esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Electrode Materials
### Other Materials  
Compounds other than carbon-based materials can be used for anode construction, with studies investigating the properties of other materials such as stainless steel, gold and titanium. However, these materials often provide a similar power outage to carbon and are considerably more expensive [88]. Other problems with metal electrodes include corrosion issues and the smooth surface that prevents bacterial adhesion [89]. Stainless steel, gold and titanium are the most commonly used metals. These types of electrodes work well in marine environments [98]. In this environment, a stainless steel cathode covered in a seawater biofilm was able to maintain 64 mW\({}^{-2}\) and a current density of 1.89 Am\({}^{-2}\)[99]. Gold and titanium have also been used in a number of studies. Gold was found to be as effective an electrode as graphite by [PERSON] et al [100]; however, uncoated titanium was found to be less effective [101]. In addition to other materials, surface treatments and coating are also used to improve electrode performance [89]. This can commonly include coating graphite in metals such as gold or palladium. This has the effect of increasing electrode performance over plain graphite [102].  
Cathode construction also plays a part in power generation, and cathodes are often constructed from the same carbon-based materials as the anode. The difference is that the cathode needs to have a high redox potential, which can be improved by using catalysts such as platinum (Pt) or lead oxide (PbO\({}_{2}\)) [88]. The expense of metals such as Pt limits the use of non-carbon cathodes.
# A Load Balancing Routing Mechanism Based on SDWSN in Smart City
## 4 SDSNLB Algorithm and Solution
### Adjustment of Link Matrices  
Through multiple Elman networks, the input of the link traffic \(Y\), the output of the traffic matrix \(\hat{X}\) does not necessarily satisfy the constraint of \(Y=A\times\hat{X}\). Therefore, some adjustments must be made to \(\hat{X}\) to obtain \(\hat{X_{0}}\).  
**Definition 5**.: _K-L distance: Let the random variable \(x\) be in the two probability distributions \(P_{(x)}\) and \(q_{(x)}\). The K-L distance is defined as:_  
\[I_{(Q\|P)}=\underset{i=1}{\overset{n}{\sum}}P_{(x)}\times\log\frac{P_{(x)}}{q _{(x)}}\]  
_where i represents the \(i-th\) value of x and p, q is the probability density function._  
**Definition 6**.: _I-Projection: Given a joint probability distribution set \(Q_{(x)}\) and \(P_{(x)}\), if \(Q_{(x)}\in Q\), and satisfy_  
\[I_{(Q\|P)}=I_{(Q\|P)}\]\(Q_{(x)}\) is I-Projection of \(P_{(x)}\).For a joint probability distribution set, if the constraint condition \(p_{(\gamma)}\) is satisfied, I-Projection of \(p_{(\gamma)}\) on \(R_{(\gamma)}\) as follows:  
\[Q_{(x)}=\left\{\begin{array}{ll}0&\mathrm{P}_{(\gamma)}=0\\ P_{(x)}\times\frac{R_{(\gamma)}}{P_{(\gamma)}}&\mathrm{P}_{(\gamma)}\
eq 0 \end{array}\right.\]  
By iterating the constraints, the probability of all elements is estimated by minimizing the K\(-\)L distance. Through iterative calculations, adjustments are made while the constraints are met, so that the final result satisfies the maximum likelihood estimate.
## 2 Materials and Methods
### Clinical Cases
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Internal Test Dataset B** & **External Test Dataset** \\  & **(n = 38)** & **(n = 49)** \\ \hline \multirow{2}{*}{Age} & 59–89 & 50–85 \\  & (median: 79.5) & (median: 72) \\ \hline Sex & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Male \\ Female \\ \end{tabular} } & 22 & 24 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Female \\ Tumor type \\ \end{tabular} } & 16 & 25 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Part-solid \\ Matrix size \\ \end{tabular} } & 38 & 49 \\  & 512 \(\times\) 512 & 512 \(\times\) 512 \\ \hline Number of slices & 120–224 & 38–515 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Pixel size (mm) \\ \end{tabular} } & 0.78–0.98 & 0.55–0.98 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Slice thickness (mm) \\ \end{tabular} } & 2.0 & 0.625–5.0 \\ \hline Histology & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Adenocarcinoma \\ \end{tabular} } & 17 & 39 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Squamous cell \\ carcinoma \\ \end{tabular} } & 1 & 10 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Unknown \\ Time to progression \\ \end{tabular} } & 20 & 0 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Range (days) \\ \end{tabular} } & 283–2329 & 12–2793 \\  & (median: 920) & (median: 1014) \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Maximum diameters of \\ solid components (mm) * \\ \end{tabular} } & 1.5–57.5 & 1.5–81.7 \\  & (median: 20.3) & (median: 32.6) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Maximum diameters of \\ whole tumors (mm) * \\ \end{tabular} } & 10.5–67.3 & 21.4–81.7 \\  & (median: 32.3) & (median: 42.4) \\ \hline \hline \end{tabular}  
* Measurement based on an iso-voxel size of 1.5 mm.  
