esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & \multicolumn{2}{c}{**Hypoxia**} & \multicolumn{2}{c}{**Ischemia**} \\ \hline  & **Cell Diameter, μm** & **Nuclear Diameter, μm** & **Cell Diameter, μm** & **Nuclear Diameter, μm** \\ \hline \hline Intact (\(n=6\)) & \(5.2\pm 0.6\) & \(2.2\pm 0.3\) & \(5.4\pm 0.4\) & \(2.1\pm 0.3\) \\ Control (PBS) (\(n=12\)) & \(8.0\pm 0.9\) & \(4.4\pm 0.6\) * & \(9.1\pm 0.9\) * & \(4.6\pm 0.4\) * \\ SRC inhibitor (\(n=6\)) & \(6.9\pm 0.6\) & \(3.9\pm 0.3\) * & \(4.7\pm 0.8\) & \(1.9\pm 0.4\) * \\ IKK\(\beta\) inhibitor (\(n=6\)) & \(9.5\pm 1.2\) * & \(4.7\pm 0.4\) * & \(9.5\pm 1.5\) & \(5.1\pm 0.2\) * \\ \hline \hline \end{tabular}  
\end{table} 
Table 4: Morphometric parameters of cerebral cortex neurons after hypoxic and ischemic exposure. Figure 8: Representative microphotographs of immunocytochemical staining of mouse sensorimotor cortex sections in the modeling of acute ischemic brain injury. (**A**)—Intact, (**B**)—Control, (**C**)—SRC inhibitor, (**D**)—IKK\(\beta\) inhibitor. Green channel—neuronal marker MAP2, red channel—astrolytic marker GFAP, blue channel—nuclear marker DAPI. Field of vision—320 \(\times\) 320 μm. Objective—Plan-Apochromat 20\(\times\)/0.8 M27. Visualization modeling of ischemia was performed on the 7 th day using magnetic resonance imaging. An analysis of the structures of the brain was carried out. Particular attention is paid to the volume of the focus of ischemic inflammation. Tomographic images of the brain of the group of Intact animals did not reveal any disorders--the cerebral hemispheres have a normal structure and intensity of the MR signal (differentiation of gray and white matter). MRI images of the Control group demonstrate pronounced areas of edema in the areas of the sensorimotor cortex and ventricles of both the left and right hemispheres. The volume of the affected area was 28.5 \(\pm\) 7.51 mm\({}^{3}\) (Figure 9). Significant changes in brain tissue were noticeable when the IKK\(\beta\) kinase inhibitor was applied. By day 7 of the postischemic period, the pronounced structural changes in the area of the cerebral cortex with a predominance of diffuse edema were observed. The edema smoothly passed to the ventricles and further into the deep layers of the nervous tissue. In this group, there was the largest volume of the affected area (38.6 \(\pm\) 18.8 mm\({}^{3}\)), while the width of the affected area was also at 6-7 mm maximum. The response of the nervous tissue to the SRC kinase inhibitor was similar to the IKK\(\beta\) kinase blockade; however, in this group, there was a less diffuse influx of fluid into the area of ischemic injury. The volume of the affected area was 35.8 \(\pm\) 6.2 mm\({}^{3}\). To sum up, the morphological analysis showed that the use of an SRC kinase inhibitor partially reduces the negative effect of hypoxic and ischemic damage on brain tissues. Blockade of the kinase IKK\(\beta\) negatively affects the morphological structure of the cerebral cortex during the AHH modeling.
## 4 Results
### Seismic Facies Analysis
#### 4.2.3 Facies C: Wavy Seismic Facies  
_Description._ Facies C consists of regular undulating reflections with overall moderate-to-high amplitude and high continuity (Figure 5G,H). These undulations measure \(\sim\)0.3-3.4 km in wavelength and 5-15 ms TWT in wave height. They exhibit apparent upslope-migration characteristics, with a thick upslope flank (backset) and a thin downslope flank (forest). Trains of undulations typically show a downslope-decreasing trend in both wavelength and wave height (Figure 5G,H). The facies is generally distributed along the flanks of the channels, with the thickness gradually decreasing away from the channels.  
