esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 3 Results and Discussion
### Computational Studies
#### 3.3.2 Prediction of Biological Activity
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **Expected Activity** & **Probability** \\ \hline \multirow{6}{*}{6} & Hepatoprotectant & 0.996 \\  & Antiinflammatory & 0.741 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Total & 0.861 \\ \cline{2-3}  & & melanoma & 0.741 \\ \hline \multirow{6}{*}{7} & Hepatoprotectant & 0.995 \\  & Antiinflammatory & 0.743 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Total & 0.893 \\ \cline{2-3}  & & melanoma & 0.764 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Colorectal cancer & 0.753 \\ \cline{2-3}  & & Colon cancer & 0.747 \\ \cline{2-3}  & & Lung cancer & 0.715 \\ \hline \multirow{6}{*}{8} & Hepatoprotectant & 0.992 \\  & Antiinflammatory & 0.811 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Total & 0.892 \\ \cline{2-3}  & & Colorectal cancer & 0.767 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Colon cancer & 0.793 \\ \cline{2-3}  & & Melanoma & 0.751 \\ \cline{2-3}  & \multirow{2}{*}{Cervical cancer} & 0.723 \\ \hline \multirow{6}{*}{9} & Hepatoprotectant & 0.995 \\  & Antiinflammatory & 0.757 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Total & 0.892 \\ \cline{2-3}  & & Melanoma & 0.769 \\ \cline{2-3}  & \multirow{2}{*}{C Colorectal cancer} & 0.743 \\ \cline{2-3}  & & Colon cancer & 0.737 \\ \cline{2-3}  & & Lung cancer & 0.722 \\ \hline \multirow{6}{*}{10} & Hepatoprotectant & 0.996 \\  & Antiinflammatory & 0.707 \\ \cline{2-3}  & Hypolipemic & 0.786 \\ \cline{2-3}  & \multirow{2}{*}{Antineoplastic} & Total & 0.881 \\ \cline{2-3}  & Melanoma & 0.776 \\ \cline{2-3}  & Colorectal cancer & 0.719 \\ \cline{2-3}  & Colon cancer & 0.712 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Predicted directions of biological activity of tested compounds calculated using PASS program version 2.0. 0.893). Additionally, analysis using the PASS program indicates potential activity against melanoma, colorectal cancer, colon cancer, lung cancer, and cervical cancer (Table 4). The presented results of the in silico study can be treated as a starting point for the initial selection of cancer cells sensitive to the tested compounds. However, this is not always reflected in activity tests in the in vitro model.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Car-Following Behavior
#### 4.2.1 Hdv  
The IDM (Intelligent Driver Model) is a car-following model widely used to simulate human driving behavior [23]. The IDM model is well-suited to human driving behavior because it incorporates natural driving tendencies and adjusts the driving style in different traffic situations. It is especially effective at capturing how human drivers follow each other in a dynamic and adaptive way.  
Despite its effectiveness, the traditional IDM does not explicitly account for the driver's PRT. Incorporating perception-reaction time into the IDM allows for a more nuanced representation of driver behavior, accounting for the inherent delays in human response. This adjustment is essential for enhancing the model's applicability in safety analyses, traffic management strategies, and the development of advanced driver-assistance systems (ADAS).  
For HDVs, \(\tau_{h}\) is used to represent their PRT, denoted as PRT_HDV. To integrate PRT within the IDM framework, the acceleration of a following vehicle is redefined to consider the driver's delayed response and physical braking constraints. The comprehensive formulation is presented below in Equation (14).  
