esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of a Polygeneration System with CO\({}_{2}\) Working Fluid
## 4 Conclusions  
The objective of this work was the investigation of a polygeneration system driven by a biomass boiler that uses only CO2 as the working fluid. This system produces electricity, refrigeration and heating at two temperature levels. The analysis was conducted with a developed model in Engineering Equation Solver. The most important conclusions of this work are summarized below:  
* In the nominal operating conditions, the system energy efficiency was 78.07% and the exergy efficiency 26.29%.
* In the nominal operating conditions, the net power production was 24.50 kW, the cooling production was 30.73 kW, the heating production at 45 \({}^{\circ}\)C was 9.24 kW and the heating production at 80 \({}^{\circ}\)C was 13.60 kW.
* The parameters that most affect the system energy efficiency are the medium pressure and the high heating temperature.
* The parameters that most affect the system exergy efficiency are the high pressure and the refrigeration temperature.
* The examined system can operate in a great range of heating and refrigeration temperatures, and so, it can be applied in numerous applications in the industrial and the building sector.
* The variation of the pressure levels and of the (TIT) can lead to significant deviations in the system energy and exergy efficiencies.  
In the future, the system can be examined with the combination of solar energy collectors and a biomass boiler. Moreover, financial analysis can be performed, as well as a life cycle investigation. Furthermore, another idea is to perform simulations of the present system with solar energy as the heat source with or without the biomass boiler. Also, a more applied study could be performed in the future by considering the mechanical efficiency in the turbo-machinery, as well as the reduction in the performance of the turbine in extremely high temperatures (close to 1000 \({}^{\circ}\)C) due to the use of cooling blades.  
E.B. investigation, software, writing and reviewing the original paper; C.T.: supervisions, writing and reviewing the original paper. Both authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning\" in the context of the project \"Reinforcement of Postdoctoral Researchers-2 nd Cycle\" (MIS-5033021), implemented by the State Scholarships Foundation (IKY).  
No ethical approval was required. The study was a simulation study; no participants were contacted and no human data have been used.
## 3 Results and Discussion  
### Characterization of NPs  
The XRD pattern of CoNiFe showed (111), (200), and (220) reflections (Figure 1a), which matched with the standard JCPDS data for FeNi with the lattice parameter a = b = c = 3.5450 A (No: 00-038-0419). The crystal structure was also found to be face-cubic centered (FCC) with the main peaks (111), (200), and (220) revealed by XRD pattern. The crystal structure of the CoNiZn sample was found to be FCC as well (JCPDS 89-4307) with the main peaks (111), (200), and (220), as shown in Figure 1b. The crystalline sizes were calculated to be around 12.0 nm using the Debye-Scherrer formula based on the full width at half maximum (FWHM) of the main diffraction peak (111).  
The SEM images of the prepared ternary alloys CoNiFe and CoNiZn are shown in Figure 2. The related size distribution diagrams were also prepared by the Digimizer software. Some sub-micrometric spheres can be seen in both figures; such spheres are composed of assemblies of smaller nanoparticles aggregated due to different reasons, such as the magnetic interaction between the magnetic nanoparticles [13; 22]. According to Figure 2b, the average particle size of the CoNiFe sample was 8 nm, while it was about 17 nm in Figure 2d for CoNiZn. So, the average particle size, as well as the diameter of the spheres, of CoNiFe NPs was smaller than of CoNiZn.  
A vibrating sample magnetometer (VSM) was used to measure the magnetic properties of the prepared samples. In brief, the synthesized sample was placed in an applied positive magnetic field H. Then, the sample was vibrated vertically, and the induced magnetic field was created so that the induced current was proportional to the magnetization of the sample. After the magnetization of the sampled saturated up to M\({}_{\rm s}\), the magnetic field was decreased to zero, but a little magnetization remained in the ferromagnetic sample. So, a negative magnetic field was applied to remove the remanent magnetization, which is called coercive field H\({}_{\rm c}\). By repeating this cycle, the M-H curve was obtained with a hysteresis loop.  
The hysteresis loop observed in Figure 3 indicates the soft ferromagnetic behavior for the prepared samples. CoNiFe sample showed the higher magnetic saturation M\({}_{\rm s}\) = 94 emu/g in comparison with CoNiZn (M\({}_{\rm s}\) = 46.6 emu/g), while it had the lower coercive field H\({}_{\rm c}\) value of about 68.7 Oe. This can be due to the smaller particle grains of the CoNiFe sample. The magnetic element Fe caused a higher magnetization of CoNiFe compared with CoNiZn. On the other hand, the non-magnetic element Zn segregated  
Figure 1: XRD patterns of the synthesized (**a**) CoNiFe, and (**b**) CoNiZn samples.
