esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation
### Experiment Setup
However, there are two datasets, namely MNIST and USPS, where our approach does not yield an improvement. In these cases, the accuracy decreases, albeit by a small amount for all DR algorithms. For the USPS model, the average accuracy reduction is consistently less than 2% across all DR methods. Similarly, for the MNIST, the decrease is slightly larger but remains below 9%. And the improvements can be considerable for some models, scaling up to more than 60% for 20 NG, the model that seems to benefit more from our approach. We can visually inspect the cases that exhibit no improvement. For instance, the USPS model does not enhance across any of the employed DR algorithms following our modification. Notwithstanding, the visualizations illustrate that the differences are not substantial, as shown in Figure 6.  
Another model that exhibits a good behavior with our system is the Coil20 model. In this case, the enhancement is common to all DR algorithms but tSNE. This improvement is evident in the projections obtained, shown in Figure 7. While certain classes remain clustered even after the modifications, several clusters that were previously either split or overlapped with others now form distinct and well-defined clusters after the modification. The tSNE appears to be comparatively less performant than the other DR algorithms. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Improved +** \\ **PaCMAP** \\ \end{tabular} & \begin{tabular}{c} **Improved +** \\ **tSNE** \\ \end{tabular} & \
\begin{tabular}{c} **Improved +** \\ **Trimap** \\ \end{tabular} &
\begin{tabular}{c} **Improved +** \\ **UMAP** \\ \end{tabular} \\ \hline
20 NG & 62.98\% & 47.62\% & - & 65.66\% \\ Cifar10 & 11.09\% & 21.64\% & 12.20\% & 4.96\% \\ Coil20 & 2.61\% & 3.48\% & 2.80\% & 6.38\% \\ Fashion-MNIST & 3.54\% & 3.12\% & 2.79\% & 2.58\% \\ FlickMaterial10 & 12.92\% & 13.25\% & 26.85\% & 16.80\% \\ Har & 9.50\% & \(-\)0.27\% & 8.08\% & \(-\)1.50\% \\ MNIST & \(-\)3.25\% & \(-\)2.51\% & \(-\)8.38\% & \(-\)2.65\% \\ Sentiment & 30.46\% & 30.11\% & 75.33\% & 22.33\% \\ Spheres & 28.83\% & 16.55\% & 13.15\% & 66.43\% \\ Svhn & 17.02\% & 30.03\% & 18.39\% & 29.76\% \\ USPS & \(-\)1.61\% & \(-\)0.89\% & \(-\)1.91\% & \(-\)1.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The table presents a comparative analysis of the enhancements achieved through various Dimensionality Reduction (DR) techniques with our modification method. It displays the mean improvement obtained by employing five distinct clustering approaches. The models that demonstrate an improvement are highlighted in blue, while those that do not are highlighted in red. It is noteworthy that some models, which exhibit no improvement, are consistent across all DR techniques. Although these models exhibit a decline in accuracy, the magnitude of such reductions is negligible. Figure 6: \(Cont.\)
## 6 Protocol Comparison and Security Analysis
### Denial-of-Service Attacks  
BAPC adds to the cryptocurrency system, applying economic costs to defend against the Denial-of-Service attacks. In the registration stage, initiating payment requests requires a small amount of upfront capital. The attacker will therefore spend a large amount of upfront capital if continuously initiating payment requests. During the initial authentication phase, authentication costs cryptocurrencies, which are converted by the cost of capital. If an attacker repeatedly initiates initial authentication, the attacker incurs significant cryptocurrency costs. In the switching authentication phase, a deposit is required before authentication. When the transaction is not verified, a certain margin will be deducted. Because the authentication request initiated by the attacker cannot be authenticated, a large margin is consumed. If normal users continuously initiate requests, the satellite node can limit the number of requests from the same user address within a certain period of time.
