esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scientific Mapping  
Table 2 shows the twenty articles selected by the search strategy.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ref.** & **Title** & **Publication Year** \\ \hline
[40] & A novel design engineering review system with searchable content: & 2017 \\  & knowledge engineering via real-time multimodal recording & 2017 \\ \hline
[41] & An Industry Case Study: Investigating Early Design Decision & 2017 \\  & Making in Virtual Reality & 2017 \\ \hline
[42] & Automatic derivation of geometric properties of components from & 2017 \\  & 3d polygon models & 2017 \\ \hline
[43] & Interaction techniques for virtual reality based automotive & 2017 \\  & design reviews & 2017 \\ \hline
[44] & VR-based operating modes and metaphors for collaborative & 2017 \\  & ergonomic design of industrial workstations & 2017 \\ \hline
[15] & A virtual reality supported 3D environment for engineering & 2018 \\  & design review & 2018 \\ \hline
[45] & Chances and Limitations of a Virtual Reality-supported Tool for & 2018 \\  & Decision Making in Industrial Engineering & 2018 \\ \hline
[46] & Comparison of the Usability of a Car Infotainment System in a & 2018 \\  & Mixed Reality Environment and in a Real Car & 2019 \\ \hline
[47] & A study of deriving usability evaluation factors on virtual & 2019 \\  & reality contents & 2019 \\ \hline
[16] & Analyzing the potential of Virtual Reality for engineering & 2019 \\  & design review & 2019 \\ \hline
[48] & Combining Virtual Reality (VR) Technology with Physical & \\  & Models—A New Way for Human-Vehicle Interaction Simulation & 2019 \\  & and Usability Evaluation & \\ \hline
[49] & Usability Evaluation of VR products in Industry—A Systematic & 2019 \\  & Literature Review & 2019 \\ \hline
[50] & User centered design of interaction techniques for VR-based & 2019 \\  & automotive design reviews & 2019 \\ \hline
[51] & User Experience-and Design-Oriented Virtual Product & 2019 \\  & Prototyping System & \\ \hline \end{tabular}
\end{table}
Table 2: List of selected articles.  
These articles were reviewed in order to answer the research questions Q2 and Q3. It is worth noting that the attributes addressed by Q2 and Q3 are not abstracted at the same level and are not always mutually exclusive in the studies reviewed; furthermore, some studies did not mention some of these aspects. Our findings are provided in the subsections below.  
Q2: In terms of the application fields, methods, hardware, and software involved, how is current knowledge on the application of virtual reality in usability testing and design review in industry defined?
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 2 General Theory
\[\overset{\rightarrow}{c}_{k+1}=\overset{\rightarrow}{c}_{k}+\Delta\epsilon _{k}\overset{\rightarrow}{V}_{k}^{*}, \tag{7}\]  
\(\overset{\rightarrow}{c}_{k}\) is the equalizer's coefficients vector for the \(k\)th iteration, \(\Delta\) is the convergence factor chosen as small enough to ensure convergence of the iterative procedure, \(\epsilon_{k}=\hat{I}_{k}-\hat{I}_{k}\) is the error signal at the \(k\)th iteration, and \(\hat{I}_{k}\) is the equalizer output at the same iteration, while \(\overset{\rightarrow}{V}_{k}\) is the equalizer signal samples input that make up the estimate \(\hat{I}_{k}\cdot\overset{\rightarrow}{V}_{k}^{*}\) is the complex conjugate of \(\overset{\rightarrow}{V}_{k}\). In our case, the channel is an optical SMF. We can model the SMF as a filter with a fractional response [15] as shown in Equation (8), or in time domain in Equation (9). These equations describe the CD impact on the signal that leads to pulses' expansion and so causes ISI. \[G(z,\omega)=\exp\left(-j\frac{D\lambda^{2}}{4\pi c}\omega^{2}z\right)\,, \tag{8}\]  
\[g(z,t)=\sqrt{\frac{c}{jD\lambda^{2}z}}\exp\left(j\frac{\pi c}{D\lambda^{2}z}t ^{2}\right), \tag{9}\]  
where \(z\) is the distance of propagation, \(t\) is time variable in a frame moving with the pulse, \(\omega\) is the frequency variable, \(\lambda\) is the wavelength, \(c\) is the speed of light, and \(D\) is the dispersion coefficient of the fiber.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 5 Analysis of Fatigue Life
### Analysis of Abrasion Fatigue  
As shown in Figure 15, abrasion occurs on contact surfaces between the flexible structure and the rigid connector. Abrasion will loosen fasteners and cause fatigue damage [13]. Therefore, analyzing the abrasion of the contact surface is necessary. The non-flatness error is applied to one of the contact surfaces in front of the flexible structure to simulate abrasion. The surface roughness of the abrasion surface is considered beyond Ra6.3 in the subsequent fatigue analysis. The analysis results showed that the fatigue life of the flexible structure is about 45,000,000 cycles.