## 4 Experimental Results and Discussion
### Correlation Between Shapley Values for Models with Different Architectures  
These tests aim to assess the degree of agreement between Shapley values corresponding to re-trained models with different architectures but with the same data.  
Correlation was calculated for all possible pairs of architectures, considering separately the results of each partition per architecture. In other words, to calculate each correlation value, a vector is constructed for each architecture with the Shapley values of each image of the test set for the models of each partition. We will therefore have 5 correlation values for each pair of architectures (one per partition).  
The results obtained are shown in Figure 7 where each boxplot has been calculated from the Shapley values between each pair of architectures for the same partitions. It can be observed, as expected, that the change of architecture has a greater impact on the calculation of the Shapley values than the change of training and validation partitions.
## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System
### FSGS Analysis Module  
The FSGS analysis module (see Figure 16) gives as a comprehensive diagnostic tool for categorizing and analyzing the various forms of FSGS. This module is designed to provide clinicians with detailed insights into the genetic and secondary causes of FSGS, enabling precise identification and treatment planning. The system offers two primary categories for analysis: genetic FSGS and secondary FSGS.  
The module presents a user-friendly interface where clinicians can select between different forms of FSGS to obtain detailed information. Upon selection, the system provides an in-depth explanation of each category, including key clinical considerations, diagnostic indications, and treatment implications. The two main categories of FSGS analyzed in this module are as follows:  
Figure 14: Overview of the operation of the treatment continuation module.  
Figure 15: Example of a treatment continuation module screen (fourth module from Figure 1).  
* **Genetic FSGS**: This category focuses on inherited forms of FSGS, which include familial, sporadic, and syndromic variants. The system provides guidance on genetic testing, appropriate clinical indications, and considerations for clinical management.
* **Secondary FSGS**: This category includes FSGS arising from secondary causes such as infections, medication-induced damage, and adaptive changes associated with glomerular hypertension. The module outlines various conditions that lead to secondary FSGS, including viral infections, certain medications, and systemic diseases.  
The FSGS analysis module integrates seamlessly into the broader clinical decision-making process. By providing detailed and structured information about the genetic and secondary causes of FSGS, it supports clinicians in making informed decisions regarding the need for genetic testing or further investigation into secondary causes.  
The next section discusses the research results in the context of the decision support and classification system as a whole.
# Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
## 3 Methodology  
This section describes the systematic literature review (SLR) approach used to answer the research questions. The SLR is based on a procedure that aims to ensure high levels of objectivity, precision, and clarity during the review process [52]. It consists of four stages adapted from the work reported in [53]: (1) research scope, (2) search limit setting, (3) article selection, and (4) result analysis and reporting, as shown in Figure 2.  
Scopus, Web of Science, Pro-Quest, Taylor & Francis, and Informs were selected for this study. Although LSS emerged around 2000 [8], I4.0 began in 2011 at the Hannover Fair [6; 54]. For this reason, only peer-reviewed papers published since 2011 were considered. Figure 2: Literature review structure. Three groups of keywords were identified within the research scope. The first corresponds to the terms relating to I4.0 [11; 44; 55; 56]. The second refers to LSS methods and tools [57; 58]. The third category includes key terms that would help answer the research questions, as detailed in Table 2.  
Search strings were created considering the terms shown in Table 2. Additionally, synonyms, alternative terms, and abbreviations were included in the search to avoid limiting the results. Once the articles were identified, a spreadsheet list was prepared to identify and eliminate duplicates. The content of the selected articles was carefully examined to ensure that they met the inclusion and exclusion criteria. A thorough reading of the title, abstract, and keywords was performed to obtain a list of articles relevant to this research. Two inclusion criteria were considered:  
1. The paper must be in English and peer-reviewed. In addition, it must pertain to business case studies, review articles, survey studies, conceptual frameworks, and focus groups;
2. Articles must focus on applying I4.0 technology with an LSS tool or method. The excluding criteria were as follows:  
1. Exclude conferences and non-peer-reviewed articles, such as books and editorials;
2. Articles were excluded when \"Lean\" was used in a field other than the lean management domain;
3. Articles that considered technologies unrelated to I4.0 were excluded. The snowballing technique was used to expand the results. In other words, the articles obtained from the search strings in Table 2 were examined for new relevant references (i.e., articles), and those meeting the inclusion criteria were added to the list of selected publications [59]. The resulting number of articles is shown in Figure 3.  
The results of the SLR are divided into two sections presented below. Section 4 discusses the selected articles' main characteristics, the identified enablers of and barriers to I4.0 and LSS integration, and the proposed actions to help practitioners to achieve this goal. Finally, Section 5 identifies a set of 'gaps' in the literature, which may help to guide future research efforts on I4.0 and LSS integration.
