esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Method  
### Finite Difference Schemes  
The Steger warming vector flux splitting method is used to decompose the physical fluxes F and G [44], and then the semidiscrete scheme of the governing equations can be written as follows:  
\[\left(\frac{\partial\Psi}{\partial t}\right)_{ij}=-\frac{1}{\Delta x}\left( \mathbb{F}_{i+1/2j}^{+}-\mathbb{F}_{i-1/2,j}^{+}\right)-\frac{1}{\Delta x} \left(\mathbb{F}_{i+1/2j}^{+}-\mathbb{F}_{i-1/2,j}^{-}\right)-\frac{1}{\Delta y }\left(\mathbb{G}_{i,j+1/2}^{+}-\mathbb{G}_{i,j-1/2}^{+}\right)-\frac{1}{ \Delta y}\left(\mathbb{G}_{i,j+1/2}^{-}-\mathbb{G}_{i,j-1/2}^{-}\right)+ \mathbb{S}_{i,j}\]  
where, \(\mathbb{F}_{i=1/2,j}^{\pm}\), \(\mathbb{G}_{i\pm 1/2,j}^{\pm}\) represent the numerical flux on grid boundary and can be obtained by the WENO reconstruction with the grid values of Fiji, Fiji. A addtive Runge-Kutta scheme method [45] is used to couple the Euler equations to the chemical reactions. According to the work of [PERSON], [PERSON], [PERSON] [46; 47; 48], additional  
Figure 1: A schematic view of the bend.  
Runge-Kutta explicit implicit algorithm can be used to handle the following ordinary differential equations:  
\[\frac{dU}{dt}=F(U)=\sum\limits_{v=1}^{N}F^{[v]}(U)\]  
In this paper, the Euler equation is divided into the convection term and the reaction term. N in the equation is set to 2.  
\[U^{(i)}=U^{(n)}+(\Delta t)\sum\limits_{v=1}^{2}\sum\limits_{j=1}^{s}a_{ij}^{[v ]}F^{[v]}(U^{(j)}),\]  
\[U^{(n+1)}=U^{(n)}+(\Delta t)\sum\limits_{v=1}^{2}\sum\limits_{j=1}^{s}b_{i}^{[ v]}F^{[v]}(U^{(i)})\]  
where \(U^{(n)}=U(t^{(n)})\), \(U^{(i)}=U(t^{(n)}+c_{i}\Delta t)\), \(U^{(n+1)}=U(t^{(n)}+\Delta t)\), \(a_{ij}^{[v]},b_{i}^{[v]},c_{i}^{[v]}\), \(v=1,2,3\cdots N\) The constant is the Butcher coefficient.  
The reaction Euler equation is written as:  
\[\frac{dU}{dt}=F_{ns}+F_{s}\]  
It refers to both the nonrigid convection term and the rigid reaction term. The IMEX ARK2 method is adopted in which, ERK (display Runge-Kutta) method are used for the nonrigid term, and the rigid term is treated by ESDIRK (rigid diagonal explicit/implicit Runge-Kutta) method.  
\[F_{ns}^{(j)}=F_{ns}(U^{(j)},t^{(n)}+c_{j}\Delta t),F_{s}^{(j)}=F_{s}(U^{(j)},t ^{(n)}+c_{j}\Delta t)\]  
\[U^{(i)}=U^{(n)}+(\Delta t)\sum\limits_{j=1}^{i-1}(a_{ij}F_{ns}^{(j)}+a_{ij}F_{ s}^{(j)})+(\Delta t)a_{ii}F_{s}^{(i)}\]  
The above equation is written as \((I-\Delta ta_{ii}F_{s}^{(i)})U^{(i)}=U^{(n)}+(\Delta t)\sum\limits_{j=1}^{i-1 }(a_{ij}F_{ns}^{(j)}+a_{ij}F_{s}^{(j)})\) and \(U^{i}\) is iteratively solved.\(I\) is the unit matrix.
