esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview of Rollups
### Optimistic Rollups  
Optimistic rollups assume that all transactions are valid by default unless challenged, from which the name optimistic comes. In this approach, L2 verifies all transactions and computes the new state Merkle root, ensuring the integrity of the rollup. However, the transactions and state transitions are verified on L1 using fraud proofs only in case a challenge is raised during the dispute period. This approach significantly reduces the on-chain computation and transaction verification on L1. However, it introduces an inevitable challenge period in which transactions can be disputed, potentially taking up to a week. Therefore, latency in transaction finality is a primary challenge in optimistic rollups. This can be a critical issue in applications requiring immediate transaction finality. Furthermore, the security of optimistic rollups relies on an actively participating network of nodes that can detect and challenge fraudulent transactions, therefore, relying greatly on network integrity and node honesty. With reference to Figure 1, the fraud-proofs are maintained in the L2 blockchain, while only the rolled-up data are posted back on the L1 one.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Optimistic Rollups** & **Zk-Rollups** \\ \hline Verification Method & Utilizes fraud proofs, which require a challenge period to contest transactions. & Uses zero-knowledge proofs to validate transactions, ensuring immediate verification without contestation. \\ \hline Data Handling & Stores full transaction data on-chain to facilitate potential fraud proofs and challenges. & Posts only transaction proofs and state differences on-chain, reducing data load. \\ \hline Withdrawal Delay & Withdrawals can take up to one week due to the required fraud-proof window. & Enables instant withdrawals due to the immediate finality of transactions. \\ \hline Transaction Finality & Transaction Finality is delayed, pending the expiration of the fraud-proof & Transaction finality is immediate as cryptographic proofs confirm validity at the time of transaction posting. \\ \hline Security Assumption & Operates under the assumption that validators are honest and will actively challenge incorrect transactions during the dispute period. & Relies on the mathematical validity of zero-knowledge proofs, assuming accuracy in proof generation. \\ \hline Developer Complexity & Implementation is less complex than ZK Rollups but still requires mechanisms to handle disputes and fraud proofs. & Typically involves more complex cryptographic operations, increasing development complexity because of advanced mathematical concepts. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Optimistic and Zk-Rollups.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Security Attacks
#### 5.2.2 Cryptanalysis  
The primary objective of cryptanalysis is to obtain partial or complete keys for an encryption algorithm to gain unauthorized access to transmitted information. According to the Kerckhoff principle [47], the security of a cryptographic system relies on the secrecy of the keys, even if all other details are known to the public. In the proposed algorithm, the security primarily depends on two keys: Key 1, which determines the scrambling of the original image using the proposed DFSCM; and Key 2, the genesis block used in the final encryption stage to convert the image into a string of characters. Now, let us consider different attack scenarios on the system. In an attack focused on secret images, suppose the attacker has a collection of watermarked images and performs statistical analyses on the watermarked positions. However, determining the exact region of the secret image becomes challenging due to the images being embedded in different layers using wavelet transforms and the random distribution of pixels resulting from chaotic matrix scrambling. Moreover, Key 2, used to decrypt the image into bits, further reduces the accessibility to the secret image. The probability of the attacker discovering Key 1 is extremely low, estimated at approximately \(\dfrac{1}{10^{0}}\). In the case of a chosen plaintext attack, the attacker has access to the encryption algorithm and can obtain encrypted images for any input image. By encrypting a certain number of related watermarked images, the attackers may attempt to discern the pattern between the input and encrypted images. However, our algorithm provides an effective defence against this type of attack due to two main reasons: (1) The complex non-linear behaviour of the proposed DFSCM, which generates a chaotic matrix with high randomness; and (2) the final encryption step that employs the SHA256 hash function, producing vastly different outputs for even minor changes in the input. Based on the above analysis, our encryption algorithm demonstrates a high level of security and proves to be resilient against differential attacks.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 4 Discussion
### Benefits of CBM in Plant Phenomics and Precision Agriculture  
Digital sensors for plant phenomics research continue to advance and boost crop genetic improvements for resource optimization, gains in yield, and overall quality. The significant outcome of this study is the ability to non-destructively estimate the plant biomass and height using an integrated ground-based sensor with an end-end pipeline in data acquisition through to IoT-based cloud uploading and processing. The development of new sensors provides opportunities for researchers to assess precise phenotypes or quality traits with a high temporal resolution, which can be used in selecting genotypes with desired performance. Moreover, high temporal resolution data provides the opportunity to study dynamic crop responses to the environment to evaluate genotype performance.  
