esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 4 Experiment and Results  
In this paper, we experiment by adding a bandpass filtering step during the data preparation stage and then use Autoencoders with three neural network architectures (MLP, LSTM, and FNN) to see how the additional bandpass filtering step affects the performance for anomaly detection.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### Strain Energy Components
The first solution corresponds to the following local discretization parameters: \(H=h\), \(P_{1}=p\), and \(Q=q\). In the second problem, set to be free of the locking phenomena, one has: \(H=h\), \(P_{2}=p_{max}=8\), and \(Q=q\). The value of \(p_{max}=8\) is taken from the research of [PERSON] [4] who demonstrated that for such a value the membrane locking disappears in shell structures regardless of the thickness. For this value also the shear locking in plate structuresdisappears. It is well known that this type of locking is weaker than the membrane one in shells (compare remarks in [5; 32], for example). The proposed criterion reads  
\[\left\{ \begin{array}{rcl}\sum_{e=1}^{E}\overset{e}{U}_{m}(\overset{e}{u}^{HP_{1 }Q})+\sum_{e=1}^{E}\overset{e}{U}_{s}(\overset{e}{u}^{HP_{1}Q})&\geq&\sum_{e=1}^ {E}\overset{e}{U}_{b}(\overset{e}{u}^{HP_{1}Q})\\ \sum_{e=1}^{E}\overset{e}{U}_{m}(\overset{e}{u}^{HP_{2}Q})+\sum_{e=1}^{E} \overset{e}{U}_{s}(\overset{e}{u}^{HP_{2}Q})&<&\sum_{e=1}^{E} \overset{e}{U}_{b}(\overset{e}{u}^{HP_{2}Q})\end{array} \right. \tag{29}\]  
The first condition detects dominance of the membrane and shear strains over the bending strains in the local problems corresponding to the global problem under consideration, while the second condition reflects the change of the dominance in the local problems. The form of the criterion corresponds to the shell structures, where coupling between shear, membrane and bending strains exists. In the case of plate structures, the coupling between the membrane strains and the shear and bending strains disappears and because of this the membrane strains have to be removed from the above criterion. Fulfillment of the above criterion means that the true nature of the problem is bending-dominated and that the locking phenomenon appears in the assessed global problem. As a consequence, the locking has to be removed through increase of the approximation order \(p\). The above criteria are necessary to confirm the results from the detection and assessment tools presented in the next sections, as those tools are not capable of distinguishing between the membrane- (and/or) shear-dominated problems and the bending-dominated ones.
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 2 System Design
### Communication System and Positioning System  
The communication system consisted of two parts, which communicated with the main control board through a Universal Asynchronous Receiver/Transmitter. The connection between the float and the computer through the duplex Bluetooth module was realized before the float was formally applied. The acrylic material used in the electronic warehouse did not block the Bluetooth signal, so data could be transmitted or read without dismantling the warehouse. The program's scheduled task could also be burned through this module. The second part was responsible for sending back the environmental data collected by the float after it rose to the surface. The float system could choose different communication modules according to different application scenarios. For example, 4G communication modules have higher single transmission capacity and higher transmission frequency but can only work in areas covered by 4G signals. Meanwhile, the satellite communication adopted by Beidou short message requires one minute between two data transmissions and a small amount of single transmission. However, Beidou short message communication is not restricted by region, so as long as it can receive satellite signals, the communication needs can be met. Therefore, the 4G communication module and GPS positioning system can be used for float communication in inland lakes or adjacent seas, whereas Beidou short message communication and positioning module can be used for communication in the ocean.
## 3 Results
### Packet Delay at the Receiver  
In the third phase of our testing, we measured packet delays at the receiving end to evaluate the system's performance under different conditions. As already mentioned in Section 2.5, this was not the primary objective in this research. This issue needs to be further investigated with measurements in different environments and with a number of different commercial WiFi devices. The initial results, based on measuring 1000 packets at the receiver for each point in the graph in Figure 11, indicate that the mean packet delay increases proportionally with the number of samples included in each packet. Although the jitter--calculated as the standard deviation of the delay--also rises with the number of samples, its rate of increase is significantly lower compared to that of the mean delay. Figure 11 illustrates these findings for a sampling frequency of 100 Hz and hexadecimal representation with bit-shifting.  
