esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 3 Proposed Method
### Weighted-Average Strategy to Forecast PM2.5 Value  
Normally, if air quality is good at a particular moment, it will probably be good in the next moment. As mentioned in the previous section, if the weather feature similarity is high between two datasets, their PM2.5 values are very likely to be similar. It is considered that air quality values are correlated from temporal and spatial perspectives. We drew a chart to model the spatial-temporal correlation at specific locations. Recent data and similar data were used as references in this section. The reference data were selected for forecasting. The \"recent data\" is defined as the last observation in the historical data prior to the target that is going to be forecasted; \"similar\" is defined as the data in history with more similar weather features to the target period. In addition, most similar data were not recent data. The construction of Figure 5 includes how recent and similar data were selected.  
A weighted-average based method was proposed for forecasting. For device \(d_{i}\), weight \(w\) between times \(t_{u}\) and \(t_{v}\) was evaluated using Equation (2). In terms of the temporal part, the preliminary forecast (\(p^{T}\)) at time \(t_{u}\) was evaluated with \(h\) references (including recent data (\(r\)) and similar data (\(s\))), in total, using Equation (3). In the spatial part, we proposed concentrating the forecast time series close to the median forecast values of nearby devices in each hour using Equation (4). The design idea of Equation (4) is illustrated with Figure 6. Figure 6 shows that the blue area, which is a distribution of the forecast value of nearby devices in an hour, is concentrated close to the median by \(\%\), and shows an orange distribution. It is expected that forecast value \(p^{S}\) of adjacent devices are similar. Notation \(D(t_{u})\) is a set of temporal PM2.5 forecastings of \(n\) adjacent devices. Further, notations \(maxD(t_{u})\) and \(minD(t_{u})\) represent the maximum and minimum temporal PM2.5 forecast of adjacent devices (\(D(t_{u})\)) around device \(d_{i}\), respectively. If temporal PM2.5 forecasting (\(p^{T}\)) is larger than or equal to \(medD(t_{u})\), \(p^{T}\) is normalized to \(c\%\) of \(medD(t_{u})\) to \(maxD(t_{u})\). Otherwise, \(p^{T}\) is normalized to \(c\%\) of \(minD(t_{u})\) to \(medD(t_{u})\), where \(medD(t_{u})\) means a median temporal PM2.5 forecast of adjacent devices \(D(t_{u})\) around device \(d_{i}\).  
\[w(t_{u},\ t_{v})=\frac{1}{WD(t_{u},t_{v})} \tag{2}\]  
\[p^{T}(t_{u})=\frac{1}{\sum_{v=1}^{h}w(t_{u},t_{v})}\sum_{v=1}^{h}w(t_{u},t_{v })\times p(t_{v}) \tag{3}\]  
\[p^{S}(t_{u})=\left\{\begin{array}{c}\frac{p^{T}(t_{u})-medD(t_{u})}{[maxD(t _{u})-medD(t_{u})]\times c^{\infty}_{u}}+medD(t_{u}),\text{if }p^{T}(t_{u})\geq medD(t_{u})\\ \frac{p^{T}(t_{u})-minD(t_{u})}{[medD(t_{u})-minD(t_{u})]\times c^{\infty}_{u} }+medD(t_{u})-[medD(t_{u})-minD(t_{u})]\times c^{\infty}_{u},\text{otherwise} \end{array}\right. \tag{4}\]  
Figure 5: Schematic diagram of the forecasting approach.
