esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Development of the TF-SPME Technique
PDMS was added to divinylbenzene dispersed in hexane, and then, after sufficiently long sonification, a drop of the resulting mixture was taken with a pipette and applied to aluminum foil. After being mechanically formed, the membrane was vacuum-dried at 120 \({}^{\circ}\)C. It was determined that the use of a twenty-percent weight addition of  
Figure 8: Diagram of the sorption process using the Riazi TF-SPME device [22]. Figure 7: [PERSON]’s membrane TF-SPME [20]. divinylbenzene to PDMS was the optimal ratio to obtain a membrane with good mechanical strength. Compared to the use of SPME fiber or a single-phase PDMS membrane, the use of the DVB/PDMS membrane, obtained by rod coating, determined the increase in the sensitivity of benzene determination in the gas matrix. In 2016, [PERSON] and co-workers used a carbon mesh to develop a TF-SPME device, onto which the extraction phase was applied in the form of dispersed DVB in PDMS [24]. The polymer-coated carbon support was cut into rectangular strips with a width determined by the dimensions of the TD unit of the gas chromatograph. The obtained micro-extraction device allowed extraction with high efficiency and had higher mechanical strength compared to previous technical solutions in the TF-SPME method. The carbon mesh strip, coated with a polymer layer as an extraction phase, became part of the first commercial microextraction kit offered by GERSTEL Inc. (Gerstel GmbH, Mulheim, Germany). The technique of microextraction on a developed surface has not just remained a theoretical concept. It has been successfully applied to a high-throughput automated analytical procedure using robotic autosamplers. In 2009, [PERSON]'s team presented a solution for simultaneous microextraction of 96 samples. The developed TF-SPME device, with the extraction phase as C18 silica applied to stainless steel plates, has proven its practicality. The silica-coated plates were combined into twelve-element batteries and then into an array containing ninety-six elements, which were, among others, used in the analysis of benzodiazep in urine [25]. This practical application underscores the real-world relevance of our research and its potential to revolutionize analytical procedures. In recent years, the development of the TF-SPME method has manifested itself in the use of a variety of advanced materials as increasingly selective sorption phases that enable microextraction to be carried out with high efficiency.
## 5 Conclusions and Future Work  
We developed a methodology to semi-automatically segment defects in 3D ultrasonic data using a convolutional neural network (CNN). The methodology consists of X-ray computed tomography and phased-array ultrasonic testing data fusion. Supervised classification was then used to train the CNN with 2D images. The use of this approach makes it possible to measure evaluation metrics such as precision, recall, F1 and IoU. The CNN results were compared to the outcomes of applying global thresholds to the phased array. According to the metrics implemented, CNN clearly outperformed the global threshold.  
One of the most challenging tasks is data fusion. The proposed solution is an image registration process for 2D images output by each technique, as 3D registration was found not to be accurate enough to train the CNN. In the absence of an algorithm to find common keypoints or any similarity measure between the two data sources, however, registration is not fully automated. This is a future line of work, where the application of deep learning based on the manually annotated data reported here could prove to be useful.  
We explored the use of two ground truths: XCT segmented voids and manual labels. The use of the XCT labels was found to output data that were too noisy for CNN training, and manual labels provided more robust training. The evaluation metrics for manual labels were twice as good. The network results--an F1 for test data equal to 0.63 and IoU equal to 0.5--were better than outcomes for the global thresholds. However, CNN is not able to distinguish between echoes produced by defects such as porosity and echoes produced by microstructural elements such as resin-rich areas. Future works should improve the signal-to-noise ratio, increase the dataset, and integrate more information from ultrasonic signals and C-scan representations.  
Conceptualization, J.-I.C. and F.S.; methodology, J.-I.C., G.C., J.C., E.M. and C.G.-M.; software, J.-I.C.; validation, E.M. and G.C.; formal analysis, J.-I.C.; investigation, J.-I.C.; resources, E.M. and J.C.; data curation, J.-I.C.; writing--review and editing, J.-I.C., F.S., G.C. and E.M.; visualization, J.-I.C.; supervision, E.M. and F.S.; project administration, E.M. and F.S.; funding acquisition, E.M. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from Madrid region under programme S2018/NMT-4381-MAT4.0-CM project.  