Figure 11: Mean packet delay and jitter at the receiver at sensor sampling frequency of 100 Hz. The mean delay is shown as a solid line, the jitter as a vertical bar.  
Figure 10: Mean time for completion of all tasks needed to acquire all sensor samples, assemble and send one packet, depending on the number of samples in one packet and sensor sampling frequency. Legend label numbers denote the number of samples in one packet, letters denote different sensor value representations: D = decimal, H = hexadecimal, HS = hexadecimal with a bit-shift.
# Independent System Calibration of Sentinel-1B
## 8 Polarimetric Characterization
### Channel Imbalance
This illustrates that the specific pattern shapes and offsets are apparently introduced during the antenna pattern correction occurring during operational SAR processing. Therefore, the antenna patterns and their application during SAR data processing should be further investigated. Figure 26: Channel imbalance between the cross- and co-polar channel derived from rainforest EW mode acquisitions for H on transmit (black) and V on transmit (blue). The channel imbalance is plotted for two cases: using the standard SLC products including the antenna pattern correction (blue and black lines) and without antenna pattern correction (green and purple lines). Figure 25: Channel imbalance between the cross- and co-polar channel derived from the transponder IRF for H- (filled diamonds HV-HH in dB) and V-polarization (open diamonds VH-VV in dB) on transmit for different modes (blue: SM, red: IW, green: EW).
# Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1
## 3 Results
### XN Showed a Synergic Effect with COL in MCF-7/ADR Cells  
To further confirm that XN is a substrate of ABCB1, we examined whether XN could increase the cytotoxicity of the ABCB1 substrate, COL, in MCF-7/ADR cells. Neither COL nor XN alone had significant inhibitory effects against cell viability at the indicated concentrations; however, when administrated with XN, COL could remarkably inhibit the growth of MCF-7/ADR cells (Figure 4A). We then conducted an isobolographic analysis and evaluated the synergistic effects. As shown in Figure 4B and Table 1, COL in combination with XN at 1 and 2 \(\upmu\)M, resulted in strong synergism between XN, as evidenced by CI values \(<\) 1, and in addition, the synergistic effects increased with increasing concentrations of XN. This result is supported by our published results showing XN exhibited synergic effects with DOX (also a ABCB1 substrate) in MCF-7/ADR cells [8]. Taken together, we confirmed that XN is a substrate of the ABCB1 transporter.  
\begin{table}
\begin{tabular}{c c c c} \hline
**XN (\(\upmu\)M)** & **DOX (\(\upmu\)M)** & **Effect** & **CI** \\ \hline
0.5 & 0.08 & 0.03 & 0.79 \\
0.5 & 0.16 & 0.04 & 1.17 \\
0.5 & 0.31 & 0.08 & 1.28 \\
0.5 & 0.63 & 0.23 & 1.04 \\
0.5 & 1.25 & 0.47 & 0.93 \\
0.5 & 2.5 & 0.79 & 0.65 \\
0.5 & 5.0 & 0.90 & 0.69 \\
1 & 0.08 & 0.06 & 0.58 \\
1 & 0.16 & 0.06 & 0.95 \\
1 & 0.31 & 0.1 & 1.16 \\
1 & 0.63 & 0.21 & 1.16 \\
1 & 1.25 & 0.49 & 0.89 \\
1 & 2.5 & 0.79 & 0.66 \\
1 & 5.0 & 0.89 & 0.75 \\ \hline \end{tabular}
\end{table}
Table 1: CI values of the combination between xanthohumol (XN) and colchicin (COL) in MCF-7/ADR cells.  
Figure 4: XN sensitizes the cytotoxicity of the ABCB1 substrate, colchicin (COL), in MCF-7/ADR cells. (**A**) MCF-7/ADR cells treated with 0.6 \(\upmu\)M COL alone, 2 \(\upmu\)M XN alone, or both; (**B**) Synergisms analyzed by median effect plot using the software, Calcusyn. MCF-7/ADR cells were co-treated with COL and XN (0.5, 1, and 2 \(\upmu\)M) for 72 h, the proliferation inhibition was detected, and the combination index (CI) values were obtained.