# Learning to Segment Blob-like Objects by Image-Level Counting
## 4 Experiments and Results
### Results  
#### 4.3.1 Results on Synthetic Images  
When using synthetic data, each model was trained and evaluated on images with \(512\times 256\) px, which are generated as described in Section 4.2.2. For the training, half of the images were generated without particles and the other half with a random number of objects between 1 and 50 from a uniform distribution. In the evaluation, we considered 1000 images with particles from the same generator. As we focused on the segmentation results, we omitted the case of 0 particles. Each training step and each evaluation step generated a new random example.  
We used the results of the training with segmentation data as a reference for the achievable result quality since we expected a high level of training information to produce better results. The direct segmentation training (Table 2) reached F1-scores of greater than 98.0% with all evaluated networks. It was noticeable that the different sizes of the same network architecture achieved the same F1-scores. Consequently, increasing the size of these architectures beyond their smaller versions did not seem to have a significant advantage for training a strongly supervised segmentation on the synthetic data. The CAM-based approach (Table 3) showed slightly lower values in most cases, while one configuration, i.e., UNet++ with 16 start filters, even exceeded the value of its segmentation counterpart. The counting training combining the counting loss, the contrast loss, and the morphological loss (Table 4) showed F1-scores above 94.0%, which were, in most cases, slightly lower than those of the CAM-based approach
## 4 Results and Discussion
### Mitigation Strategies for Dry Lentils: A Challenge and Opportunities
Utilizing lentil grain yield data from various regions in Asia and Africa [78], along with data from Marchouch, an empirical linear relationship between the grain yield (Y\({}_{\text{g}}\)) and total field water supply (W\({}_{\text{r+i}}\)) was established:  
\[\text{Y}_{\text{g}}=(2.59\pm 0.32)\:\text{W}_{\text{r+i}}\qquad\qquad\text{(r}^{2}=0.83) \tag{1}\]  
Under average rainfall conditions of 330 mm, this model estimates a lentil grain yield of 855 kg/ha, closely aligning with the average values listed in Table 1. By assuming that irrigation water is sourced from a local river to increase supplemental irrigation (SI) from 0 to 100 mm, Equation (1) estimates an increase in harvested lentil grains from 855 to 1114 kg/ha, as shown in Table 10.  
While supplemental irrigation can significantly increase lentil yields, it also has significant environmental implications, particularly in water-scarce regions. Increasing irrigation from 0 to 100 mm led to a specific water use of 0.90 m\({}^{3}\)/kg, which is intermediate between maize (0.15 m\({}^{3}\)/kg) and soybeans (1 m\({}^{3}\)/kg), and significantly lower than cotton (4 m\({}^{3}\)/kg) [79]. Under these circumstances, lentil water use efficiency was relatively high [64]. As supplemental irrigation was increased from 0 to 100 mm, the cradle-to-farm gate carbon footprint of Moroccan lentil grains reduced from 1.07 to 0.79 kg CO\({}_{\text{2e}}\)/kg, which is significantly lower than that of several animal-sourced proteins [80], yet greater than the carbon footprints estimated for USA [49] and Canada [32] cultivated lentils. Higher lentil yields led to lower land use (LU), climate change (CC), and freshwater ecotoxicity (FWET) scores, as shown in Table 10. Conversely, the water use score increased from 0.03 to 86 m\({}^{3}\) deprived from users/kg. This change caused the water use contribution to the PEF to rise from 0.12% to 80.7%, with the PEF itself increasing from approximately 208 to 787 uPt/kg. This highlights the trade-off between yield improvement and environmental sustainability. Given these results, the best environmental strategy may not necessarily involve increasing supplemental irrigation. Instead, focusing on the development and adoption of drought-resistant lentil varieties could minimize the impact of lentil production. This approach is supported by findings from studies on other crops, such as Gradoli Purgatory beans, in which the adoption of drought-tolerant variants significantly reduced the PEF
