esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
## 5 Fault-Tolerant Techniques for FPGAs Based on Reconfiguration  
This section is focused on SEUs affecting the configuration layer, as they are more likely to occur. To overcome their effects, some techniques that exploit the particular reconfigurable capabilities of the FPGAs to detect and correct persistent errors in the configuration memory are detailed next:  
* Scrubbing is a technique used to correct and prevent errors in the information stored in memory. In FPGAs, scrubbing can be used to mitigate both persistent errors in SRAM cells (i.e., the configuration memory) and transient errors in user-memory elements such as BRAMs. To perform configuration memory scrubbing, the configuration memory data must be read sequentially from the start to the end and compared to the original configuration bitstream or an error check code such as a cyclic redundancy check (CRC) [43].
* Dynamic partial reconfiguration allows run-time reconfiguration without application layer interruption. This technique cannot detect errors by itself, so it must be combined with other error detection techniques such as those based on redundancy. These correction techniques take advantage of the subdivision of the configuration memory into frames, which contain information related to the configuration of specific parts of the design.  
All the features presented in the previous sections are useful to understand the principles that drive an FPGA reliability analysis and the related fault injection tools for FPGA designers.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 1 Introduction
Since SSH is the sum of geoidal height and dynamic topography (DT), a suitable geoid model and DT estimates are required. In the current study, an essential component of the latter is the recently initiated implementation of the Baltic Sea Chart Datum 2000 (BSCD2000), a common height reference for the Baltic Sea region [25; 26]. The BSCD2000 will be realized through GNSS and geoid modeling (i.e., BSCD2000 is an equipotential surface) and is compatible with the EVRS (European Vertical Reference System) associated national height system realizations of the Baltic Sea countries. As the Baltic Sea TGs are rigorously connected to the national height systems [27; 28], the TG readings refer to the BSCD2000 at the reference epoch, implying that the contemporary TG readings can be expressed directly as DT (cf. Figure 1). However, because TG stations are generally distributed in sparse land-bound coastal locations, hydrodynamic models (HDMs) providing high spatial and temporal (hourly) resolution DT may appear an appealing offshore data source. Caution should be exercised using these models, since they usually contain a long-wavelength nature [29; 30] spatiotemporal dynamic bias (DB) relative to the used height reference [15; 30; 31] (also refer to Figure 1). Hence, it would be advantageous to employ the method developed by [15; 30]: the DB estimated at TG stations is gridded and then removed from an HDM; therefore, linking the model with height references. In these studies, exact interpolators were employed for gridding. Such an approach assumes that a DB estimate at a TG station is errorless and represents all nearby offshore locations, which may not be necessarily true. Utilizing the qualities of DB, an approach was developed for estimating DB uncertainties used during offshore DB prediction by least-squares collocation (i.e., an inexact interpolator); thus, constraining TG data errors. By combining the properties of TG data and HDMs, realistic DT can be derived for shipborne GNSS-determined SSH validation. This contributions' outline is as follows. Section 2 reviews the core theoretical principles for deriving and validating shipborne GNSS-determined sea surface heights (note that subsequent sections reveal additional details with data examples, since these reinforce the developed principles). Section 3 provides an overview of the shipborne GNSS surveys and used information. The derivation of offshore DT is examined next in Section 4; whereas, Section 5 is dedicated to shipborne GNSS data processing and validation. The paper continues with a discussion in Section 6 and concludes with a summary in Section 7.
# High-Precision Elastoplastic Four-Node Quadrilateral Shell Element
## 5 Numerical Results
### Cylindrical Shells Subjected to Impact Loads  
The cylindrical shell depicted in Figure 6 possesses the following geometric dimensions: the length is \(L=12\) mm, the central angle is \(\beta=120^{\circ}\), the radius is \(R=3\) mm, the thickness is \(a=0.125\) mm, the elastic modulus of the material is \(E=10^{7}\) MPa, the Poisson's ratio is \(u=0.3\), and the density is \(\rho=2.5\times 10^{-4}\) T/mm\({}^{3}\). The busbar boundary is fixed, while the circumferential boundary is simply supported. Additionally, a concentrated impact load is applied at the central position, which linearly increases to 10,000 N over \(0\ \mathrm{s}\sim 1.5\times 10^{-4}\ \mathrm{s}\) and decreases to 0 N over \(1.5\times 10^{-4}\ \mathrm{s}\sim 3\times 10^{-4}\ \mathrm{s}\).  
