esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
For the electrochemical method, the similarity analysis of two measurement samples using AMS and POLAR is difficult to assess. This limitation is caused by the complexity of obtaining exhaust gas samples. However, based on the measurements obtained and the values of the statistical assessment, it can be concluded that humidity has a strong effect on the accuracy of electrochemical measuring devices for NO\({}_{\text{x}}\) and SO\({}_{2}\). Moreover, the water particle presence in the probe, even after the drying process, still has a significant effect on the error. In addition, different approaches and methods demonstrate that they have their special conditions in the measurement process. According to the results, the best method to recommend is optical. The statistical analysis by [PERSON]'s method resulted in f-values that for SO\({}_{2}\) equals 6.06, for NO\({}_{\text{x}}\) 17.56, and for O\({}_{2}\) 11.11, thus rejecting the null hypothesis (Table 7). The ANOVA and \(t\)-test prove this suggestion. However, for CO with an f-value of 1.31, the null hypothesis is confirmed, which is also proved by the ANOVA test and the \(t\)-test (Table 7). In addition, the errors demonstrated in Figure 15, the measurement with electrochemical devices, has a disadvantage in comparison with the optical methods. Nevertheless, the disadvantage can be eliminated by using a multicomponent concentration analysis of the main polluting markers according to the main parameters of the operation of power plants, namely, the technological process of boiler combustion, which will be carried out in the further research. In addition, the recommendations from the current work can be summed up by saying that the electrochemical method has a high error during the measurement of SO\({}_{2}\) and NO\({}_{\text{x}}\), thus for higher accuracy during the AMS integration in the technical process it is better to use optical instruments. On the other hand, the pollution concentration determination for both methods can be improved by a multicomponent analysis of the humidity, temperature, and flow of gas. Another disadvantage of this study is the lack of data such as humidity, temperature, and exhaust velocity of the exhaust gases, since the gas concentration readings change depending on them. The inclusion of this is possible by constructing a dependence on these parameters to find the adjustment coefficients. In this case, RMD1 and RMD2 had a smaller spread, which can be explained by the small number of measurements performed. However, in the case of CO measurement, it can be noted that there are a large number of random factors that determine such a strong deviation.
## 3 Data and Methods
### Methods
#### 3.2.4 The Processing Workflow in Practical Applications  
Based on the error correction method proposed above, we developed a comprehensive processing workflow to obtain surface deformation information from RapidEye imagery SPC, as shown in Figure 5. Firstly, the initial E-W displacement, initial N-S displacement, and SNR were obtained. Then, error correction was performed. The corrected offset results underwent a coordinate transformation and resampling to generate the two-dimensional (2D) horizontal offset field of the study area. Finally, an accuracy assessment was conducted by comparing GNSS observations in the study area with the offset values. RapidEye data information on the 2019 Ridgecrest earthquake is shown in Table 5. The parameters for COSI-corr are identical to those previously mentioned.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pair Num.** & **Master Scene–Slave Scene** & **Track Num.** \\ \hline
1 & 20/04/2019–21/09/2019 & 1155 512 \\
2 & 29/06/2019–06/07/2019 & 1155 513 \\
3 & 20/04/2019–21/09/2019 & 1155 612 \\
4 & 09/03/2019–21/09/2019 & 1155 613 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The information of image pairs.  
Figure 4: The correction of attitude jitter distortions of RapidEye. (**a**,**e**) are the offset fields with attitude jitter distortions. (**b**,**f**) are the offset fields after filtering. (**c**,**g**) present the attitude jitter distortions models based on the filtered offset fields computed by Equations (2)–(4). (**d**,**h**) are the offset fields after removing attitude jitter distortions.
## 3 Results and Discussion  
### Comparison of De-Noising Results  
In order to demonstrate the effect of the proposed method, the CEEMD-based method [26], VMD--wavelet packet method [27], and ours are employed, respectively, to suppress the noise interference described in Section 2, the results of the three methods are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal** & **NCC** & **SNR** & **VTP** \\ \hline Pulse 1 & 0.9816 & 13.39 dB & 1.0886 \\ Pulse2 & 0.9970 & 19.44 dB & 1.0879 \\ Total & 0.9886 & 15.51 dB & 1.1166 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results of the proposed de-noising method.  