# Natural Photosensitizers in Clinical Trials
## 4 Natural Photosensitizers Pending Clinical Trials
### Hypocrellin  
It was isolated from the parasitic fungus _Hypocrellin hambusea_[116]. As a particular type of natural photosensitizer, hypocrellin has high singlet oxygen quantum yield, low biological toxicity, and the ability to be rapidly removed from normal tissues [117]. It shows absorption maximum at 470 nm [10]. Excited hypocrellin generates ROS that damage cellular structures, including but not limited to mitochondria, leading to a decrease in their membrane potential, release of cytochrome c into the cytoplasm, and an activation of caspases (in particular, caspase-3, -7, and -9). The activation of caspases leads to apoptosis of cancer cells. It also showed that hypocrellin can affect mitochondrial oxidative stress-related signaling pathways regulated by proteins such as JunB and BAG-4, further confirming the key role of mitochondria in the mechanism of cell death induced by this photosensitizer [118]. Hypocrellin used as a photosensitizer achieved encouraging results in A431 squamous cell carcinoma cells [119; 120], A549 lung adenocarcinoma [118], MDA-MB-231 breast cancer [121], HepG2 hepatocellular carcinoma [122], and keloid fibroblasts [123; 124] or by evaluating its bactericidal activity against _Staphylococcus aureus_[125]. Its effects on LN229 glioblastoma multiforme cells were also investigated, where the focus was on factors regulating angiogenesis. The combination of PDT with hypocrellin has been shown to induce a complex angiogenic response, which can have both beneficial and detrimental effects on tumor growth [126]. However, its poor solubility, short absorption wavelength, and low bioavailability limit its use [127; 128].
## 3 Proposed Security Model  
### Network Threat Model  
The network model is assumed as homogeneous consisting of \(N\) quantity sensor nodes in the considered DT technique. In the proposed approach, it is assumed that the random way point model for every single mobile node movement though their position and location would be different withneighbor nodes at T time period. In a fixed battery scenario, to perform a competent process such as sensing data and exchanging information in MWSN, the system needs to replace dead nodes with new mobile nodes. Moreover, it is considered that every mobile sensing node can be identified through cluster head via communicating and exchanging data (i.e., battery, key, location, ID) in the form of packets to each cluster head. A distinctive key and identity will be assigned to every mobile node earlier to deployment by network administrator and the availability of bidirectional communication links between mobile nodes is also considered. It is noted that every mobile node is capable to know its location via localization method [16; 17]. The clone threats can occur through advisory actively or passively. Passive Attacks are those in which an advisory just takes the data of the original mobile node. Active attacks are capturing and stealing all the data in addition to inserting dangerous attacks in the network. Clearly, the active attacks are more harmful and malicious. An advisory can generate numerous clones of the same mobile node. In the proposed method, we suppose that advisory cannot change the key of the nodes although it has capability of fully controlling mobile nodes. In this scenario, the attacker can use the captured nodes to perform dangerous situations, as both nodes (original and clone node) have same features.
# Reduction of Coupling Interface Degrees of Freedom in Mixed-Interface Component Mode Synthesis
###### Abstract  
A new coupling interface degrees of freedom (DOFs) reduction technique for the mixed-interface component mode synthesis (MCMS) method is proposed, which referred to as the MCMS-rid method. This approach employs a set of shape functions via the linear interpolation (LI) in finite element method (FEM) to realize interface nodal coordinate transformations for each substructure, and then only a small number of interpolation basic nodes (IBNs) will be involved in mode synthesis and the following dynamic analysis. Unlike the majority of available CMS methods that retain a full dimension of the coupling interface DOFs, the MCMS-rid method allows to reduce the coupling interface DOFs significantly and enhance the computational efficiency. Three numerical models, including a rectangular beam with two ends fixed, a non-rectangular beam with the button fixed and a simplified dam-foundation system with different material properties, are presented to demonstrate the computational accuracy and efficiency of the proposed method. The results indicate that favourable accuracy with a least number of retained DOFs involved in mode synthesis can be obtained for solving eigenvalue problems when compared with other MCMS methods. The optimal number and distribution of the IBNs are discussed on structural dynamic analysis as well. It is shown that the more the IBNs are involved in mode synthesis, the better the precision that will be received. Furthermore, when the sub-regions are nearly square, the precision is best.  
