esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of the Seismic Response of an Instrumented Building with Underground Stories
## 3 Numerical Model
### Numerical Models vs. Recorded Measurements Comparisons
#### 4.2.4 Comparison of Inter-Story Drifts and Shears  
Inter-story drifts and shears are plotted in Figure 14a,b respectively, for models 1, 2, 5, 6, 8, 9, 12, and 13. Results were similar for all the considered seismic events modeled as embedded in the foundation, regardless of the considerations of the horizontal ballast, with or without traction springs. The first-floor embedded model, however, has appreciable differences concerning the others. Although of its similar shape, it gives overestimated values for all instances. For events with a maximum acceleration lower than 0.05 m/s\({}^{2}\), inter-story drifts were greater in the underground levels with horizontal ballast models. Therefore, the soil-structure interaction should not be discarded even for small seismic events. For events with a maximum acceleration greater than 0.05 m/s\({}^{2}\), ignoring horizontal ballast yields conservative results.  
In terms of inter-story shear forces, results were similar for all the seismic events modeled as embedded in the foundation, regardless of horizontal ballast consideration, with or without traction springs. The first-floor embedded model, however, has appreciable  
Figure 14: (a) Inter-story drifts in the \(Y\)-axis (event 12). (b) Inter-story shear forces in the \(Y\)-axis (event 12).  
Figure 13: (a) Fourier transforms of on-site data and model 12, accelerometer 14, event 12. (b) Fourier transforms of on-site data and model 40, accelerometer 14, event 72.  
differences from the others, giving overestimated values, just like the behavior observed in terms of inter-story drifts. The foundation-embedded model without ballast is mostly conservative in terms of inter-story shear forces for the upper stories, except for events 37, 47, 52, 55, 57, and 62; if the shear forces of the underground stories are considered, the model is conservative only for events 71 and 72.
## 3 Results
### Soybean Yield Estimation with ML
#### 3.3.2 Using Spectral Reflectance Features as Predictor Variables  
SY was modeled using two input groups (1) using REF features, and (2) a combination of REF and VI features in the selected statistical and ML models (Table 4). Pertaining to using only REF as inputs, RF yielded the best performance at a 95:5 train-test data split (r = 0.96, rRMSE = 11.60%), followed by ANN, KNN, SLR, and PLSR. SVM yielded the least-best performance at a 95:5 data split (r = 0.83, rRMSE = 20.42%) for validation over the train dataset. PLSR was the best performer (r = 0.83, rRMSE = 20.30%) at a 50:50 train-test split ratio, followed by SVM, ANN, SLR, RF, while KNN was the least-best performer (r = 0.70, rRMSE = 26.70%) when validated over the test dataset. When the validation was conducted over the entire dataset, RF yielded the best performance (r = 0.93, rRMSE = 14.60%) at a 95:5 split followed by ANN, SLR, KNN, and PLSR, and SVM (r = 0.80, rRMSE = 21.95%) yielded the least-best performance at a 95:5 split ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{3}{c}{**Dataset: Train**} & \multicolumn{3}{c}{**Dataset: Test**} & \multicolumn{3}{c}{**Dataset: Entire**} \\ \hline \multirow{2}{*}{**Train:Test Ratio**} & **Input** & **Best** & **rRMSE** & **Best** & **rRMSE** & **Best** & **Best** & **rRMSE** \\  & **Group** & **Model** & **r** & **(\%)** & **Model** & **r** & **(\%)** & **Model** & **r** & **rRMSE** \\ \hline \multirow{2}{*}{50:50} & REFs & RF & 0.95 & 13.10 & PLSR & 0.83 & 20.30 & \multirow{2}{*}{RF} & 0.84 & 20.60 \\  & REFs+VIs & & 0.96 & 11.10 & SLR & 0.83 & 19.98 & & 0.87 & 18.00 \\ \hline \multirow{2}{*}{55:45} & REFs & 0.96 & 12.00 & PLSR & 0.81 & 20.80 & \multirow{2}{*}{RF} & 0.84 & 19.90 \\  & REFs+VIs & & 0.97 & 10.30 & SLR & 0.81 & 21.30 & & 0.88 & 17.40 \\ \hline \end{tabular}
\end{table}
Table 4: Machine learning model performances at various train–test data split ratios using reflectance and a combination of reflectance and vegetation indices as inputs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \multicolumn{2}{c}{**Vegetation Index/Reflectance Feature**} & \multirow{2}{*}{**Yield (r)**} & **Juvenile** & **Female** & \multirow{2}{*}{**FNES+JNEMS (r)**} \\  & & **Nematode (r)** & & & \\ \hline Modified Simple Ratio (MSR) & 0.22 & –0.40 & –0.30 & –0.43 ** \\ Renormalized Difference Vegetation Index (RDVI) & 0.22 & –0.38 & –0.30 & –0.41 ** \\ Transformed Difference Vegetation Index (TDVI) & 0.20 & –0.35 & –0.28 & –0.38 \\ Visible Atmospherically Resistant Index (VARI) & –0.57 & 0.05 & 0.24 & 0.14 \\ Wide Dynamic Range Vegetation Index (WDRVI) & 0.22 & –0.40 & –0.30 & –0.43 ** \\ \hline \end{tabular}  
* The reflectance in red, green, blue, red edge, and NIR images is denoted by R, G, B, RE, and NIR, respectively. FNEMS and JNEMS represent female and juvenile nematodes, respectively. At \(r\) \(p\) < 0.001, correlation coefficients are considered significant. *Highest correlations for yield. **Highest correlations with nematode populations.  
