esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Line Prefetch Covert Channel with Huge Pages
## 4 The Design of a Multi-Line Prefetch Covert Channel with Huge Pages
### Setup and Configuration  
System Configuration: We utilize a local machine with an Intel(R) Core(TM) i5-6500 CPU operating at a maximum clock speed of 3.60 GHz with the Ubuntu 24.04 OS. The system supports prefetching and utilizing huge pages, enhancing the performance and memory management capabilities.  
The Software Environment: We develop sender and receiver programs that operate on the same physical machine to eliminate external interference. The programs are implemented in C using compiler-supported prefetching instructions, such as __builtin_prefetch in GCC, to manipulate the cache states.  
Huge Page Setup: To enhance the memory access patterns and overall performance, we configured the system to use 2 MB huge pages by enabling the Linux hugepages subsystem. This included resizing the shared file to 2 MB; updating the mmap system call with the MAP_HUGETLB flag to allocate memory backed by huge pages; and mounting the hugetlbfs filesystem to support these allocations.
# Sensitivity Estimation for Differentially Private Query Processing
## 5 Experiments
### Efficiency  
We evaluated the computational efficiency of our method against RS and ES by measuring end-to-end query processing time, from query submission to returning the noised results. This includes query execution time, sensitivity computation, and noise injection time under differential privacy. Note that the offline preprocessing steps (e.g., computing join attribute frequencies for ES or constructing relation sketches) are excluded from these measurements.  
The experimental results on the TPC-H dataset in Figure 8 demonstrate that Sampling-SE consistently exhibits intermediate time efficiency between RS and ES, with the performance gap becoming particularly noticeable at larger scales. This behavior can be attributed to two key factors. First, the sampling approach significantly reduces the computational overhead of evaluating multiple residual queries. Second, certain complex residual queries involving primary-foreign key joins can be simplified to identifying the maximum frequency value within a single relation, yielding additional computational savings. This balanced efficiency-accuracy trade-off makes Sampling-SE particularly suitable for enterprise data warehouses that need to process large-scale, multi-relational queries under strict privacy constraints.  
Figure 8: Running time of queries on the TPC-H dataset.  
Figure 7: Impact of privacy budget \(\epsilon\) on the Facebook dataset.  
We also compared the efficiency of different methods on the Facebook dataset, and the results are plotted in Figure 9. We set the parameters \(\epsilon=0.8\) and \(\delta=10^{-7}\) and the sample rate at \(r=1\times 10^{-4}\). The results demonstrate that Sampling-SE performs effectively in most scenarios. While slightly less efficient than RS for Q5, which involves only three small relations and, consequently, has minimal RS computation time, Sampling-SE shows superior scalability with larger datasets and complex join conditions, where its utility advantages become particularly significant. Notably, ES and Sketch-SE benefit from the offline pre-computation of statistics and sketches, respectively, giving them inherent online efficiency advantages over methods requiring real-time computation. This makes Sampling-SE ideal for real-world applications like fraud detection and customer profiling, which require both scalable join processing and rigorous privacy guarantees.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 2 Materials and Methods
### Proposed FRA6\(\sigma\) Approach--Define Phase
#### 2.1.3 Step 3: Control Chart Plot--Analyze Phase  
After computing the statistical control limits, the next step involves visualizing the transformer's FRA data through a control chart (\(\overline{X}\)-chart). This chart provides a graphical representation of the baseline \(\overline{X}(f)\), X-chart control limits, and the \(X_{latest}\) measurement. In plotting these elements together, the deviations from the expected behavior can be easily identified, enabling early detection of potential transformer defects.  
1. Fingerprint Mean Response (\(\overline{X}(f)\)) Plot  
The first component of the \(\overline{X}\)-chart is the fingerprint (baseline) \(\overline{X}(f)\) which serves as the reference curve. This curve is obtained from the previously computed mean response at each frequency point as shown in Equation (2). This mean response acts as the central trend against which future measurements will be compared.  
