esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Bioactive Phytochemicals
#### 2.4.2 Total Antioxidant Activity and Total Carotenoids Content  
Total antioxidant activity (AA) was determined using the ABTS radical cation decolorization method as described by [PERSON] et al. [20]. The sample was diluted when needed and added to a colored solution of 2,2\({}^{\prime}\)-azinobis-(3-ethylbenzothazoline-6-sulfonic acid radical cation) (ABTS\({}^{\bullet+}\)). The initial optical density (OD) of the ABTS\({}^{\bullet+}\) solution, measured at 734 nm using a UVmini 1240 UV-vis spectrophotometer (Shimadzu, Japan), was adjusted to 700 \(\pm\) 0.020. After 6 min, the final OD was measured and the results given in ascorbic acid equivalent per 100 g DW (ascorbic acid eq./100 g DW).  
Oxygen radical absorbance capacity (ORAC) assay was performed according to the method described by [PERSON] et al. [21], with some modifications in black polystyrene 96-well microplates (Nunc, Denmark). Antioxidant (20 \(\mu\)L) and fluorescein (120 \(\mu\)L; 70 nM, final concentration in well) solutions were placed in the well of the microplate at 200 \(\mu\)L final volume. A blank (fluorescein + AAPH) using 75 mM phosphate buffer (pH 7.4) instead of the antioxidant solution and eight calibration solutions using trolox (1-8 \(\mu\)M, final concentration in well) as antioxidant were also carried out in each assay. The mixture was preincubated for 10 min at 37 \({}^{\circ}\)C. After an AAPH solution (60 \(\mu\)L; 12 mM, final concentration in well) was added. The microplate was immediately placed in the reader and the fluorescence was recorded at intervals of 1 min during 80 min with an excitation wavelength at 485 nm and the emission wavelength at 528 nm. The microplate was automatically shaken before each reading. This assay was performed with a multidetector plate reader (Synergy H1, Vermont, USA) controlled by the Gen5 Biotech software version 3.04. Both AAPH and trolox solutions were prepared daily and fluorescein was diluted from a stock solution (1.17 mM) in the same phosphate buffer. Antioxidant curves (fluorescence versus time) were first normalized to the curve of the blank corresponding to the same assay by multiplying original data by the factor fluorescence blank, t = 0/fluorescencecontrol, t = 0. Results were expressed as \(\mu\)mol TE (trolox equivalent)/100 g DW.  
Total carotenoid content was total carotenoids (TC) and assessed using spectrophotometric analysis, as described by [PERSON] et al. [22], and expressed in milligram equivalent \(\beta\)-carotene per DW (mg \(\beta\)-carotene eq./g DW).
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
## 2 Experimental
### Characterization
#### 2.4.3 Mechanical Properties  
Tensile testing was determined according to ASTM D638 using a universal tensile testing machine (EZ\(-\)LX, Shimadzu, Tokyo, Japan). Tensile properties were obtained at room temperature with a crosshead speed of 50 mm/min and a load cell of 10 N. Dumbbell shaped specimens of all the blending samples were prepared following type IV, a thickness of 3 mm, an overall width of 25.7 mm, an overall length of 115 mm, and a gage width of 6 mm. The tests were conducted to determine the properties of the materials, such as tensile strength and elongation at break. The reported values were the average of six samples.  
