esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach I: Multi-Region Selective Encryption Approach for Mammography DICOM Images
#### 3.1.4 Medical Images' Statistical Properties  
It was noticed in study [25] that the ROI of each DICOM modality was segmented using different statistical values (thresholds), which indicates that different DICOM modalities have different distributions. A dataset of different modalities and different human body parts was used to fit distributions in this paper, and it is the same one used in study [25].  
Figure 5: Multi-region encryption process.  
These distributions can be used as a pre-knowledge to determine threshold values for different modalities, as illustrated in [25].  
In general, manual segmentation performed by physicians shows that the statistical properties of the segmented ROI significantly differ from one modality to another. For example, physicians consider the brightest regions as ROI in Mammography DICOM images [32; 33]. Hence, as proof of this concept, the study conducted in this approach is restricted to Mammography DICOM images, where 75% of the Mammography dataset has Laplace distribution, and only 25% was Beta distribution. However, 99% of Mammography images are Laplace after removing the background [34]. Figure 6 shows a Mammography DICOM image and its corresponding histogram.  
The histogram in Figure 6 shows that the ROI's low pixel intensities (\(\sim\)500) dominate the distribution. Additionally, the brightest region is tiny in comparison with other regions. In the literature, it has been reported that the manual segmentation of Mammography must focus on bright regions [33]. Therefore, the proposed approach in this paper considers pixels with high intensities as the most important regions within the ROI.
## 3 Encapsulation Protocol
### Http
#### 3.1.2 Machine Learning  
To avoid the problem that the exponential growth of feature dimensions makes the accuracy of the N-gram model lower when \(n>2\), [PERSON] [28] used a feature clustering algorithm to reduce the dimensions in different feature spaces and multiple single-class SVM classifiers to vote in different feature spaces, making the model extremely accurate at detecting HTTP tunnel traffic. Thus, the author achieved an FPR of \(10^{-5}\) in DARPA'99 data [63]. The URL and payload string were extracted using a deep neural network model built with Bi-LSTM (bi-directional long and short-term memory). In private datasets, experiments [53] have shown that the accuracy of identifying tunnel traffic can reach 90%. With CNN and LSTM, [PERSON] [50] developed a deep learning model for detecting URL-based HTTP tunnels. In privately balanced datasets, the best model averages 95% accuracy. However, the model it cannot handle seriously unbalanced datasets.  
He [54] combined classical classifiers with bagging, boosting, etc., with classical classifiers such as C4.5. The packet payload size, variance, packet count, data stream length, and IAT of adjacent packets can be used to identify whether the traffic is HTTP tunnel traffic.  
It is also possible to classify passenger protocols in tunnel traffic using detection methods based on ML. Based on private datasets, [PERSON]'s [39] method can identify tunnel traffic and distinguish SMTP-HTTP and P2P-HTTP passenger protocols with 95% accuracy. What is remarkable is that the model is 100% accurate at identifying gray pigeon traffic. According to [PERSON] [16], it is necessary to analyze the second-order statistical correlations between consecutive packets, including the packet distribution difference in HTTP sessions and the entropy in N-RPP and N-RMI distances. An SVM classifier can distinguish tunnel traffic from non-tunnel traffic and identify FTP-HTTP, SMTP-HTTP, and POP3-HTTP passenger protocols. HTTP tunnel traffic is recognized with an average accuracy of 82.5% on private datasets.
## 5 Result and Discussion
### Vorticity Contours
#### 5.1.3. Trailing-Edge Vortex Variation  
After the trailing edge zone at Section E, a strong vortex forms because of the transportation of low energetic material, which is located symmetrically in the lower half-span of the blades (Figure 8, Section E). This vortex has two heads; the right-hand head is stronger than the left-hand one. The vorticity of the left-hand side was affected more by the P.s pressure, and the right one was affected more by the S.s. With the increasing pressure level from passages 6-7 and 5-6 to 10-11 (Figure 8, Section E), the vorticity magnitude grows. In equal admission, when pressure level increased by 8%, the maximum of the vorticity intensity at the center of the right head of the trailing-edge vortex (Tr. vortex), which is closer to the P.s, rises about 19%, but the left-hand side of this vortex, which is closer to the S.s, grows 12%. Therefore, it can be concluded that the Tr. vortex is more affected by the P.s pressure level than the S.s pressure.  
Investigating the passages in the partial case shows that, by sufficiently decreasing the pressure, the left-hand side of the Tr. vortex almost disappeared, but the right-hand side is affected less (Figure 9, Section E).  
Figure 9: Vorticity contours at specified sections in blade passages 3–4, 4–5, and 9–10 of the partial case in the spanwise location against the degree in circumferential location (DCL).
