esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion and Analysis
### Influence of Environmental Parameters on the Temperature Field Evolution  
#### 3.1.1 Cold Air Temperature  
This section primarily studies and analyzes the evolution and distribution of temperature during the freezing process in ballast tanks. Considering that the temperature variation range of LNG carriers during navigation in polar regions is between \(-43\)\({}^{\circ}\)C and \(-26\)\({}^{\circ}\)C, the average temperature is \(-34\)\({}^{\circ}\)C. Therefore, the temperature of the cold air is selected as \(-40\)\({}^{\circ}\)C, \(-35\)\({}^{\circ}\)C, \(-30\)\({}^{\circ}\)C, \(-25\)\({}^{\circ}\)C and \(-20\)\({}^{\circ}\)C, the temperature of the seawater is \(-2\)\({}^{\circ}\)C, and the height of the ballast water is 4.5 m. The evolution law of ballast tank freezing at different ambient temperatures is analyzed. Table 3 shows the environmental conditions under different working conditions.  
This paper presents the temperature change over time at two points, a and b. Figure 7 illustrates the temperature field distribution during the freezing process at various cold air temperatures. Figures 8 and 9 depict the temperature change curves for points a and b over time under different working conditions.  
It can be observed from Figure 7 that as the temperature of the cold air decreases, there is a significant variation in the rate of temperature change within the air domain of the ballast tank. When the cold air temperature reaches \(-25\)\({}^{\circ}\)C, there is a small temperature fluctuation above point a in the center of the ballast water level. As the cold air temperature continues to decline, the temperature fluctuations gradually shift toward the right bulkhead and penetrate deeper into the ballast water. This phenomenon occurs due to convective heat transfer between the ballast water and the air domain, which results in slight fluctuations in the temperature field above point a. As the cold air temperature decreases further, the heat exchange effect of the air domain on the ballast water intensifies, causing the right side of the ballast water surface to be affected by the cold air temperature more rapidly. Consequently, the temperature fluctuations progressively migrate toward the right side of the ballast water surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Cold Air Temperature/\({}^{\circ}\)C** & **Seawater Temperature/\({}^{\circ}\)C** & **Ballast Water Height/m** \\ \hline A1 & \(-20\) & \(-2\) & 4.5 \\ A2 & \(-25\) & \(-2\) & 4.5 \\ A3 & \(-30\) & \(-2\) & 4.5 \\ A4 & \(-35\) & \(-2\) & 4.5 \\ A5 & \(-40\) & \(-2\) & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Case of cold air temperature analysis.  
temperature at point a is approximately \(-1.34\)\({}^{\circ}\)C, and the minimum temperature at point b is approximately \(-1.1\)\({}^{\circ}\)C.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 2 Signal Model and Potential Error Sources
### Code-Carrier Divergence (CCD)  
The code-carrier divergence (CCD) anomaly mainly concerns differential errors. Differential errors are the ranging errors that occur when smoothing filters are initiated at different times. Furthermore, CCD is useful to monitor and can identify different anomalies, like MP, NLOS, and reflections. It is estimated by a second-order filter design at any time \(k\) given as  
\[d_{1}(k)=\frac{\tau-\Delta T}{\tau}d_{1}(k)+\frac{1}{\tau}(z(k)-z(k-1)) \tag{7}\]  
\[d_{2}(k)=\frac{\tau-\Delta T}{\tau}d_{2}(k)+\frac{1}{\tau}d_{1}(k) \tag{8}\]  
where  
* \(z(k)=\rho(k)-\phi(k)\) is the code-minus carrier measurement.
* \(\tau\) denotes the filter time constant.
* \(\Delta T\) indicates the sample time.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Material Test of Steel for Mono-Anchor Head
#### 4.2.2 Hardness Test of Carburized 42 CrMo4  
In the cross-section of the carburized steel, the hardness decreases linearly up to a certain depth from the surface according to the carbon content and remains constant at a depth beyond that point [21; 22]. Here, a hardness test was conducted to identify the degree of hardening according to the depth from the surface. A grip part of a circular cross-section of a tensile test specimen was cut, and the Vickers hardness was measured at 0.5 mm intervals from the surface of the test specimen, as shown in Figure 9.  
