esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Site Description  
We collected data at two study sites, Hitsujigaoka and Takayama. Hitsujigaoka is located in northern Japan at 42 \({}^{\circ}\)59'13\" N and 141 \({}^{\circ}\)23'7\" E and is 180 m above sea level. Takayama is in central Japan at 36 \({}^{\circ}\)8'46\" N and 137 \({}^{\circ}\)25'23\" E and is 1,420 m above sea level. At each site, a flux tower collected meteorological data and ecological characteristics. Averaged over five years, from 2001 to 2006 (Figure 1), the maximum temperatures at Hitsugaoka and Takayama were \(24.9\pm 1.8\)\({}^{\circ}\)C and \(22.4\pm 1.6\)\({}^{\circ}\)C in August, while the minimum temperatures were \(-12.4\pm 1.7\)\({}^{\circ}\)C and \(-11.7\pm 2.1\)\({}^{\circ}\)C in February, respectively. The mean annual precipitation and mean annual solar radiation at Hitsujigaoka were 1,047 \(\pm\) 174 mm and 12.25 \(\pm\) 0.4 MJ m\({}^{-2}\) day\({}^{-1}\), respectively, whereas at Takayama they were 2,275 \(\pm\) 165 mm and \(12.43\pm 0.3\) MJ m\({}^{-2}\) day\({}^{-1}\), respectively. Both sites experience snow cover in the winter (December to April) to a depth of 1 m.  
The main vegetation type was deciduous broadleaf natural forest, and the dominant species was _Betula platyphylla_. The stand basal area for this species accounted for around 81% and 63% of the total basal area at Hitsuijgaoka and Takayama, respectively. Tree height ranged from 14 to 28 m. Overstory trees at Hitsuijgaoka (around 100 years) were older than those at Takayama (around 50 years; [19, 20]). The mean DBH was larger at Hitsuijgaoka (23.0 cm) than at Takayama (11.4 cm; [19]). We note that the flux tower at Hitsuijgaoka was compromised and the forest around the flux tower was severely damaged by a typhoon in 2003. Data from Takayama were used for calibration, while data from Hitsuijigaoka were used for validation. The different locations did not significantly influence the model output because the two sites are located in the same climatic zone and are characterized by the same canopy species. Site data is summarized in Table 2.  
Figure 1: Average monthly meteorological data from 2001–2006 as **(a)** temperature in terms of minimum temperature at Hitsuijgaoka (open rectangular and dashed line) and Takayama (open circle and dashed line), and maximum temperature at Hitsuijgaoka (solid rectangular and solid line) and Takayama (solid circle and solid line); **(b)** precipitation at Hitsuijgaoka (solid bar) and Takayama (open bar); and **(c)** solar radiation at Hitsuijgaoka (solid rectangular and dashed line) and Takayama (solid circle and solid line).
## 4 Numerical Simulation Analysis
### Establishment of Numerical Models  
The case conditions numbered 20-1.0-2, 30-1.0-2, 40-1.0-2, 30-0.5-2, 30-2.0-2, 30-1.0-1, and 30-1.0-3 were selected for numerical model analysis. First, a non-persistent joint solid element model was constructed, and then zero-thickness cohesive elements were globally embedded in the solid element model. In terms of analysis steps, in addition to the initial analysis step, two dynamic display analysis steps were set: step 1 for applying normal load and step 2 for simulating shear loading. The numerical model is shown in Figure 10.  
Figure 10: Non-persistent joint finite element numerical model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element Type** & **Parameters** & **Unit** & **Value** \\ \hline \multirow{3}{*}{Solid elements} & Density & g/cm\({}^{3}\) & 2.08 \\  & Young’s modulus & GPa & 8.79 \\  & Poisson’s ratio & / & 0.12 \\ \hline \multirow{3}{*}{Cohesive elements} & Density & g/cm\({}^{3}\) & 2.08 \\  & Stiffness & GPa/mm & 6 \\ \cline{1-1}  & Fracture Energy & N/mm & 0.18 \\ \cline{1-1}  & Normal traction force & MPa & 12 \\ \cline{1-1}  & Tangential traction force & MPa & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical simulation parameters.  
