esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Drilling Force Characterization  
The variation curves of thrust force in drilling CFRP are roughly divided into four stages [7, 18]. To better depict the force generation, the calculated force values are extracted from the signal in the steady stage during drilling tests. The drilling thrust force as a function of hole number is shown in Figures 6 and 7. The results show that the drill force increased with the hole number for all the tools, due to the tool wear. In Figure 6, at the initial stage of drilling tests, the values are 25 N for MCD, 18 N for NCD, and 16 N for MCD/NCD coated drills, respectively. The MCD coated drill encountered more severe friction than that of NCD and MCD/NCD coated tools, because the surfaces of NCD coatings are smoother than MCD. The forces increase with more holes drilled for all the tools due to tool wear. As to Figure 7, with the given drilling parameter \(n=6000\) rpm, the force values are lower than that of the MCD and NCD coated tools with a parameter of \(n=4000\) rpm. It suggests that the higher spindle speed brings lower force for the fresh drill. In contrast, at the end of drilling tests, with lower spindle speed and feed rate, the average force is lower attributed to the increasing feed rate leading to an increase in uncut chip thickness for the worn tool. It is obvious that the MCD/NCD coated tools exhibit the lowest force value during the drilling tests, suggesting that the dual-layer composite diamond coated tools display the most excellent performance in drilling tests. For the MCD/NCD coated tool, the force value is the lowest among all the drilling conditions. Exceptionally, the force values present similar results at the 21 th hole under condition II. That may be caused by the worn surface of MCD film which becomes smooth before film delamination. It can be observed that, under the given drilling parameters \(n=4000\) rpm and \(f=0.004\) mm/r, the MCD/NCD coated tools exhibit the most excellent performance.  
Figure 5: Raman spectrum of diamond coatings. (**a**) MCD; (**b**) NCD; (**c**) MCD/NCD.
## 4 Results and Discussions
### Analysis of Intraday Optimization
To further illustrate the role of CSES in intraday optimal scheduling, charging/discharging patterns and RES utilization are analyzed. Figure 10 shows intraday  
Figure 9: Intraday power equilibrium under CO-CSES. (**a**) AC1; (**b**) AC2; (**c**) AC3. charging/discharging power and SOC under CO-CSES, and Table 11 shows the intra-day RES utilization rate of different cases. The source-load fluctuations and imbalances in intraday are more severe compared to those in day-ahead scheduling, and the RES utilization under each case has decreased to different degrees. As can be seen from Table 11, Case 1's AC2 subnetwork has the most obvious decrease and the lowest value in RES utilization, which is consistent with the trend of RES utilization in the day-ahead scheduling. CL-CSES can mitigate this phenomenon due to its larger shared energy storage capacity, but it still struggles to approach the day-ahead optimization results. On the other hand, CO-CSES maintains the highest level of RES utilization in both intraday and day-ahead stages. As shown in Figure 10, the CSES accommodates a large amount of redundant power from the AC2 subnetwork by charging in 0-6 h and then supports other subnetworks with insufficient RES power by discharging. For AC1 and AC3 subnetworks, the coordinated advantages of CO-CSES and VSC interactions enable them to achieve near-complete RES utilization (99.31% and 99.91%, respectively). Through multi-subnetwork synergy, CO-CSES flexibly coordinates the charging and discharging power allocated to each subnetwork while the total charging and discharging follow the SOC trajectory. This shows that the coordinated optimization strategy based on the dual-time-scale framework can effectively adapt to the uncertainty of RES and maintain a high level of RES utilization while guaranteeing the economy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **AC1** & **AC2** & **AC3** \\ \hline Case 1 & 96.15\% & 71.51\% & 92.47\% \\ Case 2 & 97.04\% & 79.74\% & 99.63\% \\ Case 3 & 99.31\% & 88.10\% & 99.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Intraday RES utilization rate of different cases among AC subnetworks. Figure 10: Intraday CSES charging/discharging power and SOC under CO-CSES.
# An Analytical Method for Gas Flow Measurement Using Conservative Chemical Elements
## 4 Conclusions  
In this study, we analyzed the applicability of the conservative element as a reliable method for measuring gas flow rate during a chemical reaction by comparing it with a mechanical device (MFM). The MFM can obtain a reliable flow rate by measuring the mass value of the targeted gas. However, the reliability of the MFM is affected by the various gases produced in the chemical reaction and changes in the gas composition. In contrast, the conservative element is chemically stable, and its concentration in the reaction is determined only by physical mixing with the byproduct gases generated. Because the gas flow using the conservative element is not affected by the gas composition and mixing ratio, this approach can be used without any limitations in the measurement range. We expect this approach to fundamentally overcome the limitations of the mechanical devices. However, for this method, a quantitative analysis must be conducted of the conservative species in the discharge gas, which is closely related to the data collection interval of the gas analyzer. In future study, we will investigate technology for rapidly monitoring the conservative element to obtain more reliable measurements of gas flow.  
