esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Establishment  
### Finite Element Model  
The commercial program ABAQUS was used to evaluate the influence of the duration of the pulse-type NFGMs on the structural response of a 60 m deep hydraulic arched tunnel. The detailed geometry of the three-dimensional (3D) FE model is shown in Figure 4. The outer dimensions of 15.30 m width and 17.15 m height were used in the concrete lining. Subsequently, a fluid element was added to this 3D-FE model to represent the fluid-structure-rock interaction (FSRI) system. Choosing an appropriate mesh size was crucial for the dynamic analysis. It is worth noting that the desirable range of the element size can be calculated by the seismic wave cut-off frequency range and shear wave velocity. According to the work of [PERSON] and [PERSON] [35], the largest size of the element should be between 1/10 of the transmitted wavelengths and 1/8 of the transmitted wavelengths, to minimize the element mesh size effect, which is calculated according to Equations (5) and (6). The maximum and minimum element sizes in the FSRI system are 5.0 m and 1.5 m respectively, which could reduce the element mesh size effect and capture the seismic wave progress in three geometric cases, as illustrated in Figure 4.  
\[\Delta I\leq(\frac{1}{8}\sim\frac{1}{10})\lambda_{\text{s,min}} \tag{5}\]  
\[\lambda_{\text{s,min}}=\frac{V_{\text{sp}}}{f_{\text{max}}} \tag{6}\]  
Here, \(f_{\text{max}}\) and \(V_{\text{sp}}\) are the highest frequency of the input wave and the SV waves velocity, respectively.  
To mitigate the dynamic boundary effect, this widely used technique introduces an artificial dynamic boundary and isolates the finite numerical computation area from the semi-infinite elastic medium [37; 38]. According to [PERSON] et al. [39], the viscous-spring boundary is used to model the elastic recovery capabilities of the semi-infinite medium and absorb the scattered wave during the dynamic analysis process. Additionally, when the viscous-spring boundary condition is utilized as the boundary condition, the GMs must be transformed into generalized nodal force at the truncated boundary nodes. In this study, our previous work validated the GM input mechanism, the initial load conditions, including the static loads and dynamic loads, and the numerical model [40; 41].  
Figure 4: 3D FE model of a hydraulic arched tunnel [36]. ((**a**) FE model, (**b**) enlarged plot of the local refinement diagram).
## 2 Problem Formulation
### Notations  
Section 3 provides a detailed description of the algorithm. As demonstrated, \(G(\zeta)\) for \(\zeta_{i}\leq\zeta\leq\zeta_{i+1}\) is determined solely by \(G_{i+1}\), \(G_{i}\), and \(\zeta\), meaning that only the adjacent models and the parameter \(\zeta\) are utilized.  
For convenience, the known models \(G_{i+1}\) and \(G_{i}\) are relabeled as \(G_{2}\) and \(G_{1}\), respectively. Accordingly, the \(j\)th pole of the \(G_{i+1}\) model is denoted as \(p_{j2}\), while the \(j\)th residual of \(G_{i}\) is represented as \(r_{j1}\).  
Additionally, the parameter \(\zeta\) is transformed into \(\rho\) such that as \(\zeta\) varies from \(\zeta_{i}\) to \(\zeta_{i+1}\), \(\rho\) transitions from zero to one. The relationship between \(\zeta\) and \(\rho\) is given by the following:  
\[\zeta=(1-\rho)\zeta_{i}+\rho\zeta_{i+1},\ \ \rho\in\{0,1\},\ \ i\in\{1,2,3, \dots\}. \tag{4}\]  
Thus, the interpolated model is represented as \(G(\rho)\) over the parameter range \(\zeta_{i}\leq\zeta\leq\zeta_{i+1}\). The \(j\)th pole and residual of \(G(\rho)\) are denoted as \(p_{j}(\rho)\) and \(r_{j}(\rho)\), respectively.
# A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique
## 3 Technical Background
### Acoustic Emission Burst Features  
AE signals collected from the concrete structures have special parameters that can represent AE events in a descriptive way. These AE features characterize the AE bursts that are considered to develop the crack assessment and classification methods in this study. The AE features we used in our study are the peak amplitude, AE counts, AE energy, rise time, and decay time. Altogether, these combined features will be addressed as AEB features in our study. They are highly related to concrete structural health monitoring, and they are also crucial measures for crack characterization. Figure 2 presents a pictorial view of the features. They are further defined as follows [35,36]:  
Peak amplitude: The highest voltage measured in an AE waveform is denoted the peak amplitude.  
