esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 3 Results and Discussion
### Precursors
#### 3.1.4 PAN  
PAN is one of the components of photochemical smog and forms from a sequence of reactions where aldehydes, ketones, and other oxygenated VOCs combine with OH to form an acetyl radical, which then combines with NO\({}_{2}\) to form PAN [47]. PAN formation in the three mechanisms are similar, though CB6R3 lumps acetyl nitrate output into two categories (PAN, PANX), while RACM2 uses four (PAN, PPN, MPAN, OPAN) as does SAPRC07 (PAN, PAN2, PBZN, MAPAN), depending on the precursor oxygenated VOCs. The formation rate for PAN from the acetyl radical in RACM2 is 8% less than the other two [48; 49]. PAN may be destroyed by the reversal of the same PAN-forming mechanism back to the acetyl radical as part of a cycle as well as by its interaction with OH. The cycling between PAN and its acetyl radical precursor is faster in the summer than the winter. The speed of removal of PAN via the OH channel is slow in the summer and depends on the species of the acetyl radical present in the winter. The acetyl radical may also be destroyed via interaction with NO, HO\({}_{2}\), and RO\({}_{2}\), which is fast limiting the formation of PAN [47]. Finally, RACM2 has an additional photolytic reaction removing PAN from the cycle. The net result is more varied production of PAN amongst the three mechanisms in the summer, due to the speed of formation (and destruction), the lumping process, and the additional removal of PAN in the RACM2 mechanism. The winter production of PAN is less varied as it is slower, there is less variability in the composition of the alkyl radicals due to the reduction in biogenic emissions, and the light levels are low. During summer, CB6R3 produced about 55% and 60% more PAN than SAPRC07 and RACM2, respectively. There were no significant differences between the mechanisms in winter.
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### Optimization of the \"Ring-Slot\" Structure Parameters  
For the dual-element \"ring-slot\" antenna structure mentioned above to achieve higher performance, it is necessary to optimize its parameters, including the metal frame height, ground slot length, and chip inductor.
# Comparing the Performance of Robust Controllers for Vibration Suppression in Long Rotor Systems
## 8 Insights into Motivations and Methods in This Paper  
The motivation for this theoretical article is a comparison of the performances of robust controllers that are designed both in the time and frequency domain. Such a comparison, particularly for the vibration control of long rotors, does not exist in the literature. This comparison is rigorous since the long rotor system requires multivariable models with a very high interaction index for their representation. Therefore, the level of rigorousness is high, therefore it requires a theoretical investigation.  
It is shown that the time-domain controller design, by using the robust control toolbox, theoretically can work very well, and the settling time of the response reduces substantially when compared with controllers designed in the frequency domain based on the optimisation methods.  
While the controllers found via the frequency-domain design can be simply structured, the \(H_{\infty}\) controllers designed by the toolbox (i.e., time domain) exhibit a high order indeed, such that realisability is questionable. The higher order of the time-domain designed \(H_{\infty}\) controllers are unavoidable, since they are observer-based controllers in the state space (big picture). The following table summarises the motivation.  
Apart from Table 2, the performance of the solutions can be measured by the \(\gamma\) values in (21) in Table 3 for the three types of controllers discussed in this article.  
It should be remembered that in reference [10], the rotor model is based on the mass, stiffness, and damping matrices of a rotor system. Such lumped models are easily convertible to a state-space model, but they are not accurate enough to represent a long rotor.  
In this article, the model is obtained from a connection of the distributed parameter shafts to the lumped parameter disks and bearings and a multivariable transfer function matrix is accurately estimated for such a hybrid system. Therefore, by using the approach in this paper, apart from the accuracy of the model, it is also possible to compare the performance of controllers that are designed both in the time and frequency domain based on transfer function matrices. Such a comparison is the main purpose of this paper.  
Figure 15: Response of the system with eighth order \(H_{\infty}\) controller to double-step disturbances, indicating rapid disturbance rejection.  
The dynamic stiffness \(\mathbf{K}\), \(\mathbf{D}\), and \(\mathbf{B}\) partitioned matrices and their details in (1) and (2) express the dynamic model in this paper. They are represented in the \(s\) domain and include all the dynamic parameters of shafts, disks, and bearings. This builds Equation (1a), from which a transfer function matrix can be estimated. Therefore, the dynamic model is embedded in the elements' transfer function matrix (5).
