esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Results  
The proposed paradigm has been validated through a prototype implemented in Java. Cryptographic operations involving large integers use the java.math.BigIntegerlibrary. Hash digests have been computed using the SHA-224 [25] function. Regarding the employed cryptosystems, we have chosen the following:  
* _Vendor's key-pair (Section 5.1):_ RSA with 2048 bit keys.
* _Cryptosystem for e-coin transaction signature (Section 5.2):_ ECDSA [26] with 224 bit keys.
* _Cryptosystem for product encryption (Section 5.3):_ ECIES with 224 bit keys.  
Our experiments have measured the running time of the \"Valued e-coin generation\" (Section 4.3), \"No-valued e-coin generation\" (Section 4.4), and \"Spending an e-coin\" (Section 4.5) procedures. The prototype has been run on several personal computers. Average running times from 500 executions have been measured. As expected, computers with a faster processor lead to better running times. We have also observed that the running time benefits from parallel execution mode.  
Table 1 shows the average running time of the \"Valued e-coin generation\" and \"No-valued e-coin generation\" procedures. Let us recall that the generation of valued e-coins involves both the customer and the server (which is required to compute a blind signature) while the procedure for generating no-valued ones is run entirely by the customer. The table shows that the generation of a no-valued e-coin takes some more time than a valued one. This is due to the fact that step 3 of the procedure for generating no-valued e-coins sometimes has to be run more than one time. In our experiments, in which we have implemented the ECIES cryptosystem, there is a 50% chance of having to run it again. In the fastest tested processor, in parallel mode, generation of a valued e-coin and a no-valued one takes around 3 and 4 ms, respectively, leading to generation rates of 333 and 250 e-coins per second, respectively.  
Table 2 shows the running time of the \"Spending an e-coin\" procedure at the vendor part. We focus on this part of the process due to the fact that a vendor may receive a lot of concurrent payments. We do not distinguish between spending a valued or a no-valued e-coin since the procedure is exactly the same in both cases. The fastest running time, obtained on an AMD Ryzen 7 processor in parallel mode, indicates that receiving an e-coin payment takes 2.69 ms, so that around 371 payments can be processed in a single second.
## 4 Challenges
### Threshold Limits  
Threshold-limiting methods require threshold limits to be set by the user in advance, usually based on expectations about network traffic, such as the maximum number of data and control packets transmitted during a given time period [10]. As WSNs are application-driven, their performance requirements vary with the application context. Without significant practical experience, identifying the threshold appropriate for each context is a challenge to the development of feasible trust models for WSNs.  
Closely integrating the trust model into the fabric of the WSN may help address the issue. For example, rather than comparing a node's trust metric to the corresponding threshold, the trust model can include a comparison of the trust metrics of a suitable reference set of nodes and search for outliers. The detection of a flood attack provides an illustration: the evaluating node deploys a promiscuous mode to monitor all nodes within its neighborhood and record their packet sending rate. The resulting datasets are forwarded to an outlier detection algorithm, which identifies them as malicious nodes with abnormal packet sending behavior.
## 3 Methodology
### QoE Models for Video on Demand Applications
The fitting parameters for regression were Sum of Squares Due to Error (SSE) = 0.12, R-square = 0.89 and adjusted R-square as 0.92. \[QoE=3.56*exp^{-0.02*jitter}+0.35*exp^{-0.65*jitter}+1 \tag{4}\]  
In Equation (4) jitter is measured in milliseconds and the fitting parameters for the regression were SSE = 0.11, R-square = 0.92 and adjusted R-Square = 0.94. The SSE shows the error between the original QoE data and the fitted curve. The magnitude of the fitting parameters (SSE, R-square) indicates that the models are indeed good but not optimal. We understand these errors will also result in variations of QoE at certain operating points of PLR and jitter. We will not focus on these fitting errors in this research and will concentrate on the sampling errors in QoS measurements that propagate to estimated QoE. In addition to this, there are inherent variations in QoE due to fluctuations in the subjective perception of subjects. These variations were analysed, and the observations were reported later in this paper.
