esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 3 DFT-Based LS Channel Estimation Methods
### Improvements in the Related Methods
#### 3.2.1 Methods Controlled with Noise Energy  
These type of methods compare a noise-based threshold level \(\alpha\) to the estimated energy of the particular impulse response coefficients \(\mathbf{h}_{C}\) in order to select the ones with energy amplitudes above the threshold. Consequently, the new impulse response can be described as follows:  
\[\hat{\mathbf{h}}_{N}=\mathbf{I}_{N\{\mathbb{D}\}}\hat{\mathbf{h}}_{C}, \tag{10}\]  
where  
\[\mathbf{I}_{N\{\mathbb{D}\}}(n,n)=\left\{\begin{array}{ll}1&\text{if}\quad n \in\mathbb{D}\\ 0&\text{otherwise}\end{array}\right., \tag{11}\]  
and  
\[\mathbb{D}=\Big{\{}n|n\in\mathbb{M}\wedge\Big{(}|\hat{\mathbf{h}}_{C}(n)|^{2} \geq\alpha\Big{)}\Big{\}}. \tag{12}\]  
In [14], the research on the optimal selection rule resulted in the following threshold value:  
\[\alpha=2\sigma_{W}^{2}, \tag{13}\]  
where  
\[\sigma_{W}^{2}=\frac{1}{N-M}\sum_{n\in\{0,1,...,N-1\}\backslash\mathbb{M}}| \hat{\mathbf{h}}_{LS}(n)|^{2} \tag{14}\]  
is the variance of the estimated noise component in the time domain.
###### Abstract  
This paper presents a study to evaluate the comparative positioning accuracy of Surface Vehicle (SV) using Dead Reckoning (DR), Geodetic Least-Squares Adjustment (GLSA), Geodetic Robust Adjustment (GRA), and External Kalman Filter (EKF) methods. This involved simulating the results of navigational measurements subject to errors (including gross errors) used to position the SV swimming along a given trajectory in the vicinity of three beacons. We showed an apparent increase in the SV positioning accuracy, from approximately 9 m of Root Mean Square (RMS) obtained by DR and GLSA methods, to approximately 2 m (RMS), achieved using GRA and EKF methods. We also showed that, by interchanging GRA and EKF methods, it is still possible to increase the positioning accuracy of the SV up to 1.14 m (RMS). However, such an interchange should occur after the experimentally determined limit of the mean error of the position coordinates estimated by the GRA method has been exceeded.  
navigation marks; data fusion; position accuracy; surface vehicle positioning; coastal navigation +
Footnote †: journal: 2023, 13, 2110. [[https://doi.org/10.3390/app13042110](https://doi.org/10.3390/app13042110)]([https://doi.org/10.3390/app13042110](https://doi.org/10.3390/app13042110))
## 5 The Proposed Protocol
### Registration Phase  
This procedure is carried out in a secure channel. \(P_{i}\) is allowed to register with \(S_{m}\) to become a legitimate service user. As shown in Figure 2, \(P_{i}\) and \(S_{m}\) perform the following steps for registration.  
_Step R1_: \(P_{i}\) enters the identity \(ID_{i}\), password \(PW_{i}\) and the biometrics \(B_{i}\). \(P_{i}\) selects a random number \(\sigma_{i}\) and computes \(A_{i,m}=h(ID_{i}||PW_{i}||h_{bio}(B_{i})||\sigma_{i})\). Next, \(P_{i}\) sends \(\{ID_{i},A_{i,m}\}\) to \(S_{m}\).  
_Step R2_: Upon receiving \(\{ID_{i},A_{i,m}\}\), \(S_{m}\) computes \(u_{m,i}=h(ID_{i}||prk_{m})\) and \(B_{m,i}=u_{m,i}\oplus A_{i,m}\). Next, \(S_{m}\) sends \(\{B_{m,i},puk_{m},ID_{S_{m}}\}\) to \(P_{i}\).  
_Step R3_: Upon receiving \(\{B_{m,i},puk_{m}\}\), \(P_{i}\) computes \(\varepsilon_{i}=\sigma_{i}\oplus h(ID_{i}||PW_{i}||h_{bio}(B_{i}))\) and \(V_{i}=h(h(ID_{i}||PW_{i}||\sigma_{i})||h_{bio}(B_{i}))\). Finally, \(P_{i}\) stores \(\{B_{m,i},puk_{m},ID_{S_{m}}\}\) and \(\{V_{i},\varepsilon_{i},A_{i,m}\}\) on the mobile device \(MD_{i}\) and the smart card \(SC_{i}\), respectively.
# CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
## 4 Experiments and Discussions
### Ablation Study  
We undertook additional experiments to better understand the characteristics of our method. Specifically, we tried to answer the following three questions: (**Q1**) Is our method efficient on a limited-sized dataset? (**Q2**) How efficient is our method with different transformation functions? (**Q3**) Is a TCN-based architecture the right choice for feature extraction in our method?  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Benchmark Datasets**} \\ \cline{2-10}  & **Power** & **UCR** & **ECG** & **2D-Gesture** & **PSM** & **SMD** & **MSL** & **SWaT** & **WADI** \\ \hline MAD-GAN [32] & 65.75 [5] & 91.65 [4] & 48.81 [5] & 42.47 [2] & 36.17 [5] & 91.50 [4] & 91.69 [4] & 77.00 [1] & 37.00 [1] \\ \hline DAGMM [22] & 65.72 [5] & 68.90 [4] & 82.35 [5] & 38.91 [2] & 80.08 [3] & 94.91 [4] & 84.82 [4] & 81.28 [4] & 14.12 [4] \\ \hline MSCRED [36] & 42.50 [5] & 69.76 [4] & 85.37 [5] & 60.17 [2] & 84.64 [5] & 84.14 [4] & 93.63 [4] & 80.72 [4] & 37.41 [4] \\ \hline CAE-M [28] & 95.52 [5] & 82.22 [4] & 62.32 [5] & 97.94 [5] & 72.04 [5] & 93.67 [4] & 87.33 [4] & 81.01 [4] & 41.17 [4] \\ \hline OmniAnomaly [30] & 58.71 [5] & 90.98 [4] & 78.21 [5] & 41.11 [2] & 80.83 [3] & 94.01 [4] & 87.65 [4] & 81.31 [4] & 42.60 [4] \\ \hline TranAD [43] & 49.96 [5] & 96.94 [4] & 33.33 [5] & 90.34 [5] & 73.86 [5] & 96.05 [1] & 94.94 [1] & 81.51 [1] & 49.51 [1] \\ \hline GDN [29] & 95.70 [5] & 81.58 [4] & 77.68 [5] & 91.68 [5] & 72.92 [5] & 83.42 [4] & **95.91** [4] & 81.00 [1] & 57.00 [1] \\ \hline Anomaly Transformer [42] & 96.06 [5] & 79.29 [5] & 94.47 [5] & 98.47 [5] & **97.89** [1] & 92.33 [1,6] & 93.59 [1,6] & **94.07** [1] & **69.24** \\ \hline MTAD-GAT [38] & 58.78 [5] & 87.61 [4] & 79.19 [5] & 88.87 [5] & 82.06 [5] & 86.83 [4] & 87.68 [4] & 81.01 [1] & 41.69 [4] \\ \hline USAD [39] & 95.70 [5] & 94.476 [4] & 62.33 [5] & 93.36 [5] & 73.86 [5] & 94.95 [4] & 88.22 [4] & 84.00 [1] & 42.96 [1] \\ \hline
**CL-TAD** & **98.02** & **97.57** & **97.00** & **99.93** & 95.74 & **99.62** & 95.38 & 82.53 & 39.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison on the benchmark datasets (Performance score: \(F_{1}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Training Option**} & \multicolumn{3}{c}{**Performance**} \\ \hline
**Version** & **Training Dataset** & **Testing Dataset** & **Precision** & **Recall** & \(F_{1}\) \\ \hline Version 1 & Normal\_v.0 & Attack\_v.1 & 74.00 & 92.76 & 82.32 \\ \hline Version 2 & Normal\_v.1 & Attack\_v.1 & 71.37 & 97.84 & 82.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of our model on the SWaT dataset.
