esa-sceva/satcom-chunk-collection huggingface dataset:
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 1 Introduction
Unlike the existing technical solutions, this method works with FPGA as the only processor without designing a new module or modifying the algorithm flow. This method solves the dual-constraint problem by balancing the relationship among the modules to find the optimum trade-off between resources and processing capacity. Focusing on the dual-constraint problem in on-board processing, we designed an efficient FPGA system for the InSAR coarse registration and interferogram generation process. To improve the processing capacity, we studied the data reading and writing rules of matrix transposition based on AXI conditions. Moreover, to balance the relationship between resources and processing capacity, we proposed an FPGA design method based on gradient descent theory, which could help identify the optimum trade-off scheme. Finally, the structure and method of this paper were successfully implemented on FPGA. The contributions of this paper are summarized as follows:  
* To realize on-orbit InSAR processing, we design an efficient FPGA system for coarse registration and interferogram generation flow of InSAR. * To perform matrix transposition under the AXI protocol, we analyze the data rule of matrix transposition based on the AXI protocol. * To solve the dual-constraint problem of processing capacity and resources, we propose a design method of FPGA based on the gradient descent theory. We use the InSAR design in this paper to illustrate how to find the optimum trade-off between processing capacity and resources. * We implement the efficient structure and the design method on FPGA. The results show that the efficient structure outperforms the NVIDIA GTX Titan Black GPU. The design method can not only guide the engineering design but also evaluate the optimized structure academically. The remainder of this article is organized as follows. Section 2 introduces the targeted InSAR algorithm, including coarse registration and interferogram generation, and briefly introduces the gradient descent theory. Section 3 describes the architecture, processing, and storage structure of the FPGA system proposed by this paper. Notably, the matrix transpose storage structure can be adapted to the SoC system under the AXI conditions, ensuring processing efficiency. Section 4 illustrates how to seek the balance between resources and processing capacity of FPGA using the gradient descent theory. In Section 5, this paper shows experimental results measuring the performance of the FPGA system and compares them with that of GPU and DSP. Results show that the balanced design of the FPGA proposed by this paper could achieve high processing performance while saving resources. With the design proposed by this paper, FPGA demonstrates comparable processing capacity to that of NVIDIA GTX Titan Black GPU, revealing the great potential of FPGA in the field of spaceborne real-time processing. The paper ends with conclusions in Section 6.
## 3 Calculation Results and Analysis
### Analysis of the Cell Temperature along the Optical Axis  
When the parameters of the Fresnel lens and cell are determined, the illumination along the optical axis is determined. In addition, the power generation performance of photovoltaic cells is affected by temperature, which is affected by the cooperation of the optic-electric-thermal. In order to determine the parameters of photoelectric conversion, it is necessary to calculate the temperature of the cell in the steady state. Through the optical-electrical-thermal coupling model, the temperature distribution of the receiver and the thermal response of the cell at different positions are calculated. Figure 6 shows the surface temperature distribution of the receiver at 108 mm, 109 mm, 110 mm, 111 mm, 112 mm and 114 mm when the ambient temperature is 25 \({}^{\circ}\)C and the heat transfer coefficient is 10 W/m\({}^{2}\)K. Figure 6a shows that the surface temperature of the cell reaches 49.8 \({}^{\circ}\)C at \(Z=108\) mm and the minimum temperature is 39.9 \({}^{\circ}\)C on the edge of the receiver. Figure 6b shows that the surface temperature of the cell at 109 mm is 53.4 \({}^{\circ}\)C and the edge temperature of the receiver is 42.1 \({}^{\circ}\)C. In Figure 6c, the cell is at the design focal plane of the lens where the surface temperature rises to 55 \({}^{\circ}\)C and the temperature at the edge of the receiver is 43.1 \({}^{\circ}\)C. In Figure 6d, the surface temperature of the battery continues to rise to 55.5 \({}^{\circ}\)C at \(Z=111\) mm.  
