esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Vision Transformers (ViTs), recently adapted from their successful natural language processing (NLP) counterparts, have shown great promise in image classification tasks; yet their potential in the realm of artificially produced image recognition remains underexplored. This study introduces a Vision Transformer (ViT) architecture, termed Patch-Based Vision Transformer for Identifying Synthetic Media (PV-ISM), created for the purpose of binary classification, which involves separating artificial intelligence-generated photos from genuine ones. In order to train and assess the model, the CIFAKE dataset has been used. This dataset comprises real CIFAR-10 images and synthetically generated counterparts produced via Stable Diffusion 1.4. The results of the experiments show that the suggested model performs well, highlighting the effectiveness of transformer-based architectures in the domain of image authentication. The following are this work's main contributions:  
* It is demonstrated that Vision Transformers, even without extensive pre-training or external datasets, can outperform conventional transfer learning models (e.g., ResNet50) on the task of synthetic image detection, achieving an accuracy of 96.60% versus 93.32%. * A comprehensive ablation study is conducted on hyperparameters such as patch size, image resolution, and epoch count, with their tangible influence on detection accuracy and model generalization being identified. * It is highlighted that, compared to transfer learning models, which require vast datasets and training time, PV-ISM is not only more time-efficient, but also maintainsbalanced classification performance across classes, as evidenced by a near-symmetric confusion matrix. What emerges is not just a performance model, but an adaptable framework for identifying synthetic imagery--a task becoming increasingly vital in today's digital ecosystem. Moreover, our findings suggest that the patch-based architecture of Vision Transformers aligns naturally with the structural patterns present in generated images, providing an edge in subtle feature discrimination. The following is how the paper is structured: Section 2 offers a detailed analysis of relevant research on synthetic image detection and transformer-based models. The suggested methodology is described in Section 3, along with the architectural design of the model and training procedures. Section 4 describes the experimental framework, presents the obtained results, and offers an in-depth analysis. Section 5 presents experimental studies with the proposed model by using two more datasets. Finally, Section 6 wraps up the study by summarizing the main conclusions and offering recommendations for further research paths.
## 3 Biosensor Application of Nanostructures Electrodeposited onto a Template
### Electrodeposition through Aluminum Oxide and other Templates
Gold nanotubes with edge thicknesses of 30 to 50 nm and 100 nm in average diameter were formed by electrodeposition from 10 mM of KAu(CN)\({}_{2}\) at \(-1400\) mV for 300 s. Then, the polycarbonate template was partially dissolved in CH\({}_{2}\)Cl\({}_{2}\). The gold surfaces were modified by a thiolated capture oligonucleotide. The detection of the target HPV oligonucleotide was carried out using electrochemical impedance spectroscopy (EIS), measuring the change in charge transfer resistance as a function of target DNA concentration in two mM of K\({}_{4}\)[Fe(CN)\({}_{6}\)] + 2 mM K\({}_{3}\)[Fe(CN)\({}_{6}\)] at the open circuit potential and using a 10-mV potential modulation over the range from 0.01 Hz to 100 KHz. The new and novel feature of the study was that EIS was carried out in the presence of an externally applied and separate applied electric field of +5V. It was found that the application of an external field, which promoted the DNA probe to lay flat on the gold surface, greatly increased the sensitivity of the charge transfer resistance to target DNA binding. Sensitivity for the target DNA of one fM was achieved, which was lower than all except one other report for the same target, and a good linear range from 0.01 pM to 1 \(\upmu\)M.
## 2 Materials and Methods
### Outcomes
#### 2.3.4 Implant Stability  
Implant stability was measured immediately after implant placement and at the 3-month follow-up using a resonance frequency analysis device (RFA) (Ostell ISQ, Integration Diagnostics AB, Gothenburg, Sweden). RFA is a widely used analyzer that shows implant stability quotient (ISQ) values ranging from 1 to 100. ISQ values of 1-59 indicate low stability, 60-69 indicate medium stability, and 70 or higher indicate high stability.
