esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Analysis  
In this section, some efficient parameters have been analyzed to carry out a parametric investigation. Optimized parameters are used to verify the polarization conversion ratio and 3 dB linear-to-circular polarization bandwidth. To attain the optimal parameters, we varied the values of the substrate thickness (h), angle of incidence (\(\theta\) in degree), and material properties (\(\varepsilon_{r}\)). The influence of the parameters on the phase difference and 3 dB AR of the proposed metasurface unit cell was evaluated using some parameter simulations, as only one of the components varies, while the other remains constant.  
Figure 6 illustrates the calculated 3 dB AR and phase difference of the proposed metasurface. The 3 dB AR is influenced by varying the parametric values of the substrate thickness (h = 1.55, 1.60 and 1.65 mm), the angle of the incident wave (\(\theta=0^{\lx@math@degree}\) to \(30^{\lx@math@degree}\)), and material properties (\(\varepsilon_{r}\) = 2.0, 2.1 and 2.2), as illustrated in Figure 6a-c. However, increasing the substrate thickness (from 1.55, 1.60, and 1.65 mm) will modify the bandwidth of a metasurface, so 1.60 mm is preferred as the optimal size to achieve high polarization conversion efficiency in multiband frequency, as illustrated in Figure 6a. In the meantime, the AR performance is roughly influenced by varying the angle of the incident wave from its optimal value, as shown in Figure 6b. It can be seen that at multiple frequency bands, the AR values are stable up to \(20^{\lx@math@degree}\). One more important parameter of the proposed metasurface is the material properties. Figure 6c illustrates the response from varying the '\(\varepsilon_{r}\)'. The material properties had no significant effect on the CP conversion, however, and had a  
Figure 5: The calculated results for: (**a**) \(r_{RHCP-y}\) and \(r_{LHCP-y}\), and (**b**) PCR (LP–CP) with PER (dB) (dashed line indicates the PER condition (20 dB line)).  
slight impact on the overall bandwidth of the frequency bands, as depicted in Figure 6c. By varying the substrate thickness, Figure 6d demonstrates that the phase difference is almost \(\pm 90^{\lx@math@degree}\) or an odd integral multiple of \(\pm 90^{\lx@math@degree}\). From Figure 6d, it is clear the phase difference remains the same when varying the substrate thickness.
## 7 Simulation Result
### Flickr30k  
Analyzing the baseline performance (B) in Tables 6 and 7, it is evident that Qwen-2 achieves the highest performance across most evaluation metrics. In particular, Qwen-2 records the highest scores in BLEU, ROUGE, and CIDEr, indicating its superior baseline performance in caption generation. Meanwhile, Pixtral achieves the highest performance in METEOR and also records a high MoverScore, suggesting strong semantic similarity between generated and reference captions. Phi-3.5 demonstrates a higher CIDEr score than Pixtral and Llama 3.2, but it records the lowest performance in most other metrics. When applying the greedy prompt engineering strategy (Greedy PES), the optimal model \(m^{*}\) and optimal PE strategy \(p^{*}\) are found to be Qwen-2 with the ToT technique. Notably, this combination achieves the highest performance across METEOR, S-BERT, MoverScore, and CIDEr, further confirming its effectiveness in enhancing captioning performance. Additionally, Phi-3.5, despite being the smallest model with only 4.2B parameters, demonstrates comparable performance. This suggests that Phi-3.5 could be a resource-efficient alternative for general captioning tasks, particularly in hardware-constrained environments where computational efficiency is a key requirement. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **Phi** & **Llama** & **Pixtral** & **Qwen** & **Phi** & **Llama** & **Pixtral** & **Qwen** & **Phi** & **Llama** & **Pixtral** & **Qwen** \\ \hline
**Metric** & \multicolumn{4}{c}{**BLEU**} & \multicolumn{4}{c}{**ROUGE**} & \multicolumn{4}{c}{**METEOR**} \\ \hline B & 1.9661 & 2.6143 & 3.7315 & 4.359 & 0.0811 & 0.1412 & 0.2186 & 0.2555 & 0.0988 & 0.2393 & 0.3036 & 0.2784 \\ \hline I & 24.8959 & **10.8788** & 3.803 & 3.6845 & **0.5341** & **0.3779** & 0.1774 & 0.2103 & **0.5309** & 0.3883 & 0.3541 & 0.1791 \\ \hline I, S & 17.6712 & 3.6719 & 1.5761 & 4.1518 & 0.469 & 0.2048 & 0.0943 & 0.2272 & 0.4934 & 0.2803 & 0.1855 & 0.1988 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performances of BLEU, ROUGE, and METEOR according to MLLM models and prompt engineering techniques on the FLICKR30k dataset (The boldfaced numbers indicate the highest performance for each language model across different PES variants).
