esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
This article surveys the state-of-the-art, potentials and challenges of AI techniques for providing cognitive sensing (CS) solutions in future IoT (FloT) systems. The CS concept will be used in the FloT to facilitate a smart, secure and energy-efficient data collection process. Based on research efforts over the last decade, four approaches to the cognitive sensing (CS) problem were identified. They include smart energy management (SEM), self-management (SM), cognitive security and smart data collection (SDC). In the SEM approach, the focus of research effort has been the use of machine learning (ML) to (i) forecast energy availability, (ii) predict events for optimizing duty cycling schedule and (iii) develop energy management models for IoT nodes. By leveraging AI, LPWANs can be designed to further optimize its performance, yet few works have explored this potential. For the SM approach, metaheuristics are used majorly for optimization tasks and efforts in this area are currently skewed towards optimizing data traffic between the node and the edge/cloud system. Furthermore, hybrid metaheuristics have proven to be more suitable and widely used for solving the multi-objective CS challenges due to their ability to combine the strengths of multiple algorithms.  
For cognitive security, the scarcity of structured threats/attack data is still a major obstacle in the advancement of its concept while approximate sensory DC, data aggregation and fusion, cognitive radios, smart data collection protocols, and architectures are some of the observed approaches used to promote smart DC processes in the literature. Further, we identified data availability, data privacy, modeling difficulties, resource limitation, and efficient algorithms as some of the challenges mitigating against the use of AI techniques in CS solutions. Moreover, it is observed that supervised learning techniques are used majorly for SEM solutions whereas metaheuristics are used for self-management and smart data collection solutions.  
Future research direction suggests the need for effective data management strategies that provide for privacy in IoT data. This is due to the problem that could arise when collected data are improperly managed. Hence, the development of effective data management solutions, policies or architectures that provides data owners with much-needed privacy while making such data available for AI tools are still needed from the literature. Other research directions worth exploring include data warehousing, computational offloading, energy-neutral nodes and lightweight algorithms that can run effectively on resource-constrained nodes.  
All the authors have equally contributed to this article. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Orientation of Liquid Crystalline Molecules on PDMS Surfaces and within PDMS Microfluidic Systems
## 3 Results
### LC Orientation in PDMS:PDMS and PDMS:Glass Microstructures
#### 3.2.4 Photolithography with the Use of SU-8 Negative Photoresist
This is why the channels are barely visible when aligned along the polarizers' axes, and the maximum intensity of the transmitted light in the vicinity of the channels' sidewalls is obtained after rotating the sample by 45 degrees. Additional tests were performed in the PDMS:PDMS system with 50-\(\upmu\)m-wide channels of simple (Figure 6i) and more complex (Figure 6j) geometry infiltrated with 5 CB NLC. As was previously found, they led to the conclusion that NLC molecules are perfectly vertically aligned on the surfaces inside the PDMS microstructure made from the SU-8 mold. The homeotropic anchoring on the channel walls was found to be stable in time. Figure 6: (**a–h**) The PDMS:glass-based sets of test microchannels of different widths infiltrated with 5 CB NLC and observed under the polarizing microscope. Details related to channel dimensions are given in the text. (**i,j**) POM images of the 50-\(\upmu\)m-wide channels of different geometries obtained in the PDMS:PDMS architecture using the SU-8 mold. The scale bars are 100 \(\upmu\)m in all of the photographs. (**k1–k3**) Results of the numerical simulations showing the LC molecular arrangement (determined by the orientation angle \(\theta\) in the \(x\)–\(y\) plane) in the microchannels’ cross-sections with different aspect ratios (\(h/w\)).
## 3 Description of FE Model and Verification
### Verification  
The FE analysis approach of this study was verified by comparing with the results of the LONGSTAB program [18], which has been widely used in analyzing track stability.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Span Length (m)** & **EA (kN)** & \(EI_{y}\) **(kN\(\cdot\)m\({}^{2}\))** & \(EI_{z}\) **(kN\(\cdot\)m\({}^{2}\))** & \(GJ\) **(kN\(\cdot\)m\({}^{2}\))** \\ \hline
9 & \(1.15\times 10^{7}\) & \(2.76\times 10^{6}\) & \(7.38\times 10^{6}\) & \(3.50\times 10^{5}\) \\
15.2 & \(1.87\times 10^{7}\) & \(8.42\times 10^{6}\) & \(1.52\times 10^{7}\) & \(8.04\times 10^{5}\) \\
30 & \(3.04\times 10^{7}\) & \(3.51\times 10^{7}\) & \(3.08\times 10^{7}\) & \(2.38\times 10^{6}\) \\ \hline \end{tabular}
\end{table}
Table 4: Section properties of open-deck steel plate girder bridge calculated by 3D FE analysis.  
