esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Performance of IBSA_Net on Other Crops  
While the experiments conducted in this study primarily focused on tomato disease leaves, the feature extraction and identification capabilities of IBSA_Net can be extended to other crops. To test this, we evaluated the model's performance on apple and chili pepper leaves, the dataset examples are shown in Figures 14 and 15. The apple leaf dataset comprises five categories: Alternaria leaf spot, Brown spot, Grey spot, Mosaic, and Healthy, while the chili pepper leaf dataset consists of two categories: Bacterial spot and Healthy. These datasets were obtained from [PERSON]'s paper and PlantVillage, respectively [37]. The datasets were split into an 8:2 ratio for training and testing, and the results are presented below.  
From Tables 9 and 10, it is evident that both IBSA_Net and EfficientNet outperformed the other four models. Although EfficientNet achieved 0.7% higher accuracy than IBSA_Net on the apple leaf disease dataset, IBSA_Net exhibited superior performance in terms of Precision, Recall, and F1-score evaluation metrics. Furthermore, on the pepper leaf disease dataset, IBSA_Net outperformed all other five networks in all evaluation metrics. The results obtained from the apple and pepper leaf datasets confirm the efficacy of IBSA_Net in recognizing diseases in different crops. Remarkably, IBSA_Net demonstrated remarkable disease recognition and generalization abilities, even in the presence of complex image backgrounds.  
Figure 14: Apple leaf disease dataset.  
Figure 15: Pepper leaf disease dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AlexNet** & **VGG** & **GoogLeNet** & **MobileNetV3** & **EfficientNet** & **IBSA_Net** \\ \hline Early\_blight (PlantVillage) & 0.83\% & 13.75\% & 28.56\% & 24.79\% & 41.25\% & 47.38\% \\ Early\_blight (PDDA) & 26.25\% & 11.67\% & 52.25\% & 59.83\% & 75.63\% & 78.94\% \\ Target\_Spot (PlantVillage) & 8.91\% & 12.65\% & 17.34\% & 22.34\% & 43.21\% & 40.27\% \\ Target\_Spot (PDDA) & 41.46\% & 47.59\% & 71.85\% & 67.54\% & 79.84\% & 87.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Percentage of warm and sick areas in the attentional heat map.
## 2 Magnetic Characterization Techniques
### Magnetorelaxometry (MRX)  
Another common technique for magnetic characterization of the nanoparticles is magnetorelaxometry. According to [37], the first magnetometer used for magnetorelaxometry measurement purposes was the superconducting quantum interference device (SQUID) magnetometer. This technique, in comparison with other existing techniques, is fast as every measurement needs a few seconds in order to be achieved [19], and there is no need for a high dynamic range [38; 39; 40]. Consequently, the method presents improved behavior in terms of calibration, as well as bias and temperature stability. Magnetorelaxometry is a technique that measures the relaxation of previously aligned magnetic moments of the magnetic nanoparticles when an external magnetic field with a range of 1-2 mT is applied for 1-2 s [8] and then ceases to exist [1; 20; 32; 41; 42; 43; 44; 45]. In other words, this method measures the relaxation of the magnetization of magnetic nanoparticles after switching off a magnetizing field H\({}_{\text{mag}}\)[41; 45]. The relaxation mechanisms are divided into two categories: the Brownian relaxation and the Neel relaxation. The first mechanism is related to the movement of the nanoparticles due to the temperature. The second mechanism exists because the magnetization vector rotates inside the particle [41; 46]. The time of the relaxation can determine the size of the particle.  
Comparing the magnetorelaxometry method to the magnetic susceptibility and the photon correlation spectroscopy method, the first is more accurate for the nanoparticle characterization procedure [8]. In addition, the MRX method is a rapid method, which allows investigation of both binding and aggregation kinetics and can be applied in opaque media, while enabling the study of the core properties and the hydrodynamic size distribution of the nanoparticles [8; 19; 20; 47]. Moreover, compared to ACS and PCS, this method evaluates with a limited number of nanoparticles (411) [8]. Using this method, one can achieve rapid and accurate characterization of the material, as MRX gives details about the volume of the core, the anisotropy constant, and the energy barrier distribution [46]. Another important advantage of this method is its ability to provide separation between bound and unbound nanoparticles, which is very helpful in the case of biochemical assays [45].  
