*4.2. Results*

The number of iterations is set to 4000, and Figure 6 exhibits the convergence of fitness values during iterations. When the iteration reaches 2000, the fitness value does not descend. Accordingly, the result returned at the end of the iteration is considered the optimal solution. Figure 7 presents the tree structure of optimal results. The tree structure can be simplified to Equation (4). Equation (4) is the final equation developed by GP to estimate the *UC*.

$$\begin{array}{l} \text{UC} = IC + 2T + \left(IC + Su(\sin(\sqrt{(LOP + Su)}) \\ \quad + \sin(LOP + Su)) + 3T - 2\sin(PD - T) + \sin(\sin(LOP) \\ \quad + \cos(\sin(\tan(D - T))) \end{array} \right) \tag{4}$$

**Figure 6.** The value of fitness function during iterations.

**Figure 7.** Tree structure representation of optimal results (X0 = LOP; X1 = PD; X2 = IC; X3 = Su; X4 = T).

For evaluating the performance of the developed equation, *R*2, RMSE, mean absolute error (MAE), variance account for (VAF), and A-20 index were introduced to evaluate the performance of Equation (4) in training and testing sets [46–49]. Equations (5)–(7) display the calculation equations for MAE, VAF, and A-20 index, respectively. When the MAE gets closer to 0, the model has better accuracy. When the VAF reaches 100, the predicted UC is perfectly equated to the actual. When the predicted UC is equal to the actual UC, the A-20 index is 1.

$$MAE = \frac{1}{M} \sum\_{i=1}^{M} | \mathbf{U} \mathbf{C} - \mathbf{U} \mathbf{C}' | \tag{5}$$

$$VAF = [1 - \frac{var(\mathbf{UC} - \mathbf{UC'})}{var(\mathbf{UC})}] \mathbf{U} \times 100\tag{6}$$

$$A - 20 = \frac{m20}{M} \tag{7}$$

where *var*(·) means the variance, and *m*20 is the number of samples with a ratio of the predicted value to the actual value in the range (0.8–1.2).

Figure 8 shows the predicted results and five regression indicators in training and testing tests. When the predicted UC equals the true UC, the corresponding point falls on the red line in the figure. The points falling between the two purple dotted lines indicate that the ratio of the predicted UC to the real UC is between 0.8 and 1.2. The A-20 index indicates that some of the predicted values are different from the actual values.

**Figure 8.** The performance of equation developed by GP in training and testing sets.


**6. Conclusions**

The proposed algorithm uses an adaptive feature distribution extractor for railway track segmentation by making full use of the strong linear characteristics of railway scenes and the typical categories of the local areas. A good balance between segmentation precision, recognition accuracy, calculation time, and complexity of manual operation can be achieved. By using the proposed algorithm, the railway intrusion detection system can automatically and accurately delimit the boundaries of a surveillance scene in real time and greatly improve the efficiency of the system operation. Considering the fact that, in China, there are over 29,000 km of high-speed railways and the average density of cameras on high-speed railway lines is about 2.92 cameras/km, the proposed algorithm is of great significance to improve the efficiency.

The proposed algorithm can be applied into the surveillance system of public places such as airport aprons, highway pavement, and squares. These places share some common characteristics: simple structure full of straight lines—such as airplane runways and different functional areas, vehicles and different lanes, pedestrians and sidewalk lines. Before applying this method, however, the training dataset of the simplified CNN has to include new categories in such scenes, then the proposed algorithm can segment the scene and label each local area.

**Author Contributions:** Conceptualization, Y.W., L.Z., and Z.Y.; Investigation, Y.W. and B.G.; Methodology, Y.W. and L.Z.; Project administration, Z.Y.; Software, Y.W.; Validation, Y.W.; Writing—original draft, Y.W.; Writing—review & editing, Y.W. and L.Z.

**Funding:** This research was funded by National Key Research and Development Program of China (2016YFB1200401).

**Conflicts of Interest:** The authors declare no conflict of interest.


**2.2 Network layer**

*Wireless Mesh Networks - Security, Architectures and Protocols*

of the coverage of internet services.

**2.1 Physical and datalink layers**

technologies presently used in the plane.

that uses orthogonal subcarriers [4–6].

within the TV broadcast bands [12].

(QPSK) over OFDM in every subcarrier [9–11].

communication coverage of theirs is restricted.

engineering for the Internet of Things (IoT).

The audio uses amplitude modulation (AM).

than for communication with fixed satellites [13, 14].

and OFDM [7, 8].

