esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 3 Results and Discussion
### Evaluation of the Stability of PTX-Loaded Nanoparticles to Irradiation  
The PTX-loaded nanoparticles were obtained using single solvent evaporation technique with an encapsulation efficiency of PTX of 94.2 \(\pm\) 3.0%. The \(DL\) value of PTX was 8.0 \(\pm\) 0.2%.  
Importantly, the HPLC spectra of the PTX extracted from the irradiated nanoparticles (Figure 3) did not show any additional peaks (compared to non-irradiated samples). Moreover, the areas of the peak of PTX (RT \(\sim\) 6 min) obtained from the chromatograms of the non-irradiated and irradiated samples, and recalculated with a given concentration gave an RSD value of 0.4%. This results confirm that PTX did not degrade after irradiation with both types of ionizing radiation (\(\gamma\)-rays and EB) and suggest the stability of PTX encapsulated in polymeric nanoparticles to irradiation (25 kGy).  
The resulting particles (independent of drug loading) were spherical in shape (as shown by SEM, Figure 4) and varied in dimensions. No particular differences between the particles morphology were noticed after irradiation. DLS data have shown that the average size of particles was equal to 253.5 \(\pm\) 3.9 nm with narrow particle size distribution (\(PDI<\) 0.2) independent of whether the particles were loaded or not (Table 5). No change in average particles size was noticed after the irradiation with \(\gamma\)-rays and EB (compared to non-irradiated samples). The zeta potential of PTX-loaded particles was negative (\(-\)20.83 mV) and similar to the formulations prepared without the drug. This might be explained by the lack of ionizable functional groups in PTX molecules [33]. However, after irradiation, \(ZP\) decreased and reached \(-\)36.73 mV and \(-\)33.75 mV in the case of \(\gamma\)- and EB-irradiated samples, respectively. This may be attributed to a greater amount of ionized terminal carboxylic groups [34; 35] resulting from polymer degradation, which may potentially contribute to the increase in particles stability in suspension.
## 3 Experimental Results and Analyses
### Analysis of Rock Fatigue Stiffness Degradation  
The secant modulus and Poisson's ratio are two important parameters in rock mechanics, and they are normally employed to describe the stress-strain characteristics of the rocks that incur dynamic loading damage [23; 24; 25]. The secant modulus is defined as the slope of the line connecting the upper stress point and the cycle start point for each stage, and Poisson's ratio is defined as the ratio of the radial strain to the axial strain for each cycle in the loading stage. Figure 10 shows that the secant modulus decreased continuously as the number of cycles increased, and the rate increased more clearly in the last few cycles. Thus, the stiffness of the sample changed continuously during the deformation process. This occurred because the internal fracture holes expanded and penetrated each other during the loading process, the rock structure deteriorated continuously, and the stiffness decreased until the stability was lost. In addition, the change in the secant modulus was different for each loading frequency; the smaller the loading frequency, the fewer loading stages were experienced, and the faster the secant modulus decreased.  
Figure 9: Evolution of the maximum (**a**) axial, (**b**) lateral, and (**c,d**) volumetric strains as a function of the loading stage.  
Figure 11 shows the evolution of Poisson's ratio for the increasing levels of the fatigue loading. Poisson's ratio increased rapidly at the beginning of each stage, and the whole process was composed of two stages: (1) steady growth and (2) rapid growth. Poisson's ratio can reflect the change in radial strain, and the increase in the radial strain was larger in the stage that occurred close to the time when the rock is damaged. For the rock samples subjected to the different loading frequencies, Poisson's ratio went through the different stages to reach the maximum value, but the overall trend was consistent, and it tended to reach 0.5 at the time the damage occurred.  
Figure 11: _Cont_.  
Figure 10: Evolution of the secant modulus as a function of the loading cycle for the samples subjected to the different loading frequencies. (**a–d**). The loading frequencies were 0.5 H, 0.2 H, 0.1 Hz, and 0.05 Hz, respectively).
