esa-sceva/satcom-chunk-collection huggingface dataset:
Previous studies have focused on the toxic effects of single heavy metals or single hydrocarbons on plants, whereas very few studies have evaluated the effects of hydrocarbon and metal mixtures. Oil-contaminated sites are typically contaminated by both kinds of contaminants; therefore, examining their combined effects provides a more realistic assessment of the environmental impacts of oily residues [23]. According to the current National Environmental Quality Standards for Soils class II, 0.3 mg/kg of Cd is considered safe for plants and the environment, whereas its levels in agricultural sludge are restricted to 0.8 mg/kg of Cd in the same control standard. Nevertheless, if only the chemical index of a specific pollutant is used to evaluate soil quality, the degree of environmental damage caused by the pollutant in question could be severely underestimated. Plant toxicity assays can reflect the degree of soil contamination more accurately. Evaluating heat treatment residues using plants can not only remove pollutants from the residues to make them harmless, but it can also result in them being used for desert greening, a technology that uses plants to remove pollutants and is an economically viable and sustainable biotechnology [24]. However, this technique requires a high level of plant species selection, as plants must not only be resistant to pollutants but must also be able to accumulate them in large amounts. Mung beans are grown on abandoned cultivated lands at the northern edge of Shaanxi Province. These plants exhibit stable yields and can remove toxicants in soil, wastewater, and sludge. Therefore, the mung bean plant was selected as the test plant in the present study. In this study, residues were prepared via commonly used oily sludge treatment methods, and the growth of the mung bean plants was used as an indicator of toxicity, to evaluate the effect of heavy metals and hydrocarbons in the actual oily sludge heat treatment residue on the growth of the mung bean plants as well as to determine the feasibility of using this plant to repair contaminated soil. Additionally, this approach not only removes the pollutants in the heat treatment residues and makes them harmless, but it also provides data to support residue greening.
## 2 Theoretical Fundamentals  
### Tranquilized Time-Series-Decomposition Embedding  
The embedding block (Figure 1) was the first step in the proposed fault forecasting task, and its quality directly affects diagnosis accuracy. Traditional embedding methods are discrete and unfriendly to time-series prediction, so it was replaced with tranquilized time-series decomposition embedding in the fault forecasting task. First, the fault forecast involved tranquilizing data for better prediction in the later stage, and then decomposing the tranquilization into trend-cyclical and seasonal Init parts. Then the fault forecast data is juxtaposed with these trend-cyclical and seasonal Init parts. Finally, three curves were created and transformed to the encoder input.  
For length-L input series \(X\)\(\in\)\(R^{L\times D}\), we used a different method to tranquilize the original fault forecast data.  
\[X_{Tql}=X_{n}-X_{n-1} \tag{1}\]  
where \(X_{\text{n}}\) and \(X_{\text{n-1}}\) denote the sequence value of the current time \(T_{\text{(n)}}\) and the previous time \(T_{\text{(n-1)}}\).  
Then, the time-series decomposition process is  
\[\begin{array}{c}X_{t}=AvgMov\Big{(}X_{Tql}\Big{)}\\ X_{s}=X_{Tql}-X_{t}\end{array} \tag{2}\]  
where \(X_{\text{s}}\) and \(X_{\text{t}}\in\)\(R^{L\times D}\) denote the seasonal and the extracted trend-cyclical part, respectively.  
\[\begin{array}{c}AvgMov\Big{(}X_{Tql}\Big{)}=\frac{1}{m}\sum_{j=-k}^{k}X_{Tql +j}\\ m=2k+1\end{array} \tag{3}\]  
The trend period of time \(t\) is estimated by averaging the values of the time series within 2 \(k\) periods of t. Averages eliminate the randomness of the data, instead of the trend component. The average was calculated as follows:  
\[X_{Embedding}=\Big{[}X_{Tql},X_{t},X_{s}\Big{]} \tag{4}\]  
where \(X_{Embedding}\in\)\(R^{L\times 3}\) denotes our final Embedding output.  
