esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Age-Related Differences in Muscle Synergy Patterns and Their Roles in Balance Control
#### 4.2.2 Age-Specific Muscle Synergy  
We found that the activation of the biceps femoris (BF) is a prominent component of the specific muscle synergy observed in older adults. The hamstrings, including the BF, are primarily responsible for hip extension when the knee is extended [48]. Therefore, the prominent activation of the BF during the single-limb stance phase in older adults may serve to facilitate hip extension, counteracting the greater trunk flexion. This finding supports the greater hip extension torque observed in older adults. By reducing trunk flexion through hip extension, older adults can also better prepare for the subsequent protective (swinging) leg compensatory step during balance recovery.  
In younger adults, we observed a prominent activation of the gluteus medius (Gmed) in their specific muscle synergy. The Gmed serves as a prime mover for hip joint abduction, which plays a crucial role in controlling the movement of the CoM from the lateral to medial side when the standing (perturbed) leg is fixed on the ground. Following a surface drop perturbation, the CoM shifts laterally, disrupting ML balance as it moves outside of its base of support (BoS) during the initial phase of the SLS phase.  
To restore ML balance, it is necessary to bring the CoM back to the center and within the BoS. The activation of Gmed actively contributes to this process by controlling the displacement of the CoM from the lateral to medial side, directing it towards the inside of the BoS [49]. The prominent activation of Gmed in younger adults effectively assists in stabilizing ML balance, as indicated by their reduced postural sway (SDCoMAccel) in the ML direction during the SLS phase. The observation of greater hip abduction torque in younger adults compared to older adults supports this activation pattern of the Gmed specific muscle synergy. By generating greater hip abduction torque through Gmed activation, younger adults are better able to counteract the lateral displacement of CoM and effectively restore ML balance following unexpected surface drop perturbations.
## 4 Discussion
### Ablation Study  
#### 4.5.1 Analysis on Adaptive Brightness  
To evaluate the effectiveness of the proposed adaptive brightness enhancement (ADB) in multiscale information fusion, we conducted a comparative analysis of fusion performance with and without ADB. As shown in Figure 17, the fused images produced without ADB exhibited inferior quality, demonstrating the necessity of ADB for effectively integrating multiscale contrast features in our method. The quantitative results in Table 5 further validated this observation. Across all evaluation metrics--VIF, SF, AG, CC, and EN--the ADB-enhanced approach consistently outperformed the non-ADB variant. For instance, in the \"Queens Road\" image, ADB improved VIF from 0.758 to 0.82 and AG from 2.294 to 3.683. Similarly, for \"Kaptein_1123,\" ADB increased SF from 0.03 to 0.057 and AG from 2.281 to 5.01. These results highlight the significant role of ADB in enhancing fusion performance, as it achieves superior results in all tested scenarios.  
Figure 16: Evaluation results of the eight fusion metrics for the M3 FD dataset in eight state-of-the-art methods and our method.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Image** & & **VIF** & **SF** & **AG** & **CC** & **EN** \\ \hline \multirow{2}{*}{“Queens Road”} & Without ADB & 0.758 & 0.033 & 2.294 & 0.628 & 6.103 \\  & With ADB & **0.82** & **0.045** & **3.683** & **0.675** & **6.998** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experimental results on the adaptive brightness strategy, with the best results highlighted in bold.
# SOPRENE: Assessment of the Spanish Armada's Predictive Maintenance Tool for Naval Assets
## 4 Unified Architecture
### Anomaly Detection  
Once the prediction of the engine status has been made, it is necessary to determine whether this status corresponds to a normal value or a possible failure (anomaly). Since no labeled anomalies are available, the anomaly detection process is carried out in an unsupervised manner by using the autoencoder neural network. For the distributed implementation of this method, the Sparkling Water library was used.  
By using the reconstruction error provided by the autoencoder model, we can discern between normal data (low reconstruction error) and anomalous data (high reconstruction error). This value can be presented as the mean square error of all the input variables (a single value) or its decomposition, that is, the error of each of the input variables or nodes of the network. The goal of this stage is to determine which ones are abnormal and which variables cause those abnormalities. The process is divided into three sequential phases:  
1. Detect anomalies: To determine which datum is normal or anomalous, a first filter is carried out by using the mean square error. Based on a precalculated threshold error, the data that exceed this error are classified as anomalous and the rest as normal. The user can choose whether the calculation of this threshold error is carried out using the interquartile range technique [60] (a statistical dispersion measure that allows the threshold to be calculated automatically) or by establishing a percentage of anomalous data in the set of data.
2. Independent contributions: To determine which specific variables have caused the appearance of the anomaly in the data classified in the previous phase as anomalous, the decomposition of the reconstruction error is used (see Figure 2). Taking into account that the data are now normalized, this allows us to order the variables by their reconstruction error. To determine which variables have contributed the most to the formation of the anomaly, the system uses a method that automatically selects the most anomalous variables. The method is called the Elbow Method [61] and allows starting from a set of variables and their reconstruction errors by automatically selecting those that deviate the most.
