esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Change in Bacterial Viability
#### 2.3.2 Live and Dead Staining  
S. mutans was grown in the presence of 5 ug/mL of MD-1, MF-1, clove oil and CHX for 8 h to evaluate the viability using Live/dead staining. The change in viability as observed through BacLight staining, which enumerates live and dead cells, is shown in Figures 5b and 6. Notably, clove oil does not show any significant increase in the population of dead cells. It is clear from Figures 5b and 6 that the exposure to MF-1 resulted in maximum increase in the population of dead cells (41 \(\pm\) 4%), followed by CHX, wherein 37 \(\pm\) 3% of the cells died due to the exposure to 5 ug/mL of CHX. The exposure to MD-1 led to the death of 25 \(\pm\) 2% cells.  
Figure 5: Decrease in the viability of _S. mutans_ when grown with various concentrations of Chlorhexidine digluconate (CHX), clove oil (CO) and essential oils (MD-1 and MF-1) as determined by MTT assay and through (**A**). Significant values (\(p\)-values \(>\) 0.05) are marked with an asterisk. Panel (**B**) shows the % population of Live and Dead as determined by BacLight staining.
# Risk Elements in Total Suspended Particles in Areas Affected by Opencast Mining of Brown Coal
## 2 Materials and Methods
### Determination of the Risk Elements Contents  
The dried TSP samples were digested as follows: whole samples were decomposed in a digestion vessel with 10 mL of Aqua regia (a mixture of nitric and hydrochloric acid in a ratio of 1:3). The mixture was heated in an Ethos 1 (MLS GmbH, Leutkirch im Allgau, Germany) microwave-assisted wet digestion system for 33 min at 210 \({}^{\circ}\)C. After cooling, the digests were quantitatively transferred into a 25 mL glass tube, topped with deionized water, and kept at laboratory temperature until measurements were taken. The following element concentrations were measured: As, Be, Cd, cobalt (Co), chromium (Cr), copper (Cu), manganese (Mn), Pb, vanadium (V), and zinc (Zn). The element concentrations in the digests were determined by using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700, Agilent, Santa Clara, CA, USA) equipped with an ORS 3 collision cell and hyperbolic quadrupole. An internal standard consisting of yttrium, ytterbium, and indium, each at a concentration of 20 \(\mathrm{\SIUnitSymbolMicro g}\)/L, was used for signal stability control. Blanks prepared from the reagents used without samples represented 10% of the total amount of the digests. The final element concentrations were expressed in \(\mathrm{mg}\)/\(\mathrm{m}^{2}\)/30 days. The detection limits for the individual elements were 0.02 \(\mathrm{mg}\)/\(\mathrm{m}^{2}\)/30 days for As, Zn, and Pb; 0.001 \(\mathrm{mg}\)/\(\mathrm{m}^{2}\)/30 days for Be and Cd; 0.01 \(\mathrm{mg}\)/\(\mathrm{m}^{2}\)/30 days for Co, Cr, Cu, and V; 0.05 \(\mathrm{mg}\)/\(\mathrm{m}^{2}\)/30 days for Mn; and 0.2 \(\mathrm{mg}\)/\(\mathrm{m}^{2}\)/30 days for Ni.
## 5 Satellite Stabilization Problem
### Example 2. The Control Problem with Complete Information about the State Vector  
The measuring system model is described by the following relationship:  
\[z(t)=\left(p(t),q(t),r(t)\right)^{\mathrm{T}}\]  
In this case, there is no need to use a state observer because there is complete information about the state vector at an arbitrary moment in time. In practice, this case is rarely realized, but it is of interest for the analysis of losses in terms of the value of the cost functional associated with the incompleteness of the information received. In all tests, the number of generated random initial states is \(N=27\) and \(L_{1}=L_{2}=L_{3}=2\) is the scale of truncation. Parameters of the mini-batch adaptive method of random search are \(B_{\max}=1000\), \(M=30\) and \(R=8\cdot 10^{-9}\). To synthesize the plant control \(u(t,x)\), a system of orthonormal Legendre polynomials is used.  
