esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application of Our Proposed Approach
### Case Study-1: Demand Data for Transformer Tanks
#### 5.1.3 Development of Level Prediction Model
At this step of implementation, the logical operator 'AND' is used to connect the inputs. The overall conclusion is made by the aggregation of the selected decision rules to a single fuzzy set. This aggregation is carried out by selecting the truth degree of each rule through the min-max operation. Finally, the non-fuzzy values are obtained by de-fuzzifying the fuzzy sets through Equation (8). Figure 5: The rule editor in the MATLAB platform. To perceive the defuzzification process mathematically and assess the validation of the developed level prediction model on MATLAB [56; 57], an example is presented with an input for \(L_{T}\) = 16.74, \(b_{T}\) = 0.27, \(SN_{T}\) = 1.13 and \(D_{T}\) = 20.00 corresponding to the 19 th month. The membership functions, the decision rules as well as the rule viewers of the fuzzy logic tool box (as shown in Figure 6) indicate that for the given inputs only the decision rule 11 is fired (i.e., output for this rule is non zero) and all other rules remain in the non-fired (i.e., output of these non-fired rules are zero) position. Similar to what is adopted by [PERSON] et al. [69], for this particular rule the fuzzy values of the input \(L_{T}\), \(b_{T}\), \(SN_{T}\) and \(D_{T}\) are computed by using Equation (7) and the values obtained are as follows:  
\[\mu_{V.Low}(L_{T})=0.260;\ \mu_{V.Low}(b_{T})=0.644\]  
\[\mu_{V.High}(SN_{T})=1.350;\ \mu_{Avg}(D_{T})=1.000\]  
Whereas, for rule 11, the firing strength (truth value) are determined as follows:  
\[a_{11} =\min\{\mu_{V.Low}(L_{T}),\mu_{V.Low}(b_{T})\,,\] \[\mu_{V.High}(SN_{T}),\mu_{Avg}(D_{T})\}\] \[=\min(0.260,0.644,1.350,1.00)\] \[=0.260\]  
Finally, for the defuzzification, the crisp output of the predicted level is computed as 17.34 by using Equation (8); whereas the developed fuzzy logic-based model in MATLAB as shown in Figure 6, provides an output of 17.4 for the same inputs. This shows that the obtained output of 17.34 is very close compared to the output 17.4 given by the model developed in the MATLAB fuzzy toolbox. By featuring this higher accuracy, the obtained output of the predicted level (\(L_{Tpred}\)) is used to compute the new series of forecasted demand (\(Y_{T}\)).
## 3 Results and Discussions
Analyses so far suggest that the deposition pattern directly influences  
Figure 4:0 Warpage index for the different deposition strategy. The warpage index was determined using normalized relative displacement by substrate thickness. The relative displacement was calculated at the corresponding position with respect to the minimum displacement. the temperature distribution, thermal gradient, and cooling rate, which will affect the generation of residual stress and warpage. Next, the effects of the deposition travel speed on thermal profiles are analyzed. FE simulations were carried out with four different travel speeds: 50 mm/min, 100 mm/min, 150 mm/min, and 200 mm/min. The alternate deposition pattern was chosen for the analysis. Figure 10 shows the temperature history for the different travel speeds. In the case of 50 mm/min, the peak temperature is 883 \({}^{\circ}\)C at t = 96 s, and the temperature drops below 300 \({}^{\circ}\)C at t = 126 s, which is 30 s after it reaches its peak value. Figure 10 also shows that the peak temperature decreases with an increase in travel speed. The peak temperature is calculated as T = 784 \({}^{\circ}\)C, T = 696 \({}^{\circ}\)C, and T = 662 \({}^{\circ}\)C for a travel speed of 100 mm/min, 150 mm/min, and 200 mm/min, respectively. The faster the travel speed that is applied, the less time is available for the heat accumulation, which leads to lower levels of peak temperature. In order to investigate the influence of travel speed on residual stress generation, the maximum Mises stress at the end of deposition is analyzed and plotted as a function of distance along the diagonal direction in the deposition layer as shown in Figure 4:1 The overall trend is that the generation of residual stress decreases with an increase in the travel speed. As noted above, the level of peak temperature and the amount of heat accumulation decrease with an increase in the travel speed. It is also worth noting that the rate of residual stress generation decreases as the travel speed increases. This is mainly because the thermal gradient decreases as the travel speed increases. Figure 4:2 Temperature profile for the different travel speeds of (**a**) 50 mm/min, (**b**) 100 mm/min (**c**) 150 mm/min, and (**d**) 200 mm/min.
