esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Problem Formulation
### A Multi-Agent System Architecture and Models of Agents in CPSs
In this example, the resource activity model for resource agent \(a_{1}\) is \(A_{na_{1}}^{1}\), the resource activity model for resource agent \(a_{2}\) is \(A_{na_{2}}^{5}\), the resource activity model for resource agent \(a_{3}\) is \(A_{na_{3}}^{1}\|A_{na_{3}}^{5}\), the resource activity model for resource agent \(a_{4}\) is \(A_{na_{4}}^{1}\), the resource activity model for resource agent \(a_{5}\) is \(A_{na_{5}}^{5}\), the resource activity model for resource agent \(a_{6}\) is \(A_{na_{6}}^{1}\), the resource activity model for resource agent \(a_{7}\) is \(A_{na_{7}}^{3}\), the resource activity model for resource agent \(a_{8}\) is \(A_{na_{8}}^{2}\) and the resource activity model for resource agent \(a_{9}\) is \(A_{na_{9}}^{4}\). Figure 3 shows the resource activity models for the resource agents in the motivating example. The set of resource agents for performing the \(k\)-th operation in \(\Omega_{n}\) is denoted by \(RA_{n}^{k}\). The set of resource agents for performing the operations in \(\Omega_{n}\) is denoted by \(RA_{n}=\bigcup\limits_{k\in\{\mathcal{U}\in\{1,2,\ldots,K_{n}\}|d_{nt^{k}=1}\} }RA_{n}^{k}\). A configuration for process agent \(n\) is defined by the Cyber World model, \(\Omega_{n}\), of process agent \(n\) and the Cyber World models of activities of resources agent \(a\in RA_{n}=\bigcup\limits_{k\in\{\mathcal{U}\in\{1,2,\ldots,K_{n}\}|d_{nt^{k} =1}\}}RA_{n}^{k}\) for performing the operations in \(\Omega_{n}\). **Definition 5**. : _The Cyber World model for a configuration of process agent \(n\) with the Cyber World model \(\Omega_{n}\) and the Cyber World models of activities of resources agent \(a\in RA_{n}=\bigcup\limits_{k\in\{\mathcal{U}\in\{1,2,\ldots,K_{n}\}|d_{nt^{k} =1}\}}RA_{n}^{k}\)for performing the operations in \(\Omega_{n}\) is a DTPN \(\Psi_{n}=(P_{n},T_{n},F_{n},m_{n0},\mu_{n})=\Omega_{n}\|_{k\in\{\mathcal{U}\in\{ 1,2,\ldots,K_{n}\}|d_{nt^{k}=1\}\}\wedge\mu\in RA_{n}^{k}}\), where \(n\in WA\). The firing time \(\mu_{n}(t)\) for each transition \(t\in T_{n}\) is \(t_{nn}^{k}(t)\)._  
Note that the number of different configurations of process agent \(n\) is usually not unique. For example, Figure 4a-c show three different configurations for process agent \(n\). The process-planning problem is to find the optimal configuration of the process agent \(n\) and the associated set of resource agents \(RA_{n}^{k}\) for performing the \(k\)-th operation in \(\Omega_{n}\) to compose the overall Cyber World model \(\Psi_{n}\)=\((P_{n},T_{n},F_{n},m_{n0},\mu_{n})=\Omega_{n}\|_{k\in\{\mathcal{U}\in\{1,2, \ldots,K_{n}\}|d_{nt^{k}=1\},a\in RA_{n}^{k}\}}\)\(A_{nn}^{k}\) to meet the manufacturing requirements.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 4 CRFID Operating Procedure Optimization  
In this section, we first describe the optimized CRFID operating procedure. Then, we analyze goodput optimization problem by controlling the optimal number of transmission frames and sleep time of CRFID according to the current energy harvesting.
## 3 Model Implementation
### Data Plane Implementation: TSN Devices and Switches  
The data plane comprises the TSN end-devices (sensors, actuators, ECUs) that generate and consume TT streams, and the TSN switches that forward these streams according to the GCL.  
