esa-sceva/satcom-chunk-collection huggingface dataset:
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 3 Biosynthesis of Medically Important Terpenoids  
Terpenoid biosynthesis can be divided into four main stages. In the first stage, universal building blocks isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) are synthesized via the mexolonate pathway (MVA pathway) and the methyl-erythnitol phosphate pathway (MEP pathway). MVA pathway, which exists in _S. cerevisiae_, starts from acetyl-CoA and ends up with the production of DMAPP. In the second stage, geranyl pyrophosphate (GPP), farnesyl pyrophosphate (FPP), or geranylgeranyl pyrophosphate (GGPP), which are the precursors of monoterpenoids, sesquiterpenoids, or diterpenoids, respectively, are created by condensation reactions of one molecule DMAPP with one, two, or three molecules of IPP under the action of isopentenyl transferase. On the other hand, triterpenoids are similarly generated via polymerization of two molecules of FPP. In the third stage, these linear isopentenyl pyrophosphate precursors are cyclized or rearranged by terpene synthases to form primary terpene carbon skeleton. Finally, this carbon skeleton undergoes several post-modifications and tailoring reactions (oxidations, acetylation, ester  
Figure 5: Chemical structure of (**A**) Perillyl alcohol, (**B**) D-limonene, (**C**) Geraniol, (**D**) Menthol, (**E**) Sabinene, (**F**) Patchouli alcohol, (**G**) Ginsenoside and (**H**) Betulinic acid.  
ification, alkylation, etc.) under the action of cytochrome P450s (CYPs) to form biologically active terpenoids (for recent reviews see: [5; 12; 56]).
## 3 Analysis of Influencing Factors
### Influence of Basement Length \(L\)  
The maximum vertical deformation of the tunnel, \(\mu_{\rm max}\), affected by basement length, \(L\), for three different unloading ratios (\(\alpha\) = 0.25, 0.50, 0.75) and five different tunnel cover depths (\(H_{\rm c}\) = 12 m, 24 m, 36 m, 48 m, 60 m) is analyzed. Figure 15 shows the relationship between the maximum vertical deformation of the tunnel, \(\mu_{\rm max}\), and the basement length, \(L\), for different cases, respectively. There are two transition ratios, \(L/H_{\rm c}\), for each curve. The first transition ratio occurs when \(L/H_{\rm c}\) is 3, and the increased rate of maximum vertical deformation of the tunnel begins to decrease. The maximum vertical deformation remains stable at the second transition ratio. The second transition ratio for each curve is different, and it decreases with increasing \(H_{\rm c}\). The second inflection point occurs at \(L/H_{\rm c}\) = 7 for \(H_{\rm c}\) = 12 m, while \(L/H_{\rm c}\) = 4 for \(H_{\rm c}\) = 60 m. This result indicates that the tunnel with larger \(H_{\rm c}\) is relatively less influenced by \(L\).  
Figure 14: Relationship between maximum vertical deformation of tunnel and unloading ratio.  
Figure 15: _Cont._To eliminate the influence of the unloading ratio, the maximum vertical deformation of the tunnel, \(\mu_{\max}\), was normalized in the form of \(\mu_{\max}/(\alpha H_{\mathrm{c}})\). Figure 16 shows the relationship between the maximum vertical tunnel displacement and the basement length, \(L\), after normalization. It can be seen that the maximum vertical deformation of the tunnel for an \(\alpha\) value of less than 0.6 is all distributed within a certain range, while the distribution of the maximum vertical deformations for \(\alpha\) = 0.75 goes outside this range. This is similar to previous research findings showing that tunnel deformation is less induced by excavation when \(\alpha\) < 0.6.  