The main aspects of the method are described by Algorithm 1.  
Figure 1: Embedding of fault forecast task.
## 3 Path Planning Problem
### Path Planning Objectives
#### 3.1.3 Path Smoothness  
This is designed to calculate the degree of path bending. The smoother the path, the smaller the energy loss. The average angle of path represents the smoothness objective. The best average value is \(0^{\circ}\). This means that the path is straight.  
For two consecutive segments \(p_{1}p_{2}\) and \(p_{2}p_{3}\), \(p_{i}=(x_{i},y_{i})\), \(i=1-3\), the deviation angle is calculated with Equation (4):  
\[Angle[p_{1},p_{2},p_{3}]=\pi-\cos^{-1}\left(\frac{\left(x_{2}-x_{1}\right) \left(x_{3}-x_{2}\right)+\left(y_{2}-y_{1}\right)\left(y_{3}-y_{2}\right)}{ dis(p_{1},p_{2})\times dis(p_{2},p_{3})}\right). \tag{4}\]  
Therefore, the average angle of path \(p\) can be computed with Equation (5):  
\[Smoothness(p)=\frac{1}{n}\sum_{i=0}^{n-1}\{Angle[p_{i},p_{i+1},p_{i+2}]\}. \tag{5}\]  
In this work, for the path planning problem, the model of multi-objective optimization (MOP) is as follows:  
\[\left\{\begin{array}{l}p=[S=p_{0},p_{1},p_{2},...,p_{n},p_{n+1}=T]\\ \text{Minimize }f_{1}(p)=Length(p)\\ \text{Minimize }f_{2}(p)=Safety(p)\\ \text{Minimize }f_{3}(p)=Smoothness(p).\end{array}\right. \tag{6}\]  
A collision without obstacles is the only constraint. It is permissible to touch the boundaries of obstacles. In this work, we defined the infeasible path as a path to collide with obstacles and considered the boundary of the workspace as an obstacle.  
Usually, at least two objectives conflict with each other. Approximate Pareto optimal paths can be found by solving the model illustrated in Equation (6). At present, many evolutionary algorithms have been able to solve lots of MOP problems, such as genetic algorithms, artificial bee colony algorithms, and particle swarm optimization algorithms. Although the MOP problem of path planning has been studied for many years, it is still difficult to efficiently find the approximate Pareto solutions due to the complexity of the path planning problem.
## 3 Results and Discussion
### Gypsum Plasters
#### 3.1.2 Mechanical Properties  
Figure 5 reports the results of flexural (a) and compressive strength (b). The results are compared to the mechanical properties of the reference sample (without additives). It should be noted that, in general, the increase in the percentage of bioceramics involved an improvement in the mechanical properties. For flexural strength, a slight decrease, compared to the reference mixture, was experienced when a percentage of 0.3% of bioceramics was added. When the amount of bioceramics increased, with 0.6 and 1% percentages, the flexural strength increased, achieving resistances above the reference specimens. For the GBC-1 samples, flexural strength reached an increase of 3% compared to the reference values. Regarding the compressive strength, all samples analyzed reached an increase compared to the reference sample, obtaining an increase of up to 21% in the case of the GBC-1 mixture.
## 3 Results
### Performance of Different SNRs  
SNRs are a key indicator for extracting graphical information, as excessive irrelevant signals could obscure the target signal within the noise. In addition, SNRs play a pivotal role in the retrieval process, as the \(\mathbf{S}_{\circ}\) in the IMAP-DOAS algorithm is directly tied to them. More noise leads to higher \(\mathbf{S}_{\circ}\) weighting during iterations, thereby increasing the dependence of retrieval results on the observational data. The retrieval precision of pixels gradually improves with the improvement of the SNR. The box plot of the absolute error obtained from the simulated and the retrieved \(\Delta\)XCH\({}_{4}\), as shown in Figure 7a, indicates that the absolute error decreases as the SNR increases. The results are all based on condition with a spectral resolution of 10 nm and a spatial resolution of 10 m. At the SNR of 800, the median absolute error is 16.67 ppb, whereas at an SNR of 100, the median absolute error rises to 57.89 ppb. Meanwhile, the error distribution at high SNR, such as 800, 600, and 400, is more concentrated, suggesting higher measurement precision. In contrast, as noise increases, the error distribution becomes more discrete, with a remarkable increase in outliers, suggesting more measurement error.  
