esa-sceva/satcom-chunk-collection huggingface dataset:
# Kriging Interpolation in Modelling Tropospheric Wet Delay
###### Abstract  
This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are divided into three blocks: 5 stations located approximately in the center of the networks, 5 stations on the edge of the networks and 5 stations outside the networks. The zenith tropospheric wet delays are estimated at the network and user stations through the ionosphere-free positioning model; meanwhile, the predicted wet delays at the user stations are generated by the Kriging interpolation in the use of the tropospheric estimations at the network. The root mean square errors (RMSE) are calculated by comparing the predicted wet delays and estimated wet delays at the same user station. The results show that RMSEs of the stations inside the network are at a sub-centimeter level with an average value of 0.74 cm in the sparse network and 0.69 cm in the dense network. The stations on edge and outside the network can also achieve 1-cm level accuracy, which overcomes the limitation that accurate interpolations can only be attained inside the network. This contribution also presents an insignificant improvement of the prediction accuracy from the sparse network to the dense network over 1-year's data processing and a seasonal effect on the tropospheric wet delay predictions.  
GNSS; troposphere; tropospheric delay; kriging interpolation; precise point positioning 1  
[PERSON], 1,2G, [PERSON], 1,*, [PERSON], [PERSON], 2,3 and [PERSON]
## 5 Conclusions  
In this study, we explored the spatial and temporal variations of GPP and its responses to climate changes in the YRB from 2000 to 2018. The primary conclusions can be succinctly summarized as follows:  
1. The annual average GPP in the YRB was 1153.5 \(\pm\) 472.4 g C m\({}^{-2}\) yr\({}^{-1}\) from 2000 to 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low.
2. Significant increasing trends were observed in GPP over the 19-year period, with an annual increase rate of 8.86 g C m\({}^{-2}\) yr\({}^{-1}\) per year. The GPP of the Poyang Lake Basin and the Jialing River Basin grew much faster. Savannas and forests also had relatively higher GPP increasing rates. Greater emphasis should be placed on vegetation protection in the Taihu Lake Basin, as it had decreasing GPP trends.
3. Temperature was the primary climatic driver of GPP changes in the YRB. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 \(\pm\) 13.86%, 58.87 \(\pm\) 9.79%, and 27.07 \(\pm\) 15.92%, respectively. The regions with higher sensitivities to precipitation were primarily located in the upper reaches of the Yangtze River.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f14091898/s1](https://www.mdpi.com/article/10.3390/f14091898/s1)]([https://www.mdpi.com/article/10.3390/f14091898/s1](https://www.mdpi.com/article/10.3390/f14091898/s1)). Figure S1: The spatio-temporal changes of precipitation change rates (a) and significances (b) in the YRB; Figure S2: The spatio-temporal changes of temperature change rates (a) and significances (b) in the YRB; Figure S3: The spatio-temporal changes of shortwave radiation change rates (a) and significances (b) in the YRB.  
All authors contributed to the design of this research and the writing of the manuscript. Conceptualization, C.N. and Q.Y.; methodology, C.N.; software, Y.Z.; validation, R.X., C.D. and X.C.; writing--original draft preparation, C.N. and X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Public-Interest Scientific Institution, Chinese Research Academy of Environmental Sciences (Grant number 2022 YSKY-72), the National Key Research and Development Program of China (Grant number 2022 YFC3202104) and the Yangtze River Joint Research Phase II Program (Grant number 2022-LHYJ-02-0603).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Results
### Mutagenicity Assay

Table 5: The results of bacterial reverse mutation test for DKP. 1.125 mg/mL, in the experiment without metabolic activation, the binominal B-value exceeded 0.99, which means that chances of spontaneous mutations were below 1% in these treatments. However, the primary criterion for mutagenicity was FIB; thus, these treatments were not considered mutagenic. In the experiment with TA100 without metabolic activation, FIB was also below 2 and B-value was below 0.99 for each concentration, so mutagenicity was not observed in this setting. However, for the TA100 strain with metabolic activation, N-DKP was mutagenic at the highest tested concentration (FIB was above 2 and B-value = 1). It can be, therefore, concluded that N-DKP induced a base-pair substitution mutation in Salmonella typhimurium TA100 strains following metabolic modification by mitochondrial enzymes; therefore, it was found to be a mutagen. The results from Tables 5 and 6 are graphically demonstrated in Figure 6.  