In the context of precision agriculture, crop biomass and height are valuable traits for making informed management decisions. Biomass measurements require physical destructive harvesting whereas proximal sensing systems are able to estimate the same without damaging the in-season crop. The CBM sensor that was developed in this study can be readily mounted on a tractor or boom-spray to collect field measurements. The adopted agronomic design of the small-scale field experiment enables direct transferability of the established biomass and height estimation models to a conventionally managed larger-scale farmer's field. Furthermore, the presented method of modeling biomass in wheat could be suitably extended for non-destructive in-season estimation of biomass in other grain and forage crops.
## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Vigor
#### 3.1.2 Green Vegetation, NPV, BSC, and Bare Soil Cover
For instance, hyperspectral images were thought not to be able to effectively differentiate BSCs when there was a mixed pixel with plants [73], while hyperspectral images with continuum removal crust identification algorithm (CRCIA) could work reliably for BSC identification in the presence of plant litter and plants [71], and [72] concluded that hyperspectral images can be used to monitor local or even regional changes of BSC in the southwestern deserts of the United States. Considering the inconsistent conclusion on the applicability of hyperspectral imagery, further research is needed in other arid and semiarid ecosystems, such as semiarid mixed grasslands.
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 3 GPU-Accelerated Parallel Algorithm for 3D Laplacian Smoothing
### Details and Key Issues of the Parallel Algorithm
#### 3.2.3 Iteration Procedure  
Laplacian smoothing is an iterative procedure. Unlike the serial implementation of it, the parallel version requires iterative computation of a series of nodes at the same time. It must be ensured that the previous iteration is completed before the new iteration begins, and the smoothing process will use the updated coordinates. Therefore, it is necessary to set a barrier at the end of each iteration.  
In CUDA, this kind of data dependence within a single block can be easily solved by using the barrier __syncthreads(). However, synchronization between all blocks is not designed in CUDA because it consumes a lot of GPU operations and has low returns. A feasible solution is to allocate only one block for iteration, so that each iteration is completed before the next step.  
Clearly, using only one block effectively avoids data collision, but it does not exploit the GPU efficiently. Note that in Laplacian smoothing, the current iteration pass always converges regardless of whether the coordinates of points are updated, and the results remain unchanged. Therefore, we adopted a new form using multiple blocks; see Figure 5.  
In this case, different blocks iterate over the same part of the node together, even though the coordinates of the nodes they use are not consistent. Blocks that launch earlier use old values, while those that start later use updated values. The result is always computed towards the end of the iteration. As the iteration proceeds, more and more blocks converge and exit the computation until the last block completes the iteration.
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 4 Results
### Effects of Orbit Errors on FCB Estimation  
Figure 2 shows the distributions of the a posteriori residuals of the WL FCB estimation with daily WL float ambiguities from all 40 reference stations during the seven-day period. If the ambiguities have an integer nature, the a posteriori residuals should obey a zero mean normal distribution. The distributions show that 96.6% of the WL residuals are within 0.15 cycles for GPS satellites, with a corresponding figure of 94.8% for the IGSO and MEO satellites, while more than 98% are within 0.25 cycles for both. This indicates the high consistency of the fractional parts of the WL float ambiguities for both the GPS satellites and the IGSO and MEO satellites of the BDS. However, because of the large multipath effects of the GEO satellites at low elevation angles [18; 42], the residuals of the GEO satellites of the BDS are larger, but they still have 87.8% of the WL residuals within 0.25 cycles, which can satisfy the ambiguity resolution requirements.  
