esa-sceva/satcom-chunk-collection huggingface dataset:
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 2 Structure of the Portable Smart Spectrometer
### Spectroscopic Module  
The majority of portable spectrometers are Czerny-Turner (CZ) spectrometers or variants such as the Schmidt-Czerny-Turner. The spectroscopic system of the CZ spectrometer consists of a collimating mirror, a condensing mirror, and a grating. A common problem with the portable CZ spectrometer is that the incident and exit beams are not co-axial [8]. The CZ spectrometer represents a typical off-axis system, with problems such as increased size, difficult alignment adjustment, and poor stability. If prisms are exclusively used as the dispersive elements, the dispersion of the spectrometer becomes inhomogeneous, limiting the spectral resolution of some bands. Moreover, the use of diffraction gratings or prisms alone can cause spectrum bending. Prism-grating is a combination of prism and diffraction grating. Since the direction of the spectrum bending caused by the prism is opposite to that of the diffraction grating, the prism-grating can be used to eliminate the spectrum bending. The use of prism-gratings can not only mitigate the problems of off-axis systems, but can also contribute to reducing the size of the spectrometer without compromising uniform dispersion and spectrum bending [9; 10; 11].  
Figure 3 shows the spectroscopic module of the PSS-1.0. It consists of a spectroscope, (FGS-1A, Shenzhen Fable Jewellery Technology Co., Ltd., Shenzhen, China) and a focusing lens. The FGS-1A is a compact spectroscope, integrating optical elements including a slit, collimating lens, and a prism-grating. It has a diameter of 15 mm and a length of 55 mm [12]. The image focal length of the focusing lens is 16 mm. The length of the PSS-1.0 is limited to 120 mm. As shown in Figure 3, the incident light enters the slit of the PSS-1.0. After passing through the collimator lens, it is split by the prism-grating and focused on the CCD (TCD1304 DG) by the focusing lens. The spectrum output by the spectroscopic module is finally imaged on the receiving surface of the CCD.  
Figure 3: Spectroscopic module of the portable smart spectrometer.  
Figure 2: Structure of the portable smart spectrometer.
## 4 Discussion
### Kinematic  
Continuous rotation developed a higher torque than OTR motion during the apical preparation step in the single-length technique [38], torque-sensitive (equivalent to OTR) and time-dependent reciprocating motions [47], optimum glide-path motion and stainless-steel manual preparation [48], and adaptive motion [3; 49].  
The apical force of continuous rotation was lower than that of OTR used in the crown-down technique, and conversely, apical forces of continuous rotation were higher than those of OTR used in the single-length technique [38]. In addition, continuous rotary glide-path instruments produced higher apical forces than those produced by reciprocating motion [50], stainless steel manual and optimum glide-path motion [48], and adaptive motion [51]; however, Scout Race instrument (FKG Dentaire, La Chaux-de-Fonds, Switzerland) exhibited lower apical force [50]. Time-dependent reciprocating motion exhibited the highest mean apically directed force compared with torque-sensitive reciprocating motion and continuous rotation [47]. However, studies have suggested that, rather than differences in the motion, geometric differences [4] and heated-treated alloy with smaller cross-sectional areas with fewer contact points [20] affect the screw-in force. In those studies [4; 20], the screw-in forces produced by reciprocating instruments were higher and lower, respectively, than those by continuous rotating instruments. Transline vibrating motion also exhibited lower forces than those exhibited by continuous rotation and reciprocating motion [52].  
Thus, continuous rotation exhibited higher torque than time- or torque-dependent reciprocating motions, and forces differed from motion to motion. Reciprocating motion exhibited higher forces than continuous rotation in most studies because reciprocating instruments can be pushed into the canal during the counterclockwise rotation [38]. Another possible reason could be the aggregate impact of the packing motion and the change in angular momentum between clockwise and counterclockwise rotation (and vice versa), which pushes the instruments down and produces more apically directed force.
