esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Drilling Along Pilot Holes on Drilling Performance and Effective Rock Strength +
## 3 Results and Discussion
### Impact of Pre-Cored Holes on Drilling Performance
Drilling through pre-cored intervals may require more energy because the MSE values in the pre-cored formation are substantially higher than in the un-pre-cored formation [17]. This observation may be explained by the fact that more energy was used due to the extra effort needed to penetrate the pre-cored sections. An essential component of drilling operations, the Depth of Cut (DOC) indicates the thickness or depth of material the drill bit removes with each revolution. DOC was computed in this investigation using experimental data to determine how quickly the drill bit penetrates rock under specific drilling conditions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**ROP(m/h)-PR** & **ROP(m/h)-UN** & **T(N-m)-PR** & **T(N-m)-UN** & **DOC(mm/rev)-PR** & **DOC(mm/rev)-UN** & **MSE(P)-UN@60 RPM** & **MSE(P)-PR@60 RPM** \\ \hline
5.1 & 3.6 & 191 & 173 & 0.085 & 0.06 & 1.317 \(\times\) 10\({}^{9}\) & 1.028 \(\times\) 10\({}^{9}\) \\
5.3 & 3.5 & 190 & 170 & 0.0883 & 0.0583 & 1.331 \(\times\) 10\({}^{9}\) & 9.847 \(\times\) 10\({}^{9}\) \\
5.2 & 3.6 & 192 & 172 & 0.0867 & 0.06 & 1.301 \(\times\) 10\({}^{9}\) & 1.014 \(\times\) 10\({}^{9}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Data results obtained from a single experiment with repetition in pre-cored and un-pre-cored at holes 1, 2, and 3 at 60 RPM. Drilling performance as a whole and Depth of Cut (DOC) can be understood from the collected ROP values. Greater Depth of Cut usually correlates with higher ROP values, indicating faster drilling progress. As a result, examining ROP in addition to Depth of Cut (DOC) enables a thorough evaluation of drilling performance and efficiency.
# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
## 3 The Proposed Method
### CRF Based Semantic Inference of Image Patches for ISS  
After each image patch is assigned the appropriate semantic label, the image patches belonging to the same class are put together. According to Equation (6), the PBCRF model is constructed, and the mapping issue between category labels and image patches is transformed into a problem of minimizing the energy function. The main steps of CRF based semantic label inference are shown in **Algorithm 1**:  
```
Input: Image patches \(P\) of training images, image-level semantic label and parameters Output: Semantic Segmentation Results \(\hat{y}\) Step 1: Random arrangement of training images Step 2: Constructing undirected graph \(G(V,E)\) with superpixels as nodes Step 3: Calculating unary potential energy function \(\phi_{i}(y_{i},x_{i})\) according to Equation (8) Step 4: The class correlation function \(\big{(}y_{i},y_{j}\big{)}\) and cosine similarity function \(\mu\big{(}y_{i},y_{j}\big{)}\) are calculated by Equation (10) and Equation (11), and the pairwise potential energy function \(\phi_{ij}\big{(}y_{i},y_{j},x_{i},x_{j}\big{)}\) is calculated by Equation (9). Step 5: Constructing potential energy function of patch based on CRF by Equation (6) Step 6: The semantic segmentation result \(\hat{y}\) can be obtained by minimizing the potential energy function
```  
**Algorithm 1** Semantic label inference based on CRF  
In Section 3.2, we add the category correlation and similarity information to the pairwise potential energy function to for semantic label inference.  
