esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement Uncertainty and Compliance Evaluation Applied to Natural Gas Moisture
## 4 Conclusions  
This study developed and validated an MS-Excel spreadsheet for assessing the uncertainty of natural gas moisture based on the Portable Moisture Analyzer--PM880 algorithms.  
Figure 3: Histogram for 5% significance level, moisture in natural gas.  
In this preliminary study, the sources of uncertainty considered were the dew point temperature and the natural gas pressure.  
Even with a measurement uncertainty value of around 20%, there is no risk of false compliance assessment, as the moisture levels in natural gas are significantly lower than the upper specification limit.  
Regarding future research, it is important to evaluate the impact of sampling variability on measurement uncertainty, as this factor can influence risk assessment. Furthermore, the calculations for measuring the dew point and pressure could be elaborated upon by incorporating more realistic estimates of their uncertainties. The sensitivity analysis on the uncertainty sources (dew point temperature and pressure) could also be included in cases where measurement uncertainty can compromise the compliance evaluation. Another area for future work is comparing the results between different methods.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15052482/s1](https://www.mdpi.com/article/10.3390/app15052482/s1)]([https://www.mdpi.com/article/10.3390/app15052482/s1](https://www.mdpi.com/article/10.3390/app15052482/s1)), Table S1: Uncertainty and Compliance.  
Conceptualization, R.M.M., C.L.B. and E.C.d.O.; methodology, R.M.M., C.L.B. and E.C.d.O.; software, R.M.M., C.L.B. and E.C.d.O.; validation, R.M.M., C.L.B. and E.C.d.O.; formal analysis, R.M.M., C.L.B. and E.C.d.O.; investigation, R.M.M., C.L.B. and E.C.d.O.; resources, R.M.M., C.L.B. and E.C.d.O.; data curation, R.M.M., C.L.B. and E.C.d.O.; writing--original draft preparation, R.M.M., C.L.B. and E.C.d.O.; writing--review and editing, R.M.M., C.L.B. and E.C.d.O.; visualization, R.M.M., C.L.B. and E.C.d.O.; supervision, R.M.M., C.L.B. and E.C.d.O.; project administration, R.M.M., C.L.B. and E.C.d.O.; funding acquisition, R.M.M., C.L.B. and E.C.d.O. All authors have read and agreed to the published version of the manuscript.  
The authors thank for the financial support provided by the scholarship from the Brazilian agency CNPq (305479/2021-0). This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company PETROBRAS S.A. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Machine Learning and Feature Selection Enabled IDS
### Popular Datasets Adoption for ML/FS-Enabled IDS
#### 5.3.2 NSL-KDD Dataset  
The NSL-KDD dataset was proposed by [PERSON] et al. as an upgraded version of the legacy KDDCup99 dataset, which was created to overcome the limitations of the KDDCup99 dataset [89]. KDDCup99, due to containing over 70% redundant and duplicate records, posed challenges, prompting the creation of NSL-KDD with a focus on efficiency and effectiveness. The removal of redundancies in NSL-KDD enhances its overall utility. The decision to eliminate redundancy in the training dataset serves to prevent the trained classifier from developing biases towards more frequently occurring records. Consequently, NSL-KDD offers a total of 148,517 instances, making it more feasible to evaluate the entire dataset instead of relying on smaller portions. Researchers frequently utilize the NSL-KDD dataset to assess the performance of classification models deployable within IDS. However, it is essential to note that this dataset was generated over twenty years ago and may not accurately represent current real network traffic. This limitation has been recognized by contemporary researchers working with the underlying dataset.
## 4 Results
### Accuracy of the Classification Models  
The accuracy of the SVM classification model for emotion identification is shown in Figure 8b confusion matrix. The excited, happy, neutral, sleepy, and disgusted emotions achieved 65%, 60%, 61%, 69%, and 55% accuracy of detection, respectively. The false-positive value of each emotion class was always less than 19%, except for 21% and 26% when detecting a disgusted emotion as neutral and when detecting a happy emotion as excited, respectively. This result would not greatly affect the accuracy of the final result.  
