esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment Setup
### Deep Echo State Network (DeepESN) Offline Training and Compensation  
Figure 1 also presents the configuration of the DeepESN. To address the impact of noise introduced by UWOC channels, DeepESN offline training and compensation (DeepESN processing) were directly applied to the output signal from the oscilloscope. Digital data were first converted to the training echo states ((\(\mathbf{X}(t)\in\mathbb{R}^{UL\times 1}\)) and testing echo states (\(\overline{\mathbf{X}}(t)\in\mathbb{R}^{UL\times 1}\)) at the time instance of \(t=1,\ldots,T\) for training states or \(t=T+1,\ldots,M\) for testing states. Here, \(U\) is the number of reservoir units in each reservoir layer, \(L\) is the number of reservoir layers, \(T\) is the length of the training states, and \(M\) is the total length of the states.  
For the \(i^{th}\) (\(i=1,\ldots,L\)) reservoir layer, the matrix product of the input weight matrix (\(\mathbf{W}^{(1)}\in\mathbb{R}^{U\times 1}\) or \(\mathbf{W}^{(i)}\in\mathbb{R}^{U\times U}\)) and training states (\(\mathbf{u}(t)\in\mathbb{R}^{1\times 1}\) or \(\mathbf{x}^{(i)}(t)\in\mathbb{R}^{U\times 1}\)) is fed into each reservoir layer. The echo states are calculated by [38]:  
1. For the first reservoir layer (\(i=1\)): \[\begin{split}\mathbf{x}^{(1)}(t)&=\mathbf{D}\Big{(}\mathbf{u} (t),\mathbf{x}^{(1)}(t-1)\Big{)}\\ &=\Big{(}1-a^{(1)}\Big{)}\mathbf{x}^{(1)}(t-1)+a^{(1)}\mathbf{\tanh} \Big{(}\mathbf{W}^{(1)}\mathbf{u}(t)+\hat{\mathbf{W}}^{(1)}\mathbf{x}^{(1)}(t-1)\Big{)}\end{split}\] (1)
2. For the rest of reservoir layers (\(i>1\)): \[\begin{split}\mathbf{x}^{(i)}(t)&=\mathbf{D}\Big{(}\mathbf{x} ^{(i-1)}(t),\mathbf{x}^{(i)}(t-1)\Big{)}\\ &=\Big{(}1-a^{(i)}\Big{)}\mathbf{x}^{(i)}(t-1)+a^{(i)}\mathbf{\tanh} \Big{(}\mathbf{W}^{(i)}\mathbf{x}^{(i-1)}(t)+\hat{\mathbf{W}}^{(i)}\mathbf{x}^{(i)}(t-1)\Big{)} \end{split}\] (2)  
where \(\mathbf{D}\) represents the echo states calculation function, \(\mathbf{\tanh}\) signifies the element-wise application of the hyperbolic tangent, and \(\hat{\mathbf{W}}^{(i)}\in\mathbb{R}^{U\times U}\) denotes the recurrent weight matrix. The parameter \(a^{(i)}\) refers to the leaky rate updating speed of the weight matrix in the reservoir layer and is related to the speed of reservoir dynamics in response to the input [40]. The output weight matrix \(\mathbf{W_{out}}\in\mathbb{R}^{1\times UL}\) is obtained using the singular value decomposition (SVD) method. Equation (3) calculates the testing results utilizing the trained weight matrix [38].  
\[y(t)=\mathbf{W_{out}}\overline{\mathbf{X}}(t) \tag{3}\]  
In the experiment, the DeepESN has 10 reservoir units in each layer and two layers in total. This is one of the optimized setups of the DeepESN in UWOC systems, which will be discussed in the later sections.
# Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach
## 4 Results and Discussion
### Comparison between the proposed system and existing works
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\textit{Ord}=8\)} & \multicolumn{3}{c}{\(\textit{Ord}=10\)} & \multicolumn{3}{c}{\(\textit{Ord}=12\)} \\ \cline{2-10} \multirow{2}{*}{**Class ID**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Time**} \\  & \multicolumn{1}{c}{**(Trad)**} & \multicolumn{1}{c}{**(Ours)**} & & \multicolumn{1}{c}{**(Trad)**} & \multicolumn{1}{c}{**(Ours)**} & & \multicolumn{1}{c}{**(Trad)**} & \multicolumn{1}{c}{**(Ours)**} & \\ \hline  
\hline Average & 27.70 & 12.535 & 54.7 & 29.03 & 13.11 & 54.8 & 30.06 & 14.00 & 53.4 \\ \hline Avg & & & & & & & & & \\ for 3D & 0.061 & 0.028 & 54.7 & 0.064 & 0.029 & 54.8 & 0.066 & 0.031 & 53.4 \\ object 1 & & & & & & & & & \\ \hline \hline \end{tabular}  
* 1 Avg per 3D object represents the average computation time for one 3D object. It is computed by summing all the computation time and dividing it by the number of objects (454)—the test is performed on the original dataset without augmentation.  
