esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Wind Shear Influences on the Aerodynamic Performances of a UAV Airfoil
## 3 Results and Discussion
### Effect of Shear Wind on Lift  
Both lift and lift-drag ratios are critical for a bionic UAV design, thus the effect of shear wind on lift and lift-drag ratio is further discussed in the current research. According to the previous study on velocity and pressure distributions, both lift and drag have been estimated. The lifts of the four position cases of the linear distributed shear wind are illustrated in Figure 11a and the negative case is compared in Figure 11b. It is shown that the maximum CL has decreased as \(dV_{\rm W}/dh\) increases. Compared with a wind field with zero gradient, the maximum lift coefficient was reduced by 18% in a shear wind with \(dV_{\rm W}/dh=3.05\), 13.4% for \(dV_{\rm W}/dh=2.29\) shear wind, and 8% for \(dV_{\rm W}/dh=1.52\) shear wind. So, it is verified that a positive gradient reduces the maximum lift coefficient, and the greater the gradient is, the lower the maximum lift coefficients are. A positive velocity gradient also decreases the corresponding stall angle. For an aircraft, a positive gradient at the same angle of attack usually reduces lift which is not beneficial to take-off and landing which may lead to a flight accident. Therefore, a UAV needs to increase the angle of attack to maintain lift under a positive gradient shear wind situation.  
Figure 11: Lift curve with linear wind gradient: (**a**) Positive gradient; (**b**) Negative gradient.  
Figure 10: Velocity distribution of logarithmically distributed shear wind field: (**a**) Velocity distribution near chord axis (\(L=c\), \(\alpha=7^{\circ}\)); (**b**) Velocity magnitude contour near the airfoil surface (\(\alpha=7^{\circ}\)).  
The lift curve of a negative gradient case is shown in Figure 11b. It is shown that a negative gradient increases lift which is consistent with the conclusion discussed in the previous Section 3.2. Compared to a positive gradient, a negative gradient also increases the slope of the lift curve, maximum lift coefficient and stall angle of attack. Compared to a gradient-free wind, the maximum lift coefficient of the negative gradient is increased by 19.6% and the stall angle of attack is increased to 16\({}^{\circ}\). Therefore, a negative gradient increases lift and delays stall at the same average speed, which is the reason why birds use shear wind to keep rising to gain potential energy and finally achieve long-distance gliding flight. Therefore, only a small angle of attack is needed to generate sufficient lift required for the ascent in a negative gradient wind, which is of great value for the take-off and landing of UAVs.  
Similarly, shear wind velocity studies show that logarithmic distribution reduces lift. For the same conditions of \(V_{Whm}\), \(V_{Whmxx}\) and \(V_{Whmy}\), logarithmic distribution decreases the slope of the lift curve and maximum lift coefficient, and increases stall angle. The maximum lift coefficient is also decreased by 39.9% compared to linear distribution with \(dV_{\text{W}}/dh=3.05\), as shown in Figure 12.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 5 New Research Directions and Challenges
### Using Lightweight Cryptosystems  
Using efficient lightweight schemes such as identity based cryptography (IBC) and certificateless-public key cryptography (CL-PKC) do not require Certificate Authority CA [68]. This leads to lowering the processing time (delay) and handshaking processes. Hence, it can provide more flexibility and security to cellular systems by providing many security goals at once, such as key management, authentication, integrity, confidentiality, privacy preserving, and anonymity. Therefore, we can improve the robustness against attacks: MITM (false base station), IMSI catcher, replay, impersonation, and ensure forward/backward secrecy FBS. Furthermore, this approach has low computational and communication cost with the ability to achieve handover security [69; 70].
# Thermospheric Neutral Wind Measurements and Investigations across the African Region--A Review
## 6 Conclusions  
This paper presented a brief review of thermospheric wind studies over the African region focusing on FPI measurements and comparisons with the HWM-14. Currently, FPI instruments are installed in South Africa, Ethiopia, Nigeria, and Morocco. The studies reviewed covered observations during 2018-2019, 2016-2017, 2015-2016, 2013-2016, and 1994-1995 over South Africa, Nigeria, Ethiopia, Morocco, and the Ivory Coast, respectively. We found that the HWM is more accurate in the northern hemispheric part of the African region, a result attributed to the presence of data informing the development of this empirical model. Overall, the HWM reproduces the climatological behavior of meridional and zonal winds. However, varying levels and different types of discrepancies were reported for each of the studied regions.  
