esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Analyzing the Influencing Mechanism  
#### 4.2.1 Supply and Demand Analysis of Nighttime Economic Facilities  
We measured the demand for nighttime activity in the urban agglomeration of the YRD using the NTL data. We measured the supply status of NSFs using tourist POI data. Based on fishing nets with 50 rows and 50 columns, we calculated the NTL data and the supply quantity of NSFs provided in the YRD urban agglomeration for each fishing net. Using natural breaks, we reclassified the data and divided them into five groups ranging from small to large. We assigned values of 1 to 5 to the data. After reclassification, we compared the NTL data score to the supply quantity of the NSF score (the reclassified score values of the NTL data minus the reclassified score values of the NSF supply quantity). (In Figure 7, because the range of values obtained by subtracting the reclassified score values of the NTL data from the reclassified score values of NSF supply quantity is between \(-4\) and \(3\), the figure only depicts values within this range.) When the reclassified score values of the NTL data are greater than the reclassified score values of NSF supply quantity,  
Figure 5: Nuclear density analysis of night service facilities. (**a**) Catering service facilities. (**b**) Accommodation service facilities. (**c**) Transport service facilities. (**d**) Sightseeing service facilities. (**e**) Entertainment service facilities. (**f**) Shopping service facilities.  
the demand for NSFs exceeds the supply, referred to as the relative deficit area of NSFs. Conversely, when the reclassified score values of the NTL data are less than the reclassified score values of NSF supply quantity, the demand for NSFs is less than the supply; this is referred to as the relative surplus area of NSFs.  
In general, the relative surplus area that corresponds to each type of business in the YRD urban agglomeration accounts for the smallest proportion. A relative surplus occurs in cities with a low level of economic growth, slow nighttime economic development, low population activity, and limited tourist flow. These cities include Yancheng, Chuzhou, Xuancheng, Huzhou, and Taizhou. A significant deficit exists in Shanghai, Hefei, Nanjing, Wuxi, Suzhou, Jiaxing, Ningbo, and Zhoushan, which have high levels of economic growth, high nighttime economic vitality, high population activity, and huge tourist flows. Current NSFs cannot meet the needs of residents and tourists. In addition, there are fairly well-balanced catering and shopping facilities for the NTE. However, the construction of transportation facilities must be reinforced.  
Figure 6: Standard deviational ellipse of nighttime service facilities.  
Figure 7: _Cont_.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 1 Introduction
Four different proportional-integral differential controllers were also proposed and the robustness of each was compared [48]. [PERSON] et al. [49] applied the fuzzy proportional-integral-derivative control algorithm to air suspension dampers, which can effectively reduce body vibration. The method can calculate the adaptive expansion factor of the fuzzy theory domain. Although the PID controller can achieve accurate control, the adjustment speed is not fast, whereas the fuzzy control can be adjusted quickly but without any adjustment accuracy. Accordingly, the fuzzy-PID controller, which has the characteristics of a simple structure, good stability performance, and high reliability, was selected as the upper controller in this study. In this study, the body attitude-related parameters, such as lateral camber angle and lateral acceleration, obtained from the inertial guidance system were used as input variables. The lateral load transfer ratio (LTR) was considered as the rollover evaluation index, and the appropriate target braking wheel was selected according to the LTR. Subsequently, the EHB system was combined with differential braking technology to autonomously reduce the speed of the vehicle to improve the driving stability of forest fire trucks during operation in forest areas. The main contributions of this paper are as follows: (1) Considering a forest fire truck as the research target, a 3-DOF vehicle rollover dynamics model was established, and the corresponding mathematical model was developed in MATLAB/Simulink. Subsequently, the correctness of the rollover dynamics model was verified in combination with CarSim; (2) The literature indicates that vehicle early warning research is a crucial prerequisite for vehicle rollover prevention. Therefore, a vehicle rollover prevention early warning algorithm transverse load transfer rate LTR was proposed, a braking torque distribution strategy was proposed, and a safety threshold range was selected; (3) The control strategy of differential braking was selected, and the PID and fuzzy PID control algorithms were proposed, and the control effect of these two algorithms on the braking system and whether the braking pressure of the target wheel cylinder can be calculated effectively were compared and analyzed; (4) Combined with the original hydraulic braking system of the forest fire truck, a set of wire-controlled auxiliary braking system with electro-assisted electro-hydraulic control was designed, and the overall design of the system was performed; and (5) To verify the designed wire-controlled control strategy, the vehicle was selected for control simulation under extreme operating conditions (fishhook steering test and steady-state test) and the final results were analyzed.
