esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Protein Expression and Purification  
For the expression of the PCI_Lip WT and its mutants, 15 mL LB medium containing 25 \(\upmu\)g mL\({}^{-1}\) kanamycin and 20 \(\upmu\)g mL\({}^{-1}\) chloramphenicol were inoculated with 7.5 \(\upmu\)L cryostat and incubated overnight at 37 \({}^{\circ}\)C and 180 rpm. For the main culture, 400 mL TB medium (with the same antibiotics and concentrations as in the overnight cultures) were inoculated with the whole overnight culture in a 2 L baffled shake flask. A total of 0.5 mg mL\({}^{-1}\) L-(+)-arabinose and 5 ng mL\({}^{-1}\) tetracycline were added to induce the expression of chaperones. The culture was incubated at 37 \({}^{\circ}\)C and 180 rpm to an ODof 0.8. Subsequently, the expression of the target protein was induced by adding 0.5 mM IPTG, and the temperature was reduced to 20 \({}^{\circ}\)C. After 20 h of incubation, the culture was harvested by centrifugation (3500\(\times\)\(g\) at 4 \({}^{\circ}\)C, 10 min). The _E. coli_ pellets were washed twice with 25 mL 80 mM potassium phosphate buffer, pH 7.0, and stored at \(-\)20 \({}^{\circ}\)C.  
The _E. coli_ pellets were resuspended in 5 mL 90% binding buffer (50 mM sodium phosphate buffer with 0.3 M sodium chloride at pH 7.5) with 10% elution buffer (binding buffer with the addition of 250 mM imidazol). After three times sonication for 2.5 min with a sonifier MS72 (microtip diameter 5 mm, cycle 5 and the amplitude 50%, Bandelin Electronic, Berlin, Germany,) on ice, the suspension was centrifuged (3500\(\times\)\(g\) at 4 \({}^{\circ}\)C, 10 min). The supernatant was again centrifuged (14,000\(\times\)\(g\) at 4 \({}^{\circ}\)C, 10 min) and subjected to immobilized metal ion affinity chromatography (IMAC). IMAC purification was performed at an NGC Quest 10 (Bio-Rad Laboratories) using a HisTrapTM High Performance 5 mL Ni-NTA column (Cytiva Europe, Hoegaard, Belgium). The IMAC method comprises the application of one column volume (CV) sample with a flow rate of 1.5 mL min\({}^{-1}\) prior to two washing steps with the first 5% elution buffer for four CVs and then at 30% for another four CVs. The target protein was eluted with 100% elution buffer for two CVs.  
After IMAC purification, the sample was desalted using three connected HiTrap(r) Desalting columns (Cytiva Europe) with a CV of 5 mL each. Therefore, two CV desalting buffer (80 mM potassium phosphate buffer, pH 7.0) was used at a flowrate of 2 mL min\({}^{-1}\). As the last purification step, a second size exclusion purification was performed in a centrifugal filter with a molecular mass cut-off of 30 kDa (Merck) by washing the samples up to ten times with desalting buffer. To control the successful expression of the PCI_Lip WT and the respective mutants, sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) of the purified enzyme fractions was performed according to Laemmli [27] with a 4% stacking and 12% resolving gel. The gels were stained with Coomassieie Brilliant Blue R 250. The purified sample was stored at 4 \({}^{\circ}\)C for further experiments.
## 6 Conclusions  
The presented work proposed for the first time in the field of radiometric SAR system calibration to exploit hierarchical Bayesian data analysis. It was claimed that Bayesian statistics is well suited to the analysis of calibration data because of the following key factors:  
* Within Bayesian statistics, probability distributions are used in describing model parameters. The distributions convey a meaning of uncertainty. Bayesian statistics is therefore an appropriate choice for calibration, where an estimated parameter is meaningless without a statement of its uncertainty.
* Hierarchical joint probability models are well suited to describe data that is typically acquired during an external radiometric SAR calibration campaign. During data analysis, depending on the research question, parameters often need to be estimated on different levels or for different groups. Hierarchical Bayesian modeling is well suited to derive model parameters for different interdependent parameters, especially when numerical methods like Markov chain Monte Carlo simulations are used.  
