esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Uncertainties in Our Model-Based Assessment  
We utilized the InVEST model in this study, incorporating water yield, sediment delivery ratio and nutrient delivery ratio modules, in order to assess the ecosystem services of the TRHR. However, as the resources that are required to perform model calibration and testing are scare, the results presented in this study cannot be easily calibrated against measurement data. We therefore calibrated our results by comparing them with datasets published in the literature. As a result, the most significant limitation of this study is that our model parameters are both uncertain and variable; biophysical tables were used, for example, as inputs for the nutrient delivery and sediment delivery ratio modules, while the overall InVEST model also has its own limitations [45]. Subsurface water, an important component of TRHR water resources, was not calculated by the water yield module as part of this analysis [19], while potential soil loss, included in the SDR module, included only water erosion as part of this calculation. This is a key shortcoming of this study as two other important kinds of soil erosion are also common in the TRHR, freeze-thaw and wind erosion [21].
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 6 Hardware Architecture  
A simple modification of the existing decoder hardware allows decoding of the proposed algorithm. Therefore, while maintaining the basic structure of the existing architecture, the addition of the capability to dynamically select the optimal partial H-matrix based on the channel state significantly reduces energy consumption while maintaining decoding accuracy. Through such a simple modification, the proposed decoding method can be easily integrated into existing systems, offering improved performance and energy efficiency.
# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 4 Adversarial Training for Data Debiasing  
As previously explained, removing the sensitive attribute is rarely sufficient to guarantee non-discrimination as correlations are likely to exist between other attributes and the sensitive one. In general, detecting and suppressing complex correlations between attributes is a difficult task.  
To address this challenge, our approach GANSan relies on the modeling power of GANs to build a sanitizer that can cancel out correlations with the sensitive attribute without requiring an explicit model of those correlations. In particular, it exploits the capacity of the discriminator to distinguish the subgroups indexed by the sensitive attribute. Once the sanitizer has been trained, any individual can apply it locally on their profile before disclosing it. The sanitized data can then be safely used for any subsequent task.
## 3 Results and Discussion
### NACA0012 Models
#### 3.1.4 Results of the Sharp Trailing Edge Based on Definition Formula Applying 264 Points  
The distributions of \(\mathrm{P/P_{t}}\), \(\mathrm{T/T_{t}}\), and \(\mathrm{U/U_{t}}\) of simulation data and reference data are presented in Figures 14 and 15, and Table 15 shows the corresponding mean error ratios. Figure 16 gives the optimal total mean error ratios under different configurations, and those are highlighted in red. For the far-field distance, the finest results for the three distances are 2.83%, 2.76%, and 3.45%. For the turbulence model, the minimum result of SST is 2.76%, and that of SA is 4.51%. For the flux type, ROE has a minimum value of 2.76%, and that of AUSM is 3.82%. In general, the SST + 16 m, combined with ROE has the best performance of 2.76%. Compared with optimal results of other simulation configurations, the corresponding accuracy developments are 2.39%, 38.85%, 48.88  
Figure 16: Comparison of the simulation performances of the sharp trailing edge based on 264 points formula under different simulation configurations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Simulation Configurations**} & \multicolumn{4}{c}{**Mean Error Ratios under Different Configurations**} \\ \cline{2-7}  & \multicolumn{2}{c}{**ROE**} & \multicolumn{2}{c}{**AUSM**} \\ \cline{2-7}  & **P/P\({}_{\mathbf{t}}\)** & **T/T\({}_{\mathbf{t}}\)** & **U/U\({}_{\mathbf{t}}\)** & **P/P\({}_{\mathbf{t}}\)** & **T/T\({}_{\mathbf{t}}\)** & **U/U\({}_{\mathbf{t}}\)** \\ \hline SST + 12 m & 3.44\% & 3.81\% & 1.23\% & 7.95\% & 2.81\% & 0.70\% \\ \hline SA + 12 m & 8.42\% & 3.39\% & 1.73\% & 5.19\% & 5.03\% & 4.08\% \\ \hline
**SST + 16 m** & **2.62\%** & **4.00\%** & **1.65\%** & 18.77\% & 17.84\% & 1.62\% \\ \hline SA + 16 m & 8.82\% & 4.45\% & 2.92\% & 6.81\% & 8.17\% & 1.23\% \\ \hline SST + 20 m & 6.03\% & 2.75\% & 1.56\% & 9.67\% & 8.47\% & 6.99\% \\ \hline SA + 20 m & 5.11\% & 7.58\% & 1.26\% & 13.73\% & 7.50\% & 5.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: The mean error ratios of P/P\({}_{\mathbf{t}}\), T/T\({}_{\mathbf{t}}\), and U/U\({}_{\mathbf{t}}\) of the sharp trailing edge based on 264 points.  