## 4 Experimental Results and Analysis
### Discussion  
The achieved results are more efficient than the results presented by [PERSON] et al. [32] in 2018 who introduced k-means for the segmentation of the diseased part and a multiclass SVM for classification purposes and obtained an accuracy of 96%, as shown in Table 8. However, the list of diseases they detected did not contain powdery mildew, whereas our proposed method showed an accuracy of 95.5% while detecting powdery mildew as well. None of the studies available have yet checked the vein pattern of plant leaves. As it is the most important part of the plant as concerns food and water transportation, the proposed technique is far superior to the available techniques. Furthermore, the mobile-based system proposed by [PERSON] et al. [38] in 2019 uses classification techniques and obtained an accuracy of 80% when detecting the diseases of mango plants. In comparison, the achieved accuracy of the proposed model is much better, at 95.5%.
## 1 Introduction
For this method, at least one pair of the PZT patches is used with one serving as an actuator to generate a stress wave and the other serving as a sensor to detect the stress wave. A damage or change along the wave propagating path will change the detect signal. As a real time method, many researchers have explored this method [27]. [PERSON] and [PERSON] [28] combined the ultrasonic waves and support vector machines method to detect loosened bolts. The experiments show that the proposed methods could identify the location and the level of damage of the loosened bolts. The active sensing-based method was also used in detecting damage of the bolt on a steel bridge [29], and some researchers applied ultrasonic and magneto-elastic active sensors in monitoring bolted joints in a satellite panel [30]. In our team's previous studies [31; 32], the piezoelectric active sensing method with the time reversal technique [33] was used to monitor the bolt looseness. The time reversal method reverses the response signal that is received by the sensor in its time domain. This method can make the signal energy focus on space and time. In these studies, the peak of the time reversal focused signal can represent the wave energy that transmitted through the interface of the bolted joint, and the wave energy is proportional to the true contact area of the bolted joint interface which depends on the bolt tightening force. The experiments show that the focused signal peak amplitudes increase with the increasing blot preload until reaching saturation and the focused signal peak remains stable. When there is no more deformation space at the bolted joint interface under a certain bolt preload, the true contact area does not change any more. Besides, the surface roughness of the bolted joint affectsthe saturation value; the higher roughness can postpone the saturation value of the bolt preload. When it comes to bolt connection surfaces, the contact roughness is determined by the process and working condition, which cannot be increased artificially. Even if a high roughness is used, it is hard to guarantee the higher roughness precisely for a certain machining process. Therefore, in order to resolve the problem of saturation, a new monitoring structure needs to be developed. The new proposition is that of a new washer structure for monitoring bolted looseness status based on the piezoelectric active sensing method and to deal with the saturation problem. The new washer design will control the contact area artificially so that the washer can monitor the full range of the rated preload.
## 2 Materials and Methods
### Materials
#### 2.2.4 The Weibull Model  
The field of applicability includes failure models determined by degradation processes such as fatigue, corrosion, diffusion, or mechanical abrasion, as well as phenomena concerning material strength and failure times of electronic and mechanical components [49].  