The applicability of the method for radiometric calibration was demonstrated through a case study. The data of an external calibration campaign was analyzed. The campaign goal was to derive the ERCSof DLR's C-band Kalibri transponder prototype. Due to hierarchical Bayesian data analysis, it was possible to estimate and compensate the overpass-dependent drift of the RADARSAT-2 system and to derive the transponder ERCS with a remaining standard uncertainty (according to GUM) of only 0.21 dBm\({}^{2}\).  
In order to convert the case study approach to an operational uncertainty analysis procedure for SAR missions, a database of point-target measurements should be set up. Filling the database incrementally with measurements of permanently installed reference point targets over the mission lifetime would allow one to continually derive radiometric uncertainty estimates based on Bayesian statistics.  
The authors are convinced that Bayesian statistics and hierarchical modeling are an important step toward _traceable_ radiometric SAR system calibration. Uncertainties and confidence intervals can now be derived with the same diligence that is presently applied to the estimation of calibration parameters.  
The campaign was only feasible due to the commitment and collaboration of several partners and many DLR colleagues. [PERSON] from MDA Systems Ltd. rendered the campaign possible by organizing the acquisitions and by providing the processed RADARSAT-2 datatakes. The EDMO airport in Oberpfaffenhofen and namely [PERSON] greatly simplified the campaign work by permitting the installation of all utilized 1.5 corners right on the grassland surrounding the airstrip, directly next to the DLR premises.  
On the side of DLR, the campaign was made possible by [PERSON] and the additional campaign team members [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
# Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
## 2 Related Work
OVAR aims to extract a forwarding set with maximum EPA from the adjacency graph, which excludes any hidden node. Both VAPR and OVAR adopt EPA-based candidate selection strategies similar to HydroCast, the main difference being the local neighbor information. Link-State Routing Protocol-A location unaware message forwarding technique [17] uses independent and local forwarding decisions to select next hop forwarder on-the-fly based on its link transmission reliability and reachability to the gateway, which is estimated by exchanging one-hop control packets. Moreover, an accumulate-and-forward concept is applied to deal with the large propagation delay. An enhanced version of the work in [17] proposed in [22] adopts an adaptive technique to reduce the end-to-end delay by dynamically calculating the data hold time at each node. The metrics include current buffer occupancy, the delay experienced by stored data packets, arrival and service rate, data transmissions from neighbors and reachability. A location-free link state routing (LCSR) [23] uses a greedy hop-by-hop routing by relying on the parameters such as hop count, path quality, and pressure, which is supplied by the periodical beacon. In different transmission cases, the three parameters will be used respectively to select the next-hop forwarder. As mentioned before, the geographic-based routing protocols require the underwater positioning techniques. Because of the time-varying characteristics of underwater acoustic channels and the higher propagation delay of the acoustic signals, the link-state routing protocols should be supported by the practical link-state estimation techniques. In this paper, instead of geographic information, each node can provide its depth information by the pressure sensor, similar to the depth-based routing protocols. Different from the previous works on the candidate set selection, we focus on the effect of candidates location distribution and candidate set size on path reliability.
## 2 Materials and Methods  
The commercial formulations of the tested agrochemicals were: TrekP(r) (ADAMA AGAN, Ltd., Ashdod, Israel) for the herbicide Terbuthylazine, Confidor(r) (Bayer AG, Leverkusen, Germany) for the insecticide Imidacloprid, and Ortiva(r) (Syngenta Italia, Milano, Italy) for the fungicide Azoxystrobin. The chemical structure of the active ingredients is reported in Figure 1.  
The H2O2 solution (30%) Perhydrol(r), used as a catalyst in the AOP experiments, was supplied by Merck Life Science (Milano, Italy). The LCK-385 test-in-cuvette kit for the total organic compound determination was supplied by Hach Lange (Milano, Italy). lyophilized aliquots of the luminescent bacteria _V. fischeri_ were prepared from fresh cultures at our laboratory starting from an original batch supplied by the Pasteur Institute (Paris, France). The 96-wells \"Black Cliniplate\" microplates were supplied by Thermo Scientific (Vantaa, Finland) and the luminometer was the Victor Light 1420 model from Perkin-Elmer, USA. The Istituto Zooprofialttico Sperimentale of Abruzzo and Molise \"[PERSON]\" (Teramo, Italy) supplied the freshwater microalga _P. subcapitata_ culture. Inorganic salts and nutrient broth components for the bioluminescent bacteria and algal growth were obtained from Sigma-Aldrich. Tap water was chosen as the solvent of the agrochemicals.  
