esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 3 Results and Discussion
### Comparison of Different Adjustment Schemes
#### 3.2.1 Adjustment Results of the First Phase  
Assuming that the coordinates of S8 are (500.000 m and 800.000 m), and the azimuth angle of S8-S7 is \(90^{\circ}00^{\prime}00^{\prime\prime}\), the classical free network adjustment method is used to calculate the adjustment coordinates of each point in the first phase and the root mean square error  
Figure 4: The construction scene of the jacket during the second phase of the observation of the control network.  
Figure 3: Schematic diagram of the first free station observations. STA# indicates the station’s location, # represents the station number, and different colors distinguish the observation values of different stations.  
(RMSE), as shown in Table 1, in which mx and my denote the RMSE in the direction of x and y, respectively, and \(mp=\sqrt{mx^{2}+my^{2}}\) represents the RMSE of the whole point position. The table shows that the error of each point position is below 1.6 mm, and the average value is 1.2 mm, which indicates that the internal conformity accuracy of the first-phase control network observation is high.
These limitations not only hinder accurate assessments of the environmental health risks posed by IVOCs, but also affect the ability of various parties to develop effective pollution control strategies. Therefore, a comprehensive understanding of the emission characteristics, oxidation mechanisms, and toxicity risks of IVOCs is crucial for precise source apportionment, emission control, improving atmospheric pollution governance, and uncovering potential health risks. Research on IVOCs has made some progress. [PERSON] et al. summarized major IVOC measurement techniques for use in the atmosphere [20]. [PERSON] et al. focused on IVOC sources and their contributions to SOAs [21]. [PERSON] et al. reviewed IVOC definitions, measurement techniques, field observation results, and laboratory and model simulation findings, and emphasized the significant contribution of IVOCs to SOAs [22]. [PERSON] et al. provided insights into the key role of IVOCs in SOA formation and the impact mechanisms [23]. [PERSON] and [PERSON] examined IVOC health risks in southern California in the USA and India [18; 19]. The existing review articles show clear differences in the research focus. For example, studies on the contribution of IVOCs to SOA formation vary, with some focusing on the reaction mechanisms and others on observational data analysis. These differences limit the systematic understanding of IVOCs. Additionally, most current research links IVOCs with SVOCs, and there are only a few independent studies on IVOCs. Furthermore, there is a lack of comprehensive summaries on the toxic effects and health risks of IVOCs. Adopting an innovative approach based on the full-chain research results for \"emission-exposure-transformation-risk\", this article collects a large amount of field observation data, smog chamber experimental results, and chemical transport model simulations on IVOCs, sorts out the latest domestic and international research progress, systematically summarizes IVOC source emission characteristics, ambient concentrations, SOA formation contributions, and so on, and conducts health risk assessments of typical IVOC species using the data reported in this paper. The findings of this review answer scientific questions about the characterization of IVOC emissions, the distribution of ambient concentrations, their contribution to SOA production, and their risk to human health. It can provide a scientific basis for the refined management and control of IVOCs and promote a paradigm shift in atmospheric pollution control, from \"total amount control\" to \"reactive species priority\" and \"health risk priority\".
## 4 Results and Discussion
### Choosing the Parameter \(\beta\)  
Before proceeding to the presentation and discussion of the main results, it is important to understand the impact of the parameter \(\beta\). In this regard, the proposed FSS-MFxRRLS algorithm is employed for ANC of a standard SaS process with \(\alpha=1.1\) (see Figure 5a for a typical signal drawn from such a strongly impulsive process). The FSS step size \(\mu=0.1\) has been experimentally selected for fast convergence as well as good performance at the steady state. The value of the parameter \(\beta\) has been empirically adjusted from a very small value close to zero to a large value close to but less than unity, and the corresponding MNR curves are presented (in a butterfly pattern) in Figure 6. For clarity of presentation, the curves for \(\beta=0.01\) (small value close to zero) and \(\beta=0.995\) (a large value close to but less unity) are plotted using \"thick-bold\" lines. Furthermore, a vertical panel and a wide panel (plotted inside the main figure) are used to highlight the curves during transient and at the steady state, respectively. It is interesting to note that choosing an appropriate value for \(\beta\) poses a trade-off situation: too small a value may improve the convergence speed; however, it would degrade the steady-state performance, and vice versa. Considering such a behavior, the value of the parameter is selected as \(\beta=0.75\) for the rest of the experiments presented in this paper.
