esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.3.1 Machine Learning Methods  
ML is a multidisciplinary field that encompasses a variety of specific algorithms. This study selects three ML algorithms: RF, ANN, and XGB.  
1. RF: RF is an ensemble ML method that generates multiple decision trees from random samples [36; 37]. During the construction of decision trees, the sample distribution is considered, and the bootstrap process is used for resampling. A bagging (bootstrap aggregating) process creates the final solution by averaging the results of the bootstrap trees [38]. This process improves the model's performance and robustness by mitigating overfitting and enhancing generalization. In this study, the RF algorithm is implemented using the Scikit-learn library in Python 3.11, with hyperparameters such as n_estimators, max_depth, min_samples_split, and min_samples_leaf adjusted accordingly.
2. ANN: ANNs simulate biological neural systems and typically consist of an input layer for explanatory variables, multiple hidden layers for nonlinear computation, and an output layer for producing results [39]. The weights and biases in the neural network are optimized by minimizing the cost function between actual labels and predicted values, allowing the network to learn and adapt to various data patterns for improved prediction accuracy. In this study, the ANN structure was modified from an initial two-layer neural network to a two-layer neural network with Dropout layers (rate = 0.5) inserted between hidden layers [40] to prevent overfitting and enhance generalization.
3. XGB: Introduced by [PERSON] and [PERSON], XGB is a highly efficient algorithm within the gradient boosting framework [41]. It supports parallel tree boosting [PERSON] et al. and reduces overfitting through L1 and L2 regularization [42]. XGB's innovative features, such as randomized parameter selection, leaf node proportion adjustment, and a unique tree penalty mechanism, offer superior performance for various data science tasks [43; 44]. In this study, XGB is implemented using the xgboost library in Python 3.12, with hyperparameters like n_estimators, learning_rate, max_depth,subsample, colsample_bytree, min_child_weight, lambda, and alpha optimized to enhance model accuracy and efficiency.
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
## 5 Conclusions  
Based on all the case studies presented, the pseudo-3D resistivity method has successfully helped researchers find out the distribution of relevant faults around the groundwater surveys, and provided relevant information for subsequent water exploration work. The groundwater was clearly detected with support from the borehole data as this technique has efficient exploration and flexibility.  
In order to combine the 2D resistivity method with the pseudo-3D resistivity method in groundwater exploration, the 2D resistivity method needs to be first carried out, and then the 2D resistivity method is applied to the key areas. In special terrain areas where it is difficult to carry out the 2D resistivity method or the 3D, the pseudo-3D resistivity method can be directly used.  
Conceptualization, Z.G. and X.C.; methodology, X.C.; software, C.L.; validation, Z.G., C.L. and J.L.; formal analysis, Z.G. and C.L.; investigation, C.L.; data curation, C.L. and Q.W.; writing--original draft preparation, Z.G. and X.C.; writing--review and editing, Z.G., C.L. and X.C.; visualization, J.L.; supervision, J.L.; funding acquisition, C.L. and Q.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Natural Science Foundation of China (NSFC) [42074169], Innovation-Driven Youth Team Project of Central South University [2020 CX0012], Hunan Province Natural Resources Science and Technology Project \"Key Technologies and Applications of Polluted Site Detection and Evaluation in Urban Physical Examination\" No.: 2022-25 and Regional Innovation Cooperation Programs of Sichuan province (2021 YFQ0050).  
The authors declare no conflict of interest.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Positional Encoding  
Positional encoding is an essential part of every Transformer model, as this kind of neural network does not \"recognize\" the sequential order of the data, and the order within the data sequence could be mixed up. Therefore, it is necessary to add this kind of information to the input features at the beginning of the encoder and decoder parts of the model [4].
