esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 5 Optimization Techniques  
### Automated Feature Generation  
Automated feature engineering aims to create candidate features automatically from a dataset and select several optimal features for training.  
In this section, based on aircraft flight field knowledge, a feature interaction method is used to construct new composite features, focusing on the dataset of this application scenario. Using OpenFE ([[https://github.com/IIIS-Li-Group/OpenFE](https://github.com/IIIS-Li-Group/OpenFE)]([https://github.com/IIIS-Li-Group/OpenFE](https://github.com/IIIS-Li-Group/OpenFE)), (accessed on the 10 November 2024)), it is more efficient to achieve the construction of composite features. Specifically, it includes the following aspects:  
1. Interaction between time and geographic features: the parameters involved are _TIME_R_, _LONPC_, _LATPC_, _FLIGHT_PHASE_.
2. Interaction between meteorological conditions and flight conditions: parameters involved are _RUDD_, _WIN_SPD_, _TAT_, _ALT_STD_.
3. Flight dynamic feature interaction: parameters involved are _FF1_, _GSC_.
4. Interaction between flight parameters and flight status: parameters involved are _N11_, _VB11_, _TLA1_.  
The OpenFE framework [15] can help to identify useful cross features with a certain degree of interpretability. From the feature generation section in Figure 4, it can be seen that the core idea of OpenFE is to generate possible candidate features first. Then, redundant candidate features are removed. Finally, only features that are effective for the model are retained. In the feature generation stage, the two different original features are combined and transformed into candidate features through basic computation operations, including addition, subtraction, multiplication, division, maximum and minimum, etc. Next, the generated candidate features are validated and selected by gradient boosting to computing their incremental contribution to the model, as follows:  
\[\begin{split}&\Delta=L(f)-L(f^{\prime})\\ & L(f^{\prime})=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i}+f^{\prime}(x_{i} [\gamma^{\prime}]))\end{split} \tag{8}\]  
In the above formulas, \(\gamma^{\prime}\) represents a new feature set. However, this is only a rough screening. Further consideration need to be given to the relationship between newly generated features and original features. At this point, computing incremental contribution  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Abnormal Event** & **Anomaly Label** \\ \hline No Abnormal Event Occurred & 0 \\ Large Decent Rate (2000 ft\(\sim\)1000 ft) & 1 \\ Large Decent Rate (1000 ft\(\sim\)500 ft) & 2 \\ Large Decent Rate (500 ft\(\sim\)50 ft) & 3 \\ Large Approach Roll Angle & 4 \\ High Approach Speed & 5 \\ Low Approach Speed & 6 \\ ILS Glide Slope Deviation & 7 \\ ILS Heading Deciation & 8 \\ Low-Altitude High Speed & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information of anomaly label.  
needs to be used on the entire feature set, including original features and new features. The final results are shown in Table 4.
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 4 Results
* Case 2: Attributes Recovered Cases and Death are additionally added to the input list while the output remains the same. * Case 3: The attribute Vaccinations is added to the input list of Case 2 while the output remains the same. * Case 4: The attributes related to the Event feature as explained in Section 3 are added to the input list of Case 2 and the output remains the same. Exponential Smoothing has a major limitation, which is that the model can only be used for univariate series. This means that the series should depend only on a single variable. Although it was known that the model would not work well, it was selected to establish a baseline performance, and while we expect LSTMs to fare better based on previous research, a point of comparison is needed with standard models. To test this model, we followed the authors of [28] and selected Confirmed Cases as the only input for the model. The output is the number of Confirmed Cases predicted for the next day. Alpha was varied with values of 0.8, 0.2, and 0.5. Alpha, in Exponential Smoothing, defines the weighting level of the smoothness factor. In Figure 8, we can see that the smoothing does not greatly affect the predictions and that all three models overfit at first before tending to flatline at a singular value. This result is expected as Exponential Smoothing is mostly used for data that do not depend highly on trends and seasonality, whereas COVID-19 high depends on the environment and surroundings and, hence, depends on trends and seasonality. LASSO Regression was tested on all the cases, as it works on both univariate