esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems
## 4 Numerical Verification and Comparison
### Solution from the Proposed New Group DM Approach
\begin{split}\widehat{S}_{a_{1}}&=r\overline{w}_{1}s_{a_{1 }}=6\times 0.195\times s_{0.661}=s_{6\times 0.195\times 0.661}=s_{0.773},\\ \widehat{S}_{a_{2}}&=r\overline{w}_{2}s_{a_{2}}=6 \times 0.140\times s_{0.857}=s_{6\times 0.140\times 0.857}=s_{0.720},\\ \widehat{S}_{a_{3}}&=r\overline{w}_{3}s_{a_{3}}=6 \times 0.174\times s_{0.655}=s_{6\times 0.174\times 0.655}=s_{0.684},\\ \widehat{S}_{a_{4}}&=r\overline{w}_{4}s_{a_{4}}=6 \times 0.175\times s_{0.500}=s_{6\times 0.170\times 0.500}=s_{0.525},\\ \widehat{S}_{a_{5}}&=r\overline{w}_{5}s_{a_{5}}=6 \times 0.140\times s_{0.524}=s_{6\times 0.140\times 0.524}=s_{0.440},\\ \widehat{S}_{a_{6}}&=r\overline{w}_{6}s_{a_{6}}=6 \times 0.175\times s_{0.821}=s_{6\times 0.175\times 0.821}=s_{0.863},\\ \end{split} \]  
Thus,  
\[ \begin{split} s_{\widehat{p}_{1}}&=s_{0.863},\,s_{ \widehat{p}_{2}}=s_{0.773},\,s_{\widehat{p}_{3}}=s_{0.720},\,s_{\widehat{p}_{4} }=s_{0.684},\,s_{\widehat{p}_{5}}=s_{0.525},\,s_{\widehat{p}_{6}}=s_{0.440},\\ \end{split} \]  
Based on the results of Steps 3 to 5, Equation (9) was used to calculate the overall value \(z_{i}\) of alternative \(x_{i}\):  
\[ \begin{split} z_{1}&=ILOWGA_{\overline{w},w}(s_{a_{ 11}},s_{a_{12}},\ldots,s_{a_{1n}})=ILOWGA_{\overline{w},w}(s_{0.661},\,s_{0.857},s_{0.655},s_{0.500},s_{0.524},s_{0.821})\\ &\qquad\qquad=\left(s_{\widehat{p}_{1}}\right)^{0}\otimes\left( s_{\widehat{p}_{2}}\right)^{0.067}\otimes\left(s_{\widehat{p}_{3}}\right)^{0. 333}\otimes\left(s_{\widehat{p}_{4}}\right)^{0.333}\otimes\left(s_{\widehat{p }_{5}}\right)^{0.267}\otimes\left(s_{\widehat{p}_{6}}\right)^{0}\\ &\qquad\qquad=\left(s_{0.863}\right)^{0}\otimes\left(s_{0.773} \right)^{0.067}\otimes\left(s_{0.720}\right)^{0.333}\otimes\left(s_{0.684} \right)^{0.333}\otimes\left(s_{0.525}\right)^{0.267}\otimes\left(s_{0.440} \right)^{0}\\ &\qquad\qquad\qquad=s_{0.863}\end{split} \]  
The other values were also obtained using the proposed method. \(z_{2}=s_{0.784}\), \(z_{3}=s_{0.621}\), \(z_{4}=s_{0.736}\) and \(z_{5}=s_{0.855}\). Step 7. Rank alternatives' order and provide suggestions. The results showed that the CNC machine tool selection ranking order from best to worst was \(A_{5}>A_{2}>A_{4}>A_{1}>A_{3}\).
# Quadrotor Formation Strategies Based on Distributed Consensus and Model Predictive Controls
## 5 Formation Strategies
### Formation Strategy: Leader  
Figure 3 shows the block diagram of a leader's formation strategy. First, the leader tracks the predefined waypoints and desired altitude/climb rate using the MPC. The MPC is formulated as a QP problem for the horizontal motion and LQT for the vertical motion. After the predicted trajectory is generated along with the tracking control input, a built-in algorithm generates followers' desired formation trajectories and the trajectories are transmitted to each follower.
