esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Influence of IN
### Influence of IN on ROC Curves
#### 5.2.1 Glrt  
Figure 8 shows the ROC curves for GLRT. It can be noticed, again, that the detection performance can be too optimistic if the conventional model (_C-model_) is adopted in the absence of IN. On the other hand, the performance can be pessimistic if the _C-model_ is adopted with IN present. Moreover, one can notice from Figure 8 that the detection performance under the more realistic _R-model_ suffers less influence of IN, as previously inferred visually in the shape of the received and covariance matrices (see Figure 7). It is also worth mentioning that the ranges of decision thresholds used for plotting the ROC curves under the _R-model_ were the same for the scenarios with and without IN. This is an important result, because new decision thresholds need not be computed under IN circumstances, _i.e._, IN need not be detected. New decision thresholds must be determined for the _C-model_ under IN, since the corresponding ROC curves in Figure 8, with and without IN, were plotted using very different decision threshold ranges.  
As we can see in Figure 8, with the _C-model_, moderate impulsive noise seriously degrades the performance for all false alarm probability values. However, in the case of strong concentrated IN, an inflection is evident in the ROC curves. As the threshold is increased in the case of concentrated IN, the decision statistic starts to become strongly influenced by the IN. This is because the strong concentrated IN will govern the instantaneous SNR. As a consequence of the low instantaneous SNR regime beyond a given value of the threshold, \(P_{\rm fa}\) becomes equal to \(P_{\rm d}\) all the way up to zero. The performance under the _R-model_ reveals that the signal processing tasks performed at each CR can  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{2}{c}{_R-model_} \\ \hline MA-filter length & \(L=10\) \\ AGC dynamic range & \(D=2\) \\ AGC overdrive factor & \(f_{\rm od}=8\) \\ Number of quantization levels & \(N_{\rm q}=8\) \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._increase the robustness of the spectrum sensing technique in the IN environment, as inferred from the analysis in Section 5.1. Moreover, it can be found that the detection performance values with strong concentrated IN and without IN are very close to one another in the case of the _R-model_. The better performance with strong concentrated IN, when compared with moderate IN, may be explained by the hard limiter at the AGC, which clips the high amplitudes that happen with strong IN. Similar to what had happened in the moderate IN condition, the ranges of decision thresholds for plotting the ROC curves under the _R-model_ were the same for the scenarios with and without strong concentrated IN, and very different under the _C-model_.
## 3 First Generation: Virtual Inertia Topologies
### Summary of Topologies  
A summary table which highlights the key features and weakness of various virtual inertia control topologies is presented in Table 1.
# Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve
## 2 Related Work  
### Geometric Vehicle Model  
Usual vehicle model configurations consist of geometric, kinematic, and dynamic models. The geometric vehicle model is particularly important in order to relate the vehicle's dimensions, the radius of the turn, and the radius of the curvature of the road undertaken by the vehicle during turning. It is developed based on Ackerman steering configurations, where the line perpendicular to each of the vehicle wheels should intersect at the center point of the vehicle cornering arc where the radius of turn is R (Figure 2). This model only considers the dimension and positions of the vehicle with no regard to its  
Figure 1: Clothoid-curve-based trajectory tracking control method.  
velocity, acceleration, and internal forces [2]. Therefore, compared with the kinematics and dynamics models, using this model for trajectory tracking control usually has the highest computational efficiency. However, large-tire lateral forces and lateral acceleration will be produced when the vehicle turns at high speed, which cannot be neglected; therefore, if geometric vehicle models are used for trajectory tracking control, vehicle speed must be limited when passing curves in order to ensure high trajectory tracking accuracy.
# Characterization and Prediction of Air Transport Delays in China
## 4 Statistical Analysis of Flight Delays in China  
As a first step, we perform standard descriptive analyses on the evolution of the average delay. Specifically, Figure 3 depicts the evolution of the average monthly delay, both aggregated over the whole system (top left panel), and individually for the eight most important airports (sorted according to the total number of flights in the data set). Two important facts can be observed.  
