esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Uptake Studies  
Chemicals for uptake studies were from Merck KGaA (Darmstadt, Germany) if not stated otherwise. Uptake studies with the prototypical substrate taurocholate, with remdesivir or betaine were carried out at 37 \({}^{\circ}\)C as described [93] either in the presence of sodium or in the absence of sodium with choline chloride as replacement of NaCl. Taurocholate uptake was measured in the presence of a tracer amount of 25 nM [\({}^{3}\)H(G)]-taurocholic acid (740 GBq/mmol; American Radiolabeled Chemicals Inc., St. Louis, MO, USA) and different concentrations of unlabeled taurocholate. Remdesivir (Sellekchem, Houston, TX, USA) uptake was determined by UHPLC-MS/MS as described [94]. Time-dependent uptake of taurocholate and remdesivir was assessed at a concentration of 25 nM and 5 uM,respectively. Kinetic parameters \(\mathrm{K_{m}}\) and \(\mathrm{V_{max}}\) were determined within the linear uptake ranges, i.e., 30 s for taurocholate and 10 min for remdesivir. For the determination of \(\mathrm{IC_{50}}\) values of remdesivir for inhibition of taurocholate uptake, 7 different remdesivir concentrations between 1 uM and 250 uM were used. Betaine accumulation was assessed at a concentration of 100 nM betaine [glycine-2-3H] (740 GBq/mmol, American Radiolabeled Chemicals Inc.) at 1 and 10 min.  
Transport data were analyzed with GraphPad Prism 9.3.0 (GraphPad Software Inc., La Jolla, CA, USA). Time-dependent uptake values of taurocholate and remdesivir were fitted by non-linear regression (least-squares fit) to the following equation as described: [91]\(\mathrm{c_{in}}=\mathrm{k_{in}}/\mathrm{k_{out}}\times\mathrm{c_{out}}\times [1-\exp(-\mathrm{k_{out}}\times\mathrm{t})]\) with \(\mathrm{k_{in}}\) and \(\mathrm{k_{out}}\) being the rate constants for inwardly- and outwardly-directed transport, respectively, \(\mathrm{c_{in}}\) and \(\mathrm{c_{out}}\) being the intracellular and extracellular substrate concentrations, respectively, and \(\mathrm{t}\) being the time. For determination of the kinetic parameters \(\mathrm{K_{m}}\) and \(\mathrm{V_{max}}\), uptake values were at first corrected for uptake in the absence of sodium. Data were then fitted by weighted \((1/\mathrm{y^{2}})\) non-linear regression, using the Michaelis-Menten equation: \(\mathrm{V}=(\mathrm{V_{max}}\times[\mathrm{S}])/(\mathrm{K_{m}}+[\mathrm{S}])\) with \(\mathrm{V}\) being the initial uptake velocity and [S] the substrate concentration. The remdesivir concentrations that achieved half-maximum inhibition (\(\mathrm{IC_{50}}\)) of taurocholate accumulation were also determined by non-linear regression fitting the values to the following 4-parameter equation: uptake(S) = uptake(min) + (uptake(min) \(-\) uptake(max))/(1 + (([S]/\mathrm{IC_{50}})^{\mathrm{slope}}))\) with uptake(S) being the taurocholate uptake at a given remdesivir concentration [S], uptake(min) and uptake(max) the minimal and maximal taurocholate uptake values, respectively, and slope the Hill slope. Taurocholate uptake in the absence of sodium was considered as the minimal uptake and used as a constraint in the calculation of the \(\mathrm{IC_{50}}\) values.
# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## Appendix B Joint MGRF Segmentation
### 3D Spatial Interaction MGRF  
The following Algorithm A3 provides calculations steps used in our segmentation algorithm.  
```
1. Use DTIprep software [34] for motion and eddy current corrections. Images with significant artifacts remaining after preprocessing are removed.
2. Estimate the diffusion tensors from the diffusion-weighted MRI and calculate the 5 DTI features (FA, MD, RA, \(\lambda_{\parallel},\lambda_{\perp}\)) using 3D Slicer [35].
3. Mask off non-brain tissue in the DTI volume (Algorithm A1).
4. Create the raw feature matrix \(\mathbf{B}\) including the five DTI features and b0 base volume.
5. Use a weight matrix \(\mathbf{W}\) (\(\mathbf{W}\) obtained through the training phase according to [42] to transform \(\mathbf{B}\) into H-space (Equation (4)).