In addition, in the international literature [48], another type of magnetorelaxometry is also referred to, namely temperature dependent magnetorelaxometry (TMRX). This method enables the characterization of even smaller nanoparticles and can be applied in lower temperatures, so that nanoparticles that cannot be detected at room temperature due to short relaxation time now can be detected.  
There are many types of magnetometers [17; 42; 46] that can be used for magnetorelaxometry purposes such as optically pumped magnetometers (OPM), superconducting quantum interference devices (SQUID), giant magnetoresistive sensors (GMR), Hall effect magnetometers, and fluxgate magnetometers.
# Positively Charged Lipid as Potential Tool to Influence the Fate of Ethosomes
## 2 Materials and Methods
### In Vivo Tolerability Studies  
In vivo studies were performed on healthy human volunteers using X-Rite SP60 portable sphere spectrophotometer (X-Rite Incorporated, Grandville, MI, USA), which represents a colorimeter able to detect any changes in skin color [19]. Six volunteers were broadly informed about the procedures and the aim of the study and gave their written consent to participate. The experimental protocol provided that all subjects enrolled in the study rested under room conditions (22 \(\pm\) 3 \({}^{\circ}\)C and 40-50% relative humidity) for 30 min before starting with the study. Four sites (each one 1 cm\({}^{2}\)) were outlined on the ventral surface of both forearms of each volunteer and baseline values of erythema index (E.I.) were recorded before applying the samples. Hill Top chambers (Hill Top Research, Inc. Cincinnati, Maimville, OH, USA) were filled with 1 mL of each sample and applied to the defined sites. Saline solution (0.9% \(w/v\) NaCl) was applied on one site as control, the other sites were treated with conventional ethosomes and ethosomes made of Phospholipon 90 G:DOTAP at increasing concentrations (10:0.5 and 10:1, molar ratio). Data were reported as the variation of erythema index (\(\uplambda\)E.I.) values following treatment with samples compared to baseline values. The induced erythema was monitored over 6 h, 24 h and 48 h, considering the following equation:  
\[\text{E.I.}=100\bigg{[}log\frac{1}{R560}+1.5\bigg{(}log\frac{1}{R540}+log \frac{1}{R580}\bigg{)}-2\bigg{(}log\frac{1}{R510}+log\frac{1}{R610}\bigg{)} \bigg{]} \tag{4}\]  
where E.I. represents the erythema index; 1/R is the inverse reflectance at the wavelength corresponding to the relative absorption peaks of hemoglobin (\(\uplambda\) = 540 nm; \(\uplambda\) = 580 nm; \(\uplambda\) = 560 nm) and melanin (\(\uplambda\) = 510 nm and \(\uplambda\) = 610 nm).
## 3 Experimental Validation and Discussions  
### The PP Specim Preparation  
Figure 6 shows the PP and the prepared test specimens fabricated by using Three-dimensional technology. The PP was printed with a fine layer resolution of 0.1 mm. The PP sample was designed with thickness \(L_{pp}\) = 1 mm, sample diameter 29 mm, and hole diameter \(d_{pp}\) = 2 mm. The selection of these structural parameters can better provide reasonable resistance, and the use of the appropriate perforation ratio range will also be conducive to further research. It should be noted that it was printed with hole spacing \(b_{pp}\) = 5 mm for the PP1. The printed specimen is rigorously measured and selected in order to comply with the design requirements. The measured parameters of the PP1 are listed in Table 3.  
Figure 6e shows the B and K impedance tube for measuring the sound absorption coefficient of the composite structure. Based on the principle of the transfer function, the double microphone method is used for the sound absorption experiment measurement. The ambient temperature of the laboratory is 20.1 \({}^{\circ}\)C, the relative humidity is 45%, the sound velocity in the air is 343.65 m/s, and the air density is 1.2 kg/m\({}^{3}\). The inner diameter of the impedance tube is 29 mm, and the acoustic characteristics are measured in the frequency range of 500-6400 Hz.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample Number** & **Perforation Ratio**, \(\phi_{pp}\) (\%) & **Hole Diameter**, \(d_{pp}\) (mm) & **Hole Spacing**, \(b_{pp}\) (mm) & **Plate Thickness**, \(L_{pp}\) (mm) & **Specimen Diameter**, \(D_{pp}\) (mm) \\ \hline PP1 & 6.18 & 2.02 & 5.1 & 1.02 & 28.58 \\ \hline \end{tabular}
\end{table}
Table 3: The measured parameters for PP1 test specimen.