## 2 Results and Discussion
### Saffron  
A total of 56 VOMs were identified in saffron (_C. sativus_ L.), being mainly characterized by sesquiterpenoids (87.2%), followed by terpenoids (9.8%) (Figure 1A; Table 1). The most dominant VOMs identified in saffron include dihydroquinol (#161), \(\alpha\)-curcumene (#129), \(\beta\)-sesquiphellandrene (#128), (-)-\(\delta\)-cadinene (#115) and \(\beta\)-bisabolene (#117), representing about 78.8% of its total volatile profile. VOMs such as cryophyllene isomer (#87), \(\alpha\)-phellandrene (#28), \(p\)-cymene (#42) and epizonarene (#104), were also identified, although in lower amounts (relative peak areas of 2.89%, 2.85%, 1.83% and 1.75%, respectively) (Table 1). The VOMs identified only in saffron are listed in Table S1. [PERSON] et al. (2019) conducted a comprehensive evaluation of the valotomic fingerprint of saffron with the aim of testing its authenticity and quality. Many of the VOMs identified in our samples agrees with the data provided in that work.
# \(H_{2}/H_{\infty}\) Output Tracking Control with a Performance Compensator for Aeroengines
## 4 Compensator Design
### Compensator for Nominal Controller  
Assume \(y_{r}\) is a bounded Lipschitz continuous reference such that  
\[\ddot{y}_{r}=h\dot{y}_{r} \tag{23}\]  
where \(h=diag\left(\left[\lambda_{1},\ldots,\lambda_{ny}\right]\right)\), \(\lambda_{i},i=1,\ldots,n_{y}\) is the eigenvalue of the \(i\)-th output of the above linear system. The reference is stable when \(h<0\), or equivalently, \(\lambda_{i}<0,i=1,\ldots,n_{y}\).  
If \(h\) is a known constant matrix, applying the Laplace transfer into Equation (23), we obtain  
\[G_{y_{r}}(s)=\frac{y_{r}(0)}{s}+\frac{\dot{y}_{r}(0)}{s(s-h)}\]  
where the initial reference \(y_{r}(0)\) is assumed to be zero. Three classic references are shown in Table 1. It indicates that the step reference is a special case of reference (23).  
By combining system (2) with the tracking reference (23), we obtain the following augmented system:  
\[\dot{x}_{a}=A_{a}x_{a}+B_{a}u+B_{wa}w \tag{24}\]  
where \(u=\dot{u}_{p}\), \(x_{a}=\left[\begin{array}{c}\dot{x}\\ \dot{y}_{r}\end{array}\right]\), \(w=\dot{w}_{p}\), \(A_{a}=\left[\begin{array}{cc}A&A_{I}\\ 0&h\end{array}\right]\), \(A_{I}=\left[\begin{array}{c}0\\ I\end{array}\right]\), \(B_{a}=\left[\begin{array}{c}B\\ 0\end{array}\right]\), \(B_{wa}=\left[\begin{array}{c}B_{w}\\ 0\end{array}\right]\).  
The state feedback controller \(u=\left[\begin{array}{cc}K_{1}&K_{2}&K_{3}\end{array}\right]x_{a}\) of system (24) can be re-expressed as  
\[\Delta u_{p}=K_{1}\Delta x_{p}+K_{2}\int_{0}^{t}ed\tau+K_{3}\Delta y_{r} \tag{25}\]  
Then, the diagram of the closed loop control system is shown in Figure 2.  
Different from the controller structure (10), a new term \(K_{3}\), namely compensator, is obtained in (25).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Image Function** & **Primitive Function** & \(\dot{y}_{r}(0)\) & \(h\) \\ \hline step & \(\frac{1}{s}\) & \(1(t)\) & \(\infty\) & \(-\infty\) \\ ramp & \(\frac{1}{s^{2}}\) & \(t\) & \(1\) & \(0\) \\ exponential & \(\frac{d}{s(s+a)}\) & \(1-e^{-at}\) & \(a\) & \(-a\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three classic linear references.  
Figure 2: Diagram of output tracking control with a constant \(h\).
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 5 Experiments
### Methods for Comparison  
We choose the following nine state-of-the-art methods for comparison:  
**HAN**[2]: introduces a hierarchical attention mechanism to aggregate both node-level and semantic-level information based on multiple artificially selected metapaths.  
**MAGNN**[7]: makes several improvements on HAN by using metapath-based graphs to replace metapath instances and considering information of all nodes rather than only the head and tail node in metapaths.  