## 2 Materials and Methods
### Study Groups  
Rats were grouped into six groups (\(n\) = 14 per each) and treated as follows:  
Group 1, CNTR group: Rats received physiological saline orally for 14 days. On the 14 th day, rats were injected intraperitoneally (i.p.) with normal saline, one hour following the oral administration of saline.  
Group 2, PTZ-treated group (PTZ): Rats received normal saline orally for 2 weeks. On day 14, rats received a single i.p. injection of PTZ (60 mg/kg) one hour following the oral administration of saline, as reported previously [27].  
Group 3, SAE+ PTZ-treated group (SAE + PTZ): Rats received a daily dose of SAE (250 mg/kg, orally) for 14 days, as reported previously [28]. On day 14, rats received a single i.p. injection of PTZ (60 mg/kg) one hour following the oral administration of SAE.  
Group 4, Na\({}_{2}\)SeO\({}_{3}\) + PTZ-treated group (Na\({}_{2}\)SeO\({}_{3}\) + PTZ): Rats received a daily dose of Na\({}_{2}\)SeO\({}_{3}\) (0.5 mg/kg, orally) for 14 days, as previously reported [29]. On day 14, rats received a single i.p. injection of PTZ (60 mg/kg) one hour following the oral administration of Na\({}_{2}\)SeO\({}_{3}\).  
Group 5, SAE-SeNPs + PTZ-treated group (SeNPs + PTZ): Rats received a daily dose of SAE-SeNPs (0.5 mg/kg, orally) for 14 days, as reported previously [30]. On day 14, rats received a single i.p. injection of PTZ (60 mg/kg), one hour following the oral administration of SAE-SeNPs.  
Group 6, diazepam + PTZ-treated group (diazepam + PTZ): Rats received a daily dose of diazepam (20 mg/kg, orally) for 14 days, as reported previously [31]. On day 14, rats received a single i.p. injection of PTZ (60 mg/kg) one hour following diazepam administration.  
Normal saline was used to dissolve PTZ, Na\({}_{2}\)SeO\({}_{3}\), SAE-SeNPs, and diazepam for oral administration. The doses of SAE and SAE-SeNPs were selected according to the previous study of [PERSON] et al. (2018) and [PERSON] et al. (2019), respectively, and to a preliminary study using different SAE doses (150, 200, and 250 mg/kg) and SAE-SeNPs doses (0.1, 0.25, and 0.5 mg/kg). The potent anti-seizure effects were observed at a higher dose of SAE (250 mg/kg) and SAE-SeNPs (0.5 mg/kg) (data were not shown). After one day of the last treatment, eight rats were euthanized and sacrificed, and the remaining six rats were kept to calculate the death rate within 7 days post-seizures. The cerebral cortex was immediately dissected and rinsed with physiological saline and divided into small parts. To obtain a 10% (_w/v_) homogenate for biochemical examination, parts of cortical tissues were homogenized in 10 mM phosphate buffer (pH 7.4). In order to evaluate norepinephrine (NE) neurotransmitter levels, another parts cortical tissues were blended in high-performance liquid chromatography (HPLC)-grade methanol and centrifuged for 10 min at 12,000 rpm at 4 \({}^{\circ}\)C, the supernatant was exposed to HPLC. For histopathological studies, some cortical tissues were fixed in 10% formalin, and the tissue was examined for histopathological changes.