# Analysis of Vertical Stiffness Characteristics Based on Spoke Shape of Non-Pneumatic Tire
## 3 Results and Discussion
### Effect of Change in Spoke Shape on Directional Deformation  
Table 4 shows the results of the directional deformation analysis for each model under a 4000 N load.  
In all four models, it was confirmed that a significant change in vertical displacement occurred at the part that was in contact with the ground, and that the spokes bent organically toward each other and supported the load.  
In the fillet-applied model, the vertical displacement was the lowest at 17.693 mm, whereas it was the highest in the symmetric-spoke division model at 18.921 mm.  
Compared with the reference model, the directional deformation was lower by approximately 4.13% for the fillet-applied model and greater by 2.53% for the symmetric-spoke division model.
## 4 Detailed Analysis of Tunnel and Bridge Characteristics
### Time Histories and Narrow-Band Spectra of The Bridge Vibrations
From these ground vibration measurements, the frequency-dependent surface-to-tunnel reduction \(v_{T}/v_{S}\) has been evaluated in Figure 14 where the velocity amplitudes \(v_{T}\) near the tunnel are divided by the velocity amplitudes \(v_{S}\) near the surface line. The low-and high-frequency tunnel-to-surface ratios come close to one. A significant reduction was found in the mid-frequency range between 10 and 25 Hz for the low speeds (Figure 14a) and between 12 and 50 Hz for the high speeds (Figure 14b). The reduction goes down to \(v_{T}/v_{S}\) = 0.1 or even 0.03 for certain high-speed frequencies. Figure 13: Ground vibration from the tunnel line (**a,b**) and the surface line (**c,d**), one-third octave band spectra for (**a,c**) low train speeds: \(\square\) 160, \(\circ\) 125, \(\triangle\) 100, + 80 km/h, (\(x\) = 17.5, 20 m), and (**b,d**) high train speeds: \(\square\) 280, \(\circ\) 250, \(\triangle\) 200, + 150 km/h, (\(x\) = 8, 20 m). The surface-to-tunnel amplitude ratios of other institutes are collected in Figure 14c. The Highspeed2 prediction [35] uses a similar strong surface-to-tunnel reduction between 16 and 32 Hz for high-speed traffic in deep bored tunnels (square markers). Measurements of the Karlsruhe Institute of Technology [29] at a near-surface tunnel showed a reduction down to 0.1 between 12 and 32 Hz (triangle markers). The most complete measurements were performed by the Austrian Institute of Technology [31] where shaker excitation was used to measure the transfer function from the surface or (deep) tunnel track to the measurement points at the surface. From the measured ground vibrations from train passages and the transfer functions of the soil, forces have been back calculated and a comparison has been established based on these forces. The soil on the surface line has higher transfer values between 16 and 32 Hz so that the originally lower tunnel-surface ratios have been corrected to higher values of \(v_{T}/v_{S}\) = 0.3 (circle markers). All three high-speed measurements (Figure 14c) and the measurements of BAM (Figure 14a,b) agree well in the reduction of the mid-frequency component. Moreover, all measurements show a higher high-frequency tunnel-to-surface ratio. The high-frequency ratios are around the value one, somewhat lower for the German measurements and somewhat higher for the Austrian measurements. This high-frequency range starts between 32 and 50 Hz, and it is based on significantly high amplitudes on the tunnel and surface lines.
