esa-sceva/satcom-chunk-collection huggingface dataset:
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 4 Wake-Up Radio Receiver Design
A stream of 10,000 random bits is considered, where IEEE 802.11 frames of 40 MHz of bandwidth encode WuC bits with a value of \"0\", and frames of 80 MHz of bandwidth encode bits with a value of \"1\". Finally, Figure 10a shows the signal level at filter output of the third chain for an 80 MHz and a 160 MHz IEEE 802.11 signal, being \(-\)81 dBm chosen as a threshold value (i.e., 80 MHz signal level at 1 m) and resulting in an operational range (BER = 0) from 1 m to 22 m. This range can also be observed from Figure 10b, which shows BER performance at the output of the third chain of theWuRx of Figure 6, considering a stream of 10,000 random bits, with IEEE 802.11 frames of 80 MHz of bandwidth encoding WuC bits with a value of \"0\", and frames of 160 MHz of bandwidth encoding bits with a value of \"1\". It is important to highlight that we use the aforementioned threshold values for evaluation purposes. More than the absolute numbers obtained and used in this validation, what is crucial is to achieve enough power difference between signals of different bandwidths at the filters' outputs. If the signal level at the input of the envelope detector needs to be above a minimum value, it can be achieved by adding an amplification step. Figure 8: (**a**) Signal level at filter output of Figure 5, (**b**) Bit Error Rate (BER) at WuRx output of Figure 5, vs. distance, employing 20 MHz and 40 MHz signals. With four symbols and two bits per symbol, it is necessary to choose the codification scheme. In this way, we use the codification scheme shown in Table 1, where there is one bit of difference between adjacent symbols, i.e., between a 20 MHz and a 40 MHz signal, between a 40 MHz and an 80 MHz signal, and between an 80 MHz and a 160 MHz signal. This leads to better BER performance than using the coding scheme with bits \"00\", \"01\", \"10\" and \"11\" for a 20 MHz, a 40 MHz, an 80 MHz, and a 160 MHz signal, respectively. Note that, in this last case, there are two bits of difference between a signal of 40 MHz and a signal of 80 MHz. For the case with two symbols and one bit per symbol, a 20 MHz signal is always decoded as a bit of \"0\", while a signal with higher bandwidth represents a bit of \"1\". Figure 9: (**a**) Signal level at filter output of the second chain of Figure 6, (**b**) BER at the output of the second chain of the WuRx of Figure 6, vs. distance, employing 40 MHz and 80 MHz signals.
###### Abstract  
The large number of inexpensive and energy-efficient terminals in IoT systems is one of the emerging elements of the recent landscape of information and communication technologies. IoT nodes are usually embedded systems with limited processing power devices and strict requirements on energy consumption. In this paper, we consider the design and implementation of a part of the IoT communication uplink stack, namely the error correction coding scheme, for energy-efficient operation. We examine how an efficient rate-adaptive coding scheme, namely the Raptor-like (RL) quasi-cyclic (QC) subclass of low-density parity check (LDPC) codes, can be applied. We present an encoding algorithm designed for an embedded CPU; a respective QC-RL-LDPC decoding scheme, based on typical LDPC iterative decoding; and a combined design procedure for construction of the QC-RL-LDPC parity check matrices. Next, we conduct experiments to explore the time intervals for implemented encoding and the goodput of the coding system with incremental redundancy. We provide the statistical results by combining the measured energy consumption of the encoder and the simulated radio transmitter energy consumption. We demonstrate the comparison of normalized energy consumption statistics with the fixed-rate LDPC coding case. As conclusion, we note that under an unknown channel corruption level, the short block QC-RL-LDPC code implemented in the CPU can achieve higher energy efficiency per information bit compared to a fixed-rate LDPC code. Future work will include an extension of the work to nonbinary QC-RL-LDPC coding design.  
error correction; ECC; low-density parity check codes; LDPC; QC-LDPC; IoT systems Article
## 6 Result Analysis  
### Model Verification  
To quantify the model's accuracy, the mean absolute percentage error (MAPE) was calculated. This metric provides a tangible representation of the average deviation. The MAPE formula, presented in Equation (11), uses \(Y_{i}\) to represent the actual satisfaction of the \(i\)th subject, \(\overline{y}_{i}\) for the predicted satisfaction, and n as the total number of subjects. Upon calculation, the predicted errors of the model for the four enhanced tasks are presented in  
Figure 5: Comparison between the predicted and actual user satisfaction for each function.  
