esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### System 3: The IEEE 118-Bus
#### 5.3.3 Case 3: Simultaneous Minimization of APL, VD, and VSI\({}^{-1}\)  
The weighting factors in Equation (18) are taken as \(\omega_{1}=1,\omega_{2}=0.65,\omega_{3}=0.35\), as in [32], where the simultaneous minimization of APL (OF\({}_{1}\)), VD (OF\({}_{2}\)), and VSI\({}^{-1}\) (OF\({}_{3}\)) is considered. The QODELFA is applied for three different values of power factor: Unity, 0.866 unity, and 0.82 lag. It may be noted from the results listed in Table 11 that, by applying QODELFA with unity power factor DGs (Case 3.1), the APL, VD, and VSI\({}^{-1}\) are decreased to 554.682 kW, 0.0297 p.u., and 1.1250 p.u., respectively, which are better than those from SFSA [32] and SOS [32]. The obtained results from QODELFA are also better than those from QOTLBO [29], except for the VD value. Nevertheless, the QODELFA's overall solutions have a better compromise than those of QOTLBO considering the three OFs together. By comparing the results obtained by QODELFA to those from SFSA [32] for Case 3.2 with DGs operating at PF = 0.866 lag, it can be noticed that the proposed algorithm's solutions provide a better compromise regarding APL (156.142 kW), VD (0.0067 p.u. ), and VSI\({}^{-1}\) (1.1024 p.u.) than the solutions of 176.969 kW, 0.00852 p.u., and 1.0978 p.u., respectively, from SFSA [32]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  
Figure 16: Convergence characteristics for Case 2 of the IEEE 118-bus system using QODELFA. Figure 17: Convergence characteristics for Case 3 of the IEEE 118-bus system using QODELFA. By implementing the QODELFA on the benchmark functions as well as the OPDG problem, its advantages can be concluded as follows:  
\begin{table}
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 1 Introduction
It should be pointed out that, in the QoE model based on the features of a particular service, it cannot be extended to other network services; however, network operation and maintenance centers often need to ensure that the QoE of all types of network services can be effectively evaluated, thus they can provide users with a better network experience. Therefore, the QoE evaluation model for a single specific service type does not meet the needs of the network operation and maintenance center. To solve this problem, some works have been done on the QoE evaluation algorithms that applicable to multiple service types [18; 19; 20; 21; 22; 23; 24]. In [20], [PERSON] et al. proposed an implementation of QoS/QoE mapping in software-defined networks for ISP to monitor the user-perceived QoE after accessing the service. The authors of [24] also presented some parameteric QoE evaluation for VoIP, online video, FTP services, etc., where only specific part of network parameters are used for different services, such as transmission rate factor in VoIP and rate of correctly received data in FTP. In this paper, we proposed a network traffic classification-based nonlinear mathematical QoE quantization model for universal traffic types, which can be seen as a QoS-based QoE quantization method. For a network data flow, we first classify its traffic type by using a machine learning classifier, then extract its QoS metric values into the corresponding QoE quantization model, after that the QoE score will be obtained. The proposed QoE model has a nonlinear mapping relation with QoS, which will be less influenced by the \"marginal effect\" than other existing linear mapping quantization approaches. In addition, the proposed model has earned good quantization accuracy and engineering feasibility. The main contributions of this paper can be listed as follows. (1) An end-to-end QoE score measurement method is proposed. (2) A machine learning classifier (random forest) is used to identify the type of network traffic, where the network traffics are categorized into 11 main types, including on-demand streaming media and live streaming media. (3) More elaborate QoE mathematical quantization formulae of different types of network traffic are given, the way in which different QoS metrics affect QoE is fully considered into the QoE formulas. The rest of this paper is organized as follows. Section 2 shows some preliminaries about QoS and QoE. In Section 3, we introduce the whole structure of our QoE quantization system, and then the three key procedures, namely, flow features and QoS metrics extraction, random forest classifier, and QoE quantization model, are presented. The experiment details including traffic classification results and QoE quantization results will be given in Section 4. At last, we will summarize the whole paper in Section 5 and give the conclusions.
