esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We presented a modified algorithm from previous works as a merger of two algorithms, where ECC is used to select the key pair, and then Elgamal is used for exchanging the secret key. Communication then occurs normally with secret key encryption, which is considered faster and the most secure. Moreover, fewer messages form the main key will reduce the probability of guess attacks. ECC is proven to be light; as shown in the results, the 512-bit key is equal to a 15,360 bit key in RSA, which is quite a lot bigger. We also tested the FHEEP against major well-known attacks and found it to be secure against these attacks mathematically. We also used the second level of hashing to ensure that the integrity of the message is met and that there are no replay attacks, as we are also considering the nonce in authentication messages. The algorithm is recommended for light devices that have low computation power but that possess decision capabilities. This algorithm also addresses a new type of attack called the interleaving attack, which uses a Third party or Certification authority to allow devices to act as relays, and their trust is established by the CA. FHEEP can also handle new types of attacks and will be a computationally less expensive algorithm for D2D communication in 5G. In the future, authors plan to test the algorithms over various setting such as 5G installations and do the security testing of the proposed algorithm. We also plan to do the formal analysis of the proposed algorithm to validate the security of the algorithm.  
Figure 5: Showing the Messages overhead for no of nodes.  
**Author Contributions:** Conceptualization, methodology, original draft preparation and formal analysis was done by A.A along with K.A.M; validation, supervision, and funding of the project by Z.D.  
**Funding:** This research work has been carried out in State Key Laboratory Intelligent Communication, Navigation and Micro-Nano System, School of Electronic Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China. The research work received financial supported by National High Technology 863 Program of China (No. 2015 AA124103) and National Key R&D program no 2016 YFB0502001. The authors are thankful for the financial support and acknowledge guidance and support provided by State Key Laboratory Intelligent Communication, Navigation and Micro-Nano System, BUPT.  
**Acknowledgments:** The authors appreciate and acknowledge anonymous reviewers for their reviews and guidance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Model Limitations and Future Research Directions
#### 5.2.4 Influence of Image Generation Model Iteration Numbers on Image Quality  
The iteration number is a hyperparameter in the stable model, which determines the quality of the generated image by determining the iteration numbers of the generated image. [PERSON] et al. [13] default the number of iterations to 50. Generally speaking, the more iterations, the longer it takes to generate images and the higher the quality of the generated images. Figure 6 shows the image generated by different iterations.  
It can be seen from Table 8 that with the increase of iterations, the generated pictures are more transparent and more consistent with the real world, and the details are more prosperous and more consistent with the sentence description. However, can improving the quality of generated images improve the accuracy of classification? To answer this question, we use the stable_diffusion model with different iterations to generate two groups of images on the emotion dataset. Then we directly input the two groups of images into the CLIP model to classify the images. The results are shown in the rows of Iteration50  
Figure 5: (a) Image generated using the original text. (b) Image generated using disambiguated text.  
Figure 6: The images generated using the stable_diffusion model with different iterations according to “A little cat with a white head, a black body, and a gray tail”.  
and Iteration100 in Table 8. The image generated by iteration 100 is more accurate than the image generated by iteration 50 for classification. Image quality can indeed affect the accuracy of classification. Although the accuracy here is only improved by 0.28, since we zero-shot the CLIP model directly to the task, there are no trainable parameters, so there is no training error, so the conclusion here is still credible.  
It is feasible to increase the number of iterations of the image generation model to improve the image quality. However, at the same time, the amount of calculation will also increase accordingly. All the experiments in this paper are images generated using an image generation model with 50 iterations. In future research, balancing the relationship between the generated image quality and the amount of computation required is also a problem worthy of researchers' attention.
## 2 Results and Discussion
### TFME Procedure Optimization
#### 2.2.1 Solid Phase and Desorption Solvent Selection  
Noticeably, the detector response for piperacillin was at least two orders of magnitude stronger than that for impenem at the stage of solid-phase and desorption solvent optimization. As the analysis of piperacillin was not compromised, further attention was focused on the optimization of impenem extraction.  
