esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Solution: Modelling Components of a Communication Protocol as Resources
### Functional Flows
#### 3.2.1 Phase 1--Protecting a Topic  
As we have explained before, MQTT topics are modelled as UMA resources. Right after a new topic is defined, it must be registered for protection. Under our proposal, the creator of a topic will  
Figure 3: The main diagram of the proposed solution. ADC: Analog to Digital Converter; DAC: Digital to Analog Converter; DEMUX: Demultiplexer; HTTP: Hypertext Transfer Protocol; IoT: Internet of Things; MUX: Multiplexer.  
also be the topic owner. From the UMA point of view, the topic creator will be the Resource Owner. A set of rules has to be observed when protecting a new topic:  
1. In MQTT, topics follow a hierarchical tree structure. A protected topic will be defined as a complete route from a root to a leaf.
2. A previously protected topic cannot be protected again.
3. A child topic cannot be protected if the user has not permissions over the parent topic.
4. The protection applied to a topic is applied to it and all its descendants.  
Once the owner has defined the new topic as a UMA resource, the remaining process to protect it is the same as defined by the UMA specification.
## 4 Discussion  
### Spatial and Temporal Baselines  
As presented in Section 2.2, \(|\gamma|\) decreases with larger temporal baselines due to temporal decorrelation. For the case of SAOCOM-1 \(\gamma_{tmp}\) seems to follow an exponential pattern, as suggested by Equation (9), see Figure 11(a), where the parameters \(\tau=12\) day and \(\gamma_{\rm inf}=0.125\) are considered. In particular, the latter is selected in such a way as to account for the coherence estimation bias for zero coherence. There are, however, some points that do not seem to fit totally with this exponential model. This is because this temporal series does also have high variability in the spatial baseline in comparison to Sentinel-1, and Figure 11(a),b depicts this situation more clearly. Both scatter plots are based on the same pixels of the forested area in Figure 11(a) and considered for the interferometric coherence matrices, but in this case \(|\gamma_{TVV}|\) is compared with the value of the temporal baselines and \(B_{\perp}\). These graphs show that large \(B_{\perp}\) values seem to have a negative impact on the interferometric coherence, regardless of the temporal separation between the images and the range spectral filtering applied to mitigate the spatial decorrelation. This might be explained by the volumetric decorrelation term, which is baseline-dependent as indicated by Equation (7). Another interesting observation is that in the case of Sentinel-1, \(\gamma_{tmp}\) seems to have a strong influence on the coherence values, as temporal decorrelation is larger at C-band despite the much smaller variation and values of \(B_{\perp}\), see Figure 12(a),b.  
Figure 12: SAOCOM-1 \(|\gamma_{TVV}|\) vs. baselines.  
Figure 13: Sentinel-1 \(|\gamma_{TVV}|\) vs. baselines.
## 2 Materials and Methods
### Gene Expression Analysis  
TRI reagent (Ambion(r), Life Technologies, Waltham, MA, USA) was used for total RNA extraction from PBMCs. cDNA was synthesized from 0.1 \(\upmu\)g of total RNA with a Revert Aid First Strand cDNA Synthesis Kit (ThermoScientific, Waltham, MA, USA). Relative gene expression analysis was performed with two-step real-time qPCR. SYBR Green qPCR MasterMix with ROX (KAPA SYBR FAST qPCR Kit, KAPA BIOSYSTEMS, Wilmington, MA, USA) was used for the real-time PCR reaction and primer sets were as follows: U6 F: GCTTCGGACACATATACAAAAT; R: CGCTTCACAGAATTGCGTGTCAT; HMOX1 F: TCAGGCAGAGGGTGATAGA; R: TTGGTGCATGGGTCAGC; NRF2 F: TCCAGTCAGAAACCAGTGAT; R: GAATGTCTGCGCCAAAGCTG; NF-kB F: AGAGGCTTCCGATTCGATATATG; R: GGATAGGTCTTCGGCCCTT. Real-time PCR was carried out on an Applied Biosystems(r) 7500 Real-Time qPCR instrument (Waltham, MA, USA). Gene expression levels of the genes of interest were calculated according to the \(2^{-\Delta\Delta\text{CT}}\) method, where U6 served as endogenous control.
# Emergence of Nano-Dentistry as a Reality of Contemporary Dentistry
## 4 Discussion
### Nanotechnology in the Surgical Field
#### 4.2.3 Nanotechnologies in the Correction of Deformations and Defects of Bones  
Nanophase materials have shown promising results in the treatment of various deformities and bone defects. Nanophase hydroxyapatite and nanophase carbon are the two main promising types of nanophase materials used to treat bone defects.  
