esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Explanation of Results  
From the results of all studies, it is clear that CMSB-based methods achieve improvements in resolution, contrast, and lesion detectability over DAS in CPWC ultrasound imaging. In addition, the proposed methods substantially increase the contrast and speckle quality over CMSB. The average contrast improvements obtained by LOCw-CMSB in the simulation and experiment over CMSB (\(\delta\) = 0.7) are 14.7% and 34.4%, respectively. The average gCNR and sSNR improvements obtained by CMSB (\(\delta\) = \(\delta_{ac}\)) in the simulation and experiment over CMSB (\(\delta\) = 0.7) are 3.2% and 41.7%, respectively. Owing to the combination of LOC weighting and adaptive diagonal reducing, LOCw-CMSB (\(\delta\) = \(\delta_{ac}\)) improves the average CR, gCNR, and sSNR by 22.3%, 2.4%, and 39.8% over CMSB (\(\delta\) = 1.3) in the simulation and experiment. This demonstrates the improvements in the contrast and speckle quality of the proposed methods over CMSB.  
The preliminary study of CMSB did not consider the separability of closely spaced point targets and cyst edge resolution [39], which are also important metrics for adaptive beamformers [50]. In this study, the separability of closely spaced point targets and the cyst edge resolution of different methods were evaluated. As seen from Figure 3b, CMSB-based methods can distinguish the two laterally closely spaced point targets, while DAS and DMAS cannot distinguish the two point targets. In addition, CMSB (\(\delta\) = \(\delta_{ac}\)) obtains the  
Figure 8: In vivo cross section view of carotid images reconstructed by **(a)** DAS, **(b)** DMAS, **(c)** SLSC, **(d)** CMSB (\(\delta\) = 0.7), **(e)** CMSB (\(\delta\) = 1.3), **(f)** LOCw-CMSB, **(g)** CMSB (\(\delta\) = \(\delta_{ac}\)), and **(h)** LOCw-CMSB (\(\delta\) = \(\delta_{ac}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **CR (dB)** & **gCNR** & **sSNR** \\ \hline DAS & \(-\)32.2 & 0.54 & 1.50 \\ DMAS & \(-\)48.9 & 0.35 & 1.00 \\ SLSC & \(-\)24.8 & 0.91 & 2.06 \\ CMSB (\(\delta\) = 0.3) & \(-\)63.5 & 0.79 & 1.43 \\ CMSB (\(\delta\) = 0.5) & \(-\)59.8 & 0.83 & 1.50 \\ LOCw-CMSB & \(-\)69.2 & 0.81 & 1.46 \\ CMSB (\(\delta\) = \(\delta_{ac}\)) & \(-\)59.7 & 0.89 & 1.76 \\ LOCw-CMSB (\(\delta\) = \(\delta_{ac}\)) & \(-\)68.6 & 0.88 & 1.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Image quality metrics of in vivo carotid images obtained by different methods.  
best cyst edge resolution as seen from Figures 3 and 7. This demonstrates the resolution improvement of LOCw-CMSB and LOCw-CMSB (\(\delta=\delta_{ac}\)). Note that LOC weighting and adaptive diagonal reducing scarcely have influence on the resolution performance of CMSB according to Tables 2-4.  
In comparison with CMSB (\(\delta=1.3\)), CMSB (\(\delta=\delta_{ac}\)) generally has a slight contrast degradation as seen in Tables 2 and 4. However, CMSB (\(\delta=\delta_{ac}\)) obtains an improved contrast over CMSB (\(\delta=1.3\)) at the deep region in the experimental cyst study according to Table 4. This is likely because the dynamic diagonal reducing preserves the speckle at larger depths.
# An Event-Driven Serverless ETL Pipeline on AWS
## 6 Discussion
### Pricing  
In any serverless system, cost-awareness is critical. Although calculating the pricing for a single resource is straightforward, calculating the costs for the whole system can be a daunting task. For that purpose we present a simple expression that models the costs of the aforementioned architecture. Let \(D=\sum_{i=0}^{n}p_{i}\) where \(p_{i}\) is the \(i\)th payload size of \(n\) events received over time \(t\), (S3 Req, SNSmsg, SQSreq, S3 In, S3 Out, GBs, DBWrites) be the pricing of each chargeable element accordingly, and Wcost, WRcost, Wout, and WRrecords be, respectively, the worker's/writer's duration in GB/Seconds, size of transformed output, and number of records to be written in the DB, as also provided in Table 2. Subsequently, the active costs can be summarised as:  
\[Cost =n\times(S3 Req+SNSmsg+SQSreq)+D\times(S3 In+S3 Out)+\] \[(Wcost+WRcost)\times GBSecCost+(\frac{Wout}{256\text{ KB}}+1)\times SQSreq+WRrecords\times DBWrites\]  
More specifically, the division over 256 KB chunks models the fragmentation of SQS messages to match the maximum message payload size of AWS (256 KB). Note that this is not a complete model, as it neglects passive costs and redrive policies in the case of errors. However, it affords us the observation that many of the costs have linear relationships with the pipeline inputs. Still, the lambda function charges (\(Wcost,WRcost\)) are somewhat harder to estimate, since they are affected by several factors. With this in mind, in Figure 6,  
Figure 5: Mean execution times for distinct lambda worker operation separated by Lambda function’s available memory.  
