esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradable Contrast Agents for Photoacoustic Imaging
## 4 Gold Nanoparticles as Biodegradable PA Agents  
The characteristics of gold nanoparticles (AuNPs) are inert and biocompatible and can be varied through ligand attachment and shape modification [49; 50; 51]. Although various AuNPs has been demonstrated as molecular PAI contrast agents [52; 53; 54], there are no FDA-approved AuNPs owing to concerns related to hepatic retention. To achieve efficient renal clearance, the particles should be smaller than approximately 6 nm [55]. Therefore, several attempts have been made to make biodegradable AuNPs platforms that can be degraded under specific conditions.  
Figure 2: Nitrogen-doped carbon nanodots (N-CNDs) as theranostic agents. (**A**) Synthesis of N-CNDs. (**B**) TEM image of N-CNDs (scale bar = 5 nm). (**C**) Comparison of the PA signals of gold nanorods (GNR), methylene blue (MB) and N-CNDs. (**D**) Temperature changes of different concentrations of N-CNDs under irradiation at 808 nm. (**E**) Three-dimensional PA images (upper row) and depth-resolved two-dimensional PA images (lower row) in sentinel lymph nodes (SLNs) before (control), 30 min and 150 min after the injection of N-CNDs. (**F**) PA signal changes in the SLN area over time. Reproduced with permission from [48]. Copyright Ivyspring International Publisher, 2016.
## 5 Results and Discussion
### Scenario 1: Current Scenario  
In this first scenario, the current operational model of the company under study is analyzed, using real historical data to replicate its logistics performance. This scenario provides a benchmark for understanding the actual efficiency of the existing logistics framework by comparing real and simulated travel distances. By assessing the current scenario, KPIs such as routing efficiency, vehicle utilization, and cost implications of distance travel are examined. This allows for the identification of potential inefficiencies in the company's operations, which will later be contrasted with the optimized scenario. Table 4 presents the details of the orders considered, listing essential attributes such as the assigned driver, pickup and delivery times, baggage volume, travel distance (both real and simulated), and associated costs (i.e., transportation and warehouse expenses).  
Moreover, the comparison of routing costs reveals that, in most cases, the simulated cost per kilometer is slightly lower than the real cost, suggesting opportunities for route optimization. For instance, in the order assigned to Driver A on 6 January 2023 at 10:30, the company recorded 16 km, whereas the simulation carried out the route to 19.8 km, highlighting possible real-world constraints such as traffic conditions, driver route preferences, or suboptimal path selection. Additionally, the role of warehouse storage in certain deliveries underscores the need for dynamic fleet management. Some orders required temporary storage due to scheduling constraints, leading to additional costs, such as EUR 5.00 on 6 January 2023 and EUR 10.00 on 7 January 2023. These findings emphasize that optimizing vehicle allocation strategies and minimizing reliance on storage facilities could further  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Driver** & **Pickup** & **Delivery** & **Real Distance** & **ABS Model** & **Real Cost (EUR** & **ABS Model Cost** & **Warehouse Cost** \\
**Pickup-Delivery** & **Date/Time** & **Date/Time** & **Traveled (KM)** & **(KM)** & **per KM)** & **(EUR per KM)** & **(EUR)** \\ \hline B-A & 31 December & 6 January 2023 & 11 & 9.3 & 5.50 & 4.65 & 30.00 \\  & 2  
enhance cost efficiency. Overall, the current scenario simulation effectively replicates the company's real-world operations, providing a strong baseline for comparison with the optimized scenario, where further improvements in routing efficiency, cost reduction, and resource allocation will be examined.
## 3 Results
### Method Specific AVCs
#### 3.1.1 Paired \(t\)-Test  
For every joint, the two method-specific AVCs are plotted with their standard deviation in Figure 5.1a-5.3a. Figure 5.1 addresses the hip, 5.2 addresses the knee and 5.3 addresses the ankle. The results of an SPM paired-\(t\)-test is plotted in Figure 5.1b-5.3b.  
Figure 5: From top to bottom: SPM1D output of the method-specific AVCs from the hip (5.1); knee (5.2) and ankle (5.3). From left to right: Mean and standard deviation of the motion capture system and the bar linkage (**a**); plot of the \(p\)-values (**b**).  
