esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Simulation Results
### Temperature Field Distribution of Different Lining Insulation Materials
#### 3.2.2 Incident Heat Flux  
The incident heat flux is a reflection of the heat intensity received on the surface of the material. Figure 15 shows the variation of the incident heat flux of each model at 10 m from the tunnel ceiling. It can be seen from the figure that the incident heat flux peak of the calcium silicate plate (Model 1) is the highest, and the value is between 200-350 kW/m\({}^{2}\). Followed by protective coatings (Model 2), the peak range is 125-300 kW/m\({}^{2}\); the incident heat flux peaks of MgO and SiO\({}_{2}\) gel are the smallest, at 105-200 kW/m\({}^{2}\) and 100-175 kW/m\({}^{2}\), respectively. After about 450 s, there are occasional peaks around the tunnel ceiling and side walls, but this phenomenon is not continuous.  
When comparing the effects of different thermal insulation materials on tunnel fires, it is found that the incident heat flux of tunnels using other thermal insulation materials, except for magnesium oxide and SiO\({}_{2}\) gel materials, usually shows higher incident heat flux than tunnels without any protective measures. However, from the perspective of overall fire resistance, a high incident heat flux peak is not equivalent to good thermal insulation performance. On the contrary, lower incident heat flux can effectively reduce heat transfer and improve fire resistance. Calcium silicate enhances the thermal feedback of the tunnel due to its high emissivity, resulting in an increase in temperature. In contrast, the peak value of the incident heat flux of the SiO\({}_{2}\) gel material is much lower than that of the calcium silicate, which indicates that the SiO\({}_{2}\) gel material can absorb and disperse the heat radiation more effectively, reduce the heat transfer to the tunnel ceiling and its rear structure, protect the tunnel structure from thermal stress damage, improve the overall fire resistance of the tunnel, and reduce the difficulty and cost of subsequent repair work.  
Figure 14: Temperature distribution of different materials 10 m downstream of a fire.  
In summary, it can be concluded that under the same fire intensity and other boundary conditions, the incident heat flux is proportional to the gas temperature and the emissivity of the material. Therefore, high emissivity and high gas temperature will increase the incident heat flux density and further affect the fire resistance of the tunnel.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Quantitative Accuracy Assessment
As discussed in the visual analysis of classification results, the confusion among four classes in Table 3 (particularly between highland Kimchi cabbage and cabbage) was significantly reduced, yielding increases in both PA and UA for all classes. When the August image with VNIR only was considered for classification, the similar vegetation vitality of highland Kimchi cabbage, cabbage, and weeds within the fallow class resulted in severe confusion. By accounting for texture features with spectral information, the confusion could be reduced. However, PA and UA of cabbage were relatively lower than that of other crops, indicating a persistent misclassification of cabbage to highland Kimchi cabbage. When multi-temporal images were used for classification, the accuracy values of all classes increased, particularly with cabbage. Texture features with multi-temporal spectral information proved most useful in the cabbage class because it alleviated the misclassification of cabbage to highland Kimchi cabbage. Based on all evaluation results in Figure 6 and Table 3, it can be concluded that texture information extracted by the proper kernel size can improve classification performance, and the impact of using texture features is most significant when using a single image for crop classification. The latter finding implies the usefulness of texture information when only one UAV image is available for crop classification, due to difficulty acquiring time-series UAV images in the area of interest.
## 4 Case Study
### Results and Discussion
#### 4.2.5 The Impact of the Sources of Training Data on Prediction Accuracy  
Given that the influence of a foundation pit on its surrounding ground settlements gradually weakens with the increase in the distance to the outer edge of the foundation pit, the influence zone of a foundation pit, according to the Chinese standard [49; 50], can be divided into a strong influence zone, i.e., the area with its distance to the outer edge of the foundation pit less than 0.7 times the depth of the foundation pit, and the weak influence zone, i.e., the area with its distance to the outer edge of the foundation pit larger than 0.7 times the depth of the foundation pit, as illustrated in Figure 10. The impact of the source of training data on prediction accuracy was investigated by comparing prediction accuracies of three BPNNs, namely M1, M2, and M3, where these three models were trained using data collected from the strong influence zone, the weak influence zone, and both zones, respectively. The size of the training dataset for each BPNN was 6000. In the proposed case study, 350-1, 350-2, and 350-3 were located in the strong influence zone, while 350-4 and 350-5 were located in the weak influence zone, and the prediction results for these five settlement observation points were shown in Figure 11 and Table 4.  
