esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### SAR Data Pre-processing  
We processed S-1 SAR data using the \"s1 tiling processing chain\" [72], based on the free Orfeo Toolbox (website: www.orfeo-toolbox.org). Pre-processing comprised the following steps: calibration, orthorectification, multi-image filtering [73; 74; 75], and splitting into the S-2 tiling grid that is referenced to the U.S. Military Grid Reference System (MGRS). The SAR data sets are calibrated to gamma nought backscatter and orthorectified to 10 m resolution in UTM projection that was based on the SRTM model at 30 m resolution. A multi-image filter [74; 75] is applied to decrease the speckle effect and to enhance the equivalent number of looks (ENL). A \(3\times 3\) spatial window was chosen, and each image was filtered only with the images acquired before its own acquisition date, in order to simulate NRT conditions. Further details on the pre-processing of the SAR data can be found in chapter 2.2.1 of [47].
# Evaluation of a New-Concept Secure File Server Solution
## 4 Results
### Results for 10 Gbit/s Connection
#### 4.2.2 Results of Writing Tests on 10 Gbit/s Connection  
The average transfer speeds calculated for file writing for the 1 Gbit/s bandwidth network are illustrated in Table 5 and Figure 5.  
The Ubuntu-Windows 2019 Server configuration performed best in the measurements for all four file sizes. It is clear from the results that ARDS TestServer was, on average, three times slower than the Windows server for files of 1 GiB and 256 MiB at this transfer rate because of the use of snapshotting techniques. In the case of using the 4 MiB files, the speed difference was smaller, and what is more, when transferring the smallest files  
Figure 4: Measurement results of reading on 10 Gbit/s bandwidth.  
Figure 5: Measurement results of writing on 10 Gbit/s bandwidth.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{5}{c}{**Files**} \\  & \multicolumn{2}{c}{**1 \(\times\) 1 GiB**} & \multicolumn{2}{c}{**4 \(\times\) 256 MiB**} & \multicolumn{2}{c}{**256 \(\times\) 4 MiB**} & \multicolumn{1}{c}{**2097 \(\times\) 500 kiB**} \\ \hline
**Test ID** & \multicolumn{5}{c}{**Average Transfer Speed in MiB/s**} \\ \hline
**Windows 10—ARDS TestServer** & 268.08 & 252.94 & 180.35 & 75.64 \\
**Windows 10—Windows 2019 Server** & 810.08 & 816.61 & 234.18 & 59.35 \\
**Ubuntu 22.04—ARDS TestServer** & 303.69 & 328.22 & 233.43 & 150.84 \\
**Ubuntu 22.04—Windows 2019 Server** & 843.81 & 844.27 & 558.02 & 322.65 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Writing test results for 10 Gbit/s bandwidth.  
using Windows 10, the transfer was already more than 20 percent faster when we tested the open-source file server. Furthermore, it is important to realize that the Windows server did not have shadow copy service (VSS) enabled, which would have meant a similar extra load when writing to the server as the automated snapshotting in ARDS TestServer.
## 1 Introduction  
Microstructure characteristics play the most important role in determining the mechanical properties of materials at room temperature and elevated temperatures. Many works have been dedicated to the modification of microstructures and improvement of mechanical properties in the past decade [1; 2; 3; 4]. In the case of materials used at elevated temperatures, Ni-base superalloy is widely utilized in crucial components such as blades and vanes in gas turbine engines due to its superior high-temperature properties [5]. These components are usually operated in severe circumstances combining elevated temperatures and complicated stress states, which induces significant microstructural degradation and leads to the failure of these components. To evaluate the high-temperature properties of Ni-base superalloy, the creep rupture test is primarily considered since these components are subjected to high centrifugal forces during operation [6; 7; 8].  
