esa-sceva/satcom-chunk-collection huggingface dataset:
# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 4 Experiments and Results
### RRU-Net-Transfer-Learning DRRU-Net
The reason for high accuracy in pixel units of RRU-Net is that RRU-Net classifies most of the pixels in the image as normal because it did not find the forgery regions in a situation where most regions of the image are not forgery regions. This can be seen from the _Dice_ value in Table 1.  
When the proposed DRRU-Net is trained with weights initialized randomly, the _Dice_ coefficient is 0.830 in the DEFACTO Splicing set, 0.652 in the DEFACTO Copy-Move set, and 0.547 in CASIAv2. The accuracies are 0.997, 0.984, and 0.864 for each dataset, which is better than the results of RRU-Net, but not satisfactory. Table 2 shows the results predicted with the RRU-Net, DRRU-Net, and DRRU-Net TL models by taking one Copy-Move forgery image from each set of CASIAv2 (up) and DEFACTO Copy-Move (down). As shown in Figure 2, RRU-Net fails to accurately identify forgery regions in most images of the test dataset. DRRU-Net shows unstable predictions in CASIAv2, which has undergone different processing from the training dataset. It predicts the forgery regions, but its prediction includes normal regions very often. Compared to other networks, DRRU-Net TL detects forgery regions with relatively high accuracy. In this experiment, RRU-Net, which finds clues that can only be found in RGB streams, showed poor performance, but DRRU-Net TL, which transfer-learned the weights of the poorly performing RRU-Net down-sampling section, showed the highest performance among the compared networks. Compared to DRRU-Net TL, DRRU-Net can be seen as having optimized weights to search mainly for DCT streams rather than RGB streams, as with CAT-Net. Therefore, DRRU-Net TL, which transfer-learns RRU-Net, can be interpreted as relatively balanced search in RGB and DCT streams without depending on DCT streams as in DRRU-Net.
## Materials and Methods
### Complementary Integrated Circuit Fabrication Process  
To fabricate a complementary inverter circuit, an electrode pattern was formed, in which the drains of p-type and n-type OTFTs were connected. In a ring oscillator circuit formed by connecting an odd number of inverter circuits, a via hole for the interlayer connection of electrodes is required. The via holes were formed using a YAG LASER (Light Amplification by Stimulated Emission of Radiation) system (VL-C30, V-Technology Co., Ltd., Yokohama, Japan), and the slit size for controlling the irradiation area was set to 50 \(\times\) 75 \(\upmu\)m. After opening the holes, the interlayer connection was made by filling silver nanoparticle ink using the ink-jet printing machine.  
Figure 1: Schematic of the fabrication process for the organic complementary integrated circuit. The number of material printing/depositing steps is seven. The sintering temperature of the Ag nanoparticle ink is 120 \({}^{\circ}\)C and the annealing temperature of the semiconductor is 150 \({}^{\circ}\)C.
## 2 Numerical Simulation Methodology
### Numerical Methods
#### 2.3.3 Details of the Computational Method  
The finite volume method (FVM) was applied to transform the differential equations described above into discrete equations. A second-order upwind scheme was used for differential derivatives. The semi-implicit method for the pressure-linked equations (SIMPLE) algorithm was adopted to couple the pressure and velocity fields to provide accurate gas phase parameters, while discretized equations were solved by the least squares cell-based method. Under-relaxation factors of 0.3 for pressure, 0.7 for momentum, 0.8 for \(k\) and \(\varepsilon\), 0.9 for electrical potential and 1 for energy and the pollutant were used for the convergence of all variables. The convergence criteria were set as follows: residuals for both velocity and continuity reaching 10\({}^{-4}\), and energy residual reaching 10\({}^{-6}\).
