esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems  
[PERSON] 1, [PERSON] 1,*, [PERSON] 1 and [PERSON] 2  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; [EMAIL_ADDRESS] (S.F.) [EMAIL_ADDRESS] (K.J.)  
Electric Power Dispatching and Control Center of State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-138-1145-8800  
Received: 11 February 2018; Accepted: 12 March 2018; Published: 16 March 2018  
Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.  
Renewable energy resources integration; demand response; game theory; gossip algorithm
## 5 Conclusions  
This study investigated the microstructural evolution and change in mechanical properties of the TLPS Cu-Sn skeleton microstructure using a Cu-solder-resin composite during thermal aging at 150-200 \({}^{\circ}\)C, and the mechanism of phase transformation and void formation was discussed. The following conclusions can be made:  
* In the as-bonded state, the Cu-Sn skeleton microstructure was identified as comprising Cu particles connected with Cu\({}_{6}\)Sn\({}_{5}\) and Cu\({}_{3}\)Sn IMCs partially filled with polyimide resin. In addition, submicron voids were observed at the interface between the Cu\({}_{3}\)Sn phase and Cu particles.
* After aging at 150-200 \({}^{\circ}\)C for 1000 h, the Cu\({}_{6}\)Sn\({}_{5}\) phase fully transformed into Cu\({}_{3}\)Sn except for the small amount of (Cu,Ni)\({}_{6}\)Sn\({}_{5}\) phase at the chip side interface. The phase transformation from Cu\({}_{6}\)Sn\({}_{5}\) into Cu\({}_{3}\)Sn could have a positive impact on the shear strengths owing to the better mechanical properties of the Cu\({}_{3}\)Sn. Furthermore, the number of submicron voids at the interface between Cu\({}_{3}\)Sn and Cu particles appeared to increase after aging, which should be because of the Kirkendall effects between them. The formation of submicron voids could have a negative impact on the shear strengths, suggesting a good agreement with the factography after aging.
* The averaged shear strengths were found to be 22.1 (reference), 22.8 (+3%), 24.0 (+9%), and 19.0 MPa (\(-\)14%) for the as-bonded state and the specimens aged at 150, 175, and 200 \({}^{\circ}\)C for 1000 h, respectively. The TLPS joints maintained a shear strength over 19 MPa after the thermal aging at 200 \({}^{\circ}\)C for 1000 h because of both the positive and negative impacts of the thermal aging, as mentioned above. These results indicate an excellent thermal reliability of the TLPS Cu-Sn skeleton microstructure.  
Figure 13: Mechanism model of the microstructural evolution for the TLPS layer aged at 150–200 \({}^{\circ}\)C: (**a**) as-bonded, (**b**) gradual transformation of Cu\({}_{6}\)Sn\({}_{5}\) into Cu\({}_{3}\)Sn and formation of submicron voids, and (**c**) fully-transformed structure with dominated Cu\({}_{3}\)Sn.  
**Author Contributions:** Conceptualization, H.T., Y.K. and A.H.; Investigation, H.T.; Methodology, H.T., A.L., T.M. and T.S.; Project administration, H.Y.; Resources. H.T. and H.Y.; Supervision, Y.K. and A.H.; Validation, H.T. and H.Y.; Visualization, H.T.; Writing--original draft, H.T.; Writing--review & editing; H.T., A.L., T.M., T.S., Y.K. and A.H.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion
### Input Data  
For future deployment of diagnostic models in the failure diagnosis of axial piston pumps, the present study avoids the use of traditional diagnostic models to extract deep-level features or multi-feature fusion approaches for input data. Instead, pressure signal data are collected from axial piston pump outlets and then normalized by means of minimum-maximum normalization [24]. Following this, an original signal is truncated using a sliding window of length 1024 without overlapping. After data segmentation, 2000 subsamples were generated for each dataset, of which the training set constituted 80%, with the remaining 20% retained for the test set. Subsequently, utilizing the weighted graph construction method proposed in Section 3.3 the graph data were constructed using subsamples taken from both the training set and the test set.  
To further evaluate the intra-class variability of the dataset, we conducted a statistical analysis on key signal features extracted from samples within the same fault category. Specifically, we computed the standard deviation of root mean square (RMS), kurtosis, and spectral centroid across all samples in each fault class. The results reveal notable intra-class variation, particularly in the RMS and frequency domain features, which may be attributed to operating condition fluctuations and minor inconsistencies in fault progression.  