First of all, three peaks are present in the delay evolution, around July 2016-2018. While it may prima facie appear that they are due to the increased traffic usually observed during the summer, a weak correlation is actually present between both time series--\(R^{2}=2.82\times 10^{-4}\) for the aggregated time series, with a maximum of \(R^{2}=0.146\) in the case of ZSPD (Shanghai Pudong International Airport). Considering the changes in traffic levels and delays between consecutive months (i.e., \(\hat{d}(t)=\log_{2}d(t)/d(t-1)\), with \(d(t)\) being the average delay at month \(t\)) yields a slightly higher correlation for the whole system (\(R^{2}=7.99\times 10^{-3}\)), but still not high enough to justify traffic as a major driver for delays.  
Secondly, one may focus on the inter-year evolution, to check whether the average delay has reduced over time--see Table 1 for a synthesis. The peaks in the summer 2016 are always smaller than those of 2017; in turn, delays were again reduced during the summer of 2018, thus suggesting that the summer of 2017 was characterized by exceptional situations. On the other hand, a slight decrease in the mean level can be observed for the winter months, when compared with the previous year. Nevertheless, such decreases are seldom statistically significant. As can be seen in Table 2, which reports the \(p\)-values of a series of \(t\)-tests on the average delay for each pair of seasons, only ZSPD (Shanghai Pudong International Airport) presents a statistically significant decrease in the average  
Figure 3: Delay evolution in China from May 2016 to October 2018. Each panel represents the evolution of the average (black lines) and median (green lines) of flight delays, calculated with a one month resolution; additionally, the dashed gray lines (right axis) depict the evolution of the number of flights (in thousands per month). The top left panel reports the aggregated results for the whole data set, while the other ones for the top–eight airports (ranked in decreasing number of flights).  
delay between the two consecutive winters (significance level of \(\alpha=0.01\), effective \(\alpha^{*}=3.72\times 10^{-4}\) with a Sidak correction for multiple testing).
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 2 Problem Formulation
### Topology Structure  
Define an undirected graph \(G=(V,E)\) to describe the radar network, where \(V=[1,2,\ldots,N]\) denotes the radar set. The symbol \(E\subseteq V\times V\) denotes the set of radar connections in the network. The pair \((m,n)\) is a member of set \(E\), where radar \(m\) and radar \(n\) can exchange information between each other. If two radars can exchange information, they are called neighbors. The set of radar \(m\) and its neighbors is denoted by \(E_{m}\). The degree \(d_{m}\) of radar \(m\) means the number of its neighbors. Define the adjacency matrix \(A(G)_{mn}\) as  
\[A(G)_{mn}=\left\{\begin{array}{c}1,\;if(m,n)\in E\\ 0,\;otherwise\end{array}\right. \tag{1}\]
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 2 Related Work
### Deep Learning  
In recent years, as deep neural networks show great potential for solving complex estimation problems, a rapidly growing trend of utilizing DL techniques has appeared in robotics tasks. For navigation based on visual information, [PERSON] et al. [17] used deep neural networks to learn how to recognize objects from the image, and then determined discrete actions such as turning left and turning right according to the identified information. [PERSON] et al. [18] proposed a novel deep neural network architecture, Intention-Net, to track the planned path by mapping monocular images to four discrete actions such as going forward. Since the action is discrete, the navigation behaviors in above-mentioned researches are rough and likely to be unfeasible. For navigation based on range information, [PERSON] et al. [19] used demonstrative data to train a DL model that can control the robot through inputting the data from laser sensors and the target position. However, since a large number of labeled data are required for training the above DL models, it may be unpractical for real-world applications.
## 1 Introduction
### Description of the GlobalStar Satellite Network  
The GlobalStar satellite communication system operates in Low Earth Orbit and consists of a constellation of 48 satellites positioned at an approximate altitude of 1414 km. These satellites are distributed among eight planes, each accommodating six satellites, and follow circular orbits with an inclination of approximately 52 degrees. It is worth noting that the GlobalStar constellation is currently undergoing reorganization due to bankruptcy [16].  
