esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.3.1 Assessment of Ecological Environment Evaluation Indices  
The initial phase of the research involved the evaluation and selection of appropriate indices. In 2006, China's Ministry of Environmental Protection formulated the \"Technical Criterion for Ecosystem Status Evaluation\", which was first revised in 2015 [36] (hereinafter referred to as the Criterion). This Criterion introduced the Ecological Environment Condition Index (EI), a tool primarily reliant on remote sensing technology designed to assess the ecological status of regions at or above the county level. The EI calculation is as follows:  
\[\text{EI = 0.35 \times Biodiversity Index + 0.25 \times Vegetation Cover Index + 0.15 \times Water Network Density Index + 0.15 \times (100 -\text{ Land Stress Index}) + 0.1 \times(100 -\text{ Pollution Load Index}) + Environmental Constraint Index} \tag{1}\]  
The numbers within the formula represent weights that have been subjectively assigned. Specific calculations for each index can be referenced in the Criterion [36]. Following the EI scores, the ecological environment is classified into five levels: excellent (EI \(\geq\) 75), good (55 \(\leq\) EI \(<\) 75), moderate (35 \(\leq\) EI \(<\) 55), fair (20 \(\leq\) EI \(<\) 35), and poor (EI \(<\) 20). Since its adoption, the EI has been widely used across China for environmental assessments [37; 38]. The current study presents the ecological environment assessment results for the Qinling region based on the EI of 2020 (Figure 3). The EI provides a broad-brush assessment of an area's ecological status but lacks the ability to depict the precise spatial distribution of environmental quality. Therefore, this study turns to the RSEI, which is entirely based on remote sensing technology, devoid of subjective weighting, and allows for a visual, quantitative assessment of the Qinling's ecological environment.
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 1 Introduction
### Piezoelectricity  
There are numbers of electric dipoles in every piezoelectric material. These electric dipoles will be aligned to a specific direction after a certain electric field is applied. The direction that electric dipoles aligned to are called the poling direction of the piezoelectric material. When a stress is applied along the poling direction of the piezoelectric material, the displacement of inner electric dipoles will create an electric potential between the two poles of the material. When the two poles are connected to a loading outside, the electric energy stored in the material is released. This phenomenon of converting mechanical energy into electric energy is called piezoelectricity. While the rapid development of the research of the mechanics of asphalt pavement and performance evaluations, piezoelectricity has been widely employed to establish the relation between moving vehicles and pavement deformation [16; 17].  
Piezoelectric material has been utilized in different applications, such as condition monitoring [18] and evaluating the structural health of the pavement [19; 20; 21]. Piezoelectric material has shown that it is very sensitive and responsive to external force. This characteristic makes it a great option for designing a load-measuring device. In most of the in-pavement WIM technologies, piezeceramic bars are used to measure the vehicle's weight [12]. After being developed for decades, piezoelectric materials have been fabricated into different shapes, with different compositions and properties. Piezoceramic lead zirconate titanate, known as PZT, is widely used in many designs [22] simply because it has the best cost-effectiveness among all kinds of piezoelectric materials. As a brittle material, PZT has low strain endurance, and its application is limited. [PERSON] et al. have developed a Poly Vinylidene Fluoride (PVDF) film in their work that is coated with poly (3,4-ethylenedioxy-thiophene)/poly(4-styrenesulfonate) [PEDOT/PSS] electrodes [23]. With great flexibility, PVDF is capable of enduring large strains.
# A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexopod Robots
## 4 The Three-Element Trajectory Determination Method  
For a hexapod robot, planning its foot trajectory is very important, and it will affect the robot's flexibility and stability. While moving, the robot's leg may be in the support phase in order to drive the robot towards a certain direction or off the ground and in the transfer phase. The gait is then realized by switching between the support phase and transfer phase in different sequences. For each leg, its trajectory planning is worth studying in order to enrich the movement of legged robots.  
