esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architecture of the Proposed Two-Dimensional Convolutional Neural Network (2D-CNN) Model
### Fully Connected Layer  
The fully connected (FC) layer is the final part of a regular CNN model. It works by establishing full connection between the input feature and the output layer. Mathematically, the input feature is transformed to the output layer through a weight matrix and a bias matrix, as illustrated in Equation (6):  
\[\mathbf{y}_{d}=f(\mathbf{W}_{d}\mathbf{x}_{d}+\mathbf{b}_{d}) \tag{6}\]  
Figure 5: Architecture of the proposed 2D-CNN model.  
where \(\mathbf{x}_{d}\) and \(\mathbf{y}_{d}\) denote the input and output vector of the FC layer, respectively; \(\mathbf{W}_{d}\) and \(\mathbf{b}_{d}\) denote the weight and bias matrix, respectively; \(f(\cdot)\) denotes the ReLU activation function expressed in Equation (5). Furthermore, the last convolutional layer is flattened to a one-dimensional array before the FC layer.  
In the training process, the early stopping method with limited epochs of 2000 is adopted to avoid overfitting of the proposed 2D-CNN model. The adaptive momentum (Adam) algorithm is selected as the optimizer and the learning ratio is set to \(8\times 10^{-5}\) to reach a reasonable prediction performance. The above-mentioned hyperparameters of the proposed 2D-CNN model are determined by trial and error. Several networks with different parameters of convolutional and pooling layers are examined and the effect on R\({}^{2}\) and RMSE values in Case Ani-5 are summarized in Table 2, where \(N_{CL}\) and \(K_{CL}\) denote the number and kernel size of convolutional layer, respectively. It is observed from Table 2 that the architecture of three convolutional layers with kernel size at \(2\times 2\) and average pooling, as shown in Figure 5, has the maximum R\({}^{2}\) and minimum RMSE value in predicting the uniaxial horizontal and moment bearing capacity factors.
## 4 Robust Distributed Rendezvous Control
### Handling of Dynamic Scenarios, including Faulty UUVs
#### 4.3.1 Discard Faulty UUVs  
Note that a UUV can become faulty owing to various reasons, such as hardware malfunction. We next address how to handle faulty UUVs. Let a healthy UUV denote a UUV having no faults.  
Every UUV is encountered by its child under Algorithm 2. Thus, every faulty UUV, including a faulty root, can be detected by its child. Once a healthy UUV meets with a faulty UUV, the healthy UUV sends signal to the faulty UUV. A healthy UUV responds with an acknowledge signal whenever it receives a signal. If a UUV does not respond to the received signal, then the healthy UUV can find that a fault has occurred in the responding UUV.  
Algorithm 2 is further designed to cope with a faulty UUV (including a faulty root). We discuss how to handle faulty UUVs from now on.  
We first introduce a method of discarding (dropping) faulty UUVs once they are sensed. Once a broken UUV, e.g., \(u_{B}\), is sensed, then a tree \(T\) is updated by applying Algorithm 1 in Section 4.1 without \(u_{B}\). Thereafter, Algorithm 2 runs utilizing the updated \(T\). The updated \(T\) does not contain \(u_{B}\). This implies that we discarded (dropped) \(u_{B}\).  
If there are too many faulty UUVs, then it may be impossible to build a tree \(T\) without them. If we cannot build a tree without faulty UUVs, then we cannot make every healthy UUV encounter at the root UUV.
###### Abstract  
Ultrasonic waves and acoustic emissions (AE) are important technologies to reveal rock damage. However, few studies have simultaneously monitored both types of data in the same experiment because of limitations on the experimental apparatus. In this study, an integrated ultrasonic wave and AE testing device was developed to investigate the deformation characteristics and damage development of salt rock. Fracture experiments under uniaxial compression were carried out on three samples from the Jintan Salt Mine, Jiangsu Province, China. The deformation process can be divided into five stages. In the compression fissure and linear deformation stages, P- and S-waves rose slightly to stability, and acoustic emission activity was weak (0.04% and 2.66%, respectively). Subsequently, S-wave velocity slowly declined and AE events become more active, with about 13.8% of the total in the stabilized-growth cracks stage. When the salt rock entered the accelerated-growth cracks stage, AE events increased to 75.27%; with features of an earthquake swarm, the velocities of P- and S-waves began to fall significantly. After the peak stress, salt rock produced only a small number of AE events. The beginning stress of rock damage and dilatancy were about 42-50% and 62-67% of the uniaxial compressive strength, respectively. The ultrasonic wave velocity ratio, _lb_-value, and _r_-value effectively predicted rock failure, but the _r_-value was superior owing to its sensitivity and ease of measurement.  
