esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR\(-\)2 and Sentinel\(-\)1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel\(-\)1A is 2% higher than that of ALOS/PALSAR\(-\)2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR\(-\)2. Upon comparing and analyzing the visibility, coherence, closed-loop residuals, and typical time series of landslide deformation from the a two kinds of data, it was found that the extremely low quality of available Sentinel\(-\)1 A summer data is a major factor influencing that system's performance. ALOS/PALSAR\(-\)2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling-Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling-Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling-Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring.  
Qinling-Daba Mountains; Interferometric Synthetic Aperture Radar (InSAR); ALOS/PALSAR\(-\)2; Sentinel\(-\)1A; IPTA\(-\)SBAS; landslides detection 2023
## 3 Methods
### Features of EEG Signals
#### 3.1.5 Empirical Mode Decomposition  
Empirical mode decomposition (EMD) is a signal analysis technique that involves decomposing a signal into components of different frequencies and amplitudes, called intrinsic mode functions (IMFs) [83]. This method was introduced by [PERSON] and his collaborators in 1998 and is particularly useful in analyzing nonstationary and nonlinear signals [84]. EMD is an adaptive decomposition method that adjusts to the signal's properties over time, enabling the analysis of nonstationary signals such as EEG signals [85]. In the case of epileptic seizures, these signals often exhibit frequency and amplitude variability. EMD can help extract signal components that correspond to different aspects of an epileptic seizure. EMD allows for signal analysis at different time scales, enabling the identification of both low-frequency and high-frequency signal components (Figure 9). In the case of epileptic seizures, there can be both slow changes at low frequencies and rapid changes at high frequencies. Multiscale analysis using EMD can reveal these different aspects of an epileptic seizure [85].  
The main assumption of the EMD method is that any signal can be decomposed into IMF components that satisfy two criteria: They must be \"sufficiently smooth\" at each point and the number of extrema (maxima and minima) must be equal or differ by one at most. The process of signal decomposition using EMD consists of several steps [86]:  
1. Identifying all local maxima and minima in the signal.
2. Calculating the average value between the maxima and minima for a given IMF component.
3. Generating the first IMF component by taking the difference between the input signal and the calculated average value.  
4. Checking if the IMF component meets the IMF criteria. If so, it is considered the first IMF component. If not, the process is repeated for the remaining signal after removing this component.
5. Repeating steps 2-4 for the remaining signal until obtaining all IMF components.  
After applying EMD, we obtain a signal decomposed into IMF components that represent different frequencies and amplitudes present in the original signal. These IMF components can be analyzed separately to obtain more detailed information about the signal's characteristics at different time scales. When extracting features from an EEG signal, we proceed similarly to extracting features for wavelet transform, but we calculate the features for each successive IMF.
# The Carbon Footprint of Milk Production on a Farm
## 3 Results and Discussion
### Effect of Feed, Its Origin and the Feeding System on the CF
[25] demonstrated that the introduction of pastures with HSG reduced the acidification and eutrophication potential by 11% and 6%, respectively, as compared with traditional grassland. This indirectly translates into a reduction in the CF, especially in the longer term and with improved manure management. In contrast, in Latin America, [PERSON] et al. [43] observed no apparent differences in the CF between grazing and intensive systems, despite zero-grazing systems being more productive in terms of the milk yield. This indicates that feed efficiency and local conditions play a role that is equally important to that of the type of feed itself. A study conducted in the Peruvian Amazon by [PERSON] et al. [42] demonstrated that the silvopastoral system, despite the limited proportion of external feed, was characterised by a relatively high CF value (2.26 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM), of which as much as 1.81 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM originated from enteric fermentation. This means that even under grazing systems, feed quality and herd management are of crucial importance. Under alpine conditions, [PERSON] et al. [28] observed that systems with a higher proportion of BSHC concentrated feed (Brown Swiss high concentrate) showed a lower CF value (1.14 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM) than under systems with a low proportion of AGLC concentrated feed (Alpine Grey low concentrate, 1.55 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM), with the latter systems, however, having a considerably lower impact on biodiversity. This suggests that the intensity of feeding alone is not the only factor affecting the environmental balance. [PERSON] et al. [5] noted that farms with lower feed consumption and good milk yield achieved lower CF values, even under tropical conditions. In that study, CH\({}_{4}\) from enteric fermentation accounted for 60% of emissions, and the variability in the amount of dry feed offered per kg of FPCM milk was a significant CF-differentiating factor. In summary, the feeding system affects the CF of milk production in many ways: through the diet composition, feed and fertiliser use intensity, feed quality, and manure management method. Although grazing systems often generate a lower CF, their efficiency is determined by the quality of available feed and production efficiency [9; 29; 53]. In contrast, intensive systems, despite higher emissions associated with feed and fertiliser, can exhibit a lower unit CF at high productivity if accompanied by efficient management and proper feed ration planning [5; 28]. Therefore, the assessment of the impact of the feeding system on GHG emissions should be integrated into the analysis of the entire product life cycle, taking into account local conditions, the quality of feed raw materials, and their origin [27; 41; 43].
