esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Solid State Structural Features: X-ray Crystallography  
Several of the target molecules (**1d**, **2c**, **3c** and **4c**) were examined by x-ray crystallography. Crystal data and structure refinement information for **1d** and **2c** are recorded in Figure 4 and Table 1 while Figure 5 and Table 2 contain data for **3d** and **4c**. Critical bond information is listed in Table 3. The crystallographic information files for these molecules have been uploaded to the Cambridge Crystallographic Data Center and have the following control numbers: **1d**: 854704, **2c**: 854697, **3d**: 854705 and **4c**: 854706. \begin{table}
\begin{tabular}{l l l} \hline Compound & **1d** & **2c** \\ Formula & C\({}_{18}\)H\({}_{10}\)O\({}_{3}\) & C\({}_{13}\)H\({}_{6}\)F\({}_{6}\)O\({}_{3}\) \\ Formula Weight (\(g/mol\)) & 274.26 & 324.18 \\ Crystal Dimensions (\(mm\)) & \(0.30\times 0.24\times 0.20\) & \(1.20\times 0.10\times 0.06\) \\ Crystal Color and Habit & clear prism & yellow needle \\ Crystal System & orthorhombic & monoclinic \\ Space Group & F d d 2 & P 21/c \\ Temperature, K & 173 & 173 \\ \(a\), Å & 18.0996(6) & \\ \(b\), Å & 20.9271(7) & 18.6978(12) \\ \(c\), Å & 26.0789(8) & 13.8431(9) \\ \(a\),\({}^{\circ}\) & 90.00 & 90.0 \\ \(\beta\),\({}^{\circ}\) & 90.00 & 98.964(3) \\ \hline Compound & **1d** & **2c** \\ \(\gamma\),\({}^{\circ}\) & 90.00 & 90.0 \\ V, Å3  \\ Reflections to determine final unit cell & 9975 & 9959 \\
2\(\mathrm{\theta}\) range, \({}^{\circ}\) & 5.0, 56.84 & 5.28–57.7 \\ Z & 32 & 4 \\ F(000) & 4544 & 648.71 \\ \(\mathrm{\rho}\) (\(g/cm\)) & 1.475 & 1.768 \\ \(\lambda\), Å, (MoK\(\alpha\)) & 0.71070 & 0.71073 \\ \(\mu\), (\(cm^{-1}\)) & 0.101 & 0.18 \\ Reflections collected & 103146 & 26516 \\ Unique reflections & 6360 & 3195 \\ R\({}_{\mathrm{merge}}\) & 0.0403 & 0.027 \\ Cut off Threshold Expression & \(>\)2 sigma(I) & \(\mathrm{Inet>1.0 sigma(Inet)}\) \\ Refinement method & full matrix least-sqs using F 2 & full matrix least-sqs using F \\ Weighting Scheme & 1/[\(\mathrm{sigma^{2}}\) (\(\mathrm{F_{o}}^{2}\)) + (0.0555P)2+ & 1/(\(\mathrm{sigma^{2}}\) (\(\mathrm{F}\)) + 0.0005F2) \\  & 3.0465P] where \\  & P = (\(\mathrm{F_{o}}^{2}\) + 2 Fc2)/3 & \\ R\({}_{1}\)a [FOOTNOTE:a]Footnote a: \(\mathrm{R_{1}=\mathcal{X}[\mathrm{F_{o}}]-[\mathrm{F_{c}}]/\mathcal{S}\; \mathrm{F_{o}}}\); \(\mathrm{e}\)\(\mathrm{wR_{2}=[\mathcal{X}\{\mathrm{w}(\mathrm{F_{o}}^{2}-\mathrm{F_{c}}^{2} )^{2}\}\mathcal{Y}\{\mathrm{w}\mathrm{F_{o}}^{4}\}]^{\
icefrac{{1}}{{2}}}}\); \(\mathrm{e}\)\(\mathrm{wR_{2}=[\mathcal{X}\{\mathrm{w}(\mathrm{F_{o}}^{2}-\mathrm{F_{c}}^{2} )^{2}\}\mathcal{Y}\{\mathrm{w}\mathrm{F_{o}  
Figure 5: ORTEP drawings of **3d** and **4c**. Ellipsoids are at the 50% probability level and hydrogen atoms were drawn with arbitrary radii for clarity.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### Error Rate  
The percentage of sentences that the model erroneously identified is called the error rate. Through the quantification of inaccurate predictions, it emphasizes the model's dependability.  
\[\text{Error Rate}=\frac{\text{Number of Incorrectly Recognized Sentences (E)}}{\text{Total Number of Sentences (N)}}\times 100 \tag{14}\]  
In Equation (14), E is the number of errors, and N is the total number of test sentences.  