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 3 Vehicular Communication
### Hybrid Networking  
Currently, both ad hoc and cellular networks are struggling to fulfill vehicular communication requirements that are highly application dependent. Varying the quality of service requirements can hardly be considered for communication, since only one single network is used. The mismatch between requirements and available communication technologies increases the desire for multiple network usage [29]. However, once multiple networks are combined, mutual interference must be excluded so that their coexistence can be first made possible [30]. Furthermore, the adding of simple redundancy may not be sufficient for this task. Hybrid solutions for data dissemination will need to be sophisticated in order for them to be capable of relieving the individual network and ensuring sufficient quality of service [12]. Clustering allows the network load to be reduced even further [31]; however, a trade-off in cluster design needs to be found: large clusters lead to a high network load within a cluster and low network usage between clusters. In [32], the current context of the vehicle is observed and the optimal communication technology is selected on a per-packet basis. In addition to this, a reputation system estimates trust scores for the hybrid system.
## 4 Case Study for Signal Processing of Pulsed Thermography  
In Sections 2 and 3, the basic principles of optical infrared thermography and PPT, PCT, TSR, DWT, and DAC signal processing, which are widely applied, were introduced. Section 4 introduces the latest application of each signal processing technique and some case studies. Each algorithm is used in similar cases overall but has different strengths and differences. Some cases of pulsed thermography signal processing studies are as follows.
## 2 Materials and Methods
### Modelling of the Life Cycle Inventory Phase  
The Life Cycle Inventory (LCI) phase involved collecting data on material and energy inputs, emissions and waste outputs at each stage of the production process. The data collection relied on a combination of primary and secondary sources.  
The client provided site-specific data for the production of the cosmetic cream, including material consumption, energy use and waste generation. These data were gathered through direct measurements and estimates based on operational practices. The secondary data were obtained from the Ecoinvent 3.8 database, which provided life cycle inventories for raw materials, energy production, and other processes not measured directly during the study. This database ensured that the environmental modelling captured all the relevant upstream and auxiliary processes, such as electricity generation and transportation of raw materials. The environmental impacts of cosmetic cream production were assessed using the Environmental Footprint 3.1 [11].  
The LCA was conducted using the OpenLCA 2.0.1 software, which enabled detailed environmental modelling of the cosmetic cream's life cycle. The Ecoinvent 3.8 database served as the primary secondary data source, ensuring that the inventory was comprehensive and representative of industry standards.  
The data used in the LCA were validated for consistency and accuracy. The primary data from the client were cross-checked with industry benchmarks and literature values. The secondary data from Ecoinvent 3.8 were selected based on relevance and geographical applicability. Assumptions made during the analysis include: (1) Transportation distances for raw materials were modelled based on industry averages; (2) Electricity use for production was modelled using Slovenia's national electricity grid mix, incorporating contributions from fossil fuels and renewable sources. Packaging impacts were modelled using standard data from the Ecoinvent database for plastic-based cosmetic packaging.  
The interpretation phase involved analyzing the LCIA results, to identify key environmental hotspots within the production system. Particular attention was given to the synthesizing of the AuNPs, which are expected to contribute significantly to resource depletion and ecotoxicity. Based on the results, recommendations for mitigating environmental impacts were developed, focusing on optimizing energy use and reducing emissions during production.  
The scope of this LCA is limited to a cradle-to-gate analysis, and, as such, it does not account for downstream processes such as product distribution, consumer use, or end-of-life disposal. The environmental impacts of the infrastructure were also excluded, such as the construction of production facilities and equipment. These exclusions may lead to underestimating the overall environmental burden slightly, but they align with standard LCA practice.
## 2 Materials and Methods
### Design Choices
#### 2.5.3 Naming Design and Infrastructure  
Our choice to use standalone Jupyter notebooks instantiated in individual containers, rather than an integrated environment such as JupyterHub, forced us to find an effective way to direct teams to their individual notebooks. If we had used JupyterHub, we could give all the teams a single domain name to connect to, and then they would be directed to their notebook after they authenticated. Instead, we had to direct users on an individual team to the individual container for that team. We chose to do this through DNS host names in the URL for each notebook.  
Figure 2 shows our design of the infrastructure and process for connecting teams to their notebooks. We present each team that joins a competition with a unique DNS name for their notebook in the xprize.org domain. In the example below, \"team1\" gets a URL such as \"[[http://team1.comp1.xprize.org/](http://team1.comp1.xprize.org/)]([http://team1.comp1.xprize.org/](http://team1.comp1.xprize.org/))\" as step 1. Rather than create a new DNS [32] record for each new team that was added or removed, we used a wild-card domain record for \".comp1.xprize.org that points all teams in a competition to an application load balancer of our Cloud Service Provider.  
Figure 2: Routing of web requests to team notebook container.  
An HTTPS request for a notebook arrives at the application load balancer (labeled Application gateway), and in step 2, it is sent to a proxy server. In step 3, the proxy server routes the request to the container containing the team's notebook based on the name of the container that we gave to that team.  
