esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 2 The Recognition Process  
The section presents the schematic diagram of the digital asymmetry communication system based on FEC coding recognition, as shown in Figure 1. It includes source coding, channel coding, a modulator, demodulator, data storage and abundant channel simulation modules which aim to put the simulated channel propagation model into use of simulated code data.  
Generally, a source coding technique needs to compress data in order to transmit information more quickly, while a channel coding technique is on the contrary. So as to assure the reliability of transmission, redundant codes, called supervised codes, will be added to the information codes to form a certain constraint relationship. We assume that bits of information from transmission is encoded to obtain the code words of code length \(N(N>k)\) through channel coding at the transmitter end, and then mapped to the symbol vector through BPSK modulation. In the model, the channel adopts an additive white Gaussian noise channel (AWGN), and the Gaussian noise samples are distributed to \(n\sim N(0,\sigma^{2}),\;\sigma\in R\). The SNR is defined as the bit error rate (BER) and the bit energy noise ratio (Eb/N0).  
The received codeword is defined as \(r\), which is also called the receiving codeword. Because the symbol vector is disturbed by noise through the AWGN channel, it is then defined as:  
\[r=e+v \tag{1}\]  
where \(e\) is the error pattern. In general, BPSK demodulation is performed before the types of forward error correction codes are identified. On the basis of the Gaussian distribution of channel noise, the logarithmic likelihood ratio (LLR) of the transmitted information is showed as:  
\[LLR(r)=\ln\frac{P(v=0|r)}{P(v=1|r)}=\frac{2}{\sigma^{2}} \tag{2}\]  
\(\sigma\) is the variance of the channel noise and \(r\) is the code words of reception, and the recognition model of the FEC code is used to identify the information bit vector sent by \(r\). In this work, we focus on the recognition of types of FEC codes.
The FAA established the Weather-Impacted Traffic Index (WITI) concept and model in 2004 based on live weather information and traffic demand, and this was developed by the MITRE company to measure the impact of weather on traffic flow in the US national airspace system. The WITI is widely used in airspace capacity predictions, and representative prediction models include capacity prediction using WITI based on traffic flow, predictive analysis of WITI and flight delays, and capacity prediction methods using WITI to build scenario trees [15]. However, the classic WITI model only includes the impact of weather on sector capacity under the influence of thunderstorms. In actual operation, there are many other types of bad weather conditions that affect flight operations, and these different types of bad weather will affect sector capacity to varying degrees. In 2019, [PERSON] proposed a multi-sector dynamic capacity evaluation method to solve the problem that the existing capacity evaluation technology is not comprehensive enough. Through the concept of sector-capacity influencing factors and sector coupling, a workload model of the multi-sector controller was established. Then, by quantifying the degree of congestion of the airspace unit, the availability of multiple sectors in severe weather is obtained [16]. This paper proposes a new method to address this problem. We use a modified WITI model to take into account different types of bad weather in the capability assessment and identify and quantify the effects. This is then added as a variable to the controller's workload model, which is trained using the DNN (deep neural network) algorithm to obtain more accurate predictions of the sector capacity. This approach leads to the output results being within a specified error range, which greatly improves their accuracy. In short, this new method takes into account different types of bad weather, quantifies their severity, and, more specifically, assesses the sector capacity under the condition of bad weather. The remainder of this paper is organized as follows: Section 2 optimizes the WITI model based on the Gaussian mixture clustering algorithm, classifies different types of bad weather according to different intensities of meteorological radar echoes, and calculates the weather-influence coefficient. Section 3 adopts the DNN algorithm, with input variablessuch as the selected sector flow, the number of commands, the command duration, the optimized weather-influence coefficient, and the number of diverted flights used to generate the controller workload as the output, so as to obtain a more accurate prediction of the sector capacity. Section 4 presents numerical calculations and simulation experiments. Finally, conclusions are drawn and ideas for further work are proposed in Section 5. The architecture diagram of the overall system is shown in Figure 1.
