esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 3 The Principle of Vector Observation Based on Position Loci
### Position Integration Formula  
Based on the principles of inertial navigation, the position and the velocity under the navigation frame are shown as:  
\[\mathrm{p}^{n}=\mathrm{R}_{c}v^{n} \tag{9}\]  
where \(\mathrm{p}^{n}=\begin{bmatrix}L&\lambda&h\end{bmatrix}^{\mathrm{T}}\); \(L\), \(\lambda\), \(h\) denote the vehicle's latitude, longitude, and height, respectively. The local curvature matrix \(\mathrm{R}_{c}\) is defined below.  
\[\mathrm{R}_{c}=\begin{bmatrix}0&\frac{1}{R_{M}+h}&0\\ \frac{\mathrm{sec}\,L}{R_{N}+h}&0&0\\ 0&0&1\end{bmatrix} \tag{10}\]  
where \(R_{M}\) and \(R_{N}\) denote the meridian radius and the transverse radius of curvature based on the WGS-84 reference ellipsoid, respectively.  
As is well-known, the expression of position integration can be obtained by integrating the velocity. Combining with Equation (9), the specific position integration can be calculated as:  
\[\mathrm{r}^{n}=\int_{0}^{t}v^{n}d\tau=\int_{0}^{t}\mathrm{R}_{c}^{-1}\dot{ \mathrm{p}}^{n}d\tau \tag{11}\]  
where \(\mathrm{r}^{n}(0)=0\).  
Based on the relation between position and velocity, Equations (6)-(8) can be reconstructed through integral calculation, as follows:  
\[\mathrm{C}_{b0}^{n0}\int_{0}^{t}\alpha_{v}d\tau=\int_{0}^{t}\beta_{v}d\tau \tag{12}\]  
The integral calculation formulae on both sides of the equation are shown below [22]:  
\[\alpha_{p}=\int_{0}^{t}\alpha_{v}d\tau=\int_{0}^{t}\int_{0}^{\tau}C_{b(\tau)} ^{b0}\dot{\rho}d\sigma d\tau \tag{13}\]\[\beta_{p}=\int_{0}^{t}C^{n0}_{n(\tau)}\mathbf{r}^{n}d\tau-t\mathbf{v}^{n}(0)+ \int_{0}^{t}\int_{0}^{\tau}C^{n0}_{n(\sigma)}(\omega^{n}_{ie}\times)\mathbf{r}^{ n}d\sigma d\tau-\int_{0}^{t}\int_{0}^{\tau}C^{n0}_{n(\sigma)}\mathbf{s}^{n}d \sigma d\tau \tag{14}\]  
Comparing Equation (14) with Equation (8), we notice that the measurement vectors are constructed by the GPS position information of the vehicle. Moreover, the effects of outliers, coming from the GPS position information, can be weakened through position-loci integration. Thus, the proposed algorithm is more robust than the traditional algorithms.
## 2 Materials and Methods
### Construction and Screening of Site-Saturation Mutagenesis Library  
All molecular procedures were performed as previously described [27]. The pET28a vector harboring the PbAcE gene was used as a template to generate an SSM library for each residue selected to improve substrate specificity. Polymerase chain reaction (PCR) was performed with NNK (N = G or A or T or C, K = G or T) degenerate codon primers (Table S1) using a standard protocol: 5 min denaturation at 95 \({}^{\circ}\)C, 18 cycles of 95 \({}^{\circ}\)C for 30 s denaturation, 55 \({}^{\circ}\)C for 1 min annealing, 68 \({}^{\circ}\)C for 6 min extension, and a final 7 min extension at 68 \({}^{\circ}\)C. The PCR product was then treated with _Dpm1_ for 1 h at 37 \({}^{\circ}\)C and transformed into _Escherichia coli_ (strain DH5\(\alpha\)). The plasmids were harvested and transformed into _E. coli_ BL21(DE3) for expression.  