Table 4 as follows: 19.81%, 12.55%, 9.13%, and 13.47%, with an average error of 13.74%. Consequently, the average accuracy rate is 86.26%, indicating that the PSO-BP neural network model could predict user satisfaction with high accuracy to a certain extent, affirming the effectiveness of the model.  
\[MAPE=\frac{\sum\left|\frac{y_{i}-\overline{y}_{j}}{y}\right|}{n}\times 100\% \tag{11}\]  
In order to verify the superiority of the user satisfaction model of the PSO-BP neural network, we compared the prediction results of the PSO-BP model with those of the BP neural network. The parameters of the BP neural network prediction model were consistent with those of the PSO-BP neural network. The comparison results show that the average MAPE of the PSO-BP neural network is 13.74%, while the average MAPE of the BP neural network prediction model is 31.79%. The MAPE of the PSO-BP neural network is significantly better than that of the BP neural network, which indicates that the PSO-optimized BP neural network overcame the problems of slow convergence speed and the susceptibility to local minima. Therefore, the prediction performance is greatly improved and more reliable, and it becomes an effective tool for predicting user satisfaction.
## 1 Introduction  
The renewable energy penetration rate to the power grid is rapidly increasing these days. As per the statistics available in 2018 [1], the total renewable power capacity is 1,081 GW, excluding hydropower. Wind, solar, biomass, tidal and geothermal are considered as the prime renewable sources in generating electric power, while wind energy is the most attractive one dominating the energy market. In 2017, 52 GW of wind power was added globally, making a total of 539 GW, which is more than 50% of the total power generated from various renewable resources. China, the United States, Germany, India and Spain are the top five countries investing most in the wind energy sector. Among many wind turbine manufacturers, Vestas, Siemens-Gamesa and GE have captured the majority of the wind market in 2017, having a market share of 16.7%, 16.6%, and 7.6%, respectively [1].  
Because of the growing nature of wind energy penetration, large scale wind turbines are getting popular, especially in offshore wind farms. In June 2017, MHI Vestas, a joint venture between Vestas and Mitsubishi Heavy Industries, launched a 9.5 MW offshore turbine, which is currently the world's largest wind turbine as of today, and it has been scheduled for 10 MW. Standing 187 meters tall and with 80-meter blades, this wind turbine is an upgraded version of MHI Vestas' 8 MW V164 turbine, which is already in operation at the Burbo Bank Extension and Blyth offshore wind farms in Britain. Seeing the bright future of the large scale wind turbine, GE has recently introduced the Haliade-X 12 MW, which will be the most powerful offshore wind turbine in the world to date, featuring a 12 MW capacity, 220-meter rotor, and a 107-meter blade. The Haliade-X will also be the most efficient of wind turbines in the ocean having a capacity factor of 63%--which of course will be dependent on wind conditions. This turbine is expected to be commissioned for trial by the middle of 2019 [2].
