esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison between the Use of Carbamide Peroxide and Hydrogen Peroxide
The 10% CP/PF showed higher bleaching efficacy than the 6% HP/Go; the higher bleaching efficacy of 10% CP/PF may be related to the longer application duration despite its lower HP concentration [39]. [PERSON] et al., analyzed the dental color stabilization of upper incisors and canines in 60 patients undergoing different bleaching techniques: group 1 (35% HP in-office bleaching), group 2 (in-office application of 3% HP followed by in-office bleaching using 35% HP), group 3 (3% HP in-office bleaching), and group 4 (10% CP at-home-bleaching) [40]. Differences in canine's chroma were noted in a short-term evaluation (after the 3 rd session for groups 1, 2, and 3, and 7 days after at-home bleaching): in group 1 and group 2, canine's chroma was significantly lower than in group 4. However, lightness increased in all groups with no statistical difference 60 days after treatment [40]  
Concerning TS, it is strongly linked to the presence of peroxides and their reaction products reaching the pulpal chamber causing an inflammatory response [41]. As a result of the lower concentration of HP available, CP can be used to reduce TS (Figure 3). [PERSON] et al., compared the effectiveness of whitening and its effect on TS of two different at-home bleaching techniques: 37% CP and 35% HP [41]. The results of this clinical study demonstrated that when used in a single 40 min in-office application, high concentrations of CP (37%) effectively achieve satisfactory bleaching effects [41]. Despite producing less color change than 35% HP, 37% CP reduced significantly both the risk and level of TS experienced by patients [41]. HP and CP are used in high concentrations for endodontically treated teeth (non-vital teeth) [42]. [PERSON] et al. evaluate the psychosocial impact and aesthetic perceptions of 42 patients undergoing non-vital tooth bleaching with HP (35%) and CP (37%). The color change (\(\Delta\)E) has been evaluated at 1 week, 1 month, and 3 months after bleaching [42]. The results of the current study demonstrate that both gels were highly effective, and the color change lasts at least three months [42] (Table 5). Figure 3: Chemical mechanism of bleaching systems.
## 2 Methodology
### Principles of Electrolyzer Power Selection  
To effectively integrate green hydrogen production facilities into a municipal WWTP, it is essential to select the electrolyzer power correctly. The selection of electrolyzer power found in the literature is usually based on the oxygen demand for aeration, considering that the hydrogen demand can also be assessed [13]. We propose to evaluate several main technological criteria for selecting the electrolyzer power: (1) possibilities of using the by-product of electrolysis--oxygen; (2) possibilities of using the main electrolysis product--hydrogen; (3) possibilities of using the by-product of electrolysis--heat; (4) availability of renewable electricity for green hydrogen production.  
The maximum oxygen demand is determined by the demand in WWTP's processes--aeration and sulphur removal from biogas. According to some authors, the excess oxygen content is of a relatively low value and should usually be released into the atmosphere, since the compression, storage and transport of oxygen to external users does not seem economically viable [13; 45]. Other authors state that high-purity oxygen from the electrolyzers can have a relatively high market value due to its demand in the steel and chemical industries [14].  
The minimum hydrogen demand is equal to the hydrogen consumption for methanation of CO\({}_{2}\), contained in raw biogas, or biogenic CO\({}_{2}\). The additional hydrogen could theoretically be used in WWTPogenerators for electricity production. A certain amount of hydrogen can also be used as transport fuel in WWTP's transport [13]. According to the EU hydrogen strategy [10], with the growing demand for green hydrogen in industry, energy and transport, the maximum hydrogen production volumes in the WWTP could be practically unlimited.  
The base heat demand is determined, as in the case of oxygen, by the heat demand in WWTP's processes--primarily for sludge drying and bioreactor heating. If WWTP has access to a city district heating (DH) network, then the volume of excess heat production in the electrolyzer can be determined by the needs and capabilities of the district heating system [29; 46].  
The availability of renewable electricity for green hydrogen production is one of the key factors limiting the maximum possible electrolyzer power [9].  
Thus, the optimal power of the electrolyzer must be selected based on a complex analysis of these four criteria.  
However, in individual cases, the selected power of the electrolyzer may be directly limited by any one of the criteria.
