esa-sceva/satcom-chunk-collection huggingface dataset:
# Pixel Resolution Imaging in Parallel Phase-Shifting Digital Holography
## 2 Principles and Methods  
### Hologram Recording  
Figure 1 shows the optical schematic of the PPSDH system based on the Mach-Zehnder interference structure. A polarized He-Ne laser source (Thorlabs HNL020 LB) with a wavelength of 632.8 nm, whose output amplitude is continuously adjusted by a polarizer, acts as the source beam for the proposed PPSDH system. The beam is spatially filtered and collimated to generate a uniform beam with a plane wavefront. The ratio of the p-polarized and s-polarized components of the source's linearly polarized light is continuously adjusted by a half-wave plate (HWP). The reflected beam (s-polarized) from a polarizing beam splitter (PBS) illuminates the transparent object, and the illumination light is modulated by the complex amplitude of the object to form the object beam \(u(x,y)\), while the transmitted beam (p-polarized) acts as the plane wavefront reference beam. The object diffracted beam and the reference beam are combined by a non-polarizing beam splitter (BS), and their polarization states become right-handed circular polarization and left-handed circular polarization, respectively, through a quarter-wave plate (QWP) with the fast axis oriented at \(-\pi\)4. The polarization direction of the object diffracted beam \(o(x,y)\) (right-handed) and the reference beam \(r(x,y)\) (left-handed) at the plane of the monochromatic polarization camera (FLIR BFS-U3-51S5P-C) has the same rotational angular velocity, but in opposite directions. Thus, relative phase shifts of \(\delta\) = 0, \(\pi\)/2, \(\pi\), 3\(\pi\)/2 are formed between the object beam and the reference beam through a micropolarizer filter array with the optical axis direction of 0, \(\pi\)/4, \(\pi\)/2, 3\(\pi\)/4. Due to the circular arrangement of a set of 2 \(\times\) 2 adjacent pixels with different polarization directions, the four sub-phase-shift holograms \(I_{0}(x,y;0)\), \(I_{0}(x,y;\tau/2)\), \(I_{0}(x,y;\pi)\), \(I_{0}(x,y;3\pi/2)\) in a spatially multiplexed hologram \(I_{0}(x,y;\delta)\) (SMH) recorded by the polarization camera are also cross-distributed, as in a checkerboard [7; 26; 27; 35; 36].
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 4 FPGA Implementation Details of Cache Mapping
### 2-Way Set-Associative Mapping  
N-way set-associative cache provides N blocks in each set to decrease conflict, where N is the cache's degree of associativity. For each memory address, the data can map to any block in the set. The set of the memory address can be found using \(KmodN\). In our implemented work, we designed a 2-way set-associative cache and a 4-way set-associative cache on BRAM.  
Figure 10 shows the cache layout of the 2-way set-associative cache system. According to the NetFPGA-10G team, the total capacity of BRAM is 11,664 Kbits. The memory capacity of BRAM is less than RLDRAM. However, the processing speed of BRAM is faster than that of RLDRAM. Therefore, BRAM can accelerate work further as an alternate cache. In Figure 10, the _value/data_ are shown in green, and _Tag_ is shown in red. A single set of 2-way set-associative caches contains two blocks of data using up to 1024 bits. From the accessible memory of BRAM, we create 6000 sets for a 2-way set-associative cache system.  
For example, when two memory addresses map to set 4, the 2-way cache system can accommodate data from both addresses, as shown in Figure 11. The cache miss will happen during the first loop iteration and both words are loaded into the two ways of set 4. For the next iterations of these addresses, the cache will hit. If these addresses repeat 5 times each, then the miss rate is \(2/10=20\%\); however, the miss rate will be \(100\%\) for the direct-mapped cache because the data change in a particular cache line for every repetition of addresses. In a set-associative cache system when both ways are full, the question will arise of which way to replace. In our work, we use the Least Recently Used (LRU) algorithm for the replacement of the ways [29].  
Figure 10: Two-way set-associative cache system.  
Figure 9: Flow chart of direct-mapped cache system.