\end{table} 
Table 2: Patient characteristics with imaging parameters for internal test dataset B and external test dataset for prediction of CTRs for part-solid tumor cases. Patients from our hospital were scanned while breathing freely to acquire the planning CT images using 4-slice CT scanners (Mx 8000, Philips, Amsterdam, The Netherlands; Aquilion, Toshiba, Tokyo, Japan; Aquilion PRIME, Toshiba, Tokyo, Japan). Patients from TCIA were scanned with CT scanners from Siemens and GE. A radiologist (H.Y.) determined the three tumor types based on the planning CT images with a lung window level (WL) of \(-600\) and window width (WW) of 1500 Hounsfield units (HU) [23]. All lung cancer regions on CT images were observed by the radiologist with almost the same contrast and brightness, which were manually adjusted according to the WL and WW. Since CT images are obtained from invariant physical values (linear attenuation coefficients) [24], the CT values (HU) including WL and WW could ideally be independent of CT scanners.
## 3 Results and Discussion  
### Energy Potential of the Optimal System Configuration  
This paper proposes a centralized distributed grid-connected HRES to power a group of five hotels located in the same area. The total electrical load is estimated at 1.7 GWh/yr.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **PV** \\ \hline PV module & Suntech 325 \\ \hline Panel type & Flat plate \\ \hline Maximum power & 325 W \\ \hline Cell efficiency & 15\% \\ \hline Temperature coefficient & \(-\)0.4\%/K \\ \hline Degrading factor & 85\% \\ \hline Orientation angel & 30\({}^{\circ}\) \\ \hline Lifetime & 25 years \\ \hline Capital expenditure & 950 USD/kW \\ \hline Replacement cost & 100\% of capital cost \\ \hline O\&M cost & 23 USD/year \\ \hline \multicolumn{3}{c}{**Wind Turbine**} \\ \hline Wind turbine model & Eocycle EO20 \\ \hline Axis eye & Horizontal axis \\ \hline Rated power & 20 kW \\ \hline Rotor diameter length & 15.8 m \\ \hline Hub height & 36 m \\ \hline Wind speed (Cut-in) & 2.7 m/s \\ \hline Wind speed (Cut-out) & 20 m/s \\ \hline Lifetime & 25 years \\ \hline Capital expenditure & USD 29,400 \\ \hline Replacement cost & 50\% of capital cost \\ \hline O\&M cost & 880 USD/year \\ \hline \end{tabular}
\end{table}
Table 2: The technical and economic data of the system components.  
Figure 8: Schematic diagram of the proposed energy system.  
The system boundary condition is set to a 1.0 MW PV system and five wind turbines based on the available space for establishing the HRES. The simulation results show that the optimal HRES comprises 420 kW of PV and five wind turbines with a specified rating of 20 kW, both integrated with the utility grid. Table 3 presents the performance results of the PV and wind turbines. It can be observed from this table that the PV system operates for 4384 h per year, indicating approximately 12 h of sunshine per day. The results also show that the wind turbines operate for 7762 h per year, highlighting the attractiveness of this region for solar and wind energy applications. The comprehensive performance data for the optimal system configurations are presented in Figure 9. The optimal HRES is capable of generating 1.2 GWh/yr of electric energy through PV and wind turbines, achieving a total renewable energy fraction of 58%. The amount of energy purchased is estimated to be 844 MWh/yr, accounting for 42% of the required electric energy. The amount of energy sold is estimated to be 291 MWh/yr.