we empirically present the pricing attributed to the worker lambda function throughout the experiments. Interestingly, it is observed that, in higher data volumes, tuning of the lambda functions specifications is essential, since there are cases where a more powerful lambda function costs less than an underspecified function (e.g., 128 MB/256 MB Memory at one event per minute).
## 2 Results and Discussion
### Synthesis  
The assembly of the acyclic precursor was efficiently carried out on a solid phase using optimized protocols for the individual transformations. Wang resin **1** was acylated with the \(N\)-[(9\(H\)-Fluoren-9-ylmethoxy)carbonyl] (Fmoc) \(N\)-alkyl amino acids (sarcosine, 4-OBzl-proline (Hyp(Bzl))), 2-indolinescarboxylic acid (Idc), and methyltyrosine (OrBu)), and the Fmoc protecting group was cleaved to yield resin **4** (Scheme 1, route I). Because of the limited number of commercially available \(N\)-alkyl amino acids, we also evaluated an alternative route using resin \(N\)-alkylation. Thus, Wang resin **1** was esterified with Fmoc-amino acids, the Fmoc group was cleaved, and amine **2** was reacted with 4-nitrobenzenesulfonyl chloride (Ns-Cl) (resin **3**) to facilitate Mitsunobu alkylation with alcohols [38], which introduced the \(N\)-substituent (Scheme 1, route II). This reaction sequence was designed to enhance the diversity of compounds available with this method. Removal of the Ns group yielded secondary amine **4**. Resin-bound amine **4** was then acylated with bromoacetic acid (resin **5**) and reacted with PPh\({}_{3}\) to form phosphonium salt **6**. The resin-bound phosphonium salt was not isolated, and the LC/MS analysis of trifluoroacetic acid (TFA)-cleaved sample revealed the expected molecular ion in all prepared compounds. The addition of trimethylamine (TEA) or 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) in \(N\)-methyl-2-pyrrolidone (NMP) triggered the Wittig olefination. The olefination of the ester proceeded smoothly at ambient temperature. TFA exposure released products **8**. Crude products were isolated and then purified by reversed-phase (RP) HPLC in an acidic mobile phase (aqueous formic acid or TFA), and tetamic acids **8** were fully characterized (Table 1). The structures of the building blocks are listed in Figure 1.  
Figure 1: List of building blocks (BBs) used in synthesis.  
To address the presence of tautomers **8a** and **8b**, we collected their NMR spectra in CDCl\({}_{3}\) and DMSO-\(d_{6}\). As expected and in agreement with previously reported data [39; 40], we observed the presence of one isomer (**8a**) in CDCl\({}_{3}\) based on the diagnostic proton resonances corresponding to the two methylene protons of isomer **8a**_[1,2]_ (\(\delta\) = 4.06 and 2.92 ppm) and the ketone carbon of isomer **8a**_[1,2]_ (\(\delta\) = 203.0 ppm). However, the NMR spectrum acquired in DMSO-\(d_{6}\) revealed the presence of a mixture of tautomers **8a** and **8b** (in an approximately 1:1 ratio) based on the diagnostic proton resonances corresponding to the methylene protons of isomer **8a**_[1,2]_ (\(\delta\) = 3.89 and 2.91 ppm) and the olefinic proton of isomer **8b**_[1,2]_ (\(\delta\) = 4.62 ppm). Moreover, the \({}^{13}\)C NMR spectra unambiguously showed the presence of the ketone carbon of isomer **8a**_[1,2]_ (\(\delta\) = 205.0 ppm) as well as the olefinic carbon of isomer **8b**_[1,2]_ (\(\delta\) = 93.4 ppm). The NMR spectra of all other compounds are presented in the Supplementary Materials.
