esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### NIRS Models Accuracy: Spectral Range  
Biodiverse pastures usually present a greater spectral range due to variations in the species of plants that make up the pasture and the soil background [28]. Lignin, protein, starch, cellulose and hemicellulose, pasture constituents, have fundamental molecular bonds between O-H and C-H. Absorptions above 1960 nm are responsible for the molecular bonds O-H, N-H, O=H and O-H, C-H, C-H and O-H [50], which are precisely the main bonds of pasture quality parameters [28]. [PERSON] et al. [28] consider that bands of visible region (500 to 750 nm) are those where the highest correlations with CP and NDF occur. Similarly, [PERSON] et al. [9] report that in the spectral region between 620 and 1000 nm, CP can be predicted with good results. Also, [PERSON] et al. [43] report that visible region (below 700 nm) represents the bands that correlate best with pasture CP. Furthermore, [PERSON] et al. [28] also refer to the correspondence of near-infrared (800 to 1000 nm) and infrared (1900 to 2400 nm) spectral regions with pasture quality parameters. [PERSON] et al. [44] report that the sensitive spectral bands for CP are located between 505 nm and 1000 nm, and between 2013 nm and 2420 nm, with absorption peaks at 1500 nm and 1935 nm. [PERSON] et al. [26] and [PERSON] [25] also highlight the importance of 2100 to 220 nm bands for estimating pasture CP. These data can help to explain the low R\({}^{2}\) values for CP obtained in this study, since the spectral range of useful micro = NIR is restricted to wavelengths between 1093 nm and 1670 nm.  
Despite this limitation in the range of portable spectrometer, [PERSON] et al. [51] concluded that this equipment is sufficiently robust for directly application in the field. In this study the good correlations obtained for NDF (R\({}^{2}\) of 0.73 and 0.69 for calibration and validation, respectively) open up good perspectives for practical application, given the importance of fibre in animal feed, particularly for ruminants. These better results obtained with fibre, relative to CP, may be due to the fact that the most important spectral region for the prediction of NDF varies between the 1370 nm and 1418 nm [37] spectral bands provided by the micro-NIR. Also, according to [PERSON] et al. [26] and [PERSON] and [PERSON] [25] the spectral regions between 1650-1670 nm and 2260-2280 nm are correlated with C-H molecular bonds for lignin and cellulose, which may help to explain the better correlations with NDF in this study, since lignin and cellulose are two of the main constituents of this parameter. The micro NIR sensor used in this study covers the first of these wavelengths' ranges.
## 4 Results and Discussion
### Power Consumption Evaluation of the Three DKF Algorithms: Scenario 1  
In this sub-section we examine the power consumption of the three selected DKF algorithms by presenting and discussing the results obtained from both simulations and  
Figure 4: Proposed fully distributed, real-time and low-power leak detection solution for WWPM using low-cost MEMS accelerometers.  
physical measurements. We start by presenting the results from simulations in Section 4.1.1 then validate them with results from laboratory experiments in Section 4.1.2.
# Performance of 5G Trials for Industrial Automation
## 2 Industrial Use Cases and Requirements
### Kista Trial Site  
At the Kista trial site, 5G-enabled industrial robotics are being validated in three distinct use cases, out of which the following two are most relevant for the performance evaluation carried out in this article: 5G-enhanced collaborative robots and human-robot interaction. The setup is located in an Ericsson smart factory. The setup consists of two stationary robots and a mobile robot, which in the first use case, collaboratively solve a task where material needs to be transported between to robot workstations. The robots are integrated over 5G, allowing major parts of the robot control functionality to be removed from the robot itself and to be placed into the edge cloud. Machine vision via a video system and video analysis in the edge cloud support robot collaboration.  
Interconnecting robots over 5G infrastructure and the edge cloud has several benefits, e.g., improved production organization flexibility on a factory floor, support for accelerated changes in the production processes, or a reduced robot footprint by miniaturizing the hardware that needs to stay onboard the robot units.  
