esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibration of Rotary Encoders Using a Shift-Angle Method
## 3 Experiments
### Comparison of Calibration Results Obtained Using the Shift-Angle Method and Self-Calibration  
The SelfA rotary encoder is equipped with 12 head sensors and a rotary encoder. The 12 head sensors are divided into three sets and arranged as depicted in Figure 10. The first set consists of three head sensors numbered 1, 5, and 9, which are located at an interval of 120\({}^{\circ}\). The second set consists of four head sensors numbered 1, 3, 7, and 11 and is located at an interval of 90\({}^{\circ}\). The third set consists of seven head sensors numbered 1, 2, 4, 6, 8, 10, and 12, which are located at an interval of approximately 51.42\({}^{\circ}\). The measured angles using sensors 1 and 7 are averaged and denoted by a\({}_{\text{i}}\) in Equation (9). a\({}_{\text{i}+(-1)\text{NG}/\text{NH}}\) denotes the measured angles using the other sensor heads. According to Equation (9), the angles measured using the 12 head sensors can be used to calibrate the angle errors \(\mu_{\text{i}}\) of the rotary encoder.  
The experiment is performed on the SelfA rotary encoder using self-calibration and the shift-angle method. Figure 11 shows a comparison of the experimental results obtained using the two methods. In the experiment, the shift-angles are set from 0\({}^{\circ}\) to 14\({}^{\circ}\). The angle errors range from 0\({}^{\circ}\) to 359\({}^{\circ}\) in steps of 1\({}^{\circ}\). The self-calibration method is used to calibrate the SelfA rotary encoder at the same angle. Figure 12 depicts the difference in results using the shift-angle method and self-calibration. The difference in results is less than \(\pm 0.1\)\".  
Figure 10: Schematic SelfA rotary encoder of sensor heads setup. The first set is the triangle NH of 3, the second set is the square NH of 4, and the third set is the circle NH of 7 [10].  
Figure 9: Calibration results for curve fitting at three shift-angles. \(S=0^{\circ}\), blue solid line for curve fitting and circle label for the measured values; \(S=5^{\circ}\), light blue solid line for curve fitting and star label for the measured values; and \(S=10^{\circ}\), red solid line for curve fitting and rectangular label for the measured values.
# Development and Evaluation of Light-Weight Active Noise Cancellation Earphones
## 2 ANC Algorithms for Earphones
### Adaptive Feedback ANC Algorithm  
The basic principle of the adaptive feedback ANC is to estimate the primary noise to be canceled to use it as the reference signal \(x(n)\) for the adaptive filter \(W(z)\). As shown in Figure 2, the secondary signal \(y(n)\) generated by the adaptive filter is filtered by the secondary path model \(\hat{S}(z)\) and then added with \(e(n)\) measured from the error sensor to synthesize the reference signal \(x(n)\). The secondary signal \(y(n)\) is generated as  
\[y(n)=\sum_{l=0}^{L-1}w_{l}(n)x(n-l) \tag{1}\]  
where \(w_{l}(n),\ (l=0,\,1,\cdots,L-1)\) are the coefficients of \(W(z)\) at time \(n\), and \(L\) is the filter length. These filter coefficients are updated by the filtered-X least-mean-square (FXLMS) algorithm [3] expressed as  
\[w_{l}(n+1)=w_{l}(n)+\mu x^{\prime}(n-l)e(n) \tag{2}\]  
where \(\mu\) is the step size. The filtered signal is  
\[x^{\prime}(n)\equiv\sum_{m=0}^{M-1}\hat{s}_{m}(n)x(n-m) \tag{3}\]  
where \(\hat{s}_{m}(n),\ \ (m=0,\,1,\cdots,M-1)\) are the coefficients of the FIR filter \(\hat{S}(z)\) (with length \(M\)) that is the secondary path estimate. Figure 2 clearly shows that the reference signal \(x(n)\) equals the primary noise \(d(n)\) if the perfect secondary model is available, i.e., \(\hat{S}(z)\cong S(z)\). The method of modeling the secondary path is presented in the following subsection.  