## 2 AOTF Working Principle  
Depending on the applied power level and frequency, a different optical wavelength is refracted at the output of the AOTF. Having a linear polarized light beam at the entrance, the birefringence causes two light beams to exit the AOTF at a specific angle (Figure 1). The diffracted and undiffracted light beams are created if the applied light beam is at the specific Bragg angle \(\theta_{\text{B}}\)[15]. Only those wavelengths of interests (diffracted beam under \(\theta_{\text{D}}\)) can be used for analytic purposes (e.g., imaging). The other beam under \(\theta_{\text{B}}\) is optically blocked.  
Figure 1: The setup of the AOTF, together with the RF driving system, applied to the transducer. Reproduced and adapted with permission from reference [14] by OSA Publishing.  
The intensity of the output depends on the material of the crystal and on the Diffraction Efficiency (\(DE\)) of the AOTF. The latter can be approximated by  
\[DE\cong\ sin^{2}\sqrt{\frac{\pi^{2}\ M_{2}\ L\ P_{A}}{2\ H\ \lambda^{2}}}, \tag{1}\]  
in which \(L\) and \(H\) are the dimensions of the transducer, \(\lambda\) the optical wavelength, \(P_{A}\) the acoustic power inside the crystal, and \(M_{2}\) the figure of merit [16]. The latter can be calculated as  
\[M_{2}=\frac{n_{i}^{3}\ n_{d}^{3}\ p^{2}}{\rho\ v^{3}}, \tag{2}\]  
in which \(n_{i}\) and \(n_{d}\) are the refractive indices of the incidence and diffracted light beam, respectively; \(\
u\) is the velocity of the acoustic wave; \(p\) is the photo-elastic coefficient; and \(\rho\) is the mass density of the material of the crystal.  
In order to increase the \(DE\), an optimal RF power needs to be applied to the transducer [12].  
\[P_{opt}\cong\frac{\lambda^{2}\ H}{2\ L\ M_{2}} \tag{3}\]  
For the selection of a specific optical wavelength, a dedicated frequency has to be applied to the transducer. This frequency depends on \(v\), \(\lambda\), \(\theta_{B}\), the difference \(\Delta n\) between the diffraction indices, and the angular direction \(\eta\) of the acoustic propagation [17].  
\[f\cong\frac{\Delta n\ v\ sin^{2}(\theta_{B}+\eta)}{\lambda\ sin\theta_{B}} \tag{4}\]  
Specifically for space applications, the crystal material is typically Tellurium-Dioxide (TeO\({}_{2}\)), Potassium Dihydrogen Phosphate (KDP), Mercurous Bromide (Hg\({}_{2}\)Br\({}_{2}\)), or quartz [18; 19; 20]. Depending on the used material of the crystal, and the selected optical wavelength domains (e.g., Visible (VIS), Near-Infrared (NIR), or Ultra-Violet (UV)), the applied frequency ranges can vary between 45 MHz and 250 MHz [12].
