esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, in order to provide a foundation for the novel heap leaching technology that is suitable for vegetation restoration, the permeability of the ion-adsorbed rare earth under the lateral liquid injection conditions was investigated by running a model test. The influence of the mineral particle composition on the permeability was highlighted. According to the above research, the main conclusions were obtained as follows:  
1. Under lateral liquid injection conditions, the permeability of the ion-adsorbed rare earth pile is governed by the migration of fine mineral particles. As the hydraulic head of leaching liquid increases, the motion of the fine particles is divided into four stages, stabilization, migration initiation, deposition, and remigration. Accordingly, the permeability coefficient exhibits a varying process of stabilization, gradual increase, re-stabilization, and re-increase.
2. The permeability of the rare earth pile under the lateral liquid injection conditions is non-uniform in space. The permeability coefficients near the liquid injection end and the liquid outlet end are significantly larger than those at the middle positions of the pile. This non-uniformity is caused by the fine particle migration without replenishment near the liquid injection end and the fine particle exudation from the liquid outlet end.
3. The particle composition of the rare earth has an important effect on the permeability of the rare earth pile under the lateral liquid injection conditions. For the coarse-fine particle soil, the fine particles easily migrate, and they cannot easily block the pore channels. In this case, preferential flow paths are likely to develop in the lower part of the rare earth pile, resulting in a blind zone of leaching at the top of the pile. For the fine-medium particle soil, the fine particles cannot easily within the pore channels and thereby, the permeability coefficient is relatively stable. This particle composition is conducive to the heap leaching of the rare earth under lateral liquid injection conditions.  
Conceptualization, W.J.; methodology, W.J. and J.Y.; investigation, W.J. and J.Y.; validation, J.Y.; formal analysis, W.J. and J.Y.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], C.Y. and X.Z.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON], C.Y. and X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (2019 YFC0605005 and 2019 YFC0605001) and the National Natural Science Foundation of China (52179102 and 51969015).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### 0D Thermodynamic Analysis  
The last part of the characterization consisted of using the results obtained in the previous steps as input for the final 0D model in order to obtain the uncertain parameters (CR, CW1) and the information regarding the gas thermodynamic conditions. The methodology applied in the model aimed at simultaneously finding the uncertainties by applying two criteria at the same time: the minimum residual heat release rate as well as the minimum difference between the real and simulated in-cylinder pressure.  
The deformation constant \(K_{\mathit{d}\mathit{f}}\) was adjusted in the model to obtain the same experimentally measured TDC volume deformation at 1.1 and 2.7 bar of intake pressure. In this way, considering that deformation increases linearly with cylinder pressure, the same \(K_{\mathit{d}\mathit{f}}\) was used for the other engine conditions. In Figure 13, it is possible to see the instantaneous cylinder volume near TDC for each condition considering the \(K_{\mathit{d}\mathit{f}}\) obtained from the measurements. It is possible to note how the volume is affected by the increases in cylinder pressure and the importance of considering the changes in instantaneous volume trace for each condition to avoid errors in the heat release rate calculation as well as to get a correct estimation of uncertain parameters.  
The volume derivative is also of interest because it directly affects both the heat release rate calculation as well as the IMEP calculation. Figure 14 shows the volume derivative with and without deformation as well as the difference between the two derivative volumes at an intake pressure of 2.7 bar. The biggest difference between the curves occurred from \(-20^{\circ}\) to \(20^{\circ}\) aTDC.  
Figure 15 shows the impact of the volume change in the heat release rate trace. It is known that the heat release rate in motored conditions is zero. However, some residuals in the trace are inevitable and the main idea is to try to reduce these residuals as much as possible. It is clear that the RoHR trace for the adjusted instantaneous volume was more accurate and realistic as the solid curves were closer to zero. On the other hand, the dashed curves deviation from zero increased as the intake pressure increased, indicating that there were larger errors in the RoHR calculation. The RoHR shape for the dashed lines was very similar to the difference in the volume derivative shown above. These results present good agreement with the results found in the previous study developed by [PERSON] et al. (2018).  