## 2 Numerical Methods
### Grid Independence Study  
To conduct the grid independence analysis, four sets of grids with varying sizes were chosen, each comprising approximately 1.5 million, 2 million, 2.6 million, and 4.3 million grid cells. The results of film cooling effectiveness of these grid sets along the centerline are presented in Figure 3. The results reveal discernible discrepancies at the cooling hole outlets with increasing grid numbers. Notably, upon reaching two million grids or more, no consequential variations in the calculated outcomes are observed. Thus, the grid set comprising approximately two million grids is selected for subsequent numerical simulations.  
Figure 2: Variation of phase lag between the rod and the pulsating jet.
## 3 Results and Discussion
### Comparison with Other Adsorbents  
In general, although the adsorption affinity is usually compared based on the adsorption capacity, it is difficult to evaluate each adsorbent on a parallel basis by using adsorption capacity alone. Adsorption  
Figure 9: Relative energies of (a) [Eu(H\({}_{2}\)O)\({}_{8}\)]\({}^{3+}\) adsorbed on precursor LDH and (b) Eu\({}^{3+}\) adsorbed on EDTA-intercalated LDH.  
experiments of each material are usually conducted under various initial concentrations or volumes of solution. From Table 2, although the adsorption capacity obtained at high initial concentration is larger than that at lower initial concentration, the PC at high initial concentration shows a reverse tendency. Hence, the concept of PC was also used here to compare the adsorption affinity with other reported adsorbents. The comparison of the adsorption properties of other previous adsorbents was shown in Table 6.  
From Table 6, EDTA modified LDH which synthesized in this work has relatively high adsorption capacity (145 ug\(\cdot\)g\({}^{-1}\) for L2-Eu, 124 ug\(\cdot\)g\({}^{-1}\) for L2-La) relative to only LDH. It means the chelating agents improve the adsorption affinity of LDH. However, comparing with some adsorbents which were exposed to a higher concentration of target ions measured under their adsorption experiments, it shows a lower adsorption capacity. It is difficult to compare each other under different conditions (even thousand times difference among them). This is a result of adsorption capacity sensitively to the initial concentration of target ions. Therefore, introducing the concept of PC is also important to reduce this difference. The PC of both L2-Eu and L2-La are larger in comparison with others, which can indicate that LDH intercalated by EDTA has superior adsorption performance.
# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
## 4 Conclusions  
To address the problem of poor long-sequence data prediction ability in the encoder-decoder structure for wear prediction analysis, a dual attention mechanism prediction model is proposed. The model integrates a feature attention mechanism to calculate the weight of each input feature at the current time and enhance the influence of important features on the prediction results. It also integrates a temporal attention mechanism to optimize the weighting of the hidden state information of the encoder and capture the long-term correlation of input data. To address the problem of low sensitivity of output feedback to input signal changes in neural networks, the ARIMA model is used to linearly correct the prediction results and further optimize the wear prediction results. The performance of the model with different attention mechanisms was tested on a real-world dataset, and a comparison experiment was designed between the combination prediction model and other prediction models. The results show that the combination prediction model has a significant advantage in improving wear prediction accuracy.  
Conceptualization, Z.W. and N.Q.; methodology, Z.W. and N.Q.; writing-original draft preparation, Z.W.; writing-review and editing, Z.W., N.Q. and M.L.; supervision, P.W.; project administration, P.W. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Milling cutter wear prediction fitting curve of NASA.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**C3**} & \multicolumn{4}{c}{**C9**} & \multicolumn{4}{c}{**C13**} \\ \cline{2-13}  & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline APCANet-MP-SVR & 6.0817 & 7.1130 & 10.2275 & 0.8624 & 6.4311 & 6.9712 & 9.9602 & 0.8698 & 5.8042 & 6.4223 & 9.3317 & 0.8795 \\ SE-IDANN & 6.2389 & 7.2687 & 10.4690 & 0.8577 & 6.5948 & 7.2161 & 10.1475 & 0.8590 & 6.0038 & 6.5489 & 9.5654 & 0.8753 \\ DA-E-D & 5.8122 & 5.9293 & 9.9954 & 0.8749 & 6.1608 & 6.7684 & 9.7728 & 0.8705 & 5.6481 & 6.1549 & 9.1926 & 0.8920 \\ DA + ARIMA & 2.9209 & 3.4126 & 9.1732 & 0.8973 & 2.9133 & 3.4561 & 9.5378 & 0.8884 & 2.4360 & 2.9319 & 8.9006 & 0.9149 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of different methods in NASA dataset.  