## 4 Experimental Results
### HVDC Breakdown Characteristics of HFO-1336 mzz (E) Mixture with CO2/N2 under Uniform Electric Field  
Similarly, the HVDC breakdown properties of the proposed mixture are investigated under uniform field configuration. It is evident that the breakdown strength of the test gas increases linearly with an increase in pressure and mixing ratio. Nevertheless, the 30% HFO-1336 mzz (E)/CO2 has 1.21 times higher dielectric strength than SF6 at 0.15 MPa under uniform field configuration, as can be seen in Figure 11a. The 30% base gas concentration shows a more promising result in terms of dielectric strength than SF6. The 30% base gas mixture composition can be used for medium-voltage applications. The dielectric characteristics of HFO-1336 mzz (E)/N2 for the uniform field configuration can be seen in Figure 11b. SF6 shows a linear increase in breakdown voltage, and nonlinearity can be seen in the proposed gas mixture. The 30% HFO-1336 mzz (E)/N2 mixture at lower pressure of less than 0.23 MPa shows lower breakdown strength, but higher than SF6. Above 0.23 MPa, the breakdown voltage increases with respect to SF6. The 30% HFO-1336 mzz (E)/N2 mixture at 0.30 MPa pressure reveals 1.06 times higher breakdown voltage value than SF6.  
Figure 11: Breakdown voltages of (**a**) HFO-1336 mzz (E)/CO2 and (**b**) HFO-1336 mzz (E)N2 at uniform field.  
Figure 10: Breakdown voltages of (**a**) HFO-1336 mzz (E)/CO2 and (**b**) HFO-1336 mzz (E)N2 at quasi-uniform field.
# An Efficient Two-Level-Partitioning-Based Double Array and Its Parallelization
## 4 Experiments  
### Datasets for the Experiments  
All experiments were carried out on an Intel i7-7700 CPU @ 3.60 GHz (four cores and eight hyper threads) with 16 GB memory running Ubuntu 11. CodeBlocks is used as the programming platform. Gcc-4.2 and OpenMp-3.1 are used as the default compiler and parallel library, respectively.  
We use the following three datasets in our experiments and some statistical information is shown in Table 2:  
* DBLP [31]: DBLP is a library in computer science. We extracted all the independent words in title field of DBLP. After the duplicated strings were removed, there were 211,517 independent strings in total.
* EN-WIKI [32]: We extracted title field of Wikipedia and got 347,961 independent words after the duplicated strings were removed.
* SynthDS: To verify the effects of our proposed structures and algorithms on large dataset, we also randomly generated 2 million unique strings with lengths varied from 3 to 20 as a synthetic dataset.
## 2 Problem Description
### Three-Dimensional Fuzzy Modeling  
The three-dimensional fuzzy system [21] was first established in 2007 and has been used in the domain of DPS control [22]. Recently, there has been effective development in the three-dimensional fuzzy system for modeling DPS. The relevant theory of the three-dimensional fuzzy system and black-box modeling theory were combined to form a three-dimensional fuzzy modeling framework as shown in Figure 1[10]. Unlike traditional modeling methods, the three-dimensional fuzzy modeling method can model a DPS without reduction in the dimension.  
Assume that \(K\) and \(J\) represent the order of the input variable \(Q(k)\) and output variable \(w(\bar{F},k)\), respectively. Input variables of three-dimensional fuzzy system are \(\{Q(k-1),Q(k-2),\cdots,Q(k-K)\}\) and \(\{w(\bar{F},k),w(\bar{F},k-1),\cdots,w(\bar{F},k-J)\}\). The three-dimensional fuzzy rule is given as shown in Equation (6):  
\[\begin{array}{l}\bar{V}_{l}:\\ \text{if }w(\bar{F},k-1)\text{ is }\tilde{O}_{1l}\text{ and }\cdots\text{ and }w(\bar{F},k-J)\text{ is }\tilde{O}_{ll},\\ \text{and }q_{1}(k-1)\text{ is }Q_{1}^{ll}\text{ and }\cdots\text{ and }q_{1}(k-K)\text{ is }Q_{1}^{ll},\\ \vdots\\ \text{and }q_{\underline{L}}(k-1)\text{ is }Q_{\underline{L}}^{ll}\text{ and }\cdots\text{ and }q_{ \underline{L}}(k-K)\text{ is }Q_{\underline{L}}^{kl},\\ \text{then }w(\bar{F},k)\text{ is }\varphi_{l}(\bar{F}).\end{array} \tag{6}\]  
where \(\tilde{O}_{sl}\) is a three-dimensional fuzzy set, \(Q_{g}^{ll}\) is a conventional fuzzy set, \(s=1,2,\ldots,\)\(J,h=1,2,\ldots,K,g=1,2,\ldots,L\)\(l=1,2,\ldots,\)\(Z\), and \(Z\) indicates the count of fuzzy rules.  
