esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
###### Abstract  
China's first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites' data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites' single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China's first dynamic environment satellite constellation was comparable to that of the six satellites.  
dynamic environment satellite constellation; validation; SLA; SWH; SSWS; HY-2
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 2 Related Work
### Vision Based Crack Detection Methods
proposed a model called Res-Unet by incorporating residual connections to the original U-net for detecting cracks in the concrete bridge structures [38]. [PERSON] et al. integrated a switch module named SWM with the U-net architecture to boost the running speed and reduce the computational complexity of the U-net. The SWM model allows the pixel classification result obtained by the VGG13 encoder to be passed into the decode module if there is a crack; otherwise, it just discards the result to save the computation time [39]. [PERSON] et al. considered the problem of detecting thin cracks with adverse environmental conditions in concrete structures. For this, the authors modified the U-net architecture by adding a Pyramid Pooling Module (PPM) into it, and the model successfully predicted the thin cracks [40]. [PERSON] et al. proposed a U-net model with an attention mechanism to detect cracks on concrete structures. The authors utilized the attention gate module in three different fashions (i.e., Attention U-net, Advanced Attention U-net, and Full Attention U-net). They remarked that the full attention strategy was the best for detecting cracks with less computation complexity [41]. [PERSON] et al. improved the U-net model by adding residual blocks, an atrous spatial pyramid pooling module, and an attention gate module for detecting concrete cracks. The authors tested their model with a few datasets and showed by an ablation study that the improved model outperformed U-net with no notable computational complexity [42]. With the period, researchers have developed other encoder-decoder-based architectures as well, rather than improving the U-net. For example, [PERSON] et al. proposed a model named HiSegNet to improve the inference speed of crack segmentation while preserving the crack details [43]. [PERSON] et al. tackled the challenge of high computation complexity during the training of a crack segmentation network in their work [44]. The authors developed their model based on a student-teacher framework. They utilized channel-wise distillation knowledge to make the model lightweight. [PERSON] et al. developed a model named PAF-Net by incorporating a feature fusion technique to mitigate semantic gap issues during crack segmentation in concrete structures [45]. [PERSON] et al. proposed a segmentation model named RCDNet to detect cracks on pavement structures in real-time [46]. The authors incorporated attention modules to increase the accuracy of the model without any computational head.
# Innovative Hydrogeophysical Approaches as Aids to Assess Hungarian Groundwater Bodies
## 3 Results
### Conventional and Nonconventional Geoelectric Arrays
#### 3.1.3 Study of a Fault Zone and Fracture Zones  
The previously presented array set was also tested in a sedimentary environment consisting of sand, sandy clays, clay and gravel in India to see whether they are also applicable in studying groundwater bodies in other climatic conditions. Since the measurements were carried out some days after a rain, fractures were expected to be still water-saturated, thus electrically conductive.  
The fault is clearly seen at about 5 m in the Dp section in Figure 9a, but its accurate localization is difficult. To decrease the doubtfulness, measurements were carried out also by the \(\gamma_{116}\) configuration (Figure 9b). Since the \(\gamma_{116}\) array is very close to the null arrays, it is almost insensitive to vertical variations of the electrical resistivity but very sensitive to its horizontal variations [28]. Thus, it does not present as clearly the rock blocks themselves at both sides of the fault, but it displays well the boundaries of the fault fracture zone. They are at the centers of the conductive anomalies, at 4.5 m and 6 m. These anomalies occur, namely due to the water-saturated large fractures, which delineate the fault zone from both sides.  