**Funding:** This research was funded by the Jilin Province Science and Technology Innovation Platform Construction Project (YDZJ202302 CXJD027).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data is unavailable due to commercial restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This paper is devoted to software-defined radio (SDR) implementation of frequency modulated antipodal chaos shift keying (FM-ACSK) transceiver and presents results of prototype testing in real conditions. This novel and perspective class of spread-spectrum communication systems employs chaotic synchronization for the acquisition and tracking of the analog chaotic spreading code and does not need resource-demanding cross-correlation. The main motivation of the given work is to assess the performance of FM-ACSK in real conditions and demonstrate that chaotic synchronization can be considered an efficient spread-spectrum demodulation method. The work focuses on the real-time implementation aspects of the modulation-demodulation algorithms, forward error correction (FEC) and symbol timing synchronization approach in MATLAB Simulink. The performance of the presented prototype is assessed via extensive testing, which includes measurement of bit error ratio (BER) in single-user and multi-user scenarios, estimation of carrier frequency offset (CFO) impact and image transmission over-the-air between two independent sites and comparison with classical frequency hopping spread spectrum (FHSS). The paper shows that the presented class of the spread spectrum communication systems demonstrates good performance in low signal-to-noise ratio (SNR) conditions and in terms of BER significantly outperforms the classic spread-spectrum modulation schemes which employ correlation-based detection.  
software-defined radio; chaotic communication; spread spectrum communication; frequency modulation; synchronization; communication systems; MATLAB; physical layer
## 3 Data and Methods
### Lithospheric Data
To evaluate the quality of seismic acceleration compared to a linear trend representing the background seismicity, [PERSON] et al. (1998) ([51]) introduced the C factor, which is the ratio of the sum of the squares of the residuals of the fit of \(s=s(t)\) and the same quantity of a linear fit; a C value less than 1 indicates acceleration, with lower values indicating more prominent acceleration. [PERSON] et al. ([48]) improved the technique by focusing on strain deposited on the mainshock fault by surrounding seismicity, corrected by a damping function with distance. [PERSON] et al. ([49]) further enhanced the algorithm based on 14 case studies, confirming its ability to reveal hidden acceleration in seismic sequences and provide estimates of \(t_{f}\) and expected magnitude based on parameters A and B of the functional relation. In this analysis, the algorithm is automatic: considering the \(s=s(t)\) time series backward and excluding the mainshock, it detects the time when the acceleration starts and the minimum (no attenuation) and maximum circles (with some attenuation). Therefore,  
Figure 5: Spatial distribution of ten years of seismicity around the mainshock, in a radius of 150 km from the epicenter (largest green circle). Blue and red dots (confined within blue and red circles, respectively) represent the events contributing to the acceleration found by the R-AMR analysis. The red dots are the events closer to the seismogenic fault. for the characterization of seismicity, the R-AMR algorithm was applied to the seismic catalog without taking into account any potential heterogeneity.
## 3 Results
### Model Validation
#### 3.2.3 Residual Range  
Since the correction factor \(\lambda_{tot}\) plays a crucial role in the definition of the battery available capacity and hence on the variation law of the battery SoC during a driving mission, the reduction in \(\lambda_{tot}\), evaluated with different battery pack temperature and ageing levels, was used to estimate the variation of the residual range of the BEV exploited in this paper.  
In Figures 26 and 27, the curves of battery SoC and residual driving range are charted, respectively, accounting for the four different battery ageing levels considered in Section 3.2.1 (DoD100). As far as the battery EoL was achieved (purple curve), the tendency  
Figure 24: Combined effect of the battery pack temperature and the battery ageing level on \(\lambda_{tot}\) for DM25 in the case of a battery with 400 life-cycles completed and DoD80.  