**HetSANN**[24]: leverages type-specific attention layers to directly encode node representations with the consideration of heterogeneous graph structures.  
**R-GCN**[22]: extends the homogeneous GCN to knowledge graphs with multiple relations by employing relation-specific projection matrices.  
**HGT**[8]: proposes a novel heterogeneous mutual attention mechanism to capture the information of different meta-relation triplets inspired by the Transformer architecture.  
**HGB**[11]: adopts the multi-layer GAT [6] as backbone with enhancements from the redesign of learnable edge-type embedding, residual connections, and \(l_{2}\) normalization.  
**NARS**[36]: decouples the message passing and representation update process in GNNs and trains on neighbor-averaged features for randomly-sampled subgraphs.  
**HSAGN**[35]: enhances NARS [36] with an attention aggregation mechanism and label model.  
**GAMLP**[37]: learns to capture the underlying correlations between different scales of knowledge with two novel attention mechanisms and knowledge within node labels.  
Here, we compared our proposed models to a total of nine baseline methods, including three metapath-based models (HAN, MAGNN, and HetSANN), three metapath-free models (R-GCN, HGT, and HGB), and three decoupled methods (NARS, HSAGN, and GAMLP), in order to comprehensively evaluate their performance. It should be noted that some of the baseline models are difficult to apply to large-scale datasets such as OGB-MAG due to challenges in choosing and sampling metapaths. Therefore, we only compared our models with those that are available in the online leaderboard for the OGB-MAG dataset.
# An Interpretable Method for Anomaly Detection in Multivariate Time Series Predictions
## 1 Introduction
The main contributions include the following three aspects. (1) We propose an interpretable method based on gradient optimization for anomaly detection. Although its interpreter needs to receive the predicted results of the anomaly detection model, it does not rely on the internal structure or parameters of the original model. This method can interpret anomalies in batches without affecting the performance of anomaly detection. (2) To address the issue of feature independence assumption failure, when selecting important features, we not only use the method of absolute gradient multiplied by input to measure the independent effects of data with different dimensions but also introduce KSG mutual information estimation and multivariate cross-correlation to evaluate the relation ships and mutual influences between different dimensions within the same sliding window. This dual method can dig deeper into hidden important features, thereby improving the accuracy and effectiveness of feature selection. (3) We identify the main features that cause anomalies based on the cumulative gradient and input variation values. Meanwhile, by analyzing the difference between anomalies and optimized values, we can explain the trend of component changes after being attacked. The experimental results validated the effectiveness of the proposed method. The structure of this paper is arranged as follows: Section 2 will introduce the relevant work and outline the research on interpretable methods based on gradients, Section 3 will elaborate on the design method of the proposed interpreter, and Section 4 will present the results of the experimental research and data analysis. Finally, Section 5 will summarize the entire text and provide a conclusion.
###### Abstract  
An elastic transmission error (TE) compensation method for a rotary vector (RV) speed reducer is proposed to improve its transmission accuracy based on error sensitivity analysis. Elastic and geometric TEs of the RV speed reducer can be compensated by tooth surface modification of cycloidal gears. Error coefficients of the TE of the RV speed reducer are derived to determine error factors with positive effects on TEs based on error sensitivity analysis. A total TE, including the elastic TE, is obtained by using Adams. The elastic TE compensation method is developed to calculate modification values of error factors with positive effects on the TE to decrease the elastic TE of the RV speed reducer. TE simulation results show that the elastic TE accounts for 25.28% of the total TE, and calculation results show that the maximum contact force and normal deformation of the modified prototype are obviously improved. The feasibility and accuracy of the proposed elastic TE compensation method for RV speed reducers were verified by TE experiments. TE experiment results showed that the TE of the modified RV speed reducer is 47.22% less than that of the initial RV speed reducer.  
