esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Target Tracking Method
### The Spatial Registration Method
#### 3.2.2 Spatial Registration Based on the Right-Angle Translation Method  
The location of the target in the local geographic coordinate system centered on UAV \(A\) is obtained from the direct observation data of the target at sea by UAV \(A\), which is denoted as \(\mathbf{X}_{AT}\).  
Assuming that both the sensor observation information and the platform attitude information are true, we can obtain  
\[\mathbf{X}_{ABT}-\mathbf{X}_{AT}=\mathbf{0} \tag{19}\]  
Equation (19) can be extended as follows:  
\[\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z}_{AB})+\mathbf{T }_{uts}(\mathbf{\mu}_{B})\mathbf{h}_{ctr}(\mathbf{Z}_{BT})-\mathbf{T}_{uts}( \mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z}_{AT})=\mathbf{0} \tag{20}\]  
Substitute Equations (10)-(12) into Equation (20):  
\[\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z}_{AC})-\mathbf{T }_{uts}(\mathbf{\mu}_{B})\mathbf{h}_{ctr}(\mathbf{Z}_{BC})+\mathbf{T}_{uts}( \mathbf{\mu}_{B})\mathbf{h}_{ctr}(\mathbf{Z}_{BT})-\mathbf{T}_{uts}(\mathbf{ \mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z}_{AT})=\mathbf{0} \tag{21}\]  
Considering the true value as a function of measured value and error value, the sensor measurement information and the attitude information are subjected to a first-order Taylor expansion at their measured values. Since systematic and random errors of the measured values are in small quantities compared to true and measured values, a higher order can be approximately ignored and only the first-order term is retained.  
Let  
\[\mathbf{\chi}=\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z} _{AC})-\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z}_{AT}) \tag{22}\]  
\[\mathbf{\gamma}=\mathbf{T}_{uts}(\mathbf{\mu}_{B})\mathbf{h}_{ctr}(\mathbf{Z }_{BT})-\mathbf{T}_{uts}(\mathbf{\mu}_{B})\mathbf{h}_{ctr}(\mathbf{Z}_{BC}) \tag{23}\]  
Equation (21) can be expanded to  
\[\mathbf{0}=\mathbf{\chi}+\mathbf{\gamma}+\]  
\[\begin{array}{c}\frac{\partial\mathbf{\chi}}{\partial\mathbf{\phi}_{A}}( \psi_{At}-\psi_{A})+\frac{\partial\mathbf{\chi}}{\partial\mathbf{\phi}_{A}}( \theta_{At}-\theta_{A})+\frac{\partial\mathbf{\chi}}{\partial\mathbf{\phi}_{A}}( \phi_{At}-\phi_{A})+\\ \frac{\partial\mathbf{\gamma}}{\partial\mathbf{\phi}_{B}}(\psi_{Bt}-\psi_{B})+ \frac{\partial\mathbf{\gamma}}{\partial\mathbf{\phi}_{B}}(\theta_{Bt}-\theta_ {B})+\frac{\partial\mathbf{\gamma}}{\partial\mathbf{\phi}_{B}}(\phi_{Bt}- \theta_{B})+\\ \frac{\partial\mathbf{\chi}}{\partial\mathbf{\pi}_{A}}(r_{ACt}-r_{AC})+\frac{ \partial\mathbf{\chi}}{\partial\mathbf{\phi}_{A}}(b_{ACt}-b_{AC})+\frac{ \partial\mathbf{\chi}}{\partial\mathbf{\pi}_{A}}(e_{ACt}-e_{AC})+\\ \frac{\partial\mathbf{\gamma}}{\partial\mathbf{\pi}_{B}}(r_{BCt}-r_{BC})+ \frac{\partial\mathbf{\gamma}}{\partial\mathbf{\pi}_{B}}(b_{BCt}-b_{BC})+ \frac{\partial\mathbf{\gamma}}{\partial\mathbf{\pi}_{B}}(e_{BCt}-e_{BC})\end{array} \tag{24}\]
## 7 Conclusions  
This paper investigated the problem of multi-dimensional routing, wavelength, and timeslot allocation (RWTA) in time-division based quantum key distribution optical networks (QKD-ON). We have designed a time-window-based security orchestration (TW-SO) strategy. To enable efficient utilization of wavelength resources, a relative loss of time continuous compactness-based RWTA strategy was proposed. Simulation evaluations were conducted under different scenarios, e.g., different key updating periods and different distributions on wavelength resources. Based on the results, we can draw conclusions that in terms of key success rate, key-updating delay, and blocking probability, the proposed strategy can achieve better performance compared with the baselines. Meanwhile, it is more applicable to the scenarios with diverse timeslots requirements. Moreover, the relationship between security level and network provisioning capacity was discussed, and how to get a trade-off between them requires further study.  