Figure 25: Combined effect of the battery pack temperature and the battery ageing level on \(\lambda_{tot}\) for DM25 in the case of a battery with 600 life-cycles completed and DoD80.  
of the battery SoC evolution grew progressively resulting in lower final battery SoC values. Consistently, an important reduction in the BEV residual driving range occurred with a relevant loss of both the first (from \(\sim\)60 km down to \(\sim\)46 km) and last residual range estimations (from \(\sim\)48 km down to \(\sim\)37 km) made at the beginning and at the end of the driving mission, respectively.  
Figure 27: Comparison of the residual driving range estimated for different battery aging in the case of DoD100.  
The same analysis was conducted for DoD100. The effect of the decay of \(\lambda_{tot}\) on battery SoC and residual driving range are reported in Figures 28 and 29, respectively. In this case, a smaller variation of both battery SoC and BEV residual driving range occurred until 2/3 of the entire life-span of the battery (400 life-cycles). Conversely, once the battery EoL was reached, the loss in the residual range became much more relevant.  
Figure 28: Comparison of the battery SoC trend estimated for different battery ageing levels in the case of DoD80.  
Figure 29: Comparison of the residual driving range estimated for different battery aging in the case of DoD100.  
In conclusion, despite the absolute number of kilometers that could be driven permitting a complete discharge of the battery, the DoD80 appears to be a more efficient condition for battery operation since it allows for mitigation of the negative effects related to the reduction in the correction factor of the battery available capacity.
## Penetration-Type TDR (PTDR)  
### Principle of TDR  
The dielectric constant of a medium, which represents the ability to store electrical potential energy under an electrical field, can be determined from the propagation time of electromagnetic waves. The time domain reflectometry (TDR) unit has been used to measure the reflection of electromagnetic waves along the elapsed time through the electrodes of the TDR. The dielectric constant (k\({}_{\text{a}}\)) of soils in the vicinity of the TDR electrodes is dependent on the propagation velocity of a pulse, as follows [10]:  
\[\text{k}_{\text{a}}=\left(\frac{\text{c}}{\text{v}}\right)^{2}=\left(\frac{ \text{ct}}{2\text{L}}\right)^{2} \tag{1}\]  
where c is the velocity of electromagnetic waves in a perfect vacuum (3 \(\times\) 10\({}^{8}\) m/s); v is the propagation velocity of electromagnetic waves in a tested medium; t is the travel time of electromagnetic waves in a tested medium; and L is the length of the electrodes of the TDR. The travel time is determined as the time elapsed from the reflection at the beginning of the electrodes to the reflection at the end of the electrodes. Using TDR, the dielectric constant can be determined from a known probe length and the time between the first and second reflection.  
In a TDR waveform, the identification of the first and second reflection points is critical for the determination of the dielectric constant. In general, the location of the first reflection is a fixed point, unchanged by the material surrounding the probe [31]. [PERSON] and [PERSON] [32] determined the location of the second reflection to be the intersection of the tangent line, representing the maximum value of the derivative, with a horizontal line representing the maximum value of the derivative.  
The empirical relationship provided by [PERSON] et al. [11] has been widely used to estimate the volumetric water content (\(\theta\)) from the dielectric constant (k\({}_{\text{a}}\)) [15; 16; 17; 18]. In addition, linear relationships between (k\({}_{\text{a}}\))\({}^{0.5}\) and \(\theta\) have been proposed by [PERSON] [33] and [PERSON] et al. [34].