AE counts: The number of times the AE signal exceeds a preset threshold during any selected portion of the test is defined as the AE counts.  
AE energy: Measurement of area conducted within the rectified signal envelope can be defined as AE energy.  
Figure 1: Basic acoustic emission (AE) data acquisition system for concrete beams.  
Figure 2: A pictorial representation of the considered AE features.  
Rise time and Decay time: The time between the first threshold crossing and peak amplitude is called the rise time. The decay time is the opposite of the rise time. The time when the peak value of the amplitude reduces to the lowest within the threshold is set prior.
# Effect of _Tinospora cordifolia_-Derived Phtocomponents on Cancer: A Systematic Review
## 1 Introduction  
Cancer is a growing economic burden worldwide. The Globocan 2018 database estimates 18.1 million new cancer cases and 9.6 million cancer-related deaths [1]. This frightening and dreadful statistic has motivated the hunt for efficacious anticancer agents. Current therapeutic modalitiesin cancer, including surgery, chemotherapy, and radiotherapy have shown to be associated with significant morbidity [2]. Thus, there is much interest shown in the advent of alternative therapeutics including medicinal plants. Although several studies have explored the therapeutic benefits of natural plant-based products, there is a relative deficit in the number of systematic reviews available, especially with respect to plants present in remote areas such as the _Tinospora_ genus. In the _Tinospora genus_, only _Tinospora cordifolia (Tc)_, has shown to exhibit anti-carcinogenic properties. Thus, _Tc_ was selected as the subject of the review. _Tc_ belongs to Menispermaceae family (universally named as \"Guduchi\"/ Giloy in Sanskrit). It is a deciduous climbing shrub with typical greenish-yellow flowers, found at higher altitudes like India, Myanmar and Sri Lanka. _Tc_ has shown to exhibit several unique features, distinguishing them from other closely related species in the _Tinospora_ genus, including _Tinospora malabrica (Tm)_. _Tc_ has an ash-colored cork, higher lenticels, nodes and internode, and a lower nuclage content than _Tm_. A variety of active components are derived from _Tc_ including alkaloids, steroids, diterpenoid lactones, aliphatics, and glycoside. Apart from the anti-neoplastic properties, _Tc_ is also shown to exhibit anti-diabetic, anti-periodic, anti-spasmodic, anti-inflammatory, anti-arthritic, antioxidant, anti-allergic, anti-stress, anti-leptrotic, anti-malarial, hepatotoxicity and immunomodulatory properties [3; 4]. The present review systematically analyzes the published data assessing the effects of _Tc_-derived phytocomponents on cancer.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 7 Summary  
This paper presents a formal model for representing resource-bounded IoT services. The model relies on the actor model to define services. We precisely described the syntax and operational semantics of the model, in which it represents the requirements of an IoT service as sets of temporally defined feeds, each with its currency and contextual constraints. For each feed, the service provider secures appropriate sensor data feeds and carries out the required aggregations and customizations for different clients or client groups. The paper also presented a proximity model which enables the representation of and reasoning and decision making about the dynamically evolving relationship between a service's sensing and acting capabilities and the environments in which these capabilities are exercised. The decision-making capability, in essence, will be used to drive mobility. Furthermore, we present the design of an innovative runtime environment for implementing IoT services with AI capabilities. The proposed runtime environment offers high-level primitives supported by a middleware implementing key mechanisms required by IoT services, allowing application developers to focus on application-specific concerns. The presented model and runtime environment aims to support the implementation of innovative sensor-based IoT services involving distributed and mobile devices of various types by providing high-level programming constructs.  
In on-going work, we are working on evaluating the developed runtime environment experimentally to assess its performance and scalability. In addition, we will implement deep-learning models into the IoT runtime environment to enable the intelligence of sampling, processing, analyzing and communications of sensor data. Finally, we plan to apply an AI-guided system-level optimization to this new IoT system to dynamically allocate the resources and adjust parameters based on the application scenarios.  
This research work is supported in part by the National Science Foundation (NSF) under grant # 2011330. Any opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect NSF's views.  
