esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology: Information Extraction and Teaching Intervention
### Free-Text Analysis--Machine Learning Approach  
#### 3.2.1 Framework Overview  
This study aimed to identify automatically in the NSS data the issues reported in the free-text responses that make negative comment on the open-ended questions. Figure 4 shows the overall architecture of the proposed text-mining method, consisting of two learning components:  
1. Classification: responses were grouped into smaller categories (e.g., teaching quality, assessment, etc.) to reveal the finer-grained issues to fit the NSS categories, as well as to yield a better performance in topic modeling.
2. Topic modeling: for each category, important topics were drawn and interpreted into meaningful issues upon which teaching interventions could be developed.  
Figure 4: Architecture of the proposed text-mining method to identify automatically the problem statements in the NSS free-text responses.  
Figure 3: Relative frequency (%) of negative responses per category in the annotation scheme developed.  
Specifically, the analysis process was broken down into five steps (Figure 4): Step 1 is initiated by a survey researcher manually annotating a small sample of approximately 20% of the NSS open-ended comments, according to pre-defined categories. Step 2 uses the manually annotated samples to train a classification model to 'learn' to discriminate between the various categories of problem statement. In Step 3, the trained model automatically annotates the remaining 80% of the dataset's open-ended comments. In Step 4, an unsupervised topic modeling method automatically identifies the important topics within each problem statement category. Finally, the automatically generated topics are manually inspected and validated, and the results of analysis inform the development of efficient teaching interventions to address the issues raised by the students.
## 6 Simulation Analyses
### Beneficial Region and Transmission Power Requirement
The colored region corresponds to the beneficial region. base station and the user (SNR\({}_{SU}\)) the greater the flying base station transmission power requirement (\(\eta\)). For example, if \(\gamma=2\) (Figure 5b), the flying base station is beneficial if its transmission power meets the following requirements dependent on SNR\({}_{SU}\): \(\eta>0.75\) when SNR\({}_{SU}\) = 0 dB; \(\eta>13\) when SNR\({}_{SU}\) = 10 dB; \(\eta>25.5\) when SNR\({}_{SU}\) = 20 dB. Greater \(\gamma\), (i.e., greater attenuation) also decreases the \(\eta\) requirement (i.e., smaller transmission power ratio requirement on the flying base station) for the beneficial region, which is also seen in the full-duplex case.
## 2 Materials and Methods
### Cell Culture and Treatment  
Human fetal osteoblast (hFOB) 1.19 cells (SV40 large T antigen transfected; American Type Culture Collection) (Cat# ATCC(r) CRL-11372(tm)) were cultured in complete growth media (1:1 mixture of Ham's F12 medium with L-glutamine (Cat#10-080-CV, Cellgro) and Dulbecco's Modified Eagle's Medium with 4.5 g/L glucose and sodium pyruvate without L-glutamine and phenol red (Cat# 17-205-CV, Cellgro) supplemented with 0.3 mg/mL gentamicin sulfate (Cat# 17-518Z, Lonza) and 10% fetal bovine serum (heat-inactivated at 56 \({}^{\circ}\)C) (Cat# S1105H, Atlanta Biologicals) under standard cell culture conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)) with humidified air. After the third passage, cells were seeded (50,000 cells/well) in 1 mL of complete growth media on a glass coverslip in a 24-well plate. Further, cells were incubated under standard cell culture conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)) with humidified air for 2-3 days to allow for adhesion to a 24-well plate. Experimental groups received the cells cultured in complete growth media; however, negative control groups received media only and cells cultured in complete growth media. Further, 24-well plates were aspirated.  