Keywords:salt rock; rock damage; crack; ultrasonic velocity; acoustic emissions +
Footnote †: journal: Applied Physics Letters
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.2 Execution Time: Model Selection and Inference
Therefore, in terms of practical deployment in resource-constrained settings, the meta-learning approach offers significant advantages by enabling scalable, low-latency forecasting services with minimal degradation in accuracy. In contrast, the exhaustive search method required substantially more time, with the total service latency often exceeding 50 to 100 s. This is because exhaustive search evaluates all candidate models before selecting the best one, significantly increasing the model selection time. Although the inference time of each selected model in exhaustive search was often similar to that of the proposed method, the additional overhead from evaluating all models sequentially became the dominant factor in the total latency. These results clearly demonstrate that the proposed framework achieves low-latency service not by accelerating inference itself but by eliminating the computational burden of exhaustive model selection. The model selection time of the proposed system was consistently under 0.01 s across all settings, enabling fast hand-off to the inference stage. Consequently, inference time becomes the primary latency contributor, but the overall service time remains within operational thresholds for real-time forecasting systems. Across all tested configurations, the meta-learning framework achieved a total service time speedup ranging from 15\(\times\) to nearly 19\(\times\) compared to exhaustive search, enabling practical deployment in latency-sensitive energy control environments.
# Applications of Probabilistic Forecasting in Demand Response
## 1 Introduction
Finally, we propose a specific DR strategy based on the calculation of conditional load quantiles in the very short term which enables aggregators to more accurately align the actual consumption with the purchased energy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **ML Technique** & **Specific Objective** & **Pros/Cons** \\ \hline
[19] & Deep neural network and reinforcement learning & Improved network stability and reliability & Calculation of optimal incentive rates/ Possible customer mismatch \\ \hline
[20] & Whale optimization & Fitting the actual load curve to the forecasted load curve & Cost optimization for electricity retailers/ No predefined load forecasting model. Only day-ahead optimization \\ \hline
[21] & Stacked autoencoder neural network & Support for decision-making processes for day-ahead dynamic pricing & Decision support tool for retail electric providers. It considers off-customers with different behaviors/ It is essential to incorporate prosumer customers \\ \hline
[22] & K-Nearest Neighbor (kNN) & Shift demand to periods of low demand & Calculation of optimal incentive rates/ Challenges arising from user non-participation \\ \hline
[23] & Heuristic and genetic algorithm & Development of an Intelligent Electric Power Management System & It aims to reach the lowest daily cost of electricity/ It does not account for energy storage \\ \hline
[24] & Temporal Convolution Neural Network & PV power forecast for battery energy storage management & Payback period reduction for users/ Potential users are entire buildings \\ \hline
[25] & Multi-Agent Proximal Policy Optimization & Frequency regulation & Control of residential air conditioners/ Real-time control without prior planning \\ \hline
[26] & Ensemble with neural network, random forest, support vector machine, and extreme gradient boosting & Assessing the energy flexibility of residential buildings & Useful for energy aggregators Updated forecasts one hour in advance/ No evaluation of uncertainty associated with the prediction \\ \hline This study & Quantile regression models & Fitting the actual load curve to the forecasted load curve & Probabilistic approach with uncertainty evaluation \\  & & Day-ahead and one-hour-ahead forecasts \\  & & One-hour-ahead fitting \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies exploring the integration of ML techniques with the activation of DR strategies. The paper is organized as follows. Section 2 describes the two probabilistic forecasting methods selected in the research and the dataset used to illustrate the proposed approach, as well as how to evaluate the accuracy of point and probabilistic predictions. Additionally, it develops the methodology used to simulate the effect of demand response throughout the behavior of elemental and aggregated loads. Section 3 deals with the introduction of the three main applications proposed in this paper and the analysis of the experimental results. The main findings and recommendations of the study are presented in Section 4.