and multivariate series. For this model, the time lag was set to seven days, following the logic of the previous works discussed in the literature review that seven days would cover the data accurately as it would most likely include weekdays as well as weekends. Figure 8: Prediction using Exponential Smoothing model. In Figure 9, similar to the previous model, the model has a tendency to overfit. We also see that there is not a discernible improvement with the addition of the extra variables. LASSO Regression works by adding an attribute only if it is useful to the model. More specifically, the L1 regularization component of LASSO confers a coefficient of zero to features that were completely neglected by the model to make predictions. From this, we can deduce that the addition of the vaccination, policies, and outdoor events might not be providing any additional information to the trends, at least in the case of the UAE, and hence may likely be unnecessary for the forecasting of COVID-19. Table 7 shows the MSE and \(R^{2}\) Score resulting from testing the LSTM using the different lag periods, as mentioned in Section 3. From this, a lag of five days was chosen. Although
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 4 Results
### QoE Quantization Performance
#### 4.4.3 Throughput Rate  
QoE sensitivity to throughput rate (TH):  
\[\Psi_{TH}=\frac{\partial QoE}{\partial TH}=-\frac{\pi\beta p}{TH}\cdot\exp(- \beta\cdot QoS) \tag{18}\]  
We assume that TH takes the range from 0 to 500 Kbps, the QoE changing slope values in Figure 6 can be described as \(\left\{\Psi_{TH}(\text{LV}),\Psi_{TH}(\text{OV})\right\}>\left\{\Psi_{TH}( \text{MV}),\Psi_{TH}(\text{OA}),\Psi_{TH}(\text{WB}),\Psi_{TH}(\text{LA}), \Psi_{TH}(\text{FS})\right\}\)\(>\left\{\Psi_{TH}(\text{VC}),\Psi_{TH}(\text{IM}),\Psi_{TH}(\text{LO}),\Psi_{TH}(\text{OG})\right\}\). Further, we can make the following conclusions:  
1. The values on the curves are greater than 0 in the entire analysis domain, indicating that the increase in throughput rate has a positive effect on the QoE of all network services.
2. At the initial stage of the throughput rate, the values of curves are relatively large, but then drop quickly to \(\sim\)0, except in network traffic of the video service. This implies that for all network services, QoE will increase significantly when the throughput rate starts to increase from 0. However, the increase in QoE will become less obvious when the throughput rate reaches a certain level. Therefore, we can conclude that increasing throughput rate will not lead to increasing QoE, as long as the throughput rate meeting the service requirement.  
Figure 5: Effects of the packet loss rate on QoE attenuating.
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 6 Conclusions  
This paper presents the pollution non-uniformity on insulator surface from both field contamination experiments of HVDC transmission lines and wind tunnel contamination simulation tests, as well as the mechanism of non-uniformity along insulator sting. Conclusions can be made that:  
1. The contamination on insulator surfaces is very non-uniform in operating HVDC transmission lines, the non-uniformity is mainly reflected in three aspects, such as the top and bottom surface, windward and leeward side, high voltage group and middle group area. The value of \(\mathrm{K_{T/B}}\), \(\mathrm{K_{W/L}}\), \(\mathrm{K_{H/M}}\) are 1/0.49, 1/1.45, 1/0.61 respectively. Besides this, the value of \(\mathrm{K^{*}}\) and \(\mathrm{K}\) is almost equal.
2. The effects of particles diameter, wind velocity and relatively humidity on pollution non-uniformity were quantitatively computed through wind tunnel simulation results. With the increase of wind speed (particles diameter is 250 meshes, RH is 60%), \(\mathrm{K_{T/B}}\) value remains almost  
Figure 11: The trajectory trend of charged particles.  
constant and stable at around 0.5. However, the K\({}_{\rm W/L}\) value (within 1/0.81-1/1.75) and K\({}_{\rm H/M}\) value (within 1/0.49-1/0.71) presents a downward trend. With the decrease of particles diameter (wind speed is 5 m/s, RH is 60%), the K\({}_{\rm T/B}\) value (within 1/0.51-1/0.66) and K\({}_{\rm W/L}\) (within 1/1.45-1/1.36) value are closer to 1, which means non-uniformity becomes smaller, while the K\({}_{\rm H/M}\) value (within 1/0.65-1/0.58) is the opposite. The change of relative humidity has little effect on K\({}_{\rm T/B}\) and K\({}_{\rm W/L}\). With the increase of relative humidity (wind speed is 5 m/s, particles diameter is 250 meshes), K\({}_{\rm T/B}\) and K\({}_{\rm W/L}\) values remain almost constant and stable at around 1/0.5, 1/1.44 respectively. However, with the increase of humidity, the K\({}_{\rm H/M}\) value (within 1/0.65-1/0.72) is closer to 1.