## 2 Materials and Methods
### Bacterial 165 rRNA Gene Amplicon Sequencing and Bioinformatic Analysis  
The composition of bacterial communities of samples was investigated based on the partial 165 rRNA gene amplicon sequencing approach. Initially, all replicates of each enrichment culture (within a sample) were pooled, and analysis was then processed. DNA was extracted with a FastDNA spin kit for soil (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer's recommendations. The 165 rRNA gene was amplified by PCR using the primers Bakt_341F (5\({}^{\prime}\)-CCT ACG GGN GGC WGC AG-3\({}^{\prime}\)) and Bakt_805R (5\({}^{\prime}\)-GAC TAC HVG GGT ATC TCA TCC-3\({}^{\prime}\)). The cycle parameters for the first round were as follows: an initial denaturation at 98 \({}^{\circ}\)C for 30 s, 30 cycles of 10 s at 98 \({}^{\circ}\)C, 20 s at 55 \({}^{\circ}\)C, 40 s at 72 \({}^{\circ}\)C, and a final elongation at 72 \({}^{\circ}\)C for 5 min. QS high-fidelity DNA polymerase was used for PCR (New England Biolabs, Ipswich, MA, USA). Negative controls confirmed the absence of contamination during DNA extraction and PCR. High throughput 165 rRNA gene amplicon sequencing was performed on the Illumina Miseq platform (Illumina, San Diego, CA, USA), and data analysis was performed using the QIIME software pipeline [35]. The bacterial 165 rRNA forward and reverse reads were joined and demultiplexed. The chimeric sequences were identified and filtered using the ChimeraSlayer method. The filtered sequences (>0.01%) were clustered and assigned to operational taxonomy units (OTUs) using the SILVA database [36]. Within-sample diversity values were estimated using OTU numbers, Shannon, Simpson, PD_whole_tree, Chao 1, and Fisher's indices.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Enhanced Counter Synchronization and Session Management
#### 3.5.2 Synchronization Protocol  
The protocol maintains strict ordering while allowing for network-induced irregularities:  
* Initialization: Both parties start with \(seq=0\) and \(counter=0\)
* Increment \(seq\) before sending
- Use current \(counter\) for IV generation
- Include both values in the message header
* Check if \(seq_{\text{received}}=expected_{\text{seq}}\)
- If \(seq_{received}>expected_{\text{seq}}\): request retransmission of missing messages
- If \(seq_{received}<expected_{\text{seq}}\): discard as duplicate
- Only increment local \(counter\) after successful decryption
* Error Recovery:
* Maintain sliding window of last 5 messages for replay detection
* Allow counter resynchronization if sequence numbers match but counters drift
* Session termination only after 3 consecutive authentication failures
## 4 Results and Discussion  
### Numerical Simulation Results  
In order to validate the performance of the IT2 FISs generated by gradually increasing the FOU taking into consideration symmetry over a number of steps, they were applied to near-collision between ships. At this time, one of ships was designated as the OS, and the other was designated as the TS. Navigation information of both ships were extracted from the AIS in Mokpo sea area as presented in Table 3, and Figure 13 shows the occurred area of near-collision, which the ship safety domains proposed by [PERSON] and [PERSON] were overlapped.  
Numerical simulation was conducted based on Matrix Laboratory (MATLAB). Figure 14 shows the encounter situation between the OS and the TS, and near-collision was occurred as presented in Figure 14d.  
The FOU size \"\(\pm\)0%, \(\pm\)5%, \(\pm\)10%, \(\pm\)15%, \(\pm\)20%, and \(\pm\)50%\" were measured by the linear \(R_{c}\) as shown in Figure 15 and Table 4. On the other hand, the FOU size \"\(\pm\)25%, \(\pm\)30%, \(\pm\)35%, \(\pm\)40%, and \(\pm\)45%\" were measured by the non-linear \(R_{c}\) as presented in Figure 16 and Table 5. At the initial encounter situation between the OS and the TS, the FOU size \"\(\pm\)0%, \(\pm\)5%, \(\pm\)10%, \(\pm\)15%, \(\pm\)20%, and \(\pm\)50%\" inferred the \(R_{c}\) 0.00, 0.01, 0.01, 0.00, 0.00, and 0.16, respectively. The FOU size \"\(\pm\)25%, \(\pm\)30%, \(\pm\)35%, \(\pm\)40%, and \(\pm\)45%\" inferred the \(R_{c}\) 0.00. When it comes to a point of time for a give-away vessel (i.e., \(R_{c}\) 0.33), the computed linear \(R_{c}\) based on the FOU size \"\(\pm\)0%, \(\pm\)5%, \(\pm\)10%, \(\pm\)15%, \(\pm\)20%, and \(\pm\)50%\" gave the alarm at about 7.93 nm and 1800 s left until near-collision. The computed non-linear \(R_{c}\) based on the FOU size \"\(\pm\)25%, \(\pm\)30%, \(\pm\)35%, \(\pm\)40%, and \(\pm\)45%\" gave the alarm at about 7.93 nm and 1800 s, 7.57 nm and 1710 s, 7.57 nm and 1710 s, 6.86 nm and 1530 s, and 7.21 nm and 1620 s, respectively. In near-collision situation, the FOU size \"\(\pm\)0%, \(\pm\)5%, \(\pm\)10%, \(\pm\)15%, \(\pm\)20%, and \(\pm\)50%\" computed the \(R_{c}\) 0.99, 0.99, 0.98, 0.98, and 0.97, respectively. The FOU size \"\(\pm\)25%, \(\pm\)30%, \(\pm\)35%, \(\pm\)40%, and \(\pm\)45%\" computed the \(R_{c}\) 0.98, 0.98, 0.98, 0.97, and 0.97, respectively.  