Various concentrations of MgO/Mg(OH)\({}_{2}\) NPs (1 mM, 5 mM, 10 mM, and 50 mM) were sterilized for 15 min in an ultraviolet (UV) light before exposure to hFOB 1.19 cells to prevent any microbial growth. After sterilization, each concentration of MgO/Mg(OH)\({}_{2}\) NPs was immersed in 1 mL of complete growth media in a 15 mL tube (Cat#05-539-5, Fisher brand). Control groups received 0.5 mL of freshly prepared complete growth media; however, experimental groups received 0.5 mL of various concentrations (1 mM, 5 mM, 10 mM, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs prepared in complete growth media on the 24-well plates. Samples were incubated under standard cell culture conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)) with humidified air for 30 min. Each sample was in triplicate for the reproducibility of the results. Further, 24-well plates were aspirated, and samples were rotated slightly in 1X phosphate-buffered saline (PBS) (Cat#21-040-CM, Cellgro) maintained at pH 7.4 to remove nonadherent cells. They were allowed to sit for 10 min at room temperature (RT).
# Simulation of Track-Soft Soil Interactions Using a Discrete Element Method
## 3 Results and Discussion
### Soil Shear Characteristic Verification of Track Segment  
#### 3.2.1 Comparative Analysis of the Results of the Soil Shear Test and DEM Simulation  
As the shear displacement of the track segment increases, more soil is accumulated in front of the track segment (marked by the red circle in Figure 7). Additionally, the test data collected by the force sensor include the shear force between the track segment and the soil and the resistance caused by the soil accumulation in front of the track segment. The shear stress-displacement test result has great errors without completely removing the influence of \"bulldozing\". However, it is impossible to completely remove the accumulated soil in front of the track segment in the test. As shown in Figure 17, the shear stress values of clay test data increase continuously.  
The accumulated soil in front of the track segment produced a great impact in the measurement and was difficult to remove completely. However, the accumulated soil in front of the track segment could be effectively removed by changing the calculation domain in DEM, which could modify the shear relationship model of the soil and track segment accurately.  
The MAPE of clay, sand and mixed soil were 7.5%, 11% and 9%, which indicated that the prediction model is accurate and effective. Shear stress-displacement fitting data from the test and simulation are shown in Figure 18.  
Figure 16: Friction and cohesion characteristics of different soils. The cohesion and friction characteristics of scaled-up particles were calibrated by DEM simulation. The two ordinates are the cohesion and friction of scaled-up soil particles. The abscissa is the soil composition.
## 1 Introduction  
Chitin, an aminopolysaccharide composed of N-acetyl-D-glucosamine (GlcNAc) units linked by \(\beta\)(1 4) bonds, is the second most abundant polymer in nature and can be found in fungal cell walls and the exoskeletons of arthropods (crustaceans, mollusks, insects, etc.) [1]. Total or partial deacetylation of chitin yields chitosan, a cationic polysaccharide is consisting of variable amounts of D-glucosamine (GlcN) and GlcNAc moieties [2]. In general, when the degree of acetylation of the polymer surpasses 30%, it is considered chitin, and if it is lower than 30%, it is considered chitosan [3]. To minimize the waste derived from chitin's chemical deacetylation, the development of chitin deacetylases (EC 3.5.1.41) offers a greener alternative [4].  
Both chitin and chitosan exhibit an interesting range of physicochemical and biological properties with numerous applications to the food, cosmetic, agriculture, textile and biomedical industries [5, 6]. However, their use is often limited by their viscosity and poor aqueous solubility.  
Chitooligosaccharidesarides (COS), the hydrolyzed products of chitin or chitosan, are homo- or hetero-oligomers of GlcN and/or GlcNAc linked by \(\beta\)(1\(\rightarrow\)4) bonds [7, 8]. Generally, COS has a degree of polymerization (DP) up to 20 and an average molecular weight (MW) lower than 3.9 kDa. They are water-soluble and possess many interesting biological activities [9, 10, 11, 12, 13, 14]. Unlike chitosan, COS can be absorbed in the gastrointestinal tract and enter the bloodstream [15]. The biodegradability of chitosan and their derivatives has been correlated with the presence in human tissues of lysozyme, a glycosyl hydrolase that can hydrolyze \(\beta\)(1\(\rightarrow\)4) linkages in these molecules [16]. COS are high value-added products with great potential in many areas, including agriculture, cosmetics, functional foods, pharmacy and medicine [17, 18, 19, 13].  
