esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Modeling and Validation of ET and NEE Estimation  
#### 4.1.1 Contributions of Different Input Variables  
This study evaluates the impacts of different types of input variables (vegetation growth, surface moisture, radiative energy, and others) and their combinations on the estimation of ET (evapotranspiration) and NEE (net ecosystem exchange). The results (fivefold cross-validation) are shown in Figure 6. The combination of four types of variables inputs, along with the Random Forest Regression (RFR) algorithm, achieved the highest estimation accuracy, with an R\({}^{2}\) of 0.81 and an RMSE of 1.13 mm for ET estimation, and an R\({}^{2}\) of 0.83 and an RMSE of 2.83 gC/m2 for NEE estimation. The accuracy for the combination of three types of variables was ET--R\({}^{2}\) = 0.61-0.78, RMSE = 1.18-1.62 mm; NEE--R\({}^{2}\) = 0.26-0.81, RMSE = 2.90-5.91 gC/m\({}^{2}\). The accuracy for the combination of two types of variables was ET--R\({}^{2}\) = 0.53-0.79, RMSE = 1.28-1.82 mm; NEE--R\({}^{2}\) = 0.01-0.77, RMSE = 3.08-6.34 gC/m\({}^{2}\). When using a single type of input variable, vegetation growth variables (R\({}^{2}\) = 0.50, RMSE = 1.87 mm) and other variables (R\({}^{2}\) = 0.52, RMSE = 1.93 mm) combined with the RFR algorithm provided the most accurate ET estimation. Similarly, vegetation growth (R\({}^{2}\) = 0.49, RMSE = 4.58 gC/m\({}^{2}\)) combined with the RFR algorithm also yielded the most accurate NEE estimation. Overall, vegetation growth has more potential when used in estimating ET and NEE. Given the distinct seasonal variation in ET (Figure 4a), the RFR algorithm effectively captures the nonlinear relationship between ET and DOY (Day of Year). In summary, for both ET and NEE estimation, the combination of multiple types of input variables enhances the accuracy of machine learning models, and different combinations of variables provide more informative data on ET and NEE.
## 3 Materials and Methods
### Wireless Backup and Query Service  
Three web-service functions (upload, history and real-time information service) were installed on the server for the remote backup of monitoring data and the submission of queries. The upload service is used for the transmission of data from the robot to the server, whereas the other two services enable remote users to submit queries pertaining to historical or real-time monitoring data held on the server. The robot backs up information when signing in at inspection points and when an intruder is detected.  
Figure 7: (a) Feature points of skeleton; (b) intruder detection results.  
The processor first connects to a wireless access point using a Wi-Fi module. The human-machine interface (HMI) contacts the upload web service to transmit the tag number and sign-in time to the server. After receiving the data, the server records the information within a table in the SQL database. When an image of an intruder is captured by the robot, the image data is encoded to base64 format and then transmitted (along with the time of detection) to the upload service. The base 64 data is then decoded back into an image by the server, whereupon it is saved in PNG format within a specific folder. The image file is named using the time of detection to facilitate subsequent queries. A client program was also developed to assist in data queries. This program provides real-time monitoring data as well as historical data by accessing the real-time information service or history service, respectively. The real-time information service searches for the latest data within the database, whereas the history service searches for data pertaining to the specific period of time defined by the user. The architecture of the data remote backup and querying system is presented in Figure 8.
## 3 Basic Statistical Analysis  
Descriptive statistics are fundamental to understanding and interpreting data. They are also critical for summarizing and describing the main features of data collection quantitatively and objectively [24]. They form the basis for more complex statistical analyses and are essential for exploring data, testing hypotheses, and making decisions in research and applied settings. They provide a summary of the central tendency, dispersion, and shape of the distribution of a data set [31].  