## 2 Theoretical Background
### Decoupling of Equations of Motion
To realize the decoupling of the power equation and obtain the contribution value of each mode to the response, both Ansys and MSC Patran adopt the diagonalization and normalization of the mass matrix. Accordingly, the modal, mass, and stiffness matrices are shown in Equations (9)-(11):  
\[z_{n}=\frac{1}{\sqrt{m}}\left[ \begin{array}{ccc}\frac{1}{\sqrt{3}}&\frac{1 }{\sqrt{2}}&\frac{1}{\sqrt{6}}\\ \frac{1}{\sqrt{3}}&0&-\frac{2}{\sqrt{6}}\\ \frac{1}{\sqrt{3}}&\frac{-1}{\sqrt{2}}&\frac{1}{\sqrt{6}}\end{array} \right] \tag{9}\]  
\[m_{n}=z_{n}^{T}mz_{n}=\left[ \begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array} \right] \tag{10}\]  
Figure 2: A three-degree-of-freedom system. \[\mathbf{k}_{n}=\mathbf{z}_{n}^{T}\mathbf{k}\mathbf{z}_{n}=\left[ \begin{array}{ccc}0&0&0\\ 0&\frac{k}{m}&0\\ 0&0&\frac{3k}{m}\end{array} \right] \tag{11}\]
## 4 State-of-the-Art Review
### Biological Sources  
[PERSON] and his co-author state that, adding to hydrogen being the fuel of the future mainly due to its high conversion efficiency, recyclability, and nonpolluting nature, biological hydrogen production processes (mostly controlled by either photosynthetic or fermentative organisms) are more environment friendly and less energy intensive as compared to thermochemical and electrochemical processes [123]. They concluded that the rate of fermentative hydrogen production is always faster than that of the photosynthetic production. They found that most of the biological processes are operated at ambient temperature and pressure, so rgwt are not energy-intensive processes.  
[PERSON] et al. defend biohydrogen as having the potential to replace current hydrogen production technologies heavily relying on fossil fuels [124]. In their study, it was found that attaining technical and economic efficiencies is the main drive behind employing co-cultures of pure bacteria in fermentative hydrogen production. These are the ones with the best performance at generation rates, although it is essential to first determine specific optimal operational conditions. Additionally, as seen in the previous study, side products of biohydrogen can be useful too; biodiesel wastes, oil industry wastewaters, and microalgal biomass have significant potential as sustainable feed stocks.  
However, the usefulness and practical application of biohydrogen to everyday energy problems is still unclear, according to [PERSON] et al. [125]. By first standardizing the units of hydrogen production, the authors intended to calculate the size of biohydrogen systems that would be required to power proton-exchange-membrane fuel cells of various sizes.  
They undoubtedly concluded that biohydrogen technologies are still in their infancy, and if they are to become commercially competitive with steam reforming and electrolysis, they must be able to synthesize hydrogen at rates that are sufficient to power reasonably sized fuel cells. So, further research and development aimed at increasing the rates of synthesis, optimizing bioreactor designs to rapidly remove and purify side-product gases, genetically modify enzyme pathways, and increasing overall final yields of hydrogen productionareis essential.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### Visual Navigation Algorithms for Grasping Targets
#### 4.2.2 Target Grasping Analysis  
By integrating the depth camera and laser ranging data, the UAV's and target object's coordinate information is achieved. Combined with the UAV's own pose data, the current three-dimensional spatial position of the target object in the world coordinate system is obtained. Figure 7 presents the target object grasping scenario in the aerial manipulator system, including the world coordinate system W, camera coordinate system C, and target coordinate system. Hereafter, we describe the process of transforming the target's coordinates from the camera coordinate system to the world coordinate system.  
Figure 6: Principle of laser ranging based on visual guidance.  
Figure 7: Coordinate system of the target grasping scene.  
First, the camera's attitude can be obtained from the UAV's onboard inertial navigation system. The obtained camera attitude quaternion can be denoted as \(\text{q}=w+xi+y+zk\), and its corresponding rotation matrix can be represented as [39]:  
\[R=\begin{bmatrix}1-2y^{2}-2z^{2}&2 xy+2 wz&2 xz-2 wy\\ 2 xy-2 wz&1-2x^{2}-2z^{2}&2 yz+2 wx\\ 2 xz+2 wy&2 yz-2 wx&1-2x^{2}-2y^{2}\end{bmatrix} \tag{7}\]  
Assume that the coordinates of the target object in the world coordinate system and camera coordinate system are represented as \(\text{a}_{w}=(x_{w},y_{w},z_{w})^{T}\) and \(\text{a}_{c}=(x_{c},y_{c},z_{c})^{T}\), respectively. \(T\) denotes the translation matrix between the world coordinate system and the camera coordinate system, obtained using the camera coordinates provided by the UAV's inertial navigation system. Therefore, our algorithm ultimately calculates the coordinates of the target object in the world coordinate system as follows:  
\[\text{a}_{w}=R^{-1}a_{c}+T \tag{8}\]
## 2 The Formulation of Multi-Area Economic Dispatch  
### The Traditional MAED Formulation  
The aim of MAED is to minimize the total production cost while satisfying various physical and operational constraints [10]. In this section, we employ a two-area economic dispatch formulation to illustrate the MAED problem.  