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 3 Hybrid Acoustic-RF Approaches for Positioning Energy-Neutral Devices
### Setup  
Two approaches can be followed to perform the necessary distance measurements. In the first, the anchor nodes emit the acoustic signals, which are subsequently captured by the mobile device through a microphone. In the other case, the mobile device broadcasts an acoustic signal and the anchor nodes fulfills the role of acoustic receiver.  
Given the energy constraints of the mobile device, the transmission of acoustic signals is preferably left to anchor nodes. To pick up acoustic signals at useful distances, the power consumption of speakers easily amounts to several hundred milliwatt or even a couple of watt. In contrast, MEMS microphones only have a power consumption in the order of a few milliwatt. This leaves a reception window for the mobile device that can be two or three orders larger than the time window for transmission before both energy requirements equalize. Considering a LOS distance in the order of 10 m for a typical room, such a long reception window will generally not be needed. On the other hand, some energy-demanding signal processing may be involved in the receiver contrast to transmission, e.g., for filtering or information extraction. Depending on the needs of the application, this speaker-microphone power consumption imbalance will impact the architectural decisions.  
In terms of physical dimensions, preference is again mainly given to the anchor-transmission setup. Since speakers are relatively large, a small MEMS microphone is preferred for the mobile device. Moreover, the small dimensions of the microphone cause the beam pattern to be omnidirectional, even at low ultrasound frequencies, which guarantees acoustic pick-up from all directions. At the same operating frequency, a larger speaker will exhibit a more directional beam pattern. Consequently, multiple speakers may have to be used at the mobile device to ensure acoustic transmission to sufficient anchor nodes, which increases cost and complexity.
## 2 Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms
### NSGA-II Multi-Objective Optimization and Decision Algorithm
#### 2.3.2 Multiple Objective Optimization  
In this study, the optimization objective functions \(WRMS\), \(\rho_{\max}\), and \(s\) cannot be minimized at the same time. Only three optimization objectives are weighed to obtain the Pareto optimal solution set, and then a further decision is made to obtain the optimal solution. In this study, the NSGA-II Algorithm [36] is used to solve nonlinear optimization problems, and the specific process is shown in Figure 6.  
As the core of the NSGA-II Algorithm, it is necessary to explain non-dominant sorting in more detail. For \(n\) minimization objective functions \(f_{i}(x),i=1,2,\cdots,n\), for any decision variables \(X_{a}\) and \(X_{b}\), if the following two conditions are true, then \(X_{b}\) is dominated by \(X_{a}\), and the constraint conditions are:  
1. \(\forall i\in 1,2,\cdots n,f_{i}(X_{a})\leq f_{i}(X_{b})\).
2. \(\exists i\in 1,2,\cdots n,f_{i}(X_{a})<f_{i}(X_{b})\).  
In a group of solutions, the Pareto level of the non-dominated solution is one, which is deleted from the solution set, and the Pareto level of remaining solutions is two. By analogy, the Pareto level of all solutions in the solution set is finally obtained.  
Before applying the NSGA-II Algorithm, the parameters are set as follows:  
1. Population size: In order to obtain the global optimal solution, the population size should not be too small, but the size should also not be too large considering the time complexity. In this study, the population number is set as 100.
2. Crossover probability and mutation probability: Crossover probability and mutation probability were set as 0.8 and 0.2, respectively, in this study.
3. Maximum evolutionary algebra: Matlab simulation results showed that the evolution can be completed after 30 generations. Therefore, \(MaxGen=30\).  
The lane changing longitudinal displacement \(\Delta x\) and lane changing duration \(\Delta t\) in the scenario in Section 2.1 are decision variables, and the value range and step size satisfy Equation (12).  
\[\left\{\begin{array}{l}80\leq\Delta x\leq 135\\ 4\leq\Delta t\leq 8\end{array}\right. \tag{12}\]where the step size of \(\Delta x\) and \(\Delta t\) is 0.2 m and 0.2 s, respectively.
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 6 Conclusions and Discussion  
In this paper, a DDQN with dual-agent algorithm is proposed to effectively reduce congestion at a four-phase signalized intersection. The proposed architecture includes two agents, agent \(P\) and agent \(A\), where each agent controls traffic flow with different directions. In the traffic signal control process, two agents shift behaviors from working and waiting. Through the dual-agent control algorithm, the fixed phasing sequence which is commonly ignored in some DRL methods [11, 13] is guaranteed. Due to enough intervals between each control action, the traffic light flicker is avoided, and the system computation load is alleviated. The fixed phasing sequence of traffic signal balances waiting time of vehicles for each road as well. Owing to the difficulty in obtaining data from a real traffic system, a reward function based on traffic flow and a state representation with vehicle positions are presented to accelerate the agent learning speed. The algorithm is evaluated in simulations, and results show the performance of the dual-agent algorithm in improving traffic capacity is much better than the binary action control algorithm.  
