esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations of Predictive Analysis  
There are many advantages to using EHR data, as it can contain a longitudinal medical history of patients, including symptoms, laboratory findings, procedures, prescriptions, and test results that can be easily used on a computerized platform [45; 46]. The secondary use of EHR data is faster and can collect more measurements from a larger population. However, the longitudinal dataset has significant advantages over the aggregated dataset for the early predictive model. Adapting any ML model in the health care system without further clinical trial examination, on the other hand, can be difficult. We must understand the combined effect of different variables, such as the confounding effect of risk variables, and how long it may take to develop glaucoma after exposure to such variables.  
Further, many challenges are associated with leveraging EHR data for predictive analysis because of its complex nature. Extracting information from these heterogeneous datasets requires correct data collection techniques, proper data preprocessing, reasonable dimension reduction, and suitable machine learning algorithms. In addition, building a longitudinal dataset to capture a patient's history's temporal events is challenging.  
Further, implementing data-driven analysis into the day-to-day workflow is challenging because of the different regulatory systems in various institutions, including insurance, hospital managers, security, and IT teams. In addition, longitudinal data collection requires many patients' records to capture all possible heterogeneity in the population. Though the ethics and regulations may improve over time, the EHR data also has inherent limitations, including missing, incomplete, and incorrect data. With the sparse and natural missing data of electronic health records, a longitudinal dataset analysis approach works well andyields significantly better predictions than traditional methods. However, more work needs to be done for further validation, and improvements to these methods must be made before implementation in a clinical setting.
# Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications
## 3 System Model
```
Input : Number of relays N, Number of Destinations M, Physical Network distances and Rayleigh coefficients, Social network levels \(\beta_{j}\)
1 for\(j\in N\)do
2 Calculate \(\gamma_{S,R_{j}}=\frac{P_{S_{1}|h_{S,R_{j}}|^{2}}}{\sigma_{S}^{2}d_{S,R_{j}}}\)
3 Calculate \(C_{j}=W\log_{2}(1+\gamma_{S,R_{j}})\)
4 Calculate \(T_{j}=\frac{1}{C_{j}}\)
5 for\(i\in M\)do
6 Calculate \(\gamma_{R_{j},D_{i}}=\frac{P_{R_{j}|h_{S,D_{i}}|^{2}}}{\sigma_{S}^{2}d_{S,R_{j }}}\)
7 Calculate \(C_{j,i}=Wlog_{2}(1+\gamma_{R_{j},D_{i}}\beta_{j})\)
8 end for
9 Calculate \(T_{j,J}=\frac{1}{min(C_{j,i})}\)
10 end for
11\(sort(T+T_{I})\) a vector of N elements
12 while\(k\in N\)do
13 Find \((T_{k}+T_{k,J})\)
14 if\(P_{S}T+P_{R}T_{I}<P_{S_{1}}T_{1}\)then
15 return k optimal solution of (7) and stop
16 end if
17
18 end for
19 Update the new values of \(\beta_{j}\) and SNR for each links for searching the best relay in the next transmission period
```  
**Algorithm 1** Social Relays Selection Procedure
## 3 Results and Discussion
### Extraction Efficiency  
The efficiency of extraction from a hydrosol into the organic phase is an important parameter in assessing the applicability of the method. If the extraction does not have a high yield, it is difficult to estimate the content of volatile compounds in a hydrosol based on extract analysis. We also wanted to test the hypothesis of whether the ratios among compounds change with extraction. After the first extraction, the hydrosol was re-extracted, and the areas under the peaks in the first and second extractions were compared. This was done for three compounds that occur in all samples; the results are presented in Table 3. We found that the extraction efficiency was poorer for pentan-3-one than for eucalypt and alpha-terpineol. At an extraction ratio of 1:1, the peak area of the second extract for pentan-3-one was 17% of the peak area of the first extract, with the addition of NaCl improving the extraction yield to 12%. The extraction yield was better for eucalypt and alpha terpineol, where the peak area of the second extract was less than 3% of the peak area of the first extract. At an extraction ratio of 10:1, the extraction efficiency was much poorer.  