Another use case aims to explore 5G-enhanced human-robot interaction. One aspect that is considered is whether safe real-time human-robot interaction should be allowed, e.g., a technician operates on the factory floor while mobile robots safely move around the technician. Another aspect considers enhancing real-time human-robot interactions, e.g., a technician demonstrates an arm motion to a robot by performing contactless teaching, so called lead-through programming. In both cases, robot motion planning software is deployed at the edge cloud and is fed by a video camera system, providing the robot with vision capabilities.  
Offloading robot software and advanced industrial robotics applications from its dedicated hardware to a common edge cloud platform puts stringent requirements on the reliability and latency of the 5G communication network. The requirements on bounded communication latency and communication service availability of the use cases at the Kista trial site are listed in Table 1. Further details of the use cases can be found in [8; 9].
## 5 Speed Considerations  
Our system further implements the capability to monitor the speed of passing vehicles. Considering that some countries adjust passing distance rules based on vehicle speed, this feature is significant and would offer deeper insights into cyclist and vehicle safety interactions.  
To estimate the speed of a passing car from video footage, we first need to determine the pixel-to-real-world scale for the different passing distances between the bike (on which the camera is mounted) and the car. This can be carried out by measuring a known object in the image and then dividing the pixel length of the object by its real-world length. The pixel-to-real-world scale is inversely proportional to the distance of the camera from the object. From an experiment using an OAK-1 camera and equally spaced checkerboard images on a vertical wall as shown in Figure 17, we show that the number of pixels on a camera image that corresponds to a horizontal line of length 1 m is almost inversely proportional to the distance of the camera from the wall, \(d\) in Figure 18.  
Thus, for any horizontal displacement of length \(\Delta p\) pixels on an OAK-1 camera image, the actual displacement, \(s\), as illustrated in Figure 19, is given as follows:  
\[s=\Delta p\bigg{(}\frac{1}{f}d\bigg{)}=\Delta p\bigg{(}\frac{1}{1033.8}d\bigg{)} \tag{6}\]  
Figure 17: Checkerboard images at 1 m horizontal spacing on a vertical wall. The cyan laser lines are used to guide the placement of images.  
Figure 18: A trend line through the data points shows a near-perfect inverse relationship between pixel-to-real-world scale and working distance.  
where \(d\) is the distance between the camera and the car, and f is the focal length of the camera, which can be determined from a regression analysis of the experimental data.  
From video footage of a passing car, we can extract the image frames and the time of each frame. We can then calculate the displacement of the car, \(s\), between any two frames using Equation (6). The speed of the car relative to the bike, \(v\), can be estimated by using \(v=\frac{s}{\Delta t}\), where \(\Delta t\) is the time difference between the two frames. The speed estimation can also be performed automatically using image recognition by tracking the displacement of the bounding box of the passing car (or part of the car such as the front wheel). Figure 20 highlights the minor discrepancies between manually measured distances in car images and those computed through image recognition. From experiments, the errors in the displacement estimates are less than 5%. This works out to be 2 km/h for a relative speed of 40 km/h.  
Figure 21 shows an experiment to estimate the speed of a vehicle passing a stationary camera using the methods described above and Figure 22 shows the speedometer readings at test runs 1 and 2, respectively. The calculated values show close agreement with the speedometer readings (Table 10).  
Figure 21: Estimation of vehicle speed from image frames extracted from video.  
Figure 19: Camera projection model.  
Figure 20: Comparing manually measured and image recognition-calculated displacements.