\[a_{n}(t)=\max\left[a_{\text{m}}\left(1-\left(\frac{v(t)}{v_{0}}\right)^{\mu}- \left(\frac{s^{*}(t-\tau_{h})}{s(t)}\right)^{2}\right),b_{m}\right] \tag{14}\]  
where the expected distance between two vehicles \(s^{*}(t-\tau_{h})\) is recalculated based on the vehicle's state at time \(t-\tau_{h}\), as shown in Equation (15):  
\[s^{*}(t-\tau_{h})=s_{0}+v(t-\tau_{h})T_{h}+\frac{v(t-\tau)\Delta v(t-\tau_{h} )}{2\sqrt{a_{\text{m}}b}} \tag{15}\]  
where  
\(a(t)\): Acceleration of the vehicle at time \(t\)  
\(a_{\text{m}}\) : Maximum acceleration  
\(v(t)\): Current speed of the vehicle  
\(v_{0}\): Desired (free-flow) speed  
\(\mu\): Acceleration exponent, typically set to 4  
\(s(t)\): Current headway (distance to the leading vehicle)  
\(s_{0}\): Minimum desired headway  
\(T_{h}\): Safe time headway  
\(b\): Desired deceleration The calibration of IDM parameters, as shown in Table 4, is based on empirical traffic flow data from German highways [23]. It was achieved through theoretical modeling, optimization fitting methods, and simulation validation to ensure an accurate representation of traffic flow dynamics.
## 3 Results and Discussion  
### Morphological and Structural Characterization of Wolframite  
Wolframite is a mineral of iron, manganese, and tungstate, which has a heavy grayish-black to brownish-black color. When ground into 400 mesh size grain, the color changed from black to brown. A typical SEM micrograph for a natural wolframite powder sample is shown in Figure 2a, which presents tabular and bladed crystals. Moreover, fracture surfaces and cleavage planes are prevalent on the surface of the natural wolframite, which are probably produced by the mechanical grinding. It is commonly known that the catalytic activity of catalysts is closely related to active sites. Therefore, much more active sites were provided by these fracture surfaces and cleavage planes, conferring greater potential to the processed wolframite as a low-cost catalyst than for wolframite synthetized with perfect crystal faces by complex methods. The EDX mapping demonstrated that Mn, Fe, W, and O were especially enriched in the wolframite as shown in Figure 2b-d. In addition, The EDS data reveal that the dominant elements are W, Mn, Fe, and O and their contents were 61.71-66.07, 12.94-13.36, 5.23-6.11, and 14.88-19.7 wt% respectively. The results were consistent with the chemical formula of wolframite, (Fe,Mn)WO\({}_{4}\).  
Figure 1: Schematic representation of the microbial fuel cell (MFC) with a natural wolframite-coated graphite cathode.  
The XRD pattern of the natural wolframite sample is shown in Figure 3a. Although a mixture of phases was observed, wolframite was predominant. All diffraction peaks were perfectly indexed as pure structures of MnWO\({}_{4}\) (JCPDS card no. 74-1491) and FeWO\({}_{4}\) (JCPDS card no. 71-2391). These sharp diffraction peaks suggest great crystallinity of wolframite. Some peaks assigned to trace impurities could be detected and corresponded to quartz (JCPDS card no. 33-1161).  
Figure 3b shows the Raman spectrum of natural wolframite. The bands at 129, 207, 262, 327, 398, 540, 699, and 882 cm\({}^{-1}\) correspond to the characteristic bands of wolframite, in agreement with previous studies [31; 32; 33]. In detail, the strongest sharp band at 882 cm\({}^{-1}\) was assigned to the \(\upnu_{1}\) symmetric A\({}_{\text{g}}\) mode of terminal WO\({}_{2}\). The band located at 699 cm\({}^{-1}\) was attributed to antisymmetric bridging mode linking with the tungstate chain. The bands at 262 cm\({}^{-1}\) and 207 cm\({}^{-1}\) were assigned to the \(\upnu_{4}\) bending modes in the tungstate anion, while the band at 327 cm\({}^{-1}\) is assigned to \(\upnu_{2}\) bending mode. The band at 125 cm\({}^{-1}\) was induced by lattice vibration [31].
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model  
In this section, the proposed TSCH energy model is introduced. First, all types of time slots are discussed, followed by a more detailed examination of the states in these time slots. Afterwards, the time slot energy model is presented. Finally, we explain how the slot model could be adapted for use with different hardware. The implementation of the proposed model can be found in [20].
## 2 Spectroscopy and Optical Properties  
The measurements to characterize the laser glass were performed in the R&D Center for High Power Laser Components affiliated with the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences. The absorption spectrum of NDSG with a doping concentration of 0.3 mol% is shown in Figure 1a. There is a peak at 802 nm, matching the wavelength of the LD pump, which aids pump absorption. The fluorescence spectrum of NDSG with a doping concentration of 0.3 mol% is displayed in Figure 1b. The peak wavelength is in the range of 1059-1062 nm, while the linewidth is 51.7 nm, and the fluorescence lifetime is 376 us. A large linewidth leads to advantages in ultrafast laser applications.  
