esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seawater Channel Modelling Elements  
Water channel modelling requires the development of the necessary modelling theory to identify the main modelling elements. This theory is the foundation of a comprehensive and reliable water channel model. Such a model requires the formulation of a channel model equation, which involves the deterministic or probabilistic dynamics between the influencing elements and the associated energy exchange. These elements lead to a collective or individual impact on the characteristics of the optical beam propagating through the communication linkspan at any given time and water medium volume. The first step in building such an equation is to author a dictionary that maps each participating UOWC system element to a potential equation variable governed by the optical propagation physics laws and associated limits. The most prominent physics law is the energy conservation of an optical wave travelling through a water medium. From the physics point of view, most of these variables are based on the energy exchange feeds and the inherent optical property set, which we will explain next. From a statistical point of view, these variables can be categorised into two types: deterministic and random. Deterministic variables have a fixed value range, which can be determined using deterministic algorithms, whereas random variables are described by their PDF or CDF as required.  
As summarised above, the RTE was deduced based on a logical modelling basis [5; 6; 7]. The solution of the underlying equation represents the target model from which we can compute the temporal profile of the water channel's CIR, which we term \(h_{c}(t)\) in the mathematical expression of this communication. The complexity of such an equation determines the possibility of obtaining an analytical solution. Otherwise, a numerical solution should be picked based on sound mathematical approaches. Also, analytical solutions can be found through reasonable simplifications with practical environment limits. Such simplified analytical solutions help to establish the required benchmarks for numerical solutions. The framework topology of the modelling tasks is depicted in Figure 2.  
However, to produce a UOWC propagation channel model, we first need to define the UOWC system layout, which helps furnish the transition or transformation boundaries between the input variables, water medium convolutive events/processes, and output variables. Second, we must be equipped with implementation and deployment setting/configuration use cases besides these variables. Through these use cases, we can produce a set of outputs for the corresponding use cases to demonstrate the logical and reliability aspects of the modelling solution. In this manner, we transfer the water channel model from a theoretical model to a realistic one to design and build UOWC systems.
## 4 Results
### Data Transmission  
After the conversion process, each transformed image was passed to C++ and Python for further processing. In order to evaluate the two transfer methods from Unity to the video processing algorithms, a test was carried out to measure the time elapsed from sending the converted image in Unity to receiving it in the native C++ plugin or in the Python program via MQTT. The results of the test conducted for sending 300 images are shown in Figure 5.  
In this case, irregular behaviour with fluctuations in transmission time was observed for both options, although an upper limit of about 2 ms is apparent in both approaches. The MQTT Unity-to-Python transmission method was slightly superior to the option of  
Figure 4: Processing times prior to data transmission from Unity. For C++ DLL: average, 39.46 ms; minimum, 35 ms; maximum, 57 ms. For Python MQTT: average, 6.05 ms; minimum, 4 ms; maximum, 17 ms.  
Figure 5: Data transmission times from Unity to C++ and Python. For C++ DLL: average, 1.19 ms; minimum, 0.23 ms; maximum, 1.90 ms. For Python MQTT: average, 1.01 ms; minimum, 0.14 ms; maximum, 1.99 ms.  
forwarding internally to the native plugin. Although it took more extreme minimum and maximum values, 0.14 and 1.99 ms, respectively, passing to Python took about 1 ms on average, compared to almost 1.20 ms for sending to the native DLL.
# Chaotic Communication System with Symmetry-Based Modulation
## 4 Discussion  
The proposed symmetry coefficient modulation can be used as a basic technology for the practical development of communication systems. Note that in this paper only an alphabet of two symbols was considered, while there can be many more of them by analogy with quadrature modulation, in which there can be 1024 or more symbols. For practical systems, it is of interest to estimate the channel density at different signal-to-noise ratio (SNR) levels and the data transfer rate. In this article, we fundamentally do not consider the issues of noise in the channel, since chaos-based coherent systems are mostly used with noise reduction at the receiver side [11]. As for the transmission rate, it can be calculated from the data presented in Figure 7. The results are summarized in Table 1. To fill this table, one should take the worst of achieved synchronization times for a given \(\Delta S\) or \(\Delta a\), time step \(h=0.01\) s to find the corresponding number of samples, and estimate the time needed to transfer these samples having 1 GHz of sampling frequency. The resulting table approximately shows some realistic data transfer rates, which are possible with the SCM approach using an alphabet of 2 symbols.  
