esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and methods
### Characterization of BCs  
The surface area of BCs was analyzed by using the Brunauer-Emmett-Teller (BET) method, and the area was determined using N\({}_{2}\) adsorption data at 77K (TriStar II, Norcross, Micromerities, USA). The elemental contents of C, N, and H in feedstock and BCs were determined by a Thermo elemental analyzer (FLASH EA1112, Waltham, Thermo Scientific, USA). The O content was calculated based on the difference of the C, H, N, and ash contents. In addition, O/C and H/C were also calculated by O, H, and C (mol/mol). According to the experimental method in a previous study, the pH at point of zero charge (pH,pzc) was measured to determine the surface charge of BCs [19]. XRD (X-ray diffraction) analysis (SmartLab, Rigaku Co., Japan) was conducted by scanning at a range of 2\({}^{\circ}\) to 80\({}^{\circ}\) with a step size of 0.02\({}^{\circ}\) (29) to determine the metal crystallization on the surface of BCs. The surface functional groups on BCs were analyzed by Fourier transform infrared (FTIR) spectroscopy (FT/IR-4600, Jasco, Japan) at a wavelength range between 400 and 4000 cm\({}^{-1}\). X-ray photoelectron spectroscopy (XPS) analysis (K-Alpha, Waltham, Thermo Scientific, USA) was performed to observe the binding energy of surface compositions on BCs.
## 4 Security Threats and Attacks
### Attacks on Application Layer  
The application layer performs complex and intelligent functions in the CPS system. Attackers can access important information from the computing systems via network interface cards or serial ports. The attacker can distract the computation of important components including the process scheduler and file systems. As the size of CPS increases, it becomes more complex and vulnerable to attackers. Two types of attacks occur at the application layer. The first is the hardware attack at the application layer by disturbing the working of computational components like the power supply, CPU, and DRAM of CPS. Hardware attacks create intentional faults to the hardware such as system crashes by cutting off the power supply. When the computational system stops working, it lost control over the physical system. These types of attacks are executed by intriguing malicious malware. Hardware attacks include power viruses, thermal attacks, and row hammer attacks.  
The second is the software attack that disturbs the flow and execution of applications by generating system errors. These attacks generate wrong and faulty responses to requests of the physical layer. These faulty commands lead to defective control over the system. Software attacks at the application layer include black door attacks, false-code execution, Stuxnet attack, BlackEnergy malware, and Triton. These application layer attacks cause malfunctioning of software by changing parameter settings causing degradation of performance and emergency stop of autonomous service. A cyberattack known as a \"man-in-the-middle\" (MitM) involves the interception of communications between two parties, frequently in order to acquire login credentials or personal data. Numerous cryptographic techniques, including multi-party computation, proxy re-encryption, and homomorphic encryption, are used to protect the privacy of devices and data integrity [39].
## 3 Results and Discussion
### High-Throughput Sequencing Data Analysis
Principal coordinates analysis (PCoA) was used to explore and visualize similarities between the fungal communities present in each sample. Samples ordinated closer together have a greater similarity than those that are more distant. The PCoA results with Bray-Curtis distances at the genera level showed that samples from different biodeterioration patterns were not closely grouped together. Unweighted pair-group analysis (UPGMA) based on the relative abundance of fungal communities revealed a distinct clustering pattern between samples that originated from different biodeterioration patterns. Samples L2 and L3 were the most relatable in terms of genera composition; results agree with the Venn diagram and heatmap previously presented in Figures 5 and 6, respectively. Sample L1 was slightly more separated from L2 and L3, while sample L4 showed a strong separation from all the others (Figure 7a,b). This indicates that the compositions of fungal communities in these samples differ significantly from each other (PERMANOVA \(p\)-value \(<\) 0.005). The saline environment present in sample L4 was probably the major factor shaping this unique community. Figure 6: Heatmap representing the distinct distribution pattern of the 20 most abundant fungal genera across different biodeterioration types. Dark red color blocks represent a higher prevalence. Vertical clustering denotes the similarity in genera composition among samples. Horizontal clustering indicates the similarity of their abundance. Through the SIMPER analysis, which calculates the contribution of each genus (%) to the dissimilarity between each two groups, we verified that _Needevriesia_ (20.15%) and _Cladosporium_ (6.5%) were the genera that contributed the most to the dissimilarity between samples L1 and L2/L3. Regarding sample L4, _Carpodiales_unclassified (17.5%), _Cladosporium_ (9.74%) and _Parengyodontinu_ (3.1%) accounted for more than 30% of the total differentiation from L2/L3. The dissimilarity among samples L1 and L4 was mainly due to the high impact of _Carpodiales_unclassified (15.34%), _Needevriesia_ (12.71%) and _Cladosporium_ (5.7%). These results support the theory that specific population profiles were stratified by different biodeterioration patterns.