A DR5000 spectrophotometer (Hach Lange, Milano, Italy) was employed to evaluate the active ingredients concentration and the total organic compounds (TOC) content.  
The photoreceptor prototype was assembled at the Proambiente laboratories by using materials currently employed in AOP experiments (UV lamps and stainless steel components).  
Figure 1: The chemical structure of the compounds under study: (**A**) imidacloprid, (**B**) azoxystrobin, and (**C**) Terbuthylazine.
## 3 Results
### Physico-Chemical Characterization of PG  
SEM was utilized to examine the surface structure of the material and study the morphology of the PG. The image displayed an irregular and porous structure with heterogeneous pore shapes across the entire surface of the PG sample (Figure 1).  
XRD was carried out to determine the structure of the PG. The results from XRD analysis revealed that all observed lines were attributed to calcium sulfate dihydrate (CaSO\({}_{4}\cdot\)2H\({}_{2}\)O), with three main lines located at approximately 20 = 12\({}^{\circ}\), 20.5\({}^{\circ}\), 23\({}^{\circ}\), and 29\({}^{\circ}\). The presence of a line attributed to quartz was also observed, located at 20 = 26\({}^{\circ}\) (Figure 2) [31; 32].  
Figure 1: SEM pictures of phosphogypsum (\(\times\)1.4k).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**KI** & **Compounds** & **Percentage (\%)** \\ \hline
991 & Myrcene & 1.16 \\ \hline
1005 & \(\alpha\)-phellandrene & 0.01 \\ \hline
1011 & \(\delta\)-3-carene & 0.42 \\ \hline
1189 & \(\alpha\)-terpineol & 1.43 \\ \hline KI: Kovat index. & & \\ \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: XRD spectrum of phosphogypsum.  
The XRF analysis confirmed the XRD results and identified significant percentages of sulfur trioxide (50.11%) and calcium oxide (38.52%) in the PG sample (Table 2).  
TGA analysis was used to investigate the thermal information of the PG sample. The results showed a total weight loss of approximately 21%, with DT analysis revealing two peaks. The first peak appeared around 134 \({}^{\circ}\)C and was attributed to the dehydration of gypsum, while the second peak appeared at 431 \({}^{\circ}\)C and was attributed to the oxidation of organic matter (Figure 3) [33; 34].  
FTIR analysis was used as a complementary analysis to all previous analyses. The spectrum of the PG sample showed the vibrations of the SO4 bonds at bands: 608 cm\({}^{-1}\), 672 cm\({}^{-1}\), and 1150 cm\({}^{-1}\). The vibrations of the H2O bonds appeared at the bands: 1640 cm\({}^{-1}\), 1692 cm\({}^{-1}\), and 3440 cm\({}^{-1}\) (Figure 4) [35; 36].  
Physicochemical properties of the PG material were determined, including density, pH, conductivity, organic matter content, mineral matter content, and specific surface area. These properties were measured and presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**Mineral Components** & **SO\({}_{3}\)** & **CaO** & **SiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **F** & **Na\({}_{2}\)O** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **SrO** & **K\({}_{2}\)O** & **Y\({}_{2}\)O\({}_{3}\)** & **TiO\({}_{2}\)** & **PAF** \\ \hline
**(\%)** & 50.11 & 38.52 & 2.65 & 1.22 & 1.00 & 0.25 & 0.23 & 0.06 & 0.05 & 0.05 & 0.02 & 0.02 & 0.01 & 5.81 \\ \hline \end{tabular}
\end{table}
Table 2: The identified mineral components through XRD.  
Figure 3: Thermal analysis of phosphogypsum: TGA-DT.
