esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Based on this, semi-theoretical and semi-empirical equations are proposed in combination with on-site tests or moving model measurements to describe the initial waveform of the entry compression wave with variations in time or space, written as \(p(t)\) and \(p(x)\), respectively [30; 31]. The one-dimensional model has been validated using field measurements [13; 14] and numerical simulations [32], but it still has some deficiencies. First, due to the evolution and superposition of the pressure waves, the pressure varies with time and space simultaneously, which is more accurately described as \(p(x,t)\). Second, the equations in the one-dimensional model need to calculate the pressure of any point at any time from the flow and pressure states of the adjacent positions at the previous moment using 1D finite volume methods [33; 34] or 1D finite difference [15; 35], which requires lots of iterative calculations. At the same time, it is notable that the pressure state of any point in the tunnel is the sum of the state influenced by each pressure wave individually at a given time, which is independent of the state of adjacent positions at the previous moment. Thus, based on the principle of superposition, it is possible to develop more accurate equations to describe the pressure state changes over time and space \(p_{s}(x,t)\) without iterative calculations. Based on the superposition of four PWSs in a tunnel, a one-dimensional model, referred to as the spatial-temporal distribution of the pressure state (SDPS) model, is proposed, and equations are first proposed to describe the transient pressure state at any point in a tunnel \(p_{s}(x,t)\). To identify the extreme pressure in the post-train stage, a spatial-temporal prediction method and a calculation program are proposed. Additionally, two dimensionless quantities, namely the train/wave velocity ratio \(M\) and the train/tunnel length ratio \(\varepsilon\), are proposed to describe the operating conditions based on dimensionless analysis. Then, the dimensionless spatial-temporal distribution characteristics of the extreme parameters (including the maximum positive pressure (MPP), the maximum negative pressure (MNP), and the maximum peak-to-peak pressure region (MPPR)) under different cases were analyzed, indicating the location of potential fatigue failure in tunnels. This study is helpful when attempting to understand the spatial-temporal distribution of pressure in a tunnel and to identify regions that have a high risk of fatigue failure at an early stage.
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 1 Introduction
This algorithm outperforms other point cloud reconstruction methods and accurately demonstrates the relationship between citrus tree volume and growth patterns. To address the challenges posed by the complex morphology and overlapping structure of wheat plants, ref. [11] combines point cloud data with virtual design. Basic information on wheat growth, such as the number of stems and inclination angles, is extracted from the point cloud data to build an initial 3D network model. This model is then iteratively refined by adjusting the leaf azimuth to construct a virtual 3D model of the wheat plant. This approach provides technical support for the analysis of wheat plant phenotype and functional structure. The above research methods provide high-quality data for the 3D reconstruction of object surfaces and cover many industrial areas. However, there are fewer studies applying 3D reconstruction to the dimensional inspection of rubber treads. Considering the errors and limitations of traditional tread measurement techniques [12; 13], this paper proposes a new point cloud dimensional measurement method based on RGB-D technology. Using the design data from Guilin Rubber Design Institute and its five-compound extruder linkage production line as the basis for actual production measurements, the proposed method first corrects the corner point positions of the depth camera using the checkerboard grid method. Statistical filtering is then employed to reduce image noise in the point cloud data. Finally, by comparing this method with other traditional methods and the national industry error standard, the RGB-D-based point cloud measurement method for rubber tread size is shown to offer higher measurement accuracy, lower equipment costs, stable performance, and compliance with the highest accuracy standard of the national standard.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 4 Our Research on IPMN Standards
### Standards for Indoor Map Data
#### 4.2.2 Data Collection for Indoor Maps  
Several methods, such as SLAM, Lidar, and traditional total station surveys can be used for data collection of indoor maps. Indoor maps can also be produced automatically at low cost by extracting indoor elements from the existing floor plans, which are usually in the format of CAD drawings. This specification presents a procedure and detailed techniques to obtain digital indoor maps from existing floor plan, as shown in Figure 7.  
Figure 4: Conceptual model of indoor map. (**a**) elements and their relationships of indoor map, (**b**) an illustration of building element.  
Figure 5: Content model of indoor map—Core Information Model.  
Figure 6: Content model of indoor map—Navigation Information Model.  