Two hundred colonies were initially screened for each SSM. Based on the change in color of phenol red as result of esterase activity, positive PbAcE colonies were screened [14; 28; 29; 30]. Linalyl and terpinyl acetates were mainly used as substrates. Two hundred successful transformation colonies for each SSM were inoculated into a 96-well microplate containing 500 \(\mu\)Luria-Bertani (LB) broth supplemented with 50 \(\mu\)g/mL substrate and grown at 37 \({}^{\circ}\)C overnight. Each colony was re-inoculated into the same medium, grown at 37 \({}^{\circ}\)C until the optical density (OD) at 600 nm reached 0.5. The culture was induced by 0.5 mM isopropyl-1-thio-\(\beta\)-D-galactopyranoside (IPTG) and incubated at 25 \({}^{\circ}\)C overnight. The cell lysates containing PbAcE mutant proteins were then prepared as follows: The cells were pelleted and suspended in 100 \(\mu\)L B-PER(tm) Bacterial Protein Extraction Reagent. Protein extract (100 \(\mu\)L) was transferred to a 96-well microplate (Corning, Costar, NY, USA), 100 \(\mu\)L 100 mM linalyl acetate or \(\alpha\)-terpinyl acetate in 10 mM Tris-HCl was added, the pH was adjusted to 8.0, and 0.04 mg/mL phenol-red was added with thorough mixing. The reaction mixture was incubated at 25 \({}^{\circ}\)C until wild type wells indicated a color change. The change was photographed every 5 min. All screening was repeated twice. The top 12 positive mutants for each mutant library were selected using the intensity of color change.  
The nucleotide sequence of the positive mutants was verified using DNA sequencing. The second substitutions of His93 to Pro or Asp were introduced in the Lys91 Ala, Lys91 His, and Lys91 Ser mutants, respectively, using site-directed mutagenesis. In total, six double mutants were constructed. The third mutation of Tyr182 to Val, Thr, Glu, Met, and Trp was combined with Lys91 Ala/His93 Asp or Lys91A/His93 Pro, generating 10 triple mutants. The activity of double and triple mutants was screened as described above. Finally, mutants exhibiting higher activity than that of the wild type were further examined.
# A Deep Learning Model for Data-Driven Discovery of Functional Connectivity
## 2 Materials and Methods
### Method
#### 2.2.2 Self Attention  
Using the embeddings created by the CNN encoder, we estimate the connectivity between the regions of the brain using multi-head self-attention following [19]. The self-attention model creates three embeddings namely (key, query, value) for each region, which, in our architecture, are created using three simple linear layers. Each linear layer \(\phi\) is of size 24. \(key_{i}=\phi_{k}(region_{i})\), \(query_{i}=\phi_{q}(region_{i})\), and \(value_{i}=\phi_{v}(region_{i})\). To create weights between a region and every other region, the model takes the dot product of a region's query with every other region's key embedding to obtain scores between them. Hence, \(score_{ij}=query_{i}\cdot key_{j}\). The scores are then converted to weights using softmax. \(w_{i}=Softmax(score_{i})\), where \(score_{i}\in\mathbb{R}^{1\times r}\) is a vector of scores between region \(i\) and every other region. The weights are then multiplied with the \(value\) embedding of each region  
Figure 1: BrainGNN architecture using: (**a**) Preprocessing: to preprocess the raw data with different steps (Section 2.1.2); (**b**) 1 DCNN: to create embedding for regions (Section 2.2.1); (**c**) Self-attention: to create connectivity between regions (Section 2.2.2); (**d**) GNN: to obtain a single feature vector for the entire graph (Section 2.2.3); and (**e**) Linear classifier: to obtain the final classification.  
and summed together to create new representation for a \(region_{i}\). The following equations show how to get new region embedding and weight values.  
\[\begin{split}& key_{i}=region_{i}*W^{(k)},\quad value_{i}=region_{i}*W^{(v )},\quad query_{i}=region_{i}*W^{(q)}\\ & K=||_{i=1}^{r}key_{i}^{T}=key_{i}^{T}||....||key_{r}^{T},\quad weight _{i}=softmax(query_{i}*K)\\ & new\_region_{i}=\sum_{j}^{r}(weight_{ij}*value_{j})\end{split} \tag{1}\]  
This process is carried out for all of the regions, producing a new representation of every region and the weights between regions. These weights are then used as the functional connectivity between different regions of brain for every subject. The self attention layer encodes the spatial axis for each subject and provides the connection between regions. The weights are learned via end-to-end learning of our model performing classification. This trees us from using predefined models or functions to estimate the connectivity.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 3 Validation Methodology
### Validation of the Decametric LAI Product and Reference LAIs
#### 3.1.3 Assessment of the Reference LAI  
A 6-fold cross validation was implemented to assess the reliability of the reference LAI derived from recalibrated SL2P-based and empirically based methods. All the data were randomly divided into six data subsets of the same size. In each iteration, five subsets were used as the training sets, and the remaining subset was used as the test set. The cross-validation process was repeated 6 times and all LAINet LAI were used for both calibration and test processes. Finally, all the test samples at each iteration were integrated to obtain final assessment results.