The comparisons between the HWM-14 and FPI observations showed noticeable differences, particularly in the meridional wind. The discrepancy is attributed to the historical lack of observations over the continent in the construction of the empirical model. As space weather is becoming a more pressing issue all over the world, space weather-related observations and research are becoming more common. We are seeing more FPI observations in Africa. Given that the HWM-14 is a most-used model for space weather research, the need to improve the model with data more evenly distributed around the world is more urgent. It will be very significant to use the African data to validate various first principle models as well. Additionally, coverage of different longitudes is critically important for the study of non-migrating tides with short time scales as ground-based data sample more local times than satellite observations.  
Conceptualization, D.O., A.B. and J.B.H.; investigation, D.O., A.B., J.B.H., B.R., Z.K-J., T.O., Q.W. and J.J.M.; resources, D.O., A.B., J.B.H., B.R., Z.K-J., T.O., Q.W. and J.J.M.; writing--original draft preparation, D.O., A.B., J.B.H.; writing--review and editing, D.O., A.B., J.B.H., B.R., Z.K-J., T.O., Q.W. and J.J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors appreciate the institutions hosting the FPI in Africa, namely: the Centre for Atmospheric Research (Nigeria); the South African Astronomical Observatory (South Africa); Oukaimeden Observatory (Morocco); Bahir Dar University (Ethiopia); Korhogo Observatory (Ivory Coast). We also appreciate the international partners that make the FPI available in Africa: [PERSON] of the National Center for Atmospheric Research (NCAR), USA through National Science Foundation (NSF) grant AGS-2120511; [PERSON] of the University of Illinois Urbana-Champaign, USA through NSF grants; Air Force Office of Scientific Research (AFOSR), Defense University Research Instrumentation Program (DURIP), Boston college, USA; CNET/SPI at Lannion. The authors appreciate the Atmosphere journal for offering to pay the APC for this article.  
The authors declare no conflict of interest.
## 5 Simulations  
According to [22], some commercially available modems are considered: LinkQuest, Teledyne Benthos, TriTech International, Aquatec Group, EvoLogics and DSPComm. Table 2[23] shows a number of commercial modems that are more suitable for dense networking with sensors in shallow areas,  
Figure 3: Our new threshold for algorithm DEADS.  
while Table 3 shows the technical specifications of the EvoLogics modems that will be considered in the following. In this section, it will be measured the performance of the algorithm modified by us presented in Figure 3. The network simulated has 50 nodes that are random distributed at a \(200\times 200\) m network. The initial power of each node is 4 J and the packet size to be transmitted is considered to be of 2000 bits. Any other parameters that are considered are shown in Table 3. The basic metrics that will be taken in consideration are: (a) network lifetime (the number of communication cycles until the first node in the network dies), (b) the number of packets transmitted (a communication cycle is considered for the transmission for the entire packet, if it is transmitted successfully from the source node to the intermediate node and received from the destination node); (c) acoustic channel capacity in (kbps) (the average capacity of each acoustic channel between the transmitter and a receiver for a network whose environmental parameters will vary like: temperature or salinity); (d) delay (ms) calculated as the total delay needed to send the packet from the transmitter to receiver including the transmission delay (the time it takes for the modems to transmit the packet) as well also the propagation delay (the time it takes for the packet to propagate according to its direct path for the connection between the transmitter and the receiver).  
Each graph and conclusion presented regarding the metrics mentioned, are as result of an averaging over 100 simulations.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## Preliminaries and Definitions  
### Ciphertext-Policy Attribute-Based Encryption (CP-ABE)  
CP-ABE, first proposed by Beyhencourt, is an access control scheme wildly used in cloud storage. It mainly contains four algorithms:  
**Setup\(\rightarrow\)(PK,MSK)**: This algorithm is run by the authority in the system, to generate the system public key (PK) and the master secret key (MSK), with the system parameters.  
**Encrypt(M,T,PK)\(\rightarrow\)CT**: This algorithm is executed by the owner of the data. The message M is encrypted by the system public key PK and the access structure T.  
**Key_Gen(MSK,S)\(\rightarrow\)SK**: This algorithm is performed by the authority, to generate the secret key SK through the user's attribute set S and the master secret key MSK.  
**Decrypt(CT,SK)\(\rightarrow\)M\({}^{\prime}\)**: This algorithm is executed by the user, to decrypt the ciphertext CT by the secret key SK, and recover the message M\({}^{\prime}\).
