esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### User Spacecraft Orbit Determination
#### 4.4.3 Measurement Modeling  
To adhere to the GTDS format, specific procedures are necessary to transform the data from its original non-DOWD format. The process of converting TDRS one-way Doppler measurements into DOWD format involves differencing them at each corresponding time. The reduction of one-way Doppler measurements is calculated using the equation [10]:  
\[\Delta V_{d}(t)=[v_{d}(t)]_{TDRS_{i}}-[v_{d}(t)]_{TDRS_{j}} \tag{1}\]where \(\Delta V_{d}(t)\) is the DOWD measurement at time \(t\), \([v_{d}(t)]_{TDRS_{i}}\) is the one-way Doppler measurement from \(TDRS_{i}\) at time \(t\), and \([v_{d}(t)]_{TDRS_{j}}\) is the one-way Doppler measurement from \(TDRS_{j}\) at time \(t\).  
Data accuracy and precision are of paramount importance. It is essential to consider any potential sources of bias that may affect the results of the measurements. Fortunately, the bias in the DOWD measurements is quite negligible. This is because any possible effects due to atmospheric refraction and biases in the user spacecraft tend to cancel each other out. Therefore, the resulting DOWD measurement bias is typically insignificant and does not require correction.
## 3 Results and Discussion
### Cell Viability  
The viability of Caco-2 cells treated with water and 80% ethanol extracts of Korean WS and NS germinated for 7, 9, and 11 days is presented in Figure 4. The cell viability after the treatment with the water extracts of WS and NS increased with germination time, and it was higher for the water extracts of WS than that of NS. Treatment with 80% ethanol extracts of WS decreased the cell viability with germination time, while that of NS did not exhibit a significant difference over time. The cell viability after the treatment with ethanol extract were higher for NS than WS after 9 days of germination, in contrast to the water extract. However, all extracts exhibited more than 90% cell viability, thereby implying that all extracts of WS and NS did not exhibit cell toxicity. The sample is considered non-cytotoxic if it does not reduce the cell viability below 85% [38].  
Figure 4: The viability of Caco-2 cells treated with Korean wavy and normal wheat sprouts germinated for 7, 9, and 11 days. NSW, water extracts of the wavy wheat sprouts; WSE, 80% ethanol extracts of the wavy wheat sprouts; NSW, water extracts of the normal wheat sprouts; NSE, 80% ethanol extracts of the wavy wheat sprouts. Values are shown as mean \(\pm\) SD (n \(\geq\) 2). Values with a same letter (a–g) are not significantly different by [PERSON]’s multiple range test (\(p<0.05\)).  
Figure 3: Nitric oxide production ratio in MEF cells treated with Korean wavy and normal wheat sprouts germinated for 7, 9, and 11 days. NSW, water extracts of the wavy wheat sprouts; WSE, 80% ethanol extracts of the wavy wheat sprouts; NSW, water extracts of the normal wheat sprouts; NSE, 80% ethanol extracts of the wavy wheat sprouts. Values are shown as mean \(\pm\) SD (n \(\geq\) 2). Values with a same letter (a–d) are not significantly different by [PERSON]’s multiple range test (\(p<0.05\)).  
The human intestinal epithelium is an important barrier against the invasion of microorganisms during the intake of contaminated food [41]. Therefore, the enhancement of viability or non-cytotoxicity on these cells is beneficial for human health. Wheat aqueous extract was reported to increase the cell viability with an increase in the dose of the extract [42], but there have been no reports on wheat sprout. Apigenin, a flavonoid in plants, and arabinylan, a soluble dietary fiber, are known to promote cell viability [43; 44] and are present in wheat and wheat sprout [23; 45; 43]. They may be partially responsible for the promoting effect of waxy and normal wheat sprouts on the cell viability.