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Analysis Method of Error Distribution Patterns in MLS Point Clouds
#### 3.4.3 Test Data Preprocessing  
The test data consisted of MLS point clouds acquired from the Columns. According to the assumptions of the proposed analysis method, equal point density across all four planes is required. To achieve this condition, the MLS point clouds of the Columns were manually segmented into individual planes. This plane-level segmentation is crucial for verifying the consistency of the error distribution pattern within a single MLS point cloud. Moreover, segmentation prevents inaccurate C2M distance measurements that may result from incorrect normal vector calculations near the edges of the Columns.  
Subsequently, random down-sampling was applied to each segmented point cloud. The number of down-sampled points was set to match the smallest number of points among the four planes. This parameter was determined to ensure sufficient data for analysis while maintaining an equal number of points across all planes. Therefore, the random down-sampling parameter was determined independently for each Column to maximize the number of points used in the analysis.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 2 Method  
### Principle of Gas Column Concentration Inversion  
When the multispectral infrared imaging system works, the radiation received by the detector unit, including the radiation of the background, target gas and atmosphere. Within a certain detection distance, the influence of the atmosphere on the radiation transmission process is relatively small. Therefore, the infrared radiation received by the detector mainly includes the radiation from the background and the radiation of the target gas [31]. The radiation energy received by the detector can be expressed as follows:  
\[DN_{s}=\tau_{gas}DN(T_{B})+\left(1-\tau_{gas}\right)DN\big{(}T_{gas}\big{)} \tag{1}\]  
where \(DN_{s}\) is the gray level of the detector connected to the output; \(\tau_{gas}\) is the transmittance of the target gas; \(DN(T_{B})\) is the gray value corresponding to the background radiation; \(DN\big{(}T_{gas}\big{)}\) is the gray value corresponding to the target gas radiation; \(T_{B}\) is the temperature of the background; and \(T_{gas}\) is the temperature of the target gas. According to Equation (1), the relationship between the gas absorbance and each radiation quantity is  
\[A=ln\frac{1}{\tau_{gas}}=ln\frac{DN(T_{B})-DN\big{(}T_{gas}\big{)}}{DN_{s}-DN \big{(}T_{gas}\big{)}} \tag{2}\]  
where \(A\) is the absorbance of the gas. According to Equation (2), the absorbance of the gas can be obtained if the background radiation, the target gas radiation and the radiation received by the detector are known. According to the Lambert-Beer law, the absorbance of the gas is closely related to the column concentration of the gas. Therefore, determining how to accurately obtain background radiation \(DN(T_{B})\) is the focus of gas column concentration inversion research.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 3 Experimental Setup
### Equipment Setup  
The designed five-pulley belt drive system was installed on an engine dynamometer (Figure 12). The bottom left pulley was the DR pulley, marked as pulley 1, and the top middle pulley was the DN pulley, marked as pulley 4. The remaining pulleys were as follows: pulley 2 connected to a hub load sensor for belt tension measurement; pulley 3 connected to a speedometer for belt speed measurement;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Variation** \\ \hline Pulley Radius (mm) & \(R\) & 76.2/108/148 \\ Thermal Conductivity of the Pulley (W/m/k) & \(\Lambda R_{p}\) & 55 (Steel)/0.3 (FRP) \\ Transmitted Speed (RPM) & \(\omega_{p4}\) & 2000/4000/6000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pulley ID**} & \multicolumn{3}{c}{**Layout 1**} & \multicolumn{3}{c}{**Layout 2**} \\ \cline{2-7}  & **X (mm)** & **Y (mm)** & **Radius (mm)** & **X (mm)** & **Y (mm)** & **Radius (mm)** \\ \hline
1 & 0 & 0 & 148 & 0 & 0 & 148 \\
2 & 228 & \(-\)18 & 65 & 240 & \(-\)29 & 65 \\
3 & 297 & 187 & 62 & 311 & 255 & 62 \\
4 & 212 & 189 & 76 & 205 & 185 & 108 \\
5 & 117 & 104 & 65 & 96 & 113 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layouts of tested belt drive systems.  