Nanophase hydroxyapatite shows excellent osteoblastic adhesion compared to traditional materials. Nanoparticles of nanophase hydroxyapatite (HA) are used to treat bone defects--NanOSSTM HA (Angstrom Medica, Woburn, MA, USA), Vitosso (Orthovita, Inc, Malvern, PA, USA) HA + tricalcium phosphate (tri CaP), and Ostim HA (Osartis GmbH, Germany) [86, 87].  
Unlike nanophase hydroxyapatite, carbon nanophase exhibits excellent biomechanical properties due to a combination of not only nanoscale but also similar to natural HA. Thus, making it the material with the greatest potential for the correction of maxillfacial defects and maxillofacial implant material in the future.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 3 Terminologies Used in WSNs
### Hop Count  
The hop count (HC) is widely use as a decision variable in the design of energy-efficient and energy-balanced routing protocols for WSNs. In fact, some studies [44; 45] used the hop count as an evaluation metric for comparing the performance of WSN routing protocols. The hop count is defined as the number of relay nodes traversed by the message from the source node to the destination. It is desirable to minimise the hop count during the data transmission phase in order to extend the network lifetime of WSNs as much as possible, but there is a compromise between reducing the hop count and maximizing the network lifetime of WSNs. By reducing the hop counts from the source node to the destination, some nodes are compelled to convey a large amount of load and can easily deplete their energy. Therefore, the hop count is broadly considered as a decision variable while designing a load-balanced energy routing protocol for WSNs.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 4 Method  
### Experimental Setup  
Our experiments take place within the mapped environment displayed in Figure 3. We customized this environment using insights from prior research [11]. We implemented a dynamic water environment capable of being adjusted to accommodate various wave conditions, encompassing parameters such as height, speed, and scale. The dynamic water environment is described as a floating surface containing multiple waves, and its attributes are established based on Equation (1):  
\[y_{x,z}=A\cos\left(\omega t+\left(\frac{x_{t},\ z_{t}}{K}\,2\pi\right)\right), \tag{1}\]  
where \(y_{x,z}\) denotes the magnitude of the height value for each point in a Cartesian coordinate system, \(A\) is the amplitude or maximum of height, and \(\omega\) is the wave speed value. Additionally, \(t\) is the time, \(x_{t}\) and \(z_{t}\) are the x and z positions, and K is the wave unit. This formula enables the agent to remain buoyant on the water surface. To create a dynamic environment, we input specific values into the equation, allowing us to configure the environment to have varying wave effects. Conversely, we rendered the environment static by inputting a value of 0 for 'A,' thereby eliminating the influence of waves. The experiments in this study used the input values \(v_{x}\) 2, \(v_{y}\) 3, scale \(x\) 4, scale \(y\) 6, and height 1.2. Using a larger value makes the dynamic environment more challenging to solve.
# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 6 Accuracy and Suitability Analysis
[39]: Not feasible for the author-claimed _Energy and Space Utilization_ application. The solution accuracy does not meet ADP accuracy criteria, i.e., 85% < 90%. It is a data-driven method and requires historical sensor data for developing Gaussian distribution models for diverse occupancy patterns for all monitored spaces. It is achieved via averaging the fitting results for the past twenty-four days. Ref. [40]: Not feasible for the author-claimed _Energy and Space Utilization_ and _HVAC Control and Occupant Comfort_ applications. The solution accuracy does not meet ADP accuracy criteria, i.e., 87.5% < 90%. Although the execution platforms are mature (IoT and cloud-based), the fusion algorithm is a simple aggregation algorithm that does not work well with motion sensors. The motion sensors are placed at a distance no less than 30 cm from under the desk of subjects, yet the occupancy sensing algorithm assumes that subjects not completely stationary while sitting at their desks and while not present at their desks, the office space is vacant. Ref. [42]: Suitable for the author-claimed _HVAC Control and Occupant Comfort_ and _Energy and Space Utilization_ applications. The solution is not expected to scale up as the spatial NDD is very high, i.e., 18 ft\({}^{2}\)/node. The method requires ground-truth data to label and train IHMM parameters via supervised learning. New sensor data is also needed to update the historical dataset to periodically tweak the model parameters, for accurate long-term applications, such as space usage analysis and occupancy modeling. The solution is not optimized in terms of node power consumption and IoT/Edge AI execution. The solution system level diagram is shown in Figure 5 for reference.