Table 3 also shows some physical properties of the three compounds. Pentan-3-one has a shorter retention time, smaller log P, lower boiling point, higher water solubility, and higher topological polar surface. This explains its higher affinity for the aqueous phase and thus poorer extraction into hexane. Eucalypt and alpha terpineol have similar retention times and logP, and the extraction efficiency differs slightly in the opposite direction than expected. This result can be explained by other physical properties. The boiling point would indicate better extraction efficiency for alpha-terpineol, and the water solubility and the topological polar surface explain why the extraction efficiency is lower for alpha terpineol compared to eucalypt.  
These results are important to understand the limitations of analyzing the extracts of a hydrosol. Extraction has different yields for different compounds, and the yields can differ significantly in some experimental setups, so the ratios among compounds can change substantially. However, if the aim of analysis is quantification of a compound for which the extraction yield is high, the extraction method is a good choice. An important drawback is also that highly water-soluble compounds cannot be detected. In this case, adulteration of a hydrosol with such compounds cannot be detected.
## 2 Methodology
### Evaluate Framework Using MCDA  
In this framework, we perform Multi-Criteria Decision Analysis (MCDA) to comprehensively evaluate the performance of LLMs using metrics such as human validation, precision, recall, semantic textual similarity, factual consistency, coherence, consistency, fluency, and relevance. The primary objective is to analyze and rank the LLM responses using specified weights and objectives, facilitating informed decision-making.  
**Human Validation and Gold Standard Responses (A)**: Human validation holds the highest weight, as it ensures the accuracy and reliability of results. This process involves human annotation and comparison with gold standard answers within the context.  
**Precision, Recall, and F1-Score (B)**: These core metrics evaluate the effectiveness of the LLM responses. They are equally weighted, emphasizing their critical role in determining accuracy and alignment with the gold standard answers.  
**Semantic Textual Similarity and Factual Consistency (C)**: These metrics assess contextual relevance and truthfulness. They are assigned slightly lower weights than direct performance metrics but remain essential for validating contextual alignment and factual accuracy.  
**Coherence, Consistency, Fluency, and Relevance (D)**: These qualitative metrics focus on the narrative quality and contextual appropriateness of the responses. They are assigned the lowest weights, ensuring balance in evaluating overall response quality.  
The criteria weights are defined as [1, 1, 1, 1, 0.9, 0.9, 0.85, 0.85, 0.85, 0.85], with all metrics set to be maximized. The highest weights (1) are assigned to \"Human Validation\", \"F1-Score\", \"Precision\", and \"Recall\", reflecting their impact on accuracy. Slightly lower weights are assigned to semantic textual similarity and factual consistency, while qualitative metrics receive the lowest weights.  
The Euclidean distance metric is used in TOPSIS because it effectively measures the straight-line distance in multi-dimensional space, ensuring a robust comparison of alternatives. Results are rounded to two decimal places for clarity. The function \"perform_MCDA\" processes the given DataFrame, applies the weights and objectives, and generates a comprehensive ranking of the LLMs.  
In Figure 5, the evaluation is divided into three categories:  
* **A (Gold Standard Answer + LLM Response + Context)**: Combines all elements for comprehensive validation and consistency.
* **B (Gold Standard Answer + LLM Response)**: Directly compares the gold standard answers with LLM responses to assess accuracy.
* **C (Context + LLM Response)**: Evaluates contextual suitability without considering the gold standard answer.  
