esa-sceva/satcom-chunk-collection huggingface dataset:
# On Ad Hoc Communication in Industrial Environments
## 3 Comparing Methods for the Extraction of NEPs
### Average Number of Simultaneous Encounters per Node  
With the assumption of equally distributed, randomly placed access points and randomly moving nodes on area \(A\), \(N_{\varnothing}^{XX}(N,N_{AP})\) can be determined for both acquisition methods. For the observation by trace protocol, the number of nodes that a specific node might encounter is calculated. It is expected that the \(N-1\) other nodes are randomly distributed on \(A\). The number of simultaneously encountered nodes is therefore expected to be the fraction of \(N-1\) that are present in \(A_{r}\). It follows that  
\[N_{\varnothing}^{TP}(N)=\frac{A_{r}}{A}(N-1) \tag{1}\]  
For the acquisition by means of network traces \(NT\), two different cases must be considered. The first case is that the area \(A\) is not completely covered by access points. This case is defined by \(N_{AP}A_{r}\leqslant A\). This means that the combined covered area by all access point is smaller than \(A\). Overlap of access point communication ranges is rare, due to the assumed equal distribution of access points. If the area is not completely covered, the average number of simultaneous encounters is equal to the average number of nodes, within range of an access point minus the source node (\(\frac{A_{r}}{A}(N-1)\)) times the probability, to be within the range of an access point (\(\frac{N_{AP}A_{r}}{A}\)). When assuming complete coverage of the area this probability is 1. Once the area is completely covered, the covered area per access point decreases because the nodes will tend to register at the closest access point. In reality,the chosen access point depends on the applied roaming scheme; most are based on received signal strength. This decline can be formalized with \(A_{AP}=\frac{A}{N_{AP}}\), where \(A_{r}\geq A_{AP}\). It simplifies to  
\[N_{\varnothing}^{NT}(N,N_{AP})=\begin{cases}\left(\frac{A_{r}}{A}(N-1)\right) \cdot\frac{N_{AP}A_{r}}{A},&\text{if }N_{AP}A_{r}\leq A\\ \\ \frac{N}{N_{AP}}-1,&\text{otherwise}\end{cases} \tag{2}\]  
The model for the acquisition via trace protocol is therefore equal to the model for the acquisition via network trace only for the case \(N_{AP}A_{r}=A\) and a perfect 1:1 coverage of the application area by the access points. Later results show differences even in this case.
## 3 Discrete Cu(I/II) Complexes
### Inorganic Supports  
In addition to the many examples published in the past years [88; 89], a few new supported Cu(I/II) complexes have been disclosed recently, which contain low molecular weight organic ligands covalently anchored onto (or within) a siliceous support (Figure 4). Because these materials do not contain any polymeric component, but the oxidic support itself, they will be mentioned only briefly here.  
Silica gel decorated with a sulfur-containing ligand was developed by [PERSON] et al. as a new support material for Cu(I/II) [182]. In situ reduction with sodium ascorbate of the complex obtained by stirring the modified silica with CuCl\({}_{2}\) or Cu(OTf)\({}_{2}\) afforded the corresponding CuAAC catalyst **42**. Under optimized conditions, which included _inter alia_ the use of 0.05-0.1 mol % of the triflate catalyst at r.t. in PEG\({}_{400}\)-H\({}_{2}\)O (1:1), fair to nearly quantitative yields (72%-99%) were obtained in three-component CuAAC reactions (Scheme 2a), after a reaction time comprised between 15 min and 1 h. In the most favorable case, this translated into a remarkable TOF value of 7920 h\({}^{-1}\). Unfortunately, reuse of the supported system in a series of six benchmark CuAAC runs caused some reduction of product yield (approximate 5%).  