## 4 Robotic Arm Position Detection
### Machine Learning Model  
We evaluate the captured CSI data using a set of statistical features successful at profiling static channels in recent work [33]. The amplitude data of the received raw CSI were initially inserted into the digital signal processing framework. This process commenced by utilising a Hampel filter [22] to eliminate irrelevant measurements and outliers caused by the transmission and reception hardware. Subsequently, a lowpass Butterworth FIR filter [29] was employed to eliminate both the noise generated by the movement of the human body and the high-frequency Additive White Gaussian Noise (AWGN). To ensure data smoothness, the moving average method was applied to each sample, followed by the computation of statistical estimators such as standard deviation, mean, variance, and kurtosis across the CSI amplitude values for all 52 subcarriers. The resulting feature set was then fed into a linear Support Vector Machine (SVM) classifier, which performed multi-class classification to determine which of the 26 alphabetical keys was pressed.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 7 Conclusions
In summary, with the help of sophisticated nowcast models and an extended AMAN, SINOPTICA was able to show that it is possible to support ATCOs and pilots to guide air traffic safely and efficiently around severe weather areas in challenging meteorological situations, thus making planning more reliable and predictable for all stakeholders on the ground and in the air. Conceptualization, M.-M.T. and O.G. ; methodology, M.-M.T., O.G., L.N., M.K. ([PERSON]) and O.O. ; software, L.N., M.K. ([PERSON]), O.O., K.M., H.E., N.G., V.M., M.M., L.E., R.L., E.T.R. and A.T.; validation, K.M., M.-M.T. and O.G. ; formal analysis, O.G. ; investigation, M.-M.T., O.G., M.K. ([PERSON]), L.N. and O.O. ; resources, H.E. ; data curation, O.G. and A.T.; writing--original draft preparation, M.-M.T. and O.G. ; writing--review and editing, A.P., R.B., O.G., V.M., M.M., M.L., S.F., R.C.T., L.E., M.C.L., R.B. and M.-M.T. ; visualization, O.G., L.N., N.G. and M.-M.T. ; supervision, A.P. ; project administration, A.P. ; funding acquisition, E.R., A.P., R.B., M.K. ([PERSON]) and M.-M.T. All authors have read and agreed to the published version of the manuscript. This research was funded by H2020 SESAR, grant number No 892362. Restrictions apply to the availability of these data. Data was obtained from Flight Radar24.com and are available [[https://www.flightradar24.com/commercial-services/data-services](https://www.flightradar24.com/commercial-services/data-services)]([https://www.flightradar24.com/commercial-services/data-services](https://www.flightradar24.com/commercial-services/data-services)). Further reports are available under \"[[http://sinoptica-project.eu/index.php/articles/](http://sinoptica-project.eu/index.php/articles/)]([http://sinoptica-project.eu/index.php/articles/](http://sinoptica-project.eu/index.php/articles/))\". The authors declare no conflict of interest. The founding sponsor had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussions
### Structural Characteristics and Elemental Analysis of Raw RH  
The morphological characteristics of the inner and outer surfaces of raw RH are shown in Figure 2. The inner surface of RH has a flattened and smooth structure that looks rigid, so it would be difficult for the catalysts or enzymes to penetrate the structure (Figure 2a,b), while the outer surface has a regular linear ridge structure (Figure 2c,d). It can be seen that RH has the unique features of a ridged outer surface, which is rich in silica. The surface features of RH described here are consistent with previous observations by other researchers [32; 33]. As shown in Table 2, silica was predominantly located at the tips and shoulders of the ridges and was present in lower amounts in other regions of RH. The high silica content on the outer surface provides strength and stiffness to the husk. Because the unique surface characteristics of RH and contents with a large amount of silica are known to have a significant adverse influence on the performance of enzymatic hydrolysis, a special strategy is required for the effective fractionation of RH. That is, swelling must be realized using an alkaline solution so that the catalyst can easily penetrate the internal structure of the RH, and a catalyst containing sodium ions should react with the silica in RH to form water-soluble sodium silicate, which can be dissolved and released into Fr. hydrolysate [34; 35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Component** & **Dry Solids (\%, _w/w_)** \\ \hline \multirow{3}{*}{Carbohydrate} & Glucan & 35.6 \(\pm\) 0.8 \\  & XMG (a) & 13.6 \(\pm\) 0.4 \\  & Arabinan & 1.7 \(\pm\) 0.1 \\ \multirow{2}{*}{Lignin} & Acid insoluble lignin & 22.7 \(\pm\) 0.3 \\  & Acid soluble lignin & 0.7 \(\pm\) 0.2 \\ \multirow{2}{*}{Extractives} & Water extractives & 3.5 \(\pm\) 0.1 \\  & Ethanol extractives & 0.7 \(\pm\) 0.1 \\ \multirow{2}{*}{Carbohydrate} & Ash & 15.7 \(\pm\) 0.2 \\  & Crude protein (b) & 3.2 \\ \multirow{2}{*}{Lignin} & Crude lipid (b) & 0.5 \\ \hline \hline \end{tabular} \({}^{(a)}\) XMG (Xylan + Mannan + Galactan). \({}^{(b)}\) Protein and lipid contents were analyzed by KFRI; Korea Food Research Institute. \(N\)-factor = 5.95.  
