esa-sceva/satcom-chunk-collection huggingface dataset:
# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
## Materials and Methods
### Machine Learning Models  
The different mathematical models (or black-box models) built to estimate the HLC of the analysed building are presented in this section. In this case, the three machine learning algorithms studied are XGBoost, SVR and MLP neural network.
# Design, Manufacturing, and Flight Testing of an Experimental Flying Wing UAV
## 3 Weight Estimation for an Electric UAV
### Battery Weight Estimation with Range Requirement  
The electric-powered UAV's range is optimal when the required thrust is minimum. The derivative of the battery energy with respect to range is expressed as:  
\[\frac{dE_{B}}{dS}=-\frac{T_{R_{\min}}}{\eta_{motor}\eta_{pre}\eta_{discharge}}=- \frac{W_{Total}\sqrt{4C_{D_{0}}K}}{\eta_{motor}\eta_{pre}\eta_{discharge}} \tag{16}\]The total variation in the battery energy and range between the start and end of flight can be expressed as:  
\[\int_{E_{R_{0}}}^{E_{B_{1}}}dE_{B}=-\frac{W_{Total}\sqrt{4C_{D_{0}}K}}{\eta_{motor} \eta_{pr}\eta_{discharge}}\int_{R_{0}}^{R_{1}}dS \tag{17}\]  
Here, the left-hand side gives the total variation in battery energy between the start and end states. The right-hand side gives the total variation in range between the flight start position \(R_{0}\) and flight end position \(R_{1}\), and it can be expressed as:  
\[-\frac{W_{Total}\sqrt{4C_{D_{0}}K}}{\eta_{motor}\eta_{pr}\eta_{discharge}} \int_{R_{0}}^{R_{1}}dS=-(\frac{W_{Total}}{\eta_{motor}\eta_{pr}\eta_{discharge}}\sqrt{4C_{D_{0}}K})R \tag{18}\]  
Therefore, the battery mass fraction of the aircraft's total weight for optimal range can be expressed as:  
\[\frac{W_{Battery}}{W_{Total}}=(\frac{R\sqrt{4C_{D_{0}}K}}{\eta_{motor}\eta_{pr }\eta_{discharge}\rho_{Battery}}) \tag{19}\]
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 2 Methodology
### Random Forest Algorithm  
RF is a machine learning model that utilizes an ensemble of decision trees combined with a method known as bootstrap aggregation, or 'bagging'. This approach involves creating multiple subsets of the training data by sampling with replacement from the original dataset [29]. Each subset is used to train a different decision tree, and the forest is constructed by aggregating these trees. This technique often results in higher accuracy compared to a single decision tree model while retaining the interpretability benefits of tree models [30].  
In RF, each tree consists of a sequence of binary decisions, or 'nodes', based on a single feature or a combination of features. These nodes create splits in the tree, grouping similar observations together and separating them from others. The algorithm provides measures to rank features according to their importance in predicting the target outcome, selecting those with the highest predictive ability [31].  
The RF feature-selection process involves the following steps: training the algorithm on bootstrapped datasets, computing feature importance scores for each tree and selecting features with importance scores above a predefined threshold. The detailed procedure of the RF algorithm is outlined in Algorithm 2.  
```
0: Training dataset \(X\), number of trees \(N\) and feature importance threshold \(T\)
0: Selected features \(S\)
1: Initialize: \(S\leftarrow\varnothing\)
2:for\(i=1\) to \(N\)do
3: Sample a bootstrapped dataset \(X_{i}\) from \(X\) {Randomly sample with replacement}
4: Train a decision tree \(T_{i}\) on \(X_{i}\)
5: Compute feature importance scores using \(T_{i}\)
6: Update \(S\) with features having importance scores above \(T\)
7:endfor
8:return\(S\)
```  
**Algorithm 2** Random Forest (RF)  
The RF feature-selection method is advantageous as it can identify relevant features, reduce dimensionality and improve model interpretability while maintaining high predictive performance.