The behavior of trajectories set for different sizes of mini-batch is shown in Figure 5:  
Table 5 shows the results of solving the problem depe  
Figure 5: The behavior of the satellite coordinates \(p,q,r\) for different mini-batch sizes: (**a**) \(d=1\); (**b**) \(d=10\); (**c**) \(d=20\); (**d**) \(d=27\).  
Based on the results of examples 1 and 2, we can conclude that, with the mini-batch size \(d=10\), good convergence of the estimates of the state vector coordinates to the true values is already achieved. The total execution time of the algorithm with the mini-batch size \(d=10\) was 30 min and with the mini-batch size \(d=27\) was 90 min based on an INTEL CORE i5 2.10 GHz processor. The results obtained indicate that, when using mini-batches, the required quality of transients is achieved at reasonable computational costs.
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 1 Introduction
In short, the currently implemented access control mechanisms in CDSs lack sufficient expressiveness of the policy and, therefore, granularity, as well as enforcement features [15; 16; 17]. There is a proposal of an innovative access control model [7] enabling the enforcement of fine-grained access control policies suitable for C0 and C1 CDSs, but it is mandatory to assess the technical viability and the results are more reliable and realistic if it is done experimentally. The main contribution of this paper is a feasibility assessment of such a highly expressive E2E access control model in severely constrained devices (C0 and C1 CDSs) based on an experimental performance evaluation. The experimental performance analysis, focusing on three key performance indicators, i.e., the response time, the power consumption and the memory footprint,  
Figure 1: Scenario schema, where several stakeholders playing different roles access E2E IoT applications on different IoT domains through CDSs acting both as simple publishers (thick arrows) and as tiny E2E servers (thin arrows). provides remarkable results. Based on these measurements, the performance evaluation of this proposal demonstrates the feasibility of this analyzed access control model for resource-constrained sensors. Moreover, to the best of our knowledge, no similar experimental performance analysis assessing feasibility has been conducted based on the impact of the tightness of the access control model. Therefore, the proposed test-bed implementation and performance analysis model shall be considered as a reference for future research and benchmarking. Furthermore, on the one hand, the analyzed policy language enables the granting of decisions based on local context conditions defined as rich expressions on attributes. On the other hand, it enables reacting accordingly to the requests by the execution of additional tasks defined as obligations. Consequently, the expressiveness is far beyond the rest of the specifically designed proposals for resource-constrained devices. However, the validation requires to be demonstrated even in the most severely constrained C0 and C1 devices, and this is the purpose of this paper. The remainder of the paper is organized as follows. Related works are presented in Section 2 as the state of the art. The proposed access control model is specified in Section 3. The performance evaluation model conveying an experimental prototype is described in Section 4, and the resulting performance evaluation is discussed in Section 5. Finally, the main conclusions of the paper are presented in Section 6.
## 3 Network Framework in SD-EAR
### Physical Layer (PL)  
This layer consists of several sensor nodes that connect with the SDN controller through OpenFlow protocol [4], and receive relevant flow table information from the SDN controller in that zone. Please note that nodes are divided into certain zones, and each zone is under one unique controller.
