esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definition of the Problem
A priori, the equilibrium field is detected along with the perturbed one, so evaluating the difference, between two \(\pi\) faced coils, allows taking into account the perturbations induced by odd numbers, the case of  
Figure 1: (**a**) Top view sketch of the actual arrangements of the two saddle coil systems measuring the radial field component of the magnetic field on the high field side (HFS) of axially symmetric divertor experiment—upgrade (AUG) [7]. A system composed by two saddle coils (SATwest and SATeast) has been upgraded in 2011 [7] adding four coils (Satn, Satw, Sate, Satw). (**b**) Poloidal cross sections with the coils location. interest for AUG, especially for the \(n=1\) modes. Further details can be found in the literature [4; 7]. In the rest of the paper the subscripts n,s,e,w refer to the measurements from the above specified coils. Following [3; 4], the perturbed radial component of the magnetic can be written as:  
\[B_{1}^{r}=\ \dot{B_{r}}\cos(m\chi+\phi_{0}) \tag{1}\]  
where \(\chi\ =\ \theta-\frac{n}{m}\phi\) is the helical angle and \(\phi_{0}\) the phase of the mode. In (1), the amplitude of the magnetic field is the main term defining the island width and the growth [3]. Using the system of coils described above, (2), (3) give [4]:  
\[\dot{B_{r}}\ =\ \frac{1}{2}\ \sqrt{\left(\underbrace{\int\left(\dot{B_{n}}-\dot{B_{ s}}\right)dt}_{B_{r}^{\rm ms}}\right)^{2}+\left(\underbrace{\int\left(\ddot{B_{e}}- \dot{B_{w}}\right)dt}_{B_{r}^{\rm em}}\right)^{2}} \tag{2}\]  
\[\phi_{0}=at\beta\left(\frac{B_{r}^{\rm ms}}{B_{r}^{\rm em}}\right) \tag{3}\]  
where \(B_{r}^{\rm ms}\) and \(B_{r}^{\rm em}\) stand for the integrated signal of the difference between the two measured time derivative signals of two \(\pi\) faced and coupled coils. The system at AUG is then able to detect the amplitude and the toroidal position of the mode [7]. The angular velocity (\(\omega_{0}\)) and the frequency of the mode can be obtained by the derivative of the phase in (3), i.e \(\omega_{0}\ =\ \frac{d\phi_{0}}{dt}\). However, when the amplitudes of the radial perturbations (\(B_{r}^{\rm ns},\ B_{r}^{\rm em}\)) due to the mode are small and the measurements are dominated by other effects, like those induced by instrumental behaviours, e.g., noise or drift, or by other physical phenomena even causing rapid fluctuations of the signals, then \(\omega_{0}(t)\) is strongly affected and the aforementioned derivation of \(\omega_{0}\) for the systematic tracking of the slowing down of the mode cannot practically be used. A first alternative would consist of detecting directly the frequency of the mode from the fluctuations of the measured radial component. However, this approach would require cautiousness since it implies the knowledge of the proper rotational transform [3] of a specific observed mode as well as a manual intervention to identify the actual fluctuations. Consequently, it is not compatible with real time applications. A possible way to address the problem is shown in the next section.
## 4 Data Processing Scheme  
There are a number of issues which have critical impact on POD of GNSS satellites, such as tracking geometry, force models, and estimating approaches. Thanks to the excellent activities of IGS, most of them are well-known. Here we concentrate on some of the issues which are special for the BeiDou regional system, and are achievable with the available data set. Aimed at possible improvements in BeiDou POD, we identified three topics for investigation: impact of the tracking network coverage, benefit of involving MEO satellites, and contribution of integer ambiguity-fixing. The corresponding data processing schemata are defined here and carried out for the selected data set and the results are discussed later on for possible further improvement.
## 2 Mechanism Analysis
### Non-Parasitic Motion Analysis
#### 2.4.2 Analysis of Non-Parasitic Motion of the Mechanism in the 2T1R Motion Mode  
In the 2T1R motion mode, the topological structure of branched chain 1 is SOC\({}_{1}\)[-R\({}_{11}\)-P\({}_{12}\)-S\({}_{13}\)-], the topological structure of branched chains 2 and 3 is SOC\({}_{i}\)[-U\({}_{1i}\)-P\({}_{i2}\)-S\({}_{i3}\)-] (\(i=2\), 3), and the topological structure of branched chain 4 is SOC\({}_{4}\)[-C\({}_{41}\)-P\({}_{42}\)-R\({}_{43}\)-]. The center point \(O_{1}\) of the moving platform is selected as the reference point.  
