esa-sceva/satcom-chunk-collection huggingface dataset:
# Valorization of Waste Tires by Pyrolysis and Activation Processes
## 2 Materials and Methods  
### Overview  
Four different pyrolysis/activation experiments were performed in which the presence of chemical or physical activation is varied. In Process 1, waste tire powder was pyrolyzed without the presence of either chemical or physical activation. In Process 2, waste tire powder was chemically activated and then pyrolyzed. In Process 3, pyrolysis was performed, followed by physical activation. Finally, Process 4 consisted of chemical activation followed by pyrolysis, and then physical activation. The feedstock (waste tire powder and chemically activated waste tire powder) was characterized, as well as the resulting solid, liquid, and gas products. In the following sections of the manuscript, the products obtained are identified with the initial letter of the product followed by the number of the process. For example, products from Process 1 are represented as solid: S1, liquid: L1, and gas: G1.
## 5 Discussion
### Potential Use Cases for Mobile 3D Motion Capture-Based Applications  
The findings of this research raise the question of possible application areas for human motion capture using mobile 3D motion capture frameworks such as Apple ARKit. Referring to the three categories defined by [PERSON] et al. [2], such frameworks could be applied to use cases in categories (2) interacting with software or (3) motion analysis for medical examinations or performance analysis, as it focuses on tracking single bodies rather than observing crowds. The results suggest that ARKit can track a motion's progression reliably but with relatively high error rates, depending on the joint of interest. Human motion capture using ARKit is further limited to a relatively small set of trackable joints. For example, the hand and toe joints are not actively tracked but calculated based on the angle and wrist joints, limiting the trackable joint angles to the shoulder, elbow, hip, and knee. However, mobile 3D motion capture frameworks are a promising technology for use cases that focus on tracking a specific motion of body parts rather than the exact joint position. Such use cases can be seen in category (2), such as interacting with software through gestures or other movements. Potential use cases in (3) include sports applications for amateurs or physiotherapy applications, which could focus on counting repetitions of a specific exercise. Depending on the motion and joint of interest, specific use cases relying on the exact joint position and angle data might be possible if the two main requirements for a good tracking presented at the beginning of this section can be met. For example, such use cases could include measuring the possible range of motion of a joint before and after a particular intervention and monitoring the progress in the medical field, or correcting the execution of a specific exercise in sports and physiotherapy applications. Using mobile 3D motion capture frameworks in these use cases would extend the usage of human motion capture technologies beyond professional settings and allow day-to-day usage at home, performed by consumers. ARKit and other mobile IPS systems enable new use cases, especially in mHealth, which were not possible with previous HMC systems. Our findings show how mobile 3D motion capture frameworks can be applied and how mHealth applications could leverage the software for future applications. However, the limitations of 3D motion capture frameworks and ARKit's boundaries, in particular, need to be considered and should be evaluated before applying the technology to specific use cases.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Algorithm  
In this section, the RL algorithm for the distributed mechanism is presented. All the nodes and BSs of small cells in the data plane are equipped with the RL algorithm and receive a route map from the CC proactively. This enables them to select the best route proactively based on priority levels (see Section 4.1.2), which helps to offload traffic from MC BS. In the data plane of the small cell, a source node transmits a data stream to a destination node in a selected route out of the given routes by the CC. During the transmission, if the route is interrupted by PUs, a second prioritized route is selected and the previous route is identified as a route with a high traffic intensity level. By continuing this process, a table is updated with route scores based on channel capacity at the end of each transmission cycle, which gives a clearer pattern of the random appearance of PUs. An experimental setup and configuration are shown in Figure 5. In this platform, since the network layer is the focus of this study, the physical distance between nodes, as well as phenomena, such as shadowing and fading, are not the concerns in this work. Algorithm 1 provides a general route selection scheme in which the CC sends the initial prioritized routes through the MC BS to the source node in the data plane proactively. As for CC, it is assumed that routes are readily available and prioritized based on the number of hops. ```
1:procedurehybrid route selection
2: CC sends initial prioritized routes to MC BS proactively
3: MC BS sends prioritized routes \(K\), where \(k_{1}\) has the highest and \(k_{k}\) has the lowest priority, to a source node \(fc_{s}\)
4:for(\(k_{D2D}\in K\)) do
5: Select route \(k_{1}\) if it has the least hops and traffic intensity \(\Psi\) at time instant \(t\) when PU is OFF
6:if all \(k_{D2D}\) have ON PUs then
7: Select the backbone route \(k_{bb}\)
8:endif
9:endfor
10:endprocedure
```  
**Algorithm 1** General description of the route selection by the source node. Algorithm 2 shows the distributed route selection mechanism for traffic offloading. Based on the flowchart shown in Figure 3, the source node receives a route map (i.e., a list of routes created proactively), which is prioritized based on the number of hops, from  
Figure 4: Random appearance of PU activities in D2D routes in three transmission cycles (**A–C**). the CC. The source node rearranges the list and gives priority to D2D routes in order to offload traffic from MC BS. The Q-value is dependent on traffic intensity, which is based on the channel capacity of the links of a route and the learning rate \(\alpha_{i}^{i}(s_{t}^{i},a_{t}^{i})\) that changes dynamically based on the PU activity level. Therefore, routes with lesser PU activities tend to be selected compared to those with higher PU activities.