Four different adsorbents were tested--C18, PCA, SCX, DVB--amongst which the best extraction recovery (given as a ratio of analyte amount desorbed from TFME blade to analyte amount present in initial extraction solution) was observed for DVB as solid phase (Figure 2A). Amongst the four different tested desorption solvents--acetonitrile/water (1:1), methanol/water (1:1), acetonitrile/methanol/water (1:1) and pure methanol--the best results were obtained when a mixture of methanol:water (1:1) was used, yielding 9.44% imipenem recovery.  
While optimizing desorption parameters for TFME, one needs to consider the elution power, which cannot be greater for desorption solvent compared to the mobile phase used in LC. Otherwise, chromatographic separation will be altered towards reduced retention times, and analytes with low affinity to the stationary phase (such as impenem in the applied settings) would be eluted in a dead retention time or would form more than one peak. Indeed, chromatographic separation was strongly impaired when a mixture of isopropanol:water (1:1) was used as a desorption solvent, resulting in the elution of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & **Parent Ion** & **Product Ions** & **Collision** & **Q1** & **Q3** & **Retention Time** \\  & _m/z_ & _m/z_ & **Energy (V)** & **(V)** & **(V)** & **(min)** \\ \hline \multirow{3}{*}{Imipenem} & \multirow{3}{*}{300.00} & 141.85 & \(-\)28.00 & \(-\)10.00 & \(-\)14.00 & 1.76 \\  & & 98.05 & \(-\)28.00 & \(-\)10.00 & \(-\)19.00 & \\  & & 125.95 & \(-\)20.00 & \(-\)13.00 & \(-\)19.00 & \\  & & 143.00 & \(-\)20.00 & \(-\)22.00 & \(-\)15.00 & \\ \multirow{3}{*}{ Piperacillin} & \multirow{3}{*}{518.05} & 159.95 & \(-\)19.00 & \(-\)22.00 & \(-\)30.00 & \\  & & 359.00 & \(-\)10.00 & \(-\)22.00 & \(-\)12.00 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: LC-MS/MS parameters for impenem and piperacillin.  
imipenem within the dead retention time. For this reason, isopropanol:water (1:1) was not considered in the experiments presented here. For the methanol:water (1:1) mixture, analytes formed well shaped peaks, which were separate from each other.  
Figure 1: Shewhart charts for QC of self-made TFME blades coated with DVB for imipenem (**a**) and piperacillin (**b**) extraction at concentration 0.1 mg/L. Determined peak areas for all blades are given as grey points. Red lines denote control limit (\(\pm\) 3\(\sigma\)), yellow lines denote warning limits (\(\pm\) 2\(\sigma\)) and the blue line indicates mean peak area for all blades.
# Incorporation of UV Filters into Oil-in-Water Emulsions--Release and Permeability Characteristics
## 1 Introduction
analyzed the effects of different types of emulsifiers on the permeation through the skin of the two above-mentioned sunscreen agents from o/w emulsion [23]. The results have revealed that the emulsifying system has a significant effect on the UV filter permeation from topical formulations so the vehicle for sunscreen agents should be selected carefully. [PERSON] et al. confirmed that penetration and stratum corneum retention of OMC were dependent on the type of formulation used [14]. Recent studies have indicated that avobenzene and octyl methoxycinnamate are photochemically unstable and their absorption properties are reduced due to exposure to sunlight [24; 25]. Therefore, UV filters were entrapped in different materials to increase their photostability, sun protection, and to reduce potential skin permeation. So far, ordered mesoporous silica SBA-15 has been used to decrease the transdermal permeation of Avo and OMC [26]. It was reported that mesoporous silica containing Avo minimized the possible toxicity induced by percutaneous penetration [27]. Additionally, other inorganic vehicles, such as hydrotalcites and zeolites were also tested to increase the photostability and to prevent OMC absorption through the skin [20; 28; 29]. [PERSON] et al. found that the incorporation of encapsulated octyl methoxycinnamate into the formulations reduced the accumulation of the UV filter in the skin [30]. The decrease in the penetration of OMC was achieved when it was introduced to solid lipid microspheres [31]. [PERSON] et al. reported that the loading of OMC and Avo into lipid microparticles reduced the percutaneous penetration of both UV filters [32]. In turn, [PERSON] et al. created a formulation consisting of hybrid lipid-silica nanoparticles with OMC incorporated into the hydrogel [33]. The aim of our research was the assessment of in vitro skin release and permeation of avobenzene (Avo) and octyl methoxycinnamate (OMC) from an oil-in-water emulsion. Strat-M and cellulose membranes were applied to mimic the skin barrier. Prior to the release experiments, the sunscreen formulations obtained were characterized in terms of physicochemical parameters and stability.