## 2 An ODE-Based Model for OORS
### Modelling Transition Rates
At the time of request (\(k=0\)), the probability of receiving the content is given by the probability that at least one of the neighbouring nodes already has a stored copy of the content. At time \(t_{k}=k\theta\) (with \(1\leq k\leq k_{c}\)), the probability of receiving the content through an opportunistic transmission is given by the likelihood that none of the previous \((\omega_{0}+k-1)\) encountered nodes had a copy of the requested content, while the node encountered at time \(t_{k}\) stores it. The last case (4) is the probability of receiving the requested content from the cellular network because none of the \((\omega_{0}+k_{c})\) nodes encountered before the deadline \(T_{c}\) had a copy of the requested content. Owing to Lemma 1, it is straightforward to compute the expected value \(E[\widehat{W}_{c}]\) as \(\sum_{k=0}^{k}p_{W}(k,\mathcal{I})k\theta+p_{W}(k_{c}+1,\mathcal{I})T_{c}\). Using (3) and (4), after some algebraic manipulations, we obtain that  
\[E[\widehat{W}_{c}]=\frac{\theta(1-\mathcal{I}^{*})^{\omega_{0}}\Big{\{}1-(1- \mathcal{I}^{*})^{k_{c}}\Big{[}1-\mathcal{I}^{*}\Big{(}\frac{T_{c}}{\theta}-k _{c}\Big{)}\Big{]}\Big{\}}}{\mathcal{I}^{*}}\, \tag{5}\]  
where \(\mathcal{I}^{*}=\mathcal{I}/N\). We emphasise that \(E[\widehat{W}_{c}]\) is a discontinuous function of \(\theta\) because \(k_{c}\) is obtained through a floor function. Traditional convergence analysis of numerical methods for ODE systems relies on the assumption of smooth functions. We can easily remove discontinuities by substituting the floor function with its argument. Then, expression (5) simplifies as  
\[E[\widehat{W}_{c}]=\frac{\theta(1-\mathcal{I}^{*})^{\omega_{0}}\Big{[}1-(1- \mathcal{I}^{*})^{T_{c}/\theta}\Big{]}}{\mathcal{I}^{*}}. \tag{6}\]  
It is worth pointing out that expression (6) has a nonlinear dependency on the \(\mathcal{I}(t)\) function. However, it allows us to express \(\mu(t)\) as a function of \(\mathcal{I}(t)\), which is needed to solve the system (2). Finally, we assume that \(\lambda(t)\) is constant and equal to \(\lambda_{0}\). Formally, this means that \(\lambda(t)=\lambda_{0}\) if \(t\in[0,(N+1)/\lambda_{0}]\), and zero elsewhere. It is also straightforward to derive that \(\gamma(t)=1/T_{s}\) as the sharing timeout is fixed.
## 2 Materials and Methods  
A total of forty extracted single-rooted teeth were selected for the present study to perform operative torque test. The single-rooted teeth were extracted for periodontal and orthodontic reasons. After the extraction procedures, teeth were immediately stored in saline water 0.2% until examination. Teeth presenting root caries, restorations, deformation such as resorption, immature apex, root fractures, and calcification were discarded. The inclusion criteria were as follows: teeth presenting one canal with a class I according [PERSON]'s classification and a curvature smaller than 30\({}^{\circ}\) according [PERSON]'s criteria [24; 25]. As showed in Figure 1, to clearly investigate the root-canal anatomy, the teeth were previously analyzed through the use of cone beam computed tomography (CBCT) in axial, coronal, and sagittal sections [26].  
An endodontic access and a manual glide path with a #15 K-file (Dentsply, Maillefer, Tulsa, OK, USA) to the working length was performed on all the extracted teeth [27].  
The forty extracted teeth were randomly divided into four different groups (n = 10).  
To perform the operative torque test, a brand-new endodontic file was utilized for all the tests. The files utilized in the present studies were the Edge Taper F2 (Edge Endo, Albuquerque, NM). These files present as shaped cross-sectional design, with tip 25 and taper 0.06. Before the instrumentation, each of the NTR files were inspected with a \(\times\)20 microscope to detect any sign of deformation.  
For each group, different endodontic irrigants were selected: Group A, aqueous saline solution (NaCl 0.2%, OGNA, Milano, Italy); Group B, aqueous sodium hypochlorite (NaCl5%, OGNA, Milano, Italy); Group C, aqueous ethylenediaminetetraacetate (EDTA), and Group D, paste-type combination of acetyl alcohol, EDTA, and hydrogen peroxide (RC PREP, OGNA, Milano, Italy). Before proceeding with the instrumentation of the canal, a total of 1 mL of the selected irrigant was introduced into the canal with the use of a 30-gauge syringe. Afterwards, files were rotated, according to the manufacturer recommendation, clockwise at 300 rpm with 3 Ncm maximum torque by an endodontic torque recording motor (Kavo, Biberach, Germany) [28]. This endodontic torque recording motor ensure a real-time evaluation every 1/10 s. The operative torque test was concluded when the file reached the apex [29].  
