esa-sceva/satcom-chunk-collection huggingface dataset:
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 2 Differential Precise Time and Frequency Transfer
### Single-Difference Ambiguity Estimation
When the precision of pseudorange measurement of P codes is about \(\pm 0.3\) m, the determination of the ambiguity requires about 25 epochs of pseudorange observations [24]. If the epoch number, \(l\), is reduced, the determination of SD carrier-phase ambiguity is inevitably affected by the pseudorange noise and multipath. Next, the pseudorange noise and multipath affect the time transfer performance. In the double-difference positioning solution, it is a common practice to select the satellite with the maximum elevation as the reference satellite. However, when solving the single-difference ambiguity of the reference satellite using Equation (3), it is inevitable that the influence of the pseudorange noise and multipath will be present. To further reduce the impact of the pseudorange noise and multipath on the SD integer ambiguities, we take each visible satellite as a reference satellite separately and estimate the SD integer ambiguities. As soon as a set of SD integer ambiguity samplings is available, we can classify them into groups according to their distribution. This group is also called a cluster, a region in which the density of samples is locally higher than in other regions [25]. In this contribution, the K-means clustering method is employed to determine SD integer ambiguities. The K-means algorithm aims to choose the best clusters that minimize the sum square error of the distances between a sample and its cluster centroid [25]. The problem can be expressed mathematically as:  
\[ \begin{cases}P(S\{S_{1},\ \ldots,S_{n}\})=min\sum_{i=1}^{n}\sum_{j=1}^{m}\left(x _{ij}-\mu_{i}\right)^{2}\\ \mu_{i}=\frac{1}{m}\sum_{j=1}^{m}x_{ij}\ \ i=1,\ \ldots,n\end{cases}  \tag{10}\]  
where \(S\) is the set of total samples and \(\{S_{1},\ \ldots,S_{n}\}\) is the set of n clusters from \(S_{1}\) to \(S_{n}\). For each cluster, \(S_{i}\), there are \(m\) samples, \(x_{ij}\) is the \(j\)th sample of the \(i\)th cluster, and \(\mu_{i}\) is the centroid of the \(i\)th cluster. In this study, the clustering is applied on SD integer ambiguities, and we set the initial groups according to satellite PRN. For each group, its centroids, \(\mu_{i}\), from all members, \(x_{ij}\), are calculated using Equation (10). In practice, the solving process of K-means is an optimization problem. In this study, we selected the cluster with the most data sampling points as the final SD integer ambiguity.
The experimental results confirmed that the swept-bladed fans show noise savings compared to the zero-sweep baseline model. [PERSON] et al. [8] investigated the effects of various tip structures on the flow field, losses distribution, and noise characteristics and improved fan performance by changing the blade tip structure. It was shown that the grooved blade tips improved the efficiency by reducing mixing losses between the leakage flow and mainstream but increased the fan noise. These studies [4; 5; 6; 7; 8] revealed that the characteristics of gap flow between the tip of the fan blade and the fan orifice (housing or shroud) are critical to the aerodynamic and aeroacoustic performance of fans. However, most of these studies concerned a fan unit alone or a fan in a simplified outdoor unit. The axial fan used in an outdoor unit has a complicated inflow structure, and even the same fan has different performance depending on the inflow structure affected by various devices. Notably, in the case of the outdoor unit, there are many components, not only an axial fan but an orifice, a heat exchanger, a heat sink, a motor, and a motor mount. These components can affect the flow performance as well as the noise of the outdoor unit. In this study, the whole outdoor unit, including these components, is investigated. As described above, among the components, the orifice is known to be one of the main parts affecting the flow and noise performances of an axial fan. As the cooling fan rotates in the outdoor unit, complicated tip vortex structures are created, which can adversely affect the flow performance and noise of fan [9; 10; 11]. The orifice organizes the flow by guiding the flow path between the orifice and the blade tip. In this study, the shape of an orifice is optimized to maximize the flow rate and thus to reduce the noise of an axial fan in the outdoor unit of an air conditioner.