Figure 6 graphically demonstrates the results of the Ames test for the experiments with DKP and N-DKP using the TA100 strain in the setting of metabolic activation. The raw data behind this graph are provided in Supplement S7: Mutagenicity assay results (Tables S11 and S13). The orange dashed line represents a two-fold increase over baseline, over which mutagenic activity was detected. The blue bars correspond to the mean number of revernants found in each concentration. The green bar represents the number of revernants scored for the negative control (solvent) and the pink bar is the positive control. Red asterisks indicate concentrations for which the B-value was above critical at 0.99. For concentrations marked with asterisks but below the dashed line, mutagenicity might be suspected; however, this prediction was not reliable as the criterion of FIB > 2 was the primary one. Only N-DKP, at a concentration of 4.5 mg/mL, following metabolic activation in the TA100 strain, provided a clear positive result for mutagenicity.
## 2 Materials and Methods
### Photocatalytic Degradation of Rhodamine B (Rh-B) under Visible Light  
The photocatalytic dye degradation efficiency of the as-prepared catalysts was evaluated under visible light irradiation at room temperature for Rh-B. A 350 W Xenon lamp (Lap Pu, XQ) was used as a light source and cut off filter with 3 mm thickness was used, the separation distance between the light source and the reaction mixture was kept 10 cm. A certain amount of the prepared photocatalyst was added to the Rh-B solution (100 mL, 30 mg L\({}^{-1}\)), each. Prior to the irradiation, the solution was stirred for 30 min in dark to establish adsorption/desorption equilibrium between the photocatalysts and the dye. Then, the solution was exposed to light irradiation with continuous stirring and 2 mL of the sample was taken out after each 30 min till 3 h. The photocatalyst was separated from the solution by centrifuging at 4000 rpm for 5 min. The Rh-B concentration was determined from absorption spectra (400 to 700 nm) using a UV-vis spectrophotometer at maximum absorption peak value of each sample approximately at 553 nm. Scavengers were added for the determination of reactive species and an explanation of the possible reaction mechanism that is involved in the photo-degradation of the Rh-B by P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite 1-5.
## 3 The Hierarchical Multi-Dimensional Aggregate Pyramid (MAP) Model
### Space-Time-Attribute Cube  
The massive datasets are transformed and aggregated along three dimensions--namely, \(S\) (Space), \(T\) (Time), and \(A\) (Attributes)--so that the Cartesian product \(S\times T\times A\) can metaphorically define the abstract concept of the space-time-attribute cube, as illustrated in Figure 1. In fact, in real datasets, the \(S\times T\times A\) cube is still a multidimensional (nD) structure; this is because the spatial coordinates contain longitude and latitude, and the objects have several different attributes.  
As building blocks for future aggregation, the \(S\times T\times A\) cube requires discrete and finite units in all three dimensions. However, the spatial and temporal domains are continuous; thus, the space and time should first be discretized via partitioning into suitable regions and time intervals, respectively. Here, space discretization is accomplished using a regular rectangle grid, i.e., GeoHash. In the temporal dimension, the data is divided and reorganized by a predefined granularity (i.e., day, week, or month); in the attribute dimension, moreover, the categorical values are aggregated using a hierarchical tree structure.  
The \(S\times T\times A\) cube supports two types of aggregation: cube aggregation and face aggregation. As illustrated in Figure 1, cube aggregation can be viewed as a contraction of eight cubes into a cube of coarser granularity. Cube aggregation resembles a common concept in computer graphics, namely Level of Details (LoD), in which complex objects are represented with varying levels of granularity so as to achieve a trade-off between the model fidelity and visualization performance. Cube aggregation provides the required function for hierarchical pyramid construction.  
Face aggregation can be metaphorically seen as a projection of the cube onto one of its faces along one dimension (see Figure 2). For example, for a given attribute, we can study the variation of the aggregate measures over space and time by means of this face aggregation process. Face aggregation will be used in client visualization due to the 2D display environment.
