esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study developed an LSTM-based deep-learning model to generate synthetic Vp logs of distant wellbores in the gas-hydrate-bearing-sediments in the UBGH2 field in the Republic of Korea. Sensitivity analysis results of the input combination and hyperparameters (i.e., the learning rate and batch size) produced the optimal model structure, with accuracy and generality. Inputing GR, RHOB, TNPH, and PEF logs efficiently synthesized Vp logs, and satisfactory performance was achieved in terms of the RMSE and \(R^{2}\) for 13 wellbores in the UBGH2 field. Hyperparameter analysis balanced the model's accuracy and computational cost.  
The model's generality was also examined using 10-fold cross-validation because each fold yielded an \(R^{2}\) higher than 0.8 on average. Data swapping between the training and validation demonstrated consistent test performance. The LSTM and ANN results comparison indicated that the LSTM-based model was more suitable than ANN for generating sequential well-logging data with high accuracy. Consequently, this deep-learning model is applicable to generating synthetic Vp logs at a less consolidated unconventional GH reservoir. We anticipate that the proposed deep-learning approach can be extended to restore or predict well-logging data at missing or unsampled intervals for both conventional and unconventional reservoirs.  
Conceptualization, M.J.; methodology, M.J. and B.M.; software, M.J. and B.M.; validation, M.J., S.K. ([PERSON]) and B.M.; sensitivity analysis, M.J. and M.K.; investigation, M.J.; writing--original draft, M.J.; writing--review and editing, S.K. ([PERSON]), M.K. and B.M.; formal Analysis, S.K. ([PERSON]); supervision, B.M.; project administration, B.M.; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Geoscience and Mineral Resources (KIGAM) project, \"Machine learning-based analysis on gas hydrate (No. GP2021-010)\", and the Korea Electric Power Corporation (No. R21X001-1).  
Machine learning data supporting this study's findings are available from the corresponding author upon reasonable request. UBGH field data can only be made available to researchers subject to a non-disclosure agreement due to confidentiality agreements.  
The authors declare no conflict of interest.
## 2 Framework for Coordinated Operation
### A New Framework for Coordinated Operation  
A new framework is established for CO of electricity and natural gas networks, as presented in Figure 4. It is different from the traditional DO in decision sequence as GNO-ENO-GNO. GNO as advance decision-maker provides NGFUs gas consumption  
Figure 3: The diagram of traditional DO.  
Figure 2: The structure of traditional DO.  
constraints to ENO. When there are multiple coupling units and gas supply capacity is limited, how to generate reasonable constraints for multiple coupling units is extremely important. To solve this problem, to the knowledge of the authors, it is the first time to introduce an indicator, that is, credit rating (CR) for decision making and the entire framework for CO is shown in Figure 5. There are three steps in the procedure.  
Figure 4: The diagram of the proposed CO framework.  
Figure 5: The structure of proposed CO framework.  
Step 1: Receive NGFUs' CR from the previous period, GNO calculates the maximum profit with an uncertain gas load of NGFUs, named GNO sub-optimization-A problem. GNO as advance decision-maker provides NGFUs gas consumption constraints to ENO. To generate reasonable gas consumption constraints information that deliver from GNO to ENO. This means that the natural gas network supplies the maximum capacity to NGFUs. Step 2: ENO set the maximum gas supply of NGFUs as constraints in optimization subproblem to determine the hourly dispatch, including the dispatching generation and actual gas consumption of NGFUs. Step 3: After receiving the actual gas load of NGFUs in natural gas network, GNO optimizes the gas network scheduling to achieve the minimal operation cost and updates the CR of NGFUs for the next scheduling period.  
Some insights could be summarized as follows:  
1. The GNO and ENO do not have dispatching power and networks information of another party, but the information in coupling units is available for both GNO and ENO.
2. In a natural gas network, resident natural gas load priority is higher than NGFUs gas load, GNO will cut the gas supply of NGFUs first if there is a natural gas shortage. Thus, in GNO optimization sub-problem, the resident natural gas load is the control parameters and the profit of this part is fixed.
3. In GNO optimization sub-problem-A, the cost of a gas well is ignored. It assumes that the gas purchase price of NGFUs is greater than the production cost of a gas well, GNO will always benefit from it.
