esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Potential Inhibitors of Squalene Synthas
-1-[[(3R,5S)-7-Chloro-5-(2,3-dimethoxyyphenyl)-1-(3-hydroxy-2,2-dimethylpropyl)-2-oxo-1,2,3,5-tetrahydrodro-4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(9)** and [1-[(3R,5S)-1-(3-Acetoxy-2,2-dimethylpropyl)-7-chloro5-(2,3-dimethoxyyphenyl)-2-oxo-1,2,3,5-tetrahydrodro-4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(10)**; 1-[(3R,5S)-7-chloro-5-(2,3-dimethoxyyphenyl)-1-[2,2-dimethyl-3-(propioyloxy)propyl]-2-oxo-1,2,3,5-tetrahydrodro-4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(11)**; 1-[(3R,5S)-1-[3-(Butrykov)-2,2-dimethylpropyl)-7-chloro-5-(2,3-dimethoxyyphenyl)-1-[2,2-dimethoxyyphenyl)-7-chloro5-(2,3-dimethoxyyphenyl)-1-[2,3,5-tetrahydrodro-4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(12)**; 1-[(3R,5S)-1-[3-(Butrykov)-2,2-dimethylpropyl)-7-chloro-5-(2,3-dimethoxyyphenyl)-2-oxo-1,2,3,5-tetrahydrodro4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(12)**; 1-[(3R,5S)-7-chloro-5-(2,3-dimethoxyyphenyl)-1-[2,2-dimethyl-3-(byaltyoxy)propyl]-2-oxo-1,2,3,5-tetrahydrodro-4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(13)**; 1-[(3R,5S)-7-chloro-5-(2,3-dimethoxyyphenyl)-1-[2,2-dimethyl-3-(byaltyoxy)propyl]-2-oxo-1,2,3,5-tetrahydrodro-4,1-benzoxazeppin-3-yl]acetyl]piperidine-4-aceetic acid **(14)**]**  
Zaragozic acids and derivatives of quinticolidine are well-known inhibitors of squalene synthase; however, due to their unfavorable toxicity profiles, these promising compounds have failed to enter the clinical trial phase [38]. Furthermore, computer simulations indicated strong interactions between squalene synthase and zaragozic acid with a high docking score, confirming the in vitro enzymatic results [38]. Several nitrogen- and nonnitrogen-containing bisphosphonate derivatives (BPs) were reported as inhibitors of squalene synthase. One such example includes 5-(N-[2-butenyl-3-(2-methoxyphenyl)]-N-methylamino)-1,1-penthylidenebis(phosphionic acid) trisodium salt (7) (IC\({}_{50}\): 3.6 nM), which selectively inhibited the activity of squalene synthase. In addition, a tripivaloyloxymethyl derivative (**8**) of compound 7 demonstrated inhibitory potential vis-a-vis squalene synthase with an IC\({}_{50}\) value of 39 uM [48]. However, these nitrogen-containing BPs are not metabolized and are excreted unaffected via the kidney. Notably, BPs are typically administered under fasting conditions, as food reduces their bioavailability [49]. Figure 5: Chemical structures of compounds **7-14** as potential squalene synthase inhibitors. Figure 6: Chemical structures of the potential square inhibitors **15**–**26**. Figure 7: Chemical structures of potential square synthase inhibitors **27–40**.
## 2 Materials and Methods
### Learning Phase  
The learning phase is necessary because this system can be used for different DBs and different fundus cameras. It can be mentioned that the set parameters in the LP can be stored and are not required for each use of the system. LP is usually necessary when the imaging device for the retina is changed, another retinal database is used, or other parameters are required. Therefore, in the LP, the system calibration is practically done. We used a supervised method for system training because, in this case, it performs better and requires less computational effort than an unsupervised one. To this end, a set of candidate features (CF, Table 1) for each box from a learning set (\(LS\)) was calculated. Five classes are considered: _OD_, _MA_, _EX_, _HE_ (as RoIs), and the class of the rest, _NR_ (Non-RoI). The LP block diagram is presented in Figure 4. In order to select the features used in the segmentation process, the LP is divided into two tasks: (1) the establishment of the representative value of the features for each class, and (2) feature selection and establishment of classifiers for the segmentation process of each class. Correspondingly, _LS_ is also divided into two disjoint sets: _LS_1 (learning set for Task 1) and _LS_2 (learning set for Task 2).  
