esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance of Four-System Integrated SPP with a Priori ISB Constraints
Under a cutoff elevation of 50\({}^{\circ}\), for the four-system SPP without a priori ISB constraints, the average satellite number and PDOP value are 9.3 and 7.0, respectively, while the average satellite number decreases to 8.4, and the average PDOP value increases to 8.2-8.3 for the four-system SPP with a priori ISB constraints, indicating that many unsolvable epochs suffering from the extremely adverse observation conditions become solvable after introducing the a priori ISB constraints. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Directions**} & \multicolumn{6}{c}{**Elevation Mask Angles**} \\ \cline{3-6}  & & **10\({}^{\circ}\)** & **20\({}^{\circ}\)** & **30\({}^{\circ}\)** & **40\({}^{\circ}\)** & **50\({}^{\circ}\)** \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & 3 & 5 & 7 & 22 & 25 \\  & North (\(\%\)) & 3 & 5 & 9 & 27 & 26 \\  & Up (\(\%\)) & 4 & 5 & 9 & 24 & 23 \\  & 3D (\(\%\)) & 4 & 5 & 9 & 24 & 23 \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & 1 & 3 & 4 & 20 & 23 \\  & North (\(\%\)) & 3 & 5 & 8 & 26 & 25 \\  & Up (\(\%\)) & 4 & 5 & 9 & 23 & 21 \\  & 3D (\(\%\)) & 4 & 5 & 9 & 23 & 21 \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & 0 & 1 & 2 & 18 & 22 \\  & North (\(\%\)) & 3 & 4 & 7 & 26 & 24 \\  & Up (\(\%\)) & 4 & 5 & 8 & 21 & 19 \\  & 3D (\(\%\)) & 4 & 5 & 8 & 22 & 19 \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & \(-1\) & \(-1\) & 1 & 16 & 20 \\  & North (\(\%\)) & 3 & 4 & 7 & 25 & 23 \\  & Up (\(\%\)) & 4 & 4 & 7 & 20 & 17 \\  & 3D (\(\%\)) & 3 & 4 & 6 & 20 & 17 \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & \(-4\) & \(-4\) & \(-2\) & 14 & 17 \\  & North (\(\%\)) & 2 & 3 & 5 & 23 & 20 \\  & Up (\(\%\)) & 3 & 4 & 5 & 17 & 13 \\  & 3D (\(\%\)) & 3 & 3 & 4 & 18 & 14 \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & \(-6\) & \(-7\) & \(-7\) & 10 & 14 \\  & North (\(\%\)) & 2 & 2 & 3 & 21 & 18 \\  & Up (\(\%\)) & 4 & 3 & 3 & 14 & 9 \\  & 3D (\(\%\)) & 3 & 3 & 2 & 14 & 10 \\ \hline \multirow{3}{*}{With A Priori ISB} & East (\(\%\)) & \(-7\) & \(-9\) & \(-8\) & 8 & 12 \\  & North (\(\%\)) & 2 & 2 & 3 & 20 & 17 \\ (7 days) & Up (\(\%\)) & 4 & 3 & 2 & 12 & 6 \\ \cline{1-1}  & 3D (\(\%\)) & 3 & 3 & 2 & 13 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Improvement rates of positioning accuracy in (m) for the seven four-system SPP cases considering a priori ISB constraints over the one ignoring a priori ISB constraints under different elevation masks.
