esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of a Solar-Driven Organic Rankine Cycle with Reheating
## 2 Material and Methods
### Simulation of the Developed Models  
The present investigation is completed with the software Engineering Equation Solver [18]. Two models, one for steady-state and one for dynamic analysis, were developed. In the dynamic analysis, climate conditions for the location of Athens in Greece and more information about them can be found in Ref. [26]. It is useful to state that the ETCs are selected to have a slope of 38\({}^{\circ}\), which is the same as the latitude of Athens for maximizing the incident solar potential on a yearly basis. The equations of Section 2.2 have been used in the developed models. Proper discretization was performed in order to make the results independent of the time step. The condenser temperature is slated to be 5 K over the maximum daily ambient temperature in order to achieve a proper operation of the ORC. Moreover, it is useful to state that the system was studied for 12 representative days--one day for each month in order to properly simulate the annual performance of the solar-fed configuration.  
In the first part of the analysis, a simple comparison of the two examined ORCs in steady-state conditions is performed by selecting nominal electricity capacity at 15 kW. The next step is the dynamic analysis of the two systems for a typical day. The final evaluation of the system is conducted on a yearly basis by investigating two critical parameters--the saturation temperature of the ORC and the nominal electricity capacity of the ORC. The results are evaluated in terms of energy efficiency and financial indexes such as SPP and NPV. The final global optimum choice is the selected one that maximizes the investment NPV.  
In the last part of this section, Tables 1 and 2 summarize the main data of the present simulation. Table 1 includes the input parameters of the thermodynamic analysis and the system design, while Table 2 summarizes the data of the financial investigation. An assumption of this work is the selection of the solar field area at 300 m\({}^{2}\) and the storage tank at 5 m\({}^{3}\).  
Moreover, It is critical to say that the thermodynamic model for simple ORC has been validated in our previous works [28]. However, it is important to again provide validation evidence in order to be clear about the accuracy of the developed model. More specifically, the developed model has been compared with experimental results from Reference [29]. In order to perform a suitable validation, all the proper modifications of the existing model have been done in order to be in accordance with the \"boundary conditions\" of the experimental setup in Reference [29]. Table 3 summarizes the comparative results for different working fluids and it has to be said that the deviation in the electricity yield was found close to 0.6% and thermodynamic efficiency close to 0.5%. These are small deviations that indicate the high accuracy of the developed model.
## 3 Proposed UGAVs-MDVR Protocol
### Cluster Creation (CC)  
The clusters are formed by considering the similar speeds of vehicles because the UGVs used for mine detection move together during mine detection and clearance. The direction is identical for cluster formation; otherwise, the UGV will be dropped from the cluster membership, as given in Algorithm 1 and shown in Equation (1). The cluster creation process is summarized in the following steps, as given in Algorithm 1:  
1. Cluster construction,
2. Cluster-head election,
3. Joining process execution of CMs,
4. Dropping off the CMs,
5. Modification of cluster record,
6. Cluster maintaining process.  
```
1.TO complete V\({}_{w}\) in \(\Phi\)(n) Implement // all UGVs
2.IFCLHD = NOT then //CH not elected
3.Functioncluster_creation(V\({}_{Z}\)) // cluster construction
4.FunctionCLHD_election(V\({}_{Z}\)) //CHF Function
5.IFINVC_military = TRUE && INVC_direct = SAME //the joining of incoming vehicles by considering direction
6.Functionjoining_cluster(V\({}_{Z}\)) //joining as a cluster member
7.modify_cluster(CR) //Update the cluster Record (CR)
8.ENDIF
9.IFVehicle_CMs(V\({}_{Z}\)) = CR(modify_stop) //when no change detected
10.Functiondrop_veh(V\({}_{Z}\)) //left CM
11.modify_cluster(CR) //modify the CR
12.ENDIF
13.ELSE
14.Functionmaintain_status_CLHEAD(V\({}_{Z}\)) //maintain CH
15.Functionkeep_status_CLMN(V\({}_{Z}\)) //maintain Cluster
16.Functionkeep_connection(UAV)
17.ENDIF
```  
\[Cluster\ (C)=\left\{\begin{aligned} & CMB_{1}\in[ SPD_{CMB1}|dr_{1}]\\ & CMB_{2}\in[ SPD_{CMB2}|dr_{2}]\\ &.\\ & CMB_{n}\in[ SPD_{CMBn}|dr_{n}]\end{aligned}\right\} \tag{1}\]  
where \(CMB_{1}\), \(CMB_{2}\), and \(CMB_{3}\) are cluster members that move with speed \(SPD_{CMBn}\) and direction' \(dr_{1}\)'.