## 2 Materials and Methods
### Methods
#### 2.2.3 Evaluation of Cation Exchange and Adsorption Kinetics  
The data obtained by analyzing sample aliquots at defined time intervals provided, for each metal-resin pair, the quantity of the metal ions remaining in solution. From these data, the percentage of removal after 40 min can be determined using the following Formula (1):  
\[Removal\ (\%)=\frac{(Ci-Cf)}{Ci}\times 100 \tag{1}\]  
where \(Ci\) represents the initial concentration of metal ions in the bulk of the solution at time zero, and \(Cf\) is concentration of metal ions in the bulk at equilibrium.  
These results enabled a study on the adsorption kinetics and ion exchange capacity. Generally, the adsorption data obtained for most metal-resin combinations can be adequately described through empirical mathematical models. Numerous investigations have attempted to elucidate the correlation between experimental data and pseudo-first-order (PFO) or pseudo-second-order (PSO) models, focusing on the specific type and dynamics of the adsorption processes involved. Some experimental observations, reported in the literature, indicate that at high initial solute concentrations, the adsorption process predominantly follows first-order kinetics. Conversely, at lower initial concentrations, the experimental data exhibit superior conformity to the pseudo-second-order model [30].  
An alternative interpretation of model preference is predicated on the chemical characteristics of the phenomenon. Specifically, adsorption processes that conform to the PFO model are typically associated with physical adsorption (physisorption), primarily governed by the availability of vacant sites on the material/resin surface. Conversely, processes following the PSO model are attributed to chemical adsorption (chemisorption) [31], characterized by specific chemical interactions between the metal ions and the functional groups present on the material/resin matrix.  
In this work, [PERSON]'s pseudo-first-order (PFO) (Formula (2)) and pseudo-second-order (PSO) equations (Formula (3)) were applied to model the kinetic data:  
\[\log(qt-qe)=logqe-K1t/2.303 \tag{2}\]  
\[\frac{t}{qt}=\frac{1}{K2\times qe^{2}}+\frac{t}{qe} \tag{3}\]  
These equations allow for the determination of the equilibrium adsorption capacity _qe calc._ (in mg of metal per g of material/resin), which can then be compared with the experimentally (_qe exp._) obtained value to estimate the error on _qe_ (_Error qe_).
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 5 Analysis of Computational Results  
### Comparison between Simulation Results and Experimental Measurements  
Four reference points were selected at locations identical to monitoring points K3-1, K3-2, K3-3, and K3-4, to analyze the response characteristics of pore water pressure. The locations of the reference points are shown in Figure 15.  
The calculation results are shown in Figure 16. Under the assumption of solely considering the impact of train vibration loads and not accounting for the effects of temperature increases or rainfall, the pore pressure at each reference point experienced a slight increase after the application of vibration loads. Subsequently, it gradually returned to its original value and fluctuated within a certain range before eventually stabilizing. The magnitude of pore pressure changes at different depths below the tunnel floor, and decreased gradually with increasing depth down the vertical direction as the distance from the tunnel decreased. Away from the tunnel, the pore pressure changes decreased and the changes in speed slowed down. The pore pressure in the soil is proportional to the depth, so the larger the depth, the greater the pore pressure. The overall trend in changes in reference and  
Figure 14: Train vibration load time course curve at v = 80 km/h.  
Figure 15: Reference point location map.  
measured points is basically consistent, and at K3-4, the absolute value of the average relative error of pore pressure is about 8%, within a reasonable range, verifying the reliability of the model. However, the numerical simulation results in this paper show a slightly smaller magnitude of change than actual measurements. This may be due to factors such as changes in groundwater levels due to temperature increases, rainwater precipitation, and the fact that the numerical model uses a single-tube, single-track tunnel structure, while the actual condition is a double-tube, single-track tunnel structure.
## 2 Proposed Prognostic Framework
### Damage Propagation Model  
The rate of growth of a fatigue crack can be expressed using the Paris-Erdogan equation as:  
\[\frac{da}{dN}=C(\Delta K)^{m} \tag{1}\]  
\[\Delta K=Y\sqrt{\pi a} \tag{2}\]  
where \(a\) is the crack size, \(\frac{da}{dN}\) is the crack growth rate per load/impact, \(N\) is the cumulative impact/load, \(m\) and \(C\) are Paris model parameters, \(\Delta K\) is the range of stress intensity factor, and \(Y\) is a dimensionless constant for a given stress range. The Paris model expressed in Equation (1) can be rewritten as a state transition function by Equation (3):  
\[a_{k}=C_{k}(Y\sqrt{\pi a_{k-1}})^{m_{k}}\ dN+a_{k-1} \tag{3}\]  
where \(k\) is the impact index at which the sample is scanned using the OTS technique and \(a_{k}\) represents the damage state.  