Not applicable.  
The data and source code are available upon request.  
The authors declare no conflict of interest.
## 3 Review Results
### Applications in Brain Metastases  
Brain metastases develop from the haematogenous spread of cancer cells and unlike gliomas are typically not infiltrative [36, 37]. Therefore, the main use of DTI in radiotherapy treatment planning would be to avoid salient WM tracts by including them as OAR. This method has been evaluated in several studies of cohorts including brain metastasis along with other benign neurological conditions. The largest identified study contained 20 patients with brain metastasis (and three patients with other conditions), and they compared SRT plans with and without including the CST as an OAR, finding that this significantly reduced the maximum radiation dose to the CST but not the total dose to 95% of the tract [21]. However, this study did not use the tractography-guided treatment plan for administration of the dose to the patients. There are studies that include patients with metastasis that use radiotherapy-guided treatment plans for the patients' therapy, with all of the studies finding that the maximum dose was significantly reduced to the dissected tracts [20, 23, 30]. These studies found no complications in the patients during short follow-up periods (all < 1.5 years); however, the subject numbers were small (three to nine subjects each), and no studies included a control group. However, these studies show the feasibility on integrating tractography into SRT treatment of brain metastases.
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## 3 Structure and Properties of Kirchhoff Migration
### Various Properties of Kirchhoff Migration
In this case, if we select the first \(N_{0}\) singular vectors, where \(M<N_{0}<N\), then \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\) becomes_  
\[\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\approx\left|\sum_{m=1}^{M} \left\{J_{0}(k|\mathbf{x}_{\mathrm{ANO}}^{(m)}-\mathbf{x}|)^{2}-\left[\left( \frac{\mathbf{x}_{\mathrm{ANO}}^{(m)}-\mathbf{x}}{|\mathbf{x}_{\mathrm{ANO}}^ {(m)}-\mathbf{x}|}\cdot\mathbf{e}_{1}\right)^{2}+\left(\frac{\mathbf{x}_{ \mathrm{ANO}}^{(m)}-\mathbf{x}}{|\mathbf{x}_{\mathrm{ANO}}^{(m)}-\mathbf{x}|} \cdot\mathbf{e}_{2}\right)^{2}\right]J_{1}(k|\mathbf{x}_{\mathrm{ANO}}^{(m)}- \mathbf{x}|)^{2}\right\}\] \[+\left.\sum_{s=1}^{N_{0}-M}\left\{J_{0}(k|\mathbf{x}_{\mathrm{ RND}}^{(s)}-\mathbf{x}|)^{2}-\left[\left(\frac{\mathbf{x}_{\mathrm{RND}}^{(s)}- \mathbf{x}}{|\mathbf{x}_{\mathrm{RND}}^{(s)}-\mathbf{x}|}\cdot\mathbf{e}_{1} \right)^{2}+\left(\frac{\mathbf{x}_{\mathrm{RND}}^{(s)}-\mathbf{x}}{|\mathbf{ x}_{\mathrm{RND}}^{(s)}-\mathbf{x}|}\cdot\mathbf{e}_{2}\right)^{2}\right]J_{1}(k| \mathbf{x}_{\mathrm{RND}}^{(s)}-\mathbf{x}|)^{2}\right\}\right|.\]  
_This means that a map of \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\) will identify not just the anomalies \(\mathbf{A}_{m}\) but also some random scatterers \(\mathbf{R}_{s}\). Note that a map of \(\mathfrak{F}_{\mathrm{KIR}}(\mathbf{x})\) will identify almost all the anomalies \(\mathbf{A}_{m}\) and random scatterers \(\mathbf{R}_{s}\), but the anomaly locations will show peaks of magnitude \(\tau_{m}\), \(m=1,2,\cdots,M\), while the scatterer locations will only show relatively small peaks \(\tau_{s}\), \(s=1,2,\cdots,N_{0}-M\). Otherwise, if \(N_{0}<M\), location of some anomalies \(\mathbf{A}_{m}\), \(m=N_{0}+1,N_{0}+2,\cdots,M\), cannot be identified via subspace migration. Therefore, in this case, Kirchhoff migration will provide better results than subspace migration._  
**Case 3**. : _If there are no relationships between \(\alpha_{m}\) and \(\sigma_{s}\), \(\varepsilon_{\mathbf{A}}^{(m)}\) and \(\varepsilon_{\mathbf{R}}^{(s)}\), or \(\mu_{\mathbf{A}}^{(m)}\) and \(\mu_{\mathbf{R}}^{(s)}\) for any m and s, both Kirchhoff and subspace migrations will identify a mix of anomalies and random scatterers. Hence, the detection performance of both subspace and Kirchhoff migration will be poor, even if we apply a sufficiently large k._Based on the discussion, we can conclude that the imaging performance of the subspace migration is better than the one of the Kirchhoff migration if the background is homogeneous and discrimination of nonzero singular values of MSR matrix is clear, i.e., for the imaging of small inhomogeneities (see Figure 2, [14]) for the distribution of singular values corresponding to the target shape). In contrast, the imaging performance of the Kirchhoff migration will be better than the one of the subspace migration if the background is inhomogeneous and discrimination of nonzero singular values of MSR matrix is vague.