## 2 Materials and Methods Background
### Smart Building (Smart Home) Template Design
The whole principle of a smart home is based on the exchange of data and information between individual sensors and devices, which are then used as the basis for further actions. Smart home solutions can be divided according to where various stimuli (data and information) from sensors and devices are processed. There are three possible smart home implementations: local solution, cloud solution, and hybrid solution. For the local solution, which is one application in our case, there is no need to send any information or data via the Internet to a remote server for processing, evaluation, and subsequent sending back to the sensors or devices to perform a certain action. All data is processed and evaluated locally. However, this does not mean that it cannot be controlled remotely when using one of the local solutions. It is possible, however the connection is established directly to the specific device in the household. In most cases, it requires ownership of a public IP address. Here, communication takes place via a bus to connect individual devices. Our case represents a household (flat) that is subject to a decentralized system. Communication among individual devices is peer-to-peer or direct. However, all connected devices must contain their own control unit. The independent functioning of the individual elements guarantees higher reliability of operation [41; 42; 43]. The alternative solution used for our flat (smart home) is a cloud solution. It is a solution where all data and information is sent either directly by the devices or through a  
Figure 13: Model design of building automation in IoT application. gateway via the Internet to a remote server, to a cloud, where it is processed and evaluated. Then, the appropriate commands are sent back via the Internet directly to the actuator--the device--which then takes the corresponding action. We used a cloud solution of ABB Jablonec nad Nisou, Czech Republic, for the IoT application within our smart home. Cloud services can be controlled using any web browser, and some of them can also be controlled using a mobile phone app. The advantage of using cloud services for smart home management is, for example, the possibility of connecting multiple households (flat and cottage; Smart area, Smart Cities) into one overview and control panel. Furthermore, they can be linked to other Internet services such as weather forecasting, analytics, social media, services for tracking the current location of the household user, and many others. In addition to the advantages of cloud-based home management services, they also bring some disadvantages. For example, higher security risk, the need for a constant fast and stable household connection to the Internet, and more.
## 5 Case Study: Smart Conservation of Cultural Heritage
### Evaluation and Results
#### 5.3.3 Scalability and Performance  
The experiment was initiated with a small-sized installation of approximately 50 deployed things (including sensors, beacons, mobile devices). During the first weeks, the CA middleware did not show any performance bottlenecks, which is expected due to the low volume of contextual data and rules to be checked. In the following weeks, the number of deployed things was gradually increased with artificially created sensors, beacons and mobile devices, which were emulated with scripts. Similarly, the number of rules was gradually increased, although by a lower factor, emulating the extended logic due to larger volumes of contextual data.  
For the evaluation of the performance of each week, the following indices were calculated: (a) _inference cycle time_, which is the average time that a complete check of all rules was performed and (b) _response time_, which is the average response time for an emergency. Table 6 summarizes the findings for each index during the 8 weeks.  
Figure 10: Screenshot of real time monitoring script.  
Figure 9: Rule in BSON format stored in the DB.  
The inference cycle time, which is indicative of how quickly the middleware can perform a complete scan of CA situations that require attention, follows a linear correlation to the number of rules. Figure 11 illustrates this correlation. Similarly, the inference cycle time follows a linear correlation to the number of things, which is shown in Figure 12. The response time follows a linear correlation to both the number of rules and things. Both indices indicate that the middleware scales linearly with the number of added rules and things, which means that it scales well with higher volumes of context data.  
By integrating hybrid reasoning, the proposed middleware manages to both capture and address a wide range of CA situations and show efficient scalability with increased sensor nodes. The combination of computationally heavy machine-learning methods with lightweight rules offers a solution that exhibits flexibility in representation and efficiency in performance. Overall, the findings justify the need for a scalable middleware that can address the requirements of a CH environment in terms of context representation and reasoning without performance loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Week** & **\# Things** & **\# Rules** & **Inference Cycle Time (s)** & **Response Time (s)** \\ \hline
1 & 50 & 25 & 0.15 & 0.10 \\
2 & 50 & 25 & 0.15 & 0.09 \\
3 & 100 & 40 & 0.24 & 0.13 \\
4 & 500 & 150 & 0.94 & 0.51 \\
5 & 1000 & 200 & 1.29 & 0.66 \\
6 & 5000 & 700 & 4.55 & 2.18 \\
7 & 10,000 & 1000 & 6.71 & 3.54 \\
8 & 50,000 & 4000 & 27.12 & 15.12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance results.  
Figure 11: Correlation between inference cycle time and number of rules.
