esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Mechanisms of the Effects of the ELF-EF on Sympathetic Activity  
The mechanisms of the effect of the ELF-EF on sympathetic activity cannot be clarified from the present data. Possible candidate mechanisms are presented in the following.  
It can be assumed that the EF acted on the skin surface to directly modify the distal part of the sympathetic nerve connecting to the sweat glands to produce a reduction in SC-SFs. The skin is known to generate an endogenous electric field with an intensity of several volts/m [27], and this is related to sweat production. In the present study, the exogenous ELF-EF may have affected the skin's potential to modify the SC-SFs. However, the lack of changes in the SCL found in the present study was not explained by the direct effects of the EF on the skin. The relation of SCFs to orienting and arousing responses that are possibly generated in the central nervous system would also not comply with the peripheral mechanisms of EFs.  
The skin changes induced by EFs may also be transmitted to the central autonomic area to produce a reduction in sympathetic activity. The vibration of skin and skin hair is known to generate changes in skin circulation, which can be detected by the central nervous system to alter autonomic activities [28].  
The EF may directly act on the central nervous system and generate autonomic changes. It has been reported that ELF-EFs generate alterations in EEG, which is an endogenous EF clinically used for the evaluation of brain activity [29]. Theta and alpha waves increase their power during EF treatment, suggesting the lowering of arousal levels [6]. The arousal level is intimately related to autonomic control [30]. Lowered arousal levels may be related to reductions in SC-SFs. The mechanisms of changes in EEG due to ELF-EFs have not yet been clarified. Hormonal changes induced by ELF-EFs may mediate these changes [7]. Ephaptic modulation of the nervous system by ELF-EFs may also be a candidate mechanism [31]. Future research is necessary to validate these possibilities.
## 3 Research Methodology
### Identification of Research and Selection of Studies  
The literature search was conducted using two major academic databases, Scopus and Web of Science (WoS), due to their wide coverage of the fields of digital technology, production, and industrial engineering.  
Two search queries were defined to capture the body of literature intersecting the themes of TPM and I4.0 technologies:  
**Query 1:**  
_TITLE-ABS-KEY ((\"Total Productive Maintenance\" OR \"TPM\") AND (\"Industry 4.0\" OR \"Smart Manufacturing\" OR \"Digitalization\") OR \"TPM 4.0\")._  
**Query 2:**  
_TITLE-ABS-KEY ((\"Total Productive Maintenance\" OR \"TPM\") AND (\"Predictive Maintenance\" OR \"Condition-based Maintenance\" OR \"IoT\" OR \"Artificial Intelligence\" OR \"Big Data\"))._  
The analysis was restricted to the period **from 2017 to March 2025**, selected based on the availability of relevant publications for Query 1. As mentioned in the Figure 1 below, this search resulted in the identification of a total of **475 documents** (143 for Query 1 and 332 for Query 2), distributed across both Scopus and WoS.  
Then a rigorous selection process was applied. The initial filters resulted in the exclusion of articles  
* Not written in English;
* Not classified as journal articles or conference proceedings.  
Subsequently, duplicates (137 documents) were removed. The remaining publications were assessed using additional exclusion criteria:  
* Review papers, posters, white papers, or technical reports;
* Studies not related to manufacturing contexts;
* Documents mentioning TPM/I4.0 only in the keywords or abstract, without further elaboration in the main text.  
This first phase resulted in a final corpus of **58 articles retained for bibliometric analysis**.
## 2 Related Work  
### Human Adaptation to Input Perturbations from Neurological and Psychological Perspectives  
Human beings possess a remarkable ability to understand and make sense of various forms of input, even when it is perturbed.  
From a neurological perspective, the language processing areas of the brain, including Broca's and Wernicke's areas, play a crucial role [9]. When presented with perturbed texts, these areas first work to decipher the intent of the text and attempt to correct it [10]. For example, studies using functional magnetic resonance imaging (fMRI) have shown that the brain initially registers irregularities and then attempts to map the chaotic elements into known language patterns [11; 12]. However, such self-correction reflections remain underexplored in LLMs. When disturbed text is input, the large language model will perform autoregressive generation based on the disturbed text and will not correct the text.  
Psychologically, human comprehension is dependent on context, semantic knowledge, and prior experience. The context provides valuable cues that help overcome the confusion caused by perturbations [13]. For example, in a sentence like \"The cat chased the munose\" with some letter swaps, the overall context of a typical predator-prey relationship allows the reader to infer the correct meaning. Furthermore, continuous exposure to a large amount of language over time allows human beings to develop heuristics and expectations [14]. We subconsciously predict what words are likely to follow based on the initial words in a sentence, which helps to compensate for perturbations. However, unlike the brain, LLMs' word segmentation is deterministic, making them vulnerable to perturbations such as spelling errors or irregular combinations of tokens [15]. These disruptions can fragment subword units or distort semantics, where minor input deviations rarely compromise brain comprehension.  