###### Abstract  
In the present study, the response of the crystalline scintillator LaBr\({}_{3}\)Ce when excited with X-rays at tube voltages from 50 kVp to 150 kVp was investigated, for possible use in hybrid medical-imaging systems. A single crystal (10 \(\times\) 10 \(\times\) 10 mm\({}^{3}\)) was irradiated by X-rays within the aforementioned tube-voltage range, and the absolute efficiency (AE), as well as the detective quantum efficiency for zero spatial-frequency (DQE(0)), were measured. The energy-absorption efficiency (EAE), the quantum-detection efficiency (QDE) and the spectral compatibility with various optical photodetectors were also calculated. The results were compared with the published data for the LaCl\({}_{3}\)Ce, Bi\({}_{4}\)Ge\({}_{3}\)O\({}_{12}\) (BGO), Lu\({}_{2}\)SiO\({}_{5}\)Ce (LSO), and CdWO\({}_{4}\) single crystals of equal dimensions. The AE values of the examined crystal were found to be higher than those of the compared crystals across the whole X-ray tube-voltage range. Regarding the EAE, LaBr\({}_{3}\)Ce demonstrated a comparatively better performance than the LaCl\({}_{3}\)Ce crystal. The emitted-light spectrum of LaBr\({}_{3}\)Ce was found to be compatible with various types of photocathodes and silicon photomultipliers. Moreover, the LaBr\({}_{3}\)Ce crystal exhibited excellent performance concerning its DQE(0). Considering these properties, the LaBr\({}_{3}\)Ce crystal could be considered as a radiation-detector option for hybrid medical-imaging modalities, such as PET/CT and SPECT/CT.  
Keywords:LaBr\({}_{3}\)Ce; scintillators; crystals; radiation detectors; medical-imaging applications +
Footnote †: journal:
## 4 Results and Discussion
### Sensitivity Analysis of the Meta-Ensembles
#### 4.2.2 Model Residuals  
Plot of the residuals of prediction does provide a means of validating ML models. By using residual plots, the observed errors of the best performing meta-heuristic models are assessed to ensure they are consistent with their corresponding stochastic errors (i.e., their randomness and unpredictability). Adopting this procedure is very necessary in this study given that none of the models used herein was able to produce 100% accuracy hence, these models could be regarded as inherently possessing some slight degree of errors. Another reason for using the residual plot is to assess the claims made of supposed overfitting in using the TVS validation methods and to evaluate the closeness of prediction when utilising both iterative cross validation methods--kFCV and MCCV.  
A good model must always show independence of the residuals by having random errors left when learning the dataset. Figure 10 compares the independence of the errors of the meta-heuristic models across the validation methods. It is very interesting to observe  
Figure 9: Ideal v actual intrinsic compressibility index.  
that the TVS method does show a less independence of the stochastic errors even though ML dataset training relying on it showed earlier that its performance in terms of the statistical measures was the highest. However, as indicated in Figure 10, the data points are less symmetric about the origin while there is a corresponding high density of points and some measures of trending around the zero line. Moreover, there appears to be a pattern of distribution that is linear along the horizontal axis. This is indeed a confirmation of the less accuracy of the models' prediction due to overfitting, when using the TVS method for training and testing of the dataset in this research. This also means that the models could not capture completely, the predictive information presented by the data hence, the reason there is a seepage of the data into the residuals.  
