esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Preparation  
### Experimental Dataset  
#### 3.1.1 Stanford-MIT-Toyota Dataset  
[PERSON] et al. publicly released the Stanford-MIT-Toyota dataset, which utilized LFP batteries manufactured by A123 Systems [37]. The information on the battery is shown in Table 1.  
The cells in this dataset were subjected to cycle charge and discharge experiments at a constant temperature of 30 degrees Celsius. The charging profile used was called the six-step fast charging mode. The first four steps of charging were all charged by constant current, and the total time was fixed at 10 min. The charging current was independently and randomly set in the four stages of battery SOC of 0-20%, 20-40%, 40-60%, and greater than 60%. The charging mode of the last two steps of all cells was the constant current and constant voltage (CCCV) under 1C. This paper selects the fifth batch of the dataset for research. This batch contains 9 different working conditions and a total of 45 batteries. All cells are still named according to their working condition type and channel number. For example, the cell tested in the 38 th channel of the first working condition is called g1c38, and its voltage and temperature curves are shown in Figure 4. The corresponding details of working conditions and cell numbers are illustrated in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input** & unlabeled dataset \(S\) \\ \hline
**Data** & The combined sample set \(T\) consisting of both \(T_{source}\) and \(T_{target}\) \\
1 & Initialize the weight vector \(w_{1}^{t}\) \\  & For \(t=1,\ldots,N\): \\
2 & Set normalized weight vector \(p^{t}\) \\
3 & Use the combined training set \(T\) and the \(t\)-th iteration normalized weight vector \(p^{t}\) to train on the base learner \\  & CART to obtain the hypothesized mapping \(h_{t}\). Enter \(S\) to get the label. \\
4 & Compute the adjusted error \(e_{i}^{t}\) for the label of each sample on \(S\) the: \\  & let \(D_{t}=\max_{i=1}^{t}\lvert c(x_{i})-h_{t}(x_{i})\rvert\) \\  & then \(e_{i}^{t}=1-exp(-\lvert c(x_{i})-h_{t}(x_{i})\rvert/D_{t})\) \\
5 & Compute the total adjusted error of \(h_{t}\) on \(T_{target}\): \\  & \(\varepsilon_{t}=\sum_{i=n+1}^{n+t}\frac{\lvert e_{i}^{t}\rvert}{\sum_{i=n+1}^{n }\lvert e_{i}^{t}\rvert}\) \\
6 & Let \(\beta_{t}=\varepsilon_{t}/(1-\varepsilon_{t})\) and \(\beta=1/(1+\sqrt{2\ln n/N})\). Once the value is greater than 0.5, stop iterating. \\
7 & Update the new weight vector: \\  & \(w_{i}^{t+1}=\begin{cases}w_{i}^{t}\beta^{t},&1\leq i\leq n\\ w_{i}^{t}\beta_{t}^{-t},&n+1\leq i\leq n+m\end{cases}\) \\
**Output** & \(y=h_{f}(x)\) is the weighted median of all mapping results \(h_{t}(x)\), \(t\in(1,N)\), and the mapping weight is \(\ln(1/\beta_{t})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information of battery in Stanford–MIT–Toyota dataset.
## 3 Improved Firefly Algorithm-Wavelet Support Vector Machine
### Improved Firefly Algorithm  
As mentioned above, the optimization of the firefly algorithm is achieved through mutual attraction between fireflies. With an increase in iterations, the entire swarm of fireflies will gather around the optimal solution. By this time, the distance between individual and optimal solution has been extremely small. In the traditional FA, the randomization parameter \(\alpha\) is a constant. Therefore, in the process of approaching the optimal solution, it is possible to appear that the displacement of a firefly is greater than the distance from the optimal solution. As a result, the firefly may skip the optimal solution when updating its location with Equation (25). If this happens multiple time in a row, it will have an effect on the searching ability and the convergence rate.  