Figure 9: Imperfection of the rail.  
Since the LONGSTAB program can not consider a bridge, an identical CWR track model, but without a bridge, is utilized in the comparison. The comparison results are shown in Figure 10. In Figure 10, the \(x\) and \(y\) axes represent the lateral displacement of the rail and applied temperature to the rail, respectively. From the results, the maximum and the minimum buckling temperatures are similar to each other, with less than 5% difference. Thus, it can be known that the modeling approach of this study is proper to analyze the CWR track buckling. In addition, it can be found that a critical temperature corresponding to \(f_{b,min}\) is 65.7 \({}^{\circ}\)C, which is close to 72 MPa (which is the value specified in UIC and Korean codes) of allowable additional axial compressive stress, \(f_{all}\), where \(\Delta T_{R}\) is set as 35 \({}^{\circ}\)C.
# Intra-Articular Bone Marrow Aspirate Concentrate Injection in Patients with Knee Osteoarthritis
## 3 Results
### Primary Outcome Measure  
The mean VAS score improved over time, with scores decreasing from 6 months (\(8.3\pm 1.2\)) to 5 years (\(4.7\pm 0.5\)) postoperatively (\(p<0.001\)). The most significant improvement was observed in the first 6 months (Post hoc analysis, \(p<0.001\)). Based on the severity of the arthritis, the pain improvement was significantly different according to the K-L grades. Patients with knee OA of K-L Grades III and IV had lower pain improvement than those with knee OA of K-L Grades I and II during the follow-up period (ANOVA, \(p<0.01\), Figure 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Enrolled Cases** \\ \hline Case & 25 patients (47 knees) \\ Follow-up period(month) & 76.2 (range, 62–83) \\ Age (year) & 67.5 (range, 58–85) \\ Gender (male/female) & 9/16 \\ Height (cm) & \(162.1\pm 9.3\) \\ Weight (kg) & \(69.8\pm 11.4\) \\ BMI (kg/m\({}^{2}\)) & \(26.3\pm 4.2\) \\ Smoking status & \\ smoker (\%) & 10 (40) \\ non-smoker (\%) & 15 (60) \\ Knee OA severity (by K-L grade) & \\ K-L Grade 1 & 1 (2.1\%) \\ K-L Grade 2 & 27 (57.5\%) \\ K-L Grade 3 & 12 (25.5\%) \\ K-L Grade 4 & 7 (14.9\%) \\ \hline \hline \end{tabular} Note: The time point at which the data were assessed was when all patients had a minimum follow-up of 5 years after BMAC injection. Values are presented as the mean \(\pm\) standard deviation or median (range). BMI, body mass index; OA, osteoarthritis; K-L grade, Kellgren–Lawrence grade.  
\end{table}
Table 1: Demographic data.  
Figure 1: Study cohort.  
The improvements in pain intensity showed no significant difference according to age (patients aged \(<\)60 vs. those aged \(\geq\)60 years) and sex (Table 2).
# The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network
## 4 Results and Discussion
### Blue Noise Hopfield Neural Networks in Optimization Problems
#### 4.1.1 The Effect of Blue Noise on the Ability of Neural Networks to Optimize Continuous Functions
The shortest path for these 10 cities is 2.6776, as shown in Figure 7.  
