esa-sceva/satcom-chunk-collection huggingface dataset:
# A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
## 2 System Model
This implies that we can only identify \(\widehat{\mathbf{h}}(n)\) and \(\widehat{\mathbf{g}}(n)\) up to a scaling factor. A similar discussion can be found in [15; 18] in the framework of blind identification/equalization and nonlinear acoustic echo cancellation, respectively. Nevertheless, because  
\[\mathbf{f}(n)=\mathbf{g}(n)\otimes\mathbf{h}(n)=\left[\eta\mathbf{g}(n) \right]\otimes\left[\frac{1}{\eta}\mathbf{h}(n)\right], \tag{7}\]  
the global impulse response can be identified with no scaling ambiguity. Consequently, for the performance evaluation of the identification of the temporal and spatial filters, we can use the normalized projection misalignment (NPM), defined in [24]:  
\[\text{NPM}\left[\mathbf{h}(n),\widehat{\mathbf{h}}(n)\right] = 1-\left[\frac{\mathbf{h}^{T}(n)\widehat{\mathbf{h}}(n)}{\| \mathbf{h}(n)\|\|\widehat{\mathbf{h}}(n)\|}\right]^{2}, \tag{8}\] \[\text{NPM}\left[\mathbf{g}(n),\widehat{\mathbf{g}}(n)\right] = 1-\left[\frac{\mathbf{g}^{T}(n)\widehat{\mathbf{g}}(n)}{\|\mathbf{ g}(n)\|\|\widehat{\mathbf{g}}(n)\|}\right]^{2}, \tag{9}\]  
where \(\|\cdot\|\) denotes the Euclidean norm. On the other hand, for the identification of the global filter, \(\mathbf{f}(n)\), we should use the normalized misalignment:  
\[\text{NM}\left[\mathbf{f}(n),\widehat{\mathbf{f}}(n)\right]=\frac{\|\mathbf{f} (n)-\widehat{\mathbf{f}}(n)\|^{2}}{\|\mathbf{f}(n)\|^{2}}. \tag{10}\]  
In [11], this bilinear system identification problem has been studied in terms of the Wiener filter. Therefore, the assumption was that the two impulse responses that need to be identified are time-invariant (which represents a basic assumption in the context of the Wiener filter). In practice, however, these systems could vary in time. For this reason, in this paper, we approach the system identification problem considering that the systems that need to be identified vary in time. Thus,we assume that \(\mathbf{h}(n)\) and \(\mathbf{g}(n)\) are zero-mean random vectors, following a simplified first-order Markov model, i.e.,  
\[y(n) = \mathbf{h}^{T}(n)\mathbf{X}(n)\mathbf{g}(n) \tag{3}\]0 \[y(n) = \mathbf{h}^{T}(n)\mathbf{X}(n)\mathbf{g}(n) \tag{3}\]1  
where \(\mathbf{w_{h}}(n)\) and \(\mathbf{w_{g}}(n)\) are zero-mean white Gaussian noise vectors, with correlation matrices \(\mathbf{R_{w_{h}}}(n)=\sigma_{w_{h}}^{2}\mathbf{I}_{L}\) and \(\mathbf{R_{w_{g}}}(n)=\sigma_{w_{g}}^{2}\mathbf{I}_{M}\), respectively (with \(\mathbf{I}_{L}\) and \(\mathbf{I}_{M}\) being the identity matrices of sizes \(L\times L\) and \(M\times M\), respectively). It is considered that \(\mathbf{w_{h}}(n)\) is uncorrelated with \(\mathbf{h}(n-1)\) and \(v(n)\), while \(\mathbf{w_{g}}(n)\) is uncorrelated with \(\mathbf{g}(n-1)\) and \(v(n)\). The variances \(\sigma_{w_{h}}^{2}\) and \(\sigma_{w_{g}}^{2}\) capture the uncertainties in \(\mathbf{h}(n)\) and \(\mathbf{g}(n)\), respectively.