## 7 Experiments and Results
### Effects of Fitness Function
We could see that by applying the weight of the FN predictions, the number of TP instances increased, which explained the improvements in the g-mean and F1 score values. However, it also caused an increase in the number of FP instances, which explained the decrease in the accuracy score. Next, we used majority voting ensemble learning to decrease the number of FP instances while maintaining the number of TP instances. Additionally, since the PSO and CSO produced similar results, an interesting observation was that a light optimizer with a simple mechanism to update the particles within the search space, such as the CSO, could achieve similar results when used as an optimizer for an MHOANN. Another observation was that, whereas the PSO and CSO produced similar results when using similar fitness functions, the CSO was better in terms of execution time. Using the same population size of \((50)\) and the same number of iterations (100), CSO was 22.4% faster for the dataset of Spanish companies, 34.4% faster for the dataset of Taiwanese companies, and 48.3% faster for the dataset of Polish companies. Table 11 lists the actual execution times in seconds. In this work, as discussed in Section 7.2, we noticed a direct relationship between the weight of the FN predictions and the set of metrics that were monitored. While we chose the weight that produced the best g-mean score, which meant a weight that produced a balance between sensitivity and specificity, a lower weight could produce a better specificity score and a higher weight could produce a better sensitivity score, depending on which metric the user focused on.
## 4 Discussion  
We validated the simulation model under the same conditions as described by [PERSON] and [PERSON]. [21]. We investigated the vertical vibration of the rail caused by China's high-speed track spectrum at 300 km/h and compared it to results reported by [PERSON] and [PERSON]. [21].The overall trends are consistent with the results reported by [PERSON] and [PERSON]. [21].  
However, there are several factors that may influence the simulation results: (1) Track irregularities were not considered in the vehicle-track coupled system dynamic model. Hence, the wheel-rail interactions input into the FE model may have been below their real values. (2) The vehicle-track system parameters--e.g., the stiffness and damping coefficient of the rail pad, CA layer, and support layer--in the simulation model may differ from their real values. (3) The dynamic displacements of the slab track vary as a function of time. There are phase differences between functions of acceleration and displacement in the rails and track layers because of the wheel-rail forces transmitted from the top layer to the bottom layer of the slab track in the form of stress waves. The displacement was extracted at the time of maximum rail acceleration, rather than at the time of maximum displacement.  
According to the track dynamics derived from the FE simulations, wheel polygons have a greater effect on rail vibrations than on the concrete slab layer, CA layer, and support layer. In addition, compared to the characteristics of the rail dynamic displacement and stress, the rail acceleration magnitudes relative to both OOR amplitude and harmonic order are linear and stable. Furthermore, on the basis of the range and magnitude of the rail vibration accelerations, with the maximum acceleration reaching approximately 1200 m/s\({}^{2}\) with 21 st-order wheel polygons, it is recommended that sensors with a measurable range of 1500-2000 m/s\({}^{2}\) be employed.  
The results presented in this paper demonstrate that it is feasible to monitor wheel polygons in-service based on rail acceleration, displacement, or stress measurements. However, there are several factors that influence rail vibration, such as the type of track, train speed, and track irregularities. Hence, the outcomes of this study provide guidance for determining the most suitable sensor locations across and along the track for rail vibration measurements in order to have the greatest influence on the accuracy of wheel polygon identification.  
Figure 15: Effects of running speed on the dynamic behavior of supporting track components: (**a**) vibration displacement versus running speed and (**b**) von Mises equivalent stress versus running speed.
## 5 Conclusions  
This paper proposed a vehicle load identification method based on an optimal combined strain influence line. Two types of strain sensors were installed on the deck and U-ribs to monitor the structural response for detecting the axle information and axle weight, respectively. To decrease the influence of the transverse position of the load, an optimal combined strain influence line was proposed using a genetic optimization algorithm based on an index of variation coefficient. Then, the vehicle load could be identified based on a calibrated combined strain influence line using the least squares method. From numerical and experimental results, some conclusions can be drawn as follows:  
1. The axle's information can be accurately identified based on the FAD sensors installed on the lower edge of the deck between the U-ribs.
2. The index of variation coefficient can be used to evaluate the sensitivity of the transverse position of the vehicle to the strain influence line. Based on the index, the optimized combined influence line searched by the genetic algorithm is, to some extent, insensitive to the transverse position of the vehicle.
3. Axle weight and gross weight identification errors for numerical simulation are below 2% and 1%, respectively, and that of the experimental test are below 5.8% and 4.9%, respectively. The maximal identified error of axle weight is larger than that of the gross weight, and the main cause is that the identified errors of some axles are positive while other axles are negative. The experimental test's identified error is slightly larger than that of the numerical analysis due to the welding deformation and measuring noise. However, the identification accuracy insufficient for meeting the demand of practical engineering applications.
4. Overall, the proposed optimized combined influence line algorithm can effectively and accurately identify the vehicle's load on the orthotropic deck steel box beam bridge without estimating the transverse position of the vehicle in advance, which is convenient for engineering applications.  