## 3 Battery Experiments
### Test Schedule  
Overall, the tests involved in this study were categorized into one of two types, namely, a parameterization and a validation test schedule. The parameterization test includes hysteresis voltage test and a series of HPPC (Hybrid Pulse Power Characterization) tests.  
The hysteresis voltage is a considerable factor for battery modeling. Thus, the hysteresis voltage test was conducted at various temperatures to improve the model accuracy. During the hysteresis voltage test, the cells were discharged by 1/25 C constant current, until the low cut-off voltage (2.5 V) was reached. After the 1 h period rest, the cells were recharged by the same 1/25 C rate until the upper cut-off voltage (4.2 V). This test was carried out at temperatures of 0 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C. There were 4 cycles involved in total, all the discharge and chaege cycles was carried out by constant current (CC) method.  
The HPPC test profile as referred to [29], was composed of a series of hybrid pulse power steps with 1 h rest at each SOC point. The current profile and the voltage response of a HPPC microcycle are shown in Figure 4. The HPPC test was carried out with 1 C rate under the temperatures ranging from 0 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, with an interval of 10 \({}^{\circ}\)C. In HPPC test, the model parameters of OCV, R\({}_{0}\), R\({}_{1}\), C\({}_{1}\) were identified online. The identification results were discussed in Section 3.4.  
Figure 4: Current profile and voltage response of a hybrid pulse power characterization (HPPC) microcycle.  
The purpose of the validation test was to evaluate the performance of the modified model, as well as the SOC approach, which will be introduced in the next section. Therefore, a current sequence derived from the Federal Urban Driving Schedule (FUDS) cycle was used in this study. FUDS is a dynamic current load which is designed to simulate the battery operation case in EV applications. In summary, the entire profile consisted of nine typical FUDS microcyles; a 1/3 C constant current discharge between the FUDS cycles and the constant current discharge was a rest period of one hour. To implement the FUDS to the cells, the battery size factor (BSF) was scaled down to 9.25 Wh. The cells were fully charged before the test and were discharged to the low cut-off voltage of 2.5 V at last. Therefore, the \"true value\" of SOC was calculated by the accumulative Ampere-hours flowing in and out of the battery. Figure 5 shows the sampled current and voltage of the validation test profile. The validation test was run at the temperature ranges from \(-20\)\({}^{\circ}\)C to \(50\)\({}^{\circ}\)C. It is worth pointing out that the test should be terminated immediately when the low cut-off voltage of 2.5 V was reached.  
Figure 5: The profile of validation test cycles: (**a**) current profile; (**b**) voltage response.
## 4 Multipath Service Node Selection Algorithm
### Node Selection Algorithm
The symbols used in the algorithm are shown in Table 1 and the execution steps of Algorithm 1 are as follows:  
Step 1: Initialization: \(\alpha=2,\beta=2500,p_{0}=0.03,\gamma=200,K=\{\varnothing\}\), \(\textit{KTag}=\{\varnothing\}\), \(\textit{M}=\{m\}\);  
Step 2: For each multipath service node m in M, the edge node sends the Path State Detection Information to m to obtain the bandwidth, round-trip delay, packet loss rate and other information of the path between the edge node and m, then goes to Step 3. Step 3: According to Equation (3), the link state of each path is calculated, and the nodes in M are sorted according to the path link state. Step 4: First, the node with the best path link state is selected from M, and whether the node is in the same AS as the existing nodes in K is judged according to the TAG value. If the node is not in the same AS as the existing nodes in K, it joins K; otherwise, try the next node. Step 5: Check whether all m in M has been accessed. If satisfied, go to Step 6 ; otherwise, return to Step 2. Step 6: Output the optimal candidate set K with the best path state and the lowest link overlap. The main overhead of the multipath transmission system proposed in this paper is determined by the complexity of the multipath service node selection algorithm. Assume that the number of multipath service nodes is M and the number of paths is k. The complexity of the incremental heuristic method in [38] is O (k \(\times\) M \(\times\) d), where d is the diameter of the network. O (k \(\times\) d) comes from the calculation of overlap. The method proposed in this paper uses the independence between autonomous systems to ensure the independence between paths and does not need to calculate the path overlap, so the time complexity is O(M). This shows that our method is simple and scalable.