Comparing the average prediction errors of M1 and M2, it can be found that the prediction error of M1 was smaller than the prediction error of M2 in the strong influence zone, while the opposite was true in the weak influence zone. Specifically, the values of \(\overline{E_{a}}\) of M1 and M2 in the strong influence zone were 0.80 mm and 0.94 mm, respectively, while the values of \(\overline{E_{a}}\) of M1 and M2 in the weak influence zone were 0.52 mm and 0.44 mm, respectively. This result indicates that the source of training data can markedly affect the accuracy of BPNNs in predicting ground settlements in different zones. Additionally, the average prediction error of M3 was smaller than the average prediction errors of M1 and M2 at all five settlement observation points. Thus, it can be concluded that enlarging the source of training data can aid in the improvement of BPNNs' settlement prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Settlement Observation Point**} & \multicolumn{3}{c}{**M1**} & \multicolumn{3}{c}{**M2**} & \multicolumn{3}{c}{**M3**} \\ \cline{2-7}  & \(\overline{E_{a}}\) **(mm)** & \(\overline{E_{p}}\) **(\%)** & \(\overline{E_{a}}\) **(mm)** & \(\overline{E_{p}}\) **(\%)** & \(\overline{E_{a}}\) **(mm)** & \(\overline{E_{p}}\) **(\%)** \\ \hline
350\(-\)1 & 0.87 & 6.22 & 1.02 & 7.31 & 0.78 & 5.77 \\
350\(-\)2 & 0.83 &Figure 11: _Cont._  
Figure 10: Diagram of the strong influence zone and the weak influence zone.  
Figure 11: Cont.
## 2 Materials and Methods
### Embedding Using PLMs
#### 2.2.1 ProtBERT-BFD  
ProtBERT-BFD is based on the BERT language model that is trained on the BFD [52] (Big Fantastic Database) with over 2 billion protein sequences or 393 billion tokens. The number of layers was increased compared with the original BERT model. The features are extracted (embedding) from the last hidden state of the PLM with a size of \(1024\times n\), where \(n\) is the length of the peptide sequence (\(n=25\) for Ngly dataset and 31 for Kgly dataset).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PLM** & **Dataset** & **Pretrained LM** & **\# of Layers** & **Hidden Layers Size** & **\# of Attention Heads** & **\# of Parameters** \\ \hline ProtBERT-BFD [46] & BFD & BERT & 30 & 1024 & 16 & 420 M \\ ProtBERT [46] & UniRef100 & BERT & 30 & 1024 & 16 & 420 M \\ ProtALBERT [46] & UniRef100 & Albert & 12 & 4096 & 64 & 224 M \\ ProtXLNet [46] & UniRef100 & XLNet & 30 & 1024 & 16 & 409 M \\ ESM-1b [47] & UniRef50 & BERT & 33 & 1280 & 20 & 650 M \\ TAPE [48] & Pfam & BERT & 12 & 768 & 12 & 92 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key differences between the six used protein language models.  
Figure 2: Window size of sample sequence. The size of the window is \(n=2*w+1\) in which \(w\) residues surrounding the PTM site from both left and right sides.
## 6 Discussions  
In this paper, four different methods were used to predict CLI from rock mechanical properties including UCS, BTS, density, and brittleness. For this purpose, a database of measured CLI and other rock mechanical properties was established and used in the analysis leading to the development of predictive models. These models were subsequently compared to each other via statistical indices. The analysis showed that the models offered a reasonable estimate of CLI, which can be used for the estimation of disc cutter consumption in the NTNU model. Table 14 presents the comparative statistical indices for the four methods used for developing the models.  