Compared to the previous example, the MCM-41 supported Cu(I)-NHC complex **43**, described by [PERSON] et al. [183], and especially the Cu(I)-acetylacetonate on HMS silica **44**, studied by [PERSON] et al. [184], displayed lower catalytic activity in two- or three-components CuAAC reactions (Schemes 1 and 2a). In the case of the latter system, however, the material could be used four times in a model reaction, with essentially identical results (99% \(\rightarrow\) 98% yield).  
While most of the reported approaches rely on surface functionalization of pre-formed oxide supports, [PERSON] et al. resorted to the sol-gel process for immobilizing a copper complex containing trimethoxysilane groups in a templated organosilica matrix [185]. Contrarily to most other studies in the field, testing of the catalytic properties of the material **45** was performed by adopting a Box-Behnken factorial design of experiments in order to optimize an ultrasound-promoted three-component CuAAC reaction in water with styrene oxide (Scheme 2b). The approach led to identify the best conditions in the use of 0.33 mol % of the supported catalyst, under irradiation at 40 kHz for 7 min. By adopting the same experimental set-up, different epoxides could be converted into the corresponding triazole products in fair to excellent yield (74%-99%, TOF up to 2926 h\({}^{-1}\)). In spite of the mechanical stress put on the siliceous material by sonication, the supported system maintained intact its activity in six consecutive runs (98% \(\rightarrow\) 98% yield) and no copper was reportedly detected in the filtrate by AAS.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
## 4 Conclusions  
In this paper, a constraint handing method is proposed for Unscented Hoo filtering (UHF). The method applies the projection to the sigma points. The points that do not satisfy the condition are projected into the feasible domain. It can arbitrarily constrain the state in the estimation process. Compared with the unconstrained UHF algorithm, the UHF algorithm in the constrained state performs better, which significantly improves the estimation accuracy of the filter.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.F.; validation, Y.L., Y.F. and H.L.; formal analysis, Z.H.; investigation, M.G. and Z.H.; data curation, Y.F.; writing--Original draft preparation, Y.F.; writing--Review and editing, Y.L., Z.H. and H.L.; supervision, J.L.; project administration, Z.H.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Mean Square Error** & **Max Error** & **Time** \\ \hline UKF & \(7.2030\times 10^{-4}\) & \(4.53\%\) & \(1.5007\) s \\ \hline Unconstrained UHF & \(4.6877\times 10^{-4}\) & \(3.61\%\) & \(1.7334\) s \\ \hline Constrained UHF & \(3.9919\times 10^{-4}\) & \(3.55\%\) & \(1.7298\) s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison Results Under the USABC condition.  
Figure 5: State of Charge (SOC) estimation results based on different algorithms under different driving cycles: (**a**) the HWFET driving cycle; (**b**) the USABC driving cycle.  
Figure 6: SOC estimation errors based on different algorithms under different driving cycles: (**a**) the HWFET driving cycle; (**b**) the USABC driving cycle.  
Funding: This research was funded by the National Natural Science Foundation of China with grant numbers 61701146, 61671194, and National Key Research and Development Program of China with grant number 2020 YFB1710600.
# An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background
## 2 Materials and Methods
### Wavelet Transform and Curvelet Transform
#### 2.1.4 Curvelet Transform Algorithm  
The algorithm for the curvelet transform is described as follows:  
1. Perform a J-level decomposition of the original image I to obtain the subband sequences CJ and DJ;
2. Set the initial block size (i.e., the finest scale block) to BMIN, and let B1 = BMIN;
3. For j = 1 to J: - Divide Dj into subblocks of size BJ with overlapping regions; - Apply the discrete ridgelet transform to each subblock; - If (j mod 2 = 1), set Bj + 1 = 2 Bj; otherwise, set Bj + 1 = Bj.  
The curvelet transform itself is a redundant transformation. If the number of wavelet decompositions in the first step is J, then the overall redundancy factor of the entire transform is 16J + 1. Since each step of the above decomposition is reversible, it is possible to construct the corresponding inverse transform algorithm to completely reconstruct the original image.