# Design of NO\({}_{2}\) Photoacoustic Detection System Based on Finite Element Simulation
## 3 Design of Photoacoustic System Simulation
### The Design Size of the Resonator  
The design size of a photoacoustic cell affects the resonant frequency of the cavity and the size of the photoacoustic signal, especially the size of the cavity where the photoacoustic reaction occurs. In the past, it was difficult to intuitively show the difference of different design sizes through a theoretical analysis. The COMSOL simulation platform enables intuitive and convenient analyses. The resonant cavity with a different length L and radius R was scanned with a frequency range of 500 to 3500 Hz and a scanning interval of 5 Hz, and a simulation diagram, as shown in Figure 9, was obtained.  
Figure 8: Finite element simulation of frequency response.  
The simulation of different lengths and radii of the resonator provides guidance for the design of the resonator. It can be seen from Figure 9 that the size of the signal increases with an increase in the cavity length L and decreases with an increase in the radius R. On the other hand, an increase in the length of the resonator significantly increases the size of the cavity, and an increase in the radius of the resonator causes the laser to hit the inner wall of the resonator and produce noise. Therefore, combined with the portability of the photoacoustic cell and the collimation of the laser, the length of the designed resonator L is 90 mm, and the radius R is 4 mm.
## 4 Experimental Validation  
This section considers semantic image segmentation in two use cases that significantly differ in their image acquisition set-up, which is known to affect generalization, and in the regularity of the shapes to be segmented, which directly affects the annotation strategy. For both use cases, the section considers experiments to demonstrate that assigning pseudo-labels to unlabeled data based on models trained on a few (semi-)manually annotated samples improves the quality of the resulting data-reinforced CNN* segmentation model, compared to a CNN model trained only on the manually labeled data.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Foreground IoU (\%)**} \\ \cline{2-7}
**Method** & **1 Images** & **5 Images** & **10 Images** & **15 Images** & **25 Images** & **50 Images** \\ \hline CNN & 37.4 & 58.9 & 69.9 & 72.4 & 80.4 & 81.6 \\ Random Ferns & 74.7 & 74.0 & 77.3 & 80.0 & 79.6 & 79.2 \\ CNN* 0.45 & 72.2 & 76.0 & 76.0 & 73.7 & 74.7 & 74.3 \\ CNN* 0.50 & 80.0 & 81.3 & 82.2 & 82.4 & 82.6 & 82.7 \\ CNN* 0.55 & 75.0 & 73.3 & 76.2 & 80.3 & 79.7 & 78.2 \\ CNN* (0.45,0.55) & **84.5** & **83.3** & **84.3** & **84.8** & **85.0** & **85.1** \\ CNN* \textasciitific{}CNN & 45.7 & 65.9 & 73.8 & 75.7 & 82.8 & 82.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Foreground IoU of different methods on pollen test dataset as a function of the number of manually labeled images. In total, 250 pseudo-label samples, and 50 test samples are considered.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Pixel Accuracy (\%)**} \\ \cline{2-7}
**Method** & **1 Images** & **5 Images** & **10 Images** & **15 Images** & **25 Images** & **50 Images** \\ \hline CNN & 75.2 & 86.2 & 94.6 & 95.1 & 97.3 & 97.4 \\ Random Ferns & 97.0 & 96.9 & 97.3 & 97.6 & 97.5 & 97.5 \\ CNN* 0.45 & 9
## 2 Methods
### Electrical Resistivity Tomography (ERT)
#### 2.2.2 Theoretical Background  
Resistivity is a value that reflects various parameters in the ground, as indicated in Section 1. Soil resistivity is especially well-known as an effective indicator for understanding ground water flow and the physical and chemical properties of soil and waste, and it is known that the resistivity value mainly depends on the resistivity of pore water, the porosity, and the degree of saturation. These relationships are expressed as follows by [PERSON]'s law [23], which is widely used as an empirical formula:  
\[\rho=a\cdot\rho_{w}\cdot\phi^{-m}\cdot S^{-n} \tag{1}\]  
where \(\rho\) is the apparent resistivity (\(\Omega\cdot\)m), \(\rho_{w}\) is the resistivity of pore water (\(\Omega\cdot\)m), \(\phi\) is the porosity, \(S\) is the degree of saturation, and \(a\), \(m\), and \(n\) are experience constants. This formula provides the basic theoretical framework for relating changes in the resistivity distribution obtained by ERT to changes in subsurface water content and the progress of washout in landfills.  