The container holding their notebook is in a private IP network space [33] that is not directly accessible to users on the Internet. For step 3 to work, we need a way to name the container and register that name into a private DNS space. Our Cloud Service Provider has a DNS service that can be manipulated programmatically, so we use a private DNS domain for our containers' names. In Figure 2, this host name for the container with team1's notebook is team1.privateprize.org.  
While this level of detail may seem excessive, explaining the design of the DNS namespace and architecture is important for our discussion. First, it shows how we applied the gatekeeper design pattern [25]. Second, it illustrates our use of SDI (Software Defined Infrastructure) [24], as the DNS infrastructure is allocated and loaded with domain information programmatically. Third, this infrastructure has some problematic features, and it should be understood in order to put some of the problems we experienced (to be described later) into context.
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Performance Analysis
#### 4.3.2 Tamper Sensitivity Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(T\) **= 0.02** & \(T\) **= 0.03** & \(T\) **= 0.05** & \(T\) **= 0.10** & \(T\) **= 0.20** \\ \hline MD5-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ SHA-1-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline RCF-based method & 97.9\% & 95.8\% & 84.4\% & 47.7\% & 17.6\% \\ MultiResU-Net-based method & 84.0\% & 78.3\% & 62.5\% & 35.0\% & 11.8\% \\ Attention U-Net-based method & 93.7\% & 91.3\% & 84.4\% & 68.6\% & 36.2\% \\ AAU-Net-based method & 92.8\% & 90.9\% & 83.0\% & 65.6\% & 29.5\% \\ MultiRes-RCF-based method & 98.6\% & 96.9\% & 90.9\% & 70.7\% & 38.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Tamper detection rate after \(8\times 8\) pixel tampering area is modified. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(T\) **= 0.02** & \(T\) **= 0.03** & \(T\) **= 0.05** & \(T\) **= 0.10** & \(T\) **= 0.20** \\ \hline MD5-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ SHA-1-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline RCF-based method & 32.2\% & 28.1\% & 13.3\% & 5.2\% & 0.2\% \\ MultiResU-Net-based method & 55.0\% & 43.0\% & 19.0\% & 5.3\% & 1.3\% \\ Attention U-Net-based method & 86.3\% & 78.3\% & 55.5\% & 23.5\% & 4.1\% \\ AAU-Net-based method & 28.6\% & 18.4\% & 8.5\% & 2.3\% & 0.5\% \\ MultiRes-RCF-based method & 91.6\% & 85.0\% & 64.3\% & 27.6\% & 8.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Tamper detection rate after 16 pixels have been modified. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(T\) **= 0.02** & \(T\) **= 0.03** & \(T\) **= 0.05** & \(T\) **= 0.10** & \(T\) **= 0.20** \\ \hline MD5-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ SHA-1-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline RCF-based method & 62.5\% & 56.5\% & 43.75\% & 28.0\% & 11.5\% \\ MultiResU-Net-based method & 73.5\% & 65.25\% & 45.75\% & 15.5\% & 4.0\% \\ Attention U-Net-based method & 82.0\% & 76.5\% & 60.25\% & 28.75\% & 12.25\% \\ AAU-Net-based method & 82.75\% & 80.25\% & 65.5\% & 29.5\% & 10.5\% \\ MultiRes-RCF-based method & 84.5\% & 77.25\% & 59.0\% & 29.5\% & 11.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Tampering sensitivity comparison of subject-unrelated tampering with 400 RS images. It can be seen from Table 9 that the tampering detection ability of the MultiRes-RCF-based method to the unrelated subject is roughly equivalent to the AAU-Net-based method and the Attention U-Net-based method, and even better when the threshold \(T\) is small.