* TSN End-Devices: These devices serve as the sources and sinks for data streams. As depicted in Figure 2, examples include wheel speed sensors (LeftFrontWheel), LiDAR units, cameras (FrontLeftCamera), and On-Board Units (OBUs). They are responsible for packetizing data, assigning PCP values, and transmitting frames at their designated cycle start times, potentially after performing sub-flow segmentation if dictated by the CNC's scheduling parameters (\(N_{i}>1\)). Receiving devices are responsible for reassembling sub-flows if applicable.
* TSN Switches: The TSN switches are the core enablers of deterministic forwarding in the data plane. Each switch port is equipped with multiple priority queues and a Time-Aware Shaper (TAS) mechanism. Upon receiving the GCL from the CNC, the TAS in each switch configures its egress queue gates to open and close precisely according to the scheduled time intervals for each \(PCP_{i}\). This ensures that frames from different streams are forwarded in a conflict-free manner, adhering to the computed schedule and minimizing queuing delays for TT traffic.  
The operational flow for scheduling a set of TT streams is as follows. The CNC gathers all stream requests \(F_{i}\) via the CUC. It first executes the preprocessing and filtering steps of Algorithm 1 to determine the set of schedulable streams \(\mathcal{S}_{sched}\) and their sub-flow parameters. Then, using either the SMT-based iterative approach or the acceleration heuristic, it computes the GCL that satisfies all constraints defined in Section 2.3. This GCL is then deployed to the TSN switches. When multiple data streams are assigned the same priority level and are eligible for transmission within overlapping GCL-defined open windows (a situation ideally minimized by a precise GCL), the switch typically processes them based on a First-Come-First-Served (FCFS) principle within that priority queue during that window.
## 4 Simulation Results and Analysis
In the case of conventional localization approaches, the FPE algorithm shows less localization error than other conventional approaches. However, localization using fingerprint maps is not a common approach in UWB localization systems due to the anchors and tag distance limitation. The UWB anchors and tag should maintain minimum distances for accurate localization. The most common conventional UWB localization approach is the LLSE and which gives accurate position results than the MLE and WCE approaches. The WCE approach has a high localization error and it is not suitable for our simulation scenario. From all the simulation results and analysis, the proposed LSTM approach outperforms conventional localization approaches and gives better localization accuracy for UWB localization.
## 4 Results  
### Initial State Preparation and Unitary Operation  
The initial state is prepared by superposing the states of two qubits required to define the pairing Hamiltonian. This is performed by placing a Hadamard gate on each qubit which provides the contributions of all the qubits to the measurement. The unitary operators obtained in the previous section corresponding to the XX, YY, and ZZ types of interaction are then converted into quantum gates [32] and applied to form a circuit.  
* For the XX type of interaction: Comparing with the standard \(U3\) gate (Equation (15)), the parameters for the gate are found to be \(\theta=2\hat{l}_{\parallel}t\), \(\phi=-\pi/2\), and \(\lambda=\pi/2\);
* For the YY type of interaction: The parameters obtained for the \(U3\) gate are \(\theta=2\hat{l}_{\parallel}t\), \(\phi=-\pi/2\), and \(\lambda=\pi/2\). The parameters obtained for the \(U3^{\ddagger}\) gate are \(\theta=2\hat{l}_{\parallel}t\), \(\phi=\pi/2\), and \(\lambda=-\pi/2\);  
Figure 9: (a) Circuit implementation for the different interactions of the (a) \(\hat{\alpha}_{1}^{x}\hat{\alpha}_{2}^{x}\) term where the circuit consists of two CNOT gates and one \(U3\) gate; (b) \(\hat{\alpha}_{1}^{y}\hat{\alpha}_{2}^{y}\) term where the circuit consists of two CNOT gates and one \(U3\) gate, one \(U3^{\ddagger}\) gate and two NOT gates; and (c) \(\hat{\alpha}_{1}^{z}\hat{\alpha}_{2}^{z}\) term where the circuit consists of two CNOT gates and one Rz gate.  
