esa-sceva/satcom-chunk-collection huggingface dataset:
# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 2 Research Methods
### Machine Learning Models
#### 2.3.2 AdaBoost Model  
AdaBoost [29] (Adaptive Boosting) is a classic ensemble learning method proposed by [PERSON] and [PERSON] in 1996 and belongs to the Boosting family. The core idea of AdaBoost is to combine multiple weak classifiers into a strong classifier, thereby improving the model's accuracy. It achieves this by iteratively training weak learners (such as decision stumps) and adjusting the sample weights based on the misclassified samples in the previous iteration. This process ensures that subsequent learners focus more on the misclassified samples. Finally, AdaBoost combines these weak classifiers with weighted voting to form a robust and high-performing strong classifier. Mathematically, AdaBoost minimizes an exponential loss function, which provides a theoretical guarantee of its generalization error bound. Unlike Bagging methods (e.g., Random Forest), Boosting employs a sequential training process where each round of training is influenced by the results of the previous round.  
In practical applications, AdaBoost commonly uses decision stumps (i.e., shallow trees with only one split) as weak learners. These models are simple, computationally efficient, and enable quick iterations. The training process of AdaBoost consists of three steps:  
Initialization: Initially, all training samples are assigned equal weights.  
Iteration: In each iteration, a weak classifier is trained, and its error rate is calculated. The weights of misclassified samples are then adjusted to ensure that the next iteration focuses more on these samples.  
Combination: The weight of each weak classifier in the final model is determined based on its error rate, where classifiers with lower error rates receive higher weights and make greater contributions to the ensemble. The final strong classifier is obtained by combining the results of all weak classifiers using weighted summation.  
Through this process, AdaBoost effectively reduces training error and enhances the model's generalization ability.  
AdaBoost's advantages lie in its simplicity, efficiency, and flexibility to combine with various weak learners. It is particularly well-suited for handling small to medium-sized datasets and has demonstrated excellent performance in many classification tasks. However, AdaBoost is sensitive to noisy data because the weights of misclassified samples are continuously increased, which can lead to instability when dealing with noisy data. Additionally, AdaBoost does not inherently support parallel training as it relies on the results of the previous iteration to update sample weights.  
In recent years, AdaBoost has been widely applied in fields such as face detection, text classification, and bioinformatics, establishing itself as a classic algorithm in the field of machine learning.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 3 Methods
### BILSTM Model  
#### 3.2.1 LSTM Model  
The randomness and dynamics of traffic flow data suggest that short-term traffic flows exhibit stronger correlations. In general, traffic information displays a stronger long-term dependence in terms of temporal features, as a certain time interval is required between the traffic flow passing through the adjacent detection points, especially in the case of traffic congestion. The basic recurrent neural network (RNN) utilizes the output of the preceding neuron as the input of the subsequent neuron during time series processing, such that the RNN contains a short-term memory function to process traffic data information. However, it cannot perform memory processing for long-term dependencies, namely the vanishing and exploding gradient problem. To resolve this issue, the LSTM model with a gate structure provides a new method [30]. Similar to the RNN model, LSTM is a chain structure composed of repeating unit cells. Nonetheless, the difference between the LSTM and RNN is that the LSTM cell contains four layers inside, instead of the single-layered unit construction of the RNN. The specific structure of the LSTM is illustrated in Figure 1. In principle, the LSTM selects and controls the information flow using the input gate, forget gate, and output gate, including filters that save or delete the information as required. The fundamental equations of the evaluation procedure are stated as follows:  
\[G_{f}=\sigma(W_{f}\cdot[T_{t-1},I_{t}]+b_{f}) \tag{6}\]\[G_{i}=\sigma(W_{i}\cdot[T_{t-1},I_{t}]+b_{i} \tag{7}\] \[S_{t}=\tanh(W_{c}\cdot[T_{t-1},I_{t}]+b_{c})\] (8) \[C_{t}=G_{f}*S_{t-1}+G_{i}*S_{t}\] (9) \[G_{o}=\sigma(W_{o}\cdot[T_{t-1},I_{t}]+b_{o})\] (10) \[T_{t}=G_{o}*\tanh(C_{t}) \tag{11}\]  
where \(G_{f}\),\(G_{i}\), and \(G_{o}\) represent the forget gate, input gate, and output gate, respectively; \(W\) denotes the weight, \(b\) represents the bias term, \(T_{t-1}\) refers to the input of the previous unit, \(S_{t-1}\) denotes the state of the previous memory unit, and \(\sigma\) represents the sigmoid function.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.10. Gossip Control Protocol (GCP)  
GCP [36] is a controlled flooding algorithm, using Piggy-Backing and Forwarding Control to reduce the energy consumption in mobile WSNs. Periodic hello _beacons_ are transmitted so that nodes are aware of their 1-hop neighbours. Available software version numbers are piggy-backed onto these beacons. To balance the load, and reduce energy use, each node transmits beacons a limited number of times, based on available _tokens_ (a system constant), renewed when a new software version is received. When a newer beacon is received, a node responds with its own beacon to request the new version. When an older beacon is received, the node responds with the updated software. Neighbours overhear the software update packets to benefit from _free updates_. MD5 signatures are used to verify the updates. The protocol contains no support for multiple packets in a software update.  
