esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Stability Analysis and Augmentation Adjustment of UAV
### Multi-Section Variable-Sweep Wing UAV System  
The research object of this study can be considered as a four-joint, four-angle variable-sweep wing UAV system. Based on its overall layout, it can be regarded as a multi-body system that is composed of the following five main sections: left inner wing \(W_{1}\), left outer wing \(W_{2}\), right inner wing \(W_{3}\), right outer wing \(W_{4}\), and fuselage \(FL\) (including engine, tail, wing weapon section, etc.). Because the masses of other movable parts, such as the trailing edge of the tail and the control surface of the wing, are small, they are not treated as separate rigid bodies, and only the force or moment generated by the control surface deflection is considered. \(W_{1}\) and \(W_{3}\) are hinged to \(FL\) at \(O_{1}\) and \(O_{3}\), respectively, and \(W_{2}\) and \(W_{4}\) are hinged on \(W_{1}\) and \(W_{3}\) at \(O_{2}\) and \(O_{4}\), respectively, as shown in Figure 11. \(O_{j}\) is the centroid of \(FL\), and the sweep angles of each wing-body are defined as \(\Lambda_{1}\), \(\Lambda_{2}\), \(\Lambda_{3}\), and \(\Lambda_{4}\), respectively. In this research, the coordinates \(O_{f}\), the centroid of the \(FL\), are chosen as the origin, to establish the body coordinate system. The \(O_{x}\) axis is in the symmetrical plane of the UAV, pointing forward, parallel to the axis of the fuselage; the \(O_{z}\) axis is located in the plane of symmetry, perpendicular to the \(O_{x}\) axis; and the \(O_{y}\) axis is perpendicular to the symmetry plane and points to the right. In this research, wind speed is not considered in the modeling, that is, it is assumed to be carried out in a static wind field.  
According to the dynamic modeling methods described in Section 2.1, the coordinates of \(O_{f}\) in the Earth coordinate system and the projection of pitch, yaw, and roll angles, along with the sweep angle for each wing part in the body coordinate system, are selected as generalized coordinates, which are defined as (\(x\),\(y\),\(z\),\(\theta\),\(\psi\),\(\Phi\),\(\Lambda_{1}\),\(\Lambda_{2}\),\(\Lambda_{3}\),\(\Lambda_{4}\)). The projection of the translational velocity of \(O_{f}\) and the angular velocity of rotation on the body coordinate system, and the change rate of the sweep angle of each body are selected as the system's generalized velocity, which is defined as (\(u\),\(v\),\(w\),\(p\),\(q\),\(r\),\(\eta\),\(\eta\),\(\eta_{1}\),\(\eta_{2}\),\(\eta_{3}\),\(\eta_{4}\)). To simplify the dynamic model, the following reasonable assumptions are made:  
1. Ignoring the rotation and angular velocity of the Earth, and the Earth is considered to be stationary;
2. Ignoring the curvature of the Earth's surface, and the Earth coordinate system is considered as the inertial coordinate system;
3. Ignoring the changes in the fuel weight during flight, i.e., the aircraft's weight is always fixed.  
Figure 11: Schematic diagram of the multi-joint variable-sweep UAV system. The fuselage, tail, and weapon section are treated as one rigid body.
## 2 Materials and Methods: Ten-Koh Mission Description
### Satellite Operation Modes  
The operation of the satellite is divided in the following operation modes. These are linked to the specific events from the concept of operations previously explained in Figure 7 via the number shown in parenthesis:  
* LEOP mode (1).
* Communication sessions mode (4).  
Figure 7: Satellite concept of operations as explained in sequence from 1 to 4. In Processes 3 and 4, a mission is configured to be executed and later payload and housekeeping data are downlinked. This mission configuration–execution–downlinking occurs following the execution of Steps A–D. After launch and when the nominal operations started, only Processes 3 and 4 are present as part of the nominal operation.  
* Telemetry mode (4).
* Real-time mode (4).
* Normal mode (2).
* Payload operation mode (3, 4).
* Satellite log mode (4).
* Direct command mode (4).
* Set SD card saving data address mode (4).  