The excellent high-temperature strength of nickel-based superalloy is predominantly derived from the solid-solution strengthening of refractory chemical elements, homogeneous distribution of ordered \(\gamma^{\prime}\) precipitates embedded in the \(\gamma\) matrix and precipitation of carbides [9; 10]. Under the high stress loads at elevated temperatures, the microstructure of the superalloy undergoes significant changes, such as transformation of carbides [11; 12; 13], coarsening or rafting of \(\gamma^{\prime}\) precipitates [14], formation of dislocation networks and shearing of \(\gamma^{\prime}\) precipitates by dislocation pairs [15; 16; 17], which lead to the ultimate failure of these components. Therefore, these components must be replaced when the microstructural degeneration has been accumulated to a certain extent, which is quite costly. On the other hand, rejuvenation heat treatment (RHT) provides a way to reduce the cost of this replacement [18; 19; 20; 21; 22; 23; 24]. If a degraded microstructure as well as the high-temperature property of a Ni-base superalloy can be restored to its pre-service condition by performing a specific RHT, the service life of these components can be substantially extended.  
To verify the effectiveness of RHT, one has to consider the microstructure evolutions during creep and RHT. Therefore, the rafted \(\gamma^{\prime}\) precipitates, decomposition of carbides and change of dislocation configuration must be compared at different stages. In the present study, a polycrystalline cast Ni-base superalloy K465 was used in a creep test. After heat treatment, finely distributed \(\gamma^{\prime}\) precipitates and a large amount of carbides are formed in the matrix. RHT was carried out on the crept samples with different levels of accumulated strain. The samples were tested again to evaluate the effectiveness of RHT. At each state, the microstructure in the dendrite core (DC) and in the interdendritic region were investigated respectively. The effect of RHT on microstructure rejuvenation was discussed based on the morphology of \(\gamma^{\prime}\) precipitates, area fraction of carbides and dislocation configuration.
## 1 Introduction
GW analysis methods for anisotropic materials and structures were developed [4, 28, 29, 30, 31, 32, 33, 34, 35, 36], and GW in non-linear solids started to provide a new NDE approach, especially for detecting fatigue damage [37, 38, 39]. The uses of laser devices enabled, for example, detailed evaluation of composite damages and GW fields generated by fatigue cracks [40, 41]. A recent review is a source for many other articles related to GW [42], and Imperial College London's web site [43] is a treasure trove of many doctoral theses on GW and NDE subjects. Although many advances in GW science have materialized, it is still difficult to find data on GW attenuation for most engineering materials. A recent review gathered available bulk-wave and GW attenuation studies [14], which illuminate the scarcity of attenuation data, especially for the transverse waves and Lamb waves. Some of the studies cited above are exceptions and compared calculated and measured attenuation for carbon-fiber reinforced composite (CFRP) sheets [31, 34, 35]. When the attenuation coefficients of bulk waves of the same (or similar) material are available, GW attenuation coefficients can be calculated using [PERSON]'s parametric equations [19]. For Lamb waves, [PERSON] gave needed parameters for the zeroth and first symmetric and asymmetric modes in graphic form. Here, material damping was incorporated into complex wave numbers. Another method is to use Disperse software [25, 26], which used the global matrix method. This allows a choice of hysteretic or Kelvin-Voigt damping models, but, again, bulk-wave attenuation coefficients must be supplied. Until recently, bulk-wave attenuation coefficients were of limited availability, and comparison between theoretical calculations and experimental results has been scarce. Two reports provided attenuation coefficients of longitudinal and transverse waves for over 200 materials [44; 45]. This gives an impetus for experimentally verifying the theoretical prediction of Lamb-wave attenuation [19; 25; 26]. In turn, when such verification materializes, the experimental methods utilized in the bulk-wave attenuation measurements are validated. This is an added incentive of the present study. Note also that the two attenuation studies [44; 45] showed all but a few possessing the linear frequency dependence indicative of hysteretic damping. That is, the Kelvin-Voigt damping model was inapplicable. The objective of this work is to determine the attenuation coefficients of Lamb waves of ten engineering materials and to compare the results with calculated Lamb-wave attenuation coefficients, which utilized Disperse [25; 26]. It is intended to ascertain the degree of agreement between theory and experiment. General agreements were found, but modest to large deviations were also observed. Results will be discussed in regard to possible sources of discrepancies.
