-dimensional morphologies and highly anisotropic shapes, single-crystalline Fe\({}_{3}\)O\({}_{4}\) nanorods were synthesized through the solvothermal synthesis of an iron pentcarbonyl precursor in octanol using hexadecylamine and oleic acid coordinating agents [79]. By adjusting the amount of hexadecylamine and reaction time, the sizes of nanorods or aspect ratios (length-to-diameter ratio) can be controlled. Two different sizes, length \(\times\) diameter (L \(\times\) D) of 65 \(\times\) 6.5 nm and 140 \(\times\) 12 nm, were observed with TEM, and HR-TEM images of the obtained Fe\({}_{3}\)O\({}_{4}\) nanorods are shown in Figure 6a,b [79]. Separately, [PERSON] and coworkers synthesized Fe\({}_{3}\)O\({}_{4}\) nanorods with different aspect ratios ranging from 5.6 to 11. They also reported the synthesis of Fe\({}_{3}\)O\({}_{4}\) nanorods with comparable aspect ratios but different lengths and diameters [40]. A mechanistic study of the reaction revealed that Fe\({}_{3}\)O\({}_{4}\) nanorods are formed by heteronucleation on intermediate FeO nanocubes [79]. In this reaction, Fe(CO)\({}_{5}\) is first decomposed under solvothermal conditions to generate metallic iron. Then, metallic iron is oxidized by dissolved oxygen or other oxidizing species in the solution to form FeO nanocubes. At the same time, Fe(CO)\({}_{5}\) reacts with oleic acid to form iron oleate simultaneously to the condensation of hexadecylamine and oleic acid to release water molecules for the hydrolysis of iron oleate. Combined with the dissolution of FeO, the decomposition of Fe(CO)\({}_{5}\) and the hydrolysis of iron oleate provide the source for growing Fe\({}_{3}\)O\({}_{4}\) nanorods. Applying this method, [PERSON] et al. fabricated epitaxial magnetite nanorods on a SrTiO\({}_{3}\) substrate and observed enhanced room temperature
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 3 Proposed Method  
Propose a multi-task learning and multi-branch network model (MaMNet) for the joint grading of DME and DR. The MaMNet model is shown in Figure 3, which consists of three main parts: multi-branch network, feature fusion module, and disease grading module. The MbN has a four-branch architecture, by fusing different branches which can extract the low-level features information of DR and DME in a targeted way. The feature fusion module includes SFEN, CFEN, and ASPP. By combining various features that were collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features to improve joint grading accuracy. The disease grading module is composed of a combination of GA (Global Average Pooling Layer) and FC (Fully Connected Layer).  
The proposed method can be divided into four steps:  
Firstly, color fundus images are filtered by the averaging filter, and then the original and the filtered images are weighted and superimposed to obtain the input images.  
Secondly, the input images are fed into the MbN to extract the underlying features \(F_{DME}\) and \(F_{DR}\). The results of branch 1 and branch 3 are fused to obtain the \(F_{DME}\), and the results of branch 1 and branch 4 are fused to obtain the \(F_{DR}\).  
Thirdly, we use SFEN and CFEN to get the self-feature \(S_{DME}\) and cross-feature \(C_{DME}\) of \(F_{DME}\). And then, \(F_{DME}\), \(S_{DME}\), and \(C_{DME}\) are concatenated to get the comprehensive discriminative features \(D_{DME}\). Similarly, the \(S_{DR}\) and \(C_{DR}\) are obtained from the SEFN and CFEN. The ASPP is used to extract the global lesion feature \(G_{DR}\). \(S_{DR}\), \(C_{DR}\), and \(G_{DR}\) are concatenated to get the \(D_{DR}\).  
Finally, the \(D_{DME}\) and \(D_{DR}\) are sent to the grading module of DR and DME respectively for disease grading.
