esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 5 Security and Performance Analysis
### Security Analysis
#### 5.1.4 MSR Token Sniffing  
The MSR token is calculated from a random value that is changed with every packet to inject randomness, freshness and, thus, security into the token production process. In order to pass the verification process by sniffing, the malicious sensor must perform the selective jamming attack [25] on the verifier to prevent it from accepting the claimant's token, as shown in Figure 11. Without this jamming process, the verification process will complete while the malicious sensor sniffs the token. According to the attack model, the attacker can compromise a normal sensor to launch its attack in which case it is not easy to implement selective jamming with the resource-restricted sensor.
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 4 Tests and Analysis of Results
### Performance Comparison and Analysis of Different Models  
In order to comprehensively evaluate the performances of different models in the fault identification task, a series of comparative experiments were conducted in this study. As shown in Figure 10, this chapter selects representative machine learning SVM, ran  
Figure 9: Confusion matrix plots of different models.  
dom forest, K-nearest neighbour algorithm and deep learning CNN, GAN and LSTM algorithms as the comparison object, aiming at clarifying the superiority of transformer fault diagnosis model proposed in this paper relative to other models by comparing the recognition accuracy performance indexes of the models, and providing scientific basis for its practical application.  
As shown in Figure 10,the fault diagnosis model proposed in this study achieved a high accuracy of 99.04% in the experiment. In contrast, the accuracies of all models under either machine or deep learning were not above 99.00%. The above data show that the periodic map spectrum calculated after mode decomposition by SCA-VMD was used as the feature input. And using the attention mechanism to improve GoogLeNet on the transformer fault diagnosis task showed obvious superiority, and its accuracy was much higher than other models, which provides strong support for its reliability and effectiveness in practical engineering applications.  
In order to further verify the classification performance of the proposed scheme, it was compared with methods found in the literature [19; 20]. Ref. [19] and this study transformed the transformer vibration signal into different images and extracted the effective features of the signal to achieve high-accuracy fault diagnosis under different operating conditions. Ref. [20] was similar to the initial data processing methods carried out in this study, both of which mined the fault feature information after the adaptive decomposition of the vibration signals and used a two-level diagnostic model based on SSA-KELM for fault diagnosis. Using the dataset collected in this study, the proposed method was compared with the methods used in [19] and [20].  
As shown in Table 6, upon comparison, it can be found that the accuracy of the proposed scheme applied to dry-type transformers and oil-immersed transformers was higher than that of other methods, which indicates the reasonableness of the proposed SCA-VMD adaptive decomposition to compute the periodogram as a feature after combining the improved GoogLeNet for fault diagnosis.  
Figure 10: Comparison of diagnostic accuracy of different models.
## 2 Efficiency Analysis and Derivation of Wireless Power Transmission  
Figure 2 illustrates the WPT's schematic, including the Class-E PA, the inductive power transmission link with the compensation network, and the half-wave rectifier. The link is extensively used in IMDs to transfer power. Class-E PA is commonly employed in WPT to enable the transfer of DC voltage to AC voltage due to its high efficiency performance. The efficiency of the link and Class-E PA plays a significant role in the WPT system. To achieve a high-power transmission efficiency, this section aims to derive and analyze the factors that impact the efficiency of both the inductive power transmission link and the Class-E PA.
###### Abstract  
Background: Accumulated training shots throughout a session may induce changes in electromyography (EMG) parameters of the primary muscles involved in movement in archery athletes. Thus, the aim of this study was two-fold: (i) analyze the impact of 50 and 100 archery shots on a single session on the EMG parameters of trained archery athletes; and (ii) explore the effects of training routines of the athletes to cope with fatigue induced by the accumulated shots on the EMG parameters. Methods: They were divided into two groups: those who regularly performed \(\leq\)100 shots per training session (n = 13) and those who performed >100 shots per session (n = 7). The participants were exposed to a condition involving 100 archery shots, with measurements taken at baseline, after 50, and after 100 shots. EMG was used to measure the electric potential of the deltoid (middle and posterior), trapezius (upper, middle, and lower), and infraspinatus during isometric contraction. The collected outcomes included the mean and maximal amplitude of EMG root mean square (EMGRMS, \(\mu\)V) and the median frequency of the raw surface EMG signal power spectrum (EMGD, Hz). Results: The results showed significant differences for most of the analyzed muscles analyzed, specifically in the deltoid, infraspinatus, and trapezius (\(p<0.05\)). Conclusions: Our study suggests that in most of the muscles analyzed, EMG parameters--particularly mean and maximal EMGRMS--tend to increase from baseline to 50 shots, with significant declines observed after 100 shots, indicating muscle fatigue. The training routines of the athletes do not appear to significantly influence their response to fatigue conditions.  
archery; muscle fatigue; electromyography; athletic performance Article
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Deployment Approaches
#### 4.1.1 Linear Approach  
This is the regular implementation discussed in the above section with architectural details (Figure 1). This approach comprised of all the participating networks i.e., _Source, Intermediate_ and _Destination_ networks connected with each other in linear fashion. A third party detection engine (similar to HADEC [30]) feeds the attack definitions into the destination network, which then forwards them to the neighbouring network and this process continues until the definitions reach the source.
