esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods of Analysis
### Frequency Domain Analysis  
A discrete Fourier transform algorithm was used to convert the measured signals from the time domain into the frequency domain. The major frequency content of the pressure fluctuations in a gas fluidized bed reactor is normally below 10 Hz, so that sampling at 100 Hz is sufficient to capture most flow features [33]. The power spectrum of the measured signals was calculated by the non-parametric  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Diameter Range (mm)** & **Particle Density, \(\rho_{p}\) (kg/m\({}^{3}\))** & **U\({}_{\text{mf}}\) (m/s)** \\ \hline \
\begin{tabular}{c} Calcined dolomite \\ Silica sand \\ \end{tabular} & 0.18–0.212 & 1450 & 0.041 \\
\begin{tabular}{c} 512.2 \\ \end{tabular} & 0.18–0.212 & 2520 & 0.069 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of particulate materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Campaign** & **Membranes Activated** & **Bed Material** & **Permeation Rate (L/min)** & _U (m/s)_ \\ \hline Ref. 1 & 0 & \begin{tabular}{c} Calcined dolomite \\ Silica sand \\ \end{tabular} & 0 & 0.004–0.37 \\ Ref. 2 & 0 & Silica sand & 0 & 0.004–0.37 \\ \begin{tabular}{c} \#10.0 \\ \#1-1 \\ \end{tabular} & \begin{tabular}{c} Membrane \#1-4 \\ Calcined dolomite \\ \end{tabular} & \begin{tabular}{c} Calcined dolomite \\ \end{tabular} & \
\begin{tabular}{c} 5,10 \\ \(1.5,3.0\) \\ \end{tabular} &
\begin{tabular}{c} 0.09–0.  
method. In order to decrease variations, the power spectrum was obtained by averaging a number of sub-spectra. Thus, the time series was divided into \(L\) segments of individual length \(N_{s}\), represented as:  
\[x_{i}=x(n+iN_{s})\ n=1,2,\ldots,L \tag{3}\]  
The power-spectrum of each segment is calculated as:  
\[P_{xx}^{i}(f)=\frac{1}{N_{s}U}\left|\sum_{n=1}^{N_{s}}x_{i}(n)w(n)\exp(-j2\pi fn )\right|^{2} \tag{4}\]  
where \(U\) is a normalizing factor representing power in the window function \(w(n)\), and can be expressed as:  
\[U=\frac{1}{N_{s}}\sum_{n=1}^{N_{s}}w^{2}(n) \tag{5}\]  
The averaged power spectrum is then calculated by:  
\[P_{xx}(f)=\frac{1}{L}\sum_{i=1}^{L}P_{xx}^{i}(f) \tag{6}\]  
The frequency resolution of the power spectrum is given by \(\Delta f\) = \(f_{s}\)/\(N_{s}\), where \(f_{s}\) is the sampling frequency; 8192 points of the data series in each run were used for the Fourier transform giving a frequency resolution of 0.012 Hz. With each run repeated 9 times, a smooth power spectral was obtained by averaging the data sets. A Hanning window [33] has been used as window function \(w(n)\) in this study, and can be expressed as:  
\[\mathrm{w}(n)=\frac{1}{2}\bigg{(}1-\cos\bigg{(}\frac{2\pi n}{N-1}\bigg{)}\bigg{)} \tag{7}\]
# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 3 Principles of the BiGRU-Based Receiver
### BiGRU-Based Receiver  
Our proposed acceptance scheme was based on BiGRU. The received DCO-OFDM signal sequence is denoted as \(r=[r_{1},r_{2},\ldots,r_{T}]\), where the vector \(r_{i}(i=1,2,\ldots,T)\) corresponds to the \(i\)-th bit of the received symbol. The corresponding prediction sequence is denoted as \(y=[y_{1},y_{2},\ldots,y_{T}]\), and the vector \(y_{i}(i=1,2,\ldots,T)\) corresponds to the \(i\)-th bit of the received symbol.  
