esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Analysis and Calibration Improvement of the Imaging Section in a Mueller Matrix Microscope
## 3 Application and Discussion
### General Applicability
The _t3_ values of the pixels at the edge in Figure 10a are larger than those in Figure 10b; on the other hand, the pixels corresponding to the Daphnia also have large errors, and the birefringence features at these pixels are smaller than the errors caused by the imaging section, thereby leading to an erroneous judgment of the birefringence of the sample. For instance, the values in the red dotted box approximately range from 0.15 to 0.23 in Figure 10c and from 0.29 to 0.47 in Figure 10d, and the values of the orange dotted box area approximately range from 0.15 to 0.36 in Figure 10e but from 0.03 to 0.21 in Figure 10f. The comparison results shown in Figure 10 are similar to those obtained for the air and quarter-wave plates, i.e., when the birefringence of the sample is weak, the calculated value of _t3_ is larger than its theoretical value, but _t3_ is smaller than its theoretical value for the sample with strong birefringence. Then, the polarization features of Daphnia were recovered, as shown in Figure 10b; this is unlike the results presented in Figure 10a, where the experimental results were severely distorted by the birefringence of the imaging section. Thus, the proposed method possesses considerable advantages in terms of birefringence and birefringence. Figure 10: Measurement results of _t3_ for the Daphnia sample: (**a**,**c**,**e**) after calibrating the polarizing section with the conventional method; (**b**,**d**,**f**) after calibrating both the polarizing and imaging sections with the proposed method. (**c**,**e**) correspond to enlarged views of the red and orange dotted boxes in (**a**), respectively; (**d**,**f**) correspond to enlarged views of the red and orange dotted boxes in (**b**), respectively. Figure 9: Micrograph of the Daphnia sample. improving the estimation accuracy of the birefringence caused by the internal structures of the Daphnia and retaining more details of the hierarchical structure of the tissue.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments
### Implementation Details  
The experimental environment is Intel(R)CoreTMig \(-\) 9900K [EMAIL_ADDRESS] GHz, running memory 16 G, Ubuntu 18.04, 64-bit operating system. We use CUDA10.1(NVIDIA Corporation, Santa Clara, CA, USA), CuDNN 7.6.5 (NVIDIA Corporation, Santa Clara, CA, USA), python 3.7. We performed network training on an NVIDIA GEFORCE RTX2080 Ti GPU. We trained our network with the open-source deep learning framework PyTorch. We used the Adam optimizer to update the weights of the neural network iteratively. The initial learning rate is set to 0.01 for network training. Other training parameters are shown in Table 3 below:  
The SEM rate represents the scaling parameter of the SEM fully connected layer. The High-ASPP rate and Low-ASPP rate represent the dilation rate of parallel ASPP modules. The output stride represents the output stride of the encoder structure. We maintained consistent and comparable hardware and software parameters in each training and testing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline learning rate & 0.01 & SEM rate & 16 \\ epoch & 200 & batch size & 2 \\ High-ASPP rate & [1, 6, 12, 18] & Low-ASPP rate & [12, 24, 36] \\ momentum & 0.9 & weight decay & 0.0005 \\ output stride & 8 & crop size & \(512\times 512\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters.  
Figure 10: Diagram of hard examples in the dataset. Spacecraft objects are marked with red bounding boxes.  
experiment. To focus on comparing network performance and exploring the impact of data on the network from experiments, we did not use data augmentation to expand our dataset further. Figure 11 shows the loss decline of our SISnet and our baseline method during training. It can be seen that, as the number of iterations increases, loss decreases rapidly. In the middle and later stages of training, our network loss fluctuates less and tends to stabilize more quickly.