The key focus of GCD is improving efficiency, but the code update verification through signatures could be a useful feature for autonomic updates in vulnerable situations.
## Data and Methods
### Mapping Algorithms
Therefore, HV is an additional and effective indicator to separate forests from water and cropland, but not sufficient for complete separation of forests and built-up lands. The histogram of the difference image (Figure 3c) shows that forests have low Difference values, while water has high Difference values. The histogram of the Ratio image (Figure 3d) shows that forests overlap with the other three land cover types. Based on the 95% confidence intervals of the HH, HV, Ratio, and Difference images, the threshold values for the decision tree algorithm were determined. As some uncertainty may exist in the ROIs of different land cover types, we excluded 2.5% pixels with the lowest and highest values. The threshold values were further rounded to integer numbers for HH and HV images, and to 0.5 for Ratio and Difference images. The above exploratory data analysis provides useful information for constructing a decision tree algorithm based on the threshold values from the HH, HV, HH - HV, and HH/HV images. First, water which reflects most of the backscatter through specular reflection can be easily identified because of low values in both HH and HV. Second, forests have high values in both HH and HV, and low values in Difference (HH - HV) because the L-band of PALSAR has great penetration into forests causing huge volume scattering when its incident energy interacts with large trunks and branch components. Third, the most croplands can be easily identified because the Difference (HH - HV) indicates that the forests have the low backscatter values, while the croplands have relatively high backscatter values. The thresholds of the PALSAR-based forest mapping algorithm were determined as \(-16<\mathrm{HV}<-8\) and \(2<\mathrm{Difference}<8\) and \(0.3<\mathrm{Ratio}<0.85\). The PALSAR-based forest maps (Figure 4a) often have lower NDVI causing a commission error due to some natural features like rock lands or manmade structures such as urban, buildings. The NDVImax values of forests which are more than 0.7 are greater than that of rocky lands and urban lands. The PALSAR data above can use Landsat NDVImax (>0.7) to reduce commission error of forest  
Figure 3: The backscatter signatures of different land covers in four images. (**a**) Horizontal transmission and reception (HH) polarization, (**b**) horizontal transmission and vertical reception (HV) polarization, (**c**) Difference image (HH - HV), and (**d**) Ratio image (HH/HV). maps. Finally, in this study, we combined PALSAR and Landsat NDVI\({}_{\text{max}}\) (Figure 4b) to generate annual maps of PALSAR/Landsat forest maps using the thresholds: \(-16<\text{HV}<-8\), \(2<\text{Difference}<8\), \(0.3<\text{Ratio}<0.85\), and NDVI\({}_{\text{max}}>0.7\).
## 5 Results and Discussion
Standardized frameworks for DT implementation would also be beneficial, enabling the integration of different systems and ensuring better data interoperability. The role of human factors in DT systems should be explored, as human operators significantly impact production efficiency. Including human operators in DTs would provide a holistic understanding of the production environment, considering factors such as decision variability, skill level, and fatigue. Integrating human operators and machine automation through human-in-the-loop systems would optimize machine and human performance. In addition, integrating artificial intelligence (AI) with DTs offers opportunities for process optimization. AI algorithms, particularly those using machine learning, can analyze large datasets and make real-time predictive changes, improving decision-making and operational efficiency. AI can also be applied to predictive maintenance, suggesting the incorporation of tailored maintenance programmes based on specific machine conditions. Incorporating AI to simulate human roles within a DT framework can optimize workflows and identify potential bottlenecks caused by human intervention, leading to productivity improvements.
## 3 Experimental Results
### Experiments with Large Range  
Although the range of a single rotating paraboloid is small, it cannot meet the demand of large-scale measurement in industry. A combined array composed of many rotating paraboloids was designed for large range applications, as shown in Figure 12.  
Figure 10: Comparison experiments in X direction.  
The measurement range of a combined array in this paper was 450 mm in the direction of X and Y. In particular, the distance between the center of adjacent rotating paraboloids was 50 mm. Of course, the precise distance and angle between each rotating paraboloid were calibrated before. The relative position of the center point of each rotating paraboloid on the combined array could be calibrated by laser interferometer and coordinate measuring machine (CMM) (Hexagon, Stockholm, Sweden). The schematic of detecting two positions on different rotating paraboloids is shown in Figure 13.  
Figure 11: Comparison experiments in Y direction.  