_Interpretation._ Based on their regular wavy reflections, upslope-migrated crests, and downslope-decreasing wave sizes, facies C is interpreted as sediment waves mainly formed by turbidity currents overspilling from the channels [12; 39; 48; 49; 50; 51]. More specifically, the symmetric and asymmetric waves are further interpreted, respectively, as antidunes and cyclic steps formed by unconfined supercritical turbidity currents overspilling from nearby channels [52; 53; 54; 55; 56].
## 2 Methods  
After correcting the IFPB with a linear model [16], the code and phase observations of frequency \(g(g=1,2)\) can be written as  
\[\begin{array}{l}P_{g,i}^{k}=\rho_{i}^{k}+\frac{\mu_{i}^{k}}{f_{g}^{k2}}+F_{g,i} ^{k}+e_{g,i}^{k}\\ L_{g,i}^{k}=\rho_{i}^{k}-\frac{\mu_{i}^{k}}{f_{g}^{k2}}+\lambda_{g}^{k}N_{g,i} ^{k}+e_{g,i}^{k}\end{array}, \tag{1}\]  
where \(k\) and \(i\) denote a satellite and receiver respectively, and \(P_{g,i}^{k}\) and \(L_{g,i}^{k}\) are code and carrier phase observations with corresponding wavelength \(\lambda_{g}^{k}\) and frequency \(f_{g}^{k}\). \(\rho_{i}^{k}\) is the nondispersive delay that includes geometric delay, tropospheric delay, satellite and receiver clock biases, and any other delay that affects all observations identically. The quotient \(\mu_{i}^{k}/f_{g}^{k2}\) denotes the slant ionospheric delay, \(N_{g,i}^{k}\) the integer ambiguity, and \(F_{g,i}^{k}\) the code IFB. Finally, \(e_{g,i}^{k}\) and \(e_{g,i}^{k}\) denote un-modeled code and carrier phase errors including multipath effects and noise. The phase center correction and phase window effect [32] must be considered in modeling. To eliminate the first-order ionosphere delay, the well-known ionospheric-free (IF) combination is usually used for PPP [2]:  
\[\begin{array}{l}P_{c,i}^{k}=\alpha P_{1,i}^{k}-\beta P_{2,i}^{k}=\rho_{i}^{k }+F_{c,i}^{k}+e_{c,i}^{k}\\ L_{c,i}^{k}=\alpha L_{1,i}^{k}-\beta L_{2,i}^{k}=\rho_{i}^{k}+\widetilde{N}_{c,i}^{k}+e_{c,i}^{k}\end{array}, \tag{2}\]  
where \(\alpha=f_{1}^{k2}/(f_{1}^{k2}-f_{2}^{k2})\); \(\beta=f_{2}^{k2}/(f_{1}^{k2}-f_{2}^{k2})\) and \(F_{c,i}^{k}=\alpha F_{1,i}^{k}-\beta F_{2,i}^{k}\). The related float ionosphere-free ambiguity \(\widetilde{N}_{c,i}^{k}\) is usually expressed as the combination of WL and NL integer ambiguities and their UPDs:  
\[\widetilde{N}_{c,i}^{k}=\frac{f_{2}^{k}}{f_{1}^{k}+f_{2}^{k}}\lambda_{w}^{k} N_{w,i}^{k}+\lambda_{n}^{k}(N_{n,i}^{k}+\phi_{n,i}-\phi_{n}^{k}), \tag{3}\]  
where \(N_{w,i}^{k}\) and \(N_{n,i}^{k}\) are the integer WL and NL ambiguities, respectively, with corresponding wavelengths \(\lambda_{w}^{k}\) and \(\lambda_{n}^{k}\). \(\phi_{n,i}\) and \(\phi_{n}^{k}\) are the receiver and satellite NL UPDs, respectively.