## 4 Experimental Protocol to Assess W2 reg on Multi-Class Classification with NLP Data
### Neural Network Model and Baseline Training Strategy  
Our task was to predict the occupation using only the textual data of the biography. We did this by using a RoBERTa model [8], which is based on the transformer architecture and is pretrained with the Masked Language Modelling (MLM) objective. We specifically used a RoBERTa base model pretrained by Hugging Face. All information related to how it was trained can be found in [8]. It can be remarked that a very large training dataset was used to pretrain the model, as it was composed of five datasets: _BookCorpus_[40], a dataset containing 11,038 unpublished books; _English Wikipedia_ (excluding lists, tables, and headers); _CC-News_[41], which contains 63 millions English news articles crawled between September 2016 and February 2019; _OpenWebText_[42], an open-source recreation of the WebText dataset used to train GPT-2; _Stories_[43], a dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. Pre-training was performed on these data, by randomly masking 15% of the words in each of the input sentences and then trying to predict the masked words After pre-training RoBERTa parameters on this huge dataset, we then trained it on the 400.000 biographies of the _Bio_s dataset. The training was performed with PyTorch on 2 GPUs (Nvidia Quadro RTX6000 24 GB RAM) for 10 epochs with a batch size of 8 observations and a sequence length of 512 words. The optimiser was [PERSON] with a learning rate of \(10^{-5}\), \(\beta_{1}=0.9\), \(\beta_{2}=0.98\), and \(\epsilon=10^{6}\). The computational time was about 36 h for each run. We want to emphasise that 5 runs of the training procedure were performed to evaluate the stability of the accuracy and the algorithmic biases. For each of these runs, we split the dataset into 70% for training, 10% for validation, and 20% for testing. We denote, as the baseline models, the neural networks trained using this procedure.
## 3 Results and Discussion  
### Characterization of ZIF-8@AuNPs  
The synthesis and detection procedures are illustrated in Figure 1. The MOFs were synthesized via stirring at room temperature, and AuNPs were subsequently coated inside. As shown in Figure 1a, the surface of the ZIF-8 crystal appears smooth, exhibiting a regular dodecahedral structure with high crystallinity. The average particle size was approximately 50 nm. By interrupting the growth process, the size of the ZIF-8 nanoparticles can be controlled to obtain the desired particle diameter. However, the surface ligands still possess numerous binding sites, leading to the slight aggregation of ZIF-8. The aggregation of the ZIF-8 particles encapsulates Au nanoparticles. Consequently, the diameter of the ZIF-8@AuNPs increased, as illustrated in Figure 1b. The corresponding TEM images, shown in Figure S1, illustrate that the ZIF-8 nanoparticles aggregate in a three-dimensional manner. Moreover, characteristic signals of Au were also observed in the XPS and EDS results, as shown in Figures 1c and 1d, respectively.  
The adsorption sites in ZIF-8 ensure the effective adsorption of analytes, and light compromising of the adsorption capacity was caused by the presence of AuNPs. Brunauer-Emmett-Teller (BET) tests were performed, and the results verified that AuNPs reduced adsorption capacity. N\({}_{2}\) adsorption and desorption experiments were performed at 77 K, and the pore size characterizations of ZIF-8 and ZIF-8@AuNPs were determined. Figure 2a shows that the BET surface area of ZIF-8 is approximately 1076.79 m\({}^{2}\)/g, whereas ZIF-8@AuNPs exhibits a BET surface area of approximately 1013.11 m\({}^{2}\)/g. Moreover, the total pore volume of ZIF-8@AuNPs is measured to be 0.997 cm\({}^{3}\)/g. Furthermore, the pore size distribution curve depicted in Figure 2b reveals that the average pore size of ZIF-8 is 5.118 nm, whereas that of ZIF-8@AuNPs is 3.615 nm. The decrease in pore size owing to the presence of AuNPs is also acceptable because the diameter of the analytes is much smaller than the reduced pore size.
