esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Development and Validation
#### 3.1.1 Performance Validation of the Models
Figure 5: The scatterplots of TROPOSIF and re-aggregated SIF; (**a**) for our local models and (**b**) for the universal model. Figure 6: The temporal correlations of (**a**) TROPOSIF and TSIF and of (**b**) TROPOSIF and USIF; (**c**) the difference of TSIF R\({}^{2}\) minus USIF R\({}^{2}\), R\({}^{2}\) refers to the temporal correlation; (**d**) the land cover types within the MODIS tile grid H27V05. The abbreviations are as follows: ENF, Evergreen Needleleaf Forests; EBF, Evergreen Broadleaf Forests; DNF, Deciduous Needleleaf Forests; DBF, Deciduous Broadleaf Forests; MF, Mixed Forests; CSH, Closed Shrublands; OSH, Open Shrublands; WSA, Woody Savannas; SAV, Savannas; GRA, Grasslands; WET, Permanent Wetlands; CRO, Croplands; URB, Urban and Built-up Lands; CNV, Cropland/Natural Vegetation Mosaics; PSI, Permanent Snow and Ice; BAR, Barren; WAT, Water Bodies. We also calculated the mean and the 5 th and 95 th percentile of _SIF_ of all pixels from TROPOSIF, TSIF, and USIF, respectively. As shown in Figure 7, both our local models and the universal can capture well the temporal variations in TROPOSIF, with the 5 th percentile, the 95 th percentile, and the mean of predicted _SIF_ resembling that of the original TROPOSIF. The strong consistency holds under both normal and stressed conditions. For example, in July 2019, the area within the MODIS tile grid H27V05 suffered a severe drought, and the TSIF below the normal was successfully captured. We observed some discrepancies in temporal variations between TSIF, USIF, and the original TROPOSIF. For example, on 10 June 2018, the 95 th percentile of TSIF shows a slight offset from TROPOSIF. The case is likely due to human agricultural activities (e.g., irrigation), but USIF has more such discrepancies. In general, TSIF better captures the temporal variations in TROPOSIF than USIF.
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
## 6 Conclusions  
Sand and dust will be entrained into the air under the influence of a helicopter flow field, causing the brownout phenomenon and presenting a serious hidden danger to flight safety. The brownout phenomenon of a helicopter in a sand-dust environment is numerically simulated, and the movement and distribution of sand at different hovering heights are analyzed.  
1. In the brownout phenomenon, the generation of dusty clouds and the entrainment of sand particles are directly related to the ground effect of the rotor. The main reason for all kinds of phenomena is the entrainment of dust from the ground after the blade tip vortex falls off a blade to the ground.
2. In the sand-dust environment, when the UH60 hovering height is 0.52R, the phenomenon of brownout will occur, and the pilot's field of vision will be covered by the dusty cloud, which presents a serious hidden danger to flight safety. When the hovering height is 1R, the phenomenon of brownout is not obvious. For other helicopters, this value will be slightly different.
3. The sand particles under the middle of the blade disk will be squeezed by the downwash flow field and move upward, then many particles will contact and collide with the fuselage.
4. In the brownout phenomenon, most of the sand particles still gather close to the ground. The average velocity of the particles is about 12 m/s, the height of dusty cloud from the ground will gradually increase over time, and will then remain at a certain height. The radial range of dust will keep expanding for a longer time.
5. In sand-dust environment, the particles with large radius are less susceptible to upwash than these with small radius. This paper verifies that the particles with a 50 um radius basically are left on the ground, while the particles with 10 um radius will be suspended in the air easily.
