esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Soybean TPS Gene Family Characterization  
Initially, 43 sequences were mined by co-analysis of BLAST and HMM search from the _Glycine max_ Wm82. a2. v1 genome database. After CD-Search and sequence length confirmation, 23 sequences were likely to encode full-length TPS proteins (Table S1), which was also reported by [PERSON] et al. in 2014 [PERSON] et al. (2014). To better define the 23 _GmTPSs_, they were named following [PERSON] et al. in 2014 [PERSON] et al. (2014), and a phylogenic tree was constructed together with several AtTPSs. Results showed that GmTPSs fell into different subfamilies, as narrated in Introduction, implying their functions might be divergent in terpene biosynthesis (Figure 1A). Gene structure is another important index defining the evolution or divergence of multigene family members. Consequently, an exon-intro diagram of soybean _GmTPSs_ was also constructed to detect the structural diversity. The number of introns ranged from six to fourteen in _GmTPSs_. Intriguingly, all the _GmTPSs_ from TPS-a, TPS-b, and TPS-g subfamilies had six introns except that _GmTPS11_ and _GmTPS12_ had seven introns. Comparatively, _GmTPSs_ from more ancient TPS-c and TPS-e/f subfamilies showed many more introns (Figure 1A). Furthermore, a total of 10 conserved motifs were tried to be predicted in the GmTPSs by MEME suite. Results showed that GmTPSs from TPS-a, TPS-b, and TPS-g subfamilies shared similar motif patterns (Figure 1A). Together, the intron/extron pattern and conservative motif analysis implied the potential diverse functions of GmTPSs in different phylogenetic groups. Moreover, the chromosomal locations of the 23 _GmTPS_ genes belonging to TPS-a, b, and g subfamilies were obtained from the soybean genome database. Results showed that the 23 _GmTPS_ genes were distributed on nine chromosomes accounting for nearly half of all the 20 chromosomes (Figure 1B). Obviously, there are eight _GmTPSs_ on chromosome 12, which had the highest density. Specifically, we mainly focused on the 18 GmTPSs belonging to TPS-a, TPS-b, and TPS-g subclades which were well-known enzymes responsible for volatile monoterpene and sesquiterpene biosynthesis in angiosperms. The transcripts of 18 _GmTPSs_ in different soybean tissues or organs of Wm82 were checked with earlier published RNA-seq data ([PERSON] et al., 2014). Conspicuously, trace transcripts of most _GmTPSs_ were detected in these samples. In contrast, _GmTPS12_, _GmTPS20_, and _GmTPS21_ were highly expressed in leaves (Figure 1C). Herein, the results indicated that most _GmTPS_ genes might be responsive to elicitors as stress-induced genes. Alternatively, some of the unexpressed _GmTPS_ genes might become pseudogenization during evolution.
## 5 Result
### SELMWK Algorithm Effect Comparison Experiment
#### 5.3.3 Validating the Advantages of Weighted Kernels  
The third set of comparative experiments verifies the weighted kernel's advantages relative to the single kernel. Figure 6 is a comparison chart of the recognition effects of the three algorithms on different data sets:  
Figure 5: 100 recognition results of SSELM on two datasets.  
Figure 6 shows the recognition effects of the three algorithms on Batch1, Batch2, Batch6, Batch7, Batch10 and Dataset 2. Among them are six labeled data in different batch datasets in dataset 1 and four labeled data in dataset 2. It can be seen from the figure that the SELMWK using the weighted kernel function has a good recognition effect on each data set except Batch1, and the overall recognition effect is also the best. In addition, the effect of the SKELM algorithm using the RBF kernel from Batch1, Batch2, and Batch3 is lower than that of the SKELM algorithm using the Poly kernel. Especially in Batch1, it is more obvious, and the recognition accuracy differs by about 10%.
