esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 4 In-Situ Nanoparticles
### Nanoparticles in As-Cast Cu Alloys  
Representative SEM and TEM micrographs of as-cast Cu-1.5 Fe-0.5 Co alloy [69], Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy [63; 67], Cu-1 Fe alloy [38] and Cu-3 Sn-8 Zn-6 Pb-1 Fe alloy [40] are shown in Figure 11. Apparently, high-density nanoparticles are homogenously embedded in micron-sized copper grains, forming so-called NPFG structure (illustrated in Section 3.2.1). This structure can be directly observed in SEM images when samples are in good conditions. The morphology and dimension of the nanoparticles in Cu alloys are more complicated than that in steels. Spot-like (2-20 nm), spherical (20-100 nm) and petal-like (100-500 nm) particles with different crystallographic structure can exist in the Cu matrix, simultaneously. For instance, large amount of spot-like and spherical nanoparticles are dispersed within the Cu matrix in Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy. No segregation of nanoparticles at grain boundary was found. TEM-EDS analysis indicates that Fe and Co are rich in all nanoparticles, and the content of Fe is several times more abundant than that of Co. Hence, all in-situ nanoparticles are generally denoted as iron-rich nanoparticles. Quantitative statistics results of nanoparticles in all investigated Cu alloys are listed in Table 2, and spot-like particles are not included due to their small size and the limited TEM resolution. It is obvious that with the augment of Fe content, the volume fraction and size of nanoparticles increase in Cu-Fe-Co alloys, while the number density keeps at the same magnitude. Moreover, petal-like particles appear when Fe content reaches to 2 wt. %, showing that the content of Fe strongly affects particle's morphology.  
Two kinds of crystallographic structure [71], face-centered cubic (FCC) and body-centered cubic (BCC) lattice, are detected in iron-rich nanoparticles through Cs-corrected HAADF-STEM characterization, and atomic arrangement and matching at the Cu/Fe interface are schematically shown in Figure 12. It is noted that, when incident beam is along the \([001]_{\mathrm{Cu}}\) zone axis, nanoparticle with FCC structure has cube-on-cube orientation relationship (OR) with Cu matrix. BCC iron-rich nanoparticles also exist in Cu matrix, which are identified as the pattern of BCC \(\alpha\)-Fe under \([111]_{\alpha\mathrm{-Fe}}\) zone axis and OR between nanoparticle and matrix satisfied classic K-S OR. Mismatch degrees of all tested interface are very low, so we can conclude that there is a good coherent particle/matrix interface.  
Figure 10: Bright field image (TEM) of the Ni-Cr-Mo-V-Nb-Ti ship steel in different states and average diameter and volume fraction of the nano-particles in different states [57; 59].
## 3 Database of Water Content and Hyperspectral Information
### Data Acquisition Method
#### 3.1.2. Indication of Extraction Point for Water Content and Hyperspectral Information  
In order to match the actual water content and hyperspectral information, the data extraction point within the measurement area was marked with numbers from 1 to 100 using spray lacquer, as shown in Figure 4. In general water-content measurement tests, steel cans were used as cups, but paper cups were used due to the large number of samples. The paper cups may have errors in weight measurement due to humidity and other factors. Therefore, in this test, the weight of the paper cup was measured at the site before sampling, and the weight of the empty paper cup was measured after drying to calculate the exact weight of the soil. Additionally, calibration tarps for atmospheric correction must be installed within the shooting range. The accurate measurement of hyperspectral information requires placing one or more targets of known reflectivity levels within or close to the target area from which image data will be collected [23]. This is to correct the image data for independent variables that affect the incident illuminance in the captured image. Here, the independent variables refer to solar geometry (time and latitude), air pollutants, relative humidity, and clouds.