Conceptualizatin, C.W.; software, Q.-X.Y.; validation, C.-Y.L.; methodology, C.W. and Q.-X.Y.; data curation, T.-Y.Q.; writing-original draft preparation, C.-Y.L.; writing-review and editing, C.-Y.L.; investigation, T.-Y.Q.; supervision, C.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by China Scholarship Council (CSC No. 201608420053) and the National Natural Science Foundation of China (Grant No. 51408250).  
Not applicable.  
**Data Availability Statement:** All necessary data is given in this article.  
The authors declare no conflict of interest.
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 2 Materials and Methods
### Surface Analytical Characterization by XPS  
XPS measurements were performed using a Theta Probe Thermo (Hillsboro, OR, USA) VG Scientific spectrometer. All spectra were recorded in constant analyzer energy (CAE) mode using a pass energy of 150 eV for survey and 100 eV for high resolution regions (F1s, C1s, O1s, Zn2p and ZnL3M45M45). The calibration of the binding energy (BE) scale was performed by taking a suitable signal as internal reference. In particular, for \(\phi\) = 0 and \(\phi\) = 0.05, the reference peak was the -CF\({}_{2}\) component of the C1s signal (BE = 291.8 eV) related to the polymer backbone. For \(\phi\) = 0.10 and \(\phi\) = 0.15, the C-C component of the C1s signal (BE = 284.8 eV) was chosen as reference because of the high extent of defluorination in the polymer matrix. Data analysis and curve-fit procedures were performed by the commercial software Thermo Avantage(v. 5.937, 2014) from Thermo Scientific. The absence of sample degradation upon exposure to X-rays (e.g., defluorination of the organic matrix and polymer matrix crosslinking) was evaluated by cyclic acquisition of the F1s and C1s high resolution regions, and was negligible within the acquisition time of a whole spectral set. The atomic percentage surface composition was determined by considering integrated peak areas (after Shirley background removal) and Scofield sensitivity factors.
# Plasmonic Filter and Demultiplexer Based on Square Ring Resonator
## 2 Materials, Structures, and Methods
Therefore, the transmission from the input to the drop channel can be expressed as:  
\[T(\omega)=\left|\frac{\widetilde{S}_{-3}}{\widetilde{S}_{+1}}\right|^{2}= \left|\frac{e^{j(\theta_{1}-\theta_{3})}\sqrt{\frac{2}{\tau_{b}}}\sqrt{\frac{2} {\tau_{d}}}}{j(\omega-\omega_{0})+\frac{2}{\tau_{b}}+\frac{1}{\tau_{d}}+ \frac{1}{\tau_{0}}}\right|^{2} \tag{6}\]  
Here, \(\widetilde{S}\) represents the Fourier transform of \(S\). The transmission data from CMT is also plotted in Figure 2e, which is in great agreement with the simulated result. Furthermore, if a reflector structure is utilized at the end of the bus waveguide, the drop efficiency can be improved at the cost of a wider FWHM, as presented in Figure 3a. Consequently, there is a trade-off between the transmission and FWHM. The phase shift of the SPPs when reflected back to the SRR by the reflector can be given by [7]:  
\[\phi=\frac{4\pi\text{Re}(n_{eff})l}{\lambda}+\sigma \tag{7}\]  
where \(\sigma\) indicates the phase shift that occurs when reflected back by the metal surface, and \(l\) refers to the distance between the reflector surface and the SRR. Obviously, for a particular wavelength we can obtain a \(\phi\) to indicate the incident light and reflected light constructive interfered by changing \(l\). From FDTD, the drop efficiency at 1310 nm can be enhanced to 64% when \(l=34\) nm, but the FWHM is also improved to 67 nm, as given in Figure 3b. This outcome is comparable with some previous works [4; 5; 6; 7; 8; 9]. A monitor is set behind the source to detect the reflection, showing that the reflection is only approximately 3% at the drop wavelength, as depicted in Figure 3b. Therefore, the rest of the energy is dissipated due to ohmic loss. Such intrinsic loss is normal and acceptable for an MIM system [3]. According to CMT, there will also be \(S_{+3}=0\), but \(S_{+2}=e^{-j\phi}S_{-2}\). The transmission will be as follows:  
\[T(\omega)=\left|\frac{e^{j(\theta_{1}-\theta_{3})}\sqrt{\frac{2}{\tau_{b}}} \sqrt{\frac{2}{\tau_{d}}}(1+e^{-j\phi})}{j(\omega-\omega_{0})+\frac{2}{\tau_{ b}}(1+e^{-j\phi})+\frac{1}{\tau_{d}}+\frac{1}{\tau_{0}}}\right|^{2}\
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results
### SAR Evaluations  
SAR is a measure of the rate at which energy is absorbed per unit mass by a human body when exposed by electromagnetic field. According to IEEEC95.3 standard, the allowed SAR value is 2 W/kg [10]. The safety of the proposed antenna structure is evaluated by CST's SAR calculations. The evaluations are carried out with the simulation model case shown in Figure 5b, in which the antenna-skin distance is 0 mm. An input power of 5 dBm was used in the SAR evaluations since it is the same power as was used in the measurements. With this input power, the maximum SAR value (10 g) is calculated for the selected frequency ranges, and these are summarized in Table 4. The maximum SAR values vary from 0.027 (2.45 GHz) to 0.053 (10.6 GHz), which are clearly below the standardized limit. Hence, this antenna can be considered safe for in- and on-body communications.  
