esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Algorithm Efficiency for Optimizing Experimental Designs with Correlated Data
## Materials and Methods  
### Statistical Model for Randomized Complete Block Designs  
The linear mixed model (LMM) framework for RCB designs can be expressed as \(\mathbf{y}=\mathbf{X}\boldsymbol{\beta}+\mathbf{Z}\mathbf{g}+\mathbf{e}\), where \(\mathbf{y}\) is a vector of observed phenotypes (responses); \(\mathbf{X}\) is an incidence matrix of fixed block effects; \(\boldsymbol{\beta}\) is a vector of fixed block effects; \(\mathbf{Z}\) is an incidence matrix of random treatment effects; \(\mathbf{g}\) is a vector of random treatment effects, with \(\mathbf{g}\sim MVN(\mathbf{0},\mathbf{G})\), where \(\mathbf{G}=\sigma_{g}^{2}\mathbf{A}\) for genetically correlated observations, with \(\mathbf{A}\) being the numerator relationship matrix calculated from pedigree information or molecular markers to account for additive genetic relatedness between individuals and \(\mathbf{G}\) is a variance-covariance matrix for genetic relationships. For instance, \(\mathbf{G}=\sigma_{g}^{2}\mathbf{I}\) for genetically unrelated individuals. The vector \(\mathbf{e}\) represents residual errors, with, \(\mathbf{e}\sim MVN(\mathbf{0},\mathbf{R})\), where \(\mathbf{R}\) is a variance-covariance matrix for modelling correlated errors. Most often, \(\mathbf{R}\) is modelled with an autoregressive error structure of order 1 [19]. To obtain the variance-covariance matrix of random treatment effects, linear mixed model normal equations are solved as described by [PERSON] [20] and [PERSON] et al. [21] to give  
\[\mathbf{M}(\Omega)=Var(\hat{\mathbf{g}}-\mathbf{g})=(\mathbf{Z}^{{}^{\prime}} \mathbf{R}^{-1}\mathbf{Z}+\mathbf{G}^{-1}-\mathbf{Z}^{{}^{\prime}}\mathbf{R}^ {-1}\mathbf{X}(\mathbf{X}^{{}^{\prime}}\mathbf{R}^{-1}\mathbf{X})^{-1}\mathbf{ X}^{{}^{\prime}}\mathbf{R}^{-1}\mathbf{Z})^{-1} \tag{1}\]  
from which the trace and determinant of the matrix \(\mathbf{M}(\Omega)\) are calculated based on \(A\)- and \(D\)-optimality criteria, respectively (For further details see [10]).
## 5 Conclusions  
The response of an in situ phytoplankton community in terms of size and taxonomy to humic acid (HA) addition was investigated using enclosed mesocosms for a eutrophic freshwater zone, physically divided by a sea embankment, within the Yeongsan River estuary. Phytoplankton biomass estimated by chl \(a\), especially nanosized (<20 \(\upmu\)m), increased significantly (\(p<\) 0.05) with HA addition through the seasons. The abundance of nanosized _Stephanodiscus_ sp. also significantly increased when the species dominated phytoplankton taxonomy (67%). Because the zone generally maintained its eutrophic status during the wet seasons, this response was unexpected. The pattern of phytoplankton response is similar to the results of a previous mesoccosm study of DIP addition which showed a significant increase in phytoplankton biomass (chl \(a\)) during dry seasons. The results suggest that HA can contribute to algal blooms in estuaries where HS levels are expected to increase, although the underlying mechanisms remain to be explored. The management of HS and a better understanding of HS behavior may be vital in mitigating algal blooms in eutrophic estuaries that are disturbed by engineered structures, such as the Yeongsan River estuary.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app122010223/s1](https://www.mdpi.com/article/10.3390/app122010223/s1)]([https://www.mdpi.com/article/10.3390/app122010223/s1](https://www.mdpi.com/article/10.3390/app122010223/s1)), Table S1: Phytoplankton genera/species that appeared at the sampling stations during the sampling period in the Yeongsan River estuary.  