**2. Related work**

aircraft.

This concept is complementary to the standard internet technology for aircraft like satellite web. It's potential to run web-based FIM even once the plane is outside

The authors discuss about connected researches on candidates for inter-aircraft computer networks in terms of the Open Systems Interconnection (OSI) reference model. The authors likewise outline the current radio equipment found in a regular

The authors discuss IEEE 802.11 (Wi-Fi), IEEE 802.16 m (WiMAX), and IEEE 802.15.4 and IEEE 802.22 that are standardized by the IEEE 802 standardization committee for local area networks (LAN) and metropolitan area networks (MAN). The authors subsequently present an overview of satellite internet as well as the ground-based internet. Lastly, the authors are going to touch on the communication

**IEEE 802.11a/g/n/ac (also known as Wi-Fi)** is a physical and datalink layer protocol that uses 2.4–2.5, 4.9–5.0, 5.03–5.091, 5.15–5.35, and 5.47–5.725 GHz. The modulation is performed by orthogonal frequency-division multiplexing (OFDM)

**IEEE 802.16 m (also known as WiMAX)** is a physical/datalink layer protocol that uses wireless networking for communication over a wider space than the IEEE 802.11a/g/n/ac series. It often uses 2.575–2.645 GHz UHF band

0.7–1.7 GHz (UHF band) is employed. It uses quadrature phase-shift keying

**Satellite net services** are primarily provided by geostationary satellites, although some are provided by satellite constellations who relay communications via many satellites. As fixed satellites do not handle the polar regions, therefore the

Since satellite constellations are able to work with low-earth-orbit satellites, the correspondence latency is smaller than within the net service provided by fixed satellites. Because the places of those satellites are completely dynamical, an omnidirectional antenna is usually required, and far additional power is required

The authors here shortly discuss about ground-based aircraft internet, aircraftbased internet, and latest developments in long-distance and low-speed network

References [15–25] show other popular wide-area network technologies. A voice communication channel using 118.0–136.975 MHz (VHF) is also used.

**Long-Term Evolution (LTE)** is a standard for mobile phones. In Japan roughly

**IEEE 802.15.4 and IEEE 802.22** are less famous than the above three protocols. IEEE 802.15.4 is a physical/datalink layer protocol for short-range wireless communication which uses 868.0–868.8, 902–928 MHz, or 2.4000–2.4835 GHz and direct-sequence spread spectrum (DSSS) or offset quadrature phase-shift keying (OQPSK). IEEE 802.15.4 is best widely known as Zigbee wireless network protocol. IEEE 802.22 is a standard for wireless regional space networks using white areas

**32**

In the network layer protocol, the Internet Protocol (IP) is the factual standard whenever the datalink level has enough bandwidth. Based on the IP, the Space Communications Protocol Specifications (SCPS) may also be used when the band is extremely narrow, like for communication between the earth and other planets [26, 27].


*5.1. Communication Theory—Information Science—Meaningful Information*

The concept of information as an 'immaterial' substance, involving meaning in some way, has been available since antiquity (cf. Capurro [77]). It is fascinating to note that the nature of information, often viewed today as 'just' a philosophical question, in fact requires the best available concepts of physical science of the origin and fundamental structure of the universe to be correctly addressed. The modern origin of a theory of information is often ascribed to the work of Shannon on theories of communication, in which information is related to the removal of redundancy in a formal manner. This approach was specifically intended *not* to address or define meaning, but it nevertheless gave rise to many other formal theories of which the most modern and comprehensive is that of the mathematician Mark Burgin [78].

In the 1950's however, the discussion of information was almost fatally polarized by the statement of other mathematicians, Norbert Wiener and his follower John Wheeler, to the e ffect that information was not matter or energy. This statement gives the sense that there is something truly 'di fferent' about information, but scarcely gives an indication of what it might be. Such theories are not wrong but they are incomplete in that the scientific and logical origin of the dialectical, that is, in our terms real physical interactions described is not specified. Faced with the multitude of theories of information of all kinds, Brenner gave his 'personal synthesis' [79] of a several competing theories, from which a few key benchmarks for philosophy can nonetheless be taken as discussed further in this Section.

Given their origin in communication science and its significant dependence on technology, it is not surprising that theories of information emphasize formal concepts and standard logics. From the human, *natural* philosophical standpoint—we do not separate these—communication as the transfer of information is a human activity, and its nature and changes must follow the rules and logic of the latter.