Figure 15: Relationship between maximum vertical deformation of tunnel and basement length for different cases: (**a**) \(\alpha\) = 0.25; (**b**) \(\alpha\) = 0.50; (**c**) \(\alpha\) = 0.75.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 4 Methodology
### Temporal Sequence Construction
#### 4.1.2 Feature Encoder
For example, let us assume the historical neighbor sequence of \(u_{1}\) as \(\mathcal{Q}_{u_{1}}^{t}=\{u_{1},s_{1},s_{2},s_{1},s_{3}\}\), and the historical neighbor sequence of \(s_{2}\) as \(\mathcal{Q}_{s_{2}}^{t}=\{s_{2},u_{2},u_{1},u_{1}\}\) as shown in Figure 3. The frequency at which \(u_{1}\) appears in the \(\mathcal{Q}_{u_{1}}^{t}\), \(\mathcal{Q}_{s_{2}}^{t}\) sequence is \([1,2]\), then the correlation can be denoted as \(\mathbf{Co_{u_{1}}^{t}}=\{[1,2],[2,0],[1,1],[2,0],[1,0]\}\). Similarly, \(\mathbf{Co_{s_{2}}^{t}}\) can be denoted as \(\mathbf{Co_{s_{2}}^{t}}=\{[1,1],[1,0],[2,1],[2,1]\}\). Figure 3: Example of co-occurrence features. Then, we apply a function \(f\) to generate the temporally correlated representation by  
\[\mathbf{X}_{s,Co}^{t}=f\big{(}\mathbf{Co}_{*}^{t}[:,\mathbf{0}]\big{)}+f\big{(} \mathbf{Co}_{*}^{t}[:,\mathbf{1}]\big{)}, \tag{2}\]  
where \(*\) represents \(u\) or \(s\). **Feature Fusion. ** After extracting the sequence information, we obtain the historical interactions that occurred before time \(t\) for both passengers and stations. These interactions can be used to represent the current node features. We further introduce learnable features \(\mathbf{X}_{N}^{U}\in\mathbb{R}^{n\times d_{N}},\mathbf{X}_{N}^{S}\in\mathbb{R }^{m\times d_{N}}\) for passenger nodes and station nodes to represent their inherent attributes, which will be gradually learned during the model training process. Hence, we denote \(\mathbf{X}_{u,N}^{t},\mathbf{X}_{s,N}^{t}\) as the learnable node feature sequences for passenger \(u\)'s neighbors and station \(s\)'s neighbors. Additionally, for historical interaction information \(e\), the boarding and alighting attributes of passengers are important features for representing this interaction information. Therefore, we represent the fused features as follows:  
\[\mathbf{Z}_{*}^{t}=Proj\big{(}\mathbf{X}_{*,N}^{t}||\mathbf{X}_{*,E}^{t}|| \mathbf{X}_{*,T}^{t}||\mathbf{X}_{*,Co}^{t}\big{)}, \tag{3}\]  
where \(*\) denotes \(u\) or \(s\). \(Proj(\cdot)\) represents the projection mapping function, which is implemented using a linear layer in this model. This is used to integrate the temporal feature information in the sequence, thereby generating the embedding \(\mathbf{Z}_{*}^{t}\in\mathbb{R}^{N\times d_{E}}\) of the corresponding node at time \(t\).
###### Abstract  
Magnetotelluric (MT) sounding is a geophysical technique widely utilized in mineral resource surveys, where conductivity and magnetic permeability serve as essential physical parameters for forward modeling and inversion. However, the effects of conductive anisotropy and non-zero magnetic susceptibility are usually ignored. In this study, we present a three-dimensional (3D) MT modeling algorithm using Coulomb-gauged electromagnetic potentials, incorporating a mixed nodal and edge-based finite element method capable of simulating MT responses for conductive anisotropic and magnetic anomalies. Subsequently, the algorithm's accuracy was validated in two steps: first, it was compared with analytical solutions for a 1D magnetic model; then, a comparison was made with previously published numerical results for a 3D generalized conductive anisotropic model. The results of two tests show that the maximum relative error is below 0.5% for both models. Furthermore, representative models were computed to comprehensively analyze the responses of MT. The findings illustrate the relationship between anisotropic parameters and electric fields and emphasize the significance of considering the impact of magnetic susceptibility in magnetite-rich regions.  