Conceptualization, Y.S. and S.K.; methodology, Y.S.; software, Y.S.; validation, Y.S.; formal analysis, Y.S.; investigation, Y.S and S.K.; resources, Y.S. and S.K.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, S.K.; visualization, Y.S.; supervision, Y.S.; project administration, Y.S and S.K.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
We would like to thank the laboratory staff for participating in the water sampling and mesoccosm experiments.  
The authors declare no conflict of interest.
# An Image Authentication Scheme Using Merkle Tree Mechanisms
## 2 Background
### Decentralized Storage: IPFS  
IPFS stands for Interplanetary File System. It is an open-source, peer-to-peer (P2P) distributed hypermedia protocol that aims to function as a ubiquitous file system for all computing devices. IPFS is similar to the World Wide Web but looks more like a single BitTorrent swarm that exchanges objects within a single Git repository. In other words, IPFS provides a high throughput, content-addressable (block) storage system and content-related hyperlinks. It combines a decentralized hash table, data exchange, and a self-certified namespace, which also forms a generalized Merkle architecture. IPFS has no single point of failure and nodes do not need to trust each other, distributed content delivery can also save bandwidth consumption [8; 21].
###### Abstract  
This study proposes a new algorithm for a higher-order vector finite element method based on two new types of second-order edge elements to solve the electromagnetic field diffusion problem in a 3D anisotropic medium. To avoid source singularity in the quasistatic variant of Maxwell's function, a secondary field formulation was adopted. The modeling domain was discretized using two types of quadratic edge hexahedral elements, which were obtained using the edge unification method to reduce variables on each side of two conventional quadratic edge elements. Compared with the traditional quadratic element, the number of unknowns that needed to be solved was significantly reduced. The sparse linear equation of the finite element system was solved using an open-source direct solver called MUMPS. The numerical results demonstrated that the proposed algorithm has the same level of accuracy as the conventional vector finite element method and has a significant advantage over it in terms of computational cost.  
vector finite element method; marine controlled-source electromagnetic method; quadratic edge element; hexahedral element; anisotropic Article  
A New 3D Marine Controlled-Source Electromagnetic Modeling Algorithm  
Modeling Algorithm Based on Two New Types of  
Edge Elements
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
## 3 Theoretical Methods  
The stress characteristics of the rock around the borehole in the anchorage structure are the same as those of the thick-walled cylinder. The thick-walled cylinder can be used to simulate the stress characteristics of the rocks around the borehole, as shown in Figure 8. According to the elastic theory, the tangential stress \(\sigma_{\theta}\) and radial stress \(\sigma_{r}\) of the thick-walled cylinder are respectively (it should be pointed out that the compressive stress is positive in the following Equations):  
\[\sigma_{\theta}=\frac{P_{2}R_{2}^{2}-P_{1}R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}}+ \frac{(P_{2}-P_{1})R_{2}^{2}R_{1}^{2}}{r^{2}(R_{2}^{2}-R_{1}^{2})}, \tag{1}\]  
\[\sigma_{r}=\frac{P_{2}R_{2}^{2}-P_{1}R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}}-\frac{(P_ {2}-P_{1})R_{2}^{2}R_{1}^{2}}{r^{2}(R_{2}^{2}-R_{1}^{2})}, \tag{2}\]  
where, \(r\) is the radial distance, m; \(P_{1}\) and \(P_{2}\) are the inner and external wall pressure acting on the thick-walled cylinder respectively, MPa; and \(R_{1}\) and \(R_{2}\) are the inner and outer diameters of the thick-walled cylinder respectively, m. According to Equations (1) and (2), the radial stress and tangential stress of thick-walled cylinder in elastic state are both functions of \(r\), independent of mechanical parameters such as the elastic modulus and [PERSON]'s ratio of materials. The radial stress \(\sigma_{r}\) increases with the increase of \(r\), while the tangential stress \(\sigma_{\theta}\) decreases with the increase of \(r\).  
Figure 8: Stress analysis of thick-walled cylinder.
