esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 4 Performance Evaluation
### Performance Measurement Parameters  
In order to better verify the performance of the RATP algorithm, the following metrics are used for evaluation:  
RMSE: It is used to calculate the geometrical space error between the real and the predicted position, that is, the geometric space errors between the predicted trajectory points and the actual trajectory points.  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{j=1}^{N}{\left({real_{j}-pred_{j}}\right)^{2}}}} \tag{22}\]  
where \(real_{j}\) is the real location \((x_{real},y_{real})\), \(pred_{j}\) is the predicted location \((x_{pred},y_{pred})\), and N is the number of predicted trajectory points.  
Delivery ratio: The number of packets successfully received by all the destination nodes in the network accounts for the ratio of the number of packets sent by the source node within a certain time. The higher the value, the higher the transmission success rate in the network.  
End-to-end delay: The end-to-end delay is the average time that packets takes place from the initial node to the destination node.  
Routing overhead: The total number of forwarded packets (FoM) minus the number of packets successfully received by the destination node (ReM), and then divided by the number of packets successfully received by the destination node. The formula is as follows:  
\[\text{ROR}=\frac{\text{FoM}-\text{ReM}}{\text{ReM}} \tag{23}\]
# Automatic Segmentation of Ethnomusicological Field Recordings
## 2 Materials and Methods
### Segmentation
\(S_{i1}\) starts at time 0 and ends at candidate boundary, \(B_{i1}\), \(S_{i2}\) starts at candidate boundary, \(B_{i1}\), and ends at \(B_{i2}\), \(S_{i3}\) starts at \(B_{i2}\) and ends at \(B_{i3}\), and so on. We treat each candidate boundary, \(B_{i}\), as a discrete random variable with two outcomes: Either the candidate boundary represents an actual boundary and splits the recording into two segments, or not. The probability mass function for the variable is defined as the maximum of both likelihood curves:  
\[P(B_{i}=true)=\max(p_{s}(t_{i}),\ p_{t}(t_{i})). \tag{1}\]  
In our model, the probability of each segment is only dependent on the location of the previous segment, so we can express the joint probability of all segments as:  
\[P(S_{i1})P(S_{i2}|S_{i1})P(S_{i3}|S_{i2})\ldots\ P(S_{iN}|S_{iN-1}). \tag{2}\]  
To calculate the probability of placing a segment boundary at \(S_{i}\) given \(S_{j}\), we consider all candidate boundaries within the segment, as well as its duration. If the segment is to start at time \(j\) and end at \(i\), values of all candidate boundary variables within the segment must be _false_, while the value of the candidate boundary variable at time \(i\) must be _true_. Segmentation can be further constrained by previous knowledge of the typical lengths of segments, \(D_{i}\), given their class, leading to the following formulation:  
\[P\big{(}S_{i}|S_{j}\big{)}=P\big{(}D_{i}\big{|}S_{i},\ S_{j}\big{)}P(B_{i}= true)\prod_{j<k<i}P\big{(}B_{j}=false\big{)}. \tag{3}\]  
Probability, \(P\big{(}D_{i}\big{|}S_{i},\ S_{j}\big{)}\), of the segment duration given its boundaries is dependent on the class of the segment, as calculated by the deep classifier. Probability, \(P\big{(}C_{i}=c\big{|}S_{i},\ S_{j}\big{)}\), that the segment, \(i\), belongs to class \(c\) is calculated by averaging all class labels within the segment. By analyzing durations of segments in our collection of field recordings, we estimated the means and standard deviations for all segment classes \((\mu_{c},\ \sigma_{c})\); for example, the duration of speech segments varies a lot and ranges from several seconds to over 10 min, while the average length of choir singing segments is around three minutes and their standard deviation below two minutes. By additionally enforcing minimal segment duration, \(D_{min}\), we obtain the following expression for the probability of segment duration:  
\[P\big{(}D_{i}\big{|}S_{i},\ S_{j}\big{)}=\left\{ \begin{array}{l}\sum\limits _{c}P\big{(}C_{i}=c\big{|}S_{i},\ S_{j}\big{)}G(i-j,\mu_{c},\sigma_{c})\\ 0,\qquad i-j<D_{min}\end{array} \right., \tag{4}\]  
where \(G\) is the unscaled Gaussian function. We find the sequence of segments that maximizes the joint probability as given in Equation (2) with dynamic programming, which leads to a simple and efficient solution. After segmentation is calculated, segments can be labeled by finding the class, \(c\), that maximizes \(P\big{(}C_{i}=c\big{|}S_{i},\ S_{j}\big{)}\).