The key process in Figure 7 is entity extraction and handling element geometry with a priori knowledge. Figure 8 shows some examples of element geometry extraction.  
This approach can quickly generate indoor maps in accordance with a content model at very low cost, especially in the scenarios where indoor surveying is prohibited.
## 2 Experimental Test Program
### Experimental Setup and Instrumentation  
The experimental setup and instrumentation are presented in Figure 3a,b. After performing a 90-degree counterclockwise rotation, the specimens of each beam-column joint were positioned so that the column was in the horizontal position and the beam was in the vertical position. In order to replicate the inflection points of the columns in the center, the specimen was supported with equipment that allowed rotation.  
During the testing, a constant \(N_{c}\) = 0.05\(A_{c}\)\(f_{c}\) column compressive axial load was applied (where \(A_{c}\) is the area of the column cross-section). All of the specimens were subjected to full cycle deformations that were applied by a swivel connector that was attached to the actuator near the free end of the beam. Throughout the whole loading procedure, the column axial load was maintained at a constant amount of 122.5 kN (=\(N_{c}\) = 0.05\(A_{c}\)\(f_{c}\) = 0.05 \(\times\) 250 \(\times\) 350 \(\times\) 28.0 \(\times\) 10\({}^{-3}\)) for all specimens.  
The cyclic displacement-controlled loading history at the beam end began after applying the constant axial load to the column. The specimens were subjected to increasing cyclic reversal deformation across an eight-step loading history, with maximum displacement, \(d\) = \(\pm\)8.50 mm, \(\pm\)12.75 mm, \(\pm\)17.00 mm, \(\pm\)25.50 mm, \(\pm\)34.00 mm, \(\pm\)51.00 mm, \(\pm\)68.00 mm and \(\pm\)85.00 mm.  
As indicated in Figure 4, each loading step contained three complete loading cycles for each story drift (SD) level. It is noted that \(d\) = 17 mm corresponds to SD = 1%. The loading pace was 0.05 mm/s for the initial four loading phases and then raised to 1 mm/s for the last four loading steps.  
The load was measured with an accuracy of 0.05 kN by a load cell. The displacements of the beams were measured using two string position transducers placed 0.75 m and 1.84 m from the end of the column (see also Figure 1), while the slip of the longitudinal FRP bars of the beams was measured using two linear variable differential transducers (LVDT) with 0.01 mm accuracy (see Figure 3b).  
In addition, two SPTs were inserted on the front side of the joint core to assess the shear deformation until specimen failure. Moreover, one LVDT was installed at the left end of the specimen column to monitor the support during the test. The photographs of Figure 3a,b provide additional information on test rig instrumentation.  
Figure 3: (**a**) Experimental setup and instrumentation of the full-scale beam-column joint specimens under lateral imposed cyclic reversal deformations; and (**b**) LVDT set for measuring the slip of the beams’ longitudinal CFRP bars (two LVDTs with 0.01 mm accuracy).
## 4 Experimental Results and Analysis
### Hyperparameters  
Each instance of our image denoising approach requires an average of 0.0166667 s of computational time to complete the training phase. This duration reflects the computational resources invested in training a single model. Longer training times offer the potential for refined weight optimization and improved model performance, albeit at a higher resource cost. The proposed model used a learning rate of 0.00238. The learning rate governs the step size taken by the optimization algorithm during parameter updates. This updated value aims to strike a balance between convergence speed and stability. A higher learning rate can expedite convergence, but a careful balance is necessary to prevent overshooting and instability during optimization. We trained the model for a fixed 47 epochs. An epoch denotes a complete iteration through our training dataset. The number of epochs directly affects the frequency of weight updates using the complete dataset. An optimal number of epochs is crucial to mitigate underfitting and overfitting risks. Finally, we employed 846 steps per epoch to ensure comprehensive model training. A step involves updating the model parameters based on a data batch. This parameter, coupled with the batch size, determines the frequency of parameter updates. Higher steps enhance convergence, but computational requirements increase accordingly.