6. Slotting on the swept rotor blade tip can influence on rotor wake, and then reduce the damage of brownout.  
Conceptualization, Y.C.; Data curation, G.W.; Formal analysis, Y.C. and C.J.; Investigation, C.J.; Methodology, G.W.; Project administration, C.J.; Resources, C.J.; Supervision, Y.C.; Validation, G.W.; Writing--original draft, G.W.; Writing--review & editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
In order to solve the problem of manual selection of truncation distance in the ADPC algorithm [29], dichotomy is used to determine the truncation distance adaptively. 3. To tackle the problem of privacy leakage in CDPC algorithm, this paper introduces the Laplace mechanism to add noise to the local density. Based upon these improvements, an extension of CDPC based on differential privacy (DP-CDPC) is proposed. To demonstrate the superiority of the algorithm, extensive tests and evaluations are conducted on multiple datasets. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Solved Problems** & **Proposed Algorithm** & **Characteristics** \\ \hline [PERSON]. [PERSON] et al. [16] & \begin{tabular}{c} Effectively leverage the privacy leakage issue in \\ the process of data \\ mining \\ \end{tabular} & \begin{tabular}{c} A differential privacy \\ preservation multiple \\ cores DBSCAN \\ clustering schema \\ \end{tabular} & \begin{tabular}{c} Show better efficiency, accuracy, and privacy \\ preservation effect than \\ previous schemas \\ \end{tabular} \\ \hline [PERSON] et al. [27] & \begin{tabular}{c} To solve the poor \\ performance of DPC \\ algorithm on evenly \\ distributed data and \\ avoid privacy leakage \\ \end{tabular} & \begin{tabular}{c} Optimize the clustering \\ process with \\ reachable-centers and \\ propose DP-rcCFSFDP \\ \end{tabular} & \begin{tabular}{c} Improve the clustering \\ effectiveness while \\ preserving data privacy \\ compared with \\ DP-CFSFDP \\ \end{tabular} \\ \hline [PERSON] et al. [28] & \begin{tabular}{c} To solve the sensitive \\ information leakage and \\ security risk \\ \end{tabular} & \begin{tabular}{c} Differential \\ privacy-preserving \\ density peaks’ clustering \\ based on the shared near \\ neighbors similarity \\ method \\ \end{tabular} & \begin{tabular}{c} Effectively protects the \\ data privacy and \\ improves the quality of \\ the clustering results \\ \end{tabular} \\ \hline [PERSON] et al. [29] & \begin{tabular}{c} To solve the poor \\ adaptability of \\ high-dimensional data, \\ inability to \\ automatically determine \\ clustering centers, and \\ privacy problems in \\ clustering analysis. \\ \end{tabular} & \
\begin{tabular}{c} A new density peak \\ clustering algorithm \\ with adaptive clustering \\ center based on \\ differential privacy \\ (DP-ADPC) \\ \end{tabular} &
\begin{tabular}{c} Can realize the \\ automatic selection of \\ clustering center, solve \\ the privacy problem in \\ clustering analysis, and \\ improve clustering effect \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: The summarization of the applications and improvements of differential privacy techniques to clustering algorithms. The rest of this paper is organized as follows. Section 2 summarizes the related basic theoretical knowledge. Section 3 presents the core theory of the proposed algorithm, including its principles and steps. Section 4 includes specific experiments that validate the proposed algorithm on various datasets and compare its performance with other algorithms. Section 5 is the conclusion of this paper.
# Multiplexing Perfect Optical Vortex for Holographic Data Storage
## 3 Results
### Experimental Multiplexing Process in Holographic Data Storage  
To further demonstrate the effectiveness of multiplexing, six different data pages were stored within the single POV-multiplexing hologram. These data pages correspond to topological charges of 5, 10, 15, \(-\)5, \(-\)10, and \(-\)15. Then, the POV-multiplexing hologram was illuminated by the corresponding inverse POV beams with \(\ell_{\text{reading}}=-5\), \(-\)10, \(-\)15, 5, 10, and 15, and the resulting reconstructed images were captured using CCD. These reconstructed images are presented in Figure 5.  
In Figure 5, the six point-data pages can be accurately reconstructed from the POV-multiplexing hologram by utilizing the corresponding POV reading beam. The reconstructed images showcase well-preserved POV intensity characteristics at each data point, indicating the successful retrieval of the stored information. It is important to note that each point in the point-data page has the capacity to store 2 bits of information. With a data page size of \((9\times 9)\) cells, the total storage capacity per data page amounts to \((9\times 9\times 2)\) bits. Therefore, a single data page can store a significant amount of data. In the case of the POV-multiplexing hologram, which incorporates six data pages, the total storage capacity is greatly increased. Each data page contributes \((9\times 9\times 2)\) bits, so the overall storage capacity reaches \((162\times 6)\) bits. This demonstrates a substantial enhancement in storage capacity achieved through the multiplexing of data using POV beams. Overall, the successful reconstruction of the six point-data pages and the significant increase in storage capacity validate the effectiveness of the POV-multiplexing hologram approach for high-density holographic data storage applications.