\[LS=LS1\cup LS2,\quad LS1\cap LS2=\Phi \tag{1}\]  
Each class has 10 representative boxes in _LS_1 and 10 representative boxes in _LS_2 (experimentally and arbitrarily chosen). Thus, the number of boxes is 50 for _LS_1 (5 \(\times\) 10 = 50 boxes) and also for _LS_2 (2).  
\[Card\{LS1\}=Card\{LS2\}=50 \tag{2}\]  
Task 1. For each feature \(F\) from (CF), the representative of each class is established by averaging the corresponding feature values on the representative class boxes _LS_1: \(\overline{F}_{OD},\overline{F}_{MA},\overline{F}_{EX},\overline{F}_{HE}, \overline{F}_{NR}\). The exemplification of the representative feature calculation for the _OD_ class is done in the next section (Table 2).  
Task 2. This task consists of feature selection and weight assignation for classifiers. Because the classification process takes into account heterogeneous features, it is convenient to consider the image classification based on weighted local classifiers. To this end, another set (_LS_2) of 50 boxes (10 for each class) is used. Taking into account the representatives of classes established in Task 1, each box from the learning set _LS_2 is classified based on minimum distance to the representatives of the classes for each feature. The considered distances are the absolute differences in the case of scalar features and the Minkowski distance of order 1 in the case of the Local Binary Pattern (_LBP_) histogram (Table 1).  
Figure 3: Convolutional neural network (CNN)-based architecture of blood vessel segmentation (BVS).
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems  
The extensive utilisation of automation and IT systems in modern ships provides new opportunities for hackers and malicious actors to implement different cyberattacks that could lead to catastrophic incidents and cause major safety losses [3; 6; 24]. Extensive research efforts have been made by the research community to identify vulnerabilities in the modern maritime industry [3; 6; 24; 25; 26; 27], and many successful cybercrime cases have been reported in the last few years [24; 25]. According to [24], the main common motivations for these attacks are to gain remote control over ships and vessels, to steal important and confidential information that can be used for launching further attacks, or to disrupt the ship's operations by corrupting important components and making the automated systems unavailable. In fact, most of the IT systems in modern ships are insecure and vulnerable to attack because they are considered less critical to security and performance [6]. In this section, we investigate cyberattacks on modern and autonomous ships based on the already hacked automation systems and actual incidents.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### Synergistic Development Characteristics of Logistics and Capital Flows
#### 4.4.1 Spatial Fluidity of Logistics and Capital Flows
From a temporal perspective, the logistics network of the Greater Bay Area evolved from a single-core concentration to a polycentric structure between 2016 and 2021. This  
Figure 5: Spatial patterns of capital network. shift is particularly evident in the rising significance of cities such as Zhuhai and Dongguan, reflecting the diversification of logistics organizational models and functional differentiation within the region. In terms of capital flow, Shenzhen's ascendancy in the technology and financial sectors has transformed its role from a capital recipient to a regional capital exporter and allocator. Its strengthening financial ties with Guangzhou signal the emergence of a new capital flow hub. Despite year-to-year fluctuations in Hong Kong's linkages with Guangzhou and Shenzhen, its position within the regional financial network remains firmly established. Peripheral cities, including Huizhou and Zhongshan, are increasingly participating in the regional network, notably through industrial reception and capital absorption functions. These trends collectively illustrate the dynamic evolution and spatial reconfiguration of urban economic roles across the Greater Bay Area.
## 2 Autonomous Vehicles  
Autonomous vehicles, also called self-driving vehicles or driverless vehicles, are appropriate means of transport that are capable of sensing their environment and navigating without human input [14]. Autonomous cars use tools such as a Global Positioning System (GPS), odometry, radar, laser lights, and computer vision for independent movement [15; 16]. The use of these types of technology allows vehicles to identify the route, obstacles, and barriers occurring on the road, and to read the content of signs [14; 15; 16].  