6. Construct the subject's shape prior (Algorithm A2).
7. Using the H-space features and shape prior, estimate the region labeling \(\mathbf{m}\).
8. Estimate the Gibbs potentials (Equation (10)).
9. Enhance the region map \(\mathbf{m}\) using voxel-wise Bayes classifier after integrating the three descriptors in the proposed joint MGRF model.
```  
**Algorithm A3** Segmentation Approach Algorithm
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 3 Experiment Design  
### Dataset  
To verify the improvement of the VLSM-Net, the data for this experiment are all obtained from the LUNA16 dataset and the LiTS17 dataset.  
The LUNA16 dataset is a subset of the dataset LIDC-IDRI, which is the largest public lung nodules dataset. After excluding some CT scans that were too thick, there were 888 CT scans and approximately 1,186 nodule states left. Then, preprocessing operations were performed on the data, including CT value cropping, data resampling, and cutting. After that, lung nodules labeled with the same dimensions as the CT scans in datasets were generated according to the annotation. The dataset was randomly assigned as the training set, validation set and test set in the ratio of 8:1:1.  
On the other hand, the LiTS liver dataset contains 131 CT scans. All the data are acquired from different scanners, with size of 512 \(\times\) 512 for each CT image, which are labeled as background, liver, or liver tumor. The same preprocessing operations are performed on the LiTS data, including CT value cropping, data resampling, and cutting. The number of training datasets, validation datasets, and test datasets is 88, 23, and 20, respectively.
## 3 Results and Discussions  
### Suppression of Thermoacoustic Instability  
This study investigated the evolution characteristics of thermoacoustic instability (Figure 4) and compared the intensity of thermoacoustic oscillations at different jet flow rates and gas densities. Figure 4a-d represent jet hole diameters ranging from 2.00 to 3.50 mm. With the increasing jet flow, the sound pressure intensity in the combustion chamber decreases, which is 25.18 Pa lower than the original oscillation amplitude (Figure 4). However, the amplitude of sound pressure does not decrease linearly. At a jet flow below or equal to 4 L/min, the sound pressure amplitude increases with the increasing jet flow, reaching a maximum amplitude of 24.8 Pa. As the jet flow rate exceeds 4 L/min, the sound pressure amplitude decreases, reaching a minimum sound pressure of 10.6 Pa. This indicates a significant inhibitory effect once the jet flow rate exceeds the critical value of 4.0 L/min. However, excessive jet-in-crossflow can cause a flame blowout. The diameter of the jet-in-crossflow influences the intensity of combustion oscillations. The effects of 3.00 and 3.50 mm apertures are more pronounced than those of 2.50 and 2.00 mm apertures. Therefore, this study examined the evolution characteristics of thermoacoustic oscillations at a jet aperture of 3.5 mm. The Ar/He volume ratio significantly affects the combustion oscillation amplitude, which decreases as the Ar/He volume ratio diminishes.  
To further examine the variation characteristics in the flame heat release rate, the Abel inverse transform of the CH* distribution under the stratified Ar/He jet-in-crossflow was analyzed (Figure 5). The jet flow rates are set at 1.0, 3.0, 5.0, and 7.0 L/min, with an Ar/He volume ratio of 0%/100%. The aperture of the stratified Ar/He jet-in-crossflow is set to 3.50 mm. With the increasing jet-in-crossflow rate, the intensity of the flame CH* heat release rate gradually decreases. This indicates that the stratified Ar/He jet-in-crossflow effectively mitigates flame heat release pulsation aggregation and alters the distribution of flame heat release rate pulsation. Before the jet flow is introduced, the flame heat release is concentrated near the burner nozzle. However, as the jet flow rate increases to 7 L/min, the concentrated area of the flame heat release rate becomes more diffuse. According to the variation patterns of sound pressure amplitude (Figure 4), the stratified Ar/He jet-in-crossflow can effectively disrupt the coupling between the flame, sound, and flow, thereby altering the energy transfer pathway between the flame front and sound waves. The stratified Ar/He jet-in-crossflow suppresses the persistence of thermoacoustic oscillations in the combustion chamber.  
Figure 3: Schematic of the flame under stratified argon/helium jet-in-crossflow.