# Self-Ginding Silage Knife Strengthened with Ni-WC Alloy Prepared by Laser Cladding
## 5 Conclusions  
1. In this study, a Ni-WC alloy layer was made at the surface of the cutting edge by laser cladding. A microstructure of typical rapid directional solidification was formed in the cladding layer that was dense and without defects such as cracks and gas holes. A metallurgical combination was formed between the substrate and cladding layer. Obvious composition dilution did not take place at the cladding layer;
2. A large number of hard phases formed during the laser cladding, increasing the hardness of the cladding layer. The chosen hardness value of the cladding layer was approximately 1100 HV(0.2), and the average hardness value was approximately 1000 HV(0.2). The hardness of the cladding layer was obviously higher than that of the substrate, but there was a good hard gradient between the cladding layer and the substrate;
3. The laser cladding increased the impact toughness of the knife to a large degree. When the laser power was 1600 W, the cladding layer had a combined high hardness and toughness and the best wear resistance. The friction mass loss of the 65 Mn steel was approximately four times that of the cladding layer;
4. Contrasting with common knives made with 65 Mn steel, a self-sharpening edge was formed during operation for the knives after laser cladding with the Ni-WC alloy. The sharp cutting edge of the laser cladding knives was still kept after usage for 76 h. The wear resistance and cutting performance increased sharply due to the laser cladding;
5. The successful manufacturing the self-grinding knife using laser cladding increased the working performance and cost effectiveness, while ensuring excellent work efficiency. The tool's life increased by more than double, with only a 30% cost increase. This product has a large market and strong application in agricultural machinery.  
Conceptualization, L.X.; Data curation, M.L. and M.G.; Formal analysis, Z.S.; Funding acquisition, Z.S. and M.G.; Investigation, Z.S.; Methodology, L.X. and J.G.; Project administration, F.L.; Resources, L.X.; Supervision, J.G.; Validation, Z.S., F.L., J.G., and M.G.; Writing--original draft, L.X. and M.L.; Writing--review and editing, M.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the China National Key R&D Program during the 13 th Five-Year Plan Period (grant number: 2016 YFD0701701-01), the Technical Innovation Guide Plan of Shandong Province: Shandong-Chongqing Total Cooperation Project (grant number: 2020 LYXZ021), and the Natural Science Foundation of Tianjin (grant number: 19 CYBJC19100).  
The authors declare no conflict of interest.
## 4 Model and Notations
### Constraint Programming
#### 4.2.1 Start-Based CP Model  
A formulation of the main variables is presented in Table 4 using the concepts of CP Optimizer. From now on, this model is called \(CP_{wav}\).  
\[\mathrm{Minimize}\ T=\sum_{i=1}^{n}max(0,t_{i}) \tag{21}\]\[\text{subject to:}\] \[\text{t}_{i}=\max(0,\text{endOf(itvs[C_{i}])}-\text{d}_{i}) \tag{22}\] \[\text{EndBeforeStart}\left(\beta_{j},\ \left[\alpha_{jk}\right] \right)\ \ \text{j}\in\text{j},\ \text{k}\in\text{m}_{i}\] (23) \[\text{Alternative}\left(\beta_{j},\beta_{j^{\prime}}\right)\ j,j^{ \prime}\in Ji\] (24) \[\text{noOverlap}\left(\left[\alpha_{jk}\right]\right)\ \ j\in J,\forall k\in\text{m}_{j}\] (25) \[d_{i}\geq\beta_{i}.\text{end}\ \forall i\in\text{N}\] (26) \[\text{interval}\ \alpha_{jk},\text{opt},\text{size}=P_{jk}+St_{jj^{ \prime}k},i\in V,k\in m_{i}\] (27) \[\text{endAtStart}\left(St_{jj^{\prime}k},\alpha_{jk}\right)j\in J, \forall k\in\text{m}_{j} \tag{28}\]  
The objective function is to minimize the total tardiness (21), given by the difference between the job's end value and due date (22). The EndBeforeStart constraints (23) represent the precedence constraints between interval variables. Alternative constraints (24) represent the assignment constraints stating that each operation must be performed on exactly one machine. Constraint (25) defines the nonoverlapping constraint; that is to say that, during the interval \(\left[\alpha_{jk}\right]\), which represents the assignment of an operation j to machine \(k\), the latter cannot overlap; e.g., the machine is busy during this interval.  
The constraint endAtStart (\(\alpha\), \(\beta\)) is used to state that the end of a given interval variable \(\alpha\), equals the start of a given interval variable \(\beta\). We use this constraint (28) to ensure that the end of a setup should be followed by the execution of the considered operation.
