esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### Experimental Operations  
Three experimental sets were implemented, corresponding to the three levels of constant axial force of 6 kgf, 9 kgf, and 12 kgf. At each value of axial force, there were two subsets: one for conventional drilling and another for ultrasonic-assisted drilling. Each subset used a new drill bit to make 5 holes on 5 samples. The bit totally lost its cutting ability after producing 5 holes in CD. Thus, each experimental subset includes 5 CD holes and 5 UAD holes. The experimental parameters are presented in Table 1, where the spindle speed was chosen as a typical value for drilling aluminum alloys.  
In all tests with UAD, frequency and amplitude were kept constant at 20.188 KHz and 15 \(\upmu\)m, respectively. The frequency was experimentally found by using the auto-adjust function on the ultrasonic generator. The amplitude of 15 \(\upmu\)m was obtained as the maximum value from the transducer.  
Firstly, a prepared mass with proper weights, including 6 kgf, 9 kgf, or 12 kgf, was hung on the end of the rope. A stop was placed between the fixture and the roller to prevent the fixture moving forward. The workpiece was then clamped on the fixture by using an M10 bolt. Two Bakelite sheets were used to insulate the workpiece from the fixture, preventing heat transfer from the workpiece to other parts of the fixture. After turning on the lathe, a manual effort was applied to release the stop and decelerate the fixture motion such that the workpiece came into contact with the tools smoothly. At the instant when the tool contacted with the workpiece, the fixture was totally released, and then the drill process started. It was assumed that all new drill bits have the same cutting capability. In order to consider the effects of the reduction in cutting ability of the tool on cutting performance, experimental data were labeled corresponding to the order of the holes being drilled. Hole number 1 is the first hole produced by a new drill bit and, correspondingly, holes numbered from 2 to 5 are the next holes drilled by the same tool. All signals from the LVDT, loadcell, and temperature sensor were captured, stored, and analyzed. Data from CD and UAD were compared in pairs of holes with the same number order Calibrated validations were made for all measuring devices used in the test. The LVDT was calibrated by comparing the distance with commercial calipers with a resolution of 0.02 mm. The thermal sensor was checked with a GIM 3590 commercial infrared thermometer from Greisinger.
# CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
## 5 CCFont Model
### Loss Functions  
The total loss function L of the model is expressed as the sum of adversarial loss (\(L_{ADV}\)), style loss (\(L_{s}\)), and L1 loss (\(L_{\text{L1}}\)), as shown in Equation (1). \(\lambda_{s}\) and \(\lambda_{\text{L1}}\) are hyperparameters for style and L1 loss during training and serve as weights for each loss.  
\[L=arg\,min_{G}\,max_{D}\,L_{ADV}\,(G,D)+\lambda_{s}\,L_{S}\,(G,D)+\lambda_{ \text{L1}}\,L_{\text{L1}}(G) \tag{1}\]  
The loss functions in Equation (1) are as follows:  
**Adversarial Loss (\(L_{ADV}\))**: The \(L_{ADV}\) of cGAN is expressed as Equation (2), known as min-max adversarial loss [6], where \(G\) minimizes (generator loss) and \(D\) maximizes (discriminator loss). The discriminator \(D\) determines whether the received fake image (\(G(x)\)) is real (true: 1) or fake (false: 0) and maximizes it (\(D(y)=1\)). Generator G generates a fake image (\(G(x)\)) and forces D to predict it as a real image (\(D(G(x))=1\)), such that \(G(x)\) is minimal (\(G(x)=0\)).  
\[min_{G}\,max_{D}\,L_{ADV}\,(G,D)=E_{y}\,[log\,D(y)]+E_{x}\,[log\,(1-D(G(x)))], \tag{2}\]  
where \(y\) is the real image of the character that corresponds to \(Yc\), and \(x\) equals \(Zc+Zs\). By predicting \(y\) as a real image and \(x\) as a fake image from \(G\), the discriminator \(D\) attempts to minimize this loss function. In comparison, the generator \(G\) attempts to maximize \(x\) as a true image to deceive the discriminator \(D\).  