For comparison, some of the physical parameters of three gain materials are listed in Table 1. LHG-8 (Hoya Corp, Tokyo, Japan) is a type of phosphate neodymium glass. We see that NDSG has excellent thermomechanical properties compared to the other two materials. In addition to the larger fluorescence full width at half maximum (FWHM), the thermal conductivity of NDSG is 2.2 times that of LHG-8 and the thermal shock parameter is 1.8 times that of Nd: YAG, and it has a lower nonlinear refractive index. These advantages indicate its great potential in achieving high-energy and high repetition rate lasers [4]. With the present preparation technology, the doping concentration of  
Figure 1: (**a**) Absorption spectrum and (**b**) fluorescence spectrum of NDSG with a doping concentration of 0.3 mol%.  
NDSG is low, which makes the emission cross section only 1.4 pm\({}^{2}\) at 1 \(\upmu\)m, and the energy extraction efficiency is poor. We aim to improve this in the future, and look forward to the application of NDSG in laser amplification.
# The Fate of _Yersinia pseudotuberculosis_ in Raw Fermented Meat Products
###### Abstract  
_Yersinia pseudotuberculosis_ is a foodborne pathogen with an animal reservoir, thus being able to spread via contaminated meat. The survival of _Y. pseudotuberculosis_ during the ripening and storage of artificially contaminated spreadable fermented sausage (Teewurst) and dry fermented sausage was studied, with initial counts of 8 log, 6 log, and 3 log CFU/g. While the pathogen was completely inhibited in all batches of dry fermented sausage after 4 d of ripening and was thus absent in the final product, it survived much better in the spreadable sausage, characterized by a higher pH and fat content. The counts in the Teewurst final product (after 2 d of ripening) dropped from 8 log, 6 log, and 3 log CFU/g to approx. 6.3, 2.4, and 1.4 log CFU/g, respectively. For the initial concentrations 6 log and 3 log CFU/g, at least 1 out of six samples was still positive after 20 d of cold storage. On the other hand, in the batches with the highest initial counts (8 log CFU/g), all the samples were positive at the end of the experiment (37 d). The rapid decline in pH caused by the starter culture was an effective barrier for _Y. pseudotuberculosis_ in dry fermented sausages, but the pathogen was able to persist in Teewurst.  
Teewurst; fermentation; ripening; water activity; food safety Article
## 2 Materials and Methods
### Estimation of Model Parameters
The required information is the set of parameters which are maintained constant (see Table 1), the data \(D^{R}(t_{j})\), \(D^{H}(t_{j})\), \(D^{U}(t_{j})\), \(D^{F}(t_{j})\), \(R_{im}(t_{j})\), from \(j=1\) to \(j=j_{\text{end}}\), population \(N\) and size of time-window \(W\). The results are the transmissibility \(\beta(t_{j})\) from \(j=0\) to \(j=j_{\text{end}}-W\), and the proportions \(\lambda_{*}(t_{j})\) from \(j=j_{h}\) to \(j=j_{\text{end}}-W\). ```
Input:\(t_{0}\), \(j_{h}\), \(j_{\text{end}}\),\(N\), \(\alpha\), \(\gamma\), \(\delta\), \(\varphi\), \(W\), \(\{D^{R}(t_{j})\), \(D^{H}(t_{j})\), \(D^{U}(t_{j})\), \(R_{in}(t_{j})|1\leq j\leq j_{\text{end}}\}\) Result:\(\{\beta(t_{j})|0\leq j\leq j_{\text{end}}-W\}\), \(\{\lambda_{*}(t_{j})|j_{h}\leq j\leq j_{\text{end}}-W\}\)
1 at \(t_{0}\), estimate the values \(E(t_{0})\), \(I(t_{0})\), \(\beta(t_{0})\) using (10) ; // See Section 2.3.1
2 for\(j=1\); \(j\leq j_{\text{end}}-W\); \(j=j+1\)do
3 estimate \(\beta(t_{j})\) using (16) ; // See Section 2.3.2
4 if\(j\geq j_{h}\)then
5 estimate \(\lambda_{*}(t_{j})\) using (19) ; // See Section 2.3.3
6
7 end if
8
9 end for
```  
**Algorithm 1**Parameter estimation
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 3 Results
### Comparative Analysis: Precipitation vs Groundwater Storage (2003-2023)
#### 3.2.4 Climate Anomalies
* **The 2017 Coastal El Nino (CEN)**  
The 2017 event was one of the most intense ever recorded in Peru, triggered by an abrupt warming of the Eastern Tropical Pacific Ocean. It caused extreme rainfall along the northern and central coasts, with historical accumulations. The impacts were devastating: widespread flooding, landslides, infrastructure collapse, and more than one million people affected. It exposed the country's high vulnerability to extreme climatic events and underscored the need to strengthen risk management systems [17].  