# Combination of Models to Generate the First PAR Maps for Spain
## 3 Results
The R\({}^{2}\) obtained is greater than 0.99 in all cases except Lugo-USC and Salamanca-CIALE, where the R\({}^{2}\) is 0.988. The highest RMSE values were also reached at these two stations, with 6.21% and 5.75%, respectively. Regarding the MBE results, in all stations, the MBE was close to zero, \(-0.39\%\) being the highest value (Albacete-ITAP). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **Alava-** & **Albacete-** & **Astrais-** & **Córdoba-** & **Lugo-USC** & **Salamanca-** & **Zaragoza-** \\  & **NEIKER** & **ITAP** & **SERIDA** & **IFAPA** & **Lugo-USC** & **CIALE** & **Aula** & **Dei** \\ \hline \multirow{4}{*}{PAR model} & Slope & 0.99 & 1.00 & 0.99 & 1.00 & 0.99 & 0.99 & 1.00 \\  & Intercept & 2.28 & \(-\)0.07 & 1.26 & \(-\)0.55 & 1.74 & 3.70 & 1.28 \\ \cline{1-1}  & R\({}^{2}\) & 0.997 & 0.997 & 0.995 & 0.997 & 0.988 & 0.988 & 0.998 \\ \cline{1-1}  & MBE & 0.04 & \(-\)0.39 & \(-\)0.17 & 0.10 & \(-\)0.02 & \(-\)0.38 & 0.06 \\ \cline{1-1}  & RMSE & 3.19 & 2.54 & 4.38 & 2.47 & 6.21 & 5.71 & 2.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the validation results at each station. Figure 4: Coefficients c of the PAR model for each point in the study territory. Figure 5: Validation scatterplots for each station: (**a**) Álava-NEIKER; (**b**) Albacete-ITAP; (**c**) Asturias-SERIDA; (**d**) Córdoba-IFAPA; (**e**) Lugo-USC; (**f**) Salamanca-CIALE; (**g**) Zaragoza-Aula Dei.
###### Abstract  
In this paper, we present a conceptual study on a Virtual Power Plant (VPP) architecture for the optimal management of Distributed Energy Resources (DERs) owned by prosumers participating in Demand-Side Management (DSM) programs. Compared to classical VPP architectures, which aim to aggregate several DERs dispersed throughout the electrical grid, in the proposed VPP architecture the supervised physical domain is limited to single users, i.e., to single Points of Delivery (PODs) of the distribution network. The VPP architecture is based on a service-oriented approach, where multiple agents cooperate to implement the optimal management of the prosumer's assets, by also considering different forms of Demand Response (DR) requests. The considered DR schemes range from Price-Based DRs to Event-Based DRs, covering both the normal operating functions and the emergency control requests applied in modern distribution networks. With respect to centralized approaches, in this study the control perspective is moved from the system level to the single prosumer's level, who is allowed to independently provide flexible power profiles through the aggregation of multiple DERs. A generalized optimization model, formulated as a Mixed-Integer Linear Programming (MILP) problem, is also introduced. Such a model is able to compute the optimal scheduling of a prosumer's assets by considering both DR requests and end-users' requirements in terms of comfort levels while minimizing the costs.  
virtual power plant; demand-side management; energy management system; demand response; smart grid; urban energy system; distributed energy resources; multi-agent system 2018 8432; doi:10.3390/app8030432
## 3 Results and Discussion
### Predictive Performance of ANN Model
In particular, for the steel types of (b), (d), (e) and (f), both the predicted and measured values were much smaller than the upper limit of the allowable range, so it is possible to reduce the operating time using the predicted values and prevent excessive decarburization operations. For the steel types with small target values, the decarburization operations generally ended near the target values and excessive decarburization operations were rare. Therefore, it is necessary to further reduce the prediction errors in order to determine the accurate decarburization end time in these steel types. Finally, the total prediction error of the ANN model for the test data was \(-\)2.43 ppm in the mean and 4.02 ppm in the standard deviation, which showed a good performance compared to previous studies [29; 30].
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method
#### 4.4.9 Step 9--Gateway Positioning  
In previous steps from the method, AIDA identified the best pole positions for installing routers and gateways. Initially, pole positions are selected with the help of a grid and promoted to candidate positions. From these candidate positions, some positions are selected as the most suitable for the positioning of key devices (i.e., routers and gateways). This selection of CPs is made in Step 5--SM Clustering of the AIDA method (Section 4.4.5). The CPs considered in the gateway positioning will be the ones computed by the SM clustering approach (bottom-up or top-down) that effectively minimizes the number of required CPs.  
