esa-sceva/satcom-chunk-collection huggingface dataset:
# Inland Water Level Monitoring from Satellite Observations:
## 2 Methodological Framework
### From Data to Information, towards Decision Making  
For the analysis of charted data, both quantitative and descriptive analytics were used [49]. Both analytics allow the reviewers to summarize and describe the important characteristics of the studies included in the current review, as well as to identify trends and patterns in the literature highlighting areas that have been extensively studied and areas that require further research. Therefore, for the quantitative analysis, graphical representations were created using open-source Python visualization libraries, such as Plotly and Matplotlib. Also, for the descriptive analysis, a visualization map was generated using the VOSviewer open-source software (version 1.6.20) [50]. A network of keywords and terms was created based on the included documents determining the level of relevance between predicted keywords and their frequency of occurrence.
## 3 Materials and Methods
### Sample Preparation  
Milled rice was ground using a miller into 170 mesh. The distilled water was added at the ratio of 2.3:7.7 (ground rice: water, \(w\)/\(w\)) before the ground samples were treated with commercial 0.05% (\(w\)/\(w\)) \(\alpha\)-amylase (BAN480L; Novozymes) and 0.05% (\(w\)/\(w\)) glucoamylase (AMZ1100; Novozymes, Bagsvaerd, Denmark). The enzyme saccharification was performed by shaking at 100 rpm and 63 \({}^{\circ}\)C for 20 h, following the previous method of [PERSON] et al. [13]. Then, enzyme deactivation was performed at 85 \({}^{\circ}\)C for 30 min. After enzyme deactivation, samples were inoculated with 1% (\(w\)/\(w\)) of 12 different LABs (10\({}^{\prime}\) CFU/mL), respectively (Table 3). For the present study, 12 LAB strains were isolated and selected from various Korean fermented foods, considering their tolerances to acid, salt, and glucose in a preliminary study (Table 3). Then, inoculated samples were fermented at 30 \({}^{\circ}\)C for 24 h. All samples were kept at about -70 \({}^{\circ}\)C in a deep freezer (I1 Shin Bio Base; Model No. DF8514; Dongdooheon-si, Gyeonggido, Korea). All experiments were conducted in triplicate.  
Figure 2: PCA plots of the nonvolatile volatile compounds identified in fermented rice according to strains of LABs: (**A**) score plot, (**B**) loading plot.
## 5 Performance Evaluation
### Mobility Patterns
#### 5.4.1 Random Way Point (RWP).  
RWP is a synthetic mobility model in which MNs choose a random point in the simulation area and start moving toward it with a randomly chosen velocity. In random waypoint, nodes tend to be spotted in the middle of the simulation area. Since the newly selected point is random, sharp turns may happen that are not realistic. Another phenomenon called density wave [50] is that the number of neighbors for each MN fluctuates considerably. As the MN passes through the center of the simulation area, where it is usually more crowded (by both static and MNs), it may further converge the network. On the other hand, when the MN is spotted closer to the edges, it may be subject to packet losses in blind spots.
# Disguised Full-Duplex Covert Communications
## 7 Conclusions  
In this paper, we studied a covert communications system where a source node communicates with a disguised FD destination node. Supposedly receive-only, the destination node covertly delivers critical messages to another hidden receiver while evading the surveillance of a warden node as much as possible. We identified the optimal public data rate and the transmit power of the FD destination node that maximizes the covert rate at the hidden receiver.  
The obtained closed-form solution exhibited the following: When the destination-receiver link is extremely strong, the optimal destination transmit power approaches zero since the hidden receiver cannot eliminate a source message prior to retrieving a covert message. If the self-interference is not sufficiently suppressed, the optimal destination transmit power approaches zero in this case as well since the public data rate cannot reach the required quality of service. In addition, when the destination-warden channel gain is exceptionally high, the optimal destination transmit power approaches zero since the large difference in the received power at the warden makes it easier to detect the covert link.  
The extensive numerical results presented a phenomenon that applying more (less) destination transmit power achieves a higher covert rate when the source transmit power is low (high). It was also verified that the optimal destination transmits power yields the best balance between the covert rate and minimum DEP threshold.  