## 1 Introduction
However, the complexity of the extrusion process due to the presence of the catalyst have highlighted the need to develop a suitable material that can be processed by conventional processing techniques used in the packaging industry such as extrusion. Based on these results, in this work a new active packaging material by using PB as oxygen scavenger has been developed. This new material did not incorporate a catalyst but instead uses the photo-oxidation of PB when it is exposed to UV radiation [18], since UV exposure of PB accelerates vinyl bonds oxidation and increased PB oxygen absorption capacity. From the best of our knowledge, oxygen absorption activation of PB by UV-light has not been studied yet, although UV irradiation of PB has been studied since it benefits chain scission and crosslinking reactions of the diene chains [19; 20]. In addition to the improvement of the packaged nut's shelf life, the need to develop active aroma-enhancing packages has also been identified, which allows the controlled release of the aroma from the package to the nut samples [21]. In this way, it is possible to differentiate the product from the competitors and offer new sensory experiences to the consumer. Numerous studies have been carried out to develop new food packaging materials capable of retaining an aroma in the polymeric matrix and controlling its release into the food, which is not easy in many cases due to the high volatility of the chemical compounds, and their ease of diffusion within polymeric matrices [22; 23]. However, pyrazines, the major flavor compounds present in roasted peanut aroma, are relatively low volatile compounds able to be successfully incorporated into polymer matrices [24]. Thus, the aim of this work is the development of a double-function active packaging material for nuts. The packaging solution, on the one hand, integrates PB as an oxygen scavenger and, on the other hand, it incorporates peanut aroma to improve customer'ssensorial experience. Different formulations based on low density polyethylene (LDPE), commercial peanut aroma (PA) and PB at two levels (5 wt % and 13 wt %) were obtained by cast film extrusion. The obtained films were compared in terms of their mechanical, structural, optical and thermal properties and their oxygen scavenger capacity. The aroma retention capacity into the polymer matrix was also evaluated in the different developed formulations to study the impact of the PB concentration.
# Key Factors of the Initiation and Development of Polygonal Wear in the Wheels of a High-Speed Train
## 6 Conclusions  
In this study, the characteristics of polygonal wear measured from a large number of wheels of high-speed trains were statistically analysed, and a prediction model of polygonal wear of high-speed trains was developed. Using the prediction model, the causes and the conditions of the initiation and development of the polygonal wear were studied. The main conclusions are given below.  
1. The prominent feature of polygonal wear is that the dominant order and development rate of polygonal wear are very closely related to the wheel diameter, operation speed, and the resonance frequencies of the system, such as wheelsets.
2. If the wheel circumference is nearly an integral multiple of the wavelength of the periodical wear along the rolling circle of the wheel, the polygonal wear initiates and develops very quickly. This is referred to as the integral multiple condition, which is a key or basic condition for polygonal wear. This \"integral multiple\" is on the order of the dominant polygonal wear.
3. When the excited resonance frequency of the vehicle system is determined to be related to polygonal wear, the initiation and development of the polygonal wear depends on the operation speed and wheel diameter. Their relationship diagram is provided, and it can be used to predict polygonal wear development, modify the re-profiling schedule of polygonal wheels, and eliminate the polygonal wear on the wheels in time.
4. The effect of flexibility of the wheelset is considered in the prediction model of the polygonal wear of high-speed wheels in this paper. Therefore, the high-frequency characteristics of the wheelset bending resonances can be more accurately expressed, and high-order polygonal wear related to the wheelset bending resonances at high frequencies can be numerically reproduced.  
However, the current simulation model can only reflect the actual scenario of the train to a certain extent, and some phenomena cannot be accurately reflected. In addition, it cannot account for the effect of the bogie resonances and the frequency dependent characteristics of the suspension. In short, the current simulation model needs to be improved further.  
Figure 19: Polygonal wear development with different operation speeds and different wheel diameters.  
**Author Contributions:** Conceptualization, Y.W. and X.J.; methodology, Y.W., J.H., and W.C.; validation, Y.W., X.X., and X.J.; investigation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Science Foundation of China, grant numbers U1734201 and 51805450.  