## 2 Materials and Methods  
### Preparation of SA-MIPs  
The hybrid polymer probes (SA-MIPs) were prepared according to a previously described protocol by [PERSON] et al. [4] but using deprotonated SA as the template to improve the binding properties of the MIPs. Deprotonated SA was prepared by dissolving equimolar amounts of SA and tetrabutylammonium hydroxide (Sigma-Aldrich, St. Louis, MO, USA, \(\geq\)99.0%, CAS 147741-30-8) in acetonitrile. After 30 min of mixing, the mixture was placed in a vacuum concentrator and dried overnight to give the corresponding template. The few-nanometer thin SA-MIP shells grafted on submicron-sized silica cores were equipped with nitrobenzoxadiazole (NBD) fluorescent reporter groups allowing environmentally sensitive fluorescence detection at 530 \(\pm\) 15 nm. Dried SA-MIPs were resuspended in phosphate saline buffer (PBS, Invitrogen, Waltham, MA, USA), to a stock solution of 1.0 mg/mL. The thickness of the polymer shells and the MIP's binding properties were similar to the ones described in [PERSON] et al. [4].
# Advances in Genome Sequencing and Natural Rubber Biosynthesis in Rubber-Producing Plants
## 4 Research Progress in NR Biosynthesis Regulation
HbCPTL/HbHRBP interacts with HbHRT1/2 and HbREF, potentially serving as a bridging component between HRTs and the REF, facilitating the formation of the rubber transferase complex [29, 42, 60]. HRT1 exhibits rubber transferase activity on WRP, whereas the HRBP or REF did not demonstrate such activity. The co-expression of the HRBP or the HRBP and REF with HRT1 significantly enhances rubber transferase activity but has no impact on the length of the product chain [42]. This evidence indicates that the REF may play a role in stabilizing the HRT1-HRBP complex. In addition, the ternary protein complex consisting of HRT1, the HRBP, and the REF has also been observed in _Nicotiana benthamiana_, where it is localized on the ER and ER-derived particles. In addition to CPTs, rubber elongation factors (REFs) and small rubber particle proteins (SRPPs) are also crucial for the biosynthesis of high-molecular-weight natural rubber [51]. The REF and SRPP are homologous proteins within the stress-related protein family, typically exhibiting high transcriptional abundance in the latex of rubber-producing plants. REF subfamily members exhibit variability in their N-terminal region and possess a relatively short C-terminal region beyond the REF domain. Conversely, SRPP subfamily members feature a short N-terminal region and display variability in their C-terminal region [58]. Knocking down _REF_ transcripts in _T. brevicornicultatum_ results in a significant reduction in rubber content, which correlates with lower levels of TbCPT proteins in the latex. However, the molecular mass and stability of the rubber particles remain unaffected [67]. The overexpression of _TkSRPP3_ resulted in an increase in the dry rubber content of TKS, whereas RNAi _TkSRPP3_ led to a significant reduction in both rubber content and rubber molecular weight [68]. In _T. brevicorniculatum_, the RNAi lines of _T8 RPPs_ exhibit a 40-50% reduction in dry rubber content, with no significant impact on molecular weight and polydispersity [69]. In contrast, knockdown lines of _LsSRPP4_ and _LsSRPP8_ show no effect on rubber content, molecular weight, and polydispersity in lettuce [54].