Since our work provides a performance guideline from the information-theoretic point of view, we suggest analyses with practical modulations and imperfect CSI as valuable future research topics.  
**Funding:** This research was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R11A3050126). This work was partially supported by Korea Research Institute for Defense Technology Planning and Advancement (KRIT) grant funded by the Korean government (DAPA (Defense Acquisition Program Administration)) (21-106-A00-007, Space-Layer Intelligent Communication Network Laboratory, 2022).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation) +
## 7 Synthetic Experimental Framework
### Subpaths Extraction  
A heuristic procedure that was used to obtain the \"most used subpaths\" from the GPS sample was designed. The implemented procedure was the following:  
1. From the list of paths in Visum, called PrTPaths, that were generated by the map-matching process and the links that they use, the heuristic started by selecting the most used link by paths. An example highlighted in purple is depicted in Figure 3.
2. Selecting all the paths that use the selected link, a search simultaneously found the forward and backward maximal subpaths.
3. The procedure stopped when the number of paths that were used by link \(L_{m}\) (subpath \(M=\{L_{1},\ L_{2},\ldots,\ L_{m}\}\)) was reduced to a percent stop = 20% from the initial link \(L_{1}\). The percent stop was a design parameter.
4. Once the subpath \(M=\{L_{1},\ L_{2},\ldots,\ L_{m}\}\) was selected, the links that formed this subpath were removed from the list of candidate links to define the most used paths. The list of paths and candidate links for step 1 was updated. The procedure returned to step 1.
5. The iterative procedure was executed by also considering the departing period of each PrTPath to find subpaths for each period of the simulation and capture its dynamicity. It iterated until no more paths were found; in this case, 379 subpaths were found  
Figure 2: Conceptual diagram of the synthetic data generation for computational testing.  
(127 on T1, 123 on T2, 129 on T3). The observed path travel times were calculated as the sum of their observed link travel times.  
These subpaths with the observed travel time covered the entire network, as shown in Figure 4, and they were used by many GPS paths (158 on average), which allowed for confidence in their use and their observed travel time. The average length of these subpaths was 911 m. In this network, a subpath was considered if the number of links was greater than five.
## 3 Accident Investigation Analysis
### Fault Tree Analysis (FTA)  
#### 3.2.1 What is FTA  
FTA is one of the most widely used methods in system reliability, maintainability, and safety analysis. FTA was developed by [PERSON], at Bell Telephone Laboratories, in 1962 to apply the analytical method of studying the causes of undesired event failures. As a false logic diagram, the graphical structure of the model could be used as qualitative analysis and quantitative analysis [15].  
The main purpose of the FTA is to identify potential causes of system failures before the failures actually occur. It also can be used to evaluate the probability of the top event using analytical or statistical methods. These calculations involve system quantitative reliability and maintainability information, such as failure probability, failure rate, and repair rate. After completing an FTA, the focus is on efforts for improving system safety and reliability. An FTA begins by defining the \"undesirable\"  
Figure 6: Accident case risk assessment process [10]. ALOHA: areal locations of hazardous atmospheres; FTA: fault tree analysis; PDCA: plan-do-check-act; ALARP: risks reduced to levels that are as low as reasonably practicable.  
top event, such as fire disaster, explosion, leakage, and out of control. Then, FTA can analyze the top event to basic events through intermediate events. Once the FTA has been constructed, it can be qualified and quantified [16; 17].
## 3 Results and Discussion
### PAH Content  
The PAH contents were measured in order to evaluate the potential environmental risk of BC, taking to account that these organic compounds can affect plant and human health [92]. In the case of light PAHs, the greatest contribution was from acenaphthene and fluorene for all biochars (Supporting Information Table S2). The naphthalene and acenaphtylene contents were low; in some cases, no amount was detected. The concentrations of light PAHs were low and did not exceed the 3.5 ng g\({}^{-1}\) (Supporting Information Table S2). For medium PAHs, the greatest amount belonged to phenanthrene, which had a maximum value of 24.07 ng g\({}^{-1}\) in BB300-3H. The amounts of anthracene and fluoranthene were lower in the medium PAHs (Supporting Information Table S2). In the case of heavy PAHs, no specific PAH was a major contributor, except in the case of dibenzo [a,h] anthracene in BG300-1H and BG300-3H (Supporting Information Table S2).  