Figure 15: Distributions of U/U\({}_{\mathbf{t}}\) of the sharp trailing edge based on 264 points formula at location of \(\mathrm{x}/\mathrm{L}=0.95\).
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 4 Test Results  
The prototype ADC is fabricated in SMIC 55 nm 1p8m CMOS technology. Figure 17 shows the chip micrograph and the layout zoomed-in view of the ADC core, which only occupies 0.008 mm\({}^{2}\) (102 \(\upmu\)m \(\times\) 84 \(\upmu\)m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Noise Source**} & **Comparator** & \multirow{2}{*}{**Thermal Noise**} & **Quantization** & **Reference** \\  & **Noise** & & **Noise** & **Noise** \\ \hline POWER (\(\upmu\)_V\({}_{rms}\)_) & 250 & 142 & 676 & 152 \\ Ratio (\%) & 20.5 & 11.6 & 55.4 & 12.5 \\ \hline \end{tabular}
\end{table}
Table 2: Noise contribution of the proposed SAR ADC arrangement of this design.  
Figure 16: The proposed RVB.  
During the testing, the ADC operated with a reference voltage of 1.2 V. To achieve a full-swing output, the input VPP was increased to nearly 1.1 V. This indicates a gain error of approximately 9%. At a sampling rate of 200 MHz, the SA phase and sampling phase timings are adjusted using the clock duty cycle adjustment circuit inside the test chip to meet the requirements of sampling and conversion. All subsequent tests were conducted based on a sampling rate of 200 MHz.  
Figures 18 and 19, respectively, depict the FFT plots under low-frequency input and Nyquist frequency input conditions. Under a low-frequency input of 6.2 MHz, the measured values for SNDR and SFDR are 56.3 dB and 59.3 dB, respectively, while under the Nyquist frequency input of 99 MHz, the measured values for SNDR and SFDR are 55.3 dB and 66.6 dB, respectively. Figure 20 shows the measured static performance using the code density method. The differential nonlinearity (DNL) is within \(-\)0.69/+0.62 LSB, and the integral nonlinearity (INL) is within \(-\)1.1/+1.2 LSB.  
Figure 17: Chip micrograph and layout zoomed-in view of the SAR ADC core.  
Figure 18: FFT plot with a 6 MHz input frequency at 200 MS/s.  
Figure 21 plots the measured SNR and SNDR values versus the input power when the input frequency is 99 MHz. The total power consumption of the single ADC core is approximately 2.8 mW at a 1.2 V power supply with a sampling frequency of 200 MS/s. The proposed SAR ADC achieves a small area of 0.008 mm\({}^{2}\) and a FoM of 27 fJ/conversion-step at Nyquist rate.  
Figure 19: FFT plot with a 99 MHz input frequency at 200 MS/s.  
Figure 20: Measured DNL and INL.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 3 System Description
### Hardware of the Transmitter  
The complete block diagram of the transmitter is shown in Figure 4a. It consists of a digital platform that controls the power amplifier, the Wattmeter and the antenna tuner. The system uses the WiFi for control and configuration purposes, while the Global Positioning System (GPS) and the Oven-Controlled Crystal Oscillator (OCXO) are used for time and frequency synchronization, respectively.