2. UCL and LCL Plot as Boundary Lines  
Next, the UCL and LCL are plotted to define acceptable response boundaries. These limits help identify whether the transformer remains in a normal operating state or if potential defects exist. The UCL and LCL are determined using the Equations (4) and (5).  
These boundary lines represent the threshold for acceptable FRA variations. Any deviations beyond these limits indicate potential mechanical or electrical defects.  
3. Latest FRA Measurement Overlay  
Finally, the \(X_{latest}\) measurement is plotted on the same \(\overline{X}\)-chart. This curve represents the most recent frequency response of the transformer. If this response remains within the UCL and LCL at all frequency points, the transformer is considered stable. However, if deviations occur at specific frequency ranges, it may indicate structural or electrical changes such as winding deformation, core displacement and insulation degradation.
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 6 Magneto-Active Polymers in Biomedical Applications
### MAPs for Bone and Tendon Tissue Engineering
[209] showed that the application of an external magnetic field to magneto-active scaffolds induced pre-osteoblast proliferation, attributed to both local magneto-mechanical and magneto-electrical responses of the scaffold. [PERSON] et al. [210] tried a novel approach for force transmitting to cells. They created a magnetic hydrogel by attaching MNPs directly to the mechanically activated TREK1k+ receptor of human mesenchymal stem cells. By the application of an oscillating EMF, mechanotransduction was stimulated and hydrogels showed a 2.4-fold increase in mineralisation. Regarding tendon and cartilage tissue engineering, multiple advances have been made regarding magnetic scaffolds. Due to the limitation of self-repair from articular cartilage, a great deal of efforts has been employed to overcome this limitation and deal with articular cartilage injury. The focus of cartilage tissue engineering lays on the ability of different materials to properly recruit bone mesenchymal stem cells, and to support their activity to promote cartilage repair. In this line of work, [PERSON] et al. [113] performed a study in which a magnetic scaffold was fabricated by mixing a hybrid hydrogel composed of HA, type II collagen and PEG with PVA modified Fe\({}_{3}\)O\({}_{4}\) magnetic particles by mechanical dispersion. The magnetic hydrogel showed the ability to support BMSC behaviour in vitro, although the effect of magnetic stimulation of the hydrogel on the cells was not evaluated. However, not only it is necessary to maintain bone marrow stem cell (BMSC) behaviour in vitro, but the need to promote chondrogenic differentiation poses a new challenge. This process requires the addition of cytokines, but the currently available cytokines have a short half-life. [PERSON] et al. [211] developed a cellulose nanocrystal/dextran hydrogel with MNPs. These MNPs were grafted with kartogenin (KGN), an important cytokine, which allowed for a controlled and long-term sustained release of the cytokine. This controlled release of KGN improved host cell recruitment and induced BMSC differentiation, improving in situ cartilage regeneration. However, a long-term exposure to magnetic fields may entail negative effects on osteogenic cell differentiation [201, 212]. These principles can be extended to cartilage that, due to the lack of endogenous restoration of cartilage tissue within bone joints after damage, needs further development by means of tissue engineering [189]. However, more studies are needed to explore the influence of the magnetically induced mechanical forces on the behaviour of magneto-active scaffolds [213].
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 4 Hardware Design of the Circuit Breaker Prototype
### Hall Current Sensor Circuit Design  
Shown in Figure 5a,b are the physical image and corresponding circuit diagram of the current detection module based on a Hall current sensor, respectively. In this experiment, a high-performance Hall effect current sensor CC6920 (U2) (Jia li chuang, Shenzhen, China) is used, which features high precision and low noise linear DC current measurement, with a current measurement range of 0-5 A. The equivalent output voltage signal of the Hall sensor is 0-2.5 V. To observe the output signal more intuitively and significantly, an operational amplifier LMV321 IDBVR (Jia li chuang, Shenzhen, China) is connected to the output end of the Hall sensor as shown in the circuit diagram, with the gain designed to be two times. A first-order filter circuit is connected at the terminal to eliminate high-frequency noise interference. AD1 is the signal output terminal that outputs the current acquisition signal, corresponding to the signal output terminal in the physical image. Ports 1, 2, 3, and 4 of U2 are the input and output interfaces for the current to be measured, corresponding to the input and output terminals in the physical image.