In general, the PAH contents were low and between the ranges of the acceptable limits for biochar materials according to the European Biochar Certificate guidelines [93]. In addition, greater concentrations have been detected for other biochar materials [3; 92]. [PERSON] et al. [94] stated that pyrolysis influences the PAH concentration, where low-temperature biochars have a greater PAH concentration. In this case, the trend was the same for gorse-derived biochars (Figure 2), with greater values found for total PAHs (light, medium, and heavy) in BG300-1H, followed by BG300-3H. In the broom-derived biochars, the highest value was found for BB300-3H.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Measured** & & & **Bulk** & **Solid** & **Calculated** \\  & **Porosity** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **Density (g cm\({}^{-3}\))** & **Density (g cm\({}^{-3}\))** & **Total** \\  & **(\%)** & & & & **Porosity** \\ \hline BROOM & 43.59 & 0.06 & 0.51 & 0.28 & 1.28 & 78.12 \\ BB300-1H & 56.51 & 0.08 & 0.97 & 0.18 & 1.28 & 85.68 \\ BB300-3H & 57.59 & 0.09 & 1.04 & 0.18 & 1.27 & 85.50 \\ BB600-1H & 57.80 & 0.18 & 0.88 & 0.24 & 1.47 & 83.45 \\ BB600-3H & 60.28 & 0.19 & 1.04 & 0.25 & 1.53 & 83.93 \\ \hline GORSE & 34.69 & 0.12 & 0.24 & 0.23 & 1.34 & 82.91 \\ BG300-1H & 52.15 & 0.07 & 0.77 & 0.16 & 1.29 & 87.73 \\ BG300-3H & 48.06 & 0.07 & 0.79 & 0.14 & 1.36 & 89.61 \\ BG600-1H & 57.14 & 0.13 & 0.93 & 0.17 & 1.47 & 88.72 \\ BG600-3H & 56.79 & 0.19 & 0.86 & 0.17 & 1.49 & 88.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physical properties (dry basis) of broom, gorse, and their respective biochars.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 1 Introduction
### Research Background
With the historical and technological genesis of the usage of passwords for authentication, the domain of IT over the years has seen many advancements in terms of speed of processing, size of storage, and high throughput networks. The advent of high-speed computing and the usage of algorithms like Brute Force have weakened authentication processes using the conventional means of a username and password. In turn, this has resulted in many data breaches, compromising volumes of critical information globally. Frequent re-occurrences of such incidents put a question mark on the system of usage of the user ID and password. It also highlights the weaknesses associated with providing a robust protection mechanism against unauthorized authentication leading to undesired access to restricted and protected data. The Identity Theft Resources Centre (ITRC), in its annual report for the year 2020 [16], has listed out the root causes of identity theft and its implications, showing that 38% of data breach occurrences are due to improper cloud security configuration. It also revealed that 36% of data breaches could have occurred due to pissing attacks and related disclosure of user credentials. Upon analysis of the National Vulnerability Database (NVD) (updated up to Dec 2022) with the Common Vulnerability Scoring System (CVSS) in accordance to the Cloud Security Alliance, The Treacherous 12 [17] as well as _Security Guidance for Critical Areas of Focus in Cloud Computing v4.0_[18], it was observed that insufficient Identity and Access Management (IAM), as well as IAM, contribute cloud vulnerability by 23.6% and 45.3%, respectively, as depicted in Figure 2.  
Figure 2: Contributors Towards Cloud Vulnerability.
