esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Privacy
#### 3.2.3 Location Privacy  
Pseudonymization can help in hiding the location of users. However, to avoid complications emerging from the use of a single pseudonym, a user may be connected with many pseudonyms (or addresses) to avoid transactions relating to a single user from being linked [57]. Utilizing one-time addresses for this purpose will rapidly result in a huge number of addresses per participant. Bitcoin, Ethereum, Monero, and other cryptocurrencies include privacy measures based on one-time addresses [10]. The usage of so-called stealth addresses, which were created for financial BC platforms to ensure payee anonymity, is a good example of BC technology. The payer produces a one-time address for each transaction with a single payee by suitably applying cryptographic processes. This technique is used in Bitcoin and can be applied by other platforms, such as Ethereum and Monero [10]. Moreover, the BC segregation technique can be used to ensure location privacy [10].
## 4 Test Results of the Active Steering Control
### Lateral Force of the Wheel  
Figure 15 is test data obtained by measuring the lateral force generated on the front outer wheel of the test vehicle according to the steering angle test conditions. For lateral force analysis, UIC 518 OR \"Testing and approval of railway vehicles from the point of view of their dynamic behaviour--Safety--Track fatigue--Running behaviour\", the world's railway standard, is applied [17]. When the test standard was applied to calculate the maximum permissible limit of wheel lateral force, the maximum permissible limit was 34 kN, and all measurement results were within the allowable limit.  
Figure 16 is the result of analyzing the lateral force test data by subdividing 70 m into small sections and arranging them in the cumulative distribution order to extract 99.85% of the values as the representative sections of the small sections. Table 5 shows the mean values of the small section representative values for the analysis results in Figure 15 and the reduction rate for the lateral force of each test case for the passive case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Bogie Angle (Degrees)**} \\ \cline{2-4}  & **Front** & **Rear** & **Difference** \\ \hline Passive & 1.404 & 1.055 & 0.349 \\ Active (25\%) & 1.363 & 1.118 & 0.244 \\ Active (50\%) & 1.317 & 1.151 & 0.165 \\ Active (75\%) & 1.278 & 1.211 & 0.067 \\ Active (100\%) & 1.242 & 1.228 & 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean bogie angle for the circular section of the test section.  
Figure 14: Bogie angle difference between front and rear bogies according to steering control conditions.  
Figure 16: Analysis results for wheel lateral force according to steering control test conditions.  
Figure 15: Wheel lateral force test data according to steering control test conditions.  
The mean lateral force of the wheel in the passive case with insufficient steering is 14.99 kN. On the other hand, in the active steering control test condition, the wheel lateral force decreases as the steering angle implementation level increases. In the case of Active (25), it is 7.53 kN, which is 49.8% lower than the Passive case. In the case of Active (50) and Active (75), the mean lateral forces were 4.68 kN and 3.20 kN, respectively, decreasing by 68.8% and 78.6%, respectively. In the case of Active (100) with 100% steering control, the mean lateral force is 2.46 kN, which is 83.6% lower than the passive case.  
Therefore, it was confirmed that the bogie was aligned with the radial position as the steering angle increased to meet the target steering angle as the test train passed through the curved section, and the wheel lateral force was also significantly reduced.
# A Methodological Approach to Securing Cyber-Physical Systems for Critical Infrastructures
## 5 Proposed Approaches and Solutions
### Instantiating the Reaction Component  
Firewall Reconfiguration  
As mentioned in Section 3, a variety of anomalous conditions may lead to an overload of some active network devices, firewalls in particular. An important, ongoing goal is to enable the system to automatically react to such a condition by transferring part of the load from the overloaded devices towards others without changing the overall security policy while doing so.  
In the foreseen network protection scenario, the occurrence of an alarm for anomalous device load or link bandwidth consumption triggers a _reaction_ phase, in which an automatic computation of a new security configuration of the affected devices take place. The updated configuration is then deployed in the subsequent _enforcement_ phase.  