## 4 Results and Discussion
### Multianalyzer Data Fusion Results
These pdfs provide a visualization of the range of regression weights applied to the features from each analyzer, with greater magnitudes indicating greater influence on the final classifier confidence. Figure 9 shows the regression weight pdfs for each analyzer for the two data sets. For both data sets, the XRFS features are given the most weight. Further, the range of the pdf suggests that all of the XRFS features are used for classification (i.e., the pdf does not extend towards zero). In contrast, for the New Mexico soils, the means of the RS and LIBS pdfs are near zero suggesting that many of the RS and LIBS features are given little weight during classification. However, the long tails suggest that some of the features are given greater weight, and these are the features likely used for correcting errors committed by the XRFS classification. Figure 8: Classification matrices for each individual analyzer for the New Mexico soil suite (RS, top left; XRFS, top right; LIBS, bottom left) and the data fusion (bottom right). Each row indicates the samples’ true class label and each column indicates the classifier label given to the samples analyzed from this class. Each entry is the percent (%) of samples with a row’s true label that were assigned a column’s label by the classifier. The diagonal entries indicate correctly classified samples and the off-diagonal entries indicate misclassifications. Location abbreviations are as follows: RS = Rio Salada, SH = Sedillo Hill, LSC = Lower Socorro Canyon, USC = Upper Socorro Canyon, WS = White Sands, and W = Willard. With the military installation soils, for which the RS has poor classification accuracy, the mean of the pdf for features is close to zero, and the short tail suggests that few of these features have a large impact on the classification decision. By contrast, the LIBS features are farther from zero in general, suggesting greater influence on the classification decision. These results suggest the potential for the data fusion algorithm to be robust in classification scenarios in which an analyzer is unable to discriminate the classes well. During training, the PLSDA classifier determines the lack of reliable information in the features provided by the poorly performing instrument and applies lower weights to reduce the influence of this analyzer on the classifier decision. The hypothesis that data fusion may be robust despite poor individual analyzer classification performance is supported by the fusion results for the New Hampshire Belmont soil pits that, while showing no increase in classification accuracy with data fusion, demonstrate little negative impact from including the RS spectra.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Morphology and Size of MXene Flakes
#### 3.1.3 Flake Surface Chemistry  
The surface chemistry of MXene flakes, such as the presence of functional groups or residual contaminants, can affect sticking behavior. Functionalization of MXene flakes with hydrophilic or hydrophobic moieties can alter the interfacial interactions, thereby influencing the propensity for sticking. Additionally, certain surface contaminants can act as adhesive agents, facilitating the agglomeration of MXene flakes.  
When the initial MXene has been synthesized by immersion into highly concentrated HF, the \"accordion\" structure is considered indicative of successful synthesis [47]. Nonetheless, this accordion structure, which is synonymous with MXene, does not represent the appearance of all multilayer MXene. In Figure 4, SEM images of multilayer Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) synthesized under various conditions are depicted [48]. As the HF concentration decreases, the \"accordion\" structure becomes less prominent, and the multilayer MXene more closely resembles the typical MAX structure. Essentially, as the effective HF concentration increases, the production of H\({}_{2}\) becomes more rapid, resulting in an expanded structure. Conversely, at lower concentrations (and therefore slower kinetics), there is no morphological change in the particles.
## 2 Materials and Methods
### Feature Compression
This characteristic is necessary for loop detection. In our research work, using \(k\) directly is not conducive to understanding and cannot serve as a precise standard. Therefore, based on Equation (15), we introduce the compression ratio \(\gamma\):  
\[\gamma=\frac{argE_{n}}{m}\times 100\% \tag{16}\]  
where the compression ratio \(\gamma\) is the percentage form of the ratio between the length of the truncated eigenvectors and the length of the original eigenvectors. Setting different compression ratios can allow measurement of the dimensionality-reduction capability of KL transformation in different scenarios. To validate the feasibility of the above method, we took images from the New College dataset as an example, as shown in Figure 11.  
The feature maps extracted from the conv3 of ResNet152 can be represented in matrix form as follows:  
\[X_{3}= \begin{bmatrix}131&127&\dots&137\\ 132&130&\dots&138\\ \dots&\dots&\dots&\dots\\ 95&97&\dots&116\\ \end{bmatrix} =[131,132\dots 95,127,\dots 116] \tag{17}\]  
Applying KL transformation to map \(X_{3}\) to a low-dimensional subspace, and flattening it into a one-dimensional vector:  
\[\hat{X}_{3}=[278.21,283.02,\dots 135.12,3.24,-11.29] \tag{18}\]  
From \(\hat{X}_{3}\), it can be observed that as the index increases, the elements in the vector gradually decrease in magnitude. The result of visualizing this phenomenon is shown in Figure 12. It can be seen that the main components we need to retain are primarily concentrated at the front, while the later part contains redundant information to be removed. Therefore, we can select the first \(k\) principal components for feature compression.