and pulley 5 connected to a tensioner that reduces the vibration of the belt and maintained a constant belt tension. Moreover, an insulated chamber equipped with a temperature control system is installed on this engine dynamometer to maintain the constant temperature. Two thermocouples were mounted at the two sides of the chamber to monitor the internal temperature. A case fan present inside the chamber ensured that the air temperature was uniformly distributed. In addition, thermal measurement devices were installed close to the measurement locations. Five infrared cameras were installed to measure the temperatures at the selected locations. A thermal imaging camera targeting the DN pulley was installed to capture the temperature distribution of this pulley. Furthermore, an ancillary data acquisition system was used to record the temperature values measured from the sensors.  
The thermal model used the abovementioned information related to the belt layout and operating conditions, along with the pulley geometries and thermal properties (\(h_{p}\), \(\Lambda_{p}\), \(\cdots\)) measured using standard procedures, to calculate the temperature distributions of the belt drive system under the designed operating conditions. The comparison between the experimental and analytical results is presented in the following section.
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 1 Introduction
The attention mechanism can reduce the effect of irrelevant information on the results and enhance the influence of related information by assigning different weights and improve prediction results' accuracy. 3. We compare the proposed ATT-LSTM model with the other six models on two real multivariate time series data sets based on two evaluation indicators: MAE and RMSE. The results prove the effectiveness of the model in predicting multivariate time series data. The main structure is as follows: the second part introduces the related work of the LSTM and attention mechanism. The third part explains the ATT-LSTM model. The fourth part is the part of experimenting on two data sets. The fifth part is the summary part, which summarizes the results of our work and the existing shortcomings.
## 3 Results and Discussion
### Imputation of Daily Solar Insolation Data  
According to the AWS location, the imputation process used to estimate the daily solar insolation data followed the best empirical model based on the research results. Table 13 shows the number of imputed data for each AWS. _La Josefina_ had the most imputed values; it was necessary to fill in 2870 missing data. _Botana_ had the lowest imputed values, filling 572 missing data. On average, the empirical models allowed imputing about four years of missing data; therefore, it was necessary to fill these empty gaps by using temperature data. The time series before and after the daily solar insolation imputation process for each AWS is presented in Figures 12-19.  
Figure 14: Cerro Páramo imputation.  
Figure 13: Viento Libre imputation.  
Figure 16: Botana imputation.  
Figure 15: Universidad de Naríno imputation.  
Figure 17: La Josefina imputation.  
Figure 18: Paraiso imputation.  
Figure 20 shows the monthly daily average solar insolation for all analyzed AWSs. The AWS located in the Pacific zone presented similar behaviors; namely, they registered a peak in August and September and a lower level in November. _Viento Libre_ was the only AWS that recorded values close to 4000 Wh/m\({}^{2}\) day in August. The _Botana_ and _Universidad de Narito_ AWSs in the Andean region showed a peak between October and November. The _Cerro Paramo_ and _Viento Libre_ AWSs exhibited certain energy complementarity because the lower level in the first AWS compensates the higher level of the second AWS.