Additionally, for individual plume estimation, noise is associated with the masking process that separates the plume from the background field (SNR = 600, 400, 200), as illustrated in Figure 7b-d. At a high SNR (such as 600 and 400), the plume mask boundary remains comparatively complete. However, the detected plume regions artificially shrink, and the details of the plume boundary become increasingly blurred when the SNR decreases to 200. In theory, when noise approaches the magnitude of the signal, it may result in potentially obscuring plume boundaries and make it challenging to distinguish the plume from the surrounding noise. Therefore, increasing noise enhances the likelihood of misclassifying low-concentration plume boundaries as background noise. Furthermore, if noise-induced artifacts appear within the plume, they are difficult to eliminate through mask extraction, introducing errors in subsequent quantitative analyses.  
Figure 7: With the emission rate of 1000 kg/h, (**a**) the \(\Delta\)XCH\({}_{4}\) absolute error box plot of simulated and retrieved \(\Delta\)XCH\({}_{4}\) at the spatial resolution of 10 m and spectral resolution of 10 nm, with SNRs of 800, 600, 400, and 200, 100, respectively; (**b–d**) plume masks under varying SNRs of 800, 600, 400, 200, and 100.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz
### Sub-Arrays
#### 3.2.1 Symmetric Array
With an antenna efficiency greater than 90%, a peak gain of 3.64 dBi for a single antenna element was noted. For the MIMO configuration, isolating of >10 dB between the characteristics of the antenna was attained. Additionally, the MIMO antenna design offered sufficient radiation coverage to support several mobile phone board sides, which is a crucial characteristic for upcoming 5G-capable devices [105]. A low-profile planar four-element MIMO antenna for wireless handheld devices has also been proposed. Simple planar L-shaped monopoles in the shape of each unit antenna are mounted over etched non-ground areas measuring 10 mm \(\times\) 5 mm. The improved antenna elements provided an isolation of more than 18.8 dB without any need for an extra decoupling structure for the three 5G New Radio bands that fall inside the C-band: the n-77 band (3.30-4.20 GHz), n78 band (3.30-3.80 GHz), and n79 band (4.4-5 GHz). As illustrated in Figure 23, the designed antenna was constructed on an FR-4 substrate with dimensions of 120 mm \(\times\) 65 mm \(\times\) 1.6 mm. The measured and discussed antenna properties included reflection coefficient, radiation pattern, mutual coupling, and gain. The estimated envelope correlation coefficient was less than 0.018 for the considered frequency range. The suggested MIMO antenna's simplicity and compactness leave enough room inside the handheld mobile terminal to integrate other circuits. Investigation into the integration with lower generation antennas was further conducted, and the findings show that mounting the suggested antenna system on both of the long arms of the ground plane provides enough room for the integration of a lower generation antenna without negatively impacting the performance of either [106; 107].
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 2 QoS Control Algorithms
### ACK Algorithm  
Another control method is the ACK algorithm [6]. Due to the limited power of the sensors, the state of the sensors does not change when the number of active sensors reaches the target value, which results in low power utilization efficiency. Using random access technology, the base station individually returns feedback packets to the active sensors. Therefore, sleep sensors do not need to receive feedback packets from the base station.  
The ACK algorithm does not use a reward function to control the state of the sensor. Each sensor has a state machine, as shown in Figure 3. In the ACK algorithm, the state machine has three states, and each state contains the probability of a sensor being awakened (\(P_{1}\), \(P_{2}\), and \(P_{3}\)). Sensors in the \(P_{3}\) state have a higher probability of being awakened than sensors in the \(P_{2}\) state, which have a higher probability than sensors in the \(P_{1}\) state (\(P_{3}>P_{2}>P_{1}\)). The probabilities were 1.00, 0.10, and 0.05, respectively.  