Since pore water conductivity and porosity are not considered to fluctuate rapidly during water injection tests or rainfall events, it is assumed that changes in saturation are primarily responsible for the decrease in resistivity.
## 2 Materials and Methods
### Microbial Cultures  
The biodegradation culture used in Experiment A was obtained previously from another soil polluted over the long term by petroleum hydrocarbon received from a collaborating remediation company. A portion of 1 g of polluted soil was cultivated in Erlemeyer flasks (60 mL) as described in Section 2.2. and repetitively transferred to fresh media (overall \(\sim\)3 months and 6 transfers). Before use for experiment A the bacteria were stored frozen (\(-\)40 \({}^{\circ}\)C) in sterile glycerol. The culture was identified as a mixture of _Ochrobactrum_ sp. and _Bacillus_ sp. using Sanger sequencing on an ABI3500 Genetic Analyser (ThermoFisher Scientific, Waltham, MA, USA). Total DNA from the bacterial cultures was isolated using a Fast DNA Spin Kit for Soil (MP Biomedicals, Santa Anna, CA, USA) according to the manufacturer's protocol. Polymerase chain reactions (PCR) were performed to amplify the whole gene using the primers pAf (3 \(\pi\)-GAGTTTGATCCGGCTCAG-5\({}^{\circ}\)) in position 8-18 within the _16S rDNA_ gene and pHr (3\({}^{\prime}\)-AAGGAGGGTACCAGCCA-5\({}^{\prime}\)) in position 1542-1522 within the _16S rDNA_ gene [23]. The PCR products were then purified by using the Sure Clean kit (Bioline, London, UK). A second PCR run was performed using the BigDye(tm) Terminator v3.1 Cycle Sequencing Kit with pAf and pHr primers. The products were cleaned on NucleoSeq columns (Machery Nagel, Duren, Germany) then sequenced using capillary electrophoresis on an ABI3500 Genetic Analyser. The Basic Local Alignment Search Tool (BLAST) from the National Center for Biotechnology Information (NCBI, Bethesda, MB, USA) was used for sequence classification.  
The actinobacteria _Gordonia rubripertincta_ CWB2 and _Rhodococcus erythropolis_ S43 used in Experiment B were isolated in Freiberg, Germany and partly characterized [10; 19; 24; 25; 26]. Strain S43 was obtained from a former mining area heavily contaminated with heavy metals and arsenic, while strain CWB2 was isolated from plant potting soil using styrene as sole carbon source.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 2 Methodology
### Forecasting Methods  
#### 2.3.1 Temporal Convolutional Network  
[PERSON] et al. first combined various convolution methods with a one-dimensional CNN to make the CNN more applicable to time series data, and this network is called a temporal convolutional network [30]. A TCN is made up of stacked residual modules that combine extended convolution and two-layer causal convolution, and the weights of each convolution kernel are normalized. The TCN adds the nonlinear relation between convolution layers using a rectified linear unit (ReLU) and avoids overfitting by using a dropout. In general, there are four main portions in the TCN: causal convolution, one-dimensional CNN, extended convolution, and residual connection. A detailed explanation of each portion is given below.  
Causal convolution: A TCN imparts causality to convolution, so the TCN will not produce information \"leakage\", improving its prediction accuracy. Assuming that the input sequence of the model is \(\{x_{0},x_{1},\cdots,x_{T}\}\), and the expected predicted output is, causal convolution gives predicted output \(y_{t}\) at time \(t\) only decided by \(\{y_{0},y_{1},\cdots,y_{T}\}\), and not affected by \(\{x_{t+1},x_{t+2},\cdots,x_{T}\}\).  
