esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Electrodermal Activity Events during Sleep
## 2 Materials and Methods
### EDA Event Detection  
As mentioned previously, EDA events have frequencies in the range of 0.25 Hz to 3 Hz. Within this frequency range, we only consider EDA oscillations whose amplitudes are between 50 \(\mu\)V and 500 \(\mu\)V. For event detection in the frequency domain, we segment the signal and transform individual signal segments using the FFT. To obtain the segments, the signal is sliced into non-overlapping time windows of constant duration \(T=4\) s. This allows us to detect events with a frequency as low as 0.25 Hz.  
Then, we multiply the FT coefficients by \(2/N\), where \(N\) is the number of coefficients, and compute their absolute value. If any of these values lies between 50 \(\mu\)V and 500 \(\mu\)V then the segment is marked as an event; see Figures 8 and 9. In Figure 8, the displayed signal segment has oscillations of frequencies between 0.25 Hz and 3 Hz whose amplitudes are significant. Hence, the algorithm labels this segment as an EDA event. In Figure 9, the power spectrum in the frequency range between 0.25 Hz and 3 Hz does not show oscillations of relevant amplitudes. Hence, this segment is not labelled as an EDA event.  
Figure 8: An EDA event is detected: the power spectrum of frequencies between 0.25 Hz and 3 Hz exceeds the EDA event threshold.  
Figure 7: Example of an artefact’s power spectrum. The high-frequency contribution exceeds the threshold. Thus, the segment is labelled an artefact.
###### Abstract  
Coronal preflaring NiTi files should possess proper hardness, resistance to fracture and flexibility. This study compared the bending resistance and cyclic fatigue resistance of three orifice preflaring NiTi files. HyFlex EDM Office opener (#25/12), HyFlex CM (#25/08) and One Flare (#25/09) were tested (n = 46/instrument). Bending resistance was assessed with the stress when perpendicular displacement was applied to a 3 mm point from the file tip, and was performed at either room temperature (RT, n = 10) or body temperature (BT, n = 10). Cyclic fatigue resistance was tested with an artificial ceramic root canal at RT (n = 10) and BT (n = 10), and the number of cycles to failure (NCF) was obtained. The bending resistance and the NCF results were analyzed by two-way ANOVA and Tukey's test. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) analyses were performed (n = 3, respectively). HyFlex EDM exhibited the highest bending resistance, followed by One Flare and HyFlex CM (\(p<0.05\)), irrespective of the tested temperature. At RT, HyFlex CM demonstrated the highest NCF (\(p=0.001\)), while HyFlex EDM had the highest NCF at BT (\(p<0.001\)). The tested NiTi files were composed of austenite and martensite according to the DSC and XRD results. HyFlex EDM had the highest bending resistance and NCF measured at BT.  
bending resistance; cyclic fatigue; differential scanning calorimetry; orifice opener; X-ray diffraction
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Patient-Reported Outcome Measures** & \multicolumn{4}{c}{**MAE in SVV**} & \multicolumn{2}{c}{**MAE in RFT**} \\ \hline  & **Mean** & **SD** & \begin{tabular}{c} **Correlation** \\ **(Rho)** \\ \end{tabular} & \
\begin{tabular}{c} **_p-Value_** \\ **(Rho)** \\ \end{tabular} &