## 3 Results and Discussions
### Antimicrobial Analysis  
The agar diffusion method was used to test antimicrobial susceptibility, and the results are summarized in Table 1. Antimicrobial assessment shows improved results compared to the previous studies. _K. pneumoniae_ showed resistance towards NDG with ZOI (\(14.2\pm 1.86\)) compared to the standard ampicillin ZOI (\(19.21\pm 1.39\)) (\(p\geq 0.05\)). Similarly, _P. aeruginosa_ showed resistance towards NDG with ZOI (\(17.8\pm 1.98\)) compared to the standard ampicillin ZOI (\(19.5\pm 2.75\)). Gram-positive bacteria, _B. subtilis_, _S. mutans_, _S. pneumoniae_, and MRSA showed susceptibility towards NDG with ZOI (\(25.12\pm 2.35\), \(25.7\pm 1.89\), \(22.31\pm 1.67\), \(25.45\pm 2.89\)) compared to the standard ampicillin ZOI (\(19.22\pm 1.87\), \(20.3\pm 1.12\), \(18.21\pm 1.33\), \(19.16\pm 1.72\)), respectively (\(p\leq 0.05\)). As an alternative to antibiotics, NDG is increasingly being used to target bacteria. Although the antibacterial mechanisms of NDG are understood, currently accepted mechanisms include oxidative stress induction, metal ion release, and non-oxidative mechanisms [27]. Pathogenic bacteria are the primary cause of several human infections and pose serious health risks [28]. Since its discovery in 2004, graphene has emerged as a promising nanomaterial due to its unique catalytic, optical, and electrical properties, as well as exceptional physical properties such as a large specific surface area and mechanical strength [29]. Graphene has been found to have promising antibacterial properties in several recent studies [30; 31].  
Figure 6: High-resolution transmission electron microscopy (HRTEM) image of NDG.
## 2 Materials and Methods
### Datasets
#### 2.2.1 Vegetation Indices and Related Datasets  
This study considered the MOD13A1, a 16-day composite normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS); the MOD15A2H, an 8-day temporal resolution; a combined MODIS product which combines EVI (enhanced vegetation index), FPAR (faction of photosynthetically active radiation) and LAI (leaf area index) that represents the vegetation canopy; PAR data from Multi-angle Imaging SpectroRadiometer (MISR level 3), a Japanese global observation satellite product version 4 at daily composite; MCD12C1 version 6, yearly MODIS land cover (LC) data used to discriminate different dominated vegetation types (biomes) and other land cover classes and provide some model's inputs constants and coefficients; and MOD09A1, an 8-day temporal resolution MODIS-Terra-based surface reflectance of soil water content or land surface water index (LWSI) data that were selected to represent the functional variations of shortwave infrared (SWIR) and near-infrared (NIR) electromagnetic spectrums [30]. This study also considered the MOD17A2H, a MODIS-based global GPP product version 6, at the cumulative of 8-days timescale and 500 m temporal and spatial resolutions. All of these data were acquired and freely downloaded from NASA Land Processes Distribution Active Archive Center (LP PAAC) distribution server as combined MODIS data pool and Earth-data for NASA's Terra and Aqua satellite's search engine websites as gridded products [31].
## 5 Discussion
* Defining the exact parameters and their interdependencies would require extensive data and experimental results, which are usually proprietary information of space agencies or private companies. As an open-source academic paper, this work is meant to propose a framework that can be adapted and refined based on specific mission details and as more data become available. * Performance analysis and comparisons would necessitate access to confidential and detailed data about the actual performances of various life support systems. Also, given the novelty and variability of the systems, comparing them in a meaningful way can be challenging. * A comprehensive analysis would require an in-depth understanding of the unique contexts and constraints of different mission scenarios, which often are not public knowledge. While the limitations above make it challenging to include a comprehensive exploration of the proposed model, this work provides a framework for decision-making on life support system architecture. It helps to outline the factors to consider and provides a mathematical representation of the trade-offs, which are important and useful even if their component values are not exactly known.
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 2 Materials and Methods
### Overview of the Framework  
Figure 1 shows the overall structure proposed in this study. The framework includes four steps, feature extraction, oversampling algorithm processing, model validation, and evaluation, all of which are used to address the class imbalance of the SVD and develop a VPD classification system. First, the MFCCs or LPCs extracted from the input signals are oversampled to create a balanced dataset. An important point in this work is to make new minority samples and obtain balanced datasets using various oversampling techniques for feature parameters such as MFCCs and LPCs. Then, various combinations of deep learning models are evaluated and validated using the balanced dataset as the final task.
