esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The desire to increase energy efficiency and reliability of power grids, along with the need for reducing carbon emissions has led to increasing the utilization of Home Micro-grids (H-MGs). In this context, the issue of economic emission dispatch is worthy of consideration, with a view to controlling generation costs and reducing environmental pollution. This paper presents a multi-objective energy management system, with a structure based on demand response (DR) and dynamic pricing (DP). The proposed energy management system (EMS), in addition to decreasing the market clearing price (MCP) and increasing producer profits, has focused on reducing the level of generation units emissions, as well as enhancing utilization of renewable energy units through the DR programs. As a consequence of the nonlinear and discrete nature of the H-MGs, metaheuristic algorithms are applied to find the best possible solution. Moreover, due to the presence of generation units, the Taguchi orthogonal array testing (TOAT) method has been utilized to investigate the uncertainty regarding generation units. In the problem being considered, each H-MG interacts with each other and can negotiate based on their own strategies (reduction of cost or pollution). The obtained results indicate the efficiency of the proposed algorithm, a decrease in emissions and an increase in the profit achieved by each H-MG, by 37% and 10%, respectively.  
home microgrid; demand side management; demand response; emission reduction +
Footnote †: journal: Applied
## 4 Results and Discussion
### Pre-2014 Academic Studies  
Before 2014, academic publications on \"Carbon Footprint\" and sustainable manufacturing reveal that broad topics such as \"Manufacturing Processes\" (180 articles) and \"Supply Chain Management\" (219 articles) were frequently studied. This analysis is based on data retrieved from Scopus and Google Scholar, which provided insights into publication trends and thematic focuses during this period.  
Meanwhile, more specific topics like \"ERP Systems\" (16 articles) and \"Sustainable Manufacturing\" (58 articles) demonstrate that the subject was approached from various perspectives. Notably, there were only 2 publications exploring the relationship between \"Industry 4.0 Technologies\" and \"Carbon Footprint\", emphasizing that this combination was a limited research area before 2014. This suggests that technological advancements of the era had not yet fully integrated Industry 4.0 concepts into carbon footprint management.  
Despite the limited number of studies (16 articles) on \"ERP Systems\" in the context of carbon footprint management, these publications reflect early efforts by businesses to achieve environmental sustainability goals through resource planning. The data extracted from Scopus and Google Scholar highlights the evolving research priorities and the gradual emergence of digital technologies in sustainability-focused literature.  
In conclusion, pre-2014 publications strongly reflect awareness of carbon footprint management in the context of manufacturing processes and supply chains. However, the integration of Industry 4.0 technologies into these areas was still in its infancy. The data further suggests a broad scope for future research to deeply examine the impact of digitalization on sustainability.
## 3 Results and Discussion
### Thermal Stability under In Situ TEM Heating
Further, with an increase in exposure to 10 min, point reflections from individual larger grains appear. According to the SAED patterns taken at the maximum exposure time, lines with d/n = 0.248, 0.214, 0.166 and 0.156 nm were revealed, corresponding to reflections from the (002), (112), (302) and (113) planes of the hexagonal phase h-Ta\({}_{5}\)Si\({}_{3}\) ( JCPDS 65-3598), and lines with d/n = 0.224 and 0.131 nm were revealed, which can be attributed to the reflection from the (200) and (311) planes of the c-TaN cubic phase (JCPDS 79-5712) (Figure 6). In addition to reflections from the h-Ta\({}_{5}\)Si\({}_{3}\) and c-TaN phases, a line with d/n = 0.1406 nm, corresponding to platinum, was detected (Figure 7). It is important to note that Pt was used during the preparation of the lamella. The amorphous structure of the coating probably prevented the diffusion of platinum up to 1000 \({}^{\circ}\)C, which is due to the absence of grain boundaries serving as a channel for intense diffusion [46; 47]. And at 1000 \({}^{\circ}\)C, due to the crystallization of the coating, the diffusion of platinum was activated. Figure 5 shows the dependence of the FWHM parameter on temperature for the Ta-Zr-Si-B-N coating. In the range of 20-800 \({}^{\circ}\)C, no change in FWHM was observed; the structure remained amorphous up to 800 \({}^{\circ}\)C. At 1000 \({}^{\circ}\)C, a sharp decrease in the FWHM parameter indicates crystallization of the coating. The crystallite size was ~10 nm. Thus, the Ta-Zr-Si-B-N coating began to crystallize during holding at a temperature of 1000 \({}^{\circ}\)C, which significantly exceeds the crystallization temperature of the Ta-Zr-Si-B-C coating, which is 600 \({}^{\circ}\)C.