## 2 Modeling of Initial Health Assessment Based on MSET  
### Select Feature Indicators  
Extracting the bearing vibration acceleration characteristic indicator for analysis is an important means of monitoring the bearing health state. The vibration acceleration signal itself contains a wealth of fault information, which can sensitively reflect the subtle changes in bearing performance, such as internal bearing scratches, cracks, electrical erosion, spalling and other faults, which can be reflected in the vibration acceleration characteristic indicators [2 ]. Each characteristic indicator of vibration acceleration has its own physical significance, and its sensitivity to different types of faults is different. Considering the calculation efficiency of the model and the physical meaning of each characteristic indicator, three indicators of vibration acceleration, namely root mean square value, peak value and kurtosis, are selected to characterize the degradation trend of bearing performance. These three indicators--vibration peak, kurtosis and RMS collectively capture distinct degradation signatures across bearing lifespan. They exhibit strong correlations with progressive performance deterioration: peak amplitude directly links to localized defects, kurtosis sensitizes to early-stage micro-faults, and RMS correlates with cumulative wear severity, thereby enabling multi-stage degradation characterization. The physical meanings of these three characteristics are as follows:  
The root-mean-square value is used to measure the average strength of vibration signals. This value reflects the energy level of the vibration signal over a certain time or frequency band, expressed as follows:  
\[x_{rms}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}x_{i}^{2}} \tag{1}\]The peak value refers to the single-peak maximum value of the vibration waveform, which reflects the maximum impact force suffered during the vibration process. Once the peak value is abnormally large, it indicates that the performance of the equipment has declined, which needs to be paid attention to by operation and maintenance. The expression is shown as follows:  
\[x_{p}=\max\lvert x_{i}\rvert \tag{2}\]  
Kurtosis represents the sharpness of the vibration signal waveform and measures the tail thickness of the data distribution, which refers to the frequency with which outliers occur. When running without fault, the amplitude distribution of the vibration signal is close to the normal distribution. With the occurrence and development of the fault, the probability density of the large peak value of the vibration signal increases, the distribution of the signal amplitude deviates from the normal distribution, and the kurtosis value also increases. The expression is as follows:  
\[K=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{4}}}{(n-1)\sigma^{4}} \tag{3}\]
# Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation
## 3 Implementation
### Levels of Working with Eye-Tracking Data  
Eye-tracking data can be acquired and reprocessed (recomputed and aggregated). This can be performed either non-destructively, by retaining the full information provided by the eye-tracker hardware, or destructively, by reducing the information resolution. This relies on obtaining data from the eye-tracking hardware, the ability to access the data through the eye-tracking API, and the in-engine implementation and virtual scene definition (explained in the following subsections).  
What is the minimum required level of distinction in the acquired data for it to be fully useful for evaluation? That depends on the degrees of freedom of the provided VR hardware (the headset and controllers, if any). Generally, it requires:  
* Eye-tracking calibration data (success/failure, or more detailed accuracy value);
* Timestamp for each log entry (time, up to milliseconds);
* User coordinates (positional Vector3 in the virtual world);
* User camera rotation (rotational Vector3 in the virtual world);
* Eye-tracking rotation relative to the user camera rotation or the _point of fixation_ (Vector3).  
This could be considered the bare minimum set of variables to be acquired during experimental runtime. Other derived variables may also be obtained. The question is whether it is better to acquire the derived variables during runtime (for convenience) or to recompute them later, outside experimental runtime (to conserve computational power during the runtime). At a minimum, these derived variables include:  
* Eye-tracking rotation relative to the user camera rotation and _point of fixation_--both;
* Gazed 3D object name (object.name value);
* Gaze distance from the user camera to the _point of fixation_ (in meters);
* Data loss metric, i.e., a check for missing eye inputs;
* Fixation metric, i.e., a check for whether eye-tracking fixation is occurring, based on the eye-tracker movement velocity and its near-time history, all relative to camera movement;
* Other potential physiological measurements of the eyes, if supported by the API (e.g., pupil dilation size or measurement accuracy);
* Other potential derived variables.  