Construction of the weight graph generates graph data for every 10 subsamples employed. Therefore, the resultant training set includes 160 subgraphs, and the test set comprises 40 subgraphs. Furthermore, the study considers two input types--the real-time domain (TD) input and frequency domain (FD) input, with the latter meaning preprocessing of subsamples through FFT.
## 3 Experimental Analysis
### Theoretical Background
#### 3.1.1 Modal Decay  
The reverberation field inside a room leads to the persistence of sound even after the source ceases. The duration of this sound persistence, called the reverberation time \(R_{T}\)[5], is the most commonly used measure of room acoustic quality. In practical applications, acousticians calculate \(R_{T}\) as the 60 dB decay time since source cessation and is referred to as \(T_{60}\)[43]. Typically, such estimations assume diffuse sound field conditions and average wall absorption and calculate \(R_{T}\) as a single value to characterize the room acoustics. However, in reality, the wall absorption factors change with frequency [5; 44], and hence accurate \(R_{T}\) estimates should be frequency-dependent. Furthermore, the room architecture, variations in surface materials, and source-receiver properties affect the reflection path length [44] and magnitude, which, in turn, influence the decay of different frequency components. Therefore, decay times should be a function of frequency and direction. Since an analytical solution to decay rate estimation is complex, we can derive them numerically through reflection sound field analysis.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 2 Optimized Mathematical Modeling of the Optimized Waveform
### Additional Constraints of the Pan-Function
#### 2.2.1 Restriction on the Symbol Boundary Condition  
As is shown in [11], if \(a(t)\), \(a^{{}^{\prime}}(t)\), \(a^{{}^{\prime\prime}}(t)\),..., \(a^{(n-1)}(t)\) are continuous without skipping, and \(a^{(n)}(t)\) is finite, then there is no skipping in the end point of the interval (i.e., the power spectrum of \(a(t)\) could decay rapidly with the frequency). Herein, assuming that function \(a(t)\) is within the interval \([-2T,2T]\), it should satisfy the following boundary condition [1]:  
\[a\left(\pm 2T\right)=a^{{}^{\prime}}\left(\pm 2T\right)=\ldots=a^{(n-1)}\left( \pm 2T\right)=0 \tag{3}\]
## 5 Experiments and Evaluation
### Model Performance Evaluation
#### 5.2.1 Results Analyses  
It can be seen from the Table 2, Figures 4 and 5 that compared with other methods, our model provides the best recommendation results. In Table 2, the best results are highlighted with **Bold** style.  
Furthermore, our representation model shows the best recommendation performance at all sparsity levels. Especially it can provide larger improvements under high sparsity. We compare the results under 70% and 30% data sparsity. In the Telecom dataset, when the data sparsity is 30%, the _Top3 Hitrate_, _Top3 Accuracy_, and _nDCG\({}_{3}\)_ of our model are 11.42%, 8.19%, and 6.07% higher than the best baseline model, respectively. While data sparsity is 70%, _Top3 Hitrate_, _Top3 Accuracy_, and _nDCG\({}_{3}\)_ of our model achieve at least 21.76%, 14.47%, and 14.00% higher than the best baseline model, respectively. On the TalkingData dataset, when the data sparsity is 30%, _Top2 Hitrate_, _Top2 Accuracy_, and _nDCG\({}_{2}\)_ of our model are 5.8%, 8.74%, and 7.32% higher than other models, respectively. However, our model achieves 13.95%, 18.02%, and 11.68% improvements in _Top2 Hitrate_, _Top2 Accuracy_, and _nDCG\({}_{2}\)_, respectively, under 70% data sparsity. These results show that our model can effectively deal with the data sparsity problem.  
Figure 4: Comparisons of the Recommendation Performances on Telecom Dataset under Different Sparsity Levels. (**a**) _Top3 Hitrate._ (**b**) _Top3 Accuracy._ (**c**) _nDCG\({}_{3}\)._ (**d**) _Top5 Hitrate._ (**e**) _Top5 Accuracy._ (**f**) _nDCG\({}_{5}\)_.