Conceptualization, X.Y., X.N., and Y.Z.; methodology, X.Y. and Y.Z.; software, X.N.; validation, Q.Z. and J.L.; writing--original draft preparation, X.Y. and X.N.; writing--review and editing, Q.Z., J.L., Y.Z., H.Z., and J.Z.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by NSFC project (_61971068, 61822105, and 61601052_), Fund of National Key Research and Development Program of China (2020 YFE0200600), Fund of Science and Technology on Communication Networks Laboratory (_6142104180405_), Fund of State Key Laboratory of Information Photonics and Optical Communications, BUPT (_IPOC2020 ZT04, IPOC2019 ZR01_), Fund of State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, China (_2020 GZKF012_), and the Fundamental Research Funds for the Central Universities (_2019 XD-A05, 2018 RC24_).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Flexible Substrate Antenna Design
### Measurements  
Thus far, this investigation studied the antenna operating at 2.45 GHz. The frequency range analysis was then extended to the three target frequency ranges--2.2 to 2.5 GHz, 2.5 to 5.0 GHz, and greater than 5.0 GHz--to analyze the effects of bending levels of 14 and 27 mm on the three selected antenna substrates--PET, Teflon, and PVC. These three substrates were chosen for their excellent physical, electrical, and thermal properties, as described in Table 2, which shows Teflon has relatively low deformability but is very efficient in moist conditions and has high thermal efficiency, and PVC has very high flexibility but moderate electrical properties. Moreover, PET and PTFE Teflon are commercially available in the very cost-effective form of film sheets. Subsequently, nine flexible patch antennas were fabricated on PET, Teflon, and PVC substrates, with three for each range of frequencies, to corroborate the bending impact on the resonant frequency and reflection coefficient, and the resultant impact on the loss tangent variation on the resonant frequency. Correspondingly, three antennas were designed to operate at 2.45 GHz, three at 4.25 GHz, and three at 7.45 GHz.
## 3 Feature Extraction Algorithm for Industrial Control Protocol
### Feature Byte Mining Based on Association Rules  
There may be a certain relationship between the frequent data units and their positions in the protocol because the format of the industrial control protocol is relatively fixed. This paper uses association rule [26] to extract the characteristic positions of industrial control protocol. The association rule reflects the correlation and interdependence between things, whose purpose is to extract valuable laws and connections from massive data. The process of association rule is generally divided into two steps: the first is to find out all frequent units according to the support degree and the second is to generate association rules according to the confidence degree. After segmentation through the N-Gram model and filtering by Jaccard coefficient, frequent item sets can be successfully obtained. The location information of the protocol features can be found according to appropriate association rule and confidence threshold.  
There are two events, \(X\) and \(Y\). The confidence calculation of the association rule \(X\Rightarrow Y\) is shown in Equation (7), which means the proportion of the number of events in which the \(Y\) event occurs when the \(X\) event occurs in the total number of \(X\) events.  
\[\text{confidence}(X\Rightarrow Y)=\frac{X\cap Y}{X} \tag{7}\]  
Presume the event that the frequent item appears in the protocol frame is T, and the event that the frequent item appears in at the position N of the packet is K. Define the association rule \(T\Rightarrow K\): the probability that a frequent item occurs at position N of the packet while being present in the data frame. If this probability is greater than the specified confidence threshold, it can be considered that when the frequent item appears in the protocol packet, there is a high probability of appearing at position N, which means the position N is the characteristic position of the protocol. Complex industrial scenarios may lead to many possible values of feature positions, which can result in a reduction in the proportion of each feature value. Therefore, the confidence threshold must be dynamically adjusted according to the complexity of the realistic industrial control scenarios.