Figure 1: Three types of earphones: clip earphone (**left**), earbud earphone (**middle**), and in-ear earphone (**right**).
# Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition
###### Abstract  
Emotion recognition plays an essential role in human-computer interaction. Previous studies have investigated the use of facial expression and electroencephalogram (EEG) signals from single modal for emotion recognition separately, but few have paid attention to a fusion between them. In this paper, we adopted a multimodal emotion recognition framework by combining facial expression and EEG, based on a valence-arousal emotional model. For facial expression detection, we followed a transfer learning approach for multi-task convolutional neural network (CNN) architectures to detect the state of valence and arousal. For EEG detection, two learning targets (valence and arousal) were detected by different support vector machine (SVM) classifiers, separately. Finally, two decision-level fusion methods based on the enumerate weight rule or an adaptive boosting technique were used to combine facial expression and EEG. In the experiment, the subjects were instructed to watch clips designed to elicit an emotional response and then reported their emotional state. We used two emotion datasets--a Database for Emotion Analysis using Physiological Signals (DEAP) and MAHNOB-human computer interface (MAHNOB-HCI)--to evaluate our method. In addition, we also performed an online experiment to make our method more robust. We experimentally demonstrated that our method produces state-of-the-art results in terms of binary valence/arousal classification, based on DEAP and MAHNOB-HCI data sets. Besides this, for the online experiment, we achieved 69.75% accuracy for the valence space and 70.00% accuracy for the arousal space after fusion, each of which has surpassed the highest performing single modality (69.28% for the valence space and 64.00% for the arousal space). The results suggest that the combination of facial expressions and EEG information for emotion recognition compensates for their defects as single information sources. The novelty of this work is as follows. To begin with, we combined facial expression and EEG to improve the performance of emotion recognition. Furthermore, we used transfer learning techniques to tackle the problem of lacking data and achieve higher accuracy for facial expression. Finally, in addition to implementing the widely used fusion method based on enumerating different weights between two models, we also explored a novel fusion method, applying boosting technique.  
emotion recognition; EEG; facial expressions; decision-level fusion; transfer learning +
Footnote †: journal:
# Technology Commercialization Activation Model Using Imagification of Variables
## 6 Conclusions  
Technology commercialization is an important management strategy to strengthen the competitiveness of countries and companies, which various countries are trying to revitalize. Despite their efforts, various challenges make technology commercialization difficult to revitalize. Among the various forms of technology commercialization, technology transfer, in which patents are transferred to others, is the ideal commercialization model. Many institutions strive to promote technology commercialization through technology transfer. However, analyzing the target patents for technology transfer consumes substantial time and money, and the existing quantitative models have poor prediction performance because of limitations in patent information and a lack of training data. In this study, patent information was converted into image data using DeepInsight, and patterns related to technology transfer were explored. In addition, a data augmentation technique that can be used when the data for technology transfer learning are insufficient was applied to the model. The proposed model was tested with an actual patent and showed that it can be used in industry. The model proposed in this paper can be used for predicting technology transfer related to AI. Using the proposed model, it is believed that it will be possible to identify patents with high potential for technology transfer in the AI field in advance and establish various technology commercialization strategies.  
The model proposed in this paper can help commercialize technology by predicting technology transfers in advance. However, the proposed model has the following limitations. First, the model was built focusing on only one type of technology transfer, namely, transferring patents. In actuality, technology is transferred in various ways, such as licensing and M&A, but all these methods were not reflected in the model. Next, some of the original image information, such as drawings in the patents, was not used. Patents include images such as drawings to explain the technology developed and to secure rights. The original images from these patents should be combined with the existing quantitative information. In future research, more effective quantitative technology commercialization revitalization methods should be studied by addressing the above-mentioned limitations.  
Y.K. and S.P. conceived and designed the experiments; Y.K. analyzed the data to illustrate the validity of this study; Y.K. and J.K. wrote the paper and performed all of the research steps. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R1I1A1A01062652).
