esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.2 Markov Dynamic Evaluation
The matrix \(S=\left(s_{ij}\right)_{n\times n}\) is defined as the progress matrix of the probability transition matrix \(P=\left(p_{ij}\right)_{n\times n}\). Step 3: Simulate the progress degree \(pd(s)\)  
The progress degree is used to show how much the water quality changed from \(i\) level to \(j\) level in the time of monitoring and it can be simulated using an equation as follows:  
\[pd(s)=\sum_{i,j}s_{ij}=\sum_{i,j}(i-j)\big{|}i-j\big{|}p_{ij},(i,j\in E) \tag{7}\]  
where \(pd(s)\) is the value which shows how much the water quality changed from \(i\) level to \(j\) level, \(s_{ij}\) is the weight which can figure out the extent the water quality deteriorated or improved and \(p_{ij}\) is the transition probability of level \(i\) transferred from level \(j\). The dynamic evaluation of TSS concentration in the reservoir area is based on \(pd(s)\). The \(pd(s)\) can enable reflecting on the water quality by \(\Delta L\) which is the number of levels changed by TSS. At the same time, \(\Delta L\) is related to TSS, which can intuitively reflect the transfer process of TSS levels. The corresponding relationship among \(pd(s)\), \(\Delta L\) and the transfer process is shown in Table 3. When \(pd(s)\) is less than zero, it indicates that the concentration of TSS is increased and water quality is deteriorated; when \(pd(s)\) is greater than zero, it indicates that the concentration of TSS in the reservoir area is reduced and the water quality is improved. Besides, different degrees of progress correspond to different concentrations of TSS. If \(-1<pd(s)<1\), the level of TSS has not changed; if \(1\leq pd(s)<4\), the level of TSS is increased by one level; if \(4\leq pd(s)<9\), the level of TSS is increased by two levels; and if \(9\leq pd(s)<16\), the level of TSS is increased by three levels. At the same time, the greater the absolute value of \(pd(s)\), the more obvious the difference in TSS concentration, and the greater the degree of improvement or deterioration of the water quality. Although the progress degree \(pd(S)\) depicts the pattern and trend of TSS dynamic changes within one time period, there is no direct relationship to tie the average TSS concentration to the progress degree. For this reason and the convenient comparison of the results, two kinds of TSS concentrations were input to a Markov model to evaluate the dynamic changes in water quality. Among the two kinds of TSS concentrations, one is collected from every pixel in the study area and called pixel-based TSS, and the other is the annual averaged TSS by corresponding to all pixels in this region and called the region-averaged TSS. The evaluation results are composed of the series of TSS grids, meaning that every grid mainly considers the differences in the spatial distribution of the TSS concentration, so that the results of this evaluation are representative throughout the research area.
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 7 Conclusions  
To protect the remote-sensing image, this paper presents an image encryption algorithm with AES and chaos. Simulation data display that the proposed algorithm is very strong to resist several commonly used attacks. Comparing with three similar algorithms, the superiority of the proposed algorithm is obvious in terms of the security and encryption speed.  
Conceptualization, X.Z.; Methodology, X.Z.; Software, X.Z.; Validation, X.Z.; Formal Analysis, X.Z.; Investigation, X.Z.; Resources, X.W.; Data Curation, X.Z.; Writing-Original Draft Preparation, X.Z.; Writing-Review & Editing, X.Z.; Visualization, X.Z.; Supervision, X.W.; Project Administration, X.Z.; Funding Acquisition, X.Z.  
This research was funded by the National Natural Science Foundation of China grant number [61501465].  
The research work of this paper is supported by the National Natural Science Foundation of China (61501465). Authors would like to express their sincere thanks to the anonymous reviewers and editors for their constructive comments and suggestions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Entropy-Based DDoS Attack Detection Approach  
Entropy is an algorithm for calculating the probability of a random variable occurring during a specific period [15; 23]. The entropy algorithm is most commonly used to detect DDoS attacks based on analyzing traffic statistics collected from network traffic flows [24]. This study employed the entropy algorithm to compute network traffic packet randomness by using one packet header feature of the network traffic flow, like the destination IP and IP address and the source or destination port. In addition, entropy-based statistics are utilized for analyzing network traffic flows based on the packet header features to detect abnormal packets in the network traffic flows [17; 24].  