RV speed reducer; elastic transmission error compensation; error sensitivity; tooth profile modification +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Characterization of Pulps and Blend
#### 3.3.5 Antioxidant Activity  
Antioxidant Activity via Ferric Reducing Antioxidant Power (FRAP)  
The antioxidant activity according to the ferric reducing power assay was determined using the FRAP method (Ferric Reducing Antioxidant Power) [72] with adaptations. In the absence of light, the FRAP reagent was prepared with 300 mmol/L acetate buffer (pH 3.6), 10 mmol/L 2,4,6-tris (2-pyridyl)-s-triazin (TPTZ) in a solution of 40 mmol/L HCl and 20 mmol/L FeCl3. An aliquot of the sample extract was transferred to a test tube and 0.27 mL of ultrapure water and 2.7 mL of FRAP reagent were added. The mixture was stirred and kept in a bath at 37 \(\pm\) 1 \({}^{\circ}\)C for 30 min. After cooling to room temperature (\(\approx\)25 \({}^{\circ}\)C), samples and standards were read in a spectrophotometer (Shimadzu(r), model UV-VIS UV-1280, Sao Paulo, Sao Paulo, Brazil) at 595 nm. The standard curve was plotted using iron (II) sulfate at concentrations ranging from 500 to 2000 \(\mu\)mol/L. The results were expressed in \(\mu\)mol of ferrous sulfate equivalent/g (\(\mu\)mol Fe\({}^{2+}\)/g).  
Antioxidant Activity via ABTS\(\bullet\)+ Free Radical Scavenging  
The ABTS\(\bullet\)+ cationic radical was formed through reacting a solution of ABTS (2.2-azinobis (3-ethylbenzthiazol-6-sulfonic acid)) (7 mM) with potassium persulfate solution (140 mM) incubated at room temperature in the absence of light for 16 h and then diluted in ethanol to an absorbance of 0.70 \(\pm\) 0.05 nm at 734 nm. The antioxidant activity of the samples was estimated from the mixture of 30 \(\mu\)L of the extract with 3.0 mL of the ABTS\(\bullet\)+ radical. The reading was taken after 6 min of the reaction in a spectrophotometer at 734 nm and ethanol was used as a blank. As a reference, Trolox was used to obtain the standard curve, according to the method described by [PERSON] et al. [73].  
Antioxidant Activity via DPPH\(\bullet\) Free Radical Scavenging  
The activity of free radical scavenging based on the DPPH\(\bullet\) method was performed according to [PERSON] et al. [73] with modifications. An aliquot of the extract was added to 3.0 mL of a diluted solution of DPPH\(\bullet\) (2.2-diphenyl-1-picryl-hydrazyl) in ethanol (0.0236 mg/mL), stirred and incubated for 30 min in the absence of light, and the absorbance of samples and standards were measured in a spectrophotometer (Shimadzu(r), model UV-VIS UV-1280) at 517 nm. The standard curve was performed with Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) (100-2000 \(\mu\)mol/L in ethanol). Results were expressed in \(\mu\)mol Trolox equivalent /g (\(\mu\)mol TE/g).
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 4 Analysis of ACF and PSD
### Modulation Parameter Analysis
#### 4.1.1 Effect of Number of Subcarriers \(K\) on Navigation Characteristics  
The experimental conditions are set as follows: the subcarrier interval \(\Delta f\) is 0.1 MHz, OFDM a symbol useful time \(T_{u}=1/\Delta f\), time domain sampling interval is \(\Delta t=1/N\Delta f\), cyclic prefix length is \(T_{g}=16\Delta t\), and subcarrier number \(K\) is 32, 64, 128, and 256. Figures 3 and 4 show the change law of the ACF and PSD of the four sets of OFDM navigation signals with the value of parameter \(K\). Figure 3 shows that the ACF of OFDM navigation signal has multiple peaks, with a narrow and sharp main peak. The secondary peaks on both sides of the main peak are symmetrical with respect to the main peak, and the secondary peaks on both sides of the main peak are lower than the main peak. Near the abscissa 1 us, there is a large secondary peak in the ACF, which is caused by the cyclic prefix. When other parameters do not change and the number of subcarriers increases, it can be found that the main peak of the ACF gradually narrows, and the tracking accuracy of the code loop improves. The value of the accessory peak near the main peak decreases, and the signal is not easy to mislock in the tracking process. However, at the horizontal coordinate 1 us, the value of the secondary peak caused by the cyclic prefix becomes larger, which is prone to mislocking in the process of a phase-locked loop capture; the position of the secondary peak, however, will not change. Figure 4 shows the PSD of several groups of OFDM navigation signals. It can be found that there is a depression at the center frequency point of the carrier PSD of the OFDM navigation signals, and the two sides are symmetrically distributed. There is only one set of symmetric two main lobes in the PSD, which are relatively flat. When other conditions are unchanged, increasing the number of subcarriers will gradually expand the spectrum occupied by the main PSD lobe, so that the receiver needs to use more bandwidth at the front of the receiving end and has a stronger anti-interference ability. The amplitude of the PSD function of the main lobe is reduced, and the same level of amplitude can transmit more power.  
