esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Data Similarity to Improve SSD Read Performance
## 4 Design
### Content-Aware Page Allocation Scheme
#### 4.2.1 Write Operation Procedure  
Figure 6 illustrates the process of writing data into SSD in detail. (1) When a write request is received from the host, the SSD divides each data into page-sized segments (e.g., 4 KB) and caches them in the DRAM. The first phase of content comparison involves calculating the fingerprint (i.e., SHA-1 [5]) of each data segment. This computation time overhead can be hidden by performing these steps concurrently.  
(2) The cached page-sized data segments are evicted from the DRAM and written into the NAND flash back-end. DS-FTL utilizes existing static or dynamic page allocation schemes or other widely used schemes [9; 10; 13; 17], to calculate the target PPNs for the data segments. Then, it looks up the fingerprint table of the corresponding channel, which records the fingerprints of all resident pages in that channel. It is worth noting that we maintain the fingerprint table at the channel level, as it imposes the least parallelism restriction compared to the chip or plane level. In the figure, the symbol \"#\" denotes the respective label number.  
(3) If the fingerprint of the write request matches one in the channel's fingerprint table, indicating that the same data segment is already stored in this channel, a round-robin approach is used to select the next channel or chip as a candidate until the write sub-request is serviced.  
(4) If the fingerprint does not match, we then update the mapping table and schedule the write sub-request to be written into this PPN as normal.  
Figure 5: The Design of Data Similarity aware FTL.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 4 Results and Discussion
### Performance of the PUA-LightGBM Model
Furthermore, updating the GNSS outage \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** & **RMSE** & \(\mathbf{R^{2}}\) & **Training Time (s)** \\ \hline PUA-LightGBM (\(\delta E\)) & 1.927 \(\times\) 10\({}^{-7}\) & 0.983 & 1.563 \\ PUA-LightGBM (\(\delta N\)) & 1.334 \(\times\) 10\({}^{-7}\) & 0.986 & 1.513 \\ PUA-RandomForest & 1.417 \(\times\) 10\({}^{-7}\) & 0.989 & 83.082 \\ \hline \hline \end{tabular} Note: PUA-LightGBM (\(\delta E\))— The PUA-LightGBM model for predicting the difference in longitude between two epochs; PUA-LightGBM (\(\delta N\))— The PUA-LightGBM model for predicting the difference in latitude between two epochs; _RMSE_— The root mean square error; \(R^{2}\)— The coefficient of determination.  
\end{table} 
Table 2: Performance comparison of the PUA-LightGBM model to the PUA-RandomForest model. vehicle position prediction model while the vehicle is moving has not been proposed yet, which may be useful for future research.
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 1 Introduction
### Contribution
The selection of a real-time simulator was based on its cost-effectiveness and its ability to perform physical and cyber layer co-simulation with external hardware in a loop using industry-grade communication protocols. The power system simulation software for this simulator has many standard packages and ready-to-use models and can be integrated with MATLAB/Simulink as well. Besides that, the cutting-edge network emulation/simulation software, EXata CPS software, with a pre-defined set of libraries for different communication devices and cyberattack scenarios, is used for real-life communication network emulation [32]. The software also provides flexibility to develop different communication architectures and cyberattack scenarios based on power system operation and provides a detailed analysis of network packets. Different scenarios are developed and tested under different types of cyberattacks to analyze the response and performance of the system under critical periods. To summarize, the contribution of the authors is as follows:  
1. Develop a HIL co-simulation testbed that boasts the integration of a real-time simulator, industry-grade protection, automation equipment, and a SCADA system;
2. Show an integration of protection studies in a CPS testbed;
3. Analyze different cyber threats at a granular level with network packet analysis;
4. Demonstrate the interoperability of the testbed at the communication protocol level with the use of DNP3, C37.118, and IEC 61850 for use cases;
5. Hands-on experiments to train students and industry professionals in cybersecurity. In summary, the goal of this research work is to deliver practical and theoretical knowledge of CPS vulnerabilities using experimental learning and hands-on exercises.
# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Basic Image Augmentation Methods
#### 2.1.1 Pixel Transformation
By actively introducing noise that may exist in the real scene for the image to achieve the augmentation of training samples, it can introduce interference information to the image segmentation model, and improve the generalization performance of the training model. To achieve data augmentation, there are two basic methods of noise injection: adding randomly distributed noise or adding noise with specific effects. Gaussian noise, uniform noise, gamma noise, pretzel noise, etc., are kinds of randomly distributed noise [27]. For example, by randomly sampling the Gaussian distribution, adding Gaussian noise can alter the intensity of each pixel's value in an image, simulating the interference that an actual scene can bring. In addition, it can add random algorithmic effects with physical simulations, such as RandomRain, RandomSnow, RandomSunFlare, Emboss, Image Compression, Downscale, and Upscale. Furthermore, the method of using image compression for data augmentation involves generating images of varying qualities by changing compression quality parameters to produce different degrees of compression. This approach can increase the diversity of the data set and improve the robustness and generalization capabilities of neural networks. In the actual image segmentation application, the appropriate noise amplification type should be carefully chosen according to the needs of realistic scenes.
## 4 ALD on EG With a Seeding-Layer
### Oxidized Metal Seed Layer  
Physical vapor deposition (PVD) of ultrathin metal films followed by oxidation is one of the most straightforward ways to create a seeding layer on the graphene surface [22]. [PERSON] et al. [34] investigated thermal ALD of different insulators (Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\) and Ta\({}_{2}\)O\({}_{3}\)) using oxidized metal films as seeding layers in the specific case of few layers EG/SiC(0001). Figure 7a,b shows the morphology of Al\({}_{2}\)O\({}_{3}\) grown at 150 and 300 \({}^{\circ}\)C, respectively, with an oxidized Al (AlOx) seed layer. The Al\({}_{2}\)O\({}_{3}\) film uniformity and coverage was found to be significantly improved by increasing the deposition temperature. Deposition of TiO\({}_{2}\) on EG with an oxidized Ti (TiOx) seed layer resulted in conformal and continuous films both at 120 \({}^{\circ}\)C (Figure 7c) and 250 \({}^{\circ}\)C (Figure 7d). Finally, ALD grown Ta\({}_{2}\)O\({}_{5}\) films with an oxidized Ta (TaOx) seed layer showed a high roughness for deposition temperatures of 150 \({}^{\circ}\)C (Figure 7e) and 300 \({}^{\circ}\)C and (Figure 7f). The impact of the seeded thermal ALD on the EG structural properties (i.e., the defects density) was also qualified by Raman spectroscopy. Figure 7g shows typical Raman spectra of the D and G peaks for AlOx/Al\({}_{2}\)O\({}_{3}\) deposited at 300 \({}^{\circ}\)C, TiOX/TiO  
Figure 6: **Conductive atomic force microscopy** (C-AFM) local current mapping through an Al\({}_{2}\)O\({}_{3}\) thin film deposited onto EG on axis 4H-SiC(0001). **(a)** Morphology of the probed sample area, including both uniform Al\({}_{2}\)O\({}_{3}\) on 1 L EG and Al\({}_{2}\)O\({}_{3}\) on a 2 L EG patch. **(b)** Current map collected on this area with a tip bias V\({}_{\rm tip}=6\) V. **(c)** Two representative local current-voltage characteristics collected by the C-AFM probe on Al\({}_{2}\)O\({}_{3}\) in the 1 L and 2 L EG regions. Images adapted with permission from Ref. [21], copyright [PERSON] 2019.  
deposited at 250 \({}^{\circ}\)C, and TaOx/Ta\({}_{2}\)O\({}_{5}\) deposited at 300 \({}^{\circ}\)C. These results show that ALD of high-k dielectric materials with an oxidized metal seed layer generally have little impact on the structural integrity of EG, with the highest EG defectivity obtained in the case of TaOx/Ta\({}_{2}\)O\({}_{5}\), probably generated during oxidation of the Ta seed layer.  
In spite of the limited increase in the EG defectivity, a reduction of the electron mobility has been reported in most of the cases after ALD of high-k dielectrics seeded by an oxidized metal [34]. This was ascribed to the poor structural quality and substoichiometric composition of the seed layer (typically due to an incomplete oxidation), leading to charge trapping phenomena and increased electron scattering by charged impurities.
## 2 Materials and Methods
### Outcome Measures
#### 2.5.3. Knee Joint Position Sense Test  
Static proprioception is assessed by evaluating the ability to perceive and reproduce a previously presented and passively explained joint angle.  
Before the assessment, four markers were applied to the skin with adhesive tape over the greater trochanter, the liotibial band, the head of the fibula, and the peroneal malleolus [31]. Each pair of markers represented the axis of the thigh and leg. To record the joint positions, the same camera, mounted on a tripod, was always used. It was placed in the sagittal plane in front of the knee joint, seven meters from the participant.  