The original model of the GlobalStar constellation aimed to provide cost-effective and less complex communication solutions by utilizing a hub of ground stations without inter-satellite links (ISLs). The telemetry and command units were planned to be integrated into six host gateways, selected from a total of 200 stations distributed worldwide with redundancy. With an estimated coverage of 50% of the ground stations, each satellite would establish an average of one contact per orbit, which occurs within a period of approximately 10 to 12 min [17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specifications** & **STX3** & **STINGR** \\ \hline Size & 28.70 \(\times\) 20.57 \(\times\) 4.13 mm & 45.05 \(\times\) 47.37 \(\times\) 6.3 mm \\ TX output power & 19 dBW & 17.5 dBW \\ TX mode current & 390 mA & 325 mA \\ Sleep mode current & 7 uA & 8 uA \\ Power Supply & 3 to 5 V & 3 to 5.5 V \\ Operating temperature range & \(-\)40 \({}^{\circ}\)C to \(+\)85 \({}^{\circ}\)C & \(-\)40 \({}^{\circ}\)C to \(+\)85 \({}^{\circ}\)C \\ Frequency range & 1610 to 1620 MHz & 1610 to 1620 MHz \\ Patch antenna included & No & Yes \\ GPS module included & No & Yes \\ Price (USD) & \$60 & 5/5 \\ Patch antenna price (USD) & \$35 & \$0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the STINGR communication module [15].  
Figure 3: AztechSat-1 orbit simulation.
# Comparison of Analytical Methods for Determining Methylesterification and Acetylation of Pectin
## 3 Results and Discussion  
### Preparation of Standard Pectin Samples  
In this study, we used citrus pectin (CP) as the raw material to prepare standard pectin samples. CP contains GalA (76.7%) as the major sugar, with small amounts of Gal (13.7%), Rha (3.3%), Glc (3.1%) and Ara (1.7%) (Figure 1). To prepare unesterified HG-type pectin as a standard, CP was de-esterified using alkali saponification and hydrolyzed by acid to remove neutral sugars. The obtained species, termed CP-HG, mainly contained GaIA (\(\geq\)97%). CP-HG was then methyl-esterified with sulfuric acid-methanol to yield high methyl-esterified pectin, termed CP-HM-HG. The monosaccharide composition of CP-HG was similar to CP-HM-HG.
## 4 Optimization of the Energy Management Strategy
### Optimization of Energy Distribution Strategy Based on Genetic Algorithm  
#### 4.2.1 Target Fitness Function  
The optimization goal of this study is to reduce the fuel consumption of the fuel cell without reducing the performance of the tram. Therefore, the target fitness value function used to evaluate the tram energy allocation strategy is:  
Among them, \(X\) is the set of variables related to calculating the fitness value including the tram, and \(f(X)\) is the fitness value function. \(S\) is the search space where \(X\) is located. \(m_{H_{2}}(X)\) is the mass of hydrogen consumed by trans after a complete operating condition. \(m_{chg,bat}(X)\) and \(m_{chg,sc}(X)\) are the electrical energy consumed by the grid to charge the battery and capacitor to their initial state after a complete operating condition, respectively, and the electrical energy is converted into fuel quality:  
\[m_{chg,bat}(X)=\frac{E_{chg,bat}(SOC_{init,bat}-SOC_{end,bat})\cdot\eta_{fuel, ele}}{\zeta_{dc}} \tag{21}\]  
\[m_{chg,sc}(X)=\frac{E_{chg,sc}(SOC_{init,sc}-SOC_{end,sc})\cdot\eta_{fuel, ele}}{\zeta_{dc}} \tag{22}\]  
Among them: \(\zeta_{dc}\) is the efficiency of the energy passing through the DC/DC converter when the grid is charging the battery and the super capacitor; \(E_{chg,bat}()\) and \(E_{chg,sc}()\) are the required energy calculation functions (kWh), and are related to the state of the charge of the battery/super capacitor at the beginning and the end of the journey, respectively; \(SOC_{init,bat}\) and \(SOC_{end,bat}\) are the battery states at the beginning and the end of the journey, respectively; \(SOC_{init,sc}\) and \(SOC_{end,sc}\) are the SOC of the super capacitor at the beginning and the end of the journey, respectively; \(\eta_{fuel,ele}\) is the conversion coefficient of electric energy into fuel quality, which is based on previous research, and according to a more conservative estimation, the lower price of hydrogen fuel is \(\forall 20\) per kilogram, and the higher price of industrial electricity is \(\forall 0.9\) per kilowatt-hour, so it is converted here. The coefficient is taken as \(0.9/20\); \(\zeta_{dc}\) is the efficiency of the DC/DC that the energy passes when charging the battery/super capacitor by grid.  