4. CR is a gas price-related indicator, we assume that the forecast gas contract price of all NGFUs is the same. A higher CR means GNO will provide as much natural gas as possible to this NGFU, so that the gas constraint value deliver to ENO will be greater.
## 4 Methods
### Feature Computation
If we consider a bi-dimensional image \(I\), then we can compare the intensity \(I_{p}\) of each pixel \(p\) with the intensity \(I_{j}\) of all its \(j\)th neighbour pixels that lay on a circle centred in \(p\) with radius \(r\). If \(I_{j}>I_{p}\), the \(j\)th pixel is set to 1, 0 otherwise. Next, it is possible to process all \(p\)'s neighbours in a circular direction, interpreting the sequence of 0s and 1s as a binary string and setting the value of \(p\) to the equivalent decimal value. Proceeding as described through all of the pixels of \(I\), we obtain a new image encoding the intensity distribution of each pixel respect to its neighbours that can describe the texture of the original image. The number of patterns for this 2D implementation is \(2^{P}\), with \(P\) denoting the number of local neighbouring points around the central point. In order to extend a 2D LBP to the 3D environment, a first solution consists in considering a helix neighbourhood of each voxel [42]. However, to avoid the high increase of the computational burden, associated to the very large number of patterns for volume LBP, when \(P\) increases, as \(2^{3P+2}\), we chose another 3D implementation of LBP transformation that was also shown to work fine [42]. It considers the co-occurrence on three orthogonal planes crossing the centre of the analysed volume, as depicted in Figure 3.  
It is based on the computation of three 2D LBPs, each of one is derived from each of the three planes; their histograms are then computed and concatenated to obtain a unique histogram for the specific volume. This conspicuously alleviates the computational load, since the number of patterns for TOP-LBP is \(3\times 2^{P}\). Furthermore, in our LBP implementation, we consider two more variants to cope with other two issues of 2D LBP definition. First, we computed rotation invariant LBP, i.e., all of the binary strings obtained as the circular shift of a fundamental string are considered the same. Second, we implemented a uniform version of LBP, i.e., all binary strings containing more than two crossings from 0 to 1 or from 1 to 0 are considered not uniform and coded with a specific string. In our case, setting \(P=8\), we get 48 features by computing first-order measures from the each of the three 2D LBP. The interested readers can find more details about LBPs in [42; 50]. Figure 2: Graphical representation of the method, where the different colours refer to different steps pipeline described in Section 4. List of abbreviations: VOI: Volume of Interest, LOO: Leave-One-Out, RFE: Recursive Feature Elimination, N: number of patients, GTV: Gross Tumour Volume, CTV: Clinical Target Volume, PTV: Planning Target Volume.
## 3 Results and Discussion
### Relationship between NDVI and Climatic Factors
#### 3.2.3 Spatial Pattern of Time Lags  
The climatic factors may have delayed effects on monthly NDVI, which can vary by region. Therefore, the time lags of each pixel were calculated (Figure 7). Most unmasked pixels showed significant positive correlations between monthly NDVI and corresponding monthly temperature and precipitation, with a lag time less than 6 months (99.63% and 99.17%, respectively), of which pixels with zero and one-month lag constituted a large proportion (87.40% and 76.06%). These were mainly in western, central and northern China. Precipitation was more likely to have a lag effect (74.83%) than temperature (51.90%). Monthly NDVI was remarkably related with both temperature and precipitation after more than two months in southern and southwestern China, where the monthly averages of temperature, precipitation and NDVI were all high. The spatial patterns of precipitation time lag agree with the conclusion that the response of NDVI to precipitation is faster when soils and vegetation are drier [48]. In contrast, areas with high precipitation stayed green longer when soil moisture was high [71]. Therefore, in humid areas, precipitation had a longer time-lag effect.  
Figure 6: Violin plots of correlation coefficients between annual NDVI and temperature and precipitation at each pixel in each vegetation region. Grouping results of Games-Howell post hoc pairwise comparison analysis are labeled with letters at left. Regions with an insufficient number of pixels (\(<\)100) with significant correlations are excluded from post hoc tests. Vegetation regions with the same letters have insignificant differences in correlations. Average correlations from positive to negative are labeled in alphabetical order.
# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 3 Results and Discussion
### Evaluating Topic Coherence by Qualitative Comparison  
This section will provide a qualitative comparison of the results obtained from our experiment. In our scenario, each model is configured to extract 10 topics from each dataset, with each topic comprising 10 words. The results compare extracted topics from several topic models and demonstrate how our refinement method improves the topics by correcting misaligned words. Table 11 compares extracted topics from various topic models and highlights how refinement processes improve the topics by addressing misaligned words. It showcases how misaligned words are replaced with more appropriate terms using our refinement method.  
Figure 2: Topic coherence improvement by candidate word replacement: WordNet, GPT, and combined approaches.  
The extracted topic column shows the initial topics generated by each model. While these topics represent the general themes, they often include misaligned words that do not fit well within the topic context. After refinement, the topics are improved by replacing misaligned words with contextually appropriate replacement. The last two columns highlight the specific misaligned words identified by the misaligned word detection mechanism and their corresponding replacements. These replacements are derived using WordNet and GPT, as discussed above, ensuring that the new words enhance topic coherence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **Extracted Topic** & **Refined Topic** & **Misaligned Word** & **Replacement Word** \\ \hline \multirow{6}{*}{AGNews} & LDA & **year**, u, sale, percent, share, cut, inc, profit, chins, **report** & **sales**,**event, u, inc, profit, chins, report & year, report & sales,event, \\  & NMF & president, [PERSON], state, **afp**, election, united, [PERSON], talk, [PERSON], nuclear & election, united, [PERSON], talk & af
###### Abstract  
Reliable bond of steel fiber in concrete is a key problem relating to the reinforcing effect of steel fiber on concrete matrix and for the guide in significance for the optimal design of the geometry and mechanical properties of steel fiber. In this paper, on the basis of multi-indices of evaluation for the bond properties of single hooked-end steel fiber, the indices for the evaluation of synergistic bond properties of different deformed steel fibers are proposed. The pull-out tests were carried out for different deformed steel fibers embedded in mortar wet-sieved from self-compacting SFRC with manufactured sand. Fourteen types of steel fibers were used, including six hooked-end, two crimped, four indentation, one milling, and one large-end. The bond strength, bond energy, and bond toughness of single and per unit weight steel fiber were evaluated with the correspondence to the loading status of cracking resistance, normal serviceability, and ultimate bearing capacity of concrete. Results show that the deformed steel fibers presented different bond behaviors, hooked-end, and crimped steel fibers with circular cross-sections and a tensile strength of higher than 1150 MPa have excellent effects of strengthening, energy dissipation, and toughening capacity on self-compacting concrete with a cubic compressive strength of 60 MPa at normal serviceability and ultimate bearing capacity. Indentation, milling, and large-end steel fibers are more suitable for reinforcing the concrete strength due to the rigid bond before concrete cracking. The synergistic working of steel fibers with concrete matrix should be concerned to realize the effects of only or simultaneously reinforcing the strength and toughness of concrete.  