# Robust Learning from Demonstration Based on GANs and Affine Transformation
## 2 Background  
### Movement Primitives  
Methods rooted in machine learning, such as reinforcement learning (RL), and trajectory planning techniques like GMR and movement primitives (MPs), play a pivotal role in implementing such tasks. While RL boasts a plethora of applications, its utilization in industrial settings is often hindered by the substantial computational resources it demands. Traditional trajectory planning methods such as GMR, SEDS, and MPs remain predominant in LfD algorithms, particularly given the computational limitations of mobile platform-based industrial robots. MPs, in particular, show promise as an LfD framework for adapting learned movements to diverse task scenarios, owing to their capacity to capture demonstration variability. This variability elucidates how movements can be adjusted to accommodate different task environments. MPs can be succinctly denoted by the following equation:  
\[\ddot{y}=\alpha_{y}\big{(}\beta_{y}(g-y)-\dot{y}\big{)}+f, \tag{1}\]  
where \(y\) is our system state, \(g\) is the goal, \(\alpha\) and \(\beta\) are gain terms, and \(f\) is an additional nonlinear system that represent the force over time to obtain the desired behaviors.  
Several variants of MPs have been proposed. [PERSON] et al. proposed probabilistic movement primitives (ProMPs) [25] to learn from multiple demonstrations using probabilistic method, [PERSON] et al. proposed the ProDMP [26] method using neural network for the learning of DMP, and [PERSON] et al. proposed the EditMP [27] method using GANs for the learning of high covariance demonstrations. These MPs efficiently learn movements from numerous demonstrations and adeptly adapt learned movements to task scenarios featuring varying target positions. Additionally, [PERSON] [14] proposed an algorithm that amalgamates GMM and MPs, enhancing the adaptability of MPs to multiple demonstrations. However, this technique is constrained to a limited range of conditions, as MPs can only address specific skill types and encounter challenges in generating circular trajectories. Consequently, the DMP method necessitates not only demonstrations but also a corresponding skill segmentation model [28].
## 5 Experimental Results
### Result Discussion  
The proposed WBCA program consists of several program modules. The first module is responsible for defining the primary cybersecurity awareness outcome. The second module is responsible for deriving the result of the initial check, which ensures the information security knowledge of the user. As the simple counting sort algorithm is used for verification, the program does not require significant resources, and the user does not need a powerful technique to use it. Moreover, the proposed WBCA program uses Blockchain technology to avoid any potential threat. The proposed WBCA shows higher performance than competing programs (M-TISM, ISF, FDCA, CSA, and CKS) from the perspective of consistency, quality ratio, user response time, solution efficiency, and threat avoidance capacity shown in Table 2. The proposed WBCA makes it possible to easily determine what level of information security awareness a person has. Regardless of the result, the program hints at the shortcomings of the user in terms of the testing errors and gives recommendations for studying the relevant information security topics. Additionally, a big plus for the user is that the program is free, has an open license, and will always be available for download and further use. If there are sudden problems while using this program, the user can always leave feedback through the developer's email. However, it is worth noting a couple of disadvantages regarding this program. This program only checks the basic information security awareness level of the user. At the moment, the program is not  
Figure 6: (**a**) Threat avoidance capacity of the proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS) with maximum 900 threats. (**b**) Threat avoidance capacity of the proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS) with maximum 1800 threats.  
intended for advanced information security awareness testing. However, this drawback is insignificant, and in the future, people can always add an extended database of advanced questions to it. In the end, we can say that an effective program has been created to identify the current level of information security awareness possessed by any individual.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 4 Bounds on Orientation Estimation
### Model  
In the previous section, we derived bounds on the estimation of the position of antennas \(h_{i}\) with respect to reference antennas \(g_{i}\). For \(N\) target antennas, we form \(M=\begin{pmatrix}N\\ 2\end{pmatrix}\) vectors labeled \(\overline{h_{i}h_{j}}\)\(\forall\)\(i,j\in\{1,2,\cdots,N\}\) and \(i\
eq j\). We now use these estimated positions to estimate the relative orientation of platform \(H\) with respect to platform \(G\) by observing the rotation of the vectors \(\overline{h_{i}h_{j}}\) relative to \(\overline{g_{i}g_{j}}\). Consider the normalized vectors  
\[\overline{b}_{k}=\overline{h_{i}h_{j}}/\left\|\overline{h_{i}h_{j}}\right\|^{ 1/2},\;\overline{v}_{k}=\overline{g_{i}\overline{g}_{j}}/\left\|\overline{g_{ i}\overline{g}}\right\|^{1/2},\;k\in\{1,2,\cdots,M\} \tag{25}\]  
We assume [52] that the unit vectors \(\overline{b}_{k}\) follow a von Mises distribution with mean direction \(\overline{h}_{k}\) and concentration \(\kappa_{k}\), i.e.,  
\[\overline{b}_{k}\sim\mathcal{M}(\overline{h}_{k},\kappa_{k})\quad;\quad \overline{h}_{k}=\mathbf{R}\overline{v}_{k}, \tag{26}\]  
where \(\kappa\geq 0\), \(\|\bar{\mu}\|=1\), and \(\mathbf{R}\) is a proper rotation matrix. For \(\kappa\ll 1\) the von Mises distribution reduces to a uniform distribution in which \(\overline{b}_{k}\) are uniformly distributed on a circle irrespective of the rotation matrix \(\mathbf{R}\). For all other scenarios, if the variance in estimating position \(h_{i}\) and \(h_{j}\) is \(\sigma_{h_{i}}\) and \(\sigma_{h_{j}}\), we consider the circular variance in \(\overline{b}_{k}\) as  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**GDoP** & **1** & **2-3** & **4–6** & **7-8** & **9–20** & **21–50** \\ \hline “Rating” & “Ideal” & Excellent & Good & Moderate & Fair & Poor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qualitative rating of realistic GDoP values.  