## 2 Method for Clock and Data-Acquisition Synchronization
### Overall System  
The PCs connecting to the sensor nodes are communicated via 1-Gbps Ethernet in our system. Subsequently, the PTPd was executed to synchronize the clocks of the PCs in the system, and the data-acquisition synchronization was performed using the proposed method explained in Section 2.
# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
## 4 Discussion  
### Effect of Energy and Water on ET\({}_{a}\) in Different Climatic Regions of China  
The Budyko curve shows the relationship between ET\({}_{a}\)-Pre-ET\({}_{0}\) and can reveal the limiting relationship between energy and water on ET\({}_{a}\)[79; 80; 81]. Figure 14 shows the Long-term mean values of annual ET\({}_{a}\), Pre, and ET\({}_{0}\) together with Fu's curves with the regional average values of parameter \(\omega\), where \(\omega\) is the plant-available water coefficient, representing the relative difference in the way plants use soil water for transpiration, and larger values of \(\omega\) tend to promote evapotranspiration. It can be seen that the relationship in most areas conforms to the Budyko curve, and the data are within the boundary conditions of the hydrothermal coupling assumption, with \(\omega\) in the humid region greater than in the arid region. In humid regions, ET\({}_{a}\) is limited by available energy. As it asymptotically approaches ET\({}_{0}\), in arid regions, ET\({}_{0}\) exceeds P, and where ET\({}_{a}\) is mainly controlled by water, evapotranspiration ratio (ET\({}_{a}\)/Pre) tends to 1.  
Figure 13: Trends of annual NPP and PUE with \(\Delta\)T (**a,d**), \(\Delta\)SM (**b,e**), and \(\Delta\)U (**c,f**) in different precipitation climate types.  
Evaptranspiration ratio at some stations in the arid and semi-arid zones (in the lower right dashed box) is less than 1; as such, this may be attributed to the way different kinds of vegetation use soil water and the particularity of precipitation conversion to evaporation in arid areas. In the arid region, small precipitation events are just able to wet the soil surface and are quickly evaporated, whereas large precipitation events increase potential water losses from the ecosystem through runoff or deep soil water percolation [13]. Therefore, evaporation can be lower than precipitation in arid regions, this is consistent with the study of [PERSON] et al. [82] in northern China. In addition, evapotranspiration ratio in arid regions also shows a large fluctuation, which may be related to the large variation in precipitation in arid regions [83,84]. The amount of precipitation in arid and semi-arid regions of China has a large variation [85]; the sparser the precipitation in a region, the greater the variation, and interannual fluctuations in precipitation and regional differences cause large fluctuations in evapotranspiration.
# Bias Current of Semiconductor Laser: An Unsafe Key for Secure Chaos Communication
## 5 Conclusions  
In summary, we have analyzed the security of the bias current used as a key in secure optical chaos communication. The \(\tau_{\text{RO}}\) and bias current of ECL have been studied in detail. With an increase in bias current, the \(\tau_{\text{RO}}\) of ECL always approaches that of a solitary laser. Due to this relationship, two eavesdropping scenarios have been proposed and the results have demonstrated that the bias current used as a key was unsafe in the chaos secure communication, based on the synchronization with the mutually coupled chaotic laser. Results showed that, without the knowledge of the bias current, the eavesdropper could intercept the data from the legal user.  
**Author Contributions:** Conceptualization, D.W., Y.W., and A.W.; Methodology, D.W. and L.W.; Software, P.L., T.Z.; Validation, T.Z.; Formal analysis, L.W. and P.L.; Investigation, D.W.; Resources, Y.W.; Writing--original draft preparation, D.W.; Writing--review and editing, D.W., P.L., Z.J., Z.G., Y.G., and A.W.; Visualization, L.W. and A.W.; Supervision, Y.W.; Funding acquisition, A.W. and Y.W.  