## 4 Key Technologies for MEC-Assisted Vehicular Networks
### Digital Twin
Three migration tactics are analyzed in their work: (1) the car sends the information required by the new edge device to create the digital copy; (2) during the migration, the new edge component updates information after receiving the digital version from the cloud server; (3) during migration, the new edge BS updates information after receiving the digital prototype from the old BS. Also, in ref. [92] researchers investigate security issues derived by a DT in MEC-based vehicular networks, such as privacy, time-sensitive responses, and information synchronization. Researchers in the automotive industry have introduced a simulation-driven DT approach for self-driving cars connected through 5G in a cross-border scenario [93]. The Apollo autonomous vehicle and the open-source SVL simulator can communicate and respond to real-time messages thanks to the integration of V2X communications technology [93]. The features of a DT in a MEC structure with DT support are examined in [94] for 6G V2X networks. The DT offers several innovative concepts, such as enhancing human-machine interaction through the assessment of driving style, enhancing car and pedestrian safety through knowledge-based vehicle problem detection, and assessing the dynamic traffic parameter attributes through data collection. It is possible to separate the suggested design into two areas. The roadside MEC endpoints, drivers, other physical transportation elements, and V2X network devices are included in the physical world. The DT concept is housed in a public or private cloud that has a specialized core network for communication with the physical domain. Periodically, the physical region is sampled for data by the DT domain. In the DT sector, these data will pass the stages of analysis, learning, and prediction. To support task-oriented V2X connections, the outcomes of the evaluating and prediction processes are sent back to the physical layer of the V2X infrastructure.
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
### A Non-Iterative Approach to Optimally Solving PnP Problems
The UPnP algorithm cannot match the OPnP algorithm in the Planar Case and Quasi Singular of reference points. The optDLS algorithm reduces the number of solutions and the running time of the algorithm by increasing the constraints of the objective function in OPnP. In 2018, [PERSON] et al. [22] proposed the SRPnP algorithm, based on the RPnP algorithm. The algorithm only requires solving a univariate polynomial set of the highest order of 7 and a univariate polynomial of the highest order of 4, and, finally, uses the Gauss-Newton method for one accurate positioning. This algorithm divides the solution of PnP into three stages, each of which witnesses the formation of an optimization problem, and utilizes the optimization value obtained in the previous stage for computation. In this way, the SRPnP algorithm is more accurate and faster than the RPnP in solution, elevating it into being one of the best algorithms for solving the PnP problem. [PERSON] also proposed the RDLT algorithm in 2020 [23]. This algorithm, while using the linear method to solve the PnP problem, improves the DLT algorithm by considering the imaging constraint relationship between points, which outperforms the traditional linear method in stability and accuracy, rivals the existing non-linear optimization method, and even exhibits far higher solving efficiency than the non-linear optimization method. In 2020, [PERSON] et al. [24] proposed the WIPnP algorithm, which adopted an iterative solution by setting the weight coefficient of the spatial reference points. The algorithm is still on a par with OPnP and SRPnP as to accuracy, without significant improvement.
## 2 Materials and Methods
### Physicochemical Characterization  
Dynamic light scattering (DLS, Zetasizer Nano ZS, Malvern Instruments, Malvern, UK) was used to determine the physical parameters: mean particle size (Z-Ave) and polydispersity index (PI). All samples, diluted with ultra-purified water to suitable concentration, were analyzed in triplicate. Zeta potential (ZP) was also determined in ultra-purified water with the conductivity adjusted to 50 \(\upmu\)S/cm (using NaCl solution at 0.9%, w/V) to avoid ionic effects. Laser diffraction (LD) was performed also for particle size analysis using a Mastersizer Hydro 2000 MU (Malvern Instruments, Malvern, UK).
## 5 Proposed Scheme
### Formulation of Outage Probability (\(P^{out}\))  
Outage probability is defined as the probability that the SINR drops below a predefined threshold SINR value [24]. The general expression for outage probability is defined as:  
\[P^{out}=\ P_{r}\Big{[}\gamma\ \leq\ \gamma^{Th}\Big{]} \tag{15}\]  
where \(\gamma^{Th}\) is the predefined threshold SINR value.  