## 4 Wireless Communication Protocol
### Wireless Medium Access Control Layer
#### 4.2.2 Robustness in MAC  
With the shrinking feature size of the CMOS technology augmented with on-chip wireless transmission, the issues of failure in data transmission and single event upsets (SEU) are anticipated to be significant. Furthermore, the high frequency wireless transceivers are extremely vulnerable to noise. Such high risks of random failures and vulnerabilities can result in a failure to the MAC mechanism. This can either degrade the benefits of the novel interconnect technology or corrupt wireless data due to interference. Hence, to ensure reliable and interference-free wireless communication, the MAC mechanism should be robust against such failures. To mitigate such failures, authors in [84] proposed a Token Management Unit (TMU) that can recover from the failures in a token passing MAC mechanism. The TMU has intelligence about when the token should arrive to which WI, and in case the token is not possessed by the specific WI within a certain time, the TMU regenerates the token correctly while avoiding duplication. Such MAC mechanism also can restore the performance benefit of the wireless interconnect with minimal computation and power overheads.
## 5 Results
### Reaction Time Analysis
#### 5.2.1 Comparison of AP-On and CA Functions  
In this section, we compared the response times of Auto AP-On and CA. Both functions are activated via controls on the steering wheel, but they were trained through different methods. AP-On through video-based training and CA through a paper-based user manual. Our findings reveal significant patterns in response times, as observed through both descriptive statistics and inferential analysis.  
For the AP-On function, the median response time was recorded at 3.16 s, with a relatively narrow range of 1.60 to 5.22 s. Furthermore, the data demonstrate a standard deviation of 0.96 s, indicating less variability in the response times among participants for this function. On the contrary, the CA function displayed a median response time of 4.22 s and had a broader range, ranging from 2.02 to 8.84 s. The standard deviation for the CA function is also high at 1.78 s. As illustrated in Figure 6, these descriptive statistics suggest a more consistent and potentially more rapid responsiveness for the AP-On function compared to the CA function.  
To robustly substantiate these observations, we conducted a Mann-Whitney U test, comparing the response times for AP-On and CA. The analysis revealed statistically significant differences between the two functions (\(p<0.05\)), with a resulting \(p\)-value of 0.0135. This outcome confirms that the difference in response times between AP-On and CA is statistically significant, which also implicates the training method as a contributing factor to this variation.
## 4 Experiment Results  
### Manufacture of Slits and Gratings  
Slits and gratings are core components of the spectrometers in GeoFWHIS, and their performance directly affects the spectral quality of the system. The slit is the field stop of a spectrometer, and its geometrical error and edge defect will decrease the spectral resolution, distortion, and uniformity. Slits in GeoFWHIS are long and narrow, especially in B1 and B2 bands; the length of a single slit is 61.44 mm, but the width is only 15 \(\upmu\)m, and the length ratio to width is more than 4000. It is quite challenging to ensure the slit edge's straightness, parallelism, and uniformity by traditional methods such as manual grinding, mechanical scribing, and laser processing. In this paper, long slits based on silicon substrate are manufactured by semiconductor technology [50], as shown in Figure 12. The geometric error of the slit can be strictly controlled by stepping lithography technology. Figure 12b is the micrograph of the slit in the B1 band. Its edge is straight and sharp without noticeable burr within the whole slit range. The measured width is 14.99 \(\upmu\)m, and the parallelism error of two edges is less than 0.5 \(\upmu\)m.  
Convex blazed grating is the aperture stop of the spectrometer. Its groove density, shape, diffraction efficiency, and surface error will directly affect the spectrometer's transmittance, stray light, and wavefront error. Diffraction gratings of the spectrometers in GeoFWHIS have low groove density and small blazed angles. Especially in the B5 band,the groove density is only 8.8 lp/mm, and the blazed angle is less than 3\({}^{\circ}\). It is difficult to etch sawtooth grooves with large periods and small blazed angles on a convex substrate by ion beam etching. In this paper, we jointly design the optical system and manufacturing process to optimize the grating groove suitable for current precision micro-nano processing. The manufactured gratings and their theoretical and measured diffraction efficiencies are shown in Figure 13. The vertex angle slightly deviates from the ideal right angle, being an obtuse or rounded angle. This deviation will decrease the peak efficiency of the grating. The peak efficiencies of each band are still more than 70%, which fully ensures the transmittance and SNR of the spectrometer, and the highest peak efficiency is 86.4% in the B4 band.  
Figure 12: Slits based on silicon. (**a**) B1 and B2 slits, (**b**) 10\(\times\) micrograph of B1 slit, (**c**) B3 and B4 slits, (**d**) B5 slit.  