As an example, Figure 3 presents the time series of the NL residuals for two GEO, two IGSO, and two MEO satellites in the BDS system. For the two GEO satellites, the orbit errors are so large that  
Figure 2: Distributions of the a posteriori residuals of the daily wide-lane (WL) ambiguities.  
their residuals are evenly distributed within \(\pm\)0.5 cycles. From 1:00 to 8:00, the orbit error of C07 is very small, and the corresponding residuals are almost within \(\pm\)0.06 cycles. However, from 8:00 onwards, the orbit error increases, and the residuals then exceed 0.2 cycles for some of the stations. For C11, the residuals are also a little large and are distributed within \(\pm\)0.16 cycles. In contrast, for C08 and C12, the orbit errors are so large that the residuals are distributed within \(\pm\)0.45 cycles.  
Figure 4 shows the distributions of the a posteriori residuals of the NL float ambiguities from all 40 reference stations during the seven-day period. The distributions show that 93.3% and 97.9% of the residuals are within \(\pm\)0.1 and \(\pm\)0.2 cycles for the GPS satellites, respectively. However, the residuals for the BDS GEO satellites are so large that only 60.6% of these residuals are within \(\pm\)0.2 cycles. The residuals for the BDS IGSO and MEO satellites are much smaller, but the percentages that lie within \(\pm\)0.1 and \(\pm\)0.2 cycles are still lower than those of the GPS satellites by 5.8% and 2.7%, respectively. These large residuals indicate that the precision of the estimated FCB is very poor and thus cannot be used for PPP ambiguity resolution.  
Figure 4: Distributions of the a posteriori residuals of the NL ambiguities when using the traditional method.  
Figure 3: Time series of the a posteriori residuals of the NL ambiguities when using the traditional method on day of the year (DOY) 079 of 2015.
# Investigating the Reliability of Nonlinear Static Procedures
## 3 Criteria Adopted to Assess the Reliability of the Examined Approaches
### Assessment of Fragility Curves by Nonlinear Static Analyses  
To derive the fragility curves from the NSPs, the criteria introduced in Section 3 are applied to each of the pushover curves obtained by the execution of the NLSAs on a set of 125 models generated for each of the three configurations examined for the URM case study illustrated in Section 4.  
The 125 models are generated by Monte Carlo sampling [75; 76] with the aim of estimating the influence of the uncertainty associated with the material's mechanical parameters.  
Figure 4: Comparison of the acceleration response spectra derived from actual signals (grey, in blue their average) and the conventional format of Standards (red, with \(S=1\), \(\eta=1\), \(F_{0}=2.5\)).  
As introduced in Section 2, the expressions summarized in Table 3 are inverted by imposing that \(\mathrm{d}^{*}\mathrm{max}\) is equal to the ultimate displacement associated with the attainment of NC LS (i.e., \(\mathrm{d}^{*}_{\mathrm{max}}=\mathrm{d}^{*}_{\mu,NC}\)), in order to estimate the maximum value of the PGA compatible with NL (PGANC).  
These PGA values are then sorted in ascending order and a probability value is associated with each of them. In this way, the fragility curve from NSP is defined.  
To avoid comparing a fragility curve (from NLDA) with a single deterministic estimate of the seismic capacity (from NLSA), 125 models are created through a Monte Carlo process [75; 76]. This aims to derive a fragility curve for NLSA, accounting for dispersion related solely to uncertainties in masonry mechanical properties.  
Smaller dispersion is expected for the fragility curves from NLSAs than NLDAs since, in an NLSA, there is no record-to-record variability, and the same spectrum is used on all 250 static analyses (where, instead, the models are different with regard to the mechanical parameters of the panels).