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 3 The Proposed HMIPS
### Positioning Algorithm
The inertial sensor data containing errors can cause degradation of the HIMPS positioning accuracy when used as an input as follows:  
\[HMIPS(IS_{i,t})=R_{t} \tag{5}\]  
where \(t\) is time, \(i\) represents sensor type, \(IS_{i,t}\) is the sensor reading containing error, and \(R_{t}\) is the estimated result derived from \(IS_{i,t}\). Therefore, we use error correction to derive \(R_{t}\) to be minimal. At this time, the correction value can be derived using filter \(f\), and the revised estimation error can be calculated as follows:  
\[HMIPS\left(IS_{i,t}+f\left(\sum_{r=1}^{t-1}IS_{i,r}\right)\right)-EI_{t} \tag{6}\]  
where, \(EI\) is the expected ideal result. However, the correction value of filter \(f\) converges to a negative bias value over time. This is expressed as an equation:  
\[\lim_{x\rightarrow\infty}f\left(\sum_{r=1}^{t-1}IS_{i,r}\right)=-bias_{i,t} \tag{7}\]  
Equation (7) expresses the situation in which the bias converges to a negative number because the bias becomes the same within the homogenous section of environmental factors. In the HMIPS, both the error factor and bias for the sensor input value are corrected, and only fluctuation errors remain in the end, so accurate tracking becomes possible.
# Effects of Sidewall Brightness on LED Lighting Environment and Visual Performance in Road Tunnels
## 5 Conclusions  
The impact of the improvement of tunnel sidewall brightness on the lighting environment and visual characteristics of human eyes are analyzed based on the lighting experiments in a tunnel when the tunnel sidewalls are decorated with two different types of materials and illuminated by LED lamps with five different color temperatures. According to the results, the main conclusions are as follows.  
The tunnel sidewall luminance increases if the energy-storage and luminescent coating with high reflectance is decorated on tunnel sidewalls, and the pavement luminance increases with the increasing sidewall luminance. Taking the energy-storage and luminescent coating applied in this study as an example, the pavement luminance can be increased by more than 20% compared with the tunnel in which the sidewalls are decorated with gray cloth and illuminated by the LED lamps with the same electric power. In addition, the ratio of sidewall to pavement luminance in the tunnel decorated with energy-storage and luminescent coating is 1.3-1.4, which is far greater than the demand of lighting specifications.  
The visibility of human eyes is more sensitive to the brightness than the color temperature and S/P value of the LED light sources. After the improvement of the tunnel sidewall and pavement luminance, the visible distance of a small object in the tunnel will increase to a certain extent. Furthermore, there is no discomfort for drivers during driving, and the changing of eye pupil diameter is relatively stable, which indicates that the increase in tunnel sidewall luminance has a positive effect on the visual performance of drivers; however, the possibility of a glare issue remains to be discussed in a future study.  
Conceptualization, S.F.; methodology, X.C.; validation, J.L.; formal analysis, X.C. and L.Z.; investigation, L.Z. and J.L.; resources, S.F. and H.Z.; data curation, W.M.; writing--original draft preparation, X.C.; writing--review and editing, L.Z.; visualization, L.Z. and J.L.; supervision, W.M.; project administration, H.Z.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Department of Communications of Guizhou Province, China (No. 2021-122-025), and the Science and Technology Project of Department of Communications of Hebei Province, China (No. TH-201909).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Setup
### Evaluation Metrics  
Our research focused on the model's ability to distinguish between reduced and unreduced phonemes as well as the overall phoneme recognition rate. We employed the F1 score, which is a harmonic mean of precision and recall, for comprehensive performance evaluation of vowel reduction detection. It is defined as follows:  
\[\text{F1}=\frac{2*\text{ Precision }*\text{ Recall}}{\text{Precision }+\text{ Recall}}, \tag{10}\]  
\[\text{Precision }=C_{D\cap H}/C_{D}, \tag{11}\]  
\[\text{Recall }=C_{D\cap H}/C_{H}, \tag{12}\]  
where \(C_{D}\) is the total number of \(ih_{schma}\) or \(ah_{schma}\) that output from the model. \(C_{D\cap H}\) is the number of \(ih_{schma}\) or \(ah_{schma}\) that are correctly detected after a dynamic time warping (DTW) among outputs and human transcripts. \(C_{H}\) is the total number of \(ih_{schma}\) or \(ah_{schma}\) in the test set labeled by a human.  
Considering that our proposed vowel reduction detection method can be flexibly integrated into ASR-based CAPT systems, we also counted the phoneme correct rate (PCR), which is commonly used in ASR research.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 4 Channel Coding Schemes for Error Correction in VLC in E-Healthcare Scenarios
### Run-Length Limited (RLL) Codes  
Finally, RLL codes have been specifically designed for VLC systems to mitigate flicker and optimize error correction. By controlling the maximum and minimum run lengths of consecutive identical bits, RLL codes ensure that the light intensity changes frequently enough to prevent visible flicker while maintaining strong error correction capability. Recent developments in high-rate RLL codes, based on finite-state machines, have further improved the error performance of VLC systems, as demonstrated in simulations where these codes outperformed existing RLL schemes in both error correction and flicker mitigation [71]. The optimized minimum Hamming distance in these codes plays a critical role in reducing BER, making them highly effective for VLC systems used in healthcare, where even minor interruptions in data transmission can lead to incorrect patient monitoring.  