## 2 Key Steps in a FRB Search Pipeline
### Dedispersion  
As described in the previous section, radio waves travel from the FRB event to the observer, and traverse cold plasma (in the host galaxy, the inter-galactic medium and the Milky Way). A narrow pulse from a FRB is smeared out over the observing band by an amount depending on the magnitude of the DM, resulting in a sub-optimal detection. Hence, before performing any search for FRBs in radio data, the data have to be corrected for this dispersive delay. This process is known as dedispersion. There is an approximate \"incoherent\" method and an exact \"coherent\" method (Section 2.2.3). Since we have no a priori information about the distance of the FRB, the data have to be dedispersed over a range of trial DMs. Below, we describe some of the key dedispersion algorithms.
## 3 Principles of Infrared Spectroscopic Analysis Techniques
### Near-Infrared (NIR) SPECTROSCOpy vs. Mid Infrared Spectroscopy
#### 3.1.1 Miniaturization in NIR and MIR  
The development of miniaturized, handheld instruments would not only lead to a further extension of the range of applications but also suggests that these instruments could eventually appear in non-traditional user environments. When handheld mid-IR spectrometers first appeared, the military adopted them much more quickly than industry for on-site chemical quality and process control in homeland security and antiterrorism applications.  
Figure 1: Measurement principles of (**A**) diffuse reflection, (**B**) transmission and (**C**) transfection mode.  
In the majority of the applications, the primary objective is rapid and safe on-site identification of unknown--often hazardous or toxic--materials or for authenticating the origin of goods by trained users--but not necessarily scientists [58].  
When miniaturizing an analytical instrument one of the key objectives is to ensure that the reduction in size does not compromise measurement performance and precision. It therefore follows that handheld vibrational spectrometers will only have a real impact on quality and process control if the spectra obtained from them are comparable to those produced by larger bench-top instruments [57; 59].  
New developments are driven chiefly by the potential and advantages of micro-electro-mechanical systems (MEMS) production for building extremely miniaturized devices that can perform optical-mechanical functions. Handheld mid-IR instruments are limited to attenuated total reflection (ATR) measurements and are offered as FT-IR systems or with a linear variable filter (LVF) monochromator. NIR spectrometers can operate in diffuse reflection, transmission or transfection mode and their enabling technology varies between dispersive and LVF or digital light processing (DLP) using digital mirror devices (DMD).  
Nevertheless, it is necessary to critically scrutinize the technical background of new, elaborately advertised products, particularly those offering investment opportunities and life-science applications, because several of them might lack feasibility by lacking selectivity and/or sensitivity, respectively.  
[PERSON] _et al._ described one approach how to further improve the efficiency of a miniaturized light-weight (<60 g) handheld spectrometer by the modification of an InGaAs diode array detector by utilizing a thermoelectric cooling (TEC) to ensure constant measurement conditions and a gold-coated spherical mirror as a reflector superior to conventional e.g., Spectralon. With this new measurement setup ethanol in gasoline could be analyzed down to a lower limit of detection (LOD) being 0.68% whereas with the conventional detector the LOD before was 8.68% (Figure 2) [57].
## 2 Data Acquisition and Integration
### Integration of Elevation Data  
The next step is to integrate elevation data. We first downloaded the necessary files from the Spanish National Geographic Information Center [26]. Each file consists of a grid of approximately 5700 by 3700 cells. Each cell is associated with a geographic point on the plane (x and y coordinates) and its corresponding elevation value (z coordinate). Each measuring point is 5 m away from the four nearest points to the east, west, north, and south. These measurements are significantly more accurate than those provided by NASA's SRTM service [27], whose DEM is commonly used in routing problems despite its resolution being 90 m.  