The objective function \(F\), which represents total generator fuel cost of two areas, can be written as  
\[\begin{array}{rl}\min&F=\sum\
olimits_{n=1}^{2}\sum\
olimits_{n=1}^{N_{n}}f _{mn}\left(P_{mn}\right)\\ &=\sum\
olimits_{n=1}^{2}\sum\
olimits_{n=1}^{N_{n}}\left(a_{mn}P_{mn}^{2}+b_ {mn}P_{mn}+c_{mn}\right)\end{array} \tag{1}\]  
where \(P_{mn}\) is the real power output of the \(n\)th generating unit in area \(m\). \(f_{mn}(P_{mn})\) is the fuel cost function of the \(n\)th generating unit in area \(m\) and is usually expressed as a quadratic function without considering valve-point effect, the corresponding cost coefficients for \(f_{mn}(P_{mn})\) are given as \(a_{mn}\), \(b_{mn}\), \(c_{mn}\). \(N_{m}\) represents there are \(N_{m}\) generating units in area \(m\). In addition, the objective function is minimized and subjected to the following physical constraints [11,12].  
1. Real power balance constraints without considering transmission loss _Algorithms_**2015**, \(8\)  
\[\sum_{n=1}^{N_{1}}P_{1n}=P_{1D}+P_{L} \tag{2}\]  
\[\sum_{n=1}^{N_{2}}P_{2n}=P_{2D}-P_{L} \tag{3}\]  
where \(P_{mD}\) is the total demand in area \(m\); \(P_{L}\) is the tie-line real power transfer between area 1 and area 2. \(P_{L}\) is positive when power flow transfers from area 1 to area 2. By contrast, \(P_{L}\) is negative when power flow transfers from area 2 to area 1.  
2)  Tie-line capacity constraints  
\[\left|P_{L}\right|<P_{1,\max} \tag{4}\]  
where \(P_{L,max}\) is the maximum tie-line power flow between area 1 and area 2.  
3)  Real power generation capacity constraints  
\[P_{m,min}<P_{m}<P_{m,max} \tag{5}\]  
where \(P_{m,min}\) and \(P_{m,max}\) are the minimum and maximum generation limits of the nth generating unit \(n\) in area \(m\).  
_2.2. Area-based Decentralization for MAED_  
[PERSON] proposed a coarse-grained distributed optimal power flow method in [13]. Inspired by this idea, we add a \"dummy bus \(X\)\" between area1 and area 2, the corresponding variable for this \"dummy bus\" is tie-line power flow \(P_{L}\). By duplicating the \"dummy bus\" as \(X1\) and \(X2\), we get two separated systems as shown in Fig. 1. Meanwhile, \(P_{L}\) is duplicated as \(P_{1L}\) and \(P_{2L}\), _i.e._, a new consistency constraint \(P_{1L}-P_{2L}=0\) is introduced. Then the equivalent form of traditional MAED problem can be expressed as follows.  
