esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intelligent Jamming
### Estimation of jamming Effect
We calculated the SIR for different distances between 1 and 8 m and determined the maximum efficiency of the jamming system (distance between the jamming equipment and the Digi Mobile smartphone), which corresponded to the case when SIR = 0 dB. Figure 6 presents the decreasing SIR according to the increasing distance between the system jamming and the smartphone. If we modified the system such that \(BW_{jammer}=10\)MHz, all SIR values calculated and presented in Table 3 would be half of the values presented. If we considered the maximum \(BW_{jammer}=20\)MHz and \(BW_{network}=5\) MHz, the values calculated and presented in Table 3 would be a quarter of the SIR values presented. To perform a denial of service (DoS) through jamming, it is enough to block only one connection of the mobile terminals, i.e., the downlink or uplink. To jam downlink connections, several conditions must be fulfilled, including completing a study of the availability of the network providers and the radio environment must be suitable for this kind of jamming. Even for academic purposes, jamming uplink connections is illegal, and thus, these kinds of simulations were excluded because the jamming device would introduce dangerous interferences in the mobile operator network. Figure 6: Graph of maximum effective distance of the jamming equipment. In Digi Mobil in laboratory conditions, jamming the downlink connection was successful since the strength of the network parameters did not exceed the possibilities of the Hack-RF in terms of power. The method of jamming the whole downlink band was shown not to be a reliable one because it depended very much on the network provider emitting signals and, of course, on the limited power and bandwidth of Hack-RF. After conducting the tests on downlink jamming, with all the constraints mentioned above, our efforts were concentrated on developing an intelligent jamming system for the downlink connection. This system aimed to block the access of smartphones from registering in the network by employing pulsatory jamming in the downlink bands that was triggered by the detection of data traffic in uplink bands. Very good results were obtained with this method of concentrating all the power only on the frequencies that carried traffic.
## 4 Results
### Holdout Data Results--External Validation  
When rotations were applied on a single or double axis, the baseline model significantly increased the errors when classifying what activity the participant was doing, thus decreasing the performance. The augmented model maintained high performance even when rotations were applied. This consideration was confirmed from testing outcomes on both WPS holdout and SHS data, as external validation. Figure 7 reports the obtained results for the baseline and the augmented model according to the three augmented test sets in terms of f1-score and Kappa-score. Appendix A reports detailed numerical results, such as the median and interquartile range referring to Figure 7 and single class results according to each model, each testing set, and considered participants.  
Statistical analysis was conducted on the f1-score and Kappa-score of the two models. For all data (cross-validated participants, WPS holdout participants, and SHS external validation), no significant difference was observed related to the original test set. The paired t-test was applied to test real-life test set performances, obtaining a _p_-value smaller than 0.01 for both holdout source sets. The Wilcoxon-rank test was used for the fully-rotated test set, showing a _p_-value \(<\) 0.01 for each axis of the WPS holdout data. The SHS _p_-values were below 0.01 for both the frontal and sagittal axis (X- and Z-axis), while for rotations applied along the longitudinal axis (Y-axis), no significant difference was shown.  
Additionally, Figure 8 highlights the covered area of false negative percentage profile related to treadmill walking activities. The green area belongs to the baseline model and it is generally wider than the one of the augmented model. Low-speed treadmill activities (\(\leqslant\)2.0 km/h) majorly contributed to the upper part of the green area. On the contrary, high-speed treadmill activities (\(>\)2.0 km/h) had generally fewer false negatives (lower part of the green area). This behavior was less visible in the profiles of the augmented model.  
Figure 7: F1-score (top panels) and Kappa-score (bottom panels) of the baseline and the augmented model for the three test sets: Original, real-life and fully rotated test sets. *: test sets obtained statistically different results for the baseline and augmented model with a _p_-value \(<\) 0.01.