In general, the human cognitive system demonstrates remarkable resilience to input perturbations through integrated neural and psychological adaptation mechanisms. These self-adaptive processes stand in stark contrast to the current limitations of LLMs, where deterministic tokenization and rigid pattern-matching make them vulnerable to input perturbations that humans effortlessly overcome. We posit that by emulating architecturesinspired by human self-correction frameworks, it may be possible to enhance the robustness of LLMs, particularly when processing perturbed inputs, whereas purely autoregressive generation approaches prove inadequate.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 4 Implementation of the Indoor Channel
### Recursive Model
#### 4.4.1 Recursive Algorithm and Spectral Estimator  
This block calculates the equations of the recursive model presented in Section 2. To do this, the differential power between cell \(i\) and cell \(j\) is calculated using Equation (25).  
\[dP(i,j)=\frac{\Delta Acos(\phi_{i})cos(\theta_{j})}{\pi d_{ij}^{2}} \tag{25}\]  
The result of \(dP(i,j)\) is represented as a matrix. The \(h_{n}^{k}(t)\) response is determined by computing the LoS response between the transmitter and each of the cells, the LoS response between each of the cells and the detector, and the matrix of differential power between the cells. The model computes the \(h^{k}(t)\) function for each reflection order k using \(N_{c}^{2}\) elementary computations for \(k>1\); it uses \(8N_{c}\) bytes of memory for \(k>0\). The block returns the received power at different central wavelengths (\(P_{R,1},...,P_{R,n}\)). A second part of this block estimates the spectral power distribution. Because the model assumes that the received power at the detector has a roughly Gaussian shape, Equation (18) was implemented based on the input parameters of the transmitter block and the received power for each central wavelength. In Figure 4, the estimation of the spectral power distribution of the received power is represented as \(\mathbf{S}_{R_{\lambda}}\).
## 4 Experiments
### Iterative Effect Analysis  
We conduct a detailed analysis of the effects of iterative training on the performance of our depth completion frameworks. Our approach conducts the supervised training of the NLSPN model using labeled data, designating this initial model as the \"teacher.\" Once trained, the teacher model serves as a knowledge source for training a subsequent \"student\" model, effectively facilitating the transfer of learned insights. Upon reaching an adequate level of training, the student model assumes the role of the teacher for the successive training iterations.  
This iterative training process operates cyclically, where each newly trained student model assumes the role of the teacher in subsequent iterations. This continuous cycle of role reversal and training drives a progressive improvement in the model's capabilities. The performance metrics in Table 3 indicate substantial improvements in each iteration, demonstrating a consistent upward trend. Figure 6 demonstrates that the results improve progressively with each iteration.  
The consistent and progressive performance improvement demonstrates the effectiveness of our iterative training approach. In every cycle, the model's capacity for depth completion undergoes gradual refinement, ensuring that the knowledge transferred and acquired in each iteration is expanded and optimized. This iterative strategy enhances  
Figure 6: Iterative performance outcomes based on the NYUv2 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Iterative** & **Model** &
\begin{tabular}{c} **Batch Size** \\ **Ratio** \\ \end{tabular} & **RMSE** & **REL** & \(\delta_{\mathbf{1.25}}\) & \(\delta_{\mathbf{1.25^{2}}}\) & \(\delta_{\mathbf{1.25^{3}}}\) \\ \hline
0 & NLSPN & \(-\) & 0.240 & 0.048 & 97.1 & 99.5 & 99.8 \\
1 & NLSPN & 9:1 & 0.152 & 0.027 & 98.9 & 99.9 & 100.0 \\
2 & NLSPN & 9:1 & 0.113 & 0.016 & 99.4 & 99.9 & 100.0 \\
3 & NLSPN & 9:1 & 0.090 & 0.011 & 99.7 & 99.9 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of iterative effects.  
the precision and overall quality of depth estimation and underscores the robustness and scalability of our training framework, advancing depth completion technology.
## 1 Introduction  
The identification of buried objects using Ground Penetrating Radar (GPR) is usually considered as an imaging problem. However, analyzing the image without any knowledge of the target provides only little information about the object itself. For that reason, alternative algorithms in the time and the frequency domain calculate the natural resonances of objects, which are based on the object's material and shape as a signature.  