\end{table} 
Table 7: Computation time (**in seconds**) comparison between the proposed approach (Our) and traditional method (Trad).
###### Abstract  
Monitoring of rice planting areas plays an important role in maintaining food security. With powerful automatic feature extraction capability, crop mapping based on deep learning methods has become one of the most important research directions of crop remote sensing recognition. However, the training of deep learning models often requires a large number of samples, which restricts the application of these models in areas with a lack of samples. To address this problem, based on time-series Sentinel-1 SAR data, this study pre-trained the temporal feature-based segmentation (TFBS) model with an attention mechanism (atTFBS) using abundant samples from the United States and then performed an inter-continental transfer of the pre-trained model based on a very small number of samples to obtain rice maps in areas with a lack of samples. The results showed that an inter-continental transferred rice mapping model was feasible to achieve accurate rice maps in Northeast China (F-score, kappa coefficient, recall, and precision were 0.8502, 0.8439, 0.8345, and 0.8669, respectively). The study found that the transferred model exhibited a strong spatiotemporal generalization capability, achieving high accuracy in rice mapping in the three main rice-producing regions of Northeast China. The phenological differences of rice significantly affected the generalization capability of the transferred model, particularly the significant differences in transplanting periods, which could have resulted in a decrease in the generalization capability of the model. Furthermore, the study found that the model transferred based on an extremely limited number of samples could attain a rice recognition accuracy equivalent to that of the model trained from scratch with a substantial number of samples, indicating that the proposed method possessed strong practicality, which could dramatically reduce the sample requirements for crop mapping based on deep learning models, thereby decreasing costs, increasing efficiency, and facilitating large-scale crop mapping in areas with limited samples.  
deep learning; rice mapping; generalization ability; inter-continental transfer; Sentinel-1; temporal feature-based segmentation 2023152443[[https://doi.org/10.3390/rs15092443](https://doi.org/10.3390/rs15092443)]([https://doi.org/10.3390/rs15092443](https://doi.org/10.3390/rs15092443))
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## Modeling
### Analysis and Assessment of Braking Distance
#### 3.1.4 Braking Distance  
1. Analysis of braking deceleration process of the following car  
The driver of the following car completes the whole braking process after receiving the braking signal. According to the above braking process analysis, in the whole braking process, the actual distance traveled by the following car includes four periods: the driver's reaction time \(t_{1}\), the brake braking coordination time \(t_{2}\), the braking force growth time t\({}_{3}\) and the continuous braking time t\({}_{4}\).  
As the brakes do not have an effect during t\({}_{1}\) and t\({}_{2}\), the distance (\(D_{B1}\)) traveled by car B in times \(t_{1}\) and \(t_{2}\) is  
\[D_{B1}=V_{B}\times(t_{1}+t_{2}) \tag{1}\]  
The distance traveled by car B in time \(t_{3}\) is  
\[D_{B2}=\int_{0}^{t_{3}}{(V_{B}-\frac{j_{\rm Bmax}}{2t_{3}}t^{2})dt}=V_{B}t_{3} -\frac{j_{\rm Bmax}}{6}t_{3}^{2} \tag{2}\]  
Meanwhile, at the end of time \(t_{3}\), the speed \(V_{3}\) of car \(B\) is as follows.  
\[V_{3}=V_{B}-\frac{j_{\rm Bmax}}{2}t_{3} \tag{3}\]  
The distance traveled by car B during \(t_{4}\) is  
\[D_{B3}=\frac{V_{3}^{2}-{V_{B}^{\prime}}^{2}}{2j_{\rm Bmax}}=\frac{V_{B}^{2}-{V _{B}^{\prime}}^{2}}{2j_{\rm Bmax}}-\frac{V_{B}t_{3}}{2}+\frac{1}{8}j_{\rm Bmax} t_{3}^{2} \tag{4}\]  
The total braking distance of car B is as follows:  
\[D_{B}=D_{B1}+D_{B2}+D_{B3}=V_{B}(t_{1}+t_{2}+\frac{t_{3}}{2})+\frac{V_{B}^{2}- {V_{B}^{\prime}}^{2}}{2j_{\rm Bmax}}-\frac{j_{\rm Bmax}}{24}t_{3}^{2} \tag{5}\]
2. Braking distance analysis of the front car  
Theoretically, the front car should also undergo the same deceleration process as the following car. Therefore, in theory, the braking distance \({D^{\prime}}_{A}\) of the front car is similar to that of the following car.  