Figure 13: Convex blazed gratings and their groove shapes and diffraction efficiencies. (**a**) B1 band, groove density 312.1 lp/mm, blazed angle 3.8\({}^{\circ}\); (**b**) B2 band, groove density 210.1 lp/mm, blazed angle 4.75\({}^{\circ}\); (**c**) B3 band, groove density 68.5 lp/mm, blazed angle 3.1\({}^{\circ}\); (**d**) B4 band, groove density 19.1 lp/mm, blazed angle 2.8\({}^{\circ}\); (**e**) B5 band, groove density 8.8 lp/mm, blazed angle 2.8\({}^{\circ}\).
## 4 Simulation Experiment
### Experiment 1
It further introduces the iPID method to optimize the adaptive controller, resulting in a low-order controller with superior simulation results, robustness, and control performance. Figure 4 illustrates the control input curves corresponding to each control scheme. In the iFFDL-MFA-PID scheme, all the control inputs changed smoothly in each stage. Even when the predetermined signal changed, the control input adjusted at a lower rate, effectively alleviating the chattering phenomenon of the system. On the other hand, the FFDL-MFAC scheme exhibited significant control input changes during the predetermined \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **FFDL-MFAC** & **iFFDL-MFA-PID** \\ \hline Rise time (s) & 5 & 3 \\ Adjust time (s) & 27 & 15 \\ MAE & \(3.41\times 10^{-2}\) & \(1.15\times 10^{-2}\) \\ MSE & \(7.29\times 10^{-4}\) & \(2.32\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance indexes. Figure 3: System output error curves. Figure 2: System output curves. signal-change stage. The amplitudes of these changes were large, leading to a more pronounced chattering phenomenon in the system. \[\text{MAE}=\frac{1}{nT}\sum_{s=1}^{n}\sum_{i=1}^{T}|I_{s}(i)-I_{d}(i)| \tag{42}\]  
\[\text{MSE}=\frac{1}{nT}\sum_{s=1}^{n}\sum_{i=1}^{T}|I_{s}(i)-I_{d}(i)|^{2} \tag{43}\]  
In summary, the iFPDL-MFA-PID method not only maintains the fast response characteristic of the MFAC method but also benefits from the strong adaptive ability of intelligent PID parameters, achieving an optimal control effect for systems affected by noise disturbances. Furthermore, the parameter adjustment process based on the iFPDL-MFA-PID method is simpler and less challenging.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 3 Bibliometrics Analysis
### The Performance of the Representative Methods on Public Datasets
#### 3.2.2 Performance Analysis of Representative Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**BID**} & \multicolumn{2}{c}{**CID2013**} \\ \cline{3-6}  & & **PLCC** & **SROCC** & **PLCC** & **SROCC** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Spatial \\ domain-based \\ \end{tabular} } & MLV [46] & 0.375 & 0.317 & 0.689 & 0.621 \\ \cline{2-6}  & BIBLE [35] & 0.392 & 0.361 & - & - \\ \cline{2-6}  & MGV [46] & 0.307 & 0.201 & 0.511 & 0.499 \\ \cline{2-6}  & ARISM [47] & 0.193 & 0.151 & - & - \\ \cline{2-6}  & CPBD [49] & - & - & 0.418 & 0.293 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Spectral \\ domain-based \\ \end{tabular} } & DCT-QM [60] & 0.383 & 0.376 & 0.662 & 0.653 \\ \cline{2-6}  & SC-QI [61] & - & - & - & - \\ \cline{2-6}  & FISH [62] & 0.485 & 0.474 & 0.638 & 0.587 \\ \cline{2-6}  & LPC-SI [65] & 0.315 & 0.216 & 0.634 & 0.609 \\ \cline{2-6}  & BISHARP [66] & 0.356 & 0.307 & 0.678 & 0.681 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Learning-based \\ \end{tabular} } & BIQI [72] & 0.513 & 0.472 & **0.742** & **0.723** \\ \cline{2-6}  & DB-CNN [77] & 0.471 & 0.464 & 0.686 & 0.672 \\ \cline{2-6}  & DeepBIQ [78] & - & - & - & - \\ \cline{2-6}  & SPARISH [79] & 0.482 & 0.402 & 0.661 & 0.652 \\ \cline{2-6}  & SR [81] & 0.415 & 0.467 & 0.621 & 0.634 \\ \cline{2-6}  & MSFF [87] & - & - & - & - \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Combination \\ \end{tabular} } & S3 [88] & 0.427 & 0.425 & 0.687 & 0.646 \\ \cline{2-6}  & RFSV [91] & 0.391 & 0.335 & 0.701 & 0.694 \\ \cline{1-1} \cline{2-6}  & SVC [92] & - & - & 0.425 & 0.433 \\ \cline{1-1} \cline{2-6}  & SFA [93] & **0.546** & **0.526** & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different evaluation methods on BID dataset and CID2013 dataset.
