esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Single-Particle Imaging with X-ray Lasers
### Setup and the Experiment Procedure  
Many aspects are required to be specified prior to conducting an SPI experiment. First, the proposed resolution, sample type and size should be defined. Special diagnostics required for the experiment, such as the ion/e\({}^{-}\) TOF, high speed camera, fluorescence detector and pumping laser system and the space they require should also be taken into consideration. Secondly, the ideal sample-to-detector-distance, photon energy, pulse duration, and pulse energy should be determined. Then, careful alignment of all optics, slits, KB mirrors and large-panel pixel array detectors must carefully be accomplished at the start of the beamtime. Simultaneously, beam characterization, such as the beam profile, the focused beam spot size, beam intensity and position fluctuations, wavefront or arrival time should be diagnosed for further reference. The high harmonic content of the beam should also ideally be tested. Finally, selection of a proper sample delivery method will be of great importance to the background and hit rate. The simple flow chart of the experiment and data process is shown in Figure 4. After alignment of the instrumentation, samples are delivered into the X-ray laser beam path. The forward scattering exit wavefront will propagate downstream and then to be recorded by the detector and this pattern must be oversampled properly. Data processing involves center determination, background subtraction, analog-to-digital units (ADU) to photons conversion, hit finding, pattern classification, and orientationdetermination. Finally, phasing can produce a real-space 3D image. By using PRTF, FSC or other criteria, the spatial resolution could be determined.
## 3 Nearest Neighbor Classification of Protein Structures
### Dominance between Target Protein Structures  
In order to find the target structures that are closest to a query \(q\), we have to decide for a pair of Targets \(A\) and \(B\) which one is closer. We call such a relationship between two target structures dominance:  
**Lemma 3** (**Dominance**).: _Protein \(A\) dominates protein \(B\) with respect to a query \(q\) if and only if \(d(q,A)<d(q,B)\)._  
In order to conclude that \(A\) is closer to \(q\) than \(B\), it may not be necessary to know \(d(q,A)\) and \(d(q,B)\) exactly. It is sufficient that \(A\) directly dominates \(B\) according to the following rule.  
**Lemma 4** (**Direct dominance**).: _Protein \(A\) dominates protein \(B\) with respect to a query \(q\) if \(\overline{d}(q,A)<\underline{d}(q,B)\), where \(\overline{d}(q,A)\) and \(\underline{d}(q,B)\) are an upper and lower bound on \(d(q,A)\) and \(d(q,B)\), respectively._  
Proof.: It follows from the inequalities \(d(q,A)\leq\overline{d}(q,A)<\underline{d}(q,B)\leq d(q,B)\).  
Given a query \(q\), a target \(A\) and the representative \(R_{\mathcal{F}}\) of the family \(\mathcal{F}\) of \(A\), the triangle inequality provides an upper bound, while the reverse triangle inequality provides respectively a lower bound on the distance from query \(q\) to target \(A\):  
\[d(q,A)\leq d(q,R_{\mathcal{F}})+d(R_{\mathcal{F}},A)\text{ and }d(q,A)\geq|d(q,R_{ \mathcal{F}})-d(R_{\mathcal{F}},A)| \tag{5}\]  
We define the triangle upper (respectively lower) bound as:  
\[d^{\triangle}(q,A)=\overline{d}(q,R_{\mathcal{F}})+\overline{d}(R_{\mathcal{ F}},A) \tag{6}\]  
\[d_{\bigtriangledown}(q,A)=\max\{\underline{d}(q,R_{\mathcal{F}})-\overline{ d}(R_{\mathcal{F}},A),\,\underline{d}(R_{\mathcal{F}},A)-\overline{d}(q,R_{ \mathcal{F}})\} \tag{7}\]  
**Lemma 5**.: \(d_{\bigtriangledown}(q,A)\leq d(q,A)\leq d^{\triangle}(q,A)\)  
**Proof.**\(d_{\bigtriangledown}(q,A)=\max\{\underline{d}(q,R_{\mathcal{F}})-\overline{d}(R_{ \mathcal{F}},A),\)\(\underline{d}(R_{\mathcal{F}},A)-\overline{d}(q,R_{\mathcal{F}})\}\leq|d(q,R_{ \mathcal{F}})-d(R_{\mathcal{F}},A)|\leq d(q,A)\leq d(q,R_{\mathcal{F}})+d(R_{ \mathcal{F}},A)\leq\overline{d}(q,R_{\mathcal{F}})+\overline{d}(R_{\mathcal{F} },A)=d^{\triangle}(q,A)\). \(\qed\)  
Using Lemma 5, we derive supplementary sufficient conditions for dominance, which we call indirect dominance.  
