esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Currently, there are only a few reports on the Raman response function of CHG materials with a multi-peaked structure Raman gain spectrum, and the use of a traditional single Lorentzian model can significantly impact the accuracy of the Raman response function. In this study, Ge-Sb-Se fiber was experimentally prepared and Raman spectra, refractive index, and other material parameters were obtained. According to the multiple vibrational mode model that was proposed by [PERSON] based on previous spectroscopic data and an earlier proposed model [31], we establish a multi-vibration model in Ge-Sb-Se fiber for the first time to solve the Raman response function. The Raman gain spectrum obtained by performing a Fourier inverse transform of the Raman response function, which is calculated using this model, exhibits remarkable agreement with the Raman gain spectrum derived directly from the Raman spectra. This robust correspondence serves as compelling evidence for the effectiveness of our proposed model. The Raman response function generated by the multiple vibrational mode model and single Lorentzian model are brought into the generalized nonlinear Schrodinger equation (GNLSE) to simulate SC generation, and the results showed that the SC calculated using the Raman response function obtained from the multi-vibration model exhibited a better fit with the experimental results. This multi-vibration model can also be applied to other materials with a multi-peaked structure Raman gain spectrum. It provides a more accurate solution for the Raman response function based on material characteristics and serves as an important basis for more precise simulation and optimization of SC and Raman laser output.
## 2 Modified Porosity of Smectite as a Function of Temperature
### Smetic Hydration Properties  
Montmorillonite is a swelling clay of the smectite group and the main mineral in bentonite (65-90 wt.%) [35; 36]. Na-type smectite has superior swelling and lower permeability, and thus is favored as a buffer. The isomorphic substitution of Al\({}^{3+}\) and Mg\({}^{2+}\) in smectite octahedral sites, or of Si\({}^{4+}\) by Al\({}^{3+}\) in smectite tetrahedral sites, generates an excess negative charge on the smectite structure; this is compensated for by the adsorption of cations on the smectite layers. Figure 4 shows the smectite clay mineral structure. If the interlayer of smectite adsorbs water, that water is called interlayer water. [PERSON] [37] and [PERSON] and [PERSON] [38] have stated that the smectite interlayer includes three discontinuous basal spacings of approximately 10 A, 12.6 A (one layer of water molecules), and 15.7 A (two layers of water molecules) in a subsurface hydrogeological environment. The interlayer region in the silicate structure can be considered a solvent; the water is the solute dissolved in the solvent, and the solution is called a solid solution. Using O\({}_{10}\)(OH)\({}_{2}\) as a base crystal chemical unit, [PERSON] and [PERSON] [38; 39] employed nine oxide formula units (K\({}_{2}\)O, Na\({}_{2}\)O, CaO, MgO, FeO, Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), and H\({}_{2}\)O) and chemical composition data to describe the composition of the smectite solid solution and the generalized chemical formula of the smectite composition end member as (A\({}_{0.3}\)Al\({}_{1.9}\)Si\({}_{4}\)O\({}_{10}\)(OH)\({}_{2}\), where A represents monovalent or divalent cations expressed according to their monovalent equivalent, e.g., Ca\({}^{2+}\)/2). [PERSON] and [PERSON] [38; 39] also expressed hydrated Na-smectite as (Na\({}_{0.3}\)Al\({}_{1.9}\)Si\({}_{4}\)O\({}_{10}\)(OH)\({}_{2}\cdot\)nH\({}_{2}\)O, where n is the number of moles of water in the fully hydrated Na-smectite). The chemical and thermodynamic properties of interlayer water differ from those of pore water. Interlayer water is water that bonds to a mineral to form a hydrated mineral. When dehydration occurs, the interlayer water is released from the hydrated smectite to form H\({}_{2}\)O and a homologous anhydrous 2:1 layered silicate counterpart. This behavior is analogous to the reversible intracrystallization reaction of a solid solution [38; 39].  
\[\text{hs}\leftrightarrows\text{as}+\text{nH}_{2}\text{O} \tag{3}\]  
where hs denotes hydrous smectite, as denotes anhydrous smectite, and n is the number of moles of water released from one mole of hydrous smectite. When the smectite is exposed to higher temperatures of waste decay heat, the reaction (3) tends to move to the right and the thermal conditions may cause smectite dehydration (i.e., when interlayer water is expelled at the basal spacings from approximately 15.7 to 10 A) [40].
