esa-sceva/satcom-chunk-collection huggingface dataset:
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon
### Accumulation of Radon in Indoor Environments
#### 2.3.2. Ventilation and Air Pressure  
Well-ventilated constructions tend to have lower concentrations of radon, as the gas is diluted and dispersed into the outside air [75]. However, in buildings with insufficient ventilation, radon can accumulate, resulting in higher concentrations [76]. Additionally, differences in pressure between the interior and exterior of a building can create a suction effect, attracting radon into the building [77]. In poorly ventilated spaces, the buildup of radon gas can pose a significant risk to human health, particularly in areas with high levels of radon [78]. To mitigate this risk, it is important to ensure that buildings are properly ventilated, especially in areas with high levels of radon in the surrounding soil and rock. Ventilation systems, such as air exchange systems, can be installed to maintain a healthy indoor environment and reduce the risk of exposure to radon [79]. The study by [PERSON] et al. (2018) represents an important investigation into the concentrations of radon in indoor environments, which are a potential health hazard to the inhabitants of these spaces [80]. The study focused its discussion on the diverse factors influencing daily and multi-day variations in radon concentration. There are differing views on the relative importance of these variables, which include (i) the periodic forced ventilation of homes; (ii) the temporal variation of radon exhalation from soil and building materials caused, for example, by varying moisture content; and (iii) daily and multi-day temperature and pressure patterns. Forced ventilation and unspecified radon emissions are successfully excluded from the discussion of the effects of temporal climatic conditions in this study.  
The findings show that the indoor/outdoor pressure gradient is the primary factor in any interior space's ongoing air renovation, which affects radon concentration in both daily and multi-day patterns. The day/night cycle of the indoor temperature, which is connected to an expansion or contraction of the internal air volume, is the primary cause of the daily recurrent changes in the pressure gradient. On the other hand, multi-day patterns are mostly caused by intervals of negative interior air pressure, which are brought on by intervals of high wind speeds as a result of [PERSON]'s principle.  
Another study by [PERSON] et al. (2020) underscored temperature as the principal factor responsible for the observed temporal patterns across multiple timescales, ranging from less than an hour to years [81]. The variability of radon and temperature was remarkably similar on a seasonal scale, with both peaking in the summer. Daily variations were closely tied to the temperature gradient between the interior and exterior of the test environment. The influences of temperature included temperature-driven air ventilation and temperature-driven radon migration through a function dependent on the thermal gradient within the porous medium.
## 3 Coupling Relationship between Charging Stations and Travel Route
### Potential Trip Chains Considering the Location of Charging Stations  
The user will not opt for EV for a certain journey if she/he needs to charge the vehicle many times during the journey. Therefore, we believe that for each route in the trip chain, up to one charge is acceptable, otherwise the trip is deemed a failure. Assuming that the starting and ending points of a certain trip are D\({}_{i}\) and D\({}_{i+1}\) and the shortest distance obtained via [PERSON]'s algorithm is \(d_{i,j+1}\). With D\({}_{i}\) and D\({}_{i+1}\) as the starting and ending points, the potential route passing the charging station C\({}_{k}\) comprises of the shortest path from D\({}_{i}\) to C\({}_{k}\) and from C\({}_{k}\) to D\({}_{i+1}\), and the distances are \(d_{i,k}\) and \(d_{k,i+1}\), respectively. In this case, the deviation to the shortest path \(d_{v}\) is expressed as Equation (3). The definition of the deviation constraint is shown in Equation (4). If the deviation constraint is not satisfied, the route is excluded:  
\[d_{v}=d_{i,k}+d_{k,i+1}-d_{i,i+1} \tag{3}\]  
\[d_{v}\leq\lambda R \tag{4}\]  
Figure 5: A five-node traffic network.  
where \(\lambda\) represents deviation coefficient, \(R\) is the maximum travel range of EVs and \(\lambda R\) is the maximum deviation.  
In a trip chain, we could have several potential routes for each journey among which one is the shortest route and the others are the shortest routes passing a certain charging station. In Figure 6, we find a journey with four potential routes. One is the shortest path, and the other three are routes passing the charging stations C\({}_{1}\), C\({}_{2}\), and C\({}_{3}\), respectively, where the dashed lines indicate the process of driving. Figure 7 is a schematic diagram of a set of potential trip chains where the red dots indicate the charging stations passing by.  