\end{table}
Table 3: _Cont._
## 4 Results
### Model Validity Test Results
A rigorous test for identifying discriminant validity is performed by comparing a construct's square root of the AVE with its correlation with another construct. Discriminant validity is present when the correlation is less than the square root of the AVE [64]. As shown in Table 7, all correlation pairs were less than the associated square root of the AVE; therefore, the hypothesized model showed adequate discriminant validity.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 4 Multi-WiIR
### Data Processing
#### 4.3.3 Data Interpolation  
During the data acquisition process, data loss or time delay frequently arises, necessitating data interpolation to obtain CSI data uniformly distributed in time. The linear interpolation algorithm analyzed CSI data within a finite interval of values and calculates adjacent K values to estimate the approximation of discontinuous points for interpolation, thereby achieving temporal dimension uniformity.
# PET/CT Radiomics in Lung Cancer: An Overview
## 2 Methodology
### Feature Extraction
#### 2.4.1 Shape Features  
Shape features aim at characterizing the geometry of the region of interest. They can be computed either from each slice separately (2D) or from the whole ROI (3D). In most cases, their objective is to differentiate between round, smooth, and regular lesions from spiculated, elongated, and irregular ones. Apart from volume, common shape features are compactness, elongation, rectangular fit, spherical disproportion, sphericity, surface area, and surface-to-volume ratio [24; 28; 29]. Clearly, shape features are more easily assessed at CT than PET due to the higher image resolution of the former.
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
#### 4.3.1 CEOS-LPV Best Practices  
In order to address the issue of disparity in the currently used Cal/Val practices, the CEOS-LPV sub-group is defining standardised approaches for in situ measurements,sampling and upscaling procedures. The definition of CEOS-LPV best practices started in 2014 with the LAI protocol [149], and it now includes endorsed protocols for the following variables: Albedo, Land Cover, Above Ground Biomass, Burned Areas, Soil Moisture and Land Surface Temperature [150; 151]. The definition of a Cal/Val protocol is a laborious and collective work, involving contributions from a large team of internationally recognised scientific experts. When a protocol is issued and endorsed at CEOS-LPV level, it has to be promoted within the scientific community and the consensus should be expanded outside the CEOS-LPV contributors. This is a long process, and the Space Agencies should contribute to broadening consensus within the science community. A side effect of this lengthy process is that a protocol may become obsolete, notably in case of latest technological or theoretical advances in the relevant domain. This is the case, for instance, of the LAI protocol [149]. Hence, the protocols should be considered living documents that need to be periodically reviewed and updated in case of major advances in the field. To be noted however, that not all required protocols are ready, for instance, there is not yet an agreed protocol for SR, fAPAR and phenology variables [3]. This gap of best practices is currently being tackled with high priority, in particular for SR and fAPAR within CEOS-LPV sub-group.