The attention level to the meeting was classified into two states as shown in Table 2. After training with the balanced image set for attention classification, the accuracy of the attention model was evaluated using the confusion matrix in Figure 8a. When the participant was actively engaging in the video conference, a 30% true-negative level was achieved; thus, 70% of actively engaged participants could be accurately detected. The false-positive rate was 34%; thus, 66% of participants were correctly identified as not engaged in the meeting. This model had an F1 score of 0.6725 and accuracy of 0.6770.
## 5 Conclusions  
Polyelectrolyte complexes of chitosan and alginate loaded with TM-nanoemulsion as a medicine model were prepared by a casting/solvent evaporation method. FTIR spectra indicated formation of alginate-chitosan PEC films loaded with thyme oil. The homogeneity of the Thyme oil alginate-chitosan PEC films were confirmed by optical and scanning electron microscopy. The pH sensitivity test indicated that swelling ratio of both PEC films decreased as pH increased. The final PEC films had good mechanical properties, and the WVTR of the films were in the range of an ideal wound dressing. MTT assay showed lower toxicity of TM-PEC %1 in comparison with that of TM-PEC %0.4. TM-PEC films could limit the growth of both gram-negative _E.coli_ and gram-positive _S. aureus_ bacteria. Drug release from the TM-PEC %1 film was much faster than that of TM-PEC %0.4 film. It was understood that dissolution was the main factor in the drug release mechanism for all systems especially in the initial hours. Our results suggest that the prepared TM-PEC films may be appropriate for use in the controlled release of drugs or smart wound dressings when loaded with antibacterial essential oils like thyme oil.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/18/3933/s1](http://www.mdpi.com/2076-3417/9/18/3933/s1)]([http://www.mdpi.com/2076-3417/9/18/3933/s1](http://www.mdpi.com/2076-3417/9/18/3933/s1)), Figure S1: Optical Microscopy images of (**a**) PEC 0.4-TM and (**b**) PEC 1-TM, Table S1: Interpretation of diffusional release mechanisms.  
Conceptualization, H.H. and S.M.; methodology, H.H.; validation, H.H. and S.M.; formal analysis H.H. and S.M.; investigation, H.H. and S.M.; resources, A.E.T. and S.M.H.; data curation, A.E.T.; writing--original draft preparation, H.H. and S.M.; writing--review and editing, H.H., S.M., A.E.T. and S.M.H.; supervision, A.E.T.; project administration, H.H. and S.M.; funding acquisition, A.E.T. and S.M.H.  
This research received no external funding.  
Conflicts of Interest: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Release** & **Kinetic** & **Peppas** & **Zero-Order** & **First-Order** & **Higuchi** & **Hixson-Crowell** & **Time Range** \\  & **Exponent, n** & **Constant, k** & **Peppas** & **Zero-Order** & **First-Order** & **Higuchi** & **Hixson-Crowell** & **(hr)** \\ \hline TM-PEC \%1 & 0.50 & 0.24 & 1.00 & 0.93 & 0.98 & 0.89 & 0.97 & 0–6 \\ TM-PEC \%1 & 0.06 & 0.59 & 0.97 & 0.87 & 0.90 & 0.87 & 0.89 & 7–150 \\ TM-PEC \%0.4 & 0.49 & 0.06 & 0.99 & 0.92 & 0.93 & 0.88 & 0.93 & 0–6 \\ TM-PEC \%0.4 & 0.08 & 0.14 & 0.91 & 0.83 & 0.83 & 0.83 & 0.84 & 7–100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of release mechanism and regression coefficients of different models fitted to the release of thyme oil.
## 5 Results and Analysis  
### Energy Management System  
In order to illustrate the performance of the proposed framework, various case studies are provided. These cases are implemented on the IEEE 34-node test system (Figure 7) [31]. Note that different nodes are denoted by numbers beside the vertical lines. The feeder is modified by adding a photovoltaic (PV) unit and BESS. PV and BESS are connected to the DC bus, and the DC bus is connected to node 28 through a DC/AC converter. Table 2 shows the parameters of the BESS. To make configuring the BESS model for simulation and performance analysis more accessible for the user, a parameter configuration menu was included. It is easy to understand the description of the parameter. The maximum charge and discharge power in kW that the BESS power conversion system is generally capable of handling is indicated by the battery energy storage system-rated power specifications. The nominal battery capacity reflects the battery's energy capacity in kWh. The initial charge in the battery prior to the simulation commencing is represented by the initial battery state of charge parameters. Pre-charging the battery to a certain charge level prior to commissioning the BESS is standard procedure. Users can specify the converter efficiency in percentage terms using the PCS efficiency. The BESS technical specification contains this information. State of charge minimum and state of charge maximum are the lowest and highest battery charge states.  