Figure 5: Performance manifold defined by Equation (20) as a function of ranging precision and GDoP for N = 4.  
\(\sigma_{\bar{b}_{k}}^{2}=\sigma_{h_{i}}^{2}+\sigma_{h_{j}}^{2}\); hence, the mean resultant length \(\rho_{k}\) of the wrapped distribution and the concentration factor \(\kappa_{k}\) are given by  
\[\rho_{k}=e^{-\sigma_{h_{k}h_{j}}^{2}/2}\quad;\quad\kappa_{k}=A^{-1}(\rho_{k}) \tag{27}\]  
where \(A(\cdot)\) is a ratio of modified Bessel functions.
## 2 Materials and Methods
### Monitoring System
#### 2.2.1 Precursor Gas Measurement  
NH\({}_{3}\) gas concentrations were monitored via a 6-channel gas sampling doser (INNOVA1403) connected to an INNOVA1512i photoacoustic NH\({}_{3}\) gas analyzer (INNOVA1403 & 1512i, LumaSense Technologies, Ballerup, Denmark). Four channels were used to monitor NH\({}_{3}\) concentrations along the length of the manure composing agitation hall. In addition, two gas-sampling channels were installed outside the facility, one located at a height of 3 m above ground level adjacent to a window on the outer wall of the manure composing agitation hall and one located at ground level next to the instrument station. The INNOVA gas-sampling system provided NH\({}_{3}\) concentrations (measurement range: 0-500 ppm, sensitivity: 1 ppb) sequentially across the 6 channels at approximately 45 s intervals. Except where explicitly mentioned, internal NH\({}_{3}\) emissions are reported as averages of the data from the four internal channels.  
Sulfur dioxide (SO\({}_{2}\)) and nitrogen oxide (NO\({}_{\mathrm{x}}\)) gas concentrations (measurement range: 0-500 ppb, sensitivity: 0.1 ppb) were sampled at five-minute intervals at single points inside the manure composing agitation hall, using KENTEK gas analyzers (KENTEK Co., Ltd., Daejeon, South Korea). NO\({}_{\mathrm{x}}\) was measured on a KENTEK MEZUS 210 instrument via chemiluminescence. SO\({}_{2}\) was measured by a KENTEK MEZUS 110 instrument, using an ultraviolet fluorescence method. NO\({}_{\mathrm{x}}\) gas concentrations were only available prior to 4 July 2020. While heated traces were used to avoid moisture condensation inside the Teflon sampling tubes, condensation buildup after this date caused abnormal NO\({}_{\mathrm{x}}\) data readings after 4 July.  
The presented precursor gas concentration datasets measured at the facility were transformed to 15 min averages to reduce noise and to aid cross comparison between data sampled at different time intervals. For comparison with environmental variables and regional PM and gas concentration measurements, precursor gas concentrations were converted to hourly average values to match the data resolution of these externally sourced data.  
Regional hourly SO\({}_{2}\) and nitrogen dioxide (NO\({}_{2}\)) gas concentration data for the duration of the study, measured at local government-operated air quality monitoring sites in Paju, were downloaded from the Air Korea government website ([[https://www.airkorea.or.kr/index](https://www.airkorea.or.kr/index)]([https://www.airkorea.or.kr/index](https://www.airkorea.or.kr/index)) (accessed on 6 September 2020)).  