\[{D^{\prime}}_{A}=D_{A1}+D_{A2}+D_{A3}=V_{A}(t_{1}+t_{2}+\frac{t_{3}}{2})+\frac {V_{A}^{2}-{V_{A}^{\prime}}^{2}}{2j_{\rm Amax}}-\frac{j_{\rm Amax}}{24}t_{3}^{2} \tag{6}\]  
During actual driving, the driver of car B is sure that the front car has implemented braking after the brake lights of car A come on. Therefore, the distance covered by car A during the braking process should only be the distance driven by the car in the braking force growth stage and the continuous braking stage. Therefore, the braking distance \(D_{A}\) of car A during \(t_{2}\) and \(t_{3}\) can be obtained as follows:  
\[D_{A}=D_{A2}+D_{A3}=V_{A}\frac{t_{3}}{2}+\frac{V_{A}^{2}-{V_{A}^{\prime}}^{2} }{2j_{\rm Amax}}-\frac{j_{\rm Amax}}{24}t_{3}^{2} \tag{7}\]
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 3 Analysis of ATM Big Data Sharing
### Foundation of Aviation Big Data Sharing
#### 3.2.1 Data Classification and Participants  
ICAO has systematically standardized global ATM data-sharing requirements through documents including Annex 11 \"Air Traffic Services\" and Doc 4444 \"Procedures for Air Navigation Services--Air Traffic Management\" (abbreviated as PANS-ATM) to ensure flight safety, efficient airspace utilization, and cross-regional coordination.  
From the perspective of air traffic management operations, the main participants in data sharing include air traffic service providers (such as air traffic control centers and regional control units), airlines and operators (providing flight plans and dynamic data sharing), airport operators (providing takeoff/landing slots and ground operation data), aeronautical information service providers (supplying aeronautical charts and airspace restriction information), airspace users, meteorological service providers (offering real-timeweather and wind shear data), military aviation departments (for airspace coordination and temporary restricted area information), civil aviation regulatory authorities (conducting safety audits and operational compliance data), and international organizations (such as ICAO, Eurocontrol, and IATA).  
Regarding shared content, this paper classifies seven categories of shared data based on application scenarios and functions: core operational data, airspace and traffic flow management data, communication-navigation-surveillance data, aeronautical information data, meteorological data, emergency data, and support data. Note that the aviation big data referred to in this paper specifically pertains to ATM data.
# Solution for the Mathematical Modeling and Future Prediction of the COVID-19 Pandemic Dynamics
## 3 Types of Mathematical Models Used
### Transfer Function Model
In order for the system to be practically feasible, the following conditions: \(n\geq m_{1}\), \(n\geq m_{2}\), \(n\geq m_{3}\), \(n\geq m_{4}\) and \(n\geq m_{5}\) have to be simultaneously accomplished. By applying to (4) the Laplace transform for zero initial conditions, the following equation results:  