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 3 The Proposed Decoder Architecture
### Memory Blocks  
LDPC decoder architecture requires two storage options: one for the \(\widetilde{\gamma}_{n}\) values (AP-MB) and another for the \(\beta_{m,n}\) messages (CN-MB). The number of memory units of the AP-MB equals the number of columns of the base matrix. Each unit contains \(Z\) memory locations with a width of \(\widetilde{q}\)-bits. Accordingly, the AP-MB contains \(n_{b}\) memory units that hold \(n_{b}\times Z=8832\) received-AP information of \(\widetilde{q}\)-bits, for a total of \(n_{b}\times Z\times\widetilde{q}=52\),992 bits. The \(\widetilde{q}\times Z=1152\)-bit AP messages are sent sequentially into AP-MB. That means \(n_{b}\) clock cycles are needed to store all of the AP messages. Such \(n_{b}\) AP groups are stored in \(n_{b}\) memory units and read simultaneously from the AP-MB in a single clock cycle.  
In the proposed decoder, the second minimum should not be calculated and stored in the CN-MB, which is the main difference from the other conventional decoders. The CN-MB is used for the storage of the compressed \(\beta-\)message. Since \(\beta-\)messages from all layers must be kept, the depth of the CN-MB is equal to the number of layers. Consequently, a total of \(m_{b}\times Z\times(q-1+\left[\log_{2}(d_{\text{cmax}})\right]+d_{\text{cmax}} )=124\),416 bits of the CN-MB is required.  
Table 3 shows the size of the proposed and conventional CN-MB. Without storing the second minimum, the size of the proposed CN-MB could be reduced by 10%. These CN-MBs' outputs are sent into the Decompress block. This compressed \(\beta-\)message consists of four parts: 19 MSBs between 26-8 bit-position representing the signs of 19 CNU input  
Figure 6: The proposed VNU/APB architecture.  
messages, the next 7-5 bit-positions corresponding to the magnitude of \(min1\) and the last 5 Least-Significant-Bits (LSBs) of 4-0 bit-positions representing the index of \(min1\). The overall memory of the LDPC decoder is mostly determined by the size of the AP-MB and CN-MB. According to the calculations above, the AP-MB requires 52,992 bits and the CN-MB stores 124,416 bits. Therefore, the total memory is 173.25 kb.
# Joint Energy Supply and Routing Path Selection for
## 3 A Joint Algorithm of Energy Supply and Routing Selection
### Routing Selection
\(\hat{e}_{i,t}\), \(\hat{e}_{j,t}\) are the estimated remaining energy of nodes \(i\) and \(j\) (here, the node energy consumption during charging is ignored) after being charged, expressed as:  
\[\hat{e}_{i,t}=e_{i,t}+\left(t^{\prime}_{i}-\gamma_{i,t}\right)*\Lambda_{c}\eta \tag{8}\]  
In above equation, \(e_{i,t}\) is the current remaining energy of node \(i\), \(t^{\prime}_{i}\) is the charging time allocated to node \(i\), \(\gamma_{i,t}\) denotes how long node \(i\) has been charged in the current \(T_{c}\) interval. Here, considering the effect of energy supply on the energy level of nodes, and in order to balance the residual energy of each node at the end of each charging duration as much as possible to avoid the death of some nodes too early due to overuse, we adopt the nodal estimated residual energy instead of the current energy when defining the routing cost. What's more, the weight \(u_{i,j}\) in Equation (7) expresses the common effect of the state of current node and the next hop node on the packet delivery, whose performances are affected by the relative sizes of the two. Note that, in this paper, we introduce harmonic coefficient \(\alpha\) and \(\beta\), which satisfy:  
\[\alpha+\beta=1 \tag{9}\]  
From Equations (7) and (9), we can see that regulating the ratio of harmonic coefficient can achieve different network performances during routing selection. When \(\alpha\) is larger, the state of current node \(i\) is mainly considered, whose transmission energy consumption and remaining energy after being charged will largely affect the choice of the next-hop node. At this point, the transmission energy consumption may decrease and the network hops increase. When \(\beta\) is larger, it strives to transmit data to the sink node within the allowable range in the fastest time, so that the number of hops is small but the energy consumption per hop may increase. Therefore, the dependency of data packets transmission on the two decision factors can be adjusted by changing \(\alpha\) and \(\beta\), which can achieve the expected network performances according to different requirements. After obtaining the routing weight of each forward neighbor node, node \(i\) selects the node \(j\) with the smallest composite cost as the next-hop node, which is:  
\[j=\underset{k}{min}[u_{i,k}],\;k\in NF(i) \tag{10}\]  
Finally, under the influence of transmission energy consumption, forward energy consumption of the next-hop node and estimated energy of sensor node, we can find all optimal routes to the sink for each node, and they are also the routes that have the potential ability of balancing the survival time and performances for the network.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 3 The Proposed Method
### Tracking Failure Prediction by Analyzing Correlation Values
The accuracy versus the (\(\alpha\), \(\beta\)) plane is shown in Figure 4.  