## 4 Experimental Studies  
The proposed method was implemented using MATLAB version 2023b on a PC with an Intel i7-4710 MQ processor (Intel Corporation, Santa Clara, CA, USA), operating at 2.50 GHz, with 16 GB RAM and Windows 10 Home Premium (SP1) as the operating system. The performance of iaABC was initially evaluated on single-objective numerical optimization problems for statistical analysis. Subsequently, the method was applied to three-dimensional Wireless Sensor Networks (WSNs). The performance of iaABC for both CEC 2022 and WSNs was compared with ABC optimization algorithms (standard ABC and iABC) and modern algorithms such as the Adaptive Flower Pollination Algorithm (AFPA) [11], Composite Proactive Particles in Swarm Optimization (Co-PPSO) [39], Salp Swarm Algorithm (SSA) [40], and Sine Cosine Algorithm (SCA) [41]. The preferred methods for positioning in 3D WSNs are generally versions of PSO. In this context, we selected the Co-PPSO algorithm for comparison, as it has also successfully produced solutions for the CEC 2022 test suits. The strategy employed in our study is based on the approach outlined in reference [1]. Consequently, we chose the AFPA from the relevant study for comparison. Other methods highlighted in our research are recognized as state-of-the-art algorithms.
## 4 Discussion
### Strengths and Limitations  
Traditionally, the influence of environmental factors on users of different types of urban green spaces has been studied based on users' socio-demographic characteristics, often through surveys or interviews. However, this study takes a novel approach, integrating the social-ecological model with qualitative analysis based on social media data, to comprehensively investigate the social and physical factors affecting leisure experiences along urban river corridors. This innovative method has proven to be meaningful [141; 142], offering a fresh perspective on the subject. By categorizing and analyzing social media data thematically, this study delves into individuals' subjective experiences and perceptions. Utilizing extensive text data mining techniques, the research not only rapidly captures environmental conditions and recreational experience demands but also helps bridge the gap in public participation in landscape design processes. Ultimately, the study aims to facilitate the provision of higher-quality urban river corridor services that meet citizen expectations, promote urban sustainable development, and enhance the overall well-being of urban residents.  
While this study provides valuable insights, it also highlights the need for further research. Firstly, the study primarily focuses on Shijiazhuang, an inland Chinese city characterized by distinct seasons and limited water resources, raising concerns about the generalizability of the findings to other cities. Although some common attributes may apply across cultures based on evolutionary foundations, further research is necessary in different regions. Secondly, social media users are predominantly composed of young and middle-aged individuals and may not fully represent all demographic groups, excluding certain populations who do not use applications. According to statistics from 2022 on the age structure of Chinese internet users, individuals aged 60 and above account for only 11.3% [143], indicating that the majority of older adults do not use the internet. Consequently, relying solely on social media data may result in sample bias, capturing only a portion of the population's viewpoints and feedback. In future research and analysis, it is essential to carefully consider this limitation and integrate other data sources to ensure a more comprehensive and accurate understanding of different age groups, social backgrounds, and usage habits. Additionally, due to the diversity of big data, the data sources used in our study are limited. Therefore, future research should incorporate multiple data sources to assess and improve the accuracy of the results. Your potential contribution to this field is crucial in addressing these limitations and advancing our understanding of urban river corridors.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 3 Results
### Hyperparameter Study
#### 3.2.1 Influence of Model Structure and Input Presentation  
Two networks are investigated to predict the concentration field of the injected human hepatic artery. The first model uses the binary image presented injection location and the SDF presented vascular geometry as input, which, together, are fed into the single convolutional path in the coding part and then encoding and decoding are successively performed to generate a drug concentration distribution, as shown in Figure 4a. The network prediction, numerical simulation and relative error of the concentration field at t = 2.1 (t/T) are summarized in Figure 10 for the case of injection site at \(-\)0.25 R. The maximum error of the first model on the prediction of the drug concentration field is less than 6%. Although the model performed well, greater accuracy desired for clinical translation. As presented in Figure 4b, the second network has two paths of convolutional layers in the coding part, where the injection position and blood vessel geometry are separate inputs for encoding and then the extracted features, once stacked, are input to the decoding layers. Moreover, SDF is employed to represent both the injection site and the blood vessel geometry, as presented in Figure 3. Comparing the prediction result of the first model in Figure 10 and of the second model in Figure 9 in the previous section, one can see that Model 2 performs better than Model 1, the average concentration field prediction error being about 0.012% and 0.038%, respectively. The probable reason is that when the injection position and blood vessel geometry are superimposed into the coding layer, they easily interfere with each other and there are fewer weights that the model can learn, while when the two inputs are convolved separately, the model can obtain more effective and learnable information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **CNNs Training (s)** & **CNNs Prediction (s)** & **Numerical Simulation (s)** & **Grid Quantity** & **Speedup** \\ \hline Straight vessels & 1508 & 0.066 & 698 & 24,200 & 10,576 \\ \hline Single branch vessels & 2202 & 0.061 & 2075 & 39,600 & 34,016 \\ \hline Hepatic artery vessels & 2478 & 0.062 & 8242 & 143,730 & 132,935 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumption of the network model prediction and numerical simulation.
