esa-sceva/satcom-chunk-collection huggingface dataset:
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 2 Related Work
### Statistic-Based Methods  
Statistical anomaly detection techniques are based on the following key assumptions. Normal data instances occur in high probability regions of a stochastic model, while anomalies occur in low probability regions of the stochastic model [12]. Based on the assumed distribution, the statistical methods can be further classified as follows:  
Gaussian-based models: They assume that the data are generated by a Gaussian distribution. The distance between a data instance and the expected value of a Gaussian distribution is the anomaly score of the given instance. A threshold is set over the anomaly score to discriminate between normal and anomalous data. For example, a simple anomaly detection technique, three-sigma rule of thumb is to declare all data instances that are more than \(3\sigma\) distance away from the distribution mean \(\mu\), where \(\sigma\) is the standard deviation for the distribution. The \(\mu\pm 3\sigma\) region contains 99.7% of the data instances. This technique is mostly applicable to univariate and multivariate continuous data. A box plot is a way of summarizing data measured on an interval scale and is often used for exploratory data analysis. The box plot rule has been applied to detect outliers in univariate and multivariate medical data by [PERSON] et al. [13]. However, these methods are mostly used for monovariate time series and can generally only handle local outliers with simple distributions and large anomalous deviations.  
Regression-based Models: There are two main steps in anomaly detection technology: Firstly, the regression model is fitted to the data. Then, for each test case, the residual is used to determine the exception score. For example, [PERSON] et al. [14] proposed the method of traffic characterization and detection of traffic anomalies using sFlow analysis by incorporating two different models, Autoregressive Integrated Moving Average model (ARIMA) and Holt-Winters, into a behavior-based system. Another variant that detects anomalies in multivariate time series data generated by an Autoregressive Moving Average (ARMA) model was proposed by [PERSON] et al. [15]. In this technique, the authors transform the multivariate time series into a univariate time series by linearly combining the components of the multivariate time series.  
The disadvantages of statistical techniques are that they rely on the assumption that the data are generated from a particular distribution. This assumption often does not hold true, especially for high-dimensional real datasets.
## 4 Measurement Scenario
### Measurement Scenario 1: Lake  
The measurement scenario 1 corresponds to flights over a lake of approximately 12,000 square meters and a maximum depth of 12 m. The UAV was configured to perform straight-line flights from a 5 m point horizontally from the BS. The height and speed of measurements can be seen in Table 3.  
Figure 3 shows images taken from the UAV over the lake at the time of flight at different heights. Figure 3c presents the direction of the flight. The wind was blowing on the side of the aircraft and in the opposite direction of the flight.
## 2 Background
### The Collision Problem  
The general problem of collision in RFID can be classified into two different situations:  
* Reader collision occurs when the reader attempts to make communication with tags that are in the coverage area of another reader [7]. This type of collision is shown in Figure 1. This collision causes two different problems:
* Signal interference occurs when the fields of two or more readers overlap and interfere. This problem can be solved by programming all readers to read at fractionally different times.
* Multiple reads of the same tag occur when the same tag is read once by every overlapping reader.
* A tag collision occurs when more than one tag attempts to transmit its ID at the same time: the reader will receive a mixture of the tags' signals and cannot understand it. This type of collision is shown in Figure 2.  
Simultaneous responses from numerous tags prevent the reader from correctly translating the signal, which decreases throughput. No tag is aware of the activity of any other tag, and so they cannot prevent the simultaneous transmission of tags. The transmissions of three tags, shown in Figure 1, are not synchronized, but in many cases, the reader is synchronized with at least one tag in the interrogation zone. In the presented illustration, the reader is prevented from decoding the entire transmission and it has experienced a collision. To solve this problem, anti-collision protocols are very influential.
## 3 Results
### Staged Deployment of LEO Constellation  
The deployment strategy of the constellation will significantly impact the enhancement level of performance indicators within the service area. Specifically, with each launch of a certain number of satellites, there will be a steady improvement in the performance indicators of the constellation for the service area. The LEO navigation constellation may undergo multiple stages of task requirements, such as regional navigation constellation, global navigation augmentation constellation, and global independent navigation constellation. It is essential to comprehensively consider the task requirements at each stage of the constellation in order to successfully execute staged deployment of the LEO navigation constellation.  