# Dynamic Stability Assessment of High-Speed Railway Bridges Using Numerical Model Updating
## 4 Dynamic Stability Assessment for High-Speed Railway Bridges
### Algorithm for Numerical Model Updating Using the Univariate Search Method
If the variable related to the \(n\)-th target value \(\mathrm{D}_{n}\) in the \(k\)-th forward analysis of model updating is set as \(\mathrm{P}_{n,k}\), the variable to be used in the \(k+1\)-th forward analysis, \(\mathrm{P}_{n,k+1}\) can be expressed by using the search direction \(S_{n}\) and step distance \(\lambda_{n,k}\) of the univariate search method, as shown in Equation (11),  
\[\mathrm{P}_{n,k+1}=\mathrm{P}_{n,k}\times\left(1+S_{n}\lambda_{n,k}\right) \tag{11}\]  
where \(S_{n}\) is the search direction determined by the relationship between the \(n\)-th target value and the variable, and can be expressed as Equation (12). \(\lambda_{n,k}\) can be expressed as Equation (13) by using Equations (8)-(10). \[S_{n}=\left\{ \begin{array}{ll}1,&\left(d_{n,k,i}^{A}-d_{n,k-1,i}^{A}\right) /\left(\mathrm{P}_{n,k}-\mathrm{P}_{n,k-1}\right)\geq 0\\ -1,&\mathrm{Otherwise}\end{array} \right. \tag{12}\]  
\[\lambda_{n,k}=1-\sum_{i=1}^{N}f_{i}\cdot d_{n,k,i}^{A}/d_{n,i}^{M}/\sum_{i=1} ^{N}f_{i} \tag{13}\]  
The variables and target values can be iteratively updated through the forward analysis, and numerical model updating can be completed when the relative error of Equation (7) is equal to or less than the allowable error. The algorithm for numerical model updating based on the univariate search method can be expressed, as shown in Figure 6.  
Figure 6: Numerical model updating algorithm based on the univariate search method.
# A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining
## 1 Introduction
Non-dominated Sorting Genetic Algorithm (NSGA)-II [22] is a way to regard multi-objective functions rather than a single-objective function. [PERSON] et al. [23] developed an algorithm by adapting the NSGA-II model for data sanitization in PPDM. The multi-objective particle swarm optimization (MOPSO) algorithm [24] was extended from a conventional particle swarm optimization (PSO) algorithm [25], but handles multi-objective problems to find a set of Pareto solutions. However, the MOPSO framework cannot be directly applied to handle the problem of PPDM because the dominance relationships should be utilized to obtain the optimized transactions for deletion. In this study, we utilize the MOPSO framework and present a grid-based algorithm called GMPSO. The major contributions of the study are summarized below. * This is the first work regarding the design of an MOPSO-based framework in PPDM, which shows a better performance in terms of side effects compared to a conventional single-objective approach and the NSGA-II-based model. * We present two updating strategies for the global best (_gbest_) and personal best (_gbest_) solutions during the updating process of the MOPSO framework, which can be utilized in the PPDM problem for obtaining un-dominated solutions. * To speed up the evolutionary process, the pre-large concept is utilized here to speed up the process of the evaluated solution, and thus multiple database scans can be greatly avoided. * Experiment results demonstrate the performance of the designed GMPSO in terms of the time cost and four side effects as compared to a traditional single-objective algorithm and the NSGA-II-based approach. The rest of this paper is organized as follows. A literature review is provided in Section 2. Preliminary information and a problem statement are given in Section 3. The developed GMPSO with two updating strategies is described in Section 4. An illustrated example is provided in Section 5. Several experiments conducted on varied datasets are detailed in Section 6. Finally, some concluding remarks and areas of future study are discussed in Section 7.
## 6 Conclusions  
This paper proposes a novel technique for MFI by applying a convolution neural network in an FSO link. Four widely used modulation formats (OOK, BPSK, QPSK, and 16 QAM) were comprehensively investigated. The recognition effect of our training network demonstrated robustness against atmospheric optical turbulence and suggested immunity to additive noise. Successful identification with over 99.98% test accuracy was achieved for studied scenarios.  
Although the CNN algorithm achieved high recognition accuracy, the recognition accuracy cannot reach 100% when the modulation format types are similar. Therefore, in the future work, we hope to explore the application of recognition for more format types and feature-based data set construction, to improve the training time cost and accuracy. We believe that the proposed technique has the potential to be embedded in space networking and satellite-to-ground laser communication links.  
Writing--original draft preparation, Y.G.; Writing--review and editing, Y.G.; Visualization, X.L. and R.T.; Supervision, S.M. and T.J.; Project administration, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
This study did not report any data.  
This work is supported by the Research Project of Scientific Research Equipment of Chinese Academy of Sciences. The authors also gratefully acknowledge the Optical Communication Laboratory of CIOMP for the use of their equipment.  