3. The electric field around insulators is unevenly distributed around the insulator surface, causing different electric field force and the acceleration on particles in vertical direction at high voltage group and middle group area, which is the cause of the non-uniform distribution of contamination along the insulator string.  
[PERSON] and [PERSON] conceived the overall outline of the manuscript and all the authors contributed to the writing and revision of the paper. This research was funded by Project [No. 2018 CDYJSY0055] Supported by the Fundamental Research Funds for the Central Universities and Science and Technology Foundation of the State Grid Corporation of China [No. SGTYHT/14-JS-188]. The authors declare no conflict of interest.
## 2 Performance Indicators of System Response
### Deb's Rules  
Some algorithms are able to include objective and violation functions, e.g., the artificial bee colony algorithm [12]. In such a case, the mechanism of comparing two solutions must be defined. One of the most commonly used methods is Deb's rules. It defines the feasible solution when constraints are not violated and the infeasible solution otherwise. The rules are as follows:  
* Two feasible solutions: Select the one with better objective function value;
* Feasible and infeasible solutions: Select the feasible solution;
* Two infeasible solutions: Select the one with the lower constant function value.  
One of the advantages is that the method does not require a weighting factor and ensures that an optimized solution will be feasible (if possible).
## 3 Receiver Design Based on Fractional Fourier Transform
This means that the conventional receiver must have the parallel structures. In this paper, we propose the FrFT receiver using only one transform order. In the proposed method, when the frequency-hopping interval is \(f_{wd}\), the vector \(u_{hn}\) regarding the hopped frequency \(f_{hn}\) is written as  
\[f_{hn}=f_{m}+nf_{nd}. \tag{14}\]  
Then,  
\[ \begin{array}{l}u_{hn}=(f_{hn}s)\sin(\alpha_{opt}\frac{\pi}{2})\\ =[(f_{m}+nf_{wd})s]\sin(\alpha_{opt}\frac{\pi}{2})\\ =u_{0}+(nf_{wd}s)\sin(\alpha_{opt}\frac{\pi}{2}),\end{array}  \tag{15}\]  
where \(n\) represents the number of hopped intervals. The signals hop due to \(f_{wd}\), which means that they are spaced at regular intervals in the FrFT spectrum. Using the above property, regardless of the frequency band of the hopping, a 'decision section' for estimating a symbol can be formed because  
Figure 6: FrFT spectrum comparison between (**a**) the proposed method, and (**b**) the conventional method. the FrFT bin formed for each symbol is always regular. That is, the FrFT bin is obtained for the received signal through Equation (15), and the symbol is estimated by the demapping method which distinguishes the FrFT bin in the decision section. Since the estimated symbol can be implemented as an ideal form of the signal, it is possible to perform a matched filter between the received signal and the ideal signal. The synchronization error can be obtained between the results of the above procedure and the results of the matched filter in the ideal case. When compensating for the next symbol along with the weight, it is possible to implement symbol synchronization to correct the errors associated with the Doppler shift. Figure 7 is a plot of the FrFT spectrum for all frequency-hopping bands in a single space. From the relation given in Equation (15), it can be seen that the spectrum is formed regularly in proportion to the frequency-hopping interval. Based on this, even if an arbitrary frequency hopping is performed, it will be completed in the above spectrum section. That is, in the case that any hopped symbol is input, it can be estimated through the section of the spectrum.
## 3 RPL Routing Attacks  
The protocol design of the standard RPL incorporates limited security support against external security attacks. It comes in multiple basic security modes, namely the insecure, preinstalled, and authentication modes. The protocol operates without security support in the insecure mode whereas preinstalled security keys are utilized for the establishment of secure data communication in the preinstalled mode. In the authentication mode, a security key needs to be obtained from an authentication authority before joining an RPL network and establishing data communication [10].  