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 5 Conclusions  
In this study, the coastal zone of the Yantai northern region was extracted by remote sensing every 10 years from 1990 to 2018. The coastal zone information of the study area from 2015 to 2018, which can obtain high resolution image data in the past four years, has been extracted more precisely. Then, the changes in the coastline and coastal zones of eight counties and municipalities were analyzed.  
1. In the past 40 years, the spatial and temporal differences of the coastline in the study area are significant: from 1990 to 2018, the length of coastline increased greatly. The increased areas were mainly concentrated in Laizhou, Longkou, Penglai, and Zhifu. The increased coastline types were mainly artificial boundary lines. The length of the natural boundary line decreased, and a large number of natural boundary lines were transformed into artificial boundary lines. Human activities have seriously affected the development of the coastline. Among them, the range of coastline change in the study area decreased significantly from 2015 to 2018.
2. Human activities affect not only the coastline changes but also the types and areas of surface coverage in the coastal zone. In recent years, with the rapid development of the regional economy and urbanization in the northern coastal areas of Yantai, the types of land surface coverage in the coastal zone are mainly transferred from desert/bare land and cultivated land to structure and forests/grasses cover. Influenced by human activities, the monitoring area of various types is increasing, especially the area of structures, manually excavated land, and water area, then health problems in coastal zones increase with changes in coastal zones.
3. The changes of resources and environment in the coastal zone will affect the types and areas of surface coverage, and then affect the types of coastline landforms, so that the types of coastline landforms will change with the changes of the types of surface coverage near the coastline, especially the types of structures and roads constructed to the sea.  
[PERSON] and [PERSON] were responsible for the overall design of the study and contributed to the proofreading of the manuscript. [PERSON], [PERSON], and [PERSON] extracted and processed the data by remote sensing. [PERSON] and [PERSON] contributed to designing the study and the proofreading of the manuscript. [PERSON] and [PERSON] analyzed and interpreted the data. [PERSON] and [PERSON] contributed materials.  
This research was funded by the Shandong Natural Science Foundation (ZR2019 MD041), the National Natural Science Foundation of China (41676171), the Shandong Natural Science Foundation (ZR2015 DM015), and the Development and Construction Funds Project of National Independent Innovation Demonstration Zone in Shandong Peninsula (ZCQ17117).  
We gratefully acknowledge the help of the Geographic Information System Group of Ludong University in collecting and extracting the data.  
The authors declare no conflicts of interest.
###### Abstract  
To achieve fast and stable scanning on lightweight shipborne electro-optical platforms, we propose a rotating dual-grating two-dimensional scanning system with inertial navigation feedforward and Fuzzy PID control. First, a two-dimensional scanning model using rotating dual grating with inertial navigation feedforward technology was proposed; in that system, the required positional information for the two gratings is determined based on the target location data. Then, the scanning model was combined with the Fuzzy PID algorithm to design a servo system controller that controls the dual gratings to perform beam deflection and scanning, then simulation. Finally, an experimental platform was built for validation. Under a sea-surface disturbance of 10\({}^{\circ}\) at 0.1 Hz, the scanning error of the entire process was controlled within 0.328 mrad (RMS) and the scanning of a 15\({}^{\circ}\) (half-angle) circular region was completed within 28 s. Compared with traditional control systems, significant improvements were achieved, with the scanning accuracy increased by more than 40.85% and the scanning speed improved by over 21.42%. This paper provides a reference for the application of a rotating dual grating electro-optical scanning platform on shipborne platforms.  
