esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries and System Model
### CSI and Buffer State-Based Scheme  
Channel state information (CSI) represents the characteristics of a communication link between the transmitter and receiver. It describes how a propagating signal from the transmitter to the receiver will be affected by effects such as fading, scattering, and power decaying with increasing distance. The knowledge of CSI makes the transmitter adapt to current channel conditions, which results in a reliable communication at a higher data rate. There can be more than one category of CSI that is instantaneous and statistical. The instantaneous case is acquired if the channel is varying slowly. In the case where the channel is varying very fast, we go for statistical CSI, since the mean and variance remain constant for a relatively longer time. In most cases, the receiver can accurately track the instantaneous state of the channel from pilot signals that are typically embedded within the transmissions. However, for the transmitter, there are two methods to obtain CSI from the receiver.  
Figure 4: Classification of interference-identifying techniques.  
Figure 5: Geo-location-based interference management.  
The first one is based on the channel reciprocity principle, and the second one uses feedback from the receiver. In frequency-duplexed systems, where the up-link and down-link are apart in frequency, the link fading is not reciprocal, and thus, the CSI must be conveyed through feedback, which may incur round-trip delays that are not negligible with respect to the coherence time of the CSI being reported. Therefore, the transmitter is usually deprived of instantaneous CSI. In time-duplexed systems, in contrast, the links are reciprocal as long as the coherence time of the fading process exceeds the duplex time. Thus, the transmitter may have access to reliable CSI at low and moderate levels of mobility. In our work, the channel is sensed, and CSI at the receivers of relays are exchanged among the relays. With regards to instantaneous CSI or statistical CSI, in each case, the approach is different. For simplicity, the model in this work assumes that the location of the primary receiver and instantaneous CSI are available. In this work, a secondary power threshold is adapted for simulations. Figures 6 and 7 show the sequence of CSI exchange among the relays and primary network, in a centralized and distributed manner, respectively [9].
## 7 Validation and Use of the Open-Loop Metrics
### Case of Loss of Lock  
In order to test the goodness of the open loop approach in the presence of a loss of lock, the same case study reported in Section 4 is analyzed. It has to be remarked how \(R_{4}\), being based on an open loop architecture, is not prone to LoL, and it can work also in case of very strong scintillations. Figure 11  
Figure 10: Comparison of \(S_{4}\) and \(R_{4}\) parameters after filtering: (**a**) GPS PRN 1, (**b**) GPS PRN 3.  
Figure 9: Comparison of \(S_{4}\) and \(R_{4}\) parameters: (**a**) GPS PRN 1, (**b**) GPS PRN 3.  
reports the value of the \(R_{4}\) filtered metric, obtained by processing the raw signal samples by means of the open loop receiver. This is compared to the values of the \(S_{4}\) obtained by the second order closed loop receiver, as reported in Figure 3a, where a LoL occurred after about five minutes. In order to benchmark the results, a reference \(S_{4}\), obtained by a commercial scintillation monitoring receiver, is plotted, corresponding to the \(S_{4}\) estimate obtained running a robust third order loop, as shown in Figure 3b.  
As already observed, the \(S_{4}\) estimate from the closed loop cannot be trusted after minute 5, due to the incorrect values of the I and Q correlator outputs. On the contrary, the \(R_{4}\) metric derived from the open loop processing follows the same trend of the reference \(S_{4}\), thus confirming the validity of the proposed technique in the presence of strong amplitude scintillations inducing losses of lock in traditional receiver architectures.