\begin{tabular}{c} **Correlation** \\ **_p-Value_** \\ \end{tabular} \\ \hline Falls in last 3 months & 1.31 & 1.46 & 0.168 & 0.226 & 0.033 & 0.813 \\ \hline Fibromyalgiagia Impact Questionnaire FIQ (0–100) & 67.94 & 14.02 & 0.399 & 0.003 ** & 0.147 & 0.289 \\ \hline Numeric Pain Rating Scale (0–10) & 6.20 & 1.96 & 0.291 & 0.033 * & 0.235 & 0.087 \\ \hline Central Sensitization Inventory CSI (0–100) & 64.96 & 11.3 & 0.115 & 0.408 & 0.091 & 0.514 \\ \hline Pain Catastrophizing Scale PCS (0–52) & 25.8 & 13.43 & 0.417 & 0.002 ** & 0.147 & 0.290 \\ \hline Pain Catastrophizing Scale Rumination (0–16) & 8.61 & 4.61 & 0.347 & 0.010 * & 0.176 & 0.202 \\ \hline Pain Catastrophizing Scale Magnification (0–12) & 5.17 & 3.52 & 0.346 & 0.010 * & 0.032 & 0.820 \\ \hline Pain Catastrophizing Scale Hopelessness (0–24) & 12.02 & 6.14 & 0.440 & 0.001 ** & 0.136 & 0.326 \\ \hline Tampa Scale of Kinesiophobia TSK (11–44) & 26.78 & 6.77 & 0.113 & 0.417 & 0.148 & 0.285 \\ \hline Dizziness Handicap Inventory Total DHI (0–100) & 57.19 & 20.86 & 0.376 & 0.005 ** & 0.069 & 0.620 \\ \hline Dizziness Handicap Inventory Emotional (0–36) & 16.19 & 9.6 & 0.319 & 0.019 * & 0.075 & 0.588 \\ \hline Dizziness Handicap Inventory Functional (0–36) & 20.67 & 8.17 & 0.412 & 0.002 ** & 0.051 & 0.715 \\ \hline Dizziness Handicap Inventory Physical (0–28) & 20.33 & 5.23 & 0.291 & 0.033 * & 0.076 & 0.583 \\ \hline Activities-Specific Balance Confidence Scale ABC (0–100) & 48.33 & 20.83 & \(-\)0.367 & 0.006 ** & \(-\)0.168 & 0.226 \\ \hline Falls Efficacy Scale International FES-I (16–64) & 36.65 & 10.22 & 0.246 & 0.073 & 0.162 & 0.243 \\ \hline Fatigue Severity Scale FSS (9–63) & 52.37 & 8.2 & 0.280 & 0.040 * & 0.150 & 0.278 \\ \hline \end{tabular}
\end{table} 
Table 2: Spearman correlations between visual verticality perception test and Patient-Reported Outcome Measures. Figure 4: Correlations between the MAE-SVV and the MAE-RFT with ABC (**A**), DHI (**B**), FIQ (**C**), and PCS (**D**), IGEO-JAEN (**E**) and ISR-JAEN (**F**), respectively. MAE, Mean Absolute Error in degrees; SVV, [PERSON]’s Rho non-parametric correlation coefficient, *\(p\)\(<\) 0.05; **\(p\)\(<\) 0.01.
## 2 Background
### Server-Exhausted Attacks and Detection Methods  
SEAs exploit server vulnerability and generate a large number of requests to quickly exhaust the targeted server's resources. Therefore, a server lacking resources cannot respond to legitimate requests and may even crash. A typical SEA includes SYN flooding attacks [8; 10; 11], DNS flooding attacks [9; 12; 13], and HTTP flooding attacks [14; 15; 16].  
SYN flooding attacks that transmit TCP connections and ACK packets are not returned, causing the server to store all these requests and, eventually, run out of resources. Therefore, [8] calculates the number of SYN packets collected by a server and analyzes the response rate of an ACK packet, thereby representing the level of internal server resource damage. Reference [10] calculates the entropy between the source IP and destination IP of a TCP packet and analyzes the network usage behavior. Entropy represents the degree of concentration of information under a SEA, and a large number of centralized nodes request the target server. Reference [11] uses Network Function Virtualization (NFV) to design a dynamic resource allocation mechanism to detect attacks.  
DNS flooding attacks send a large number of nonexistent domain-name requests to the DNS, causing DNS server failure. To detect DNS flooding, [9] calculates the repetition rate of the source IP and the destination IP of flows through a DNS. Reference [12] uses Supervised Machine Learning (SML) to analyze all requests for accessing a DNS and determines those sending a large number of IP locations for nonexistent domain names to block these requests. Reference [13] uses machine learning to implement a system to detect abnormal packets and automatically block attack packets.  
An HTTP flooding attack means that the clients interact with the server and send HTTP requests to compel the server to allocate as many resources as possible to serve the attack, thus denying legitimate users access to the server's resources. Reference [14] uses server logs to train which IP is abnormal. Reference [15] uses the correlation between IP address and traffic for detection in a Named Data Network (NDN). Reference [16] builds a model using anomaly-based detection approaches by tracing browsing logs to detect attacks.