Conceptualization, S.-H.K.; methodology, G.L.; investigation, G.L.; data curation, G.L., S.-H.K.; writing--original draft preparation, S.-H.K.; writing--review and editing, S.-H.K., G.L.; project administration, S.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Korea Environment Industry & Technology Institute (KEITI) through a project to develop eco-friendly new materials and processing technology derived from wildlife programs, funded by Korea Ministry of Environment (Project No. RE202101524) and the Ministry of Trade, Industry, and Energy Technology as Innovation Program (Project No. 20013038).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Gas flow relation between Methods I and II with respect to syngas generation.
## 2 AE Monitoring at the 105-C Reactor Facility
### Installation of Acoustic Emission Systems
#### 2.2.1 Crane Maintenance Area  
The wired AE system was installed to monitor the activity in this area of building 105-C with ten sensors including five resonant sensors (type R6I) and five broadband sensors (type WDI). The sensors were installed at three different locations. The first location was near a column to roof interface (referred to as the'vertical column to roof interface location'). This area had been visually assessed by the Savannah River Nuclear Solutions/Savannah River National Laboratory (SRNS/SRNL) personnel and is known to have deteriorated in comparison to the majority of the structural system comprising the 105-C reactor building. Spalling occurred in this area in the recent past and ongoing corrosion activity was suspected. The area has undergone at least one repair activity in the past. A total of six sensors (three resonant and three broadband) were installed at this location, which is shown in Figure 2. The locations of the sensors were chosen to be near the exposed reinforcing bars showing visual signs of corrosion damage. The locations of the sensors with respect to the red dot shown in Figure 2 are provided in Table 1.  
The second location was chosen on a horizontal beam that forms the connection with the previously described column (referred to as the 'horizontal beam location'). Two sensors (one resonant and one broadband) were installed at a distance of 12 inches below the beam to roof interface where signs of deterioration were visually observed (Figure 3a). The spacing between the sensors was 6 inches. The third location was chosen at an area where no signs of damage were observed (referred to as the 'control location'). Two sensors (one resonant and one broadband) were installed at this location, which is shown in Figure 3b. The horizontal distance between the two sensors is 6 inches. The data collected from the control location was used to evaluate the effectiveness of data reduction approaches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type-Channel** & **Horizontal Dimension (Inches)** & **Vertical Dimension (Inches)** \\ \hline WDI-9 & 0 & \(-\)10.5 * \\ R6I-10 & 8.5 & 16 \\ R6I-11 & 5.5 & 9 \\ WDI-12 & 11 & 5.5 \\ R6I-13 & 14.5 & 16 \\ WDI-14 & 15.5 & 8.5 \\ \hline \hline \end{tabular}  
* Positive dimension indicates below the red dot are shown in Figure 2. Negative dimension indicates above the red dot are shown in Figure 2.  
\end{table}
Table 1: Location of sensors shown in Figure 2.
# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 2 Materials and Methods
### Crop Type Maps  
The reference crop type maps used in this study were made available by the Tuscany Regional Agency for Agriculture ([[http://dati.toscana.it/organization/artea](http://dati.toscana.it/organization/artea)]([http://dati.toscana.it/organization/artea](http://dati.toscana.it/organization/artea)), accessed on 17 October 2020) for the years from 2016 to 2019. This study focused only on the main crop types of the arable land, excluding permanent crops such as vineyards and olive groves. Selected crop typologies were grouped into 8 classes, taking the temporal pattern of the crops in the study area into account: winter cereals, clover and alfalfa, maize, sorghum,sunflower, rapeseed, horticultural crops, and soy. The centroid of each crop parcel polygon in the reference maps was used to query the raster predictors generated from the satellite images.
## 10 Results and Discussion
### Attack-Detection Rate  
The attack-detection rate measures the proportion of actual attacks or malicious events that are correctly detected and flagged by the security mechanism as positive. The attack-detection rates for the BCPS-RPL and GBG-RPL mechanisms within a CPS are shown in Figure 11.  
The bar graph shows an increasing trend in attack-detection rates over time for both the BCPS-RPL and GBG-RPL techniques. The findings show that, in terms of attack=detection rate, the GBG-RPL mechanism beat the BCPS-RPL mechanism. The attack-detection rate is directly dependent upon the identification of the malicious nodes. Once the attacker is removed from the CPS network, the procedure is finished. Throughout the simulation period, the GBG-RPL detects attacks more frequently than the BCPS-RPL. This is because the trickle timer's configuration and trust parameters were chosen with care. As a result, the GBG-RPL has a higher overall attack-detection rate, which improves network life and reliability.