## 3 Modeling and Parameter Estimation in CSADT
### Acceleration Degradation Process Modeling with Measurement Errors  
In reality, it is easy that certain measurement errors might be brought in due to the stochastic influence of measurement tools and the environment. In this case, a generalized model of Wiener process degradation considering measurement errors can be shown in Equation (17).  
\[\begin{split} Y(t)&=X(t)+\sigma_{\rm e}\varepsilon\\ &=\lambda\Lambda(t)+\sigma_{\rm B}B(\Lambda(t))+\sigma_{\rm e} \varepsilon\end{split} \tag{17}\]  
Among them, \(Y(t)\), \(X(t)\) express the observed degradation value and the actual degradation value of the products at time \(t\), respectively. \(\sigma_{\rm e}\varepsilon\) represents the measurement error which is implemented to be iid random variables with \(\sigma_{\rm e}\varepsilon\sim N\big{(}0,\sigma_{\rm e}^{2}\big{)}\) and independent of each other in most related studies.  
Assuming that the stress in CSADT is the temperature stress, the acceleration model is the Arrhenius model. Considering random effects of drift parameter, and its equation is used to reflect the relation between stress \(S_{k}\) and drift parameter \(\lambda\).  
\[\lambda_{k}=\eta\varsigma_{k}=\eta\exp(-b/S_{k}),\eta\sim N\Big{(}\mu_{\eta}, \sigma_{\eta}^{2}\Big{)} \tag{18}\]  
Then, we have  
\[\lambda_{k}\sim N\Big{(}\mu_{\eta}\exp(-b/S_{k}),\sigma_{\eta}^{2}\exp(-2b/S_{ k})\Big{)} \tag{19}\]
## 2 Modelling Description
### Approximation of Stator Windings  
To streamline the thermal analysis and to diminish computational demands, the simulations employ an approximated representation of stator windings using an anisotropic lumped material model. This approach simulates the combined effects of the composite coils, restricting airflow solely to the windings' exterior surfaces, thereby eliminating airflow between individual conductors. The lumped material model represents a composition  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rated** & **Number of** & **Slot Fill** & **Stator** & **Stator** & **Stator OD** & **Rotor ID** & **Stator** & **Winding** \\
**Power** & **Stator Tech** & **Factor** & **Length** & & & **Slot Area** & **Length** \\ \hline
250 kW & 72 & 43.4\% & 50 mm & 404.4 mm & 300 mm & 193 mm\({}^{2}\) & 63.4 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical details of the selected PM machine.  
Figure 1: (**a**) Demonstration of the active part of the PM machine. (**b**) An extracted unit used in the simulation, including the stator core back, end winding, slot liner, and rotor. Red solid lines in (**a**) highlight the unit extracted from the entirety of the active area for focused simulation analysis.  
comprising copper, enamel, and an impregnating agent (varnish, in this instance). It acknowledges that in a realistic scenario, the thermal resistance is affected by the presence of air pockets, a consequence of manufacturing variations. Additionally, the model incorporates a slot liner to reflect the application of Nomex paper in the model, measuring 0.25 mm in thickness.  
In representing the copper loss within the windings, a loss density value of 106 W/m\({}^{3}\) is used, correlating to an equivalent current density of about 12.0 ampere/mm\({}^{2}\). The winding exhibits anisotropic thermal characteristics, defined along axes parallel and perpendicular to the conductors. The equivalent anisotropic thermal conductivity values \(\left(k_{x},k_{y},k_{z}\right)\) are determined in Cartesian coordinates, aligning with the principal directions \(\left(\overset{\rightharpoonup}{e_{x}},\overset{\rightharpoonup}{e_{y}}, \overset{\rightharpoonup}{e_{z}}\right)\). The conductivity matrix is formulated as follows:  
\[k_{ij}=k_{x}e_{xi}e_{xj}+k_{y}e_{yi}e_{yi}+k_{z}e_{zi}e_{zj} \tag{1}\]  
This method, however, does not adequately account for the unique geometry of the end windings, whose curved structure introduces additional complexities in the simulation. To address these challenges, the model adopts a local cylindrical coordinate system, aligning the system's axis with the central axis of the end winding on either side. Within this framework, the equivalent anisotropic thermal conductivity is recalibrated to reflect radial, tangential, and axial orientations, with the adaptations detailed in Figure 2.
## 2 Literature Review
### LoRa  
LoRa stands for Long Range Radio, in 2012 the Semtech company developed this new wireless technology, specifically designed for long-range communication and low-power applications, and thus very suitable for IoT networks [35]. LoRa is suitable for operating limited energy devices (battery-based) and needs to transmit very low data at a time [36]. A single LoRa gateway can cover a distance of up to 5 kms in urban areas and up to 15 km in a rural area depending upon the environment, obstacles, and line of sight [37].  