Figure 8: _Cont._  
Figure 7: The overall flow of the proposed de-noising method.  
From Figure 8, it is easy to find that, due to the serious frequency aliasing, the CEEMD algorithm can distort the waveform after using the threshold method. In the VMD threshold method, because the threshold cannot be changed according to the noise, the result still contains a large number of noise components. The proposed method takes advantage of VMD to remove periodic narrowband interference, and employs wavelet packet decomposition to remove the residual white noise. The noise level in extracted PD signal is greatly reduced, and the waveform has a high consistency with the simulation waveform. Quantitative results of different method are shown in Table 4.  
It can be seen from Table 4 that the proposed method in this paper has the best ability to suppress complex noise. For pulse 1 and pulse 2, it achieves SNR of 13.3906 and 19.4398, respectively, which are much high than other two methods. The extracted PD signal has the highest similarity with the simulated signal, especially the double exponential decay signal, up to 0.9970, and the distortion is small. The results are obviously better than the other two methods, because the proposed adaptive variational mode decomposition can separate periodic narrowband component with high-efficiency. In summary, the method proposed in this paper can better suppress the noise and extract the PD signal in the online cable monitoring system.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
###### Abstract  
In recent years, skeleton-based human action recognition (HAR) approaches using convolutional neural network (CNN) models have made tremendous progress in computer vision applications. However, using relative features to depict human actions, in addition to preventing overfitting when the CNN model is trained on a few samples, is still a challenge. In this paper, a new motion image is introduced to transform spatial-temporal motion information into image-based representations. For each skeleton sequence, three relative features are extracted to describe human actions. The three relative features are consisted of relative coordinates, immediate displacement, and immediate motion orientation. In particular, the relative coordinates introduced in our paper not only depict the spatial relations of human skeleton joints but also provide long-term temporal information. To address the problem of small sample sizes, a data augmentation strategy consisting of three simple but effective data augmentation methods is proposed to expand the training samples. Because the generated color images are small in size, a shallow CNN model is suitable to extract the deep features of the generated motion images. Two small-scale but challenging skeleton datasets were used to evaluate the method, scoring 96.59% and 97.48% on the Florence 3D Actions dataset and UTKinect-Action 3D dataset, respectively. The results show that the proposed method achieved a competitive performance compared with the state-of-the-art methods. Furthermore, the augmentation strategy proposed in this paper effectively solves the overfitting problem and can be widely adopted in skeleton-based action recognition.  
skeleton-based human action recognition; relative coordinate; data augmentation; motion image +
Footnote †: journal: Applied Sciences  
13 October 2021  
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**Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Comparative Analysis of Discrete Subtraction and Cross-Correlation for Subpixel Object Tracking
## 1 Introduction
The method was verified using a 4-f imaging system, and the minimum translation was 0.27 pixels [17]. Since it does not require numerical interpolation, it will not be affected by the peak-locking error, which is a major advantage over the classical correlation approach. On the negative side, it needs precise calibration of the parameters TC and TR, and its robustness against noise and image artifacts has yet to be studied and characterized. In this manuscript, we propose a validation and evaluation of the DSM technique in comparison to cross-correlation between images, utilizing analytical fitting of the correlation peak. We conducted several sequences with different targets. By employing a micrometric linear motorized stage and a properly calibrated camera, we can achieve apparent displacements of 0.002 pixels on the camera sensor between frames, surpassing the theoretical detection limit for 8-bit images [18] and being 100 times smaller than the previous test [17]. Since our aim is to assess the ideal performance of the methods in the laboratory, we utilized simple target shapes such as a rectangle, a circle, and a Gaussian