The next step is to assign each node in our graph its elevation value. Since not all our nodes are located precisely on a measuring point, in this case, we have decided to impute the height of the nearest point. Applying complex interpolation methods for grids of elevation values with a resolution of fewer than 10 m is unnecessary, as the results do not show statistically significant differences [7]. The impute elevation values have a maximum horizontal error of \(\sqrt{12.5}\approx 3.53\) m. Figure 2 presents the elevation map of Madrid, with heights ranging from 550 to 800 m. Finally, we add the slope of each segment, calculated from the elevation values at the source and destination nodes.  
The last preprocessing stage is to obtain and integrate historical traffic data of the city of Madrid [28]. We generate a series of snapshots, one for each instant measured in hours: 24 snapshots per day. There are 4719 measuring points scattered throughout the city. Each snapshot contains the values of average vehicle speed measurements at each measuring point. The average speed of vehicles in a given segment is obtained as the average of the speeds at the measuring points closer to each segment ends. When traffic data are not available, we assign as average speed the maximum speed associated with each segment.  
Figure 1: Example of a map-based graph representation.  
We have generated a heatmap where each segment's mean speed is calculated from the values from measuring points within an area of effect. In this way, we can obtain a reliable image of the traffic state for each snapshot; see Figure 3. It is worth noting that warmer colors do not necessarily represent traffic jams since the speed mean depends on variables such as weather, speed limits, street widths, number of lanes, or accidents.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 1 Introduction
### Objectives and Research Questions  
This study's major goal is to give a thorough review of domain adaptation methods as they apply to computer and robotic vision. We address the following research questions (RQ) to achieve this goal:  
* RQ1: What are the primary issues and causes of the domain shift issue in robotic and computer vision?
* RQ2: What are the distinctive benefits provided by each category and how do traditional domain adaptation techniques and deep learning-based approaches vary from one another?
* RQ3: What conclusions may be taken from domain adaptation methodologies' cross-domain analysis when dealing with a variety of source and destination domains?
* RQ4: How do the performance and generalization capabilities of computer and robotic vision systems, with the incorporation of domain adaptation techniques, differ from those of baseline models?
* RQ5: What are the challenges and insights encountered during the process of domain adaptation for robotic and computer vision?  
We seek to offer a thorough and technical understanding of domain adaptation in computer and robotic vision, supporting developments in these fields and paving the way for greater performance in real-world applications. To this end, we rigorously address these research issues.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 8 Conclusions  
In this paper, we propose the RUL prediction method of mechanical bearings combining model-based and data-driven approaches. The adaptive sliding window method optimizes the local model parameters of the degradation model based on the ridge regression of the time series health indicator to estimate the RUL value. We compare the performance of the proposed scheme to existing Bayesian approaches for predicting the RUL value of the experimental data of the PHM data challenge problem. The adaptive sliding window method provides the smallest mean absolute error for 12 cases out of 17 cases. Furthermore, it achieves around 28% improvement in terms of mean absolute error and mean absolute percentage error with respect to the ones using Bayesian approaches. One interesting finding is that predicting the health indicator is not enough to estimate the RUL value due to the complex interaction between machine components with high uncertainty during the whole experimental times. Even though some existing Bayesian approaches estimate the health indicator well, the performance considerably depends on the degradation model. The adaptive sliding window approach provides significant performance improvement and robustness against the possible errors of the degradation model with respect to the ones using Bayesian approaches. The robustness to the  
Figure 8: RUL MAE and MAPE obtained by adaptive sliding window method, Bayesian methods using the exponential and the linear degradation models, and adaptive Bayesian method for all cases.  
model error is crucial to deploy the actual algorithm in industrial systems since it is not feasible to derive the universal degradation model even for the same bearing.  
In practical industrial systems, a full coverage of representative data of all possible failures and their combinations is typically not available, while it is a critical factor for the accurate RUL estimation. For instance, our degradation model is not general enough to capture various behaviors due to many uncertainties in practice. The RUL estimation is essentially the behavior forecasting problem of the time series data using the sparse and delayed measurement with high noise and uncertainty. Furthermore, each failure mode typically has long-term dependencies along with short-term ones of time series data. Future research will focus on bridging the gap between the traditional knowledge of mathematical models and the deep learning techniques to estimate RUL using the time series data.  