## 4 Design and Implementation
### Proposed HFSPDM Modulation  
The proposed Hybrid Frequency Shift-Pulse Duration Modulation technique is discussed in this section. Figure 6 shows the arrangement of frequencies to distinguish data pattern from start/stop bit pattern. Here, \(F1\), \(F2\), and \(F3\) represent three distinct frequencies that satisfy specific relations. For example, \(F1=4\times F3\), \(F1=2\times F2\) and \(F2=2\times F3\). Here, \(F1\) represents start bit pattern frequency. Identification of this frequency enables us to estimate the next frames. The data bit pattern is divided into two subparts to map with the desired bit pattern. The combination of two frequencies represents four 2-bit patterns such as 00, 10, 01, and 11. The data bit pattern is enclosed within two consecutive start bit patterns. Table 1 shows the mapping of four 2-bit patterns to their respective frequencies, duty cycles, and pulse shape. The modulation method is a Hybrid of FSK and PDM since frequency duration is used for decoding bits.
## 4 Results
### Experimental Setup
#### 4.1.2 Evaluation Metrics  
To assess the effectiveness of the proposed influence and engagement model, the mean absolute error (MAE) of the predicted follower and engagement growth rate was calculated. The following metrics were adopted to evaluate the influence and ability of the bot to evade detection. \(N(u)\) represents the number of users who will receive tweets from the bot and reflects its influence. The network influence ratio, i.e., the ratio of \(N(u)\) to the total number of users \(|V|\), was used in previous studies to evaluate influence [10; 11]. Since the scalability of the proposed bot strategy allows us to use a large social network reconstructed from a real-world dataset--rather than further dividing it into subgraphs--\(|V|\) remains consistent throughout experiments. Therefore, \(N(u)\) was used as one of the metrics for evaluating influence. Additionally, by considering the number of tweets, \(P^{i}\), or the number of views, assesses the impact caused throughout the simulation. The survival rate reflects the bot's ability to evade detection [7]. Considering that the passing threshold can be adjusted, \(D(u)\)known as the bot score, was used to evaluate the difficulty of discovering them by lowering the passing threshold. The standard deviation of \(D(u)\), \(\sum N(u)\), and \(\sum P^{i}\) of the surviving bots may also be presented for the assessment of robustness.
## 3 Discussion of Analysis Results
### Torque and Thrust Force  
Figure 10a,b present torque and thrust force data acquired during EPB shield tunnelling simulation. When the rotational speed of the screw conveyor is relatively fast compared with the penetration rate, the torque and thrust force decrease as tunnelling advances (case 2). Conversely, tunnelling with a rapid penetration rate compared with the rotational speed of the screw conveyor leads to steadily increasing torque and thrust force (case 3). However, if the proper penetration rate and rotational speed of the screw conveyor are applied, torque and thrust force are maintained without large variations (cases 1 and 4). During automatically controlled operation, the torque and thrust force settled within the defined operational range in response to variation of the operational conditions (Table 7, case 5). Figure 11a,b give the average torque and thrust force after the first 100 mm of advance to eliminate the effect of initial unstabilized data. With reference to the results in Figure 10, to increase the rate of TBM advancement (case 2) or to reduce unexpected downtime (case 3), the operational conditions should be optimized by moderating torque and thrust force during tunnelling (cases 1, 4, and 5). Accordingly, the simulated torque and thrust force data indicate that determining the optimal penetration rate and rotational speed of the screw conveyor is important for enhancing the performance of a TBM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Advance [mm]**} & \multirow{2}{*}{**Time [s]**} & \multicolumn{2}{c}{**Penetration Rate,**} & \multicolumn{2}{c}{**Rotational Speed of Screw**} & \multirow{2}{*}{**Note**} \\  & & \(PR\) [mm/s] & & \(\omega_{screw}\) [rpm] & \\ \hline
0 & 0 & 0.5 & 10 & Initiation of tunnelling \\
48.96 & 97.92 & 2 & 10 & Lower bound of thrust force \\
164.15 & 155.52 & 2 & 2 & Lower bound of chamber pressure \\
255.69 & 201.29 & 0.2 & 2 & Upper bound of torque \\
287.87 & 362.19 & 0.2 & 20 & Upper bound of chamber pressure \\
300 & 422.84 & 0.2 & 20 & End of tunnelling \\ \hline \hline \end{tabular}
\end{table}
Table 7: Variation of TBM operational conditions during EPB shield tunnelling simulation (case 5).  
