esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Specim Preparation  
The specimens were prepared in the Advanced Technology Dental Research Laboratory at the Faculty of Dentistry, King Abdulaziz University. Using a diamond disc under a cooling system, the crowns of the selected teeth were trimmed. The working length was determined to be 1 mm short of the visible k-file #10 from the apical foramen. The root canals were then chemo-mechanically prepared using a F5 ProTaper Gold rotary file (Dentsply, Ballaiques, Switzerland) accompanied by 5.25% sodium hypochlorite irrigation. Finally, the root canals were irrigated with 5 mL of 17% ethylenediamine tetra-acetic acid (EDTA) and then with 5 mL of distilled water. The prepared roots were obturated with single-matched gutta-percha cones, size F5 (Dentsply, Ballaiques, Switzerland). According to the sealer used, the root canals were randomly divided into three main groups: Bioceramic Endosequence (BC, Brasseler Boulevard, Savannah, GA, USA), AH Plus bioceramic (AHP-Bio, Dentsply Sirona, Tulsa Dental, Johnson, Republic of Korea) or AH Plus epoxyresin (AHP, Dentsply, Maillefer, Switzerland). All obturated roots were incubated at 37 \({}^{\circ}\)C and 100% humidity for a week until complete setting of the sealer.  
The roots were vertically embedded in blocks made of clear self-cure acrylic resin. Using a precision cutting machine (Buehler, Lake Bluff, IL, USA), the roots were trimmed perpendicularly to their long access into 1 mm thick slices. To standardize the diameter of the tested discs, three discs were obtained from the middle region of each root (n = 20), each with nearly identical diameters [20]. To guarantee consistency in the specimens' dimensions, the thickness of each slice and canal diameter were verified using a digital caliper (RexBetti, China).  
Each main group was divided into two subgroups: they were either kept dry or kept in vials containing 5 mL of phosphate-containing tissue fluid (Serum, Gibco, Life technologies, Carlsbad, CA, USA) for 30 days (n = 10) [21; 22]. The serum was refreshed every 3 days.
## 2 Related Work
### Techniques for Improving the Computational Performance of NMPC  
A barrier to the widespread adoption of NMPC is its computational burden, particularly for nonlinear optimization problems that need to be solved in real-time [25]. Numerical methods such as direct multiple shooting are commonly employed to solve NMPC problems [26; 27]. Efficient structure-exploiting approaches, such as block structured factorization and condensing, offer distinct computational advantages depending on the problem characteristics [27].  
To accelerate the solution process, warm-start initialization is a key strategy. [PERSON] et al. discussed real-time iteration schemes for NMPC, where warm-starting is a crucial component for achieving efficiency [6]. The real-time iteration scheme leverages shifting and warm-starting of previous solutions to enable fast convergence, even for nonlinear problems [28]. The general NMPC literature also acknowledges the benefits of warm-starts in reducing computation time for successive optimization problems. While the computational benefits of warm-starting are recognized in the general NMPC context, its specific impact on long-cycle smart agricultural applications, when systematically compared with different disturbance prediction strategies, requires more detailed investigation.  
As the above review demonstrates, while prior studies have made important advances in NMPC, AI-based disturbance prediction, and real-time optimization techniques, a unified, long-term comparative evaluation integrating these strategies under practical smart greenhouse scenarios remains missing. By addressing this gap, the present work delivers a comprehensive analysis and actionable insights for both research and practical greenhouse management.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 2 Thermal Conductivity Measurements Overview
### Transient Hot Wire Method  
In this technique, a very thin metal wire, usually of platinum or tantalum, is used as both heat source and sensor. The wire is submerged on the NF, and a certain voltage is applied through the wire, heating it. The heat will then be transferred to the surrounding liquid at a rate depending on the thermal conductivity of the liquid. The resistance variations measured in the wire can be correlated with the temperature variations and, therefore, the thermal conductivity is calculated. Systematic errors that might affect the measurements, such as errors resulting from natural convection, can be corrected experimentally [5; 24].
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 3 Experimental Results and Discussion  
To experiment with the power of the proposed scheme for FPRCI, the CWRUD was used in this study. The vibration sensor was selected for data collection. This sensor collects signals in the following four classes: NS, RFS, IFS, and OFS. Moreover, the crack sizes for the RFS, IFS, and OFS are 0.007, 0.014, and 0.021 inch. Furthermore, the motor torque load varies from 0 to 3 hp for all four classes. The experimental results have three main sections: digital twin test and analysis, residual signals assessment and investigation, and classification analysis.