**Funding:** This research was funded by the National Natural Science Foundation of China under grants 61822509, 61671316, 61731014, and 61475111, the National Cryptography Development Foundation under Grant MMJJ20170207, the Program for the Innovative Talents of Higher Learning Institutions of Shanxi, the International Science and Technology Cooperation Program of Shanxi under grant 201603D421008.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: BER as a function of \(\Delta I\) between the transmitter and the eavesdropper: (**a**) Eve\({}_{\text{A}}\) and (**b**) Eve\({}_{\text{B}}\). The blue and yellow shaded areas denote the cracked areas.
# Recognition of Driving Behavior in Electric Vehicle's Li-Ion Battery Aging
## 3 The Recognition of Driving Behavior
### Deep Learning Driving Behavior Recognition
#### 3.3.1 Recurrent Neural Network (RNN)  
An RNN is a form of neural network that uses cycle connections between nodes to trace the input's antecedent. An RNN takes into account the input sequence, in contrast to an ANN. Given that the driving actions are not independent in pure distribution but rather temporally connected, this is similar to driving patterns. Therefore, the LSTM, a variation of RNN, showed an accurate classification rate for driving behaviors when applying the data from the IMU and GPS [49; 50; 51]. In fact, the RNN forecasts anything in the BEV-related indicators. The temperature of the Li-ion battery is predicted accurately by the BEV time series motions. [PERSON] et al. [52] successfully estimated the surface temperature with a maximum absolute error of 0.75 \({}^{\circ}\)C using the LSTM method and gated recurrent unit (GRU). The Li-ion SOC for the BMS is predicted by [PERSON] et al. [53] with an RMSE of 1.527%. References [54; 55; 56] demonstrate the data-driven methodology for SOH prediction using data on the voltage and current from Li-ion batteries. In spite of this, references [57; 58] depict the RUL prediction on the Li-ion battery.  
The RNN may also instantaneously offer relevant responses or suggestions to assist with driving. An LSTM is created by [PERSON] et al. [59] to measure the level of comfort while riding using three inputs: velocity, longitudinal acceleration, and yaw rate. The data are initially divided into the designated zones, with the peak period and traffic flow taken into account as the input for the LSTM neural network. The LSTM network may then offer driving assistance through real-time suggestions. Similarly, [PERSON] et al. [60] have presented an advanced stacked LSTM model to forecast the energy consumption from the acceleration and deceleration characteristics. They discover that the crucial markers were the frequency and duration of the acceleration and deceleration tendencies. Thus, using LSTM models, safety motion prediction based on trajectories can be accomplished. In the above investigations, the crucial model creation is moved to the preprocessing phases. It is vital to formulate the input based on the objectives. These techniques, which directly impact the model's accuracy rate, include data resampling, segmentation, cyclical encoding, and data windowing [97].  
Figure 5: The overview of the deep learning approach in driving behavior detection. 1. data is transformed into an image or nonlinear picture using the RP formula; 2. extracted features can be seen by activation heat map; 3. CNN results are concatenated or directly input into RNN models; and 4, attention mechanisms are used after RNN for classifications.
## 3 Experimental Verification
### Measurement and Construction of the \(p-\theta\) Diagram  
The thickness components and safety devices in case of leakage around the cylinder do not permit the installation of a pressure sensor measuring the dynamic pressure in the cylinder. The non-destructive test (NDT) based on the force-induced strain on the piston was used to measure and construct the \(p-\theta\) diagram. The pressure in the working chamber equals \(F_{g}\) divided by \(A_{p}\), and then \(F_{g}\) can be reconstructed based on the main acting forces analysis in the piston (Equation (4)).  
\[F_{g}=F_{p}-F_{ri}-F_{f} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & \\ \hline Crank radius & mm & 170 \\ Connecting rod length & mm & 800 \\ Speed & rpm & 200 \\ Piston diameter & mm & 80 \\ Clearance volume & \% & 31 \\ Substance & - & Ethylene \\ Suction/discharge pressure & MPa & 100/180 \\ Suction/discharge temperature & K & 294/317 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the hyper-compressor system.  