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 4 Results
The ACS module does not require a lot of hardware resources. The execution time of the ACS module was below 240 ms for a single input pair of images. Tests were performed on an Intel Core i7-7700 central processor unit manufactured in 2017 by Intel Corporation whose headquarters are located in USA. Only a single thread was used. The module was implemented in version C++11 of the C++ language using the 3.4.13 version of the OpenCV  
Figure 4: (a) BMP with respect to _g:_ (b) average error in the disparity value with respect to \(g\) (red—without the ACS module; blue—with the ACS module). Figure 5: (a) BMP with respect to \(\omega\); (b) average error in the disparity value with respect to \(\omega\) (red—without the ACS module; blue—with the ACS module). library. The implementation did not use a GPU for calculations. As far as memory usage is concerned, the application used less than 49 MB in total. The module only processes two images, meaning memory usage is not high. The module is intended for use with images that are not over- or underexposed. In the case of it being necessary to process these kinds of images, additional filtering is required to normalize brightness. As far as the computational complexity of the ACS module is concerned, the SUFT algorithm has a greater influence. [PERSON] et al. reported that the computational complexity of SUFT is O(n \(\times\) m + K), where n and m refer to the size of an image, and K is the number of identified points [39]. All other steps in the ACS module have a lower computational complexity, including the step in which an image is translated and rotated. Translation and rotation require performing an operation for every point of an image; therefore, the computational complexity of these steps is O(n \(\times\) m). The calculations of parameters c and \(\theta\) used in the ACS depend on the number of keypoints; therefore, its computational complexity is O(K).
# Smart Sensing of PSC Girders Using a PC Strand with a Built-in Optical Fiber Sensor
## 1 Introduction
### Precast Segmental PSC Girder for Railway Bridge  
On the other hand, previous studies [13] have presented a new-type PSC I-girder and evaluated its dynamic performance for railway bridge applications in Korea. This new-type PSC I-girder improved competitiveness with a longer span length and thinner deck depth by applying a bulb-T-shaped half-deck (BH girder). Figure 4 shows the shape and dimensions of a full-scale BH girder bridge prototype for testing. This study fabricated a full-scale PSC I-girder specimen of previous studies [13]. This specimen was fabricated in three precast segments, prestressed since its assemblage, and was completed by placing the deck in concrete. Its construction involved a change of the prestressing force at each stage. Moreover, after the completion of the bridge, a short-term variation of the prestressing force occurs due to the loads and vibrations generated during the crossing of trains, as well as a long-term variation of the prestressing force is caused by seasonal changes. Any mismanagement of such changes in the prestressing force would result in the collapse of the bridge, as shown in Figure 1. Accordingly, the present study intends to check the viability and measure the performance of the developed OSPC strand in tracking the change of the prestressing force in a PSC-I girder bridge.
# Named Entity Recognition Networks Based on Syntactically Constrained Attention
## 5 Conclusions  
In this paper, we propose a syntactically constrained attention-based neural network for named entity recognition, in order to address the problem of traditional attentional mechanism divergence in the NER task. First, by converting the NER task into an MRC task, the model can understand the deeper meaning of the named entities. Then, the syntactic information is introduced into the attention mechanism, in order to guide the model to focus on contextual information that is highly relevant to the task, thereby alleviating the attention-scattering problem in traditional attention models. The experimental results show that SCAN-Net is more competitive than the existing nested NER models.  
Future research can explore the following directions. Firstly, while queries can provide valuable prior knowledge for named entity recognition, the knowledge provided may still be limited. Therefore, researchers can consider using knowledge graphs for data augmentation, as well as training a query knowledge base tailored for this task by using neural network models. Secondly, as the maximum input length for BERT is limited, the model may still struggle with processing extremely long sentences. To address this issue, researchers can explore the use of multi-hop reading comprehension techniques.  
Conceptualization, S.L.; methodology, W.S.; investigation, Z.J.; visualization, L.K.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Xinjang Uygur Autonomous Region (No. 2019D01C060) and the National Natural Science Foundation of China (No. 61966034).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 5: Case study of SCAN-Net model.
