esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 5 Conclusions  
In this paper, we have proposed an improved model for PM\({}_{2.5}\) forecasting. The model consists of two phases. In the first phase, a temperature inversion classifier is trained. We chose CART and CNN as the potential classifier for comparison on the Puli dataset. In the second phase, the test day is labeled by the employed classifier as either the temperature inversion class or the other. If it is in the temperature inversion class, the regression trained with the temperature inversion dataset is adopted to make the PM\({}_{2.5}\) forecast. Otherwise, the regression trained with the seven days prior to the test day is used to predict the PM\({}_{2.5}\) concentration. The experimental results show that CART outperforms CNN for temperature inversion classification on the Puli dataset. With the classifiers, the test day for PM\({}_{2.5}\) forecast is subject to the regression trained with the correct class. The forecast experiments tested on the Puli meteorology dataset during 2017 to 2019 show that the models considering the temperature inversion factor can reduce the forecast errors by 4-9% and 2-7% in terms of RMSE and MAE, as compared to the original model. However, there are some limitations regarding this research. The main meteorological patterns leading to temperature inversion vary from place to place. The classifier should be retrained with the dataset collected for the investigated location. Furthermore, our current forecast model based on temperature inversion classification may under-forecast PM\({}_{2.5}\) when external pollution sources arise. Our future research is directed to finding correlated PM\({}_{2.5}\) monitoring stations along the pollution transportation route as forecast regulators to adjust our model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hou r** & **Puli PM\({}_{2.5}\)** & **Lunbei PM\({}_{2.5}\)** & **Wind Speed** & **Wind Direction** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Relationship between pollution events in Puli and Lunbei.  
**Author Contributions:** Conceptualization, P.-Y.Y. and R.-F.D.; methodology, P.-Y.Y. and R.-I.C.; software, Y.-C.L.; validation, C.-Y.H.; writing--original draft preparation, P.-Y.Y. and R.-I.C.; writing--review and editing, P.-Y.Y. and R.-I.C.; visualization, Y.-C.L.; funding acquisition, P.-Y.Y. and R.-F.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Science and Technology of ROC, under Grant MOST 107-2410-H-260-015-MY3, Grant MOST 110-2420-H-163-001-MY2, and Environmental Protection Administration of ROC, under Grant EPA-108-A154.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Spatial Distribution from the Field Margins  
Most of the arthropod species experience their habitats at spatial scales beyond the plot level, and there is a spillover of natural enemies across the crop-non-crop interface [20]. In this study, the abundance and richness of arthropods according to the distance from the field margins were evaluated during the sampling period to analyze potential distribution patterns related to the proximity to the crop borders. The results indicated a temporal and spatial distribution of the arthropods. In June, the total arthropod abundance (N) and the richness (S) were significantly higher in the outer than in the inner samples (N June: w = 110, \(p\)-value = 0.030; S June: 114.5, \(p\)-value = 0.014). In July, no differences were found in N between inner and outer samples, but S was significantly higher in outer samples (N July: w = 57, \(p\)-value = 0.402; S July: w = 114.5, \(p\)-value = 0.034). Both N and S were not significantly different between inner and outer samples in August and September (N August: w = 51.5, \(p\)-value = 0.248; S August: w = 88, \(p\)-value = 0.357; N September: w = 65.5, \(0.729\); S September w = 74.5, \(p\)-value = 0.906) (Figure 4).  
This could indicate the colonization by arthropods along the sample period and agrees with several studies that found colonization of predators that overwinter in the field margins and disperse into the crop in the spring in latitudes where the spring is the rainy and favorable season for arthropods [8].  
Figure 4: Boxplots of the abundance and richness of arthropods in the pit-fall traps closer (inner) and further (outer) from the limit of the crop during the months of study. Different letters indicate significant differences in the Wilcoxon rank-sum test.
# POL-MUX System for Noncoherent Optical Networks
## 4 Proposal of a Wide-Band Passive Optical Network (WDM-PDM-PON)
### Simulation Analysis
Simulations confirm that polarization demultiplexing results in different states of polarization for different wavelengths but without any negative impact on signal quality. Figure 16: Poincare spheres of polarization demultiplexed signals from OLT1 (**a**) and OLT2 (**b**). Figure 15: Poincare spheres of the multiplexed signals from OLT1 (**a**) and OLT2 (**b**).