Figure 10: Residual plots.  
Figure 10 also shows that the residuals of ML prediction given by the models with cross validation performed using both kFCV and MCCV techniques are quite independent. As could be observed, there are no forms of trending of the data points around the origin rather, there is much scatter and disordered patterns. Nevertheless, Figure 10 shows that that ML prediction of intrinsic compressibility with the cross validation carried out by using MCCV, provides the most accuracy given the symmetric and random distribution of residuals (no observable trends) about the origin for both voting and stacking models. Again, this diagnostic test has further validated the training, testing and validation of the meta-heuristic models performed using the MCCV as the best technique at least within the confines of this study.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### Analysis of the Survey
#### 3.5.4 Additional Feedback  
The participants were also asked to provide their ratings on usage likelihood, perceived power augmentation, training requirements, and impact on autonomy. Usage likelihood, or the intention to reuse the exoskeleton, was rated 4.44 out of 10, suggesting that while the overall experience was positive, there is room to increase the willingness to reuse the device. Perceived power augmentation, or empowerment, received a rating of 4.82 out of 10, indicating that the exoskeleton moderately enhanced the user's sense of strength and capability. The training requirements were rated very low at 2.41 out of 10, reflecting that minimal training was needed to use the exoskeleton effectively. Impact on autonomy, measured as loss of autonomy, was also rated low at 2.45 out of 10, suggesting that the exoskeleton did not significantly hinder the user's sense of control and independence.  
Overall, the feedback suggests that while the exoskeleton had a positive impact on reducing physical exertion and supporting safer lifting practices, further refinement in its design could improve flexibility, comfort, and broader utility in various manual handling tasks. These insights are crucial for the ongoing development and optimization of exoskeletal devices in occupational settings.
## 2 Materials and Methods
### Wrf  
The Weather Research and Forecasting (WRF) model is a numerical weather forecasting (NWP) system developed by the US National Center for Atmospheric Research (NCAR) to meet both atmospheric research and operational forecasting requirements. WRF features two dynamic solvers, a data assimilation system, and a software architecture that allows for parallel computation and system scalability. This model can serve a wide range of meteorological applications over scales ranging from a few meters to thousands of kilometers. WRF allows researchers to create simulations that reflect real-world data (observation, analysis) or ideal atmospheric conditions. WRF provides two dynamic solvers for the calculation of atmospheric-governing equations, and variants of the model consist of WRF-ARW (Advanced Research WRF) and WRF-NMM (non-static intermediate-scale model).
## 3 Materials and Methods
### Method for the Generation of High Temporal Resolution Time Series  
The high temporal resolution Landsat-like time series were generated on the basis of the ESTARFM fusion algorithm [3] but with several modifications of the methodology to make the algorithm applicable to processing of large areas, characterized by heterogeneous landscapes and frequent cloud cover, in an automated way.  
The original ESTARFM algorithm produces synthetic Landsat images at the dates of MODIS observations. For this purpose, one pair of simultaneous Landsat and MODIS images before (\"left shoulder\") and one pair after the prediction date (\"right shoulder\") is used. ESTARFM first establishes for each pixel a relationship between MODIS and Landsat of both image pairs via linear regression. The regressions are fitted for the central pixel in a moving window by taking into account all pixels in the window that are spectrally similar (similar pixels) to the central pixel. Spectral similarity is defined by the following threshold:  
\[|R_{s}-R_{c}|\ \leq\ \sigma\left(Band\right)\ \times\ 2/m \tag{1}\]  
where \(R_{s}\) is the reflectance of the similar pixel candidate, \(R_{c}\) is the reflectance of the central pixel, \(\sigma\left(Band\right)\) is the standard deviation of the band within the entire image and \(m\) is the number of classes in the entire image, as estimated by the user.  
The slope of the regression is used as a conversion coefficient between Landsat and MODIS. This coefficient allows transfer of the reflectance changes between the MODIS scenes (pair dates-prediction date) to the Landsat scene at the prediction date. For more information on the original ESTARFM fusion algorithm, see [PERSON] _et al._[3].  
However, ESTARFM has several limitations, as previously outlined by [PERSON] _et al._[3], which hinder automated processing and its application to larger areas. These limitations are addressed and overcome in this study as described in the following.