# Hybrid Simulation of Seismic Responses of a Typical Station with a Reinforced Concrete Column
## 7 Conclusions  
Although the hybrid simulation technique with RC specimens could be applied to reproduce the seismic behavior of the typical subway station, there were still some shortcomings that need to be overcome. The first shortcoming was the application of thrust. As thrust was not applied in the test, gravity analysis was not performed. Such a loading condition did not coincide with the real condition. The second shortcoming was the assumption of the inflection point. The bottom half column was adopted as the physical substructure on the condition that the position of the inflection point would not change during the test. Limited to the laboratory conditions, such an assumption was made to meet the requirements of the hybrid test. The third shortcoming was the constitutive model of the deposit. The parameters of the soil constitutive model should be calibrated with the corresponding test results. However, such work was not promoted in this paper. Hence, the aforementioned three factors could be used as predictors to further improve the accuracy of the hybrid simulation and estimate the seismic behavior of the typical station.  
The hybrid simulation technique was shown to be a feasible solution of conducting large-scale or full-scale seismic tests of underground structures. In this paper, a series of tests, composed of hybrid tests and quasi-static tests, were performed on the central column of the typical subway station. The physical substructure was an RC column, while the numerical substructure was the rest of the model. After the definition of the experimental beam-column elements in OpenFresco, the lateral stiffness of the central column was represented. With the proposed evaluation index, the test results showed a good agreement with the numerical results in the elastic stage. Therefore, hybrid simulation was proven to be a suitable way to assess the seismic performance of a typical subway station. It should be noted that in the hybrid simulation cases of 0.22 g and 0.58 g, the ultimate shear forces were smaller than the corresponding analytical results whilst the peak drifts were larger. The stiffness of the central column decreased, and the central column gradually entered its plastic state in the aforementioned hybrid simulation cases.  
Conceptualization, C.Y. and Y.Y.; Data curation, X.C.; Investigation, X.C.; Methodology, C.Y. and X.C.; Software, X.C.; Supervision, Y.Y.; Writing--original draft, X.C.; Writing--review & editing, C.Y. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Committee of Science and Technology (16 DZ1200302).  
The authors declare no conflict of interest.  
Figure 15: Reduction of the secant stiffness.
## 2 Materials and Methods
### Preparation of MIP Membranes  
Polypropylene hollow fibre membranes were received in pieces of 53.2 cm and were cut into small 6 cm pieces and cleaned with acetone for 5 min in an ultrasonic bath. Then, one end was sealed by mechanical pressure, and polymeric microspheres were introduced at the other end. For that purpose, a slurry of 100 mg of MIP homogenized in 1 mL of ACN with an ultrasonic bath was first prepared. Subsequently, the slurry was injected into the hollow fibre with the help of a medical syringe with needle. The solvent leaked trough the pores of the membrane leaving the MIP microspheres inside the lumen. After filling, the other end of the fibre was closed by mechanical pressure, and both ends were immediately tied together with a thread to ensure the seal. The same procedure was performed for NIP microspheres. Packed fibres were stored at room temperature, being stable both in dry conditions or immersed in toluene for at least three months.  
Hollow-Fibre Membrane-Protected Molecularly Imprinted Microsolid-Phase Extraction (HFM-Protected-MI-MSPE)  
All the steps for the extraction procedure were performed under orbital stirring in a Vibramax 100 agitator (Heidolph, Schwabach, Germany). For conditioning, the packed fibres were immersed in a conventional 4.5 mL screw cap glass vial (Supelco, Belefonte, PA, USA) containing 4.5 mL of toluene and stirred at 750 rpm during 5 min. Then, the conditioned fibres were immersed in 4.5 mL of toluene soil extracts for 45 min at 750 rpm. Subsequently, fibres were washed for 5 min with 4.5 mL of a mixture Toluene / ACN 98:2 (\(v/v\)) at 750 rpm. Then, fibres were dried at 60 \({}^{\circ}\)C for 30 min and introduced in HPLC inserts containing 450 \(\upmu\)L of a mixture of MeOH:HOAc 99:1 (\(v/v\)) for elution for 30 min at 1050 rpm.