The number of possible routes for the \(m\)-th journey is recorded as \(n_{m}\), therefore, the total number of the potential trip chains can be obtained via Equation (5), where \(n_{t}\) is daily trips. Then, we judge the feasibility of each potential trip chain; if the trip is a success, the process will terminate, otherwise, it will continue with the next trip chain until all are analyzed:  
\[n_{l}=\prod_{m=1}^{n_{t}}n_{m}, \tag{5}\]
# Consensus Big Data Clustering for Bayesian Mixture Models
## 5 Further Extensions for Big Data Systems
```
1:Input: Observations \(\mathbf{y}\), Hyperparameters \(\alpha\), \(m\), MCMC iterations
2:Output: Clusters \(j\) and their \(\mu\) and \(\sigma^{2}\)
3: Divide the observations \(\mathbf{y}\) into \(P\) partitions
4:Initialize clusters and assign observations:
5: Assign all observations in each partition into a cluster
6: Set hyperparameter values for \(\alpha\) and \(m\)
7:for\(mcmc\) in \(1\) to \(MCMC\)do
8:Parallel Processing:
9:for\(p\) in \(1\) to \(P\)do
10:for\(i\) in \(1\) to \(n\) in partition \(p\)do
11:if\(c_{i}\) is NOT a singleton then
12: Draw a new value from \(c_{-i},y_{i}\) from Equation (27)
13:else
14: Draw a new value from \(c_{i}\mid c_{-i},y_{i}\) from Equation (27)
15:endif
16:if any cluster is removed then
17: Adjust the labels to maintain a sequence from \(1\) to \(k\)
18:endif
19:endfor
20: Update mean and variance for each cluster \(j\) in partition \(p\)
21:endfor
22: Merge the updated \(\mu\) and \(\sigma^{2}\) from partitions to obtain the global values
23: Update mean and variance for base distribution \(\mu_{0}\) and \(\sigma_{0}^{2}\)
24:endfor
25:Return: Clusters \(j\) and their \(\mu\) and \(\sigma^{2}\)
```  
**Algorithm 3** Bayesian nonparametric clustering in MCMC on Apache Spark \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Algorithm** & \(\boldsymbol{\alpha}\) & \(\boldsymbol{m}\) & **Clustering Accuracy** & **Time (s)** \\ \hline \multirow{6}{*}{CIFAR-10} & \multirow{3}{*}{BNP-MCMC} & 1.0 & 1.0 & 0.75 & 120 \\  & & 1.5 & 1.0 & 0.78 & 130 \\  & & 1.0 & 1.5 & 0.77 & 125 \\ \cline{2-6}  & \multirow{3}{*}{Parallel BNP-HMC} & 1.0 & 1.0 & 0.76 & 100 \\  & & 1.5 & 1.0 & 0.80 & 110 \\  & & 1.0 & 1.5 & 0.79 & 105 \\ \cline{2-6}  & \multirow{3}{*}{BNP-Spark} & 1.0 & 1.0 & 0.74 & 90 \\  & & 1.5 & 1.0 & 0.78 & 95 \\  & & 1.0 & 1.5 & 0.76 & 92 \\ \hline \multirow{6}{*}{MNIST} & \multirow{3}{*}{BNP-MCMC} & 1.0 & 1.0 & 0.85 & 150 \\  & & 1.5 & 1.0 & 0.88 & 160 \\  & & 1.0 & 1.5 & 0.87 & 155 \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{Parallel BNP-HMC} & 1.0 & 1.0 & 0.86 & 130 \\  & & 1.5 & 1.0 & 0.89 & 140 \\  & & 1.0 & 1.5 & 0.88 & 135 \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{BNP-Spark} & 1.0 & 1.0 & 0.84 & 120 \\  & & 1.5 & 1.0 & 0.88 & 125 \\  & & 1.0 & 1.5 & 0.86 & 122 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results for real-world datasets using different algorithms and hyperparameters.