In order to overcome the drawback and provide an effective solution, the randomization parameter \(\alpha\) should decrease gradually as the optimal solution is approaching. For this reason, it is modified as:  
\[\alpha(t+1)=\left(\frac{\alpha_{\infty}}{\alpha_{0}}\right)^{\frac{1}{\max}} \alpha(t) \tag{26}\]  
where \(\alpha_{\infty}\) is the final randomization parameter while \(\alpha_{0}\) is the initial value; \(t_{\max}\) is the maximum number of iterations.  
Additionally, it is worth pointing out that there is an extreme case when \(\gamma\rightarrow\infty\) for the FA. If \(\gamma\rightarrow\infty\), the attractiveness \(\beta\big{(}r_{ij}\big{)}\) decreases sharply and almost becomes zero in the sight of other fireflies. In this case, the fireflies roam in a completely random way. Hence, the traditional FA corresponds to a completely random search method. To reduce the impact of \(\gamma\) on \(\beta\big{(}r_{ij}\big{)}\), the improved attractiveness is designed as:  
\[\widetilde{\beta}\big{(}r_{ij}\big{)}=(\beta_{0}-\beta_{b})\exp\Bigl{(}- \gamma r_{ij}^{2}\Bigr{)}+\beta_{b} \tag{27}\]  
where \(\beta_{b}\) is the basic attractiveness free from impact of light absorption.  
On the basis of the equations above, the firefly location update method is rewritten as:  
\[\mathbf{x}_{i}(t+1)=\mathbf{x}_{i}(t)+\widetilde{\beta}\big{(}r_{ij}\big{)}\bigl{[}\bm {x}_{j}(t)-\mathbf{x}_{i}(t)\bigr{]}+\alpha(t+1)(rand-0.5) \tag{28}\]
## 3 Results  
Two hundred and two falls were considered in the analysis. Patients' median age was 77 years. Almost all falls occurred when the patients were hospitalized (95%) while they were alone (48%) in his hospital bedroom (70.1%) or in the toilet of his hospital bedroom (21.4%) during the afternoon (39.6%). About two-thirds of patients were at risk of fall (66.2%) according to the STRATIFY tool [28], but only in a small proportion of patients, a personalized fall prevention plan (27.5%) was developed (Table 1).  
\begin{table}
\begin{tabular}{l c} \hline \hline Setting: Inpatient & 191 (95.0) \\ People witnessing the fall: None (the patient was alone) & 97 (48.0) \\ The patient was able to provide information about the fall: Yes & 152 (76.0) \\ The patient was at fall risk: Yes & 129 (66.2) \\ Full prevention plan & 53 (27.5) \\ Personalized fall prevention plan & 84 (43.5) \\ Standard fall prevention plan & 56 (29.0) \\ None & 5 (29.0) \\ Previous falls (during the same hospitalization) & 183 (91.5) \\ No & 13 (6.5) \\ Yes, once & 4 (2.0) \\ Yes, twice & 4 (2.0) \\ Time of the day & 141 (70.1) \\ Morning & 52 (25.7) \\ Afternoon & 80 (39.6) \\ Evening & 35 (17.3) \\ Night & 35 (17.3) \\ Location & 8 (4.0) \\ Outpatient clinic & 8 (4.0) \\ Hospital corridor & 6 (3.0) \\ Outside & 3 (15.5) \\ Tolet & 43 (21.4) \\ Hospital bedroom & 141 (70.1) \\ Fall out of & 2 (1.0) \\ the stretchers & 6 (3.0) \\ the wheelchair & 36 (18.0) \\ the bed (raised bed rails) & 23 (11.5) \\ chair & 36 (18.0) \\ The patient was upright & 97 (48.5) \\ Mechanism & 13 (6.5) \\ Stumbing & 11 (5.5) \\ Losing consciousness & 58 (28.9) \\ Off balance & 95 (47.3) \\ Shipping & 