# Detection of Waste Containers Using Computer Vision
## 2 Related Work
### Convolutional Neural Networks  
The CNNs in the field of computer vision, regarding object detection, can be split in two types: region-proposal-based and regression/classification-based. Regression/classification-based CNNs have better-performing networks with respect to computational cost while maintaining a level of accuracy that is on par with other CNNs. For this reason, this section will be focused on the use of the regression CNN known as you only look once (YOLO) [20], mainly the last two iterations, which are considered to be state-of-the-art regression-based CNNs [6].  
The CNNs YOLOv2 [21] and YOLOv3 [22] were built over YOLO, the first instance of this object detection CNN. This section will be based on the YOLOv2 article [21], and will delineate its advantages versus other state-of-the-art CNNs with regard to the specific task of object recognition. The YOLOv3 changes are briefly stated at the end of the section.  
YOLO performs object detection and classification. In practice, it yields results by surrounding the detected object with a squared box and the predicted class. Regarding complexity, YOLO employs a single neural network to perform predictions of bounding boxes and class probability in one evaluation (hence the name _you only look once_), which makes it possible to achieve outstanding speed during recognition. Its concepts can be boiled down to three main ideas: (1) the unification of separate common components in object detection into one single neural network; (2) making use of features of an entire image to predict bounding boxes; and (3) the concurrent prediction of all bounding boxes for each class. The inner workings of YOLO will be explained without going deep into the details; this explanation is intended to give the reader a broad idea of its procedure.  
YOLO starts out by dividing an input image into a W \(\times\) W grid cell. Each cell is responsible for detecting an object if the object center falls into it. Then, each cell predicts B bounding boxes and confidence scores for the B predicted boxes. These scores indicate the certainty of an object being present in the box as well as how precise the model believes the box's prediction is. If there are no objects, the scores should evaluate to zero. Each cell also predicts C conditional probabilities (Class! Object). These probabilities are limited to a cell containing an object.  
YOLOv3 is the latest version of this CNN. It is not the primary focus of this study for two reasons: it has a deeper network architecture, and [22] states that this CNN performs worse when detecting medium-to-large objects. The first reason affects the training and detection phase; a deeper network means that a longer time is necessary for training, and with the current resources the process of training would take a long time to reach a good detection point. The second reason hinders the main goal of this paper, i.e., the detection of large objects.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
###### Abstract  
This work presents the theoretical design and finite element modeling of high-sensitivity microcantilevers for biosensing applications, integrating piezoelectric actuation with novel ultrananocrystalline diamond (UNCD) structures. Microcantilevers were designed based on projections to grow a multilayer metal/AlN/metal/UNCD stack on silicon substrates, optimized to detect adsorption of biomolecules on the surface of exposed UNCD microcantilevers at the picogram scale. A central design criterion was to match the microcantilever's eigenfrequency with the resonant frequency of the AlN-based piezoelectric actuator, enabling efficient dynamic excitation. The beam length was tuned to ensure a \(\geq\)2 kHz resonant frequency shift upon adsorption of 1 pg of mass distributed on the exposed surface of a UNCD-based microcantilever. Subsequently, a Gaussian distribution mass function with a variance of 5 \(\upmu\)m was implemented to evaluate the resonant frequency shift upon mass addition at a certain point on the microcantilever where a variation from 600 Hz to 100 Hz was observed when the mass distribution center was located at the tip of the microcantilever and the piezoelectric borderline, respectively. Both frequency and time domain analyses were performed to predict the resonance behavior, oscillation amplitude, and quality factor. To ensure the reliability of the simulations, the model was first validated using experimental results reported in the literature for an AlN/nanocrystalline diamond (NCD) microcantilever. The results confirmed that the AlN/UNCD architecture exhibits higher resonant frequencies and enhanced sensitivity compared to equivalent AlN/Si structures. The findings demonstrate that using a UNCD-based microcantilever not only improves biocompatibility but also significantly enhances the mechanical performance of the biosensor, offering a robust foundation for the development of next-generation MEMS-based biochemical detection platforms. The research reported here introduces a novel design methodology that integrates piezoelectric actuation with UNCD microcantilevers through eigenfrequency matching, enabling efficient picogram-scale mass detection. Unlike previous approaches, it combines actuator and cantilever optimization within a unified finite element framework, validated against experimental data published in the literature for similar piezo-actuated sensors using materials with inferior biocompatibility compared with the novel UNCD. The dual-domain simulation strategy offers accurate prediction of key performance metrics, establishing a robust and scalable path for next-generation MEMS biosensors.  