In [75], a new RLL code, called 5B10B, was developed, which is not specified in the 802.15.7 standard, but is used instead of the well-known 8B10B code. This new code, in addition to preserving the desirable characteristics of traditional RLL codes for VLC, allows for improved error correction capability. Figure 2 presents a schematic diagram of the VLC system with RLL code.
## 3 System Model
### IDW Interpolation Method  
Smartphones consume considerable power during WiFi scanning, necessitating limitations on scanning frequency due to battery constraints. Manufacturers adjust sampling frequencies accordingly; for instance, the Mi 5 and Nexus 5 have frequencies of 0.125 Hz and 0.5 Hz, respectively [36]. To address the challenge posed by low sampling frequencies, an effective strategy is to utilize interpolation methods.  
The fingerprint database is calculated based on the average \(RSS\) measured at the \(i\)-th position. As the number of positions sampled (N) increases, the amount of data that needs to be collected increases. Gathering a substantial volume of WiFi \(RSS\) data is a time-intensive and labor-intensive process. To alleviate this burden, IDW interpolation is employed to expand the WiFi \(RSS\) database [37]. \(RSS\) interpolation is calculated as follows.  
\[RSS_{interp}=\frac{\sum_{i=1}^{k}\left(\frac{1}{d_{i}}\cdot RSS_{i}\right)}{ \sum_{i=1}^{k}\frac{1}{d_{i}}} \tag{2}\]  
Figure 2: Schematic diagram of WiFi fingerprinting in an indoor environment.  
where \(RSS_{interp}\) is the interpolated \(RSS\) value at a specific location for which the actual \(RSS\) value is unknown, \(d_{i}\) represents the distance from the \(i\)-th known point to the location where the \(RSS\) value is being interpolated, and \(RSS_{i}\) is the known \(RSS\) value at the \(i\)-th location. The following Equation (3) is the definition of \(d_{i}\).  
\[d_{i}=\sqrt{\left(x_{target_{i}}-x_{base}\right)^{2}+\left(y_{target_{i}}-y_{ base}\right)^{2}} \tag{3}\]  
where \(d_{i}\) denotes the distance between the \(i\)-th known location and the target location for which the \(RSS\) value is being interpolated, \(x_{target_{i}}\) and \(y_{target_{i}}\) represent the \(x\)- and \(y\)-coordinates of the target, \(x_{base}\) and \(y_{base}\) denote the \(x\)- and \(y\)-coordinates of the database, respectively.  
As shown in Figure 3, IDW interpolates not only the vicinity of RP in the fingerprint database but also the vicinity generated by PSO particles. As a result, the fingerprint database becomes denser and cover areas where fingerprints were not measured.
## 3 Results
### Results of Partial Discharge Inception Voltage Measurements  
In this section, the PDIV measurement results made on the capacitor are shared. The threshold PD charge value for PDIV measurement was determined as 5 pC. The measurement results obtained are shared in Figure 14. Three different measurements are depicted in different colors and shown in bar view.  
The measurements were shared as the average value of measurements repeated three times on three different samples. When the values in Figure 14 are examined, the PDIV value of the PLA capacitor was determined as 2.325 kV, 2.191 kV and 2.258 kV for three different samples, respectively. In this context, it can be said that the average PDIV value for the PLA capacitor is 2.25 kV. The PDIV measurement results of the ABS capacitor were obtained as 2.155 kV, 2.250 kV and 2.203 kV.  
Based on these results, the average PDIV value of the ABS capacitor was determined to be 2.2 kV. In the ASA capacitor, partial discharge events started at 2.474 kV, 2.510 kV and 2.492 kV. Thus, the average PDIV value of the ASA capacitor was determined to be 2.5 kV. The PDIV value of the PETG capacitor was measured as 2.444 kV, 2.468 kV and 2.456 kV, respectively. As a result, the average PDIV value of the PETG capacitor was 2.45 kV. As a result of the measurements, it is seen that ASA material comes to the fore in the capacitor evaluation made according to the PDIV value. In light of the information obtained from PDIV measurement results, the materials can be ranked from best to worst as follows: ASA, PETG, PLA, ABS.  
Figure 14: Results of partial discharge inception voltage measurement.