**Style Classification Loss (\(L_{s}\))**: To create a one-to-many style-converted image, \(D\) determines whether the style is the same as the target style while discriminating the real from the fake style (\(D(y_{S})\)). To maintain the font style of the image, \(D\) predicts the font style (0-1) and feeds it back to \(G\) to reduce loss and lets \(G\) generate a font with the correct style. This concept is the same as adversarial loss, and it applies to character and is the same as Equation (3), which maximizes \(D(y_{S})\) and minimizes \(G(x_{S})\).  
\[min_{C}\,max_{D}\,L_{S}\,(G,D)=Ey\,[log\,D(y_{S})]+Ex\,[log\,(1-D(G(x_{S})))] \tag{3}\]  
**L1 Loss (\(L_{\text{L1}}\)):**\(L_{\text{L1}}\) makes the two images equal to \(G\), generating a fake image (\(G(x)\)) and reducing the mean absolute error (MAE) compared with the pixel in the target image (\(\gamma\)), which can be expressed as Equation (4):  
\[L_{\text{L1}}=E_{x,\,y}\,[\,|\,|\,|y-G(x)|\,|\,|\,] \tag{4}\]
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 4 Dataset Generation  
### Dynamics  
In this study, the gravitational dynamics model embedded in the EKF is given by  
\[\ddot{\boldsymbol{r}}=-\frac{\mu}{\boldsymbol{r}^{3}}\boldsymbol{r}+ \boldsymbol{p}, \tag{23}\]  
Figure 3: SLFN training process.  
where \(\mu=398\),600.4 km\({}^{3}\)s\({}^{-2}\) is the Earth's standard gravitational parameter and \(\mathbf{p}\) is the \(J_{2}\) perturbation acceleration in km \(\cdot\) s\({}^{-2}\), expressed as  
\[\mathbf{p}=\frac{3}{2}\frac{J_{2}\mu R^{2}}{r^{4}}\Big{[}\frac{x}{r}\Big{(}5\frac{z^ {2}}{r^{2}}-1\Big{)}\hat{\mathbf{i}}+\frac{y}{r}\Big{(}5\frac{z^{2}}{r^{2}}-1 \Big{)}\hat{\mathbf{j}}+\frac{z}{r}\Big{(}5\frac{z^{2}}{r^{2}}-3\Big{)}\hat{ \mathbf{K}}\Big{]}. \tag{24}\]  
In Equation (24), the value for the Earth's oblateness factor is \(J_{2}=0.00108263\) and the \(x\)-, \(y\)-, and \(z\)-coordinates are given in the ECI reference frame in km, whose directions are given by the unit vectors \(\hat{\mathbf{i}}\), \(\hat{\mathbf{j}}\), and \(\hat{\mathbf{K}}\); \(R=6378.14\) km the is the Earth's mean radius. While in the EKF model only the \(J_{2}\) perturbation was included, the ground truth orbit was propagated by also considering the \(J_{3}\) effect, atmospheric drag, and third-body attraction of the Sun and Moon. This was performed to increase the fidelity of the propagated orbit while keeping the implemented model of the EKF as simple as possible, in the same fashion as what was performed for the magnetic field modeling. Additionally, for the ground truth model, the acceleration due to the atmospheric drag \(\mathbf{a}_{drag}\) was modeled using the following equation.  
\[\mathbf{a}_{drag}=-\frac{1}{2}\rho(h)v_{rel}\Big{(}\frac{C_{d}A}{m}\Big{)}\mathbf{v}_{ rel}, \tag{25}\]  
where \(\mathbf{v}_{rel}\) is the spacecraft's velocity with respect to the atmosphere, \(C_{d}\) is the dimensionless drag coefficient, \(A/m\) is the area-to-mass ratio, and \(\rho(h)\) is the air density at the altitude \(h\), given by an exponential atmospheric model:  
\[\rho(h)=\rho_{0}e^{-h/h_{0}}, \tag{26}\]  
where \(\rho_{0}\) and \(h_{0}\) are the exponent density pre-factor and the atmospheric scale height, respectively. Table 3 reports the values used in this work.  
The dynamical model was developed in the Python environment using the Poliastro package [22].
