esa-sceva/satcom-chunk-collection huggingface dataset:
# Food Risk Entropy Model Based on Federated Learning
## 4 Experimental Setup
### Experimental Results  
#### 4.2.1 The Risk Entropy of Pesticide Residues  
The risk entropy and detection indicators are shown in Figure 4, and the residual rate and detection indicators are shown in Figure 5. The horizontal axis contains 58 pesticide detection indicators, and the vertical axis contains 51 agricultural products. In order to better observe the distribution of the risk entropy and find \"products + indicators\" with high-risk. The high-risk products are placed below and the detection indicators are placed on the right by the value of the risk entropy.  
Figure 4: Heat map of the risk entropy of pesticide, the row labels are the names of 51 agricultural products and the columns are labelled with 58 indicators of pesticide residue detection. Darker colours indicate higher values of the risk entropy.  
We can find that there are obvious differences between Figures 4 and 5. The following example illustrates the differences clearly. The residual rate of carbendazim in white mushrooms are up to 34.40%, but the residues in the vast majority of samples are in the safe range. In other words, although the residual rate is high, the risk is not serious, and the risk entropy is only 0.26. In contrast, the residual rate of \"leaf mustard + chlorothalonil\" is only 1.28%, and the risk entropy is up to 0.94. The reason for this phenomenon is that the residues of mustards with chlorothalonil are very large, and if the product is unfortunately eaten, it will lead to a serious food safety threat.  
It can be seen from the above example that the residual rate can only qualitatively indicate whether the product corresponds to a residue of the test indicator. In fact, residue levels are critical in food safety risks. Current evidence supports the view that risk entropy can quantitatively describe potential risks in products and metrics, and that risk scales can be identified with greater accuracy than traditional metrics.
## 2 Setup and Modeling
### Modeling
#### 2.2.4 RSOA-BFL Output  
The power of the encoded signal that emerges from the RSOA and BFL cascade is derived by convolving the RSOA output from (3) with the BFL response from (5). Since (5) is wavelength dependent while (3) is time dependent, it is necessary to transfer (3) in the spectral domain and subsequently convert the convolution product back in the time domain. This is achieved by taking the Fourier transform and its inverse, respectively, as follows:  
\[E_{BFL}(t)=F^{-1}\{F\big{[}E_{RSOA}(t)\big{]}T_{BFL}(\lambda)\} \tag{7}\]  
where \(E_{BFL}(t)\) is the electric field of the encoded signal at the BFL output, while the operators \(F\{\cdot\}\) and \(F^{-1}\{\cdot\}\) stand for the Fast Fourier Transform (FFT) and Inverse FFT, respectively, which are both available and executed in Matlab. Finally, the power of the encoded signal after the BFL is  
\[P_{BFL}(t)=|E_{BFL}(t)|^{2} \tag{8}\]
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 5 Discussion
### Simulation Results  
The objective of the simulation study was to evaluate the performance of the three change-point detection methods (AR1 seg and strucchange and the proposed CE.AR) under various scenarios looking at two aspects of the methods' detection accuracies: the number of estimated change-points and their locations. We use tables to summarize the estimated number of change-points, while histograms are used to measure the detection accuracy in change-point locations. The simulation study  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{\(\hat{N}\)} \\  & **0** & **1** & **2** & **3*** & **4** & \(\geq\) **5** \\ \hline AR1 seg & 3\% & 1\% & 10\% & 23\% & 63\% & 0\% \\ strucchange & 0\% & 0\% & 90\% & 9\% & 1\% & 0\% \\ CE.AR & 0\% & 0\% & 100\% & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Estimated number of multiple change points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline AR1 seg & strucchange & CE.AR \\ \hline
73 & 46 & 39 \\
73 & 30 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hausdorff distance for each algorithm and two choices of the parameters.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{\(\hat{N}\)} \\  & **0** & **1** & **2** & **3*** & **4** & \(\geq\) **5** \\ \hline AR1 seg & 26\% & 8\% & 25\% & 10\% & 31\% & 0\% \\ strucchange & 22\% & 68\% & 9\% & 1\% & 0\% & 0\% \\ CE.AR & 74\% & 13\% & 10\% & 3\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimated number of multiple change points.  
