esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Diagenetic Sequences
#### 5.2.1 The Early Diagenetic Stages  
At the very early diagenetic stage, the WF-LMX sediments were relatively loose and not completely consolidated, and the primary porosity was high. Framboidal pyrite can easily form in anoxic and sulfidic water columns [57]. Pyrite framboids precipitate when both pyrite and iron monosulfides are supersaturated in pore waters [58]. Therefore, framboidal pyrite generally formed during the very early diagenetic stage.  
For siliceous shale (Figure 15), with the continuous increase in burial depth and temperature, skeletal fragments of siliceous zooplanktons were dissolved and reprecipitated, converting opal-A into opal-CT, and then forming microcrystalline authigenic quartz [55; 59]. The rigid framework formed by recrystallized quartz can protect primary intergranular pores from collapsing due to increased mechanical compaction. In addition, numerous inter-crystalline pores were generated between microcrystalline quartz crystals. At this stage, organic matter is immature, and no secondary organic matter pores were developed.  
Figure 14: The correlation between clay content and pore volume (micropore, mesopore, macropore), surface area (micropore, mesopore, macropore) in the Wufeng–Longmaxi shales.
## 4 Results
### Experimental Validation  
For the developed bore surface deformation and straightness detection system, the distance between the front and rear wheels of the borescope trolley is 150 mm, with a maximum drive speed of 36 mm/s. The system is configured to sample once every 6 mm of movement, resulting in a maximum capture frequency of 6 frames per second. After setting up the detection system, the trolley is first controlled to move to the other end of the bore. Meanwhile, the two-axis turntable is adjusted so that the laser beam emitted by the dot laser can continuously illuminate the reflector on the trolley's front during its movement. Then, the borescope trolley is controlled to scan the inner bore.  
Figure 7 shows the registration results of 833 sets of bore profile point clouds from a single measurement. In the coarse registration algorithm, the required arc point clouds (\(x_{\rm p},y_{\rm p}\)) are extracted based on the criterion \(\,1\,x_{\rm p}\,\)\(\,  
the measuring tool \(\delta_{\rm g}\) conforms to a uniform distribution and the random errors of the test \(\sigma_{i}\) conform to a Gaussian distribution, the testing accuracy can be expressed as  
\[Acc=\rm E(\mu_{i})\pm 1.96\sqrt{\left(\delta_{\rm g}/\sqrt{3}\right)^{2}+Var(\mu_{ i})+E(\sigma_{i}^{2})} \tag{29}\]  
According to (29), the accuracy of the borescope trolley under different front wheel lift heights is shown in Table 1.  
Figure 9 shows the bore surface deformation detection results calculated according to (5). It can be observed that the extracted bore surface deformation exhibits a trend of symmetrical distribution along the axial direction \(z\). Specifically, the rail depressions are concentrated in the middle position along the horizontal normal direction \(x\), primarily caused by the high-speed current-carrying friction between the armature and the rail, leading to fusion and erosion wear damage. Conversely, the insulator depressions are concentrated on both sides along the vertical normal direction \(y\), mainly resulting from the splashing and erosion of aluminum liquid towards the sides due to the melting of the armature under the combined effect of Joule heat and frictional heat.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Front Wheel Lift Height** & **0 mm** & **3 mm** & **5 mm** \\ \hline Calculation accuracy (\(\upmu\)m) & 0.0 & 8.0 & 22.2 \\ Testing accuracy (\(\upmu\)m) & 0.5 \(\pm\) 4.8 & 8.6 \(\pm\) 5.3 & 22.6 \(\pm\) 6.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of the borescope trolley under different front wheel lift heights.  
Figure 8: Comparison of calculated and tested values for surface deformation detection deviation.  
Figure 7: The registration results of the bore profile point clouds.  
Figure 10 shows the bore straightness calculated according to (8) after two detections of the same bore section. The reliability of the results is evaluated using the intraclass correlation coefficient (ICC) [35]. The ICC values for side straightness and arch straightness are 0.982 and 0.994, respectively, indicating excellent reliability in the repeated detections.