###### Abstract  
Long Term Evolution (LTE) is the prominent technology in Fourth Generation (4G) communication standards, which provides higher throughput and better Quality of Service (QoS) to all users. However, users in the cell-edge area are receiving comparatively low QoS due to the distance from eNodeB (eNB) and bad channel conditions. The Conventional Modified Largest Weighted Delay First (MLWDF) algorithm is unable to resolve this issue, as it does not consider the location of the user. This paper proposes an extended MLWDF (EMLWDF) downlink scheduling algorithm to provide better services to the cell-edge user as well as to the cell-center user. The proposed algorithm divides the eNB cell area into inner and outer regions. It includes the distance of the user from attached eNB, received Signal to Interference plus Noise Ratio (SINR) and error probability into the original algorithm. The simulated results are compared with other well-known algorithms and the comparison shows that the proposed algorithm enhances overall 56.23% of cell-edge user throughput and significantly improves the average user throughput, fairness index, and spectral efficiency.  
LTE; scheduling algorithm; MLWDF; cell-edge user; SINR +
Footnote †: journal:  
Article
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 3 Results  
### GWS Trend in Peru (2003-2023)  
The percentage values are based on GRACE-DA-DM GLV3.0 percentile maps (Figure 3a,b), which represent relative groundwater storage conditions in reference to a climatological baseline (2003-2023). Calculated monthly following the methodology [40], these percentiles assess the magnitude of hydrological anomalies, identifying conditions ranging from extreme drought (percentiles 0-10) to extreme surplus (percentiles 90-100), as shown in Figure 3a,b.  
The cited percentage values (e.g., decreases from 70% to 32%) do not represent absolute volumes, but rather relative groundwater storage conditions normalized against the 2003-2023 period. This analysis, based on spatial-temporal trends derived from satellite gravimetry, does not consider a fixed total aquifer capacity.  
The most critical declines occurred during 2007-2010 and 2015-2023, driven by ENSO-related variability and anthropogenic extraction. Appendix A (Tables A4-A6) provides detailed regional trends, highlighting the Northern, Central, and Southern Zone.  
Figure 3: \(Cont\).
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats
### Feature Extraction  
For formats other than tables, specific approaches have to be used to extract features. Due to patient records in the form of raw text being a common data format, NLP and semantic analysis seem like logical choices to turn to when it comes to text analysis and feature extraction. When importing a table from a new source, it will be organized unlike our existing schema. However, due to a lot of labels often resembling natural language phrases or words, it might be possible to find a similar phrase to them and transform them into analysable phrases. In turn, these could provide hints about which features are equivalent and how data should be transformed (Figure 2).  
A similar logic could be applied to patient record-based data. As the raw text gets processed, it is interpreted by semantic analysis algorithms such as NER and word vectors, which help recognize entities and find related terms. As a possibility, the words for units measuring the same thing could be related in the model.  
Feature extraction through NLP requires the application of multiple steps (Figure 3 [10, 16, 17, 18, 19, 22]):  
1. Find all entities in column names;
2. For each entity, check the existing labels for similarity using stemming and/or lemmatization. If they do, move on to step 3, otherwise assume there is no equivalent feature, fill the missing values with defaults and proceed to step 6;
3. If nearby words exist, perform semantic analysis. They might contain units. If the target dataset also has a unit specified in column name, utilize word vectors to establish a relation between them;
4. Assign a dependency between the two features, then proceed to step 6;
5. Repeat step 2 until all columns either have a dependency, or are marked to not have it;
6. Save the dependency list for possible future usage and merge the tables in accordance with it.  
With the merge complete, the entire dataset can now be saved for later usage by the core system.