consists of three examples. In the first example, there is no change-point in the AR(1) process; the CE.AR method achieves 100% exact detection rate under each of the three choices of the autocorrelation coefficient, 0.1, 0.5, and 0.9. The second example includes two scenarios, in which we study how a change in either the mean or the autocorrelation coefficient affects the performances of the algorithms. In scenario 1, it can be seen that the detection accuracy of the CE.AR method significantly improves as the shift in the mean increases from 1 to 3. In scenario 2, the autocorrelation coefficients differ between adjacent segments; we have considered six cases, whose profiles are shown in Figure 5. The cases with weak changes in the autocorrelation coefficient, such as \(\rho_{1}=-0.5\), \(\rho_{2}=0.1\); \(\rho_{1}=0.5\), \(\rho_{2}=0.9\); or \(\rho_{1}=0.1\), \(\rho_{2}=0.5\), remain a challenge for the CE.AR method; it showed a heavy under-segmentation. In the third example, we consider a combined effect of the mean shift and the autocorrelation coefficient variation on piecewise AR(1) with multiple change-points. The two scenarios in this example show that the CE.AR method tends to disregard the first small segment, even if the mean or the autocorrelation coefficient changes dramatically. The histograms and Hausdorff distance show a low variation in estimated locations of change-points for the CE.AR method.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 5 Simulation Results
### Comparative Performance Evaluation
#### 5.3.2 Different Transmission Angles  
Next, we use three different transmission angles, 30, 60 and 90, to show the performance for different schemes. Figure 12 shows the charging time for 30 devices and the offset value, 10. We observe that the charging time of DE, RAN, RR decreases with smaller transmission angles. Because the antenna gain is related to the transmission angle, a small transmission angle can provide a narrow transmission beam to increase the antenna gain as well as the amount of energy received. Thus, the received energy of 30 degrees is higher than that of 60 and 90 degrees. However, a small transmission angle also results in more sectors. If the number of sectors increases, our DE algorithm may consume considerable computation time owing to the lengthy solution. All of these methods also require more  
Figure 9: The hybridization probability of our DE algorithm.  
charging iterations for all sectors. In particular, the charging time of DE is 54.1, 40.9, 31.9 s for 90, 60, and 30 degrees. The Greedy algorithm can still shorten the charging time effectively. Fix can improve its charging time efficiency with narrow transmission angle, so do RAN and RR.  
We further show the overflow energy in Figure 13. We can see that the greedy algorithm also outperforms the Fix method because of the optimization of the charging time. Additionally, our DE algorithm can lower the ratios of overflow energy of the greedy algorithm by 12.9%, 16.4%, and 20.4% for 90, 60, and 30 degrees. It can thus decrease the number of fully charged devices by reducing overflow energy while maintaining a low charging time. A narrow transmission degree can significantly reduce the amount of energy wasted compared to that of other methods. Both RAN and RR also suffer from more overflow energy due to their inefficient charging schedules.
## 3 Results
### Parameter Correlations
#### 3.3.3 Lighting Interactions  
The correlation between lighting conditions and other indoor environmental quality (IEQ) parameters, such as thermal comfort and indoor air quality (IAQ), is a critical area of interest within educational environments. Based on our analysis of the correlation matrix in Table 3, visual comfort shows notable relationships with several other IEQ parameters.  
Most significantly, visual comfort demonstrates a negative correlation with air velocity (\(-0.294\)), suggesting that increased air movement may adversely affect visual comfort in educational spaces. This could be attributed to effects such as increased eye dryness or disturbance of materials (papers, etc.) that might interfere with visual tasks in learning environments.  