\[ \begin{array}{ll}\left(\sum\limits_{i=0}^{n}a_{i}\cdot s^{i}\right)\cdot Y(s)& =\left(\sum\limits_{j=0}^{m_{1}}b_{1j}\cdot s^{j}\right)\cdot X_{1}(s)+ \left(\sum\limits_{k=0}^{m_{2}}b_{2k}\cdot s^{k}\right)\cdot X_{2}(s)+\left( \sum\limits_{p=0}^{m_{3}}b_{3p}\cdot s^{p}\right)\cdot X_{3}(s)+\left(\sum \limits_{c=0}^{m_{4}}b_{4c}\cdot s^{c}\right)\cdot X_{4}(s)\\ &+\left(\sum\limits_{c=0}^{m_{5}}b_{5c}\cdot s^{c}\right)\cdot X_{5}(s),\end{array} \] (5 In (6), the following notations are made:  
\[H_{1}(s)=\frac{\sum_{j=0}^{m_{1}}b_{1j}\cdot s^{j}}{\sum_{i=0}^{n}a_{i}\cdot s^{i}}, \tag{7}\]  
\[H_{2}(s)=\frac{\sum_{k=0}^{m_{2}}b_{2k}\cdot s^{k}}{\sum_{i=0}^{n}a_{i}\cdot s^{i }}, \tag{8}\]  
\[H_{3}(s)=\frac{\sum_{p=0}^{m_{3}}b_{3p}\cdot s^{p}}{\sum_{i=0}^{n}a_{i}\cdot s^{i }}, \tag{9}\]  
\[H_{4}(s)=\frac{\sum_{k=0}^{m_{4}}b_{4k}\cdot s^{c}}{\sum_{i=0}^{n}a_{i}\cdot s^{ i}}, \tag{10}\]  
\[H_{5}(s)=\frac{\sum_{p=0}^{m_{5}}b_{5e}\cdot s^{e}}{\sum_{i=0}^{n}a_{i}\cdot s^{ i}}, \tag{11}\]  
The \(H_{t}(s)\) (\(f\in\{1,2,3,4,5\}\)) notation is referring to the transfer function which mathematically connects the \(X_{t}(s)\) input signal with the \(Y(s)\) output signal. Considering these notations, (6) can be rewritten under the form:  
\[Y(s)=H_{1}(s)\cdot X_{1}(s)+H_{2}(s)\cdot X_{2}(s)+H_{3}(s)\cdot X_{3}(s)+H_{ 4}(s)\cdot X_{4}(s)+H_{5}(s)\cdot X_{5}(s), \tag{12}\]  
Furthermore, (12) can be rewritten in the vector form, as:  
\[Y = \mathcal{H}\cdot\mathcal{X}, \tag{13}\]  
where the output vector, is:  
\[Y = Y(s), \tag{14}\]  
and the input vector, is:  
\[\mathcal{X}= \begin{pmatrix}X_{1}(s)\\ X_{2}(s)\\ X_{3}(s)\\ X_{4}(s)\\ X_{5}(s)\end{pmatrix}  \tag{15}\]  
In (13),  
\[\mathcal{H}= \begin{pmatrix}H_{1}(s)&H_{2}(s)&H_{3}(s)&H_{4}(s)&H_{5}(s)\end{pmatrix}  \tag{16}\]  
represents the transfer vector of the MISO model, more exactly, the mathematical model of the considered process.
## 3 Methods and Data Sources
### Regression Model Building and Testing
#### 3.2.3 Kriging Testing  
The Kriging interpolation method is a spatial interpolation technique designed to estimate the attribute values of any point in space based on known discrete data points. In this study, we employed the Kriging interpolation method to develop a predictive model for waste generation. According to research by [PERSON] et al. [19], the Kriging interpolation method demonstrated a more accurate representation in modeling the distribution of chemical contaminants, such as antimony (Sb), in soil. Consequently, after obtaining the predictive model for waste generation, we applied Kriging spatial interpolation to assess the accuracy of our model and identify any significant prediction errors. Via this approach, we are able to gain deeper insights into the spatial distribution characteristics of waste generation and provide reliable data support for subsequent waste management decisions.
# Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
## 4 Experiments
### Numerical Experiments of DHS
#### 4.1.2 Sensitivity Analysis of Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & & _par = 0.1_ & _par = 0.3_ & _par = 0.5_ & _par = 0.7_ & _par = 0.9_ \\ \hline \multirow{2}{*}{Ackley} & Mean & \(3.04\times 10^{-15}\) & \(1.54\times 10^{-13}\) & \(5.74\times 10^{-10}\) & \(5.64\times 10^{-9}\) & \(3.02\times 10^{-8}\) \\  & Stdv & \(5.02\times 10^{-16}\) & \(6.46\times 10^{-14}\) & \(1.21\times 10^{-9}\) & \(4.21\times 10^{-9}\) & \(4.19\times 10^{-8}\) \\ \hline \multirow{2}{*}{[PERSON]} & Mean & \(1.03\times 10^{-3}\) & \(2.23\times 10^{-9}\) & \(3.36\times 10^{-6}\) & \(5.70\times 10^{-5}\) & \(1.11\times 10^{-4}\) \\  & Stdv & \(3.37\times 10^{-3}\) & \(1.35\times 10^{-8}\) & \(1.06\times 10^{-5}\) & \(1.83\times 10^{-4}\) & \(2.98\times 10^{-4}\) \\ \hline \multirow{2}{*}{[PERSON]} & Mean & \(0\) & \(1.64\times 10^{-7}\) & \(1.88\times 10^{-5}\) & \(8.43\times 10^{-2}\) & \(4.59\times 10^{-1}\) \\  & Stdv & \(0\) & \(1.16\times 10^{-6}\) & \(9.86\times 10^{-5}\) & \(2.67\times 10^{-1}\) & \(6.11\times 10^{-1}\) \\ \hline \multirow{2}{*}{Rosenbrock} & Mean & \(3.36\times 10^{-1}\) & \(1.05\) & \(1.14\) & \(1.89\) & \(2.18\) \\  & Stdv & \(3.88\times 10^{-1}\) & \(1.17\) & \(1.35\) & \(1.45\) & \(1.54\) \\ \hline \multirow{2}{*}{Sphere} & Mean & \(1.03\times 10^{-40}\) & \(1.82\times 10^{-28}\) & \(1.92\times 10^{-21}\) & \(2.81\times 10^{-19}\) & \(7.43\times 10^{-19}\) \\  & Stdv & \(1.08\times 10^{-40}\) & \(1.59\times 10^{-28}\) & \(4.61\times 10^{-21}\) & \(3.63\times 10^{-19}\) & \(2.10\times 10^{-18}\) \\ \hline \multirow{2}{*}{Schwefel} & Mean & \(7.58\times 10^{-21}\) & \(2.85\times 10^{-12}\) & \(2.21\times 10^{-8}\) & \(6.46\times 10^{-8}\) & \(7.54\times 10^{-8}\) \\  & Stdv & \(5.31\times 10^{-21}\) & \(1.62\times 10^{-12}\) & \(1.07\times 10^{-8}\) & \(2.73\times 10^{-8}\) & \(3.71\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The effect of _par_ on the means and standard deviations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & & _fw_ = 0.001_ & _fw_ = 0.004 & _fw_ = 0.007 & _fw_ = 0.01 \\ \hline \multirow{2}{*}{[PERSON]} & Mean & \(3.11\times 10^{-15}\) & \(2.97\times 10^{-15}\) & \(3.04\times 10^{-15}\) & \(2.82\times 10^{-15}\) \\  & Stdv & \(0\) & \(7.03\times 10^{-16}\) & \(5.02\times 10^{-16}\) & \(9.74\times 10^{-16}\) \\ \hline \multirow{2}{*}{[PERSON]} & Mean & \(2.24\times 10^{-3}\) & \(2.41\times 10^{-3}\) & \(1.24\times 10^{-3}\) & \(2.95\times 10^{-3}\) \\  & Stdv & \(4.77\times 10^{-3}\) & \(5.48\times 10^{-3}\) & \(3.73\times 10^{-3}\) & \(5.86\times 10^{-3}\) \\ \hline \multirow{2}{*}{[PERSON]} & Mean & \(1.90\times 10^{-5}\) & \(0\) & \(0\) & \(0\) \\  & Stdv & \(1.34\times 10^{-4}\) & \(0\) & \(0\) & \(0\) \\ \hline \multirow{2}{*}{Rosenbrock} & Mean & \(4.10\times 10^{-1}\) & \(3.37\times 10^{-1}\) & \(4.42\times 10^{-1}\) & \(3.03\times 10^{-1}\) \\  & Stdv & \(6.34\times 10^{-1}\) & \(5.74\times 10^{-1}\) & \(8.63\times 10^{-1}\) & \(4.94\times 10^{-1}\) \\ \hline
# Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values
## 2 Base Action Set Generation Algorithm Focusing on Undiscovered Sensor Values
### Discard of Extracted Action Fragments  
The length of used \(I_{i}\) is denoted as \(u_{i}\) and the length of the contributed parts of \(I_{i}\) to find the undiscovered sensor values is denoted as \(v_{i}\) among all the generated actuator output signals. The parts of \(I_{i}\) that contributed to finding the undiscovered sensor values are the parts of \(I_{i}\) between time \(t-l_{u}\) to time \(t\), where the new sensor value is found at time \(t\). Therefore, \(v_{i}\) is equal to the summation length of the contained parts of action fragment \(I_{i}\) among all the generated action fragments.  
Here, we introduce a mechanism that evaluates each action fragment \(I_{i}\) to identify and discard any fragment that has not contributed to finding new sensor values. We define discard criterion \(w_{i}\) for action fragment \(I_{i}\) as  
\[w_{i}=\frac{v_{i}}{u_{i}} \tag{18}\]  
Action fragment \(I_{i}\) that satisfies \(w_{i}<w\) is discarded from extracted action fragment set \(U_{I}\), where constant \(w\) is a discard criterion threshold.  
