esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### ElGamal Encryption and Decryption Algorithm
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* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] Digital image authentication and encryption using digital signature. In Proceedings of the 2015 International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, 19-20 March 2015; pp. 332-336. [CrossRef]
* [PERSON] and [PERSON] (1996) [PERSON]; [PERSON] [PERSON] Copyright protection of images using robust digital signatures. In Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, Atlanta, GA, USA, 9 May 1996; Volume 4, pp. 2168-2171. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Medical Image Integrity Control Combining Digital Signature and Lossless Watermarking_; Springer: Berlin/Heidelberg, Germany, 2010; pp. 153-162. * [PERSON] et al. (2000) [PERSON]; [PERSON]; [PERSON] ElGamal-like digital signature and multisignature schemes using self-certified public keys. _J. Syst. Softw._**2000**, _50_, 99-105. [CrossRef]
* [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON] ElGamal Digital Signature Algorithm of Adding a Random Number. _J. Netw._**2011**, \(6\), 774. [CrossRef]
* [PERSON] (2023) [PERSON] FEI Face Database. 2012. Available online: [[https://fei.edu.br/~cet/facedatabase.html](https://fei.edu.br/~cet/facedatabase.html)]([https://fei.edu.br/~cet/facedatabase.html](https://fei.edu.br/~cet/facedatabase.html)) (accessed on 11 June 2023). * [PERSON] (2014) [PERSON] _IIT Delhi Touchless Palmprint Database (Version 1.0)_; IIT Delhi: New Delhi, India, 2014. * [PERSON] (2008) [PERSON] _IIT Delhi Iris Database (Version 1.0)_; IIT Delhi: New Delhi, India, 2008. * [PERSON] (2000) [PERSON] The Opencv Library. _[PERSON]'s. J. Softw. Tools_**2000**, _25_, 122-125. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. An image encryption scheme based on chaotic logarithmic map and key generation using deep CNN. _Multimed. Tools Appl._**2022**, _81_, 7365-7391. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A color image encryption technique using exclusive-OR with DNA complementary rules based on chaos theory and SHA-2. _Optik_**2018**, _159_, 348-367. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] A new color image encryption scheme based on DNA sequences and multiple improved 1D chaotic maps. _Appl. Soft Comput._**2015**, _37_, 24-39. [CrossRef]  
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# Arginine-Mediated Self-Assembly of Porphyrin on Graphene: A Photocatalyst for Degradation of Dyes
## 1 Introduction  
Supramolecular self-assembly of porphyrin-based materials has attracted intensive interest from scientists as an effective protocol to design functional structures during the past decade [1; 2; 3]. \(\pi\)-Conjugated porphyrins have been extensively studied as building blocks for the construction of solid-state nanostructured materials [4; 5; 6; 7; 8], which can be utilized in many applications such as optoelectronic nanodevices, energy storage, catalysis, photodynamic therapy, sensors and photonics [9; 10; 11; 12; 13]. Supramolecular nanostructures via self-assembly are usually produced via non-covalent interactions such as hydrogen bonding, electrostatic, van der Waals interactions, \(\pi\)-\(\pi\) interactions, and coordination. These soft materials are fabricated by self-assembly protocols including ionic self-assembly [14], surfactant-assisted self-assembly (SAS) [15], and reprecipitation [16; 17]. Many well-defined and controlled nanostructures such as nanoprisms, nanotubes, nanofibre bundles, nanorods, nanosheets, nanospheres, nanoclovers, nanowires and nanoparticles have thus beenreported [14; 16; 18; 19; 20; 21; 22]. Recently; visible-light photocatalytic applications of self-assembled porphyrin nanostructures have been extensively reported [23; 24; 25]. [PERSON] and colleagues reported the dependence of photocatalytic performance on various hierarchically structured porphyrin nanocrystals such as nanosheets, octahedrons and microspheres [26]. More recently, we successfully fabricated porphyrin nanobelts via arginine-induced self-assembly of tetrakis (4-carboxyphenyl) porphyrin (TCPP), and used the structure for the photodegradation of model pollutant Rhodamine B (RhB) under visible-light irradiation [27; 28].  