# Retrofit Fatigue Cracked Diaphrag Cutouts Using Improved Geometry in Orthotropic Steel Decks
## 3 Geometry Optimization
### Discussion on Stress Concentrations  
To reduce the stress concentration in zone A of the diaphragm, a stress flow method is used. According to stress flow theory, there are certain criteria for drawing the paths of stress flows. Firstly, stress flows do not disappear in a mechanical system due to obstructions, which, in fact, lead to stress flow concentrations. Secondly, stress flows tend to follow the shortest paths and the second derivative of these paths is continuous. When the direction of stress flow turns sharply, the stress flow becomes highly dense at the corner, as shown in Figure 8, which results in concentrated stress.  
The vectors of the major principal stresses are shown in Figure 9a, under the most unfavorable wheel load case on OSDs. Since the stress concentration is in zone A, the vectors of the major principal stresses are concentrated around the diaphragm cutout; the vector direction near the U-rib is 45\({}^{\circ}\) and is tangential to the arc near the cutout. The transmission path and direction of stress flow can be described as follows: the wheel load acting on the bridge deck is mainly transmitted to the diaphragm in the form of shear stress through the U-rib, as displayed by the black arrows in Figure 9b. The process of the local stress flow diffusion at the diaphragm cutout is blocked due to the cutout, and as a consequence, the stress flow follows the principles of shortest path, resulting in a high stress concentration, at the diaphragm cutout, as shown in Figure 9b.  
Through the above stress flow analysis, the methods of reducing the stress concentration effect in zone A of the diaphragm cutout are suggested as follows: (1) reduce the obstruction to stress flow by the diaphragm cutout under wheel loads, and (2) diminish the directional change of stress flow around the diaphragm cutout. Therefore, more research needs to be conducted to optimize the geometry of diaphragm cutouts to reduce the stress flow concentration, which includes the optimization of the arc radius, the length of the straight segment, and the inclination angle of the straight segment.  
Figure 8: Stress flow characteristics.  
Figure 7: Stress around the diaphragm cutout: (**a**) Mises equivalent stress (Pa), (**b**) principal compressive stress (Pa).
## 4 Case Study-Accelerated Corrosion Test of Bars in Concrete Cover with Different Thicknesses
### Detection of Corruded Bars in Reinforced Concrete Floor  
We compared and discussed the corrosion potential and the reflected voltage of the corroded bars in the reinforced concrete, which was scanned by the ground penetrating radar at the frequency of 1 GHz. Based on the analysis of the reflected voltage of the ground penetrating radar, the average corrosion potential on the corroded plate on the 3 rd floor was \(-280\) m V (Area C). Based on ASTM C876, the steel bars in the concrete had moderate corrosion. The reflected voltage analysis of the electromagnetic waves concluded that areas A and B had moderate corrosion, which is consistent with the results from ASTM C876, as shown in Figure 23.  
Figure 21: Building Corrosion Inspection.  
Figure 22: Steel Bar Corrosion in Concrete Column.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 4 Discussion
### Effect of Point Number of Target Scale Contour  
In CCSM, the shape change of the intermediate simplified contours is related to the initial and target contours. To verify whether the point number of the target scale contour affects the simplified results, two contours with different shapes were randomly selected from the initial contour set. Each target contour had 100, 50, 40, 30, 20, 10, and 2 points as shown in Figure 14(a),b. The target contour with only two points was selected given its representativeness of extreme simplification.  
Figure 13: Similarity between the simplified and initial contours with different mesh parameters. (**a**) CCS-35. (**b**) CCS-2.  
Figure 16a,b show the relationship between the similarity and scale of these two contours. The similarity here represents the similarity between the simplified and initial contours, whereas scale represents the estimated map scale of the simplified contour. Figure 16 shows that the similarity gradually decreases when the number of points is relatively large and significantly decreases when the number of points reduces to 2. However, regardless of the number of points in the target contour, the trends between scale and similarity were generally very consistent and orderly and demonstrated continuous and monotonous characteristics. Moreover, the simplified contour lines generated by the CCSM were continuous and multi-scale. In other words, our proposed method can be applied to different points of target contours.  