In Figure 8, different conditions are used to compare four models: HGE-ACN, DDST-GCNN, MSeqGraph, and BAE. With an error rate of 5.2% under typical circumstances, HGE-ACN outperforms BAE, which has the highest at 11.3%. When the difficulty level rises, so does the rate of inaccuracy for every model. While BAE's error rate is the greatest at 15.8% under a variable signing speed, HGE-ACN performs the best with a rate of 6.5%. With error rates of 7.2% for HGE-ACN and 16.2% for BAE, respectively, signal variability and environmental noise affect performance.
## Improvement of Anaerobic Digestion of Lignocellulosic Biomass by Hydrothermal Pretreatment
###### Abstract  
Lignocellulosic biomass, comprising of cellulose, hemicellulose, and lignin, is a difficult-to-degrade substrate when subjected to anaerobic digestion. Hydrothermal pretreatment of lignocellulosic biomass could enhance the process performance by increasing the generation of methane, hydrogen, and bioethanol. The recalcitrants (furfurlats, and 5-HMF) could be formed at high temperatures during hydrothermal pretreatment of lignocellulosic biomass, which may hinder the process performance. However, the detoxification process involving the use of genetically engineered microbes may be a promising option to reduce the toxic effects of inhibitors. The key challenge lies in the scaleup of the hydrothermal process, mainly due to necessity of upholding high temperature in sizeable reactors, which may demand high capital and operational costs. Thus, more efforts should be towards the techno-economic feasibility of hydrothermal pre-treatment at full scale.  
Keywords:lignocellulosic biomass; anaerobic digestion; hydro-thermal pretreatment; biofuels +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Methods
#### 3.1.2 Sif/Npp Ratio Index  
In this study, our initial progress was made by demonstrating the presence of a certain relationship between solar-induced fluorescence (SIF) and net primary productivity (NPP). However, an accurate quantitative relationship between the two was elusive. To address this, we developed an index that quantifies the relationship between SIF and NPP. By analyzing the fluctuations of this index, we can explore the potential of SIF monitoring for dynamic changes in NPP [52]. Several studies have provided compelling evidence that the SIF/GPP ratio index offers a scientifically robust approach to assess the relationship between SIF and gross primary productivity (GPP) [53; 54; 55]. Drawing from this accomplishment, we developed a ratio index between SIF and NPP to quantify their association:  
\[ratio_{ij}=\frac{\Delta SIF_{i,j}}{\Delta NPP_{ij}} \tag{8}\]  
where \(ratio_{ij}\) represents the ratio on day \(j\) of year \(i\), \(\Delta SIF_{i,j}\) represents the image element average of the standard anomaly GOSIF on day \(j\) of year \(i\), and \(\Delta NPP_{ij}\) represents the image element average of the standard anomaly GLASS NPP on day \(j\) of year \(i\).  
The process of plant respiration represents an important factor that affects the linear relationship between SIF and NPP [56; 57; 58]. Nevertheless, our experimental findings, derived from the analysis of long-term spatiotemporal correlations between SIF and NPP, demonstrate a consistent and stable relationship. Consequently, when constructing the SIF/NPP ratio index, we omitted the consideration of plant respiration's impact.  
To evaluate the trend in SIF/NPP changes intuitively, we selected the SIF/NPP multi-year average value that reflected the data trend as the measurement indicator (the average ratio of SIF/NPP from 2010 to 2020 was 1.5189). When the SIF/NPP ratio is greater than the multi-year average ratio, the dynamic change trend in SIF is greater than that of NPP. When the SIF/NPP ratio is less than the multi-year average ratio and greater than 0, the overall trend in SIF change is smaller than that of NPP. When the SIF/NPP ratio is negative, the change trend in SIF is opposite to that of NPP.
## 3 Methods
### Segmentation of the Lung Anatomical Structures
#### 3.1.9. Segmentation of Nodule Candidates  
Nodules are soft tissue lesions exhibiting high potential to be cancer. In Figure 9a, we can see an example of a nodule (red-bordered) and other nodule-liked false lesions (yellow-bordered). For a clear visualization, a zoomed-in view of Figure 9a is provided in Figure 9b. Nodules can be divided into three types--solid, non-solid, and partly solid--depending on their radiological density. The first type, solid nodules, is found most frequently, and has a soft tissue density if isolated. In the case of vessel attachment, their contours become obscure. The second type, non-solid nodules, is inferred by vessel density and appears as a ground-glass structure. The last type, partly solid nodules, has a hybrid density of soft tissue and vessel.  