24 (11.9) \\ Not available & 25 (12.8) \\ Action at time of fall & 10 (5.5) \\ Standing up/Sitting down & 1 (0.5) \\ Living on a stretcher & 47 (24.1) \\ Walking & 5 (2.6) \\ Sleeping & 11 (7.2) \\ Getting out from bed & 1 (0.5) \\ Getting on bed & 53 (27.2) \\ Using loiette & 36 (18.5) \\ Not available & 13 (6.7) \\ Extrinsic cause (patient) & 10 (8.0) \\ Clothes & 48 (88.4) \\ Slippers (no shoe style) & 13 (10.4) \\ Medical devices (e.g., surgical drains) & 13 (10.4) \\ Walking aids & 8 (6.4) \\ Walking barefoot & 46 (36.8) \\ Extrinsic cause (environment) & 7 (9.0) \\ Bed not adjustable & 8 (10.3) \\ Poor lighting & 4 (5.1) \\ No handrail in the toilet & 9 (11.5) \\ Confined space & 11 (14.1) \\ Lowered bed rails & 22 (28.2) \\ Raused bed rails & 17 (21.8) \\ Intrinsic causes (number) & 73 (50.3) \\ One & 49 (33.8) \\ Two & 14 (6.7) \\ True & 9 (6.2) \\ Concomitant medications & 44 (29.1) \\
1-2 concomitant medications & 5 (3.3) \\
3 concomitant medications & 102 (67.5) \\ Fall-related injury: No & 119 (61.3) \\ If any, injury injury & 46 (51.7) \\ Contusion & 41 (46.1) \\ Wound & 2 (2.2) \\ Fracture & 2 (2.2) \\ Number of diagnostic tests & 141 (75.0) \\ None & 39 (20.7) \\ One & 6 (3.2) \\ Two & 2 (1.1) \\ There & 2 (1.1) \\ Injury treatment & 24 (13.0) \\ Dressing & 4 (2.2) \\ Saturure & 4 (2.2) \\ None & 157 (84.9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Falls characteristics (2016–2017). Data are absolute numbers (percentage).
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 2 Materials and Methods
### Method
#### 2.1.1 Estimation of GPP and NPP
The leaf, stem, fine root and coarse root respiration items were calculated separately, according to the BEPS model. Growth respiration (\(R_{g}\)) was considered to be proportional to the difference between GPP and maintenance respiration (\(R_{m}\)):  
\[\text{GPP}=\varepsilon\times\text{FPAR}\times\text{PAR} \tag{1}\]2where \(\gamma\) is the growth respiration coefficient and is defined as 0.25.
## 3 Results and Discussion
### Comparison to Other Methods  
To evaluate the effectiveness of the crack defect detection method in MPI for bearing rings, this paper compares the algorithm with other algorithms commonly used in MPI images. Table 4 show a qualitative comparison with traditional methods based on the SIFT features and SVM (SIFT + SVM) [25], shape features and SVM (Shape + SVM) [34], Hu invariant moment, and BP neural network (Hu + BP). One hundred MPI images of bearing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN Models** & **Accuracy** & **Parameters** & **Time** \\ \hline VGG16 & 90.5\% & 105 M & 0.869 s \\ ResNet34 & 92.2\% & 81.3 M & 0.780 s \\ GoogleNet & 93.0\% & 39.3 M & 0.737 s \\ MobileNetV3 & 91.8\% & 16.2 M & 0.748 s \\ Improved MobileNetV3 & 94.9\% & 12.6 M & 0.675 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The testing results of the CNN models.  
Figure 9: (**a**) The accuracy of validation; (**b**) The value of cross entropy loss function.  
rings are selected as the test set to verify their performance. Some of the detection results of the test set are shown in Figure 10.  