## 5 Decentralization of VANETs
### Threshold Signature Mechanism
\begin{table}
\begin{tabular}{l p{284.5 pt}} \hline \hline
**Paper** & **Main Idea** \\ \hline \multirow{6}{*}{AHM2022 [62]} & \multicolumn{1}{p{284.5 pt}}{Presents a new approach that combines a blockchain-based incentive trust management model with a privacy-preserving threshold ring signature method for VANETs. The proposed solution aims to tackle several difficulties such as malicious assaults, privacy leakage, and lack of cooperation in traffic event validation. The system guarantees the authenticity of messages and the privacy of vehicles. It encourages participation by offering incentives. It also uses a consensus technique that can tolerate Byzantine faults, exhibiting both security and efficiency in VANET contexts. \\ \hline \multirow{6}{*}{ZSJ2021 [63]} & \multicolumn{1}{p{284.5 pt}}{Examines cryptographic primitives and presents two approaches for threshold key management, allowing stakeholders to collectively and safely retrieve secrets efficiently, especially in situations involving data sharing. This technique improves the security and functionality of blockchain in ITS. \\ \hline \multirow{6}{*}{TC2021 [64]} & \multicolumn{1}{p{284.5 pt}}{Introduces a security method that employs physical layer functions, such as encoders and decoders, along with shared keys, to build a model where communication between authorized parties is protected from unauthorized interception. The paper presents a method for creating threshold-secure codes using linear block codes, with a specific emphasis on ReedMuller codes. It also showcases a very efficient implementation with quasi-linear time complexity, which can be adjusted to different key lengths. \\ \hline \multirow{6}{*}{HIC2019 [65]} & \multicolumn{1}{p{284.5 pt}}{Introduces a robust authentication and key management system for VANETs, employing edge computing and consortium blockchain to tackle challenges related to secure transmission and key management in diverse VANET contexts. The approach utilizes certificate-less authentication, employing individual session keys for cars and implementing efficient group key updating. Its security and efficiency have been demonstrated through rigorous security proofs and performance studies. \\ \hline \multirow{6}{*}{JSS2016 [66]} & \multicolumn{1}{p{284.5 pt}}{Addresses the task of determining the most effective threshold value for key reconstruction in threshold cryptography in cloud computing environments. The paper provides a framework for choosing this value, supported by experiments conducted with CloudSim to model the cloud environment and quantify the duration of key distribution and reconstruction procedures.} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._member. It also offers a cost-effective, secure, and decentralized approach for validating transactions and reaching consensus among members.
## 2 Materials and Methods
### Follow-Up Evaluation  
One week after the surgery, the patients scored their postoperative pain from zero to 10 at a visual analog scale (VAS) and registered their painkiller consumption.  
Ultra-low dose cone beam computer tomography (CBCT) images were taken preoperatively and 1 year after the surgery with 100-micrometer voxel sizes (Promax 3D Mid, PlanmecaOy, Helsinki, Finland). The CBCT images were examined with DataViewer software ([PERSON], Kontik, Belgium) by two investigators experienced in radiology blinded toward the groups and their results were averaged.  
The qualitative analysis included the examination of the structure of trabecular and cortical bone formation at the surgical sites and the presence of graft remnants or sequestration. The density of the newly formed bone and graft remnants were measured in Hounsfield Units (HU).  
Micromorphometric analysis was performed at the 1-year images with CTAn software ([PERSON], Kontik, Belgium). Bone surface/volume ratio (BS/BV), trabecular thickness (Tb.Th), trabecular number (Tb.N), and total porosity (Po(tot)) were compared.  
To follow the changes of the vertical and horizontal dimensions of the alveolar ridge, the pre-and postoperative CBCT images were set to the same position with the help of anatomical lines as reference. The vertical bone height was measured from the highest point of the buccal ridge to the lowest point of the mandibular base at the coronal image, 5 mm distally from the cemento-enamel junction (CEJ) at the midline of the second molar. The horizontal bone width was measured between the buccal and lingual cortical at the axial image, 5 mm distally from the second molar, 3 and 5 mm lower than the CEJ of the second molar.  
MBL was measured at the distal site of the second molar, from the CEJ to the deepest point of the surrounding bone, both in the pre-and postoperative CBCT.  
Statistical analysis was performed with IBM SPSS Statistics software (IBM Corporation, New York, NY, USA). All data were expressed as mean \(\pm\) standard deviation. Shapiro-Wilk test was used to access the normality of data distribution. For pairwise comparison the data were analysed by One-way ANOVA test in case of normal distribution and independent-samples Mann-Whitney-U test was used in case of non-normal distribution.