## 5 Energy Storage for Demand-Side Resources
In the work by the authors in [92], a stochastic economical flexibility evaluation method based on the envelope of the feasible operation region to the uncertain space was proposed. The work provided insights into the impact of different uncertain sources on the flexibility quantification process while taking into account the distribution network's operation cost. The flexibility that storage systems provide to the distribution networks is quantified economically, and sensitivity analysis is performed to evaluate how battery storage system capacity impacts the flexibility index. Energy storage for demand-side resources offers several benefits and presents certain challenges. One of the main advantages is the ability to provide a reliable and stable energy supply during peak demand periods. This helps to ensure grid stability and reduce the need for additional power generation infrastructure. Energy storage also enables demand response programs, allowing consumers to actively manage and optimize their energy usage. Additionally, it facilitates the integration of renewable energy sources by smoothing out fluctuations in supply. However, there are challenges associated with energy storage, such as high upfront costs and limited technological advancements. The challenges related to thermal storage are examined in [93], where a state of the art of present applications of thermal storage for demand-side management are examined. The effectiveness of thermal energy storage for managing the mismatch between the availability of renewable electricity and the demand for electricity in buildings where hot water, heating, and cooling are delivered by heat pumps and air cooling is investigated. Results showed a reduction of 46.7% in the peak demand and a shift of approximately 37% of the total energy consumption from the peak and shoulder period to the off-peak period. In the future, energy storage for demand-side resources is expected to undergo several trends. Firstly, there will be a shift towards more advanced battery technologies, such as lithium-ion and flow batteries, which offer higher energy densities and longer lifespans. Secondly, there will be a greater emphasis on decentralized storage systems, allowing individual consumers to store and manage their own energy. This will be facilitated through the use of smart grids and advanced control systems. Additionally, the integration of renewable energy sources with energy storage will become more prevalent, enabling a more reliable and sustainable energy supply. The next section introduces recent technologies for the control and automation of active distributed networks.
# Combining Machine Learning and Logical Reasoning to Improve Requirements Traceability Recovery
## 4 Evaluation
### Research Questions  
To fully evaluate our study, we propose a series of research questions in this section.  
* **RQ1**: In Phase 1 machine learning recovery, does the addition of the class attribute, method parameter, and method return features to the traceability link vector definition improve traceability recovery?
* **RQ2**: Which is the most appropriate machine learning algorithm for our approach?
* **RQ2.1**: For the traceability recovery task, which classification algorithm is most suitable for traceability recovery?
* **RQ2.2**: What is the most suitable value of parameter \(k\) when using the KNN classification algorithm for traceability recovery?
* **RQ2.3**: When using SVM classification algorithm for traceability recovery, which kernel can obtain better experimental results?
* **RQ3**: Which structural information features between source code classes are effective for predicting traceability links based on logical reasoning?
* **RQ4**: Can using logical reasoning rules based on relationship features between use cases further improve traceability recovery?
* **RQ5**: What is the interaction between structural information features of source code components and relationship features of use cases?
* **RQ5.1**: Is the structural information feature of source code components more important than the relationship features of use cases for traceability recovery?
* **RQ5.2**: Can the combination of source code and use case relationship features further improve traceability recovery?
* **RQ6**: Can our proposal outperform existing traceability link recovery approaches?  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Project**} & \multicolumn{1}{p{56.9 pt}}{**Source Artifact(\#)**} & \multirow{2}{*}{**Target Artifact(\#)**} & \multirow{2}{*}{**Correct Links**} & \multirow{2}{*}{**Description**} \\ \hline eTour & Use cases (58) & Source Code Classes (116) & & & \\ \hline SMOS & Use cases (67) & Source Code Classes (100) & & & \\ \hline Albergate & Use cases (17) & Source Code Classes (55) & & & \\ \hline eANCI & Use cases (140) & Source Code Classes (55) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Details of the datasets.
## 4 Morphing Leading Edge Application  
In the framework of the EU-funded Clean Sky 2 REG-IADP AG2 project, the Reference Aircraft (RA) is a 90 px, twin prop Green Regional Aircraft (GRA). The role of an innovative aircraft development platform dedicated to regional aircraft is to validate the integration of new technologies at a high level of maturity into future aircraft. European regional aircraft attempt to be highly competitive in terms of performance by installing advanced devices on the reference wing, which is a Natural Laminar Flow (NLF) wing and optimized by ONERA inside the AirGreen2 (AG2) consortium. The CAD model of the RA is shown in Figure 7. Different morphing concepts are considered such as morphing droop nose, morphing flap, and a morphing winglet. PoliMi is in charge of several activities including the development of active systems for load control and alleviation, and the design and integration of a fully compliant structure for the morphing leading edge.  