In Figure 6e, the cell is located at 112 mm, the temperature of the cell surface is a maximum of 57.4 \({}^{\circ}\)C and the temperature of the receiver edge is 44.4 \({}^{\circ}\)C. In Figure 6f, the cell temperature decreases and the surface temperature decreases to 51.4 \({}^{\circ}\)C. The minimum temperature of the receiver decreases to 40.9 \({}^{\circ}\)C. From the temperature change trend of the cell, it can be noticed that when \(Z<112\) mm, the temperature of the cell gradually increases in the direction away from the lens. The temperature of the cell is the highest at 112 mm. With the distance is further, the temperature of the cell gradually decreases. It can be seen that the temperature change in the cell surface is basically consistent with the change in radiation power because the surface temperature distribution of the receiver is related to the heat dissipation. The illumination non-uniformity near the focal position is the most serious thus causing photoelectric efficiency to decrease. The value of temperature is the highest for \(Z=112\) mm. In addition, the temperature difference between the cell and theedge of the receiver at 116 mm is 7.5 \({}^{\circ}\)C while the temperature difference at 112 mm is 13 \({}^{\circ}\)C. The higher the heat dissipation, the greater the temperature gradient inside the cell, resulting in a greater temperature difference between the cell and the edge of the receiver.
# Round Window Reinforcement-Induced Changes in Intracochlear Sound Pressure
## 2 Materials and Methods
### Numerical Approach to Assess the Effect of RWM Reinforcement on Cochlear Hydrodynamics  
Numerical simulations using two models of the cochlea [8; 11] were conducted to support our experimental data from the ICSP measurements after the RWM reinforcement. The simulations enabled us to estimate the RWM impedance after reinforcement and its components, including inertia and stiffness. Quantifying the RWM impedance by the simultaneous measurement of \(P_{ST}\) and the RWM volume velocity (\(U_{RWM}\)) is challenging, since the RWM motion modes differ from simple piston movement across frequencies [20; 21]. Additionally, after the reinforcement process, the RWM is occluded and the reliable measurement of the \(U_{RWM}\) is not possible.  
Both numerical models considered in the present study are LEMs that rely on the analogy between electrical, mechanical, fluidic, and acoustic domains to describe the behavior of a specific physical system as a circuit of lumped elements. The analogy only holds if the wavelength of the sound wave in the given system's fluidic or solid medium is much larger than the largest geometrical dimension of the system [22]. A LEM of the cochlea transforms its fluid dynamic behavior into an electrical system and represents the fluidic characteristics by electrical components: inductors (L), capacitors (C), and resistors (R). The sound pressure (P) and volume velocity (U) are equivalent to the voltage (V) and current (I), respectively, of an electrical system.  
The model published by [PERSON] et al. [11] used the stages volume velocity (\(U_{STAP}\)) to calculate the \(Z_{RWM}\) of a normal RWM using the assumption that \(U_{STAP}=U_{RWM}\)[20]. Due to the RWM reinforcement, it is doubtful whether this assumption is still valid, as other anatomical channels (vestibular aqueduct (VA) and cochlear aqueduct (CA)) can be activated to relieve the pressure. Considering this model limitation, [PERSON] et al. (2016) [8] presented an extended model of the cochlea by including VA and CA leakage pathways into their model to act as high-impedance pressure-relieving channels when the RWM is stiffened. Despite the mentioned weakness of the [PERSON] model if applied on a cochlea with reinforced RWM, we still considered it for comparison with the experimental data due to its simplicity and the need for fewer modeling assumptions compared to the [PERSON] model. A detailed description of both models, the corresponding model parameters, and a discussion of the results from the numerical simulations and the experiments are added to Section 4.3.
## 1 Introduction  
Many studies have been conducted on deformation bands in sandstones or other types of rocks to unravel the structural development and conditions in deformed zones at shallow levels of the continental crust ([1; 2; 3] for a review). Deformation bands have also attracted the interest of petroleum geologists because permeability, which is key for exploring oil reservoirs, is modified by the development of deformation bands [4; 5]. One interesting aspect of deformation bands is that they form at places of strain localization. In fact, many deformation bands have been reported from fold-and-thrust belts in association with thrusts or blind thrusts [4; 5; 6; 7; 8; 9]. Further, from the viewpoint of the development of fault zones, they develop at the tip of faults in so-called \"process zones\" [2; 10] and in the damage zones of fault zones [11; 12]. This is because deformation bands formed under slightly lower stresses below the critical stresses for faulting and thus can be considered as precursory structures to faults [13], where only a small amount of displacement occurred.  