When the ACK algorithm starts to operate, each active sensor randomly selects a state as the initial state. Each sensor selects an active or sleep state based on the probability \(P_{i}\) of its current state. The base station receives packets from active sensors, counts the number of packets received, and sends feedback packets to the active sensors individually. \(Q_{1}\) refers to the total number of packets received, and \(Q_{0}\) is the target value. The feedback packet contains the relationship between \(Q_{0}\) and \(Q_{1}\).  
The base station, according to the relationship between the number of active sensors and the target value, indicates the sensors' states to change. When the number of packets (total number of active sensors) is higher than the target value, the feedback packet indicates that the sensors should move to the left of the state machine. Conversely, if the number of packets is less than or equal to the target value, the feedback packet indicates that the sensor should move to the right side of the state machine. A round is complete when all active sensors receive the feedback packet. In the next round, the sensor decides between the ON and OFF states according to probability \(P_{i}\). After several runs, the number of active sensors gradually reaches the target value. Figure 4 shows the experimental results, where the total number of sensors \(m\) was set to 100 and the target value was set to 35. The experimental results show that the active sensors oscillate in the range of 34-42.
## 5 Analysis of Energy Consumption
As characterized in Phase IV, since the clusterhead has the capability of updating the sub-cluster members, a cluster member, say sensor \(i\), may become an active supplementary sub-cluster member or leader when receiving the message, which contains the IDs of new supplementary sub-cluster member or the leader (_i.e._, \((i\bigcap\overline{M}(j)\
eq\O)\) or leader ID), and then joint the tracking task. Note that \(\overline{M}(j)\) is the index set of new supplementary sub-cluster members. Therefore, we obtain  
\[S_{T}^{f}(j) = \sum_{i\in M^{\prime\prime}}N_{i}^{H}+1 \tag{20}\] \[S_{R}^{f}(j) = N_{CH(j)}+\sum_{i\in M^{\prime\prime}}\sum_{h=1}^{N^{H}}N_{I_{ R_{i}}^{(h)}} \tag{21}\]  
where \(M^{\prime\prime}=\{i:\operatorname*{argmin}_{i}\sum N_{i}^{H},i\in(\overline{ M}(j)\bigcap I_{N_{CH(j)}})^{c}\bigcup(L_{id}^{(j+1)}\bigcap L_{id}^{(j)})^{c}\}\) and \(S_{T}^{f}(j)\) and \(S_{R}^{f}(j)\) are the number of transmissions and receptions for leader handoff, respectively. Nonetheless, when the leader ID is zero (_i.e._, \(L_{id}^{(j+1)}=0\)), the sub-cluster members are assigned to be inactive sensors and the procedure of selecting a new sub-cluster will be triggered. Accordingly, the total energy consumption of transmission and reception for tracking target is \(E^{TCAT}=\sum_{j=1}(E_{T}\cdot(S_{T}^{c}(j)+S_{T}^{p}(j)+S_{T}^{f}(j))+E_{R} \cdot(S_{R}^{c}(j)+S_{R}^{p}(j)+S_{R}^{f}(j)))\).
## 3 Materials and Methods
### Boundary Feature Refinement Module (BFRM)  
Most of the classical colorectal polyp segmentation networks use a combination of up-sampling and down-sampling, which is prone to blurred boundaries when performing polyp segmentation. The Convolutional Block Attention Module (CBAM) [26] is an attention mechanism module designed for CNNs, which aims to improve the expressiveness of the model by enhancing its attention to important features such as boundaries. Inspired by this, the BFRM was proposed, which incorporates four CBAMs to process the feature maps generated by the previous layer. Subsequently, the channel dimension is adjusted using a 1 \(\times\) 1 convolutional head, followed by bilinear interpolation upsampling to restore the four sharpened feature maps \(P_{i}\) to their original spatial resolution. Finally, these multi-scale maps are aggregated element-wise by summation and normalized using sigmoid to generate the final segmentation mask, as shown in Equation (5).  