## 4 Conclusions  
This paper presents a method in which 256-level phase CGHs were loaded into the SLM to dynamically generate tunable axicons. Based on the principle of convolution and Fresnel diffraction, the intensity distributions of beams passing through these axicons were analyzed. The diffraction patterns were obtained as zero-order Bessel beams and annular hollow beams corresponding to the positive and negative axicons, respectively. The experimental results proved the theoretical analysis. Using SLM to simulate axicons has many advantages. It can dynamically generate tunable axicons for the demand of different transmission distances, and it can obtain better Bessel beams than those generated by the real axicon, which has inevitable manufacture errors. With the aid of high-level phase CGHs, the simulated positive and negative axicons will be widely used in the fields of precision measurement and laser processing. The limitation of the proposed method using SLM to generate axion is that the SLM cannot cope with high lase power r, which may damage the liquid crystal inside.  
Tunable axicons are easily generated by the SLM, and one of significant advantages of the axicon is that it can generated tunable Bessel beams, which is expected to apply in laser dicing, laser grooving, and laser drilling. However, the application is constrained by the worst uniformity of the Bessel beams along their axial propagation length, as shown in Figure 8. Further work is needed to improve axial intensity distribution and make sure that the beam has a constant intensity along its propagation length.  
[PERSON] and [PERSON] deduced the formulas, conducted the simulations, and wrote the manuscript. [PERSON] and [PERSON] contributed the main conceptual ideas. All authors read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 51575164, 51405143), the Science and Technology Research Project of Department of Education of Hubei Province (No. D20161406), the Open Fund of State Key Laboratory of Precision Measurement and Instrument (No PIL1602), the Fund of Hubei University of Technology (Nos. HBSKFZD2014007, BSQD13048), the China Postdoctoral Science Foundation (Grant No. 2016M602269), and the Natural Science Foundation Youth Project of Hubei Provincial (Grant No. 2018 CFB290).  
The authors declare no conflict of interest.
## Security Analysis
### Against Known Attacks
#### 4.5.3 Side-Channel Attack  
In the proposed scheme, it is hard to retrieve the private key via a side-channel attack. For every message that sends from the sender, the sender needs to select a random number and compute a parameter. For example, in the authentication phase, the \(AP\) selects a random number \(r_{1}\), and computes the following parameter:  
\[R_{AP_{1}}=g^{r_{1}}\text{mod}p \tag{143}\]  
Then \(AP\) uses the hash function and \(R_{AP_{1}}\) to compute the parameter \(h_{AP_{1}}\):  
\[h_{AP_{1}}=H(R_{AP_{1}}\cdot Puk_{AP}(^{M_{Access}+Prk_{AP}})||H(M_{Access})) \tag{144}\]  
The \(AP\) also uses \(r_{1}\) to compute the signatures as follows:  
\[Sig_{AP_{1}}=1-r_{1}^{-1}\cdot Prk_{AP}\cdot H(M_{Access})\text{mod}p \tag{145}\]  
\[Sig_{AP_{2}}=M_{Access}+Prk_{AP}\text{mod}p \tag{146}\]  
Therefore, it is hard for the attacker to attack due to the randomized number.
# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 2 Theory  
### Features of the Eyelids with Eyes Closed  
In the closed-eye state, the specific position of the eyeball cannot be localized directly by visual and conventional optical methods because the eyelid completely covers the eyeball, which poses a great challenge for eye movement tracking. However, the texture of the eyelid surface is rich in information that offers the possibility of indirectly localizing eye movements. The skin texture of the eyelid has unique features, including subtle folds, pore distribution, and tissue elasticity, which are affected by the cornea at the front of the eye and change accordingly during eye movements [33]. A sagittal cross-section of the eyeball and eyelid is shown in Figure 1. When the eye rotates, the anterior-most protruding cornea pushes against the eyelid, causing the texture of the eyelid to stretch or compress in localized areas. Such changes can be captured by high-precision image acquisition systems and then quantified using analytical techniques. Based on this principle, we can indirectly infer the trajectory and position of the eye by monitoring and analyzing the dynamic changes in the texture of the eyelid surface in real time. This approach not only avoids direct contact with the eye and the use of complex imaging equipment but also enables high-precision eye movement tracking under non-invasive conditions.  
