esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The tectonic processes leading up to an earthquake and the occurrence of the earthquake itself will cause local changes in the geophysical field (geomagnetic field, stress field, etc.). In this paper, the variation characteristics of the tectonic stress field (TSF) and local geomagnetic field (LGF) before and after the Yangbi Ms 6.4 earthquake are studied. The regional stress tensor damping inversion method was used to invert the TSF using focal mechanism solutions (FMSs). The change characteristics of the TSF before and after the earthquake were analyzed. An annual variation model of the LGF was constructed, and the variation of the horizontal vector was analyzed. The azimuth and plunge of the maximum principal compressive stress axis of the TSF in the epicentral region before and after the earthquake were \(-4.4^{\circ}\) and \(2.7^{\circ}\), \(172.7^{\circ}\) and \(6.6^{\circ}\), respectively. The variations in the declination, inclination and total intensity of the epicenter one year before and one month after the earthquake were \(-0.20^{\prime}\) (\(0.07^{\prime}\)), \(0.29^{\prime}\) (\(-0.12^{\prime}\)), and \(-1.7\) nT (\(-1.9\) nT), respectively. The epicenter is located at the boundary of the \"weak variation region\" of the horizontal vector. This research is of great significance concerning the TSF background and incubation mechanism of earthquakes.  
Keywords:focal mechanism solution; stress field inversion; meta-instability theory; local geomagnetic field variation; Yangbi earthquake +
Footnote †: journal: Applied
# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
## 4 Discussion  
Lung cancer is the leading cause of cancer death in the world, as nearly one-quarter of all cancer deaths worldwide are due to lung cancer. Most of the inoperable early-stage lung cancer cases are treated with radiotherapy alone or radiotherapy with chemotherapy [21]. While the distribution of FDG on a post-radiation PET/CT is affected by the inflammatory changes caused by radiation itself, FDG PET/CT remains the most common modality for identifying the treatment response of lung cancer after radiation therapy [7; 22]. Recent advancements in radiomics have led to the development of models capable of extracting complex information from CT scans--information that includes features not readily visible or quantifiable to the human eye. These models [11; 23] have shown promise in detecting local cancer recurrence following stereotactic body radiation therapy (SBRT) for lung cancer, indicating radiomics' potential to identify early changes linked to local recurrence, which are often overlooked in conventional medical assessments. Similarly, the CNN models, known for their capability in image recognition and classification, have also been developed to address the challenge of detecting recurrence after lung SBRT [24], demonstrating their capability to predict patient outcomes.  
In our study, we aimed to enhance the reliability of post-radiation lung cancer treatment response assessment based on post-radiation PET/CT images by integrating the predictions of a radiomics model and a CNN model through the ER approach. This integration seeks to address the limitations of using either model in isolation by leveraging their complementary strengths. Our model was able to achieve a predictability with AUC of 0.72, in order to differentiate PET/CTs with only benign post-radiation changes from PET/CTs with malignant lesions. The 0.02 increase in AUC compared to the individual models indicates that the fusion model has the potential to make more accurate predictions in cases where malignancy detection is challenging. While the model showed a notable improvement in accuracy and AUC, further studies are needed to validate its performance in larger and more diverse datasets. In addition, although this level of accuracy (~70%) may not yet meet the standards for direct clinical application, our comprehensive literature review revealed no prior attempts to leverage AI for this specific purpose. Therefore, we consider the radiomics model, CNN model, and their fusion as promising prototypes for the development of more sophisticated AI models in this domain.
## 1 Introduction
Ideally, a Moon-based sensor would observe the Earth and collect the angular sampling data continuously. However, in fact, the sensor observes the Earth at a certain time sampling interval. The selection of time sampling intervals will greatly affect the sampling of observations and thus the richness of angular combination. A large time sampling interval will result in the lack of effective angular combination coverage, whereas a small time sampling interval will lead to redundancy in the angular sampling. Furthermore, the effect of the time sampling interval on the angular combination differs for different observed points. Therefore, it is necessary to study the effect of the time sampling interval on the angular combination characteristics for Moon-based Earth observations with respect to different observed points. The aims of this study were to explore the angular combination characteristics of a Moon-based platform and the effect of time sampling interval for different observed points. We derived an analytical equation for the observational angles from the orbital elements of the Moon and Sun. We also proposed an angular combination number index (ACNI), which can be used to characterize the performance of the angular combination. Finally, the effects of the time sampling interval for different observed points are revealed.