In accordance with Equation (13), the POC sets of the branched chains in the 2T1R motion mode are as follows:  
\[M_{b_{1}}=\begin{bmatrix}l_{r}^{2}\\ r^{3}\end{bmatrix};M_{b_{2}}=M_{b_{3}}=\begin{bmatrix}l_{r}^{3}\\ r^{3}\end{bmatrix};M_{b_{4}}=\begin{bmatrix}l_{r}^{3}\\ r^{1}\end{bmatrix} \tag{17}\]  
In accordance with Equation (14), the POC set of the moving platform in the 2T1R motion mode is  
\[M_{Pa}=M_{b_{1}}\cap M_{b_{2}}\cap M_{b_{3}}\cap M_{b_{4}}=\begin{bmatrix}l_{ r}^{2}\\ r^{3}\end{bmatrix}\cap\begin{bmatrix}l_{r}^{3}\\ r^{3}\end{bmatrix}\cap\begin{bmatrix}l_{r}^{3}\\ r^{1}\end{bmatrix}=\begin{bmatrix}l_{r}^{2}\\ r^{1}\end{bmatrix} \tag{18}\]  
The number of degrees of freedom of the mechanism is 3. Since the number of independent elements of the POC set of the moving platform is not greater than the number of degrees of freedom of the mechanism, the POC set of the moving platform can have three independent elements. It can be seen from the calculation of \(M_{pa}\) that there are two independent translations and one independent rotation of the moving platform, and there is no non-independent parasitic motion.  
Based on the above analysis, when branched chain 1 is in the PS mode and the vA pair is in the universal joint mode, the mechanism is a 1T2R parallel mechanism without parasitic motion. When branched chain 1 is in the RPS mode and vA pair is in the revolute-pair mode, the mechanism is a 2T1R parallel mechanism without parasitic motion.
## 4 Numerical Assessment of Operating Point \(OR=0.05\)/\(Pr=0.20\)
### Spectral Proper Orthogonal Decomposition  
Because the duration of experimental measurements often greatly exceeds the elapsed time of CFD simulations, the assessment of repeating structures is of major importance. Due to the unsteadiness of the flow, statistical methods are essential, of which Proper Orthogonal Decomposition (POD) has become an established method for many CFD applications over the last decades. Because the pure POD only compares a set of snapshots, the resulting POD modes are only representations of dominating flow patterns within the dataset. For non-periodic but repeating structures, Spectral Proper Orthogonal Decomposition (SPOD) proposed by [PERSON] et al. [7] combines the power of POD with the frequency analysis. While a pure Fourier Transformation often leads to insufficient results for turbulent flows due to changing amplitudes and frequencies, temporal extinctions and excitations or variations in the direction of propagation, SPOD has shown to be a powerful tool for the reconstruction of dominant structures.  
The SPOD methodology was described in details by [PERSON] et al. [7]. As a brief summary, the algorithm processes a dataset of arbitrary dimensions which in the presented study is the spacial pressure or flow field and transforms it into the frequency space with dominant amplitudes and phase angles. At first, the dataset is reorganized into multiple (time) segments which may overlap. Window functions can be applied to the (time) segments and good results were obtained using the _Hamming_-Window (default). Inthe second step, frequency spectra are computed for all spacial points within each (time) segment. As a result, the flow is transformed from the time into frequency space for each segment. Finally, a POD algorithm is applied onto the frequency spectra, which are now treated as snapshots for the POD. The resulting number of SPOD modes therefore equals the number of segments while the frequency resolution depends on the time step between two results of the initial dataset and the duration over which each segment extends. Compared to a pure Fourier Transformation, the frequency resolution is therefore reduced by the division into (time) segments.  
The result of the SPOD is a set of SPOD modes which each represent a spacial distribution of amplitudes and phase angles for all discrete frequencies. Using the SPOD modes, key features of the flow can be reconstructed.  
Due to the computational costs, a trade-off between frequency resolution (length of the individual segments) and number of SPOD modes (number of segments) is necessary. Taking into account the frequencies of interest, a block length of 10 ms was used covering 5 periods at 500 Hz and 30 at 3000 Hz.  
The whole algorithm was published by [PERSON] (1998) as a MATLAB(r)-Code, which was used for the presented study.
