esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 5 Performance Evaluation
### Evaluation Results and Analysis
#### 5.2.4 Experiments in The Different Sizes of Substrate Network Scenario  
The slice nodes are connected by a probability of 0.5 in this scenario. Figures 8 and 9 show the results of the slice acceptance ratio and slice provisioning revenue performance when the number of substrate network nodes is 50, 100, and 150, which represent small-, medium-, and large-sized physical network, respectively.  
Figure 8 shows that, when the size of the physical network gets larger, all the algorithms have higher slice acceptance ratio. This is because the physical network with larger size has sufficient resources to host slice requests, which makes it easier to accept more slice requests. In the scenario with different sizes of substrate network, RT-CSP+ always has best slice acceptance ratio. For instance, when the substrate network has 150 nodes, the slice acceptance ratio of RT-CSP+ is 98.30%, which is 6.02%, 6.58%, 7.92%, and 17.44% higher than those of RT-CSP, VNE-DCC, NRM-VNE, and CC, respectively. The reason is that RT-CSP+ can efficiently provision slice requests based on the resource and topology attributes. In accordance with better slice acceptance ratio, our algorithms can produce better revenue performance as shown in Figure 9. From another aspect, the better performance of our algorithm in this scenario verifies its the scalability.  
Figure 8: Slice acceptance ratio in the different sizes of substrate network scenario.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Data Management Based on the Pixel-Level Grid  
#### 2.2.1 Database Connection and Initialization  
Database connection and initialization are necessary to ensure that ODC is connected to the PostgreSQL [40] open-source database so that the metadata of the imagery data and the attribute information of the band data can be stored in it. To store the data, the proposed framework automatically generates a database schema containing five database tables (dataset, dataset_location, dataset_source, dataset_type, and metadata_type) during the initialization of the PostgreSQL database. The relationship between the five database tables is shown in Figure 2. As seen in Figure 2, the dataset table is the core that stores all the data records added to the database. Since each data record contains the relevant metadata information, data type information, data location information, and data source information after the data ingestion, this information is separately recorded in the other four tables.  
Figure 2: The UML diagram of the five-table ODC structure.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 5 Substrates of Ypk1
### Control of Intracellular Glycerol Concentration
Thus, inactivation of TORC2-Ypk1 signaling upon hyperosmotic shock has two coordinated consequences that work synergistically to cause glycerol accumulation and promote cell survival, outcomes that work in conjunction with, but in a much more rapid and mechanistically distinct manner from, the processes evoked by activated Hog1. First, in less than one minute, loss of TORC2-Ypk1 signaling alleviates inhibition of Gpd1--which, combined with transcriptional induction of _GPD1_, _GPP1_ and _GPP2_--greatly increases the rate of glycerol production. Second, loss of TORC2-Ypk1 signaling also rapidly closes the Fps1 channel, thereby allowing the glycerol produced to be retained. These findings defined the underlying molecular basis of a previously uncharacterized mechanism for responding to hypertonic conditions [24]. Moreover, the fact that a minor sequence change at an analogous phosphorylation site placed two closely-related metabolic enzymes, Gpd1 and Gpd2, under the control of two distinct classes of stress-activated protein kinases (Ypk1 and Snf1, respectively) suggests that phosphorylation site divergence can be a contributory driving force for functional specialization of the products of paralogous genes during evolution.
# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 3 Materials and Methods
### Semantic Feature Extraction
#### 3.2.2 Bidirectional Gated Recurrent Unit  
A gated recurrent unit (GRU) [28] is another RNN variant commonly used to process sequential data, similar to long short-term memory, but with a simpler structure, as shown  
Figure 5: LSTM structure diagram.  
in Figure 6. GRU integrates the forgetting gate and the input gate into an update gate, and controls when to update the hidden state, thus realizing long-term dependent learning.  
The GRU unit consists of the update gate and the reset gate, which together determine the hidden status update mode of the current time step.  
