esa-sceva/satcom-chunk-collection huggingface dataset:
# InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro
## 3 Optimized Product Quantization and InMemQK Based on Time-Domain MAC  
### Background: Product Quantization  
Product quantization (PQ) [23] is commonly employed to address similarity search or nearest neighbor search problems. Its fundamental approach involves compressing data by decomposing the original vector space into a Cartesian product of multiple low-dimensional vector spaces. As illustrated in Figure 3a, a D-dimensional vector is split into C (where C = 3 for demonstration purposes) distinct V-dimensional subvectors, and M centroids are derived by performing K-means clustering on N subvectors (as shown in Figure 3b, where M = 3 for demonstration purposes).  
Figure 3c illustrates the two-step approximate MatMul process based on product quantization. Taking the product quantization of the key as an example, we obtain a codebook(key). In the first step, the input subvector sub-K [1.5, 0.2, 3.0] is multiplied and accumulated (MAC) with the centroids in the codebook(key), and the maximum value 3.4 is found via argmax, with its index recorded. In the second step, the corresponding value is found in the codebook(key) based on this index, followed by a MAC operation with subvector sub-Q [0.5, 2.0, \(-\)0.5], ultimately obtaining an approximate multiplication result of 3.5 for sub-Q \(\cdot\) sub-K. Finally, summing all subvector inner products provides the final  
Figure 3: Schematic diagram of product quantization. (**a**) Vector splitting. (**b**) Clustering and representation. (**c**) Two steps of approximate MatMul.  
Figure 2: (**a**) Voltage-domain MAC based on Ohm’s law. (**b**) Time-domain MAC based on current integration. (**c**) The normalized energy of different interfaces, where \(\varepsilon_{DAC}\), \(\varepsilon_{ADC}\), \(\varepsilon_{DTC}\), and \(\varepsilon_{TDC}\) denote the energy of one DAC, ADC, DTC, and TDC, respectively.  
approximate inner product result for \(Q\cdot K^{T}\). Given that the centroids are known, query vectors or key vectors can be pre-quantized into codebooks and subsequently written into NVM arrays.
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
###### Abstract  
Active remote sensing of atmospheric XCO\({}_{2}\) has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO\({}_{2}\)-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO\({}_{2}\) globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system's retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO\({}_{2}\)-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading.  
IPDA; atmospheric CO\({}_{2}\); performance evaluation; remote sensing satellite +
Footnote †: journal: Remote Sensing, 2017
## 2 Materials and Methods
### SERS Measurements  
A Horiba LabRam Raman spectrometer was used for SERS measurements with two different excitation sources of 532 and 633 nm wavelength. The excitation beam was focused on the surface of the sample with a 50\(\times\) lens in both cases. For reference measurements, the analyte solution was applied to a clear glass plate under the same conditions. For riboflavin and R6G, the measurement parameters are summarized in Table 2.  
Eighteen different gold nanoisland SERS substrates were investigated in this study. The SERS and normal Raman spectra shown in the following figures were measured on the same gold nanoisland substrate, obtained using the production parameters mentioned in Table 1. Each sample, each analyte with each excitation laser were examined in 10 different points. The presented results are the average value of the EF, and the standard deviation of these values was found to be 1-3%.  
The contact angles of the two analytes' aqueous solutions were analyzed on pure glass and gold nanoisland substrates. A DSA 30 Drop Shape Analyzer (Kruss GmbH, Hamburg, Germany) was used for the contact angle measurements. The sessile drop method was applied to measure the water, rhodamine, and riboflavin contact angle (CA) on glass and gold nanoisland glass surfaces at room temperature (25 \({}^{\circ}\)C). The rhodamine and riboflavin droplets (5 \(\mu\)L) were deposited on glass and modified glass surfaces with a manual dosing system, the water droplets (5 \(\mu\)L) were deposited with an automatic dosing system. The diameter of a used needle was 0.5 mm. The contact angles were calculated by Kruss Advance software by fitting the captured droplet shape to the degree calculated from the Young-Laplace equation. The mean contact angles were calculated from 10 individual droplet measurements (n = 10).