## 2 Circuit Implementation  
A block diagram of the proposed overall structure is shown in Figure 2. The proposed overall circuit consists of the continuous-time input CM current-feedback loop, ECG monitoring channel, and current/voltage (I/V) reference. As low power is an important feature in portable ECG monitoring, the proposed circuit was designed in the sub-threshold region.
## 3 Results
### Fluctuations of Phytoplankton  
Figure 3 illustrates the changes in the total cell counts and the taxon composition of phytoplankton in the surface water samples at Stations 1-3. The total numbers of phytoplankton cells at Stn. 1 were 4.7 \(\times\) 10\({}^{4}\)-2.0 \(\times\) 10\({}^{6}\) cells L\({}^{-1}\) (Figure 3a). The centric diatoms _Chaetocros_ spp. and _Thalassiosira_ spp. (Bacillariophyta) appeared at high densities at Stn. 1 during April before the growth of _Z. marina_, but these groups became minor in May and thereafter. The occurrences of Euglenoids were more frequent at Stn. 1 (Figure 3). Pennate diatoms became predominant from the second week of May, accounting for a maximum of 99% of the phytoplankton community (3 rd week of June). The increase in pennate diatoms appeared to be coincident with the growth of _Z. marina_. The appearance of dinoflagellates was confirmed at this station, and most of them were heterotrophic _Protoperidinium_ spp. (Miozoa, Dinophyceae). The toxic dinoflagellate _A. catenella_ (Group I) was not confirmed.  
At Stn. 2, the total cell numbers of phytoplankton fluctuated within a relatively low-density range at 2.7 \(\times\) 10\({}^{4}\)-5.0 \(\times\) 10\({}^{5}\) cells L\({}^{-1}\) (Figure 3b). The taxonomic composition varied greatly. The centric diatoms predominated (13-50%) in April, and then the ratios of Euglenoid and pennate diatoms increased in May (42-97%). The appearance of the genus _Alexandrium_ was not confirmed at Stn. 2.  
The total numbers of phytoplankton cells at Stn. 3 fluctuated over a wide range at 7.8 \(\times\) 10\({}^{4}\)-1.6 \(\times\) 10\({}^{6}\) cells L\({}^{-1}\) (Figure 3c). The proportion of pennate diatoms and Euglenoids increased slightly from April to May and decreased thereafter. Centric diatoms were overall predominant throughout the survey period, and most of them were _Chaetocros_ spp. At this station, the most important point was the appearance of _A. catenella_ with a confirmed presence 7 times out of 11 samplings, and the detected maximum cell density reached 1.0 \(\times\) 10\({}^{3}\) cells L\({}^{-1}\) (Figure 4).  
Figure 3: Temporal changes of cell densities (\(\Circle\)) and phytoplankton communities in composition (histograms) in the surface water collected at Stn. 1 (**a**), Stn. 2 (**b**), and Stn. 3 (**c**).
## 2 Materials and Methods
### Pre- and Post-Processing of Images  
The DICOM images store shade information using 12 bits, the aim of which is to save as much detailed information in the scanned data as possible, while most algorithms for image processing are used to work with gray-scale images coding the information on 8 bits. Therefore, in order to process the data, the depth of the color was reduced, and the images were converted to 8-bit color coding. This operation makes it possible to compute the texture feature maps with the standard approach to texture processing and has been proven to remove some of the noise [27].  