## Appendix A Variable Retrieval
### Soil Moisture Retrieval
#### a.1.2 Zenith Total Delay Retrieval from InSAR
The Permanent Scatterer Interferometry (PSI) makes use of long stacks of SAR images, repeating the same geometry, both to identify the stable targets and to separate the two contributions due to APS and long term deformations. This separation is carried out by a spatio-temporal filtering that assume APS low pass in space and incorrelated in time, from crustal deformations, correlated over long term or fast local deformations, like infrastructures [63]. The APS maps are then generated by converting phases into delays by inverting (10). The reconstruction of the absolute phase field needs a further processing step, phase unwrapping, to solve for phase ambiguity [64]. These methods are based on the assumption that the spatial variation of the phases, from target to target, is smooth respect to the ambiguity, that, from 10 is \(\phi_{a}=2\Delta R/\lambda\) approximately 2.8 cm in the case of Sentinel-1. As PS are man-made structures but also exposed rocks and stones, their density is pretty large in urbanized areas, ranging from tens to thousands PS per square kilometer, preventing wrapping errors is most cases. However, no reliable measure can be achieved in forest and highly vegetated areas, whereas all water bodies are pure noise, lacking temporal stability. PSI solves at one time the problem of target stability, by definition of PS, of precise location, by space time filtering and of satellite precise location, by using PS as ground control points, that is items 1,2 and 4 in the list above. The ionospheric contribution, is solved by using frequency diversity, similarly to GNSS, while angular diversity can be used to estimate and remove scintillations [65; 66]. In the end, a set of time-differentiated maps can be generated, representing the delay variation, due to troposphere, projected along the satellite line of sight, that corresponds to incidence angles range from 20\({}^{\circ}\) to 45\({}^{\circ}\), depending on satellite and acquisition mode. The characteristics of the APS maps that can be generated by SqueezeSAR, a technique that provides consistent improvements respect to PSI over vegetated areas, are summarized in Table 11. Despite the SAR temporal resolution and timeliness do not comply with the OSCAR requirements for Integrated Water Vapor (IWV), it is the only instrument whose horizontal resolution meets the OSCAR requirements at the goal level. Hence, it is worth using the InSAR technique to produce APS maps.
## 3 Results
### Special Role of PRI in the SIF-GPP Relationship for Corn and Soybean
#### 3.2.4 Partial Correlation Analysis between the Ratio of GPP to SIF and PRI  
As shown by the results above, PRI plays an important role in the \(SIF_{t}\)-GPP relationship. For corn and soybean, the ratio of GPP to \(SIF_{t}\) increases with PRI (Figure 8). In contrast to soybean, corn showed a clear upward tendency of the ratio of GPP to \(SIF_{t}\) with PRI. It can be interpreted that \(SIF_{t}\) is more sensitive to PRI for soybean (Figure 8) and that the division of GPP by SIF reduces some impacts of PRI on the ratio of GPP to \(SIF_{t}\) for soybean.  
It is worthy to note that complex structural-physiological factors and environmental factors have an influence on the \(SIF_{t}\)-GPP relationship. To better interpreter the potential impact of PRI on the \(SIF_{t}\)-GPP relationship, the correlation of PRI with the ratio of GPP to \(SIF_{t}\) and its relationship to other influencing variables should be analyzed in detail.  