Graphene, due to its large surface area, high number of adsorption sites, and good charge transfer properties has been demonstrated to be a good platform for semiconductor materials to enhance the photocatalytic activity [29; 30; 31; 32; 33; 34; 35; 36]. However, only a few studies have been reported so far on combination of porphyrin nanostructures with the graphene for photocatalytic applications. For example, [PERSON] et al. employed vacuum filtration methods to integrate porphyrin nanoparticles into reduced graphene oxide (RGO) for use in photocatalysis [24]. In another study, [PERSON]'s group studied visible-light photocatalytic activity of a one-dimensional porphyrin formed by self-assembly assisted by graphene oxide [37]. However, these studies faced disadvantages, being poor integration or poor distribution of the porphyrin nanostructures on the graphene substrate. Recently, we reported well-dispersed TCPP nanorods on graphene for enhanced photocatalytic performances [38].  
Herein, we fabricate a graphene nanoplates (GNPs)@porphyrin nanofiber composite via arginine-mediated reprecipitation of TCPP on the graphene surface, and use the material as a photocatalyst for degradation of a dye.
## 3 Computational Examples and Analysis
### Case Study II: Tri-Objective Optimization
#### 3.2.1 Data Description  
Raw materials for asphalt mixture with diatomite and basalt fibre included A-90# asphalt, coarse and fine aggregates, mineral filler, diatomite, basalt fibre. The corresponding technical properties as well as origins have been illustrated in the previous studies, which could satisfy the requirements of the Chinese specification JTG F40-2004. In this study, Marshall cylinder specimens were prepared following JTG E20-2011, in which the median gradation of AC-13 was selected.  
In the previous study [53], the orthogonal experiment L\({}_{16}\) (\(4^{3}\)) was designed as a three-factor and four-level experiment. The diatomic content (X\({}_{1}\)), basalt fibre content (X\({}_{2}\)), and asphalt-aggregate ratio (X\({}_{3}\)) were regarded as orthogonal input variables; _VA_ (Y\({}_{1}\)), _VFA_ (Y\({}_{2}\)), _VMA_ (Y\({}_{3}\)), _MS_ (Y\({}_{4}\)) and splitting strength at \(-10\)\({}^{\circ}\)C (\(S_{b}\), Y\({}_{5}\)) were used as output variables. These 16 designed groups of asphalt mixture specimens were tested and the orthogonal experimental results including volumetric properties as well as high-temperature and low-temperature performances are listed in Table 6.  
Figure 8: Performance verification between the RSM and GPR-AWMOPSO algorithm: (a) high- and low-temperature performances; and (b) moisture stability.
## 2 Methods
### Technical Details
#### 2.4.1 Methods for Forecasting
The average of all trees' predicted value is then considered as the final predicted value [38]. From a forecast perspective, RF regression ability can be used for a time series data set for value prediction purposes, where the performance can be improved by tuning a set of parameters and a variable selection (future selection). As a predictor RF is based on growing \(M\) randomized regression trees, where a predicted value is denoted by a random variable that re-samples a fitting set before growing \(M\) trees. Wavelet-Artificial Neural Network (W-ANN): The artificial neural network is a deep learning model that operates as a universal approximator and can accurately predict values. The model consists of a network structure with input, hidden, and output layers and considers non-linearity between the data points. This mentioned strength enables ANN to forecast future time series values [39; 40]. However, to decrease and smooth the time series data and further increase the predicted accuracy, wavelet transformation can be used in combination with an ANN model. Wavelet transformation enables a counter approach to decompose the series and cope with local trends and seasonality. We refer to [41; 42] for detailed discussion on W-ANN. The main advantages are ANN's ability to learn, comprehend, and approximate solutions to learning problems that are complex in nature, but the methodology is computationally intensive and hyper-parameter sensitive [43]. Wavelet-Long Short-Term Memory (W-LSTM): In the field of deep learning are, LSTM models, which are an extension of Recurrent Neural Network (RNN). An LSTM model enables the benefit of handling non-linearity and is able to cope with sequential data while capturing long-term dependency. The LSTM model consists of a memory cell and an output gate where an activation function controls the output [44]. Hence, the LSTM model does incorporate additional parameters in comparison with the ANN model [45], but as previously mentioned, the wavelet transformation allows for the decomposition of a time series data set, which is the main argument for using WT on the LSTM model [41]. While we evaluated performance, we tested standard LSTM and W-LSTM. We found LSTM offers the advantage of handling long data sequences and complex learning patterns, but the method is more prone to overfitting, especially when training data are limited. Although LSTMs were designed to mitigate the vanishing gradient problem, in practice, they are still prone to this issue.