For any three-dimensional fuzzy rule \(\bar{V}_{l}(l=1,2,\ldots,\bar{Z})\), its preceding set 'if \(w(\bar{F},k-1)\) is \(\tilde{O}_{ll}\) and \(\cdots\) and \(q_{\underline{L}}(k-K)\) is \(Q_{\underline{L}}^{kl}\) corresponds to the time coefficient \(w_{l}\). The resulting set 'then \(w(\bar{F},t)\) is \(\varphi_{l}(\bar{F})\)' corresponds to the space base function \(\varphi_{l}(\bar{F})\), and space-time separation is ingeniously achieved through three-dimensional fuzzy rules. If \(Z^{\prime}\) three-dimensional fuzzy rules are excited, the time coefficients and space base functions of \(Z^{\prime}\) three-dimensional fuzzy rules will naturally realize space-time reconstruction. The final result is \(\sum_{l=1}^{Z^{\prime}}w_{l}\varphi_{l}(\bar{F})\).  
The three-dimensional fuzzy modeling framework can be divided into three parts:  
1. Acquire the preceding set of fuzzy rules in order to derive temporal coefficients.
2. Acquire knowledge about the space base functions of the resulting set.
3. Obtain the predicted output through space-time reconstruction.  
The study will explore an innovative approach to three-dimensional fuzzy modeling within this specific framework.
## 3 Results and Discussion
### Overview of Metabolites Discovered in Filed Soil (Contaminated Site)
#### 3.2.3 Metabolite for Protecting Cell Membrane in Bacteria  
Fatty acid synthesis metabolism is one of the bacterial defense responses resisting a damaged cell membrane by a chemical solvent. Malonyl-[acyl-carrier protein], founded in field soil, belongs to fatty acid biosynthesis in soil bacterial metabolism. Fatty acids are known as one of the regulators related to stress and toxicity. According to [30], the production of fatty acid in the bacterial cell cytoplasm is generally derived from acetyl-CoA and NADPH. Acetyl-CoA commonly is transformed into fatty acids with the ATP (adenosine triphosphate) energy production process. Fatty acids are the main element of cell membranes, a physical and chemical wall that has a cell protection function in response to environmental stress [30]. Therefore, upregulation of metabolite related to fatty acid production in bacteria implies a defensive behavior against hydrophobic chemical's penetration into the bacterial cell membrane barrier by a virulent chemical such as unburned toluene or its combustion products. Herein, there is another study explaining metabolite production for self-protection. According to [31], hydrophobic compounds such as aromatic molecules can penetrate the cell membrane and cell wall in the cytoplasm. Once an organic pollutant such as a combustion product goes through the cytoplasmic membrane, it induces the membrane to loosen and be damaged. This causes a malfunction of the ion pumping channel importing and exporting a proton and potassium across the cell membrane and makes it difficult for the bacteria to preserve the energy [32]. Bacteria start acting to decrease the fluidity of the cell membrane by trading off bilayer against non-bilayer consisting of phospholipids [33]. The ratio changes of the cytoplasmic membrane allow the toxic chemicals to stop invading and dissolving the cell membrane. On the other hand, if bacteria rigidifies the cell membrane, no more metabolites related to fatty acid synthesis are discovered in field soil, and this means biological recovery in the environment.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 3 Materials and Methods  
### Seed Collection and Preparation  
Cones were harvested on 20 September 2021 from one plantation of half-sib silver fir _Abies alba_ (Hajnowka forest district (Wilczy Jar sub-District, compartment 416 ad) with geographical coordinates of E 23 deg39'17'', N 52 deg42'33'' (Figure 1). These trees were established in 1996 from 4-year-old seedlings derived from seeds collected in 1992 in the Tisovik reserve (Belarus) of the Bialowieza Forest [50]. There were 11 mother trees giving rise to half-sib families of undetermined paternity.  