According to their composition, COS can be classified into three different types (Figure 1) [20]: fully acetylated chitooligosaccharides (_fa_COS) (composed exclusively of GlcNAc residues), partially acetylated chitooligosaccharides (_pa_COS) (formed by GlcN and GlcNAc units), and fully deacetylated chitooligosaccharides (_fd_COS) (composed of GlcN residues only). Interestingly, COS can exhibit different degrees of polymerization (DP), degrees of deacetylation (DD) and patterns of acetylation (PA), which results in different biological properties [2]. COS obtained by hydrolysis of chitin or chitosan are heterogeneous mixtures of products, which explains the poor reproducibility and divergent results when their biological properties are tested. Hence, the preparation of well-depicted COS is necessary for a better understanding of their biological properties and the analysis of structure-activity relationships and decipher the molecular mechanisms behind them [3].  
This review is focused on the different approaches for the enzymatic preparation of COS, their characterization, and the influence of COS composition on their anti-inflammatory properties.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 2 Literature Review and Marginal Contributions
### Literature Review
#### 2.1.3 Blockchain Technology  
[PERSON] (2018) first proposed the concept of Bitcoin, and blockchain technology began to emerge and develop on this basis. With its decentralized structure, distributed nodes, consensus algorithms, smart contracts, and asymmetric encryption, blockchain technology has the potential to enhance network security [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018), transparency [PERSON] et al. (2018), and visibility [PERSON] et al. (2018). It has been widely recognized as a technology that has almost unlimited applications in various industries [PERSON] et al. (2018). A large amount of literature has been conducted on the application value of blockchain technology in specific industries [PERSON] et al. (2018), especially fields like finance [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018), medicine [PERSON] et al. (2018), healthcare [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018), and agriculture [PERSON] et al. (2018). [PERSON] et al. (2018) developed a blockchain-enabled supply chain coordination system that facilitated the alignment of diverse decisions made by stakeholders in an off-site construction supply chain. [PERSON] et al. [PERSON] et al. (2018) applied a Stackelberg game model to study the feasibility of using blockchain technology in a fresh agricultural product supply chain composed of e-commerce platforms (B2C or O2O) and offline retailers. [PERSON] et al. [PERSON] et al. (2018) explored the current status of the agri-food supply chain and how blockchain technology could be useful in addressing existing challenges. [PERSON] et al. [PERSON] et al. (2018) examined the impact of blockchain adoption on online game supply chain pricing and enterprise investment decisions under information asymmetry and found that whether enterprises adopt blockchain technology depends on the comprehensive impact of market demand and input-output on quality. [PERSON] et al. [PERSON] et al. (2018) proposed that blockchain technology is more effective in facilitating the financial resilience of a supply chain under the moderating influence of environmental dynamism compared to artificial intelligence. Similar to the above literature, our paper also captures the characteristics of BCT, such as improving data security. However, most of the operations management literature focuses on the application of BCT in supply chains for areas such as agricultural products, medicine, healthcare, and luxury products, and few studies have focused on the application of BCT in satellite supply chains. This paper fills this gap and explores the value of BCT in satellite business models from an OM perspective. Table 1 shows a comparison between this paper and the relative literature.
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 4 Proposed Algorithm
### Centralized Q-Learning in the BBU Pool  
To recommend the communication mode and transmission power, based on Equation (8), we use a Q-learning agent in the BBU pool. For Q-learning in the BBU pool, the state, action, and reward can be defined as follows. The state of the BBU pool at time \(t\) is defined as  
\[S_{BBU}^{t}=\Big{\{}l^{t},\ p^{t}\Big{\}}, \tag{11}\]  
where \(l^{t}\) is the binary level of the average received SINR of the devices at time \(t\), with a value of \(1\) when the average SINR is greater than the target SINR, \(\gamma_{min}\), and \(0\) when the average SINR is smaller than \(\gamma_{min}\). \(\rho^{t}\) is the available RB at time \(t\). The action of the BBU pool at time \(t\) is defined as  
\[A_{BBU}^{t}=\Big{\{}m^{t},p^{t}\Big{\}}, \tag{12}\]  
where \(m^{t}\) is the transmission mode at time \(t\), and \(p^{t}\) is the transmission power at time \(t\). The transmission power \(p^{t}\) is divided into discrete intervals, \((0,\ p_{max}]\). If the transmission mode \(m^{t}\) is the cellular mode \(m_{c}\), the transmission power \(p^{t}\) will be set to \(p_{max}\). The reward of the BBU pool at time \(t\) is defined as  
\[R_{BBU}^{t}=EE. \tag{13}\]  
The centralized Q-learning is summarized in Algorithm 1.  