## 6 Experimental Evaluation
### Physical Machine and VM Reservation Modeling  
Tables 2 and 3 represent the model of physical machines with real power consumption and the configurations of heterogeneous systems. This is taken from the Standard Performance Evaluation Corporation (SPEC) power benchmark [47], which is considered for simulation. A small-scale datacenter with 50 systems is considered for algorithm evaluation. The VM characteristics given in Table 4 are used to model the virtual machine reservations. It is based on Amazon EC2 compute unit (ECU) inspiration but customized according to physical machine characteristics to avoid the fragmentation of resources. Normally, the storage and network requested by the VM are not confined to the physical machine in which it is placed. Therefore, the storage and network bandwidth do not influence the placement of VM on a particular PM. The actual location of data storage is decided by the provider in a timely manner. In this real scenario, a feasibility check involves placing VM on a particular PM, mainly based on the availability of memory and the CPU clock cycle which has a strong bond with each other in computing node. The arrival time of VM is modeled using Poisson distribution. Lambda is  
determined by the average number of requests submitted in a reservation cycle. Uniform distribution in the interval (rmin, rmax) is used to model the execution time (\(\tau\)) of the VM, where rmin and rmax are the minimum and maximum execution times (VM's reservation cycles) of 10 and 60, respectively.
## 2 Related Work
### Underwater MIMO Signal Detection  
MI-based MIMO technology is a standard method for improving channel capacity, but it can lead to challenges in signal demodulation. Therefore, designing a signal detection algorithm for the MI-MIMO system is critical. The zero-forcing (ZF) method is typically used to eliminate interference in underwater channels, but it amplifies noise during the solution process, leading to signal recovery problems. In contrast, the minimum mean square error (MMSE) approach solves the noise amplification problem but with higher complexity than ZF. The maximum likelihood (ML) method has the highest decoding success rate but involves a significant amount of computation, limiting its practical application. Therefore, developing an MI-MIMO detection algorithm with low complexity and a low bit error rate is necessary. [PERSON] [36] investigated signal detection for MI-MIMO systems with misalignment coils, no signal crosstalk, and available channel state information and found that using a singular value decomposition (SVD) could provide superior detection results. [PERSON] et al. [37] applied the enhanced MMSE approach to the underwater acoustic MIMO-OFDM system, which resulted in suboptimal signal identification accuracy and complexity. [PERSON] et al. [38] decomposed a large matrix into smaller ones to enable fast detection in underwater MIMO systems. [PERSON] et al. [39] used a deep learning network for signal detection in an underwater acoustic system, which gave better detection results than the maximum ratio combining (MRC) but lower than ML. [PERSON] et al. [40] used the sparse Bayesian function for iterative signal detection in an underwater acoustic MIMO system, achieving good BER performance with limited iterations. [PERSON] et al. [41] used a deep network for signal detection in optical MIMO systems and found that it outperformed error minimization methods that use least squares and root-mean-square values. [PERSON] et al. [42] developed a generalized likelihood block detection algorithm in underwater MIMO systems, which enables fast signal detection without channel information. [PERSON] et al. [43] used the MI-MIMO system for wireless power transmission, estimated the magnetic induction channel, and performed beam allocation to improve the efficiency of MI-MIMO power transmission. [PERSON] et al. [44] studied low-density parity-check coded underwater acoustic communications using m-MIMO and single carrier with frequency domain equalization transmission technique. Their results show that the proposed system is an excellent model to achieve the future evolution of UWA communications, even in the presence of impulsive noise. [PERSON] et al. [43] proposed an underwater MIMO OFDM system and employed M-ZF, a modified signal detection algorithm based on ZF, for the system. Their simulation results show that M-ZF has lower time costs and better BER.