There is a certain regularity between the changing pattern of the eyelid texture and the position of eye movements, which makes it possible to further optimize the localization accuracy of the eye position using deep learning algorithms. Deep learning algorithms are capable of automatically extracting features from large amounts of data and learning complex nonlinear relationships [34]. Therefore, they have a significant advantage when dealing with the mapping problem between eyelid texture and eye movements. In this paper, we implemented target detection based on this principle to train a model using a large number of eyelid images labeled with eyeball positions, thus enabling the prediction of eyeball positions based on eyelid texture.
## 2 Materials and Methods
### Preprocessing Pipeline  
The collected signal was preprocessed using the following procedures and parameters set on Net Station software: filtration with 1 Hz high-pass and 45 Hz low-pass filters. Then, the standards for Net Station interpolation and noisy channel removing algorithms were applied as well as automatic and, in some cases, manual artifact removal. Then, the signal was divided into 1 s epochs, and noisy epochs were removed in Net Station using the AutoReject toolbox. See Figure 1.
## 2 Materials and Methods
### Static Gas Adsorption Measurements  
For textural characterization purposes, N\({}_{2}\) adsorption/desorption isotherms at \(-\)196 \({}^{\circ}\)C and CO\({}_{2}\) adsorption isotherms at 0 \({}^{\circ}\)C were acquired using an ASAP 2020 gas sorption analyzer from Micromeritics (USA). Samples (around 120-180 mg) were previously degassed under dynamic vacuum conditions to constant weight at a temperature of 150 \({}^{\circ}\)C. From the N\({}_{2}\) adsorption/desorption isotherms  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activated Carbon** & **Biomass Source** & **Pyrolysis Conditions** & **Activation Conditions** \\ \hline _AC\_VS\_600_ & Vine shoots & \(HTT=600\)\({}^{\circ}\)C; \(p=101\) kPa & With CO\({}_{2}\) at 800 \({}^{\circ}\)C and \\ _AC\_VS\_600_ & Vine shoots & \(HTT=600\)\({}^{\circ}\)C; \(p=500\) kPa & atmospheric pressure; \\ _AC\_WS\_500_ & Wheat straw & \(HTT=500\)\({}^{\circ}\)C; \(p=101\) kPa & 1 h holding time \\ _AC\_WS\_500_ & Wheat straw & \(HTT=500\)\({}^{\circ}\)C; \(p=500\) kPa & 1 h holding time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the preparation procedure for the physically activated carbons (ACs) tested in the present study.  
at \(-196\ ^{\circ}\)C, we estimated the apparent specific surface area (\(S_{BET}\)), micropore volume (\(V_{mic}\)), mesopore volume (\(V_{mes}\)), and pore size distribution (PSD) for pore sizes above 0.9-1.0 nm. The data from the CO\({}_{2}\) adsorption isotherms at 0 \({}^{\circ}\)C were used to estimate the ultra-micropore volume (\(V_{ultma}\), for pore sizes lower than 0.7 nm) and the PSD for narrow micropores. Further details regarding the procedures used to estimate the above-mentioned parameters are given in the Supplementary Material.  
The single-component adsorption isotherms (for both CO\({}_{2}\) and N\({}_{2}\)), at temperatures of 25 and 50 \({}^{\circ}\)C, on the activated biochars (at pressures ranging from 0 to 101 kPa) were measured using the same above-mentioned device as well as the same degasification procedure.
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 2 FBG Hoop Strain Sensor  
A novel FBG-based hoop strain sensor was developed in our previous work to monitor the hoop strain drop induced by pressure reduction [15]. This sensor has the advantages of optical sensing technology, such as high accuracy, long-term stability, multiplexibility and immunity to electromagnetic interference. This part briefly reviews the design and encapsulation of the FBG hop strain sensor, as well as its performance in a preliminary leakage experiment on a model pipeline.