Figure 7: The optimal 10-city path. When solving the TSP problem with the Hopfield neural network model with blue noise perturbation, the parameters were initialized as [47]: \(A=B=1\), \(D=1\), \(\varepsilon_{0}=0.02\), and randomly generated \(U\). The solution was performed in simulation experiments using different signal-to-noise ratios, and the experimental results were compared and analyzed. Table 2 shows the simulation test data for 200 randomly assigned initial values when the colored noise parameter is taken to different values and compared with the simulation results from the literature [41]. As shown in Table 2, it is completely feasible to introduce noise into the neural network using the method of this paper. Moreover, the moderate introduction of noise signals does not have much impact on the solving ability of Hopfield neural network, and it can be seen through extensive testing that when the \(SNR\) is less than 50, the ability of this network model to solve the TSP problem is decreasing significantly, while when the \(SNR\) is greater than 90, the noise on the neural network's ability to influence is basically negligible or even beneficial. When compared with the literature [41], it is found that by introducing blue noise and properly correcting the parameters, Hopfield obtains better optimization ability when the \(SNR\) is greater than 60, but due to the intrinsic characteristics of blue noise, which allow the network model to obtain better solution effects in white noise when the \(SNR\) is less than 60, it also provides a reference interval for subsequent researchers to ensure the \(SNR\) of the input signal sequence and the range in which the parameters are best controlled when using the Hopfield neural network.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 7 Enhancement Methods to Optimize and Increase the Efficiency of FBEHs
### Nonlinear FBEHs  
Energy harvesters with linear structures face issues like susceptibility to damage and the need for high critical wind speeds to operate effectively. To overcome these, researchers have introduced nonlinear mechanisms, such as incorporating a nonlinear magnetic force, introducing nonlinear spring forces in the energy harvester design, establishing geometric nonlinear configurations, and exploiting bistable or multistable harvester designs. These nonlinear structures enhance energy conversion efficiency, broaden operational wind speed  
Figure 11: (**a**) Illustration of wake galloping phenomenon reproduced with permission from [136] Elsevier, 2016. (**b**) Characteristic response of wake galloping EH. (**c**) Wake galloping with different interference cylinder cross-sections reproduced with permission from [144] Elsevier, 2019. (**d**) Schematic of iced-conductor-inspired wake galloping reproduced with permission from [146] Elsevier, 2020.  
ranges and improve durability. As a result, these achieve better performance under variable and lower wind speeds for environmental monitoring applications. This review focuses on nonlinear energy harvesters based on piezoelectric transduction techniques only, as these are widely explored in the literature for their compact design, scalability and higher energy density compared with other methods. Based on the FIV mechanism, several PE nonlinear energy harvesters are explained and compared in the following sections.
## 2 Principle
### Principle of Two-Dimensional Convolutional Neural Networks
The primary metric is MSE:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-\hat{y_{i}})}^{2} \tag{30}\]  
In this formula, \(y_{i}\) is the actual value, \(\hat{y_{i}}\) is the predicted value, and n is the total number of samples. Cross-entropy loss measures how different two probability distributions are and is often used in classification tasks, especially when using the softmax activation function to create probability distributions. In the context of binary classification problems:  
\[L=-\frac{1}{n}\sum_{i=1}^{n}[y_{i}\log(\hat{y_{i}})+(1-y_{i})\log(1-\hat{y_{i} })] \tag{31}\]  
For multi-classification problems:  
\[L=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{C}y_{ij}\log(\hat{y_{ij}}) \tag{32}\]  
where \(C\) denotes the number of categories. We assessed mean squared error and cross-entropy loss during the training process. The MSE quantifies the average squared deviation between predicted and actual values, thereby assessing the model's predictive capability for signal recovery and continuous signal values. If signal recovery, continuous signal value forecasting, or constellation diagram production is unnecessary, using cross-entropy as the loss function may enhance classification outcomes and reduce bit-error rates. We evaluated three optimizers throughout the training process: stochastic gradient descent (SGD), Adam, and AdamW [35]. Optimizers adjust parameters autonomously according to rules derived from gradients generated by backpropagation, which reflect changes in loss associated with the parameters. SGD, the fundamental optimizer, iteratively adjusts model parameters with mini-batch gradients. The formula is:  
\[\theta_{t+1}=\theta_{t}-\eta\
abla_{\theta_{t}}L(\theta_{t}) \tag{33}\]  
The advantages include straightforward implementation, little processing burden, limited hyperparameters, and effective performance with small datasets or uncomplicated models. The learning rate dictates the speed of learning and may result in entrapment inside local minima, rendering the process unstable. Adam modifies the learning rate via momentum by averaging gradients and their squares. The approach accelerates convergence in complex data distributions, exhibitsinsensitivity to the initial learning rate, and requires few parameter adjustments. Adam employs more memory and may encounter convergence issues. AdamW, an enhanced version of Adam, updates parameters without gradient computation and incorporates weight decay for regularization. This technique enhances model generalization and stability by removing the effects of momentum and adaptive learning rates on classic Adam's L2 regularization. The automatic modification of the learning rate and weight-decay coefficient mitigates overfitting. This study carefully looks at different neural network algorithms and traditional DSP algorithms through organized experiments; measures how things like model complexity, optimizers, and activation functions affect phase-recovery performance; and finds the best model design and settings for the D-band RoF system in these situations.