## 3 Results and Discussion
### Extraction of Chitosan  
The dried _Portunus trituberculatus_ shell and _Illex argentines_ squid gladius were used as raw materials respectively. After demineralization, chitosan was directly prepared by high pressure heating in alkali water mixture. Deproteinization and Deacetylation were carried out simultaneously by \"one pot cooking\". The specific preparation scheme of chitosan is presented in Figure 1. Chitosan yields (Table 1) of _Portunus trituberculatus_ shell and _Illex argentines_ squid gladius were 11.8% and 26.1% respectively on the basis of dry weight calculation. [PERSON] et al. [34] reported that the yields of chitosan obtained from shrimp, crab and cuttlefish were 14.9%, 5.3% and 1.2%, respectively. [PERSON] et al. [31] reported that the yield of \(\beta\)-chitosan from European squid (_Loligo vulgaris_) was 19.5% based on wet weight. [PERSON] et al. [18] reported that the yield of chitosan extracted from gladius of _Illex argentines_ squid was 23.5%-34.5% under different pressure conditions.  
Figure 1: Flow scheme for the isolation of marine-derived chitosan.
# Molecular Subtyping and Outlier Detection in Human Disease Using the Paralique Algorithm
## 3 Experimental Results
### A Search for Unrecognized Subtypes
#### 3.2.4 Colorectal Cancer  
The incidence of colorectal cancer has been in decline since the mid 1980s [55]. Despite this significant drop in prevalence, it still accounts for both the third highest number of new cases of cancer, and the third highest number of cancer deaths each year [56]. As with breast cancer, there are known hereditary links to this disease. For example, a mutation of the gene APC is responsible for two syndromes, Familial Adenomatous Polyposis and Hereditary Nonpolyposis Colorectal Cancer, that each carry a significant increase in the risk of developing colorectal cancer [57].  
GEO series GSE9348 data were again derived from the Affymetrix U133 Plus 2 array, and were intended to search for transcriptomic signatures of early stage colorectal cancer that is prone to metastasis [58]. The study contains gene co-expression data that were taken from 70 colorectal cancer patient tumors, as well as tissues from 12 healthy subjects who were matched by age and ethnicity.  
Filtering reduced the number of probes from 54,675 to 22,968. At a threshold of 0.87, our tools produced two paralogues of size 63 and 5, covering all but two of the case samples. The list of 100 genes most differentially expressed between these two putative subtypes include Cd24, identified as a prognostic marker for colorectal cancer [59] as well as OLPM4, indicated in as a marker for tumor differentiation and progression [60; 61].
## 5 Conclusions  
We determined the MK model parameters \(\alpha_{0}\), \(R_{n}\), and \(r_{d}\) by minimizing the variance between the reported in vitro SFs of HSGc-C5 cells exposed to monoenergetic \({}^{4}\)He-, \({}^{12}\)C-, \({}^{16}\)O-, and \({}^{20}\)Ne-ion beams and the calculated SF values based on the measured microdosimetric lineal energy spectra of these ion beams, when the \(\beta\) value obtained from 200 kVp X-rays SF studies was adopted. For the SOBP of \({}^{4}\)He-, \({}^{12}\)C-, \({}^{16}\)O-, and \({}^{20}\)Ne-ion scanning beams, planned by the TPS to attain a 10% SF of the HSGc-C5 cells over the target, the microdosimetric spectra were measured along the SOBP utilizing the SOI MicroPlus-mushroom microdosimeter. The SF of HSGc-C5 cells exposed to each SOBP of the beam was able to be estimated based on the measured doses and microdosimetric lineal energy spectra. Our result proved that the SOI MicroPlus-mushroom microdosimeter is capable of predicting the biological response of HSGe-C5 cells for each ion beam in conjunction with the \(y\) -based MK model and can be used for RBE-related beam quality assurance in multi-ion therapy.  
Conceptualization, T.I.; methodology, T.I.; software, L.T.T., A.B.R., V.P. and T.I.; validation, S.H.L., K.M., S.Y., T.I. and M.S.; formal analysis, H.M., T.N., S.M. and R.K.; investigation, S.H.L. and T.I.; resources, M.S., A.K. and M.T.; data curation, A.B.R. and T.I.; writing--original draft preparation, S.H.L.; writing--review and editing, L.T.T., A.B.R. and T.I.; visualization, S.H.L.; supervision, T.I.; project administration, T.I., Y.I. and T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JSPS KAKENHI, Grant Number JP20K16776.  