All network functions are thus kept alive while the causes of the anomaly are investigated, leading to the selection and application of more specific countermeasures if needed. Once the offending traffic pattern ceases, either naturally or as a consequence of the countermeasures just mentioned, the system may return to its original configuration.  
In contrast to the current state of the art outlined in Section 2, as well as the approach thoroughly discussed in [46], the practical relevance of this method heavily relies on the ability to deal with real-world device configuration. Being able to handle, for instance, IPTABLES firewall configuration as specified by the authors of [48] rather than abstract, simplified ones is indeed a challenging and important step.  
Figure 5 is a specialization of the Open Platform Architecture of Figure 1, tailored to dynamic network reconfiguration. It shows how the Reaction Module calculates and enforces a new security configuration, taking alarms from the target system as input. As shown in the figure, the target system model, built from a suitable data model, helps to identify the optimal set of devices to be affected by load redistribution. The current security configuration, to be transformed and enforced by the Reaction Module, is also stored in a data model.
## 4 Signal Processing Algorithms
### Windowing Signal  
Applying a windowing function to the spectral interferogram can improve the sensitivity of FFT spectral-analysis techniques and reduce spectral leakage and side-lobes amplitude, resulting in a greater SNR. There are various types of windowing functions available, and the appropriateness of a window function depends on the frequency content of the measured signal. For example, if measuring a multi-layered reflecting sample results in signal components close to one another, then a window function with a narrow main lobe should be used to prevent spectral resolution degradation. Various windows should be compared against one another to find the best fit for the measurement situation. Table 2 shows four commonly employed windowing functions applied to an LCI signal demonstrating an increase in PSF FWHM compared with the original unwindowed signal but also a substantial increase in SNR.
###### Abstract  
The teleoperation of robot arms can prevent users from working in hazardous environments, but current teleoperation uses a 2D display and controls the end effector of robot arms, which introduces the problem of a limited view and complex operations. In this study, a teleoperation method for robot arms is proposed, which can control the robot arm by interacting with the digital twins of objects. Based on the objects in the workspace, this method generates a virtual scene containing digital twins. Users can observe the virtual scene from any direction and move the digital twins of the objects at will to control the robot arm. This study compared the proposed method and the traditional method, which uses a 2D display and a game controller, through a pick-and-place task. The proposed method achieved 45% lower scores in NASA-TLX and 31% higher scores in SUS than traditional teleoperation methods. The results indicate that the proposed method can reduce the workload and improve the usability of teleoperation.  
human-robot interaction; mixed reality; teleoperation; robotic manipulation Article
# What Is a Causal Graph?
## 1 Introduction
### Outline  
In Section 2, we describe how a DAG can be used to represent properties of probabilistic independence between variables using precise semantics based on the method of moralisation. An alternative representation, involving functional rather than probabilistic dependencies, is also presented. Section 3 discusses some informal and imprecise ways in which a DAG might be considered as representing causal relations. In preparation for a more formal approach, Section 4 describes an understanding of causality as a reaction to an external intervention in a system and presents an associated language based on an extension of the calculus of conditional independence for expressing and manipulating causal concepts. Turning back to the DAG model, in Section 5, we introduce precise semantics for its causal interpretation, again based on moralisation but this time used to express extended conditional independence properties. To this end, we introduce augmented DAGs, which contain nodes for non-stochastic intervention indicators as well as for stochastic domain variables. In Section 6, we describe the causal semantics of a \"Pearlian\" DAG [4] and show how these can be explicitly represented by an augmented DAG, where again, a version involving functional relationships--the structural causal model (SCM)--is available.  
Section 7 considers a different type of causal problem that is not about the response of a system to an intervention but rather aims to assign to an observed putative cause responsibility for the emergence of an observed outcome. This requires new understandings and semantics that cannot be represented by a probabilistic DAG but can be by using a structural causal model. However, this is problematic, since there can be distinct SCMs that are observationally equivalent but lead to different answers.