## 1 Introduction
Since References [1; 2; 3; 4] are based on Apache Hadoop, which is a disk-based and IO optimized system, they inherit the performance limitations of Apache Hadoop. Although the methods in References [5; 6; 7; 8; 9] are based on Apache Spark, they do not perform well due to its overhead such as scheduling, distributed coordination, and lack of memory for large amount of data. As described above, Geomesa is designed to use various NoSQL systems as its storage engine. Specifically, it uses Apache Accumulo, which uses memory table, cache, and write ahead logs to alleviate IO overhead. It exploits the data distribution feature of Apache Accumulo for indexing spatiotemporal data with space-filling curve techniques. However, to our knowledge, Geomesa may suffer performance degradation since its indexing method is not pipelined and the resolution of space-filling curve is static [18]. In this paper, we propose a real-time parallel ingestion method for big spatiotemporal data by using Apache Accumulo like Geomesa. In addition, a spatiotemporal query processing method that maximizes parallelism of query processing is proposed. The proposed parallel query processing and ingestion methods are based on the table split feature of Apache Accumulo and use in-memory storage. The spatiotemporal data is classified into cells in a dynamic grid index by clients, and the data is parallelly ingested into the partitioned tables (tablets) distributed to the nodes using the threads corresponding to each cell. Our dynamic grid index can be adapted to the data distribution. Query processing is also performed in a similar manner. We identify the cells that need to be accessed during query processing and perform query processing on the tablets corresponding to each cell in parallel. This paper is organized as follows. Section 2 describes the existing distributed spatiotemporal data processing methods, and Section 3 presents the architecture of the proposed spatiotemporal data processing system and its application for location tracking for mountain hikers. In Section 4, the performance evaluation results of the proposed system are given, and finally, we conclude this paper in Section 5.
## 4 Factors Affecting the Quality of User Experience
### An Interaction Model of Paired Factors Affecting QoE\({}_{i}\)  
Figure 2 graphically shows an interaction model between individual factors that affect the \(QoE_{i}\) value. As can be concluded from the figure, there are two-way relations of influence according to the each with each principle of the paired factors and their corresponding input variables, including transitory subjective-user variables of satisfaction and/or dissatisfaction [15]. The value of \(QoE_{i}\) as a dependent variable is obtained via a mathematical model, directly based on users' ratings of satisfaction and dissatisfaction indicators, while independent variables serve as inputs to \(QoE_{i}\) prediction and classification models.
## 5 Security Analysis
### Authentication and Integrity  
The term integrity refers to protecting information from unauthorized changes during communication, while authentication aims to prevent attackers from impersonating authorized users. In the proposed key management system, a digital signature is employed to address both authentication and integrity concerns. The digital signature authenticates the user before communication by utilizing a signing process with both public and privatekeys. Consequently, this ensures data integrity and prevents unauthorized intruders from impersonating authorized users.
## 2 Materials and Methods
### Cell Viability Assays  
The cytotoxic effect of SHE concentrations, from 0 to 200 \(\upmu\)g/mL on RAW 264.7 cells, was measured by the thiazolyl blue (MTT) assay to reference the protocol. In short, RAW 264.7 cells (2 \(\times\) 10\({}^{5}\) cells/mL) were cultured onto a 96-well plate. After 24 h, the wells were treated with SHE at different concentrations (0 to 200 \(\upmu\)g/mL) and incubated under normal cell culture conditions for 24 h. Next, 10 \(\upmu\)L of MTT solution was mixed to each cell and further incubated for 4 h at 37 \({}^{\circ}\)C. Then, 80 \(\upmu\)L of the cell culture medium was discarded, and 100 \(\upmu\)L dimethyl sulfoxide was mixed to the cells. After 30 min, the measurement of absorbance was calibrated using a microplate reader at 595 nm (Bio-Rad Inc., Hercules, CA, USA), and the value was analyzed using Microplate Manager 6 software (Bio-Rad Inc., Hercules, CA, USA).
# Mapping Abandoned Cultivated Land in China: Implications for Grain Yield Improvement
## 2 Materials and Methods  
### Data Source  
The datasets in this study include the land use change survey data from 2015, the national geo-information survey data from 2017, remote sensing image data, and total grain yield data spanning the years 2015 to 2017 in China. The land use change survey data is obtained at the county level by the resources management department every year according to the change of agricultural land conversion. It can reflect land use type and ownership change in urban and rural areas, providing accurate and reliable information for different land use status quos. Therefore, these data can be used as the spatial range of abandoned land extraction. Data from the National Geoinformation Survey, provided by China's National Geomatics Center, boasts an impressive accuracy level of 1:10,000. The census data on geographical conditions is a comprehensive integration of GNSS, remote sensing technology, and geographic information system (GIS) technology. By employing techniques such as image fusion, orthorectification, and seamless mosaic, the fundamental components of surface configuration, surface coverage, water systems, and road networks are accurately captured and represented, as well as key objects such as wetlands, glaciers, and deserts, which are visually interpreted and quantitatively monitored. This study also uses a remote sensing image sourced from the Geospatial Data Cloud (available at: [[https://www.resdc.cn](https://www.resdc.cn)]([https://www.resdc.cn](https://www.resdc.cn)) [accessed 10 March 2020]), which acquires Landsat 8 satellite imagery as its primary data source, offering a resolution of 30 m. It is mainly used for the verification of abandoned cultivated land. The total grain yield data (including rice, wheat, corn, bean, potato, etc.) from 2015 to 2017 is obtained from the China Rural Statistical Yearbook. We can directly obtain the datasets of total grain yield and grain yield per unit area at the provincial level from this statistical yearbook.