\[\begin{array}{l}\min\ F=\sum\
olimits_{n=-1}^{2}\sum\
olimits_{n=1}^{N_{n} }f_{mn}\left(P_{m}\right)\\ \text{\emph{subject to}}:\ \ \sum\
olimits_{n=1}^{N_{1}}P_{1n}=P_{1D}+P_{1L}\\ \
# Peridynamics for Fracture Analysis of Reflective Cracks in Semi-Rigid Base Asphalt Pavement
## 2. Peridynamics  
### 2.1. Peridynamics Theory  
PD theory assumes that an object occupies a space domain R, and at a certain moment t, there is an interaction between any material point x in the space and other material points x\({}^{\prime}\) within a certain radius of the surrounding space, which is called the integral domain, also the horizon. Such interaction is called a bond, that is, the force between points is transferred through the bond, as illustrated in Figure 1. If the interaction force is f, the motion state of a material point is the result of the joint action of the external force and all the material points within the range of its space domain. Then,  
\[\rho\ddot{\mathrm{u}}(\mathrm{x})=\int_{\mathrm{H_{x}}}f\big{(}\mathrm{x},\, \mathrm{x}^{\prime},\,\mathrm{u}(\mathrm{x},\,\mathrm{t}),\mathrm{u}\big{(} \mathrm{x}^{\prime},\mathrm{t}\big{)}\big{)}\mathrm{d}\mathrm{V}+\mathrm{b}( \mathrm{x},\,\mathrm{t}) \tag{1}\]  
where \(\rho\) is the mass density, \(\mathrm{u}\) is the displacement vector, \(\ddot{\mathrm{u}}\) is the acceleration, \(\mathrm{b}\) is the external applied force density, and \(\mathrm{H_{x}}\) is the horizon or the interaction domain of a material point located at position x, which can be expressed by the following formula:  
\[\mathrm{H_{x}}=\mathrm{H}(\mathrm{x},\,\delta)=\big{\{}\mathrm{x}^{\prime}- \mathrm{x}\,\leq\,\delta\big{|}\mathrm{x}^{\prime}\in\delta\big{\}} \tag{2}\]  
where \(\delta\) is the horizon region.  
Figure 1. Interaction between material points.  
The relative position vector of the PD point is:  
\[\xi=x^{\prime}-x \tag{3}\]  
The relative displacement vector in the deformed configuration at time t is:  
\[\eta=u\left(x^{\prime},\;t\right)-u\left(x,\;t\right) \tag{4}\]  
Then, the expression of the force between PD points can be simplified as:  
\[f=f\left(x,\;x^{\prime},\;u\left(x,\;t\right),u\left(x^{\prime},\;t\right),t \right)=f\left(\eta,\;\xi\right) \tag{5}\]  
For linear elastic materials, f is the derivative of the microelastic strain density \(\omega\) with respect to the relative displacement vector \(\eta\), that is:  
\[f(\eta,\xi)=\frac{\partial\omega\left(\eta,\;\xi\right)}{\partial\eta}\;\forall \eta,\;\omega \tag{6}\]  
The microscopic elastic strain energy of the bond is:  
\[\omega\left(\eta,\;\xi\right)=\frac{cs^{2}}{2}|\xi| \tag{7}\]  
where the micromodulus parameter c is the stiffness of the bond, \(\left|\xi\right|\) denotes the magnitude of vector \(\xi\), and the scalar bond stretch is defined as:  
\[s=\frac{\left|\eta+\xi\right|-\left|\xi\right|}{\left|\xi\right|} \tag{8}\]  
Therefore, the constitutive function of the PD model can be obtained:  
\[f=\left\{\begin{array}{c}cs\mu\frac{\xi+\eta}{\left|\xi+\eta\right|}\;, \left|\xi\right|\;\leq\;\delta\\ 0,\;\left|\xi\right|>\;\delta\end{array}\right. \tag{9}\]  
where c is the micromodulus and \(\mu\) is the characteristic function of the bonding state. \(\left|\xi+\eta\right|\) and \(\left|\xi\right|\) are the deformed length and initial length of the bond, respectively.
# An Automatic Detection Method for Cutting Path of Chips in Wafer
## 2 Materials and Methods  
### Wafer Cutting Principle  
Wafer cutting refers to using a cutter or laser to separate the chips arranged on the wafer and finally form a separate chip. With the iterative upgrade of production technology, to improve the utilization rate and production efficiency of wafers, reduce the spacing between chips, improve the arrangement density of chips, or reduce the size of chips, the chip size can reach hundreds of microns or even smaller, and larger-sized chips are on the millimeter level. As chips become smaller, streets become narrower, and chip spacing becomes closer, the possibility of chip damage during cutting becomes greater, so it is more important to accurately locate streets and provide the appropriate cutting path. The wafer-cutting principle is shown in Figure 1. Figure 1a shows the wafer model, the rectangular blocks arranged on the surface are chips, and the wafer street is defined as the red shaded area in the amplification area.  
The wafer dicing machine is shown in Figure 1b. The dicing machine is composed of a rotation console, a microscopic vision system, a transparent stage, a ring light source, a laser scriber, an \(x\), \(y\) translation stage, and a computer. The microscopic vision system is composed of a charge-coupled device (CCD) camera, an automatic zoom lens, and a large magnification metallographic objective lens. For chips of different sizes, the chips within the field of vision can be adjusted to an appropriate size by combining objectives of  
Figure 1: Wafer cutting principle. (**a**) Wafer model; (**b**) the composition of the wafer dicing machine.  
different magnifications and changing the magnification of the automatic zoom lens. The magnification of the microscopic vision system can be adjusted between 10 and 200 times.  