# A New Look at the Stress State Across the Bohai Strait, China
## 4 Results  
### Stress Magnitudes  
Based on the above in-situ stress data, we first analyzed the distribution characteristics of principal stresses with depth across the Bohai Strait. Linear regression was applied to calculate the depth-dependent variations of \(S_{\rm H}\) and \(S_{\rm h}\) (Table 2 and Figure 5). The CD borehole dataset is limited, introducing some uncertainty in its results. However, its stress distribution trend aligns with other boreholes, supporting the reliability of our data. The results show that \(S_{\rm H}\) and \(S_{\rm h}\) in the PD borehole range from 5.81 to 11.49 MPa and from 4.11 to 8.59 MPa, with the gradients of 0.90 MPa/100 m and 0.86 MPa/100 m, respectively. \(S_{\rm H}\) and \(S_{\rm h}\) in the GZ borehole range from 4.38 to 14.44 MPa and from 3.10 to 9.78 MPa, with the gradients of 1.15 MPa/100 m and 1.01 MPa/100 m, respectively. \(S_{\rm H}\) and \(S_{\rm h}\) in the XL borehole range from 7.78 to 28.79 MPa and from 5.31 to 17.84 MPa, with the gradients of 4.29 MPa/100 m and 2.70 MPa/100 m, respectively. \(S_{\rm H}\) and \(S_{\rm h}\) in the CD borehole range from 3.67 to 6.73 MPa and from 2.39 to 4.02 MPa, with the gradients of 7.58 MPa/100 m and 4.01 MPa/100 m, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Borehole** & \(S_{\rm H}\) & \(R^{2}\) & \(S_{\rm h}\) & \(R^{2}\) & \(S_{\rm h}\) & \(R^{2}\) & \(S_{\rm V}\) \\ \hline Gaizhou & \(S_{\rm H}=0.0115D+4.67\) & 0.4448 & \(S_{\rm h}=0.0101D+3.03\) & 0.7237 & \(S_{\rm V}=0.0265D\) \\ Changdao & \(S_{\rm H}=0.0758D-0.64\) & 0.4689 & \(S_{\rm h}=0.0401D+0.08\) & 0.4807 & \(S_{\rm V}=0.0265D\) \\ Xiangli & \(S_{\rm H}=0.0429D+4.00\) & 0.5447 & \(S_{\rm h}=0.0270D+2.58\) & 0.6479 & \(S_{\rm V}=0.0265D\) \\ Pingdu & \(S_{\rm H}=0.0090D+5.89\) & 0.6051 & \(S_{\rm h}=0.0087D+3.67\) & 0.8302 & \(S_{\rm V}=0.0265D\) \\ North China Block & \(S_{\rm H}=0.0229D+4.00\) & & \(S_{\rm h}=0.0170D+2.58\) & & \(S_{\rm V}=0.0272D\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The fitting results for principal stress with depth in the boreholes across the Bohai Strait.
## 3 Results
### Satellite Resource Allocation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mission ID** & **Satellite** & **Start Time** & **End Time** \\ \hline T1 & S3 & 13:47:54 & 13:48:17 \\ T2 & S2 & 04:15:30 & 04:17:09 \\ T3 & S2 & 13:21:54 & 13:22:58 \\ T5 & S1 & 08:23:09 & 08:24:30 \\ T6 & S3 & 05:56:35 & 05:58:01 \\ T7 & S2 & 06:12:08 & 06:15:14 \\ T8 & S2 & 12:07:57 & 12:09:14 \\ T9 & S1 & 05:22:59 & 05:23:47 \\ T10 & S3 & 12:18:23 & 12:19:38 \\ T11 & S1 & 01:35:14 & 01:36:40 \\ T12 & S3 & 07:05:36 & 07:08:13 \\ T13 & S2 & 09:03:54 & 09:05:53 \\ T14 & S2 & 10:08:57 & 10:11:51 \\ T15 & S1 & 13:41:37 & 13:42:34 \\ T16 & S3 & 06:38:45 & 06:41:48 \\ T18 & S2 & 03:24:29 & 03:25:40 \\ T19 & S3 & 09:28:23 & 09:31:33 \\ T20 & S1 & 04:49:34 & 04:51:04 \\ T21 & S2 & 07:26:13 & 07:29:05 \\ T22 & S1 & 07:04:12 & 07:05:14 \\ T23 & S3 & 11:57:16 & 11:59:27 \\ T24 & S1 & 12:06:45 & 12:09:13 \\ T25 & S3 & 10:16:18 & 10:18:27 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Initial scheduling plan. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mission ID** & **Satellite** & **Scheduling Operation** & **Influenced Mission** & **Start Time** & **End Time** \\ \hline T26 & S3 & Reallocation & T10 & 09:35:56 & 09:37:22 \\ T27 & S3 & Insertion & None & 00:49:25 & 00:50:58 \\ T28 & - & Deletion & None & - & - \\ T29 & S2 & Replacement & T21 & 09:03:54 & 09:04:38 \\ T30 & S1 & Replacement & T15 & 13:41:03 & 13:43:05 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Scheduling plan of newly arrived missions.