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 3 Virtual Design and Construction (VDC)  
### VDC Project Management Framework  
VDC is focused on product, organization and process models and has been taught with the framework presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Function** & **Form** & **Behavior** \\ \hline \multirow{2}{*}{**Product**} & How the building & Building elements & How is the building \\  & should work & and systems & working \\ \multirow{2}{*}{**Organization**} & What the organization & Companies, areas, equipment, people & How well the organization is achieving the desired function \\ \multirow{2}{*}{**Process**} & What processes should be carried out by the building & Activities, milestones, stages, workflows & How well the organization is achieving the desired function \\ \hline \hline \end{tabular}
\end{table}
Table 1: POP matrix, adapted from the studies by [PERSON] et al. [16], [PERSON] et al. [20] and [PERSON] [12].  
Figure 1: VDC framework, adapted from [PERSON] et al. [35].  
A diagram is obtained through the framework presented in Figure 1 that attributes a specific term (ICE, BIM, PPM) for each element and for the main objectives and production objectives. An organized and hierarchical structure supports the framework to align each element and actor with the client and project objectives.  
From the POP and the VDC frameworks, the following correlations can be found: the product (P) design is constituted using Building Information Modeling (BIM) and the organization (O) design is developed through the practice of Integrated Concurrent Engineering (ICE) and the process (P) models are developed based on Project Production Management (PPM). Similarly, the objectives are present in both frameworks.  
The relationship between POP components and VDC elements is illustrated in Figure 2. The product, organization and process represent the BIM, ICE and PPM elements. The project function column is closely related to the objectives that the project must achieve for the client. Finally, the expected and real behavior will be measured based on the production objectives implemented in each element.  
Hence, VDC proposes vital elements for each of the original POP models. However, the framework is not subject to the same elements (ICE, BIM, or PPM) but adapts to today's technologies, methods and tools. Moreover, Figure 2 demonstrates the consistency in VDC structuring since each framework and element is equivalent, allowing the user to visualize, integrate, simulate, plan, execute and operate a project properly. Finally, the VDC framework enables the project team to exceed the client's expectations under an integrated, concurrent and collaborative working environment.  
Next, the elements BIM, ICE and PPM are explained in the context of the VDC framework.
# Framework for BIM-Based Repair History Management for Architectural Heritage
## 2 Literature Review
Furthermore, studies have investigated the incorporation of cutting-edge technologies and equipment, such as laser scanners, photogrammetry, and visual programming, into HBIM [19; 20; 21; 22]. In the realm of the facility management of heritage buildings, there have been studies on data utilization methods to support decision making for building maintenance. These include structural [23; 24] and condition analyses [25; 26] of heritage buildings by integrating lifecycle data into HBIM. Methods to ensure data sharing and mutual compatibility for planning maintenance and restoration actions based on the integrated information of historic buildings for BIM have also been researched [27; 28]. Furthermore, diverse web platforms were developed to promote cooperation and collaboration among the different stakeholders involved in the restoration and maintenance activities of built and cultural heritage sites, by integrating historical and geometric information and heritage documentation databases [29; 30; 31]. These HBIM case studies address issues related to modeling complex architecture, accurately documenting historic buildings, and creating data structures suitable for modern HBIM for an online platform. Although various technologies and applications have been explored in numerous studies, there is still a research gap in the application of BIM to preserve and manage heritage buildings. Most studies related to HBIM maintenance involve different technologies and application cases, but few focus on creating an organized BIM-based maintenance database, transferring non-shape data, or establishing maintenance procedures through attribute information, all of which are advantages of BIM. Furthermore, the discussion on the use of BIM for the preventive and repair maintenance of heritage buildings is limited. This study presents a framework for managing repair history based on BIM to address current practice gaps. To demonstrate practical implementation, the proposed framework and system modules are applied to heritage building cases. Figure 1: Literature analysis Venn diagram.