## 3 Results
### Random Forest Models for Carrot Monitoring and Yield Management  
To identify the most suitable VI for the prediction of carrot yield (YP, t/ha), the actual carrot yield (Y\({}_{\text{A}}\)) and the corresponding VIs were subjected to linear regression modeling, performed using XLStat (Ver. 19), a statistics software program, compatible with MS Excel. Linear regression results showed three models with a significant correlation between the VIs and the Y\({}_{\text{A}}\), with R\({}^{2}\) values ranging between 0.51 and 0.66 (Figure 6). Out of the studied VIs, the RDVI, the SIPI, and the GNDVI were identified as the most suitable VIs for the growth assessment and yield of carrot crops. Subsequently, RDVI and GNDVI along with field-measured SPAD values were used as inputs in the execution of machine-learning algorithms, such as random forest (RF), for the determination of the crop growth and productivity (yield) management zones of the studied carrot fields. A summary of the dynamics of the carrot crop was monitored using S2 data, and yield prediction maps were generated based on the five scenarios (Figure 7).  
Figure 6: A correlation plot that shows the dependence between the applied predictors based on the Pearson correlation coefficient. Dark blue colors indicate positive correlation, dark red colors indicate negative correlation, and near-white colors indicate weak or null correlation.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Training Model  
In this work, we propose an end-to-end training approach to simultaneously learn the SU-level and fusion center models using a set of training data, consisting of a sequence of vectorized signals \(R_{t}\) with labels \(y_{t}\). After completing the forward pass through the DNN model, a class score vector of size \(2\times 1\) is obtained at the output, which is normalized using the Softmax function. This is represented by Equation (7).  
\[f_{\theta}(R_{t})=\begin{bmatrix}f_{\theta|H_{0}}(R_{t})\\ f_{\theta|H_{1}}(R_{t})\end{bmatrix} \tag{7}\]  
with  
\[f_{\theta|H_{0}}(R_{t})+f_{\theta|H_{1}}(R_{t})=1 \tag{8}\]  
where \(\theta\) and \(f_{\theta}(\cdot)\) are the model's parameters and expression, respectively. This output reflects the probabilities of the hypotheses. \(f_{\theta|H_{t}}(R_{t})\) is the corresponding expression of \(H_{i}\) (i.e., the class score of \(H_{i}\)). As is known in supervised learning, training a neural network involves maximizing the likelihood. The goal function of the proposed DNN is based on likelihood maximization, which can be found in Equation (9).  
\[L(\theta)=\prod_{t=1}^{T}f_{\theta|H_{0}}(R_{t})^{1(y_{t}=0)}\cdot(1-f_{\theta |H_{1}}(R_{t}))^{1(y_{t}=1)} \tag{9}\]  
where \(1(\cdot)\) is the indicator function. Since the cross-entropy between the training data and the model's distribution is equivalent to the negative log-likelihood of the data under the model, the neural network loss function \(J(\theta)\) can be obtained by taking the logarithm of the likelihood and normalizing it with the training set size. It is defined in Equation (10).  
\[J(\theta) =-\frac{1}{T}\sum_{t=1}^{T}(1\,(y_{t}=0)\log f_{\theta|H_{0}}(R_{t}) \tag{10}\] \[+1\,(y_{t}=1)\,\log(1-f_{\theta|H_{1}}(R_{t})))\]  
Therefore, the maximum likelihood can be reached by seeking the network parameters that minimize the loss function as  
\[\theta^{*}=\arg\min_{\theta}J(\theta) \tag{11}\]  
The loss function is differentiable and, therefore, \(\theta\) is updated gradually via backpropagation until the optimal parameter \(\theta^{*}\) by conducting gradient descent on the loss function. There are several optimization algorithms commonly used to perform this adaptation: stochastic gradient descent (SGD), momentum, Adagrad, root mean square propagation (RMSprop), adaptive moment estimation (Adam), Adadelta, Nadam and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). The choice of optimizer depends on factors such as the specific characteristics of the data, the architecture of the neural network and the computational resources. We adopt the Adam method to calculate the optimal parameters that minimize the loss function and improve the accuracy of the network. It is a powerful stochastic optimization algorithm that requires only first-order gradients, uses an adaptive learning rate, gives overall good performance and uses a moderate amount of memory.