## 2 Materials and Methods
### Depletion of MICA and HLA-A Transcripts Using the shRNA Gene Knockdown System  
The shRNA lentiviral system was used to create SNU719 cells with the depletion of the MHC class I chain-related protein A (_MICA_) transcript or _HLA-A_ transcript. The shRNA sequence was designed through a web tool based on the shRNA design algorithm ([[https://portals.broadinstitute.org/gpp/public/seq/search](https://portals.broadinstitute.org/gpp/public/seq/search)]([https://portals.broadinstitute.org/gpp/public/seq/search](https://portals.broadinstitute.org/gpp/public/seq/search))) accessed on 1 August 2021. The sequence used to knockdown _MICA_ was 5\({}^{\prime}\)-GGCCATGAACGTCAGGAATT-3\({}^{\prime}\), and the sequence used to knockdown _HLA-A_ was 5\({}^{\prime}\)-CTCCACTCCATGATGATTT-3\({}^{\prime}\). An shRNA targeting human _MICA_ and an shRNA targeting human _HLA-A_ were designed and cloned into the pLKO.1 vector (catalog number 8453, Addgene). The resultant pLKO.1 vectors were named pLKO.1-MICA shRNA and pLKO.1-HLA-A shRNA vectors, respectively. The pLKO.1 vector, pLKO.1-MICA shRNA vector, and pLKO.1-HLA-A shRNA vector were cotransfected with lentiviral packaging plasmids (psPAX2 and pMD2.G) into HEK293T cells for 36 h using Lipofectamine 2000 transfection reagent (11668019, Invitrogen). The viral supernatant was harvested, filtered, and then used to infect SNU719 cells for 24 h at an MOI of 1. The viral supernatant was then replaced with fresh RPMI medium, and infected cells were selected by treatment with 1.5 \(\upmu\)g/mL puromycin every 2 to 3 days.  
The knockdown efficiency of MICA was assessed using reverse-transcription qPCR (RT-qPCR) and Western blot analysis, which confirmed significantly reduced levels of mRNA and protein expression, respectively (Supplementary Figure S1C,D). Similarly, the knockdown efficiency of HLA-A was confirmed using the same methods, showing decreased HLA-A expression (Supplementary Figure S1E,F).  
The resultant SNU719 cells with the depletion of _MICA_ and _HLA-A_ transcripts were named SNU719_MICA(\(-\)) and SNU719_HLA-A(\(-\)) cells, respectively. SNU719 cells infected with a lentivirus and packaged only with the pLKO.1 vector as a control group were named SNU719_PLKO.1 cells.
## 1 Introduction
[PERSON] [14] and [PERSON] et al. [15; 16] conducted 32 fatigue tests on M20 and M30 high-strength bolts under constant amplitude and collected 35 more test data points generated by Chinese scholars. They determined the S-N curve by unified regression analysis and devised the corresponding constant-amplitude fatigue design method. The allowable nominal stress amplitude for a fatigue cycle (\(N\)) of 2 \(\times\) 10\({}^{6}\) was 34.0 MPa. In the meantime, the variable-amplitude fatigue tests were conducted on 21 specimens under five loading patterns, and the Corten-Dolan theory estimated the fatigue life under variable amplitude. [PERSON] [17] and [PERSON] et al. [18] performed 71 constant-amplitude and 25 variable-amplitude fatigue tests on M30 and M39 high-strength bolts with an MTS fatigue testing machine. They determined the related S-N curves and devised the corresponding constant-amplitude fatigue design method. As a result, based on a fatigue cycle of 2 \(\times\) 10\({}^{6}\), the allowable nominal stress amplitude of M30 and M39 bolts was 58.91 and 40.82 MPa, respectively. In summary, current studies have focused on the high-strength bolts below M39 used for bolt-sphere joints, and the study on the fatigue performance of high-strength bolts with a huge diameter over M39 is still lacking. However, the M60 high-strength bolt has a much larger diameter than bolts of M39 and below. Due to the influences of size effect and manufacture technique, M60 high-strength bolts always feature a discrepancy in fatigue performance. Furthermore, there is also no relevant stipulate in any specification, which restricts the application of large-diameter high-strength bolts in industrial plants. Therefore, it is necessary to study the fatigue performance of large-diameter high-strength bolts. In addition, there is a lack of fatigue design methods in relevant Chinese and foreign codes such as [19]. Therefore, this paper conducted experimental studies on the fatigue performance of M60 high-strength bolts with a huge diameter under constant amplitude and expected to establish their fatigue design method so as to enhance the vigorous development of bolt-sphere joints of suspended crane-equipped grid structures and used in industrial construction. Eurocode 3 [20] did not validate the S-N curve for high-strength bolts larger than 36 mm in diameter, but this test could fill this gap. It was not including the connection types of bolt-sphere joint of the grid structure in Members and Connections for Fatigue Calculation of Standard for Design' of Steel Structures (GB50017-2017) [21]. The above reasons restrict the development of bolt-sphere grid structure. This test filled in the relevant content of the above specifications, accumulated the fatigue performance data of high-strength bolts in bolt-sphere joint grid, which was helpful to the prediction of fatigue life and provided a basis for structure designers. The results of this test could promote the development of bolt-sphere joints, especially in large plants with suspension cranes.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 4 Results
### Results of the Instantaneous Self-Assessment  
Tables 10 and 11 show average and maximum ISA values along the whole contingency simulation period and within the period in which the RPAS was actively controlled by an ATCO (scenarios from (c) to (f)). ISA values corresponding to the baseline scenario (a) can also be observed in those tables, being complemented by previous results for scenarios (a) and (b) depicted in Table 12. The ISA values represent the ATCO self-assessed workload perception recorded during the simulation. Note that, in addition to the ISA values, the aforementioned tables also report the ATCO reaction time from when it was queried to report the ISA value to the time it responded.  
The ISA reported values show that the ATCO controllers worked with normal, low, or very low workload levels. In all cases, the contingency-related scenarios (c)-(f) have acceptable workload levels, with a 2.64 average maximum in scenario (f) and values for the rest of the scenarios between 1.4 and 2.4.  
Three factors support the conclusion that the ATCO workload increase while managing RPAS contingencies remains within an acceptable range. First, the average ISA values when the RPAS is in the managed sector show minimal increases over the whole average values (limited to around 0.2-0.3 points). Just in one case, the workload when the RPAS was in the sector was smaller than the average value. Second, the maximum registered ISA values when the RPAS was in the sector were not much larger than the average values and, in some cases, they were even smaller than the maximum values registered for the whole simulation. Finally, if we compare with the baseline values in Table 12, we can observe that the recorded contingency-related ISA values are not significantly larger than those baseline values.  
The ISA values in Table 12 also show that for baseline scenario (b) and for the periods in which the RPAS was actively managed, the Run1 average values are larger than the Run2 values. Moreover, those Run1 values are much larger than those later attained in the scenarios from (c) to (f). This ISA decrease is mainly attributed to the familiarisation of the ATCO with the system interfaces and especially to the operational traffic flows employed in the simulation sectors. Note that familiarisation with traffic should not be considered a negative factor; on the contrary, the ATCOs need to train in their assigned sectors to understand the existing traffic flows and become proficient in their management.  
A secondary factor to be considered is the ATCO response time, which shows the short average and maximum response times. Even further, the response times recorded for the whole duration of the simulations show longer response times than when the RPAS is suffering a contingency. Even though long response times are not necessarily associated with high-workload situations, observing short response times seem to indicate that the workload levels remain reasonable.