Visual comfort also shows a modest positive correlation with acoustic comfort (0.259), indicating that spaces designed with good acoustic properties often have better visual comfort characteristics as well. This may reflect overall design quality, where attention to multiple comfort factors is present.  
Interestingly, visual comfort exhibits weak negative correlations with both thermal comfort (\(-0.060\)) and indoor air quality (\(-0.123\)), suggesting that these parameters operate somewhat independently. This independence could be attributed to the fact that visual comfort is primarily influenced by factors like light distribution, glare control, and lighting quality, while thermal comfort and IAQ depend on temperature, humidity, ventilation, and air pollutant levels.  
The relatively weak correlations between visual comfort and other IEQ parameters highlight the importance of addressing each aspect of environmental quality independently in educational building design, while still considering their potential interactions. Future research could benefit from investigating these relationships more deeply, particularly in specifically designed educational environments where multiple comfort parameters are carefully controlled and measured.
# Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence
## 2 Data and Methods
### Data
#### 2.2.1 NDVI Data  
As for the NDVI data, the GIMMS NDVI3g (8 km, bi-weekly, 3 rd Generation) was used with preprocessing for the specific requirements of the Arctic region affected by snow and cloud cover. Pixels flagged as snow or cloud cover were excluded from the analysis. The annual maximum NDVI values were obtained from the bi-weekly data [(41)]. The MODIS NDVI values for the study area were obtained from the MOD13Q1 (250 m, 16-day, Collection 6) NDVI layer. Only the pixels flagged as \"good data\" in the Summary Quality Assurance (QA) layer were used for the study. The annual maximum NDVI values of MOD13Q1 were derived from the 16-day NDVI layer.
## 3 Methods
### Introduction of the SAES-DNN Model
\[J(W,b)=\left[\frac{1}{N}\sum_{i=1}^{N}\left(\frac{1}{2}\parallel h_{W,b}\Big{(} x^{(i)}\Big{)}-y^{(i)}\parallel^{2}\right)\right]+\frac{\lambda}{2}\sum_{l=1}^{ n_{l}-1}\sum_{i=1}^{s_{l}}\sum_{j=1}^{s_{l+1}}\left(W_{ji}^{(l)}\right)^{2} \tag{3}\]  
\[J_{\text{sparse}}(W,b)=J(W,b)+\beta\sum_{j=1}^{m}KL\big{(}\rho\parallel\hat{ \rho}_{j}\big{)} \tag{4}\]  
We added a softmax supervised classifier based on SAE unsupervised training to form the deep learning SAES-DNN method in this paper. For each class of vectors, the softmax function output formula and loss function of the whole network model are defined as follows:  
\[p_{i}=p(Y|Q)=\frac{e^{w_{i}Q+b_{i}}}{\sum_{k=1}^{N}e^{w_{k}Q+b_{k}}} \tag{5}\]  
\[J(w,b)=-\frac{1}{m}\left[\sum_{j=1}^{m}\sum_{k=1}^{N}l\left\{Y_{j}=i\right\} \log\frac{e^{w_{i}Q_{j}+b_{i}}}{\sum_{k=1}^{N}e^{w_{k}Q_{j}+b_{k}}}\right] \tag{6}\]  
where \(\{Y_{j}=i\}\) is the indicative function, and the true value is 1 for the output with consistent fault and high probability, otherwise it is set to 0. \(Q\) is the softmax output vector, and \(w_{i}\) and \(b_{i}\) are the weights and offsets that softmax needs to learn. The softmax classifier is a generalization of multi-classification problem solving based on logistic regression. The output probability value of each class of feature vectors can be evaluated by Equation (5), and the SAES-DNN loss function is shown in Equation (6). The basic training process of the SAES-DNN model is as follows:  
1. Original input data was preprocessed, and network structure was initialized. 2. Pre training. The encoder was trained greedily layer by layer, and data was reconstructed by a decoder decoding process to minimize reconstruction error. 3. The output layer of the trained stacked automatic encoders was discarded and the middle-hidden coding feature layer was reserved. 4. Output data of the last layer of the automatic encoder (the extracted hidden layer of the feature representation) was taken as the input data of the classifier. 5. Fine tuning. According to the output and expected error, the parameters were fine-tuned by a back propagation algorithm to optimize the performance of the whole deep neural network. We used a set of samples to train the overall SAES-DNN network parameters (\(w_{i}\), \(b_{i}\)), calculate the activation value of each layer in the network, and obtain the final output through the softmax model of the last layer. Using the sample label and network output, the cost function was formed, which is shown in Equation (6). The partial derivatives of each parameter in the cost function were then obtained, and the gradient descent method was used to iterate to the convergence of the cost function. In each iteration, the derivative of the cost function with respect to the network parameters of the first n\(-\)1 layer was calculated according to the forward backward propagation algorithm, while the derivative of the cost function with respect to the parameters of the last layer was obtained by Equation (6).