## 3 A Post-Processing Algorithm to Enforce Traction Continuity along the Interface
### Approximating the Displacements with Local Polynomials  
In order to modify the displacements near the interface for the \(j\)th column of pixels, we approximate the displacement fields \(u_{2}^{+}\), \(u_{1}^{+}\), \(u_{2}^{-}\), and \(u_{1}^{-}\) with local polynomials in terms of both spatial coordinates. The polynomials are defined over rectangles of \(n_{x1}\times n_{x2}\) pixels above and below the interface, which are centered at the \(j\)th column of pixels (Figure 3). The pixels within each rectangle are divided into a group of \(n_{x1}\times n_{x2,inter}\) pixels that are closer to the interface, and a group of \(n_{x1}\times n_{x2,cent}\) pixels that are closer to the center of the subset. To represent the spatial variations in stresses, we approximate the displacement fields with cubic polynomials with \(n=10\) coefficients as  
\[\begin{split} u_{2}^{p+}(x_{1},x_{2})&=a_{2}^{10+ }{x_{1}}^{3}+a_{2}^{9+}{x_{1}}^{2}{x_{2}}+a_{2}^{8+}{x_{1}}^{2}+a_{2}^{7+}{x_{ 1}}{x_{2}}^{2}+a_{2}^{6+}{x_{1}}{x_{2}}+a_{2}^{5+}{x_{1}}+a_{2}^{4+}{x_{2}}^{3 }+a_{2}^{3+}{x_{2}}^{2}\\ &\qquad+a_{2}^{2+}{x_{2}}+a_{2}^{1+}\\ u_{1}^{p+}(x_{1},x_{2})&=a_{1}^{10+}{x_{1}}^{3}+ \cdots+a_{1}^{1+}\\ u_{2}^{p-}(x_{1},x_{2})&=a_{2}^{10-}{x_{1}}^{3}+ \cdots+a_{2}^{1-}\\ u_{1}^{p-}(x_{1},x_{2})&=a_{1}^{10-}{x_{1}}^{3}+ \cdots+a_{1}^{1-}\end{split}. \tag{11}\]  
Note that \(n_{x1}\) and \(n_{x2}\) should be chosen small enough to avoid smoothing local patterns in the displacement fields.
# Detail-Preserving Shape Unfolding
## 3 Technical Details
### Rigid Transformation between Two Cells  
Given a cell \(\mathcal{C}_{i}\) corresponding to vertex \(i\), and its deformed version \(\mathcal{C}^{\prime}_{i}\) (see the black lines in Figure 1), the approximated rigid transformation between them is defined by observing the edges emanating from the vertex \(i\) in \(\mathcal{S}\) and \(\mathcal{S}^{\prime}\). If the deformation is rigid between \(\mathcal{C}_{i}\) and \(\mathcal{C}^{\prime}_{i}\), then there exists a rotation matrix \(\mathbf{R}_{i}\) for all \(j\in\mathcal{N}_{i}\) such that  
\[\mathbf{p}^{\prime}_{i}-\mathbf{p}^{\prime}_{j}=\mathbf{R}_{i}(\mathbf{p}_{i} -\mathbf{p}_{j})\,. \tag{1}\]  
If the deformation is not rigid, in the least squares sense, we still could find the best approximating rotation matrix \(\mathbf{R}_{i}\) by minimizing  
\[\mathcal{L}(\mathcal{C}_{i},\mathcal{C}^{\prime}_{i})=\sum_{j\in\mathcal{N}_{ i}}w_{ij}\|\mathbf{p}^{\prime}_{i}-\mathbf{p}^{\prime}_{j}-\mathbf{R}_{i}( \mathbf{p}_{i}-\mathbf{p}_{j})\|^{2}\,, \tag{2}\]  
where, \(w_{ij}\) is the per-edge weight. According to [19], the optimal rotation \(\mathbf{R}_{i}\) can be easily solved by eigen decomposition. The transformations solved from Equation (2) make sure the local rigidity of \(\mathcal{S}\) is preserved and mesh details can not be discarded.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Model Performance Comparison
#### 3.4.1 Comparison of Model Visual Observations
The edges of the lake water body in Figure 9 are uneven, and for the images in columns (a), (b), and (c), the R50A3-LWBENet can extract the water body boundaries more clearly compared to U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet, and MU-Net. The lake water bodies in columns (d) and (e) have tiny boundaries, but U-Net, AU-Net and RU-Net are not fine enough to extract the tiny boundary regions at the edges of the water bodies, and ARU-Net makes mistakes in judging the water body regions. SER34 AUNet and MU-Net still have small errors in edge detail. R50A3-LWBENet has a strong ability to express semantic features of the water bodies, which can refine the edge contours of the lake water bodies, realize the extraction of the boundary information of the water bodies more accurately, and have obvious advantages in boundary pixel classification. The above visual observation reveals that the R50A3-LWBENet model performs better in dealing with multi-scale features, noise interference and boundary blurring, and the model is more robust with better generalization ability. Figure 9: Performance comparison of different models for boundary blurring. (**a–e**) represent 5 images of lake areas containing boundary blurring. The red circles represent key segmentation areas. R50A3-LWBENet is the water body segmentation model proposed in this paper.