In this study, we used Partial correlation analysis to distinguish the impacts of environmental variables (PAR, Ta, VPD, CWSI) and structural-physiological factors (Gs, NIRv, NDVI) on the relationship of PRI and the ratio of GPP to \(SIF_{t}\) (Table 2). By controlling the structural variables (NIRv, NDVI), the PRI is less strongly correlated with the ratio of GPP to \(SIF_{t}\) than it is with other variables (Gs, PAR, etc.). It can be explained by the fact that PRI contains some information about structure or chlorophyll content. In addition, NDVI or NIRv is also related to leaf growth and leaf age, which affect the dynamic variations in NPQ. It should be noted that the relationships between the ratio of GPP to \(SIF_{t}\) and PRI were significant when Gs was controlled for both corn and soybean, which may be due to the fact that PRI contains some information about plant responses to stress (Figure 6) and that there was no severe stress for the crops that could have caused an imbalance between the light and dark reactions. Overall, the relationships between PRI and the ratio of GPP to \(SIF_{t}\) were almost all significant in both the Partial and Pearson correlation analyses, regardless of controlling structural-physiological and environmental variables.  
Figure 8: Relationships between the ratio of GPP to \(SIF_{t}\) and PRI for corn ((**a**) for half-hourly data; (**c**) for daily data) and soybean ((**b**) for half-hourly data; (**d**) for daily data). The blue line is the trend line, and the shaded area represents the 95% confidence interval.
## 3 Results and Discussion
Both of these parameters were the highest for biomass obtained from the winter harvest from the six-year plantation, which was statistically confirmed; they were 16.2 MJ kg\({}^{-1}\) d.m. (calorific value) and 17.5 MJ kg\({}^{-1}\) d.m. (combustion heat), respectively. On the other hand, the lowest statistically confirmed values obtained for these two parameters were found for biomass obtained from the summer harvest of the annual plantation (calorific value--14.6 MJ kg\({}^{-1}\) d.m. and combustion heat--16.1 MJ kg\({}^{-1}\) d.m.) (Figure 11A,B). These values are comparable to those obtained by [PERSON] et al. (2018) and [PERSON] (2018), but lower than the values obtained by [PERSON] et al. (2018), [PERSON] (2018), [PERSON] et al. (2018), [PERSON] et al. (2018), and [PERSON] et al. (2018). Also, in comparison to other plants used for energy, the calorific value of biomass from reed canary grass was significantly lower. An example is the results of [PERSON]'s research ([PERSON], 2018), where the calorific value of miscanthus biomass was on average 19.1 MJ\(\cdot\)kg\({}^{-1}\) d.m. and that for willow biomass was on average 20.3 MJ\(\cdot\)kg\({}^{-1}\) d.m. Figure 10: Ash content (**A**) and moisture content (**B**) of reed canary grass (_P. arundinacea_) biomass depending on the harvest time and plantation age. Means followed by same letter or symbol do not significantly differ (_p_ = 0.05, Tukey’s HSD). Ash content: interaction years \(\times\) harvest time = 7.75, standard deviation = 2.73. Moisture content: interaction years \(\times\) harvest time = 0.84, standard deviation = 0.30.