Autonomous vehicles have nomenclature specific to this group. This begins with the definition of the group of autonomous vehicles themselves: automatic or automated. However, it is worth  
Figure 1: Diversity of car-sharing systems.  
explaining the detailed differences between specific terms. The main difference between the terms is related to the degree of involvement of human support in the functioning of the car [17]. A vehicle referred to as \"automatic\" or \"automated\" is an automatic machine that does not have intelligent systems [17]. Another case is an autonomous vehicle. That kind of car, unlike an automatic vehicle, is equipped with intelligent systems based on which it will be able to make decisions about the route, appropriate traffic behaviour, and others [17]. An automatic car will only carry out commands [17].  
Another issue related to autonomous vehicles is related to the terms \"self-driving\" and \"driverless\". Self-driving is considered to describe all vehicles that are not as technologically advanced as the driverless type [17].  
These concepts differ in the level of technology. The most popular classification of autonomous vehicles was presented by the National Highway Traffic Safety Administration (NHTSA) [18]. That classification is called an American point of view [18]. The classification includes a 5-point scale. It includes level 0 as well as four further levels of vehicle autonomy. The details of the classification are presented in Figure 2.  
In the case of the European Union, two main definitions have been established for autonomous and automatic vehicles [19]. An automatic vehicle is considered to be a car equipped with technology that enables the driver to transfer some of their driving responsibilities to on-board systems [20]. In turn, an autonomous vehicle is defined as being a fully automated car equipped with technologies enabling the system to perform all functions related to driving without human intervention [20].  
A separate classification can be made according to the International Society of Automotive Engineers (ISAE) from 2014 [21]. This division covers five levels of autonomous driving. The division also takes into account a zero level. The details of the classification are shown in Figure 3.  
Figure 2: Autonomous driving levels—National Highway Traffic Safety Administration (NHTSA) classification.
# Evaluation of Disassembling Process Inference Based on Positional Relations Matrix
## 2 Disassembling Process Inference Based on Positional Relations Matrix
### Generating Optimal Disassembling Process  
A disassembling sequence of \(n\) moves can be expressed as \(S_{n}=[(j_{1},k_{1}),\)\((j_{2},k_{2}),\)\(\cdots,\)\((j_{n},k_{n})]\) where \(j_{n}\
eq j_{B}\) (\(\alpha\
eq\beta\)), which represents to remove part \(j_{1}\) in direction \(k_{1}\), then part \(j_{2}\) in direction \(k_{2}\), and so on. The \(m\)th move \((j_{m},k_{m})\) (\(1\leq m\leq n\)) is feasible in the case that for any \(i\) (\(i=1,\ \cdots,N\)), \(M_{j_{m},k_{m}}=0\) or \(i\in\{j_{1},\ \cdots,j_{m-1}\}\); that is, for any part \(i\), it does not impede the move \((j_{m},k_{m})\) or it is already removed as a part of the preceding moves. Disassembling sequence \(S_{n}=[(j_{1},k_{1}),\)\((j_{2},k_{2}),\)\(\cdots,\)\((j_{n},k_{n})]\) is a feasible disassembling process for part \(j_{n}\), in the case that all the moves \((j_{m},k_{m})\) (\(m=1,\ \cdots,n\)) are feasible.  
A number sequence \(j_{1},\ \cdots,j_{n}\) can create a feasible disassembling process for part \(j_{n}\), in the case that there exist \(k_{1},\ \cdots,k_{n}\) which make all the moves \((j_{1},k_{1}),\)\(\cdots,(j_{n},k_{n})\) feasible. Accordingly, for a disassembling problem of \(N\) parts, examining all the permutations of \(1,\ \cdots,N\) as \(j_{1},\ \cdots,j_{N}\), checking the possibility of making its partial sequence \(j_{1},\ \cdots,j_{n}\) (\(1\leq n\leq N\)) feasible disassembling sequence where \(j_{n}=J\), and evaluating the optimality of the sequence, we can obtain the optimal disassembling process for any specified part \(J\). It should be noted that the computation cost required for this optimal disassembling process generation is \(\mathcal{O}(N!)\).