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 2 Theory  
### Turbulence Model  
To determine the unknown Reynolds stress tensor, a transport equation was derived to develop a relationship between the Reynolds stress and known quantities of the mean flow. The Reynolds stress was modeled with Reynolds averaging process by an eddy viscosity model (EVM) to employ the Boussinesq hypothesis (19; 20; 21; 22). Reynolds stress can be expressed as follows:  
\[\tau_{ij}=-\rho\overline{u}_{i}\overline{u}_{j}=\mu_{t}\left(\frac{\partial U_{ i}}{\partial x_{j}}+\frac{\partial U_{j}}{\partial x_{i}}\right)-\frac{2}{3} \rho k\delta_{ij} \tag{1}\]  
where \(k\), \(\rho\), \(\delta_{ij}\), \(\tau_{ij}\) and \(\mu_{t}\) represent the turbulent kinetic energy, density, Kronecker delta tensor, turbulence Reynolds stress, and eddy viscosity respectively. The velocity components and the mean velocity component in the \(x_{i}\) direction are denoted by \(u_{i}\) (\(i\) = 1, 2, 3) and capital \(U_{i}\), respectively.
###### Abstract  
Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.  
2023
###### Abstract  
Alpine grasslands on the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and human disturbance. The mechanism, direction and magnitude of climatic and anthropogenic influences on net primary productivity (NPP) of various alpine pastures remain under debate. Here, we simulated the potential productivity (with only climate variables being considered as drivers; NPP\({}_{\text{P}}\)) and actual productivity (based on remote sensing dataset including both climate and anthropogenic drivers; NPP\({}_{\text{A}}\)) from 1993 to 2011. We denoted the difference between NPP\({}_{\text{P}}\) and NPP\({}_{\text{A}}\) as NPP\({}_{\text{P}}\) to quantify how much forage can be potentially consumed by livestock. The actually consumed productivity (NPP\({}_{\text{AC}}\)) by livestock were estimated based on meat production and daily forage consumption per standardized sheep unit. We hypothesized that the gap between NPP\({}_{\text{P}}\) and NPP\({}_{\text{AC}}\) (NPP\({}_{\text{gap}}\)) indicates the direction of vegetation dynamics, restoration or degradation. Our results show that growing season precipitation rather than temperature significantly relates with NPP\({}_{\text{gap}}\), although warming was significant for the entire study region while precipitation only significantly increased in the northeastern places. On the Northern Tibetan Plateau, 69.05% of available alpine pastures showed a restoration trend with positive NPP\({}_{\text{gap}}\), and for 58.74% of alpine pastures, stocking rate is suggested to increase in the future because of the positive mean NPP\({}_{\text{gap}}\) and its increasing trend. This study provides a potential framework for regionally regulating grazing management with aims to restore the degraded pastures and sustainable management of the healthy pastures on the Tibetan Plateau.  
alpine grassland conservation; anthropogenic disturbance; ecological policies; climate change; grazing exclusion; grazing management; regional sustainability +
Footnote †: journal: Remote Sensing
## 3 Application of the COR Values--A Case Study
### Rockfall Simulation Results
#### 3.3.2 Kinematic Analysis  
Figure 14 shows the velocities of rockfalls at different locations. Notably, a rockfall's position significantly affects the starting time of the falling movement. In addition, some rockfalls moved suddenly at 52-63 and 103-107 s after resting at the bottom of the slope. The subsequent rockfall may hit the immobile rockfall during the rolling process, resulting in a short-term movement.  
Figure 15 shows the relationship between each rockfall movement process's total and component velocities. The movement process could be divided into accelerated fall and lateral movement stages. In the accelerated fall stage, due to the slope of the study area up to 80\({}^{\circ}\) and less collision in movement, the rockfall speed could be close to 50 m/s. The  
Figure 14: Velocity of rockfall at different locations. The maximal velocity was approximately 50 m/s, and the movement time of each rockfall was 22–29 s.  