In the gateway positioning step, described in this section, the candidate positions selected for a region (see example in Figure 5) are evaluated to determine whether they will be considered as positions for installing routers or gateways. In principle, all selected candidate positions are considered valid positions for router placement.  
Subsequently, a clustering process is used to determine the set of routers that will be connected to the same gateway. For this, the weighted K-means algorithm [41] is used to select the list of routers that have to be included in the same cluster and to establish the best position for the gateway on each cluster. Figure 6 illustrates the result of the clustering process of this step. The input parameters of this clustering algorithm include the list of selected CPs (already considered router positions), the number of meters connected to each candidate position, and the maximum connections accepted by a gateway. The number of meters connected to each CP defines the weight of each CP. The clustering process aims to group to the same cluster a list of the closest CPs whose sum of meters connected to each one does not exceed the limit of connections established for the gateway. This limit is a parameter determined for the execution of the method (varying according to the technical characteristics of the equipment).  
In some situations, it may occur that a given group computed by the weighted K-Means algorithm only contains one selected candidate position. This may happen because the number of meters connected to the CP is equal to or near the limit of meters established for the cluster. In this case, this position will be classified as a gateway. In other cases, the group will be formed by a set of routers and a gateway (selected among the positions of CPs in the cluster).  
After this clustering process, the positions of the key devices are established, and the AIDA method is concluded.
## 3 Operating Characteristics of the ICE-ORC System
### Influence of the Expander Efficiency  
In the previous analysis, the measured data for the expander efficiency in [PERSON] et al. [42] were used. The maximum expander efficiency was only 45%. If the mechanical design and manufacturing of the single-screw expander were improved, a maximum efficiency of 60% could be realized, close to what a positive displacement expander normally could achieve [43]. According to the measured data in [42], the expander efficiency can be plotted as a surface with regard to the mass flow rate and the pressure ratio. In this section, this surface for the expander efficiency was assumed to be improved by a geometric translation so that the maximum expander efficiency equals 60%. Then, the influence of the expander efficiency on the ORC system was investigated.  
Figure 7 shows a comparison of the net power and the thermal efficiency between the original and improved data of the expander efficiency. If the expander efficiency was improved, the variation tendencies for the net power output and the thermal efficiency were similar with those using the original low expander efficiency. However, the net power was increased by 0.6-0.7 kW for each operation point (19.68-53.66% compared with the original data). Accordingly, the thermal efficiency was improved by 25.53-51.6%. The improvement was more significant in the region with a low net power output. Therefore, keeping the expander efficiency in a high level could improve the system performance apparently. The following analysis will employ the improved data of the expander efficiency to evaluate the energy efficiency of the ICE-ORC combined system.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 1 Introduction
By introducing additional high-matching and high-quality reference frames, the temporal redundancy is greatly reduced, resulting in a significant reduction in bits for coding residuals, so that the compression efficiency of cloud gaming videos with a large number of rotation movements can also be improved. Figure 1: Relationship between camera motions and transmission bits in H.265/HEVC. In this paper, we present how we take advantage of the camera motions to optimize video encoding for cloud gaming videos. Overall, using the camera motion information and the known reconstructed frames in the reference frame list, we generate a high-quality enhanced reference frame with high relation to the current frame through a deep learning network. Then the enhanced frame is added to the reference frame list to participate in the coding process. The operation at the decoder side is consistent with the encoder side, which recovers the updated reference frame list using the transmitted camera motion information and decodes the frames based on it (That is to say, the proposed scheme is not compatible with the H.265 standard). Specifically, to generate the enhanced reference frame, we propose a camera motion-guided network referred to as the CMGNet, which consists of three parts: the camera motion-guided prediction module, the alignment and fusion module, and the reconstruction module. We first estimate more accurate pixel-level motion information and fusion masks through the camera motion-guided prediction module, then align input frames to the target frame and merge them to acquire the fused feature. Finally, the reconstruction module is used to generate the corresponding high-quality reference frame. The main contributions of this paper can be summarized as follows:  
* We propose a coding optimization algorithm for cloud gaming videos applying deep learning techniques to optimize traditional video codecs, which generates an enhanced reference frame with high relation to the frame to be encoded and adds it to the reference frame list for better compression;
* Our proposed CMGNet for generating enhanced reference frames uses camera motions as guidance to estimate more accurate pixel-level motions for frame alignment, significantly enhancing the quality of the reference frame;
* We have established a game video dataset containing sufficient rendering frames and camera motions to promote research on game video compression;
* Experimental results demonstrated the effectiveness of the proposed coding optimization algorithm for cloud gaming videos. The rest of the paper is organized as follows. Related work is summarized in Section 2. The proposed coding optimization algorithm for cloud gaming videos together with the proposed CMGNet for the generation of enhanced reference frame is detailed in Section 3. Experimental results are shown in Section 4 to verify the expected improvements. Finally, a brief summary of this paper is given in Section 5.