###### Abstract  
We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.  
Keywords:classification algorithms; data processing; machine learning; computational methods; predictive models; rainfall forecasting; extreme gradient boosting (XGBoost); random forest (rf) +
Footnote †: journal: Applied Sciences
## 5 Results
The SRT protocol's ability to adaptively manage these parameters allows for fine-tuning based on specific application requirements, such as the need for low-latency video conferencing or high-quality live streaming. This parameterized control enables stable, low-latency video delivery over long distances, as the protocol can adaptively manage transmission characteristics (Figure 8). Moreover, we evaluated the system under adverse network conditions and compared it with a conventional RTMP-based approach. The results demonstrate that our proposed method achieves a tenfold improvement in frame delivery compared with the baseline.
## 3 Results
### Efficacy Assessment
#### 3.3.2 Antioxidant Effect  
The antioxidant effect was evaluated through the quantification of intracellular ROS, 8-OHdG (in DNA and mtDNA) and CPDs. The latter two evaluations were conducted to assess the capacity of the sample to protect DNA from oxidative damage. Figure 6A demonstrates a significant increase in ROS levels for the positive control (CTR+) in comparison to the negative control (CTR\(-\)). Treatment with PLG-GRAPE resulted in a reduction of ROS levels, approaching those observed in the negative control group, thereby indicating a high degree of antioxidant activity.  
Figure 5: Evaluation of cell viability: (**a**) Results obtained from HaCat cells; (**b**) Results obtained from BJ cells. Values are shown as mean \(\pm\) st. dev (* \(p\) < 0.05 _t_-test vs. negative control), based on three replicates.  
Figure 6: Evaluation of antioxidant activity on HaCat cells through: (**A**) Intracellular ROS quantification; (**B**) CPDs quantification; (**C**) 8-OHdGquantification in total DNA; (**D**) 8-OHdGquantification in mtDNA. Values are shown as mean \(\pm\) st. dev, based on three replicates. (* \(p\) < 0.05 _t_-test vs. negative control).  
Figure 6B shows higher levels of CPDs in the CTR+ group compared to the CTR\(-\) group. Treatment with PLG-GRAPE led to a reduction in CPD levels, thereby indicating partial protection of the DNA.  
Figure 6C demonstrates elevated levels of 8-OHdG in the CTR+ group. Treatment with PLG-GRAPE resulted in a significant reduction of these levels, highlighting their capacity to mitigate oxidative damage to DNA.  
Furthermore, Figure 6D reveals increased 8-OHdG levels in CTR+ and demonstrates that PLG-GRAPE effectively reduces these levels, thus, showing protection against mitochondrial DNA oxidative damage.
