esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 3 Results
### Block Average and Group-Level Results
Positive \(t\)-values (i.e., red color scale) indicate activation sites, while negative values (i.e., blue color scale) indicate inhibition. However, changing patterns were seen relevant to \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SNR Left Grasp**} & \multicolumn{2}{c}{**SNR Left Grasp**} & \multicolumn{2}{c}{**SNR Right Grasp**} & \multicolumn{2}{c}{**SNR Right Grasp**} \\  & **Left Hemisphere [dB]** & **Right Hemisphere [dB]** & **Left Hemisphere [dB]** & **Left Hemisphere [dB]** & **Right Hemisphere [dB]** \\ \cline{2-8}  & \(\Delta[HbO_{2}]\) & \(\Delta[HbR]\) & \(\Delta[HbO_{2}]\) & \(\Delta[HbR]\) & \(\Delta[HbO_{2}]\) & \(\Delta[HbR]\) & \(\Delta[HbO_{2}]\) & \(\Delta[HbR]\) \\ \hline SC-MARA-spline & 5.24 & 4.37 & 14.67 & 11.84 & 13.27 & 10.14 & 4.29 & 3.54 \\ MC-MARA-spline & 3.98 & 3.40 & 10.99 & 9.94 & 13.5 & 9.53 & 7.34 & 4.75 \\ SC-MARA-tPCA & 8.22 & 4.72 & 13.11 & 11.47 & 11.78 & 11.01 & 4.97 & 2.75 \\ MC-MARA-tPCA & 5.77 & 3.11 & 12.53 & 9.55 & 9.9 & 9.48 & 5.18 & 5.08 \\ Hybrid MARA-spline SG & 5.41 & 5.15 & 11.54 & 9.92 & 12.29 & 11.12 & 4.16 & 3.66 \\ Hybrid MARA-tPCA SG & 5.35 & 4.35 & 11.16 & 10.01 & 10.93 & 9.97 & 6.4 & 6.04 \\ TDDR & 4.52 & 2.43 & 8.99 & 11.15 & 9.17 & 9.60 & 3.69 & 3.50 \\ Wavelet & 5.39 & 3.52 & 11.51 & 9.62 & 13.77 & 10.94 & 6.63 & 5.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Median value of block-averaged responses of SNR [dB] across all subjects, motor task conditions, and hemispheres. SNR was computed over motor channels displayed in Figures 7 and 8 and considered \(t_{activation}\) in the 6–12 s range after stimulus onset, with \(t_{baseline}\) in 24–30 s.  
other regions, such as secondary motor areas, frontal cognitive areas, and sensory, parietal, and occipital ones. Clearly, further research is needed to extend detection robustness and reliability to ancillary areas, which play a central role in the study of neuro-plasticity mechanisms addressed in neurological clinics.
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 3 Results
### Water Quality Index and Vulnerability Assessment  
The WQI provides a single value of expression, which outlines various parameters and gives a measure of the water quality. A number of 15 physico-chemical parameters were used for the calculation of the WQI (pH, TDS, Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), Na, Mg, Ca, Cu, Zn, Pb, Fe, Ni, Mn and As). The results ranged from 17.7 to 103 with a mean value of 31.9 (for all 30 samples), and the mean water quality was rated as \"good\" in general. Spatially, 21 samples were ranked with excellent quality, three samples with good quality (A14, A15, A16), four with medium quality (A9, A10, A11, A13), one sample was characterized by poor quality (A7) and one sample unsuitable for usage (A12). Thus, the WQI scores. were significantly different among the four spatial groups (Figure 8).  
Figure 7: Spatial variation of heavy metal pollution index HPI (**a**) and heavy metal evaluation index HEI (**b**).  
The WQI results were relatively high in Group I (with a mean of 19.1). The second group was characterized by a mean value of 62.9, with only one sample with a WQI score lower than 50 (WQI < 50). Group II area is localized nearby a tailing impoundment and an industrialized location. The WQI has a mean value of 28.2 in Group II. Water quality improves as the distance from mine drainage sources increases in Groups III and IV. According to the WQI classification, the general water quality state characterizes Group I with excellent quality, Group II with moderate quality and Groups III and IV with good and excellent quality.  
The WQI scores obtained from different studies from Africa (Limpopo) range between 121 and 4644, indicating very poor quality water and not suitable for drinking due to the rich precipitation, high ionic activities and sedimentation [56]. In Turkey (Kirmir Basin), according to the WQI results, the studied water samples are characterized by very poor quality due to urbanization and anthropogenic activities; WQI ranged from 7.4 to 38.1 [57]. There were considerable spatial disparities in the assessed vulnerability of surface water resources in the study area.  