The morphing leading edge is a full-span and 16% chordwise configuration that is composed of two regions: inboard and outboard separated by the nacelle. The main goal is to delay the wing stall in take-off and landing conditions, without affecting the NLF wing aerodynamic performances of the reference configuration. This target must be obtained by keeping the skin surface seamless and smoothed to avoid producing any anticipated loss of laminarity, and this requirement forced the adoption of an innovative solution to avoid the adoption of a classical slat. The requested \(C_{L,max}\) enhancements are equal to 2.4% and 1.7%, considering both the flap and leading edge morphing effects in take-off and landing, respectively.  
Starting from these general requirements, the design procedure described in Section 3 has been applied. The approach here is adopted for the design of the morphing leading edge and is based on a fully compliant structure, composed by a complete skin and by a series of internal compliant ribs, which once actuated, force the skin to assume the target shape optimized for aerodynamic purposes. The definition of compliant skin for a morphing device is one of the most challenging design phases, while the main difficulty in the design internal compliant structure of a morphing leading edge, compared with a trailing edge, is the different chord extension. This approach involves a higher level of internal stresses to achieve the desired deflection. Moreover, in the case of the morphing leading edge, the certification also requires bird impact compliance, but this part of the work is not described in this article because the major aim of this application is the validation of the optimization design procedure.  
The following sections describe the activities conducted to apply the different levels of the proposed procedure, with a special focus on the outboard leading edge only, as the same approach has been adopted on the inboard region.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 4 Demineralized Dentin Matrix (DDM)
### Clinical Studies
#### 4.2.2 Radiologic Outcomes  
Regarding radiologic outcomes, the studies presented favorable results. The mean density of the graft decreased with time, with the architecture of the DDM becoming increasingly more similar to that of the surrounding bone, suggesting a satisfactory bone healing [40; 50; 67].
## 5 Simulation Results and Analysis
### Comparison with Back-Stepping Control Method  
In order to illustrate the advantages of the proposed method, the proposed method is compared with the back-stepping control method [41]. The simulation conditions are set the same as Section 5.2, and the comparison curves of simulation results are shown in Figures 12-14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Value** & **State** & **Value** \\ \hline \(V\) (ft/s) & 7820 & \(\eta_{1}\) & 0.5099 \\ \(\gamma\) (deg) & 0 & \(\eta_{1}\) & 0 \\ \(h\) (ft) & 85,000 & \(\eta_{2}\) & \(-\)0.0493 \\ \(\alpha\) (deg) & 1.6444 & \(\dot{\eta}_{2}\) & 0 \\ \(Q\) (rad/s) & 0 & \(\eta_{3}\) & \(-\)0.0136 \\  & & \(\eta_{3}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: States at initial trim conditions.  
Figure 12: Comparison of trajectory tracking curves under dynamic neural network based dynamic inverse control (dnn-dic) and back-stepping control (bsc).  
It can be seen from Figure 12 that both methods can track the given reference signals, and the local magnifications show that the proposed method has smaller steady-state tracking errors. The states of the HVs all converge under the two methods from Figure 13, and the back-stepping control method requires greater control initially from the local magnification of Figure 14.
## 4 Experimental Validation
### Dynamic Model Experimental Results
The results of our research show that the end position curve obtained from the experiments with a camera and the positions from the simulated model agree well, as shown  
Figure 19: Plotting of the manipulator end tip for the simulation and the experimental results. Figure 18: Plotting for the angles of the joints versus time for the manipulator: experimental and simulation responses for \(\theta_{3}\). in Figure 19. The average errors in the \(x\), \(y\), and \(z\) directions were 6.24 mm, 6.4 mm, and 2.93 mm, respectively, demonstrating reasonable accuracy between the experimental and simulated data. However, further analysis of the model is required to increase accuracy. A sample of the experimental and simulated data and the error between them can be found in Table 4.  