## 2 Classification of Localization Algorithms  
### Classification Based on Sensor Nodes' Mobility  
As shown in Figure 1, localization algorithms are classified into three categories. (1) In stationary localization algorithms, all sensor nodes are static. They are attached to surface buoys or ocean floor units which have fixed locations. (2) In mobile localization algorithms, all sensor nodes are mobile. They freely drift with water currents or use propelled equipments, e.g., Autonomous Underwater Vehicle (AUV), to control their movements. (3) In hybrid localization algorithms, stationary and mobile sensor nodes coexist. These three categories are further divided into subcategories of centralized and distributed localization schemes.
## 2 Materials and Methods
### Batch Adsorption Studies  
The adsorption experiments were carried out by adding 5.0 mg of BC into a conical flask (100 mL) that contained 20.0 mL of dye solution with the desired concentrations. The adsorption of the dye solution both pre- and post-adsorption was measured using a 752 UV-Vis spectrophotometer (Shanghai Jinghua Technology Instrument Co., Ltd., Shanghai, China) in a wavelength-specific manner (664 nm for MB, 370 nm for AYR and 542 nm for NR) at room temperature, and dye concentrations were calculated through the calibration equations.  
The adsorption of MB onto BCs prepared at different temperatures was measured at 25 \({}^{\circ}\)C for 90 min. The optimization of adsorption conditions was carried out using CPP200, which exhibited the largest adsorption capacity. Effects of contact time, temperature, initial MB concentration, dosage of adsorbent and solution pH value on the removal of MB by CPP200 were investigated in detail. The effect of contact time (5 min, 10 min, 15 min, 30 min, 60 min, 90 min, 120 min, 150 min) on CPP200 (5.0 mg) toward 20.0 mL of MB (50 mg/L, pH = 7.32) was observed at 25 \({}^{\circ}\)C. Effects of initial MB concentrations (10 mg/L, 20 mg/L, 30 mg/L, 40 mg/L, 50 mg/L) and temperatures (25 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 45 \({}^{\circ}\)C) on the adsorption capacity of CPP200 for MB were investigated (equilibrium time = 90 min, 5.0 mg of CPP200, 20.0 mL MB, initial pH = 7.32). Effects of solution pH values (3.33, 4.34, 5.35, 6.30, 7.32, 8.34, 9.36) on CPP200 for MB solution (20.0 mL, 50 mg/L) at 25 \({}^{\circ}\)C were observed over periods of 90 min. Aqueous hydrochloric acid (HCI, 0.1 mol/L) and sodium hydroxide (NaOH, 0.1 mol/L) solutions were used for pH adjustment. The solution pH values were measured through a pH meter (Model: PHS-3C; Shanghai INESA Scientific Instrument Chemical Co., Ltd., Shanghai, China). Effects of adsorbent dosages (5.0 mg, 10.0 mg, 15.0 mg, 20.0 mg, 25.0 mg) for MB (20.0 mL, 50 mg/L, pH = 7.32) at 25 \({}^{\circ}\)C were examined in periods of 90 min. For the regeneration experiments, 50.0 mg of CPP200 was shaken in 20.0 mL of MB (10 mg/L) at 25 \({}^{\circ}\)C for 90 min under oscillatory conditions, and the desorption of MB from CPP200 was implemented using 40.0 mL of HCl (0.1 mol/L).  
Batch adsorption experiments were conducted, and the results of three parallel experimental studies were recorded. Equation (1) was applied to calculate the adsorption capacity of BCs, and the adsorption rate (R, %) was calculated according to Equation (2):  
\[q_{e}=\frac{(C_{0}-C_{e})\times V}{m} \tag{1}\]  
\[R=\frac{C_{0}-C_{e}}{C_{0}}\times 100\% \tag{2}\]  
where \(C_{0}\) (mg/L) is the initial MB concentration; \(C_{e}\) (mg/L) is the equilibrium concentration of MB; \(V\) (L) and \(m\) (g) are the volume of MB solution and the mass of BC added to the solution, respectively; \(q_{e}\) (mg/g) represents the equilibrium adsorption capacity of the BC.
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 5 Results and Analysis
### Fully Autonomous Recovery Verification Experiment
#### 5.4.2 Test Results of Autonomous Recovery Time for UAVs  
Validation tests were performed under different environment conditions. The time that the manipulator put the UAV into the storage place after hooking the cable was tested. The experimental process is shown in Figure 20. The tests were conducted more than 10 times, and the average time taken for the manipulator to grasp and place the UAV was less than 80 s. In addition, the average time for the UAV alignment adjustment was 30 s, and the total average recovery time was less than 120 s. Tests show that the system has high recovery efficiency in both day and night environments.  