# Targeting KRAS in PDAC: A New Way to Cure It?
###### Abstract  
Pancreatic cancer is one of the most intractable malignant tumors worldwide, and is known for its refractory and poor prognosis. Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. KRAS is the most commonly mutated oncogene in PDAC. It has been considered the \"untargetable\" oncogene for decades until the emergence of G12C inhibitors, which put an end to this dilemma by covalent binding to the switch-II pocket of the G12C mutant protein. However, G12C inhibitors showed remarkable efficacy against non-small-cell lung cancer (NSCLC), while the G12C mutation is rare in PDAC. Based on the successful experience of G12C inhibitors, targeting KRAS G12D/V, which forms the majority of KRAS mutations in PDAC, is gradually being regarded as a potential therapy.  
Pancreatic cancer is one of the most intractable malignant tumors worldwide, and is known for its refractory nature and poor prognosis. The fatality rate of pancreatic cancer can reach over 90%. In pancreatic ductal carcinoma (PDAC), the most common subtype of pancreatic cancer, KRAS is the most predominant mutated gene (more than 80%). In recent decades, KRAS proteins have maintained the reputation of being \"undruggable\" due to their special molecular structures and biological characteristics, making therapy targeting downstream genes challenging. Fortunately, the heavy rampart formed by KRAS has been broken down in recent years by the advent of KRAS\({}^{\rm G12C}\) inhibitors; the covalent inhibitors bond to the switch-II pocket of the KRAS\({}^{\rm G12C}\) protein. The KRAS\({}^{\rm G12C}\) inhibitor sorotasib has been received by the FDA for the treatment of patients suffering from KRAS\({}^{\rm G12C}\)-driven cancers. Meanwhile, researchers have paid close attention to the development of inhibitors for other KRAS mutations. Due to the high incidence of PDAC, developing KRAS\({}^{\rm G12D/V}\) inhibitors has become the focus of attention. Here, we review the clinical status of PDAC and recent research progress in targeting KRAS\({}^{\rm G12D/V}\) and discuss the potential applications.  
PDAC; KRAS; drug resistance; autophagy; combination therapy 2022
## 4 Experimental Results and Security Analysis
### Statistical Random Number Tests
#### 4.3.2 Random Test Result under SP800-22  
For the random test of SP800-22, we select different random keys and different random initial values to produce 100 output keystreams. The length of each keystream output for test is 10,000,000 bits. Table 4 shows the test results of SP800-22 for the improved CML, and compound CML, respectively.
## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.3 Performance Metrics for Code Generation Models  
CodeBLEUCodeBLEU is a metric specifically designed to evaluate the quality of machine-generated code in code generation tasks, particularly in the context of natural language description-to-code generation. It extends the BLEU metric, commonly used in machine translation tasks, to measure the similarity between the generated code and the reference code. Notably, models such as CodeBERT [114] and CodeT5 [115] have utilized the CodeBLEU metric to assess how closely the generated code aligns with the reference code.  
Exact Match (EM)Exact match is a binary metric commonly used in code generation tasks to determine whether the generated code perfectly matches the desired output or reference code. It provides a measure of accuracy by evaluating if the generated code is an exact match. CodeT5 [115] models, for example, often employ the exact match metric to assess the level of accuracy in generating code that aligns precisely with the reference code.  
Pass@metricPass@metric is a widely used performance evaluation metric in code generation tasks, particularly for evaluating language models or systems that generate code based on natural language descriptions. The metric measures the percentage of generated code that successfully passes a given set of evaluation tests or test cases. It provides insights into the system's ability to produce functionally correct code that meets the desired specifications. Models like GPT language model (Codex) [116] and pre-trained transformer-based language models like Alphacode [35] have been evaluated using the Pass@metric to assess the success rate of the generated code.  