# Inner-Loop Controllers for Grid-Forming Converters
## 4 Discussion and Conclusions  
This study investigates the small-signal stability of grid-forming converters with LC filters, employing both virtual impedance (Thevenin equivalent) and virtual admittance (Norton equivalent) for voltage control and current limiting. While virtual impedance effectively stabilizes voltage during transients by emulating adjustable reactive characteristics, its steep phase drop in the high-frequency range can exacerbate instability risks in multi-inverter microgrids.  
Small-signal analysis reveals that the Norton equivalent, implementing virtual admittance, demonstrates superior high-frequency phase stability compared to the Thevenin equivalent (Figure 13). This translates to enhanced resilience in scenarios involving multiple DERs, where interactions and potential resonances are heightened. Under steady-state conditions, both virtual impedance and virtual admittance control strategies demonstrate comparable performance. However, during transient short-circuit events, a discernible difference emerges (Figures 15 and 16). Specifically, implementations utilizing a Norton equivalent model exhibit a tendency for increased output current. This phenomenon aligns with the fundamental principle that virtual impedance primarily modulates current output, whereas virtual admittance primarily influences voltage dynamics.  
This paper introduces a flexible framework that enables the implementation of both virtual impedance and virtual admittance techniques. The optimal selection between these methods is determined by the application's specific needs, with a focus on ensuring high-frequency stability and robust fault tolerance. Future research should investigate the impact of varying LC filter parameters on the stability margins of these control strategies to provide valuable insights for practical implementations.  
Conceptualization, A.T.P. and H.P.; methodology, A.T.P. and H.P.; software, A.T.P. and A.P.M.; validation, A.T.P. and A.P.M.; formal analysis, A.T.P. and H.P.; investigation, A.T.P.; resources, A.T.P.; data curation, A.T.P. and A.P.M.; writing--original draft preparation, A.T.P.; writing--review and editing, A.T.P., A.P.M. and H.P.; visualization, A.T.P.; supervision, A.P.M. and H.P.; project administration, A.T.P. and H.P.; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript.  
Figure 16: HIL simulation results showing the voltage and current measurements of a DER operating in parallel during a 0.05 s short circuit with the inductive Droop as primary controller and predominantly inductive virtual admittance.  
**Funding:** This research was funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico--CNPq 4076542021-6.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Conclusions
### Limitations and Future Research  
While this study offers valuable insights, it is subject to certain limitations. The data was collected from UAE government entities, which may limit the broader applicability and generalizability of the findings to regions with varying socioeconomic or cultural contexts. Additionally, this study primarily focused on direct service delivery organizations, potentially excluding insights from supporting departments or non-customer-facing entities. These limitations warrant further investigation in future studies by broadening the scope to incorporate a broader range of public sector organizations across diverse geographical and cultural contexts. Longitudinal studies could explore how these challenges transform over time and in response to specific context interventions.  
The network construction relied solely on correlation coefficients, which indicate associations among the challenges. Future research could address this limitation by validating the network structure using expert panels or other qualitative approaches to confirm and enrich the findings. Furthermore, the analysis considered only two centrality metrics, betweenness centrality and eigenvector centrality, to assess the importance of nodes in the network. Future studies could incorporate additional network metrics to provide a more comprehensive understanding of node significance and network structure.  