Figure 2: Operation procedure.
# Numerical and Field Investigations of Tremors Induced by Thick-Hard Strata Fracture
## 2 Numerical Simulation
### Results and Analysis
#### 2.3.2 Evolution of Stress, Displacement and Fracture  
The variations in maximum principal stress around the roadway due to the disturbance of a mining-induced tremor are shown in Figure 6.  
Figure 5: Kinetic energy curves of roadway and source after mining-induced tremor.  
Figure 6: Evolution of maximum principal stress around the roadway: (**a**) 0 ms; (**b**) 100 ms; (**c**) 200 ms; (**d**) 300 ms.  
As seen in Figure 6, the stress on the surface of both sides of the roadway transformed from the compressive to tensile state after the stress wave reached the roadway at 100 ms. At 200 ms, the tensile stress on the roadway surface gradually increased. When the tensile stress reached the tensile strength of the surrounding coal and rock, the coal mass on the left side ruptured. Subsequently, it fell and ejected into the roadway, and a rockburst inevitably occurred. At 300 ms, the stress of the surrounding coal and rock mass of the roadway redistributed, and the compressive stress continued to transfer to the roadway surface and return to the initial stress state. Figure 7 shows the variations in vertical stress, displacement, and crack number around the roadway during the whole process of tremor disturbance.  
As seen in Figure 7, the stress, deformation velocity, displacement of the roadway, and the crack number around the roadway during the roof caving process (the monitoring points are shown in Figure 1) were recorded. Obviously, the initial vertical stress on the right side of the roadway was only 40 MPa, and began to rise gradually when the primary roof began to fracture and sink. Due to the high load, the surrounding coal and rock of the roadway were gradually damaged, the crack number began to increase, and the displacement of the roadway also increased accordingly. When the calculation step was 76,000, the movement of the primary roof had basically reached stability. At this time, the vertical stress did not increase and basically remained stable. As the calculation step reached 157,000, the stress wave arrived at the roadway. The vertical stress of the monitoring point dropped sharply, which may have been caused by the tensile stress generated by the stress wave. The vertical stress at this point was originally compressive stress, and decreased after tremor disturbance. Subsequently, the total number of cracks suddenly increased, and the corresponding vibration velocity fluctuated violently, indicating that the monitoring point was subjected to the repeated tensile and compressive actions of the focal stress wave. Finally, the number of cracks around the roadway reached a peak, which indicated that the damage to the roadway was more serious.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 4 Performance Evaluation
Nevertheless, when the number of PUs was high, the values on the Markov chain tended to be similar. Thus the prediction also did not perform optimally. However, in this scenario, EGSD-G and EGSD-R also outperform MVSD by 46.6% and 44.9%, respectively. We also included a scenario where PUs had a favorite channel in the overhead evaluation. Figure 10 shows the overhead when the numbers of maximum PUs are varied. When PUs have no favorite channel, the overheads of EGSD-G and EGSD-R are similar and increase as the number of PUs increases. Overall, the proposed EGSD has higher overhead than MSDV by 4.9%, on average. We predicted this result because MVSD is a centralized method in which the overhead is fixed, whereas EGSD is a distributed method in which the overhead depends on the network condition. The overhead in EGSD depends on the number of PUs because when the number of PUs increases, the probability of the operating channel becoming obsolete also increases, resulting in more frequent C modes. (In C mode, the time spent on coordination is higher than in D mode.) However, when the PUs have  
Figure 8: Network lifetime when the maximum number of PUs is **(a)** 30; **(b)** 60; and **(c)** 90, where PUs do not have a favorite channel. a favorite channel, the EGSD-G and EGSD-R outperform MVSD by 31.7% and 26.8%, respectively. The reason for this improvement is that when the PUs have a favorite channel, the probability of an arbitrary operating channel of a sensor node being claimed by the PUs is lower, except that the operating channel used is the favorite channel. Hence, the portion of D mode is higher than that of C mode. Moreover, in this result, we observe a slight improvement of EGSD-G over EGSD-R. Last but not least, we determine that there is a case where the distributed method may lead to lower overhead than the centralized method. Figure 10: Network overhead when (**a**) PUs do not have a favorite channel and (**b**) PUs have a favorite channel. Figure 9: Network lifetime when the maximum number of PUs is (**a**) 30; (**b**) 60, and (**c**) 90, where PUs have a favorite channel.