## 4 Results and Discussions
### Discussions
#### 4.2.3 Character-Wise Discussion
It can be verified from the confusion matrix given in Figure 8a on the axis of \"Actual value\" that the total 500 samples can be observed in the row of character \(\overline{\mathfrak{q}}\) (class no. 25), out of which 483 samples were correctly classified as \(\overline{\mathfrak{q}}\); and some of the samples of character \(\overline{\mathfrak{q}}\) were falsely classified as \(\overline{\mathfrak{q}}\) (class no. 2), \(\overline{\mathfrak{q}}\) (class no. 3), \(\overline{\mathfrak{q}}\) (class no. 14), \(\overline{\mathfrak{q}}\) (class no. 20), \(\overline{\mathfrak{q}}\) (class no. 28), and \(\overline{\mathfrak{q}}\) (class no. 34) for 2, 5, 1, 5, 1, 2, and 1 times, respectively, i.e., total 17 false-negative predictions. The higher the recall value, the lower were the false-negative predictions. Referring to Table 9, for Dataset 15, the F1 score value of character \(\overline{\mathfrak{q}}\) is 0.96 i.e., 96%, which represents a single score for corresponding precision and recall values (refer to Equation (27)). A higher value of the F1 score ensures lower false-positive and false-negative predictions, which is desirable. Referring to Tables 9 and 12, the cells with the highest F1 score were highlighted for each character class to see the highly efficient dataset in recognition of the given character. For example, Table 12 shows that Datasets 11, 12, 14, and 15 responded excellently to the SVM classifier in recognition of character \(\overline{\mathfrak{q}}\). Similarly, we can analyse the precision, recall, and F1 score values produced by the MLP (Tables 7, 9 and 12, resp.) and SVM (Tables 10-12, resp.) classifiers for various character classes associated with different datasets. The average value of precision, recall, and F1 score were estimated on the basis of all the 15 datasets. The top 10 and bottom 10 entries w.r.t. F1 score value (refer to Tables 9 and 12) are summarized in Tables 15 and 16. The characters are arranged in descending order of their mean F1 score.
## 4 Results  
### Settings  
The dataset we used is KDDCup99 ([[http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)]([http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)), accessed on 2 November 2022), a stream-based dataset enriched with additional information from packet-based data or host-based log files, so its data is adequate and representative.  
This experimental section involves three main parts. First, we explain the environment and settings we used; afterward, we compare our method with others regarding accuracy and speed. Finally, regarding carrying out other experiments, we analyze them in light of the above.  
We experimented on a seven-device cluster where each device was a GeForce RTX 2080 Ti GPU. The implementation was based on a PyTorch distributed library. In conjunction with the use of the PyTorch distributed library, we simulated the situation in an edge network, with distributed training through multiple GPU devices.  
In terms of evaluation indicators, we considered two main aspects. Firstly, for NIDS, its detection and correctness rates for errors are significant. We used TPR, Precision, and Recall to evaluate this. In statistics, True Positives (TP) are the correctly predicted positive values, which means that the value of the actual class is yes and the value of the predicted class is also yes. True Negatives (TN) are the correctly predicted negative values, which means that the actual class's value is no and the predicted class's value is also no. False Positives and False Negatives occur when the actual class contradicts the predicted class. False Positives (FP) indicate the situation when the real class is no and the predicted class is yes. False Negatives (FN) indicate the case when the real class is yes, but the predicted class is no. In the evaluation index of the binary-classification results, we used the following parameters:  
\[\mathrm{TPR}\ =\frac{\mathrm{TP+TN}}{\mathrm{TP+TN+FP+FN}} \tag{5}\]  
\[\mathrm{recision}\ =\frac{\mathrm{FP}}{\mathrm{TP+FP}} \tag{6}\]  
\[\mathrm{Recall}\ =\frac{\mathrm{TP}}{\mathrm{TP+FN}} \tag{7}\]  
Furthermore, for multi-classification networks, correctly identifying the type of attack is fundamental. We call this the type detection rate (TDR):  
\[\mathrm{TDR}\ =\frac{\mathrm{TD}}{\mathrm{TOTAL}} \tag{8}\]  
TD is the number of packets for which the type of attack was correctly identified. TOTAL is the total number of packets.  