## 3 Experiments
### Dataset Preparation  
Extracting represented features for the input layer is a crucial step to achieve good prediction. Data extraction is separated into two parts in this study. The FFT transform method is used for extracting the raw data, and the other approach of 1-D CNN automatically extracts the raw vibration signals as the input data. The raw data before being fed into FFT extractor is 10k samples per second, corresponding to a total of 50k samples for a sampling time of 5 s. After FFT operation, the spectral data is reduced to 5000 Hz as the feature data of the input layer. The spindle speeds of 116.6 Hz (7000 rpm) captured by the accelerometer, as shown in Figures 7 and 8, appear to be very small compared with others, indicating that the spindle rotatory machine has balanced performance. The spectrum was extended to approximately 1500 Hz based on a factor of 10 used to account for the vibration spectrum of the machine milling process. The machine tool with four flutes used in the milling process strikes the workpiece approximately 465 times per second (116.6 Hz \(\times\) 4), resulting in the higher spectral amplitude depicted in Figures 7 and 8. Other features in the spectral distributions are considered as important signal features for training the studied models. In addition, 1D CNN is employed to automatically extract the raw vibration signal data; thus, the total number of samples of data for analysis is 10,000.  
In this experiment, the 50 workpiece datasets arranged at special milling process conditions are used to obtain 50 sets of vibration signal data for evaluation of the surface roughness prediction using deep learning neural networks.
# Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain
## 3 Results and Discussion  
### DOT Data Filtering  
The optical signals propagating through the brain contain several spontaneous fluctuations originating from cardiac pulsation, respiration, and change of blood pressure, which contaminate the signals measured by DOT and induce spatial and temporal changes that may lead to false interpretation of brain activations. The Bayesian method explained in Section 2.3 was applied to raw DOT data to filter out the physiological noise from the hemodynamic response. Figure 2 shows the time series for DOT data both without filtering and after filtering with the Bayesian algorithm at a wavelength of 760 nm. Moreover, it shows the power spectrum density (PSD) for the respiratory fluctuations extracted from the Bayesian method versus the PSD for unfiltered data. Physiological noise and its harmonics are both mixed with the neural signal and they cannot be distinguished from the respiratory component in the pre-filtered data. However, the signal extracted by the Bayesian filter shows the respiratory component around 0.15 Hz. Since cardiac fluctuations vary in a range of 1-2 Hz and the sampling rate used for this example was 1.81 Hz, these recordings are not represented here.  
The raw DOT data were filtered while using a band pass filter with a cutoff range between 0.01-0.3 Hz, the most commonly used frequencies in literature [23], in order to test the reliability of Bayesian-filtering in the optical channels. Figure 3 shows both wavelengths (760 nm and 830 nm) unfiltered, Bayesian filtered, and band pass filtered. Reconstructed DOT volumes from linearly filtered data do not show statistical significance during the GLM processing (\(p<0.05\)), therefore only the differences at the wavelength level are shown.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 4 Proposed Method
### Template Embedding and Extraction Network
where \(V\) is the size of the output of the extraction network and is set to 64. \(K_{V}^{\prime}\) denotes the output of the network, i.e., the extracted template, and \(K_{V}\) denotes the original template. \(i\) and \(j\) denote the horizontal and vertical coordinates of the template, respectively. \(G^{T}\) indicates a geometric transformation using the ground-truth parameters. Since the geometric distortion that occurs in the image occurs equally in the inserted template, we define the loss function so that the extracted template is also subjected to this geometric distortion. This loss, \(L_{e}\), trains the extraction network to improve the extraction accuracy of the template. The total loss using the losses of the template generation and extraction networks is defined as,  
\[L_{t}=\lambda L_{g}+(1-\lambda)L_{e}, \tag{5}\]  
where \(\lambda\) is the trade-off parameter between invisibility and template extraction accuracy. The larger the \(\lambda\), the higher the invisibility but the lower the extraction accuracy. To use the middle frequency as the template noise similar to the existing watermarking technique, the template generation network is pre-trained before the whole network training. First, as in the spread-spectrum watermark [1], we generate a middle frequency noise with a size of \(512\times 512\). This noise is generated by substituting a pseudo-random sequence from \(1/4\) to \(3/4\) of the zigzag-scanned DCT coefficients, and is defined as \(T_{m}\). The pseudo-random sequence is set with an average value of 0 and a variance of 1. Then the template generation network is pre-trained until \(\sum_{i,j=1}^{U}(T_{n}^{o}(i,j)-T_{m}(i,j))^{2}/U^{2}\) is less than 0.1. The template extraction network is initialized by the Xavier uniform initializer [22].