## 4 The Improved GA
### The Problem Description  
We assume that there are \(N\)-\(1\) CHNs and \(1\) SN when implementing the GA to search for the optimal paths. The SN is the destination node. The CHN that needs to transmit data becomes the source node. The relay node is chosen from CHNs. Let \(x_{ij}\), \(c_{ij}\), \(d_{ij}\), and \(l_{ij}\) denote the link indicator, the link energy cost, the link delay, and the link length between node \(i\) and node \(j\), respectively. \(T_{ti}\) is the time duration for the node \(i\) to transmit packets and \(T_{rj}\) is the time duration for the node \(j\) to receive packets. \(D_{tmax}\) presents the maximum delay of the path. The value of \(x_{ij}\) is \(1\) when a link exists between node \(i\) and node \(j\). Otherwise, the value of \(x_{ij}\) is \(0\). We regard the search process of multi-hop paths as a combinatorial optimization problem, finding the optimal path with the minimum cost. The objective function is given by:  
\[\text{minimize}:\ F_{obj}=\sum_{i=1}^{N}\sum_{j=1}^{N}c_{ij}x_{ij} \tag{6}\]  
\[\text{where }c_{ij}=T_{ti}P_{0}A(l_{ij})+T_{rj}P_{0} \tag{7}\]  
\[\text{subject to}:\ \sum_{i=1}^{N}\sum_{j=1}^{N}d_{ij}x_{ij}<D_{tmax} \tag{8}\]  
The constraint Equation (8) makes sure that the total transmission delay is limited to a certain value so that it will not be too high.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.22 Steering Mode Selector Switch  
The ship is steered by the Track Control System or Heading Control System in auto mode. Steering modes are divided into three types, namely \"Auto\", \"Non-Follow Up (NFU)\", and \"Follow Up (FU)\". Both NFU and FU may be called manual steering modes [79]. Whereas the NFU is an open-loop control mode, the FU is a closed-loop control mode [79]. In the NFU mode, the rudder steers whilst the steering lever is held to the starboard side or port side of the vessel and remains at its current angle when the steering lever is released [80]. In the FU mode, the rudder of the vessel steers according to the given command by the helmsman [79]. The steering mode selector switch provides the transition among such modes.
## 3 Results  
### The Mdm2-inhibitory Peptide (MIP) Does Not Elevate p53 Levels in HCT116 Cells  
We started by testing the Mdm2-inhibitory peptide MIP (PRFWEYWLRLME), which was selected _in vitro_ from large libraries of random peptides using mRNA display [37]. MIP binds Mdm2 and also MdmX with higher affinity than other known peptides, such as DI [41]. We cloned the MIP peptide into a freely accessible loop of _Escherichia coli_ thioredoxin, a small and stable protein commonly used as scaffold for presenting and stabilizing peptides. mCherry was added to the construct to consent easy determination of transfection efficiency.  
As a control, we cloned the 3A peptide (LTAEHYAAQATS) in which three key hydrophobic residues of the DI peptide are mutated to alanine, thereby abrogating binding to Mdm2 or MdmX.  
To test the effect of the peptide we selected HCT116 cells, a human colon cancer cell line with wild type p53, which has been shown to accumulate upon inhibition of Mdm2 [42]. Expression of Trx-MIP-mCherry did not lead to changes in p53 protein levels, which remained similar to those in mock and Trx-3A-mCherry transfected samples (Supplementary Figure S1). This is in contrast with previously published results [37]. The raw data used to quantify protein levels are found in the Supplementary Materials (Figures S2-S5).