## 5 Enhancing QoS and QoE in ICN
### SDN-Based ICN Solutions
An advantage of this strategy is the ability to make informed decisions regarding caching, routing, and load balancing across the entire network. For instance, [PERSON] et al. [94] uses a central controller to determine popular content and optimal caching locations to reduce retrieval time and bandwidth consumption. This centralized control directly improves QoS and QoE by reducing delays and increasing network efficiency. However, a potential limitation is the scalability of the central controller in very large and dynamic networks, as well as the risk of a single point of failure. The second category focuses on SDN and NFV for flexible and coexistent ICN deployments, which emphasizes the practical aspects of deploying ICN incrementally. Architectures like ContentSDN [95] utilize NFV to deploy caching functionalities dynamically, allowing for flexible adjustments based on demand and policies. SDNDN, proposed in [96], directly addresses the coexistence of ICN (specifically NDN) with existing IP networks by extending OpenFlow switches to handle both types of packets. The primary advantage here is the flexibility and ease of incremental deployment without requiring a complete overhaul of the network infrastructure. This approach improves QoE by bringing content closer to users in a manageable and scalable way and by enabling the use of ICN benefits for specific traffic types.
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 4 Numerical Experiments
### Discussion
#### 4.3.2 MTC-HVs Proportion: The Most Critical Factor Among Vehicle Types  
In Figure 5(b), each curve is horizontal, indicating that when the proportion of MTC-HVs is fixed, changes in the proportion of ETC-HVs (with the CAVs proportion simultaneously varying) have no effect on total passage time. Thus, since all MTC-HVs choose the ETC  
Figure 7: Total travel time variation with entry-direction flow under fixed exit-direction flow. (**a**) Lane allocation under optimization model; (**b**) current lane allocation.  
lane, CAVs and MTC-HVs can be considered homogeneous in terms of total passage time or optimal solutions.  
In Figure 5(a), the curve with a low CAVs proportion (lighter color) does not intersect the vertical axis, meaning that at this CAVs proportion, the ETC-HVs proportion cannot be zero. A low ETC-HVs proportion combined with a high MTC-HVs proportion will cause system failure. In Figure 5(b), the rightmost point of each curve represents the maximum value of ETC-HVs, i.e., \(1-p_{2}\), indicating that no line exists where \(p_{2}\) is less than 0.1. These findings indicate that there is a threshold for the MTC-HVs proportion. The threshold implies that, given the current traffic demand (vehicle arrival rate), the MTC-HVs proportion should be reduced to below this threshold; otherwise, the toll station queuing system will collapse, leading to severe congestion.  
In Figure 5(a), the rightmost point on each curve represents the maximum possible proportion of ETC-HVs, i.e., \(1-p_{1}\). The minimum value occurs when the ETC-HVs proportion is at its maximum, where the sum of CAVs and ETC-HVs proportions equals 1, and no MTC-HVs exist. In Figure 5(b), the curve with zero MTC-HVs proportion (brightest color) shows the shortest total passage time. This indicates that the queuing system has the lowest total passage time when no MTC-HVs are present.  
Figure 5 illustrates the optimal solution corresponding to the optimal value curve in Figure 6. In Figure 5(b), we observed that when the proportion of MTC-HVs is fixed, the optimal value remains unchanged, leading us to suspect that the optimal value is constant. Evidently, this hypothesis is confirmed in Figure 5. This result differs somewhat from intuitive expectations. To explain this intriguing numerical finding, we derive the relaxed analytical solution of the bi-level programming model using the Lagrangian relaxation method in Appendix B, as shown in Equation (A5).  
In conclusion, the MTC-HVs proportion should be the most critical factor in toll station lane planning and management.