### Tar Concentration
#### 3.2.4 Variation of Weight Hourly Space Velocity \(n_{\text{WHSV}}\)  
Figure 9 shows the tar concentration against the weight hourly space velocity \(n_{\text{WHSV}}\). For the gravimetric tars no clear trend was observed. It also has to be noted that the point at \(n_{\text{WHSV}}\) = 1.3 h\({}^{-1}\) was conducted with a higher fuel mass flow of around 14 kg h\({}^{-1}\) compared to around 7 kg h\({}^{-1}\) (see Table 4) for the other points. Despite that, the tar concentration for the points at \(n_{\text{WHSV}}\) = 1.2 h\({}^{-1}\) and 1.3 h\({}^{-1}\) were found to be similar.  
Figure 8: Gravimetric and GC tar concentrations on dry, N\({}_{2}\)-free and STP basis at different steam to carbon ratios \(n_{\text{SC}}\).  
Figure 7: Gravimetric and GC tar concentrations on dry, N\({}_{2}\)-free and STP basis at different gasifier temperatures.  
When comparing the two gravimetric tar concentrations lower \(n_{\text{WHSV}}\) with the two ones at higher \(n_{\text{WHSV}}\), a decrease can be seen. For the GC tars no trend is visible. However, a more comprehensive study is needed to fully assess the influence of \(n_{\text{WHSV}}\) on tar concentration.  
In literature [15] for steam-air gasification it was reported that tar concentration is slightly increased with higher \"turnover rate\" respective higher \(n_{\text{WHSV}}\). For steam gasification, [56] found slightly increased gravimetric tar yields but constant GC tar yields for increasing \(n_{\text{WHSV}}\) which is consistent with this work.
## 3 Analysis of CWR Fracture Parameters near Ventilation Shafts Using Machine Learning
### Analysis of Parameter-Influencing Factors
#### 3.2.3 Machine Learning Analysis Using Proposed Parameters  
Figure 5 shows the correlation heatmap based on the final training dataset, which includes newly selected independent variables. All learning factors were included to verify the adequacy of multicollinearity, and the VIF was analyzed based on independent variables (parameters).  
As shown in Figure 5, the heatmap and scatter plot analysis indicate that rail fractures near ventilation shafts are highly correlated with temperature variations, particularly the ambient temperature (minimum, maximum) and the distance from the ventilation shaft to the fracture location. In addition, track geometry conditions, including the curve radius and inclination, were found to have a strong correlation with rail fractures. The VIF was analyzed based on variables with strong correlations and without multicollinearity; the results are summarized in Table 6.  
Figure 5: Heatmap of correlations between parameters (proposed training dataset).
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation  
Preliminary experiments were carried out to evaluate the system's performance. Prior to testing outdoor navigation, the user interface was first verified as being able to transmit discernible tactile information. This section presents the two evaluation stages of the prototype.
## 3 Results  
### Effect on the Expression of Inducible Nitric Oxide Synthase (iNOS) and Cyclooxygenase (COX)-2  
The CHA dose was determined based on the regulation of LPS-induced iNOS protein expression. Serial concentrations ranging from 5 to 400 \(\upmu\)M were used to treat LPS-stimulated macrophages via instantaneous delivery. Inhibitory effects on the induction of iNOS protein expression were observed with CHA concentrations of 100, 200, and 400 \(\upmu\)M. Therefore, 100 \(\upmu\)M was selected as the low dose, and 400 \(\upmu\)M as the high dose for further analysis (Figure 2).  
Low doses of CHA showed higher inhibition of LPS-induced expression of both iNOS mRNA and protein when administered via sustained delivery than that via instantaneous delivery. However, this difference was not observed at a high dose.  
In contrast to iNOS expression, CHA treatment did not affect COX-2 protein expression. Additionally, its mRNA level was elevated by low-dose CHA in the sustained delivery model and by high-dose CHA in both instantaneous and sustained delivery models.