## 1 Introduction
Ion pairing is affected by many factors, such as ionic strength, the size of electrical charge on the ions, the size of the solvent dielectric constant, and the size of the ions. The Bjerrum theory [15,16] of ion pairing states that ion pairing (1) decreases with the increasing ionic strength of the solvent, (2) increases with increasing magnitude of electric charge on the ions, (3) increases with decreasing solvent dielectric constant [17], and (4) increases with decreasing ionic size [17]. Figure 1: Schematic representation of (**a**) a micelle (**b**) a reverse micelle and (**c**) a part of reverse micelles with different locations. Plus represents Na\({}^{+}\) ions [6,10]. Transition metal hexacyanoferrates and pentaaminecobalt (III) complexes are extremely stable coordination compounds [18; 19]. Both the hexacyanoferrates and pentaaminecobalt (III) complexes are water soluble and exhibit redox properties [20]. So, transition metal hexacynoferrate complexes have attracted attention due to their structural attributes and various properties, such as electrochemical and ion-exchanging properties [18]. In this paper, we discuss the results of the investigation of the effect of the size of the reverse micelles on the outer sphere electron transfer rate constant (k\({}_{\text{et}}\)) and ion-pairing constant K\({}_{\text{IP}}\) in the following reaction. \[\text{Co(NH}_{3})_{5}\text{(H}_{2}\text{O})]^{3+}+[\text{Fe(CN)}_{6}]^{4-} \rightarrow[\text{Co(NH}_{3})_{5}\text{(H}_{2}\text{O})]]^{2+}+[\text{Fe(CN)}_{6}]^{3-}\]  
Co (III) \[\text{Fe(II)}\] Co (II) \[\text{Fe(III)}\]
## 4 Results and Discussion
### Effects of Base Motions
#### 4.1.1 Base Rolling Motion  
Figure 10 illustrates the frequency-response characteristics in the horizontal direction at SFD and disk nodes under various base rolling angular velocities, with unstable solutions indicated by dashed lines. Analysis of primary resonance behavior demonstrates that the base rolling angular velocity exhibits minimal influence on the amplitude of resonance peaks, with the system maintaining hardening characteristics. When the base rolling angular velocity is co-directional with rotor rotation, a slight frequency downshift of the resonance peak occurs with increasing base rolling speed. According to Equation (3), this phenomenon can be attributed to the modification of system natural frequencies through the additional stiffness terms induced by base rolling motion.  
Figure 11 illustrates the spectral composition of frequency-response characteristics in the horizontal direction at both SFD and disk nodes under a prescribed base rolling angular velocity \(\omega_{bx}=10\) rad/s. The results indicate that the responses at both nodes remain dominated by the fundamental frequency component \(\Omega\), with negligible superharmonic contributions \(2\Omega\) and \(3\Omega\). This frequency composition indicates that the introduction of  
Figure 10: Amplitude–frequency characteristics at SFD node during base rolling motion (\(C/R=0.485\%,\ U=144.02\)\(\mathrm{g\cdot mm}\)): (**a**) complete response curves; (**b**) magnified view.  
Figure 9: Frequency–response curves of harmonic components under fixed base conditions (\(C/R=0.485\%,\ U=360.04\)\(\mathrm{g\cdot mm}\)) at nodal points of: (**a**) SFD; (**b**) disk.  
base rolling motion preserves the intrinsic nonlinear characteristics of the SFD, maintaining the predominance of the fundamental frequency response.
## 3 Results and Discussion
### Volatile Profiles of Fruits in Four Different Apricot cultivars
Figure 2: The Venn diagram of volatile compounds in four different apricot cultivars. DBY, Dabaiyou apricot; LPH, Luopuhongdaike apricot; YOU, You apricot; XB, Xiaobai apricot. important contributors to the green and fresh notes [34]. 2-Pentenal was abundant in the DBY, LPH and YOU cultivars (40.31, 9.81 and 6.50 \(\upmu\)g/kg, respectively) and was considered the major aldehyde compound in this study. It is possible that some volatile compounds at low levels may also have an important impact on the flavor of apricot [35]. A lower amount of benzaldehyde, involved in basic floral and fruity notes [36], was found in the four apricot cultivars. It was reported that benzaldehyde was an important component of essential oil in Japanese apricots [37]. The most abundant ketone recorded by previous work was 3-hydroxy-2-butanone [38], which was not in agreement with our research. In this work, 3-hydroxy-2-butanone was not detected in the four apricot cultivars. The contents of linalool and \(\alpha\)-terpineol all reached up to 190.00 \(\upmu\)g/kg and above in the DBY, LPH and YOU cultivars, while these compounds were present in XB apricots with a low concentration. Previous work suggested that linalool contributes to citrus, lemon, fragrant and sweet flavors, while \(\alpha\)-terpineol provides fragrant, floral as well as lilac notes. \(\upbeta\)-linalool was the main terpene compound in many apricot cultivars from Romania [39]. [PERSON] et al. [1] also described that linalool and \(\alpha\)-terpineol were responsible for the flowery note in apricots.