## 3 Results
### Primary Outcomes  
For the hamstring flexibility, the time factor (\(p\)! 0.001), condition factor (\(p\) = 0.04), and time factor \(\times\) condition factor (\(p\)! 0.001) were significant. In the post hoc test, participants demonstrated significant improvement after FB intervention relative to their baseline data (immediately, percentage change: +5.62%, \(p\)! 0.001, ES = 0.8; 20 min later, percentage change: +4.95%, \(p\)! 0.001, ES = 0.72), but did not exhibit significant improvement after EB use (immediately, \(p\) = 0.079; 20 min later, \(p\) = 0.689; Table 1). Furthermore, compared with the EB intervention results, the FB results indicated significant improvement both immediately (\(p\)! 0.001) and 20 min later (\(p\)! 0.001). The difference between the conditions was associated with a large effect size in favor of FB intervention immediately (ES = 2.09) and 20 min later (ES = 1.58; Table 2). For quadriceps flexibility, the time factor (\(p\) = 0.642) was not significant, but the condition factor (\(p\) = 0.006) and time factor \(\times\) condition factor (\(p\) = 0.038) were significant. In the post hoc test, participants demonstrated significant improvement after the FB intervention relative to their baseline data (immediately: percentage change +2.24%, \(p\)! 0.001, ES = 0.32; 20 min later: percentage change +2.23%, \(p\)! 0.001, ES = 0.33), but they did not demonstrate significant improvement after EB intervention (immediately: \(p\) = 1; 20 min later: \(p\) = 1; Table 1). Furthermore, compared with EB intervention, FB intervention resulted in significant improvement both immediately (\(p\) = 0.01) and 20 min later (\(p\) = 0.03). The difference between conditions was associated with a medium-to-large effect size in favor of FB intervention immediately (ES = 0.76) and 20 min later (ES = 0.84); Table 2).
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### Search for Peaks Using Fast Fourier Transformation  
For the purpose of calculating the 2D search for peaks in the spectrum, we leverage the Fast Fourier Transformation (FFT) to calculate the azimuth angle on a set of given elevation angles.  
According to Equation (27), we let  
\[V(\phi;\beta)=v^{H}(\phi)J(\beta)(W\text{$U_{n}$}U_{n}^{T}W^{H})J(\beta)v(\phi) \tag{28}\]  
donate the null spectrum at the elevation specified by \(\beta\). We also let \(Q_{\beta}=J(\beta)(W\text{$U_{n}$}U_{n}^{T}W^{H})J(\beta)\), and then we can get  
\[V(\phi;\beta)=\sum_{l=-(M^{\prime}-1)}^{M^{\prime}-1}a_{\beta}(l)e^{jl\phi}, \tag{29}\]  
where \(a_{\beta}(l)=\sum_{i,j:-i=l}Q_{\beta}(i,j)\). The matrix \(Q_{\beta}\) is Hermitian, so \(a_{\beta}(-l)=a_{\beta}^{*}(l)\). Now, we can express the null spectrum in terms of the discrete time Fourier transform of the \(M^{\prime}\) point sequence:  
\[a_{\beta}^{\prime}=\{a_{\beta}(0),2a_{\beta}(-1),\dots,2a_{\beta}(-M^{\prime} +1)\}. \tag{30}\]  
Finally, we get  
\[V(\phi;\beta)=Re\{A_{\beta}^{\prime}(\phi)\}, \tag{31}\]  
where \(A_{\beta}^{\prime}=\sum_{l=0}^{M^{\prime}-1}=a_{\beta}^{\prime}e^{-jl\phi}\).  
For a given elevation angle, the null spectrum can be evaluated at \(L\) equi-spaced azimuth angles \(\phi_{l}=\frac{2\pi l}{L},L=0,1,\dots,L-1\) using an \(L\) points FFT of the sequence \(a_{\beta}^{\prime}\), and when \(L>M^{\prime}\), \(a_{\beta}^{\prime}\) is appropriately zero padded.
## 6 Interpretation and Results
### Line 3 and Line 4  
The length of the L3 line is 680 m, with 17 measuring points spaced 40 m apart. L3 is running NW-SE with an azimuth angle of 105 degrees. The electric sounding inversion results of the L3 line indicate that the survey line predominantly exhibits the typical contact between two stratigraphic layers. The upper section primarily consists of argillaceous  
Figure 10: Hydrogeological profile of lithological log ZK01 and the surrounding research area.  