This systematic MCDA approach provides a transparent and reliable evaluation of the LLMs, identifying the most suitable model for extracting patient characteristics from free-text eligibility criteria descriptions. Building upon this methodology, the subsequent section presents the evaluation results, offering a detailed analysis of model performance across multiple health-related categories.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
###### Abstract  
High Dynamic Range (HDR) and Wide Color Gamut (WCG) screens are able to render brighter and darker pixels with more vivid colors than ever. To assess the quality of images and videos displayed on these screens, new quality assessment metrics adapted to this new content are required. Because most SDR metrics assume that the representation of images is perceptually uniform, we study the impact of three uniform color spaces developed specifically for HDR and WCG images, namely, \(I\)_ICCp_, \(J_{z}\)_a\({}_{2}\)_b\({}_{z}\) and \(HDR\)-Lab_ on 12 SDR quality assessment metrics. Moreover, as the existing databases of images annotated with subjective scores are using a standard gamut, two new HDR databases using WCG are proposed. Results show that MS-SSIM and FSIM are among the most reliable metrics. This study also highlights the fact that the diffuse white of HDR images plays an important role when adapting SDR metrics for HDR content. Moreover, the adapted SDR metrics does not perform well to predict the impact of chrominance distortions.  
image quality; High Dynamic Range; color gamut; image representation; image analysis; image processing +
Footnote †: journal: Imaging Imaging
# Video-Based Parking Occupancy Detection for Smart Control System
## 3 Experimental Results
### Model for Reality Situation  
We created a model to simulate real scenes according to camera images detecting moving objects. The streetlight switched from the dim to the high state when a car entered a roadside parking space; then, the occupancy detection model detected and recorded when the car occupied the parking space. In the model, three LEDs on both sides of the street represent the streetlight. A 3.1-3.4 V white LED was soldered to a GPIO on the Jetson TX2. The status of parking grids was identified through the YOLO model, IOU, and overlapping judgment; the voting mechanism ensured that the target stopped and occupied the parking space. All these processes were executed on the Jetson TX2. Figure 14 depicts the final demonstration of the proposed model comprising two parts, namely the streetlight and parking occupancy detection sections. The automatic streetlight system switches to the dim setting at nighttime and the high setting during vehicle movement. In the daytime, no LEDs glowed. Time was the key that prompted the streetlight to switch between day and night settings. Figure 14a shows that at nighttime, no motion was detected by the camera, and therefore, the streetlight remained in the dim state. The proposed model, illustrated in Figure 14b-d, was designed only for the detection model to detect the object's presence from real-time images and glow more brightly; then, the remaining LEDs maintain their dim state. In Figure 14b, for instance, the first set of LEDs glowed at the high setting when an object was detected by YOLO, the second set glowed before the object approached, and the remaining LEDs stayed in the dim state because it was nighttime. Moreover, when the object moved to the second set, the second set of LEDs remained in the high setting, the first set reverted to the dim state, and the third set switched to the high state for prelight mode, as displayed in Figure 14c. These results demonstrate the efficiency of the proposed idea and validate the proposed model.  
Figure 15 presents parking status detection using the identify model. In the sequence of images received from the camera, for each frame, the detection model detected objects and calculated the  
Figure 13: Illustration of smart streetlight system.  
IOU and overlapping ratio to each parking space area. In the sequence of images, the green grids represented free parking spaces and the red ones represented occupied spaces. Figure 15a reveals that the red car drove to one of the parking spaces, the detection model detected the red car intersecting with the parking space. Then, the red car received a point from the voting mechanism until the score of the red car exceeded the threshold. The detection model then switched the parking grid from green to red, and the car was considered to be occupying the parking space.
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 4 Signaling Pathways Mediating the Link between Exercise, Irisin, and Insulin Sensitivity
### Exercise Regulating Irisin Secretion
However, no significant changes in FNDC5 mRNA levels in skeletal muscle were detected in the same subjects after 45 min of acute exercise (at 70% VO2 max), even though PGC-1\(\alpha\) mRNA levels were significantly increased by approximately 7.4-fold [77]. Additionally, in vitro experiments using electrical pulse stimulation to induce contraction of human skeletal muscle cells showed that FNDC5 mRNA levels were not changed with the upregulated PGC-1\(\alpha\) expression [18]. These results suggest that FNDC5 may not be a direct target gene of PGC-1\(\alpha\). There are also different reports regarding circulating irisin levels in response to exercise intervention in humans. [PERSON] et al. conducted a program of 6 months, 1 h daily exercise intervention among healthy subjects. The level of serum irisin increased by approximately 23.1% and 20.2% in the strength training and endurance exercise groups, respectively [78]. However, [PERSON] et al. demonstrated that 26 weeks of aerobic endurance or strength endurance training had no effect on circulating irisin levels in healthy subjects [79]. The divergent results might have resulted from the use of different ELISA kits that are the most common approach for measurement of serum irisin, and the ELISA kits need to be validated against tandem MS [19; 23]. On the other hand, there may be differences in individuals, races, and time of collection.