The largest difference for the hip is located in the region between 168\({}^{\circ}\) and 216\({}^{\circ}\), though it is not significant (\(\alpha\) = 0.05, \(t\) = 2.607). The largest difference for the knee is located in the region of 36-96\({}^{\circ}\) and of 192-276\({}^{\circ}\), though are not significant (\(\alpha\) = 0.05, \(t\) = 2.667). The ankle has three zones with a significant difference (\(\alpha\) = 0.05, \(t\) = 3.59, \(p\) < 0.001) located in the region of 336-108\({}^{\circ}\), 144-216\({}^{\circ}\), and 264-312\({}^{\circ}\).
## 3 Results
### Antioxidant Activity  
The investigation into the antioxidant potential of elderberry hydrolate yielded significant insights (Figures 7 and 8). The analysis revealed a concentration-dependent relationship between elderberry hydrolate and the antioxidant activity of ABTS, as measured by Trolox equivalents (Figure 7). With increasing concentrations of elderberry hydrolate (ranging from 0% to 15%), there was a proportional rise in the Trolox equivalent values, demonstrating a corresponding escalation in the antioxidant capacity.  
Figure 8: Relationship between the elderberry hydrolate concentration and the antioxidant activity by DPPH inhibition in \(\mu\)mol Trolox. The tested groups are distinguished by abbreviations denoting the constituent ingredient (EbH: elderberry hydrolate) and the respective elderberry hydrolate concentration (1:1%, 5:5% and 15:15%). The double asterisk stands for significant differences between ‘C+’ and ‘EbH1’. The triple asterisk stands for significant differences between ‘C+’ and ‘EbH15’.  
Figure 7: Relationship between the elderberry hydrolate concentration and the antioxidant activity by ABTS inhibition in \(\mu\)mol Trolox. The tested groups are distinguished by abbreviations denoting the constituent ingredient (EbH: elderberry hydrolate) and the respective elderberry hydrolate concentration (1:1%, 5:5% and 15:15%). The single asterisk stands for significant differences between ‘C+’ and ‘EbH15’. The triple asterisk stands for significant differences between ‘C+’ and ‘EbH15’.  
The impact of the varying concentrations of hydrolate on the antioxidant activity was also investigated using the DPPH assay. The Trolox equivalent values were determined at concentrations of 0%, 1%, 5%, and 15% hydrolate. As expected, elderberry hydrolate shows antioxidant activity because, during the extraction process, some of the antioxidant compounds (e.g., furfural, phenylacetaldehyde and (_E_)-beta-damascenone) are released from the berry and dissolved in the water. Surprisingly, the results revealed a non-linear relationship between the hydrolate concentration and the antioxidant activity. At 1% concentration, the Trolox equivalent value significantly increased compared to the baseline, suggesting a positive influence of hydrolate on the antioxidant properties. However, at 5%, a decrease in the Trolox equivalent values was observed, indicating a complex concentration-dependent effect. Strikingly, the highest antioxidant activity was noted at a 15% hydrolate concentration, underscoring the presence of an optimal concentration for maximum efficacy.
###### Abstract  
The error matrix has been adopted as both the \"de facto\" and the \"de jure\" standard way to report on the thematic accuracy assessment of any remotely sensed data product. This perspective assumes that the error matrix can be considered as a set of values following a unique multinomial distribution. However, the assumption of the underlying statistical model falls down when true reference data are available for quality control. To overcome this problem, a new method for thematic accuracy quality control is proposed, which uses a multinomial approach for each category and is called QCCS (quality control column set). The main advantage is that it allows us to state a set of quality specifications for each class and to test if they are fulfilled. These requirements can be related to the percentage of correctness in the classification for a particular class but also to the percentage of possible misclassifications or confusions between classes. In order to test whether such specifications are achieved or not, an exact multinomial test is proposed for each category. Furthermore, if a global hypothesis test is desired, the Bonferroni correction is proposed. All these new approaches allow a more flexible way of understanding and testing thematic accuracy quality control compared with the classical methods based on the confusion matrix. For a better understanding, a practical example of an application is included for classification with four categories.  