rotating dual grating; two-dimensional scanning model; servo control; Fuzzy PID; scanning accuracy +
Footnote †: journal: Marine Science and Engineering
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 3 Results  
### The Performance of Purposed Method  
#### 3.1.1 Fault Diagnosis Rate (FDR) and False Positive Rate (FPR)  
As shown in Table 3, in the training sample set, the average FDR of the training dataset was 0.8932 and the corresponding average FPR was 0.1157. The diagnostic model exhibited good diagnostic rates (over 90%) for all defects on the training set. In the test sample set, the average FDR was 89.32% and the corresponding average FPR was 0.1157, showing that the model is effective (here for the test set). The diagnostic model demonstrated good diagnostic rates (above 90%) for all problems on the test set, except faults 3, 9 and 15. As shown in Figure 5, faults 3, 9, and 15 have low FDR and prominent FPR (high FPR indicates poor performance). It is well-recognized that faults 3, 9, 15 and 16 are a long-standing challenge in chemical fault diagnostics and a problem that must be overcome in studying. The average FDR is 89.32%, while the average FPR is 0.1157, demonstrating that the model is effective.  
\[\text{FDR}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FN}+\text{ FP}} \tag{27}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{28}\]  
F1-score is a measure of classification problems. In some machine-learning competitions for multi-classification problems, the F1-score is often used as the final evaluation method. It is the harmonic average of precision and recall. Recall and precision are equally important in F1-score. The maximum is 1 and the minimum is 0. Table 4 shows the four values of the confusion matrix. The values of true positive (TP) and true negative (TN) represent the number of observations representing the correct classification, while the false positive (FP) and false negative (FN) represent the number of misclassifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault** & **FDR** & **FPR** \\ \hline
1 & 0.9969 & 0.0038 \\
2 & 0.9750 & 0.0300 \\
3 & 0.7354 & 0.2963 \\
4 & 1.0000 & 0.0000 \\
5 & 0.9844 & 0.0188 \\
6 &
# Variance-Triggered Two-Step GPS Acquisition
## 5 Experiments and Results
### False-Positives Rate  
The rate of false positives was measured for all algorithms for different levels of C/N0. For the VTTS acquisition, the false-positive rate was measured for 1, 5, 10, and 16 rounds.  
The VTTS technique achieved the lowest false-positive rates, as can be seen in Figure 9. In the worst case, the false-positive rate was about 15% after one round of the CS step. For several rounds larger than 7, this value was always lower than 10%. Overall, the false-positive rates of the VTTS acquisition were always better than the rates of the SA algorithm.  
Figure 8: Success rate.
# Virtualized MME Design for IoT Support in 5G Systems
## 4 Traffic Models  
We assume three classes of traffic: Mobile Broadband traffic (MBB), massive M2M (mM2M) and low latency M2M (IM2M) traffic. In the next subsections, we explain each one.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 1 Introduction
### Ground-Based Instruments
A survey on the state-of-the-art rainfall intensity gauges, with the relevant physical principles, is presented in Section 2.2 of [11]. The response time of an instrument plays an important role in assessing its suitability for rainfall intensity measurements. The dynamic response of conventional rainfall intensity gauges is shown in Figure 23 of [11], which shows that the response time and/or time delay with respect to the applied intensity step can vary from 1 up to 5 min. Also from Figure 23, it can be noted that the step response (and also the accuracy of the measurement) is strongly dependent on the intensity, with a more apparent delay effect when operating in the presence of medium-to-low rainfall intensities.
## 3 Performance Analysis
### Strictly Positive Secrecy Capacity  
The SPSC metric measures the probability of a guaranteed secrecy capacity of the communication system. SPSC can be mathematically calculated as [43] [Equation (17)]  
\[\text{SPSC}=\Pr[\mathcal{C}_{s}(\gamma_{c2e},\gamma_{E})>0]=1-\text{SOP}_{L}|_ {\mathcal{R}_{s}=0}, \tag{33}\]  
where \(\text{SOP}_{L}\) is given in (30).
