esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 2 System Model
### Network Model
#### 2.1.2 Link Model
However, looking at Figure 3d, it can be seen that at an altitude of 150 m an AUE is not necessarily assigned to the closest BS due to the shape of the 3D antenna radiation patterns and the AUE is not in the main lobe of the radiation pattern.
## 3 The Effect of Coupling
### Enhancement of Amplitude  
For the two-component Manakov system in the defocusing regime [31], the range of amplitude and configurations of the rogue waves in the two components are identical. Either both wave profiles are eye-shaped dark rogue waves (EDRW) with the main displacement below the background, or both patterns are four-petal-shaped rogue waves (FPRW) with two local maxima and two local minima. Moreover, the maximum and minimum values attained are identical in both components.  
For the three components case, the scenarios are drastically different as the various components in Equation (1) can exhibit distinct forms of rogue waves and the maximum displacements can be different (Figure 1). Interestingly, there must be at least one component in the form of an EDRW where the minimum amplitude is bounded below by about 0.4 (See Proof in Appendix A). However, the rogue wave solution cannot take the form of an eye-shaped bright rogue wave with the main displacement above the background (See Proof in Appendix A). As compared to the two-component system, the nonlinear coupling of the third component can enhance the rogue wave in two ways: increasing the maximum amplitude and'squeezing' a bright rogue wave.  
The maximum amplitude can be greater than \(\sqrt{2}\rho\) (Figure 1a), which is the upper bound of amplitude for the two-component Manakov system [31]. Similar increment in amplitude due to coupling was also found in other coupled systems such as the long wave-short wave resonance model with two short wave components [41] and a system of coupled derivative nonlinear Schrodinger equations [38].  
Although the formation of eye-shaped bright rogue wave is also prohibited [31], a tendency towards the formation of bright rogue wave can be observed in the three-component system. Such bright type rogue wave has a four-petal configuration where the saddle point is above the background and is closer to the maximum than the minimum. The \(u\)-component in Figure 1a exhibits a bright four-petal configuration where the amplitude at the saddle point is about 1.3 and the rogue wave ranges from 0 to about 1.5. Such geometry closely resembles the widely studied eye-shaped bright rogue wave.
# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
###### Abstract  
Carbon dioxide (CO\({}_{2}\)) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO\({}_{2}\) emissions. Quantifying anthropogenic CO\({}_{2}\) emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO\({}_{2}\) concentration. In this study, we propose an approach for estimating anthropogenic CO\({}_{2}\) emissions by an artificial neural network using column-average dry air mole fraction of CO\({}_{2}\) (XCO\({}_{2}\)) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO\({}_{2}\) anomalies (dXCO\({}_{2}\)) derived from XCO\({}_{2}\) and anthropogenic emission data during 2010-2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO\({}_{2}\) in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO\({}_{2}\) emissions especially in the areas with high anthropogenic CO\({}_{2}\) emissions. Our results indicate that XCO\({}_{2}\) data from satellite observations can be applied in estimating anthropogenic CO\({}_{2}\) emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO\({}_{2}\) uptake and emissions, from satellite observations.  
**Keywords:** anthropogenic CO\({}_{2}\) emissions; GOSAT; atmospheric CO\({}_{2}\) concentration
## 3 Datasets and Evaluation Metrics
### Datasets
#### 3.1.2 Synthetic or Simulated HDR Datasets Based on CRF  
In the field of HDR reconstruction from synthetic or simulated LDR images, the dataset by [PERSON] et al. [40] provides 502 image sequences, both real and synthetic, with their corresponding HDR images. The images have a resolution of 512 \(\times\) 512 pixels and cover a variety of indoor and outdoor scenarios. This dataset is often used in research aimed at solving the challenge of HDR reconstruction from a single LDR image, which is a key issue in deep learning.  
Another important set is that created by [PERSON] et al. [81], which contains 1352 synthetic image sequences in 4K resolution designed for research into converting LDR to HDR in videos. This dataset was created to explore the performance of neural networks in both dynamic and static scenarios and is a relevant choice of data in studies seeking to optimize the compression and quality of HDR video.  
The SI-HDR [82], on the other hand, offers 181 simulated HDR images with different exposures, with a resolution of 1920 \(\times\) 1280 pixels. This dataset simulates the responses of different cameras using the camera response function (CRF), allowing HDR images to be generated from multiple frames. SI-HDR proves useful in studies that optimize the fusion of multiple frames and investigate the capture of the full dynamic range of a scene.  
[PERSON] et al. [31] present a dataset focused on capturing and reconstructing HDR in videos, called DeepHDRVideo. This dataset consists of real 1080p videos and was created to evaluate HDR video reconstruction algorithms in complex scenarios, offering a robust environment for testing performance and image quality in multiple lighting conditions.  