The meaning of this operation is explained as follows. Discard criterion \(w_{i}\) can be transformed as Equation (19)  
\[w_{i}=\frac{v_{i}}{u_{i}}=\frac{\frac{v_{i}}{l^{\prime}}}{\frac{u_{i}}{l^{ \prime}}}=\frac{P(\mathbf{I_{i}}\cap A)}{P(\mathbf{I_{i}})}=P(A|\mathbf{I_{i}}), \tag{19}\]  
where \(l^{\prime}\) is a length of the whole generated actuator output signals. These probabilistic formulations have the following meanings.  
* \(P(\mathbf{I_{i}})\) : probability to use action fragment \(\mathbf{I_{i}}\) in a process of actuator output signal generation.
* \(P(\mathbf{I_{i}}\cap A)\) : probability that a robot both uses \(\mathbf{I_{i}}\) in a process of actuator output signal generation and finds undiscovered sensor values.
* \(P(A|\mathbf{I_{i}})\) : probability that a robot finds undiscovered sensor values when it uses action fragment \(\mathbf{I_{i}}\) in a process of actuator output signal generation.  
Hence, \(w_{i}\) expresses the probability that a robot finds undiscovered sensor values when it uses action fragment \(\mathbf{I_{i}}\) in a process of actuator output signal generation. This operation discards actions fragments when the probability \(P(A|\mathbf{I_{i}})\) is below the discard criteria threshold \(w\).  
A flowchart of the proposed algorithm is shown in Figure 3.  
Figure 2: Motion extraction and motion generation. (**a**) Method to extract effective motion for finding undiscovered sensor values; (**b**) Motion generation using extracted effective motion.  
Validation of Base Action Set Generation Process Using the Proposed Algorithm through Experiments with a Differential Wheeled Robot
# Reality Check: Experimental Studies on Microplastics Lack Realism
## 2 Shape (Spheres vs. Fibers)
### Human Effects  
In humans, relevant MP shapes will depend on exposure source (i.e., air, water, food), as well as how particles degrade over time in the environment and in the body. One of the earliest signs of MPs in human tissues came in 1998, when plastic microfibers were identified in biopsies of human lung tissue [18]. This study indicated two things: humans are exposed to MPs from the environment, and these MPs become embedded in our tissue. This study also importantly brought to our attention the relevance of plastic microfibers, as compared to more commonly studied spherical MPs. A more recent study built upon this work, showing that a combination of plastic particles and fibers were present in 13 of 20 human lung tissue samples examined [19].  
Although inhaling airborne MPs is a major route of exposure, MPs also enter through ingestion and can become embedded in other organs. In a screening of 11 human colon samples, 96.1% of embedded particles were plastic filaments or fibers [20]. Furthermore, as was widely publicized, MPs were recently identified in human placentas, with a total of 12 irregularly shaped MP fragments being found in 4 placenta samples [21].  
Interestingly, in a characterization of excreted MPs in human stool samples, mostly fragments and films were identified [22]. As few plastic microfibers were found in human stool samples and fibers are a major class of environmental MPs, one can infer that plastic microfibers that enter our bodies are becoming embedded in our tissues. This conclusion is in line with the aforementioned data.  
Through experimental studies, it is known that ingested MP spheres (5 \(\upmu\)m) hamper metabolism and gut microbial function. Furthermore, these spheres accumulate in the gut [23]. Spherical MPs have also been used in new in vitro models and organoid cultures of the human intestines. Similar to mammalian models, these systems show varied effects, ranging from no significant cytotoxicity to high levels of cytotoxicity and a strong immune response [24]. The advantage of these in vitro approaches is that they have the potential to more closely resemble how humans might respond to MP exposure. However, the lack of available evidence surrounding how both mammalian models and in vitro systems respond to plastic microfibers as compared to spherical MPs leaves much to be desired.  
A more recent studied began to explore this question, studying how MPs of varied shape relates to cytotoxicity in human cell lines. They found that MPs with rigid, sharp, or irregular edges appeared to be more harmful than those with smooth, round edges [25]. Future studies should find which shapes of MPs are most prevalent in various tissues, and then test the impacts of MPs with those same shapes in mammalian models or in vitro cultures for the corresponding cell types.