\(X\) is subject to the following constraints:  
\[SOC_{bt\_min}\leq SOC_{bt}\leq SOC_{bt\_max} \tag{23}\]  
\[SOC_{uc\_min}\leq SOC_{uc}\leq SOC_{uc\_max} \tag{24}\]  
Among them, \(SOC_{bt\_min}\) and \(SOC_{bt\_max}\) are the minimum and maximum values of the allowable range of the battery state, which are taken as 40% and 90% in this study, respectively; and \(SOC_{uc\_min}\) and \(SOC_{uc\_min}\) are the minimum and maximum values of the allowable capacitor state, which are taken as 18% and 96% in this study, respectively (100% in simulation).
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 3 The Improved Multipath Routing Algorithm  
The routing algorithm is improved mainly to optimize the latency and packet loss rate and to enhance the communication performance of the network system. In this section, first, the characteristics of urban rail transit ad hoc networks are analyzed, and it is explained that configuring static routes can improve the route discovery algorithm; then, with a suitable mathematical equation, it is explained that the method of constraining the number of paths according to different service latency requirements and selecting paths based on the link cost can improve the transmission efficiency and reliability; finally, in order to improve the reliability of communication and address the issues of high repair costs and long repair times in traditional route maintenance schemes, a local route repair scheme is discussed.  
The following notation is used for formulating the proposed approach.
## 3 Results
### Irrigation Advice Results from Agro-Hydrological Modeling  
The irrigation advisory service of MAGDA provides farmers with forecasts of daily Irrigation Water Requirements (IWR) in m\({}^{3}\)/ha/day for short-term (2-day ahead) and medium-term (5-day ahead) forecast windows. Although sub-daily resolution may be of high interest under certain circumstances, this temporal resolution for outcomes was not explicitly requested by local farmers during the earlier phases of the research in which they were surveyed regarding their specific needs. According to the MAGDA irrigation approach adopted, IWR estimates represent the supplementary water that farmers may apply as irrigation to meet the crop's daily water needs, based on weather forcings, crop development stage, and current soil moisture levels in the root zone.  
Tables 4 and 5 present irrigation recommendations--i.e., IWR values--issued at the SCDAB and Tetto-Bernardo pilot sites for the selected testing periods. The relative contribution of IWR to the crop reference evapotranspiration (IWR/\(ET_{c}\) ratio) is also reported for comparison purposes.  
In SCDAB, with a higher sample size of forecasts than in Tetto-Bernardo, estimates of IWR and IWR/\(ET_{c}\) ratios derived from weather forecasts with data assimilation (DA) were consistently higher than those retrieved for the benchmark case (OL) for both forecast windows. However, in Tetto-Bernardo, no clear difference was observed during the single testing period evaluated.  
The results show that, in the SCDAB testing site, short-term (2-day ahead) forecasts of IWR and IWR/\(ET_{c}\) were consistently higher than in medium-term (5-day ahead) forecasts (0.32-0.23 vs. 0.29-0.19, for DA-OL configurations, respectively) in the SCDAB testing site. No differences were detected in the Tetto-Bernardo site.  
Recommendations for irrigation derived using the long-term DA forecast were in the same order of magnitude as irrigation quotas actually applied at the farm level following the traditional ICITID method (Table 6). Although not fully comparable due to the large conceptual differences between methods, in general, the ICITID method provided recommendations 35% lower than the MAGDA solution. These differences highlight the importance of accurately simulating the soil moisture dynamics and the climate forcings that control the water balance in the atmosphere-crop-soil continuum. The MAGDA solution may provide a clear advantage against current practices that rest on coarse approaches applied at county and regional scales.