## 3 The Model of Flexible Arm
### Relationship Between Lever Arms and Deformation Angles
\begin{array}{l}\Delta r_{x}^{p_{1}}\left(\theta_{z}^{p_{1}}\right)= \Big{(}\frac{\sin\theta_{x}^{p_{2}}}{\theta_{x}^{p_{2}}}-1\Big{)}r_{x0}^{p_{1} }-\Big{(}\frac{1-\cos\theta_{x}^{p_{2}}}{\theta_{x}^{p_{2}}}\Big{)}r_{y0}^{p_{1 }}\\ \Delta r_{y}^{p_{1}}\left(\theta_{z}^{p_{1}}\right)=\Big{(}\frac{1-\cos\theta_ {x}^{p_{2}}}{\theta_{x}^{p_{2}}}\Big{)}r_{x0}^{p_{1}}+\Big{(}\frac{\sin\theta_ {x}^{p_{2}}}{\theta_{x}^{p_{2}}}-1\Big{)}r_{y0}^{p_{1}}\end{array} \right. \tag{22}\]  
Figure 3: Schematic Diagram of Flexural Deformation Caused by \(\theta_{x}^{p_{1}}\). Combine equations above, expand the trigonometric functions, and neglect higher-order small quantities. Then:  
\[\left\{ \begin{array}{l}\Delta r_{x}^{p_{1}}=\Delta r_{x}^{p_{1}}\left(\theta_{y} ^{p_{1}}\right)+\Delta r_{x}^{p_{1}}\left(\theta_{z}^{p_{1}}\right)\approx \frac{\theta_{y}^{p_{2}}}{2}r_{z0}-\frac{\theta_{z}^{p_{2}}}{2}r_{y0}=\theta_ {y}^{p_{1}}r_{z0}-\theta_{z}^{p_{1}}r_{y0}\\ \Delta r_{y}^{p_{1}}=\Delta r_{y}^{p_{1}}\left(\theta_{x}^{p_{1}}\right)+\Delta r _{y}^{p_{1}}\left(\theta_{z}^{p_{1}}\right)\approx-\frac{\theta_{z}^{p_{2}}}{2 }r_{z0}+\frac{\theta_{z}^{p_{2}}}{2}r_{x0}=-\theta_{x}^{p_{1}}r_{z0}+\theta_{ z}^{p_{1}}r_{x0}\\ \Delta r_{z}^{p_{1}}=\Delta r_{z}^{p_{1}}\left(\theta_{x}^{p_{1}}\right)+ \Delta r_{z}^{p_{1}}\left(\theta_{y}^{p_{1}}\right)\approx\frac{\theta_{z}^{p _{2}}}{2}r_{y0}-\frac{\theta_{y}^{p_{2}}}{2}r_{x0}=\theta_{x}^{p_{1}}r_{y0}- \theta_{y}^{p_{1}}r_{x0}\\ \end{array} \right. \tag{23}\]  
Therefore, the change in the length of the lever arms caused by the flexural deformation angle can be expressed as:  
\[\delta r^{p_{1}}=\mathbf{L}_{0}\theta^{p_{1}} \tag{24}\]  
where:  
\[\mathbf{L}_{0}=\left[ \begin{array}{ccc}0&r_{z0}^{p_{1}}&-r_{y0}^{p_{1}}\\ -r_{y0}^{p_{1}}&0&r_{x0}^{p_{1}}\\ r_{y0}^{p_{1}}&-r_{x0}^{p_{1}}&0\\ \end{array} \right]=-(r_{0}^{p_{1}}\times)\]  
Differentiating the equation above, we obtain:  
\[\delta\dot{x}^{p_{1}}=\mathbf{L}_{0}\dot{\theta}^{p_{1}} \tag{25}\]
# Study of Electrical Neural Stimulation Effects Using Extraneural and Intraneural Electrodes
## 1 Introduction  
Upper-limb amputees' quality of life can be greatly improved with neuroprosthetic devices. In this field, different solutions allowing interaction between these devices and the nervous system have been proposed. Commercial myoelectric upper-limb prostheses do not enable information to be derived from interactions with the environment [1]. These devices can be controlled by the users by means of electromyographic (EMG) sensors, positioned on the forearm, which can decode muscular signals. The principal motivation for the abandonment of myoelectric prostheses by amputees is the lack of afferent information. To improve dexterity, the embodiment of the prosthesis, and the interaction with the environment, restoring sensory feedback is among the main properties to be included in new generations of upper-limb prosthesis [2].  
