esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Future Opportunities and Research Directions
### Reconfigurable and Adaptive RF Harvesting Systems  
Conventional RF energy harvesters are typically designed for fixed-frequency operation and static environmental conditions. However, real-world RF environments are dynamic, with fluctuating signal strengths, frequencies, and interference patterns. Reconfigurable and adaptive RFEH systems, capable of dynamically adjusting their operating parameters in response to environmental changes, represent a critical future direction.  
Recent works have proposed reconfigurable antennas and tunable matching networks controlled by varactor diodes and MEMS switches, allowing dynamic frequency adaptation and optimal power transfer [119]. Furthermore, the concept of cognitive RFEH, where harvesters can sense the RF spectrum and opportunistically adapt to available energy sources, is gaining momentum. Adaptive systems could also leverage machine learning models to predict optimal operating parameters, enhancing overall system resilience and energy yield in heterogeneous environments [120]. An example of such a system is the implementation of a frequency-agile rectenna integrated with a spectrum-sensing module, capable of detecting spectrum occupancy and retuning its antenna and impedance matching circuit accordingly using a closed-loop feedback controller. This allows the system to adapt in real time to multi-band RF sources such as 5G base stations, Wi-Fi, and Bluetooth and ensures optimal power transfer, even in highly variable spectral environments [121]. Furthermore, research prototypes have demonstrated the feasibility of using reinforcement learning algorithms, such as Deep Q-Networks (DQN), to optimize harvesting policies based on real-time signal strength and usage statistics from surrounding RF bands. This integration of AI enables autonomous decision-making in selecting the best frequencies and configurations to maximize energy harvesting under complex urban scenarios. Future roadmaps could involve the development of integrated RF harvesting modules with built-in AI chips (e.g., edge TPU) for low-power spectrum analytics, coupled with reconfigurable meta-surface antennas that can electronically steer or reshape their radiation patterns in response to environmental feedback, enabling the development of highly adaptive, location-aware harvesting architectures [122].
## 4 Optimized LADRC Approach Using TD3
### Agent Design for Powered Parafoil System  
Based on the introduction to the TD3 agent mentioned earlier, we will proceed with the design of the state space, action space, and reward function of the agent to address the powered parafoil system's trajectory tracking control problem.  
The action variables within the agent are unquestionably parameters that require optimization, namely, \(k_{p1}\) and \(k_{d1}\) for the horizontal controller and \(k_{p2}\) and \(k_{d2}\) for the altitude controller. Then, the four-dimensional action space can be expressed as  
\[\left\{a_{1},a_{2},a_{3},a_{4}\in A\big{|}a_{1}=k_{p1},a_{2}=k_{d1},a_{3}=k_{p2 },a_{4}=k_{d2}\,\right\} \tag{28}\]  
Figure 4: Structure diagram of the TD3 agent.  
State variables are pieces of information that the agent can directly acquire from the environment. For the trajectory tracking problem, the trajectory tracking errors directly indicate the controller's performance. Therefore, the state space is constructed as  
\[\{s_{1},s_{2},s_{3},s_{4}\in S|s_{1}=y,s_{2}=\dot{y},s_{3}=z-z_{d},s_{4}=s_{3}\} \tag{29}\]  
In Equation (29), the impact of horizontal trajectory tracking is observed through variable \(y\) in Equation (17), with an expected value of 0. Based on the state space, it is necessary to design the reward function. The fundamental principle guiding the design of the reward function is to assign a reward when the state value approximates the desired state, otherwise applying a penalty. In this paper, the reward function is designed as follows:  
\[r=-|s_{1}|-5|s_{3}|+a+b \tag{30}\]  
where  
\[a=\begin{cases}2\tanh(1/|s_{1}|),\text{If }\exists\ (s_{1}\leq 0.5\&s_{2}\leq 0.05)\\ 2\tanh(1/|s_{3}|),\text{If }\exists\ (s_{3}\leq 5\&s_{4}\leq 0.5)\\ 0,\text{Otherwise}\end{cases} \tag{31}\]  
\[b=\begin{cases}-2,\text{If }\exists\ s_{1}s_{2}>0\|s_{3}s_{4}>0\\ 0,\text{ Otherwise}\end{cases} \tag{32}\]  
In the designed reward function, the terms \(a\) and \(b\) correspond to the reward and penalty components, respectively. The two terms are employed to encourage minimal trajectory tracking errors.  
Once the action space and state space have been established, the number of input and output neurons for both the critic network and actor network of the TD3 agent can be determined. To enhance the visual representation of the network structure, Figure 5 is utilized to present it more intuitively.
## 7 Results and Discussion
### Evaluation Metrics
#### 7.1.5 Network Lifetime  
Network lifetime is the number of alive nodes in the network during simulation time or after a specified scenario comes to an end.