\[\hat{y}=\text{argmin}_{y}E(x,y) \tag{12}\]
# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
###### Abstract  
The transmission of marine data is an urgent global challenge. Due to the particularity of underwater environments, the efficiency and reliability of data transmission in underwater acoustic communication are severely restricted, especially in long-distance and large-scale data transmission situations. This study proposes a dual-link data transmission method based on the Internet of Vessels, utilizing the powerful communication capabilities and flexibility of ships as relay nodes for data transmission. By constructing both above-water and underwater dual-link collaborative transmission, the method effectively improves data transmission rates and stability. Additionally, a spatial crowdsourcing allocation algorithm based on Bayesian reputation selection is designed to assess the capability of ships to complete tasks, and an integrated scoring function is used to select the optimal relay ship, solving the problems of relay ship selection and transmission path selection in the data transmission process. Furthermore, this study introduces an incentive mechanism for data transmission based on the Internet of Vessels, which maximizes the stability of data transmission. Experimental results show that the dual-link data transmission method of the Internet of Vessels significantly improves the reliability and transmission speed of underwater communication, providing a novel and practical solution for long-distance, large-volume data transmission in maritime environments.  
Internet of Vessels; dual-link data transmission; spatial crowdsourcing task allocation; multi-hop +
Footnote †: journal: Sensors  
Article
## 4 Discussion
### Comparison of Segmentation Methods
#### 4.1.2 Class Imbalance  
The needleleaf and broadleaf forests in the study area are unevenly distributed, the broadleaf forests account for a relatively small proportion of the total, and there is a serious class imbalance problem. Class imbalance, also called data skew and data imbalance, refers to the situation in which the number of training samples of different classes in the segmentation task varies greatly. To address this problem, we focus the training sample range on the region with a relatively uniform distribution of needleaf and broadleaf forests. Furthermore, Pix2 Pix outperforms U-Net in class imbalance for segmentation, which reflects the advantage of image-to-image semantic segmentation. It may be that the Pix2 Pix algorithm is mostly used for image translation, and the grasp of details is better than that of segmentation methods such as U-Net. When class imbalance is not considered, the intersection over union (IoU) of the broadleaf forest in each algorithm is lower than 30%. After solving the class imbalance problem with the transfer learning domain adaptation method, the OA of U-Net substantially advanced from 71.65% to 83.65%, indicating a 12% enhancement. Furthermore, the IoU of the broadleaf forest escalated from 10.58% to 60.92%, denoting a remarkable improvement of approximately 50.34%. Similarly, the OA of Pix2 Pix accelerated from 70.95% to 84.85%, and its associated IoU augmented from 10.48% to 66.93%, signifying a significant boost of 56.45%.
## 4 Evaluation
### Results and Discussion
#### 4.2.1 Interest Packet Loss  
Interest packet loss is an important criterion in mobility management schemes in NDN, especially for low-latency-sensitive applications that require a negligible packet loss during the handoff to ensure that data remain relevant and useful. The Input loss rate is defined as the total number of Inputs sent by all the consumers minus the number of Data packets that arrived over the total number of Inputs sent. Figure 8 presents a comparison of the average packet loss rates of different mobility support schemes; the \(y\)-axis represents the average packet loss rates, while the \(x\)-axis represents the speed of the mobile producer (ranging from 1 m/s to 30 m/s). As a first observation, we notice that with the increase in the producer's speed, there is a corresponding rise in Input loss due to increased handoff events. Nevertheless, the result shows that the multi-RV KITE scheme has the highest packet loss rate. This is because the producer receives consumers' Inputs after TI/TD messages are exchanged to establish a new trace path, which means that the Inputs received at the time of trace path creation will be considered lost. Furthermore, multi-RV KITE suffers from the stale path problem caused by frequent handoffs that occur when the producer moves to a new PoA, and the old trace path may stay active. The DMMS and MAP-ME achieve similar packet loss rates at producer mobility speeds below 5 m/s. However, in high-speed mobility scenarios, the DMMS with high producer's location prediction accuracy exhibits  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Producer Speed (m/s)** & 1 & 5 & 10 & 15 & 20 & 25 & 30 \\ \hline
**RMSE (m)** & 0.23 & 1.52 & 3.17 & 4.41 & 5.94 & 7.72 & 9.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE of Kalman filter for various producer speeds.  
significantly lower packet loss than both multi-RV KITE and MAP-ME. Even with low prediction accuracy, the DMMS maintains an acceptable packet loss ratio.  