Figure 13: The final resting position of the rockfalls. Rockfalls are densely deposited in low-lying threatened areas. The maximal rockfall movement distance was 620 m, and two-thirds of the rockfalls moved 300–400 m.  
rockfall velocity in the Z direction contributed to most of the total velocity, but each collision between the rockfall and the slope increased the velocity in the horizontal direction. When the rockfall moved to the bottom of the slope, the sudden decrease in the slope negated the influence of the acceleration of gravity on the velocity in the Z direction for a long period. Therefore, the rockfall entered a lateral transfer movement stage dominated by the velocity in the horizontal direction. At that time, the rockfall speed could reach 20-25 m/s and pose a threat to safe transportation. Then, as the number of collisions with the ground increased, the energy gradually dissipated; eventually, the rockfall came to a rest.  
Figure 16a shows the angular velocity change during the rockfall movement process, with a maximal value of approximately 20 rad/s. The angular velocity of the rockfall increased during the accelerated fall stage and gradually decreased during the lateral movement stage, which is consistent with the change in horizontal velocity. Figure 16b shows the ratio of the kinetic energy to the rotational energy of the rockfall. The maximal kinetic energy was approximately 18 MJ, and the maximal rotational energy was approximately 1 MJ. Rotation accounted for less energy in the entire rockfall movement process and displayed a relatively consistent trend with kinetic energy changes. Therefore, most of the gravitational potential energy evolved into kinetic energy.  
Figure 15: Total velocity and component velocity of rockfalls. (**a**) Bottom of rock A; (**b**) middle of rock A; (**c**) bottom of rock F; (**d**) top of rock F.
## 5 Establish the Short-Term Load Forecasting Model  
### The Selection of the Parameter \(\alpha\) and \(\lambda\)  
In Equation (10), the selection of the parameters \(\alpha\) and \(\lambda\) have an important influence on the performance of the model. Before calculating the parameters, the input variables are centralized and standardized first. This allows different variables describing different characteristics to be calculated on the same scale.  
The parameter \(\alpha\) is equally-spaced and traverses the values in the range of \((0,\,1]\). The smaller the value spacing, the higher the calculation accuracy and the longer the calculation takes time. According to the calculation experience, the value spacing is selected as 0.02, and then determines the value of \(\alpha\) according to the Mean Square Error (MSE) of the prediction result.  
For \(\lambda\), \(\lambda_{\max}\) needs to be calculated first. Given characteristic variables matrix \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{m}]\), where \(\mathbf{x}_{m}=[x_{1m},x_{2m},\ldots,x_{Nm}]^{T}\). Observed matrix \(\mathbf{Y}=[y_{1},y_{2},\ldots,y_{N}]^{T}\). First, the average value of each column of the matrix is obtained [41]:  
\[\begin{array}{l}\mathbf{X}_{ave}=[x_{ave1},x_{ave2},\ldots,x_{avem}]\\ Y_{ave}\end{array} \tag{14}\]  
Subtract the average value of the corresponding column from each element of matrix \(\mathbf{X}\) and matrix \(\mathbf{Y}\):  
\[\begin{array}{l}\mathbf{X}_{d}=[\mathbf{x}_{d1},\mathbf{x}_{d2},\ldots,\mathbf{x}_{dm}]=[\bm {x}_{1}-x_{ave1},\mathbf{x}_{2}-x_{ave2},\ldots,\mathbf{x}_{m}-x_{avem}]\\ \mathbf{Y}_{d}=\mathbf{Y}-Y_{ave}\end{array} \tag{15}\]  
then obtain:  
\[\begin{array}{l}\lambda_{\max}=\frac{\max[\mathbf{X}_{d}^{T}\times\mathbf{x}_{d}]}{N \alpha}\\ \lambda_{\min}=\varepsilon\lambda_{\max}\end{array} \tag{16}\]  
The typical value of \(\varepsilon\) is 0.001. In the range of \([\lambda_{\min},\lambda_{\max}]\), the equally-spaced 100 values are taken into Equation (10), and then the optimal \(\lambda\) is selected through five-fold cross-validation.