## 3 Evolution of Wireless Communication Technology (1G-6G)  
Wireless communication technology has evolved over the years intending to provide high-speed, reliable, and secure communication. Figure 2 shows the evolution of wireless network development from 1G to 6G, including the year of proposing and that of deploying a particular network generation over time. In the following, we explain each evolution along with its applications and shortcomings.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
###### Abstract  
Many novel, effective, and efficient applications and networking services are being developed for the Social Internet of Things. Recently, [PERSON] proposed a more secure and efficient authentication scheme with roaming service and user anonymity for mobile communications. The security analysis and discussion of the agreement phase is sufficiently safe; however, an attacker can intercept the identity of a mobile user's home agent in the authentication phase. By using this information, the attacker can mount distributed denial-of-service attacks in the roaming phase through replay attacks targeting the network's foreign agent and mobile user's home agent by using their corresponding session keys. [PERSON]'s method also has some shortcomings regarding anonymity that we aim to address. To overcome these issues, this study proposes an elliptic curve-based wireless roaming anonymous login method for the authentication phase. The problems faced in the roaming phase are resolved, and this approach provides balanced session key computation between senders and receivers. Burrows-Abadi-Needham logic (BAN-logic) is used to verify the security of the proposed scheme. The proposed scheme affords good security, efficiency, and integrity and maintains anonymity.  
roaming; anonymity; mutual authentication; elliptic curve discrete logarithm problem; wireless networks; BAN-logic +
Footnote †: journal: Accepted: 24 November 2016; Published: 30 November 2016
## 3 Methodology
### PointNet Series Model  
The PointNet series encapsulates several network architectures built on the PointNet [47] framework, specifically encompassing PointNet, PointNet++ [48], and PointMLP [61]. These three network models bear profound similarities. PointNet was the inaugural deep learning framework to accept points directly as input, aptly mapping the XYZ coordinate points in the spatial coordinate system to high-dimensional features through multiple traditional MLP layers, followed by maximum pooling to finish feature aggregation. Its successor, PointNet++, enhances this base with a hierarchical Set Abstraction (SA) layer, uses farthest-point sampling to establish the anchor points of the point cloud, and then adopts a spherical query approach to determine each point's k-nearest points. This approach segments the point cloud into repeating point plots, each conducting local feature extraction independently. The PointMLP architecture employs multiple layers of shared Multi-Layer Perceptrons (MLPs) to construct the model. Its core principle involves the progressive extraction of local features through successive residual layers, resulting in a computationally efficient feature extractor. Prior to inputting points into the feature extractor, a geometric affine module is applied. This module addresses the challenges associated with the sparsity and irregularity of geometric structures in local regions.
## 3 Proposed Method
In case the _crypto codes_ generated are of a length less than or greater than 8 bits, they should be completed or reduced to 8 bits using the same approach uniformly over all blocks. Likewise, the watermark (i.e., information to be hidden) is partitioned into segments comprising 8 bits each and then encrypted with _crypto codes_ using the XOR operation. Thereafter, the least significant bits (LSBs) from the corresponding encrypted watermark are applied to each coefficient of the watermarked block. Finally, an inverse DWT is then applied to the modified \(LL\) sub-band, then combined with the original ones \(HL\), \(LH\), and \(HH\) to reconstruct the watermarked medical image. For watermark extraction, the reverse operations of the embedding process should be executed. This involves first applying the DWT to the watermarked medical image to decompose it into sub-bands (\(LL\), \(HL\), \(LH\), \(HH\)). The modified \(LL\) sub-band is then divided into non-overlapping \(n\times n\) blocks. From each block's coefficients, the least significant bits (LSBs) are extracted to retrieve the encrypted watermark segments. These segments are decrypted using the corresponding _crypto code_ with the XOR operation. Finally, the decrypted segments are combined to reconstruct the original watermark. By following these reverse operations, the embedded watermark can be accurately extracted from the watermarked medical image.
## 4 Simulation  
To validate the effectiveness and reliability of the multi-motor coordinated control and power distribution strategy proposed in this paper, a co-simulation is conducted using MATLAB/Simulink (version 2019b) and AMESIM (version 2020.1). A drive system model composed of six permanent magnet direct drive motors is built in MATLAB/Simulink, and the output torque of each motor is transmitted to AMESIM. Based on the Kelvin-Voigt  
Figure 8: Power change curve. (**a**) Increase curve. (**b**) Power reduction curve.  