## 2 Statistical Distribution
### Normal Distribution  
The most frequently employed statistical distribution is the normal distribution, also referred to as the Gaussian distribution. A normal distribution characterizes data distributions through the use of the mean and standard deviation. The probability density function (PDF) of a normal distribution is expressed as  
\[\mathrm{f}(\mathrm{t})=\frac{1}{\sigma\sqrt{2\pi}}\mathrm{exp}\{-\frac{1}{2} \left(\frac{\mathrm{t}-\mu}{\sigma}\right)^{2}\}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distribution** & \multicolumn{2}{c}{**When to Use**} & **Characteristics** \\  & **(Failure Rate Type)** & **Real-number range** \\ \hline \multirow{2}{*}{Normal} & Wear-out & **Real-number range** \\  & (IFR) & Could be unsuitable for lifetime analysis** \\ \multirow{2}{*}{Log-normal} & Wear-out & **Failure of natural deterioration** \\  & (IFR) & **Failure of natural deterioration** \\ \multirow{2}{*}{Exponential} & Useful life & **Constant probability of failure** \\  & (CFR) & **Failure rate changes according to time** \\ \multirow{2}{*}{Weibull} & Early, Useful, Wear-out & **Failure rate changes according to time** \\  & (DFR, CFR, IFR) & **Failure rate changes according to time** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Purpose and characteristics by statistical distribution.  
where \(\mu\) and \(\sigma\) represent the mean and standard deviation, respectively. Equation (2) delineates the reliability function of a normal distribution. The reliability function signifies the probability of a failure occurring at time t. Conversely, the unreliability function, calculated as one minus the reliability function, denotes the probability of failure occurring at time t. Particularly, in the analysis of lifetime data, cumulative distribution function (CDF) values may become less dependable.  
\[R(t)=\int_{ln(t)}^{\infty}\frac{1}{\sigma^{\prime \sqrt{2\pi}}}exp\{-\frac{1}{2}\bigg{(}\frac{x-\mu^{\prime}}{\sigma^{\prime}} \bigg{)}^{2}\}dx, \tag{2}\]  
In the lifetime data analysis, a normal distribution is frequently applied, particularly in regions related to wear-out failure. However, since a normal distribution is defined over the entire real-number line, caution must be exercised when handling negative values in the PDF for a lifetime analysis.
## 2 Methods
### Active Maturity Classification
#### 2.2.3.1 Initial Maturity Classification  
Pepper maturity was classified into mature or immature using a random forest (RF) classifier based on features (Section 2.2.2) from the initial viewpoint (defined as viewpoint 0). The RF model hyperparameters were selected using the validation dataset. The selected RF classifier was composed of 1000 trees with 1 randomly sampled variable at each node for red peppers and 2 variables for yellow peppers.  
Training was performed using the color features from different combinations of viewpoints (viewpoint 0, all six viewpoints combined, and all combinations of two viewpoints--each of the five viewpoints combined with viewpoint 0). The classification certainty, \(p_{class}\), was derived from the percentage of trees holding the same classification.  
Additional Viewpoint Decision  
An additional viewpoint is acquired when the expected revenue from the additional viewpoint is higher than the costs to take the viewpoint, according to the following rule:  
\[(p_{adv}-p_{class})\cdot c_{pepper}>t_{av}\cdot c_{robot}, \tag{1}\]  
where \(p_{class}\) is the classification certainty from the initial viewpoint, \(c_{pepper}\) is the cost of misclassifying one pepper, \(c_{robot}\) is the cost of one second of robot operation time, and \(t_{av}\) is the time of adding an additional viewpoint (viewpoint specific, measured on forehand).  
Adding a viewpoint improves the classification accuracy; however, it does not bring the classification error to zero [31]. The lower the classification certainty in viewpoint 0, the more improvement in classification accuracy can be expected when adding an additional viewpoint. The classification certainty when using the additional viewpoint,  
Figure 6: Dynamic viewpoint selection process.  
\(p_{adv}\), is predicted using a linear regression model using \(p_{class}\) and is trained on the training set. The cost of misclassifying one pepper, \(c_{pepper}\), is set to the market price of a single pepper. This price is highly variable with prices for red sweet peppers in 2019, ranging from EUR 0.048 to EUR 0.599 per piece with an average of EUR 0.196 per piece [41]. Yellow peppers prices ranged from EUR 0.056 to EUR 0.542 per piece with an average of EUR 0.202 per piece in 2019 [41]. With an expected cost of a sweet-pepper-harvesting robot between EUR 75,000 and EUR 100,000, an operational time of 20 h/day and payback time of 7 years [42], the costs of one second of robot operation, \(c_{robot}\), is estimated between EUR \(4.08\times 10^{-4}\) and EUR \(5.44\times 10^{-4}\) with an average of EUR \(4.76\times 10^{-4}\).
