esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Condylar Morphological Evaluation Using Bilateral CBCT Scans of TMJ  
Pre-and posttreatment bilateral CBCT (NewTom, Verona, Italy) images of the TMJ were used to assess condylar position, detect any existing signs of internal derangement and monitor condylar morphological changes following the orthodontic treatment. Exposure parameters were identical for all subjects. CBCT data of all patients were evaluated in sagittal and coronal sections by two examiners blinded for the clinical diagnosis (neither knowing if the CBCTs were taken pre-or posttreatment). In case of disagreement a third examiner was asked in order to reach a final agreement. The most frequent osseous changes were examined in the CBCT images of the right and left TMJ areas according to criteria used in previous studies at [26; 27]:  
1. Flattening of the articular surface- loss of an even convexity of the joint (Figure 2a-d)
2. Surface erosion (decreased density of the cortical and adjacent bone) and/or condylar surface irregularity (loss of continuity of condylar cortex) (Figure 2b)
3. Absence of condylar anterosuperior position and/or apparent irregular TMJ joint space (Figure 2c)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Definition** \\ \hline ANB angle & A point–Nasion–B point angle \\ \hline Palatal plane–Mandibular plane angle & Angle between Posterior nasal spine–Anterior nasal spine and Gonion-Menton \\ \hline Facial Axis angle by Ricketts & Angle between Nasion–Basion and the facial axis (Pterygomaxillare-Gnathion) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definitions of cephalometric measurements used in this study.  
Sclerosis (increased density of cortical bone extending into the bone marrow) (Figure 2d)
# Multifunctional Hyperelastic Structured Surface for Tunable and Switchable Transparency
###### Abstract  
We leverage the crucial hypereelastic properties of a multifunctional structured surface to optimize the reconfigurability of the electromagnetic transmission under large nonlinear mechanical deformations. This multiphysics, multifunctional, hypereelastic structured surface (HSS) offers two simultaneous intriguing functionalities; tunability and switchability. It is made of copper resonators and a Polydimethylsiloxane (PDMS) substrate, which is one of the most favorable deformable substrates due to its hypereelastic behavior. The proposed HSS is fabricated via an original cost-effective technique and the multiphysics functionalities are captured in both experimental tests and numerical simulations. Leveraging the hypereelastic behavior, we demonstrate up to 8% percent shift in the resonance frequency in the GHz range, for average applied mechanical strains of around 17%. The hypereelastic deformations can continuously increase/decrease the magnitude of the scattering parameter S\({}_{21}\) in the frequency range of 10.9 GHz to 11.8 GHz by more than 40 dB, changing from being largely transparent to opaque and vice versa. The potential of hypereelastic behavior to account for the multifunctionality of the HSS is validated experimentally.  
metasurface; structured surfaces; microwave; mechanically tunable; switchable; optimized design; fabrication +
Footnote †: journal: Applied Sciences
# Intelligent Space Object Detection Driven by Data from Space Objects
## 4 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Panel** & **Body** & **Antenna** & **Optical-Load** & **Antenna-Rod** & **Accuracy** \\ \hline YOLOv3 & 0.915 & 0.931 & 0.934 & 0.892 & 0.905 & 0.927 \\ YOLOv4 & 0.979 & 0.98 & 0.965 & 0.936 & 0.952 & 0.963 \\ YOLOv5l & 0.941 & 0.939 & 0.933 & 0.892 & 0.963 & 0.934 \\ YOLOv6l & 0.975 & 0.975 & 0.957 & 0.945 & 0.957 & 0.962 \\ YOLOv8l & 0.98 & 0.982 & 0.968 & 0.955 & 0.95 & 0.969 \\ YOLOR & 0.979 & 0.981 & 0.96 & 0.948 & 0.96 & 0.966 \\ YOLOv7 & 0.978 & 0.983 & 0.962 & 0.938 & 0.959 & 0.964 \\ YOLOv7-R & 0.986 & 0.99 & 0.972 & 0.974 & 0.993 & 0.983 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of training results between YOLOv3 through the ResNet53 network architecture, YOLOv7, and YOLOv7-R. The difference between the two lies in the backbone module, with YOLOv7 as the ELAN module and YOLOv7-R as the ELAN-R module. In other YOLO models, the network structures with a similar number of parameters are adopted.