During the payload mode, the MGS sends a command or a series of commands for executing payload operation according to the following mission experiments:  
* CPD: measurements of CPD are sampled and stored it in the ECU's SD card.
* DLP: measurements of DLP are sampled and stored it in the ECU's SD card.
* MM: measurements of MM are sampled and stored it in the MM's SD card.
* ADS: measurements of ADS (magnetometer, gyroscopes and Sun sensors) are sampled and stored it in the ADS controller's SD card.
* UCP experiment: measurements of UCP are sampled and stored it in the UCP's SD card.
* Thermal switch: measurements of the thermal switch are sampled and stored it in the UCP's SD card.  
Depending on the payload instrument to be operated, the configuration for the operation must be included in the uplink command for the experiment to run. For the cases of CPD, DLP and ADS, the payload mode uses a definition of two operating windows along the orbit, which allows for the CPD and DLP cases to sample data from each instrument in two different locations of the satellite orbit. For instance, the two missions can be performed over the north and south poles, or in two opposite regions over the equator, as shown in Figure 8.
## 4 Experimental Results
### Measurements with Modulated Signal
In fact, the results shown in Figure 12a are even better in. However, in both cases, the PA needed to be linearized to meet the ACLR specifications for these average channel power levels. Altogether, with the lowest bias currents, the best PAE and ACLR results were achieved with the same or better output channel power than the higher bias currents. The only notable disadvantage of using the lowest bias here is the reduced gain, as explained in Sections 4.1 and 4.2.
## 6 Discussion
Furthermore, most recent research results achieved in the ATSC based on the multi-agent DRL are compared not just with the traditionally used ATSC algorithms but with the other inferior DRL approaches. The growing maturity of DRL applications in ATSC is additionally proven by the transition from the synthetic traffic simulations towards the simulations which are tuned by using real-world traffic data (e.g., the Monaco traffic network use case study presented in [9] and the Sants area case study in the city of Barcelona presented in [20]). The most noticeable limits of DRL frameworks applied in ATSC are the following: (1) slow and computationally expensive learning; (2) difficulties in optimal reward design for stochastic control environments, e.g. multi-intersection network; (3) over-fitting to unusual patterns which can be common in traffic flow, e.g. effects of aggressive overtaking or lane changing, impacts of lesser incidents, etc. ; (4) absence of explanation how the control decision was made; and (5) control decision can be unstable and hard to reproduce. Additionally, the DNN models in DRL frameworks can digest only adequately pre-processed raw traffic data. The efficiency of DRL framework strongly depends upon the positional arrangement of raw data in image-like input structures suitable for the DNN model digestion. A large network of intersections require aggregation of raw traffic data up to a certain level in order to be arranged in the image-like format which can represent overall traffic state at such networks. Furthermore, all obtained traffic data should be processed/formatted by using the standardized image-like format and be freely shared in line with the Open Traffic Data concept. The process of sharing should be done through the local traffic data centers to achieve flexible and broader scalability over various DRL oriented ATSC approaches. The DOTD as a conceptual framework for processing and exchanging standardized higher-level image-like formats will be essential for more extensive application of DNN based approaches in the traffic engineering such as prediction of traffic demand [60; 61], detection of traffic anomalies [62], optimization of transportation processes [63], and application of DRL in real-world traffic control problems [64]. The DOTD framework potentially can be a data platform for establishing interoperability between all mentioned traffic engineering fields related to deep learning.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method
### View Embedding Module  
Since view information is an imperative condition in gait recognition, the view embedding module is introduced to build view estimation and view angle is embedded into the previous two-path spatial-temporal feature fusion module, which can not only greatly minimize the intra-class caused by the view differences, but also improve the recognition ability of gait recognition.