## 3 Results and Discussion
### Optimization of Single HAP Conditions
#### 3.1.4 The Volume Ratio of Antisolvent to Solvent  
Another important factor is the volume ratio of antisolvent to solvent, and the influence of it on the crystal growth has been evaluated, with MPS as the response. The results shown in Figure 3d illustrated that a sharp decrease in the MPS of spiramycin from 1220 \(\pm\) 55 nm to 270 \(\pm\) 9.5 nm with the volume ratio of antisolvent to solvent ranged from 1 to 5. However, no significant effect was obtained when the volume ratio of antisolvent to solvent further increased up to 11. For a given temperature, the addition of antisolvent contributes to reducing the drug solubility in the mixed solution and enhances the supersaturation, thus leading to the reduction of the drug particle size. By contrast, the change in the supersaturation with the further increase of the volume ratio of antisolvent to solvent is non-significant, followed by a minor change in particle size. In addition, for further comparison, the spiramycin particles were centrifuged and dried to calculate the yield of spiramycin, and the maximum yield (95% \(\pm\) 2.2%) was obtained with the volume ratio of antisolvent to solvent of 5 mL/mL. Therefore, with the antisolvent amount enhanced, not only the supersaturation was increased for improving yield, but also the drug concentration in the crystallization system and nucleation rate were reduced. Thus, a volume ratio of antisolvent to solvent in the range of 3-7 mL/mL is used in the further optimization study.
# A Novel Particulate Matter 2.5 Sensor Based on Surface Acoustic Wave Technology
## 3 Technique Realization  
### Sensor Detecting Probe  
The air current microchannel in Figure 2 is composed of an upper shell (1) and a lower shell (2). For the upper shell, a 1 \(\upmu\)m Al was deposited on a silicon substrate by using an electron beam evaporator. Then, the photoresist (PR) was spin-coated, exposed and patterned. By using the inductively coupled plasma (ICP) technology, the window was etched off. For the lower shell, the fabrication process was similar to the upper one. First, a 1 \(\upmu\)m Al was deposited on a silicon substrate and the PR was patterned. Then, the air inlet, outlet and window were etched off. Second, we repeated the first step and etched the wafer to a depth of 200 \(\upmu\)m by using the ICP. Lastly, the two silicon wafers were bonded together into an individual die. The fabricated air current microchannel is shown in Figure 11, where (a) is the upper shell and (b) is the lower shell.  
To realize a stable and constant temperature gradient, two Peltier elements were utilized. One was placed in the hole of the upper shell, and the other was pasted on the back of the lower shell. When a direct current (DC) current was applied to a Peltier element, a heat flux was created, causing one side of the Peltier element to become warmer and the other side colder [28]. The difference in temperature was proportional to the current applied. By using two Peltier elements in parallel, the desired temperature gradient of 4.5 \(\times\) 10\({}^{5}\) K/m was realized in the microchannel.  
To fabricate the SAW device, a 1600 A Al was deposited on the ST-X quartz substrate using an electron beam evaporator. Then, the PR was spin-coated, exposed and patterned for the IDTs and the adjacent shorted grating reflectors. Lastly, the Al was wet-etched and the PR was dissolved in acetone. The fabricated resonator is shown in Figure 12a. Measured by a network analyzer (E5071B, Keysight, Palo Alto, CA, USA), the frequency response \(S_{12}\) of the SAW resonator is shown in Figure 12b. The center frequency was 311.625 MHz, the insertion loss was 5.0 dB and the quality value was 4371.  
Figure 11: Photograph of a fabricated air current microchannel: (**a**) the upper shell; (**b**) the lower shell.  
Figure 12: Fabricated SAW resonator: (**a**) photograph; (**b**) measured frequency response.  
Finally, the Peltier element and the SAW resonator were placed into the air current microchannel together to form the final detecting probe. The hot source was over against the surface of the SAW sensor chip. To guarantee sealing of the air channel, a silicone sealant was used to adhere around the cracks. Exposing the bonded probe to the air for about 24 h, the sealant was solidified continuously from within. By optimizing the fabrication process, a detecting probe was successfully prepared. Figure 13 gives a fabricated exploded detecting probe prototype, where the SAW sensor chip is inserted in the channel. The dimensions of the detecting probe were 50 mm \(\times\) 15 mm \(\times\) 3 mm.