The first layer was the input layer. Moreover, for each symbol \(r_{i}(i=1,2,\ldots,T)\), the current symbol \(r_{i}\) was wrapped with its \(k\) pre-symbols and \(k\) post-symbols to form \(x(i)=[r_{i-k},\ldots,r_{i+k}]\), which was used as the input sequence of the BiGRU network. The second layer was the BiGRU model layer. This study set the BiGRU model cyclic time step to \(2k+1\). The hidden state \(h_{t}\) contains the flow of symbolic information between the cyclic time steps. The output of the BiGRU model layer was entirely connected to the fully connected layer. To effectively solve the nonlinear problem, two fully connected layers were used, where the latter contained the same number of nodes as the number of classes that send signals. As this study focuses on the intensity of the de-DC signal, the tanh function was selected for the output layer, and the output was the predicted value corresponding to the current symbol \(r_{i}\). Thus, the predicted value \(\widetilde{y_{i}}(i=1,2,\cdots,T)\) corresponding to the \(i\)-th symbol of \(r_{i}(I=1,2,\ldots,T)\) was obtained.
## 3 Materials and Methods
### Evaluation Metrics  
Finally, 5-fold cross-validation was used to estimate the classification model's skill. One way to evaluate machine learning models is through measurements. These measurements, commonly called evaluation metrics, allow us to measure certain aspects, trends, and results. Thus, for classification problems, the most common metrics are the Prediction Accuracy Rate (PAR), True-Positive Rate (TPR), Sensitivity, True-Negative Rate (TNR),  
Figure 6: Pearson correlation coefficient for feature relationships.  
Specificity, F1-score, and AUC [40]. A Confusion Matrix (CM) was used to observe the estimates of the classification possibilities of the respective True (\(T\)) and False (\(F\)) values and the Positive (\(P\)) and Negative (\(N\)) predicted classes [40].  
The metrics described in [8] were used to evaluate the effectiveness of the models developed in this study. The Specificity (\(SPE\)) was calculated by dividing the number of correct Negative predictions \(TN\) by the total number of Negatives \(F\). The Sensitivity (\(SEN\)) was calculated by dividing the number of accurate Positive predictions \(TP\) by the total number of Positives \(T\). The accuracy (\(ACC\)) was calculated by dividing the sum of two accurate predictions, \(TP\) + \(TN\), by the total number of data \(P\) + \(N\). The elapsed time (\(Et\)) in seconds was used to calculate the training time and validate the models. The Equations for these metrics are provided in (4), (5), and (6), respectively:  
\[SPE=\frac{TN}{TN+FP}=\frac{TN}{N}, \tag{4}\]  
\[SEN=\frac{TP}{TP+FN}=\frac{TP}{P}, \tag{5}\]  
\[ACC=\frac{TP+TN}{TN+TP+FN+FP}=\frac{TP+TN}{P+N}. \tag{6}\]  
These were the evaluation metrics used in this study for specific reasons. \(ACC\) validates the correctness of the predictions of the developed models. \(SPE\) helps us determine whether risk levels are correctly excluded from non-risk events. In other words, it allows us to distinguish between events that appear risky but are not. Finally, \(SEN\) allows us to determine whether an event is risky. It enables us to assess whether a driver is driving safely or engaging in risky driving behavior.
## 2 Materials and Methods
### Bunch Structure Measures  
One day prior to harvest, four (2014) and six (2015, 2016) bunches per FACE ring were collected to examine bunch parameters for both cultivars. Bunches were sampled at the second position of the shoot along the cane, excluding first and last shoot position. Only healthy bunches were taken for Riesling in 2014, 2015, and 2016, and Cabernet Sauvignon in 2015 and 2016. Length, width, and bunch weight were recorded. Afterward, bunches were destemmed to record the number of berries and total berry weight (TBW). Berries of each bunch were classified according to their diameter with a stainless-steel sieve column (ISO 3310-1). The column provided the following seven groups of berry sizes: (1) \(<\)6.3 mm, (2) 6.3-8 mm, (3) 8-10 mm, (4) 10-12.5 mm, (5) 12.5-14 mm, (6) 14-16 mm, and (7) 16-18 mm. For each bunch, the berry number of each berry size was counted.