###### Abstract  
Agile Earth observation can be achieved with responsiveness in satellite launches, sensor pointing, or orbit reconfiguration. This study presents a framework for designing reconfigurable satellite constellations capable of both regular Earth observation and disaster monitoring. These observation modes are termed global observation mode and regional observation mode, constituting a reconfigurable satellite constellation (ReCon). Systems engineering approaches are employed to formulate this multidisciplinary problem of co-optimizing satellite design and orbits. Two heuristic methods, simulated annealing (SA) and genetic algorithm (GA), are widely used for discrete combinatorial problems and therefore used in this study to benchmark against a gradient-based method. Point-based SA performed similar or slightly better than the gradient-based method, whereas population-based GA outperformed the other two. The resultant ReCon satellite design is physically feasible and offers performance-to-cost(mass) superior to static constellations. Ongoing research on observation scheduling and constellation management will extend the ReCon applications to radar imaging and radio occultation beyond visible wavelengths and nearby spectrums.  
Earth observation; remote sensing; satellite constellation; reconfigurability; repeat ground tracks; simulated annealing; genetic algorithm +
Footnote †: journal: Sensors
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Software for the Data Monitoring
#### 3.2.1 Control Algorithms of Modules
The CRC cannot correct data, but is the most efficient way and with the highest probability to detect mistakes in the messages. In this case the CRC analysis is implemented by software in order to simplify the printed circuit board but at the cost of increasing the complication of the program code. That is because the speed is not an important factor in the design.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating Their Impact on Water Ecosystem
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Possible Effects on the Ecosystem** & **Observed Effects in an FPV Plant** \\ \hline \multirow{4}{*}{pH} & pH is an index of the healthiness of water. Normally & A slight, although non-statistically \\  & between 6.5 and 8.5, it is modified as a result of the & significant, decrease under the FPV plant is \\  & presence of pesticides or by the organic & reported in Ref. [63]. \\ \hline \multirow{4}{*}{Conductivity (S/m)} & It may be correlated to various parameters such as & \\  & salinity, i.e., the presence of positive and negative & \\  & ions. Its variation may be an indication of & A certain decrease is claimed in Ref. [64]. \\ \cline{1-1}  & water contamination. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: Risks and contamination pathways of a floating system [65]. scenarios. More generally, unmanned systems for aquaculture have been discussed in [70] and they have also been proposed for the case of floating buildings [71]. Finally, it is interesting to recall here an innovative approach, although not related to FPV plants but to fish farms, proposed by [PERSON] and co-authors, in which satellite data and local data are combined to obtain real-time modeling of reservoir conditions [72].
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 4 Results and Discussion
### Fatigue Life of Wind Turbine Blades with GPLRC  
Figure 8 shows the results of comparing fatigue life between the wind turbine blades with GPLRCs and traditional materials. From locations 1 to 4, the fatigue life of GPLRCs was evaluated to be higher than or similar to that of traditional materials. Between locations 5 and 8, however, the fatigue life of GPLRCs was evaluated to be lower than that of traditional materials. At location 5, the fatigue life of GPLRCs was notably lower than that of other materials. This is likely due to the maximum stress occurring at this location, combined with the fact that the fatigue failure stress (S-N curve) and tensile strength of GPLRCs are significantly lower compared to conventional materials, as shown in Figure 6 and Table 6. It is known that the non-uniform distribution of GPLs decreases the ultimate tensile strength of GPLRCs if the weight fraction of GPLs exceeds a certain level [42,43]. In this study, it was assumed that GPLs were ideally distributed inside the nanocomposite. If this criterion is not satisfied in the actual manufacturing process, the fatigue life of GPLRCs could be even lower than the results of this study.  
Table 9 summarizes the results of this study and the previous studies [15,16] by the authors of the present study. It compares the weight, material cost, natural frequency, and  
Figure 8: Comparison of fatigue lives between GPLRC and conventional wind turbine blades.  
