esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
###### Abstract  
With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model--the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.  
deep learning; anomaly detection; convolutional autoencoder; attention mechanism; unsupervised learning +
Footnote †: journal: Applied sciences
## 4 Results
### Network Emulation  
Network emulation is beneficial for increasingly complex networks and can minimize the investment required for concurrently measuring multiple devices' performance. As shown in Figure 6, networks with increasingly complex topologies can be utilized to simulate on-device capabilities while outputting live packets for network emulation. However, a limitation of this setup is the ability to measure latency accurately within the simulation. Compared to the measured latency presented in the previous subsection, the packet latency in our initial network emulation tests always measured over 400 ms. Given this limitation, external tools such as tShark were utilized to compare and contrast measured results and ensure accurate measurements for the device under test.  
Figure 5: Response time to Modbus packets.  
In this example, each IED labeled as an 'MbServer' has characteristics of the physical device we tested on. Each device also has the ability to act with an external interface but only simulates packets. However, 'MbServer3' utilizes an external TCP socket created as outlined in Section 3 of this paper. This device acts as an endpoint for the network emulation, and any packet sent to this device's address forwards the packet from simulation to a physical interface. As shown in Figure 7, these packets are transmitted on a physical interface in the same manner as the physical testbed.  
The packet shown in Figure 7 contains critical information for correct device operation, such as transaction identifier, protocol identifier, and unit identifier. In this configuration, the source port, 44,402, is not filtered by the destination device, and thus, the destination port is the only element required to have a specific value--in this case, a value of 502 indicating Modbus/TCP.  
The presented formal model shows that vulnerabilities within the protocol may occur during packet processing--characterized by the central state in the formal model. Upon testing with the network shown in Figure 7, the DUT was always able to recover from attack traffic, but did occasionally respond to packets that did not contain the correct unit ID. This demonstrates the existence of a potential implementation issue, which in turn may be viewed as a vulnerability in the implementation of Modbus on the device, thereby validating this evaluation framework's effectiveness and accuracy. However, we would be remiss if we did not mention that under regular operating conditions, traffic like this is unlikely to occur without being detected in production environments.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 6 Conclusions  
The rapid development of acoustic vector sensor technology has led to widespread use of sonar based on vector signal processing in acoustic communication. The directional reception technology can increase spatial reuse and reduce the conflict probability of packets, which is beneficial to improve the performance of UASNs. Therefore, research on underwater sensor network protocols based on directional reception is urgently needed.  
In this paper, a cross-layer protocol for low interference and low congestion based on directional reception has been presented and discussed in detail. At the MAC layer, the CLIC uses directional beams to establish data transmission links with minimal interference and maximum capacity. At the routing layer, the CLIC obtains routes with low collision and congestion by weighing the key factors to improve network performance. Simulation results show that the CLIC has higher PDR and higher energy efficiency compared to the QELAR, VBF, and CITP protocols.  
Figure 14: Comparison of energy efficiency versus packet generation rate.  
Figure 13: Comparison of PDR versus packet generation rate.  
The CLIC still has some unresolved routing issues, such as a void region. Due to the limitation of length, we have not addressed the void problem in the manuscript. In future work, we will address this issue.  
[PERSON] proposed the main ideas, wrote the paper, designed the description framework, conducted the simulations for final approval of the version to be published; [PERSON] provided guidance for the work and substantial contributions to the analysis; [PERSON] reviewed and modified the paper and gave final approval of the version to be published; [PERSON] provided guidance for the work and acquired funding and gave final approval of the version to be published. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation for Distinguished Young Scholars of China (Grant No. 62125104).  
Not applicable.  
The authors would like to thank the anonymous reviewers for their time towards the manuscript.  
The authors declare no conflict of interest.