Moreover, the system variables' identification, especially the stiffness and damping coefficients related to flexibility, is essential for the development of a more accurate model. Taking these key factors into account leads to a more accurate representation of the system and, therefore, even more reliable results. By investing the necessary effort and resources in improving these modeling techniques, we can ensure that the system meets the highest standards and fulfills the most stringent requirements.
# Electrons and X-rays to Muon Pairs (EXMP)
## 4 Simulation Results for Muon Beams
The effective laser parameter is \(a_{0}=4.3\frac{\lambda_{0}}{m_{0}}\sqrt{U(I)/\sigma_{t}(ps)}\) with \(\lambda_{0}\) the wavelength, \(w_{0}\) the focal spot size, \(U\) the energy, and \(\sigma_{t}\) the RMS pulse length of the laser. In the assumption that the FEL beam behaves as a single mode, TEM\({}_{00}\) laser mode, we checked that non-linear effects due to field intensity of the FEL photon beam could be considered negligible, since \(a_{0}\), defining the non linearity of the electron-e.m. field interaction, is quite small, definitely below \(10^{-2}\). However, since EXMP is operated in a very large recoil regime (being X in excess of \(10^{5}\)), the combination \(\chi=a_{0}X\) comes out to be much larger than 1, nearly \(3\times 10^{3}\). This is linked to the fact that the FEL e.m. field of the focused FEL beam is in the range of \(10^{15}\) V/m, which, in the electron rest frame, is transformed to \(10^{21}\) V/m, almost 3 orders of magnitude larger than Schwinger's limit. Further studies are needed to investigate potentials of this high recoil regime with respect to amplification of the electron-photon interaction strength.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 6 Discussion  
AUVs must carry out a variety of tasks, including launching, exploration, movement, recovery, and battery charging, to conduct long-term missions. A different set of capabilities are needed for each mission. As such, a thorough understanding of the system's performance capabilities for each mission is vital.  
The MDD-based method determines the performability of each phase by converting phased FTs into a single MDD. The proposed approach, in contrast, computes the performability of each sub-FT within the phased FT and then uses the UGF to determine the overall performability of the system. It can be claimed that utilizing a UGF to integrate each module's performability differs from conventional performability analysis methods. The findings shows that the proposed approach better captures the variations in performance across phases and emphasizes how performability at the functional module level can be changed, with more effects on the overall system performability.  
In an earlier work, performance and reliability were taken into consideration when designing the FT of an AUV, and the health of the system was assessed using a performance reliability index that took the performance's weights into account. Previous studies evaluated AUV health by taking into account the performance and failure of each component over time.  
Due to component failures or performance degradation, it is challenging to determine whether the current operation or a specific mission can be completed using the methodologies currently in use. The real operability of the system may be assessed differently because different missions have varying degrees of importance or severity when a component fails. Other types of missions may still be possible even if the current one is challenging to complete.  
The system performance in this paper was determined using an arithmetic weighted average, but this approach is insufficient to accurately determine the system's performance when compared to the conventional model-based performance calculation method. Con  
Figure 8: EIP results of scenarios.  
sequently, it is thought that more research is required in order to more precisely depict the system's performance based on an FT. The real-time performance of the proposed method was not thoroughly studied in this paper, but it was studied in Python and ROS to ultimately analyze the system's state in real time by being used by the AUV during executed missions.
## 2 Material and Methods
### Standalone LAES System-Baseline Case  
The thermodynamic performance of various LAES systems has been investigated by many researchers, particularly after 2010. Despite some design differences, almost all recently proposed LAES plants share a common architecture [9; 28; 29]. A typical configuration of a standalone LAES plant was retained (as depicted in Figure 2), which corre  
Figure 1: Overall methodology for simulating and quantifying the proposed hybrid system for the synergistic operation of an LAES plant and a food factory.  
sponds structurally and parametrically to the configuration proposed by [PERSON] et al. [28] and investigated in detail by [PERSON] et al. [29]. This configuration represents the baseline case for the present study (scenario A).  