The reset gate \(r_{t}\) controls the influence of the hidden state \(h_{t-1}\) of the previous time step on the current candidate hidden state \(\overset{\sim}{h_{t}}\). When \(r_{t}\) approaches 0, it ignores the previous hidden state and focuses on the current input \(x_{t}\); conversely, when \(r_{t}\) approaches 1, it means more information about the previous time step needs to be considered to generate the current candidate hidden state, which means that there is a strong relationship between the current input and the historical state.  
The update gate \(z_{t}\) determines whether the hidden state needs to be updated in the current time step. When \(z_{t}\) is close to 1, the model tends to retain the hidden state \(h_{t-1}\) of the previous time step and ignore the current input \(x_{t}\). On the contrary, when \(z_{t}\) is close to 0, the new hidden state \(h_{t}\) is closer to the candidate hidden state \(\overset{\sim}{h_{t}}\). The specific calculation formula is:  
\[r_{t} =\sigma(W_{r}\cdot[h_{t-1},x_{t}]+b_{r}) \tag{13}\] \[z_{t} =\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z})\] (14) \[\overset{\sim}{h_{t}} =\tanh(W\cdot[r_{t}*h_{t-1},x_{t}]+\text{b})\] (15) \[h_{t} =(1-z_{t})*h_{t-1}+z_{t}*\overset{\sim}{h_{t}} \tag{16}\]  
where \(\sigma\) is the sigmoid function, \(W_{r}\) and \(b_{r}\) are the weight and bias of the reset gate, and \(W_{z}\) and \(b_{z}\) are the weight and bias of the update gate. tanh is the hyperbolic tangent function, and \(W\) and \(b\) are the weights and biases associated with the candidate hidden state.  
The combination of these two gates helps the GRU efficiently deal with the problem of disappearing gradients, especially to capture important long-range dependencies in longer sequences. By flexibly regulating the flow of information, GRUs can effectively retain long-term dependencies without losing short-term information.  
Bidirectional gated recurrent unit (BiGRU) is a neural network model that combines GRU and bidirectional propagation. By considering both forward and backward information of the sequence data at each time step, it is able to capture the context in the sequence more comprehensively. BiGRU consists of two GRU layers: a forward GRU and a backward GRU. The output of each time step is a concatenation of the output of these two GRU layers, which is similar in structure to BiLSTM.  
Figure 6: GRU structure diagram.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 5 Results and Discussion  
Network Simulator 2 (NS2) is a simple event-driven simulation program that has proven to be beneficial in researching the dynamic behavior of communications infrastructure. It could be used to simulate wired and wireless network functions and standards. Generally, NS2 allows the user to select network protocols and simulate their behavior. As a result, we are employing the NS2-based simulation for protocol simulations, in addition to providing significant support for TCP, routing, and multicast protocols over wired and wireless networks. The NS2 code is developed in C++ (to define the core mechanisms of the simulation objects) and TCL (to start the scheduler events), resulting in a NAM output file. Then, it displays the output of the nodes talking with each other, which comprises two tools, plotting the nodes in a place given by the code script. All frequently deployed IP protocols are included in the network simulator (NS2). The simulation analysis is done with various network scenarios, such as nodes of 20, 40, 60, 80 and 100. Five different scenarios are conducted and the graphic results are shown in the upcoming sub-sections.  
In this simulation, our protocol is compared with the earlier models, such as GPSR, MM-GPSR [23] and GEO-LU [25]. The parameters, which are calculated for the result analysis, are Network Energy Efficiency (NEE), Network Energy Consumption (NEC) and Successful Packet Transmission (SPT).