## 2 Literature Review  
### LNG Technology  
The choice of alternative fuel by vessel owners is mainly driven by investment costs and advanced technology. [PERSON] et al. stated that both natural gas and hydrogen are already used [PERSON] et al. (2016). However, compared to natural gas, hydrogen has safety issues. The advanced technology depends on the availability of a bunker and related infrastructure. In 2019, DNV identified some alternative fuels used in shipping companies, such as LNG, LPG, methanol, biofuel and hydrogen. LNG is the most popular and promising alternative fuel because its technology is developed correctly [PERSON] et al. (2016). It has been developed through significant innovation, hence, its ability to reduce the high content of fuel emissions. They also stated that the capability to reduce sulfur and nitrogen levels is due to the use of marine fuel, such as LNG, in a diesel engine [PERSON] et al. (2018). Clean and renewable energies are ideal, although, in practice, LNG is usually selected by owners [PERSON] et al. (2016). LNG is categorized as the leading alternative fuel, followed by methanol and biofuel (2018).  
LNG technology on board vessels depends on the fuel gas supply system (FGSS). [PERSON] et al. stated that the fuel tank needs to be kept in the liquid phase at \(-163\)\({}^{\circ}\)C. Furthermore, it is designed to supply gas to dual-fuel engines under the required temperature and pressure. It also needs to avoid being over-pressurized due to its ability to improve fuel efficiency [PERSON] et al. (2019). Most vessel technology uses dual fuel systems, while the boil-off gas produced in the LNG tank is used for steam turbines [PERSON] et al. (2019). In 2022, the Maritime Executive stated that the retrofit concept reduces the cost of LNG conversion operations [PERSON] et al. (2020).
## 2 Materials and Methods
### Acquisition of Remote Sensing Data  
A very high spatial resolution (0.15 m) multispectral image of the study area was acquired on 15 th October 2020 at low tide (0.4 m) by ArborCarbon using the airborne ArborCam sensor. The seven adjustable band placements of the ArborCam sensor were configured for vegetation measurement. The bands capture reflected solar irradiance in the red, green, blue, red-edge and near-infrared (NIR) regions of the electromagnetic spectrum (Table 2). ArborCarbon provided the image orthorectified (0.15 m RMSE) and calibrated to surface reflectance. ArborCarbon also provided a proprietary vegetation height product derived from the image.  
The PlanetScope sensor images at 3 m spatial resolution and has 4 bands that capture reflected solar irradiance in the red, green, blue and NIR regions of the electromagnetic spectrum [31]. The spatial resolution is coarser than the ArborCam sensor, and there are fewer bands, particularly in relation to red and red-edge wavelengths (Table 2). Twelve orthorectified (<10 m RMSE) PlanetScope surface reflectance images were selected; they were acquired at approximately monthly intervals from April 2020 to March 2021. The images were selected based on the absence of cloud cover and coincidence with low tides (Table 3) as tidal flooding of saltmarsh areas will significantly impact vegetation reflectance, resulting in a reduction in the NDVI [37].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Saltmarsh Species**} & **Aboveground Biomass** & **Carbon Content** \\  & **(Mg ha\({}^{-1}\))** & **(\% C)** \\ \hline _Sarcocornia quinqueiflora_ & 6.88 & 34.4 \\ _Samolus repens_1  & 5.51 & 42.5 \\ _Sporobolus virginicus_ & 10.12 & 38.0 \\ _Juncus kraussii_ & 15.97 & 42.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Published values for aboveground biomass and carbon content for saltmarsh species in Jervis Bay [35].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **ArborCam Bands** & \multicolumn{4}{c}{**PlanetScope Bands**} \\  & **Name** & **Centre (nm)** & **Number** & **Name** & **Range (nm)** \\ \hline
1 & Blue & 450 & 1 & Blue & 455–515 \\
2 & Green-1 & 530 & 2 & Green & 500–590 \\
3 & Green-2 & 570 & & & \\
4 & Red-1 & 655 & 3 & Red & 590–670 \\
5 & Red-2 & 680 & & & \\
6 & Red-edge & 720 & & & \\
7 & NIR & 780 & 4 & NIR & 780–860 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Band numbers, names, and regions of the ArborCam sensor and PlanetScope sensors [31].  