Figure 14: Encounter situation between the OS and the TS: (**a**) head-on situation at relative distance “6.86 nm”; (**b**) head-on situation at relative distance “4.75 nm”; (**c**) head-on situation at relative distance “2.74 nm”, and (**d**) near-collision at relative distance “0.75 nm”.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 3 Data Processing
### Position Differences between Kinematic PPP and IGS Daily Solutions  
To assess kinematic position estimates differences among PPP scenarios, epoch-wise kinematic coordinate estimates are compared with the IGS daily estimates. In each daily kinematic solution, the epoch-wise coordinates after the first 1 h (120 epochs) are used for statistics analysis. Figure 4 shows for all 11 stations the magnitude distribution of 3D RMS of kinematic coordinate differences. The mean 3D RMS are of 28.1, 10.9, 9.2 cm and median 3D RMS are of 17.0, 8.2, 6.5 cm for the BDS-only, GPS-only and GPS/BDS combined solutions. The magnitude distribution also shows a higher percentage of more accurate estimations in GPS/BDS combined solutions.  
Kinematic PPP of station JFNG on DOY (Day of Year) 028, 2014 is shown in Figure 5. It shows that combined GPS/BDS kinematic PPP has much better stability and shorter convergence, while both GPS-only and BDS-only kinematic PPP show big scatters as large as 0.5 m. The number of tracked satellites is shown in the bottom-right subfigure of Figure 5, where number of available satellites of GPS/BDS combination almost doubles that of GPS-only and BDS-only cases. Statistics show that for JFNG, GPS-only kinematic PPP has a better accuracy than that of BDS, even if BDS-only kinematic PPP occasionally performs better that GPS when BDS has better satellite coverage with observations of good quality, such as JFNG at DOY 028, 2014.  
Figure 3: Magnitude distribution of 3D RMS of coordinate differences between daily static PPP and IGS daily solutions. Subplot presents the GPS/BDS combined, GPS-only and BDS-only solutions from left to right, respectively. The text of each subplot shows the median and mean value of 3D RMS.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**BCI Competition III**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Dataset IIIa**} & \multicolumn{4}{c}{**Dataset IVa**} \\ \hline Sub & A1 & A2 & A3 & B1 & B2 & B3 & B4 & B5 \\ \hline CSP & 95.56 & 61.67 & 93.33 & 66.07 & 96.43 & 47.45 & 71.88 & 49.6 \\ \hline EFA & 53.33 & 48.33 & 63.33 & 98.21 & 78.57 & 86.94 & 62.5 & 75 \\ \hline CDC-EFA & 100 & 98.33 & 98.33 & 90.18 & 96.43 & 94.05 & 92.86 & 100 \\ \hline \multicolumn{10}{c}{**BCI Competition IV**} \\ \hline Sub & C1 & C2 & C3 & C4 & C5 & C6 & C7 & C8 & C9 \\ \hline CSP & 88.89 & 51.39 & 96.53 & 70.14 & 54.86 & 71.53 & 81.25 & 93.75 & 93.75 \\ \hline EFA & 52.78 & 52.78 & 54.56 & 60.42 & 57.64 & 50.69 & 54.17 & 56.94 & 53.47 \\ \hline CDC-EFA & 100 & 100 & 98.61 & 99.31 & 99.31 & 97.22 & 49.31 & 97.92 & 94.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy comparison results when using CSP, EFA, and CDC-EFA methods. Figure 5: Average and maximum accuracy for each method. As shown in Tables 4 and 5, the CDC-EFA method showed improved accuracy compared with the CSP and EFA methods. We can observe that the CDC-EFA method not only improves the average accuracy, but also obtains a stable high accuracy with a low standard deviation. Table 6 indicates the feature classification outcomes for the EEG data for the BCI Competition IV dataset IIa. The CDC-EFA method was recorded with 100% accuracy for the BCI Competition IV dataset IIa, except for the seventh subject (45.14%). Therefore, the proposed CDC-EFA method showed superior and more stable performance compared to the existing method.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 6 TWT in Wi-Fi 6
### Target Wake Time (TWT) Mechanism in Wi-Fi 6
#### 6.2.5 Multi-User TWT  
TWT leverages the multi-user feature of OFDMA in Wi-Fi 6 to maximize efficiency. The multi-user operation mode reduces the overhead of the control packet and makes packet aggregation viable [13]. The access point may assign different RUs to the stations in an SP. Multiple stations wake up synchronously within one SP and the BSS can multiplex the transmissions over different RUs. Thereby, the access point maximizes energy efficiency by limiting idle listening without impacting network capacity. Moreover, multiple Target Wake Times (TWTs) may be scheduled in parallel on different RUs in a single TXOP, providing more freedom in scheduling.