## 5 Conclusions  
In this paper, the time-varying distributed formation control of a multi-agent system via limited communication was studied. The proposed aperiodic sampling law based on the dynamic event-triggered algorithm possibly alleviated the computational burden in the inner loop controller, as well as the requirements of the sampling rate of the sensors. When it comes to multiple mechanical systems, the distributed protocol has been widely utilized to remove centralized tasks at a ground station. Many works such as computation, communication, and control input update need to be executed in each agent. Further, in case each agent shares a mutual remote sensor system, the fixed base station periodically transmits numerous preprocessing data to all agents. To decrease burdens of each agent significantly, we proposed the event triggering mechanism to determine the next actions in each agent. Instead of executing inputs after the elapsing of a periodic fixed time, the devoted algorithm solely updates the next inputs when the constructed events occur. Moreover, by establishing the new control Lyapunov function, the considered multi-agent system not only exponentially asymptotically achieved the predefined time-varying formation, but also completely excluded the Zeno behavior, which allowed the practical ability of this method. As a result, our algorithm was capable of guaranteeing a satisfactory system, as well as addressing practical problems. In future work, we plan to study the switching topology and/or collision avoidance among a group of quadcopters. Another interesting topic is to consider communication delays and packet losses on the formation flight of multiple quadcopter systems.  
Conceptualization, A.T.N.; methodology, A.T.N. and T.B.N.; software, A.T.N.; validation, A.T.N., T.B.N., and S.K.H.; formal analysis, A.T.N.; investigation, A.T.N. and T.B.N.; resources, S.K.H.; data curation, A.T.N.; writing, original draft preparation, A.T.N.; writing, review and editing, A.T.N., T.B.N., and S.K.H.; visualization, A.T.N. and T.B.N.; supervision, S.K.H.; project administration, S.K.H.; funding acquisition, S.K.H. All authors read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01423) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
## 3 Results and Discussion
### Effect of Genotype on Total and Individual Anthocyanins  
Cyanidin-3-glucoside (cyano-3-glc, m/z [M\({}^{+}\)] 449, MS\({}^{2}\) 287), pelargonidin-3-glucoside (pel-3-glc, m/z [M\({}^{+}\)] 433, MS\({}^{2}\) 271), pelargonidin-3-rutinoside (pel-3-rut, m/z [M\({}^{+}\)] 579, MS\({}^{2}\) 433, 271), cyanidin-3-(6-O-malonyl)-glucose (cyano-3-mal-glc, m/z [M\({}^{+}\)] 535, MS\({}^{2}\) 287), and pelargonidin-3-(6-O-malonyl)-glucose (pel-3-mal-glc, m/z [M\({}^{+}\)] 519, MS\({}^{2}\) 271) were assigned by their mass spectra as the major strawberry anthocyanins (Tables 2-7). Over all, the pel-3-glc showed the highest concentrations (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Can**} & \multirow{2}{*}{**Cle**} & \multirow{2}{*}{**Flo**} & \multirow{2}{*}{**Fri**} & \multirow{2}{*}{**Gar**} & \multirow{2}{*}{**Son**} & \multicolumn{2}{c}{_Yearly_**} \\  & & & & & & & & & _Mean per Location_ \\ \hline \multirow{2}{*}{**Norway**} & **2017** & 212.6 \(\pm\) 25.6 & 284.6 \(\pm\) 30.5 & 374.5 \(\pm\) 30.4 & 453.4 \(\pm\) 42.4 & 263.1 \(\pm\) 58.2 & 191.6 \(\pm\) 16.2 & _296.6 B_ \\  & **2018** & 133.8 \(\pm\) 33.8 & 195.2 \(\pm\) 48.7 & 405.5 \(\pm\) 43.8 & 451.6 \(\pm\) 68.8 & 198.9 \(\pm\) 25.8 & 145.6 \(\pm\) 30.4 & 255.1 \(\it B\) \\  & _mean_ & _173.2 ab_ & _239.5 c_ & _390.0 d_ & _452.5 e_ & _231.0 bc_ & _168.6 a_ & \\ \hline \multirow{2}{*}{**Poland**} & **2017** & 245.9 \(\pm\) 53.0 & 310.1 \(\pm\) 34.3 & 359.1 \(\pm\) 89.4 & 438.0 \(\pm\) 58.8 & 235.0 \(\pm\) 34.9 & 210.2 \(\pm\) 27.2 & 299.7 \(\it B\) \\  & **2018** & 225.0 \(\pm\) 40.8 & 249.6 \(\pm\) 22.4 & 448.5 \(\pm\) 68.5 & 397.9 \(\pm\) 74.6 & 217.6 \(\pm\) 40.1 & 222.6 \(\pm\) 35.4 & 293.5 \(\it B\) \\ \cline{1-1}  & _mean_ & _235.5 a_ & _279.9 a_ & _403.8 b_ & _417.9 b_ & _226.3 a_ & _216.4 a_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of growing location and year on total anthocyanin content as the mean of two picking dates in fruits of six strawberry cultivars.