Multi-Turn Programming Benchmark (MTPB)MTPB is an evaluation benchmark employed to assess the performance of code generation systems like CODEGEN [50], specifically in multi-turn programming scenarios. This benchmark involves multiple interactions between a human and the code generation system, where the human provides natural language descriptions, queries, or instructions, and the system generates code accordingly. The quality of the generated code is evaluated based on correctness, efficiency, and adherence to the specified requirements.
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 4 Case Study
### Scenario and Application
This strategy effectively improves safety and efficiency, providing valuable guidance for replication in other critical infrastructures. During the case study, several significant challenges were faced that impacted the implementation of criticality analysis on the rail network. One of the main challenges was data integration and data quality. The effectiveness of criticality analysis depends on accurate, complete, and up-to-date data. However, the available data were fragmented and came from different sources and systems that were not always integrated, making it difficult to use effectively. Another major challenge was the complexity and heterogeneity of the railway network. The network comprised various interdependent components, each with its technical characteristics, criticality levels, and maintenance requirements. This interdependence required a methodology that could consider the criticality of each element and the system as a whole. An example of the criticality matrix obtained is shown in Figure 4.  
In addition, the technological infrastructure needed to implement advanced criticality analysis presented significant challenges. Robust data management systems, specialized software, and advanced analysis tools such as digital twins and machine learning algorithms were required. Procurement, installation, and maintenance of this infrastructure proved costly and technically complex. Finally, training and change management were critical challenges. Adopting new methodologies and technologies involved a significant change in how asset managers and operational staff carried out their work. This change required a considerable commitment to training and organizational change to ensure that staff were technically trained and aligned with the strategic objectives of the data-driven approach.
## 3 Results and Discussion
### Mixed Time Partitioning Methods vs. Sub-Cycling and Partitioning Methods
#### 3.1.2 Computational Efficiency of Partitioning Methods  
In this section, we discuss the case where different time integration schemes are used with the same time step used in both domains: the casting and mold. We consider two cases: (i) explicit time stepper used in the cast domain with implicit time stepper used in the mold domains and (ii) implicit time stepper used in the cast domain with explicit time stepper used in the mold domains. We include the total run times in the Table 5. Our investigations indicate the shortest total run time for \(E-I^{m}\) combination, comparing to the \(I-E^{m}\). The difference in the run times originates from the difference in the way boundary conditions are applied, and the system of equations is solved. The introduction of  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Description** & \(\boldsymbol{t[s]}\) \\ \hline \(E-I^{m}\) & Explicit in cast domain \(-\) Implicit in other domains & 1053.95 \\ \(I-E^{m}\) & Implicit in cast domain \(-\) Explicit in other domains & 1539.86 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A total time \(t\) of computations obtained for the mixed time partitioning methods (\(m=15\)). The different integration methods and the different time step.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Description** & \(\boldsymbol{t[s]}\) \\ \hline \(I-I^{m}\) & Implicit in cast domain \(-\) Implicit in other domains & 1535.98 \\ \(E-E^{m}\) & Explicit in cast domain \(-\) Explicit in other domains & 1225.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A total time \(t\) of computations obtained for the sub-cycles methods (\(m=15\)). The same integration methods and the different time step.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Description** & \(\boldsymbol{t[s]}\) \\ \hline \(E-I^{m}\) & Explicit in cast domain \(-\) Implicit in other domains & 3268.53 \\ \(I-E^{m}\) & Implicit in cast domain \(-\) Explicit in other domains & 3557.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A total time \(t\) of computations obtained for the partitioning methods (\(m=1\)). The different integration methods and the same time step.  
the natural (contact) boundary condition is carried out by means of elements which are the boundary's discretization. The introduction of the above boundary condition in implicit time stepper requires modifying the coefficients of matrix and right-hand vector while in explicit time stepper only the coefficients of the right-hand vector. It is the reason for extended a run time when we use implicit schemes in a casting domain.