# Augmented Reality for PCB Component Identification and Localization
## 5 Results
### Experiment 1
#### 5.1.2 Localization Times for Different Component Sizes (Run 2)  
This phase of the experiment analyzed localization times for two components, the SD card and the resistor, to examine the effect of component size on localization efficiency between the traditional and HoloLens(tm) methods:  
* Comparison of Methods: As illustrated in Figure 11, the traditional method resulted in shorter localization times compared to the HoloLens(tm) method:
* SD Card: The traditional method recorded a median time of 3 s, while the HoloLens(tm) method recorded a median time of 32.9 s.
* Resistor: The traditional method had a median time of 17 s, compared to 22.5 s for the HoloLens(tm) method.
* Variability in the HoloLens(tm) Method: Analysis of the HoloLens(tm) method revealed increased variability, most prominently for the SD card component, as evidenced by the wider interquartile range in the boxplot. This suggests potential limitations related to unstable marker tracking and subjective differences in user interpretation of the AR interface, which collectively contribute to greater inconsistency in task execution times.  
The data show that the traditional method was faster than the HoloLens(tm) method for both components. For the SD card, this difference was statistically significant (\(p=0.00018\)), with the traditional method demonstrating consistently shorter and more stable localization times. The HoloLens(tm) method exhibited greater variability and longer completion times, suggesting usability challenges with smaller or less visually prominent components. For the resistor, although mean localization times were similar across methods, the difference was not statistically significant (\(p=0.621\)). These results indicate that, while AR holds potential as an alternative to traditional methods, further refinement is needed, particularly to improve consistency and efficiency for Small component identification tasks like the SD card.
## 1 Introduction
### Organization  
The rest of this paper is organized as follows: Section 2 introduces the system composition and explains the details of the protocol design. Section 3 analyzes the system throughput and channel utilization by building a two-dimensional Markov chain model. Furthermore, fairness is analyzed. Section 4 gives the numerical results. Finally, we conclude the paper in Section 5.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 4 Proposed System for Massive Uplink Scenario
### Wake-Up Frame and Data Transmission for Triggering Uplink Transmission from Multiple Users
#### 4.1.3 Scheduled Operation for IoT Devices (Multicast-Scheduled)  
Although MU transmission reduces the transmission delay significantly, the repetition of the entire transmission procedure results in a large end-to-end delay because the AP waits for a long wake-up time in each group. To reduce the inefficiency due to the wake-up delay, the AP may transmit a wake-up packet to another group during the wake-up delay. In this scheme, as in Figure 10, the AP schedules the MU data transmission procedure of the previous group and the wake-up packet of the next group in the wake-up delay. For example, in Group 2's wake-up delay, the data transmission procedure of Group 1 and wake-up frame to Group 3 are scheduled. Using the scheduling procedure, the wake-up delay of each group is cascaded and the entire data transmission delay can be reduced.
## 3 Numerical Results and Discussion
### Effect of Random Cumulative Pitch Deviations on Modified HGRS
#### 3.2.2 Dynamic Responses Analysis  
Considering random cumulative pitch deviations, the DTEs of the helical gear pair with and without profile modification are given in Figure 15. For the unmodified gear pair, the amplitude of shaft frequency in DTEs is the largest. Comparing Figure 15a with Figure 15b, it can be found that the amplitudes of MFIHs in DTEs decrease remarkably due to profile modification, but the corresponding sideband frequencies around MFIHs show a slight increasing trend. Considering random cumulative pitch deviations, for bearing 1, the vibration acceleration along y direction are given in Figure 16. As similar as DTEs, the amplitudes of MFIHs decrease significantly, but the amplitudes of sideband frequencies around MFIHs tend to be increased notably.  
Considering different amplitudes of random cumulative pitch deviations, the root mean square (RMS) values of DTEs of the helical gear pair with and without profile modification are shown in Figure 17. The input speed changes from 25 r/min to 8000 r/min with a step of 25 r/min. For case 1 to case 4, the amplitudes of random cumulative pitch deviations are 0 \(\upmu\)m, 5 \(\upmu\)m, 10 \(\upmu\)m, and 15 \(\upmu\)m, respectively. From Figure 17a, it can be observed that the RMS values of DTEs of modified helical gear pair decrease significantly in all input speed conditions when compared with the unmodified gear pair. From Figure 17b, it can be found that profile modification shows a well effectiveness in reducing vibration of the HGRS when the input speed is less than about 5500 rpm, but the RMS values of DTEs of the modified gear pair are larger than that of unmodified one in some relatively higher input speed  
Figure 16: Bearing vibration acceleration along y direction considering random cumulative pitch deviations.  
