esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast and Interactive Positioning of Proteins within Membranes
## 2 Materials and Methods
### Original Reference Method for Integral Membrane Protein and Lipid Association (IMPALA)
#### 2.1.2 Hydrophobic Restraint Term  
Membrane insertion is described by empirical data characterizing the physical property of hydrophobicity of the molecule. This property drives insertion by pushing the hydrophobic regions into the membrane and, conversely, driving the hydrophilic regions outward. Each atom type is associated with a transfer energy term in kJ-mol\({}^{-1}\).A \({}^{-2}\). This energy term represents the energy that must be supplied to an atom of a given type to be transferred from a hydrophilic to a hydrophobic environment, given its accessible surface area in A\({}^{2}\). An accurate description of the hydrophobic effect takes into account that the solvent interacts only with accessible atoms and not with those buried in the molecular structure. The hydrophobic restraint term is consistent with two experimental facts: the hydrophobic effect is related to the nature of the solute and to the surface area of the solute in contact with the solvent. The transfer energies per atom were derived from the assumption that each exposed surface region contributes equally to the total transfer energy of the residue, which was taken from the work of [PERSON] and [PERSON] [21].  
The restriction term is calculated as  
\[\mathrm{E_{int}}=-\sum_{i=1}\mathrm{N}\;\mathrm{S_{(i)}}\;\mathrm{E_{tr(i)}} \;\mathrm{C_{(zi)}}, \tag{2}\]  
and acts only at the interface. Figure 2 shows this restriction term applied to a sp3 carbon type with an accessible surface area of 56.78 A\({}^{2}\). The arrows at each interface show the derived forces that would push the carbon into the hydrophobic core of the membrane. The central curve in black on the \(z\)-axis shows on the \(y\)-axis the amplitude of the force acting on that atom, which is negative on the left side and positive on the right side of the \(z\)-axis.  
Figure 2: Illustration of the hydrophobic constraint term and the direction of the resulting forces in the headgroup regions of the lipid bilayer.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Maximum Number of Vehicles
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**QoS Scenario** & **Maximum Range (m)** & \multicolumn{3}{c}{**Vehicles**} \\ \cline{3-5}  & **Urban** & **Highway** \\ \hline Q1, Q3 & 350 & 214 & 321 \\ \hline Q2, Q4 & 200 & 114 & 171 \\ \hline Q5, Q6 & 500 & 285 & 428 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Maximum number of vehicles in the maximum range for each QoS scenario. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**QoS Scenario** & **Maximum Latency** & **BCJR** & **logBCJR** & **maxlogBCJR** & **SOVA** & **LDPC** & **Polar** \\ \hline Q1 & 1000 & 62 & 66 & 104 & 187 & 109 & 185 \\ \hline Q2 & 1000 & 114 & 114 & 114 & 114 & 114 & 114 \\ \hline Q3 & 100 & 214 & 214 & 214 & 214 & 214 & 214 \\ \hline Q4 & 1000 & 6 & 6 & 10 & 19 & 11 & 19 \\ \hline Q5 & 1000 & 285 & 285 & 285 & 285 & 285 & 285 \\ \hline Q6 & 20 & 285 & 285 & 285 & 285 & 285 & 285 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Maximum number of vehicles achieving maximum end-to-end latency requirements for each decoding algorithm and QoS scenario in an urban environment. the maximum end-to-end latency is three times the number of vehicles compared to the BCJR and logBCJR algorithms. The maxlogBCJR and LDPC algorithms behave similarly and can serve just over half as many vehicles compared to polar coding. The results in a highway environment are similar, except for scenario Q6, where BCJR and logBCJR cannot reach the maximum possible vehicle bound of Table 9.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Data Splitting  
The dataset split plays a fundamental role in the development of a reliable model, ensuring adequate learning from the data and generalization to unseen data. Typically, datasets are divided into three subsets--training, validation, and testing [34]--which split the data as follows.  
* Training Set (60%): Utilized for fitting the model's weights. It must be large enough to capture the data's patterns and complexity.
* Validation Set (20%): Applied for hyperparameter tuning and intermediate evaluation.
* Test Set (20%): Reserved for the final assessment, it provides an unbiased measure of the generalization capability of the model.  