Notched Izod impact testing was determined following ASTM D256. Samples for the Izod impact test were prepared using the compression molding process. The dimensions of a standard specimen for ASTM D256 are 63.5 \(\times\) 12.7 \(\times\) 3.2 mm (thickness \(\times\) width \(\times\) length). All specimens were notched prior to testing. The impact tester (GOTECH, GT\(-\)7045\(-\)MD, Taichung, Taiwan) was used with a pendulum energy of 2.7 J at room temperature. The impact strength was reported as energy lost per unit cross-section area at the note (J/m\({}^{2}\)). The test result was an average of four specimens for each condition.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 5 Simulation Results and Analysis  
In this section, the proposed algorithm is verified by a large number of simulation experiments in MATLAB, and compared with two multi-attribute algorithms: TOPSIS based MCRP algorithm and fuzzy theory based FLEOR algorithm. In order to avoid the contingency of the experimental results, all sensor nodes of different network size (i.e., number of sensor nodes) randomly distributed in the monitoring area, and take the average of the experimental results. The specific simulation parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**FN(i)**} & \multirow{2}{*}{**Indexes**} & **Next Hop** & **Non-Next Hop** & **Fuzzy** \\  & & **t = 1** & **t = 2** & **t = 3** \\ \hline  & \(m_{1}\big{(}A_{t}^{a_{1}}\big{)}\) & 0.0025 & 0.9900 & 0.0075 \\ \hline \(a_{1}\) & \(m_{2}\big{(}A_{t}^{a_{2}}\big{)}\) & 0.3775 & 0.2055 & 0.4170 \\ \hline  & \(m_{3}\big{(}A_{t}^{a_{1}}\big{)}\) & 0.0099 & 0.9900 & 0.0001 \\ \hline Fusion results & \(m\big{(}A_{t}^{a_{1}}\big{)}_{final}\) & 0.00999625 & 0.9900 & 0.00000375 \\ \hline  & \(m_{1}\big{(}A_{t}^{a_{2}}\big{)}\) & 0.9666 & 0.0110 & 0.0224 \\ \hline \(a_{2}\) & \(m_{2}\big{(}A_{t}^{a_{2}}\big{)}\) & 0.8125 & 0.0624 & 0.1251 \\ \hline  & \(m_{3}\big{(}A_{t}^{a_{2}}\big{)}\) & 0.2307 & 0.3077 & 0.4616 \\ \hline Fusion results & \(m\big{(}A_{t}^{a_{2}}\big{)}_{final}\) & 0.9924 & 0.0055 & 0.0021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The routing decision result.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Definition**} & **Value** \\ \hline Simulation area (\(L\times L\)) & \(100\times 100\) m\({}^{2}\) \\ \hline Network size (\(N\)) & 100–300 \\ \hline Sink & (50, 50) \\ \hline Maximum communication radius (\(R\)) & 30 m \\ \hline Packets size & 1024 bits \\ \hline Buffer size & 20 packets \\ \hline Initial energy (\(E_{0}\)) & 0.5 J \\ \hline Data generation rate & 1024 bits/round \\ \hline Energy consumed in electronics (\(E_{elc}\)) & 50 nJ/bit \\ \hline Amplifier energy dissipation in free space (\(E_{fs}\)) & 10 pJ/bit/m\({}^{2}\) \\ \hline Amplifier energy dissipation in multipath (\(E_{mp}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline Energy consumed in data aggregation (\(E_{DA}\)) & 5 nJ/bit/signal \\ \hline Distance threshold (\(d_{th}\)) & 87 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
In this model, both the multipath fading and free space channel models are taken into account [34; 35]. All the simulation parameters are not constant. We can change some parameters according to different scenarios.
## 3 Results
### Medicine
#### 3.3.1 Cardiac Activity Analysis  
There are many applications of BCG/SCG in cardiac activity analysis, such as diagnosis of cardiovascular diseases [61; 62; 63; 64], monitoring of cardiovascular status of a patient [65; 66; 67; 68; 69], and assisting in evaluation of human cardiovascular health level [70; 71; 72]. It should be noted that ECG, as the golden standard for the means of cardiovascular monitoring, always takes the leading position [73; 74], therefore, BCG/SCG research, often with reference of ECG measurement results, assists waveform reference point in signal analysis [75; 76].
## 2 Materials and Methods  
### Study Design  
In this in-vitro experimental study, mold tubes from bovine compact bone, which simulated the root canal environment, were sealed with ProRoot MTA (Dentsply Tulsa Dental, Tulsa, OK, USA). Bovine tubes with internal diameter of 2 mm, approximate height of 5 mm, and wall thickness of 0.8 mm (further referred as \"Thin\"), 1.2 mm (\"Medium\"), and 1.6 mm (\"Thick\") were used. Seven specimens were analyzed in each of these three experimental groups. Two mechanical properties of the studied material were measured for each specimen, namely the indentation hardness (gigapascal, GPa) and reduced modulus of elasticity (gigapascal, GPa) [18]. These properties were measured in two steps.  