# Calibrated Phase-Shifting Digital Holographic Microscope Using a Sampling Moire Technique
## 5 Experiment
### Experimental Conditions  
In the experiment, an Nd:YAG laser working at 532 nm and 30 mW output power was used as the light source. Two complementary metal-oxide semiconductor (CMOS) cameras (VCXU-50, Baumer, Inc., Frauenfeld, Thurguau, Switzerland) with a resolution of 2448 \(\times\) 2048 pixel and a 3.45 \(\upmu\)m pixel pitch were used to record the holograms and interferograms. A transmission-type test target (1951 USAF resolution test chart) was set as the object. The system utilized an infinity-corrected optical microscope that consists of an objective lens with 4 \(\times\) magnification and a tube lens with a 200 mm focal length. The object image was placed close to the hologram plane via the infinity-corrected optical microscope, and four phase-shifted holograms with \(\pi/2\) phase-shifting amount were then recorded by driving a PZT (PAZ005, Thorlabs, Inc., Newton, NJ, USA).
## 3 Results and Discussion
### Analysis of the Microporous Body-Reaction Combustion Simulation
#### 3.1.1 Body-Reaction Temperature Field  
The simulation results of the combustion temperature field in the micropore are shown in Figure 3. For the bulk reaction, combustion mainly occurs near the entrance, and the temperature in the orifice is the highest, which exhibits a decreasing trend along the depth of the hole. Furthermore, the temperature inside the hole is slightly lower; however, because the size is nanoscale, the overall temperature difference is not large.
## 2 Materials and Methods
### The Effect of Carvacrol on Mycelial Weights and Water-Retention Rate  
The effects of carvacrol on the wet and dry weights as well as the water-retention rate of _P. digitatum_ mycelial were determined by the method described by [PERSON] et al. with some modifications [23]. Briefly, 100 \(\upmu\)L of _P. digitatum_ containing 10\({}^{6}\) spores mL\({}^{-1}\) was inoculated into 50 mL of potato dextrose broth (PDB) medium and then was incubated in a rotary shaker (150 rpm) at 25 \({}^{\circ}\)C. After shake incubating for 48 h, the carvacrol solution at final concentrations of 0 (control), 0.03125, 0.0625, 0.125, 0.25 and 0.5 mg\(\cdot\)mL\({}^{-1}\) were added into the above-mentioned PDB and then incubated for 24 h at 25 \({}^{\circ}\)C in a rotary shaker. The mycelia from the carvacrol treated and control PDB was collected by filtering used a Buchner funnel and washed three times with sterile water. The wet weights of the mycelia were measured, the mycelia were dried at 70 \({}^{\circ}\)C for 12 h, and the dry weights were then measured using an analytical balance (Ms105, Mettler Toledo, Greifensee, Switzerland). The water-retention rates of the carvacrol-treated and control groups were calculated using the following equation:  
\[\text{Water-retention rate }(\%)=\frac{Ww-Wd}{Ww}\times 100\]  
where \(Ww\) and \(Wd\) were the mean of wet and dry weights in carvacrol treated and control sets, respectively.
## 2 SSA-SVR Prediction Model
### Principle of SSA-SVR Prediction Model
By eliminating the units and ensuring that the calculation result falls between 0 and 1, R\({}^{2}\) provides a quantitative description of the explanatory power of the regression model, making it easier to intuitively compare the fit of different models. However, the coefficient of determination by itself cannot provide information about whether the model is overfitted and needs to be combined with other indicators to make a comprehensive judgment. Figure 3: SSA-SVR algorithm procedure.