The mean, often referred to as the average, is a measure of central tendency and is calculated by summing all the values in a data set and dividing by the number of values. It is the most common measure used to determine the center of a data set [32]. The standard deviation is a measure of the amount of variation or dispersion in a set of values. It quantifies how much, on average, the values in the data set deviate from the mean. A larger standard deviation indicates that the data points are more spread out from the mean, while a smaller standard deviation indicates that they are closer to the mean [32]. The min and max values are the smallest and largest values in the data set, respectively. They are used to understand the range of the data and can help identify outliers or extreme values in a data set. Percentiles are indicators of the distribution of data. They represent the value below which a given percentage of observations in a group of observations falls [33].  
The 25 th percentile is known as the first quartile, the 50 th percentile is the median (the middle value of the data set), and the 75 th percentile is the third quartile. These measures help to understand the spread and skewness of the data. The median, like the 50 th percentile, is particularly useful in skewed distributions because it is less affected by extreme values than the mean. The interquartile range (IQR), which is the difference between the 75 th and 25 th percentiles, provides a measure of statistical dispersion and is less sensitive to outliers than the range [33].
## 2 Methodology  
### Data Acquisition and Preprocessing  
The system began with the acquisition of heterogeneous sensor data and expert input provided by human experts from each department (steel mill, logistic, rolling mill, and sales). Over a period of three years, these experts contributed their decisions and corresponding quality labels, which served as the foundation for the system's classification tasks. In parallel, the experts developed ontological rules based on the decisions they made throughout this period through the use of statistical software. These rules were formulated to codify their expert knowledge in some way and guide the automated decision-making process. All of this is illustrated in Figure 1.  
The data, provided by the manufacturer in anonymized form due to NDA agreements, consisted of 278,312 samples, each containing readings from multiple sensors positioned along the production process; however, due to anonymization, it was not possible to identify the specific sensor corresponding to each variable. Certain production stages are equipped with a greater number of sensors and provide more extensive measurement data than others; for example, it is obvious that there are more sensors in stages where the material is being transformed (like steel mill and rolling mill) than in stages where the product is being stored or transported. The sensors present in these stages provide information such as the following:  
* Minimum, maximum, and average water flows.
* Minimum, maximum, and average temperatures in the different lamination blocks.
* Minimum, maximum, and average temperatures of the RSM (finishing blocks)
* Tundish oscillation frequency.  
The data types present in the dataset collected can be seen in Figures 2-5.  
Figure 1: Flow chart of the dataset used for the analysis.  
Figure 2: Steel mill dataset.  
The raw sensor data were collected in a single CSV file during the three-year period and segmented according to production stages, adding the defects information provided by the EDDYEyes system of ISEND S.A, shown in Figure 6. This is an eddy current system that includes visual information about the defects and is capable of measuring the severity of them; this type of technology is one of the most valuable in the steel industry in defect detection [15; 16; 17]. In this \"defectology dataset\", the Quality Indicator (QI) is a critical metric that quantifies the quantity and severity of defects for each billet/roll.  
Figure 4: Logistic dataset.  
Figure 5: Sales dataset.  
Regarding data preprocessing, outliers that were deemed too obvious were eliminated using an interquartile range method. Moreover, the data were processed to ensure that they were easily interpretable by any algorithm (through standard normalization of the dataset and encoding of some variables). Notably, only one value per variable was recorded for each product, as the values hardly vary during the manufacture of each billet or roll. This uniformity greatly facilitated both the analysis and subsequent processing of the data.