mixed-interface component mode synthesis (MCMS); finite element method (FEM); coupling interface degrees of freedom; interface nodal coordinate transformation; interpolating scheme +
Footnote †: journal: Applied Mechanics and Physics Letters
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 4 Experimental Validation  
In order to verify the correctness of the above theory, first, the optimal solution of the curvature of finite element analysis was selected and the beam with a curvature of 40 m\({}^{-1}\) was prepared. The width of the prepared composite beam was 8 mm, the height was 0.2 mm, the length of the straight section was 20 mm, the radius and chord length of the arc part were 25 mm and 29.4 mm, respectively, and the arc height was 9.5 mm. The width of the PVDF pasted on the composite beam was 8 mm, the thickness was 0.11 mm, the volume width of the end mass was 8 mm, the height was 8 mm, and the length was 5 mm, as shown in Figure 13. Then, the experimental platform was built. In the experiment, the excitation signal was set by the computer and the sinusoidal signal was sent out by the vibration controller (VT-9008), which was amplified by the power amplifier (GF-20) and output to the vibration table (E-JZK-5T). The vibration table operated according to the preset excitation signal. The experimental device is shown in Figure 14.  
Under the given excitation conditions, the output end of the PEH-C was connected directly to the oscilloscope probe (open circuit) and the required output voltage signal was obtained through the oscilloscope. First, the excitation amplitude of the exciter table was set to 5 m/s\({}^{2}\) and the excitation frequency to 10-18 Hz and a frequency sweep experiment was carried out under simple harmonic excitation. Figure 15 shows the frequency-voltage diagram collected by the oscilloscope. It can be seen from the diagram that when the frequency was 14.5 Hz, the output voltage reached 24 V. After calculation, the resonant frequency measured in the experiment was 3% different from the simulation result. The maximum voltage was 4.2% different from the simulation result. After that, the excitation amplitude of the exciter table was set to 5 m/s\({}^{2}\) and the excitation frequency to 14.5 Hz and a dwell experiment was carried out under the condition of the PEH-C resonance. The experimental results are shown in Figure 16. At a frequency of 14.5 Hz, the open-circuit output voltage amplitude was stable at about 24 V. Compared with the PEH-C device, with a curvature of 100 m\({}^{-1}\), the open-circuit output voltage amplitude of the PEH-C with a curvature of 40 m\({}^{-1}\) was increased by 9%.  
Figure 12: Output voltage.  
Figure 14: Experimental platform: (**a**) control section; (**b**) vibration table with PEH-C.  
Figure 13: PEH-C with 40 m\({}^{-1}\) curvature.  
Figure 15: Frequency sweep experiment.
## 2 Materials and Methods
### Assessment of Fouling Potential in NF  
A newly developed modified fouling index (MFI) approach using a multiple membrane array system was applied to evaluate the fouling potential of RO feed water described in our previous reports [27; 28; 29; 30]. In this method, feed water (i.e., raw water from Indonesia) was first filtered by a microfiltration (MF) membrane to determine the fouling potential of particulate matter larger than 0.45 \(\upmu\)m. The permeate obtained from the first MF membrane (0.45 \(\upmu\)m, Millipore Corp., Burlington, MA, USA) was then fed to the next ultrafiltration (UF) membrane (100 kDa, Millipore Corp., Burlington, MA, USA) to assess the fouling potential of nanocolloids. Finally, feed water containing mainly organic foulants was introduced into another tight UF membrane (10 kDa, Millipore Corp., Burlington, MA, USA) with a surface area of 0.00138 m\({}^{2}\). In this method, feed water was first filtered by a MF membrane to determine the fouling potential of particulate matter larger than 0.45 \(\upmu\)m, named as particle-MFI (P-MFI). The permeate obtained from the first MF membrane was then fed to the next UF membrane named by colloid-MFI (C-MFI), which assessed the fouling potential of nano-colloids. Finally, feed water containing mainly organic foulants was introduced to the tight UF membrane by organic-MFI (O-MFI) [28; 31].  
According to the standard specified by ASTM, the MFI can be described using Equation (2) at dead-end measurements:  
\[\frac{t}{V}=\frac{\mu R_{m}}{\Delta PA}+\frac{\mu I}{2\Delta PA^{2}}V \tag{2}\]  
where \(t\) is the filtration time; \(V\) is the filtration volume; \(\Delta P\) is the applied transmembrane pressure [Pa]; \(\mu\) is the solution viscosity [Pa-s]; I is the resistivity [m\({}^{-2}\)]; \(R_{m}\) is the membrane resistance [m\({}^{-1}\)]; and \(A\) is the membrane surface area [m\({}^{2}\)] [31; 32].