## 2 Materials and Methods
### System Calibration Test Procedure  
The Photron cameras were set up to achieve the field of view outlined in Table 1 and aligned to share a common centre point at a distance of 930 mm from the lens. Two ARRILUX 400 'Pocket Par' (Munich, Germany) spotlights were positioned either side of the cameras to achieve appropriate lighting of the measurement volume. Lighting position and intensity were adjusted to achieve sufficient, uniform image contrast, comparable across both cameras when configured with a lens aperture of f16 and a camera shutter speed of 1/10,000 s. System calibration was completed in line with the manufacturer's guidelines. The SPB panels were mounted on a tripod at the centre of the camera views, as shown in Figure 3a, and sequential images of each deformation stage were captured by changing the SPB. SPBs were only placed in front of lights for short periods of time so as to minimize any effects of humidity and heating of the pattern boards. To determine if the accuracy was affected  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deformation** & **Average Measured** & **Machine Error** & **Corrected Average** & **Designed** & **Average Error** \\
**Stage (\% Strain)** & **Length (mm) (\(\pm\)1 SD)** & **(mm)** & **Length (mm)** & **Length (mm)** & **(mm)** \\ \hline
0 & 70.706 (\(\pm\)0.002) & 0.002 & 70.704 & 70.556 & 0.148 (\(\pm\)0.002) \\
10 & 77.740 (\(\pm\)0.002) & 0.002 & 77.738 & 77.611 & 0.127 (\(\pm\)0.002) \\
20 & 84.821 (\(\pm\)0.002) & 0.003 & 84.818 & 84.667 & 0.151 (\(\pm\)0.002) \\
30 & 91.861 (\(\pm\)0.002) & 0.003 & 91.858 & 91.722 & 0.136 (\(\pm\)0.002) \\
40 & 98.930 (\(\pm\)0.002) & 0.003 & 98.927 & 98.778 & 0.149 (\(\pm\)0.002) \\
50 & 106.035 (\(\pm\)0.002) & 0.003 & 106.032 & 105.833 & 0.199 (\(\pm\)0.002) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pattern Length Measurements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Def. Stage (\% Strain)** & **Measured Length (\(\pm\)** **Edge Detection) (mm)** & **Length Minus Edge** & **Length Plus Edge** & **Min Material Measure** & **Max Material Measure** \\
**Domain (\% Strain)** & **Max Material Measure** & **Deformation (\% Strain)** & **Deformation (\% Strain)** \\ \hline
0 & 70.704 (\(\pm\)0.026) & 70.678 & 70.730 & 0 & 0 \\
10 & 77.738 (\(\pm\)0.026) & 77.712 & 77.764 & 9.87 & 10.02 \\
20 & 84.818 (\(\pm\)0.026) & 84.792 & 84.844 & 19.88  
by the position within the measurement volume, the test was repeated with the SPBs placed at the locations illustrated in Figure 3b.  
The pattern was positioned approximately parallel to the camera image plane; as a 3D system was being used, more precise alignment was not deemed necessary. Recorded images were imported into the ARAMIS software and processed with a square subset size of \(20\times 20\) pixels and a computational step size of 13 pixels.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 2 Experimental Setup
### LED Panel Driver and Characteristics  
We utilised the existing LED panel driver and designed a bias-tee circuit, as illustrated in Figure 3, where the AC port of the bias-tee module is connected to an AWG for intensity modulation of the LED panel. Alternative LED drivers with low ripple/noise can be used, depending on the system requirements, but with increased cost and complexity. To measure the _V-I_ curve of the LED panel, two low-noise voltage sources were connected in series, as shown in Figure 4a, to generate high enough voltages to turn on the LED strips. Because the turn-on voltage of each LED is around 2 V, the turn-on voltage for the entire LED string can be estimated to be \(\sim\)24 V. Therefore, the first voltage source is set to a fixed voltage of 24 V, while the second is set to sweep voltage and monitor the corresponding current. The measured _V-I_ curve of the LED panel as a lumped element is shown in Figure 4b. From the datasheet, the LED panel is biased at 0.9 A, with a corresponding voltage of around 34.5 V.  