Covering \(\approx\) 68% of all the study area, the vulnerability level was generally reduced, especially in the western and eastern regions. In the central area of the Aries catchment, high and very high vulnerability levels were observed, overlapping with the discharge area of the tailing impoundments, washing water flux and all the streams influenced by their tributary's poor-quality status. Similar to the water quality classification based on the WQI, a medium vulnerability level was overlying the area characterized by a quality improvement trend (Group III).  
Surface water resources in the Apuseni Mountains are abundant, while the ratio between regional supply and water demand in the study area was relatively small. Due to the historical and current mining activities, agricultural water consumption, and low efficiency of water use, the vulnerability of water resources in the Aries catchment remains high.
## 1 Introduction
In the sub-problem considered, the MAB player will be the nearest survival GBS, the arms will be the post-disaster clusters, and the rewards are the aerials' achievable data rates. * To ensure fairness in clusters coverage based on users' densities, we propose that GPS localization will be used to pre-estimate users' locations, which will be refined through the aerials' exploration during the proposed combinatorial MAB game. * In the second sub-problem and after distributing the aerials among clusters, a genetic algorithm (GA) approach [20] will be utilized to optimize the aerials' TX powers and LEO-Sat's bandwidth resources based on the aerials' traffic coming from users' loads in their covered clusters. GA is a well-known optimization algorithm that can effectively address constrained non-linear optimization problems by means of penalty hypothesis. * Extensive numerical analysis is conducted to confirm the effectiveness of the envisioned approach against benchmark schemes, including basic MAB approaches. The remainder of this paper is arranged as follows: Section 2 summarizes LEO-Sat aerial-related works. Section 3 previews the envisioned system model of LEO-aerial integration followed by optimization problem formulation. Section 4 figures out the proposed \"CB-FBC\" algorithm and \"GA\"-based aerials' powers and LEO-Sat's bandwidth resource allocation. Section 5 discusses the numerical simulations conducted, and Section 6 delivers the concluding remarks of this paper.
## 3 Background
### Graph Attention Network  
GAT [21] is a graph neural network developed for processing graph-structured data, with the aim of learning the relationships between nodes and their local structural information. As shown in Figure 2, GAT combines the principles of self-attention mechanisms with graph convolutional neural networks, effectively capturing the complex nonlinear relationships between nodes by learning the interactions between each node and its neighbors.  
The core idea of GAT is to utilize attention mechanisms to dynamically compute the weights between a node and its surrounding nodes, allowing for more effective aggregation of information from its neighbors. In each layer, GAT calculates an attention coefficient for each node, reflecting its importance in relation to its neighbors. These attention coefficients are then used to compute a weighted sum of the features from the neighboring nodes, resulting in a new representation for each node. The formula for GAT is as follows:  
\[h_{i}^{(l+1)}=\sigma\Big{(}\sum_{j=1}^{N}\alpha_{ij}^{(l)}W^{(l)}h_{j}^{(l)} \Big{)}, \tag{1}\]  
where \(h_{i}^{(l)}\) is the feature representation of node \(i\) in layer \(l\), \(W^{(l)}\) is the weight parameter for layer \(l\), \(\alpha_{ij}^{(l)}\) is the attention coefficient from node \(i\) to node \(j\), and \(\sigma\) is the activation function, typically ReLU or LeakyReLU. The attention coefficient \(\alpha_{ij}^{(l)}\) is calculated as follows:  
\[\alpha_{ij}^{(l)}=\frac{\exp\Big{(}\mathrm{LeakyReLU}\Big{(}a^{\top}\Big{[}W^ {(l)}h_{i}^{(l)}\Big{|}\Big{|}W^{(l)}h_{j}^{(l)}\Big{]}\Big{)}\Big{)}}{\sum \limits_{k\in\mathcal{N}(i)}\exp\Big{(}\mathrm{LeakyReLU}\Big{(}a^{\top} \Big{[}W^{(l)}h_{i}^{(l)}\Big{|}\Big{|}W^{(l)}h_{k}^{(l)}\Big{]}\Big{)}\Big{)} }, \tag{2}\]  
where \(a\) is the self-learned attention parameter and LeakyReLU is an activation function that enhances the nonlinear characteristics of neural networks. \(\mathcal{N}(i)\) represents the set of neighboring nodes of node \(i\), and \(||\) denotes the concatenation operation.  