## 3 Result
### Convolutional Neural Network Model: Testing
After that, the third and fourth convolutional layer with 64 filters, (3, 3) kernel size, (1, 1) strides, ReLU activation, and \"same\" padding, added one batch normalization as well. After the second max-pooling layer, the last two convolutional layers had 28 filters, (3, 3) kernel size, (1, 1) strides, ReLU activation, and \"same\" padding, and added one batch normalization and one max pooling layer. We also used \"adam\" as the optimizer and \"sparse categorical crossentropy\" as the loss function [40]. After the \"flatten\" layer, we had two general Neural Network layers, one has 1024 neurons with ReLU activation, and the output layer has 10 neurons with Softmax activation [40]. Figure 5: Basic CNN Model Testing. (**A**). Prediction losses as a function of epochs. (**B**). Prediction accuracy as a function of epochs. We can see that using the advanced model, after 50 epochs of training, the testing accuracy of the advanced CNN model is 99.86 and rarely volatile (i.e., random shifts in performance) (Figure 7A,B; Table 3). Figure 7C shows the confusion matrix associated with the testing data. Numbers in the figure correspond to frequency counts. Numbers on the diagonal are correct classifications and numbers off the diagonal are incorrect classifications. For example, it can be seen that [PERSON] was incorrectly classified twice: once as [PERSON] and once as [PERSON]. Figure 6: Model structure corresponding to the advanced CNN model implemented in the current study (see code in the Appendix A). Figure 7: Advanced CNN Model Testing. (**A**). Prediction losses as a function of epochs. (**B**). Prediction accuracy as a function of epochs. (**C**). Confusion matrix of the Advanced CNN model in the test set (10% of the data). Numbers that fall on the diagonal are correctly classified voices. Numbers that fall off the diagonal are incorrectly classified voices. Label = Actual Voice. Prediction = Predicted Voice.
## 3 Results and Discussion
### Nmr  
The structure of CMSC is shown in Figure 6 Ai. The \({}^{1}\)H-NMR spectrum of CMSC (Figure 6 Aii) exhibits peak between \(\delta\) = 3 ppm and 4 ppm were assigned to H 2, H 1 of the glucose subunit. The peaks at positions \(\delta\) = 4 ppm and 4.5 ppm corresponded to the H 3-6 in the cellulose backbone, respectively. The peaks between \(\delta\) = 3.88 ppm and 4.41 ppm at position H represent protons of methylene from carboxyl groups. The \({}^{1}\)H-NMR spectrum confirms the carboxymethyl sag cellulose was synthesized. The \({}^{13}\)C-NMR spectrum of CMSC is seen in Figure 6 Aiii with a range between 179 and 59 ppm. A prominent peak at 178 ppm is assigned to the CO carbon (C-8) of the carboxymethyl group. At 96 ppm, it is assigned to C-2 whereas, at 80 ppm, this peak is assigned to C-4. C-2 and C-3 are at 71 and 73 ppm, respectively. As for C-5, the signal appears at 73 ppm. The electron withdrawing effect imposed by oxygen in the carboxymethyl substituent, C-6s (68 ppm) causes a downfield shift of \(\sim\)7.4 ppm compared to C-6 (61 ppm). The C-7 represents the methylene carbon atoms of the carboxymethyl group appear at three major peaks (71, 70, 69 ppm) as carboxymethyl substitution would take place in three different positions [53].  
For cellulosic carbon (Figure 6 Bi), the \({}^{13}\)C-NMR spectrum Figure 6 Bii illustrates six singlets corresponding to \({}^{13}\)C chemical shifts of cellulose carbons, C1 (105 ppm), C4 (83-97 ppm), C2/C3/C5 (72-80 ppm), and C6 (62-69 ppm) of the anhydroglucose units of cellulose [54]. To determine the  
Figure 6: **(A) (i)** CMSC structure; **(ii)**\({}^{1}\)H-NMR and **(iii)**\({}^{13}\)C-NMR spectra of CMSC (DS = 0.6) in D\({}_{2}\)O (**iv)** Contour plots of COSY spectra of CMSC (DS = 0.6) in D\({}_{2}\)O recorded at 343 K. and **(B) (i)** cellulose structure; **(ii)**\({}^{13}\)C-NMR spectra of cellulose.  
cellulose crystallinity, C4 resonance region is most likely to be used [55]. The changes of specific hydrogen bonding with hydroxyl groups in adjacent cellulose chains would reflect the changes of C6 resonance region. Broad shoulder signals that occur at 83 ppm (C4 region) whereas 63 ppm (C6 region) indicate the presence of amorphous cellulose [56], which could be attributed to the presence of hemicellulose and lignin as they appear amorphous in nature. However, cellulose in Figure 6 Bii indicates sharp signals at 84 ppm and 63 ppm, which, respectively, show the existence of crystalline cellulose. A strong signal from 56 ppm reciprocates to aromatic methoxyl (-OCH\({}_{3}\)) resonance in lignin [57]. This peak does not appear in the cellulose spectrum indicating the absence of lignin.