## 5 Experimental Design  
In order to assess the effectiveness of the RVCE approach outlined above, an experiment considering the time link length was devised, the data spanned from MJD 57981 to MJD 57986. Five GNSS stations equipped with stable external time and frequency signals were examined for this experiment. These stations are all able to track the GPS, BDS and Galileo satellite constellations (Figure 2). Two of the stations (NTS1 and BRUX) are located at two time laboratories, the National Time Service Centre (NTSC), and Observatoire Royal de Belgique (ORB)), which are connected with the UTC (NTSC) and UTC (ORB) time and frequency signals, respectively. Another three stations are equipped with external H-maser references. The attributes of these five stations are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Site Name** & **GNSS Receiver** & **Antenna** & **Frequency Standard** \\ \hline NTS1 & SEPT POLARXtR & SEPTCHOKE, MC & UTC (NTSC) \\ BRUX & SEPT POLARXtR & JAVRINGANT\_DM & UTC (ORB) \\ KAT1 & SEPT POLARX5 & LEIAR25,R3 & EXTENNAL H-MASER \\ CEDU & SEPT POLARX5 & AOAD/M\_T & EXTENNAL H-MASER \\ ONS1 & TRIMBLE NET9 & LEIAR25,R3 & EXTENNAL H-MASER \\ \hline \end{tabular}
\end{table}
Table 1: Attributes of stations used in this study.  
Figure 1: Primary implementation procedure using robust Helmert variance component estimation (RVCE) for time and frequency transfer.  
Figure 2: Layouts of multi-GNSS stations, capable of tracking GPS, BDS and Galileo constellations.  
When the time and frequency transfer was employed, the time link was formed between the two stations, which was the primary unit in the area of time and frequency transfer. We therefore established four time links using the five stations with geodetic distances from 883.7 to 7537.5 km, and each link has at least one UTC signal provided by UTC (NTSC) or UTC (ORB), which are summarized in Table 2.
## 3 Proposed Algorithm and Formulation
### Proposed Algorithm
#### 3.2.3 Phase 3: Clustering  
In the clustering phase, each route is generated by grouping operations which allow maximizing the saving considering the time constraint. Based on the saving matrix, a list \(L(i,j)\) is created and sorted in descending order by merging savings. With list \(L(i,j)\), road \(i\) and road \(j\) are selected to be tested with the feasibility of mergence. If the mergence is feasible, these two roads are merged together and a new merged route is created and the saving matrix is updated with new routes. If no feasible mergence is found or it exceeds the given calculation time, the process is terminated.  
In the example described above (Figure 4), we need to consider:  
* Time window of operation performed in \(v_{1}\) and \(v_{2}=TW_{1}\)
* \(k\) is an operator
* \(AT_{k}\) is the available time of the worker \(k\) expressed in (min)
* Policy \(a\):
* Time necessary to go from node \(v_{0}\) to \(v_{1}\) (\(T_{01}\)) and vice versa expressed in (min)
* Time necessary to go from node \(v_{0}\) to \(v_{2}\) (\(T_{02}\)) and vice versa expressed in (min)
* or Policy \(b\):
* Time necessary to go from node \(v_{1}\) and \(v_{2}\) (\(T_{12}\)) expressed in (min)
* Time of the operation performed in \(v_{1}\) (\(T_{1}\)) expressed in (min)
* Time of the operation performed in \(v_{2}\) (\(T_{2}\)) expressed in (min)  
Therefore, a cluster is composed of the route \(v_{0}\)-\(v_{1}\)-\(v_{2}\)-\(v_{0}\) (Policy \(b\)) if:  
\[T_{01}+T_{1}+T_{12}+T_{2}+T_{02}\leq AT_{k} \tag{16}\]  
\[T_{01}+T_{1}\,\epsilon\,\,TW_{1} \tag{17}\]  
\[T_{12}+T_{2}\,\epsilon\,\,TW_{1} \tag{18}\]  
Otherwise, if the time constrain expressed by Equation (16) is not verified, two routes are created and assigned to different operators (Policy \(a\)):  
* \(v_{0}\)-\(v_{1}\)-\(v_{0}\)
* \(v_{0}\)-\(v_{2}\)-\(v_{0}\)  
The output of this phase is the definition of the first solution, which can be improved through the next phase.
## 2 The Method to Download Clear-Sky Data Volume during Rainfall  
We briefly recall the theory of the method developed in [6], which was applied there to GEO satellites. We extend its theory in Section 4.  
First, we define the channel efficiency \(\eta\) and then we use this parameter to determine the power margin and the bandwidth expansion necessary for designing a channel that delivers the same data volume as would be delivered if there were no rainfall.