For the analysis of seed germination viability, a sample was taken from the fraction of clean seeds. Approximately 49 kg of the mixture of seeds with hulls dried at room temperature 20-25 \({}^{\circ}\)C were sent to the Kostrzyca Forest Gene Bank. From this mixture, 7.61 kg of pure seeds were obtained. X-ray analysis has been performed in Kostrzyca (Figure 4 to verify the quality of seeds and the presence of insects. A sample of 400 cleaned seeds, consisting of 4 replicates of 100 seeds each was tested [51] for germination dynamics and seed quality.  
Figure 4: X-ray pictures of silver fir seeds with the examples marked (**a**) correctly shaped, (**b**) empty, (**c**) insect larva (photo [PERSON], The Kostrzyca Forest Gene Bank).  
Figure 3: Silver fir cones of a half-sib family N17 (**a**) (photo [PERSON]) and collected seeds (**b**) (photo [PERSON]).  
Figure 2: Cones of silver fir trees from which seeds were collected in September 2021 (photo [PERSON]). The cones ranged in length from 8.5 to 18.2 cm and width from 3.1 to 4.8 cm.  
Investigated seeds were surface sterilized by immersion in 75% propanol for thirty seconds and then rinsed with sterile distilled water for one minute. Six hundred seeds were chosen, and lots of up to 30 seeds were placed in 9-cm diameter Petri dishes on moist sterile paper towels. The plates were lidded but not tightly sealed. The papers towels were periodically moistened with sterile distilled water. Twenty Petri dishes were prepared in this way.
## 4 Results and Discussion
### Dimensional Analysis and Regression Equation for \(C_{d}\)  
Considering a squared arrangement of cylinders in an element of bed with emergent vegetation and looking at Figure 2, we assumed \(D,\ s,\ h,\ \varrho,\ g,\ v\) and \(U\) as independent variables, and \(F^{\prime}_{D}\), drag force exerted on the stems per unit bed area, as a dependent variable.  
Thus, we obtained the following relationship:  
\[F^{\prime}_{D}=\psi_{F}(D,\ s,\ h,\ \rho,g,\ v,U) \tag{25}\]  
being \(\psi_{F}\) a generic function. By means of dimensional analysis, we obtained:  
\[\frac{F^{\prime}_{D}}{\rho U^{2}}=\psi_{F}\bigg{(}\frac{s}{D},\ \frac{h}{D},\ F _{D},\ Re_{D*}\bigg{)} \tag{26}\]  
where \(Fr_{D}=U/\sqrt{gD}\), that is the stem Froude number. Since it is possible to assume only one non-dimensional parameter between \(Fr_{D}\) and \(Re_{D*}\), the drag coefficient can be expressed as follows:  
\[C_{D}=\psi\big{(}\psi_{reg},\ Re_{h},\ Re_{D*}\big{)} \tag{27}\]  
where \(Re_{h}=Uh/\
u\), that is the flow Reynolds number with a characteristic length \(h\). One can observe that \(Uh=q\) remains constant along a given free surface profile. To try and find a regression equation for \(C_{D}\), among the 26 tests with a linear arrangement, we considered the following 16: T1, T2, T3, T4, T6, T7, T10, T11, T17, T18, T19, T21, T24, T25, T27, T29.  
Figure 8 shows the \(C_{D}\) values (here identified as \(C_{D-n}\)) versus \(Re_{D*}\).  
Figure 8: Drag coefficients \(C_{D-n}\) computed by means of the best Manning coefficient versus \(Re_{D*}\) for 16 experiments.  