## 2 Frequency and Active Power Control Schemes
### Proposed Method
For this reason, a maximum BESS participation factor is proposed as a constraint in the dispatch scheduling problem. Such a limitation might be useful to ensure availability of energy reserves in storage units, as concluded by [PERSON] et al. (2018). As noted in [57], if action is not taken before SOC reaches its limits, the BESS would suddenly shut down by its battery management system (BMS), possibly resulting in a critical situation for the system. Although an island system is used as a study case, the proposed coordinated active power control method is also applicable in large-scale interconnected power systems (where tie-line power flows will also enter in area control error formation), without losing its simplicity and minimum signals/control modules required. In fact, the proposed method is built upon an established standard AGC scheme implemented in power systems around the world. The required real-time calculations of participation factors using Equations (1)-(3), as well as of the term \(P^{*}_{gi,atfr,soc}\), are simple and straightforward, while the three required signals per storage station (\(P^{*}_{bi,soc}\), \(c_{bi,up}\), \(c_{bi,dn}\)) add no burden to the SCADA infrastructure. Figure 2: Calculation of \(c_{bi,up}\) and \(c_{bi,dn}\) parameters. The need for storage systems to transmit SOC-related information to the system operator is already established in grid codes of island systems (e.g., Cyprus, [58]). The proposed local BESS controller is also easy to implement, as it only requires a SOC measurement, already available to the BMS, and very simple calculations. The BESS converter controller is illustrated in Figure 3. More detail is provided in [59]. The BESS active power reference \((P_{bi}^{*})\) comes from the local BESS controller of Figure 1, while the reactive reference is the output of a proportional voltage controller. Between the two, active power is prioritized in the current limiting module, although this could be reverted, depending on system needs. BESS current limits are dictated by its rated capacity, as well as by its BMS monitoring various operating parameters. An L filter is used to interface the voltage source converter (VSC) to its output step-up transformer.
# Design of a Laser Driver and Its Application in Gas Sensing
## 2 Structure of the Laser Driver
### Macro-Model of DFB-LD  
In terms of working conditions, both laser diodes and light-emitting diodes require a small driving current (tens of mA). However, the internal structure of laser diodes is far more complicated than that of light emitting diodes. According to the rate equation, the internal equivalent structure of the laser diode contains parasitic resistance, capacitance, and current sources, which are closely related to the stable operation of the driving circuit. In order to ensure the guiding significance of the simulation circuit for practice, it is necessary to establish the SPICE model of the laser diode specifically, rather than simply using the light-emitting diode to replace it. The optical power meter is not provided in TINA-SPICE software. For the purpose of processing optical signals (optical power) as easily as electrical signals, the optical signals must be represented by circuit variables (current or voltage). Therefore, we need to extrapolate two virtual optical ports in the macro-model of DFB-LD to output the optical signal related to the aforementioned circuit variables [23]. These optical ports and the electrodes constitute the four-port model of the DFB-LD (see Figure 2). It is crucial to emphasize that the equivalent circuit (left) is only used to facilitate the intuitive understanding of the electrical and optical circuits of the laser, while the sub-circuit symbol (right) generated by importing the SPICE model file (see Appendix A) is for simulation. Representative electric symbols appearing in the equivalent circuit of DFB-LD were summarized and explained in Table 1. The SPICE model file contains the definition of parameters and functions as well as the units and various operators.  
Figure 1: Flow chart of the code for regulating the laser driver.  
A test circuit shown in Figure 3 was designed for validating the macro-model of DFB-LD. The sweep range of IS1 was set within 0 to 100 mA with an accuracy of 0.1 mA, resulting in 1000 sampling points. Here the optical power is measured through a voltmeter, which is just an equivalent processing in a purely mathematical sense. The voltage obtained from measurement and the optical power of the laser model is only numerically equal, and the units of them are different.
# Validation of a Wind Tunnel Propeller Dynamometer for Group 2 Unmanned Aircraft
## 4 Discussion  
### Comparison of BEMT and Experimental Results  
As indicated in Figure 11, there is a significant difference between the BEMT code and APC results, likely due to different analytical methods and application of airfoil data. The APC published data have been found inconsistent by other studies: [PERSON], 2014 [28] and [PERSON]'s master thesis, 2009 [29]. Figure 12 furthermore shows that the BEMT code produces more reliable data for validating dynamometer experimental data, which is likely due to the treatment of Reynolds number effects. Increasing the Reynolds number results in increased thrust coefficient and propeller efficiency because an increase in the Reynolds number increases the sectional lift coefficient and decreases the blade drag coefficient [11]. The BEMT analysis captures these effects by incorporating airfoil data, rendering a more conservative prediction for both propeller efficiency and thrust coefficient.  