## 4 Mitigation Measures for Road Traffic PM Emissions and Its Impact on Carbonaceous Emissions
### Carbonaceous Composition of PM Emissions at the Exhaust Pipe
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Euro Standard** & **PM\({}_{2.5}\) EF (mg km\({}^{-1}\))** & **BC/PM\({}_{2.5}\) (\%)** & **OM/BC (\%)** & **Uncertainties (\%)** \\ \hline \multirow{8}{*}{Petrol PC and LCV} & PRE-ECE & 2.2-2.3 & 2 & 4900 & 50 \\ \cline{2-6}  & EEC 15 00/01 & 2.2-2.3 & 5 & 1900 & 50 \\ \cline{2-6}  & EEC 15 02/03 & 2.2-2.3 & 5 & 1900 & 50 \\ \cline{2-6}  & EEC 15 04 & 2.2-2.3 & 20 & 400 & 50 \\ \cline{2-6}  & Open loop & 2.2-2.3 & 30 & 233 & 30 \\ \cline{2-6}  & Euro 1 & 2.2-2.3 & 25 & 250 & 30 \\ \cline{2-6}  & Euro 2 & 2.2-2.3 & 25 & 250 & 30 \\ \cline{2-6}  & Euro 3 & 1.1-2.2 & 15 & 300 & 30 \\ \cline{2-6}  & Euro 4 & 1.1 & 15 & 300 & 30 \\ \hline \multirow{8}{*}{Diesel PC and LCV} & Conventional & 220.9-356 & 55 & 70 & 10 \\ \cline{2-6}  & Euro 1 & 84.2-117 & 70 & 40 & 10 \\ \cline{1-1} \cline{2-6}  & Euro 2 & 54.8-117 & 80 & 23 & 10 \\ \cline{1-1} \cline{2-6}  & Euro 3 & 39.1-78.3 & 85 & 15 & 5 \\ \cline{1-1} \cline{2-6}  & Euro 4 & 31.4-40.9 & 87 & 13 & 5 \\ \cline{1-1} \cline{2-6}  & Euro 34,5 equipped with & & & & \\ \cline{1-1} \cline{2-6}  & DPF and fuel additive & - & 10 & 500 & 50 \\ \cline{1-1} \cline{2-6}  & Euro 3,4,5 equipped with a & & & & \\ \cline{1-1} \cline{2-6}  & catalysed DPF & - & 20 & 200 & 50 \\ \hline \multirow{8}{*}{Diesel HDV} & Conventional & 333–491 & 50 & 80 & 20 \\ \cline{1-1} \cline{2-6}  & EURO I & 129–358 & 65 & 40 & 20 \\ \cline{1-1} \cline{2-6}  & EURO II & 61–194 & 65 & 40 & 20 \\ \cline{1-1} \cline{2-6}  & EURO III & 56.5–151 & 70 & 30 & 20 \\ \cline{1-1} \cline{2-6}  & EURO IV & 10.6–26.8 & 75 & 25 & 20 \\ \cline{1-1} \cline{2-6}  & EURO V & 10.6–26.8 & 75 & 25 & 20 \\ \cline{1-1} \cline{2-6}  & EURO VI & 0.5–1.3 & 15 & 300 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PM\({}_{2.5}\) emission factors, split of PM in elemental (BC) and organic mass (OM) from Ntziachristos and Samarras [216]. The OM/BC ratio is larger for gasoline vehicles and declines from old to modern vehicles. For diesel HDV EURO VI, it is noteworthy to see the drop of BC/PM\({}_{2.5}\) ratio with a surge of the OM/BC ratio certainly due to soot mostly removed by the DPF and the dominance of OM formed during the cooling. They are associated to the lowest PM\({}_{2.5}\) ELV.