The pattern of \(C_{D}\) is, in practice, linear with \(Re_{D*}\). Since from the dimensional analysis we obtained Equation (27) by means of a regression analysis, we can define the following linear equation:  
\[C_{D-UC}=10.83\phi_{vcg}+0.2043\ \frac{Re_{D*}}{1000}-0.0675\frac{Re_{h}}{10,000}+0.1765 \tag{28}\]  
where \(C_{D-UC}\) stands for University of Calabria drag coefficient with a determination coefficient \(R^{2}\) = 0.83. Figure 9 shows the computed \(C_{D-UC}\) values versus the experimental ones (16 tests with 310 values of the drag coefficient were considered; few points of test T18 were not included because they were, probably, affected by measurement errors). Equation (28) shows that, as expected, \(C_{D-UC}\) increases with \(\phi_{vcg}\) and \(Re_{D*}\) and decreases with \(Re_{h}\); this equation gives a maximum error of 20%, even though most of the points lay into the 10% error band (Figure 9).
## 3 Results and Discussion
### Factors Affecting the Removal of 4-CP by the Combined MPHAC/PS System
#### 3.3.2 Effect of MPHAC dose on 4-CP Adsorption and Degradation  
In general, the catalyst plays an important role in removing pollutants from contaminated waters. Figure 5 shows the performance of the MPHAC/PS system at different doses of MPHAC for 4-CP removal. As it can be seen, the removal efficiency of 4-CP with different MPHAC doses 250, 750 and 1250 mg/L is 79.9, 83.6 and 94.7% at a reaction time of 90 min, respectively. The increase of the 4-CP removal with increasing MPHAC dose is due to the enhancement of the number of active sites on the catalyst surface, which increases the likelihood of adsorption and degradation of 4-CP.  
With the increase of catalyst, the iron ions existing on the surface of the catalyst increase, leading to more activation of PS and production of more sulfate radicals [41]. Moreover, by increasing the amount of the catalyst, the adsorption process acquired a more effective role in 4-CP removal than the degradation process because the amount of PS is maintained constant for the different considered doses of the catalyst. In other words, although there are more active sites in higher catalyst doses in the MPHAC/PS system, not enough PS is present to produce sulfate radicals and the relative degradation process is reduced [42]. These results are similar to the ones obtained by [PERSON] et al. [43], who found that by applying an appropriate dose of the catalyst, 97.8% of reactive red M-3 BE was removed after 20 min reaction time.
## 3 Methodology  
The detailed technical approach used in this study is shown in Figure 2. We used NRCS and DCA data from Sentinel-1 level-2 OCN VV polarization product as the observation field, while incorporating ERA5 reanalysis data to construct the background wind field. In the wind retrieval phase, we adopted two strategies: the direct inversion method and VAM. The direct inversion method retrieves the wind field directly using the CMOD5.N model, while the VAM derives wind field by minimizing three different cost functions: VAM1 (\(I_{NRCS}\)), utilizing only NRCS with background wind field; VAM2 (\(I_{DCA}\)), employing only DCA with background wind field; and VAM3 (\(J_{NRCS+DCA}\)), which comprehensively integrates both NRCS and DCA with background wind field. To verify the reliability of the retrieval results, we compared the retrieved wind fields with wind speeds and wind directions measured by buoys.  
Figure 2: Flowchart of SAR wind field inversion with combined use of NRCS and DCA.
