esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 4 Measurement Scenarios and Data Preparation
### Data Collection Process
#### 4.2.2 Separation of the Training, Validation, and Test Dataset  
Two independent test datasets were used in our experimental evaluations. The first test dataset was separated from the measurement environments provided in Figure 2(b), i.e., the environments from which the training data came. This is the typical scenario for the presentation of classification results in most UWB-based literature [10; 11; 21]. In some cases in the literature, the test dataset was separately collected by intentionally switching the subject of the experiment, i.e., a person who carried the UWB device in [11]. However, the environment of the measurement stayed unchanged in the evaluation. As already mentioned in Section 2, the propagation of the measured UWB signal can be affected by several environmental factors such as the refractive index of materials, the placement of objects in the measured circumstances, etc. To examine this incident in our results, we collected a second test dataset that was independent and different from the training environments (Figure 2(c)). This second dataset was solely split out for the purpose of testing in our evaluation. The results using both test datasets are discussed in Section 7.  
For training the evaluated ML models including the validation process, more data were gathered in the laboratory room, the hall, and the first corridor (Figure 2(b)). In particular, one-hundred eighty-five-thousand seven-hundred ninety observations in total were gathered after balancing the three classes in this scenario. This meant each class belonged to 61,930 data points. For testing purposes, thirty percent of the data points were left out by random shuffling in each trial conducted in Section 7. The results using this test scenario in Figure 2(b) are expressed as a scenario when test and training are in the same condition (Section 7).  
In contrast, the measurement campaign, particularly for testing purposes, was conducted in different scenarios from the training. These measurements were carried out in a different room with various items of furniture and three different corridors (Figure 2(c)). The results achieved from these second test scenarios are expressed in Section 7 as a scenario where the test and training conditions were different. The total number of 36,015 data points, 12,005 for each class, was used for conducting this test scenario in our evaluation after balancing equally the three classes.
## 3 Results and Discussion
### Neutron Talbot-Lau Interferometry at HZB  
Neutron Talbot-Lau interferometry at HZB showed a slight difference in the dark-field images (autocorrelation length \(\xi\) near 1.97 \(\upmu\)m) between charged and discharged, 0.5 mm thick 15 mAh Li-po batteries (Figure 4).  
Figure 3: Neutron images of a worn, fresh, and slightly worn 43 mAh battery. The autocorrelation length corresponds to \(\xi=2.59\)\(\upmu\)m for the top images and \(\xi=2.92\)\(\upmu\)m for the middle images. On the bottom is a plot of the mean scattering values across the charged and discharged batteries. The color bar represents the dark-field signal acquired through each battery. The reported uncertainty is the one-sigma root mean square uncertainty of the nonlinear least squares fit.  
When both the fresh and worn batteries are charged, there are only a few regions where the scattering signal changes within the samples. The major difference occurs at the top of the fresh battery, where the higher scattering values (around 0.65) are associated with electrolytic material. Also of note are the vertical scattering features in both batteries, specifically in the middle and edges of the sample. When the battery is discharged, the scattering values increase to 0.29 for the fresh battery and 0.27 for the worn battery.