* For the ZZ type of interaction: Comparing with the standard _U1_ gate, \[\begin{array}{c}U1=\begin{bmatrix}1&0\\ 0&e^{i\lambda}\end{bmatrix}\\ \lambda=2J\dagger t,\end{array}\] (17)
# Gemini Surfactant as a Template Agent for the Synthesis of More Eco-Friendly Silica Nanocapsules
## Materials and Methods
### Methods
#### 2.2.2 Ecotoxicity Tests  
The short-term toxic effects of the two silica nanocapsules were evaluated on marine microalgae species (_Nannochloroppsis gaditana_, _Phaedactylum tricornutum_, _Tetraselmtis chuii_) and on a microcrustacean species (_Artemia salina_), following the standard protocols OECD 201 (2011) [28] and ISO 10253 (2016) [29], respectively, with some adaptations fully described by [PERSON] et al. [25] Briefly, tests were run with 0.45 \(\upmu\)m filtered artificial seawater (ASW) and for each compound, five concentrations plus one negative control (ASW only) were tested (n = 4 for microalgae; n = 3 for crustaceans). Range-finding tests were run for exposure concentrations ranging from 0.01 mg/L to 100 mg/L. Definitive exposure tests included the following exposure concentrations: _Nanochloroppsis gaditana_ and _Tetraselmtis chuii_: 2 mg/L, 4 mg/L, 6 mg/L, 8 mg/L, 10 mg/L; _Phaedactylum tricornutum_: 2 mg/L, 4 mg/L, 6 mg/L, 8 mg/L, 10 mg/L for SiNC_QSB2-12 and 0.5 mg/L, 1 mg/L, 2 mg/L, 4 mg/L, 6 mg/L for SiNC_CTAB; _Artemia salina_: 6.25 mg/L, 12.5 mg/L, 25 mg/L, 50 mg/L, 100 mg/L. Microalgae growth inhibition was monitored for 72 h through fluorescence daily measurements whereas crustaceans mortality or immobilization was checked after 48 h of exposure. The median lethal (LC\({}_{50}\)) and median growth inhibition (IC\({}_{50}\)) concentrations were determined by a non-linear regression model with the software _Graphpad Prism_ v.6.0. Then, toxicity endpoints were categorized according to the EC Directive 93/67/EEC scheme adapted by [PERSON] et al. [30] for nanomaterials: non-toxic (L/IC\({}_{50}\) > 100 mg/L), harmful (10 > L/IC\({}_{50}\) \(\geq\) 100 mg/L), toxic (1 > L/IC\({}_{50}\) \(\geq\) 10 mg/L), very toxic (0.1 > L/IC\({}_{50}\) \(\geq\) 1 mg/L) and extremely toxic (L/IC\({}_{50}\)\(\leq\) 0.1 mg/L).  
I/LC\({}_{50}\) values estimated for both nanomaterials were statistically compared. A table containing the logL/IC\({}_{50}\), associated standard error and degrees of freedom (data extracted from each non-linear regression report) for both SiNC (QSB2-12) and SiNC (CTAB) was prepared for each tested species. Then, the null hypothesis that both best fitting datasets were similar was tested (\(p\) < 0.05) through a \(t\)-test, followed by an F-test to compare the data variance (total of 4 independent tests).  
Figure 2: Scheme for the silica nanocapsules synthesis and a quantity of surfactants used.
## 3 Results and Discussion
### Feature Extraction and Selection
#### 3.2.2 Wavelet Energy Value  
Wavelet energy value (WEV): The db3 wavelet function was used to perform 4-layer wavelet decomposition with the original data of the sensors. Each sensor obtained 7 approximate coefficients as its characteristics, and the sum of squares of each coefficient was considered as the energy value of each sensor.  
\[WEV=\sum\
olimits_{y=1}^{m}\alpha_{4y}^{2} \tag{4}\]  
where \(\alpha_{4y}\) is the yth decomposition coefficient in the set of approximation coefficients after four-scale decomposition of the signal, and m is the total number of coefficients in the set of approximation coefficients.