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### Optimization of the \"Ring-Slot\" Structure Parameters
#### 4.3.3 Optimization of the Chip Inductor  
In Section 4.3, a chip inductor is connected in series within the \"ring-slot\" structure to optimize the impedance matching of the entire structure. Next, we shall investigate the impact of the chip inductor on the transmission coefficient between the ring antenna and the slot antenna, as depicted in Figure 37.  
As the chip inductor increases from 8.5 nH to 8.9 nH (in increments of 0.2 nH), the transmission coefficient between the ring antenna and the slot antenna initially increases and then decreases. When the chip inductor is selected as 8.7 nH, the isolation between the two antennas is maximized at around \(-\)26 dB at 3.5 GHz, and the isolation remains superior to \(-\)22 dB within the LTE band 42 frequency range. Therefore, after optimization, the chip inductor should be chosen as 8.7 nH.
## 2 Related Work
### Type of Data and Predictive Output
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Outline** & **Technique** & **Drawback** \\ \hline \multirow{3}{*}{[11]} & SSL is employed to classify COVID-19 from images. & COVIDCon algorithm, consisting of data augmentation, consistency regularization, and multi-contrastive learning. & COVIDCon does not consider domain knowledge nor perform regression and explain the output. \\ \cline{2-4}  & SSL algorithm is proposed to classify drug structure. & “MultiCon” algorithm, consisting of data augmentation, consistency regularization, and multi-contrastive learning. & Domain knowledge, regression, and output explanation are not taken into account. \\ \hline \multirow{3}{*}{[53]} & SSL method is proposed to address imbalanced data problem in tabular security datasets. & “MCoM” method, consisting of triplet mixup data augmentation, contrastive and feature reconstruction loss, pseudo-labeling, and downstream task. & Cyber security domain knowledge, regression, and classification output explanation are left unaddressed. \\ \hline \multirow{3}{*}{[54]} & Labels are attributed to the unlabeled data using semi-supervised training. & SPEA2 was employed for semi-supervised training of LSSVM. & LSSVM is a data-driven approach without any domain knowledge. Regression and output explanation are also left unaddressed. \\ \hline \multirow{3}{*}{[55]} & Synthetic, labeled data generation method was proposed with focus on accuracy and density. & Accuracy and density of generated data are dealt with by NSGA-II. KNN is employed to classify the synthetic data. & KNN does not contain any domain knowledge. Classification output is not explained by the proposed method. \\ \hline \multirow{3}{*}{[56]} & Pseudo-labeling of the unlabeled data is taken as an optimization problem of the Genetic Algorithm. & GPR is used as base learner to assign the pseudo-labels to unlabeled data. & GPR has no domain knowledge. Diversity of the unlabeled data and output explanation are not taken into account by this method. \\ \hline \multirow{3}{*}{[57]} & Semi-supervised classification method is proposed with both labeled and unlabeled data. & SVM with modified Z-score based on fuzzy logic and cluster assumption. & SVM does not deal with domain knowledge. BRBES is superior to fuzzy logic in terms of uncertainties due to ignorance. Regression and output explanation are also left unaddressed by this method. \\ \hline \multirow{3}{*}{[58]} & SSL-aided evolutionary approach is presented to classify workers based on their risk perception. & MLP is a data-driven approach with no domain knowledge. This method neither performs regression nor provides explanation for classification output. \\ \cline{2-4}  & & & MLP is a data-driven approach with no domain knowledge. This method neither performs regression nor provides explanation for classification output. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categorization of existing literature.
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 4 Experiments and Analysis
### Dynamic Steering Test  
The driving condition of double lane change (DLC) is used to test the proposed strategy performance of the ARIS system under a dynamic uncertain environment. The mass of vehicle is 95 kg. The vehicle speed is 12 km/h. Figure 15 shows the steering reference angle of the rear axle wheel under the DLC condition. L2 represents the left rear wheel and R2 represents the right rear wheel. Figure 16 shows the driving status of the vehicle. In order to compare and analyze the control performance, the vehicle is tested under the condition of with and without synchronization control.  
Figure 16 shows that in 3.4s, the lateral acceleration and yaw rate reach the maximum value. In the case of no synchronization control, the external uncertain disturbance caused by the lateral acceleration will further increase the steering tracking error and synchronization error of the left and right wheels. As shown in Figure 17, the maximum synchronization error reaches 0.45 deg. It can be seen from Figure 18 that the synchronization error is significantly reduced and the maximum synchronization error is less than 0.15 deg with the hierarchical synchronization control strategy. This is because the disturbance on either side of the left and right wheels will be fed back to the dynamic model of VRSM.  
Figure 16: The vehicle state under the condition of DLC. (**a**) The lateral acceleration of the vehicle; (**b**) the yaw rate of the vehicle.  
Figure 14: The control performance comparison of three cases.  
Figure 15: The rear axle wheels steering reference angle of the ARIS system under the condition of double lane change (DLC).  