# Autonomous Wireless Sensor Networks in
## Materials and Methods
### Communication Module
#### 2.3.2 Satellite Communication  
The Rockblock Mk2 module for satellite communication [34] was chosen to test the integration abilities of satellite communication in the PentaSense smart gateway. Both evaluation modules are based on the Iridium 9603(N) Short Burst Data Module which can be configured through a serial interface by means of AT-commands, UART interface. The RockBlock 9603 module has a serial interface over USB which allows for configuration and communication with the module. The module supports a two-way communicaiton with global coverage. The power supply needs to be of minimum 100 mA at 5 V DC, however, it can also run the unit directly from a 3.7 V DC LiPo/Lilon battery.  
Figure 2: Basic architecture of the PentaSense WSN node.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Our Proposed Algorithm  
A node's degree, transmission power, mobility, starting out energy of each sensor node, trust value node and distance from the base station to each sensor node are all taken into account when determining how fit a node is to be a cluster head. Our clustering technique takes into account the following factors:  
* [noitemsep,topsep=0 pt]
* Elections for cluster heads take place in a parodic nature.
* Ideally, only M nodes may be supported by each cluster head. The maximum node degree is M.
* It is more stable as a cluster head if the node's degree is greater. Degree difference DD as \(\mid\text{di-M}\mid\), where di is the practical degree of node i and M is the maximum degree. The better node I is as a cluster head, the smaller the i.
* Trust level value: value assigned to an anode to anticipate its behavior.
* Mobility: In choosing who will be the cluster head, mobility is a key consideration. Choosing a cluster leader who is less mobile is a good idea.
* If two nodes are within a particular transmission range of one other, it requires less power to communicate with each other, i.e., the initial energy can be efficiently used within a certain transmission range. Due to the additional obligations that cluster heads have to perform for their members, they consume more battery power than an ordinary node would do.
* It is also vital to note that the distance between the base station and each sensor node is a key factor in the cluster head section process.
Antimicrobial Efficacy of Propolis in Comparison to Chlorhexidine against _Enterococcus faecalis_: A Systematic Review and Meta-Analysis  
[PERSON]  
1 Department of Restorative Dental Science, Division of Endodonties, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Restorative Dental and Endodonties, College of Dentistry, King Faisal University, Al-Ahsa 31982, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Community Dentistry, [PERSON] Institute of Oral Health Science, Dow University of Health Sciences, Karachi 74200, Pakistan; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Restorative Dental Science, Division of Endodonties, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Restorative Dental Science, Division of Endodonties, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
4 Department of Prosthetic Dental Science, Research Chair for Biological Research in Dental Health, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
4 Correspondence: [EMAIL_ADDRESS]; Tel.: +966-581674914  
et al. [7] reported high efficacy of chlorhexidine in contrast to calcium hydroxide against _E. faecalis_. Similarly, [PERSON] et al. [8] found significant inhibition of _E. faecalis_ by 2% chlorhexidine medicament gel in endodontic re-treatment procedure using RT-qPCR.  
In recent years, a paradigm shift from synthetic to natural medicament has been noticed. Various researches have focused on natural intracanal medicament that is deleterious to all bacteria and free from the unwanted effect of synthetic medicaments [9]. One of the newly emerging natural intracanal medicaments is Propolis. Propolis is a wax cum resin substance that contains 58 to 78% polyphenic compounds prepared by honeybees to protect their honey from contamination of microorganisms [10]. It has an array of antibacterial, antioxidant, and anti-inflammatory properties that render its use for multiple indications in dentistry [11]. Numerous studies have recommended the use of Propolis as an intracanal medicament due to its effectiveness against _E. faecalis_[11; 12; 13]. [PERSON] et al. [13] and [PERSON] et al. [12] showed that Propolis was more efficient than calcium hydroxide against _E. faecalis_, while [PERSON] et al. [14] reported the efficacy of Propolis as 100% against _E. faecalis_ following a 7-day application.  