\[\text{BFRM\_out}=sigmoid(\sum_{4}^{i=1}Up(Conv_{1\times 1}(CBAM(N_{i}))) \tag{5}\]  
In Equation (5), the _Up_ function represents the upsampling of four feature maps with scaling factors of 4\(\times\), 8\(\times\), 16\(\times\), and 32\(\times\) through bilinear interpolation. The CBAM function represents the CBAM processing process, which consists of two stages: channel attention (CA) module and spatial attention (SA) module, as illustrated in Figure 4. For the input feature map \(N_{i}\), the CA module performs both average pooling and max pooling simultaneously and inputs it into an MLP to generate two 1 \(\times\) 1 \(\times\) C channel attention maps. These two maps are then summed element-wise and activated by sigmoid function to obtain the refined channel attention map \(N_{i}^{\prime}\), as formalized in Equation (6). The SA module performs max pooling and average pooling along the channel direction on \(N_{i}^{\prime}\) to obtain two feature maps of size 1 \(\times\) H \(\times\) W, concatenates these two feature maps, and then generates the final attention map through a 7 \(\times\) 7 convolution and sigmoid operation, as formalized in Equation (7).  
\[N_{i}^{\prime}=sigmoid(MLP(AvgPool(N_{i})))+sigmoid(MLP(MaxPool(N_{i}))) \tag{6}\]  
\[CBAM(N_{i})=sigmoid(Conv_{7\times 7}\big{(}[AvgPool(N_{i}^{\prime});MaxPool(N_{i}^{ \prime})]\big{)}) \tag{7}\]  
Figure 3: Structure of BEB.
# Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-\(\omega\)
## 3 Description of the Proposed Algorithm
### Overview of QPGPSO-\(\omega\)
In the final decision block of the algorithm, a similar approach is followed. If the Gbest value is achieved, the algorithm will terminate and the final solution will be displayed (case of the branch \"yes\"). Otherwise, the velocity of each particle is updated based on its current velocity, Pbest and Gbest values, followed by the position update (case of the branch \"no\"). The iterative process continues until the termination criterion is satisfied and the algorithm provides the most optimal solution for the considered dispatch problem. The QPGPSO-\(\omega\) methodology is demonstrated through a pseudocode [53] (Algorithm 1). ```
Initialize N, d-dimensional velocity vectors \(V_{i}(j)\), and solutions \(X_{i}(j)\) within the specified range of the search space. Initialize iteration \(j=0\) for1 st iteration do  initialize \(G_{pi}(0)=X_{i}(0)\) endfor for\(i=1\):N do  Calculate the initial fitness of each particle \(fit(G_{B}(0))=\min\left(fit(G_{pi}(0)\right)\right)\) \(G_{B}(0)\) corresponding to index \(fit(G_{B}(0))\) endfor for\(j=1\):maxiter do  Compute \(w(j)=0.9-0.5/\textit{maxiter}\)  Update \(V_{i}(j)\) and \(X_{i}(j)\)  Calculate the opposite number \(X_{0}\) and generate corresponding quasi-opposite population \(X_{qoi}\)  Update \(X_{i}(j)=\left\{ \begin{array}{ll}X_{qoi}&\textit{if }fit\big{(}X_{qoi}\big{)}<fit(X_{i}(j))\\ X_{i}(j)&\textit{else}\\ \end{array} \right.\)  Update \(G_{pi}(j)=\left\{ \begin{array}{ll}X_{i}(j)&\textit{if }fit(X_{i}(j))<fit \Big{(}G_{pi}(j-1)\Big{)}\\ G_{pi}(j-1)&\textit{else}\\ \end{array} \right.\)  Update \(fit(G_{B}(j))=\min\Bigl{(}fit(G_{pi}(j)\Big{)}\Bigr{)}\)  Update \(G_{B}(j)\) corresponding to \(fit(G_{B}(j))\) endfor  Print best solution \(G_{B}(\textit{maxiter})\) and best fitness \(fit(G_{B}(\textit{maxiter}))\)
```  
**Algorithm 1** QPGPSO-\(\omega\) Pseudocode  
The QPGPSO-\(\omega\) methodology is demonstrated through a pseudocode [53] (Algorithm 1).