# Non-Local Effects in Brillouin Optical Time-Domain Analysis Sensors
## 3 Mitigation of Non-Local Effects  
As it has been explained, dual-probe BOTDA setups mitigate non-local effects originating from pump pulse depletion. However, other methods have also been introduced over the years to mitigate this impairment. An early proposal was the use of a post-processing technique for temperature/strain profile reconstruction [15]. This was based on searching the BFS distribution that matched a given measurement data by using a multidimensional minimization algorithm to determine the coefficients of the parametrized unknown profile. However, the applicability of this method to realistic measurement scenarios is rather limited. Another proposal was a time-multiplexing method that is based on pulsing the probe wave so as to limit the interaction length with the pump pulse [16; 17]. According to (1), this reduces the pump pulse depletion. The drawback of this method is that the measurement time is greatly increased as the length of the interaction lengths is reduced to achieve significant mitigation of non-local effects. Finally, we also introduced a technique tolerant to non-local effects that is based on a phase-modulated probe wave and radio-frequency (RF) demodulation [18]. The tolerance to pulse depletion-induced impairments of this method is based on the characteristics of the detection process, which generates a RF phase-shift signal that has been shown to be largely independent of the Brillouin gain magnitude. Nevertheless, probably the simplest and the most successful alternative to mitigate non-local effects due to pump depletion is the use of the dual-probe setup. In the following, we review the techniques that have been proposed to enhance dual-probe BOTDA setups so as to be able to overcome the constraints imposed by second-order non-local effects as well as other limitations.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
###### Abstract  
The sixth generation (6G) satellite twin network is an important solution to achieve seamless global coverage of 6G. The deterministic geometric topology and the randomness of the communication behaviors of 6G networks limit the realism and transparency of cross-platform and cross-object communication, twin, and computing co-simulation networks. Meanwhile, the parallel-based server-less architecture has a high redundancy of computational resource allocation. Therefore, for the first time, we present a new hypergraph hierarchical nested kriging model, which provides theoretical analysis and modeling of integrated relationships for communication, twin, and computing. We explore the hierarchical unified characterization method which joins heterogeneous topologies. A basis function matrix for local flexible connectivity of the global network is designed for the connection of huge heterogeneous systems to decouple the resource mapping among heterogeneous networks. To improve the efficiency of resource allocation in communication, twin, and computing integrated network, a multi-constraint multi-objective genetic algorithm (MMGA) based on the common requirements of operations, storage, interaction, and multi-layer optimal solution conflict is proposed for the first time. The effectiveness of the algorithm and architecture is verified through simulation and testing.  
6G; satellite communication; twin network; integrated network framework; multi-objective optimization +
Footnote †: journal:  
**Keywords**: 27 June 2022  
Accepted: 29 July 2022  
Published: 4 August 2022  
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## 3 Results and Discussions
### Sensitivity Study and Model Validation
Furthermore, three values of the shape coefficient of nucleation \(\alpha\) are selected (\(\alpha_{\text{wall}}\) on the wall and \(\alpha_{\text{ice}}\) on the ice are set to be the same as 0.01, 0.001, and 0.0001, respectively). The comparisons between the numerical results simulated by the present method and the Neumann solutions of the classical Stefan problem are presented in Figures 5 and 6. Figure 4: Sensitivity study on mesh number (**a**) and time step (**b**). Figure 3: Contour of ice phase fraction and location of water–ice interface. Figure 5 compares the result of interface position evolution by the present method (in three different \(\alpha\) values) with that obtained through the theoretical Neumann solutions. It can be found from the figure that along with the shape coefficient of nucleation \(\alpha\) decreasing, the numerical results of interfacial position evolution gradually tend to be the same and coincide well with the theoretical solutions. The max relative difference between the numerical results of \(\alpha\) = 0.0001 and the theoretical solutions at various moments are about 6.58%. Figure 6 shows the comparison of the temperature distribution at t = 6 s and 9 s obtained by the present method (\(\alpha\) = 0.0001) and the theoretical Neumann solutions, respectively. It is found that the numerical result agrees well with the theoretical solutions and the max relative differences at 6 s and 9 s are about 4.01% and 4.74%, respectively. The comparison results in this section proved that the newly-proposed model could effectively predict the process of water freezing within a satisfying accuracy.