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 3 Results and Analysis
### Analysis of the Results of the Flat-Curve Embankment Model
#### 3.2.3 Distribution Characteristics of Sand Accumulation on the Embankment  
When the simulation time is t = 60 s, the sand accumulation around the subgrade slope does not change much. Therefore, the sand cloud map of t = 60 s is selected for analysis, as shown in Figures 12 and 13. It can be seen that the convex windward curve embankment has a lateral diversion effect on the wind-sand flow, and the wind-sand flow spreads to both sides before reaching the embankment, and the sand accumulation in the middle of the line on the windward side is less than that on both sides. However, when the radius is small, the sand particles on both sides of the line will accumulate on the road due to the excessive concentration, affecting driving safety. With the increase in the radius of the flat curve, the distribution of sand accumulation in the line gradually becomes uniform, and the large amount of sand accumulation on both sides is significantly reduced.  
The phenomenon of sand accumulation on the concave windward embankment is opposite to that of the convex windward. The sand accumulation is mainly distributed in the middle area along the road. The reason for this is that the sand accumulates in the middle part along the road after reaching the embankment. Therefore, it is easy to generate sand accumulation; when the radius is 400 m, the gathering effect of the middle part along the road is strong, and when the radius of the curve increases, the distribution of sand accumulation along the road is gradually uniform, and the large amount of sand accumulation in the middle area is significantly reduced.  
To sum up, the convex windward embankment is conducive to the outward drainage of the wind-sand flow, while the concave windward embankment is easy to make the wind-sand flow gather inward.  
Figure 11: Wind-speed variation on the windward side of the concave curve at \(Z=1.5\) m.
## 5 Examples of Forward-Inverse Modeling
### Sunglint: A Nuisance or Can Can It Be Used to Our Advantage?
Also, few studies have focussed on validation of realistic 2D BRDF implementations due to the general lack of a complete set of reflectance measurements that would be suitable for testing purposes. To enable more realistic simulations of ocean glint reflectance, [PERSON] et al. [80] developed a RT model with a 2D BRDF to mimic the nature of actual sea surface slope distributions (see Section 4.1.3 for details). Reflectance measurements obtained by an instrument deployed on a National Aeronautics and Space Administration (NASA) aircraft were used for validation. The goal was to match simulated reflectances with those measured by the instrument deployed on the NASA airplane, and to use RT simulations as a forward model to invert the measured reflectance in order to retrieve wind direction, sea surface slopes in the crosswind and upwind directions, and aerosol optical thickness. These parameters are important for atmospheric correction, which is the largest source of error and uncertainty in determining water-leaving radiance from space.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 3 Results and Discussion
### Classification with CNNs  
Several experiments were carried out to choose the best strategy for preparing the dataset that allowed fine-tuning AlexNet, to classify diatoms among 80 possible taxa (i.e., classes). The experiments considered the original dataset and three more:  
* Segmented dataset obtained by ROI segmentation to remove the background,
* Normalized dataset produced by histogram matching, and
* Original dataset augmented with the normalized one.  
Each dataset was used with three versions depending on the images/their class (300, 700, and 1000). Table 6 summarizes the results obtained by 10-fold cross-validation for the assessment of the classification with AlexNet trained with each dataset--and their three versions.  
After experimentation and parameter adjustment, the best result of 99.51% average accuracy (with a standard deviation of 0.048) was obtained with the augmentation of the original dataset with the normalized one. For this model, the sensitivity in the top 30 error-prone classes--the most difficult to classify--never fell under 93%. Moreover, the best performance was obtained always with the higher number of images per class.  
In a computer equipped with a GPU NVIDIA GTX 960 with 6 GB of VRAM, the training process lasted for an hour, while the inference to classify a single image took 7 ms on the same computer. The implementation tested for AlexNet was the development deployed with the MATLAB Deep Learning Toolbox.  
Although very good results were achieved, the current main controller (Intel NUC) does not provide enough computing power to deal with training and inference with AlexNet. Thus, automated classification has not been integrated in our system yet. We consider it future work to substitute the main controller with an NVIDIA JetSon module, an embedded AI computing platform capable of high-performance, low-power computation for deep learning and computer vision applications.