# _Applied_
## 5 Feature Selection for both ML Approaches
### Features Information Gain  
Figure 4 contains the most relevant features of the three datasets. These features are evaluated using the InformationGain algorithm, which uses entropy as a metric. The entropy values are used to identify the most relevant attributes of the selected features. For the ESC-10 dataset, a decrement below 0.3 occurs from the attribute _mel 84_ til a value of 0.05 for the attribute _mel 117_. Similar results are achieved when evaluating the BDLiB dataset. For that dataset, these attributes range from 0.32 up to 0.1 for the attribute _mel 123_. The number of relevant attributes is strongly linked to the dataset. While the ESC-10 and BDLib datasets present similar ranking for the evaluated features, the UrbanSound dataset presents a lower number of relevant attributes. As a result, Figure 4 shows how the relevance of valuable attributes for the ESC-10 or BDLIB datasets decreases for UrbanSound dataset. In fact, most of the attributes present an entropy lower than 0.3. Figure 4 allows us to conclude that there is a decrease from the attribute _mel 38_ in all cases, with a higher degree of decline for the UrbanSound dataset.  
Figure 4: Information gain of the attributes for the ESC-10, BDLib and UrbanSound datasets.  
This feature analysis is used in the comparison of classifiers to evaluate the impact of the set of features based on the classifiers and the datasets. Based on the _librosa_ available features, a total of up to 170 attributes can be used when ten features are considered. Nonetheless, for a complete analysis towards its consideration for embedded systems, a reduced set of seven features (up to 24 attributes) is also considered, expecting a reduced feature extraction time. Figure 5 summarizes which features are used and how many dimensions they have. All the features used are from the _librosa_ library and are spectral features. Even though certain features are multi-dimensional (MEL, SCT, MFCCs, CHROMA and TZ), they are transformed into one-dimensional arrays by taking the average over the frequencies. These one-dimensional arrays are then put together in 1 large one-dimensional array, which represents the feature group of one audio frame. These one-dimensional arrays are the input for the classifiers. Notice that each array is used as a single training or test sample and not combined with other arrays from the same audio file to generate a combined feature group per audio file.
## 6 Results
### Heart Rate  
Table 2 shows the performance scores for the heartbeat analysis. Each row corresponds to a single test subject. As mentioned in Section 4.3, several scores are calculated for each test subject. F1 score, sensitivity and precision are calculated from the number of TP, FP and FN values. Additionally, the reference as well as the predicted number of single heartbeats and the overall measurement time are given. Using the scores of each test subject, the mean performance can be calculated. This can either be done by calculating the mean over all persons (micro mean) or summing up the number of TP, FP and FN first and calculating the scores based on these values (macro mean). For the first case, the standard deviation is also reported. The F1 micro mean for all test subjects is 93.14% with a standard deviation of 10.74%. The F1 macro mean is slightly lower with 92.82%. The macro means for sensitivity and precision are 92.73% and 92.90%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & \(\mathbf{\hat{R}_{xyz,max}}\) & \(\mathbf{m_{max}}\)a  & \(\mathbf{RMSE_{ACF}}\)b  & \(\mathbf{RMSE_{EC}}\)b  & \(\mathbf{RMSE_{PS}}\)b  & \(\mathbf{RMSE_{FFT}}\)b [FOOTNOTE  
F1, sensitivity and precision range from approximately 50% up to 100%. In accordance with common practice, scores from test subjects whose F1 scores deviate more than two standard deviations from the mean F1 value are omitted. In this case, scores from test subjects with an F1 score lower than 71.66% are excluded. This is only the case with test subject 14. The scores after exclusion can be seen in Table 3.  
The overall scores clearly improve by up to almost two percentage points. The F1 macro mean is improved from 92.82% to 94.72%. The F1 micro mean now is 94.63% instead of 93.14%. Furthermore, the standard deviation is reduced from 10.74% to 7.14%.