## 4 Case Analysis
### Interpretation of Result
The traditional wave speed test method can only roughly judge the sound speed change of rock at different strata depths, which reflects the comprehensive results of rock at the same depth, and does not have directional characteristics. As a result, the traditional wave speed test method can only roughly judge the position of the weak interlayer, which can not fully reflect the distribution and contour characteristics of the structural characteristics, and has a limited ability to identify a thin, weak interlayer. Therefore, to meet the limitations of traditional optical imaging and wave speed testing methods, this paper proposes the combination of optical imaging and sound wave testing to comprehensively reflect and analyze the weak interlayer. This paper adopts the borehole optical and acoustic combination measurement technology, combines optical imaging and acoustic scanning modes, and effectively synchronously obtains the optical image information of the borehole rock wall and the acoustic full pulse reflection information of 360 degrees around the borehole circle, capturing the more comprehensive optical image information and acoustic scanning data. This technology also has some limitations. Because this technology uses acoustic testing technology, which needs to be carried out in the presence of water, if there is no water in the test borehole, it may be difficult to directly use this technology. When the distribution area of groundwater is not clear, the depth change of the groundwater level in the borehole can be determined through acoustic testing technology, and the surrounding rock data in the borehole area can be effectively collected when there is water. When there is no water in the test borehole, the optical image of the borehole wall can be collected normally, and the borehole can be filled with water through an external water supplement to achieve the data acquisition of borehole acoustic wave. When carrying out optical imaging in the presence of water, it is necessary to ensure the uniformity of water, that is, the water needs to have a certain light transmittance. The clearer the water is, the higher the optical image quality is. If it is muddy water or there are a lot of particles in the water, it is necessary to conduct borehole washing to ensure the water is clear.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 4 Taxonomy for Services Composition
### User Perspective
#### 4.2.3 Semi-Automatic Web Service Composition
The second method of community involvement is to extract the generated knowledge in a community or a specific domain in order to define a set of rules considered as \"best practices\" in this community or specific domain [77]. These rules are used to build recommendation systems to assist users in the composition process. In this regard, the authors in [78] provide an interesting introduction to domain-knowledge for services composition. First, they explain the observed lack of it in the services composition structural approach (UDDI, WSDL, SOAP). In fact, the latter does not address the issue of the coordination and scheduling of services. Several industry standards such as BPEL and WSFL offer solutions for \"a priori\" composition. According to the authors, this is unsuited to a domain-specific approach (targeting a specific area; for instance, scientific computing). The other approach addressing this problem is the semantic approach based on ontologies, which enables a \"sophisticated\" service discovery. Some researchers describe the possibility of using this technique of discovery (semantic matching) to manage the service composition. The lack of this method is due to the indeterminism that may arise during the selection phase, which is based on the semantic description of service functionalities. The authors stress the fact that the e-science domain implies certain dynamic processes that the structural and semantic approaches cannot cover. Hence, the domain-specific knowledge is established to support the services' dynamic selection and configuration. Methods such as CommonKADS and OilEd have been introduced to interpret the domain-specific knowledge provided by experts in a list of rules and actions. This list allows for building a service recommendation system. These recommendations can be provided to a software agent (such as that proposed in [79]) or the end-user through the development environment. This will help to proactively improve the services selection and composition processes. Returning to [78], the author proposed a prototype for the specific domain of engineering design search and optimization (EDSO) for the modeling, analysis, and optimization of an aerodynamic object. This prototype helps less or more experienced users to build (compose) a suite of EDSO algorithms represented in Web service form to meet their specific needs. Another study [80] suggested the same approach for service composition by upgrading this process using domain-specific knowledge (in this case, it refers to a life-science domain).
## 3 Proposed Methodology
### Calculating FRPNs with Fuzzy Logic  
Fuzzy logic is employed to transform the risk parameters into an FRPN. Fuzzy logic allows for the modeling of human reasoning using linguistic terms. It is a perfect tool for mapping the relationship between system inputs and the output we want [52]. This approach has proven useful across a range of areas including automatic control, decision analysis, and expert systems [53]. The structure of a fuzzy logic system mainly consists of four components including a fuzzifier, a knowledge base, an inference engine, and a defuzzifier. The interconnections among these components and the process are illustrated  
\begin{table}
\begin{tabular}{l l l} \hline
**Linguistic Variable** & **Triangular Fuzzy Number** & **Description** \\ \hline Very low (VL) & \((0,0,2.5)\) & Almost certain to be detected. \\ Low (L) & \((0,2.5,5.0)\) & Easy to detect. \\ Medium (M) & \((2.5,5.0,7.5)\) & Detected occasionally. \\ High (H) & \((5.0,7.5,10.0)\) & Difficult to detect. \\ Very high (VH) & \((7.5,10.0,10.0)\) & Almost impossible to detect. \\ \hline \end{tabular}
\end{table}
Table 4: Fuzzy linguistic variables regarding detection.  
in stage 3 of the proposed methodology flowchart (see Figure 1). Initially, the fuzzifier employs membership functions to transform crisp inputs into fuzzy sets. Following that, the fuzzy inference engine conducts fuzzy reasoning to generate fuzzy values by using the knowledge base. As a final step, the obtained fuzzy values are converted into a crisp output by using the defuzzifier.  