Since the damage growth area was split into two regions, two different exponential models with different parameters are necessary to represent the damage propagation. Hence Equation (3) is modified as:  
\[a_{k}=\left\{\begin{array}{ll}C_{1}^{k}(Y\sqrt{\pi a_{k-1}})^{m_{1}^{k}}\ dN+a_{k-1};if\ N_{k}\leq N^{*}\\ C_{2}^{k}(Y\sqrt{\pi a_{k-1}})^{m_{2}^{k}}\ dN+a_{k-1};if\ N_{k}\geq N^{*} \end{array}\right. \tag{4}\]  
where \(N_{k}\) is the current cumulative impact energy and \(N^{*}\) is the threshold jump energy beyond, which the temporal dynamics of damage area evolution turn into Region 2.
## 3 Design of a Resonator and Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline \multirow{4}{*}{Capacitance} & \(C_{ex}\) & 1.86 & pF & \multirow{4}{*}{Characteristic impedance} & \(Z_{ex}\) & 127 & \(\Omega\) \\  & \(C_{in,m,n}\) & 9.72 & pF & & \(Z_{in,m,n}\) & 200 & \(\Omega\) \\  & \(C_{p1}\) & 8.81 & pF & \multirow{4}{*}{Characteristic impedance} & \(Z_{p1}\) & 41.7 & \(\Omega\) \\  & \(C_{p2}\) & 9.36 & pF & & \(Z_{p2}\) & 81.2 & \(\Omega\) \\  & \(C_{S}\) & 6.43 & \(\mu\)F & & \(Z_{qp}\) & 4.17 & \(\Omega\) \\  & \(C_{qp}\) & 1.20 & mF & & \(Z_{T}\) & 86.6 & \(\Omega\) \\ \hline \multirow{4}{*}{Inductance} & \(L_{ex}\) & 2.208 & H & \multirow{4}{*}{Characteristic impedance} & \(Z_{c2}\) & 75.0 & \(\Omega\) \\  & \(L_{in,m,n}\) & 4.43 & H & & \(\theta_{TW}\) & 49.59\({}^{\circ}\) & deg \\  & \(L_{T}\) & 11.0 & mH & & \(\theta_{TV}\) & 27.27\({}^{\circ}\) & deg \\  & \(L_{S}\) & 5.02 & H & & \(\theta_{ex1}\) & 16.6\({}^{\circ}\) & deg \\ \hline \multirow{4}{*}{Physical length} & \(l_{TH}\) & 3.00 & mm & \multirow{4}{*}{Electrical length} & \(\theta_{in1}\) & 32.8\({}^{\circ}\) & deg \\  & \(l_{ex1}\) & 1.00 & mm & & \(\theta_{in2}\) & 11.2\({}^{\circ}\) & deg \\  & \(l_{ex2}\) & 0.52 & mm & & \(\theta_{in3}\) & 6.72\({}^{\circ}\) & deg \\  & \(l_{in1}\) & 2.05 & mm & & \(\theta_{in4}\) & 2.08\({}^{\circ}\) & deg \\  & \(l_{in2}\) & 0.70 & mm & & \(\theta_{in5}\) & 4.64\({}^{\circ}\) & deg \\The fabrication of the proposed resonator is shown in Figure 1:7 Specifically, the fabricated resonator employs a Teflon substrate with a low dielectric constant of 2.54, height of 0.54 mm, and thickness (\(t\)) of 0.018 \(\upmu\)m, and the size of the resonator is 3.0 mm \(\times\) 1.65 mm. The simulated value of Q\({}_{\rm L}\) is 683 at 10.0 GHz for this resonator, and the measured result is 632 at 10.008 GHz. Figure 1:8 Q\({}_{\rm L}\) change process in the resonator design stage. Figure 1:9 Q\({}_{\rm L}\) change according to stub length adjustment.
# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 3 Facial Recognition-Based Voting System  
As illustrated in Figure 2, this section outlines the methodology used in the development of a facial recognition-based voting system. The proposed system comprises two main phases: enrollment and authentication.  
During the enrollment phase, the system captures a facial image of each voter using a deep learning approach. This image is then compared with the features of facial images stored in the facial database. To assess similarity, we utilize the Mahalanobis distance [22], which computes the similarity between the captured image and the stored features, allowing the system to assign the most relevant labels to each image. If the calculated similarity value falls below a predetermined threshold, the system will return an error indicating that the individual cannot be identified. This mechanism underscores the principle of facial recognition: If two individuals are identical, their features will be exceedingly similar [18].  
The facial recognition process typically consists of four key modules: facial image acquisition, data pre-processing and augmentation, feature extraction using ResNet50, and feature comparison. In addition, a facial database is employed to store feature vectors and facilitate the comparison of facial features.
## 3 Discussion
### Acute HCV Infection Related Kegg Pathways  
RNA sequencing analysis identified several host response pathways, including adipocyte signaling, insulin signaling, and RIG-I signaling, that were not identified in prior analysis of acutely HCV infected Huh 7.5 cells [9,10]. TGF-beta signaling was activated, as in gene array studies of acute HCV infection, which has been implicated in pathological effects of chronic infection [9,22].  
More than ten genes associated with RIG-I signaling were increased during acute HCV infection in our analysis. This was surprising in view of the mutation in RIG-I in Huh 7.5 cells that ablates RIG-I signaling [55]. The upregulation of multiple genes downstream from _IPS-1_ (_G1P2_, 3.4-fold; _IL8_, 2.9-fold; _TRAF3_, 1.9-fold, and _TRAF2_, 1.9-fold) suggests crosstalk between other molecular pathogen recognition receptors and the RIG-I pathway in Huh 7.5 cells.  
Previous reports provide evidence that HCV perturbs glucose metabolism, leading to insulin resistance that accelerates liver fibrogenesis, impairs the response to interferon treatment and increases the risk of HCC [56]. Our analysis found additional genes upregulated during acute HCV infection, not previously identified, such as _HKDC1_ (14.7-fold), _IRS2_ (2.9-fold), _RHOQ_ (2.2-fold), _SKIP_, (1.9-fold), _IRS1_ (1.6-fold) and insulin receptor substrate-2 (_IRS2_), the main effector of insulin signaling in the liver [57] (Supplemental Figure 4). Since _IRS-2_ is involved in activation of lipogenic genes, it may play a role in the formation of lipid droplets on which HCV replication complexes assemble [58,59].  
_IRS2_ overexpression has also been reported in the early phase of murine liver tumorigenesis and in human HCC pathological specimens and HCC lines [59].  
Approximately 200 genes were found to be differentially expressed by RNA-seq analysis are involved in embryonic development and more than seventy percent of them were not previously reported to be differentially expressed during acute HCV infection. They include _FGF2_ and _FZD9_ (both 4-fold increase), _CEBPB_ (2.7-fold increase), _LNK_, _MAFG_ and _TFEB_ (all 2.5-fold increase), _PKD1_ and _CELSR1_ (both 2.3-fold increase), _JAG2_ and _NOTCH1_ (both 2.1-fold increase). We provide the first evidence that the Hedgehog and Notch signaling pathways are increased during acute HCV infection. The Hedgehog pathway is a critical regulator of embryonic development, is increased in chronic HCV and HBV hepatitis, and plays a key role in fibrogenesis and the development of HCC [60; 61; 62]. A recent study provides evidence that Hedgehog signaling also plays a role in creating an environment supportive of HCV replication in cell culture [63]. These results provide evidence for the Hedgehog signaling pathway playing a role in during the establishment of HCV infection and its long term pathological effects during chronic infection.
## 4 Evaluation
### Success Evaluation  
The usefulness of the ProMaRC method is determined by its contribution to increasing efficiency in product development. Since efficiency is influenced by a large number of influence factors and the effects of proactive change management cannot be measured directly, an investigation of the actual influence of ProMaRC application requires a comprehensive empirical benchmark study. This must examine the change in key performance indicators in comparison with development projects not using ProMaRC. This needs to be implemented on the basis of a sufficiently high number of development projects with comparable characteristics (e.g., degree of innovation, product context or stakeholders involved) and for different performance levels of the existing change management.  
