esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Artificial Ecosystem Algorithm for Economic Dispatch with Combined Heat and Power Units
## 2 ED Problem with CHPUUs  
The key players in the ED in efforts to supply the electricity and heat loads in the facilities and buildings are depicted in Figure 1. The core purpose of the ED combining CHPUUs would be to identify the economic potential rate for heat generated by HOUs, power generated by POUs, and both power and heat generated by CHPUUs, such that fuel costs are maintained to a minimum while heat and power needs and restrictions are met [34]. Thus, the generation cost objective (\(F\)) may be stated as:  
\[F=\sum_{m=1}^{N_{CPU}}C_{m}(Pg_{m})+\sum_{n=1}^{N_{HUI}}C_{n}(Hg_{n})+\sum_{k =1}^{N_{CHPU}}C_{k}(Pg_{k},Hg_{k}) \tag{1}\]  
where  
\[C_{m}(Pg_{m})=\alpha 1_{m}(Pg_{m})^{2}+\alpha 2_{m}Pg_{m}+\alpha 3_{m}+| \alpha 4_{m}\sin(\alpha 5_{m}(Pg_{m,\min}-Pg_{m}))| \tag{2}\]  
\[C_{n}(Hg_{n})=\varphi 1_{n}(Hg_{n})^{2}+\varphi 2_{n}Hg_{n}+\varphi 3_{n} \tag{3}\]  
\[C_{k}(Pg_{k},Hg_{k})=\beta 1_{k}(Pg_{k})^{2}+\beta 2_{k}Pg_{ki}+\beta 3_{k}+ \beta 4_{k}(Hg_{k})^{2}+\beta 5_{k}Hg_{k}+\beta 6_{k}Hg_{k}Pg_{k} \tag{4}\]  
Furthermore, the inequality requirements of this problem should always be fulfilled in regard to the capacity of the POUs, HOUs, and CHPUUs, as shown in Equations (5)-(8):  
\[Pg_{m}^{\min}\leq Pg_{m}\leq Pg_{m}^{max}\quad m=1:N_{CPU} \tag{5}\]  
\[Hg_{n}^{\min}\leq Hg_{n}\leq Hg_{n}^{max}\quad n=1:N_{HUI} \tag{6}\]  
\[Pg_{k}^{\min}\leq Pg_{k}\leq Pg_{k}^{max}\quad k=1:N_{CHPU} \tag{7}\]  
\[Hg_{k}^{\min}\leq Hg_{k}\leq Hg_{k}^{max}\quad k=1:N_{CHPU} \tag{8}\]  
Furthermore, the equality requirements of this problem should always be satisfied in the perspective of heat and power balance, as addressed in Equations (9) and (10), and as described in the following:\[\sum_{i=1}^{N_{CHPU}}Hg_{i}+\sum_{j=1}^{N_{UH}}Hg_{j}=Heat_{D} \tag{9}\] \[\sum_{i=1}^{N_{CHPU}}Pg_{i}+\sum_{k=1}^{N_{CHU}}Pg_{k}=Power_{D} \tag{10}\]  
The power loss represents an important phenomenon that occurs in power system networks due to the flow of the output power from the generation units to the customers. It is of great importance since it is usually modelled in a highly non-linear form that represents further complexity to the ED model incorporating CHPUUs. Thus, the integration of transmission losses might result in additional non-convexity for the issue, which is described in Equation (11) as a proportion of the output power of the POUs, HOUs, and CHPUUs:  
\[P_{Loss}=\sum_{j=1}^{N_{CHU}}\sum_{i=1}^{N_{CHU}}B_{ji}Pg_{j}Pg_{i}+\sum_{j=1} ^{N_{CHU}}\sum_{i=1}^{N_{UH}}B_{ji}Pg_{j}Hg_{i}+\sum_{j=1}^{N_{HIP}}\sum_{i=1} ^{N_{CHPU}}B_{ji}Hg_{j}Hg_{j} \tag{11}\]  
where \(P_{Loss}\) is the total losses; \(B_{ji}\) is the coefficient element in the \(B\)-matrix that describes line losses correlating the units.  
Accordingly, Equation (10) can be reformulated as follows:  
\[\sum_{i=1}^{N_{CHPU}}Pg_{i}+\sum_{k=1}^{N_{CHU}}Pg_{k}=Power_{D}+P_{Loss} \tag{12}\]  
Figure 1: Key players in the ED problem with CHPUs [35].