## 4 Simulations and Results
### Case V  
In this case, five out of nine sub-bands of unequal bandwidth are allocated to the PUs, designated as regions I, III, V, VII, and IX. Four sub-bands of unequal bandwidths allocated to SUs, designated as regions II, IV, VI, and VIII, are shown in Figures 12 and 13. We consider that the SU operating in regions II, IV, VI, and VIII has 16, 32, 64, and 128 OFDM subcarriers, respectively. The comparison of the proposed technique is done with the existing techniques. _i.e._, CC [14], CC using GA and DE [17], ACC [18], and ASW [21]. Two CCs have been taken on both sides of the data subcarriers in all cancellation carrier techniques. In terms of normalized power spectral density, Figures 12 to 13 show that the proposed technique achieved significant suppression when compared to the existing techniques in all regions of PUs.  
Suppression of sidelobe power level achieved in a spectrum-sharing scenario at regions allocated to PUs, using either the existing techniques or the proposed technique, is given in Table 5, which shows that the proposed technique gave outstanding results compared with the existing techniques in all regions of PUs.
# An Efficient UV-C Disinfection Approach and Biological Assessment Strategy for Microphones
## 3 Results  
### UV-C Device Design and Validation  
A UV-C device was specifically tailored for the disinfection of microphones (Figure 2). The device is equipped with two Osram Puritec Germicidal lamps (HNS 16 W G5 with 254 nm as the maximum intensity wavelength). The device includes a drawer that slides out of a protective casing to provide access to the inner chamber that is equipped with a removable tray holding the microphones. Once closed, there is no UV-C radiation leakage outside the box. Further, the mode of operation is such that the only possible variation is the time of irradiation, which simplifies the operation of the device. Although different microorganisms require different intensities of irradiation, such requirements can simply be met by lengthening the irradiation time as the process is cumulative. Thus, to achieve twice the irradiance, it will suffice to double the irradiation time, keeping in mind that the intensity is indeed high enough to inflict alterations.  
Microphones are complex pieces of equipment in terms of shape. Hence, careful design of their placement within the UV-C disinfection chamber is needed in order to assure that everywhere within the device, the irradiance is high enough to effectively eliminate contaminating microorganisms, while keeping the irradiation times short enough to remain practical. As is shown in Figure 2, the UV-C sources were placed at one end of the chamber  
Figure 2: Basic design of the UV-C disinfection device that can contain up to three microphones at the same time. The microphone support slides into a shielded casing that contains two UV-C lamps. The efficiency of irradiation is enhanced by the usage of UV-C reflecting walls.  
and the walls of the inner chamber were made to reflect UV-C to minimize loss due to UV-C absorption. Consequently, the items that most effectively absorb the irradiation are the microphones, of which up to three can be placed within the chamber for disinfection. The irradiance (\(\mu\)W/cm\({}^{2}\)) of the UV-C was measured at various locations within the device according to an external accredited agency (Opsytec Dr. Groebel GmbH) (Supplementary Information S1), and it was shown to range from 523 to 7185 \(\mu\)W/cm\({}^{2}\) depending on the position within the device. Different measurements at the same locations showed variations of 100-150 \(\mu\)W/cm\({}^{2}\). Judged by the lowest measured irradiance (i.e., 373 \(\mu\)W/cm\({}^{2}\), Supplementary Information S1), the theoretical efficacy for eliminating the most common pathogens would be >99.9% upon 1 min irradiation (according to DIN 5031-10: 2018-03). While inside the UV-C device, the irradiance is high enough for effective disinfection, the measured UV-C values outside the device were below 9 \(\mu\)W/m\({}^{2}\) (Supplementary Information S2), which is far below the acceptable limits (max dose: 1042 \(\mu\)W/m\({}^{2}\) for 8 h continuously). Therefore, we conclude that the device is safe to use.