# Low Temperature Combustion Modeling and Predictive Control of Marine Engines
## 5 Conclusions  
In this work, a cycle-to-cycle model-based control system was developed for dual-fuel marine engines operating in RCCI mode. The control-oriented model was calibrated against the Wartsila's W31 DF engine for steady state operating conditions. Transient conditions were generated with an state-of-the-art fully predictive thermo-kinetic multizone model. The work makes the following conclusions:  
* A sparse experimental dataset was augmented by using a higher fidelity combustion model to generate a broad dataset of clean sweeps and transient simulations. * The developed physics-based dynamic nonlinear RCCI COM achieved a feasible absolute and trendwise accuracy in terms of main combustion performance indicators within a 5% error margin target compared to the detailed UVATZ model. * A novel LPV-model was coupled with an adaptive MPC to control the COM. \(\theta_{50}\) and \(\mathrm{IMEP}_{\mathrm{gross}}\) were controlled with \(E_{\mathrm{fuel}}\) and \(\Delta\mathrm{BR}\). A tracking error within a 5% error margin target was achieved regardless the additive model uncertainties for nominal mid and high engine load operating conditions and their transient. * \(\theta_{50}\) achieved the reference value much faster than the \(\mathrm{IMEP}_{\mathrm{gross}}\). However, despite this, the steady state error remained low. The current stage of the developed control system establishes solid foundations for adopting RCCI combustion in commercial marine engines. The results showcased a feasible accuracy with real-time simulation capabilities, which further advances a model-based framework towards experimental testings. Conceptualization, methodology, development of COM, simulations, writing--original draft preparation, A.M.; Development of RTM, methodology, X.S. ; Implementation of adaptive MPC, A.-M.S. ; Development of UVATZ, methodology, A.V. ; Advising and supervising the paper and methodologies, K.Z. ; Consulting on methodologies and modeling, providing the experimental data, J.H. ; Advising, supervision, project administration, M.M. All authors have read and agreed to the published version of the manuscript. This work was conducted in framework of Clean Propulsion Technologies with financial support from Business Finland (ref. 43918/31/2020). Not applicable. The datasets presented in this article are not readily available due to commercial restrictions. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
BD & Burn duration  
BDM & Burn duration model  
BR & Blend ratio  
BTE & Brake thermal efficiency  
CA & Crank angle  
CAD & Crank angle degree  
CAX & Crank angle corresponding to X% energy released  
CC & Combustion centroid  
CFD & Computational fluid dynamics  
CHR & Cumulative heat release  
CN & Cetane number  
CO & Carbon monoxide
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 5 Conclusions  
The performance of the blue-sky albedo algorithm over heterogeneous landscapes and with coarser aerosol products shows that landscape changes can be well quantified by these efforts. We evaluated impact of three AOD sources on blue-sky albedo calculation using the full-expression approach that is necessitated by the vast snow cover in the circumpolar region. The operational blue-sky Landsat-8 albedo product produced using MODIS AOD data performed well with a RMSE of 0.0475 and a bias of \(-\)0.00026. The accuracy improved (RMSE of 0.0395 and mean bias of 8.67 \(\times\) 10\({}^{-5}\)) when solar zenith angle was restricted to under 70\({}^{\circ}\). The evaluation supports the feasibility of using MODIS AOD to produce operational full-expression blue-sky albedo products that will meet the increasing interests in studies of a snow-dominated but fast-changing Arctic-Boreal zone. While using local AOD information from AERONET produces slightly better accuracy in blue-sky albedo than using MODIS AOD, the much lower geographic coverage by the AERONET measurements than the MODIS AOD data warrants the use of MODIS AOD data rather than AERONET for the operational production of blue-sky albedo. Additionally, the results support the further expansion and development of these products to Sentinel-2 and other similar spatial-resolution sensors which would result in a suite of surface albedo products at further improved spatial and temporal resolutions.  