Figure 2: GAT principle data flow diagram.
# Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations
## 2 Previous Work  
The scarcity of emotional audio data for training SER systems has led to the application of the cross-corpus strategy in this field. However, the results obtained with this strategy have been inconclusive, with some studies reporting positive outcomes. For example, [PERSON] et al. [7] and [PERSON] et al. [8] report improvements of around 5% when a cross-corpus strategy is applied. On the other hand, [PERSON] et al. [5] report no difference or a reduction of 1-2% in system accuracy when using a cross-corpus strategy.  
Early research focused on determining the most effective approach for applying the cross-corpus strategy. [PERSON] et al. [7] compared two different methods: voting and pooling. The voting method involved training individual systems for each database and combining the final scores, while the pooling method trained a single system using data from all databases. The results demonstrated that the pooling method consistently yielded significantly better performance than voting. In a different context, [PERSON] et al. [8] applied the cross-corpus strategy to address data scarcity in non-English languages. They achieved notable improvements in accuracy by combining databases in English, German, Italian, and Urdu.  
However, not all studies have observed accuracy improvements when employing the cross-corpus strategy. [PERSON] et al. [5] did not find significant enhancements in their work, which could be attributed to the larger size of the databases used compared to previous studies.  
Various approaches have been explored to implement the cross-corpus strategy. [PERSON] [9] and [PERSON] [10] utilized Domain Adversarial Neural Networks to learn invariant feature sets across databases, aiming to create a model that is independent of the specific database. [PERSON] [11] proposed a domain-adaptive subspace learning technique to derive a common subspace for all databases.  
In the field of SER, most studies have utilized spectral [12] and cepstral [5] parameters, with a few incorporating handcrafted [7] features, such as GeMAPS [13] and eGeMAPS. Additionally, only a limited number of works have explored the use of SS representations for SER tasks. For example, [PERSON] et al. [14] compared the performance of the eGeMAPS parameter set [13] with that of Wav2 Vec2 [15] and found that the SS representation outperformed eGeMAPS, although their study did not employ the cross-corpus strategy. The results obtained in this study are a recall of 66.3% for the IEMOCAP database. In this work, we utilize two SS representations, HuBERT [16] and WavLM [17], which have demonstrated superior performance to Wav2 Vec2 in speech processing tasks.
## 3 Case Study  
### Study Area and Dataset  
A parabolic variable-thickness double-curved arch dam located in China was considered as the research object, as shown in Figure 6. The crest elevation of the dam is 109 m, and the bottom elevation is 40.2 m. The dead water level of the reservoir is 64 m, and the corresponding storage capacity is 1.52 million m\({}^{3}\). This study selected dam deformation data as the analysis data source, which is mainly affected by the combined influence of the reservoir water level and temperature. For the arch dam, studying the deformation law for a reasonable prediction is the primary objective during the service of the arch dam.  
Since the first deformation of the dam in 2009, four monitoring points have been configured on top of the dam, namely G0-HY, G1-HY, G2-HY, and G3-HY, which are measured by the Global Navigation Satellite Systems (GNSS). All the points contain complete records of deformation data during the past years, and the absolute radial displacement is selected as the object of analysis and prediction in this study. Figure 7 presents the absolute radial displacement data of the reservoirs, where the data for the arch dam were collected during the period from June 2009 to December 2019. Through observation, we find that the overall deformation data process line of the dam fluctuates regularly and is relatively stable, with, mostly, no evident saltation. Moreover, the deformation at the measuring points in the middle of the dam is greater than that at the measuring points located at both ends of the dam, and all the measuring points have a trend of convergence and periodicity. In this case, data from the initial 60% and 30% were used for training, respectively. The other data were used for testing. All the modules were implemented using Python.  
Figure 6: Panorama of the studied dam project.
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
## 4 Engineering Validation of the Differential Detection Method
### Detection of Karst Geological Conditions  
A survey of karst geological conditions was conducted in a specific area using a differential data acquisition mode. Figure 13 illustrates the topography of the survey area and the instrumentation used for data collection, with distinct karst geomorphological features clearly visible. Borehole data reveal the presence of three strata in this region: (1) the first layer consists of miscellaneous fill with a thickness of 1.0-5.5 m, (2) the second layer comprises red clay with a thickness ranging from 7 to 12 m in thickness, and (3) the third layer is dolomitic limestone. The actual survey line used an equal-offset profiling method with a near-source offset of 7 m and an inter-trace spacing of 0.5 m. The completed two-trace acquisition data profiles are depicted in Figures 14 and 15.  