## 3 Implementation and Validation of the Proposed Framework through a One-Inch Block Experiment
### Effect of Image Enhancement on the Object Identification in the Close-Range Photogrammetry
The photogrammetry using output images from each enhancement method was completed, with results given in Table 5. Principal point locations \(u_{0}\) and \(v_{0}\) were determined by the projected positions of the light rays through the lens center that are perpendicular to the image plane. The length of the perpendicular line is the principal distance that is equal to the focal length at infinity focus. It is related to the HS hardware system setting for the validation tests; therefore, the variations are negligible and estimated as 0.23% within all methods. Furthermore, the principal point locations are known to have correlations with other internal camera parameters such as distortion coefficients. A strong correlation with other camera internal parameters resulted in higher variations in the principal point locations within each method, which were computed as \(-18\%\) and \(-24.64\%\), respectively. Overall, the global image enhancement method implemented in the study may still have some limitations in automatically identifying and registering objects. In addition, the careful selection of the images that need to be enhanced is required early in the process in the bundle adjustment procedure. However, as demonstrated from the results in this section, the method is still valid when the enhanced images are carefully selected to be included in the bundle adjustment procedure so that the process is complete and proper camera system parameters can be obtained (Table 5).
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 6 Coverage, Capacity, and Coordination in LTE-U
The centralized scheme in [81] achieves the optimum resource utilization in the time and spatial domain along with optimized energy efficiency. An important issue from a coordination perspective in LTE-U is to decide between a frame based equipment (FBE) or load based equipment (LBE). In an FBE, the LTE-U frames align with the existing LTE frame structure. Alternatively, an LBE can contend for the channel as soon as it has data to transmit. From LTE-U perspective, it has been discussed in [69] that an FBE can result in excessive delays for high system load. Intra-operator and inter-operator synchronization in LTE-U with FBE can result in multiple LAA cells observing the same resources as free causing frequent collisions [69]. Thus, an LBE is more suitable for LTE-U. A distributed scheme with a mix of LBT and CSAT has been considered in [82] for channel reservation during the spectrum sensing period and LTE-U transmissions. The LTE-U transmission in [82] is followed by the reserved duration for WLAN transmissions. It has been shown in [82] that a reasonable level of WLAN protection can be achieved by adapting the LTE-U parameters. In [49], a local interaction game based approach has been used for distributed channel selection in LTE-U. Furthermore, an optimal spatial adaptive play (SAP) based LTE-U channel selection algorithm is proposed in [49]. Distributed channel access mechanisms have been evaluated in [6; 67; 68]. Distributed channel selection has been examined in [44; 46; 60]. Next, we analyze the existing LTE-U studies from the perspective of the environment, traffic type, and scenarios considered.
## 2 Materials
### Image and Reference Data  
In the present study, time series of sentinel-1C products (Image Collection ID: COPERNICUS/S2_SR) with 10 m spatial resolution and Sentinel-2A MSI Level-2A products with less than 15% cloud coverage between January 2019 and January 2020 were utilized (Table 1). Several preprocessing steps (i.e., orbit file correction, radiometric calibration, terrain correction, and speckle noise reduction) were initially applied for SAR data. Then, the multi-look parameterization of vertical-vertical (VV) and vertical-horizontal (VH) polarizations were obtained using Sentinel-1A images. Moreover, ten spectral bands of Sentinel-2 from Band 2 to Band 8A, Band 11, and Band 12 were utilized in this study. Next, using the nearest neighbor method, bands 5, 6, 7, 8A, 11, and 12 were resampled to 10-m resolution from 20-m pixel size, achieving the exact resolution as the other bands.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sites**} & \multicolumn{3}{c}{**Number of Scenes**} \\ \cline{2-3}  & **Sentinel-1** & **Sentinel-2** \\ \hline Coastal & 208 & 146 \\ Cropland & 89 & 145 \\ Desert & 92 & 102 \\ Mountain & 118 & 91 \\ Plain & 118 & 140 \\ Semi-Arid & 117 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data set.  
Figure 1: (**A**) The Location of six different landscapes. (**B**) The RGB color composites of the study areas.  
A reliable training dataset with enough quantity and accuracy is needed for training any supervised ML classifier [3]. In this study, very-high-resolution images available in Google Earth\({}^{\text{TM}}\) and ArcGIS and raw Sentinel-2 data were visually interpreted to produce the reference dataset. Based on the extent of LC types, 840, 759, 846, 818, 703, and 1077 samples were, respectively, produced for Site-1, Site-2, Site-3, Site4, Site-5, and Site-6 (Table 2). The obtained datasets were then divided into two parts; one was used to train ML classifiers (training dataset). The other was used to assess the accuracy of the LC maps (validation dataset). More precisely, 60% of the original GCPs were used for training, and the remaining 40% were used for accuracy assessment.
