esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Glare Value PGSV  
According to the data analysis results of the field test, the difference of the brightness contribution ratio provided outside the tunnel is too large, the brightness distribution is not uniform, and the luminance is not suitable within the field of view of 20\({}^{\circ}\). From the definition of glare, glare is easy to occur in the visual environment of the access zone with uneven luminance distribution.  
Most of the studies on glare adopt the unified glare value URG in the standard for lighting design of buildings [24] for glare analysis. Because the URG value is limited to indoor light-source lighting, large light sources like indirect lighting and luminous ceiling lighting, it is not suitable for the glare calculation in the access zone of a road tunnel. After comparing the daylight index (DGI) proposed by [PERSON] [25] with the predicted glare sensation vote (_PGSV_) mentioned by [PERSON] and [PERSON] [26], the _PGSV_ daylight calculation model was selected to analyze the glare effect in a 20\({}^{\circ}\) field of view of a road tunnel. A tunnel entrance with low luminance in the 20\({}^{\circ}\) field of view is regarded as the dark background, and the indirect glare formed by the surroundings and road surface outside the east-west oriented tunnel is regarded as the bright background in order to analyze the glare phenomenon. Part of the measured tunnel light and dark block diagram is shown in Figure 14. The _PGSV_ formula of glare value is shown in Equation (2):  
\[PGSV=3.2 logL_{s}-0.64 log\omega+(0.79 log\omega-0.61)logL_{b}-8.2 \tag{2}\]  
where, \(L_{s}\) is the luminance of light source (cd/m\({}^{2}\)), \(L_{b}\) is the luminance of background (cd/m\({}^{2}\)), and \(w\) is the solid angle (sr) formed by the light source to the human eye.  
_PGSV_ is mainly determined by three factors: light source, background, and solid angle of sight. As calculated by the formula _PGSV_, _PGSV_ = 0 is perceived glare, _PGSV_ = 1 is acceptable glare, _PGSV_ = 2 is uncomfortable glare, and _PGSV_ \(\geq\) 3 is intolerable glare [27]. When _PGSV_ is negative, it indicates acceptable glare to the human eye [28].
## 3 Results
### Ndvi  
Lower daily NDVI values were detected for the July-September harvest cycles when compared with the May-June harvest cycles reflecting poorer growth during this period (Figure 6). Alfalfa plants regrowth after harvest was slower during the summer period than the May-June months. The heat could be the main explanation for this result. The results illustrated that the average peak daily NDVI values at full canopy could be a good indicator to represent average crop coefficient values of alfalfa fields in each harvest cycle. For instance, the average maximum daily NDVI at site D was 0.93, 0.91, 0.85, and 0.83 during the harvest cycles of May through August, respectively. These values are relatively similar (\(\pm\)2.3% points difference) to the average crop coefficient values determined for the corresponding harvest cycles at this site.  
Figure 5: Soil profile representations of mean EC\({}_{e}\) distribution of observed values at the different sites. Ece is soil electrical conductivity of the saturation extract.
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
## 2 New High-Speed on/off Valve Structure and System Modeling
### System Dynamic Modeling
As shown in Figure 6, where \(R_{\rm p}\) is the diameter of the armature, \(R_{\rm t}\) is the width of the magnetic guide ring, \(R_{\rm c}\) is the thickness of the outer shell, \(H_{\rm t}\) and \(H_{\rm b}\) are the thickness of the upper and lower magnetic guide rings, \(H_{\rm s}\) is the height of the outer shell, \(G_{\rm I}\), and \(G_{\rm 2}\) are the thickness of the upper and lower nonworking air gaps, and \(G_{\rm w}\) is the thickness of the working air gap. \(S_{\rm 1}\) is the thickness of the pressure-resistant shell. Simulations can be performed by assigning various values to each design parameter in Figure 6a, thereby obtaining the corresponding system flow output and the currents in the coil winding. The range of values for the design parameters is presented in Table 1.  
