esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
## 2 System and Channel Models
### THz Link
 \tag{17}\]\[\mu_{7 Hz}=E[Z]=\frac{g_{ij}^{2}\Gamma\left(\frac{2}{\pi_{7 Hz_{2},i}}+ \frac{\pi_{7 Hz_{2},i}\mu_{7 Hz_{2},i}}{\pi_{7 Hz_{2},i}}\right)}{\Gamma(\mu_{7 Hz_{2 },i})\left(2+g_{ij}^{2}\right)}\left(\frac{\hat{\mu}_{f}A_{ij}\hat{\mu}_{l,7 Hz_{ 2},i}}{\mu_{7 Hz_{2},i}\frac{1}{\pi_{7 Hz_{2},i}}}\right)^{2}\times\left[1-exp \left(-\frac{\psi_{AoA,i}^{2}}{2m_{i}\sigma_{i-1 jangle}^{2}}\right)\right] \tag{18}\] \[+exp(-\frac{\psi_{AoA,i}^{2}}{2m_{i}\sigma_{i-1 jangle}^{2}}),\]  
\[\Omega_{7 Hz}^{2}=E[Z^{2}]-(E[Z])^{2}=\frac{g_{ij}^{2}\Gamma\left( \frac{4}{\pi_{7 Hz_{2},i}}+\frac{\pi_{7 Hz_{2},i}\mu_{7 Hz_{2},i}}{\pi_{7 Hz_{2},i }}\right)}{\Gamma(\mu_{7 Hz_{2},i})\left(4+g_{ij}^{2}\right)}\left(\frac{\hat{ \mu}_{f}A_{ij}\hat{\mu}_{1,7 Hz_{2},i}}{\mu_{7 Hz_{2},i}^{2}}\right)^{4} \tag{19}\] \[\times\left[1-\exp\left(-\frac{\psi_{AoA,i}^{2}}{2m_{i}\sigma_{i- 1 jangle}^{2}}\right)\right]+\exp\left(-\frac{\psi_{AoA,i}^{2}}{2m_{i}\sigma_{i -1j,angle}^{2}}\right)-\mu_{7 Hz}^{2},\]  
\[\mu_{SUMHz_{2},i}=\tilde{\tau}_{7 Hz_{2},i}\times\mu_{7 Hz}\times W, \tag{20}\] \[\Omega_{SUMHz_{2},i}^{2}=\tilde{\gamma}_{7 Hz_{2},i}^{2}\times \Omega_{7 Hz}^{2}\times W. \tag{21}\]
## 4 Results and Discussion  
This study evaluated the utilization and effectiveness of an enhanced power smoothing method, specifically the R-R filter, by varying its ramp-rate percentage (%R-R) through a fuzzy logic controller (FLC). A comparison was conducted with two previously tested methods in [25], R-R and MA filters, aiming to validate and highlight the proposed improvements. The study was divided into two distinct case studies.  
In the first case, comparisons between existing methods and the proposed enhancement were made under conditions based on the SoC percentage of the Li-Ion BESS. In the second case, the new method was implemented with a configuration prioritizing the charging of the vehicle's Li-Ion BESS.  
* Case 1: Li-Ion BESS at Medium SoC: In this scenario, it was required that the Li-Ion BESS's SoC be maintained within the range of 20% to 80% as an initial condition.
* Case 2: Li-Ion BESS at Minimum SoC: In this specific undercharge condition, the Li-Ion BESS started its operation with a value below 20% of its maximum storage capacity, prioritizing the vehicle's charging.  