**Acknowledgments:** The authors wish to thank CSR Tangshan Rolling Stock Co., Ltd. for their assistance in conducting the experiment. We also would like to thank Editage for English language editing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Simulation Results
As the element spacing is kept unchanged, the IOS with \(2M\) elements has a larger aperture size, it can be observed that the IOS with \(2M\) elements slightly outperforms the IOS with \(M\) elements due to the additional beamforming gain from the increase in the element number and the aperture size. Similarly, it outperforms the BIOS when \(M_{y}\) is small for the same reasons. However, with the increase in \(M_{y}\), the performance of the IOS with \(2M\) elements remains significantly inferior to that of the BIOS due to the coupling between the reflected and refracted beams. Finally, to visualize the SIC capability of the BIOS and verify the theoretical lower bound discussed in Section 4, Figure 5 shows the residual SI power as a function of \(M_{y}\) in SISO FD systems with different \(d_{\text{BB}}\). Specifically, the solid line represents the residual SI power, and the dashed line of the same color represents the theoretical lower bound of \(M\) for efficient SIC in the system with the same \(d_{\text{BB}}\). It can be observed that the residual  
Figure 4: SR performance of BIOS/IOS-assisted systems with different \(M_{y}\) (\(P_{\text{s}}=10\) dBm, \(P_{\text{u}}=7\) dBm, \(d_{\text{TR}}=3\lambda\), \(d_{\text{BB}}=1.5\lambda\)). Figure 3: DL/UL rate performance of BIOS/IOS-assisted systems with different \(M_{y}\). SI power with the proposed scheme monotonically decreases as \(M\) increases due to the beamforming gain. Meanwhile, despite the approximation, the theoretical lower bound calculated according to (37) is consistent with the simulation result.
## 4 Ice
### Sea Ice  
Regarding sea ice surfaces, SARAL/AltiKa offers mainly two advantages relatively to conventional Ku-band radar altimeters:  
* First, its smaller footprint, higher vertical resolution (\(\sim\)30 cm) and higher horizontal sampling (\(\sim\)180 m) allow a better discrimination between ice floes and open sea ice fractures (generally referred to as leads), which strongly improves the measurement of sea level and freeboard height. These improvements are somehow counter-balanced by the technical issue of waveform saturation while the altimeter overflight surfaces with highly variable scattering. This effect tends to degrade the range estimation during the approach phase of specular surfaces like leads. However, the reactivity of the Attenuation Gain Controller can be easily corrected in future altimeter version.
* The second advantage of SARAL/AltiKa lies on its higher radar frequency: Unlike Ku-band radar altimeters, the Ka-band radar signal of AltiKa penetrates only part of the snowpack and possibly less than 3 cm [43]. The latter study uses this difference of penetration depth between Ku- and Ka-band altimeters to estimate a proxy of snow depth at the top of sea ice by combining SARAL/AltiKa (Ka-band) and CryoSat-2/SIRAL (Ku-band) (Figure 25). As the uncertainty related to the impact of snow depth on the freeboard-to-thickness conversion can be up to 100 %, this opportunity of measuring snow depth from Ka- and Ku-band radar altimetry represents a real breakthrough. In addition to the freeboard-to-thickness conversion, the snow depth measurement could be of hight interest for the quantification of energy, transfer between the atmosphere and the ocean as well as for the estimation of freshwater fluxes in the ocean.  
The potential of combining Ka and Ku-band measurements to estimate snow depth at the top of sea ice is currently further investigated. In particular, the CryoVex airborne campaign carried, for the first time in March 2017, a Ka-band altimeter (Karen) in addition to the usual Ku-band altimeter (ASIRAS). The analysis of these highly accurate and colocated measurements will allow to provide more evidences on the interest of combining Ka and Ku band radar altimetry and could be a basis for the development of future polar altimetric missions.
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
###### Abstract  
As a fundamental and challenging problem, non-rigid structure-from-motion (NRSfM) has attracted a large amount of research interest. It is worth mentioning that NRSfM has been applied to dynamic scene understanding and motion segmentation. Especially, a motion segmentation approach combining NRSfM with the subspace representation has been proposed. However, the current subspace representation for non-rigid motions clustering do not take into account the inherent sequential property, which has been proved vital for sequential data clustering. Hence this paper proposes a novel framework to segment the complex and non-rigid motion via an ordered subspace representation method for the reconstructed 3D data, where the sequential property is properly formulated in the procedure of learning the affinity matrix for clustering with simultaneously recovering the 3D non-rigid motion by a monocular camera with 2D point tracks. Experiment results on three public sequential action datasets, BU-4 DFE, MSR and UMPM, verify the benefits of method presented in this paper for classical complex non-rigid motion analysis and outperform state-of-the-art methods with lowest subspace clustering error (SCE) rates and highest normalized mutual information (NMI) in subspace clustering and motion segmentation fields.  