To begin, the participants were seated on an examination table, wearing shorts and with their legs hanging, but not touching the floor. Their eyes were blindfolded to eliminate any visual feedback. Starting with a knee flexion of 90\({}^{\circ}\), as measured by a goniometer, the examiner slowly moved the leg from the initial position to the test position, with an angle of 120\({}^{\circ}\) of flexion. Subsequently, the player maintained this position for five seconds to memorize it. Then, the examiner instructed the player to actively reposition the leg to the initial position and immediately actively extend the knee in an attempt to reproduce the test position with the same limb. The subject then returned to the perceived test position and signaled this to the examiner. This position was held for five seconds, and upon the command \"return,\" the subject returned to the initial position and repeated the repositioning two more times. In this way, subjects performed three repetitions to reproduce the passively positioned knee angle. This protocol was repeated, starting from an initial position of 135\({}^{\circ}\). Finally, each player was placed in prone position and the process was repeated, obtaining two variables per position (starting from 90\({}^{\circ}\) and 135\({}^{\circ}\)), defined as the proprioceptive error in angles. The angles were photographed and measured using the SAPO(r) computer program. The SAPO(r) software version 0.69 has excellent reliability (ICC = 0.96) when used to assess knee flexion angles [32].  
A previous study on football players showed an ICC of 0.91, an SEM of 0.42\({}^{\circ}\), and a minimum detectable change (MDC) of 1.16\({}^{\circ}\) for this method of assessing knee joint repositioning ability [31].
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 4 Discussion
### Application in Optical Communication  
VCDM can compensate for wavefront distortion caused by atmospheric turbulence in wireless optical communication. Figure 16 shows the application of VCDM in an optical communication system. The beam is disturbed by atmospheric turbulence as it travels through free space. The light beam is coupled to the fiber by a lens at the receiving terminal. The coupling efficiency affects the bit error rate (BER). In free-space optical communication systems, it is necessary to improve the coupling efficiency to reduce the BER. An adaptive optics system is added in front of the coupling lens, and a VCDM P3 is used to compensate for the distorted wavefront. Given a signal beam wavelength of 650 nm, a VLC scenario is constructed. The communication method uses pulse-phase modulation, and the bit rate is 4 Gbps. The receiving aperture is 12 mm, and the mode field diameter is 8 \(\upmu\)m. The comparison of the coupling coefficient before and after correction is shown in Figure 17. After correcting the complex random aberrations of the signal beam by VCDM, the coupling coefficient increases from 61.9%, 62.58%, 65.34%, 64.37%, 65.08%, 68.7% to 84.19%, 83.62%, 83.7%, 83.62%, 83.35%, and 83.57%, respectively. It shows that the VCDM P3 can effectively improve the coupling efficiency of the VLC system and reduce the BER.  
Figure 14: Stroke of P3. (**a**) Tip stroke. (**b**) Defocus stroke.  
Figure 15: Fitting errors of the first 14 Zernike terms. (**a**) Comparison among four VCDMs listed in Table 4. (**b**) Comparison between P3 and DM69-25.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 5 Results
### Signal-to-Noise Ratio Impact on Radar Sensing
#### 5.2.2 Root Mean Square Error of Velocity Estimation  
Figure 10 presents the RMSE of the velocity estimation. It is apparent that PRS configurations with two PRS resources achieve satisfactory RMSE values (under \(10^{-1}\) m/s) earlier than others. This happens because they occupy more REs in the same slot. The best results are obtained by the PRS-2-2-2-RES, PRS-4-4-2-RES, and PRS-6-6-2-RES configurations, where the first two achieved satisfactory RMSE values at \(-3\) dB, and the last  
Figure 9: PNFR for different PRS configurations (solid lines with asterisk markers) together with velocity RMSE as a reference (dashed lines).  