According to the cutting principle of the dicing machine, the wafer is placed upside down on the stage, the microscopic vision system is adjusted to the appropriate magnification according to the size of the chip in the wafer, the captured wafer image is transmitted to the computer, and the computer calculates the wafer position and attitude. The DD motor adjusts the wafer position and attitude according to the calculation results. Calculation of the cutting path starts after the wafer has been aligned. During the processing, the image coordinate system needs to be converted to the spatial coordinate system. The \(x,y\) translation stage drives the laser to the designated position of the wafer for cutting.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 3 Result and Discussion
### Methylene Blue Adsorption
#### 3.2.5 Desorption Studies  
pH\({}_{\text{ZFC}}\) tests show that the adsorption of MB with YL-M-L-AC at acidic pH is not favorable, therefore, the HNO\({}_{3}\) solution was checked as one of theuents. Moreover, MB is an ethanol-soluble organic dye, which can be a good desorbing agent for the regeneration of the biocomposite. Water as an eluent was tested for its ability to remove MB from YL-M-L-AC due to the various mechanisms of dye binding with the biocomposite. The desorption capacity of MB on YL-M-L-AC in five cycles is shown in Figure 11. The best results for the degree of desorption are shown by the solution of nitric acid (V), achieving desorption in the range of 89.8-84.3%, ethanol, 78.5-64.1%, and deionized water, 2.12-1.85%. The multiple usability can be justified by the physical nature of the interactions between the material and MB. Lowering the pH causes protonation of the adsorbent surface, which leads to a reduction in the electrostatic forces attracting the positively charged MB. This, in turn, facilitates the diffusion of the dye from the adsorption sites into the solution [39]. YL-M-L-AC can be considered an effective adsorbent for methylene blue dye removal because it can be repeatedly regenerated and used for adsorption, slightly losing its adsorption efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{T}\) [\({}^{\circ}\mathbf{C}\)] & \(\mathbf{q_{e}}\) [mg/g] & \(\mathbf{K_{ML}}\) & \(\Delta\mathbf{G}^{\circ}\) [k/mol] & \(\Delta\mathbf{H}^{\circ}\) [k/mol] & \(\Delta\mathbf{S}^{\circ}\) [J/molK] \\ \hline
25 & 32.10 & 914.63 & \(-\)16.894 & & \\ \hline
30 & 26.02 & 719.04 & \(-\)16.566 & & \\ \hline
35 & 23.67 & 564.12 & \(-\)16.238 & & \\ \hline
40 & 23.64 & 453.15 & \(-\)15.910 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of thermodynamics for MB adsorption on YL-M-L-AC, C\({}_{0}\) = 50–250 mg/dm\({}^{3}\), t = 24 h.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Real-Time Processing
#### 5.3.2 Existing Solutions  
Regarding edge computing, there are two practical approaches. The first involves performing AI reasoning tasks on the edge while conducting AI training tasks in the cloud. The second approach is to perform part or all of the AI training and reasoning tasks at the edge [109]. Integrating edge computing with artificial intelligence enables efficient gathering, storage, and processing of IoT data, facilitating rapid real-time analysis and decision making. This synergy empowers decentralized, low-latency, and reliable application services [110]. Although edge computing already significantly reduces latency, some models can further optimize it. For example, Smart-Edge-CoCaCo is proposed to minimize latency by jointly optimizing the wireless communication model, the collaborative filter caching model, and the computing offloading model [109]. In practical applications, Smart-Edge-CoCaCo can be used in smart city traffic management systems, where it analyzes traffic data in real time to optimize traffic signals, thus reducing congestion and improving traffic flow efficiency. Regarding fog computing, while its transmission delay may be higher than that of edge computing, its storage and computing capacities are superior [111]. Existing fog models have limitations, often prioritizing either precision or faster response times, but not both. Some research projects propose architectures to address this problem, such as integrating quartet DL with edge computing devices [112]. For instance, in intelligent medical monitoring systems, quartet deep learning models can process large volumes of physiological data, such as heart rate and blood pressure, at the fog computing layer, providing accurate health monitoring and early warning while ensuring quick response times.