These case studies were designed to comprehensively evaluate the proposed enhancement under various operational conditions, allowing for a deeper understanding of its performance and effectiveness in practical situations involving V2G applications.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 2 Materials and Methods  
### The System Architecture of RSIMS  
As illustrated in Figure 1, RSIMS adopts a modular and layered architecture. The top layer is RSIAPI which hides the complex and diverse inner storage schemas and provides users a set of uniform interfaces for remote sensing images storage, access and retrieval. The bottom layer is divided into two relatively independent modules: RSIMeta and RSIData. RSIMeta, as shown in the right part of Figure 1, employs the reliable and efficient PostgreSQL database cluster to store the structured metadata of remote sensing images and then index these images with the distributed spatial index built based on multi-level Hilbert grids. RSIData, as shown in the left part of Figure 1, stores the remote sensing image files in the form of objects in CEPH, which is a distributed object storage system with high I/O performance and good capacity scalability, and then provides users two types of GDAL compatible I/O interfaces.  
The following content will be divided further into three parts to introduce RSIMS. The first part introduces the details of distributed spatial index building and multi-source heterogeneous remote sensing images integration which are key to the performance of  
Figure 1: The overall architecture of RSIMS.  
RSIMS. The second part introduces the details of remote sensing image files storage and GDAL compatible interfaces design. The last part introduces the usage of RSIAPI.
# _remote sensing_
## 3 Results
### Clock Error  
The clock offset information in the D1 and B-CNAV navigation messages of BDS-3 MEO was the same. Since the D1 navigation message had more epochs and was more complete, we used it to evaluate the accuracy of the clock error. According to one epoch per hour of the BDS-3 MEO broadcast ephemeris, the clock offset differences of each satellite between the broadcast ephemeris and precision ephemeris were calculated and are shown in Figure 5. The figure shows the annual clock error evolution process of every satellite. The mean value and standard deviation of the clock error of the entire MEO constellation were 0.004 m and 0.256 m, respectively. In terms of the mean value, the systematic deviation caused by different reference clocks was eliminated for the constellation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TYPE** & **Along** & **Cross** & **Radial** & **3D** \\ \hline Diff & 0.000 & 0.000 & 0.012 & 0.002 \\ Imp & 0.00\% & 0.00\% & 19.06\% & 0.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The RMS statistic and comparison of orbit errors between two types of navigation messages (unit: m).  
Figure 3: The RMS statistic of every satellite orbit error of the D1 navigation message (unit: m): 3D RMS (black), along RMS (green), cross RMS (blue), and radial RMS (red).  
As new hydrogen (C25 C26 C27 C28 C29 C30 C34 C35 C41 C42 C43 C44) and rubidium atomic clocks (C19 C20 C21 C22 C23 C24 C32 C33 C36 C37 C45 C46) are carried on BDS-3 MEO satellites, we distinguish them when analyzing clock error accuracies. Table 3 shows a statistical analysis of the mean value, standard deviation, and RMS of the annual clock error of the BDS-3 MEO broadcast ephemeris. At the same time, Figure 6 gives the annual accuracy statistic of hydrogen and rubidium atomic clocks for each satellite. It can be observed in Table 3 that the accuracy of the BDS-3 MEO broadcast ephemeris clock error was 0.541 m. The standard deviations of two new types of atomic clocks showed that the stability of the hydrogen atomic clock was better than that of the rubidium atomic clock. For a single satellite, the average clock error was not zero, which resulted in a systematic deviation. Due to the existence of systematic deviations, the clock error accuracy of the hydrogen atomic clock was 0.563 m, and it was lower than the accuracy of the rubidium atomic clock, which was 0.519 m. Figure 6 shows that the satellite of C29 had the best accuracy and stability with respect to the hydrogen atomic clock, while C37 had the smallest systematic deviation and the best accuracy with respect to the rubidium atomic clock. For a single satellite, the accuracy of the satellite clock error was greatly affected by the obvious systematic deviation. The systematic deviation was related to the delay deviation of the equipment, and it can be eliminated by adjusting the TGD parameter of the navigation message [10].
