esa-sceva/satcom-chunk-collection huggingface dataset:
# High Cellulose Purity by Acid Hydrolysis Pretreatment on Kenaf Outer Bast
## 3 Result and Discussion
### Effects of Acid Concentration  
Sulfuric acid as a catalyst cleaved the glycosidic linkages in polymeric carbohydrates from polysaccharides into monosaccharides [24]. Meanwhile, the acid concentration is one of the profound influences on exhibitor reaction. From the acid hydrolysis reaction,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temp a** (\({}^{\circ}\)C)} & \multirow{2}{*}{**Acid Con b** **(N)**} & \multirow{2}{*}{**Time** **(min)**} & \multirow{2}{*}{**Yield (\%)**} & \multicolumn{3}{c}{**Chemical Composition**} \\ \cline{5-6}  & & & & **Cellulose (\%)** & **Hemicellulose (\%)** & **Lignin (\%)** \\ \hline
110 & 0.25 & 60 & 68.8 \(\pm\) 0.4 & 55.0 \(\pm\) 0.1 & 13.2 \(\pm\) 0.2 & 0.6 \(\pm\) 0.2 \\ \hline
120 & 0.25 & 60 & 63.3 \(\pm\) 0.1 & 52.6 \(\pm\) 0.2 & 10.1 \(\pm\) 0.2 & 0.6 \(\pm\) 0.1 \\ \hline
130 & 0.25 & 60 & 54.4 \(\pm\) 0.1 & 51.4 \(\pm\) 0.1 & 2.5 \(\pm\) 0.1 & 0.6 \(\pm\) 0.5 \\ \hline \hline \end{tabular}  
* Temperature; b Acid concentration.  
\end{table}
Table 2: Chemical composition of kenaf outer basl with different reaction temperatures of acid hydrolysis.  
Figure 2: Anomeric hydrogens in the NMR spectrum of monosaccharides in kenaf outer basl.  
xplan hydrolysis was faster than cellulose hydrolysis. Furthermore, the acid concentration had been one of the variables in this experiment as an important parameter affecting the hydrolysis treatment to obtain a high yield of cellulose and selective removal of xylan [43]. Figure 3 shows the effect of different sulfuric acid concentrations in the NMR spectrum. The xylose peak decreased with increasing acid concentration. As shown in Table 3, the range of sulfuric acid concentration from 0.25 to 1.00 N remains from 2.2% to 0.8% of hemicellulose. Throughout that reaction, the yield of cellulose decreased from 50.2% to 47.9% at similar conditions. As supposed, increasing acid concentration tends to produce decreased hemicellulose content in the hydrolyzed solid residue. Xylan from hemicellulose decreased from 9.2 to 4.2%, and cellulose content decreased from 65.2 to 61.4%, as sulfuric acid concentration increased from 0.25, 0.5 to 1.00 N at 100 C on 60 min with jute past fiber [6]. The hydrolysis was optimized to dissolved xylan to xylose with assistance from acid concentration as the first step order reaction [44]. Meanwhile, the rate of glucose released slightly increased with the acid concentration. This can be associated with the high catalytic effect at high acid concentrations and the increased acid concentration [38]. Therefore, hemicellulose is more readily hydrolyzed and favorable to increasing the purity of cellulose on mass balance value and producing high cellulose content [43].
# Automatic Brix Measurement for Watermelon Breeding
###### Abstract
The destructive SSC measurement methods are usually performed using the refractive index method, which is accurate and stable enough for breeding tasks [25]. However, sampling watermelon juice out of the pulp is a time-consuming process, which includes manually cutting the watermelon into halves, crushing the pulp to extrude juices, and sampling juice via pipettes. It is labor-intensive and error-prone when handling a huge number of samples. In this study, we developed an automatic SSC measurement system for watermelon breeding in order to improve its efficiency and decrease costs.
