esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Analysis
### Numerical Experiments
In practice, these results indicate that direct calculation of the ATE using the datasets of Cases 2-4 would lead to a significant overestimation of the effect of high-intensity physical activity on BMD loss, due to its confounding with calcium supplementation, while methods such as NN-Dragonnet would lead to much lower estimation error (e.g., 0.094 instead of 0.29 for Case 2). Such differences are important, especially given that BC patients often suffer from additional conditions, such as cardiovascular disease or chronic fatigue. Having accurate information about the risks and benefits of each treatment on each of the multiple existing conditions enables clinicians to reach optimal treatment decisions. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} & \multicolumn{2}{c}{**Case 3**} & \multicolumn{2}{c}{**Case 4**} \\
**Model** & \(|\mathbf{e_{ATE}}|=\mathbf{0}\) & \(|\mathbf{e_{ATE}}|=\mathbf{0}\) & \(|\mathbf{e_{ATE}}|=\mathbf{0.29}\) & \(|\mathbf{e_{ATE}}|=\mathbf{0.29}\) & \(|\mathbf{e_{ATE}}|=\mathbf{0.29}\) \\  & \(|\mathbf{e_{ATE}}|\) & **PEHE** & \(|\mathbf{e_{ATE}}|\) & **PEHE** & \(|\mathbf{e_{ATE}}|\) & **PEHE** & \(|\mathbf{e_{ATE}}|\) & **PEHE** \\ \hline LR1 & 0.154 & 4.378 & **0.141** & 2.762 & **0.155** & 2.756 & 0.189 & **2.743** \\ LR2 & 0.062 & 3.896 & 0.201 & **2.336** & 0.220 & 2.330 & 0.157 & 2.790 \\ R-Forest & 0.128 & 3.912 & 0.281 & 2.453 & 0.285 & 2.463 & 0.088 & 3.179 \\ BART & 0.427 & 3.966 & 0.175 & 2.565 & 0.182 & 2.496 & 0.169 & 2.751 \\ TARnet & 0.034 & 4.020 & 0.227 & 2.373 & 0.224 & 2.406 & 0.056 & 2.833 \\ Dragonnet & **0.011** & **3.752** & 0.265 & 2.440 & 0.237 & 2.365 & 0.045 & 2.800 \\ NN-Dragonnet (C) & 0.112 & 4.096 & 0.182 & 2.404 & 0.229 & 2.396 & 0.141 & 3.050 \\ NN-Dragonnet (E) & 0.197 & 4.220 & 0.176 & 2.386 & 0.275 & 2.342 & 0.151 & 3.085 \\ NN-Dragonnet (M) & 0.216 & 4.306 & 0.154 & 2.409 & 0.221 & **2.324** & **0.001** & 2.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on test set. Top results are specified in bold, while second best are underlined. The calculated ground truth ATE of “intense exercise” on “BMD loss” is 0.58.
## 1 Introduction
### Research Objective  
The development of sensor networks to monitor and control indoor environments relies on DT technology, IoT devices that enhance real-time data collection and management [30; 31]. A DT framework enables the creation of a virtual replica of physical space, allowing precise monitoring and optimization of key environmental and energy parameters [32].  
An IoT-based sensor network is proposed to enable the seamless integration of data acquisition, real-time visualization, predictive analysis and automated control within intelligent indoor environments. The system consists of three basic components: sensor layers, gateways and back-end processing platforms. Sensors collect key data such as energy consumption, indoor air quality (IAQ), thermal comfort, lighting and occupancy, while gateways transmit this information to a centralized back-end system. The back-end infrastructure uses advanced data processing, machine learning algorithms and visualization tools to enable real-time assessment and decision-making to optimize indoor environmental conditions [33].  
A critical aspect of the proposed DT model is the interoperability of communication protocols, ensuring compatibility across multiple IoT devices. The system uses MQTT and HTTP for real-time data transfer, Modbus for energy meters and ONVIF for video surveillance. In addition, the back-end infrastructure is designed for highly efficient data storage and processing, using PostgreSQL, InfluxDB and Power BI dashboards for data visualization and long-term storage [34].  
The proposed DT-based framework aims to improve energy efficiency, environmental quality and space use in smart buildings. By integrating real-time monitoring, predictive analytics and automated control mechanisms, the system contributes to improved sustainability, occupant comfort and operational efficiency in indoor environments.