Figure 7: Comparison of the fatigue lives between the present method and results of Resor [26].  
fatigue performance between the wind turbine blades made of traditional materials and GPLRCs. According to the comparison results, the total material cost of the wind turbine blade with GPLRCs is slightly higher. However, it is similar to traditional materials, as the difference is approximately 6%. On the other hand, the total weight of the blade with GPLRCs was more than 20% lower when compared to those of traditional materials, making it possible to significantly improve wind power generation efficiency. Furthermore, this is expected to reduce the cost of constructing support structures for wind turbines owing to the reduction in self-weight. In the case of wind turbine blades, the natural frequency is designed to be higher than the excitation frequency. Thus, it can be considered that GPLRCs with a higher natural frequency are safer in terms of resonance. The fatigue performance of GPLRCs, however, was found to be poor compared to that of traditional materials. Therefore, it is necessary to prepare measures that can supplement this at the design stage, such as developing hybrid materials by mixing conventional fiberglass composites with GPLs and optimizing the content and reinforcement location of GPLs. These measures are expected to ensure safety and durability while fully utilizing the potential of GPLRCs.
## 5 Experimental Results
### Cascade Approach
#### 5.4.4 Comparison
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**Feature Set**} & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Decision Tree**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Naive Bayes**} \\ \cline{2-11}  & & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** \\ \hline Airplane & & & & & & & & & & \\ Helicopter & & & & & & & & & & \\ Jackhammer & & & & & & & & & & \\ Dolling & & & & & & & & & & \\ Cats & & & & & & & & & & \\ Dogs & Set 3 & 52.264 (3.88) & 16.301 (0.82) & 56.981 (6.03) & 0.804 (0.17) & 28.869 (3.91) & 0.857 (0.43) & 32.169 (6.21) & 1.063 (0.65) & 48.962 (4.48) & 1.752 (0.14) \\ Humans & & & & & & & & & \\ Children & & & & & & & & & \\ Cars & & & & & & & & & \\ Motorcycles & & & & & & & & & \\ Car horn & & & & & & & & & \\ Siren & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classifiers’ accuracy and timing using an equivalent non-cascade dataset. The Standard Deviation (SD) is in brackets. The disadvantage of the cascade approach is the execution time when only using the same type of classifier for all stages. Although the feature extraction is common for both approaches, with values rounding 150 ms for Set 3 (Table 7), the cascade approach requires the computation of two classifiers. Based on the classifier, the timing reported in Table 11 ranges from around 1.2 ms to more than 40 ms for the ANN (Root Dataset \(\Rightarrow\) Airport) and for the k-NN (Root Dataset \(\Rightarrow\) Residence) classifier, respectively. Due to the limited computational resources, the overall execution on the embedded system is expected to be significantly higher. There is a trade-off between time and accuracy. Additional stages increase the supported number of categories but also the overall execution time. Nonetheless, the ultimate objective is to embed this cascade approach in an FPGA, where it can be computed in pipeline, or in an embedded device with a multi-core processor, where each stage is executed concurrently in a different thread.
## 1 Introduction
Instead, previous works have considered different algorithmic solutions and different optimization targets, such as linear programming for ensuring fairness in [16], Lagrangian multipliers for minimizing delay in [17], Lagrangian Dual Decomposition for optimizing goodput in [18] or a heuristic algorithm for maximizing the number of served UEs in [19]. Following the trend of applying machine learning for different problems in the RAN [21] and for different applications [22], the motivation to consider DRL in MC is that DRL techniques are useful for optimizing dynamic decision-making problems in the absence of an accurate mathematical model of the operational environment. Moreover, thanks to their capability of generalizing from past experience, DRL techniques are efficient in problems that depend on a large number of input variables and in which both the inputs and the decision-making outputs can take a large range of possible values, as it is the case of the MC problem considered here. Based on a first version of the DQN model presented in our recent work [23] and further evolved in [24], this paper significantly extends these works by presenting the detailed formulation and algorithmic solution of the DQN model, the architectural framework based on the open RAN (O-RAN) architecture [25] for supporting the proposed solution, and by providing a much more exhaustive performance assessment of the algorithm considering different evaluation conditions. The O-RAN based framework for supporting the implementation of the proposed approach constitutes another novelty of the paper with respect to previous works. The rest of the paper is organized as follows. Section 2 presents the system model, formulates the considered multi-connectivity problem and presents the O-RAN-based architectural framework. Section 3 presents the proposed DQN-based solution. Different performance results are provided in Section 4. Finally, Section 5 summarises the conclusions.