## 5 Numerical Examples
### Application to Lunar Lander with a Rover
Moreover, when the IRFs of the substructures are known, the total CPU times of NMI-IBS, NMI-IBS-FEM and full Newmark-\(\beta\) time integration are 38.63 s, 173.96 s and 2685 s, respectively, under the same computer hardware conditions (CPU: Intel Core i5-2300, RAM: 8 GB), shown in Table 6. The most time-consuming process for the NMI-IBS is the calculation of IRFs. However, in practice, this preparation time for the IRFs can be reduced because the columns of the IRF matrix can be calculated simultaneously and the calculation is applicable to all. When facing design optimization or multiple load cases, by using NMI-IBS, efficiency can be drastically improved. NMI-IBS-FEM takes more time than NMI-IBS, but it could still improve the calculation efficiency. Moreover, these two methods do not need to calculate IRFs in advance, which might be more efficient than NMI-IBS when the number of cases is not too large. \begin{table}
\begin{tabular}{c c c c c} \hline  & **NMI-IBS** & **NMI-IBS-FEM** & **NMI-IBS-CB** & **Full Newmark-\(\beta\)** \\ \hline CPU time & 38.63 s & 173.96 s & 158.87 & 2685 s \\ \hline \end{tabular}
\end{table} 
Table 6: CPU time used for responses of the lunar lander. Figure 13: Comparison among NMI-IBS, NMI-IBS-FEM, NMI-IBS-CB (all modes retained) and the reference for the lunar lander (down for the response point 1 and up for the response point 2). This example shows that the multi-domain substructure methods can manage the practical assembly of substructures with non-matching interfaces conveniently and efficiently, with high accuracy.
## 6 Results
### Optimized Recommendation Module Prediction Accuracy
#### 6.1.1 Topic Class Prediction Accuracy  
In Figure 9, the graph presents the results of the prediction accuracy of our proposed approach with other baseline approaches. We have compared LSTM-LDA (RNN-LDA) with a basic NN model and LDA based NN model. We can observe that for LSTM-LDA, the prediction accuracies are better, i.e., approximately 96% as compared with NN (approximately 92%) and NN-LDA (about 95%).  
Figure 8: Flow chart for optimized recommendations based on PSO.  
Though NN-LDA and LSTM-LDA accuracy are pretty close, LSTM-LDA has a more stable pattern as compared with NN-LDA. The prediction accuracy for RNN-LDA gets steady after 300 epocs.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Data
#### 2.2.1 HAND Dataset  
Flood-proneness can be measured by the GIS-based flood index FSI (flood susceptibility index) [36] or a topographic index, among them GFI (geomorphic flood index) [27] or HAND (height above nearest drainage) [28]. The advantage of the HAND index is that it can be calculated from a DEM without hydrology data.  
A set of HAND values covering the entire globe is already available in GEE [37]. DEMs used are SRTM (30 m) and Viewfinder Panoramas (90 m) [38] for latitudes above 60 degrees, where SRTM data are lacking.  
Figure 1: Track of Cyclone Batsirai as it barrelled across southern Madagascar from the north-east to south-west on 5 and 6 February 2022 and location of the calibration and validation sites. Source: Track of Cyclone Batsirai, NCEI [35].  
To obtain the HAND, it is assumed that water in a given pixel flows towards the next downstream pixel following the steepest slope. Any given pixel may receive water from zero, one or several pixels upstream of it. Water can thus accumulate in a pixel, since water can only flow into a single pixel downstream. The direction of water flow in each pixel is first deduced from the DEM. The water accumulation rate is then calculated for each pixel (the number of upstream pixels feeding water into it). All pixels with a water accumulation rate above a given threshold, called the flow accumulation threshold (FAT), are considered part of the halweg network. These are valley lines along which water flows and accumulates when it rains. Once all points in the halweg network are identified, a matrix connecting each pixel in the study zone to the first point in the network receiving water from this specific pixel is established. This connection matrix, combined with the DEM, enables the value of the HAND parameter to be deduced by calculating elevation difference between each of the connected two points.  
Two values of FAT were considered for the HAND dataset available in GEE: 100 for local drainage basins and 1000 for large drainage basins.
###### Abstract  
Transportation infrastructure is central to economic development and the daily lives of citizens. However, rapid urbanization, increasing vehicle ownership, and growing concerns about sustainable development have significantly heightened the complexity of managing these systems. Although digital twin (DT) technology holds great promise, most current research focuses on specific areas, lacking a comprehensive framework that spans the entire lifecycle of transportation infrastructure, from planning and construction to operation and maintenance. The technical challenges of integrating different DT systems remain unclear, which to some extent limits the potential of DT technology in the management of transportation infrastructure. To address this gap, this review first summarizes the fundamental concepts and architectures involved in DT systems for transportation infrastructure, such as roads, bridges, tunnels, and hubs. From a lifecycle perspective, DT systems for transportation infrastructure are categorized based on functional scope, data integration methods, and application stages, and their key technologies and basic frameworks are outlined. Subsequently, the potential applications of DT in various lifecycle stages of transportation infrastructure--planning and construction, operation and maintenance, and decommissioning and renewal--are analyzed, and current research progress is reviewed and discussed. Finally, the challenges and future directions for achieving a full lifecycle DT system for transportation infrastructure, encompassing technical, operational, and ethical aspects, are discussed and summarized. The insights gained herein will be valuable for researchers, urban planners, engineers, and policymakers.  