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 3 Methodology for Quantifying Uncertainties
### Measurement Error  
The measurement error covariance matrix \(\mathbf{S_{e}}\) stems from the uncertainties of the measurement instrument. \(R_{\text{rs}}\) is derived from the Rayleigh-corrected TOA radiances \(L_{rc}\) which are measured by ocean color satellite sensors. Our measurement error would then come from the per-pixel radiometric uncertainty for satellite L1B data. This information is currently not available because it is difficult to model noise from photonic, detector, and electronic sources [43]. Therefore, we assumed the value of one percent for radiometric uncertainty because it is consistent with Gaussian noise added to the OC-SMART neural network training dataset. Details on the neural network training data, such as formation, testing, and validation are discussed elsewhere [14].  
The measurement error covariance matrix \(\mathbf{S_{e}}\) is calculated by taking the square of 1% of the Rayleigh-corrected TOA radiances \(L_{rc}\).  
\[\mathbf{S_{e}}=0.01^{2}\times L_{rc}{}^{2}. \tag{12}\]  
This value is likely a conservative estimate of radiometric uncertainty. The use of signal to noise ratios as an adequate uncertainty has been criticized [43; 44] because it is not rigorously quantified and does not provide any information on possible spectral covariance in the radiometric noise. These are valid concerns and are topics of ongoing investigations within the ocean color remote sensing community. We have chosen to define measurement error as stated above because it is applicable to all sensors compatible with OC-SMART.
## 5 Experimental Results
### RQ1. Can We Find a Better Ranking Model to Prioritize Test Cases for CAD Software?  
The goal of our work PriorCadTest is to re-rank test cases to trigger defects early. This can reduce the time cost of frequently running test cases. PriorCadTest relies on a ranking model, which is a binary classifier in machine learning. In RQ1, we check the effectiveness of six different classifiers based on the dataset of Project ArtOfIllusion.  
**Method**. The evaluation method in the section is to compare the results of the proposed approach, PriorCadTest, when we change the machine learning algorithms (including deep learning algorithms). First, we extracted functions and their corresponding test cases from Project ArtOfIllusion and constructed a test coverage matrix between functions and test cases; second, we vectorized functions and test cases using the CodeZhi tool and merged the functions and test cases according to the test coverage matrix; finally, we trained six learning-based ranking models, respectively. The six ranking models are the decision trees (CART), the random forest (RF), the SVM, the Bayesian networks (BN), the convolutional neural networks (CNN), and the recurrent neural network (RNN).  
**Result and analysis**. We trained six ranking models and evaluated the effectiveness using \(Precision\), \(Recall\), \(F\text{-}score\), \(Accuracy\), \(Time\), and \(Memory\). Table 4 shows the evaluation results on the six ranking models for eight rounds of evaluation. As shown in Table 4, the CART and the random forest outperform the SVM, the Bayesian networks according to the values of four evaluation metrics in all the test sets. As shown in Table 4, the CART and the random forest outperform the SVM, the Bayesian networks, the CNN, and the RNNaccording to the values of four evaluation metrics (_Precision_, _Recall_, _F-score_, and _Accuracy_) in all the test sets. When the SVM model ranks the test cases, it predicts all functions as non-defective functions. Thus, the values of _Precision_ and _F-score_ are not available (N/A) for the SVM model when we use Module object as the test set. The values of _Precision_ and _F-score_ are N/A for the SVM model, the CNN model, and the RNN model when we use Module object and Fold 4 as the test set. In terms of _Precision_, the CART model is lower than the random forest model in all test sets. This shows that the random forest model is better at truly predicting defects for three different test sets. The random forest model does not perform well in terms of memory consumption and time consumption based on two different datasets. In terms of _Recall_, the CART model is higher than the random forest model. The CART model is also slightly higher than the random forest model when we compare the values of _F-score_ and _Accuracy_. The evaluation metrics of the random forest model also performs well in the evaluation.  
**Conclusion**. The main purpose of this paper is to trigger defects in CAD software by test cases in the early stage. The results in Table 4 show that the CART model and the random forest model are better than the other ranking models.