It is also essential for NIDS to work efficiently. We compared efficiency as a metric by using the same dataset and recording the running time of each method.  
The backbone of the neural networks we used in our experiments is shown in Figure 3.
## 2 Existing Durability Model in Chlorine Environment
### Chloride Ion Diffusion Model  
The process of chloride ions from the concrete surface is an unstable diffusion process, which can be characterized using [PERSON]'s second law [17]. When considering a nonstationary diffusion process, the partial derivative of a concentration (C) to time (\(t\)) is equal to the negative value of the diffusion flux (\(J\)) to the partial derivative of distance:  
\[\frac{\partial C}{\partial t}=-\frac{\partial J}{\partial x} \tag{1}\]  
Further, in the process of unstable diffusion, the diffusion flux can be written as the negative value of the concentration change rate with distance, and the above equation can be rewritten as follows:  
\[\frac{\partial C}{\partial t}=D_{Cl}\frac{\partial^{2}C}{\partial x^{2}} \tag{2}\]  
where \(D_{Cl}\) is the unstable diffusion coefficient of chloride ions. When analyzing the diffusion of chloride ions into concrete, it can be considered that this unstable diffusion of chloride ions is one-dimensional. Thus, it can take the following boundary conditions for Equation (2):  
\[C\left|{}_{x=0}\right.=C_{s},\;C\left|{}_{x>0}^{t=0}=C_{0}\right. \tag{3}\]  
The analytical solution of the PDE (Partial Differential Equation) can be obtained from the boundary conditions and considering the initial chloride content:  
\[C(x,t)=\varepsilon_{1}\cdot\Big{[}C_{0}+(C_{s}-C_{0})\Big{[}1-erf\Big{(}x/ \Big{(}2\sqrt{D_{Cl}t}\Big{)}\Big{)}\Big{]}\Big{]} \tag{4}\]  
where \(C_{s}\) is the chloride ion concentration on the surface of concrete, expressed in % by mass of the cementitious material; \(C_{0}\) is the initial chloride ion content inside the concrete, with a unit of % cementing material mass; \(D_{Cl}\) is the apparent chloride diffusion coefficient in concrete, with a unit of \(\text{m}^{2}/\text{s}\); \(\varepsilon_{1}\) is the uncertainty coefficient of the model, which is a random variable that reflects significant uncertainty related to the chloride concentration model. It can be calculated by the ratio of actual measured values to the calculated values of \(C(x,t)\); \(C(x,t)\) is the chloride concentration of concrete along the depth \(x\) and with time \(t\), measured in % by mass of cementitious material; \(erf(\cdot)\) is an error function expressed as follows:  
\[erf(u)=\frac{2}{\sqrt{\pi}}\int_{0}^{u}e^{-t^{2}}dt \tag{5}\]  
This analytical solution provides a method for calculating the chloride concentration within a certain distance from the concrete surface after time \(t\). Currently, Equation (4) is widely used for calculating the diffusion distribution of chloride ions in concrete structures under chloride environments, and its results can serve as a basis for predicting the durability life of concrete structures.
## 2 Methodology
### Lstm  
LSTM can predict the future by extracting the obvious characteristics of the collected data set, and it has become one of the most feasible tools in the task of data prediction. LSTM is a special type of recurrent neural network, which is an improved type of RNN network. Through the deliberately designed neural network structure, it can save more long-term information, which solves the problem of model failure due to gradient explosion and gradient descent in the traditional RNN algorithm. The key to LSTM is the cell state, which is similar to a conveyor belt. It is free from the interference of other information while information flows, so as to achieve the function of long-term memory and excellent generalization ability.  
The core of the design of LSTM is to add a structure called a gate, which is a method of selecting information. The structure of the LSTM algorithm is shown in Figure 3. LSTM has a total of three gates to control the addition or deletion of the content of cells. The first gate is the forget gate, which will read the output of the previous unit state \((X_{t})\) and the input information \((h_{t-1})\) at the current moment, and then decide to transmit or lose the information at the previous moment.  
\[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{3}\]  
Here, \(\sigma\) is the logistic sigmoid function, which outputs the values in range from 0 to 1, \(f_{t}\) is the forget gate, \(W_{f}\) is the weight of the forget gate and \(b_{f}\) is the bias of the forget gate.  
The second gate is the input gate, and this structure is divided into two parts. The first part is the sigmoid layer, which determines the content that needs to be updated in the  
Figure 3: LSTM architecture.  
