esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Visual Exploration System with Fault Tolerance Structure
## 6 Experiments
### Evaluation of VLM  
We run multiple times on one dataset to compare VLM with ORB-SLAM. As for experimental indicators, we record the number of new keyframes in ORB-SLAM before each tracking failure, and calculate the average (A-KFs) and the standard deviation (SD-KFs) of that number. In addition, the same metrics are recorded for our system. The result is shown in Table 3.  
To demonstrate the excellence of the parallel framework in the front-end, the average frame rate and RMSE (root mean squared error) of the experiments are calculated. For comparison, firstly, the system is run on one thread; secondly, the parallel framework of front-end and back-end (P-F&B) is added, which is similar to ORB-SLAM; lastly, the parallel constraints idea is added to the front-end for parallel multiple constraints building (P-MC). The comparison result is indicated in Table 4.  
Regarding the submap-based back-end for overcoming the failure of tracking, we record the moment of tracking lost and loop-closure detection, as well as the error. The result is shown in Figure 14, where the deep blue curve is the localization error, the light blue curve is the response of tracking lost and the red curve is the response of loop-closure detection. In such a back-end, the mapping is always conducting within one of the submaps, and the error curve reduces when submaps are merged. In addition, the error does not increase after a certain duration of exploration, and a new frame is always able to be tracked within one of the submaps.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Non-Parallel** & **P-F\&B** & **P-MC+P-F\&B** \\ \hline Framerate & 12.57 & 24.66 & 32.60 \\ RMSE & 0.407 & 0.411 & 0.407 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Frame rate comparison of parallel multi-constraint front-end, where a higher frame rate is obtained by our parallel framework. Such a higher frame rate is beneficial to data association due to a shorter baseline.  
Figure 13: Left figure, turning at the corner; mid figure, obstacle avoidance in morning; Right figure, obstacle avoidance at night. The blue dotted array is the motion before avoidance, and the red array is the motion during obstacle avoidance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **A-KFs** & **SD-KFs** \\ \hline UA-CSC1 & ORB & 13.88 & 2.37 \\  & VLM & 1069.20 & 23.60 \\ \hline UA-CSC2 & ORB & 33.54 & 3.04 \\  & VLM & 836.80 & 20.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Continuous tracking performance, our submap-based system is able to map with a much higher A-KF than ORB-SLAM, since a fault recovery design is involved in VLM.
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology
### State Evaluation of Protection System Based on Matter-Element Extension  
In actual operation, the protection system may cause equipment defects due to operational faults, environmental factors, and switching operations, etc., which affect the working performance of the equipment.  
Matter-element extension method is an evaluation method used to describe things, which combines qualitative and quantitative information. Its principle is to select the characteristics of things, and quantitatively evaluate the current state of things according to the actual data of characteristic quantity indicators. The evaluation process is shown in Figure 4. To achieve real-time evaluation of the protection system state, Section 3.2.1 collates and selects nine characteristic quantities for protection system status evaluation, and uses their frequency of occurrence to judge the operating state of the system. A classical domain for state evaluation is proposed in Section 3.2.2, which can classify the state of the system into \"normal\", \"attention\", and \"severe\". Since the occurrence of each characteristic quantity has a different impact on the equipment, the nine characteristic quantities are assigned weights in Section 3.2.3 and can be adjusted in real time according to their degradation degree. In Section 3.2.4, the determination of the state level is achieved by calculating the relative closeness.  
Figure 3: Workflow of Digital Twin Model Layer.  
