esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Testing Activities on MDTP
### Discussion of the Development Work  
As it has been shown, the undertaken development works show the feasibility of the protocol that has been put forward. This version of the protocol includes more complex features that have been codified for the integration stages of the SWARMs project, which constitutes the framework used for the development of MDTP. Even with them, the performance of the protocol regarding transmission of information at the data level has been proven satisfactory, as data are transmitted at a pace acceptable by both the deployed hardware and any potential human operator. Even when more complex information is transmitted, such as GPS coordinates, the protocol will handle the data without issues or errors and will be transmitted and understood correctly at both ends of the communication. In addition to these, there are some other features that have a major importance for the development works that have been taken into account for the deployment of the protocol:  
1. A nominal bitrate of 1 kbps is quite common for underwater communications. While limitations on the bandwidth for underwater communications have been explained in this manuscript, it must be taken into account that 1 kbit per second data transmission are the usual ones present in acoustic communications. It is because of this reason that PDUs cannot be larger than a certain limit of bytes, or otherwise data will be lost. At the same time, trying to send information in single PDUs that do not relay on other ones to provide all the information requested is also something to consider, as such a procedure will maximize the chances of having all the data correctly delivered to the destination.
2. The simulator that has been used to mimic the behaviour of underwater communications in the open sea is based on what is used in the SWARMs project, that is to say, the model S2C18/34 manufactured by Evologics (Berlin, Germany), which matches the required performance for transmission of data over relatively large distances (up to 3.5 km) with data rate up to 13.8 kbps (nominally). Although this bitrate will usually fall well below this figure when communications take place in shallow waters (due to reflections in the acoustic waves), it will still be usable to transmit information.
3. If one acoustic modem is responsible to transmit the periodic data sent by several AUVs, the period for the transmission of periodic data will be no lower than 10 or 15 s in order to have the acoustic modem working correctly. This is due to the fact that, due to the characteristics of the environment where data transmissions take place, it is not advisable to establish periodic data transfers at a rate of milliseconds, especially if there are several vehicles transmitting information at the same time, because they might overall the modem, regardless of the strategy used to send that information (for example, if too many PDUs gather at a point, the modem will give priority to the first data received, in a First-In-First-Out fashion).
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
###### Abstract  
Recently, cellular networks' energy efficiency has garnered research interest from academia and industry because of its considerable economic and ecological effects in the near future. This study proposes an approach to cooperation between the Long-Term Evolution (LTE) and next-generation wireless networks. The fifth-generation (5G) wireless network aims to negotiate a trade-off between wireless network performance (sustaining the demand for high speed packet rates during busy traffic periods) and energy efficiency (EE) by alternating 5G base stations' (BSs) switching off/on based on the traffic instantaneous load condition and, at the same time, guaranteeing network coverage for mobile subscribers by the remaining active LTE BSs. The particle swarm optimization (PSO) algorithm was used to determine the optimum criteria of the active LTE BSs (transmission power, total antenna gain, spectrum/channel bandwidth, and signal-to-interference-noise ratio) that achieves maximum coverage for the entire area during the switch-off session of 5G BSs. Simulation results indicate that the energy savings can reach 3.52 kW per day, with a maximum data rate of up to 22.4 Gbps at peak traffic hours and 80.64 Mbps during a 5G BS switched-off session along with guaranteed full coverage over the entire region by the remaining active LTE BSs.  
energy efficiency; energy harvesting; eco-friendly networks; BSs' switching off/on; green BSs +
Footnote †: journal: Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
# Towards a Real-Time Description of the Ionosphere:
## 3 Measured and Modeled Data Used for Validation
### IRI and IRTAM Models Runs  
In this study, we focus on the F2-layer peak characteristics modeled by the IRI-2016, which is the current version of the IRI model. Specifically, IRI was run for the same time periods and locations covered by the ionosondes and COSMIC datasets by using the IRI Fortran code available at the IRI website ([[http://irimodel.org/](http://irimodel.org/)]([http://irimodel.org/](http://irimodel.org/)), accessed on 30 July 2021). In this way, a one-to-one comparison between measured and modeled _fo_F2 and _hm_F2 values is guaranteed.  