Not applicable.  
Two dataset from this research are available: the unregistered set of 2D UT/XCT images with its associated manually annotated keypoints, and the set of ultrasonic, XCT, and manual labels images used in the training of the CNN. Everything will be provided openly. They can be shared upon request as well with explanations.  
**Acknowledgments:** Special thanks to [PERSON], his work during his bachelor final project helped in the development of the CNN network.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
The purpose of this study was to assess the potential of multitemporal and multisensor EO data (Sentinel-1A and SPOT-5 Take-5) for crop parameter retrieval and crop type classification for agricultural water management in Angola.  
The integration of microwave data (Sentinel-1A) with optical data (SPOT-5 Take-5) did not reveal any improvement in land cover mapping. From the two supervised classification methods used, NN had the highest accuracy of approximately 88% (with a kappa coefficient of approximately 73%) compared to that of the SVM classifier (85% and 68% for the overall accuracy and kappa coefficient, respectively). Higher classification accuracies were expected when using SAR data; however, the unavailability of images for more than half of the crop cycle (only the end of mid-season until harvesting was available) did not permit a better discrimination among crops. A multitemporal NN classification using just the first seven SPOT-5 Take-5 images produced a late mid-season map with an overall accuracy of 91% (with a kappa index of 81%).  
The improved temporal resolution of the SPOT-5 Take-5 images, used in this study to simulate the ESA Sentinel-2 time series, is relevant for a better identification of the different crop growth cycle stages that are often imperceptible when using more sporadic data. Higher temporal resolution time series allow the retrieval of realistic values for \(K_{cb}\) instead of the standard values proposed by FAO 56 [13] and, consequently, a better estimation of the crop's irrigation requirements. However, this aspect was not fully exploited for the same reason mentioned previously, _i.e._, the lack of EO data for the complete growing season. Moreover, the consistency observed between the optical and microwave time series for all crop types enables the replacement of optical data affected by clouds with microwave data to increase the temporal resolution of the time series, providing a proxy measurement of crop development.  
This study was developed in the scope of the ESA Alcantara initiative project (Ref: 14-P13) and the SPOT-5 Take-5 project (ID: 29142).  
[PERSON] and [PERSON] designed the study and did most of the writing. [PERSON] performed most of the pre-processing steps. [PERSON] contributed to the results analysis and to the writing. [PERSON], [PERSON] and [PERSON] reviewed the paper.  
The authors declare no conflict of interests.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 5 Discussion, Future Work and Conclusions
The authors consider this key for continuous development and in-orbit update of the payload software. With experience from operating the satellite and scheduling captures, which is the subject of a different publication, the authors envision a more simple, modular and extensible on-board processing pipeline framework using functionality provided by the Linux OS. This more flexible approach would implement every pipeline stage as its own application and have them integrated into a pipeline via an on-ground generated and uplinked shell script. Developing a script generator for the pipeline shell scripts hides the complexity of pure shell scripting, reduces user errors, and can guarantee valid configurations. A potential disadvantage is that this framework might require the designer to put more work into making pipeline modules intercompatible and defining their input and output data format explicitly, since the handover of the data is no longer built into a monolithic pipeline application. The results of Ethernet throughput testing in Figure 8 are unexpected, and still unexplained. The result of transmission with almost no packet loss around 420-440 Mbit/s in Figure 8 was consistent across tests, but since the packet loss percentage does not reach exactly 0%, picking a frame rate to achieve this data rate could still lead to dropped frames and thus irregular sampling, making processing of the data difficult.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 2 Review Methodology
### Eligibility Criteria  
For the collected unique papers, the exclusion criteria used to determine eligibility that resulted in 298 papers included in the review were as follows:  
* **Out of scope**: irrelevant topics outside of industrial applications or not of combinatorial optimization (221);
* **Insufficient details**: irreproducible papers without all needed explicit descriptions of agent-environment interactions (99);
* **Reviews and surveys** (64).  
To collect these data, one author manually checked for sufficient descriptions of agent-environment interactions. This information, together with the title and abstract, was also used to evaluate if papers were in the scope of this review.  