A matter-element is the basic unit for describing a thing. A thing \(N\), a characteristic \(C\), and a quantity \(V\) about the characteristic \(C\) form an ordered triplet, denoted as \(R=\ \left(N,\ C,\ V\right)\). The expression of the protection system as an \(n\)-dimensional matter-element \(R_{n}\), which requires a comprehensive evaluation of several indicators, is  
\[R_{n}=\left(N,C_{n},V_{n}\right)=\begin{bmatrix}N&C_{1}&V_{1}\\ &C_{2}&V_{2}\\ &\vdots&\vdots\\ &C_{n}&V_{n}\end{bmatrix} \tag{1}\]  
where \(N\) is the protection system, \(C\) is the characteristic quantity of the protection system, and \(V\) is the range of values of the characteristic quantity.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### Multi-Source Data Fusion Framework
Additionally, requirements for decision timeliness, accuracy, and interpretability must be considered to ensure the practicality and reliability of fused decisions. From development trends, multi-source data fusion is evolving toward higher levels, deeper degrees, and broader scopes. The applications of new technologies such as edge computing, cloud computing, and artificial intelligence are providing stronger technical support for multi-source fusion. Simultaneously, trends toward standardization, platformization, and service orientation are driving multi-source fusion from technical research toward industrial applications. A comprehensive analysis of the development status of ETC data-based traffic sensing and feature extraction technologies reveals that this field has formed relatively complete technical systems, achieving important progress in data processing, feature extraction, and fusion analysis. However, facing future development requirements, continued in-depthresearch in algorithm innovation, system integration, and application expansion remains necessary to achieve the maximum utilization of ETC data value.
## 6 User Preselection Algorithm
```  
**Algorithm 2**User preselection algorithm. After the user preselection, the joint user scheduling and hybrid beamforming design is for the preselected users. Therefore, the dimension of the LEO satellite communication system will be reduced, and the algorithm complexity will be reduced. Although the algorithm performance has a slight loss, compared with the decoupling design of user scheduling and beamforming, the performance is greatly improved. In conclusion, the joint user scheduling and hybrid beamforming design with the user preselection step is a better choice after balancing performance and complexity.
## 2 Materials and Methods
### Laboratory Investigations
#### 2.3.2 Isolation of Bacterial and Fungal Strains  
Four different culture media were used for cultivation analysis, two for photosynthetic microorganisms and two for heterotrophic microorganisms: BG-11 and Bold Basal Medium (BBM) (Sigma-Aldrich, Merck S.p.A., Milan Italy), Tryptic Soy Broth Agar (TSA), and Reasoner's 2A agar (R2A). Considering that surface patinas are exposed to atmospheric oxygen, inoculated plates were incubated aerobically at room temperature for 30 days. Culture-based analysis was performed on the alteration phenomena identified as \"white turf (S\({}_{2}\), S\({}_{8}\)), pink discoloration (S\({}_{4a}\), S\({}_{5a}\), S\({}_{6a}\)), dark green (S\({}_{3}\)), and light green (S\({}_{11}\))\" (see Table 1).  
Molecular identification of each bacterial isolate (S\({}_{4a}\), S\({}_{5a}\), S\({}_{6a}\)) was performed as previously described [41]. The 165 rRNA full-length gene was amplified with primer pairs 27f (5' AGAGTTTGATCMTGGCTCAG 3') and 1492 r (5' CGGTTACCTTGTTACGACT 3'). The Forward and Reverse sequences obtained from the amplicons of each 165 rRNA gene were manually merged, resulting in approximately 1400 nucleotide (nt) length sequences. BLASTn [42] was used on the NCBI RefSeq database to identify the closest relative species for each isolate and to determine the number of variable sites within their 16S rRNA sequences. Molecular identification of fungal isolates (S\({}_{2}\) and S\({}_{8}\)) was performed as previously described [43]. To amplify the ITS barcode region, the universal primers ITS4 (5'-TCCTC CGCCTTATTGATATGC-3')/ITSS (5'-GGAAGTAAAAGTCGTAACAAGG-3') were used. PCR reactions were performed using BioMix (BioLine, Luckenwalde, Germany). In each 25 \(\mu\)L reaction solution, 5 pmol of each primer and approximately 40 ng of template DNA were added. Amplifications were carried out using MyCycleT(tm) Thermal Cycler (Bio-Rad Laboratories, Munich, Germany), applying the following protocols: (ITS) initial denaturation step for 3 min at 95 \({}^{\circ}\)C, 35 cycles of 95 \({}^{\circ}\)C for 30 s, annealing at 55 \({}^{\circ}\)C for 30 s, extension at 72 \({}^{\circ}\)C for 32 s, followed by a final extension at 72 \({}^{\circ}\)C for 5 min. Sequences were performed by Macrogen (Milan, Italy) and assembled using ChromasPro v.1.41 (Technely-sium, Southport, Queensland, Australia). Similar searches were performed using BLASTn (NCBI, National Center for Biotechnology Information), excluding from the comparison \"uncultured/environmental sample sequences\" and referring mainly to CBS collection strains, preferably to ex-type strains.  
