esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Three-Dimensional Global/Local Analyses  
Three-dimensional investigations have been also carried out with the aim to have an estimate of how much computation time could be saved employing a Global/Local two-way approach. In fact, in general, Global/Local approaches become useful and advantageous, from a computation time point of view, when large three-dimensional models must be investigated.  
For the three-dimensional model obtained by means of an extrusion along the chamber z axis direction, thermal and structural loads (convective heat fluxes for the thermal model and temperatures and pressures for the structural model) are considered constant along such a direction; then, the resulting temperatures, displacements, and stresses will vary only in the x and y directions.  
Table 11 summarizes the number of nodes of the finite element models analyzed and the CPU time needed to perform one iteration of the Global/Local analysis. For Local Model 2, the adoption of the iterative Global/Local approach is very advantageous since only four iterations are necessary to obtain a displacement residual \(\varphi\) lower than \(10^{-4}\); then, the total computation time is 104 s when adopting conformal meshes at the interface and 88 s for non-conformal meshes. Those CPU times are considerably shorter than those needed for a single thermo-mechanical analysis of the Reference Model. With regard to Local Model 3, there is no CPU time saving when adopting non-conformal meshes with respect to conformal meshes. However, the present iterative method allows for a considerable time saving since convergence is reached after only two iterations, that is, 100 s for the Global/Local analysis and 150 s for the analysis of the Reference Model. Similar considerations can be conducted for Local Models 4 and 5. On the contrary, Local Model 1 needs 10 iterations to reach convergence for conformal meshes and 18 iterations for the non-conformal meshes; then, the CPU time for both conformal and non-conformal mesh configurations becomes much longer than that required for a single analysis of the Reference Model, equal to 500 s and 684 s, respectively. Then, for the Local Models built considering an interface with the Global Model in an area where plastic strains are expected, the adoption of non-conformal meshes does not provide appreciable benefits with respect to the conformal mesh configurations. Moreover, the computational burden for the Reference Model resolution is not relatively high, considering that a relevant number of iterations is needed in the Global/Local analysis to obtain results as accurate as those achieved with the Reference Model. In order to make the iterative approach advantageous, acceleration techniques should be implemented to minimize the number of iterations, e.g., making them, in this case, less than three.
## 3 Results
### Optical Characterization
The exceptional properties of hybrid nanostructures could explain the enhancement in the emission of TiO\({}_{2}\)-nb/TiO\({}_{2}\)-nc and TiO\({}_{2}\)-ns/TiO\({}_{2}\)-nc, and the higher surface area in those nanostructures [16; 28; 29; 30]. Figure 5a shows the band gap energy of all studied TiO\({}_{2}\) nanostructures. The optical band gap was calculated by the Kubelka-Munk function (F(R)), as shown in Equation (2) [31]. According to [PERSON] et al., \(F(R)\) is proportional to absorption coefficient and was determined from the reflectance using Equation (3) considering indirect transitions [31]. \[F(R)(hv)=A\big{(}hv-E_{g}\big{)}^{2} \tag{2}\]  
\[F(R)=\frac{(1-R)^{2}}{2R} \tag{3}\]  
where \(R\) is the reflectance, \(A\) is a constant, \(h\) is Planck constant, and \(v\) is the photon frequency. In Figure 6a it can be observed that the band gap of TiO\({}_{2}\)-nt is close to 3.2 eV, this energy concurs with other reports that study materials with anatase phase [15; 27; 32]. An important reduction of the band gap for other nanostructures was noticed, the TiO\({}_{2}\) film  
Figure 5: (a) The band gap energy of TiO\({}_{2}\)-nt, TiO\({}_{2}\)-nc, TiO\({}_{2}\)-nb/TiO\({}_{2}\)-nc and TiO\({}_{2}\)-ns/TiO\({}_{2}\)-nc obtained by Kubelka-Munk function. (b) Band gap energy and oxygen vacancies area for each film. with the narrowest band gap (3.093 eV) is TiO\({}_{2}\)-ns/TiO\({}_{2}\)-nc. In contrast, the band gap of TiO\({}_{2}\)-nc (3.179 eV) is lower than TiO\({}_{2}\)-nt but larger than TiO\({}_{2}\)-nb/TiO\({}_{2}\)-nc (3.156 eV). The band gap narrowing can be related to amount of oxygen vacancies in the TiO\({}_{2}\) films, as shown in Figure 5b, since oxygen vacancies create localized states within the band gap, which operate as electron donor states [15; 22; 27]. It is clear that the band gap energy reduces as the number of single-ionized oxygen vacancies increases.