## 3 Signal Model
As a consequence, directly applying the STAP algorithms to suppress interference (DP clutter and MP clutter) requires more degrees of freedom than merely canceling DP clutter [8]. Additionally, the detection performance of STAP degrades seriously when targets fall within the MP clutter area [8]. The MP clutter cancellation method prior to STAP can overcome the influence of contaminated reference signals effectively. However, the presence of ICM-MP fluctuation disturbs the sparse model of MP clutter, and it transforms the reconstruction issue into a joint optimization problem. Consequently, both the existing SRA and LSA suffer from a performance loss in MP clutter estimation, thereby degrading the target detection ability of STAP. As a result, it is significant to devise an MP clutter suppression algorithm with ICM-MP fluctuation mitigation.
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results
### Results on SST  
#### 5.2.1 Input Sequence Length  
This experiment indicated the performance of comparison methods by considering the length of the sequential input. Figure 9 shows the average RMSE from a sequence length of one to six. The ConvGRU and the ConvLSTM showed the competitive results as did the SP-CRN (GRU) and SP-CRN (LSTM), which used the zero nearby-time representation as the periodicity component, which can improve prediction accuracy of base models by the SST value of around two K. The increasing input sequence length slightly decreased the RMSE of the SP-CRN (GRU) and the SP-CRN (LSTM) for SST prediction. Except for the length of one compared to two, the RMSE dropped significantly. The results demonstrated the superiority of the periodicity component over the base models. However, the length of the sequential input improved the prediction accuracy slightly.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 6 Conclusions  
An optimal active power dispatch strategy was developed in this paper to improve the power generation capacity and get an efficient trade-off between power production and fault protection. First, the power limit, which can protect the fault from further damage, was calculated by the thermal model of the generator. Then ODS was proposed to optimize the power references to the WTs by PSO with the consideration of the wake effect and the power limit of the faulty WT. A multi-objective optimization problem was addressed by adding power demand tracking, power fluctuation, and the power difference between the power reference and power output to the objective function. Two general fault-handling methods and PDS were compared with the proposed strategy, and the result showed the effectiveness of the proposed strategy. This idea can be used for the other heat-related faults of the WT, as well. Further research should consider the maintenance schedule and other common faults of the generator, such as the turn-to-turn short-circuit fault of the generator. In addition, the WFC considering the lifetime of the parts in the WT will further improve the reliability of the WF and reduce the maintenance costs.  
Conceptualization, K.M., J.Z. and M.S.; writing-original draft, K.M.; writing-review and editing, J.Z., M.S., A.H. and Z.C.  
The authors gratefully acknowledge financial support from Aalborg University and Otto Monsteds Fond.  
The authors declare no conflict of interest.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 3 Methodology  
In order to guarantee the effectiveness of waterline detection for the USVs mounted with a general digital camera sailing in varied inland water environments, especially concerning their accuracy and robustness, we propose a deep-learning-based paradigm termed DeepWL, which also cares about the efficiency of waterline detection for the inland USVs. As illustrated in Figure 1, the paradigm comprises two collaborative deep neural networks, in which the above one, termed WLdetectNet, is devised as the mainstay of this paradigm that acts as the main network of carrying out the task on vision-based waterline detection, and the other one below, termed WLgenerateNet, serves as the auxiliary network of WLdetectNet. Thus, in this section, we first highlight the architectural details of the two significant deep networks, and then describe their training methods. Finally, based on the proposed paradigm, we present an algorithm to achieve waterline detection.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 2 Materials and Methods
### Heavy Metals Analysis Using Spectroscopy  
Upon digestion of the sand samples collected for this study, atomic absorption spectroscopy (AAS) was used to analyse the content of six heavy metals present in the sand. This is achieved by measuring the concentration of metals in the material according to the amount of light being emitted at a specific wavelength that matches the element under review. AAS can produce measurements with an accuracy in the range of 0.5% to 5% [19]. Contamination of equipment and storage used are likely sources of error; hence, the preservation of pH and trace elements in solution is imperative. Samples were stored in a cool environment in perfluoroalkoxy (PFA) sterile containers for a short period of time to keep conditions stable during analysis. Several method blanks were produced using the same process used for sample processing.  