In the proposed scheme, we assume that the average SINR is calculated by dividing transmit power to noise power as below.  
\[\widetilde{\gamma}=\frac{\textit{Transmit power of a user}}{\textit{Noise power}}=\ \frac{P}{P_{N}} \tag{16}\]  
Considering Equations (5-15) and substituting \(\gamma=\gamma^{Th}\), the outage probability for both D2D pairs and cellular user can be obtained as:  
\[P_{j}^{out}=1-\frac{1}{1+\frac{\left(\frac{\gamma_{i}-\delta_{j} ^{-a}+\gamma_{j}^{-a}+\sum_{a\
eq A1}P_{ac}-\alpha_{a\
eq A}^{-a}}{P_{j}^{-a} +\sum_{a\
eq A1}P_{ac}-\alpha_{a\
eq A}^{-a}}\right)\gamma^{Th}}}\mathrm{e}^{ \left(-\frac{2T\hbar}{\gamma}\right)}{\gamma} \tag{17}\] \[=1-\frac{\frac{P_{j}\cdot I_{j,B}^{-a}}{P_{j}\cdot I_{j,B}^{-a}+ \left(P_{k}\cdot I_{j,B}^{-a}+P_{j}\cdot I_{j^{\prime},B}^{-a}+\sum_{a\
eq A1} P_{ac}-\alpha_{a\
eq A}^{-a}\right)\gamma^{Th}}}{\mathrm{e}}^{\left(-\frac{P_{N} \cdot\gamma^{Th}}{\gamma_{j}}\right)}\]  
\[P_{j^{\prime}}^{out}=1-\frac{1}{1+\frac{\left(\frac{\gamma_{i}- \delta_{j}^{-a}+\gamma_{j}^{-a}+\gamma_{i}^{-a}+\sum_{a\
eq A1}P_{ac}-\alpha_{ a\
eq A}^{-a}}{P_{j}^{-a}+\sum_{a\
eq A1}P_{ac}-\alpha_{a\
eq A}^{-a}}\right)\gamma^{Th}}} \mathrm{e}^{\left(-\frac{2T\hbar}{\gamma}\right)}{\gamma} \tag{18}\] \[=1-\frac{\frac{P_{j}\cdot I_{j,B}^{-a}}{P_{j}\cdot I_{j,B}^{-a}+ \left(P_{k}\cdot I_{k,B}^{-a}+P_{j}\cdot I_{j,B}^{-a}+\sum_{a\
eq A1}P_{ac}- \alpha_{a\
eq A}^{-a}\right)\gamma^{Th}}}{\mathrm{e}}^{\left(-\frac{P_{N} \cdot\gamma^{Th}}{\gamma_{j}}\right)}\]  
\[P_{k}^{out}=1-\frac{1}{1+\frac{\left(\frac{\gamma_{i}\cdot\delta_{j}^{-a}+P_{ j}\cdot I_{j,B}^{-a}+\sum_{a\
eq A1}P_{ac}-\alpha_{a\
eq A}^{-a}}{P_{k}\cdot I_{b,B}^{-a}} \right)\gamma^{Th}}{\mathrm{e}}^{\left(-\frac{2T\hbar}{\gamma}\right)}}{\gamma \cdot I_{k,B}^{-a}+\left(P_{i}\cdot I_{j,B}^{-a}+P_{j}\cdot I_{j,B}^{-a}+ \sum_{a\
eq A1}P_{ac}-\alpha_{a\
eq A}^{-a}\right)\gamma^{Th}}}\mathrm{e}^{ \left(-\frac{P_{N}\cdot\gamma^{Th}}{\gamma_{k}}\right)} \tag{19}\]
# Optimal Gliding Trajectories for Descent on Mars
## 2 Formulation of the Optimal Control Problem
### A Brief Description of the Aerodynamic  
The aerodynamic accelerations due to lift (\(l/m\)) and drag (\(d/m\)) are a function of the spaceplane area-to-mass ratio (\(A/m\)), the relative velocity to the flow, its respective coefficients of lift (\(C_{l}\)) and drag (\(C_{d}\)), and the density (\(\rho\)) of the atmosphere, which is a function of the altitude. This last point is modeled as an exponential (\(\rho=\rho_{0}exp^{(-h/H)}\)), using the data available from the planetary data sheet [13; 14; 23], where the scale height (\(H\)) is 11.1 km and the density at the surface \(\rho_{0}\) is 0.02 kg/m\({}^{3}\). The aerodynamic accelerations are as follows:  
\[\frac{l}{m}=0.5C_{l}\frac{A}{m}\rho V^{2}, \tag{7}\]  
\[\frac{d}{m}=0.5C_{d}\frac{A}{m}\rho V^{2}. \tag{8}\]  
The lift and drag coefficients were modeled from the data of the X-34 spaceplane in continuum flow [24], which, in this case, is selected only to be a function of the angle of attack (AOA). The effective angles for the control are from \(-4\) deg to 28 deg, and the maximum performance is reached at 11 deg, generating a \(l/d\) of 2.2, as presented in Figure 1.