# Two-Photon Excitation of Azobenzene Photoswitches for Synthetic Optogenetics
## 2 Photoswitches  
At the heart of photoswitches lies a light-sensitive core [20]. Of the different kinds, the most prevalent moiety used in biological applications is the azobenzene (Figure 1b) [21]. Its widespread use arises from favorable features, such as high quantum yield, minimal photobleaching, and fast responsivity at biologically-relevant conditions, namely pH, temperature and solubility in water-based solvents [21; 22; 23]. However, its ability to undergo _trans_-to-_cis_ photoisomerization is undoubtfully the main reason; giving rise to robust changes in geometry and end-to-end distance of the molecule (Figure 1b).  
Azobenzene photoswitches commonly include additional chemical groups placed on either side of the light-sensitive core. These can be quite diverse, such as drugs, chemical- or biological tethers, dyes, as well as lipidic structures [9]. The nature of these groups will dictate the mode of action of the photoswitch and whether it is to remain diffusive or immobilized to its target. Diffusive photoswitches (photochromic ligands; PCL) are, in principle, reversible caged-compounds [24]. In contrast, immobilized photoswitches (photoswitchable tethered ligand; PTL [25]) include a unique chemical tether that can conjugate specific residues of the protein (e.g., maleimide to bind cysteines [26]; benzylguanine to a SNAP domain [27]). These restrict the binding (thereby the effect) of the photoswitch to a defined molecule. Importantly, specific amino acids (a.a.) or domains can be genetically-encoded, thereby giving users access to defined populations of cells. Today, most targets for PTLs require genetic modifications, with cysteines as the preferred choice. However, recent advances have made it possible to conjugate photoswitches to endogenous, non-modified receptors by use of strong electrophilic moiety in the PTL to couple with reactive amines and hydroxyl groups, naturally present in several a.a. side chains [28]. Another scheme to bypass protein modification is by attaching the photoswitch next to its intended target. This may include expression of membrane anchors (e.g., a transmembrane  
Figure 2: Graphical illustration of ‘synthetic optogenetics’ emerging in the scientific literature. Cumulative plot of number of publications across years was performed by searching for specific terms (chemical-optogenetics, photopharmacology, optopharmacology, synthetic optogenetics, optogenetic pharmacology, photoswitch, azobenzene and photo), as well as by searching for publications from recognized researchers in the field (e.g., [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], etc.). Search was continuously performed in various search engines, notably PubMed Central, Google Scholar, and bioRxiv. Last query was performed on November 2019.  
domain with the tether facing the extracellular) to conjugate the photoswitch which can then modulate adjacent endogenous proteins [29].
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.1.2. Memetic Flower and Energy-Efficient Pollination Routing Protocol (MFE2 PRP)  
In [131], the Memetic flower and energy-efficient pollination routing protocol (MFPRP) was introduced to improve network quality of service (QoS). The proposed protocol's major goals are to select the route that maximizes data packet ratio while minimizing end-to-end latency in UWSNs. The MFE2 PRP fitness value enhances the network's ability to convey  
Figure 12: Classification of energy-efficient routing protocols.  
communicating data packets through secure connections with little energy consumption. The suggested routing system in [131] was evaluated and compared with other routing protocols, including the balanced multi-objective optimized opportunistic routing protocol (BMOORP) and the QoS aware evolutionary routing protocol (QERP). The suggested routing system achieves successful outcomes in terms of data packet delivery ratio due to the selection of optimum links for data packet communication.