Figure 16: Relationship between the scale and similarity of the target contours at different points. (**a**) Relationship between the scale and similarity of contours in Figure 15a. (**b**) Relationship between the scale and similarity of contours in Figure 15b.  
Figure 15: Two selected contours and the corresponding target contours with different point numbers. (**i**) Initial contour. (**ii**–**viii**) Target contours with 100, 50, 40, 30, 20, 10, and 2 points, respectively. (**a**,**b**) represent two contours with different shapes randomly selected from the initial contour set.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 3 Experimental Architecture and Results  
The above-mentioned analysis and the capabilities of a 5G-UAV were evaluated using a real 5G stand-alone network. That 5G network is part of an innovation network at the premises of Lulea University of Technology. The network is configured with the parameters given in Table 1. The base station utilized was an Ericsson DOT. The robot is a custom-built quadrotor equipped with the following sensors: a d455 Intel Realsense RGB depth camera, a puck Velodyne lidar, a Vectornav industrial sensor, and two sonar sensors. This sensor combination establishes a platform that can support a wide range of applications, from enhanced teleoperation for beyond visual line-of-sight missions to fully autonomous missions. This 5G-UAV platform can produce a mean uplink data rate of \(\sim\)2.4 Gbps and is regarded as a moderate platform in terms of data production, adequate for most modern applications. This section presents experiments considering the aforementioned 5G-UAV sensing platform and examines what are the capabilities of this platform. Initially, stationary experiments with all sensors were carried out without the UAV undertaking any flight-mission operations. Next, an autonomous flight-mission operation was performed and evaluated in two scenarios: (1) with the transmission of sensor data from multiple sensors so that they can be used for process offloading; (2) with intensive background network load.
## 1 Introduction
* Some research papers consider a single international energy retrofitting program as the main focus and ignore the others, for example, considering LEED retrofitting measures and ignoring retrofitting measures from other programs, such as Breeam and ARRA [17; 18]. * The interdependence between the retrofitting strategies should be considered. For instance, changing a building's envelope (i.e., wall insulation) will result in a need to change its HVAC system. * Generating many retrofitting plans without a ranking process makes it difficult for the decision maker to select the one that best reflects their expectations from the retrofitting project [14]. The primary objective of this study is to bridge the gap in the literature by developing a framework that includes the following:  
* Collecting data:
* Retrofitting measures that include building envelope measures as well as lighting and electrical equipment;
* Life cycle cost (LCC): To calculate the LCC, all cost components of the retrofitting measures, including investment, operation, maintenance, replacement, and salvage, will be considered. * Building a mathematical model:
* Definition of the decision variables and parameters;
* Definition of the constraints that will consider the thermal and visual comfort, as well as the interdependence between the HVAC and the building envelope. * Building energy consumption forecasting: Developing the simulation model for the building and running simulations for different building envelopes. * Generating different retrofitting plans: For each retrofitting plan, calculating the energy savings as well as the return on investment and the payback period. * Selecting a retrofitting plan: Finding the best retrofitting plan that meets the decision maker objectives using the analytic hierarchy process (AHP). The rest of the paper is structured as follows:* Framework: The different components of the framework as well as the case study are presented. * Results and Discussion: In this part, different retrofitting scenarios, along with their energy savings and financial indicators, are discussed. After introducing the different retrofitting plans, the AHP will be applied to select the best one according to the decision maker objectives. At the end, conclusions about the developed framework and the case study are presented, as well as recommendations for future studies.
# Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI
## 2 Literature Review
### Artificial Intelligence Algorithms For Building Energy Prediction
#### 2.1.2 Artificial Neural Network (ANN)  
ANN is an information processing method similar to the structure and function of the human brain. Proposed in the 1940s, it has the characteristics of non-linear and parallel distribution structure. The algorithm is simple and accurate, so it is widely used in many fields. Many scholars have conducted studies on the prediction of building energy consumption. [PERSON] et al. used ANN and EnergyPlus to collect data of the Administration Building of Sao Paulo University as an example to predict building energy consumption. They use parameters such as building facade and weather data to make predictions [4]. [PERSON] et al. developed an ANN-based program to simulate energy consumption according to different exterior wall materials and trained dynamic simulation data [5]. [PERSON] et al. established an ANN energy consumption prediction model for HVAC systems in office buildings, but the model has fewer input parameters [6]. [PERSON] et al. proposed a method based on ANN for energy consumption prediction and evaluation of indoor comfort, and optimized it through genetic algorithms [7].  
Back propagation (BP) is a typical acausal neural network method, which is currently most widely used. The least mean squares (LMS algorithm) and model make the prediction result of BP neural network reliable, which makes BP very different from the earlier Madaline network algorithm. BP neural network has multi-level characteristics. Typical structures include an input layer, calculation layer, and output layer (Figure 1). The working principle is, in the process of acausal propagation, if the result of the output layer meets the conditions, it means that the training is passed, and the calculation of the next neuron can be performed. If the output result does not meet the conditions, the reverse operation is performed to return the signal to the original path. Then, it has to regenerate a result by modifying the corresponding parameters or go to the next neuron next to it. In a typical BP structure, a neuron in each layer can only affect the two layers adjacent to it and cannot execute across layers until the global optimal solution is output.  
Since the typical BP neural network structure uses offline historical data, the input results may have limitations. To address this limitation and make the results more accurate, [PERSON] proposed to add a dynamic compensation mechanism to the BP neural network structure [8]. This reversed mechanism can take the output result as a new condition, import it into the input layer and start a new  
Figure 1: Typical BP Neural Network Structure.  
round of dynamic adjustment (Figure 2). [PERSON] et al proposed improvement and correction measures for the error defect of the model [9].
## 2 Mobility Models in FANETs
### Group Mobility Models
#### 2.2.2 Exponential Correlated Random  
Exponential Correlated Random (ECR) [50] is described as a group of mobility models that depicts correlated dynamic motion of a group of nodes. ECR employs the motion function to model all of the group's conceivable movements to control it. This is accomplished by predicting the group's new placements in the next available timeframe, as shown in Figure 9. ECR model could be used in conjunction with FANETs to control and avoid collisions among a large group of UAVs [51]. The ECR model can be adopted in mobility-aware connectivity of 5G cellular networks [52].
## 5 Discussions
### Different Natural Gas Accumulation Models of Various Anticline Structures  
By comparing the main controlling factors of natural gas enrichment processes of the three typical anticline structures (Z, N, and Q) under different compressive stress backgrounds, three different natural gas accumulation models of the Central Anticline Belt in the Xihu Sag can be established (Figure 14).  
(1) \"Integral anticline\" accumulation model in the northern Medium-Strong Compression Zone (Structure-Z): The compressive intensity of the Longjing Movement was relatively moderate, and a complete anticline structure was developed. The NE-trending faults were highly active. Meanwhile, although the EW-trending faults developed in the late stage in the anticline core, they did not cut through to the main reservoir  
Figure 14: Differential natural gas accumulation models in different compression zones of the Central Anticline Belt in the Xihu Sag.  
Figure 13: The natural gas enrichment process pattern of the hydrocarbon migration and accumulation occurring simultaneously, followed by later hydrocarbon adjustment under the Longjing movement.  
strata. Therefore, the trap preservation conditions were good, enabling the formation of an integral large-scale gas field.
2. \"Local enrichment\" accumulation model in the southern Strong Compression Zone (Structure-N): Under the strong compression background of the Longjing Movement, a series of giant anticline structures were formed, with large amplitudes of the anticlines and strong fault activities and natural gas migration. However, the late EW-trending fault systems were commonly developed in the anticline cores, generally cutting through the main reservoir strata, such as the Huagang Formation, resulting in the large-scale escape of natural gas. Only a part accumulated effectively again in the shallow Longjing Formation; thus, the gas reservoir showed the characteristics of a small-scale and scattered distribution.