## 4 Application of CRISPR/Cas9-Based Genome Editing Technology in _A. orgyaeae_ and _A. niger_  
The clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system is a highly efficient and revolutionary genome editing technology, originally derived from the adaptive immune system of bacteria and archaea [40]. This system is primarily composed of two core components: a single guide RNA (sgRNA) and the Cas9 nuclease. The sgRNA directs the system to target specific DNA sequences through complementary base pairing, while the Cas9 nuclease executes double-stranded DNA cleavage at the designated sites, enabling precise genome editing [40; 144]. In recent years, CRISPR/Cas9-mediated genome editing technology has been effectively utilized in both fundamental research and industrial production of natural products and recombinant proteins in the genus _Aspergillus_, with _A. niger_ and _A. orgyae_ emerging as the most extensively utilized species [40; 145; 146].
## 4 Simulation and Experiment Analysis
When the waveforms of the \(\mathrm{I_{DF2}}\) current waveform and the \(\mathrm{I_{DR2}}\) current waveform are examined, one of them shows a current flow while the other does not, and when the pulse is triggered, while the \(\mathrm{I_{DM1}}\) and \(\mathrm{I_{DR2}}\) currents flow, the \(\mathrm{I_{DF2}}\) current does not flow. When the pulse is off, the \(\mathrm{I_{DR2}}\) and \(\mathrm{I_{M1}}\) currents do not flow; on the contrary, the \(\mathrm{I_{DF2}}\) current flows. The \(\mathrm{I_{L2}}\) current is continuous, and is the sum of the \(\mathrm{I_{DF2}}\) and \(\mathrm{I_{DR2}}\) currents, according to [PERSON]'s current law. As for the \(\mathrm{I_{DM}}\) current, we can see from Table 1 that it is a 70 mA rms current. Since this waveform contains high noise at small currents, it is not given as an additional figure. Figure 10: (**a**) \(\mathrm{I_{DF2}}\) current waveform at 80 V under full load of proposed converter; (**b**) \(\mathrm{I_{DR2}}\) current waveform at 80 V under full load of proposed converter; (**c**) \(\mathrm{I_{M1}}\) current waveform at 80 V under full load of proposed converter; (**d**) \(\mathrm{I_{L2}}\) current waveform at 80 V under full load of proposed converter. Figure 9: (**a**) Waveform formed at voltage \(\mathrm{V_{o1}}\) during the transition from full load to half load; (**b**) waveform formed at voltage \(\mathrm{V_{o1}}\) and current Io during the transition from zero load to full load. Finally, in this research, a different approach and method were used compared to other studies, as shown in Table 13. Transformer and diode selections were made meticulously. It is understood that 0.3% cross-regulation has been observed at 50% loading.
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 1 Introduction  
With the continuous expansion of urban space scale and population, public transportation has become a travel choice for more and more people. On ordinary weekdays, the number of public transport trips in Beijing is about 6.1 million people/day, accounting for 30% of the city's permanent resident population, and on weekends, it is about 4.4 million people/day. Among them, the proportion of the direct bus travel is only 43.1% [1]. However, the lack of regional public transportation coordinated dispatching tends to result in a consequent worsening of both the magnitude and variability of the average waiting time. This in turn impacts heavily impacts the level of service. Owing to the inconvenience of transfer, public transportation in China is not attractive to travelers. Some studies have shown that transfer is one of the main factors that affect the travel rate of public transportation [2]. The travel time with transfer is obviously longer than the travel time without transfer in Beijing. The average time of a trip in the public transportation system is about 1 h, and the transfer time is more than 10 min [1, 3]. So, it is necessary to study the coordinated dispatching problem of regional public transportation to reduce the transfer waiting time of passengers and improve the service level of public transportation.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Electrical Quality Metric  
As previously described, critical charge is an indicator of how robust SRAM is against soft errors and is also a metric for evaluating the usefulness of SRAM cells for space applications. However, in conventional SRAM design, the design parameters described in the previous sections, such as the RSNM, RAT, WAT, WWTV, and HPWR, should also be considered. Hence, it is preferable to evaluate the overall performance of the SRAM cell through a design metric that can measure the overall performance rather than considering individual parameters, since each parameter is in a trade-off relationship with the others. [PERSON] et al. proposed the Electrical Quality Metric (EQM) in [4] to estimate the overall performance of SRAM, and the equation for the EQM is given as follows:  
\[EQM=\frac{Q_{C}\times RSNM\times WWTV}{H_{PWR}\times WAT\times RAT\times Area} \tag{3}\]  
The design parameters shown in (3) were measured for all comparison cells and summarized in Table 1, showing the values at VDD = 1 V. If the overall performance of the cell increases, the EQM value should increase. For comparison, the relative EQM of various cells (\(EQM_{comparison}/EQM_{RHHS16T}\)) was measured and is presented on a log scale in Figure 12. Moreover, although the proposed RHHS16T cell can recover from soft errors above 100 fC, the EQM was calculated with \(Q_{C}\) set to 50 fC to demonstrate the superiority of the RHHS16T cell. As shown in Figure 12, the proposed RHHS16T cell shows the highest EQM, indicating that it shows overall better performance compared to the comparison cells.