## 2 System Model and Problem Formulation
### System Model
We consider the linear minimum MSE (LMMSE) channel estimation from the observation vector \(\mathbf{y}\) in (5). In classical estimation theory [26], it is well known that the LMMSE estimate of the channel \(\mathbf{h}\) is given by  
\[\mathbf{\hat{h}}=\mathbf{C}_{h}(\mathbf{P}^{*}\otimes\mathbf{I}_{M_{r}}) \Big{[}\mathbf{C}_{n}+\Big{(}\mathbf{P}^{T}\otimes\mathbf{I}_{M_{r}}\Big{)} \mathbf{C}_{h}(\mathbf{P}^{*}\otimes\mathbf{I}_{M_{r}})\Big{]}^{-1}\mathbf{y}, \tag{6}\]  
where  
\[\mathbf{C}_{h}=\mathbb{E}[\mathbf{h}\mathbf{h}^{H}]=\mathbf{T}^{T}\otimes \mathbf{R}\]  
Figure 1: An illustrative example of 3 MIMO system under consideration, to which the proposed scheme is applicable. and  
\[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]0  
are the channel and interference covariance matrices, respectively. Also, we have  
\[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]1  
to represent the interference correlation at the transmitter side. In practice, the matrix inversion for the LMMSE channel estimation in (6) can be approximated or replaced by several low-complexity alternatives suggested in [27] based on the matrix polynomial expansion with arbitrary degrees of freedom. This approach indeed significantly reduces the computational burden of the matrix inverse operation from a cubic complexity to a square complexity. The MSE of the LMMSE channel estimate \(\mathbf{\hat{h}}\) given the parameters \(\mathbf{P}\), \(\mathbf{T}\), \(\mathbf{R}\), and \(\mathbf{Q}\) can be obtained as [26]  
\[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]2 \[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]3 \[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]4  
**Remark 1**. : _When the additive noise term \(\mathbf{W}\in\mathbb{C}^{M_{\tau}\times L}\) is considered (i.e., not ignored), the total interference-plus-noise term can be written as_  
\[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]5  
_Suppose that \(\mathbf{S}_{k},\forall k\), and \(\mathbf{W}\) are independent of each other and that the covariance matrix of \(\mathbf{W}\) takes the following form:_  
\[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]6  
_Then, it still follows that \(\mathbf{C}_{n}=\mathbb{E}[\mathbf{n}\mathbf{n}^{H}]=\mathbf{Q}^{T}\otimes \mathbf{R}\), with \(\mathbf{Q}\) given by_  
\[\mathbf{H}=\mathbf{R}^{1/2}\mathbf{H}_{w}\mathbf{T}^{1/2}, \tag{2}\]7  
_In a similar fashion, it is also possible to cover a multi-user or multi-cell scenario by treating \(\mathbf{W}\) as the intra-cell or inter-cell interference, respectively._
## 5 Numerical Modelling of Light Embankment Piling
### The Finite Element Model  
The numerical analysis was performed using the FE code PLAXIS 3D 2017 [43]. Figure 6 shows a cross section of half of the FE model. Note that the cross section shown in the figure is cut along the symmetrical vertical axis (left-hand side in the figure). The model width (transversal direction of the road) and depth were set to 100 and 31.7 m, respectively, in order to obtain realistic boundary conditions. The model length (longitudinal direction of the road) was equal to three pile rows (Figure 3), i.e., \(2s\) with an additional \(1/2s\) at each end for the square arrangement, and \(2.5s\) with an additional \(1/4s\) at each end for the triangular arrangement. The model length of three pile rows was chosen to model the stress distribution between the piles in both transversal and longitudinal road directions. The groundwater was allowed to flow due to consolidation through the upper horizontal boundary of the model as well as through the vertical outer model boundaries on either side of the road. All vertical boundaries were normally fixed. The ground surface boundary was fully free, and the bottom horizontal boundary fully fixed. A 10-node tetrahedral element mesh was refined in steps until there was no