esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles
## 5 Environment
### Experimental Setup  
For visualization, interface, and its highly dynamic physics engine, Gazebo is chosen. The first step is to start it. Then the ROS is used to control the drone. Here, OpenAI Gym, which provides the learning agents, is implemented inside the ROS to control the drone. The drone simulator in Gazebo creates the environment and sends several data sensors to give feedback to the agent, and the agent must send actions (Figure 3).  
First, the drone is trained with a certain number of episodes. The training is expected to move the drone from one location to a predetermined desired destination. Data from the drone simulator contain the positions X, Y, and Z that will be sent to the ROS, and the reinforcement learning algorithm was trained in controlling the drone to fly directly to the desired position. The agent will send one of the ten commands (actions) in Table 4.  
The final goal is to fly the drone autonomously. In the training process of the drone, reinforcement learning algorithms are used, which involves several parts, agents, and environments.  
The first condition from the drone is on the floor, Initial Drone Position (IDP), where the coordinated ground truth is X = 0.0, Y = 0.0, Z = 0.0, as shown in Figure 4. The goal is then to move it to the Desired Drone Position (DDP), such as X = 9.0, Y = 0.0, Z = 1.0 (Figure 4).  
The training starts with the agent sending the take-off command. The drone environment must take off with an altitude of 1 m from the floor and send a message to the agent, \"take-off success.\" Then every 0.3 s, the agent sends action and receives a reward feed point as feedback from the environment.  
The training session is divided into episodes, each containing 100 steps to arrive from the initial to the desired location. The agent accumulates each reward it receives per step  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Action** & **Velocity Linear X** & **Velocity Linear Y** & **Velocity Linear Z** & **Velocity Angular Z** \\ \hline
0 = Forward & = Speed Value & - & - & 0.0 \\
1 = Turn Left & 0.05 & - & - & = Speed Value \\
2 = Turn Right & 0.05 & - & - & = Speed Value \\
3 = Up & 0.05 & - & - & = – Speed Value \\
4 = Down & - & - & = – Speed Value & 0.0 \\
5 = Backward & = \(-\) Speed Value & - & - & 0.0 \\
6 = Fly To Left & - & = Speed Value & - & 0.0 \\
7 = Fly To Right & - & = – Speed Value & - & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Velocity commands.  
Figure 3: State, reward, agent, and environment interaction.  
and calculates the average reward at the end of the episode, which it then uses to learn and adopt.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 1 Introduction
Within the framework, we introduce a MobileNet-based CTI identification model designed for low computational complexity. This model estimates the presence and average power of CTI signals in each slot from the detected spectrum, accurately capturing CTI signal characteristics in the time, frequency, and power domains through the application of short-time Fourier transform (STFT). This approach enables estimation of the power of each CTI signal, supporting efficient CTI-aware rate adaptation. 3. We adapt our framework to two common WLAN interference mitigation approaches, one based on explicit channel measurements and the other leveraging packet error-based implicit interference mitigation, resulting in the M-CARA and P-CARA algorithms, respectively. Both algorithms utilize information obtained from the CTI identification model to project interference occurrence times according to each CTI type's pattern, and then adjust their responses based on interference intensity. Sim ulation results show that both algorithms outperform existing methods in terms of packet error rate, goodput, and latency.
## 3 Classification Based on Function
### Communication Support Sensors
#### 3.4.1 Space Satellite Communications  
The communication satellite plays an important role in earthquake rescue because of its large communication range and good communication effect. It can be quickly deployed and opened within a short period of time and has the working characteristics of mobility and flexibility, strong environmental adaptability, etc. Its communication network covers a large area, is in real time, and receives a lot of unexpected information, which can provide information and communication security services between all levels of command at the earthquake site and complete the earthquake emergency rescue work. Satellite communication is self-contained and has low power requirements, requiring only small generators or solar cells to be provided at the terminal for communication services [71].