## 1 _Article_
Furthermore, the cytopathic effect of the herpes simplex virus was considerably reduced in vitro by nontoxic concentrations of AuNPs [24]. On the other hand, the phytofabricated AuNPs displayed premium free radical scavenging performance [25]. Plant extracts have been widely used to produce AuNPs; for example, _Aloe vera_, _Azadirachta indica_, _Cinnamomum campora_, _Medicago sativa_, _Terminalia catappa_, and _Coriandrum sativum_ have been reported as highly efficient agents for the synthesis of AuNPs [26]. It is noteworthy that AuNPs are generally considered less cytotoxic than alternative metal NPs [27; 28]. This was another motivation for investigating plant-extract-mediated synthesis of AuNPs. Ginger (_Zingiber officinal_; Zingiberaceae) is a herbaceous, perennial plant that has been used for centuries as a continent for food and beverages, as well as in folk medicine for recovery from cough and flu and gastrointestinal diseases, as well as its anti-inflammatory, antinausea, and digestion-aiding activity, in addition to promoting the body's immunity level [29]. Ginger contains many active metabolites, including volatile oils, as well as non-volatile compounds, such as paradols, zingerone, shogaols, and gingerols, which are believed to be responsible for the distinctive biological and medicinal characteristics of ginger [30]. Scientific reports have proven the pharmacological effects of ginger as antibacterial, anticancer, antioxidant, antidiabetic, analgesic, and anti-inflammatory activities [29; 31; 32]. In addition to helping treat and prevent arthritis, it has hepatoprotective and nephprotective effects [33; 34; 35]. The propensity of ginger extract to facilitate the synthesis of metal nanoparticles was previously reported for copper [36], silver [37], and gold [38]. The current study investigates the biogenic synthesis of AuNPs by using aqueous extract of fresh ginger rhizome for the reduction of HAuCl\({}_{4}\). After evaluating the physicochemical characteristics of the phytosynthesized nanoparticles, we explore their potential biological activity in terms of antioxidant, antimicrobial, and cytotoxic effects. The objective of this study is to evaluate the possible fields of application of AuNPs and their limitation for practical uses.
## 3 On-Site Measurements
### Embankment Section
#### 3.2.3 Force Density Spectra  
From the admittance and immission measurements, the force density spectra were determined, as described in Section 2.3. Figure 15 shows the results for different train types on the embankment track. The solid lines indicate the average spectrum and the surrounding hatched area the range between the minimum and maximum amplitudes for each class.
# Risk-Based Access Control Model: A Systematic Literature Review
## 6 Discussion
In addition, in the access control context, the security risk value will be used to determine the access decision whether granting or denying access, which requires having a precise and accurate quantitative/numeric risk value. This section answers the third research question by investigating and reviewing various risk estimation techniques utilized in risk-based access control models of retrieved/analyzed publications, as shown in Table 6.  
As discussed earlier, one of the challenges of implementing a reliable and effective risk-based access control model is to determine the risk estimation technique that produces accurate and precise risk values to determine the access decision. However, due to the unavailability of datasets that describe risk likelihood and its impact, most publications did not discuss a clear method to assess the security risks of each access request. This reflected on having 18 publications from retrieved papers without a risk estimation process. On the other hand, there are 8 publications that proposed a mathematical equation based on relationships between input and output variables to estimate the risk. However, these mathematical equations are variable dependent and cannot be adopted in different environments. In the same way, there are 7 publications that proposed the fuzzy logic system for the risk estimation process. However, the major issue in that method is the subjectivity and the need for domain experts to define fuzzy variables and build fuzzy rules. In addition, there are 7 publications that utilized the risk assessment to determine risks and assign them priorities. However, the risk assessment itself cannot provide a numeric risk value that can be used to make the access decision. For the machine learning and game theory as risk estimation methods, there are a few publications that discussed these methods. This is due to the lack of datasets that are required for training and testing phases in machine learning and for building appropriate strategies in game theory. As discussed, providing a dataset that describe risk likelihood and its impact on a specific context is one of the key issues of implementing risk-based access control models. We encourage various researchers to build and share different datasets regarding risk-based access control models that can improve the performance and add learning ability to current risk-based access control models. Having datasets that consider different risk factors in different domains can help researchers improve and optimize their current risk-based models. There are no specific criteria for a dataset for the risk-based access control model except it should provide quantitative values of risk likelihood and its impact for a set of access control scenarios in a specific context with the specifying risk factors adopted.