* **Groundwater Recharge Potential During the 2017 Events**  
The CEN events of 2017 created favorable conditions for aquifer recharge, though with regional variability. This confirms that extreme events like El Nino can provide significant but temporary recharge.  
In the **northern zone**, recharge was likely high due to sustained heavy rainfall, alluvial soils, and the presence of seasonal watercourses. However, urbanization in cities like Piura limited infiltration.  
In the **central zone**, the recharge potential was moderate, with intense rains in 2023 associated with Cyclone Yaku. Rural areas and inter-Andean valleys may have benefited, but impermeable urban surfaces and steep slopes reduced effective infiltration.  
In the **southern zone**, recharge was low due to lower rainfall intensity and duration, compacted soils, and high surface runoff.  
This analysis highlights the importance of considering physical and land-use conditions when evaluating the actual contribution of extreme weather events to groundwater resources. The evolution of groundwater storage is not solely dependent on rainfall amount. Evidence suggests that overexploitation from human activities (intensive agriculture, urban growth) is a key factor, especially in regions with stable precipitation. Additionally, climate change may be disrupting natural recharge cycles.
# Quantifying Auditory Presence Using Electroencephalography
## 5 Conclusions  
This paper presented an analysis of auditory presence using eight loudspeakers to reproduce urban soundscapes as programs. Two experiments were conducted where  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**EEG Index** & **Cohen’s Kappa** & \(p\) **for Kappa** \\ \hline Theta & \(0.125\) & \(0.302\) \\ Alpha & \(0.059\) & \(0.486\) \\ Beta & \(0.03\) & \(0.62\) \\ T/A & \(-0.123\) & \(0.362\) \\ T/AB & \(-0.111\) & \(0.551\) \\ T/B (Experiment 1) & \(0.636\) & \(0.006\) * \\ A/B & \(0.256\) & \(0.126\) \\ T/B (Experiment 2) & \(0.56\) & \(0.005\) * \\ \hline \hline \end{tabular}  
* Significance level of \(0.05\).  
\end{table}
Table 6: Kappa between presence score and EEG indices.  
presence was measured both subjectively with questionnaires and objectively with EEG. For subjective evaluation, the presence score showed significant differences between loudspeaker setups. For objective measurement, beta power and T/B extracted from EEG signals showed significant differences in the meantime. Correlation between presence score and EEG indices was analyzed, and T/B demonstrated a significant correlation with presence score.  
The results of the present work suggest that the results of EEG measurement and subjective measurement both showed significant difference between different loudspeaker setups, and they were correlated to a great extent. This indicates the possibility of EEG to be a potential tool for the objective measurement of presence. Among all indices extracted from EEG signals, T/B has the largest potential to be the objective marker for the measurement of presence. Future work could focus on building a model for predicting presence by measuring the T/B only once, which may further make it easier to measure presence.  
Conceptualization, S.Z.; methodology, S.Z. and X.F.; software, S.Z.; formal analysis, S.Z.; investigation, S.Z. and X.F.; resources, X.F. and Y.S.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, X.F. and Y.S.; visualization, S.Z.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China 2018 YFB1403800.  