## 3 Proposed Algorithm
### Node State Transformation Phase  
In the TSEP algorithm, nodes are randomly deployed in the network model. Therefore, some areas may have many nodes. The areas with a high density of nodes are more likely to cause transmission interference and select too many cluster heads. In the data transmission phase, too many cluster heads send data to the base station. This phenomenon wastes a lot of energy and does not prolong the network life cycle. Therefore, we use a node state transformation mechanism to control the number of cluster heads in high-density node areas. This process requires two steps, that is, selecting the neighbor nodes and checking the nodes' status. We have first introduced the selection process of the neighbor nodes. Initially, each node broadcasts a message within its range that contains the information about the identity label of node, residual energy, and current status. After receiving the message with the node information, the closest corresponding neighboring node stores the neighbor nodes information. A node may fail to find a neighbor node and can become an isolate node, and this could occur if the minimum distance between the isolate node and the other node is outside its range. We then set half of the pairs of nodes to be active and set the other half of the pairs of nodes to be asleep. The isolate nodes are set to be active all the time. After selecting the neighboring node, we should check the status of the node to determine what the node should do. If the node is in active mode, nodes will participate in the election of cluster heads. If the node is in sleep mode, nodes will only become a member node and will not become a cluster head in this round.  
Figure 1: The flow diagram of Threshold-Sensitive Stable Election Protocol (TSEP) algorithm. HWSN: heterogeneous wireless sensor network; TDMA: time division multiple access.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 2 Dynamic Measuring Principle and Device of Atom Gravimeter
### Dynamic Measurement Principle of Atom Gravimeter
#### 2.2.2 Kalman Filter  
When the measured gravity data is used for navigation, detection, and other scenarios, real-time performance is more emphasized. Therefore, teams such as French iXBlue and ZJUT have adopted another scheme, i.e., using data fusion algorithms such as the Kalman filter to estimate the gravity value in real-time. The effect of vibration acceleration \(a_{\text{vib}}\) on the reflecting mirror of an atom gravimeter is equivalent to the introduction of an additional phase shift in gravity phase shift  
\[\Delta\phi=(k_{\text{eff}}\cdot g-2\pi\alpha)T^{2}+\phi_{\text{vib}}. \tag{7}\]  
The accelerometer can be firmly connected to the mirror to measure the vibration, and the vibration can be converted into the corresponding phase shift to correct the gravity phase shift:  
\[\phi_{\text{vib}}=k_{\text{eff}}\int_{-T}^{T}a_{\text{acc}}(t_{\pi}+t\prime)h( t\prime)\text{d}t\prime. \tag{8}\]  
The fringe parameters \(P_{0}\), \(C\) and the gravity value \(g\) are taken as state vector, i.e., \(\mathbf{X}=\begin{bmatrix}P_{0}&C&g\end{bmatrix}^{\text{T}}\). The evolution of the state vector over time is  
\[\mathbf{X}_{k}=\mathbf{F}\cdot\mathbf{X}_{k-1}+\mathbf{w}, \tag{9}\]  
where, \(\mathbf{X}_{k}\) and \(\mathbf{X}_{k-1}\) are the states at \(k\) and \(k-1\) moment respectively, and \(\mathbf{F}\) is the state evolution matrix:  
\[\mathbf{F}=\begin{pmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{pmatrix}. \tag{10}\]  
\(\mathbf{w}\) is the process noise of the state vector \(\mathbf{X}\), and its covariance matrix can be represented by a diagonal matrix:  
\[\mathbf{Q}=\begin{pmatrix}\sigma_{P_{0}}^{2}&0&0\\ 0&\sigma_{C}^{2}&0\\ 0&0&\sigma_{g}^{2}\end{pmatrix}, \tag{11}\]  
where \(\sigma_{P_{0}},\sigma_{C}\) and \(\sigma_{g}\) are a priori statistics of the standard deviation of \(P_{0}\), \(C\) and \(g\), respectively.  
The population of the atom gravimeter \(P(k)\) is observed, and the observation noise \(v\) can be expressed by the diagonal matrix \(\mathbf{R}\). The dimension of \(\mathbf{R}\) is 1, and the value is \(\sigma_{P}^{2}\), i.e., the prior variance of the population \(P\). The observation matrix \(\mathbf{H}\) is the Jacobian matrix of the state vector with respect to the observation equation:  
\[\mathbf{H}=\begin{pmatrix}1&\\ -\frac{1}{2}\cos[(k_{\text{eff}}\cdot g-2\pi\alpha)T^{2}+\phi_{\text{vib}}]\\ \frac{C+k_{\text{eff}}T^{2}}{2}\sin[(k_{\text{eff}}\cdot g-2\pi\alpha)T^{2}+ \phi_{\text{vib}}]\end{pmatrix}^{\text{T}}. \tag{12}\]  
Under the framework of extended Kalman filter, the gravity value can be estimated in real-time by iterative recursion [38; 42; 49].