Figure 8: Segmentation of ribs and bones: (**a**) input 2D CT slice, (**b**) 2D segments of the ribs and bones, (**c**) isotropic structure of the ribs and bones.  
Figure 9: Segmentation of the nodule candidates: (**a**) input 2D CT slice, (**b**) zoomed-in view of the parenchymal internal structures, (**c**) 2D nodule candidate segment, (**d**) isotropic structure of the segmented nodule candidates, (**e**) isotropic structure of blob enhancement lesions, (**f**) isotropic structure of the true tumor nodule.  
To ensure the detection of all nodule types, the lesions inside the parenchyma were first segmented first using the threshold value \(-800\) HU, as illustrated in Figure 9c. Then, an isotropic image of these internal lesions Figure 9d was reconstructed from 2D segments. Using this isotropic image, we attempted to extract possible nodule lesions, also known as nodule candidates. A nodule is a round or oval-shaped structure in a 2D CT scan, but it forms a blob-shaped structure in volumetric view. Thus, a blob enhancement filter can be applied to extract nodules. In Figure 9e, we can see the isotropic structures of the segmented nodule candidates. In this step, we cannot directly segment the true tumor nodules because there are many nodule-like structures in the lungs. These structures interfere with and make it troublesome to detect the real nodules. Since nodules are the signs of lung cancer, wrong or missed detection can seriously affect the patient. For this reason, nodule candidates are segmented first in this step, and then actual nodules are segregated with the help of a machine learning algorithm in the next detection phase.
## 6 Conclusions  
In this paper, an intelligent battery pack balancing algorithm intended for use in an agricultural drone was proposed. The use of a lithium polymer battery is associated with difficulties when analyzing the system because this type of battery generates electricity by way of a chemical reaction. To resolve this, we proposed an intelligent balancing algorithm using fuzzy control because such a method does not require analysis with a mathematical model. The method of communication was designed using AFSK and RF instead of the more commonly used ZigBee to ensure that there was sufficient bandwidth for the required data and to cover a larger area. This system allows the voltage data values that describe the status of the battery cell to be sent efficiently and rapidly. In order to apply fuzzy control to the proposed battery pack module balancing algorithm, a fuzzy controller was developed by analyzing and adapting the general fuzzy control theory. A field test was also conducted to determine the efficacy of the battery pack module algorithm proposed in this paper, and it was confirmed that it was able to control the discharge current by controlling the output voltage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Division** & **Only Battery** & **BMS** & \
\begin{tabular}{c} **Improvement** \\ **Efficiency** \\ \end{tabular} &
\begin{tabular}{c} **Measuring** \\ **Instrument** \\ \end{tabular} \\ \hline Charging time & 240 min & 210 min & 14\% & Charge-discharge \\ Discharge time & 9 min & 12 min & 33\% & Device \\ Flight time 9 min & 550 s & 580 s & 5\% & Agricultural Drone \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of outcomes with the battery only and the BMS.  
Figure 20: Comparison of the voltage deviation of each battery cell.  
In this study, we found that the efficiency improved by 5% when the developed BMS was installed compared to when only a battery was used. In actual flight, the 5% efficiency improvement could be improved even further during certain climate and temperature conditions, showing an efficiency exceeding 10% while charging and discharging. We have anticipated that if the proposed battery pack and integral BMS are commercialized, it will be proven to be useful during the operation of agricultural drones given the extended battery lifetimes and reduced charge times.  
Contributions, Conceptualization, S.-B.K. and S.-H.L.; methodology, S.-B.K. and S.-H.L.; software, S.-B.K.; validation, S.-B.K. and S.-H.L.; formal analysis, S.-H.L.; investigation, S.-B.K. and S.-H.L.; resources, S.-B.K. and S.-H.L.; data curation, S.-B.K.; writing--original draft preparation, S.-H.L.; writing--review and editing, S.-B.K.; visualization, S.-H.L.; supervision, S.-B.K. All authors have read and agreed upon the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 3 Results
### Validation of Diurnal Radiation
#### 3.2.1 Validation under a Daily Clear-Sky Condition  
On days classified as clear (as shown in Figure 5A), the median R\({}^{2}\) consistently exceeds 0.90 in all the regions except for the locations in the EU (median R\({}^{2}\) > 0.75), with African locations consistently surpassing 0.97. Under such conditions, the average median MB values for both EU locations were nearly zero (though they have the highest variability among all the locations) while the average of the median MB for both Japanese locations was \(-\)3.5 W\(\cdot\)m\({}^{-2}\), ranging from \(-\)77.3 to +50.6 W\(\cdot\)m\({}^{-2}\) (Figure 5B). Similarly, the median MB indicates an overall underestimation for both African locations, at \(-\)27.7 W\(\cdot\)m\({}^{-2}\) in GOB and +7.6 W\(\cdot\)m\({}^{-2}\) in VUW. In the KSA location, the median value of the MB is \(-\)7.2 W\(\cdot\)m\({}^{-2}\), with a range from \(-\)10.1 to \(-\)0.5 W\(\cdot\)m\({}^{-2}\) for the 25 th to 75 th percentiles of clear days' data. In India, the MB in clear days varies from \(-\)77.3 to 73.8 W\(\cdot\)m\({}^{-2}\).  