Keywords:magnetotelluric; anisotropy; three-dimensional; magnetic; forward modeling; finite element method; Coulomb-gauged potentials +
Footnote †: journal: Applied sciences
# Opportunities and Limitations in Recycling Fossil Polymers from Textiles
## Recycling Fossil Polymers
### Polyester
Degradation is the most important problem in re-extrusion, particularly thermal degradation, thermo-oxidative degradation, hydrolysis and mechanical degradation [101]. Hydrolytic degradation (Figure 7a) consists of the decrease in PET molecular weight due to chain scission resulting in an increase in melt fluidity and is favoured by the presence of nucleophilic additives, such as humidity (which must be removed before extrusion through an appropriate drying step) and metal catalysts [102]. Thermal chain scission is also possible (Figure 7b). The PET waste fibres are light; therefore, they cannot flow easily from a hopper to an extruder by gravity alone, and the solid conveying rate may be insufficient due to low bulk density for supplying material to the next stages, which are plasticizing and melt conveying [103; 104]. The excessive friction can cause a heat increase on the material, leading to thermal degradation. Another important feeding problem is bridging which is crossing of fibres from the hopper to the entrance of the extruder. This problem can be solved by special screw designs and force-feeders. Often, the fibre waste is also mixed with PET waste bottle scraps to limit this problem. Hydrolytic and thermo-oxidative degradation are also important in PET fibre re-extrusion. To eliminate hydrolytic degradation, drying must be carried out before each processing operation in the melt. A special screw design to reduce stress shearing and nitrogen feeding can be recommended to prevent the thermal oxidation. To overcome the previous issues about feeding fibrous material to extruders, the work group in the ReSynt Project [105] have studied and developed a methodology for compacting recycled polyester frays and fabrics to easily feed the extruders by employing the classic feeders used for polymeric granules. In fact, the compacted material turns  
Figure 7: (**a**) Hydrolytic and (**b**) thermal chain scission of PET. out to have a bulk density of 0.5 kg/L. In the Resynt project, recycled polyester-based compounds were developed by reactive extrusion to produce final injection moldable or thermoformable objects. Furthermore, the ReSynt project approached the possibility to track the recycling material and enable the consumer to know whether, and in what percentage, his product is actually recycled, by adding an appropriate tracer dye.
## 4 The Role of Dielectrophoresis in the Characterization of Skin Tumor Cells
### Clinical Applications  
Among CTCs and skin tumor cells detection methods, DEP emerged as a potential alternative technique for currently available conventional approaches. Some major strong points of DEP remain the possibility to perform label-free particle discrimination [96] and to isolate single cells [97]. Via DEP, cells' evolution in real time can be monitored, based solely  
Figure 2: Skin cancer cells vs. normal cells—relevance for dielectrophoretic analysis. on the cell's electrical properties [98]. Moreover, DEP has higher specificity compared to other alternative approaches, such as density gradient centrifugation [99]. Despite DEP's incommensurable advantages, a series of drawbacks (low selectivity and limited reproducibility) has limited this technique to reach large scale deployment. Table 3 highlights the most important advantages and limitations of DEP, in a pragmatic comparison to other cellular detection/separation methods. Strictly related to skin cancers, DEP was used first in melanoma cells more than 30 years ago, however, with limited further studies until the present. Table 4 summarizes the most relevant up-to-date results regarding the utility of DEP in human and different animal skin cells analysis. Human skin cancer cells have specific electrical properties that facilitate separation when using DEP. One important aspect revealed since the earliest experiments [109], is the role of melanin content in modulating melanoma cells' behavior in a non-uniform electrical field. Modifications in cell structure due to normal ageing or destructive changes under different drug therapies will alter the cell movement under DEP, facilitating or impeding single cell separation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AoA & Angle of Arrival \\ AWGN & Additive White Gaussian Noise \\ CDF & Cumulative Distribution Function \\ CFR & Channel Frequency Response \\ CLF & Concentrated Likelihood Function \\ CRLB & Cramér-Rao Lower Bound \\ DA & Data-Aided \\ \end{tabular}  
Figure 9: RMSE vs. the SNR in dB and the angular separation \(\Delta_{\theta}\) in degrees with \(Q=2\), \(R=100\), and \(T_{H}=100\).