# Effect of Postharvest UVB Irradiation on the Fruit of cv. Dottato (_Ficus carica_ L.)
###### Abstract  
Exposing fruits and vegetables to UVB radiation post-harvest is a technique used to modify secondary metabolites and prolong their shelf life. The aim of the present study was to evaluate the effects of UVB irradiation on the chemical and physical characteristics of fig cv. Dottato fruits. The UVB irradiation was 2.26 Wm\({}^{-2}\). Two exposure times were carried out: 10 and 60 min resulting in a UVB dose of 1.4 and 8.1 kJm\({}^{-2}\), respectively. In the control, the UVB was eliminated by a polyester film (control - UVB). After treatment, the fig fruits were stored and analyzed at different times until decay. Quality parameters (decay, weight loss, color, chlorophyll, and firmness) and physicochemical parameters (soluble solids content, pH parameters, and titralable acidity) were positively influenced by irradiation. Total and individual sugars increased gradually during the storage period in both the skin and the flesh, with glucose being higher after 10 days in the UVB treated samples. Total carotenoid content increased gradually during the storage period, with a marked increase in the +UVB fruit. The content of total and individual polyphenols was positively influenced by UVB treatment, with the UVB treated samples showing the highest values at both 7 and 10 days. The study showed an increase in by-products in both the skin and the flesh. This research confirms the effectiveness of UVB radiation in improving the nutritional qualities and shelf life of _Ficus carica_ fruits.  
Keywords:UV radiation; fig fruit; postharvest; qualitative parameters; bioactive compounds +
Footnote †: journal: Applied  
_Article_  
Effect of Postharvest UVB Irradiation on the Fruit of cv. Dottato (_Ficus carica_ L.)
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion
### Regeneration Performance  
H protons can exchange metal ions attached to the carboxyl group and the amino group by protonation under acidic conditions. Therefore, we use 0.05 M HNO\({}_{3}\) as an eluent to elute the Cd\({}^{2+}\)-loaded Alg-EDTA to achieve regeneration of the adsorbent. As shown in Figure 12, the adsorption rate of Alg-EDTA to Cd\({}^{2+}\) still lies in the range of 92.0-95.0% after nine adsorption-desorption cycles, although the morphology of Alg-EDTA changed slightly (from spherical to flat). We propose that the as-prepared Alg-EDTA has stable adsorption performance after the repetition of the usage acting as a potential low-cost and highly-efficient heavy metal ion adsorbent. Moreover, we further analyzed the chemical composition of the Alg-EDTA using an elemental analyzer. Elemental analysis result shows that the C%, N% and H% of initial Alg-EDTA is 35.27%, 15.21% and 3.84%, respectively. The C%, N% and H% of Alg-EDTA after nine adsorption-desorption cycles is 34.36%, 14.47% and 4.05%, respectively. The chemical composition of Alg-EDTA after repeated use is consistent with that before adsorption.  
Figure 11: Plausible adsorption mechanism of Alg-EDTA combined with heavy metal ions. M represents heavy metal ions such as Cd\({}^{2+}\), Pb\({}^{2+}\), Cu\({}^{2+}\), etc.
# Novel Beam Scan Method of Fabry-Perot Cavity (FPC) Antennas
## 2 Materials and Methods
### Deployment of a PRS Superstrate and an AMC Ground Plane  
Figure 4 shows the deployment of the proposed PRS superstrate and the AMC ground plane. As we can see in Figure 4a, the strip lines of the PRS are symmetric to the \(y\)-axis, which is intended for a symmetrical beam-scan performance on the H-plane (\(xz\)-plane). Moreover, they are tapered toward the edges so as to suppress an SLL [17]. By tapering the widths of the strip lines, their reflection magnitudes are lowered to the edges. Thus, while fields in the cavity propagate to lateral openings experiencing multiple reflections between the PRS and the AMC, most of them should be transmitted through the PRS before leaking out of the openings. Consequently, the SLL gets decreased thanks to the reduced leaky components contributing to the undesirable sidelobe. In our work, the proposed PRS has three tapering reflection magnitudes of 0.86, 0.72, and 0.50 toward the edges.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(n^{th}\)**AMC Cell** & **Length [mm]** \\ \hline
1 & 22.53 \\
2 & 27.61 \\
3 & 27.57 \\
4 & 27.24 \\
5 & 25.88 \\
6 & 25.56 \\
7 & 25.56 \\
8 & 25.53 \\
9 & 25.84 \\
10 & 25.99 \\
11 & 25.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lengths \(a_{n}\) (mm) of each AMC cell determined for a scanned beam in \(\theta_{t}=30^{\circ}\).  
Now, we move on to the AMC ground plane in Figure 4b, which is employed for the aforementioned phase control. The AMC ground plane consists of 76 unit cells arranged with a period \(p\) of 35 mm. Each AMC cell has the required reflection phase calculated by (3) and (4) at the design frequency, and their corresponding lengths are listed in Table 1. In Figure 4b, the same-colored cells have the same reflection coefficients, and there are eleven different colors in total. In addition, since the proposed antenna is designed for a scanned beam along the \(x\)-axis, the phase distribution of the cells is symmetric with respect to the \(x\)-axis. In Figure 4, the regions enclosed by the red and the blue lines are related to polarization modes incident on the cells, which will be explained in the following subsection in more detail.  