Calculate the displacement of the center under the concentrated impact load using \(8\times 16\), \(16\times 32\), and \(32\times 64\) TQuadShell elements, respectively. The results from the \(16\times 32\) and \(32\times 64\) TQuadShell elements align closely with those obtained from the \(8\times 16\) Shell281 element in the ANSYS (2021) finite element software (Figure 7). The Shell281 element in ANSYS employs an eight-node quadrilateral element. When the mesh density of the TQuadShell element is four times that of the Shell281 element, the number of nodes in both elements becomes equal. To ensure calculation accuracy, the TQuadShell element utilizes a medium grid. The accuracy and reliability of the TQuadShell element calculations are satisfactory.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Node No.** & **u** & **v** & \(\omega_{Z}\) \\ \hline
5 & 1 & 1 & 1 \\
6 & 1 & 1 & 1 \\
7 & 1 & 1 & 1 \\
8 & 1 & 1 & 1 \\ \hline \end{tabular}
\end{table}
Table 4: Bending patch test results (displacement normalization relative to analytical solution).  
Figure 6: Cylindrical shell.
# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 2 Related Work
### Gait Data Collection  
A common problem encountered in gait recognition is the lack of enough gait data. To address the problem, researchers [6; 17; 18] constructed various gait database with different emphases. The SOTON database [19], mainly for gait recognition, contains hundreds of subjects with limited covariates such as views, shoes, clothing, and walking speed. The USF dataset [17], as one of the most frequently-used gait recognition dataset, is composed of 122 subjects only. The CASIA database [18] considers additional factors, such as night scenario, multi-views, different clothing, and carrying status, but contains a small number of 124 subjects. The OU-ISIR database [6], targeting age estimation, contains 60,000 subjects covering different ages. A common issue with these databases is that they are still too small to contain enough variation in those involved factors. Such variations are essential aspects of the statistically reliable evaluation in many gait analysis applications. A recent work [20], described as so far the world's largest gait database, mainly targeted performance evaluation of gait-based age estimation. There are also some general performance capture datasets [21] being used in gait motion synthesis. Some works [22] synthesized new data by distortion or blending of an existing one. However, such methods can only mimic limited and low-quality variations in viewpoint. Our method can also be considered a synthetic method but in a more intrinsic way. In addition to synthesis from images, we build a complete 3D framework to simulate factors involved in gait analysis such as environments, anthropometric measures, outfit clothing, and motion styles.  
Using synthetic data for model training is a recently popular strategy to address the lack of sufficient data in learning-based methods such as object tracking [7], optic flow [23], scene understanding [24; 25], pedestrian detection [26], human information estimation [27], action recognition [28], etc. However, the _reality gap_ prevents the strategy from becoming practically useful: a model trained in a simulated environment may not be applicable to real-world scenarios due to numerous discrepancies between the two environments. Two solutions have been proposed to bridge the gap: extreme realism [7; 8] and domain randomization [29; 30]. The former attempts to improve the simulation similarity with the real-world environment, while the latter aims for exposing the model to a vast range ofsimulated environments at the training stage instead of just a single synthetic one. Our work shares inspirations with [31] on the necessity to balance intra-subject and inter-subject variations. While [31] focused on face synthesis, which is different from gait, and the dynamic and subtle nature of gait increases the difficulty.
