esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Clustering Discovery of Illegal Websites  
In the process of clustering the 3.7 million data, the previously trained multi-layer perceptron clustering model is used. To handle the large amount of data, 10,000 data points are read at a time for clustering, and the clustering results are aggregated. Afterward, a manual review is conducted to remove a small number of normal websites, resulting in a dataset consisting entirely of illegal websites. The selected illegal website dataset includes the attributes listed in Table 2.  
After applying the clustering model, we identified 467,382 data points as classified illegal websites. From these results, we further screened out 431,820 potential illegal website data points based on threshold criteria. Subsequently, through manual screening,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attributes** & **Describes** & **Data Type** \\ \hline ip & IP address & character string \\ port & port number & continuous data \\ server & web container & discrete data \\ domain & domain name & text (domain name) \\ title & site title & text \\ org & organization & discrete data \\ country & country & discrete data \\ city & city & discrete data \\ html & HTML original code & text \\ screen & website screenshot & image \\ header & Web response header information & text \\ subject.CN & Common name information for SSL certificates & text (domain name) \\ subject.N & SSL certificate subject optional name & text (list of domain names) \\ links & Site external link & text (list of domain names) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attributes description.  
Figure 8: Comparison of experimental results.  
we selected a total of 397,275 illegal website data points. Each attribute of these illegal website data points is stored in a JSON file. The \"screen\" attribute and \"html\" attribute are stored separately as independent files, residing in two distinct directories. In the JSON file, only the relative paths to these files are stored.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 4 Discussion
### Model Limitations  
The proposed porosity prediction model based on CNN-BiLSTM-Transformer demonstrates strong temporal feature-capturing capabilities, effectively incorporating neighborhood information and well logging curve characteristics for comprehensive porosity prediction. However, as the number of network layers increases, the model becomes more complex, requiring greater computational resources. Additionally, the model relies on large, continuous datasets to capture temporal features effectively, thereby enhancing prediction accuracy and generalization ability. The study area provides an extensive dataset of continuous well logging curves and experimental measurements, enabling the CNN-BiLSTM-Transformer model to perform exceptionally well. However, in regions with limited or lower-quality data, the effectiveness of this method requires further validation. The model has certain limitations, including high computational resource consumption, sensitivity to data quality, and complex hyperparameter optimization. Future research could integrate geological background information or seismic data to further refine the network structure, improve model interpretability, and expand its applicability.
# Distributed GNE-Seeking under Partial Information Based on Preconditioned Proximal-Point Algorithms
## 5 Numerical Studies
### Cournot Market Competition  
Each firm \(i\) decides the quantity \(x_{i}\in\mathbb{R}^{n_{i}}\) of the commodity for \(n_{i}\leq m\) markets, subject to \(\mathbf{0}^{n_{i}}\leq x_{i}\leq X_{i}\). The maximum capacity of each market \(l=1,\ldots,m\) is \(r_{l}\). Hence, we have a shared affine constraint \(Ax\leq r\), where \(r=\operatorname{col}\left(\left(r_{k}\right)_{l=1,\ldots,m}\right)\) and \(A=[A_{1}\ldots A_{N}]\). The matrix \(A_{i}\in\mathbb{R}^{m\times n_{i}}\) indicates which markets firm \(i\) enters. Specifically, \([A_{i}]_{l,j}=1\) if firm \(i\) enters market \(l\), and \([A_{i}]_{l,j}=0\) otherwise, for all \(j=1\ldots,n_{i}\) and \(l=1,\ldots,m\).The profit function of each firm \(i\) is \(J_{l}(x_{i},\boldsymbol{x}_{-i})=f_{i}(x_{i},\boldsymbol{x}_{-i})+g_{i}(x_{ i})\), where \(f_{i}(x_{i},x_{-i})=p(Ax)^{\top}A_{i}x_{i}\) and \(g_{i}(x_{i})=-x_{i}^{\top}Q_{i}x_{i}-q_{i}^{\top}x_{i}\). \(g_{i}(x_{i})\) is the production cost of firm \(i\), and \(Q_{i}\in\mathbb{R}^{n_{i}\times n_{i}}Q_{i}>0,q_{i}\in\mathbb{R}^{n_{i}}\) are given parameters. The market price for each market and \(l=1,\ldots,m\) is given by \([p(x)]_{l}=\bar{P}_{l}-\chi_{l}[Ax]_{l}\), where \(\bar{P}_{l},\chi_{l}>0\) are constants.  