Keywords:thematic accuracy; quality control column set; multinomial test +
Footnote †: journal: Applied Sciences
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method
### Least Mean Square Parameter Estimation Algorithm Based on Virtual Frequency Characteristics  
Because the real frequency characteristic expression is more complex than the virtual frequency characteristic expression, the derived algorithm is more complex in form, so this paper uses the least mean square parameter estimation algorithm based on virtual frequency characteristics. In the frequency response identification experiment, a sine signal \(u(t)=U\sin(\omega t)\) with frequency \(\omega\) and amplitude \(U\) is applied to the input end of the system to be identified, and theoretically \(\omega\in[0,\infty]\). The frequency characteristic observation data \(\{M(\omega_{k}),N(\omega_{k})\}\) corresponding to each discrete frequency point \(\omega_{k}\) (\(k=0\), \(1\), \(2\),... ) are collected. According to Equation (25), the criterion function \(J^{*}(\theta)\) based on virtual frequency characteristics is constructed by using the observation data of virtual frequency characteristics \(\{N(\omega_{k}),k=0,1,2,\cdots\}\), which is defined as the square of the difference between the observation data of virtual frequency characteristics \(N(\omega_{k})\) and the model output \(Im[G(j\omega)]\). \[J^{*}(\theta)=\frac{1}{2}\{N(\omega_{k})-Im[G(j\omega)]\}^{2} \tag{27}\]  
Transform the expression of virtual frequency characteristics in Equation (25) into the following form:  
\[ \begin{split}&\Big{\{}\big{[}1+\psi^{T}(\omega)\alpha_{2}\big{]}^{2 }+\big{[}\varphi^{T}(\omega)\alpha_{1}\big{]}^{2}\Big{\}}Im[G(j\omega)]=\\ &\big{[}1+\psi^{T}(\omega)\alpha_{2}\big{]}\varphi^{T}(\omega) \beta_{1}-\big{[}n_{0}+\psi^{T}(\omega)\beta_{2}\big{]}\varphi^{T}(\omega) \alpha_{1}\end{split}  \tag{28}\]  
Since the virtual frequency characteristic observation data \(N(\omega_{k})\) contains measurement noise, there is an error between the virtual frequency characteristic observation data \(N(\omega_{k})\) and the model output \(Im[G(j\omega)]\). Therefore, according to Equation (28), the equation error expression is defined as:  
\[ \begin{split} v_{i}(\omega_{k})&=\Big{\{}\big{[}1+ \psi^{T}(\omega_{k})\alpha_{2}\big{]}^{2}+\big{[}\varphi^{T}(\omega_{k})\alpha _{1}\big{]}^{2}\Big{\}}N(\omega_{k})-\\ &\big{[}1+\psi^{T}(\omega_{k})\alpha_{2}\big{]}\varphi^{T}( \omega_{k})\beta_{1}+\big{[}b_{0}+\psi^{T}(\omega_{k})\beta_{2}\big{]}\varphi^ {T}(\omega_{k})\alpha_{1}\end{split}
## 2 Control Algorithm
### Parameter Tuning
#### 2.5.1 Effect of \(\Phi\)  
\(\Phi\) is the parameter to determine the switching method and reduce the chattering phenomenon, as shown in (21). However, the larger the value of \(\Phi\), the more sluggish the response. As \(\Phi\rightarrow\infty\), \(\text{sat}(s,\Phi)=s/\Phi\). As \(\Phi\to 0\), \(\text{sat}(s,\Phi)=\text{sgn}(s)\), which usually causes chattering phenomenon. Consider the case \(\Phi\rightarrow\infty\), so that \(\text{sat}(s,\Phi)=s/\Phi\). Since \(s\) is finite in general, \(|s|/\Phi<1\). The smaller the \(|s|\), the smaller the \(|s|^{2}/\Phi\). The decreasing rate of the Lyapunov function is influenced by  
\[\dot{V} \leq -\eta\frac{s^{2}}{\Phi}=-\eta\frac{|s|^{2}}{\Phi} \tag{33}\]  
The original saturation function \(\text{sat}(s,\Phi)=\text{sgn}(s)\) brings \(\dot{V}\leq-\eta|s|\). The condition to have a more greatly decreasing rate of the Lyapunov function is given by  
\[\eta\frac{|s|^{2}}{\Phi} \geq \eta|s| \tag{34}\]or equivalently, \(|s|>\Phi\). Therefore, a large \(\Phi\) is not helpful in speeding up the converge of the system. Figure 2 demonstrates the comparison. \(\Phi=100\) in Figure 2a, whereas \(\Phi=0.1\) in Figure 2b. Other parameters are set identical. It is obvious that the responses with smaller \(\Phi\) are faster, regardless of the type of controls.
## 2 Informative Frequency Band Selectors
### Negentropy  
Entropy is a general concept that measures the disorder in a system. In the thermodynamic theory, the strength of the energy is uniform over the whole system (called the state of equilibrium) and it is characterized by constant temperature and maximum entropy. In the signal processing terminology, it means that the system is stationary. One can find the usage of the entropy measure in [66; 67].  