## 6 Experimental Evaluation and Discussion
### Data Recovery  
It is our most important goal to both ensure users communicate normally and protect user privacy. In other words, defense against WFP attacks must be based on the premise that users can work properly, so we must ensure the reliability of user data transmission. Here, we simulate the data slice transmission process through a simulator, and during the experiment. We artificially discard some data or interfere with the slice transmission sequence to evaluate the final data transmission success rate. Redundant slicing is an important basis for SMART to ensure the reliability of data transmission. We designed three control experiments with different redundancy values **r**, including (r = 0, 5, 10, 15, and 20), and different numbers of circuits (m = 1, 3, 5, and 7). Respectively, the process of slicing and the recovery of 1000 data was repeated five times. The data recovery is observed by fixing the number of redundant slices **r** constant or the number of **m** constant, and Figure 9 is the average data recovery rate (transmission success rate). We observe that the data recovery capability gradually increases when the proportion of the anonymous multi-path rises; the success percentage of multi-path transmission also gradually increases with the increase of redundant slices **r**.  
Figure 8: Comparison with the other four state-of-the-art defenses.  
In the case of data loss, the data recovery time of TrafficSliver without the redundant slicing function will far exceed our defense. This is because TrafficSliver must reorganize data sequentially and, when one packet is lost, the packets to be sent must queue up for the lost one to be resent, thus reducing the overall transmission speed. The redundant slicing policy we designed allows a certain percentage of data loss in the Tor network, but still recovers the complete data without retransmission, with a recovery rate of nearly 97%. This design not only protects user privacy but also guarantees the reliability of communication by users using the Tor network.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Measurement Using the Fuzzy Bayesian Network
#### 3.4.1 Reliability Quantification for the Employed Experts
In a heterogeneous group, the individual experience of each expert receives considerable attention;
* With respect to the education and experience of the experts in a field, the longer they have focused on a subject (academic or practical subject), the more accurate their intuitionistic judgement is;
* With respect to expert familiarity with a subject, especially through practical experience, an experienced specialist can theoretically master every detail of the subject. Based on the abovementioned criteria, a heterogeneous group of five experts (their information is listed in Table 7) was developed to comment on the various disruption and resilience capacities associated with the Arctic NSR. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Item** & **Age** & **Occupation** & **Educational Level** & **Certificate Rank** & **Job Tenure** \\ \hline Expert 1 (E1) & 53 & Senior seafarer & Bachelors of navigation & Senior Captain & He has been working on board a ship for nearly 25 years; as a senior captain, he sailed the Arctic Northeast Route recently. \\ \hline Expert 2 (E2) & 50 & Senior seafarer & Bachelors of navigation & 2 nd Officer & He has been working on board a ship for nearly 15 years; currently, he is certified as a Chief Officer working on a ship capable of navigating the NSR. \\ \hline Expert 3 (E3) & 48 & Professor & Ph.D. of navigation & Senior Captain & He has been working on board ships since 1991 and obtained the certificate of senior captain; currently, he is an associate professor focusing on Arctic sea transport. \\ \hline \end{tabular}
\end{table} 
Table 7: Details of the experts. Figure 8: Bayesian network (BN) established for measuring the resilience of the NSR. The weight for each expert was assessed based on their education level, expertise, professional position, work experience, and age [46; 73; 74; 75]. In addition, the competency certificate level of the selected experts was also regarded as an important indicator. Finally, the rating criteria were developed, as presented in Table 8.  
Based on the information from the employed experts shown in Table 7, the capacity was first scored by every individual expert according to the rating criteria presented in Table 8; then, pairwise comparison matrices were derived. Finally, the weight for each expert was obtained by applying Formulas (8)-(11), as summarized in Table 9.
## 3 Optimization Model and Evaluation Indexes
### Degwo-Bpnn  
#### 3.3.1 Concept of DEGWO-BPNN  
Because the grey wolf algorithm has the defect of premature stagnation, a hybrid differential evolution grey wolf optimizer algorithm (DEGWO) was proposed on account of the GWO algorithm. DE algorithm is applied to mutate the population, making GWO jump out of the search stagnation state, accelerating the convergence speed, and improving its performance. DE algorithm performs mutation, crossover, and selection operations on the population updated by the GWO algorithm to keep the diversity of the population. Using the powerful search ability of the DE algorithm, the \(\alpha\), \(\beta\), and \(\delta\) wolves are selected from the population position updated by DE, thus jumping out of the situation where the GWO algorithm falls into search stagnation. The optimal weight and threshold of BPNN can be searched in DEGWO, and the prediction performance and generalization ability of the BPNN model can be greatly improved.