The long-range and low-power characteristics of LoRa make it suitable for smart sensing technology in civil infrastructures (such as health monitoring, smart metering, environment monitoring, etc.), as well as in industrial and construction applications.  
LoRa Protocol Stack has two layers, the physical layer, and the MAC layer. The physical layer allows low-power, low-data, and long-distance communication. Depending upon the country of operation, it operates in different frequency bands 470-510 MHz in China, 865-867 MHz in India, 867-869 MHz in Europe, 902-928 MHz in North America, and 920-925 MHz in Korea and Japan. Each transmission of LoRa can have a payload in the range of 2-255 octets and data speed can go up to 50 Kbps if channel aggregation is used. Semtech patented the modulation technique used in LoRa. The MAC control mechanism is  
Figure 2: Different applications of Zigbee Communication Technology.  
provided by Long Range Radio Wide Area Network (LoRaWAN), many end-devices can communicate with the gateway simultaneously using LoRa modulation through MAC. The LoRa Alliance has developed the open standard, LoRaWAN [38].
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 7 Lattice Properties  
The orders \(\sqsubseteq^{\mathrm{avg}},\sqsubseteq^{\mathrm{max}}\) and \(\sqsubseteq^{\mathrm{pv}}\) are all reflexive and transitive (i.e., preorders), but not anti-symmetric (i.e., not partial orders). This is due to the fact that there exist channels that have \"syntactic\" differences but the same semantics; e.g., two channels having their columns swapped. However, if we are only interested in a specific type of leakage, then all channels such that \(A\sqsubseteq B\sqsubseteq A\) (where \(\sqsubseteq\) is one of \(\sqsubseteq^{\mathrm{avg}},\sqsubseteq^{\mathrm{max}},\sqsubseteq^{\mathrm{pv}}\)) have identical leakage, so we can view them as the \"same channel\" (either by working on the equivalence classes of \(\sqsubseteq\cup\sqsubseteq\) or by writing all channels in some canonical form).  
Seeing now \(\sqsubseteq\) as a partial order, the natural question is whether it forms a lattice that is whether suprema and infima exist. If it exists, the supremum \(A\lor B\) has an interesting property: it is the \"least safe\" channel that is safer than both \(A\) and \(B\) (any channel \(C\) such that \(A\sqsubseteq C\) and \(B\sqsubseteq C\) would  
Figure 3: Refinement of mechanisms under \(\sqsubseteq^{\mathrm{avg}}\) for \(5\times 5\) channels. The \(x\)-axis represents the \(\varepsilon\) on the LHS of the relation, and the \(y\)-axis represents the one on the RHS. The top graph represents refinement of the truncated geometric mechanism (that is, \(TG\sqsubseteq^{\mathrm{avg}}\)), the middle graph is refinement of randomized response (\(R\sqsubseteq^{\mathrm{avg}}\)), and the bottom graph is refinement of the exponential mechanism (\(E\sqsubseteq^{\mathrm{avg}}\)).  
necessarily satisfy \(A\lor B\sqsubseteq C\)). If we wanted a channel that is safer than both \(A\) and \(B\), \(A\lor B\) would be a natural choice.  
In this section, we briefly discuss this problem and show that--in contrast to \(\sqsubseteq^{\text{avg}}\)--both \(\sqsubseteq^{\text{max}}\) and \(\sqsubseteq^{\text{prv}}\) do have suprema and infima (i.e., they form a lattice).
# Analysis and Applications of GlobeLand30: A Review
## 3 User Analysis
### Application Fields
Given that reliable information on cities' changes at a global scale will become increasingly important with climbing urban populations [50], Peking University investigated the spatial-temporal variation of habitat quality patterns of Beijing-Tianjin-Hebei area based on the analysis of GlobeLand30 from 2000 to 2010 [51]. Many research institutions have used GlobeLand30 for biodiversity and ecosystem, sustainable urban development, water resource management, and climate change. For instance, the National Geomatics Center of China analyzed the distribution of built-up areas, change rate, and increased proportion on a global scale by GlobeLand30 [52]. The National Climate Center of China used GlobeLand30 as a basic input parameter in the Beijing Climate Center Climate System Model (BCC_CSM) to assess the effects of land cover dataset on land surface and climate simulations [53]. Government departments mainly used GlobeLand30 for analysis and research in disaster resilience, energy and mineral resources management, and sustainable urban development. Most applications in Non-Government Organizations (NGOs) and the UN are food security and sustainable agriculture, biodiversity and ecosystem sustainability, and public health surveillance. Timely and accurate geographic information on the global cropland extent is critical for applications in the fields of food security and agricultural monitoring. Thus, [PERSON] et al. [54] advocated for a shared definition of cropland, as well as validation datasets that are relevant for the agricultural class by studying different land cover data.