Conceptualization, A.M.E., Z.L., Z.W., Q.S., I.P. and C.S.; methodology, A.M.E., Z.L. and Q.S.; validation, A.M.E. and Z.L.; formal analysis, A.M.E.; investigation, A.M.E. and Z.L.; resources, C.S.; data curation, A.M.E.; writing--original draft preparation, A.M.E.; writing--review and editing, A.M.E., Z.L., Z.W., Q.S., I.P. and C.S.; visualization, A.M.E.; supervision, C.S.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the following grants: USGS grant number 140G0118C0010 and 140G0118C005 for Landsat Science Team Support; NASA grant 80 NSSC18K0642 for MODIS Science Team support; and NASA grant 80 NCSSC18K0479 for Land Cover and Land Use Change Circumpolar Albedo support.  
**Data Availability Statement:** The data will be archived and available in accordance with MDPI policies upon publication.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON], Finnish Meteorological Institute, Helsinki, P.O. Box 503, 00101, Finland, for their support on providing in situ albedo data from the Sodankyla tower site. Additionally, we would like to thank the NOAA/ESRL/GML/GRAD, the GML-Radiation group for the radiation data from the Summit tower site.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### QAP Results  
Each of the 50 QAP instances consists of a 50 by 50 cost matrix and a 50 by 50 distance matrix, with integer costs and distances generated uniformly at random from \([1,50]\).  
The results are visualized in Figure 3. We earlier saw that FDC suggested that swap, \(\text{Cycle}(3)\), and \(\text{Cycle}(4)\) would likely perform better than the others, while the search landscape calculus suggested that swap and all forms of cycle mutation are appropriate for the QAP. Consistent with the fitness landscape analysis, scramble, reversal, and insertion all perform poorly for QAP within both the EA and SA. For the longest SA runs (Figure 3b), there is little difference in solution cost among swap and the various cases of cycle mutation. For runs of \(10^{4}\) to \(10^{5}\) SA evaluations, swap and \(\text{Cycle}(0.25)\) find solutions with lower values of the cost function than the others at statistically significant levels.  
The EA results are especially interesting (Figure 3a). Swap suffers from a premature convergence effect at \(10^{4}\) evaluations, while all cycle mutation cases continue to make substantial progress, minimizing the cost function. Furthermore, \(\text{Cycle}(0.25)\) and \(\text{Cycle}(3)\)'s rates of convergence are equivalent to swap up to that point. The superior convergence effect is attributed to cycle mutation's larger neighborhood, especially in the case of \(\text{Cycle}(\alpha)\). We do not see the same behavior with SA (Figure 3b) because SA has a built-in way of handling local optima, allowing swap to continue to improve solution quality despite swap's much smaller local neighborhood.  
Figure 2: Results on the TSP for (**a**) \((1+1)\)-EA, and (**b**) SA.
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 3 Screening for Sleep Apnea in Hospitalized Patients
### Polysomnography  
PSG in a lab setting is the gold standard test for diagnosing OSA [56]. It is usually performed in outpatient settings, and few centers have inpatient sleep lab facilities that can perform full-scale PSG for hospitalized patients. PSG (Type I or Type II) (Table 2) frequently requires dedicated lab equipment or patients to be moved to the sleep lab overnight for testing, which could be in a remote part of the hospital or an adjacent outpatient clinic facility. This might not be feasible for acutely ill patients. With the advent of home sleep apnea testing devices (Type III devices), there have been several studies that have successfully used home testing devices in hospitalized patients for the diagnosis of OSA [23; 57]. These results have been validated with follow-up polysomnography [23; 57]. Few hospitals have set up sleep apnea testing in the hospital setting, as these tests are not reimbursed during inpatient admissions. However, inpatient testing with HSAT to prescribe PAP therapy to facilitate discharge in healthcare systems with managed healthcare might be cost-effective as it may reduce LOS while compensating for testing costs [58]. Insmaller centers and community hospitals, overnight pulse oximetry followed by referral to a sleep center might be the only cost-effective method.