## 2 Results
### Cloning and Functional Validation of cDNAs Encoding a Second DXS Isoform, CMS, CMK and HDR
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Enzyme** & **Identity** & **PSORT** & **Predotar** & **ChloroP** & **MitoProt** & **TargetP** \\ \hline  & 74\% & ER (membrane) 0.550 & P 0.45 & & & & \\ DXS/DXS2.1 & 64\% & Peroxisome 0.102 & M 0.01 & Y (0.509) & 0.636 & cTP (0.375) \\  & 41\% & ER (lumen) 0.100 & E 0.51 & & 35 residues & 37 residues & mTP (0.217) \\  & 65\% & P (stroma) 0.570 & P 0.91 & & & & \\ DXS2.2 & 56\% & M (matrix) 0.475 & M 0.06 & Y (0.565) & 0.327 & cTP (0.849) \\  & 34\% & Cyto 0.450 & E 0.08 & 62 residues & 16 residues & mTP (0.301) \\  & & P (stroma) 0.520 & P 0.30 & & & \\ DXR & 89\% & Cyto 0.450 & M 0.01 & Y (0.509) & 0.596 & cTP (0.303) \\  & & M (matrix) 0.360 & E 0.69 & 83 residues & 66 residues & mTP (0.049) \\  & & P (stroma) 0.865 & P 0.07 & Y (0.576) & 0.5259 & cTP (0.932) \\ CMS & 76\% & P (thy mb) 0.515 & M 0.20 & 66 residues & Not & cTP (0.083) \\  & & P (thy space) 0.461 & E 0.71 & & predictable & mTP (0.083) \\  & & P (stroma) 0.899 & P 0.84 & Y (0.582) & 0.949 & cTP (0.895) \\ CMK & 75\% & P (thy mb) 0.595 & M 0.01 & 57 residues & 59 residues & mTP (0.084) \\  & & P (thy space) 0.595 & E 0.16 & & & \\  & & Plasma m 0.700 & P 0.97 & & & \\ MECS & 85\% & P (thy mb) 0.547 & M 0.03 & Y (0.582) & 0.5829 & cTP (0.955) \\  & & M (inner mb) 0.415 & E 0.03 & 55 residues & 58 residues & mTP (0.024) \\  & & ER (lumen) 0.850 & P 0.66 & & & \\ HDS & 87\% & Plasma mb 0.790 & M 0.02 & Y (0.526) & 0.009 & cTP (0.496) \\  & & Peroxisome 0.300 & E 0.33 & 40 residues & 18 residues & mTP (0.098) \\  & & M (matrix) 0.850 & P 0.80 & Y (0.530) & 0.104 & cTP (0.827) \\ HDR & 79\% & Peroxisome 0.322 & M 0.02 & 34 residues & Not & cTP (0.129) \\  & Nucleus 0.300 & E 0.20 & & predictable & mTP (0.129) \\ \hline  & & & & & & \\ DXS/DXS2.1 (CAA09804, [39]); DXS2.2 (ABI35993, this work); DXR (AAF65154, [40]), CMS (ACI16377, this work); CMK (ABI35992, this work); MECS (AAF65155, [40]), HDS (AAO24774, [41]); HDR (ABI30631, this work). For DXS2.1 and DXS2.2, the percent of identity has been established with DXS/CLA1, DXS2/DXL1, DXS3/DXL2, respectively. For the PSORT and Predotar predictions of subcellular localization, the three most favorable localizations are indicated with the corresponding score. For the ChloroP, MitoProt and TargetP predictions, scores of transit peptide presence and transit peptide length are given. cTP, chloroplast transit peptide; Cyto, cytoplasm; E, elsewhere; ER, endoplasmic reticulum; M, mitochondria; mb, membrane; mTP, mitochondria transit peptide; P, plastid; thy, thylakoid. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Identity of the _C. roseus_ MEP pathway enzymes excluding transit peptides with the corresponding _A. thaliana_ orthologs and prediction of subcellular localization of the full sequences.