Figure 2: Load-time history.  
old cell. In the second part, a new candidate value is generated in the tanh layer, and the sigmoid layer and the tanh layer are used to update the cell state at the same time.  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{4}\]  
\[\widetilde{C}_{t}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{5}\]  
where \(W_{i}\) and \(W_{c}\) represents the corresponding weight, \(b_{i}\) and \(b_{c}\) represents the corresponding bias, tanh is the hyperbolic tangent activation function, and the output range is \(-1\) to \(1\).  
Combine the preparations made in the first two steps to update the state of the cell and obtain a new cell state (\(C_{t}\)).  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{6}\]  
The third gate is the output gate, which determines the output value based on the neuron state. The sigmoid layer of the output gate will first determine the unit state to be output, and then the neuron state to be output will be multiplied by the output of the tanh layer and the sigmoid layer to obtain the output value.  
\[O_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{7}\]  
\[h_{t}=o_{t}*tanh(C_{t}) \tag{8}\]  
where \(W_{o}\) and \(b_{o}\) respectively represent the weight and bias of the output gate.
###### Abstract  
Renal hypouricemia (RHUC) is caused by an inherited defect in the main (reabsorptive) renal urate transporters, URAT1 and GLUT9. RHUC is characterized by decreased concentrations of serum uric acid and an increase in its excretion fraction. Patients suffer from hypouricemia, hyperuricosuria, urolithiasis, and even acute kidney injury. We report the clinical, biochemical, and genetic findings of a pediatric patient with hypouricemia. Sequencing analysis of the coding region of _SLC22A12_ and _SLC2A9_ and a functional study of a novel RHUC1 variant in the _Xenopus_ expression system were performed. The proband showed persistent hypouricemia (67-70 umol/L; ref. range 120-360 umol/L) and hyperuricosuria (24-34%; ref. range \(7.3\pm 1.3\%\)). The sequencing analysis identified common non-synonymous allelic variants c.73G \(>\) A, c.844G \(>\) A, c.1049C \(>\) T in the _SLC2A9_ gene and rare variants c.973C \(>\) T, c.1300C \(>\) T in the _SLC22A12_ gene. Functional characterization of the novel RHUC associated c.973C \(>\) T (p. R325W) variant showed significantly decreased urate uptake, an irregular URAT1 signal on the plasma membrane, and reduced cytoplasmic staining. RHUC is an underdiagnosed disorder and unexplained hypouricemia warrants detailed metabolic and genetic investigations. A greater awareness of URAT1 and GLUT9 deficiency by primary care physicians, nephrologists, and urologists is crucial for identifying the disorder.  
SLC22A12; URAT1; hypouricemia; uric acid transporters; excretion fraction of uric acid 1
## 4 Results and Discussion
### Steady-State Analysis
#### 4.1.2 Phase 2: Suppressing WG and Using e-Turbo to Control Boosting  
Suppressing the wastegate of an engine leads to extremely high pre-turbine pressures at medium to high loads. Figure 13 presents the pre-turbine pressures and peak engine torques achieved for various turbine sizes and the wastegate valve closed. As shown from the figure, a turbine 30% larger than the original size can provide or even increase the maximum power targets of the baseline engine without violating the pre-turbine pressure limit. A torque deficit occurs at 1000 rpm, but this is mainly due to a poor model convergence at this specific engine speed near the surge line and not the incapacity of the engine to achieve the targeted torque.  
The results in Figure 14 show that with this configuration, extremely high amounts of energy, more than 15 kW, can be harvested at high engine speeds and loads. The system can harvest energy up to 10 kW at medium to high engine speeds. However, as can be seen, the larger turbine results in considerable amounts of energy that need to be provided by the motor for meeting, if possible, the targeted maximum torque at low engine speed conditions. This is happening due to the lower pre-turbine pressures and increased turbocharger's inertia at a non-boosting area of the engine's map.  
The next stage in this phase was to investigate the potential of reducing the amount of energy needs to be provided by changing the compressor's size. For this reason, five different compressors (multipliers of 0.8 to 1.2) were tested, and the results are presented in Figure 15.  
It is clearly shown in Figure 15 that a small compressor could reduce the power level needs to be provided by the motor from 0.6 down to 0.2 kW at 1600 rpm with no effect on the engine's maximum torque. However, a smaller compressor would also reduce the amount of energy that can be harvested (from 18.5 down to 17 kW) at high engine speeds; but foremost, it would struggle to meet the maximum engine's torque demands.  