Such empirical investigation of the success is omitted in this contribution. Before conducting such extensive benchmark study, it should be evaluated first if indicators suggest sufficient usefulness of the method. As a primary indicator, expert assessments are used. Experts are able to assess whether the method addresses leverage points in industrial practice and transfer the expected effects on other contexts. To do so, expert assessments of industrial pilot users are recorded in a questionnaire after pilot application (cf. Appendix C; questions 28 to 33). Measurable success factors on the basis of which the questions are structured were derived from an influence model (cf. Appendix D). In this way, an initial estimate of the success can be made, which will have to be substantiated in subsequent benchmark studies.  
Expert assessments indicate the usefulness of ProMaRC to develop complex technical systems more efficiently, by risk assessment and support in selecting appropriate proactive measures (cf. Appendix C). Within the interviews, dependency analysis was highlighted as very helpful and promising. Today, high manual effort to identify and document dependencies is a barrier to fulfill development standards (cf. SC-10) but also to reach sufficient system understanding in early development stages. Automated dependency analysis unlocks new action space such as impact analysis (as part of proactive as well as reactive change management). It also spares developers with rare competencies (for instance, requirements engineering). Feedback on the overall method indicates that integrating ProMaRC into superordinate risk and change management procedures helps to consider requirement changes in development projects more accurately and more comprehensively. To encourage the usage of ProMaRC, the software prototype needs to be connected to common requirements engineering and project management software tools and further developed beyond the prototype stage.
# High-Speed Photodetectors for Microwave Photonics
## 3 Integrated Photodiode-Antenna Emitters
### Summary of Results Reported in the Literature  
Figure 11 gives an overview of the radiation power of photonic emitters from 50 to 150 GHz that have been reported in the literature. In [26; 27; 28], a horn antenna with high directional gain was connected to the PDs. In refs. [27; 29; 30; 31; 32; 33], either a Teflon or Si lens was used to improve the directional gain of the antenna. Planar antenna structures include Yagi [34; 35], bow-tie [18; 24], and slot [29; 35], however, impedance matching between PD and antenna was not reported for these structures. In contrast, the integrated photonic emitters reported in refs. [23; 25] have planar structures and include an impedance matching network. Due to the high-power handling capability of the MUTC PD, these integrated photodiode-antenna emitters have the highest radiation power, both, at 60 GHz, and 100 GHz.
## 4 Comparison between VESC and HRSC  
For the comparison between VESC and HRSC, a full-length encasement in the case of VESC and horizontal reinforcement at equal intervals throughout the depth of the column was studied for both types of geotextiles taken for the study. A graph was plotted for the diameters of 50, 75 and 100 mm, as shown in Figures 8a-c and 9a-c. As it can be seen from the graph, VESC for the full-length encasement with the G2 type of the geotextile for a 100 mm diameter of store column was most desirable among all. The maximum value of load ratio was found to be 1.80 for the 100 mm diameter of the stone column for the G2 textile for full-length VESC, whereas it was 1.72 for the HRSC for the same specification. The values of load ratio for 50 mm and 75 mm diameter columns for the G2 type of textile for VESC were 1.41 and 1.64, and those for HRSC were 1.33 and 1.54, respectively, as observed from Figure 10a-c.  
Vertical encasing reinforcement, in contrast to horizontal reinforcement sheets, necessitates using specialised equipment. In comparison to VESCs, HRSCs do not require improvement initiatives. In this study, the amount of geosynthetic used in HRSCs is much lesser than that used in VRSCs. As a result, compared to VESCs, HRSCs save more than half of the reinforcing material. HRSCs may be one of the cost-effective alternatives, particularly in big projects, due to the ease of constructing horizontal sheets as a reinforcing element in HRSCs. They are also a good approach for reinforcing stone columns, enhancing the ultimate capacity of columns, and decreasing ground settling.  
Figure 7: Load Ratio-Settlement variation of VESC for various L\({}_{\tau}\) on single stone column with diameters (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.  
Figure 8: Load Ratio-Settlement variation of HRSC for various arrangements on single stone columns with diameters (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.  
Figure 9: Comparison of Load-Settlement behaviour between VESC and HRSC for L\({}_{\tau}\) = L on single stone column with diameters (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.