## 2 Study Area
### SAR Data  
The Sentinel-1 SAR data includes 25 TOPS mode acquisitions from May 2017 to September 2017, with 6 revisit days. This is dual-polarization (VV and VH) data, leading to 50 images in total. First of all, a reference image was selected and all images are coregistered to it by taking into account the TOPS acquisition [41]. Five-look (in range) intensity radar data are generated and radiometrically calibrated for antenna gain, range spreading loss, normalized reference area and the calibration constant using information from Sentinel-1 SAR header.  
Reliable estimates using backscattering coefficients require that the estimated number of looks (ENL) is large. To increase the ENL, speckle filtering can be used with loss of spatial resolution. In this paper, we improve the time series SAR Sentinel-1 dataset by exploiting a temporal adaptive filtering to reduce speckle while keeping as much as possible the fine structures present in radar images [42].  
Finally, all images are orthorectified into map coordinates. This can be done by simulating SAR image from the SRTM DEM 30 m and using it to do coregistration. The image pixel size of the final data is 20 m. The Sentinel-1 SAR data are processed by using the TomoSAR platform (i.e., a platform supports the entire processing from SAR, Interferometry, Polarimetry, to Tomography (so-called TomoSAR)) developed by [PERSON] and [PERSON] [43]. In details, the full description of this SAR dataset can be found in [30].
# Efficient LDPC Encoder Design for IoT-Type Devices
### Relevant Research Works  
While numerous hardware implementations of modern ECC codecs, including LDPC codecs, have already been developed, typically they are devoted to high-throughput demanding communication standards; thus, they are based on high-volume custom ICs [8; 9; 10], FPGAs [11; 12; 13; 14], or GPU devices [15; 16]. On the other hand, the typical physical-layer protocol stack for an IoT-type device is developed with low memory, device complexity and processing time requirements as the most important goals.  
LDPC codes [6; 7] are a class of linear block error-correcting codes that are known to achieve a performance near the capacity limit [17]. Extensive research in the field of LDPC coding includes issues such as graph-theoretic representations of LDPC codes [18; 19], parity-check matrix design and construction methods [20; 21; 22; 23; 24], efficient encoding [25] and decoding [26; 27; 28] algorithm development, ECC coding system design for different use cases [29; 30], hardware implementations in ASIC and FPGA equipment [9; 11; 13; 31; 32], and the design of nonbinary codes and codecs [22; 33; 34; 35; 36]. While LDPC codes have already been applied in a number of communication standards, such as WiMAX [37], Wi-Fi [38], and 5G [39], in this article, we show that LDPC coding can also be considered for devices with highly constrained computational resources. Despite the recent advances in digital circuit technologies, the process of codec design is still not trivial.  
The research on LDPC efficient encoding implementation is quite rich already, with a number of issues that have been considered. For example, in [40], an efficient FPGA architecture for subclass of CCSDS (Consultative Committee for Space Data Systems) codes, recommended for channel coding in near-Earth and deep-space communications, is presented. The proposed architecture is based on a series of convolutional encoders, leveraging the QC structure inherent parallelism. In [41], LDPC encoding for a 5G new radio (NR) codes is considered, and a high-throughput encoder architecture is proposed. In [42], an encoding scheme is developed, involving pruning the full-base matrix of 5G NR code for a computationally efficient encoding in a high-throughput hardware architecture. The 5G NR code encoders are also considered in [16], where a GPU implementation is developed.
## 3 Results and Discussions
### Enzymatic Thermostability  
The thermal stability of the WT and mutant enzymes was evaluated as described in the Section 2. As illustrated in Figure 4a, the activities of both WT and mutant luciferases decreased with increasing temperatures. After incubation at 40 \({}^{\circ}\)C for 10 min, the enzymatic activities of WT and _Mut1_ luciferases were almost completely lost. In contrast, _Mut2_ and _Mut3_ showed significantly enhanced thermal tolerance, retaining approximately 76% and 62% of their activity, respectively. The inactivation half-lives of the WT and mutant luciferases at 50 \({}^{\circ}\)C were also monitored (Figure 4b). The inactivation curves revealed that _Mut2_ and _Mut3_ had the slowest inactivation rates. After 30 min at 50 \({}^{\circ}\)C, _Mut2_ and _Mut3_ still maintained residual activities of 64% and 38%, respectively, compared to only about 7% for WT luciferase, while _Mut1_ almost completely lost its activity.  
