esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Satellite-Based Evapotranspiration
## 3 Methods  
### Surface Temperature (T\({}_{s}\)) Estimation  
The T\({}_{\text{s}}\) was calculated for ETM+ and TIRS through the single-channel algorithm [48; 49]  
\[\text{T}_{\text{s}}=\gamma\left[\frac{1}{\varepsilon_{\lambda}}(\text{$\psi_{1 }$}\text{L}_{\text{sen}}+\text{$\psi_{2}$})+\text{$\psi_{3}$}\right]+\delta \tag{1}\]  
where L\({}_{\text{sen}}\) [W m\({}^{-2}\) sr\({}^{-1}\) \(\upmu\)m\({}^{-1}\)] is the radiance of the sensor in the thermal spectrum obtained by the radiometric calibration of the digital numbers of band 6 from ETM+ and band 10 from TIRS. \(\varepsilon_{\lambda}\) was obtained from ASTER GED. The \(\psi_{1}\), \(\psi_{2}\) y \(\psi_{3}\) variables are atmospheric functions, which depend on \(\uptau\), L\({}_{\text{d}}\) (W m\({}^{-2}\) sr\({}^{-1}\) \(\upmu\)m\({}^{-1}\)) and L\({}_{\text{u}}\) (W m\({}^{-2}\) sr\({}^{-1}\) \(\upmu\)m\({}^{-1}\)). The \(\gamma\) and \(\delta\) are parameters that depend on L\({}_{\text{sen}}\) and the brightness temperature of sensor (T\({}_{\text{b}}\)) (K). The validation of T\({}_{\text{s}}\) was carried out following the method proposed by [PERSON] et al. [50].
## 4 Results
### Impact of Temperature and Precipitation on EQI  
From 2000 to 2020, the growing season temperature in Anhui Province ranged from 21.58 to 23.93 \({}^{\circ}\)C, with a multi-year average of 22.89 \({}^{\circ}\)C, showing a slow first decreasing and then an increasing trend. Precipitation ranged from 698.63 to 1398.72 mm, with a multi-year average of 1015.95 mm, fluctuating slowly upward (Figure 9). Spatially, the average temperature decreases from south to north, with the lowest temperature in the Dabie Mountains and the highest temperature in the south of Anqing. The amount of precipitation tends to decrease from the south to the north, and the maximum precipitation is observed in the cities of southern Anhui and Dabie Mountains.  
The partial correlation coefficient between EQI and average temperature ranged from \(-\)0.86 to 0.73, with a predominantly negative correlation (Figure 10a). The regions with significant negative correlation accounted for 10.61% of the province, mainly in the western region and Southern Anhui Mountains. The significant positive correlation was 0.44%, sporadically distributed in the Huaibei Plain and the upper reaches of the Yangtze River. The correlation between EQI and precipitation is strong, with the number of partial correlation coefficient ranging from \(-\)0.91 to 0.85, with a predominantly positive correlation (Figure 10b). The significant positive correlation accounted for 28.56% of the province's land area, and the significant negative correlation accounted for only 9.87%, mainly in Southern Anhui Mountains and the Yangtze River basin.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & **Extremely Significant** & **Significant** & **Not** & **Significant** & **Extremely Significant** \\  & **Reduction** & **Reduction** & **Significant** & **Increase** & **Increase** \\ \hline Huaiibei Plain & 1.78\% & 0.95\% & 63.94\% & 14.39\% & 18.95\% \\ Jianghuai hills & 1.21\% & 0.51\% & 23.88\% & 13.54\% & 60.86\% \\ Riverside Plain & 5.75\% & 2.35\% & 41.50\% & 11.80\% & 38.60\% \\ Dabie Mountains & 0.73\% & 0.38\% & 40.78\% & 17.45\% & 40.66\% \\ Southern Anhui Mountains & 1.85\% & 1.02\% & 65.96\% & 8.50\% & 22.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Area ratio of each EQI trend.  
Figure 9: Average temperature and precipitation of growing season in Anhui Province, 2000–2020.