## 3 ANN Drag Model and Training Strategy
### The Optimal Contribution Rate Algorithm (OCRA)
#### 3.2.1 Contribution Rate Analysis Algorithm  
We assume that the input samples in the training model of ANN are represented by:  
\[\mathbf{X}=(\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{n})=\begin{pmatrix}x_{11}&x_{12}& \cdots&x_{1n}\\ x_{21}&x_{22}&\cdots&x_{2n}\\ \cdots&\cdots&\cdots&\cdots\\ x_{n1}&x_{n2}&\cdots&x_{nn}\end{pmatrix}, \tag{14}\]  
where \(\mathbf{x}_{j}\) denotes the \(j\)-th input sample  
\[\mathbf{x}_{j}=(x_{1,j},x_{2,j},\cdots,x_{n,j})^{\mathrm{T}}, \tag{15}\]  
where \(x_{i,j}\) represents the \(i\)-th index of the \(j\)-th input sample. The process of obtaining the data contribution rate and the cumulative contribution rate of training samples is divided into four steps:  
1. Considering the logarithmic characteristics of drag distribution in the polydisperse gas-solid system (see Table 2) and the following contribution rate analysis, in which the input data are required by principal component analysis to maintain a standardized and linear property [58; 59], we propose a kernel function to process the input samples as follows: \[X^{\prime}_{ij}=\exp\biggl{\{}\frac{X_{ij}-M_{i}}{S_{i}}\biggr{\}}.\] (16) In the above formula, \(X^{\prime}_{ij}\) is the \(j\)-th sample of the \(i\)-th index after that the original matrix \(X_{ij}\) was standardized and linearized. \(M_{i}\) is the statistical average of the \(i\)-th sample, and \(S_{i}\) is the standard deviation of the \(i\)-th sample.
2. To explore the independence between training samples, we need to construct the covariance matrix of the standardized sample set [57] as: \[\mathbf{R}=\mathrm{cov}(\mathbf{x}^{{}^{\prime}}_{i},\mathbf{x}^{{}^{\prime}}_{j})= \begin{pmatrix}r_{11}&r_{12}&\cdots&r_{1n}\\ r_{21}&r_{22}&\cdots&r_{2n}\\ \cdots&\cdots&\cdots&\cdots\\ r_{m1}&r_{m2}&\cdots&r_{mn}\end{pmatrix}.\] (17)* Perform similarity transformations on the covariance matrix \(R\) and calculate the eigenvalues of the covariance matrix to demonstrate the property of independence for each training sample [60] \[\mathbf{P}^{-1}\mathbf{R}\mathbf{P}=\mathbf{\Lambda},\] (18) where \[\mathbf{\Lambda}=\begin{pmatrix}\lambda_{1}&&&\\ &\lambda_{2}&&\\ &&\ddots&\\ &&&\lambda_{n}\end{pmatrix}\] (19) is a diagonal matrix with the eigenvalues \(\lambda_{1},\lambda_{2},\cdots,\lambda_{n}\) of matrix \(R\).
* Finally, the contribution rate and the cumulative contribution rate of the training sample and training sample set will be calculated by \[C_{r}=\frac{\lambda_{k}}{\sum\limits_{k=1}^{n}\lambda_{k}},\] (20) \[C_{rc}=\sum\limits_{k=1}^{n}\frac{\lambda_{k}}{\sum\limits_{k=1}^{n}\lambda_{ k}}.\] (21)  
Through the above four steps, we can obtain the contribution rate and the cumulative contribution rate of the training sample and training sample set.
## 2 Problem Statement
### Vehicle Dynamics and Platoon Control Objective  
Within our framework, the longitudinal dynamics of the vehicles are denoted as  
\[\left\{\begin{array}{l}\dot{r}_{i}(t)=v_{i}(t)\\ \dot{v}_{i}(t)=a_{i}(t)\\ \dot{a}_{i}(t)=-\frac{1}{T}a_{i}(t)+\frac{1}{T}u_{i}(t)\end{array}\right. \tag{6}\]  
where \(r_{i}(t)\), \(v_{i}(t)\), \(a_{i}(t)\) are the position, velocity, and acceleration of the \(i\)-th vehicle, respectively. \(T>0\) is the time constant of the drivetrain. The propelling force \(u_{i}\) represents the control input.  