To validate the dynamic simulation model, experimental tests were performed on the valve model using the REXROTH 7920 (Bosch Rexroth, Lohr am Main, Germany). Under a 12 V power supply, the HSV valve core was actuated from the minimum to the maximum opening using three distinct duty cycle driving signals. Figure 6 illustrates the coil current measured during this process. The valve model's simulation provides the same three distinct duty cycle driving signals, with the simulated coil current depicted in Figure 7. Due to the coil's inductance, the current gradually increases and then stabilizes, influenced by the back electromotive force generated by the armature's movement. When the armature contacts the valve seat, the back electromotive force diminishes, causing the coil current to continue rising rapidly. As the armature's total displacement and mass are measured values, if the model's coil current matches the experimental results, it demonstrates the accuracy of the modeling for inductance, back electromotive force, and valve dynamics. Figure 6 demonstrates that the current profiles for both the model \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(R_{\rm p}\) (mm) & \(R_{\rm T}\) (mm) & \(R_{\rm c}\) (mm) & \(H_{\rm b}\) (mm) & \(H_{\rm m}\) (mm) & \(H_{\rm s}\) (mm) \\ \hline Range & [3,10] & [3,6] & [3,10] & [3,10] & [5,20] & [20,50] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Range of values for undetermined design parameters. Figure 6: (a) Simplified diagram of the system model, (b) Illustration of magnetic circuit division. essentially identical across all three duty cycles, highlighting the model's high precision at various valve core positions. It is worth noting that in the 100% duty cycle curve after 5 ms, the measured and modeled values are different but remain unchanged. This is due to the limitation of the sensor range; according to the trend of past data, the current in the actual coil continues to increase.
# Interval Feature Transformation for Time Series Classification Using Perceptually Important Points
## 2 Related Work  
A feature-based method relies on extracting meaningful features from a time series. In time series classification tasks, some researchers choose to extract features over the global time domain while others prefer to extract features from data sets in which the raw time series is already segmented. Some time series classification problems involve class differences in time series properties that are restricted to specific discriminative time intervals. Interval classifiers seek to learn the location of discriminative subsequences and the features of separate different classes, which can be learned by computing simple features across many subsequences, and then building classifiers by searching over both features and time intervals [6].  
[PERSON] et al. [20] fully exploited the amplitude and trends information of time series. In their method, the universe of the discourse of time series was firstly pre-divided into several intervals according to the predefined number of intervals to be partitioned. Then, information granules were constructed in the amplitude-change space based on the data of the time series belonging to each of the intervals and their corresponding change (trends). [PERSON] et al. [12] used statistics, such as the mean, slope, and variance to build a tree-based ensemble classifier that significantly outperformed one-nearest-neighbor classifiers with dynamic time warping. [PERSON] et al. [21] proposed a feature extraction approach that was beneficial for very large-size data, extracting approximately 9000 distinct features from data, including correlation structure, distribution, entropy, stationarity, and scaling properties, and fitted to a range of time-series models. [PERSON] et al. [22] used the mean, standard deviation, skewness, and kurtosis of the time series and its successive increments to represent and classify control chart patterns. [PERSON] et al. [23] introduced a histogram-based similarity measure, and this method counted the frequency of occurrences of each pattern in the time series, then compared the frequencies (or the histograms) of patters in the time series to obtain a (dis)similarity measure.  
Since many time series are non-stationary with the variability of amplitude and frequency over time, new frequency domain features extracted from original time series may help to improve the performance of classification models. Techniques for frequency domain feature extraction include the Fourier transform (FT) [24], continuous wavelet transform (CWT) [25], Hilbert-Huang transform [26], ensemble empirical mode decomposition (EEMD) [27], the least-squares wavelet analysis (LSWA) [1] and so on.
content in mammalian hair (such as human hair and wool) and bird feathers exceeds 80% [5].  
Keratin is a cysteine-rich fibrous protein, structurally divided into \(\alpha\)-keratins and \(\beta\)-keratins. \(\alpha\)-keratins are the primary constituent of soft tissues such as wool, hair, hooves, nails, horns, and the stratum comeum, whereas harder \(\beta\)-keratins are present in feathers, avian claws and beaks, reptilian claws, and scales. The significant presence of disulfide and hydrogen bonds confers to keratin some appealing mechanical properties, such as strength, stability, rigidity, and resistance to proteolytic degradation. Furthermore, keratin is characterized by unique aminoacidic sequences, such as \"Arg-Gly-Asp\" (RGD) and \"Leu-Asp-Val\" (LDV), that specifically bind vitronectin integrin receptors which, for instance, are overexpressed by several cancer cells. Therefore, keratin could represent a remarkable breakthrough in the field of cancer nanomedicine [6].  
To increase its poor water solubility, several methodologies have been investigated and setup, allowing the extraction of keratin from different sources. Extraction can be achieved only by cleavage of disulfide covalent bonds and hydrogen inter-chain bonds, and the so far known extracting procedures can be divided into denaturing and hydrolyzing methods [7; 8]. Denaturing processes allow for obtaining keratin with its native molecular weight distribution and amino acid composition. On the other hand, hydrolyzing processes (acidic, alkaline, enzymatic, and ionic liquid hydrolysis) give access to the isolation of a mixture of low molecular weight proteins and polypeptides, both having negligible sulfur content.  