[PERSON] and [PERSON] conceived the main idea and the network model; [PERSON] and [PERSON] contributed to data analysis and simulation. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported in part by the Basic Research Laboratory of the National Research Foundation under Grant NRF-2020R1A4A2002021 and in part by Institute of Information & Communications Technology Planning & Evaluation (IITP) under Grant No. 2020-0-00187 funded by the Korea government.  
The authors declare no conflict of interest.
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
###### Abstract
It adds a slow-moving section to the ECG signal that we do not want [17]. * Power line interference: This kind of noise is caused by electricity sources like power lines. It tends to show up at 50 or 60 Hz, even though we cannot always know when it will appear or how strong it will be [18]. * Motion artifacts: If the sensors move away from where they should be on the skin, it results in these unwanted changes in the signal. These represent a problem because we cannot predict how they will look or how often they will occur. * Muscle noise: This occurs because of muscle movements and is similar to the ECG signal in terms of its energy. To ensure consistency and accuracy in the ECG readings, it is crucial for all clinical technicians to follow the same standardized method for obtaining a 12-lead ECG. This standardization guarantees that the ECG data collected are reliable and precise. These high-quality ECG data then serve as the foundation for feeding into AI-based systems. The AI-based system capitalizes on these reliable ECG data to carry out the analysis. Employing advanced algorithms, the system can recognize patterns, anomalies, and indicators within the ECG signals. This analysis yields valuable insights that can assist in diagnosing cardiac conditions and making informed medical decisions.
## 5 Algorithm Design  
The resource allocation problem in the SAGIN scenario is essentially a multi-objective optimization problem (MaOP), which proves to be an NP-hard problem. This scenario results in a much more complex environment than a ground network alone due to the highly dynamic nature of satellites and vehicles.  
To solve this problem, we propose DRL-G, which is a hybrid algorithm combining deep reinforcement learning and the heuristic algorithm. At present, the effect of deep reinforcement learning in solving combinatorial optimization problems is as good as that of high-performance heuristic algorithms [25]. However, due to the fact that the deep reinforcement learning algorithm cannot fully explore all actions, we use the heuristic algorithm to conduct a local search based on its results so as to obtain better results than the original algorithm. The specific algorithm flow is shown in Figure 4. After receivingthe service request, DRL-G first predicts the placement scheme through the sequence-to-sequence model within the allowed response time and then finds the best strategy through the greedy algorithm.
# Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization
###### Abstract  
For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.  
gait phase; feature optimization; time-domain feature; time window; genetic algorithm; Bayesian optimization
# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
## 2 Materials and Methods  
The hypothesis is that the network is able to detect an impersonating device via RF signals without using the layers of the protocol and stack for other more enhanced security mechanisms to do this at the physical layer itself. Therefore, it is possible that the indeed each device has a unique characteristic that imparts those characteristics onto the signal. If we learn the characteristics of those transmitted signals we will know who the device is. Hence, the method is broadly described as RF fingerprinting to detect these fingerprints or these characteristic signatures inside a radio's electromagnetic transmitted signals. Further, the main concept for identifying network devices underlying radio fingerprinting is to find distinctive features (patterns) and utilize them as device signatures. For radio fingerprinting, a number of features at the upper layers, medium access control (MAC), and physical (PHY) layer have been used [3]. International mobile station equipment identity (IMEI) numbers, MAC addresses, and IP addresses are examples of straightforward unique identifiers that are simple to spoof. Environmental and mobility changes can affect location-based aspects including channel state information (CSI) and radio signal strength (RSS).  
The representativeness and balance of the dataset are critical considerations when working with deep-learning neural networks. A dataset that is both representative and balanced is essential for training a model that generalizes well to real-world data. Further, a representative and balanced dataset is fundamental to the success of deep learning models. It ensures that the model can generalize well to unseen data, make accurate predictions, and avoid biases or skewed performance. Balancing classes, data augmentation, and thoughtful dataset curation are essential steps to achieve this. Here are some key aspects to consider:  
* Dataset Size: The dataset should be large enough to cover the diversity of real-world scenarios. A small dataset may result in overfitting, where the model learns to perform well on the training data but fails to generalize to unseen data. In this work, we generated a 5000 Non-HT dataset of WLAN beacon frames for each router.
* Class distribution: Simulating a static Rayleigh fading channel with a specific delay profile and average path gains effectively applies a known transformation to the data. This introduces variations in the received signal characteristics while keeping the channel static. The fading channel effectively acts as a data augmentation technique to create diversity within the dataset. By introducing variations, we generate additional samples for the minority class and improve the performance of the deep-learning model when dealing with class imbalance.