###### Abstract  
In order to increase industrial production quality and efficiency, it is essential to understand how the aeration and no-aeration condition affects liquid and solid material mixing in the stirred tank. Due to complicated shear flows, the related mass-transfer mechanism confronts numerous difficulties. This paper put forward an improved computational fluid dynamics and discrete element method (CFD-DEM) modeling approach to explore the effect mechanism of aeration conditions on liquid-solid material mixing. Firstly, a mass-transfer dynamic model is set up with a volume of fluid and piecewise linear interface construction (VOF-PLIC) coupling strategy to explore flow modes and vorticity evolution trends under aeration control. Then, a self-developed interphase coupling interface is utilized to modify the coupling force and porosity of the porous media model in the DEM module, and random dispersion properties of the particle phase under non-aeration and aeration are obtained. Results show that the aeration and flow-blocking components transform fluid tangential speeds into axial and radial speeds, which can improve the material mixing quality and efficiency. The mixed flow field can reach a greater turbulent process under the impeller rotation, making the particles have an intensive disorder and complex flow patterns. The enhanced motion efficiency of the vortex clusters encourages their nesting courses and improves cross-scale mixed transport. It can serve as some reference for the three-phase flow mixing mechanism, vorticity distribution law, and particle motion solution and has a general significance for battery homogeneous mixing, biopharmaceutical processes, and chemical process extraction.  
three-phase particle flow; mass transfer; porous media model; self-developed interphase coupling interface; CFD-DEM modeling +
Footnote †: journal: Applied Sciences
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 6 Simulation Results
### Number of Additions Per Matrix Entry
#### 6.3.2 IID Uniform Target Matrices  
Consider now a random matrix \(\mathbf{T}\) whose entries are uniform IID within \([0,1)\). If the same algorithm is used as before, the performance degrades significantly, as such a matrix is not a good codebook. Since all entries are positive, all codewords are constrained to a single orthant of the full space. A uniform distribution within \((-1,+1)\), on the other hand, would hardly be an issue.  
We write the \(K\times N\) target matrix as \(\mathbf{T}=\tilde{\mathbf{T}}+\frac{1}{2}\mathbf{1}\). We deal with the matrix \(\tilde{\mathbf{T}}\) whose entries are uniformly distributed within \([-\frac{1}{2},\frac{1}{2})\) in exactly the same way as in Section 6.3.1. We calculate the term \(\frac{1}{2}\mathbf{1}x\) directly and add the result to \(\tilde{\mathbf{T}}x\). In addition to the linear computation coding for \(\tilde{\mathbf{T}}x\), this requires \(N-1\) additions for \(\mathbf{1}x\) and \(K\) additions for adding \(\tilde{\mathbf{T}}x\) to \(\frac{1}{2}\mathbf{1}x\). Since the norm of \(\tilde{\mathbf{T}}\) is half the norm of \(\mathbf{T}\), the SQNR for \(\tilde{\mathbf{T}}\) may be 6 dB lower than for \(\mathbf{T}\). The results are shown in Table 2. A comparison to Table 1 shows that this procedure only causes a minor degradation, if any at all, that is more pronounced for small matrices.  
For comparison, Table 2 also shows the number of additions required for the CSD representation. Here, the rows CSD and CSD\({}_{\text{a}}\) refer to the same methodology as in Table 1. The last two rows indexed by an additional \"h\", refer to the hybrid approach of decomposing \(\mathbf{T}\) into its mean \(\frac{1}{2}\mathbf{1}\) and \(\tilde{\mathbf{T}}\). This approach is not only useful for linear computation coding, but also for the CSD representation.  
For the \(4096\times 512\) matrix of Examples 1 and 2, this time with uniform entries within \([0,1)\), we use the same cut into 32 submatrices of size \(4096\times 16\). With the results of Table 2, this results in 1.575 additions per matrix entry for 96 dB SQNR. Compared to the four variants of CSD representation shown in Table 2, this means savings of 76%, 69%, 75%, and 68%, respectively.
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
## 5 Results
### Comparison with Other Methods  
For comparison, a subset of classical forecasting models was selected for comparison included the Naive, Standard Exponential Smoothing (SES), Holt-Winters and ARIMA models. The details are described in the following sections. The prediction accuracy of the conventional FCMs models was calculated. The prediction accuracy was calculated and listed in Table 2. As can be seen from Table 2, the prediction precision of the time series with FCM learning by the Particle swarm optimization (PSO) or Genetic algorithm (GA) methods is slightly higher than the developed LSFCM prediction model, but less than the LSFCM-ref model. There are no significant differences for all the time series in these models: PSO-FCM, GA-FCM and LSFCM. However, the results with LSFCM-ref model are more accurate than others.  
Figure 6: The performance of the fuzzy cognitive map (FCM) model with different parameters.  
