esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Kinetics  
The adsorption-removal rate \(RP\) (%) and the unit adsorption capacity \(q_{e}\) of a soil mixture (mg/g) concerning the heavy metal Zn\({}^{2+}\) are computed using Equations (2) and (3), respectively:  
\[RP=\frac{C_{0}-C_{e}}{C_{0}}\times 100\% \tag{2}\]  
\[q_{e}=\frac{C_{0}-C_{s}}{m_{s}}V \tag{3}\]  
where \(C_{0}\) is the initial contaminant concentration (mg/L), \(C_{e}\) is the equilibrium concentration (mg/L), \(m_{s}\) is the adsorbent mass (g), and \(V\) is the solution volume (mL).  
The adsorption capacity \(q_{t}\) (mg/g) concerning heavy metal Zn\({}^{2+}\) at time \(t\) is expressed as:  
\[q_{t}=\frac{C_{0}-C_{t}}{m_{s}}V \tag{4}\]  
where \(C_{t}\) is the Zn\({}^{2+}\) concentration (mg/L) of the centrifuged supernatant at time \(t\).  
Four equations, the Langmuir model, the Freundlich model, the D-R isothermal adsorption model, and the Henry model, are frequently utilized to describe the isothermal adsorption of metal ions in solution by solid adsorbents. The model calculation common is shown below.  
1. Langmuir model:  
The model assumes isotropic adsorbent surface and monolayer adsorption with homogeneous adsorption. The model is usually applied to the whole isothermal adsorption process and can reflect the amount of soil adsorbed.  
\[\frac{c_{e}}{q_{e}}=\frac{1}{k_{L}q_{m}}+\frac{c_{e}}{q_{m}} \tag{5}\]  
where \(K_{L}\) is the Langmuir isotherm parameter (L/mg), and \(q_{m}\) is the maximum adsorption capacity (mg/g).  
2. Freundlich model  
The model assumes that the adsorbent is anisotropic and multilayered for the low-concentration phase of isothermal adsorption.  
\[q_{e}=k_{F}C_{e}^{\frac{1}{n}} \tag{6}\]  
where \(K_{F}\) is the adsorption capacity-related constant (L/g), and \(n\) is an empirical constant, usually greater than 1. A value greater than 1 indicates a predominantly physical adsorption process, and a larger value corresponds to a more nonlinear adsorption model.  
3. D-R isothermal adsorption model  
The model assumes that the adsorbed solute fills in the pores  
\[\ln q_{e}=\ln q_{m}-k{e}^{2} \tag{7}\]\[\varepsilon=RT\ln\left(1+\frac{1}{c_{e}}\right) \tag{8}\]  
\[E=-\frac{1}{\sqrt{2k}} \tag{9}\]  
where, \(k\) correlation model constant (mol\({}^{2}\cdot\) k\({}^{-2}\)), maximum adsorption (mol\({}\cdot\) g\({}^{-1}\)), \(\varepsilon\) is the Polanyi potential, which is related to the equilibrium concentration (kJ \(\cdot\) L \(\cdot\) mol\({}^{-2}\)), \(R\) ideal other constants, \(T\) thermodynamic temperature (K), \(E\) average free energy of adsorption (kJ \(\cdot\) mol\({}^{-1}\))  
\(\lx@sectionsign\)1 Henry model  
\[q_{e}=k_{d}C_{e} \tag{10}\]
# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
###### Abstract  
In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environment, while there exists technology drawbacks and a lack of mature systems. Aiming to solve the crowd counting problem with high-density under complex environments, a feature fusion-based deep convolutional neural network method FF-CNN (Feature Fusion of Convolutional Neural Network) was proposed in this paper. The proposed FF-CNN mapped the crowd image to its crowd density map, and then obtained the head count by integration. The geometry adaptive kernels were adopted to generate high-quality density maps which were used as ground truths for network training. The deconvolution technique was used to achieve the fusion of high-level and low-level features to get richer features, and two loss functions, i.e., density map loss and absolute count loss, were used for joint optimization. In order to increase the sample diversity, the original images were cropped with a random cropping method for each iteration. The experimental results of FF-CNN on the ShanghaiTech public dataset showed that the fusion of low-level and high-level features can extract richer features to improve the precision of density map estimation, and further improve the accuracy of crowd counting.  