## 4 Machine Learning (ML) Algorithms and Results
### Presentation of the Results Obtained for Classical ML Models
Further reduction of the class number, referring to the reduction of the angular resolution to 9 degrees, sustained the percentage advantage of the ANN models (narrow, medium and bilayered, reaching similar accuracies within 1% of each other) over the ensembled ones (Subspace Discriminant and Bagged Trees). However, in this case, the SVM models (especially the linear and quadratic ones) were up to just 3 percent behind the ANN models. Finally, in the last case (of the smallest number of classes and the angular resolution of 21.6 degrees), the order of the best models did not change, but the difference of the accuracy between them decreased and did not exceed 3% this time. Moreover, as it can be seen, the best accuracy result was obtained for an angular resolution of \(21.6^{\circ}\) and the worst for an angular resolution of \(1.8^{\circ}\), which is as expected. Utilizing greater angular steps increases the proportion of correctly identified steel grades and magnetic directions (the angles of the transducer alignment with respect to TD and RD). This is because the classification process has been obviously simplified and the number of potential classes has been decreased (there were 510 classes for \(1.8^{\circ}\) step size, but only 50 for \(21.6^{\circ}\)). However, it is crucial to achieve both the highest possible classification accuracy and the greatest number of detected directions, which, in this case, define the resolution of the classification operation. An accuracy of 50.9% was obtained for an angular resolution of \(3.6^{\circ}\), which was stated in the previous study [26] as the most optimal for identifying grades of GO sheets and their angles (according to the average misalignment angle between the RD and easy magnetic axis of the GO steels).
# Influence of the Tip Clearance on the Aeroelastic Characteristics of a Last Stage Steam Turbine
## 2 Research Methodology  
### Energy Method  
The energy method is widely applied in the investigation of turbomachinery aeroelastic characteristics [9; 10], which assumes that the aeroelastic modes are traveling wave modes and calculates the total aerodynamic force during a blade oscillation period. The energy method uses a one-way fluid-structure interaction calculation, in which the blade is vibrating at the selected traveling wave mode and the aerodynamic work done on the blade surface \(W_{aero}\) by fluid perturbations is integrated. Validation of the energy method and the 3D computational fluid dynamics (CFD) simulation setups applied in this paper have been presented in [8; 11; 12] by comparison with the result from the influence coefficient method and other CFD codes.  
The main procedures in the numerical simulation using the energy method are listed below:  
1. Calculate the blade mode shape and the natural frequency of the mode;
2. Set up the traveling wave mode and interpolate the blade surface displacement on the aerodynamic mesh;
3. Simulate the unsteady flow field with blade vibration by 3D-CFD simulations;
4. Integrate the aerodynamic work done to the blade during a blade oscillation period.  
The aerodynamic damping coefficient calculated from the energy method was applied as the non-dimensional parameter to describe the blade aeroelastic stability, which is defined as Equation (1),  
\[\Xi=\frac{-W_{aero}}{\pi b\alpha^{2}c^{2}p_{ref}} \tag{1}\]  
where \(W_{aero}\) is the aerodynamic work per blade oscillating period done to the blade, and the reference pressure \(p_{ref}\) is equal to the average total pressure minus the average static pressure at the inlet of the rotor. If the integrated aerodynamic work done on the blade is positive in a blade vibration period, then the aerodynamic damping coefficient is a negative value, and the rotor is aeroelastically unstable.  
To resolve the distribution of aerodynamic work on the blade surface, the aerodynamic work coefficient calculated from the unsteady pressure was used as in Equation (2).  
\[W=\frac{-h\cdot\overline{n}p_{i}}{\alpha^{2}cp_{ref}} \tag{2}\]  
where \(W\) is the aerodynamic work coefficient, \(h\) is the local displacement factor, \(\overline{n}\) is the local normal vector, \(\alpha\) is the ratio between maximum blade vibration amplitude and chord length, and \(p_{i}\) is the imaginary component of the local unsteady pressure. The \(W_{aero}\) in Equation (1) is the integrated value of the local aerodynamic work coefficient along the blade surface. A negative aerodynamic work coefficient on the blade surface indicates that a stabilization effect is performed due to the flow field, and vice versa.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 5 Performance Evaluation  
The objective of the proposed framework is to establish and maintain a multi-hop access network that would allow a population of clients to send and receive information to/from a central location (root) and by extension to/from each other if required. A subsidiary goal is to create a tree topology to ensure unambiguous routing.  