## 4 Discussion  
### Crop Yield Prediction  
Crop yield prediction is a new research area for RTM applications. The limited number of studies (15) of RTM applications did not provide enough information for a comprehensive systematic review. It is, however, indicative for researchers to employ RTMs specifically to crop monitoring and CYP. These studies mainly focused on LAI use for crop monitoring or linkage for integration with other models. Investigation of the retrieval of other crop traits, particularly of net primary productivity or above-ground biomass exploration towards applications for RTM-based CYP, could provide further benefits in reliability and accuracy enhancement. Similarly, developing CGMs having more crop traits coupling abilities, like APSIM or better, can provide more accurate input information for RTMs. Most CGMs are limited to simulating LAI that can be used as an input in RTMs for CYP.  
The availability of repeated research provides information about reliability and improvement. RTM-based CYP lacks reputative studies for evaluation of the integration impacts in different environmental scenarios. Only two studies have used APSIM (CGM) and 6S (RTM) [51; 52] for maize yield loss, having high accuracy. Cereals (wheat, rice and maize) are priority crops under investigation due to their linear relationship between LAI and crop yield. The many crops that have a nonlinear relationship, like cotton, are another research area to be addressed by researchers.  
Research study [48] relevant to CYP was the most cited study, with a per-year average citation of 17.2. This study worked on winter wheat regional estimates using the integration of the WOFOST and PROSAIL models. Although model efficiency was good, with an RMSE of 595 kg/ha, it had an R\({}^{2}\) of 0.30. Similarly, study [51] had a high R\({}^{2}\) of up to 1 in some provinces of China, but had a limited average citation rate of 3.75 per year. This study used the integration of APSIM and 6S models for the assessment of maize production. It is therefore a vigorous study in this area that will not only improve the efficiency of models in different scenarios but will also be helpful in the development of new models.
## 4 Results and Discussion
### Stabilization of Graphite
#### 4.1.3 Impact of pH Value on Viscosity  
The rheological behavior of the graphite dispersions as a function of dispersant content was investigated for two different pH value ranges (see Figures 7 and 8). For PVP, the pH value that is determined in the zeta potential measurements lies within the pH value range identified from the literature. The measurements were repeated at a lower pH value, more towards the neutral range. For TX-100, the assumed pH value range, based on PVP, does not correspond with the one that was determined from the zeta potential measurements. Therefore, the second analysis was performed in the pH value range that resulted from the zeta potential evaluation. The standard deviation was based on 61 single point measurements.  
Figure 7: Viscosity as a function of PVP content for aqueous dispersions containing 10 m\(\%\) graphite nanoparticles at varying pH values **(a)** pH = 9.0–9.5 (see Table 1) and **(b)** pH = 7.8 (see Table 1).  
It can be seen that the progression of the viscosity displays similar pH values with both PVP and TX-100, respectively.  
There are small differences in the viscosity values for a low dispersant content. With a small amount of PVP, the viscosity values are slightly lower for a pH value of 7.8 than for a pH value of 9.0, which resulted from the zeta potential measurements. This can be explained in terms of the Peclet number, which represents the relationship between convective and diffusive transport in dispersions [46]. A Brownian dispersion is present with small Peclet numbers, which are primarily determined by small particles, according to this characteristic value. It is likely that the particles no longer sedimented due to gravity, with a reversible equilibrium structure being able to establish itself [36]. Small particles floating in the aqueous medium resulted in increased viscosity. At a low TX-100 content, the viscosity values for both pH values were subject to a high standard deviation. For this reason, no definite trend can be identified. However, in the plateau area, the viscosity values were almost identical for both dispersants and pH values. This provides strong evidence that the pH value does not have a significant impact on the stabilized dispersions.