## 5 Results and Discussions
### Trajectory Optimization for Collision Avoidance  
It is assumed that a user segment satellite (Figure 5b) is present in an orbit that intercepts the orbit of the tracked RSO that was tracked in both the tracking scenarios, indicating a possible collision event. In order to avoid the total uncertainty volume, the spacecraft performs a collision avoidance manoeuvre based on the values obtained from Tables 5 and 7 to reorient its initial orbit to a new orbit with a semimajor axis increased to 4.7 km in the SBSS scenario and 5.4 km in the ground-based tracking scenario (in-plane); failing to do so may result in a collision. It is assumed that the spacecraft that performs the orbit-raising manoeuvre is equipped with Nano Avionics EPSSC1 [95], which can generate a thrust of 1 N with a specific impulse of 213 seconds. An orbit raising manoeuvre (in-plane) is preferred over the out-of-plane manoeuvre (inclination change) due to its optimality in terms cost and manoeuvre time. Table 8 tabulates the orbital parameters of the spacecraft before and after the manoeuvre.  
The control parameters for the constant thrust directions are tabulated in Table 9 for both ground-based and space-based tracking scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cartesian Co-Ordinates** & \(\mathbf{x_{t}}\) **(km)** & \(\mathbf{y_{t}}\) **(km)** & \(\mathbf{z_{t}}\) **(km)** & **R (km)** \\ \hline
**RSO-state parameters** & 732.38 & \(-\)2240.1 & 11,285 & 2818.1 \\ \hline
**Total Error (\(\mathbf{\sigma^{\prime}s}\))** & 5.4 & 7.6 & 21.55 & 7.78 \\ \hline \hline \end{tabular}
\end{table}
Table 7: RSO position estimates and the corresponding errors.  
Figure 12: Uncertainty volumes around the tracked RSO in ground-based scenario.  
According to the results illustrated in Figures 13 and 14, the total reorientation time in the SBSS and the ground-based scenarios is 100 min and 130 min, respectively. It can be observed that the thrust vector angles start from 0\({}^{\circ}\) at time t = 0 and return to 0\({}^{\circ}\) at the reorientation time, indicating the end of the orbit raising manoeuvre. The trajectory change from the initial trajectory to the final optimal trajectory is illustrated in Figures 15 and 16.  
The proposed approach results in orbit raising manoeuvres that require minimum \(\Delta\)V and manoeuvre time taking into account actual conditions, all modelled perturbations, and any chosen thrust profile, thereby granting the spacecraft with responsive trajectory planning and navigation capabilities.  
Figure 16: Change in semimajor axis from initial to final trajectory (ground-based scenario).  
Figure 15: Change in semimajor axis from initial to final trajectory (SBSS).
# An LLC Converter with Capacitive Insulation
## 3 Design Considerations
### Design of Resonant Tank  
By considering the unpredictable parasitic impedance caused by PCB traces, in practice, the voltage gain operating point at full load is usually set to be slightly lower than 1, generally 0.98. Based on \(N_{P}=28\), the equivalent load impedance \(R_{ac-FL}\) at full load and the equivalent load impedance \(R_{ac-LL}\) at light load can be determined as follows:  
\[R_{ac-FL}=\frac{8\cdot N_{P}^{2}}{\pi^{2}}\cdot R_{o}=190.6\;\Omega, \tag{2}\]  
\[R_{ac-LL}=10.\frac{8\cdot N_{P}^{2}}{\pi^{2}}\cdot R_{o}=1960.5\;\Omega, \tag{3}\]  
Compared to conventional LLC converters, which generally use a film capacitor as a resonant capacitor, the capacitance of the film capacitor has a low temperature coefficient of \(\pm\)5%, so the capacitance can be considered a constant value when designing a conventional LLC converter. The proposed circuit uses Y1 capacitors for the resonant tank. Considering that the capacitance of the Z5U grade capacitor will change with the temperature, the higher the temperature is, the lower the capacitance. According to its data sheet [21], the capacitance variation curve is built in Figure 4 to establish that the capacitance of the 4.7 nF Y1 capacitor at a high temperature of 70 \({}^{\circ}\)C and 85 \({}^{\circ}\)C will decline by 35% and 50%, respectively, which is a critical point for designing the resonant tank.  