Water-rock interactions (i.e., dissolution-precipitation reactions) are enhanced at fracture interfaces [14; 15; 16; 17]. However, this aspect of so-called structural diagenesis [18] has been little explored in the studies of deformation bands [19]. Such interactions among fluid flow, alteration and deformation in deformation bands and surrounding host parts have recently been shown in the fault damage zones of a few sedimentary basins: e.g., near the Dombjerg Fault, in the NE Greenland rift system [20; 21; 22]; near the Eisenstadt Fault in the Vienna Basin (Austria) [23]. Further, once a significant amount of clay minerals forms at the fracture interfaces in rocks, not only is the frictional strength significantly reduced (e.g., the internal coefficient of friction is 0.1 for smectite [24; 25], typical authigenic clay minerals formed by weathering), but the permeability is decreased by the swelling of such clay minerals [25]. Therefore, a landslide could be triggered by a large amount of rainfall due to the buildup of pore fluid pressure as well as low frictional strength in such weathered rocks [26].  
In the present study, we investigated microstructures in deformation bands in sandstones, which occurred in forearc coal-bearing Paleogene strata located in eastern Hokkaido in northern Japan. We first documented these deformation bands in sandstones developed under a special structural setting, where almost vertical strata suddenly become horizontal (i.e., flexure or monoclinic, referred to as flexure in the present paper). We then conducted detailed microstructural analyses of deformation bands in core and outcrop sandstone samples. We found that the deformation bands were characterized by cataclass and alignment of phyllosilicate, which plays a key role in the formation of deformation bands in the sandstones. We further showed that alteration (i.e., weathering) was induced in phyllosilicate grains by the formation of deformation bands, which was enhanced by water-rock interactions.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Domains**} & \multicolumn{2}{c}{**Conventional Brackets. C (\(n=60\))**} & \multicolumn{2}{c}{**Low-Friction Brackets. LF (\(n=60\))**} \\ \cline{2-5}  & **Mean** & **SD** & **Mean** & **SD** \\ \hline
**Functional limitation** & 0.4 & 0.6 & 0.5 & 0.6 \\ \hline
**Physical pain *** & 1.6 & 0.6 & 1.1 & 0.8 \\ \hline
**Psychological discomfort *** & 0.6 & 0.8 & 0.9 & 0.8 \\ \hline
**Physical disability *** & 0.5 & 0.7 & 0.3 & 0.5 \\ \hline
**Psychological disability *** & 0.2 & 0.4 & 0.4 & 0.7 \\ \hline
**Social disability** & 0.1 & 0.3 & 0.2 & 0.4 \\ \hline
**Handicap** & 0.0 & 0.0 & 0.1 & 0.1 \\ \hline
**Total OHIP** & 3.4 & 2.0 & 3.3 & 2.6 \\ \hline \end{tabular} Comparison between groups applying the Student’s \(t\)-test. * Statistically significant differences at \(p<0.05\).
* Statistically significant differences at \(p<0.01\).  
\end{table} 
Table 5: Comparison of quality of life measures between treatment groups (\(n=120\)). statistically significant differences in the impact of orthodontic treatment on OHRQoL when evaluating conventional and self-ligating brackets [33; 34]. The literature is scarce regarding information assessing the influence of bracket slot size on pain and OHRQoL in orthodontic patients. The methodology used in this study has been similar to that used in other scientific studies that analyze pain and OHRQoL in orthodontic treatment [6; 10; 20; 22; 23; 26; 29; 30; 31; 32; 33; 34]. Depending on the results obtained, we can give the patient information before and during the orthodontic treatment about which factors will have the greatest impact on their quality of life. Therefore, we can achieve greater cooperation from the patient and take preventive measures that will help to have greater success in future fixed orthodontic treatments. This study has several limitations in terms of sample size, the short follow-up period in relation to the duration of orthodontic treatment, and the absence of OHRQoL evaluation before starting orthodontic treatment. The possibility of generalizing these results could be limited as they are carried out in the same centrer; therefore, we would need to carry out a multi-center study with a larger sample size. In the future, a larger sample and randomized studies will be required to evaluate pain and improvements in OHRQoL following orthodontic treatment. It may be helpful to stratify the randomization by age and analyze young people and adults separately in future studies. Further multicenter clinical trials with longer treatment periods are necessary to eliminate the effect of the bias for the results.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 3 Data Acquisition and Preprocessing
### Remote Sensing Data Acquisition and Preprocessing
#### 3.1.3 Soil Data  
Since soil conditions directly affect the growth of grassland vegetation, especially in semi-arid grassland ecosystems, key factors such as soil organic carbon, nitrogen, and phosphorus content significantly influence the spatial distribution of above-ground biomass. In this study, soil data from the global soil database (HWSD 2.0) published by the Food and Agriculture Organization (FAO) and the International Institute for Applied Systems Analysis (IIASA) were selected ([[https://doi.org/10.4060/cc3823](https://doi.org/10.4060/cc3823) en]([https://doi.org/10.4060/cc3823](https://doi.org/10.4060/cc3823) en), accessed on 16 August 2024). Data on soil organic carbon, total nitrogen, total phosphorus, and pH value at a depth of 0-20 cm with a spatial resolution of 90 m were extracted [31]. The data were then resampled to a 30 m resolution using bilinear interpolation, a method chosen for its ability to preserve the spatial continuity of soil properties (e.g., organic carbon, nitrogen) that typically exhibit gradual spatial gradients rather than abrupt changes. This approach minimizes artificial discontinuities between adjacent pixels and aligns with the gradual variation characteristics of soil attributes in semi-arid grasslands. Following resampling, the data were clipped to the study area to ensure spatial consistency with other datasets.