## 4 Model Development
### Model Selection  
The aim of the model selection step was to identify the architecture of the best NN model with the best generalization properties that had the minimum error and displayed the most optimal behavior with new data. The best generalization was accomplished by utilizing the model with complexity that was the most appropriate to produce a high enough quality fit of the data.  
The two algorithms used were (1) order selection and (2) input selections. The former one was responsible for figuring out the optimal number of hidden neurons, while the second one was responsible for figuring out the input variables' optimal subset. The algorithm of the order selection that was used for the development of the model was \"incremental order,\" which began with the minimum number of neurons (order), as seen in Figure 8a, and then increased with a certain number of perceptions in each iteration. The algorithm selected the neurons (order) with the minimal selection loss. It is worth noting that with a greater the number of neurons, the error of selection increased because of overfitting since it was a complex model. The achieved election error was MSE = 0.0159995. The optimal number of neurons (order) was three, as depicted in Figure 8b.  
On the other hand, the algorithm for input selection, which was used for the developed model, was \"growing inputs,\" in which the inputs were increased according to its correlations with the targets.  
Figure 8a displays the error history for the different subsets during the selection process of the \"incremental order.\" The line in blue gives the training error, while the line in orange illustrates the selection error. On the other hand, Figure 8b displays the error history for the different subsets during the growing inputs selection process. The blue line illustrates the training error, while the orange line illustrates the selection error. The figure shows that the best architectural neural network was the one with three hidden neurons. Figure 9 illustrates the architecture of the optimal selected model, in, where the circles in yellow depict the scaling neurons, the circles in blue depict the neurons, and the circles in red depict the unscaling neurons.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Final parameters norm & 2.34 \\ Final training error & 0.00132 \\ Final selection error & 0.00233 \\ Final gradient norm & 0.000798 \\ Epochs number & 77 \\ Elapsed time & 0:00 \\ Stopping criterion & Gradient norm goal \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the model training using the quasi-Newton method.
## 3 Results
### Results of kNN Model for PV Soiling Losses Estimation
#### 3.2.1 kNN Model Evaluated on CIESOL Test Set  
First, the estimations obtained with the kNN model trained with the data from the CIESOL station are displayed graphically along the observed (or real) values (see Figure 11) for comparison. The model tends to overestimate, with estimated values generally being higher than the observed ones, resulting in an MBE of 0.19%, the highest value amongst all Local Models. The RMSE is 0.84% of SL.  
Figure 12 compares the estimated values against the observed values, revealing a clear positive relationship with a coefficient of determination of 0.78. The red line represents the scenario in which the estimates perfectly match the observed values, which would result in an \(R^{2}\) of 1.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Group** & **Model** & **MBE [\%]** & **nMBE [\%]** & **RMSE [\%]** & **nRMSE [\%]** & **R2** \\ \hline Local & kNN CIESOL & 0.19 & 6.28 & 0.84 & 27.65 & 0.78 \\ Models & LightGBM CIESOL & 0.05 & 1.66 & **0.68** & **22.46** & **0.86** \\  & CatBoost CIESOL & 0.06 & 1.88 & 0.73 & 24.04 & 0.83 \\  & ANN CIESOL & **0.01** & **0.44** & 0.88 & 29.04 & 0.76 \\ \hline Extrapolated & kNN CIEMAT & 0.39 & 19.60 & 1.53 & 76.32 & \(-\)0.12 \\ Models & LightGBM CIEMAT & **0.02** & **0.79** & 1.38 & 68.74 & 0.09 \\  & CatBoost CIEMAT & 0.19 & 9.64 & 1.41 & 70.34 & 0.05 \\  & ANN CIEMAT & \(-\)0.15 & \(-\)7.26 & **1.34** & **66.73** & **0.14** \\ \hline Global Model & ANN & **0.09** & **3.41** & **1.02** & **40.50** & **0.63** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics of machine learning soiling models for daily soiling losses estimation.
## 3 Experimental Results and Discussions
### Experimental Setup
#### 3.1.3 Model Definition  
The model used during this experiment is based on Proposition 1 and is defined by the following hyperparameters:  
* Smoothing parameter: The smoothing parameter \(\alpha\in(0,1]\), which is fixed to \(\alpha=1\), corresponding to a Laplacian smoothing.