3. Build anomaly mask: From the selection of the previous phase, a matrix or output mask of dimensions \(m\times n\) is built and \(m\) is the number of rows or records and \(n\) is the number of columns or variables, where the anomalous variables are marked with a one and the normal variables with a zero. This information will be used by the subsequent diagnostic module.
## 2 Materials and Methods  
This study employs a structured narrative literature review to synthesize and critically evaluate recent advancements in dental imaging technologies, focusing on CBCT, AI, AR, and VR in diagnostic imaging, surgical planning, and dental education.  
Search Strategy: We conducted a comprehensive search using PubMed, Scopus, Web of Science, and Google Scholar for English-language peer-reviewed articles published between 2010 and 2025. The following keyword combinations were used: \"dental imaging\", \"CBCT\", \"artificial intelligence in dentistry\", \"augmented reality\", \"virtual reality\", \"3D printing\", and \"digital dentistry\". Boolean operators were applied to refine the search.  
Selection Process: From the 2015 initial results, duplicates were removed, and 134 full-text articles were screened. Of these, 68 studies met the inclusion criteria and were included in the final synthesis.  
Inclusion Criteria: Studies were included if they:  
* Focused on the application of CBCT, AI, AR/VR, or related digital imaging technologies in dentistry;
* Provided empirical data (clinical trials, observational studies, or systematic reviews) or were technical papers with clear relevance to diagnostics, surgery, or education;
* They were published in peer-reviewed journals.  
Exclusion Criteria: We excluded non-English studies, abstracts without full-text access, conference proceedings, non-peer-reviewed materials, and papers lacking clinical applicability.  
Thematic Grouping: Included studies were grouped into three primary domains: (1) Diagnostics and Imaging; (2) Surgical Planning and Intraoperative Support; and (3) Dental Education and Simulation. Subthemes within each domain were identified during full-text analysis.  
Quality Consideration: Although formal bias assessment tools (e.g., PRISMA, AM-STAR) were not employed due to the narrative nature of this review, studies were evaluated qualitatively based on:  
* Relevance to dental clinical practice,
* Level of evidence (e.g., randomized controlled trials, cohort studies, technical validation),
* Technological innovation and clinical impact.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 3 Results and Discussion
### Impact Resistance of SCC after Exposure  
Figure 7a,b presents the impact energy failure and the corresponding number of blows for the SCC specimens (FA, GGBFS, FA/EPA, and GGBFS/EPA) subjected to elevated temperatures. Compared to the heated specimens, the SCC specimens not exposed to heat, including FA, GGBFS, FA/EPA, and GGBFS/EPA, demonstrated higher impact energy levels. The impact energy for the unheated specimens was 913.9 J, 2234.0 J, 812.4 J, and 2132.4 J, respectively. At 30 min of heating duration (821 \({}^{\circ}\)C), the failure impact energy was 182.8 J and 223.4 J for the FA 30M (no. of blows: 9) and GGBFS 30M specimens (no. of blows: 11), respectively. SCC specimens with EPA content showed increased failure impact energy value, with the FA/EPA 30M and GGBFS/EPA 30M specimens recording 243.7 J (no. of blows: 12) and 284.3 J (no. of blows: 14), respectively. A decrease in failure impact energy was observed as the heating duration reached 60 min (925 \({}^{\circ}\)C), with the FA 60M and GGBFS 60M specimens having failure impact energy values of 121.9 J (no. of blows: 6) and 182.8 J (no. of blows: 9), respectively.  
The FA/EPA 60M and GGBFS/EPA 60M SCC specimens exhibited a similar fractional impact energy failure, measured at 203.1 J (number of blows: 10). After 90 min of heating (986 \({}^{\circ}\)C), the failure impact energy decreased to 101.5 J (number of blows: 5) for the FA 90M specimen and increased to 3162.5 J (number of blows: 8) for the GGBFS 90M specimen. The FA/EPA 90M and GGBFS/EPA 90M specimens demonstrated a failure impact energy of 182.8 J. The inclusion of the EPA showed a crucial role in mitigating the reduction in impact energy caused by temperature exposure exceeding 30 min. Further increasing the duration to 120 min (1029 \({}^{\circ}\)C) led to a notable decrease in failure impact energy to 40.6 J, 142.2 J, 60.9 J, and 162.5 J for the FA, GGBFS, FA/EPA, and GGBFS/EPA specimens, respectively. SCC mixes incorporating GGBFS (GGBFS 0M and GGBFS/EPA 0M) exhibited delayed setting times but enhanced impact resistance. The overall raise in strength can be attained by the latent hydraulic properties of the material, which reacted with the (Ca(OH\({}_{2}\))) formed during the hydration process [37].
