esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 5 Conclusions  
Due to the lack of sufficient anomaly data relative to normal data, we introduce a novel method for anomaly detection using only normal data. The work can be summarized as follows:  
Figure 8: Results of ablation experiment. Blue is the proposed method, orange is the condition with input–output residuals as anomaly scores, and grey is the condition without local constraint.  
Figure 7: Comparison of qualitative results. (**a**) test images with anomalies; (**b**) ground truth of test images; (**c-f**) segmentation results for MSE-based AE, SSIM-based AE, AnoGAN and proposed method, respectively. Blue areas are anomaly-free, red areas are anomalies.  
* Using VQ-VAE to construct a discrete latent space. Then, the latent space distribution of the normal image is modeled using PixelSNAIL.
* During anomaly detection, the discrete latent code out of the normal distribution is resampled by PixelSNAIL. After this resampling, the index table is reconstructed to a restored image by the decoder. The greater the distance between the restored image and the image reconstructed directly using VQ-VAE, the more likely the region is anomalous.
* The method is evaluated on the industrial inspection dataset MVTec AD that contains 10 objects and five textures with 73 various anomalies. The results show that the proposed method achieves better performance compared to other methods.  
Our motivation can be intuitively explained as keeping the normal portions intact while restoring the abnormal portions of images. Since the time required for resampling and inference is related to dimension of the latent space, this may constrain real-time performance of our method. In addition, it is possible to collect a small number of anomalous images in reality, and introducing this anomalous information may improve the performance of the model. Future work will focus on real-time performance and semi-supervised learning.  
Project administration, Y.H., L.W.; Validation, D.Z., L.W., and J.G.; investigation, L.W., D.Z.; resources, L.W., D.Z.; visualization, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research is sponsored by the National Key Research and Development Program of China (Grant Nos. 2018 YFB0704400, 2018 YFB0704402, 2020 YFB0704503), Natural Science Foundation of Shanghai (Grant No. 20 ZR1419000).  
The authors would like to thank [PERSON] for valuable discussions.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Results and Discussion
### Physiological Analysis of Comfort  
The participants' physiological values were scored using the Likert-type scale (Table 7). For ear temperature, the score for the normal vests was five-seven points, and that for the cooling vests was four-seven points, but this difference was nonsignificant (\(p>0.05\)). For heart rate, the score for the normal vests was four-seven points, and that for the cooling vests was five-seven points, but this difference was nonsignificant (\(p>0.05\)). For diastolic blood pressure, the score for the normal vests was five-seven points, and that for the cooling vests was four-seven points, but this difference was nonsignificant (\(p>0.05\)). For systolic blood pressure, the score for the normal vests was five-seven points, and that for the cooling vests was four-seven points, but this difference was nonsignificant (\(p>0.05\)). For average skin temperature, the score for the normal vests was four-seven points, and that for the cooling vests was two-five points, which is a significant difference (\(p<0.05\)). This finding indicates that the cooling effect of the vests was substantial.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Degree of Perceived Thermal** & **a. General Vest** & **b. Cooling Vest** & \(P\) & **Unplanned** \\
**Sensation Questionnaire** & & & & **Comparison Result** \\ \hline Effects of weight on motion & 0.50 \(\pm\) 0.58 & 0.54 \(\pm\) 0.81 & 0.000 * & b \textgreater{} a \\ Humid feeling of vests & 0.56 \(\pm\) 0.59 & 0.53 \(\pm\) 0.36 & 0.000 * & a \textgreater{} b \\ Effects of sufulness of vests in the experiment & 0.52 \(\pm\) 0.87 & 0.58 \(\pm\) 0.62 & 0.000 * & b \textgreater{} a \\ Satisfaction with the looseness of & & & & \\ vests in the experiment & 0.55 \(\pm\) 0.72 & 0.58 \(\pm\) 0.61 & 0.000 * & a \textgreater{} b \\ Coolness of vests in the experiment & 0.50 \(\pm\) 0.81 & 0.31 \(\pm\) 0.56 & 0.000 * & b \textgreater{} a \\ \hline \hline \end{tabular}
* stands for significant difference, \(p<0.05\).  