In the two specimens, for each variable, the Vickers hardness is distributed as shown in Figure 10. In addition, Table 3 shows the maximum, minimum, and average values according to the carburizing parameters. The hardness of the carburized specimens was approximately 40% higher than that of the non-carburized specimen. For the carburized specimens, a linear decrease of hardness was present up to a depth of 1 mm. Therefore, the carburized specimen can be divided into an inner layer and an outer layer according to the effective hardening depth, inferring that each layer has different yield strength and tensile strength values.  
Figure 10: Hardness distribution according to the distance from the center.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Carburizing** & **Maximum** & **Minimum** & **Average** \\ \hline H0 & 330.28 & 228.85 & 256.03 \\ H1 & 452.83 & 269.39 & 353.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardness test results (unit: HV).  
Figure 9: Vickers hardness test.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 3 BLIP-2 Optimization Efficiently Using LoRA
### Q-Former Optimization for Domain Adaptation
By incorporating LoRA, the original weight matrices \(W_{Q}\) and \(W_{K}\) are kept frozen, and only the low-rank matrices \(B_{Q}\) and \(B_{K}\) are updated during training. This significantly reduces the number of trainable parameters while preserving the model's performance. The LoRA-enhanced self-attention mechanism improves parameter efficiency and enables effective domain adaptation in the Q-Former module of BLIP-2. By focusing exclusively on low-rank updates, our method achieves efficient adaptation while retaining the pre-trained knowledge of the original model. Subsequently, these refined queries \(Q^{\prime}_{learned}\) and visual features \(V_{img}\) undergo cross-attention:  
\[V^{\prime}_{img}=\text{Cross-Attention}(Q^{\prime}_{learned},V_{img}). \tag{5}\]  
Next, the input text is refined via self-attention using LoRA-efficiently fine-tuned Q, K, and V matrices in the same manner as Equation (4) to obtain \(T_{txt}\):  
\[T_{txt}=\text{Self-Attention}_{LoRA}(\text{Input Text}). \tag{6}\]  
These two outputs, \(V^{\prime}_{img}\) and \(T_{txt}\), are entered together into the LLM decoder. Finally, the LLM decoder generates a contextually rich caption (Output Text). The proposed method contributes to enhanced environmental understanding, situational awareness, and decision-making capabilities for autonomous vehicles. Owing to the integration of LoRA and Q-Former, our method significantly enhances the generation efficiency and accuracy of contextually rich captions for driving environments. By selectively optimizing the self-attention layers, the model is able to focus more effectively on road-specific cues while minimizing computational overhead. This parameter-efficient strategy is all about efficiency. Ultimately, our method enables au  
Figure 3: BLIP-2 optimization for describing autonomous driving scenes. The vision encoder extracts visual features from the input image, which are then processed by the Q-Former. Using LoRA, only the self-attention layers are optimized, allowing the model to adapt efficiently without modifying the full architecture. The cross-attention mechanism enables learned queries to interact with the extracted features. The LLM decoder (OPT) generates textual descriptions based on the processed information. This approach retains the pre-trained knowledge while supporting parameter-efficient adaptation for vision–language tasks. tonomous vehicles to interpret their surroundings more effectively, leading to improved situational awareness and decision-making.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.3 Naive Bayes (NB) Classifier  
The naive Bayes classifier is a machine learning algorithm that uses probability to classify data. It is based on [PERSON] theorem and assumes that features are independent (naive) [46]. The algorithm first estimates the prior probabilities of each class using training data and then calculates the conditional probabilities of each feature given to each class. To make predictions, it combines the prior probabilities with the likelihood of the observed features for each class and selects the class with the highest posterior probability. Naive Bayes is efficient with high-dimensional data, but may not capture complex relationships between features. The NB classifier uses the following [PERSON] theorem presented in Equation (4):  
\[P(C|\mathbf{x})=\frac{P(\mathbf{x}|C)\times P(C)}{P(\mathbf{x})} \tag{4}\]where \(P(C|\mathbf{x})\) is the probability of a data point belonging to class \(C\) given its features \(\mathbf{x}\), \(P(\mathbf{x}|C)\) is the probability of features \(\mathbf{x}\) given class \(C\), \(P(C)\) is the prior probability, and \(P(\mathbf{x})\) is the marginal probability.