In this paper, we design a three-element trajectory determination method to help the robot plan its foot's movement. The three elements used are the start point in support phase \(P_{0}=(x_{0},y_{0},z_{0})\), the end point in support phase \(P_{1}=(x_{1},y_{1},z_{1})\), and the joint angle changes in transfer phase \(\Delta\theta=[\Delta\theta_{1},\Delta\theta_{2},\Delta\theta_{3}]\). \(P_{0}\) and \(P_{1}\) are used to control the height, distance, and the direction of the movement, while \(\Delta\theta\) is used to make decisions on the lifting motion of the leg. Applying this method to legged robots, this paper transfers the six variables of \(P_{0}\) and \(P_{1}\) to another three variables that are related to the movement of legged robots, which are height, direction, and distance. Such a transfer is based on some restrictions such as symmetry, equal altitude, vertical landing, etc. \(\Delta\theta\) is set according to the feedback information from the foot. The trajectory planning methods in the support phase and transfer phase are different, and they are elaborated separately.  
Figure 5: The leg’s diagram for the solution of inverse kinematics.
## 3 Performance Evaluation
### Network Connectivity  
[PERSON]'s algorithm is an iterative procedure for building a networking tree with minimal accumulated costs across all routing paths in the network. Every iteration results in the addition of another node to the forming networking tree. The iterations are over when all nodes are added to the routing tree. A node can never be left outside of the routing tree, _i.e._, [PERSON]'s algorithm would always find relay nodes to carry signals from any node to the AP. The only case where the [PERSON]'s algorithm would fail in reaching all nodes is if during a specific iteration there are no nodes to be reached using the maximum transmission power of nodes already in the forming routing tree. This represents a power limitation constraint and is true regardless of the link-cost function being used. Forming a routing tree could fail due to severe channel attenuations as dictated by the dynamic multipath fading environment and body-shadowing.  
To address network connectivity, we define the outage ratio, \(P_{\mathit{out}}\), as the ratio between [PERSON]'s failed routing attempts over the overall routing attempts. A failed attempt occurs when any node is unable to join the [PERSON]'s forming network tree due to a transmission power limitation. To evaluate \(P_{\mathit{out}}\), A set of captured RSSI tables were fed to the simulation, which proceeded to run the routing algorithm under a transmission power constraint assumption. To obtain generic results, the maximum transmission power, \(P_{\mathit{tx}}^{\mathit{max}}\), was normalized to target RSSI to obtain a maximum power ratio, \(P_{\mathit{ratio}}^{\mathit{max}}\).  
\[P_{\mathit{ratio}}^{\mathit{max}}=\frac{P_{\mathit{tx}}^{\mathit{max}}}{RSSI_{T}} \tag{4}\]  
Simulations for evaluating \(P_{\mathit{out}}\)were ran while sweeping over various values of \(P_{\mathit{ratio}}^{\mathit{max}}\). Referring to the power control mechanism for meeting the link budget in Equation (1), failed routing attempts correspond to channel instances where \(\alpha_{j,k}<(P_{\mathit{ratio}}^{\mathit{max}})^{-1}\). The simulation is evaluating the probability for this event to occur.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 4 Source Anonymity-Based Lightweight Secure AODV Protocol
### Source Anonymity-Based Lightweight Secure AODV Protocol
#### 4.1.2 Lightweight Tamper-Proof Scheme  
The RSA digital signature in the SAODV protocol guarantees that the data is not tampered with, and the shortest key of the RSA digital signature is 1024-bit. Its computation and storage for the resource-constraint node are not a small overhead. In comparison, the deployment of Cyclic Redundancy Check (CRC) could also achieve a similar functionality--tamper-proof. What's more, the polynomial of CRC-4 is only 5 bits. Meanwhile, the computational complexity of CRC is much lower than that of the RSA digital signature. So, we propose an Energy-efficient secure AODV protocol, which could effectively reduce the storage space and energy consumption of SAODV protocol, increasing the energy efficiency by using CRC instead of a RSA digital signature to test whether the data has been changed.  