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 3 The Overall Structure of SDADM
To expand the path hopping space and reduce the complexity of exploring paths, no constraints were applied during the generation of the path hopping space. If used directly, it will cause problems such as repeated use of paths, repeated occurrence of key nodes, and unavailability of paths, reducing the diversity and unpredictability of path hopping. Therefore, this module, based on the path in communication, constrains the paths in the hopping space in terms of similarity, usage time, remaining path bandwidth, and remaining path node capacity. Constraining the similarity and usage time of the path is to avoid the same or similar paths being used repeatedly over multiple hopping cycles, and to enhance the unpredictability of path hopping. Constraining the remaining path bandwidth and the remaining path node capacity is to ensure the availability of the path and to avoid problems such as data loss due to the bandwidth or node capacity not meeting the demand. The weights of the paths in the alternative space are calculated separately from the remaining path bandwidth and key path nodes as the probability of path selection. At the same time, because the current communication path is different, its similar paths and usage time are also different, and the remaining path bandwidth and node capacity are constantly changing, so this hopping space is also dynamically changing. (4) Hopping execution module: Based on the security status of the network, it adaptively switches between two hopping strategies weighted by key nodes and remaining path bandwidth, dynamically changing the hopping cycle. It strives to select the path that is most divergent from the current path, satisfies the current network status, and has not been used recently.
###### Abstract  
Millimeter-wave (mmWave) communications and visible light communications (VLC) are proposed to form hybrid mmWave/VLC systems. Furthermore, channel modeling is the foundation of system design and optimization. In this paper, we compare the propagation characteristics, including path loss, root mean square (RMS) delay spread (DS), K-factor, and cluster characteristics, between mmWave and VLC bands based on a measurement campaign and ray tracing simulation in a conference room. We find that the optical path loss (OPL) of VLC channels is highly dependent on the physical size of the photodetectors (PDs). Therefore, an OPL model is further proposed as a function of the distance and size of PDs. We also find that VLC channels suffer faster decay than mmWave channels. Moreover, the smaller RMS DS in VLC bands shows a weaker delay dispersion than mmWave channels. The results of K-factor indicate that line-of-sight (LOS) components mainly account for more power for mmWave in LOS scenarios. However, non-LOS (NLOS) components can be stronger for VLC at a large distance. Furthermore, the K-Power-Means algorithm is used to perform clustering. The fitting cluster number is 5 and 6 for mmWave and VLC channels, respectively. The clustering results reveal the temporal sparsity in mmWave bands and show that VLC channels have a large angular spread.  
visible light communications (VLC); mmWave communications; channel modeling; channel propagation characteristics; path loss; delay spread (DS); Ricean K-factor; cluster characteristics
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 2 System and Channel Modeling
### Channel Modeling
#### 2.2.2 Pointing Error Loss
 \tag{22}\]  
where \(\delta(\cdot)\) is the Dirac delta function and \(F_{\theta_{AoA|\theta_{xz}}}(\cdot)\) is defined as  
\[F_{\theta_{AoA|\theta_{xz}}}(\theta_{FOV}){=}\int_{0}^{\theta_{FOV}}\!\!\!f_{ \theta_{AoA|\theta_{xz}}}(\theta_{AoA})d\theta_{AoA}. \tag{23}\]  
Finally, the distribution of AoA fluctuation can be derived as  
\[ \begin{split} f_{X_{pa}}\big{(}X_{pa}\big{)}& =\exp\left(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}}\right) \delta\big{(}X_{pa}\big{)}\\ &+\left[1{-}\exp\!\!\left({-}\frac{\theta_{FOV}^{2}}{2\sigma_{AoA }^{2}}\right)\right]\delta\big{(}X_{pa}-1\big{)}.\end{split}  \tag{24}\]
## 1 Introduction
### Cultural Heritage Annotation and Classification
#### 1.2.2 Feature Learning Approaches  
The majority of feature learning approaches related to the visual identification of cultural heritage assets are based on deep learning. CNNs, which represent the state-of-the-art in visual recognition, are used to categorize and annotate images. These approaches are more straightforward than those based on feature extraction as they do not require any prior feature extraction step. Instead, the developed models learn to identify patterns and features directly from the data. However, deep learning approaches often suffer from overfitting, where the model is unable to accurately classify samples that were not used in the training stage. Several deep-learning-based approaches have tackled the challenge of cultural heritage classification and annotation. The most remarkable contribution was made by PigeoNET [28], as it delivered a reliable confirmation that deep learning approachesoutperform handcrafted feature-based approaches for the categorization of paintings. Indeed, the results of this contribution surpassed the baseline results of the Rijksmuseum challenge set in [11]. The authors of [29] studied the use of multiple image patches to improve the accuracy of CNNs for the classification of paintings using transfer learning. In [30], the authors tried to use both painting images and brush stroke information for the identification of paintings using two parallel deep residual networks. The approaches in [31; 32] explore Siamese convolutional networks for painting matching and retrieval in large databases. Both solutions can be used to capture visual links between assets and thus cluster assets sharing the same visual features. A large cultural dataset, called OmniArt, consisting of more than half a million assets in addition to a multitask classification approach, is published in [33]. Architectural heritage classification and annotation are addressed in [8; 13].  