\end{table}
Table 1: Chemical composition of rice husk.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 1 Introduction
### Organization  
The remainder of the paper is organized as follows: Section 2 summarizes some terminologies, notations related to FHS sets and the concept of \(q\)-ary \(m\)-sequences and their \(d\)-decimated sequences. Section 3 presents three new constructions of LHZ FHS sets with an optimal MPHC. Lastly, the conclusions of this paper are outlined in Section 4.
# ICT Skills in the Deployment of 21 st Century Skills:
## 3 A Definition of ICT Skills in the Context of 21 st Century Skills  
In order to define and revisit developmental antecedents for ICT skills in young children, a conceptualization of ICT skills in early childhood is needed. One comprehensive way of defining and conceptualizing ICT skills is in the context of 21 st century skills, since 21 st century skills, by their very nature, are usually applied through the use or the integration of digital technologies. Thus, the use of ICT devices is one form of how information can be accessed, which might influence 21 st century skills and can inform how they can be fostered at all ages [3]. Additionally, [PERSON] and colleagues [4] state that ICT skills and 21 st century skills are often viewed as interrelated, but a clear definition of this interrelation is lacking. Thus, the authors suggest seven 21 st century skills that invite the addition of a digital component, allowing the support of ICT skills. According to them, (1) Technical skills refer to the basic skills needed to operate digital devices. (2) Information skills encompass the search, evaluation, and organization of digital information [27]. (3) Communication skills include the skills needed for digital social interactions, such as contacting others or sharing information via e-mail, social media, and messenger apps [6]. (4) Collaboration skills regard the skills needed for collaboration in digital environments, e.g., simultaneously working on a document or online project or sharing ideas via messengers [6]. (5) Critical thinking skills involve making informed decisions, which can be achieved by closely analyzing, synthesizing, and evaluating received information [6]. In a digital context, critical thinking entails the skills to quickly filter and assess online information and differentiate between true and fake news through critical assessment of the contents [4]. (6) Creative skills include the development and creation of new ideas with the help of digital tools [28; 29]. (7) Problem-solving skills encompass the skills to formulate problems in a way that digital devices can process, search for (multiple) solutions for familiar and unfamiliar problems, and generalize knowledge to new problems [30; 31].  
These considerations provide a basis for understanding how ICT skills play an important role in the context of 21 st century skills. As stated, the ECEC context has three specific challenges that need to be considered. First, a fruitful implementation of ICT skills in ECEC demands knowledge about young children's developmental processes. Thus, cognitive, emotional, and social antecedents need to be addressed. Second, it remains unclear how the implementation of ICT skills can build on these antecedents while also fostering the antecedents adequately. Third, ECEC should be developmentally appropriate [23]. ECEC mostly relies on playful learning [25]. Thus, play might be a way to foster ICT skills in the context of 21 st century skills in young children.