# Detection of Personality Traits Using Handwriting and Deep Learning
## 6 Discussion
### Comparison with State of the Art  
Since there are no public databases that correlate handwriting samples with personality traits, the comparisons presented below are based on the data obtained and published by each author.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MBTI** & **Accuracy** \\ \hline Extraversion (E) vs. Introversion (I) & 91\% \\ Sensing (S) vs. Intuition (N) & 85\% \\ Thinking (T) vs. Feeling (F) & 88\% \\ Judging (0) vs. Perceiving (P) & 83\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: MBTI prediction accuracies.  
Figure 7: Handwriting sample text written by one of the subjects.  
In [PERSON] (2016) [32], a handwriting feature detection algorithm using multilayer perceptron (MLP) is used. The features analyzed are character size, line spacing, word slant, and horizontal-to-vertical ratio of characters. The personality traits are obtained via Minnesota Multiphasic Personality Inventory tests. The results presented are based on tests applied to 70 subjects (50 for training and 20 for validation) and have an accuracy between 69% and 76%.  
In [PERSON] (2018) [9], a feed-forward neural network is used to determine the so-called Big Five personality type. The results presented are obtained based on handwriting samples obtained from 128 people. The handwriting characteristics analyzed are the baseline, the slope, connecting strokes between characters, and certain characteristics of the letter f. The results obtained range between 77% for Agreeableness and Conscientiousness and 84% for Openness to Experience.  
In [PERSON] (2019) [3], an algorithm based on Support Vector Machine is presented. The features analyzed are the text margin, baseline, character size, and letter t analysis. The database used consists of 1890 handwriting samples obtained from a group of students aged between 20 and 24 years old. The accuracy of the results is 93%.  
In [PERSON] (2022) [20], text feature extraction is performed using a Semi-supervised Generative Adversarial Network (SGAN) and a graph-based character representation. The model used is the Big Five personality traits. The database built for this application contains 1038 instances (800 for training and 208 for testing). The accuracy of the algorithm varies between 86% and 91%.  
A summary of these results is presented in Table 8.
## 5 Discussion and Comparison of the Implementation of the Proposed Techniques
Previous systems achieved a sensitivity between 72% and 98.7%, while our system achieved a sensitivity of 99.54%. Previous systems achieved a precision range of 80% to 96.1%, while our system achieved a precision of 99.68%. The previous systems achieved a specificity between 82% and 93.7%, while our system achieved a specificity of 100%. Finally, the previous systems achieved an AUC between 85.02% and 99.5%, while
## 2 Calculation Method for Surface Settlement
### Total Surface Settlement  
The surface settlement induced by the sequential excavation of a large-section tunnel under pipe-roof protection consists of three components: settlement due to tunnel excavation (u\({}_{1}\)), settlement caused by pipe-roof construction (u\({}_{2}\)), and settlement induced by groundwater level drawdown (u\({}_{3}\)). The total surface settlement can be calculated using Equation (32):  
\[\begin{split}\text{u}=\bigg{(}\frac{\gamma h}{E_{1}I_{1}}\psi_{1 }+\frac{\gamma h}{E_{2}I_{2}}\psi_{2}+\frac{\gamma h}{10k}\bigg{)}+\psi_{3} \psi_{4}+(b_{n}t_{n}\Delta h+\psi_{5})\\ \psi_{1}=\bigg{(}1+\frac{1}{10}\bigg{)}\bigg{(}\frac{5a^{4}}{384} -\frac{\lambda a^{2}b^{2}}{16}\bigg{)}\psi_{2}=\frac{L^{4}}{384}\\ \psi_{3}=\bigg{[}\frac{1-3\alpha}{p-p_{0}+\beta/(3\alpha)}\bigg{]} -\frac{1-3\alpha}{3\alpha}\\ \psi_{4}=\sum\limits_{j=1}^{n}\bigg{[}1+M\bigg{(}1-\frac{x_{j}}{ i}\bigg{)}\bigg{]}\times\frac{3\pi r^{2}\alpha}{2i\sqrt{2\pi}G}\psi_{5}=\frac{b_{ 1}{t_{1}}^{2}}{2}+\frac{1}{2}\sum\limits_{i=2}^{n-1}b_{i}\Big{[}(t_{1}+\cdots+ t_{i})^{2}-(t_{1}+\cdots+t_{i-1})^{2}\Big{]}\end{split} \tag{32}\]
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
## 3 Functional Connectivity Analysis Methods
### Model/Seed Based Methods  
Brain FC analysis among a priori regions of interest (ROIs) or voxels (11) is the most widely applied model-driven method. There are three significant steps in these methods:  
_Step 1_: Assessment of positions and shapes of ROIs or locations of voxels.  