## 5 Discussion
### Comparative Analysis with Previous Studies Using MQTTset Dataset  
In this section, we compare our results to prior studies that used different numbers of features from the same dataset, MQTTset.  
[PERSON] et al. [23] utilized 33 features and demonstrated that models like RF and DT achieved the highest accuracy of 91.59%. Notably, their study required substantial training times, highlighting a potential issue for real-time applications. [PERSON] et al. [5] using 33 features showed improvements with ensemble methods such as stacking, which achieved an accuracy of 95.43% but at a high computational cost.  
[PERSON] and [PERSON] [4] reduced the feature set to 10 and showed that models like RF could achieve an accuracy of 96.33%. The training times were also relatively lower than those of [PERSON] et al. [23] and [PERSON] et al. [5]. However, it is essential to note that the training time may depend on the available computational resources, which can vary significantly across different studies.  
Our study reduced the feature set from 33 to only 5 using SMDT. The selected five features still provide high performance with simpler models, as evidenced by the performance metrics. We achieved competitive accuracy rates with models such as RF, DT, GB, and XGBoost, reaching 95% in binary classification and 91% in multiclass classification. Without sacrificing performance, the significant reduction in feature dimensionality demonstrates SMDT's potential for enhancing computational efficiency. Table 10 summarizes this comparative analysis with previous studies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Features** & \begin{tabular}{c} **Best** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **F1-Score** \\ \end{tabular} & \
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **Time (s)** \\ \end{tabular} \\ \hline
[23] & 2020 & 33 & \
\begin{tabular}{c} (multiclass) \\ RF, DT: 91.59\% \\ \end{tabular} &
\begin{tabular}{c} RF, DT: \\ 91.40\% \\ \end{tabular} & 2298.2762 & 125.8504 \\ \hline
[5] & 2023 & 33 & \
\begin{tabular}{c} (binary classes) \\ Stacking: 95.42\% \\ \end{tabular} &
\begin{tabular}{c} Stacking: \\ 95.42\% \\ \end{tabular} & 250.05 & 0.94 \\ \hline
## 4 Results and Discussion  
### Experiment Results by SAFRAN  
Data were acquired from a ground test using experimental hardware that included a civil aircraft engine with damaged bearings and sensor locations. The general overview of the tested engine and its accessory gearbox is given in [31], where shafts L1-L5 are labeled. The engine had two main shafts and an accessory gearbox with equipment such as pumps, filters, alternators, and a starter. The accessory gearbox was linked to the high-pressure shaft HP by a radial drive shaft and a horizontal drive shaft. The database holds the healthy-state record (labeled as ACC1) and bearing-failure record (ACC2); both signals had a sampling frequency of \(50\,\mathrm{kHz}\). The tachometer signal is also provided. Due to ACC2 data being acquired closer to the L5 location, the outer race fault was expected to be more influential on this shaft than that in L1 (placed more distantly). Table 3 presents the fault frequencies of the bearing gearbox, referenced to L5, as detailed in [31].  
Figure 4 displays the spectrogram of the bearing-failure record estimated within the entire frequency range \([0,25]\,\mathrm{kHz}\) (see left plot), indicating that the IAS signal had the highest energy arising at the 62 nd harmonic. For illustration, the left plot shows the spectrogram of the downsampled version of ACC2 (by 16 times) within \([0,1.56]\,\mathrm{kHz}\) (right plot), for which IAS had a low signal-to-noise (SNR) ratio, having the highest energy of the third harmonic but hiding other harmonics.  
Figure 5 depicts the resulting envelope spectra of the considered filtering scenarios, showing that the SK approach had a sharp peak at failure frequency (see Figure 5a), but harmonics became less visible. In the case of SE, failure frequency was missed (first harmonic), and the third harmonic was then instead clearly observed (see Figure 5b). Regarding SSK (Figure 5c) and SSE (Figure 5d), either short-term approach displayed the envelope spectrum more accurately with a dominant presence of the fault frequency and all its harmonics. Hence, the short-term filtering approach improved failure diagnosis in the angular domain.  