low rank representation; subspace clustering; non-rigid structure-from-motion +
Footnote †: journal: Applied Sciences
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 5 Data Aggregation Overview
### Data Fusion
In a complex environment where multiple targets are present, and the sensor data might include false alarms or noise, it becomes critical to correctly associate each observation with its respective target. Figure 4: Sensor fusion framework. * State estimation is a technique that tries to determine the state (such as position) of a moving target. The challenge arises from the fact that the observations are often noisy, and it is not always clear which observations correspond to the target being tracked. * Decision fusion is a technique used to draw high-level conclusions about detected events and activities based on input from various data sources. These methods typically operate on symbolic information and aim to account for uncertainties in the reasoning process [61]. Choosing the most suitable technique depends on the nature of the problem and the underlying assumptions of each method. In recent years, significant effort has been dedicated to researching data fusion. The study by [PERSON] and [PERSON] [65] offers a framework designed to predict activities by analyzing the sequences of actions detected by sensors installed in a smart building. These activity sequences can be predicted by analyzing the correlation between successive actions, as users generally follow consistent patterns that remain relatively stable depending on the specific activity that they are performing. [PERSON] et al. [66] employ consistent metadata for building modeling: this approach involves utilizing sensor ontologies, subsystems, and relationships to guarantee seamless interoperability and portability. Sensor data fusion enables thorough and consistent analysis of data from individual sensors, while combining data from multiple sensors improves the precision, reliability, and clarity of the resulting dataset. Nonetheless, this process encounters several challenges, including the imperfection and correlation of data, inconsistencies, and the heterogeneity of the datasets, which could be alleviated by employing an infrastructure-as-a-service approach [67] for initial validation and baseline data acquisition.
# Preface: Land Surface Processes and Interactions--From HCMM to Sentinel Missions and Beyond
## 1 Introduction  
The scientific understanding of the energy and water fluxes between land and atmosphere primarily predicates our capacity to describe, model, and predict the highly complex Earth system, which is formed by mutually interlinked components (land, atmosphere, and ocean). A credible representation of land surface processes and interactions, including energy, mass and momentum fluxes, as well as biogeochemical cycles, in Earth System Models (ESMs) is crucially important.  
Recognizing the importance to better understand these complex processes and interactions, the World Climate Research Programme (WCRP) launched the Global Energy and Water Cycle Experiment (GEWEX) in 1990, which has been currently evolved into Phase III as Global Energy and Water Exchanges (GEWEX), with its mission to measure and predict global and regional energy and water variations, trends, and extremes, via improved observations and modelling of land, atmosphere, and their interactions [1].  
Tremendous efforts have been devoted to Earth observations (i.e., remote sensing and in situ sensing) of land-atmosphere interface characteristics, including heat fluxes, soil moisture, land and sea surface temperature, precipitation, snow, and vegetation, as boundary conditions for climate modelling studies. One of the pioneering satellite missions was the Heat Capacity Mapping Mission (HCMM), which was an experimental satellite program launched on 26 April 1978, to observe simultaneously reflected solar radiation and emitted thermal radiation in its visible and infrared channels, respectively, either during the day or at night [2]. HCMM was the first cooperation project between the United States and Europe in the field of space-borne remote sensing [3]. These pioneer efforts were primarily scientific, to lay the foundations for later practical applications of satellite imaging technologies.  
More recently, a new fleet of satellite constellations called Sentinels, particularly designed for the Copernicus programme (previously known as Global Monitoring for Environment and Security (GMES) services), includes radar and multi-spectral imaging instruments, meeting various observational needs for Essential Climate Variables (ECVs) for land, ocean, and atmosphere, as well as the interactions between them [4]. The aim of this fleet is to provide systematic data services for value-added products, based on the progress of Earth observation science.
## 4 Results
### Feature Reduction
#### 4.1.1 Linear Regression  
In our dataset, all features are numerical except one feature, date, which is removed, and the Linear Regression is constructed based on the full feature set. As depicted in Figure 1, the fitness of Linear Regression is under question since the normal distribution of Label as the dependent variable is not satisfied. Therefore, the suggested features for omission are not considered for omission. Also, Figure 1 shows some levels of multicollinearity among features, homoscedasticity, and the existence of outliers.