## 2 Data and Methods
### Analyses  
Statistic indices, including coefficient of determination (R\({}^{2}\)), root mean square error (RMSE) as a percentage of the mean value (RMSE(%)), and bias (the difference between simulations and observations), were employed to evaluate the model performances.  
\[\mathrm{RMSE}=\frac{100}{\overline{\mathrm{X}}_{\mathrm{obs}}}\times\sqrt{ \frac{1}{n}\times\sum_{i=1}^{n}\bigl{(}\mathrm{X}_{\mathrm{sim\_i}}-\mathrm{ X}_{\mathrm{obs\_i}}\bigr{)}^{2}} \tag{16}\]  
here, \(\overline{\mathrm{X}}_{obs}\) is the average of the observed values; \(\mathrm{X}_{\mathrm{sim\_i}}\) and \(\mathrm{X}_{\mathrm{obs\_i}}\) are the \(i\)th simulated and observed values, respectively; and \(n\) is the total number of samples.
###### Abstract  
Post-harvest deterioration of fruit quality represents a significant challenge in the dried fruit industry, particularly affecting the preservation of nutritional compounds and sensory attributes during the drying process. This research examined the potential protective effects of exogenous melatonin supplementation on the preservation of selected quality metrics and antioxidant characteristics in sun-dried apricots, utilizing a comparative analysis across disparate melatonin concentrations (10, 100, and 1000 \(\upmu\)M). Our research findings demonstrated that melatonin treatment, particularly at 100 \(\upmu\)M concentration, significantly enhanced quality preservation in sun-dried apricots. Specifically, the treatment resulted in improved color retention (increased L*, a*, and b* values), reduced oxidative stress markers (MDA and H\({}_{2}\)O\({}_{2}\)), and optimized sugar composition (glucose: 18.99 g/100 g, fructose: 12.58 g/100 g, sucrose: 15.52 g/100 g). The melatonin treatment at 100 \(\upmu\)M concentration proved particularly effective, revealing the most significant results. Specifically, this concentration resulted in the highest \(\beta\)-carotene levels, reaching 223.07 mg/kg. These findings suggest promising applications for commercial-scale implementation through either dipping or spraying methods. The non-toxic nature of melatonin and its demonstrated efficacy in preserving fruit quality parameters position it as a valuable post-harvest treatment option in the fruit supply chain. This research contributes significantly to advancing sustainable post-harvest preservation strategies, though further investigation into melatonin stability and standardization of application protocols remains necessary for optimal commercial implementation.  
H\({}_{2}\)O\({}_{2}\); dried apricot; MDA; melatonin; oxidative stress; color 1
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## 3 Research Methodology
### Correlation Analysis between Vegetation GPP and Meteorological Factors  
In this paper, the Pearson correlation coefficient was used to calculate the correlation between precipitation, air temperature, and vegetation GPP at the pixel scale.  
The Pearson correlation coefficient was calculated using the following formula:  
\[r_{xy}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{ \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y} )^{2}}} \tag{20}\]  
where \(x_{i}\) and \(y_{i}\) denote the value of GPP and climate factor in year \(i\). \(\overline{x}\) and \(\overline{y}\) denote the multi-year average GPP and multi-year average climate factor values; n is the length of the time series.  