# Impact of Chitosan-Genipin Films on Volatile Profile of Wine along Storage
## 2 Materials and Methods  
### Chemical Reagents and Standards  
Chitosan of medium molecular weight (150 kDa) from shrimp (CAS#9012-76-4) was used. It was 88% deacetylated and had a purity of 95%, determined as described by [PERSON] et al. [21]. This polysaccharide was composed by 6.2% of water-soluble material with molecular weight lower than 12-14 kDa (determined by dialysis) and 17% of alkali soluble material (determined by ammonium precipitation). Chitosan and glycerol (99.5%, CAS#56-81-5) were provided by Sigma-Aldrich (St. Louis, MO, USA). Genipin (\(\geq\)99.0%, CAS#6902-77-8) was supplied from Challenge Bioproducts Co. (Taiwan, China). The films of chitosan crosslinked with genipin were prepared by solvent casting using 1.5% (\(w/v\)) chitosan in 0.1 M acetic acid, 0.05% (\(w/v\)) genipin, and glycerol as plasticiser, has reported by [PERSON] et al. [21].  
The standards used in model solutions were purchased from 4 different suppliers: benzaldehyde (CAS#100-52-7, \(\geq\)99%) provided by Fluka, Steinhem, Germany; arabinose (CAS#28697-53-2, \(\geq\)99%), furfural (CAS#98-01-1, \(\geq\)98%), phenylacetaldehyde (CAS#122-78-1, \(\geq\)98%), phenylalanine (CAS#673-06-3, \(\geq\)99%), and octan-3-one (CAS#106-68-3, \(\geq\)98%), provided by Sigma-Aldrich, St. Louis, MO, USA; ethyl acetate (CAS#141-78-6, 99.9%), provided by VWR, France; ethyl hexanoate (CAS#123-66-0, 99%), hexanoic acid (CAS#142-62-1, 99.5%), hexanal (CAS#66-25-1, 99%), hexanol (CAS#111-27-3, 99%), leucine (CAS# 61-90-5, \(>\)99%), and 3-methylbutanal (CAS#590-86-3, 97%) provided by Aldrich-Chemie, Steinhem, Germany. All other reagents (acetic acid, ethanol, sodium chloride, sodium hydroxide, and tartaric acid) used were analytical grade.  
The SPME holder, for manual sampling, included a fused silica fibre coating with divinylbenzene/carboxen/poly(dimethylsiloxane) (DVB/CAR/PDMS) with 50/30 \(\upmu\)m of thickness and 1 cm of length, was supplied from Supelco (Bellefonte, PA, USA). Prior to initial use, SPME fibre was conditioned for 60 min at 270 \({}^{\circ}\)C in the gas chromatography injector as recommended by manufacturer. The retention index probe (n-alkane series of C8 to C20 straight-chain alkanes, in hexane) was purchase by Fluka (Buchs, Switzerland).
# Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China
## 5 Experiment of Cost Estimation Ontology Reasoning
### A Case Application  
The case application is to illustrate the cost estimation ontology on a real bill quantity of building construction. The proposed ontology is applied in the construction cost estimation analysis of the concrete structure engineering, which consists of 22 cost items.  