# Analytical Modeling and Application for Semi-Circular Notch Flexure Hinges
## 3 General Analysis Modeling
### Stress Characteristics Modeling  
The stress and stress characteristics of the flexure hinge are the important characteristics in the design and practical application of the hinge. For the semi-circular flexure hinge, the strain is small considering the length and the structure. Therefore, the stress characteristics of the hinge are mainly analyzed. The stress of the flexure hinge under different rotation angles can be obtained from the FEA results. The relationship between the rotation angles and the corresponding stress for the flexure hinge with different ratios of \(t\) and \(L\) is presented in Equation (11):  
\[\frac{\sigma}{E}=\sum_{i=0}^{4}b_{ij}(\frac{t}{L})^{i}\theta^{j}\ (i+j\leq 4) \tag{11}\]  
In particular, for a given maximum rotation angle \(\theta_{\text{max}}\), the \(\sigma\) can be expressed as the \(\sigma_{\text{s}}\) while the stress \(\sigma\) is the yield strength \(\sigma_{\text{s}}\) of the material. Equation (11) can be expressed as in Equation (12) which can be utilized in the selection of material in the design of flexure hinge for the given value of \(t/L\) and maximum rotation angle:  
\[\frac{\sigma_{\text{s}}}{E}=\sum_{i=0}^{4}b_{ij}(\frac{t}{L})^{i}\theta^{j}\ \ (i+j\leq 4) \tag{12}\]  
The corresponding coefficients for Equations (11) and (12) are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Coefficients** & **Value** \\ \hline \(a_{0}\) & 63.91 \\ \(a_{1}\) & \(-85.78\) \\ \(a_{2}\) & 43.27 \\ \(a_{3}\) & \(-9.73\) \\ \(a_{4}\) & 2.205 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coefficient for Equations (9) and (10).
## 4 Result
### Comprehensive Analysis of Remote-Sensing Interpretation Results
Moreover, geological data analysis reveals that the area is underlain by weak lithologic strata, leading to a preliminary assessment that it is potentially susceptible to landslide hazards. Validation via the online platform (Table 4(i)) did not reveal direct, significant landslide indicators. This suggests that the area may be in the early stage of deformation accumulation, indicating its potential value as a target for early warning and monitoring. Overall, hazard point C is currently in a creeping deformation stage, presenting identifiable landslide risk despite limited direct optical evidence. Continued monitoring is recommended to assess long-term deformation evolution and implement appropriate preventive measures;acteristics, geological conditions, and stability status of each hazard site. The analytical results indicate that InSAR technology demonstrates significant advantages in monitoring ground-deformation rates and identifying deformation areas, enabling the precise quantification of surface displacements and effectively revealing deformation trends. However, in certain areas, due to data decorrelation effects or gaps in interpretation, the accurate delineation of landslide boundaries can be challenging. Therefore, incorporating optical remote-sensing imagery allows clearer delineation of landslide boundaries, intuitive identification of surface-damage features, and provides high-resolution imagery of the deformation zones. This integrated approach facilitates more comprehensive and accurate identification and assessment of potential landslide hazards. Collectively, the analyses of hazard points A, B, and C indicate that InSAR is particularly effective for monitoring gradual deformation and quantifying landslide movements, whereas optical remote sensing provides superior boundary delineation and morphological characterization. Single-method approaches exhibit inherent limitations; thus, combining both methodologies substantially improves landslide hazard identification and monitoring precision, increasing the reliability of geological hazard early warning systems. This comprehensive remote-sensing approach is scientifically robust, providing valuable technical support for future landslide prevention strategies, risk assessment, and proactive disaster management.
###### Abstract  
In the present paper, a new type of surface-bonded fiber Bragg grating (FBG) dynamometry bolt is designed. It is assumed that the adhesive layer is a linear viscoelastic material and its creep mechanical behavior is expressed by the standard linear solid model. The shear strain transfer model of the surface-bonded FBG sensor is established. Additionally, the instantaneous and quasistatic strain transfer functions of the surface-bonded FBG sensor are obtained. The functions are validated by a uniaxial tensile test and a long-term constant-load tensile test. The test results show that the strain measured by the FBG sensor has a proportional relationship with the strain measured by the resistance strain gauge. Furthermore, under the fixed load for a long period of time, the strain of the FBG sensor has a tendency to drift and the strain reduction rate is about 40.5%. Finally, the field application is carried out in a mining area. It has been proved that the ground pressure online monitoring system based on the FBG sensing technology can successfully monitor the stress of the rock bolt.  