## 2 Methodology
### Model Accuracy Performance Evaluation
#### 2.3.2 Regression Coefficient (R\({}^{2}\))  
R-squared (R\({}^{2}\)), also referred to as the coefficient of determination, quantifies the extent of variance in the dependent variable (output/target) that can be explained by the independent variable (input features). R\({}^{2}\) values range from 0 to 1. Scoring the R\({}^{2}\) value 1 indicates that the input features correlate with the target 100% and provide the best fit. In contrast, 0 indicates no correlation between the input and target. Mathematically, R\({}^{2}\) is computed as:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{N}}\left(y_{i}^{\text{predicted}}-y_{i }^{\text{Actual}}\right)^{2}}{\sum_{i=1}^{\text{N}}\left(y_{\text{Actual}}^ {\text{Mean}}-y_{i}^{\text{Actual}}\right)^{2}} \tag{16}\]
## 2 Materials and Methods
### Control System  
The chamber must allow the barometric pressure to be changed by up to \(\pm\)15 hPa with respect to the current ambient barometric pressure. The control system must allow a given setpoint of barometric pressure to be reached and maintained but must also comply with a specific rise/fall time (i.e., a ramp) defined by the user. According to the requirements defined by the entomology team, the pressure control system was designed for experiments consisting of four stages. In stage 1, the barometric pressure is slowly adjusted to the 'acclimation pressure' complying with a rise or fall time defined by the user; in stage 2, the acclimation pressure is held steady for the duration defined by the user in order to enable acclimation of specimens during the experiment; in stage 3, the barometric pressure is increased or decreased to the 'test pressure' complying with a rise or fall time defined by the user; lastly, in stage 4, the test pressure is held steady until the experiment is finished.  
For pressures higher than the local barometric pressure, the pressure control valve allows the control of air flow into the chamber. On the other hand, the vacuum control valve is used to obtain barometric pressures lower than the local values.  
A Proportional-Integral (PI) controller is employed in all stages to control the barometric pressure. During stages 2 and 4, the control system maintains a fixed setpoint of barometric pressure. During stages 1 and 3, when control must comply with the rise or fall time, the setpoint is set in steps of 0.2 hPa following a linear function. Based on the input values defined by the user, the control system defines a linear equation for the setpoint as a function of time that allows calculation of the values of the setpoint every time the controller output is computed.  
The PI controller was tuned empirically through several attempts in which the controller parameters were tested until a satisfactory performance was achieved for the entomology application. When the vacuum control valve is in use, the barometric pressure becomes inversely proportional to the aperture of the vacuum valve and consequently the controller action is reversed; hence its parameters become negative. The PI controller reads the barometric pressure transmitter and computes new output values every 0.2 s when the control routine is enabled.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 4 Simulation Verification
### Experimental Results and Analysis  
This study presents the results of simulation investigations using sinusoidal steering and double-lane-changing scenarios. Under these scenarios, the system's driving state undergoes significant changes, thus effectively demonstrating the effectiveness of the method proposed in this paper.  
\[RMSE=\sqrt{\frac{1}{M}\sum_{k=1}^{M}(\hat{x}_{k}-x_{k})^{2}} \tag{67}\]
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 5 Implementation and Experimental Evaluation
### Experimental Results
We can see that the efficient management of proxies provided by the DML results in a better performance compared to the other two alternatives. This fact is explained by the nature of ProxySelection+, which receives as input the \(L_{\max}\). Number of proxies used: We compare the three methods with respect to the number of special nodes that they use. The DML is using proxies; the non storing mode is using the controller \(C\); and the storing mode is using LCAs. The use of special nodes is wasteful of resources. For example, the proxies store the data requested and the correspondence of producers and consumers, and the LCAs hold routing information about their descendants. In Figure 5(c), we can see that the DML is performing really well compared to the storing mode and uses less special nodes. Of course, the non-storing mode is using just one special node, but this has a severe impact on the latency achieved, as shown in Figure 5(b). Even when the DML uses more proxies to guarantee worst-case latencies, their number is comparable to the case of storing mode. However, the DML drastically reduces the latency in this case, thus achieving a much more efficient use of proxies.