## 2 Materials and Methods
### Workflow Description  
With the vector and satellite data available, a methodology was proposed that can be explained in four stages (Figure 3). The first stage consisted of searching for information on the location of _Polylepis_ stands in the study area and integrating it with a land use and land cover (LULC) map, which generated a base map from which the target classes of the classification model were determined (green section). In the second stage, satellite data were selected and prepared through the GEE platform, including S2 and PlanetScope spectral data (bands and indices) and textures of the NIR band of PlanetScope image, S1 radar data, and the Digital Elevation Model (DEM) obtained from the Sigtierras project [52] (yellow section). In the third stage, the scenarios to be evaluated were defined, considering the spatial resolution and the combination of different types of variables (orange section). The final stage consisted of comparing different scenarios and variables to determine the combination that produces the highest reliability in detecting _Polylepis_ stands. Metrics from each scenario were evaluated, and the best-performing scenario was selected. Using this scenario, a _Polylepis_ map was generated and validated by forestry specialists in the region (blue section).  
Figure 1: Sentinel-2A image mosaic covering tiles 17 MPS, 17 MPT, 17 MQS, and 17 MQT (24 August 2020) of the study area and location of _Polylepis_ stands.  
Figure 2: _Polylepis reticulata_ tree (**a**) and stand (**b**) in Toreadora lagoon zone.
## 3 Experimental Results  
### Data Description  
Several high-resolution remote sensing images from GF-2 and aerial images with cloud shadows are tested. As shown in Figures 6a-8a, three presentative images are selected to show the cloud detection and compensation method. Figure 6 shows an aerial remote sensing image with a resolution of 20 cm located in a mountainous area of China. Since the resolution is high, the cloud area is enormous, and the information loss is profound. Figure 7 shows a GF-2 satellite image in a plain rural area of China with a resolution of 0.8 m. Under a cloud shadow, different plants are shaded. Figure 8 shows a satellite remote sensing image of the urban area.  
Figure 5: Results of shadow detection and compensation via LCC. (**a**) Original image #1. (**b**) Shadow detection result. (**c**) LCC compensation result. (**d**) Original image #2. (**e**) Shadow detection result. (**f**) LCC compensation result. Redpixels in (**b**,**e**) represent detected shadows. Yellow circle in (**c**,**f**) are regions with over compensation. Blue frames in (**c**,**f**) are regions with insufficient compensation.  
Figure 6: \(Cont.\)  
Figure 6: Comparison of shadow compensation results in aerial image #3. (**a**) Original aerial image. #1. (**b**) Initial shadow detection result. (**c**) Final shadow detection result. (**d**) LCC result. (**e**) HM result. (**f**) GT result. (**g**) CSR result. (**h**) WLC result. (**i**) The proposed method’s result. Red pixels in (**b,c**) are detected shadows.  
Figure 7: _Cont._
## 3 High-Dimensional Mapping Entropy
### High-Dimensional Mapping Entropy Algorithm  
HDME combines kernel functions and high-dimensional mapping theory to improve SE and achieve an up-dimensional description of reconstructed information in space. The specific steps are as follows:  
1. For a given \(N\) point time series \(X=\{x_{i}\}\) with a pre-determined embedding dimension \(m\), perform m-dimensional reorganization to obtain an \(N-m+1\) vector: \[X_{m}(i)=\{x(i),\cdots,x(i+m-1)\}\ \ \ (i=1\,\sim\,N-m)\] (12)
2. Map the recombinant vector samples to a high-dimensional kernel space using non-linear mapping based on the kernel method.  
