esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, HR satellite data with short revisit intervals are used to predict high spatial resolution LAI values over the area of the Huailai experimental region in Hebei Province, China. A historical low-resolution MODIS product was used as background information to extract the stable phenological changes in vegetation, which were used to estimate the HR LAI. The HR data and low-resolution data are then used for recursive update training, resulting in LAI data with a spatial resolution of 30 m. This study demonstrated the feasibility of frequent observations at high spatial resolution for crop monitoring. The estimation results of our proposed recursive update model are compared with ground-measured data, and the two show good consistency. The results demonstrate that the new method can produce temporally continuous and smooth LAI profiles from HR time series observations. The main advantage of the recursive approach is the ability to update model dynamics. Additional studies should be conducted to improve phenological information extraction and disturbance detection in subsequent dynamic monitoring. With the increase in HR remote sensing data, we will further study the method of normalizing multisource remote sensing data, and we plan to obtain high-precision surface reflectance data and to improve the accuracy of the time series estimation of LAI.  
[PERSON]. ([PERSON]) conceived and designed the study. [PERSON]. ([PERSON]) contributed to the conception of the study, performed the data analysis, and wrote the paper. [PERSON], [PERSON] and [PERSON] aided with the discussion and the manuscript revision. [PERSON]. ([PERSON]) reviewed and edited the manuscript. All authors read and approved the manuscript.  
This research was supported by the National Basic Research Program of China under grant No.2013 CB733403, the National Natural Science Foundation of China under grant No. 41171263, and the National Natural Science Foundation of China under grant No. 41801242.  
The authors declare no conflict of interest.
## 3 Envelope Demodulation
### Verification of Envelope Demodulation  
To verify the effectiveness of the envelope demodulation algorithm, this paper takes the two-tone amplitude modulation signal as the research object and represents its mathematical expression form as  
\[y(t)=(1+0.3\cos(2\pi\cdot f_{1}t)+0.5\cos(2\pi\cdot f_{2}t))\cdot\cos(2\pi \cdot f_{c}t) \tag{4}\]  
where modulating signal \(f_{1}=20\), modulating signal \(f_{2}=30\), and carrier signal \(f_{c}=30\).  
Figure 1 demonstrates the validation results of the envelope demodulation algorithm, which verifies the correctness of the envelope demodulation algorithm and indicates that the envelope demodulation algorithm can be used to separate the rotation signal of the bearing from the vibration signal of the bearing failure and correctly extract the vibration fault signal of the shaft failure. The spectrum in Figure 1b shows that \(100\pm 20\) Hz and \(100\pm 30\) Hz are the spectra of the modulated signal \(y(t)\). The spectra of Figure 1d are 20 Hz and 30 Hz, which are the spectra of the modulated signal.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Methodology** & **Advantage** & **Limitation** \\ \hline [PERSON] et al. (2025) [15] & Envelope analysis + WPT with SVM classification & Effective fault type discrimination & Sensitivity to wavelet basis selection; high computational load \\ [PERSON] et al. (2025) [13] & Hybrid QWPEE and t-SNE for feature extraction & Enhanced pattern recognition for bearing faults & Dependency on parameter tuning \\ \hline \end{tabular}
\end{table}
Table 1: Cont.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum Encoder
#### 3.3.1 Quantum Bit Mapping  
Each classical bit \(c_{i}\in\{0,1\}\) from the polar-encoded codeword is first mapped to a quantum state in the computational basis, as in Equations (2) and (3).  
\[0\rightarrow|0\rangle=\begin{pmatrix}1\\ 0\end{pmatrix} \tag{2}\]  
\[1\rightarrow|1\rangle=\begin{pmatrix}0\\ 1\end{pmatrix} \tag{3}\]  
This step initializes each qubit in a pure basis state, representing classical bits in quantum form.
## 3 Results and Discussion  
### XRD and SEM Analysis  
The XRD spectra of different ZnO NPs is shown in Figure 1. Three major peaks, observed at 20 \(\approx\) 31.8\({}^{\circ}\), 34.5\({}^{\circ}\), and 36.3\({}^{\circ}\), can be assigned to the (100), (002) and (101) lattice planes, respectively, which is in a good agreement with the standard JCPDS file No. 36-1451 for ZnO [34]. This reveals that Sample A and Sample B both belong to the hexagonal wurtzite structure with the space group P63 mc [35]. No change occurred in the structure of ZnO during the vacuum treatment. The average grain sizes of the two samples, according to the [PERSON]'s equation, were both about 60 nm.  
