esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Assimilation Experiments  
We performed three groups of assimilation experiments for both single-site and multi-site cases. In the first experiment (denoted as'sm'), we assimilated only SMOS L3 soil moisture data, in the second experiment (denoted as 'fapar'), we assimilated only JRC-TIP FAPAR data, and in the third experiment (denoted as'sm+fapar'), we assimilated both SMOS L3 soil moisture and JRC-TIP FAPAR data. As mentioned, for two of the selected sites, DK-Sor and FI-Sod, no soil moisture data were available for assimilation due to the filtering of the SMOS L3 data set with high RFI. Therefore, only JRC-TIP FAPAR data were assimilated at these two sites. This resulted in 29 assimilation experiments (8 for the single-site'sm', 10 for the single-site 'fapar', 8 for the single-site'sm+fapar', 3 for the multi-site assimilation).  
We note here the different spatial resolutions of the remote sensing products used in the assimilation (\(\sim\)25 km for the SMOS soil moisture and \(\sim\)1 km for the JRC-TIP FAPAR as mentioned in the previous section) and the even smaller footprint (\(\sim\)few hundred meters) of the eddy covariance FLUXNET data used for validating our results. We also note the difference in the temporal resolutions of the SMOS soil moisture product (daily) and the JRC-TIP FAPAR product (16 daily). Hence, the number of soil moisture data points was higher than the FAPAR data points (6887 versus 1218 in the'sm+fapar' multi-site case). However, our objective was to use all the observational information at the temporal and spatial scales available and not to achieve an equal balance between observational data streams in terms of numbers of observations. In any case, the weight of an individual observation in the cost function calculation was a consequence of the specified uncertainty for this observation. In that sense, it was difficult to judge the constraint of an observational data streams on the model by the number of data points alone. The temporal resolution of a dataset determines the temporal scales that can be resolved by the data. The daily SMOS L3 product will be able to capture this temporal variability and constrain surface soil moisture and the associated hydrological processes on this time scale. However, phenological quantities (such as LAI) show less high temporal variability than soil moisture, therefore, the 16 daily FAPAR observations were of sufficient temporal resolution to constrain the phenology part of the model.
# Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN
## 3 Simulation Model
### MSRDPFSP Model Construction
#### 3.2.5 Statistical Modules and Summaries  
In the Drain module, by setting the SimTalk script function, it accurately records the time when each workpiece finishes processing and leaves the system. Concurrently, it tags the workpiece with its originating workshop and its completion sequence within that workshop. Based on this recording mechanism, real-time statistics on the completion of each workpiece in each workshop can be achieved. Specifically, the time when the last workpiece exits each workshop is extracted as the makespan of the workshop. The makespan of each workshop effectively captures the load distribution and production bottleneck information of each workshop during the scheduling process, reflecting not only the operational pressure within individual workshops but also revealing the overall efficiency of system resource utilization and coordination levels. Makespan as a feature input can provide richer and more meaningful state information for the subsequent reinforcement learning algorithms. This can help the intelligentsia accurately perceive the actual impacts of different scheduling schemes on the system performance, optimizing the resource allocation and job sequencing more efficiently during the decision-making process, and improving the convergence speed and policy quality of the learning.  
Figure 14: Failure rate setting.  
Figure 13: MTTR parameter setting.  
The introduction of assembly lines as well as workers introduces workpiece transfer time between stations, thereby increasing the computational complexity on the shop floor. The incorporation of AGV trolleys, paths, and robotic arms introduces time costs for workpiece distribution, thereby elevating the computational complexity off the shop floor. The first three parts are the embodiment of multi-scenarios. In each simulation, the same scheduling sequence remains consistent across scenario changes, while the introduction of failure rates leads to different simulation results for the same sequence, which embodies the model robustness.
## 4 Overview of GNSSs
### Glonass
#### 4.3.1 GLONASS System Architecture  
The GLONASS system architecture consists of three segments:  
* Space segment;
* Control segment;
* User segment.  
All of these segments work together to provide users around the world with accurate three-dimensional positioning, timing and velocity data [70].
# Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons
## 4 Validation Case Study
Concerning the computational burden of the compared control algorithms, the execution times were measured on a PC having base frequency of 2 GHz and clock precision of \(5\times 10^{-8}\) s. The allotted controller computing time for the entire braking scenario experiment lasting for 1.5 s is \(T_{LSMC}=0.068\) s, \(T_{RSMC}=0.021\) s, \(T_{ADC}=0.23\) s, \(T_{RL}=0.461\) s, \(T_{MSMC}=0.345\) s, \(T_{PSSMC}=0.99\) s. These measured times correspond to 1500 calls of the controller calculation function with sampling period of 1 millisecond. Meaning that even in the case of the RL controller, the time required to run one call falls below 1 millisecond. The measured allotted computing times include all necessary double-precision operations for calculating the control input, including numerical evaluation of the integrals and derivatives, multiplications, and additions. With proper optimizations, these controllers can be implemented on modern hardware. From computational perspective, the RSMC and LSMC are more efficient by almost an order of magnitude in terms of measured allotted computing times. While the NN-based RL controller is not the most demanding due to the many operations involved in NN evaluation, the PSSMC appears the most time consuming due to its complexity and many operations. In a following step, the best achievable performance of the LSMC and RSMC controllers is verified. To this end, the parameters \(\delta,v_{\max}\) of the LSMC and the parameter \(k\) of the RSMC are searched by optimizing for the test scenario under aforementioned conditions (180 rad/s initial braking speed and 0.15 slip set point obtained by first order lag filtering). The optimization is performed using a genetic algorithm (GA). The values of the parameters are searched for in the domains \(\delta\in[0.01;1],v_{\max}\in[0.1;10]\) for the LSMC and in the domain \(k\in[0.01;20]\) for the RSMC. These domains also act as lower and upper bounds for the optimization variables throughout the numerical search process. Other settings were: a number of 50 search agents, 5% out of the total number of agents is the number of individuals to survive the next generation, crossover fraction is 80%, maximum 5000 generations. The GA solver parameters were set to ensure convergence to approximately the same solution on five runs. The best values of the performance index are \(I_{test}=5.9858\times 10^{-4}\) for the LSMC (for \(\delta^{*}=0.5032,v_{\max}=0.012\)) and \(I_{test}=6.0758\times 10^{-4}\) for the RSMC (obtained for \(k^{*}=15.46\)). On one hand, the best performance values are not very far from those obtained with the initial settings of the LSMC and RSMC parameters. Yet, the result shows that the LSMC structure has slightly better control capability over the RSMC structure. Nevertheless, the control performance reveals itself as a robust one and undoubtedly superior over the other control strategies proposed for comparisons.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 2 Methods
### Thruster Output Calculation
#### 2.2.2 Thruster Output Calculation  
The force required to control the longitudinal movement of the ship is \(F_{X}\), and the force required for berthing at speed \(U\) is \(F_{Y}\) (Equation (11)). The output of the bow and stern thrusters, located at distances \(a\) and \(b\) from the ship's center of gravity, respectively, and needed to keep the vessel parallel to the pier while considering the turning moment, is given by Equation (12).  
\[\begin{split} F_{X}=(F_{TF}+F_{TA})sin\theta_{T},\\ F_{Y}=F_{TLF}+F_{TLA},\\ M=aF_{TLF}+bF_{TLA},\end{split} \tag{11}\]  
where \(F_{TF}\) represents the bow thruster output, \(F_{TA}\) represents the stern thruster output, \(F_{TLF}\) represents the lateral component of the bow thruster output, \(F_{TLA}\) represents the lateral component of the stern thruster output, \(a\) represents the distance between the center of gravity and the bow thruster, and \(b\) represents the distance between the center of gravity and the stern thruster.  
As \(a\) and \(b\) are known in advance, \(F_{TLF}\) and \(F_{TLA}\) are calculated using Equation (13). Considering the external force (\(F_{X}\)) acting longitudinally on the hull, the thruster output angle is required. Assuming that the output angles of the bow and stern thrusters are the same, the thruster output angle (\(\theta_{T}\)) and outputs (\(F_{TF}\), \(F_{TA}\)) for the bow and stern required  
Figure 3: Coordinate system.  
to maintain the hull parallel to the pier and achieve the desired berthing speed under the given external force conditions are provided in Equations (14) and (15), respectively. Additionally, \(F_{TF}\) and \(F_{TA}\) can be converted to 100 horsepower (hp) per 1.0 ton-force or 100 kW per 1.5 ton-force.  
\[\begin{array}{l}F_{TLF}=\frac{M-bF_{Y}}{a-b},\\ F_{TLA}=F_{Y}-F_{TLF},\\ \theta_{T}=\text{atan}\bigg{(}\frac{F_{X}}{F_{Y}}\bigg{)},\\ F_{TF}=F_{TLF}\;sec\theta_{T},\\ F_{TA}=F_{TLA}\;sec\phi_{T},\end{array} \tag{13}\]  
where \(F_{TLF}\) represents the lateral component of the bow thruster output, \(F_{TLA}\) represents the lateral component of the stern thruster output, and \(\theta_{T}\) represents the thruster output angle.