During the charge stage, air is compressed in a two- or three-step process with intermediate cooling. Released compression heat is recovered by thermal oil, which is stored at a relatively high temperature in a hot storage section with a two-tank arrangement. Compressed air is afterwards cooled by the returning air from the air separator and by cold fluids supplied from the cold storage section, before flowing into a cryoturbine. The expansion in the turbine produces a vapour-liquid mixture that is collected and separated into a gas stream and a liquid stream in the air separator. The produced liquid air is then stored in an insulated tank at approximately 80 K and at atmospheric pressure.  
During the discharge stage, the liquid air is pumped from a tank to high pressure and heated to a near-ambient temperature by the cold fluid circulating between the cold storage and the evaporator. This configuration permits some cold energy obtained during air regasification to be employed for cooling during the liquefaction stage, which is a highly energy-consuming process. Pumped air flows into a recuperator, where it is heated by the air leaving the expansion train. The expansion is usually divided into three stages with intermediate superheating, using thermal oil from the hot storage tank. The thermal oil is then cooled in a heat exchanger and returned to the hot storage section at an ambient temperate. This heat exchanger is essentially the only component of the plant where heat is rejected to the environment, since air is discharged from the recuperator at temperatures very close to the ambient one.
## 4 Conclusions  
The findings of the study suggested that NIR spectroscopy exhibits the potential to discriminate the morphology of BSFL (instars) as well as to trace the feed source used to grow the larvae. Further studies should be carried out to evaluate the ability of NIR spectroscopy to trace BSFL when reared on organic side streams such as food waste, municipal waste, abattoir waste and other heterogeneous matrices. Overall, NIR spectroscopy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Laval**} & \multicolumn{3}{c}{**Feed Source**} \\ \cline{2-4}  & & **Spent Brewers\({}^{\prime}\) Grains** & **Soy Waste** \\ \hline
5 th instar & Spent brewers’ grains & 100\% (34/34) & 0 \\  & Soy waste & 3\% (2/74) & 97\% (72/74) \\ \hline
6 th instar & Spent brewers’ grains & 96\% (64/67) & 4\% (3/67) \\  & Soy waste & 0 & 100\% (57/57) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear discriminant analysis (LDA) confusion matrix, percentage of correct classification. In brackets is the number of samples.  
Figure 6: Partial least squares optimal loadings used to predict the source of stream feed utilised to rear the black soldier fly samples analysed using near infrared spectroscopy.  
exhibits the potential to be used as a non-invasive rapid tool for the real-time traceability of BSFL. A traceability system for BSFL will aid in risk evaluation and the identification of hazards associated with it, thereby assisting in improving the safety and quality of BSFL intended to be used by the animal feed industry. Although the results of the present study are promising, further research is still needed to validate the existing PLS-DA and LDA models, as well as to include chemical parameters associated with quality, such as crude protein and lipid content.  
S.A. and D.C.: sample preparation, analysis, draft preparation. L.C.H., S.M.O.M., D.M., P.J., O.Y., D.C. and S.A.: writing-review and editing of the manuscript. S.M.O.M., D.M., P.J., O.Y. and D.C.: supervision and project administration. L.C.H.: funding acquisition. All authors have read and agreed to the published version of the manuscript.  
The work has been supported by the Fight Food Waste Cooperative Research Centre (Project 2.4.1), whose activities are funded by the Australian Government's Cooperative Research Centre Program.  
Not applicable.  
The authors acknowledge [PERSON], [PERSON], and [PERSON] from Flyfarm for their help and support with sample preparation and collection.  
The authors declare no conflict of interest.