Figure 9: Multi-motor power distribution flow chart.  
viscoelastic mechanics model, the conveying structure model is built in AMESIM, and the speed of each contact surface between the conveyor belt and the motor is fed back to MATLAB/Simulink. The multi-motor permanent magnet motor drive system is shown in Figure 10. The simulated wavelength is 1 \(\times\) 10\({}^{-5}\) s.  
In the simulation experiment, the operation process of the permanent magnet motor in the bearing section of the belt conveyor is simulated, and the contact point between the motor and the conveyor belt is taken as the observation point. The rated parameters of the six permanent magnet motors are the same, and the basic parameters of the distributed permanent magnet direct drive belt conveyor are shown in Table 1.  
The tension caused by gravity on the conveyor belt:  
\[f_{g}=Lg(m_{melt}+m_{bulk})\sin\beta=2803.5N \tag{21}\]  
The tension caused by frictional resistance on the conveyor belt:  
\[f_{s}=cfLg[m_{roll}+(m_{melt}+m_{bulk})\cos\beta]=1737.7N \tag{22}\]  
The motor speed is set to start according to a sine wave curve. The mathematical expression is the following:  
\[n(t)=n_{0}+\frac{\Delta n}{2}(1-\cos\frac{\pi t}{t_{a}}) \tag{23}\]  
where is the initial speed; \(\Delta n\) is the speed change value; and \(t_{a}\) is the start time. In this paper, the initial parameters of the simulation settings are the following: \(t_{a}\) = 5 s, \(n_{0}\) = 0 rpm, and \(t_{a}\) = 80 rpm.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter (Unit)** & **Value** & **Parameter (Unit)** & **Value** \\ \hline Nominal capacity (t/h) & 2300 & The rated speed of the motor (r/min) & 80 \\ \hline Nominal speed (m/s) & 4.2 & Coefficient of secondary resistances (-) & 1.09 \\ \hline Mass of belt per unit length (kg/m) & 30.1 & Slot angle (\({}^{\circ}\)) & 3 \\ \hline Mass of materials per unit length (kg/m) & 152.12 & Artificial friction coefficient (-) & 0.025 \\ \hline Mass of rollers per unit length (kg/m) & 35 & Gravitational acceleration (m/s\({}^{2}\)) & 9.8 \\ \hline Permanent magnet motor radius (m) & 0.5 & Spacing of motor (m) & 30 \\ \hline Rated power of motor (kW) & 20.9 & Rated torque of motor (kN\(\cdot\)m) & 2.5 \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of distributed permanent magnet direct drive belt conveyors.  
Figure 10: Co-simulation of distributed permanent magnet direct.
# A New Mechanical Specific Energy Model for Composite Impact Drilling
## 1 Introduction  
With the continuous development of oil and gas exploration and development to deep and ultra-deep wells, problems such as low rock-breaking efficiency of a PDC bit (Poly-crystalline Diamond Compact Bit) and head failure due to stick-slip vibrations are becoming more and more frequent in deep hard formations. As a drilling tool that can convert hydraulic energy into periodic axial and circumferential impact force, the composite impactor can effectively reduce the stick-slip vibration of the drill bit and achieve the purpose of increasing the ROP (Rate of Penetration) ([PERSON] et al. 2016) [1]. MSE refers to the mechanical work required to destroy a rock per unit volume ([PERSON] 1965, [PERSON] 2014) [2, 3], which can be used as an objective method to evaluate drilling efficiencies. As early as 1965, [PERSON] proposed a calculation model of MSE based on a large number of experiments ([PERSON] 1965) [2], which was also the original model of MSE calculations. Later, various conditions and working conditions were considered by [PERSON] and [PERSON] (1992) [4], [PERSON] et al., (2005) [5], [PERSON] and [PERSON] (2005) [6], [PERSON] (2012) [7], and [PERSON] (2014) [8]. The MSE model was adjusted to improve its accuracy. As a new speed-up method for deep hard formations, composite impact drilling technology has achieved good application effects in the field (Su 2019) [9]. Researchers have also done some kinematics and dynamics studies on this technology ([PERSON] 2019) [10], but there is no relevant research on the MSE model of drilling with composite impact technology. Therefore, based on the theory of MSE, a new MSE model of composite impact drilling is established by considering the effect of a composite impactor on the drill bit's axial impact force and torsional impact force during operation. This model can be used to evaluate and analyze the drilling efficiency and working condition of drill bit in the process of compound impact drilling, optimize the drilling parameters and further improve the drilling speed.