The fractures seemed to reach very close to the surface because even the shallowest \(\gamma_{116}\) profile of apparent resistivity (Figure 9c) displayed very well the fracture zone. The two large positive-negative anomalies, which are due to large fractures, delineate the fracture zone from both sides (at 4.6 and 6.1 m, at their inflexion points). Two other large anomalies appeared at 1.2 m and 8.6 m. Both of them were also indicated in the \(\gamma_{116}\) ERT section as conductive zones (Figure 9b). The other significant fracture anomalies are all inside the fracture zone, demonstrating how highly fractured it is. In summary, while the Dp array gave a very good overview of the study site, the \(\gamma_{116}\) ERT section delineated the fracture zone well, but the shortest \(\gamma_{116}\) array delineated it the best. In addition to the good delineation of the fault fracture zone, the set of methods Dp ERT, \(\gamma_{116}\) ERT and the shallowest \(\gamma_{116}\) profile also enabled the detection of two other significant fractures.  
Figure 9: Fault zone characterization: (**a**) Dp electrical resistivity tomography (ERT) section; (**b**) \(\gamma_{116}\) ERT section; (**c**) the near-surface apparent resistivity profile measured by the \(\gamma_{116}\) array, green rectangle delineates the fracture zone according to the \(\gamma_{116}\) profile, red lines mark two other main fractures (Section 3.1.3).
## 2 Proposed Multimodal Reconfigurable Sensor Readout Circuit
### Reconfigurable Structure
Assuming the capacitive sensor in Figure 3 has a parasitic capacitance, the output voltage is expressed using the following equation:  
\[\text{V}_{\text{oc1}}=\text{V}_{\text{DD}}\times\frac{\left(2\Delta\text{C}+ \text{C}_{\text{P1}}-\text{C}_{\text{P2}}\right)}{\text{C}_{1}} \tag{4}\]  
In the above equation, if C\({}_{\text{P1}}\) and C\({}_{\text{P2}}\) are ideally the same such that there is no effect on the output voltage. However, if C\({}_{\text{P1}}\) and C\({}_{\text{P2}}\) are different, and the difference of the value is large, V\({}_{\text{oc1}}\) has an offset regardless of the input capacitance. To attenuate this difference, the 8-bit capacitive digital-to-analog converter (DAC) is applied. The C\({}_{\text{dac1}}\) and C\({}_{\text{dac2}}\), the capacitance of 8-bit capacitive DAC, can be adjusted from 0.1 pF to 12 pF to compensate for the difference in parasitic capacitances C\({}_{\text{P1}}\) and C\({}_{\text{P2}}\). The CDS configuration of the PGA in the second stage is shown in Figure 4. The offset can be reduced by the CDS configuration of the second stage. If the amplifier operates in a capacitive-sensing mode, the offset can be reduced twice through two CDS configurations. The CDS operation of the PGA is driven by non-overlapping clocks, P\({}_{1}\) and P\({}_{2}\). In the P\({}_{2}\) clock, the input and offset are stored in C\({}_{2}\), and in the P\({}_{1}\) clock, the offset stored in C\({}_{2}\) is removed, such that a low offset can be achieved [18]. The input voltage of the PGA is amplified by the ratio of C\({}_{2}\) to C\({}_{3}\). The programmable capacitor C\({}_{3}\) enables adjustment of the gain. The gain is expressed using the following equation:  
\[\text{V}_{\text{oPGA}}=\frac{\text{C}_{2}}{\text{C}_{3}}\times\text{V}_{ \text{ox1}} \tag{5}\]  
Consequently, the output voltages of the readout circuit according to the sensing mode (voltage/resistive-sensing mode, current-sensing mode, and capacitive-sensing mode) are respectively defined by the following equations:  
\[\text{V}_{\text{ovPGA}}=\frac{\text{C}_{2}}{\text{C}_{3}}\times\left(1+\frac{2 \text{R}_{1}}{\text{R}_{\text{mid}}}\right)\times\text{V}_{\text{idv}} \tag{6}\]\[\mathrm{V_{oilPGA}}=\frac{\mathrm{C_{2}}}{\mathrm{C_{3}}}\times\mathrm{R_{1}} \times\mathrm{I_{in}} \tag{7}\]  
Figure 1:0  
Figure 1:1 CDS configuration of the programmable gain amplifier (PGA) in the second stage. Figure 1:2 Configuration of the capacitive-sensing mode. CDS: correlated double sampling, DAC: digital-to-analog converter.