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 2 Numerical Model Description  
### Wind Model  
The CCMP (Cross Calibrated Multi-Platform) sea surface wind field is the global surface wind field assimilation data launched by NASA in 2009. It adopts the enhanced variational assimilation analysis method and combines the sea surface wind field data collected on many ocean passive microwave and scatterometer remote sensing platforms. The CCMP wind field, with high precision and high spatial and temporal resolution, can meet the needs of oceanic and atmospheric research [PERSON] et al. (2018). In this paper, this wind field is selected as the background wind field in the driven wind field of the SWAN model [PERSON] et al. (2018); [PERSON] et al. (2018). The third-generation wave model SWAN constructs a mathematical wave model by using the dynamic spectrum balance equation based on the principle of energy conservation and the theory of linear random surface gravity waves. This model was first developed by Delft University in the Netherlands. After years of version iterations, it has become a numerical model commonly used in the world to simulate wind waves, swell waves, and mixed waves. The data used in this study are sourced from ESE (NASA Earth Science Enterprise), offering a temporal resolution of 6 h and a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The dataset spans from July 1927 to December 2020, covering a spatial range is 78.375\({}^{\circ}\) S to 78.375\({}^{\circ}\) N and a longitude range from 180\({}^{\circ}\) W to 180\({}^{\circ}\) E.  
The wind field calculation grid adopts a rectangular distribution, and the grid points are arranged as 131 \(\times\) 131. The grid spatial resolution is 6\({}^{\prime}\)\(\times\) 6\({}^{\prime}\), and it is distributed in the area of 17.625\({}^{\circ}\) N-30.625\({}^{\circ}\) N, 115.625\({}^{\circ}\) E-128.625\({}^{\circ}\) E. In the process of setting the SWAN model, white wave dissipation, wave breaking, bottom friction, nonlinear interactions, and other physical processes are considered. Every calculation takes 6 h.
## 2 Background
### Code-Based GNSS Inter-User Ranging Techniques
#### 2.1.2 Extrapolation of Aiding User's Observables to the Aided User's Timestamps  
It has to be noticed that both users have an independent clock such that the GNSS measurements from the aiding user are not synchronous with the GNSS measurements from the aided user. The offset between their respective measurements timestamp is \(\Delta t_{ij,k}=t_{j,k}-t_{i,k}\), where \(t_{*,k}\) is the receiver timestamp associated with a given set of pseudorange measurements after the correction of the corresponding receiver clock bias \(\delta t_{*,k}^{r}\) for user \(*\) (which denotes the user ID, \(i\) or \(j\)). One way to retrieve such a clock bias estimate \(\delta t_{*,k}^{r}\), and the one considered in the present analysis, is to estimate it via the Least Squares (LS) SPP, as will be explained in Section 2.1.3.  
To compensate for this timestamp offset \(\Delta t_{ij,k}\), the measurements of the aiding user have to be extrapolated at the timestamp of the aided user \(t_{i,k}\). This is achieved by using, for example, the range-rate information that can be inferred by the Doppler measurements. This aspect is of particular importance in the case of a space scenario involving orbiters. Indeed, given the high user velocity, the users might have traveled a long distance over the timestamp offset \(\Delta t_{ij,k}\) duration, and hence the ISR value would be affected by a significant error if no such time-alignment was performed. The technique to extrapolate the measurements of the _aiding user_ to the _aided user_ measurement timestamps, as introduced in [51], is based on the Doppler frequency shift of the _aiding user_\(j\) with the satellite \(s\), \(\phi_{j}^{s}(t_{j,k})\) so that  
\[\begin{split}\hat{\rho}_{j}^{s}(t_{i,k})&=\rho_{ j}^{s}(t_{j,k}-\Delta t_{ij,k})\\ &=\rho_{j}^{s}(t_{j,k})-\Delta t_{ij,k}\cdot\lambda_{f_{c}}\cdot \phi_{j}^{s}(t_{j,k}),\end{split} \tag{4}\]  
where \(\lambda_{f_{c}}\) is the wavelength associated with the carrier frequency of the GNSS signals, 0.2549 m for L5/E5a, being the focus frequency band of the present contribution, given its inherent advantages for spaceborne GNSS users, as discussed in [42]. L5/E5a has indeed some clear advantages given its wider transmission antenna pattern beamwidth and its lower carrier frequency when compared to E1/L1 resulting in better signal availability and lower free space path loss. Also, in the case of Global Positioning System (GPS), the L5 signal is preferred over the L1 signal given its narrower bandwidth leading to a decrease in receiver noise and an increase in ranging accuracy.