## 2 Literature Review  
Monitoring construction sites continuously is very important to facilitate workers in a safe environment with fewer hazardous incidents, improving their physical safety and health. A hybrid wearable sensor network system based on the IoT is realized for safety and health monitoring and it employs Wearable Body Area Network (WBAN) to accumulate worker data and a low-power wide-area network (LPWAN) to connect the WBAN to the Internet [18]. A study has established a WBAN for workers' health, with wearable devices to monitor physical parameters, such as body temperature, heart rate, blood pressure, etc. [19; 20]. In addition to the physical parameters, a study deployed devices based on WBAN on the construction site to observe environmental parameters, such as temperature and humidity around the workers [21]. A wearable sensor-based system is able to monitor ambient conditions, location, and physical attributes in order to generate early warning information about worker safety hazards on a construction site [22].  
Radiofrequency identification (RFID) and magnetic field proximity sensing systems were examined, using interaction scenarios involving pedestrians, employees, and construction equipment, and the magnetic system exhibited a significant reduction in coverage range while still delivering reliable coverage measurements using a set of testing that was more dynamic than other sets of tests [23]. Wireless technology using a radio frequency of 700 Mhz was utilized and implemented for sensing the proximity of heavy machines on the construction sites and warning to reduce the risk [24].  
An IoT-based intrusion monitoring system is implemented with the integration of five components to enhance the finding error, detection error and alarm delay with portable RFID triggers and intelligent hardhats [25]. A novel safety method centered on the IoT is created to provide real-time to recognizes real-time staff safety issues such as, to lower accident rates [26]. An IoT-enabled network is proposed to co-locate low-cost sensors for building site monitoring and the network performance is increased by better system dependability in terms of improved reliability, security, and safety in the real-time monitoring of environmental factors [27]. A novel architecture for an autonomous system based on the IoT is implemented to monitor, localize, and alert the site laborers who work within risk zones, with the wearable device working on a GPRS module [28]. A hybrid wearable sensor network system based on being IoT connected is realized for health and safety monitoring, and it uses WBAN to accumulate worker data and a low-power wide-area network (LPWAN) to connect the WBAN to the Internet [29].
# Aika: A Distributed Edge System for AI Inference
## 4 Experimental Setup and Evaluation
### Micro-Benchmarks  
To gain insight on the performance and overhead of the individual component of Aika and how time is spent on the various tasks, we ran several micro-benchmarks. These benchmarks are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Micro-Benchmark** & **Time (milliseconds)** \\ \hline Local Controller Initial Startup & 300 \\ \hline Local Controller Further Startup & 150 (per local controller after the first) \\ \hline Agent Startup & 20–40 \\ \hline Pass integer item from agent to agent & 7–9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results from micro-benchmarks.  
We experienced that the cluster controller took approximately 300 milliseconds to start a single local controller on a separate node with an SSH client. After setting up one local controller, the time increases with approximately 150 milliseconds per additional local controller. The local controller, however, takes approximately between 20 and 40 milliseconds to start an agent. This demonstrates that the local controller not only can be used to offload the workload of the cluster controller, but can also manage to recover agents in a shorter amount of time, leading to a more efficient recovery procedure. This is especially relevant if agents shut down often.  
Passing integer items from one agent to the next in a pipeline takes approximately 8 milliseconds. During this time, the item moves through two persistent queues and one TCP stream.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 7 Future Directions
### Advanced Technology Integration
#### 7.1.2 Knowledge Representation and Semantic Technologies  
Recent advances in knowledge representation for Digital Twins have focused on semantic web technologies and knowledge graphs as effective frameworks for modeling complex system relationships. Semantic models employing ontologies like SAREF, SAREF4 BLDG, and the Flow Systems Ontology (FSO) enable a detailed representation of the building components and their interactions, supporting enhanced interoperability between different data sources [126]. The integration of semantic technologies allows for the transformation of traditional structured data into rich knowledge graphs that capture both the physical relationships and operational dynamics of building systems. For example, recent work has demonstrated how ontology-based semantic models can automatically generate and calibrate building energy models while maintaining a high accuracy across multiple measuring devices [53]. Knowledge graphs particularly excel in representing highly interconnected building data, as they can model complex relationships between components, sensors, and control systems while supporting reasoning mechanisms and logic-based methods [37]. This approach enables more sophisticated queries and analytics compared to traditional data structures, allowing Digital Twins to not only store data but also infer new knowledge about system behavior and relationships. The semantic layer also facilitates the integration of disparate data sources and enables automated reasoning about building systems, supporting more intelligent decision-making and control strategies.