An example in Table 1 shows that SCM may outperform the parameter modulation in terms of the transmission rate, having another advantage in its spectrum stability resulting in a higher level of secrecy. A more detailed comparison of these systems, as well as with systems without chaos, is not the subject of this study, since transfer rate depends on many practically important factors: the used alphabet, the information encoding method, the number of users in the communication channel, the SNR, the physical transmission medium, and so forth. Some numerical characteristics of implemented chaotic communication systems can be found in the Introduction. Another important practical characteristic of a communication system is a bit error rate (BER). Likewise, the study of the communication system noise resistance is not the subject of this study, since in depends on the encoding method and the denoising techniques. Meanwhile, the reader may be interested in BER estimation for communication systems utilizing chaos, and this issue is addressed in several studies. For example, [PERSON] et al. [24] compare chaos-based DS-CDMA (direct-sequence code division multiple access) system with a conventional DS-CDMA system based on Gold codes and finds that the chaos-based DS-CDMA system outperforms the conventional DS-CDMA system when the spreading factor is low. [PERSON] et al. [25] estimate BER of binary phase shift keying (BPSK) and chaotic-pulse-position modulation (CPPM) communication systems and finds that the chaotic communication system is slightly inferior to the conventional one.
# A Comprehensive Methodology for Investment Project Assessment Based on Monte Carlo Simulation
## 3 Application of the Methodology on a Virtual Investment Project
### Timeseries Forecasting of Demand for Products A, B, C  
One of the stochastic input variables is the demand for products. Based on time series data (Table 1), quantitative forecasting of future demand is performed by software. Figures 2-4 show the demand forecast in monthly periods for one year. The forecasting has also considered the characteristics of the time series in terms of seasonality and trend.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Period (Month)**} & \multicolumn{3}{c}{**Products (t/Month)**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline
1 & 72.1 & 9.5 & 11.2 \\
2 & 73.5 & 9.3 & 23.8 \\
3 & 81.9 & 10.0 & 18.2 \\
4 & 72.8 & 10.5 & 13.3 \\
5 & 78.4 & 9.7 & 23.5 \\
6 & 74.9 & 10.7 & 18.9 \\
7 & 74.2 & 10.3 & 14.8 \\
8 & 83.3 & 10.9 & 16.0 \\
9 & 79.1 & 10.1 & 13.4 \\
10 & 77.7 & 11.3 & 19.3 \\
11 & 74.9 & 10.8 & 12.9 \\
12 & 74.2 & 10.9 & 24.6 \\ \hline Total & 91.0 & 124.0 & 210.0 \\ \hline Preliminary production plan & 850 & 130 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of orders for the last year (in a monthly period).  
In Figure 2 it can be seen that demand for product A is seasonal, and the most accurate forecast is by the SARIMA (1,0,0)(1,0,0) method. The forecast error, assessed according to MAPE (mean absolute percentage error), is 1.59%. Demand A is stable and forecastable with high accuracy. In the case of product B, it is a demand with an upward trend (Figure 3) and is relatively stable.  
Forecast error (forecast by the Double Exponential Smoothing Method) is 4.15%, according to MAPE. Product C is characterized by high dynamic demand with no seasonality or trend (Figure 4). The higher demand variability may be due to the unstable price of the product, replicating input prices, and market behaviour. The resulting forecast values for the next 12 months are presented in Table 2. The forecast errors according to the MAPE, MAD, and RMSE methods are presented in Table 3. The forecast errors should also be taken into account when drawing up the production plan, which must account for deviations in demand.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{3}{c}{**Forecast: Products Demand (t/Month)**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline
13 & 82.71 & 11.06 & 16.60 \\
14 & 78.88 & 11.18 & 17.50 \\
15 & 77.59 & 11.31 & 17.50 \\
16 & 75.03 & 11.44 & 17.50 \\
17 & 74.38 & 11.56 & 17.50 \\
18 & 82.19 & 11.69 & 17.50 \\
19 & 78.67 & 11.81 & 17.50 \\
20 & 77.50 & 11.94 & 17.50 \\
21 & 75.14 & 12.06 & 17.50 \\
22 & 74.55 & 12.19 & 17.50 \\
23 & 81.71 & 12.31 & 17.50 \\
24 & 78.48 & 12.44 & 17.50 \\ \hline Total & 936.84 & 140.99 & 209.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forecast of products A, B, C demand.  
Figure 4: Demand forecast for product C.