The IEEE 34 bus data are provided in [31; 32], and allowable voltage limits are 0.95 p.u. to 1.05 p.u., respectively. Moreover, PV capacity is 2 MW, and annual peak load is 1 MW.  
The proposed EMS is implemented, and the results are discussed below. Figure 8 shows daily load and PV output power profile in a sample day in January.  
The battery SOC as well as charge/discharge power profiles are shown in Figures 9 and 10. According to the presented EMS, during 0:00-9:00, PV power is lower than load. Therefore, the BESS should be discharged; however, the SOC is at the minimum level thus, discharge is not possible. Hence, as can be observed, SOC remains at 5% until 9:00 when PV power exceeds the load. At this time, the BESS starts to charge with maximum power until reaching the maximum SOC at 12:00. Subsequently, the BESS remains at 95% SOC, until 17:00. PV output power becomes less than load at 18:00, and BESS starts to discharge to  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Elements** & **Setting** \\ \hline Rated Charge Power & 200 kW \\ Rated Discharge Power & 200 kW \\ Battery Size & 500 kWh \\ Initial State of Charge & 5\% \\ Conversion System Efficiency & 93\% \\ State of Charge MIN & 5\% \\ State of Charge MAX & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: BESS parameters.  
Figure 8: Load and PV power daily profile.  
Figure 7: Modified IEEE 34-bus system [31].  
compensate the difference. BESS continues to discharging until 20:00, when it reaches the minimum SOC, as can be seen in Figure 9. The SOC level remains at 5% until the end of the day. The charge/discharge power profile can be seen in Figure 10.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Evidence Representation Based on Spatiotemporal Structure
#### 4.2.2 Evidence Spatial Structure Representation Unit  
To capture the local spatial structure characteristics of evidence posts' interactions in message propagation, inspired by graph attention networks (GATs) [44], we adopted attention mechanisms on the information dispersion network. Although GATs can automatically capture the contributions (attention weights) of different nodes to the target node when aggregating neighboring nodes, it ignores the credibility of neighborhood information. Therefore, such an aggregation may result in excessive noise. We introduced the credibility of posts in the process of spatial neighborhood aggregation to enhance the reliable representation of evidences, which can be formulated as  
\[\beta_{ij}=\mathbf{softmax}(Leaky\ ReLU(\mathbf{\alpha}^{T}[\exp(r_{i})\mathbf{c}_{i};\exp(r_ {j})\mathbf{c}_{j}])), \tag{8}\]  
\[\mathbf{m_{i}}=\mathop{\big{|}}_{k=1}^{K}\sigma(\sum_{j\in N_{i}}\beta_{ij}^{k} \mathbf{W^{k}}\mathbf{c}_{j}). \tag{9}\]  
Equation (8) demonstrates how to model the semantic contribution of neighborhood posts to target posts (evidence). Specifically, we used a feed forward network as an attention function, which contained a single hidden layer with a LeakyReLU, and used the global credibility weights to optimize the local semantic contribution of information. In the equation, \(\mathbf{\alpha}\in R^{2d}\) is a learnable parameter weight vector and \(\beta_{ij}\) denotes the semantic contribution of neighborhood node \(j\) to target node \(i\). Equation (9) demonstrates how to aggregate spatial neighborhood information according to the calculated contributions. The spatial neighborhood representation of a piece of evidence is obtained by a weighted summation over all spatial neighborhood semantic representations. To capture diversified representations of spatial structure relationships, attention is expanded to multi-head attention. Specifically, Equation (8) (i.e., the attention operation) is repeated K times, then the learned representations are concatenated. \(\mid\mid\) denotes a concatenation operation, \(\sigma(\cdot)\) denotes the exponential linear activation function, \(N_{i}\) denotes the collection of posts directly connected with evidence \(i\) in the information dispersion network, \(\mathbf{W^{k}}\in R^{q\times 2d}\) is a learnable shared parameter matrix that acts on each node in the network, and \(\mathbf{m_{i}}\in R^{Kq}\) is the semantic representation of the spatial structure of evidence \(i\) (when \(i\) = 0, it represents source information).