## 4 Discussion
However, different controlling factors were also detected in the subregions. The climatic factors of PRE, TEM, and SUN were dominant controllers of FVC changes in the LOP area, while in the QBM area, the topographical factors of RIF, ALT, and SLO were dominant (Figure 8). The LOP area was characterized by a fragile ecosystem with lower rainfall and temperature compared with other subregions, as well as the steeper slopes and broken terrain. Therefore, soil and water conservation projects and revegetation were essential measures to consistently improve FVC in the LOP  
Figure 10: Interannual variation of climatic factors in Shaanxi Province from 2000 to 2018.  
area. Conversely, the QBM area possesses superior climate conditions, with higher rainfall and heat supplement being beneficial to vegetation growth, but the large gullies, altitude difference, thin soil layer, and steep slopes restrict vegetation distribution. Reduced human disturbances and enhanced soil and water conservation will be helpful to maintaining high FVC in the QBM area. There are some limitations in our research. The MODIS NDVI data used in this study is only available from 2000; however, our aim was to monitor the benefits of the GGP over the past 20 years, since its implementation in 1998. Therefore, our research could not reveal the spatial distribution characteristics and variation trend of FVC before 2000 and on a longer time scale beyond 2018. In the future, with the availability of more data collection plateaus and efficient mathematical methods, subsequent studies could consider adding more time-series data to further improve accuracy. In this study, only major climatic and topographic factors such as temperature, precipitation, sunshine duration, altitude, slope, aspect, and relief were selected, but geological background, soil texture, and plant species were not considered. However, this study does provide useful information on FVC and could be improved further by eliminating some of the limitations in future work.
# Limitations in Maximum Intensity Front Crawl in Swimmers with Down Syndrome
## 3 Results
### Body Pitch  
Table 3 compares the angles of body pitch relative to the water plane between the two groups at each length. The two-way ANOVA showed that time has no effect (differences between lengths: \(p\) = 0.83), but the difference between groups is highly significant (_p_ = 0.0004), with no interaction between lengths and groups (_p_ = 0.19). In the post-hoc analysis, the differences between groups were significant for both the 0-25 m (_p_ = 0.016) and the 25-50 m (_p_ = 0.0002) lengths.  
In particular, SDS swimmers had higher body pitch angles in both lengths.  
Figure 3 displays the relationship between the body pitch angle and lap time, as estimated through linear regression analysis. This analysis was performed separately for the 0-25 m and 25-50 m segments of the 50 m swimming test. The regression results were not significant in the CNT group, but they were positive and statistically significant in the first length for the SDS group. However, in the second length, the significant relationship was completely lost for the SDS athletes (_p_ = ns).  
Figure 2: HR values at rest (REST), immediately after the 50 m front crawl test (50 m END), and after 30 (30 s REC) and 60 s (60 s REC) of post-test recovery. Data are presented as the mean \(\pm\) SD. Black squares and continuous line: CNT group; White circles and dashed line: SDS group. The * indicates differences between the SDS vs. CNT groups significant at \(p\) < 0.001 in the post-hoc analysis of two-way ANOVA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SDS (_n_ = 11)** & **CNT (_n_ = 11)** & \(p\)1 ** \\ \hline Body pitch 0–25 m (\({}^{\circ}\)) & 12.78 \(\pm\) 3.43 & 9.39 \(\pm\) 2.08 & 0.016 \\ Body pitch 25–50 m (\({}^{\circ}\)) & 13.73 \(\pm\) 3.14 & 8.08 \(\pm\) 3.75 & 0.0002 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Body pitch angle (\({}^{\circ}\)) (m \(\pm\) SD) between the SDS and CNT group.
###### Abstract  
This paper summarizes an engineering experience of solving the problem of thermal cracking in mass concrete by using a large project, Zhongguancun No.1 (Beijing, China), as an example. A new method is presented for controlling temperature cracks in the mass concrete of a foundation. The method involves controlled cycles of water circulating between the surface of mass concrete foundation and the atmospheric environment. The temperature gradient between the surface and the core of the mass concrete is controlled at a relatively stable state. Water collected from the well-points used for dewatering and from rainfall is used as the source for circulating water. Mass concrete of a foundation slab is experimentally investigated through field temperature monitoring. Numerical analyses are performed by developing a finite element model of the foundation with and without water circulation. The calculation parameters are proposed based on the experiment, and finite element analysis software MIDAS/CIVIL is used to calculate the 3D temperature field of the mass concrete during the entire process of heat of hydration. The numerical results are in good agreement with the measured results. The proposed method provides an alternative practical basis for preventing thermal cracks in mass concrete.  
circulating water; mass concrete; 3D temperature field; temperature monitoring; simulation +
Footnote †: journal: Applied Physics Letters B