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 4 Proposed Method
### Convolutional Neural Network  
Figure 5 illustrates the structure of our 1D-CNN module, where the input 1 \(\times\) 512 sequence data are indexed from positions 504 to 1016 of the original 1 \(\times\) 1016 CIR data using a fixed-size window. The core concept of 1D-CNN is the convolution step, which can be viewed as a correlation process. Our 1D-CNN module consists of three convolutional filters with batch normalization (BN), the kernel sizes for Conv1 to Conv3 are 16 \(\times\) 3, 32 \(\times\) 3, and  
Figure 4: The network structure diagram of 1D-CLANet.  
Figure 3: CIR curve from typical (**a**) LoS, (**b**) other NLoS scenarios.  
\(64\times 3\), respectively. All Maxpooling layers have a kernel size of \(1\times 2\). The convolutional filters are defined as follows:  
\[\mathbf{u}_{\text{C}}=\mathbf{v}_{\text{C}}*\mathbf{Z}=\sum_{s=1}^{C}\mathbf{v}_ {\text{C}}^{\text{S}}*z^{s} \tag{10}\]  
where \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) is the input CIR signal with length \(L=512\) and \(\mathbf{U}=[\mathbf{u}_{1},\mathbf{u}_{2},\dots,\mathbf{u}_{\text{C}}]\)\(\in\mathbb{R}^{H\times W\times C}\) represents the output feature map after convolution operation. The feature map has spatial dimensions \(H\times W\) and \(C\) channels. \(\mathbf{V}=[\mathbf{v}_{1},\mathbf{v}_{2},\dots,\mathbf{v}_{\text{C}}]\) is the set of learned filter kernels, where each filter \(\mathbf{v}_{\text{C}}\) is defined as \([\mathbf{v}_{\text{C}}^{1},\mathbf{v}_{\text{C}}^{2},\dots,\mathbf{v}_{\text {C}}^{L}]\). The kernels are applied with a stride of \(1\) and padding set to \(1\).  
Normalizing the input can significantly reduce the number of epochs required for training. It also has a regularization effect, which helps to reduce generalization error. The batch normalization layer is defined as follows:  
\[\mathbf{\hat{u}}_{\text{c}}=\gamma_{BN}\bigg{(}\frac{\mathbf{u}_{\text{c}}- \mu}{\sqrt{\sigma^{2}+\varepsilon}}\bigg{)} \tag{11}\]  
where \(\gamma_{BN}\) and \(\beta\) are learnable parameters, \(\mu\) and \(\sigma^{2}\) are the mean and variance of the batch, and \(\varepsilon\) is a small constant added to the variance for numerical stability (commonly \(\varepsilon=10^{-5}\)).  
After the batch normalization operation, the outputs \(\mathbf{\hat{U}}=[\mathbf{\hat{u}}_{1},\mathbf{\hat{u}}_{2},\dots,\mathbf{ \hat{u}}_{\text{C}}]\) are passed through the ReLU activation function. Pooling layers are typically added after CNN modules to perform spatial downsampling of the feature maps. Here, the ReLU activation function and Maxpooling layer work together to introduce sparsity and enhance nonlinearity.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 3 Results
### SVM-Based Classifier with Max-Normalized Raw Signals  
The confusion matrix in Table 5 demonstrates that max normalization of raw Bluetooth signals leads to the best classification performance of all preprocessing methods: diagnostical accuracy reaches 86.21% and effective accuracy is a perfect 100.00%, meaning every column's highest probability lies on the diagonal. Most device classes become almost flawlessly distinguishable. In the Uzundurukan smartphone set, Classes 8, 15, and 16 each achieve a diagonal entry of 1.00, indicating zero misclassification for those twin units. Class 9 also performs exceptionally well with 0.97 on the diagonal and only a small off-diagonal spill (0.03) into Class 6. Similarly, the Rusins wearable devices show near-perfect separability. Class 1 attains 0.93 on its diagonal, Class 2 and Class 3 both reach 1.00, and Class 18 also scores 1.00, fully resolving the mutual confusion observed under other normalization schemes. Classes 4, 6, and 22 likewise achieve diagonals of 1.00, while Class 5 improves substantially to 0.87 and Class 7 to 0.90. A few residual confusions remain among some Uzundurukan twins. Class 10 reaches 0.83 on its diagonal but still records 0.07 of its samples as Class 14 and Class 15. Class 12's diagonal is 0.53 with a notable 0.10 assigned to Class 13, and Class 13 reaches 0.70 while misclassifying 0.37 of its instances as Class 12. Class 14 scores 0.87 yet shows 0.23 in Class 7, suggesting some lingering overlap.  