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 4 Results
### ABM's Outputs and Traffic Analysis Results
A comparative inspection reveals that the intervention leads to a more proportionate distribution of trips across age groups, particularly among younger cohorts (e.g., 18-35), who exhibit increased activity in the updated model. This correction likely reflects the influence of latent behavioral variables captured by the Cox model with frailty, which were previously underrepresented in the original workflow. Taken together, these figures demonstrate the multidimensional value of incorporating advanced duration modeling into synthetic activity generation. Not only do they enhance temporal realism and spatial accuracy, but they also improve the demographic representativeness of the synthetic population. These improvements are of particular relevance for transport planning applications where precision in demand modeling across time, space, and user profiles is critical for robust policy evaluation and infrastructure design.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 6 Possible Applications and Improvements
### Possible Future Improvements of the Channel Models  
Existing channel models can be improved in many aspects. The channel model in [8] can be improved by considering the multipath effect caused by fluctuations of sea waves. In [8], multipath effects of the sea surface may be incorporated into the channel model in the future to make it more reliable. The channel model proposed in [9] can be improved by integrating elevated duct-based measurements. The channel model in [10] does not have a deployment scenario and does not take into account the elevated duct, even though the UAVs were involved in the channel model. Roughness of the sea was ignored while designing the channel model proposed in [12]. For further improvement, sea-roughness can be included as a parameter. The channel model proposed in [5] does not have a path loss model. This can be included for further improvement of the channel model. In [13], only the surface duct is considered as an atmospheric channel. To make it robust, other types of ducting effects must also be considered. The channel model proposed in [15] ignored many of the important channel evaluation parameters, such as mean excess delay, fade margin, etc. They can be considered in the future design of the channel model. Again, the channel model proposed in [17] can be integrated to support nLoS and bLoS propagation. Both the channel models proposed in [19; 20] can be improved by incorporating the fade margin calculation. Lastly, the channel model proposed in [21] can be improvised by changing the sea surface design from two-dimensions to three-dimensions. Possible future improvements of all discussed channels are summarized in Table 5.
## 3 Methods
### Multi-Scale Attention Block  
The MSAB structure is illustrated in Figure 2, which primarily consists of a combination of different types of convolutions and a unique self-attention head. The utilization of different types of convolution operations enables the capturing of multi-scale lesion features in retinal fundus images. The distinctive self-attention head replaces the linear projection in traditional methods [23] with a 1 \(\times\) 1 convolutional layer for channel contraction and expansion. This replacement helps reduce the number of hyperparameters. Overall, this block is designed to capture cross-channel multi-scale lesion features in the retinal fundus image and build long-range dependency relationships.  
Figure 2 shows that the output of the feature encoder \(F_{i}\) is passed to the MSAB. The MSAB utilizes two convolutional layers with kernel sizes of 1 \(\times\) 1 and 3 \(\times\) 3 to capture multi-scale feature information, resulting in \(F_{i}^{1}\) and \(F_{i}^{2}\). Subsequently, the MSAB employs self-attention heads to dynamically establish long-range dependency relationships within \(F_{i}^{1}\) and \(F_{i}^{2}\). Each self-attention head consists of three convolutional layers with a kernel size of 1 \(\times\) 1, followed by a spatial flattening operation, which generates Query, Key, and Value. The matrix multiplication with Query and Key to obtain attention \(Att_{i}\) process is as follows:  
\[Att_{i}=Q(F_{i})\otimes K(F_{i})^{T},i\in[1,\dots,5] \tag{1}\]  
Figure 1: MLNet.  