## 3 Experimental Setup  
Figure 6 illustrates the experimental setup to obtain the PAM4 optical signal. At the transmitter (Tx) side, a 1550 nm wavelength distributed feedback (DFB) laser with an output power of 6 dBm is launched into a silicon photonic (SiPh) chip with an SiMRM. The SiMRM was fabricated by the multi-project wafer (MPW) scheme in CUMEC. The electrical PAM4 signal is generated by an arbitrary waveform generator (AWG, Keysight M8194A) with 45 GHz analog bandwidth. Subsequently, the signal is amplified by a 60 GHz radio-frequency (RF) amplifier. The Tx digital signal processing (DSP) includes PAM4 symbol mapping, pre-distortion, upsampling, channel estimation, and pre-emphasis. The pre-distortion and pre-emphasis serve to alleviate non-linear distortion and tackle issues related to high-frequency roll-off, stemming from the limited bandwidth of the AWG. The optical PAM4 signal is produced via a SiMRM with a bandwidth \(\sim\)67 GHz and operated at \(-\)3 V bias, measured by a lightwave component analyzer (LCA; Keysight N4373D). At the receiver (Rx) side, the optical PAM4 signal is detected by a 70 GHz bandwidth PD connected to a real-time oscilloscope (RTO, Keysight UXR0802A) with 80 GHz bandwidth and 256 GSa/s sampling rate. To evaluate transmission performance related to different received optical powers, a variable optical attenuator (VOA) is employed. The Rx DSP invovles time synchronization for ensuring proper alignment of the received signal with the transmitted signal, resampling to adjust the signal sampling rate to match with the neural network, the proposed ONN processing, symbol demapping, and BER evaluation. Inset of Figure 5 shows the photo of the SiMRM with diameter of \(\sim\)10 \(\upmu\)m. It was fabricated on a silicon-on-insulator (SOI) platform with a staring wafer of 220 nm silicon layer and 2 \(\upmu\)m buried oxide layer (BOX). The SiMRM has a loaded Q of \(\sim\)3000.  
Figure 5: The structure of electro-optic nonlinear activation functions. MZI: Mach–Zehnder Interferometer; DC: directional coupler; PD: photodetector.  
Figure 6: The experimental setup to obtain the PAM4 optical signal. AWG: arbitrary waveform generator; DFB: distributed feedback laser diodes; PC: polarization controller; EDFA: erbium-doped fiber amplifier; VOA: variable optical attenuator; PD: photodetector; RTO: real-time oscilloscope. Inset: photo of the SiMRM.
At present, VANET mainly executes the routing algorithm via each vehicle node, and establishes the decision of the next-hop neighbor node through the exchange of messages between nodes. Although the load of a single routing host is dispersed, under the distributed routing architecture, several nodes may possibly select a specific vehicle node to forward packets to at the same time because they lack knowledge on the result of each other's routings. This may easily cause congestion and the unsatisfactory arrangement of the packet delivery route.
# Secure NIfTI Image Authentication Scheme for Modern Healthcare System
## 2 Literature Review
They enhanced the watermark's security by using the Arnold transform. In this particular system, the author began with a DWT that had three levels. The Z transform was then applied to the HL and HH sub-bands that they had selected in the previous step. After that, the Z-transformed component was subjected to BSVD, and the bits of an encrypted watermark were inserted into the S matrix's singular values. [PERSON] and [PERSON] [28] applied SVD and BSVD to separate images, and inverse SVD and BSVD were used to rebuild them. When the PSNR values acquired by these two approaches are compared, it is evident that the PSNR values obtained by BSVD are greater than those obtained by SVD. [PERSON] et al. [29] proposed a hybrid technique for watermarking. This approach combines the DWT and DCT wavelet transformations with the SVD to break down the components. The medical image is first exposed to three levels of DWT and then to DCT throughout the HH band. The DCT coefficients are then decomposed using SVD, and the singular values are watermarked. [PERSON] et al. [30] described a method for watermarking medical images based on DWT and SVD. The author created the watermark for this approach by combining patient and image data. The procedure of adding the watermark then commenced. At this stage, the author employed the DWT first, followed by the SVD on the LL sub-band. Finally, the watermark is placed in the singular value matrix's least significant bits. [PERSON] et al. [31] suggested a technique for NIfTI pictures using LWT and QR decomposition, in which the watermark is inserted into the first slice of the image. This approach is good, but the quality of watermark extraction is less resistant to attack. After thoroughly analyzing the literature and the various contributions, we found that most medical image watermarking approaches are developed for natural images or some for DICOM images. However, there has been no state-of-the-art contribution for NIfTI images. Based on earlier findings, this study proposes an approach for authenticating NIfTI images by combining the Slantlet, lifting wavelet transform, and Arnold Cat Map. Because of their low computational complexity, the LWT and HD were chosen. To successfully include the watermark, the proposed technique employs a hybrid combination of four complicated functions, namely LWT, Slantlet Transform, and ACM. It is a simple approach for scrambling the watermark before insertion, which helps in the security of the watermark. If an unsanctioned person extracts the watermark, they would be unable to deduce anything from it. Watermarking algorithms are open to the public.