Figure 5: Schematic of the hyper-compressor test system.  
The reciprocating inertial force \(F_{ri}\) and friction force \(F_{f}\) can be obtained with known variables (Equations (5) and (6)). The piston rod load \(F_{p}\) has only one independent unknown variable: stress \(\sigma\) on the piston, which can be obtained from the measured strain (Equation (7)).  
\[F_{ri}=M_{s}r\omega^{2}\cos\theta+\lambda M_{s}r\omega^{2}\cos 2\theta \tag{5}\]  
\[F_{f}=\kappa\cdot(M_{s}g-\frac{F_{p}\lambda}{\sqrt{1-\lambda^{2}\sin^{2} \theta}}) \tag{6}\]  
\[F_{p}=\frac{\pi{D_{p}}^{2}}{4}\cdot\frac{\sigma_{up}+\sigma_{down}}{2} \tag{7}\]  
where \(M_{s}\) is the reciprocating inertial mass and \(\kappa\) is the dynamic friction coefficient.  
Considering the factors of temperature, gravity, and extremely high pressure, two two-axis 90-degree angle strain gages were installed in diametrically opposite positions to measure the axial and circumferential strain, which had a quarter bridge with temperature compensation.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.4 Postequalization TFDNet  
The commonly used ML-based equalizers in VLC systems often aim at fitting the waveform of the transmitting signal, which is a time-domain-serial signal. It is expected that the well-learned received signal should have the same spectrum as the transmitted one. However, waveform-fitting ML equalizers would sometimes cause the spectrum difference between the equalized signal and the original one. This suggests that we should take both time- and frequency-domain information into consideration to obtain a better equalization performance.  
A novel postequalizer, namely joint time-frequency deep neural network (TFDNet), is reported in [56] to compensate for the nonlinear distortions in the VLC system. TFDNet could reveal comprehensive information of nonstationary signals received in the VLC system by considering both time and frequency domain information simultaneously. TFDNet can be divided into three main procedures: (1) the received one-dimensional (1D, time domain) signal goes through a short-time Fourier transformation (STFT) operation and would be transferred into a two-dimensional (2D, time-frequency domain) signal, which is a matrix and could be denoted as Y; (2) then, the obtained STFT matrix Y is fed into the NN to be trained. The labels could always be obtained by manipulating the original transmitting signal. If we assume that each row of Y represents a certain frequency component, then Y would be fed into the following network column by column; (3) finally, after the NN finishes the training progress, the reconstructed transmitting signal could be obtained by carrying out the inverse STFT (ISTFT) operation, where the analysis window must satisfy the COLA constraint [57]. Experimental results in [56] also confirm that the proposed TFDNet could resist severe nonlinear distortions and achieve a 0.1 Gbps and 0.2 Gbps data rate gain for VLC system compared to other nonlinear compensators such as Volterra and DNN.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multirow{2}{*}{\begin{tabular}{c} _Material_ \\ _Reference_ \\ _to Failure_ \\ \end{tabular} } & \multicolumn{6}{c}{**Playing Tuning (End Tune)**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**B2 (S) (123.5 Hz)**} & \multicolumn{2}{c}{**C3 (DO) (130.8 Hz)**} & \multicolumn{2}{c}{**D3 (RE) (146.8 Hz)**} \\ \cline{3-10} \multirow{-2}{*}{_Structural Components_} & \multirow{2}{*}{\