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## Materials and Methods
### Determinations of Limonoid Aglycones in BJ  
The determinations of limonoid aglycones in BJ were conducted according to [PERSON] et al. [25]. Briefly, 10 mL of the juice sample was centrifuged at 2500 g for 10 min and loaded on Millipore C18 Sep Pak cartridge rinsed with 2 mL of methanol and then 5 mL of deionized water before the use. The cartridge was further washed with 20 mL of deionized water and limonoid aglycones were eluted with 50 mL of methanol. The extracts were dried using a rotary evaporator (IKA RV8, IKA(r)-Werke GmbH & Co. KG, Staufen, Germany) and resuspended in 1 mL of acetonitrile.  
The limonoid aglycones were quantitated by RP chromatography using a Surveyor LC pump, a Surveyor autosampler, coupled with a photodiode array detector (PDA (Thermo Finnigan, Waltham, MA, USA), equipped with Xcalibur 3.1 software (Thermo Fisher Scientific, Waltham, MA USA).  
Separations were achieved using a C-18 column (Phenomenex Luna 5 \(\upmu\) C18, 250 x 4.60 mm) and monitoring the wavelengths at 250 nm. The mobile phase consisted of a gradient program that began at methanol 10%, acetonitrile 0%, and water 90% and ended at methanol 41%, acetonitrile 10%, and water 49% in 45 min. The flow rate was 1 mL/min and the injection volume was 5 \(\upmu\)L. Sample peak identifications were achieved by comparing retention times from the sample peak with those of standards run under identical conditions. Concentrations were determined using the external standard method.
## 4 Discussion  
This study has systematically advanced the evaluation of CPAM material properties. A comprehensive performance assessment framework encompassing both construction adaptability and long-term durability has been established. Through CT-based reconstruction technology, quantitative relationships between micro-porosity structures and macroscopic performance indicators have been developed. The findings provide enhanced scientific support for CPAM selection.  
The research adopts the working hypothesis that CPAM damage mechanisms can be structurally modeled, where material composition parameters (e.g., aggregate size, asphalt content) exhibit predictable influence pathways on performance indicators (e.g., molding strength, water stability) within defined ranges.  
However, the study incorporates several idealized assumptions, including the following: (1) negligible binder effects on CPAM performance due to the lack of access to binder composition or properties in the long-stored commercial mixtures used, (2) standardized patching procedures in field applications, and (3) exclusion of service-period factors like material aging and thermal deformation. While these assumptions facilitate modeling, they may introduce discrepancies in practical implementation.  
Future research should prioritize field validation under diverse construction conditions, incorporating variables such as pavement structure, pothole geometry, and traffic loading patterns. Long-term performance monitoring is recommended to optimize the evaluation framework and develop performance-based CPAM specifications.  
Figure 14: Analysis of contact points and strength indicators: (**a**) mean contact number fitted to Marshall strength; (**b**) mean contact number fitted to penetration strength.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans
### Wireless Ad Hoc Network (WANET)
#### 4.6.3 Underwater Ad Hoc Network (UANET)  
It is costly to deploy infrastructure for constructing underwater networks so that UANETs are often adopted to avoid such construction. Typical such network are underwater acoustic (sensor) networks [66], which can be used to connect underwater things to construct the IoUT [2]. With acoustic underwater communication, it is necessary to minimize the use of handshake to avoid long latency due to slow signal propagation speed and transmission rates. To improve the performance of a single UANET, it is necessary to jointly optimize the design of the data link layer and network layer, especially medium access control (MAC) [67; 68] and routing protocols [69] as well as end-to-end reliable transfer protocols [70]. Several surveys on underwater networking issues can be found in the literature (e.g., [71; 72]).
## 5 Application to a Specific Case Study: ITER Divertor Remote Handling System
### ITER Software Lifecycle Definition  
Due to the complexity of the proposed requirements (Annex 1), interaction with numerous external agents, modular integration across different environments, and maintainability over long periods, an iterative software development cycle was chosen. It offers numerous benefits, including increased flexibility, risk mitigation, and stakeholder satisfaction, making it a preferred approach for many software development projects. Integrating an iterative software development cycle within a MAMIC architecture involves structuring the development process to align with the principles of iterative development while leveraging the modular and flexible nature of the MAMIC architecture.  
Several steps are necessary to be carried out for the culmination of an iterative software cycle, as has been followed in the case of ITER:  
1. Incremental development: Breaks down the software development process into smaller increments or iterations, each focused on delivering specific functionality or features. This aligns with the iterative approach, allowing for continuous feedback and improvement.