* Number of segmentation: The number of segments used depends on the number of features (i.e., dimensions of the vocabulary) of the dataset concerned.
* Number of partialization layers: A single layer of partialization is used, and the number of partial class posteriors in the layer is equal to the number of feature segments.
## 5 Conclusions  
This paper presents an MCSK-VTRM technique, which is applied to UWA communications. In this communication method, the M-ary technique and the CSK technique are combined to improve the amount of information carried by one spread spectrum sequence, so the efficiency of communications is improved. VTRM is used to equalize UWA channels to improve the reliability of communications and avoid the inter-symbol interference caused by UWA multipath channels. First, this paper establishes an MCSK system model and compares it with traditional spread spectrum communications. Then, we introduce the application of TRM in UWA communications and analyze the focusing performance and applicable conditions of TRM in detail by theoretical formula derivation and MATLAB simulations. Finally, simulations and poor experiments verify that MCSK-VTRM communications can maintain robust reliability and improve the communication rate.  
Conceptualization, F.Z.; methodology, B.L.; software, G.Y.; validation, W.Z. and D.M.; writing--original draft preparation, B.L.; supervision, D.N.; project administration, F.Z. and D.N.  
This research was funded by the National Natural Science Foundation of China under Grant No. 61431004, C051917003, 61571151, 61501134, 11774074, 61601136, 61771152, 61801139 and U1806201; by the National Key Research and Development Program of China under Grant No. 2017 YFC0305702 and 2018 YFC0308500; and by the Fundamental Research Funds for the Central University of China under Grant No. HEUCFG201825.  
The authors declare no conflict of interest.
## 3 Results
Four studies performed conventional transfer without splinting of the components [20; 21; 23; 24]; one study did not make precise the method used for transfer [4]. Regarding the digital group transfer, three studies performed splinting of the scan bodies [6; 8; 20], while four authors did not perform splinting of the scan bodies [9; 21; 22; 24]. Three studies do not make it clear whether or not the scan bodies were splinted [3; 4; 23]. [PERSON] et al. [25] used adhesives containing hemispheres along the palate during scanning as an auxiliary strategy for scanner recognition on the edentulous ridge. Regarding the marginal adaptation of prostheses, eight studies performed analyses on this issue [3; 4; 6; 9; 20; 21; 23; 25], indicating 100% adaptation. However, one study presented 80% adaptation of the infrastructure pieces [9], and another presented 86.7% adaptation [25], both in a digital group. Regarding technical and biological complications, a study with a full-arch prosthesis was identified that presented screw loosening in the conventional group [4]. Other studies presented infrastructure misadaptation, peri-implantitis, fracture of the provisional prosthesis, chipping and fracture of zirconia, screw loosening, fracture of the definitive prosthesis, implant loss [8; 9; 20; 23; 25], and decementation of zirconia [23]. [PERSON] et al. [9] indicated a fracture of the overdenture tooth in two patients. In general, there were fewer complications for the conventional impression and digital groups, as observed in Table 2[3; 4; 6; 8; 9; 20; 21; 22; 23; 24; 25]. All included studies indicated the viability of digital scanning as an alternative to conventional impressions. However, [PERSON] et al. [25] emphasized that the number of implants and type of scan body used in the scanning procedure imply a difference in the accuracy of implant positioning when compared to the digital technique with the conventional one. Likewise, [PERSON] et al. [9] highlighted the need for rescanning in 20% of the cases in their sample.