This method of dividing the data allows good performance in both training data and new unseen data during testing.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 2 Related Work
### Paradigm of Self-Training  
Self-supervised pre-training is a significant research topic aimed at learning generalizable and shareable knowledge from large-scale data, which further benefits downstream tasks. The pretext tasks of self-supervised learning can be broadly divided into contrastive learning and masking modeling. In recent years, with the maturation of contrastive learning and masking modeling techniques, several self-supervised pre-training methods designed for log sequences have emerged.  
In log sequences, the masking modeling paradigm is commonly applied, where the model is optimized by reconstructing the masked content from the unmasked portions. LogBERT [19] enhances the self-supervised training approach based on BERT [9] by introducing two novel self-supervised training tasks. To adapt to log sequences, masked log key prediction (MLKP) is proposed to enable the network to learn patterns of normal log sequences. The NSP of BERT is transformed into the volume of hypersphere minimization (VHM). LAnoBERT [20] utilizes masked language modeling based on BERT to train the model and employs the masked language modeling loss function for each log key during testing, enabling unsupervised anomaly detection. The LogFiT [21] model utilizes the RoBERTa model to handle log datasets containing up to 512 tokens per sample, while the Longformer model is employed for datasets where the token count per sample exceeds 512. Similarly, it employs BERT-based masked language modeling to learn the language patterns of normal log data. The encoder of BERT and its self-supervised paradigm enable it to generate contextually sensitive log keys for the input log sequences and reconstruct the masked log sequences. However, directly masking log sequences disrupts the inherent structure of log sequences, making reconstruction overly challenging and unable to guide representation learning. In contrast to the direct reconstruction approach used in previous works, MMSM proposes a novel masking modeling task, which involves reconstructing the original log sequence from multiple randomly masked log sequences.
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
###### Abstract  
This work evaluates the yarn (_Dioscorea rotundata_) drying performance of solar adsorption drying (SAD) compared to solar drying (SD) and open sun drying (OSD). The evaluation concerns the drying cycle for batches of yam, the effect on vitamin C, color and composition. As SAD can be applied during the night, by using air dehumidified by passing through a bed of silica gel, the cycle time for drying batches of yam can be reduced over 20-25% compared to SD and halved compared to OSD. SAD is thereby most effective during the first nights of drying when the driving force is the highest. Composition was not affected by drying methods but did affect vitamin C content. Vitamin C retention is not significant different for SD and SAD, but with a slight preference for SD. For three yam cultivars ('Pona', 'Lilli' and 'Dente'), 'Lilli' dried fastest and 'Dente' drying slowest. SAD maintained 16 the whitish color of yam better than the other drying methods.  
Yam; adsorption drying; solar drying; vitamin C; _Dioscorea rotundata_
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Cosmetic Industry  
Kaolin is extensively utilized for its desirable properties in a range of formulations related to the cosmetic industry [93; 116]. Its primary function is as an absorbent agent, effectively removing excess oils and impurities from the skin, which makes it a valuable component in facial masks and powders [117]. The fine, platy structure of kaolin contributes to a smooth texture in cosmetic products, enhancing their application and feel on the skin.  
Kaolin's mildness and inert nature render it suitable for use in products designed for sensitive skin. It helps in stabilizing emulsions and preventing the separation of components in creams and lotions. Additionally, its high absorption capacity is advantageous in controlling the oiliness of cosmetic formulations, which improves their performance and longevity [118]. Recent advancements have also explored kaolin's role in the formulation of mineral-based cosmetics, in which its inclusion contributes to the opacity and coverage of products. The widespread use of kaolin in cosmetics highlights its versatility and efficacy in enhancing both the functional and aesthetic qualities of personal care products. Major cosmetic brands, such as _L'Oreal_ and _Estee Lauder_, incorporate kaolin in their products for its beneficial effects in skin care.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 7 Conclusions  
Motivated by the contradictory relationship between the freshness and fidelity of the received data, we studied the joint optimization of the two metrics by adjusting the number of sensor nodes. Explicit expressions of average AoI and MMSE were derived, based on which a closed-form sub-optimal solution was obtained via feasible approximations. Numerical results validate that our proposed sub-optimal number of sensor nodes is correct and can achieve the best data freshness and fidelity tradeoff with negligible errors.  