## 3 Results
### Final Models
#### 3.2.2 Support Vector Regression  
We used a radial-based kernel function to conduct an epsilon regression. The final model's parameters achieving the best fit were \(Cost~{}=~{}1\), \(\gamma~{}=~{}0.0833\), \(\varepsilon~{}=~{}0.1\), and \(number~{}of~{}support~{}vectors~{}=~{}3641\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameter** & **Coefficient** \\ \hline Intercept & \(-7.7668\times 10^{3}\) \\ Tcell & \(8.2463\times 10^{1}\) \\ Eff & \(-3.3805\times 10^{1}\) \\ Tamb & \(-9.8509\times 10^{1}\) \\ P & \(8.9286\times 10^{0}\) \\ WS & \(7.8149\times 10^{0}\) \\ WD & \(-7.2022\times 10^{-1}\) \\ TOA & \(7.8705\times 10^{-2}\) \\ CSDHI & \(2.3547\times 10^{-2}\) \\ GHI & \(4.4554\times 10^{-2}\) \\ DHI & \(2.5494\times 10^{-1}\) \\ BNI & \(2.0092\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression coefficients of the final model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** \\ \hline Tcell & \(0.41\) & \(0.01\) & \(0.24\) & \(0.09\) & \(0.83\) & \(0.11\) \\ Eff & \(0.17\) & \(0.18\) & \(0.43\) & \(0.21\) & \(0.75\) & \(0.04\) \\ Tamb & \(0.26\) & \(-0.56\) & \(0.08\) & \(-0.28\) & \(0.62\) & \(0.13\) \\ RH & \(-0.42\) & \(0.08\) & \(-0.02\) & \(-0.02\) & \(-0.77\) & \(-0.02\) \\ P & \(0.10\) & \(0.88\) & \(-0.11\) & \(-0.24\) & \(0.04\) & \(-0.08\) \\ WS & \(-0.02\) & \(-0.17\) & \(0.00\) & \(0.94\) & \(0.10\) & \(0.08\) \\ WD & \(-0.01\) & \(-0.10\) & \(0.04\) & \(0.08\) & \(0.11\) & \(0.98\) \\ Irr & \(0.75\) & \(-0.12\) & \(0.33\) & \(0.04\) & \(0.46\) & \(0.13\) \\ TOA & \(0.73\) & \(-0.06\) & \(0.64\) & \(0.01\) & \(0.21\) & \(0.02\) \\ CSGHI & \(0.77\) & \(-0.04\) & \(0.59\) & \(0.02\) & \(0.19\) & \(0.02\) \\ CSBHI & \(0.84\) & \(0.00\) & \(0.46\) & \(0.04\) & \(0.18\) & \(0.02\) \\ CSDHI & \(0.34\) & \(-0.16\) & \(0.84\) & \(-0.05\) & \(0.19\) & \(0.02\) \\ CSBNI & \(0.75\) & \(0.22\) & \(0.44\) & \(0.12\) & \(0.25\) & \(-0.04\) \\ GHI & \(0.89\) & \(-0.04\) & \(0.36\) & \(-0.06\) & \(0.24\) & \(0.01\) \\ BHI & \(0.94\) & \(-0.04\) & \(0.12\) & \(-0.08\) & \(0.24\) & \(0.00\) \\ DHI & \(0.29\) & \(-0.05\) & \(0.91\) & \(0.02\) & \(0.13\) & \(0.04\) \\ BNI & \(0.87\) & \(0.12\) & \(0.01\) & \(-0.06\) & \(0.34\) & \(-0.06\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of PCA.  
Scatterplots (see Figure 4) revealed more information about the model's effectiveness. Figure 4 shows the scatterplots of predicted values vs. actual ones. For a suitable model, all points should be near to the diagonal line and show no practical dependencies.