## 3 Results
### Analysis of the Results
* The complexities of the partial and full churn problems might be different, and the latter might be harder to predict. However, this specific question is beyond the scope of the current research, because it would require double the amount of computations without clear practical usefulness in the context of the current article. We will ignore the slight improvement obtained when using partial churn with a threshold above 0.5, as it is insignificant and could be a natural result of nonlinearity of the model and the fact that we averaged the values over the time windows. As for the full churn scenario, the F1 score increased from 0.624 with threshold value 0.5 to 0.643 with threshold value 0.61. Next, in Figure 13 we present the F1 score values for different modeling moments before and after the correction of threshold value. As can be observed, in the majority of cases, the correction improved the results; however, an anomaly with a single but significant drop of a single curve point can be observed. In order to analyze this result, we present the values for all confusion matrices as graphs in the Appendix. In Figure 14, it can be clearly observed that some spikes appear in the separate false/true negative/positive values. The expected result of the signals used to make decision being weakened (as a result of the higher threshold value) is due to the decrease in positive predictions and increase in  
Figure 12: F1 performance with different model thresholds. negative predictions (both true and false ones). Thus, the F1 score improvement is due to the increase in true negative values and decrease in false positive values. The non-smooth spiky behavior of the false/true negative/positive values obtained for some custom values suggests that such an approach may be unreliable in practical applications. However, it is also evidence that the use of a stronger signal for churn prediction can improve the performance of the model, solving the full churn prediction problem. Thus, instead of engineering a modified custom decision threshold, assigning increased weights to the churn class could be a preferred approach, although it is beyond the scope of the current research. Future research might consider such a possibility.
## 4 Experiment and Analysis
### Backbone Improvement Experiments  
In this experiment, we explored the impact of replacing different backbone networks within the RT DETR framework on model performance. Four configurations were compared: RT DETR-ResNet18, RT DETR-Faster, RT DETR-EfficientViT, and RT DETR-MobileNetv3. We evaluated these models in terms of total parameters, GFLOPs, FPS, and two accuracy metrics, mAP50 and mAP50-95, to assess their performance in resource consumption, speed, and accuracy.  
The experimental results, as shown in Table 2, indicated that RT DETR-MobileNetv3 was the lightest in terms of total parameters, with 9,546,660 parameters and 23.6 GFLOPs, indicating higher computational efficiency. In terms of real-time performance (FPS), this model ranked first with a result of 76.4. Despite having fewer parameters, the MobileNetv3 version showed strong performance with a mAP50 of 0.857, surpassing RT DETR-EfficientViT and being close to RT DETR-Faster's 0.879. For the more comprehensive accuracy metric mAP50-95, RT DETR-MobileNetv3 achieved 0.591, which was very close to RT DETR-Faster. This result demonstrated MobileNetv3's contribution to overall model performance when replacing the backbone, especially in maintaining high efficiency and good accuracy while ensuring real-time performance.  
Figure 7: Comparison of the detection performance of different models.
## 3 Analysis Methods  
### Data Processing  
The present study relies on publication records of scholars who participated in the Mobile Health Training Institute (mHTI). Given the relatively long duration from research ideation to publication [28], we focus on the 2015 to 2022 scholar cohorts to effectively capture the lagged impact of program training (excluding 2020 due to the COVID-19 pandemic). As a result, we collected a total of 229 scholars' publicly available publications up to January 2024 from Google Scholar.  
Google Scholar's wide-ranging coverage across various disciplines, quick indexing, and easy accessibility [29] make it an essential tool for compiling thorough publication records for our analysis. However, collecting public publications through Google Scholar presents several challenges, due to name disambiguation and the need for complete publication records. To overcome these issues, we started by identifying scholars with clearly distinguishable personal profiles on Google Scholar. To improve search accuracy and increase the number of scholars with identifiable profiles, we supplemented verification searches with additional information such as institution and email domains. As a result, a total of 176 mHTI scholars were matched with their Google Scholar profiles, while the remaining 53 scholars, who could not be reliably identified due to insufficient or unclear information with the described approach, were excluded from the analyses. Table 1 shows the demographic information of the 176 mHTI scholars included in our analysis sample. The scholars primarily come from psychology, computer science, statistics, engineering, and data science, comprising 66% of the total. Additionally, most scholars are in mid-career, primarily assistant or associate professors.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variable** & **Frequency** & **Variable** & **Frequency** \\ \hline
**Cohort** & & **Career stage** & \\
2015 & 23 & Early-career & 55 \\
2016 & 24 & Mid-career & 110 \\
2017 & 30 & Late-career & 8 \\
2018 & 19 & Others & 3 \\
2019 & 25 & **Gender** & \\
2021 & 29 & Female & 107 \\
2022 & 26 & Male & 69 \\
**Discipline** & & **Region** & \\ CS/Stats/Engineering/Data science & 53 & Midwest & 37 \\ Medicine/Nursing & 35 & Northeast & 49 \\ Psychology & 64 & Southeast & 30 \\ Public health & 15 & Southwest & 12 \\ Others & 9 & West & 32 \\  & & Others & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic distribution of the 176 mHTI scholars (2015–2022) included in our analysis.  