## 3 Results and Discussion
### Analysis of Hilbert Spectrum
(2) As the elevation of the monitoring points increases, the frequency distribution range of the Hilbert energy at the monitoring points in both models appears to broaden significantly, presumably due to the elevation amplifying the high-frequency portion of the Hilbert energy. It should be noted that the enhancement of Hilbert energy for the homogeneous slope is not obvious and the increase in energy is small (Figure 11), whereas the Hilbert energy for the SBRS (Figure 10) undergoes an enhancement of the energy from 10 Hz to 20 Hz, and the increase in energy increases with the increase in the number of weak layers. This leads us to hypothesize that the reason for this discrepancy is the reflection and refraction of seismic waves via weak strata as they propagate. Furthermore, we discovered that, with a maximum frequency of 30 Hz and a spike of Hilbert energy in the high-frequency portion, monitoring point M19 on the SBRS had the widest Hilbert energy frequency band. This suggests that the top of the slope is likely to sustain damage first during seismic activity because the weak layer of the slope amplifies the Hilbert energy more than the elevation does, and the amplification effect on the high-frequency part of the Hilbert energy is more pronounced. Figure 10: Time history curves and 3D Hilbert spectra of steep bedding rock slope at different monitoring points under action of 0.1 g seismic wave.
## 2 Reliability Analysis Method
### Method Validation  
To verify the accuracy and effectiveness of the ISSA-Kriging-MCS method, two numerical examples were selected to compare with other classical algorithms. Taking the calculation result of the MCS as the standard value, a comparative analysis was conducted between the number of limit function calls \(N_{call}\) and the relative error \(\zeta\). The MCS technology generated \(10^{6}\) candidate sample points for predictive evaluation. On the premise of ensuring calculation accuracy, 200 initial sample points were selected for evaluation using the LHS method. The formula is as follows:  
\[\zeta=\frac{\left|P_{f}-P_{f}^{\text{MC}}\right|}{P_{f}^{\text{MC}}}\times 100\% \tag{16}\]  
where \(\zeta\) is the relative error between the failure probability \(P_{f}\) of each reliability analysis method and the failure probability \(P_{f}^{\text{MC}}\) of the MCS method.
## 8 Conclusions  
As observed in clinical studies, the greatest advantage of delpazolid over linezolid is the potential for delpazolid to be used in long-term therapies. The development of delpazolid has focused on TB treatment, as this disease requires long-term treatment. In December 2016, LegoChem Biosciences entered into a license agreement with RMX Biopharma for the development, manufacture, and commercialization of delpazolid in China. In addition, delpazolid received an FDA orphan drug designation, a Qualified Infectious Disease Product Designation, and was selected as a Fast Track target drug.  
On October 30, 2019, at 'The 50 th Union World Conference on Lung Health,' held in Hyderabad, India, LegoChem Biosciences released the interim efficacy and safety results of a phase 2a study on delpazolid. Particularly, the results of a clinical phase 2a early bactericidal activity trial involving 79 Korean patients with drug-susceptible tuberculosis were reported. The findings will be further analyzed to determine the doses appropriate for different patient populations to guide further phase 2a studies. The phase 1 trial revealed that myelosuppression can be reduced, and phase 2a results suggested that delpazolid can replace linezolid as a therapy for TB and reduce the treatment period.  
Conceptualization, Y.L.C. and J.J.; writing, J.J.; review and editing, Y.L.C. and J.J.; visualization, Y.L.C. and J.J.; project administration, Y.L.C. and J.J. All authors have read and agreed to the published version of the manuscript.  
We thank all those who provided important information that was part of this work. We also thank [PERSON] for his assistance in figure preparation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**NTM Species** & \multirow{2}{*}{**Antibiotics**} & **Range** & **MIC\({}_{\text{50}}\)** & **MIC\({}_{\text{90}}\)** \\ (**no. of strain tested**) & & **(ug/mL)** & **(ug/mL)** & **(ug/mL)** \\ \hline \multirow{4}{*}{_Mycobacterium avium_ (22)} & Delpazolid & 8-0.125 & 2 & 8 \\  & Linezolid & 8-0.125 & 2 & 8 \\  & Clarithromycin & \textgreater{}128-\textless{}0.125 & \textgreater{}128 & \textgreater{}128 \\  & Delpazolid & 8-0.25 & 2 & 8 \\  & Linezolid & 16-0.5 & 4 & 8 \\  & Clarithromycin & 128-\textless{}0.125 & \textless{}0.125 & 1 \\  & Delpazolid & 2-0.25 & 1 & 2 \\  & Linezolid & 8-0.5 & 2 & 8 \\  & Clarithromycin & 8-\textless{}0.125 & 0.25 & 4 \\  & Delpazolid & 2-0.25 & 1 & 2 \\  & Clarithromycin & 0.125-\textless{}0.125 & \textless{}0.125 & \textless{}0.125 \\  & Delpazolid & 4-0.25 & 1 & 2 \\  & Linezolid & 8-0.5 & 2 & 4 \\  & Clarithromycin & 0.2-\textless{}0.025 & 0.1 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: MICs of antibiotics against clinical isolates of NTMs.