where \(K(.)\) and \(Q(.)\) denote the Key and Value generators, respectively, and \(\otimes\) denotes matrix multiplication. \(Att_{i}\) further utilizes the generated Query and its corresponding Value to generate the attention feature map \(F_{i}^{mid}\). The process is as follows:  
\[F_{i}^{mid}=V(F_{i})\otimes\phi(Att_{i}),i\in[1,\dots,5] \tag{2}\]  
where \(V(.)\) denotes the Value generator, and \(\phi(.)\) denotes the softmax function. Then, the matrix \(F_{i}^{mid}\) is de-flattened and multiplied element-wise with trainable scaling factor \(s\). The results are then added element-wise to \(F_{i}^{1}\) and \(F_{i}^{2}\), yielding \(F_{i}^{att1}\) and \(F_{i}^{att2}\). Subsequently, \(F_{i}^{att1}\) and \(F_{i}^{att2}\) are added element-wise and undergo upsampling and decoding to generate \(F_{i}^{MSAB}\). The entire process can be represented as follows:  
\[F_{i}^{MSAB}=U\Big{[}\left(sF_{i}^{mid1}\oplus F_{i}^{1}\right)\oplus\left(sF _{i}^{mid2}\oplus F_{i}^{2}\right)\Big{]},i\in[1,\dots,5] \tag{3}\]  
where \(\oplus\) denotes element-wise addition operation, \(s\) is the trainable scaling factor and is enclosed as S in Figure 2, and \(U\) denotes upsampling and decoding.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 4 Methodology  
### Overview of Our Method  
To address the SCE task, this paper conceptualizes SCE as a sequence labeling task. This perspective allows for a systematic approach to identifying sentiment cues across varying textual instances.  
Given an instance \(X=\{x_{1},x_{2},\cdots,x_{n}\}\), our objective is to assign a corresponding label set \(Y=\{y_{1},y_{2},\cdots,y_{n}\}\), where \(y_{i}=1\) signifies that the element constitutes a significant sentiment cue. For example, regarding Instance 1, the corresponding \(X\) and \(Y\) are as illustrated in Equations (1) and (2), respectively.  
\[X=\{\textit{``Very\",``friendly\",``customer\",``service\",``.\"}\}, \tag{1}\]  
\[Y=\{0,1,0,0,0\}. \tag{2}\]  
However, a principal challenge within this work is the absence of annotated data for the SCE task, meaning that \(Y\) is unknown within the dataset. To address this, we introduce a Self-Supervised Sentiment Cue Extraction (SS-SCE) method that employs self-supervised learning to tackle the SCE task. In the SS-SCE framework, we utilize a sentiment classification model, which has been widely labeled, to generate pseudo labels for the SCE task. These pseudo labels, derived from samples \(X\) in the sentiment classification dataset, serve as inputs and outputs for constructing the SCE training dataset, thereby enabling the training of an SCE sequence labeling model. The fundamental steps of this approach are depicted in Figure 1.  
Figure 1 illustrates the basic workflow of our method. Initially, we train a sentiment classification model based on a sentiment classification dataset. Building on this, we generate candidate sequences of pseudo labels (referred to as the Candidate Sequences in the figure) for an instance within the dataset and then use the sentiment classification model to select one sequence from these candidates as the pseudo label. Thus, by taking an instance as input and using the obtained pseudo label as the label, we can form a sequence labeling instance (referred to as the Generated Instance in the figure). By generating such generated instances for other instances in the sentiment classification, we can compile a  
Figure 1: This figure illustrates the workflow of our self-supervised sentiment cue extraction method. “Input” and “Label” represent the roles of “An Instance” and “Pseudo Label” within the “Generated Instance”, respectively. The bold arrows indicate the process of training the corresponding models using the dataset.  
dataset (referred to as the Generated Dataset in the figure) that is suitable for training the SCE model. Based on this dataset, the SCE model can then be trained.  
In the following sections, we will introduce our method in detail.