## 3 Normalization Algorithms
### Pseudo-Invariant Point Feature-Based Normalization  
Pseudo-invariant polygon feature-based normalization was conducted using pseudo\(-\) invariant point features. One crucial procedure for point feature\(-\)based radiometric normalization is to accurately select pixels that indicate the same geometric position in different images [22]. In our study, eight different surface types with diverse reflectance were selected to generate the regression equation. To avoid point number weight effects, we chose the same number of samples for each surface class in this process of radiometric normalization based on pseudo-invariant point features.
## 3 The Influence of Structure Parameters
### The Diameter of Nanodisk  
From previous studies [5], we know that the diameter of nanodisk is the main factor affecting the structural color. The influence of the gap size between nanodisks on the structural color is less important because light can be tightly confined in the nanodisk due to its high refractive index. The influence of the diameter of nanodisk on the distribution of the reflectance spectra is discussed in the following section. Keeping the center-to-center distance of the nanodisks unchanged, that is, \(p\) is kept fixed, we observe the variation of reflectance spectra by changing the diameter of the nanodisk \(D\) from 30 nm to 200 nm. The results show that the center wavelength of the maximal reflection peak lies in the blue band when the diameter is minimum, as shown in Figure 3a. As the diameter \(D\) increases, the maximal reflection peak of the spectra gradually shifts to longer wavelengths and reaches about 642 nm with full width at half-maximum (FWHM) around 95 nm when \(D\) = 200 nm, as shown in Figure 3b. In the above simulation, the period \(p\) is invariable, so the gap size between nanodisks \(g\) gradually decreases as the diameter of nanodisk \(D\) increases, where \(g=p-D\). In order to verify whether the diameter of nanodisk \(D\) is also a main factor affecting structural color when gap size between nanodisks \(g\) remains unchanged, we performed the simulation again. The difference is that the period \(p\) changes as \(D\) varies, but \(g\) is invariable. The results show the same variation trend compared with simulations of \(p\) unchanged, except that the spectral distribution is slightly different, as shown in Figure 3c,d. The above simulation results confirm that the diameter of the nanodisk has a decisive effect on the structural color, compared with the gap size between nanodisks.  
Figure 3: The reflection spectra of different \(D\). (**a**) The reflection spectra with \(D\) = 30 nm when \(p\) is invariable. (**b**) The reflection spectra with \(D\) = 200 nm when \(p\) is invariable. (**c**) The reflection spectra with \(D\) = 180 nm when \(p\) is variable. (**d**) The reflection spectra with \(D\) = 200 nm when \(p\) is variable.
# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 5 Experimental Results
### The Real-to-Virtual Gap and Closing  
The real-to-virtual gap is a common problem in most methods that use synthetic data to solve real world problems. To study the problem in our paradigm, we train the GaitSet model [5] with real-world and synthetic data. GaitSet regards gait as a set of gait silhouettes. It takes a set of gait silhouettes as input, and then uses a CNN to extract frame-level features from each silhouette independently. After that, a so-called set pooling operation is used to aggregate frame-level features into a single set-level feature. Finally, a structure called horizontal pyramid mapping is used to map the set-level feature into a more discriminative space to obtain the final representation, from which gait can be recognized.  
Training was conducted under three different settings as was done in [5]: small-sample training (with 24 subjects), medium-sample training (with 62 subjects), and large-sample training (with 74 subjects). We generate the training set by first sampling the area corresponding to walking on the motion manifold, and then classifying them into different subjects according to ID embedding. We picked up 74 subjects out of 124 subjects in the CASIA-B dataset [18] as real-world training data. They will be mixed with synthetic data according to specified proportions to form the whole training set. The rest of the CASIA-B and the OU-MVLP dataset [6] were used for testing of both the real data-trained system and the synthetic data-trained system.  
From Figure 8a,c, we can observe a clear gap that was narrowed down when we gradually added real world data when training the synthetic model. Generally, the synthetic  
Figure 5: Parameterized editing of a human body: (**a**) Original model; (**b**) Height + 10; (**c**) Girth \(-\) 30; (**d**) Hip + 20.  