## 3 Case Study  
The most critical issue in this case study was the on-time vaccine delivery, and the vaccine was to be transported from the distribution center in _Montemor-o-Vello_ to the vaccination centers considered for this study. The matrices were developed using the distance in minutes between the distribution center and the vaccination centers, and between each vaccination center, for each cluster. Google Maps and each vaccination center's location were used to develop temporal matrices. While the matrices were being elaborated, it was considered that:  
* The fastest and safest route is always chosen under normal traffic conditions (highway, national roads, and main itineraries). In other words, although it is faster in some cases to take the national route, the highway route was chosen because it is safer. Tacit knowledge was also used (sometimes the routes shown on Google Maps were not be the best for getting around);
* The travel time between two places is always the same (i.e., the travel time from, for example, _Leiria_ to _Condeixa-a-Nova_ is the same as the travel time from _Condeixa-a-Nova_ to _Leiria_);
* The vehicles drive at the maximum speed allowed by the law;
* These conditions were established to minimize the risk of vaccine loss due to unstable roads. This risk increases considerably when using national or municipal roads. It is known that the price per vaccine loss (due to broken vials and contamination of the entire vaccine box) may be high. The matrices obtained are shown in the following figures, which present a color scale indicating whether the time is high (red), intermediate (yellow), or short (green). The main goal of these matrices is to make all the different levels of time between the distribution center and the vaccination centers, as well as between the two vaccination centers, visible. The aim is always to choose the shortest time. The times in the matrix that have the same origin and destination (\(i=j\)), should not be considered for this case study.  
Figure 2 shows the times obtained in _Visent_'s cluster.  
Figure 2: _Visent_’s Cluster.  
Figure 3 shows the time matrix relative to _Aveiro_'s cluster.  
Figure 4 shows the time matrix of _Leiria_'s cluster.  
Figure 4: _Leiria_’s Cluster.  
Figure 3: _Aveiro_’s Cluster.  
Figure 5 shows the time matrix of _Castelo Branco_'s cluster.  
Figure 6 shows the time matrix of _Cailbra_'s cluster.  
Figure 5 shows the time matrix of _Cailbra_'s cluster.  
Figure 6 shows the time matrix of _Cailbra_'s cluster.  
Figure 5: _Castelo Branco_’s Cluster.  
Figure 6: _Cailbra_’s Cluster.  
Figure 7 shows the time matrix of _Guarda_'s cluster.  
Time in vaccination centers must also account for the unloading time of the vaccines. Therefore 20 min (which corresponds to the average stopping time in each center) were added to all the matrices presented with the times between two points.
## 2 FE Model Validation
### Numerical Model
#### 2.2.5 Analysis Method  
The calculation of the model is performed in ABAQUS using the central difference method. The resultant forces on the nodes (i.e., the difference between the external force vector \(\mathbf{P}\) and the internal force vector \(\mathbf{I}\) of elements) are the product of the mass matrix \(\mathbf{M}\) and the acceleration vector of nodes \(\ddot{\mathbf{u}}\):  
\[\mathbf{M}\ddot{\mathbf{u}}=\mathbf{P}-\mathbf{I} \tag{6}\]  
The acceleration at time is \(t\) is  
\[\ddot{\mathbf{u}}\Big{|}_{(t)}=(\mathbf{M})^{-1}.(\mathbf{P}-\mathbf{I})\Big{|}_{(t)} \tag{7}\]  
Assuming that the acceleration is constant, the velocity at the midpoint of the current increment step is determined by  
\[\dot{\mathbf{u}}\Big{|}_{(t+\frac{M}{2})}=\dot{\mathbf{u}}\Big{|}_{(t-\frac{M}{2})}+ \frac{(\Delta t\Big{|}_{(t+\Delta t)}+\Delta t\Big{|}_{(t)})}{2}\ddot{\mathbf{u}} \Big{|}_{(t)} \tag{8}\]  
The displacement at the end of the incremental step is obtained as  
\[\mathbf{u}\Big{|}_{(t+\Delta t))}=\mathbf{u}\Big{|}_{(t)}+\Delta t\Big{|}_{(t+\Delta t )}\times\dot{\mathbf{u}}\Big{|}_{(t+\frac{M}{2})} \tag{9}\]  
The advantage of the explicit integration method is that the state at the end of the incremental step depends only on the displacement, velocity, and acceleration at the beginning of the incremental step. As a result, simultaneous equations do not need to be solved, so the computational cost of each incremental step is low. In this paper, the calculation method of automatic stable time step increment of ABAQUS/explicit module was used for analysis and calculation. According to the ABAQUS/explicit user document, the calculation of the stability time step is related to the element length, the elastic modulus and the density.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
###### Abstract  
Farmers in the Brazilian Cerrado are increasing grain production by cultivating second crops during the same crop growing season. The release of PlanetScope (PS) satellite images represents an innovative opportunity to monitor double cropping production. In this study, we analyzed the potential of six PS monthly mosaics from the 2021/2022 crop growing season to discriminate double cropping areas in the municipality of Goiatuba, Goias State, Brazil. The four multispectral bands of the PS images were converted into normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green-red normalized difference index (GRNDI), and textural features derived from the gray-level co-occurrence matrix (GLCM). The ten most important combinations of these attributes were used to map double cropping systems and other land use and land cover classes (cultivated pasture, sugarcane, and native vegetation) of the municipality through the Random Forest classifier. Training and validation samples were obtained from field campaigns conducted in October 2021 and April 2022. PS mosaic from February 2022 was the most relevant data. The overall accuracy and Kappa index of the final map were 92.2% and 0.892, respectively, with an accuracy confidence of 81%. This approach can be expanded for mapping and monitoring other agricultural frontiers in the Cerrado biome.  