Overall, deep learning approaches are effective, however there are still many challenges related to the effectiveness of model training, such as the need for powerful computing platforms, the time required to train networks and the risk of overfitting. In comparison, feature extraction approaches have lesser requirements in terms of computational resource requirements [21].
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 4 Results  
Planted Wheat Area and Growth Periods Derived from Satellite Remote Sensing in South Central Shanxi Province  
The estimated wheat-planting area in south central Shanxi Province was 774,538 ha during 2010 to 2011, slightly higher than that determined by the census data (710,100 ha). This estimated area was then used as a basis for the subsequent classification of irrigated and rainfed wheat.  
Nine sowing and maturity dates were estimated with HJ-1A/B satellite data, corresponding to the nine agrometeorological stations, respectively (Table 3). The differences between the estimated results (sowing and maturity dates) and ground-based observations were within 12 days, less than 13.5 days of the average revisit interval of HJ-1A/B imagery, indicating that the estimation accuracy of the wheat growth period is acceptable. The estimations at Jiexiu and Ruicheng stations exhibited larger errors due to the small wheat plots, and hence, the associated mixing of wheat pixels with those of other land use types. The average differences in sowing and maturity dates between HJ-1A/B and ground-based estimates were within six and two days respectively, indicating that the method used in this study is robust for estimating the wheat growing period.
## 4 Experiment and Verification
### Component Assessment Modeling  
The momentum wheel is a key actuating component of the satellite attitude control system. It regulates the speed and direction by controlling the current. Changes in the current parameter have a direct impact on the performance of the momentum wheel.  
Figure 7: Comparison of trend extraction methods.  
Therefore, if the current parameter is unstable or too large or too small, it may lead to an unstable rotational speed of the momentum wheel, increased attitude control errors, or failure to achieve the required attitude adjustment. At the same time, abnormal changes in temperature parameters can indirectly affect the performance of the momentum wheel by influencing the friction characteristics, inertia characteristics, and motor performance. In a closed-loop satellite attitude control system, monitoring and control of temperature are very important to ensure the proper operation and accurate attitude adjustment of the momentum wheel. Based on the above considerations, the bearing temperature and current of a momentum wheel component are selected. Seven years of whole-life data are used to assess and analyze the validity of the methodology presented in this study. Figure 8 shows the original data for these two health characteristics after preprocessing with wild-value rejection and resampling.  
As can be seen in Figure 8, the current data are extremely stable for the first two years, with no significant changes in amplitude. Around day 700, there is significant performance degradation, with a decreasing trend in the current amplitude. The temperature data show a relatively gentle downward trend overall.  
Using the first cycle data as the health state benchmark, the two parameters are normalized positively. The results are shown in Figure 9. The weights are calculated using the entropy weighting method and CRITIC method; the mean value is taken as the combined weight. The results are shown in Table 1. The details of the method can be found in [15] and will not be repeated here.  