To coordinate two agents, we set a penalty factor in reward functions in previous experiments. It is used to avoid vehicles waiting too long in the other agent (waiting agent) control lanes. However, we find that the penalty has little effect on reducing traffic congestion and average waiting time. This is probably because the busy agent cannot hold an action for a long time in our simulation environment, which enables steady traffic streams in all directions. Therefore, the optimal control policy is to let the number of crossing vehicles be as large as possible. Besides, to avoid the action changes frequently, we set the minimum time interval for each agent. This setting may cause the control policy to fail to reach the desired results. Thus, we will aim to investigate how to set an optimal duration in traffic signal control based on DRL algorithms.  
Conceptualization, J.G. and Y.F.; Data curation, J.G. and Y.F.; Methodology, J.G. and Y.F.; Supervision Y.F., Z.S. and P.W.; Writing--original draft J.G.; Writing--review and editing J.G., Y.F., Z.S. and P.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (61673253, 61901254) and the key support Projects of Shanghai Science and Technology Committee (16010500100).  
The authors declare that there is no conflict of interest regarding the publication of this article.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 4 Photocatalytic Properties
### Temperature Effect  
We studied the temperature effect on the photodegradation of MO. Figure 10 shows the variation of C/Co with time of the photocatalytic reaction for the ZnO-Cr\({}_{1}\) sample at room temperature (RT) and at 45 \({}^{\circ}\)C. Here, PCA decreased with increasing temperature, as did the degradation percentage of MO. The latter reached 53% at 45 \({}^{\circ}\)C after 100 min, which is lower than that obtained after the same time at RT (\(\approx\)98.8%). This dramatic retraction is mainly due to less adsorption of dye molecules on the surface of the photocatalyst. In fact, when the temperature increases, the exothermic adsorption of reactants becomes disfavored, thus limiting the photodegradation reaction [56]. In addition, with increasing temperatures, the enhanced kinetic energy of dye molecules might allow them to escape from the photocatalyst surface [24], leading to decreased photodegradation efficiency. On the other hand, it is known that for semiconductor photocatalysts like ZnO, the PL  
Figure 9: Photocatalytic mechanism for Cr-doped ZnO nanorods under UV-visible light.  
intensity decreases with increasing temperature due to the dissociation of excitons (thermal quenching), which also leads to less generation of electrons and holes that contribute to the creation of an OH\(\bullet\) radical and O\({}_{2}^{\bullet-}\) superoxide. Therefore, a limitation of the photodegradation process is obtained.
###### Abstract  
Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two weeks). Cloud cover further reduces the number of useable observations, and the utility of these data for routine ET or LE monitoring is limited. Moderate-resolution satellite imagery is available multiple times per day; however, the spatial resolution of these data is too coarse to enable the estimation of ET from individual agricultural fields or variations in ET or LE. The objective of this study is to combine high-resolution satellite data collected in the visible and near-infrared (VNIR) bands with data from the MODIS thermal-infrared (TIR) bands to estimate subpixel surface LE. Two temperature-sharpening methods, the disaggregation procedure for radiometric surface temperature (DisTrad) and the geographically-weighted regression (GWR)-based downscaling algorithm, were used to obtain accurate subpixel land surface temperature (LST) within the Zhangye oasis in China, where the surface is heterogeneous. The downscaled LSTs were validated using observations collected during the HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration) project. In addition, a remote sensing-based energy balance model was used to compare subpixel MODIS LST-based turbulent heat fluxes estimates with those obtained using the two LST downscaling approaches. The footprint validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all, leading to significant errors (i.e., the root mean square error is 73.15 W\(\cdot\)m\({}^{-2}\)) in LE, whereas the errors in the LE estimates obtained using DisTrad and GWR were 45.84 W\(\cdot\)m\({}^{-2}\) and 47.38 W\(\cdot\)m\({}^{-2}\), respectively. Furthermore, additional analysis showed that the ability of DisTrad and GWR to capture subpixel LST variations depends on the value of [PERSON]'s diversity index (SHDI) and the surface type within the flux contribution source area.  