## 3 Results
The dependences of the capacitance normalized by the maximum value of the capacitors under study on the strength of the control electric field are shown in Figure 5. It follows from the figure that the capacitor based on the non-annealed film exhibits minimal tunability, which is explained by the composition of the solid solution BaTi\({}_{0.4}\)Sn\({}_{0.6}\)O\({}_{3}\), most of which is a linear dielectric BaSnO\({}_{3}\). The tunability of capacitors formed on the basis of annealed films of BaTi\({}_{0.8}\)Sn\({}_{0.2}\)O\({}_{3}\) composition depends on film thickness. For films of 400 nm thickness, it increases from 2.8 to 3.4 with an increase in the deposition temperature from 650 to 850 \({}^{\circ}\)C. The tunability of capacitor based on 800 nm film is 6.8 times (85%), which is the best result for BTS capacitors today. For a capacitor with a planar structure, the total capacitance is composed of three components: air, BTS film and substrate. In this configuration, tunability only comes from BTS film since the substrate shows no tunability, being the linear dielectric. Then the thickness of the FE film will affect the tunability, because in the case of a thin FE film, some part of the field will penetrate into the substrate with a low dielectric constant. Figure 4: ACM images of BTS films on alumina before (**a**,**b**) and after (**c**,**d**) annealing. The dependences of the Q-factor of the same capacitive structures on the strength of the control field, measured at a frequency of 2 GHz, are shown in Figure 6. Three groups of curves can be distinguished here. (1) The relatively high Q-factor of a capacitor based on a non-annealed film is explained by 60% of barium stannate in a solid solution. (2) The Q-factor of capacitors based on films deposited at a temperature of 800 \({}^{\circ}\)C and above increases from 10 to 55-60 under the action of an applied field, which can be explained by their well-formed predominantly oriented crystal structure (see Figure 3). (3) The low Q-factor of capacitors based on films formed at substrate temperatures of 650-700 \({}^{\circ}\)C is due to the polycrystalline structure of these films when the grain boundaries make an additional contribution to the dielectric losses of the capacitor. Figure 5: Dependencies of the capacitances of the BTS films based planar capacitors on control electric field strength. Figure 6: Dependencies of the Q-factor of the BTS based planar capacitors on control electric field strength.
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
## 3 Proposed AOA Estimation Approach  
In this section, phase shifts providing difference beams are designed to facilitate the phase offset estimation between adjacent subarrays. Two AoA estimation schemes are proposed for direct AoA acquisition according to the value of \(N/K\), where \(K\) is the number of different phase shifts for any symbol.
## 3 Manufacturing Techniques for Wearable Antennas
### Rigid Wearable Antennas  
The manufacture process of rigid wearable antennas follows the conventional techniques of printing or constructing antennas for example by using etching. Rigid antennas worn on the body can be miniaturized so as to minimize the inconvenience to the user as conventional antennas are not practical, see Figure 2. These rigid antennas sometimes need to be manufactured in a curved contoured shape so as to meet the wearable requirement [20]. Another difficulty is the small size of these antennas and their complex structure [21].
# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## Appendix A
### Comparison  
The first model considered was a simple extension of our U-Net to a Siamese paradigm, following the structure presented in [20]. Tiny-CD (EfficientNet v0) [42] was the second model considered for our comparison. It effectively utilizes low-level features and minimizes the number of model parameters. The model introduces a novel strategy for feature mixing, enabling spatio-temporal correlations with low computational complexity. Additionally, it incorporates a fast attention mechanism (MAMB) that refines low-resolution results using localized features and a pixel-wise classifier to generate precise change masks. Finally, ChangerEx (ResNet18) [43] was the last change detection model taken into account. In this work, the model incorporated an \"exchange\" interaction that enabled the exchange of bi-temporal feature maps. This exchange process occurred in either the spatial or channel dimension, allowing for the mixing of the exchanged features as they passed through the subsequent convolution layers. As a result, the contextual information of the bi-temporal features could be perceived through mutual learning, facilitated by the feature exchange and subsequent mix layers.  
We compared the architectures and evaluated their impact on the performance of the open-pit mine change detection task, as summarized in Table 11. All the models showed a negative impact on task performance. We identified that the main factor contributing to this decline in performance was the limited number of training patches available for these models. As mentioned in Section 4.1, it was noticed that the performance of the U-Net model trained with site B (188 training patches) demonstrated superior generalization on the validation set compared to the other configuration using site A for training (828 training patches). This observation can be attributed to Site B's complex and heterogeneous land use types. However, the other architectures exhibited the opposite trend,indicating that their performance depended more on the amount of training data available. Indeed, it can be seen that the F1 score values were higher when Site A was used for training. The scarcity of training data poses a challenge for these architectures, as they require more diverse data to learn the patterns and characteristics of open-pit mine changes effectively. The insufficient number of training patches restricted the models' ability to generalize and adapt to different variations and complexities in the open-pit mine environment. The models struggled to accurately detect and classify the changes without an adequate representation of the diverse scenarios and changes. These results underscore the importance of the availability and diversity of training data in achieving optimal performance, in addition to the complexity of the chosen model. A comprehensive and diverse training dataset is crucial for capturing the complex variations and characteristics or open-pit mines, enabling deep learning models to generalize effectively and deliver accurate change detection results.
