esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Recognizing the Wear State of Milling Cutters under Variable Conditions Based on Optimized SDP
### A Residual Network with an Integrated Attention Mechanism
The Process of Recognizing the Wear State of Milling Cutters under Variable Conditions Based on Optimized SDP  
In actual milling operations, working conditions vary widely, and a large amount of data from these conditions lacks labels, with different distributions between conditions. Utilizing transfer learning to find shared parameter information between source and target domains can reduce the model's reliance on extensive target domain data and enhance the model's generalization ability, thereby achieving the recognition of milling cutter wear states under variable conditions. The specific implementation steps are as follows. 1. Collect vibration signals of milling cutters in the initial wear, normal wear, and severe wear stages under different working conditions, and measure the corresponding milling cutter wear amount;  
Figure 8: Diagram of CBAM. Use cluster analysis to extract template images for each wear state, with the maximization of differences between template images of each wear state as an indicator, and employ DE to search for the optimal parameter combination [_l_, _g_] for DDP transformation of milling vibration signals;
3. Use the optimal parameter combination [_l_, _g_] to perform DDP transformation of milling vibration signals, merging vibration signals from three-axis directions into visualized DDP images;
4. Utilize the ResNet-50 model pre-trained on the ImageNet dataset, introduce CBAM, and construct the CBAM-ResNet-50 model;
5. Replace the final layer in the CBAM-ResNet-50 model, perform representational learning on DDP images of complete data under source conditions, and fine-tune to form a generalized milling cutter wear state recognition model;
6. Classify DDP images of vibration signals under target conditions to identify the wear state of milling cutters in variable condition scenarios. The structure diagram for recognizing the wear state of milling cutters under variable conditions based on optimized SDP is shown in Figure 9.
## 3 Results
### Meteorology  
In this study, the MOD_GMAO GPP algorithm followed the standard MOD17A2H GPP algorithm, which used the GMAO meteorology reanalysis data. The GMAO meteorology reanalysis data were replaced with the site meteorological data for the MOD_Tower GPP algorithm. The MOD_GMAO GPP and MOD_Tower GPP algorithms were thus compared to determine the influences of the meteorological data on the GPP estimates. The results showed that the MOD_Tower GPP algorithm did not estimate eight-day GPP more reliably than the MOD_GMAO GPP algorithm (Figure 1a,b; Table 2). Conversely, estimates of annual GPP from the MOD_Tower GPP algorithm were inferior to thoseof the MOD_GMAO algorithm (Figure 3a,b; Table 3). We directly compared the daily GMAO meteorology reanalysis data and the site meteorological data (Figure 4). The two datasets were highly significantly correlated for T\({}_{avg}\) (R\({}^{2}\) = 0.97) and T\({}_{min}\) (R\({}^{2}\) = 0.94) (Figure 4a,b), which implied that the GMAO meteorology reanalysis data could be reliably used to estimate the on-site temperature conditions. Additionally, VPD\({}_{avg}\) was also statistically significantly correlated (R\({}^{2}\) = 0.88) (Figure 4c), which indicated that the on-site moisture variations could also be dependably estimated from the GMAO meteorology reanalysis datasets. With respect to PAR, the GMAO meteorology reanalysis data generally overestimated the site meteorological data (Figure 4d), and the correlation (R\({}^{2}\) = 0.85) was stronger than that of VPD\({}_{avg}\), but weaker than those of T\({}_{avg}\) and T\({}_{min}\)
## 2 Materials and Methods
### Quality Control Strategy  
Quality control is critical in the AMVs algorithm. The pixels at the edge of image may move out of the area or the OF tracked by some pixels may be different from the overall tendency of the surroundings due to the limitations of the OF method. These situations need to be considered in the quality control process, which should follow the objective principles. The commonly used AMVs quality identifications are related to clouds [11; 34], while our research is not restricted by clouds. Therefore, the following quality control schemes are proposed (Table 3). The wind vector that exceeds the simulated area is identified as 1. The wind vector of pixels with abnormal water vapor is identified as 2. The acceleration of u or v component exceeding 10 m/s\({}^{2}\) indicates that the wind vector is inconsistent with the flow tendency, which belongs to the abnormal situation of OF identification, so it is identified as 3 or 4, respectively. If the two conditions are met at the same time, it is identified as 5. For the wind vector whose wind speed is too low (the threshold here is set to 0.5 m/s), we identify it as 6. Good quality wind vectors are identified as 0.  
Figure 4: Schematic diagram of image pyramid method with an original size at the bottom and the resizing rate of 0.5.