## 4 Experimental Evaluation
### Results and Analysis  
#### 4.3.1 Comparison with Baseline Method  
The experimental results on the GEO dataset with 10% of the training set are shown in Table 4. Comparing the results with the baseline methods, our method achieves exciting results on the F1-score (all geological words) and the F1-score (complex geological words), maintaining competitive performance on the F1-score. With an impressive F1-score (all geological words) of 85.57%, GeoCWS outperforms Conv-SEC (66.70%), BERT (82.02%), ZEN (79.84%), WMSEG (81.91%), and WeiDC (80.52%), which demonstrates GeoCWS exceptional ability to identify and segment geological words. Notably, in the complex geological word category, GeoCWS achieves an exceptional score of 90.32%, which is an increase of 27.21%, 9.09%, 9.88%, 11.54%, and 12.14% compared to the baseline methods. The GeoCWS can accurately identify and segment complex geological words with strong geological domain characteristics, which demonstrates the effectiveness of GeoCWS in acquiring geological domain knowledge to improve CWS in geoscience.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 4 Results
### Quantitative Comparisons  
SemanticKITTI dataset: This experiment compares LFNet with existing models for the SemanticKITTI public dataset, and the statistical results of each method are shown in Table 2. LFNet outperforms most existing models in terms of mIoU metrics. Compared to projection-based 2D spatial methods (including SqueezeSegv3 and PolarNet), our model achieves a 16.9-18.5% performance improvement. Compared with the STPC method based on raw points, the mIoU of LFNet improves by 18.2%. Compared to Cylinder3D, SPVNAS, and FusionNet, which are based on voxel partitioning and 3D convolution, LFNet improves by 5-11.5% in terms of mIoU.  
LFNet improved by only 2.5% in mIoU compared to RPVNet, which incorporates all three model structures. The RPVNet model uses three types of input data: voxelized point clouds, raw point clouds, and depth maps. It performs feature learning and information fusion on different forms of data through the three networks. It is able to fully mine semantic information from multiple data sources using a more complex fusion network. However, due to its high degree of complexity and computational costs, the RPVNet model cannot achieve the real-time operation of a system.  
NuScenes dataset: In addition, LFNet is compared with the existing models for the NuScenes dataset, and the statistical results of each method are shown in Table 3. LFNet outperforms most existing models in terms of mIoU. Compared with the fusion methods based on raw points and voxelization (including JS3C-Net and AF2S3 Net), LFNet improves by 6.2-17.6% in terms of mIoU. Compared to multi-view-based methods (including PolarNet and SalsaNext), LFNet improves by 8.8-21.3% in terms of mIoU. Compared to Cylinder3D and SPVNAS, which are based on voxel partitioning and 3D convolution, LFNet improves by 3.7-5% in terms of mIoU.  
Compared to LidarMultiNet, LFNet has a 1.6% reduction in mIoU. LidarMultiNet extracts both 2D and 3D information through different branches and processes the semantic segmentation results of the first stage through a second-stage refinement module. However, due to the complexity of the model structure, LidarMultiNet cannot be applied to real-time semantic segmentation.  
Figure 3: LFNet visualization results for the NuScenes dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Method**} & \multicolumn{13}{c}{**\(  
Our model adds a feature-learning and fusion module to the 3D-Unet model. The LFNet performance is significantly improved. In the SemanticKITTI dataset, the segmentation performance of LFNet was optimal in 10 out of 19 categories compared to the existing models. In the NuScenes dataset, the segmentation performance of LFNet was optimal in 6 out of 16 categories. The experimental results demonstrate the effectiveness of the proposed LFNet for semantic segmentation.