As the advance ratio increases to a magnitude of about 0.6 at either low airspeed or low rotational speeds, the propeller is expected to encounter a sufficiently low Reynolds number that it is susceptible to boundary layer separation. The resulting effect is a sharp decline in propeller efficiency and near zero thrust coefficient as the flow relative to the propeller may render a negative angle of attack. Under this condition, flow is expected to separate around the bottom (pressure side) of the propeller. The experimental data from the dynamometer is consistent with this expectation, having better agreement with the BEMT results than those of APC. Thus, the BEMT code is shown to be accurate and useful to validate dynamometer performance over a range of advance ratios from about 0.3 to 0.6.  
Figure 14: BEMT and experimental \(C_{T}\) and \(\eta_{P}\) for APC propellers 18 \(\times\) 12E, 16 \(\times\) 12, and 14 \(\times\) 12.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 4 Experimental Result
### CSE-CICIDS2018 Scenario 3 Results  
In the CSE-CICIDS2018 dataset, scenario 3 requires differentiation between \"Benign\" traffic and \"Ddos Attacks-LOIC-HTTP\". This analysis evaluates the model's ability to categorize these traffic types. We draw insights from Tables 9 and 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** \\ \hline Benign & 0.99995 & 0.99836 & 0.99915 \\ Ftp Bruteforce & 0.99919 & 1 & 0.99959 \\ Ssh Bruteforce & 0.99896 & 0.99975 & 0.99936 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The evaluation performance of scenario 1 in the proposed model per category.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Recall** & **F1-Score** \\ \hline Benign & 0.99965 & 0.999 & 0.99933 \\ DoS\_GoldenEye & 0.99925 & 0.9997 & 0.99947 \\ DoS\_Slowloris & 0.9995 & 0.9997 & 0.9996 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The evaluation performance of scenario 2 in the proposed model per category.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** \\ \hline Benign & 0.99965 & 0.999 & 0.99933 \\ DoS\_GoldenEye & 0.99925 & 0.9997 & 0.99947 \\ DoS\_Slowloris & 0.9995 & 0.9997 & 0.9996 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation classification of scenario 2 results of the proposed model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Results** \\ \hline Accuracy & 0.99947 \\ Precision & 0.99947 \\ Recall & 0.99947 \\ F1-Score & 0.99947 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The evaluation performance of scenario 3 in the proposed model per category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** \\ \hline Benign & \(1\) & 0.98527 & 0.99258 \\ Ddos Attacks-LOIC-HTTP & 0.98573 & 1 & 0.99282 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The evaluation performance of scenario 3 in the proposed model per category.  
Table 9 assesses the model's performance in scenario 3. It provides a detailed account of each category's precision, recall, and F1-score. The model demonstrates perfect precision (1.00) in identifying \"Benign\" traffic with a recall of 98.527%, resulting in an F1-score of 99.258%. These metrics are proof of the model's remarkable accuracy and reliability in recognizing benign activities without misclassification.  
Table 10 summarizes the overall performance metrics for scenario 3. From Table 10, it can be seen that scenario 3 resulted in an accuracy rate of 99.27% and consistent precision, recall, and F1-score values of around 99.28%.
## 2 Carrier Tracking Loop and the Phase Error  
As the signal acquisition process in a GNSS receiver only provides rough estimates of the Doppler frequency and code delay of the satellite signals in view [11], a circuit with a feedback loop namedtracking loop is introduced following the acquisition in order to get more precise carrier and code phase estimates. A basic analog domain phase-locked loop structure is shown in Figure 1, including a phase discriminator, a loop filter and a local numerical controlled oscillator (NCO). The carrier tracking loop uses the In-phase and Quadrature channel integrator outputs to calculate the phase error, then a loop filter is introduced to eliminate the high frequency noise, and the result is used as a controlled quantity to tune the frequency of the carrier NCO.  