\end{table}
Table 6: Perceived thermal sensation (Likert-type scale; mean \(\pm\) standard deviation; and unit: %).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Physiological Value Measurement** & **a. General Vest** & **b. Cooling Vest** & \(P\) & **Unplanned** \\  & & & **Comparison Result** \\ \hline Ear temperature & 0.54 \(\pm\) 0.71 & 0.54 \(\pm\) 0.65 & 0.000 * & a \textgreater{} b \\ Heart rate (HR) & 0.54 \(\pm\) 0.73 & 0.53 \(\pm\) 0.87 & 0.000 * & a \textgreater{} b \\ Blood pressure—diastolic blood & 0.51 \(\pm\) 0.69 & 0.59 \(\pm\) 0.38 & 0.000 * & a \textgreater{} b \\ Blood pressure—systolic blood & & & & \\ pressure & 0.50 \(\pm\) 0.69 & 0.58 \(\pm\) 0.23 & 0.000 * & a \textgreater{} b \\ Mean skin temperature (TSK) & 0.52 \(\pm\) 0.36 & 0.30 \(\pm\) 0.21 & 0.000 * & b \textgreater{} a \\ \hline \hline \end{tabular}  
* stands for significant difference, \(p<0.05\).  
\end{table}
Table 7: Physiological measurements (Likert-type scale; mean \(\pm\) standard deviation; and unit: %).
## 3 Antenna Design and Parameters Study
### Design and Simulation of the EBG MPAs

Table 2: The antenna parameters. Figure 5: The simulated S\({}_{\mathbf{II}}\) values of the original and the CP EBG MPAs. Figure 7 depicts the normalized far-field antenna radiation patterns of the original and the EBG antennas in polar coordinates. The real values of the main lobe magnitude were mentioned under the drawings. As observable in Figure 7a,b, the shape of the CP EBG antenna radiation pattern is more directive than that of the original MPA, with the 3 dB half-power beamwidth (HPBW) value of 28.9\({}^{\circ}\). The surface current distribution over time of the antenna before and after adding the EBG is demonstrated in Figure 8. It is apparent from this figure that at the phase of \(\omega t\) = 90\({}^{\circ}\), the near-field level at the lower edge of the original MPA is 1 A/m while that value at the same position of the CP EBG MPA is 0.2 A/m, which corresponds to the dB-scale values of 0 dB and \(-\)14 dB. The formula to convert the values from A/m to the dB scale is as follows:  
\[E_{dB}=20 log_{10}(E) \tag{1}\]  
Figure 6: The simulated gains over frequencies of the original and the CP EBG MPAs. Figure 7: The simulated far-field radiation patterns of (**a**) the original MPA and (**b**) the CP EBG MPA. The red lines are the main pattern curves and the blue lines mark the 3 dB angular widths. This means that there is a surface wave suppression at the antenna edges and the unwanted edge radiation is significantly reduced by approximately 14 dB. As a result, more energy is directed into the main radiation beam, therefore improving the antenna gain. The axial ratio over frequencies of the CP EBG MPA are presented in Figure 9. As depicted in this figure, the AR at 8.483 GHz is around 1.6 dB, which is below the 3 dB threshold value for a CP antenna. The 3 dB ARBW is around 190 MHz, from 8.34 to 8.53 GHz.
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 2 Methodology of the Multi-Classification Multi-Leak Identification (MC-MLI) Scheme  
In this paper, an MC-MLI for WDI is proposed, and its development flow is illustrated in Figure 1. First, pressure sensors and flow meters are deployed and initialized in WDI. Data collection from practical WDI is then performed. The measured detection parameters, such as mean value, peak-to-peak value, and the variance in flow rates and pressures, are collected. Furthermore, the water behavior of the WDI determined by the WDI structure, the pipe properties and sensor locations, etc., are modeled as a hydraulic model. The estimated detection parameters can then be obtained based on the hydraulic model. Afterward, data analysis and feature extraction are performed to design the adaptive kernel, which is the core of classification. It is worthwhile to point out that the proposed multi-classification is a parallel structure in which the status of each pipe section is identified by a unique classifier. Feature combination will influence the design of the adaptive kernel and the detection performance. The proposed scheme is adaptive in that various classifiers might have different feature combinations and kernels to accomplish the desired detection performance. For instance, sensitivity and detection time take priority over accuracy and specificity for an important pipe section. Multiple classifier scenarios with varying feature combinations are created and evaluated by multi-criteria decision-making (MCDM). Afterward, the overall scoring of each classifier scenario is obtained and becomes the reference to select the best classifier under certain system requirements. Finally, MLI is obtained by integrating all selected classifiers in a parallel structure.