Figure 5 shows the geometry of the source feeder that is a \(y\)-polarized U-slot microstrip patch antenna, whose feeding point is _fp_ far from the center. The U-slot is introduced to improve impedance matching of our antenna, which is difficult due to the strong influence of reflected fields in the cavity.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 1 Introduction
For instance, many new mobile IoT applications that can only be supported under 5G environments, such as drones with HD cameras and augmented reality/virtual reality (AR/VR)-based evacuation guidance, can be used for the first time on the fire scene. Furthermore, due to the 5G characteristics of massive capacity and connectivity, the data collection from a wide range of sensor sources can also become possible. These data sources include temperature, smoke, states of various fire protection systems, and many other sensor sources, providing a solid basis for understanding and decision making of the building evacuation under fire emergencies. Therefore, there is no doubt that the IoT-aided building fire evacuation will truly play a role in the upcoming 5G era. To facilitate the understanding and research of IoT-aided building fire evacuation, we conducted a literature review of the state-of-the-art smart building fire evacuation system. After that, based on the benefits and insufficiencies, the system architecture of an  
Figure 1: The overview of 5G usage scenarios and services. IoT-aided building fire evacuation control system was designed. Finally, new insights that 5G technologies can bring to the building fire evacuation were discussed. The article is organized as follows:  
* Section 2 introduces the concept of IoT in smart building applications. * Section 3 describes a review of the state-of-the-art smart building fire evacuation system. * Section 4 suggests a preliminary design of an IoT-aided building fire evacuation control system. * Section 5 discusses the opportunities in the 5G era. * Section 6 provides a discussion. * Section 7 provides a concluding remark.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 4 The Methods of Decoupling
### Internal Decoupling
#### 4.2.2 Characteristic Modes Analysis  
The characteristic modes analysis (CMA) was first proposed by [PERSON] [64] in 1965. Then, its field of use expanded from PEC to dielectric or magnetic material objects. Now, CMA can be carried out in most commercial EM simulation software as a tool to guide antenna decoupling design. With the help of CMA, the characteristic modes of arbitrary conductive structures are revealed, and the best location with good isolation of radiators can be found easily.  
The research [65] excited two different characteristic modes to realize pattern diversity based on CMA. A monopole element acted as an electric source, which was placed on the maximum point of one mode E-field maximum points to excite it. Similarly, a loop element acted as a magnetic source placed on the other mode H-field maximum point, and it was generated successfully. It can be observed from radiation pattern that the modes on the two antennas were orthogonal. Therefore, an orthogonal polarized MIMO was established with isolation better than 20 dB.  
The CMA also can be used to find feed points to obtain desired resonant frequency [66; 67]. Moreover, on the optimization of structures, a parametric analysis can be performed based on CMA [68; 69]. In [68], a twelve-port MIMO system with dual-band depicted in Figure 7 was proposed. The characteristic current distributions of each radiation branch were shown by CMA. Thus, the isolation enhancement was achieved by adjusting the length of the relevant branches, and it was better than 12.5 dB. The eight-port MIMO was placed on the bezel in [69]. Its wide band was generated by coupled feeding and CMA optimization, which was applied for 5G mobile devices. According to characteristic current distributions, a patch branch with a weak current was chosen to modify. After optimization, the number of characteristic modes became more, which bandwidth was expended and isolation realized better than 15 dB between eight elements.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 4 Simulation Environment
### Simulation Setup for Testing  
The simulation environment was developed to closely mimic the actual mining conditions at Pilbara iron ore mine. In particular, the surface levels of the simulated area closely resemble the overall topography of the Pilbara region, which has a flat topography typical of open-pit mining. This configuration allows the swarm robots to be tested in a controlled environment without the difficulties caused by uneven terrain, allowing their ability to locate, collect, and move ore to be effectively assessed. The following colour-coding is used in the simulation: white sections represent mineral-rich areas in the simulation, green blocks represent high-grade hematite, and red blocks represent low-grade goethite. The diagram highlights the ongoing challenges faced by mining operations at the Pilbara iron ore mine, which are increasingly reliant on mineral processing and beneficiation methods due to the depletion of the high-grade ore. Depending on the considered swarm model, the arrangement of the nested areas in which the swarm robots operate differs. For example, in the ant model, a large blue circle called a \"cache\" represents the coordination required to collect and transport ore. The honeybee model, on the other hand, mimics the communication process inspired by the waggle dance behavior of bees by using four rectangular areas referred to as \"dance floors\", close to the central base. These areas facilitate the exchange of information between the robots about resource locations and task updates. In this environment, swarm robot models can be thoroughly tested under simplified and controlled conditions, focusing on core functionalities such as ore detection and transportation. While certain complicating factors typical of real-world mining, such as environmental variability and physical obstacles, have been omitted for clarity, the simulation still provides valuable insights on how these models can be used to improve the robustness and efficiency of large-scale mining operations. Figure 9 illustrates these complex configurations and shows how the simulation environment closely resembles the considered real-world mining scenario at Pilbara.