In this simulation, we set the total number of participants to 20 and the number of markets to 7, i.e., \(N=20,m=7\). We defined the market structure using Figure 0(a), which does not show the actual spatial relationships and distances among markets and firms. We also set \(n_{i}=1\) for all \(i\in\mathcal{I}\). Then the arrows in Figure 0(a) represent only the participation of firms in the markets; therefore, we have \(x=\operatorname{col}(\left(x_{i}\right))_{i\in\mathcal{I}}\in\mathbb{R}^{n}\).  
We considered \(m\) markets distributed across seven continents. Individual firms could not communicate with all other firms because of geographical location, communication technology, or company systems. The firms could communicate only with their neighbors on the communication graph \(\mathcal{G}_{c}\), shown in Figure 0(b). Only connected firms \(i\) and \(j\) in the undirected communication graph \(\mathcal{G}_{c}\) could exchange their information. We randomly selected \(r_{l}\in[1,2]\), \(Q\) with diagonal elements in [1,8], \(q_{i}\in[1,2]\), \(\bar{P}_{l}\in[10,20]\), \(\chi_{l}\in[1,3]\), and \(X_{i}\in[5,10]\) for all \(i\in\mathcal{I}\) and \(l=1,\ldots,m\).  
Figure 1: (**a**) Network Nash–Cournot game. (**b**) Communication graph \(\mathcal{G}_{c}\).
## 2 Experimental Procedure
### Characterization  
The optical transmittance spectra of the coated glasses were measured by using a UV-VIS-NIR spectrophotometer (Hitachi U-4100, Tokyo, Japan) at room temperature. The refraction index and film thickness were measured by a spectroscopic ellipsometry (SENTECH SE800 PV, Berlin, Germany). The surface morphologies and Young's modulus of the coatings were determined by atomic force microscope (Dimension Fastscan, Bruker, Billerica, MA, USA). The scratch resistance was evaluated by the pencil hardness test. The pencil hardness test was operated according to ASTM D 3363-2005. The optical microscope images of the scratches were taken by metallurgical microscope (LEICA DM2500M). FTIR spectra were recorded on the infrared spectrometer (Vertex70 Hyperion3000, with a diamond crystal plane (single reflection) Attenuated Total Reflection (ATR) attachment), with a resolution of 4 cm\({}^{-1}\) and range of 4000-400 cm\({}^{-1}\), to measure the possible groups on the coatings. Thermogravimetric analysis was performed by using thermogravimetry (TG209F1 libra) from 30 \({}^{\circ}\)C to 710 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min in the air to measure the decomposition temperature of the coating. The water contact angles were measured atroom temperature by an optical contact angle system (OCA 20, Databases) with a droplet volume of 5 \(\mu\)L.
## 2 Appendix Segmentation with Double-Layered Outlier Rejection FCM
### Double-Layered Outlier Rejection FCM (DORFCM)
#### 2.3.2 Layer-2 Learning (Step 5 to Step 7)
(2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] Development of a weighted fuzzy c-means clustering algorithm based on JADE. _Int. J. Numer. Anal. Model._**2014**, \(5\), 113-122. * [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] A new feature weighted fuzzy c-means clustering algorithm. In Proceedings of the Multi Conference on Computer Science and Information Systems (MCCSIS 2009), Algarve, Portugal, 17-23 June 2009; pp. 11-18. Available online: [[http://www.iadisportal.org/digital-library/a-new-feature-weighted-fuzzy-c-means-clustering-algorithm](http://www.iadisportal.org/digital-library/a-new-feature-weighted-fuzzy-c-means-clustering-algorithm)]([http://www.iadisportal.org/digital-library/a-new-feature-weighted-fuzzy-c-means-clustering-algorithm](http://www.iadisportal.org/digital-library/a-new-feature-weighted-fuzzy-c-means-clustering-algorithm)) (accessed on 1 March 2022). * [PERSON] and [PERSON] (2002) [PERSON]; [PERSON] Alternative c-means clustering algorithms. _Pattern Recognit._**2002**, _35_, 2267-2278. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation. _Turk. J. Electr. Eng. Comput. Sci._**2013**, _21_, 1801-1819. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Automatic segmentation of liver/kidney area with double-layered fuzzy C-means and the utility of hepatorenal index for fatty liver severity classification. _J. Intell. Fuzzy Syst._**2020**, _39_, 925-936. [CrossRef]