Not applicable.  
We would like to thank the Accelerator Engineering Corporation for operating the HIMAC accelerator proficiently and for their passionate support. We thank the Australian Research Council (ARC) for the assistance of this project with a Discovery Project Grant \"Development of radiation detectors to better understand ion interactions\" (DP 170102273).  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Sensory Flavor Differences in SDT Across Different Regions  
Based on the comprehensive analysis of the review results, a flavor wheel (Figure 1A) and corresponding aroma and taste radar charts (Figure 1B,C) for the Yunnan large-leaf variety of SDT were developed. The analytical results revealed significant regional variations in the color, aroma, and taste profiles of SDT across the 13 investigated regions in Yunnan Province. Regarding chromatic characteristics, the dry tea leaves exhibited a predominant coloration spectrum ranging from dark green to brownish-green, with tightly rolled and substantial leaf morphology. The tea infusion demonstrated a characteristic bright yellow to orange hue, while the brewed leaves displayed a yellowish-green to brownish-green coloration with a soft and lustrous appearance. In the context of tea quality assessment, flavor and aroma profiles emerged as critical evaluation parameters, constituting primary determinants in the comprehensive quality evaluation system of SDT. These sensory attributes were found to play a predominant role in the overall quality assessment framework, underscoring their significance in the organoleptic evaluation of SDT.  
Figure 1: Sensory flavor characteristics of SDT: flavor wheel (**A**); taste radar graph (**B**); aroma radar graph (**C**).  
In terms of the taste of the tea, the BDao, GFZ, BKN, SH, MA, MN, and XN samples exhibited heavy and thick attributes, of which the BDao, GFZ, and BKN samples had a strong sweet aftertaste, the MA, MN, and XN samples had an obvious or stronger bitterness and astringency, and the SH sample had a more astringent taste. The BS, PS, WLS, BD, DBS, and DG samples exhibited sweet and mellow attributes, with a strong sweet aftertaste. Among these, BS and PS had a sweet, mellow, and slippery taste with some astringency; BD, DBS, and DG had a sweet and smooth taste, with BD and DBS being more mellow and brisk.  
In terms of the aroma of the tea, the WLS, MA, and MN samples predominantly exhibited clean sweetness and a honey aroma. The GFZ and PS samples predominantly exhibited clean sweetness, along with floral and honey aromas. The BKN sample predominantly exhibited a clean and refreshing aroma, while SH and XN were mainly characterized by sweet and honey aromas. The BD and DBS samples predominantly exhibited sweet, floral, and honey aromas, while BS had a mainly clean sweetness. The DG and BDao samples predominantly exhibited sweet and tender aromas.