Keywords:temperature sharpening; energy balance; heterogeneous surface; evapotranspiration; MODIS; [PERSON]'s diversity index (SHDI) +
Footnote †: journal: Remote Sens. **2017**, 9, 836; doi:10.3390/rs9080836
## 4 Overview of GNSSs
A particular area of interest in terms of research and development by the Japanese satellite industry market is the provision of navigation services to address the problem of insufficient visibility of GNSS satellites in metropolitan areas, which, according to research, is the biggest problem in the use of GNSS positioning [74; 75]. GNSS standards specify the required signal accuracy in space for various operating conditions. They specify in detail the accuracy limits for warnings. If these limits are not exceeded, the user will not receive a message. Space signal accuracy requirements are also defined for SBAS and GBAS. The requirements for GNSS must match those of other systems that are currently in use [76]. Figure 10 shows a simplified way of locating objects in area.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Position Accuracy  
**Position accuracy** is the maximum positioning error tolerated by the application [105]. 5G should ensure accurate positioning of the device outdoors [108] with accuracy from 10 m to <1 m on 80% of occasions and better than 1 m in indoor deployment [212]. Accuracy positioning is very important in uMTC monitoring-based activities (e.g., monitoring remote cameras), and uMTC managing/controlling-based activities (e.g., driving) [213]. Moreover, the mMTC tracking-and ticketing-based activities pose high performance requirements in terms of position accuracy.
# Research on Blockchain Data Availability and Storage Scalability
## 6 Research on Data Availability and Storage Scalability
### Data Redundancy Metrics  
Different blockchain networks have different data redundancy scales. To eliminate the impact of network scale on data redundancy comparison and analysis, it is necessary to normalize data redundancy and convert it into the same benchmark value or range.  
Full nodes store complete block data with complete verification capability. The ratio of stored data to total data is 1, and the verification capability is 1. Degraded nodes store partial block data and have partial verification capabilities. For example, SPV nodes only store block headers, and the stored data accounts for approximately 0.008% of the total data volume; the verification function in SPV nodes relies on full nodes, and have a verification capability of 0. ESPV nodes store the latest 1800 blocks and have 90% verification capability. Data availability refers to the probability of obtaining target data, and is not specific to transaction verification capabilities. Assuming that nodes of the same type store the same amounts of data, storage redundancy is defined as follows:  
Definition of storage redundancy: Storage redundancy, which is the normalized data redundancy, is the accumulation of the proportion of data stored by different node types and the proportion of each node type in the blockchain network. The calculation formula to calculate it is:  
\[R=\sum_{i=1}^{l}c_{i}\times q_{i} \tag{8}\]  
where \(R\) represents the storage redundancy of the blockchain, \(l\) represents the number of different node types in the blockchain, \(c_{i}\) represents the ratio of the data stored by node type \(i\) to the total data, and \(q_{i}\) represents the proportion of the number of node type \(i\) in the blockchain.  
Therefore, the storage redundancy of SSFL and SSFE can be obtained when the number of degraded nodes has different proportions, as shown in Table 1.  
When the proportion of degraded nodes is 0, these two storage schemes are called SSF, with a storage redundancy of 1. When the proportion of SPV nodes is 100%, the blockchain is full of SPV nodes without full nodes, and there is no usable data, it is determined that this situation does not exist, it is indicated by \"\(\overline{\text{---}}\)\" in the table.  
From Table 1, it can be seen that with the same proportion of degraded nodes, the storage redundancy of SSFE is higher than that of SSFL. From the experimental results in Figure 5, it can be seen that within a time limit, under the same proportion of degraded nodes, the data availability of SSFE is higher than that of SSFL.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 3 Methodology
### Wavelet Transform  
The frequency characteristics of the multipath effect were determined using the wavelet transform, which allows for a full analysis of the signal in terms of both time and frequency. The wavelet transform is used to decompose the signal into components of individual frequencies [6]. Using inverse wavelet transform, it is possible to reconstruct the signal for components of selected frequencies, e.g., those typical of the multipath effect [17]. The wavelet transform used in the research on the multipath effect was presented in [PERSON] and [PERSON] [24], [PERSON] et al. [36], [PERSON] and [PERSON] [37]. [PERSON] et al. [17] summarized the advantages of this method relative to the STFT when analyzing processes such as the multipath effect.  
Continuous wavelet transform is a conversion of the signal \(f(t)\) to the form of \(F(a,b)\), which is a function of translation \(a\) and scale \(b\) coefficients of the mother wavelet \(\psi\) given by the following:  
\[F(a,b)=\int_{-\infty}^{\infty}f(t)\psi_{a,b}(t)dt \tag{29}\]  
Wavelets, unlike the infinite sine waves used in STFT, are single wavelets of finite length and energy centred around a single point. The wavelet transform consists of comparing an appropriately scaled mother wavelet with the signal, with the visualization of its results being called a scalogram, showing the amplitude of the wavelet as a function of time and frequency.  
There is an infinite number of types of mother wavelets, and selecting the appropriate type largely determines the results obtained. This paper uses the Morlet wavelet, a sinusoid multiplied by the Gaussian window. This type of mother wavelet is commonly used in the multipath analysis [36; 38; 39] due to the simple relationship between wavelet scale and Fourier period. The wavelet transform was used to independently analyze the time series of observations of the CMC combinations, the DD residuals, and the time series of coordinates.