## 2 Materials and Test Programs
### Test Programs
#### 2.2.2 SEM Test  
In order to collect the microscopic data under the same conditions as the macroscopic mechanical parameters, the specifications and manufacturing methods of the micro-test samples were the same as those of the static triaxial test described above. First, the F-T samples were hand-opened to expose the fresh undisturbed surface and cut into small pieces with a certain distance from the opening surface for the SEM test. Subsequently, the cut samples had to be dried to allow them to be completely gold-plated, and the air-dried samples were adhered to the SEM observation table with conductive tape, followed by vacuuming and gold spraying. Finally, an electron microscopic was used to observe them.  
Each observed sample was photographed with 3 points during the SEM test, so as to select better image extraction data to reduce the error when performing image processing later. The images, whose magnifications were selected as 25\(\times\) and 50\(\times\), could only show the whole structure of the sample with the inconspicuous form of bonding between particles. When the magnifications were 200\(\times\), 500\(\times\) and 1000\(\times\), the local characteristic information of the sample or a few unrepresentative particles only could be displayed. However, when 100\(\times\) magnification was adopted, the particle state of the images and the microscopic feature information were more comprehensive and abundant. Therefore, 100\(\times\) images were taken for the study and analysis (Figure 1).  
Finally, the microscopic images should be subjected to image preprocessing, image segmentation, and image morphology processing to obtain the \"particle image\" and \"porosity image\" by the MATLAB program. Then, the microscopic data could be acquired by counting and measuring the processed images by Image Pro-Plus (IPP).
## 5 Discussion
### Limitations and Recommendations for Future Research  
Some limitations and research directions for future studies are noted as follows. First, this study focuses on SST in convenience stores, and therefore the results cannot be fully generalized to other types of SST in the retail industry. This is because most SST functions are relatively simple, such as beverage vending machines. However, SSTs in convenience stores provide a variety of functions. For some specific functions (e.g., mobile data purchases), they are relatively more complex to operate than a self-service vending machine. To further test the generalizability of this study, future research can apply this research model to examine the use of SSTs in other retail industries. Second, the main idea of SST in convenience stores is to serve customers of all ages. In this study, we focused on university students' perceived values of using SST in the context of convenience stores. More specifically, the empirical data of this research were collected from universities in the NorthTaiwan metropolitan area. Taiwan has the second highest density of convenience stores in the world, especially in urban areas; they can be found on almost every street, especially in large cities in northern Taiwan. For future research, data collection could be expanded to include students in post-initial programs and students in other regions (e.g., suburbs) or counties to obtain a more stratified sample. Future studies could be conducted to validate the current findings. It is also suggested that consumers of other age groups be included to determine their online behaviors. Finally, the transitional effect of offline to online use of SST was not reflected in the perceived convenience, satisfaction, and continued use of in-store physical SST. About 30% of respondents used ibon less than once a week, which may have also affected their willingness to use the app. Therefore, follow-up research could separately examine the transitional effect of high-frequency and low-frequency users from offline to online to verify and compare the conclusions of this study.
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
## 3 Results
### Search Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Article** & **Device** & **FR (Hz)** & **A (mm)** & **Vibration Protocol** \\ \hline P1. [PERSON] et al. & & & & Anterior tibial and gluteus medius \\
2004 & Custom Device & 83 & NR & \begin{tabular}{c} \\ 1 single session for 14 s \\ \end{tabular} \\ P2. [PERSON] et al. 2009 & Thrive MD-01 & 91–108 & 1 & \begin{tabular}{c} Palm flexor tendon and biceps brachii \\ 1 single session for 5 min \\ \end{tabular} \\ P3. [PERSON] et al. 2010 & Horus & 120 & 0.01 & \