The longitudinal dynamics of the \(i\)-th vehicle can also be written in the state-space expression:  
\[\dot{x}_{i}(t)=Ax_{i}(t)+Bu_{i}(t) \tag{7}\]where \(x_{i}(t)=[r_{i}(t),\;v_{i}(t),\;a_{i}(t)]^{\rm T}\) and  
\[A=\begin{bmatrix}0&1&0\\ 0&0&1\\ 0&0&-\frac{1}{T}\end{bmatrix},\;\;B=\begin{bmatrix}0\\ 0\\ \frac{1}{T}\end{bmatrix}\]  
It is assumed that both the sensing delay and communication delay exist in the vehicular platoon. The objective of the platoon control under PF topology can be described as:  
\[\left\{\begin{array}{l}r_{i}(t-\tau_{1})\to r_{i-1}(t-\tau_{1})-d-hv_{i}(t- \tau_{1})\\ v_{i}(t-\tau_{1})\to v_{i-1}(t-\tau_{1})\\ a_{i}(t-\tau_{2})\to a_{i-1}(t-\tau_{2})\end{array}\right. \tag{8}\]  
where \(\tau_{1},\tau_{2}\) are the sensing delay and communication delay, respectively. Our proposed delay structure is based on a realistic scenario in which the position and velocity can be sensed by the neighboring vehicles, while the acceleration is communicated through V2V communication. Then, these two different ways of acquiring information introduce two different delays [30]. To focus on deriving the ETDMs, we assume that \(\tau_{1},\tau_{2}\) are both constant. In reality, one may use a buffer memory to ensure that the sensing delay \(\tau_{1}\) and communication delay \(\tau_{2}\) are further delayed intentionally (if required) to be constant. Therein, we choose the CTH policy as the inter-vehicle spacing policy. \(d\) and \(h\) are the desired standstill spacing and time headway between two successive vehicles, respectively.
# A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
## 1 Introduction
The results show an increased correlation between simulated surface soil moisture and in situ observations [19]. Recent studies have demonstrated that data assimilation techniques have great potential for time series reconstruction. [PERSON] implemented a Kalman filter approach to apply temporal constraints on the retrieval of surface emissivity and temperature from radiance measurements, and the results show that the Kalman filter strategy can retrieve temperature simultaneously with an accuracy of \(\pm\)0.2 K [20]. [PERSON] proposed a new fusion strategy for generating high-quality, all-weather LST data based on cumulative distribution function (CDF) matching and multiresolution Kalman filtering (MKF) [21]. [PERSON] reconstructed hypothetical clear sky LST with missing or possibly cloud-contaminated pixels by assimilating high-quality satellite retrieval data into a time-evolution model built from reanalysis data through a Kalman filter data assimilation algorithm [22]. However, regarding the current reconstruction of land SEB parameters, the most prominent problem is the lack of systematic analysis and assessment methods for the reconstruction effect, including analyses of the \"true value\" determination and scale effects [23; 24; 25; 26]. This study selects land surface temperature, one of the land surface energy parameters, as the object;addresses adoptability problems common to current remote-sensing inversion methods and the instability in the data accuracy; and uses the Kalman filter for data assimilation to take land surface in situ data continuously, improve the forecast accuracy, and ensure that the reconstructed land surface temperature series are more similar to the in situ data. We also extend this technique to the entire study area using the derived linear regression equation, thereby obtaining a complete time series of the land surface temperature in the study area, which can be used to obtain the refined long-term land surface temperature at larger scales.
## 7 Simulation Results Discussion: Analysis and Evaluation
### Key Metrics Analyzed  
Our study conducted a comprehensive analysis of key factors to enhance the understanding of vehicular communication networks in rural areas:  
* Vehicle Count (CV): We meticulously recorded the total number of vehicles at each second during the simulation.
* Vehicles with Pre-Signature (CPV): Special emphasis was placed on scenarios in which vehicles, upon encountering an RSU, received an updated RV or were accessible through overnight connectivity.
* Total Number of Communications (TC): Indicates the total number of V2V communications.