Convolutional Neural Network (CNN); feature fusion; crowd counting; density map +
Footnote †: journal: Applied sciences
## 2 System Model  
### System Components  
The International Telecommunication Union (ITU) has designated 156.025-162.025 MHz of Very High Frequency (VHF) bands as the maritime special frequency band. The vast majority of spectrum resources can be used for analog voice intercom. At present, the digital communication bands approved by the International Maritime Organization (IMO) are only two channels used by the Automatic Identification System (AIS) with the bandwidth of each channel being only 25 kHz. The next-generation maritime digital communication systems only extend the bandwidth to 400 kHz, but are still evolving. These systems are still unable to meet the higher data rate requirements in the future. Since the VHF band has a long history and a large number of legacy systems, it is difficult to re-divide the spectrum resources. Therefore, MCRCS is the best choice for solving the limitations of the maritime broadband wireless communications.  
In the MCRCS, the PUs are the analog voice intercoms in the VHF maritime frequency bands and the legacy system equipment outside the maritime band. The SUs are the communication devices with cognitive radio technology installed on the ships.  
The VHF wireless signals are transmitted in a straight line. Considering the curvature of the Earth in the maritime environment, the transmission range of VHF signals is limited. In the region, the SU monitors the working status of the PU and uses the spectrum resources of the PU to exchange data with other SUs. In order to improve the accuracy of detection and solve the competition between SUs, we built the MCRCS using a centralized spectrum allocation scheme. In this scheme, the SU first sends the spectral detection results, before the spectrum lease requests to the DC through an additional signaling channel. Following this, the DC allocates the spectrum using the EDSAA. Finally, the allocation results are transmitted to the SU through the signaling channel.  
According to the distance between the network region and the DC which is built on land, the system is divided into two typical regions: close to shore and deep sea. In the region close to the shore, the SUs can communicate directly with the DC through the signaling channels. In the deep-sea region, the SUs exchange data with the DC through satellite links. The physical composition of the system is shown in Figure 1.  
An important consideration of MCRCS is that the PU must be well protected. These bands are usually used for the transmission of important information, such as rescue in the case of disasters, vessel navigation, weather broadcasting and so on. Therefore, the overall communication efficiency in the region of MCRCS is the most important.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.4 Evapotranspiration  
\(ET\) was obtained from the global MOD16 dataset, which estimates \(ET\) from the Penman-Monteith equation parameterized with land surface and vegetation properties [66]. Daily meteorological reanalysis data and remotely sensed vegetation property dynamics from MODIS are used as inputs. The algorithm calculates \(ET\) considering the sum of daytime and nighttime components and separating the dry canopy surface from the wet [71]. The MOD16 ET dataset is available on an 8-day (MOD16A2) basis at a spatial resolution of 500 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Data** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Data** \\ **Availability** \\ \end{tabular} &
\begin{tabular}{c} **Main** \\ **Reference** \\ \end{tabular} \\ \hline Land surface temperature (\(LST\)) & MOD11 & 1 km & 8 days & 2003–2016 & [63] \\ Precipitation (\(P\)) & IMERG & \(\sim\)11 km & Monthly & 2003–2016 & [64] \\ Surface soil moisture (\(SM\)) & & & & & \\ Subsurface soil moisture (\(SSM\)) & & & & & \\ Evapotranspiration (\(ET\)) & MOD16 & 0.5 km & 8 days & 2003–2016 & [66] \\ Terrestrial Water Storage & & & & & \\ Anomalies (\(TWSA\)) & & & & & \\ _EVI_ & & & & & \\ _GPP_ & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthesis of remote sensing datasets.