## 3 Results  
### Exploratory Data Analysis and the Preprocessing of Input Features  
For our experiments, we used an artificially generated data set: after generating the data set for a random multi-label classification problem, using the _sklearn.datasets.make_multilabel_classification_ package, we adapted the data set to the students' learning-style classification problem. This approach was applied due to the paucity of informative and representative data available in the actual Moodle environment. Samples were expertly labeled, manually, on the basis of the knowledge acquired in the course of our previous research, the results of which were published in [15]. In that research, we investigated the relationships between factors deduced from the students' interactions with a virtual learning environment, by means of tracking each student's behavior and learning style.  
During the exploratory data analysis, the main statistical characteristics of the set of input features (activities of the student in the VLE) were identified--the results are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Navigation** & **Navigation** & **Skip** & **Forum** & **Forum** & **Video,** & **Content** & **Feedback** & **No. of** & **Quiz** & **Question** \\  & **Deep** & **Overview** & **Visit** & **Post** & **Pictures** & **Text Stay** & **No.** & **or Links** & **Reviewsion** & **Details** \\ \hline mean & 7.3737 & 8.5858 & 8.2727 & 10.7272 & 4.6868 & 8.8585 & 5.3737 & 5.3333 & 8.0808 & 10.7373 \\ std & 5.7792 & 6.0170 & 6.1125 & 7.0230 & 3.7  
The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The standard deviation (\"std\" in the Table 1.) is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The minimum (\"min\" in the Table 1) is the smallest value in the data set. Names of the input features are presented in the headings of the table--they are explained further in the Section 3.2.  
The correlation matrix, once computed, shows 2 pairs of input features that tend to be strongly correlated (marked in dark red colour). The labels are not correlated (see Figure 1).  
It is appropriate to note that, in general, depending on the data set available, it may be necessary to use input feature preprocessing techniques for cleaning up the data [38], feature scaling, imputing the missing values, for oversampling or under-sampling in the case of an imbalanced data set, encoding the categorical data, etc.
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 5 Simulation and Analysis
### Results
#### 5.4.3 SER Performance of Improved 4 PPSK  
Compared with BP [7], SVM [8] and JAWD [9], the detection performance of the improved 4 PPSK system with the SIF followed by the proposed stacked DSAE network is shown in Figure 6. The system parameters are \(M=4\); \(K=2\); \(N=10\); \(f_{c}=10\times 10^{6}\) Hz; and a sample rate \(f_{s}=100\times 10^{6}\) Hz, and the training database is noise-free. Corresponding to the set of system parameters, the ratio of the training and testing sets is 60,000:100,000, and the structure of stacked DSAE network is 100-60-20-4. There are four layers in the network, with 100 nodes in the input layer; 60 nodes in the first hidden layer; 20 nodes in the second hidden layer; and 4 nodes in the output layer.  
Figure 5: SER of the stacked DSAE network for the improved 2 PPSK system.  
As shown in Figure 6, the SER of the proposed scheme based on the improved MPPSK modulation in Equation (1) is better than that based on the existing MPPSK [5, 6]. It is consistent with the expected results. Furthermore, the proposed scheme performs approximately 3 dB better than the SVM at the SER of \(10^{-4}\).  
We also obtained the performance of the structure that employs the SIF, a coherent processing block, and LPF followed by the proposed stacked DSAE network in the receiver. The major system parameters are the same as above, except that the training database contains both noiseless and \(-3\) dB noised datasets. The performances of the BP, SVM, JAWD detectors, and this stacked DSAE network are shown in Figure 6, separately.  