Figure 9: Comparison of uniaxial compression test numerical and experimental results.
## 2 Methodology
### Determination of Displacement Monitoring Index for Rockfill Dams
#### 2.2.1 Hybrid Monitoring Model
Previous research has indicated that the parameters \(K_{b}\), \(\varphi_{0}\), and \(n\) in the E-B model have a significant influence on dam displacement when compared to other model elements [38; 39; 40; 41]. Consequently, to enhance the efficacy of the analysis, this study focuses on the random field simulation of the parameters \(K_{b}\), \(\varphi_{0}\), and \(n\) in each rockfill zone of the dam. The remaining parameters are adopted from laboratory geotechnical tests. The specific steps for constructing the water pressure component considering material uncertainty are as follows:  
(1) The material parameters and their statistical eigenvalues for the random field simulation must be determined, and Latin hypercube sampling (LHS) must be used to generate an independent standard normal random sample matrix, X, of rockfill parameters by n times random sampling. (2) For the upstream rockfill zone of the dam body, the Gaussian correlation function is selected, and the equivalent cross-correlation coefficient matrix and equivalent autocorrelation coefficient matrix in standard normal space are obtained using Equation (3). Subsequently, Cholesky decomposition is performed for its mapping in three directions, and the associated log-normal random field is obtained via Equations (4) and (5). (3) Repeating steps (2) enables the simulation of the parameter random fields for the main and secondary rockfill zones of the dam, respectively. The random results are assigned to the corresponding finite element unit to obtain the parameter random field of the overall dam body. (4) The dam measuring-point displacements of the above n-parameter random fields at different water levels are calculated via FEM. The displacement results are then subjected to statistical analysis, and the water pressure component is obtained by fitting the variation of the mean displacement at the measuring point with the reservoir level through Equation (7). (5) Inputting the aforementioned water pressure component, in conjunction with the temperature component and the time-dependent component, into Equation (10) results in the construction of the stochastic finite element method-based monitoring model (SFEMM model) of the rockfill dam. The measured data from the pertinent dam measuring point are selected for partial least squares fitting, and the coefficients to be determined in each component are solved to obtain the trained SFEMM model of dam displacement. In general, when the SFEMM model is evaluated, its computational accuracy is higher than that of traditional statistical models and conventional FEM models. When the accuracy of the SFEMM model is lower than that of conventional FEM models or traditional statistical models, it indicates that there is a significant deviation between the material parameters or random field parameters and the actual values. Experimental analysis can be conducted to further determine the parameter values. This issue should be analyzed specifically for practical engineering, and this paper does not delve into it in depth.
## 4 Results
### SAM vs. SAM 2
#### 4.1.2 Model Performance Deltas  
Focusing on improvement differences, or deltas, instead of absolute values could shed more light on this comparison. The distribution of these deltas for each dataset and metric appears in Figure 7, without outliers. These deltas are shown in Table 2 in parentheses.  
Coronacases and Radiopaedia deltas for both metrics are centered around zero, suggesting that the central tendency of the delta values is neutral, with no significant overall increase or decrease. However, the Montgomery dataset shows a slight negative skew (around 1%), indicating that the improvement in this dataset is more extreme than in the other two.  
The Interquartile Range (IQR), represented by the box, is small for Coronacases and Radiopaedia deltas for both metrics. It shows that the middle 50% of the data lies close to \(\pm\)1%. The whiskers extend close to \(\pm\)3% except in the Jaccard index for Radiopedia,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Execution Time (s)** & **SAM (Delta)** & **SAM 2 (Delta)** \\ \hline
**[PERSON] et al. [19]** & 0.039 & 0.876 (+0.837) & 0.685 (+0.646) \\
**[PERSON] et al. [26]** & 11.2 & 0.96 (\(-\)10.24) & 0.81 (\(-\)10.39) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Execution time of SAM 2 vs. state-of-the-art medical image segmentation models.  
