esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Strengths and Limitations  
This study successfully employs an artificial neural network (ANN) to detect starch adulteration in milk, demonstrating high accuracy, precision, sensitivity, and F1-scores across various testing scenarios. One of the primary strengths of this research lies in its innovative use of multispectral sensor technology combined with ANN, which allows for rapid and accurate detection of adulteration in a field setting. This method significantly enhances the feasibility of real-time, on-site testing without the need for elaborate laboratory setups.  
However, the study also has certain limitations that must be acknowledged. The data set, while robust for initial testing, is limited to controlled adulteration levels and does not cover the full spectrum of potential real-world scenarios where milk might be adulterated in different ways or mixed with other substances not tested in this study. Further, the reliance on spectral data means that any optical interference or sensor calibration issues could affect the accuracy of the results. Future research should aim to expand the variety and complexity of the test samples and consider external validation of the methodology to reinforce the generalizability of the findings.
## 2 PV Modeling and Problem Formulation
### The Model of a Solar Cell  
#### 2.1.1 SDM  
For demonstrating the real-time characteristics of PV systems, their mathematical modeling is required under practical considerations. A PV array can be modeled by using the cell as its basic unit. SDMs are widely used due to their simplicity and ease of implementation. According to Figure 1a, the equivalent circuit for the SDM consists of a parallel resistor, a series resistor, a diode, and a current source. Calculating the output current can be accomplished using the following formula [40]:  
\[I_{o}=I_{p}-(I_{sh}+I_{D}) \tag{1}\]  
where \(I_{p}\), \(I_{sh}\), and \(I_{D}\) are the photogenerated, shunt resistor, and diode currents, respectively.  
Calculating \(I_{sh}\) and \(I_{D}\) can be accomplished using [PERSON]'s voltage law (KVL) and [PERSON]'s equation as follows:  
\[I_{sh}=\frac{V_{o}+R_{s}I_{o}}{R_{sh}} \tag{2}\]  
\[I_{D}=I\bigg{[}\exp\left(\frac{V_{o}+R_{s}I_{o}}{uv}\right)-1\bigg{]} \tag{3}\]  
where \(u\) represents the non-physical diode ideality factor, whereas \(I\) represents the diode reverse saturation current, \(V_{o}\) represents the cell output voltage, \(R_{sh}\) represents the shunt resistance, and \(R_{s}\) represents the series resistance.  
The junction thermal voltage can be calculated using the electron charge \(q\) (\(1.60217646\times 10^{-19}\) C) the junction temperature \(T\), and [PERSON]'s constant \(k\) (\(1.8865033\times 10^{-23}\) J/K) as follows:  
\[v=\frac{kT}{q} \tag{4}\]  
Combining Equations (1) and (4) will result in the cell output current (\(I_{o}\)) for the SDM as follows:  
\[I_{o}=I_{p}-\frac{V_{o}+R_{s}I_{o}}{R_{sh}}-I\bigg{[}\exp\bigg{(}\frac{V_{o}+R _{s}I_{o}}{uv}\bigg{)}-1\bigg{]} \tag{5}\]
## 3 Results and Discussion  
### Data Preprocessing  
This paper uses the historical photovoltaic data from February 2018 to January 2019 in a certain region in northwest China for simulation and selects the valid data for a total of 12 h during the day from 6:00 to 18:00. Eight users with high correlation of photovoltaic output in this area were selected, and then four power-stealing users were constructed according to the characteristics of common power-stealing methods to form the sample data of this paper.  
Before the simulation, the sample data was pre-processed, and the missing values in the data were filled using piecewise linear interpolation. At the same time, considering that the numerical ranges of different eigenvalues are different, the standard deviation standardization method was used to process the data, and the processed data presented a normal distribution with a mean value of 0 and a standard deviation of 1. The formula for standard deviation standardization is shown in Equation (11), where: \(x_{imen}\) is the mean value of the original data samples, and \(x_{ivar}\) is the standard deviation of the original data samples.  
\[x_{ij}^{\prime}=\frac{x_{ij}-x_{imen}}{x_{ivar}}. \tag{11}\]
## 4 Parallel Design and Implementation of DMAS Algorithm
### PDMAS Implementation Using Spark  
The proposed PDMAS is implemented using PySpark API for Spark. Algorithm 1 provides the pseudo-code for parallel implementation of the DMAS algorithm using Spark. The master node reads the input data and antennas location in Cartesian coordinates. These antenna location are used to estimate the signal propagation time using Equation (1). The estimated delay values along with the input data are converted into RDDs and distributed to all workers nodes to accelerate each calculation. Similarly, an empty matrix is created to store the subsets of pixel values and is broadcasted. This matrix is shared by different worker nodes. The worker nodes calculate the subsets of pixel values using the map operation from the MapReduce programming model. These subsets of pixel values being computed in parallel are integrated using the reduce operation and returned to the master node. Finally, the master node transforms the matrix of pixel values into an image using a simple Python function and and the results are stored.  