# A Large-Scale Model of Lateral Pressure on a Buried Pipeline in Medium Dense Sand
## 4 Conclusions  
This research simulated the lateral earth pressure on an HDPE buried pipe induced by a landslide. It also investigated the stresses along the buried pipe and observed the surface cracks under the lateral soil displacement effect. The results from the experiment indicated that the highest earth pressures occurred at the unstable soil boundaries upon reaching the soil strength at failure. The section of the buried pipeline located in the landslide area experienced the greatest stress [30]. Generally, lateral earth pressure rose steadily along the buried pipe as the ground was being mobilized. It was discovered that there was a strong relationship between lateral pressures on the buried pipe and soil surface deformation. Some of the findings provided from this research can be generally applicable for cases related to HDPE water mains backfilled with sands. Understanding the active and passive pressures surrounding the pipeline during landslides is important for the subsequent assessment of pipeline deformation and risk models. The authors intend to extend this research with a fully instrumented pipeline of strain sensors, in particular, using distributed fiber-optic sensing for a better understanding of the soil-pipe interaction model. Factors such as different soil types, water presence, and different pipe properties shall be considered in future experiments of landslide -induced pipe bending.  
Conceptualization, H.M.; methodology, H.A.; validation, H.M.; formal analysis, H.A.; investigation, H.A.; resources, H.M.; data curation, H.A.; writing--original draft preparation,H.A. and N.F.N.; writing--review and editing, A.D.R. and B.W.; visualization, M.Y.; supervision, H.M.; project administration, H.M.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by YUTP Fundamental Research Grant Scheme, grant number 015 LC0-034.  
The authors are grateful to Universiti Teknologi PETRONAS (UTP) for the financial support given through the graduate assistantship (GA) scheme.  
The authors declare no conflict of interest.  
Figure 8: Surface displacement of the soil in increasing 50 mm stages by the movement of a plate. Soil cracks are observed at different stages and the pipe bends.
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 5 Simulation Results  
Simulation results show the error performance and the complexity. As simulation parameters, the size of FFT was 128 and the size of CP was 32. Also, transmit symbols go through a Rayleigh fading channel with seven multi-paths and channels are modelled through the exponential power delay profile as follows,  
\[P(u)=\frac{1}{P_{sum}}e^{-\frac{u}{P_{sum}}}(u=1,\cdots,U), \tag{15}\]  
where \(P(u)\) is the element of exponential power delay profile, \(P_{sum}\) is the total exponential power delay profile, \(u\) is the index of multi-paths and \(U\) is the number of multi-paths. The simulation is conducted under the assumption that the MIMO channels are independent and identically distributed random variables. Also, the convolutional coding was used for the simulation to correct the error bits and to improve the BER performance. It consisted of the \(2\times 2\) and \(4\times 4\) MIMO-OFDM system with 16-quadrature amplitude modulation (QAM) and 64-QAM.
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 4 Case Study and Analysis  
The initial model selected to be applied to the system is YOLOv5s (hereafter, defined as 'basic'). Hyperparameters set up for model learning are shown in Table 2. Epoch, Batch size, Image size, and Learning rate are set to 300, 16, 640 \(\times\) 640, and 0.01. The hyperparameter value is not changed to identify the tendency of the technique used in this study, and model training is conducted by applying the basic value. Afterwards, performance analysis according to changes in hyperparameters is conducted for further model optimization. The model learning environment is presented in Table 3.  