The highest RMSE for clear sky was observed in the CNR location, with a median value of 108.6 W\(\cdot\)m\({}^{-2}\). In contrast, clear days presented lower RMSE values for both Japanese locations, averaging a median of 52.5 W\(\cdot\)m\({}^{-2}\) (Figure 5C). In African locations, particularly on clear days, the median RMSE value hovers around 41.6 W\(\cdot\)m\({}^{-2}\), with both GOB and VUW exhibiting similar error bars, ranging from approximately 7 to 115 W\(\cdot\)m\({}^{-2}\). In the KSA site, the RMSE values were notably lower, ranging from 16.2 W\(\cdot\)m\({}^{-2}\) to 58.7 W\(\cdot\)m\({}^{-2}\) (Figure 5C). The Indian locations showed the highest variability of RMSE values (0 to 250 W\(\cdot\)m\({}^{-2}\)) under clear-sky conditions among all the locations analyzed in this study.
# Integrating Cork Biochar as a Green Filler via Photopolymerization
## 3 Results and Discussion
### Mechanical Properties of Cork Biochar Polymer Composites (CBPCs)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CBPC**} & **\%\(w\)/\(w\) Cork** & **Young’s Modulus** & **Tensile Strength** & **Elongation at Break** \\  & **Biochar** & **(E) (MPa) (\(\pm\)SD)** & **(\(\sigma_{t}\)) (MPa) (\(\pm\)SD)** & **(\(\varepsilon_{x}\)?) (\(\pm\)SD)** \\ \hline Resin & 0 & 2280 \(\pm\) 80 & 58.1 \(\pm\) 2.7 & 4.1 \(\pm\) 0.7 \\ CBPC-0.15 & 0.15 & 2190 \(\pm\) 200 & 40 \(\pm\) 5 & 2.20 \(\pm\) 0.04 \\ CBPC-0.25 & 0.25 & 2300 \(\pm\) 130 & 40 \(\pm\) 5 & 2.0 \(\pm\) 0.3 \\ CBPC-0.35 & 0.35 & 2090 \(\pm\) 70 & 38 \(\pm\) 4 & 2.3 \(\pm\) 0.2 \\ CBPC-0.5 & 0.5 & 1987 \(\pm\) 29 & 32 \(\pm\) 4 & 1.8 \(\pm\) 0.3 \\ CBPC-1 & 1 & 1520 \(\pm\) 80 & 20.1 \(\pm\) 1.5 & 1.5 \(\pm\) 0.1 \\ CBPC-1.5 & 1.5 & 1220 \(\pm\) 130 & 14 \(\pm\) 2 & 1.3 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mechanical property values of CBPCs. CBPCs-0.25%\(\omega\)\(\upsilon\)/\(w\), and CBPCs-0.35%\(w\)/\(w\) series show no significant differences when compared to each other, like [PERSON]'s modulus observations. The ANOVA indicates a significant difference in the percentage elongation at break between the resin and the full range of CBPCs, which is similar to the results observed for the tensile strength. Additionally, there are no significant differences among CBPCs-0.15%\(w\)/\(w\), CBPCs-0.25%\(w\)/\(w\), CBPCs-0.35%\(w\)/\(w\), and CBPCs-0.5%\(w\)/\(w\) when compared to each other. However, for CBPCs-1.5%\(w\)/\(w\) and CBPCs with lower percentages of biochar, significant differences exist. Above 0.5%\(w\)/\(w\), there are none. This suggests that once a specific percentage is attained, the modification of the percentage elongation at break is consistent across all cases.