achieved it at \(-\)2 dB of the input SNR. It was obtained together with PNFR at the level of \(\sim\)12.5 dB and \(\sim\)13 dB, respectively. The next four best are the configurations that had 1 PRS resource configured. PRS-12-12-1-RES achieved a satisfactory RMSE at \(-\)1 dB in conjunction with PNFR at \(\sim\)12 dB, along with PRS-6-6-1-RES. PRS-2-2-1-RES and PRS-4-4-1-RES were also very close to each other, reaching a satisfactory RMSE at 0 dB of the input SNR with simultaneous \(\sim\)13 dB of the PNFR. The worst-performing configurations were those with only two REs allocated for PRS per slot (PRS-12-1-2-RES) and one RE allocated for PRS per slot (PRS-12-1-1-RES). The fact that they occupied the least amount of REs allowed us to allocate more resources for the communications tasks; nevertheless, this implies that they require the best channel conditions for successful radar sensing. The configuration with two PRS resources achieved a satisfactory RMSE at 8 dB of the input SNR with simultaneous \(\sim\)13.5 dB of PNFR and a PRS-12-1-1-RES configuration with 1 resource at 10 dB of the input SNR with simultaneous PNFR at the level of \(\sim\)13 dB. Overall, the configurations with 1 and 2 PRS resources were close to each other in terms of performance. The difference in SNR when achieving a satisfactory RMSE was only 2 dB, while two PRS resources occupied twice more resources in the 5G grid. Considering this, configurations with one PRS resource are much better in terms of performance/overhead ratio.
## 2 Materials and Methods
### Data Processing
#### 2.5.3 Solar-Induced Chlorophyll Fluorescence (SIF) Data Processing  
The retrieval of SIF signals is based on algorithms derived from the classical Fraunhofer Line Discrimination (FLD) method [32]. In this study, we employed the Spectral Fitting Model (SFM) algorithm. This method performs least-squares fitting using multiple spectral bands over a relatively wide wavelength window based on the natural spectral characteristics of both SIF and surface reflectance to reconstruct a continuous SIF spectral curve. It assumes that surface reflectance and the fluorescence spectrum conform to specific mathematical functions (e.g., linear, polynomial, or Gaussian functions), enabling spectral fitting and inversion of the SIF signal [30; 33]):  
\[L(\lambda)=\frac{r_{MOD}(\lambda)\cdot E(\lambda)}{\pi}+F_{MOD}+\varepsilon( \lambda)=L_{MOD}(\lambda)+\varepsilon(\lambda) \tag{4}\]where \(r_{\text{MOD}}(\lambda)\) is the modeled reflectance, \(F_{\text{MOD}}(\lambda)\) is the modeled fluorescence, \(L_{\text{MOD}}(\lambda)\) is the modeled apparent radiance, and \(\varepsilon(\lambda)\) is the residual between the observed and modeled values at each wavelength, representing the model fitting error.  
The SFM algorithm was adopted as the standard fluorescence retrieval algorithm for the FLEX mission. It was widely used in top-of-atmosphere SIF retrievals in the O\({}_{2}\)-A and O\({}_{2}\)-B bands, and its performance and accuracy were extensively evaluated. Compared to the FLD family of algorithms, the SFM method had higher spectral resolution requirements but offered the advantage of retrieving full-spectrum fluorescence within the fitting window and greater resistance to noise.
## 3 Finite Element Modeling of the Positive and Negative Stiffness Components
### Eigenfrequency Study  
From the geometrical parameters and mechanical properties indicated above, a preliminary eigenfrequency computation is performed on a three-dimensional finite element model of the assembly. Fixed boundary conditions are specified on the left, right, top, and bottom boundaries of the assembly to mimic clamped conditions with the frame. This results in the modal shapes illustrated in Figure 10. The modes of interest are the ones having in-plane kinematics, and particularly the first one associated with the symmetrical bending of curved beams on each side of the mass, resulting in its vertical motion. For this specific kinematic to occur, it is outlined that the negative stiffness introduced by the curved beam should overcome the positive stiffness produced by the four supports. The second mode presenting the rotation of the mass is not of interest in this contribution, although it is in some applications related to structure stabilization and damping in joints of reticulated structures.
## 6 Conclusions  
To solve the problem of target flange identification and positioning in the automatic docking system of a small crane tube, this paper proposes a method of flange port detection applicable to the automatic docking system of small crane tubes of tank cars. The system framework, algorithm principle, and realization process of this method are introduced. The space pose detection program of the flange end face is written. Additionally, the method was tested for circle center detection calibration, flange end face image detection, and the detection of the flange port of on-site tank cars. The experimental results show that the maximum error of the circle center detection accuracy is 1.503 mm and the maximum error of the diameter detection accuracy is 2.895 mm, which are less than the 3 mm range required by mechanical installation tolerance, meeting the needs of automatic on-site butt joint. At present, the detection system is limited by the requirements of explosion proof application, and the real-time image acquisition experiment is difficult to carry out on site, but we are carrying out the assembly of explosion-proof facilities in the laboratory. After the installation of explosion-proof facilities in the laboratory meets the requirements, we will carry it out on-site.  