## 2 Methodology
### Dynamic Allocation of Satellite Resources for Emergency Scheduling
#### 2.4.1 Initial Scheduling Plan of Satellite Resources  
The genetic algorithm and tabu search are common algorithms for solving combinatorial optimization problems [32; 33; 34]. The genetic algorithm is a highly parallel, stochastic and adaptive optimization algorithm based on the biological evolution and selection mechanism, and it has strong robustness and high global search capability. However, in the late stage, the convergence speed and the computational efficiency are low, and it is easy to converge to a local optimum. Tabu search is a neighborhood search algorithm that prohibits the repetition of previous work and avoids the local optimum. However, tabu search also has some limitations, such as strong dependence on the initial solution, serial operation for only one solution, and low search efficiency. Based on the characteristics above, a hybrid genetic tabu search algorithm is designed in this paper to allocate satellite resources to the mission set. The algorithm is used to solve the combinatorial optimization model and obtain the initial scheduling plan. The core concept of this algorithm is as follows: (1) the genetic algorithm is used for large-scale searches in the global space. The initial population traverses most of the solution space in parallel. At the end of iteration, the result is stable in the region where the solution space is better; and (2) starting from each individual in the optimization region, the tabu search is applied in a small-scale search of the local space to delay or avoid the genetic algorithm converging to a local optimum, thereby improving the ability to search for the best solution. The process of the hybrid genetic tabu search algorithm is shown in the following processing chain (Table 4):
## 3 Results and Discussion  
### Learning Capabilities  
To manage the dynamicity of the environment and support efficient set-up of new IDIVSs, we suggest including online machine learning capabilities in IDIVSs. The online learning mechanism is to cater for the situation of the set of available sensors being dynamic, and that the interpretation of environmental states may change over time. We also suggest using data-driven approaches, i.e., black- or grey-box modelling, to facilitate the span of different application areas. If an IDIVS is to use traditional batch learning, the IDIVS is trained offline before it is deployed and thereafter adapted via online training mechanisms. However, an efficient batch learning approach is often not viable due to, e.g., insufficient training data being available. Further, given that the IDIVS concept concerns streamed data, resembling a sensor constantly producing a sensor value, the availability of relevant historical label data may not be natural for some applications. An IDIVS should ideally have learning capabilities that can manage the following:  
* Cold start (no or very little initial training data);
* Dynamicity of available sensors;
* Concept drift.  
Our primary feature in the IDIVS concept to cope with these aspects is to make use of human users that can provide feedback, or even teach. Moreover, this learning situation can be characterized as being online and incremental.  
In previous work, several aspects of the learning element of IDIVS have been explored and illustrated: online learning and cold start setting [21], the dynamic aspect of available sensors [22; 23], and the interactive aspect of human-in-the-loop, including both interactive strategies [21] and the human factor [24]. In the following, we highlight the primary findings and connections to the IDIVS' capabilities and design aspects.
## 2 Regularized MoLE
### Definition  
The MoLE follows the divide and conquer principle. In the learning phase, the input space is divided into soft regions and a linear \"expert\" is learned for each region, while the gates assign soft boundaries to the experts' domains. The output for a new data sample is given by the weighted combination of the experts' outputs, where the new data is assigned to a specific or overlap of regions. The MoLE output is given by  
\[\hat{g}(\mathbf{x}_{i})=\sum_{p=1}^{p}g_{p}(\mathbf{x}_{i})\,\hat{g}_{p}( \mathbf{x}_{i}), \tag{1}\]  
where \(\mathbf{x}_{i}\) is the vector of input variables, \(P\) is the total number of experts, \(\hat{g}_{p}(\mathbf{x}_{i})\) is the output of expert \(p\), and \(g_{p}(\mathbf{x}_{i})\) is the gate output for expert \(p\). The gates assign mixture proportions to the experts, and have the following constraints \(\sum_{p=1}^{p}g_{p}(\mathbf{x}_{i})=1\) and \(0\leq g_{p}(\mathbf{x}_{i})\leq 1\). From now on, \(\hat{g}(\mathbf{x}_{i})\), \(\hat{g}_{p}(\mathbf{x}_{i})\), \(g_{p}(\mathbf{x}_{i})\), are denoted by their shortened versions \(\hat{y}_{i},\hat{y}_{pi}\) and \(g_{pi}\), respectively. Expert \(p\) has the linear form \(\hat{y}_{p}(\mathbf{x}_{i})=\mathbf{x}_{i}^{T}\theta_{p}\), where \(\theta_{p}\) is the vector of regression coefficients of linear expert \(p\); the bias has been omitted to simplify the derivation. For the gate, the following softmax function will be employed:  
\[g_{pi}=\left\{\begin{array}{ll}\frac{1}{1+\sum_{i=2}^{p}\exp(\mathbf{x}_{i}^{ T}\mathbf{w}_{i})},&p=1,\\ \frac{\exp\left(\mathbf{x}_{i}^{T}\mathbf{w}_{p}\right)}{1+\sum_{i=2}^{p}\exp \left(\mathbf{x}_{i}^{T}\mathbf{w}_{i}\right)},&p=2,\ldots,P,\end{array}\right. \tag{2}\]  
where \(\mathbf{v}_{p}\) is the gate parameter of expert \(p\). This softmax function follows the required gate constraints. The MoLE formulation fits perfectly for multimode-multiphase processes, where each different mode can be modeled by an expert \(p\), while the gates define the boundaries of the different modes. MoLE can infer the number of modes, or can use the expert information to define the number of modes. It has a very flexible format, and established learning algorithms. However, MoLE models cannot deal with collinearity or irrelevant variables. The next section will discuss how to apply regularization to MoLE models.