The Normalized Biparametric Weibull Distribution  
The biparametric Weibull distribution can be expressed in a more advantageous form by substituting the parameter \(\lambda\) and normalizing the time by a constant \(\eta\) which represents the real scale parameter, \(\beta\) being the shape parameter. It is denoted by:  
\[\lambda=1/\eta^{\beta}\text{or}\;\eta=1/\sqrt[6]{\lambda}. \tag{17}\]  
The probability density function will take the form:  
\[f(t/\eta;\beta)=[\frac{\beta}{\eta\left(\frac{t}{\eta}\right)^{\beta-1}}] \cdot exp\left[-(t/\eta)^{\beta}\right]. \tag{18}\]  
The distribution function, namely, the probability that the product will fail in the time interval (0, \(t\)), will be:  
\[F(t/\eta;\beta)=\int_{0}^{t}f(t/\eta;\beta)dt=1-exp\left[-(t/\eta)^{\beta} \right]. \tag{19}\]  
The probability that the product will not fail in the time interval (0, \(t\)) will be:  
\[R(t/\eta;\beta)=1-F(t/\eta;\beta)=exp\left[-(t/\eta)^{\beta}\right]. \tag{20}\]  
The failure rate becomes:  
\[z(t/\eta;\beta)=[\beta/\eta]\cdot\left(exp\left[-(t/\eta)^{\beta}\right] \right)^{\beta-1}. \tag{21}\]  
The mean time to failure for the two-parameter Weibull distribution will be:  
\[m=\eta\cdot\Gamma(1/\beta+1). \tag{22}\]  
The Three-Parameter Weibull Distribution  
This distribution model constitutes a complete variant using three parameters: \(\beta\)--the shape parameter; \(\eta\)--the scale parameter or characteristic life, i.e., the lifespan up to which 63.2% of failures will occur; and \(\gamma\)--the minimum lifespan, or the location, position, or initialization parameter. The probability density function will have the form:  
\[f(t;\eta,\beta,\gamma)=\{\frac{\beta}{\eta}\left[\frac{(t-\gamma)}{\eta} \right]^{\beta-1}\}exp\left\{-\left[(t-\gamma)/\eta\right]^{\beta}\right\}. \tag{23}\]where: \(t>\gamma\), \(\beta>0\), \(\eta>0\) si\(-\infty<\gamma<\infty\).  
The distribution function, namely, the probability that the product will fail in the time interval (0, \(t\)), will be:  
\[F(t;\eta,\beta,\gamma)=1-exp\Big{\{}-[(t-\gamma)/\eta]^{\beta}\Big{\}}. \tag{24}\]  
The reliability function, namely, the probability that the product will not fail in the interval (0, \(t\)) will be:  
\[R(t;\eta,\beta,\gamma)=exp\Big{\{}-[(t-\gamma)/\eta]^{\beta}\Big{\}}. \tag{25}\]  
The failure rate becomes:  
\[z(t;\eta,\beta,\gamma)=[\beta/\eta]\cdot(t/\eta)^{\beta-1}. \tag{26}\]  
The mean time to failure will be:  
\[m=\gamma+\eta\cdot\Gamma(1/\beta+1). \tag{27}\]
## 3 Results and Discussion
### Analysis of GC-IMS  
#### 3.2.1 Topographic Analysis  
As shown in Figure 4A,B the topographic plots obtained for the four samples analyzed, which overlay the 2D spectra. It was a two-dimensional map in which Y axis represented the retention time in the chromatographic column (in seconds), and X axis represented the drift time in the drift tube (in milliseconds). Each spot in the topographic plot represented a chemical compound. The color intensity signified the concentration of compound, which meant lower content by white and higher content by red [16]. The deeper the color, the higher concentration it had. As could be seen from the overall qualitative map (Figure 4A), a total of 55 volatile substances were identified in samples from the four processing stages, including monomer and dimer. With the depth of processing, the number and color of spots in the samples changed significantly, the number increased and the color was more intense (Figure 4B), indicating that both number and content of volatile compounds had increased. The spot distribution of braised chicken was higher than that of raw and fried chicken, indicating that the types of volatile compounds in the braising stage were abundant, which was consistent with the results of GC-MS analysis in 3.1 of this paper and [PERSON] et al. [5]. However, the spot distribution was similar for 1 h and 2 h of braising, indicating that the release of volatile compounds gradually reached equilibrium after braising for 1 h.
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame
### Optimization of the Parameters for the Eight-Element MIMO Array Antenna
#### 5.2.3 Optimization of the Spacing between the \"Ring-Slot\" Unit Modules  
The spacing (d) between two different modules has a significant impact on the overall isolation of the MIMO system. As shown in Figure 45, as the spacing (d) increases, the resonance frequencies of the ring antenna and slot antenna shift towards higher frequencies. When the spacing (d) increases from 73.5 mm to 77.5 mm, the isolation between antenna 1 and antenna 2 decreases. Simultaneously, as the spacing (d) increases, the distance between antenna 1 and antenna 3, which are located in different modules, also increases, leading to improved isolation between antenna 1 and antenna 3. After considering the isolation within modules and between modules, the optimal spacing (d) is chosen as 75.5 mm.