Figure 19: Autonomous recovery UAV experiment process. (**a**) UAV catapult takeoff; (**b**) UAV in flight; (**c**) hook up the arresting cable; (**d**) UAV speed drops rapidly and falls on the recovery net.
## 5 Conclusions  
In general, probiotics exhibit potential as a safe and effective option for managing oral health, as they effectively reduce VSC levels, organoleptic scores, and cariogenic bacteria counts, as well as improving saliva quality and salivary flow. To sustain the reduction in VSC levels, tongue scraping in addition to probiotic intake can be recommended. Furthermore, the combination of prebiotics with probiotics (symbiotics) has been shown to decrease tongue coating. Moreover, probiotic administration seems to positively influence oral-health-related quality of life. Nevertheless, different bacterial strains have been used in the included studies; hence, the conclusions cannot be generalized, being one of the main limitations of this review. Additionally, further research on the probiotics' potential to persist in the oral cavity after cessation of treatment, as well as studies with prolonged follow-ups with standardized methodologies, is essential to establish their efficacy conclusively.  
The research highlights the significant potential of probiotics in managing oral health, particularly in reducing VSC levels, improving saliva quality, and enhancing oral-health-related quality of life. This suggests that probiotics could be a viable, non-invasive adjunct to traditional oral health treatments. However, a key limitation of the study is the variation in bacterial strains used across different studies, which prevents the generalization of the findings. Additionally, there is a need for further research to explore the long-term persistence of probiotics in the oral cavity post-treatment and to employ standardized methodologies to conclusively determine their efficacy.  
Future ExpectationsFuture research with larger sample sizes, prolonged follow-up periods, and a more standardized mode of probiotic administration is advised, as those were limitations observed in the included studies. Furthermore, it would be interesting to develop a concept on how to provide access to probiotics to the general population, both from an economic as well as an educational standpoint.  
Conceptualization, F.R.-V.; methodology, M.A.; investigation, A.R.G.; data curation, C.G.; writing--original draft preparation, V.O.; writing--review and editing, R.L.G.; supervision, J.N.; project administration, C.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 5 Testing and Experimental Configuration
### Test Area  
The environment used to evaluate the coverage of the LoRa network was in the rural community of Chirinche Bajo, Salcedo, Ecuador. The geographic location of the gateway and end nodes were deployed in an area of 50 hectares, as illustrated in Figure 7.  
As is characteristic of the Andean zones, the Chirinche Bajo community has different environmental particularities such as agricultural areas with altitudes ranging from 2910 to 3040 m above sea level, slopes of 0.3% to 15%, terrain with a hard orography, irregularities in the soil and lush Eucalyptus trees in the community boundaries. Most of the people in the community are dedicated to farming potatoes, corn, onions, pasture, and caring for domestic animals such as cattle, pigs and poultry.  
To plan the deployment, it is important to obtain some insights into the communication range [41]. The satellite map in Figure 8 illustrates the position of the gateway and end nodes distinguished by blue and red points, respectively. Table 2 shows the approximate straight-line distance of the different positions from the gateway.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Position** & **Distance (m)** \\ \hline Pos A & 50 m \\ Pos B & 496 m \\ Pos C & 508 m \\ Pos D & 619 m \\ Pos E & 821 m \\ Pos F & 875 m \\ Pos G & 465 m \\ Pos H & 254 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Node positions and distance (approximate in a straight line) from the gateway.  
Figure 7: Spatial distribution of nodes and gateway on the digital surface elevation model, Chirinche Bajo community, Salcedo, Ecuador.  
Figure 8: Longitudinal cross-sectional profile of link Nodes—Gateway.