## 5 Experiments  
For the purpose of objectively evaluating the proposed method's predictive performance, we conducted three experiments using the 10-fold cross-validation methodology:  
* CNN, where the conventional approach for training the adapted VGG19 CNN model was used;
* CNN-TL, where the transfer learning methodology was used with handpicked hyper-parameters for fine tuning; and
* CNN-TL-DE, where our proposed method which utilizes the DE for the optimization of hyper-parameters for fine tuning of the transfer learning was exploited.
## 7 Discussion
However, it should be noted that the model's effectiveness relies upon the quality and volume of historical data, and there is noticeable variability in performance across different project datasets, suggesting a potential issue with the generalizability of the models. It is essential to understand the distribution characteristics of the dataset used in certain projects. In some projects within the TAWOS dataset, the distribution of SPs is heavily skewed, with many values clustering around the mean and a few extreme outliers. Thisskewed distribution poses a significant challenge for predictive models, as they attempt to learn from both the central tendency and the outliers simultaneously. Standard estimation methods, such as the mean estimator and median estimator, naturally perform well in these scenarios because the mean and median minimizes the overall error for a central cluster of data points. When the majority of SPs are close to the mean or median, standard estimation methods can appear deceptively effective. This elucidates why, in some cases, the proposed advanced models, which aim to capture more complex patterns and nuances, may not always outperform simple naive estimates. These findings hold significant implications, as they underscore the efficacy of the proposed model in accurately estimating SPs, a crucial aspect of project management in software development. By achieving lower error rates and higher SA compared to both traditional and advanced models, this study not only validates the use of robust transformers in feature extraction but also emphasizes the importance of sophisticated estimation techniques. The relevance of this study extends beyond academic circles, carrying practical implications for project managers and software development teams seeking more reliable estimations of effort and time. In conclusion, this research makes a substantial contribution to the field of software development estimation by combining advanced NLP techniques with ML models. The comprehensive comparisons with both standard and state-of-the-art methods not only highlight the quantitative potential of the proposed model for more efficient and effective project management but also encourage further exploration into the integration of these techniques for broader applications in software development and project estimation.
## 2 Materials and Methods  
### Pushover Analysis  
In the field of structural engineering, the pushover analysis method is used to estimate how a structure will respond to increasing lateral forces and a consistent vertical load distribution until a target displacement is achieved. The process involves multiple sequential elastic analyses that together form an approximation of the force-displacement curve for the entire structure. To begin, a two- or three-dimensional model is created that includes load-deformation diagrams for all lateral force-resisting elements. Vertical loads are applied first, followed by a pre-defined lateral load pattern that is distributed throughout the building height. The lateral forces are gradually increased until some of the structural elements begin to yield. At this point, the model is adjusted to account for the reduced stiffness of the affected elements, and the lateral forces are increased again until additional elements yield. This process is repeated until a controlled displacement at the top of the building reaches a certain level of deformation, or the structure becomes unstable. The resulting data are plotted to create a capacity curve that shows the relationship between roof displacement and base shear [47]. There are two types of static pushover analysis: force-controlled and displacement-controlled. In the force-controlled pushover procedure, the specific load combination is applied as directed. This procedure should be used when the load is already known, such as in the case of gravity loading. However, numerical issues can arise with this procedure, affecting the accuracy of the results. The target displacement may be associated with very small positive or even negative lateral stiffness due to the development of mechanisms and P-delta effects [48].  
Pushover analysis, which has a flowchart shown in Figure 5, has become the preferred method for evaluating seismic performance of structures by codes due to its ease of concept and computation. This analysis allows for the monitoring of the overall capacity curve of the structure as well as the sequence of yielding and failure at the elemental and structural levels. The goal of pushover analysis is to estimate critical response parameters as close to those estimated by nonlinear dynamic analysis as possible [49; 50; 51; 52].  
Pushover analysis can provide valuable insight into the elastic and inelastic behavior of structures during earthquakes, given that the structure is appropriately modeled, the lateral load model is selected carefully, and the results are interpreted meticulously. However, for low- and medium-rise buildings with a dominant fundamental mode response, pushover analysis is more suitable. For specialized and high-rise buildings, pushover analysis should  
Figure 4: Heavily damaged column element and bond-slip due to 90\({}^{\circ}\) hooked stirrups.  
be complemented by other evaluation procedures, as higher modes can significantly impact the response [53; 54; 55].