Figure 16 presents the equal-offset profile after differential suppression of Rayleigh or transverse waves from the common source recording data. In the differential processing, the profile with a 7.5 m offset is regarded as the main profile, while the data from the 7 m offset profile are adjusted for Rayleigh wave time delay and amplitude attenuation before being added in reverse to the main profile data. The Rayleigh wave correction time delay calculation employs a correlation analysis algorithm, with different differential times applied to the dual-trace common source recordings at various points.  
Figure 13: Karst geological exploration: survey line deployment and field instrumentation configuration. (**a**) Field photograph of karst terrain with seismic survey line location. (**b**) Field data acquisition instruments and equipment.  
Figure 16: Data processing profile of common-shot, common-offset (7 m) and trace interval of 0.5 m after two-channel differential suppression of Rayleigh waves.  
Figure 14: Common shot, common-offset (7 m) and trace interval of 0.5 m collection data profile.  
Figure 15: Common shot, common-offset (7.5 m) and trace interval of 0.5 m collection data profile.  
The processed profiles demonstrate effective attenuation of Rayleigh waves, leading to a more polished and distinct seismic record, with a marked increase in dominant frequency. Based on these profiles, additional data processing and geological interpretation of the seismic operation data can be undertaken, including determining bedrock reflection interfaces and karst features.  
It is crucial to recognize that the equal-offset profile does not retrieve velocities from the reflection wave signals. As a result, it cannot currently perform time-depth conversion. This limitation represents a key area for future research.
# Isolation and Characterization of Starch from Different Potato Cultivars Grown in Croatia
## 3 Results and Discussion
### The Color of Potato Starches  
Starch color is an important parameter of starch quality. Table 3 shows the color parameters of the isolated potato starches. In the CIELab system, the intensity of white color--i.e., the brightness of starch--is expressed through the L* value. The values of the L* parameter range from 0 (black) to 100 (white), and the values obtained for all of the starches were close to 100, which indicates a white color of high intensity. The parameter a* describes the color domain; if the value is positive, it is in the domain of red, and if the value is negative, it is in the domain of green. As the table shows, all of the obtained values are negative, which means that the samples are in the domain of green. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Dry Matter** & **Protein** & **Fat** & **Ash** & **Starch** & **Crude Fiber** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline Saprodi & 25.05 \(\pm\) 0.29 \({}^{\rm c}\) & 2.53 \(\pm\) 0.11 \({}^{\rm c}\) & 0.09 \(\pm\) 0.01 \({}^{\rm a,b}\) & 1.24 \(\pm\) 0.01 \({}^{\rm d,e}\) & 19.78 \(\pm\) 0.17 \({}^{\rm b}\) & 1.08 \(\pm\) 0.01 \({}^{\rm d}\) \\ Sofista & 24.43 \(\pm\) 1.99 \({}^{\rm b,c}\) & 2.15 \(\pm\) 0.13 \({}^{\rm b}\) & 0.09 \(\pm\) 0.00 \({}^{\rm a,b}\) & 1.24 \(\pm\) 0.01 \({}^{\rm d,e}\) & 19.91 \(\pm\) 0.12 \({}^{\rm b}\) & 0.88 \(\pm\) 0.04 \({}^{\rm a}\) \\ Stilleto & 28.54 \(\pm\) 0.69 \({}^{\rm d}\) & 3.47 \(\pm\) 0.16 \({}^{\rm d}\) & 0.10 \(\pm\) 0.00 \({}^{\rm b}\) & 1.02 \(\pm\) 