Figure 14: S21 channel parameter between the flexible antenna and UWB capsule.  
Figure 13: (**a**) UWB capsule antenna, (**b**) UWB capsule endoscope model.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 4 Proposed Scheme
### Preamble Codeword Set Selection Algorithm
#### 4.1.2 Preamble Codeword Utilization Rate Analysis Model  
We define \(N_{fs}\) as being the number of transferable preamble sequences that are broadcasted by the BS in the first RA slot, and \(N_{chosen}\) as being the number of actually transmitted preamble sequences by devices in the first RA slot. A device transmits a random preamble sequence among the \(N_{fs}\) preamble sequences. Therefore, \(N_{chosen}\) can be calculated as follows:  
\[N_{chosen}=N_{fp}\Bigg{(}1-\Bigg{(}1-\frac{1}{N_{fp}}\Bigg{)}^{v}\Bigg{)}. \tag{5}\]  
In (5), the value \(v\), which means the number of active devices in the current RA cycle, is a large value in mMTC. We assume that \(N_{fs}\simeq N_{chosen}\) since \(\Big{(}1-\frac{1}{N_{fp}}\Big{)}^{v}\) has a value close to zero. According to the above assumption, the probability that a specific preamble codeword is chosen by at least one device is calculated as follows:  
\[P_{chosen}=1-\Bigg{(}1-\frac{1}{MN_{fp}}\Bigg{)}^{v}. \tag{6}\]  
The expected number of codewords chosen by at least one device can be calculated as follows:  
\[E[N_{codeword}]=P_{chosen}\cdot M\cdot N_{fp}=M\cdot N_{fp}\Bigg{(}1-\Bigg{(} 1-\frac{1}{MN_{fp}}\Bigg{)}^{v}\Bigg{)}. \tag{7}\]  
In a specific situation where the preamble codeword length \(L_{c}=2\), the expected number of preamble codewords selected in the first and second RA slots can be calculated as follows:  
\[\mathbb{E}[N_{fp}]=N_{fp}\Bigg{(}1-\Bigg{(}1-\frac{1}{N_{fp}}\Bigg{)}^{v} \Bigg{)} \tag{8}\]\[\mathbb{E}[N_{sp}]=M\bigg{(}1-\bigg{(}1-\frac{1}{M}\bigg{)}^{v}\bigg{)}. \tag{9}\]  
The average number of preamble codewords deduced by the BS is calculated as follows:  
\[\mathbb{E}[N_{de}]=\mathbb{E}[N_{fp}]\times\mathbb{E}[N_{sp}]=M\cdot N_{fp} \bigg{(}1-\bigg{(}1-\frac{1}{M}\bigg{)}^{v}\bigg{)}\Bigg{(}1-\bigg{(}1-\frac{1} {N_{fp}}\bigg{)}^{v}\bigg{)}. \tag{10}\]  
As the preamble codeword utilization refers to the ratio of the actually transmitted preamble codewords among the preamble codewords deduced by the BS, the preamble codeword utilization can be calculated using Equations (7) and (10) as follows:  
\[\mathcal{E}=\frac{\mathbb{E}[N_{codeword}]}{\mathbb{E}[N_{de}]}=\frac{\Big{(} 1-\Big{(}1-\frac{1}{MN_{fp}}\Big{)}^{v}\Big{)}}{\Big{(}1-\Big{(}1-\frac{1}{M} \Big{)}^{v}\Big{)}\Big{(}1-\Big{(}1-\frac{1}{N_{fp}}\Big{)}^{v}\Big{)}}. \tag{11}\]
## 6 Conclusions and Indicative Extensions  
### Conclusions  
This study provides a bibliometric and systematic review of manufacturing-as-a-service (MaaS), analyzing research trends, technological enablers, and adoption challenges. Key findings include the following:  
* Mapping the research landscape--highlighting annual trends, key contributors, and the interdisciplinary nature of MaaS research.