## 4 Results
### Experienced System Characteristics and Automated Driving Style Preference
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Nr.** & **Rated Driving Situation Categories** & **Meaning** \\ \hline
1. & Reaction to Speed Limit & Timing and intensity of deceleration in front of a speed reducing speed limit sign and acceleration behind an increasing speed limit sign. \\ \hline
2. & Deceleration to Lead Vehicle: Intensity & Deceleration intensity when approaching a slower lead vehicle. \\ \hline
3. & Driving Path in Curve & Driving paths taken in curves on motorway feeder roads, on- and off-ramps. The driving paths varied between driving in the lane center and driving closer to the inside of the curve. \\ \hline
4. & Deceleration in Curve & Timing and intensity of deceleration in front or while driving through curves on motorway feeder roads, on- and off-ramps. \\ \hline
5. & Lane Selection & Appropriateness of lane selection decision. In this category, no lane change was performed. Participants were expressing either their satisfaction with the choice of lane or dissatisfaction when they wished to switch lanes to the left for the purpose of driving faster or responding to a potential cut-in vehicle, or to the right in order to comply with the rule of driving on the right-hand side. \\ \hline
6. & Overtaking on the Right & Appropriateness or correctness of overtaking on the right side. Note: The AV always applied with traffic regulations and only overtook on the right if conditions to legally do so were fulfilled. \\ \hline
7. & Lane Change Right: Decision & Appropriateness of the decision to change to the right lane. \\ \hline \end{tabular}
\end{table} 
Table 4: Explanation of the Rated Driving Situation Categories. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Rating Criteria** & & **Selected ADS** & **Unselected ADS** & & \\
**Rating Criteria** & **Driving Situation Category** & **M (SD)** & **M (SD)** & **U** & **_p_** \\ \hline \multirow{6}{*}{Comfort} & Reaction to Speed Limit & 2.36 (0.97) & 1.36 (1.87) & 1512 & 0.013 * \\  & Deceleration to Lead Vehicle: Intensity & 2.26 (1.01) & 0.79 (1.93) & 228 & 0.027 * \\  & Driving Path in Curve & 2.46 (0.71) & 1.71 (1.47) & 1937 & 0.012 * \\  & Deceleration in Curve & 1.88 (0.93) & 0.75 (1.54) & 147 & 0.037 * \\  & Lane Selection & 0.21 (1.22) & \(-\)1.21 (1.28) & 453.5 & \textless{}0.001 *** \\ \hline \multirow{6}{*}{Safety} & Reaction to Speed Limit & 2.68 (0.77) & 2.06 (1.48) & 1502.5 & 0.006 ** \\  & Deceleration to Lead Vehicle: Intensity & 2.70 (0.70) & 2.07 (1.14) & 221 & 0.027 * \\ \cline{1-1}  & Driving Path in Curve & 2.33 (0.80) & 1.67 (1.46) & 1882 & 0.033 * \\ \cline{1-1}  & Overtaking on the Right & 2.60 (0.97) & 0.45 (2.21) & 84.5 & 0.023 * \\ \cline{1-1}  & Lane Change Right: Decision & 2.76 (0.60) & 1.95 (1.73) & 661 & 0.038 * \\ \hline \end{tabular} Note: * \(p\) \(<\)0.05, **_p_ \(<\)0.01, *** \(p\) \(<\)0.001.  
\end{table} 
Table 3: Comparison of comfort and safety ratings between group Selected ADS and group Unselected ADS in different driving situation categories.
## 2 Materials and Methods
### Land Cover Data  
The Moderate Resolution Imaging Spectroradiometer (MODIS) annual land cover type products (MCD12Q1) were utilized to identify vegetation types within the study area. These products, which have a spatial resolution of 0.05\({}^{\circ}\), can be downloaded from [[https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)]([https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)) (accessed on 11 March 2022). The land cover was classified into 17 categories based on the International Geosphere-Biosphere Programme (IGBP) classification schemes. The data were re-gridded to a spatial resolution of 0.083\({}^{\circ}\)\(\times\) 0.083\({}^{\circ}\) to align with NDVI data, allowing us to estimate the NDVI-GPP relationship across different PFTs. We concentrated on the following vegetation types: ENF (evergreen needleaf forest), EBF (evergreen broadleaf forest), DNF (deciduous needleaf forest), DBF (deciduous broadleaf forest), MF (mixed forests), CSH (closed shrulands), OSH (open shrulands), WSA (wo woody savannas), SAV (savannas), GRA (grasslands), WET (wetlands), CRO (croplands), and CNVM (cropland/natural vegetation mosaics) (Figure 1a).  