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## Appendix A Wave-Current Interaction Processes
### Momentum and Energy Flux from the Wave Model
As shown above, using the full spectral wave model, it is possible to estimate both the momentum energy and energy fluxes directly from the wave breaking source terms [11; 18; 25].
## 3 Numerical Simulations
### Numerical Method  
Ansys-fluent is a CFD solver based on finite volume, which is used to discretize the equations introduced in the previous section. In the calculation model, the heat generation of the positive and negative plates of the battery is ignored. The cooling air of the battery module is the ideal gas. The turbulence intensity of the medium at the mass flow inlet boundary of is 5%, and the inlet temperature of cooling air is set to 26.85 \({}^{\circ}\)C. The pressure outlet boundary is used for the outlet, the heat transfer of the contact surface between the battery and the heat sink adopts the coupled heat transfer condition, and other walls are regarded as adiabatic conditions. The specific boundaries are shown in Figure 2. The purpose of this paper is to improve the air-cooling effect of the battery using aluminium foam. Therefore, to shorten the simulation time, the maximum heating power at 3 \(C\) discharge rate is 30 W, which is set as the average heating rate of the battery, and contact thermal resistance is ignored. The specific parameters of aluminium foams used in this work are shown in Table 2; the parameters of the heat spreader and cooling air are listed in Table 3. The energy equation and momentum equation are discretised by the second-order upwind scheme, and the coupling of pressure and velocity is solved by the SIMPLE algorithm [37]. For all simulation cases, the convergence criterion is that the residual of each governing equation is less than \(1.0\times 10^{-6}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Pores Per Inch (PPI)** & **Ligament Diameter (mm)** & **Porosity** \\ \hline
1 & 10 & 0.406 & 0.918 \\
2 & 10 & 0.406 & 0.794 \\
3 & 10 & 0.406 & 0.682 \\
4 & 20 & 0.203 & 0.924 \\
5 & 20 & 0.203 & 0.774 \\
6 & 20 & 0.203 & 0.679 \\
7 & 40 & 0.102 & 0.923 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of aluminium foam [35]. Reproduced with permission from [Applied energy]: published by [Elsevier], (2017).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Materials** & \(\rho\) **(kg m\({}^{-3}\))** & \(\epsilon_{p}\) **(J kg\({}^{-1}\) K\({}^{-1}\))** & \(\lambda\) **(W m\({}^{-1}\) K\({}^{-1}\))** \\ \hline Base fluid & Ideal gas & 1.24 & 1006.43 & 0.027 \\ Heat spread & Aluminium & 2719 & 871 & 202.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the heat spreader and cooling air.  
Figure 2: Boundary conditions of the battery module.
## 8 Conclusions  
Using the most recently introduced optimization algorithm (SMA), a unique approach for NSDI was proposed in this study as a one-stage procedure. There are always uncertainties in the measured modal parameters in typical applications of health monitoring approaches, which can lead to unreliable and inaccurate structural damage prediction. As a result, uncertainties are included in the developed NSDI process. An SMA method is updated to increase its performance in dealing with problems involving a large number of variables. An interval analysis is adopted for use with the ESMA to consider the uncertainties using the interval bounds of the uncertainties in the damage variables of the metaheuristic optimization model.  
The numerical results demonstrate that the proposed approach is capable of accurately detecting the location and severity of various damage scenarios. Based on the results it can be concluded that the proposed interval optimization method procedure can obtain accurate PoDE and DMI and can provide robust damage identification results. In summary, the proposed non-probabilistic method is able to provide accurate damage detection results and has a small prediction cost since it does not require the utilization of surrogate models. Despite these advantages, this approach may need further improvements to be implemented in the real-time recorded response of a structure, such as adding the structural identification (SI) stage, in order to convert the acceleration response to the frequency response. Consideration of uncertainty in loadings will be pursued in our upcoming papers.  