## 3 Results and Discussion  
Ferrite can be divided into two main categories, namely, soft ferrite and hard ferrite, according to its applications. Soft ferrite is defined as having an Hc not larger than 200 Oe, when magnetization occurs; hence, the smallest externally applied magnetic field can be used to reach the maximal magnetization strength. The features of soft ferrite include a lower coercive force and higher magnetic permeability; that is, it can be easily magnetized and de-magnetized and is therefore is widely used for electronic materials. Hard ferrite is obtained after the material is magnetized by an externally applied magnetic field, and its magnetic properties are maintained in the long term. The features of hard ferrite include a higher coercive force (the magnetic field strength for the residual magnetization is reduced to zero after the magnetic material is magnetized and de-magnetized), higher residual magnetic flux density (Br), higher residual magnetization (Mr), higher stability (to environmental variation factors, such as externally applied magnetic field and vibration), and higher maximum energy product (Bh).  
In this work, a co-precipitation method was used to prepare nanoscale ferrite, and a metal solution was added with NaOH as a precipitation agent. Then, air was passed to heat up and oxidize the product so that the hydroxide was changed into ferrite with a spinel structure. [M\({}_{x}\)Zn\({}_{1-x}\)]Fe\({}_{2}\)O\({}_{4}\) (M=Cu or Ni) was prepared at different conditions, and the effect on crystal phase, surface morphology, average particulate diameter, and magnetic properties was investigated.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### Fingerprint Feature Extraction  
It is worth mentioning that effective and reliable feature extraction is the most crucial and substantial issue to the final accuracy of the feature matching process, which heavily depends on the perfection of the previous pre-processing steps [20]. The minutiae-based feature extraction and matching approach suffers from critical issues, such as its computational complexity being usually very high. In addition, this approach requires high-quality fingerprint samples, so that when the samples are seriously degraded, this usually results in significant degradation in matching accuracy. On the other hand, the image-based approach can effectively tackle these problems, where the feature detection algorithms are applied to extract features locally from candidate fingerprint images. In this work, an optimized combination of the Harris algorithm and SURF algorithm is presented, which is potentially expected to generate a set of most represented features with high repeatability and excellent matching properties [21].
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 3 Research on the Heat Transfer Model of the Subsea Connector
### Heat Transfer Model of the Seawater Layer between the Hub and Lenticular Sealing Gasket  
Figure 5a shows a schematic diagram of heat transfer between the hub inner surface and the lenticular sealing gasket. The heat transfer between the hub inner surface and the lenticular sealing gasket includes the thermal conduction \(q_{1}\) between the lenticular sealing gasket and seawater, the convective heat transfer \(q_{2}\) of the seawater, and the thermal radiation \(q_{3}\) from the lenticular sealing gasket to the top and bottom hub.  
The gap is narrow and the flow of seawater is extremely low because of the sealing state between the hub and the lenticular sealing gasket. Convective heat transfer on the surface has less influence than thermal conduction and radiative heat transfer, so it can be ignored. As shown in Figure 5b, the model of heat transfer is simplified as a composite heat transfer combining thermal conduction and thermal radiation. The heat transfer  
Figure 4: Relationship between \(\lambda_{e1}\) and the temperature.  
Figure 5: Heat transfer between the hub and the lenticular sealing gasket. (**a**) Schematic diagram of heat transfer; (**b**) Simple diagram of heat transfer.  
quantity of the seawater layer between the lenticular sealing gasket and the hub, \(Q_{3-4}\), can be expressed as:  
\[Q_{3-4}=2\pi L_{2}\cdot\lambda_{k4}\cdot\frac{T_{3}-T_{4}}{\ln(r_{4}/r_{3})}+2 \pi r_{3}\varepsilon_{s}L_{2}\varphi_{3-4}C_{0}\Bigg{[}\left(\frac{T_{3}}{100} \right)^{4}-\left(\frac{T_{4}}{100}\right)^{4}\Bigg{]} \tag{6}\]  
where \(\lambda_{k4}\) is the thermal conductivity of seawater at temperature \(T_{4}\)[18]; \(T_{3}\) is the temperature of sealing gasket outer surface; \(T_{4}\) is the temperature of the top hub inner surface; \(r_{3}\) is the outer diameter of the sealing gasket; \(r_{4}\) is the inner surface of the hub; \(C_{0}\) is the black-body radiation coefficient; \(\varphi_{3-4}\) is the shape factor from the sealing ring outer surface 3 to the flange inner surface 4, and the calculation method is the same as that in Equation (1). The equivalent thermal conductivity of the heat transfer model, \(\lambda_{c2}\), can be expressed as:  
\[\lambda_{c2}=\lambda_{k4}+r_{3}\varepsilon_{s}\varphi_{3-4}C_{0}\Bigg{[}\left( \frac{T_{3}}{100}\right)^{4}-\left(\frac{T_{4}}{100}\right)^{4}\Bigg{]}\frac{ \ln(r_{4}/r_{3})}{T_{3}-T_{4}} \tag{7}\]  
The relationship between equivalent thermal conductivity \(\lambda_{c2}\) and temperature is shown in Figure 6.