Based on the deployment model, this section examines the constellation index as PDOP, with the least orbital plane of the constellation serving as the constraint function and \(\gamma=5^{\circ}\) representing the elevation angle of the constellation. Analysis of the simulation Table 3 data revealed that 66 satellites are initially deployed to mitigate MEO service performance degradation during emergencies such as satellite retirement. It includes4 orbital planes within the tilt constellation (cyan) and 2 orbital planes within the polar constellation (blue). In the second stage, 156 satellites are deployed for navigation augmentation constellation, and the tilt and polar constellations are expanded to accommodate 10 and 4 orbital planes, respectively. In the third stage, 288 satellites are deployed, independently operating their own systems to provide comprehensive PNT services [27]. Figure 13 illustrates the staged deployment spatial configuration of the LEO hybrid navigation constellation.  
\"Event\" denotes the PDOP of the constellation in different scenarios, where 24 MEO + \(tilt_{10}+polar_{4}\) denotes the Walker 24/3/1 serviced in conjunction with LEO navigational enhancement constellation, and \(tilt_{10}+polar_{4}\) denotes the 10 orbital planes of the LEO tilt constellation and 4 orbital planes of the polar constellation.  
Figure 12: Ten modes of three-satellite failure.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 2 Distributed Beamforming
The required phase shift for each node, \(\theta_{n}\), can be calculated based on its distance from the receiver, as defined by Equation (5):  
\[\theta_{n}=\frac{(d_{max}-d_{n})\times 2\pi}{\lambda} \tag{5}\]  
\(d_{n}\) represents the distance between the \(n_{th}\) sensor and the receiver, and \(d_{max}\) is the maximum distance between any sensor and the receiver, serving as a reference. This phase adjustment ensures that the signals from all nodes arrive at the receiver with the appropriate phase shift, enabling coherent summation and maximising the received signal strength. Due to the propagation distance, each transmitted signal experiences path loss, \(\beta_{n}\), which can be modelled using the Friis free-space path loss Equation (6):  
\[\beta_{n}=\left(\frac{4\pi\times d_{n}}{\lambda}\right)^{2} \tag{6}\]  
Considering the ideal phase shifts calculated previously, the received signal, \(r(t)\), is obtained by summing the contributions from all sensors, taking into account the respective path losses:  
\[r(t)=\sum_{n=1}^{N}\frac{s_{n}(t)}{\sqrt{\beta_{n}}}. \tag{7}\]  
This section has outlined the fundamental principles of distributed beamforming in WSNs. By synchronising transmissions and applying appropriate phase shifts to the signals from individual nodes, as defined by Equation (3), it is possible to achieve constructive  
\[d_{F}\geq\frac{2D^{2}}{\lambda} \tag{1}\]0 Position of the elements in a distributed WSN. interference at the receiver, enhancing the received signal strength and extending the communication range. However, the performance of distributed beamforming is significantly influenced by node position accuracy and path loss. In the following section, we investigate the impact of the number of nodes on the phase error variance, employing the Cramer-Rao Lower Bound (CRB).
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 2 NOx Formation Mechanism
### Thermal NOx Route  
The thermal NOx route could also be called the Zeldovich way, which was first proposed in 1964 [21]. The details of its elementary reactions include the following:  
\[\text{O}\cdot\,+\,\text{N}_{2}\rightarrow\text{NO}\,+\,\text{N}\cdot \tag{1}\]  
\[\text{N}\cdot\,+\,\text{O}_{2}\rightarrow\text{NO}\,+\,\text{O}\cdot \tag{2}\]  
With the situation of the production existing water, the thermal pathway could expand as follows:  
\[\text{N}\cdot\,+\,\text{OH}\rightarrow\text{NO}\,+\,\text{H}\cdot \tag{3}\]  
The strength of the thermal NO route is mainly affected by temperature. The higher the reaction temperature, the more thermal NO route is generate. In the research of [PERSON] et al. [22], a simplified NOx generation mechanism was built through the steady-state approximation and showed a good agreement in a wide range of conditions with the detailed mechanism. Based on this simplified mechanism, the different percentages of each route during CH4 combustion are shown in Figure 1. The results showed that the thermal NO route would be crucial when the temperature is above 1600 \({}^{\circ}\)C, which is the same as the practical test. The reason is that, at the beginning of the chain reaction, R1 needs to break the chemical bound of N-N, which needs activity energy of about 318 kJ/mol.  