The NTIRE2021 dataset [83] consists of 1761 images, including real HDR images and synthetic LDR images, with a resolution of 1900 \(\times\) 1060 pixels. It covers 29 scenes and includes data from multiple frames and single frames. This dataset was created for the NTIRE 2021 HDR challenge, aimed at advancing the state of the art in HDR reconstruction from LDR images, and is suitable for training and evaluating HDR reconstruction algorithms using real and synthetic data.  
An interesting resource in the context of synthetic data for HDR is GTA-HDR [84], which is a dataset made up of 40,000 HDR images taken from scenes in the game GTA V (Grand Theft Auto V). The images, with a resolution of 512 \(\times\) 512 pixels, simulate a wide variety of lighting conditions and dynamic environments, making them valuable for testing HDR reconstruction techniques in synthetic and controlled environments.  
The dataset by [PERSON] et al. [85] is another example of a multi-dimensional HDR application, containing 880 scenes of cholangiocarcinoma images obtained by hyperspectral microscopy and high-resolution color images (2304 \(\times\) 1728 pixels). Originally designed for medical research, this dataset can also be useful in exploring advanced image processing techniques, including HDR reconstruction in scenarios with significant illumination variations.
## 7 Evaluation Results  
The evaluation of our text/non-text pixelwise segmentation in Table 3 showed that the text elements on the page were almost fully recognized, with a macro-averaged per-pixel F1-score of 99.8%. When compared to the results of the Kraken engine, which achieved a macro-averaged per-pixel F1-score of 99.5% on our datasets, this showed that our method provided a slightly improved text recognition on the tested datasets. When comparing the text line instance segmentation performance, our results showed a clear improvement with a macro-averaged F1-score of 99.3% with the default settings compared to 88.4% for the Kraken system. Especially on the GW5051 dataset, which features a two-column layout, the results showed the highest difference between the two methods. A review of the results suggested that Kraken often failed to separate nearby text lines such as marginalia from the main text or merged the lines from the two columns. The evaluations for the layout recognition followed our evaluation scheme as described in Section 5. The results, as presented in Table 4, were all achieved using the same baseline detector model. The detailed evaluations require knowledge for splitting or merging the baselines and the classification of the text lines types. As described in Section 2.3, we used general background knowledge and special configurations about the existing layout types for each dataset representing one book or several books printed in the same layout. Our default configuration was an automatic column detection with marginalia, a header and a footer line, and dropped capitals and images. The configurations used for the datasets were:  
* CamStd: no changes to the standard configuration;
* CamMarg: no changes to the standard configuration;
* GW5051: two-column layout, no marginalia, no dropped capitals, split of the top line into headline and header;
* GW5056: no dropped capitals;
* CamManual: no changes to standard configuration. The main results can be summarized as follows:  
* CamStd: nearly perfect layout recognition;
* CamMarg: confusion of the very few dropped capitals with text elements, nearly perfect normal text lines' recognition, and good footer, header, and marginalia recognition (between 97.5% and 99.0% precision and recall);
* GW5051: very good column separation (precision 99.8%, recall 99.9%), some problems with footers (due to very short footers causing problems for the baseline detection), and good results for separation of the top line into header and text (headline);
* GW5056: very good results for the separation of marginalia and text (despite ragged side), good results for header, and similar to GW5051, some problems with footer due to very short footers;
* CamManual: overall good results despite difficult layout and dropped capitals, as well as unexpected elements (handwritten artifacts and underlines, astrological symbols).
## 1 Introduction
Literature [14] proposed a system protection method for an integrated pipe gallery power cabin based on multisource heterogeneous data fusion. The method initially integrates multiple distributed data sources from the pipe gallery power cabin using middleware technology for data layer fusion. Then, the data with similar characteristics are divided into subspaces, and the proposed LGP method is used to extract features in each subspace. Finally, the features extracted in each subspace are fused, fully using the spatial correlation of multiple distributed data sources. In fact, using data correlation for data cleaning cannot sufficiently discriminate dirty data from time or space, and the correlation of data should be measured from the dual correlation of time and space [15]. Literature [16] used the two-way comparison method to identify and process abnormal data in spatial power load forecasting. This method uses the load variation between the loads at the previous moment as the criterion for judging whether the data are abnormal for horizontal comparison; it also simultaneously processes the abnormal data. For multiyear data, after judging the abnormal values in the load data of each year at the same time, the average value of the normal data is used as the correction value for horizontal comparison. Literature [17] proposed a multilevel cleaning and identification method of measurement data based on the spatial-temporal correlation characteristics of data. The second-level data identification is carried out according to the time-series correlation of the measurement data, and the convolutional neural network is used to establish a spatial-physical correlation model for the third-level data identification. However, these articles on the spatiotemporal correlation of measurement data ignore the measurement deviation caused by the different spatial positions of the measurement equipment when considering the spatial correlation of the measurement equipment. Toward this end, in this paper, a joint spatiotemporal cleaning technique for distribution network measurement data based on CC-VC-UKF is proposed. In the presence of spatial measurement deviations, the FTU measurement sequence is filtered based on the CC-VC-UKF algorithm with the TTU measurement data sequence as the reference sequence, and the FTU measurement data with higher accuracy can be obtained from the filtered data sequence, and measurement deviations according to the network topology. The rest of this paper is organized as follows: The related work is discussed in Section 2; the proposed CC-VC-UKF method is presented in Section 3; combined spatiotemporal cleaning of measurement data in the distribution network based on the CC-VC-UKF algorithm is presented in Section 4; extensive simulations are conducted in Section 5; and finally, the conclusion is given in Section 6.