Overall, max normalization dramatically enhances the discriminative power of the SVM classifier across both smartphone twins and wearable devices. The near-perfect or perfect diagonals for most classes underscore its effectiveness, although the few remaining misclassifications point to the potential benefit of more advanced feature-extraction or classification techniques for the closely matched RF fingerprints.
## 3 Results and Discussion
### Quality Factor of Graphene Plasmonic Trimers  
The quality factor (\(Q\) factor) of graphene plasmonic trimers is calculated as a function of the vertex angle (\(\theta\)), shown in Figure 5. It can be seen that the figures of the \(Q\) factor are identical to those of the energy states. In general, the \(Q\) factor relies on both the confinement and loss of the plasmon. As we know, total loss of graphene is composed of two parts, radiation loss and absorption loss; therefore, the \(Q\) factor can be written as \(1/Q=1/Q_{abs}+1/Q_{rad}\)[44]. Within the frequency range of interest in  
Figure 4: Plasmon energy diagram of trimeric graphene molecules with various vertex angles.  
this work, when the plasmon energy increases, the confinement capability of graphene is enhanced, so the radiation loss decreases, while the \(Q_{rad}\) increases. The quality factor of absorption can be defined as \(1/Q_{abs}=\gamma(\beta)/(2\,|\,i(\beta)\,|\,)\), where \(\gamma(\beta)\) and \(|\,i(\beta)\,|\,\) are the real part and the imaginary part of the propagation constant of the SPP mode, respectively. The real part of the propagation constant \(\gamma(\beta)\) is expressed as \(k_{0}\cdot n_{\textit{eff}}\), where \(n_{\textit{eff}}\) represents the effective mode index of SPPs, indicating the confinement ability of graphene, while the imaginary part of the propagation constant indicates the loss of SPPs. The effective mode index (\(n_{\textit{eff}}\)) as a function of plasmon energy with a range from 0.225 to 0.272 eV is shown in Figure 5b. Therefore, the \(Q\) factor increases linearly with the plasmon energy.
## 2 Field Monitoring Program and Data Collection  
### Test Station  
The study was conducted at a 161 kV test station located in a severely polluted industrial area in Taiwan, very close to the Taiwan Strait. The test station is situated 1.60 km from the Taiwan Strait and 10 km from a coal-fired power plant, separately. The location of the test station is shown in Figure 1.  
Therefore, in this challenging environment, the insulators tested at the test station suffer from heavy pollution and salt contamination. Especially in winter, strong occasional  
Figure 1: The location of the test station in the industrial area.  
monsoons bring plenty of salt contamination and industrial pollution, making it suitable for studying the pollution performance of the insulators.  
The study utilized a string of ceramic insulators coated with silicone grease because it can provide longer creepage and good anti-fog performance in the test station. Throughout the entire test period, the insulators were energized at 161 kV, and the leakage current of the string of testing insulators was recorded through a measurement device. The specifications of the leakage current measuring device are shown in Table 2. The test station is depicted in Figure 2.  
In addition to establishing a leakage current measurement device at the test station, a weather collection system was built to record weather parameters, including temperature, humidity, wind direction, wind speed, and ultraviolet radiation. In this study, measurement data for a period of thirty consecutive months were recorded from June 2016 to December 2018. These data encompass not only different seasons and natural washing by rain but also various special conditions such as typhoons, salt contamination, and industrial pollution. Figure 3 illustrates the weather data collection system.
# Rain Removal of Single Image Based on Directional Gradient Priors
## 3 Proposed Deraining Method
### Proposed Model
#### 3.2.2 Initialization of Multi-Scale Dictionaries  
Considering the size difference of raindrops or rain streaks in rain images, we propose the sparse coding based on multi-scale dictionaries to reconstruct a rain layer and constrain the layer solved in each iteration. In this paper, we define the multi-scale dictionaries with different sizes (15 \(\times\) 15, 11 \(\times\) 11, 7 \(\times\) 7, and 3 \(\times\) 3), which can be regarded as convolution filters to extract the information of the rain streaks with different sizes. The multi-scale dictionaries are first constructed by utilizing the initial rain layer, and then updated to encode the rain layer during each iteration. The initialization procedures of the dictionaries are described as follows:  
1. Input the initial rain layer R and define a dictionary template with size K, which is the identity matrix of (15 \(\times\) 15, 11 \(\times\) 11, 7 \(\times\) 7, and 3 \(\times\) 3).