**Lemma 6** (Indirect dominance).: _Protein \(A\) dominates protein \(B\) with respect to query \(q\) if \(d^{\triangle}(q,A)<d_{\bigtriangledown}(q,B)\)._  
**Proof.**\(d(q,A)\stackrel{{\text{Lemma\ref{lem:bound}}}}{{\leq}}d^{ \triangle}(q,A)<d_{\bigtriangledown}(q,B)\stackrel{{\text{ Lemma\ref{lem:bound}}}}{{\leq}}d(q,B)\). \(\qed\)
## 3 2D Positioning Control
### Dynamic Characterisation of the System
Additionally, 2K\({}_{\mathrm{M}}\) is the slope of the total thrust force, F\({}_{\times}\), generated by the pair of Halbach linear motors around the equilibrium position in the X-axis (Figure 4). Since the actuator distribution in the moving platform is symmetrical, the same equations are considered valid for the Y-axis. The mass of the moving platform is known (13.25 kg), and the value of K\({}_{\mathrm{M}}\) depends on the reference value set for the vertical force at the stable equilibrium position, F\({}_{\mathrm{zref}}\). This value defines the amplitude of the sinusoidal distribution of the forces along the axis of motion, while the spatial period is defined by the design [9]. For instance, when F\({}_{\mathrm{zref}}\) is set to 1 N, K\({}_{\mathrm{M}}\) is approximately 205 N/m, and when F\({}_{\mathrm{zref}}\) is set to 2 N, K\({}_{\mathrm{M}}\) is 410 N/m. Therefore, K\({}_{\mathrm{M}}\) and \(m\) are known, whereas the viscous-friction factor \(b_{X}\) must be obtained experimentally. On the other hand, rotation around the Z-axis (\(\theta_{\mathrm{z}}\)) is generated by torque acting on the central point of the moving platform. This torque (T\({}_{\mathrm{z}}\)) is the sum of the torques generated by each motor in the plane of motion (Figure 3b). The 2D plane mirror laser interferometer system requires the rotation of the moving platform to be less than \(\pm 1.2\times 10^{-4}\) rad, according to the manufacturer's specifications, to prevent misalignment between the laser beam and plane mirror. Therefore, to rotate the moving platform by angle \(\Delta\theta_{\mathrm{z}}\), each motor would have to perform a displacement of the same magnitude, equal to \(\Delta\theta_{\mathrm{z}}\)-R, in the direction that favours that rotation. According to this, the torque generated by the four motors of the NanoPla to perform a rotation of \(\Delta\theta_{\mathrm{z}}\) can be calculated as follows:  
\[\mathrm{T_{z}}\ =\ 4K_{\mathrm{M}}\mathrm{R^{2}}\Delta\theta_{\mathrm{z}} \tag{6}\]  
It is worth mentioning that the angular position \(\theta_{\mathrm{z}}\) is considered to be a stable equilibrium angular position when, at that angular position, the platform remains still and after a disturbance, it comes back to the same angular position. This stable equilibrium position is created when the moving platform is perfectly aligned in the X- and Y-axes--that is, \(\theta_{\mathrm{z,eq}}=0\). Thus, as in the previous case, the transfer function that relates the final angular position of the stage \(\theta_{\mathrm{z}}\)(s) with the defined stable equilibrium angular position \(\theta_{\mathrm{z,eq}}\)(s) is defined by Equation (7), where \(\mathrm{b_{\theta}}\) is the damping factor in \(\theta_{\mathrm{z}}\), and I\({}_{\mathrm{z}}\) is the inertia of the moving platform. Figure 4: Generated forces by one (F\({}_{\mathrm{M,x}}\)) and two motors (F\({}_{\mathrm{x}}\)) along the X-axis of movement. \[\mathrm{F_{y}}\ =\ \mathrm{F_{M3}}+\mathrm{F_{M4}} \tag{2}\]0