fiber Bragg grating; dynamometry bolt; strain transfer efficiency; pressure online monitoring system +
Footnote †: journal: Applied Sciences
# Geovisualization of Buildings: AI vs. Procedural Modeling
## 2 Materials and Methods
### What Can Be Achieved with Today's Technology and Also by Combining Different Technologies?
A control group of buildings, modeled using traditional methods, should be included to provide a baseline for comparison. This will allow for a robust assessment of how well the procedural models replicate the variety found in real-world structures. The second hypothesis, which suggests that this modeling can be achieved through different approaches such as AI and machine learning, requires the implementation of AI-based techniques to test its validity. Specifically, AI methods like deep learning for computer vision and Natural Language Processing (NLP) for parsing architectural descriptions can be employed to create 3D models. The experimental design for this hypothesis should involve gathering input data in the form of images and textual descriptions of buildings. AI models should then be trained using a subset of this data to learn to recognize and replicate architectural features. The effectiveness of these AI-generated models should be evaluated by comparing them with manually createdmodels, focusing on their accuracy, level of detail, and the time efficiency of the modeling process. A comparative analysis should be conducted to assess which approach--AI-based or procedural--better accommodates the diversity of buildings. This analysis will provide critical insights into the strengths and weaknesses of each method. To further strengthen the validation process, a hybrid approach can be tested, wherein procedural modeling is enhanced with AI-generated inputs. This combined method could involve using AI to generate initial modeling scripts that are subsequently refined through procedural techniques. The experimental design for this approach would involve creating a workflow that integrates AI and procedural modeling and then analyzing the effectiveness of this hybrid method in handling complex architectural structures. The resulting models can be compared to those produced by AI or procedural methods alone to determine the added value of combining these techniques. Throughout the validation process, it is important to employ statistical methods to assess the significance of the results. Hypothesis testing, such as t-tests or ANOVA, can be used to evaluate whether the differences between models generated by various methods are statistically significant. This statistical validation will ensure that the conclusions drawn from the experiments are robust and reliable.
## 1 Introduction
The proposed forecasting system incorporates a framework of sensor systems with three factors [15], modeling (identifying relations among sensors), profiling (identifying events and their characteristics), and managing trade-offs (training and tuning). This paper further examines this sensor system's framework by extending it to _in-situ_ sensing for space weather forecasting based on sensors from multiple satellites. In our model, the prediction of space weather is done on the satellite network. Our proposed model assumes that satellites record the observation data and they exchange information of anomaly detection of the sensor data. A related study [16] has investigated the Standard Radiation Environment Monitor (SREM) that provides radiation hazard alarms to the host spacecraft. The SREM units are on-board INTEGRAL and Rosetta of the satellites for monitoring protons. In this study, the demonstration study showed that several SREMs (monitoring different energy bands) units successfully generated alarms of the radiation hazard for the satellites. On the contrary, space weather forecasting also can be done on a computer (_i.e._, workstation) at Earth. A study [17] investigated a possibility of early warning of radiation hazards by physics-base model. In these circumstances, the two kinds of forecasting manner can be considered in space weather. Our study aims to investigate a possibility of space weather forecasting on the satellite network with _in-situ_ sensing and adaptive information processing. After explaining the data used in Section 2.1, Section 2 then examines modeling (Section 2.2) and profiling (Section 2.3). Managing trade-off is conducted by an SVM in Section 2.2. To extend the framework to _in-situ_ sensor systems from multiple satellites, Section 3 compares the performances of the four-sensor (from two satellites) model and the eight-sensor (from four satellites) model. Two typical examples are tested with the proposed model: a coronal hole and CMEs. Section 4 discussed a significance of our proposed model. Furthermore, this paper states remained issues for realizing space weather forecasting system based-on a self-organizing network. Section 5 provides summary and concludes this paper.