Furthermore, the comparison between the proposed approach and the classical prediction approach (Naive, SES, Holt-Winters and ARIMA) is presented in Table 3. As can be noted, the LSFCM approach outperformed the classical prediction models and the LSFCM has the minimum RMSE compared with the other prediction methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **PSO-FCM** & **GA-FCM** & **LSFCM** & **LSFCM-Ref** \\ \hline (a) (c = 10) & 11.66 & 12.4 & 11.48 & 10.47 \\ (b) (c = 10) & 38.5 & 38.4 & 41.6 & 37.46 \\ (c) (c = 7) & 8328 & 8386 & 8999 & 7753 \\ (d) (c = 7) & 23.5 & 23.6 & 26.1 & 22.8 \\ (e) (c = 7) & 79.28 & 80.39 & 80.55 & 73.23 \\ (f)(c = 6) & 11.8 & 13.1 & 12.89 & 10.51 \\ (g)(c = 9) & 34.14 & 35.02 & 34.58 & 33.21 \\ (h)(c = 10) & 543.82 & 545.83 & 542.79 & 518.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with other FCM models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Naive** & **ARIMA** & **SES** & **Holt-Winters** & **LSFCM** & **LSFCM-Ref** \\ \hline (a) & 13.12 & 10.51 & 22.13 & 13.14 & 11.48 & 10.47 \\ (b) & 61.92 & 60.67 & 62.79 & 64.02 & 41.6 & 37.46 \\ (c) & 11,435 & 10945 & 14320 & 10605 & 8999 & 7753 \\ (d) & 27.31 & 28.50 & 28.33 & 28.78 & 26.1 & 22.8 \\ (e) & 82.34 & 82.65 & 82.34 & 105.27 & 80.55 & 73.23 \\ (f) & 13.14 & 12.63 & 13.15 & 13.65 & 12.89 & 10.51 \\ (g) & 46.13 & 46.49 & 96.55 & 46.75 & 34.58 & 33.21 \\ (h) & 949.61 & 939.64 & 1464.15 & 1042.99 & 542.79 & 518.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with the classic prediction models.  
Figure 7: Adjustment concepts. Black star points: the initial position, red circles: the final position.
# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 5 Conclusions  
IoMT is a significant and promising technology and provides an ease of access to real-world health data instantly. Currently, the number of IoMT devices is increasing exponentially, which intensifies the requirements. In the IoMT environment, the study of ICN caching policies is involved in terms of content placement strategies. IoMT contents are distributed in terms of scalability and cost-effectiveness. With the rapid growth of the IoMT network traffic, it is preemptory to be a suitable framework to address the challenges. This article introduces a dynamic caching strategy designed for the IoMT. By integrating IoMT with ICN, we propose a dynamic caching scheme aimed at reducing energy consumption within information-centric IoMT networks. This study considers key caching policy parameters, such as cache-hit ratio, stretch, and latency, to enhance the IoMT network performance. The proposed framework is evaluated on selected parameters through simulations while exploring various configurations involving network topology, cache size, and content popularity. An analysis of the results demonstrates the superior performance of the proposed caching framework, known as DCS, compared to others. The dynamic nature of the DCS approach yields significant results as compared to existing methods. In the future, it is intended to explore the pertinence of DCS in more scenarios and further optimize the data exchange processes. The proposed policy can be improved by including other related parameters including memory management of sensor nodes, security, etc.  
Conceptualization, M.S., A.A. and M.T.; methodology, M.S. and A.A.; software, M.T.; validation, M.T., M.S.K. and M.S.; formal analysis, M.S. and M.S.K.; investigation, M.T.; resources, A.A. and M.T.; data curation, M.S.K., M.T. and M.S.; writing--original draft preparation, M.T.; writing--review and editing, M.S., M.S.K. and A.A.; visualization, M.S.; supervision, M.T.; project administration, A.A. and M.S.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Scientific Research, Qassim University under project (2023-FFT-2-HSRC-37648).  
The datasets used are available publicly.  
Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding publication of this project.  
There is no conflict of interest to report.
## 2 Research Methodology
### General System
#### 2.3.4 End-Users  
End-users are users that receive services from original servers and edge servers. On the client-side, end-users typically interact with the user interface of the service or application, and their satisfaction with the performance, usability, and functionality of the service can greatly impact their overall experience. This is why network service and application providers often focus on optimizing their offerings to meet the needs and expectations of end-users.