## 7 Conclusions  
Using the Morse molecular force field theory, the interaction relationships and underlying mechanisms between the target vehicle and adjacent vehicles are analyzed. The concept of risk fields is introduced, considering the time and spatial risk field superposition effects under the influence of various factors such as vehicle position, speed, and acceleration. A visualized risk quantification method is proposed for the connected and mixed traffic flow environment.  
The risk quantification method can be applied to various interaction behaviors of vehicles in the same lane and across lanes (such as following and lane changing), ensuring that the risk assessment results exhibit smooth transitions at the start and end of driving behavior switches, and provide real-time risk levels faced by the target vehicle during these transitions.  
The method quantitatively computes and visualizes the risk fields generated by the target vehicle and interacting vehicles. After comparing the proposed _MRF_ index with _PET_, _MTTC_, _DRAC_, and _SPF_, it is found that the _MRF_ index can compensate for the shortcomings of traditional risk quantification indicators, which may fail to provide continuous results at conflict points. Additionally, due to the introduction of the half-life parameter, the method can switch between more conservative and more aggressive risk assessments, allowing for a dynamic evaluation of the objective driving risks of drivers with different driving styles.  
The results of interactive vehicle trajectory prediction were applied in the modeling of the driving risk field, enabling the prediction of the future trajectories of interacting vehicles. This allows the model to possess the capability to predict potential driving risks in future time periods. The deviation between predicted risks and actual risks was approximately 5%, indicating that this model can significantly enhance driving safety.  
This study has certain limitations that require further improvement. The applicability of this method in more complex traffic scenarios needs to be further explored, as well as the impact of factors such as road width, curvature, and gradient on the quantification results. Building upon this research, a systematic, multi-dimensional, and comprehensive driving risk quantification framework could be established, providing a safer and more reliable driving environment for connected autonomous vehicles within intelligent connected mixed-traffic flows.  
Conceptualization, Y.C. and D.Q.; methodology, Y.C.; software, T.W.; validation, Y.C., D.Q. and S.C.; investigation, D.S.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [52272311].  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Limitations and Future Work  
We proposed a grip strength reflection method that reflects muscle activity as a new approach to improve VR interfaces. Our findings showed that the proposed VR interface improved users' satisfaction and immersion. However, there were some clear limitations and areas for improvement. First, data acquired from the FDP in this study may have been affected by unintended actions owing to noise from the experimental environment or interference from bodily artifacts [13]. To address these issues, a proposed method to correct the data could be to acquire EMG signals from muscles that may interfere. Several errors caused by body rotation could then be adjusted by attaching IMU sensors. To resolve these issues, we plan to apply the proposed methods to improve the system and develop a system that includes sensory feedback using actuators such as motors or air pumps. In addition, the most explicit limitation was that our database was collected under a controlled laboratory environment with only six subjects. The proposed system should be verified with different subject groups such as age, gender, race, etc., and more subjects should be recruited to generalize the results. In future research, we will verify the system's effectiveness with more subjects and improve the system. Finally, although our research experiments focused on assessing users' satisfaction with the system, inferential statistical methods should be used to evaluate the effectiveness of the UI more reliably to strengthen our conclusions in future work [14; 15].