Mean values of torque generated during intracanal instrumentation (Ncm) were recorded for all the groups taken in exams. In addition, the quantity and the maximum value of all peaks (average peak torque) were recorded for all instrumentation. Afterwards average peak torque (APT) mean value were calculated for each instrument. Data for all the groups were recorded and analyzed using a SPSS 17.0 software (SPSS Incorporated, Chicago, IL, USA). One-way ANOVA was performed for all the tests with a level of significance set at \(p<0.05\). Afterward, a post hoc Bonferroni between groups test was performed.
## 4 Results and Discussion
### Hysteresis Effect of 8 Axle  
The experimental results obtained under the testing condition of group 1 are shown in Figure 9, including the original data curve and the calculated delay time of eight axles based on Formula (1). The standard deviation is used to weigh the random error. Moreover, the delay times of eight axles under the testing conditions of all the testing groups are calculated as shown in Table 3. From this table, we know that the delay time of each loop is different from each other in both the brake process and brake release process. The asynchronous response of the multi-axle vehicle leads to different braking torques on different axes at the same braking moment, which may seriously affect the braking stability. The hysteresis effect in brake release process is relatively more obvious. This effect may lead to an unexpected once-more brake action due to a slow gas exhaust of the loop, which will result in difficult restarting of the vehicle. Therefore, when developing the hysteresis compensation control strategy for multi-axle vehicle, different delay times of every loop both in the brake process and brake release process should be taken into consideration.  
Figure 8: The working procedure of the PBS.
## 3 Results and Discussion
### TPC and TFC  
A review of the literature shows that in most studies on _H. perforatum_, extraction was carried out using toxic solvents such as methanol and acetone, which are not compatible with food, cosmetic, or pharmaceutical applications. Moreover, industrial-scale production is costly and harmful to the environment. The most commonly used solvents are pure ethanol and methanol, but they may be even more efficient when mixed with water [41]. Other authors report the polyphenol content of the methanolic extracts as follows: 505.7 [35], 461.19 [42], 355.01 [23], and 64.08 [43] mg GAE/g dw. The values reported using ethanol or ethanol-water solvents vary within a vast range, from 15.54 [33] and 64.4 [44] to 104.18 mg GAE/g dw [45]. However, due to the different solvents used in the cited articles and the different units in which the results are presented, it is difficult to compare them with the results obtained in the present paper. This study was carried out using non-toxic extraction media (GL and PEG). The results of TPC and TFC determination in GL-WE and PEG-WE of _H. perforatum_ are compared in Table 3. A higher concentration of polyphenols was observed in GL-WE. Glycerol-water solvents have been successfully used to extract polyphenols from various plants [46; 47; 48; 49]. [PERSON] et al. [34] demonstrated that an extraction yield of total polyphenols (89.9 mg GAE/g dw) was achieved using 10% (\(w/v\)) aqueous glycerol, which was significantly higher than that attained with water. However, the use of GL for the preparation of plant extracts is not as common as the use of water or ethanol. GL is a suitable extraction solvent due to its low cost, lack of toxicity, imcombustibility, low volatility, and easy blending with water (which allows for the extraction of polyphenic compounds) [50]. According to bibliographic data for _Hypericum_ spp. GL-WE, the following TFC values were obtained: 86.6 mg GAE/g dw [34] and 54.83 mg GAE/g dw [51].  
It should be noted that polyphenols can be easily solubilized in polar protic media, such as ethanol-water and glycerol-water mixtures [34]. Higher yields have been obtained by increasing the polarity of the solvent, indicating that mainly hydrophilic compounds were extracted. Therefore, extraction using the tea method or a mixture of water and organic solvents resulted in higher yields compared with pure organic solvents or continuous boiling with water for extended periods [44].
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 4 Study on the Mechanism of GO Adsorption Process
### Influence of External Environment on Adsorption  
The adsorption reaction of GO to metal ions is usually carried out in an aqueous solution, so the pH value, temperature and interfering ions of the aqueous solution will affect the adsorption effect. In this paper, the influence of various influencing factors on the adsorption process was analyzed through DFT calculation so as to lay a foundation for quickly finding the appropriate reaction conditions during the experiment.  