IRI _fo_F2 values were modeled through the URSI coefficients [31], while _hm_F2 values were modeled through the [PERSON] et al. [44] option. Moreover, the IRI model was run with the storm option [39; 40] \"ON\" to take into account the magnetic activity disturbance effect on modeled _fo_F2 and _hm_F2 values.  
Likewise, the IRTAM procedure was also run for the same time periods and locations covered by the ionosondes and COSMIC datasets. The IRTAM runs were made by using the IRTAM Fortran package available on the Global Assimilative Model of Bottomside Ionosphere Timeline (GAMBIT) Consortium website ([[http://giro.uml.edu/GAMBIT/](http://giro.uml.edu/GAMBIT/)]([http://giro.uml.edu/GAMBIT/](http://giro.uml.edu/GAMBIT/)), accessed on 30 July 2021), with the application of the GAMBIT coefficients ([[https://ulcar.uml.edu/GAMBIT/GambitCoefficients/](https://ulcar.uml.edu/GAMBIT/GambitCoefficients/)]([https://ulcar.uml.edu/GAMBIT/GambitCoefficients/](https://ulcar.uml.edu/GAMBIT/GambitCoefficients/)), accessed on 30 July 2021) for modeling both _fo_F2 and _hm_F2. GAMBIT coefficients are the URSI ones corrected by IRTAM through the NECTAR method (see Section 2.2) on the basis of assimilated data. Since GAMBIT coefficients are available from the beginning of 2000, the validation analysis here presented is restricted to years 2000-2019. It has to be pointed out that the number of stations assimilated by IRTAM has changed over the years due to the number of available GIRO Digisondes able to stream information in real-time to GAMBIT. This number has changed from about 10 at the beginning of 2000 to about 60 at the end of 2019. As a consequence, we should expect different performance of IRTAM for different years; specifically, an increase of its performance as time goes by.
## 2 Instrument Design
### Additional Sensors
#### 2.2.3 Multispectral Camera  
One more sensor is a multispectral camera that has two configurable modes: Red-Green-Blue (RGB) or Color-Infrared (CIR). Multispectral cameras are commonly used in many remote sensing applications. In particular, the CIR configuration is chosen to study vegetation-covered areas because vegetation is highly reflective at Near-Infrared (NIR) band ([31], p. 7); whilst the RGB configuration is preferred in applications such as analyzing man-made objects, performing atmospheric and deep water imaging, or studying vegetation structures.  
The multispectral camera model is a DuncanTech MS4100 (DuncanTech, Auburn, CA, USA) [32] with a FOV of 60\({}^{\circ}\), a detector resolution of 1920 \(\times\) 1080 pixels, and a maximum image frequency of 10 frames per second. The camera has 3-CCD image sensors that can work in either in RGB, or in CIR mode, and hence, it can measure electromagnetic radiation at four optical frequency bands: Blue (350-500 nm), Green (500-600 nm), Red (600-700 nm), and NIR (750-850 nm).
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 3 Simulation and Discussion
### Simulations on Second-Order System
#### 3.1.2 Case 2 (Effectiveness against Low-Frequency Perturbation)  
In the second case, the simulation was performed with disturbance (\(d\)). The sinewave was introduced as disturbance (Figure 10). The frequency of the sinewave was 0.9 rad/s (low frequency). As described in Section 2.2.3, our proposed algorithm is effective against low-frequency perturbation. The proposed method has the ability to minimize the effect of low-frequency perturbation (Figure 11). The control input can be observed in Figure 12. The input disturbance had a low frequency. Therefore, the ESO estimated it accurately (Figure 13). The absolute average error of the ESO-based PIDSMC was smaller than that of the ESO-based SMC (Table 3).  