###### Abstract  
In the past few decades, there have been numerous attempts to add plastic aggregates composed of polymeric materials to cementitious composites, either as an alternative to using natural aggregates or as fillers and fibers. However, the addition of plastic aggregates often results in cementitious composites with lower mechanical performance. In this paper, we attempt to address this issue by applying gamma irradiation technology to restore the mechanical performance. We aimed to determine the optimal gamma irradiation and mixing combinations by comparing the experimental results with information summarizing the recent literature related to the use of gamma-irradiated plastic aggregates within cementitious composites. To this end, the effects of changes in the physical and chemical properties of plastics due to irradiation with gamma irradiation on the strength of cementitious composites were evaluated using irradiation doses of 25, 50, 75, and 100 kGy and various plastic materials as key parameters. In the compressive strength test, it was found that adding gamma-irradiated plastic increased the compressive strength of the cementitious composites compared to the nonirradiated plastic. This suggests that the irradiation of plastic aggregates with gamma rays is an effective method to recover some of the strength lost when plastic aggregates are added to cementitious composites. In addition, modifications in the microstructure and chemical properties of the gamma-irradiated plastic were analyzed through SEM and FT-IR analysis, which allowed the determination of the strength enhancement mechanism. The results of this study show the possibility of the state-of-the-art performance improvement method for using plastic aggregate as a substitute for natural aggregate, going further from the plastic performance improvement technology for limited materials and radiation dose presented in previous studies.  
lightweight aggregate; plastic aggregate; gamma irradiation; irradiated plastic; compressive strength +
Footnote †: journal: Applied Sciences  
1
# See-Through Near-Eye Display with Built-in Prescription and Two-Dimensional Exit Pupil Expansion
## 2 Design Rules
### Multiplexed Slanted Grating  
Santed grating, a type of asymmetric grating, lends itself to folding the optical path of see-through NEDs for the reason that the energy of diffracted light can be concentrated to a certain diffraction order [22]. Its cross-sectional profile is outlined in Figure 4, where \(p\) is the grating period, \(h\) the grating height, \(w\) the grating width, and \(\theta_{g}\) the slant angle. To couple light into and out of the lens, both the ISG and OSG were constructed as the transmission gratings, whose allowable diffraction angles shall satisfy [22]  
\[p(n_{i}sin\theta_{i}-n_{e}sin\theta_{m})=m\lambda \tag{7}\]  
where \(n_{i}\) is the refractive index of the incident medium, \(n_{e}\) the refractive index of the exit medium, \(\theta_{i}\) the incident angle (relative to the grating normal), \(\theta_{m}\) the diffraction angle of \(m^{\rm th}\) order, \(m\) the diffraction order, and \(\lambda\) the wavelength. As Equation (7) indicates, for a single-period slanted grating, both of its spectral and angular bandwidths were intrinsically narrow. To solve this issue, a multiple of waveguides with different gratings can be stacked together for the full color [15]. As an alternative solution, we resorted to the multiplexing of plural gratings on a single layer [23; 24]. The multiplexed grating can be decomposed into three sub-gratings of the same slant angle but of different periods, widths and heights, as shown in Figure 5. Each sub-grating was designed for certain wavelength and incident angle. Say the periods of sub-gratings are \(p_{A}\), \(p_{B}\), and \(p_{C}\), respectively, then the collective period \(P_{m}\) of the multiplexed grating shall be the least common multiple of the above three. To minimize the reflection at the interface between the grating and the lens, it was desirable to etch the slanted gratings out of the lens so that there is no mismatch in their refractive indices. Speaking of the fabrication, photolithography, electron-beam lithography, and focused ion beam are among the feasible lithography techniques [25].  
\begin{table}
\begin{tabular}{c c c} \hline
**Object** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Microdisplay} & \(D_{m}\) (diagonal) & 0.233 inch \\  & \(D_{m}\) (horizontal/vertical) & 0.165 inch \\  & _Resolution_ & \(640\times 640\) \\  & _Pixel size_ & 6.5 μm \\  & _CR_ & 100,000 \\ \hline \multirow{4}{*}{Projection lens} & \(f\) & 10 mm \\  & FOV (diagonal) & \(33^{\circ}\) \\ \cline{1-1}  & FOV (horizontal/vertical) & \(24^{\circ}\) \\ \cline{1-1}  & Aperture & 6 mm \\ \cline{1-1}  & _ER_ & 12 mm \\ \cline{1-1}  & _EP_ & 1 mm \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the pico projector.  
Figure 4: Cross-sectional profile of slanted grating. \(p\) is the grating period, \(h\) the grating height, \(w\) the grating width, \(\theta_{g}\) the slant angle, \(\theta_{i}\) the incident angle (relative to the grating normal), and \(\theta_{m}\) the diffraction angle of \(m^{\rm th}\) order.