Figure 15: DTEs of modified and unmodified helical gear pair considering random cumulative pitch deviations.  
conditions. This is because the effects of random cumulative pitch deviations on dynamic responses are not the same in different input speeds due to natural frequencies of gear systems and frequency components of mesh excitations. As the amplitude of random cumulative pitch deviations increases, the influences of cumulative pitch deviations on the effectiveness of profile modification in reducing system vibration become more obvious. When the amplitude of random cumulative pitch deviations is larger than 10 \(\upmu\)m, the vibration of the modified HGRS becomes stronger than that of the unmodified one in most input speed conditions, as shown in Figure 17c,d. Thus, as a long-period component of gear errors, cumulative pitch deviations not only affect the kinematic accuracy of gear pairs, but also have significant effect on vibration responses of HGRS.
## 3 Simulation Results
### Powertrain Mode Selection  
The concept of power-weighted efficiency represents an important discriminant factor to evaluate and select the most suitable powertrain mode and satisfy the minimum energy consumption criterion, for each vehicle operating condition (vehicle speed and acceleration,  
Figure 5: Efficiency maps, maximum (black solid lines) and minimum (black dashed lines) torque characteristics for the engine (**left**), the MG1 (**center**) and the MG2 (**right**) of the EVT two-mode hybrid system.  
Figure 6: Angular speed (**left**), output shaft torque (**center**), and output shaft power (**right**) maps of the ICE (**top**), MG1 (**middle**) and MG2 (**bottom**) to achieve the lower penalized power-weighted efficiency for the Input Split mode and road slope \(\alpha=0\).  
road slope). In particular, the hybrid system analyzed within this paper, whose architecture is depicted in Figure 1, can work with the Input Split or the Compound Split modes whose resulting maps, for a fixed road slope \(\alpha=0\), are reported in Figures 6 and 7, respectively.  
The first main difference between the two modes is that the Input Split mode can manage higher vehicle accelerations while the Compound Split mode is able to run up the vehicle at higher speeds. All maps are obtained by limiting the maximum speed to 150 km/h. The output power requested to the three actuators at the maximum speed with the Compound Split mode is lower than the corresponding value for the Input Split mode, since the vehicle can reach higher velocities with the Compound Split mode. For high request of output power, in terms of vehicle speed and accelerations, the MG1 tends to work mainly as an electric motor during both Input Split and Compound Split modes. The MG2 operating range is fully exploited during the Input Split mode, meanwhile its output power is nearly for the Compound Split mode. The ICE mainly provides high torque at lower engine speeds, thus working within its most efficient condition. The torque and angular speed maps of each actuator are overlapped on their corresponding efficiency characteristics, as shown in Figure 8, thus proving that the power-weighted efficiency methodology tends to bind the working conditions within a high efficiency range.  
The procedure here proposed relies on the minimum energy consumption criterion, which leads to a charge depleting of the battery. Indeed, a large contribution of battery power, shown in Figure 9 together with ICE fuel consumption map, is requested during high speed acceleration.  
Figure 7: Angular speed (**left**), output shaft torque (**center**), and output shaft power (**right**) maps of the ICE (**top**), MG1 (**middle**), and MG2 (**bottom**) to achieve the lower penalized power-weighted efficiency for the Compound Split mode and road slope \(\alpha=0\).  
Finally, the selection between Input Split and Compound Split modes is carried out by considering the one that guarantees the highest value of power-weighted efficiency in the whole range of the vehicle operating condition, as shown in Figure 10.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 3 System Model
### Network Model  
**Definition 1**.: _Edge Node: An edge device is a tuple EdgeN = (Id, Loc, F, EC, EM, ES), where:_  
1. _Id represents the unique identifier of the edge device;_
2. _Loc represents the geographic location of the edge device, which includes the longitude and latitude;_
3. _F represents the state of the edge device, which is divided into normal and fault states;_
4. _EC represents the CPU capacity of the edge device;_
5. _EM represents the memory capacity of an edge device;_
6. _ES represents the storage capacity of the edge device._  
**Definition 2**.: _Container Service: A container service is a tuple Caas = (Id, CC, CM, CS, CD), in which_  
1. _Id represents the unique identifier of the container service;_
2. _CC indicates the size of CPU resources required by the container;_
3. _CM indicates the size of memory resources required by the container;_
4. _CS represents the size of storage resources required by the container;_
5. _CD represents the size of data resources transmitted by the container._  
**Definition 3**.: _Edge Network: An edge network is a tuple EN = (DN, CN, FEN), where_  
1. _DN represents all the devices in the network;_
2. _CN represents all the containers in the network;_
3. _FEN represents all the predicted failure devices in the network._  
In this edge network architecture, edge devices are evenly distributed in the network, and services are provided in the form of containers, which can be deployed simultaneously on the same device. Each container can only host one service in it; there is a predicted failure group of edge devices in the network, and the containers running on this group of devices will be migrated to the remaining healthy devices in the form of live migration.