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 4 Energy-Efficient Routing Protocol for Reliable Data Transmission in WBAN
### Routing Process
#### 4.2.3 Data Forwarding  
When the best next hop node is selected, data is transmitted. The source node sends the data to the next hop node, and then repeats the above steps until the data is sent to the sink. When a node is selected as the next hop by two other nodes at the same time, this node first forwards P1 priority data to ensure timely transmission of emergency data. Assuming that the initial energy of the node is the same, when the residual energy of a node is less than \(E_{min-th}\), the node only transmits its own data.
## 2 Materials and Methods
### Liquid Media Growth Assay  
_P. ostreatus_ and _P. eryngii_ were inoculated by adding three 5-mm fungal plugs from a 7-day-old malt extract agar (MEA) culture into 500-mL Erlenmeyer flasks with 125 mL of 3% malt extract (ME) in the presence or absence of TCE (14 or 140 mg L\({}^{-1}\)). The fungi were grown for 14 days at 28 \({}^{\circ}\)C under orbital agitation (120 rpm) in the dark. Fungal growth was monitored daily by visual observation. One-milliliter aliquots were sampled every four days to analyze ligninolytic activity (lactase and MnP).  
Lacase activity was spectrophotometrically determined by the oxidation of 10 mM 2,6-dimethoxy phenol in 50 mM sodium acetate pH 5.0 at 477 nm (\(\epsilon\) = 14,600 M\({}^{-1}\) cm\({}^{-1}\)) [36]. Mn peroxidase (MnP) activity was assayed by the oxidation of 1 mM MnSO\({}_{4}\) in 50 mM sodium malonate buffer (pH 4.5), in the presence of 0.1 mM H\({}_{2}\)O\({}_{2}\). Manganic ions, by Mn\({}^{3+}\) form a complex with malonate, which absorbs at 270 nm (\(\epsilon\) = 11,590 M\({}^{-1}\) cm\({}^{-1}\)) [37]. One unit of enzyme activity (IU) is defined as the amount of enzyme that produces 1 umol of product per minute under the assay conditions.  
At the end of the assay, fungal pellets were studied by scanning electron microscopy (SEM, Hitachi S-3000N, Tokyo, Japan) to evaluate any potential damage to the structure of the hyphae by the presence of TCE in the liquid broth (14 or 140 mg L\({}^{-1}\)). The fungal pellets were firstly washed in distilled water, then fixed in a modified Karnovsky's fixative consisting of a solution of glutaraldehyde 2.5% in 0.1 M phosphate buffer (PBS), pH 7-7.4  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Chemical Analysis** & & & **Microbial Enzymatic Analysis (U g\({}^{-1}\))** \\ \hline
**pH H\({}_{2}\)O** & **pH KCl** & **E.C. (μs cm\({}^{-1}\))** & **O.M. (\%)** & **N Kjeldhal (\%)** & **Hydrolase** & **Dehydrogenase** & **Urease** \\ \hline \(8.14\pm 0.01\) & \(7.3\pm 0.2\) & \(449\pm 2\) & \(3.1\pm 0.2\) & \(0.20\pm 0.03\) & \(1.5\pm 0.6\) & \(11\pm 1\) & \(3142\pm 28\) \\ \hline \end{tabular}
\end{table}
Table 1: Chemical and biological characterization of soil. Data present the mean \(\pm\) standard deviation of three replicated analysis.  
for 24 h, then rinsed three times with PBS for 5 min, and dehydrated by an increasing concentration of ethanol series for 5 min [38] before critical point drying; they were then sputter-coated with gold using a Sputter Coater Quorum, Q150T-S.