The thorough analysis of keratins structure, distribution, and extraction from different sources will not be discussed in the present report, being already exhaustively reviewed by other authors in reviews and book chapters [7; 9; 10; 11; 12; 13; 14].  
The purpose of the current contribution is the description and discussion of keratin-based nanoparticles for drug delivery purposes. In particular, keratin nanocarrier production of different origins will be described along with available biological data.
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 2 Materials and Methods  
### BC and HC Preparation  
BC and HC were obtained through the pyrolysis and thermal hydrolysis of sugar cane bagasse, respectively. The biomass underwent an initial shredding process resulting in fragments measuring between 2 and 3 cm in length. The study involved the implementation of slow pyrolysis at 300 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 700 \({}^{\circ}\)C for a duration of 60 min within a fixed-bed reactor. The BC produced at these temperatures was labelled as BC300, BC500, and BC700, respectively. The experimental setup comprised a temperature controller cabinet, a quartz tube reactor with an inner diameter of 2.5 cm, and an electric furnace heater. The BCs that were acquired underwent a grinding process and were subsequently rinsed with deionised water. Afterward, they were dried in a furnace at a temperature of 105 \({}^{\circ}\)C for a duration of 24 h. The BCs were then stored in airtight containers at room temperature until they were ready for further use.  
HC was produced by means of a 2G Parr agitated pressure reactor featuring a removable glass liner. The experimental procedure involved the addition of 200 g of sugar cane bagasse and 1 L of distilled water into the glass liner, followed by thorough mixing for a duration of 30 min. The vessel was hermetically sealed and subjected to a continuous flow of nitrogen gas for a duration of 10 min. The experiment involved subjecting the vessel and its contents to a temperature of 250 \({}^{\circ}\)C while simultaneously agitating them for 30 and 180 min. This process yielded two distinct samples, namely, HC30 and HC180. Subsequently, the vessel along with its contents underwent rapid cooling in a cold-water bath maintained at room temperature. The HCs were isolated through the process of vacuum filtration, followed by multiple washes with distilled water. The resulting HCs were then subjected to drying in an oven set at 105 \({}^{\circ}\)C for a period of 24 h. Finally, the HCs were stored in plastic receptacles for further use.  
Activated carbon (AC) from Sigma-Aldrich was subjected to characterisation and subsequently employed in sorption experiments to facilitate a comparison between the BCs and HCs.
## 2 Dynamic Model Analysis
### Dynamic Model of Spur Gear Pair Considering the Meshing State of Multiple Pairs of Teeth
\begin{array}{ll}m_{e}\ddot{x}+\Big{(}c^{(j-1)}(t)&+c^{(j)}(t)+c^{(j+1)}(t) \Big{)}\dot{x}+k^{(j-1)}(t)f^{(j-1)}\Big{(}x-e^{(j-1)}(t)\Big{)}+k^{(j)}(t)f^{ (j)}\Big{(}x-e^{(j)}(t)\Big{)}\\ &+k^{(j+1)}(t)f^{(j+1)}\Big{(}x-e^{(j+1)}(t)\Big{)}-c^{(j-1)}(t)\dot{e}^{(j-1 )}(t)-c^{(j)}(t)\dot{e}^{(j)}(t)-c^{(j+1)}(t)\dot{e}^{(j+1)}(t)\\ &=F_{m}\\ &\text{where $I_{1}$ and $I_{2}$ are rotary inertia of driving and driven wheels, respectively $\big{(}\$kg\cdot m^{2}$\big{)}$; $m_{e}$ is the equivalent mass $\big{(}\$kg)$, $m_{e}$ =I_{1}I_{2}/\big{(}I_{1}R_{b2}^{2}+I_{2}R_{b1}^{2}\big{)}$; $F_{m}$ is the equivalent applied load $\big{(}\$N)$, $F_{m}=T_{1}/R_{b1}=T_{2}/R_{b2}$. \end{array} \]  
Considering the actual meshing state of a pair of gears, the above dynamic equations can be sorted as follows:  
\[m_{e}\ddot{x}+c_{m}(t)\dot{x}+W_{m}(t)=F_{m} \tag{3}\]  