To derive optimal \(\alpha\) and \(\beta\), we provided a video sequence containing a frame of tracking failure. We labeled the frame of tracking failure as 'failure' and the others as'success'. The accuracy of \(T_{1}\) was investigated at every frame for a specific pair of (\(\alpha\), \(\beta\)). As shown in Figure 4, accuracy of (\(\alpha\), \(\beta\)) is set to zero if the pair cannot detect tracking failure. After experiments using Visual Tracker Benchmark dataset [29], we found that the optimal \(\alpha\) is 0.85 and \(\beta\) is \(-2.4\). Though there are slight differences among video sequences, (\(0.85-2.4\cdot\sigma\)) values are around 0.6 and \(\frac{c_{t}(p,d)}{N_{\text{aver}}}\) values at tracking failure points are around 0.6 as well. Therefore, we performed experiments by setting \(T_{1}\) and \(T_{2}\) as 0.6.
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 3 Methodology
### Model Architecture
Before concatenation, we reshape the features from different encoder blocks in a particular spatial size using max pooling of different levels (see Figure 3). We reshape the features of different encoder blocks using multi-kernel max pooling (e.g., \(2\times 2\), \(4\times 4\), \(6\times 6\), \(8\times 8\)), i.e., using multiple receptive fields. We obtain several sub-feature maps from the encoder blocks and capturing pixel information from multi-scale local ranges. As a result, while passing fine-grained low-level information to the proceeding encoder layers, a scale-invariant model is also being developed to share more pixel information. Moreover, the model can share information from multiple scales and increase the architecture's robustness to extract essential redundant features for detecting cracks of different sizes. The decoder module's structure is shown in the right dotted box. The decoder module also consists of 4 decoder blocks. Each block in the decoder consists of a merged layer (see paste colored block in Figure 3), and then the structure contains two repeated \(3\times 3\) convolution blocks like the encoder. The merged layer includes two inputs: one is the feature map from the corresponding encoder layer (black arrow in Figure 3), and the other one is the concatenation (yellow circle in Figure 3) of the outputs of the previous encoder blocks. The merged layer also includes a bridge as we also construct the short linkages in the decoder module. Before concatenating, we upsampled the features from different blocks in the same spatial size. For example, the merged layer of the last block of the decoder module receives the feature map from the first encoder block, whose shape is 224 \(\times\) 224 \(\times\) 16. So, the other input must be in the same shape. We upsampled the feature maps from all of the previous decoder blocks whose initial shapes were 14 \(\times\) 14 \(\times\) 256, 28 \(\times\) 28 \(\times\) 128, 56 \(\times\) 56 \(\times\) 64, 112 \(\times\) 112 \(\times\) 32 and converted them in the shape of 224 \(\times\) 224 \(\times\) 16 by using different upsampling levels. After getting the same size, we concatenated them and provided them as the second input of the merged layer. In this strategy, the decoder blocks are not only considering the feature maps of the corresponding encoder blocks, but also using the feature maps of all the previous decoder blocks and the bridge. As a result, a better flow of pixel reconstruction has been established, and the U-net model has become more efficient. Finally, a \(1\times 1\) convolution was utilized to categorize the \"Crack\" and the \"Non-crack\" pixels at the last layer.