## 2 Materials and Methods  
### Experimental Mobile Laboratory Setup  
The mobile laboratory [18] designed for evaluating the performance of UV photocatalysis was used in this study. The mobile laboratory consisted of a series of 12 flow-through chambers (7.2 \(\times\) 0.9 \(\times\) 2.4 m), and each chamber (0.5 \(\times\) 0.9 \(\times\) 2.4 m) was divided by vertical baffles to maximize the UV dose. Each chamber was equipped with 11 wall panels coated with TiO\({}_{2}\) (nanostructured TiO\({}_{2}\) anatase at 10 \(\mu\)g\(\cdot\)cm\({}^{-2}\) from PureTi, Cincinnati, OH, USA) on all sides. Here, 2 fans (I-Fan Type 40, Fancom, Panningen, The Netherlands) were installed to control the treated airflow through the mobile laboratory. The airflow was measured with the anemometer fan (ATM, Fancom, Panningen, The Netherlands), and the internal airflow was controlled in real-time using the fan monitoring system (Lumina 20/21, Fancom, Panningen, The Netherlands), the 2 fans and the anemometer fan.  
The mobile laboratory and filtration unit were installed at a swine farm (Figure 1). The rationale for using the filtration ahead of UV treatment was to separate UV and filtration effects on the mitigation of odorous gases. While farm-scale UV treatment is relatively novel, mechanical filtration is also rarely researched. It is essential to note that the PM is a carrier of sorbed odorous compounds [22] and pathogens. The filtration kept the UV chambers clean for the initial phases of this research. However, the filtration was later removed to test the effectiveness of UV treatment with a realistic PM load in the treated air (see Results).  
The mobile laboratory was connected to the airflow from the pit fan to a T-shape connector (Figure 1) capable of discharging the excess air. A flexible duct was used to channel the treated air into the filtration unit and the mobile lab. The minimum treated airflow was 0.28 m\({}^{3}\).s\({}^{-1}\) (facilitating 52 s UV treatment time from inlet to outlet in the mobile laboratory). The maximum treated airflow was 0.78 m\({}^{3}\).s\({}^{-1}\) (enabling 19 s UV treatment time from inlet to outlet in the mobile laboratory).  
The UV-A (light-emitting diode; LED) lamps installed inside the mobile lab were the same as the previous pilot-scale experiment [18]. Additional 110 lamps were installed in chambers #2 and #3, and a total of 50 lamps were installed in the remaining ten chambers (#1 and #4-#12). The treatment was controlling the UV dose (a product of treatment time and UV irradiance). The effect of UV wavelengths was investigated by installing different UV wavelength lamps in chamber #2 only and using that chamber for side-by-side comparisons, similarly to the process described in the previous pilot study [19]. Figures S2-S7 (Supplementary Material) illustrate the details of the UV mobile lab.
