esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## Appendix A Machine Learning Models and Hyperparameters Optimization
### Support Vector Regression  
Support vector regression (SVR) is a widely used regression algorithm that works on the principle of the support vector machine (SVM). While Lasso and Ridge regressions only support linear regressions, SVR allows for both linear and non-linear regressions. The main concept of SVR is to create a hyperplane with the smallest possible distance between it and the sample points.  
The main hyperparameter of SVR is the kernel, which converts the observed data into a higher-dimensional dataset so that it can linearly separate the high-dimensional space. There are some widely used standard kernel functions including the linear kernel, the polynomial kernel, and the Gaussian kernel. The selection of the kernel function has a great impact on the performance of the regression algorithm. In this work, the polynomial kernel with a \(d\) of three is determined to be the most optimal kernel function through tuning, which can be expressed as:  
\[k(x_{i},x_{j})=\left(x_{i}Tx_{j}\right)^{d} \tag{10}\]  
where \(x_{i}\) is the feature in the original dataset and \(x_{j}\) is the feature after its transformation into a higher dimensional space, \(d\) is the integer parameter and \(d\geq 1\).
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 6 Simulation Results and Analyses  
We conduct some simulations to evaluate the game model and the algorithm we proposed. Suppose, in the scenario shown in Figure 2, there are seven UAVs flying in a certain AP coverage. They shoot videos independently and send seven different videos back to UCDC via a wireless network. Their rate ranges and characteristics are marked in Table 1. Their motions can be divided into three categories: slow, medium and fast. The slower the motion is, the less transmission resources the video occupies. In addition, the rate is also related to the complexity of the scene. Complex scenarios will also take up more bandwidth. In a word, the seven videos here represent different typical shooting scenarios. In practice, the number of videos may be more, but the principle and workflow of the algorithm is the same.
# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 3 Methods  
We define the topological map as follows. \(G(S,E)\) consist of the global targets set \(S=\{s_{1},s_{2},\ldots,s_{n}\}\) and the undirected edges set \(E=\{e_{1},e_{2},\ldots,e_{m}\}\). \(s\) denotes a global target corresponding to a corner or intersection in the environment. \(e_{k}=(s_{i},s_{j})\) is an undirected edge, connecting \(s_{i}\) and \(s_{j}\), representing a straight-line scene, such as a road or corridor. With the support of a prior topological map, we can obtain a global exploration strategy \(O=\{\overline{\sigma_{0}},\overline{\sigma_{1}},\overline{\sigma_{2}},\ldots,\overline{\sigma_{n}}\}\) for the given region by customizing or solving the Chinese Postman Problem (CPP) [24], which is a priority queue. \(\overline{\sigma_{k}}=(s_{i},s_{j})\) denotes a directed line segment from \(s_{i}\) to \(s_{j}\), corresponding to the exploration guidance. Define \(f\) as the frontier cluster, and \(F=\{f^{1},f^{2},\ldots,f^{n}\}\) as a set of remaining frontier clusters in a scene. We adopt the incrementally frontier information structure (FIS) proposed by FUEL to update frontier clusters efficiently [12]. Viewpoint sequential queue \(VP^{k}=\{vp^{k}_{1},vp^{k}_{2},\ldots,vp^{k}_{m}\}\) of frontier cluster \(f^{k}\) is extracted by random sampling, where \(vp^{k}_{1}\) is a viewpoint with the largest reward of \(f^{k}\) and will replace \(f^{k}\) in constructing PCATSP.  
Figure 1 shows an overview of the proposed method, which operates upon a voxel grid map. We employ a hierarchical architecture to plan the exploration path, which consists of global path planning (Section 3.1) and local path planning (Section 3.4). The global path planning module takes a prior topological map, global exploration strategy, and frontier clusters as input to plan the global exploration path based on the Priority Constrained Asymmetric Traveling Salesman Problem (PCATSP). Nodes with priority in PCATSP will be extracted (Section 3.2), and the movement cost of some frontier clusters will also be updated (Section 3.3). Then, the global exploration path is given to the local path planning module, which refines the input path based on rewards and costs of each viewpoint candidate to improve the stability of exploration efficiency. Finally, the exploration path will output to the trajectory generation module. The exploration task will be completed when no frontier clusters can be extracted from environment.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques  
The fundamental recommendation question is to check if a user \(u\in U\) will be interested in item \(i\in I\), for \(U\) and \(I\) as the domain of users and items, respectively. The most common ways to answer such questions are:  
* To find out the set of items that \(u\) liked previously and then find the similarity between them and \(i\);
* To find out people who like \(i\) and try to compute their similarities with \(u\).  