The accession number (s) for our nucleotide sequences have been provided by GenBank: for pink alteration (S\({}_{6a}\)) **PV466827** (isolate 1); **PV466828** (isolate 2); **PV466829** (isolate 3);  
**PV466830** (isolate 4); **PV466831** (isolate 5); **PV466832** (isolate 6); white turf alteration **PV470090** (isolate A); **PV47009** (isolate B); **PV47009** (isolate C).
# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 2 Materials and Methods
### Reinforcement Learning (RL)
#### 2.2.1 Double Deep Q Networks (DDQN)  
DDQN could effectively cope with the uncertainty and complexity in autonomous driving environments, enabling autonomous vehicles to make appropriate decisions when facing constantly changing road and traffic conditions. DDQN was an improved algorithm based on Deep Q-Network (DQN) [19], designed primarily to solve the problem of overestimation of target Q-values in DQN. The core idea of DDQN was to separate action selection from target Q-value estimation by introducing a prediction network and a target network to reduce estimation bias. First, the corresponding action \(a_{\text{max}}\) was selected through the prediction network \(Q(s,a;\theta)\). The formula is expressed as follows:  
\[a_{\text{max}}=\text{argmax}_{a}Q(s_{t+1},a;\theta) \tag{1}\]  
In the formula, \(s_{t+1}\) is the state at the time \(t+1\), \(a\) is the action taken, and \(\theta\) is the online weight parameter of the network used in action selection.  
The calculation of the target Q-value relied on the collaboration between the target network and the prediction network, and its formula is as follows:  
\[y_{i}=r_{t+1}+\gamma Q\big{[}s_{t+1},\text{argmax}_{a}Q(s_{t+1},a;\theta_{t}); \theta_{t}^{-}\big{]} \tag{2}\]  
In the formula, \(r_{t+1}\) is the immediate reward obtained by the agent from the environment after taking action \(a_{t}\) at time \(t\), \(\gamma\) is the discount factor that controls the influence of future rewards on current decisions, and \(\gamma\in[0,1]\); \(Q[s_{t+1},a;\theta_{t}]\) is the Q-value estimate of taking action \(a\) at time \(t\) in state \(s_{t+1}\), predicted by the prediction network. \(\theta_{t}\) is the parameter of the prediction network at time \(t\), and \(\text{argmax}_{a}Q(s_{t+1},a;\theta_{t})\) represents the action with the maximum Q-value among all actions \(a\) in state \(s_{t+1}\), calculated based on the parameters \(\theta_{t}\) of the prediction network at time \(t\). The parameters \(\theta_{t}^{-}\) of the target network are periodically updated by copying the parameters \(\theta_{t}\) of the prediction network; thus, \(\theta_{t}^{-}=\theta_{t}\).  
The agent interacted with the environment and generated and stored tuples \((s_{t},a_{t},r_{t+1},s_{t+1})\) in the experience replay buffer. The prediction network selected actions using the \(\varepsilon\)-greedy strategy. After the prediction network determined the actions, the target network collaborated to calculate the target Q-values. The prediction network's parameters were updated through the loss function, and the parameters of the prediction network were synchronized to the target network every fixed number of steps. This process was repeated iteratively to optimize the agent's decision-making ability while reducing the overestimation of target Q-values by separating the prediction network and the target network. The framework of the DDQN algorithm is shown in Figure 2.
## Appendix D External Validation for Sounding Model  
We acquired additional electricity generation data for external validation from reporting agencies in Turkiye and India, as described in Appendix A.4. A comparison to our models is shown in Tables A2 and A3. Since India reports only at the daily level, not hourly, it could only be used to validate the generation and annual-level models, not the sounding models that require hourly generation data. Some plants in the US, Europe (ENTSO-E), and Australia were excluded from training due to the strict filters set in Section 3.4; however, they do meet the looser requirements also described in that Section. There are very few external validation plants for FGD as compared to NDT because the plant filters were relaxed for NDT but not FGD at inference time.  