## 2 Materials and Methods
### Analysis of Wine Volatiles  
This study quantified major volatile compounds (those with levels \(\geq\) 10 mg L\({}^{-1}\)) and polyols using a gas chromatograph Agilent 6890 (Agilent Technologies, Santa Clara, CA, USA) equipped with a flame ionization detector (FID) and a CP-WAX 57 CB capillary column (60 m; 0.25 mm; 0.4 \(\upmu\)m film). Sample preparation involved adding 1 mL of an internal standard solution (4-methyl-2-pentanol, 1018 mg L\({}^{-1}\)) and 0.2 g of calcium carbonate to 10 mL of the wine sample, followed by ultrasonication, centrifugation, and GC injection of 0.7 \(\upmu\)L to the supernatant. Compounds such as methanol and higher alcohols, acetaldehyde, acetoin, ethyl esters, glycerol, and 2,3-butanediol were measured using a calibration table constructed with standard solutions of known concentrations.  
Minor volatiles (levels \(<\) 1 mg L\({}^{-1}\)) were analyzed using stir bar sorptive extraction-thermal desorption-gas chromatography-mass spectrometry (SBSE-TD-GC-MS). A coated PDMS stir bar extracted volatiles from a prepared wine solution buffered to pH = 3.2, which were then desorbed and analyzed in a GC-MS system. The oven temperature profile and mass spectrometer settings were optimized for compound identification. All analyses were performed in triplicate, with quantification based on calibration tables of standard solutions of known concentrations. Peaks were quantified using Chemstation software (ChemStation v. 02. 02. 1431, ChemStation International, Inc., Dayton, OH, USA) algorithms that process the chromatographic signal obtained for each compound and calculate its area. The relative area, calculated by dividing this area by the area of the internal standard, is then used in the appropriate calibration equation to calculate the absolute concentration of each compound.  
The contribution of volatile compounds to the aroma properties of wine is influenced by their concentration and odor perception threshold (OPT). The Odor Activity Value (OAV), calculated as the ratio between the concentration of a compound and its OPT, is commonly used to assess the impact of a compound on the overall aroma. A more detailed description of the analytical methods for volatiles has been described in a recent paper [26].
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Convergence in the Solution
An estimate for general quadrature schemes gives  
\[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]1 \[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]2  
For the time-integration error we linearise \(j(t_{n}^{(k)},\mathbf{u}(t_{n}^{(k)}))\) about \((t_{n}^{(k)},\mathbf{u}_{n}^{(k)})\) and use Equation (16) to get  
\[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]3  
Using this for the time-integration error gives  
\[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]4 \[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]5Summing up quadrature and time-integration error results in  
\[\theta^{\text{ref}}(t_{n}):=\left(\frac{\phi_{-}^{\min}}{\|\mathbf{ \phi}_{-}(t_{n-1},\mathbf{u}(t_{n-1}))\|}\right)^{1/(\hat{p}+1)}, \theta^{\text{ref}}(0)=\left(\frac{\tau}{c_{0}^{\text{ref}}} \right)^{1/(\hat{p}+1)},\,(c_{0}^{\text{ref}}>0),\]6  
**Remark 2**. : _Using linear interpolation to compute solutions at \(t\in(t_{n},t_{n+1})\), one gets at most \(\mathbf{u}_{h}(t)-\mathbf{u}(t)=\mathcal{O}(\tau^{2/q})\). Higher orders are achievable using, for example, dense output ([11], p. 188)._  
Our idea is to use a goal oriented local error estimate to obtain step-sizes more suitable to address the error in the QoI. We first derive our error estimate and controller, for the resulting goal oriented adaptive method we show convergence in the QoI with Theorem 3. We make an analysis to predict performance in Section 4.3.
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
###### Abstract  
Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number \(t\) as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.  
sparrow search algorithm; chaotic mapping; adaptive weight; t-distribution mutations; multi-strategy; global optimization +
Footnote †: journal: Applied Sciences