* Reputable Communications (RC): We focused on'reputable communications', where vehicles with an updated RV successfully sent a message.
* Online Reputable Communications (ONRC): The extent of online communication availability was evaluated, signifying instances where vehicles with an RV communicated within the RSU's range. Here, reputation could be accessed via the RSU, meaning that--while the communication is reputable--our scheme was not necessary to accomplish this.
* Offline Reputable Communications (ORC): A pivotal element of our research was 'offline reputable communications,' referring to the exchange of RVs between vehicles located outside the RSU's range. These represent communications where a valid up-to-date reputation is available thanks to our scheme, where it otherwise would not be.  
Figure 12: V2V Reputable Communication: Within RSU Range.  
Figure 13: Offline V2V Reputable Communication: Outside RSU Range.
## 3 Reliability Modelling and Evaluation
### Reliability Modelling and Evaluation under Triggering Mechanism 1
,\]  
where \(\mathbf{P}_{1_{(\gamma-1)\times(\gamma-1)}}\) is a transition probability matrix between each transient state, \(\mathbf{Q}_{1_{(\gamma-1)\times 1}}\) is the column matrix from each transient state to the absorption state, \(\mathbf{0}_{1_{1\times(\gamma-1)}}\) is a zero matrix, which is the transition probability matrix from the absorption state to transient state, and \(\mathbf{E}_{1_{1\times 1}}\) is an identity matrix representing the probability from the absorption state to the absorption state. Let \(N_{1}\) denote the total number of shocks before the system failure. We can derive some probabilistic indices related to the shock numbers \(N_{1}\) by the following equations. The distribution function of the shock numbers \(N_{1}\) is  
\[P\{N_{1}\leq l\}=\sum\limits_{j=1}^{l}\mathbf{\alpha}_{1}\mathbf{P}_{1}^{j-1} \mathbf{e^{\prime}}_{1_{\gamma-1}}=1-\mathbf{\alpha}_{1}\mathbf{P}_{1}^{l}\mathbf{ e^{\prime}}_{1_{\gamma-1}}, \tag{1}\]  
where \(\mathbf{\alpha}_{1}=(1,0,\dots,0)_{1\times(\gamma-1)}\), and \(\mathbf{e}_{1_{\gamma-1}}=(1,\dots,1)_{1\times(\gamma-1)}\). The probability mass function of the shock numbers \(N_{1}\) is  
\[P\{N_{1}=l\}=\mathbf{\alpha}_{1}\mathbf{P}_{1}^{l-1}\mathbf{Q}_{1}. \tag{2}\]  
The expected number of shocks \(E(N_{1})\) is  
\[E(N_{1})=\mathbf{\alpha}_{1}(\mathbf{I}_{1}-\mathbf{P}_{1})^{-1}\mathbf{e^{\prime }}_{1_{\gamma-1}}. \tag{3}\]  
We can derive the probability density function of \(T\) when \(N_{1}=n\):  
\[f_{1}(t)= \begin{cases}\lambda^{n}t^{n-1}\exp(-\lambda t)/(n-1)!,&t\geq 0\\ 0,&t<0.\end{cases}  \tag{4}\]  
In addition, we can obtain the probability density function of \(T\) by using the Law of Total Probability:  
\[f_{1}(t)= \begin{cases}\sum\limits_{l=1}^{\infty}\Bigl{[}\left(\lambda^{l}t^{ l-1}\exp(-\lambda t)/(l-1)!\right)\times P\{N_{1}=l\}\Bigr{]},&t\geq 0\\ 0,&t<0.\end{cases}  \tag{5}\]  
The expected system life \(E(T_{1})\) is  
\[E(T_{1})=\int\limits_{0}^{\infty}tf_{1}(t)dt \tag{6}\]  
Then, we can show the reliability function of the system as  
\[ \begin{array}{rl}R_{1}(x)&=P\{T>x\}\\ &=P\left\{\sum\limits_{i=1}^{N_{1}}X_{i}>x\right\}\\ &=1-\int_{0}^{x}\sum\limits_{l=1}^{\infty}\Bigl{[}\left(\lambda^{l}t^{l-1}\exp (-\lambda t)/(l-1)!\right)\times P\{N_{1}=l\}\Bigr{]}dt.\end{array}  \tag{7}\]
# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 4 Data Preparation
### Cleaning Discrepant Data  
In order to facilitate the training process, discrepant samples were identified and removed using the quantiles method. Samples which are beyond the \(Q_{1}-3\times std\) or \(Q_{3}+3\times std\) are replaced by the mean value. The extreme values were replaced with the average. Figure 3 shows the same variables after discrepant data samples were removed.  