# Teleseismic Tomography for Imaging the Upper Mantle Beneth Northeast China
## 5 Conclusions  
In this paper, the method of data acquisition and reliability evaluation is verified based on the travel time tomographic imaging of distant earthquakes by fast marching method, and then the three-dimensional velocity structure of the upper mantle in Northeast China is obtained by using this method. The results of this study are as follows:  
(1) In this study, three-dimensional P-wave velocity disturbance images in the depth range of 750 km in the northeast of China are obtained, showing the high-velocity anomaly and an obvious low-velocity anomaly widely distributed in the upper mantle of the Songliao basin. The phenomenon of partial melting of the upper mantle exists in the lower part of the Songliao basin and Wudalianchi.  
Figure 14: Section results of the study area, (**a**) 45\({}^{\circ}\) N; (**b**) 44.5\({}^{\circ}\) N.  
Although the low-velocity anomalies beneath the mountain are from the lower mantle, different from other volcanoes, they may come from the independent mantle plume on the west side of the mountain, but the mantle convection caused by the mantle wedge is weak.  
(2) There are obvious low-velocity anomalies in the mantle transition zone beneath the Changbaishan volcano group and Jingpohu volcano. These low-velocity anomalies come from the deeper lower mantle and have a continuous connection with the upper mantle through the plate gap. The geographical location of the volcano has a good corresponding relationship with these low-velocity anomalies. This indicates that the location of the plate gap restricts the location of the Changbaishan volcano group and Jingpohu volcano. The thermal material provided by the plate, together with the heat source provided by the dehydration in the mantle wedge, plays a decisive role in the formation of the volcano.  
(3) The low-velocity anomalies widely distributed in the upper mantle are more or less connected, and the small independent low-velocity anomalies are scattered under the Songliao basin, which indicates that there is more active convection in the upper mantle in the northeast, which suggests that the Cenozoic volcanoes in the northeast have a higher affinity  
Conceptualization, methodology, investigation and writing, Z.J.; formal analysis, project administration, G.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by the Major Projects of the National Science and Technology of China (2016 YFC0600505) and the National Natural Science Foundation of China under Grant 41574109, 41504085 and 41874136. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
CIT Computed Tomography  
FMM Fast Marching Methods  
LET Local Earthquake Tomography  
FLGPL Five-linked-great-pool Lake  
WLVZ West-Low-Velocity-Zone  
LVZ Low-Velocity-Zone  
HVZ High-Velocity-Zone  
ELVZ East-Low-Velocity-Zone  
NSGL North-South Gravity lineament
## 4 Conclusions  
In this study, a PC strand with an embedded FBG fiber sensor (optical sensor PC strand; OSPC strand) was developed and the tensile strength was determined, which was followed by tensile strength, creep, and relaxation tests. The following conclusions were made by performing long-term measurement of prestress force on the PSC structure for more than 1600 days and by comparing the experimental results with the analytical results obtained in the finite-element analysis.  
(1) Tensile test results indicated that prestress force measurement was performed smoothly from the elastic section to the fracture seed using the OSPC strand developed in this study, and the ultimate strength of the OSPC strand was about 2200 MPa, demonstrating a performance that is similar to or better than a general PC strand at 1860 MPa.  
(2) As a result of carrying out the creep test, to keep the load constant, and the relaxation test, to keep the displacement constant, for more than 1000 h, the long-term performance of the OSPC strand alone was also superior to that of the general PC strand.  
(3) From the results of measuring the change in prestress force for more than 1600 days after manufacturing a PSC beam specimen by installing an OSPC strand and introducing prestress force, the change in the prestress force was well represented according to the change in the external temperature. Furthermore, considering the properties of the optical fiber sensor, which was significantly affected by temperature, separate test specimens were produced by being divided into composite and non-composite specimens depending on whether the optical fiber sensor was embedded in concrete, and experiments were performed by varying the temperature to calculate the coefficient of thermal expansion for each case.  
(4) By performing finite-element analyses, the long-term change in prestress force according to standards such as ACI, CEB-FIP, and KS was numerically expressed over a long period of time, and the suitability was determined by comparing the analytical results with the prestress force data of the actual beam specimens, considering the thermal expansion coefficient.  
Long-term performance verification of the OSPC strand was completed, and the tension of the PC strand inside an actual structure was efficiently measured. This will enable the efficient maintenance of PSC structures.  
**Author Contributions:** Conceptualization, S.-T.K., Y.-H.P. and S.S.; Performing experiments, C.-H.Y., finite-element analysis and data analysis, Y.-S.P.; writing--original draft preparation, S.-T.K. All authors have read and agree to the published version of the manuscript.  
**Funding:** This research was supported by a grant from a Strategic Research Project (Project No. 20210177-001) funded by the Korea Institute of Civil Engineering and Building Technology.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.