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 3 Proposed Method
### Noise Removal Process  
The proposed algorithm uses a non-local mean technique to remove the co-amplified noise during the enhancement process of low-light images. Unlike a typical local mean filter, the non-local mean technique uses an average of the pixels around the filter center to remove noise, weighted by how similar the surrounding pixels are to the center. This results in much better sharpness after filtering and less loss of detail in the image compared with the local mean filter. However, non-local mean techniques rely on similarity to set weights, which means that the structural features of the filtering region are not taken into account. To address these issues, this paper proposes a modified non-local mean algorithm based on a local steering kernel. Steering kernels are characterized by considering gradients and analyzing the radiosity of pixels in the local region, and typically take the following form [13].  
\[K_{x,y}(i,j)=\frac{\sqrt{det(C_{x,y})}}{2\pi h^{2}}exp\left(-\frac{[i\,j]^{ \mathrm{T}}C\,i\,j]}{2h^{2}}\right),\quad-k\leq i,j\leq k \tag{19}\]  
Here, \(C_{x,y}\) refers to the covariance matrix of size \(2\times 2\) computed on a square local region centered at pixel \(x,y\)[17]. \(i,j\) refers to the relative coordinates away from the center of the steering kernel. \([i\,j]\) represents a matrix representation of the coordinates, and \(k\) is a constant that determines the size of the steering kernel. \(h\) is a smoothing constant to control the scope of the steering kernel [18]. The weights for denoising are calculated by comparing the similarity of the center and periphery regions to the steering kernel. The similarity comparison is calculated using Euclidean distance, and to convert this result to weights \(u_{x,y}(i,j)\), we use the following formula.  
\[u_{x,y}(i,\,j)=\frac{1}{z_{x,y}}exp\left(-\frac{\|w(x,y)-w(x+i,y+j)\|_{2}^{2}} {h^{2}}\right) \tag{20}\]\[z_{x,y}=\sum_{i,\ j\in K_{x,y}}exp\left(-\frac{\|w(x,y)-w(x+i,y+j)\|_{2}^{2}}{h^{2} }\right) \tag{21}\]  
Here, \(w(x,y)\) means the center window centered at \(x,y\), and \(w(x+i,y+j)\) means the comparison window set at a distance of \(i,j\) from the center window. \(z_{x,y}\) is the normalization constant used to convert the results of the similarity comparison into weights. The denoising result \(out(x,y)\) is calculated by convolving the pixel value with the steering kernel \(K_{x,y}(i,j)\) and the weight \(u_{x,y}(i,j)\), as shown in the following equation.  
\[out(x,y)=M_{x,y}(i,j)\ *\ K_{x,y}(i,j)\ *\ u_{x,y}(i,j) \tag{22}\]  
Here, \(*\) refers to the convolution operator and \(M_{x,y}(i,j)\) is the pixel value in the same region as the local steering kernel.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 2 Signal Models  
In a typical GPS receiver, the received signal model and the correlation signal model are key to understanding the behavior of satellite signal transmission. By analyzing the characteristics of the correlation signal model, an appropriate spoofing detection strategy can be formulated. This section introduces these two signal models.
## 3 Simulation Environment and Approaches  
### Model in the Loop  
Extending the ideal simulation environment developed in [25], the Model-in-the-Loop (MIL) environment depicted in Figure 4 integrates comprehensive attitude and orbit dynamics. This setup incorporates the World Magnetic Model (WMM) 2020-2025, the DE405 solar system model, and a conic eclipse model to predict sunlit, umbra, and penumbra conditions [43]. The MIL environment employs various sensors: the MMC5603 NJ magnetic field sensor (16-bit resolution per axis), the SLCD-61N8 Coarse Sun Sensor (12-bit resolution, restricted by the STM32F411 RE MCU), and the L3 GD20 gyroscope (16-bit resolution per axis). These sensors are subjected to white noise, bias, misalignments, nonorthogonalities, quantization, and a sampling time of 0.01 s.  
Figure 4: Model-in-the-Loop environment. Sensor noises, quantization, sample times, and encoding and decoding functions are considered for this simulation approach.  
