esa-sceva/satcom-chunk-collection huggingface dataset:
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
###### Abstract  
Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.  
tool wear monitoring; milling; complex part; deep learning; autoencoder; deep multi-layer perceptron Article
## 5 Performance Evaluation
### Dataset
#### 5.1.3 Dataset Preparation  
The dataset is collected from benign residential consumers. However, practical data collection processes can lead to issues such as erroneous data, missing values, and unintentional anomalies (outliers). These issues can arise from equipment failures, inaccuracies, aging components, transmission errors, and poor connections. Therefore, we first use the preparation methods outlined in [50; 51; 52] to ensure that this dataset is clean and free from these issues. Then, we use the cleaned dataset in our experiments to detect intentional cyber-attacks. Due to a lack of publicly available malicious consumption datasets, we use the continuous attack \(f_{1}\), specified in Equation (1), to generate corresponding malicious samples from the benign samples.  
For the IRISH dataset, we consider only \(\mathcal{C^{\prime}}_{irish}\) = 200 consumers from the total residential consumers (\(\mathcal{C}_{irish}\)). As a result, we have a total of 214,400 samples, divided into 2 categories: 107,200 benign samples and 107,200 malicious samples (generated using the continuous attack function \(f_{1}\)). For the PRECON dataset, we used all consumers \(\mathcal{C}_{precon}\), applying the continuous attack function \(f_{1}\) to generate corresponding malicious samples for these consumers. Thus, the data are balanced, meaning that there is an equal number of benign and malicious samples in both datasets. The IRISH dataset was utilized to evaluate both the classification performance and the robustness of our proposed detector. In contrast, the PRECON dataset was employed specifically to assess the classification performance of the detector. This is because the PRECON dataset contains a limited number of consumers, preventing the effective partitioning of the data into separate attacker and defense sides.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results
### Pupil and Glint Detection  
#### 3.2.1 Pupil Detection  
The circular ring rays location (CRRL) method [77] is utilized for pupil center detection, for the reason that it is more robust and accurate than conventional detection methods. As shown in Figure 5, in the CRRL method, improved Otsu optimal threshold binarization is utilized on a gray-scale eye image to eliminate the influence caused by illumination change. Through an opening-and-closing operation, rough location of pupil area, and circular ring rays, and pupil boundary points and center can be detected accurately when interference factors such as eyelashes, glint, and natural light reflection are located on the pupil contour. The CRRL method contributes to enhance the stability, accuracy, and real-time quality of a gaze tracking system.
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
## 3 Test Design of the Stabilizer Component  
### Test Design  
The corresponding tests of the component of SBS-modified bitumen stabilizer were designed based on the basic principle of the vulcanization reaction and the particularity of bitumen: sulfur, vulcanization accelerator, vulcanization activator, antioxidant and other active ingredients.  
For the convenience of testing, the experimental raw material samples were labeled with English letters in lower case, and the definition was as follows: a, sulfur; b, tetramethylthiuram disulfide; c, zinc oxide; d, butylated hydroxytoluene. SBS-modified bitumen was prepared by using different Component No. stabilizers (stabilizer content was 0.1% of bitumen quality), and the 48-h segregation softening point was tested and analyzed. Then, the median of each component range was calculated. The details of the experimental design are shown in Table 1.  
Figure 3: Rubber mass performance for different times of the vulcanization reaction process [25]. 1, Tensile strength; 2, tear strength; 3, resilience; 4, hardness; 5, 300% stretching strength; 6, elongation; 7, permanent deformation.  
According to the components in Table 1, firstly, Component Nos. 1-4 were tested, and the value of a (the content of S) was determined based on the test results and analysis. Secondly, Component Nos. 7-12 were tested, and the value of b (the content of TMTD) was determined based on the test results and analysis. Thirdly, Component Nos. 13-18 were tested, and the value of c (the content of ZnO) was determined based on the test results and analysis. Lastly, Component Nos. 19-20 were tested, and the value of d (the content of BHT) was determined based on the test results and analysis. a:b:c:d comprised the basic components of the stabilizer.
## 3 Results and Discussion
### Coefficient of Friction (COF)  
The variation of COF as a function of load for AlSi5 Cu1 Mg + 0 wt %, 0.3 wt %, 0.6 wt %, and 0.9 wt % La at a sliding velocity of 0.19 m/s is depicted in Figure 8; the figure shows that the COF of all alloys increased with the increase of the normal load. For AlSi5 Cu1 Mg + 0 wt %, 0.3 wt %, 0.6 wt %, 0.9 wt % La alloys, when the normal load increases from 1.3 MPa to 6.3 MPa, the COF increased by 21.4%, 13.5%, 14.7%, 14.3%, respectively. This may be because the real contact area of friction increased as the load increases. When two nominally flat surfaces are placed in contact, surface roughness causes contact to occur at discrete contact asperity. The sum of the areas of all the contact asperity constitutes the real area of contact. When the normal load increases, the height and angle of the asperity flattened because of the plastic deformation. The increase of the actual area leads to an increase in the shear resistance in the rubbing direction.  