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 4 Discussion
### Effects of Reforestation on Land Cover Change  
The sandy lands are mainly located in the windward direction of each bay (e.g., Changjiang Bay, Haitan Bay, and Tannan Bay). The area of sandy land decreased by 13.22 km\({}^{2}\) between 1990 and 2007, and the reason can be explained by policy-making that aims at sand control on Haitan Island. Local forestry bureau, Fujian Academy of forestry, and other relevant institutions carried out reforestation projects in Pingtan County [27]. The coastal forests have an advantage in windbreak and sand fixation, which protects Haitan Island's ecosystems and the environment from wind damage. Nevertheless, in 2007-2019, the area of sandy land increased by 3.81 km\({}^{2}\). The fact may be explained by the possible conversion of other land use types into bare land because of the development process to support the tourism and built-up land development [38] and the construction of roads and wind power stations near the location of wind gaps. Massive construction reduces the windproof ability of shelter forests and forms new wind gaps, which push the development of sand [27].  
Reforestation can contribute to local ecological restoration, coastal protection, and urban green space expansion [39]. In this study, forest land had experienced significant growth over the past three decades. The maximum NDVI showed Haitan Island was greener in 2019 than before (Figure 7), which indicated the effects of reforestation. Overall, forest change on Haitan Island reflects the ability of anthropogenic activities to transform natural landscapes. Although the pattern of forest change was discussed in this study, forest species and quality on Haitan Island remain unknown. According to incomplete statistics based on the forest survey data, black pine was the dominant tree species in Pingtan Island for wind prevention in the 1990s, but beach sheoak subsequently became the major tree species [27]. In addition, due to the continuous reforestation processes and in favor of the goal  
Figure 7: The maximum NDVI: (**a**) 1990, (**b**) 2000, (**c**) 2007, and (**d**) 2019 on Haitan Island.  
of international tourism, varieties of tree species were cultivated on Haitan Island in recent years. Therefore, the change of forest species and quality on Haitan Island needs to be further studied.
## 5 Simulation Study  
### Simulation Environment  
In our experimental environment, we assume the simulation area is a 400 m \(\times\) 400 m region with a total of 60 addresses on the map. More specifically, 33.3% addresses are homes, 25% addresses are schools, 25% addresses are offices and 16.7% addresses are other types of places, e.g., restaurants or shops. In our simulation scenario, there are three types of nodes: the first type is students whose first activity must be go to school; the second type is workers whose first activity must be go to work and the third type is others, whose first activity could be any of activities except going to work and going to school. The time of the first activity for all the nodes is between 6 AM and 8 AM. Furthermore, every node goes back to its home before midnight. The number of activities of any kind of nodes is between 1 and 10.  
We assume the network bandwidth is 5 kbps, and the simulation runs for three days. Other simulation parameters and their default values are summarized in Table 3. In particular, we assume that in flooding protocol, only messages newly generated by the source node are allowed into the queue after the queue is full, while messages transmitted by other sensors are definitely dropped. The performance metrics we used in our simulations are:  
**Data delivery ratio**, which is the ratio of the data received by the sink node to the sum of data generated by all the sensors in the network.  
**Data delivery delay**, which is defined as the duration from the very beginning of data generation until it is received by the sink node.  
**Data delivery overhead,** Energy consumption of sensors is due mainly to data transmission. Thus, the more duplicated copies generated, the higher the data delivery overhead.  
**Network lifetime,** which is defined as the duration from the very beginning of the network operation until a half of sensor nodes die in our simulation.  
All the simulation results are averaged over 100,000 independent runs.
## 4 Performance Evaluation
### Network Cost  
Capital expenditure (CAPEX) consists of both initial network equipment and network installation costs [25; 26]. In this work, the power cable line comprises the electrical collection cables and optical  
Figure 12: Reliability block diagram for Full protection architecture.  
Figure 13: Mean down time for different architectures for a small (wind power farms) WPF.  
fiber cables. Since the communication network part is integrated with the electrical system, we consider only the network equipment cost to evaluate and compare different network architectures.  