Figure 9: Lithological map and drilling verification borehole chart of the ZK01 well.  
-slate characterized by low resistivity and anomalous reaction, whereas the lower section is predominantly composed of quartz sandstone exhibiting high resistivity and anomalous reaction (Figure 11). An anomalous response in the resistivity between the measuring points (160-120) suggests the presence of cracks in this particular region.  
The length of the L4 line is 640 m, with 16 measuring points and the distance between them is 40 m L4 is running N-S with an azimuth angle of 10 degrees. The findings of the electrical sounding conducted on L4 indicate that the upper section of the profile primarily consists of argillaceous slate with a low resistivity. Conversely, the lower section is characterized by a higher resistivity, primarily attributed to the presence of quartz sand slate. There is speculation regarding the presence of fissures within the contact interface of the two strata.  
The ZK04 verification hole was drilled to investigate the potential for aquifers in the area based on electrical sounding data and the results from previous verification holes. The hole was found to have four resistivity layers, with the first layer being a low-resistivity layer of quaternary and argillaceous slate. The second layer was a high-resistivity sandy slate, while the third layer had reduced resistivity and was believed to have a high argillaceous composition. The fourth layer was another high-resistivity sandy slate (Figure 12). Although the lithological layers were identified as expected, the core of the hole had no obvious traces of water passage, and the pumping test yielded only 30 tons per day, which was unsatisfactory. Analysis of the verification results indicated that the relatively flat contact surface of the lithological layers hindered the formation of crushing, leading to the low water yield. To improve results in future verifications, it is recommended to choose areas with more favorable lithological interfaces that are easier to form broken contact surfaces.  
After employing the electrical resistivity imaging method in the Nanshan Huarong groundwater survey, it was determined that the survey area contains indications of faults. In addition, there are two areas of structural damage, which are presumed to be the result  
Figure 11: Line 3 (**a**) shows the resistivity distribution with depth and (**b**) shows the hydrogeological profile of the survey line.  
of the intersection of distinct directional faults, causing rock fragmentation, an increase in water content, and a decrease in resistivity.
## 2 Materials and Methods
### Optimization Function  
To determine optimal sensor positions, an optimization function is required, combining the environment and sensor models based on the selected metric. This function  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{r_{\mathrm{p}}}\) [m]} & \multicolumn{2}{c}{**Environment 1**} & \multicolumn{2}{c}{**Environment 2**} & \multicolumn{2}{c}{**Environment 3**} \\  & **2D** & **3D** & **2D** & **3D** & **2D** & **3D** \\ \hline
1 & 36 & 216 & 20 & 152 & 23 & 138 \\
0.5 & 115 & 1265 & 85 & 1079 & 88 & 968 \\
0.1 & 2331 & 116,550 & 2117 & 115,530 & 2099 & 104,950 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of voxels for two-dimensional and three-dimensional representations of used environment models.  
represents a minimization problem suitable for various optimization algorithms that are non-derivative, nonlinear and constrained.  
The metric chosen for this optimization is the area coverage ratio, which indicates the proportion of the target area that is covered by the sensors. The goal of the optimization is to minimize the global loss value, which is essentially the complement of the coverage.  
The loss \(l_{\text{vs}}\) associated with each sensor-voxel pair is calculated as the complement of their visibility:  
\[l_{\text{vs}}(v_{i},s_{j})=1-v_{\text{vs}}(v_{i},s_{j}). \tag{5}\]  
Since a voxel can be visible from multiple sensors, the voxel loss \(L_{\text{v}}\) is calculated as the product of the losses from each of the \(m\) sensors within the set \(S\):  
\[L_{\text{v}}(v_{i},S)=\prod_{j=1}^{m}l_{\text{vs}}(v_{i},s_{j}). \tag{6}\]  
The global loss value \(L\) is then obtained by averaging the individual voxel losses \(L_{\text{v}}\) over all \(n\) voxels within the environment \(V\):  
\[L(V,S)=\frac{1}{n}\sum_{i=1}^{n}L_{\text{v}}(v_{i},S). \tag{7}\]  
Sensor positions, yaw and pitch angles are characterized by continuous values, while the voxel positions adopt discrete values derived from the environment's rasterization parameter. Although the proposed solution utilizes a loss minimization function that aims to reduce the loss value \(L(V,S)\), a coverage value \(C(V,S)\) is used in the results:  
\[C(V,S)=1-L(V,S). \tag{8}\]  
Moreover, this coverage value \(C(V,S)\) can be used for optimization problems that utilize a maximization function.