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
###### Abstract  
While State-of-Charge (SOC) estimation for supercapacitors has been extensively studied, most research focuses on single units. However, the recent introduction of reconfigurable circuits significantly alters system dynamics, rendering existing SOC estimation techniques inadequate. This paper addresses this challenge by employing a switching systems approach to estimate the SOC of supercapacitors with reconfigurable circuits. We first establish an RC model for the supercapacitor integrated with the reconfigurable circuit and thoroughly analyze the state continuity and observability of the resulting switched system. Subsequently, we propose a switching observer and evaluate its convergence properties by comparing its performance against other observer techniques. Experimental validation on a hardware platform demonstrates the superiority of our proposed observer for accurate SOC estimation in this context.  
switching observer; linear switching system; reconfigurable circuits; state of charge; supercapacitors +
Footnote †: journal: Applied sciences
# Experimental Studies on the Spraying Pattern of a Swirl Nozzle for Coal Dust Control
## 2 Research Method  
### Numerical Study of Both Spray Nozzles  
In recent years, the application of computational fluid dynamics (CFD) in spray atomizer design and analysis has received more and more attention from researchers and engineers [35], as it can provide detailed and useful information about the fluid flows and particles that are usually difficult to produce by experimental methods. CFD has been used to simulate the two-phase flows in both swirl nozzles and traditional nozzles in order to gain understanding of the differences in flow characteristics between the two nozzles. The major difference between the swirl nozzle and traditional nozzle structurally is that swirl nozzles force the mass flow into eddy chambers in a tangential direction while the traditional nozzle does this in an axial direction.  
In this research, the standard k-\(\varepsilon\) turbulence model by [PERSON] [36] was used. The computational domain was an eddy chamber of 6 mm in radius and the spray nozzle was 1.5 mm in diameter. A full 3-D simulation was carried out in order to generate the overall flow path line when the conditions of the two-nozzle model were identical including boundary conditions.  
The computational fluid dynamics (CFD) software Fluent 18.2 was used to simulate the water jet generation in both spray nozzles. The mesh for the computational domain was generated using software ANSYS Workbench 18.2. There are 12,257 hexahedral cells and 65,194 nodes. In this model, several boundary conditions for both nozzles are defined. The inlet boundary condition was specified by velocity-inlet at the speed of 21 m/s and outlet was specified by pressure-outlet at the atmospheric pressure. Both inlet and outlet turbulent intensity are specified by a standard value of 5%. All the wall boundary conditions were derived for no slip stationary walls.
## 2 Data and Methodology
### Detrended Fluctuation Analysis and Hurst Exponent  
The DFA scaling coefficients is one of the most employed methods to measure \(H\), as it allows its calculation using the slope of a variability function \(F(m)\) plotted versus \(m\) on a log-log scale, where \(m\) are the time slots that divide the TS [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018).  
The \(H\) exponent was introduced by [PERSON] (1972) and is related to the concepts of Brownian motion (Bm) and fractional Brownian motion (fBm) being often used to quantify the long-range, or long-memory dependence in the TS [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018).  
The \(H\)  
Thus, the closer the \(H\) value is to 1, the higher the probability for the next change to be positive, if the last change was also positive and vice versa.  
The calculation of the \(H\) index using the DFA involves several steps. The first step [59; 62] consists on the following estimation,  
\[P(i)=\sum_{i=1}^{N}(P_{t}-P), \tag{1}\]  
where \(N\) is the number of observations in the TS, \(P_{t}\) represents the value (price) observed at the time instant \(t\), and \(P\) denotes the arithmetic average of the price. The second step [59; 62] calculates the quantity \(F(m)\), that is, the root-mean-square error (RMSE), by means of  
\[F(m)=\sqrt{\frac{1}{N}\sum_{i=1}^{N}[P(i)-P_{m}(i)]^{2}}, \tag{2}\]  
where \(P_{m}(i)\) is the ordinary least squares value that is subtracted from \(P(i)\), for removing any trend.  