pull-out behavior; deformed steel fiber; wet-sieved mortars; bond strength; pull-out energy; pull-out toughness Article
## 2 ULMA-Based borehole PEC Signal Model
Considering the noise generated in each receiver, we stacked the EMFs induced in the \(M\) receivers into a vector as follows:  
\[\mathbf{u}(t,d_{0})=[U_{1}(t,z_{1},d_{0}),\cdots,U_{m}(t,z_{m},d_{0}),\cdots,U_ {M}(t,z_{M},d_{0})]^{T}+\mathbf{n}=-\frac{\xi\chi}{t_{of}}\mathbf{Vg}^{T}(t,d_ {0})+\mathbf{n} \tag{3}\]  
where \(\mathbf{V}=[\mathbf{v}(z_{1}),\)\(\mathbf{v}(z_{2}),\)\(\ldots,\)\(\mathbf{v}(z_{M})]^{T}\) and \(\mathbf{n}=[n_{1},\)\(n_{2},\)\(\ldots,\)\(n_{M}]^{T}\) is a Gaussian white noise vector with a mean of 0 and a variance of \(\sigma^{2}\). According to Equation (3), the phase varies with TRD, so the loss can be obtained by directly summing the EMFs [21]. To reduce the loss and ensure a satisfactory casing effect, the phase should be compensated and the noise should be suppressed. Therefore, the following LCMV cost function is established:  
\[\left\{ \begin{array}{l}\min\mathbf{w}^{T}\mathbf{R}_{u}\mathbf{w}\\ s.t.\mathbf{w}^{T}\mathbf{V}=\mathbf{f}\end{array} \right. \tag{4}\]  
where \(\mathbf{R}_{u}=E[\mathbf{u}\mathbf{u}^{T}]\) denotes the auto-correlation matrix of \(\mathbf{u}\), and \(\mathbf{f}=[1,1,\)\(\ldots,\)\(1]\in\mathbb{Z}^{1\times P}\) is a row vector with all \(P\) elements equal to 1. The weight is optimized through the Lagrange multiplier methodology as  
\[\mathbf{w}=\mathbf{R}_{u}^{-1}\mathbf{V}(\mathbf{V}^{T}\mathbf{R}_{u}^{-1} \mathbf{V})^{-1}\mathbf{f}^{T} \tag{5}\]  
As shown in [21], Equation (5) can compensate for the phase shift at each receiver with a different TRD. The output SNR is then enhanced to improve the corresponding casing result. However, within the limited space of downhole casings, the number of receivers \(M\) is usually far smaller than \(P\), so the number of constraints in Equation (4) is considerably larger than the dimension of \(\mathbf{w}\), and the inverse of \(\mathbf{V}^{T}\mathbf{R}_{u}{}^{-1}\mathbf{V}\) becomes ill-conditioned. Although the inverse of \(\mathbf{V}^{T}\mathbf{R}_{u}{}^{-1}\mathbf{V}\) can be computed through the DL method, the selection of the DL factor is based on empirical values and the process entails a large number of calculations, making the method lack practical usability. To avoid the ill-conditioning of Equation (4) and improve the NDE effect, we established a sparsity-based cost function that reduced the number of constraints in Equation (4).
## 4 Case Study of Crash Scenarios
### Concept  
A good V2P system must be able to effectively operate its three phases, i.e., _detection, tracking and trajectory prediction, and action_. _Detection_ phase requires the first contact between a vehicle and VRU [14]. Similarly, _tracking and trajectory prediction_ requires sufficient number of messages exchanged, and _action_ phase requires sufficient amount of time for reaction in order to stop the vehicle. Our study evaluates the mechanisms of active and passive participation to understand the first contact by VRU, available response time, and number of messages received by the vehicles from the VRU involved in the potential crash.
## 1 Introduction
### Strategies for Edge Computing  
Recently, tiny machine learning has emerged as a promising approach for implementing DNNs on microcontrollers [5; 6; 7; 8]. Despite limited resources, this concept enables real-time, energy-efficient processing on embedded devices powered by microcontrollers, enhancing privacy and reducing reliance on external computing resources. The migration from fog or cloud computing to on-device computation is commonly referred to as edge computing [9], relying on two main strategies:  
1. Optimization of DNN architectures during the design phase. This process maximizes the accuracy on a target dataset, while adhering to specific constraints, such as embedding size, kernel/stride size, hidden layer size, the number of model parameters, and the storage space required for intermediate computations--all of which significantly impact the memory requirements [10; 11]. Quantization-aware optimization is performed by using specialized frameworks [12; 13] that simulate the quantization (e.g., mapping weights and activations to 8-bit integers) but compute gradients in floating-point to preserve training accuracy. This approach ensures that the final model keeps its accuracy while deployed on the real device [14].
2. Application of compression techniques to pre-trained DNN models, such as post-training quantization, binarization, pruning, and clustering [8]. Post-training quantization freezes a pre-trained DNN and reduces memory usage and latency by converting floating-point parameters to lower-precision integers, often with minimal accuracy loss [15; 16; 17; 18]. Binarization, an extreme form of quantization, reduces weights and activations to a single bit for maximum compression [19; 20]. Pruning eliminates unnecessary parameters, either through structured pruning, which removes channels or filters to improve inference speed at some accuracy cost, or unstructured pruning, which zeroes out individual weights with minimal accuracy impact [21; 22]. Clustering replaces similar weights with a smaller set of common centroid values, significantly reducing model size while preserving performance [23].