## 2 Materials and Methods
### Methods
#### 2.3.2 Standardized Anomaly Index  
In order to obtain signals related to water stress, we calculated the spatial distribution of standardized anomalies of each variable at the pixel level in this study ([PERSON] _et al._, 2021; [PERSON] _et al._, 2021; [PERSON] _et al._, 2021). The pixel (i, j) values of 2019 are compared with the average pixel (i, j) values for the period 2011-2020, and then standardized according to their standard deviation (SD). The specific calculation formula is as follows:  
\[\mathrm{Var}_{\mathrm{2019~{}anomaly}}(\mathrm{i,j})=\frac{\mathrm{Var}_{\mathrm{ 2019}}(\mathrm{i,j})-\mathrm{mean}(\mathrm{Var}_{\mathrm{2011-2020}}(\mathrm{ i,j}))}{\mathrm{SD}(\mathrm{Var}_{\mathrm{2011-2020}}(\mathrm{i,j}))} \tag{1}\]  
where \(\mathrm{Var}_{\mathrm{2019~{}anomaly}}(\mathrm{i,j})\) denotes the standardized anomaly of the pixel (i, j) in 2019; \(\mathrm{Var}_{\mathrm{2019}}(\mathrm{i,j})\) denotes the original value of pixel (i, j) in 2019; \(\mathrm{mean}(\mathrm{Var}_{\mathrm{2011-2020}}(\mathrm{i,j}))\) denotes the mean value of pixel (i, j) during 2011-2020; \(\mathrm{SD}(\mathrm{Var}_{\mathrm{2011-2020}}(\mathrm{i,j}))\) denotes the standard deviation of pixel (i, j) during 2011-2020. The calculated results indicate the degree to which each month of the drought year (2019) deviates from its associated multi-year (2011-2020) average, representing the drought conditions for each pixel.
## 2 Materials and Methods
### Cell Culture and Measurement of Intracellular Calcium Influx in TRPV1-Overexpressing HEK293T Cells  
A TRPV1-overexpressing HEK293T (TRPV1-HEK293T) cell line was purchased from Creative Biogene (New York, NY, USA) and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 2 ug/mL puromycin and 10% fetal bovine serum (Gibco, Waltham, MA, USA) at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). At 80% confluence in a T75 flask, the cells were detached using trypsin EDTA (Gibco) and sub-cultured or seeded.  
The experiment was performed as previously described [6; 20; 21; 22; 23]. Briefly, the TRPV1-HEK293T cells were treated with capsaicin (10 \(\upmu\)M) and troxerutin (1, 10, 50, and 100 \(\upmu\)g/mL) for 3 min, incubated with calcium-specific fluorescent dye Fluo-3/AM (5 \(\upmu\)M, Invitrogen, Waltham, MA, USA) and 0.1% Pluronic F-127 (Invitrogen) for 40 min, and the medium was washed using 1X NBS (normal bath solution, 140 mM NaCl, 5 mM KCl, 2 mM CaCl\({}_{2}\)/EDTA, 0.5 mM MgCl\({}_{2}\), 10 mM glucose, and 5.5 mM HEPES, pH 7.4). Intracellular calcium fluorescence responses were measured using a Leica DMi8 inverted microscope  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **CAS No.** & **SMILES** & **Molecular Weight (g/mol)** \\ \hline SB452533 & 459429-39-1 & O=C(NCCN(CC)C1=CC=CC(C)=C1)NC2=CC=CC=C2 Br & 376.29 \\ JTS-653 & 942614-99-5 & O[C@H]1N(C(N=C)=CC=C2C)C3=CC=CC(C(NC(C=C)) & 474.43 \\  & =CN=C40 CC(P)(F)F=O)-C30C1 & & \\ Mavatrep (JNJ-39439335) & 956274-94-5 & O[C](C)C1=CC=CC=CC=C1C2=CC=C3N=CC(/C=C/C4 & 422.44 \\  & =CC=CC(F)(F)C-C4)NC3=C2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on known transient receptor potential vanilloid 1 (TRPV1) antagonists used as anchors for grouping predicted compounds with the molecular transformer drug–target interaction (MT-DTI) model.  
Figure 1: Overview of the affinity and bioactivity prediction process using the drug–target interaction (DTI) model.  
(Leica Microsystems, Wetzlar, Germany) at excitation and emission wavelengths of 488 and 515 nm, respectively. Microscopic images were captured over 180 s at 1.5 s intervals. Intracellular calcium influx was determined using three independent experiments. Changes in intracellular calcium levels were expressed as an F/F0 ratio, where F0 is the initial fluorescence intensity, which was calculated using ImageJ (National Institutes of Health, Bethesda, MD, USA) with custom script. The dosage of 10 uM capsaicin resulted in a fully activated response both in the present and a previous study [20].