In each of the above two cases, the similarity values are used to measure the degrees to which \(u\) is interested in \(i\).  
Many researchers and industries have used different approaches to build robust RSs that intelligently recommend learning or teaching materials to users. The most used techniques are collaborative filtering, content-based filtering, knowledge-based, and hybrid-based recommendation techniques. Other RSs use these techniques to make recommendations, such as context-aware, multi-criteria RSs, deep learning, and machine learning techniques. This section sheds more light onto various RS techniques.
# Analysis and Applications of GlobeLand30: A Review
## 5 Discussion and Outlook
### Comprehensive Validation  
The quality or accuracy assessment is an integral part of GLC mapping and applications. Third-party researchers or users have analyzed the quality of GlobeLand30 via sample-based validation or comparison with existing land cover or other datasets. An average accuracy of 80% for all classes or one single class was given by most published papers or documents [14; 29; 37; 86]. [PERSON] [87] reported that the quality of GlobeLand30 was at a similar level to CORINE data. [PERSON] et al. [7] summarized the reported accuracy of 23 global and 41 regional LC products, in which GlobeLand30 products have relatively high overall accuracy (78.6% and 80.3% for 2000 and 2010, respectively) compared with GLC2000 (68.6%), GlobCover2005 (73.1%), GlobeCover2009 (67.5%), UMD (65.0%), MODIS (71.6%), GLCNMO V2 (77.9%), FROM-GLC (63.69%), GeoWiki hybrid 1 (87.9%) and so on. GlobeLand30 is then recommended for diverse applications, including climate change studies, land change research, and ecosystem analysis [28; 88]. In particular, GlobeLand30 is considered a useful product for developing countries or regions in which good land cover maps are difficult to find [14; 89].  
However, these accuracy assessments were conducted either within 10% map sheets selected from a global scale [24] or in some individual countries [14; 86]. Areas remain in which the uncertainty of GlobeLand30 must be validated and documented. An international validation was initiated by GEO Global Land Cover Community Activity (GEO CA-01) in 2016 with three tasks to promote more comprehensive validation of GlobeLand30 and other finer-resolution GLC datasets. First, a technical specification was formulated to describe the appropriate approaches and procedures for sampling design, response, and analysis protocols at 30 m resolution and global scales. A landscape shape index (LSI)-based sampling approach was developed to consider the high spatial heterogeneity of land cover in large areas [37]. Two web-based validation tools were developed to facilitate online and collaborative validation processes. One tool is GLCVal [90] developed by the National Geomatic Center of China, and the other is LACO-Wiki [91] developed by the International Institute for Applied Systems Analysis (IIASA). Approximately 40 countries have joined this GEO-led activity and completed the validation for their own territory. More countries are expected to join the validation of GlobeLand30 and other finer-resolution GLC datasets in the near future.
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Self-Optimization Positioning Method for Base Stations
#### 3.3.3 C-Type Transition Area  
Taking Figure 3 as an example, when the coordinates from the previous moment are within the C-type transition area, that is, within the radius \(\mathrm{x_{a}}\) of a certain base station node, the four closest base station nodes to that base station node are selected, namely nodes labeled 2, 5, 7, and 10. By permuting and combining these, four positioning coordinates with associated weights can be obtained. Finally, the weighted average Formula (6) is applied to calculate the final positioning coordinates.
## 4 Static Network Performance of a TFBN
### Cost Parameters
#### 4.2.3 Static Cost  
The node degree multiplied by the diameter equals the static cost (Equation (2)). Similar to the hop distance, a low static cost is preferable for any interconnection network. Table 1 summarizes the static cost analysis for TFBN. The table shows that a 2D-Mesh requires the highest static cost and TFBN network requires the lowest static cost with 256 nodes. However, with 4096 nodes, 2D-Mesh fails to improve its previous position, and TESH leads the static performance due to its low node degree (4) compared to TFBN with a node degree of eight.  
\[\textit{Static cost}=\textit{Node degree}\times\textit{Diameter}. \tag{2}\]  
The cost parameters and hop distance parameters of the TFBN had been reported in the earlier work. However, we need these parameters again for the evaluation and analysisof the trade-off between traffic congestion and packing density of interconnection networks for an MPC system. Table 1 demonstrates that both the node degree and wiring complexity of the TFBN is high, which yielded quite low values of the hop distance parameters in comparison with other networks considered in this study.