## 3 Results
### Surface Coating  
The coating of PASA polymers on the nanoparticle surface was demonstrated by recording an FT-IR absorption spectrum (bottom spectrum labeled III in Figure 5a). In addition, FT-IR absorption spectra of bare Gd\({}_{2}\)O\({}_{3}\) nanoparticles and PASA were taken (top and middle spectra labeled I and II in Figure 5a, respectively) as reference. The observed absorption frequencies are provided in Table 2. Characteristic absorption peaks of PASA [23; 24] were also observed in the FT-IR absorption spectrum of PASA-coated Gd\({}_{2}\)O\({}_{3}\) nanoparticles, confirming the surface coating. The frequency difference of \(\sim\)196 cm\({}^{-1}\) between symmetric and asymmetric COO\({}^{-}\) stretches indicated that COO\({}^{-}\) groups were electrostatically bonded to Gd\({}^{3+}\) on a nanoparticle surface through a bridge bond [25], as drawn in Figure 5b. Considering numerous COO\({}^{-}\) groups per PASA (i.e., \(n=\sim\)72), numerous bridge bonds are expected per PASA with a nanoparticle although only one bond is presented in Figure 5b.  
Figure 4: XRD patterns of PASA-coated Gd\({}_{2}\)O\({}_{3}\) nanoparticles before (bottom) and after (top) TGA. Vertical bars below the peaks after TGA indicate peak positions of cubic Gd\({}_{2}\)O\({}_{3}\) and only the strong peaks were assigned to (_hkl_) Miller indices.  
Figure 5: (**a**) FT-IR absorption spectra of bare Gd\({}_{2}\)O\({}_{3}\), PASA, and sample (i.e., PASA-coated Gd\({}_{2}\)O\({}_{3}\) nanoparticles): Subscripts s, as, and b indicate stretch, antisymmetric stretch, and bend, respectively. (**b**) Bonding structure of a PASA with a Gd\({}_{2}\)O\({}_{3}\) nanoparticle. Only one bridge bond is shown, but numerous bridge bonds are expected per PASA with a nanoparticle and approximately four PASA are bonded to a nanoparticle. (**c**) TGA curve of PASA-coated Gd\({}_{2}\)O\({}_{3}\) nanoparticles.  
The coating amount of PASA polymers per nanoparticle was estimated in weight percentage (%) from a TGA curve (Figure 5c). The initial mass drop of 5.5% between room temperature and \(\sim\)110 \({}^{\circ}\)C was due to water desorption, and the next mass drop of 63.2% was due to PASA burning through its reaction with flowing hot air. The final mass drop of 31.3% was due to the remaining Gd\({}_{2}\)O\({}_{3}\) nanoparticles. To estimate the average number of PASA polymers coating a nanoparticle per unit surface area, a grafting density (\(\sigma\)) [26] was calculated to be 0.29 nm\({}^{-2}\) using a molecular mass of PASA (i.e., 9900 amu), the average particle diameter estimated from HRTEM (i.e., 2.0 nm), and the bulk density of 7.407 g/cm\({}^{3}\)[27] for Gd\({}_{2}\)O\({}_{3}\). By multiplying \(\sigma\) with the average nanoparticle surface area (= \(\pi\)d\({}_{\rm avg}\)), the average number of PASA polymers coating a nanoparticle was estimated to be \(\sim\)4. The results are provided in Table 1.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 4 Experimental Results
### Subjective Assessment  
In order to further demonstrate the effectiveness of the proposed multi-color-channels-based GSR model, Figures 3 and 4 respectively show the visual butterfly and girl restoration results of the proposed model and some state-of-the-art models with an 80% pixel missing rate. As can be seen from Figures 3 and 4, the SALSA model cannot restore sharp edges and fine details. The BPFA model can have better visual effects than the salsa model, but it still has undesirable visual artifacts, such as ringing artifacts in Figure 4d. The GSR, JPG-SR and GSRC-NLP models produce over-smoothing effects. Compared with the model proposed in this paper, the MAR-LOW has a similar visual effect, which is not easy to distinguish with the naked eye. However, according to the experimental results in Tables 1-3, the proposed model has better objective evaluation results. In general, the visual effects of Figures 3 and 4 show that the GSR model based on multi-color channels proposed in this paper performs best in image restoration. It not only preserves fine details, but also effectively removes visual artifacts.  
Figure 3: The restoration results of butterfly with pixel missing rate of 80%. (**a**) Butterfly, (**b**) butterfly with pixels missing rate 80%, (**c**) SALSA [38] (PSNR = 22.85), (**d**) BPFA [4] (PSNR = 21.06), (**e**) GSR [13] (PSNR = 26.03), (**f**) MAR-LOW [41] (PSNR = 30.86), (**g**) JPG-SR [14] (PSNR = 26.58), (**h**) GSRC-NLP [27] (PSNR = 26.78), (**i**) our model (PSNR = 30.90).