## 1 Introduction  
Living in the era of an always-connected society has created great opportunities for developing technological tools to support distributed curation in cultural heritage. The most known \"place\" for distributed curation is social networks, also called user-generated content (UGC). In the last decade, an increasing number of cultural heritage venues have started to adopt the use of social networks to facilitate cultural contributions from everyday visitors [1]. As a result, there is an continuously increasing amount of UGC on the Web.  
Despite the \"explosion\" of UGC, only a small portion of people contribute to generating new content. There is an emerging rule of thumb that suggests that, if you get a group of 100 people online, then one will create content, 10 will \"interact\" with it (commenting or offering improvements), and the other 89 will just view it [2]. Furthermore, although social media is currently being used by many cultural institutions to reach their audiences, our findings show that there is one mayor issue; the large amount of information provided and available online is problematic for an efficient reach to potential audiences. To our knowledge, however, distributed curation has not been fully exploited with regard to cultural heritage promotion and integration in people's everyday lives.  
This paper reports on an exploration of potential design opportunities for social media and technology to identify issues and challenges in involving people in distributed curation through 2 studies. In the first study, we took an empirical and critical approach using data from in-depth, semi-structured, and audio-recorded interviews to identify potential design opportunities. In the second study, we aimed to support the interview results using a case study. In the case study, we explored how user-generated content could contribute to visitor's engagement around built heritage in an \"everyday life\" context using 3D sound narratives. Overall, this work is conducted as part of a larger project called _PLUGGY_[3], which aims to allow users to share their local knowledge and everyday experience with others, and alongside cultural institutions. PLUGGY aims to build extensive networks around a common interest around cultural heritage.  
In the following sections, we first introduce an emerging paradigm shift to contextualise our approach. We show a number of recent examples of projects in Human-Computer Interaction (HCI) that aimed to support distributed curation using social media. We then present the methods and outcomes of a series of in-depth semi-structured interviews. Themes are discussed on how existing social media could be better utilised to improve user engagement and to encourage distributed curation within a cultural heritage context. We then present the case study where we evaluated the experience of a mini audio tour with user-generated (i.e., personal stories from a local audience) vs. non user-generated (i.e., professional stories including facts) narratives.
## 3 Improved Grey Wolf Optimizer Algorithm to Optimize Support Vector Machine  
### Support Vector Machine  
The SVM is essentially a supervised machine learning method that combines statistical learning and structural risk minimization [44]. The algorithm has apparent advantages in dealing with the classification and recognition of nonlinear and small samples. The SVM is used initially to solve linearly separable binary classification problems. It assumes that in a specific feature space, there is a sample training set \((x_{i},y_{i})\)\(i=1,2,\ldots,M\), \(x_{i}\)\(\in\)\(R^{m}\), \(y_{i}\)\(\in\)\(\{1,-1\}\). \(M\) is the number of samples in the training set, \(m\) is the dimension of any sample, and \(y_{i}\) represents the sample category. The key of the SVM algorithm is to find an optimal classification hyperplane that can meet the requirements of classification and recognition. Solve the function:  
\[\left\{\begin{array}{l}\min\varphi(w)=\frac{\|w\|^{2}}{2}\\ s.t.\;\;y_{i}(w\cdot x_{i}+b)\geq 1\end{array}\right. \tag{11}\]  
where \(w\) is the normal vector of the optimal classification hyperplane, \(b\) is its offset, and \(wx+b=0\) is the hyperplane.  
The SVM algorithm can use the nonlinear mapping method \(\Phi(x)\) to transform the linearly inseparable samples into a linearly separable problem on the high-dimensional space structure. Establish the optimal classification surface expression as:  
\[\left\{\begin{array}{l}\min\varphi(w)=\frac{\|w\|^{2}}{2}+c(\sum\limits_{i= 1}^{M}\xi_{i})\\ y_{i}(w\cdot\Phi(x_{i})+b)\geq 1-\xi_{i}\end{array}\right. \tag{12}\]  
where \(c\) is the penalty factor, which has an essential influence on the learning accuracy and generalization ability of the SVM algorithm, and \(\xi_{i}\) is the slack variable.  