## 4 Results
### Application Planes  
This layer is the plane that specifically conveys the outsourced programming to the customer. Clients do not have to pay money for the installation of the required software, they just need to pay the rent for its use. The security issues for this plane are end-to-end.  
Data security is also one of the concerns of the application plane. A generic security platform is needed inside the cloud to get high visibility and end-to-end control over data. The complex administrative and security related issues are minimized by MacAfee and Intel with the introduction of end device visibility. Moreover, in the application plane, the organizational data is stored at the data centers of SaaS. Additionally, the cloud providers focus on the high availability, and they make backups and data replications across multiple sites. The vendors of Amazon, Google App and Elastic Compute Cloud (EC2) integrate the cryptographic algorithms for login access with Secure Shell (SSH). This can possibly avoid the malicious access and vulnerability issues that are caused by unauthorized access to the security model.
## 4 Experimental Demonstration
### Result  
#### 4.4.1 Experiment I: Preliminary Experiments on the CWRU Dataset  
We conducted experiments to compare five models. They are the traditional MLP model, the FCN model, the 1 DCNN+LSTM, the 1 DCNN+BiGRU model without the self-attention mechanism, and the improved 1 DCNN+GRU model released by [PERSON] et al. last year [20].  
We can see the accuracy of each model on the test set in Figure 9. Although there was still room for growth in the effect of the traditional MLP model, the effect was not ideal after 20 iterations. The FCN model had better results than the MLP, but the training was not stable overall. 1 DCNN+LSTM model has a good performance compared with FCN model and MLP model. However, the improved 1 DCNN+GRU model by [PERSON] et al. [20] had better performance, reached an accuracy of more than 95% in the 10 th epoch, and was still growing slowly. The red solid line represents our model; at its fourth epoch, it had obtained 98% accuracy; compared with the 1 DCNN_BiGRU model from which the attention mechanism has been removed, our model converges more quickly. model from which the attention mechanism has been removed, our model converges more quickly. However, after the eighth iteration, the two curves almost overlap. Its accuracy fluctuated up and down to 99.9%, with a small range. Here, we can see the excellent performance of the 1 DCNN_BiGRU_Att model.  
Figure 9: Accuracy of the individual models in CWRU.  
To achieve a better view of the data distribution across the models at each iteration, we grouped the box plots of the models. The four parameters in the box plot are also indicators of our evaluation of the model. Since the MLP and FCN models performed poorly in the accuracy plots above, only the box plots of the remaining four models are compared here. The specific information is shown in Figure 10. The accuracy, precision, recall, and F1-score of each model are evident in these four bin plots, along with the outliers, median, upper quartile, and lower quartile of these parameters. Our model performed very well in general, with the smallest outliers of all the models, and its upper quartile, lower quartile, and median almost coincide and are close to 1.0. Through the internationally recognized bearing dataset--although there are only two types of bearings and the dataset is not balanced--it can also be preliminarily proved that the model has a good effect on the fault diagnosis of multiple types of bearings under complex working conditions.
## 3 The Proposed Solution
### Detailed Protocol Description
#### 3.3.4 Revoking  
Revocation is a necessary process of the protocol so as to administer misbehaviour of authorized vehicles. Supposing that a vehicle \(V\) is misbehaving, e.g., the message \(m\) is false or repeated multiple times etc. The RSU can forward the tuple to the SA which can locate the corresponding line of the misbehaving driver by using the timestamp \(t_{cur}|rand\). Then all information can be forwarded to the CA requesting for further instructions. According to a predefined policy actions can be taken. If necessary the CA can instruct the SA to simply delete the appropriate line from \(BB_{SA}\), thus removing \(V\) from the list of authorized users. No revocation list is needed for the expired, deleted credentials.