## 5 Experimental and Results
### Results and Discussion
The disturbance response of the output is too slow to achieve the ideal response. The motor speed does not recover immediately to the steady-state condition when the motor is loaded and is affected by a disturbance. High-responsivity and optimal disturbance rejection cannot be achieved when using the initial PI-P gain controller parameters. Figure 9 shows the \(q\)-axis current \(i_{q}\) and the plant input current \(i_{q}^{*}\). This is the minor current response using the initial PI-P gain controller. The transfer function of the minor current loop is assumed as one, and this paper mainly focuses on the speed control loop. The errors between the reference \(M(k)\) and the initial output \(y_{0}(k)\) are minimized using Equation (45), where the errors between the reference \(M(k)\) and \(y_{0}(k)\) are:  
\[\widetilde{e_{My}}=M\left(k\right)-y_{0}\left(k\right), \tag{45}\]  
The initial output data \(y_{0}(k)\) are composed of the step response and disturbance response data; it assumed that:  
\[y_{0}\left(k\right)=M_{1}\left(z\right)\widetilde{r}\left(q,k\right) \tag{46}\]  
The errors between the reference \(M(k)\) and \(y_{0}(k)\) are thus:  
\[\widetilde{e_{My}}\left(k\right)=M\left(k\right)-M_{1}\left(z\right) \widetilde{r}\left(q,k\right) \tag{47}\]  
To obtain the optimal parameters for the optimal step response and disturbance response, the performance index is minimized using:  
\[J\left(q\right)=\sum_{k=1}^{N}\left(\widetilde{e_{My}}\left(k\right)\right)^{2} \tag{48}\]  
\[J\left(q\right)=\sum_{k=1}^{N}\left(M\left(k\right)-M_{1}\left(z\right) \widetilde{r}\left(q,k\right)\right)^{2} \tag{49}\]  
Reference model data \(M(k)\) are composed of step reference data \(M_{1}(k)\) and disturbance reference data \(M_{2}(k)\). For step reference data \(M_{1}(k)\) in the range \(1\leq k\leq 3286\) (\(r(k)=\)1 and \(d(k)=0\)), the performance index is evaluated from reference \(r(k)\) to controlled output \(y(k)\) using step reference model \(M_{1}(2)\) and fictitious reference signal \(\widetilde{r}\left(q,k\right)\). For disturbance data \(M_{2}(k)\) in the range \(3287\leq k\leq 5001\) (\(r(k)=0\) and \(d(k)=1\)), disturbance moves virtually to the reference using \(r\left(k\right)^{\prime}=\frac{d(k)}{C_{1}\left(q_{1}z\right)}\), so the performance index is evaluated from reference \(r\left(k\right)^{\prime}\) to controlled output  
Figure 9: Initial input data \(u_{0}(k)\) and \(q\)-axis current \(i_{q}\). \(y(k)\) using step reference model \(\frac{M_{1}(z)}{C_{1}(q_{1},z)}\) and fictitious reference signal \(\widetilde{r}\left(q,k\right)\), where \(\frac{M_{1}(z)}{C_{1}(q_{1},z)}\) is the disturbance reference model \(M_{2}(z)\). The performance index is minimized for both step response data and disturbance response data at the same time, as shown in Equation (49) in the range \(1\leq k\leq 5001\), so optimal controller parameters can be obtained for step response and disturbance response following the step reference model and disturbance reference model.
## 4 Parameters Sensitivity Analysis
### Mobility Ratio Between Outer and Inner Regions  
Figures 6 and 7 present the impact of mobility ratio between outer and inner region regions on the production performance curves and pseudotime factor respectively. In these scenarios, we keep the fracture permeability \(k_{\text{fh}}\) the same while changing the value of \(k_{\text{m2h}}\) in order to create different mobility ratio. It can be seen that with all other parameters kept constant, the production curves and pseudotime curves exhibit similar characteristics as in Figures 4 and 5. The larger the value of \(M\), the