Enzymes from cold-adapted organisms typically exhibit greater flexibility, a characteristic feature of psychrophilic proteins. As the temperature increases, the flexible regions of these enzymes begin to fold to accommodate the changes in entropy, which is often a contributing factor to their poor stability. Luciferases catalyze two essential enzymatic steps for bioluminescence: (i) adenylation of D-luciferin; and (ii) oxygenation of adenyl-luciferin. In the second step, luciferase functions as an oxygenase, catalyzing the oxygenation of adenyl-luciferin to form an energy-rich dioxetanone intermediate. Throughout this process, luciferase undergoes significant conformational changes. Although _Mut1_ (F374C-G399C) is located in a highly flexible loop region and is more likely to unfold at an early stage; it is also in close proximity to the active pocket region. The excessive local rigidity caused by steric contacts reduces the enzyme's ability to fold at high temperatures, negatively impacting its activity. This suggests that increasing local rigidity can enhance thermal stability; it may also interfere with the necessary conformational changes required for enzyme activity, highlighting the delicate balance needed in protein engineering for improved thermal stability without sacrificing function.
## 3 Results and Discussion
### Influence on the Energy Dissipation  
The areas surrounded by the hysteresis loops normally indicate the energy dissipated by the viscoelastic material. In Figure 4, the areas surrounded by the hysteresis loops of initial condition and that of the submerged viscoelastic tapes are also very similar, which implies that the seawater has little influence on the damping effect of the viscoelastic material.  
To further quantify this issue, the energy dissipated per cycle is computed as follows:  
\[\Delta\ W\ =\ \int F\delta\ d\delta \tag{4}\]  
Figure 4: Hysteresis loops initially and submerged in seawater.  
where \(\Delta\ W\) denotes the dissipated energy; \(F\) is the impact force and \(\delta\) denotes the deformation of the viscoelastic material. Due to the limitation of the experimental apparatus, the intensity of impact is not precisely controlled. Since \(\Delta\ W\) can be influenced by the impact intensity, a normalized parameter \(\Delta\overline{W}\) is defined as:  
\[\Delta\overline{W}\ =\ \Delta\ W/\delta_{\text{max}} \tag{5}\]  
where \(\delta_{max}\) is the maximum impact depth.  
Figure 5 shows the dissipated energy as a function of submerged time. The blue triangle denotes \(\Delta\overline{W}\) of each sample. The red round is the average \(\Delta\overline{W}\), since three specimens were tested each month. In the initial condition, the viscoelastic material dissipated an average energy of 80.5 J/m per cycle. During the five years of submerging, \(\Delta\overline{W}\) varies from 73 to 98 J/m per cycle, which is still at the same level compared with the initial condition. This implies that the seawater has little influence on the damping capacity of the viscoelastic material.
# Research on Double-Arc Cutting Tool Design and Cutting Performance
## 2 Design of DACT
### Cutting Geometry Model of DACT
#### 2.2.2 Cutting Residual Height of DACT
Figure 9: Comparison schematic diagram of cutting scallop height. When \(\alpha\in[0,\pi/6]\), \(L\leq\sqrt{9r^{2}-27r^{2}\tan^{2}\alpha}\), the cutting scallop height \(h_{ee}\) of the DACT is:  
\[h_{ee}=\frac{6r-\sqrt{36r^{2}-L^{2}}}{2} \tag{13}\]  
When \(\alpha\in[0,\pi/6]\), \(\sqrt{9r^{2}-27r^{2}\tan^{2}\alpha}\leq L\leq\sqrt{16r^{2}-12r^{2}\tan^{2}\alpha}\), the cutting scallop height \(h_{er}\) of the DACT is:  
\[h_{er}=3r-r\sec\alpha\Big{(}\sin\theta+\sqrt{3}\Big{)} \tag{14}\]  
where \(\sqrt{9r^{2}-27r^{2}\tan^{2}\alpha}\leq L\leq\sqrt{16r^{2}-12r^{2}\tan^{2}\alpha}\). When \(\alpha\in[0,\pi/6]\), \(\sqrt{16r^{2}-12r^{2}\tan^{2}\alpha}\leq L\leq 4r\), the cutting scallop height \(h_{er}\) of the DACT is:  
\[h_{er}=3r-\frac{\csc\alpha\sqrt{16r^{2}-L^{2}}}{2} \tag{15}\]  
When \(\alpha\in[\pi/6,\,\pi/2]\), \(L\leq 2\sqrt{3}\)\(r\), the cutting scallop height \(h_{rr}\) of the DACT is:  
\[h_{rr}=r-r\sec\alpha\sin\theta \tag{16}\]  
where \(4r^{2}(\cos\theta+1)^{2}-4r^{2}(\sin\theta\tan\alpha+1)^{2}-L^{2}=0\). When \(\alpha\in[\pi/6,\,\pi/2]\), \(2\sqrt{3}\)\(r\leq L\leq 4\)\(r\), the cutting scallop height \(h_{rc}\) of the DACT is:  