Sensor signals are simulated and packed into a 32-byte vector. This data packet is then transmitted to the attitude estimator, where it is unpacked, and the attitude estimation algorithm is applied. The controller computes the required three-axis magnetic moment (12 bits per axis, constrained by the MCU's PWM resolution), which is then packed and sent back to the simulated space environment. This framework is crucial for the systematic integration of subsequent simulation approaches and allows for flexible integration with various communication protocols. The following subsections delve into the developed simulation models for both sun sensors and magnetorquers, considering the constraint of achieving real-time execution.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Computational Scheduling Kernel Pipeline Design
#### 2.2.3 Partial Loading and Partial Calculation of Data within Layers
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Input Tensor/Output** & **Partial Loading Input** & **Occupy the Data** & **Occupy Data Storage** & **Occupy the Parameter** \\  & **Tensor** & **Tensor/Output Tensor** & **Loading Area Depth** & **Area Depth** & **Storage Area Depth** \\ \hline CONV1 & \(448\times 448\times 3/\) & \(56\times 56\times 3/\) & 784 & 6272 & 18 \\  & \(448\times 448\times 16/\) & \(56\times 56\times 16/\) & & & \\ \hline POOL2 & \(448\times 448\times 16/\) & \(56\times 56\times 16/\) & \(1568\) & 1568 & 0 \\  & \(224\times 224\times 16/\) & \(28\times 28\times 16/\) & & & \\ \hline CONV3 & \(224\times 224\times 16/\) & \(28\times 28\times 32/\) & 784 & 3136 & 72 \\  & \(224\times 224\times 32/\) & \(28\times 28\times 32/\) & & & \\ \hline POOL4 & \(224\times 224\times 32/\) & \(28\times 28\times 32/\) & 784 & 784 & 0 \\  & \(112\times 112\times 32/\) & \(14\times 14\times 32/\) & & & \\ \hline CONV5 & \(112\times 112\times 32/\) & \(28\times 28\times 32/\) & 784 & 6272 & 288 \\  & \(112\times 112\times 64/\) & \(28\times 28\times 64/\) & & & \\ \hline POOL6 & \(112\times 112\times 64/\) & \(28\times 28\times 64/\) & \(1568\) & 1568 & 0 \\  & \(56\times 56\times 64/\) & \(14\times 14\times 64/\) & & & \\ \hline CONV7 & \(56\times 56\times 64/\) & \(14\times 14\times 64/\) & & & \\ \hline POOL8 & \(56\times 56\times 128/\) & \(14\times 14\times 128/\) & 784 & 784 & 0 \\  & \(28\times 28\times 128/\) & \(7\times 7\times 128/\) & & & \\ \hline CONV9 & \(28\times 28\times 128/\) & \(14\times 14\times 128/\) & 784 & 1568 & 1152 \\  & \(28\times 28\times 256/\) & \(14\times 14\times 64/\) & & & \\ \hline POOL10 & \(28\times 28\times 256/\) & \(14\times 14\times 256/\) & \(1568\) & 1568 & 0 \\  & \(14\times 14\times 256/\) & \(14\times 14\times 256/\) & & & \\ \hline CONV11 & \(14\times 14\times 512/\) & \(14\times 14\times 256/\) & \(1568\) & 784 & 1152 \\  & \(14\times 14\times 512/\) & \(2\times 2\times 512/\) & & & \\ \hline POOL12 & \(7\times 7\times 512/\) & \(1\times 1\times 512/\) & 128 & 64 & 0 \\ \hline CONV13 & \(7\times 7\times 512/\) & \(7\times 7\times 512/\) & \(7\times 7\times 1024/\) & 896 & 98 & 1152 \\  & \(7\times 7\times 1024/\) & \(7\times 7\times 1024/\) & & & \\ \hline CONV14 & \(7\times 7\times 1024/\) & \(7\times 7\times 1024/\) & & & \\ \hline CONV15 & \(7\times 7\times 1024/\) & \(7\times 7\times 1024/\) & & & \\ \hline CONV15 & \(7\times 7\times 1024/\) & \(7\times 7\times 1024/\) & & & \\ \hline FC17 & \(1\times 1\times 50,176/\) & \(1\times 1\times 50,176/\) & \(1\times 1\times 2/\) & & \\ \hline FC18 & \(1\times 1\times 256/\) & \(1\times 1\times 256/\) & & & \\ \hline FC19 & \(1\times 1\times 4096/\) & \(1\times 1\times 4096/\) & \(1\times 1\times 4096/\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: TinyYolo loading strategy for each layer part.