## 3 Value Assessment Framework for Waste Household Appliances
### Cem\(-\)MRC Algorithm Design
#### 3.4.3 Maximum Ratio Combination Method  
The maximum ratio combination (MRC) method is different from the two combination methods mentioned above. It adopts the idea of maximizing the signal-to-noise ratio from communication and is therefore an optimal solution.  
Let \(X_{est1},X_{est2},X_{est3},X_{est4},X_{est5}\) be the output results of different training models for 5500 data samples in the training set, as represented by column vectors. \(X_{ACC}\) represents the true price of the training set, and \(N_{1},N_{2},N_{3},N_{4},N_{5}\) represent the errors between the predicted values and the true values.  
\[\begin{split} X_{est1}=X_{ACC}+N_{1}\\ X_{est2}=X_{ACC}+N_{2}\\ X_{est3}=X_{ACC}+N_{3}\\ X_{est4}=X_{ACC}+N_{4}\\ X_{est5}=X_{ACC}+N_{5}\end{split} \tag{16}\]  
Let \(Y\) be the aggregated matrix composed of the output results of different training models on the training set; then,  
\[Y=\left(\begin{array}{c}X_{est1}^{T}\\ X_{est3}^{T}\\ X_{est4}^{T}\\ X_{est5}^{T}\\ \end{array}\right)=\left(\begin{array}{c}1\\ 1\\ 1\\ 1\\ 1\\ 1\\ \end{array}\right)X_{ACC}^{T}+\left(\begin{array}{c}N_{1}^{T}\\ N_{2}^{T}\\ N_{3}^{T}\\ N_{4}^{T}\\ N_{5}^{T}\\ \end{array}\right) \tag{17}\]\[H=\left(\begin{array}{c}1\\ 1\\ 1\\ 1\\ 1\\ \end{array}\right),N=\left(\begin{array}{c}N_{1}^{T}\\ N_{2}^{T}\\ N_{3}^{T}\\ N_{4}^{T}\\ N_{5}^{T}\\ \end{array}\right) \tag{18}\]  
Let \(W=\left(\begin{array}{cccccc}w_{1}&w_{2}&w_{3}&w_{4}&w_{5}\end{array}\right)\) represent the weights of different models, where \(w_{i}\) is scalar. Then,  
\[\begin{array}{c}WY=\left(\begin{array}{cccccc}w_{1}&w_{2}&w_{3}&w_{4}&w_{5} \end{array}\right)\left(\begin{array}{c}X_{est1}^{T}\\ X_{est2}^{T}\\ X_{est3}^{T}\\ X_{est5}^{T}\\ X_{est5}^{T}\\ \end{array}\right)\\ =w_{1}X_{est1}^{T}+w_{2}X_{est2}^{T}+w_{3}X_{est3}^{T}+w_{4}X_{est4}^{T}+w_{ 5}X_{est5}^{T}\\ \\ WY=WHX_{ACC}^{T}+WN\end{array} \tag{20}\]  
The signal-to-noise ratio (SNR) can then be expressed as:  
\[SNR=\frac{WHX_{ACC}^{T}X_{ACC}H^{T}W^{T}}{WNN^{T}W^{T}} \tag{21}\]  
To maximize SNR, \(X_{ACC}^{T}\) is a known fixed value. Thus, \(HX_{ACC}^{T}X_{ACC}H^{T}\) is a constant, and the above equation is equal to maximizing the following formula:  
\[max\frac{WHH^{T}W^{T}}{WNN^{T}W^{T}}=\frac{(w_{1}+w_{2}+w_{3}+w_{4}+w_{5})^{2} }{w_{1}^{2}N_{1}^{T}N_{1}+w_{2}^{2}N_{2}^{T}N_{2}+w_{3}^{2}N_{3}^{T}N_{3}+w_{4} ^{2}N_{4}^{T}N_{4}+w_{5}^{2}N_{5}^{T}N_{5}} \tag{22}\]  
As this is a weighted method, we have \(w_{1}+w_{2}+w_{3}+w_{4}+w_{5}=1\). To maximize the above formula (22), the denominator needs to be minimized:  
\[min\{w_{1}^{2}N_{1}^{T}N_{1}+w_{2}^{2}N_{2}^{T}N_{2}+w_{3}^{2}N_{3}^{T}N_{3}+w _{4}^{2}N_{4}^{T}N_{4}+w_{5}^{2}N_{5}^{T}N_{5}\} \tag{23}\]  
Then, we obtain  