## 4 Simulation Experiments
### Performance of the Communications
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & ns-3, SimBlock \\ \hline Blockchain framework & Ethereum \\ \hline Consensus algorithm & Proof of Work \\ \hline Routing protocol & VNIBR-VNAODV+-IGRP \\ \hline Simulation scenario & Manhattan type urban area \\ \hline Propagation model & Hybrid buildings propagation loss model \\ \hline Intersections number & 64 \\ \hline Intersections separation & 100 m \\ \hline Number of PNs (vehicular density) & 128-256-512 \\ \hline Number of VNs & 421 \\ \hline mRSUs number & 8-16-32 \\ \hline VaNetChain transactions & 100-1000-10,000-100,000 \\ \hline VaNetChain packet size & 1 MB \\ \hline Transmission rate & 6 Mbps \\ \hline MAC & IEEE 802.11p \\ \hline Transport protocol & UDP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main parameters of our simulations. Figure 4: Packet delivery ratios measured against different numbers of vehicles in the VANET. Finally, because the mRNAs play a key role in mining and validating new blocks, we considered varying their number and checking how this affects the responsiveness of the VaNetChain. According to the criteria set out in [64], we found that the optimal number of mRNAs was 16; therefore, we changed it to 8 and 32 to establish a point of comparison. For this scenario, owing to the results of the previous experiments, VNIBR was established as a routing protocol. The vehicle density was set at 256 and the number of transactions was varied. The results are shown in Figure 6.  
To begin with, it is interesting to note the stability shown by the curve corresponding to 8 mRNAs: although the number of transactions increases, the measured times range between 386 and 394 ms. The explanation lies in the fact that a lower density of mRNAs implies the participation of a greater number of virtual nodes in the transactions. Moreover, as the number of transactions increases, a small increase in the latency curve is due to the queuing caused by the pre-validation mechanisms. When using 16 and 32 mRNAs, similar results were obtained up to 1000 transactions (around 350 ms), representing an improvement over 8 mRNAs, and therefore fewer virtual nodes involved in the retransmissions. From that point on, the delays with 16 mRNAs start to fall, reaching 308 ms with 100,000; in constrast, the delays increase progressively with 32 mRNAs, reaching 407 ms.  
Figure 5: Latencies measured against different numbers of blockchain transactions. Figure 6: End-to-end delays measured with the VNIBR-based VaNetChain configuration in processing different numbers of transactions, with different numbers of mRNAs. An excessive density of miners, therefore, can have a detrimental effect because it implies more mRNAs wasting resources in hash computation races won by others.
## 3 The Conceptual Framework Proposed
### Analyzing the Real-Use Environments and Implementing Living Labs
#### 3.2.3 Physical Context  
The physical context of product use refers to the natural and artificial environment of the place where a product is used [35; 39]. Mostly, products are used indoors, outdoors, or both. For example, heating curtains and directional speakers are indoor products, while reflective pavement markers and IoT-based manhole tops are installed on the road. The use environments of these two categories of products are very different in terms of the natural environmental factors such as weather (e.g., rainy or sunny), temperature, humidity, time (e.g., day or night, seasons), and natural objects (e.g., trees), and the artificial environmental factors such as noise, social events, artifacts (e.g., buildings, facilities).  
The framework in Figure 3 shows how these natural and artificial environmental factors can have different significances according to whether or not a product is mainly used in a specific place or condition. For example, workers are supposed to wear respirator masks and welding helmets in a welding factory where various kinds of welding fumes are generated and float in the air, and the temperature is kept high. On the other hand, heating curtains are a new product in which carbon heating wire is inserted into the ordinary curtain to prevent cold air from outside the window and to warm the indoor air. Although both welding helmets and heating curtains are used indoors, in contrast to welding helmets, heating curtains are installed in ordinary houses or offices, where the temperature and air conditions remain normal, and artificial environments, such as indoor structure and furnishings, exist in various forms.  
In this manner, using the framework, the key components of the physical real-use context of a product can be drawn according to whether a product is used indoors or outdoors, and whether the product is used in a special or normal environment. Moreover, the information about the specific locations of a product (i.e., where the product is installed or used) is inevitably used to understand the physical real-use environment of the product, because depending on the location, the natural and artificial environments can vary significantly.  
Figure 2: The Framework for the Context Analysis of a Product (Technological Context).
## 4 Case Study--Safety Assessment of the Rockfill Specim in Triaxial Test
### Numerical Modeling
#### 4.4.2 Material Parameters' Calibration  
To calibrate the parameters of a Mohr-Coulomb model, each of the twelve triaxial tests from the preliminary investigation is reproduced in the FEM environment using Rocccience software RS2 [36]. The model calibration is conducted under the following assumptions:  
* Experimental results point to a non-cohesive material structure--expected for rockfill--so effective cohesion is neglected (\(c^{\prime}=0\)). Strength is therefore controlled solely by the effective friction angle \(\varphi\)'.
* The value of \(\varphi\)' is assigned according to the maximum axial stress \(\sigma^{\prime}{}_{1}\) attained in the test; any post-peak softening is ignored.
* An elastic modulus equal to the secant modulus E\({}_{50}\), measured at 50% of \(\sigma^{\prime}{}_{1}\)max on the \(\sigma^{\prime}{}_{1}\)-\(\epsilon_{1}\) curve, is adopted. The secant modulus E\({}_{50}\) provides a balanced, single-value approximation of soil stiffness in the majority of the stress path of triaxial loading, especially in the part representing working stress levels (analogy with the real structure safety assessment).
* [PERSON]'s ratio (v) and the dilation angle (\(\psi\)) are determined by least-squares optimization so that the model reproduces the observed dilatant behavior evident in the volumetric-axial strain curve.  
Figure 12 provides an example of the test recreation based on one of the triaxial tests from the preliminary investigation. The calibrated parameters are listed in Table 1. More details on the triaxial test recreation procedure are presented in [38].  
Figure 12: Test recreation example. (**a**) axial strain vs. axial stress; (**b**) axial strain vs. volumetric strain.  
Figure 13 illustrates how the calibrated material parameters change with the confining stress level. In line with the behavior observed during testing, the calibrated parameters indicate a decrease of the effective friction angle (non-linear failure criterion), an increase in stiffness, and a reduction in dilatancy as the confinement level rises. [PERSON]'s ratio shows no clear dependence on stress level.
## 4 Results and Discussion
### Determination and Analysis of c and \(\varphi\) Values of Carbonaceous Mudstone
#### 4.3.2 Analysis of c and \(\varphi\) Values of Carbonaceous Mudstone  
By organizing and analyzing the calculation results in Table 2, the following can be obtained:  
(1) The maximum horizontal thrust Pmax and minimum horizontal thrust Pmin of the carbonaceous mudstone specimens after saturated water immersion decreased significantly compared with that in the natural state, which reflected the decrease of c and \(\varphi\) values of the carbonaceous mudstone after saturated water immersion in a macro level: the average cohesion of Group B samples is 33% lower than that of Group A samples in their natural state, and the internal friction angle is also about 15% lower than that of Group A samples. The main reasons for this result are as follows: (1) When the carbonaceous mudstone samples are saturated with water, part of the clay minerals soften in contact with water and adsorb more water molecules, which makes the carbonaceous mudstone particles gather more water, resulting in reduced friction and cohesion. (2) The organic matter existing inside the carbonaceous mudstone is easily lost in reaction with water and oxygen under the action of microorganisms, which also weakens the original inter-particle adhesion and friction effect, making the c and \(\varphi\) values significantly lower.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Specimen** & **Maximum** & **Minimum** & **Slider** & & & & & \\
**Number** & **Thrust/kN** & **Thrust/kN** & **Weight/kN** & \(\sum_{g}\)_icosa\({}_{i}\)_/kN** & \(\sum_{g}\)_icins\({}_{i}\)_/kN** & \(\sum_{i}\)_/kN** & \(\sum_{i}\)_/kPa** & \(\varphi\)**/(\({}^{\circ}\))** \\ \hline A1 & 22.01 & 12.03 & 2.75 & 2.63 & 0.67 & 0.84 & 22.01 & 14.77 & 52.33 \\ A2 & 18.02 & 11.53 & 2.07 & 1.90 & 0.71 & 0.86(2) Because the push-shear direction of specimens A2 and B2 is consistent with the inclination of the rock layer, the average cohesion of these two specimens is 34% lower than that of the other specimens, and the average angle of internal friction is also lower than that of the other specimens by about 7%. Since the natural weak surface of the rock layer may have fissures or interlayer structures with weak bond, which makes the shear strength of the specimens along the inclined portion of the carbonaceous mudstone significantly lower, priority should be given to the portion along the inclined portion of the slope when reinforcing carbonaceous mudstone slopes in the actual field.