## 5 Performance Metrics  
The following are the parameters considered for analysis.  
True Positive Rate: The ratio of actual positive cases to the sum of true positive and false negatives that are correctly identified by a classifier.  
\[TPR=\frac{TP}{TP\ +\ FN} \tag{7}\]  
False Positive Rate: The proportion of actual negative cases to the sum of true negative and false positives that are incorrectly classified as positive by a classifier.  
\[FPR=\frac{FP}{FP\ +\ TN} \tag{8}\]  
Precision: The fraction of positive instances that a classifier accurately predicts out of all positive instances.  
\[Precision=\frac{TP}{TP\ +\ FP} \tag{9}\]  
Recall: The fraction of correctly predicted positive instances among all actual positive instances.  
\[Recall=\frac{TP}{TP\ +\ FN} \tag{10}\]  
F1-score: The harmonic mean of precision and recall, providing a balance between the two metrics.  
\[F1\_Score=2\times\frac{(Precision\ \times\ Recall)}{(Precision\ +\ Recall)} \tag{11}\]  
Accuracy: The ratio of correctly classified instances (true positives and true negatives) among all instances.  
\[Accuracy=\frac{TP+TN}{overall\ samples}\times 100 \tag{12}\]  
Error: The ratio of incorrectly classified instances (both false positives and false negatives) among all instances.  
\[Error=\frac{FP\ +\ FN}{overall\ samples} \tag{13}\]
## 4 Equivalent Block Diagrams for GNSS and Spoofing Signals  
Section 3 identified the main sources of the transmitter feature. This section will present, to the best of our knowledge for the first time in the literature, two equivalent  
Figure 6: Examples of characteristics of the band-pass filter at the transmitter for a genuine (**a**) and spoofer (**b**) transmitter.  
simplified models of a genuine GNSS transmitter and a spoofer GNSS transmitter, by taking into account all five HW impairments identified and discussed in the previous section. These equivalent models will serve as the bases for addressing RFF in the context of GNSS, as they clearly identify the places of various HW features and point out situations where the same type of feature (e.g., phase noise) can affect multiple blocks. In order to build these equivalent transmitter block diagrams, we gathered information from the Galileo standards and manufacturer brochures, e.g., as in [51] and from software-defined radio GNSS transmitter sheets such as those in [52].
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 5 Conclusions  
This study successfully achieved its main objective of automatically handling and processing various CAMs and DENMs through a series of defined test scenarios using specialized tools Karlos and C-ITS SIM. The testing focused on evaluating the performance and compliance of third-party OBUs in handling these messages in terms of their content.  
In this study, four test scenarios were designed and executed to capture and analyze communication. The tests revealed several warnings and one error, underscoring the necessity of using tools like Karlos and C-ITS SIM in the realm of C-ITSs. These tools are essential for thorough evaluation and ensuring compliance with standards.  
Future research will need to address the challenges associated with the penetration of vehicles in C-ITSs, which includes the possibility of testing multiple units simultaneously. Field testing is also a critical area for exploration, given the authors' existing experience in this domain. Furthermore, ongoing research into new types of cooperative messages will be essential to ensure that our tools can evaluate these effectively.  
The outcomes of this study contribute significantly to the field of C-ITSs by demonstrating the effectiveness of automated tools in assessing the handling of CAMs and DENMs. The automated processing capabilities of tools like Karlos and C-ITS SIM not only streamline the evaluation process but also enhance the efficiency of identifying issues and ensuring compliance. The identification of warnings and errors highlights the need for ongoing refinement and calibration of both on-board units and testing tools to ensure reliable performance in real-world conditions. Moving forward, integrating these findings into the development and deployment phases of C-ITS technologies will be crucial for enhancing their robustness and ensuring their alignment with evolving standards and operational requirements.  
Conceptualization, Z.L. and M.V.; methodology, Z.L., M.V. and M.S.; software, Z.L. and M.S.; validation, M.S., M.Z. and R.H.; formal analysis, M.S., M.Z. and M.V.; writing--original draft preparation, M.V. and M.S.; writing--review and editing, M.S. and M.V.; project administration, Z.L. and S.-C.H.; funding acquisition, Z.L., S.-C.H. and R.H. All authors have read and agreed to the published version of the manuscript.  