Figure 11: Tracking-error comparison (case 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ESO-Based SMC** & **ESO-Based PIDSMC** \\ \hline
0.279947 & 0.182187 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absolute average error in degree (case 2).  
Figure 12: Control inputs of both algorithms (case 2).  
Figure 16: Output-error comparison (case 3).  
Figure 17: Control inputs of both algorithms (case 3).  
Figure 15: External disturbance of the system (case 3).  
The performance of the designed controller depended upon the accuracy of the estimation of the perturbation. The relation between the actual and the estimated perturbation was a low-pass filter. By increasing the observer gain, the estimation accuracy was improved. In case 4, the frequency of the disturbance was high (close to and greater than the breaking frequency of the low-pass filter). The designed algorithm did not effectively estimate the high-frequency perturbation (Figure 21).  
The absolute average error of the designed algorithm (in the first three cases) was lower than that of the ESO-based SMC (Figure 22). This suggests that the developed technique is more robust against the low-frequency elements of perturbation.  
Figure 20: Output error in the presence of variable disturbance (case 4).
## 3 Finding Models to Predict Energy Consumption
### Benchmarks  
Two additional benchmarks were selected for comparison purposes. The first benchmark is the manual technique used by the case site providing the dataset for this study. The second benchmark is an ARIMA model. ARIMA was selected because of its widespread use in energy forecasting and, in particular, in related works [6].  
The manual technique used by the case site is performed by a simple calculation as per Equation (5), where the energy consumption of a given day, \(C_{predicted}\), is the planned production flow (\(F_{planned}\)) and the TECI (\(n_{previous}\)) based on measured data collected on the previous day.  
\[C_{predicted}=F_{planned}n_{previous} \tag{5}\]  
Figure 8: MAE grid search result for (**a**) SVR and (**b**) Random Forest.  
Figure 6: RMSE grid search result for (**a**) SVR and (**b**) Random Forest.  
Figure 7: MAPE grid search result for (**a**) SVR and (**b**) Random Forest.  
The choice of the ARIMA model for this study was based on the time-series nature of our dataset (data numbers and the output variable relates to your past data). Equation (6) [49] represents the mathematical expression for the autoregressive part.  
\[x(t)=\sum_{i=1}^{p}\alpha_{i}x(t-i) \tag{6}\]  
where \(t\) is the index represented by an integer, \(x(t)\) is the estimated value, \(p\) is the number of autoregressive terms, and \(\alpha\) is the polynomial related to the autoregressive operator of order \(p\).  
Equation (7) [49] reflects the dependency of time-series values on the errors of previous estimates, i.e., the errors of the forecast are taken into account when estimating the next value in the time series.  
\[x(t)=\sum_{i=1}^{q}\beta_{i}\varepsilon(t-i) \tag{7}\]  
where \(q\) is the number of moving average terms, \(\beta\) is the polynomial related to the moving average operator of order \(q\), and \(\varepsilon\) is the difference between the estimated and actual values of \(x(t)\).  
Equation (8) [49], a combination of Equations (6) and (7), represents the ARIMA model (\(p\) and \(q\)) used as a benchmark for this study.  
\[x(t)=\sum_{i=1}^{p}\alpha_{i}x(t-i)-\sum_{i=1}^{q}\beta_{i}\varepsilon(t-i) \tag{8}\]  
After empirical analysis, the selected ARIMA model presented the order of the autoregressive (\(p=1\)), the degree of differencing (\(d=0\)), and the order of the moving average (\(q=1\)).