## 4 Results
### Q5: What Are the Challenges/Future Work Proposed in the Studies Analyzed?  
Table 16 presents the challenges and future work outlined in the analyzed studies, focusing on providing a deeper definition and conceptualization of terms related to SD, such as: (i) happiness, (ii) unhappiness, (iii) motivation, (iv) well-being. Additionally, there is an emphasis on exploring and understanding technical debt from a more human perspective, valuing software developers not only for their work but also as individuals influenced by their environment, emotions, and interpersonal relationships. The goal is to standardize and formalize these concepts to establish a common language for further study.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline \# & **Challenge** & **No. Appearances** & **Ref.** \\ \hline \multirow{3}{*}{1} & Lack of studies that explore technical debt from an & \multirow{3}{*}{1} & \multirow{3}{*}{S21} \\  & individual perspective by focusing on its social & & \\  & and psychological aspects. & & \\ \hline \multirow{3}{*}{2} & Provide a better and deeper understanding of the & \multirow{3}{*}{1} & \multirow{3}{*}{S21} \\  & relationships between appearances and the & & \\ \cline{1-1}  & management of technical debt in developer morale. & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: Challenges and future work proposed in the studies analyzed.  
Figure 13: Classification of causes and consequences of (un)happiness in software development communities.
## 4 Results
### Actual Data Experiment  
To verify the effect of the algorithms, we used the echo data collected from an actual ultrasound imaging system for an experiment. Compared with the simulated echo data, the actual echo data suffers from low Signal-to-Noise Ratio and focus error due to the sound velocity error and energy attenuation in complex tissue, as well as the inaccurate system parameters. Thus, the actual echo image is weaker than the simulation result. The echo images processed by each algorithm are shown in Figures 7 and 8 shows the lateral variation at the depth of 75.6 mm and 88 mm. Table 3 lists the lateral resolution, contrast and gCNR of images with different beamformers. Since it is difficult to distinguish the scattering point and cyst in the image with DS, we only analyze the images with SA, GSC, and GSC-CROSS in the following text. Comparing the gCNR, the value of GSC-CROSS is higher than that of GSC and SA. Thus, the lesion is easier to detect by GSC-CROSS and the contrast is improved indeed. Compared with SA and GSC, the lateral resolution with GSC-CROSS is improved by 56.9% and 54.8%, and the contrast with GSC-CROSS is improved by 65.3% and 97.1%. Moreover, observing the beamforming responses of GSC and SA, we can see that their performance is closed. This is because we set a small value of L, so there are more subarrays used to estimate the covariance matrix, which degrades the GSC beamformer. This experiment shows that even in the situation of high noise, GSC-CROSS can still obtain better lateral resolution and contrast. Moreover, as the subarrays increase, GSC-CROSS can also maintain a good performance which means that it can maintain good performance and stability together. The actual data experiment results prove the effectiveness of the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Mean Intensity in**} & \multirow{2}{*}{**Mean Intensity in**} & \multirow{2}{*}{**Contrast (dB)**} & \multirow{2}{*}{**gCNR**} \\  & **the Cyst Region (dB)** & & & \\ \hline DS & \(-\)45.71 & \(-\)20.02 & 25.69 & 0.968 \\ SA & \(-\)60.09 & \(-\)21.19 & 38.90 & 0.998 \\ GSC & \(-\)58.89 & \(-\)27.34 & 31.55 & 0.996 \\ GSC-CROSS & \(-\)93.54 & \(-\)41.95 & 51.59 & 1.000 \\ \hline \hline \end{tabular} Contrast = mean intensity in the background–mean intensity in the cyst region.  
\end{table}
Table 2: Contrast and gCNR of beamforming responses for different beamforming algorithms.  
Figure 6: Lateral variation at z = 40 mm of Figure 5.
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 2 The Icelandic Power System & the Storm Events
### Extreme Weather Event of December 2019  
There are numerous exogenous threats to the Icelandic power system, most of which are weather phenomena [16]. Those threats that are most relevant to the December 2019 storm are high intensity winds, ice loading, and salt pollution. Analysis by meteorologists in the days prior to the storm anticipated it to be the worst northerly storm within the last 50 years, based on pressure differentials at various altitudes [17].  