Random Forest; gray-level co-occurrence matrix; tropical savanna; land use and land cover mapping: satellite constellation
## 5 Experiments and Discussion
### Evaluation Metrics  
The evaluation metrics for the RMSDC model with ConvLSTM block for predicting road maintenance needs with climate change data depend on the specific objectives. Some suggested evaluation metrics that could be used are as follows:  
1. Mean Absolute Error (MAE): MAE calculates the average absolute difference between the predicted and actual values. It is commonly employed for time series forecasting and regression tasks.
2. Root-Mean-Square Error (RMSE): RMSE computes the square root of the average squared difference between the predicted and actual values. It is similar to MAE but gives more weight to significant errors.
3. Precision and Recall: Precision and recall are valuable metrics for evaluating classification models. Precision measures the proportion of true positive predictions among all positive predictions, while recall gauges the proportion of true positive predictions among all actual positive cases.
## 2 Methods  
### MGEX Tracking Network  
As of the middle of 2022, the MGEX tracking network contains about 520 stations, more than 300 of them are capable of tracking BDS. However, not of all these stations are applicable for BDS satellite POD, mainly due to deficiency of frequencies or pseudo-random noise (PRN) number supporting capability. Based on the statistics of multi-GNSS observations during July 2022 from the Crustal Dynamics Data Information System (CDDIS) [32], 129 MGEX stations with the capability of tracking all BDS-3 B1I, B3I, B1C, B2a and B2b signals are recognized and selected for the BDS-3 satellite POD experiment, and Figure 1 demonstrates the station distribution. One month observations from 1 July 2022 to 31 July 2022 are selected for the POD experiment.  
Figure 1: Tracking stations selected for BDS-3 POD. Sites are marked by green diamonds, blue circles and red five-pointed stars for Javad receivers, Septentrio receivers and Trimble receivers, respectively.
## Appendix B Detailed Description and Background Data of the Calculation Model and Hourly Simulation
### Road Transportation Hydrogen Demand  
Annual hydrogen consumption for road transportation H\({}_{\text{road}}\) (kg H\({}_{2}\)/year) (blue rectangle in Figure 3) of the passenger cars, vans, trucks, tractor-trailers, and buses is calculated in Table 10 using the German and Spanish national average annual distance driven d [57; 119; 120] and the estimated vehicle fuel economy, specific energy consumption SEC\({}_{\text{T}}\) (kg H\({}_{2}\)/100 km), in the Near Future and the Mid Century scenario [36]. For Hamburg, this results in H\({}_{\text{road}}\) of 479,909 kg H\({}_{2}\)/year in the Near Future scenario and decreases to 316,129 kg H\({}_{2}\)/year in the Mid Century scenario. For Murcia, this results in H\({}_{\text{road}}\) of 545,192 kg H\({}_{2}\)/year in the Near Future scenario and decreases to 381,732 kg H\({}_{2}\)/year in the Mid Century scenario. H\({}_{\text{road}}\) is then multiplied by a normalized repeating weekly fueling profile [260].  