## 3 Materials and Methods
### Retrieval of Biophysical Variables from Sentinel-2 LIC and L2A Images  
As a proof-of-concept of the developed TOA radiance retrieval algorithms, a S2-A image was selected for both the TOA L1C and BOA L2A reflectance products. The chosen image was acquired by S2A on 22 August 2018 at 12:56 h (UTC time +2 h) over the area of Barrax (Spain). Barrax is a sparsely vegetated site located in Spain between 38.75\({}^{\circ}\) N and 39.75\({}^{\circ}\) N and 1.73\({}^{\circ}\) W and 3.00\({}^{\circ}\) W. It is predominately flat with a mean elevation of 700 m above sea level (a.s.l.), although there is some rugged terrain in the northeast reaching 1185 m a.s.l. For the given location and acquisition date, The image was illuminated with a mean SZA of 30.8\({}^{\circ}\). Since the focus here is retrieval over vegetated surfaces, a subset over the Barrax agroecosystem was chosen (600 \(\times\) 600 pixels). This region is characterized by large agricultural fields with center pivot irrigation systems. Main crops are wheat, alfalfa, rapeseed, sunflower and garlic. In August, the non-irrigated areas are bare soil or senescent vegetation. In addition, an AOT at 550 nm of approximately 0.15 was determined from the AERONET stations of Aras de los Olmos (at 130 km north-east) and Murcia (100 km south-east) at the time of observation.  
As described in the section above (Section 3.5), we applied the GPR retrieval algorithms to S2 L1C and L2A data for the GSA-identified dominant thus retrievable variables. To do so, the S2 L1C TOA reflectance data first had to be converted to TOA radiance data, which is done in the SNAP toolbox. In addition, only the 10 m and 20 m bands were used at 20 m resolution without the broadband B8 as it is overlapping with B8a (see Figure 1).  
Further, in an attempt to make the models better fit to process real S2 data, Gaussian noise was added to the _retrieval_ TOC and TOA training datasets. The addition of noise to the RTM generated spectral bands has multiple purposes: it simulates errors of radiometric calibration, atmospheric noise and residuals from the atmospheric correction, but to some extent also bridge between the simplified representation of the RTM and the actual radiometric behaviour of the canopy [89]. Generally, noise prevents the retrieval model from over-fitting on the training database. However, an accurate quantification of all error terms in the sensing process remains difficult [89]. While for the TOC training dataset noise levels can be obtained from S2 surface reflectance studies as in [50], for TOA that is not the case. After some testing of additive and multiplicative noise levels, eventually, a 2% multiplicative Gaussian noise was used. The GPR model development and image processing were done in the MLRA toolbox. Finally, to account for the non-vegetated surfaces, 20 distinct bare soil spectral signatures were added to the L1C and L2A training datasets.
# Policy Similarity Measure for Two-Player Zero-Sum Games
## 5 Experiments
### Results and Analysis  
#### 5.3.1 Non-Transitive Mixture Model  
The exploration trajectories and final exploitability values for the non-transitive mixture model environment are presented in Figure 1. The trajectory of explored strategies visually illustrates the ability of each algorithm to navigate the Gaussian humps. PSM-PSRO exhibited a well-distributed exploration pattern, effectively covering the policy space compared to baseline methods. In quantitative terms, PSM-PSRO achieves the lowest exploitability (Exp = 2.83 \(\pm\) 0.91), which is approximately 7% lower than that of the best-performing baseline method (Exp = 3.06 \(\pm\) 0.80). This highlights the effectiveness of PSM-PSRO in mitigating exploitability by reducing policy redundancy and promoting diversity.  
The trends of the PSM and UDM during training are shown in Figure 2. In Figure 2a, the PSM value for PSM-PSRO is lower than those of all baseline methods, indicating reduced redundancy within the policy population. Figure 2b reveals that UDM values for PSM-PSRO stabilize at higher levels than other methods, confirming its superior ability to promote and sustain diversity throughout the training process. This result supports the PSM method, demonstrating that minimizing payoff vector similarity leads to more diverse and robust policy sets.  