First, calculate the Euclidean distance of each vector from the rest of the and construct the distance matrix \(d_{k}\):  
\[d_{ij}^{m}=\|X_{m}(i)-X_{m}(j)\|\ \ (1\leq i,j\leq N-m) \tag{13}\]  
Second, convert the distance matrix into a kernel matrix:  
\[D_{k}=\begin{bmatrix}d_{11}^{m}(X_{1},X_{1})&\cdots&d_{1N-m}^{m}(X_{1},X_{N-m} )\\ \vdots&\ddots&\vdots\\ d_{N-m1}^{m}(X_{N-m},X_{1})&\cdots&d_{N-mN-m}^{m}(X_{N-m},X_{N-m})\end{bmatrix} \tag{14}\]  
Then, substitute the generated kernel matrix into different kernel functions. Sum the matrix to obtain the mapping points of corresponding vectors in space, denoted as:  
\[K_{1\times N-m}=\sum_{i=1}^{N-m}exp\{-\gamma{D_{k}}^{2}/\sigma^{2}\} \tag{15}\]  
\[\text{or}\ K_{1\times N-m}=\sum_{i=1}^{N-m}(\gamma{D_{k}}+C)^{d} \tag{16}\]  
Finally, match the initial m-dimensional reconstruction vector with the matrix \(K_{1\times N-m}\) to obtain the high-dimensional spatial projection points;  
1. To calculate the Chebyshev distance between two vectors \(X_{m}(i)^{\prime}\) and \(X_{m}(j)^{\prime}\) in a high-dimensional space \(X_{m}\), we need to determine the absolute difference between the corresponding elements of the two vectors:\[d[X_{m}(i)^{\prime},X_{m}(j)^{\prime}]=\underset{k=0,\cdots,m}{max}|x(i+k)-x(j+k)| \ \ (i,j=1,2,\cdots,N-m,i\
eq j) \tag{17}\]
4. Pre-set the value of the similarity tolerance \(r\) and count the \(d[X_{m}(i)t,X_{m}(j)t]\leq r\) of the number of distances, denoted as \(B_{i}\), and compare this result with the total number of distances \(N-m-1\) to obtain the ratio \(B_{i}^{m}(r)\): \[B_{i}^{m}(r)=\frac{1}{N-m-1}B_{i}\ \ (i=1,\cdots N-m)\] (18)
5. Calculate the above \(N-m\) of \(B_{i}^{m}(r)\) of the above, denoted as \(B^{m}(r)\): \[B^{m}(r)=\frac{1}{N-m}\sum_{i=1}^{N-m}B_{i}^{m}(r)\] (19)
6. Repeat the above steps 1-5 when increasing the reconstruction dimension to \(m+1\), and similarly obtain \(B^{m+1}(r)\): \[B^{m+1}(r)=\frac{1}{N-m}\sum_{i=1}^{N-m}B_{i}^{m+1}(r)\] (20)
7. This results in the high-dimensional mapping entropy of this time series: \[HDME(m,r,sigma,N)=\underset{N\rightarrow\infty}{lim}\left[-ln\frac{B^{m+1}(r )}{B^{m}(r)}\right]\] (21)  
However, in practice \(N\) is finite in value, so the entropy of the high-dimensional mapping entropy is denoted as:  
\[HDME(m,r,sigma,N)=\left[-ln\frac{B^{m+1}(r)}{B^{m}(r)}\right] \tag{22}\]  
The process of feature extraction based on high-dimensional mapping entropy is shown in Figure 3.
# Cross-Domain Access Control Model in Industrial IoT Environment
## 5 Experimental Verification and Analysis
### Throughput  
Throughput measures the ability of a system to process requests or transactions per unit of time. Throughput is expressed in terms of the number of transactions per second calculated using Formula (20):  
\[TPS=Summations/\Delta t \tag{20}\]  
where \(Summations\) is the number of transactions stored in the blockchain ledger during the time period \(\Delta t\). To observe the changes in the \(TPS\), the experimental design was as follows.  
Two comparison groups were set up. In the first comparison group, all schemes were master-slave chain structures. We conducted six experiments separately. Each experiment created a block every 10 s. First, the scheme of this paper was compared with the schemes of all other papers. The scheme in [21] used the PBFT consensus, and the scheme in [23] used the proof of work (pow) consensus for the master-slave chain. Figure 9 shows that the average throughputs of the scheme in [21] and the scheme in [23] were 79.2 and 106.9, respectively. The throughput of the proposed scheme in this paper is the highest--1.67 and 1.24 times higher than those of the scheme in [21] and the scheme in [23], respectively.  
The second comparison group was between master-slave chains and single chains. The throughput variation between master-slave and single chains was determined by sending access requests at different rates. The results are shown in Figure 10. At lower access request volumes, the difference in throughput between the master-slave and single chains was relatively tiny. However, as accesses increased, the transactions within the domain did not need to be completed through the master chain as in the master-slave chain. The master chain only needed to be responsible for cross-domain access control, making it possible for the master and slave chains to work together. Therefore, the throughput of the master-slave chain was higher than that of the single chain.  
Figure 9: Throughput comparison between master and slave chains. – this paper; – Scheme [21]; Scheme [23].