The challenge is to do so exclusively through local decision-making by individual mobile nodes, in the face of an arbitrary and heterogeneous distribution of clients.  
We measure the swarm performance by plotting the LQM values and the swarm size over time. We are using the LQM as a first-order approximation of performance/QoS (used  
\begin{table}
\begin{tabular}{c c c c c} \hline \(l_{j}\backslash l_{i}\) & **1** & **2** & **3** & **4** \\ \hline
1 & — & 1.06 & 1.3 & 1.5 \\
2 & 1.06 & 1.3 & 1.5 & 1.68 \\
3 & 1.3 & 1.5 & 1.68 & 1.84 \\
4 & 1.5 & 1.68 & 1.84 & 1.98 \\  & & & & \\
9 & 2.25 & 2.37 & 2.49 & 2.6 \\ \hline \end{tabular}
\end{table}
Table 1: Shown are, for any node \(i\), The ratio that the distance from \(i\) to the new drone \(j\) must be better (shorter) than the distance to the current parent node \(p\) before \(i\) will consider re-connecting from \(p\) to \(j\). The ratios, shown here for different _levels_ of the nodes \(i\) and \(j\), increase rapidly for increasing level disparity. See Equation (9).  
only to determine the parent node); we are not evaluating the QoS from a communication point of view, nor are we including the operational costs for the network (or consider different traffic volumes). Emphasis is placed on demonstrating the ability of the swarm to realize a MANET covering most (and ideally: all) mobile clients without a central command module i.e., through self-organization. Other potential performance indicators for the algorithm, such as, e.g., energy efficiency, delay, or throughput, are not considered in this article and were not the focus of our work.
## 2 Proposed Enhanced PNCC Algorithm
### Noise Suppression
#### 2.2.1 Large-Time Power Calculation  
In the proposed method, the large-time power filter is used as an average low-pass average filter, similar to the medium-time power filter, but it is applied on a higher number of frames. This filter  
Figure 2: Normalized 25 gammatone filter banks.  
is applied by computing the running average power \(P[m,\;l]\) for \(2M\;+1\) consecutive frames as in the following equation:  
\[Q[m,l]=\frac{1}{2M+1}\sum_{m^{\prime}=m-M}^{m+M}P[m^{\prime},l] \tag{3}\]  
where \(m\) represents the frame index, and \(l\) is the gammatone channel index. As shown in Figure 3a, the power values of the gammatone spectrogram for clean speech does not change rapidly along with consequent frames. On the other hand, the power values of additive environmental and white noise change more frequently. Thus, these noises cause a sudden variation in speech values over consequent frames of each gammatone channel, as shown in Figure 3b,c. These sudden changes vary in each gammatone channel according to each type of 110 noise. Additionally, pre-emphasizing causes boosting of the additive noise values with speech at high frequency. Therefore, in this stage, the average filtering over each gammatone channel will cause smoothing of these sudden changes in the power values. Furthermore, the number of average window frames \(2M\;+1\) affects recognition performance. Using a large average window causes the 115 blur effect that can destroy the speech information, which degrades the system performance, especially in the case of undistorted speech utterances. Moreover, it is computationally demanding. On the other hand, small average window size leads to incomplete removal of sudden changes in power that are associated with some background noise conditions. Therefore, in this paper, the value of 120 coefficient \(M\) is set to be equal to 5, and it was chosen experimentally by selecting the value that provides high performance at different noise levels.
# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 5 Conclusions  
Considering the growing requirements of underwater target tracking, the IKCF was proposed to track targets in underwater sonar image sequences. For the first time, in order to solve the problem of the poor training effect with a fixed-size base in traditional KCF, a base sample with a dynamically continuous scale was proposed. Then, an adaptive filter update strategy based on PSR was adopted. In the process of filter model updating, the PSR was used to judge whether the model needs to be updated, which can reduce the computation and solve the filter drift problem caused by the target disappearing and appearing again. The visual and quantitative experimental results have shown that the proposed IKCF has a higher tracking accuracy and better effectiveness. Therefore, the proposed method can provide better underwater target tracking and has important theoretical and practical value.  
Conceptualization, X.W., Y.Z. and G.W.; Methodology, Z.Z. and B.D.; Software, B.D.; Investigation, Z.Z. and B.D.; Resources, X.W.; Writing--original draft preparation, G.W. and B.D.; Writing--review and editing, X.W., G.W., Y.Z. and Z.Z.; Visualization, Z.Z.; Supervision, X.W. and Y.Z.; Project administration, X.W.; Funding acquisition, X.W.  
This research was supported in part by the National Natural Science Foundation of China grant number 41876110 and in part by the Fundamental Research Funds for the Central Universities of China grant number HEUCF180601.  
The authors declare no conflict of interest.