By attaching the gotten CRC check code at the back of the next original data and then making a transmission together, the receiver divides the data, and it represents data that has not been tampered with when there is no remainder.  
CRC is an error detection code, which is usually used for detecting the unexpected data change in storage devices or the Internet. The data uses a CRC algorithm to obtain a check code, and this code is attached at the back of the transferred original data. Then, the receiver reuses the same CRC algorithm to check whether the data is being changed. Generally, the errors caused by environmental factors such as interference or harasses could use the CRC algorithm to effectively detect them in transmission. However, if the transmitting data and its CRC code is artificially manipulated at the same time by malicious nodes, it cannot not be detected whether the data has been tampered with the CRC algorithm.  
In order to defend against the tamper attack, the polynomials are generated by a pseudorandom. Usually, the polynomial that is generated by the CRC algorithm is a fixed pattern, and the malicious nodes could be easy to tamper with the data and its CRC simultaneously, while if the pseudorandom polynomials are implemented, it could reduce the possibility of tampering with the data and its CRC. The transmitter and the receiver only need to negotiate and make the using sequence of the polynomial generated by pseudorandom in advance.  
In addition, we could introduce the checksum (CHK) for verifying the CRC's integration. Meanwhile, the checksum (1 byte) is encrypted by sharing the key, and thus the ability to defend against the tamper attack can be enhanced.  
Figure 2: Non- consecutive pseudonym single mapping.
## 2 Strain-Gauge-Integrated Smart Geosynthetics
Despite recent advances in the design and production of different high-performance strain gauges, almost all strain-gauge-based smart geosynthetics have involved a limited number of gauges installed at different geosynthetic points. Because geosynthetics are planar-shaped reinforcements, the stress and, consequently, the strain are distributed in different directions on the geosynthetic surface, which sometimes distorts the reinforcement planar elements. A low installation resolution and the inability of strain gauges to detect strain in different directions and distortion modes hinder or render almost impossible the development of an integrated continuous monitoring system. The assembly of strain gauge circuits required to do so, the complex calibration processes, and the strain gauge sensitivity and vulnerability to destructive environmental agents have limited the application of strain gauges as practical long-term solutions to develop smart geosynthetics. Indeed, corrosive factors, soil moisture, freeze-thaw and wet-dry climatic cycles, and strain gauge sensitivity to damage during installation, transportation, and construction continuously reduce strain gauge efficiency in the field.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 3 Results
### Auto-Navigation Performances in Orchard  
In order to verify the reliability and rationality of the automatic navigation system mounted on the sprayer, a linear navigation test was carried out in the orange orchard, as shown in Figure 12. The spacing of planting rows was 5 m, and the ridge length was about 35 m. Considering the outstretched part of the branches of orange trees, the actual width of the walkable area between ridges was 2 m, and the width of the body was 0.62 m. Therefore, the minimum safe distance was 0.69 m, which satisfied the path planning requirements.  
Figure 11: The positioning results of the spraying robot in the straight-line running experiments. (**a**) Positioning measurements using BDS; (**b**) Heading angle measurements using BDS; (**c**) Positioning measurements using BDS/IMU integrated system; (**d**) Heading angle measurements using BDS/IMU integrated system.  
Two continuous ridges of orange orchards were randomly selected as the test site. Before the test, the high-precision positioning board was used to collect the coordinates of points 1-4 on the planned path, the path planning scheme shown in Figure 9 was employed for the orchard path tracking test and the automatic navigation driving speed of the sprayer was 0.4 m/s. The experiment was conducted at the fruit tree Innovation Park of the Horticultural Research Institute of Hunan Academy of Agricultural Sciences, Changsha City, Hunan Province on 7 April 2022.  