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 4 Simulation Tests
### The Impact of Weighted Moving Average Smoothing  
Here, the rectangular (Figure 2a) and dipping (Figure 2b) prism are adopted to verify the effectiveness of smoothing. To simplify the discussion, the norm of regularization term is set to 2 for two type of prism models. Additionally, considering that the smoothness of obtained models is controlled by the values of \(s_{f}\) factor, the different \(s_{f}\) values will be selected. In this part, \(s_{f}\) is set as 0, 1, 2 and 3, while \(s_{f}=0\) means no smoothness is applied in the inversion process. The inverted results for these two models are shown in Figures 3 and 4, respectively. Obviously, according to the models obtained with different \(s_{f}\) values, incorporating the smoothness technique with mutation strategy of DE can help strengthen the continuity among the variables.  
Figure 3: The inversion results of the model shown in Figure 2a with different \(s_{f}\). (**a**) \(s_{f}=0\), (**b**) \(s_{f}=1\), (**c**) \(s_{f}=2\), (**d**) \(s_{f}=3\).  
Figure 2: The cross\(-\)section through the \(x\)-axis of the 2D models with density \(1\,\mathrm{g}/\mathrm{cm}^{3}\). (**a**) rectangular prism, (**b**) dipping prism, (**c**) parallel prism, (**d**) U prism.  
The fitting process to each \(s_{f}\) for the rectangular and dipping prism is shown in Figure 5.  
Obviously from Figure 5 when \(s_{f}\
eq 0\), which mean smoothness is applied in the inversion process, the inversion can be significantly accelerated.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 4 Results
### Experimental Data
#### 4.2.4 Parameter Setting  
The model training process involves the following steps:  
1. Dataset division: Randomly select 10% of the training set as the validation set and use the remaining 90% for training;
2. Training strategy: Train the model on the four datasets for up to 50 epochs. If the validation loss does not decrease for five consecutive epochs, the training is halted;
3. Model structure: The BGF model comprises 2 layers of GCN and 12 layers of BERT. The word embedding dimension is set to 200.  
With these methods and settings, we ensure the reliability and reproducibility of the accuracy rate as an evaluation metric, verifying the effectiveness and superiority of the BGF model in text categorization tasks. These measures guarantee the credibility of our research conclusions through dependable and replicable experimental results.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 4 Simulation Experiments on Robustness
### Experiment Results and Analysis
#### 4.2.2 Robustness with Different Tolerance Parameter \(\tau\)  
In order to explore the influence of different tolerances on the dependence characteristics of the DGCN of CSOS, the tolerance coefficient is varied in this experiment. Because the dependency scale of the group dependency is always 5, the value of the tolerance coefficient is varying at an interval of 0.2, and the robustness of the DGCN of CSOS when different model networks are used for simulation is shown in Figure 9. It can be seen from the figure that the experiments under the three model networks have a consistent conclusion: the smaller the tolerance coefficient, the worse the robustness of the system. When the tolerance coefficient reaches a certain threshold, there is a certain upper limit for the change of robustness, and the robustness drop curves no longer improve.
# On Ad Hoc Communication in Industrial Environments
## 2 Related Work
They acquired NEPs using network traces [29, 30] from the campus networks of several universities. These traces contain the complete communication within the examined infrastructure network, including the registration of mobile devices at certain access points. With the assumption that two nodes registered at the same access point can also directly communicate with each other, NEPs are extracted from the network traces [31]. The presented work, in contrast, utilizes a novel protocol to directly record NEPs. This enables a more precise observation of the network without assumptions about the connectivity of clients. No previous work has analyzed the ad hoc communication channel of mobile clients in an industrial environment. With ongoing trends like Industry 4.0, the importance of mobile clients and wireless communication will increase. Therefore, this investigation is highly relevant. Additionally, this work uses the established tool NEP to examine the ad hoc channel and proposes and publishes a new method to record these NEPs, which can be applied with other communication technologies in industrial scenarios, but also in non-industrial applications.