Figure 1: The proposed analytic framework for interdisciplinary training evaluation.  
Using the scholarly package [30] with Python 3.10.14, we systematically gathered publication data for each scholar, including article titles, abstracts, author lists, and publication years. Data collection resulted in a set of 19,083 publication records from Google Scholar. To ensure data quality, we removed records with missing or excessively short abstracts, keeping a final set of 16,580 publication records. This dataset served as the foundation for the topic modeling analysis elaborated in the subsequent subsection.
# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
###### Abstract  
In this study, a neural network-based power prediction for a photovoltaic system was conducted using a multi-parameter approach, considering radiation, temperature, wind speed, humidity, and cloud cover. Photovoltaic systems are highly popular renewable energy sources due to their robust, modular, and environmentally friendly characteristics. Although photovoltaic systems offer many advantages, their dependency on irradiation for energy generation and their sensitivity to meteorological parameters pose a significant disadvantage, leading to intermittent energy production. Since these parameters affect the quality of power generated at the plant, they introduce uncertainty in power systems. Therefore, it is crucial to consider these factors in energy planning and management. In this study, to mitigate uncertainty in power systems and contribute to energy planning by predicting power production, power data obtained from a power plant, along with meteorological data, were used in Single Layer Perceptron Neural Network. The predicted power values obtained from the proposed model were compared with the actual values, and the results of this comparison were presented. Furthermore, to demonstrate the model's performance, the R and MSE values were provided as 0.98 and 0.03, respectively, indicating a strong correlation between predicted and actual values and a low prediction error.  
artificial neural network; photovoltaic; power prediction; renewable energy Article
# BIM Policy Trends in Europe: Insights from a Multi-Stage Analysis
## 3 Results
### BIM Policy in Europe
#### 3.2.29 International Initiatives  
Standardization in the field of structured sematic information management with BIM in the AEC industry is addressed by the international Technical Committee CEN/TC442, which was established in 2015. It comprises 10 working groups; for example, WG 9, which deals with digital twins. Many standards have been developed in cooperation with the ISO/TC 59/SC 13 committee under the CEN/ISO Vienna Agreement, and these standards are utilized in numerous countries beyond Europe. In 2020, the CEN/TC442 Business Plan defined the measurement of BIM maturity according to the following indicators: content, digitalization, interoperability, and collaboration [115].  
A significant development for the Member States of the European Union in BIM implementation was the publication of Directive 2014/24/UE [32]. This directive introduces the possibility of requiring the use of BIM in public procurement, which also serves a promotional role. Since its issuance, some Member States have mandated the use of BIM. As part of the European Commission's efforts, strategies and programs have also been developed to digitalize the economy, including that of the construction industry [116; 117; 118; 119]. The next step in promoting the requirement for BIM in public procurement is a handbook on the related cost-benefit calculations [27].  
The European Commission's Rolling Plan for ICT Standardisation, in support of the Green Deal, has identified trends in the areas of ICT environmental impact and circular economy and sustainability. It plans to expand standardization efforts in the field of digital twin technology.  
Among other initiatives, mention must be made of DigiPLACE, a European construction platform supporting a common language, digital construction standards, and legal standards. Additionally, BIM4 EEB provides a BIM-based toolkit for the efficient renovation of residential buildings. The Digital Building Logbooks initiative aims to propose a model for digital construction logbooks. Furthermore, the EUnet4 DBP constitutes a network of researchers and stakeholders dedicated to devising a unified strategy for digitalizing the building permit issuance process.  
Equally crucial are initiatives related to Open BIM, which are based on open standards and workflows. These allow all project participants to engage in a coordinated and simultaneous manner, irrespective of the software tools they utilize [120; 121]. The principal advocate and proponent of the solutions and standards it delivers in the digital transformation is building SMART International, which maintains branches in numerous countries, thus facilitating local initiatives.