Figure 12: L-shaped rotating paraboloid checker.  
The distance between the measuring points \(A\) and \(B\) can be expressed as:  
\[l_{x}=(x_{B}-x_{A})*0.68+(x_{o2}-x_{o1}) \tag{16}\]  
\[l_{y}=(y_{B}-y_{A})*0.68+(y_{o2}-y_{o1}) \tag{17}\]  
where (\(x_{o1}\), \(y_{o1}\)) and (\(x_{o2}\), \(y_{o2}\)) are the positions of the \(O_{1}\) and \(O_{2}\) center points on the calibrated L-type combined array.  
The L-type combined array was placed on a platform. The slope sensor was driven by two-dimensional moving stages and moved in the X or Y direction above a rotating paraboloid in a step of 0.2 mm. Similarly, the relative position of the slope sensor was monitored by a laser interferometer. Figure 14 shows the displacement measurement in the Y direction. The measurement errors could be obtained by comparing the detection values of the laser interferometer with the detection values of the slope sensor. Figures 15 and 16 show the error values of every measurement point in the X and Y direction. The curves of five colors indicated that the experiment was repeated five times.  
Figure 14: Measurement of large range displacement in the Y direction.  
Figure 13: Schematic of large range measurement.  
According to Figures 15 and 16, it can be seen that the measurement error was approximately \(\pm 2\)\(\upmu\)m when measured within the range of 450 mm. The measurement error in each set of rotating paraboloids was still 0.4 \(\upmu\)m, in contrast, the measurement error between the different rotating paraboloids was larger. The main cause of the larger error was due to the calibration error of the relative position of each free surface on the array. The accuracy of the system could be improved by improving the calibration accuracy of the L-type combined array.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
###### Abstract  
An unmanned aerial vehicle (UAV), with the advantages of mobility and easy deployment, serves as a relay node in wireless networks, which are known as UAV relay networks (URNs), to support user equipment that is out of service range (\(U_{o}\)) or does not have a direct communication link from/to the base station (BS) due to severe blockage. Furthermore, URNs have become crucial for delivering temporary communication services in emergency states or in disaster areas where the infrastructure is destroyed. The literature has explored single transmissions from one BS to a UAV to establish a wireless backhaul link in the URN; however, there exists a possibility of \(U_{o}\) outages due to severe interference from an adjacent BS, causing an overall throughput degradation of user equipment (UE) in the URN. In this paper, to improve the signal-to-interference-plus-noise ratio (SINR) of a wireless backhaul link, avoid an outage of \(U_{o}\), and guarantee a reliable relay transmission, simultaneous transmissions from multiple BSs (e.g., macrocell BSs (mBSs) and small cell BSs (sBSs)) is considered. An outage probability is analyzed, and an optimal transmit time allocation algorithm is proposed to maximize the throughput of the UE and guarantee a reliable relay transmission. Simulation results demonstrate that simultaneous transmissions from multiple BSs in the URN leads to higher throughput and reliable transmission without an \(U_{o}\) outage compared to a single transmission in the URN from a single BS (e.g., mBS or sBS), and the optimization of transmit time allocation is essential in the URN.  
U  
A +
Footnote †: journal: Electronics  
**Citation:** [PERSON] Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks. _Electronics_**2023**, 12, 4053. [[https://doi.org/10.3390/electronics12194053](https://doi.org/10.3390/electronics12194053)]([https://doi.org/10.3390/electronics12194053](https://doi.org/10.3390/electronics12194053))
## 4 Conclusions  
Simple solar radiation models, which consider only the day of the year as the input values, were calibrated and verified with the measured data for the solar climate of Poland. As expected, the goodness of fit of the tested models, expressed by the determination coefficient (R\({}^{2}\)), is slightly worse than in other climatic zones, yet it is maintained within 0.94-0.97 and can be considered satisfactory.  
Regarding long-term monthly average daily solar radiation, values of statistical parameters (RMSE, MABE, MAPE) demonstrate slight differences between the tested models, (1)-(3). More significant differences can be observed between individual stations analyzed.  
The best prediction accuracy was obtained for Equations (1) and (3). The lowest values of statistical parameters were most often observed for Equation (3), but considering that the differences are negligible and Equation (3) is more complex, the authors recommend Formula (1) as a simple tool for the prediction of long-term monthly average daily solar radiation in temperate climate when meteorological data is not available. The results indicate also that the general equation for Poland as a whole gives satisfactory accuracy and can be applied to estimate mean monthly daily solar radiation.  
Conceptualization, D.M. and M.K.; methodology, D.M., M.K., and M.W.; software, D.M., M.K., and M.W.; investigation, D.M. and M.K.; resources, D.M. and M.K.; data curation, M.K.; writing--original draft preparation, D.M. and M.K.; writing--review and editing, M.W.; visualization, M.K. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data was made available by the Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB) in Poland.  