Although RPL ensures limited resilience against external attacks [11], it lacks sufficient support to defend against internal routing attacks [12; 13]. RPL has no mechanism to address the common attacks such as sinkhole, wormhole, and blackhole attacks nor the RPL-specific ones including VN, rank, and WP attacks [14; 15; 16; 17; 18]. This would magnify the vulnerability of the protocol and makes it an attractive target for different routing attacks. Moreover, these attacks can be easily launched by any node in RPL networks. For example, a malicious node can initiate a VN attack by updating the VN value being disseminated in the DIO message without adhering to the standard protocol operation. As a result, a new illegitimate DODAG update is carried out, leading to a full topology reconstruction of the RPL network. Such an attack in addition to other types of attack would cause an adverse impact on network stability and incur a noticeable increase in network overhead and power consumption. The following sub-sections provide a brief overview of four critical routing attacks that can be easily and effectively initiated in RPL networks [14; 15; 16; 17; 18].
## 3 Proposed SDRE Nonlinear Observer-Controller
### SDRE Observer  
Consider the following nonlinear system:  
\[\begin{array}{c}\dot{x}(t)=f(x(t))+B(x(t))u(t),\ \ x(0)=x_{0}\\ y(t)=h(x(t))\end{array} \tag{5}\]  
That \(y\in\mathbb{R}^{q}\) is system output, \(h(x(t))\ :\ R^{n}\to\ R^{n\times q}\) is known as a function of the system state. The problem is to estimate the \(x(t)\) state vector assuming the measurement of \(y(t)\). If \(x(t)\) estimation is shown as \(\hat{x}(t)\), according to theorem one in [21], the estimation error \(e(t)=x(t)-\hat{x}(t)\) generated by the SDRE observer asymptotically tends to be zero. The structure of the SDRE observer is as follows:  
\[\dot{\hat{x}}(t)=A(\hat{x}(t))\hat{x}(t)+B(\hat{x}(t))u(t)+L(\hat{x}(t))(y(t)- C(\hat{x}(t))\hat{x}(t)) \tag{6}\]  
in which \(f(\hat{x}(t))=A(\hat{x}(t))\hat{x}(t)\), \(h(\hat{x}(t))=C(\hat{x}(t))\hat{x}(t)\) and \(L(\hat{x}(t))\) are observer gains, which are obtained from equation \(L(\hat{x}(t))=\Sigma(\hat{x}(t))C^{T}(\hat{x}(t))V^{-1}\). In this case \(\Sigma(\hat{x}(t))\), the solution of the Riccati equation depends on the following condition.  
\[A(\hat{x}(t))\Sigma(\hat{x}(t))+\Sigma(\hat{x}(t))A^{T}(\hat{x}(t))-\ \Sigma(\hat{x}(t))C^{T}(\hat{x}(t))V^{-1}C(\hat{x}(t))\Sigma(\hat{x}(t))+W=0 \tag{7}\]  
According to theorem one in [21], in order to reach the stable SDRE observer, it is necessary that the pair \(\bigl{(}A^{T}(\hat{x}(t)),C^{T}(\hat{x}(t))\bigr{)}\) is stable at all points for all \(\hat{x}(t)\in R^{n}\). The weight matrices \(V\in R^{q\times q}\) and \(W\in R^{m\times n}\) are symmetric and determined by the designer. It should be noted that \(V\) and \(W\) are, respectively, positive-semidefinite integrand point and positive, respectively. It should be noted that the purpose of the SDRE observer's stability is that the estimation error \(e(t)\) asymptotically tends to zero.
## 3 Results and Discussion
### Effects of the Operating Conditions on the SCWG of Cellulose
#### 3.1.3 Effect of Cellulose Initial Concentration  
Figure 6 shows the hydrogen yields obtained in gas phase depending on the initial concentration of cellulose within the range 0.05-0.55 g/L. All the experiments were carried out at 250 bar, 535 \({}^{\circ}\)C and with n = 0.4.  
It can be seen from Figure 6 that hydrogen yields decrease at high cellulose feed concentration. Some researchers have previously studied the effect that organic matter concentrations have on gas yields and the same trend has been observed [48]. [PERSON] et al. [51] suggested that it could be attributed to lower reaction rates in water gas shift reaction because of a shorter number of water molecules as the number of organic molecules are increased. Their results are in agreement with the results obtained in  
Figure 5: Effects of the oxidant on the SCWG of cellulose at 535 \({}^{\circ}\)C, 250 bar and with an initial concentration of cellulose = 0.3 g cellulose/L.  