## 3 The CPN Model
### The CPN Formalism
#### 3.1.2 Timing Extensions  
Now, we address the second extension of Petri nets, where the transitions are not timeless but timed. The provision of time gives our model the opportunity to describe the temporal details of a system in a precise manner. The approach combines three characteristics--(1) Each token carries one _time stamp_, a timing value used to determine the firing time of the transitions fed by the place this token is located (2) a transition may fire and produce new tokens with a delay, and (3) a guard expression can be used when we need to assign conditions regarding the time of firings. When we consider time, there is always a global clock for keeping the time. Time is usually advances in _time units_ and not in real time metrics (hours, minutes, seconds, etc.). For transition firings, it is a common practice to add a delay, which is analogous the time required for the system changes to take effect.  
To determine the firing time of a transition, we first examine all the input places and from each place, we find the token with a minimal time stamp (that is, the next token to leave this place). In case of a draw between time stamps residing in a place, the system chooses the one with the smaller Job_Id. Thus when a transition has \(b\) input places, we take into account \(b\) tokens, one token with the minimum time stamp from each of these places. Then, the firing time is equal to the maximum of the selected time stamps, \(t_{i\max}\) indicating that the last condition required to enable transition \(t_{i}\) became true at time \(t_{i\max}\). This procedure will be clarified with an example in Section 3.2.4, where the firing time of our model is described.  
When the firing time is determined, a delay time stamp determines the time of birth for the new token in its next place (the transition's output place). In other words, the time stamp of the newly produced token is the sum of the determined firing time and the introduced delay. The delay is an indication that the proper time has elapsed before the new token is produced and located to its new place. This means that we have to take into account the time required for an event to complete and affect the system's state.
## 3 Results  
### Muscle Activity (EMG)  
#### 3.1.1 Effect of Passive Exoskeleton on EMG  
Wearing an exoskeleton led to a statistically significant decrease in muscle activity in the lower-limb muscles (rectus femoris: RF, biceps femoris: BF, and tibialis anterior: TA), while the effects on the back and upper-limb muscles were not statistically significant (Table 1). In the case of the RF, wearing the CEX (W\({}_{\text{CEX}}\)) significantly reduced muscle activity by about 68.8-69.7% compared to the cases without exoskeletons (WO) and wearing the Chairless Chair (W\({}_{\text{CC}}\)). There was no significant difference in RF muscle activity between WO and W\({}_{\text{CC}}\). In the case of the BF, there was a significant decrease in muscle activity when wearing the CC (W\({}_{\text{CC}}\)) and CEX (W\({}_{\text{CEX}}\)), by 78.5% and 71.5%, respectively, compared to WO. TA muscle activity was also decreased significantly by 67.1% and 57.9% when wearing the CC (W\({}_{\text{CC}}\)) and CEX (W\({}_{\text{CEX}}\)), respectively, compared to without exoskeletons (WO).
# Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails
## 2 Literature Review
### Definitions of Phishing and Spam  
As previously mentioned, phishing intends to deceive users and can be used as a technique to steal information or gain unauthorised access to a network. On the other hand, spam is typically nuisance emails that in many cases will be sent immediately to the junk or spam folder. [PERSON] et al. [3] categorise both spam and phishing as types of malicious emails which can be detected using machine learning.  
Current research demonstrates that there are many ways of delivering phishing and spam, but via email is the most common. This is discussed by [PERSON] et al. [4] who state that email is a targeted form of communication which corresponds with the threat of phishing where individuals or organisations are targeted by threat actors, hoping to inherit personal information from their victims. Even though [PERSON] et al. [5] mainly focus on spam emails, they highlight that the rate of exchanging information via emails is significantly increasing. [PERSON], [PERSON] and [PERSON] [6] illustrate that this could be a result of the COVID-19 pandemic in recent years, necessitating organisations to work remotely and threat actors proceeding to use phishing and spam emails. Therefore, this research aims to explore phishing and spam.  