The same naive baselines used in Tables 3 and 4 were applied for the external validation set as a comparison to the ML approach: simply predicting the mean off/on value or mean capacity factor over the training set. For NDT, these baselines produced an RMSE of 0.326 to 0.343 and MBE of 0.119 to 0.180. For FGD, the baselines were 0.436 to 0.461 and \(-0.406\) to \(-0.299\) for RMSE and MBE, respectively.  
In external validation, our NDT models achieved a regression performance comparable to the cross-validation results but performed more poorly for classification with mAP ranging from 0.72 to 0.78.  
For FGD, our external validation results were significantly poorer. Classification mAP ranged from 0.55 to 0.78, and regression RMSE did not always outperform the naive baseline. Even more concerning, there was a large negative bias ranging from \(-0.299\) to \(-0.406\). However, it must be noted that the set of FGD plants available for external validation was extremely limited: only 3 plants compared to 42 available for NDT. Therefore, additional external validation of FGD is needed. Furthermore, we suspect that the high error and large negative bias for those three Turkish FGD plants could be because the average annual capacity factor of those three plants is around 0.8, yet our FGD training set consists of only European (ENTSO-E) and US plants with an average annual capacity factors around 0.4 and 0.5, respectively.
## 6 Results and Discussion
### Descriptive Statistics of the Experimental Dataset Variables
#### 6.1.1. Diagonal Histograms  
Water content concentrates between 4% and 9%, confirming the moderate moisture range noted previously.
* Porosity predominantly ranges from 12% to 17%, with only a few samples exhibiting values above 25%.
* Density is tightly clustered near 2.3-2.5 g/cm\({}^{3}\).
* UCS shows a dominant band from 30 MPa to 50 MPa, matching the earlier histogram.
## 3 Datasets and Features
### Unified Set with Meaningful Features  
The above datasets vary in the collections of features they consist of. Their comparison requires that they all share the same collection of features. To address this issue, the new version of these sets was proposed in [8]. In the new versions, each dataset contains 43 unified features (see Table 2 for their list). The suffix 'v2' was added to set names to differentiate new sets from old ones. Following [8], in our study, the original data was preprocessed to remove the features that could unjustifiably influence the classification results--the misleading features and redundant--highly correlated--features. In particular, all features that are flow identifiers, like source and destination IP addresses and port numbers (4 features), time-related features (5), and TTL-based features (3), were removed. The collection of features after this step consists of 31 items. Next, the correlation analysis was performed to minimize the number of features by removing the redundant ones. Computing the correlation matrix of all feature pairs allowed for finding the strongest correlations. The pairs of very strong correlation, i.e., with correlation coefficient \(\geq\) 0.8, contained candidates for possible removal. The one with the lowest correlation with the other features was finally removed among each of the two candidates. All the removed features, along with appropriate correlations, are listed in italics in Table 3. As there were seven such features, the ultimate number of features is 24. The complete list of traffic features are shown in Table 2; removed ones are printed in italics. \begin{table}
\begin{tabular}{l l l} \hline  & **Feature name** & **Description** \\ \hline _1 (m)_ & _IPV4\_SRC\_ADDR_ & _IPv4 source address_ \\ _2 (m)_ & _L4\_SRC\_PORT_ & _IPv4 source port number_ \\ _3 (m)_ & _IPV4\_DST\_ADDR_ & _IPv4 destination address_ \\ _4 (m)_ & _L4\_DST\_PORT_ & _IPv4 destination port number_ \\ \hline \hline
5 & PROTOCOL & IP protocol identifier byte \\
6 & L7\_PROTO & Application protocol as a number \\
7 & IN\_BYTES & Incoming number of bytes \\
8 & IN\_PKTS & Incoming number of packets \\
9 & OUT\_BYTES & Outgoing number of bytes \\ \hline _10 (r)_ & _OUT\_PKTS_ & _Outgoing number of packets_ \\ \hline
11 & TCP\_FLAGS & Cumulative of all TCP flags \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of features in the ‘v2’ versions of datasets. Items that are not used in experiments printed in italics. Letter after the ordinal number indicate the reason for removal: m—misleading, r—redundant feature.