Here, \(c_{m}(t)\) is comprehensive meshing damping, which can be described as:  
\[c_{m}(t)= \begin{cases}c^{(j-1)}(t)+c^{(j)}(t)&nt_{2}\leq t<nt_{2}+(t_{h}-t_{2} )\\ c^{(j)}(t)&nt_{2}+(t_{h}-t_{2})\leq t<nt_{2}+t_{z}\\ c^{(j)}(t)+c^{(j+1)}(t)&nt_{2}+t_{2}\leq t<nt_{2}+t_{h}\end{cases} \quad n=0,1,2,\ldots \tag{4}\]  
Figure 2: Dynamic model for gear system considering the meshing state of multiple pairs of teeth: (a) \(j-1\)th and \(j\)th pairs of teeth; (b) \(j\)th pair of teeth; (c) \(j\)th and \(j+1\)th pairs of teeth. where \(t_{z}\) is the meshing period \((s)\), which is related to the number of teeth \(z_{1}\) and speed \(n_{1}\) of driving wheel, namely \(t_{z}=60/z_{1}n_{1}\); \(t_{h}\) is the meshing time of a pair of teeth from engagement to disengagement \((s)\). \(W_{m}(t)\) is the comprehensive internal incentive and we can write its expression as follows:  
\[W_{m}(t)= \begin{cases}k^{(j-1)}(t)f^{(j-1)}\left(x-e^{(j-1)}(t)\right)+\\ k^{(j)}(t)f^{(j)}\left(x-e^{(j)}(t)\right)-c^{(j-1)}(t)\hat{e}^{(j-1)}(t)-c^{(j) }(t)\hat{e}^{(j)}(t)\\ k^{(j)}(t)f^{(j)}\left(x-e^{(j)}(t)\right)-c^{(j)}(t)\hat{e}^{(j)}(t)\\ k^{(j)}(t)f^{(j)}\left(x-e^{(j)}(t)\right)+\\ k^{(j+1)}(t)f^{(j+1)}\left(x-e^{(j+1)}(t)\right)-c^{(j)}(t)\hat{e}^{(j)}(t)-c^{ (j+1)}(t)\hat{e}^{(j+1)}(t)\end{cases} nt_{z}\leq t<nt_{z}+(t_{h}-t_{z}) \tag{5}\]  
Here, \(f^{(j-1)}\left(x-e^{(j-1)}(t)\right)\), \(f^{(j)}\left(x-e^{(j)}(t)\right)\), \(f^{(j+1)}\left(x-e^{(j+1)}(t)\right)\) is the backlash function of \(j-1\text{th}\), \(j\text{th}\) and \(j+1\text{th}\) pair of teeth, respectively. For example, the expression of the backlash function of the \(j\text{th}\) pair of teeth is:  
\[f^{(j)}\left(x-e^{(j)}(t)\right)= \begin{cases}x-e^{(j)}(t)-b^{(j)}(t)&x>e^{(j) }(t)+b^{(j)}(t)\\ 0&e^{(j)}(t)-b^{(j)}(t)\leq x\leq e^{(j)}(t)+b^{(j)}(t)\\ x-e^{(j)}(t)+b^{(j)}(t)&x<e^{(j)}(t)-b^{(j)}(t)\end{cases}  \tag{6}\]
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were conducted using SPSS 28.0.1 (IBM Corp., Armonk, NY, USA) with the alpha significance level set at 0.05. Descriptive characteristics for all outcome measures of interest are displayed as mean \(\pm\) standard deviation according to the participant's sex. Data were tested for normality using the Shapiro-Wilk's tests and independent samples \(t\)-tests were conducted to examine possible sex differences. One-way repeated measures (RM) ANOVAs were performed for men and women separately to determine any significant differences in outcome measures according to body composition assessment method (i.e., within-subject factors): BIA versus DXA. The effect sizes of significant one-way RM ANOVA test results were reported using partial eta squared values (n\({}_{\text{p}}\)\({}^{2}\)). Sex-specific [PERSON]'s r correlation coefficients between assessment methods were computed, with the absolute value of the correlation demarcated as follows [21]: negligible correlation (r < 30), weak correlation (r = 0.30-0.50), moderate correlation (r = 0.50-0.70), strong correlation (r = 0.70-0.90) and very strong correlation (r > 90). The Fisher r- to z-transformation was used to determine if these correlation coefficients between body composition assessment methods significantly differed between both sexes. Analyses were conducted for the whole-body, right arm and right leg test outcomes, respectively. The bias and 95% limits of agreement (95% LOA) were calculated for each outcome measure according to sex and graphically depicted using Bland-Altman plots [22], with the x-axis, representing the mean of both body composition assessment methods, and the y-axis, representing the difference between the two methods, compared. Finally, one sample \(t\)-tests were performed to determine whether or not the mean difference between both methods was significantly different from zero.