###### Abstract  
The application of solar energy as a renewable energy source has significantly escalated owing to its abundance and availability worldwide. However, the intermittent behavior of solar irradiance is a serious disadvantage for electricity grids using photovoltaic (PV) systems. Thus, reliable solar irradiance data are vital to achieve consistent energy production. Geostationary satellite images have become a solution to this issue, as they represent a database for solar irradiance on a massive spatiotemporal scale. The estimation of global horizontal irradiance (GHI) using satellite images has been developed based on physical and semi-empirical models, but only a few studies have been dedicated to modeling GHI using semi-empirical models in Korea. Therefore, this study conducted a comparative analysis to determine the most suitable semi-empirical model of GHI in Korea. Considering their applicability, the [PERSON], [PERSON], [PERSON], and Perez, models were selected to estimate the GHI over Seoul, Korea. After a comparative evaluation, the Hammer model was determined to be the best model. This study also introduced a hybrid model and applied a long short-term memory (LSTM) model in order to improve prediction accuracy. The hybrid model exhibited a smaller root-mean-square error (RMSE), 97.08 W/m\({}^{2}\), than that of the Hammer model, 103.92 W/m\({}^{2}\), while producing a comparable mean-bias error. Meanwhile, the LSTM model showed the potential to further reduce the RMSE by 11.2%, compared to the hybrid model.  
global horizontal irradiance; satellite images; semi-empirical models; cloud index; clear-sky model +
Footnote †: journal: Applied Sciences
## 2 Methods
### Matching Cost Computation
The final combined matching cost is derived by merging the two cost components mentioned above:  
\[C(p,d)=\rho\Big{(}C_{GRAD}^{CLAHE},\lambda_{GRAD}\Big{)}+\rho\Big{(}C_{CTg}, \lambda_{CTg}\Big{)} \tag{5}\]  
\[\rho(c,\lambda)=1-exp\Big{(}-\frac{c}{\lambda}\Big{)} \tag{6}\]  
Here, \(\lambda\) is a normalizing parameter to control the influence of outliers, and the exponential function is used to normalize each cost component to the range [0, 1] and ensure that the final matching cost won't severely bias to one of the matching costs. \(C(p,d)\) is the final used matching cost that combines the enhanced image gradient-based matching cost (\(C_{GRAD}^{CLAHE}\)) with the improved census transform-based matching cost (\(C_{CTg}\)). Figure 2 presents the visual results of stereo matched disparity map using the proposed enhanced gradient-based matching cost and improved census transform-based matching cost on the Tsukuba of the Middlebury dataset [32]. The original gradient-based and Census transform-based matching cost are also shown in Figure 2 for the convenience of comparison. In order to make the result convincing, we use the same cost aggregation strategy as [23] and no refinement process is applied. The comparison of results obtained from the original gradient-based and enhanced gradient-based is shown in the first row. Further, it can be found that the enhanced gradient-based cost produces a better disparity map than the raw gradient-based cost at image boundaries (red circle mark). In the second row, the proposed modified census transform-based cost and the original census transform-based cost are also compared. According to the disparity map, our modified census transform-based matching cost performs visibly better than the original census transform-based matching cost at repetitive local structures (blue circle mark in Figure 2). Figure 3 compares the proposed combined matching cost computation method with common individual cost methods, such as the absolute difference of intensity (AD), the absolute difference of image gradient and traditional census transform-based cost measurement. Tests are implemented on the two image pairs of the Middlebury stereo dataset (i.e., Tsukuba, Teddy). All of the disparity maps are initial stereo matching results without any post-processing and generated by the same cost aggregation method, which would be mentioned in the next section, and the winner takes all disparity computation strategy. From Figure 3, it can be found that the comprehensive performance of our proposed cost method obviously outperforms AD, gradient-based matching cost, and traditional census transform-based matching cost. AD cannot handle large textureless regions effectively, the gradient-based matching cost cannot do well with tiny boundaries, while the traditional census transform-based matching cost fails at repetitive or similar local structures.