# Water Impermeability of Self-Compacting Fly-Ash-Containing Concrete
## 3 Results and Discussion
The same strength was achieved with self-compacting fly-ash-containing concrete at a cement consumption of 430 kg and a fly ash consumption of 150 kg/m\({}^{3}\) with a specific surface area of 290 m\({}^{2}\)/kg. Thus, the cement savings amounted to 135 kg per 1 m\({}^{3}\) of self-compacting concrete. The experimental and calculated data obtained indicate that it is possible to reduce cement consumption by 20-23% by adding the optimal amount of fly ash to the compositions of self-compacting concrete while ensuring the specified strength. Grinding fly ash to the optimum specific surface area allows additional cement savings of 8-10%. Increasing the dispersion of fly ash positively affects water separation (Figure 1). For example, at a consumption of 250 kg of fly ash with a specific surface area of 290 m\({}^{2}\)/kg, the water separation rate is 0.45 g/L. With an increase in the specific surface area to 390 m\({}^{2}\)/kg, it decreases to 0.15 g/L. The sensitivity of water separation to increased fly ash consumption at its higher specific surface makes it possible to use fly ash at a high water-cement ratio at a rational dosage of 150-200 kg/m\({}^{3}\) as a water-retaining additive after its additional grinding. Based on the obtained experimental data, a linear relationship between the compressive strength and water impermeability of self-compacting fly-ash-containing concrete was obtained (Figure 7). It is described by the following equation:  
\[W=0.0155\cdot f_{c}+0.1103 \tag{9}\]  
Figure 7: Dependence of water impermeability on compressive strength of self-compacting fly-ash-containing concrete. The correlation analysis made it possible to establish a correlation coefficient between the strength and water resistance of self-compacting fly-ash-containing concrete, which is 0.969. The obtained mathematical models make it possible to determine the nature of the relationship between the studied properties and the possibility of predicting the water impermeability of self-compacting fly-ash-containing concrete based on correlation dependencies. To verify the relevance of the experimental and calculated values of water impermeability, a special experiment was performed, the results of which are shown in Table 6. The previously specified materials were used in the experiments. The results in Table 6 show that the accuracy of calculating the water impermeability values using Equation (9) is sufficiently good.
## 3 Omnidirectional Robotic Platform. Hardware Design
### Device Selection  
In the final version, the robot will be equipped with the following sensors:  
* Two 2D LIDARs for object detection. They will be used to prevent the robot from crashing with obstacles, with the walls, and with the accelerator. The model UST-20 LX, by Hokuyo, is used.
* Cameras for teleoperation and optionally for visual odometry. The model UI-3080 CP Rev. 2, by IDS, is used.
* A point cloud generator sensor for the self-localization. It can be a 3D LIDAR or an RGBd camera (only for small environments). The model HDL-32E of Velodyne has been used for localization and environment reconstruction tests with good results. However, it is recommendable to use another sensor like Puck Lite or Ultra Puck from the same manufacturer, since the size of this sensor is too big for our spatial constraints, the vertical field of view is focused down (problematic for low and small robots like ours) and it has moving parts (recommendable to avoid them). In this paper, we include the 3D LIDAR Puck Lite, by Velodyne.
* An inertial measurement unit, IMU, for the localization system. The model VMU931 is used.
* A radiation sensor.
* An ultrasonic sensor, SONAR, to determine the distance between the radiation sensor and the radiation source. The model MB7040 I2 CXL-MaxSonar-WR Ultra compact Housing, by MaxBotix is used.
* A WiFi antenna for communication.
* A robotic arm to bring the radiation sensor in the particular application of the radiation survey, and to bring a specific tool in other cases. The Jaco by Kinova is used.  
Furthermore, the onboard computer is the NUC i5-8259U, 8 GB, DDR4-SDRAM, 256 GB SSD Mini, by Intel, which is the computer that supports all the logic behavior. The devices are connected to the NUC as shown in Figure 4. We have decided to connect the cameras through Ethernet, whereas the robotic arm through USB drivers. However, both the cameras and the arm may use Ethernet or USB. With these devices, the system fulfills the needs to comply with the last requirement of Section 1.  
Figure 4: Electric diagram where all the devices are connected to the NUC for its processing. Blue wires represent power cables, yellow ones represent USB cables, where both termination types are indicated, green ones indicate Ethernet alternatives, and the purple one is a specific cable from Velodyne that communicates the LIDAR with the interface box throughout a given protocol.