In this context, the Singularity Expansion Method (SEM), initially proposed by [PERSON] in 1971 [1], states that the electromagnetic Complex Natural Resonances (CNRs) of an object can be calculated from the backscattered late time response in the time domain [2, 3]. This has been used in the evaluation of geological formations, buried objects, and other radar operations, for example [4, 5]. In Equation (1), which is the starting SEM time-domain equation, the signal \(y(t)\) is decomposed in complex residues \(R_{i}\) and poles \(s_{i}=\alpha_{i}+\mu\omega_{i}\), each with a damping factor and a complex frequency. This Singular Value Decomposition (SVD) based data processing has been used before as preprocessing for data sets with hidden eigenvalues in large data sets; here, this decomposition is also used to analyze data [6].  
\[y(t)\approx\sum_{i}R_{i}e^{-s_{i}t}. \tag{1}\]  
A comparison of CNRs' extraction methods in the time and frequency domain is presented in [7]. These methods are currently being used in the study of landmine detection for humanitarian purposes; so, the validation of the poles and residues of GPR backscattered signals through the accuracy of the reconstruction for buried objects is critical for this research. The Complex Natural Resonances that lead to target identification are considerably sensitive to the signal-noise ratio (SNR). Then, the recovery of the backscattered signal (not only the reflection of the incident wave) before the relevant information disappears into the noise is crucial for the pole validation stage and the posterior object identification problem [8].
# Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes
## 5 Conclusions  
This paper provides a method to reduce the error probability of the quantum convolutional code by enhancing the fidelity of the encoded states using quantum neural networks. Considering that the quantum gates used by the encoding and decoding circuits have noise, we use a quantum neural network to optimize the quantum state after the encoding circuit. A quantum neural network can convert a quantum state with lower fidelity into a target quantum state with higher fidelity through the unitary transformation obtained by learning. Through quantum neural networks, the fidelity of a large number of noisy quantum states is improved from a discrete state to a state whose fidelity is close to 1.  
The increase in qubits to be processed will bring exponentially increased complexity to the quantum neural network. When too many qubits are input to the QNN once, the gradient will collapse, and the fidelity cannot be effectively improved. Therefore, we divide the encoding circuits that need to be optimized for processing, and divide the quantum state with more qubits into multiple quantum states with fewer qubits for quantum neural network optimization. After the quantum neural network optimization, the BER is greatly improved compared with the unoptimized case. After simulation, under the condition of 3 dB SNR, the BER of 0.45% in the case of noisy quantum gates can be reduced to 0.19%.  
In the case of assuming no noise, the signal-to-noise ratio at 3 dB is 0.057%. There is still a certain gap after quantum neural network optimization. The reasons include: (1) the output quantum state of the decoding circuit is unknown and cannot pass through the quantum neural network. However, the quantum gate of the decoding circuit also has noise; (2) the training results of the quantum neural network for more complex quantum states are poor, and the effect of completely improving the fidelity to 1 cannot be achieved. Thus, there is a certain gap with the ideal situation.  
Figure 9: The BER of the [6, 2, 2] quantum convolutional codes applying the QNN compared with BER before training and the noiseless quantum gate.  
**Author Contributions:** Formal analysis, H.X.; Supervision, X.C.; Writing--review & editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 6 Discussion
### Practical Implications for Metro Construction  
The findings provide actionable insights for engineers and project managers involved in similar underground projects:  
1. Central Pillar Monitoring: Given the pillar's higher vibration impact, priority should be given to monitoring and reinforcing core load-bearing components during blasting operations.
2. Optimization of blasting Parameters: The study confirms that delayed detonation and proper spacing of blast holes can effectively control vibration levels. Adopting such techniques ensures that vibrations remain within safe limits, minimizing risks to both structures and operations.
3. Model Validation and Predictive Planning: The use of FLAC3D not only provided accurate predictions for unmeasurable areas, such as the roof, but also demonstrated the value of numerical simulations in early risk assessments. Future projects can use similar models to identify potential hazards and refine blasting designs before construction begins.
# Performance Prediction of Composite Marine Propeller in Non-Cavitating and Cavitating Flow
## 4 Conclusions  
The purpose of this study was to compare the performance of specific composite propellers with different ply angles from a CIS perspective. FSI analysis of model-scale composite propellers was performed using a coupled CFD-FEM to examine the influence of the lamination direction on the deformation of the composite propeller. Additionally, the effects of the stacking method on the performance of composite propellers were discussed and a hydroacoustic analysis of the noise generated and propagated by a composite propeller in non-cavitating and cavitating flows was conducted. Simulations of the hydrodynamic noise of composite propellers under cavitation conditions were performed to clarify the delay in the CIS speed due to the deformation. The results were as follows:  
1. Some deformed parameters of the propeller, such as the pitch and rake, affected the performance of the propeller. In particular, it was confirmed that the deformation pattern of the pitch is directly related to the change in the strength of the vortical flow near the tip region.
2. The deformation of the composite propeller itself did not have a significant effect on the sound pressure level, but the volume change of cavitation caused by the pitch change had a decisive effect on the variation of sound pressure level radiated from the composite propeller.  