## 4 System Design Based on Software Engineering
### Class Diagram  
The incorporation of object-oriented programming (OOP) methods is imperative in both the design and implementation phases of the FELL system, aimed at forecasting the health status of electric vehicle batteries. OOP provides a structured and modular framework for system development, facilitating the organization of data and functionality into discrete objects capable of interacting seamlessly. As illustrated in Figure 2, the class diagram of the FELL system underscores the pivotal role of OOP principles in orchestrating and managing system components. Through the delineation of entities such as ElectricVehicle, ElectricVehicleBattery, and Model as distinct classes interconnected via composition and aggregation relationships, OOP enables the representation of real-world entities and their interactions in a lucid and intuitive manner. Furthermore, OOP fosters code reuse, scalability, and maintainability, essential attributes in the evolution of intricate systems  
Figure 1: System architecture diagram.  
like FELL. The strategic adoption of object-oriented programming ensures the coherence, flexibility, and extensibility of the FELL system, establishing a robust foundation for the integration of extended Kalman Filter and deep learning models to augment electric vehicle battery health prediction capabilities.  
Figure 2 depicts a class diagram of the FELL system, structured upon principles of object-oriented programming. This diagram encompasses eight distinct categories: ElectricVehicle, ElectricVehicleUser, ElectricVehicleBattery, BatteryData, Duration, State-OfHealthPrediction, Model, and EvaluationResult. Notably, ElectricVehicle exhibits a composition relationship with ElectricVehicleBattery, signifying that an ElectricVehicle encompasses an ElectricVehicleBattery within its structure. Additionally, ElectricVehicleUser is linked with ElectricVehicle, illustrating the potential ownership of an electric vehicle by a user. Furthermore, StateOfHealthPrediction is depicted with an aggregation relationship to Model, indicating its role in managing a collection of prediction models. Similarly, Model is portrayed with a composition relationship to BatteryData, suggesting that each model is intricately linked with specific battery data utilized for training and prediction purposes. Moreover, StateOfHealthPrediction is associated with Duration, detailing the time duration specified for SoH prediction. Lastly, Model is shown to possess an aggregation relationship with EvaluationResult, implying the generation of an evaluation result during the process of assessing the model's performance.
## 3 Domain Decomposition and Application
### Domain Decomposition with Proper Orthogonal Decomposition  
The idea of POD is to extract a basis matrix from a matrix of _snapshots_ and reduce the global system by a projection on the basis [27]. In this article, the matrix of _snapshots_ is chosen as the solutions in each cell and the basis function is obtained by singular value decomposition on the matrix of _snapshots_  
The reduced system is defined as  
\[M^{t}\mathbf{K}Mv_{r}=M^{t}f, \tag{16}\]  
Figure 6: The application of DD for traverse case.  
with the basis matrix \(M\) on our example defined by  
\[M=\begin{bmatrix}M_{1}&&&\\ &M_{2}&&\\ &&\ddots&\\ &&&M_{8}\end{bmatrix}, \tag{17}\]  
where the reduced basis \(M_{j}\) for the cell \(j\) is obtained by singular value decomposition on the matrix of _snapshots_\(S_{j}\) in the cell \(j\). Let \(S_{j}=U\Sigma W^{t}\); then, \(M_{j}\) is made of first columns of \(U\) and the number of columns of \(M_{j}\) is chosen based on energetic singular values criterion [28].  
To initialise the matrix of _snapshots_, one solves the equation without magnetic source imposed:  
\[\mathbf{K}_{j}V_{j}=0, \tag{18}\]  
with Dirichlet conditions imposed in \(x\), \(y\), and \(z\) boundaries in each cell \(j\), which means the input and output voltage are imposed on the boundary of cell in \(x\), \(y\), and \(z\) directions, and obtains the solution \(V_{j}^{x}\), \(V_{j}^{y}\), and \(V_{j}^{z}\).  
For each cell, the matrix of _snapshots_\(S_{j}\) is initialised as  
\[S_{j}=\begin{bmatrix}\mathbf{1},V_{j}^{x},V_{j}^{y},V_{j}^{z}\end{bmatrix}, \quad\text{with}\quad\mathbf{1}=\begin{bmatrix}1,1,\dots,1\end{bmatrix}^{t}. \tag{19}\]  
The constant vector allows a translation of the solution in each cell. It is not essential, but it gives a better initialisation.  