As shown in SEM images (Figure 2), ZnO samples are composed of particles with a hexagonal rod shape and relatively uniform particle size distribution. The crystal structure and morphology ofthe ZnO NPs are generally unchanged after the vacuum treatment. This is consistent with the results obtained by XRD (Figure 1). However, the average particle size (\(\sim\)150 nm) of Sample B is slightly smaller than those (\(\sim\)200 nm) of Sample A, owning to a decrease in particle aggregation--the smaller the particle size, the larger the specific surface area. The BET measurement shows that the specific surface areas of Sample A and Sample B are 4.29 and 6.34 m\({}^{2}\)/g, respectively.
# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 4 Experiment
### Evaluation Metrics  
Evaluation metrics refer to the use of various data indicators to determine the detection performance of the model. The metrics adopted in this paper include mean intersection over union (MIoU), F1-score, mean pixel accuracy (MPA), and accuracy.  
Figure 8: Results of random color adjustment of the dataset.  
Figure 7: Results of random flipping of the dataset.  
Figure 9: Loss change and pixel accuracy change curves: (**a**) loss change; (**b**) pixel accuracy change.  
In crack detection, the MIoU metric can help evaluate the performance of the model in image segmentation, especially in capturing the degree of overlap between the detected crack area and the actual label. Its calculation formula is as follows:  
\[MIoU=\frac{1}{N}\sum_{l=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{7}\]  
Among them, N denotes the total number of categories; \(TP_{i}\) denotes the true positives of the i-th category, that is, the number of pixels that the model correctly predicts as this category; \(FP_{i}\) denotes the false positives of the i-th category, namely, the number of pixels that the model wrongly predicts as this category; and \(FN_{i}\) denotes the false negatives of the i-th category, which means the number of pixels that the model fails to predict as this category.  
The F1-score represents the balance coefficient between the precision and recall of the model, providing a comprehensive evaluation of the overall performance of the trained model.  
\[Precision=\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{8}\]  
\[Recall=\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{9}\]  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{10}\]  
As an intuitive and straightforward evaluation metric, mean pixel accuracy (MPA) can comprehensively consider the classification accuracy of an image segmentation model at the pixel level. It provides an intuitive understanding of the overall performance of the model and is applicable to the evaluation and comparison of various image segmentation tasks.  
\[MPA=\frac{\sum_{i=1}^{N}TP_{i}}{\sum_{i=1}^{N}(TP_{i}+FP_{i})} \tag{11}\]
## 3 MIGOU Platform  
### Design Criteria  
The design of our platform has been based on the following criteria:  
* _Hybrid radio_: The platform must be able to operate as a traditional node, using a highly integrated transceiver, and as an SDR system when a high radio flexibility is required for specific CR tasks. It should be noted that when no special task or customization is required, the use of an exclusively SDR system results in a considerable penalty in terms of power consumption, resource usage, and development time. This idea is further detailed in a previous work [31].
* _Low-power programmable logic_: The platform must include programmable logic resources where high-speed communication and/or processing tasks can be offloaded. This is especially important for applications with strict timing constraints, where it has been shown that these tasks can affect each other negatively [32]. Besides, these logical resources must support some low-power consumption mode for when they are not in use.
* _Flexible hardware-software (HW-SW) boundary:_ The communication interface between the MCU, where the application runs, and the programmable logic must be as fast as possible to avoid bottlenecks, thus ensuring a flexible HW-SW boundary. This is an important feature for communication and processing tasks with precise timing requirements.
* _Agile application development_: Being an experimentation-oriented platform, the development of new applications should be as agile as possible. In this sense, the use of an OS is considered an essential aspect of the platform. In addition, it should have development and expansion interfaces that maximize its possibilities for use.
* _Consumption measurement_: The platform should integrate power consumption metering tools that allow the developer to evaluate the energy performance of the methods and algorithms being tested on it.