## 4 Numerical Experiments  
### Case Settings  
The case study is performed by Gurobi 11.0 using the Python 3.11 API on a PC with an Intel Core i7-8700 CPU running at 3.20 GHz and with 32 GB of memory. All feasible solutions produced by Gurobi in the solution process are recorded.  
1. Power system setting: The three systems are taken from [10], and their sizes are reported in Table 3. Each of these systems comprises solely thermal generating units.
2. Demand setting: There are 365 loads from the first day of the year to the 365 th day of the year, which are findings that come from [28]. In order to match the loads to the target systems, the loads for the 365 days, _origin loads_, are mapped to the capacity of the corresponding systems, _scaled loads_. Specifically, for each system, the upper (\(sys_{up}\)) and lower (\(sys_{low}\)) bounds of the target load mapping interval are determined based on the system's maximum generation capacity scaled by factors \(s_{up}\) and \(s_{low}\):  
\[sys_{up}=\text{maximum generation capacity}\times s_{up} \tag{21}\]  
\[sys_{low}=sys_{up}\times s_{low} \tag{22}\]  
_origin loads_ is then normalized and scaled to _scaled loads_ to fit the target system:  
\[scaled\ loads=\frac{origin\ loads-min(origin\ loads)}{max(origin\ loads)-min( origin\ loads)}\times(sys_{up}-sys_{low})+sys_{low} \tag{23}\]  
A simple rule is applied for dataset partitioning: days ending with the digit seven are assigned to the validation set, those ending with two are assigned to the testing set, and the remaining days are assigned to the training set. And each load will be perturbed to expand the dataset.
3. Benchmark setting: The latest Gurobi solver 11.0 was used with the following settings: threads set to 1 and MIPGap set to 1 \(\times\) 10\({}^{-6}\). All other settings were kept at their default values.
###### Abstract  
The reasonable expression of live load and its accuracy are important to the safety and design rationality of highway bridge structures. In this study, the optimization issue of the traffic load model for the suspenders of large-scale suspension bridges is studied. Taking a 2300-m main span suspension bridge as an example, a method for suspender classification based on the geometric feature of the influence lines is proposed, and the extreme traffic load effect scenarios are analyzed and used as an optimization reference. Multi-objective optimization based on a genetic algorithm is used to explore the improvement of the traffic load model of the suspender. The traffic load model of the suspender is optimized with three objectives, i.e., accuracy, convenience, and improvement, and the optimization results regarding the load value and loading length are obtained. The value of the uniformly distributed load of the optimized model ranges from 6.4 kN/m to 8.9 kN/m, and the maximum value of the concentrated force could reach 1433 kN. By comparing the obtained optimized model with the current specification model and the extreme load effect scenario model, the improved applicability of the optimized model in the analysis of the load effect of the suspender can be verified. The optimized method and relevant conclusions can provide useful references for the improved design and operation management of similar bridge structures.  
traffic load model; multi-objective optimization; super-long-span suspension bridge; suspender; influence line; extreme load effect scenario
## 2 Materials and Methods  
### Cold Atmospheric Pressure Plasma Jet Device  
A medical syringe needle was inserted inside the quartz tube, and the tube was wrapped with a copper pad as shown in Figure 1a. Alternative sine wave electricity with a frequency of 60 kHz and voltage of 1 kV\({}_{\text{rms}}\) (DPO4034 and P6015 A, Tektronix, Beaverton, OR, USA) was directly applied to the needle, and the copper pad was grounded. Ar gas (99.99%) was inserted through the syringe needle into the quartz tube. The length of the ground electrode was optimized experimentally as 100 mm to make a plume long, and the length of the quartz tube to the end was optimized as 10 mm. The flow rate was selected as 0.3 L/min to ensure a Reynolds number 2000, which has been reported to be the best value for increasing the plume length [16]. The discharge current was 15 mA, and the gas temperature was about 30 \({}^{\circ}\)C at 15 mm distance. Samples were placed below 15 mm from the end of quartz tube in all the experiments.  