## Appendix A Experiments Results

Table 5: Performance Metrics for Phishing Detection Models On TREC_06 Dataset (Training Time in Seconds denoted as **S**). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (S)** \\ \hline Naive Bayes & 97.47\% & 99.57\% & 95.92\% & 97.71\% & 0.5992 S \\ \hline Logistic Regression & 99.04\% & 98.66\% & 99.66\% & 99.16\% & 4.5619 S \\ \hline SGD Classifier & 99.10\% & 98.66\% & 99.66\% & 99.20\% & 5.3173 S \\ \hline XGBost & 98.75\% & 98.28\% & 99.52\% & 98.90\% & 29.1080 S \\ \hline Decision Tree & 98.23\% & 97.91\% & 98.97\% & 98.44\% & 250.7007 S \\ \hline Random Forest & 99.15\% & 99.00\% & 99.50\% & 99.25\% & 170.5102 S \\ \hline Extra Tree & 99.28\% & 99.25\% & 99.47\% & 99.36\% & 421.4103 S \\ \hline CNN & 99.39\% & 99.59\% & 99.31\% & 99.45\% & 99.6245 S \\ \hline RNN & 99.27\% & 99.18\% & 99.52\% & 99.35\% & 1148.2347 S \\ \hline LSTM & 99.12\% & 98.75\% & 99.68\% & 99.21\% & 1454.6466 S \\ \hline BERT & 99.66\% & 99.72\% & 99.65\% & 99.69\% & 877.7004 S \\ \hline DistilBERT & 99.68\% & 99.68\% & 99.75\% & 99.71\% & 493.6949 S \\ \hline RoBERTa & 98.99\% & 99.21\% & 98.96\% & 99.09\% & 937.3521 S \\ \hline GCN & 73.25\% & 73.15\% & 73.25\% & 73.13\% & 1345.6267 S \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance Metrics for Phishing Detection Models On CEAS_08 Dataset (Training Time) in Seconds denoted as **S**). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (S)** \\ \hline Naive Bayes & 99.23\% & 100.00\% & 97.73\% & 98.85\% & 0.0639 S \\ \hline Logistic Regression & 98.79\% & 100.00\% & 96.43\% & 98.18\% & 2.6391 S \\ \hline SGD Classifier & 99.34\% & 100.00\% & 98.05\% & 99.02\% & 0.6771 S \\ \hline XGBost & 98.68\% & 99.33\% & 96.75\% & 98.03\% & 22.6479 S \\ \hline Decision Tree & 95.06\% & 89.25\% & 97.08\% & 93.00\% & 7.9171 S \\ \hline Random Forest & 99.34\% & 99.67\% & 98.38\% & 99.02\% & 7.2300 S \\ \hline Extra Tree & 98.79\% & 97.75\% & 98.70\% & 98.22\% & 19.4767 S \\ \hline CNN & 99.45\% & 99.55\% & 99.40\% & 99.47\% & 60.3413 S \\ \hline RNN & 98.18\% & 97.35\% & 99.25\% & 98.29\% & 62.5940 S \\ \hline LSTM & 98.82\% & 99.39\% & 98.35\% & 98.87\% & 363.3502 S \\ \hline BERT & 99.61\% & 99.53\% & 99.69\% & 99.61\% & 150.1970 S \\ \hline DistilBERT & 99.37\% & 99.37\% & 99.37\% & 99.37\% & 84.3074 S \\ \hline RoBERTa & 99.45\% & 99.69\% & 99.22\% & 99.45\% & 152.0265 S \\ \hline GCN & 72.06\% & 72.16\% & 72.06\% & 72.04\% & 200. 3135 S \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance Metrics for Phishing Detection Models On Nazario_5 Dataset (Training Time) in Seconds denoted as **5**).