## 2 Data Sources
The data over the various sites were acquired from different satellite tracks, with incidence angles, \(\theta\), ranging from 28 to 42 degrees depending on the track. The spatial resolution of this product, with 150 MHz chirp bandwidth, is 3.3 meters in azimuth and 2.0 m in ground range (at \(\theta=36^{\circ}\)) [18], and the pixel size is 0.90 m \(\times\) 1.95 m (slant range \(\times\) azimuth). Along the central section of the west coast, we derived ice velocities from 46-day repeat pass data of ALOS PALSAR acquired in FBS mode, spanning the periods 15 September to 31 October 2008 and 20 November 2009 to 5 January 2010. The FBS mode data have a swath width of about 70 km. The 28 MHz chirp bandwidth results in a ground range resolution of about 7 m at \(\theta=34^{\circ}\). The nominal azimuth resolution is 5 m [19].
# A Road Defect Detection System Using Smartphones
## 2 Related Work
### Vibration Sensor-Based Machine Learning Method  
[PERSON] et al. [31] introduce a novel approach for visualizing road pavement anomalies using fog computing in a vehicle-to-infrastructure (V2I) network and machine learning. In this research, they propose a method to efficiently process real-time road condition data and visualize surface anomalies promptly using fog computing, enhancing the ability to detect and respond to road anomalies. The focus here lies in detecting speed bumps using features derived from accelerometer data and optimizing this process with a genetic algorithm. Additionally, they propose a method for detecting road anomalies by comparing data windows of varying lengths using Dynamic Time Warping (DTW). Expanding on the DTW method, they introduce a system named Quick Filter Based Dynamic Time Warping (QFB-DTW) that utilizes a series of accelerometer data points to discover anomalies on the road surface [32]. [PERSON] et al. [22] propose an automated machine learning system using Random Forest to detect potholes on the road using smartphone sensor data. The significance of this research lies in the use of widely accessible smartphones and their built-in sensors to collect data that machine learning algorithms can process for road anomaly detection. [PERSON] et al. [24] explore optimized machine learning techniques for road monitoring. They perform sensitivity analyses of three machine learning models, including Support Vector Machines (SVM), Decision Trees (DT), and Multilayer Perceptrons (MLPs), to test the effectiveness of feature selection. This contributes to an ongoing conversation about selecting and effectively applying algorithms for efficient and reliable road monitoring systems, which is crucial for assessing road conditions. They also describe a Smart Pothole Detection System that utilizes a One-class Support Vector Machine (OCSVM) instead of an SVM. This research explains the integration of sensor technology for vehicle-based road condition monitoring and advanced computational methods, showcasing potential directions for intelligent transportation systems [33]. [PERSON] [34] develops a classification method to enhance context awareness in autonomous vehicles. They classify driving environments using IMU sensors and energy consumption data without relying on computer vision. They evaluated 13 classification algorithms to select the optimal method. These results demonstrate the applicability of autonomous driving technology advancements in path planning and safety.
## 1 Introduction
Due to its intrinsic relationship with vegetation photosynthetic activity, SIF has also been used to estimate V\({}_{\text{cmax}}\) to better simulate GPP and SIF using the SCOPE model [41]. Several studies have evaluated the sensitivity of SIF and GPP to this parameter [39; 41; 44], and have shown that the SIF-V\({}_{\text{cmax}}\) relationship was not stable and instead varied between different versions of the SCOPE model [19; 39; 45; 46]. Two recent studies showed that SIF was less sensitive to V\({}_{\text{cmax}}\) in SCOPE v1.6 [19; 44]. However, the sensitivity of SIF to V\({}_{\text{cmax}}\) in the SCOPE v1.7 model across different biomes has not been examined yet. In addition, no studies have used satellite SIF observations and flux tower GPP data to jointly constrain the parameters of the SCOPE model for improving SIF and GPP simulations. Several previous studies have used satellite SIF products (e.g., GOSAT and GOME-2) to improve the GPP estimation in cropland and grassland (e.g., [20; 41]), which has demonstrated the potential of SIF observations in constraining GPP [19; 41]. However, the satellite-derived SIF products from GOSAT or GOME-2 have coarse spatial resolution (e.g., 10 km diameter for GOSAT, 40 \(\times\) 80 km\({}^{2}\) for GOME-2), leading to large scale mismatch between satellite grid cells and eddy covariance (EC) flux tower footprints [2]. The OCO-2 satellite, launched in 2014, has enabled the retrieval of finer-resolution (1.3 \(\times\) 2.25 km\({}^{2}\)) SIF than the previous satellites/sensors [47]. The ground area of the OCO-2 SIF soundings is close to the typical footprint of EC flux towers, and therefore the OCO-2 SIF data can be better matched with EC observations to examine the relationship between SIF and GPP or to simultaneously constrain models [5]. To date, only a few studies have used the OCO-2 SIF products to evaluate or improve the SIF simulation of the SCOPE model (e.g., [44]). Here, we used SIF observations in the far red region from OCO-2 and GPP data from flux towers to improve the performance of the SCOPE model for simulating SIF and GPP by optimizing the key uncertain parameters in the model. We chose two temperate forests (Park Falls and Willow Creek in the USA) as our study sites. The objectives of this study were to (1) identify the key parameters of the SCOPE model in predicting SIF and GPP; and (2) examine the effects of parameter optimization on the simulation of SIF and GPP, and assess how well the SCOPE model simulations can be constrained by OCO-2 SIF and flux tower GPP, both separately and together. Our results are useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and revealing the potential of improving the SIF and GPP estimation by incorporating the SIF products and flux tower data.