This research is supported by a joint research project funded by Czech Technical University in Prague and National Taipei University of Technology, Project number: NTUT-CTU-113-01.  
Not applicable.  
The data used in this article consist of custom-recorded logs and data from on-board units collected during the study. Due to the nature of the data and potential privacy concerns, the dataset cannot be publicly shared. However, the authors are willing to provide access to the dataset upon official request for research purposes. Interested researchers may contact the authors directly to discuss access and receive the data following appropriate procedures.  
The authors declare no conflicts of interest.
# Monitoring of Evolving Laser Induced Periodic Surface Structures
## 4 Results
### Influence of Geometrical Properties of the Surface Structure
#### 4.1.2 Influence of Structure Period  
As described in the experimental section, similar fringe patterns were observed for titanium and Si\({}_{3}\)N\({}_{4}\) samples although the structural parameters were very different as well as the dielectric properties of the samples. Therefore, we have investigated the role of the structure period for the 2D electric field distribution. The resulting fringe pattern only slightly depends on the structure period: Fourier analysis of the field distribution yields fringe periods of \(\sim\)1.55 \(\upmu\)m, 2.01 \(\upmu\)m, 2.17 \(\upmu\)m and 2.35 \(\upmu\)m for nanostructured titanium samples with a structure height of 300 nm and structure periods of  
Figure 6: Structure-induced intensity changes to the far-field distribution at 50 \(\upmu\)m distance, that is, the difference between the far-field distribution of reflected light from structured and unstructured samples. (**a**) For an entirely structured sample surface. (**b**) For a sample where only the central spot (30 \(\upmu\)m diameter) is structured.  
200 nm, 600 nm, 700 nm and 800 nm, respectively. The highest contrast of the fringe pattern is observed for the 800 nm structure.
## 4 Numerical Simulation Based on MIDAS FE Platform
Then, by feeding the stresses measured at the engineering site on B-B and C-C cross-sections (i.e., 4.1 MPa and 6.6 MPa, respectively) to the linear surrogate model obtained, the applied pre-stresses to segments 1 are identified for #6 pier and #7 pier, which are 1.56 \(\times\) 10\({}^{9}\) N/m\({}^{2}\) and 3.04 \(\times\) 10\({}^{9}\) N/m\({}^{2}\), respectively. Finally, by substituting the design value of the applied pre-stress to segment 1 in the initial FE model with the identified value, model updating for the stage that pre-stresses are applied to segments 1 is completed. After the model updating for the stage that pre-stresses are applied to segments 1 is completed, model updating for subsequent construction stages is undertaken progressively following the construction sequence with reference to the stress data measured on the location (see Figure 8). Model updating for subsequent stages basically follows the practice presented above for model updating for the stage that pre-stresses are applied to segments 1, and the identified pre-stresses to segments 1-7 are shown and compared with the initial design value in Figure 11. As can be seen, all the identified pre-stresses are notably greater than the initial design values, especially at the late stages in the construction process. This indicates that the analyses using the initial FE model without model updating might lead to inauthentic results. Finally, the updated numerical model of Huangsha Harbor Bridge is obtained for use by incorporating the identified pre-stresses. It should be emphasized that due to the advantage of the proposed model updating practice, these uncertain parameters with the initial FE model of Huangsha Harbor Bridge are easily and accurately identified using single-variable linear regression models without the need to establish a complicated multi-variable surrogate model and using advanced optimization techniques, e.g., the sensitivity-analysis-based parameter identification. After model updating, the stress sequences on B-B and C-C cross-sections in accordance with the construction process are calculated using the updated FE model of Huangsha Harbor Bridge and compared with the corresponding stress sequences measured on location in Figure 12. As can be seen, the numerical results for the updated FE model agree well with the full-scale measurement data. This indicates the effectiveness of the proposed model updating practice. In addition, the stress sequences calculated using the initial model without model updating are also added in Figure 12. As can be seen, the numerical results for the initial FE model are noticeably smaller than the full-scale measurement data, suggesting that the structural analyses utilizing the FE model with design parameters might lead to unsafe results. Figure 11: Design pre-stresses applied to segments 1–7 and the identified values. (**a**) #6 pier; (**b**) #7 pier. Figure 12: Stress sequences calculated using the FE model and measured on location. (**a**) B-B cross-section; (**b**) C-C cross-section.