###### Abstract  
This work focuses on the use of a char produced during the pyrolysis of a mixture of non-recyclable plastics as a precursor for the preparation of porous activated carbon with high developed adsorption uptake of lead in water. Physical and chemical activation was used to enhance the porosity, surface area, and surface chemistry of char. The final activated carbon materials were deeply characterized through N\({}_{2}\) adsorption isotherms, scanning electron microscopy, Fourier transformed infrared spectroscopy, analysis of the metal content by inductively coupled plasma mass spectroscopy, and pH of point zero charge. The native char displayed a Pb adsorption uptake of 348 mg Pb-g\({}^{-1}\) and considerably high leaching of carbon, mainly organic, ca. 12%. After stabilization with HCl washing and activation with basic character activators, i.e., CO\({}_{2}\), NaOH, and KOH, more stable adsorbents were obtained, with no organic leaching and a porous developed structure, the order of activation effectiveness being KOH (487 m\({}^{2}\)-g\({}^{-1}\)) > NaOH (247 m\({}^{2}\)-g\({}^{-1}\)) > CO\({}_{2}\) (68 m\({}^{2}\)-g\({}^{-1}\)). The activation with KOH resulted in the most effective removal of Pb in water with a saturation adsorption uptake of 747 mg Pb-g\({}^{-1}\).  
pyrolytic char; chemical activation; adsorption; lead; plastic waste Article
## 6 Biological Properties
### Anti-Tumour and Anti-Viral Activity  
Anti-tumour activity is one of the fields that has contributed to more research on the effects that the bioactive compounds of _A. altissima_ exert. Several studies with the dried bark, show that its active compounds exert antitumor effects in several cell lines, for example in A549, MDA-MB-231, LAPC4, A375, B16, and SGC-7901 among others. These in vitro studies lead us to state that ailanthone specifically may be a good inhibitor for cancers such as melanoma, acute myeloid leukaemia, lung, liver, breast, bladder, osteosarcoma, and prostate cancer [22,64,65]. In the work of [PERSON] et al. [65] the different mechanisms of the action of ailanthone that justify its inhibitory effects were described and explained. The main basis of these effects is the mechanism of apoptosis. Apoptosis can be triggered by regulation of Bcl-2 family proteins, transcription factors such as \(\beta\)-catenin, tumour suppressor genes, and by the PI3K/AKT/mTOR and JAK/STAT3 pathways. Arylanthone induces apoptosis by the downregulation of Bcl-2 and the downregulation of the Bax proteins. It also induces this cell death through the downregulation of p53 (tumour suppressor protein) and miR-195, which consequently leads to inhibition of the JAK/STAT3 signalling pathway. On the other hand, the signalling pathway which is responsible for cell proliferation, differentiation, and metabolism that can lead to anti-apoptosis is PI3K/AKT/mTOR. In this pathway ailanthone has a suppressive effect, inducing apoptosis through the phosphorylation of PI3K and AKT. The review work of these authors is very interesting in that they warn of the problem that there are few studies evaluating the cytotoxicity of ailanthone in normal cells. Importantly, a preclinical study by [PERSON] et al. [66] assessed the safety of ailanthone in Kumming rats and classified the mean lethal dose of ailanthone (27.3 mg/kg) as level 2 (severe) from the Globally Harmonised System of Classification and Labelling of Chemicals.  
Furthermore, they warn that research into this active compound from _A. altissima_ is still very early, as there are no studies on the bioavailability and side effects of ailanthone. Researchers are advised to focus on comparing the efficacy of ailanthone with other existing chemotherapy drugs and also to conduct in vivo and clinical trials [65].  
Regarding the antiviral activity, some studies demonstrated that the main compound responsible for this effect is the main compound of _A. altissima_: ailanthone. In the review by [PERSON] et al. [22] the authors summarize all the existing pharmacology considering the dried bark of this species. They state that the active compound shows moderate inhibitory effects against the tobacco mosaic virus (TMV). In a study conducted by [PERSON] et al. [67] these effects reached IC\({}_{50}\) values of 0.30 mmol/L. However, they mention that the mechanism of action leading to this activity is not yet well elucidated.