this work and corroborate their consistency. On the other hand, no significant yield differences have been found between 6 and 10 s residence times. This probably indicates that minor changes in residence time are not relevant with regards to yields when the temperature remains constant. Moreover, the LHV of the gases obtained was lower when the cellulose feed had a concentration of 0.05 g/L (794.5 kJ/Nm\({}^{3}\)) than when the concentration was 0.55 g/L (7034.1 kJ/Nm\({}^{3}\)) (Table 2). These results could suggest that thermal cracking and steam reforming are weaker when high concentrations of cellulose are fed, and this results in a lower LHV [54]. It is, therefore, confirmed that lower cellulose feed concentrations favor larger hydrogen yields (92.02 % with 0.08 g/L and only 6 s). Furthermore, [PERSON] and [PERSON] [55], similarly to other research teams, have experimentally and theoretical verified the kinetic and thermodynamic aspects of SCWG. In this sense, due to the Le Chatelier principle, hydrogen yields can be increased when there is a low concentration biomass and, therefore, a greater amount of water is fed into the system. This proportion provides a better thermodynamic equilibrium that favors hydrolysis and water-gas shift reactions. Even though hydrogen yields are in fact reduced when cellulose concentration rates are increased, it is important to highlight that, for the same really short residence times, gasification efficiency can still reach over 50%.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods
### Methods
#### 2.4.1 Temporal Method  
In this study, the temporal method, linear time interpolation and smoothing filtering were used.  
Following the procedure described by [PERSON], 2010 [30] and [PERSON] et al., 2015 [20], the invalid pixel was taken as the center, and the period of the effective pixel at the same position was determined and used to derive a linear equation to estimate the LST value of the invalid pixel. Because it is rare for a pixel to have an invalid LST value for two consecutive 10-day periods, most estimations were completed by taking the average of two values that were close to one another in time.  
The smoothing filters used in this study were the HANTS and SG filters. The core algorithms of HANTS are the least squares method and Fourier transform. The points in the LST value that are greatly affected by cloud pollution are removed through iterative fitting by means of the least squares method, and the curve decomposition and reconstruction are performed with the help of forward and inverse Fourier transforms in the time and  
Figure 2: Flowchart of validation and comparison of different reconstruction methods. CAAW indicates the method based on classification and the adaptive window.  
frequency domains in order to achieve cloud removal and the reconstruction of a time-series of remote sensing images. There are 5 parameters to be input into the HANTS algorithm: the number of frequencies (NOF), high/low suppression flag (SF), valid data range (VDR), fit error tolerance (FET), and degree of over-determinedness (DOD) [31]. A value of 'Low' was used for SF, since undetected clouds result in unusually low LST values. A range of \(-50\)\({}^{\circ}\)C to \(70\)\({}^{\circ}\)C was set for VDR to omit unusual temperatures during the computational process. Values of 6 \({}^{\circ}\)C and 7 \({}^{\circ}\)C were used for the FET and DOD, respectively.  
The core idea of the Savitzky-Golay (S-G) filter is to filter the data in the window by means of weighting, but the weightings are determined by means of the least-squares fitting of a given high-order polynomial. The advantage of this is that it is possible to preserve the change information of the signal more effectively while filtering and smoothing. There are 5 parameters to be input into the S-G algorithm: Nleft, Nright, Order, and Degree. Nleft and Nright represent the number of data points to the left and right of each point to be included in the filter, respectively, the default of which is 5. Order is an integer specifying the order of the desired derivative. For smoothing, an order of 0 is used. To find the smoothed first derivative of the signal, an order of 1 is used; for the second derivative, order 2 is used, etc. Degree is an integer specifying the degree of the smoothing polynomial. Typical values are 2 to 4. Lower values for Degree will produce smoother results but may introduce bias, while higher values for Degree will reduce the filter bias, but may 'overfit' the data and give a noisier result.
# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
## 3 Simulation Results and Discussion
The drawback of the TD-Drone Dijkstra algorithm is that the route is calculated using the information about the operating drones and stations at the moment of route calculation and, if a drone or a station from the route is down on this route, the message does not reach the destination. All the routes passing through the station or drone that is down are compromised until the fault is detected. Moreover, this problem reappears when the drone/station is fixed until the moment this information is updated. However, the chance of this problem occurring is low and, if it appears, it may be fixed in time following the repair of the drone/station or when the current status of the network is updated. Using the example of the Spray and Wait or Epidemic algorithms presented in this paper, any message has the chance to reach the destination even if drones or stations are down because a copy of the message is spread in the network.