Figure 3: Electrostatic potential diagram of GO with different functional groups in stable configuration: (**a**) GO(OH)\({}_{s}\), (**b**) GO(O), (**c**) GO(OH)\({}_{b}\), (**d**) GO(COOH).
## 4 Conclusions  
Vibration signals of rolling bearings have the problems of overload information of time-frequency image and difficulty in fault diagnosis. To solve the problems, we propose a fault diagnosis method based on time-frequency compression fusion and residual time-frequency mixed attention network. The proposed method is verified on the bearing dataset of the University of Ottawa, and carries out the performance tests under different sample sizes, sampling times and sampling frequencies. The experimental results show that the time-frequency information of fast, slow and diffuse signals all contribute to the fault identification of the model and the TFCF time-frequency image can give full play to the performance of the diagnosis model. The residual time-frequency mixed attention module reduces the information loss after feature matrix weighting, and focuses on the important time-frequency information from the three dimensions of the TFCF image channel, time and frequency, which accelerates the convergence speed of the model training and improves the recognition accuracy to 99.86%. The proposed diagnosis model can not only solve the fault diagnosis under normal working conditions, but also maintain good performance under small sample size, short sampling time and small sampling frequency, and has broad application prospects.  
Conceptualization, G.S.; data curation, X.Y., Y.H., C.X. and M.L.; methodology, X.Y., Y.H. and C.X.; project administration, G.S. and X.Y.; writing--original draft, X.Y., Y.H. and C.X.; writing--review and editing, G.S., X.Y. and C.X.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grant 51775177).  
Not applicable.  
Data Availability Statement: Data was obtained from the bearing dataset of the University of Ottawa in March 2022 and are available [[[https://data.mendeley.com/datasets/v43](https://data.mendeley.com/datasets/v43) hmbwxpm/1]([https://data.mendeley.com/datasets/v43](https://data.mendeley.com/datasets/v43) hmbwxpm/1)] with the permission of the University of Ottawa.  
The authors declare no conflict of interest.  
Figure 9: Experimental results of model performance test.
## 1 Introduction  
One of the most widely used methods for evaluating the usability of a product, software, or system is a think-aloud protocol [1]. In usability testing, a think-aloud protocol is a method of verbalizing what participants experience, think, act, and feel while performing a task [2]. This method helps to discover usability problems and to examine improvements because it provides direct information on a user's thinking and cognitive processes, which are challenging to examine through mere observation or questionnaire surveys. For this reason, the think-aloud protocol is often taught in courses that train usability and user experience (UX) specialists [3, 4, 5]. In addition, [PERSON] et al. [6] and [PERSON] et al. [7] surveyed usability and UX practitioners and reported that most of the respondents frequently used a think-aloud protocol for usability testing and considered it to be the most common method for finding usability problems. Therefore, a think-aloud protocol is a standard method for usability evaluation [8].  
However, there are some challenges associated with implementing and analyzing a think-aloud protocol [7]. In particular, even if qualitative verbal and observational data are obtained through usability testing, it is often difficult to determine how to analyze the data to identify usability problems. The analysis is time-consuming, and the quality of the results varies depending on an analyst's skills. In addition, usability testing is not necessarily conducted only by expert analysts. When the resources of ergonomic experts are limited, a usability evaluation is conducted by non-expert analysts. In such cases, the analysis of a think-aloud protocol becomes more difficult.  
One of the reasons for this challenge is the lack of a clear method for analyzing qualitative data (video and text data) obtained from a think-aloud protocol. Once a formulaic analysis procedure is established, analysts can perform textual data analysis easily and efficiently. They can analyze the data without hesitation after data acquisition, and there are fewer omissions. In addition, it is expected that the procedure would be easy to use, even for novice designers.  
The purpose of this study is to propose a formulaic analysis procedure for the think-aloud protocol that specifies the procedure for analyzing verbal responses after user testing. The aim is to develop a method that can extract usability problems and user requirements with few omissions.