profile. Additionally, we introduced a more complex target to evaluate the accuracy of both methods for non-symmetrical objects. Through these tests, we analyzed not only the methods' ability to register subpixel displacements but also factors such as tracking noise and systematic deviations, including tracking errors. Our findings demonstrate that DSM is a fast and accurate method for tracking objects with subpixel precision, and it may complement the correlation method in some precise tasks. The manuscript is structured as follows: In Section 2, the materials and methods for the experiments are described. In Section 3, the main results are presented and discussed. Finally, in Section 4, we discuss the main conclusions obtained with our tests.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 4 ADRC Design for Attitude Tracking
### ESO Design for the Attitude Dynamics  
Let \(\bar{e}=\mathbf{\omega}_{e}-\hat{\mathbf{\omega}}_{e}\) be the estimation error, through (11), and we propose the following extended state observer:  
\[\Sigma_{ESO}:=\left\{\begin{array}{l}\dot{\mathbf{\omega}}_{e}=\mathbf{J}^{-1} \mathbf{\Gamma}+\eta_{1}+L_{2}\bar{e}\\ \dot{\eta}_{1}=\eta_{2}+L_{1}\bar{e}\\ \dot{\eta}_{2}=L_{0}\bar{e}\end{array}\right. \tag{13}\]  
where \(\hat{\mathbf{\omega}}_{e}\), \(\eta_{1}=\mathbf{J}^{-1}\xi\) and \(\eta_{2}\) are the angular velocity error estimation, the disturbance estimation, and its time derivative, respectively, and \(\mathbf{\Gamma}\) is the control input. The set of matrices \(L_{2}=\text{diag}(l_{2},l_{2},l_{2})\), \(L_{1}=\text{diag}(l_{1},l_{1},l_{1})\), \(L_{0}=\text{diag}(l_{0},l_{0},l_{0})\) is selected with the assistance of a desired closed-loop Hurwitz polynomial of the third order. Then, the following result is obtained.  
**Proposition 1**.: _Consider the ESO (13) and the angular dynamics error (11). Then, the estimation error behavior satisfies the ISO property (input-to-output stability) [54], i.e., the solutions for the error dynamics converge to a sphere centered at the origin of the estimation error phase space with radius_  
\[\rho=\frac{K_{0}}{J_{1}\lambda_{M}} \tag{14}\]  
_where \(\lambda_{M}\) depends on the selection of the set of coefficients of the matrices \(\{L_{2},L_{1},L_{0}\}\). Furthermore, the error dynamics exhibit asymptotic stability to \(\bar{e}=0\) for \(\xi=0\)._  
The proof is given in Appendix A.
## 2 RT-Induced Toxicities and Their Clinical Management
Current RT guidelines limit the tolerated dose for minimizing the toxicity risk in the tumor-surrounding normal tissues at 5 years after RT treatment. In the case of side effects, the applicable therapeutic 'window' of RT should be re-adjusted. However, lowering the RT alone is not sufficient to reduce side effects and even with low radiation doses, the risk of complications cannot be avoided. For example, among 5-year survivors of childhood cancer, there appears to be no threshold dose below which radiation to the cardiac substructures does not increase the risk of cardiac diseases [19]. Consistently, in breast cancer patients, even lower doses of irradiation are correlated with cardiovascular manifestation [20]. Several measures are considered to alleviate the unwanted damage and side effects among patients undergoing RT. Periodic fasting or fasting-mimicking diets (FMDs), which are plant-based diets, low in protein and sugar but relatively high in fat, have demonstrated potent anticancer effects in mouse cancer models, including enhancing the efficacy of chemoradiotherapy and targeted cancer drugs, as well as triggering anticancer immune responses. Combining periodic FMD cycles with standard cancer treatments holds the highest potential for promoting cancer-free survival in patients, as fasting or FMDs can disrupt alternative metabolic strategies adopted by cancer cells, prevent resistance acquisition, and protect normal cells from the side effects of cancer drugs [21]. Based mostly on the experimental model's data, renin-angiotensin-aldosterone system (RAAS) inhibitors show promise in mitigating the progression of radiation nephropathy (RN), and epoxyeicosatrienoic acids can attenuate renal vascular dysfunction in patients undergoing abdominal or pelvic radiation [22]. To reduce radiation-induced damage to normal tissues, a plethora of radioprotective agents and radiomitigators have been investigated [23]. For example, amifostine is used to protect normal tissues from radiation damage. The level of protection from amifostine varies among different tissues, with higher protection factors observed in the hematopoietic system and salivary glands compared with the lung, kidney, and bladder, while central nervous system is not protected at all due to the blood-brain barrier [24].