Figure 13: Instantaneous volume for each condition taking into account the measured deformation.  
is very similar in the optical and metal engines, however, the deformation will be much larger in the optical engine due to the extensor (it is an implicit term in \(\Delta V_{p}\) in Equation (3)).
# Marine Internet for Internetworking in Oceans: A Tutorial
## 6 Challenging Issues for Marine Internet
### End-to-End Reliability and Security  
Since marine Internet consists of heterogeneous and mobile network nodes that a network path may go through, typically including various types of vessels, airborne vehicular and satellites. How to ensure end-to-end transmission reliability in terms of connectivity, network resource allocation as well as QoS is very challenging. Although similar issues exist and have been addressed for conventional mobile ad hoc networks (MANETs) and opportunistic networks [75], they are not comparable to marine Internet in terms of network scale and heterogeneity of network nodes with different communication and power capacities as well as networking capabilities. Different from terrestrial environments in which there are many cost-effective networks other than MANETs, in oceans, so far there is no alternative to marine Internet for provisioning cost-effective networking services, and these issues have to be studied.  
Similarly, how to ensure end-to-end networking security is another important and difficult issue. In oceans, it is necessary to encourage more collaboration between different network nodes in order to maximize network performance at low cost. However, oceans are insecure cooperative environments, in which friendly and hostile nodes co-exit, and most of marine Internet users are transient from different places through vessels. In this case, how to carry out the authentication for users and network nodes, and how to defend marine Internet from security attacks are two challenging issues especially when there is no guarantee on always-on connectivity to the authentication or management center located on the land.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 6 Performance Evaluation
### Performance of the PM2M Overlay with Backoff Error  
We evaluate the performance of PM2M with backoff error for both traffic classes under the same conditions as in the previous experiment, except that the number of nodes was ranging between 300 and 1320. As before, these values were chose to result in the decrease of the probability of successful access to the medium in the range similar to the case without backoff error, as considered in the previous subsection.  
We found that we can accommodate up to about 1320 nodes for each traffic class in the same range of transmission success probability, which represents a 12% decrease of overlay network capacity. The transmission success probability for 1320 nodes and a packet arrival rate of two packets per second is close to 0.83 and 0.87 for Traffic Classes 0 and 1, respectively. The mean number of backoff periods in slots for Classes 0 and 1, shown in Figures 11b and 12b, reaches 33 slots and 100 slots, respectively, under the highest load. The medium access probability (Figures 11c and 12c) is about 10-15% lower for class \(TC_{0}\) than for the higher priority class \(TC_{1}\). Figure 12d shows that the throughput of \(TC_{1}\) is almost three-times higher than \(TC_{0}\) (Figure 11d) under the highest load. Regarding the mean number of backoff attempts, Figure 11e shows that lower traffic class \(TC_{0}\) has to perform slightly more backoff attempts than higher priority traffic class \(TC_{1}\) (Figure 12e), as we expected with the presence of backoff error.  
Figure 10: Performance of the PM2M overlay for Traffic Class 1 without backoff error.  
Figure 11: Performance of the PM2M overlay for traffic class \(TC_{0}\) with backoff error.  
Figure 12: Performance of the PM2M overlay for traffic class \(TC_{1}\) with backoff error.
# Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction
## 3 Experimental Verification
### Evaluation Indicator  
In this paper, the Root Mean Square Error (\(RMSE\)) and Mean Absolute Error (\(MAE\)) between the actual and predicted scores are used to measure the computational errors of different algorithms. The smaller the value of the evaluation index is, the smaller the prediction error and the higher the accuracy rate, and the formula is defined as follows:  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{11}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}|y_{i}-\hat{y}_{i}| \tag{12}\]The _F1-Score_ value is used to measure the recommendation effectiveness of different algorithms. The higher the value, the higher the recommendation accuracy. The _F1-Score_ is calculated from _Precision_ and _Recall_ and is defined by the following formulas:  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
\[F1-Score=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{15}\]  
Among them, TP (True Positive) means the positive class is a positive class. FN (False Negative) means missed report. A positive class is decided to be a false class. FP (False Positive) means false report, false class is decided as a positive class. TN (True Negative) means a false class is a false class.  