## 4 Results
### Penetration Outcome
#### 4.2.2 GNSS Jamming  
The jamming attacks produced results fulfilling our expectations yet without great surprises. As the vehicle requires radio communication systems, it is conspicuous that blocking such signals implies that the vehicle will be disrupted or forced to a halt. This point implies a fundamental modification of the vehicle program through the implementation of a fully automated offline mode. In fact, jamming attacks are frighteningly easy to set up despite the legal constraints on their use. Therefore, in the current configuration, any owner of an SDR platform is capable of completely blocking the operations of the vehicle as it is set to stop immediately when the signal is lost. A remote control system can be considered to support the circumvention of such situations; however, the use of radio waves alone would not solve the problem since it would again be possible to jam this particular connection and thus prevent remote troubleshooting. Therefore, a fully automated offline mode allowing the vehicle to move to the side of the road or to an area suitable for dropping off its passengers should be considered. This system, therefore, leaves the door open for various improvements with other radio communication information.
## 3 Results and Discussion  
### Graprefruit Essential Oil Composition  
The GC-MS analysis of the volatile compounds of the GF-EO is presented in Figure 2. It was possible to identify 35 compounds, accounting for 97.11% of the total peak area (Table 1). The major volatile compounds were identified as D-limonene (24.90%), nootkatone (24.33%), \(\beta\)-pinene (7.71%), \(\gamma\)-Terpinene (7.52%), trans-caryophyllene (4.40%), and \(\alpha\)-pinene (3.98%). Nootkatone was the main oxygenated sesquiterpene present in the GF-EO, while limonene, \(\beta\)-pinene, \(\gamma\)-Terpinene, and \(\alpha\)-pinene were the main non-oxygenated monoterpenes present. D-limonene was the main prominent compound, which is also reported by other authors [21; 42]. D-limonene is the principal component of citrus EOs (orange, lemon, mandarin, lime, and GF) [43]. However, these studies reported a higher quantity of D-limonene, at about 70%. However, a recent study from [PERSON] et al. [44] reported that EOs extracted from GF peels cultivated in India, depending on the variety, had 1-15% of limonene. These differences observed between studies in the compound % may be explained by the geographical area of GF cultivation, mainly by the edaphoclimatic factors. Daily sun exposure, air humidity, and soil microbiota are the parameters mostly responsible for the changes observed in secondary metabolites [45]. Additionally, the maturity of the fruit, the harvest season, and the condition of the extraction method can impact the concentrations of the GF-EO. For instance, in Turkish GF-EO, the main compounds present were shown to be the same as those reported in our study [46; 47], especially limonene, \(\beta\)-pinene, \(\alpha\)-pinene, and nootkatone. However, opposite to our results, in the Turkish GF-EO study, a high concentration of myrcene and sabinene was found. D-Limonene showed several biological effects, including antioxidant, anti-inflammatory, anticancer activity, and immune modulatory effects [48]. For instance, the study in [49] showed the antioxidant and anti-lipid peroxidation activities of D-limonene (100 mg/kg body weight) for Streptococcus-induced alterations in male Albino Wistar rats after 45 days of administration. Overall, D-limonene significantly decreased (\(p<0.05\)) the thiobarbituric acid reactive substances, lipid hydroperoxides, and conjugate dienes in the plasma, liver, and kidney and improved (\(p<0.05\)) the tissue antioxidants, namely superoxide dismutase, catalase, and vitamin C. In addition, at a concentration of 0.04%, D-limonene significantly (\(p<0.01\)) decreased nitrite levels and reduced LPS-induced PGE2 production in the murine macrophage cell line RAW 264.7 [50]. Moreover, the D-limonene treatment led to an enhanced protein expression of TNF-\(\alpha\) (increased by 30%), IL-1B (increased by 50%), and IL-6 (increased by 20%) compared to the LPS-induced cells left untreated.