There are several approaches for restoring sensory feedback in upper-limb amputees. Electrical nerve stimulation, utilizing neural or surface electrodes on peripheral nerves, is one of the most effective. The use of neural electrodes can provide forms of sensory feedback, in terms of tactile sensations, that are more similar to natural ones because of the high selectivity. In 2019, [PERSON] et al. conducted an experimental study on sensory feedback restoration via invasive electrical neural stimulation on an upper-limb amputee [2]. Force and slippage sensations were elicited using extraneural (i.e., cuff) and intraneural (i.e., ds-FILE) electrodes implanted for 11 weeks in a woman with transradial amputation. The approach demonstrated natural-like force and slippage sensations, allowing the user to enhance handling abilities with the hand prosthesis. However, these devices, bothintraneural and extraneural, are quite invasive for patients. For this reason, it is very important to study the problem in a simulation environment, considering the interaction between nerve and electrode to estimate the efficacy and safety of the implant. To this purpose, computational multiscale approaches have been used in the literature, also to evaluate stimulation parameters and waveforms [3] and to study the interaction between nerve and electrode [4]. To perform a computational multiscale study, the finite element method (FEM) and mathematical axon models have been considered [4; 5; 6; 7; 8; 9].
## 5 Evaluation
### Threats to Validity  
One can argue that the above experiments provide anecdotal evidence of the benefits of the delegated policy evaluation. Indeed, it is not straightforward to generalize the obtained results or to draw strong conclusions that would also hold for other applications or use cases. The experimental results are subject to:  
* The complexity of the authorization policies
* The computational power for each of the microservices
* The network capacity between each microservice  
In our experimental setting, each microservice ran in a docker container with a virtual network connection between the microservices, creating ideal network operating conditions. By artificially reducing the bandwidth of virtual connections, the added value of delegated policy evaluation would be even more outspoken.
# Photovoltaics and Electrification in Agriculture
## 2 Special Issue Overview: General Topics
### Precision Agriculture and Photovoltaics  
The electrification of the tasks associated with agriculture encompasses not only the more traditional methods, such as tillage or pumping, but also those related to the so-called precision agriculture.  
There are many activities that benefit from the application of intelligent systems, such as selective spraying and fertilization, which can be applied with robots that include an internet connection (internet of things IoT), and which benefit from autonomy that provides them with a photovoltaic power system like the one proposed by [PERSON] et al. in their study [11]. The document concludes that this field still has a long way to go and that there are still few experiences, which is why it is an area of great interest for research and the application of results to the automation of cultivation tasks and the improvement of precision in agriculture.  
In agricultural applications, an important aspect is the remoteness from the connection points when implementing the Internet of Things (IoT), which influences the energy consumption of transmission systems and forces to study strategies to optimize the use of data and power. This aspect is addressed by [PERSON] et al. in their work [12], where they study a long-range system applying Matlab algorithms. In their conclusions, they determine the improvement that occurs by including hybrid systems in terms of range and then addressing real-time systems. All this is applicable to the new generation of agriculture 4.0.
## 4 Training Methodology
### Statistical Analysis of the Performance of the Models  
To further evaluate the proposed models, a data subsampling method was applied to allow the study of the consistency and variability of the models. This technique evaluates the performance of algorithms in the case of unseen or independent datasets. In the performance prediction of the models considered, models were only updated with new data, and previously trained data were not considered. By using the subsampling method, distortions in the dependency structure of a time series caused by the block bootstrap can be avoided [45]. This selected approach is useful for detecting patterns and ensuring that the model has not been overfitted to a specific section of the data. In this study, it was implemented by splitting the entire time series (in this particular case, using a separate year as a test set) into multiple segments, and each segment was used to validate the models independently.  