The ArborCam image and 12 PlanetScope images were uploaded to GEE for analysis as described below.
## 3 Results and Discussion
### Extraction Process Optimization  
It has been reported in many investigations that the variables \(R_{L/S}\), \(C_{\rm DES}\), and \(T\) play an important role regarding the extraction of phenolic compounds in general [29]. In order to investigate the combined effect of the independent variables on OLL polyphenol extraction, a BBD process was applied to find the optimized values of the variables \(R_{L/S}\), \(C_{\rm DES}\), and \(T\). Fifteen experiments with 3 center points were carried out for different combinations of the variables using statistically designed experiments; the results are shown in Table 3, which includes the design with the experimental and predicted values. The distribution of the data was analyzed for normality using the Shapiro-Wilk test and also for the presence of outliers. The analysis reported zero outliers among all the data, and the normality conditions were only proven for GL-Lys and GL-Pro model values (\(p\) = 0.98, \(p\) = 0.72, respectively), whereas a significant \(p\) = 0.0147 was observed for GL-Arg. Bias in the coefficient estimates and standard errors are some of the consequences if the normality assumption is not satisfied. Consequently, this may lead to wrong conclusions regarding the confidence intervals and significance tests [30]. In order to fit a Gaussian distribution, the \(Y_{TP}\) values of the GL-Arg model were subjected to a mathematical transformation. One of the statistical goals for transforming data is to obtain symmetrical values that help in conditioning and enabling easier data analysis for subsequent stages [31]. The appropriate transformation is chosen through trial and error until the values have a normal distribution. In this study, the \(Y_{TP}\) values of the GL-Arg model were converted using the Box-Cox transformation [32; 33]. The transformation has the following form:  
\[Y(\lambda)=\left\{ \begin{array}{ll}\frac{y^{\lambda}-1}{\lambda},\mbox{if } \lambda\
eq 0;\\ \log y,\mbox{if }\lambda=0.\end{array} \right. \tag{3}\]  
where \(Y\) is the transformed value, the parameter lambda (\(\lambda\)) defines a particular transformation, which can be a square root when lambda = 0.5 or natural log when lambda = 0. The optimal lambda was chosen within a [\(-\)2, 2] interval with jumps of 1/10 [34]. On the basis of the smallest standard deviation obtained, the optimal value of lambda was found to be at (\(-\)1.2). The application of this conversion allowed an insignificant \(p\)-value (\(p\) = 0.09468) for the Shapiro-Wilk test; therefore, it had normalized the data and satisfied the assumption of the test. All subsequent analysis regarding the GL-Arg model was based on the transformed values.
## 3 Impulse Detection Methodology Using Deep Learning Flexible Boundary Regression  
Since the time lengths of the AE impulse signal excited by the fuel stream are variable, the proposed method must adapt to predict boundaries of different sizes. This research introduces the DLFBR impulse detection model, which considers an impulse as an object to be analyzed and recognized. This model improves the 1D-CNN to adapt for hit detection in the AE signal. The research reframes hit detection as a straightforward regression issue, directly from the AE signal vector to the boundary box coordinates. The proposed method takes an AE signal as the input, feeds it through a neural network structure that looks similar to a 1D-CNN to integrate the boundary regression layer, and receives a vector of box coordinates around the impulse position in the output. The basic idea of the detection algorithm includes two steps: preprocessing the signal to extract the shape signal and a flexible boundary detector.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Type of Setup.** & \multicolumn{2}{c}{**Detailed Specification**} \\ \hline \multirow{4}{*}{Card PCI 2} & - & 18-bit 40 MHz ADC \\  & - & Sensor testing: AST built-in \\  & - & Dynamic range: \(>\)85 dB \\ \hline \multirow{4}{*}{AE sensors (WDI-AST)} & - & Peak sensitivity [V/ubar]: \(-\)25 dB \\  & - & Directionality: \(\pm\)1.5 dB \\  & - & Working range: from 200 to \\  & - & 900 kHz \\  & - & Resonant frequency: 650 kHz \\ \hline \multirow{4}{*}{Measurement type} & - & Acoustic emission \\  & - & Vibration \\  & - & Pressure \\  & - & Microphone \\ \hline Pinhole size & \multicolumn{2}{c}{0.6 mm, 1 mm, 2 mm} \\ \hline \multirow{4}{*}{Test cases} & - & No operation \\  & - & Without fake powdered coal \\  & - & With fake powdered coal \\ \hline AE sampling rate & \multicolumn{2}{c}{1 MHz} \\ Signal length for each case & \multicolumn{2}{c}{3 min to 5 min} \\ Number of datasets & \multicolumn{2}{c}{4} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data acquisition system and data specification.  