Figure 13: Engine’s performance results for various turbines sizes and the wastegate completely shut: (**a**) pre-turbine pressure; (**b**) engine torque.
## 3 Models for Parameters' Identification and UFLS Schemes' Optimization  
### A Review of Particle Swarm Optimization (PSO)  
The PSO algorithm was developed by [PERSON] and [PERSON] in 1995 [19]. PSO is defined as an Evolutionary Computation (EC) method that searches the space to determine the optimal solution for an objective function [20]. The PSO algorithm evaluates itself based on the travels of each particle as well as the swarm cooperation. Each particle starts to traverse randomly accordingly to its own best knowledge and the swarm's experience. The basic regulation of PSO method can be explained by the following:  
1. Evaluating the objective value of each particle;
2. Updating the local and global best objective and positions;
3. Updating the velocity and the position of each particle.  
For each iteration, the velocity vector of the \(i^{\text{th}}\) particle is updated based on the following Equation:  
\[V_{i}^{(k+1)}=wV_{i}^{(k)}+c_{1}r_{1}(X_{\mathit{pbest}}^{(k)}-X_{i}^{(k)})+c _{2}r_{2}(X_{\mathit{gbest}}^{(k)}-X_{i}^{(k)}) \tag{3}\]  
In Equation (3), \(w\) is the inertia weight factor, \(V_{i}^{(k)}\) is the previous velocity of the particle, \(V_{i}^{(k+1)}\) is the present velocity of the particle, \(c_{1}\) and \(c_{2}\) are weighting acceleration constants, \(X_{\mathit{gbest}}^{(k)}\) is the best position that a particle of the group has achieved up to now, \(X_{\mathit{gbest}}^{(k)}\) is the best position that the whole group has achieved up to now, and \(r_{1}\) and \(r_{2}\) are random parameters ranging from [0,1], with values produced for each particle in each iteration.  
After updating the velocity of the \(i\)th particle, the particle moves toward its new position from its present position by:  
\[X_{i}^{(k+1)}=X_{i}^{(k)}-V_{i}^{(k+1)} \tag{4}\]  
Figure 7 illustrates the search processes of the PSO utilizing the modified velocity and position of the individual based on Equations (3) and (4).  
Figure 6: Structure of the simplified SFR model with UFLS.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 2 Materials and Methods
### Methods
#### 2.3.1 Model Introduction
Equations (8) and (9) are representations of DFC attention, which aggregate pixels in two dimensions horizontally and vertically, respectively. To improve inference speed, they exploit partially shared transformation weights and implement them with convolutions, avoiding time-consuming tensor operations. To adapt to input images of different resolutions, they use two depthwise convolutions of size \(1\times K_{H}\) and \(K_{W}\times 1\), independent of the feature map size. This method enables fast inference on mobile devices with tools such as TFLite and ONNX, and its theoretical complexity is O(\(K_{H}HW+K_{W}HW\)). Figure 7a,b show the bottleneck structure of GhostNet and GhostNetV2, respectively. On the basis of GhostNet, GhostNetV2 introduces the DFC attention mechanism on the parallel branch of the first Ghost module to enhance the expansion ability of features.
## 4 Energy Management Systems
### Rule-based Energy Management Systems
#### 4.1.6 Mutative FLC  
The FLC was improved by incorporating a third input, which is a FC degradation function. The extent of degradation, termed the \"state of degradation\" (SOD), was defined with 0 representing the start of its life cycle and 1 indicating its end. The objective of this controller is to extend the FC lifespan. These changes exhibit a common trend of lowering the average FC power output and reducing its responsiveness to load changes.  
In assessing the degradation of fuel cells, several key indicators are considered crucial. Primarily, the efficiency of power generation serves as a fundamental metric; a decline in this efficiency often signals deterioration in the fuel cell's performance. Another critical indicator is the voltage value; a reduction in the voltage output of a fuel cell is a direct manifestation of its diminished capacity to efficiently convert chemical energy into electrical energy. Additionally, factors such as increased internal resistance, changes in the electrochemical surface area, and the presence of impurities or contaminants in the fuel cell components also serve as vital signs of degradation. These indicators collectively provide a comprehensive understanding of the fuel cell's condition, enabling more accurate predictions of its lifespan and operational efficiency.