## 3 Results and Discussion
### Test Pulse Characterization  
An XFROG trace was measured for the test pulse using the reference pulse shown in Figure 5. The trace consisted of two spectral components (Figure 6a), each of which was generated by the cross-correlation between the reference pulse and one of the two spectral components in the test pulse. The FROG signal measured at 400 nm was generated by sum-frequency mixing between the reference pulse (800 nm) and the pulse at 800 nm contained in the test pulse (hereafter denoted as pulse A). In contrast, the signal at 480 nm was generated by sum-frequency mixing between the reference pulse and the other pulse in the test pulse (1180 nm, hereafter denoted as pulse B).  
Figure 4: (**a**) Temporal intensity and phase profiles of the reference pulse (800 nm) retrieved from SHG FROG (broken line) and SD FROG (solid line) traces; (**b**) Spectral intensity and phase profiles of the reference pulse.  
Figure 5: (**a**) SD FROG trace and (**b**) retrieved temporal profile of the reference pulse.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 3 DGNSS Positioning Infrastructure
### DGNSS Based on NMEA Messages
The position correction in Equation (20) is determined by the following vectors: \(\mathbf{G}\), \(\hat{\mathbf{1}}\), \(\hat{\mathbf{B}}\), and \(\hat{\mathbf{T}}\), as well as \(\mathbf{PR}\mathbf{C}\). The distance from the satellite and the receiver can be as large as 20,000 km., and the accuracy of the satellite position and the receiver position required has limited effects on the positioning results, especially for the meter level positioning. Therefore, the geometry matrix \(\mathbf{G}\) can be calculated from navigation messages, and it can also computed using the GSV sentences [17; 20]. The propagation delays \(\hat{\mathbf{1}}\) and \(\hat{\mathbf{T}}\) are computed with the standard models. The satellite clock error is estimated using the navigation messages. The position correction is valid when these vectors are based on the set of satellites, which are utilized in determining the standalone GNSS position. As shown in Figure 1, a critical step of DGNSS based on NMEA messages is that, for each satellite ID that appears in GSA sentences, we compute the propagation delays, the pseudorange correction, and the estimate of the satellite clock error based on the data from the reference station. The above vectors are then formed on the order of satellites that appear in the GSA sentence.
## 4 Discussion  
### Influence of the Direct Radiation Infilling Effect on the SIF Retrieval  
As mentioned in Section 3.2, the diurnal variation in the atmospherically corrected SIF still shows a \"U\" shape, with negative values at noon. This is partly due to the so-called \"direct radiation infilling effect\", which was analyzed by [PERSON] et al. [37]. [PERSON] et al. [38] also pointed out that changes in the sun and observation geometry and in atmospheric properties have a significant influence on the reflectance spectra and, consequently, on SIF retrieval. According to the study by [PERSON] et al. [37], the \"direct radiation infilling effect\" is proportional to the difference between the direct and diffuse radiation reflectance. Therefore, we simulated the diurnal variation in the direct and diffuse radiation canopy reflectance using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model [39], as shown in Figure 8. Here, the Leaf Area Index (LAI) was set to be 4 and the leaf inclination distribution function (LIDF) was spherical. The diffuse radiation canopy reflectance is constant during the day, while the direct radiation reflectance shows a \"U\" shape and most values are lower than the diffuse reflectance. According to [PERSON] et al. [37], the lower reflectance for the direct radiation will lead to a negative \"infilling\" effect at the oxygen absorption bands, which can partly explain the \"U\" shape of the diurnal variation and also the negative values of the retrieved SIF for a mature maize canopy.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 2 Problem Setting & Research Questions  
This paper tackles the problem of modelling, visualizing, and querying large-scale spatio-temporal data using traditional relational database versus graph databases approaches. We investigate query performance, storage and computational cost, as well as ease and efficiency of simulation of real-world applications. In particular:  
_How to store spatio-temporal data efficiently?_ Coordinates are comprised of latitude and longitude and time adds a third dimension. The accessing times of secondary storage is an issue when housing large volumes of data. The use of R-Trees and a geo-graph (a grid-based geospatial system where vertices are grids and edges connect other vertices to these grid vertices to indicate their physical location) are two techniques investigated for efficiently indexing multidimensional data.  