## 6 Conclusions  
Many illegal users and services hide their location and communications through Tor and Tor Hidden Service, so-called Dark-Web. Tor introduced several PT to improve users' anonymity and help them across the censorship. Multiple traffic classification methods have been developed to de-anonymize Tor and PT. Snowflake is the latest release of Tor PT based on the WebRTC, a multimedia peer-to-peer communication technology. It applies UDP as the transport layer framework protocol, which defeat previous traffic classification methods. In this paper, we propose a Tor-Snowflake traffic identification framework F-ACCUMUL, which can identify if a user is connecting Tor through Snowflake PT and whether he is accessing a hidden service. Specifically, we use a rule-matching method to pre-identify traffic quickly. Then, we extract some fingerprint features of the Snowflake DTLS handshake to identify the snowflake traffic accurately. On the basis of this, the first 300 messages of the Snowflake DTLS data transmission phase are extracted and the accumulative payload length are sampled (ACCUMUL). Finally, the Tor-Snowflake HS traffic is identified. The results show that our proposed Tor-Snowflake traffic identification framework can identify Snowflake traffic quickly and accurately. In addition, the feature extraction ACCUMUL method can efficiently identify Tor-Snowflake HS access activities by extracting additional features in combination with statistical features related to traffic identification when choosing the parameter combination of (300, 40).  
There are still some deficiencies in our research. The F-ACCUMUL Snowflake traffic identification framework is able to identify Snowflake traffic in the network quickly and accurately. In addition, it can effectively identify the activity of accessing HS in Tor-Snowflake scenarios. However, our proposed identification method based on Snowflake handshake fingerprinting can be resisted by PT developers by eliminating fingerprint information specific to the Snowflake DTLS handshake phase or morphing. This may lead to a long-term offensive and defensive stalemate. In addition, our proposed framework uses machine learning algorithms and does not involve deep learning algorithms. Therefore, in the future, we need to propose a more robust approach for Tor-Snowflake traffic identification that can adapt to the changes of Snowflake version updates. We will also test the effectiveness of our proposed ACCUMUL method to identify HS access activities on other Tor PTs.  
Conceptualization, J.C. and G.C.; methodology, J.C. and H.M.; software, J.C.; validation, J.C. and H.M.; formal analysis, J.C.; investigation, J.C.; resources, J.C.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C. and H.M.; visualization, J.C.; supervision, G.C.; project administration, G.C.; funding acquisition, G.C. All authors
### Dustproof Technology
In other words, the original solid-gas interface of the dust particles is replaced by the solid-liquid interface, which greatly improves the wettability of the liquid to the dust, thereby improving the dust reduction efficiency. In order to improve the dust-proof effect of water injection and spraying, and the wetting ability of water to coal, many countries have added wetting agents. In the late 1970s, researchers from some scientific research institutions in my country began to study chemical dust suppressants. Significant progress has been made since the 1980s. At present, more than 20 kinds of mine dust suppressants with patent rights are identified in China. Germany, the Soviet Union, Japan, the UnitedKingdom, the United States and Pakistan have also developed and applied a variety of dust suppressants. The advantages, disadvantages and dust removal efficiency of various technologies are shown in Table 2 below. In order to prevent and control mine dust, a variety of dust reduction technologies such as the coal seam water injection, the spraying, and the dust collectors have been widely adopted. These technologies have significant dust reduction effects and play an important role in improving the underground working environment, protecting the health of workers and preventing the occurrence of mine dust accidents. However, these technologies are far from being effective in the dust reduction on the excavation face, resulting in a serious mine dust disaster in my country. Therefore, it is of great significance to conduct further research on the key technology and equipment of foam dust reduction in fully mechanized mining and excavation face, conduct industrial tests, and promote the application for improving the scientific and technological level of dust prevention and control, improving the underground production environment and realizing the construction of green mines [17].
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Algorithm Complexity
#### 9.3.2 Pseudonym-Changing Protocol Complexity  
The pseudonym-changing protocol takes various road network parameters to update vehicles pseudonyms in a group manner. For the pseudonym-changing algorithm's complexity, we should consider the selection of virtualizer, messages creation, and pseudonym update process. Let \(D_{n}\) be the distance calculation time between GH and group member and \(V_{n}\) be the number of vehicles taking part in the virtualizing process; the time complexity of the vehicle virtualizing selection process is \(O(D_{n}*V_{n})\). The selected vehicles will create duplicate messages with virtual pseudonyms. Let \(MSG_{n}\) be the number of messages designed for the virtual pseudonym-changing process and \(V_{n}\) be the vehicles that make duplicate messages, then the computation complexity of messages creation is \(O(MSG_{n}*V_{n})\). Now all the group members cooperatively update pseudonyms. Let \(PID_{n}\) be pseudonyms, and \(V_{n}\) is the number of vehicle update pseudonyms. The time complexity of pseudonyms update is \(O(PID_{n}*V_{n})\). The overall computation cost of pseudonym-changing protocol is given below.  