The model quantitatively assesses ecosystem service functions and presents them spatially, thereby illustrating the distribution and variation in ecosystem services and aiding in the identification of regions with inadequate ecosystem service functions. The technique has been effectively implemented in the Domer River in the Netherlands, Ecuador, Venezuela, Colombia, the coast of Belize City, Indonesia, Tanzania, Ohio, Hawaii, and other locations. It offers significant references for regional ecosystem services, watershed management, natural capital preservation, ecosystem service compensation, and alterations in ecosystem productivity [13]. In China, researchers have extensively sought to assess ecological services like water conservation, water supply, water yield, biodiversity, and carbon storage through the application of this model. Scholar [PERSON] [14] employed the InVEST model to examine the spatial-temporal variations in water conservation in the Xiaoluan River Basin. [PERSON] et al. [15] examined the spatio-temporal variations and determinants of water production in Xinjiang from 1990 to 2018, whereas the InVEST model was extensively utilized in the study of ecological conservation reserves in China. This research utilizes the \"annual water volume\" module, referencing other studies, to perform operational simulations of water production in the study area from 2010 to 2018. Previous studies have extensively examined the evolutionary patterns of precipitation, surface water, and groundwater resources; however, significant issues persist regarding the distribution characteristics of groundwater resources in the Mudan River Basin [16]. The primary manifestations are as follows: (1) Prior research has concentrated on quantifying regional replenishment, with very few investigations into the spatio-temporal dispersion characteristics. (2) The prior groundwater recharge calculation method predominantly relies on the water balance approach, substituting numerous environmental variables with a singular coefficient, resulting in significant inaccuracies. (3) Previous studies mostly focused on groundwater levels, water content of the aquifer, and lateral recharge, with limited research on regional recoverable amounts, recharging efficiency, quantification, and regional visualization. Consequently, drawing from the aforementioned research context and prior experience, and after validating the feasibility of the SWAT and InVEST models, this paper incorporates the groundwater runoff modulus method and ArcGIS spatial analysis to enhance the accuracy of recharge calculations, visualize the distribution of groundwater reserves in the study area, and quantify the recoverable volume and recharge efficiency. This serves as a reference for research methodologies in this area and the management of groundwater resources in the Mudan River.
# A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM
## 3 LSTM Structure and Principle  
The creation of LSTM networks solves the long input sequence-related problem that causes gradients in recurrent neural networks (RNN) to disappear. Compared to traditional RNNs, LSTMs introduce memory cells that can decide which states should be kept and which should be forgotten, thus being able to deal with long-term dependencies.  
Input, output, and forget-ting gates, along with memory cells, make up the majority of LSTM. The three gates all have Sigmoid activation mechanisms. The forgetting gate regulates whether the previously recorded historical knowledge is maintained, whereas the input gate and output gate each control the neuron's input and output information in turn at any given time.  
The following Figure 1 is an expanded view of the LSTM network, where \(X_{t}\) stands for the input at the time t, and \(h_{t}\) represents the state at this moment t.  
The calculation formula of LSTM is expressed as follows:  
\[i_{t} =\sigma(W_{i}\cdot[h_{t-1},X_{t}]+b_{i}) \tag{3}\] \[f_{t} =\sigma(W_{f}\cdot[h_{t-1},X_{t}]+b_{f})\] (4) \[o_{t} =\sigma(W_{o}\cdot[h_{t-1},X_{t}]+b_{o})\] (5) \[C_{t}^{\prime} =\tanh(W_{C}\cdot[h_{t-1},X_{t}]+b_{C})\] (6) \[C_{t} =f_{t}*C_{t-1}+i_{t}*C_{t}^{\prime}\] (7) \[h_{t} =o_{t}*tanh(C_{t}) \tag{8}\]  
Among them, \(W_{i}\), \(W_{f}\), \(W_{C}\), and \(W_{o}\) are the weight matrices of the input, forget, update, and output gates, respectively. \(b_{i}\), \(b_{f}\), \(b_{c}\), and \(b_{o}\) are their corresponding biases, which can help calculate the output \(h_{t}\) at time t and the updated cell state \(C_{t}\).