\begin{tabular}{c} _Material_ \\ _to Failure_ \\ \end{tabular} } & \multicolumn{2}{c}{**FEA Eq. Stress_} & \multicolumn{2}{c}{_Factor of_} & \multicolumn{2}{c}{_FEA Eq. Stress_} & \multicolumn{2}{c}{_Factor of_} & \multicolumn{2}{c}{_FEA Eq. Stress_} & \multicolumn{2}{c}{_Factor of_} \\ \cline{3-10}  & & \multicolumn{2}{c}{_Output_} & \multicolumn{2}{c}{_Safety (FoS)_} & \multicolumn{2}{c}{_Output_} & \multicolumn{2}{c}{_Safety (FoS)_} & \multicolumn{2}{c}{_Output_} & \multicolumn{2}{c}{_Safety (FoS)_} \\ \cline{3-10}  & & \multicolumn{2}{c}{**(MPa)**} & \multicolumn{2}{c}{**(r)**} & \multicolumn{2}{c}{**(MPa)**} & \multicolumn{2}{c}{**(r)**} & \multicolumn{2}{c}{**(r)**} & \multicolumn{2}{c}{**(MPa)**} & \multicolumn{2}{c}{**(r)**} \\ \hline Soundbox & 48.20 & 1.675 & 28.78 & 1.856 & 25.97 & 2.249 & 21.43 \\ Soundboard & 31.50 & 3.111 & 10.12 & 3.559 & 8.85 & 4.616 & 6.82 \\ Neck & 57.00 & 0.532 & 107.16 & 0.575 & 99.06 & 0.67 & 84.20 \\ String hole & 67.20 & 12.822 & 5.24 & 14.440 & 4.65 & 18.264 & 3.68 \\ Bridge & 54.00 & 12.883 & 4.19 & 14.545 & 3.71 & 18.325 & 2.95 \\ Nut & 50.50 & 10.179 & 4.96 & 10.993 & 4.59 & 12.879 & 3.92 \\ \hline \hline \multicolumn{10}{c}{**Playing Tuning (End Tune)**} \\ \cline{3-10} \multirow{-2}{*}{
\begin{tabular}{c} _Material_ \\ _Reference_ \\ _to Failure_ \\ \end{tabular} } & \multicolumn{2}{c}{**B2 (S) (123.5 Hz)**} & \multicolumn{2}{c}{**C3 (DO) (130.8 Hz)**} & \multicolumn{2}{c}{**D3 (RE) (146.8 Hz)**} \\ \cline{3-10}  & & \multicolumn{2}{c}{_Factor of_} & \multicolumn{2}{c}{_Section Stress_} & \multicolumn{2}{c}{_Factor of_} & \multicolumn{2}{c}{_Section Stress_} & \multicolumn{2}{c}{_Factor of_} \\ \cline{3-10}  & & \multicolumn{2}{c}{_Safety (FoS)_} & \multicolumn{2}{c}{_Safety (FoS)_} & \multicolumn{2}{c}{_Safety (FoS)_} & \multicolumn{2}{c}{_Safety (FoS)_} \\ \cline{3-10}  & & \multicolumn{2}{c}{**(MPa)**} & \multicolumn{2}{c}{**(r)**} & \multicolumn{2}{c}{**(r)**} & \multicolumn{2}{c}{**(r)**} & \multicolumn{2}{c}{**(MPa)**} & \multicolumn{2}{c}{**(r)**} \\ \hline Lower string group & 2860 & 1502.49 & 1.90 & 1693.47 & 1.69 & 2131.92 & 1.34 \\ Middle string group & 2670 & 669.16 & 3.99 & 756.42 & 3.53 & 953.16 & 2.80 \\ Upper string group & 2860 & 1198.85 & 2.39 & 1342.31 & 2.13 & 1692.03 & 1.69 \\ (Cha.0.16 mm) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Factor of safety (FoS) calculations by components.
# The Significance of Calcium in Photosynthesis
## 5 Ca\({}^{2+}\) Is Involved in the Mechanisms of Regulating Photoproduction  
Under drought and other environmental stresses, a large number of stomata in plants will be closed. Thus, the reduction in CO\({}_{2}\) entering the stomata, which cannot meet the demand of photosynthesis, is called a stomatal limitation factor. Stomatal movement is related to Ca\({}^{2+}\) as described above. Non-somatal limitation factors induce the decrease in photosynthetic efficiency caused by damage of photosystems under moderate or severe stress. Photoinhibition is also divided into two aspects: photodamage and photoprotection [88]. The photosynthetic proteins are damaged under light stress in an event called photodamage, such as the net loss of D1 protein.  