The authors declare no conflict of interest.
## 2 Related Works
The primary goal is to minimize the combined impact of offloading delay and power usage. This goal is divided into rewards for each time slot. Next, in [33], the author proposed an algorithm based on Deep Deterministic Policy Gradient (DDPG) combined with replay memory strategy. This approach aims to achieve a flexible offloading decision plan and effective coordination of wireless resources between the backhaul and fronthaul. On the other hand, the joint computation offloading and resource allocation-based MEC computing systems are utilization schemes to handle the multiple task computing paradigms. In [34], a NOMA-based MEC system was considered for facilitation in terms of multiple computing tasks from user devices. Heuristic algorithms have been proposed to solve the problem, where the aims to trouble the computation allocation for NOMA-based MEC servers and time allocation are optimized by bisection search allocation. With that, in [35], since massive devices in IoT typically have a limitation resource, the authors proposed a novel NOMA scheme, optimal to handle the low-complexity sub-option to the total offloading throughput maximum. However, a NOMA was proposed that combines the merits of Time-Division Multiple Access (TDMA) and traditional approach NOMA. Therefore, based on the proposed NOMA scheme, the problem of maximizing the total offloading throughput by optimizing the device scheduling, the time allocation, and the computation resource allocation was considered. Hence, MEC resource allocation could be considered to be time allocation, and channel allocation for minimizing the overall delay of all tasks was investigated. In the context of cooperative task offloading resources, the author of [36] proposed the UAV-enabled MEC system to serve as MEC service for handling multiple UEs. The computation and communication resource responsibility to minimize the weighted sum energy consumption of UEs and UAV was investigated. The proposed approaches aim to provide optimization of UAV trajectory as well as the computation and communication resources based on the Successive Convex Approximation (SCA) method optimized via the Largangian Dual methods. In [37], the authors proposed a UAV assisted with a Fog-IoT network framework that ensures network delay-sensitive tasks and energy consumption for disaster management. Therefore, they proposed to optimally adjust the 3D placement of the UAV-FNs in terms of delay constraint and energy saving to prolong the network lifetime through witlessly transferring power. In [38], the authors proposed an ensemble of semi-parametric models based on the kernel-based Probabilistic Neural Network (PNN) for resource optimization of IoT fog networks. The paper aims to provide the fog computing benefits in facilitating real-time IoT application [39] with the goal to optimize response times in mission-critical tasks.
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## 4 Result and Analysis
### Streamline  
Being the particle's displacement path in the case of steady flow, streamline (also referred to as flowline for the steady-state velocity field in some literature) is constructed to verify the local orientation of the velocity field [62]. Usually the hypothetical particle tracks are constructed as the streamline by interpolating from the current velocity field. Setting the seed point's time marker as \(t(0)\), the location of \(t(i+1)\) can be computed iteratively [38]. If the spatial variation of ice flows is abnormal, the streamline can terminate abruptly rather than ending up in the ocean. Usually, the velocity  
Figure 2: MOA-based Antarctic Surface Ice Velocity with stretched MOA 2009 as background, the seven divisions are combined from [PERSON]’s 2012 Antarctic Drainage System [60] (their names are just for notation in latter analysis, rather than exact demarcation of geographical entities), Landsat-8 Rock Outcrop areas, and all tie-points distribute all over the continent. Brown enlarged cross mark exemplify two neighboring tie-points in surface crevasse of the Larsen-C shelf in the close-up above. The floating velocity field of Drygalski Ice Tongue is enlarged in the close-up below. Digital numbers denote glaciers and ice shelves discussed in Section 4: 1� the Drygalski Ice Tongue, 2� the Cook Ice Shelf, 3� the Mertz Ice Tongue, 4� the Totten Glacier, 5� the Shackleton Ice Shelf, 6� the West Ice Shelf, 7� the Fimbul Ice Shelf, 8� the Brunt Ice Shelf, 9� the Pine Island Glacier, and 1� the Thwaites Glacier.  
magnitude's profiles along the streamlines show a monotonous increasing trend as the elevation decreases. Dozens of start points are settled in Larsen-C, Filchner-Ronne, Ross, and other smaller ice shelves. The streamlines align quite well with the flow features on the ice surface, implying that there has been no or little directional change in ice flow during the last several decades. Most of them can move along a local orientation, forming smooth curves, as shown in Figure 3, although a few are trapped in local extrema, a data void, or a drastic shear zone and thus cannot wind their way to the ocean or converge to the mainstream. Specifically, among 97 start points, 19 streamlines break off halfway, and most of them, such as the Lambert Glacier in Figure 3b, are trapped in upstream narrow bottlenecks through visual inspection. Even so, it is difficult to detect the onset of tributary flow from this result since successful tracking depends both on the absolute velocity magnitude and on topography and morphology.