The relationship between \(C_{r1}\), \(C_{r2}\) and \(C_{r}\) is as follows:  
\[C_{r1}=C_{r2}=2C_{r}, \tag{4}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Specification** \\ \hline Rated input voltage (\(V_{in}\)) & 370 V\(\sim\)390 V \\ \hline Rated output voltage (\(V_{o}\)) & 12 V \\ \hline Rated output current (\(l_{o}\)) & 10 A \\ \hline Resonant frequency (\(f_{s}\)) & 230 kHz \\ \hline Minimum switching frequency (\(f_{sw(\mathrm{min})}\)) & 210 kHz \\ \hline Magnetizing inductance divided by resonant inductance (\(k\)) & 4 \\ \hline Maxima duty cycle (\(D_{max}\)) & 0.42 \\ \hline Estimated efficiency (\(\eta\)) & 96\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Circuit specifications.  
Since the maximum capacitance of the Y1 capacitor is 4.7 nF, considering the capacitance and volume, both \(C_{r1}\) and \(C_{r2}\) are designed as 9.4 nF (two 4.7 nF Y1 capacitors paralleled) in this prototype. Then, the value of \(L_{r}\) can be worked out as follows:  
\[L_{r}=\frac{1}{2\cdot\pi^{2}\cdot f_{r}^{2}\cdot C_{r}}=160\ \mu\text{H}, \tag{5}\]  
The value of \(L_{m}\) and the value of \(L_{lk}\) are as follows:  
\[L_{m}=4\cdot L_{r}=640\ \mu\text{H},\ L_{lk}=17.6\ \mu\text{H}, \tag{6}\]  
Eventually, the resonance frequencies \(f_{\text{s}}\) and \(f_{p}\), and the corresponding quality factors can be calculated as follows:  
\[f_{\text{s}}=\frac{1}{2\pi\sqrt{L_{r}\cdot C_{r}}}=229.7\ \text{kHz}, \tag{7}\]  
\[f_{p}=\frac{1}{2\pi\sqrt{(L_{m}+L_{r})\cdot C_{r}}}=102.7\ \text{kHz}, \tag{8}\]  
\[Q_{FL}=\frac{\sqrt{\frac{L_{r}}{C_{r}}}}{R_{\text{ac}-FL}}=1.211, \tag{9}\]  
\[Q_{LL}=\frac{\sqrt{\frac{L_{r}}{C_{r}}}}{R_{\text{ac}-LL}}=0.1211, \tag{10}\]
# Microbial Precipitation of Calcium Carbonate for Crack Healing and Stabilization of Sandy Soils
## 4 Discussion  
### Healing of Desiccation Cracks on the Soil Surface Using MICP  
This study investigated the healing efficiency of MICP for cracks on the surface of sandy soils produced during the drying process. It was found that these cracks were healed by the microbiologically induced precipitation of CaCO\({}_{3}\). In the treatments usingmicroorganisms (CS + B) and microbial metabolites (CS + BM), the crack-healing effects were about 71% and 80%, respectively. These results indicate that biological additives can improve CaCO\({}_{3}\) precipitation even without microbial activity. According to previous studies, soluble microbial products (SMPs) act as templates and create a local environment that may favor the attraction of Ca\({}^{2+}\), leading to carbonate saturation [33]. In addition, CaCO\({}_{3}\) has been shown to precipitate even when only the biological additives from cultures of actinobacteria were employed (e.g., mycelium pellets, culture supernatant, and spent cultures) [24]. This is the result of interactions between organic and/or inorganic compounds with an organic matrix that do not require extracellular or intracellular biological activity [34]. This mechanism could be described in terms such as biologically mediated mineralization or organomineralization processes [34; 35]. However, the mechanisms underlying the effect that acidic organic molecules (such as polysaccharides, proteins, and amino acids) have on the composition, microstructure, shape, and size of biomarkers require more investigation [36]. Overall, microbial metabolites have the potential to promote CaCO\({}_{3}\) precipitation for the purpose of crack healing or the stabilization of sandy soil in polluted or extreme environments where microbial activity is very low or absent.