## 3 Standardization
#### International Standards  
International standards, such as those depicted in Figure 5, harness technology to ensure smooth interoperability while meeting information exchange requirements (e.g., EIRs and BEPs). The ISO 19650 [16] series, introduced in 2018, lays the groundwork for BIM information management by defining key concepts and introducing LOIN--a framework that specifies the minimum necessary geometric, alphanumeric, and documentary details per EN 17412 [28]. Similarly, the ISO 29481 [29] standard defines IDMs, which connect construction processes with precise information specifications, leading to improved data quality and availability. Together, these standards work to create a clear, organized BIM  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Problem** & **Solution** \\ \hline Implementation of energy model & \(\bullet\) & Standards for BIM and BEM \\ properties to BIM tools is time- and & \(\bullet\) & Exchange Information Requirements \\ resource-consuming & & \\ Post-processing the BIM output for inputs & \(\bullet\) & BIM should define all required data \\ of the BEM tools is time- and & \(\bullet\) & Standards for BIM and BEM \\ resource-consuming and only for & & \\ professionals & & \\ BIM-based BEM is only useful if design & \(\bullet\) & Quick results playback and \\ variation can be easily and quickly & comparison of design alternatives \\ compared and evaluated during the first & (e.g., via dashboard) \\ design stage & \(\bullet\) & Immediate graphical result output \\ Tools are not compatible with architects’ & Tools and workflow need to be easy \\ working methods and needs, especially in & to use \\ the early design phase & \(\bullet\) & Research has to include AEC industry \\  & & more \\ Interoperability of exchange information & \(\bullet\) & Further extension of IFC and gbXML \\  & \(\bullet\) & Standards for BIM and BEM \\ \hline \hline \end{tabular}
\end{table}
Table 1: BIM2 BEM: problems and solutions.  
workflow that minimizes misunderstandings. While not every aspect of BIM can be standardized, focusing on exchange properties is essential for achieving true interoperability, especially between BIM and BEM.  
The standards provide a framework for defining and describing exchange requirements. Complex, time-consuming, and often manual processes can lead to potential errors. Typically, companies enrich these requirements during their initial BIM projects and then update them as needed. Despite these efforts, interoperability continues to pose significant challenges in both BIM and BIM-to-BEM workflows [23]. Addressing these issues early in the project lifecycle reduces complications in later stages. Rather than having project partners independently define exchange requirements, supporting standardized platforms and predefined exchange criteria can save time, reduce costs, and minimize errors [14]. Although standardized properties alone do not fully guarantee interoperability, they significantly reduce the need for later adjustments related to geometry, topology, and semantics caused by mismatched interfaces and translation issues [32].
###### Abstract  
Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts.  
GWOAD-LSTM; pigment fading; hLC color attribute; prediction accuracy Article
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 4 Predicting Signal Strength  
In the previous section, we saw that at epochs where the positioning solution is not fixed and becomes float, code or navigated, there is a degradation in the C/N\({}_{0}\) of some satellites, hence this can suggest a correlation between C/N\({}_{0}\) and the quality of the positioning. Looking at C/N\({}_{0}\) helps us to better detect the challenging points and can function as an indicator for our prediction.  
In the following subsections, we see that, first, we need to predict the satellite visibility, and after that evaluate the link budget based on the status of the signal.
# Enhanced Gain Difference Power Allocation for NOMA-Based Visible Light Communications
## 3 Proposed Power Allocation Scheme  
In this section, we propose an enhanced gain difference power allocation (EGDPA) scheme that is based on the differences in channel gains among all users. Then, based on the residual allocation principle, the corresponding analytical expressions in terms of achievable data rate, energy efficiency, and error probability are presented accordingly.