Randomness is measured during the abnormal packet detection stage to calculate the probability of an attack in the network. The attack probability is calculated as a percentage, as shown in Equation (1):  
\[p_{xi}=\frac{x_{i}}{n} \tag{1}\]  
where \(x_{i}\) is the frequency of each unique source IP address of the total number among all source IP addresses that occurred in the total number of \(n\) packets during time \(t\).  
As stated before, an entropy algorithm is a statistical means to compute incoming packet randomness in network traffic flows to detect DDoS attacks of various traffic rates (i.e., low or high attack traffic rate), which is this work's primary goal. The entropy algorithm depends on the probability calculation of the source IP address, and the presence of randomness in the behavior of packets is considered a sign of DDoS attacks. The formula for the entropy-based detection approach is expressed in Equation (2) [25].  
\[H(X_{i})=-\sum_{i=1}^{n}P(x_{i})\log_{2}P(x_{i}) \tag{2}\]  
\(P(x_{i})\) denotes the probability of a specific feature from the packet header features within the time interval \(t\), \(n\) represents the total number of features in network traffic at a given time, and \(H(x_{i})\) denotes the entropy value. In the DDoS attack detection approach, the entropy calculating the incoming traffic packets' probability to the source IP address based on the IP frequencies \(i=1\), \(2\),..., \(n\), \(P(x_{i})>0\).  
Attack and normal traffic have different probability distributions derived by computing the source of packet header features' probabilities and are considered a variable of entropy, which, in turn, is used to measure the randomness and uncertainty in the IP source address variable. The higher the uncertainty, the higher the entropy, which is a factor contributing to detecting DDoS attacks in a network. Indeed, the probability is considered an indicator of an attack against the controller. Thus, if a controller receives a large volume of packets, the probability value will be high, indicating abnormal behavior in the network traffic. Therefore, the probabilities of source IP addresses \(P(x_{i})\) that launch the attack towards a distinct destination in 5 s are used as input to calculate the entropy value using Equation (2). Figure 2 presents the entropy-based detection approach's flowchart.
# Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values
## 2 Base Action Set Generation Algorithm Focusing on Undiscovered Sensor Values
### Experiment Using a Differential Wheeled Robot  
We validated the process of base action set generation using the algorithm in a simulation experiment with a differential wheeled robot. The experimental environment is shown in Figure 4a. The experimental field of 10 m \(\times\) 10 m was surrounded by four walls, and the robot was placed in the center. It had two drive wheels and one caster wheel. The robot weighed 10 kg, and each drive wheel could generate 10 Nm torque at maximum. Boxes of 1 kg were placed around it. The boxes were moved by robot locomotion. The field was divided into the nine spaces shown in Figure 4b, and the regions in which each box was present were calculated. The robot received this information as a sensor input value. For example, it received sensor value \(s(t)=\{4,7,2\}\) at time \(t\) when Box 1, 2, and 3 were present in regions 4, 7, and 2, respectively. The corresponding bin of \(s(t)\) was \(b(4,7,2)\) and \(s(t)\) was an undiscovered sensor value if \(n_{b}(4,7,2)=0\) at time \(t-1\). The idea here is to have the algorithm determine which actions will change the external environment and then use them repeatedly to find undiscovered sensor values. A screenshot of the simulation experiment is shown in Figure 5.  
Figure 3: Flowchart of proposed algorithm.  
1 step of the physics simulation was 0.02 s in this experiment. It is too difficult for the robot to maneuver when boxes are near a wall, so the positions of the boxes and robot were reset to their initial ones every 50,000 step = 50,000 * 0.02 s = 1000 s. Each trial in this experiment was conducted until 200 position resets. We compared three algorithms: A random motion generation algorithm (Algorithm Random), our proposed algorithm(Algorithm A) and Algorithm A without the mechanism for discarding action fragments defined in Section 2.5 (Algorithm A). Ten trials were executed for each algorithm. In this case, Algorithm Random was the same as Algorithm A without the action fragment extraction and combination mechanism; in other words, Algorithm A maintained its initial state. A three dimensional histogram was prepared for sensor values in Algorithm A and A'. Each dimension of this histogram was divided into nine regions, as shown in Figure 4b, and each sensor value was allocated to a corresponding bin. Thus, this histogram had \(9^{3}=729\) bins.  