This method starts with initializing random solutions and uses fitness to assess the quality of the solutions. After multiple iterations, it finds the best overall solution, with advantages including ease of operation, good stability, and high search efficiency [8]. Therefore, combining the Dingo Optimization Algorithm with the Support Vector Machine to establish a DOA-SVM classification model and using the DOA to quickly optimize the key parameters of SVM within the definition domain can effectively improve the model's classification performance. Despite numerous studies on welding defect detection, challenges remain in balancing accuracy and model complexity. In this work, a defect recognition method combining the NRS with the DOA-SVM in a multisensory framework is proposed to address these challenges effectively. This paper first establishes a multi-information fusion experimental platform for Gas Metal Arc Welding (GMAW), collecting and extracting features from welding process visual images, arc signals, and vibration signals, to build an information system for welding quality defect identification and prediction based on feature-level fusion (original dataset). Secondly, the Neighborhood Rough Set's feature reduction is applied to analyze the original dataset of the information system, obtaining a dataset containing fewer or minimal features. Furthermore, using the streamlined dataset to optimize the training of the DOA-SVM model, a welding defect identification and prediction model is obtained. Lastly, the model is validated with test samples, and its classification performance is compared and analyzed.
## 3 The Proposed Method  
### Improvement on FOA  
It can be seen that basic FOA search optimal solution is in the neighborhood of previous group location only. In other words, the location of each fruit fly during the iteration process is determined according to previous swarm coordinate and the preset fly distance range. The optimization is therefore simplified further to shorten the program code and decrease the convergence time. Unfortunately, an excessive pursuit of local searching ability has also reduced the diversity of the fruit fly swarm. The basic FOA can easily to fall into a local extreme due to poor global searching capacity. In order to balance the optimization process, a variation coefficient \(C_{v}\) and a disturbance coefficient \(C_{d}\) are introduced during the process of producing new fruit flies to enhance the group diversity. The principle of the two coefficients is presented in Figure 1.  
The smell concentration of each individual is calculated to search the maximal fitness in Step 3.3. Then, the concentrations are ranked as (\(\mathit{smell}_{1}\), \(\mathit{smell}_{2}\), \(\mathit{smell}_{3}\),..., \(\mathit{smell}_{i}\),..., \(\mathit{smell}_{p}\)), where \(\mathit{smell}_{1}\) is the biggest and \(\mathit{smell}_{p}\) is the worst. For any \(\mathit{smell}_{i}>C_{v}\), the location of the corresponding fruit fly is updated according to Equation (11). That is to say, the fly moves towards the best one. If \(\mathit{smell}_{i}<C_{v}\), the variation operation is conducted and the fly obtains its location in a new way, which can be expressed as follows:  
\[\left\{\begin{array}{l}X_{i}=\mathrm{rand}(LR^{\prime})\\ Y_{i}=\mathrm{rand}(LR^{\prime})\end{array}\right. \tag{15}\]  
where \(LR^{\prime}=\mathrm{max}(LR,\,X_{fg},\,Y_{fg})\), the reason lies in the coordinate range of the fruit fly swarm may exceed the original scope with the optimization process. The location of the variation fly is updated in a bigger range randomly to search appropriate solution.  
Figure 1: (a) Principle of the variation operation; (b) principle of the disturbance operation.  
Moreover, the disturbance coefficient \(C_{d}\) is introduced when the position of the group remained unchanged for several iterations. Assuming that the _smellbest_ after \(m\)-th iteration could be presented as _smellbest\({}_{m}\)_ and _smellbest\({}_{n}\)_ for \(n\)-th, if _smellbest\({}_{m}\)_ = _smellbest\({}_{n}\)_ and \(n\) - \(m\) > \(C_{d}\), then the location of each fruit fly can be obtained as follows:  
\[\left\{\begin{array}{l}X_{i}=X_{fg}+10\text{rand}(FR)\\ Y_{i}=Y_{fg}+10\text{rand}(FR)\end{array}\right. \tag{16}\]  
where \(i\) = 1, 2, 3,..., \(P\). The aim of the disturbance operation is to expand the searching area when the preset fly distance range does not work in finding a better smell concentration for the fly swarm. The flowchart of the proposed IFOA is shown in Figure 2.  
Figure 2: Flowchart of the improved fruit fly optimization algorithm (IFOA).