```
0: RF Sensors Data
0: Image Reconstruction
0: Master Node:
1: Read input data along with antenna locations
2: Estimate \(\tau_{r}(x,y)\): signal propagation time for all focal points in the imaging domain using Equation (1)
3: Create a matrix \(I_{MN}\) for storing pixel values
4: Convert the input data and delay values into RDDs and broadcast it to each worker node
0: Worker Node:
5:for subset of \(t_{a}\) < range do
6:for subset of \(t_{a}\) < range do
7:\(I_{mn}\gets I_{mn}+S_{ta,ra}(\tau_{r})\times S_{ta,ra+1}(\tau_{r})\): Calculate the subset of pixel values in parallel using Map operation
8:endfor
9:endfor
10: Integrate \(I_{mn}\) to \(I_{MN}\) using Reduce operation
11: Return \(I_{MN}\) to master node
0: Master Node
12: Convert \(I_{MN}\) into image and save.
```  
**Algorithm 1** PDMAS algorithm using Spark.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### Upper Layer Challenges
#### 7.2.1 Connectivity Challenges  
Here, we highlight challenges that may prevent CR-LPWAN devices from establishing connection in CR-LPWAN networks as follows:  
1. **Connectivity compatibility issue:** The challenge of connectivity compatibility, also termed interoperability issue, stems from conditions wherein different users adopt different connectivity technologies for different CR-LPWAN systems. This is due to the present large IoT market space, wherein technologies are developed from different vendors. These technologies are often proprietary in nature, which compounds further the issue of compatibility. It is thus difficult for users to migrate easily or interconnect devices obtained from different vendors. This will cause a divide in the market, which will ultimately limit the range of CR-LPWAN systems and stifile user satisfaction.
2. **Maintainability issue**: With different products being available for use, different CR-LPWAN systems will have different reliability and durability levels. Consequently, devices may develop faults, shut down, or batteries may expire at different rates, limiting connectivity in CR-LPWAN systems. In such cases, maintenance should be made easy to reduce the length of down times.
3. **Signalling**: Different technologies have different communication modes, which may hamper signalling between CR-LPWAN end nodes and different gateways. For example, bidirectional signalling may be required to ensure effective data delivery in CR-LPWAN networks. However, due to signalling mismatch between different CR technologies, there may be breakdown in connectivity.
4. **Bandwidth**: Bandwidth usage and management is a factor in ensuring effective connectivity in CR-LPWAN networks. With different products available for IoT applications, there may be more bandwidth requirement in an application over another. Thus, inability to meet minimum bandwidth requirements across a variety of applications may cause increase transmission error rates, which will limit connectivity.  
5. **Transmission Power levels**: Increasing transmission distance often depends on the transmission power level of CR-LPWAN end nodes. Thus, deploying devices with disparate transmission power levels can hamper the connectivity of CR-LPWAN networks. Consequently, effort is required to develop CR-LPWAN nodes that meet specific power transmission levels in order to ensure effective information transfer over CR-LPWAN networks.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling  
### Problem Description  
The study of VNE focuses on methodologies that can be proven capable of providing not only successful but also optimal bindings between the computational and networking virtual demands of an incoming VNR and the corresponding substrate resources of the physical underlay. This process is portrayed in Figure 2.  
The computational and networking demands of the incoming VNR#1 and VNR#2 are allocated over the residual resources of the depicted SN. Specifically, virtual nodes \"b\" of VNR#2 and \"a\" of VNR#1 share the same host, physical node \"N1\". Similar is the case for the virtual links connecting virtual nodes \"a\" and \"b\" of VNR#1 and VNR#2 respectively. Both are bound to the physical link \"L1-2\". However, it is feasible to combine different substrate resources to accommodate individual virtual demands. Such is the implementation that we encounter by the formation of the network path, synthesized by the substrate links \"L2-3\" and \"L3-4\", in order to connect the already embedded but distant virtual nodes \"a\" and \"c\" of VNR#2.  