[11] used a self-developed CO\({}_{2}\) dynamic alteration device to soak rocks for 1, 3, 5 and 7 days, revealing the alteration damage mechanism of CO\({}_{2}\). [PERSON] et al. [12] immersed granite in a carbon dioxide solution for 6 h, 24 h and 168 h, and found that the injection of carbon dioxide promoted the dissolution of granite and the precipitation of secondary minerals. Carbon dioxide dissolves rocks, and thus changes their mineral composition [13; 14; 15; 16; 17]. The interaction between a brine solution and rock affects the fracture mechanical properties of reservoir and sealed rock [18; 19; 20; 21; 22; 23]. [PERSON] et al. [24] immersed the sandstone of the Three Gorges reservoir in a brine solution for five cycles, and observed two typical macroscopic damage modes, surface spalling and internal cracking, as the cycle increased. A saline solution can affect fracture propagation patterns in rocks. [PERSON] et al. [25] soaked gypsum in salt water for up to 80 days and found that its uniaxial strength decreased to different degrees with time. The saline solution degrades the mechanical properties of the rock [26; 27]. A saline solution also has an impact on rock mineral composition [28; 29; 30]. [PERSON] et al. [31] carried out CT scanning of the triaxially loaded sandstone after immersion in a saline solution, analyzed the damage evolution mechanism of sandstone, and proposed a damage model based on the influence of chemical corrosion and the CT value. To sum up, as shown in Table 1, although many scholars have conducted in-depth studies on the effects of carbon dioxide and a salt water solution on the mechanical properties of rocks soaked in the short term, it is of great significance for the oil and gas injection and production of deep salt water reservoirs in the short term. But what about long-term oil and gas production? In this case, is it the same as the combined effects of brine, supercritical and carbon dioxide on the mechanical properties of sandstone reservoirs in the short term? Therefore, it is necessary to further study the long-term effects of a salt water and carbon dioxide environment on the mechanical properties of sandstone. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference Serial Number** & **0** & **1–5** & **6–10** & **11–17** & **18–24** & **25** & **26–30** \\ \hline Soaking medium & Brine–CO\({}_{2}\) & & CO\({}_{2}\) & & & Brine \\ \hline Maximum soaking time & 120 days & 30 days & 2 days & 7 days & Close to 5 days & 80 days & Close to 3 days \\ \hline Influence mechanical property (compressive strength, etc.) & First decrease, then increase, then decrease & Simple reduction & / & / & / & Simple reduction & / \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The previous reference experimental scheme design and comparison of the results with this study (where “/” indicates minor research or very little research and reference “0” represents the content of this study).
## 3 Enhanced Frequency Response Strategy of a DFIG
### De-Loaded Operation of the DFIG  
To emulate the primary frequency response, the DFIG should retain the required reserve. To address this demand, the power coefficient (\(c_{p}\)) of the DFIG should be reduced by the pitch angle control of wind turbine and over-speeding of the DFIG.  
Figures 2 and 3 illustrate the de-loaded curves with \(d\%\) reserve power when the DFIG performs over-speeding and pitching, respectively [12; 14]. The black and red solid lines indicate the mechanical power curve and electrical output power of the DFIG when performing MPPT operation. The red dotted line represents the de-loaded curves when performing pitching and/ or over-speeding. Operating points 1 and 2 are the MPPT and de-loaded operating points, respectively.  
Figure 3: De-loaded operation by pitching strategy.  
Figure 2: De-loaded operation by over-speeding strategy.  
To achieve the required reserve for support frequency response, the operating point of the DFIG moves from operating point 1 to operating point 2 by over-speeding and/or pitching, as illustrated in Figures 2 and 3. It should be pointed that unlike pitching strategy; over-speeding retains more kinetic energy in the rotor when retaining the reserve. Furthermore, when supporting frequency, the response of the pitch angle control is relatively slow in comparison with the response by releasing kinetic energy from the rotating masses of the DFIG.  
As in [12, 14], the required reserve \(d\%\) is given as:  
\[d\%=P_{opt}-P_{de}\Big{/}P_{opt}\times 100\%, \tag{7}\]  
where \(P_{opt}\) and \(P_{de}\) indicate the maximum available and de-loaded powers of the DFIG, respectively.  
Assuming that the wind speed is constant, the power coefficient for the de-loaded operation, \(C_{p,deload}\), is represented as:  
\[C_{p,deload}=(1-d\%)C_{p,max}, \tag{8}\]  
where \(C_{p,max}\) indicates the coefficient of the MPPT operation and that achieved at the MPPT operation mode.  
Thus, the power reference during the de-loaded operating mode (\(P_{de}\)) can be given as:  
\[P_{de}=0.5\rho\tau R^{2}C_{p,deload}, \tag{9}\]  
Based on the wind speed, the de-loaded operation is divided into three areas: low wind speed area, medium wind speed area, and high wind speed area, as follows [19]:  
1. Low wind speed area: the required reserve is achieved by over-speeding strategy.
2. Medium wind speed area: the required reserve is achieved by the combination of the over-speeding and pitching strategies.
3. High wind speed area: the required reserve is achieved by pitching strategy.