## 5 Performance Evaluation
### Gather  
In the Gather experiment, the data type was Integer and the size of transferred data was 16 MB. Figure 9 shows the results of Gather communication times. Like the Broadcast experiment, in the Figure 9 graphs, C represents MPI communication in C environment, Gather_B represents MPI buffer communication in Python environment, and Gather_O represents MPI object communication in Python environment.  
Figure 8: Broadcast communication time results according to the experiment environment: (**a**) Host; (**b**) one Docker Container; (**c**) two Docker Containers; (**d**) four Docker Containers.  
Figure 9: Cont.  
Figure 9 is the result of performing Gather communication. Figure 9a is the Host environment, Figure 9b is the one Docker Container environment, Figure 9c is the two Docker Containers environment, and Figure 9d is the four Docker Containers environment. Similar to the Broadcast experiment in Figure 8, communication in MCDRAM of Flat mode was the fastest and DDR4 of Flat mode was the slowest. In the Gather experiment, the data size is smaller than that of Broadcast and AllReduce. Gather communication collects data from many processes in a single process. This feature of Gather communication generates Out Of Memory when the data size is large. The reason that the performance comparison between experiments seems small is that the data size is small. The feature of this experiment is that the case of Gather_B in MCDRAM of Flat mode shows faster communication performance than C.  
It was confirmed that the Gather_B communication in MCDRAM of Flat mode has a performance improvement of up to 65% compared to the C communication in MCDRAM of Flat mode. In addition, the Gather_B communication in MCDRAM of Flat mode was able to confirm up to 74% performance improvement over the Gather_B communication in DDR4 of Flat mode. It can be seen that Gather_B communication in Cache mode is up to 13% faster than Gather_B communication in DDR4 of Flat mode. Like Broadcast, the Host environment (Figure 9a) and the one Container environment (Figure 9b) showed similar performance. In addition, poor communication performance was confirmed in the increase of the number of Containers.
# Smart Low-Cost Control System for Fish Farm Facilities
## 2 Identification of Critical Factors for Control Systems for Aquaculture
### Factors Affecting Fish Wellbeing and Performance
#### 2.1.2 Factors of the Species Abiotic Domain  
Regarding the Abiotic Domain, most of the factors are related to the water. Focusing on the water, we can identify the factors linked to water quality, such as the water temperature [46; 47; 48], DO [49; 50; 51], Salinity [52; 53], Turbidity [54; 55], Water Colour [56], pH [57; 58], Oxidation/Reduction Potential [59; 60], and pollution. All the aspects of water quality affect the fish's performance directly, but some of them also affect them indirectly. The hatcheries, where larvae and fry fish growth occur, are the aquaculture facilities in which water quality is most important to ensure no deformities and the correct starting of feed intake. Pollution has been included as a general term which can include the presence of external pollution sources such as hydrocarbon [60], pesticides [61], colored organic matter [62], and harmful algal blooms [63], among others. Nevertheless, pollution can also be caused by abnormal situations in production, such as an increase in ammonia [64] and uneaten feed or fish feces [65]. Other factors related to the water are the water current or water velocity [53; 66], waves [66; 67; 68], and tides [69]; some of them affect the fish performance directly, while others affect them indirectly by affecting the cage structure and net shape.  
Besides the water, some other abiotic factors affect aquaculture performance, such as the atmosphere and light. The wind speed and direction, which causes irregular feeding [70; 71], air temperature [72], and rain or storms, and their effect on fish escapes and water turbidity [32; 73], are the most relevant factors related to the atmosphere. Regarding the light, the intensity and color, as well as the photoperiod, are extremely important in hatcheries [52; 54; 74]. The aforementioned factors of the Abiotic Domain, identified for  
Figure 2: Factors of the Production Domain. The managers of the aquaculture facilities define the factors of the Production Domain, some of them cannot be modified, such as the location. The feed and feeding procedure greatly impacts fish performance and well-being and can be adapted to the grown species and the other Domains.  
the proposed Smart Low-Cost Control Systems for Aquaculture Facilities, are depicted in Figure 3.