Conceptualization, Z.C., M.X., M.W. and Y.J.; methodology, Z.C. and M.X.; software, M.X.; validation, M.W. and Y.J.; formal analysis, M.X.; investigation, Z.C. and M.X.; resources, all authors; data curation, M.W. and Y.J.; writing-original draft preparation, Z.C. and M.X.; visualization, M.X.; supervision, Z.C.; project administration, Z.C.; funding acquisition, Z.C. All authors approved the final manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grant 61901066, Grant 61971077, and in part by Natural Science Foundation of Chongqing, China under Grant cste2019 cyj-mssmX0575. It is also sponsored by the State Key Laboratory of Integrated Services Networks (Xidian University) under grant ISN2-17. The authors declare no conflict of interest.
# Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method
## 3 Simulated Signal Evaluation
The process for setting parameters is as follows:  
Figure 8: The envelope spectrum of bearing fault simulation signals. (**a**) CEEMD. (**b**) LMD. (**c**) ITD. (**d**) VMD. Figure 9: Teager energy signal of bearing fault simulation signal. Step 1: CTEO is used to improve the impact component in the raw signal. Step 2: The relevant parameters of the 3D composite chaotic map are initialized with \(\theta=0.99\), \(B=[2,4000]\). The evaluation function of the ICFOA is determined during the optimization procedure. Step 3: The parameters of the fruit fly population are initialized with \(I_{C1 max}=120\), \(I_{C2 max}=80\), \(Ns=30\), \(D=10\), and \(S=3\)D-LSCCM. Step 4: Some combinations \([K,\alpha]\) corresponding to population number are induced by 3D-LSCCM as the reference locations of individual flies. Calculate the standard deviation STD of the simulation signal, set the updating step length of the VMD algorithm to \(\tau=0.003\) based on STD, and set the fault tolerance threshold of convergence to \(\varepsilon=1\times 10^{-7}\). Then, the 3D-LSCCM is used to update the locations of the individuals in a global search. Then, the five optimization algorithms in Table 2 are applied to individually select from the optimum group \([K,\alpha]\) for VMD. The effects of the optimization are shown in Figure 11 and Table 2. The proposed ICFOA outperforms other algorithms in terms of iterations and convergence rate. Then, ICFOA is used to search for the optimum group of VMD parameters. Finally, the VMD is reset and used to demodulate the fault signal of the simulated bearing failure. The fundamental frequency (8 Hz) of the fault and its frequency multiplications (2\(\times\)-7\(\times\)) can be distinctly detected, as shown in Figure 11b. On comparing the envelope spectrum with Figure 10d, it shows much better clarity and distinguishability on spectrum lines. The compared results indicate that the proposed method is effective in extracting sensitive features. Figure 10: Teager energy enhanced envelope spectrum. (**a**) CEEMD, (**b**) LMD, (**c**) ITD, and (**d**) VMD.
## 4 Methodology
### Threshold Fine-Tuning Procedure
#### 4.1.2 Finding an Optimal Threshold  
To design an automated procedure for finding an optimal threshold value, we need to check the quality of a thresholded attribution map \(I_{\tau}\). This can be done by asking the target classifier we use to generate attribution maps. That is, for a masked input \(\hat{x}_{\tau}\) created according to (6), we measure how much the model's predicted class probability has increased due to masking: our conjecture is that if the masking has been successful, it will remove class-irrelevant features, and therefore the model will output a higher probability for the given class.  
To be more specific, we define the relative probability increase (RPI) for the quality measure as follows:  
\[\text{RPI}(\hat{x}_{\tau},x):=\frac{\max\{\hat{y}^{c}-y^{c},0\}}{y^{c}}, \tag{7}\]  
where \(\hat{y}^{c}\) is the prediction probability of a masked input \(\hat{x}_{\tau}\) and \(y^{c}\) is the prediction probability of the original image \(x\), with respect to the class \(c\). RPI measures the increase of class probability due to masking, relative to the original probability.  
We compute the RPI values for increasing values of \(\tau\) so that \(\hat{x}_{\tau}\)s generated in the process will contain progressively increasing numbers of features. Then, we choose the best \(I_{\tau^{+}}\) for which we have the largest RPI value. Algorithm 1 shows our procedure, called threshold fine-tuning (TFT), which uses GPU-based batch processing to accelerate the computation of multiple forward passes (the lines 9 to 11 in Algorithm 1).  