Figure 4: Spectrograms of ACC2 record estimated within full frequency range of \([0,25]\,\mathrm{kHz}\) (**left**), and for downsampled version (by 16 times) within \([0,1.56]\,\mathrm{kHz}\) (**right**).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **L1 (L5)** & **L4 (L5)** & **L5 (L5)** \\ \hline Speed & 1.34 & 0.984 & 1 \\ \hline FTF & 0.55 & 0.40 & 0.43 \\ BSF & 3.46 & 2.44 & 3.56 \\ BPFI & 7.95 & 5.87 & 10.24 \\ BPFO & 5.45 & 3.97 & 7.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fault bearing frequencies of supporting shafts L1, L4, L5, with speed of L5 as reference.
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Experimental Platforms for Sixth-Generation (6G) Communication Channels
#### 6.1.2 Channel Sounders  
These are crucial for establishing standardized channel models across global coverage, spectra, and full applications in 6G. Channel sounders, composed of Rx, Tx, and a data acquisition unit, actively recognize channels by exploiting unknown propagation environments [236]. Key properties for evaluating channel sounders include delay range, bandwidth, channel snapshot (CS) repetition rate, and dynamic range [237]. Channel sounders are used to investigate properties of channels in various scenarios, spanning sub-6 GHz, mmWave, THz, and optical wireless communication (OWC) frequency bands.  
Channel sounders find applications in diverse scenarios such as outdoor mobile, urban macro, indoor office, mmWave path loss investigation, and maritime communication. Moreover, they cater to specific channel measurement frequencies and scenarios, including those for UAV, maritime, and mine environments. These sounders also serve applications like massive MIMO, RIS, intelligent sensing and access control (ISAC), and the Industrial Internet of Things (IIoT).  
The exploration of 6G channels through testbeds, spanning simulation, and active measurement is vital for shaping the standardization and deployment of future communication systems. The establishment of these testbeds across various frequencies and scenarios ensures a robust foundation for 6G research and development [24].
## 5 DTSCS Scheme for MUCS  
According to the analysis and requirements of Section 2.3, we design the DTSCS scheme for closed search. In the first stage, the CPIO algorithm proposed in the Section 3 is used as the search algorithm for MUCS. In the return stage, the ST approach proposed in the former section is used to obtain the shortest path with OC to return to the airport. The two stages are coupled in time. The pose of UAV at the end of the search stage is the starting pose for the return stage, which in turn determine the time steps needed for UAV to return to the starting airport.  
In the search stage, \(UAV_{i}\) needs to calculate its remaining distance and the time steps needed to return to the airport in real time. Assume the RC of \(UAV_{i}\) is \(T_{i}\). \(T_{i,1}(k)\) represents the total time steps of the search stage. \(T_{i,2}(k)\) is the total time steps of the return stage. Assume that \(UAV_{i}\) has searched for \(k\) time steps and \(UAV_{i}\) can still safely return to the airport. Before executing the next-step search, \(UAV_{i}\) need first to calculate the position to be reached at the next step and how long it will take from the current cell returning back to the airport. If at the next step, the calculated fuel can still guarantee \(UAV_{i}\) safely returning back to the airport, then the \(UAV_{i}\) search forward a time step. Otherwise, the \(UAV_{i}\) stop searching and return directly to the airport. Therefore, the time for \(UAV_{i}\) returning to the airport should satisfy the following relationships:  
\[T_{i,1}(k)+T_{i,1}(k)\leq T_{i}, \tag{23}\]  
\[T_{i,1}(k+1)+T_{i,1}(k+1)>T_{i}, \tag{24}\]  
where Equation (23) means that the sum of \(T_{i,1}(k)\) and \(T_{i,2}(k)\) at any time step cannot exceed the RC. Equation (24) specifies the condition for the return of the \(UAV_{i}\). The DTSCS scheme can not only ensure that UAV gets the maximum search rewards, but also maximize the range. Besides, it is not limited to the fixed range \(T_{i}\). It can be changed during the task. We can specify the searching time for the first stage, or we can issue a return order at any time during the search. All of these scenarios are possible cases in the application of MUCS.  