When the sprayer was spraying in the orange orchard, the straight-line navigation automatic driving experiments were carried out five times. The maximum lateral tracking error was as low as 8.58 cm, if the path tracking algorithm is stable. However, the maximum lateral tracking error of the spraying robot was 10.77 cm due to the orchard field soil environment, such as side slip resulting from field ridge and steep hill. However, the tracking accuracy is acceptable because of its large operation width. We can conclude that the orchard soil condition and ground-surface undulation, especially for heavier spraying robots when the medicine box is filled with pesticides. Since the heading is only employed to correct the positioning error, the lever-arm error coming from pitch and roll is not compensated. The navigation deviation information on the fourth route was shown in Figure 13. The results of the five groups of experiments were summarized in Table 1. Through calculation, the error of the average absolute value was 3.55 cm, and the overall root mean square error of the lateral deviation absolute value was 1.19 cm. The field tests showed that the maximum offset error was no more than 10.77 cm. Take a certain navigation line to perform linear trajectory fitting, and the navigation effect is shown in Figure 14.  
Figure 12: Field test of the spraying robot navigation system.  
Figure 13: The diagram of the lateral and heading deviation.
## 3 DQN-Based Solution
### Policy Learning Process
When the dataset is full (i.e., \(l\) experiences are stored), old experiences are removed from the dataset to save new ones. It is worth mentioning that during a number of InitialCollectSteps of the data collection, the actions are selected completely randomly by forcing \(\varepsilon=1\) in order to explore several states and start filling the dataset with experiences. The process of updating the weights \(\theta\) of the evaluation DNN is executed in every time window in parallel to the data collection and it makes use of the experiences stored in the experience dataset. Specifically, for each update a mini-batch \(U(D)\) of \(l\) past experiences is firstly selected randomly from the dataset. The selected experiences are denoted as \(e_{j}\), \(j=1\),..., \(J\), and the components of tuple \(e_{j}\) are denoted as \(<s_{j},a_{j}r_{j},s_{j}^{*}>\). Then, the update is performed based on the mini-batch gradient descent process. First, it computes the average mean squared error (MSE) loss \(L(\theta)\) over all the \(J\) experiences in the mini-batch as:  
\[L(\theta)=\underset{e_{j}\in U(D)}{\mathbb{E}}[(r_{j}+\tau.\underset{a^{ \prime}}{max}\ Q({s_{j}}^{*},a^{\prime},\theta^{-})-Q({s_{j}},a_{j},\theta))^{2 })] \tag{7}\]  
Then, the mini-batch gradient descent of \(L(\theta)\), denoted as \(\
abla L(\theta)\), is obtained by differentiating \(L(\theta)\) with respect to \(\theta\), which yields:  
\[\
abla L(\theta)=\underset{e_{j}\in U(D)}{\mathbb{E}}[(r_{j}+\tau.\underset{ a^{\prime}}{max}Q({s_{j}}^{*},a^{\prime},\theta^{-})-Q({s_{j}},a_{j},\theta)) \cdot\
abla_{\theta}Q({s_{j}},a_{j},\theta)] \tag{8}\]  
Then, the weights of the evaluation DNN \(Q(\textbf{s,}\textbf{a,}\theta)\) are updated as:  
\[\theta\leftarrow\theta+\alpha\cdot\
abla L(\theta) \tag{9}\]  
where \(\alpha\) is the learning rate. After each update of \(\theta\), the resulting \(Q(\textbf{s,}\textbf{a,}\theta)\) is used for triggering new actions. Moreover, after \(P\) updates of \(\theta\), the weights of the target DNN are updated as \(\theta^{*}=\theta\). The training operation of the DQN-agent associated to the \(u\)-th UE is summarized in Algorithm 1, which includes the data collection (lines 3-12) and the update of the weights \(\theta\) of the evaluation DNN (lines 13-21 of Algorithm 1). The training duration in time steps is given by parameter MaxNumberOfTrainingSteps.