Firstly, a large overview (Study-I) matrix consisting of three 7 \(\times\) 3 matrices was placed at the apical, middle, and coronal part of simulated root canal cross section (see red rectangles in Figure 1). The main aim was to evaluate the effect of the distance from simulated apical foramen and root canal wall thickness on mechanical properties of MTA. Secondly, to obtain particularized information about gradient of altering mechanical properties, the denser 7 \(\times\) 14 matrix (Study-II) was standardly placed approximately 1.5 mm from simulated apical foramen on 2 areas in the central part of simulated root canal cross section and 2 interfacial areas (see yellow rectangles in Figure 1). The main feature of Study-2 is its significantly higher spatial resolution.
###### Abstract
One reason for this is that the effect of calibration on the derivation of the Level 2 surface reflectance products that are necessary to produce time series of Level 3 products, such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FaPAR), is unknown. Calibration of sensors to obtain accurate Level 1 products is generally done without consideration of downstream products, such as surface reflectance, and their derivatives. This rather large void in our understanding can limit the information obtainable from these data sets significantly. Furthermore, the advent of sensor systems such as those on Landsat and Sentinel 2 that are quite similar in their basic designs begs the question of how we can calibrate these systems so that the accuracy, or at least the consistency, of downstream Level 2 and Level 3 products can be enhanced. In an attempt to address the effect of calibration on the interoperability of data from similar sensor systems, a 11/2 day \"expert-panel\" workshop was conducted. The workshop was held in conjunction with the PECORA 20 conference in Sioux Falls, SD, on November 13-14, 2017. Key to the success of the workshop was an appropriate blend of expertise so that all aspects of the problem could be considered and solid recommendations could be developed. Since both Landsat and Sentinel 2 fly science-grade sensor systems and produce free and openly available data, they were selected for the focus, but with the goal being that the workshop results would not only be applicable to Landsat and Sentinel 2, but also useful to the broader remote-sensing satellite community. Key to the problem analysis was a discussion involving both the engineers calibrating the data and the scientists using the data. Thus, a 10-member panel was selected that included Landsat calibration experts, Sentinel 2 calibration experts, and applications experts spanning a variety of disciplines. As seen in Table 1, this group of individuals forms a well-rounded body of expertise to address both calibration and its effect on data interoperability from several perspectives. The workshop was designed to maximize the contribution from each panel member and to emphasize discussions among the panel members. To that end, each panel member was allowed 30 min to present his perspective on the subject, and the rest of the workshop focused on discussions among panel members. This paper reports on the results generated from the workshop and is organized as follows. An outline of calibration procedures for Landsat and Sentinel 2 is provided with extensive references to detailed information for the interested reader. After this introduction to the sensors, a calibration comparison of the two instruments is presented. The next section of the paper presents the current status and limitations of data operability with these sensors based on four perspectives. Lastly, the recommendations produced by the panel are presented and the paper closes with a short summary.
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 1 Introduction  
The world is constantly developing information and communication technology and infrastructure, and cyberspace is recognized as the core basis for information administration services and the operation of major national infrastructure. With the recent surge in state-based cybercrime and terrorism, systematic cyberattacks are posing serious challenges to national security.  
The Department of Defense (DoD) separates cyberspace from other spaces such as land, sea, air, and space, and has announced the DoD Strategy for Operating in Cyberspace, defining cyberspace as the fifth battlefield. Accordingly, the DoD is building a cyber warfare response system [1]. In addition, JP 3-12 classifies cyberspace operations (CO) as cyber network operations (CNO), defensive cyberspace operations (DCO), and offensive cyberspace operations (OCO) [2].  
As the world has recognized the cyberspace domain as an operational and mission domain in earnest, we have been faced with the current trends in which we need to secure the ability to conduct COs from a macroscopic perspective in order to secure \"cyberspace dominance.\" The COs conducted by military forces require active and positive cyberspace actions, as well as cyberspace defense. To perform cyber intelligence, surveillance, and reconnaissance (ISR) in an enemy's cyberspace during peacetime and identify and manage targets, active COs are essentially required. The identified potential targets are recommended through the target development and prioritization process, which is the second stage of the targeting process. In this second stage, basic and intermediate target development is performed, and a list of verified and approved targets is created as a cyber target list. Finally, target priorities must be given.  