As the figure shows, the lines are now smoother and easier to read. Figure 4 shows that the samples are evenly distributed after the withdrawal of discrepant data.  
The predictive models to be used are robust and tolerant to noise. However, the cleaner data are expected to show better results. As an example, a provisional experiment to train  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **C. Intensity** & **Hydraulic** & **Torque** & **VAT** & **Velocity** & **Temperature** \\ \hline mean & 30.26 & 75.90 & 15.28 & 18.25 & 4.59 & 38.22 \\ std & 1.36 & 4.54 & 0.69 & 2.67 & 0.98 & 1.62 \\ min & 26.34 & 62.93 & 13.59 & 9.67 & 1.27 & 33.19 \\ Q1—25\% & 29.30 & 72.86 & 14.90 & 17.13 & 3.92 & 37.17 \\ Q2—50\% & 30.46 & 75.53 & 15.43 & 18.72 & 4.57 & 38.33 \\ Q3—75\% & 31.28 & 79.52 & 15.78 & 19.97 & 5.28 & 39.35 \\ max & 34.26 & 88.97 & 17.09 & 26.17 & 7.87 & 43.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical parameters of the dataset variables, before processing: C. intensity, hydraulic unit oil level, torque, VAT pressure, velocity, and temperature.  
Figure 4: Distribution of data points of all the sensors, with lowly and highly discrepant data cleaned. oil level, torque, VAT pressure, velocity, and temperature.  
Figure 3: Plot of the dataset values after cleaning discrepant data. The variables are current intensity, hydraulic unit oil level, VAT pressure, velocity, temperature, and torque.  
a neural network LSTM model with a historical window of 70 samples and 40 LSTM unit cells showed higher and undetermined errors. The model was not able to learn or predict some variables, as shown in Table 2. With clean data, there were better and determinable results, as shown in Table 3. The tables show the MAPE and MAE for all input variables, as determined in the test set. They also show the RMSE, as calculated in the train and test sets, globally for all variables.
## 4 Simulation Results
### Noise Tolerance Analysis  
Until this point, the background noise radiance had been set to a constant value that corresponded to limited noise contribution. In this sub-section, we shall discuss the effect of the background noise in the key rates when assuming different satellite orbits. Since our analysis takes into consideration only night-time conditions, we varied the background  
Figure 6: End-to-end link loss and normalized SKR over time for the different i-QBER values of (**a**) \(D_{rx}=1\) m and SNSPD with SMF coupling, (**b**) \(D_{rx}=1\) m and SPAD with SMF coupling, and (**c**) \(D_{rx}=1\) m and SPAD without SMF coupling.  
Figure 7: End-to-end link loss and normalized SKR over time for \(D_{rx}=1\) m and SNSPD with SMF coupling.  
noise radiance to correspond to four distinct scenarios, namely clear night, new moon, full moon, and urban night.
## 2 Theoretical Formulation  
The formulation proposed by two different standards, ISO 14136 and VDI 2081-1, is used in this work in order to compare them with each other as well as with the numerical and experimental results. In this section, the authors introduce the general equations specified in both standards regarding the calculation of the pressure loss (for more detailed information about this formulation, we urge the reader to consult the original references).