The initial model of the system refers to a model without any technique applied. Its mAP and inference time are approximately 95.3 and 1.6 ms, respectively. We set the initial model as a performance comparison criterion for future performance-enhanced models. We aimed to improve the performance using three performance enhancement techniques (structure modification, model scaling, and lightweight design) to find the optimized model for the system. In Section 3, learning conditions and comparative analysis of results obtained by applying three techniques are presented. Conditions for each technique are set up directly, and the reason why there are various conditions is to analyze the tendency of the technique. The hyperparameters and hardware are equally used in the learning in each case.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Hyperparameters**} \\ \hline Epoch & 300 \\ \hline Batch size & 16 \\ \hline Image size & 640 \(\times\) 640 \\ \hline Learning rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model learning environment (hyperparameters).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Hardware**} \\ \hline Graphic & GeForce RTX 3090 \\ \hline CPU & i9-10900X \\ \hline Memory & 64 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model learning environment (hardware).  
To analyze the trend of model performance that is revealed through the change in pruning value and find the suitable model, 4 cases of pruning values--10%, 30%, 50%, and 70%--were analyzed.  
Figure 16 shows the graph summarizing the results of the four cases. When the pruning values were 10%, 30%, 50%, and 70%, mAP values were approximately 95%, 93.5%, 90.2%, and 0.0601%, and the inference times were 1.6, 1.6, 1.7, and 1.6 ms, respectively.  
In conclusion, the performance tended to decrease as the lightweight value increased. Thus, the best performance was exhibited when the lightweight technique was set to 10%, which is the most suitable model for the system.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 2 Fault Detection and Isolation Scheme  
The proposed fault detection scheme is summarized in Figure 1, which consists of two phases: (1) offline training; and (2) online fault detection. The two phases and techniques applied in these two phases are explained in detail in the following sections.  
In general, the fault detection scheme is based on the comparison and analysis of the reconstruction error between the actual monitoring data and its reconstructed output obtained from the trained normal reference model. Changes in the reconstruction error could indicate the occurrence of faults. The reconstruction errors of monitored variables are shown on a two-dimensional contribution map, which stacks multiple observations (time point) into one image to clearly illustrate the contribution of the variables over the entire faulty data times series. On the contribution map, a high reconstruction error will be identified as anomalies or faults.
## 2 Materials and Method
### Uptake Experiment
A relative pressure sensor (GC30, Nagano Keiki Co., Tokyo, Japan) and a temperature and relative humidity sensor (TA502 RW, Toplas Engineering Co. Ltd., Tokyo, Japan) were set at the outlet of the vessel to monitor the pressure and temperature of the standard gas outflow. The analog signal output from these sensors were logged via a PC data logger (NR500 and NRTH08, KEYENCE, Osaka, Japan). The standard GEM gas was generated using a permeation device (PD-1B-2, GASTEC, Ayase, Japan) with liquid mercury enclosed by a permeation tube (VICI AG International, Schenkon, Switzerland). The temperature of the device was set to 323 K all the time, and the flow of mercury-free air into the permeating chamber was set to 0.82 L min\({}^{-1}\). No split flow was set; therefore, the inflow into the permeation device corresponded to the outflow from the device. The concentration of GEM was determined by sampling and measuring the GEM standard gas with a conventional gold trap (Nippon Instruments Corp., Osaka, Japan) and a CV-AFS, which was calibrated with saturated GEM under a given temperature (MB-1, Nippon Instruments Corp.). The GEM sampling was done at the inlet of the glass vessel because pilot tests showed almost no difference in the GEM concentrations between the inlet and outlet of the glass vessel during exposure. The GEM sampling was done at least before and after exposure in each experiment. The GEM standard gas from the permeation device was continuously introduced from one end of the glass vessel and flowed out from another end, which was open to the air. During exposure, the magnetic stirrers were ceased. For safety reasons, all uptake experiments were carried out in a fume hood. After exposing the solution to the GEM standard gas for a given period of time, the introduction of the standard gas was stopped, and the solution was gently stirred using the magnetic stirrers for 3-5 min. Approximately 290 mL of the homogenized uptake solution was gently siphoned from the bottom of the vessel into a 375 mL PFA impinger (Savillex, Minneapolis, MN, USA) using an approximately 60 cm long, 6 mm i.d. \(\times\) 8 mm o.d. PFA tubing (Yodoflon, 8 mm o.d.\(\times\) 6 mm i.d., Yodogawa Hu-Tech Co., Ltd., Osaka, Japan). The mass of the uptake solution sample in the impinger was weighed using a balancer (ML4002T, Mettler-Toledo GmbH). The analysis of mercury in the uptake solutions described in the following subsection was carried out as soon as the solutions were decanted in the impinger.