higher production rate, as \(M\) reflects the flow capacity of region 2 compared to region 1. Thus, there will be more supplement into region 1 to hold a high production rate when \(M\) is bigger. With the increase of mobility ratio, the diffusivity ratio between the outer and inner regions will also increase, and the pressure wave propagates faster, which leads to a notable decrease of pseudotime factor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Reservoir temperature, T (K) & 350 \\ Initial reservoir pressure, \(p_{\text{i}}\) (MPa) & 45 \\ Inner region radius, \(R_{\text{1}}\) (m) & 100, 200, 300, 400 \\ Reservoir radius, \(R_{\text{e}}\) (m) & 800 \\ Reservoir thickness, \(h\) (m) & 30 \\ Well perforation height, \(h_{\text{w}}\) (m) & 5, 10, 15, 20 \\ Initial fracture horizontal permeability, \(k_{\text{hi}}\) (\(10^{-3}\)\(\upmu\)m\({}^{2}\)) & 1.5 \\ Initial matrix horizontal permeability, \(k_{\text{m2 hi}}\) (\(10^{-3}\)\(\upmu\)m\({}^{2}\)) & 0.2 \\ Interporosity flow coefficient between fracture and matrix, \(\lambda\) & 0.01 \\ Storativity ratio of natural fracture, \(\omega\) & 0.05 \\ Mobility ratio between outer and inner regions, \(M\) & 0.1, 0.2, 0.3, 0.4 \\ Diffusivity ratio between outer and inner regions, \(\delta\) & 0.05 \\ Permeability anisotropy factor for fracture, \(\kappa_{1}\) & 1, 10, 100, 1000 \\ Permeability anisotropy factor for matrix in outer region, \(\kappa_{2}\) & 20 \\ Permeability stress-sensitivity coefficient, \(\gamma_{\text{g}}\) (MPa\({}^{-1}\)) & 0.01, 0.03, 0.05, 0.07 \\ Specific gravity, \(\gamma_{\text{g}}\) & 0.6 \\ Critical pressure, \(p_{\text{c}}\) (MPa) & 5 \\ Critical temperature, \(T_{\text{c}}\) (K) & 195 \\ Wellbore radius, \(r_{\text{w}}\) (m) & 0.1 \\ Well bottom-hole pressure, \(p_{\text{wf}}\) (MPa) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The reservoir, fluid, and well parameters for sensitivity analysis.  
Figure 4: The effect of inner region radius on production performance curves.  
Figure 5: The effect of inner region radius on pseudotime factor.
## 3 Validation
### Evaluation of Numerical Performance (in Terms of DOFs Reduction)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{_ka = 2.0_} & \multicolumn{2}{c}{_ka = 3.0_} & \multicolumn{2}{c}{_ka = 4.0_} & \multicolumn{2}{c}{_ka = 5.0_} \\ \hline
**Array Size** & \(DOF_{FE-BE}\) & \(DOF_{FullFE}\) & \(DOF_{FE-BE}\) & \(DOF_{FullFE}\) & \(DOF_{FE-BE}\) & \(DOF_{FullFE}\) & \(DOF_{FE-BE}\) & \(DOF_{FullFE}\) \\ \hline \(N_{C}=24\), \(N_{R}=4\) & \(0.5576\times 10^{5}\) & \(0.1943\times 10^{7}\) & \(1.232\times 10^{5}\) & \(0.5055\times 10^{7}\) & \(2.197\times 10^{5}\) & \(1.109\times 10^{7}\) & \(3.408\times 10^{5}\) & \(2.102\times 10^{7}\) \\ \hline \(N_{C}=36\), \(N_{R}=6\) & \(1.251\times 10^{5}\) & \(0.5805\times 10^{7}\) & \(2.763\times 10^{5}\) & \(1.621\times 10^{7}\) & \(4.931\times 10^{5}\) & \(3.642\times 10^{7}\) & \(7.648\times 10^{5}\) & \(6.968\times 10^{7}\) \\ \hline \(N_{C}=48\), \(N_{R}=8\) & \(2.220\times 10^{5}\) & \(1.287\times 10^{7}\) & \(4.907\times 10^{5}\) & \(3.744\times 10^{7}\) & \(8.757\times 10^{5}\) & \(8.517\times 10^{7}\) & \(13.58\times 10^{5}\) & \(16.38\times 10^{7}\) \\ \hline \(N_{C}=60\), \(N_{R}=10\) & \(3.467\times 10^{5}\) & \(2.411\times 10^{7}\) & \(7.664\times 10^{5}\) & \(7.198\times 10^{7}\) & \(13.68\times 10^{5}\) & \(16.51\times 10^{7}\) & \(21.22\times 10^{5}\) & \(31.84\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Degrees of freedom (DOFs) of the FE-BE coupling model and full FE model. respectively. As shown in Figure 16, higher frequency and a larger size of the array result in higher values of \(DOF_{FullFE}/DOF_{FE-BE}\).