This review focuses on industrial combinatorial optimization problems. Thus, we are mostly concerned with problems that deal with resource allocation, such as entity distribution or selection. Common examples include manufacturing, scheduling, and network allocation. Papers are considered out of scope if the application mainly deals with control or energy management systems. For these examples, the action is often a continuous control signal.  
The resulting 298 papers contain verified and thorough implementations of RL for industrial combinatorial optimization problems. The state representation, action space and reward design are clearly explained in every paper selected, and the following sections group and categorize the different approaches to each of the three agent-environment interactions. The distribution of publications over time is presented in Figure 3. It can be seen that while little interest was given prior to 2015, in the last 10 years the number of publications has kept growing.  
Besides manually describing and categorizing agent-environment interactions, algorithms used, etc., which were then used to populate the respective tables and images of this document, no other synthesis methods were used. No certainty assessment measures were used, no effect measure outcomes were used, and no missing results risk of bias or certainty assessment was performed.  
Figure 3: Distribution by publication date. The earliest paper is from 2002.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 3 Transmission Error Frequency Analysis
### Fundamental Characteristic Frequencies  
The fundamental motion characteristic frequencies of the six primary moving components, the gear shaft, planet gear, crankshaft, cycloidal gear, pin gear, and flange, are essential for analyzing the TE in an RV reducer. For convenience, we define the motion characteristic frequency of the input gear shaft, \(f_{\rm sun}=\omega_{\rm sun}/(2\pi)\), as the base frequency to analyze the characteristic frequencies of the other components.  
Since frequency is directly proportional to rotational speed, the characteristic frequencies of the planet gear \(f_{\rm pla}\), cycloidal gear \(f_{\rm cyc}\), and pin gear \(f_{\rm pin}\) are related to the gear shaft characteristic frequency \(f_{\rm sun}\) as shown in Equations (6) and (7):  
\[\frac{f_{\rm pla}-f_{\rm cyc}}{f_{\rm sun}-f_{\rm cyc}}=\frac{z_{\rm sun}}{z_{ \rm pla}}, \tag{11}\]  
\[\frac{f_{\rm pin}-f_{\rm cyc}}{f_{\rm pla}-f_{\rm cyc}}=\frac{z_{\rm pin}-z_{ \rm cyc}}{z_{\rm pin}} \tag{12}\]  
When the flange is fixed (Condition 2):  
\[\left\{\begin{array}{l}f_{\rm cyc}=0\\ f_{\rm pla}=\frac{z_{\rm sun}}{z_{\rm pla}}f_{\rm sun}\\ f_{\rm pin}=\frac{z_{\rm sun}}{z_{\rm pla}}\frac{z_{\rm pin}-z_{\rm cyc}}{z_{ \rm pin}}f_{\rm sun}\end{array}\right. \tag{13}\]  
When the pin gear is fixed (Condition 1):  
\[\left\{\begin{array}{l}f_{\rm pin}=0\\ f_{\rm pla}=\frac{z_{\rm cyc}z_{\rm sun}}{(z_{\rm pin}-z_{\rm cyc})z_{\rm sun} +z_{\rm pin}z_{\rm pla}}f_{\rm sun}\\ f_{\rm cyc}=\frac{(z_{\rm pin}-z_{\rm cyc})z_{\rm sun}}{(z_{\rm pin}-z_{\rm cyc })z_{\rm sun}+z_{\rm pin}z_{\rm pla}}f_{\rm sun}\end{array}\right. \tag{14}\]  
Since frequency has no direction, the negative sign \"\(-\)\" indicating the opposite rotation direction is omitted in Equations (11)-(14).
## 5 Solution and Analysis of Examples  
To evaluate the performance of the algorithm and strategy established above, the latitude and longitude locations of a merchant and its 80 consumers are shown in Table 2. The real distance between different locations would be calculated according to Equation (6)  
Figure 5: The travel distance fair strategy.  
The route for each rider was optimized based on the improved ant colony algorithm. Before each route optimization, the merchant node was added to each cluster to guarantee that the rider start node was included in the optimal route of each cluster. The optimized routes for each cluster are presented in Figure 7. It can be observed that the improved ant colony algorithm performed well in finding the best route for the rider. It can be observed from Table 3 and Figure 7 that the route length of each rider finished has a substantial difference, which means that some riders would ride longer distances than others. In Figures 7b, 8b and 9b, the \(y\)-axis represents the normalized route length, defined as \(Normalized\)\(Length=\frac{Actual\ Path\ Length}{Reference\ Length}\). The reference length (100 km) represents the standardized scaling distance for comparative visualization of route optimization results.  