# A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach
###### Abstract  
Insights into flood dynamics, rather than solely flood extent, are critical for effective flood disaster management, in particular in the context of emergency relief and damage assessment. Although flood dynamics provide insight in the spatio-temporal behaviour of a flood event, to date operational visualization tools are scarce or even non-existent. In this letter, we distil a flood dynamics map from a radar satellite image time series (SITS). For this, we have upscaled and refined an existing design that was originally developed on a small area, describing flood dynamics using an object-based approach and a graph-based representation. Two case studies are used to demonstrate the operational value of this method by visualizing flood dynamics which are not visible on regular flood extent maps. Delineated water bodies are grouped into graphs according to their spatial overlap on consecutive timesteps. Differences in area and backscatter are used to quantify the amount of variation, resulting in a global variation map and a temporal profile for each water body, visually describing the evolution of the backscatter and number of polygons that make up the water body. The process of upscaling led us to applying a different water delineation approach, a different way of ensuring the minimal mapping unit and an increased code efficiency. The framework delivers a new way of visualizing floods, which is straightforward and efficient. Produced global variation maps can be applied in a context of data assimilation and disaster impact management.  
Keywords:object-based change detection; graphs; SAR; floods +
Footnote †: journal:
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 3 Performance Analysis
### Average Bit Error Rate  
The average BER is an intuitive way to evaluate the impact of modulation scheme on communication system. Here, we employ the hybrid modulation to improve the performance of the system. PPM-BPSK-SIM is a combination of PPM and BPSK-SIM schemes, where the information symbols are modulated into parallel signals by the PPM encoder, converted into high-rate serial signals and then transferred to the BPSK modulator. For \(L\)-PPM, a data symbol consists of \(L\) time slots, where \(L\) is the average symbol length. The conditional BER of \(L\)-PPM-BPSK-SIM is expressed as \(P_{e-L-PPM-BPSK-SIM}=\dfrac{1}{2}erfc\Big{(}\sqrt{\gamma L\text{log}_{2}L} \Big{/}4\sqrt{2}\Big{)}\)[41; 42; 43], where \(erfc(\cdot)\) is the complementary error function. The DF scheme is employed in the UAV relay of the mixed RF/FSO UAV dual-hop system, so the average BER is expressed as [44]:  
\[P_{e}=P_{1}+P_{2}-2P_{1}P_{2}, \tag{16}\]  
where \(P_{1}\) and \(P_{2}\) are the average BER of the RF link and FSO link, respectively. By using \(erfc(x)=\dfrac{1}{\sqrt{\pi}}G\)\(\dfrac{2,0}{1,2}\)\(\Bigg{(}x^{2}\Bigg{|}\)\(\dfrac{1}{0,1/2}\Bigg{)}\) and \(e^{-x}=G\)\(\dfrac{1,0}{0,1}\)\(\Bigg{(}x\Bigg{|}\)\(-\Bigg{)}\)[45], the average BER of the RF link using hybrid \(L\)-PPM-BPSK-SIM can be obtained as:  
\[\begin{split} P_{1}&=\int_{0}^{\infty}\dfrac{1}{2 }erfc\Big{(}\dfrac{\sqrt{\gamma L\text{log}_{2}L}}{4\sqrt{2}}\Big{)}f_{\gamma _{1}}(\gamma)\,d\gamma\\ &=\dfrac{1}{2\sqrt{\pi}(N-1)!}G\dfrac{2,1}{2,2}\bigg{(}\dfrac{L \text{log}_{2}LB\bar{\gamma}_{1}}{32}\bigg{|}\dfrac{1-N,1}{0,1/2}\bigg{)}.\end{split} \tag{17}\]
###### Abstract  
This study provides a detailed analysis of the convergence criteria for dynamic mode decomposition (DMD) parameters, with a focus on sampling frequency and period in high-Reynolds-number flows. The analysis is based on flow over an idealized road vehicle, the [PERSON] body (\(Re=7.7\times 10^{5}\)), using computational fluid dynamics (CFD) data from improved delayed detached eddy simulation (IDDES). The pressure and velocity spectrum analysis validated IDDES's ability to capture system dynamics, consistent with existing studies. For a comprehensive understanding of the contributions of different components of the circle, the [PERSON] body was divided into three regions: (a) front; (b) side, lower, and upper surfaces; and (c) rear fascia. Both pressure and skin-friction drag were analyzed in terms of frequency spectra and cumulative energy. Key findings show that a 90% contribution to the pressure drag comes from modes with a frequency of less than 26 Hz (\(St=0.187\)), while the friction drag requires 84 Hz (\(St=0.604\)) for similar energy capture. This study highlights the significance of accounting for intermittency and non-stationary behavior in turbulent flows for DMD convergence. A minimum of 3000 snapshots is necessary for the convergence of DMD eigenvalues, and sampling frequency ratios between 5 and 10 are needed to achieve a reconstruction error of less than 1%. The sampling period's convergence showed that \(T^{*}=250\) (equivalent to 20 cycles of the slowest coherent structures) stabilizes coherent mode shapes and energy levels. Beyond this, DMD may become unstable. Additionally, mean subtraction was found to improve DMD stability. These results offer critical insights into the effective application of DMD in analyzing complex vehicle flow fields.  
DMD; modal decomposition; CFD; IDDES; flow over ground vehicles; Ahmed body +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion
### Critical Discussion
#### 5.8.2 Enhancement or Simplification of the Achievement of Security Requirements  
The proposed framework offers an approach to address the security challenges in the manufacturing industries while performing operations remotely. Integrating and collaborating mobile industrial robots with industrial machines lessen the labor force and enhance productivity. It is mandatory to tackle security concerns. The proposed framework models a real-world manufacturing environment to provide a clear visualization of how these security measures operate in tandem, allowing manufacturers to pinpoint and rectify security attacks, thus reducing complexity and simplifying the achievement of security requirements. The proposed framework also helps those industries prioritize the implementation of smart manufacturing.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 5 Conclusions  
This paper proposes a Petri net modeling approach to Ising model formulation for quantum annealing. A Petri net is an outstanding method for modeling target optimization problems because it can represent the structure and behavior of problem instances using simple rules; the color and time extension provides powerful modeling capability. Moreover, for Ising model formulation, we can use various properties extracted systematically from Petri net models. These properties are well-defined in the Petri net theory.  
Figure 6: Colored Timed Petri Net Model for Job-Shop Scheduling created by using CPNTools [24].  
Figure 7: Job-Shop Schedule obtained with the Fujitsu Digital Annealer.  
We introduced a new class of colored Petri nets, binary quadratic nets, which are equivalent to the Ising models. Thus, our modeling approach has the following steps:  
1. Model the target combinatorial optimization problem with a problem-domain Petri net such as a timed and colored Petri net;
2. Extract constraints and objective functions as properties from problem-domain Petri net models and construct a binary quadratic net incrementally;
3. Convert the binary quadratic net to the specified format of the corresponding quantum annealing machine;
4. Invoke the annealing process with the converted Hamiltonian.  
Although our method requires users to model their optimization problems with problem-domain Petri nets, this process can be carried out in a relatively straightforward manner if we know the target problem and the simple Petri net modeling rules. Therefore, we can drastically reduce the difficulty of the Ising model formulation.  
