esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
###### Abstract
The mismatch between the weight allocation and the channel perception capabilities of node devices causes significant discrepancies between the channel perception results and the actual channel states, leading to errors in message transmission decisions. This, in turn, increases the probability of conflicts and reduces network throughput. To address the shortcomings of existing collaborative channel perception, enhance the rationality of weight allocation, accurately reflect channel states, and improve the transmission performance of UAV networks, this paper integrates the data fusion algorithm from evidence fusion theory into channel state perception for data links. The advantages of data fusion are applied to assessing the traffic pulse statistical capabilities of network node devices. Typical characteristic parameters that describe the traffic pulse statistical capabilities of node devices are considered as sets of evidence parameters within the recognition framework. By calculating the credibility and falsity, and measuring the differences and conflicts of characteristic parameters among neighboring node devices within a single-hop range, a comprehensive evaluation of the traffic pulse statistical capabilities of node devices can be achieved. Based on this evaluation, the geometric mean method was used to calculate channel perception weights for each node within a single-hop range, in order to form a new and reasonable weight allocation strategy. This new strategy is called cooperative channel perception based on data fusion, abbreviated as CCP-DF. This new method can accurately perceive the true state of the channel and provide a reliable basis for decision making in data link message exchange, thereby reducing the probability of conflict and enhancing the throughput of data link networks. This paper applies the advantages of multi-source data processing in data fusion theory to the measurement of network node channel perception capability. It rationally allocates network node data fusion weights, provides technical support for enhancing the UAV cooperative channel perception capability, and subsequently improves the radio spectrum sensing theory.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## Bus Depot Alsterdorf  
### Electrical Infrastructure  
Bus depot Alsterdorf is the newly planned depot in the city of Hamburg and the biggest electrical bus depot from the company Hochbahn. Figure 1 shows the diagram of the depot with its carports and a detailed representation of the Carport 6.  
Two redundant transformers are connecting the depot to the grid. The medium voltage grid within the depot consists of four rings, operating at 20 kV. One ring is used for the emergency power supply (10 kV grid) and the self-consumption (administration building and workshop). The remaining three rings are connected to carports. Carports have four modules with a medium voltage transformer and 10 fast charging DC-charging stations per module. This makes a total capacity of 40 buses per Carport and 240 buses on the depot. Previous studies estimated the peak power demand of this depot to 16.56 MW [24]. Due to its high power demand, it is connected to the 110 kV high voltage grid.
## 2 Basic Theory of HSDTPEs
### Incompatible Polynomial Approximation over Each Triangular Element
As mentioned above, a shear correction factor \(k\) is required to properly compute the transverse shear stiffness in the FiSDTPE with the assumption of FSDT, which causes constant transverse shear strains and stresses across the thickness and violates the conditions of zero transverse shear stresses on the top and bottom surfaces of plates [16]. Usually, \(k\) is taken as \(k=1.2\) for the special case of FiSDTPE in Equation (13) according to the principle of the equivalence of strain energy. However, the high-order shear deformation theory gives a parabolic distribution of the transverse stresses/strains directly and avoids the use of a shear correction factor, and thus \(k\) is taken as \(k=1.0\) in Equation (13) for the rest of the HSDTPEs. Therefore, the strain energy of element \(e_{i}\) can be derived as:  
\[\Pi^{e}=\frac{1}{2}(\mathbf{a}^{e})^{\mathrm{T}}\int_{-h/2}^{h/2}\left(\iint \limits_{\Delta e_{i}}\mathbf{B}^{\mathrm{T}}\mathbf{D}\mathbf{B}dxdy\right) dz\ \mathbf{a}^{e} \tag{14}\]  
For the plate made of Functionally Graded (FG) materials which is created by mixing two distinct material phases, the composition of the FG materials is in general assumed to be varied continuously from the top to the bottom surface. There are many kinds of FG materials made from all classes of solids. But for the sake of simplicity and convenience, only a ceramic-metal composite is considered and implemented to test the performance of the HSDTPEs in the present study, and the power-law [25; 32; 45] is used to describe the through-the-thickness distribution of FG materials, which is expressed as:  
\[V_{c}(z)=\left(\frac{1}{2}+\frac{z}{2}\right)^{n}\quad(0\leq n\leq\infty) \tag{15}\]  
\[P(z)=(P_{c}-P_{m})V_{c}+P_{m} \tag{16}\]  
where \(n\) is the volume fraction exponent, \(V_{c}\) is the volume fraction of the ceramic, \(P_{m}\) represents the material property of the metal, \(P_{c}\) represents the material property of the ceramic, and \(P\) denotes the effective material property. In this work, the Young's modulus \(E\) in Equation (13) varies according to Equation (16) and the Poisson ratio \(v\) is assumed to be constant for the analysis of functionally graded plates.
## 3 A Resilient Routing Protocol  
Our proposed resilient routing protocol enhances the stability, efficiency, and reliability of MANETs by integrating UL, the AHP, and DRL within an SDN framework. This integration leverages the strengths of each component to address inherent challenges in MANETs, such as dynamic topology changes, limited energy resources, and varying link qualities.
## 2 Notation and Background
### The Objective Function of Hybrid DL  
In recent years, the hybrid DL [16; 19; 25; 26] has been getting more and more attention in the classification problem. The hybrid dictionary has been shown to perform better than the other types of dictionaries, as it can preserve both the class-specific and common information of the data. To learn such a dictionary, we can define the objective function of Hybrid DL as follows.  
\[\begin{split}\min_{D,Z}&\sum_{i=1}^{C}\|X_{i}-D_{0 }Z_{0}^{i}-D_{i}Z_{i}^{i}\|_{F}^{2}+\lambda_{1}\psi(D)+\lambda_{2}\phi(Z)\\ s.t.&\|d_{j}\|^{2}=1,\ j=1,\cdots,k_{0},\cdots,K \end{split} \tag{3}\]  
where \(D=[D_{0},D_{1},\cdots,D_{C}]\in\mathbf{R}^{m\times K}\), \(D_{0}\) is the dictionary shared by all the classes, \(D_{i}\left(i=1,\cdots,C\right)\) is the \(i\)th class sub-dictionary, \(Z_{0}^{i}\) and \(Z_{i}^{i}\) are the coding coefficients of the samples from the \(i\)th class over \(D_{0}\) and \(D_{i}\), and \(\psi(D)\) and \(\phi(Z)\) denote the functions about the hybrid dictionary and the coding coefficients, respectively. The constraint of \(\phi(Z)\) typically adopts \(l_{1}\)[26] norm or \(l_{2,1}\)[25; 27] norm for sparse coding.
###### Abstract  
Body composition as a meaningful factor can result in physiological responses in both the physical body and general health status. Nevertheless, the schemes for establishing cut-off points for identifying the classifications of the body fat percentage of athletes competing in combat sports still include gaps. The aim of this study was, by using bioimpedance, to calculate the percentiles for the classification of body fat percentages in Lithuanian professional male athletes (n = 52) competing in combat sports with weight classes. A total of 52 Lithuanian professional male athletes competing in combat sports with weight classes were evaluated using a multi-frequency bioelectrical impedance analysis method with frequencies ranging from 1 kHz to 1000 kHz. Percentiles P3, P10, P25, P50, P75, P90, and P97 were used to determine the classification. As a consequence, the following classification categories were assigned: 6.6-7.8% (extremely low); 7.9-10.9% (very low); 11.0-14.7% (below normal); 14.8-18.8% (normal); 18.9-21.5% (above normal); 21.6-29.3% (very excessive); and \(\geq\)29.4% (extremely excessive). The assessment of body composition in combat sports athletes identified an inverse association between higher body fat levels and a decrease in the muscle-to-fat ratio (\(\beta\) -13, 95% confidence interval (CI): -1.5; -1.0, \(p\) < 0.001). The relationship between lower body fat percentage and lighter weight categories in which combat sports athletes from different combat sports were competing has been identified (\(\beta\) 0.3%, 95% CI: 0.2; 0.3, \(p\) < 0.0001). The established cut-off points may assist sports medicine professionals and sports dietitians in monitoring the adiposity of combat sports athletes.  
Keywords:combat sports; athletes; body composition; adiposity; athletes' health +
Footnote †: journal: Applied Sciences
# Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride
## 3 Results
### Dislocation Motion
#### 3.2.3 Yield Stress Estimate  
Based on the definition of the threshold Schmid stress (also known as the critical resolved shear stress (CRSS)), \(\tau_{S}\), as the stress at which the dislocation moves with a velocity of at least 1 m/s [55], it is found that for the edge dislocation in UN, \(\tau_{S}\) = 179 MPa, whereas for the screw dislocation, \(\tau_{S}\) = 197 MPa. Although this definition of the CRSS is based on studies on FCC and HCP metals, we assume its validity for UN. According to [PERSON] and [PERSON] [56], an upper-limit estimate of the uniaxial yield stress of a polycrystal can be calculated from  
\[\sigma_{y}=3.06\tau_{S} \tag{5}\]  
which gives \(\sigma_{y}\) = 548-603 MPa. Unlike the GPa-range yield stresses calculated earlier, which are only valid for single-crystal UN, \(\sigma_{y}\) = 548-603 MPa is comparable to the yield stress of macroscopic samples at room temperatures. However, to the best of our knowledge, no experimental stress-strain measurements of UN exist in the open literature.  
Another estimate of the yield stress for macroscopic polycrystalline UN can be calculated using the model of [PERSON] et al. [57], which has been used to give a good prediction of the yield stress of \(\alpha\)-U [50]. This model applies the crack formation criterion of brittle fracture [55] to obtain a value of the critical stress at which the elastic strain energy imposed upon an elongated single crystal exceeds the surface energy of the crystal. This critical stress correlates with the yield stress because, although UN will fail plastically, its brittle fracture provides a guide to the stress at which the elastic limit is reached. For UN, this critical stress can be calculated as [50; 57]  
\[\sigma_{c}=2\sqrt{\gamma C_{11}/w} \tag{6}\]  
where \(\gamma\) is the (001) surface energy, \(w\) is the sample width, and \(C_{11}\) is the elastic constant. According to the DFT study of [PERSON] et al. [58], the (001) surface energy is \(1.44\,\mathrm{J/m^{2}}\). The sample width, \(w\), can be estimated as the size of a 10 \(\upmu\)m single crystal [50; 57], which is a typical average grain size. The value of \(C_{11}\) = 423.9 GPa is taken from experiments [40]. Using these values, \(\sigma_{c}\) is about 494 MPa, which agrees within 10% with the lower limit of the range calculated earlier (i.e., 548 MPa) using Equation (5), which is a good agreement considering the widely different approaches used to arrive at the two values. Based on this simple analysis, a rough estimate of the yield stress of UN can be given as 550 MPa.  
Figure 8: The variation in the average screw dislocation velocity in UN with shear stress (**a**) on a linear scale, and (**b**) on a semi-log scale. The error bars represent one standard deviation, and the orange lines represent piecewise curve fits.
_Applied_  
_sciences_  
_Article_  
**Prognostic Assessment of the Viability of Hydrothermal Liquefaction as a Post-Resource Recovery Step after Enhanced Biomethane Generation Using Co-Digestion Technologies**  
**[PERSON] 1, [PERSON] 1,* and [PERSON] 2**  
1 Department of Physics, University of Otago, PO Box 56, 9016 Dunedin, New Zealand;  
[EMAIL_ADDRESS]  
2 Department of Food Science, University of Otago, 9016 Dunedin, New Zealand;  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +64-3-479-7812  
Received: 8 October 2018; Accepted: 16 November 2018; Published: 19 November 2018  
**Featured Application: The proposed integrated system investigated in this paper may provide the basis for simultaneous sterilization and enhanced resourced recovery from organic waste streams in future studies.**  
**Abstract:** In line with global efforts at encouraging paradigm transitions from waste disposal to resource recovery, the anaerobic co-digestion of substrates of wet hydrolyzed meat processing dissolved air flotation sludge and meat processing stock yard waste was investigated in the present study. It was demonstrated that the co-digestion of these substrates leads to the introduction of co-digestion synergizing effects. This study assessed biomethane potentials of the co-digestion of different substrate mixtures, with the preferred substrate mixture composed of stockyard waste and wet hydrolyzed meat processing dissolved air flotation sludge, present in a 4:1 ratio on a volatile solid mass basis. This co-digestion substrate mix ratio presented an experimentally determined cumulative biomethane potential of 264.13 mL/gVSadded (volatile solid). The experimentally determined cumulative biomethane potential was greater than the predicted maximum cumulative biomethane potential of 148.4 mL/gVSadded, anticipated from a similar substrate mixture if synergizing effects were non-existent. The viability of integrating a downstream hydrothermal liquefaction processing of the digestate residue from the co-digestion process, for enhanced resource recovery, was also initially assessed. Assessments were undertaken via the theoretical based estimation of the yields of useful products of biocrude and biochar obtainable from the hydrothermal liquefaction processing of the digestate residue. The environmental sustainability of the proposed integrated system of anaerobic digestion and hydrothermal liquefaction technologies was also initially assessed. The opportunity for secondary resource recovery from the digestate, via the employment of the hydrothermal liquefaction process and the dependence of the environmental sustainability of the integrated system on the moisture content of the digestate, were established. It is anticipated that the results of this study will constitute an invaluable basis for the future large-scale implementation of the proposed integrated system for enhanced value extraction from organic waste streams.  
**Keywords:** anaerobic co-digestion; resource recovery; synergizing effects; hydrothermal liquefaction; meat processing industry
# Towards Sustainable Water Use in Two University Student Residences: A Case Study
## 3 Results and Discussion
### Water Use Characterization  
Based on the characterization of the water devices and users water use patterns, the estimated current average total annual water consumption in Residence I and II were 325.67 and 318.80 m\({}^{3}\)/year, respectively. These estimates were aligned with the annual water bills provided by the university managers. The research carried out in student residences and dormitories in Belgium, Malaysia, and the USA found that the daily per capita water demand ranged from 89.5 L [33] to over 300 L [30; 32]. Residences I and II had a daily water consumption of 224 and 219 L per user, respectively, which are among the highest values reported in these international studies.  
The most water-consuming devices were showers, accounting for 46% and 61.41% of water consumption in Residences I and II, respectively, followed by kitchen taps (31.51% in Residence I and 11.52% in Residence II). Although the flushing cisterns were water consumption-efficient, the consumption concerning these devices was still 7.02% in Residence I and 13.22% in Residence II (Figure 1). Estimates for washbasin timed flow taps were not visually represented in Figure 1 because they accounted for only 0.17% of the water consumed in Residence I and 0.23% in Residence II.  
The literature review conducted by [44] on the impact of gender on residential water demand found mixed results. It revealed that water consumption behavior is also influenced by various factors such as social, economic, cultural, and ideological elements [44; 45]. Nevertheless, the present research is aligned with most existing studies, indicating that women generally consume more water than men [22; 30; 32; 44]. The higher water consump  
Figure 1: Average monthly and annual water consumption by use category in Residence I (**A**) and II (**B**). * Not estimated for maximum occupancy.  
tion by women is attributed to longer showers [46] and differences in restroom fixtures (urinals use less water than toilets) [44]. It is not clear whether there were differences in water usage with regard to kitchen taps between genders, as kitchen taps have different water flows, and students have access to low-cost meals in university canteens. Surveys have indicated that students rarely cook at residences. When they do, they tend to use ready-to-eat food, which requires less residential water for preparation.
## 2 Materials and Methods
### Wear Measurement Algorithm for the Contact Strip
#### 2.3.2 Image Processing for Wear Measurement
\[\sum_{i=1}^{n}{r_{i}}^{2}=\sum_{i=1}^{n}(y_{i}-f(x_{i}))^{2} \tag{6}\]  
The proposed estimation function involves transforming the determined polynomial for the laser line into a matrix in the form given by Equation (7). To obtain matrix X, the parameter of the estimation model that minimizes the residual sum of squares is derived using pseudo inverse as in Equation (8). \[AX=B \tag{7}\]  
\[X=prinv(A)B \tag{8}\]  
The fitted line obtained by applying the least squares method for the pantograph used in our study is shown in Figure 12. Figure 12a shows the first fitted line of reference obtained based on the laser line shown in Figure 6, using our estimation method with a five-degree polynomial for fitting, while Figure 12b shows the second fitted line obtained using the same estimation method, but with the first base line as input instead of the acquired laser line. Figure 12: Fitted line estimation results for the (**a**) first line and (**b**) second line. Figure 13 shows the fitted lines obtained from the degree of the polynomial in the estimation method in order to confirm the accuracy of the used polynomial function. As shown in Figure 13b, it is confirmed that the fitted curve obtained using the fifth-order polynomial is suitable as the contact strip. After determining the fitted line of the contact strip, the wear size of the contact strip is measured using the post-processing method based on blob detection. Blob detection performs labeling from a target pixel to a neighboring pixel based on the subpixel in a binary image [23]. The neighboring pixels are grouped in eight directions by setting the threshold of the target image. In this study, the threshold is set to 0 and 255. Pixels are expressed in terms of wear size by converting \(\upmu\)m/pixel into mm/pixel. As shown in Figure 14a, the contact strip is indicated by overlapping the laser line and the fitted line. The pixels' wear size is shown through the overlap of two lines, along with Figure 14b. Furthermore, the wear size is determined through the blob process as shown in Figure 14c. The wear size in the blob processing of the image is computed by substituting the physical value into Equation (9). Here, \(PS_{wa}\) is the pixel size of the physical wear measurement range, \(D\) is the physical wear measurement range, and \(P_{wa}\) is the number of pixels in the physical wear measurement range. In this study, the measurement range is applied to the width considering the structure of the contact strip. \(D\) is 40 cm, \(P_{wa}\) is 410 pixels which is measured with the visual check, and \(PS_{wa}\) is computed at 976 \(\upmu\)m/pixel as shown in Equation (9). The pixels of the camera are corrected according to the wear measurement range, converted to \(10^{-3}\) units, and this unit is shown in the blob box of the image. \[PS_{wa}=\frac{D}{P_{wa}} \tag{9}\]  
Figure 13: Fitted lines obtained by applying polynomials of the (**a**) third, (**b**) fifth, and (**c**) sixth degrees in the estimation method.
###### Abstract  
Anthocyanins are flavonoids with an antioxidant effect. They are the pigments that give rich colours to berries, red onions, pomerangates, and grapes. In addition to acting as antioxidants and fighting free radicals, anthocyanins may offer anti-inflammatory, anti-viral, and anti-cancer benefits. Among various types of fruits, blackberries and grapes are distinguished by their rich content in polyphenols, including anthocyanins. The purpose of this study was the identification and quantification of the anthocyanins in black grape skins and blackberries, but also the determination of the total phenolic content and total antioxidant capacity. The grape skins and blackberry extracts were prepared by an ultrasound-assisted acidified ethanol and methanol extraction method, with the 80% methanol solution being the most effective. Alcoholic extracts of blackberries and grape skins were analysed by the HPLC-DAD-MS method. There were five glycosylated anthocyanin compounds in blackberries, eight glycosylated anthocyanins compounds, and seven fragments of anthocyanin derivatives in grape skins identified. It was concluded that the anthocyanin profile of blackberries and grapes revealed mainly anthocyanin monoglycososides and acetylglycosides. Cyanidin-3-glucoside was the main component (86.49%) in blackberries, while, in the grape skins, the main component was delphinidin-3-O-glucose (about 40.64%). Principal component analysis (PCA) was carried out on the basis of the 13 identified compounds in order to separate the extracts and describe the anthocyanins characteristics of different groups, the findings being in agreement with the experimental results. Compared to methanol extracts, ethanol extracts showed higher antioxidant activity, being related to the total phenolic content for the blackberries. Overall, the obtained results indicated that the blackberries and grapes skins possessed a high antioxidant content, similar to other berries, highlighting their potential use as fresh functional foods or fruit-derived products.  
Keywords:antioxidant capacity; anthocyanins; blackberries; grapes; polyphenols +
Footnote †: journal: Applied
## 2 Virtual Surgical Planning Protocol Between Surgeons and Bioengineers
### Setting the Resection Margin  
The surgeon sets the resection margin based on the Hounsfield unit (HU) threshold-based mandible model and the lesion model. Adding a safety margin offset to the 3D lesion model can assist in setting the resection margin. The HU threshold-based mandibular model, including internal bone structures, is critical in determining whether to preserve or sacrifice adjacent teeth and the inferior alveolar nerve. It also allows the surgical approach (intraoral or submandibular) to be determined and instrument accessibility to be considered. This three-dimensional approach to structures and lesions allows planning of a safe yet conservative resection margin. Saving the resection margin as a plane is beneficial for subsequent reconstruction planning and collaboration between the surgeon and the bioengineer.  
Surgeon's role: Define resection margins based on clinical judgment and 3D visualization.  
Bioengineer's role: Implement resection margins in the 3D model, highlight potential problems.