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 5 Practical Sources of Errors
### Memory, Aging, Fabrication Variations  
Noise is sensitive to the spatial correlations and their time variations in the molecular lattice of the sensor. This is the reason why FES is a much more sensitive and information rich tool than classical sensing based on resistivity changes. However, these enhancements come at a price: the noise may be too sensitive thus it may reflect on also memory, aging, and variations in fabrication. All these facts must be taken into the account and carefully monitored during applications. For marketable FES systems, the detailed study of aging and memory are yet to be done, similarly to many other sensing methods in the exploratory phase.  
We underline that similar drawbacks limit both the FES method and the resistivity measurements. One important issue is time stability and unavoidable drifts of physical properties [65]. This difficulty is reduced by considering changes in the recorded physical properties, including statistical parameters. We have observed similar stability of noise measurements as for resistivity, at least in some of the tested gas sensing layers. The same remark is valid for repeatability of the measurement results delivered by noise phenomena. Data repeatability can be improved by cleansing, by using heating pulse, or UV-light irradiation.
Sensors_**2013**, _13_, 14261-14276; doi:10.3390/s131114261  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Application of Collocated GPS and Seismic Sensors to Earthquake Monitoring and Early Warning**  
**[PERSON] 1,2, [PERSON] 1,* and [PERSON] 1**  
1 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (B.G.)  
2 German Research Centre for Geosciences (GFZ), Telegrafenberg, Potsdam 14473, Germany; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-27-6875-8512; Fax: +86-27-6877-8371.  
_Received: 14 August 2013; in revised form: 14 October 2013 / Accepted: 17 October 2013 / Published: 24 October 2013_  
**Abstract:** We explore the use of collocated GPS and seismic sensors for earthquake monitoring and early warning. The GPS and seismic data collected during the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes are analyzed by using a tightly-coupled integration. The performance of the integrated results is validated by both time and frequency domain analysis. We detect the P-wave arrival and observe small-scale features of the movement from the integrated results and locate the epicenter. Meanwhile, permanent offsets are extracted from the integrated displacements highly accurately and used for reliable fault slip inversion and magnitude estimation.  
**Keywords:** real-time high-rate GPS; seismic sensor; integrated displacements; earthquake monitoring; earthquake early warning
###### Abstract  
The current study aimed to assess the effect of including the lateral-step or sidestep exercise with elastic bands on multidirectional speed abilities (linear, change of direction, and curve sprinting) and navicular drop and to know the impact of navicular drop changes on multidirectional sprinting changes in young male football players. Thirty-two young male football players (age: 14.7 \(\pm\) 0.82) were randomly divided into group A (n = 16) or group B (n = 16). Using a crossover design trial (ABBA), a group performed three sets of six repetitions per side in the lateral-step exercise with elastic bands attached in a low vector, three days per week, plus their regular training sessions, while the other continued with their regular football training sessions during the first 3 weeks, changing the role during the next 3 weeks of the intervention. Pre-intervention and three and six weeks after the beginning of the intervention, a navicular drop test, a 10 m linear sprint test, a multiple change of direction test (V-cut), and a curvilinear sprinting test were assessed. The sidestep exercise with elastic bands significantly improved the ability to sprint multidirectionally in only three weeks of training (\(p\) < 0.05, \(n^{2}\) = 0.56 to 0.74), and there was a trend in the impact of multidirectional sprinting performance through those changes reported in the navicular drop test (r = \(-\)0.23 to 0.45).  