## 3 Challenges and the State of the Art Progresses
### Analytics on Chaincode Data  
Blockchain provides more benefits to data analytics due to its security and redundant features. With blockchain, data cannot be forged and the fraud actions can be prevented in the real-time transactions. Combined with big data analytics, it is possible to identify behavior patterns and identify risky transactions in real-time. Blockchain also greatly improves transparency in data analytics, thus the behavior patterns identification is likely to be more accurate. Furthermore, with the redundant architecture of blockchain, data are more valuable and reliable.  
On the other aspect, transactions are globally published and are not encrypted in most blockchain applications. Hence, there is raised concern of lacking privacy. One solution is to store only encrypted data in the blockchain, which leads to a security problem if the key to decrypt is lost.  
Furthermore, for on-chain data, only the current states are recorded in the Merkel tree, and other transactional history in stored in the blocks which do not support fast query. Therefore, querying historical data is very time-consuming and expensive. In [51], [PERSON] et al. presented two models for efficiently processing temporal queries on Hyperledger Fabric. The first model creates a copy of each transaction event and stores temporally close transactions together on Fabric. The second model tags metadata to each transaction and makes temporally close events share the same metadata. In the follow-up work [52], [PERSON] et al. presented variants on the two models which can handle the skew in Fabric data.  
Figure 4: The time consumption with the increased documents in Ethereum.
## 2 Error Transformation Matrix of Axis TF  
For shaft-hole fit (Figure 1), the cylinder tolerance zone is acquired by expressing clearance value at the axis, so the variation of shaft in the hole is equivalent to the variation of the axis in the cylinder tolerance zone in Figure 2. During tolerance analysis, the error transformation matrix \(D_{i}\) reflects variation of TF [18]. \(D_{i}\) is gained from homogeneous transform matrix (HTM) based on DOF. According to TTSR(Technologically and Topologically Related Surfaces) theory [9], the axis TF in Figure 2 has four DOFs in the Cartesian coordinate system \(O\)-\(XYZ\), including the horizontal displacement \(u_{i}\), \(v_{i}\) along \(X\) and \(Y\) axes as well as rotation \(\alpha_{i}\), \(\beta_{i}\) around \(X\) and \(Y\) axes. Basic HTMs on these four DOFs are:  
\[\begin{split}\textbf{Trans}(u_{i})=\left[\begin{array}{cccc}1&0& 0&u_{i}\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\,,\textbf{Trans}(v_{i})=\left[\begin{array}{cccc}1& 0&0&0\\ 0&1&0&v_{i}\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\\ \textbf{Rot}(\alpha_{i})=\left[\begin{array}{cccc}1&0&0&0\\ 0&\cos\alpha_{i}&-\sin\alpha_{i}&0\\ 0&\sin\alpha_{i}&\cos\alpha_{i}&0\\ 0&0&0&1\end{array}\right],\textbf{Rot}(\beta_{i})=\left[\begin{array}{cccc} \cos\beta_{i}&0&\sin\beta_{i}&0\\ 0&1&0&0\\ -\sin\beta_{i}&0&\cos\beta_{i}&0\\ 0&0&0&1\end{array}\right]\\ \end{split} \tag{1}\]  
Figure 1: Shaft-hole fit and cylinder tolerance zone.  
Figure 2: Variation of axis tolerance features (TF) in cylinder tolerance zone.  
Due to small errors, \(\sin\alpha_{i}\approx\alpha_{i}\) and \(\cos\alpha_{i}\approx 1\). These are similar to \(\beta_{i}\). Therefore, the corresponding error transformation matrix \(\mathbf{D_{i}}\) is:  
\[\mathbf{D_{i}}=\mathbf{Trans}(u_{i})\mathbf{Trans}(v_{i})\mathbf{Rot}(\alpha_{i})\mathbf{Rot}(\beta_{ i})=\left[\begin{array}{cccc}1&0&\beta_{i}&u_{i}\\ 0&1&-\alpha_{i}&v_{i}\\ -\beta_{i}&\alpha_{i}&1&0\\ 0&0&0&1\end{array}\right] \tag{2}\]  
In fact, pose decoupling of axis TF determines the relationships between \(u_{i}\), \(v_{i}\), \(\alpha_{i}\), \(\beta_{i}\) and the tolerance value and clearance value. Here, the analytic relationships of the tolerance value and clearance value of shaft-hole fit with error variables on each DOF were disclosed according to the T-Map followed by interpolation of area coordinate system in the Finite Element Theory. In this way, pose decoupling of axis TF in the tolerance domain is realized.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 6 Discussions
### Influence of the Number of Test Sets on Training Results  
Reasonable allocation of the proportion of training set and test set has a certain influence on the results. This paper discusses how to set the proportion of test set and training set. The training set and test set are usually divided into 7:3; with a validation set, they are divided into 6:2:2. When the data magnitude is below ten thousand, or for a specific problem to be solved, appropriately changing the ratio of the test set and the training set can improve the accuracy of the prediction model to some extent.  