## 2 Materials and Methods
### Mixture Ratio Design  
According to the \"Technical Guidelines for Construction of Highway Roadbases\" (JTG/T F20-2015) [26], the lime dosage of lime-stabilized materials is not less than 10%. In addition, many scholars have performed research on the effects of mixing lime as an additive with phosphogypsum. [PERSON] [27] used lime to improve the red clay content of the Jiangxi Expressway, and the improvement was optimal when the lime content was 5-10%. [PERSON] [28] conducted research on gypsum fly ash and lime binder and found that when the lime content was 6-8%, the strength was the best, and the material could be used as a pavement subbase material. [PERSON] [29] used quicklime to improve high liquid limit soil, and the results showed that when the lime content was 4-6%, the high liquid limit soil exhibited good road performance and met the requirements for highway subgrade filling. Therefore, the lime content was initially set at 6%, 8%, and 10%.  
Based on research [30; 31] designed to set two series of ratios, the first one was set with lime to phosphogypsum ratios of 1:1, 1:2, and 1:3. In order to use more phosphogypsum, the second series was set with phosphogypsum to soil mass ratios of 1:1, 1:2, and 1:3. The results are shown in Table 6. The proportions in the mixing ratios represent the percentage of each substance in the total mass of the mixture. According to the method designed for the mixture ratios in Table 6, unconfined compressive strength tests, expansion tests, and shrinkage tests were carried out indoors to analyze the mechanical properties of lime-phosphogypsum-stabilized red clay. On this basis, XRD and SEM were used to further explore the microstructure of the mixture.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**L/\%** & **P/\%** & **T/\%** & **K/\%** & \(\omega\)/\% \\ \hline  & 6 & 88 & & 27.11 \\  & 12 & 82 & & 28.85 \\
6 & 18 & 76 & & 28.42 \\  & 24 & 70 & & 27.55 \\  & 31 & 63 & & 28.52 \\  & 47 & 47 & & 23.98 \\ \hline  & 8 & 84 & & 30.24 \\  & 16 & 76 & & 28.01 \\
8 & 24 & 68 & & 27.41 \\  & 23 & 69 & 90, 92, 93, 94, 95 & 27.84 \\  & 31 & 61 & & 27.59 \\  & 46 & 46 & & 25.94 \\ \hline  & 10 & 80 & & 28.16 \\  & 20 & 70 & & 29.46 \\
10 & 30 & 60 & & 28.55 \\
23 & 67 & & 29.12 \\  & 30 & 60 & & 28.55 \\  & 45 & 45 & & 22.11 \\ \hline \end{tabular} \(L\) = Lime, P = Phosphogypsum, T = Red clay, K = Compaction degree, and \(\omega\) = Optimal moisture content.  
\end{table}
Table 6: Mixture ratio design.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Substance** & **CaO** & **SiO2** & **Na2O** & **MgO** & **SO3** & **Al2O3** & **Fe2O3** \\ \hline Mass fraction (\%) & 97.95 & 0.460 & 0.439 & 0.384 & 0.382 & 0.122 & 0.0831 \\ \hline SrO & Cl & CeO2 & Ag2O & P2O5 & TiO2 & Dy2O3 & other \\
0.0325 & 0.0279 & 0.0222 & 0.0172 & 0.0145 & 0.0139 & 0.0106 & 0.0411 \\ \hline \end{tabular}
\end{table}
Table 5: Chemical composition of lime.