## 7 Nanotechnology in Food Science
### Packaging with Nanostructures
#### 7.2.1 Smart Nano-Packaging  
Innovative packaging systems go beyond preserving and protecting food from spoilage. They seek to improve the food's attributes and the packaging functionality, as well as adding new features. In this regard, smart packaging tries to merge all these concepts. These things considered, it is classified into two groups: active packaging and intelligent packaging. This area is in full development and can provide answers regarding the prevention of foodborne infections as well as sustainability concerns. Smart packaging is described as packaging that incorporates both active and intelligent systems that work in tandem. The food validity period can be bound using some factors from food product data, such as saturated and unsaturated fatty acid content, enzyme activity, water activity, pH, or protein content [71].
## 3 Results
### Phases in Parallel
#### 3.2.1 Single Oscillator in Parallel
to the dispersion Equation (16), except in the neighbourhood of the singularity point \((\eta_{m}/\delta)\Omega=1\), where a competing effect between \(\xi_{m}\) and \(1-(\eta_{m}/\delta)^{2}\Omega^{2}\) arises. In contrast, in Figure 8 it is shown that the impedance ratio \(\xi_{m}\) governs the transition between a discrete-continuous-like behaviour, for large negative values of \(\ln(\xi_{m})\), and a continuous-like one for large positive values of \(\ln(\xi_{m})\). In particular, when the elastic impedance of the oscillator tends to infinity, namely for \(\ln\xi_{m}\to-\infty\), the leading order term in Equation (20) is:  
\[[(1+\xi_{c})\sin\phi_{+}\,+(1-\xi_{c})\sin\phi_{-}]\,\frac{\frac{\eta_{m}}{ \delta}\Omega}{1-\big{(}\frac{\eta_{m}}{\delta}\big{)}^{2}\Omega^{2}}. \tag{21}\]  
In this case, each unit cell behaves as the clamped two-phase system of finite size, sketched in the inset in Figure 8a, whose discrete eigenfrequencies are the roots of the term within square brackets in Equation (21). These values correspond to the central frequencies of the tiny bands for large negative \(\ln\eta_{m}\). Note the presence of an infinite number of bands due to the continuous phases, but band intervals of increasingly negligible size, as a result of the discrete oscillator. When the elastic impedance of the oscillator is relatively small, namely for large \(\ln(\xi_{m})\), the ratio \(1/\xi_{m}\) makes the contribution of the term (21) in Equation (20) small, except in the vicinity of the characteristic frequency \(\Omega=\delta/\eta_{m}\). In such a case, the continuous-like  
Figure 8: Oscillator in parallel. Band distribution as a function of the impedance ratio \(\xi_{m}\). Normalised frequency \(\Omega\) is given for \(\xi_{c}=0.22\) and \(\eta_{c}=0.5\). (**a**) \(\eta_{m}=1.26\), (**b**) \(\eta_{m}=0.525\). Dashed lines indicate the characteristic frequency of the oscillator. Figure 7: Oscillator in parallel. Band distribution as a function of the characteristic frequency ratio \(\eta_{m}\). Normalised frequency \(\Omega\) is given for \(\xi_{c}=0.22\), \(\eta_{c}=0.5\) and \(\xi_{m}=8.71\). Dashed lines indicate the two cases analysed in Figure 8, \(\eta_{m}=1.26\) and \(\eta_{m}=0.525\), respectively. behaviour is similar to the one in Figure 7. The transition between the different behaviours described above is roughly in the interval \(-5\leq\ln(\xi)\leq 0\), and the interaction between the continuous and the discrete phases can be attributed to the expression (21) divided by \(4\xi_{m}\) in Equation (20). The width of the stop band generated by the oscillator with \(\eta_{m}=1.26\) (Figure 8a) increases monotonically as \(\xi_{m}\) decreases, while the propagating band created by the oscillator with \(\eta_{m}=0.525\), namely the first optical pass band, has maximum bandwidth in the transition zone, for \(-3\leq\ln(\xi_{m})\leq 0\) (Figure 8b).