Figure 1: Fraction of each NO formation route during CH4 combustion, as obtained with the simplified reaction scheme. Inlet concentration: 4 vol% CH4, 10 vol% O2, rest N2 [22].  
Owing to the temperature of the chamber being higher than 1800 K, the thermal NOx route plays a leading role in all pathways. The thermal NO route contributes to the main emission of NOx in the high-temperature area [23; 24; 25].  
The second influence factor on the thermal NOx pathway is residence time. NOx is continuously generated during the flue gas flowing in the combustion chamber. Restricted by the reaction rates of NOx generation, the partial chemical equilibrium would not be achieved in a short time. This is quite different from the fuel decomposition process, which is considered fast chemistry. Hence, the more residence time, the more NOx production exists [26].
## 3 The Proposed Octree-Hiding Scheme
### The Extraction Process
#### 3.1.1 Semi-Blind Extraction Approach  
The receiver should be able to extract the secret data following the reverse order of the procedures described earlier on the sender side. Figure 4 shows a general illustration of the secret extraction process.  
Once the carrier video is received, the homogeneous regions are extracted using the same segmentation technique used at the embedder. Next, the quantization process is performed on the DCT vector to locate the indices of the insignificant DCT coefficients that contain the secret data. Finally, the secret data are re-scaled back to the natural original range.  
The previously described method of extraction is the semi-blind extraction version of the proposed hiding system. The advantage of the semi-blind approach extraction is the low communication cost since the sender will need to send a few parameters. Table 1 lists the parameters that need to be sent to the receiver to be able to extract the hidden data. This semi-blind way of extraction can work if the carrier video has a very high quality and is close to being identical to the host video. In this case, the extraction algorithm will be able to generate the same exact regions generated at the embedder, and thus, the secret data will be extracted in acceptable quality.  
Based on Table 1, the receiver must receive the threshold parameter \(T_{quad}\) to be able to segment the regions of the carrier signal using the same threshold value. Moreover, the scaling factor \(k\) is needed to be able to reconstruct the original secret message. Moreover, the proposed Octree segmentation method needs two additional parameters. The first parameter is the minimum block size to be able to obtain the same Octree cubes obtained at the sender. The second parameter is the C\({}^{\prime}\) parameter to be able to locate the insignificant coefficients in each cube.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameters** & **Quantity** \\ \hline \multirow{3}{*}{Semi-Blind} & Min. block size & \\ \cline{2-3}  & Parameter C\({}^{\prime}\) & One value for the entire video segment \\ \cline{2-3}  & Scaling Factor \(k\) & \\ \hline \multirow{3}{*}{No Error Correction} & The Octree structure & One structure for the entire segment \\ \cline{2-3}  & Parameter C\({}^{\prime}\) & \\ \cline{2-3}  & Scaling Factor \(k\) & \\ \hline \multirow{3}{*}{Error Correction} & The error bit & One value for each error \\ \cline{2-3}  & Parameter C\({}^{\prime}\) & \\ \cline{2-3}  & Scaling Factor \(k\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters that need to be sent to the receiver to be able to extract the hidden data.  