## 3 Computer-Aided Analytical Modeling of Class-E Series-Tuned RFPAs
### Maximum Value of \(Q_{s}\)  
To further investigate the effect of the quality factor \(Q_{s}\) of the series resonator, the efficiency versus angular frequency is plotted. The variations on the x-axis are expressed as a \(\pm\) percentage deviation from the designed angular frequency, using a normalized scale, as shown in Figure 5.  
Figure 4: Efficiency vs. \(Q_{s}\) with curve fitted approximation for \(Q_{min}\) calculation for (**a**) Topology 1 (**b**) Topology 2.1, and (**c**) Topology 2.2. Additionally, performance metrics are given in Table 1, including the maximum peak voltage and current and the maximum output power capability, \(c_{p}\) defined by Equation (32), as follows:  
\[c_{p}=\frac{P_{out}}{V_{peak,n}\cdot I_{peak,n}} \tag{32}\]  
where \(P_{1 out}\) is the output power [28]. Harmonic levels up to the third order are also included. These metrics were also compared with mathematical models reported in previous literature studies to validate the accuracy of the simulation. Device stress is normalized relative to the supply voltage and is a crucial metric for assessing device reliability, as the peak voltage and peak current at the device can be up to \(V_{peak,n}\) multiplied by the supply voltage and \(I_{peak,n}\) multiplied by the supply current, respectively. Without loss of generality, the device referred to here is a transistor. If the breakdown voltage of the device is lower than this, the device is unsuitable for constructing the Class-E RFPA. Based on these observations, an additional parameter to determine the upper bound of \(Q_{s}\), referred to as \(Q_{\mathbf{sat}}\) in this work, is introduced. This upper bound ensures that the RFPA achieves its maximum output power capability (\(c_{p}\)). It is evident from Table 1 that while efficiency levels reach at least 99% at \(Q_{\min}\), the RFPA was not operating at its maximum \(c_{p}\). Additionally, harmonic suppression was insufficient, highlighting the need to introduce an upper bound for \(Q_{\text{sat}}\). As shown in Figure 6, \(c_{p}\) also exhibits exponential behavior leading to saturation. Therefore, a similar methodology was employed using curve-fitting techniques to determine this upper bound but with some additional constraints. Mathematically, \(c_{p}\) is modeled using Equation (33), as follows:  
\[c_{p}=a+be^{-cQ_{s}} \tag{33}\]
## 4 The Operating Mechanism of the ICSPB-IoT
### \"Information Flow\" to \"Information Network\"  
An ICSPB-IoT is a complex dynamic system integrating various information flows such as people flow, things flow, and control flow [47]. Moreover, from a vertical perspective, the practice of ICSPB-IoT can be divided into various stages including design, component production, transportation, lifting, and product delivery. The information flows drive the operation of ICSPB-IoT, ensuring the cooperation and coordination across stages and participants, and are critical to the achievement of different goals such as an integrated design, lean production, transparent transportation, and visual assembly. Furthermore, in each stage of the construction process, information flows in two ways, that is, each participant not only transmits information to others, but also obtains information from others.  