The most commonly established notions from the related literature suggest a uniform model regarding the formulation guidelines of VNE [18, 22, 23]. Table 1 provides the semantic terminology used in the analysis of the following sections. Consequently, a VNE strategy can be decomposed into the main phases presented below.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Formula** & **Description** \\ \hline SN = \((N_{s},L_{s})\) & SN is a substrate network, consisting of sets of nodes \(N_{s}\) and links \(L_{s}\). \\ \(G^{V}=(\text{VN}^{i})\) & \(G^{V}\) represents a VNR as a set of \(i\) Virtual Networks. \\ \(\text{VN}^{i}=(N^{i},L^{i})\) & \(\text{VN}^{i}\) denotes the \(i^{\text{th}}\) Virtual Network of an incoming VNR, consisting of nodes \(N^{i}\) and links \(L^{i}\). \\ \(\widehat{R}=\prod_{j=1}^{m}R_{j}\) & \(\widehat{R}\) is the vector space of all the available resources, \(R_{1},\dots,R_{m}\). \\ \(rc:N_{s}\cup L_{s}\rightarrow\widehat{R}\) & \(rc\) is the function that assigns a capacity to an element of the substrate network. \\ \(rd^{i}:N^{i}\cup L^{i}\rightarrow\widehat{R}\) & \(rd^{i}\) is the function that assigns a demand to an element of \(\text{VN}^{i}\). \\ \(f_{i}:N^{i}\to N_{s}\) & \(f_{i}\) is the function that maps a virtual node of \(\text{VN}^{i}\) to a substrate node. \\ \(g_{i}:L^{i}\rightarrow\text{SP}\subseteq\text{SN}\) & \(g_{i}\) is the function that maps a virtual link of \(\text{VN}^{i}\) to a path (SP) in the substrate network. \\ \(M(G^{V})=(f_{i},g_{i})\) & \(M\) is the generalized function, that maps a VNR denoted by \(G^{V}\) onto the substrate network SN. \\ \hline \hline \end{tabular}
\end{table}
Table 1: VNE formulation terminology.  
Figure 2: Visualization of VNR mappings.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 1 Introduction
Due to the lower working temperature and multiple vapor escaping holes, the evaporation rate was increased to 0.62 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\). Despite the efforts in developing SCE, the performance was only analyzed with a small-scale laboratory device where the wastewater pond was simulated with a small container (35 mm \(\times\) 35 mm \(\times\) 50 mm). The performance potentials of the scaled-up SCE device are still unclear as there are several factors which need deeper investigations. First, the coupled heat and mass transfer in a scalable solar evaporation device may vary significantly from that of the laboratory test rig. For instance, boundary conditions in both air and water  
Figure 1: Schematic of the SCE. The red region in bulk water represents the penetration depth of infrared thermal radiation (\(\sim\)10 \(\upmu\)m). domains are different [37; 38]. Second, the previous laboratory study overlooked the impact of the configurations and designs of vapor escaping holes. Further parametric investigations on structural design are needed for evaluating the performance potentials of the scalable device. In this study, a detailed numerical modelling framework of the SCE process is developed to study the conjugated heat and mass transfer mechanism in the evaporation and transport processes and to reveal influential factors for optimizing the evaporation rate. The modelling framework is validated by lab-scale experiments and applied to the scalable solar evaporation scenario, through boundary condition variations. Results show that the evaporation rate of scalable SCE (0.313 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\)) is much higher than that of the laboratory evaporation with a container (0.139 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\)). Through parametric optimization and energy flow analysis, two major heat-loss mechanisms can be identified, including the heat conduction through the water bottom (16.2% of the total energy input) and heat convection on the solar absorber surface occupy (36.8% of the total energy input). Therefore, thermal insulation for heated water and solar absorber can effectively reduce the heat loss. With proper design and thermal insulation, the evaporation rate of scalable SCE could be further enhanced to 0.797 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\), indicating a 473.3% performance enhancement potential than the laboratory SCE. The energy conversion efficiency is 50.3%, which is the highest one in the SCE process.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 2 Methods
### Innovation 1: Underwater Light Propagation Model  
Underwater images suffer from color distortion due to the scattering and absorption of light by water [31], as shown in Figure 3. The longer wavelengths (red, orange, and yellow) are absorbed faster, while shorter wavelengths (blue and green) tend to scatter, leading to a blue-green tin in underwater images. This phenomenon makes it difficult for traditional image segmentation models to accurately detect features, particularly cracks, which may blend into the background or be obscured by poor lighting conditions. To address this, integration of an underwater light propagation model is proposed in the preprocessing stage of the DeepLabV3+ pipeline. This model simulates the scattering and absorption of light in water and compensates for the resulting color imbalance. By adjusting the image's color balance and enhancing contrast, this preprocessing step ensures that the model receives cleaner and more representative input, improving the accuracy of crack detection.