# Continuous Control Set Predictive Current Control for Induction Machine
## 6 Discussion  
A method for induction machine speed tracking is shown in this paper. The main part of the method is model predictive current control without the need for calculation of the weighing factors for the inner control loop (they are simply set to one). Speed tracking is achieved by simple _PI_ regulator of the outer control loop. Speed and rotor flux estimator is implemented and it is successfully utilized for sensorless control and calculation of the angle for coordinate system transformation. Simulation is carried out, and the method shows great performance during transients and steady state. At the loading instance there is a speed overshoot of 20 rpm (1.4% of reference speed), and around 2 Nm in torque (7% of the reference torque). Additionally, there is improvement compared to the finite control set method, which is visible from frequency spectrum of the generated torque on the rotor shaft. The controllability analysis that was carried out showed that the system is fully controllable, which suggests that pole placement could be conducted and behavior of the system could be manipulated. In the future work, pole placement will be further investigated, along with the experiment that will be conducted on the real time test bench.  
Conceptualization, T.V.; methodology, T.V.; software, T.V.; validation, T.B. and V.J.S.; formal analysis, M.B. and V.J.S.; investigation, T.V.; resources, T.V.; data curation, T.B.; writing--original draft preparation, T.V.; writing--review and editing, T.B. and V.J.S.; visualization, T.V.; supervision, M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Croatian Science Foundation under the project number UIP-05-2017-8572.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 10: Fourier analysis of torque for continuous and finite control set _PCC_.
# PFAS: The Journey from Wonder Chemicals to Environmental Nightmares and the Search for Solutions
## 4 Remediation and the Future Use of PFAS
### Possible PFAS Alternatives and Manage PFAS in Sewage  
Considering the number of PFAS-related products and benefits, eliminating PFAS from the world or making a PFAS-free world is unrealistic. Instead of an immediate ban on PFAS, researchers are suggesting phasing out, where people can limit unnecessary use [95]. They recommend differentiating thousands of PFAS-related products into three major categories:  
1. Non-essential--Products that are not essential for the functioning of society, health, and safety (ex: ski waxes)
2. Substitutable--Products that perform important functions but alternatives with an equivalent property have been developed (ex: some water-resistant textiles, Fluorine-free foam, non-fluorinated water repellents)
3. Essential--Products that are essential for the functioning of society, health, and safety, and the alternatives have not developed yet (ex: some medical devices)  
A few potential alternatives are the use of fluorine-free foam such as F3 instead of AFFF, choosing untreated fabrics or fabrics with an alternative stain-resistant coating that does not contain PFAS, and using cookware made from stainless steel, cast iron, ceramics, or glass instead of Teflon coated products. It is also important to encourage and support PFAS-free alternatives in their manufacturing processes. Estimating and treating the PFAS in sewage from households and businesses is an important process to protect public health and the environment [96]. The initial and most important step of this is to control the source by  
Figure 4: PFAS remediation techniques.  
phasing out the PFAS products, generating regulations, and educating the public about the PFAS-free alternatives. It is necessary to improve wastewater treatment by implementing advanced filtration technologies and using effective technologies to destroy PFAS in sludge. Sludge management is important as sewage sludge with high PFAS concentrations should be disposed of in a way that minimizes environmental contamination. It is also important to do regular monitoring of PFAS levels in the wastewater and treated effluent and invest in research and development of cost-effective and efficient PFAS removal technologies.