## 2 Materials and Methods
### Performance Evaluation  
Performance is evaluated using an independent balanced test set (78 images for each class) and there are no excluded images due to motion and projection artifacts or to image quality. The CNN prediction output is compared to the ground truth set by the expert reader (A.M.). Four statistical metrics are used to report classification performance [33]:First, the accuracy that generally describes how the model performs across all classes. It is obtained as the ratio between the number of correct predictions to the total number of predictions:  
\[\text{Accuracy}=\frac{\text{True}_{\text{positive}}+\text{True}_{\text{ negative}}}{\text{True}_{\text{positive}}+\text{True}_{\text{negative}}+\text{False}_{\text{positive}}+\text{False}_{\text{negative}}}\]  
Then the precision, that is calculated as the ratio between the number of positive samples correctly classified to the total number of samples classified as positive. The precision measures the model's accuracy in classifying a sample as positive:  
\[\text{Precision}=\frac{\text{True}_{\text{positive}}}{\text{True}_{\text{ positive}}+\text{False}_{\text{positive}}}\]  
The recall is calculated as the ratio between the number of positive samples correctly classified as Positive to the total number of positive samples. The recall measures the model's ability to detect positive samples. The higher the recall, the more positive samples detected:  
\[\text{Recall}=\frac{\text{True}_{\text{positive}}}{\text{True}_{\text{ positive}}+\text{False}_{\text{negative}}}\]  
Finally, the F1-score is a way of combining the precision and recall:  
\[\text{F1-score}=\frac{2\ \times\ \text{Precision}\ \times\ \text{Recall}}{ \text{Precision}+\text{Recall}}\]  
Confusion matrix and area under the curve (AUC) of the receiver operating characteristic (ROC) and precision-recall (PRC) curves (ROC-AUC and PRC-AUC) are supplied. Additionally, multiple class activation maps are generated to analyze the feature attribution and understand the CNN predictions.
# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 3 Proposed Methodology
### Optimal Sweeping Path Planning Based on Simulated Annealing Algorithm
#### 3.2.4 Static and Dynamic Obstacle Avoidance Strategies  
Static and dynamic obstacles will affect the unmanned sweeper to perform the task, the function of detecting obstacles and predicting their future trends through sensors, and performing path planning according to certain algorithms so as to avoid obstacles is essential. The unmanned sweeper can obtain the point cloud information in the environment by LiDAR and match and analyze the obstacles in the environment in real time, and the objects that do not exist in the high-precision map will be judged as obstacles. Obstacles without positional changes are static obstacles, while the positional changes in obstacles in different frames of data characterize the historical motion trajectories of dynamic obstacles. In order to make the robot safer during obstacle avoidance, we will make a reasonable expansion of obstacles.  
For static obstacles, if the expanded static obstacles appear on the paths of the two cities planned by SA, we will replan the paths of this section of the road based on the A star algorithm [18].  
For dynamic obstacles, we use particle filters to make trajectory predictions for them. The historical trajectories of dynamic obstacles are pre-stored into a passive mode behavioral planner, then all feasible trajectories are extracted from the map based on the embedded traffic rules, then the particles are sampled from the behavioral model, the weights are computed for each sampled particle, and finally the weights are normalized and the likelihood of each predicted trajectory is computed. The trajectory with the highest likelihood is passed into the local planning and used for path replanning.
## 5 Experiment and Result Analysis
### Model Training Strategy  
The MRAB-CNN model proposed in this paper takes 11 input variables, with the output being the predicted value of component content. After finalizing the model structure, the MRAB-CNN model underwent training. For comparison, we used the RAB-CNN and Deep Neural Network (DNN) models. The DNN model was configured with five layers, each containing 64 neurons, while the parameters for the RAB-CNN model were kept consistent with those of the MRAB-CNN model.  
The training strategy is outlined as follows. Initially, the data were standardized. Subsequently, the Adam algorithm was employed as the model optimization algorithm and the Mean Square Error was used as the loss evaluation function. The initial learning rate was set at 0.0005 and the activation function was uniformly set as Relu. Considering the small size of the model's data, the batch size was set to 2, and the model was trained over 350 iterations. The loss curve during the training process is depicted in Figure 9. As can be discerned from the figure, the MRAB-DNN model designed in this paper achieves the convergence effect of the DNN model in 200 rounds when the training times is 20 rounds. This reflects the rapid convergence of the residual attention block, which can amplify the effective features' weights and accelerate the model's convergence during training. The convergence rate of the MRAB-DNN model with a large residual structure is slightly higher than that of the RAB-DNN model during training. By assessing the convergence of each model in Figure 9, it can be seen that the MRAB-DNN model can reach a state of convergence within a limited number of training times. Therefore, it can be applied to the prediction of element component content in the rare earth extraction process.