## 2 Methods
Residual power generation is optimized by minimizing total marginal costs among the dispatchable power plants to be brought online, as follows:  
\[\text{min}(MC_{\text{tot}})=\text{min}{\sum_{i}P_{\text{i}}\cdot MC_{i}} \tag{3}\]  
The net capacity of the \(i\)-th power plant to be brought online (\(P_{\text{i}}\)) is known. For an \(i\)-th power plant, marginal costs (\(MC_{\text{i}}\)) account for fuel costs (\(C_{\text{fuel,i}}\), \(\epsilon\)/MWh\({}_{\text{th}}\)), \(CO_{2}\) allowances (\(C_{\text{CO2}}\), \(\epsilon\)/\(t_{\text{CO2}}\)), and the variable part of operating and maintenance costs (\(C_{\text{V}}\), \(\epsilon\)/MWh\({}_{\text{th}}\)), all divided by the efficiency of the power plant (\(\eta_{\text{i}}\)) as follows:  
\[MC_{\text{i}}=\frac{C_{\text{fuel,i}}+C_{\text{CO2}}\cdot e_{\text{fuel,i}}+C_ {\text{V,i}}}{\eta_{\text{i}}} \tag{4}\]  
The emission intensity of a fuel is denoted with \(e_{\text{fuel,i}}\) (t\({}_{\text{CO2 eq}}\)/MWh\({}_{\text{th}}\)). The country-specific emission intensities are 0.368 t\({}_{\text{CO2 eq}}\)/MWh\({}_{\text{th}}\) for hard coal, 0.312 t\({}_{\text{CO2 eq}}\)/MWh\({}_{\text{th}}\) for fuel-oil, and 0.232 t\({}_{\text{CO2 eq}}\)/MWh\({}_{\text{th}}\) for natural gas [41]. These emission factors include total greenhouse gases emissions (CO2, CH4, and N2O) from production to consumption (mining, transport, distribution, and fuel combustion). The full load efficiency of the \(i\)-th power plant is known. However, when the power plant operates at partial load, the efficiency decreases. Thus, partial load operation increases fuel consumption and carbon emissions per unit of generated electricity. Marginal costs increase as a consequence. Using data from [42; 43; 44], correlations were built to determine the partial load efficiency (\(\eta_{\text{i}}\)) of a combined cycle gas turbine (CCGT), open cycle gas turbine (OCGT), and oil- and coal -fired power plants. The partial load efficiency (\(\eta_{i}\)) is calculated relatively to the full-load efficiency (\(\eta_{0}\)), the full-load capacity (\(P_{0}\)), and the partial load (\(P_{i}\)) capacity, as follows:  
\[ \begin{array}{l}\text{CCGT}:\ \eta_{i}=\eta_{0}\cdot[0.231\cdot\ln(P_{1}/P_{0})-0.055]\\ \text{OCGT}:\eta_{i}=\eta_{0}\cdot[0.321\cdot\ln(P_{1}/P_{0})-0.473]\\ \text{Coal and oil}-\text{fired}:\eta_{1}=\eta_{0}\cdot[0.0826\cdot\ln(P_{1}/P_{0})+ 0.62]\end{array}  \tag{5}\]  
The share of renewable energy (\(f_{\text{RES}}\)) in the electricity demand is calculated as the ratio between net renewable energy (\(E_{\text{RES}}\)) and the net electricity demand (\(E_{\text{demand}}\)) over an observed period of time (\(j=1\ldots t\)), as follows:  
\[f_{\text{RES}}=\frac{\sum\limits_{j=1}^{t}E_{\text{RES},j}}{\sum\limits_{j=1}^ {t}E_{\text{demand},j}}\cdot 100\% \tag{6}\]  
The net electricity demand (\(E_{\text{demand}}=P_{\text{load}}\cdot t\), in MWh) is the product of the net public load (\(P_{\text{load}}\), in MW) and the observed period of time (\(t\), in hours).
## 2 Literature Review
### Dual-Tree Complex Wavelet Transform  
DTCWT is suitable for image processing applications [9]. In [30], DTCWT is used to create an effective contrast enhancement method and achieves a good performance in low illumination image enhancement. A new method for the interpolation of a full high-definition (HD) image which applied DTCWT to decompose the low-resolution image into different sub-bands was presented in [31]. To acquire good performance, the Wiener filter is used to remove the noise component of the dual-tree complex wavelet transform (DTCWT) coefficients [32]. In [33], the DTCWT coefficients are preprocessed by median filtering. [PERSON] et al. [34] presented a novel biometric image encryption algorithm based on DTCWT and compressed sensing. To produce better fusion effects, Q-shift DTCWT was proposed to simplify the construction of filters in DTCWT [35].