Negentropy, the opposite of entropy, was proposed by [PERSON] [48] as a measure of impulsiveness, in the time domain (in SE--Squared Envelope) and in the frequency domain (in SES--Squared Envelope Spectrum), which are additive and their average was proposed as a fault (impulsive and cyclical impulses) detection tool. The associated quantities displayed as functions of the frequency and frequency resolutions form \"SE infogram\", \"SES infogram\" and \"average infogram\" [48]. The estimator of negentropy for the vector \(x=(x_{1},...,x_{N})\) is defined as follows:  
\[\hat{I}(x)=\frac{1}{N}\sum_{i=1}^{N}\Big{(}\frac{{x_{i}}^{2}}{\frac{1}{N}\sum _{i=1}^{N}x_{i}}\ln\frac{{x_{i}}^{2}}{\frac{1}{N}\sum_{i=1}^{N}x_{i}}\Big{)}. \tag{7}\]  
In order to obtain the \(SE\) and \(SES\) infograms (\(I_{SE}\), \(I_{SES}\)) the vector \(x\) in the Equation (7) has to be substituted by the corresponding \(SE\), or in case of the \(SES\) infogram by \(|SES|\), of the filtered signal for the tested frequency band \(f\) +/- \(\Delta f\). Then, using the formula (8) the average infogram (called infogram) has been proposed [48]:  
\[\Delta I(f,\Delta f)=\frac{I_{SE}+I_{SES}}{2}. \tag{8}\]  
Infogram considers both specific features of repetitive transient states: impulsiveness in the time and frequency domains. Hence, it distinguishes oneself among the other selectors. However, the energy from the envelope and the energy from the envelope spectrum have no upper bounds and their scales may differ significantly, therefore the average of both can be exposed to scoring the result, for which the scale is larger.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 2 Formation Mechanism and Value of Vertical Crack
### Failure Mode of Slope with Crack  
In order to understand the influences of tensile cracks on stability of slope, three types of slope failure were developed in this study. In the stability analysis of clay slope, the slip surface of slope is composed of tension crack and shear surface, and the shear surface is assumed to be circular. According to the location of shear surface outlet, the failure surface can be divided into three types: toe circle slope, base circle slope, and face circle slope, as shown in Figure 2. The location of the center of the circular arc is \(O\left(a,b\right)\), \(a\) and \(b\) are the coordinates of center of circle, and \(r\) is radius. When \(r=\sqrt{a^{2}+b^{2}}\), it is defined as toe circle slope; when \(r>\sqrt{a^{2}+b^{2}}\), it is defined as base circle slope; when \(r<\sqrt{a^{2}+b^{2}}\), it is defined as face circle slope, \(h\) is the distance from shear outlet to ground [37; 38].  
Thus, the main assumptions of this research are as follows:  
(1) There exists no shear strength for soils in the crack area.  
(2) The soil materials follow Mohr-Coulomb failure criterion and relevant flow rule, and the tensile strength of soils is neglected.  
(3) The slip body slides along the circular surface with center \(O\left(a,b\right)\), and there appears a vertical crack at the slope crest, which forms a combination of the slip surface and the shear slip surface.  
(4) The soil in the crack area is simplified as surcharge, and acts on the top of the circular slip surface.  
(5) The optimal crack depth is determined according to the principle of minimum safety factor.
## 1 Introduction
It has been shown that the oxidation method of heating surfaces in boiler units can be used to clean them from ash deposits [13]. The authors also suggested that this method will allow for the reduction in surface contamination with ash during the boiler operation by smoothing the metal surface roughness due to the formed protective oxide film. This should weaken the adhesion of ash and resinous deposits. However, the effect of this processing method on the fuel combustion processes has not been studied. The influence of the metal surface roughness on the ignition and combustion characteristics has been studied with the development of the detonation theory of condensed explosives [14]. It has been found that surface roughness increases the flame propagation velocity near the heating surface [14]. A similar result has been established in [15; 16] when studying the effect of metal surface roughness on the ignition characteristics of OCOWF droplets during their interaction with a heated surface under a conductive and mixed (conductive, convective, and radiative) heat supply. The flame shape has been found to depend on the roughness of the surface where the OCOWF droplet burns [16]. The flame is elongated in the vertical direction on smooth surfaces. The greater the roughness of the heating surface, the more elongated the flame shape along the horizontal direction due to the higher velocity of dispersed products near this surface [16]. It is also known that the ignition and burnout times can be controlled by changing the surface roughness. Laser processing is the most promising method for modifying heating surfaces in order to control the combustion characteristics of OCOWF droplets since it allows controlling the ignition time and burnout time in a wide range [15]. In addition, it contributes to a significant reduction in the deposition of solid fuel combustion products on the steel surface [15]. The effect of metal surface roughness on the ignition and combustion characteristics of liquid fuels was studied in [15] where the texture was formed by two methods, including abrasive processing and laser irradiation. It should be noted that the surface layer modification of metals was conducted in [15; 16] without changing the texture configuration, i.e., without creating regular textures on the surface, e.g., in the form of ordered grooves, rectangles, or craters [17; 18]. The presented work is aimed at studying the effect of the texture configuration in the form of microchannels on the ignition and combustion characteristics of slurry (OCWF), emulsion, and oil droplets under conditions that correspond to the operation conditions of power-generating equipment in the startup mode (a mixed heat supply to the droplet, including convection, radiation, and conduction) and the nominal operation mode (mainly conductive heating of a fuel droplet during its interaction with the surface).