When an image is of low quality, particularly when it lacks sharpness and contrast, the histogram equalization operation can be applied to ensure that the whole color range is used, thereby rendering objects more easily visible, as shown in Figure 2b. However, this approach is prone to failure when used on images that contain both very dark and very light objects, as is often the case with plain radiographic  
Figure 2: Various aspects of image preprocessing. (**a**) Original data. (**b**) The same image after histogram equalization shows improved contrast but does not depict the structure clearly in the tooth region. (**c–f**) Examples of feature maps derived from a digital intraoral radiographic image via various texture analysis methods. CLU, clustering with k-means approach; DIR, digital intraoral radiography; FOF, first-order features; GTDM, gray-tone difference matrices; LBP, local binary patterns.  
images. Hence, there is a need for more sophisticated methods to depict the data in a more informative manner. Nevertheless, application of the histogram equalization (HEQ) method in the preprocessing stage (e.g., before the texture feature map is computed) was tested, and when followed by the RLM technique, proved to be useful because the texture feature map quality improved significantly.  
On the other hand, the contrast of some textural feature maps was low and did not present the content clearly. For those, histogram stretching after the final result was applied which aims in scaling the pixel values in order to assure use of the full range of 8-bit color coding (0-255). This transformation does not change the image content but makes it easier for a radiologist to evaluate. There were also some cases where application of histogram equalization gave a better effect.
## 3 Methodology
### Dataset Description  
#### 3.2.1 Data for Hurricane Intensity Estimation  
The hurricane image database consists of the collection of satellite images (HURSAT) obtained from the National Centers for Environmental Information that are used for computing intensity prediction [43]. All the images are downloaded in NetCDF format. The hurricane is located at the center of all the images present in the database. Another dataset, called the best track data, is downloaded from the HURDAT2 database available at the National Hurricane Center [44]. Hurricanes of the Atlantic and Pacific oceans with the wind speed at 6-h interval data are present in the best track dataset. HURSAT2 data contains images of all the locations in the world, and best track data contains only wind-speed hurricane details of Atlantic and Pacific oceans. The images that match with the HURDAT2 database are only downloaded for all the years. All the satellite images are cropped and resized as a 50 \(\times\) 50 square, which retains all the information without loss and also speeds up the data augmentation and model training process.  
Each satellite image contains the information such as hurricane name, data, and time of satellite image, but it does not provide the hurricane wind speed details. By searching in the best track dataset by providing the name of the hurricane, the wind speed of that particular image is retrieved. Similarly, all the wind speed of the images are obtained from the HURDAT2 database. Finally, the images are labeled with the corresponding wind speed. Data augmentation is done, since the number of images of the weak tropical cyclones is greater than the number of images of strong tropical cyclones. Data augmentation is used to balance the dataset and to improve the performance. HURDAT2 best track data contains features such as location of the tropical cyclone, maximum sustained wind speed in Knots (kts), year of occurrence, and latitude and longitude at the center.
## 2 Probabilistic Non-Linear Computation
### Sampling Methods
The process steps of the FBS procedure are as follows, see Figure 3: (a) generate the LHS-field; (b) perform simulated annealing for correlation; (c) define the leading basic variable (on subset of fractiles) on the sample field with correlation the leading parameter \(X_{L}\); (d) determine those realizations which are closest to the pre-defined fractiles (e.g., 5%, 15%,..., 95%); (e) select the sample sets associated with the pre-defined fractiles of the leading parameter \(X_{L}\); (f) perform the deterministic simulation of each subset; (g) collect the results of the system response \(X_{R}\) and order it according to the pre-defined fractiles; (h) perform a PDF-fitting; (i) perform sensitivity analysis to (I) the original LHS-field \(\rho_{x_{LHS}}\) and (II) the reduced FBS-field \(\rho_{x_{FBS}}\); (f) check leading parameter \(|\rho_{x_{LHS}}-\rho_{x_{FBS}}|\leq error\). More details regarding the appropriate selection of the leading basic variable are provided in [29]. The Estimation of Coefficient of Variation of Resistance method (ECOV) seems to be a further reduction to two \(p_{i}\)s as it is proposed in the FBS, but it is a simplified probabilistic procedure in which the random variation of resistance is estimated using only two samples. It is based on the idea that the random distribution of resistance, which is described by the COV, can be estimated from the mean and characteristic values e.g., 5%-fractile of resistance, see Figure 4. The method is not based on an LHS sampling field and hence does not take into account the correlation between basic variables but takes from all basic variables the, e.g., 5%-fractile or 50%-fractile. Figure 2: Latin Hyper Cube Simulation (LHS) method including and not including correlation between basic variables. Figure 3: Fractile Based Simulation (FBS) method procedure including and not including correlation between basic variables—including the process steps.
# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**KNN** & **Fencing** & **Walk** & **Punch** & **Smoke** & **Cartwheel** \\ \hline Brush\_hair & HOF: 0.7424 & HOF: 0.7679 & HOF: 0.8125 & HOF: 0.5606 & HOF: 0.8915 \\ \cline{2-6}  & Variance: & Variance: & Variance: & Variance: & Variance: \\  & q = 3: 0.8730 & q = 3: **0.7857** & q = 3: 0.8016 & q = 3: 0.7143 & q = 3: 0.9683 \\  & q = 5: 0.8492 & q = 5: 0.7381 & q = 5: 0.8095 & q = 5: **0.7460** & q = 5: 0.9603 \\  & q = 10: 0.8492 & q = 10: 0.7778 & q = 10: 0.8095 & q = 10: **0.7460** & q = 10: 0.9762 \\ \cline{2-6}  & More info: & More info: & More info: & More info: \\  & q = 3: **0.8889** & q = 3: **0.7857** & q = 3: **0.8174** & q = 3: 0.7222 & q = 3: **0.9921** \\  & q = 5: 0.8809 & q = 5: 0.7540 & q = 5: 0.7778 & q = 5: 0.7301 & q = 5: 0.9524 \\  & q = 10: 0.8810 & q = 10: 0.7619 & q = 10: 0.7778 & q = 10: **0.7460** & q = 10: 0.9365 \\ \hline  & & HOF: 0.5865 & HOF: 0.7778 & HOF: 0.6212 & HOF: 0.8605 \\ \cline{2-6}  & & Variance: & Variance: & Variance: & Variance: \\  & q = 3: 0.6594 & q = 3: **0.8043** & q = 3: 0.6742 & q = 3: 0.8254 \\  & q = 5: **0.6812** & q = 5: 0.6667 & q = 5: 0.6364 & q = 5: 0.8571 \\ Fencing & & q = 10: 0.6304 & q = 10: 0.7246 & q = 10: 0.5454 & q = 10: 0.8571 \\ \cline{2-6}  & & More info: & More info: & More info: & More info: \\  & q = 3: 0.6739 & q = 3: 0.7681 & q = 3: **0.7273** & q = 3: **0.9127** \\  & q = 5: 0.6739 & q = 5: 0.7464 & q = 5: 0.6364 & q = 5: 0.7698 \\  & q = 10: 0.5797 & q = 10: 0.7681 & q = 10: 0.5303 & q = 10: 0.8333 \\ \hline  & & HOF: 0.6827 & HOF: **0.7637** & HOF: **0.8248** \\ \cline{2-6}  & & Variance: & Variance: & Variance: \\  & q = 3: 0.5800 & q = 3: 0.5530 & q = 3: 0.8174 \\  & q = 5: 0.6600 & q = 5: 0.6061 & q = 5: 0.7857 \\ Walk & & q = 10: 0.6067 & q = 10: 0.5682 & q = 10: 0.7222 \\ \cline{2-6}  & & More info: & More info: & More info: \\  & q = 3: 0.6333 & q = 3: 0.5303 & q = 3: 0.8016 \\  & q = 5: **0.6867** & q = 5: 0.6818 & q = 5: 0.8174 \\  & q = 10: 0.5800 & q = 10: 0.5000 & q = 10: 0.7778 \\ \hline  & & HOF: **0.6805** & HOF: **0.9078** \\ \cline{2-6}  & & Variance: & Variance: \\  & q = 3: 0.5682 & q = 3: 0.8730 \\  & q = 5: 0.5757 & q = 5: 0.8651 \\ Punch & & q = 10: 0.5303 & q = 10: 0.8968 \\ \cline{2-6}  & & More info: & More info: \\  & q = 3: 0.6061 & q = 3: 0.8492 \\ q = 5: 0.6364 & q = 5: 0.8730 \\ q = 10: 0.5227 & q = 10: 0.8492 \\ \hline  & & HOF: **0.8295** \\ \cline{2-6}  & & Variance: & \\  & q = 3: 0.7778 \\  & q = 5: 0.8016 \\  & q = 10: 0.7460 \\ \hline  & & More info: & \\  & q = 3: 0.8254 \\  & q = 5: 0.7619 \\  & q = 10: 0.7698 \\ \hline \end{tabular}
\end{table} 
Table 2: K Nearest Neighbors (KNN) classifier results.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 4 Analysis of Vibration Isolation Structure