significant difference in the results. The final mesh, with soil layers, is shown in Figure 6. The final general target element dimension was 5.2 m, with 0.3 m and 0.5 m large elements in the embankment and in the pile group, respectively. The simulation of the piled embankment was divided into several stages of construction, in-situ conditions, excavation, pile installation, embankment and traffic load, and followed by a final consolidation simulation phase until the excess pore pressures reached \(1\) kPa (assumed as full consolidation). Note that no installation effect of the piles was simulated. The traffic load of \(15\) kN/m\({}^{2}\), required by TK Geo 13 [16], was added as a static load after 45 days of consolidation. The settlements were reset to zero at the start of the consolidation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(s\) (m) & 0.8 & 1.0 & 1.2 & 1.3 & 1.4 & 1.5 & 1.6 & 1.8 & 2.0 \\ \hline \(n_{piles}\) per row (\(n_{piles/row}\)) & 16 & 13 & 11 & 10 & 9 & 9 & 8 & 7 & 7 \\ \(n_{piles}\) per metre road (\(n_{piles/m}\)) & 20 & 13 & 9.2 & 7.7 & 6.4 & 6.0 & 5.0 & 3.9 & 3.5 \\ \hline \end{tabular}
\end{table}
Table 1: Number of piles, \(n_{piles}\), for each \(s\).
## 3 Results
### Sensory Analysis of Beers Dry-Hopped with Selected Samples  
Dry-hopped beers were prepared using the four selected hops to verify how sensory attributes are transferred to beers. The base beer (non-dry-hopped) was used for comparison. Data from the quantitative descriptive analysis of base and dry-hopped beers are presented in Figure 2, this provides the global sensation perceived (olfactory and gustatory) when the product is evaluated. Beers dry-hopped with PT05, PT11 and PT14 hops presented a significant increase in spicy, non-citrus fruits, and citrus perception, respectively. Although, in general, these results match with CATA sensory analyses of these hop cones (Table 2), the association between the sensory perceptions of hops and the respective dry-hopped beers is not always clear. Hops from the same sensory clusters would be expected to produce dry-hopped beers with similar sensory perceptions, but this was not observed. Dry-hopped beers from PT11 (G1) and PT14 (G2) hops presented similar hoppy and fruity intensities. Additionally, some hops attributes, such as floral in PT14 and non-citrus fruits in PT14 and PT15, were noted in hops but not in the respective dry-hopped beers.  
In a recent study, authors calculated the probability of perceiving a particular hop aroma in beer given by perceiving the aroma in hops. High probabilities were demonstrated for spicy, resinous, herbal, and grassy (100%), but only medium probabilities for citrusy (40%), floral (38%) and fruity (43%) aromas [21]. This is in general accordance with our results, where some positive associations, as well as some divergences, were demonstrated between the sensory analysis of hops and respective dry-hopped beers, mainly for floral and non-citrus fruit sensations. Therefore, although sensory analysis of hops can be useful for predicting some attributes of dry-hopped beers, it cannot be used to comprehensively  
Figure 1: Agglomerative Hierarchical Clustering and Correspondence Analysis of Portuguese wild hops sensory attributes.  
anticipate their final sensory attributes. Combinatory, competing, masking, antagonistic, and synergistic chemical effects may occur and be the reason for some odours to be present in hops but not in beer, as well as the opposite [3; 4]. Moreover, different aroma compounds responsible for the specific sensory attributes are expected to have different extraction patterns. The quantification of individual volatile compounds is important to have a better understanding and interpretation of sensory analysis results. In that sense, the aroma composition of dry-hopped beers and hops was evaluated to understand the aromatic characteristics that Portuguese wild hops impart to dry-hopped beer.