As seen in Figure 10, a comparison of the results shows that the crack defects are incompletely and incorrectly detected by the other three methods. In the methods based on shape features and SVM, the crack regions are extracted by thresholding, which causes inaccurate location and missed detection, as shown in Figure 10b. In the method based on Hu and BP and the method based on SIFT and SVM, although cracks are detected, the results also contain false indications. As shown in Table 4, the proposed method has a recall rate of 96.5%, a precision rate of 91.7%, and an average detection time of one image of 9.33 s, which shows better real-time performance than the three traditional methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall** & **Precision** & **Time** \\ \hline Shape features + SVM & 73.1\% & 45.2\% & 19.27 s \\ Hu + BP & 76.5\% & 50.7\% & 26.88 s \\ SIFT + SVM & 88.8\% & 79.2\% & 18.75 s \\ Our method & 96.5\% & 91.7\% & 9.33 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the proposed method and other traditional detection methods.  
Figure 10: Detection results using four methods. (**a**) Original image; (**b**) The method based on shape features and SVM; (**c**) The method based on Hu moment invariant feature and BP; (**d**) The method based on SIFT features and SVM; (**e**) Our method.
# Three-Dimensional Scaffolds Designed and Printed Using CAD/CAM Technology: A Systematic Review
## 4 Discussion
### Materials Used and Their Properties  
Synthetic grafts are usually ceramics based on calcium phosphates, such as hydroxyapatite (HA), beta-tricalcium phosphate (\(\beta\)-TCP) or a combination of both [22].
## 3 Results and Analysis  
### Test Function and Evaluation Indicator  
In order to verify the performance of the hybrid algorithm proposed in this paper, 11 typical test functions are selected for simulation experiments, as shown in Table 1, including the function name, search range, optimal position, and optimal value. In addition, Table 2 is a supplement to Table 1, which is used to show function expressions. \(f_{1}\)-\(f_{4}\) are unimodal functions, which are used to test the optimization ability of the algorithm for a single extreme value function. \(f_{5}\)-\(f_{10}\) are multimodal functions, which are used to test the global search ability of the algorithm under multiple local extreme values and the ability to solve complex optimization problems. \(f_{2}\) and \(f_{10}\) are non-partitioned functions with a control relationship between dimensions; \(f_{4}\) is a noise function with random interference; and \(f_{11}\) is a step function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Function Name** & **Search Range** & **Optimal Position** & **Optimum Value** \\ \hline \(f_{1}\) & Sphere & \(x_{c}[-100,100]\) & (0,0......) & 0 \\ \(f_{2}\) & Rosenbrock & \(x_{c}[-50,50]\) & (1,1....) & 0 \\ \(f_{3}\) & Schwefel222 & \(x_{c}[-10,10]\) & (0,0......) & 0 \\ \(f_{4}\) & Quartic & \(x_{c}[-18,128]\) & (0,0......) & 0 \\ \hline \(f_{5}\) & Rastrigin & \(x_{c}[-5.12,5.12]\) & (0,0......) & 0 \\ \(f_{6}\) & Greuxuxh & \(x_{c}[-60,600]\) & (0,0......) & 0 \\ \(f_{7}\) & Ackley & \(x_{c}[-32,32]\) & (0,0......) & 0 \\ \(f_{8}\) & Levy and Montalvo 2 & \(x_{c}[-5,5]\) & (1,1....) & 0 \\ \(f_{9}\) & Schwefel226 & \(x_{c}[-500,500]\) & (420.968746,... 420.968746) & \(-418.98288\times\) D \\ \hline \(f_{10}\) & Schafer & \(x_{c}[-100,100]\) & (0,0....) & 0 \\ \hline \(f_{11}\) & Step & \(x_{c}[-100,100]\) & (0,0....) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark test functions.  
Figure 4: Flow chart of FAS-MMSBAS algorithm.  
Three indicators are selected in the experiment to evaluate the performance of the algorithm:  
1. The mean, which is the average value of the algorithm run many times, reflecting the quality of the solution. The closer the average value is to the optimal value, the better the algorithm's results.
2. The standard deviation, which is the standard deviation of the optimal value and can reflect the stability of the algorithm. The smaller the standard deviation, the better the stability of the algorithm.
3. The running time, in seconds, which is the average time of multiple runs of the algorithm under the same environment and parameters, reflecting the running efficiency of the algorithm.  