Taking the M-CM-GA-ELM solution model as an example, the number of test samples was set to 14 (20% of the total sample), 21 (30% of the total sample), 28 (40% of the total sample), and 35 (50% of the total sample). The prediction results are in Table 4.  
When the subsidence coefficient was estimated, under a certain data level, the MeaRE value and RMSE value showed an increasing trend with the decrease in learning samples, indicating that the prediction performance was reduced, which shows that at a limited data level under the circumstances, increasing the number of learning samples can greatly improve the expected effect.  
In general, it takes 1-2 years of observation to obtain the surface subsidence coefficient, so it is difficult to obtain more training samples. If a better distribution ratio can be obtained, it is of great significance to improve the prediction efficiency.
## 3 Results and Discussion
### Analysis of the Mineralization System by Sulfate and Nitrate Markers
On the other hand, the formation of complexes between Fe(III) and aromatic byproducts, such as aminophenols generated during SMX oxidation, can influence the persistence of aromaticity in treated water samples (Figure 5c). If a significant proportion of these Fe-aromatic complexes are formed, a greater persistence of residual aromaticity in the treated water and a decrease in the mineralizing efficiency of the process can be observed. Figure 6: Proposed pathway for SMX degradation by photo-Fenton. This can be explained by the fact that the ferric ion is sequestered outside the catalytic cycle. Aromatic compounds are not easily cleaved if they are protected by the metal complex, and the opening of the aromatic ring is hindered. These complexes exhibit high stability and can inhibit the cleavage of aromatic rings by protecting them from hydroxyl radical attack. As a result, a greater retention of aromatic structures in the aqueous matrix is observed, evidenced by a smaller decrease in UV absorbance at 254 nm. This phenomenon, in addition to compromising the complete mineralization of the contaminant, could contribute to the formation of less biodegradable and potentially ecotoxic byproducts. Therefore, controlling iron concentration and suppressing pathways that favor the formation of stable complexes is crucial to maximizing the efficiency of the photo-Fenton process. Finally, Figure 5d shows the color of the treated water samples once a steady state was reached. In the present study, a direct relationship was observed between ferrous ion concentration and the color of the treated water after SMX oxidation by the photo-Fenton process. Color in wastewater, especially after oxidative treatments such as photo-Fenton, may be due to the formation of partially oxidized aromatic intermediates (quinones, anilines, aminophenols, catechols, etc.) and the formation of colored complexes between Fe\({}^{3+}\) and these compounds. Operating at concentrations of [Fe(II)]\({}_{0}\) = 0-3.0 mg/L, the water color progressively decreased, reaching a minimum value at 3.0 mg/L, suggesting a high efficiency in the degradation of aromatic intermediates and a low formation of colored species. Iron does not accumulate in the form of colored complexes, and colorless or minimally colored waters are obtained. However, by increasing the concentration of [Fe(II)]\({}_{0}\) = 3.0-5.0 mg/L, a linear increase in the color of the treated waters was evident. Excess iron favors the formation of Fe(III)-aromatic intermediate complexes, such as those with aminophenols and quinones, which are highly colored (brown-yellowish). These complexes have conjugated structures and strongly absorb in the visible, linearly increasing the water color. The accumulation of these complexes, in addition to altering the color, can compromise the quality of the treated water, especially in reuse applications or discharge into receiving bodies.