# Review on the Recent Numerical Studies of Liquid Atomization
## 3 Interface Capturing
### Application Cases
A total of 3.2 billion cells were found enough with the assist of adaptive mesh refinement method (AMR), though a refinement level between eleven to thirteen has to be employed, which is also a huge computer burden. With their inhouse numerical code ARCHER, [PERSON] et al. [45] performed a simulation on pre-filming atomization with the CLSVOF method and synthetic turbulence model. A total of 3.3 million grids were created in a zone with a scale of 6 mm. The simulation results showed agreement with the experiment of [PERSON] et al. [46] qualitatively in ligament formation process, but under-predicted the formation frequency. With the CLSVOF method and LES model, [PERSON] et al. [47] calculated the jet atomization in a crossflow within a rectangular channel of length, width, and height of 55\(D\)\(\times\) 30\(D\)\(\times\) 30\(D\), where \(D\) is the nozzle diameter. Subsonic air speed of 180 m/s was used to mimic a combustion situation in a cruising airplane. A total of 3.4 million primary cells were created, which was adaptive to 8.6 million during calculation with an AMR level of three. The trajectory of the jet was comparable to an experimental observation, and they also demonstrated the KH instability and RT instability dominate the breakup in the downside and upside of the liquid column, respectively. Table 2 compares the commonly used mathematical modeling methods mentioned above. It can be seen from the above review that LES or DNS are usually adopted as the turbulence model in order to fully resolve the gas-liquid interface in atomization. This is because the interfaces evolve very rapidly in scales as small as the turbulence vortices, and the Reynolds-averaged Navier-Stokes (RANS) turbulence models only resulting in time-average information are not competent. However, LES or DNS would require grid numbers in the magnitude of hundreds of millions or billions within just a calculation zone of a few millimeters. This unbearable burden even for most supercomputer centers indicates impossibility for engineering design purposes.
# Contrastive Learning Based on Transformer for Hyperspectral Image Classification
## 4 Experimental Descriptions and Result Analysis
### Datasets' Description  
The experiments were conducted on three publicly available datasets, Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SV), as indicated in Figure 5.  
The IP dataset was collected by the AVIRIS sensor in Northwestern Indiana. The scene size is \(145\times 145\). After removing the bands absorbed by water, the number of bands reduces to 200. The 10,249 labeled pixels are designed into 16 classes.  
The UP scene was gathered by the ROSIS sensor over Pavia, Northern Italy. UP has \(610\times 610\) pixels. Each pixel consists of 103 spectral bands. The 42,776 labeled pixels are divided into nine categories.  
The SV image was acquired by the AVIRIS sensor over Salinas Valley, California. The image size is \(512\times 217\) with 204 available spectral bands. The 54,129 labeled pixels are partitioned into 16 categories.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **Dim** & **Heads** & **MLP Dim** & **Head Dim** & **Parameters** \\ \hline
2 & 256 & 16 & 512 & 32 & 1,612,288 (SV\&UP) \\  & & & & & 1,646,848 (IP) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of our transformer.
## 1 Introduction
The state and input constraints of a strict-feedback nonlinear tracking system are tackled by introducing an integral barrier Lyapunov function into the backstepping procedure and extending the system input as a system state in [27]. However, the aforementioned integral barrier Lyapunov function technique is only able to handle the time-invariant constraints on the system states, namely this method neither constrains system tracking errors nor handles time-varying constraints. Therefore, we need to improve the above integral barrier Lyapunov function such that the time-varying appointed-time performance constraint function can combine with it to deal with the performance constraints on the tracking errors. Motivated by the above observations, an appointed-time time-varying integral barrier Lyapunov function-based trajectory tracking control method for a hovercraft is proposed that guarantees an appointed-time performance constraint on the tracking errors and addresses model uncertainties. This paper proposing the appointed-time performance constraint function and time-varying integral barrier Lyapunov function is unprecedented. Accordingly, the major contributions of this paper are summarized as:  
Figure 1: Photo of the hovercraft. (1) A new appointed-time performance constraint function is first proposed for the purpose of ensuring the transient and steady performance of the tracking system. Different from the existing prescribed performance bound technique [12; 13; 14; 15; 16; 17; 18; 19; 20], the appointed-time performance bound technique does not need the complex error transformation. In addition, compared with the traditional finite/fixed-time control [21; 22], the trajectory tracking system in this paper can track the desired targets within the appointed time, and the tracking time can be user-appointed arbitrarily subject to a physically possible range of the controlled system's actuators. (2) Compared with the previous integral barrier Lyapunov function in [23; 24; 25; 26; 27], a new integral barrier Lyapunov function, which can confine system tracking error and handle time-varying constraints, is proposed to guarantee the tracking errors that would not exceed the time-varying appointed-time performance constraint bounds. (3) It is proven that, under the proposed controller, user-appointed-time convergence of the tracking errors is ensured, despite the presence of the model uncertainties. The simulation results represent the advantages of the proposed approach. The remainder of this paper is arranged as follows: preliminaries and problem description is introduced in Section 2. Section 3 is devoted to the appointed-time time-varying integral barrier Lyapunov function-based trajectory tracking controller design for a hovercraft. In Section 4, numerical simulation results are analyzed to evaluate the control performance of the proposed approach. Finally, conclusions are drawn in Section 5.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 4 Mobile Radiation Detection Systems  
Since gamma rays can travel long distances in air (ranging from 65 m for 186 keV in \({}^{235}\)U to 110 m for 662 keV in \({}^{137}\)Cs), they are used for RN material detection and fingerprint. Neutrons also travel long distances in air, and normally result from spontaneous fission of heavy nucleus or generated in other nuclear reactions, like the absorption of alpha particles by certain nucleus. Beta and alpha particles can also be measured with mobile radiation detection systems; however, their short range in air (a few meters for beta and a few centimeters for alpha particles) makes detection difficult [89]. Therefore, in this article, it will be emphasized the detection of gamma and neutron radiation.  