**Keywords:** microcantilever; ultrananocrystalline diamond (UNCD); aluminum nitride; 1 picogram; biosensor; quality factor; resonant frequency shift
## 5 Parametric Analysis
### Effect of Horizontal Distance between the Parallel Adit and Tunnel  
Figure 11 illustrates the relationship between the pressure head of the tunnel crown and horizontal distance \(S_{\rm h}\) with different parallel adit discharges. The ordinate represents the relative pressure head at the crown of the tunnel (\(H_{\rm tc}\) is the pressure head of the tunnel crown when the collaborative drainage system works, and \(H_{\rm tc}^{0}\) is the pressure head at the crown when the tunnel and parallel adit are all fully waterproofed). The horizontal distance \(S_{\rm h}\) is linearly positively correlated with the tunnel crown pressure head. For the three different parallel adit drainage conditions (\(Q_{\rm p}\) = 0.95 \(Q_{\rm p,max}\), \(Q_{\rm p}\) = 0.75 \(Q_{\rm p,max}\), \(Q_{\rm p}\) = 0.55 \(Q_{\rm p,max}\)), \(S_{\rm h}\) changes from 1.9 \(D_{\rm t}\) to 0.6 \(D_{\rm t}\), and the relative pressure head decreases from 0.47, 0.59, 0.70 to 0.38, 0.51, 0.64, respectively. The pressure head on the lining can be reduced to a certain extent by reducing \(S_{\rm h}\); however, considering the construction conditions and surrounding rock stability, the distance between the parallel adit and tunnel should be no less than the tunnel diameter.
## 2 Fundamental Concept  
The Riemann-Liouville and Caputo fractional derivatives, respectively, are defined as [1]  
\[\frac{d^{\alpha}f(t)}{dt^{\alpha}}=\frac{1}{\Gamma(1-\alpha)}\cdot\frac{d}{dt} \int_{0}^{t}f(\tau)\cdot(t-\tau)^{-\alpha}d\tau,\ \ \ (0\leq\alpha\leq 1) \tag{1}\]  
\[\frac{d^{\alpha}f(t)}{dt^{\alpha}}=\frac{1}{\Gamma(1-\alpha)}\int_{0}^{t}\frac {df(\tau)}{d\tau}(t-\tau)^{-\alpha}d\tau,\ \ \ (0\leq\alpha\leq 1) \tag{2}\]  
where \(f(t)\) is a given function; \(\Gamma(t)\) is the Gamma function; \(\alpha\) is the fractional order; \(t\) is the time; and \(\tau\) is a integral variable. The main problem of these two derivatives is that the kernel of power function is singular at the end point (i.e., \(\tau=t\)) of the interval. To avoid this problem, [PERSON] and [PERSON] recently proposed a new fractional derivative using a kernel of exponential function [27]. This derivative (namely Caputo-Fabrizio fractional derivative) is defined as [27, 28]  
\[\frac{d^{\alpha}f(t)}{dt^{\alpha}}=\frac{\omega(\alpha)}{1-\alpha}\int_{0}^{t }\frac{df(\tau)}{d\tau}e^{-\frac{t}{\alpha}(t-\tau)}d\tau,\ \ \ (0\leq\alpha\leq 1) \tag{3}\]where \(\omega(\alpha)\) is a normalized function and it can be reasonably given as \(\omega(\alpha)=1\), for detail see [28]. According to this definition, it is obvious that the new kernel has no singularity. For the case of \(\alpha\to 0\), [PERSON] and [PERSON] [27] state that Equation (3) reduces to  
\[\lim_{\alpha\to 0}\frac{d^{\alpha}f(t)}{dt^{\alpha}}=\lim_{\alpha\to 0}\frac{\omega( \alpha)}{1-\alpha}\int_{0}^{t}\frac{df(\tau)}{d\tau}e^{-\frac{\alpha}{1-\alpha }(t-\tau)}d\tau=f(t)-f(0) \tag{4}\]  
On the other hand, for the case of \(\alpha\to 1\), Equation (3) degenerates into  
\[\lim_{\alpha\to 1}\frac{d^{\alpha}f(t)}{dt^{\alpha}}=\lim_{\alpha\to 1}\frac{ \omega(\alpha)}{1-\alpha}\int_{0}^{t}\frac{df(\tau)}{d\tau}e^{-\frac{\alpha}{1 -\alpha}(t-\tau)}d\tau=\frac{df(t)}{dt} \tag{5}\]  
in addition  
\[\lim_{\alpha\to 1}\frac{1}{1-\alpha}e^{-\frac{1}{1-\alpha}(t-\tau)}=\delta(t-\tau) \tag{6}\]  
where \(\delta(t)\) is the Dirac delta function.  