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
###### Abstract  
This study shows a simplified approach for calculating emissions associated with forest fires in Mexico, based on different satellite observation products: the biomass, burnt area, emission factors, and burning efficiency. Biomass loads were based on a Mexican biomass map, updated with the net primary productivity products. The burning efficiency was estimated from a Random Forest Regression (RFR) model, which considered the fuel, weather and topographical conditions. The burned areas were the downloaded Maryland University MCD64c6 product. The emission factors were obtained from well-known estimations, corrected by a dedicated US Forest Service and Mexican campaign. The uncertainty was estimated from an integrative method. Our method was applied to a four-year period, 2011-2014, in three Mexican ecoregions. The total burned in the study region was 12,898 km\({}^{2}\) (about 4% of the area), producing 67.5 (\(\pm\)20) Tg of CO\({}_{2}\). Discrepancies of the land cover maps were found to be the main cause of a low correlation between our estimations and the Global Emission Database (GFED). The emissions were clearly associated to precipitation patterns. They mainly affected dry and tropical forests (almost 50% of all emissions). Six priority areas were identified, where prevention or mitigation measures must be implemented.  
gas emissions; remote sensing; machine learning +
Footnote †: journal:
## 4 Digital Twin Modelling Methodology
### Model Training Algorithm  
#### 4.4.1 Support Vector Regression (SVR)  
SVM is a supervised learning method widely used in statistical classification and regression analysis. The SVM model provides the best separation hyperplane in feature space (FS) such that the negative and positive sample intervals in the training set are the largest. With the help of the SRM inductive principle, SVR improves its generalization ability using only a limited number of patterns [2]. Therefore, the SVM technique as a regression analyzer, SVR, is applied in this research to tackle regression estimation problems for TBM performance prediction.  
The main objective of SVR is to find a function f(x) with the minimum deviation between the predicted and actual values. SVM makes all sample points approach the hyperplane by minimizing the total deviation between the sample points and the hyperplane [58]. The linear approximating function of SVR, f(x), is represented in Equation (5).  
\[\text{f(x_{i})}=(\text{w}_{i},\text{x}_{i})+\text{b} \tag{5}\]  
where w identifies the weight vector having a unit length at a right angle with the hyperplane and bias (b) corresponds to the threshold coefficient.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 5 Methodology
### Hypotheses
#### 5.1.4 Model Performance  
Specialized models that have been finetuned on healthcare data are expected to have a better understanding of domain-specific nuances and terminology, leading to more accurate and precise FHIR bundles. General-purpose models, while powerful, may struggle with the specific requirements of healthcare data conversion. Additionally, models like Mistral, which retain more of the original text, may introduce challenges in standardizing the information for FHIR conversion.  
**H4:**: _Specialized models finetuned on healthcare data (e.g., Llama3-Med42) will perform better than general-purpose models (e.g., Mistral, Gemini) in terms of precision and resource mapping accuracy. However, Mistral and Gemini will exhibit similar performance characteristics, with Mistral retaining more original text, potentially affecting standardization._
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 3 Results
### Spatial Trends in the Human Impact on FEF  
In order to explore the spatial evolution of the factors influencing forest ecological function and to obtain their global trends, we used the trend analysis tool in Arc GIS 10.8 to draw a three-dimensional perspective map. As is shown in Figure 5, the X-axis curve indicates the longitudinal trend, and the Y-axis curve indicates the latitudinal trend.  
In the east-west direction (X direction), the influence of LIVE and PROT increases linearly from west to east. FEF has the highest sensitivity to changes in LIVE, whose extent is much higher in the east than in the west. The influence of AGRI, PDS, URB, and PDUC on FEF is distributed in a U-shape. There is significant spatial differentiation in the impacts of AGRI, URB, and PDS, with AGRI and URB having greater impacts on the central region and PDS on the western counties. Additionally, the effects of PDUC are not significantly different from west to east; neither are those of EDS. In contrast, INDS has an inverted U-shaped influence on FEF. We also find that the impacts of the natural factors are all inverted U-shaped with significant spatial variations. PREC plays an important role in the central region, while TEMP, ELVE, and SOIL have higher impacts in the east of YRB.  