Since the output of the models, CLI, is sensitive to each rock property, obtained models give very similar results and it is not easy to choose the best among them. However, some of the models offer superior predictions compared to others depending on the number of input variables. For example, the models having two inputs including D and BI can offer reasonable results with only two parameters, however, BI requires punch penetration testing that is not very common. Yet, most of the models produced acceptable estimates of CLI.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Inputs**} & \multicolumn{2}{c}{**BI**} & \multirow{2}{*}{**UCS**} & \multicolumn{4}{c}{**UCS**} \\ \cline{2-2} \cline{5-8}  & & **BTS** & & **UCS** & & **D** & **UCS** & **BTS** & **D** \\ \cline{2-2} \cline{5-8}  & **D** & **D** & & **BTS** & & **UCS** & **BI** & **BI** & **BI** \\ \hline Linear & 1 & 2 & 4 & 4 & 5 & 6 & 7 \\ \hline R & 0.84 & 0.82 & 0.73 & 0.77 & 0.65 & 0.77 & 0.78 \\ R\({}^{2}\) & 0.71 & 0.67 & 0.53 & 0.60 & 0.42 & 0.59 & 0.62 \\ MSE & 135.97 & 148.57 & 212.52 & 171.47 & 289.83 & 201.65 & 159.59 \\ RMSE & 11.66 & 12.19 & 14.58 & 13.09 & 17.02 & 14.20 & 12.63 \\ VAF & 70.53 & 67.48 & 53.26 & 59.74 & 42.03 & 58.56 & 61.62 \\ \hline Non-L & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ \hline R & 0.85 & 0.83 & 0.79 & 0.76 & 0.69 & 0.81 & 0.79 \\ R\({}^{2}\) & 0.72 & 0.69 & 0.62 & 0.58 & 0.47 & 0.66 & 0.62 \\ MSE & 124.88 & 138.22 & 154.67 & 167.51 & 235.07 & 159.15 & 154.71 \\ RMSE & 11.18 & 11.76 & 12.44 & 12.94 & 15.33 & 12.62 & 12.44 \\ VAF & 72.33 & 68.86 & 62.39 & 57.58 & 47.30 & 66.01 & 62.07 \\ \hline ANN & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ \hline R & 0.75 & 0.83 & 0.76 & 0.80 & 0.73 & 0.73 & 0.84 \\ R\({}^{2}\) & 0.57 & 0.68 & 0.57 & 0.64 & 0.54 & 0.53 & 0.71 \\ MSE & 168.89 & 137.79 & 173.64 & 186.01 & 193.45 & 301.09 & 145.67 \\ RMSE & 13.00 & 11.74 & 13.18 & 13.64 & 13.91 & 17.35 & 12.07 \\ VAF & 56.78 & 68.12 & 57.31 & 64.12 & 53.75 & 51.36 & 69.99 \\ \hline FL & 1 & 2 & 4 & 4 & 5 & 6 & 7 \\ \hline R & 0.82 & 0.79 & 0.70 & 0.77 & 0.68 & 0.75 & 0.79 \\ R\({}^{2}\) & 0.68 & 0.63 & 0.48 & 0.60 & 0.47 & 0.57 & 0.63 \\ MSE & 170.22 & 195.74 & 272.03 & 212.35 & 280.70 & 226.94 & 197.14 \\ RMSE & 13.05 & 13.99 & 16.49 & 14.57 & 16.75 & 15.06 & 14.04 \\ VAF & 67.71 & 62.87 & 48.39 & 59.72 & 46.75 & 56.95 & 62.60 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of accuracy of the models based on obtained statistical performance indices.
## 2 Materials and Methods  
### Pulsed Terahertz Measuring System  
The imaging device utilized in this work is presented in Figure 1. It is a pulsed terahertz system (TDS) based on Tray-4000 spectroscopic of Picometrix (Ann Arbor, MI, USA). The main elements of the system are: a photoconductive transmitter and receiver, optical delay line, and femtosecond laser. As shown in Figures 1 and 2, measuring heads operate in reflection mode. The THz transmitter (based on a bowtie antenna with photoconductive gap, Figure 2) is excited by the femtosecond laser pulses. This causes a pulsed flow of antenna current resulting in electromagnetic wave induction. The terahertz wave generated by the bowtie antenna is collimated by a hemispherical silicon lens and focused in the vicinity of the imaged structure surface by a plano-convex dielectric lens. This electromagnetic pulse is partly transmitted through the examined object and partly reflected. In the presented setup the reflected field is detected by the receiving antenna. Based on the measured time domain response it is possible to gain information about the internal structure of the object under test (similar to ultrasonic testing or sonar/radar applications).  
Figure 1: Utilized pulsed terahertz imaging system: (**a**) photo; and (**b**) simplified scheme; Tr—photoconductive transmitter, Rx—photoconductive receiver, OUT—object under test, Fl—focusing lens, B—beam splitter, PCA—photoconductive antenna.  
Figure 2: Structure of the photoconductive antenna and the basic measuring setup.  