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 3 Proposed System
### Process Flow of Proposed System
#### 3.2.1 Sensing Data Request  
Figure 5 shows the process flow from the data requester sending the sensing data request to the \(SP\) until other users accessing the request. (1.1) The data requester sends the description and schema of the required sensing data, the amount of reward for providing the data, and the data requester ID \(K_{pub}\big{(}u_{dr}\big{)}\) to the \(SP\). (1.2) The \(SP\) stores the received request information in the request database and randomly selects one of the data processing nodes that are processing the fewest requests, and it allocates the request. Therefore, it is possible to allocate the requests evenly to all data processing nodes and maximize the processing efficiency. (1.3) The \(SP\) sends the request information to the assigned data processing node, which uses the data requester ID to transmit the encrypted data after the sensing data are collected. (1.4) The \(SP\) discloses the request information and the address of the allocated data processing node in the request database so that the user can access the request.  
Figure 4: Process flows of proposed system.  
By randomly assigning a sensing data provision request, an owner of a malicious data processing node is less likely to be assigned a specific request and, therefore, sensitive personal information included in the corresponding sensing data cannot be obtained.
# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 3 Digital Twin  
The DT is the practical, discrete-event simulation model in the industrial company. This model deals with supplying the production lines using AGVs.  
Large parts are supplied by trailers, and it is not possible to load a big number of these parts to satisfy the needs of the production line for a longer time. Hence, more trailers must be used for transport at one time. It is also not possible to control the supply in a way that if the supply falls below a certain level, then a requirement would be generated for transport from the warehouse (except for a limited number of some parts). This is caused by the transport time which is longer than the time of consumption of the parts transported to the production line.  
The whole system of supplying the production lines is based on a simple principle: a tractor with trailers continually transports the parts and after unloading the parts it goes to the warehouse or to pre-production for new parts and then transports them immediately to the production lines.  
The limited capacity of the buffer (parts storage) on the production line is a regulator in this case. Each tractor has a defined path using different loading and unloading stations which must be passed. The various types of parts are loaded and unloaded at different stations in the company. The parts can be loaded on the trailer at the loading stations in the warehouse or at the various production departments in the company. Each production line has several unloading stations for various parts. A schematic layout of the loading and unloading stations is shown in Figure 5[77].  
The decision variables are the number of each AGV type in the DT. The following figure illustrates a situation where a collapse of the whole transport system occurred. One AGV trailer blocks another AGV conveying parts to another production line--see Figure 6.  
The optimisation process of the AGV transport is to utilise the tractors with trailers (conveying the small and big parts from the warehouse and the finished products from the production lines) at maximum capacity. The aim is also to utilise all the production lines.  
Figure 5: Simple layout of loading/unloading stations for AGV.  
Figure 6: Sample of AGV collapse.  
The objective function is composed of the following function definitions (the average use of the production lines is superior to the average use of the trains using the coefficients in the objective function):  
\[F(\mathbf{X})=\frac{\sum_{i=1}^{m}(10-N_{i}(\mathbf{X}))}{1000}+\sum_{i=1}^{n}U_ {i}(\mathbf{X}) \tag{9}\]  
where  
* \(F(\mathbf{X})\) denotes the resulting objective function (maximisation);
* \(N_{i}\) denotes the number of AGVs of the same type;
* \(m\) denotes the number of AGVs of different types;
* \(U_{i}\) denotes the utilisation of the \(i\)-th production line;
* \(n\) denotes the number of production lines.
## Appendix B Liquid Injection and Flow Rate Verification  
Since the motorized syringe pump and the mass flow controller are key instruments to generate the concentration of acetic acid in the standard gas generation system, the reliability of these instruments was verified. To verify that the motorized syringe pump provided a correct rate of injection, a known volume of water was injected into the empty vial. The mass of the vial was weighed before and after injection. The results of the mass of injected liquid and the set point were compared, and the difference between them did not exceed 3%. The rate of injection was constantly verified visually during the experiments.  