The calculation formulas of the three evaluation indicators are shown in Formulas (4)-(6):  
\[\text{Mean}=\left|\frac{1}{N}\sum\limits_{i=1}^{n}f(x_{i})\right| \tag{4}\]  
\[\text{Standard}=\sqrt{\frac{1}{N}\frac{n}{\sum\limits_{i}^{n}(f(x_{i})-Mean)^{ 2}}} \tag{5}\]  
\[\text{Time}=\frac{1}{N}\sum\limits_{i}^{n}t_{i} \tag{6}\]
## 1 Introduction
### Related Works
Finally, recent studies employed wearable sensors to monitor the movements and physiological data of elderly dementia patients with the goal of identifying behavioral symptoms of dementia. The tailored methods used in upcoming digital research are presented by the median AUC of 0.87 acquired from the machine learning models [15]. Internal and external factors have been the focus of aggressive behaviors in people with dementia and have been identified to highly contribute to aggressive behaviors. Non-pharmacol interventions are recommended in order to manage these symptoms and reduce caregiver stress [16]. Moreover, in another investigation, verbal non-aggressivebehaviors were the most frequent, and physical aggression behaviors were the most disruptive [17]. Considering aggressive and violent behaviors by patients with dementia reported in nursing homes and by caregivers, a review revealed that person-centered care and non-mechanical techniques can reduce the use of physical and pharmacological restraints. This highlights the further need for research to understand these behaviors [18]. In a review, [PERSON] and [PERSON] addressed the clinical assessment and management of aggressive behaviors in patients with dementia based on personality traits, environmental conditions and cognitive impairment [19]. In another study, researchers [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] explored relationships between psychiatric symptoms and behavioral problems in dementia. It was found that physical aggression is predicted by hyperactivity in persecutory ideas and depressive appearance. The study suggested a distinction of syndromes of non-cognitive symptoms that persist throughout the course of the illness [20]. Subsequently, in the another review [21], the exploration of conceptual challenges considering the definition of aggressive behaviors in dementia patients highlighted psychiatric intervention as a significant cause along with medication and hospital admission. Similarly, in another review [22], the researchers evaluated management strategies from various factors such as pharmacological, environmental and person-centered approaches, raising concerns about the lack of an effective approach. A study conducted by [PERSON] et al. on 126 patients with dementia revealed more than 80% exhibited troublesome behaviors, with aggressiveness being one of them. Based on the severity of the illness, these behaviors exhibited different levels of severity. The findings highlighted that aggressiveness is a significant aspect of dementia [23]. Moreover, another publication proposed a novel algorithm for classifying Alzheimer's Disease Class Rules with the goal of improving the early diagnosis of dementia. The Alzheimer's Disease Cognitive Resilience (AD-CR) algorithm used data from Alzheimer's Disease and showcased high accuracy and performance with specificity above 90%. The algorithm's effectiveness on this application highlights the need for further development in the fields of recognition and prediction using machine learning in such illnesses [24].