## 6 Simulation Analysis
### Basic Data
#### 6.2.2 Scheduling Results
Figure 10: The MECS scheduling results on a typical day under the LLC mode: summer (**left**) and winter (**right**). SPC was 10.84 MW\(\cdot\)h and 7.36 MW\(\cdot\)h higher, and UCC was 2.55 g/kW\(\cdot\)h and 0.94 g/kW\(\cdot\)h higher than on a typical summer day in the OEE mode and LLC mode, respectively. Overall, different ESD operation modes and seasons directly influence the MECS scheduling. Figure 12 shows the power output structure of MECS scheduling under the OEE mode. According to Figure 12, the load demand is mainly supplied by the TPPs, WPP, and HS. The complementarity between WPP and HS was significant. On a typical summer day, the outputs of the HS were 5487 MW-h and 5478 MW-h higher than the outputs of WPP in the OEE/LLC modes, respectively. However, on a typical winter day, the output of HS were 6996 MW-h and 6729 MW-h lower than the outputs of WPP in the OEE/LLC modes, respectively. Moreover, complementarity also appeared in the thermal power units. For the maximum economic benefit, TPP1 and TPP2 were primarily scheduled for high capacity power output. In contrast, TPP3 and TPP4 were primarily scheduled for the low capacity reserve service. Based on the above analysis, the output structures of different power sources in the MECS operation plan were determined. On both typical summer and winter days, the TPP, the WPP, and the HS were the major power sources. The output proportions of the WPP and HS were 14% lower and 17% higher on a typical summer day than that on a typical winter day. The total output proportions of the PV and the ESD were the same, 9%. Overall, the complementarity of TPPs, WPP, PV, and HS was significant. The TPPs and the HS were the major power sources for a typical summer day, whereas the TPPs and WPP were the major power sources for a typical winter day. Complementarity should be utilized to ensure safe and stable operation.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 8 Theoretical Analysis and Simulations of Hollow-Core Fibers
### Models
Numerical results show that the coupled mode model is able to analyze the loss and dispersion properties of the HCF, and enables a better understanding of the influence of fiber geometry on the propagation properties of the waveguide.
## 1 Introduction  
In orthodontics, obtaining a precise full-arch intraoral scan is essential for accurate diagnosis, treatment planning, and determining treatment outcomes. Traditionally, indirect impression techniques, such as plaster or digital models, have been preferred over direct intraoral scans due to concerns about time efficiency and the necessity of patient presence. However, the current gold standard for measurements remains the electronic caliper on -plaster models, which is widely used due to its accuracy, practicality, portability, and cost effectiveness. Despite these advantages, plaster models pose several limitations, including the need for extensive storage space, susceptibility to damage, and the accumulation of errors from multiple processing stages, ultimately affecting measurement accuracy [1, 2, 3, 4, 5, 6].  