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 3 Results
### Prediction of Time Series at Single Sites  
To further compare the prediction results, the trained models were used to make prediction at each site using the remote sensing records and reanalysis meteorological data for the location of the site. After prediction, the bias between predictions and observations was calculated (Figure 5). The median biases of all three carbon fluxes were close to zero in the experiments, which means that the models remained unbiased during the prediction.  
In order to evaluate the modelling performance, three ML models (RF, ANN, and CNN) were used to predict the time series of carbon fluxes for four selected sites in different climate zones. Figure 6 shows the time series of GPP of these sites, including observed and predicted values by the models. CNN could lead to the best predictions at all sites, except that it had slightly lower R\({}^{2}\) (0.78) at NL-Loo than RF (0.83). All three models showed a similar trend in the predicted time series, but CNN tends to be closer to the observations. For the low values, all three models could make reliable estimates in most cases. However, for some peaks, RF and ANN showed significant overestimates, especially for ANN at FI-Hyy and CH-Dav (see red circles in Figure 6).
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
###### Abstract  
With the widespread use of smartphones, there is a surging demand for localization in indoor environments. The main challenges are the requirement of special equipment (e.g., a map database and Wi-Fi access points) and error accumulation for indoor localization. In this paper, we propose a novel collaborative indoor positioning method to reduce error accumulation. Estimated positions are corrected using the collaborator's positions when an encounter is detected by communication based on Bluetooth Low Energy (BLE). In addition, a map is obtained by taking photos of information boards. Therefore, the proposed method needs smartphones only; other equipment is not required. We obtained an accurate localization comparison using a machine learning model. The experimental results showed that the proposed method achieved reliable encounter communication in eight facilities. The collaborative localization method successfully enhanced position estimations. Specifically, the proposed method outperformed the existing baseline method by 13.0% in accuracy of indoor positioning.  
Bluetooth Low Energy; collaborative localization; encounter communication; indoor localization; particle filter
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 4 A Numerical Example
Figure 4: Errors between target agent and each tracking agent. Figure 3: Tracking trajectories of the system. According to Equations (17)-(20), the value of \(\gamma\) is related to the convergence rate. It can be seen from the Equation (11) that the value of \(\gamma\) is also positively correlated with maximum dwell time of unstable subsystems. Thus, there exists a trade-off in \(\gamma\) between the convergence rate and the maximum dwell time of unstable subsystems. In this example, with the trade-off factor \(\gamma=0.1\), we obtain \(\tau_{max}^{us}=1.2\), \(\tau_{min}^{s}=0.3\), \(\bar{\lambda}\approx 0.47\), \(\bar{\lambda}\approx 0.05\), \(N_{\sigma}^{-}(t_{0},t)=23\), \(N_{\sigma}^{+}(t_{0},t)=11\), \(N_{\rm s}=3\), \(N_{\rm hs}=2\), \(\lambda^{\bigstar}\approx 0.67\), \(\lambda^{\ast}\approx 0.05\). \(\frac{\tau_{max}^{us}}{\tau_{min}^{s}}=4\), \(\frac{((\bar{\lambda}-\gamma)\left\lceil\frac{N_{\sigma}^{-}(t_{0},t)}{N_{\rm s }}\right\rceil-\lambda^{\bigstar})N_{\rm s}}{((\bar{\lambda}+\gamma)\left\lceil \frac{N_{\sigma}^{+}(t_{0},t)}{N_{\rm hs}}\right\rceil+\lambda^{\ast})N_{\rm hs }}\approx 4.29\), then \(\frac{\tau_{max}^{us}}{\tau_{min}^{s}}<\frac{((\bar{\lambda}-\gamma)\left\lceil \frac{N_{\sigma}^{-}(t_{0},t)}{N_{\rm hs}}\right\rceil-\lambda^{\bigstar})N_{ \rm s}}{((\bar{\lambda}+\gamma)\left\lceil\frac{N_{\sigma}^{+}(t_{0},t)}{N_{ \rm hs}}\right\rceil+\lambda^{\ast})N_{\rm hs}}\), which apparently satisfies the condition in Theorem 1. As analyzed, the stability of the multi-agent tracking systems with quasi-cyclic topologies is related to the ratio of the maximum dwell time of unstable subsystems and the minimum dwell time of stable subsystems, which effectively verifies the correctness of theoretical results. Figure 5: Switching signal of the tracking process. Figure 6: Norm of tracking errors.