These results can improve the feasibility, conceptual design, performance, and manufacturing methods for composite propellers.  
Conceptualization and writing, J.-H.K.; methodology, H.L. and S.-H.K. of Hyundai Heavy Industries (HHI); resources, H.-Y.C. and Z.-H.H. of Korea Shipbuilding and Offshore Engineering (KSOE); validation, H.-S.S. of Korea Research Institute of Ships and Ocean Engineering (KRISO). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not involve humans.  
Figure 20: Sound pressure levels of model 1 in non-cavitating and cavitating flows.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
This research was supported by Hyundai Heavy Industries (HHI) and Korea Shipbuilding and Offshore Engineering (KSOE). In addition, I appreciate Korea Research Institute of Ships and Ocean Engineering (KRISO) for technical support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Conclusions  
The effects of L treatment temperature on the mechanical properties of A 707 Grade L5 modified steel were investigated, and the mechanisms of the effects were also discussed in terms of the reverse transformation behavior and precipitates. In addition, L treatment was applied to a full-scale long part product, and the weldability of the product was evaluated. In conclusion:  
* The strength-toughness balance was obviously improved by L treatment at 1068 K (near the A\({}_{\text{C3}}\) point of the steel), and an investigation of the mechanical and fracture toughness properties of the overall product revealed that L treatment resulted in high quality characteristics of the forging for use in an offshore structure.
* The in situ EBSD measurement results indicate that a fine and complicated microstructure is formed by L treatment at higher temperatures between A\({}_{\text{C1}}\) and A\({}_{\text{C3}}\). The complicated structure seems to indicate that the crystal grain after the L treatment become extremely fine. Moreover, it is clear that the necessary L treatment time is at least 100 min in order to stably obtain the mechanical property-improving effect.
* TEM-EDS analysis shows that coarse Cu precipitates are observed in the not-transformed \(\alpha\) phase. Thus, the strengthening effect of the L treatment temperature is relevant to the area ratio of the not-transformed \(\alpha\) phase and the transformed \(\gamma\) phase during L treatment. The strength, especially the Y.S., seems to be decreased by the not-transformed \(\alpha\) phase acting as a softer phase.
* The EBSD results indicate that the improvement of toughness is due to the refining of the EBSD grain size by the transformed \(\gamma\) phase that is generated during L treatment.  
Figure 17: Relationship between CTOD value and heat input for SAW and GTAW weld joint at F.L. (test temperature: 273 K).  
* The long part forgings of Cu-containing low alloy steel have good weldability, since the maximum hardness of HAZ is less than 300 HV, and the HAZ of steel has a good CTOD property with less than 2.3 kJ/mm of heat input using GTAW.  
[PERSON] and [PERSON] conceived and performed the experiments; [PERSON] wrote the paper; [PERSON] reviewed and supervised the work.  
This research received no external funding.  
The authors declare no conflict of interest.
# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 2 Related Work  
### Three-Dimensional Gaussian Splatting  
Three-dimensional Gaussian Splatting (3 DGS) [7] is an emerging technique for scene representation and novel view synthesis, enabling high-quality, photorealistic renderings. It reconstructs complex 3D scenes by representing them with a set of parameterized 3D Gaussian points. These Gaussian points include parameters such as position, covariance matrix, opacity, and spherical harmonic coefficients that represent colors in 3D space. Three-dimensional Gaussian models learn these parameters from images captured from multiple viewpoints, allowing for fast and efficient scene rendering. A growing number of research has explored the integration of 3D Gaussian Splatting in various applications, demonstrating its versatility and effectiveness across different domains. For example, [PERSON] et al. [10] integrated 3D Gaussian Splatting into monocular SLAM systems, achieving efficient tracking, mapping, and rendering through a unified representation. [PERSON] et al. [12] introduced 4D Gaussian Splatting, modeling Gaussian motion and shape changes to enable efficient rendering of dynamic scenes. [PERSON] et al. [24] proposed Pixel-GS, which incorporates pixel-aware gradients to control density, effectively reducing artifacts and enhancing rendering quality. DreamGaussian [25] developed a model capable of generating novel view rendering from arbitrary angles based on a single image. Furthermore, the explicit representation of 3D Gaussian Splatting facilitates direct editing of 3D scenes in the parameter space, paving the way for advanced scene manipulation techniques. Some works [26; 27] have made some significant advancements in the field of editable 3D Gaussian models. Therefore, the lightweight design of 3D Gaussian Splatting models plays a crucial role in advancing practical applications across various domains. To reduce storage requirements, we adopt the 3D Gaussian Splatting model as the foundation, achieving compression through pruning redundant Gaussian points and optimizing the representation of Gaussian parameters.