Figure 4: A general illustration of the extraction process.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Land Degradation and Development Index (LDDI)  
To calculate LDDI, it is necessary to process the original NDVI and GPP data. Firstly, the maximum NDVI value is extracted from the GIMMS-NDVI 3g.v1 dataset for each half-month. Next, the half-month data is converted into monthly data, and the NDVI values for 12 months are averaged to obtain the annual NDVI data. The original GPP data consists of 46 files per year, with daily averages from 1 to 45. To obtain the annual GPP data, the daily data from 1 to 45 is multiplied by 8 days, and the last data point is multiplied by 5 or 6 days, depending on leap year conditions. Finally, all the data is summed to obtain the total annual GPP. After processing the original NDVI and GPP data, NDVI and GPP are normalized using the following equation:  
\[NNDVI=\frac{NDVI-NDVI_{\text{min}}}{NDVI_{\text{max}}-NDVI_{\text{min}}} \tag{1}\]  
\[NGPP=\frac{GPP-GPP_{\text{min}}}{GPP_{\text{max}}-GPP_{\text{min}}} \tag{2}\]  
where \(NGPP\) and \(NNDVI\) represent the normalized values of \(GPP\) and \(NDVI\), respectively. \(NGPP_{\text{min}}\) and \(NDVI_{\text{min}}\) represent the minimum values of \(GPP\) and \(NDVI\), respectively, while \(NGPP_{\text{max}}\) and \(NDVI_{\text{max}}\) represent the maximum values of \(GPP\) and \(NDVI\), respectively. In the normalization process, the maximum and minimum values of \(NDVI\) and \(GPP\) are the maximum (small) values in the time series of the study period. Then, \(LDDI\) is calculated based on the Euclidean distance method using the normalized \(NDVI\) and \(GPP\) values [12; 42].  
\[LDDI=\sqrt{\left(NNDVI-NNDVI_{\text{min}}\right)^{2}+\left(NGPP-NGPP_{\text{ min}}\right)^{2}} \tag{3}\]  
Since both \(NNDVI_{\text{min}}\) and \(NGPP_{\text{min}}\) are 0 after normalization, \(LDDI\) can be simplified as:  
\[LDDI=\sqrt{NNDVI^{2}+NGPP^{2}} \tag{4}\]
# Design of 4 \(\times\) 1 Power Beam Combiner Based on MultiCore Photonic Crystal Fiber
## 5 Conclusions  
In this article, we have shown a new approach of designing specific air holes in a multicore PCF structure that are utilized to obtain an optical 4 \(\times\) 1 power combiner.  
Through BPM simulation results, we showed that the energies of four optical signals at 1 \(\upmu\)m wavelength can be combined into one source output in a PCF structure with dimensions of 38 \(\upmu\)m \(\times\) 38 \(\upmu\)m \(\times\) 2.6 mm. The amount of total power at the output is about 99.6% of the input power, the coupling losses between core 1-core 2/3/4/5 is about 0.01% of the input power, and the optical bandwidth value is 220 nm for the output port. Thus, this combiner device can be used in high power and parametric applications.  
This combiner can be fabricated by stacking and drawing techniques which are simpler than other known fabrication techniques [20]. It is important to emphasize that all of these combiners were demonstrated using simulation.  
In addition, a higher level 8 \(\times\) 1 combiner was demonstrated using two units of 4 \(\times\) 1 connected to a 2 \(\times\) 1 combiner in a cascade configuration.  
This research could be expanded to optical combiners with larger numbers of input fiber laser sources, such as 16 \(\times\) 1 and 32 \(\times\) 1.  
[PERSON] envisioned the project. [PERSON] provided guidance and funding. [PERSON] designed the device and performed simulations with support of [PERSON], [PERSON], [PERSON], and [PERSON] wrote the manuscript text. [PERSON] made the figures and all authors reviewed the manuscripts. The authors declare no conflict of interest.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.6 HTTP Rest  
Representational State Transfer (REST), often referred to as HTTP REST, is an architectural-style protocol widely employed in web development [61]. It adheres to the principle of statelessness, where every HTTP request includes all the essential information needed to comprehend and handle the request. REST employs GET, POST, PUT, and DELETE methods for various operations. This protocol is well suited for scenarios where a vehicle node periodically requests information from a server or sends occasional updates. [PERSON], [PERSON], and [PERSON] [62] present a GET method to disseminate and visualise real-time traffic data over REST vehicle web service. This architecture is characterised using clear client-server segregation, with the client handling the user interface and experience and the server managing data [63]. Refer to Figure 8 HTTP REST Architecture.  