Figure 6: Effect of neighborhood size \(k\): (**a**) k = 1; (**b**) k = 3; (**c**) k = 6; (**d**) k = 10; (**e**) zoom in.  
Figure 7: Effect of interpolation methods: (**a**) linear interpolation; (**b**) zoom in; (**c**) dual quaternion interpolation.  
model can gain accuracy in competing with the real model when about 30% percent of real data are added in the training. However, when running both models on the OU-MVLP test set, we found that there is a significant decrease in the accuracy but the gap was not so apparent. This observation indicates that the gap may be due not to the real-to-virtual but to the translation of the learned model to another domain. Another interesting phenomenon was that when we introduced real data in the training of the synthetic model, the accuracy could even outperform the real model. The reason behind this could be found in the hypothesis of domain randomization [29]: if the variability in the simulation is significant enough, models trained in simulation will generalize to the real world with no additional training. By mixing real and synthetic data together, we increased the variability of training data and thus led to an increase in performance.
## 2 Materials and Methods
### Analysis  
#### 2.4.1 Detection of the NDVI and Climate Trends from 1982 to 2011  
Using the NDVI data from 1982 to 2011, trends in the NDVI were estimated in each grid and each subcontinental region in a manner similar to an existing study [33]. The Mann-Kendall trend test [66] was used for statistical analysis because this technique does not require an assumption of normality in variance and is more robust against anomalous outliers compared with linear regression analysis. The slopes of the trends were calculated based on the Kendall robust line-fit method [67], which is a method for robust linear regression that chooses the median slope among all lines through pairs of two-dimensional sample points. The method is a non-parametric method and is relatively insensitive to outliers.  
To avoid including changes in non-vegetation effects (e.g., snow) in the calculation of temporal changes, we focused on the annual average during the vegetation growing season in each grid cell. The vegetation growing season is defined in each grid as those months in which the 1982-2011 monthly average air temperatures were greater than 5 \({}^{\circ}\)C and the monthly average NDVI was greater than 0.1.  
Climate trends (temperature and precipitation) were also estimated from 1982 to 2011 to interpret the NDVI trends using the same approach as for the NDVI analysis, that is, the [PERSON] robust line-fit method [62]. We used the growing season average for temperature and the annual total for precipitation in the trend estimation.
## 3 Examples and Discussion
### Effect of the Mainshock-Aftershocks on the Seismic Resilience
#### 3.4.2 Function Curves under Mainshock and Mainshock-Aftershock
An analysis of the resilience curve of a structure under the influence of different recovery times reveals that a shorter time interval between the mainshock and aftershocks leads to a smaller enclosed area between the functional function curve and the coordinate axis, indicating decreased resilience and a weaker ability of the system or structure to return to its initial state. Conversely, a longer time interval between the mainshock and aftershocks results in a larger enclosed area between the functional function curve and the coordinate axis, indicating increased resilience and a stronger ability of the system or structure to recover to its initial state. This enhanced recovery capability also reduces the required amount of resources and manpower. Using the Monte Carlo method, increasing the number of simulation iterations enhances the convergence of resilience values.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 1: RGB Preprocessing  
RGB preprocessing was performed to adequately expose the water surface which remained underexposed. Through Otsu's thresholding and clean-up operations, the under-exposed regions were highlighted, after which the exposure levels were adjusted based on the underexposed coordinates, thus forming the ROI.  