One of Landsat's annual targets is to achieve an Average Outage Duration Index (SMS) below 50 min., which is formally defined in [18,19] as:  
\[\text{SMS}=\frac{\sum_{i}E_{i}}{E_{Alls}}\times 8760\times 60\] [min/yr] (1)  
where,  
\(E_{i}\) \(\;\;\;\) unserved energy resulting from each disturbance \(i\) [MWh];  
\(E_{Alls}\) \(\;\;\) total annual served energy [MWh/yr].  
Landsnet's annual performance report for 2019 contains a dedicated subsection describing the storm events [14]. The December storm event was responsible for 81.7 system minutes of the 91.2  
Figure 1: The Icelandic power system, with two 220 kV regions connected by a 132 kV ring.  
system minutes recorded for 2019, which greatly exceeds the annual target. Notably, 80.5 system minutes were attributed to the loss of radially connected loads. In total, 103 transmission poles/towers were damaged in the storm, with additional damage in two substations. The damage to transmission infrastructure was mostly located in the north-west of Iceland, where the wind and icing loads were the greatest. Damage to substations occurred in the North-West and South-East, due to icing and salt pollution. A more detailed analysis of overhead line disturbances during the storm can be found in [3]. Ref [14] reports the most significant outages experienced during the storm.
# Distributed Architectures and Constellations for \(\gamma\)-ray Burst Science
## 4 The Role of Distributed Architectures in Tomorrow's Multi-Messenger Astrophysics
### An Incomplete List of Pathfinder/Precursor Missions
#### 4.3.1 CAMELOT and GRBAlpha  
One of the concepts developed to perform all-sky monitoring and timing-based localization of gamma-ray transients is called CAMELOT: Cubesats Applied for MEasuring and LOcalising Transients [24]. It is a constellation of 3U CubeSats equipped with large and thin (150 \(\times\) 75 \(\times\) 5 mm) CsI(Tl) scintillators read out by SiPM detectors, called multi-pixel photon counters (MPPCs), by [PERSON]. The detectors are placed on two perpendicular walls of the satellites to maximize the effective photon collecting area on a CubeSat of this size.  
The detector concept developed for the CAMELOT mission was first demonstrated on a 1U CubeSat, named GRBAlpha [25]. It carries a smaller, 75 \(\times\) 75 \(\times\) 5 mm CsI (Tl)scintillator, which provides one-eighth of the expected effective area of the 3U CubeSats envisioned for the CAMELOT mission. GRBAlpha was successfully launched on 22 March 2021 to a sun-synchronous polar orbit with a Soyuz 2.1 rocket from Bajkonur. Following a short commissioning phase, the detector was switched on and regular measurements began. The on-board data acquisition software stack is periodically updated in orbit, continuously improving the capabilities of the science payload. The degradation of the SiPM detectors, which are protected by a 2 mm thick lead shield, is being monitored. About half of the polar orbit is plagued by a high particle background, and thus the duty cycle of the detector cannot be better than 50%, even if it is operated continuously. The satellite communicates on amateur radio frequencies and its ground segment is supported by the radio amateur community. The mission also takes advantage of the SatNOGS network for increased data downlink volume. Figure 1 shows GRBAlpha before the launch.  
GRBAlpha became the first nano-satellite to detect multiple confirmed GRBs. At the time of writing, the mission detected five GRBs (four long and one short GRBs [26; 27; 28; 29; 30]), two of them over the course of a single night from 18 to 19 October. Figure 2 shows the light curves of GRB211018A observed by GRBAlpha in different energy bands [30]. GRB detectors developed for the CAMELOT mission are also going to be launched on VZLUSAT-2, which is a technological 3U CubeSat built by the Czech Aerospace Research Centre. The satellite will carry two perpendicular detectors the same size as GRBAlpha. VZLUSAT-2 is expected to be launched in January 2022 into a sun-synchronous polar orbit on a Falcon 9 rideshare mission.  
Figure 1: GRBAlpha, a 1U CubeSAT equipped with a gamma-ray detector (visible on the top, see [25] for details).  
Figure 2: Light curves of GRB 211018A observed by GRBAlpha in different energy bands [30].