2. The rain layer is divided into many patches with a size of 15 \(\times\) 15, and the step size is 1. Then, the average value of each block is calculated, and the difference blocks are obtained by subtracting the average value from the pixel values of each block.
3. The difference blocks are reshaped into the column vectors, and the column vectors of the blocks are multiplied by their transposed vectors to obtain matrices with a size of 225 \(\times\) 225.
4. SVD is applied to the matrices obtained in (3) to extract the decomposed components and initialize the four dictionaries. Because rain streaks generally have the brightest intensities and their values are nonnegative, we extract the nonnegative data of the first \(K\) column vectors as the values of the dictionaries and then import the values into the dictionary template.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 2 Popular Power Curve Models
### Nonparametric Models  
For a wind turbine, there is a nonlinear relationship between power output and the other variables, such as wind speed and wind direction. It is usually described by a wind turbine power curve and expressed by the following function, namely.  
\[P=f(V,\mathbf{\theta})+\zeta \tag{1}\]  
where \(\mathbf{\theta}\) denotes the set of variables that affect the power output of a wind turbine, such as wind direction and pressure, \(\zeta\) is the error term, \(f(\cdot)\) is the nonlinear function.  
Due to the superior nonlinear fitting ability of many artificial intelligence methods, they have been widely utilized in wind turbine power curve modeling, namely estimating the unknown nonlinear function \(f(\cdot)\). Some popular ones are SVR [20], GP [21, 22], and ANNs [19, 26].
## 3 Results and Discussion
### Two Revised DNNs Applied to the IDRC 2002 Data Sets  
Figure 5 shows the revised U-Net DNN applied to the IDRC 2002 data sets. The threshold is set \(R^{2}\geq 0.95\). The training process was based on IDRC 2002 set 1. The results are shown on the top subplots of Figure 5. One can see that the training, validation, and testing results are sufficient. The calibration model obtained from IDRC 2002 set 1 was then applied to IDRC 2002 set 2 and the results are shown in the bottom subplots of Figure 5. The results were also sufficient. Thus, the revised U-Net DNN can fulfill the goal of calibration transfer.  
Figure 6 shows the application of the revised attention mechanism DNN applied to the IDRC 2002 data sets. The training process was applied to the IDRC-2002 set 1. The results are shown on the top subplots of Figure 6. The bottom subplots show the calibration transfer results using the obtained calibration model from IDRC 2002 set 1 applied to the IDRC-2002 set 2. The results were still acceptable and the calibration transfer was satisfactory.  
One can see from Figures 5 and 6 that some samples appear to be outliers. We did not make further efforts to determine the issues associated with them because it was outside the scope of the present work. The important thing is that we could use a small batch size of 12 in the training process and evaluate the threshold \(R^{2}\). Furthermore, the obtained calibration model can be effectively applied to the whole data sets. Thus, this strategy can obtain a calibration model with good generalization capability.  
Figure 5: Predicted versus measured quantities of the active substance of the Escitalopramt tablets based on the revised U-Net DNN.
# Application of Simulation in the Optimization of the Blood Plasma Storage Process
## 3 Card Manufacturing Process for Blood Plasma Separation  
The output of the analyzed process is a plasma separation card called Cobas HIV-1, which can be seen in Figure 1. Cobas HIV-1 is an in vitro nucleic acid amplification test for the quantification of human immunodeficiency virus type 1 (HIV-1) in EDTA plasma or from a dried plasma spot infected with HIV-1. This test is intended for use in conjunction with the clinical picture for the clinical treatment of patients infected with HIV-1. This test can be used to confirm HIV-1 infection in antibody-reactive individuals and to assess a patient's prognosis by measuring baseline HIV-1 levels or to monitor the effects of antiretroviral therapy by measuring changes in HIV-1 RNA levels during treatment.  
The card fundamentally changes the way plasma samples are collected and processed, enabling reliable quantitative testing of HIV patients living in remote areas--even in areas of extreme heat and humidity--while meeting WHO's requirements to determine HIV viral load before starting treatment.