The process is repeated for distinct values of \(m\) and the slope of the line relating \(\log(F(m))\) with \(\log(m)\) determines the scaling \(H\) exponent.  
If \(k\in N\), then the \(k^{th}\) order auto-covariance is defined as  
\[\gamma(k)=Covariance[P_{t},P_{t+k}], \tag{3}\]  
and the \(k^{th}\) order autocorrelation can be determined as  
\[\rho=\frac{\gamma(k)}{\sqrt{Var(P_{t})}\sqrt{Var(P_{t+k})}}=\frac{\gamma(k)}{ \gamma(0)}. \tag{4}\]  
[PERSON] [63] explored an important relation between \(H\) and the autocorrelation function \(\rho\), given by  
\[\rho=2^{2H-1}-1. \tag{5}\]  
In this work, the value of \(H\) is used as a component to calculate the \(EI\) index for the ethanol and the cointegrated agricultural commodities. If we obtain \(H>1/2\), then it indicates the long-term memory in the TS [47]. However, the index can be affected by short-term memory bias or distributional properties [64]. Therefore, \(H\) values deviating from the theoretical value of \(H=1/2\) do not necessarily indicate the absence of random walk phenomenon. To mitigate this problem, other measures are proposed in addition to \(H\) in the follow up.
###### Abstract
[23; 24] introduced finite difference method to obtain the 3D temperature field during cutting operation, and simulation result was validated by measurement with an infrared camera. They indicated that the proposed method was time efficient and without losing accuracy when compared with finite element-based method. [PERSON] et al. [25] came up a method to predict the tool-chip temperatures for different machining conditions, based on cutting forces, [PERSON]'s energy partition function and Rapier's equation. The predicted cutting temperature was demonstrated using a K-type thermocouple and an infrared pyrometer. [PERSON] et al. [26] developed a device, which could capture the temperature field variations in high speed intermittent cutting, based on infrared thermography. They pointed out the temperature along time in each cutter revolution could be approximately approached by exponential Heating/Cooling curves. Moreover, they indicated that thermal cycling could damage the cutter, and uncut time played an important part in cutting temperature. [PERSON] et al. [27] proposed an approach to estimating the temperature of cutter tooth and workpiece during intermittent cutting process, and the predicted results agreed well with the experiment measurement. Then, they confirmed that [PERSON]'s hypothesis was true based on the proposed model and the experiment in cutting 1045 steel. [PERSON] et al. [28] proposed a model to predicting the cutter temperature in end milling. The cutting cycle was divided into temperature increase and decrease phases, and they were studied separately. Finally, an experiment was provided to validate the effectiveness of the proposed method. Previous researches on the cutting force, trochoidal toolpath, and stability of trochoidal machining are of great importance. They have demonstrated that trochoidal milling is able to extend cutter life, improve cutting efficiency, control cutting force, machine difficult-to-cut materials effectively. As has been discussed, cutting temperature plays an important role in tool life. Unfortunately, there were few researches focused on the cutting temperature during trochoidal milling, and this will hinder its further application. In this work, a practical approach to predicting the cutting temperature in trochoidal machining was established. First, the heat flux that flows into the cutter during milling process was studied based on the numerical simulations performed on Deform 2D v11. After that, an analytical approach to predicting the cutting temperature in milling processed was proposed, and it was verified by compared with an elaborated research. Then, the engagement between the cutter and workpiece in trochoidal machining was analyzed, and the cutter edge temperature history in trochoidal machining was predicted for the first time. Finally, the cutter edge temperature patterns during trochoidal and side milling were extensively analyzed, and the reasons for the extended cutter life in trochoidal machining were illustrated in detail.
## 3 Research Method
### 3D Effect Analysis  
When the measuring line measurement violates the assumption of the 2D electrical resistivity method that the formation resistivity property is homogeneous in the direction normal to the measuring line, the 3D effect is generated, influencing the measurement result. As the electrical resistivity measurement is the current field solving in 3D space, this study used the universal finite element analysis software COMSOL 4.2a-AC/DC module to build a 3D direct computation model (COMSOL group, 2011). This study assumed that the strata had pipelines, and the measuring line distance was changed to discuss the interaction. Figure 10 is the schematic diagram of the 3D effect model. The pipeline resistivity was set as R2 in the model, and the soil layer resistivity was R1; if the resistivity ratio was n = R2/R1, (R1, R2) = (1000, 100), the section size of pipeline was \(\mathrm{d}\mathrm{p}\mathrm{y}\times\mathrm{d}\mathrm{z}\mathrm{p}\), set as 1.5 m \(\times\) 2 m, and the depth of burying dep was set as 2 m. There were 5 to 6 measuring lines laid in parallel with the pipeline. The distance dy between measuring lines was set as 3 m, the electrode spacing dx was set as 3 m, and the measuring line length was L = 42m. As the dipole-dipole has better resolution capability for lateral variation, the measuring line configuration was dipole-dipole in this study. Table 9 shows the parameters of the 3D effect model.  