The transmission setup shown in Figure 3a is utilized if there is an access to both sides of evaluated structure. This restriction is no longer valid in case of reflection arrangements presented in Figure 3b,c. The \"V\" setup provides a relatively high signal to noise ratio (SNR), but because of the high number of degrees of freedom, accurate arrangement can be problematic, especially for long focal lengths of focusing lenses and significant changes of lift-off distance within a single imaging procedure. The reflection setup with the beam splitter enables a less complex setup and the direction of THz pulse propagation is perpendicular to OUT's front surface. An important disadvantage of this setup is significant reduction of SNR caused by non-ideal behavior of beam splitter. In case of proposed double-sided imaging algorithm the setup presented in Figure 3d is utilized. This is an extension of arrangement shown in Figure 3c and similarly just reflected signals are acquired. This application is possible using a two-channel Tray 4000 spectroscope. A more time consuming equivalent of the proposed system is the measurement using the reflection setup from both sides of OUT separately. The algorithm presented in the next paragraph can be utilized in both cases.
# The Nuclear Receptor PXR in Chronic Liver Disease
## 1 Introduction  
Nuclear receptors (NRs) are components of a well-known superfamily of transcription factors involved in the modulation of different biological processes, including inflammation, apoptosis, cell cycle arrest, and drug metabolism [1, 2]. In humans, the 48 members of this superfamily are classified in seven subfamilies (NR0-NR6), based on their mechanism of action [3]. Even though the 31% of NRs are considered \"orphan\" receptors, meaning that their ligands have not been identified yet, the main part of these receptors are regulated by well-known endogenous substances and/or xenobiotics [2, 3]. Endogenous ligands for NRs are lipophilic hormones able to enter the cell. Some examples of these endocrine NRs are provided by the thyroid hormone receptors (TR\(\alpha\) and TR\(\beta\)) and the androgen (AR) and estrogen (ER\(\alpha\) and ER\(\beta\)) receptors [2, 4].  
The pregnane X receptor (PXR) belongs to the 1I subfamily and is a ligand-activated transcription factor, the classification of which is still controversial because a number of scientists still consider PXR an orphan receptor, although many ligands have been notified, since it binds a variety of endogenous compounds and xenobiotics at high (micromolar) concentrations [4, 5]. However, regardless of its classification, PXR plays a key role in regulating hepatic homeostasis and responses to drugs, functioning as a so-called _xenosensor_, being activated by toxic compounds, thereby helping detoxification [6, 7]. In the liver, PXR is highly expressed by hepatocytes, but its expression has been detected also in Kupffer and hepatic stellate cells (HSCs) [8, 9, 10, 11]. Moreover, it has been reported that hepatic PXR expression is variable during development. Indeed, being low during fetal and neonatal stages, PXR expression in the liver increases during infancy, reaches a peak at puberty, and then decreases to fetal levels in the elderly [12]. Even though PXR expression seems to be not essential for development and its genetic deletion does not affect phenotypic traits, itshepatic activity and regulation have been related to the pathophysiology of many liver diseases.  
This review summarizes the latest findings about PXR expression and its role in the hepatic environment, focusing on its connection to chronic liver diseases of different etiologies and underlining the inconsistencies in the results that have sometimes been obtained by studies conducted with different approaches. In the last part, a focus on gender-related differences in PXR activation and their relationship with disease development is reported.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
###### Abstract  
Cloud radio access network (C-RAN) is a promising technology for the Internet of Things (IoT). In C-RAN, the remote radio head (RRH) and baseband unit (BBU) in the conventional base station are separated, and each BBU is backward centralized into a virtual BBU pool. In this paper, we consider the uplink transmission for the two-user C-RAN with two RRHs under a block fading channel. A novel rateless coded transmission scheme is designed. During each transmission round, each user keeps transmitting to the RRHs using Raptor code until the BBU pool feeds back an acknowledgement (ACK). With the proposed scheme, each user does not require the instant channel state information, which greatly reduces the system overhead. We also design the quantizer at the RRHs and the iterative multi-user detector and decoder at the BBU pool, based on the belief propagation (BP) algorithm. For the Raptor code applied at each user, we optimize the corresponding output node degree profile, based on extrinsic information transfer (EXIT) analysis for the decoding process at the BBU pool. The resulted degree profiles are optimal in an average sense under all possible channel states. The simulation results show that the rateless coded transmission scheme with the optimized degree profiles outperforms the benchmark degree profile in both bit error rate and average system throughput. Moreover, the achieved performance is close to the theoretical limit.  