This paper conducted research on the automatic docking of bottom loading tank cars with a small crane tube via flange space position detection. In the future, when this method is applied in complex conditions, we should further improve the speed and accuracy of the algorithm and the stability of the system in order to meet the high demands of the automatic docking of bottom loading tank cars.  
Data curation, Z.L., Z.M. and M.X.; Formal analysis, Z.L.; Funding acquisition, M.L. and S.L.; Project administration, M.L.; Software, Z.L. and J.L.; Writing--original draft, Z.L.; Writing--review & editing, M.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key R & D Plan Industry Prospect and Key Core Technology Project of Jiangsu Province (BE2021066), Open Fund Project of Jiangsu Institute of Marine Resources Development (JSIMR201904, JSIMR201810), and Regional Leading Research Center of NRF and MOCIE (NRF-2019R1A5A8083201).  
Not applicable.  
This work is supported by the key R & D plan industry prospect and key core technology project of Jiangsu Province (BE2021066), the open fund project of the Jiangsu Institute of Marine Resources development (JSIMR201904, JSIMR201810), and the Regional Leading Research Center of NRF and MOCIE (NRF-2019R1A5A8083201).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation** & **Circle Center** & **Flange** & **Diameter** & **Running Time** \\
**Example** & **Accuracy Error** & **Detection Outer** & **Accuracy Error** & **(s)** \\  & **(mm)** & **Diameter (mm)** & **(mm)** & **(s)** \\ \hline A & 1.503 & 162.105 & 2.895 & 0.694 \\ B & 0.803 & 165.755 & 0.755 & 0.690 \\ C & 1.212 & 163.649 & 1.351 & 0.709 \\ \hline \hline \end{tabular}
\end{table}
Table 2: On-site flange detection results.
## 4 Application
### Ranking Alternatives Using the MCDM Methods
For 2012, the top three were Singapore, Hong Kong, and Finland, whereas the lowest three were Djibouti, Haiti, and the Republic of Congo. Based on the 2014 results, Germany, the Netherlands, and Belgium emerged as the leading countries, while the Democratic Republic of Congo, the Republic of the Congo, and Afghanistan were among the poorest performers. In 2016, Germany, Luxembourg, and Sweden were the top three performers with LPI scores of 4.23, 4.22, and 4.2, whereas the Syrian Arab Republic, Haiti, and Tajikistan were at the opposite end of the spectrum with LPI scores of 1.6, 1.72, and 2.06. The 2018 results showed Germany, Sweden, and Belgium as high-performing countries, while Afghanistan, Angola, and Haiti were the lowest-performing. For 2023, Singapore and Finland occupied the top two positions, with Denmark, Germany, the Netherlands, and Switzerland sharing the third place. Cuba, Angola, Cameroon, Haiti, Afghanistan, and Libya were classified as the lowest-performing countries. The rankings from MCRAT, SAW, TOPSIS, and FUCA generally differ from the LPI rankings; however, the position of the top-ranked alternative has remained unchanged. Specifically, for the years 2010, 2014, 2016, and 2018, Germany has been the top-ranked country across all methods. In 2012 and 2023, Singapore was the highest-performing alternative in all rankings. The six-year averages of the rankings from the four MCDM methods and the LPI are presented in Figure 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Max-Based** & **Sum-Based** & **Vector-Based** & **Peldschus-** \\  & & **MCRAT** & **MCRAT** & **MCRAT** & **Based MCRAT** \\ \cline{2-6}  & 2010 & 1 & 0.9999 & 0.9999 & 0.9947 \\  & 2012 & 1 & 0.9999 & 1 & 0.9950 \\  & 2014 & 1 & 0.9999 & 0.9999 & 0.9959 \\  & 2016 & 1 & 1 & 1 & 0.9943 \\  & 2018 & 1 & 1 & 1 & 0.9957 \\  & 2023 & 1 & 0.9999 & 1 & 0.9959 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The correlation coefficients between the normalized and non-normalized MCRAT results. Figure 3 reveals that the rankings obtained using MCDM methods are very similar to each other. Although the MCDM and LPI rankings show a consistent trend, the country rankings differ between the two approaches. Despite some variations in the results averaged over six years, a notable common finding is that Germany consistently ranks first across all rankings, while Afghanistan is positioned at the bottom in each case.