One-dimensional CNN: The TCN uses one-dimensional CNN to generate an output sequence with an equal length to the input sequence [31], which can retain the information contained in the entire input data and construct long-term memory.  
Extended convolution: The TCN uses extended convolution to obtain a larger receptive field. For a one-dimensional sequence \(x\in R^{n}\) and convolution kernels \(f:\{0,1,\cdots,k-1\}\), the extended convolution equation \(F\) on the sequence element \(M\) is defined as:  
\[F(s)=\sum_{i=0}^{k-1}f(i)\cdot x_{m-v\cdot i} \tag{6}\]  
where \(v\) is the expansion coefficient, \(k\) is the size of the convolution kernels, and \((m-v\cdot i)\) indicates that the \((m-v\cdot i)\)_th_ element of upper layer is used.  
From Equation (6), it can be seen that enlarging the convolution kernel size \(k\) or increasing the expansion coefficient \(v\) can increase the receptive field of the network. Generally, the number of extended networks at layer \(i\) is:  
\[v=O\Big{(}2^{i}\Big{)} \tag{7}\]  
Residual connection: The TCN guarantees the accuracy of the deep network by residual connection. We define the expression of residual connection with \(x\) as the input and \(y\) as the output as follows:  
\[y=\eta(x,\rho)+x \tag{8}\]  
where \(\eta\) represents a series of convolution operations and \(\rho\) is the weight matrix of the convolution kernel.  
According to Equation (8), the output of the residual module combines the input information with the output information of the convolution calculation, which ensures the accuracy of the deep TCN network model.
## 4 Results
### Application to the Solar Cadaster of the Greater Geneva Area  
From monthly and yearly irradiation raster outputs, post-processing is carried out with GIS to calculate indicators by roof section for solar PV, and by building for solar heating and domestic hot water purposes. Indicators include: geometry (orientation, slope, available and not shadowed roof surface area, potential solar panels areas), monthly and yearly solar energy production, building energy needs coverage ratio, peak power for PV, investment and yearly maintenance costs, subsidies, and CO\({}_{2}\) savings.  
GIS layers of the results are displayed through two systems:  
1. Official \"geoportail\" of the carton of Geneva [19] (also centralized data for the Greater Geneva area) that displays and delivers the full set of the spatial layers. Experts and professionals can download them for their own use in GIS.
2. The online web interface [20] illustrated in Figure 5 is addressed to the broader public (building owners, public authorities, policymakers, etc.). It displays the main indicators and key information about the thermal and solar photovoltaic potential and gives the best and unshaded locations for solar installations. It aims at raising awareness among building owners about the existing solar potential and motivating them to invest in solar energy.  
Results can be also aggregated to areas like municipalities as illustrated in Figure 6, supporting policy makers in devising energy planning and strategies at different scales. For example, urban centers with a higher number of building roofs often have the highest potential, but also some peripheral and rural areas may offer interesting potential on farm roofs.  
As an example of helpful information for energy planners and public authorities, overall in the Greater Geneva area, potential PV peak power on building roofs totals 3800 MW. Based on statistics on electricity consumption in the Canton of Geneva, PV solar panels on roofs could potentially cover 38% of the consumption (with a potential of 1000 MW over the 62 MW already installed in 2019).
# A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects
## 2 Materials and Methods
### ResNet-50 and Transfer Learning
#### 2.2.2 Transfer Learning  
TL involves leveraging knowledge acquired from one or more source tasks to facilitate feature extraction in a target domain, particularly when data samples are limited. In this study, we loaded a TL-ResNet50 model pre-trained on ImageNet data using the PyTorch third-party library, fine-tuning the output feature parameters while keeping the remaining layers frozen. Specifically, the TL-ResNet50 model trained on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 dataset, a dataset that contains over one million natural scene images with a size of 224 \(\times\) 224 pixels. To accommodate the six types of steel defects, the original output feature of the pre-trained TL-ResNet50 model, a 2048-dimensional vector, needs to be modified to 6 dimensions. TL aids in identifying steel surface flaws' edges, textures, and shapes by extracting more generic features, thus reducing training time.