In light of existing literature, controversy exists in the efficacy of Propolis and chlorhexidine against _E. faecalis_. It is hypothesized that Propolis exhibits comparable antimicrobial efficacy in comparison to CHX against _E. faecalis_. Therefore, the aim was to perform a systematic review that can evaluate the antibacterial activity of Propolis in comparison to chlorhexidine against _E. faecalis_ in extracted human teeth.
###### Abstract  
Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTetro robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.  
reconfigurable mechanism; floor cleaning robot; area coverage; path planning; feature mapping +
Footnote †: journal: Sensors  
182585  
doi:10.3390/s18082585
###### Abstract  
Background: Grape pomace is highly attractive for the food industry as it contains numerous bioactive molecules relevant for human health. However, in order to exploit pomace flour as a functional food ingredient for food industry, it is important understand how long-term storage affects the stability of both bioactive molecules and volatile compounds, in addition to color. To this end, we analyzed whole pomace flour from red grape during a six-month storage period in the dark, either at 4 \({}^{\circ}\)C or 25 \({}^{\circ}\)C. Methods: The specific parameters monitored of grape pomace flour included: antioxidant activity (TEAC assay), total phenol content (Folin-Ciocalteu assay), phenol composition (high performance liquid chromatography), fatty acid composition (gas chromatography-mass spectrometry), volatile compound profiles (headspace-solid phase micro-extraction) and color. Results: Prolonged storage did not significantly affect total phenol content, antioxidant activity and characterized bioactive molecules (polyphenols, fatty acids). The only detected effect of storage was a slight whitening of the pomace flour and a small increase of volatile long chain esters and ketons after 6 months at 25 \({}^{\circ}\)C. Conclusions: The activity of several health-relevant bioactive compounds remained stable following storage of pomace flour for 6 months at 4 \({}^{\circ}\)C, supporting its possible use as a functional food ingredient.  
g rape pomace flour; polyphenols; fatty acids; shelf life; functional food +
Footnote †: journal: Applied
## 3 Results
### Variability and Drivers
#### 3.3.2 EOF Analysis of the Deseasoned Versus Detrended and Deseasoned Data  
The first three principal components of the deseasoned data explain just under 40% of the variance (Table 3), with PC1 explaining approximately 27%. Spatial loadings for PC1_deseasoned show a northward gradient (Figure 3e), with the north/northwest Caribbean having higher loadings than the southern Caribbean Sea bordering South America, where sea level variability is in part driven by the Caribbean Current and the Panama-Colombia Gyre [20; 23]. The PC1_deseasoned timeseries shows an interannual signal superimposed on a long-term rising trend (Figure 3f). A high and significant correlation with Carib SST (+0.63) reinforces the idea that Caribbean SSTs are an important driver of the long-term sea-level trend (see also Figure 4a). The PC1_deseasoned timeseries also shows a 4-5 year cycle. This 4-5 year cycle may be associated with ENSO, which has a 3-5 year cycle [74] and which has been shown to impact sea levels in the Caribbean on the interannualtime scale [17; 21; 23; 24; 48]. Significant correlations exist between PC1_deseasoned and both NINO3 (+0.31) and NINO3.4 (+0.33).  