# Application of Surge Arrester in Limiting Voltage Stress at Direct Current Breaker
## 3 Calculation of the Current Zero-Crossing Time
### Stage 3 (\(t_{2}\) through \(t_{3}\))  
Capacitor C2 stops conducting current during this period, while switch SW1 still conducts current, even though it has been triggered, due to the operational delay. As shown in Figure 3b, the stored energy in the downstream inductor is now discharged in an RL circuit. From the figure, the KVL yields  
\[I_{d3}R_{d}+L_{d}\frac{dI_{d3}}{dt}=0 \tag{2}\]  
where \(I_{d3}\) is the downstream current in Stage 3, and \(L_{d}\) and \(R_{d}\) are the downstream inductance and resistance, respectively. Thus,  
\[I_{d3}(t_{32})=I_{0}e^{-\frac{R_{d}}{L_{d}}t_{32}} \tag{3}\]  
where \(I_{0}\) is equal to the final current of the last stage \(I_{0}=I_{d2}(t_{2})\). \(t_{32}\) is the SW1 operational delay.  
In addition, the upstream circuit is another RL circuit, comprising the supply voltage, the surge-arrester blocking voltage (\(V_{SAB}\)), \(L_{u}\), \(R_{u}\), and the upstream inductance and resistance, respectively. Thus,\[I_{3u}R_{u}+L_{u}\frac{dI_{3u}}{dt}=V_{S}-V_{SAB} \tag{4}\]  
where \(V_{S}\) is the supply voltage. Therefore, the upstream current decays according to  
\[I_{u3}(t_{32})=\frac{V_{S}-V_{SAB}}{R_{u}}\Big{(}1-e^{-\frac{R_{u}}{L_{u}}t_{32} }\Big{)}+I_{0}e^{-\frac{R_{u}}{L_{u}}t_{32}} \tag{5}\]  
Note that the surge-arrester voltage \(V_{SAB}\) is greater than that of the source, and thus the upstream current decays.
## 3 Results
### Visually Improved Radar Composites  
Two examples of radar composites with and without the quality information are presented in Figure 4. The panel shows the composite of seven (nine) radars in the left (right) column (each corresponding to a different weather event), first without the quality information (top), then with the QI algorithm described within this paper (center), and finally, a composite field of QI (bottom) is presented. The difference between the first two pairs of radar composites is clear. The disturbing, non-meteorological echoes, mostly caused due to local wi-fi routers with their frequencies interfering with the radar signals, were removed. Consequently, the radar composite with the quality information is evidently cleaner and offers a more reliable overview of the current meteorological situation.  
In the examples presented in Figure 4, the overall QI was constructed on the basis of all specific QIs but the one based on the beam height above the terrain since its usage contradicts with the concept of the radar product CAPPI 2 km.  
The visual improvement of the radar composites is particularly appreciated in aviation. Pilots, dispatchers, and air traffic specialists put value on being able to make decisions on the basis of 'clean', QI-based radar maps and not being forced to mentally filter out the unwanted information from the old radar composites with no quality information included. Thus, cleaner radar images enhance aviation safety; they allow for the more convenient and precise planning of flight routes as well as the necessary maneuvering of air traffic in real time operations in situations of the quick development of dangerous weather phenomena.  
A QI-based radar composite that is updated in real time is an excellent tool as a separate layer on the air traffic management radar display at an ATC (air traffic control) workplace. A radar display that has the ability to measure distances by mouse and integrated radar data as a background represent invaluable support compared to separate radar displays at an ATC workplace, for instance to meet the regulations that an aircraft should fly at least 10 km from a cumulonimbus cloud, etc. In addition to this, clean radar composites do not purely offer visual advantages: a higher quality radar image has the potential to produce fewer false alarms by automatic warning systems.