The reactive species generated from the CAP were measured by analyzing the optical spectra of the plasma plume. An optical fiber (R400-7-UV-VIS, Ocean Optics) was placed normally at a distance of 5 mm from the plasma plume. The spectra were measured using a spectrometer (HR4000 CG-UV-NIR, Ocean Optics) having a resolution of 0.75 to 1.00 nm and a detection range of 200-1100 nm. The acquisition was triplicated and averaged. A petri dish was placed 15 mm below the tube. The 'CON' experiment was executed with an empty petridish; the 'DI' was executed with 1 mL deionized (DI) water; and the 'H\({}_{2}\)O\({}_{2}\)' was executed with 1 mL 1% H\({}_{2}\)O\({}_{2}\). Figure 1b shows the optical emission spectrum (OES) of CAP with Ar gas. Most peaks were from excited Ar molecules (between 700 and 900 nm), and OH- (309 nm) and O (777 and 844 nm) emission lines were also found.
## 3 Parameter Analyses
### Structural Stability  
Considering that the FCRA is located on water like ships [38; 39], the structural stability should be guaranteed to avoid capsizing. Herein, only the transverse stability and initial stability are investigated with respect to the structural characteristics and working requirements. Assuming that a small heel angle \(\delta\) occurs as shown in Figure 4, G, B, and B\({}_{1}\) are the centers of gravity, buoyancy without heeling, and buoyancy with a slight heeling angle. According to the position of G and B\({}_{1}\), it can be seen that the condition GBsin\(\delta<\) BB\({}_{1}\) can return the equilibrium under its own restoration moment after the heeling.  
Before the FCRA heels, combining with Appendix A Equation (A4), the distance between the center of gravity and the initial floating center is calculated as Appendix A  
Figure 4: Left view before and after heeling.  
Equation (A5). It should be mentioned that the position of G may be lower than the position of B when the mass of the trolley accounts for a large proportion of the mass of the FCRA. In this case, Appendix A Equation (A5) is still valid for the stability of the FCRA.  
After the FCRA has a slight heeling, in accordance with the principle of the center of gravity movement and Appendix A Equation (A4), the distance of the floating center movement can be written as Appendix A Equation (A6). Then, the condition to ensure the FCRA is restored to the horizontal floating state with the heeling angle \(\delta\) can be described as Appendix A Equation (A7). It can be approximated that cos\(\delta\approx 1\). Then, the stability parameter \(S\) is defined as  
\[S=b^{3}-4b\sin^{3}\frac{\theta}{2}+4n(2a+3 ab+3a_{1}b_{1})+6n\pi(a+ab+a_{1}b_{1} )\frac{c}{1-c}>0 \tag{4}\]  
Hence, the stability of the FCRA increases with the increase in the value of \(S\), which indicates the restorability of the FCRA when a small heeling occurs.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 4 Performance Evaluation and Discussion
### Effect of Transmitter Molecule Volume on Communication Performance  
We evaluated the BER for a model that considers the volume of the transmitting molecule. Here, the distance between the transmitting and receiving molecules was estimated based on the maximum likelihood estimation as expressed in (17). The oversampling was set to \(s_{L}=40\). Figure 15 shows the BER when the distance between transmitting and receiving molecules is estimated. Also, Table 6 summarizes the required SNR based on the results in Figure 15.  