Figure 10: _Cont._
## 5 Case Study
### What-If Analysis
Such functions represent performances with competitive priorities, that means the improvement of one \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Control Factors** & **Level 1** & **Level 2** & **Level 2** & **Summary** \\ \hline
1 & Minimum distance & 500 mm & 700 mm & 900 mm & Minimum distances between two generic resources \\
2 & Robot speed & 0.25 m/s & 0.5 m/s & 0.75 m/s & Speed adopted by the robot to carry out the inspection \\
3 & Logistic spaces & Same side & Consecutive sides & Opposite sides & Relative position of the infeed and outfeed spaces \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Control factors and selected levels for the Design of Experiments. another. They are reported with the SMART function in Table 6 with their expressions and the descriptions. The _impact on space_ compares the envelope of the occupied area (\(A_{r}\)) with the reference value (\(A_{t}\)) representing the total available workplace area. In this study, the reference value is 49 m\({}^{2}\). The _time_ is the sum of the single time of all the sequential tasks (\(t_{i}\)). The _HRC relevance_ is provided by the percentage of the collaborative time (\(T_{c}\)) on the total time (\(T_{t}\)). Finally, the _cost_ is the sum of the costs of each resource (\(c_{i}\)) on the workplace multiplied by the number of them (\(n_{i}\)). All the 27 configurations generated by means of the proposed approach are firstly evaluated by the four functions. Consequently, the SMART is adopted to obtain a synthetic score. It is provided by the weighted sum of the other functions once they are normalized on a scale between 0 and 100, where the worse value corresponds to 0 and the best value to 100. The necessary weights (\(a_{i}\)) assigned to the normalized functions (\(f_{i}\)), in accordance to the relative importance of each function on the collaborative workplace performances, are the following:  
* Impact on space: 38%;
* HRC relevance: 13%;
* Time: 13%;
* Cost: 38%. Indeed, the industrial experience suggests to pose a higher attention on the minimization of spaces and costs.
## 3 Rotor-Side External Retrofit Techniques
### Crowbar Method  
The crowbar is the most prevalent LVRT protection circuit adopted in DFIG-WTs. A crowbar circuit comprises a set of paralleled resistors installed between rotor windings and the AC-side of the RSC [18; 20], as shown in Figure 4. When rotor overcurrent or DC-link overvoltage occurs, the crowbar is triggered and a low-resistance path is created to allow high currents to flow through. In this operation mode, the RSC is disconnected from rotor windings via the closed crowbar switch [21] and the damage of overcurrent can be avoided. With the crowbar circuit triggered, the DFIG-WT is transformed into a standard induction motor and the excitation control is lost. In this case, the DFIG consumes a large amount of reactive power from the power grid via stator windings, which may deteriorate the terminal voltage dynamics. In general, removing the crowbar in a timely manner can make the RSC provide reactive power to the grid as soon as possible to accelerate the grid voltage recovery. However, more crowbar connection time is needed to ensure that the natural flux is damped up to a desirable level so that the RSC can recover the DFIG control [22]. Therefore, the crowbar connection time should be chosen carefully to improve the transient stability of DFIG-WTs. Another important factor which influences the crowbar performance is the crowbar resistance. On one hand, low crowbar resistance can lead to serious rotor overcurrent and electromagnetic torque with a large peak [22]. On the other hand, increased crowbar resistance can lead to rotor overvoltage and DC-link overvoltage, which may cause damage to rotor windings. Thus, an appropriate value of the crowbar resistance should be selected to meet the aforementioned issues.  
Figure 4: Crowbar protection circuit.  