C-RAN; uplink transmission; rateless code; block fading; degree profile optimization Article
###### Abstract  
In this paper, we present a monitoring program of loss control prevention for airlines to enhance aviation safety and operational efficiency. The assessments of dynamic stability characteristics based on the approaches of oscillatory motion and eigenvalue motion modes for jet transport aircraft response to sudden plunging motions are demonstrated. A twin-jet transport aircraft encountering severe clear-air turbulence in transonic flight during the descending phase was examined as the study case. The flight results in sudden plunging motions with abrupt changes in attitude and gravitational acceleration (i.e., the normal load factor) are provided. Development of the required thrust and aerodynamic models with the flight data mining and the fuzzy logic modeling techniques was carried out. The oscillatory derivatives extracted from these aerodynamic models were then used in the study of variations in stability characteristics during the sudden plunging motion. The fuzzy logic aerodynamic models were utilized to estimate the nonlinear unsteady aerodynamics while performing numerical integration of flight dynamic equations. The eigenvalues of all motion modes were obtained during time integration. The positive real part of the eigenvalues is to indicate unstable motion. The dynamic stability characteristics during sudden plunging motion are easily judged by the values in positive or negative. The present quantitative assessment method is an innovation to examine possible mitigation concepts of accident prevention and promote the understanding of aerodynamic responses of the jet transport aircraft.  
flight data mining; plunging motion; clear-air turbulence; stability characteristics; motion modes; eigenvalues +
Footnote †: journal: Applied Sciences
## 2 Starch Modification Processes
### Chemical Modifications  
Chemical modification involves the introduction of functional groups into the starch molecules. The chemical modification techniques depend on the starch source, reaction conditions such as concentration, reaction time, pH, the presence of a catalyst, and the type of substituent [60]. Examples of chemical modification processes are etherification, esterification, crosslinking, grafting, and decomposition (acid or enzymatic hydrolysis) [1]. However, the key issue with chemical modification is the generation of toxic residues. Figure 5 shows the reaction mechanisms of different types of chemical modification pro  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Physical Modification**} & \multicolumn{1}{c}{**Description**} & **References** \\ \hline \multirow{4}{*}{1. Pre-gelatinization} & \(\bullet\) & Results in instant starch & \multirow{4}{*}{[53]} \\  & \(\bullet\) & Heat and dry under gelatinized conditions & \\  & & without allowing molecular re-association & \\ \hline \multirow{4}{*}{2. Heat Moisture Treatment (HMT)} & \(\bullet\) & A hydrothermal process & \multirow{4}{*}{[54]} \\  & \(\bullet\) & Heat starch granules in a sealed vessel at a temperature above the glass transition & \\  & 2. Heat Moisture Treatment (HMT) & & temperature of starch. & \\  & & \(\bullet\) & Strengthens starch granules under higher \\  & & temperatures and low moisture environment & \\ \hline \multirow{4}{*}{3. Annealing} & \(\bullet\) & A hydrothermal process & \multirow{4}{*}{[53]} \\  & 3. Annealing & \(\bullet\) & Hold starch granules in excess water at a temperature between the T\({}_{\text{g}}\) (25 \({}^{\circ}\)C) and \\  & & gelatinization temperature of starch (100 \({}^{\circ}\)C) & \\ \hline \multirow{4}{*}{4. Microwave (MV) heating} & \(\bullet\) & Materials absorb microwave energy, and the energy is converted into heat through & \\  & & molecular vibration and friction & \\ \hline \multirow{4}{*}{Non-thermal treatments} & \(\bullet\) & Use the mechanical force to change the & \multirow{4}{*}{[56]} \\  & 1. Milling & \(\bullet\) & Altier granule morphology, crystallinity, \\  & & solubility, and swelling properties & \\ \cline{1-1} \cline{2-4}  & & \(\bullet\) & Two types: (i) Static type or Ultra & \multirow{4}{*}{[56]} \\  & 2. High-Pressure Treatment (HPT) & & High-Hydrostatic Pressure (HHP) treatment & \\ \cline{1-1}  & 2. High-Pressure Treatment (HPT) & \(\bullet\) & Use homogenizers to produce turbulence, & \\ \cline{1-1}  & & high shear, and cavitation in starch slurry & \\ \cline{1-1}  & & through the high pressure & \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary of physical starch modification processes.  
cesses. The following section provides a comprehensive description of different chemical processes commonly used for starch modification and their applications.