\begin{tabular}{c} Peroneus longus and tibialis anterior \\ 4 weeks (30 min/day, 3 day/week) \\ \end{tabular} \\ P4. [PERSON] et al. 2010 & Custom Device & 60 & NR &
\begin{tabular}{c} Forearm extensor muscles \\ 1 single session for 5 min \\ \end{tabular} \\ P5. [PERSON] et al. & & & & \\
2011 & NEMOCO & 100 & 0.2–0.5 &
\begin{tabular}{c} Flexor carpi radialis, biceps brachii \\ 3 consecutive days (30 min/day) \\ \end{tabular} \\ \hline P6. [PERSON] et al. & & & & \\
2011 & Custom Device & 70 & NR & \
\begin{tabular}{c} Forearm flexor tendons \\ 1 single session with 40 trials (5 s/trial) \\ \end{tabular} \\ P7. [PERSON] et al. 2012 & Thrive MD-01 & 91–108 & 1 &
\begin{tabular}{c} Palm flexor tendon, biceps brachii \\ 1 single session for 5 min \\ \end{tabular} \\ \hline P8. [PERSON] et al. & & & \\
2012 & CROSYSTEM, NEMOCO & 100 & 0.2–0.5 & \
\begin{tabular}{c} Pectoralis minor, biceps brachii and flexor carpi \\ 3 consecutive days (30 min/day) \\ \end{tabular} \\ \hline P9. [PERSON] et al. 2013 & NR & 90 & 0.015 &
\begin{tabular}{c} Achilles tendon, tibialis anterior \\ 6 weeks (30 min/day, 3 day/week) \\ \end{tabular} \\ \hline P10. [PERSON] et al. & & & \\
2013 & Horus & 120 & 0.01 &
\begin{tabular}{c} Biceps brachii and flexor carpi ulmaris \\ 2 weeks (30 min/day, 5 day/week) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Vibration devices, parameters, and protocols used in the included articles.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
###### Abstract  
A major challenge for the Air Traffic Management system is the need to boost airspace capacity, which is near saturation in some situations. Separation minima are one of the factors related to airspace capacity, and the SESAR program promotes research into advanced separation modes. Ad Hoc separation, a novel separation mode, involves applying different pairwise separation minima in the same volume of airspace depending on a set of factors, such as encounter geometry, aircraft models, and flight level, among others. This research examines the impact of implementing this concept in different en-route scenarios. The goal is to determine whether applying this concept proves advantageous or, conversely, results in an increase in the complexity of the system without significant benefits in the key performance areas of capacity, environment, and cost-efficiency. Fast Time Simulations are conducted in RAMS software, with the concept being implemented in the LECMZMU, LECMTLU, and LECMDGU sectors of the Madrid ACC. The results reveal favorable capacity outcomes with increases of around 2% and the LECMZMU sector exhibits the most significant environmental and cost-efficiency benefits. Furthermore, implementing the Ad Hoc concept in a larger scenario could yield even greater environmental and cost-efficiency benefits.  
separation minima; ad hoc separation minima; airspace capacity; environment; cost-efficiency +
Footnote †: journal: Applied sciences  
Article
# Survey for Soil Sensing with IOT and Traditional Systems
## 5 Future Aspect  
In terms of hardware, while spectrometers offer an exact resolution for spectral data, their high cost (exceeding $15k) and stringent environmental requirements significantly limit their applicability in field studies for soil reflectance spectroscopy. Meanwhile, the rise of RF-based soil sensing technology presents an enticing potential for soil property sensing due to its low-cost equipment and wide coverage area. Many of these RF systems can also remain operational for long periods when deployed in the soil. However, currently, RF-based soil sensing primarily detects soil moisture levels only. As a result, a Printed Circuit Board (PCB)-based soil sensing system could be an optimal solution in the future, striking a balance between device cost and sensing capabilities. From a preprocessing perspective, it is essential to simplify these steps to transition from laboratory-based experiments to real-world applications. To accomplish this, we need to address the influences of particle size and soil moisture levels on reflectance spectroscopy and ensure the stability of prediction models for targeted soil attributes. On the computational methods front, Partial Least Square Regression (PLSR) is widely acknowledged as the go-to calibration technique used in spectral data analysis. However, Least Square-Support Vector Regression (LS-SVR) also presents acceptable, and at times superior, prediction results compared to PLSR. Further advancements in neural network methodologies will undoubtedly benefit these computational approaches.