The RGB stream shown in Figure 1(a) experienced chromatic aberration due to overexposure in the bottom right corner, while in contrast, the water streams were not exposed properly. To properly process the frame, the image was captured through the colour sensor in 16-bit greyscale format before being processed for thresholding. The 16-bit greyscale format captured 65,000 different shades of grey, allowing for greater information in a frame when converted into a black and white image. After thresholding, regions which were not of interest were inverted to a black background colour by their coordinate area size, as shown in Figure 1(b). Regions outside the ROI were cleaned up using a kernel that passed over the image, leaving a giant patch to determine the source of the ROI. The kernel size was set to be big enough to clean up noise outside the ROI. The ROI was selected based on the median values from the pool of coordinates in the giant white patch. The size of the ROI was insignificant as long as the streams were properly exposed for the depth extraction process. The selected ROI coordinates were fed into the depth pipeline to correctly expose the water streams before capturing the depth frames. The preprocessing stage was carried out during the day, since it was not needed at night due to the lack of light causing any interference with the depth pipeline or the autoexposure.  
Figure 1: Block diagram of the proposed real-time flood monitoring system.  
Figure 2: (a) Captured frame from camera experiencing chromatic aberration in bottom right of the scene. (b) Thresholded region which forms the basis for the ROI coordinates.
## 6 Integration in Production  
After finalization of the RL model, it was deployed using containerization to ensure a consistent and scalable runtime environment. Docker was used to encapsulate the application [45], along with all necessary dependencies. This containerized deployment enabled portability across different operating environments and minimized compatibility issues. For real-time interaction, the model was served via an API built using FastAPI [46]. The interface supported POST requests, allowing external systems to submit input data and receive predictions. FastAPI's asynchronous architecture is particularly well suited to high-throughput environments, enabling non-blocking communication and improved response times.  
The deployed model was hosted on an Amazon Web Services (AWS) EC2 instance with fixed compute specifications. API endpoints were documented and created using OpenAPI Generator [[https://github.com/OpenAPITools/openapi-generator](https://github.com/OpenAPITools/openapi-generator)]([https://github.com/OpenAPITools/openapi-generator](https://github.com/OpenAPITools/openapi-generator)) (accessed on 15 January 2025), facilitating seamless integration with computer-integrated manufacturing systems. A higher-level system architecture is illustrated in Figure 8. This setup allows automated decision-making by process engineers, leveraging RL-driven optimization in real time. To ensure reliability and robustness in deployment, a preliminary out-of-distribution (OOD) monitoring mechanism was implemented using the Isolation Forest algorithm [47] to detect unfamiliar inputs. This safeguard helped maintain the integrity of the predictions during live operation.  
Latency tests showed that the average response time for a prediction request was approximately 300 milliseconds, making the system suitable for near real-time applications in production workflows. Model training was conducted offline and did not interfere with the deployed inference system.
## 3 Evolution Towards Next Generation Networks (NGN)
### NGN Architecture: From Vertical Stovepipes to a Layered Architecture  
The following architectural requirements for NGN have been recognized and need to be covered:  
* High-capacity packet transfer (these days, mostly based on IP) within the transmission infrastructure, with a possibility to interwork with existing and future networks, both packet- and circuit switched, both connection-oriented and connectionless, and both fixed and mobile.
* Separation of managing functions from transmission features. Separation of service provisioning from the network and ensuring open access to services via an open interface and thus a flexible, open and distributed architecture (see Figure 2).
* Support for a wide range of services and applications by using the mechanisms based on the modular and flexible structure of elementary service building blocks.
* Broadband capabilities, while complying with the requirements for QoS (Quality of Services) and transparency. Possibility of a complex network management should be available.
* Various types of mobility (users, terminals, services). Unlimited access to a variety of service providers.  
* Various identification schemes and addressing which can be translated to the target IP address for the purposes of routing in the IP network. (Flexible addressing and identification, authentication).
* Converged services between fixed and mobile networks (as well as voice, data and video convergence). Various categories of services with the need of different QoS and Classes of Services (CoS).
* Conformance to regulatory requirements, such as emergency calls and security requirements in terms of personal data protection.
* Cheaper and more effective technologies compared to current technologies.