## 2 Related Works
### Multiple Access with Collision Avoidance (MACA)-Based Power Control  
[PERSON] et al. put forward their idea of a MAC protocol for Underwater Wireless Sensor Networks (UWSN) [27]. The authors describe a layered infrastructure where a physical, MAC/link, route, transmission and application layers are suggested as the levels present in such an infrastructure. The authors establish a classification to distinguish two different kinds of protocols (contention-based and contention-free). One kind of contention-based MAC protocol that uses handshaking is MACA, even though it is not adapted by default to underwater communications since it was not designed for such an environment. Thus, the authors show a MACA-based power control protocol (referred to as MACA-PC) in an underwater scenario with all the expectable interferences. Three concepts have been defined: the _transmission rage_ (fixes the range at which packages can be received and decoded), the _interference range_ (range at which nodes are capable of sending sender transmissions and can be interfered) and the _interference zone_ (range where nodes from a UWSN can sense a transmission but cannot decode it). It was found out that MACA-PC offered a similar performance for a one-hop scenario as the output provided by regular MACA (and better than the one providing just basic power control, which is prone to have data collisions during transmissions) with a lower power consumption. A multi-hop scenario presented the same results.  
The solution that is presented by the authors of the paper solves many of the issues that are found in underwater communications, but it cannot be applied to the application domain of this manuscript, due to the fact that the ideas that are put forward here involve interoperability at a higher level, where actual data rather than bits are transferred.
## 2 Modified Hammerstein-Like (MHL) Model and Its Identification Method
### Parameter Identification  
Since the MHL model is a cascade structure of three parts, the parameters can be identified sequentially. (1) The time delay \(e^{-\tau s}\) is obtained by measuring the step response of the FSM system, as shown in Figure 5. (2) By employing the identification method presented in [23], the rate-dependent model \(H_{D}(v(t))\) can be established. (3) Based on the least square method, we take the output \(w(t)\) of the rate-dependent term as the input and the actual output \(y(t)\) as the output to identify \(G(s)\). It is worth noting that \(w(t)\) is a virtual variable which is the output of the actual input signal \(u(t)\) after passing through the time delay and rate-dependent term. Therefore, it is necessary to first identify \(\tau\) and \(H_{D}(v(t))\).  
Figure 4: MHL model structure schematic diagram of FSM system.  
Figure 5: Step response of time delay phenomenon.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 4 Data Source and Preprocessing  
### Data Source  
The data utilized in this study were sourced from the hydroelectric power units and generator sets managed by the Hua Dian Electric Power Research Institute. They include readings from sensors attached to the hydroelectric generator units, capturing three critical types of data: unit flow rate, guide vane opening, and maximum water guide tile temperature.  
Unit Flow Rate (m3/s): This parameter represents the volume of water passing through the turbine per unit time, which is essential for assessing the operational capacity of the hydropower unit.  
Guide Vane Opening (degrees): This measurement indicates the aperture of the guide vanes that regulate the inflow of water to the turbines. It effectively controls the turbine output by modulating the flow volume that forms a circumferential quantity during operation.  
Maximum Water Guide Tile Temperature (\({}^{\circ}\)C): This parameter indicates the highest temperature recorded across multiple guide tiles. Monitoring this temperature is vital for assessing the thermal condition of the equipment.
## 6 Discussion
### Performance Analysis  
In this section, the reconstruction performances and computation complexities using different signal reconstruction algorithms were compared. For each sparsity level \(K\) in the different measurements \(M\) and compression ratio \(R\), we consider a Monte Carlo draw by repeating the same experiment 100 times for the same value of \(K\), \(M\) or \(R\)[10].  