Due to their limited coverage (0.01% for closed shrulands, 0.03% for open shrulands, and 0.04% for permanent wetland), these vegetation types were excluded from the analysis to avoid potential data distortion. As depicted in Figure 1a, grasslands dominate the Inner  
Figure 1: (**a**) Land use types in China in 2010, including ENF (evergreen needleleaf forest), EBF (evergreen broadleaf forest), DNF (deciduous needleaf forest), DBF (deciduous broadleaf forest), MF (mixed forests), CSH (closed shrulands), OSH (open shrulands), WSA (woody savannas), SAV (savannas), GRA (grasslands), WET (wetlands), CRO (croplands), UB (urban and built-up), CNVM (cropland/natural vegetation mosaic), PSI (permanent snow and ice), BSV (barren of sparsely vegetated), and WAT (water bodies). (**b**) Spatial pattern of aridity index (AI) in the study area. (**c**) The classification of the Koppen-Geiger climatic zones. Spatial distribution of annual mean NDVI (**d**) and GPP (**e**) during the growing season from 1982 to 2018.  
Mongolian Plateau, the Loess Plateau, and the Tibetan Plateau. Forestlands are mainly prevalent in southern China and northeastern China, while croplands are concentrated in the North China Plain, the Northeast Plain, the Loess Plateau, and the Sichuan Basin. Woody savannas and savannas are mainly distributed in southern China. The Koppen-Geiger climatic zones over China were shown in Figure 1c. The AI patterns shown in Figure 1b and spatial distribution of annual mean NDVI and GPP in China during the growing season from 1982 to 2018 were illustrated in Figure 1d,e.
## 3 Results and Discussion
### Post-Multiply FV Method
In contrast, the other rods completely disappeared, since their intensity values were less than the selected threshold limit of the 3D surface. Therefore, the combination of primary 3D NRF-CT images with high-resolution 3D gamma-CT images obtained by the FV technique provided a beneficial improvement in the image resolution and the isotope-selective capability in three dimensions. The numerical treatment of the FV technique can be used in different approaches for primary 3D-CT images. Most of these approaches are similar to the post-multiply FV method, except for a few minor differences. One of the approaches is to directly overlap a 2D NRF-CT image with a 2D gamma-CT image without the 2D gamma-CT image segmentation process according to the following equation:  
\[\left[\text{2D FV NRF}-\text{CT}\left(x_{i},z_{j}\right)\right]=\left[\text{2D NRF}^{\sim}-\text{CT}\left(x_{i},z_{j}\right)\right]\times\left[\text{2D gamma}-\text{CT}\left(x_{i},z_{j}\right)\right] \tag{2}\]  
Figure 10: A shot of the three-dimensional visualization of the fused CT-image of the NRF attenuation caused by the distribution of \({}^{208}\)Pb (3D FV NRF-CT image). See corresponding Movie S2 in the Supplementary Materials. The 2D FV NRF-CT image obtained by this approach in the current study had the required quality. However, it could not be used for isotope quantification because the intensity at each pixel of the NRF-CT image was not conserved. Furthermore, even if the background noise were significantly reduced, it would be impossible to eliminate all noise. Another approach is the post-sum FV method, for which the overlapping is as follows:  
\[\left[\text{2D FV NRF}-\text{CT}\left(x_{i},z_{j}\right)\right]=\left[\text{2D NRF}^{\sim}-\text{CT}\left(x_{i},\ z_{j}\right)\right]+\left[\text{2D gamma}-\text{CT}\left(x_{i},\ z_{j}\right)\right] \tag{3}\]  
The fused image obtained using the post-sum FV method shows the change in the intensity at each pixel but retains some of the background noise, so this method is useless for isotope quantification. Figure 11 shows a comparison between the 2D FV NRF-CT images for the layer L4 obtained by (a) the post-multiply FV method, (b) the post-multiply FV method without 2D gamma-CT image segmentation, and (c) the post-sum FV method. These results show that the alternative approaches may change the intensity of each pixel, so that they are unable to maintain the quantity of the isotope of interest. Furthermore, the noise cannot be completely removed even if it can be greatly decreased. Therefore, we recommend the post-multiply FV method as the most beneficial method for NRF-CT imaging that is likely to be effectively applicable in a variety of fields, including nuclear engineering and nuclear safety.