3. \"Fault-sandbody coupling\" accumulation model in the western Weak Compression Zone (Structure-Q): The degree of compression was relatively weak, the amplitude of the anticline was low, and no late-stage destructive faults were developed in the anticline core. Therefore, the preservation conditions of the structural traps were good. However, due to the weak activity of the oil-source faults trending northeast, the fault activity and the coupling degree with the sand body determined the enrichment degree of the gas reservoir.
## 2 Target Detection Method
### Improved Faster R-CNN
#### 2.3.1 Resnet50 Network Replacing VGG16 Network  
The VGG16 network is used as the feature extraction backbone network by the original faster R-CNN model, and the features output by the final convolution output layer of VGG16 are used as the shared features of the RPN network and the RoI pooling layer. This algorithm is used mainly to detect everyday objects, and it is suitable for the coco data set. When this algorithm is used in the insulator data set, there are mainly the following shortcomings:  
1. VGG16 uses a single-layer feature layer output to be suitable for the detection of single-sized targets. Because of the different sizes of insulators in the image, it is easy to cause missed detections and misjudgments;
2. Due to the different scales of aerial insulator images, many insulators have become small targets related to the entire picture. In order to identify the insulators more accurately, the feature extraction backbone network needs to be improved.  
The Resnet50 network [21] consists of 49 convolutional layers and 1 fully connected layer. The innovative introduction of residual blocks makes the network depth increase, and deeper features can be extracted while effectively avoiding gradient disappearance and gradient explosion. The authors in [21] claimed that the residual network with a depth of up to 152 layers on the ImageNet data set has still lower complexity than the VGG16 network. Therefore, comparing with VGG16, Resnet50 increases the depth of the network but does not increase the complexity of the network.
## 3 Results
### Sensitivities of Calculation of Soil Fractal Dimensions to Different Soil PSC  
To further analyze the effects of PSC systems on calculating volume fractal dimensions, results of \(\mathrm{Lg}\) (\(V/V_{T}\)) and \(\mathrm{Lg}\) (\(R_{i}/R_{\mathrm{max}}\)) change under different PSC systems were analyzed. The variation trend indicated that refining clay and sand scales could lower slope (\(k\)) of the regression equation, then \(D\)s increased (in Figure 9). Accordingly, \(D\)s decreased while silt scales were refined. Among all PSC systems, Blott & Pye (2012) PSC system contained the most information comparing to the other five PSC systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PSC Systems** & **China (1987)** & **Katschinski (1957)** & **USDA (1993)** & **ISSS (1929)** & **ISO14688 (2002)** & **Blott \& Pye (2012)** \\ \hline China (1987) & 1 & 0.993 ** & 0.983 ** & 0.993 ** & 0.998 ** & 0.998 ** & 0.998 ** \\ Katschinski (1957) & 0.993 ** & 1 & 0.968 ** & 0.977 ** & 0.991 ** & 0.997 ** \\ USDA (1993) & 0.983 ** & 0.968 ** & 1 & 0.991 ** & 0.985 ** & 0.979 ** \\ ISSS (1929) & 0.993 ** & 0.977 ** & 0.991 ** & 1 & 0.995 ** & 0.988 ** \\ ISO14688 (2002) & 0.998 ** & 0.991 ** & 0.985 ** & 0.995 ** & 1 & 0.997 ** \\ Blott \& Pye (2012) & 0.998 ** & 0.997 ** & 0.999 ** & 0.988 ** & 0.997 ** & 1 \\ \hline \hline \end{tabular}
**\({}^{**}\)**Correlation is significant at the 0.01 level (2-tailed).  
\end{table}
Table 2: Pearson’s correlation coefficients among \(D\)s under different PSC systems.  
Figure 8: Relationships between \(D\)s and forest density of MPPs. Linear regression analysis was applied. Determination coefficients (\(R^{2}\)) of the linear regressions are also shown.