In the Near Future and the Mid Century scenario, the average annual distance driven is assumed to remain constant. The number of tube trailer tractors for hydrogen transportation and their driven kilometers are assumed to be included in the number of road tractors and the number of kilometers they are driven each year. The road vehicles are owned by either the residential or the services sector, and the road transportation energy is consumed in or between smart city areas. The final energy consumption for motorcycles is not included as it currently represents only about 1% of the total road transportation final energy consumption.  
At an average annual speed of 45 km/h for passenger cars [129] in Europe and the average annual distance driven d (Table 10), there are only about 305 and 280 driving hours per year per car for, respectively, Hamburg and Murcia, mostly occurring during daylight hours [129]. For most of the non-driving time, passenger cars are mostly parked at home, the office, or close to a services sector building like a supermarket or hospital [129; 334].
## 3 Materials and Methods
### Feature Engineering Techniques
#### 3.1.2 Transformers  
Transformer architectures are widely used for solving classification problems. Initially, their application comprised natural language processing, but through their effectiveness, they have spread to such areas as image classification, language modeling, summarization, and translations. The most well-known transformer models are BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and T5 (Text-To-Text Transfer Transformer) [31]. The architecture uses an encoder-decoder structure consisting of multiple layers aiming at capturing the dependencies between the elements in a sequence, making it well suited for the classification tasks where the relationships among the input elements are crucial [32]. The main concept in this architecture is the attention mechanism, which, according to the paper [33], calculates a weighted sum of the input features with the weights referred to as attention scores, which are dynamically determined by the input data. This mechanism is a key component emphasizing the crucial or relevant features while restraining the irrelevant or less important ones.
# Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging
## 4 Discussion  
Lung ultrasonography (LUS) is a non-invasive imaging technique that allows real-time assessment of pulmonary structures. It has proven to be effective in diagnosing and treating various lung diseases, including pneumonia, pleural effusion, lung consolidation, and pneumothorax. LUS is recommended as a point-of-care examination for patients with symptoms in the chest. We presented a web-based LUS simulator developed to provide a comprehensive learning tool that visualizes ultrasound images, allows probe manipulation, and presents different disease scenarios. The simulator utilizes WebGL technology and a combination of vector-based and ray-marching techniques to simulate ultrasound waves and generate realistic ultrasound images. It incorporates various artifacts commonly encountered during lung examinations, such as shadows, A-lines, B-lines, pleural breaks, pleural sliding, and consolidation. By simulating these artifacts, the simulator helps users develop skills in identifying and interpreting ultrasound findings. The simulator has been tested for compatibility and performance, demonstrating its usability and effectiveness in providing practical experience in lung ultrasound interpretation.  
The LUS simulator developed as part of the COVID-19 Rapid Response Projects and made available free of charge constituted a significant stride in responding to the COVID-19 pandemic while simultaneously aiming to encourage the utilization of ultrasonography in diagnosing COVID-19 patients. Faced with the challenges imposed by the pandemic, this innovative tool facilitated the learning and practice of ultrasonography from the comfort of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Framebuffers Size** & **Max FPS** & **Min FPS** \\ \hline
4096 \(\times\) 4096 & 2.6 & 1.4 \\
2048 \(\times\) 2048 & 78.3 & 74.3 \\
1024 \(\times\) 1024 & 215 & 181 \\
512 \(\times\) 512 & 261 & 239 \\
256 \(\times\) 256 & 271 & 241 \\
128 \(\times\) 128 & 274 & 247 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frames per second (FPS) for different sizes of framebuffers.  
Figure 8: The LUS simulator, patient examination screen.  
one's own home, offering immense value to aspiring medical professionals and physicians. The unrestricted access to the simulator not only democratized the acquisition of this crucial skill but also facilitated the commencement of ultrasonography education, which gained paramount importance in the context of COVID-19.