_How to query a graph topology effectively?_ Basic graph pattern matching is popular when extracting data from graph databases, but there are a range of graph query languages (such as Gremlin, Cypher, and GSQL) implementing more complex graph patterns too. The conciseness of querying graphs versus traditional SQL is briefly addressed in the conclusion (Section 7).  
_Can the respective database components be easily integrated into applications?_ Due to the rise of popularity in web-based applications, the ease of incorporating three typical databases into applications will be investigated with a Flask [7] back-end and Angular [8] driven front-end. This will show how appropriate it is in real-world applications and production settings to use graph database technologies and any challenges encountered during implementation versus using a classical SQL database back-end.  
PostgreSQL was chosen as our control database due to its industry-proven suitability for spatio-temporal data when coupled with PostGIS. We chose two graph databases with differing underlying architectures, namely Java and C++, and compared how they performed against our control database and with respect to each other. Despite this choice in databases, there are other notable mentions such as GeoMesa [9] and TerraLib [10] as proven suitable alternatives for the analysis of spatio-temporal data.
## 3 Method Architecture
### Feature Fusion Coding
#### 3.5.2 POS-Semantic Fusion  
The design of the POS-semantic fusion part is similar to that of the pinyin-semantic fusion part, but it focuses on the correlation between POS features and semantic features. Through the multi-head attention mechanism, features with interactive information are mined from the pinyin features and semantic features, recorded as interactive features A and interactive features B. Then, the correlation vector between the POS features and the semantic features is obtained, the obtained correlation information vector is used as the weight  
Figure 2: The interactive fusion network diagram.  
matrix to weight the POS features themselves, and the corresponding semantic information is given to the POS encoding; at the same time, the correlation vector between the semantic features and the POS features is obtained, the obtained correlation information vector is used as the weight matrix to weight the semantic features themselves, and the corresponding POS information is given to the semantic encoding. Then, the two weighted vectors are concatenated and fused to form a context feature that contains high-order semantic associations between semantic features and POS features. Finally, dimensionality reduction is applied to obtain the final fused feature vector. Here, \(H_{\text{pos}}=[h_{pos}^{1},h_{pos}^{2},\dots,h_{pos}^{i},\dots,h_{pos}^{n}]\) represents the sequence POS embedding feature, \(H=[h_{x}^{1},h_{x}^{2},\dots,h_{x}^{i},\dots,h_{x}^{n}]\) represents the sequence semantic embedding feature, and the POS-semantic fusion is shown in Formula (5):  
\[H_{\text{pos}}^{h}=Cross-ATT\big{(}H,H_{pos}\big{)} \tag{5}\]  
By interactively fusing POS features and semantic features, the high-order correlation between semantic information and POS information can be retained to a large extent, thereby improving the model's ability to understand text.
## 1 Introduction  
Nile Red (9-diethylamino-5-benzo[a]phenoxazine-5-one, NR) is a polar dye consisting of an electron donor (diethylamino group) and an electron with a drawing aromatic system connected by a flexible single bond [1]. It exhibits a change in its electronic emission as a function of the local polarity of its surroundings. These molecule fluorescence properties in the presence of organic solvents were previously determined, resulting in very soluble and strong fluorescence in organic solvents, and these properties were properly exploited for biochemical applications [2, 3].  
Due to its sensitivity to the local environment, NR has been advantageously applied to probe the local polarity in the study of various heterogeneous systems, including micelles [4], reverse micelles [5], dendrimers [6, 7, 8], liposomes [9, 10], proteins [11, 12], and ionic liquids [13]. Furthermore, NR in solution self-associates owing to strong intermolecular van der Waals attractive forces between molecules. The formation of supramolecular structures is generally observed in aqueous solutions, and many studies have been devoted to their characterization and properties. In the literature, there is an enormous quantity of information concerning the forces taking part in the interactions between moleculesmediated by different environments connected with biological and medical applications, such as, for example, liposome systems [14; 15; 16; 17; 18; 19]. It was recently demonstrated that alkan-1 ol solvents are significantly more polar than expected based on the measured H-bonding properties of \"monomer\" alkan-lols in diluted solution [20]. This prompted us to consider the aggregation process of NR in an alcoholic solution.  