The stacked DSAE network, based on both the noisy training database and noiseless training database, performs the best in Figure 7. In the proposed scheme, the performance with \(-3\) dB noised training database is approximately 5 dB better than that with the noiseless training database at the SER of \(10^{-4}\). This is consistent with the results in Tables 1 and 2 and Figure 5. This is because, in the case of the \(-3\) dB, noised training data can be extracted with more signal features than with the noiseless training data.  
Compared with the SER performance of the 4 PPSK system with only SIF in Figure 6, the required SNR is reduced by approximately 2 dB at the SER of \(10^{-4}\) in the structure with an SIF, a coherent processing block and an LPF in Figure 7.  
Figure 6: SER Comparisons of the 4 PPSK system.
## 4 Hyperplane Estimation
### Hyperplane Hypothesis Generation and Evaluation by Singular Value Decomposition (SVD)  
In order to fit data to a model for hyperplane estimation, a method is needed to generate hypothetical hyperplanes for evaluation against the data set. A widely used technique to solve a linear system of equations is the least squares solution from the components of singular value decomposition (SVD) [23], a technique with which to describe a matrix in a decomposed form in terms of other matrices. A given matrix, \(M\), can be described, after SVD has been performed, to generate \(U\) (the real orthogonal matrix), \(D\) (the rectangular diagonal matrix, whose diagonal entries are the singular values of \(M\)) and \(V\) (the real orthogonal matrix) as:  
\[M=UDV^{T} \tag{2}\]  
The details of the SVD calculation are not given here.  
However, one is able to solve a linear system described by a matrix, \(M\), by taking the SVD and subsequently taking the right-hand column of the calculated \(V\) as the solution.  
For example, in two dimensions, given the equation of a line:  
\[ax+by+c=0 \tag{3}\]Along with two points (\(xI\ yI\ 1\)) and (\(x2\ y2\ 1\)), in homogeneous form, one can construct the matrix:  
\[M=\left(\begin{array}{ccc}x1&y1&1\\ x2&y2&1\end{array}\right) \tag{4}\]  
and vector:  
\[t=\left(\begin{array}{ccc}a&b&c\end{array}\right) \tag{5}\]  
Thus, the system is described as:  
\[Mt^{T}=0 \tag{6}\]  
Taking the SVD of \(M\) and using the values in the right-hand column of \(V\), a solution for \(t\) is obtained. For two rows, this will solve Equation (6) exactly, but as more rows are added, it will give the least squares solution.  
In order to extend this beyond two dimensions, we simply increase the size of the vector described in Equation (5); needing, where \(n\) denotes the number of dimensions, \(n\) rows in \(M\), in order to uniquely identify a hyperplane in this way.  
Thus, \(M\) can be thought of as the minimal set required to describe a hyperplane. This minimal set is used as an agent's hypothesis.  
Finally, a method is required for evaluating the hypothesis against a data point. This Euclidean distance measure is called the residual distance of a point from a hyperplane. To calculate this, we must first convert the hyperplane to Hessian normal form [24], which then allows us to easily calculate the orthogonal distance from the hyperplane by taking the dot product of the point (with homogeneous coordinates) with the vector of hyperplane parameters, \(t\). This also applies identically in \(n\) dimensions [24].
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Experiment Details
Based on these results, we chose to retain the default value of 2 for minNumObj. To validate MIPART's performance, a 10-fold cross-validation approach was utilized as the test option, dividing the multi-instance dataset into 10 subsets. In each fold, one subset was used as the test set while the remaining nine served as the training set, providing a comprehensive assessment across all data partitions. To thoroughly evaluate the model's performance, we employed a set of common metrics: accuracy, precision, and sensitivity. These metrics, of which the formulas are detailed in Equations (5)-(7), each offer specific perspectives on the model's classification effectiveness. Their calculations are in the basis of key indicators--true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN)--providing insights into different aspects of predictive accuracy and reliability. * Accuracy: Represents the fraction of instances correctly classified, given by \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (5)
* Sensitivity: Assesses the method's capability to accurately detect true positive instances, calculated as \[Sensitivity=\frac{TP}{TP+FN}\] (6)
* Precision: Denotes the fraction of predicted positives that are actual positives, expressed as \[Precision=\frac{TP}{TP+FP}\] (7)  
Additionally, we conducted various non-parametric statistical tests, including the Friedman aligned ranks and Wilcoxon signed-rank tests, to assess the statistical significance of the experimental results, ensuring reliability with appropriate \(p\)-values.