transportation infrastructure; digital twin (DT); project lifecycle; DT use +
Footnote †: journal: Applied sciences  
[PERSON]  
1 National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China 12 Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China 2
## 3 Relevant Research in Wind Farm Design and Optimization
### Objective Functions
#### 3.1.2 Cost of Energy (CoE) and Levelized Cost of Energy (LCoE)  
The Cost of Energy (CoE) [S/kWh] is defined as the cost per kWh of energy (_i.e._, effective expected electrical energy) converted from the wind and represents the price of energy at which the wind farm project won't have neither economic profits nor economic losses over its operational lifetime (_i.e._, the breakeven price of energy). The general mathematical expression used to quantify CoE is shown in Equation (9):  
\[\text{CoE}=\text{Cost}_{\text{Farm}}/\text{AEP}_{\text{Farm}} \tag{9}\]  
Several definitions of CoE were identified in the literature, differing in the way the total cost of the wind farm (\(\text{Cost}_{\text{Farm}}\) ) is quantified. More than one third of the total reviewed works (representing more than 80% of the works that adopted the CoE as the wind farm performance metric) [28; 54; 55; 84; 137; 138; 161; 181; 219; 230; 231; 232; 233; 234; 235; 236; 237; 238; 239; 240; 241; 242; 243; 244; 245; 246; 247; 248; 249; 250; 242; 244; 245; 246; 247; 248; 249; 251; 252; 253; 254; 255; 256; 257; 258; 259; 260; 261; 262; 263; 264; 265; 266; 267; 268; 269; 270; 271] considered the Mosetti's cost function, in which the total cost of the wind farm is assumed to be an exclusive function of the number of WTs (\(N\)) constituting the wind farm, as shown in Equation (10):  
\[\text{Cost}_{\text{Mosetti}}=N(2/3+1/3\ e^{-0.00174N^{2}}) \tag{10}\]  
It should be noted that Equation (10) is dimensionless and represents only a fraction of the total capital costs of wind farms, while disregarding all other variable costs. [PERSON] _et al._[28], who first proposed the approximation, assumed a dimension of cost/year [S/y], which is inconsistent. In addition, it is very unlikely that WT manufacturers adopt this simplified WT cost function, in which for \(N\!>\!35\)the function value converges to \(2/3N\). The reason for using Equation (10) derives primarily from the wish of the authors of the reviewed works to compare their results with the results of the work of [PERSON] _et al._ In such works, the main objective was to develop better numeric solution procedures instead of improving the wind farm performance modeling. Although the acquisition of WT represent the major part of the total capital cost of the wind farm [55, 135], other fixed and variable costs cannot be neglected [3, 75, 76]. More complex cost models, which include additional fixed and variable costs, are discussed in Section 3.2.6. Equivalent measures of the CoE are given by the Levelized Cost of Energy (LCoE) or the Levelized Production Cost (LPC) [$/kWh]. Both the LCoE and LPC are similar in definition to the CoE, but both make a clear distinction between fixed and variable costs affecting the profitability of the wind farm and might include the time variation of the value of money, which in turn includes the concepts of interest (e.g., related to the additional payments required to pay loans) and the inflation rate.
## 2 Design Thinking and Communication
### The Concept of CCS in Taiwan  
The New Taipei City Government in Taiwan aims to consistently transfer information during manual review and to improve the effectiveness of reviewing architectural design. In promoting BIM, the government has developed CCS, required architects to adopt regulatory templates in their designs using BIM in the design stage, and applied CCS in the review stage to examine BIM submitted by architects to reduce cognition gaps while reviewing design communications. According to \"The report of Code-checking System for building design review in New Taipei City\" [10], published by the Chinese Public Works Engineering Information Association and commissioned by the New Taipei City Government, the results, based on building code logicalization, were directly classified into three categories: pass, checked, and fail. The audit report was produced by a cloud-based system (Figure 2). The results would be used to establish communication guidelines to help subsequent manual review.  