* Identifying technological enablers--such as cloud computing, blockchain, AI, and digital marketplaces that facilitate MaaS adoption.
* Categorizing research themes, classifying existing literature into theoretical models, platform architectures, and data-driven innovations.
* Recognizing adoption challenges--including trust mechanisms, pricing models, interoperability concerns, and SME integration barriers.
* Highlighting future research directions--emphasizing the need for empirical case studies, advanced decision-support systems, and industry-wide adoption strategies.  
This review directly answers the research question, showing that MaaS is evolving through Industry 4.0 technologies but faces adoption hurdles. Further research should refine trust models, pricing strategies, and sector-specific applications to accelerate MaaS integration.  
MaaS enhances Industry 4.0 adoption by enabling flexible, cost-efficient manufacturing through cloud computing, AI, and blockchain. Industries like automotive and healthcare benefit from on-demand production and supply chain agility, reducing costs and lead times. Adapting digital infrastructure and workforce skills is key to maximizing its potential.
## 3 Results  
### Evaluation of Simulated ET and GPP  
Figure 2 displays the evaluation conditions of the modeled ET and GPP in this study versus the 72 ET and GPP observational records from seven flux sites (Figure 1) between 2003 and 2009. The R\({}^{2}\), RMSE, and NSE values shown in Figure 2 indicate that the constructed remote sensing-based ecohydrological model accurately simulates the ET over China. For example, the R\({}^{2}\) of the simulated monthly ET based on the total records in seven flux sites is 0.82; the RMSE is 15.98 mm month\({}^{-1}\) and the NSE is 0.79 by comparison against the observed ET. Interestingly, different land covers perform differently. For the three forest field sites (DHS, QYZ, and CBS), the RMSEs, R\({}^{2}\)s, and NSEs, respectively, range from 10.47 mm month\({}^{-1}\) to 14.94 mm month\({}^{-1}\), from 0.76 to 0.91, and from 0.75 to 0.90. The CBS site outperforms the others. For the two grass fields (NMG and DX), the RMSEs, R\({}^{2}\)s, and NSEs are 15.52 mm month\({}^{-1}\) and 20.80 mm month\({}^{-1}\); 0.70 and 0.91; and 0.56 and 0.73, respectively. The DX site has better performance than NMG. The shrubland site (HB) and cropland (YC) also perform well as per their R\({}^{2}\) values of 0.91 and 0.79, NSE values of 0.76 and 0.74, and RMSE values of 17.63 mm month\({}^{-1}\) and 19.21 mm month\({}^{-1}\), respectively.  
In addition to ET, the total GPP was also simulated accurately, with an R\({}^{2}\) of 0.81, RMSE of 48.07 g C m\({}^{-2}\) month\({}^{-1}\), and NSE of 0.80 (Figure 2). Among the forest sites, CBS still outperforms QYZ and DHS, with an R\({}^{2}\) of 0.91, RMSE of 38.92 g C m\({}^{-2}\) month\({}^{-1}\), and NSE of 0.90. For grassland sites, DX still outperforms NMG (R\({}^{2}\)  
Figure 2: Simulated monthly ET and GPP versus EC observations from 2003 to 2009.  
11.36 g C m\({}^{-2}\) vs. 17.18 g C m\({}^{-2}\), and NSE 0.72 vs. 0.75). The shrubland site (HB) also has an accurate GPP, with an R\({}^{2}\) of 0.96, RMSE of 34.95 g C m\({}^{-2}\) month\({}^{-1}\), and NSE of 0.80. Compared with other vegetation types, although the cropland site (YC) has a larger RMSE of 96.96 g C m\({}^{-2}\) month\({}^{-1}\), it still presents good consistency (R\({}^{2}\) of 0.74 and NSE of 0.67). The general consistency between the simulations of the three validation indicators and the EC observations suggests that the proposed model is accurate enough to support our subsequent analyses.