## 2 Materials and Methods
### Statement of the Problem
#### 2.1.2 Spatial Conflicts and Contractor Coordination  
In the construction projects considered here, the work areas are managed by different contractors, necessitating coordination to mitigate spatial incompatibilities and ensure efficient execution. Inappropriately addressed conflicts can lead to suboptimal schedules, rework, and potential project delays.  
Resolving spatial conflicts through conventional manual approaches is frequently a time-intensive and resource-demanding process. While techniques such as adjusting activity dates in Primavera P6 or modifying component positions within 3D models can address certain conflicts, the process is inherently iterative. Teams must repeatedly review and update the 4D simulation, which combines scheduling data with 3D models, until an acceptable solution is achieved. This iterative cycle can be time-consuming and inefficient, particularly for complex projects with intricate spatial constraints. In addition, they are prone to errors, as the manual analysis and coordination processes may overlook intricate interdependencies between activities, resource allocations, and spatial constraints. This necessitates the development of automated and optimized solutions to facilitate seamless coordination among contractors and enhance project execution efficiency.
## 3 Application of Diagnosis  
### Main Air Blower  
In this study, we applied the proposed fault diagnostics procedure to the main air blower (MAB) in the fluid catalytic cracking (FCC) process, which is one of the plant processes. The MAB is a rotating device for blowing air in the FCC process to the reaction tower; moreover, it is a major piece of equipment, and can cause significant losses in the event of a failure. Figure 5 shows a conceptual diagram of MAB equipment. It is composed of various equipment, such as compressors, turbines, etc. To monitor the condition of the equipment, a total of 236 sensors, such as temperature, pressure, flow rate, displacement, etc., are attached, as shown in Table 2.
## Materials and Methods  
### Study Area  
The study was conducted in pan-Arctic regions at latitudes over 60\({}^{\text{o}}\) north. A total of 11 sites in this area comprising 102 site-years (Figure 1 and Table 1), with at least 3 years of available GPP and satellite data, were selected. These sites included four biomes, according to the International Geosphere Biosphere Project (IGBP): permanent wetlands (WET), open shrulands (OSH), grasslands (GRA), and evergreen needleleaf forests (ENF). These sites represent a relatively wide diversity of regions, climates, and ecosystems in the pan-Arctic. The vegetated portion of the Arctic includes 26% erect-shrub tundra, 18% peaty graminoid tundra, 13% mountain complexes, 12% barren, 11% graminoid tundra on mineral soils, 11% prostrate-shrub tundra, and 7% wetlands and lakes, according to the land cover based on the vegetation map for the Arctic [34].  
Figure 1: Spatial distributions of the 11 eddy covariance flux sites. Figure generated using a conformal projection center at the North Pole with ArcMap 10.3 ([[http://www.esri.com/](http://www.esri.com/)]([http://www.esri.com/](http://www.esri.com/))).
## 5 Analysis on Bearing Capacity of PHC Modular Pile
### Effect of Nodular Length  
Two typical types of the PHC nodular piles (named as type A and type B) are normally used in engineering projects, as shown in Figure 8. For type A PHC nodular pile, the diameter/length ratio of nodule is relatively large, as shown in Figure 8a. For type B PHC nodular pile, the nodule length is large, and the diameter/length ratio is relatively small, as shown in Figure 8b.  