The authors declare no conflict of interest.  
Figure 11: SNR vs. test accuracy for weak, moderate, and strong turbulence strength.  
Figure 10: Confusion matrix for test data for different turbulence strength: (a) weak, (b) moderate, and (c) strong.
# A Brief Survey of Fixed-Parameter Parallelism
## 5 Implementation Aspects
### Input Modification  
Adding or removing an element of the universe to or from a set is a constant time operation since the corresponding cell has to be updated. Doing this operation for all sets or for a group of sets is also \(FPP\) since we have \(O(n*m)\) processors, one for each cell. Adding or deleting a set of elements can be done in a similar manner.  
Changing the universe, however, is different. Deleting an element or set of elements from the universe of elements requires just ignoring the processors and cells related to these elements. On the other hand, adding elements to the universe requires creating new columns in the input representation for the introduced elements. These new cells will also require additional processors to handle them. If the number of introduced elements remains linear in \(m\), then the existing processors can handle a constant number of cells and simulate the work of a constant number of additional processors. The same applies to adding a new set to the collection of sets in the input. A new row will be required in this case, in addition to new processors. However, a simple observation can solve this case and the previous one. We should also note that composition of addition and deletion operations are also \(FPP\) by following the same steps.  
It is safe to assume that the number of sets and number of elements will not exceed polynomial functions of \(n\) and \(m\) (say \(p(n)\) and \(p^{\prime}(m)\)), respectively. If we initially know an upper bound for \(p(n)\) and \(p^{\prime}(m)\), which is generally the case, then we can assume the input initially has \(O(p(n))\) sets and \(O(p^{\prime}(m))\) elements, and the algorithm will remain \(FPP\) with as many processors as needed since the number of processors (\(O(p(n)*p^{\prime}(m))\)) is polynomial. If there is no polynomial bound for how large the input can grow, then the algorithm is not \(FPT\) in the first place, therefore it cannot be \(FPP\).  
Many algorithms tend to have the above input modification features (of deletion or addition to the input). Examples include the well known Vertex Cover algorithm, where vertices included in the solution are deleted. Other modification methods may employ reductions that consist of adding vertices to the input to transform it into an instance of another problem, before applying an FPP algorithm.
# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 3 War: Neural Computing against Cryptography
### Detecting Malicious Encryption  
Detecting and locating malicious encrypted data can help detecting ransomware and other kinds of malware. Some researchers have focused on the applications of NNs in this area [36]. Some research works focusing on malicious encryption detection in software code and network traffic are briefly reviewed in the following.  
Cryptography can be used to hide malicious data until it is needed. When encrypted data is detected and it cannot be decrypted, there is normally no way to tell if it is malicious or not. It has been proposed to use machine learning models to identify encrypted files and network traffic as malicious.
## 2 Materials and Methods
### CD44 Expression by Flow Cytometric Analysis  
The level of CD44 positivity was evaluated using a flow cytometer. Cells (1 \(\times\) 10\({}^{6}\)) were cultured in 25 cm 2 flasks and treated with 100, 200 and 400 ug/mL of the peel extract for 48 and 72 h. After treatment, cells were collected and then incubated for 1 h with anti-mouse monoclonal CD44 antibody (2 ug/mL in Phosphate buffer saline, PBS) (HCAM Antibody DF1485-Santa Cruz Biotechnology, Inc. Bergheimer, Heidelberg, Germany). For negative controls, cells were incubated with mouse IgG1 isotype immunoglobulins at a concentration of 2 ug/mL in PBS. The samples were then washed with ice-cold PBS containing 10 mM NaN\({}_{3}\) and 1% BSA (Sigma) and incubated for 30 min at 4 \({}^{\circ}\)C with a secondary goat anti-mouse IgG Alexa-Fluor 488-conjugate (Invitrogen, Eugene, OR, USA) at 1:50 dilution. After two washes in cold PBS, the cells were labeled with propidium iodide (PI, Sigma, final concentration 40 ug/mL) and immediately analyzed by a flow cytometry instrument (Gallios). Fluorescence emissions were collected through 530 nm and 570 nm band-pass filters for Alexa-Fluor and PI signals, respectively. At least 10.000 cells per sample were acquired in log mode, and only PI-negative cells were considered for analysis. The data were analyzed using Kaluza software (version 2.2), (Beckman Coulter, Milan, Italy) and expressed as mean \(\pm\) standard deviation from three independent experiments.