The method introduced the Lagrange multiplication operator to process the above Formula (12) and obtained the optimal classification surface function as:  
\[f(x)=\text{sgn}\left\{\sum\limits_{i=1}^{M}\alpha_{i}y_{i}K(x_{i},x)+b\right\} \tag{13}\]  
where \(\alpha_{i}\) is the Lagrange multiplier and \(K(x_{i},x)\) is the kernel function.  
The commonly used kernel functions mainly include the radial basis kernel, the polynomial, the linear kernel, and the Sigmoid kernel. Because the radial basis kernel function has few parameters, fast convergence speed, and convenient optimization, it was chosen to construct the SVM algorithm model. The expression of the radial basis function is:  
\[K(x_{i},x)=\exp(-\frac{\|x_{i}-x\|}{g^{2}}) \tag{14}\]  
where \(x\) is the center of the kernel function, and \(g\) is the radial basis radius.
# Use of Social Media Data in Disaster Management: A Survey
## 7 Methodology
### Methodologies Used for Data Management
proposed a system design [53] to find a secure place locator (SPL), which covers a system information engineering aspect. It involves combining multiple data sources, such as location databases, governmental information, and information from the community, as it uses a relational database model to store and process the data. [PERSON] et al. performed a case study on emergency knowledge management and social media technologies [54]. The study investigated social media and related tools for effective knowledge management. It discussed how US government agencies use social media data as an informal information dispersal mechanism, and also studied how visual information layering helped the disaster management scenario. Apart from text information, the use of multimedia data, such as images, audio, and video, in extreme event management [55] remains challenging due to the variety and complexity of social media content. Such events require sophisticated techniques to represent and analyse the multimedia contents to better understand extreme events. The authors of [56] proposed a novel data model based on a hypergraph structure to manage the massive amount of multimedia data produced by social media. The proposed data model comprises three different entities--users, multimedia objects, and annotation objects--to represent the variety and complexity of relationships of the multimedia contents. This approach enables merging of social media contents from different social media platforms in a single data structure. Here, the influence diffusion algorithm [57] was proposed to investigate social media users who have significant interactions on a particular social media object.
# Substrate-Integrated Waveguide Microwave Sensor for Water-in-Diesel Fuel Applications
## 6 Cross-Slot SIW Applicator Characterization, Water-in-Diesel Background
Considering the VNA N9927A resolution \(\Delta S_{21}=0.01\) dB, the proposed set-up is able to measure a minimum variation in water concentration of \(\Delta\rho\approx 7\) ppm [41]. The SIW applicator is intriguing for its compactness and performances even when compared with the literature [14; 15; 22]. It could be employed with a single frequency source and an MW power meter in order to obtain an online and low-cost system for diesel quality detection. The response of the microwave sensor is practically immediate due to the instantaneous interaction of the microwave with the sample. It is worth noting that a potential industrial application for on-time/online measurement could be feasible if the water-in-diesel solution flowed in a microwave-transparent tube, in contact with the sensor like the bag used in this paper. In this case, the tube walls should be continuously washed and in contact with the flow to be monitored, thus allowing an instantaneous sensor response. The influence of temperature on the applicator response is not significant for relatively large changes. In fact, the thermal expansion causes a negligible variation in the sensor size and, as a consequence, a negligible variation in the chosen resonant frequency. This slight variation does not affect the applicator performance since it can operate over the wide frequency range of \(f=9-11\) GHz. Due to the temperature dispersion of the complex dielectric constant of the samples, a calibration could be required for large temperature changes. The main limitation of the proposed applicator is the low selectivity. Generally, microwave sensors are characterized by a low selectivity, since they are based on the dielectric constant variation of the measured sample. Dielectric constant variation can depend on a number of potential contaminants/concentrations for a given wavelength. However, water contamination is the predominant one occurring in the diesel production/supply chain, and the proposed microwave monitoring is interesting in practical application. Table 8 reports the comparison between the proposed applicator and the literature results. The proposed sensor exhibits the best sensitivity \(\Delta|S_{21}|/\Delta\rho\)[14; 15; 22]. Figure 20: Measured modulus of the scattering parameters \(S_{21}\) as a function of water concentration \(\rho\) at frequency \(f=9.76\) GHz.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Case Study Overview  
* Objective: To evaluate the system's ability to predict machine faults and optimize job schedules effectively in a simulated manufacturing environment.
* Scope: A production line with three machines operating continuously for 24 h, processing five predefined jobs with diverse task durations and constraints.
* Significance: Highlights the potential for improving production efficiency and reliability through the proposed integration.