### Vibration Isolation Ratio  
We define the vibration isolation rate \(\varphi\) as Equation (6). The larger the value \(\varphi\) is, the better the vibration isolation effect is. We extract the peak acceleration values of each layer and compare them as shown in Table 6.  
After the vibration isolator is installed, the structure's peak acceleration of each floor is reduced. The maximum vibration isolation rate is 60.53% and the average vibration isolation rate is 37.72%. The overall vibration trend of the vibration isolation structure is gentle.  
\[\varphi=\frac{a_{1}-a_{2}}{a_{1}}\times 100\% \tag{7}\]  
where \(a_{1}\) is the vertical acceleration peak value of the ordinary structure; \(a_{2}\) is the vertical acceleration peak value of the vibration isolation structure; and the unit is mm/s\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Floors** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{\varphi}\) \\ \hline
1 & 24.53 & 11.64 & 52.54\% \\
2 & 31.42 & 12.40 & 60.53\% \\
3 & 27.45 & 14.25 & 48.09\% \\
4 & 15.79 & 15.19 & 3.80\% \\
5 & 17.35 & 15.26 & 11.44\% \\
6 & 32.86 & 16.46 & 49.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Acceleration peak and vibration isolation ratio of the structure.  
Figure 15: Structural vibration response: (**a**) vertical acceleration of the first floor; (**b**) vertical acceleration of the third floor; (**c**) vertical acceleration of the sixth floor; (**d**) spectra of the first floor; (**e**) spectra of the third floor; (**f**) spectra of the sixth floor.
## 2 Materials and Methods
### Data
#### 2.2.4 Fluxnet Site, Hegyhatssl  
FLUXNET is a global network of micrometeorological tower sites that measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere using eddy covariance methods [38]. Hegyhatssl is a FLUXNET site [39] located in the flat region of western Hungary (Figure 3), surrounded by semi-natural grass fields (hay meadows), agricultural fields (mostly crops), and forest patches. The soil type of the region is brown forest soil.  
For the long-term monitoring of the surface-atmosphere exchange of nitrous oxide in the region, an eddy covariance system was added to the instrumentation of the Hegyhatssl tall tower greenhouse gas monitoring station. The eddy covariance system is operated at 82 m and 3 m height [40; 41; 42]. In addition to the daily continuous carbon dioxide and water vapor exchange measurements, weekly LAI and daily soil moisture (derived from 10-30 cm depth) observations are also conducted.  
Figure 1: Raw (**a**) and extrapolated (**b**) GEOV1 Leaf area index from the Copernicus Global Land Service project on 13 April 2008.  
Figure 2: Surface soil moisture evolutions for 2008 at Hegyhatssl (West-Hungary) for model (red), raw ASCAT satellite observations (purple) and ASCAT seasonal CDF rescaled (blue) observations.