# Research on Human Exposure to Transport-Induced Vibration in Buildings
## 2 The Measurement Methodology and Evaluation of Human Exposure to Vibration in Buildings
### Signal Recording and Analysis
The vibration acceleration signal is filtered in 1/3 octave bands and the root mean square value of vibration accelerations is determined for each center frequency. This approach allows for the presentation of the root mean square value in the frequency domain. This provides information not only about the exceedance of vibration perception threshold values, but also about the frequency band in which this exceedance occurred. In the assessment of the impact of vibrations on people staying in buildings, carried out in accordance with the RMS method, the following assessment factors are taken into account: the purpose of the room, the time of day when the vibrations occur, the nature and repeatability as well as the direction of the vibrations and the position of the human body receiving the vibrations in the room. The values of the n coefficient (vibration perception threshold multiplier [23]) relating to the purpose of the room, time of day and the nature of vibrations are shown in Figure 4.  
Figure 3: Illustration of vibrational interval taken to analysis acc. [20]. In the revised Polish standard [23], a new index has been introduced to express the assessment result of the impact of vibrations on people in buildings. This index, called the Human Perception of Vibration Index (HPVI), in relation to vibrations transmitted to humans in the \"z\" direction, can be calculated using the following formula:  
\[\mathrm{HPVI}=max\bigg{(}\frac{a_{RMS}}{a_{zRMS}}\bigg{)} \tag{2}\]  
where \(a_{RMS}\) is the root mean square value of vibration accelerations obtained from the analysis and \(a_{zRMS}\) is the root mean square value of vibration accelerations equal to the vibration perceptibility threshold for humans in the \"z\" direction in the same frequency band in which \(a_{RMS}\) was determined. The second common method of evaluation is the vibration dose value VDV [23]. This method is primarily used for general and local vibrations that actively affect humans. Its sensitivity to the occurrence of peak values in the recorded signal arises from the formula used to calculate it, where vibration acceleration is raised to the fourth power, as shown below:  
\[\mathrm{VDV}=\bigg{[}\int_{0}^{T}a_{w}^{4}(t)dt\bigg{]}^{\frac{1}{2}} \tag{3}\]  
The transformation of the signal using Equation (3) results in the vibration dose value (VDV) being expressed in units of m/s[175]. The procedure for determining the VDV is similar in essence to the procedure used in the RMS method. Figure 4: Comfort levels depending on room destination: (**a**) in z direction, (**b**) in x and y directions acc. [23]; (e) direction definition. Table 1 presents threshold values for the probability of complaint occurrence according to the appendix of the Polish standard [23].
## 2 Methods
### Segmentation Network  
The segmentation network of ASGASN is a horizontal U-shaped asymmetric deep learning model, with the upper end performing image input and the lower end directly outputting building extraction results. The detailed structure is given in Figure 2 and Table 1. As we can see from Table 1, it contains eight encoder blocks, one ASPP module and nine decoder blocks. The encoder (blocks 1-8) consists of convolutional blocks and several residual blocks that can extract feature mappings at different levels from global and local contextual information. The decoder (blocks 10-18) consists of convolutional blocks, residual blocks and global convolutional blocks. Convolutional blocks are used to resize feature maps and the change the number of channels, residual blocks are used to deepen the network layers to prevent network degradation, and upsampling is used to recover the image details. A skip connection fuses each upsampled mapping with the corresponding size feature mapping in the encoder. The fusion output is sequentially and progressively fused with the multilevel feature map and restored to the size of the original input map. Finally, there is an output layer after the encoder that performs pixelwise classification. ASPP (block 9) is used as a bridge between upsampling and downsampling, while extracting multiple scales of semantic information from the small size feature map, so that better global learning of the feature map can be performed. ASGASN's detailed blocks are shown in Table 1: 'Conv' stands for Convolutional, 'ReLU' stands for Rectified Linear Unit, 'BN' stands for Batch Normalization, and 'GlobalConv' denotes Global Convolutional. Figure 1: The structure of our proposed ASGASN. A are convolutional + batch normalization + Leaky rectified linear unit (Relu) layers; B is Residual block; ASPP is Atrous Spatial Pyramid Pooling; C is transposed convolution + batch normalization + Relu layers; D is upsample; E is Global convolutional block+ batch normalization+ Relu layers; F is concatenation operation; G is skip-connection.
## 3 Empirical Analysis
### Forecasting of Energy Consumption in China Based on EEMD-ISFLA-LSSVM Model  
In order to predict energy consumption in China accurately, we first take the ensemble empirical mode decomposition of the original energy consumption sequence to achieve data de-noising. By EEMD, three IMFs and one residual signal are obtained, which are shown in Figure 5.  