In comparison to MAP-ME, DMMS reduces the packet loss rate by approximately 10%, and when compared to multi-RV KITE, the reduction is even more significant, at 20%. This result is attributed to the proactive mobility management that notifies the Anchor about the predicted location of the producer when handoff happens. Consequently, Inputs are redirected proactively toward the producer's new PoA.
# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 1 Introduction
They generalized the propositional logic in two directions: propositions as fuzzy and logical variables as many-valued. In [43], the authors proposed a new reasoning based on intuitionistic fuzzy propositional logic. They developed two classification methods of intuitionistic fuzzy propositional logic (truth table and figure of equivalence). In [44], the authors explored the propositional part of strict finitistic logic. In this paper, we proposed a novel fuzzy model for reasoning and predicting the academic performance of students. Our model aims to address the limitations of previous studies by incorporating propositional logic with a fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. The main contribution of our paper is the development of a new fuzzy model that improves upon the existing approaches in terms of accuracy and interpretability. In addition, our model provides interpretability by generating linguistic rules that can be easily understood by domain experts. The proposed fuzzy model can be applied to a wide range of educational datasets and can be used to identify students who are at risk of academic failure, thereby enabling interventions to improve their academic performance. Overall, our paper presents a significant contribution to the field of educational data and provides a promising direction for future research in this area and other areas. The rest of this paper's structure goes as follows: Section 2 presents the proposed model (FPM). Section 3 presents the experiment. Section 4 presents the result and discussion. The final part of the paper reports the conclusions. \begin{table}
\begin{tabular}{l l} \hline \hline
**Area** & **Methods or Techniques and the Study’s Reference** \\ \hline \multirow{7}{*}{Prediction} & Neuro-fuzzy [11; 18; 19; 26; 31; 33] \\  & Fuzzy decision tree [14] \\  & Genetic fuzzy [13; 16] \\  & Fuzzy logical model [16; 30] \\  & Fuzzy expert system [16] \\  & Fuzzy clustering + regression (integrated) [23] \\  & Fuzzy learning (fuzzy based on ML concepts) [24] \\  & Mamdani fuzzy [32] \\ \hline \multirow{2}{*}{Classification} & Neuro-fuzzy [12; 18] \\  & Qualitative link analysis [28] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The related work.
# A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
## 4 Usages and Test Cases
### Performance of the Experience Reuse Method
#### 4.3.2 Experimental Results  
Figure 9 shows the changes in IGD values and HV values of non-dominated solutions for BDFDA3 at time steps \(t=1.0\), \(t=2.0\), and \(t=3.0\). Compared with the randomly initialized population, the initial population obtained using the experience reuse method has a considerably higher quality at the beginning of the optimization. The initial population is very close to the non-dominated solutions obtained after the algorithm's convergence. In addition, the algorithm can converge significantly faster with the help of the experience reuse method. The subgraphs in Figure 10 are sampled from the BDDF6 random update process when time steps \(t=1.0\), \(t=2.0\), and \(t=3.0\) occur. It is clear that when the problem is updated randomly, the initial population modified from the previous PS is not only far from the approximate optimal PS of the new problem but also that the quality and diversity of the non-dominated solutions of the new problem obtained after the algorithm converges are somewhat different from the best case. In contrast, the algorithm that introduces the experience reuse method can still achieve good performance in this case. The convergence speed of the algorithm, the quality of the final non-dominated solutions, and the diversity of the final population are better than the strategy used for comparison.  
Figure 9: Convergence curves of Dynamic NSGA-II with experience reuse method or with restart strategy 1 on BDFDA3 at time steps 1.0, 2.0, 3.0. (**a**) IGD, \(t=1.0\). (**b**) IGD, \(t=2.0\). (**c**) IGD, \(t=3.0\). (**d**) HV, \(t=1.0\). (**e**) HV, \(t=2.0\). (**f**) HV, \(t=3.0\).