\[\begin{split} Time\ complexity(Pseudo-Update)=O(D_{n}*V_{n})+O(MSG_{n}*V_{n})+O(PID_{n}*V_{n})\\ =O(D_{n}+MSG_{n}+PID_{n})V_{n}\\ =O(D+MSG+PID)V(n)\\ =O(n)\end{split} \tag{63}\]
## 2 Acoustic Characteristics of Honeycomb Sandwich Structures
### Sound Transmission Loss  
Figure 2 shows a typical profile, highlighting the variation in STL occurring as a function of frequency. Such characteristic STL curves of a panel can be divided into four regions according to the frequency range involved. These are generally referred to as the: (I) stiffness; (II) resonance; (III) mass; and (IV) coincidence regions.  
1. In this region, the STL value is mainly controlled by the stiffness of the materials. Over this frequency band, the larger the stiffness of the materials, the larger the STL value.
2. In region II, the STL value is controlled by the natural resonance behavior of the materials. Structure is subjected to great displacements due to resonance and a large amount of acoustic energy is transmitted into the receiving side. As a result, the STL curve shows noticeable valleys throughout the frequency band, and the damping can weaken the influence of resonance.
3. After the resonance region, the characteristic STL curve is mainly controlled by the mass of the materials. STL increases in accordance with increases in the frequency.  
Figure 2: A typical sound transmission loss (STL) curve showing the stiffness, resonance, mass, and coincidence regions.  
4. When the frequency increases to a certain specific value, the bending waves caused by bending deformation of the panel coincides with the wavelength of the sound, leading to the 'coincidence effect'. At this point, there is a great reduction in the STL. For the normal plane wave incidence on a regular panel, there is no coincidence effect in STL due to the fact that the trace wavenumber is 0.  
It can be seen in the research discussed above that stiffness, mass, damping effect, and occurrence of resonance are the main factors influencing the transmission of sound by materials [29; 30]. As honeycomb sandwich structures have relatively complex parameters, there are numerous factors affecting their STLs (including the surface density of the panel, face-sheet thickness, and core thickness). Therefore, it is necessary to investigate the effect of dimension parameters on sound transmission losses in honeycomb sandwich core material for its acoustic and noise reduction applications.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 5 Performance Analysis
### Security Requirements Comparison  
As shown in Table 4, proposed scheme provides all listed security requirements. [PERSON] et al.'s [30] and [PERSON] et al.'s [71], and proposed schemes utilize hierarchical system structure. [PERSON] et al.'s [30] and [PERSON] and [PERSON]'s [4] only achieve one security requirement. [PERSON] et al.'s [71] scheme achieves mutual authentication and anonymity, and [PERSON] et al.'s [5] scheme achieves mutual authentication, session key confirmation, and anonymity. None of mentioned previous schemes achieve traceability of anonymity, unforgeability, and non-repudiation except proposed scheme.
## 1 Introduction
After the optimization of the cooling channels, the warpage decreased, and the temperature uniformity increased. Optimization strategies involving the use of various algorithms for designing the shape and layout of cooling channels have been frequently discussed in the literature [12; 13; 14]. Moreover, many studies have used DOE to set optimal process parameters for injection molding [15; 16]. However, these two issues have rarely been integrated in the literature. This study combined finite element analysis with optimization algorithms to analyze the temperature field during the cooling stage. Subsequently, the entire injection molding process was conducted using DOE to obtain the best process parameters. The aim of this study was to uniformly cool melt within a cavity. An optimization was conducted to design conformal cooling channels, which alleviated the thermally induced residual stress formed during the manufacturing of optical lenses and solved the uneven shrinkage problem. Such optimization can also effectively shorten the cooling time and enhance the cooling efficiency during the manufacturing process, which can improve the image quality of plastic optical lenses.