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
## 3 Test Results and Discussion
### Deformation Characteristic  
Table 3 shows the peak strain of SSC under triaxial compression. We can see that under triaxial compression, the strains in the three directions are not equal (\(\epsilon_{1}\
eq\epsilon_{2}\
eq\epsilon_{3}\)) and strain \(\epsilon\)'s value depends on the stress ratio. The influence of stress ratio \(\sigma_{2}/\sigma_{3}\) on the peak strain is shown in Figure 6. In the \(\sigma_{3}\) direction, compare with strain \(\epsilon_{m}\) under uniaxial stress, the peak strain \(\epsilon_{3}\) under triaxial compression is much higher, and increases first and then decreases as \(\sigma_{2}/\sigma_{3}\) increases. The maximal value of \(\epsilon_{3}\) is obtained at the stress ratio \(\sigma_{2}/\sigma_{3}=0.5\). In the \(\sigma_{2}\) direction, the peak strain \(\epsilon_{2}\) increases approximately linearly with the increase of \(\sigma_{2}/\sigma_{3}\) and reaches the maximum compressive strain at \(\sigma_{2}=\sigma_{3}\), where \(\epsilon_{2}=\epsilon_{3}\). When \(\sigma_{1}/\sigma_{3}=0.05\), all of the strain \(\epsilon_{2}\) are compressive strain, but when \(\sigma_{1}/\sigma_{3}=0.1\), the \(\epsilon_{2}\) is positive at the stress ratio \(\sigma_{2}/\sigma_{3}=0.25\), and changes to negative at \(\sigma_{2}/\sigma_{3}=0.5\). When intermediate stress \(\sigma_{2}\) is small, tensile stress occurs in the \(\sigma_{2}\) direction because of the \(\sigma_{3}\) direction's extrusion pressure. In the \(\sigma_{1}\) direction, the value of \(\epsilon_{1}\) depends on the stress ratios \(\sigma_{1}/\sigma_{3}\) and \(\sigma_{2}/\sigma_{3}\), and increases as \(\sigma_{2}/\sigma_{3}\) increases but decreases as \(\sigma_{1}/\sigma_{3}\) increases. When the stress ratio \(\sigma_{1}/\sigma_{3}\) is small, the growth rate of \(\epsilon_{1}\) is faster. As the stress ratio \(\sigma_{1}/\sigma_{3}\) increases, the growth rate of \(\epsilon_{1}\) slows down. The strain \(\epsilon\) of SSC was also affected by the replacement ratio. It can be seen that \(\epsilon_{3}\), \(\epsilon_{2}\), and \(\epsilon_{1}\) increased first and then decreased with the increase in the replacement ratio. When the stress ratio is constant, the general trends of \(\epsilon_{3}\), \(\epsilon_{2}\), and \(\epsilon_{1}\) with replacement ratio were SSC-70 \(>\) SSC-30 \(>\) SSC-100, which means that the strain of \(\epsilon_{3}\), \(\epsilon_{2}\), and \(\epsilon_{1}\) reach the maximal value at a replacement ratio of 70%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Stress Ratio**} & \(\sigma_{1}\) & \(\sigma_{2}\) & \(\sigma_{3}\) & \(\sigma_{3}\)/\(f_{\rm m}\) & \(\epsilon_{1}\)/\(10^{-3}\) & \(\epsilon_{2}\)/\(10^{-3}\) & \(\epsilon_{3}\)/\(10^{-3}\) \\  & & (MPa) & (MPa) & (MPa) & & & & \\ \hline \multirow{8}{*}{SSC-3
# Study on the Ash Deposition Characteristics for Co-Combustion of Zhundong Coal with Cotton Stalk
## 2 Structure of the Boiler and Computational Model
### Ash Deposition Model Model  
Generally, there are three particle adhesion theories to determine whether ash particles rebound from or adhere to surfaces: the critical viscosity model, the critical velocity model, and the molten fraction model [19; 27]. [PERSON] et al. [20] used the critical viscosity model and the melt fraction model calculated by chemical equilibrium to conduct numerical simulations of the ash depositions from two kinds of coal and verified the results through experiments. The results demonstrated that the molten fraction model based on chemical equilibrium calculations exhibited the smallest deviation from actual observations. The proportion of molten components in ash particles was a critical parameter in determining their adhesion or rebound. [PERSON] et al. [28] conducted ash deposition experiments in a pulverized coal furnace and calculated the content of molten substances in ash at different temperatures using Factsage. The comparative analysis revealed a positive correlation between the ash deposition tendency of coal samples and the proportion of molten ash fractions. Consequently, the molten fraction model was employed to evaluate the ash deposition behavior during the co-combustion of Zhundong coal and cotton stalks. The content of molten components was determined through thermodynamic calculations using the Factsage software (version 8.2), while the adhesion probability of the ash particles was calculated based on Equations (1) and (2).  
\[\mathrm{P}_{stick}=p_{l}/(p_{l}+p_{s})\times 100\% \tag{1}\]  
\[\eta_{stick}=P_{stick}+W_{stick}\left(1-P_{stick}\right) \tag{2}\]  
Here, \(p_{l}\) represents the liquid phase fraction in the ash particles, \(p_{s}\) denotes the solid phase fraction in the ash particles, \(P_{stick}\) is the molten phase fraction of the ash particles at a specific temperature, \(\eta_{stick}\) is the adhesion probability of ash particles, and \(W_{stick}\) represents the molten phase fraction of the ash particles at the wall temperature.