Figure 2: Detailed setup of the data acquisition system.
# Impact of Device Topology on the Performance of High-Speed 1550 nm Wafer-Fused VCSELs
###### Abstract  
A detailed experimental analysis of the impact of device topology on the performance of 1550 nm VCSELs with an active region based on thin InGaAs/InAlGaAs quantum wells and a composite InAlGaAs buried tunnel junction is presented. The high-speed performance of the lasers with \(L\)-type device topology (with the largest double-mesa sizes) is mainly limited by electrical parasitics showing noticeable damping of the relaxation oscillations. For the \(S\)-type device topology (with the smallest double-mesa sizes), the decrease in the parasitic capacitance of the reverse-biased p\({}^{+}\)n-junction region outside the buried tunnel junction region allowed to raise the parasitic cutoff frequency up to 13-14 GHz. The key mechanism limiting the high-speed performance of such devices is thus the damping of the relaxation oscillations. VCSELs with \(S\)-type device topology demonstrate more than 13 GHz modulation bandwidth and up to 37 Gbps nonreturn-to-zero data transmission under back-to-back conditions at 20 \({}^{\circ}\)C.  
vertical-cavity surface-emitting lasers (VCSELs); wafer fusion; optical modulation; long wavelength; short cavity; 1550 nm; MBE
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Ranking of Flights and Further Research
#### 5.6.1 The Number of Flights per Weekday, Month, and Hour  
The variable \"Origin_WDMH_size\" captures the total number of flights scheduled from the origin airport, broken down by weekday, month, and hour. This metric provides valuable insights into peak traffic periods and potential congestion, which may contribute to delays. The detailed breakdown is presented in Table 15.  
It can be observed that big airports with a large number of flights in the same weekday, hour, and months, as planned time at origin, have a tendency towards decreasing delay rates (Figure 8) over the last 20 years, but the smallest airports with the least traffic, 322 partial score, have an increasing trend.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Score** & **Condition** & **DR [\%]** & **Share [\%]** & **All** & **Delayed** & **On Time** \\ \hline
310 & 29,288.5 \(\leq\) Origin\_WDMH\_size \(<\) 33,556.5 & 7.0 & 4.9 & 60,968 & 4295 & 56,673 \\
316 & 9612.5 \(\leq\) Origin\_WDMH\_size \(<\) 29,288.5 & 5.6 & 34.5 & 425,769 & 23,946 & 401,823 \\
322 & Origin\_WDMH\_size \(<\) 9612.5 & 4.4 & 57.5 & 708,763 & 31,369 & 67,394 \\
324 & 33,556.5 \(\leq\) Origin\_WDMH\_size & 4.1 & 3.1 & 37,993 & 1560 & 36,433 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Number of flights per weekday, month, and hour (Origin_WDMH_size).  
Figure 8: Delayed rate (DR) as a function of time for the variable of the size of Origin_WDMH_size. Source: Authors’ own elaboration.