## 3 Results and Discussion
### Photometric Growth Inhibition Experiments
However, even total dissociation of the complex in a 0.5 mM solution would give a citrate concentration of around 0.19 mg/mL, far lower than the reported minimum inhibitory concentrations for even the most sensitive bacteria [29]. This, together with the fact that the pH is not in the range at which citric acid shows the strongest antimicrobial activity [30], means that free citrate can be eliminated as a contributory factor to the antimicrobial activity of the solutions containing Co-cit. The Co(II) ion in the form of various salts was also investigated in aqueous cell growth media and found to be not bioavailable for evaluation of its toxicity due to inherent solubility problems at the pH of the media, so free cobalt is also unlikely to play a role (data not shown). The lag phases of _L. monocytogenes, S. aureus_, and _E. coli_ were, with a few exceptions, not greatly affected by combinations of Co-cit, added preservatives, and acids (Tables 1-3). However, all Co-cit -preservative combinations substantially increased the lag phases of _P. aeruginosa_ (Table 4). This suggests that resistance to Co-cit in _P. aeruginosa_ may be an adaptive response rather than an inherent characteristic.
## 2 Experiment and Data Preprocessing  
### Acquisition of Aging Samples  
This experiment used Karamay #25 naphthenic mineral oil and 0.2 mm thick cellulose kraft paper as the insulation oil and insulation paper, respectively [17]. In accordance with the IEEE standard, a temperature of 130 \({}^{\circ}\)C is chosen as the accelerated thermal aging temperature for the oil-paper insulation samples to ensure that it does not exceed the flash point of the transformer insulating oil [18]. After vacuum drying, the insulating paper was placed into an incubator with an oil-paper ratio of 10:1 along with a copper sheet for the thermal aging experiment. During the first 10 days of the experiment, sampling occurred every 2 days. Between the 10 th and 20 th days, data were selected every 4 days. Subsequently, from the 20 th to the 30 th day, sampling took place every 5 days. After each procedure of sampling, Raman spectroscopy and degree of polymerization tests were conducted on the samples. The degree of polymerization of the insulation paper at different thermal aging days was measured according to the IEC60450 standard, as shown in Figure 1[19].  
According to Figure 1, the aging characteristics of samples obtained from the accelerated thermal aging experiment were consistent with those of transformer oil-paper insulation. Once the aging duration increased, the paper polymerization degree rapidly declined during the early stages of aging and then gradually decreased in the middle and later stages.  
Depending on the polymerization degree of the insulating paper, the aging stages of oil-paper insulation samples were categorized into four and ten groups conforming to DL/T 984-2018 guidelines [20]. The specific categorization is presented in Table 1.
# A Multi-Scale Video Longformer Network for Action Recognition
## 4 Experiments
### Visualization  
In Figure 14, we present examples of correct and incorrect predictions by our MSVL on the HMDB51 and UCF101 datasets, and display both RGB frames and the feature maps of the four stages of MSVL. We observe that MSVL performs well on videos where the human body occupies a significant portion of the frame. However, for videos with a small proportion of the human body region, prediction errors may occur. This phenomenon could be attributed to the attention mechanism involving global and local tokens, which makes MSVL focus more on the overall global features and may neglect local characteristics. We calculated the real-time performance of the network. Our MSVL took a total of 16 min and 56 s to perform inference on 3996 videos, with each video extracting 8 frames as input to the network; this resulted in our MSVL achieving 31.45 FPS on a 3090 GPU.  