Using the Caputo-Fabrizio operator, a fractional-derivative element can be defined as  
\[\sigma(t)=\eta\frac{d^{\alpha}\varepsilon(t)}{dt^{\alpha}}=\frac{E}{1-\alpha} \int_{0}^{t}\frac{d\varepsilon(\tau)}{d\tau}e^{-\frac{\alpha}{1-\alpha}(\frac {t-\tau}{\lambda})}d\tau,\quad(0\leq\alpha\leq 1) \tag{7}\]  
where \(\sigma(t)\), \(\varepsilon(t)\), \(E\) and \(\eta\) stand for the applied stress, corresponding strain, elastic modulus and viscosity coefficient of the fractional-derivative element, respectively; and \(\lambda=\eta/E\) is a creep time. It is clearly observed that this element will reduce to a linear elastic spring as \(\alpha=0\), whereas becomes an ideal Newtonian dashpot as \(\alpha=1\).
## 4 Analysis and Results
### Dynamic Analysis
#### 4.2.4 Case IV: Full Hand Model  
A series of virtual experiments on the Full Hand Model was additionally carried out. In these simulations, the carpal bones were entirely removed, and fixed supports were added at the location of the carpometacarpal joints, and the location of the wrist. The removal of these joints results in reduced mobility, hence representing a harsher scenario where the motion of the hand is constrained at that location. In comparison with the index finger model, the full hand model can absorb more kinetic energy from the end effector owing to its increased bone and tissue mass.  
Figure 11: Abduction: Maximum Principal Stress.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S\#** & **Robot Size** & **Hyperextension** & **Flexion** & **Abduction** \\ \hline
1 & UR3; Effective Mass: 3.6 kg & 1520 mm/s & 1360 mm/s & 1000 mm/s \\ \hline
2 & UR5; Effective Mass: 7.2 kg & 1300 mm/s & 1250 mm/s & 850 mm/s \\ \hline
3 & UR10; Effective Mass: 14.4 kg & 1200 mm/s & 1150 mm/s & 800 mm/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Robot Speed Limits.  
Figure 13 shows the hyperextension of the digital hand model under impact at 1.0 m/s velocity, by a robot with an effective mass of 14.4 kg (UR10 or similar). The shear elastic strain exceeded the pain threshold limit of 0.85 m/m at 16 ms. The maximum principal stresses developed in the index finger was 104 MPa at 32 ms, around the time where the strain energy of the hand reached its maximum value of 6.17 Joules, i.e., maximum energy is transferred from the robot system to the hand. Overall, the maximum principal stress was observed in the little finger at the position shown in Figure 13d. For impact with a robot with an effective mass of 7.2 kg (UR5 or similar), the peak principal stress in the index finger dropped to 75 MPa at 24 ms. The digital hand model was able to absorb the kinetic energy of the end effector with relative ease.  
Figure 12: (a) Maximum Principal Stress (MPa); (b) Maximum Shear Elastic Strain.  
Figure 13: Impact of UR10 end effector at velocity of 1 m/s under hyperextension. (a) Impact Geometry, (b) Energy Transfer, (c) Shear Elastic Strain, and (d) Principal Stress.  
Figure 14 shows the results for impact of the UR10 end effector at a velocity of 2.2 m/s under flexion. This represents a very harsh condition, with high velocity and high equivalent mass. The maximum principal stresses and shear elastic stress and strain were higher than the acceptable thresholds. The highest principal stress and equivalent (von-Mises) stress occurred at 21 ms from the start of impact, in the first metacarpal bone, in this scenario.