In this study, a target importance rank (TIR) algorithm based on the PageRank algorithm was used to explain the items to be considered when assigning priorities to the cyber target list and calculating the cyber target importance (CTI) scores for the items to be considered. The TIR algorithm was derived based on the PageRank algorithm to calculate the CTI score. This score is used to suggest a way to assign priorities.
## 2 Materials and Methods
### Geographic Polarization Suppression Model
#### 2.2.1 Real-Time Positioning Information Solution Method
Therefore, the expression for the solar and lunar altitude angles is shown in Equation (11), and the light incidence angle is shown in Equation (12). \[f_{1}(\alpha,\varphi,T_{time})=\arcsin(\sin\varphi\sin\delta+\cos\varphi\cos \delta\cos t) \tag{11}\]  
\[\theta=90^{\circ}-f_{1}(\alpha,\varphi,T_{time}) \tag{12}\]  
Here, \(\alpha\) represents the longitude value of the observation point; \(\varphi\) represents the latitude value of the observation point; \(\delta\) represents the solar and lunar declination angle; \(t\) represents the solar and lunar hour angle; \(T_{time}\) represents the time information; and \(\theta\) represents the solar and lunar light incidence angle.
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 3 Experimental Results
### Feature Importance  
We used the SHAP technique to extract the important features considered by the five regression models during the evaluation of walkability. The 15 most important features out of 150 were visualized in Figure 5. In Figure 5a, for multiple regression, it was determined the higher the proportion of features such as \"building\", \"tree\", \"sky\", \"road\", and \"car\", the higher score was predicted. Figure 5b shows that ridge regression was affected by the \"road\", \"sidewalk\", \"sky\", and \"car\" features, in that order, and Figure 5c shows that LASSO regression was affected by the \"road\", \"sidewalk\", \"tree\", and \"sky\", in that order. Figure 5d shows that the high prediction scores of SVR were related to \"sidewalk\", \"road\", \"building\", \"sky\", and \"tree\". Additionally, Figure 5e shows that \"road\", \"building\", \"sky\", \"sidewalk\", and \"tree\" are significant features on XGBoost. Table 6 contains 30 features that are highly dependent on the model's training process. Table 7 shows the feature importance obtained 66 features by removing common features from the feature importance of each model. After examining the top 5 feature importance in five regression models, \"road\" and \"sky\" were extracted, and upon examining the top 10, \"tree\" and \"sidewalk\" were also extracted. The fact that the following features were significantly extracted can be observed because road images were used in the walkability evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Multiple** & **Ridge** & **LASSO** & **SVR** & **XGBoost** \\ \hline
1 & building & road & road & sidewalk & road \\
2 & tree & sidewalk & sidewalk & road & building \\
3 & sky & sky & tree & building & sky \\
4 & road & building & sky & sky & sidewalk \\
5 & car & car & flag & tree & tree \\
6 & sidewalk & tree & runway & car & car \\
7 & plant & wall & stairs & pole & wall \\
8 & wall & pole & path & earth & pole \\
9 & fence & streetlight & grandstand & wall & plant \\
10 & earth & fence & refrigerator & grass & grass \\
11 & bus & truck & fireplace & signboard & fence \\
12 & grass & mountain & skyscraper & field & earth \\
13 & pole & earth & sand & ceiling & signboard \\
14 & signboard & trade name & sink & person & streetlight \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature importance by each model (ranked 1–30).  
Figure 5: Feature importances of the five regression models. (**a**) multiple linear, (**b**) ridge, (**c**) LASSO, (**d**) SVR, (**e**) XGBoost.
# An Activity Theory-Based Approach for Context Analysis, Design and Evolution
## 4 Testing Ideas through Experimentation
### Results and Findings  
For each context modeling approach reviewed by groups \(\alpha\) and \(\beta\), 840 evaluations were collected. The initial estimation is quite simple: 60 evaluators \(\times\) 14 assessment sub-items = 840. Thus, 1680 evaluations were totaled, which is 840 from group \(\alpha\) plus 840 from group \(\beta\).  
As explained above, for each factor rated, its sub-items were weighted according to a value scale between 1 and 5. Thus, each factor evaluated was classified with its corresponding value, according to its category ([A], [B], [C], or [D]). From this classification, overall averages were obtained for each factor. These final averages represent the evaluators' assessment for the examined approach in terms of the factors considered. Thus, these results are summarized and shown in Figure 6.  