0.01 \({}^{\rm b}\) & 22.49 \(\pm\) 0.71 \({}^{\rm c}\) & 0.96 \(\pm\) 0.00 \({}^{\rm b,c}\) \\ Dartiest & 21.95 \(\pm\) 1.14 \({}^{\rm a,b}\) & 2.37 \(\pm\) 0.06 \({}^{\rm b,c}\) & 0.10 \(\pm\) 0.00 \({}^{\rm b}\) & 0.96 \(\pm\) 0.01 \({}^{\rm a}\) & 17.42 \(\pm\) 0.00 \({}^{\rm a}\) & 1.08 \(\pm\) 0.01 \({}^{\rm d}\) \\ Sereno & 24.48 \(\pm\) 0.76 \({}^{\rm b,c}\) & 3.37 \(\pm\) 0.03 \({}^{\rm d}\) & 0.09 \(\pm\) 0.00 \({}^{\rm a}\) & 1.20 \(\pm\) 0.01 \({}^{\rm d}\) & 17.63 \(\pm\) 0.43 \({}^{\rm a}\) & 1.18 \(\pm\) 0.04 \({}^{\rm e}\) \\ Sentaa & 21.66 \(\pm\) 0.97 \({}^{\rm a}\) & 1.79 \(\pm\) 0.09 \({}^{\rm a}\) & 0.08 \(\pm\) 0.00 \({}^{\rm a}\) & 1.11 \(\pm\) 0.01 \({}^{\rm c}\) & 17.36 \(\pm\) 0.32 \({}^{\rm a}\) & 1.07 \(\pm\) 0.05 \({}^{\rm d}\) \\ Scala & 23.57 \(\pm\) 0.84 \({}^{\rm a,b,c}\) & 2.15 \(\pm\) 0.08 \({}^{\rm b}\) & 0.12 \(\pm\) 0.00 \({}^{\rm c}\) & 1.14 \(\pm\) 0.02 \({}^{\rm c}\) & 19.06 \(\pm\) 0.70 \({}^{\rm b}\) & 0.90 \(\pm\) 0.03 \({}^{\rm a,b}\) \\ SL 13–25 & 23.48 \(\pm\) 0.72 \({}^{\rm a,b,c}\) & 2.33 \(\pm\) 0.09 \({}^{\rm b,c}\) & 0.16 \(\pm\) 0.00 \({}^{\rm d}\) & 1.25 \(\pm\) 0.03 \({}^{\rm e}\) & 17.26 \(\pm\) 0.37 \({}^{\rm a}\) & 1.03 \(\pm\) 0.04 \({}^{\rm c,d}\) \\ \hline \hline \end{tabular} Values with different letters in the same column are significantly different at \(p<0.05\).  
\end{table} 
Table 1: Chemical composition of the investigated potato cultivars.
# Towards the Quantification of 5f Delocalization
## 3 Results and Discussion
### XES and 5f:6p Peak Ratios
The 5f/6p\({}_{3/2}\) peak is neglected in the simulation. The real point of this exercise is not to confirm the analysis for the localized n = 2 case but rather to project what the n = 3 delocalized case would be. To that end, setting n = 3 and applying the equations again, the plots in Figures 8 and 9 were obtained. Interestingly, the M\({}_{4}\) spectrum corresponding to the n = 3, delocalized case (n\({}_{5/2}\) = 2.1, Green or possibly n\({}_{5/2}\) = 2.4, yellow) is not substantially different to the experimental spectrum for the n = 2  
Figure 8: Shown here is the experimental M\({}_{4}\) XES spectrum of UF\({}_{4}\) and the simulated spectra for n = 3 and various values of n\({}_{5/2}\). The simulation used normalized Lorentzian line shapes, with a half-width at half-max of \(\Gamma\). For the 6p\({}_{1/2}\): \(\Gamma\) = 3.5 eV. For the 5f peak, \(\Gamma\) = 4 eV. The 5f/6p\({}_{3/2}\) peak is neglected in the simulation. Figure 7: Shown here is the experimental M\({}_{5}\) XES spectrum of UF\({}_{4}\) and the simulated spectra for n = 2 and various values of n\({}_{5/2}\). The simulation used normalized Lorentzian line shapes, with a half-width at half-max of \(\Gamma\). For the 6p\({}_{3/2}\), \(\Gamma\) = 3.5 eV. For the 5f peak, \(\Gamma\) = 4 eV. localized case, the UF\({}_{4}\) result. This is because the n\({}_{5/2}\) value for the delocalized n =3 case would only be about n\({}_{5/2}\) = 2.23, not very different from the n = 2 localized case. (Table 2) One the other hand, the M\({}_{5}\) projection shows a very clear and substantial change. The n\({}_{5/2}\) = 2.1 (green) spectrum, and even the n\({}_{5/2}\) = 2.4 (yellow), are very different to the experimental M\({}_{5}\) spectrum for UF\({}_{4}\). This is because the major change in the delocalization is to significantly populate the n\({}_{7/2}\) states, which in turn is manifested in the M\({}_{5}\) spectrum, not the M\({}_{4}\).