2.1 Relationships between the Reconstruction Error and the Compression Ratio for the Reconstruction of Different Gas Signals with Different Sparseness Degrees Using the TVS-MH Algorithm  
Figure 10 illustrates the relationship between the reconstruction error and the compression ratio for different gas signals with different sparseness degrees during which the signals were reconstructed using the TVS-MH algorithm and the signal length was 512 (\(N=512\)). It can be observed that for K-sparse signals, the greater the value of K is, the higher the reconstruction error is.  
Figure 9: The relations of the compression ratio with the reconstruction error and iteration number for the reconstruction of three different gas signals using the TVS-MH algorithm.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
###### Abstract  
A novel portable underwater acoustic modem is proposed in this paper for covert communication between divers or underwater unmanned vehicles (UUVs) and divers at a short distance. For the first time, real dolphin calls are used in the modem to realize biologically inspired Covert Underwater Acoustic Communication (CUAC). A variety of dolphin whistles and clicks stored in an SD card inside the modem helps to realize different biomimetic CUAC algorithms based on the specified covert scenario. In this paper, the information is conveyed during the time interval between dolphin clicks. TMS320C6748 and TLV320 AIC3106 are the core processors used in our unique modem for fast digital processing and interconnection with other terminals or sensors. Simulation results show that the bit error rate (BER) of the CUAC algorithm is less than \(10^{-5}\) when the signal to noise ratio is over -5 dB. The modem was tested in an underwater pool, and a data rate of 27.1 bits per second at a distance of 10 m was achieved.  
underwater acoustic communication; bionic; underwater acoustic modem; micro-modem; covert; dolphin sounds +
Footnote †: journal: Sensors
## 3 Results and Analysis
### Migration Law of Wetting Front
#### 3.2.2 Influence Law of Substrate Thickness on Wetting Front
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{\(V=a\cdot t^{b}\)} \\ \cline{2-7}  & \multicolumn{3}{c}{**Top of the Slope**} & \multicolumn{3}{c}{**Middle of the Slope**} \\ \cline{3-7}  & \multicolumn{2}{c}{**\(a\)**} & \(b\)** & \(R^{2}\)** & \(a\)** & \(b\)** & \(R^{2}\) \\ \hline \multirow{3}{*}{Substrate thickness} & 0 & 3.4177 & \(-\)0.4103 & 0.9992 & 3.0130 & \(-\)0.3620 & 0.9847 \\  & 1 & 2.1927 & \(-\)0.3402 & 0.9936 & 2.0976 & \(-\)0.3085 & 0.9677 \\  & 3 & 0.9644 & \(-\)0.2058 & 0.9678 & 1.8500 & \(-\)0.2978 & 0.9605 \\  & 5 & 0.7978 & \(-\)0.2002 & 0.9961 & 1.4126 & \(-\)0.2746 & 0.9634 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fitted table of wetting front transport rate \(V\) versus time \(t\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{\(Y=m\cdot x^{n}\)} \\ \cline{2-7}  & \multicolumn{3}{c}{**Top of the Slope**} & \multicolumn{3}{c}{**Middle of the Slope**} \\ \cline{2-7}  & \(m\) & \(n\) & \(R^{2}\) & \(m\) & \(n\) & \(R^{2}\) \\ \hline a & 2.1822 & \(-\)0.6799 & 0.9918 & 2.1358 & \(-\)0.2083 & 0.8443 \\ b & \(-\)0.3364 & \(-\)0.3705 & 0.9551 & \(-\)0.3109 & \(-\)0.0643 & 0.8263 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Fitting of different substrate thicknesses with power function parameters. non-linear relationship combined with the non-linear relationship between the migration rate and time of wetting fronts on the slope, the migration characteristics of wetting fronts can be predicted, which will be beneficial to the protection of ecological slopes.