The solution \(V_{\text{POD}}\) projected in the material is calculated as  
\[V_{\text{POD}}=Mv_{r}. \tag{20}\]  
Then, the system (21) is repeated in each cell until the convergence with the potential obtained by POD in the precedent iteration is imposed on contact nodes in neighbouring cells.  
\[\mathbf{K}_{j}V_{j}^{i}=f_{j}-(\mathbf{K}_{jn_{1}}V_{POD}^{i-1}+\mathbf{K}_{jn _{2}}V_{POD}^{i-1}+\dots+\mathbf{K}_{jn_{6}}V_{POD}^{i-1}). \tag{21}\]  
Further, the matrix of _snapshots_ in each cell is uploaded with the solution obtained by system (21) as  
\[S_{j}^{i}=[S_{j}^{i-1},V_{j}^{i}]. \tag{22}\]  
Then, the basis \(M\) is uploaded and the reduced system (16) is uploaded to obtain the new potential \(V_{POD}\) for the next iteration.  
To summarise, the algorithm of DD-POD is as follows:  
1. Initialisation: initialise the matrix of _snapshots_ as Equations (18) and (19).
2. Iterative resolution: * Solve the reduced system as Equation (16) and project reduced solution on full domain with (20); * Solve Equation (21) in each cell with the solution on full domain by POD imposed and update the matrix of _snapshots_ as Equation (22).
3. Repeat the second step until convergence.
## 3 Results
### Model Validation
#### 3.2.2 Partial Discharge (DoD80)  
The combined effect of the variation of the battery pack temperature and the battery ageing level is presented in this section for DM 25 in the case of DoD80. Recalling Figure 9, four different values of the life-cycles completed by the battery were considered, specifically 0, 200, 400 and 600 (EoL). The charts from Figures 22-25 report the results obtained for 0 (i.e., \(\lambda_{5 oH_{DoD80}}=1\)), 200 (i.e., \(\lambda_{5 oH_{DoD80}}=0.985\)), 400 (i.e., \(\lambda_{5 oH_{DoD80}}=0.94\)) and 600 life-cycles (i.e., \(\lambda_{5 oH_{DoD80}}=0.8\)), respectively.  
Figure 21: Combined effect of the battery pack temperature and the battery ageing level on \(\lambda_{tot}\) for DM25 in the case of a battery with 400 life-cycles completed and DoD100.  
Figure 20: Combined effect of the battery pack temperature and the battery ageing level on \(\lambda_{tot}\) for DM25 in the case of a battery with 300 life-cycles completed and DoD100.  
The general trend shown by the results of Section 3.2.1 recurred in the case of a partial discharge with DoD80. In fact, the average value of \(\lambda_{tot}\) started decreasing at around 1/3 of the battery EoL (-0.85) and kept decreasing until the EoL was achieved (-0.67). Once again, the influence of the variation of \(\lambda_{t_{bp}}\) did not show relevant effects on the global correction factor.  
The battery SoH had much greater impact than a relatively contained variation of the battery pack temperature on \(\lambda_{tot}\) and hence on the available battery capacity, in both DoD100 and DoD80 scenarios. This result could also be expected in the cases of Scenarios 2 and 3 of Table 7, because the average value of \(\lambda_{t_{bp}}\) would be higher with respect to Scenario 1 since the battery pack temperature would be closer to the optimal operating temperature. Therefore, Scenario 1 can be considered as the most demanding from a battery temperature perspective, and hence, no results related to Scenario 2 and 3 are reported for conciseness.  
Figure 23: Combined effect of the battery pack temperature and the battery ageing level on \(\lambda_{tot}\) for DM25 in the case of a battery with 200 life-cycles completed and DoD80.  