Figure 6: XMPP architecture.  
Figure 7: AMQP architecture.
###### Abstract  
The plasma electrolytic oxidation (PEO) surface modification technique was employed for improving the mechanical and anti-corrosion properties of the AZ63 magnesium alloy. Different PEO processing times (5, 10 and 20 min) in a 10 g/L NaAlO2 electrolyte, with no other additives, led to the formation of ceramic coatings with mean thicknesses between 15 and 37 microns. Scanning electron microscopy (SEM) showed that the porosity of the coatings decreased with processing time, but an increase in roughness was observed. X-Ray diffraction phase analysis indicated a coating structure composed of majority magnesium aluminate spinel. The corrosion rate of the coated samples decreased with an order of magnitude compared with the bare alloy. The average micro-hardness values of the PEO-coated samples was up to five times higher than those of the AZ63 alloy.  
surface modification; PEO; AZ63 magnesium alloy; corrosion; micro-hardness Article  
The R&D Center for Innovative Materials, Processes and Products for the Automobile Industry, University Pitesti, 110040 Pitesti, Romania 1
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 5 Conclusions  
In this paper, global GPP and NPP were firstly estimated with GLASS data using the MuSyQ-NPP algorithm, and an eight-day global GPP and NPP product with a resolution of 1 km was developed. Secondly, GLASS GPP/NPP estimates were validated against FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP/NPP products. Then, the temporal and spatial variation in global GPP and NPP from 2004 to 2012 was analyzed. Lastly, the advantages of the MuSyQ-NPP algorithm and limitations of this study were discussed. The results of this study indicate that GLASS data and the MuSyQ-NPP algorithm have great potential in regional and global GPP and NPP estimates.  
Generally, time series of estimated GLASS GPP in 2004, 2008 and 2012 agreed well with the time series of FLUXNET GPP and MODIS GPP. In addition, the time series of estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories compared to MODIS time series. Estimated GLASS GPP matched better with FLUXNET GPP in DBF, EBF, ENF and MF. But in CRO, estimated GLASS GPP and MODIS GPP were lower than FLUXNET GPP within the growing season. Second, estimates of global GPP/NPP in our study achieved higher precision than MODIS GPP/NPP. In DBF, EBF, CRO, GRA and MF, RMSEs were lower between our estimated GLASS GPP and FLUXNET GPP than those between MODIS GPP and FLUXNET GPP. As for NPP, the RMSE between estimated GLASS NPP and BigFoot NPP was only 160.16 g C\(\cdot\)m\({}^{-2}\).yr\({}^{-1}\), much lower than the RMSE between MODIS NPP and BigFoot NPP. Thirdly, the global total GPP decreased by 1.41% from 2004 to 2008 and increased 11.58% from 2008 to 2012. In addition, global NPP decreased by 1.64% from 2004 to 2008 and increased by 15.59% from 2008 to 2012. GPP and NPP in 2012 were higher than in 2004 and 2008 for almost all land cover types. Finally in 2008, GLASS GPP and NPP reached the minimum value of the period analyzed.  
The following are available online at www.mdpi.com/2072-4292/10/2/327/s1.  
**Acknowledgments:** This work was supported by the National Key R&D Program of China (2016 YFB0501502), the National Natural Science Foundation of China (41531174, 41471349 and 61661136006001) and the National Key R&D Program of China (2017 YFA0603002).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] proposed the ideas; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] preprocessed and analyzed the data; [PERSON] and [PERSON] prepared the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Number of GSTs Visible per Day (\(n_{\text{GST}}\))
Figure 13: The GSTs developed in various countries as Paraguay, Malaysia, Taiwan, and Mongolia from left to right. The number of visible GSTs in a day, \(n_{GST}\), derived from the simulation results is taken as 16 and the average elevation is taken as 56 degrees. Assuming a proportional elevation increment during a satellite pass of 10 min of 56-degree elevation, using Equation (4), the communication time \(\tau(i)\) between satellite and GST is calculated as approximately 4 min.