## 3 Experimental Analysis
### Bias in Code Multipath Combination of BDS
It is also suggested that the multipath effects of the GEO and non-GEO satellites might need discriminative treatments, taking their remarkably different behaviors into account. For example, the method of elevation-dependent model might be feasible but only for the non-GEO satellites. The distance between the F713 and CUT0 stations is about 10 km, and the surroundings of two stations are distinctly different [11]. Thus, the above results partly confirm that the elevation-dependent bias is related to satellite instead of station. In this sense, the code multipath combination of BDS satellites might not only contain multipath effects from the surroundings of the stations, but also contain some multipath-like biases from the satellites. One of the possible explanations could be that the BDS signals subject to multipath effects caused by reflections of the satellite components, such as the solar panels. In addition, the internal hardware delay could also be the contributor to the multipath-like biases. However, no data are available to verify this at the moment. Although high cut-off elevation angles could, to some extent, help circumvent the effects from multipath, it will lead to the loss of the availability [14]. An alternative method of dealing with the biases in multipath combination is to correct it with a proper model, considering the abovementioned high correlation between multipath combinations and the elevation of BDS non-GEO satellites. [PERSON] and [PERSON] (2014) introduced an elevation-dependent correction model for BDS IGSO and MEO satellites. Shown in Figure 9 are the multipath combinations of BDS satellites C09 (IGSO) and C14 (MEO) after applying the elevation-dependent correction model. The similar results can be obtained for the other IGSO and MEO satellites at other stations. With the correction, the elevation-dependent bias in multipath combination disappears. It reminds us that the precision of positioning could be improved by applying a proper elevation-dependent correction model to the raw observables of BDS IGSO and MEO satellites. Fortunately, the relationship of elevation and the bias for each non-GEO satellite is relatively stable, so an elevation-dependent correction model is expected to be also applied for kinematic or real-time cases. Therefore, efforts should be made to work out a delicate elevation-dependent correction model for each non-GEO satellite.
## 4 Accuracy Evaluation  
### Evaluated Parameters  
In order to be able to compare different approaches to calibration and evaluate the quality of the suggested solution, it is necessary to set the criteria for comparing two calibration results and a criterion that allows us to evaluate the accuracy of calibration. These should be, as far as possible, objective criteria that can be measured.  
For the evaluation of the camera calibration quality, it is a reprojection error. In fact, this error is proportional to the difference in the distance between the real physical point and the corresponding measured one. In other words, it describes how close to the real environment the object's coordinates can be calculated and thus how accurately this environment can be recreated.  
This value, in itself, has no dimension, and the closer this value is to zero, the better the calibration. Thus, by comparing the error value obtained as a result of the suggested algorithm and the classical manual approach, one solution evaluation can be compared to another.  
To calibrate the wheels, the evaluation quality criterion needs to be determined. Unlike the camera, the result of the wheel calibration is not a matrix, but just one number--a coefficient. In fact, it determines how many times one robot's wheel should rotate faster than another, so that when the command \"move straight\" is received, the robot really moves straight. However, unlike the camera, it is not so easy to determine the deviation of the obtained coefficient from the true one. The fact is that this coefficient is generally unique for each robot.  
Thus, the only way to assess the quality of the wheel calibration is to compare the calibration coefficient with a known correct one. The so-called \"correct\" coefficient is determined by the method of manual value selection and is also not absolutely accurate. The calibration may also be affected by a coating and even a surface tilt. It is not always possible to reproduce exactly the same conditions with automatic and manual calibrations. Therefore, it is simply incorrect to evaluate how much the resulting coefficient differs numerically from the reference. A better approach is to evaluate and compare the robot's behavior using different calibration factors.  
To evaluate the robot's behavior using this coefficient, and since the primary behavior is movement in a straight line, the comparative test should determine the quality of the robot's movement in a straight line. Two approaches can be used for this: distance approach--when the quality can be evaluated as the robot's deviation from the straight line, along which it begins to move when the robot passes a certain fixed distance, and a time approach--when the quality can be evaluated as the robot's deviation from the straight line, along which it begins to move when the robot passes a certain fixed time. To evaluate the suggested solution, the distance approach was used, since it makes it possible not to depend on the speed of robot's movement. A distance of 2 m was chosen according to the calibration validating algorithm in the Duckietown project [11].