\[h_{rc}=r(1+\csc\alpha)-\frac{\csc\alpha\sqrt{16r^{2}-L^{2}}}{2} \tag{17}\]  
The cutting scallop height \(h_{BEM}\) of the BEM is:  
\[h_{BEM}=R-\frac{\sqrt{4R^{2}-L^{2}}}{2} \tag{18}\]  
When \(\alpha\in[0,\pi/6]\), the cutting contact point \(O^{\prime}\) of the DACT is located on the bottom arc; the relationship between the cutting scallop height \(h\) and the tool path interval \(L\) is shown in Figure 10a. The radius of the DACT and BEM is 5 mm, as shown in Figure 9.0  
When \(\alpha\in[\pi/6,\,\pi/2]\), the cutting contact point \(O^{\prime}\) of the DACT is located on the ring arc. Figure 10b shows the relationship between the cutting scallop height \(h\) and the tool path interval \(L\) at this time. The dotted line in Figure 10 is the cutting scallop height curve of the BEM. As shown in Figure 10, the cutting scallop height of the DACT is always smaller than that of the  
Figure 9.1 Relationship between \(L\) and \(h\): (**a**) cutting contact point \(O^{\prime}\) is located on the bottom arc; (**b**) cutting contact point \(O^{\prime}\) is located on the ring arc. BEM. Meanwhile, the smaller the \(\alpha\), the greater the difference between the BEM and DACT. Therefore, the DACT is more suitable for machining surfaces with a small curvature. With the same cutting depth, the residual height of the DACT is smaller than the BEM, and therefore the processing quality will be better.
# Effect of Surface Micro-Hardness Change in Multistep
## 3 Experimental Results and Analysis  
Based on many scholars' researches on the cutting process, the temperature and stress gradients in local areas during the formation of the machined surface layer has been investigated by finite element simulation. The cutting forces cause stress gradients in local areas instantaneously [12; 13; 14] during cutting, and the temperature at the contact area between the tool tip and the machined surface would be different [15; 16; 17; 18]. On this basis, the unit feed periodic machining was proposed. It was considered that in the ideal state, along the feed direction, the cutting force (the magnitude and direction of force were same)  
Figure 4: The schematic diagrams of friction and wear. (**a**) Experiment equipment. (**b**) The wire-cut sample to be rubbed. (**c**) Front view. (**d**) Left side view.  
Figure 3: The schematic diagrams of the micro-hardness measurement along the feed direction. (**a**) Roughing micro-hardness at the depth of 100 \(\upmu\)m below the machined surface (cross section). (**b**) The finishing surface micro-hardness (top view).  
and the cutting temperature were the same at the corresponding point, for example, points A and B in Figure 5a. The cutting force and temperature were different near the adjacent points in the unit feed, as shown by the curve _abc_ in Figure 5a. At the same time, the surface integrity also showed periodic changes along the feed direction. For example, the surface topography showed a certain regular peak and valley, and the valley was a path on which the tool passed in the cutting direction, as shown in Figure 5b. The differences of cutting force and temperature in the unit feed resulted in the differences of micro-hardness in local areas.  
Normally, the unit feed periodic machining did not take into account the factors such as tool wear and cutting vibration during the cutting process. These factors caused the periodicity of surface integrity to change along the feed direction. To minimize the effects of tool wear, the new tools were replaced before next machining. At the same time, in order to improve the roundness, each sample was subjected to multipass to reduce the cutting vibration before the experiment. Because the degree of work hardening of the vibrating turning [19] was higher than that of ordinary turning, and the difference of surface layer micro-hardness in the local area would cause the turning vibration in the ordinary turning process, which in turn caused a significant change in the finishing surface micro-hardness of the localized area.  
In this paper, the effect of roughing surface layer micro-hardness on the finishing surface micro-hardness was studied. Additionally, then the effect of finishing surface micro-hardness change on the friction and wear properties of titanium alloy were investigated along the feed direction.