As a result, the left and right rear wheels are subject to the same level of total disturbance in the virtual model, which further makes the tracking controller show the same tracking error characteristics. Therefore, the synchronization error is reduced.  
The above experiments prove that the control strategy proposed in this paper can significantly reduce synchronization error under uncertain disturbance condition and is superior with the properties of disturbance rejection and robustness.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 5 Short-Term Weather Forecast  
Weather forecasting aims to predict atmospheric phenomena within a short time-frame, generally ranging from one to three days. This information is crucial for a multitude of sectors, including agriculture, transportation, and emergency management. Factors such as precipitation, temperature, and extreme weather events are of particular interest. Forecasting methods have evolved over the years, transitioning from traditional numerical methods to more advanced hybrid and machine-learning models. This section elucidates the working principles, methodologies, and merits and demerits of traditional numerical weather prediction models, MetNet, FourCastNet, and PanGu.
## 5 Experimental Results
### Discussion  
To summarize, we demonstrated that a projection-based method that might appear to introduce potential information loss during rendering can precisely segment challenging cases in 3D dental shapes when thoughtfully designed. The results demonstrate that the method outperforms several established point- and edge-based methods [8; 9; 17], as well as a Graph Cut-based conventional method specifically tailored for this task, indicating its highly competitive performance. The proposed method does not produce errors where, for example, half of the tooth is not segmented due to the low amount of geometric details or scanned dental appliances. This is the case with conventional segmentation methods, and it is considerably time consuming to fix when employed in actual clinical work; our method could decrease this overhead. The results hold for both maxilla and mandibles, as well as among individual tooth types (incisors, canines, premolars, and molars), without any significant variance in performance.  
Although our projection-based approach achieves high-quality results, it has certain limitations in handling cases with very narrow inter-dental spaces. Our network is limited in precisely segmenting corresponding cells if the space is extremely narrow due to occlusions.  
In the future, our work could be extended in several ways. Recent work [5] has demonstrated the benefit of splitting the geometry and curvature information into two  
Figure 7: Demonstration information preservation during the (un)projection process. (**a**) Visualization of the segmentation results of one high-resolution test case. (**b**) Two generated masks from the case in parametrization space (unwrapped to 2D). In most cases, the generated teeth regions are consistent without any holes. Occasionally, masks contain small projection errors near the tooth–gingiva boundary, see blue arrows.  
streams and fusing their complementary information to learn more discriminative shape representations. It would be intriguing to see if the same applies to projection-based approaches that employ depth and normal maps, like ours.  
Looking forward, further attempts could combine the benefits of projection and geometric methods. Such a combination could lead to a more expressive encoding of the input tooth shapes by analyzing visual and geometric aspects as in [28]. We believe that such fusion could effectively suppress the errors of our method since the inter-dental space could be segmented based on geometric information.  
Future research should further develop and confirm the initial findings by comparing the results of our projection-based method with more point-, edge- and graph-based approaches.  
Our framework can also be applied to other areas of digital dentistry. For instance, a method inspired by our approach could be used to automate tasks such as margin line detection or tooth preparation segmentation in automatic crown design [29].
## 2 Related Works
### Resource Allocation Strategies in Heterogeneous Networks
The paper [18] introduces the concept of MEC applications aimed at enhancing users' overall experience quality when accessing streaming services through popular video caching management and video quality adaptation. The paper [19] proposes a method combining the advantages of in-network caching and MEC to boost mobile network throughput and user QoE. This method also puts forward a flexible transcoding strategy to provide users with low-latency video streaming services in MEC networks, given limited storage, computing, and spectrum resources. In the study [20], the authors focus on the flow table management issue in software-defined satellite-terrestrial networks and propose a multi-strategy flow table management scheme. The paper [21] also introduces a software-defined satellite-terrestrial integrated network for the joint management and orchestration of network, caching, and computing resources. The authors of [22] propose a traffic engineering scheme in software-defined RAN based on real-time video traffic, addressing the downlink transmission multipathtraffic engineering problem under backhaul and wireless access constraints to achieve greater throughput and QoE gains. The paper [22] first introduces an online method for dynamically estimating the effective rate of video streams to provide a satisfactory quality of experience. Second, it proposes a traffic engineering method that considers the characteristics of video streams. Finally, the paper discusses a radio coordination method for providing stable video rates across cells. The study [23] presents a novel mechanism that jointly considers buffer dynamics, video quality adaptation, in-network caching, video transcoding, and transmission to optimize energy consumption and QoE metrics in video streaming. In summary, existing research has demonstrated that implementing edge computing technology, in-network caching, and software-defined networking in heterogeneous satellite networks can further enhance network performance. Additionally, these technologies have shown effectiveness in video services to some extent. However, current studies lack research on the integration of heterogeneous satellite networks, edge caching, in-network caching, and software-defined networking for video services, which requires further in-depth exploration.