The make-up of a phishing or spam email depends on the sender. [PERSON] et al. [2] outline examples of phishing emails such as fake invoices and purchase orders or security alerts and account suspension. Many threat groups distributing phishing emails will employ their own techniques to hook individuals, whereas spam emails tend to be generalised and less personal. Phishing emails will typically contain URLs or attachments, as well as employing certain words encouraging the user to believe what is written.  
Regarding the consequences of phishing emails, they can act as a gateway to additional threats which [PERSON] and [PERSON] [7] have illustrated, as they state phishing emails have been used to deploy file-encrypting ransomware such as CryptoLocker. Furthermore, [PERSON] [8] outlines that the MITRE framework, which highlights threat actor behaviour, recognises that threat actors tend to use a paraphishing link or an attachment to gain initial access into an organisation's digital estate. The consequences of spam emails can slightly differ to phishing emails in that companies may be fined if spam emails are sent to customers. In the case of American Express, they were fined for sending 4 million nuisance emails to customers who had opted out [9], thus outlining that spam emails can be sent to users for promotion purposes and not necessarily to gain information from them.  
Overall, as both phishing and spam still present themselves as a threat, this research aims to provide a detection solution for detecting both phishing and spam emails.
## 3 Reactivity of Oxygen Carrier with Different Reaction Times
### Lifetime Assessment of Oxygen Carrier  
It has been demonstrated that the iron oxidation state of Fe\({}_{2}\)O\({}_{3}\) is applicable for industrial chemical looping combustion transforming to Fe\({}_{3}\)O\({}_{4}\), although it has different states (Fe\({}_{2}\)O\({}_{3}\), Fe\({}_{3}\)O\({}_{4}\), FeO and Fe) [45]. For the oxygen carrier, there was a conversion destruction during a longtime operation. The lifetime of oxygen carriers can be assessed by the following equation:  
\[t_{lf}=\frac{t_{op}}{\Delta X_{\text{OC}}}, \tag{5}\]  
where, \(t_{lf}\) is the lifetime of oxygen carrier in CLC; \(t_{op}\) is the total operation time; \(\Delta X_{\text{OC}}\) is the conversion destruction of oxygen carrier during longtime operation.  
The conversion of oxygen carrier can be calculated by the CO\({}_{2}\) produced in the redox cycles. Usually, the conversion of oxygen carrier \(X_{\text{OC}}\) is defined as follow:  
\[X_{\text{OC}}=\frac{m_{ox}-m}{m_{ox}-m_{red}}, \tag{6}\]  
where, \(m_{ox}\) is the mass of oxygen carrier in totally oxidized state; \(m\) denotes the instantaneous mass of oxygen carrier particles; \(m_{red}\) represents the mass of oxygen carrier when it is totally reduced. In these tests, the Fe\({}_{2}\)O\({}_{3}\) in oxygen carriers was not totally converted to Fe\({}_{3}\)O\({}_{4}\). Thus, the total reduced state of Fe\({}_{2}\)O\({}_{3}\) was chosen as Fe\({}_{3}\)O\({}_{4}\).  
The conversions of the oxygen carrier in the 1 st and 100 th cycles under different conditions are shown in Figure 8.  
The conversions of the oxygen carriers in the first cycles were 9.0%, 7.6% and 6.2% separately while they were 8.3%, 7.4% and 6.0% in the 100 th cycles. The declines of the conversions were 0.7%, 0.2% and 0.2% separately in these tests. To assess the lifetime of oxygen carrier, the theoretical times of 100 cycles were adopted as operation times which were 800 min, 500 min and 500 min in the test 1 to test 3. The calculated lifetimes of oxygen carriers under the conditions of test 1 to test 3 were 1888 h, 4234 h and 4475 h separately, which demonstrated that the reduction time had a great effect on the oxygen carrier's lifetime.