Figure 10: Schematic diagram of the 3D effect model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Impact Factors** & **n** & **R1** & **R2** & **dx** & **dep** & **dyp** & **dzp** & **L** \\ \hline Influence distance & 1000 & 100 & 3 & 3 & 2 & 1.5 & 2 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Parameter table of the 3D effect model.  
Figure 9: Schematic diagram of the boundary effect model.
## 4 Results and Discussion
### Energy Balance  
Figure 9a,b depicts the energy content for the ABAQUS/Explicit model of beams S1-2 and S1-4, respectively. The results indicate that the ratio of kinetic energy (ALLKE) to internal energy (ALLIE) was found to be less than 1%, with the total energy (ETOTAL) approaching zero. These findings provide evidence for the suitability of the explicit solver in simulating RC beams under quasi-static four-point bending. The decreases in internal energy observed in the RC beams are attributed to crack propagation in the concrete and plastic deformation of the reinforcements. Cracks in the material release stored energy, and plastic deformation of reinforcements results in a loss of their ability to store energy.  
Figure 8: Mesh convergence in the ultimate load of different mesh sizes in simulation.  
Figure 9: Comparison of different types of energy in the simulation by using the ABAQUS/Explicit: (**a**) beam S1-2; (**b**) beam S1-4. Note: ALLWK denotes the external work, ALLIE denotes internal energy, ALLKE denotes kinetic energy, and ETOTAL is the total energy of the model.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Human Signal Sources
#### 2.1.6 Gait Sequences  
Human gait refers to the walking style of a person. Alongside uniquely identifying the walker, it can also be used for detecting the walker's emotions [44]. Kinetic or motion data collected by motion capture sensors, in combination with neural network algorithms, can be very effective in recognizing human emotions from gait sequences [45; 46]. Traditionally, gait was measured by motion capture systems, force plates, electromyography, etc.; however, the emergence of modern technologies, such as accelerometers, electrogoniometers, gyroscopes, in-shoe pressure sensors, etc., has made gait analysis much easier and more efficient [47].
# Re-Visiting Acoustic Sounding to Advance the Measurement of Optical Turbulence
## 3 Results and Discussion
For comparison, a profile of \(C_{n}^{2}\) at 24.5 m as derived from \(C_{T}^{2}\) (mean = 9.8 \(\times\) 10\({}^{-15}\), std dev = 1.9 \(\times\) 10\({}^{-14}\)) on 14 July 20 at Wright-Patterson AFB. The \(C_{n}^{2}\) plots using \(C_{T}^{2}\) and those obtained using \(C_{v}^{2}\) at 24.5 m match quite well, but do not show pronounced quiescent dips near sunset (15/0000 UTC to 15/0130 UTC) and sunrise (15/1130 UTC) to 15/1230 UTC). The DELTA plot, however, does, as it did for 14 July, show significant minima of \(C_{n}^{2}\) just prior to sunset on 15 July (UTC, 14 July local time) and just after sunrise on 15 July. The DELTA's lower quiescent period \(C_{n}^{2}\) values again brought its mean \(C_{n}^{2}\) value for the day to less than half the mean value found using both sonic anemometer methods. Figure 12 below shows DELTA camera images at two points in time, from which differential tilt variances are derived to arrive at the \(C_{n}^{2}\) profile shown in Figure 11 (DELTA images and data were not available after 12 UTC on 15 July). The image at 15/0006 UTC in Figure 12 looks nearly identical to the one at 14/0006 UTC in Figure 10. This suggests that the limited feature separation distances resulted in the lower DELTA \(C_{n}^{2}\) value being influenced more by the more quiescent air near the receiver camera that was ~30 m higher than the air volume the sonic anemometers were sampling at 10-25 m above the ground at the 30-meter mast (see Figure 3). However, the 15/1122 UTC image in Figure 12 also looks identical to its counterpart in Figure 10--this indicates that even with the multiple feature separation distances available, the DELTA weighting functions cannot be expected to provide exactly comparable data to that measured at a point along the path where the weighting function may peak. Additionally, the turbulence induced random motion of the high contrast features on the target board against relatively darker background areas in the morning results in some spurious banding effects in the images. This probably caused issues with tracking and error in estimating \(C_{n}^{2}\).