Photoproduction refers to the capacity of preventing damage to the photosystem under excess energy, including the consumption of excess light energy and the removal of reactive oxygen species. During photosynthesis, the captured light energy is mainly used by photochemical electron transfer, chlorophyll fluorescence emission, and heat dissipation [89]. Photochemistry electron transfer is associated with the synthesis of photosynthetic products, and chlorophyll fluorescence emission is only rarely part of light energy consumption. Thus, heat dissipation is an important way to consume excess light energy and prevent photodamage. The process of dissipating harmlessly excess excitation energy as heat is called NPQ.  
Some reports showed NPQ is regulated by Ca\({}^{2+}\)[90]. The recombination of the PS2 reaction center complex is a common possible mechanism of NPQ, which involves reversible inactivation of D1 protein and synthetic regeneration [35]. High contents of D1 protein have been detected in Ca\({}^{2+}\)-treated plants [10], and the turnover of protein components of photosynthetic reaction centers is regulated by CaM, an important component of Ca\({}^{2+}\) signal transduction pathway [91]. Moreover, Ca\({}^{2+}\)-binding sites may exist on D1 protein [36]. PsbS, a nuclear encoded PS2 subunit protein, also plays a key role in NPQ [37], because Ca\({}^{2+}\) can induce the aggregation of PsbS in vitro [38]. In addition, the synthesis of violaxanthin de-epoxidase (VDE) is affected by Ca\({}^{2+}\), and CaM mediates the expression of the _VDE_ gene in the presence of Ca\({}^{2+}\) to improve the xanthophyll cycle [10].  
Actually, ROS are not only signaling molecules to regulate stomatal movement, but their excessive accumulation under stress can cause damage to plant cells. The ROS scavenging system can help plant cells maintain the balance of the ROS content. ROS can inhibit D1 protein recombination [92]. Exogenous Ca\({}^{2+}\) can activate ascorbate peroxidase (APX), catalase (CAT), and superoxide dismutase (SOD) during heat stress [91]. Thus, the Ca\({}^{2+}\) signal transduction pathway is involved in regulating ROS balance and protecting the photosystem.
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 5 Performance Evaluation
### Simulation Results
With TEFR, even if the radio range increases, since only the location information on some neighbor nodes of the planar graph after the planarization is transmitted to the message transfer node, the amount of the location information is not increased by as much as with SBFR. The experimental results show that the proposed face routing did not increase the transfer time, unlike with GFG and SBFR, even when the number of nodes increased, and the transfer time lessened as the radio range increased. A message was able to be sent to the destination within a certain number of hops regardless of the number of deployed nodes as long as the network was the same size. As the radio range became wider, the hop count was gradually reduced because a message can be directly forwarded to a node located at a longer distance away. In comparison with face routing using local information, more position information needs to be transmitted because the planar graph information of the neighbor is additionally used. However, since the neighbors of the message transfer node only  
Figure 14: Hop counts according to radio range (deployed nodes: 500). Figure 15: The quantity of transferred location information according to the number of nodes (radio range: 100 m). transmit information on some nodes after planarization, it uses less location information than face routing using 2-hop node information. Through our experimental results, we can see that the proposed face routing was able to forward a message at a faster and more constant rate in a high density WSN than the existing face routing methods, and it was able to forward messages to the destination within a certain number of hops. Furthermore, as the radio range was enlarged, the transfer time was reduced because the messages were forwarded by traveling via a smaller number of nodes from the source to the destination. Therefore, transfer efficiency can be further improved by adjusting the radio range when applying the proposed face routing in practice.