Take ring beam, for example: the bill quantity item \"_Ring Beam of Cast-in-Place Concrete_\", coded as \"_010503004_\", represents a set of beams that have the same \"_Material Feature_\" and \"_Geometry Feature_\" with a \"_Cast-in-Place Concrete_\" and \"_Ring_\" feature. After the decomposition, if building product is known to have the type of \"_Beam_\", material of \"_Cast-in-Place Concrete_\", and the section shape in geometry feature of \"_Ring_\", it can be classified into the bill quantity item of \"_Ring Beam of Cast-in-Place Concrete_\". In the cost item, the three optional numbers of specific ring beam changes with the specific building construction. The \"_Ring Beam of Cast-in-Place Concrete_\" has strength grade with a \"_C20_\", then it is coded in \"_010503004001_\". Accordingly, a reasoning process can be abstracted for this bill quantity item, as shown in Figure 15. The reasoning rule is as follows:  
The cost of work items is a relatively important indicator in the process of cost estimation, and it includes the price of resource, material, labor, construction equipment, enterprise management fee, and profit. Estimators start to price each work item by using the construction company's historical records, which include cost information from previously completed projects. Therefore, many companies have established unit cost databases to support the query, calculation, and update of unit cost. Unit cost changes continuously, but the current estimating applications do not have the built-in capability to update their unit cost databases. Therefore, estimators have to obtain the latest construction costs information and update the unit cost databases manually, which is a time-consuming process. Providing direct access to the latest construction costs would eliminate the need for manual updating and has the potential to improve estimating efficiency [58]. Estimators can extract the quantities of work items from the cost estimation ontology, and the unit cost can be assigned to a work item by the unit cost database.  
The reasoning results can be applied in the process of cost estimation, as shown in Figure 16. The reasoning process is reflected in 1 and 2 Estimators need to extract the unit cost from the unit cost database 3 and find the quantity of work item from drawing or BIM 4. Then, unit cost is combined to work item quantity 5 to form the bill quantity.  
Figure 15: Reasoning process of bill quantity item “Ring beam C20 of cast-in-place concrete” codes as “010503004001”.
# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
## 4 Example Analysis
### Algorithm Optimization Comparison  
To verify the effectiveness of the APSOGA fusion intelligent optimization algorithm, a two-stage optimization was performed on the above example. The number of front warehouses and alternative points are determined through K-means clustering analysis, and based on this, the location and end path optimization of front warehouses are carried out. The final iteration goal is the total upper cost. A comparison of the solution results between the APSOGA fusion algorithm and the traditional single intelligent algorithm is shown in Figure 6. In Figure 6, the horizontal axis represents the number of iterations, and the vertical axis represents the objective function value.  
The optimal solution curve in Figure 6 represents the convergence of different intelligent optimization algorithms for the same example. The total objective function values calculated by a single intelligent optimization algorithm, PSO and GA, are CNY 1.0614 million and CNY 974,700, respectively, while the total objective function obtained by the APSOGA fusion algorithm is CNY 925,600. Compared to a single algorithm, the total cost decreased by CNY 134,900 and CNY 48,200, respectively. Therefore, compared to solving with a single heuristic algorithm, the adaptive algorithm has a better solving effect. Compared with the other five heuristic algorithms, APSOGA has the lowest total cost, reducing the total costs by 25.75% and 25.74%, respectively, compared to using the adaptive particle swarm optimization algorithm (APSO) and the adaptive genetic algorithm (AGA), and reducing the total cost by 25.84% compared to using the GA. This confirms the effectiveness  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Distribution Routes** & **Transportation Mileage/km** & **Delivery Cost/CNY** \\ \hline \multirow{3}{*}{1} & 7-28-29-35-18-32-25-7 & 288.518 & 4327.764 \\  & 7-27-24-26-23-37-7 & 322.272 & 4834.075 \\  & 2-21-20-19-9-16-15-13-12-10-8-2 & 459.597 & 6893.953 \\  & 2-22-14-11-72-7 & 215.979 & 3239.684 \\  & 6-36-44-49-31-40-48-6 & 344.550 & 51,682.456 \\  & 6-33-45-47-30-34-38-6 & 346.577 & 51,986.601 \\  & 4-55-54-51-52-53-52-43-42-41-4 & 590.014 & 8850.211 \\  & 4-46-56-39-54-4 & 429.683 & 6445.242 \\  & 5-67-65-62-61-66-58-5 & 624.889 & 9373.334 \\  & 5-63-60-59-57-68-64-5 & 666.192 & 9992.882 \\  & 3-69-70-72-74-3 & 287.399 & 431.098 \\
6 & 3-71-73-75-3 & 319.541 & 479.311 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Distribution routes and electric vehicle mileage within each region.  
Figure 6: Iterative convergence graph of different algorithms.  
of the APSOGA in solving this type of LRP. The APSOGA has a faster solving speed than the traditional algorithms PSO and GA, as shown in Table 6.