Figure 22: Combined effect of the battery pack temperature and the battery ageing level on \(\lambda_{tot}\) for DM25 in the case of a new battery and DoD80.  
As a final step, the effects of the decay of \(\lambda_{tot}\) were further investigated by analysing the evolution of the battery SoC and the residual driving range estimated along the driving missions. These results are reported in the next section.
# The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem
## 2 Transport System Analysis  
The transport system is one of the main components of:  
* SC and communities that is a partnership across the areas of (i.) transport, (ii.) information and communication and (iii.) energy [26];
* ITS resulting from the integration of knowledge in the areas of (a.) decision support systems (DSS) in transport and (b.) information and communication technologies (ICT).  
SC and ITS require transport systems planning that considers the sustainability components (I. economic, II. environmental, III. social), to achieve the 17 goals of the 2030 Agenda for sustainable development of the United Nations.  
The VRP is a problem to be solved in a wide area of transport system planning that also requires demand management actions to be implemented in a long, medium, short and real-time time horizon by different interacting actors (Figure 2).
## 3 Routing Problems
### Vehicle Routing Problem  
A generalization of the Travelling Salesman Problem is the _Vehicle Routing Problem_ (VRP) [63; 64; 65; 66] where a desired quantity of goods needs to be delivered from a central depot to customers by vehicles of a certain capacity.  
We are looking for closed routes of individual vehicles that start and end at the depot, each customer is served exactly once by exactly one vehicle, the requirements of all customers are met and the total transport costs are minimal.  
Consider the following notation:  
* number of customers
* depot (start and end of each vehicle's route)\(K\)... number of (identical) vehicles \(d_{j}\geq 0\)... request of the \(j\)th customer (for depot \(d_{0}=0\)) \(Q\)... vehicle capacity \((KQ\geq\sum_{j=1}^{n}d_{j})\) \(c_{ij}\)... the cost of transport from \(i\) to \(j\) (\(c_{ii}=0\)) \(x_{ij}\)... binary decision variable equal to \(1\) if \(j\) is immediately followed by \(i\) on the route, \(x_{ij}=0\) otherwise \(\delta_{i}\)... the load left in the vehicle after visiting customer \(i\).  
\[z=\sum_{i=0}^{n}\sum_{j=0}^{n}c_{ij}x_{ij}\rightarrow\min \tag{10}\]  
subject to  
\[\sum_{i=0}^{n}x_{ij}=1,\;j=1,\,\ldots,\,n \tag{11}\]  
\[\sum_{j=0}^{n}x_{ij}=1,\;i=1,\,\ldots,\,n \tag{12}\]  
\[\sum_{i=1}^{n}x_{i0}=K \tag{13}\]  
\[\sum_{j=1}^{n}x_{0j}=K \tag{14}\]  
\[0\leq\delta_{i}\leq Q-d_{i},\;i=1,\,\ldots,\,n \tag{15}\]  
\[\delta_{i}-\delta_{j}+Qx_{ij}\leq Q-d_{j},\;i\
eq j,\;i=1,\,\ldots,\,n,\;j=1, \,\ldots,\,n,\;\text{such that }d_{i}+d_{j}\leq Q \tag{16}\]  
\[x_{ij}\in\{0,1\},\;i=0,\,\ldots,\,n,\;j=0,\,\ldots,\,n \tag{17}\]  
In the model, (11) and (12) ensure that exactly one vehicle arrives at each customer (11) and exactly one vehicle leaves it (12). Equations (13) and (14) ensure that all \(K\) vehicles return to the depot (13) and all \(K\) vehicles leave the depot (14).  
Equation (16) is analogous to the MTZ constraints in the Travelling Salesman Problem preventing the formation of partial circuits and at the same time ensuring that the requirements of customers \(i\) and \(j\) can be met when traveling from \(i\) to \(j\)[67].  
The Vehicle Routing Problem has many other specific formulations, e.g., there may be a larger number of depots available, and customers are only ready to receive delivery of goods at certain time intervals. For more details, see the sources listed at the beginning of this section.