```
1:an input image \(x\), an attribution map \(I\), and a classifier \(f(x)\)
2:an array \(T\) of increasing threshold values in \([0,1]\)
3:Initialize RPI as a \(|T|\)-dimensional array with the zero values.
4:Initialize \(\hat{X}\) as a \(|T|\)-dimensional array with the zero values.
5:for\(i=1:|T|\)do
6:\(\tau\gets T[i]\).
7: Compute \(I_{\tau}\) and \(\hat{x}_{\tau}\) according to (4) and (6).
8:\(\hat{X}[i]\leftarrow\hat{x}_{\tau}\).
9:endfor
10:Transfer \(\hat{X}\) to a GPU.
11:GPUStart\(\triangleright\) Batch processing of all masked images at once.
12:\(\hat{\gamma}^{c}\leftarrow\) the prediction probability of \(\hat{X}\) for the class \(c\).
13:GPUEnd
14:for\(i=1:|T|\)do
15:RPI\([i]\leftarrow\) the quality of \(\hat{x}_{\tau}\) according to (7) based on \(\hat{\gamma}^{c}[i]\).
16:endfor
17:\(\tau^{*}=T[\arg\max_{i=1,2,\ldots,|T|}\text{RPI}[i]]\).
18:Return \(I_{\tau^{*}}\).
```  
**Algorithm 1** Threshold Fine-Tuning (TFT) Algorithm
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 4 Experiment and Result
### Real-World Tasks
#### 4.2.3 Real-World Task Result  
The experimental data of flight trajectories in senses 1 and 2 are shown in Figure 13, where the solid blue line is the trajectory of the UAV. Figure 14 is a series of screenshots of the actual flight video of the UAV. It can be seen that the UAV can avoid obstacles and smoothly fly to the preset destination. The maximum flight speed of the UAV in sense 1 is 1.7 m/s, and the maximum acceleration is 1.5 m/s\({}^{2}\), and the maximum speed of the UAV in sense 2 is 1.98 m/s, and the maximum acceleration is 2.0 m/s\({}^{2}\). The experimental results show that the trajectory generation algorithm proposed in this paper generates a safe and smooth trajectory that meets the dynamic requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Obstacle** & **Position (m)** & **Size (m)** \\ \hline Obstacle 1 & [2.0, 1.0] & [0.7, 0.7, 2.1] \\ Obstacle 2 & [5.5, 1.0] & [0.7, 0.7, 2.1] \\ Obstacle 3 & [11.0, 0.0] & [0.7, 0.7, 2.1] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Obstacle information of sense 2.  
Figure 13: Real-world task result. (**a**) Sense 1. (**b**) Sense 2.
## 3 The Consecutive Iteration Based on Signal Cancellation (SCCI) Algorithm
### Adaptive Variable Convergence Factor
#### 3.2.1 Parameter Settings  
The error between the input signal power spectrum \(P(f)\) and the model estimate \(Y_{e}(f)\) is defined as follows:  
\[\begin{array}{l}e(a,b)=\sum\limits_{f=1}^{N}\left(Y_{e}(f)-P(f)\right)\\ =\sum\limits_{f=1}^{N}\left(aGs(f)+b-P(f)\right)\end{array} \tag{2}\]  
where N is the number of FFT points, \(Y_{e}(f)\) represents the estimated power spectrum of the input signal, \(G_{s}(f)\) denotes the power spectrum of the signal, and the mean square error is provided by  
\[\begin{array}{l}e(a,b)^{2}=\sum\limits_{f=1}^{N}\left(aGs(f)+b-P(f)\right)^{ 2}\\ =a^{2}\sum\limits_{f=1}^{N}Gs^{2}(f)+Nb^{2}+2 ab\sum\limits_{f=1}^{N}Gs(f)\\ +\sum\limits_{f=1}^{N}P^{2}(f)-2a\sum\limits_{f=1}^{N}Gs(f)P(f)-2b\sum\limits _{f=1}^{N}P(f)\end{array} \tag{3}\]  
The gradients of a and b can be obtained as follows:  
\[\begin{array}{l}\
abla a=2a\sum\limits_{f=1}^{N}Gs^{2}(f)+2b\sum\limits_{f=1 }^{N}Gs(f)-2P(f)\sum\limits_{f=1}^{N}Gs(f)\\ =2\sum\limits_{f=1}^{N}Gs(f)(aGs(f)+b-P(f))\\ =2\sum\limits_{f=1}^{N}Gs(f)(Ye(f)-P(f))=2\sum\limits_{f=1}^{N}Gs(f)e_{f}(a,b )\\ \end{array} \tag{4}\]  
\[\begin{array}{l}\