Figure 4: Experiment settings. (**a**) Experimental environment (Unit: meters); (**b**) Sensor division area of boxes.  
Figure 5: Screenshot of experiment.
## 2 Methodology  
### Research Framework  
Figure 2 illustrates the research framework. The framework proposed in this study is mainly composed of four parts. The first part is data collection using a wearable device from participants (i.e., a chest-mounted camcorder for collecting ground truth, a pocket held smartphone for tracking the participant's location using a global positioning system (GPS), and an ankle attached IMU sensor for securing 3-degree of acceleration and angular velocity respectively). Second, the experimenter attaches the equipment and walks along the set path at the test site, and the attached equipment collects data in this process. The data collection process in this study can be divided into two main categories. In the author's previous study, 101,607 gaits were collected from 64 participants on the same site. The previously collected data is used to train and test the cascaded LSTM-based DRNN model. In addition, to check whether the cascaded LSTM-based DRNN model constructed in this study is feasible even for new data, 31,142 gaits collected through other subjects in the same experiment were collected. All data collected were obtained from [PERSON] et al. After classifying them into individual gaits through the gait detection algorithm proposed by [PERSON] et al. [50], they were labeled as normal gaits and abnormal gaits according to the ground truth and gait pattern. The third part determines the number of hidden layers by inputting the gait data collected in the previous study into the cascaded LSTM-based DRNN model. The data collected in the previous study are processed by dividing it into training data (80%) and test data (20%) through random sampling. The last part performs activity classification on the proposed model by using the newly collected data in this study as test data and finally confirms the relationship between the classification results and the environmental barrier. In the following sections, the details of each part will be elaborated on.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 2 Ultra-Tight GPS/INS Integration and RISS  
The ultra-tightly coupled integration, which is the state-of-the-art for GPS/INS integration, has been investigated over the past two decades. In this approach, as can be seen in Figure 3, the GPS raw measurements come directly from the front end of the GPS receiver, in the form of In-phase (I) and quadrature (Q) signal samples [24; 25]. The correlator outputs I and Q are fed into the integration filter as measurements. Thus, estimated Doppler can be used to assist the tracking loops during GPS signal outages or under high receiver dynamics [26; 27]. The integration of INS derived Doppler feedback to the carrier tracking loops provides a vital benefit to this system; the INS Doppler aiding removes the vehicle Doppler from the GPS signal; hence, it results in a major reduction in the carrier tracking loop bandwidth [22]. The bandwidth reduction, in turn, improves the anti-jamming performance of the receiver and, hence, increases the post correlated signal strength. In addition, due to lower bandwidths, the accuracy of the raw measurements is likewise increased [19].  
It was briefly mentioned above that the proposed method uses a 3D Reduced Inertial Sensor System (RISS) to develop a low-cost, simple, and efficient ultra-tightly coupled GPS/INS integrated system. Details about the origins of the adopted RISS system can be found in [11; 12; 13]. The RISS system uses measurements from one gyroscope, two accelerometers, and the built-in car odometer to generate a 3D navigational solution. The gyroscope is aligned with the vertical axes of the vehicle frame; while, the two accelerometers are mounted to the forward and transversal directions, respectively. This simple configuration is popular due to the fact that it provides superior performance over traditional 3D IMU systems at a lower cost [28].
## 3 Survey Results
### General Perceptions by Area of Expertise and by Years of Experience  
To further understand whether there were different perceptions depending on the participants' background or experience, we stratified their responses to Q12, Q17, Q18, Q22, Q23, and Q27 by these two factors (Figure 3a by area and Figure 3b by experience).  
We anticipated that when examining replies by area of expertise, we would see different patterns depending on whether the participants came from a clinical or technological background. However, this was not really the case as both of these groups exhibited similar trends (Figure 3a): they see privacy and security favourably (Q18, Q22, Q23), have mixed views on interpretability and reporting (Q12 and Q27), and perceive dataset availability and balance between IP rights and transparency unfavourably (Q17 and Q26).  