## 2 Methods
### Phase-Based Effective Connectivity Estimation Approaches Considered in This Study  
#### 2.3.1 Phase Transfer Entropy  
We obtain phase TE values through three different estimators that allow computing TE from individual signal pairs. First, the proposed kernel-based [PERSON]'s phase TE estimator (TE\({}_{\kappa a}^{\theta}\)), defined in Equation (9). Second, the Kraskov-Stogbauer-Grassberger TE estimator (TE\({}_{\kappa S}^{\theta}\)), a method that relies on a local approximation of the probability distributions needed to estimate the entropies in TE from the distances of every data point to its neighbors [33; 34]. Thirdly, an approach termed symbolic TE (TE\({}_{Sym}^{\theta}\)) that relies on a symbolization scheme based on ordinal patterns. The symbolization scheme allows estimating the probabilities involved in the computation of TE directly from the symbols' relative frequencies [35].  
In all cases, \(\theta^{x}\) and \(\theta^{y}\) are obtained by convolving the real-valued time series with a Morlet wavelet, defined as  
\[h(t,f)=\exp(-t^{2}/2\xi_{t}^{2})\exp(i2\pi ft), \tag{10}\]  
where \(f\) stands for the filter frequency, \(\xi_{t}=m/2\pi f\) is the time domain standard deviation of the wavelet, and \(m\) defines the compromise between time and frequency resolution [8].
## 4 Simulation Experiments
### Case Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of parallel machines at each stage & [1, 2, 1, 2, 1, 1], A total of 11 machines \\ Processing time of each operation & An integer follows uniform distribution between [1,10] \\ Processing power of machines at each stage & [8, 8, 8, 5, 8, 7, 8, 6] (Unit: kw/h) \\ Idle power of machines at each stage & [1, 3, 1, 5, 1, 4, 4, 3] (Unit: kw/h) \\ Effective service time of lubricants on the machines at each stage & [5, 9, 4, 4, 4, 7, 4, 5, 5, 9, 5] (Unit: h) \\ The amount of lubricants used on the machines at each stage & [0.23, 0.33, 0.36, 0.36, 0.29, 0.30, 0.32, 0.22] (Unit: L) \\  & [88.5311, 107.1938, 108.0180, 139.7903, 93.3949, 186.1302, \\ Delivery time of each job & 110.8196129.5210, 117.1030, 116.3252, 123.3349, 92.1824, \\  & 180.2294, 142.0430, 143.8744] (Unit: min) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Relevant parameters in factory 2. Figure 7: Pareto optimal fronts obtained by each algorithm. (**a**) 3D map of Pareto fronts. (**b**) Comparison of Makespan and Total carbon emissions. (**c**) Comparison of Total tardiness and Total carbon emissions. (**d**) Comparison of Makespan and Total tardiness. Taking the first solution set in Table 12 as an example, this paper draws scheduling Gantt charts of jobs in factory 1 and factory 2, which are shown in Figure 8. The make span of jobs in factory 1 is 126 min, the total carbon emissions are 40.2161 kgCO\({}_{2}\), and the total tardiness is 120.1580 min. The make span of jobs in factory 2 is 140 min, the total carbon emissions are 67.3113 kgCO\({}_{2}\), and the total tardiness is 41.4895 min. The make span of all jobs is 140 min, which is the maximum of make span in two factories. The total carbon emissions are 107.5274 kgCO\({}_{2}\), which are the sum of total carbon emissions in two factories, and the total tardiness is 161.6475 min, which is the sum of total tardiness in two factories. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Total Carbon** & \multicolumn{2}{c}{**Total Tardiness**} & \multirow{2}{*}{**Make Span**} & \multicolumn{2}{c}{**Scheduling Scheme**} \\ \cline{3-4} \cline{6-