PC1 explains 54% of the detrended and deseasoned data. The explained variability is comparable to the upper range of the variance explained by interannual variability (53%) presented in [23]. The spatial loadings show the north-south gradient exhibited by PC1_deseasoned but with the high loadings in the northwest Caribbean, including around Jamaica and Cuba, being more pronounced (Figure 3g). The timeseries captures the interannual signal minus the long term trend (Figure 3h). Pacific SST indices exhibit strong correlations with PC1_des_det. The correlations are +0.55 with NINO3, +0.60 with NINO3.4, and +0.54 with PDO. This again supports the idea of ENSO being a significant driver of interannual variability in the Caribbean. There are, however, also high correlations with wind related indices (0.71 with Jamaica wind and 0.57 with CLLJ) and the Pacific-Atl gradient (0.66). These latter correlations increase with lag (PC1 lagging the index), e.g., to 0.75 with Jamaica wind (lag of 4 months), 0.68 with CLLJ (lag of 5 months), and 0.68 for the  
Figure 4: (a) PC1_deseasoned and Caribbean Sea Surface temperature anomalies (Corr = 0.65), (b) PC1_des_det and Deseasoned + Detrended Kingston altimetry data (Corr = 0.86).  
Pacific-Atl SST gradient (lag of 2 months). The PC1_des_det correlates strongly (+0.80) with the deseasoned and detrended timeseries of Jamaica altimetry data (Figure 4b). Interannual variability of sea levels off the south coast of Jamaica is, therefore, linked to variations in the abovementioned basin wide and large-scale climate and ocean phenomena.
# Learning Weighted Forest and Similar Structure for Image Super Resolution
## 4 The Proposed Super Resolution Method
### The Weighted Predictive Model based on Random Forest  
As there are T trees in random forest, for each LR test patch, T different candidate HR patches are obtained. Considering that these LR test image patches have different degrees of fitting error in each prediction model, the application of weighted prediction can estimate HR image patches more accurately.  
Firstly, use the k-d tree method to quickly search the K-nearest neighbors' patches for each LR test image patch \(x_{l}\) in its leaf node. The number of neighborhoods is expressed as \(N_{K}(x_{l})=\{n_{1},n_{2},\ldots,n_{k}\}\). Secondly, calculate the cumulative fitting error of its K-nearest neighbor image patches as the approximate error of the test image patch in the local regression model. Then, perform the weighted prediction to estimate the HR image patch according to the approximate error in each regression model. The predictive HR image patch is expressed as follows:  
\[\widetilde{y_{h,t}}=m_{l(t)}(x_{l}) \tag{6}\]  
where \(\widetilde{y_{h,t}}\) is the estimation HR patch from the \(t\)-\(th\) tree. Its SR reconstruction error is described as below:  
\[e_{t}=\|\widetilde{y_{h,t}}-y_{h}\|^{2} \tag{7}\]  
then the cumulative fitting error of the K-nearest neighbor image patches in leaf node for LR test patch \(x_{l}\) is approximated as:  
\[e_{N_{K}(t)}=\frac{\sum\limits_{k=1}^{K}\frac{1}{k}e_{t,n_{k}}}{\sum\limits_{k =1}^{K}\frac{1}{k}} \tag{8}\]  
where \(\frac{1}{k}\) can be exploited to adjust the contribution of neighbors to calculate cumulative error, \(e_{t,n_{k}}\) denotes the reconstruction error resulting from the kth neighbor image patch, and K stands for the number of neighbor image patches.  
Finally, implement the weighted prediction for the desired HR image patch according to the cumulative fitting error sum of the K-nearest neighbor image patch in each local regression model:  
\[y_{h}=\frac{\sum\limits_{t=1}^{T}w_{t}m_{l(t)}(x_{l})}{\sum\limits_{t=1}^{T}w _{t}}=p(x_{l}) \tag{9}\]  
where \(p=\frac{\sum\limits_{t=1}^{T}w_{t}m_{l(t)}}{\sum\limits_{t=1}^{T}w_{t}}\) represents the weighted projection model, and \(w_{t}=\frac{\frac{1}{N_{K}(t)}}{\sum\limits_{t=1}^{T}\frac{1}{N_{K}(t)}}\) satisfied \(\sum\limits_{t=1}^{T}w_{t}=1\). The weighted prediction mode ensures that each local regression model with a smaller error has a higher weight, which improves accuracy of the weighted prediction.