* [PERSON] (1981) [PERSON] _Pattern Recognition with Fuzzy Objective Function Algorithms_; Plenum Press: New York, NY, USA, 1981. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] A new segmentation method of cerebral MRI images based on the fuzzy c-means algorithm. _Turk. J. Electr. Eng. Comput. Sci._**2017**, _25_, 3215-3226. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] [PERSON] MR brain image segmentation using an improved kernel fuzzy local information c-means based wavelet, particle swarm optimization (PSO) initialization and outlier rejection with level set methods. _Int. Arab. J. Inf. Technol._**2018**, _15_, 683-692. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] An efficient segmentation and classification system in medical images using intuitionist possibilistic fuzzy C-mean clustering and fuzzy SVM algorithm. _Sensors_**2020**, _20_, 3903. [CrossRef] [PubMed]
## 4 Discussion  
The results demonstrate that PTFE is preferred over HDPE for use as a matrix material due to the lower porosity of PTFE pellets. The PTFE samples have a high tensile strength, indicating that they are structurally robust when compacted at \(>\)392 MPa, allowing for repeated analysis and ease of handling.  
The optimal compaction pressure for fabricating PTFE samples with the compaction simulator appears to be greater than 350 MPa to avoid lamination or capping. However, a lower compaction pressures should be used with a hydraulic press if the operator controlled pressure release is well regulated to prevent the sample from being broken. The dwell time utilised by the compaction methods must also be considered, as a lower compaction pressure with a sufficiently long dwell time will achieve the same performance as a higher pressure with shorter a dwell time.  
The particle size of the material has a significant effect on tensile strength, axial relaxation, sample porosity and scattering (Figure 7). Smaller particle sizes would be preferable for scattering reduction and higher tensile strength.  
Comparing the performance of the compaction simulator and hydraulic press suggests that the hydraulic press is superior in creating lower porosity samples. This is primarily driven by the effect of dwell time as the compaction simulator was set to a shorter dwell time compared with the hydraulic press where the operator directly controls the dwell time. No noticeable advantage was observed by applying a vacuum to the die (Figure 7) in regards to the final porosity values. This is attributed to the axial relaxation after compaction, as a higher compaction pressure causes more significant axial relaxation [32] and greater changes in porosity. Analysis immediately after compaction is not recommended as changes in porosity will lead to a reduced reproducibility. However, utilising a vacuumed die for PTFE samples would be the preferred choice for minimising the effect of porosity if THz-TDS analysis must be carried out immediately after compaction and repeated analysis is not required.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Tensile Strength (MPa)** \\ \hline PTFE & 3.162 \(\pm\) 0.050 \\ PTFE & 3.162 \(\pm\) 0.050 \\ HDPE2 & 0.846 \(\pm\) 0.058 \\ \hline \end{tabular}
\end{table}
Table 3: Tensile strength of PTFE, HDPE1 and HDPE2 pellets produced using the compaction simulator. Pellets were compacted at 392 MPa. Calculated values are the mean \(\pm\) one standard deviation (\(n=5\)).  
Finally, there is a significant difference between the optical parameters extracted from the two HDPE grades. It is therefore advisable to create reference samples of pure matrix polymer for each batch of material.
# Adhesive Single-Lap Joint Evaluation Using Ultrasound Guided Waves
## 3 Materials and Methods  
### Simulation Model  
The k-Wave functions used for simulation need different input structures, namely, the properties of the computational grid (representing the medium through which the acoustic waves propagate) and the properties and locations of both acoustic sources and sensors. A perfectly matched layer (PML), commonly used to truncate computational regions in numerical methods, is used to absorb acoustic waves in the limits of the computational domain. The schematic of the 3D simulation setup is presented in Figure 1, and its dimensions are: X = 126 mm, Y = 32 mm, and Z = 18 mm. The spatial resolution is 150 \(\upmu\)m. Each plate is 90 mm in length, 30 mm in width, and 4 mm in thickness. The section of the bonded region is 60 mm \(\times\) 30 mm. The adhesive thickness is 150 \(\upmu\)m. The emitter and receiver transducers are identical, measuring 25 mm in diameter. The incidence and reception angles are equal and given by Equation (9). The defects were simulated by introducing air (in the absence of adhesive) in a circular shape in the center of the joint region.  