Figure 7: SAM 2 vs. SAM segmentation delta comparison. (**a**) CoronacasesJaccard index. (**b**) Radiopaedia Jaccard index. (**c**) Montgomery Jaccard index. (**d**) Coronacases Dice score. (**e**) Radiopaedia Dice score. (**f**) Montgomery Dice score.  
which is close to \(\pm\)5%, suggesting that the changes are relatively small for better and worse segmentation results. Again, Montgomery shows a more significant delta, with the IQR extending close to \(\pm\)5% and the whiskers close to \(\pm\)15% Jaccard index, and close to \(\pm\)3% and the whiskers close to \(\pm\)10% Dice score, reinforcing a more significant improvement in this dataset.  
The notch around the median suggests that we can be 95% confident that the actual median lies within this notch. The notch does not overlap with the ends of the box, suggesting that the median is distinct and significantly different from the IQR endpoints.  
Overall, the Jaccard index changes for the Coronacases and Radiopaedia datasets appear to be centered around zero, with a small degree of variability and no significant outliers. The distribution is relatively symmetric and well-behaved.  
In any case, the box plots appear symmetric around the median, indicating that the distribution of the delta values is relatively balanced. A slightly more prolonged whisker at the upper end might suggest a minor positive skew. This skew is reinforced by the average value (represented by the discontinuous green line) being consistently higher than the median (represented by the solid green line) in every instance.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Global Navigation Satellite System (GNSS)
#### 3.1.1 Assisted Global Positioning System (AGPS)  
An alternative solution for utilizing the GPS under NLoS or poor satellite coverage conditions, is the assisted global positioning system (AGPS), shown in Figure 12. It aims to improve the GPS performance by using the cellular radio network. The AGPS receivers obtain information via the cellular radio network, which improves the time-to-first-fix (TTFF), at the expense of higher power consumption and processing time [70]. It is extensively used for the emergency numbers 112 (Europe) and 911 (USA) requirement, thus making the cellular phone location data available for the emergency call dispatchers.  
Figure 12: Assisted GPS (AGPS), reproduced with permission from Djuknic, Geolocation and Assisted GPS, published by IEEE, 2001.
## 3 Results and Discussion  
### Effect of the Cathode Current Density on Sodium Expansion  
Figure 2a,b show the sodium expansion measurements of semigraphitic cathode materials with the two levels of a cathode current density at 20 min and 120 min, respectively. At the beginning of electrolysis, as the penetration of the electrolyte and sodium and the formation of the graphite-sodium intercalation progresses, sodium expansion develops at a faster rate. The reason is that the interaction of alkali ions with carbon cathodes generates internal stresses within the electrode structure and leads to an increase in the interlayer space [11, 12]. The macroscopic strain generated in the carbon cathode is closely related to changes in the graphite interlayer spacing at atomic scales. In addition, the formation of intercalation compounds in this stage exacerbates the development of stresses and strains in the carbon cathodes [13, 14].  
Figure 1: Schematic diagram of a modified Rapoport. (1) Stainless steel cover (2) Argon gate (3) C-Ni-Cr alloy cathode rod (4) High purity graphite crucible. (5) Electrolyte (6) Semigraphitic cathode (7) Boron nitride plate. (8) Furnace (9) Graphite mechanical support. (10) Argon inlet.  
However, the decreasing tendency of the sodium expansion rate is observed with the continuous electrolysis, but the sodium expansion approaches asymptotically to a constant. The reason is that the macroscopic expansion developed in the carbon cathode has a close relationship with changes in the interlayer space at the atomic length scales, as different amounts of intercalation compounds are formed between the graphite layers [15; 16; 17]. In addition, the higher the current densities, the faster the sodium concentration increases in a cathode sample [7]. Therefore, the sodium expansion of the carbon cathode is bigger with higher current densities at the beginning of electrolysis.  
Figure 2: Sodium expansion of the semigraphic cathode sample at the two values of current density with various testing times (**a**) 20 min (**b**) 120 min.  