## 1 Introduction
The radial turbine speed is 100,000 rpm, and preliminary results show that the compressor consumes less power in the S-CO\({}_{2}\) gas turbine. In summary, the application of efficient and compact S-CO\({}_{2}\) Brayton cycle system to solar power generation is a new development direction of green energy. As the key equipment of the whole power cycle, the aerodynamic performance and the off-design performance of the turbine have a decisive influence on the cycle efficiency. Therefore, the S-CO\({}_{2}\) turbine is the core to promote the green power generation technology. The output power of the axial turbine varies over a larger range than the radial turbine. Besides, multi-stage turbine has higher operating efficiency than single-stage one. However, there are three main difficulties in the design of multi-stage S-CO\({}_{2}\) axial turbines at present: (1) the structure of the multi-stage axial turbine is complicated and the design is difficult; (2) based on the special physical properties of S-CO\({}_{2}\), the conventional axial turbine design principles and methods are no longer applicable, which makes the design difficulty further increased; (3) solar power generation systems are severely affected by geographical distribution, seasonal variations and day-night cycles, good off-design performance of turbines is the most critical factor. Based on the research background above, this paper designs a 10 MW three-stage axial turbine for solar energy S-CO\({}_{2}\) Brayton cycle power generation system, and studies its aerodynamic performance and off-design performance. It provides a theoretical basis for the application of S-CO\({}_{2}\) turbines in the field of solar power generation.
## 2 Methods
### Effectiveness Dependencies  
For the purpose of analyzing energy-intensive applications (i.e., gas power plants and air conditioning systems), a simplified method is applied: the performance of the systems will be simulated using effectiveness curves. These methods were proven to be effective for the analysis of these types of systems in multiple studies [14; 15; 16].  
A water-cooled chilled water unit (chiller [16]) was assumed as the example air conditioning system for the purpose of this comparison. For the purposes of analysis of the air conditioning system, an EER curve vs. supplied cooling water temperature and level of nominal capacity will be used (Figure 3). EER is defined using the following equation:  
\[\text{EER}=\frac{Q_{C}}{N},-\]  
where:  
\(Q_{C}\)--cooling capacity obtained from the chilled water unit, W  
\(N\)--power necessary to produce the required cooling capacity (for compressors and other supplementary equipment), W  
Dew-point and wet-bulb cooling towers can be used to supply cold water to remove the heat from the condenser of the chilled water unit. Depending on the temperature of the water obtained from the tower and the percent of nominal capacity that is used by the system, the chilled water unit will obtain a different EER  
Figure 3: Coefficient of energy efficiency of the water-cooled refrigeration unit as a function of performance and cooling water temperature.  
directly to the energy consumption (the unit can supply the same capacity with lower energy consumption with a higher EER; it can also supply a higher cooling capacity with the same energy consumption when it is needed).  
For the purposes of analyzing the gas power plant, a gas turbine capacity curve is going to be used (Figure 4). The curve shows the dependence of the capacity that can be obtained from the gas turbine for different air temperatures at the inlet to the compressor. The capacity is referenced to the nominal capacity for an inlet air temperature equal to 15 \({}^{\circ}\)C--when the supplied air is characterized by temperatures lower than 15 \({}^{\circ}\)C, the cooling capacity proportionally increases. When supplied air is characterized by temperatures higher than 15 \({}^{\circ}\)C, the capacity decreases.  
Cooling towers can be used to supply the cold water to initially pre-cool the airstream before entering the compressor, which can generate energy savings. When air with a lower temperature is supplied, the gas turbine can generate the same capacity while consuming less fuel or produce more power using the same amount of fuel. Depending on the temperatures supplied by the cooling towers, the performance of the turbine will change accordingly.