The acoustic properties of the materials used in the simulation are presented in Table 1. For water and air, the values were taken from literature [67]. For aluminum and epoxy, the properties were obtained experimentally using a conventional pulse echo technique. For aluminum, a 10 MHz transducer was used due to the small thickness of the plates. As the thickness of the epoxy layer is very small, a cylinder with a 20 mm height and a 30 mm diameter was constructed to allow velocity measurements. The densities were taken from the manufacturer's specifications.
## 6 Simulation and Results Discussion
### Results and Discussion  
#### 6.2.1 Bandwidth vs. End-to-End Delay  
It refers to the data bandwidth and end-to-end delay; it measures the amount of data transferred in the link by the service provider, which is not yet received by the service requester. Our model QoSBS shows that the overall delay is reduced to 25 Mbps/s. Figure 6 shows the bandwidth and end-to-end delay for both the securities-enabled QoSBS and other existing algorithms. In this case, our proposed work reduces the number of data lost to the desired level, as depicted in Table 3. The outcome of the proposed algorithm deliberates better performance in providing minimal end-to-end delay compared to other existing algorithms.
## 3 Preliminaries
### FHE-Based Secure Search  
[PERSON] et al. proposed the definition of _Secure Search_[20], i.e., using FHE to search encrypted data. The client encrypts the data through FHE and uploads them to the cloud server for storage. When it needs to retrieve the data, it sends an encrypted query request \(\llbracket q\rrbracket\). Due to the complete homomorphism of FHE, the server can correctly perform the search. First, the client runs the homomorphic encryption key generation algorithm to generate a public key \(pk\) and a private key \(sk\). The client publishes the public key \(pk\), while saving the private key \(sk\). Next, the client uploads \(n\) encrypted items \(x=(x_{1},x_{2},\cdots,x_{n})\) to the server using the public key \(pk\). The encrypted data stored in the server are denoted as \(\llbracket x\rrbracket=(\llbracket x_{1}\rrbracket,\llbracket x_{2} \rrbracket,\cdots,\llbracket x_{n}\rrbracket)\). When the client wants to make a query, they send an encrypted query \(\llbracket q\rrbracket\) to the server. The server then performs homomorphic evaluation on each record \(\llbracket x_{i}\rrbracket\) to obtain the encrypted matching result \(\llbracket b\rrbracket=(\llbracket b_{1}\rrbracket,\llbracket b_{2} \rrbracket,\cdots,\llbracket b_{n}\rrbracket)\). If record \(\llbracket x_{i}\rrbracket\) satisfies the query \(\llbracket q\rrbracket\), then \(b_{i}\) is 1; otherwise, it is 0. After computing \(\llbracket b\rrbracket\), the server can then fetch the matching record by homomorphically computing \(\llbracket i^{*}\rrbracket\) and obtaining \(\llbracket x_{i}\rrbracket\). The server sends \((\llbracket i^{*}\rrbracket,\llbracket x_{i^{*}}\rrbracket)\) to the client for decryption. Here, \(i^{*}\) corresponds to the index of the first matching record.  
The bottleneck of the FHE-based security search framework is the homomorphic multiplication in the acquisition step. Homomorphic multiplication in [20] was \(O(nlog^{2}n)\), which was subsequently optimized to \(O(n\log n)\) in [21]. [PERSON] et al. [22] proposed a new \(LEAF\) protocol using three methods of positioning, extraction, and reconstruction, which reduced the homomorphic multiplication times to \(O(n)\), which is more advantageous for weak power device deployment. The LEAF protocol is used in the scheme proposed in this paper.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
###### Abstract  
As an important consideration of road traffic facilities, bridges play a crucial role in daily traffic operations, and changes in their status can have an impact on traffic operation. The existing research mainly focuses on monitoring the status of bridges themselves or analyzing the operation status of road traffic, and rarely considers the changes in traffic operation status caused by changes in bridge status. Therefore, in order to evaluate the impact relationship between the two, this article designs an algorithm that combines the Analytic Hierarchy Process (AHP), the Delphi method, and Singular Value Decomposition (SVD) based on the traditional evaluation of bridges and road traffic operation status, and establishes a bridge traffic operation status impact assessment model. Then, simulation analysis and actual data verification will be conducted based on the specific situation of Ma'anshi Bridge on the Chongqing Wuhan Expressway. The experimental results show that the evaluation model established in this paper conforms to the characteristics of traffic operation, can reflect the impact of bridge state changes on traffic operation status well, effectively promote the automation level of bridge traffic impact management, and has high reliability and accuracy.  