Then, the COF AlSi5 Cu1 Mg + 0.6 wt % La alloy vs. sliding velocity test in 1.3, 3.8, 6.3 MPa normal load, are shown in Figure 9, respectively. As shown in the figure, when the normal load is consistent, the COF of the alloy decreases as the sliding velocity increases. It can be attributed to the large temperature rise of friction surface because of the raise of the sliding velocity. The high temperature of the friction surface is far more than the recovery and recrystallization temperature of the alloy. Furthermore, it is even possible to bring about dynamic recovery of recrystallization phenomenon [35], due to the fast sliding velocity of friction, then resulting in the grain of friction surface recrystallization and micro-melting. Repeated phase change and deformation, resulting in grain refinement of the top friction surface, so that the COF decreased. Moreover, the decrease in COF with increase in sliding velocity is attributed to two factors [7]: reduced adhesion due to increase oxidation encouraged by high sliding surface temperature caused by localization of the heat and reduced time available for the formation of cold junction at interface, which, in turn, reduces the force required to shear off weld joints at sliding interfaces to maintain the relative motion.  
Figure 8: The Coefficient of Friction (COF) of alloys at sliding velocity of 0.19 m/s.  
Figure 7: Wear rates of AlSi5 Cu1 Mg + 0.6 wt % La alloy variation (**a**) at the sliding velocity of 0.19 m/s versus the normal loads; (**b**) under the normal loads of 1.3, 3.8, 6.3 MPa versus the sliding velocity.
## 5 Conclusions  
Based on the obtained results, we can infer that the SPME technique exhibits a greater extraction capacity according to the outlined procedures. It is noteworthy that the SPME technique holds a distinct advantage due to its lack of sample treatment procedures, a step required in micro-QuEChERS, which necessitates a processing time of 2 to 3 h. The utilization of automated SPME extraction leads to a reduction in instrumental error associated with sample manipulation. Additionally, it allows for the potential reuse of the sample for morphological analyses and insect characterization, if necessary, as the applied heating temperature is insufficient to generate significant structural alterations in the visible morphology of the insect. This preservation not only facilitates method replication but also permits future taxonomic and ecological investigations regarding these specimens.  
The identification of pesticide residues in insect samples from lettuce crops underscores the complexity of pesticide exposure in agricultural systems. The significant presence of chlorpyrifos, along with its distribution across various concentration ranges, as revealed by the SPME method, emphasizes the ongoing need for the vigilant monitoring and management of pesticide usage to mitigate its ecological and human health repercussions.  
Conceptualization, C.V., C.M.S.-N., C.S. and R.O.A.; methodology, C.V. and R.O.A.; formal analysis, C.V.; investigation, C.V., C.M.S.-N., C.S., M.T.M.-Q., M.M. and R.O.A.; resources, C.V. and R.O.A.; data curation, C.V., M.M. and R.O.A.; writing--original draft preparation, C.V., M.T.M.-Q. and R.O.A.; writing--review and editing, C.V., C.M.S.-N., C.S., M.T.M.-Q. and R.O.A.; project administration, R.O.A.; funding acquisition, R.O.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the \"Agencia Nacional de Investigacion y Desarrollo (ANID)\", Chile, through the project \"FONDECYT Iniciacion\" n\({}^{\circ}\) 11200014; the \"Subsecretaria de Agricultura\", Chile, and through the project \"Programa de reduceton de uso y iresgo de plagucidas en la produccion comercial de mortalizas para la pequefa y mediana agricultura\", codigo INIA n\({}^{\circ}\) 502453-70.  
The data used in this research are not available for ethical reasons; any requests for data availability should be directed to the author via correspondence.  
The authors express their gratitude to [PERSON], [PERSON], [PERSON], and [PERSON] for their contributions to various field activities related to this investigation.
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 6 Conclusions  
In this paper, a wireless sensing node using satellite communication is proposed for surface wave testing of soil layers. Unlike previous work in cabled geophone, which is connected to the specific seismic instrument and limited by wires, the proposed method uses a MEMS accelerator and wireless sensing to upload the data to the server. In doing so, a low-cost portable sensing node can be achieved as a promising alternative to the expensive instrument. It can be densely and rapidly deployed throughout the measured area. Another important contribution of the study is the application of CS algorithm, an effort against data lost in wireless communication caused by environmental interaction. The recovery from lost data is achieved based on the encrypting algorithm and decrypting \(l_{1}\) optimization.  