**4. Discussion**

Observed copper concentrations in water were consistent and followed similar trends to the observed values for most of the sampling time points. An exception to that is the observed values in the October 2020 sampling time points, where the copper concentration values were significantly higher than that of the trend following the WASP simulated values. This could be attributed to roadwork and bridge construction in the area, which would have been impossible for the model to cover. In sediment samples, the majority of the WASP simulated values were underestimated compared to the observed values, with a few exceptions. This could be the result of the continuous accumulation of metals in sediments over many years, which was not accounted for in the current modelling exercise.

Modelled nickel concentrations in water samples across 2020 and 2021 also followed a similar trend, without major discrepancies from the observed values. For sediment samples, there were a few instances where the observed values were higher than those of the modelled progression. The model tended to underestimate the nickel concentrations for sediment samples, following a similar trend to the copper concentrations for sediment samples.

During simulations, the model was found to be underestimating the concentrations at downstream segments. Additional diffuse loads were added to compensate for matching the simulated values with measured values. This significantly improved the accuracy for both metals. For water samples, additional loads (~1–3 kg/day) were included at major segments (sampling stations) as diffuse loads. The loads were increased 100-fold for the calibration of sediment samples. Calibration process determined these amounts. Copper and nickel are both known to sequester in the sediment bed and are less likely to be suspended in water. Copper, as mentioned above, enters the environment mainly through anthropogenic sources [4]. The release and remobilization of sediment-bound copper have been well-documented in various studies; however, the overall impacts are minor [39,40]. Mobility of nickel, on the other hand, is site-specific and depends on the soil type and pH [41]. These observations can be attributed to the higher observed values of nickel and copper in the sediment samples in the current study.

The model used in the current study is an extension of the previously developed model 1D WASP and HEC-RAS coupling [32] and quasi-2D [33,34]. In the study [38], the mixing of vanadium and sediment was modelled with a quasi-2D modelling approach in the lower Athabasca River. The authors observed a marked increase in vanadium concentration along the river. The model was successful in capturing the transverse mixing from tributary water with mainstream water.

As mentioned earlier, modelling freshwater systems is challenging, involving many complex processes that affect contaminants' transport and fate [42], which demands sensitive methods to obtain accurate results for different metals. With limited data availability, understanding metal speciation becomes difficult in such systems [43]. The current study for the South Saskatchewan River system serves as a preliminary study focusing on trace metal transport and fate. This study also provides an understanding of how the concentrations of trace metals (Cu and Ni) vary across upstream and downstream of the South Saskatchewan River. There were a few CCME ISQG exceedances for Cu concentrations, and there are no guidelines outlined for Ni (as of now). Although there may be no immediate danger just yet, contamination of aquatic systems cannot be excluded. Further

study involving more metals in the model with sufficient input parameters would be an ideal recommendation.

#### **5. Conclusions**

The aim of this study was to better understand trace metal fate and interaction in the South Saskatchewan River. Two trace metals, copper (Cu) and nickel (Ni), were selected on the basis of their presence in the region. Copper is used in a wide range of products such as construction, pipes, industrial equipment, etc., resulting in widespread anthropogenic contamination with Cu. In contrast, nickel has a geogenic background in Saskatchewan and is transferred to the aquatic environment by effluents, leachates, and runoff from mining activities and the general land surface.

A 1D modelling approach was successfully applied in studying the trends and patterns of trace metal fate in the river and sediment. The simulations provided interesting data for both the trace metals and gave an insight into how the metals travel and react between water and sediment in the South Saskatchewan River.

A number of parameters needed to be assumed due to the lack of availability of consistent raw data for the modelling of these two trace metals. Improved availability of these parameter values would help make the model more efficient and accurate in predicting future trends with increased/decreased flows.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/w15020265/s1, Trace metal concentrations can be found in the spreadsheet attached Table S1: Heavymetal concentrations.xlsx.

**Author Contributions:** Conceptualization, S.P., P.S., M.B. and K.-E.L.; methodology, S.P. and P.S.; software, S.P. and P.S.; writing—original draft preparation, S.P. and P.S.; writing—review and editing, M.B. and K.-E.L.; supervision, M.B. and K.-E.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded University of Saskatchewan's New Faculty Graduate Student Support Program (NFGSSP), Global Water Futures (GWF), Canada First Research Excellence Funds, and the Canada Foundation for Innovation (CFI)—38581.

**Data Availability Statement:** All the research data supporting reported results can be found in the Supplementary Materials.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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