## 2 Theory Background
### Machine Learning Model
#### 2.4.2 Artificial Neural Network (ANN)  
The ANN can improve the data representation and function fitting ability of the model by increasing the number of hidden layers [24]. The multilayer network structure is helpful for the extraction and representation of input features, but it also leads to a sharp increase in the number of parameters of the model, which greatly increases the training time of the model, which is not conducive to industrial applications.  
As shown in Figure 3, in order to avoid too many model parameters and overly long model training times, this paper chooses the back propagation (BP) neural network with four hidden layers.  
Figure 3: ANN network structure.  
Figure 2: Random forest structure.  
The calculation is shown in Equation (7):  
\[y_{i}=\varphi_{active}(w^{T}x_{i}+b) \tag{7}\]  
where \(\varphi_{active}\) is the activation function, \(w^{T}\) is the weight matrix, and \(b\) is the bias matrix.  
Combined with Figure 3, the formula has a more understandable form:  
\[\begin{array}{l}Y_{1}=X_{in}\\ Y_{2}=\varphi_{active}(W_{2}Y_{1}+B_{1})\\ Y_{i}=\varphi_{active}(W_{i}Y_{i-1}+B_{i-1})\\ Y_{out}=Y_{6}=W_{6}Y_{5}+B_{5}\end{array} \tag{8}\]  
where \(Y_{i}\) is the output matrix of the \(i\)-th layer.  
The execution process of the ANN can refer to the pseudocode in Algorithm 2.  
```
Input: Training set \(D=\left\{\left(x^{(n)},y^{(n)}\right)\right\}\), validation set \(V\), learning rate \(\eta\). Output: weight matrix \(W\), bias matrix \(b\).
```  
**Algorithm 2** ANN
# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 4 Performance Evaluation
### Network Lifetime Comparison  
Figure 5 compares the network lifetime under different radii. It is clear from figure that network lifetime decreases with the increase in network radius. This is due to increase in distance between nodes which increases the energy consumption. Moreover probability of void hole increases with increase in network radius which leads to short lifespan. LiMHA has 15% higher network lifetime as compared to ODTS on average and 45% improved lifetime as compared to LAEHA. In LiMHA network is divided into sectors and presence of each super node in a sector decreases the void hole probability. Super node has the capability to receive the data from normal nodes as well as to send the collected data from normal node to downstream super node. In the start when network radius ranges from 100-200 m, the performance of ODTS is observed better in terms of maximizing network lifetime except with the case of 100 m radius. ODTS uses ant colony optimization for achieving better lifespan in sparse network. It chooses a path that is more energy efficient as a result data is transmitted through the shorter path. For large area network the performance of ODTS in terms of lifetime degrades as compared to LiMHA. LiMHA uses super nodes for data transmission. If normal nodes unable to locate neighboring nodes in their transmission range they transmit the data to their respective super node in their sector without any hesitation. Thus probability of transmission of data increases which increases the overall network lifetime. With the increase in network area traffic load increases. In LiMHA, super nodes have enough energy to bear the burden of high traffic load, however in ODTS high traffic load on nodes decreases the network lifetime. Same is the case for LAEHA where high data load on nodes near the sink decreases the lifetime of sensor nodes due to high energy required for sending data to the sink. Thus network lifetime tends to decrease rapidly.  
For better visualization, we have compared our protocol with cluster based heterogeneous schemes N-SEP and P-SEP. The network lifetime is the metric to validate the legitimacy and effectiveness or our proposed work with respect to the baseline strategies for energy efficiency. We have  
Figure 5: Network Lifetime at Various Radii.  
increased the transmission radius for all schemes for fair comparison, where it is evident that with the increase in radii, the network nodes have to transmit data over long distances, that is well known with the increase in distance directly proportional to energy tax. Authors in both N-SEP and P-SEP have used small communication distances, however, when we increased the communication distance for all schemes as shown in Figure 5. The network lifetime gradually starts declining due to uneven battery dissipation over long transmission ranges. Hence, it is well grounded that our proposed is suitable for both small and large communication ranges. While, other schemes perform well enough, but not as much needed.