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 5 Two-Dimensional Slope Stability Analysis  
### Results of Slope Stability  
A two-dimensional stability analysis of Huasushu slope was carried out using the Morgenstern-Price method based on seepage field data. The stability factor for Case 1 was 1.077. A slope was considered stable when its stability factor exceeded 1.0. Case 1, therefore, represented a stable state under hydrostatic conditions; that is, the slope remained stable when the reservoir water level was not changing.  
Figure 20: (**a**) Equivalent plastic strains under Case 1. (**b**) Equivalent plastic strains under Case 2. (**c**) Equivalent plastic strains under Case 3. (**d**) Equivalent plastic strains under Case 4. (**e**) Equivalent plastic strains under Case 5.  
The stability coefficients under other working conditions were calculated using the finite element model, which coupled the saturated-unsaturated seepage and stress fields to analyze the impact of reservoir water level fluctuations and rainfall. The results are shown in Figure 21.  
Figure 21a shows that the slope stability coefficient demonstrates a continuous decay trend with time when the reservoir level slowly decreases from 175 m to 145 m. The slope stability coefficient for the 50-year rainfall conditions decreased from 1.077 to 1.022, indicating that rainfall infiltration significantly increased the risk of slope instability. The mechanism can be attributed to the dynamic response of the seepage field triggered by the coupling of the reservoir water level decline and rainfall infiltration. Notably, the effect of rainfall on slope stability is not obvious at its early stage, showing that the effect of rainfall intensity on slope stability at this stage has a time lag characteristic.  
As shown in Figure 21b, the slope stability coefficients still show a decreasing characteristic when the reservoir water level decreases from 162 m to 145 m. Comparative analysis shows that the slope stability coefficient under the 50-year rainfall conditions is significantly differentiated after 2 d. Compared with the single reservoir water level condition, the stability coefficient under the coupled rainfall condition intensifies up to 1.03, revealing the lag effect mechanism of the unsaturated matrix suction dissipation under the decreasing water level conditions.  
The coupling analysis showed that the most dangerous condition for the Huasushu slope's stability was Case 3 (a large decrease in reservoir water level from 175 m to 145 m combined with 50 years of rainfall). However, under these conditions, the minimum value of the slope stability factor was still 1.022, indicating that the slope was essentially stable.
# Research on Permanent Magnet Synchronous Motor Control System Based on Adaptive Kalman Filter
## 2 Theoretical Analysis of the EKF
### The EKF Model of the PMSM
\begin{array}{l}K_{k}=P_{k|k-1}H^{T}[HP_{k|k-1}H^{T}+R]^{-1},\\ \hat{x}_{k}=\hat{x}_{k|k-1}+K_{k}(y-H\hat{x}_{k|k-1}),\\ P_{k}=(I-K_{k}H)P_{k|k-1}\end{array}  \tag{10}\]  
where \(\hat{x}_{k|k-1}\) is a priori estimate at the time k, \(P_{k|k-1}\) is a priori estimation covariance matrix, \(Q\) is the state noise covariance matrix, \(K_{k}\) is the Kalman gain matrix, \(R\) is the measurement noise covariance matrix, \(\hat{x}_{k}\) is the optimal estimate at time \(k\), \(P_{k}\) is the optimal estimated covariance matrix. According to the EKF model of PMSM, the EKF observer can divide into two steps. The first is the prediction link corresponding to Equation (9). According to the optimal estimation value of the state variable obtained at time \(k-1\), the system state variable at the current time is estimated a priori. The prior estimation value \(\hat{x}_{k|k-1}\) of the system state variable can be obtained. Then, the covariance matrix \(P_{k|k-1}\) of a priori estimation is calculated to obtain the Kalman gain matrix. The second is to update the state variable, which corresponds to Equation (10). The observation error and the minimum variance principle are used to modify the prior estimate of the prediction process. Then the optimal estimate value \(\hat{x}_{k}\) and the optimal estimate variance matrix \(P_{k}\) of the state variable are obtained. However, the speed estimation of the control system based on the PMSM mathematical model requires accurate EKF parameters.