_AUC_ (Area Under Curve) is used to measure the sorting ability of the algorithm. _AUC_ is the area of the ROC (Receiver Operating Characteristic) curve and the coordinate axis. The larger the value, the better the recommendation effect. The formula is defined as follows:  
\[AUC=\frac{\sum I(P_{p},P_{n})}{M*N} \tag{16}\]  
\[\sum I\big{(}P_{p},P_{n}\big{)}=\left\{\begin{array}{l}1,p_{p}>p_{n}\\ 0.5,p_{p}=p_{n}\\ 0,p_{p}<p_{n}\end{array}\right. \tag{17}\]  
where \(M\) denotes the number of positive samples, \(N\) denotes the number of negative samples, and \(M*N\) denotes the logarithm of samples.  
The experiments randomly divide the dataset into a training set (80%), test set (10%), and validation set (10%) in the ratio of 8:1:1, using the training set to train the model. And use the experimental results of the algorithm on the validation set to evaluate the prediction effect of the algorithm. The vector length of Doc2 Vec is 100 dimensions; the embedding dimension is set to 64. The number of feature dimensions of GRU is 5, and the number of neuronal nodes is 32. The number of hidden layers in _DNN_ is 3, with 32 neuronal nodes in each layer, and the layer is fully connected and calculated without activation function. The Batch Size is set to 256 and 1024 on the two datasets, the learning rate is set to 0.001, the optimizer uses Adam, and the activation function is used when the epoch is set to 7. In the output layer, the activation function is ReLU, the loss function adopts using Logarithmic loss function, and to prevent over-fitting of the experimental model training, the L2 regularization factor is used.
## 5 Conclusions and Prospective
### Summary of Critical Insights Drawn from the Literature Review  
From the analyzed information, a summary of issues and best practices in the JSP problem was given. From the point of view of the type of problem, most of the papers reviewed described their own type of JSP problem, which in essence reduces to a particular variant of a more general type of JSP. This can create a problem because, as in the case of searching for a particular problem, it can be more complex to find other approaches because it does not fit the particularities of the cases presented. On the other hand, it is worth noting that the majority of the papers reviewed had two or at most three objectives, which is helpful for decision making (the selection of the solution in the estimated Pareto front). As for the non-energy objectives, the makespan was the main objective, which conforms to real-world evidence since the makespan--or any other measure of production efficiency--still remains as the primary concern for the manufacturing industry.  
From the point of view of the constraints and considerations, some of them repeatedly appeared in the reviewed articles, such as the assumption that machines must be available at the beginning, the machine does not start the next job until it has finished the previous one, and the existence of infinite buffers. However, it is striking how considerations such as  
Figure 8: Reference front for the ta12 problem. The different symbols correspond to the contributions of each algorithm.  
Figure 9: Schedules of the ta12 instance with the extreme solutions (best energy solution and best makespan solution).  
waiting times, machine speeds, or possible machine shutdowns have not been taken into account in the vast majority of the articles reviewed.  
For the optimization features, the usually crossover and mutation operators were in the usual values: high probability of crossover and low for mutation. However, this information was not always provided, which jeopardizes the reproducibility of many of the studies. It is also surprising to notice that many authors opted to generate new algorithms to find solutions to the multi-objective problem for energy and makespan, but they omitted considerations of energy consumption in real environments such as those produced by waiting times. To test the performance of these algorithms, a benchmark was made against existing algorithms, where the best-known ones, such as NSGA-II, MOEA/D, SPEA-2, etc., were usually found. Furthermore, the authors focused their efforts on developing new algorithms that were not used again in studies contributed thereafter. This may be due to the low reproducibility that existed in the articles: undefined operators, source code not present in public repositories, etc.