Figure 3: The ACF of OFDM navigation signals with a different number of subcarriers.
## 5 Conclusions  
Intensive simulations performed for the reference and proposed physical layer network coding detection algorithms applied in the multiple access phase at the relay of two-way relaying systems have proven that the proposed improved PNC detection algorithm can be a valuable alternative to the typical, regular one at the price of higher computational requirements in the relay, mostly in the form of separate channel decoding of both data streams generated by the end terminals. Previous experiences of the authors indicate that the proposed detection algorithm outperforms the standard successive interference cancellation detector, in which, in detection of a stronger signal, a weaker signal is treated as noise. Such configuration is often assumed by other researchers when SIC receivers are considered in detection of both received signals. The performance of the proposed and reference algorithms has been demonstrated on the example of a typical OFDM transmission system. It is worth mentioning that, in contrary to many previous papers in which ideal AWGN channel model with perfect signal synchronization and power control is applied, our research is supported by simulations in which multipath channels typical for WiFi transmissions are modeled. This results in much more realistic PNC configuration on each OFDM subcarrier.  
Individual contributions of the authors to the paper are the following: Conceptualization, H.S.G. and K.W.; methodology, K.W.; software, H.S.G.; validation, H.S.G., and K.W.; formal analysis, K.W.; investigation, H.S.G.; resources, H.S.G., and K.W.; writing--original draft preparation, H.S.G.; writing--review and editing, K.W.; visualization, H.S.G.; supervision, K.W.; project administration, K.W.; funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Poznan University of Technology, grant number 0312/SBAD/8145.  
The authors declare no conflict of interest.
## 3 Methodology
### Machine Learning Models
#### 3.4.3 Artificial Neural Network (ANN)  
The current ANN algorithms are based on the backpropagation neuron proposed in [57], i.e., on the mathematical formulation of the averaged backpropagation multilayer neuron. The ANN demonstrated its usefulness in the evaluation of landslide susceptibility phenomena, and its predictive capacity was compared with other algorithms [23; 24].  
ANN has a high resilient power and fault tolerance, although it is incapable of interpreting learning. The latter is supposed to be a limiting factor since understanding the operation is necessary to validate its prediction in certain fields (e.g., the banking sector).  
Among the wide variety of ANNs, feed-forward networks were used in this study, particularly following a model-averaged neural network (avNNet) [58; 59]. The idea be -hind avNNet is to train multiple neural network models with different configurations or initializations, and then average their predictions to obtain a more robust and accurate final prediction. Each individual model can have different architectures or hyperparameters (e.g., number of hidden layers, neurons, and iterations/epochs), allowing for the exploration of different approaches and avoiding reliance on a single model. Averaging the models helps to reduce overfitting and improves the generalization ability of the final model. By combining the predictions of multiple models, the impact of individual decisions is diminished, leading to a more reliable consensus.  
The stopping criterion plays a crucial role in ANN performance: it indicates when to stop the training process. In this study, cross-validation was used as the stopping criterion [60]. This technique involves dividing the data into training and validation sets and then evaluating the model's performance on the validation set at each stage of training. To do this, the loss and val-loss curves are plotted throughout different training epochs. As these curves descend, the model correctly generalizes. The training process stops when the loss curves start to increase because the following epochs cause an overfitting of the model.  
It is also important to perform a sensitivity analysis to assess the robustness and influence of the different variables on the model's response. In binary classification, an accuracy metric can be used to evaluate different sets of predictors. This allows for examining how the model's accuracy varies when modifying the feature sets or input variables.  
ANNs are flexible and can work with various types of data, such as images, text, and numerical data. Their main limitation is the need for large amounts of data to train effectively, especially when dealing with deep architectures. Additionally, their complexity and the need to adjust multiple hyperparameters can make their implementation and optimization challenging [61].