## 3 VRDeepSafety Simulation Environment
### Dataset Generation
#### 3.2.1 Real-World Complexity  
VRDeepSafety tackles critical challenges AVs face in real-world environments, such as poor visibility caused by adverse weather (e.g., fog, rain) or nighttime conditions. The platform simulates these scenarios to rigorously test AV performance in low-visibility situations. Additionally, it replicates dense traffic dynamics--including city streets, highways, lane changes, sudden braking, and merging--to evaluate decision-making, path planning, and collision avoidance.  
Dynamic agents (vehicles, pedestrians, cyclists) with unpredictable behaviors, such as erratic lane changes or jaywalking, are integrated to assess the AV's adaptability and real-time safety responses.  
As shown in Figure 1, VRDeepSafety generates a comprehensive training dataset by combining synthetic and real-world data. The VRDeepSafety simulation engine creates synthetic data by varying four key parameters: driving scenarios, weather conditions, traffic densities, and agent behaviors, enabling the simulation of a wide range of realistic driving situations. Specifically, the synthetic data, which contains sensor data, as well as ground truth (GT) V2X data (e.g., locations, velocity, and IDs of other vehicles, pedestrians, and infrastructure), is used to augment the real-world dataset, which contains only sensor data. This combination provides the VRFormer model with more comprehensive input for training. In detail, the augmented data are created by using the feature space generated from real-world sensor data as a basis and appending the synthetic data, so that the positional data from a real-world LiDAR point cloud are augmented with the GT positional data from the V2X simulation. This is conducted for all sensor data and the V2X data. This fusion approach ensures that the trained VRFormer model is robust and generalizable, by leveraging real-world sensor data, as well as providing the model with access to GT data, which would otherwise be difficult to access, and allows for a consistent format between both datasets. The rationale behind this method is to leverage the realism of the simulated data alongside the GT of the V2X data.  
Figure 1: VRDeepSafety data generation philosophy diagram.  
By using immersive virtual reality, VRDeepSafety creates interactive training environments, exposing AVs to complex driving scenarios that are difficult or unsafe to replicate in real-world environments. This exposure improves the resilience of the vehicle's perception of algorithms to perform consistently, even under unknown or adverse conditions.
## 4 Numerical Examples
### A System with Similar Eigenvalues  
The third example is a simple \(3\times 3\) eigenvalue problem, which is used to discuss the stability of the methodology in the case of \"close modes\" (with very similar eigenvalues). The mass and stiffness matrices of the system are  
\[\begin{array}{c}M=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right],\\ K=\frac{1}{p^{2}+1}\left[\begin{array}{ccc}p+\frac{p^{2}}{2}+\frac{p^{4}}{2} &-\frac{p}{2}+p^{2}-\frac{p^{3}}{2}&0\\ -\frac{p}{2}+p^{2}-\frac{p^{3}}{2}&\frac{1}{2}+\frac{p^{2}}{2}+p^{3}&-\frac{p}{ 2}+p^{2}-\frac{p^{3}}{2}\\ 0&-\frac{p}{2}+p^{2}-\frac{p^{3}}{2}&\frac{3}{10}+\frac{p^{2}}{2}+p^{3}\end{array}\right]\end{array} \tag{51}\]  
At \(p=0.99\), the eigenvalues of \((K,M)\) are \(\lambda_{1}=0.8890\), \(\lambda_{2}=0.9900\) and \(\lambda_{3}=0.9901\). It is clear that \(\lambda_{2}\) and \(\lambda_{3}\) are very close. Table 3 presents the eigenvector sensitivities computed by the proposed method and the modal superposition method. From Table 3, one can see that the proposed method can obtain the same calculation results as those of the modal superposition method. To a certain extent, these results show that the proposed method can be used to solve the eigenvalue problem with close modes.