The communication network cost can be divided into active device costs (OLT, ONU, and Ethernet switch) and passive component costs (POS and fiber). The total network cost for the EPON-based and switch-based architectures can be represented as follows:  
\[\text{C}_{EPON}=\text{C}_{OLT}+\text{C}_{FF}+\text{C}_{POS}+\text{C}_{DF}+\text {C}_{ONUI}\times\text{N}_{ONUI} \tag{9}\]  
\[\text{C}_{Ethernet}=\text{C}_{ESW}+\text{C}_{FF}+\text{C}_{DF} \tag{10}\]  
where, \(\text{C}_{OLT}\), \(\text{C}_{POS}\), \(\text{C}_{ESW}\), and \(\text{C}_{ONUI}\) represent the equipment of the OLT, Ethernet switch, POS, and ONU, respectively. CFF and CDF represent the costs of optical fiber cable of the feeder fiber and distributed fiber, respectively. \(\text{N}_{ONUI}\) represents the number of WT-ONUs.  
The equipment costs used in our model are detailed in Table 5. We consider that the OLT cost is $12,100, and the ONU cost is $350. As expected, unprotected EPON is the most economic deployment solution for EPON-based architectures, compared with the conventional switched-based architectures.  
In Figure 14, results show that the network cost of architectures (A, C, and D) with full protection is lower than unprotected architecture for conventional switched-based architectures. Using the results obtained of reliability analysis and network cost, an effective and highly reliable WPF architecture can be chosen and deployed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Cost (US \$)** \\ \hline OLT & 12100 \\ ONU & 350 \\ ESW & 1800 \\ Splitter (1 \(\times\) 2) & 50 \\ Splitter (1 \(\times\) N) & (50 per port) \\ Fiber (per km) & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Component cost (US \(\$\)) [22].  
Figure 14: Network cost for WPF architectures.
## 4 Experiments and Analyses
### Parameter Setting and Evaluation Metrics  
In our experiment, precision (P), recall (R) and F1 score (F1) are used as evaluation metrics. Only when the model correctly recognizes the boundary and type of entities at the same time can it represent correct recognition. In order to avoid experimental error and contingency, we tested each experiment ten times, and calculated the average values of the evaluation metrics as the final experimental results.  
We manually adjusted the hyperparameters according to the recognition performance of the model on the development set. At the representation layer, we used BERT, a pre-trained language model released by Google, to generate a contextual representation and follow its default Settings. For CBHNN, we follow the settings in Section 3.1.2. For CMTE, \(d_{k}\) is set to 64, and the numbers of crossmodal attention heads and layers are set to 8 and 2, respectively. The hidden size of BiLSTM in the semantic encoding layer is set to 512. The maximum sentence length is 200, the batch size is 32 and the dropout rate is 0.4 for all datasets. The training epoch is set to 100 for the Weibo dataset, and 20 for OntoNotes 4. Adam is adopted to optimize the model, with a learning rate of 0.0004. The model with the highest F1 score on the development set will be the best, and the performance evaluation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Type** & **Train** & **Dev** & **Test** \\ \hline \multirow{3}{*}{Weibo} & Sentence & 1.35 k & 0.27 k & 0.27 k \\  & Character & 73.78 k & 14.51 k & 14.84 k \\  & Entity & 1.90 k & 0.39 k & 0.42 k \\ \hline \multirow{3}{*}{OntoNotes 4} & Sentence & 15.72 k & 4.30 k & 4.35 k \\  & Character & 491.90 k & 200.51 k & 208.07 k \\ \cline{1-1}  & Entity & 13.37 k & 6.95 k & 7.68 k \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
will be conducted on the test set. All the experiments are conducted on an NVIDIA 3090 GPU. Table 2 shows the selection range of hyperparameters.  
Table 3 shows the evaluation results on the development set of different kernel sizes (context window size) in CBHNN and different hidden sizes in the semantic encoding layer. We can find that the kernel size is set to 3 and the hidden size is set to 512, which are superior to other optional parameters. Therefore, our model selects the kernel size of 3 in CBHNN and the hidden size of 512 in the semantic encoding layer as the final parameters.