### Significance of Work
It is evident from the network visualization mapping that in recent years, a lot of research has been carried out in the domain of the LEM. Before 2016, researchers identified the potential benefits of LEMs in addressing the shortcomings of the existing centralized electricity system, including grid inflexibility, unreliability, load curtailment, high costs, environmental impact, and security vulnerabilities. Subsequently, a surge in project initiatives led to a significant increase in the number of publications in the field.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Anticancer Properties
#### 3.5.2 Molecular Pharmacology  
PPAR-\(\gamma\) is classified as a nuclear receptor which, after binding with its ligand, translocates to the nucleus, heterodimerizes the retinoid X receptor (RXR), and acts as gene regulator via its interaction with the peroxisome proliferator response element (PPRE) of selected genes. The latter is related to metabolism in physiological as well as cancer cells [43]. As such, the receptor possesses the capability of affecting key cancer cell processes related to proliferation, progression, differentiation, metastasis, apoptosis, etc. Activation of PPAR-\(\gamma\) receptors suppresses tumorigenicity through their interaction with insulin growth factor, as illustrated in Figure 3. Additional molecular mechanisms of therapeutic significance are summarized in Table 1.  
Figure 3: Pharmacological effect of pioglitazone facilitated through the inhibition of thromokane A2 (TX-A2) and growth factor receptor pathways. The former regulates the expression of phospholipase A2 (PLA2), arachidonic acid (AA), cyclooxygenase-2 (COX-2), prostaglandin E2 (PGE2) and prostacyclin I2 (PGL2). The latter regulates the expression of vascular endothelial growth factor (VEGF) through the phosphoinositide 3-kinase pathway (PI3K) (created using [[https://biorender.com](https://biorender.com)]([https://biorender.com](https://biorender.com)), URL accessed on 25 January 2025).
###### Abstract
Then, data certification is ensured by storing hashes on an EVM-compatible blockchain via smart contracts. This dual-layered approach, which combines blockchain and cloud storage, is known as off-chain data storage, a well-known technique that provides scalability, security, and transparency, while reducing the costs related to the blockchain [19; 20; 21]. Later, the authenticity of recorded events can be checked by comparing the hashes computed on the data stored on a cloud server with those stored on the blockchain [22]. To assess the practicality of our solution, we performed a comprehensive evaluation of our prototype software system using a substantial data set extracted from the incident dispatch dataset provided by the NYC OpenData initiative [23]. Our findings demonstrate the efficacy of our approach, making it a cost-effective strategy for aggregating the numerous data points and timestamps that would otherwise necessitate more recordings per day. The main contribution of this paper is the development of a decentralized framework that leverages blockchain technology to certify the integrity and authenticity of incident data. By employing a client-server and blockchain-based software architecture, our framework guarantees that all recorded events become certified; thus, it holds a proof of the recorded timing. This solution facilitates secure and transparent data handling and enhances trust among actors. Additionally, we performed a thorough cost assessment to demonstrate the framework's economic viability, highlighting its cost-effectiveness in real-world scenarios. This combination of features provides emergency system authorities and patients with a trusted third-party mechanism to certify incident-related data efficiently and effectively. The remainder of the paper is structured as follows. Section 2 reviews the most relevant approaches in the literature and compares them with our proposed method. Section 3 describes the architecture and methodology for preserving data integrity using blockchain. Section 4 presents experimental results from real-world simulations. Section 5 discusses the results and limitations of our approach. Finally, Section 6 draws our conclusion.
# Sensitivity Analysis of a Numerical Model for Percutaneous Auxicular Vagus Nerve Stimulation
## 3 Results
### Sensitivity of the Stimulation Thresholds for Single Nerves
#### 3.2.1 Effect of the Fiber Diameter  
Figure 3 shows the effect of the fiber diameter on the stimulation thresholds for N1 and N2. The axon fiber diameter was changed from 7 \(\upmu\)m to 12 \(\upmu\)m in steps of 0.5 \(\upmu\)m. Results show that the stimulation thresholds decrease with increasing fiber diameter. Biphasic pulses require slightly less amplitude to stimulate the nerves than the monophasic pulses (0.24%-0.88% and 0.28%-2.54% less for biphasic for N1 and N2, respectively). N2 is stimulated with lower amplitudes than N1 for the two types of pulses (with a fiber diameter of 8 \(\upmu\)m, for example, the stimulation thresholds are 0.89 V and 0.88 V for N1 and 0.82 V and 0.80 V for N2). The sensitivity index for this parameter and for each configuration is in average equal to SId_axon_1 = 13% and SId_axon_2 = 17% for N1 and N2, respectively. N2 is more sensitive to the axon fiber diameter.