Advancements in digital technology have introduced intraoral scanners (IOSs) as an alternative to conventional impression methods, offering enhanced accuracy and efficiency in capturing digital impressions of patients' teeth and oral structures. This process involves three main steps: (1) the direct or indirect digitalization of the oral cavity, (2) data processing, and (3) fabrication using computer-aided design and computer-aided manufacturing (CAD/CAM). By enabling direct in-mouth scanning, IOSs improve data storage and retrieval while minimizing inaccuracies caused by material deformation and multiple handling stages [7, 8, 9].  
Among the latest intraoral scanners, the CEREC Primescan system (Dentsply Sirona, Charlotte, NC, USA) stands out as a cutting-edge device designed for various digital procedures. Utilizing high-resolution sensors and short-wave light, this system captures detailed digital impressions of tooth surfaces, allowing for seamless data transfer to the lab via Connect software version 5.2. Integrated with CEREC 5 software version 5.2, the Primescan system supports both single-tooth and full-arch restorations, enhancing workflow automation and precision. Compared to other IOSs on the market, such as the iFero Element (Align Technology, San Jose, CA, USA) and the TRIOS series (3 Shape, Copenhagen, Denmark), the CEREC Primescan offers superior scanning speed, enhanced accuracy, and optimized software integration. However, comprehensive studies comparing its performance with existing alternatives are still limited, necessitating further validation of its clinical effectiveness [10].  
This study aimed to assess the validity of digital measurements using the CEREC Primescan system compared to conventional plaster model measurements in terms of accuracy and reliability. We hypothesized that direct intraoral scanning with Primescan would provide comparable measurement accuracy to conventional methods while offering greater efficiency and convenience in clinical practice.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 4 Experimental Results and Discussion
### Ablation Experiments  
In order to validate the effectiveness of each module of the MFFPN algorithm, ablation experiments were conducted for experimental validation. MFFPN-L was selected for validation, and the proposed CST module, CSE module, and ISPP module were tested. First, experiments were performed for each module separately, and the proposed modules were added in sequence for the experiments. Then, it was judged by precision, recall, and mAP metrics, and the final results are shown in Table 3.  
Through comparative analysis, the MFFPN-L algorithm proposed in this paper improved by 3.5%, recall improved by 3.8%, and mAP improved by 4% relative to the initial model precision, and the experimental results show that the MFFPN-L algorithm proposed in this paper can greatly improve the model's ability to perform spatial small target feature extraction and improve the recognition accuracy of spatial small targets, which proves that the MFFPN algorithm has superior performance and is adapted to the task of spatial small target detection.  
To further validate the effectiveness of the MFFPN algorithm in spatial small target detection, the test set in this dataset was visualized and some of the detection results are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** \\ \hline PANet & 0.937 & 0.935 & 0.941 \\ BiFPN & 0.933 & 0.929 & 0.954 \\ MFFPN-S (ours) & **0.971** & **0.953** & **0.973** \\ MFFPN-L (ours) & **0.972** & **0.973** & **0.981** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of multiple feature pyramid algorithms on spatially small target datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Large** & **ISPP Module** & **CSE module** & **CST module** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** \\ \hline  & & & & 0.937 & 0.935 & 0.941 \\ ✓ & & & & 0.946 & 0.945 & 0.953 \\ ✓ & ✓ & & & 0.948 & 0.948 & 0.963 \\ ✓ & ✓ & ✓ & & 0.966 & 0.956 & 0.974 \\  & ✓ & ✓ & ✓ & 0.971 & 0.953 & 0.973 \\ ✓ & ✓ & ✓ & ✓ & **0.972** & **0.973** & **0.981** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison table of ablation experiment results.
## 4 Simulation and Measurements Results
### Results for the 3.5 GHz Array
An uplink data rate of DR = 21 Mbit/s is anticipated for the embedded measured n78-band 2 \(\times\) 2 square patch array, feasible for numerous wireless connected-vehicle applications.