## 6 Results and Discussions
### Simulation of the Monitoring and Control of Parking Lots in the North of a City
#### 6.2.1 Simulation Set-Up  
A MacBook computer (macOS Sierra version 10.12.3, 2 GHz Intel Core i5, 8 GB of RAM) with Ptolemy II 10.0 and VisualSense was used to run the files with the different simulation scenarios. Additionally, the Google cloud infrastructure as a service (IaaS) was the support for the deployment of the message brokers and the web application with its microservices. These containerized components were deployed in a Kubernetes Cluster with n1-standard-2 type virtual machines (2 vCPUs, 7.5 GB of memory).  
Besides, a test file was configured with Apache JMeter to evaluate the performance of reservation service [38]. In these tests, 1000 was used as the maximum total number of reservations, so we always get valid answers from the service.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Mathematical Modeling of the ISD
#### 2.3.7 Energy Balance of the Bottom Layers for the Heating and Drying Area  
As the ISD does not have an insulation material, the influence of the ground layers \(i\) (asphalt, soil, and absorber) is considered in the model following our previous research [34], in which the sun drying practice was modeled. The energy balance of the bottom layers under the ISD is driven by heat conduction. Figure 4 shows the thermal network across the bottom layers. The rate of thermal energy flow into the bottom layers is given as:  
\[C_{i}.\frac{dT_{i}}{dt}=K_{i}.\left(T_{top}-T_{i}\right)+K_{i}.\left(T_{bottom }-T_{i}\right) \tag{14}\]  
where \(T_{i}\) is the material temperature of the respective layer \(i\), \(C_{i}\) is the thermal capacity of the material, calculated by the specific heat capacity \(c_{i}\), and the thickness \(\delta_{i}\) of the layer \(i\):  
\[C_{i}=\ c_{i}.\delta_{i} \tag{15}\]  
The thermal conductance \(K_{i}\) is given as:  
\[K_{i}=\frac{\lambda_{i}}{\frac{1}{2}.\delta_{i}} \tag{16}\]  
where \(\lambda_{i}\) is the thermal conductivity of the material \(i\) (asphalt, soil, and absorber). The temperatures \(T_{top}\) and \(T_{bottom}\) of the layers are known boundary conditions between the layers and are given as:  
\[T_{top}=\frac{K_{i}.T_{i}+K_{i+1}.T_{i+1}}{K_{i}+K_{i+1}} \tag{17}\]  
\[T_{bottom}=\frac{K_{i}.T_{i}+K_{i-1}.T_{i-1}}{K_{i}+K_{i-1}} \tag{18}\]
## 4 Results and Analysis
This also indicates that the channel 4 was near to the crack location, as the signals for this channel encountered little attenuation during all the four PLB tests. Figure 11: Planer diagram of the estimated location of the crack. Figure 12: Acoustic emission signals obtained from the pencil lead break test (PLB) 1. Figure 14: Acoustic emission signals obtained from the pencil lead break test (PLB) 3. Figure 10.0 Acoustic emission signals obtained from the pencil lead break test (PLB) 2. Moreover, to further check whether the estimated location of the crack is correct, we calculated the distance of the crack based on time of arrival and velocity of the AE signals for PLB 1. The time of arrival is calculated by using the following expression:  
Figure 10.1  
where \(v\) represents the velocity of the signals, \(d\) is the distance of a certain sensor from the crack location and \(t\) is the time of arrival of the signal to the sensor. In this case, the velocity of the AE signal was 3,032,407 mm/s. Moreover, the time of arrival of the signals to the four channels was also known, as presented in Figure 10.2  
By incorporating the velocity and time of arrival information in (7), the location of the leak can be estimated. The estimated distances of the crack from the four channels are given in Figure 10.3 It is obvious from the figure that the distance between the estimated location of the leak and channel 4 was minimal compared to others. It was also observed that channel 4 received the signal well before the other channels from the time of arrival given in Figure 10.4 Therefore, based on all the information, it can be assumed that the leak was in the vicinity of the channel 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Channel Number (Sensor)** & **Time of Arrival (milli-seconds)** \\ \hline
**1** & **0.27** \\ \hline
**2** & **0.24** \\ \hline
**3** & **0.34** \\ \hline
**4** & **0.14** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time of arrival of the AE signals for four channels (sensors). Figure 15: Acoustic emission signals obtained from the pencil lead break test (PLB) 4.