A similar verification for flow rate was completed to assure that the system did not leak. Measurements for three different flow rates were compared with the mass flow controller and the flowmeter. The difference between readings on the flow controller and the flowmeter depended on the flow rate. Smaller flow rates yielded a higher difference between readings on the two instruments.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Proposed Design** & **[22]** & **[23]** & **[24]** \\ \hline Frequency Band & 3.5-3.8 & 1.68-4.15 & 2.8–5.13 & 3.3–6.0 \\ Gain (dBi) & 4.5–5 & Not specified & 0.6-3.7 & 5–6 \\ Isolation (dB) & \(<\)–30 & 16 & Not specified & \(>\)15 \\ Size (mm\({}^{3}\)) & 75 \(\times\) 75 \(\times\) 18.2 & 130 \(\times\) 130 \(\times\) 30 & 86 \(\times\) 86 \(\times\) 1 & 140 \(\times\) 140 \(\times\) 1.5 \\ MIMO Ports & 4 & 4 & & 16 \\ Substrate & FR-4 & Not specified & F4 BME & FR-4 \\ Real-World & 200 Mbps & None & None \\ Validation & throughput & None & None \\ Complexity & Low & High & Moderate & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Sub-6 GHz Indoor Base Station Antennas. above the MIMO antenna, the broadband gain was nearly doubled, achieving a stable range of 4.5-5 dB across the entire target frequency band, compared to the initial 1.2-3 dB. Furthermore, this configuration substantially reduced mutual coupling to below \(-\)30 dB for adjacent elements. 3. The proposed MIMO antenna underwent rigorous real-world evaluation using an open-source 5G core and centralized unit/distributed unit (CU/DU) platform. Additionally, a walk test was conducted to benchmark its performance against commercial off-the-shelf (COTS) designs. These tests confirmed the antenna's suitability for deployment in 5G indoor base stations. The paper continues with the following sections. Section 2 details the design and results of the designed unit cell of the antenna, MTM, and array design. Section 3 provides an analysis of the proposed antenna, accompanied by an SDR of ORAN as a proof of concept for indoor wireless communication and a walk test for suitability of deployment. In the last section, the paper concludes with the Section 4.
## 2 Background
### Maintainability Index Variants
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{l}{**Computational Complexity**} & \multicolumn{2}{l}{**Logical Complexity**} & \multicolumn{2}{l}{**Software Size**} & \multicolumn{2}{l}{**Code Documentation**} \\ \cline{2-7}
**Maintainability** & **Halstead’s** & **[PERSON]’s** & **[PERSON]’s Cyclomatic** & **Lines of** & **Number of** & **Percent of Lines of** \\
**Index Variant** & **Effort** & **Volume** & **Complexity** & **Code** & **Statements** & **Comments** \\ \hline \(Ml_{orig.}\) & X & & X & X & & \\ \(Ml_{orig.(CM)}\) & X & & X & X & & X \\ \(Ml_{impr.}\) & X & X & X & & & \\ \(Ml_{impr.(CM)}\) & & X & X & X & & X \\ \(Ml_{LOC}\) & & & & X & & \\ \(Ml_{IH}\) & & X & X & & X & \\ \(Ml_{IH(CM)}\) & & X & X & & X & \\ \(Ml_{VS}\) & & X & X & X & & \\ \hline \hline \end{tabular}  
* Mark “X” is used to indicate the inclusion of a software metric for the computation of a specific Index variant.  
\end{table} 
Table 1: The inclusion of software metrics for the computation of Index variants and software aspects the metrics measure. Dependencies. [PERSON] [17] proposed the Index using the object-oriented Martin's metrics suite, where Distance From the Main Sequence, Afferent Coupling, Number of Concrete Classes, and Efferent Coupling are needed for computation. The author also proposed the Index using the object-oriented Chidamber and Kemerer metrics suite, where Coupling Between Objects and Response For a Class are considered for computation. The evolution of the initially proposed Maintainability Index into numerous variants has resulted in a varying usage of Index variants in the context of maintainability assessment. This issue has already been noted by systematic literature reviews conducted in the research field [43,45]. The most widely used variants and their example usage in the literature regarding object-oriented software are summarized in Table 2. The table highlights that different Index variants are used across the research community, while using software based on various object-oriented programming languages. The Index variants are used for several purposes, including maintainability monitoring [10,22,23,24,25,26,27,62], exploration of the relationship between maintainability and software metrics [14,15,16,20,28,29,63], characterization of architectural design patterns in view of maintainability [64,65,66,64], investigation of the impact of community patterns on software maintainability [67], technical debt detection [30], and machine-learning-based fault, change proneness, and maintainability trend prediction [31,32,33,34,35].