We expected to see experienced professionals with a less favourable opinion of AI in healthcare versus those with less experience due to the relative novelty of AI and its technological, methodological, and ethical requirements and implications for clinical practice. Nonetheless, this does not seem to be the case either (Figure 3b): participants with more expertise indicated that existing AI solutions met their demands for privacy, security, and patient security (Q18, Q22, and Q23) in general, but those with fewer years of experience were slightly more doubtful. Furthermore, individuals with fewer years of expertise criticised AI more regarding interpretability, dataset availability, the balance of IP rights and openness, and reporting (Q12, Q17, Q28, and Q27) than the more experienced ones. A relationship between perception and years of experience does not appear to exist for these four elements.  
Regardless of expertise and years of experience, there are unmet expectations regarding interpretability, dataset availability, balance between IP rights and transparency, and reporting (Q12, Q17, Q26, and Q27). Worryingly, our findings are consistent with and backed up by ongoing conversations [17; 31; 35; 36; 55].
## 3 Morphology Similarity and Coplanarity Measurement  
### Morphology Similarity Measurement  
Usually, the distance between two series, such as the Manhattan distance [31], Euclidean distance [32], and dynamic time warping (DTW) distance [33], is used to evaluate the similarity. The smaller the distance is, the greater the similarity is. Both the Manhattan distance and Euclidean distance are incapable of morphology recognition because these two distances are calculated from the absolute differences of vectors. In Figure 2b, The \(Z\)-axis coordinates of the coal seam floor series vary largely from 0 to about 70 m. Both the Manhattan and Euclidean distance will be very large even if the floor series have similar morphologies. DTW is usually used to calculate the shape similarity between two series with different lengths. DTW distance is computed by first finding the best warped path to align the two series. It is only because of the warped path that the position information of the floor series is eliminated. It is obvious that DTW distance is not an appropriate evaluation index for the morphology similarity of the floor series.  
The gradient of the coal seam floor series is not only the key parameter describing the morphology, but also the target of horizon control. The PLR of the floor series including gradient information can be used to calculate the morphology similarity distance. Figure 4 shows the schematic of the morphology similarity measurement according to the PLR of the floor series. PLR\({}_{i}\) and PLR\({}_{j}\) are the PLR series of two floor series, F\({}_{i}\) and F\({}_{j}\), at different positions on the longwall face. The subscripts \(i\) and \(j\) represent different \(X\)-axis values. The solid circles on each PLR series represent turning points, while the hollow circles represent alignment points that are projected from corresponding turning points on the other PLR series. The distance between F\({}_{i}\) and F\({}_{j}\), \(D(\)F\({}_{i}\), F\({}_{j}\)), is calculated according to the follow formula,  
\[D(\text{Fs}_{i},\text{Fs}_{j})=D(\text{PLR}_{i},\text{PLR}_{j})=\sqrt{\sum_{k= 1}^{n-1}\Bigl{\{}(rad_{ik}-rad_{jk})^{2}+(\Delta z_{ik}-\Delta z_{jk})^{2} \Bigr{\}}} \tag{1}\]  
where \(n\) is the length of the alignment PLR of the floor series. The smaller the distance is, the greater the morphology similarity is. If the distance equal zero, the two series are the same or similar.