To investigate the influence of nodular length on the bearing capacity of the PHC nodular pile, the nodular length L was designed as 25 mm, 75 mm, 125 mm, 175 mm and 225 mm, respectively. The calculated load-displacement responses of the PHC nodular pile with different nodular lengths are arranged as shown in Figure 9. It can be seen in Figure 9 that the bearing capacity of the PHC nodular pile would decrease with the increase in nodular length, and the pile head displacement of the PHC nodular pile with different nodular length all increased stably with the pile head load. When the pile head displacement reached 40 mm, the ultimate bearing capacity of the PHC nodular piles were 2984 kN, 2357 kN, 2067 kN, 1920 kN and 1827 kN, respectively, as the nodular length increased from 25 mm to 225 mm. The ultimate bearing capacity of the PHC nodular pile decreased by 39% as the nodular length increased from 25 mm to 225 mm. Hence, the increase in nodular length would hamper the bearing capacity of the PHC nodular pile, and the type A PHC nodular pile was more suitable to improve pile capacity.  
Figure 8: Schematic of two types of PHC nodular piles. (**a**) Type A, (**b**) Type B.  
Figure 9: Effect of nodular length on bearing capacity of PHC nodular pile.
## 3 Results
### Monitoring of Land Use and Occupation
The lower availability of water may have reduced the natural regeneration capacity of vegetation, contributing to the decrease in vegetation cover, between 2018 and 2023. If the downward trend in precipitation continues, it is possible that the impact on vegetation will intensify, exacerbating the effects of anthropogenic activities, such as mining, and affecting the long-term health of vegetation. Water areas also decreased by 0.44 km\({}^{2}\), representing a 22.80% reduction compared to the area in 2018 and 0.09% of the total area. Bare soil areas showed a decrease of 20.31 km\({}^{2}\), which corresponds to an 11.52% reduction compared to 2018 and 4.42% of the total area. Conversely, built-up areas increased by 30.43 km\({}^{2}\), representing a 142.53% increase compared to 2018 and 6.62% of the total area. Some areas classified as deforested under the influence of mining were identified as artisanal gold mining activities through Google Earth validation, supporting the accuracy of the classification results. In these regions, it was observed that farmers converted areas originally designated for agriculture and grazed into clear-cut zones with extensive excavations in search of gold. These actions are directly linked to the presence of the Serrita-Cedro Project in the region, which has drawn significant attention from informal gold miners. The conversion of agricultural and pastureland into mining sites has caused significant impacts on land use and land cover, along with environmental and social consequences, highlighting the need for continuous monitoring and proper regulation.
# Investigation of Microservice-Based Workflow Management Solutions for Industrial Automation
## 3 Challenges and Requirements Set by the Industry for the Workflow Lifecycle
### Requirements for Workflow Implementation  
Workflow implementation is the stage where the allocation of resources and services is determined. Services are allocated to tasks in the managed domain of the workflowaccording to service-level agreements and operational conditions as required. This phase precedes workflow execution. The following requirements become particularly relevant during workflow implementation.  
**Challenge 3. Collaboration**: This requirement implies that candidate systems must be designed to generate and manage workflows in collaboration with other systems and their associated resources. The systems must standardize the output and communication between resources, meaning that the requirements for the partial workflows produced in terms of syntax and semantics must be met by each contender. There is also a need for the orchestration of workflows that involve multiple organizations. Once again, architectural constraints appear at this level. Heterogeneous edge cloud implementations affect the orchestration of resources from multiple administrative domains.
**Openness**: A system must be able to process workflow statements generated according to other partners' specifications and be tolerant of elements of expression that are outside the domain's and service's expertise. Distributed workflow orchestration is essential to exploit the potential of edge cloud architectures, where workflow elements manage different domains and coordinate in a hierarchical or peer-to-peer manner. In this environment, the concept of openness serves to assure that a service provider that encounters elements in a workflow that it cannot process does not: (1) fail with regard to the incomprehensible elements, (2) modify those elements, or (3) remove or discard those elements. Moreover, the complexity of heterogeneous application development and deployment processes needs to be hidden and a sophisticated level of abstraction has to be proposed. The solution must provide toolkits that simplify not only the tasks of resource/service discovery and monitoring but also the management of end-to-end workflows. In addition, usage policies and an adaptive migration mechanism need to be established [58; 59].
**Completeness**: A system should provide a complete set of services for the generation and management of workflows. In case a system provides only a part of the required services during implementation, it must be extended through services from other providers that compensate for the lacking capabilities.
## 6 Conclusions  
The following objectives have been achieved in this paper:  
* Detailed data analyses of the assumed data were performed. It was found that 2.5% of the data contained outliers using the IQR method. These outliers were removed;
* The best regression model for depicting load was found to be Decision Tree with an RMSE, \(R^{2}\), MSE, MAE and training time of 0.087, 0.85, 0.0077, 0.05, and 1.32, respectively;
* The best optimizer for hyperparameter tuning was achieved using Grid Search with a leaf size of 6 and a reduced MSE of 0.0067861.  