abla b=2 Nb+2a\sum\limits_{f=1}^{N}Gs(f)-2\sum\limits_{f=1 }^{N}P(f)\\ =2\sum\limits_{f=1}^{N}e_{f}(a,b)\end{array} \tag{5}\]  
Therefore, the updated formulas for a and b are provided by  
\[a_{n+1}=a_{n}-\mu_{n}\
abla a(n)=a_{n}-2\mu_{n}\sum\limits_{f=1}^{N}Gs(f)e_{f} (a_{n},b_{n}) \tag{6}\]  
\[b_{n+1}=b_{n}-\mu_{n}\
abla b(n)=b_{n}-2\mu_{n}\sum\limits_{f=1}^{N}e_{f}(a_{ n},b_{n}) \tag{7}\]In introducing the hyperbolic tangent function \(f(x)=1-\frac{2}{1+\exp(2x)}\) and controlling the shape of the convergence curve through parameters \(\alpha\) and \(\lambda\), the relationship between the convergence factor and the error function is provided by  
\[\mu(n)=\alpha(1-\frac{2}{1+\exp(\lambda|e(a_{n},b_{n})|)}) \tag{8}\]  
Simultaneously, due to the inevitable introduction of some noise in the error function \(e(a_{n},b_{n})\), the correlated values of the error \(e(a_{n},b_{n})e(a_{n-1},b_{n-1})\) are used instead of \(e(a_{n},b_{n})\) to suppress the impact of noise on the convergence performance. Substituting into Equation (8) yields Equation (9).  
\[\mu(n)=\alpha(1-\frac{2}{1+\exp(\lambda|e(a_{n},b_{n})e(a_{n-1},b_{n-1})|)}) \tag{9}\]  
Equations (6) and (7) represent the iterative updated formulas for parameters a and b. Equation (9) introduces the adaptive variable convergence factor, which is influenced by parameter \(\alpha\) to control the convergence speed. Parameter \(\lambda\) affects the shape of the function and achieves control over the convergence accuracy.
# A Novel Method for Assessing Regional Tendon Stiffness and Its Significance
## 3 Results
### Reliability Test  
Test-retest reliability of patellar tendon shear modulus indicated good reliability for the lateral half (ICC = 0.94; 95% CI = 0.75 \(-\) 0.99) and medial half (ICC = 0.81; 95% CI = 0.17 \(-\) 0.96).
## 3 Results and Discussion
### Technological Features of L. lactis
#### 3.7.2 Rheological Properties of Milk Clot  
To assess the suitability of the A13 and A14 strains for cheese making, the clotting rate during lactic fermentation was investigated in comparison to two commercial starter cultures, S1 and S2, used in the dairy industry. Dynamic rheometry can monitor the gelation process without destruction of the gel network by measuring properties at low-strain rates [42]. Changes in complex modulus (G*) are shown in Figure 3. Complex modulus shows the viscoelastic behaviour of the sample. The higher the G* values, the stronger the gel [43]. The rheological analysis showed that fermentation with only one strain added to the milk yielded lower complex modulus values, compared to milk fermented with two strains. This is probably an effect of synergistic reactions between the applied strains. It is a very common phenomenon in diary practice. Commercial production of sour cream, yoghurt, kefir, and quark relies on the addition of at least two different strains [7]. At the end of the fermentation process, the wildtype _L. lactis_ A13 and A14 strains were characterized by very similar values of G* to those exhibited by the commercial starters S1 and S2. When the strains were combined, a spectacular increase in the complex modulus value was noted. The results suggest that the wildtype strains can be successfully applied commercially for curd formation.  
Figure 3: Changes in complex modulus during the fermentation of skimmed milk by two wildtype _L. lactis_ strains, A13 and A14, in comparison to commercial starter cultures, S1 and S2, and in combined application.