We implemented our method with Python incorporated using well-known Petri net tools, CPNTools, and SNAKES. We can systematically generate the Ising models for optimization problems, such as scheduling, vehicle routing, and portfolio optimization, once we model the target optimization problems with Petri nets.  
In the future, we aim to introduce a mechanism to reduce the Ising variables and their interaction densities because they can affect the performance of quantum annealing. Petri net reachability analysis can contribute to this process as a pre-processing step for quantum annealing. The binary quadratic net model can also be used to analyze the quantum annealing process by attaching an additional subnet that simulates the annealing process. This tool may contribute to parameter tuning for annealing, which is an essential task used to expand the quantum annealing technology, mentioned in the Introduction.  
Conceptualization, M.N.; methodology, M.N. and K.K.; software, K.K.; validation, T.Y. and M.N.; formal analysis, M.N. and K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 2 Materials and Methods  
### Samples Preparation and GC-FID Procedure  
To evaluate the performance of the SPME-GC-FID method for ethanol detection, a series of aqueous solutions containing different concentrations of ethanol were prepared. The solutions were prepared by adding known amounts of ethanol (purity > 99%) to 2 mL of ultrapure water (UPW). The ethanol concentrations were selected to cover a range of 0.1-10 mg/L, which are considered relevant for forensic toxicology, clinical diagnosis, food and beverage industries, as well as for environmental monitoring. Sodium chloride (Sigma-Aldrich, Milwaukee, WI, USA) 30% (\(w/v\)) was added to the samples followed by magnetic stirring for 1 min at room temperature. A volume of 0.5 mL of each solution was transferred to a 20 mL glass vial for SPME analysis. Artificial sweat samples and PBS samples containing different concentrations of ethanol were also prepared for comparison with the ultrapure water samples, being subjected to the same time and temperature of extraction.  
Summing up, the SPME-GC-FID optimized procedure involved four main steps, including (Figure 1):  
* Sample preparation: the samples were prepared as mentioned before;
* Extraction: the SPME fiber was exposed to the sample matrix headspace and the volatile compounds adsorbed into the fiber coating for 20 min at 30 \({}^{\circ}\)C;
* Desorption: after extraction, the fiber was inserted into the GC injection port, where the compounds were thermally desorbed from the fiber coating and transferred to the GC column;
* Separation and detection: the separated compounds were detected by a flame ionization detector (FID).  
Figure 1 presents the scheme of the main steps mentioned above.
## 3 Results
### Model Accuracy  
While the RFE determined the optimal combination of environmental predictors for each soil property, variable selection effectively reduced the model processing time. The performance of the RF and XGBoost models was indicated by the mean values of the _RMSE_, _MAE_, and \(R^{2}\) for 100 iterations based on a 10-fold cross-validation (Table 4). Generally, a low accuracy was observed for the models that contained all 40 environmental covariates (hereafter referred to as 'all-variable models'). In particular, the XGBoost model for the SAP  
Figure 4: Distribution of soil organic carbon (SOC) (**a**), total phosphorus (STF) (**b**), and available phosphorus (SAP) (**c**) under different land use types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Soil Property**} & \multicolumn{2}{c}{**Variables before**} & \multicolumn{2}{c}{**Variables after Selection**} & \multirow{3}{*}{\(R^{2}\)} \\  & \multicolumn{2}{c}{**Selection**} & & \\  & **Number** & **Type** & **Number** & \\ \hline \multirow{5}{*}{SOC} & \multirow{5}{*}{40} & Soil & 3 & \\  & & Climate & 1 & \\  & & Organisms & 2 & \\  & & Relief & 1 & 0.86 \\  & & Remote sensing & 2 & \\  & & Human activity & 3 & \\  & & Soil & 1 & \\  & & Climate & 1 & \\  & & Organisms & 2 & \\ STP & 40 & & Relief & 1 & \\  & & Remote sensing & 1 & \\  & & Human activity & 2 & \\  & & Organisms & 2 & \\ SAP & 40 & & Relief & 1 & 0.91 \\  & & Remote sensing & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance of recursive feature elimination for soil properties.  
had the lowest accuracy, with an _RMSE_ of 11.81 mg\(\cdot\)kg\({}^{-1}\), _MAE_ of 9.02 mg\(\cdot\)kg\({}^{-1}\), and \(R^{2}\) of 0.15.  