Figure 1: Non-transitive mixture model. Exploration trajectories during training. The final exploitability \(\times\)100 (Exp) is reported at the bottom for each method.The bold values in the figure represent the best-performing results.  
The final policy similarity matrices, visualized as heatmaps in Figure 3, further validate the efficacy of PSM-PSRO. Unlike baseline methods, which exhibit significant clusters of high-similarity policies (highlighted in red), PSM-PSRO generates a more heterogeneous policy population, as indicated by the dominance of cooler tones (blue) in its heatmap. This visual evidence aligns with the quantitative findings, demonstrating that PSM-PSRO effectively reduces intra-population redundancy while fostering a diverse policy set.  
This improvement can be attributed to the susceptibility of existing diversity algorithms to producing redundant policies, as evidenced by the visual results in Figures 1 and 3. In contrast, PSM-PSRO effectively alleviates policy redundancy by penalizing excessively similar strategies, thereby fostering a more diverse and robust policy population.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 6 Results
### Qualitative Analysis
#### 6.6.2 Other Factors Influencing Bundle Generation  
There are notable differences in the completeness and complexity of the bundles generated for the skin care and mammography clinics. The bundles generated for the skin care clinic are generally more complete and complex, often including detailed patient information, diagnostic notes, and treatment plans. By contrast, the bundles generated for the mammography clinic sometimes include only patient and practitioner names, lacking the comprehensive details required for a full FHIR representation. This disparity may be attributed to the nature of the clinical reports from each clinic as observed in Section 6.2, with the skin care reports containing more structured and detailed information.  
Few-shot prompting helps the models to identify more information and generate more complete bundles. By providing examples, the models are better guided in understanding the expected format and content of the FHIR bundle. However, a notable issue with few-shot prompting is that the models sometimes follow the example too closely, reproducing the values as they appear in the example without updating them with the actual values from the clinical report. For instance, the models might retain placeholder values like \"DateTime: YYYY-MM-DD\" instead of inserting the correct date and time from the document. This overreliance on the example can lead to errors and inaccuracies in the generated bundles. The impact of temperature on the LLMs' performance appears to be minimal. Temperature settings, which control the randomness of the models' output, do not seem to significantly affect the resource mapping accuracy or the completeness of the bundles. This suggests that the models' performance is more influenced by the prompting technique and the presence of examples rather than temperature variations.  
Overall, there are few significant differences between Mistral and Gemini, except that Mistral tends to retain more of the original text in Spanish. This characteristic of Mistral may be beneficial in preserving the nuances of the original clinical reports but can also introduce challenges in standardizing the information for FHIR conversion. Gemini, on the other hand, appears to be more adept at translating and structuring the information in a way that aligns with FHIR standards.
## 2 Experimental Details
### Selection of the Radiographic Examination Technique  
RT testing of pipes using radiation is categorized into double-wall single images (DWSIs) and double-wall double images (DWDIs) based on the pipe's outer diameter. As shown in Figure 1, the DWSI technique is used for medium and large pipes with an outer diameter of 80 A (3.5 inches or larger). The radiation source is placed close to the welded area, penetrates the first pipe wall, and images the weld on the opposite side [11].  
DWDI is used for small pipes. Due to the small outer diameter, inspection is performed at a long source-to-film distance (SFD) on a flat elevated state without wrapping the film or DDA. This study focused on developing an inspection device for large pipes in the field and selected a technique that can automatically perform DWSI inspection.
## 3 Method
### Data Reduction and Evaluation  
The raw data sequences (respiratory data and HR data) were downsampled to 4 Hz, i.e., in total 1200 samples for each sequence. After adjusting to zero-mean, both respiratory and HR data were filtered with a band-pass finite impulse response (FIR) filter of length 256 of 3 dB-bandwidth 0.1-0.5 Hz. The first and last data samples were corrupted from the transient of the filter, and, as data for the further analysis, the middle 960 samples (4 min) were used.