The atomic absorption spectrophotometer used was a continuous flame-type AA-7000 model produced by Shimadzu Europa GmbH. Lamp mode BGC-D2 setting was used for testing Cu, Fe, Pb, Mn, and Zn, with a non-BGC lamp setting used to read Sr concentrations. Burner height was set to 7 mm for all elements tested except for Fe, where burner height was set to 9 mm. Burner lateral and burner angle were set at 0 pulse and 0 degrees, respectively, and supply flow rate for the air-acetylene flame used was set in the range of 1.6-2.8 L/min. Wavelengths emitted by the respective lamps used included 324.8 nm for Cu, 248.3 nm for Fe, 283.3 nm for Pb, 279.5 nm for Mn, 460.7 nm for Sr, and 213.9 nm for Zn.  
Absorbance values were obtained for all samples prepared for analysis for all elements under review. Calibration curves plotting absorbance against concentration were created using the standard addition method and sample analyte solution was used to control matrix effects in the results. Concentrations computed were checked using percentage spiked recovery analysis to ensure matrix effects were quantified. Calibration checks were performed throughout the analysis approximately every 10 sample readings. It was ensured that the Percentage Relative Standard Deviation (% RSD) was less than 2% for readings collected by preparing standards of the same concentration to evaluate precision in results.
## 4 Bridge Degradation Identification and Model Update Results
### Analysis of Crack Detection Results
#### 4.2.1 Determination of the Threshold Range for Crack Detection Parameters
However, in each run where cracks were detected, no clearly defined regions were identified along the width dimension. Furthermore, in conjunction with the manual inspection report of the bridge, no cracks were found in the locations indicated by the experimental results. It can be concluded that the detection of sparse areas was caused by uneven sampling rates and operational deviations during data collection. Therefore, the detection results based on the method proposed in this paper indicate that there are no significant structural cracks in the bridge, which is consistent with the results obtained using conventional methods.
## 4 Field Test and Validation
### Horizontal Displacement of Soil Adjacent to Pipeline  
The unloading of soil in the foundation pit will also have an effect on the pipeline. Since the horizontal displacement monitoring point could not be directly placed on the pipeline, the influence of soil excavation in the foundation pit on the pipeline horizontal displacement was studied by analyzing the horizontal displacement of soil mass around the pipeline. Therefore, five horizontal displacement monitoring points were set in the soil near the foundation pit and pipeline, as shown in TST-1 to TST-5 in Figure 16.  
Figure 14: Layout of measuring points.  
Figure 16: Comparison of horizontal displacement of measuring points. (**a**) TST-1; (**b**) TST-3; (**c**) TST-5.  
Figure 15: Measuring instrument: level gauge.  
Figure 16 shows a comparison of the actual horizontal displacement and the simulated displacement of soil at the measuring points of TST-1, TST-3, and TST-5. It can be seen from the figure that the numerical simulation results kept a trend of consistency with the actual measured results, and the overall change law of horizontal displacement at the three measuring points remained similar. With the increase in excavation depth, the horizontal displacement peak of each measuring point also increased. However, the position of the horizontal displacement peak point at each measuring point declined; at the beginning of excavation, the horizontal displacement peak point was located 2.5-3.0 m below the surface, and the horizontal displacement peak point dropped to the maximum depth of the measurement range (6 m below the surface) after excavation was completed. In practice engineering, the reasonable arrangement of internal support near the maximum point of horizontal displacement can help better control the deformation of soil, give full play to the self-confining effect of foundation pit excavation, and mitigate the disturbance to soil, thereby indirectly achieving the effect of pipeline protection. For TST-1, TST-3, and TST-5, their actual displacement peak increased from 1.85 mm to 8.19 mm, from 2.17 mm to 14.09 mm, and from 2.31 mm to 11.23 mm, with increases of 6.37 mm, 11.92 mm and 8.92 mm, respectively, and their distance to the edge of the foundation pit was 9.21 m, 2.35 m, and 5.13 m, respectively. This means that as the measuring point approached the foundation pit, the horizontal displacement peak became greater. According to the actual situation, the minimum clear distance between the pipeline and the edge of the foundation pit was 5.81 m, and the distance of between the buried site and the surface was 1.53 m. Therefore, on the basis of the analysis of the horizontal displacement of each measuring point, it can be speculated that the actual maximum horizontal displacement of the pipeline was lower than 6.5 mm, which is within the safety control limit of pipeline horizontal displacement.