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 1 Introduction
It is shown in [24] that the CMODH-retrieved wind speeds are in good agreement with the buoy measurements, with an RMSE of 1.66 m/s for Radarsat-2 and 2.05 m/s for Sentinel-1A/B. In short, with the accumulation of sea surface remote sensing data, more and more accurate GMFs are proposed, which provides a guarantee for marine applications. On 23 November 2021, China successfully launched a 1-meter resolution C-band SAR satellite, also known as Gaofen-3 02 (GF3-02), at the Jiuquan Satellite Launch Center. The satellite is China's first SAR operational satellite, marking the transformation of the SAR satellite business from scientific experiment to business application, further enhancing China's operational observation capabilities for marine remote sensing. The satellite operates in a solar synchronous regression orbit at 755 km. The primary payload is C-band SAR, with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR. Together with the GF-3 SAR satellite [25], it forms the constellation of China's ocean surveillance and monitoring satellites. The satellite revisit and coverage capabilities have been significantly improved, marking the initial formation of China's ocean surveillance and monitoring satellite constellation. The purpose of this paper is to show the first wind speed retrieval results of GF3-02 SAR and to preliminarily verify the sea surface wind speed retrieval capability of GF3-02 SAR. Section 2 introduces the GF3-02 SAR data and wind field data used and the classical co-polarization wind speed retrieval model. The wind speed retrieval results and discussions for each model are given in Section 3. Then, Section 4 discusses the sources of wind speed retrieval errors and the comparison of ERA5 wind speeds and HY-2B SCA wind speeds with NDBC buoy wind speeds. Finally, Section 5 gives the conclusions.
## 5 Conclusions  
An improved binary particle swarm optimization and deep neural network has been proposed to monitor OSNR for optical performance monitoring in next-generation optical networks. Through the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Signal**} & **ANN** & **IBPSO-DNN** & **SVM** \\ \cline{2-4}  & **Average/Maximum Error (dB)** & **Average/Maximum Error (dB)** & **Average/Maximum Error (dB)** \\ \hline PM-RZ-QPSK & 0.33/0.54 & 0.29/0.37 & 0.34/0.61 \\ PM-NRZ-16 QAM & 0.48/0.65 & 0.37/0.48 & 0.47/0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average and maximum estimated errors of PM-RZ-QPSK and PM-NRZ-16 QAM signals using ANN, IBPSO-DNN, and SVM.  
Figure 11: True versus estimated OSNRs and errors for PM-NRZ-16 QAM signal with (a) 100 bin numbers and (b) 65 bin numbers using PSO-DNN in different OSNRs.  
Figure 12: Average estimated errors for (**a**) PM-RZ-QPSK signal and (**b**) PM-NRZ-16 QAM signal processed by artificial neural network (ANN), improved binary particle swarm optimization and deep neural network (IBPSO-DNN), and support vector machine (SVM).  
improvement of binary particle swarm optimization, a sparse amplitude histogram is selected as input data to train and test the monitoring system based on PSO and DNN for OSNR estimation in an optical communication network. Experimental results show that the sparse signal set and original signal set can achieve relatively consistent estimation accuracy in the OSNR monitoring system. The sparse amplitude histogram retains the key information of the original data and eliminates the redundant parts. Compared with the ANN-based and SVM-based algorithm, the proposed algorithm achieves better performance. The structure of the required neural network is simpler, and the requirements for hardware are reduced, thus achieving cost reduction and improving the estimation efficiency. Neural network optimization based on particle swarm optimization and its extended algorithm will become a powerful tool in the complex global search of optical performance monitoring in the future.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed the simulation software and experimental facilities; [PERSON] and [PERSON] wrote the paper. This work was supported by the Frontier Science and Technology Innovation Project in the National Key Research and Development Program under Grant No.2016 QY11W2003, Natural Science Foundation of Hunan Province under Grant No.2018 JJ3607, Natural Science Foundation of China under Grant No.51575517 and National Technology Foundation Project under Grant No.181 GF22006. The authors declare no conflict of interest.