The transfer function of the PLL above can be given as follows:  
\[H(s)=\frac{\theta_{o}(s)}{\theta_{i}(s)}=\frac{KF(s)}{s+KF(s)} \tag{1}\]  
where \(\theta_{i}(s)\) is the input signal phase, and \(\theta_{o}(s)\) is the loop output phase, \(K=K_{d}K_{o}\) represents the loop gain, \(F(s)\) is the loop filter, which decides the order of the loop. The transfer function of a frequently-used 3 rd PLL loop is as follows:  
\[H(s)=\frac{2\omega_{n}s^{2}+2\omega_{n}^{2}s+\omega_{n}^{2}}{s^{3}+2\omega_{n} s^{2}+2\omega_{n}^{2}s+\omega_{n}^{3}} \tag{2}\]  
Here \(\omega_{n}\) is the natural frequency. The PLL output phase error mainly includes thermal noise \(\sigma_{w}\), dynamic stress error \(e(t)\) (induced by signal dynamics), vibration-induced oscillator phase noise \(\sigma_{v}\) and Allan deviation oscillator phase noise \(\sigma_{A}\). Considering all these error sources, one metric that is normally used to determine if a PLL keeps in lock is whether the total phase jitter \(\sigma_{\theta}\) satisfies the following expression:  
\[\sigma_{\theta}=\sqrt{\sigma_{w}^{2}+\sigma_{v}^{2}+\sigma_{A}^{2}}+\frac{e(t )}{3}<15^{\circ} \tag{3}\]  
Each of the phase jitter components and their impact on the carrier tracking loop parameters is discussed in the following subsections.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
### Future Development Directions  
With the rapid development of network technology and information technology, future emergency networks will develop in the direction of intelligence, integration, popularization, and low cost. This will mainly introduce network intelligence and integration, and lead to the development trend of space-air-ground-sea integration networks.
# New Methods of Series Expansions between Three Anomalies
###### Abstract  
The calculation of satellite orbit involves some very complex formula derivations and expansions, which are very difficult to manually derive and prone to errors. And the efficiency of manual derivation is not high. We can use computer algebra systems to derive complex formulas related to satellite orbits. This can avoid some of the drawbacks of manual derivation and significantly improve computational efficiency and accuracy. In the past, the relationship among three anomalies was generally represented in the form of a trigonometric series with the first eccentricity \(e\) as the parameter. In this paper, the trigonometric series with the parameter \(m=\frac{1-\sqrt{1-e^{2}}}{e}\) is used, as determined by the Lagrange conjugate series. We can use the formula of the Lagrange conjugate series to derive the relationship between the true anomaly and elliptic anomaly. And the relationship between the elliptic anomaly and the mean anomaly is derived by using the symbolic iteration method. In this research paper, we calculated the accuracy of the trigonometric series expansion among three types of anomalies at the first eccentricity \(e\) equal to values of 0.01, 0.1, and 0.2. The calculation results indicate that the accuracy of the trigonometric series expansion with \(m\) as the parameter is better than \(10^{-5}\). Moreover, in some cases, the trigonometric series expansion among the three anomalies with \(m\) as a parameter is simpler in form than the expansion expressed with parameter \(e\). This paper also derived and calculated the symbolic expressions and extreme values of the difference among three anomalies and expressed the extreme values of the difference in the form of a power series of \(e\). It can be seen that the extreme value increases with the increase in eccentricity \(e\). And the absolute values of the extreme value of the difference between the elliptic anomaly and the mean anomaly, the true anomaly and the elliptic anomaly, and the true anomaly and the mean anomaly increase in this order. When the eccentricity is small, the absolute value of the extreme value of the difference between the true anomaly and the mean anomaly is about twice as large as the elliptic anomaly and the mean anomaly and the true anomaly and the mean anomaly and the mean anomaly are the mean anomaly and the mean anomaly.  
Lagrange conjugate series; anomaly; satellite orbit; Kepler's equation; symbolic iteration method; extreme value +
Footnote †: journal: Applied sciences
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 5 Development of 1-D CNN Models
### 1-D CNN Model Using Time-History Response
#### 5.1.2 Training Procedures of Time-History Responses  
Figure 14 shows the loss values of the CNN model trained by time-history responses. Training loss decreased in the first five iterations and then converged until the end of training. Validation losses sharply decreased in the initial five iterations and maintained stable status until the end at 40 th iteration. In general, the training and validation losses performed well and converged after only a few epochs.