## 3 Formulation and Numerical Modeling
### Governing Equations
In this fluid model, the electron transport and the motion of ionic and neutral species are solved by this set of equations in the time domain. The convection of electrons due to fluid motion is neglected. For the microwave plasma interactions, due to the time scale difference between electron and ion motions, only electron heating by microwave is considered in the frequency domain. In the microwave plasma interface, the high frequency electric field \(\overline{\mathbf{E}}\) is computed using the Helmholtz equation:  
\[\frac{\partial n_{e}}{\partial t}+\
abla\cdot\Gamma_{e}=R_{e}, \tag{1}\]4  
where \(\mu_{r}\) is the relative permeability, assumed to be 1 in this work, \(k_{0}\) is the wave number in the vacuum, \(\varepsilon_{r}\) is the relative permittivity, the same as defined in the Poisson equation but assumed to be 1 for the plasma region, \(\omega\) is the angular frequency of microwave, \(j\) is the unit of imaginary number, and \(\sigma\) is the plasma conductivity. The relationship between the plasma current density \(\overline{\mathbf{J}_{p}}\) and the electric field \(\overline{\mathbf{E}}\) is defined as:  
\[\frac{\partial n_{e}}{\partial t}+\
abla\cdot\Gamma_{e}=R_{e}, \tag{1}\]5  
and  
\[\frac{\partial n_{e}}{\partial t}+\
abla\cdot\Gamma_{e}=R_{e}, \tag{1}\]6  
where \(v_{m}\) is the collision angular frequency of the electrons with background neutral gases. This formula is obtained by assuming a Maxwellian distribution function and taking the first moment of the Boltzmann equation, the mean momentum equation described above. One can refer to Ref. [43] for a more detailed derivation. Solving the Helmholtz equation with appropriate boundary conditions allows for the power transferred from the microwave to the electrons to be calculated by  
\[\frac{\partial n_{e}}{\partial t}+\
abla\cdot\Gamma_{e}=R_{e}, \tag{1}\]7  
where \(\overline{\mathbf{J}}\) is the total current density, i.e., the plasma current plus the displacement current density, and \({}^{*}\) denotes the complex conjugate, and this microwave heating term is coupled back to the electron plasma fluid via Equation (2), and then the updated electron density \(n_{e}\) is coupled to the Helmholtz equation, Equation (14), via the plasma conductivity \(\sigma\) in Equation (16), forming a self-consistent set of microwave plasma fluid models.
# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 1 Introduction
[7] addressed human body detection for UAVs in harsh conditions by applying a data hybrid weighting method to integrate visible and infrared images, thereby improving the accuracy of human target detection. [PERSON] et al. [8] combined a twin deep learning network for semantic segmentation of RGB images with data fusion techniques to generate LiDAR point clouds with semantic labels, enabling the detection of obstacles in landing areas and accurate distance measurements. Although multi-sensor data fusion significantly enhances UAV target detection and offers new opportunities for pedestrian re-identification, it still faces issues such as changing viewpoints [9], diverse pedestrian appearances [10], and complex backgrounds [11]. Despite these advancements, existing methods struggle with extreme viewpoint variations and the complexity of pedestrian behaviors, which severely limit identification accuracy. To overcome these obstacles, this study proposes a multi-scale graph convolutional network (MU-GCN) combined with a non-local attention mechanism for Transformer-based Person Re-identification (MNTReID). The contributions of this paper are summarized as follows:  
* The design of the multi-scale graph convolution network (MU-GCN): The MU-GCN is integrated into the stitching branch of the Transformer. This network capturesdetailed local features of pedestrian images using multi-scale convolutional kernels. It further enhances these features through graph convolutional networks. This approach is particularly effective at adapting to different feature scales, which is crucial for handling variable pedestrian poses and size changes in UAV views. * The integration of the non-local attention mechanism: The non-local attention mechanism is introduced into the Transformer's global branch. This mechanism overcomes the limitations of the traditional self-attention mechanism in capturing long-range dependencies. It significantly improves the model's ability to integrate global contextual information, thereby enhancing performance in complex environments. * Experimental validation on the UAV aerial image dataset: Experiments were conducted on a UAV aerial image dataset. The results show that the proposed model improves the mean Average Precision (mAP) by 9.5% and Rank-1 accuracy by 4.9% compared to the baseline model. These findings demonstrate the effectiveness of the proposed approach.