## 3 Results  
### Shear Behavior  
Figure 5 depicted the relationship between deviator stress \(q\) and the axial strain \(\varepsilon_{1}\) for samples with optimum water content \(w_{\text{opt}}\) = 27.5%, dry side \(w_{\text{d}}\) = 19%, and wet side \(w_{\text{w}}\) = 35%. Figure 5a-c showed that for each sample, as \(\varepsilon_{1}\) increased, generally \(q\) exhibited a trend of initial growth followed by a reduction, defining a typical strain-softening pattern. This characteristic was also observed by [PERSON] et al. [10]. This mechanical response resulted from a dual-phase deformation mechanism: at the initial loading phase, the soil sample was densified through rearrangement of soil grains, ultimately reaching peak deviator stress \(q_{\text{max}}\). Subsequently, the initiation and development of microcracks within the soil matrix triggered a progressive reduction in soil skeleton, resulting in macroscopic stress deterioration. In addition, this strain-softening characteristic was less pronounced at higher confining pressure \(\sigma_{3}\), manifested by a smaller attenuation of post-peak stress. This \(\sigma_{3}\)-dependent characteristic can be explained by the more enhanced soil fabric densification under higher \(\sigma_{3}\) level, which more effectively restrained the fabric degradation. Additionally, Figure 5a-c demonstrated that at each \(\sigma_{3}\) value, the curve of the sample with higher water content lay above that of the sample with lower water content. Furthermore, the curve showed a more significant rise when \(w\) increased from 27.5% to 35%, compared with the increase in \(w\) from 19% to 27.5%.  
Regarding the volumetric strain \(\varepsilon_{\text{v}}\), Figure 6 presents a visual view of samples after shearing under \(\sigma_{3}\) = 700 kPa. It could be observed that the increase in \(w\) favored dilation behavior. In this study, negative and positive values represent dilatancy and contraction, respectively. Figure 7 presents the \(\varepsilon_{\text{v}}\)-\(\varepsilon_{1}\) relationship at different \(\sigma_{3}\) levels. As can be observed, at a confining pressure \(\sigma_{3}\) = 30 kPa, each sample initially contracted and then exhibited dilation behavior. In addition, the increase in \(w\) led to a decrease in contraction and an increase in dilation. When \(\sigma_{3}\) increased, the sample displayed increased contraction and diminished dilation. This \(\sigma_{3}\) effect was also observed by [PERSON] et al. [23]. At \(\sigma_{3}\) = 120 kPa, the sample with \(w\) = 19% even only presented pure contractive behavior without any dilation.  
Figure 5: Deviator stress–axial strain curves: (**a**) \(\sigma_{3}\) = 200 kPa; (**b**) \(\sigma_{3}\) = 700 kPa; (**c**) \(\sigma_{3}\) = 1200 kPa.
## 4 Simulation Environment
### Calculation of UE Rate  
The final stage of the main simulation loop is the calculation of the user throughput at the considered time slot. This is performed based on \(SE_{mcs}\), which is associated with the selected MCS, and the calculated \(\text{BLER}_{i,mcs}\). The throughput of the \(i\)-th UE is given by:  
\[v_{i,mcs}=\frac{SE_{mcs}\cdot 12\cdot 14\cdot(1-\text{BLER}_{i,mcs})}{T_{ \text{slot}}}. \tag{13}\]  
The numbers 12 and 14 are related to the fact that, according to the 3 GPP specification [27], within a single RB, there are always 12 subcarriers in the frequency domain and 14 OFDM symbols within a single time slot. It should be noted that a UE is not always granted with RBs. Thus, to observe a reliable rate, the throughput should be averaged over multiple time slots.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**MCS**} & \multirow{2}{*}{
\begin{tabular}{c} \(SE_{mcs}\) \\ (bit/Hz) \\ \end{tabular} } & \multirow{2}{*}{\(\beta_{mcs}\)} & \multicolumn{2}{c}{**Required**} & \multirow{2}{*}{\(b_{mcs}\)} & \multirow{2}{*}{\(c_{mcs}\)} \\  & & & & EESM (dB) & \\ \hline
1 & 0.15 & 4.73 & \(-6.3\) & 0.19 & 0.04 \\
2 & 0.23 & 2.48 & \(-4.5\) & 0.31 & 0.05 \\
3 & 0.38 & 1.13 & \(-2.8\) &