## 5 Discussion
### Worldwide Potential Applicability  
Global hydrogeological characteristics change with the development of geology and geomorphology. The proposed RS and GIS method was implemented in GCTs, as a case study for tectonic movement of Taiwan. On the basis of in-situ investigation, the key parameters conducive to groundwater were determined. Similar regions worldwide include Japan, the Philippines, the western and eastern United States, western South America, western Asia, southern Europe, and so on. The adjustment of parameters obviously needs to be considered. For example, the ranking of slate rock type is lower in the ancient platform due to lower groundwater storage and poor permeability, but it is higher in the developed fracture network of Taiwan. In this case, the weathering zone (i.e., soil and regolith) may be one of the selected parameters because it has a greater impact on the shallow groundwater potential than the rock type of bedrock. By applying the developed method worldwide and discussing the results, it will be useful to understand the impact parameters of groundwater potential.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 2 Materials and Methods
### Quantification of CBZ  
CBZ analysis was performed using a Shimadzu HPLC equipment (Shimadzu Corporation, Kyoto, Japan) equipped with an LC-20 AB pump, a DGU-20A5 degasser, a SIL-20A automatic injector, a CTO-20 AC column oven, and an SPD-M20A diode-array detector. The separation was performed with a LUNA C18 column (particle size 5 \(\upmu\)m, 150 \(\times\) 4.60 mm), using a C18 precolumn (particle size 5 \(\upmu\)m, 4 \(\times\) 2.0 mm), both from Phenomenex (Torrance, CA, USA). Ultrapure water with 0.1% formic acid (A) and acetonitrile (B) were used as eluents. The chromatographic program is described in Table 1. The control of the chromatographic system and the acquisition and processing of chromatographic data were made using LC solution version 1.25 SP2 software. CBZ quantification was carried out using external calibration. For that, different solutions with concentrations ranging from 2.5 to 20,000 \(\upmu\)g/L were prepared in water from the respective stock solution. The concentration of CBZ assessed in each sample, resulting from triplicate injections, was expressed as mg/L.
## 3 Results and Discussion  
### Density and Moisture Content  
The density and the moisture content of the experimental hybrid composites are presented in Figure 4.  
The highest density value was obtained for the panels with date palm fibres (1012 kg/m\({}^{3}\)) due to the fact that the quantity of resin applied was about 30% greater than the resin amount applied on the layers of the other two composites. The density of composites was not influenced by the densities of the fibres, which were almost in the same range of values: 1.44 g/cm\({}^{3}\) for Kevlar fibres, between 0.9 g/cm\({}^{3}\) and 1.2 g/cm\({}^{3}\) for date palm fibres and 1.5 g/cm\({}^{3}\) for cotton fibres [17]. The density of DP with polyester was about 44% lower than similar composites with epoxy resin as the matrix [44]. Both resins are generally used as matrices for natural fibre composites that cure at environmental temperatures. The lower density of the hybrid composites with polyester could be an advantage in applications where lightness is essential, such as in aerospace and automotive applications.  
Furthermore, there were no big differences between the moisture content values, these being similar to those found in the literature [45].  
According to the statistical examination, the investigation revealed the absence of statistically significant distinctions in both density and moisture content values across all three types of hybrid composites, with findings established at a 95% confidence level. The computed \(p\)-values and F-values for density and moisture content were 0.354, 0.182, 1.17 and 1.97, respectively. Additionally, the outcomes derived from the Tukey test corroborated with the aforementioned findings, categorizing all three variants of hybrid composites into identical groups concerning both density and moisture content.
# Lower-Body Positive Pressure Treadmill Training for Pediatric Gait Disorders: A Scoping Review
## 4 Discussion
### Anti-Gravity Training: Protocols and Settings  
Overall, anti-gravity treadmill training is based on the principles of motor learning and neuroplasticity through a mass-practice and task-specific intervention that aims to promote long-lasting neuromuscular adaptation [34]. However, optimal training frequency and duration for gait rehabilitation with the anti-gravity treadmill remains unknown due to the large variability between protocols in published studies. According to the recommendations of [PERSON] et al. [35], longer interventions with progressive intensities (e.g., duration: 8-16 weeks; frequency: 2 or 4 sessions/week) may be needed to experience meaningful motor-function improvements in children with CP. Almost all the studies included in this review meet such recommendations.  
Regarding the progression in training settings, it has been suggested that in order to get closer to normative gait patterns, very low speeds and high levels of BWS should be avoided when possible [36; 37]. Depending on the study, initial BWS levels were adjusted between by 30 and 50%. The treadmill speed was set to 0.7-1.5 km/h and gradually increased. The effect of treadmill inclination is less obvious and still requires further investigation [38; 39; 40]. In this review, all studies had set this parameter to 0 deg. In general, the progression in these settings was poorly described. Moreover, no study reported individual data on setting progression. The disparate training schedules, combined with the lack of information about setting adjustments, limit our understanding of the impact of anti-gravity training on children with locomotor impairments and may preclude clinical reproduction of the proposed protocols [41].