As presented in Table 6, by comparing Methods C and D, it is confirmed that the transmission improvements correspond to 11.4 dB when the transmitting molecule is a point source, 11.1 dB when \(a_{TX}=0.10\)\(\upmu\)m, and 12.1 dB when \(a_{TX}=0.20\)\(\upmu\)m. As shown  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Demodulation Method** & **Ambient Environment Parameters** & **Required SNR [dB]** \\ \hline Method C & point source & 16.0 \\ Method C & \(a_{TX}=0.10\)\(\upmu\)m & 18.8 \\ Method C & \(a_{TX}=0.20\)\(\upmu\)m & 25.9 \\ Method C & \(a_{TX}=0.30\)\(\upmu\)m & - \\ Method D & point source & 4.6 \\ Method D & \(a_{TX}=0.10\)\(\upmu\)m & 7.7 \\ Method D & \(a_{TX}=0.20\)\(\upmu\)m & 13.8 \\ Method D & \(a_{TX}=0.30\)\(\upmu\)m & - \\ Method F & point source & 6.0 \\ Method F & \(a_{TX}=0.10\)\(\upmu\)m & 7.0 \\ Method F & \(a_{TX}=0.20\)\(\upmu\)m & 8.6 \\ Method F & \(a_{TX}=0.30\)\(\upmu\)m & 10.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of the volume of transmitting molecule on Required SNR.  
Figure 15: BER characteristics with respect to the volume of the transmitting molecule.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Demodulation Method** & **Ambient Environment Parameters** & **Required SNR [dB]** \\ \hline Method C & left 35 \({}^{\circ}\)C, right 30 \({}^{\circ}\)C & 20.2 \\ Method D & left 35 \({}^{\circ}\)C, right 30 \({}^{\circ}\)C & 21.9 \\ Method E & left 35 \({}^{\circ}\)C, right 30 \({}^{\circ}\)C & 16.1 \\ Method F & left 35 \({}^{\circ}\)C, right 30 \({}^{\circ}\)C & 12.1 \\ Method C & left 30 \({}^{\circ}\)C, right 35 \({}^{\circ}\)C & 22.5 \\ Method D & left 30 \({}^{\circ}\)C, right 35 \({}^{\circ}\)C & 27.7 \\ Method E & left 30 \({}^{\circ}\)C, right 35 \({}^{\circ}\)C & 15.8 \\ Method F & left 30 \({}^{\circ}\)C, right 35 \({}^{\circ}\)C & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Required SNR in heterogeneous temperature distribution.  
in Figure 15, communication characteristics also improve even when \(a_{TX}=0.30\)\(\upmu\)m. Furthermore, in Table 6, a comparison of Methods D and F indicates that the transmission characteristics are also improved by 0.7 dB and 5.2 dB when \(a_{TX}=0.10\)\(\upmu\)m and \(a_{TX}=0.20\)\(\upmu\)m, respectively. The results indicated that the MLSE method can improve the communication characteristics even when actual volume of the transmitting molecule is assumed.
## 4 Navigation Integration Processing and Performance Evaluation
Comparisons of navigation accuracies for the standard UKF and IMMUKF, and for the IMMUKF and STUKF, are shown in Figures 10 and 11, respectively. Both the IMM and STA demonstrate the adaptation capability. Comparison of IMMUKF and STUKF in Figures 11 is included for better understanding the performance of the two algorithms, _i.e._, parametric adaptation and structural adaptation. The proposed FUZZY-IMMUKF has two main features. First, it employs the IMM mechanism for adjusting the adequate model based on the dynamic characteristics. Furthermore, the FLAS is adopted for automatically determining the lower and upper bounds of \(\mathbf{Q}_{k}\). In is seen that substantial estimation accuracy improvement can be obtained by using the proposed method. In addition, to inspect the correctness of the filter switching capability, the mode probability of the FUZZY-IMMUKF needs to be checked, which is depicted in Figure 14. The soft-switching capability enables the filter to capture the adequate vehicle dynamic. Figure 15 provides comparison of east and north position root mean squared errors via the three approaches: UKF, IMMUKF and FUZZY-IMMUKF. Table 2 shows the comparison of navigation RMS (root mean square) errors for the three approaches. Figure 9: Membership functions of input fuzzy variables \(\xi\) (top) and \(\zeta\) (bottom). Some remarks can be made as follows:  