Beyond eye-tracking, experimental runtime can also process other algorithms. To measure user behavior, other logs can be produced, such as user movement logs, interface usage logs, interaction logs, etc. Such interaction logs (event logs) are important, as their execution (based on a temporal event, spatial event, etc.) allows segmentation of the eye-tracking data. An event log is a demarcation that states that _from this event here until another event there, an experimental phase occurs_, and, therefore, _this eye-tracking data section is suitable for further extraction and processing_.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 6 Simulation Analysis  
In this paper, the similarity coefficient and performance index (PI) are utilized to quantify the separation performance, while the mean square error (RMSE) is employed to measure the accuracy of direction of arrival (DOA) estimation. The equation is presented as follows.  
\[\zeta_{ij}=\zeta(s_{i},y_{j})=\left|\frac{\sum\limits_{t=1}^{N}y_{j}(t)s_{i}(t) }{\sqrt{\sum\limits_{t=1}^{N}y_{j}^{2}(t)\sum\limits_{t=1}^{N}s_{i}^{2}(t)}}\right| \tag{45}\]  
\[PI=\frac{1}{m(m-1)}\left[\sum\limits_{i=1}^{n}\left(\sum\limits_{j=1}^{n}\frac{ \left|g_{ij}\right|}{\max\limits_{k}\left|g_{ik}\right|}-1\right)+\left(\sum \limits_{i=1}^{n}\frac{\left|g_{ij}\right|}{\max\limits_{k}\left|g_{kj}\right| }-1\right)\right] \tag{46}\]\[\text{RMSE}\ =\ \sqrt{\frac{1}{K}\sum_{j=1}^{K}\left(\hat{\theta}_{j}-\theta_{j} \right)^{2}} \tag{47}\]  
The true and estimated values of the \(i\)-th source signal are \(s_{i}\) and \(y_{j}\), respectively. The closer the similarity coefficient is to 1, the more similar the source signal is to the separated signal, and the higher the separation accuracy. \(G\) is the system matrix, \(G\) = \(\boldsymbol{B}\boldsymbol{A}\), where \(\boldsymbol{B}\) is the separated matrix and \(\boldsymbol{A}\) is the mixing matrix. The \(j\)-th element of \(\boldsymbol{G}\) is denoted as \(g_{ij}\); the maximum element value of the \(i\)-th row vector in this matrix is denoted as \(\underset{k}{\max}|g_{ik}|\). Similarly, \(\underset{k}{\max}|g_{kj}|\) represents the maximum element value of the \(j\)-th column vector. \(\theta_{j}\) represents the true DOA value, \(\hat{\theta}_{j}\) represents the estimated value, and \(K\) is the number of DOAs.
## 4 Federated System
### Simulation Results
#### 4.3.3 Test 3--Three-Client System with Synchronism Loss  
The third evaluation test of the proposed FDRL system involves a scenario where one of the participating clients loses synchrony with the system. As shown in Figure 10, the system consists of three clients, with two of them participating in the aggregation process throughout all the implemented rounds. Client 1, however, joins the system at the beginning of Round 2 but subsequently loses synchrony and does not rejoin until the end of Round 4. At that point, the server transmits the global model, formed through the aggregation of the remaining clients, to Client 1.  
Figure 9: Reward obtained in Test 2 by Client 1 (**a**), Client 2 (**b**), and Client 3 (**c**).  
After confirming in the previous tests that convergence in the global model could be achieved after three rounds of local training with two clients, the number of training steps was halved in this third test. Figure 11 presents the results obtained in terms of the reward achieved.  
Figure 11: Reward obtained in Test 3 by Client 1 (**a**), Client 2 (**b**), and Client 3 (**c**).  
Figure 10: Federated system scenario (Test 3).  
As observed in the previous tests, Clients 1 and 2, which participated in all rounds, achieved a global model that converged after the third round. In contrast, the results for Client 3 indicate that when it lost synchrony, the global model had not yet reached convergence. Despite obtaining the maximum reward at the end of the second round, this model is not considered to meet the convergence criterion, as the reward was achieved within less than 10% of the total training period. However, once Client 3 reconnected to the system, it received the global model from the server and trained further to achieve the maximum reward.  
Another indication of the nonconvergence of the global model after the second round of training is that only Client 3 achieved the maximum reward. This occurred because the global model formed at this stage was better aligned with the characteristics of Client 3's environment but remained suboptimal for the other clients.