Under the government-established system of intelligent construction for prefabricated buildings and the IoT industry standard, contractors establish subsystems that receive and transmit information to one another, forming a stream of \"information flow\". Moreover, the \"information flow\" is crisscrossed and interwoven to form an \"information network\", as presented in Figure 2. With the advancement of  
Figure 1: Conceptual framework of Intelligent Construction System for Prefabricated Buildings (ICSPB-IoT).  
the construction process, the number of participants corresponding to the network nodes increases, and industry standards promote the unification of each node, ensuring the success of information reception and transmission. Compared with the information flow in traditional prefabricated buildings, the \"information network\" of ICSPB-IoT has real-time dynamics, complete accuracy, traceability and visibility. Based on the ICPSB-IoT \"information network\", this study further discusses the operation mechanism of the ICPSB-IoT as follows.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 8 Conclusions  
The cost-aware framework demonstrates its efficacy in managing the allocation and execution of fog jobs in a cloud-fog computing environment. Scheduling and load balancing decisions are frequently triggered at run-time such that quality and service obligations of fog jobs are fulfilled. It is shown that the framework emphasizes the notion of energy-efficient scheduling based on the QoS penalty of fog jobs, in which the formulations of scheduling decisions in the framework tolerate risks of delays and energy on the cost performance.  
The scheduling mechanisms employed in the framework demonstrate the effectiveness of decisions in incorporating the impacts of SLA obligations and energy incurred due to communication, service, and waiting performance metrics on cost reduction. Such decisions mitigate the cost of energy and cost of QoS penalty required to execute fog workloads, as well as cope with heterogeneity and variations in IoT workloads experienced in the cloud computing environment with considerations to SLA obligations for each fog job.  
Figure 5: Cost of energy penalty scheduling for a virtualized queue of 30 jobs across the cloud–fog computing environment.  
Figure 6: Cost of energy penalty scheduling for each server in the cloud environment. (**a**) Virtualized queue of 16 jobs. (**b**) Virtualized queue of 9 jobs. (**c**) Virtualized queue of 5 jobs.  
The improvement in energy cost at the level of the cloud tier has reached around 55%, which results in around a 40% enhancement in the QoS penalty. At the queuing level of the tier, the improvements in cost and penalty reach around 52% and 45%, respectively.  
The genetic-based approach utilized in the framework shows a great enhancement in forming near-optimal schedules in a reasonable time, and the approach improves the cost performance of energy and QoS penalties as well. It is shown that an optimal schedule with a reduced cost of energy penalty is formulated at the queuing level of the cloud tier by utilizing only 200 genetic iterations. In addition, only 3000 genetic iterations are employed to mitigate the cost of the energy penalty at the tier level of the cloud environment. Future directions include proposing a resource allocation framework, in which the goal is to decide on an optimal set of resource configurations and setups such that QoS requirements are met. It involves proposing an SLA penalty and profit models based on workloads' heterogeneity and client demands for resources, that are to be utilized by the framework so that client satisfactions are maximized.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Gas Chromatography-Mass Spectrometry  
The analysis of urinary VCs was performed using a Hewlett Packard HP5890 series II GC coupled to an HP5971, single quadrupole Mass Selective Detector (MSD) with an HP Chemstation (Hewlett Packard, Bracknell, UK). Chromatographic separation was performed on a Stablewax DA capillary column, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m (Restek, Benner Circle, Bellefonte, PA, USA). The carrier gas was 99.9995% pure helium (AirProducts, Crewe, UK) with a constant flow rate of 1.2 mL/min. The GC was operated under the following conditions: temperature program, 40 \({}^{\circ}\)C with 2 min of hold time, ramping at 4 \({}^{\circ}\)C/min to the final temperature of 245 \({}^{\circ}\)C and then held at 245 \({}^{\circ}\)C for 6 min, giving a total run time of 59.25 min. The injection port was operated in splitless mode (purge off 0.7 min) at 245 \({}^{\circ}\)C. After a solvent delay of 4.6 min, mass spectra were acquired in full scan mode with a scan  
Figure 3: Flow diagram of the one-factor-at-a-time design of extraction optimization. The orange chart refers to conditions which were compared at a time.  
range of _m/z_ 35-450 used for data acquisition. The operating conditions for the MS system were as follows: electron ionization mode at an energy of 70 eV; transfer line and ion source temperatures were 280 \({}^{\circ}\)C and 180 \({}^{\circ}\)C, respectively. Total ion chromatograms (TIC) were analyzed and peaks were integrated automatically using the Turbomass software (PerkinElmer, Inc., Waltham, MA, USA) with an initial detection threshold of 10.0. The analyst reviewed the automated integration and made adjustments and manually integrated the peaks, if it was necessary, keeping the proper judgement. Moreover, the chromatograms were analyzed using the free Automated Mass Spectral Deconvolution and Identification System (AMDIS) software by the National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA). The deconvoluted peaks were tentatively identified, where possible, by comparison of the mass spectra with the NIST/EPA/NIH Mass Spectral Library (version 2.2, 2014, Gaithersburg, MD, USA). For identification, only the components with a match factor >80% were listed.