The inhomogeneous sodium concentration in the carbon causes the occurrence of the diffusion-induced stresses in the carbon cathode. Such heterogeneities are even more serious in the carbon and induce rapid stress and strain gradients that finally result in the chemical and physical degradation of the carbon cathodes [18,19]. However, the sodium concentration at each point of the carbon cathode keeps a constant value after sodium saturation, thus, the sodium expansion of the carbon cathode has the same value in the end with various current densities. In a word, with fewer sodium and melt penetrated into the carbon cathode, the strain response is also decreased. In order to further study the effect of the current density on the microstructure of the carbon cathodes, high-resolution transmission electron microscopy, given in Figure 3, shows the atomic arrangement of graphite crystals at testing 20 min with various current densities.
# Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation
## 1 Introduction
introduced the concept of secondary electron emission with a Poisson distribution for each dynode of the PMT, which optimizes the performance of airborne lidar for cloud measurement [18]. Variance is a measure of the spread of a distribution in probability theory and statistics, and equality between the variance and mean is the necessary condition for a Poisson distribution [19]. Estimation of the variance is a good method to evaluate data characteristics in the applications of distance sampling and terrestrial laser scanning [20; 21; 22]. [PERSON] et al. utilized the variance estimator to estimate the stand-level volume in the single-scan mode of terrestrial laser scanning [21]. [PERSON] et al. implemented a variance component estimation procedure to enhance the lateral precision of image data and the precision of laser scanner range measurement in a terrestrial laser scanner to determine 3D coordinates of an object [22]. In this paper, to understand the appropriate data characteristics of the lidar measurement profiles, the calculation models of the temporal variance and spatial variance were constructed and executed under consideration of the sample distributions of lidar data traces. Based on the analysis of the variance-mean scatter distribution of analog data and PC data, the dead time and the threshold voltage of the PC system and the linear working range of the photo-electric converter were estimated, which improved the lidar system performance. Moreover, a novel gluing method between analog data and PC data is presented, based on calculation of the variance distribution.
## 4 Experimental Results and Analysis
### Performance Evaluation Metrics  
In designing the object detection model for this experiment, consideration was given to the actual application needs of the production line, with false positive rate and miss rate as core metrics used for evaluating the performance of the detection system. For the production line, a miss implies that defective products could potentially proceed to subsequent stages or reach the market, causing serious quality issues and economic losses. Therefore, reducing the miss rate is the primary objective of the detection system. Simultaneously, the false positive rate directly affects the proportion of good products misidentified as defective, where a high false positive rate leads to additional re-inspection costs and resource wastage, decreasing the efficiency and cost-effectiveness of the production line.  
Figure 15: Sliding window diagram.  
This paper uses accuracy, miss rate, false positive rate, and precision as metrics to evaluate the performance of the model. The formulas for calculating miss rate, false positive rate, and precision are as follows:  
\[\text{Miss rate}=\frac{F_{P}}{T_{N}+F_{P}}\times 100\% \tag{2}\]  
\[\text{False alarm rate}=\frac{F_{N}}{T_{P}+F_{N}}\times 100\% \tag{3}\]  
\[\text{Accuracy}=\frac{T_{N}+T_{P}}{T_{N}+F_{P}+T_{P}+F_{N}}\times 100\% \tag{4}\]  
True Positive (TP): the inspected component is OK, and the detection result is also OK, correctly identified.  
False Positive (FP): the inspected component is NG (Non-Good), but the detection result is OK, a missed detection.  
False Negative (FN): the inspected component is OK, but the detection result is NG, a false alarm.  
True Negative (TN): the inspected component is NG, and the detection result is also NG, correctly identified.  
Accuracy is a fundamental performance metric for classification models, used to measure the proportion of correctly classified samples among all samples. It represents the percentage of correct predictions out of all predictions. High accuracy indicates that the model can correctly classify most samples, making it suitable for scenarios with relatively balanced class distributions. Conversely, low accuracy suggests that the model makes numerous classification errors and requires further optimization.