Future investigations may explore a multi-layer network model to analyze the dynamic relationship between critical challenges and their connections with stakeholders, organizational units, or innovation outcomes. This approach would enable a more nuanced understanding of how the various dimensions of challenges interact with the PSI system. Moreover, in today's rapidly evolving technological landscape, there is a need to examine how advanced technologies, such as digital platforms and AI, play a pivotal role in overcoming challenges, fostering innovation, and enhancing government service agility [116, 117]. Initiatives aimed at accelerating service delivery, such as the Zero Government Bureaucracy program [54], also merit further exploration to understand their potential benefits in addressing these challenges. While this study focuses on the organizational challenges, future research could examine how significant external factors, such as global crises, influence public service innovation [118].  
Conceptualization, A.A. and H.B.; methodology, A.A. and H.B.; software, A.A.; validation, H.B.; formal analysis, A.A.; investigation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, A.A. and H.B.; visualization, A.A.; supervision, H.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
## 3 Results
### Subsection pH Fluctuations and Ion Release Dynamics During 3-Month Aging  
The pH analysis of the extraction media over a 90-day period (Figure 5) revealed material-specific variations in chemical stability between TT and DD zirconia systems immersed in A2 shade coloring liquids. TT zirconia, treated with ZirColor ST, exhibited a dynamic pH trajectory, peaking at 1.30 on day 28, followed by a gradual decline to 1.03 by day 70, and a slight rebound to 1.05 at day 90. The overall mean pH value for TT was 1.14, indicating greater fluctuation over time. Conversely, DD zirconia, immersed in DD Shade Liquid, maintained a more consistent and lower pH profile, ranging from 0.90 on day 14 to a minimum of 0.82 on day 56, with a moderate increase to 1.01 by the end of the observation period. The mean pH value for DD remained at 0.88, underscoring its relatively stable chemical environment.  
Figure 4: Lightness ratio (L*) of TT and DD zirconia specimens with different thicknesses after immersion in coloring solutions aged for 3 months. TT: TT ONE Multilayer; DD: cubeX\({}^{2}\) ML.  
Figure 3: Colorimetric results (\(\Delta\)E L*, a*, b*) of coloring solutions after 3 months of use, measured following immersion of (**a**) TT and (**b**) DD zirconia specimens. TT: TT ONE Multilayer; DD: cubeX\({}^{2}\) ML.  
Complementary analysis of ion concentrations using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) revealed distinct temporal patterns in the release of Fe and Er ions during the 3-month immersion period (Figure 6). In both TT and DD groups, Fe concentrations increased over time, with TT showing a steady rise and DD exhibiting a more pronounced surge at the 3-month mark, ultimately exceeding TT's Fe levels. In contrast, Er ion concentrations in both zirconia systems markedly declined by the third month (Figure 6b).
# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 5 Building Data Analysis  
This study validates the superiority and effectiveness of the proposed method using one year of hourly energy consumption data from a building in Shanghai, including feature attributes such as pressure, temperature, and wind speed.  
In terms of data processing, this paper first traverses and restructures the dataset, allocating 90% of the data to the training set and the remaining data to the test set. Then, the'mapminmax' function is applied to normalize the data, mapping it to the [0,1] range. This ensures that the features contribute more evenly to the loss function during model training, avoiding the issue of certain feature values being excessively large or small, which could negatively impact the training process. This also improves the convergence speed of the neural network. The calculation formula is as follows (Equation (20)):  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}, \tag{20}\]  
where \(x\) is the origin data, \(x^{\prime}\) is the normalized data, and \(x_{\text{min}}\) and \(x_{\text{max}}\) are the minimum and maximum values of the original data.  
The convolutional layer, ReLU activation function, GRU layer, and self-attention layer are built to construct a deep learning model, and the Adam optimization algorithm is used to train the network. Training options include the learning rate, gradient threshold, and maximum training iterations. The trained network is used to predict the test set, and the predicted results are denormalized to restore the original data.  
Next, the Golden Jackal Algorithm is applied to optimize the network's hyperparameters. The optimization goal is to minimize the network's Mean Squared Error (MSE) by adjusting the learning rate, number of GRU neurons, key values in the attention mechanism, and regularization parameters.  