## 4 Model Description  
### Numerical Model  
In order to reproduce the measured characteristics of the wake field, an LES model is employed. This allows for resolving the large (energy-containing) motions, whereas the effects of the smaller eddies are modelled. To achieve this, the Navier-Stokes equations are decomposed into a filtered (or resolved) component and a residual (or subgrid scale, SGS) component. The classic Smagorinsky model [43] is used for the parametrization of the residual scales. There, the subgrid viscosity \(\
u_{\text{\tiny{KS}}}\) is assumed to be proportional to the Smagorinsky coefficient \(C_{s}\) and the local cell length \(\Delta=(\Delta_{x}\Delta_{y}\Delta_{z})^{1/3}\) and the resolved strain-rate \(\overline{S}_{ij}\) as \(\
u_{\text{\tiny{KS}}}=(C_{s}\Delta)^{2}\sqrt{2\overline{S}_{ij}\overline{S}_ {ij}}\). The value of \(C_{s}\) is set to 0.168, which comes from adjustments made to reproduce decaying homogeneous turbulence [44]. This model is chosen for its ubiquity and because the absence of its best known disadvantage, associated to bounded flows: overdissipation close to walls [40; 45]. The interpolation scheme for the convective term consists of a dynamic blend where, depending on the velocity flux and the magnitude of the velocity gradients at the cell faces, an amount of up to 20% upwind is used in combination with the linear interpolation. This scheme is called _filteredLinear_ within OpenFOAM v2.1. In this way, the upwind part is employed only  
Figure 1: Representation of the measurement positions of the hot-wire. The turbulence is generated by a grid of spacing \(M\). The reported values of TI are measured at \(x=0.5\) m from such grid, where the ADs are subsequently located. This position is referred to as \(x_{D}\). Time-series of the velocity are recorded at various positions along vertical lines at \(3D\), \(4D\) and \(6D\).  
in regions of steep velocity gradients while the flow maintains the second-order accuracy of the linear scheme elsewhere. The PISO algorithm is employed for the solution of the pressure-velocity equations.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 4 Conclusions  
The global incidence and emergence of drug-resistant microbes has created a need for the development of novel strategies to tackle microbial infections. Among new approaches, the screening or development of biofilm inhibitors is considered a novel target. In this  
Figure 7: (**A**) Inhibition of EPS production in _E. coli_, _P. aeruginosa_, and _S. aureus_ by ZnO-NPs. (**B**) Eradication of established biofilms of _E. coli_, _P. aeruginosa_, and _S. aureus_ by ZnO-NPs. Data is presented as average of three replicates, and error bar is standard deviation. One-way ANOVA was performed using Tukey test at significance level of 0.05. Different letters in treatment groups represent different significance groups at \(p\)-value = 0.05 and are in ascending/descending order of values starting from the letter “a”.  
study, ZnO-NPs were synthesized using the aqueous extract of _P. zeylanica_ and then characterized. ZnO-NPs inhibited the biofilms development and EPS production of _P. aeruginosa_, _S. aureus_, and _E. coli_ in a dose-dependent manner. Moreover, ZnO-NPs altered the biofilm's architecture and reduced the attachment and colonization bacteria onto the solid surface. The eradication of biofilms was also achieved in the presence of ZnO-NPs. The results of this study show the potency of ZnO-NPs against the biofilm-forming ability of bacterial pathogens that may be used as a biofilm inhibitor after careful in vivo investigation, at least for topical applications and coating of medical devices.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12020710/s1](https://www.mdpi.com/article/10.3390/app12020710/s1)]([https://www.mdpi.com/article/10.3390/app12020710/s1](https://www.mdpi.com/article/10.3390/app12020710/s1)), Figure S1: SEM images of ZnO-NPs synthesized using aqueous extract of _Plumbago zylanica_.  
Conceptualization, I.A. and F.M.H.; methodology, F.M.H., F.A.Q., M.J.H., M.H.B., J.M.K., and N.A.A.-S. software, F.A.Q., and F.M.H.; formal analysis, F.M.H., F.A.Q., M.J.H., M.H.B., J.M.K., and N.A.A.-S.; investigation, F.M.H., F.A.Q., M.J.H., M.H.B., J.M.K., and N.A.A.-S.; data curation, F.M.H., F.A.Q., M.J.H., M.H.B., J.M.K., and N.A.A.-S.; writing--original draft preparation, F.M.H., F.A.Q. and I.A.; writing--review and editing, F.M.H., F.A.Q. and I.A.; supervision, I.A. and F.M.H. All authors have read and agreed to the published version of the manuscript.  
The research received no funding.  
Not Applicable.  
The data presented in this study are available on request from the corresponding author.  
Authors are grateful to the Researchers Supporting Project Number (RSP-2021/360), King Saud University, Riyadh, Saudi Arabia.  
The authors declare that there are no conflict of interest associated with this study.