strength activation; change of direction; curve sprint; sprint +
Footnote †: journal: Applied sciences  
1
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Optimal Input Selections
#### 2.4.2 Overview of Gamma Test (GT)  
The Gamma Test (GT) is an advanced approach for evaluating the variance of the noise, or the mean square error (MSE), which could be fulfilled without over-fitting. This method is very advantageous for assessing the nonlinear relationship between two random variables (input and target). [PERSON] (1997) [37] and [PERSON] et al. (1997) [38] were the first reporting gamma test method and later many investigators have developed and detailed [35,36,39]. Only summarized information about GT is discussed here. Details for mastering the art of GT are presented in the aforementioned articles for the interested readers. By using necessary conditions detailed in the above-mentioned papers, the variance of the noise is specified by the bias of the regression between \(\gamma(k)\) and \(\delta(k)\), where \(1\leq k\leq p\). This variance is dubbed \(\Gamma\). \(\gamma(k)\) and \(\delta(k)\) are presented in Equations (9) and (10):  
\[\delta(k)=\frac{1}{M}\sum_{i=1}^{M}\Bigl{|}x_{N[i,K]}-x_{i}\Bigr{|}^{2} \tag{9}\]  
\[\gamma(k)=\frac{1}{2M}\sum_{i=1}^{M}\Bigl{|}y_{N[i,K]}-y_{i}\Bigr{|}^{2} \tag{10}\]  
\(x_{N[i,K]}\) is the \(k\)th nearest neighbor of \(x_{i}\), and \(y_{N[i,K]}\) is the corresponding target. In order to calculate \(\Gamma\), a least squares fit line should be carried out for the \(p\) points (\(\delta(k)\), \(\gamma(k)\)). Afterward, the bias of the regression line will be easily estimated, which represents the gamma statistics parameter \(\Gamma\). It is worth mentioning that \(\Gamma\) provides useful findings for building an accurate model. The smaller the value of \(\Gamma\), the more appropriate is the input  
Figure 2: Map of the study area and collected boreholes sites.  
set. In addition, \(V_{ratio}\) presents an important indication to assess the predictability of the selected target depending on utilized inputs, and illustrated as:  
\[V_{ratio}=\frac{\Gamma}{\sigma^{2}(\mathrm{y})} \tag{11}\]  
where \(\sigma^{2}(\mathrm{y})\) is the output variance. Overall, in the current paper, by using the least value of \(\Gamma\) and \(V_{ratio}\), the best-input combinations were chosen.
# Study About the Performance of Ascon in Arduino Devices
## 2 The Ascon Cipher Suite
### Ascon as Authenticated Encryption  
For the authenticated encryption process, each Ascon design has an encryption algorithm, denoted by \(\mathcal{E}_{k,r,a,b}\), and a decryption algorithm, called \(\mathcal{D}_{k,r,a,b}\). These algorithms have the following parameters:  
* The master key, \(K\), which has a length \(k=|K|\leq 160\) bits.
* The rate, \(r\), defining the data block size.
* The number of the permutation rounds, \(a\), in the initialization and finalization phases.
* The number of permutation rounds, \(b\), in the intermediate phase.  
The input of \(\mathcal{E}_{k,r,a,b}\) takes \(k\) bits of \(K\), \(128\) bits of a nonce, \(N\), associated data of arbitrary length, \(A\), and a plaintext of arbitrary length, \(P\). Its output consists of an authenticated ciphertext of the same length as \(P\), denoted by \(C\), in addition of a \(128\)-bit authentication tag, which authenticates both the associated data and the encrypted message, i.e.,  
\[\mathcal{E}_{k,r,a,b}(K,N,A,P)=(C,T). \tag{1}\]  
The input of the \(\mathcal{D}_{k,r,a,b}\) algorithm takes the key, \(K\), the nonce, \(N\), the associated data, \(A\), the ciphertext, \(C\), and the tag, \(T\), giving as output the plaintext, \(P\), if the verification process of the tag, \(T\), is correct, and returns an error, \(\bot\), if the verification process fails, such that:  
\[\mathcal{D}_{k,r,a,b}(K,N,A,C,T)\in\{P,\bot\}. \tag{2}\]  
There are two initial versions of Ascon: Ascon-128 and Ascon-128a, and due to the quantum threat posed by Shor [11] and Grover [12] algorithms, a variant of Ascon has been added, which is expected to be secure against potential attacks of a quantum computer: Ascon-80 pq. Table 1 shows the three authenticated encryption algorithms of the Ascon suite and their parameters.