## 2 Test Materials and Test Methods  
### Test Materials  
For a brief analysis of the basic physical properties of soft soils in the South China coastal region, undisturbed soil samples were collected at 11 soil extraction points (as shown in Figure 1 in the South China coastal region, and the results of the basic physical properties are shown in Table 1. The data in Table 1 are not much different, and the basic performance is similar. Soft soils in coastal and river areas of Southern China are generally characterized by high water content (above 50%), high liquidity index (above 1.2), high compressibility, low permeability and high organic matter content.  
In the present work, the coastal soft soil in South China is taken from Zhongshan City, Guangdong Province (Extraction point 11 in Figure 1). The sample depth of the soft soil is 6-8 m. The specific physical and mechanical properties are shown in Table 1. The cement used in the present work is P.O 42.5R ordinary Portland cement, and the main chemical composition is shown in Table 2. The composition was evaluated by X-ray fluorescence (XRF). The water used for sample preparation is fresh water. The sand used in this paper is natural river sand, and three particle size combinations of 0.075-0.25 mm (fine sand), 0.25-0.5 mm (medium sand), and 0.5-1.0 mm (coarse sand) are sieved and prepared. Both freshwater and seawater are used for the dry-wet cycle test. The seawater is artificially configured in the laboratory, and the specific components are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Extraction** & **Location** & **Water** & **Density** & **Void** & **Liquid** & **Plastic** & **Liquidity** & **Plasticity** & **Organic** \\
**Points** & **Content/\%** & **/G/cm\({}^{3}\)** & **Ratio E** & **Limit/\%** & **Limit/\%** & **Index** & **Index** & **Content/\%** \\ \hline
1 & Foshan & 73.7 & 1.63 & 1.83 & 42.5 & 25.6 & 2.85 & 16.9 & 3.80 \\
2 & Nanhai & 70.7 & 1.65 & 1.72 & 49.9 & 28.1 & 1.95 & 21.8 & 3.65 \\
3 & Panyu & 64.0 & 1.72 & 1.51 & 43.8 & 26.9 & 2.20 & 16.9 & 0.90 \\
4 & Jiangmen & 52.7 & 1.69 & 1.41 & 47.6 & 29.6 & 1.28 & 18.0 & 3.60 \\
5 & Guangzhou & 72.7 & 1.55 & 1.93 & 46.7 & 28.0 & 2.37 & 18.7 & 0.23 \\
6 & Guangzhou & 70.9 & 1.58 & 1.86 & 50.6 & 29.3 & 1.95 & 21.3 & 1.82 \\
7 & Zhongshan & 65.7 & 1.69 & 1.61 & 53.4 & 26.7 & 1.46 & 26.7 & 4.62 \\
8 & Zhuhai & 64.3 & 1.68 & 1.59 & 52.4 & 26.5 & 1.46 & 25.9 & 3.82 \\
9 & Dongguan & 68.9 & 1.54 & 1.90 & 58.1 & 32.6 & 1.42 & 25.5 & 2.38 \\
10 & Shenzhen & 65.7 & 1.69 & 1.57 & 47.9 & 25.5 & 1.79 & 22.4 & 1.65 \\
11 & Zhongshan & 66.7 & 1.71 & 1.58 & 55.1 & 26.6 & 1.39 & 29.5 & 4.13 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of South China coastal soft soil in different districts.  
Figure 1: Distribution map of soil collection points.