_Step 2_: Measurement of ROI or voxel representative time series.  
_Step 3_: Connectivity evaluations for different ROIs or connecting seeds (ROI or voxel) to other voxels in the brain.  
The FC strengths that arise represent the temporal fluctuation relationships between the chosen voxels or regions. These ROIs are also referred to as seeds. As the starting step, a \"seed\" area is chosen. It is usually a precise area of interest in the brain to which we wish to measure connectivity or a collection of regions whose time courses reflect the closest resemblance to an experiment's protocol (e.g., box-car waveform). Then, a functionally-linked network is defined as regions correlating with the seed time course that surpasses a predefined threshold. For a waveform \(s\) of seed region under time course \(t\), the correlation coefficient \(r\) can be calculated as [20]:  
\[r=\ \frac{\sum\left(t-\overline{t}\right)\left(s-\overline{s}\right)}{\sqrt{ \left(t-\overline{t}\right)^{2}\left(s-\overline{s}\right)^{2}}} \tag{1}\]  
Here, \(\overline{t}\) are the means of individual time course and \(\overline{s}\) means of their respective waveform. If the value of \(r\) is 1, then it shows the perfect correlation, and the 0 value shows no correlation. The negative value (\(-\)1) of \(r\) means a perfect anti-correlation among the referenced regions.  
There are two critical aspects of seed-based analysis. First, this technique needs previous knowledge of the seed area or an external reference function (not necessarily from inside the brain), which typically requires knowledge of an experiment's methodology. The second characteristic is selecting the threshold for correlations directly connected to the significance level for correlation values. Obtaining a significant correlation coefficient depends on the signal of interest having the most significant variability when compared to experimental noise, as well as the number of time samples utilized. When conducting group analysis/multiple participants, the seed sections and threshold values should be carefully considered. Model/Seed based analysis approaches, though straightforward and easy to understand, are restricted since they are highly influenced by manual seed selection and, in their most basic form, can only show one functional system at a time. The selection of the seed region is user-dependent. However, there are several methods to measure the FC, such as cross-correlation analysis (CCA), coherence analysis (CA), and statistical parametric mapping.
The obstacle avoidance instruction is instantly fed back to the control system, achieving real-time monitoring of the space ahead and intelligent obstacle avoidance. Finally, through the development and testing of prototypes, the system's adaptability and reliability are validated. Results indicate that the system can accurately and effectively identify and avoid obstacles in the detection direction. Moreover, it can not only qualitatively detect obstacles present in the environment but also output accurate quantitative results for their spatial relative intrusion size. This lays the informational groundwork for subsequent prediction and health management over the full lifecycle of the tunnel.