The proposed sensing node consists of one computational core, a portable power supply, MEMS accelerometer for sensing ground motion, and a satellite-based communication device. The functionality of the proposed architecture was validated in the surface wave testing outside of the laboratory. Five of the sensors were employed to measure the ground motion, encrypt the measurements, and upload  
Figure 8: GPS information provided by the satellite-based sensing node.  
Figure 7: Recovered data of the five satellite-based sensing nodes.  
the data to a cloud-based server via satellite communication. The acceleration data were successfully decrypted with limited error in the server even when up to 16% data was lost during communication.  
The proposed computational wireless seismic sensing system is a game-changing technique for its low-cost hardware and software architecture, easy deployment, accurate measurement, and reliable communication. Future work will continue the research on both geological research and earthquake engineering applications. In the future, we will also develop this satellite-based geophone to record a seismogram as a seismometer for long-term monitoring of ground movement.  
[PERSON] and [PERSON] contributed equally to this work. [PERSON] and [PERSON] conceived and designed the hardware architecture; [PERSON] coded the algorithm; [PERSON] measured and analyzed the data; [PERSON] and [PERSON] wrote the paper.  
This work is supported by the Postdoctoral Foundation of Heilongjiang Province of China (grant no.: 17 LJ0072).  
The authors declare no conflict of interest.
## 3 Free Surface Waves in a Half-Space  
The possibility of a wave traveling along the free surface of an elastic half-space such that the displacements decay exponentially with distance from the surface was first considered by [PERSON] [29]. Additional related studies can be found, e.g., in the pages 187-194 of the textbook by [PERSON] [28]. A surface wave, also called a Rayleigh wave, is defined by an angular frequency \(\omega\) and a wavenumber \(k=\omega/c\), where \(c\) is the surface wave velocity; other values include the Lame constants \(\lambda,\mu\) and the mass density \(\rho\). For a Cartesian coordinate system \((x,y,z)\), the displacements of the incident surface waves along the half-space \(z\geq 0\) in the positive \(x\)-direction are of the forms:  
\[u_{x}=iA\big{(}d_{1}e^{-\imath yz}+d_{2}e^{-\imath yz}\big{)}e^{i(kx-\omega t )},\ \ u_{z}=A\big{(}d_{3}e^{-\imath yz}-e^{-\imath yz}\big{)}e^{i(kx-\omega t)}, \tag{1}\]  
where \(A\) is the complex amplitude; \(t\) indicates time; and \(d_{1},d_{2},d_{3},p,q\) are dimensionless quantities. For an elastic half-space with the material properties given, the well-known equation for the phase velocity of surface waves (see page 32 of Achenbach [28]) has a unique solution independent of the wavenumber. These dimensionless quantities, therefore, depend only on the material properties of the half-space. They are not presented here for the purpose of simplicity but curious readers can find their expressions in, for example, [PERSON] et al. [30]. Note that the stress components can be easily calculated from the displacements with the help of Hooke's law.
## 3 Experimental Setup
### Image Acquisition  
The thermographic image is conducted with a mobile system for free-field measurements on wind turbines in operation and consists of an infrared camera of the type imageIR 8300 from the manufacturer _InfraTec_. The actively cooled camera has a global shutter (snap-shot detector) and an InSb  
Figure 4: Sketch of the parameter used for calculating the relative length LFR\({}_{i}\) of premature turbulent flow in column \(i\) normalized to the distance of laminar flow if no premature flow transition would occur: the distance between leading edge \(y_{\text{LE},i}\) and natural flow transition \(y_{\text{tr,nat,i}}\). The laminar flow reduction (LFR) is a figure of merit for the reduction of the laminar flow regime surface area due to premature flow transitions triggered by surface defects.  
Figure 5: Experimental set-up for field measurements for the thermographic flow visualization containing an IR-camera, an optical trigger camera and the measurement unit.  
focal plane array with a format of 640 px \(\times\) 512 px with a pixel size of 15 \(\upmu\)m. The wavelength spectrum of the thermographic camera is sensitive between 2 \(\upmu\)m and 5 \(\upmu\)m, and the noise equivalent temperature difference (NETD) is less than 25 mK at 30 \({}^{\circ}\)C. The dynamic range is 14 bit and the integration time is set to 16.000 \(\upmu\)s.  
In order to maximize the spatial resolution of the thermographic images for the typical measurement distance between 100 m and 300 m a 200 mm teleclens optic is used. The lens has an angular aperture of 2.7\({}^{\circ}\)\(\times\) 2.2\({}^{\circ}\) and with the given detector size the instantaneous field of view (IFOV) results in 0.08 mrad. For the typical measurement distance this results in a spatial resolution between 8.8-20 mm. The field of view along the longer axis with 640 px is between 5 and 14 m.  