The difference in order quantity and delivery distance for the riders would result in unfair workload distribution. Therefore, each rider's order quantity and delivery distance balance were considered when designing a strategy for redist  
Figure 6: The clusters distributed by the K-means algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Number of Nodes** & **Center Location** & **Optimal Route Length (km)** &
\begin{tabular}{c} **Standard Deviation** \\ **of the Route** \\ **Length (km)** \\ \end{tabular} \\ \hline
1 & 20 & (126.652731, 45.731321) & 11.59 & 0.0043 \\
2 & 23 & (126.623411, 45.725812) & 13.87 & 0.0038 \\
3 & 16 & (126.649456, 45.712240) & 8.42 & 0.0038 \\
4 & 21 & (126.672374, 45.716193) & 15.18 & 0.0044 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The clustering result using the K-means algorithm.  
Figure 7: The optimal result for the rider using an improved ant colony algorithm. (**a**) The optimal route for the rider. (**b**) The convergence process of the algorithm.
# Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet
## 3 Setup for Numerical Simulation  
The method of FEM coupled with SPH was used to simulate the process of rock breaking under the multiple forces of the TBM disc cutter and the high-pressure water jet. In the numerical simulation, the rock and disc cutter are set up based on FEM, while the water jet is built by SPH. Although FEM cannot reflect inherent cracks in the rock, it can clearly show the rock stress distribution during the process of rock breaking. Due to its advantage of being meshless, SPH can effectively solve the large deformation problem of the water jet.
## 2 Description of the Models
### Vertically Excited Parametric Pendulum with a Torque
The effect of the torque can also be observed, since \(\
u=0\) in (**a**,**a**’), and \(\
u=0.6\) in (**b**,**b**’). X and Y represent angular displacement and velocity, respectively. of direct separation of motions [25], we introduce slow \(z(\tau)\) and fast coordinates \(\psi(\tau,\tau_{1})\), where \(\tau=\omega t\) is the dimensionless time that we use in order to simplify the equations:  
\[\phi(\tau_{0})=z(\tau_{0})+\left(\frac{1}{\omega}\right)\psi(\tau_{0},\tau_{1}), \tag{6}\]  
where a slow time \(\tau_{0}=\tau\), while \(\tau_{1}\) is a fast time \(\tau_{1}=(1/\omega)\tau\) and the time derivatives are defined respectively  
\[\frac{dx}{d\tau}=\frac{\partial x}{\partial\tau_{0}}\frac{d\tau_{0}}{\partial \tau}+\frac{\partial x}{\partial\tau_{1}}\frac{d\tau_{1}}{\partial\tau}=\frac {\partial x}{\partial\tau_{0}}+\frac{1}{\omega}\frac{\partial x}{\partial\tau _{1}}=\dot{x}+\frac{1}{\omega}x^{\prime}, \tag{7}\]  
where \(\partial x/\partial\tau_{0}\equiv\dot{x}\) and \(\partial x/\partial\tau_{1}\equiv x^{\prime}\). Additionally, the fast coordinate should fulfil the vanishing average condition:  
\[\overline{\psi}(\tau)=\frac{1}{2\pi}\int_{0}^{2\pi}\psi(\tau,\tau_{1})\mathrm{ d}\tau=0 \tag{8}\]  
By substituting Equations (6)-(8) into Equation (4) with the Taylor expansion of \(\sin(z+\frac{1}{\omega}\psi)\) and some algebra, we obtain:  
\[ml^{2}\ddot{\phi}+k\dot{\phi}+m(gl-l\dot{x})\sin\phi=N, \tag{3}\]0  
After averaging (and using Equation (8)), the final equation of motion for a slow motion can be expressed as (see [25], where \(\upsilon=0\)):  
\[ml^{2}\ddot{\phi}+k\dot{\phi}+m(gl-l\dot{x})\sin\phi=N, \tag{3}\]1  
producing new equilibria at \(z=\pm\pi\) corresponding to an inverted pendulum. In our more general case, the effective potential \(V_{eff}(z)\) in the limit of very high frequency \(\omega\) can be written as:  
\[ml^{2}\ddot{\phi}+k\dot{\phi}+m(gl-l\dot{x})\sin\phi=N, \tag{3}\]2  
which is plotted in Figure 6b. \[ml^{2}\ddot{\phi}+k\dot{\phi}+m(gl-l\dot{x})\sin\phi=N, \tag{3}\]3 The unperturbed (Equation (5)) potential \(V(\phi)\) is shown in panel (**a**), and in panel (**b**) the effective potential \(V_{eff}(z)\) of the vertically excited parametric pendulum with a torque (Equation (11)) in the limit of large \(\omega\) (for \(\gamma\omega=3\)) and \(\upsilon=0.2\). In that case, the fast oscillations stabilize the motion around \(z\approx 0\) and \(\pm\phi\) (inverted pendulum). The effective potential is asymmetric because of a nonzero torque \(\upsilon\
eq 0\). \(z_{1}\) and \(z_{2}\approx\pi\) (modulo \(2\pi\)) denote the new minima for the effective potential corresponding to the inverted pendulum.