## 5 Role of Triterpenoids in Cancer Treatment  
Triterpenoids are structurally diverse organic compounds. More than 20,000 triterpenoid varieties are formed by multiple modifications of the basic backbone structure. Several triterpenoids such as avicin, betulinic acid, boswellic acids, celastrol, diosgenin, madecassic acid, mashinic acid, momordin, saikosaponins, platycodon, pristimerin, unsolic acid, CDDO, and withanolide, have been shown in our laboratory and others to possess anticancer and anti-inflammatory activities. Preliminary data from ongoing studies indicate that some synthetic triterpenoids are being developed with improved anticancer activity. Several triterpenoids are now in clinical trials at different phases (Table 4; www.clinicaltrials.gov). Synthetic triterpenoid, CDDO, is being tested in patients with various cancers. Betulinic acid ointment is under evaluation for the treatment of dysplastic nevi that have the potential to transform into melanoma. Triterpenoids are highly multifunctional and thus have promise as agents in the treatment of cancer because of their ability to block the NF-kB activation, induce apoptosis, and inhibit proliferation, invasion, metastasis and angiogenesis.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Triterpenoids** & **Cancer** & **Phase** & **Status** & **Sponsors** \\ \hline CDDO-Me & Solid tumors or & I & Terminated & MDACC \\  & Lymphoid malignancies & & & \\ CDDO & Solid Tumors & I & Completed & NCI \\  & or Lymphoma & & & \\ CDDO-Me & Liver disease & I/II & Terminated & RPI \\ Ginsenoside & Breast cancer & II & Ongoing & SIU \\ Ginsenoside & Hypertension & II & Completed & SMH \\ \hline \end{tabular}
\end{table}
Table 4: List of triterpenoids in clinical trials.
# Ensemble-Based Correction for Anomalous Diffusion Exponent Estimation in Single-Particle Tracking
## 3 Results
### TA-MSD Correction Method Test  
In this subsection, we perform an experimental comparison of the proposed corrected time-averaged MSD method described in Formula (17) and the classical TA-MSD method,  
Figure 1: Dependence of MAE(\(\mu\)) on \(\mu\) for different values of \(\sigma\), comparing methods for estimating the anomalous diffusion exponent (through averaging \(\alpha\) values for ensemble trajectories and the TEA-MSD method).  
the Whittle method, and the \(\alpha\) exponent estimation obtained using the RANDI neural network approach.  
Ensemble trajectories were generated with \(\alpha\) following a truncated normal distribution N(\(\mu\), \(\sigma^{2}\)), bounded between 0 and 2. Applying the TEA-MSD approach for \(\mu\) estimation and trajectory correction based on the dependence of the TA-MSD method estimation variance on T, we experimentally evaluate the effectiveness of the correction compared to conventional trajectory \(\alpha\) estimation methods for various \(\mu\) and \(\sigma\) values. The trajectory length of T = 25 was specifically chosen to ensure compatibility with the RANDI neural network model, which is trained on trajectories of this particular length.  
The results (Figure 2) show that for small and moderate values of \(\sigma\), corrected estimates outperform other methods in terms of the MAE metric. However, as \(\sigma\) increases, the truncated normal distribution over [0,2] becomes progressively flattened, leading to a less distinct peak and a shape that deviates increasingly from a typical Gaussian profile. In such cases, the effectiveness of the correction method diminishes significantly, and the conventional TA-MSD method becomes more reliable. This is because attempting to correct trajectories that are not normally distributed only worsens the results, demonstrating the limitations in the correction method's application  
Figure 2: Dependence of MAE of ensemble trajectory \(\alpha\) estimates on \(\mu\) and \(\sigma\) for estimation methods and the correction method.
# Insider Threat Detection Using Machine Learning Approach
## 4 Experimental Results
### Anomaly Detection  
Anomaly detection is the process of detecting deviations from normal behaviour in the data. We used two distinct anomaly detection methods, one-class SVM and isolation Forest. We train the models on the negative class, i.e., the normal class, which represents most users. We test the models' efficiency using the positive class with a subset of the negative class.  
Recall that our dataset contains 1000 users, 30 of whom are abnormal or positive class. We divide the dataset into 80% and 20% for training and testing, respectively. The 80% for training contains all negative class users. We used 20% of the training set as a validation set. The 20% for testing has all 30 users as well as the remaining negative class. Table 5 shows the results on the CERT4.2 dataset after employing DFS and PCA on OCSVM and isolation Forest.