(1) In the four time intervals, 0-300, 701-800, 901-1,200 and 1,401-1,800 s, the vehicle is not maneuvering and is conducting constant-velocity straight-line motion. For this case, all the UKF, IMMUKF and FUZZY-IMMUKF provide equivalently good results. The navigation accuracies based on the three approaches have relatively small difference. By use of the T-S fuzzy logic, the FLAS senses smaller values of DOD parameters, and then reduces the process noise covariance. As a result, the navigation accuracies based on the UKF, IMMUKF and FUZZY-IMMUKF are equivalent. (2) In the three time intervals, 301-700, 801-900 and 1,201-1,400 s, the vehicle is maneuvering. The mismatch of the model leads to larger navigation error while the FLAS timely detects the increase of DOD parameter, and determines a large process noise covariance to maintain good estimation accuracy. It has been verified that, by monitoring the innovation information, the FUZZY-IMMUKF has good capability to detect the change in vehicle dynamics and adjust the process noise covariance for preventing the divergence and achieving better navigation accuracy. Figure 11: Comparison of the navigation accuracy for IMMUKF and STUKF. Figure 10: Comparison of the navigation accuracy for UKF and IMMUKF. Figure 14: Model probability of FUZZY-IMMUKF. Figure 12: Comparison of the navigation accuracy for IMMUKF and FUZZY-IMMUKF. Figure 13: Comparison of the navigation accuracy for three approaches: FUZZY-IMMUKF, IMMUKF and UKF.
## 2 Physical and Mathematical Models
### Mathematical Model Verification  
The numerical results will be verified according to the experimental results in reference [23]. The classroom model is illustrated in Figure 2. Figure 3 shows a comparison between the experimental and simulated values. It can be observed that the experimental values are close to the simulation values in the same position (\(x=9\) m, \(y=1.8\) m, \(z=0\) m), and the maximum volume concentration differences of CO\({}_{2}\) are less than 0.005%. On the positions of teacher and student 2.1, several measuring points are distributed in the vertical direction. The maximum temperature differences between experimental values  
\begin{table}
\begin{tabular}{c c c} \hline \(\phi\) & \(\Gamma\) & \(S_{\phi}\) \\ \hline \(u\) & \(\eta_{eff}=\eta+\eta_{t}\) & \(-\partial p/\partial x\) \\ \(v\) & \(\eta_{eff}=\eta+\eta_{t}\) & \(-\partial p/\partial y\) \\ \(w\) & \(\eta_{eff}=\eta+\eta_{t}\) & \(-\partial p/\partial z+\rho g\beta(T-T_{c})\) \\ \(T\) & \(\eta/\eta_{t}/\sigma_{t}\) & \\ \(k\) & \(\eta+\eta_{t}/\sigma_{k}\) & \(G_{k}+P_{k}-\rho\varepsilon\) \\ \(\varepsilon\) & \(\eta+\eta_{t}/\sigma_{c}\) & \(c_{1}\rho S\varepsilon-c_{2}\rho e^{2}/(k+\sqrt{\
u\varepsilon})\) \\ \hline \end{tabular} Values of various coefficients in Table 1: \(c_{\mu}=0.09\), \(\sigma_{k}=1.0\), \(\sigma_{\varepsilon}=1.2\), \(c_{2}=1.9\), \(c_{1}=\max\{0.43,\eta/(5+\eta)\}\), \(\eta=Sk/\varepsilon\), \(S=(2S_{ij}S_{ij})^{1/2}\), \(S_{ij}=(\partial u_{i}/\partial x_{j}+\partial u_{j}/\partial x_{i})/2\).  
\end{table}
Table 1: Coefficients in the control equation.  
Figure 1: Physical model of the train.  
and simulated values are less than 0.5 \({}^{\circ}\)C. The reliability of the numerical method can be confirmed by the above results.