**Remark 4**: _Under return conditions, UAV may have surplus fuel after returning to the airport, which is due to the OC of itself._  
**Definition 5**: **(Range utilization)**_. The proportion of time-of-flight of UAV in \(T_{i}\) is defined as:_\[\eta_{i}=\frac{T_{i,1}(k)+T_{i,2}(k)}{T_{i}}\times 100\%. \tag{25}\]  
Set the RC for the \(UAV_{i}\) to be 100 steps. Taking an example shown in Figure 5, the remaining range at this time step is assumed to be six steps. If \(UAV_{i}\) continues to search, it will take at least nine steps, as shown in black arrow, for \(UAV_{i}\) to return to the airport at the next step. According to Equations (23) and (24), \(UAV_{i}\) should return to the airport at this time step, as shown in red arrow. In this case, \(\eta_{i}=95\%\). Algorithm 3 describes the detailed flows of the DTSCS scheme.
## 1 Introduction
However, it also brought about problems such as increased power consumption and complex logic code, which is often unacceptable in some scenarios with low power consumption, light miniaturization, and high reliable computing requirements [20]. Therefore, optimizing the design based on the parallel multi-processing core and the calculation characteristics of the matrix multiplication of forward and inverse solutions of the robotic arm to meet the real-time processing performance is of great significance. In practical engineering applications, real-time computing requirements for forward and inverse kinematics solutions reach milliseconds. For example, autonomous path planning based on force feedback and visual feedback requires a single planning time at the microsecond level in fine control. In previous studies, Xilin's XC5 VLX330T was adopted to design a calculation module for path planning based on a 40-bit single-precision floating point, and the calculation time was less than 2.3 milliseconds [21]. This method was only an engineering design with no optimization in the algorithm itself. Based on the results of [21], this paper presents a method for calculating high-speed matrix cascade multiplication for robotic arm control, which includes three parts: equivalent order reduction calculation, optimal order calculation, and parallel dot product calculation. The equivalent order reduction calculation is implemented to remove unnecessary multiplication and accumulation operations in the matrix with a large number of zero vectors by the statistical partition of the zero vector of the matrix. When the row vector in the multiplication matrix or the column vector in the multiplication matrix is 0, the multiplication and accumulation operation of the vector is considered unnecessary. The optimal order calculation is calculated according to the equivalent reduced-order matrix to obtain the optimal multiplication order. Through parallel dot product calculation, the efficiency of practical vector dot product calculation is improved, thereby improving the calculation speed of matrix cascade multiplication. The arrangement of this paper is as follows. The calculation model of matrix cascade multiplication is given in Section 2. In Section 3, the equivalent reduced-order simplification method is presented for the application process of robotic arm control. Section 4 discusses the experimental verification, the result analysis, and the comparison with the previous designs. Section 5 summarizes the thesis.
# Preparation and Electron-Beam Surface Modification of Novel TiN Material for Medical Applications
## 3 Results and Discussion
### Electrochemical Tests and Cell Growth  
For materials developed as implant components or coatings, their surface properties, and especially their behavior in contact with biological liquids (such as saline solutions) are of prime importance. That is why, upon preparation and surface characterization, the samples produced in this work were electrochemically tested. As mentioned above, contacts with copper wires were first provided, after which the substrate area of the samples was insulated, leaving only a coated surface available for contact with electrolyte. Thus prepared electrodes, together with counter and reference electrodes, were then immersed in a model saline solution (inorganic fraction of DMEM) and connected to electrochemical system.  
First, the open circuit potential (OCP or E\({}_{\text{OC}}\)) which characterizes processes taking place on the surface immersed in a solution was measured. The OCP shows how the material undergoes corrosion in the medium in which it is immersed. Second, electrochemical impedance spectroscopy (EIS) was applied to comprehend possible mechanisms involved into electrochemical processes. To trace changes occurring over long-term contact with saline, both OCP and EIS were monitored for the same samples stored in saline for 1, 3, 5, 7 and 10 days. The results are presented and discussed in the following section.