## 2 Materials and Methods  
Mg/Si\({}_{3}\)N\({}_{4}\) nanocomposites, by varying the volume percentage (0%, 0.5%, 1% and 1.5 vol%) of Si\({}_{3}\)N\({}_{4}\) nanoparticles in the pure Mg matrix, were fabricated using the ultrasonic-assisted stir casting method. The ingots of magnesium were measured out beforehand, and then they were melted in a resistance heating furnace at 750 \({}^{\circ}\)C. Once a homogenous melt had been achieved, preheated Si\({}_{3}\)N\({}_{4}\) nanoparticles were added, and a mechanical stirrer was used to mix the composite mixture for 10 min. After this, ultra-sonication was carried out for 5 min, for the uniform distribution of the nanoparticles. Finally, the molten material was transferred into a steel mould.  
The wire cut samples were polished and etched to investigate the Si\({}_{3}\)N\({}_{4}\) particle distribution in the Mg matrix using a scanning electron microscope, equipped with energy dispersive spectroscopy (Vega LMU 3, TESCAN, Brno, Czechia). An X-ray diffractometer was used to perform the phase analysis of the samples. The experimental density was determined using Archimedes principle and the theoretical density using the rule of mixtures. The ignition temperature of the samples was evaluated using a thermogravimetric analyzer (TGA/DSC3+, Mettler Toledo, Mumbai, India). Three samples for each composition with the dimensions of 2 \(\times\) 2 \(\times\) 1 mm\({}^{3}\) were tested in an aluminium crucible from 30 to 750 \({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C/min. Furthermore, the samples were subjected to compression testing in accordance with ASTM E9-09. A universal testing machine (model M-50, Fine Spray India, Bangalore, India) was used to test five samples for each composition with a length to diameter ratio of 1, at a strain rate of 8.33 \(\times\) 10\({}^{-5}\).
## 1 Introduction
### Nonlinear Numerical Model  
The precast segmental concrete beam (PSCB) prestressed with internal un-bonded CFRP tendons (PSCB-IUCFRP) is a newly developed design scheme to improve the flexural performances of PSCB [21], as shown in Figure 1. Compared with the widely used PSCB externally prestressed by steel tendons, it shows the comprehensive benefits of rapid construction, better-carrying capacity, and no corrosion property of CFRP tendons. Previously, most studies focused on the flexural behaviors of internally un-bonded prestressed monolithic beams [30; 31; 32; 33; 34; 35] rather than the PSCB-IUCFRP. The available experimental tests for PSCB-IUCFRP [21; 22] are insufficient for the study of flexural design equations.  
Numerical model analysis is a practical method generally adopted as an alternative to expensive, time-consuming experimental tests. In previous studies [25; 36], a high-efficiency nonlinear computational procedure for the numerical analysis of PSCB-IUCFRP was developed in OpenSees 2.5.0 software [37] by the authors [25]. The modeling scheme of the proposed model is shown in Figure 2. The features of this model are as follows:  
1. The model included four kinds of elements: free-slip tendon elements, segment elements, joint elements, and rigid beam connection elements.
2. The free-slip tendon element was a newly developed element type for un-bonded tendons. Considering the un-bonded phenomenon, the tendon stress was uniformly distributed along the whole tendon length, as shown in Figure 3a.
3. The segment and joint element were all modeled by the displacement-based fiber beam element, as shown in Figure 3b. For the joint element, considering the joint discontinuity, the fiber section contained no reinforcement fiber, and the tensile strength of the concrete fiber was also ignored.
4. The rigid beam connection element was employed to model the interaction between the beam and free-slip tendon elements.
5. All elements were one-dimensional element types, reducing the number of degrees of freedom and significantly improving the computational efficiency. We considered all critical mechanical behaviors in the model, such as material nonlinearity, geometric nonlinearity, open joint discontinuity, and tendon free-slip effects. Details on the finite element formulas, constitutive model, and developed procedure can be seen in the previous studies [25; 36].  
Figure 1: Schematic diagram of internally un-bonded prestressed PSCB.