## 4 Discussion  
In the reviewed literature (Section 1), mapping innovations that combine with big data, satellite images and machine learning are excelling [10]. However, their potential to complement official statistics (for NSOs) is largely unexplored. To solve the remaining challenges, the key is the promotion of collaboration and engagement between data scientists, NSOs and different potential user groups of EO-data and derived mapping products and hence, progress towards sustainability.  
Summarising the major findings of the cases, and in general, of the EO and Statistics conference, we find these to be some of the important next steps to foster a continuous evolution in terms of data ecosystems and to achieve high-resolution sociodemographic maps and to advance poverty mapping and the monitoring of SDG 11 indicators:  
* Strengthening local capacities to use and sustain these methods through methods that are easily reproducible and the promotion of training. In particular, those relevant  
Figure 10: DIMD (data-driven index of multiple deprivations) for the QS locations (**left**) and socio-economic variability (SoEeVa) index (**right**).  
in case of crisis and disasters, providing readily available data for fast responses. For example, such data have been mostly absent for COVID-19 responses (further discussed in Section 4.1).
* Improving data infrastructure through data standards and formats that promote spatial interoperability, Analysis Ready Data (ARD), and scalable workflows like cloud computing [10]. For example, in support of local and national SDG monitoring (further discussed in Section 4.2).
* Accelerating validation of promising new approaches and assessing their cost/benefit and suitability for purpose, as well as account for uncertainties in data (further discussed in Section 4.3).  
To better understand, monitor, promote and pursue this progress towards unlocking the sociodemographic knowledge with EO data, it is important to begin by performing a diagnosis. Each element will have a different impact depending on the particular region or institution considered but identifying these gaps and needs is an opportunity to create or reinforce the ideal collaborative partnerships that promote capacity building and education and, in this way, bridge the geo-spatial digital divide.  
The main advances are focused on the innovation in data, ideas, techniques and methodologies. It is vital that these and other important developments, at the same time, align to those standards that allow them to spread worldwide, and yet still be able to be calibrated to the specificity of local needs.  
Activities involved in the integration of statistical and geo-spatial information can evolve through the implementation of these and other techniques but will continue to address the need to deliver high-quality and timely decision-ready-data to the decision-makers; nevertheless, the better and faster they are informed, the quicker the right steps are taken in the road towards inclusive and sustainable development, and in particular towards achieving SDG 11.
## 3 Results and Discussion
### Comparative Analysis of Performance with Other Model Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sub-** & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Fmax**} & \multirow{2}{*}{**AvgPr**} & \multirow{2}{*}{**AvgRe**} & \multirow{2}{*}{**AUPR**} & \multirow{2}{*}{**MCC**} \\
**Ontology** & & & & & & \\ \hline \multirow{8}{*}{MF} & MMSNet (Max Pooling) & 0.660 \(\pm\) 0.033 & 0.690 \(\pm\) 0.045 & 0.632 \(\pm\) 0.028 & 0.700 \(\pm\) 0.055 & 0.619 \(\pm\) 0.043 \\  & MMSNet (Average Pooling) & 0.618 \(\pm\) 0.044 & 0.652 \(\pm\) 0.057 & 0.584 \(\pm\) 0.036 & 0.645 \(\pm\) 0.069 & 0.575 \(\pm\) 0.062 \\  & MMSNet (GlobalMax) & 0.732 \(\pm\) 0.025 & 0.776 \(\pm\) 0.041 & 0.696 \(\pm\) **0.026** & 0.831 \(\pm\) 0.021 & 0.738 \(\pm\) 0.025 \\  & MMSNet (GlobalAverage) & 0.754 \(\pm\) 0.031 & 0.798 \(\pm\) 0.046 & 0.710 \(\pm\) 0.032 & 0.845 \(\pm\) 0.024 & 0.754 \(\pm\) 0.028 \\  & MMSNet (ResidualAttention) & **0.764 \(\pm\) 0.014** & **0.818 \(\pm\) 0.025** & **0.720 \(\pm\) 0.028** & **0.849 \(\pm\) 0.015** & **0.762 \(\pm\) 0.014** \\ \hline \multirow{8}{*}{BP} & MMSNet (Max Pooling) & 0.536 \(\pm\) 0.014 & 0.600 \(\pm\) 0.052 & 0.488 \(\pm\) 0.016 & 0.485 \(\pm\) 0.017 & 0.450 \(\pm\) 0.015 \\  & MMSNet (Average Pooling) & 0.518 \(\pm\) 0.015 & 0.566 \(\pm\) 0.026 & 0.476 \(\pm\) 0.021 & 0.456 \(\pm\) 0.012 & 0.420 \(\pm\) 0.013 \\  & MMSNet (GlobalMax) & 0.628 \(\pm\) 0.019 & 0.696 \(\pm\) 0.024 & 0.574 \(\pm\) 0.016 & 0.634 \(\pm\) 0.019 & 0.579 \(\pm\) 0.025 \\  & MMSNet (GlobalAverage) & 0.638 \(\pm\) 0.015 & 0.698 \(\pm\) 0.031 & 0.584 \(\pm\) **0.008** & 0.643 \(\pm\) 0.016 & 0.583 \(\pm\) 0.021 \\  & MMSNet (ResidualAttention) & **0.654 \(\pm\) 0.011** & **0.728 \(\pm\) 0.020** & **0.592 \(\pm\)**0.015** & **0.661 \(\pm\) 0.009** & **0.603 \(\pm\) 0.009** \\ \hline \multirow{8}{*}{CC} & MMSNet (Max Pooling) & 0.746 \(\pm\) 0.012 & 0.794 \(\pm\) 0.021 & 0.708 \(\pm\) 0.032 & 0.734 \(\pm\) 0.018 & 0.668 \(\pm\) 0.015 \\  & MMSNet (Average Pooling) & 0.732 \(\pm\) 0.020 & 0.764 \(\pm\) 0.029 & 0.700 \(\pm\) 0.021 & 0.712 \(\pm\) 0.030 & 0.650 \(\pm\) 0.024 \\ \cline{1-1}  & MMSNet (GlobalMax) & 0.790 \(\pm\) 0.009 & 0.816 \(\pm\) 0.022 & 0.764 \(\pm\) 0.008 & 0.816 \(\pm\) 0.008 & 0.729 \(\pm\) 0.012 \\ \cline{1-1}  & MMSNet (GlobalAverage) & 0.794 \(\pm\) **0.005** & 0.814 \(\pm\) **0.014** & **0.778 \(\pm\)**0.010** & 0.818 \(\pm\) 0.007 & 0.734 \(\pm\) 0.008 \\ \cline{1-1}  & MMSNet (ResidualAttention) & **0.800 \(\pm\)** 0.007 & **0.824 \(\pm\)**0.017** & 0.776 \(\pm\) **0.005** & **0.819 \(\pm\) 0.005** & **0.739 \(\pm\) 0.007** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the model component comparison.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 4 Key Distribution
### Communicating the Registration URL  
Providing that we have physical access to the device, communicating the registration URL to the device is very straightforward. It could be hard-coded into the device or we could manually enter the URL in full or by using a public or private URL shortening service (assuming we trust such a service, of course). We could also use a non-textual encoding of the URL, such as is used within the advertising industry. For example, this could be in the form of a QR-code [22] or other two-dimensional bar-code; or it could use radio-frequency communications via an Near-Field Communication (NFC) tag [23] or a Bluetooth Low Energy (BLE) [24] beacon protocol such as Eddystone [25].  
For a system where it is not possible (or desirable) to have physical access to the device at the time of registration, we would need to fall-back to the hard-coded approach of providing this initial registration URL in the device's software or firmware. This is clearly less desirable as an overall solution since it constrains the device to only be usable in the context of one specific system; however, for many systems, this may be considered a desirable feature to lock the device in the system in question.  
Whatever the means of providing the URL, it is to be expected that in the majority of cases, the registration process will take place before the device has been deployed. This means that despite the potentially limited bandwidth available to the device in the field, the registration operation can be assumed to occur without specific bandwidth constraints; and hence, the additional overheads of a HTTP connection are not regarded as an issue.  
The process also takes place entirely outside of the SRUP protocol.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 3 The Developed Fault Location Algorithm
The symmetrical components are taken into account in the proposed method just for calculating the updateddownstream current in the faulty section. From Equations (3), (6) and (13), it can be seen that the unbalanced feeder can be analyzed because its effect on the current and voltage is known. Furthermore, in practice, the phases voltage and current can be obtained from the Over Current/Earth Fault and Over/Under Voltage relay which is installed at the beginning of the feeder.