# Efficient Zero-Sequence Impedance Measurement in Autotransformers Using Low-Voltage Excitation
## 1 Introduction
This paper presents a zero-sequence impedance measurement method for autotransformers based on low-voltage excitation and disconnection tests for the field measurement. Firstly, a model for calculating the zero-sequence impedance of autotransformers was derived by combining the disconnection test and the disconnection position, and from this, the formula for calculating the zero-sequence impedance under different test conditions was obtained. Then, combined with the remaining two sets of open circuit and short circuit tests on the LV side, a complete set of methods for measuring the zero-sequence impedance of autotransformers on the LV side is formed. Finally, the feasibility of this method is demonstrated by comparing the results with standard test results through simulation verification of different test conditions. The method in this paper allows the measured value of the high-voltage side short circuit test in the standard method to be obtained through the low-voltage side test, which is applicable to all autotransformers and traditional transformers that are not Y-connected. As a result, the method greatly reduces the cost of field testing and the requirement for a site.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 3 Design of AMP-RTR Model  
### Metric Requirements of AMP-RTR Model  
In order to develop the Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR), we propose four main indicators according to the existing literature [7; 8; 18; 23; 24; 25] and the actual situation of the manufacturing industry, which can be summarized as follows:R1 (normalization): The data update time of product attributes usually has different periodic, and the data update periodic of different attributes may vary greatly. However, the length of the update cycle is not directly related to the timeliness of the data, so it is necessary to standardize the update cycle according to different application scenarios. The time series can be normalized to twice the set period. For example, when the period \(T=7\) is set, the time series can be normalized to \([0-14]\).  
The sequence \(T=\{t_{1},t_{2},t_{3},\ldots,t_{k}\}\) is normalized to \([a-b]\).  
\[t_{z_{i}}=a+\frac{(b-a)\times(t_{i}-\min(T))}{Max(T)-\min(T)} \tag{1}\]  
R2 (interval scale): In order to make the difference between the timeliness scores updated by the two iterations meaningful, we require the iteration to adopt interval scale. For example, the change of timeliness score value from 0.8 to 0.6 at the beginning of the recurrent update should indicate the same degree of data timeliness attenuation compared with the change from 0.6 to 0.4. At the same time, the timeliness score value iterating from 0.6 to 0.7 should represent the same degree of data timeliness improvement as that iterating from 0.5 to 0.6.  
R3 (interpretability): The process monitoring alarm must be easy to understand and allow users to effectively find abnormal conditions. In other words, the Anomaly Monitoring of Process on the Recurrent Timeliness Rules (AMP-RTR) issues an abnormal warning, which should indicate that there is a high probability of an anomaly in the product production process. On the contrary, if the AMP-RTR model method does not monitor abnormalities (i.e., does not remind the user), it should indicate that the product production process has no abnormalities in a high probability.  
R4 (availability): In the manufacturing process, a large amount of data will be generated recurrently. It must be ensured that the time series of data updates can be transmitted to the processor timely and accurately. At the same time, the processor can effectively process the time series and feedback the processing results to the user in time.