Next, we measured the frequency response of the LED panel at the bias point of (34.5 V, 0.9 A) using the setup that is shown in Figure 5a. Here, we used two low-noise voltage sources connected in series as in the _V-I_ measurement to mitigate the light intensity  
Figure 3: The bias-tee combining the LED driver and arbitrary waveform generator (AWG) to drive the LED panel.  
Figure 2: **(a)** Illustrative view, and **(b)** schematic of the light-emitting diode (LED) panel in use.  
to relax the alignment requirement between the LED panel and the receiver. A transimpedance amplifier (TIA) circuit converts the detected current from the photodiode into a voltage signal with a gain of 10 k\(\Omega\) for the following data acquisition by the OSC. The optical receiver was implemented compactly using a printed circuit board (PCB) inside an aluminium enclosure. A photo of the optical receiver is given in Figure 6 with a closeup of the PCB inside.  
Figure 7 depicts the receiver's relative signal strength at varying incident light angles, from which a half angle of \(\pm 35^{\circ}\) can be observed. Here, the half angle is defined as the incident angle at which the strength is reduced to half of that generated when the incident light is perpendicular to the photodiode. We also observed that the optical receiver works robustly as long as the LED panel shines it in our practical measurements. In addition, using a high-bandwidth laser diode (Osram, PL450B) as the light source, we measured the 3-dB bandwidth of our receiver as around 20 MHz, which satisfies our system specification.  
Figure 6: (**a**) Optical receiver and (**b**) the printed circuit board (PCB) inside.  
Figure 7: Measured directivity of the optical receiver.
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 4 MSC-Based Therapy for OA
### Sources of MSCs
#### 4.2.2 AD-MSCs for OA Therapy
These findings indicate the potential efficacy of AD-MSCs in facilitating tissue repair and alleviating pain [226]. In a phase IIb knee OA clinical study that was active-controlled and double-blinded, investigators successfully used 5107 AD-MSCs to treat the patients. After 12 months, the majority of patients in the group that was given AD-MSCs showed a 70% improvement. In addition, MRI findings at 12 months showed a substantial increase in articular cartilage volume in the AD-MSC group compared to the HA group [227]. [PERSON] et al. [228] found that 16 separate studies had reported employing AD-MSCs as a therapy for OA using various approaches. AD-MSCs that were isolated from the IPFPand injected into an OA knee showed improved mobility and function and a reduction in pain levels. There were no adverse effects from the treatment. After two years of follow-up, patients reported significantly reduced levels of discomfort and had cartilage regeneration, as demonstrated by MRI [229]. [PERSON] et al. discovered that orbital AD-MSCs have higher concentrations of CD73, CD90, CD105, and CD146, but lower concentrations of CD31, CD45, and HLA-DR [215]. [PERSON] et al. demonstrated an increase in HLA-ABC and HLA-DR expression in AD-MSCs after IFN treatment, which raises concerns about the usage of allogenic AD-MSCs [230]. A phase lib randomized, placebo-controlled clinical investigation of AD-MSCs was conducted on 24 patients with knee OA. After six months of follow-up, the AD-MSCs-treated group had a higher WOMAC score, better function, and less pain, demonstrating the therapeutic potential of AD-MSCs [231]. In the NCT01585857 trial, it was found that patients receiving injections of 2106 cells had a higher baseline pain and WOMAC score than those receiving more significant dosages [232]. Importantly, these trials indicated that autologous AD-MSCs transplantation has a low risk of adverse events and does not cause graft rejection or malignancy in recipients, making it a feasible treatment option for OA [210]. To fully comprehend the defining phenotypes and enhance the therapeutic efficiency of these MSCs, additional research is required on donor-matched AD-MSCs from various isolation sites and their associated properties [200; 215].