Generally, Nile Red is used to localize lipid droplets within cells. This molecule is non-fluorescent in water and other polar solvents but undergoes fluorescence enhancement and large absorption and emission blue shifts in nonpolar environments. Hydrophobic and hydrophilic solvents contribute two major intermolecular forces between hydrophobic nonpolar and hydrophilic polar sites, referring to guest molecules. Nile Red is an uncharged hydrophobic molecule whose fluorescence is strongly influenced by the polarity of its environment, and its position as a function of its distribution represents an important tool for sensing applications.  
This research aims to obtain a deeper insight into the dye-alcohol and dye-dye interactions of NR to elucidate the role of hydrophobic interactions and to correlate the spectroscopic behavior of NR to the alcohol structure. For this purpose, the UV-Vis absorption and fluorescence emission spectra of NR in alkan-1 ol H-(CH\({}_{2}\))\({}_{n}\)-OH were studied both as functions of \(n\) and of NR concentration. Furthermore, the hydrophilicity of the medium was increased by adding water and monitoring the spectra of the NR-alkan-1 ol-water ternary systems.
## 3 Experiment Verification
### Evaluation Metrics  
To assess the accuracy of the model's predictions, a variety of evaluation metrics are utilized. These metrics aid in gauging the quality of the predicted images. For image prediction, metrics such as MSE, mean absolute error (MAE), SSIM, and peak signal-to-noise ratio (PSNR) [35] are employed. MSE and MAE measure the absolute pixel error between predicted and actual images, SSIM evaluates the similarity of structural information within spatial neighborhoods, and PSNR represents the ratio of the maximum possible power of a signal to the power of distortion noise. In the field of meteorology, evaluation metrics such as POD, false alarm rate (FAR), and CSI [32] are relied upon. These metrics are calculated by first setting a precipitation threshold, and then classifying grid points within and outside the threshold in both predicted and actual images as rain points (marked as 1) and no-rain points (marked as 0), respectively. For example, when the threshold is set to 40, the processing method is as illustrated in Figure 11.  
In order to assess the accuracy of the prediction results, the following process is employed. The predicted and actual images are compared pixel by pixel. If the predicted value is 1 and the actual value is 1, it is recorded as a hit. If the predicted value is 1 and the actual value is 0, it is recorded as an alarm. If the predicted value is 0 and the actual value is 1, it is recorded as a miss. The counts for hits, alarms, and misses are then totaled. The formulas for calculating these three metrics are provided in Equations (9)-(11) [32].  
\[\text{CSI}=\frac{n_{\mathit{hits}}}{n_{\mathit{hits}}+n_{\mathit{alarms}}+n_{ \mathit{misses}}} \tag{9}\]  
\[\text{FAR}=\frac{n_{\mathit{alarms}}}{n_{\mathit{hits}}+n_{\mathit{alarms}}} \tag{10}\]  
\[\text{POD}=\frac{n_{\mathit{bits}}}{n_{\mathit{hits}}+n_{\mathit{misses}}} \tag{11}\]  
In Equations (9)-(11), \(n_{\mathit{hits}}\) represents the number of hits, \(n_{\mathit{alarms}}\) represents the number of alarms, and \(n_{\mathit{misses}}\) represents the number of misses. Generally, higher values of  
Figure 10: Radar data noise filtering.  
CSI and POD indicate more accurate predictions, while lower values of FAR indicate fewer false alarms.
## 2 Preliminaries and Models
### Data Model  
Let \(x_{d}^{i}\) be the meter reading of the \(i\)th (\(1\leq i\leq N\)) user node at the \(d\)th (\(1\leq d\leq T\)) fine-grained timestamp, where \(N\) is the number of user in a BAN (a neighborhood-wide grid), and \(T\) is a billing period. At each fine-grained time index \(d\), a neighborhood grid (over the entire BAN) spatially aggregated utility usage can be expressed as:  
\[AS(d)=\sum_{i=1}^{n}x_{d}^{i};\;d=1,2,\ldots,T \tag{1}\]  
Figure 1: System model under consideration.  
At the end of a billing period (\(d=T\)), a temporally aggregated utility usage data for the \(i\)th user is expressed as:  
\[AT(i,T)=\sum_{d=1}^{T}x_{d}^{i};\;i=1\;\text{to}\;N \tag{2}\]