## 3 Experiments and Analyses
### Dictionary Construction Time Comparison  
As shown in the previous subsection, the fused images of different multi-focus fusion methods are compared by objective evaluations. Dictionary-learning based image fusion methods, including SR-KSVD and the proposed SCC method, achieve the best performance. However, the dictionary construction process usually takes a very long time. The efficiency of dictionary construction is an important feature of image fusion method. Both K-SVD [39] and the proposed SCC are sparse-representation based dictionary learning methods. So it compares the dictionary construction time of K-SVD and the proposed SCC. K-SVD is one of the most popular dictionary learning methods in recent years. It uses an iterative algorithm to reduce dictionary learning errors and can describe the underlying structure of the image perfectly. To verify the low computation cost of the proposed method, four pairs of images are used for testing computation time. The time consumption of K-SVD and SCC are shown in Figure 12 and Table 5. SCC uses low computation times, that are marked in bold, in four group experiments. The experimental results demonstrate that SCC has a much better performance of computation time than K-SVD.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **EI** & \(\mathbf{Q^{AB/F}}\) & **MI** & **VIF** \\ \hline LE & 128.0512 & 0.8478 & 4.1059 & 0.4113 \\ DWT & **131.5033** & 0.8355 & 3.5696 & 0.3779 \\ DT-CWT & 96.3171 & 0.8548 & 3.8049 & 0.4035 \\ CVT & 122.5616 & 0.8448 & 3.8799 & 0.4678 \\ NSCT & 69.4987 & 0.9850 & 7.6092 & 0.8187 \\ SR-DCT & 122.4806 & 0.8518 & 4.1766 & 0.4784 \\ SR-KSVD & 128.4125 & 0.8625 & 4.8410 & 0.5118 \\ SR-SCC & 128.5106 & **0.8629** & **4.8901** & **0.5291** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Objective Evaluations of Multi-focus “Bottle” Fusion Experiments.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **EI** & \(\mathbf{Q^{AB/F}}\) & **MI** & **VIF** \\ \hline LE & **94.1343** & 0.9443 & 6.7677 & 0.7920 \\ DWT & 94.8093 & 0.9430 & 6.1765 & 0.7629 \\ DT-CWT & 80.2643 & 0.9470 & 6.2083 & 0.7636 \\ CVT & 93.5736 & 0.9359 & 6.4109 & 0.8068 \\ NSCT & 93.6767 & 0.9447 & 7.1940 & 0.8245 \\ SR-DCT & 76.9935 & 0.7343 & 3.3002 & 0.7291 \\ SR-KSVD & 93.5276 & **0.9450** & 7.4607 & 0.8269 \\ SR-SCC & 93.6448 & 0.9448 & **7.5420** & **0.8283** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Objective Evaluations of Multi-focus “Leopard” Fusion Experiments.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **EI** & \(\mathbf{Q^{AB/F}}\) & **MI** & **VIF** \\ \hline LE & 55.0926 & 0.7964 & 5.0724 & 0.6299 \\ DWT & **56.7262** & 0.8795 & 4.6665 & 0.5885 \\ DT-CWT & 44.6470 & 0.8196 & 5.0999 & 0.6025 \\ CVT & 53.5711 & 0.8438 & 4.7937 & 0.6367 \\ NSCT & 53.5958 & 0.8055 & 5.3111 & 0.6625 \\ SR-DCT & 48.8765 & 0.6332 & 3.1122 & 0.3385 \\ SR-KSVD & 53.0461 & 0.8791 & **5.7345** & 0.6766 \\ SR-SCC & 53.0304 & **0.8826** & 5.6656 & **0.6893** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objective Evaluations of Multi-focus “Lab” Fusion Experiments.
## 5 Limitation  
The proposed analytical model is not without limitation. One of the major limitations is that the heterogeneity of CGCW cannot be taken into consideration since the model is one-dimensional. It has been proved that the compaction and uneven density of the CGCW materials generated during the production and installation can significantly affect the porosity and hydraulic conductivity at different locations in the wall [38]. The actual contaminant concentrations behind the CGCW can be larger or lower than the results predicted by the model. Another limitation is that the parameters of the SBCW are assumed not to change with time. However, the materials of the cut-off wall can be easily affected by contaminants during the service life [39]. This can result in a gradual increase in the hydraulic conductivity. Further numerical simulations are recommended to investigate the effects of the heterogeneity and parameter time-variation on the CGCW performance.  
The empirical equations proposed in this manuscript also have limitations. The parametric sweep feature is used to simulate the leakage of the GMB holes under different situations. However, the effects of the parameter uncertainty on the leakage and CGCW performance cannot be taken into consideration. It has been proved that the analysis of parameter uncertainty can offer significant helps in the engineering design since it can help in further identifying critical factors that influence the leakage behavior [31]. Uncertainty quantification feature of COMSOL is expected to be used in the future study to quantify the uncertainty in model outcomes caused by uncertainties in input parameters.