# Performance Prediction of Composite Marine Propeller in Non-Cavitating and Cavitating Flow
## 2 Methodology and Results
### Flow Simulation
#### 2.2.2 Simulation Set-Up  
The numerical simulation domain is shown in Figure 3, where \(D_{B}\) is the maximum diameter of the submerged body. Flow analysis was performed prior to the FSI analysis without considering the deformation in a non-cavitating flow. A convergence test was performed to select the number of grids for numerical analysis, as shown in Table 2 (\(K_{T}=T/\rho n^{2}D^{4}\)). The final selected grid number was approximately 23.2 million, and the unsteady analysis was performed in two stages.  
First, the solution was performed using the moving reference frame approach. Second, a time-accurate solution was completed with the sliding mesh method every 0.5\({}^{\circ}\) per time step. For the comparison of the results with the model test later, the flow analysis was carried out under the test condition of Table 3, which is the same with the experiments carried out in a Large Cavitation Tunnel (LCT) located at the Korea Research Institute of Ships and Ocean Engineering (KRISO). The noise reduction effect of the composite propeller was attributed to the difference in the development of cavitation caused by the deformation of the blade. In particular, it can be predicted that the development characteristics of TVC, which determine the CIS of the propeller, will be greatly influenced by the amount of deformation of the composite propeller. The propeller rotating component (interface area) was expanded to simulate the vortical flow generated from the propeller tip region, and the mesh refinement area was constructed as shown in Figure 4. The extracted volume regions with specific vorticity and pressure values are included in the rotating component. The RANS method has low accuracy in simulating vortical flow generated from the propeller tip region due to the averaging of the Navier-Stokes equation and excessive numerical diffusion [19]. However, applying large or detached eddy simulation methods excessively increases the time required for FSI analysis. Therefore, rather than using a quantitative comparison of the cavitation area, we found a compromise that simulated the TVC as much as possible by considering the time required for FSI analysis. Thus, the Eulerian multiphase model was employed with the volume of fluid method for the cavitation modeling. The Schnerr and Sauer cavitation model was also used [20], which is based on the asymptotic form of the Rayleigh-Plesset equation. Here, the bubble dynamics, viscous diffusion, and surface tension were not considered.
# Removal of Sodium Dodecylbenzenesulfonate by Macroporous Adsorbent Resins
## 3 Results
### Effect of the Amount of Adsorbents  
The effect of the amount of adsorbents on the adsorbed amount (\(q\)) and removal of SBDS was investigated at an SDBS concentration of 0.1 mg/mL after 7 h adsorption time (Figure 4). The \(q\) value and the removal efficiency were much higher for HP20 than for HP2 MGL (more than 10 times for \(q\), and about 20% higher removal efficiency). A maximum amount of 80 mg/g SDBS was adsorbed with 1 mg/mL HP20. The efficiency of SDBS removal using HP20 increased from 80 to 94% with an increase in the amount of HP20 used (from 1 to 50 mg/mL), and a lower maximum efficiency of SDBS removal (80%) was observed when using HP2 MGL under the same conditions. In addition, the maximum amount of SDBS adsorbed by HP2 MGL was only 9 mg/g when 3 mg/mL of HP2 MGL was used. The large difference in the adsorption indicated that HP20 had a better adsorption capability for SDBS than HP2 MGL, presumably because of the interaction between SDBS and the phenyl rings in St in HP20, which the MMA-based HP2 MGL does not contain. The same behavior was reported, describing that a molecule having aromatic structure could be adsorbed via strong electron \(\pi\)-\(\pi\) interaction between adsorbate and adsorbent [23].