## 1 Introduction
For each \((s,t)\in R\), if both vertices \(s\) and \(t\) are on a same path of the arborescence, then vertex \(s\) has to be visited before vertex \(t\). The optimization seeks to find an arborescence of minimum total cost such that all the precedence constraints are satisfied. Several models for the problem, all based on Mixed Integer Linear Programming (MILP), were proposed. We refer the interested reader to Chou et al. (2018). The _Precedence-Constrained Minimum-Cost Arborescence problem with Waiting Times_ (PCMCA-WT)--which is the object of the current paper--was first introduced in Chou et al. (2018), where the problem was shown to be \(\mathcal{NP}\)-hard through a reduction to the _Rectilinear Steiner Arborescence_ problem (Shi and Su (2018)), and different MILP models were proposed. The problem is about retrieving an arborescence where traveling times are present among vertices and temporal precedences relative to the time of visit have to be fulfilled among pairs of vertices. Waiting times at vertices are allowed to enforce such precedences, but these waiting times are accounted for in the objective function together with travel times. The optimization is to minimize such an objective function. The organization of the paper is as follows. The PCMCA-WT is formally defined in Section 2. Section 3 describes a new family of compact models for the problem (characterized by a polynomial number of variables and constraints). Section 4 discusses the results of a vast experimental campaign where the new models are compared to those previously disclosed in the literature. Some conclusions are the content of Section 5. The contributions of the paper can be summarized as follows:  
* New models for the PCMCA-WT that are polynomial in size and are characterized by a substantially smaller memory footprint compared to the known models are introduced. This result is achieved by exploiting some theoretical properties emerging from the current study and previously unobserved. * The new models are solved both with MILP and Constraint Programming (CP) solvers. The experimental results substantially improve the state of the art for the instances commonly adopted in the literature. Out of the 88 open instances from the literature, improved lower bounds are provided for 71 instances and improved upper bounds are provided for 80 instances. Finally, seven instances are closed for the first time.
# Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging
## 3 Methodology
### BMA Implementation Procedure  
For a better understanding of the principles of the BMA approach, we provide the implementation procedures of BMA:  
Step 1: Determination of model space. This step is to determine a set of candidate models as the model space. The details are described in Section 3.3.  
Step 2: Parameter estimation and evaluation of candidate models. According to the parking duration dataset, the parameters of candidate models are estimated using different methods. More specifically, the maximum likelihood estimation and the expectation-maximum methods are applied to estimate the parameters of single models and mixture models, respectively. Then, several goodness-of-fit metrics are calculated, including the log-likelihood, Akaike information criterion (AIC), and Bayesian information criterion.  
Step 3: Calculate the posterior model probability. According to the AIC values from step 2, the posterior model probability of each candidate model can be calculated using Equation (7). Step 4: Generate the probability density function of BMA. As per the posterior model probability and the probability density functions of candidate models, the derived probability density function of BMA can be obtained using Equation (1).  
To sum up, the derived probability density function of BMA integrates the probability density functions of candidate models by assigning weights (i.e., posterior model probability) that can overcome the model uncertainty in depicting parking duration distribution characteristics.
## Materials and Methods
### Characterization Tests
#### 2.3.2 Mechanical Tests  
Three-point flexural strength tests as well as compression tests were performed at a speed rate of 0.5 mm/min with a [PERSON] 4000 kN test machine (Wolpert, Neu Ulm, Germany), at 23 \(\pm\) 1 \({}^{\circ}\)C and 65 \(\pm\) 10 RH%. The span length for the flexural tests was 70 mm with a support radius of 2 mm. At least three samples were tested in flexural mode and six for compressive mode after 28 days of curing at 23 \(\pm\) 2 \({}^{\circ}\)C in sealed plastic bags. Apart from the reported parameters, the procedure followed the instructions of the EN 196-1 Standard, which is formulated for Portland cement composites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Code** & **Metakaolin** & **Na\({}_{2}\)SiO\({}_{3}\) + NaOH** & **Water** & **Fibers** & **wt\% *** \\ \hline Metakaolin & MTK-0 & 100 & \(15+15\) & 5 & 0 & 0.00 \\ Metakaolin + 1\%Kenaf & Ke1 & 100 & \(15+15\) & 5 & 1 & 0.73 \\ Metakaolin + 2\%Kenaf & Ke2 & 100 & \(15+15\) & 5 & 2 & 1.46 \\ Metakaolin + 3\%Kenaf & Ke3 & 100 & \(15+15\) & 5 & 3 & 2.17 \\ Metakaolin + 10\%Hemp & He1 & 100 & \(15+15\) & 5 & 1 & 0.73 \\ Metakaolin + 2\%Hemp & He2 & 100 & \(15+15\) & 5 & 2 & 1.46 \\ Metakaolin + 3\%Hemp & He3 & 100 & \(15+15\) & 5 & 3 & 2.17 \\ Metakaolin + 1\%Bamboo & Ba1 & 100 & \(15+15\) & 5 & 1 & 0.73 \\ Metakaolin + 2\%Bamboo & Ba2 & 100 & \(15+15\) & 5 & 2 & 1.46 \\ Metakaolin + 3\%Bamboo & Ba3 & 100 & \(15+15\) & 5 & 3 & 2.17 \\ Metakaolin + 4\%Bamboo & Ba4 & 100 & \(15+15\) & 5 & 4 & 2.88 \\ \hline \hline \end{tabular} All amounts are expressed in grams. * Last column shows the wt% fiber amount in the composite.  