# Selected Antioxidants in Organic vs. Conventionally Grown Apple Fruits
###### Abstract  
The apple (_Malus domestica_ [PERSON]) is one of the most widely cultivated temperate fruits globally, gaining scientific interest as a rich source of antioxidants with a demonstrated beneficial human health impact. Since a growing number of consumers are increasingly seeking safe and healthy food options, alternative fruit production systems such as organic farming, and their potential to provide safe and nutritious foods, have been gaining increasing attention in the last decades. The aim of the presented study was, therefore, to analyse and to compare the concentrations of selected health-promoting antioxidants, such as phenolic acids, flavonoids, and vitamin C, in fruits of three apple cultivars (Champion, Gala, and Idared) grown in conventional and certified organic orchards in Poland. All analyses were performed using the high-performance liquid chromatography (HPLC) method. Organic apples tested within the study, compared to the conventionally grown ones, were characterised by significantly higher concentrations of phenolic acids (av. \(>\)31%) and flavonoids (av. \(>\)66%) with the identified differences being consistent in all three cultivars and two seasons. The greatest production system effect was observed in the case of Idared. Significant cultivar and production season effects on the concentration of the measured fruit phenolics, with strong interactions between the two factors, were also identified. Vitamin C content in the fruits was strongly dependent on the year-to-year differences in the fruit growing conditions. The study suggests that the organic production system has a potential to provide apple fruits rich in selected health-promoting phenolic antioxidants.  
apple; _Malus domestica_; organic production; bioactive compounds; HPLC; phenolic compounds; phenolic acids; flavonoids; vitamin C
## 4 Experimental Results and Analysis
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **SSDT** & \begin{tabular}{c} **SAS** \\ **Branch** \\ \end{tabular} & **SS3 FCN** & **VHIS** & \begin{tabular}{c} **SS3 FCN-** \\ **VHIS** \\ \end{tabular} & \begin{tabular}{c} **PCA/PCA-** \\ **ON** \\ \end{tabular} & \
\begin{tabular}{c} **GAN/PCA-** \\ **ON** \\ \end{tabular} &
\begin{tabular}{c} **3 DCNN** \\ \end{tabular} \\ \hline \multirow{2}{*}{SalinaV} & OA & **82.27** & 81.19 & 81.32 & 64.2 & 74.60 & 76.67 & 75.87 & 69.72 \\  & AA & **87.85** & 86.77 & 86.17 & 64.7 & 76.00 & 78.25 & 77.45 & 69.09 \\ \hline \multirow{2}{*}{PaviaU} & OA & **83.81** & 82.2 & 79.89 & 73.26 & 76.23 & 73.84 & 71.46 & 70.07 \\  & AA & **83.55** & 80.55 & 76.6 & 62.08 & 70.6 & 62.71 & 62.83 & 60.18 \\ \hline \multirow{2}{*}{HoustonU} & OA & **85.91** & 81.04 & 83.3 & / & 76.28 & / & / & / \\  & AA & **83.89** & 81.86 & 83.69 & / & 77.71 & / & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The overall performance between different methods (%). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**NO.** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** \\ \hline SSDT-net & 91.37 & 87.20 & 98.97 & 93.08 & **99.28** & 91.36 & **87.08** & **66.37** & 76.59 \\ SS3 FCN [19] & **94.67** & 85.45 & 98.60 & 92.90 & 96.26 & 75.97 & 86.25 & 66.37 & **78.09** \\ SS3 FCN-VHIS & 90.18 & **87.78** & 98.39 & 93.87 & 94.60 & 89.63 & 77.21 & 58.71 & 73.48 \\ \hline
**C10** & **C11** & **C12** & **C13** & **C14** & **C15** & **OA** & **AA** & **K \(\times\) 100** & \\ \hline
72.46 & **80.02** & 68.51 & 54.53 & **97.14** & **95.77** & **85.97 \(\pm\) 2.21** & **83.89 \(\pm\) 2.53** & **84.76 \(\pm\) 2.37** & \\
**81.78** & 76.29 & **68.52** & **68.74** & 94.37 & 91.04 & 83.30 & 83.69 & / \\
65.01 & 65.15 & 41.39 & 41.85 & 91.01 & 97.26 & 76.28 & 77.71 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The result (%) of the Houston University dataset.