The developmental framework of the CCS is mainly divided into two parts. The first part is developing the design template, including building codes logicalization and a parameter analysis of the design template. The next step is to establish the review system engine, including testing the analysis of parameters, transferring plans to IFC, and system development of review project. These are described as follows:
# A Unified Database Solution to Process BIM and GIS Data
## 3 Results
### Implementation of the Common Database Environment for IFC and CityGML Data  
The architecture of the proposed solutions assumes the use of several schemas in a single database. Separate IFC and CityGML schemas are dedicated to storing BIM and GIS data, respectively. GIS applications only use data via the CityGML schema, while BIM applications can access data via IFC schema. IFC and CityGML schemas are treated as temporary datasets. The data stored in the IFC schema is converted into a BIGI-S, which allows for lossless saving of the data from the IFC model. Similarly, the data saved by GIS applications in the CityGML schema are also converted into the BIGI-S. It should be emphasized here that the BIGI-S does not mean, in this case, a completely new conceptual data model for the existing BIM and GIS data but, above all, refers to appropriately related IFC and  
Figure 2: General scheme of importing IFC data into the database. SQL: Structured Query Language. CityGML source schemas. The integrated data model is materialized in the form of a set of tables and stored procedures that are located in the BIGI-S. The differences in the GIS and BIM data conceptual models were taken into account when building the integrated data model. Table 1 shows selected examples, as well as differences between the methods of storing building data in the CityGML and IFC models. It requires a number of data transformations to convert data between these models via an intermediate model but is, with some simplifications, possible. The stored procedures implemented in the BIGI-S allow to save and read data between the CityGML and IFC models and the integrated model. The presented concept assumes that the data are stored in the BIGI-S in common tables with the use of the geometric data types typically used in GIS systems.
## 3 Graph Structure Abstraction for Malicious Attracts
### Traffic Interaction Attribute Graph
#### 3.2.1 Construction of TIAG
```
Input: Packet sequence \(P=\{p_{1},p_{2},\ldots,p_{N}\}\) in \(\Delta T\) Output: A \(TIAG_{t}\)
1: Traffic = Group_Flow(\(p_{1},p_{2},\ldots,p_{N}\)), len = len(Traffic) // group flows
2: Initialization(\(V_{SIP}\), \(V_{DIP}\)) // Initialize a vertex set and edge set
3: Initialization_E(\(Edges_{lm\times len}\)) // Initialize a largest directed edge record array
4: s = d = 0 / Initialize location records
5:for flow /*/ \(\mathit{T}\mathit{i}\mathit{e}\)do
6:\(SIP_{i}\) = flow_source_IP, \(DIP_{i}\) = flow_destination_IP //Mapping nodes to IP
7:if\(SIP_{i}\
otin V_{SIP}\)then\(V_{SIP}\) = add(\(SIP_{i}\),\(V_{SIP}\)), index_s = s; s = s + 1
8:else index_s = get_index(\(SIP_{i}\)) // Find the first index of \(SIP_{i}\) node
9:if\(DIP_{i}\
otin V_{DIP}\)then\(V_{DIP}\) = add(\(DIP_{i}\), \(V_{DIP}\)), index_d = d, d = d + 1
10:else index_d = get_index(\(DIP_{i}\))
11:\(e_{index\_s,index\_d}=e_{index\_s,index\_d}+1\)
12:endfor
13:len_SIP = len(\(V_{SIP}\)), len_DIP = len(\(V_{DIP}\)) //Calculate the number of non-duplicate IPs
14:\(Edges_{len\_SIP\times len\_DIP}\) = Delete(\(Edges_{len\times len}\), len_SIP, len_DIP) //Reduce the array size
15:Return\(TIAG_{t}\)
```  
**Algorithm 1** Construction of TIAG  
Algorithm 1 takes as input a sequence of packets at \(\Delta T\). It first groups flow, processes the disorder, and calculates the number of flows (line 1). Then, it initializes the source and destination nodes, the directed edge record array, and the location record (lines 2-4). For each flow, Algorithm 1 records the source and destination IP (line 6), and if the node does not exist in the corresponding node-set, algorithm 1 joins the node and records the location index of the node in the corresponding node-set, and update the location records (lines 7-9). If the node already exists, it finds the position index of the first occurrence of the node in the set (lines 8-10), and the index obtained in the source nodes is used together with the index obtained in the destination nodes to update the directed edge records array (line 11). After the flows are processed, it calculates the number of unduplicated IPs in the two node sets (line 13) and reduces the size of the directed edge record array (line 14). Finally, the two node sets and the directed edge record array obtained construct the TIAG (line 15).