## 4 Design and Implementation
### OOK Modulation
#### 4.1.1 Encoding in OOK Modulation  
The algorithmic implementation of OOK Modulation using a programmable board is as shown in Algorithm 2. Here the \(Setup\) function is used to initialize the LED-connected pins and a predefined test message. \(Loop\) function is used to perform repetitive tasks, which includes sending the start bit pattern for synchronization before each byte is sent, followed by transmission of each bit of a byte. Furthermore, the \(Send\) function encodes the received bit into Manchester format and controls the LED states, respectively. For example, if the received bit is 1, post encoding, it becomes 0, 1, represented by Off states followed by On state of the LED. A minimum delay is decided, which should satisfy the flicker-free characteristic of light at the transmitter and the observable width of the stripe at the receiver. Depending on this value of \(x\), the data rate and width of the observable stripes in the image are controlled. The size of transmitter and distance from the receiver also affects the number observable stripes.
## 3 Proposed Solution
### Stage 1  
Algorithm 1 emulates the operational behavior of a vehicle within a VANET, emphasizing the significance of real-time data transmission, collision detection, and surveillance of vehicle trajectories. It establishes essential parameters, including vehicle velocity, geographic location, and directional orientation, in addition to parameters pertinent to K-means clustering, which facilitates the identification of potential collisions predicated on the spatial proximity of adjacent vehicles. During each computational iteration, the vehicle's positional coordinates and speed are recalibrated, data are disseminated to RSUs or proximal vehicles, and the amalgamated data are subjected to clustering techniques to ascertain collision hazards. In instances where a collision is anticipated, notifications are generated for both the driver and surrounding vehicles. Furthermore, the vehicle's velocity and trajectory are continuously scrutinized, and driving-related data are relayed to centralized entities, including insurance providers and traffic regulatory authorities.  
```
1:Initialize vehicle parameters: ID, speed, location, direction, and nearby vehicles.
2:Set K-means parameters for collision detection.
3:while vehicle is running do
4: Update vehicle's speed, location, and direction.
5: Transmit data to nearby vehicles and RSUs.
6: Receive and combine data from nearby vehicles.
7: Perform K-means clustering on vehicle locations.
8: Check if any cluster is within collision threshold.
9:if collision detected then
10: Alert driver and notify nearby vehicles.
11:endif
12: Send data to central systems.
13: Simulate vehicle running status and pause.
14:endwhile
```  
**Algorithm 1** An AI-enabled VANET-based collision detection system
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 5 Evaluation
### Testing with Indoor/Outdoor and Semi-Indoor Sample Test Set with Proposed Data Processing  
Figure 14 shows the accuracy for indoor and outdoor cases obtained after applying the proposed preprocessing. The preprocessing phase increases the I/O detection accuracy significantly (approximately 4% for the worst case and 2% for the best case). The machine learning techniques clean the input data, as well as propose a better structure for the learning model.  
Figure 14: I/O detection accuracy obtained with proposed preprocessing.  
Figure 13: Preprocessing flow.  
The performance of the proposed approach was compared to traditional technologies commonly used for I/O detection. The first comparison was against I/O detection using GPS. The proposed approach was compared to three different studies [1,9,11]. The proposed method is clearly better because it uses more information, has better preprocessing, and applies suitable Machine Learning model.  
Based on the overall scheme, other classifier methods, such as LSTM and Meta-classifier are applied. However, they are not suitable for the I/O detection scheme and the accuracy is also lower than KNN. In proposed scheme, I/O detection focused on the simple and effective methods, as it does not require mobile resources. Based on this approach, the machine learning methods are more suitable for the scheme. In contrast, both LSTM and Meta-classifier required large resources for both training and testing phases, so that they are not considered as promised methods for proposed scheme. Another reason is that the accuracy of LSTM and Meta-classifier is lower than KNN. LSTM reaches 91% overall accuracy and Meta-classifier is 94%, which cannot be considered in the proposed scheme.  
The machine learning model will be evaluated based on the accuracy of test sets, as well as training coverage. We use offline training for the application; thus, training coverage may not be important. Nonetheless, it will remain a criterion used to evaluate the machine learning model.