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
## 2 Experimental Conditions
### Binder and Mortar Preparation  
MK was used as the benchmark precursor to first examine the influence of several compositional factors and establish adequate synthesis conditions for the baseline system. To evaluate the feasibility of using CRC as an alternative precursor and identify optimal MK replacement dosages, different binder compositions were produced with CRC steps of 25.0% up to total MK substitution. The details of the mixture proportions and synthesis factors examined are presented in Table 1. All binder formulations were designed to respect the following molar ratios: (i) SiO\({}_{2}\)/(Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)) = [3.75-5.15]; (ii) (Na\({}_{2}\)O + K\({}_{2}\)O)/(Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\)) = [1.00-1.30]; (iii) (Na\({}_{2}\)O + K\({}_{2}\)O)/SiO\({}_{2}\) = [0.20-0.30]; and (iv) H\({}_{2}\)O/(Na\({}_{2}\)O + K\({}_{2}\)O) = [10.0-15.0] and SiO\({}_{2}\)/(Na\({}_{2}\)O + K\({}_{2}\)O) = [0.50-0.80] in the solution; calculated assuming the precursors complete dissolution and participation in the polymerization reactions. The binder specimens were produced following the procedure described elsewhere by [PERSON] et al. (1979, 2000). In short, it involved: (a) homogenization of sodium silicate and sodium hydroxide solutions at 60 rpm for 300 s; (b) manually mixing the powdered precursors in a plastic container; and (c) mixing the binders' solid components and the alkaline solutions at 60 rpm for 600 s in a mortar mixer. Then, the resulting slurries were transferred to plastic molds (\(\O\approx\) 20 mm; height \(\approx\) 40 mm), placed inside sealed plastic containers, and left to cure in the mold for 24 h according to the conditions described in Table 2. Alkali activated mortars were prepared following the same procedure but with two additional mixing steps: (i) 30 s mixing step at 60 rpm for introduce the aggregates; and (ii) 60 s mixing step at 95 rpm to further guarantee a proper homogenization. Mortar specimens (4 \(\times\) 4 \(\times\) 16 cm\({}^{3}\)) were produced with a binder-to-aggregate ratio of 1.0 and cast as prescribed by EN 1015-11 (2007). Mortars were coded after their parent binder using M to mark the difference between them (e.g., R50 represents a binder produced as per Table 1, while MR50 will be used to refer to mortar specimens produced with R50 binders).
## 3 Results and Discussions
Lastly, we conduct the performance comparison of the proposed nodule detection with some of the state-of-the-art techniques discussed in Section 2.1. While these techniques worked on the same dataset (LIDC-IDRI), they used a different number of samples and different performance assessment measurements. For a fair comparison, we quantitatively compared each method based on the same assessment measurements, as demonstrated in Table 5. From the table, we can analyze that the techniques in [13] and [25] achieved higher and the same accuracy values, compared to our proposed method, but their performances were assessed using fewer samples. Moreover, the performance of [24] is also quite high, using the same number of samples. However, [24] applied the CNN, which consumes a high computation cost and is applicable in some specialized computers that have good GPUs. Unlike their methods, our proposed method applied the improved random forest by firefly (RF + FA), which is preferable and applicable in any standard computers. Moreover, the proposed automated nodule detection is promising for the detection of variant size and type of nodules in a high detection accuracy. \begin{table}
\begin{tabular}{l c} \hline \hline
**Function Name** & **Total Time** \\ \hline Reading input images (Maximum: 764 slices) & 1.53 s \\ \hline Lung regions segmentation (Automated seeded region growing) & 11.49 s \\ \hline Border reconstruction (3D chain coding) & 99.68 s \\ \hline Preliminary screening & 18.56 s \\ \hline Feature extraction & 10.40 s \\ \hline False reduction (RF+FA) & 2.74 s \\ \hline Entire program & 144.40 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The total execution time of our proposed nodule detection. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**References** & \multicolumn{1}{c}{**Method**} & **Exams** & \begin{tabular}{c} **Acc** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Sen** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Spec** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **FP** \\ **(per exam)** \\ \end{tabular} \\ \hline \multirow{4}{*}{[9]} & \(\bullet\) & Multiple thresholding & & & & \\  & \(\bullet\) & Labeling & & & & \\  & \(\bullet\) & Rule-based & 200 & 90.12 & 98.03 & 87.71 & 2.45 \\ \cline{1-1}  & \(\bullet\) & SVM (optimized by & & & \\  & particle swarm) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of proposed nodule detection with previous methods.