# A High-Precision Active Vibration Isolation Control System: Experimental Study
## 4 Experimental Study  
### System Implementation  
The implementation of the proposed control is summarized as follows:  
* The System Identification toolbox of Mathworks MATLAB is used to identify the system parameters such as the nominal stiffness and damping ratio of the springs. The system matrices are subsequently obtained. * The feedforward control law is formulated as in Section 3.1. * The weighting functions are obtained as in Appendix B and, then, the controller matrix \(\mathbf{K}(s)\) is acquired by applying the function'mixsyn' in MATLAB. The Model Reducer toolbox is used to reduce the order of the controllers. The balanced truncation method is applied (Hankel singular values) with a frequency range focused from 0.01 to 100 [Hz]. The elements of the controller matrix \(K(s)\) are listed as follows:  
\[ \begin{array}{l}K_{x}=\frac{412.33^{3}+6.156\times 10^{4}s^{2}+1.64\times 1 0^{5}s+1.419\times 10^{7}}{s^{6}+4.99\,92s^{2}+266.5\,s+9928}\\ K_{y}=\frac{317.3^{5}+5.805\times 10^{5}+1.35\times 10^{5}+1.35\times 10^{5}+1.352\times 10^{ 4}}{s^{6}+1.80\times 10^{5}s^{2}+266.48+1.021\times 10^{4}},\\ K_{z}=\frac{2970s^{6}+8.538\times 10^{5}s^{5}+1.476\times 10^{4}s^{14}+1.699\times 10^{ 3}+1.097\times 10^{2}s^{2}+4.36\times 10^{3}s^{5}+5.41\times 10^{13}}{s^{6}+287.5 ^{5}s^{4}+4.961\times 10^{4}s^{5}+5.717\times 10^{8}s^{3}+3.681\times 10^{8}s^{ 2}+1.463\times 10^{16}s^{1}+1.76\times 10^{10}},\\ K_{yx}=\frac{9.2155+2.8125+6.907\times 10^{5}+4.69\times 10^{5}+4.93\times 10^{5}+3.7\times 10^{7}s^{2}+2.954\times 10^{11}s^{2}+2.004\times 10^{2}}{s^{6}+1.20 5\times 5.00\times 10^{5}s^{4}+5.73\times 10^{8}s^{3}+5.81\times 10^{5}+1.453 \times 10^{18}},\\ K_{yy}=\frac{22.74s^{6}+5.286\times 10^{5}+5.73\times 10^{4}+6.69\times 10^{3}+3.341\times 10^{3}+3.12\times 10^{3}+1.087\times 10^{6}s^{1}+1.074\times 10^{ 2}}{s^{6}+2324s^{5}+3.206\times 10^{8}s^{4}+2.94\times 10^{8}s^{3}+1.508\times 10^{ 2}s^{2}+4.76\times 10^{4}s^{4}+4.545\times 10^{5}},\\ K_{yz}=\frac{23.65s^{6}+5.488\times 10^{4}s^{5}+7.578\times 10^{5}s^{4}+6.951\times 10^{ 4}s^{2}+3.576\times 10^{3}s^{2}+1.13\times 10^{16}s+1.116\times 10^{7}}{s^{6}+ 2320s^{5}+3.2\times 10^{8}s^{4}+2.937\times 10^{8}s^{3}+1.507\times 10^{12}s^{ 2}+4.762\times 10^{14}s+4.555\times 10^{5}},\end{array}  \tag{15}\]  
In order to compare with the proposed control system, a proportional-derivative (PD) controller is also implemented for the AVIS. The PD control is chosen over the conventional PID for this vibration isolation problem thanks to the integral behavior of the springs in the system. An additional integral involves the velocity outputs along with their measurement noise and bias while it does not contribute to enhance the stability of the system. The PD controller has the form of  
\[\mathbf{U}_{p}=diag\{\mathbf{K}_{P},\mathbf{K}_{D}\}\dot{\mathbf{X}} \tag{16}\]
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 2 Target Peak Enhancement Using Time Reversal Processing
### Detection Performance Comparison
#### 2.3.1 Conventional Matched Filtering  
After matched filtering, the clutter signal intensity \(x\) has the K-distribution. Thus, the conditional pdf (probability density function) under the null hypothesis \(H_{0}\) is given by  
\[p(x\left|H_{0}\right.)=\frac{4}{\sqrt{\lambda}\Gamma(\alpha)}\left(\frac{x}{ \sqrt{\lambda}}\right)^{\alpha}K_{\alpha-1}\left(\frac{2x}{\sqrt{\lambda}} \right)\,, \tag{7}\]  
while the pdf under the alternative hypothesis \(H_{1}\) is  
\[p(x\left|H_{1}\right.)=\frac{4}{\sqrt{\lambda}\Gamma(\alpha)}\left(\frac{(x-P _{l})}{\sqrt{\lambda}}\right)^{\alpha}K_{\alpha-1}\left(\frac{2(x-P_{l})}{ \sqrt{\lambda}}\right)\,, \tag{8}\]  
where \(\lambda\) and \(\alpha\) are the scale and shape parameters, which are dependent on the average size of the clutter and the number of the scatter in the resolution cell of the sonar, respectively. \(\Gamma(\cdot)\) represents the gamma function.  