Figure 4: The sample data.  
The data from 1990 to 2009 are taken as the training set, and the test set is the data from 2010 to 2016. The ISFLA-LSSVM model is adopted to predict the IMF components and the residual signal obtained above. The algorithm parameters settings are as follows: the frog population is 500. The sub-population is 50. The sub-population search number is 10. The regularization parameter search range of LSSVM is [0.1, 150], and the RBF kernel parameter is [0.01, 30]. The maximum number of iterations is 150. The prediction results and the residuals are shown in Figure 6.  
Figure 5: The IMFs of EEMD.  
Figure 6: _Cont._  
The prediction results of the IMFs and the residual signal are reconstructed. Then the final prediction result of energy consumption are obtained, which are shown in Figure 7.  
Figure 6: The forecasting results of IMFs.  
The calculation equation of the relative error \(RE\) is shown in Equation (12):  
\[RE=\frac{|\hat{y}_{i}-y_{i}|}{y_{i}}\times 100\%. \tag{12}\]  
The relative errors of each prediction point are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Actual Value (104 \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{  }}}}}}}}}}}}}\)**ce)** & **Forecasting Value (104 \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}\)**ce)** & **RE (\%)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: The relative error.  
Figure 7: The prediction and residual figures of the final prediction result.  
According to Figure 7 and Table 2, using the EEMD-ISFLA-LSSVM model to forecast the energy consumption, the fitting degree between the prediction curve and the actual curve is very good, and the prediction effect is significant. The relative errors of all prediction points are not more than 5%.
## 1 Introduction
This is achieved by iteratively optimizing the state vector including the following geophysical parameters: five layered CO\({}_{2}\) and H\({}_{2}\)O concentration profiles, the pressure (i.e., height), scattering optical thickness at 760 nm, and the Angstrom exponent of a scattering layer, solar induced chlorophyll fluorescence (SIF), and polynomial coefficients describing the spectral albedo in each fit window. In this publication, we confront the _3-Scat_ retrieval setup with actually measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO\({}_{2}\) data product. This includes preprocessing (Section 2), adaptation of the noise model (Section 3.1), zero level offset correction (Section 3.2), post-filtering (Section 4.1), bias correction (Section 4.2), comparison with CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model (Section 5.1), comparison with NASA's operational OCO-2 XCO\({}_{2}\) product (Section 5.2), and validation with ground based Total Carbon Column Observing Network (TCCON) data (Section 5.3). The complete retrieval chain including adaptations, pre-, and postprocessing will be referred to as FOCAL v06 in the following.
## 3 Experimental Results  
The experiments evaluate three aspects of the proposed method: the effectiveness of the focus measure, the quality of image fusion, and the computational efficiency of the algorithm.  
Figure 5: The final decision map.  
For the evaluation of the focus measure method, a comparative experimental approach was adopted. Four representative classical focus evaluation metrics were selected as baseline references: the variance of gray levels [21], gradient energy [22], Laplacian energy [23], and spatial frequency [24]. These methods quantify image sharpness from different perspectives and are widely used and theoretically grounded in focus region extraction.  
For image fusion quality and algorithmic efficiency, six types of sample images were selected for the experiments, as shown in Figures 6-11. To simulate real-time video stream processing, each sample consisted of nine consecutively acquired images at different focal planes, containing both focused and blurred regions. The magnification for the cell slice samples was 10\(\times\), and for the circuit board samples, it was 4.2\(\times\) or 8\(\times\). The nine images were fused sequentially as follows: two images were first fused to generate a result, which was then fused with the third image, and the new result was fused with the fourth image, and so on, until all nine images were fused. The proposed algorithm was compared with eight commonly used fusion algorithms in terms of image quality and computational efficiency: dense scale-invariant feature transform (DSIFT) [25], Laplacian pyramid (LP) [26], contourlet transform (CVT) [27], dual-tree complex wavelet transform (DTCWT) [28], non-subsampled contourlet transform (NSCT) [29], discrete wavelet transform (DWT) [30], guided filtering (GF) [31], and convolutional neural network (CNN) [17] methods. All comparative algorithms were implemented using the parameter settings specified in their respective reference literature.