To exclude the interference of variables, the partial correlation coefficient was introduced to study the effect of precipitation (air temperature) on vegetation GPP after accounting for the interference of air temperature (precipitation). The coefficient is calculated as follows:  
\[R_{xy,z}=\frac{R_{xy}-R_{xz}R_{yz}}{\sqrt{\left(1-R_{xz}{}^{2}\right)}\sqrt{ \left(1-R_{yz}{}^{2}\right)}} \tag{21}\]  
where \(R_{xyz}\) is the partial correlation coefficient between the independent variable \(y\) and the dependent variable \(x\) after the independent variable \(z\) is fixed. The significance of the partial correlation coefficient is tested by a \(t\)-test, which is calculated as follows:  
\[t=\frac{R_{xy,z}\sqrt{n-m-1}}{\sqrt{\left(1-R_{xy,z}{}^{2}\right)}} \tag{22}\]  
where \(n\) is the number of samples and \(m\) is the number of independent variables.  
Considering that the change of an element is influenced by multiple factors, the compound correlation coefficient is introduced to study the correlation between temperature, precipitation, and vegetation GPP, and the formula is calculated as follows:  
\[R_{x,yz}=\sqrt{1-(1-R_{xy}{}^{2})(1-R_{xz,y}{}^{2})} \tag{23}\]  
where \(R_{xy}\) denotes the linear correlation coefficient between \(x\) and \(y\); \(R_{xz,y}\) is the partial correlation coefficient between the independent variable \(z\) and the dependent variableafter the independent variable \(y\) is fixed; and \(R\)\({}_{x,y,z}\) is the complex correlation coefficient of the dependent variable x and the independent variables \(y\) and \(z\).  
The significance of the complex correlation coefficients was tested by the \(F\)-test, which was calculated as follows:  
\[F=\frac{R_{x,y,z}{}^{2}(n-k-1)}{k(1-R_{x,y,z}{}^{2})} \tag{24}\]  
where \(n\) is the number of samples and \(k\) is the number of independent variables.
## 4 Experimental Results
### Filter and Haar Evaluation  
Facial regions occupy a significant portion of the dataset's images. Therefore, to enhance the feature extraction capability of the CNN for large objects, we added a larger filter to our CNN model. Furthermore, we aimed to direct the CNN model's focus more towards the facial regions. To achieve this, we employed Haar cascade classifiers to segment the facial areas. The result is illustrated in Table 2.  
In Table 2, we first show the result of the adjustment composition of filters in the CNN. Filter composition was reconfigured to (64, 128, 256, 512), and accuracy improved by 1.71%. We posit that the addition of an extra convolutional layer and filter led to an improvement in accuracy. Subsequently, we evaluated the impact on the system by focusing on facial regions using the Haar cascade classifier. In terms of results, the Haar cascade classifier contributed to a 1.36% improvement in accuracy. We attribute this enhancement to the reduction of background noise, enabling the model to concentrate more on extracting facial features.
## 3 Research Design of the Fieldwork and Results: Comparing Three Paradigms
### Research Results: Comparative Analysis of Three Paradigms  
The results of this study, drawn from immersive fieldwork and ongoing collaboration with decentralized governance initiatives, highlight the transformative potential of Web3 technologies in reshaping traditional nation-state structures. The analysis reveals three distinct paradigms--Network States, Network Sovereignties, and Algorithmic Nations--that are characterized by their varying ideological, governance, economic, technological, and territorial attributes. Network States represent a crypto-libertarian, market-driven approach that emphasizes individual sovereignty, the financialization of social relationships, and startup-like governance structures. In contrast, Network Sovereignties adopt a commons-centric worldview that prioritizes collaborative governance, ethical technology use, and stewardship of public goods, rejecting the radical individualism of Network States. Finally, Algorithmic Nations emerge as a culturally rooted, emancipatory model, leveraging algorithms and data-driven governance to empower communities while promoting transnational cooperation and solidarity. This paradigm particularly focuses on ethical and inclusive governance models, where technology serves the community's cultural and social needs rather than purely economic objectives. These findings provide a comparative framework for understanding how decentralized technologies are influencing the reconfiguration of governance, sovereignty, and territoriality, offering insights into potential future developments in global governance.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 3 Proposal Architecture
### Architecture  
PlaIMoS consists of two hardware devices: the PlaIMoS fixed measurement station which measures patient blood oxygen saturation, respiration rate and galvanic resistance, which are identified by radio frequency identification (RFID) sensor and a PlaIMoS remote node that obtains patient body temperature, electrocardiogram, heart rate and fall data. The fixed measurement station is connected to the Internet by means of its Wi-Fi interface which sends all information directly to the PlaIMoS database, located in the cloud. The PlaIMoS remote node employs the IEEE 802.15.4 standard to transmit information to the WSN infrastructure. The Web service receives the information from the WSN infrastructure and sends the data to the PlaIMoS database. All information stored in the database is used by the PlaIMoS API (Web service) and presented in JavaScript object notation (JSON) format for the different apps. The iOS, Android and Windows apps display the information to the participating patients, doctors and caretakers and push alerts when the medical parameters exceed the ranges established by the doctor. This architecture is shown in Figure 1.
# Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings
## 2 Problem Formulation
### Graph Model  
One of the classical techniques in scheduling is representing a problem instance (or a solution) as a graph. For Flow Shop problems, both approaches are widely used in a form of disjunctive [38] and solution graphs [39]. The latter can be used e.g., to check solution feasibility or for the goal function evaluation purpose [40]. In this subsection, we will introduce graph \(G(\pi)\), constructed for solution \(\pi\).  
First, let us introduce a solution graph for a classical, non-permutation FSSP, given by \(G^{\prime}(\pi)=(V,\;E_{1}\cup E_{2})\), where  
\[V=\bigcup_{\begin{subarray}{c}i\in\mathcal{M}\\ j\in\mathcal{J}\end{subarray}}\{(i,j)\}, \tag{5}\] \[E_{1}=\bigcup_{\begin{subarray}{c}i\in\mathcal{M}\\ j\in\mathcal{J}\setminus\{1\}\end{subarray}}\{\big{(}(i,j-1),\;(i,j)\big{)} \},\quad E_{2}=\bigcup_{\begin{subarray}{c}i\in\mathcal{M}\setminus\{1\}\\ j\in\mathcal{J}\end{subarray}}\{\big{(}(i-1,I_{i-1}(\pi_{i}(j))),\;(i,j) \big{)}\}. \tag{6}\]  
The vertices in \(V\) represent operations and will be placed on a grid for an easier visualization. The operation of job \(j\) on machine \(i\) will be represented by a weighted vertex in \(j\)-th \"column\" and \(i\)-th \"row\", denoted by \((i,j)\) and with weight \(\rho_{ij}\). The arcs of graph \(G^{\prime}(\pi)\) are weightless and represent problem constraints (\(E_{1}\) represents machine constraints, and \(E_{2}\) -- technological constraints). Finally, the length of the longest path to vertex \((i,j)\) equals \(C_{i,j}\). _Length of the path_ is defined in this paper as a sum of weights of its vertices and arcs. Thus, the value of the makespan \(C_{\max}(\pi)\) is equal to the length of the longest path in the graph \(G^{\prime}(\pi)\) (critical path). We denote length of a path \(c\) by \(\text{len}(c)\).  
We will now move onto extended graph \(G(\pi)\). To reflect minimum idle times in the graph, we will use the same technique as for setup times [40]. Each arc \(((i,j),\;(i,j+1))\)  
Figure 1: The left-shifted schedule for the instance from Example 1 and \(\pi=((1,2,3,4,5),\;(2,4,3,5,1),\;(1,2,3,5,4))\).  
in \(E_{1}\) now has a weight of \(\hat{r}_{i}\). Secondly, we will introduce another set of arcs, to reflect maximum idle time [37]  
\[E_{3}=\bigcup_{i\in\mathcal{M}}\bigcup_{j\in\mathcal{J}\setminus\{n\}}\big{\{} \big{(}(i,j+1),\ (i,j)\big{)}\big{\}}. \tag{7}\]  
Any \"return\" arc \(((i,j+1),\ (i,j))\) in \(E_{3}\), has weight \(\hat{D}_{i,j}\), defined as  
\[\hat{D}_{i,j}=-\rho_{i,j}-\rho_{i,j+1}-\hat{d}_{i}. \tag{8}\]  
A structure of graph \(G(\pi)\) for the solution \(\pi\) from Example 1 is shown in Figure 2. For more readability, arcs from sets \(E_{1}\), \(E_{2}\) and \(E_{3}\) are marked in green solid, blue dashed, and red dotted lines, respectively: