esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanofluids
#### 3.3.6 External Magnetic Field  
The NFs can incorporate several magnetically sensitive metal or metal oxide NPs. In such cases, the magnetic particles can have a diversity of configurations depending on the nature of the particles and on the magnetic field magnitude or strength. If the field is strong enough, the small-dimensioned particle will form connected networks or chains that tend to get oriented toward the magnetic field direction. This alignment effect will move the particles nearby and promotes a larger number of physical contacts and interaction between each other and, thus, resulting in enhanced thermal conductivity and heat transfer capability of the NF. This can be observed in the experimental works performed with Fe\({}_{2}\)O\({}_{3}\) or Ni and carbon Nanotubes [68, 69]. Also, these studies have demonstrated that as the magnitude of the applied magnet field decreases, the time to reach the maximum peak value of the thermal conductivity will increase.
# Evaluating the Physicochemical Properties of Some Kosovo's and Imported Honey Samples
## 3 Results and Discussion
### Hydroxymethylfurfurral (HMF)  
The HMF content in honey samples from Kosovo analyzed in this study ranged between 7.41 and 166.43 mg/kg of honey (mean value \(\pm\) SD = 29.48 \(\pm\) 32.26). According to both the Codex Alimentarius Commission and European Union, 28 honey samples (85%) met the HMF directive, while five honey samples (15%) exceeded 40 mg/kg, which is the maximum content of HMF set in the above-mentioned standards [9; 10]. The honey samples KH4 (forest honey, Gjakova region), KH8 (Acacia, Prishtina region), KH12 (mixed honey, Ferizaj region), KH17 (Acacia honey, Gjakova region), and KH33 (flower honey, Ferizaj region) exceeded the maximum content of HMF set in honey standards. Generally, HMF is not present in fresh honey and its content is used to evaluate the quality and freshness of honey [52; 53; 54]. Although only HMF is not strong evidence for extensive heating or a prolonged period of storage and adulteration [55], the samples that exceeded 40 mg/kg indicates excessive heating or inadequate storage conditions. The content of HMF was below 10 mg/kg in six samples (KH6, KH7, KH10, KH19, KH25, and KH26), indicating a medium degree of freshness. In the other 22 samples, the HMF content ranged between 11.25 and 38.04 mg/kg. The HMF content in imported honey samples ranged between 9.20 and 67.25 mg/kg (mean value \(\pm\) SD = 34.76 \(\pm\) 16.96). In 4 out of 12 (33%) of the samples, the HMF content exceeded the maximum limit (40 mg/kg) set by the honey standards. The imported honey samples IH1 (Acacia honey, Munchen/Germany), IH6 (Acacia honey, Manavlar Gida San/Turkey), IH11 (chestnut honey, Korce/Albania), and IH12 (chestnut honey, Korce/Albania) exceeded the maximum content of HMF set in honey standards. No significant differences were shown when comparing the HMF values of local and imported honey samples. Although most of Kosovo's honey samples (85%) met the standards with regard to HMF, researchers from Rumania and Moldova reported that all samples analyzed had an HMF content below 40 mg/kg [56; 57].  
A summary of the results of the acidity, HMF, and soluble solids of local and imported honey is presented in Figure 2. The results presented in the figure show the increased values of free acidity, HMF, and low soluble solid contents.
## 4 Experiment Results
### Operation of User Interface (UII)  
A user interface (UI) is the bridge of communication between systems and users. Understanding the user's habits and interaction with the system is necessary. The intuitive design can be used to meet the needs and usage of users. At the same time, the designer must also have a basic knowledge and understanding of how the system works. Letting users operate intuitively and formulating interfaces according to user's needs and preferences maximize the benefits of the system. Therefore, when developing the user interface, all the functions of the system were divided into the Robot Status interface and the Palletizing interface. All relevant functions of the robotic arm will be included in the arms control interface, such as model selection, connection establishment, communication transmission, and control. To allow users to use the interface intuitively, there are small windows in the interface to classify all functions.  
In the interface of Palletizing, as shown in Figure 31, the main function is to input the effective area of the pallet, the size of the object and the database form, and to issue the execution command. Although general pallets have standard specifications, it is difficult to ensure that there are special specifications for internal use in the factory; therefore, the manual data input is used instead of the drop-down menu. In addition, a simulation of palletizing is also included in the interface, which means that the user will know how the objects are going to be palletized from the interface in advance so that the user can understand the array of the objects.  
After opening the user interface, you must first select the brand of the robotic arm and enter the I/O and port numbers to establish a connection with the robotic arm. UR10 robotic arm is used in this research, and Modbus TCP is used to communication protocols. After the connection is established, the robotic arm's basic data and other information can be obtained through the interface. After inputting the simulated size of the pallet and object into the palletizing interface, it can be seen that the first arrangement starts at the coordinate (0, 0) and the arrangement distance is (240, 210) from the single-layer simulation result, as shown in Figure 32. On the horizontal axis is two horizontally arranged objects with one vertically arranged object, and on the vertical axis is one vertically arranged object with two horizontally arranged objects. There will be overlap in the first arrangement because the number of horizontal arrangements of Block 1 is 2 (N1 Overload = 2) and the number of horizontal arrangements of Block 2 is 1 (M1 Overload = 1). After calculating, the result of the first arrangement is to add eight objects to the pallet. The second arrangement of  
Figure 30: Communication structure.  
the system is to arrange the distance from (90, 60) to (60, 90). The result of the second arrangement is to add one object to the pallet. The coordinates of each object are (X, Y, R), R is the axis of rotation, 0 is the horizontal arrangement, and 1 is the vertical arrangement.
## 1 Introduction  
Various kinds of hologram printers have been studied [1; 2; 3], and holographic stereogram printers have been studied for many years and commercialized by several companies [1; 2]. The holographic stereograms are basically made from an array of 2D images and phase added stereograms [4], which include phase information to improve image quality and are used to generate a photo-realistic hologram [5]. With the development of the computer-generated holograms (CGH) of 3D objects, it becomes easier to calculate and print the plane transmission hologram [3]. For CGH printing, an electron beam printer [6] and a laser lithograph [7] provide quality printing. Since these are both high cost, some studies on low cost hologram printers have been proposed [3]. Although the plane transmission hologram is easy to print, it has a chromatic dispersion, which causes image blur with white light illumination. On the contrary, the volume reflection hologram has wavelength selectivity and causes no chromatic dispersion and it is easy to show 3D images with white light illumination. Although the transmission hologram can be optically transferred to a volume hologram manually [8], the automatic volume reflection hologram printer still has been studied [3; 9; 10; 11; 12]. The main principle of a volume hologram printer is a reconstructed image from the plane CGH which is optically transferred to a photo-sensitive material as the volume reflection hologram [3]. The volume hologram printer is also used to fabricate a holographic optical element for a laser 3D display [13]. Since the resolution of the spatial light modulator (SLM), which is used to display plane CGH, is usually of the megapixel order, the optically transferred volume hologram's size will be in mm order. Then, all of the mm order's transferred holograms are tiled to make a larger hologram. This tiling manner is recorded by using an X-Y motorized stage, which will cause phase discontinuity and image degradation in reconstruction by a diffraction effect at each small hologram. Therefore, if the size of a sub-hologram is too large, the split lines and cells of sub-holograms are visible, which reduce the image quality [11]. For this case, a fully overlapping approach [11; 12] is proposed where the overlap ratio is 50% in both horizontal and vertical directions. Nevertheless, the printing speed is reduced by one quarter. To improve image quality with little loss in printing speed, a partially overlapping approach was proposed in the conference paper [14]. If the mm order's transferred holograms are simply overlapped partially, there will be a strong intensity non-uniformity (higher intensity in the overlapped parts than the non-overlapped parts) that causes the split lines and reduces the quality. With this in mind, a digital filter is applied to the overlapping area to control image intensity. In the present paper, the partial overlapping is described in more detail, and the results of the numerical simulation show that the digital filter is feasible to improve the image quality.
## 2 Theoretical Background  
### Concept of Tunnel-lining Crack Index  
The conceptual diagram of TCI [12] was shown in Figure 1. One span of the tunnel shown in the left side of the figure was spread out to a flat surface as shown in the right side. The basic formula of TCI is shown in Equation (1):  
\[F_{ij}=\frac{1}{A}\sum_{k=1}^{n}\left(t^{(k)}\right)^{\alpha}\left(l^{(k)} \right)^{\beta}\cos\theta_{i}^{(k)}\cos\theta_{j}^{(k)} \tag{1}\]  
where \(A\) is the total area of the lining; \(n\) is the number of cracks; \(t\) is the width of crack, \(k\); \(l\) is the length of crack, \(k\); \(\theta_{i}\) and \(\theta_{j}\) donate the angles formed by the normal vector of the crack with the \(x_{i}\) axis and \(x_{j}\) axis; \(\alpha\) and \(\beta\) donate the weighting coefficients of crack width and length.  
\(F_{11}\) and \(F_{22}\) obtained by the Equation (1) indicated the longitudinal component and the transverse components of the TCI, respectively. The crack index \(F_{0}\) was expressed as the sum of longitudinal and transverse components as shown in Equation (2). Associating Equation (1) with Equation (2), the crack index \(F_{0}\) can be derived as Equation (3).  
\[F_{0}=F_{11}+F_{22} \tag{2}\]  
\[F_{0}=\frac{1}{A}\sum_{k=1}^{n}\left(t^{(k)}\right)^{\alpha}\left(l^{(k)} \right)^{\beta} \tag{3}\]  
It is an objective evaluation method without depending on the technical force of the inspection engineer. The width, length, and direction of cracks were considered. This method has been widely used in the soundness evaluation of tunnel lining in Japan.  
However, the relevance of tunnel lining stability and the cracks density in the lining was questionable. On the one hand, the intersection and distribution of cracks were not considered in TCI. On the other hand, some approximations have to be used in the calculation of crack index. For example, the cracks were always not straight lines, but they were divided into small segments and considered as straight lines to simplify the calculation. In addition, the calculation of TCI based on the statistical methods is extremely time consuming.
## 3 DPD Coefficients Recognition Algorithm
### Coefficient Estimation Algorithm  
Classical adaptive estimation algorithms include the LS algorithm, the least mean square (LMS) algorithm, and the recursive least squares (RLS) algorithm. These methods are also widely applied in adaptive equalization [38; 39]. The LMS algorithm has the advantages of no matrix inversion, simple structure, and easy implementation. Still, it is unsuitable for DPD application scenarios since it is prone to poorly conditioned covariance matrices, resulting in extremely slow convergence. The RLS algorithm is more computationally intensive and may also have poorly conditioned covariance matrices leading to unstable convergence. Therefore, in this paper, we employ the batch processing LS algorithm to estimate model coefficients.  
The core idea of the LS algorithm is to search for the best function of the data for matching by minimizing the sum of squares of the errors. By replacing \(x(n-l)\) and \(y_{GMP}(n)\) of Equation (13) with \(\hat{y}_{GMP}(n-l)\) and \(z(n)\), respectively, we can write the equation as  
\[\mathbf{z}=\mathbf{U}\mathbf{c}, \tag{14}\]  
where  
\[\mathbf{z}=[z(N),z(N-1),...,z(1)]^{\text{T}}, \tag{15}\]  
\[\mathbf{U}=[\mathbf{u}(N),\mathbf{u}(N-1),...,\mathbf{u}(1)], \tag{16}\]  
\[\mathbf{u}(n)=[u(n),...,u(n-L_{c}+1)|u(n-L_{c}+1+M_{c})|^{K_{c}}]In addition, it is worth noting that the GMP model has as many as eight parameters, while the MP model has only two. Parameter selection is usually performed scan-by-scan to obtain a set of parameters to achieve a trade-off between the modeling error and model size. This approach is appropriate for the MP model, but for the GMP model with eight parameters, it takes a lot of combinations and time to obtain the proper set of parameters. There have been studies, such as the simulated annealing (SA) method, the particle swarm optimization with the Akaike information criterion (PSO-AIC) method [40], to develop fast and efficient strategies to determine the optimal parameters, allowing a suitable balance of performance and computing complexity.
## 3 Preliminaries and Problem Statement
### Greedy Algorithms
However, the merged solution may not be the optimal solution of the moving object coverage problem--that is, the solution corresponding to the minimum farthest traveled distance. In this paper, we use the merged solution as an approximation solution. Our proposed greedy bottleneck matching Algorithm 2 is presented as follows. ```
0:\(P\), \(m\), \(n\)
0: A set \(M\) of bottleneck matchings of sensor positions
1: Partition \(P\) into \(V_{1}\), \(V_{2}\),..., \(V_{h}\) zones, where \(h=\frac{n}{m}\) and \(V_{i}=\{(i-1)\cdot m+1,\)..., \(i\cdot m\}\), \(1\leq i\leq h\)
2:\(M=\varphi\)
3:\(i=1\)
4:while\(i<h\)do
5: Construct a weighted complete bipartite graph \(G_{i}=(V_{i}\), \(V_{i+1}\), \(E)\), where \(E\) represents the weighted edges between \(V_{i}\) and \(V_{i+1}\). 6: Find the bottleneck matching \(M_{i}\) of \(G_{i}\) by using the Hopcroft-Karp algorithm
7:\(M=M\cup M_{i}\)
8:\(i=i+1\)
9:endwhile
10:return\(M=\{M_{1},\)\(M_{2},\)..., \(M_{h-1}\}\)
```  
**Algorithm 2**Greedy_bottleneck_matching  
As demonstrated in [40], the time complexity associated with the Hopcroft-Karp algorithm for solving the bottleneck matching problem of a weighted complete bipartite graph is \(O(m^{1.5}\log m)\). Therefore, the total time complexity of the greedy bottleneck matching algorithm, designated as Greedy_BM, is \(O(n\sqrt{m}\log m)\).
## 4 Outage Probability Analysis
### Delay Analysis  
While considering buffer-aided relaying for a delay-tolerant network (DTN), like the data network, outage and packet drops are considered to be important performance metrics and not the delay; however, in time-sensitive communications, like voice communication, delay considerations are of prime importance compared to packet drops. In such networks, we are more concerned with the average delay, which is measured in time slots and is calculated by adding the number of time slots that a transmitted packet resides in the buffers (either at the source or at the relays). Unlike in [14], we considered end-to-end delay, i.e., from source-to-destination, from the time packet was generated (at the source or received at the source) to the time it reaches its final destination; we will call it the network or system delay. Based on [PERSON]'s law, the average packet delay of the system is given by Equation (34):  
\[\mathbb{D}_{Total}=\mathbb{D}_{T}=\mathbb{D}_{S}+\mathbb{D}_{R}=\frac{L_{s}}{ \eta_{s}}+\frac{L_{k}}{\eta_{k}}, \tag{34}\]  
where \(L_{S}\) and \(L_{k}\) are the average queue lengths at source and the relay, respectively, and \(\eta_{S}(source\to relay)\) and \(\eta_{K}(relay\to\ destination)\) are the average throughputs. \(K\), \(L\), and \(l\) are number of relays, the buffer size of each relay, and the buffer state, respectively. As we are considering virtual duplex mode, we used different relays for transmission and reception to avoid loop interference (LI). Thus, a new source packet can be served in each time slot, as in FD relay systems; so that the average delay can be  
Figure 10: State transition flow diagram, showing one or more arrival and departure.  
approximated as the average of the two delays, that is \(\mathbb{D}_{average}=\frac{(\mathbb{D}_{S}+\mathbb{D}_{R})}{2}\). The average queue lengths at the buffers can be determined as:  
\[L_{K}=\sum_{l=1}^{(L+1)^{N}}\pi_{l}\ R_{l}\ (B_{K}), \tag{35}\]  
where \(R_{l}(B_{K})\) gives the number of packets (or the buffer length) of buffer \(B_{K}\) at state \(S_{l}\) and \(\pi_{l}\) is the stationary probability of that state. On the other hand, because the probability of selecting any of the relays is the same, the average throughput at relay (\(R_{k}\)) is given by:  
\[\eta_{k}^{*}=\frac{\eta^{*}}{N}=\frac{(Average\ throughput\ of\ whole\ system)}{(Number\ of\ relays)}. \tag{36}\]  
where \(\eta^{*}\) is the average throughput of the overall relay network. For delay-sensitive transmission, the average throughput \(\eta^{*}\) is obtained as:  
\[\eta_{k}^{*}=R\times(1-P_{out}), \tag{37}\]  
where \(R\) is the average data rate of the system (without considering the outage probability). In the proposed scheme, on average, every packet requires one time slot (not necessarily consecutively) to reach the destination due to virtual duplexing, we have \(R=1\), and thus, \(\eta_{k}^{*}=\frac{1-P_{out}}{(1\times N)}\); finally, the total delay is given below.  
\[\mathbb{D}^{*}=\frac{N\sum_{l=1}^{(L+1)^{N}}\pi_{l}\ R_{l}(B_{K})}{(1-P_{out})}. \tag{38}\]
## 3 Results and Discussion
### The Environmental Impact Analysis of Each Phase  
Figure 5 shows the proportion and variation of environmental impacts from each stage during the construction of slab systems. This study shows the similar contribution of environmental impact from all of the indicators for the OSS, the FPS, and the VDS. Materials production is the highest environmental impact contributor in all indicators during the construction of the three slab systems. There are a number of studies to assess carbon dioxide emissions in terms of strengthening the building materials, utilisation of by-products and recycled materials during the construction stage [24; 25; 28; 29; 30; 31]. Although few studies have evaluated the environmental indicators of building materials, this study assessed GWP, AP, EP, ODP, POCP and ADP of three different slab systems. The results of this study would be expected to be helpful in evaluating GWP, AP, EP, ODP, POCP and ADP for other construction methods and building materials. Transporting building materials to the construction site is the second largest environmental impact source amongst the three slab systems. In this study, transporting the building materials was influenced by the distance and the fuel efficiency of the transporting method. The three environmental impacts from transporting building materials show similar results, which are that the ordinary reinforced concrete is the highest, while the VDS is the lowest. In particular, the environmental impacts for transportation stage are lower than other systems, even though it was noticeable that the VDS required more building materials than other slab systems. The reason that the environmental impacts of transporting building materials are lower is that the number of required vehicles was small since the weight of steel docking and void formers is lighter than other building materials in this study. Although the construction period is relatively longer than the other stages, the reason for the low environmental impact in all three cases might seem to be closely related to the unique characteristics of the construction and building industry. It is commonly recognised that one of the distinctive features of modern industries is automation. However, the construction and building industry is comparatively behind such trend. In this study, in all three cases, the consumption of fuel from construction machinery was low, and the environmental impact was considered to be lower than for the other stages, since the number of utilised items of construction equipment was small and limited. In the case of the VDS, the environmental impact from utilising the construction equipment was lower than that of the other two slab systems, although two more building materials were applied to construct the VDS.
## 2 Materials and Methods
### Numerical Simulation
#### 2.2.4 Finite Cell Mesh  
As mentioned above, the mesh is automatically generated using the \"mesh\" module of the ANSYS Workbench R22 software. It is assumed that the mesh consists of tetrahedral-shaped cells. It is found that the creation of a proper mesh in the case of the investigated crane is possible for an approximate minimal face size of less than \(l_{e}\) = 0.025 m. Therefore, for further simulations, it is assumed that the minimal face size on the crane surfaces equals \(l_{e}\) = 0.02 m. To estimate the sensitivity of the numerical solution to the cell size, for the arbitrarily chosen crane configuration (\(\theta\) = 90\({}^{\circ}\)), the simulations are carried out for the smaller cells, namely, \(l_{e}\) = 0.0175 m and \(l_{e}\) = 0.015 m. The maximal size of the cells, \(l_{eMAX}\), also varies correspondingly to \(l_{e}\), and \(l_{eMAX}\) = 5.0 m, 4.5 m, and 4.0 m, respectively. It is worth noting that it is necessary to perform about 100 solver (pressure-based, steady-state analysis) iterations to obtain a convergent solution. The obtained results are collected in Table 1. This table shows the aerodynamic force and moment coefficients, whose strict definition will be given later. The \(M_{tip}\) is an overturning moment causing the overturning of the studied crane. Figure 5 shows the location of the tipping line. The overturning moment \(M_{tip}\) is determined concerning this line.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(L_{t}\) **(m)** & \(L_{MAX}\) **(m)** & **Nodes** & **Cells** & \(F_{s}\) **(N)** & \(M_{tip}\) **(Nm)** & \(C_{s}\) & \(C_{M}\) \\ \hline
0.0150 & 4.0 & 13,097,189 & 73,925,163 & 7740.040 & 255,158.070 & 2.319 & 2.017 \\
0.0175 & 4.5 & 9,979,287 & 56,384,955 & 7737.990 & 255,105.940 & 2.318 & 2.017 \\
0.0200 & 5.0 & 7,959,409 & 44,895,413 & 7783.727 & 256,793.930 & 2.332 & 2.030 \\ \hline \end{tabular}
\end{table}
Table 1: Results of the convergence test.  
Figure 5: Crane and tipping line—the line between two supports of the crane.  
As can be observed, the represented results are very similar. The greatest values of the aerodynamic forces are obtained for \(l_{e}\) = 0.02 m. Therefore, it seems that the choice of \(l_{e}\) = 0.02 m and \(l_{eMAX}\) = 5.0 m ensures the appropriate accuracy of the calculations and safety of estimation. Moreover, the time of computations and necessary space on the hard disk are also optimal. In Figure 6, there are depicted details of the mesh on the surface of the studied crane.