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### COBIT 5 for Cloud Security
#### 3.1.2 Strengths
Continual Improvement: COBIT 5 encourages a culture of continuous development in cloud control and assurance. It promotes businesses to evaluate their control system, measure performance, and identify opportunities for improvement. This iterative strategy assists organizations in adapting to changing cloud security concerns and continuously improving their control environment.
## 4 Efficient Intersection Solver
### Ray Tracing for Intersection  
The ray tracing problem can be solved in the rotated space given the right facet, as shown in Figure 6. The task is to calculate intersection \(Q\) of the ray extending unit vector \(\overset{\rightarrow}{\mathbf{m}}=[1;0;0]\) and the facet \(P_{1}P_{2}P_{3}\). Assume \(\overset{\rightarrow}{\mathbf{n}}\) is normal vector to facet \(P_{1}P_{2}P_{3}\), and the length of vector \(\overset{\rightarrow}{\mathcal{O}}\) is \(t\). Then, it is obvious that:  
\[\overset{\rightarrow}{P_{1}}\overset{\rightarrow}{Q}\cdot\overset{ \rightarrow}{\mathbf{n}}=0, \tag{8}\]  
therefore,  
\[\left(P_{1}-t\overset{\rightarrow}{\mathbf{m}}\right)\cdot\overset{\rightarrow} {\mathbf{n}}=0, \tag{9}\]  
such that,  
\[t=\frac{P_{1}\cdot\overset{\rightarrow}{\mathbf{n}}}{\overset{\rightarrow}\mathbf{{m}}\cdot\overset{\rightarrow}{\mathbf{n}}}, \tag{10}\]  
Note that, \(P_{1}\) and \(\overset{\rightarrow}{\mathbf{n}}\) are known given the facet. \(t\) is the length of the vector \(\overset{\rightarrow}{\mathcal{O}}\). Since the rotation transformation does not change magnitude, \(t\) is therefore \(\|\mathbf{a}\|\) in Equation (2). The margin factor in Equation (2) is therefore calculated as:  
\[\lambda=\frac{t}{\|\overset{\rightarrow}{\mathbf{r}}\|}. \tag{11}\]  
We can repeat the process for each RMS vertex to calculate our final cost function in Equation (3). Note that, although the process contains several steps, each step requires minor computation power compared to solving a set of linear equations in higher dimensions, reducing the total amount of computation power needed.  
Figure 5: D origin-in-triangle check. For a convex problem, there is only one intersection between the polyhedron and the query direction. Therefore, only the intersection facet will contain the origin in the rotated space thus can be determined by this check.  
Figure 6: The ray tracing problem known as in the field of computer graphics, which provides a fast and efficient way of calculating the intersection between a query direction and a facet in space.