We also observed an intriguing phenomenon: the feature map patches exclusively contain global features in models trained for an extended number of epochs. In contrast, models trained for fewer epochs tend to contain predominantly local features, as illustrated in Figure 15. We posit that this phenomenon arises because global tokens only transfer these global features to each local token through attention calculations after undergoing multiple epochs of training. Furthermore, in the feature maps of the subsequent video  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**HMDB51**} & \multicolumn{2}{c}{**UCF101**} & \multirow{2}{*}{**FLOPs/G**} & \multirow{2}{*}{**Params/M**} \\ \cline{2-2} \cline{6-7}  & **Top-1/\%** & & & & **Top-5/\%** & \\ \hline C3D [11] & 51.6 & 77.4 & 82.3 & 95.1 & 38.5 & 78.4 \\ TSN [19] & 56.1 & 84.2 & 83.1 & 96.9 & 102.7 & 24.3 \\ I3D [12] & 66.4 & 86.5 & 91.8 & 98.2 & 59.3 & 35.4 \\ R(2 + 1)D [20] & 66.6 & 86.8 & 93.6 & 99.2 & 53 & 63.8 \\ TSM [44] & 69.5 & 88.2 & 92.6 & 99.1 & 32.8 & 23.8 \\ divST [16] & 72.7 & 93.6 & 94.7 & 99.7 & 196 & 122.2 \\ Swin-S [33] & 72.2 & 93.4 & 97.6 & 99.7 & 166 & 49.8 \\ MSVL (Ours) & 72.9 & 89.4 & 97.6 & 99.8 & 158 & 27.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison with the state-of-the-art on UCF-101 and HMDB-51 datasets.  
frames, we can also observe shadows of human actions from the previous video frame, similar to those that global tokens extract. This further indicates that global tokens can pass the extracted spatiotemporal features to local tokens, thereby maintaining global features of action information from previous video frames and effectively integrating temporal and spatial features.
## 2 Methodology
### PCA-Based Image Reconstruction  
Different regions, such as the crack, the coil, and the surrounding sound areas, present distinctive thermal profiles in IR images. Therefore, we can take each region as an independent signal source and separately enhance the features of cracks. Here, PCA is used for source separation for IR images.  
Principal component analysis (PCA) [20; 21; 22] is a common image processing method usually used in thermography to improve the sensitivity of crack detection. A detailed algorithm of PCA  
Figure 1: The detection mechanism for cracks on a polycrystalline diamond compact (PDC) bit: (**a**) principle of eddy current pulsed thermography (ECPT); (**b**) temperature distribution excited by an annular coil.  
can be found in a previous work [19]. Figure 2 shows the schematic diagram of PCA-based image reconstruction method. Firstly, the IR image sequences captured by the IR camera are converted to gray-level image sequences. Here, \(X(N_{x},\ N_{y},n)\) represents the pixel value of gray-level image sequences, which is a 3D matrix. The \(N_{x}\times\ N_{y}\) means the resolution of camera, and n is the number of images. Next, the 3D matrix \(X\) is transferred to a 2D matrix \(U(N_{x}\times N_{y},n)\) as the input data of image processing. Then, the matrix \(U\) is processed by PCA, generating the principal components (PCs). Finally, the appropriate PCs are transferred to 2D arrays to form new images, which can improve the detection sensitivity and increase the signal-to-noise ratio of raw IR images.  
The solution of PCA is mainly based on eigenvalue decomposition of whitening matrix (EVD), as shown in the following equation.  
\[E\{U^{\prime}(t)U^{\prime}(t)^{T}\}=EDE^{T}=ED^{0.5}D^{0.5}E^{T}=W_{\text{PCA}} ^{-1}E\{X^{\prime}{}_{\text{PCA}}(t)X^{\prime}{}_{\text{PCA}}(t)^{-T}\}W_{ \text{PCA}}^{-T}. \tag{1}\]  
\(E\) is the orthogonal matrices of eigenvectors, \(D=\text{diag}(\lambda_{1},\cdots,\lambda_{N})\) represents the eigenvalue matrix, and \(\lambda_{1}\geq\cdots\geq\lambda_{N}\) is the eigenvalues.  