To improve the control effect based on these important factors, many crowbar modifications were proposed in terms of both configuration and strategy. An analytical expression for crowbar resistance was derived in Reference [23]. By taking grid impedance into consideration, the appropriate crowbar resistance can be calculated, which shows desirable performance in practical applications. Crowbar resistance chosen via the traditional method is close to the margin of LVRT security region. The parallel R-L configuration crowbar with series R-L circuit, as shown in Figure 5, was proposed in Reference [20]. During the transient period, the parallel R-L configuration crowbar is activated and maintains a connection to the terminal of rotor windings and the RSC. The smooth transition can be guaranteed through the injection of the proposed circuit.
## 2 Materials and Methods
### Spiking Network Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Symbol** & **Value** & **Unit** \\ \hline
**Cellular Properties** & & & \\   Leak conductance & \(g_{L}\) & 10 & nS \\   Leak reversal potential & \(E_{L}\) & \(-65\) & mV \\   Membrane capacitance & \(C_{m}\) & 200 & pF \\   Resting voltage & \(v_{rest}\) & \(-65\) & mV \\   Action Potential & \(v_{thr}\) & \(-50\) & mV \\   threshold & \(T_{refr}\) & 5 & ms \\   Refractory period & \(T_{refr}\) & 50 & ms \\   Adaptation & \(\tau_{w}\) & 500 & ms \\   time constant & & & \\  _Excitatory Neuron_ & & & \\   Spike sharpness & \(\Delta_{e}\) & 2 & mV \\   Adaptation current & \(b_{e}\) & varies & pA \\   increment & & & \\   Adaptation conductance & \(a_{e}\) & 4 & nS \\  _Inhibitory Neuron_ & & & \\   Spike sharpness & \(\Delta_{i}\) & 0.5 & mV \\   Adaptation current & \(b_{i}\) & 0 & pA \\   increment & & & \\   Adaptation conductance & \(a_{i}\) & 0 & ns \\
**Synaptic Properties** & & & \\  _Excitatory Neuron_ & & & \\   Reversal potential & \(E_{e}\) & 0 & mV \\   Quantal conductance & \(Q_{e}\) & 1 & nS \\   Decay time of & & & \\   synaptic conductance & \(\tau_{e}\) & 5 & ms \\
**Inhibitory Neuron** & & & \\   Reversal potential & \(E_{i}\) & \(-80\) & mV \\   Quantal conductance & \(Q_{i}\) & 5 & nS \\   Decay time of & \(\tau_{i}\) & 5 & ms \\   synaptic conductance & & & \\
**Network Properties** & & & \\   Total network size & \(N\) & 10,000 \\   Connectivity probability & \(p\) & 0.05 \\   Fraction of & & & \\   inhibitory cells & \(g_{ei}\) & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 3 Addition of Stabilization
### Problem Definition  
The problem of adding of stabilization begins with a program \(p=\langle S_{p},\delta_{p}\rangle\), its invariant \(S\) and an environment \(\delta_{e}\). The goal is to change the set of program transitions so that starting from an arbitrary state, the program recovers to \(S\). In addition, we do not want to change the behavior of the program inside its invariant. Thus, during the addition, we are not allowed to change the set of program transitions in the invariant (i.e., \(\delta_{p}|S\)). Also, we introduce parameter \(\delta_{r}\) that identifies additional restrictions on program transitions. As an example, consider the case where a program cannot change the value of a sensor, that is, it can only read it. However, the environment can change the value of the sensor. In this case, transitions that change the value of the sensor are disallowed as program transitions but they are acceptable as environment transitions. Thus, the problem statement is as follows:Given program \(p\) with state space \(S_{p}\) and transitions \(\delta_{p}\), invariant \(S\), environment \(\delta_{e}\), set of program restriction \(\delta_{r}\) and \(k>1\), identify \(p^{\prime}\) with state space \(S_{p}\) such that:  
\begin{tabular}{l l}
**C1** & \(p^{\prime}|S=p|S\) \\
**C2** & \(p^{\prime}\) is \(k\)-stabilizing for invariant \(S\) in the environment \(\delta_{e}\). \\
**C3** & \(\delta_{p}^{\prime}\cap\delta_{r}=\emptyset\) \\ \end{tabular}