## 2 Data Acquisition
### Neonatal HI Micro-Scale Sharp Waves  
EEG sharp wave patterns in preterm infants are known to be originated from central and occipital regions [29], typically with amplitudes ranging from 10 to 75 \(\mu\)V around 30 weeks of gestational age. These amplitudes can escalate to higher levels, reaching 260 \(\mu\)V [30] or even 300 \(\mu\)V [29]. Additionally, epileptiform waveforms with lower amplitudes of 10-25 \(\mu\)V have been identified in the EEG of sick preterm human babies [31]. In clinical practice, fast epileptiform events lasting <100 ms (mostly 20-70 ms [32]) are categorized as spike transients, while sharp waves are defined as patterns with durations of 100-200 ms [33]. This definition differs from the conventional one for epileptiform seizures, where events are described as repetitive and rhythmic waves with various frequencies and/or amplitudes lasting for at least 10 s [34].  
We have previously demonstrated the 'translational' value of micro-scale sharp-wave EEG patterns, characterized by amplitudes between 20-80 uV and durations between 70 and 250 ms, within the latent phase of fetal sheep data [11, 13, 14, 35]. Additionally, we have established their existence during the critical early postnatal period in clinical recordings from profoundly premature neonates within the early hours of birth, making them promising candidates to serve as a reliable marker for HIE [11, 13, 14, 35]. For consistency with our definitions for sheep HI EEG, we define neonatal HI sharp waves in preterms with similar characteristics, superimposed on a suppressed HI EEG background. These patterns occur in the frequency range of 4 to 14.3 Hz, encompassing the \(\theta\) (4-8 Hz), \(\alpha\) (8-12 Hz), and lower-beta \(\beta\) band (i.e., 12-14.3 Hz). Our research indicates that EEG sharp waves hold predictive value for the latent phase of injury following an HI insult. A higher number of sharp waves within the first 30 min post-HI correlates with enhanced subcortical neuronal survival in the caudate nucleus (r = 0.8). Conversely, increased sharp-wave activity between 2-4 h after HI is associated with more damage and reduced neuronal survival in the same subcortical region (r = \(-\)0.83) [13]. These observations underscore the significance of micro-scale EEG waveforms in a suppressed background as early indicators of HIE, emphasizing the need to explore the development of automated methods to accurately capture their presence in bedside monitoring at birth. We refer to these HI micro-scale sharp waves as'sharp waves'.
## 6 Discussion
### Practical and Theoretical Implications  
#### 6.1.1 Practical Implications  
It can be concluded that the extension of the RPI model to risk dimensions greater than three was successfully performed, which allowed the inclusion of an additional risk variable in the FMEA analysis.  
The innovative approach of the proposed model is to split the detectability variable from the traditional FMEA model into two components. One component relates to the degree of ability to detect failure modes, and the other component relates to the degree of ability to mitigate failure modes. In this way, the proposed model allows the FMEA-like analysis to consider not only the ability to detect, but also the ability to mitigate. The conventional FMEA approach does not explicitly consider the ability to mitigate, i.e., it assumes that it is always possible to mitigate or eliminate a particular failure mode once it is detected. This may not be the case at the beginning of the project or at some point during project implementation.  
In this sense, the proposed model of FMEA analysis allows the inclusion of the mitigation capabilities of the stakeholders in the risk scenario under study, making the risk analysis closer to the reality. In addition to this feature, the proposed model also allows for the creation of a range of outcomes, the boundaries of which are defined by a QPI of 1% (minimum or zero performance) and a QPI of 100% (maximum performance). This information is extremely important for decision making when making investments to improve mitigation capacity and/or eliminate outages. In addition to this aspect, this outcome boundary also allows for the identification of the available scope for mitigation improvements that actually impact the risk associated with the scenario under study.  
Furthermore, the model developed can be applied to any type of project, since any type of project can go wrong. Typically, projects are divided into tasks that depend on technical and human requirements that may be missing or whose performance may vary over time. By analyzing these requirements, it is possible to identify failure modes that impact performance and find ways to mitigate these failure modes. With this information and the application of the proposed model, it is possible to identify the critical failure modes associated with the project and thus implement risk management measures to address the potential causes of performance degradation.