## 3 Results
Table 3 shows the differences between machines and between intensity percentages in the complete run, Table 4 shows the concentric phase, and Table 5 shows the eccentric phase. No significant differences were found in the activation of any muscle group when comparing the SLP and BIOFIT-LP machines at the same intensity (60% 1 RM vs. 60% 1 RM or 75% 1 RM vs. 75% 1 RM), neither in the complete run, in the concentric phase, nor in the eccentric phase (\(p>0.05\)), except for the biceps femoris in the eccentric phase at 75% 1 RM, which showed a greater significant activation in SLP as compared to BIOFIT-LP (ES = 0.60; moderate effect size; \(p<0.\) 01); in the tibialis anterior, where there was a greater activation at both intensities in the BIOFIT-LP machine in the full stroke (ES = 0.53-0.86; moderate effect size; \(p<0.01\)-0.001) and eccentric phase (ES = 0.69-0.89; moderate effect size; \(p<0.001\)), but not in the concentric phase (ES = 0.3-0.5; moderate effect size; \(p>0.05\)); and gastrocnemius, which showed a greater activation in SLP than in BIOFIT-LP at both intensities and all phases (ES = 0.68-0.91; moderate effect size; \(p<0.05\)-0.001).
# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 3 Proposed Algorithm
### Classification Using an Optimal width of Image Histogram  
Using the fast Fourier transform algorithm mentioned earlier, we classify 3D film pattern images into good or bad by cutting them into patterns. The algorithm for determining the quality of the pattern image is as follows. First, convert the image to a gray image, and then obtain the image histogram of the gray image. Then, determine the specific height of the image histogram \(\alpha\), and find the maximum pixel value and the minimum pixel value corresponding to \(\alpha\), and then obtain the width of the corresponding height \(\alpha\). The equation for calculating the width of the image histogram \(W(\alpha)\) is as follows:  
\[W(\alpha)=H(\alpha)_{max}-H(\alpha)_{min}, \tag{7}\]  
where \(H(\alpha)_{max}\) and \(H(\alpha)_{min}\) are the maximum value and the minimum value for a specific height \(\alpha\) of the image histogram, respectively. \(W(\alpha)\) represents the width of the pixel, which is the difference between \(H(\alpha)_{max}\) and \(H(\alpha)_{min}\). Figure 4 shows the process of obtaining width \(W(\alpha)\). After calculating the width \(W(\alpha)\), the 3D film image is classified as a good 3D film image or a bad 3D film image based on the predetermined threshold \(\beta\). The decision can be defined as follows:  
\[R=\begin{cases}1,&W(\alpha)>\beta\\ 0,&otherwise\end{cases} \tag{8}\]where \(R\) is the class label of the image. When \(R=1\), it is a good image, and when \(R=0\), it is a bad image. The procedure of the proposed algorithm is shown in Figure 5.
## 3 Results and Discussion  
### Stable Operation  
Figure 5 shows the point for which displacements were analysed. During the operation of the rotor, the foils of the foil bearing moved cyclically. The bumps of the bump foil usually supported the top foil firmly and there was no free space between these foils. Sometimes, when the shape of the bump foil differed significantly from the ideal profile (which could be due, for example, to manufacturing accuracy) or there was a large clearance in the bearing for some reason, some changes in the gap height between the foils were observed. The bearing components were cyclically pressed against each other by the rotating and moving shaft, resulting in changes in the geometry of the gaps.  
Figure 4: Vision Research’s v2511 ultrahigh-speed camera (**left**). Extension tubes that enable achieving higher macro magnification (**right**).  
Figure 5: Point used to analyse the displacement (on the bump foil).  
The displacement in the horizontal direction of the point located on top of the bump of the bump foil (Figure 5) is shown in Figure 6. The data come from the time course registered during operation at a rotational speed of 24,000 rpm. With each rotation, it can be seen that the bump is pressed by the top foil (during the shorter and larger displacements). In this case, the irregular operation associated with inaccurate manufacturing affects the dynamic behaviour of the foil. All of this also translates into the different nature of the operation of the rotor supported by foil bearings. It should be emphasised here that a properly functioning bearing was analysed, where almost all bumps supported the structure steadily. The fragment analysed constitutes only a small portion of the bearing. The example presented can illustrate why it is so difficult to analyse foil bearings numerically. In such analyses, most researchers assume that the bearings are perfectly made.  
Figure 7 shows the vibration of the top foil located between the bumps. The area is similar to that shown in Figure 5 but the measurement was carried out under different operating conditions. This is the stable operation of the properly manufactured foil bearing. The frequency of the vibration is equal to the rotational frequency. The peak-to-peak amplitude of the vibration is equal to 0.5 \(\upmu\)m. This kind of movement can be observed quite frequently in a foil bearing.  
Figure 6: Displacement of the foil at a rotational speed of 24,000 rpm. One bump is not manufactured perfectly.  
Figure 7: Displacement of the foil at a rotational speed of 24,000 rpm.