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 4 QEPAS Sensor Based on Custom QTF
### QerPAS Sensor Employing a Custom Small-Gap Qtf  
In the QEPAS technique, the acoustic wave generated by the reaction between gas molecules and laser at the gap of the two prongs can be regarded as spherical wave oscillation. The spherical wave propagation decreases with the cube of the distance, which means that the energy of the acoustic wave will attenuate rapidly as the distance from the generation point of the acoustic wave increases. Based on the above analysis, employing a QTF with a small gap will increase the strength of the acoustic wave acting on the prongs of QTF. Therefore, the signal level of QEPAS would be enhanced. The schematic of the laser beam, a QTF, and the generated acoustic wave is shown in Figure 8. The first custom QTF with a small gap of 200 \(\upmu\)m used in the QEPAS sensor was reported by [PERSON] [75]. Using the COMSOL software, a finite element modeling (FEM) was constructed, and the displacement of QTF with different micro-resonators (mR) was calculated theoretically. For comparison, a standard QTF with a 300-\(\upmu\)m gap was also investigated. The calculated results are shown in Figure 9. An experimental evaluation was carried out by employing a distributed feedback diode laser with emitting wavelength of 1.39 \(\upmu\)m and choosing water vapor (H\({}_{2}\)O) as the target analyte. The detailed parameters of QTF and experimental results are displayed in Table 2. It can be seen that using a custom QTF with a small gap of 200 \(\upmu\)m improved the QEPAS sensor performance significantly. This improvement can be explained as follows. Firstly, due to a lower energy loss of propagation, the QTF with a small gap of 200 \(\upmu\)m allowed the acoustic wave to push the fork prongs more efficiently. Secondly, a decrease of QTF mass compared to that of a standard QTF would result in bigger displacement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Small-Gap QTF** & **Standard QTF [63]** \\ \hline Length & mm & 3.42 & 3.6 \\ Width & mm & 0.3 & 0.6 \\ Thickness & mm & 0.44 & 0.36 \\ Gap & \(\upmu\)m & 200 & 300 \\ Detection limit & ppm & 1.85 & 5.9 \\ NMEA & cm\({}^{-1}\)W/Hz & \(2.02\times 10^{-8}\) & \(7.73\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions and parameters of two QTFs.  
Figure 7: Schematic of the custom QTF, fundamental flexural mode, and the first overtone flexural mode.
## 8 Conclusions  
In conclusion, we reported the recent advances introduced by the L3 MATRIX project in Silicon Photonics and in photonic-electronic integration. Our advances in the areas of system architecture and design, light sources, fabrication of individual Silicon Photonics components and conventional and slow-light modulators provide a toolbox of technologies and architectures to address related challenges. All parts have been designed having two important restrictions in mind: compatibility with high-volume manufacturing and with the CMOS technology. In addition, we have developed our strategy for a full 3D vertical assembly approach and our proposed system architecture. We highlighted its motivation in view of the challenges described to introduce considerable power and cost savings in conjunction with a significant decrease in the number of DC switching layers in a co-packaged system. Progress and open questions based on the technical status of the project are discussed in detail as well. Our results provide a step toward future low-power and low-cost Data Centers.  
Conceptualization, K.H. and A.M.; silicon photonics, J.K. and V.S.; mask layout integration and design methodology, K.L. and R.G.B.; quantum well light sources, M.S., Y.B., F.H. and B.J.O.; quantum dot light sources, K.P., D.R.S., M.T., A.J.S. and H.L.; modulators, A.B., A.Z. and P.S.; RF modulator electrodes design, B.S., Y.E., H.O. and T.T.; system testing, N.T., G.M.-A., M.M.-P., T.M., N.P. and K.V.; system integration and assembly, K.H. and A.M.; coordination of reporting current results, K.P. and D.R.S.; general project coordination B.S., T.T. All authors have read and agreed to the published version of the manuscript.  
The L3 MATRIX project is co-funded by the Horizon 2020 Framework Programme of the European Union with Grant Agreement Nr. 688544. L3 MATRIX project is an initiative of the Photonics Public Private Partnership.  
Not applicable.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding authors upon reasonable request.