A knowledge base, which contains a set of if-then rules, is built to map the relationship between the inputs and the output. A representative linguistic fuzzy rule is as follows [54]:  
\[\text{Rule }i\text{: If }x_{1}\text{ is }A_{i1},x_{2}\text{ is }A_{i2},\ldots, \text{ and }x_{r}\text{ is }A_{ir},\text{ then }y_{i}\text{ is }C_{i}, \tag{11}\]  
where \(i\) = 1, 2, \(\ldots\), \(M\) (\(M\) is the total number of fuzzy rules), \(x_{j}\) (\(j\) = 1, 2, \(\ldots\), \(r\)) is the input variable, \(y_{i}\) is the output variable, and \(A_{ij}\) and \(C_{i}\) are fuzzy sets modeled by the membership functions \(\mu_{A_{ij}}(x_{j})\) and \(\mu_{C_{i}}(y_{i})\), respectively. The Mamdani fuzzy inference engine is employed for its advantages of intuitive formation, widespread acceptance, and compatibility with human input [55]. Thus, for a collection of \(M\) fuzzy rules, the obtained output can be expressed as follows [56]:  
\[\mu_{c_{i}}(y_{i})=\max\bigl{\{}\min\bigl{[}\mu_{A_{i1}}(x_{1}),\ \mu_{A_{i2}}(x_{2}),\ldots,\ \mu_{A_{ir}}(x_{r})\bigr{]}\bigr{\}}. \tag{12}\]  
To derive the crisp output for the fuzzy inference result, the center of gravity is adopted for defuzzification.
## 2 Materials and Methods
### Data Acquisition Systems and Calibration
In the case of VNIR imaging, we collected the separated dark and white reference images (3D data cubes) at the beginning of the experiment each day and the experiment was performed for 5 days. Therefore, we had 5 pairs of reference images for the VNIR images. Calibrated hyperspectral images were calculated by applying the following equation to each pixel (Equation (1)). \[\mathrm{Rc}=(\mathrm{Rm}-\mathrm{D})/(\mathrm{W}-\mathrm{D})\times 100 \tag{1}\]  
where \(\mathrm{Rc}\) was the calibrated reflectance value (%), \(\mathrm{Rm}\) was the uncalibrated measured value, \(\mathrm{D}\) was the dark current value, and \(\mathrm{W}\) was the white reference value. Due to system noise, only 400-1000 nm (467 bands, resolution of 1.27 nm) was used from the VNIR HSI system and 1005-2503 nm (198 bands, resolution of 7.40 nm) was used from the SWIR HSI system. A spectrometer system was also used in this study for spectroscopic data analysis and for comparing the spectrometer data with the spectral responses of the hyperspectral images at the VNIR and SWIR ranges. The spectrometer system consisted of a spectrometer (ASD LabSpec 4 Hi-Res Analytical Instrument, Malvern Panalytical, Ltd., Malvern, UK), a contact probe with a 10 mm spot size, and a computer to control and acquire reflectance spectra using Indico(r) Pro software (Malvern Panalytical, Ltd., Malvern, UK). Before collecting the spectra of the samples, a white reference spectrum and a dark current were collected for the calibration of the measurement. A diffuse reflectance panel with 99% reflectivity (Spectralon, Labsphere, North Sutton, NH, USA) was used to collect the white reference spectrum. The white panel was also used as a background throughout the spectrometer measurements with samples. The surface of the FMs was cleaned before collecting their spectra and the probe was thoroughly cleaned between samples. Each spectrum was an average of 50 scans with 1 nm intervals over the wavelength range of 350-2500 nm.
## 4 Performance Evaluation Results
### Impact of Dataset Split Ratio  
Figure 7(a)-c shows the four algorithms' beam prediction accuracy results for data splits {80:20}%, {70:30}%, and {60:40}%, for \(M=8\) for the considered scenarios. The result patterns follow similar trends across the different splits and show that the data split ratios do not strongly influence the beam prediction accuracy. There are no significant changes in the results for the considered data split ratios.