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
## 2 DCSK Based on First Order Hybrid System  
### The First Order Hybrid Chaotic System  
The first order hybrid chaotic system contains a continuous state \(u\left(t\right)\in\Re\) and a discrete state \(s\left(t\right)\in\left\{-1,+1\right\}\), given by [32]  
\[\frac{du}{dt}=u-s, \tag{1}\]  
where \(t\) is a continuous time. The discrete state determined by a switching condition detected at every intervals \(T\) is given by  
\[s\left(nT\right)=\left\{\begin{array}{cc}+1,&u\left(nT\right)>0\\ -1,&u\left(nT\right)\leq 0\end{array}\right., \tag{2}\]  
where \(n\in\mathbb{Z}\) and the discrete state \(s\) remains unaltered until the next switching pulse. Figure 1a shows the switching pulse waveform for the hybrid system with \(T=\ln 2\). Figure 1b shows the continuous state, the corresponding discrete symbols and the states at the switching interval by blue solid line, red dotted line, and black dot marks, respectively. The dynamics of the hybrid system are chaotic, characterized by the positive Lyapunov exponent \(\lambda=1\), obtained both analytically and from a time series [32; 34]. The auto-correlation function of the continuous signal is given in Figure 2, which has a typical pulse-like auto-correlation function. These quantities indicate that the signal generated from the first order hybrid dynamical system is chaotic.  
Figure 1: The waveform states of the hybrid system. (**a**) The periodic switching pulse; (**b**) The continuous state, discrete state, and state at the switching point of the hybrid system.
# Numerical Analysis of an Upstream Tailings Dam Subjected to Pond Filling Rates
## 4 Discussion  
It is concluded by [PERSON] et al. (2017) that the stability of an upstream tailings dam could be improved by utilizing rockfill berms as supports on the downstream side. The stability analyses obtained in this study agree with this conclusion; refer, for example, to Figures 4 and 6. In addition, it was found from this study that, in order to keep the stability of an upstream tailings dam, the exceptionally high pond filling rate is not recommended. However, the recommendation is only applicable in the case of 20 m rockfill width and the tailings type used in this study. In practice, larger rockfill width could be added next to dikes or on the downstream side to enhance the slope stability and hold the production  
Figure 11: Concept of excess pore water pressure build-up during the stage of construction (point B and \(PFR\) = 10 m/year).  
Figure 12: Effect of the pond filling rates (\(PFR\)) on the build-up of \(EPP\) during the stage of construction at: **(a)** point A and (b) point B (defined in Figure 1b).  
speed. In this case, key questions remain to be answered: (i) which design of rockfill berms is needed for different production speeds to maintain the stability of a tailings dam, and (ii) how can the volume of rockfill berms be minimized (i.e., for minimum cost) while still maintaining the stability and production speed?  
In practice, an upstream tailings dam is typically constructed by placing dikes on top of previously deposited soft saturated tailings, which could lead to the excess pore water pressure gradually increasing during pond filling and dam raising. Therefore, an adequate pond filling rate is required to allow excess pore water pressure to dissipate gradually during the consolidation process. The numerical results from this study showed that excess pore water pressure increased in each raising phase and decreased in the subsequent consolidation phase. However, some of the excess pore water pressure remained after every consolidation phase (i.e., the build-up of excess pore water pressure) (Figure 11). The build-up excess pore water pressure varied depending on the pond filling rates. The dam with a high pond filling rate had a shorter time for the excess pore pressure to dissipate during every consolidation phase than the dam with low ones. Therefore, the highest value was found in the dam with the pond filling rate of 10 m/year in this study (Figure 12).  
Even though the numerical results obtained are encouraging, some additional work will need to be performed in the future to simulate other conditions involved in the responses of an upstream tailings dam under cyclic and seismic loading.