This study investigated the difference in gas concentrations during daytime and nighttime and between weekdays and weekends. This was because manure agitation only occurs during the daytime at weekdays. Daytime was defined in this study as between 7 am and 7 pm, while nighttime was from 7 pm to 7 am. Weekends were defined as the 48 h from 7 am on Saturday until 7 am on Monday.
## 4 Implementation System and Results
### Unipolar Pulser Generator  
As we mentioned above, the pulse optimization for the probe was done in two parts. The first part, performed on the included PCB design, ensured the integrity of the trigger signals from the FPGA to the STHV800. Since the transducer impulse had a duty of only a few nanoseconds, the rising time portion was also a value of a few nanoseconds. We applied techniques such as calculating the characteristic impedance of the circuit, as in Equations (1) and (3), and calculating the length of the printed circuit and used thevenin resistors to reduce the reflection of the signal. (Altimu Designer software also  
Figure 14: Set-up of a scanning acoustic microscopy (SAM) system with a P/R device. (**a**) A block diagram of the SAM system; (**b**) A real SAM system.  
Figure 13: Block diagram of the program inside the FPGA. (**a**) The main program; (**b**) The pulser program with an internal trigger = 10 kHz.  
provide the signal integrity simulation feature to shorten the time and improve the efficiency of the design). Besides that, we placed decoupling capacitors near the FPGA and the STHV800 to reduce the noise interference when they were working. For the second part, we used a damping resistor for the signal from the STHV800 to the transducer. The value of the damping resistor had been determined by tests because the impedance of the transducers may have differed.  
The trace width and resistor values used in the design are described in Table 3.  
In this test, we measured the unipolar pulse signal generated from the FPGA chip to excite the STHV800 chip and evaluated the effectiveness of the techniques used in the PCB design with this signal. As mentioned above, we used the schematic described in Figure 8 to design the signal from the FPGA to the STHV800 and set the signal of the excitation transducer 50 MHz. The measurement point is denoted as Test_Point. We used a KEYSIGHT oscilloscope, model MSO-X2024A, to monitor the signal.  
After a thorough analysis and comparison of the unipolar pulse signals in Figure 15, we can see that this signal improved. The undershoot part of this signal had been reduced significantly, which was essential in increasing the transducer signal's bandwidth. In Figure 15b, we see clearly that the damping resistor effect reduced the signal amplitude, but at the same time, the ringing part of the signal was also clearly reduced. This had important implications for detecting different layers of the object to be scanned because for many samples with many layers, there are often many echo signals received, and creating the vertical resolution of the sample depends on these echo signals. In Figure 15a, we compare the signal before and after adding damping resistors, the signal from the FPGA to the STHV800 had reduced overshoot and ringing with damping resistor. We could easily change the pulse width by changing the variables in the FPGA with the software to make a signal suitable for different transducers.
## 3 Results and Discussion
### Microbiological Evaluation  
Microbiological loads of treated and non-treated deer meat samples are shown in Figure 1. Initial aerobic plate count of non-treated and treated samples ranged between 3.39 and 3.25 log cfu/g, with counts increasing to a range of 5.30 and 4.55 log cfu/g after 21 days in vacuum packaging. Microbiological limit of shelf-life 7 log cfu/g was not reached in any of the samples.  
The vacuum-packed venison samples treated with 2% LA and 2% AA mixture showed significantly lower aerobic plate count as compared to vacuum-packed non-treated samples at day 21, which can lead to increased time on the shelf. However, in treated samples, slightly lower aerobic plate counts were recorded on days 1, 7, and 14 of the display.  
The difference in the reductions in the microbial loads in different studies by LA and AA might be due to various factors such as dissimilarities in sampling site, sampling methods, acid concentration, temperature of the solution applied, method and type of application, handling, and initial surface load [46]. Therefore, similar results cannot be achieved from the same decontaminant in different studies. In this study, only aerobic bacteria were counted, but [PERSON] et al. [47] showed that lactic acid treatment decreased the number of both spoilage and pathogenic bacteria. Another research study, performed by [PERSON] et al. [48], reported that spraying 2% lactic acid on pre-eviscerated carcass decreased Enterobacteriaceae count by 1 log cfu/g, and Listeria by <1 log CFU/g. [PERSON] et al. [49] showed that microbial populations were not reduced by treatment with ascorbic acid.