# Design Considerations Concerning an Innovative Drive System for a Manual Wheelchair
## 2 Materials and Methods
### Electronic Control System  
Once the prototype was built, work began on designing the automatic gear ratio change system. The developed system operates using a DC servomotor; a T22/10 NM torque meter (Hottinger Bruel & Kjaer GmbH, Darmstadt, Germany) working in the current standard; an HY38-500 push-pull type incremental encoder (Termipol, Lublinic, Poland) working at 5 VDC; as well as communication with the user via a touch HMI display (Nextion, Shenzhen, China). The first step in building the electronic system controlling the derailleur was to develop an electrical diagram. The system's central point is the STM32L432 microcontroller (STMicroelectronics, Geneva, Switzerland). The power supply is provided by two adjustable pulse converters set to two voltage ranges: 12 V for the torque meter and the microcontroller module, and 6 V for the servo. A pack of 4 lithium-ion cells, i.e., Li-Ion Samsung INR18650-25R 2500 mAh cell (Samsung SDI Co., Ltd., Yongin, Republic of Korea) was selected as the voltage source. They can be easily replaced in the event of discharge. They can also be expanded to a larger capacity if a longer operating time is needed. The control system's block diagram is presented in Figure 4.  
The assembly of the ratio shifting mechanism is shown in Figure 5. The servomotor (21) is connected to the derailleur tensioner lever (22) by means of a handle (23). The derailleur tensioner lever (22) is mounted on a mandrel (24) connected to the servo mount (25), which is screwed to its body.  
The control module can operate in two modes: semi-automatic, controlled by the user using a touch panel, or automatic. In the latter operating mode, the shift occurs when the set value of the drive torque measured by a torque meter installed on the pushrim hub shaft is exceeded. The ranges of drive torque for a given gear ratio are fully editable through data entered into the controller. This will enable optimal range adjustment in the future based on the results collected based on tests carried out on the test group, taking into account muscle tension tests in order to verify the reduction in their load [27,28]. The changes in the center of gravity [29] and the range of movements of individual upper limb parts [30], which, as the authors prove, has a significant impact on the driving torque generation by the user's musculoskeletal system, must also be taken into account.  
Figure 3: Prototype view: (**A**) isometric view from the left side, (**B**) isometric view from the right side, (**C**) assembly for the left wheel drive system—rear view, (**D**) assembly for the right wheel drive system—front view. The footrests have been removed to increase visibility of the drive system.
# A Study on the Effect of Gear Hobbing Process Parameters on the Residual Stress of the Tooth Root
## 5 Conclusions  
In this paper, the effect of hobbing process parameters (hob speed, axial feed speed, and radial cutting depth) on the residual stress of the tooth root was researched by an orthogonal test and a single-factor test. Based on the above analysis results, the following conclusions were drawn:  
1. The process parameters of hobbing have a significant influence on the residual stress of the root, and the importance is successively given to the hob speed, axial feed speed, and radial cutting depth. In each process parameter range, the best process parameter combination is low hob speed, low axial feed speed, small path cutting depth.
2. The influence of the hob speed on the residual stress of the tooth root is the greatest. According to the LASSO feature importance analysis, the respective importance values assigned to the hob speed, axial feed speed, and radial cutting depth in relation to the residual stress of the tooth root are 0.460, 0.278, and 0.262, which are consistent with the range analysis results.
3. Under the cutting conditions of the experiment, the residual stress of the tooth root was measured as residual tensile stress. According to the analysis of the single-factor experiment, with the increase in the hob speed and axial feed speed, the residual tensile stress of the tooth root shows an increasing trend. With the increase in the radial cutting depth, the residual tensile stress at the tooth root exhibits a decreasing trend.
4. If the residual compressive stress is to be obtained, the process parameters of hobbing should be optimized. It is necessary to replace the empirical method of process parameter selection with calculation algorithm.  
Conceptualization, Y.W. and H.X.; methodology, Y.W. and H.X.; software, H.X.; validation, Y.W. and H.X.; formal analysis, Y.W., H.X., H.S., G.W. and Z.W.; investigation, H.X., H.S., G.W. and Z.W.; resources, Y.W. and H.X.; data curation, Y.W. and H.X.; writing--original draft preparation, H.X.; writing--review and editing, Y.W.; visualization, Y.W. and H.X.; supervision,Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Defense Basic Scientific Research program of China, grant number JCKY2019427D002.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.