In the north-south direction (Y direction), the influence of EDS, AGRI, URB PDUC, and PROT on FEF shows an inverted U-shape. There is a significant spatial differentiation in the effect of LIVE. EDS, PDUC, and LIVE have a more remarkable influence in the north. Factors such as INDS, PDS, and PROT show a U-shaped impact on FEF, with PDS and PROT having greater influence in the north and INDS in the south. The degree of influence of AGRI on FEF decreases from north to south and is more sensitive to differences between north and south regions. As for natural factors, the effects of TEMP and ELEV are declining from north to south, both of which are much more pronounced in the north. The opposite is true for PREC and SLOP. The effect of SOIL is inverted U-shaped and increases from north to south.  
In general, the influence of human activity factors on FEF is more evident in the east-west direction than in the north-south direction, with LIVE, PDS, and PROT on FEF being the most sensitive in the east-west direction and AGRI, PDS, and URB being the most sensitive in the north-south direction. The degree of influence of natural factors varies considerably in both directions.
## 4 Discussion
### Comparison and Analysis with Other Methods
In the imbalanced classification task for water heater reviews, the Sac-BiLSTM model achieves an Accuracy of 94.9874, matching the results of the test set. Additionally, in the imbalanced food delivery comment dataset, the Sac-BiLSTM model still outperforms other models. The accuracy variations of different algorithms on the validation set of the product review dataset at different iteration numbers are shown in Figure 8. From Figure 8, it can be observed that the static single-channel Static-CNN algorithm has the lowest accuracy and significant fluctuations. Other methods that optimize using non-static and multi-channel approaches show improved accuracy and stability. The proposed Sac-BiLSTM algorithm achieves significantly higher accuracy than other algorithms, with smaller fluctuations. It rapidly converges within the first five iterations and reaches a stable accuracy level after the tenth iteration. Furthermore, the proposed algorithm introduces a novel dual-channel structure that fully utilizes the feature extraction capabilities of CNN combined with dilated convolutions. This allows the algorithm stabilization at a high level within a small number of iterations. Additionally, the Sac-BiLSTM incorporates Self-Attention units in the BiLSTM module, enabling the model to weight the importance of different words and better capture key information for sentiment expression. Compared to a regular BiLSTM, this further improves accuracy. Additionally, the accuracy of Sac-BiLSTM is higher than that of BiLSTM-CNN (series and parallel) in different categories. In the tablet and fruit categories, the accuracy of Sac-BiLSTM reaches 88.5293 and 87.3329, respectively. On the other hand, the accuracy of the BiLSTM-CNN (series) is 84.6752 and 83.9825, and the accuracy of the BiLSTM-CNN (parallel) is 85.6842 and 86.5796.
## 7 Discussion  
The results of this study demonstrate that the CVNN model can effectively predict wave conditions such as wave direction, height, and period based on the motion data of floating structures. The predictions showed high accuracy, particularly for wave direction, where errors were consistently below 2%. The errors for wave height and period predictions were also relatively low, with the maximum error being less than 10% and most predictions showing errors of less than 5%.  
This study holds significant value as it is the first to apply the CVNN model for predicting wave conditions surrounding floating structures. The use of CVNN is a novel approach in this context, allowing for the effective representation of both the amplitude and phase information of waves, including wave direction data in polar coordinates. This is particularly important as traditional models, such as RVNNs, often struggle to capture such information accurately. Therefore, this research makes a substantial contribution to the field by introducing a powerful and efficient method for wave prediction.  
However, it is important to note that the model was tested using pseudo-measurement data generated through numerical simulations, which may not fully represent real-world conditions. The performance of the model could be further improved if actual measurement data were used, as the quality of the input data plays a significant role in prediction accuracy.  
Additionally, the study was conducted under simplified conditions, comprising a simple rectangular floating body with zero forward speed and long-crested wave conditions. These assumptions may limit the generalizability of the model to more complex real-world scenarios, where structures may have more intricate geometries, non-zero forward speeds, and short-crested wave conditions. Future studies should explore these factors to assess the model's robustness and potential for real-world applications.