## 2 Materials and Methods
### Biocompatibility Studies  
#### 2.4.1 Indirect Cytotoxicity  
The indirect cytotoxicity of the samples was evaluated following the ISO 10993-5 standard [56] with human foreskin fibroblasts (hFFs, Merck Millipore Corporation, Bedford, USA). Details on the protocol can be found elsewhere [29]. Briefly, the non-treated and plasma-treated samples were sterilized in ethanol for 10 min, washed three times with Phosphate Buffer Saline (PBS) and immersed in DMEM for 72 h at 37 \({}^{\circ}\)C. The hFFs were cultured at a cell density of 5 \(\times\) 10\({}^{3}\) cells/well on a 96 well tissue culture polystyrene (TCPS) plate for 24 h. Dilutions of the sample extracts (1:1, 1:10, 1:100, 1:1000) were used to incubate the cells for 24 h, and afterward, the cells were lysed with mPER in order to quantify the cell viability with the LDH kit.
## 4 Experimental Results
### Comparison with Mean RSS Imputation  
The proposed system applies the EM imputation method to deal with the missing data in the training set. This section explores the performances of the two missing data imputation methods. The only difference is the missing data in the training samples are replaced with the average RSS value for the corresponding AP.  
Table 6 gives good averaged results even using the mean RSS imputation for the stop and go movements, although the accuracy is always worse than the one with the EM imputation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Trajectory** & **Mismatch with Mean RSS Imputation** & **Mismatch with EM Imputation** \\ \hline
1 & 5.10\% & 2.51\% \\
2 & 3.53\% & 1.84\% \\
3 & 3.68\% & 1.96\% \\
4 & 4.05\% & 2.98\% \\
5 & 3.85\% & 2.95\% \\
6 & 3.29\% & 2.50\% \\
7 & 3.96\% & 2.80\% \\
8 & 4.32\% & 3.01\% \\
9 & 3.94\% & 2.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean RSS imputation vs. EM imputation.  
Figure 13: Optimal K in dependence of matching accuracy for Trajectory 6.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 5 Mesh Networking with IEEE 802.15.4
### Native Routing  
The native routing approach proper of LWMesh performs route discovery during the normal traffic delivery, thus having a very low overhead associated with the condition of not having a route, hence similar to the overhead associated with the transmission of a regular broadcast frame. Therefore, the obtained route entries will be employed for subsequent transmission without any additional route discovery. If each node has a sufficient memory to save the routing table, all nodes are able to discover all possible (active) routes in the network, even with the possibility to maintain multiple options for the same destinations. In detail, a routing table is updated upon transmission or reception of a frame, while nothing is done if the source node's MAC address demonstrates that the sending device is not able to perform routing capabilities and, thus, it can not be marked as a link of the route. Thanks to the presence of the Link Quality Indicator (LQI), the entry is updated and the source node becomes the next-hop _iff_ (i) the routing table contains an entry towards the source node's address, (ii) the next-hop address differs from the source MAC address of the received frame, and (iii) the received frame has a LQI better than the LQI contained in the routing entry  
At the end, the native routing maintains the routes with the best LQI on each link and operates local optimization, even if it does not guarantee best routes. Finally, native routing admits the alteration of the routing entries in the following cases (with that, normally the record in the routing table never times out or expires): (i) a record is superseded with a new one if there is no available space and a new entry has to be placed in the routing table (in this case, considering the rank field as metric, the least active entries are discarded first); and (ii) a record is deleted from the routing table when it reaches a score value equals to 0, as discussed before.
## 2 Methods
### Box Plot Anomaly Detection Mechanism
The energy consumption data are analyzed using a rolling window approach, where a box plot is constructed for each window of data. Anomalies are flagged when the energy consumption falls outside the range defined by (5). As the data are continuously updated, the IQR for anomaly detection adapts accordingly to account for the natural variability in consumption, ensuring that the detection thresholds evolve over time.