b 2024 14 9327 10.3390/app14209327
# Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations
## 5 Coordinate Errors Due to Quarter Cycle L2 Biases
They reach centimeter level in north and east, and the level of a few centimeters in the height component. Jumps in the time series are due to changes in satellite constellation caused by rising or setting satellites or by missing observations above the elevation mask angle of 5 deg. Due to the daily repetition of the GPS satellite orbits, the described geographical distribution of daily coordinate errors is stable as long as the GPS satellite constellation remains unchanged. In the near future, we will experience replacements of older GPS satellites by Block III satellites, which will enlarge the already existing majority of L2C-capable space vehicles. This will reduce the coordinate errors produced by defective RINEX-2 files. Figure 6: Coordinate errors of 24 h static positioning in early 2018 due to L2 quarter cycle biases: ionosphere-free linear combination with ambiguities being fixed, estimation of tropospheric zenith delays enabled, based on simulated stations in a 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) grid and on seven real stations.
# Band Gap Analysis for Materials with Cookie-Shaped Auxetic Microstructures, Using Finite Elements
## 2 State of the Art and Purpose of the Study  
Classical composite materials are usually designed by using the rule of mixture, where the actual microstructure of inclusions within a matrix does not play a role. The response in dynamic excitation is much more complicated and, in general, cannot be described in a similar way. Classical composites are known to have band gap properties. Waves of certain frequencies are blocked and cannot be transmitted through the composite medium. Phononic band gaps in periodic cellular materials have been considered in various publications [14].  
Furthermore, even in statics, some effects of composite materials cannot be captured by the simple method of mixtures. This is the case with auxetic materials, as the negative Poisson ratio cannot be discovered by using the rule of mixtures on two classical constituents with a positive Poisson ratio.  
Various microstructures have been proposed for the explanation of auxetic behavior and subsequently for the optimal design of auxetics, these include, star-shaped [2; 6; 10; 15; 16; 17] and chiral [7; 11; 18; 19] hybrids, such as star-chiral [20], perforated continua [21], chiral [22; 23] and mechanisms. Cookie-shaped microstructures have been proposed to explain the auxetic effect and have certain advantages [12]. They are easier to construct and they do not have sharp corners with stress concentrations and possible fatigue effects.  
Further information on auxetics can be found in [1; 2; 3; 4; 21; 22].  
Similarly, continua with microstructures exhibit band gap properties. A band gap analysis of star-shaped honeycombs with varied Poisson's ratios is studied in [15], which can be used as a seminal work for the optimal design with respect to the band gaps. Other auxetic microstructures have also been investigated with respect to band gap formation, see [7; 12; 16; 22].  
For the systematic study of wave propagation in composites, the works of [PERSON] and [PERSON] [24; 25] can be used. On a representative volume element (unit cell), all directions of wave propagation and all frequencies of interest must be considered. Wave propagation is defined by the conditions based on the Floquet theorem [26]. The propagation of the wave in two-dimensional periodic lattices is described in detail in [26]. The lattices are designed within a desired band by using the Floquet-Bloch principles in order to study the propagation of the wave in three regular honeycomb lattices. The results indicate the efficiency of the proposed method and provide the dispersion curves for the different 2-D topologies of the studied unit cells. The interaction of the microstructure and band gap formation is not straightforward. Classical or topology optimization can be used for the optimal design [27; 28; 29; 30; 31]. Further proposals to influence the band gap behavior have been published, based on contact [32] or nonlinear effects or prestressing [33]. The investigation of auxetics with strong circular inclusions in the present paper was inspired by these investigations.