Finally, comparison charts of predicted values from different algorithms, including radar charts, are plotted to evaluate the performance of the original and optimized models. This study uses metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) to quantify the model's performance on the test set.
## 4 Numerical Simulation
### Simulation Results and Comparative Analysis
#### 4.2.2 Spatial Effect Analysis
In addition, it can be found that with the increase of the excavation depth, the difference of horizontal displacement of piles in adjacent cases keeps increasing, so the influence range of the spatial effect tends to increase with the increase of the excavation depth. Figure 19: Horizontal displacement of piles. Figure 18: Horizontal displacement of piles top along long side. The shape of ground settlement with displacement, as shown in Figure 20, is similar to the results obtained by [PERSON] et al. [29]. The settlement curve is a single-peaked curve with a \"trough\" shape. In each case, the settlement curves are compared and analyzed. The settlement grows in each case, and the shape of the settlement curve is virtually the same in each case. In general, the excavation has an impact on surface settlement in the range of about 30 m. The settlement steadily expands with a distance in the range of 0 to 6 m. The settlement reaches a maximum at 6 m from the edge and, after exceeding 6 m, it gradually decreases and finally stabilizes. When the foundation pit was excavated to a depth of 19 m, the settlement reached a maximum of 11.23 mm. This is consistent with the change rule of horizontal displacement of the pile. With the increase of excavation depth, the settlement difference of adjacent cases also increases continuously. It can be found that the maximum horizontal displacement of the pile and the maximum ground settlement occurs in the same section. Therefore, it can be seen there is a correlation between the spatial effect of settlement and the horizontal deformation of the pile.
## 7 Limitations and Challenges
### Lack of Studies on Suitable Consensus Mechanisms  
The reviewed literature revealed a clear deficit in research on consensus mechanisms tailored for specific blockchain applications in PARFSET. While existing mechanisms such as proof of work (PoW) and proof of stake (PoS) are widely used in other applications of blockchains, their suitability for the PARFSET domain remains underexplored. [PERSON] et al. stressed the importance of selecting efficient and context-appropriate consensus mechanisms, as the choice directly impacts scalability, security, and energy consumption [35].  
The consensus mechanisms used in blockchain applications for PARFSET must align with the unique characteristics of this field. The substantial volume of data generated in PARFSET settings, combined with the absence of currency token exchanges (unlike cryptocurrency systems), necessitates mechanisms that prioritize low energy consumption and minimal operational complexity. Traditional PoW is unsuitable due to its excessive computational effort and high energy demands. PoS and delegated proof of stake (DPoS) offer low energy consumption; however, PoS relies on validators committing stakes, which is not suitable for a blockchain without a native coin. Similarly, DPoS introduces governance challenges, as participants must elect delegates, adding further complexity. Practical Byzantine fault tolerance (PBFT) is energy-efficient but relies on extensive communication through multiple message exchanges, which may struggle to scale in PARFSET environments with a diverse and large user base. Proof of authority (PoA) is an energy-efficient mechanism that assigns validation rights to trusted authorities. However, it demands reputational effort (managing trusted validators) and carries a risk of centralization, which may not be desirable in the personal and decentralized nature of PARFSET applications. Proof of location (PoL) relies on location, offering a low computational footprint, which makes it well-suited for PARFSET cases, as these are based on individuals in specific locations. However, concerns remain regarding both individual privacy and the accuracy of the approach [36].Ultimately, blockchain implementations in PARFSET must balance energy efficiency with the effort required for governance, communication, and stakeholder management to ensure a scalable and practical solution, and a more thorough investigation is required to identify suitable alternatives.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### OFDM-OQAM Transceiver