[PERSON], the first author, was responsible for developing the methodology introduced in this work and carrying out the majority of the research project. Both analytical derivations and statistical methods were closely tested and confirmed by [PERSON] and [PERSON], who suggested the procedures utilized. [PERSON] provided his expertise and technical background in AI and assisted [PERSON] with carrying out the numerical analyses and implementation of algorithm. [PERSON] with his expertise in statistical methods and feature extraction worked closely with [PERSON] and [PERSON] throughout the project. [PERSON] worked closely with [PERSON], [PERSON], [PERSON] and [PERSON] in the reviewing and the editing phase and final verification of the analytical derivations. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The support for the participation of SC Kuok was provided by the National Natural Science Foundation of China (Grant No. 52178115), the Science and Technology Development Fund, Macau SAR under research grant SKL-IOTSC-2021-2023, and the Guangdong-Hong Kong-Macau Joint Laboratory Program under grant number 2020B1212030009. These generous supports are gratefully acknowledged.  
Not applicable.  
This research received no Data Availability Statement.  
The authors declare no conflict of interest.
## 4 Results
### Features for a Calibration Provider  
The creation tool for DCCs was implemented in two ways. The first one was a conversion tool that was added to an existing in-house-developed calibration certificate creation software that allowed the creation of a DCC in a similar way to the conventional certificate. The second method was by using a simple data input form that was developed for manually creating DCCs.  
The signing of the documents was implemented using organizational seals, which are digital signatures that identify the organization authenticating the document instead of the person creating it. In the POCs, the identifications of the persons who performed the calibration were stated in the DCC. The organizational seals are well suited for organizations that already have established personnel authentication methods that can be applied to authorize the rights to seal DCCs. As the rights are managed based on the existing authentication system, the issuing or removing of the signing rights happens automatically as a part of organization's normal onboarding and offboarding processes. Depending on the system used by the calibration provider, two versions for sealing were implemented:  
* A physical signing server using an Intel NUC mini PC, allowing the sealing to be done locally and separated from the cloud platform;
* A cloud-based sealing tool as a part of the platform, enabling the sealing and sharing of the document with the same user interface.  
The seals for the DCCs were created and validated using public and private key pairs designated to each organization issuing the DCCs in the POCs. For the authorization of the keys, a simple prototype PKI was set up at Aalto University, with the root key being securely stored in a safe. The keys issued for the testing were safely either stored in the NUCs or in the cloud depending on which system the organization was using. In either case, the authorization of key use was managed by the corresponding organization. For testing purposes, also separate, universally usable test users were created to enable using the platform without individual AD credentials.  
Figure 6: An illustration of the DCC Platform-based exchange of DCCs using the APIs of the platform.  
To ensure that the format of the DCC was following the agreed structure, the DCC was validated against the DCC schema or good practice schema before a seal was added to the file. To further ensure that the information was interpretable, the files were canonicalized as a part of the seal creation process to remove non-meaningful variances in the documents. The signing service also included a function for validating the seals.
## 2 Experiments Details
### Characterization of NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb, Er NPs-PMMA
materials was at 1530 nm, corresponding to the \({}^{4}\)I\({}_{13}\)/2\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) energy level transition of Er\({}^{3+}\). Nanoparticles coated with a shell had a stronger emission than cubic-phase NaLuF\({}_{4}\) of the same size. The fluorescence performance was significantly improved by nearly six times. The full width at half-maximum (FWHM) of the NaYF\({}_{4}\)/NaLuF\({}_{4}\): Er, Yb NPs fluorescence spectrum was broadened to 62 nm. The absorption spectra of the core film were characterized by a Cary500 Scan UV-Vis-NIR spectrophotometer, as shown in Figure 4d. The typical transitions of ground-state Er\({}^{3+}\) to each excited-state energy level corresponded to the absorption peaks in the figure. Yb\({}^{3+}\) was codoped in, and the absorption peak at 976 nm was much stronger than that of pure doped Er\({}^{3+}\), which facilitated the absorption and improved the efficiency rate of pump light. Figure 4: (**a**) AFM 2D images and (**b**) AFM 3D images of the NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb, Er NPs-PMMA; (**c**) Emission spectrums of NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb, Er NPs and NaLuF\({}_{4}\): Yb, Er NPs under 980 nm excitation; (**d**) Absorption spectrum of NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb, Er NPs-PMMA. Figure 3: (**a**) Schematic images of core-shell NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb\({}^{3+}\), Er\({}^{3+}\); (**b**) Schematic images of nanoparticles copolymerized with MMA.