A Decision Tree is one of the best methods for solving the issue of STLF because the load has a dynamic nature. It changes with seasons, days, and months, among other parameters. Linear Regression methods did not perform well, with a very low \(R^{2}\) value, which implies it is difficult to establish linear relations among input and output parameters. Since the data set is large, Neural Networks also produced reasonably good results but, because of their computational cost, they lag behind Decision Trees. As far as hyperparameter tuning is concerned, Grid Search is considered the best method because of its certainty. Bayesian Optimization has more reliability than Grid Search but it requires strong, pre-computed values which might not be available with data sets. Randomized Search for hyperparameter tuning can be highly unpredictable since it depends on random values.  
Based on the proposed research work, the following areas can be promising future aspects of the research:  
* The forecasted load active power can be scheduled with constraints obtained from Distributed Energy Sources integrated with the grid;
* Calculation of Localized Marginal Pricing (LMP) and Distributed Localized Marginal Pricing (DLMP) can be carried out with forecasted load;
* A more robust comparison with Deep Learning methods can be carried out and the advantages and disadvantages of each individual method can be presented;
* Peer to Peer trading using forecasted load can be analyzed.  
**Author Contributions:** Conceptualization, B.V.S.V., I.S. and P.T.; methodology, B.V.S.V., M.K., I.S., P.T. and N.D.B.; software, B.V.S.V. and I.S.; validation, M.K., P.T. and N.D.B.; formal analysis, B.V.S.V.; investigation, B.V.S.V. and I.S.; resources, B.V.S.V., M.K., I.S. and N.D.B.; data curation, B.V.S.V.; writing--original draft preparation, B.V.S.V., M.K., I.S., P.T. and N.D.B.; writing--review and editing, B.V.S.V., M.K., I.S., P.T. and N.D.B.; visualization, B.V.S.V., I.S. and N.D.B.; supervision, M.K. and N.D.B.; project administration, M.K. and N.D.B.; funding acquisition, M.K. and N.D.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Case Study: Open Pit Gacko, Continuous Parts of the Combined System  
The lignite basin Gacko is located in the north-eastern part of Herzegovina, a region of Bosnia and Herzegovina, and has an area of about 30 km\({}^{2}\). It is divided into four exploitation fields: Western, Central, Eastern, and Southern. Coal mining in this basin began in 1954, with a smaller capacity, and since 1982, with a capacity of 1.8 Mt/year for the needs of the power plant. Coal production currently takes place with a capacity of 2.3 Mt/year. Exploitation has been completed in West Field, it is currently taking place in Central and South, and it is planned to continue in East Field. The coal mining system is combined with hydraulic bucket excavators, with a bucket volume of 4-6 m\({}^{3}\), truck transportation to the crusher, with a load capacity of 110 t, and further transport with belt conveyors. Two continuous systems with rotary excavators with a theoretical capacity of 1600 m\({}^{3}\)/h and suitable belt conveyors and spreaders are engaged in the excavation of the overburden. In addition to continuous systems, hydraulic excavators, buckets with a bucket volume of 10-12 m\({}^{3}\), trucks with a load capacity of 110 t, a crusher with a theoretical capacity of 2000 m\({}^{3}\)/h, appropriate conveyors, and a spreader within the combined system are engaged in the excavation of overburden. The geological structure of the deposits and the great variety of mining equipment make exploitation conditions more complex.  
The CCS system (crusher-belt conveyors-landfill) consists of two semi-mobile primary crushers, SB 1315 and SB 1515, and belt conveyors TU-3, TU-2, TU-1, and PTU. Coal brought by truck to the SB 1515 crusher is directly shaken onto the same rake, and that brought to the SB 1315 crusher is deposited at the landfill and dosed to the rake using a loader where, after crushing and pulverization by the conveyor system, it is handed over to the power plant. Figure 1 shows the position of the CCS system at the open pit Gacko. Figure 2 shows a view of the open pit Gacko.  
Figure 1: View of the CCS system and position of open pit Gacko (Source: Google Earth, [[https://philarcher.org/diary/2013/euromap/](https://philarcher.org/diary/2013/euromap/)]([https://philarcher.org/diary/2013/euromap/](https://philarcher.org/diary/2013/euromap/)), accessed on 10 July 2024).  
Figure 2: Gacko open pit (photographed by the author of the article: N.S.).