The likelihood ratio test with threshold \(\gamma\) can be expressed as follows:  
\[\text{Select}\,H_{1}\text{ if }\frac{p(x\left|H_{1}\right.)}{p(x\left|H_{0}\right.)} = \left(1-\frac{p_{t}}{x}\right)^{\alpha}\frac{K_{\alpha-1}\left(\frac{2(x-P_{t })}{\sqrt{\lambda}}\right)}{K_{\alpha-1}\left(\frac{2x}{\sqrt{\lambda}}\right)} > \gamma\,. \tag{9}\]  
Then, the corresponding probability of detection is described by  
\[P_{D}=\int_{\gamma}^{\infty}\frac{4}{\sqrt{\lambda}\Gamma(\alpha)}\bigg{(} \frac{(x-P_{t})}{\sqrt{\lambda}}\bigg{)}^{\alpha}K_{\alpha-1}\left(\frac{2(x-P _{t})}{\sqrt{\lambda}}\right)dx\,, \tag{10}\]  
with the false alarm probability  
\[\int_{\gamma}^{\infty}\frac{4}{\sqrt{\lambda}\Gamma(\alpha)}\bigg{(}\frac{x}{ \sqrt{\lambda}}\bigg{)}^{\alpha}K_{\alpha-1}\left(\frac{2x}{\sqrt{\lambda}} \right)dx=P_{FA}\,. \tag{11}\]
## 3 The Sphere-to-Sphere TCP Calibration Model
To achieve optimal results, we seek to minimize the 2-norm of the objective function. This optimization problem transforms into a nonlinear least squares problem. \[F(X_{i})=\min\lVert f(i)\rVert^{2} \tag{6}\]  
Meanwhile, during the actual calibration and the summary of the calibration data, we identified the following characteristics:  
Figure 4: The robot sphere-to-sphere calibration process. The obtained calibration results must be the global optimum. If the algorithm converges to a local optimum or fails to converge, it can lead to catastrophic consequences for subsequent measurement processes, rendering it unacceptable. 2. During the calibration process, the function involved may be a multi-dimensional, non-convex continuous function. 3. Controllable noise is mixed into the calibration data. When the noise level is low, the high-dimensional space housing both the global optimal solution and other local optimal solutions becomes more concentrated. 4. Based on the volume of calibration data collected, a termination criterion for assessing result convergence can be established through the norm of residuals in the least squares method. A large norm suggests a deviation from the global optimal solution or issues with the calibration data, leading to the rejection of the final iteration results. Conversely, a small norm indicates successful convergence of the results. When addressing the aforementioned NLS optimization problem, the function may exhibit multiple local minima. If the algorithm fails to achieve global convergence, it can result in inaccurate calibration outcomes. Hence, the dependable determination of the global optimal solution for the function, while maintaining a balance between computational efficiency and result accuracy, becomes crucial. This entails devising appropriate solution methods derived from the aforementioned characteristics, and it holds significant importance in achieving precise TCP calibration results.