## 8 Fixed and Adaptive Wave Domain Equalization
### Transform Domain Approaches  
WDAF utilizes a set of transformations that transform the multichannel adaptive equalization problem into a transformed domain. This basic idea of applying a spatial transformation has also been applied to non-adaptive room equalization aiming at a large listening area. In [206], the sound field  
Figure 10: Block diagram illustrating the concept of wave domain adaptive filtering (WDAF)-based room equalization. The driving signals for the \(N\) loudspeakers—denoted by \(\underline{\mathbf{d}}^{(N)}\)—are transformed into the wave domain using the spatio-temporal transform \(\mathcal{T}_{1}\), resulting in \(M\) transformed components \(\underline{\underline{\mathbf{d}}}^{(M)}\). These are filtered in the wave domain by the MIMO matrix \(\underline{\underline{\mathbf{\zeta}}}\) of equalization filters, resulting in the pre-filtered loudspeaker driving signals \(\underline{\underline{\mathbf{w}}}^{(M)}\), which are then transformed back by \(\mathcal{T}_{2}\). The acoustic paths between the \(N\) loudspeakers and \(M\) control points (microphones) are combined into the MIMO room transfer matrix \(\underline{\underline{\mathbf{R}}}\). The signals at the control points \(\mathbf{l}^{(M)}\) are transformed into the wave domain using transformation \(\mathcal{T}_{3}\), resulting in the transformed control signals \(\underline{\mathbf{l}}^{(M)}\). The desired free-field propagation is modeled in the wave domain by the MIMO matrix \(\underline{\underline{\mathbf{\zeta}}}\) of free-field transfer functions, resulting in the transformed desired signals \(\underline{\underline{\mathbf{s}}}^{(M)}\) at the control points. The error \(\underline{\underline{\mathbf{s}}}^{(M)}\) used for adaptation of the compensation filters is given by the difference of the transformed desired signals \(\underline{\underline{\mathbf{a}}}^{(M)}\) and the actual signals \(\underline{\mathbf{l}}^{(M)}\) at the control points.  
has been decomposed into circular/cylindrical basis functions for a concentric setup of loudspeaker and microphone array. This is essentially a two-dimensional problem. The equalization filters have been computed by least-squares optimization in the transformed domain. Room equalization has also been considered in the context of multizone synthesis by formulating the three-dimensional problem in the spherical harmonics domain (e.g., [207]).  
A rather different approach is discussed in [208]. Here the original HIE is interpreted such that the sound field exterior to a spherical loudspeaker array is attenuated by the usage of variable directivity loudspeakers. The attenuation of the exterior sound field leads to less reflections traveling back into the listening area. Although such loudspeakers have not yet been realized, the simulation results look promising. The equalization problem is considered in the spherical harmonics domain, where the filters are computed by least-squares optimization.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination
#### 2.5.3 Higher-Order Statistics  
Higher-order statistics describe the higher-order numerical characteristics of a signal. Higher-order moments can be represented as follows:  
\[\mu_{k}=E\Big{[}(X-E(X)^{k}\Big{]},\text{for k}\geq 2 \tag{3}\]  
where k represents the order of the moment, and \(\mu_{k}\) represents the value of the kth order moment. The first-order moment \(\mu_{1}=E[X]\) represents the mean; the second-order moment represents the variance, describing the degree of dispersion; the third-order moment represents skewness, used to measure the symmetry of the distribution; the fourth-order moment represents kurtosis, used to describe the degree of peakedness in the signal distribution.
## 2 Materials and Methods
### Quantification  
The water signal was used as the internal reference to quantify metabolite concentrations in the volume of interest (VOI) of the tissue. The internal reference is more stable than the external reference, as it is not associated with considerable large inter-individual variability [30; 31]. The internal reference spectra must be generated by the same brain VOI that generates the metabolite spectra [25; 32]. Water reference is a very strong signal that can be quickly acquired, even though it must be done without water suppression in a separate scan [33]. The area under the water MR spectra is calculated and multiplied to a correction factor to calculate water intensity:  
\[W_{Int}=W_{area}/\left(e^{-\frac{TE}{T_{2w}}}\right)\left(1-e^{-\frac{TE}{T_{ 2w}}}\right), \tag{12}\]  
where, \(W_{area}\) is the area under the water peak, TE and TR are the echo time and the repetition time, respectively, and \(T1_{w}\) and \(T2_{w}\) are the water relaxation times. Equation (2) was obtained from [34].  