## 5 Conclusions  
In order to simulate the carbon cycle of the Hangzhou forest ecosystem for 20 years, from 2001 to 2020, we integrated the forest phenology inversion data into the InTEC model. The key findings from the study are as follows:  
(1) The spatial and temporal dynamics of GPP revealed a distinct pattern, with higher productivity in the southeast and northwest and lower productivity in the southwest. However, a consistent declining trend was observed in the annual means of GPP, NPP, and NEP across the study period, indicating a weakening carbon sink capacity.  
(2) Phenological changes emerged as a critical regulator of forest carbon fluxes. Specifically, EOS enhanced GPP, NPP, and NEP, while an earlier SOS suppressed them. These relationships underscore the importance of phenological timing in governing annual carbon uptake.  
(3) Structural equation modeling revealed that both climate and phenology jointly affect the carbon cycle, but through different pathways. Climate change exerted a stronger direct effect, primarily through Tmax influencing NPP. In contrast, phenology exerted a direct effect of 0.85, with EOS strongly influencing GPP and LOS being pivotal for NEP.  
Overall, this study highlights that phenological shifts--often overlooked in large-scale carbon assessments--play a substantial and quantifiable role in modulating forest carbon dynamics. Our results demonstrate that incorporating phenology into ecosystem models is essential to improving the accuracy of carbon cycle simulations, particularly in the context of ongoing climate change.  
M.H.: Writing--original draft, Data curation, Methodology, Software, Validation, Visualization. X.L.: Writing--original draft and analysis. H.D.: Visualization and revised the manuscript. G.Z.: Visualization and revised the manuscript. F.M.: Software and analysis. Z.H.: Contributed data survey and analysis. J.X.: Contributed data survey and analysis. Y.Z.: Contributed data survey and analysis. All authors have read and agreed to the published version of the manuscript.
* The research was supported by the National Natural Science Foundation of China (No. 32201553, 32171785, U1809208), the Key Research and Development Program of Zhejiang Province (Grant Nos. 2022C03039; 2021C02005).  
**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 conflict of interest.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 2 Approaches to Modeling, Determining, and Mitigating Errors in MLS Point Clouds
### Calibration  
One crucial quality assurance measure is the calibration of offsets and rotations of laser scanners to the IMU. Calibration minimizes systematic errors significantly. [PERSON]. [15] presented a procedure to estimate the boresight angles and the range finder offset during in-flight calibration. The idea was to use tie planes instead of tie points. The benefit of this approach was that no interpolation of airborne MLS data was necessary. As the planes' geometry parameters were unknown, the adjustment estimated them simultaneously with the four calibration parameters. [PERSON]. [16] presented a method for post-mission quality control of airborne laser scanner data. A transformation of overlapping lidar strips minimizes systematic discrepancies at conjugate points in the normal direction. It turns out that biases in the system components are derivable from these transformation parameters.  
Another strategy for calibration of land-based MLS is to match individual points from point clouds of several runs and to minimize the deviations between them. Ref. [17] attributed the determined differences directly to boresight angles since they assumed lever-arm offsets to be known and position errors to be negligible. For correspondence matching of overlapping point clouds, they used the iterative closest point (ICP) algorithm. In contrast Ref. [18] subdivided the point clouds into profiles and estimated the boresight angles and lever-arm offsets from the corresponding profile points. They reported a reduction in misalignment from the meter to centimeter levels.  