## 4 Experimentation and Results  
### Wavelet Denoising  
According to the methodology depicted in Figure 3, the first step involves the application of the wavelet denoising stage. Figure 6 illustrates the performance of the denoising stage. In Figure 6a, the noisy signal is shown in both the time domain and its time-frequency representation. As observed, the background noise (i.e., the existing noise when the transformer in not connected) from 0 to 0.5 s exhibits a considerable amplitude, which is also evident across the time-frequency plane (see the red arrows). Conversely, in Figure 6b, the background noise has been substantially removed (see the blue arrows), demonstrating the effectiveness of the proposed denoising stage.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 2 Kalman Filtering GNSS/INS Fusion Navigation Basic Principles
### Mathematical Model of GNSS/INS Fusion Navigation
#### 2.2.2 Observation Equations
\right] \tag{29}\]  
The coefficient calculation formula is as follows:  
\[b_{j1} =-e_{j1}\sin\lambda+e_{j2}\cos\lambda \tag{30}\] \[b_{j2} =-e_{j1}\sin L\cos\lambda-e_{j2}\sin L\sin\lambda+e_{j3}\cos L\] \[b_{j3} =e_{j1}\cos L\cos\lambda+e_{j2}\cos L\sin\lambda+e_{j3}\sin L\]  
The observation equation for the combined pseudorange and pseudorange rate system is as follows:  
\[Z(t)= \begin{bmatrix}H_{\lx@paragraphsign}(t)\\ H_{\lx@paragraphsign}(t)\end{bmatrix} X(t)+ \begin{bmatrix}V_{\lx@paragraphsign}(t) \\ V_{\lx@paragraphsign}(t)\end{bmatrix}  \tag{31}\]
## 3 Calculation Methods
### FEM with Trefftz-Type Basis Functions (FEMT)  
The inverse heat transfer problem described by Equations (1)-(9) was solved, as in [20], by a special calculation procedure based on FEM, combined with Trefftz functions in the domain \((\Omega_{H}\cup\Omega_{M})\times\langle 0,t_{K}\rangle\), divided into space-time cuboidal and 8-nodes elements. This calculation method consists of interpolating the unknown temperature function in the nodes of elements, using the time-dependent Trefftz functions, determined for the Fourier-Kirchhoff equation [20] and for the heat equation [21]. When determining the Trefftz functions for the heat equation, the parabolic velocity profile was taken into account as follows:  
\[v_{x}(y,t)=\left(\delta_{M}y-y^{2}\right)\frac{\delta\mathrm{v}_{F,ave}(t)}{ \delta_{M}{}^{2}} \tag{10}\]  
where \(\mathrm{v}_{F,ave}\)--mean fluid velocity obtained based on total mass flow rate (see Table 1) and the density of the fluid.  
By minimizing the functional describing the mean square error of the approximate solution at the boundary, at the initial time and along the common edges of adjacent subdomains, unknown values of the temperature function at the nodes were determined [22].
## 4 Evaluation
### Drawbacks of the Evaluation  
To establish the effectiveness of the proposed model in the symbolic domain, we evaluated the model for the pattern discovery task, where a comparison between the SymCHM and other approaches is based on the JKU PDD and JKU PTD datasets. To avoid diminishing the MIREX's position of being an evaluation exchange and not a benchmarking framework, we focused our evaluation on the two variants of the compositional model we developed, the SymCHM and SymCHMMerge, as shown in Table 2.  
As thoroughly discussed by [PERSON] [30], this MIREX task possesses many drawbacks and thus might not be the optimal tool for an algorithm comparison. However, it is rather difficult to create an experiment which would provide a clearer evaluation of the algorithm's performance. First, a definition of a pattern is vague; there are several sources gathered in the JKU datasets. Some of the patterns in the ground truth represent themes, while others represent entire sections. Without any prior knowledge about the goal (length of pattern, perhaps a ratio between the length and the variation within the pattern occurrences), the metrics are logically leaning towards awarding the approach which finds most occurrences of the discovered pattern. It seems impossible to design an algorithm capable of finding a \"pattern\" when the definition of a pattern varies among the annotators. The three-layer F score proposed by [PERSON] is a step towards a metric which provides the balance between the establishment and the occurrence metrics otherwise provided. Second, the size of the dataset presents a limitation: the combined JKU PDD and JKU PTD datasets represent ten (classical) musical pieces in total. It is thus difficult to claim the datasets provide a representative sample of any kind of music or genre. However, we acknowledge the incredible effort put in the creation of the datasets and the tasks; we believe the size of the datasets is affected by the effort needed. Nevertheless, we believe the MIREX discovery of repeated themes and sections task is currently the best currently available approximation of a performance evaluation for the pattern discovery in music.