## 3 Methodology
### Tunnel Primary Support Deformation Prediction Model  
A new fusion deep neural network (FDNN) model based on multidimensional feature extraction is developed to predict the settlement of the vault and horizontal convergence deformation of the initial support structure. The structure of the model is shown in Figure 9. The input layer of the model consists of four parts of the data, and the features are extracted based on the physical attributes of the data, respectively. The four types of input data were selected at 0.25 times the tunnel width intervals in the axial direction of the tunnel. The data were selected within 2 times the tunnel width ahead and 3 times the tunnel width behind the predicted section to form the database. The geometrical data are selected as an array with a size of 4 \(\times\) 20. The geological data, construction data, and support data are encoded as an array with a size of 8 \(\times\) 12.  
The deformation in the initial support phase of the tunnel is directly influenced by the geometric parameters before and after the specific cross-section. To capture this spatial influence, we employ long short-term memory (LSTM) to extract the geometric parameter information ahead and behind the prediction location. Furthermore, for geological, support, and construction data, we utilize Convolutional Neural Network (CNN) to extract distinctive features across the longitudinal span of various segments. These features, once extracted from both LSTM and CNN, are flattened and seamlessly integrated before being channeled into the deep neural network (DNN) layers. After undergoing comprehensive processing through five sequential DNN layers, the model produces a prediction for tunnel deformation. The LSTM block consists of four LSTM layers, while the CNN block comprises convolutional pooling operations. Building upon the data encoding format introduced in Section 2.2, we construct a labeled database. This database serves as the foundation for training the proposed FDNN model. As a result of this training, the LSTM and CNN blocks become adept at extracting pertinent features related to initial support deformation for each respective data type.
# A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
## 3 Results
A and B correspond to two randomly selected samples. Figure 10: Confusion Matrix: (**a**) U-Net, (**b**) SegNet, (**c**) DeepLabv3+/ResNet50, (**d**) DeepLabv3+/ResNet18, and (**e**) Mobilenetv2. tions is due to the reduction in MiniBatchSize to 16 samples for SegNet, compared to the MiniBatchSize of 32 samples used for the other networks. Notably, when employing a MiniBatchSize of 32 samples, the performance of SegNet decreases to approximately 91% to 93%, emphasizing the need to reduce the MiniBatchSize to 16 for optimal performance. Despite the longer training time associated with the reduced MiniBatchSize, SegNet's final accuracy remains competitive, showcasing its ability to achieve high performance even with a smaller batch size. As shown in Figures 7-9 achieving maximum accuracy during training requires only a few iterations for DeepLabv3+ segmentation networks. The encoder that leads to the shortest training time is ResNet18, which has the fewest layers among the three. However, MobileNetV2 exhibits slightly superior results to the other networks, achieving excellent scores in all metrics as indicated in Table 4.  
During the training process of the segmentation model, anomalies or irregularities that might occur in individual images are likely to diminish or be addressed as the model learns from a diverse set of images. The learning process, driven by probabilities, helps the model to generalize and effectively segment objects or regions of interest in images, even in cases where there might be variations or anomalies in the data. In this case, the model might not learn extensively about these imperfections due to their limited occurrence in the training data.
## 4 Security Analysis
### Informal analysis
#### 4.2.3 Session Key Agreement  
According to the proposed protocol, the CCS and UAV negotiate the master key and then combine the refresh key and counter to calculate the session key and authentication key, rather than having one party generate and distribute them alone. Thus, the AKA protocol enables session key agreement.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Label Datasets
#### 3.1.2 Local Marmousi Models  
In this section, a rectangular window measuring 128 \(\times\) 192 was used to extract velocity models from the Marmousi model, as shown in Figure 4. The maximum velocity of the model was constrained to 4000 m/s. The window was systematically moved from left to right and top to bottom with a horizontal step size of 120 and a vertical step size of 150. Following this process, a total of 1700 velocity models that held practical significance were chosen from the extracted models. Figure 5 shows six representative velocity models selected from the aforementioned set. These models differ in terms of layer numbers, dip angles, and velocity ranges. The velocity distributions demonstrate nonlinearity, contributing to the complexity of the models. To enhance the neural network model's generalization capability and mitigate the risk of overfitting, data augmentation techniques were used. As shown in Figure 6, horizontal and vertical flips were applied to each of the 1700 velocity models. Consequently, the labeled dataset consisted of 5100 velocity models after augmentation. The expanded labeled dataset encompasses a wider range of velocity models, thereby facilitating the neural network's adaptation to diverse samples and application scenarios.