\end{table}
Table 1: Composition and labelling of the investigated composites.
## 2 Geometric Modeling Approaches
### Modeling of Dielectric Constant of the Medium
#### 2.3.2 Dielectric Property Modeling of Soil
The variation in the soil complex dielectric constant with moisture content is shown in Figure 6.  
As shown in Figure 6, at a frequency of \(f=6\) GHz and temperature \(T=15\)\({}^{\circ}\)C, the real part of the soil dielectric constant gradually increases with increasing soil moisture content, while the imaginary part increases rapidly as the soil moisture content rises.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 4 Discussion
### Limitations and Challenges  
Although this study has established a foundational framework for multi-source sentiment computing in rural visual landscapes, several notable limitations remain. First, the geographic concentration on three villages in Dalian constrains the generalizability of cultural and ecological insights to other regions. Future research should therefore focus on diversifying application contexts and expanding the geographical scope. Second, the participant pool--comprising exclusively students and teachers--exhibited a degree of homogeneity. Subsequent studies should include a broader demographic spectrum, such as rural residents and tourists, to more accurately evaluate the model's applicability across different user groups and to strengthen the generalizability of the findings. Finally, the current experiment employed two-dimensional images as stimuli, limiting emotional extraction to flat visual environments. To improve the ecological validity of emotional assessments, future investigations should consider developing a multimodal virtual or augmented reality platform that incorporates environmental variables such as seasonal transitions and dynamic lighting conditions. This would foster more immersive experimental settings and enable dynamic, adaptable evaluations. Collectively, these advancements will offer critical technical support for refining the emotional assessment framework and enhancing its applicability in complex real-world environments.
###### Abstract  
Phase change materials (PCMs) have been used in various fields including the materials of buildings. In this research, mixed shape-stabilized PCMs (Mixed SSPCMs) were prepared by impregnating coconut oil and _n_-hexadecane into exfoliated graphite nanoplatelets (xGnP) through a vacuum impregnate method. Coconout oil is fatty acid ester PCM which is relatively economical in comparison to other PCMs, and _n_-hexadecane is paraffin PCM that has high latent heat capacity. Drawbacks include leakage in a liquid state and low thermal conductivity resolved by xGnP. When preparing Mixed SSPCMs, coconut oil and _n_-hexadecane were impregnated at different proportions, namely 70:30, 50:50, 30:70 wt %. Mixed SSPCMs were analyzed through SEM, FT-IR, DSC, TGA and TGi. As a result, we confirmed the microstructure, chemical stability, thermal properties, thermal stability and thermal conductivity of Mixed SSPCMs. Latent heat capacity of Mixed SSPCMs were 89.06, 104.30 and 124.50 J/g while those of SSPCMs containing single PCM were 82.34 and 96.40 J/g. Thermal conductivity of Mixed SSPCMs was more than 284% higher than that of pure coconut oil and _n_-hexadecane. Finally, we confirmed that coconut oil and _n_-hexadecane were impregnated into xGnP, and the Mixed SSPCMs have high thermal durability.  
mixed SSPCM; fatty acid ester PCM; paraffin PCM; thermal performance; thermal storage; xGnP; impregnation Article
## 3 System Model and Problem Formulations
### Capacity and Coverage Dimensioning  
We carry out coverage and capacity dimensioning based on the link budget profile. For coverage dimensioning, we calculate the maximum radius that the given specification allows. For that, we calculate the path loss as specified by 3 GPP TR-38.901, and then we calculate the area covered by one RRU\({}_{\text{m}}\) and RRU\({}_{\text{M}}\). After calculating the area covered by each RRU\({}_{\text{m}}\) and RRU\({}_{\text{M}}\), we calculate the required number of RRU\({}_{\text{m}}\) and RRU\({}_{\text{M}}\) that ensures coverage of all UEs. No UEs in that area are in a dead zone. Next, we carry out the capacity dimensioning based on the link budget analysis for both uplink and downlink and place RRU\({}_{\text{m}}\) and RRU\({}_{\text{M}}\) properly.