## 6 Discussion and Conclusions
The hybrid model, which combines both approaches, provides high completeness similar to TF-IDF while showing moderate accuracy. However, a significant disadvantageof the hybrid method is the significantly longer processing time due to the additional computational complexity of integrating semantic and syntactic analyses. The practical applicability of each method depends on the specifics of the task. For applications where processing speed is critical, the TF-IDF method may be preferred, whereas for tasks requiring comprehensive and detailed identification of different types of borrowings, the hybrid model is a more suitable choice, despite the increased computational cost. In the future, it is advisable to conduct further research on the integration of specialized embeddings for the Kazakh language, as well as the adaptation of deep language models such as BERT, which will improve detection quality by taking deeper account of semantic and contextual features of the text. Additionally, extending testing to cross-lingual datasets will help address the problems associated with the diversity of morphological structures in related languages. Thus, the proposed hybrid model is a promising solution for textual duplicate detection in low-resource agglutinative language contexts, thereby contributing to the improvement of accuracy and reliability in academic integrity monitoring and textual data mining systems. Conceptualization, S.B., A.T. and O.K. ; methodology, O.K. and A.T.; software, A.T. and Y.A. ; validation, A.B., A.T. and Y.A. ; formal analysis, S.T. ; investigation, S.B. and A.B. ; writing--original draft preparation, A.T., O.K. and S.T. ; writing--review and editing, A.M. and S.S.; visualization, A.T.; supervision, A.B. ; project administration, S.B. All authors have read and agreed to the published version of the manuscript. This paper was written in the framework of the state order to implement the research project, IRN. AP23490123 <<Development of a system to detect plagiarism using combined methods, models for finding near-duplicate, focusing on the Kazakh language>>. Not applicable. 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.
## 5 Performance and Simulations of Instantaneous Frequency Rate Tracking Approach
### Tracking Threshold Evaluation
\begin{split}\mathrm{var}\Big{(}\hat{f}\Big{)}=\big{(}1-P_{acq2} \big{)}\times\mathrm{var}\Big{(}\hat{\hat{f}}|outlier\Big{)}+P_{acq2}\times \mathrm{var}\Big{(}\hat{\hat{f}}|no\ outlier\Big{)}\geq\big{(}1-P_{acq2}\big{)} \times\\ \frac{f_{2}^{2}}{3}+P_{acq2}\times\frac{90}{n^{2}}\frac{1}{SNR \times T_{s}^{2}N^{3}},\end{split}  \tag{62}\]  
where \(P_{acq2}\) is the probability of correct frequency rate estimation. Comprehensive simulations have been made to evaluate the tracking performance for the whole EDL flight. The results show that the theoretical analysis works well for the aerobraking flight, while for parachute deployment, the dynamic response of the algorithm behaves poorly when the coherent integration time is as large as 0.25 s. To improve the dynamic response of the algorithm, the integration time is reduced for parachute deployment. Thus, the tracking threshold is evaluated separately for aerobraking flight and parachute deployment.