The combined weight of the current was 0.499 and the combined weight of the bearing temperature is 0.501. The assessment model for the momentum wheel component is shown in Equation (14):  
\[\text{f}(\xi)=0.499\text{f}(\xi_{1})+0.501\text{f}(\xi_{2}) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Weighting of Current** & **Weighting of Bearing Temperature** \\ \hline CRITIC method & 0.512 & 0.488 \\ Entropy method & 0.486 & 0.514 \\ \hline Combined weighting & 0.499 & 0.501 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the calculation of the parameter weights.  
Figure 8: The current (**a**) and bearing temperature (**b**) data of the momentum wheel after preprocessing.
# Application of Image Recognition Methods to Determine Land Use Classes
## 4 Results
### Post-Processing Results  
After obtaining the classification results and conducting a detailed analysis, values were manually determined to set confidence thresholds, below which a pixel is considered unreliable and assigned to the \"Unclassified\" class. Figure 12 presents an example of a confidence map, which is utilized for the elimination of pixels below the defined threshold.  
The figures illustrating the operation of the satellite image post-processing algorithm, both prior to and subsequent to the processing, are presented in Figure 13.  
Figure 12: An example of a confidence map.  
Figure 13: Example of post-processing process. (**a**) Input classified satellite image for post-processing. (**b**) Result (output) of post-processed satellite image.
# Tilt-Induced Error Compensation with Vision-Based Method for Polarization Navigation
## 3 Polarization Orientation Error Compensation Method
\begin{array}{l}\delta_{s}=0.3723+23.2567\sin\sigma_{s}+0.1149\sin 2\sigma_{ s}-0.1712\sin 3\sigma_{s}\\ -0.758\cos\sigma_{s}+0.3656\cos 2\sigma_{s}+0.0201\cos 3\sigma_{s}\end{array}  \tag{29}\]  
where \(\sigma_{s}=2\pi(D-D_{0})/365.2422\) is the day angle, \(D\) is the day of year, and since 1985, the vernal equinox time \(D_{0}\) expressed in days is  
\[D_{0}=79.6764+0.2422\times(Y-1985)-\text{INT}[(Y-1985)/4] \tag{30}\]  
Figure 7: Celestial sphere. where \(Y\) represents the year and INT represents rounding to zero. The solar hour angle \(T_{S}\) of the observation site O is calculated as follows, where the local time \(S_{d}\) of the observation site is  
\[S_{d}=S_{Z}+\{F_{Z}-4[120^{\circ}-(Lon_{D}+Lon_{F})]\}/60 \tag{31}\]  
where \(S_{Z}\) and \(F_{Z}\) represent the hour and minutes of local Beijing time, whereas \(Lon_{D}\) and \(Lon_{F}\) represent the longitude and latitude. The calculation formula of the time difference \(E_{t}\) is as follows:  
\[E_{t}=0.0028-1.9857\sin\sigma_{s}+9.9059\sin 2\sigma_{s}-7.0924\cos\sigma_{s}- 0.6882\cos 2\sigma_{s} \tag{32}\]  
Using \(E_{t}\) to modify \(S_{d}\) to get true solar time \(S_{t}\):  
\[S_{t}=S_{d}+E_{t}/60 \tag{33}\]  
To solve the local solar hour angle \(T_{S}\) according to \(S_{t}\):  
\[T_{S}=\left(S_{t}-12\right)\times 15^{\circ} \tag{34}\]  
Through the above formulas and solving steps, the solar altitude angle \(h_{s}\) and solar azimuth angle \(\varphi_{gS}\) can be calculated finally. Solar position can be expressed by solar altitude angle and solar azimuth angle as  
\[\mathbf{S}_{n}=\left[\cos h_{s}\cos\varphi_{gS},\cos h_{s}\sin\varphi_{gS}, \sin h_{s}\right] \tag{35}\]  
Thus, all remaining variables in Equation (22) have been solved. Substituting the acquired horizontal attitude angle into Equation (22) allows for the compensation of the polarized light heading information.