2. Feedback loops: Establishes feedback loops at the end of each iteration to gather input from stakeholders, users, and team members. This feedback informs the next iteration, guiding adjustments and refinements to the system.
3. Modular design: Utilizes the modularity of the MAMIC architecture to encapsulate different components and functionalities within distinct modules. Each module represents a self-contained unit with well-defined interfaces, facilitating incremental development and testing.
4. Separation of concerns: Maintains a clear separation of concerns within the MAMIC architecture, with distinct layers for domain logic, application logic, and infrastructure. This separation ensures that changes and updates can be made to specific components without affecting the entire system.
5. Continuous integration and verification and deployment: Implements automated testing and continuous integration and verification practices to ensure that changes introduced during each iteration are seamlessly integrated into the codebase. This allows for rapid feedback and the early identification of potential issues.
6. Adaptability and flexibility: Embraces the flexibility of the MAMIC architecture to accommodate changes and updates throughout the development process. The modular design and separation of concerns enable developers to respond quickly to evolving requirements and priorities.
7. Iterative refinement: It continuously refines and improves the system with each iteration based on feedback and lessons learned. This iterative refinement process ensures that the software evolves incrementally, gradually meeting the desired objectives and quality standards.  
Taking into account a classic software lifecycle and the considerations carried out in the MAMIC architecture, the following iterative development methodology (IDM) is presented in Figure 4.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Multipath Error Envelope
#### 4.1.2 BOC(1,1)  
The area of BOC(1,1) signal multipath error envelope is shown in Table 7. Figure 34 shows the multipath error envelope for the BOC(1,1) signal of infinite bandwidth. It is interesting that the envelopes of the coherent and non-coherent discriminator of the LS method are the same, like the BPSK(1) signal case. The W2 and double-delta correlator multipath error envelope are about the same, as well, but the double-delta correlator has two additional side lobes at 0.5 chips due to the false lock points at that position. In all, the coherent LS discriminator has a minimum envelope area, which is the same as the non-coherent LS discriminator envelope, 60.5% of the W2 envelope and 38.7% of the double-delta correlator envelope.  
The multipath error envelopes of the BOC(1,1) signal with the 6.138-MHz bandwidth are shown in Figure 35. The CCRW W2 discriminator has a zero-crossing point offset of 0.02 chips. The double-delta correlator has a minimum envelope area. The LS discriminators have side lobes at the position of 0.5 chips, due to the fluctuations of the S-curves in the left side to the linear region. However, the non-coherent discriminator still has a smaller area of the multipath error envelope than the BGP discriminator. In all, the double-delta correlator discriminator has a minimum envelope area, which is 17.0% of the coherent LS discriminator envelope, 46.8% of the non-coherent LS discriminator envelope, 75.7% of the W2 envelope and 41.1% of the BGP envelope.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bandwidth** & **Coherent** & **Non-Coherent** & **W2** & \(\Delta\Delta\) \\ \hline Infinite (chip\({}^{2}\)) & 0.0044 & 0.0046 & 0.0093 & 0.0098 \\
16.368 MHz (chip\({}^{2}\)) & 0.0066 & 0.0098 & 0.0097 & 0.0103 \\
8.184 MHz (chip\({}^{2}\)) & 0.0067 & 0.0086 & 0.0082 & 0.0088 \\
4.092 MHz (chip\({}^{2}\)) & 0.0253 & 0.0287 & 0.0273 & 0.0302 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The multipath error envelope of the BPSK(1) signal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bandwidth** & **Coherent** & **Non-Coherent** & **W2** & \(\Delta\Delta\) & **BGP** \\ \hline Infinite (chip\({}^{2}\)) & 0.0046 & 0.0046 & 0.0076 & 0.0119 & \\
24.552 MHz (chip\({}^{2}\)) & 0.0219 & 0.0140 & 0.0081 & 0.0129 & 0.0181 \\
12.27 MHz (chip\({}^{2}\)) & 0.0192 & 0.0086 & 0.0060 & 0.0095 & 0.0157 \\
6.138 MHz (chip\({}^{2}\)) & 0.0676 & 0.0246 & 0.0152 & 0.0115 & 0.0280 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The multipath error envelope of the BOC(1,1) signal.  
Figure 33: Multipath error envelopes for the S-curves of the BPSK(1) signal (4.092-MHz bandwidth).
# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
## 5 Conclusions  
This study applied two CNN processes to drill bit defect diagnosis. In the results, both the 2D CNN and the 1D CNN provide high diagnostic accuracy of over 96%. Short-time Fourier transform was performed to convert the time-domain vibration signals into a 2D image to perform the 2D CNN process. The generated spectrum images were directly used as inputs to perform the 2D CNN. In order to perform the 1D CNN, the vibration signals were reconstructed based on the STFT spectrogram to obtain a 1D data set. The reconstructed data set contains 1536 data points. Compared to the raw data set, which contains 32,770 data points, the reconstruction process significantly reduces the data size while keeping the necessary features.  
By comparing the two CNN algorithms, we found that the 2D CNN process achieves a higher diagnosis accuracy with a longer computational time. For the 1D CNN, the classification accuracy can be achieved at a similar level to that of the 2D CNN; however, the computational time of the 1D CNN is shorter than the 2D CNN due to its low computational complexity. This process reduces the hardware requirements for computation and can be beneficial to system integration and real-time analysis. It is worth mentioning that both CNN processes provide high accuracy in distinguishing \"healthy\" conditions from damaged conditions. In other words, the processes can detect whether the drill bits are damaged. For fault diagnosis purposes, the overall diagnostic accuracy of the 1D CNN is lower than that of the 2D CNN, but it does provide a high calculation speed and an accurate diagnosis.  
Conceptualization, Y.Y. and W.Z.; methodology, Y.Y., Q.L. and W.Z.; original draft preparation, Y.Y.; review and editing, Q.L., B.S.H. and W.Z.; supervision, W.Z.; project administration, B.S.H. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Agency for Science, Technology, and Research (A*STAR) and Schaeffler for project entitled \"Condition Monitoring and Fault Prognosis of Robot Joints based on Fiber Optic Sensing and Deep Learning\" through grants #020956-00003 and #021003-00003.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**1D CNN**} & \multicolumn{2}{c}{**2D CNN**} \\ \hline
**Epoch Number** & **Accuracy (\%)** & **Time (s)** & **Accuracy (\%)** & **Time (s)** \\ \hline
**1** & 27.7 & 0.42 & 29.33 & 1 \\
**50** & 60 & 22.1 & 28.0 & 52 \\
**100** & 81.8 & 30.3 & 57.33 & 96 \\
**150** & 81.8 & 57.7 & 94.67 & 156 \\
**200** & 87.2 & 93.8 & **97.33** & **206** \\
**250** & 90.9 & 121.3 & 97.33 & 252 \\
**300** & **96.6** & **147.5** & 97.33 & 310 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diagnostic accuracy with different epoch numbers for the 1D CNN and the 2D CNN.
## 2 Methodology  
### Improved Mass-Conserved and Interface-Corrected DI Method  
#### 2.1.1 Original Cahn-Hilliard Model  
In the original DI method, the Cahn-Hilliard model is often adopted to trace the interfaces in the calculation. The governing equation of the original model can be written as:  
\[\frac{\partial C}{\partial t}+\
abla\cdot(\mathbf{u}C)=\Gamma\
abla^{2}\mu_{C}, \tag{1}\]  
where \(C\) is the volume fraction of heavier fluids for two-phase flows in the range of [0, 1], which is also named as the order parameter; \(t\) is time; \(\mathbf{u}\) is the velocity of the fluid; \(\Gamma\) is the mobility; and \(\mu_{C}\) is the chemical potential obtained from the free energy in the interfaces, and it can be written as:  
\[\mu_{C}=2\beta C(C-1)(2C-1)-\kappa\
abla^{2}C, \tag{2}\]  
Here, the constants \(\beta\) and \(\kappa\) are obtained from the surface tension coefficient \(\sigma\), as well as the interface thickness parameter \(\xi\):  
\[\kappa=\frac{3}{2}\sigma\xi;\ \beta=\frac{12\sigma}{\xi}, \tag{3}\]  
Based on the order parameter \(C\), the local density \(\rho\) of the fluid can be obtained from a linear combination of the heavier and lighter fluids (\(\rho_{H}\) and \(\rho_{L}\)):  
\[\rho=\rho_{L}+C\Delta\rho;\ \Delta\rho=\rho_{H}-\rho_{L}, \tag{4}\]  
Many numerical methods [10; 32] have been developed to discretize the Cahn-Hilliard Equation (Equation (1)) for tracking fluid interfaces. However, numerical and/or modeling errors, such as diffusion and dissipation, flatten or compress the fluid interfaces such that unphysical results can be obtained during numerical simulations. In the present study,inspired by the Hamilton-Jacobian equation used in the level set method, a new correction term is introduced into the original Cahn-Hilliard model to maintain the shape of the fluid interface. In addition, to guarantee mass conservation, a mass correction term, introduced previously by [PERSON] et al. [35], is also applied.