The metabolite concentrations are calculated, while using area of a peak by integrating over a fitted basis model function for that metabolite. Specifically, the intensity of a peak, \(p\), of metabolite, \(m\), from MRS data derived from a PRESS pulse sequence is given by:  
\[I_{m,p}=[m]N_{m,p}Ke^{-\frac{TE}{T_{2w}}}\left(1-e^{-\frac{TR}{T1_{m,p}}} \right), \tag{13}\]  
where \([m]\) is the concentration of metabolite, \(m\); \(I_{m,p}\) is the intensity of metabolite \(m\) in peak \(p\); \(N_{m,p}\) is the number of hydrogens in the molecular group of m that gives rise to peak, \(p\), \(K\) is a scale factor that takes into account the MR machine, \(TE\) and \(TR\) are the echo and repetition times of the PRESS sequence; and, \(T1_{m,p}\) and \(T2_{m,p}\) are the longitudinal and transverse relaxation times of the hydrogen in the molecular group of m that gives rise to peak, \(p\). Hence, the absolute quantification of metabolite concentration is achievable using a water reference concentration, yielding:  
\[[m]=2[W_{Int}]e^{-\frac{TE}{T2_{2w}}}\left(1-e^{\frac{TE}{T1_{W}}}\right) \frac{\sum_{p}{}^{\,{}^{\,{}^{\,{}^{\,{}^{\,{}^{\,{}^{\,{}^{\,{}^{\,{}^{ \,}^{\,{}^{\,}^{\,}^{\,}^{\,{}^{\,}^{\,}^{\,}^{\,}^{\,{}^{\,}^{\,}^{\,}^{\,}^{ The in vivo MRS and MRI data were collected from two different regions of four healthy volunteers. Two brain regions of each subject that are commonly affected by [PERSON]'s disease, the Inferior Precuneus (IP) and the Posterior Cingulate Cortex (PCC), were scanned [36; 37]. For one of the subjects, in addition to PCC and IP, three other regions (left and right hippocampus, as well as ventral posterior cingulate gyrus) were also scanned, in order to assess water concentration variability in different brain regions of an individual.
## 4 Results and Discussion
### Comparison Results
(2) In terms of the highest TS, the error distribution of the proposed MDRN model falls in the range of less than 5% (accounting for 95%) and 10% (accounting for 100%) for Case 1, and 5% (accounting for 95.5%) and 10% (accounting for 99.5%) for Case 2. However, the error distributions of the comparison model are scattered in many ranges (even 4%-7% errors beyond TS10 for Case 1, and 3%-5% errors beyond TS10 for Case 2). (3) In terms of all of the criteria, the rankings for prediction performance, from best to worst, are as follows: MDRN, AE-based DRN, LSSVR, and BPNN. Therefore, according to the qualitative analysis and the quantitative analysis, the proposed model MDRN, which combines manifold dimension reduction and deep feature learning, is beneficial for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Performance**} \\ \cline{3-7}  & & **MAPE (\%)** & **RMSE** & **TS\({}_{\textbf{1}}\) (\%)** & **TS\({}_{\textbf{5}}\) (\%)** & **TS\({}_{\textbf{10}}\) (\%)** \\ \hline \multirow{4}{*}{1} & Proposed model & 1.943 & 0.022 & 27 & 95 & 100 \\  & AE-based DRN & 2.388 & 0.033 & 25 & 91 & 96 \\  & LSSVR & 2.543 & 0.034 & 24 & 91 & 96 \\  & BPNN & 3.210 & 0.044 & 26 & 83 & 93 \\ \hline \multirow{4}{*}{2} & Proposed model & 1.861 & 0.023 & 35.5 & 95.5 & 99.5 \\  & AE-based DRN & 2.201 & 0.031 & 34.5 & 93 & 97 \\ \cline{1-1}  & LSSVR & 2.291 & 0.032 & 32 & 93 & 97 \\ \cline{1-1}  & BPNN & 2.757 & 0.038 & 30.5 & 85.5 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the prediction performances using different models. Figure 8: Prediction results of Case 2 using different models. (**a**) AE-based DRN with 19-40-40-1, (**b**) LSSVR, and (**c**) back propagation neural network (BPNN) with 19-12-1.  
exploring the sophisticated relationships between manufacturing multi-parameter inputs and quality, and displays a better prediction capacity for manufacturing quality. Based on the Friedman test at a 5% level [42], the differences between the proposed MDRN model and the comparison models are statistically significant. Specifically, the values of significance (asymptotic) are 0.029 (Case 1) and 0.001 (Case 2), respectively, or less than 0.05 in both cases. Hence, the proposed MDRN model, which has a significant difference with other candidates, is an effective attempt for the multi-parameter manufacturing quality prediction.