## 5 Conclusions  
There are many categories of abnormal data in the collected data due to the failure of sensors or other factors for SHM, and the data in each category is often imbalanced. To better facilitate the anomaly detection of SHM data and solve the problem of having low accuracy in identifying samples with a small number of images because of the unbalanced dataset, this paper proposes the data migration method for identifying SHM abnormal data.  
The process of data migration reveals that there are some similarities in the image features of different bridge data, and other bridge data can be migrated to the target dataset. The models trained on the target bridge dataset with data migration perform better overall than the models trained without data migration for different categories of abnormal data.  
Figure 6: The accuracy and loss function curves of C bridge: (**a**) The accuracy curve; (**b**) The loss function curve.  
Figure 7: Confusion matrix recognition accuracy comparison chart: (**a**) A bridge training; (**b**) A + C bridge training (1—normal, 2—drift, 3—local gain, 4—noise, 5—missing, and 6—outlier).  
In this paper, the test conditions showed a 30% improvement in some categories. The recognition accuracy of the categories with fewer images has been improved.  
The bridge data used for data migration can be selected from the dataset of other bridges. So long as the model trained on this dataset has good recognition ability for its own bridge data, it can be migrated and expanded to the dataset constructed by the target bridge, and this method is generalized.  
The proposed method in this paper also has some limitations. When data migration is performed, a manual judgment of the suitability of image features between bridges is required. This process is not automated.  
Validation, C.Q. and L.H.; Formal analysis, H.Z.; Investigation, R.Z.; Resources, C.Q. and H.L.; Data curation, S.Z.; Writing--original draft, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research work was jointly supported by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2020D32), National Natural Science Foundation of China (Grant No. 52078100), Natural Science Foundation of Liaoning Province of China (Grant No. 2023-MS-106), Fundamental Research Funds for the Central Universities (Grant No. DUT22 JC19), Joint Opening Fund of State Key Laboratory of Coastal and Offshore Engineering (2022-KF-18-06).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
GPP and ET are very important carbon and water fluxes in terrestrial ecosystem, and they determine several ecosystem functions, such as respiration and growth. Accurate simulation of these fluxes will be helpful for quantifying ecosystem carbon-water exchange and their response to climate change. In this study, flux measured data of eight sites provided by ChinaFLUX were used for validating the uncertainties of MODIS-GPP and -ET products in different land cover types over China. Results showed that MODIS-GPP generally underestimated measured GPP and MODIS-ET overestimated measured ET in China. The performance in evergreen forests is very poor for MODIS-GPP. MODIS-ET, in contrast, provides more reliable estimates than that of MODIS-GPP in evergreen forests. MODIS-GPP performs well in croplands with \(R^{2}\) of 0.80, but it overestimated flux derived GPP in non-growing season and underestimated flux derived GPP in growing season; while MODIS-ET overestimated measured ET of croplands. MODIS-GPP and -ET algorithms provided very good products in grasslands and mixed forests. By designing four experimental groups and taking GPP as an example, we differentiate the impacts of max light use efficiency, climate factors and LAI/FPAR on the estimations. The MOD17 maximum light use efficiency for croplands and EBF need to be optimized, otherwise it will significantly underestimate estimated GPP, especially in croplands. But the MOD17 maximum light use efficiency for other land cover types are comparable with the optimized maximum light use efficiency based on flux observed calibration. Statistical analysis showed that the differences between the Data Assimilation Office (DAO) meteorological data and site measured meteorological data had little impact on GPP, but the remote sensing input data (FPAR/LAI) could be more sensitive to the estimate of GPP and ET. Although there are a few uncertainties, our results suggest that MODIS-GPP and -ET algorithms can effectively estimate the seasonal dynamics of GPP and ET over China. They are effective algorithms for estimating GPP and ET at regional and global scales. Meanwhile, high accuracy of LAI and FPAR data can help improve the estimating accuracy of GPP and ET. In these cases, the improved algorithms of remote sensing products for cloud detection and removal are necessary.