## 3 The Proposed Scheme
### Motivation  
The main objective of the feature weighting of sentiment analysis is to assign an appropriate weight for every single word in order to reflect its relative significance in the feature space, which in turn allows accurate prediction of the sentiment. Generally, many supervised feature weighting approaches assume that the inter-category distribution of a feature plays a major role in feature weighting, while ignoring its intra-category distribution. Nevertheless, the researchers have been questioning feature weighting based on only the inter-category distribution of the features. It has been proved that incorporating the two distributions is superior to using only one with respect to the measurement of the significance of the feature, and as a result yields a better classification performance. The inter-category and intra-category distribution of the features are equally important in feature weighting.  
Intuitively, for a specific word, the more evenly a word is distributed in the feature space, the weaker it distinguishes the category. For example, even though conjunction words widely appear in a document, they hardly represent any category. It is generally believed that the words of large CDS mostly occur in only one or a few categories. Note that adjective words more likely appear in the given category to express the sentiment. Therefore, it is highly likely that the words of non-uniform distribution in the entire categories represent a specific category, and thus a larger weight needs to be assigned. While inter-category distribution is important to characterize the significance of a word in the decision of the weight, intra-category distribution of the word is also crucial in measuring its significance. Specifically, the distribution of a word within a certain category reflects its dependency on that category. More accurate weight could be obtained by incorporating the dependency. Thus, in the proposed scheme, CDS is used to measure the inter-category distribution of the word, while the property of intra-category distribution is formulated using the Intra-Distribution Measure (IDM). The feature weight is then finally computed based on the two measures.
## 2 A Hypothetical Example Application of the V-GSI Chart in a Tunnel
### Calculating Volumetric Fracture Counts (VFC) and Confidence Limits  
As shown in Equation (5) below, the inverse of the mean true spacings for each set provides the bias-free _VFC_ contribution for the two sets shown in Figure 3, i.e., the VFC contribution of set J1 is 0.65 fractures/m\({}^{3}\) (1/1.54), and bedding contributes 0.44 fractures/m\({}^{3}\) (1/2.25) to the overall rock mass fracture count in the example in Figure 3.  
\[VFC_{i}\ (contribution)=\frac{1}{\
abla\overline{S}t_{i}} \tag{5}\]  
where \(\overline{S}t_{i}\) is the mean true spacing for the \(i\)th discontinuity set, and \(\
abla\) implies the use of geometrical means or normal (Gaussian) means, depending on the spacing distribution. In this example, a normal distribution is assumed to keep the example simple and because there is insufficient data to determine an alternative spacing distribution. It is, however, well known that discontinuity sets frequently have negative exponential spacing distribution, particularly for joint sets that are tectonic in origin.  
The bias-free _VFC_ for the rock mass in the tunnel is then simply the sum of the individual _VFC_ contributions for the two sets (0.65 + 0.44 = 1.09), but in general is calculated with the use of Equation (6), irrespective of the number of \(i\) discontinuity sets (_NDS_).  
\[VFC=\sum_{i=1}^{i=NDS}\left(\frac{1}{\
abla\overline{S}t_{i}}\right) \tag{6}\]  
The 95% confidence limits on the true spacing data are then used to calculate the _VFC contribution (cont.)_ for each individual set \(i\) using Equations (7) and (8) as follows:  
\[Lower\ limit\ VFCi\ cont.=\frac{1}{\overline{S}t_{i}+1.96\times\frac{ \partial\overline{S}t_{i}}{\sqrt{n}}} \tag{7}\]  
\[Upper\ limit\ VFCi\ cont.=\frac{1}{\overline{S}t_{i}-1.96\times\frac{ \partial\overline{S}t_{i}}{\sqrt{n}}} \tag{8}\]  
where \(n\) is the number of spacing observations recorded for each set \(i\). The overall lower limit and upper limit on the mean _VFC_ is then the sum of the individual _VFCi_ contributionsfound using Equations (7) and (8). In more formal terms, the 95% confidence limit on the mean _VFC_ can be calculated using Equation (9):  
\[VFC_{limits}=\sum_{i=1}^{i=NDS}\left(\frac{1}{\overline{S}_{ti}\pm 1.96\times \frac{\partial S_{ti}}{\sqrt{n}}}\right) \tag{9}\]  
Table 3 provides a summary of the mean _VFC_ and confidence limits calculated for the example tunnel face in Figure 3 using Equations (1)-(9).