## 4 Methodology
### Land Cover Classification Framework
#### 4.2.2 Image Labeling  
According to [12], Band 10 is especially for cirrus recording and thus being omitted in this study. The remaining twelve bands are selected as features for pixel-wise classification. Ground-truth labeling is necessary in supervised learning tasks in order to build the model. Thus, this image is labeled based on manual interpretation of the original Sentinel-2 satellite image (in false-color RGB format) along with Google map images and on-site checking. The ground-truth of five classes including building (No. 1), tree (No. 2), road (No. 3), water (No. 4), and crop field (No. 5) are labeled in Matlab software (2017b) using polygons of different shapes for each class (see Figure 3) and 'Un' denotes the unlabeled data. By using the labeled pixels, the average reflectance over five different land cover classes can be compared and shown in Figure 4, which lays the foundation for discriminating various classes by various machine learning based classifiers.  
Figure 2: The developed framework for land cover classification by using Sentinel-2 satellite and machine learning based classifiers.  
In order to compare the discrimination ability of visible images and multispectral images [20], two training datasets are separately selected to evaluate the performance of various methods: RGB features (e.g., only Red, Green, and Blue bands) and full 12 band features. In addition, the labeled dataset is divided into the training dataset and testing dataset to avoid the problem of over-fitting, which is a common issue in machine learning based classifiers. In this study, the proportions of training data and testing data are set to be 80% and 20%, respectively.
# Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm
## 2 Construction of Evaluating Indicator System and Grading Standards
### Construction of Evaluating Indicator System
The rock dip angle affects the path of karst water seepage, supply, and discharge by influencing the anisotropic characteristics of rock permeability. Generally speaking, the permeability parallel to the rock layer is much greater than that perpendicular to the rock layer. However, when the dip angle of the rock layer is too large, the area of the water-rich surface is small, leading to less groundwater circulation and poor erosivity. If the dip angle of karst is too small, it is not conducive to the smooth infiltration of groundwater into karst strata. 4. Negative terrain area ratio B4: The negative terrain area ratio determines the recharge capacity of groundwater. Landforms such as depressions, subsidence, and sinkholes on the surface make it easier for surface water to accumulate and recharge to the run-off area of groundwater through seepage. 5. Surrounding rock grade B5: The surrounding rock grade reflects the integrity, strength, and deformation resistance of the surrounding rock. When the surrounding rock grade is high, the integrity, strength, and deformation resistance of the surrounding rock are poor, which leads to higher groundwater permeability and the formation of water conducting channels. Moreover, the surrounding rock with high grade often appears in weak zones, such as fractured zones and dissolution cavities, which can easily cause water inrush disasters in water-rich tunnels. 6. Hydrodynamic zonation B6: The groundwater system is one of the decisive factors in the occurrence of karst in tunnels. The possibility and characteristics of water inrush vary depending on the karst hydrodynamic zonation in which the water-rich tunnel is located. The hydrodynamic zonation in which the tunnel is located is an important factor determining the characteristics and water inflow of water inrush.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 2 Materials and Methods
### Research Methodology  
The objective of this study is to profile the operating cost distribution across the mine value chain of an open-pit copper mine from drilling to flotation. To this end, costs were estimated across the entire mining-to-processing workflow by means of process costing. In other words, a cost-accounting method was used to calculate the average operating expenses (OPEX) incurred by each unit or operation to produce a metric ton of ore extracted or processed over a specific period.  
The data used for this study were derived from historical operational records, financial reports, and production data of the mine over a period of five years (i.e., 2018-2022). Operating costs across the value chain were estimated as a five-year average of annual costs reported for each key unit.  
In terms of the implementation of the process costing methodology, costs were gathered and allocated to specific units of the value chain following the four steps below:  
* In the context of this study, the mine value chain was first segmented into mining and processing. Mining operations or units included drilling, blasting, loading, hauling, stockpiling, and blending. Processing operations, on the other hand, encompassed comminution and flotation. Each operation represented a distinct phase of the value chain, with costs being tracked individually for each unit. Figure 1 provides an overview of the different units across the value chain as defined in this study;Costs were then accumulated for each unit over one year. Cost accumulation included equipment depreciation, labor expenses, fuel and energy consumption, maintenance and repair costs, consumables such as explosives, flotation reagents, wear parts and other chemicals, and contingency funds (10%) for unexpected expenses. This step ensured that all relevant costs were comprehensively captured for each operational unit, providing a clear view of their individual contribution to the financial performance of the value chain;
* Once the costs were accumulated, the average operating cost per unit was calculated by dividing the total accumulated costs by the metric tons extracted or processed over a one-year period;
* In the final step, the average operating costs per unit in the entire mine value chain were summed up to determine the total operating cost. This provided insights into the overall cost structure for mining and processing a metric ton of material. By aggregating costs across all units, from extraction to final processing, the holistic assessment of total operating expenses could highlight areas for potential cost reduction.  
Ultimately, the average operating costs per unit across the entire mine value chain were compared with published industry benchmarks to provide context for the research findings.