Due to the length of the rotor blade being more than twice the length of the maximum field of view of one image, the blade surface has to be captured in segments. Therefore, the IR-camera is triggered by an external optical system that signals the infrared camera the moment in which the rotor blade has a horizontal position parallel to the ground, resulting in an image acquisition of each rotor segment at the time of the same rotor blade orientation.
## 5 Discussion  
Since the responsiveness of the PD used in our system is small (0.8 A/\(W\)), the conversion efficiency is relatively low, and we did not take the microwave upconverter and electrical filter (MUC + EF) into consideration. Thus, the whole conversion loss should be larger than 35 dB. There are several solutions to further improve the conversion efficiency. One is to use an active microwave upconverter [32] which could provide additional conversion gain (\(\sim\)3 dB), the other is to use electrical amplification after the PD, but the increased noise power and appearance of spurious signals will be another problem.  
Bandwidth is another factor that must be considered when the proposed scheme is used in radar systems. In addition to the space field, the bandwidth of the L-Ku band is sufficient for most radars, for example, warning radar [33], and civil aviation radar [34; 35]. On the other hand, in this paper, we sacrifice nearly half of the bandwidth in order to obtain high in-band spurious rejection (0.8-18 GHz). The main limiting factors of bandwidth come from MZM and PD, and today's modulators [36] and detectors [37] can achieve a bandwidth of over 100 GHz. Therefore, for the application in the space field, we just replace the MZM and PD in this experiment with the modulator and detector of higher bandwidth.  
In practice, the DAC/ADC may be located in an office away from the radar. The effects of fiber dispersion should be considered when the fiber has sufficient length [38]. When we consider analog signal transmission, fiber dispersion creates a time delay \(\Delta\tau\) between adjacent signals. That will increase phase difference \(\Delta\varphi=2\pi f\Delta\tau\), and then cause the amplitude of the synthetic wave to drop. The higher the modulation frequency \(f\), the larger the phase shift and the loss. Therefore, this limits the transmission bandwidth. To solve the problem introduced by dispersion, dispersion compensation techniques, such as Bragg grating dispersion compensation [39], chirp compensation [40], and dispersion compensation fiber (DCF) [41], are necessary in the application.
## 5 Experiments
### Experimental Settings
#### 5.1.4 Parameter Settings  
In our experiment, the objective timestep \(Q\) is set to 12, i.e., estimating wind speed in the next 12 h. We set the historical timestep \(P\) to 48, i.e., wind speed in the last 48 h. The length of historical timesteps is selected based on the reference [20]. We consider [PERSON] as an optimizer, aiming to minimize the MSE loss over 64 epochs on the DSWE 1 and NREL datasets. The batch size is 32. Additionally, \(D\) is equal to 32. STGPT2 UGAN has some hyperparameters containing the spatial attention layer \(L_{SA}\), the temporal attention layers \(L_{TA}\), the number of attention heads \(N_{z}\), the dimensions of each head \(d=D/N_{z}\), the number of STA modules \(B\), and the learning rate \(L_{\tau}\). After repeated attempts, we find that the highest prediction precision appears when the hyperparameters \(L_{SA}\), \(L_{TA}\), \(N_{z}\), \(d\), \(B\), and \(L_{\tau}\) are set to 1, 1, 4, 8, 2, and \(1\times 10^{-3}\), respectively.  
It is worth noting that both STGPT2 UGAN and advanced benchmarks are implemented in Python 3.7. In detail, the two-layer LSTM is reproduced by the Keras library. We leverage the Pytorch library to construct our proposed model and other deep learning benchmarks. We reproduce SVR and ARIMA utilizing the sklearn package and statsmodels package, respectively.
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 4 Results
### Preliminary Intercomparisons
#### 4.3.2 VAD and Dual Setup  
For this comparison, we merge the dual-setup reconstructed wind speed data with a 100% actual availability in the chosen range gate with the filtered data described in the previous section, which results in 2886 10 min means. We choose Bura because it is the VAD lidar furthest inland (pos. 5) and so the dual-setup reconstructed wind speed is more accurate due to the angle between Koshava, Sterenn, and the scanned point. However, Bura is not at the northward position of the lines scanned by the dual setup and so we are comparing two rather different flows.  
Figure 7: Actual availability of reconstructed wind speed components as a function of eastward position. The numbers 1, 2, and 3 refer to the vertical level of measurements of the PPI and dual setups. The dashed line indicates position 2 and the solid line is the height of the terrain (not to scale).  
Figure 9 shows the scatter plot for a height of 100 m ASL. Slightly more scatter can be seen compared to those in Figure 8 (6% bias and \(R^{2}=0.98\)). This is also partly due to the effect of having only four samples in the dual setup, whereas Bura samples every 4 s within the 10 min intervals.