## 3 Mathematical Models
### Classification and Regression Trees (CART)  
Classification and Regression Trees (CART) constitute a fundamental machine learning technique that holds its roots in the realm of decision trees. This method embodies a versatile approach capable of addressing both classification and regression tasks. The origins of CART can be attributed to its introduction by [PERSON] et al. (2014); [PERSON] et al. (2014) in the 1984 book titled \"Classification and Regression Trees,\" where they elaborated on its principles and applications. In essence, a CART algorithm partitions the feature space into a hierarchical structure of binary decisions, resulting in a tree-like structure wherein each internal node represents a feature and a threshold that conditions the data's traversal along one of its two branches ([PERSON], 1984). Through a recursive process, this partitioning culminates in terminal nodes, or leaves, which correspond to the predicted outcome for a given input instance. The efficacy of CART lies not only in its interpretability, owing to its intuitive representation, but also in its capacity to accommodate various data types and handle non-linear relationships between features and target variables ([PERSON], 1984; [PERSON], 1984).  
The CART algorithm operates through an iterative procedure aimed at optimizing its splitting criteria, typically based on metrics such as Gini impurity for classification tasks and mean squared error for regression tasks ([PERSON], 1984). By evaluating potential splits and selecting the one that minimizes the chosen metric's value, the algorithm generates a hierarchy of decision rules that sequentially refine the prediction process. This process is governed by pruning strategies to mitigate overfitting and ensure generalizability. Although it is susceptible to certain limitations like instability and sensitivity to data variations, CART's inherent adaptability and ability to handle missing values contribute to its enduring relevance and applicability in diverse domains, ranging from medical diagnosis to financial forecasting.  
Figure 4: Histogram of output in used database.
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 5 Results and Discussion
Our results are in agreement with the literature, confirming that multiple temporal aggregation can improve the accuracy of conventional forecasting, also being more effective in general than simple temporal aggregation. This conclusion remains true when reviewing both the benchmarks and the variants of the proposed meta-learner. Another important finding refers to the extent of the accuracy improvements depending on the particularities of the data set. As seen in Table 3, multiple temporal aggregation is more effective in fast-moving data than in slow-moving series. This can be attributed to the variation and intermittency of the M5 data that render the extraction of time series patterns more challenging, even at high aggregation levels. In contrast, temporal aggregation manages to extract hidden patterns in the M4 data, where the series are less noisy and are characterized by trend and seasonality. In order to obtain a better understanding of the differences of the examined forecasting approaches, we apply the multiple comparisons with the best (MCB) test that compares whether the average ranking of a forecasting method is significantly better or worse than the other methods [63]. The results are shown in Figure 4. If the intervals of two methods do not overlap, this indicates a statistically different performance. Thus, methods that do not overlap with the gray interval of the figures are considered significantly worse than the best method, and vice versa. Focusing on the fast-moving series, it is confirmed that conditional multiple temporal aggregation significantly outperforms the rest of the methods, with CMTA-PW and CMTA-PSTR being, however, of similar accuracy. MTA-STR follows in terms of average rank, being significantly more accurate than CTA and CON. Interestingly, MTA-EW has the lowest performance, possibly due to the seasonal shrinkage it implies. Regarding the slow-moving data, CMTA-PW is again the top-ranked method and significantly better than the rest of the forecasting approaches. However, in this case it is followed by MTA-EW, which is reasonable given the lack of strong seasonal patterns in the M5 data set. Yet, MTA-EW is not significantly better than the approaches that build on structural scaling, being superior only to conventional forecasting and CTA. We also observe that in both data sets, CON has a better average rank than the \(\text{Comb}/\text{Com}_{\text{b}}\) and Theta/SBA forecasting approaches. Therefore, we conclude that ETS has been correctly identified as the most accurate method for producing base forecasts, enhancing the performance of the methods considered for applying (multiple) temporal aggregation.