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 3 Calibration of SMAP-R Signals
### Direct Power Information
For instance, the dynamic range of freeze/thaw states is comprised within a 4-7 dB range, depending on the landscape [32]. Proper calibration is also important for applications related to vegetation water content estimation; the variability caused by changes of 2-3 kg/m\({}^{2}\) in vegetation water content corresponds to a change of approximately 5 dB in the reflected GPS signal [33]. These geophysical sensitivities are within the same order of magnitude than the GPS transmitter variations alone. Therefore, GPS transmitter variations must be accounted for before science research is undertaken. In addition to the reflected signal, SMAP-R also measures a direct-like signal through an antenna's back lobe. Due to its unpredictable incidence, angle is not a stable reference and is, therefore, not used for calibration. For this reason, to calibrate the GPS signal variability, we propose to collocate CYGNSS direct power and antenna gain information. Ancillary information from CYGNSS can be used for calibrating SMAP-R data by using GPS transmitted power constrained to the same day of the acquired measurements and the GPS antenna gain for the same incidence angle. By doing this, we are assuming the GPS antenna pattern is symmetric in the azimuthal direction, which leads to some remaining calibration errors. In order to evaluate the impact of this assumption, we have analyzed the combined \(P_{tx}\) and \(G_{tx}\) values of four GPS satellites for a period of 12 months, with results shown in  
Figure 7: Combined GPS Equivalent Radiated Isotropically Power (EIRP) (\(P_{tx}G_{tx}\)) for the measured range of incidence angles (37.3\({}^{\circ}\) to 42.7\({}^{\circ}\)) and for the 32 satellites of the constellation. The main information of this plot is in the total variation range of those transmitted power. Each color corresponds to a different GPS satellite. (**a**) GPS block IIF satellites are represented by circles, (**b**) GPS block IIRM satellites are represented by squares, and (**c**) GPS block IIR are represented by crosses. Figure 8. A period of 12 months of observation ensures azimuthal variability in the measurements. In Figure 8, the mean variability in the vertical axis represents the antenna gain variability with respect to the incidence angle. The standard deviation of the mean variability of each GPS transmitter is due to azimuthal gain variations in its antenna pattern. The azimuthal variability has very low impact (\(<\)0.2 dB) on the combined \(P_{tx}\) and \(G_{tx}\) over a period of 12 months for the four GPS satellites analyzed. This error is much lower than the required sensitivity for most of the geophysical parameters. Therefore, the GPS transmitter information from CYGNSS metadata can be collocated with SMAP-R measurements and used as a source of information for our calibration methodology.
## 4 Discussion
### Isotropic Coordinate System for Binary Patterns  
The following are the characteristics of transects R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\):  
* R\({}_{\rm j}\) is a straight line for j = 1, N;
* R\({}_{\rm j-1}\) is parallel to R\({}_{\rm j}\) for j = 2, N;
* The distance between R\({}_{\rm j-1}\) and R\({}_{\rm j}\) remains constant for j = 2, N.  
Characteristics (a) through (c) suggest that it is possible to consider transects R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\) as a layered pattern with an isotropic structure (DStr = 0) and of an isotropic size (DSize = 0), irrespective of the angle of R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\) rotation. Consequently, R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\) is a reference isotropic layer pattern. That is why the DStr and DSize are the measure of deviation the structure and size of an arbitrary pattern under study from the structure and size of a layered isotropic pattern. At the same time, R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\) is the coordinate system (CS) used for the construction of the EM, CS = (R\({}_{1}\),..., R\({}_{\rm j}\),..., R\({}_{\rm N}\)).  
We assume that transect R\({}_{\rm j}\) is a straight line that is not parallel to the rest of the transects. This transect is denoted as R\({}_{\rm angle}\). The rest of the transects in the sequence R\({}_{1}\),..., R\({}_{\rm j-1}\) and R\({}_{\rm j+1}\),..., R\({}_{\rm N}\) are straight parallel lines with identical distance between R\({}_{1}\),..., R\({}_{\rm j-1}\) and between R\({}_{\rm j+1}\),..., R\({}_{\rm N}\). In this case, the coordinate system CS-1 = (R\({}_{1}\),..., R\({}_{\rm j-1}\), R\({}_{\rm angle}\), R\({}_{\rm j+1}\),..., R\({}_{\rm N}\)) represents a layered pattern with an isotropic structure (DStr = 0) and of anisotropic size (DSize > 0). If transect R\({}_{\rm j}\) is a zigzag line (denoted by R\({}_{\rm zigzag}\)), then CS-2 = (R\({}_{1}\),..., R\({}_{\rm j-1}\), R\({}_{\rm zigzag}\), R\({}_{\rm j+1}\),..., R\({}_{\rm N}\)) is the coordinate system with an anisotropic structure and of anisotropic size. CS-1 and CS-2 could be used for the construction of two different empirical models, EM(CS-1) and EM(CS-2), of binary patterns with two different reference patterns, which, consequently, would give the DStr and DSize a different meaning. The non-isotropic CS could be potentially used together with the isotropic CS to reveal minor differences between patterns of complicated structure and size. In addition, the non-isotropic coordinate system could be used for pattern encrypting and decrypting [19].