In this regard, for factors [A] _context richness_ and [D] _traceability_, the activity theory-based approach shows exceptional values. Its lower value is identical in factors [B] _understandability_ and [C] _collaboration_.  
Although a more detailed discussion of this situation will be provided, it is noteworthy that the lowest values identified in factors [B] and [C] in the activity theory-based approach are higher than the maximum values for factors [A], [B], and [C] of the UML-based approach (see Figure 6).  
Regarding the UML-based approach, the value of factor [D] is exceptional in that it is the most outstanding; however, it is still far from the value obtained by the activity theory-based approach in the same factor considered.
## 2 Brief Overview of Geographic CA Modeling and Tools
### Typical CA Transition Rule Mechanisms  
Transition rules combine key geographic CA elements--the structural grid of cells, possible cell states, and neighborhood compositions--to mechanize geospatial processes that generate changes over time [8]. However, transition rules seldom rely exclusively on a cell's current state and those of its neighbors for propagating changes [47]. Instead, change events are typically produced or constrained based on current cell states, neighborhood characteristics, outcomes of other spatial or aspatial analyses, or global-level settings [11; 48; 49]. One now-ubiquitous strategy in geographic CA modeling is to integrate products of other spatial or aspatial analyses in transition mechanisms. For instance, studies have guided transition rule behaviors with suitability or susceptibility maps outputted by techniques like multi-criteria evaluation (MCE) [48; 49], or probability maps produced by machine learning (ML) classifiers [20; 38; 50]. Other studies involved estimated values produced with geographically weighted regression (GWR) to influence allocation of new changes [51]. Likewise, simply adding stochasticity to CA rule mechanisms was used to produce non-deterministic simulation outcomes [10; 44; 52], to portray the inherent uncertainty of real-world processes and anthropogenic activities [53], and to accommodate lack of complete information about system behaviors [9]. Alongside suitability maps and stochastic terms, global limits on the number of changes were also set to constrain a model's behavior [50; 54; 55] and used to implement \"what-if\" scenarios [37]. Other studies set spatial constraints to prevent changes within certain locations [55] and to examine the effects of policies [56]. Therefore, a general-purpose modeling tool should support implementations of CA transition rule extensions without requiring users to abandon transparency unless they choose to.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Availability
#### 3.3.2. Redundant Storage  
Traditional data storage media, such as solid-state storage, are subject to failure or data errors due to wear-out. A solution to these problems is the use of a distributed redundant array of independent drives (RAID) or other redundant data systems [39]. In such systems, if a data device fails, the data of the failed device are restored with the help of parity devices. On the other hand, if a parity device fails, the data in the data devices are used to restore the parity data.  
The Redundant Storage pattern [40] is presented as a solution to the problem of failure of cloud storage devices. In cases that consumers are unable to access data and/or services become unavailable, a secondary redundant cloud storage device is synchronizing its data with the data in the primary cloud storage device. Thus, when the latter fails, a storage service gateway diverts requests to the former, ensuring uninterrupted service delivery.
## 3 Results
### Evaluation Methods and Outcomes  
Fourteen studies used questionnaires for evaluation [18; 19; 22; 23; 24; 25; 27; 34; 35; 36; 37; 38; 39; 42], including the use of Likert scales in nine studies. The data were presented as the mean \(\pm\) SD. The Mann-Whitney U test, Kruskal-Wallis test, and \(t\)-test were used to compare group differences [22; 24; 25; 32; 37; 39].  
Eleven studies showed that the 3D-printed models were considered highly realistic in terms of anatomical representation, including visual and tactile feedback, and were useful for training [18; 19; 23; 24; 25; 27; 34; 35; 36; 37; 39]. Six studies regarded the 3D-printed models as cost-effective, reusable, and durable tools [18; 23; 24; 29; 35; 36]. Three studies showed that the time to complete the training procedure with the 3D models decreased significantly with repeated trials [24; 25; 27]. Two studies found that the participants' knowledge and confidence showed significant improvements after the use of 3D-printed simulator training [18; 38]. Six studies showed that 3D models helped surgeons understand the anatomical relationships of complex cases [20; 26; 28; 31; 33; 40].