## 3 FPGA Implementation  
To implement the proposed NN pre-distorters, an FPGA has been used for the benefits they offer [31; 32]. Indeed an FPGA enables a higher sampling frequency, tolerates higher  
Figure 5: Multilayer perceptrons neural networks pre-distorter.  
data rates and provides real-time processing [33]. Since the training of the NN pre-distorters is carried out off-line, only the real-time part, i.e., their layers, will be implemented on an FPGA, without the need of implementing the learning algorithm.  
Figures 6 and 7 represent the architecture of a neural networks, of the AM/AM pre-distorter for TWTA and SSPA, respectively, implemented using Xilinx system generator [34]. To achieve the proposed implementations signed fixed-point representation has been adopted, allowing a better computational speed and minimal resources consumption at expenses of a reduced degradation. The number of bits has been optimized to obtain the best trade-off between speed, space and degradation. Each sample is encoded on 16 bits: 5 bits are reserved for the integer part, 10 bits for the fractional part and one bit of sign. It should be noted that the number of bits at the transmitter side is not usually a problem and it is fixed to the maximum number of bits at Digital to Analog Converter (DAC) to maximize the dynamic range of the transmitted signal. It is worth noting that for AM/PM pre-distorter, the same NN architecture implementation is adopted, the difference is that the weights and bias take a different values, for TWTA and SSPA, respectively. It should be noted that complex multipliers are needed to operate in the output layer, which corresponds to a four real-valued multipliers pipe-line architecture in the implementation as a trade-off between complexity, efficiency and resources.  
Figure 8 represents the Register Transfer Level (RTL) schematic of Figures 6 and 7, while Figures 9 and 10 represent a zoom on both entities AM/AM and AM/PM of Figure 8.  
Figure 6: Implementation for NN AM/AM TWTA pre-distorter.  
Figure 7: Implementation for NN AM/AM SSPA pre-distorter.  
Figure 8: RTL Schematic for NN TWTA and SSPA pre-distorters.  
Figure 10: A zoom on NN AM/PM entity.  
Figure 9: A zoom on NN AM/AM entity.
# Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems
## 4 Results and Discussion
### Economic Functions  
Based on the equations presented in Table 3, as previously assumed, OPEX are accounted for by taking a fixed percentage of the capital cost (\(OPEX=f\cdot CAPEX\)), after some mathematical transformation, it can be found that the task of minimization of LCoE, maximization of IRR and minimization of DPT can be considered as a task to minimize the ratio between CAPEX and the AEP:  
\[LCoE|_{\min} \Rightarrow \frac{CAPEX}{AEP}(\frac{1}{a}+f)|_{\min} \tag{2}\] \[IRR|_{\max} \Rightarrow \frac{AEP\cdot p_{kwh}}{CAPEX}-f|_{\max}\] (3) \[DPT|_{\min} \Rightarrow \frac{1}{\frac{AEP\cdot p_{kwh}}{CAPEX}-f}|_{\min} \tag{4}\]  
Consequently, optimum LCoE, IRR and DPT occur at the same turbine configuration while optimum NPV is achieved at an entirely different design point. Maximum NPV is achieved when the margins between total discounted income and total discounted costs are maximized. As seen in Figures 12 and 13, the optimum NPV occurs at a larger blade length than the optimum LCoE.  
Figure 16 illustrates the variation of total discounted incomes of different wind turbines as a function of their total discounted costs. The dashed line in the middle is the break-even line, while  
Figure 15: DPT against blade length.  
turbines above the line are economic and those below the line are not. Two points have been highlighted on the cost-income front. Point A, with total discounted costs of $3.9 m, has the highest net profit margin between incomes and costs which implies the maximum NPV is achieved. The investment returns of designs with costs higher than point A decline. Point B, with total discounted costs of $3.5 m has the maximum slope which indicates that the ratio between incomes and costs is maximized. This point corresponds to minimum LCoE. In this optimization problem, a wind turbine designed with the objective of minimum LCoE tends to choose a smaller-scale investment compared with the objective of maximum NPV.  
The choice between LCoE and NPV is a complex economic problem. Investment size and incremental internal rate of return may also be taken into consideration to make the decision that most meets the desire of the designers. Further exploration is beyond the scope of this research. Nonetheless, the result obtained herein, that the optimal blade length of a certain turbine should be situated between the value of optimal LCoE and optimal NPV, may be valuable to wind turbine manufacturers.