## 2 Data and Methodology
### Pre-Processing of Landsat Time Series and Selection of VIs for CCDC  
We used surface reflectance data obtained from 1 January 2017 to 31 December 2019 by the Enhanced Thematic Mapper Plus (ETM+)/Landsat-7 and Operational Land Imager (OLI)/Landsat-8. Despite the data gaps associated with the Scan Line Corrector (SLC) failure in ETM+ imagery, the remaining pixel observations are useful because they maintain the same radiometric and geometric corrections as data collected prior to the failure. All the available images from both sensors in the period were considered in the data analysis, including 469 images from four tiles: 215 images from Landsat-7 and 254 images from Landsat-8.  
On the Google Earth Engine (GEE) cloud-computing platform [32], the reflectance of the ETM+ bands was radiometrically adjusted to that of the OLI bands using slopes and intercepts reported by [PERSON] et al. [33] This procedure is recommended to face the spectral differences in spectral response functions of the bands from both sensors. Such differences tend to produce, for instance, greater NDVI values for OLI than for ETM+ [33]. Therefore, statistical functions to adjust the band reflectance between the sensors ensure temporal continuity of the time series generated from ETM+ and OLI. Moreover, we excluded pixels under clouds and cloud shadows using the CF-mask algorithm [34; 35].  
From the surface reflectance data, we calculated seven VIs for detecting disturbance (savanna clearing and burned areas). Equations and references of the VIs are listed in Table 1[29; 36; 37; 38; 39; 40]. They were selected because of their sensitivity to different biophysical parameters of the savannas. For instance, the Enhanced Vegetation Index (EVI) and the Soil-Adjusted Vegetation Index (SAVI) are the VIs more sensitive to changes in vegetation structure produced by disturbance. They were proposed to reduce constraints of the NDVI that are mainly associated with signal saturation over dense vegetation, or with soil background influence over sparse vegetation. On the other hand, the Green-Red Normalized Difference (GRND) and the Moisture Stress Index (MSI) may detect changes in old/new foliage and leaf/canopy water stress resulting from gradual processes of savanna clearing or degradation, respectively. Finally, we also considered in the data analysis the Normalized Burn Ratio (NBR) and its variant (NBR2), which are VIs sensitive to fire disturbance. They use near-infrared (NIR) and shortwave infrared (SWIR) bands on their formulations (Table 1). Therefore, besides their sensitivity to different biophysical parameters of the savannas, such VIs explore different band combinations of OLI and ETM+ in the visible, NIR, and SWIR spectral regions.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Supflix Tree-Based Alignment Methods
#### 2.1.2 Other Sequence Comparison Approaches Based on the Suffix Tree Method
It can perform its sequence comparison alignment-free approach for 29 primates' genomes (ranging from 14 to 25 Mbp) in just 66 s.  
Another algorithm based on the suffix tree method is the multiple sequence alignment algorithm (MSA) [23]. The MSA algorithm starts by detecting the similar substrings between the sequences. Then, it uses an approach called the center star strategy in order to calculate the pairwise optimal alignment distance between the set of inputs and select the central sequence which has the highest degree of similarity with other sequences in the set [24]. Finally, the algorithm runs a pairwise alignment between the aligned center and the other sequences to construct the final multiple alignment. This algorithm can perform an alignment of 67,200 strains, all longer than 10,000 bps, in 9 min, and its quality has been evaluated based on alignment accuracy, biological relevance, and statistical significance, through metrics or scoring schemes such as sum-of-pairs score (scores the sum of pairs of aligned residues), total column score (scores based on the similarity of residues to a consensus sequence), and consensus identity (scores based on concepts of information theory like entropy and mutual information [25]. This algorithm is a fundamental problem in bioinformatics, where the goal is to align three or more biological sequences (DNA, RNA, or protein) to identify conserved regions, functional motifs, and evolutionary relationships. Let us say, for a given number of sequences \"N\", Seq1, Seq2,..., Seqn, the goal is to find an optimal alignment that maximizes the similarity of the sequences by inserting gaps to account for insertions, deletions, and substitutions. However, there are a number of important challenges regarding this algorithm, such as computationally expensive, accurate alignment and selecting a gap penalty that accurately reflects biological reality [26]. MSA algorithms play a crucial role in various fields of bioinformatics, allowing analysis of sequence similarities and evolutionary relationships among sequences. Understanding the principles and techniques used in MSA is essential for researchers working in these domains. Current research in MSA has focused on improving alignment accuracy, improving scalability to handle large datasets, and incorporating evolutionary models and structural information into the alignment process [26].