Eigenvalue decomposition can be expanded as  
\[E\{U^{\prime}(t)U^{\prime}(t)^{T}\}=EDE^{T}=ED^{0.5}D^{0.5}E^{T}=W_{\text{PCA}} ^{-1}E\{X^{\prime}{}_{\text{PCA}}(t)X^{\prime}{}_{\text{PCA}}(t)^{-T}\}W_{ \text{PCA}}^{-T}. \tag{2}\]  
Among them, \(E\{X^{\prime}{}_{\text{PCA}}(t)X^{\prime}{}_{\text{PCA}}(t)^{-T}\}=I\), so Equation (2) can be simplified as  
\[E\{U^{\prime}(t)U^{\prime}(t)^{T}\}=W_{\text{PCA}}^{-1}IW_{\text{PCA}}^{-T}=W_ {\text{PCA}}^{-1}W_{\text{PCA}}^{-T}. \tag{3}\]  
Combining Equations (2) and (3), we can obtain the Equation (4) as follows:  
\[W_{\text{PCA}}=(ED^{0.5})^{-1}=D^{0.5}E^{T}. \tag{4}\]  
Finally, the signal vector matrix \((N_{x}\times N_{y},\text{n})\) is separated by PCA:  
\[X^{\prime}{}_{\text{PCA}}(t)=W_{\text{PCA}}\ U^{\prime}(t). \tag{5}\]  
Each column of the matrix \(X^{\prime}{}_{\text{PCA}}(t)\) is a principal component, which can be transferred to a 2D matrix and then used to conduct image reconstruction.  
Figure 2: Schematic diagram of principal component analysis (PCA)-based image reconstruction.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 4 Experimental Results and Analysis
### 500 MB PageRank Experiments
#### 4.1.2 Results with Changing RDD Caching Options  
First of all, _Distinct_ stages are not affected by changing the RDD caching policy but only by the memory usage. The stages that are affected by the RDD caching option are _flatMap_ stages since during the shuffle phase, cached RDDs are used again.  
In Figure 4, the graph is normalized by the N_1 option that does not cache the RDD and _1 memory configuration to check the performance difference. When comparing only the graphs of _1, in the order of M_1, M&S_1, and S_1, there is a 32% performance degradation in M_1 and 30% and 20% performance improvements with M&S_1 and S_1, respectively. With the M_1 option, the reason for relatively poor performance is that RDDs are cached unevenly due to the lack of storage, which will result in uneven scheduling as we previously mentioned. This means that the JVM heap space is insufficient for shuffling the data and saving the RDDs.  
In order to address this problem, we spread the RDDs to cache both on the memory and SSD, which can actually improve the performance as shown with the M&S_1 option. Caching the RDD on memory improves the access speed for the RDD, and caching the RDD on the SSD can avoid the shuffle spill by effectively extending the available shuffle space in memory. With the S_1 option that has shown a 20% performance improvement, the RDD is cached on the SSD only. The shuffle spill is reduced by caching the RDD on the SSD. However, it has achieved a lower performance improvement than M&S_1, where the RDD is mainly cached on memory and reused from the memory.  
In the default configuration of Spark, which is option _3, we can see that in the order of M_3, M&S_3, S_3, and N_3, overall performance decreases. In the default configuration, the storage of the JVM heap is enough for the RDD to be cached with balance. Therefore, the overall performance mainly depends on the performance of the memory device used. However, we can still see the best performance with the M&S_1 option since we can effectively reduce the GC time and shuffle spill by caching the RDD _both_ on the memory and SSD.