## 4 Conclusions  
In this work, we compared the use of ZnO:Al films instead of the usual ITO as the front TCO in rear emitter SHJ solar cells. The results of ZnO:Al and ITO layers grown on glass with a variation in the oxygen content during the sputtering deposition demonstrate that for optimized layers with the same sheet resistance, the light absorption is very similar. As a result, the conversion efficiency of the best ZnO:Al-based solar cells of 22.6% is only slightly below that of the best ITO-based solar cells of 22.8%. Optically, the ZnO:Al front TCO even outperforms the ITO, as demonstrated by \(\approx\) 0.3 mA/cm\({}^{2}\) higher j\({}_{\rm sc}\)s. However, it was found that the higher series resistance originating from a higher contact resistance between ZnO:Al and the screen-printed silver grid leads to a slightly lower efficiency of the ZnO:Al-based solar cells due to an increased R\({}_{\rm s}\) and thereby lowered fill factor. These results demonstrate that ZnO:Al layers can be used in rear emitter SHJ solar cells with only a very small efficiency loss, confirming published simulation studies [5; 6]. An indium-free monofacial cell achieved \(\eta\) = 22.5% showing that the replacement of ITO/Ag by ZnO:Al/Ag as a back contact produces cells with practically the same efficiency.  
[PERSON] is the main author, and he carried out the experiments, analyzed the data, planned and wrote the original draft. [PERSON] helped with the preparation of the TCOs, their characterization and the analysis of the results. [PERSON]-V. processed the solar cells and contributed to their characterization. [PERSON] conceptualized the study. [PERSON] and [PERSON] supervised the study, advised on the use of methods and data analysis, acquired funding and were responsible for project administration. All authors participated in the discussion of the results, reviewed the manuscript, and provided input for editing.  
This research was funded by the German Ministry of Economic Affairs and Energy (BMWi) in the framework of the HERA project (reference #03258251).  
Figure 6: External quantum efficiency (EQE) and 1-R\({}_{\rm tot}\) vs. wavelength of the solar cells with the highest power conversion efficiency of three groups. Monofacial and bifacial ZnO:Al solar cells (red and green lines, respectively) compared to a bifacial ITO reference solar cell (black line). For the 1-R\({}_{\rm tot}\) spectra, only one of the ZnO:Al curves is shown since there is no difference between the monofacial and the bifacial cell on the UV-VIS reflection measurement of the front side.  
**Acknowledgments:** The authors would like to thank their colleagues of HZB for their help with the realization of this work: [PERSON] and [PERSON] for the sputtering depositions and their useful advice. [PERSON] for help with the analysis of the layers on glass and experiment planning. [PERSON] and [PERSON] for screen printing. [PERSON] and [PERSON] for their help with the EQE measurements. [PERSON] and [PERSON] for the discussions about the results. [PERSON] and [PERSON] for proofreading the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Characterization of Nanoluminophore  
X-ray pattern diffraction (XRD) was performed on a BRUKER D8 ADVANCE diffractometer (Billerica, MA, USA). Calculation of the unit cell parameters was performed using POWDER 2.0 software (Moscow State University, Moscow, Russia). Particle shape and size were established from a scanning electron microscopy (SEM) image. For this aim, a Carl Zeiss NVision 40 microscope (Zeiss AG, Oberkochen, Germany) connected with Oxford Instruments XMAX (80 mm\({}^{2}\)) set-up (Oxford Instruments plc, Abingdon, UK) (using ImageJ software) was used. Hydrodynamic diameter distribution of nanoparticles was determined using Zetasizer Ultra (Malvern Panalytical, Malvern, UK). Registration of particle luminescence was completed using fiber-optic spectrometer USB2000 (OceanOptics, Orlando, FL, USA). The sample was placed inside the integrating sphere Cintra 4040 (GBC Scientific, Braeside, Victoria, Australia) and irradiated by a 50 mW IR light diode (\(\lambda\) = 975 nm).
## 4 Conclusions  
This paper introduced a large-scale Korean spontaneous speech corpus for speech recognition. We call this KsponSpeech, which contains 969 h of 622,545 utterances spoken by 2000 Korean native speakers. We present the baseline performance of an end-to-end model trained with KsponSpeech. Here, we proposed a new evaluation metric, space-normalized word error rate, handling the Korean word-spacing variation. To validate the effectiveness of our corpus, we investigated the performance of standard end-to-end models, the effectiveness of the data augmentation technique, and the number of sub-word units suitable for Korean. As a result, we confirmed that the syllable-based Transformer model trained using the data augmentation technique showed the best performance and presented it as the baseline performance. We also explored approaches to generating clean transcription from disfluent speech. We confirmed that an end-to-end model trained with KsponSpeech can be used to generate the clean transcriptions.  
We are releasing the KsponSpeech corpus on the AIHub open data hub site. This corpus can contribute to building a high-quality end-to-end ASR model for Korean spontaneous speech, which can be applied to various AI fields, such as AI assistants, dialog robots, and AI tutors. We expect that KsponSpeech will be widely used as a benchmark corpus for Korean speech recognition.  