## 2 Materials and Methods
### Evaluation of Photocatalytic Activity  
The photocatalytic activity was characterized in mineralization of benzene. The source of UV light was a high pressure Hg lamp (125 W, \(\upl_{\text{nm}}>\) 340 nm, light spectrum around 365, 400, 440, 550 and 580 nm), which was obtained from Shanghai Yaming, China. There was a 15 cm distance between the lamp and sample. In these tests, the UV light region on the surface of sample was 320-400 nm and the UV intensity was 0.96 mW/cm\({}^{2}\).  
Samples were put in a closed cylindrical stainless steel gas-phase reactor (5.6 L) with a quartz window. Gaseous environment was synthetic air and humidity was indoor humidity. For each test, liquid benzene (2 \(\upmu\)L) was injected and evaporated in the reactor. After complete evaporation of benzene in the reactor (shown by concentration in gas chromatograph), a sample of PCM or 5 g PCS was started to be irradiated by the UV lamp. Then, the concentration of benzene in the photocatalytic progress was measured every 15 min by gas chromatograph (GC9560, Shanghai, Huai, China).  
Figure 1: Diagram of the vacuum-ultrasonic-negative pressure devices.  
The PCSs with coating concentrations of TiO\({}_{2}\) 1-5 g/L were used in recycled experiments. The approach is as follows: after the first photocatalytic activity test, the sample was irradiated under a UV lamp for 1 h with the quartz window open to ensure the pollutants absorbed on the surface of sample removed completely. Then, seal the reactor again and start the next test. PCM-5 was applied in long-term photocatalytic effect test. The sample was placed on the roof (State Key laboratory of Silicate Materials for Architecture, Wuhan University of Technology, Luoshi Road, Wuhan, Hubei, China) with the face up for 6 months (September 2015 to March 2016). The outdoor temperature was \(-\)6-38 \({}^{\circ}\)C and the relative humidity conditions range from 35 to 90%. The month average rainfall was 118.9 mm. After 6 months of atmospheric environment exposure, the photocatalytic activity of PCM-5-6 months was evaluated under same experimental conditions with other samples. The control group was also tested after 1 month of atmospheric environment exposure.
# Regular Wave Effects on the Hydrodynamic Performance of Fine-Mesh Nettings in Sampling Nets
## 2 Material and Methods
### PIV Measurement Experiment for Field Distribution Around the Netting  
A schematic representation of the PIV system is provided in Figure 2. The experimental setup comprised a high-resolution CCD (charge-coupled device) camera (TSI POWERVIEW Plus 4 MPE, TSI Incorporated, Shoreview, Minnesota, USA), a dual Nd:YAG laser, a synchronizer, an articulated optical arm, and a computer-based image acquisition and processing system [42; 43]. To visualize the flow, polyvinyl chloride (PVC) tracer particles--chosen for their near-neutral buoyancy--were introduced into the flow field both upstream and downstream of the netting. These particles were assumed to accurately follow the fluid motion. The dual-pulsed Nd:YAG laser, with a wavelength of 532 nm and a maximum energy output of 200 mJ per pulse at a repetition rate of 14.5 Hz, generated a thin laser sheet oriented vertically through the central longitudinal plane of the netting. This laser sheet illuminated the tracer particles, enabling flow visualization in a 300 \(\times\) 300 mm\({}^{2}\) measurement area. The CCD camera, positioned orthogonally to the laser plane, recorded sequential images of the scattered light with a spatial resolution of 2360 \(\times\) 1776 pixels\({}^{2}\) and a frame rate of 5 Hz. The laser pulses and camera operation were precisely synchronized via a dedicated timing controller to ensure accurate capture of particle displacement between image pairs.  