In the release and/or contamination by radioactive material, it is important to determine the radioisotopes present and the intensity of radiation in a given area. The field of radiation monitoring can fall into two distinct categories: (i) the location, identification, and quantification of a radioactive source; and (ii) the mapping of a contaminated area--aims at mapping the distribution of the radiation field over a pre-defined area [17]. This information allows quantification and scanning along space (geographical area in study) and evaluating, along time (study possible variations along the time), areas of risk, becoming a tool to support the decisions of governments and authorities. For example, the fast mapping of a contaminated area (e.g., post-disaster scenario) can provide valuable information for the safety of a task force and for the population evacuation plan.  
This section is divided into five subsections: (i) brief review of the recent advancements in algorithms for the detection and search of radioactive sources, (ii) radiation detection and gamma spectrometry for ground and air surveys, (iii) gamma imaging, (iv) combination of neutron and gamma detection systems, and (v) dual particle (neutron and gamma) imaging systems.
## 4 Dynamic Response Characteristics of the TDP-VSG
### VSG Control with Transient Damping Characteristics  
The control block diagram of the transient damping VSG employing a first-order lead-lag compensator is illustrated in Figure 5[31]. It leverages the high-frequency anti-interference properties and low-frequency attenuation characteristics of the first-order lead-lag element within the damping control loop. This configuration ensures that the damping properties of the VSG are manifested exclusively during transient oscillations, without influencing the steady-state output of the VSG.  
Utilizing the TDP-VSG control block diagram presented in Figure 5, the rotor motion equation for the TDP-VSG is derived as articulated in Equation (9) below:  
\[P_{\text{m}}-P_{\text{e}}=J_{\omega}\omega_{0}\frac{d\omega}{dt}+\frac{D_{ \text{s}}\omega_{0}T_{\text{d}}S}{T_{\text{d}}S+1}(\omega_{\text{g}}-\omega_{ 0}) \tag{9}\]  
Figure 5: Small-signal model control block diagram of TDP-VSG.  
where \(T_{\text{d}}\) is the transient damping decay time constant, and \(D_{\text{s}}\) is the transient damping coefficient.  
From Figure 5, the closed-loop small-signal model under given power disturbances and grid frequency disturbances is derived as follows:  
\[\left\{\begin{array}{l}G_{\text{PD}}(s)=\frac{K_{\text{T}}(T_{\text{d}}s+1)}{T_ {\text{d}}J_{\text{d}}\omega_{0}s^{3}+a_{1}s^{2}+b_{1}s+K_{\text{T}}}\\ G_{\omega\text{D}}(s)=\frac{-K_{\text{T}}((T_{\text{d}}s+1)(J_{\text{d}}\omega_ {0}s+D_{\text{s}}\omega_{0}+K_{\omega})-D_{\text{s}}\omega_{0})}{T_{\text{d}}J_ {\text{d}}\omega_{0}s^{3}+a_{1}s^{2}+b_{1}s+K_{\text{T}}}\end{array}\right. \tag{10}\]  
where \(G_{\text{PD}}(s)\) is the closed-loop small-signal model of the TDP-VSG's grid-connected output power under a given power disturbance; G\({}_{\omega\text{D}}(s)\) is the closed-loop small-signal model of the TDP-VSG's grid-connected output power under grid frequency disturbances; the coefficient \(a_{1}=J_{\omega}\omega_{0}+T_{\text{d}}(D_{\text{s}}\omega_{0}+K_{\omega})\), \(b_{1}=K_{\omega}+T_{\text{d}}K_{\text{T}}\).