**Author Contributions**: Data curation, S.Y., J.-U.B., Y.-J.L., and M.-Y.C.; software, J.-U.B., M.-K.L., and S.-H.K. ([PERSON]); validation, S.-H.K. ([PERSON]), Y.-J.K., and S.Y.; investigation, J.-U.B. and D.-H.K.; resources, S.Y.; writing--original draft preparation, J.-U.B.; writing--review and editing, J.P., Y.-J.L., and S.Y.; project administration, S.-H.K ([PERSON]); all of the authors participated in the project. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korea government (20 ZS1140 Development of Core Conversational AI Technologies).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 3 AM Process Control Architecture  
Most powder-based AM fabrication processes use two monitoring cameras. The first continuously monitors the chosen characteristics of the melt-pool. The second produces global images of the build surface before and after exposure of each layer. Pyrometers are used frequently to measure the temperature of the melt-pool. Actual laser-power levels and scan-speed rates can also be collected during the fabrication process. These two parameters directly affect the quality of the final product.  
To utilize the capabilities of the monitoring systems in Appendix A, we propose a multi-loop, time-based approach to AM-process control. Our proposed control architecture is shown in Figure 1. It has two internal temporal loops: a real-time loop and a near-real-time loop. The major input to this multi-loop controller is a build plan, which involves process-specific and machine-specific tasks. Process-specific tasks include choosing the required support lattices and build orientation. Machine-specific tasks include converting the build plan into the formats required by (1) the controller of the AM machine that will build the product and (2) the monitoring systems that come with that machine. Today, that conversion is usually done by machine-specific, software tools.  
The real-time loop uses individual samples of in situ, melt-pool data to monitor and control the process parameters. Changes in those parameters will be needed if the analysis of that sample data indicates an unstable process. The \"sample-based\" feedback data includes melt-pool temperatures and sizes, sampled at up to 20 kHz. The sample-based, process-control parameters include energy power and material-feed rates. As of today, only a few AM machines have the capabilities to execute this real-time, control loop. The near-real-time, layer-wise loop determines the scan path and process parameters for the next layer based on the entire collection of sample-based monitoring data.  
Process monitoring includes functions to curate, fuse, and communicate in situ sensor/measurement data for both control loops. These functions are computationally rigorous outside the competence of the traditional, edge-computing nodes, such as PLCs or embedded controllers. Today, physics-based predictive models are rarely used in AM. Therefore, the predictive models used for both inner- and outer-loop control are previously trained, offline, data-driven AI/ML models. The major output of the loop-based monitoring functions, therefore, are the information models needed as inputs to those AI/ML models [12; 13; 14; 15; 16; 17].  
In the following sections, we provide more details about the functions inside each control loop and the some of the offline functions needed to provide the models used in those functions.
## 1 Introduction
[16] built a tool that allowed the user to analyze and define the possible existing correlation surfaces, units, and faults based on an interactive 3D analysis environment. [PERSON] et al. [17] built 3D geological modeling with commercial software to study boring log data. However, [PERSON] and [PERSON] [18] pointed out that the current geo-databases do not provide sufficient 3D data modeling or analyzing techniques. As a result, more efforts are needed for the development of a3D geological system. The rapid update of smartphones has led to the development of geological surveys using mobile devices. This provides new methods for engineers to conduct geological surveys in the field. The famous GIS tools, e.g., Google Maps and ArcGIS, have basic functions such as displaying maps and drawing polylines on maps and are helpful for field work to a certain extent. However, they are not specifically used for geological surveys and lack professional functions, such as recording geological information in a specified format, conducting spatial analysis of geological data, and transmitting data to a geological database. An android application named GeoTool [19] was developed to measure the directions, strike, and dip of bedding planes. An IOS application named Smart Compass-Clinometer [20] was also developed to analyze geological stability. [PERSON] [21] developed a tablet application named SedMob for creating sedimentary logs. The accuracy of the compasses on different platforms (IOS and Android) is studied [22]. It is convenient to save and extract logging information and easy to perform data synchronization in the remote server. However, all these tools or systems are mainly focused on recording data and can only provide two-dimensional perspectives; thus, engineers cannot develop a stereoscopic understanding of terrains and surrounding environments. Additionally, they lack spatial analysis functions and cannot help engineers figure out geological conditions effectively. In addition, the data fields present in them are not aimed at hydraulic engineering, so they are not suitable for the geological survey of hydraulic engineering. Many geological objects need to be recorded and analyzed, such as lake water quality [23], boreholes [24], groundwater and surface water [25], and earthquakes [26] ([PERSON] et al. 2016). Different modeling, recording, and analyzing methods are adopted for different geological objects, which can be described better by combining multiple methods [27; 28]. In this research, we developed an integrated tool for the quick recording and analysis of geological objects. A quick evaluation of the geological condition can be performed using multiple methods, including geological object recording, interpretation of the 2D images, and 3D analysis of the geological condition. Moreover, in the 3D model, the accumulation of and the information extracted from images can be loaded into 3D modeling software for further analysis, such as GOCAD [29]. The tool in this research has a practical value to geological surveys.