Due to its ease of practical use, the plane is the geometric primitive of choice in the literature for calibrating land-based MLS. The combination of plane orientations and MLS passages is essential for calibration quality, though. Ref. [19] simulated different scenarios and proposed configurations in which the systematic errors magnify and can thus be reliably determined. Ref. [20] provided a similar approach by replicating data collections of a virtual calibration field. They assessed different configurations based on parameter correlations and partial redundancies. However, practical evaluation of two MLS passages in opposite directions of a castle revealed remaining systematic deviations. The authors attributed these to trajectory uncertainties in the evaluation drive.  
While [15; 16] saw the possibility of creating suitable conditions for calibrating airborne laser scanning systems, this is not necessarily the case for land-based MLS systems. Ref. [21] estimated drifts due to positioning uncertainty along with calibration parameters, but as reported by [15], there is a risk of introducing further parameter correlations with this approach.
# The Role of Advanced Manufacturing Technologies in Production Process Performance: A Causal Model
## 5 Discussion, Conclusions and Industrial Implications
### The Most Important Effects and Their Industrial Implications
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{H_{i}}\) & **Level** & **Dependent Variable** & **Condition** & **Independent Variable** & **Level** & **Probability** \\ \hline \multirow{4}{*}{H\({}_{1}\)} & + & _Intermediate Systems_ & If & _Stand-Alone Systems_ & + & 0.459 \\  & + & _Intermediate Systems_ & If & _Stand-Alone Systems_ & – & 0.000 \\  & – & _Intermediate Systems_ & If & _Stand-Alone Systems_ & + & 0.027 \\  & – & _Intermediate Systems_ & If & _Stand-Alone Systems_ & – & 0.446 \\ \hline \multirow{4}{*}{H\({}_{2}\)} & + & _Integrated Systems_ & If & _Intermediate Systems_ & + & 0.534 \\  & + & _Integrated Systems_ & If & _Intermediate Systems_ & – & 0.018 \\  & – & _Integrated Systems_ & If & _Intermediate Systems_ & + & 0.017 \\  & – & _Integrated Systems_ & If & _Intermediate Systems_ & – & 0.702 \\ \hline \multirow{4}{*}{H\({}_{3}\)} & + & _Integrated Systems_ & If & _Stand-Alone Systems_ & + & 0.500 \\  & + & _Integrated Systems_ & If & _Stand-Alone Systems_ & – & 0.000 \\  & – & _Integrated Systems_ & If & _Stand-Alone Systems_ & + & 0.027 \\  & – & _Integrated Systems_ & If & _Stand-Alone Systems_ & – & 0.482 \\ \hline \multirow{4}{*}{H\({}_{4}\)} & + & _Production Benefits_ & If & _Stand-alone Systems_ & + & 0.338 \\  & + & _Production Benefits_ & If & _Stand-alone Systems_ & – & 0.054 \\  & – & _Production Benefits_ & If & _Stand-alone Systems_ & + & 0.027 \\  & – & _Production Benefits_ & If & _Stand-alone Systems_ & – & 0.411 \\ \hline \multirow{4}{*}{H\({}_{5}\)} & + & _Production Benefits_ & If & _Intermediate Systems_ & + & 0.345 \\  & + & _Production Benefits_ & If & _Intermediate Systems_ & – & 0.035 \\  & – & _Production Benefits_ & If & _Intermediate Systems_ & + & 0.052 \\  & – & _Production Benefits_ & If & _Intermediate Systems_ & – & 0.333 \\ \hline \multirow{4}{*}{H\({}_{6}\)} & + & _Production Benefits_ & If & _Integrated Systems_ & + & 0.491 \\  & + & _Production Benefits_ & If & _Integrated Systems_ & – & 0.030 \\  & – & _Production Benefits_ & If & _Integrated Systems_ & + & 0.018 \\ \cline{1-1}  & – & _Production Benefits_ & If & _Integrated Systems_ & – & 0.394 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Conditional probability (If). In other words, managers must start the AMT implementation process with _Stand-Alone Systems_, and after acquire experience from those technologies, then _Intermediate Systems_ can be performed, and once again they must be focused on obtain enough experience for finalizing the implementation process with _Integrated Systems_ to guarantee _Production Benefits_.