The RF and XGBoost models based on variable selection performed notably better than the all-variable models (Table 4). Specifically, the \(R^{2}\) values of the models for the SOC, STP, and SAP all substantially increased after variable selection. The \(R^{2}\) values of the RF and XGBoost models with variable selection reached 0.79 and 0.84 for the SOC, respectively; the corresponding \(R^{2}\) values were 0.70 and 0.77 for the SAP. Compared to the SOC and STP models, the STP models showed the greatest optimization and the highest accuracy after variable selection (RF_R\({}^{2}\) = 0.86; XGBoost_R\({}^{2}\) = 0.89). When the machine learning models were integrated with variable selection, the XGBoost model was improved in its prediction accuracy (\(R^{2}\)) for the soil properties by 3.5%-10.0% compared to the RF model. Both of the two models showed an excellent prediction performance, with minor difference in their accuracy.
# The Influence of Automated Machining Strategy on Geometric Deviations of Machined Surfaces
## 3 Measurement of Geometrical Characteristics  
The most popular method of geometrical dimension control is the coordinate measuring technique. This measurement strategy plays an important role in the accuracy of obtained results [23; 24]. The number of points collected during the measurement, the calculation of geometric element, filtering and outlier eliminations are the most influencing factors. The setting of these parameters is mostly left to the operator, and significantly affects the value of the flatness and the associated uncertainty [13]. The measurement of surface quality obtained from the CAM system SolidCAM and control system Heidenhain TNC 426 was conducted. After that, geometric characteristics of the produced part were measured with the use of the coordinate measuring machine Carl Zeiss Contura G2 with an RDS articulating probe holder and VAST XXT scanning sensor. The maximum permissible  
Figure 3: Surfaces used for measuring.  
error (\(MPE_{E}\)) of machine was calculated according to ISO 10360-2, where \(L\) is the measured length dimension in mm.  
\[MPE_{E}=\bigg{(}1.8+\frac{L}{300}\bigg{)}(\mu\text{m}) \tag{1}\]  
The elements used in the measurement are defined in Figure 3.  
Dimension and geometric tolerance evaluation was performed with a coordinate base system consisting of PlaneA (Spatial rotation, Z origin), the PER-A plane (X origin) and the symmetry of the B1-PER_long and B2-PER_long planes (Planar rotation, Y origin). The origin is shown in Figure 1 (green color). In the analysis of production, one length dimension in four ways and 20 geometric tolerances were evaluated. The measured surfaces (Figure 3) describe their identification, surface types and deviations. Two styli were used for the measurement of test samples.  
The 1 st stylus was used for the measurement of all dimensions and geometrical tolerances except cylindricity. The probe diameter = 1 mm, and the length of stylus = 20 mm. For the measurement, a 50 mm extension was used, because the minimum recommended stylus length is 40 mm. The 2 nd stylus with a diameter of 3 mm and length of 50 mm was used for measurement of cylindricity.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 2 SuDoRM-RF++  
The SuDoRM-RF++ method is depicted in Figure 1a. SuDoRM-RF++ adheres to the ESD architecture, which comprises three main components: the Encoder, Separator, and Decoder. The encoder is responsible for converting a mixture \(\mathbf{x}\in\mathbb{R}^{T}\), which contains audio signals from \(K\) speakers and interferences into a representation \(\mathbf{v_{x}}\) that characterizes the signal. Subsequently, the separator produces \(K\) latent representations \(\{\mathbf{\hat{v}}_{k},k=1,2,\cdots,K\}\) for each speaker in the mixture. Finally, the decoder proceeds to reconstruct the separated \(K\) source signals in the time domain, with each signal being denoted as \(\hat{s}^{k}\in\mathbb{R}^{T}\).