## 3 Results and Discussion
### Vehicle Volume Measurement (VVM)  
Figure 6 shows the number of vehicles per VVM point, considering the east-west (EW), west-east (WE), and EW + WE directions. As can be appreciated, the highest concentration of vehicles was located in the central VVMs, that is, 4-10, with the maximum at point 5. This figure presents the average values for each VVM, therefore, the maximum and minimum values of traffic during peak hours are not shown, and similar values were obtained for both directions.
## 3 Results and Discussion
### Nutritional Profile
Cohesiveness is the degree of resistance of food to undergoing a second deformation with regard to the resistance in the first deformation, being expressed as a percentage. In this parameter, all formulations presented statistically significant differences between them, with values of 0.386% (B_DW), 0.518% (B_BMW), and 0.654% (B_TW), with B_TW presenting the highest cohesion percentage. The results obtained by [PERSON] and colleagues [62] for this parameter corroborate the results obtained in this work, with a cohesion value for the control bread of 0.34%. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **B\_DW** & **B\_BMW** & **B\_BMWns** & **B\_TW** & **B\_TWns** \\ \hline Hardness (g) & 11,701.2 \(\pm\) 171.4 b & 8068.3 \(\pm\) 822.3 a & 11,847.9 \(\pm\) 299.1 b & 11,624.3 \(\pm\) 1187.8 b & 8269.1 \(\pm\) 1023.4 a \\ \hline Springiness (\%) & 0.592 \(\pm\) 0.012 ab & 0.712 \(\pm\) 0.062 cd & 0.549 \(\pm\) 0.024 a & 0.788 \(\pm\) 0.044 d & 0.676 \(\pm\) 0.036 bc \\ \hline Cohesiveness (\%) & 0.386 \(\pm\) 0.016 a & 0.518 \(\pm\) 0.056 b & 0.325 \(\pm\) 0.011 a & 0.654 \(\pm\) 0.014 c & 0.547 \(\pm\) 0.029 b \\ \hline Gumminess (\%) & 4522.9 \(\pm\) 252.3 b & 4273.0 \(\pm\) 52.6 b & 3957.4 \(\pm\) 334.6 b & 6475.3 \(\pm\) 210.3 c & 2480.6 \(\pm\) 143.1 a \\ \hline Chewing (\%) & 2706.1 \(\pm\) 161.1 a & 3084.4 \(\pm\) 185.0 ab & 2889.9 \(\pm\) 218.4 ab & 4386.6 \(\pm\) 589.6 c & 3577.5 \(\pm\) 206.9 bc \\ \hline Resilience (\%) & 0.138 \(\pm\) 0.009 a & 0.171 \(\pm\) 0.004 b & 0.165 \(\pm\) 0.011 b & 0.235 \(\pm\) 0.006 c & 0.163 \(\pm\) 0.004 b \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Texture parameters of the developed bread formulations. Gumminess is the force required to chew a semisolid food to the point of deglutition. On the other hand, chewiness is generally defined as the energy needed to chew solid foods until swallowing. Both gumminess and chewiness are secondary parameters resulting from hardness, cohesiveness, and elasticity [63]. With respect to gumminess and chewiness parameters, the B_TW showed significantly higher values than B_DW with percentages of 6475.3%, 4522.9%, and 4386.6% to 2706.1%, respectively. Resilience is similar to elasticity, although it measures both the speed and the forces involved in the recovery of food when a deforming force is removed, also being measured in percentage [63]. For this parameter, all formulations presented statistically significant differences between them, with values between 0.138% (B_DW), 0.171% (B_BMW), and 0.235% (B_TW). Resilience, which simulates how a slice of bread maintains its initial height, was significantly higher in the B_TW formulation (0.235%) than other formulations.