# Quantum Antenna as an Open System: Strong Antenna Coupling with Photonic Reservoir
## 3 Dynamics of Quantum Antenna: Qualitative Analysis  
The strong coupling regime corresponds to the condition \(\Omega_{R}>>\Delta\), which gives \(g\approx 1\). For simplicity, we will analyze the antenna dynamics in the regime of exact resonance (zero detuning). In this case the approximations \(\gamma_{12}\approx 3\Gamma(\omega)/2\), \(\gamma_{21}\approx\Gamma(\omega)/2\), \(\gamma_{11}\approx\gamma_{22}\approx\Gamma(\omega)\) become appropriate. Therefore, the system (9)-(11) simplifies to  
\[\dot{\rho}_{11}=-\frac{\Gamma(\omega)}{2}\rho_{11}+\frac{\Gamma(\omega)}{4} \tag{25}\]  
\[\dot{\rho}_{12}=-i\bigg{(}2\
u-i\frac{3}{4}\Gamma(\omega)\bigg{)}\rho_{12}- \frac{\Gamma(\omega)}{4}\rho_{21}-\frac{\Gamma(\omega)}{2} \tag{26}\]  
\[\dot{\rho}_{21}=i\bigg{(}2\
u+i\frac{3}{4}\Gamma(\omega)\bigg{)}\rho_{21}- \frac{\Gamma(\omega)}{4}\rho_{12}-\frac{\Gamma(\omega)}{2} \tag{27}\]  
The system (25)-(27) is equal to the equations of resonant fluorescence [1]. The temporal evolution described by this system goes to the steady state defined by the condition \(\dot{\rho}_{\alpha\beta}=0\). For the strong fields such that \(\Omega_{R}>>\Gamma\) the last two terms in [1] have been neglected. As a result, the steady state values \(\rho_{11}(\infty)=\rho_{22}(\infty)=1/2\) and \(\rho_{12}(\infty)=\rho_{21}(\infty)=0\). We are keeping these terms and obtain the steady state as  
\[\rho(\infty)=\left(\begin{array}{cc}1/2&\rho_{12}(\infty)\\ \rho_{12}^{*}(\infty)&1/2\end{array}\right) \tag{28}\]  
where  
\[\rho_{12}(\infty)=\frac{\Gamma(\omega)\Big{(}2i\
u-\frac{\Gamma(\omega)}{2} \Big{)}}{2\Big{(}4\
u^{2}-\frac{\Gamma^{2}(\omega)}{2}\Big{)}} \tag{29}\]  
As it follows from (20), the antenna emission has not vanished, while the emission properties are independent on its initial state.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 4 Discussion
### General Discussion  
Ultimately, it was deemed that the use of active sampling with TD tubes outperforms SPME in terms of the overall VOC coverage and recovered amounts of plant volatiles. The use of active sampling with TD tubes presents other advantages as well, including an easier quantification of target analytes due to the removal of the differential and unknown uptake rates of compounds by SPME, and the ease with which TD tubes can be transported and stored for the long term [28; 29].  
One of the largest benefits of the _in vivo_ active sampling technique is the ability to sample whole plants without destruction. Through keeping the plant alive, it becomes an enhanced model for how the plant naturally exists and can therefore provide a more accurate profile of plant VOC production with minimal stress (or when specific experimental stressors are applied to the plant, e.g., changes in nutrients, the presence of a pest, etc.). This system can be used for several applications to analyze plant volatiles. With the human population growing rapidly, there is concern for food shortages, especially those caused by insect pests [58]. Thankfully, there are plants that can naturally deter insect herbivores by releasing VOCs, including wheat, maize, and tomato species [5; 60; 48; 59; 61]. To further understand the relationship between plant volatiles and pests, this _in vivo_ sampling system can be utilized to characterize plant headspace volatiles that may be responsible for pest deterrence by collecting samples with and without exposures to pests or by characterizing different varieties of the same plant species with known deterrent properties. Another application of the _in vivo_ sampling system is in identifying compounds responsible for pollinator attraction. Understanding how plants are being pollinated and by what species could enhance current agricultural practices, as certain volatiles attract specific classes of pollinators, which can drastically influence the reproductive success of plants. Other areas of research that this system can be applied to is studying the interactions between intra- and interplant species, monitoring the production of VOCs throughout the life cycle of a plant, assessing the effects of environmental or physical stressors on plants, analyzing genetic modifications, adapting the system to collect plant VOCs at different locations, and several other fields. With the use of the _in vivo_ plant sampling system, plant biology and chemistry can be further understood across several plant species.