## 5 Conclusions  
Recently, neural network models based on dependency trees have attracted widespread attention in ABSC. However, due to the imperfect parsing performance of the dependency tree, the noise information will be updated to the sentence representation during the process of introducing syntactic information. Therefore, we propose an AEGCN model with multi-head attention. This model uses a multi-head self-attention mechanism to obtain input semantic information, using attention-enhanced graph convolutional network and dependency tree to obtain input syntactic information. The multi-head interactive attention mechanism integrates semantic and syntactic information to obtain the final feature vector to predict the sentiment polarity. The experimental results on five datasets show that the interactive integration of sentence syntactic information and semantic information can indeed effectively improve the performance of the model.  
This paper aims to strengthen the interaction between syntactic information and semantic information, make rational use of the advantages of graph convolutional network and attention mechanism, and use the semantic information and syntactic information of sentences for aspect-based sentiment prediction. However, we only interacted semantic information and syntactic information in the last layer of the model. Future research can consider building a multi-layer network architecture, interacting semantic information and syntactic information at each layer.  
Conceptualization, G.X. and P.L.; funding acquisition, P.L.; methodology, G.X.; project administration, Z.Z.; resources, P.L. and Z.Z. Writing--original draft, G.X.; writing--review and editing, J.L., F.X.; visualization, G.X.; investigation, G.X.; validation, G.X. All authors have read and agreed to the published version of the manuscript.  
Our work was supported by the Science Foundation of the Ministry of Education of China (no.14Y)C860042), the National Social Science Fund (19 BYY076), and the Shandong Provincial Social Science Planning Project (no.19 BKJ51/18 CXWJ01/18 BYJ04/16 CFXJ18).  
Not applicable.  
Figure 4: Impact of the AEGCN layers.
## 4 Numerical Example
### Modeling of the Beam-Column Joint
The amplitude of the end-reflected wave packet from S\({}_{1}\)-S\({}_{3}\) decreased as the bolt looseness severity increased, whereas the trend was reversed for the wave path S\({}_{1}\)-S\({}_{4}\). The relative amplitude change was not quite distinct compared to those  
Figure 9: Received signals under different transducer configurations (DInc: directly incoming wave packet; ERef: end-reflected wave packet). (**a**) S\({}_{1}\)–S\({}_{3}\) (_l_ = 150 mm); (**b**) S\({}_{1}\)–S\({}_{4}\) (_l_ = 150 mm); (**c**) S\({}_{1}\)–S\({}_{3}\) (_l_ = 200 mm); (**d**) S\({}_{1}\)–S\({}_{4}\) (_l_ = 200 mm); (**e**) S\({}_{1}\)–S\({}_{3}\) (_l_ = 250 mm); (**f**) S\({}_{1}\)–S\({}_{4}\) (_l_ = 250 mm). of the transmitted waves in the author's present work [44]. So the bolt looseness severity estimation methods based on amplitude change may not be suitable in this case. The amplitudes of other wave packets situated between the directly incoming and end-reflected wave packets also exhibited changes, which could potentially carry information about bolt looseness as well. In addition, slight wave shifts were noticeable in the received signals.
## 4 Optimization Problem
### Finite Element Model  
Three dimensional parametric model of rail wheel is prepared in order to solve optimization problem. The model is fixed and loaded as shown in Figure 4. The 20-node SOLID186 finite elements are used to generate mesh within volumes. CONTA174 and TARGET170 elements are used to define contact between the fixed points and cylindrical surfaces of holes in the wheel web. A symmetry constrain on the wheel surface is set since half of the wheel geometry is considered. Displacements of the inner surface of wheel web are locked in the x direction. Radial load \(F_{R}\) and pressure representing bolt pretension are applied. It is identified that the maximum principal stress \(\sigma_{max}\) occurs at the edge of the upper or lower bolt hole, and the maximum stress amplitude \(\Delta\sigma\) is achieved when the wheel is rotated by 180\({}^{\circ}\). Therefore, in order to calculate the stress amplitude exactly in the same nodes of mesh, two load steps with radial load applied on opposite sides of the rim are defined. Lateral loads are not included in the analysis under the assumption that the speed of the vehicle on the track is relatively  
Figure 3: Permitted radial load of hydraulic motor drive shaft. (Dotted lines refer to the permitted radial load and offset range for maximum vehicle mass).  