## 3 Channel Model
### FSO Channel
#### 3.1.2 Turbulence-Induced Fading  
The impact of atmospheric turbulence leads to power losses and unpredictable fluctuations in both signal intensity and phase, thereby necessitating a suitable model for characterizing turbulence, ranging from weak to strong levels. In this regard, the gamma-gamma distribution emerges as an appropriate choice. The probability density function (PDF) of turbulence \(h_{j}^{t}\) can be mathematically expressed [15].  
\[f_{h_{j}^{t}}\Big{(}h_{j}^{t}\Big{)}=\frac{2\big{(}\alpha_{j}\beta_{j}\big{)}^ {(\alpha_{j}+\beta_{j})/2}}{\Gamma\big{(}\alpha_{j}\big{)}\Gamma\big{(}\beta_ {j}\big{)}}h_{j}^{t\frac{\alpha_{j}+\beta_{j}}{2}-1}K_{\alpha_{j}-\beta_{j}} \Big{(}2\sqrt{\alpha_{j}\beta_{j}h_{j}^{t}}\Big{)},j\in\{SR_{i},R_{i}D\}, \tag{13}\]  
where \(K_{v}(\cdot)\) is the order-modified Bessel function of the second kind and \(\Gamma(\cdot)\) is the gamma function. Additionally, the parameters that represent large-scale fading \(\alpha_{j}\) and small-scale fading \(\beta_{j}\) are expressed as  
\[\alpha_{j}=\bigg{[}\exp\!\left(0.49\sigma_{R_{j}}^{2}/\left(1+1.11\sigma_{R_{j }}^{12/5}\right)^{7/6}\right)-1\bigg{]}^{-1}, \tag{14}\]  
\[\beta_{j}=\bigg{[}\exp\!\left(0.51\sigma_{R_{j}}^{2}/\left(1+0.69\sigma_{R_{j }}^{12/5}\right)^{5/6}\right)-1\bigg{]}^{-1}, \tag{15}\]  
where \(\sigma_{R_{j}}^{2}=1.23C_{n}^{2}K^{7/6}L_{j}^{11/6}\) is the Rytov variance, which depends on the refractive index \(C_{n}^{2}\), \(K=2\pi/\lambda\) is the wave number; and \(\lambda\) is the wavelength.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Alkaline Solutions  
Cellulose is prone to swelling in alkaline solutions, such as sodium hydroxide or lithium hydroxide, but because of its amphiphilic character, it is not suitable to dissolve it completely [47]. Additionally, in the presence of NaOH, cellulose tends to form aggregates. In order to avoid this, the addition of chemicals preventing aggregation, e.g., urea, thiourea, and polyethylene glycol (PEG), is employed [48]. However, even in the presence of these agents, alkaline solutions are not very efficient at dissolving cellulose, e.g., in a NaOH/PEG mixture (a concentration of NaOH of 9%), only 13% of the biopolymer was dissolved [30; 49; 50].  
Although cellulose dissolution in alkaline solutions is not effective, these solvents are capable of dissolving lignin. For instance, in an 18% NaOH solution, 66.6% (wt.%) of lignin from eucalyptus chips could be dissolved [51]. Hydroxyl groups, present in an alkaline solvent, can interact with lignin phenolic monomers, which results in polymer fragmentation to smaller molecules. Moreover, in alkaline conditions, linkages between lignin and polysaccharides can break [51; 52]. However, because of the major disadvantages of alkaline solutions, which make them less attractive from an ecological point of view, they are not considered future solvents for biomass processing.
# Damage Identification in Warren Truss Bridges by Two Different Time-Frequency Algorithms
## 2 Models for Numerical Simulations
### Representation of the Damage
#### 2.2.1 Damage of Diagonal Elements in the Bridge Side Wall  
The first damage considered represents a degradation of the connection between a diagonal member and the lower chord. The structural components involved in the damage are highlighted with the dashed green circle shown in Figure 2. In case of total damage, the lateral element does not contribute to the global stiffness matrix of the bridge, since it does not transmit any axial action, but its mass still remains in the model, sustained by the upper connection. This damage can then be modelled by removing the diagonal element from the finite element model of the bridge, and by applying a concentrated inertia at the connection of the removed element with the upper chord (indicated with the black circled marker in Figure 2).  
A less severe damage condition, which might occur in the case of partial degradation, is modeled through a reduction in the Young's modulus of the damaged diagonal member in the FEM model. Clearly this way represents only the overall effect of the damaged connection on bridge dynamics. Three different defect entities have been considered, with a reduction in the Young's Modulus equal to 30%, 50% and 70%, respectively, with respect to the nominal value.  
For each damage type, the simulation plan is carried out by modelling the defect at different positions along the bridge span: this was done in order to assess whether and how the damage location affects the outcome of the detecting algorithms, either in terms of damage detection (i.e., false negative or false positive) or of accuracy in damage localization.