## 2 Fatigue Strength Behavior of the A572 Gr. 50 Steel
### Proposed Method to Determine the A572 Gr. 50 S-N Curve  
This proposed method to determine the S-N curve is based on the experimental data given by the [PERSON] et al. (2011) research, where the CSA G40.21 Gr. 350W steel and the A572 Gr. 50 steel were considered to present the same properties. The steps are as follows.  
_Step 1_. Determine the \(S_{y}\), \(S^{\prime}\), \(S_{e}\), \(S_{e}\) and \(S_{ut}\) values and the strain hardening exponent \(n\) value of the A572 Gr. 50 steel. These values can be found in the engineering handbook [8] Appendix A, Table A-22, [9] Chapter 12, or based on the [PERSON] et al. (2011) methodology.  
_Step 2_. Take the material strain \(\epsilon_{ut}\) value as the exponent \(n\) value determined in step 1.  
_Note 1_: Remember that \(\epsilon_{ut}=n\) holds only on the \(S_{ut}\) coordinate. For any other coordinate, the corresponding \(\epsilon_{i}\) value must be estimated.  
_Step 3_. By using the material strain \(\epsilon_{ut}\) value of step 2 in Equation (2), determine the true strain \(\epsilon_{i}\) value that corresponds to the \(S_{ut}\) value.  
_Step 4_. By using the true strain \(\epsilon_{t}\) value of step 3 in Equation (3), determine the corresponding true stress \(\sigma_{t}\) value.  
_Step 5_. By using the \(\sigma^{\prime}_{t}\), \(S_{e}\) values and \(N=10^{6}\) cycles in Equation (5), determine the fatigue slope \(b\) value.  
_Step 6_. By using the \(\sigma^{\prime}_{t}\), \(b\) values and \(N=10^{3}\) cycles in Equation (4), determine the fatigue strength \((S^{\prime}_{f})_{10^{3}}\) value.  
_Step 7_. By using the above data, draw the S-N curve.  
Now to determine the probabilistic S-N curve percentiles for the A572 Gr. 50 steel, let us first, based on the maximum \((S^{\prime}_{f})_{10^{3}}\) and on the minimum \((S^{\prime}_{f})_{10^{6}}\) values, determine the corresponding Weibull shape \(\beta\) and scale \(\eta\) parameters on which we determine the corresponding S-N percentiles. The analysis is as follows.  
**3. Weibull/S-N Fatigue Strength Analysis for A572 Gr. 50 Steel**  
The Weibull parameters \(W(\beta,\ \eta)\) used to determine the corresponding probabilistic S-N percentiles are estimated as follows.
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 2 Drive Test Campaign
### Measured QoS and Signal Metrics
SS-RSRP is the equivalent of RSRP used in LTE [53]. * RSRQ-5G (dB)--Reference Signal Received Quality (RSRQ) for 5G NR is named as a Second Synchronization-RSRQ (SS-RSRQ) [50; 51]. It is defined as the ratio of N\(\times\)SS-RSRP/NR carrier Received Signal Strength Indication (RSSI), where N is the number of resource blocks in the NR carrier RSSI measurement bandwidth. The measurements in the numerator and denominator shall be made over the same set of resource blocks. The SS-RSRQ measurements can be used for cell selection, cell reselection, and mobility procedures. The RSRQ-5G range is defined from -43 to 20 dB [52]. SS-RSRQ is used in 5G NR networks to determine the quality of the radio channel. RSRQ, unlike RSRP, also includes interference level due to the inclusion of RSSI in the calculation. This parameter is also used for cell selection and handover if the RSRP is insufficient. It happens mainly in border parts of a cell. It is similar to RSRQ determined in LTE [53]. * SINR-5G (dB)--SINR for 5G is often called a Second Synchronization-SINR (SS-SINR) [50; 51]. It is defined as the linear average over the power contribution (in watts) of the resource elements carrying secondary synchronization signals divided by the linear average of the noise and interference power contribution (in watts). In5G networks, SINR-5G is reported as a coded value via measurement report to a base station. The reported range is from 0 to 127, total 128 values, where values 0 and 127 represent SINR \(<-23\) dB and SINR \(>40\) dB, respectively [52]. The method of determining the radio signal quality parameters in the 5G-NR system is defined by the 3 GPP standard, particularly by the specification [50,51].