low and the vehicle runs on short sections of straight tracks between level crossings. Furthermore, there are no axles between the wheels in the rail drive system of the analyzed road-rail vehicle. Each wheel is driven separately by an independent hydraulic motor, which eliminates slippage and reduces lateral forces. The use of independent wheel drive and a conical wheel profile results in continuous centering of the vehicle in the track. For the nominal wheel position on the rail, the cone angle of the wheel profile is very small. The resulting lateral force due to the conicity of the wheel is therefore negligible compared to the vertical forces.
###### Abstract
The microphone time signals are recorded with a sampling rate of 51.2 kHz according to the Nyquist sampling theorem, because the upper-limit frequency of interest in this part is around 20,000 Hz. The cross spectral matrix is computed using 1024 data points with a Hanning window and 50% overlap, resulting in a frequency resolution of 50 Hz.
## 3 Results and Discussion
### Surface Hydrophobicity  
Protein surface hydrophobicity is determined by the content of hydrophobic residues on the surface of a molecule and is, therefore, an indicator of protein unfolding. In collagen, these hydrophobic residues are hidden inside the triple helix; however, when the gelatin forms disordered polypeptide chains, it already has a higher hydrophobic surface due to new residues exposed on the surface. In this study, the different treatments of HIU showed a significant increase in surface hydrophobicity of the control without treatment (\(p<0.05\)) (Table 1).  
In the present study, the highest hydrophobicity values corresponded to the treatments UPG-52 and UPG-84 (treatments for 2 min); this behavior was in agreement with the previous study for HIU for the giant squid protein, with an amplitude of 40% for 90 s, as well as for wheat proteins when using 31 W/cm\({}^{2}\) for 20 min and scallop proteins when using 3361.64 W/cm\({}^{2}\) for 1 h [7; 8; 22]. All of these studies reported the highest surface hydrophobicity for the most intense and extended treatment. The results of this investigation could be attributed to the unfolding of polypeptide chains by the interruption of non-covalent interactions, allowing for an increase in the exposure of hydrophobic gelatin residues.
###### Abstract  
This study evaluates an innovative reinforcement method for cold-formed steel (CFS) upright sections through finite element assessment as well as prediction of the normalized ultimate load and deflection of the profiles by artificial intelligence (AI) and machine learning (ML) techniques. Following the previous experimental studies, several CFS upright profiles with different lengths, thicknesses and reinforcement spacings are modeled and analyzed under flexural loading. The finite element method (FEM) is employed to evaluate the proposed reinforcement method in different upright sections and to provide a valid database for the analytical study. To detect the most influential factor on flexural strength, the \"feature selection\" method is performed on the FEM results. Then, by using the feature selection method, a hybrid neural network (a combination of multi-layer perceptron algorithm and particle swarm optimization method) is developed for the prediction of normalized ultimate load. The correlation coefficient (R), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and Wilmot's index of agreement (WI) are used as the measure of precision. The results show that the geometrical parameters have almost the same contribution in the flexural capacity and deflection of the specimens. According to the performance evaluation indexes, the best model is detected and optimized by tuning other algorithm parameters. The results indicate that the hybrid neural network can successfully predict the normalized ultimate load and deflection.  
Keywords:cold-formed steel; upright; finite element method; feature selection method; multi-layer perceptron; particle swarm optimization; neural network +
Footnote †: journal: Applied