Furthermore, to determine the statistical significance of the results obtained, the analysis of variance (ANOVA) test was applied to the metrics to compute the associated F-statistics and \(p\)-values. These statistical tools will ensure that the results obtained are not due to chance. A low \(p\)-value (lower than the significance threshold of 0.05) suggests that there are significant differences in the observed results that are unlikely to be due to chance, which reinforces confidence in the validity of the model, whilst a high value of the F-statistic indicates that the model provides a significant improvement in prediction compared to a model with no predictor variables. A \(p\)-value lower than 0.001 is considered highly statistically significant.
## 7 Implementation and Evaluation
### Multimedia Workflows
#### 7.4.1 Startup Time  
From the users' point of view, a high quality streaming experience requires a fast startup time. To check the quality of our approach, we constructed workflows of different length. As stated before, the maximal length of workflows we used was 12 services. To simulate a worst case scenario all services are scaling services. Scaling services are the most expensive ones, as seen above.  
\begin{table}
\begin{tabular}{l|c c c c c c c c} \hline \hline _\# Services_ & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** \\ \hline Real time & 100 & 100 & 99 & 90 & 55 & 36 & 20 & 9 \\ Non-real time & 0 & 0 & 1 & 10 & 45 & 64 & 80 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Success and error rates in %.  
Figure 15: Server load for 12 s chunks.  
The startup time has been determined as the time passed from pressing the play button on the display device until the first chunk was completely received at the publisher service. The pull concept of our PUMA framework, _i.e._, the client getting the adapted media file stream from the publisher service, is providing an additional average delay not greater than 190 ms for all chunk sizes.  
Figure 16 shows the startup time depending on the workflow length. It is notable that each increase in the workflow length adds a constant delay. A further analysis of the composition of the startup time shows, e.g., for a workflow with one service, the startup time of 1995.28 ms with 1 MB chunks is composed of 181.01 ms network delay, 50.66 ms Input/Output (IO) delay and 1763.61 ms for content processing (see Figure 17).  
Overall, a startup time of around 4 s for 2 MB chunks is remarkably fast compared to startup times of about two minutes as recently reported in P2P streaming applications [14].  
Knowing that the content processing is responsible for 88% of the delay in this case we conducted another experiment measuring the processing time depending on chunk size and load. Similar to the  
Figure 16: Startup time at client.  
Figure 17: Input/Output and network delay.  
previous load experiment, we used a single four CPU machine running up to 17 parallel scaling processes with varying chunk sizes.  
The results depicted in Figure 18 show a nearly linear dependency between chunk size and processing time. The linear increase of processing time is smaller for small chunk sizes, due to the servers' more complex threading behavior for bigger chunks. Again we can see that the processing time is greatly influenced by the server load. This renders the application of P2P scenarios, where peers usually have limited processing power, useless for content editing workflows.
# Optimal Mixture Designs for Heavy Metal Encapsulation in Municipal Solid Waste Incineration Fly Ash
## 3 Results and Discussion
### Heavy Metal Leaching
#### 3.3.4 The Encapsulation of Zn  
The Zn concentrations and the encapsulation percentage values are shown in Figure 7. All mixtures show Zn amounts orders of magnitude lower than the EPA drinking water limit, in contrast with the MSWIFA and water mixture, which showed values somewhat comparable with the limit. This suggests that all groups strongly encapsulate Zn (encapsulation percentages all over 97%). For groups BM and CB, the concentration of the leachate first reduces and then increases during the hydration time from 3 to 180 days. Generally, groups with LPS show slightly greater encapsulation than groups without. Zn encapsulation mechanisms in this study can be explained based on the literature [6; 11; 29; 32; 35; 37; 38].  
Figure 6: The (**a**) Cu concentration and (**b**) encapsulation percentage for the leachate in all groups at all ages.  
During the hydration process, Zinc forms hydroxides at high pH conditions (>8) [38]. The anionic nature of these hydroxides prevents their adsorption onto the negatively charged C-S-H surface, but they can form the complex CaZn\({}_{2}\)(OH)\({}_{6}\):H\({}_{2}\)O [32; 37] or become otherwise physically encapsulated in the bulk. Others have postulated that the Zn could be fixed in a metallic C-S-H phase [37].