#### 4.1.1.3 Receiver Description  
After passing through the fading channel under NPR conditions, the received signal as represented in Figure 19 over the \(k\)th sub-carrier is given by [10]:  
\[s[n,k]=\ddot{\mathbf{H}}x[n,k]+\eta[n,k], \tag{19}\]  
where \(\ddot{\mathbf{H}}\) is an \(M\times M\) diagonal matrix that contains the fading channel coefficients \(\dot{H}[\ddot{k}]\), and \(\eta[n,k]\) is a Gaussian noise process with variance \(\sigma_{\eta}^{2}\) expressed by:  
\[\eta[n,k]=w[n,k]*f[\dot{n},k]. \tag{20}\]  
The expression \(w[n,k]\) in Equation (20) signifies the mutually independent zero-mean complex AWGN process with equal variances \((\sigma_{w,k}^{2})\). In addition, the variance of \(\eta\)\((\sigma_{\eta,k}^{2})\) is given as:  
\[\sigma_{\eta,k}^{2}=\sigma_{w,k}^{2}\sum_{j=0}^{L_{p}-1}f^{2}[j,k], \tag{21}\]  
while the receiver AFB impulse response is defined as [131; 134]:  
\[f[\dot{n},k]=g^{*}[(L_{p}-1-\dot{n}),k]. \tag{22}\]
## 3 System Description
### Proposed Integrated Design  
A conceptual scheme is proposed to combine the WTEU and the CFPU to use the energy from MSW, as illustrated in Figure 3. In this integrated design, most of the energy generated by MSW incineration is transferred to the steam-water system of CFPU, increasing total power generation significantly. The followings are the description of the connection between WTEU and CFPU.  
1. Remove ST, EG, RHs and condenser (CON) of the WTEU. Both WTEU and CFPU use the steam turbine generator set of CFPU for electricity generation.
2. The saturated steam of WTE boiler drum is transported to the second stage extraction steam to heat the feedwater flowing into the RH2.
3. The SH of WTEU no longer heats the saturated steam from boiler drum but heat partial exhaust steam from the HPST of CFPU. Then the steam from the SH outlet of WTE boiler returns to the reheater of coal-fired boiler for further heat absorption.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter** & **Unit** & **Value** \\ \hline \multirow{4}{*}{Cold side} & Inlet/outlet fluid & - &
\begin{tabular}{c} Urea solution/ \\ Hydrolytic gas \\ \end{tabular} \\  & Inlet/outlet temperature & \({}^{\circ}\)C & 50.0/150.0 \\  & Mass flow & kg/s & 0.67 \\ \hline \multirow{4}{*}{Hot side} & steam pressure & MPa & 2.13 \\  & steam temperature & \({}^{\circ}\)C & 473.9 \\ \cline{1-1}  & steam flow & kg/s & 0.58 \\ \cline{1-1}  & Attempering water pressure & MPa & 1.10 \\ \cline{1-1}  & Attempering water temperature & \({}^{\circ}\)C & 25.0 \\ \cline{1-1}  & Attempering water flow & kg/s & 0.14 \\ \hline \multirow{4}{*}{Hot side} & LMTD & \({}^{\circ}\)C & 76.9 \\ \cline{1-1}  & Heat load & MW & 1.41 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the UHU of CFPU.  
Since the steam employed in the original urea hydrolyzed derives from the third stage extraction steam of STs of CFPU, decreasing the work output and efficiency of STs. An additional evaporator (AE) is deployed in the ECO space of WTE boiler to supply the heating steam to the urea solution hydrolysis.
5. The fluid entering the ECO of WTEU is the feedwater from the DEA outlet of CFPU, and the fluid getting into AE extracts from the feedwater of the suggested WTE system.
6. The heat source of PAH2 and SAH is changed from the first stage extraction steam of ST of original WTEU to the feedwater from DEA outlet of CFPU, while the heat source of PAH3 and PAH1 remains unchanged. Then the cooling water from PAH1, PAH2 and SAH gathers and flows into the CON of CFPU with the drain water of RH8.  
In general, the available energy from MSW combustion is transferred to the steam-water cycle of CFPU by integrating with the CFPU system, contributing to a remarkable augmentation in total electricity generation. Meanwhile, several facilities of the WTEU are removed, saving a large number of investment and operation costs.  
Figure 3: Diagram of the proposed integration design.