\[w_{1}^{2}:w_{2}^{2}:w_{3}^{2}:w_{4}^{2}:w_{5}^{2}=\frac{1}{N_{1}^{T}N_{1}}: \frac{1}{N_{2}^{T}N_{2}}:\frac{1}{N_{3}^{T}N_{3}}:\frac{1}{N_{4}^{T}N_{4}}: \frac{1}{N_{5}^{T}N_{5}} \tag{24}\]  
Finally, the maximum combined output value is  
\[p=\sum_{i=1}^{N}w_{i}p_{i} \tag{25}\]
## 2 Results
### DisA and CdaA Prevent Spontaneous Mutagenesis in Growing B. subtilis Cells  
After demonstrating that mutant strains _AlaA_, _AdaA_ and _AdaA_/_AdisA_ are decreased in the levels of c-di-AMP, we investigated the contribution of these factors to mutagenic processes taking place on exponentially growing _B. subtilis_ cells. To this end, the spontaneous mutation frequency to RifR was determined for strains with single or double disruptions in _disA_ and _cdaA_. The results revealed no significant differences in mutagenesis levels between the _disA_ and WT strains; however, the disruption of _cdaA_ or both genes did increase \(\sim\)2 and \(\sim\)3 times the mutagenesis levels, respectively, compared to the parental YB955 strain (Figure 2).  
Figure 1: Intracellular c-di-AMP levels of _Bacillus subtilis_ YB955 (open bar), _cdaA_ (dark grey bar), _cdaA_ (black bar) and _cdaA_/_cdaA_ (hatched bar) were determined in vegetative cells (grown to an OD\({}_{6000\text{nm}}=1.0\)) by competitive ELISA quantification as described in Materials and Methods. Data show the average from three independent determinations \(\pm\) standard deviations. *** represent significant statistical differences compared to the parental strain, as determined by a one-way analysis of variance (ANOVA) and Tukey Honest Significant Difference (HSD) test (\(p=2.07\times 10^{-8}\), \(p=3.43\times 10^{-9}\), \(p=1.28\times 10^{-9}\), respectively).  
Figure 2: Spontaneous mutation frequencies to rifampicin resistance (Rifßß) of _Bacillus subtilis_ YB955 (open bar), _cdaA_ (dark grey bar), _cdaA_ (black bar) and _cdaA_/ _cdaA_ (hatched bar) were determined during exponential growth. Complemented strains _cdaA amyE:_Phs-disA_ (light stippled bar), _cdaA amyE:_Phs-cdaA_ (horizontal stripped bar), _cdaA_/ _cdaA amyE:_Phs-cdaA_ (vertical stripped bar), and _cdaA_/ _cdaA amyE:_Phs-disA_ (dark stippled bar) were also tested. Data show the average from three independent experiments, each one in sextuplicate \(\pm\) standard deviations. All strains were supplemented with IPTG 0.25 mM. Different letters (a, b, c) represent significant statistical differences between means; the same letter denotes no significant difference, as determined by a one-way ANOVA and multiple mean comparison by Tukey HSD test (all \(p\)-values < 0.001).  
These mutagenic effects can be attributed to the disruption of _cdaA_, or both _cdaA_ and _disA_, as the expression of genes encoding these proteins from the IPTG-inducible _Plts_ promoter restored the mutagenic levels to RifR in the _cdaA_ and _cdaA_/_disA_ mutant strains (Figure 2). Altogether, these results suggest that DisA, CdaA and c-di-AMP counteract spontaneous GAM in _B. subtilis_.