## 3 Design of the Direct Yaw Moment Control System
### Feedback Control  
The feedback control part of the proposed DYC system is proportional to the absolute vehicle yaw rate. There are several ways to determine the value of yaw rate feedback gain. To make the determination of the yaw rate feedback gain model-based and adaptable to various vehicle velocity conditions, this paper selects a method that the yaw rate feedback gain \(k_{r}\) is determined in a way that the yaw rate gain of the controlled vehicle with respect to the steering angle matches the one in the case of unloaded vehicle. This means that DYC system will affect the vehicle motion in the way that the yaw rate gain, with respect to the steering angle, will be suppressed, resulting in improved vehicle stability. The yaw rate gain of the controlled vehicle with respect to the steering angle can be computed from the linear vehicle model as indicated in the following equation.  
Figure 5: Feed-forward compensation parameters dependency on velocity and loading condition.  
\[G_{DYC}(0)=\frac{r_{DYC}(0)}{\delta_{f}(0)}=\frac{a_{21}b_{11}-a_{11}b_{21}}{a_{1 1}(a_{22}+b_{22}k_{r})-a_{12}a_{21}}. \tag{25}\]  
Next, the yaw rate gain of the unloaded vehicle as the desired vehicle yaw rate can be calculated as indicated in the following equation.  
\[G_{des}(0)=\frac{r_{des}(0)}{\delta_{f}(0)}=\frac{a_{21}^{\prime}b_{11}^{\prime }-a_{11}^{\prime}b_{21}^{\prime}}{a_{11}^{\prime}a_{22}^{\prime}-a_{12}^{ \prime}a_{21}^{\prime}} \tag{26}\]  
where, the coefficients \(a_{ij}^{\prime}\) and \(b_{ij}^{\prime}\) are determined from the unloaded condition vehicle parameters. By allowing the yaw rate gain in both equations to be equal to each other, the yaw rate feedback gain can be theoretically calculated as indicated in the following equation.  
\[k_{r}=\left(\frac{a_{11}^{\prime}a_{22}^{\prime}-a_{12}^{\prime}a_{21}^{\prime }}{a_{21}^{\prime}b_{11}^{\prime}-a_{11}^{\prime}b_{21}^{\prime}}-\frac{a_{11 }a_{22}-a_{12}a_{21}}{a_{21}b_{11}-a_{11}b_{21}}\right)\frac{a_{21}b_{11}-a_{ 11}b_{21}}{a_{11}b_{22}}. \tag{27}\]  
By substituting all coefficient values, the yaw rate feedback gain can be expressed by the vehicle parameters as the following equation.  
\[k_{r}=-\big{(}A^{\prime}-A\big{)}\frac{2l^{2}K_{f}K_{r}}{K_{f}+K_{r}}V \tag{28}\]  
where, \(A\) and \(A^{\prime}\) indicate the vehicle stability factor, as defined by Equation (10), for loaded and unloaded vehicles, respectively. Typical values for the yaw rate feedback gain and its dependency on vehicle velocity is shown in Figure 6.
## 3 Rural Area Features and Topology Design
The position of the multisensors is initially saved in a central server, but it could be changed at any time. Although in our initial implementation the multisensors are not mobile, we can implement mobile sensors that can be monitored using GPS or wireless positioning based systems [16]. The wireless multisensor sends a fire alarm through the access points to a central server if the combination of its physical sensors gives that there is fire. The input variables of the multisensor are fire infrared radiation and smoke, but we are planning to add temperature (and the quick changes of temperature), humidity and CO\({}_{2}\)[17]. The field of vision of the wireless cameras covers the whole  
Figure 2: Fire detection and verification design proposal. geographical area where the multisensors are placed (the cameras can rotate 270\({}^{\circ}\) horizontally and 90\({}^{\circ}\) vertically). The central server has a database that relates each multisensor with a certain horizontal and vertical direction of any one of the closest wireless IP cameras. Then, the server sends a frame to the closest cameras. They move their objective toward the multisensor that detected the fire. The images are seen from all cameras placed near the affected zone to a computer placed in the firefighter control room. All devices have an IP address and these images and information could be accessed from Internet. Because the data registered by the sensors are combined with the decisions of the firefighters after they have seen the images taken from of the cameras, this system offers full information to the firefighter squads, facilitating the extinction tasks. Moreover, the affected area can also be visualized using the controls of the camera at the time. It can also be used to improve fire control and surveillance.