## 4 Conclusions  
Aiming at the jumping error in the traditional multi-frequency heterodyne phase unwrapping, the phase principal values of three frequencies are obtained by the four-step phase-shifting technique, and the corresponding threshold value is set according to the difference between the actual value of the decimal part calculated from the phase principal value and the theoretical value of the decimal part derived from the principle of multi-frequency heterodyne phase unwrapping. The number of cycles is adjusted by the threshold value, so as to reduce the jumping of the absolute phase. The experimental results show that compared with the traditional dual-frequency heterodyne phase unwrapping, the elimination rate of the jumping points of the technique proposed in this paper can reach more than 96.8%, and the technique is not susceptible to noise interference, which has strong noise resistance and practicality. Furthermore, whether the measured object has a simple continuous surface or a complex steep surface, this method can measure its complete 3D contour surface, which proves the certain universality of the proposed method and provides a certain reference value for the subsequent 3D reconstruction based on fringe projection.  
Figure 8: The 3D point cloud image measured by the method in this paper. The 3D point cloud of flange (**a**). The 3D point cloud of oil gasket (**b**). The 3D point cloud of U-card (**c**).  
**Author Contributions:** The contribution of the authors are as follows: Conceptualization, Methodology, Writing--Original draft, Z.M.; Software, Validation, Writing--Original draft, Z.L.; Validation, Y.D.; Writing--Review and editing, Supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (12172001), The University Synergy Innovation Program of Anhui Province (GXXT-2022-019) and Anhui Provincial Natural Science Foundation (2008085 QA23).  
**Institutional Review Board Statement:** The study did not require ethical approval.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Due to privacy reasons, the data is not publicly available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 1 Introduction
### Our Work as Cross-Layer Approach  
State-of-the-art communication algorithms are already optimized for efficient implementation on edge devices by trading-off communication performance against hardware complexity. In contrast, most research in ANN-based communication only focuses on the algorithmic level without considering the feasibility of the implementation. However, an efficient hardware implementation is mandatory for a fair comparison to conventional systems. This challenge of designing an efficient architecture for ANN-based communication systems can be abstracted into three design layers: Communication Layer, ANN Layer and Hardware Layer. Those layers are not independent but highly influence each other; therefore, a joint analysis is of high importance for efficient cross-layer design. In our work, we target this challenge by taking into consideration the relations between the layers during our design process. We evaluate how the convergence latency of the ANN layer is related to the signal-to-noise ratio (SNR) of the communication layer and additionally provide a link to the hardware layer in terms of DOP. We exploit this interaction in a framework that calculates the optimal degree of parallelisms (DOPs) for given application requirements. This allows us to tackle the challenge of optimizing communication performance, while taking into account the limitations and constraints posed by the hardware device, in our case the limited FPGA resources.  
In summary, the novel contributions of this paper are:  
* A cross-layer approach that relates the application requirements (SNR, bit error rate (BER)) to the ANN architecture (time of convergence for training the ANN) and to the hardware implementation capabilities (DOP, utilization of the FPGA resources);
* The evaluation of the hardware performance of a trainable demapper in the context of AE-based communication systems that enables online fine-tuning to the latest noise conditions in wireless channels. To the best of our knowledge, such an FPGA implementation is unprecedented;
* A parameterizable framework that enables the adjustment of the DOPs to satisfy the application requirements, to ensure environmental adaptation and optimize latency or power;
* A benchmark of the highly efficient FPGA design by comparing the performance to a PyTorch implementation on a high-performance GPU, an embedded GPU, and an embedded CPU.
## 3 TVWS-DTV Coexistence: Field Measurements  
The objective of the field tests was to complement the laboratory results. These tests allowed the evaluation of the operational limits of DTV STBs in the presence of interfering signals in adjacent channels in an outdoor scenario with wireless transmission, unlike the laboratory tests, which exclusively used wired transmission. The field tests were divided into short distances and long distances.