The study was conducted according to the guidelines of the Declaration of Helsinki.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors would like to thank [PERSON] for the suggestions for this study, and all listeners who participated in the listening test.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
EEG electroencephalography  
T/A  
the power ratio of theta/alpha  
T/B  
the power ratio of theta/beta  
T/AB  
the power ratio of theta/alpha+beta  
A/B  
the power ratio of alpha/beta
## 7 Conclusions and Future Studies  
This paper described a winter road surveillance system in mountain areas, and it is necessary to establish a longer radio transmission range and to reduce radio noise from the obstacles. Therefore, this paper proposed AAA controls through image recognition to realize the longer radio distance with quick responses, and beam-forming adjustments with the Kalman filter algorithm to reduce the radio noise. Then, implementations of the prototype AAA system to realize longer wireless connections with a high-frequency radio system such as IEEE802.11b were presented in this paper. Then, the quickness of the prototype AAA system was evaluated through experiments with the prototype system, and the experiment results were considered for network simulations using the actual GIS data of the local area.  
The experiment results indicate the effective beam-forming controls in the winter road surveillance system in the local areas, and it is considered that a higher data delivery rate and lower latency are expected in the actual fields. However, the additional experiments are considered to be necessary for the evaluation of the DTN with the data triage methods, and the additional implementations of the DTN devices in the vehicles are also necessary for future works. Moreover, we are planning to confirm the experiment in actual fields, and also to deal with the additional functions for V2V communication, such as the group functions for the radio connections or the FEC (forward error correction) for future studies.  
[PERSON] designed the study, analysis, and interpretation of data, and he wrote the initial draft of the manuscript. [PERSON] contributed by assisting in the preparation of the manuscript. All other authors contributed to data collection and interpretation and critically reviewed the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.  
This research received no external funding.  
This paper is the extended version of \"Remote Voltage Controls by Image Recognitions for Adaptive Array Antenna of Vehicular Delay Tolerant Networks\" in the Advances on P2P, Parallel, Grid, Cloud and Internet Computing. (3 PGCIC2018), Oct. 2018 [26]. Further, this work was supported by SCOPE (Strategic Information and Communications R&D Promotion Programme) Grant Number 181502003 by Ministry of Internal Affairs and Communications in Japan.  
The authors declare no conflicts of interest associated with this manuscript.  
Figure 13: Results of the latency in the experiments.
## 2 Materials and Methods  
### FAPAR Product  
FAPAR is a biophysical variable indicative of vegetation photosynthetic status [30; 31; 32; 33]. It is directly related to primary productivity and is widely used in vegetation productivity modeling based on the light use efficiency theory [30; 31]. Different from vegetation indices (VI), FAPAR has a clearer physical meaning [34], although some VIs (e.g., the enhanced vegetation index and the wide dynamic range vegetation index) have been found to have strong correlation with crop instantaneous productivity (e.g., GPP and NPP) [35; 36]. Several studies have also demonstrated that cropland productivity is related more to FAPAR than to NDVI or leaf area index (LAI) [32; 37; 38]. FAPAR is also a critical component of the land-surface radiation budget that is useful in investigating the impact of climate variability on vegetation dynamics and has been recognized as one of the Essential Climate Variables (ECV) in the Global Terrestrial Observing System (GTOS) [39]. In this study, time series FAPAR from the MODIS FAPAR/LAI product (MOD15A2, collection 5) was employed to extract phenological metrics and derive cropland productivity. MODIS FAPAR is derived using physical models as the main algorithm and empirical relationships between NDVI and FAPAR as a backup algorithm when the physical models fail [40]. The product has a spatial resolution of 1 km with 8 days' temporal composition to reduce cloud contamination. Validation studies have shown that it is sufficient for agricultural monitoring and cropland productivity estimation [41; 42]. A total of 14 years' MOD15A2 data from 2000 to 2013 were downloaded from the MODIS website ([[http://modis.gsfc.nasa.gov/](http://modis.gsfc.nasa.gov/)]([http://modis.gsfc.nasa.gov/](http://modis.gsfc.nasa.gov/))). The product was processed using the MODIS re-projection tool (MRT, [[https://lpdaac.usgs.gov/tools/modis_reprojection_tool](https://lpdaac.usgs.gov/tools/modis_reprojection_tool)]([https://lpdaac.usgs.gov/tools/modis_reprojection_tool](https://lpdaac.usgs.gov/tools/modis_reprojection_tool))) to mosaic and extract FAPAR data.