In the single-frame PIV measurements, two consecutive particle images were captured within a single frame and subsequently transmitted to a computer for post-processing using the INSIGHT 4G\({}^{\text{TM}}\) version 11.1.0 software. For each experimental condition, a total of 100 pairs of instantaneous particle images were sequentially acquired, enabling the calculation of time-averaged mean flow fields around the netting. During image analysis, interrogation windows measuring 64 \(\times\) 64 pixels [2] were employed, with a 50% overlap between adjacent windows to ensure adequate spatial resolution.
### Effective Wind Speed and State Observer  
The presented observer structure is based on the previously introduced wind turbine model which is extended by a disturbance model of the effective wind speed to be estimated. In control theory, this is a recognized method in which unknown inputs (such as wind speed in this case) are combined with a model and measurements to form a disturbance observer [10]. To do this, we start with the disturbance model  
\[\dot{v}=-\frac{1}{\tau_{v}}v+\frac{1}{\tau_{v}}\,v_{0} \tag{19}\]  
proposed in [18], where \(\tau_{v}\) denotes the time constant of disturbance model and \(v_{\Sigma}\) represents the mean wind speed. With the assumption that the mean values as reference wind speed change slowly, it can be calculated over an appropriate time period \(T_{p}\) (e.g., 10 min) from the anemometer sampled wind measurement  
\[v_{\Sigma}=\sum_{i=1}^{N_{s}}v(t-i\,T_{s})\,,\quad T_{b}=N_{s}\,T_{s}\,, \tag{20}\]  
where \(T_{s}\) denotes the sampling time and \(N_{s}\) the number of samples in \(T_{b}\). Furthermore, the mean wind speed can be obtained by additional prior knowledge by means of static maps which contain relations of the equilibrium points  
\[v_{0}=v_{0}(\beta,\omega_{g})|_{\{\beta_{r}^{\prime},\tau_{r}^{\prime}\}}\,. \tag{21}\]  
where \(v_{\Sigma}=v_{0}\). The actual model for the wind speed and state estimator is then obtained by expanding the state vector (12) by  
\[\bar{x}=(\,x^{T}\,,\,v\,)^{T}\,. \tag{22}\]Hence, from Equations (17), (19) and (22), the augmented state space observer model results in  
\[\begin{split}\dot{x}&=\sum_{i=1}^{N_{r}=8}h_{i}(z)\, \left(\bar{A}_{i}\,x+\bar{B}_{i}\,u\right),\qquad\tilde{x}_{0}=\tilde{x}(t_{0}) \,,\\ y&=\tilde{C}\,x\end{split} \tag{23}\]  
with  
\[\bar{A}_{i}=\begin{pmatrix}A_{i}&0_{4\times 1}\\ 0_{1\times 4}&-\frac{1}{1_{0}}\end{pmatrix},\qquad\bar{B}_{i}=\begin{pmatrix} B_{i}\\ 0_{1\times 2}\end{pmatrix},\qquad\tilde{C}=\begin{pmatrix}C&0_{3\times 1}\end{pmatrix}\,. \tag{24}\]  
Based on this model, an observer can now be specified in TS form also known as TS Luenberg observer  
\[\begin{split}\dot{\hat{x}}&=\sum_{i=1}^{N_{r}=8}h_{i}(\hat{z}) \left(\bar{A}_{i}\,\hat{x}+\bar{B}_{i}\,\tilde{u}+L_{i}(y-\hat{y})\right),\\ \hat{y}&=\tilde{C}\,\hat{x}\,,\end{split} \tag{25}\]  
with \(\hat{z}=(\,\hat{\omega}_{r}\,,\,\beta\,,\,\hat{\vartheta}\,)^{\mathrm{T}}\). The observer provides the required estimate of the effective wind speed in front of the rotor and the estimated states of the wind turbine divided into five individual rotors  
\[\hat{x}=(\,\hat{x}\,,\,\hat{\vartheta}\,)^{\mathrm{T}}\,, \tag{26}\]  
which are directly fed into the two control laws for the partial-load and full-load operating region, see the block diagram in Figure 5.